&EPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-96/082 y"
July 1996
Bioindicators for Assessing
Ecological Integrity of Prairie
Wetlands
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EPA/600/R-96/082
July 1996
Bioindicators for Assessing Ecological Integrity
of Prairie Wetlands
by
Paul R. Adamus
ManTech Environmental Research Services Corp.
U.S. Environmental Protection Agency
National Health and Environmental Effects Research Laboratory
Western Ecology Division
Corvallis, OR 97333
Contract Number: 68-C4-0019
EPA Project Officer-
Mary E. Kentula
U.S. Environmental Protection Agency
National Health and Environmental Effects Research Laboratory
Western Ecology Division
Corvallis, OR 97333
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Western Ecology Division, Corvallis, OR 97333
Printed on Recycled Paper
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Notice
The research described in this report has been funded by the U.S. Environmental Protection
Agency. This document has been prepared at the EPA National Health and Environmental
Effects Research Laboratory, Western Ecology Division, in Corvallis, Oregon, through Contract
68-C4-0019 to ManTech Environmental Research Services Corp. and Contract number
5B6075NATA to Ann Hairston (JB Enterprises). It has been subjected to the Agency's peer and
administrative review and approved for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
Preferred citation:
Adamus, P.R. 1996. Bioindicators for assessing ecological integrity of prairie wetlands.
EPA/600/R-96/082. U.S. Environmental Protection Agency, National Health and Environmental
Effects Research Laboratory, Western Ecology Division, Corvallis, OR.
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Table of Contents
Figures vi
Tables vii
Acknowledgments viii
Executive Summary ix
1. Introduction 1
1.1 Need for This Document 1
1.2 Document Organization 3
1.3 Cumulative Effects of Stressors 6
1.4 Glossary, Abbreviations, and Place Names 12
1.5 Statistical Analyses: Objectives and Methods 13
2. Algal and Microbial Communities as Indicators of Prairie Wetland Integrity 18
2.1 Ecological Significance and Suitability as an Indicator 18
2.2 Potential Indicator Metrics 21
2.3 Previous and Ongoing Monitoring in the Region 22
2.4 Response to Stressors 23
2.4.1 Algal and Microbial Communities as Indicators of Hydrologic Stressors . . 23
2.4.2 Algal and Microbial Communities as Indicators of Changes in Vegetative
Cover 25
2.4.3 Algal and Microbial Communities as Indicators of
Wetland Salinity 26
2.4.4 Algal and Microbial Communities as Indicators of Sedimentation
and Turbidity 27
2.4.5 Algal and Microbial Communities as Indicators of Excessive
Nutrient Loads and Anoxia 27
2.4.6 Algal and Microbial Communities as Indicators of Pesticide and
Heavy Metal Contamination 29
2.5 Monitoring Techniques 31
2.5.1 Direct Sampling 31
2.5.2 Indirect Sampling Through Measurement of Processes 31
2.5.3 Time-Integrating Methods 34
2.5.4 Bioassay Methods 34
2.6 Variability and Reference Points 34
2.6.1 Spatial Variability 34
2.6.2 Temporal Variability 36
2.7 Collection of Ancillary Data 36
2.8 Sampling Design and Required Level of Sampling Effort 37
2.9 Summary 37
3. Vascular Plants as Indicators of Prairie Wetland Integrity 39
3.1 Ecological Significance and Suitability as an Indicator 39
3.2 Potential Indicator Metrics 40
3.3 Previous and Ongoing Monitoring in the Region 41
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3.4 Response to Stressors 41
3.4.1 Vascular Plants as Indicators of Hydrologic Stressors 42
3.4.2 Vascular Plants as Indicators of Changes in Vegetative Cover Condition . 46
3.4.3 Vascular Plants as Indicators of Wetland Salinity 47
3.4.4 Vascular Plants as Indicators of Sedimentation and Turbidity 48
3.4.5 Vascular Plants as Indicators of Excessive Nutrient Loads and
Anoxia 49
3.4.6 Vascular Plants as Indicators of Pesticide and Heavy Metal
Contamination 51
3.5 Monitoring Techniques 53
3.5.1 Ground-based Sampling 53
3.5.2 Aerial Methods 53
3.5.3 Potential or Historical Vegetation 54
3.5.4 Bioassay Methods 55
3.5.5 Bioaccumulation 55
3.6 Variability and Reference Points 55
3.6.1 Spatial Variability 55
3.6.2 Temporal Variability 58
3.7 Collection of Ancillary Data 58
3.8 Sampling Design and Required Level of Sampling Effort 59
3.8.1 General Considerations 59
3.8.2 Asymptotic Richness: Results of Analyses 60
3.8.3 Power of Detection: Results of Analyses 61
3.9 Summary 62
4. Invertebrates as Indicators of Prairie Wetland Integrity 64
4.1 Ecological Significance and Suitability as an Indicator 64
4.2 Potential Indicator Metrics 66
4.3 Previous and Ongoing Monitoring in the Region 66
4.4 Response to Stressors 67
4.4.1 Invertebrates as Indicators of Hydrologic Stressors 67
4.4.2 Invertebrates as Indicators of Changes in Vegetative Cover 71
4.4.3 Invertebrates as Indicators of Wetland Salinity 75
4.4.4 Invertebrates as Indicators of Sedimentation and Turbidity 76
4.4.5 Invertebrates as Indicators of Excessive Nutrient Loads
and Anoxia 76
4.4.6 Invertebrates as Indicators of Pesticide and Heavy Metal
Contamination 77
4.5 Monitoring Techniques 80
4.5.1 General Considerations 80
4.5.2 Sampling Equipment 80
4.5.3 Time-Integrating Methods 84
4.5.4 Bioassay Methods 84
4.5.5 Bioaccumulation 84
4.6 Variability and Reference Points 85
4.6.1 Spatial Variability 85
4.6.2 Temporal Variability 89
4.6.3 Spatial vs. Temporal Variability 91
4.7 Collection of Ancillary Data 92
4.8 Sampling Design and Required Level of Sampling Effort 92
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4.8.1 General Considerations 92
4.8.2 Asymptotic Richness: Results of Analysis 94
4.8.3 Power of Detection: Results of Analysis 95
4.9 Summary 98
5. Amphibians as Indicators of Prairie Wetland Integrity 100
5.2 Potential Indicator Metrics 100
5.3 Previous and Ongoing Monitoring 100
5.4 Response to Stressors 100
5.5 Monitoring Techniques 101
5.5.1 General Considerations 101
5.5.2 Equipment and Methods 101
5.5.3 Bioaccumulation 102
5.6 Variability and Reference Points 102
5.7 Collection of Ancillary Data 103
5.8 Sampling Design and Required Level of Sampling Effort 103
5.9 Summary 104
6. Birds as Indicators of Prairie Wetland Integrity 105
6.1 Ecological Significance 105
6.2 Potential Indicator Metrics 106
6.3 Previous and Ongoing Monitoring 107
6.4 Response to Stressors 107
6.4.1 Birds as Indicators of Hydrologic Stressors 107
6.4.2 Birds as Indicators of Changes in Vegetative Cover 109
6.4.3 Birds as Indicators of Wetland Salinity 111
6.4.4 Birds as Indicators of Sedimentation and Turbidity 112
6.4.5 Birds as Indicators of Excessive Nutrient Loads and Anoxia 113
6.4.6 Birds as Indicators of Pesticide and Heavy Metal Contamination 114
6.5 Monitoring Techniques 116
6.5.1 General Surveys 116
6.5.2 Reproductive Success 117
6.5.3 Time Budget Analysis 117
6.5.4 Bioassay Methods 117
6.5.5 Bioaccumulation 118
6.6 Variability and Reference Points 118
6.6.1 Spatial Variability 118
6.6.2 Temporal Variability 120
6.7 Collection of Ancillary Data 122
6.8 Sampling Design and Required Level of Sampling Effort 123
6.8.1 Asymptotic Richness: Results of Analysis 124
6.8.2 Power of Detection: Results of Analysis 125
6.9 Summary 126
7. Synthesis and Recommendations for Indicators 128
Bibliography 131
Appendices 172
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Figures
Figure 1. Paths of effects that can result from increases in water levels above the long-
term annual norm in a prairie wetland . ^ 8
Figure 2. Paths of effects that can result from decreases in the density and percent
cover of emergent vegetation in prairie wetlands 9
Figure 3. Paths of effects that can result from increases in sediment deposition in a
prairie wetland 10
Figure 4. Paths of effects that can result from increases in nutrient loading to a prairie
wetland 11
Figure 5. The difference between two means (of the number of Conchostraca in sweep
nets) that can be detected by various sample sizes 17
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Tables
Table 1. Species diversity by basin type and month in the Cottonwood Lakes area 36
Table 2. Summary evaluations of possible algal and microbial indicators of
stressors in prairie 38
Table 3. Summary evaluations of vascular plant indicators of stressors in prairie wetlands. . 63
Table 4. Response of nektonic and benthic invertebrate herbivores/detritivores and
predators to water level manipulations in the Delta Marsh 69
Table 5. Response of taxonomic richness of nektonic and benthic invertebrates to
water level manipulations in the Delta Marsh 70
Table 6. Response of density and biomass of nektonic and benthic invertebrates to water
level manipulations in in the Delta Marsh 72
Table 7. Comparison of studies on species richness 86
Table 8. Summary evaluations of possible invertebrate indicators of stressors in
prairie wetlands 99
Table 9. Summary evaluations of possible invertebrate indicators of stressors in
prairie wetlands 127
Table 10. Representative, sample symptoms of changes in the ecological integrity
of prairie wetlands 129
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Acknowledgments
This effort was conducted in support of the Risk Reduction component of EPA's Wetlands
Research Program. Mary E. Kentula was the EPA Project Officer. Brooke Abbruzzese
and Stephanie Gwin were the project managers for ManTech Environmental Research
Corp. (MERSC). William Sanville and Naomi Detenbeck of the USEPA Environmental Research
Laboratory-Duluth, and P. James Wigington of the USEPA National Health and Environmental
Effects Research Laboratory-Western Ecology Division, were instrumental in focusing the study
objectives. Ted Ernst performed the statistical analyses. Carol Roberts helped prepare the
figures. Cynthia Chapman of MERSC and Ann Hairston of JB Enterprises edited the document.
Miriam Pendergraft of National Asian Pacific Center on Aging (NAPCA) proofed the final
document. Patty Adkins (NAPCA) did the final word processing and formatting of the document,
and prepared the camera-ready copy.
The document benefitted greatly from the review comments of Loren Bahls, Walter Duffy, Chip
Euliss, Susan Galatowitsch, Mark Gloutney, Hal Kantrud, Gary Krapu, Mark Hanson, Judy
Helgen, Henry Murkin, and Louisa Squires. The statistical analyses presented in this document
would not have been possible without suggestions from Scott Urquehart, Oregon State
University, and the gracious sharing of data sets by the following scientists:
Sam Droege,
Breeding Bird Survey, National Biological Service, Washington, DC
Walter Duffy,
Cooperative Fish and Wildlife Research Unit, National Biological Service,
Brookings, SD
Ned (Chip) Euliss and David Mushet,
Northern Prairie Science Center, National Biological Service, Jamestown, ND
Susan Galatowitsch,
University of Minnesota, Minneapolis, MN
Mark Hanson,
Minnesota Dept. Natural Resources, Bemidji, MN
Lawrence Igl and Douglas Johnson,
Northern Prairie Science Center, National Biological Service, Jamestown, ND
Henry Murkin,
Institute for Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba,
Canada
Louisa Squires,
Santa Clara Water Resources District, San Jose, CA (formerly Iowa State University)
The following scientists were especially helpful in informing me of relevant ongoing studies during
a visit to the Northern Prairie Science Center during 1994: Lew Cowardin, Ray Greenwood, Larry
Igl, Gary Krapu, and Jeff Price.
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Executive Summary
This document is designed to assist State agencies and other users in developing programs to
monitor biological communities of prairie pothole wetlands and ultimately to develop biological
criteria appropriate for protecting this valued resource. The document emphasizes one aspect of
biocriteria development: the selection (targeting) of assemblages of biological indicators
(bioindicators) and metrics as a basis for designing and conducting biosurveys. Before
meaningful biocriteria can be developed and implemented, appropriate bioindicators must be
identified and tested. Bioindicators are species, species assemblages, or communities whose
presence, abundance, and condition is indicative of a particular set of environmental conditions.
As a foundation for developing biocriteria, the document is a compilation of current knowledge
regarding responses of various organisms to natural and anthropogenic stresses, and a
summary of the utility of various organisms as indicators of these stresses. Information is
organized by chapters covering each of the major assemblages of related species: microbes,
algae, vascular plants, invertebrates, amphibians, and birds. For each, the document reviews
past and ongoing biological monitoring programs in the prairie region's wetlands. The document
provides quantified estimates of spatial and temporal variability of various biological groups from
a limited number of existing data sets. As an additional aid to future monitoring, the document
broadly describes field-sampling methods potentially applicable to the region's wetlands. By
documenting the ecological roles of each biological group, the document also attempts to clarify
understanding of interactions among ecosystem components and justify the use of particular
biological groups as indicators. Several appendices of the document are in electronic format and
provide a database of information on environmental tolerances, life history, habitat preferences,
and other characteristics of individual species as well as tabulate results of the analyses of
indicator variability.
The document concludes that in most prairie wetlands the possibility of ongoing or recent past
exposure to excessive sedimentation is probably best indicated by species composition of algae
and invertebrates, with emphasis on the epibenthic forms (organisms that live on the top
surfaces of the sediment). Epibenthic and epiphytic (attached to plants) algae and invertebrates
are also useful indicators of excessive enrichment, removal of vegetative cover, and turbidity that
is occurring either currently or during past years as determined by analysis of decay-resistant
remains. Ongoing or recent past changes of water regime and salinity as well as overgrazing in
individual wetlands are perhaps best indicated by species composition of vascular plant
communities. Longer-term changes in these factors can be inferred by examining seed banks
and decay-resistant remains of invertebrates. Exposure to pesticides and heavy metal
contaminants can sometimes be inferred from species composition of invertebrates and from
various biomarkers in amphibians and birds. For bioaccumulative contaminants, tissues of
individual plants and birds can be examined. Birds are also uniquely valuable for spatially
integrating information on the hydrologic stresses to wetlands across entire regions.
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1. Introduction
1.1 Need for This Document
Under the Clean Water Act (CWA), wetlands are legally considered "waters of the State." Thus,
States are required to adopt narrative standards and criteria for protecting quality of wetlands
(USEPA 1987, 1989), just as States have developed standards and criteria for other surface
waters.
The US Environmental Protection Agency (USEPA) is responsible for developing regulations,
policies, and guidance to help States implement a water quality standards program. USEPA
policy requires that States adopt biological criteria as part of their water quality standards for
wetlands. USEPA recommends that States use biological criteria to supplement the chemical and
physical water quality standards they have used traditionally. USEPA has taken this approach
because biological criteria measure the actual time-integrated response of the resident aquatic
community to all environmental stresses, rather than inferring biological impairment from a
comparison of values derived from laboratory bioassays with instantaneous field measurements
of the same or similar contaminants (USEPA 1990).
To satisfy USEPA requirements for biocriteria development, State agencies need technical
information. Specifically, they need to know which biological resources to monitor in wetlands,
how to monitor them, how to analyze and interpret the data, and what it costs. To adequately
monitor wetlands, develop sound criteria and standards, and evaluate ecological risks, States
also need information on what levels of various contaminants (or other regulated stresses) will
impair the integrity of various kinds of wetland communities. USEPA is mandated to provide
States with such technical guidance and information, drawn from comprehensive synthesis of
literature, research, and expert knowledge.
This document is intended as a contribution to the effort to establish biocriteria in one region of
North America: the prairie region. The document compiles biological information on a single
wetland type in this region: prairie potholes. These are wetlands that during most years are
unconnected by surface water to lakes, rivers, or streams. The acreage of prairie pothole habitat
has declined dramatically over the years as a result of human activities (Dahl 1990, Dahl et al.
1991), underlining the importance of monitoring and maintaining the quality of what habitat
remains.
Processes for developing biocriteria may include:
• developing and testing consistent and biologically meaningful classifications of
ecoregions and wetland types
• designing and conducting biosurveys, e.g., to establish and characterize reference sites
and conditions
» developing and calibrating sample metrics
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evaluating data to assess environmental effects
* analyzing collected data to devise biocriteria.
This document assumes that the reader is generally familiar with these processes, and thus it
does not discuss all of them in depth. Rather, the document emphasizes just one aspect of
biocriteria development: the selection (targeting) of assemblages of biological indicators
(bioindicators) and metrics as a basis for designing and conducting biosurveys. Before
meaningful biocriteria can be developed and implemented, appropriate bioindicators must be
identified and tested.
Bioindicators are species, species assemblages, or communities whose presence, abundance,
and condition is indicative of a particular set of environmental conditions. Bioindicators can
include assemblages of species that are important because they play pivotal roles in wetland
ecosystems or assemblages that show outstanding sensitivity to, or strong correlation with,
particular anthropogenic or natural factors (stressors). Bioindicators can be used both to assess
wetland condition and to help measure and diagnose the actual causes of impairment.
Bioindicators that are cost-effective and sensitive to particular stressors are useful for measuring
attainment of other wetland management objectives as well, such as criteria for successful
restoration of wetlands. The most useful bioindicators are likely to be ones that can distinguish
between natural variation (e.g., phenological changes, annual wet-dry cycles) and anthropogenic
stresses because the latter often mimic (and are overlaid upon) natural stresses, varying only in
terms of relative magnitude.
Once bioindicator data have been collected, efforts are often made to integrate the data into
metrics (e.g., density estimates, species counts) and ultimately to combine multiple metrics into
indices of ecosystem condition. However, in the case of prairie wetlands, our knowledge of the
relative performance of various metrics is severely limited, and no attempts have been made yet
to devise and test indices of wetland integrity. For ecosystems generally, several excellent texts
describe methods for developing and interpreting metrics and indices of condition (e.g., Green
and Vascotto 1978, Gauch 1982, Pielou 1984, Isom 1986, Jongman et al. 1987, Ludwig and
Reynolds 1988, Magurran 1988).
When developing biocriteria, it is seldom practical to address the environmental needs of all
species within a particular assemblage of organisms (Landres 1992). Thus, many past efforts
have focused on identifying functionally similar assemblages (or "guilds") of species and life
stages. "Functionally similar" generally means similar with regard to reproductive strategy, food
habits, and/or habitat preference. Examples of assemblages specific to wetland or aquatic
species are discussed by Merritt and Cummins (1978) (macroinvertebrates), Dean-Ross and
Mills (1989) (bacterial communities), Short (1989) (birds), Boutin and Keddy (1993), and Hills et
al. (1994) (plants). A limitation of the functional assemblage approach is that much of the basic
natural-history information needed to validate the appropriateness of the assemblages and
classifications for prairie wetlands is currently lacking.
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1.2 Document Organization
Biological monitoring (biomonitoring) generally focuses on one or more broad assemblages of
related organisms. For this reason, this document uses common taxonomic nomenclature for
designating assemblages of organisms (algae and microbes, vascular plants, invertebrates,
amphibians, birds) as main section headings. Fish and mammals were not discussed in this
document because of their relatively low diversity in prairie wetlands and the paucity of
information. No attempt was made to give equal coverage to all topics in this document because
availability of data varies greatly. The document provides information on each of the major
assemblages of organisms in separate subsections:
x.1 Ecological Significance and Suitability as an Indicator: This describes why the particular
assemblage is important to a wetland's functioning. That is, the subsection provides a rationale
for using the assemblage as an assessment endpoint. The subsection also summarizes
advantages and disadvantages of using the assemblage as an indicator of the ecological
integrity of wetlands, and it identifies assemblages that are conventionally defined as subsets of
the larger taxonomic assemblage.
x.2 Potential Indicator Metrics: This subsection lists the metrics (measurable aspects that
summarize biological structure or function, e.g., species richness) that show promise as
indicators of the ecological integrity of wetlands when applied to the taxonomic assemblage.
Metrics were included if they had been used previously in prairie wetlands and/or were judged by
the author to show promise (due to sensitivity, cost, and other factors) for reflecting wetland
integrity. These lists of metrics are by no means definitive. Readers should not assume
because a metric is listed, that it has been "proven" by research, and they should be aware that
the listing is not all-inclusive. Many indicators deserve considerably more investigation and fine-
tuning before they are used routinely. Whenever possible, users should obtain assistance from
local wetland scientists when using the indicators to interpret wetland condition.
x.3 Previous and Ongoing Monitoring in the Region: From a review of over 400 publications,
this subsection summarizes studies in the region that have addressed the taxonomic
assemblage and the most common themes among these studies. Some ongoing research (circa
1994) is also noted, but listings are not necessarily comprehensive. This subsection is provided
to help readers understand the relative extent of knowledge about different taxa and stresses.
Understanding the extent of the knowledge base allows users to exercise proper caution in
interpreting statements in this document. Such an understanding also can help focus future
research on important topics that previously have been understudied.
x.4 Response to Stressors: This is the largest of the subsections, and for each major
taxonomic assemblage (primary headings), it compiles and organizes all available prairie wetland
literature according to various stressors (secondary headings) and metrics (tertiary headings).
Information on both anthropogenic and natural stressors is presented together because few
prairie studies have reliably distinguished any differences in the responses of biological
communities to the effects of these. Stressors are not necessarily "bad" for maintaining wetland
resources and functions of interest to humans. Indeed, some degree of disturbance or stress,
whether natural or anthropogenic, is vital to the evolution and sustainability of prairie wetlands,
whereas excessive levels (too much or too little) of a stressor can spell the eventual loss of
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wetland function. Anthropogenic stressors are of special interest because they not only affect
the structure and function of biological communities but also influence an ecosystem's ability to
rebound from natural stresses. They are also, by definition, more amenable to human control.
Information in the stressor subsections describes how each metric (e.g., biomass, richness)
responds to each stress, with the caveat that much of this information is derived only from single,
perhaps unrepresentative, studies. When information is sufficient, this subsection gives
physiological thresholds for impacts occurring at broad taxonomic levels, e.g., the level of salinity
at which most wetland plants are incapable of reproducing.
Stressors discussed in this subsection are factors that are most likely to impair the ecological
integrity of prairie wetlands when present at levels (or times) that differ greatly from their usual
natural occurrence, and belong to the following categories. Six categories of stressors have been
recognized:
x.4.1 Hydrologic Stressors: Changes in levels of surface water or water tables, i.e.,
drought or flood conditions, whether natural or aided by anthropogenic factors, e.g.,
drainage, groundwater withdrawl, global climate change.
x.4.2 Vegetative Cover Conditions: Changes in areal cover and density of vascular
plants, whether natural, e.g., from muskrat consumption, or aided by anthropogenic
factors, e.g., grazing, burning, mowing, herbicide application.
x.4.3 Salinity: Changes in total dissolved solids in the water column, soils, or sediments,
whether natural or aided by anthropogenic factors.
x.4.4 Sedimentation and Turbidity: Physical changes in a wetland's benthic (bottom)
substrate and/or changes in light penetration caused by introduction or resuspension of
living or (especially) non-living matter as aided by either natural or anthropogenic factors,
e.g., tillage, erosion.
x.4.5 Excessive Nutrient Loads and Anoxia: Occurrence of available phosphorus and
nitrogen at greater-than-natural-background levels, usually because of the introduction of
animal fecal material or application of fertilizers, and the resultant spread of anoxic
conditions (i.e., lack of dissolved oxygen) throughout sediments and the water column.
x.4.6 Pesticide and Heavy Metal Contamination: Occurrence of insecticides,
herbicides, fungicides, heavy metals (e.g., mercury), and selenium at greater-than-
natural-background levels usually because of intentional application to crops or leaching
from drained, irrigated, or mined soils. The relative ecological risks of these stressors to
all wetland functions (not just biota) were assessed in an earlier comprehensive review of
the prairie pothole literature (Adamus 1992). Also, it is important to recognize that few
stressors act alone; cumulative interactions among stressors are usually important, and
these interactions are summarized in Section 1.3.
x.5 Monitoring Techniques: This subsection describes techniques, equipment, and general
considerations for sampling the particular assemblage of organisms. This is intended to be a
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general description rather than a prescriptive manual or standard operating procedure.
Information is intended to be sufficient to allow users to make choices among various types of
equipment and protocols.
x.6 Variability and Reference Points: This subsection summarizes what is known about the
spatial and temporal variability of each metric, e.g., the degree to which species richness
changes within a wetland, among wetlands, within a year, and among years. Also, this
subsection summarizes published maximum numeric values or ranges of numeric values for
several metrics, e.g., densities of macroinvertebrates, in order to provide crude reference points
that are useful for planning a monitoring program, calibrating wetland models, conducting realistic
simulations, interpreting monitoring data, evaluating the success of restoration projects, and
(perhaps) defining "optimum" conditions. However, these referenced numeric values are not
necessarily representative of the prairie wetland population generally because the studies from
which they are drawn were not located according to a probability-based sampling scheme.
These numeric values reflect only the wetland that was studied, the season and year it was
studied, and the equipment and techniques used to study it. Typically there is insufficient detail
in descriptions of study areas and methods to permit meaningful comparisons among values or
to distinguish natural variation from anthropogenic effects. Moreover, species richness values
are notoriously difficult to compare because of additional biases introduced by variation in
sample sizes, sampling frequency, and the levels of resolution in identification.
x.7 Collection of Ancillary Data: This subsection describes key variables that affect each
assemblage because biomonitoring data are easiest to interpret when collected simultaneously
with data on other (mostly abiotic) variables.
x.8 Sampling Design and Required Level of Sampling Effort. The level of effort and costs of
sampling depend directly on the number and layout of samples within dr among wetlands as well
as the sampling frequency and duration (i.e., the sampling design). The sampling design that is
most appropriate for a particular objective depends on the desired precision and accuracy. This
subsection summarizes some of the sampling designs used previously to monitor the taxonomic
assemblage in prairie wetlands.
x.9 Summary;The highlights of the section are presented and key findings from the research are
reported in a readily usable format. Another useful feature of this document is the series of
appendices at the end, which list vascular plants (Appendices A and I), invertebrates (Appendix
B), birds (Appendix C), and algae (Appendix H) that occur in prairie wetlands. These lists are not
comprehensive; they primarily include species that were identified in the literature as being
numerically or functionally dominant in at least one prairie wetland during at least one sampling
period. The appendices, A - O, were prepared in similar format so they can be linked and cross-
referenced using commercially available database software if users so desire. They have
several uses. First, they can be a source of ideas for candidate species for laboratory toxicity
testing; thus, bioassays that are used to help establish water quality criteria would be realistic
because they would be run on species indigenous to the wetland type and region. Second,
vegetation information in the appendices could be used as an aid in classifying wetlands during
the development of State water quality criteria. Third, the information can be used to help
develop site-specific criteria, e.g., as an information source for the "recalcitration" or "resident
species" procedures described in USEPA's water quality program guidance (USEPA 1991).
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Fourth, the lists can serve as an aid in linking species composition with wetland condition. For
example, users can compare the species they find in a particular wetland (e.g., a reference
wetland or other wetland for which a State is developing a "wetland profile") with species listed in
the appendices. Then, by noting in the appendices the conditions usually associated with those
species, users can make inferences about the ecological integrity of the wetland and the possible
identity of its stressors. As a result, the appendices help fulfill the recommendation of Smith
(1991) for developing "natural community databases.. .for evaluation of changes in plant
community structure to determine the biotic integrity of specific habitat types," and serve as a
foundation for a "habitat requirements approach" to biocriteria development as demonstrated in
the Chesapeake Bay by Dennison et al. (1993).
1.3 Cumulative Effects of Stressors
When symptoms of change are noted in prairie wetlands, it is not always possible to attribute the
symptoms to a single stressor (i.e., an agent of stress) because many stressors in prairie
wetlands act in concert or manifest themselves similarly (Larson 1994). Thus, if species
composition, richness, density, biomass, or other metrics are to be interpreted unambiguously
and used as a basis for biocriteria, it is important to understand which stressors are most likely to
influence each other or exert a similar influence on a particular metric. The following examples
are intended to further the understanding of the most frequently encountered interactions:
• Hydrologic stressors can aggravate or mitigate the effects of several other stressors.
Specifically, drought (or water level drawdown) aggravates the effects of salinity, turbidity,
excessive nutrient enrichment, and contamination (chemicals within a wetland become
concentrated and bottom sediments are more likely to be disturbed by wind mixing).
However, floods (or water level increases) also can decrease salinity in wetlands (Neill
1993). Floods can increase turbidity and nutrient enrichment in wetlands if they deliver
chemicals and sediments to the wetland via runoff and groundwater input.
• Changes in vegetative cover are almost always the result of changes in hydrology,
salinity, sedimentation/turbidity, nutrient enrichment, and/or contaminants. Droughts can
decrease cover by allowing greater access to the center of usually flooded wetlands by
vehicles, livestock, and fire, or can increase cover in the long term by increasing the
dominance of "drawdown" species (plants whose germination depends on periodic
absence of water or shallow conditions). Floods usually decrease cover by drowning
rooted wetland plants.
• Turbidity can increase the toxicity of some herbicides (Hartman and Martin 1984, 1985)
but can reduce the availability of others. Excessive sedimentation can increase the
frequency of drought experienced by a wetland by decreasing wetland depth and isolating
the wetland substrate from the water table. Flooding can increase, however, in
downslope wetlands as water is displaced to these wetlands.
Thus, users who desire to fully know, for example, the biological effects of hydrologic alteration
(or natural hydrologic cycles) will remember to look not only in the hydrologic stressor subsection
of this document, but also in subsections on salinity, excessive nutrient enrichment,
sedimentation and turbidity, and pesticide and heavy metal contamination.
6
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Figures 1-4 also illustrate some of these relationships, and a more complete analysis can be
derived from the qualitative models of prairie wetlands detailed by Adamus (1992).
7
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Water level
Salinity
Tillage
Water
persistence
Germination of
dormant emergent
seeds / x
Benthlc
Invertebrates
Submersed
plant cover
Diversification of
monotyplc
vegetation
stands
Transport of seeds and
Invertebrates Into
wetlands
Use by waterblrds and other
consumers
Figure 1. Paths of effects that can result from increases in water levels above the long-term annual norm
in a prairie wetland.
+ = increase in the variable, - = decrease in the variable, 0 = no change in the variable
This diagram simplifies the processes involved. The extent and actual probability of these effects occurring
may depend partly on the wetland type (e.g., semipermanent vs. temporary), initial condition (e.g., the point
in a long-term wet-dry cycle the wetland is currently in), seasonal timing, presence/absence offish, and
characteristics of the specific water level process that trigger the effects (e.g., the type, frequency, duration,
intensity, and timing of water level changes). See text for citations of supporting literature.
8
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Emergent Vegetation
percent cover
Algae
(except epiphytic)
Shoreline
erosion
Light
penetration
Invertebrate
attachment and
grazing area
Zooplankton
Aeration of
water column
Turbidity
Macroinvertebrates
Figure 2. Paths of effects that can result from decreases in the density and percent cover of emergent
vegetation in a prairie wetland.
+ = increase in the variable, - = decrease in the variable
This diagram simplifies the processes involved. The extent and actual probability of these effects occurring
may depend partly on the wetland type (e.g., semipermanent vs. temporary), the initial condition (e.g., the
point in a long-term wet-dry cycle the wetland is currently in), seasonal timing, presence/absence offish,
and characteristics of the specific vegetation removal process that trigger the effects (e.g., the type,
frequency, duration, intensity, and timing of herbicide application, grazing, fire, water level increase, etc.).
See text for citations of supporting literature.
9
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¦aval
Zooplankton
Nutrient
loading
Phytoplankton
growth
Emergent
plant bloraass
Light penetration
of water column
accumulation
Utter
column
Ufa by waterbirds
and other consumers
Submersed plants,
epiphytic and
eplpelle algae
Figure 3. Paths of effects that can result from increases in sediment deposition in a prairie wetland.
+ = increase in the variable; - = decrease in the variable
This diagram simplifies the processes involved. The extent and actual probability of these effects occurring
may depend partly on the wetland type (e.g., semipermanent vs. temporary), the initial condition (e.g., the
point in a long-term wet-dry cycle the wetland is currently is), seasonal timing, and characteristics of the
specific sediment deposition processes that trigger the effects (e.g., the type, frequency, duration, intensity,
and timing of deposition). See text for citations of supporting literature.
10
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Deposition
Benthlc
Invertebrates
Access Into wetland
Mobility of burled
Plant species
unpalatable to cattle
waterblrds and
contaminants
other consumers
Figure 4. Paths of effects that can result from increases in nutrient loading to a prairie wetland.
+ = increase in the variable; - = decrease in the variable; 0 = no change in the variable
This diagram simplifies the processes involved. The extent and actual probability of these effects occurring
may depend partly on the wetland type (e.g., semipermanent vs. temporary), overall water chemistry, initial
condition (e.g., the point in a long-term wet-dry cycle the wetland is currently in), seasonal timing, and
characteristics of the specific nutrient loading process that trigger the effects, such as the frequency,
duration, intensity, and timing of increased inputs (e.g., more fertilizer or greater seasonal runoff) or
increased mobilization of nutrients previously immobilized in sediments. See text for citations of supporting
literature.
11
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1.4 Glossary, Abbreviations, and Place Names
For the sake of maintaining brevity, this document uses broadly certain terms that conventionally
have a more narrow definition;
basin. In the prairie landscape, a topographic depression that normally lacks a
permanent natural connection to larger rivers or lakes and contains a wetland, at least
during the first weeks of the growing season.
biological criteria (biocriteria). Standards that use the condition of an organism or
assemblage of organisms to describe the ecological integrity of unimpacted, least-
impacted, or representative ("reference") areas. Biocriteria may be expressed in numeric
or narrative terms.
biomarker. A measurement, generally of biological tissue or physiological byproducts,
that indicates previous or ongoing organism response and/or exposure to general or
specific environmental stresses (Huggett et al. 1992).
cover ratio. The percent open water in a wetland, where "open water" is any part of the
wetland that lacks a canopy of emergent vegetation and contains water during at least
part of the growing season.
density. The number of individuals per unit area or volume.
ecological (or biological) integrity. The condition or "health" of an area as defined by
comparison of community structure and functions to those of unimpacted, least-impacted,
or representative ("reference") areas.
macrophytes. Plants generally visible to the unaided eye, including vascular plants and
some of the larger algae.
permanent basins. Prairie pothole depressions that retain surface water throughout the
year, as classified by Stewart and Kantrud (1971), and that contain wetland vegetation
and soils. Used synonymously with "permanent (or permanently flooded) wetland."
seasonal basins. Prairie pothole depressions that retain surface water for much of the
growing season (e.g., sometimes into July), as classified by Stewart and Kantrud (1971),
and that contain wetland vegetation and soils. Used synonymously with "seasonal (or
seasonally flooded) wetland."
semipermanent basins. Prairie pothole depressions that retain surface water
throughout most of the growing season, as classified by Stewart and Kantrud (1971), and
that contain wetland vegetation and soils. Used synonymously with "semipermanent (or
semipermanently flooded) wetland."
temporary basins. Prairie pothole depressions that retain surface water only during the
first weeks of the growing season, as classified by Stewart and Kantrud (1971), and that
12
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contain wetland vegetation and soils. Used synonymously with "temporary (or
temporarily flooded) wetland."
species composition. The identity and relative abundance of species in a biological
community. Used synonymously with "community composition."
species richness. The number of species (or any other taxonomic denomination) per
sample, per wetland, per number of individuals. Used synonymously with "taxa richness"
and "family richness" because many data sets combine a variety of levels of taxonomic
resolution.
Throughout this document several place names and abbreviations are used without elaboration
to maintain brevity. These are defined as follows:
Cottonwood Lakes. The Cottonwood Lakes Long-Term Environmental Monitoring site, a
large and varied complex of prairie pothole wetlands located in Stutsman County, North
Dakota, in which data on waterfowl, climate, and vegetation dynamics have been
collected for decades.
Delta Marsh. A large lacustrine marsh in south-central Manitoba, Canada, a portion of
which has been used by the Marsh Ecology Research Program (MERP) of Ducks
Unlimited to conduct over 80 multi-year experiments using ten, 5-ha marsh cells, each
with independent water-level control.
EMAP. USEPA's Environmental Monitoring and Assessment Program, a long-term
program intended to regularly monitor the ecological condition of ecosystems (including
wetlands) throughout the Nation using a probability-based sample design, and generate
estimates of status and trends in ecosystem condition by region and ecosystem (e.g.,
wetland) type.
NPSC. The Northern Prairie Science Center, the Federal research facility in Jamestown,
North Dakota, that has investigated wetland ecology of the prairies for decades, run by
the National Biological Service (formerly by the US Department of the Interior, Fish and
Wildlife Service, FWS).
1.5 Statistical Analyses: Objectives and Methods
One objective of this project was to estimate the probable number of samples needed to satisfy
various purposes. To achieve these estimates, eight existing data sets were obtained from
investigators in the region and were analyzed statistically.These data sets were selected based
on their availability. Two of the data sets pertained to wetland plants, four to
macroinvertebrates, and two to birds. Detailed descriptions of the data sets are found in
Appendix L. Data were analyzed to address two questions of practical relevance to sampling
prairie wetlands:
13
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1. How many samples need to be collected to find 50%, 75%, 90%, 95%, and 99% of the
taxa found in the full suite of samples collected by a particular study? (asymptotic
richness)
2. Given a particular number of samples containing information on biomass, number of
individuals, or number of taxa, what size difference between two means (e.g., from
different wetlands or different dates) can be detected at usual levels of statistical
significance? (power of detection or "precision")
Information of this type is essential to estimating costs and levels of effort required for monitoring
programs. The results are presented in Sections 3.8, 4.8, and 6.8.
Information on asymptotic richness is needed to help determine if a population has been
oversampled or undersampled with regard to detecting most taxa that are present. To address
this objective, we used a bootstrap subsampling technique to quantify species accumulation
rates (Szaro and King 1990). This reflects a basic principle of diminishing returns: as one
samples a population, the number of taxa in samples at first rises rapidly, but then levels off as
additional samples add only a few new taxa.
A computer program was written in SAS to estimate species accumulation rates. The program
first tallied the number of taxa in the entire data set. Samples that had been collected were then
selected randomly without replacement until the number of taxa they cumulatively contained
reached one of the specified points (50%, 90%, 95%, 99% of species total from all samples).
However, the number of samples needed to reach a particular point depended on the order in
which the samples were combined. Thus, the random selection process was repeated 100
times, and the median, mean, and standard deviation of sample sizes estimated from the 100
runs were used to represent requisite sample size. Two assumptions were made when
implementing the statistical analysis: 1) 100 runs were sufficient to stabilize the estimates of
requisite sample size, and 2) the number of samples originally collected was sufficient to capture
nearly all taxa in the target population.
Addressing the power of detection at first seemed straightforward, inasmuch as many papers in
the published literature (and recently, several software programs) have defined power of
detection through use of elementary equations and simplifying assumptions (Downing 1979,
Schwenneker and Hellenthal 1984, Canton and Chad wick 1988, Riddle 1989, Downing 1989,
Niemi et al. 1993). Although coefficients of variation calculated for all data sets (Appendix N)
might have been used in such equations, the use of simplified approaches limits the generality of
the results. Thus, a more involved approach (Components of Variance) was used. Variance
component estimates for random factors were calculated using the SAS MIXED procedure for
fitting mixed linear models (experimental designs having both fixed and random effects). The
estimated variance components were used as estimates of the population variances in the
following equations. Estimated variances were obtained by incorporating the variance
component into the expected mean square for the random effect of interest. We made statistical
comparisons only within data sets, not between them, e.g., to determine which taxon is least
variable, or which metric varies the most seasonally. Further, we did not transform any values or
test assumptions that routinely underlie the analysis of variance (ANOVA). The analyzed data
represent samples that are subject to uncontrolled influences such as weather. In the future, it
14
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might be informative to use data collected over several years as independent experiments to
address the issue of the effect of temporal variation on the estimates. We approximated the
degrees of freedom (c^) for obtaining F values by using Satterthwaite's effective df (Steel and
Torrie 1980):
Effective df,
(«,2 ' n,)2
(s22 / n2)2
("r1)
(n2"1)
where dfA = effective degrees of freedom for the error term,
s2 = estimated variance with subscripts to identify the sample,
and
n = sample size with subscripts to identify the sample.
We used two methods to calculate precision (i.e., detectable difference) over a range of sample
sizes. The first equation provides an "optimistic" estimate for the difference between two means
that is detectable at a given sample size. The second equation is more conservative and takes
into account the assurance that the study has the desired precision (Steel and Torrie 1980):
P = t
\ W2„>,
n \
s2
2— ,
rn
where P1n = optimistic precision estimate for the nth sample,
t = value from f-table,
df = effective degrees of freedom for the error term for the nth
sample,
s2 = estimated variance,
r = number of replicates for the nth sample, and
a = probability of a Type I error (falsely rejecting the null
hypothesis).
Precision is defined as the absolute difference that is detectable. Thus, Fp provides greater
assurance that the difference between means in future experiments will be no greater than the
estimated ability to detect the specified difference in the means:
Pl, F(M'vWi,>.
2— .
r
15
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where Pin = conservative precision estimate for the nth sample.
F = value from F-table,
df = effective degrees of freedom for the error term,
t = value from f-table,
s2 = estimated variance,
r = replicate,
a = probability of a Type I error (falsely rejecting the null
hyopthesis), and
(5 = probability of a Type II error (accepting a false null
hypothesis).
We present the results of applying the equations over a range of n values. Specifically, P? and
P2 were calculated by varying the replicates and subsequent degrees of freedom over a range of
values. Curves of the calculated values of P1 and P2 were plotted on the same graph for each
random-effect variable from each of six data sets (Figure 5). The upper curve on each plot
represents the conservative precision estimates and the lower curve the more optimistic
estimates. These curves make it possible to assess relationships between the number of
replicates and the precision estimate. Because of the large number of curves generated, results
have been summarized tabularly (Appendix M).
Readers should understand that the values presented in this document, while generally realistic,
are not intended as exact estimates of requisite sample sizes. The requisite number of samples
or resultant levels of precision could differ if sampling is done according to a design or under
conditions (e.g., weather, season, wetland type, equipment) that differ from those upon which
these estimates were based.
16
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Precision Estimates for Sample Sizes of 2 to 100
W
CO
3
.E
"O
0
*>
o
'-O
c
c
0
i_
0
o
a=
i_
b
0
0)
E
_Q
3
CO
z
o
c
0
M—'
CO
0
0
D
2
90-
80
70
60
50
40
30
20
10
0
I | I I I—I | I i I I | I I I I—| I I I
10 20 30 40 50
Sample Size
I I ! I | I I I I
60
70
80
I I I I | I I
90
100
Figure 5. The difference between two means (of the number of Conchostraca in sweep nets) that can be
detected by various sample sizes.
The upper curve is based on an equation that estimates precision conservatively, whereas the lower one
estimates precision optimistically. See explanation on the preceding page. Results from curves for all major
taxa and metrics are compiled tabularly in Appendix M.
17
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2. Algal and Microbial Communities as Indicators of Prairie Wetland Integrity
2.1 Ecological Significance and Suitability as an Indicator
In prairie wetlands, four algal assemblages are commonly defined: phytoplankton (algae
suspended in the water column), metaphyton (unattached and floating or loosely associated with
substrata), and two assemblages of attached algae, namely epiphytic algae (attached to plants)
and benthic algae [attached to sediments; however, the more specific meaning of benthic for
wetlands is epipelic (attached to substrate rocks) (Crumpton 1989)]. The regional pool of
species is probably largest for the benthic and phytoplanktonic forms, although individual
samples of communities are usually taxonomically simple. Microbes prevalent in prairie wetlands
include bacteria, viruses, yeasts, and microscopic fungi.
Because they form the basis of the food chains, algal and microbial communities are of critical
ecological importance in prairie wetlands. The prairie wetland animal community depends on
solar energy that has been converted by photosynthesis to biomass by algae or vascular plants
(Neill and Cornwell 1992). The algae and vascular plants provide a substrate and food source
for microbial communities, and the microbes in turn are consumed extensively by a wide variety
of detrivorous invertebrates. Algae, and to a lesser degree vascular plants (Campeau et al.
1994), are also consumed directly by invertebrates. In some prairie wetlands, the levels of algal
and microbial productivity approach those of vascular plants.
The relative importance of microbes vs. algae as supporters of prairie wetland invertebrates
depends on interactions between microbes and algae, season and water regime, and chemistry
of a particular wetland. Based on a statistical analysis of invertebrate biomass in submersed
plant beds in 11 eastern Canadian lakes, Lalonde and Downing (1992) concluded that aquatic
invertebrate biomass was highly correlated with the biomass of littoral phytoplankton, epiphyton,
and vascular plants, a situation also noted in prairie wetlands (Murkin et al. 1991). However,
Murkin et al. (1992) suggest that algae's role in determining the horizontal distribution of
invertebrates within a prairie wetland might not be as great as it would seem. They based this
conclusion on their failure to find spatial overlap between maximum density of acroinvertebrates
and maximum epiphytic biomass (measured as chlorophyll) in Delta Marsh. Similarly, in most of
the Cottonwood Lake wetlands that LaBaugh and Swanson (1993) studied, the seasonal and
annual changes in microinvertebrate abundance were not statistically related to the abundance of
algae. They speculated that microinvertebrates might be depending more on microbial biomass.
Ducks which feed on wetland invertebrates also do not congregate in wetlands that have the
most algae, although ducks that use algae-rich wetlands were found in a Saskatchewan study
(Gloutney 1993) to spend more time feeding. Using isotope ratios to investigate food webs in
Delta Marsh, Neill and Cornwell (1992) found that emergent vascular plants and the algae and
microbes attached to them, rather than submersed macrophytes or metaphyton, were the most
important sources of organic matter to the invertebrate consumers that were most abundant
during June.
In addition to their value as food for wetland animals, algae influence invertebrates and vascular
plants in at least five other ways: 1) phytoplankton and metaphyton reduce light penetration of
the water column; 2) epiphytic and benthic algae mediate the levels and flux of nutrients,
18
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contaminants, and oxygen across ecotones (e.g., sediment-water column) and plant surfaces; 3)
all algae (when they respire, die, and decompose) can diminish dissolved oxygen in the water
column and sediments; 4) some blue-green algae fix (add) gaseous nitrogen to the water column,
thus enriching wetlands; 5) some blue-green algae are toxic to other organisms. Any of these
phenomena can alter the basic habitat structure of prairie wetlands, and thus, the populations of
invertebrates, amphibians, fish, and waterbirds. Moreover, microbial populations can alter the
structure of habitat available to invertebrates because they are the major assemblage
responsible for decomposition, and decomposition is usually the primary factor responsible for
offsetting excessive accumulation of plant litter in prairie wetlands. Excessive plant litter
accumulation with associated increase in microbial densities can create oxygen deficits in
sediments and surface waters (Baird and Mathias 1979, Barica 1984, Barica et al. 1987),
conditions that can impair seed germination, damage invertebrate communities, and further
retard decomposition.
Certain microbial communities also can detoxify some chemicals. For example, microbes
associated with wetland plants can detoxify some synthetic organic compounds (Hodson 1980)
such as pentachlorophenol (Pignatello et al. 1985), the herbicides glyphosate (Goldsborough and
Beck 1989) and atrazine (W. Crumpton, personal communication, Iowa State University, Ames,
I A), as well as detergents (Federle and Schwab 1989).
Particular microbial communities also can reduce, via the process of denitrification, the
overenrichment of wetlands. This is important at landscape and regional scales not only
because wetlands are among the most effective ecosystems for removing nitrogen (Groffman
and Tiedje 1989) but also because they intercept much of the runoff and groundwater before it
reaches larger, more permanent waterbodies. Protection and enhancement of nitrogen removal
functions of wetland microbial communities is important to maintain and restore important public
uses of downslope lakes, rivers, and aquifers. In semipermanent wetlands of Iowa, Davis and
van der Valk (1978b) reported removal of 86% of the runoff inputs of nitrate and 78% of the
inputs of ammonia. Two open wetland complexes in North Dakota removed 13% and 58% of the
tributary nitrate, as compared to a drained wetland complex in which there was a > 10-fold
increase in nitrate (Malcolm 1979). An unvegetated wet basin in South Dakota that was loaded
with municipal wastewater removed 1765 kg N/ha (White and Dornbush 1988). On a regional
basis, Jones et al. (1976) in northwestern Iowa found that among 34 watersheds, those with a
large percentage of land as wetlands had less nitrate in streamflow than those with a small
percentage of wetlands.
Denitrification rates may be equal or greater at the beginning and end of the growing season than
during mid-summer (Christensen 1985, Myrold 1988, Zak and Grigal 1991). Thus, denitrification
functions in wetlands may be of greatest value in removing nitrate during years when runoff
inputs occur early or late in the growing season. However, if runoff resulting from the spring
melting of snow surrounding wetlands occurs prior to ice-out in wetlands, the runoff flows under
the wetland ice, purging the basins of anoxic, ammonia-rich water which can subsequently be
released into receiving waters (if seasonal connections exist) without being substantially
denitrified, thus causing water quality problems (NDDHCL 1990). This adverse impact might be
more likely to occur in landscapes dominated by semipermanent and permanent basins because
they tend to remain frozen longer than temporarily flooded wetlands.
19
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As indicators of wetland integrity, microbial communities have several characteristics usually
considered to be advantages (Adamus and Brandt 1990):
• tight linkage to fundamental processes (e.g., decomposition, denitrification, respiration)
• easily collected and transported samples
• immediate response to contamination
• measurable in wetlands year-round and in wetlands that lack surface water
• sensitive to presence of some contaminants and available assay protocols (e.g., Ames
test, Microtox test).
In adddition, USEPA protocols are available (but may need adaptation to wetlands), and
sufficient information is published to identify a few "indicator taxa" that clearly are associated with
particular stresses.
Characteristics usually considered disadvantages when using microbial communities to
determine wetland condition include:
• lack of an identifiably stressor-specific response
• laborious and slow identification (plate culture); process measurements difficult to
interpret with regard to ecological significance
• rapid turnover of individuals of most species requires frequent sampling; microbes do not
integrate conditions over time very well
» naturally great micro-spatial variation
Moreover, microbial communities are impractical for detecting bioaccumulation.
Algae have several characteristics usually considered advantages for monitoring ecosystem
integrity (Adamus and Brandt 1990):
• pivotal in food webs, and tight linkage to fundamental processes (e.g., photosynthesis,
respiration)
» rapid reproduction rates; short life cycles;sensitivity to short-term impacts
• generally immobile and thus reflect conditions at a particular site; useful for in situ
exposure assessments and whole-effluent bioassays
• decay-resistant remains (diatom frustules and pigments) provide a means for establishing
historical reference conditions in a wetland.
20
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For researchers, standardized collection procedures are available and impact the monitored
wetland only minimally. Also, tolerances and indicator value are well-known, particularly with
regard to nutrients. Finally, USEPA has protocols, which may need modification to wetlands, that
are available for sampling both structure and function of algal communities.
Characteristics usually considered disadvantages when using algae to determine wetland
condition include:
rapid turnover, requiring frequent sampling
relative insensitivity to heavy metals and insecticides (Hellawell 1984)
need for laborious identification of many taxa
complicated interpretation of spatial patterns because of drifting cells of unattached
species
difficulty in linking responses of many algal taxa to a specific stressor
poorly documented linkages to other components of the food chain in prairie wetlands.
2.2 Potential Indicator Metrics
Various measurements and metrics can be applied to algal or microbial samples, for use in
characterizing conditions in reference wetlands, identifying the relative degree of past
disturbance of a prairie wetland or assessing the current inhibition of key processes:
• richness of species and functional groups (per unit volume of sample, or per thousand
randomly chosen individuals)
• number and biomass of cells per unit sample; chlorophyll-a (C55H72MgN405) concentration
per unit volume
• proportional density and richness of species reputedly tolerant to a named stressor
• degree of temporal variability in richness, density, and/or biomass (expressed as
coefficient of monthly or weekly variation)
• month during which maxima of richness, density, and/or biomass occur (generally, or for
particular groups, e.g., blue-green algae, benthic algae)
• litter decomposition rate (or less precisely, the depth of fibric litter and proportional weight
of dead vascular plant vegetation), used as an indicator of microbial activity
• denitrification enzyme activity (DEA)
21
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The specific ways some of these metrics have been or could be interpreted as indicators of
stressed conditions are described in Section 2.4.
In situ methods of measuring algal production (e.g., uptake rates of carbon radioisotope, oxygen
production) are not considered here. These methods are described generally by Stevenson and
Lowe (1986), and protocols applicable to prairie wetlands are given in Hooper and Robinson
(1976), Britton and Greeson (1988), and Gurney and Robinson (1989b). Estimates of algal
production are strongly influenced by the choice of method used to estimate production, and
temporal variability of measurements can be enormous.
2.3 Previous and Ongoing Monitoring in the Region
Field studies of algae have been published in at least 19 papers covering at least 124 prairie
wetlands. A survey of five wetlands by LaBaugh and Swanson (1988) sampled only the water
column; surveys of about 50 wetlands by Kling (1975) and two wetlands by Shamess et al.
(1985) included benthic and epiphytic algae as well. Information on species composition of
metaphytic, epiphytic, and benthic algal communities also is very limited. Apparently no
published studies have described the species composition of microbial communities in prairie
wetlands.
Most algal and microbial studies in the region have considered only the physiological processes
(e.g., respiration, production) associated with algae or microbial communities in general. Almost
without exception, these studies have been completed during a single year's growing season,
and in nearly all cases in permanent prairie wetlands or lakes. In a few cases (Barica 1975,
Hickman and Jenkerson 1978, Barica et al. 1980), these process-based studies have incidentally
mentioned the dominant taxa that were found. Identified assemblages of microbes in the region
apparently have not been cultured in the laboratory to determine their functional characteristics,
e.g., potential as denitrifiers or their role in methanogenesis. Decomposition processes have
been measured in a few instances, generally without identifying the microbial taxa responsible
(e.g., Davis and van der Valk 1978a,b; Neely and Davis 1985b; Wrubleski et al. 1993). Two
studies (LaVeglia and Dahm 1974, Johnson 1986) measured process rates in the microbial
communities of a few prairie wetlands using various physiological and chemical indicators;
respiration, monophosphatase activity, electronic transfer system (dehydrogenase) potential,
glucose mineralization, ammonium production, nitrification, and sulfur oxidation. Species
responsible for the measured processes were not identified.
Montana's water quality agency (State Department of Health and Environmental Sciences)
currently uses epiphytic and benthic algae (specifically, diatoms) on a trial basis as an indicator
of the condition of about five prairie wetlands that represent varying degrees of potential
impairment. The information collected on species composition could yield valuable insights into
sampling variability and habitat relationships. Ongoing research on algae in prairie wetlands by
the National Hydrologic Research Institute in Saskatoon, Saskatchewan, is focusing on transfer
of algal energy to zooplankton and effects of herbicides on algae (Appendix K).
22
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2.4 Response to Stressors
The following subsections describe responses of the algal and microbial communities to
hydrologic stressors, vegetative cover conditions, salinity, sedimentation/turbidity, excessive
nutrient loads/anoxia, and pesticide and heavy metal contamination.
2.4.1 Algal and Microbial Communities as Indicators of Hydrologic Stressors
Declining water levels in a wetland often raise the water temperature and concentrate dissolved
nutrients that exist in the water column as well as mobilize some of the nutrients from shoreline
sediments and from plant litter that has become exposed. Such increases in nutrient
concentration sometimes cause algal blooms in the remaining surface water (Schoenberg and
Oliver 1988). Inundation can have the opposite effect, sometimes diluting and chemically binding
nutrients to bottom sediments, cooling the water column, increasing algal competition with
vascular plants, and thus reducing biomass of some algal taxa. However, inundation typically
increases the leaf surface area available for colonization by some algae and provides increased
opportunities for dispersal of some algal species into and out of a wetland.
Species Composition
Changes in the density of phytoplankton—as compared with metaphytic, epiphytic, and benthic
algae—might suggest that water levels in a wetland have changed within recent days or perhaps
weeks. Specifically, recent (within a year) inundation often decreases the ratio of phytoplankton
biomass (per unit area) to biomass of the other algal community components (Hosseini and van
der Valk 1989a,b). This occurs as higher water levels reduce canopy coverage of vascular
plants, increase light penetration and the area of substrate available for colonization, and dilute
the levels of nutrients that otherwise would support the proliferation of rapidly growing
phytoplankton (Hooper and Robinson 1976, Gurney and Robinson 1988). Increased water levels
can also differentially reduce phytoplankton density and productivity by creating habitat space for
zooplankton, which graze selectively on the phytoplanktonic forms of algae. During the first year
after flooding of one wetland, the biomass and productivity of both metaphyton and attached
algae increased, whereas only the metaphyton continued this increase into the second year
(Hosseini and van der Valk 1989a,b). Long-term changes in wetland water regimes might be
inferred by collecting diatom remains using sediment cores (see Section 2.5.1) and determining
what proportion of the found species (or pigments) are ones that are characteristically associated
with drought or wet conditions, as inferred from salinity tolerances given for 143 taxa by Fritz et
al. (1993) and for 62 taxa by Blinn (1993).
Species Richness
Data are insufficient to characterize the response of algal richness to changes in water levels of
prairie wetlands. Sampling of five wetlands in the Cottonwood Lakes area identified 245 taxa in
the two semipermanent wetlands, 159 in two seasonal wetlands, and 98 in a saline wetland
(LaBaugh and Swanson 1988). The collective list from all five wetlands totalled 306 taxa, and
80% of these were present in the two semipermanent wetlands, 52% in the two seasonal
wetlands, and 32% in the saline wetland.
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Density, Biomass, and Productivity
Phytoplankton density arid productivity can be lower in temporarily flooded wetlands than in
persistently flooded prairie lakes (Robarts et al. 1995), Because flooding creates additional
habitat space for both planktonic and epiphytic algae, the total biomass and production of algae
in a wetland can increase in response to flooding, even if production of phytoplankton per unit
area drops (Hosseini and van der Valk 1989a,b). Thus, total algal biomass or productivity of a
prairie wetland, even if it could be measured accurately, would be a confusing indicator of water-
level change.
Decomposition
One of the few studies conducted on this topic in a prairie wetland (Wrubleski et al. 1993) found
that leaching and decomposition of aboveground litter was more rapid in six of eight plant
species in a flooded wetland than in a dry wetland; only the litter of cat-tail (Typha) and common
reed (Phragmites) showed no difference between wet and dry treatments. The depth of flooding
was inconsequential, but there were important differences in decay rate among taxa, with
Chenopodium decomposing the slowest and Scolochloa and Scirpus lacustris the fastest. The
nutrient content of litter from Phragmites actually increased over time, probably indicating the
flood-related development of a rapidly growing microbial and epiphytic algae community, a
phenomenon noted in other prairie wetlands as well (e.g., Neely and Davis 1985b). The relative
extent of plant litter, as estimated coarsely from low-altitude photographs, was found to be a poor
indicator of past hydrologic conditions in one prairie wetland (van der Valk and Squires 1992).
Other Microbial Processes
Hydrologic conditions affect denitrification, a microbial process that is of considerable importance
because it improves water quality by removing excessive amounts of dissolved nitrogen.
Although effects of changing water levels on denitrification have not been studied in prairie
wetlands, two recent landscape-scale studies of Saskatchewan fields (Elliott and de Jong 1992,
van Kessel et al. 1993) highlight the key role of soil moisture:
Soil water content was the most dominant factor controlling denitrification activity,
followed by the concentration of ammonium, total soil respiration, and nitrate (van
Kessel et al. 1993).
Measurements of denitrification in a South Dakota wetland soil indicated that conditions of less
than 22% volumetric soil moisture completely inhibit denitrification (Lemme 1988). A wetland
does not have to be exposed to runoff for very long to reach these moisture levels and remove
nitrate (i.e., convert and export nitrogen as a gas). Microbial communities that support
denitrification develop rapidly in newly created wetlands (Duncan and Groffman 1994).
It remains unclear under which water regime denitrification is greatest. For example, Kantrud et
al. (1989) state, "It would seem that temporary and seasonally flooded wetlands would be
especially efficient in removal of excess nitrogen." There are at least two reasons why this might
be so. First, fluctuating water levels that typify temporary and seasonal wetlands might be
expected to enhance denitrification so long as 1) anaerobic conditions still occur, 2) moisture
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levels in the upper soil layers are not too severely depleted (i.e., pore space is 30%-60% water-
filled; Linn and Doran 1984, Lemme 1988), 3) carbon supplies also are not limiting (Fraser et al.
1988), and 4) salinity conditions are not extreme. Second, soil temperature might be expected to
be warmer in temporary and seasonal wetlands during much of the year because of their shallow
depths.
However, other logic suggests that semipermanent and permanent wetlands might be more
effective than temporary and seasonal wetlands for removing nitrate. Because semipermanent
and permanent wetlands are usually groundwater discharge or flow-through systems, they are
less susceptible to drought, and by definition, they remain saturated and thus favorable to
denitrification for longer periods. Prolonged drought in temporary wetlands not only results in
moisture deficits inhospitable to denitrifying microbes but also can result in loss (via
mineralization) of organic matter essential for sustaining denitrifiers. Organic matter content of
soils in semipermanent and permanent wetlands generally seems to be greater than in temporary
and seasonal wetlands [however, Loken (1991) reported less organic matter in soils of
semipermanent groundwater discharge wetlands; he attributed this to high salinity of these
basins inhibiting their productivity].
2.4.2 Algal and Microbial Communities as Indicators of Changes in Vegetative Cover
Species Composition
Algae and microbes respond quickly and persistently to changes in vegetative cover. As
grazing, mowing, fire, and other factors decrease the amount of plant litter in prairie wetlands, the
composition of algal and microbial communities can shift from characteristically epiphytic species
to benthic or phytoplanktonic species. Long-term changes in plant cover of a wetland might be
inferred by collecting diatom remains using sediment cores (see Section 2.5.1), and determining
what proportion of the found species are ones that are characteristically shade-tolerant.
Species Richness
Species richness of algal communities sometimes declines with removal of vegetative cover
(Seelbach and McDiffett 1983).
Density and Biomass
Algal and microbial biomass and density can either decrease (Rabe and Gibson 1984) or
increase (Seelbach and McDiffett 1983) as vascular plant cover becomes sparser.
Decomposition, Other Microbial Processes
Decomposition rates can be retarded somewhat by wetland plant litter that has accumulated
excessively (Godshalk and Wetzel 1978). However, microbial density and denitrification are
generally greater in unplowed prairie soils than in plowed soils where plant litter is mostly
removed (Linn and Doran 1984). Some rooted plants are capable of enhancing microbial
populations and processes by 1) transferring nitrates from the sediment into aboveground tissues
and eventually into the water column; 2) providing a carbon substrate (e.g., plant litter);
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3) speeding the diffusion of oxygen (via roots) into otherwise anaerobic subsurface zones,
especially during mid-growing season; and 4) increasing diurnal and seasonal fluctuations in the
water table, and consequently the oxidation status, as a result of evapotranspiration. Densities
of denitrifying microbes might be greatest where soil organic matter reaches a maximum just
below the soil surface, but above the depth limit of the root zone (Parkin and Meisinger 1989). In
this zone, impeded lateral flow increases the time available for nitrate loads to interact with
prolific microbial populations present in the surrounding root masses.
2.4.3 Algal and Microbial Communities as Indicators of Wetland Salinity
Species Composition
In the prairie region, a salinity threshold of about 1000 mg/L separates algal species that are
relatively salt-tolerant from ones that are not (Prepas and Trew 1983). Long-term changes in
salinity of a wetland might be inferred by collecting diatom remains using sediment cores (see
Section 2.5.1), and determining what proportion of the found species are ones that are
characteristically salt-tolerant. However, this approach is unreliable in highly saline wetlands due
to rapid dissolution of diatom and chrysophyte remains (Walker et al. 1995). Salinity limits and
optima for 142 diatom taxa found in inland lakes of North America are presented by Fritz et al.
(1993) and Blinn (1993) and these species might be used for reconstructing past salinity
conditions in a wetland.
Species Richness
In saline lakes of western North America, the richness of diatom taxa is negatively correlated with
specific conductance, with greatest richness corresponding to specific conductance of less than
45 mS (Blinn 1993). Diatom richess is greatest in waters where specific conductance is primarily
the result of NaCI, or where concentrations of MgS04 are intermediate rather than where
carbonate ions are dominant (Blinn 1993). Because local groundwater regimes play a major role
in determining the ion chemistry of prairie wetlands, slight changes in groundwater flow might
noticeably alter diatom species composition and richness.
Density, Biomass, and Production
Algal productivity increases with conductivity up to about 3000 pS/cm, and it decreases at higher
salt concentrations (Reynolds 1979). Moreover, chlorophyll-a, an indicator of algal biomass,
occurs at lower concentrations in highly saline prairie lakes than in fresher ones, i.e., ones with
< 1000 mg/L total dissolved solids (Barica 1978). An empirical model is available for predicting
the nutrient status of saline prairie lakes, given information on their conductivity and chlorophyll-a
content, but the model is not accurate where the ratio of total nitrogen to total phosphorus is < 12
(Bierhuizen and Prepas 1985, Campbell and Prepas 1986, Evans et al. 1995).
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Decomposition
Apparently no studies of the effects of salinity on decomposition have been conducted in prairie
wetlands. One study elsewhere found that decomposition was slower in inland wetlands having
greater salinity (Reice and Herbst 1982).
2.4.4 Algal and Microbial Communities as Indicators of Sedimentation and Turbidity
A relatively low biomass and density of algae and microbes can indicate wetlands that receive
chronically elevated inputs of sediment. Even more indicative might be a shift in species
composition.
2.4.5 Algal and Microbial Communities as Indicators of Excessive Nutrient Loads and Anoxia
Species Composition
Algae and microbes respond more quickly to nutrient additions than do submersed vascular
plants (Crumpton 1989). Among algal assemblages in prairie wetlands, phytoplankton and
epiphyton respond immediately to small, repeated nutrient additions, whereas metaphyton
demonstrate a delayed but large and enduring response, and benthic algae respond hardly at all
(Murkin et al. 1994b). When nutrients are added in only a single dose, phytoplankton show a
stronger response than epiphyton (Gabor et al. 1994).
Species composition of algal communities (especially diatoms) has a long history of use as an
indicator of the relative state of enrichment of a water body. Moreover, composition of algal
species reflects not only the total level of nutrients but also the ratio of two nutrients, phosphorus
and nitrogen. One study of a prairie wetland (Barica et al. 1980) showed that a large ratio of
biomass of green algae (Chlorophyta) to blue-green algae (Cyanophyta) can indicate that an
oversupply of nitrogen, relative to phosphorus, has occurred within a few months. This pattern
has been supported by wetland studies in other regions (e.g., Michigan bogs, Hooper 1982) that
found that Euglenophytes (one-celled, mobile green algae) in particular respond to increases in
ammonium and Kjeldahl nitrogen (rather than to nitrate alone), as well as to other substances
associated with decomposing organic matter. The indicator status of a large variety of algal taxa
with regard to enrichment is cataloged in several publications (e.g., Prescott 1968, Lowe 1974,
Richardson and Schwegler 1986, Leclercq and Maquet 1987, Descy and Coste 1990). From
such listings, the long-term changes in nutrient status of a wetland might be inferred once diatom
remains or pigments from sediment cores (see Section 2.5.1) are collected and analyzed to
determine the proportion of the found species (or pigments) that are characteristically associated
with eutrophication.
Among microbial assemblages, photosynthetic microbes appear to respond more immediately to
nutrient additions than do most other microbes (Pratt and Cairns 1985). In some wetlands,
enrichment increases the number of facultative-anaerobic bacteria (e.g., streptococci,
enterobacteriaceae and aerobic spore forms, e.g., Bacillus spp., Pseudomonas alcaligenes, and
Aeromonas spp.). Mesotrophic ponds can have elevated numbers of fluorescent
pseudomonads, whereas oligotrophic waters can have more denitrifiers (Pseudomonas
fluorescens and Vibrio spp.) (Schmider and Ottow 1985).
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Species or Form Richness
Algal or microbial species-richness is not a precise indicator of enriched conditions because it
can either increase (e.g., Pratt et al. 1985, Morgan 1988) or decrease (e.g., Hooper 1982,
Schindler and Turner 1982) in response to nutrient addition.
Biomass or Density
Algal (Murkin et al. 1991) and microbial (Tate and Terry 1980, Schmider and Ottow 1985)
biomass or density are strong indicators of a wetland's degree of enrichment. Increasing
duration and frequency of algal blooms can be a sign of increasing enrichment of a wetland.
Decomposition
Microbial populations, and consequently decomposition, are at least temporarily accelerated by
enrichment in some wetland types (e.g., Dierberg and Ewel 1984). However, it is not apparent
that relatively high rates of decomposition are a sign of atypical enrichment in prairie wetlands,
and over the long term, enrichment could reduce decomposition rates in a wetland if it results in
anaerobic conditions becoming widespread.
Other Algal Indicators of Enrichment
In prairie wetlands, Hooper-Reid and Robinson (1978a) found statistical relationships between
nutrient enrichment (or impoverishment) and various physiological indicators: alkaline
phosphatase activity, nitrogenase activity, ratio of protein to carbohydrate and lipid, and silica
uptake rate. The strength of these relationships varied within the growing season, and in
contrast, Murkin et al. (1994b) found no such statistical relationships. Formation of
polyphosphate bodies within algal cells has also been used as an indicator of phosphate
oversupply (Stevenson and Lowe 1986). However, many anatomical and physiological
approaches are relatively labor-intensive and are often more appropriate for use in research than
in routine monitoring.
Other Microbial Processes
The activity of denitrifying microbes is probably greater in wetlands of greater fertility (e.g.,
moderately alkaline clays with adequate organic matter). For example, microbial biomass in soils
of North Dakota was found to be greater in areas underlain by siltstone than in areas underlain
by less fertile sandstone or shale parent material (Schimel et al. 1985). Denitrification rates
might be greater in wetlands that have been exposed to nutrient runoff than in relatively pristine
wetlands (personal communication, J. Kadlec, Utah State University, Logan). Tillage and
fertilization of soils over time also might increase the suitability of remaining soil carbon as an
energy source for denitrifying microbes (Groffman et al. 1992).
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2.4.6 Algal and Microbial Communities as Indicators of Pesticide and Heavy Metal Contamination
Species Composition
Algal blooms commonly occur in wetlands following the application of herbicides to kill vascular
plants. Benthic algae sometimes are the first to increase because they benefit from the opening
of the canopy. By stabilizing bottom sediments somewhat and thus reducing turbidity, their
establishment can pave the way for metaphyton such as Chara, which can reduce turbidity even
further (Crawford 1981). A shift in community composition from large filamentous chlorophytes
(green algae) to smaller diatom species and blue-green algal species—particularly those of the
order Chaemaesiphonales—is another possible sign of herbicide effects on a wetland
(Goldsborough and Robinson 1983, Herman et al. 1986, Hamilton et al. 1987, Gurney and
Robinson 1989a). However, whether this occurs can depend on the particular herbicide that is
applied. Limited data from laboratory assays (Peterson et al. 1995) suggest that 1) glyphosate
might differentially inhibit diatoms, a key food of snails and midge larvae; 2) diquat might cause a
shift from diatoms and blue-green algae to unicellular green algae; and 3) atrazine, hexazinone,
simazine, and tebuthiuon might allow nuisance filamentous blue-green algae to become more
dominant than other algal assemblages.
Effects of heavy metals and selenium on algae and microbes have been studied elsewhere (e.g.,
Crane et al. 1992), but they have received little study in prairie wetlands. Algal taxa that might
be potential indicators of heavy metal contamination are identified in several studies from other
regions (Lange-Bertalot 1979, Maeda et al. 1983, Denisegeret al. 1990), and microbial taxa that
are potential indicators of contaminants are documented by Baath (1989) and Dean-Ross and
Mills (1989).
Species Richness
Algal and microbial species richness is probably a weak indicator of wetland contamination with
toxic substances, but species richness remains untested in prairie wetlands. Microbial diversity
sometimes declines with exposure to hydrocarbon pollutants (Atlas et al. 1991) but not
necessarily in response to heavy metals (Dean-Ross and Mills 1989).
Biomass and Density
In response to contaminants, the total biomass or density of algae and microbes can either
decrease (e.g., Whitton 1971) or increase. Decreases are due generally to inhibition of
reproduction and growth, whereas increases typically occur when contaminants are differentially
toxic to animals that otherwise would graze on algae (e.g., Hurlbert et al. 1972), or to plants
whose shading otherwise limits algal growth. Algae that inhabit sediments (benthic algae)
appear to remain inhibited by some pesticides for a longer period than are algae that are
attached to substrates above the sediment surface (Gurney and Robinson 1989a). This
suggests that the ratio of benthic species to non-benthic species might be a useful indicator of
persistent, sediment-adsorbed contaminants. However, the total biomass or density of algae and
microbes is a poor indicator of contamination. This is especially true if the numbers of cells,
rather than their volume, is the monitored indicator (Gurney and Robinson 1989a).
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It cannot be assumed that contaminants that are harmless or harmful to vascular plants will
usually have the same effect on algae. Many algal species are more sensitive than vascular
plants to particular contaminants, especially those that inhibit photosynthesis (Fletcher 1990).
Effects of contaminants on algal and microbial communities of prairie wetlands specifically have
only recently been studied, beginning with a mesocosm study by Johnson (1986). A widely used
herbicide, atrazine, was found to reduce algal productivity and growth by more than 40% when
present at concentrations > 1 mg/L (Johnson 1986). Some evidence suggests that atrazine
concentrations as low as 0.001 mg/L might be capable of altering algal species composition
(deNoyelles et al. 1982) and biomass (Herman et al. 1986); effects may depend on duration of
exposure (Jurgensen and Hoagland 1990). Some attached algae can develop resistance to
atrazine after exposure to 0.050 mg/L (Detenbeck et al. 1993). Atrazine concentrations of up to
0.008 mg/L were found in a survey of 42 prairie wetlands in nine South Dakota counties (R.
Ruelle, personal communication, FWS, Pierre, SD), and concentrations of 0.001-.005 mg/L
occur most of the time in agricultural streams entering the Great Lakes (Frank et al. 1979). A
concentration of 0.413 mg/L would be expected to occur immediately after a 0.5-ha prairie
wetland is sprayed at recommended dosages (Sheehan et al. 1987). Reviewing other toxicity
data, Sheehan et al. (1987) concluded that the expected in-wetland concentrations of 7 of 21
herbicides used in the prairie region could be toxic to algae if wetlands were sprayed directly.
Recent laboratory testing of 23 pesticides (20 herbicides, 2 insecticides, 1 fungicide) at realistic,
environmentally expected concentrations resulted in impacts to a wide range of algal species
from nine of the pesticides, five of which were triazine herbicides (Peterson et al. 1995). Least
damaging to algae were the fungicide propiconazole and the herbicides picloram, bromoxynil,
and triclopyr. Field assays indicate triclopyr might be relatively nontoxic to wetland vascular
plants as well (Gabor et al. 1993). Johnson (1986) also found that two other herbicides (triallate
and treflan) actually stimulated photosynthetic productivity by 20%-30% two weeks after
application. However, triallate can be highly persistent under some conditions (Sheehan et al.
1987), and long-term effects were not determined. Carbofuran also mildly stimulated algal
growth when present at concentrations of 10 and 100 mg/L. Phorate showed no effects, and
fonofos inhibited algal growth only after 30 days, suggesting that a degradation product was
responsible for toxicity. After applying another popular herbicide, glyphosate (Roundup), to
prairie wetland mesocosms at a typical rate (2.5 L/ha), Shaw (1992) also reported a mild
stimulatory effect on phytoplankton productivity at concentrations <0.1 mg/L. However, greater
concentrations depressed algal productivity (as measured by 14C uptake) in 3 of 4 wetlands, and
the author noted that lower concentrations of glyphosate could be just as toxic to algae in waters
of relatively low calcium and magnesium content. In a survey of 10 other Saskatchewan
potholes, Shaw (1992) found a glyphosate concentration > 0.1 mg/L in only one.
Decomposition
Even when applied at concentrations 50 and 100 times normal field rates, one soil insecticide
(AC 92,100) had no apparent effect on decomposition rates in a prairie hydric soil (LaVeglia and
Dahm 1974). No data are available for other pesticides.
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Other Microbial Processes
In a prairie wetland mesocosm, a dosing study of six herbicides (atrazine, fonofos, carbofuran,
phorate, treflan, triallate) found that none had a significant impact on indicators of microbial
functions (glucose mineralization, oxygen consumption, alkaline phosphatase activity, respiratory
electron transfer system/dehydrogenase activity) (Johnson 1986). Similarly, an insecticide
dosing study of an Iowa hydric soil found no impacts on some other indicators of microbial
function (LaVeglia and Dahm 1974). Applying 50-100 potentially toxic contaminants to microbial
communities and a variety of other organisms in laboratory bioassays, Blum and Speece (1991)
found that chemicals that were highly toxic to a popular test organism—fathead minnow—were
almost always toxic to a major denitrifier, Nitrosomonas. Two microbial assemblages responsible
for decomposition (aerobic heterotrophs and methanogens) were less sensitive to the same
contaminants.
Bioaccumulation
Apparently no studies have examined the role of algal and microbial communities as sinks for
heavy metals or pesticides in prairie wetlands.
2.5 Monitoring Techniques
Methods for monitoring a'gal or microbial communities are described by Stevenson and Lowe
(1986), Britton and Greeson (1988), and Aloi (1990). Microbial communities, especially
assemblages of bacteria, are notoriously difficult to characterize because of the selectivity of
culture techniques (Atlas 1984). Nonetheless, various bacterial strains can be placed in
assemblages that likely have ecological significance (Mills and Wassel 1980).
Algae can be sampled at any season, but algal biomass is often greatest during the later part of
the growing season (e.g., Hooper-Reid and Robinson 1978a, Crumpton 1989). In semipermanent
wetlands, it may be advisable to sample metaphyton during sunny weather because sunlight
makes the metaphyton mats more buoyant and thus easier to see and sample.
2.5.1 Direct Sampling
Chlorophyll-a is sometimes sampled from the water column as an indicator of algal biomass.
Some studies in prairie wetlands (e.g., Hickman and Jenkerson 1978) show it being only weakly
correlated with measures of algal biomass (dry weight) and productivity, while others (Hosseini
and van der Valk 1989a,b, Labaugh 1995) report stronger correlation. Cell volume is also
sometimes used as an indicator of production (e.g., Shamess et al. 1985), but cell surface area
seems to be a more accurate surrogate (Hooper-Reid and Robinson 1978b).
Algal communities in wetlands are generally collected using certain methods from sediment
samples, water column samples, artificial substrates, or natural organic substrates.
Sediment sampling. Algae and microbes can be sampled from sediment surfaces in all types of
prairie wetlands. Piston corers, plastic syringes, or other suction devices can be used. For
example, Shamess et al. (1985) used a plexiglass corer to remove the top 2 cm of sediment
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when sampling benthic algae in a Manitoba wetland. In Florida cypress swamps, Dierberg and
Brezonik (1982) sampled the nitrifying bacteria of surface sediments using a sterile piston corer
and a plastic syringe with an attached tube.
Water column sampling. Whenever standing water is present for more than a few days, algae
and microbes can be counted from samples of the water column of prairie wetlands (Robarts et
al. 1992). Volumetric tube containers (Gurney and Robinson 1988) or fine-mesh nets have been
used to collect samples. Vertically integrating, automated samplers also can be used
(Schoenberg and Oliver 1988). Surface microlayers (top 250-440 nm) can be sampled using
fine nets or screens mounted on a frame (Estep and Remsen 1985).
Artificial substrates. Artificial substrates (initially sterile materials placed in a wetland and
subjected to natural colonization) sometimes integrate the algal and microbial assemblages from
a large variety of microhabitats (Henebry and Cairns 1984, Goldsborough and Robinson 1986).
Microbes or algae can be monitored by installing plexiglass plates or similar inert, sterile surfaces
in prairie wetlands at any time when surface water is likely to be present for several days. The
substrates are colonized by attached algae and microbes during this period, then retrieved for
analysis. In prairie wetlands, cellulose acetate substrates roughened with sandpaper were used
by Hooper and Robinson (1976), Hooper-Reid and Robinson (1978a,b), and Shamess et al.
(1985); acrylic rods were used by Hosseini and van der Valk (1989a,b), and Murkin et al. (1992).
Natural substrates. Epiphytic and benthic algae can be sampled using a quadrat approach, in
which a frame is placed over a standard-sized area of bottom and substrates are scraped
(Hooper and Robinson 1976). Frame sizes of 10 x 10 cm (Atchue et al. 1982) and 1-2 m2
(Schoenberg and Oliver 1988) have been used in other regions.
To accurately estimate algal and microbial density, the surface area of substrate must be
quantified. This can be a daunting task in the case of epiphytic algae, where plant surface areas
need to be measured. Some investigators have approached this by measuring surface areas of
a random sample of plants, sometimes with the use of a digital scanner, then measuring their
volumes (by displacement) or dry weights and developing area-volume or area-weight calibration
curves. The curves can be used to estimate plant surface area from future, simpler
measurements of the volume or weight of other plants of the same species.
Bacterial and fungal abundance are usually estimated as colony forming units (CFU) using plate
count techniques. However, concerns have been raised about the validity of this technique for
monitoring fungi; use of low-nutrient culture media (rather than the typical enriched media) is also
recommended (Baath 1989).
Use of more than one sampling method is recommended because different taxa occupy different
habitats. For example, the data of Shamess et al. (1985) indicate that species richness of one
prairie wetland ranged from 18 to 35 species, and that of another ranged from 26 to 41 species,
depending on which of five components of the algal community were sampled, and how they
were sampled. The methods were acetate colonization substrate (smooth, roughened),
epiphyton sampling (scraped from Typha stems), epipelon sampling (from core samples), and
phytoplankton sampling (using a tube sampler). Each wetland was sampled 17 times during the
growing season by each method. When results of sampling all five components were pooled,
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species richness of the first wetland was 78 and that of the second was 80; thus, no single
component of the algal community contained more than 45%-51% of the species. The number
and proportion of species that were unique to one component of the algal community was
smallest for the smooth substrate-colonizing component (1 endemic species, constituting 4% of
all species on smooth substrate) and greatest for the phytoplankton component (17 endemic
species, constituting 41% of all phytoplanktonic species).
2.5.2 Indirect Sampling Through Measurement of Processes
Algal and microbial communities can be monitored indirectly by monitoring processes such as
decomposition and denitrification.
Decomposition
Procedures for measuring rates of decomposition in prairie wetlands are detailed by Murkin et al.
(1989) and Davis and van der Valk (1978a,b). In the latter instance, the authors collected fresh
standing-litter immediately after first frost. They clipped six, 1 *1 m quadrats of each species, at
15-m intervals along a transect parallel to shore. They placed plant litter in nylon mesh bags on
the sediment surface or suspended in the water column. They then removed a few bags
periodically for about 1 year (more often at first). Silt and invertebrates were removed and
samples were dried to a constant weight. The investigators noted that, by excluding litter-
processing invertebrates, the bags might not precisely represent the natural rate of
decomposition.
Denitrification Enzyme Activity (DEA)
A method for determining the relative activity level of important denitrifying bacteria in soils was
applied to wetlands by Groffman and Tiedje (1989). Requirements include a laboratory with a
gas chromatograph, a gas manifold (to make samples anaerobic), and facilities to do chloroform-
incubation methods of carbon analysis, chloramphenicol microbial inhibition, and nitrogen gas
measurement, from samples brought in from the field. The initial laboratory investment is
approximately $20K, and exclusive of the gas analysis tasks, one person can run 50-100
samples per day, with a cost of $150/month for expendable supplies (P. Groffman, personal
communication, Institute of Ecosystem Studies, Millbrook, NY).
Other Measures That May Reflect Microbial Processes
Respiration and other functional activities of microbial communities can be estimated by a variety
of indirect methods. Methods for measuring respiration of entire ponds or wetlands are available
(Madenjian et al. 1990). Probably the best-known microbial bioassay technique is the Microtox
Standard Assay Procedure, which has been used to measure microbial stress in prairie wetlands
potentially exposed to pesticides (Ruelle and Henry 1993). Measurements of the relative rates of
lipid biosynthesis (Fairchild et al. 1984) are another expression of microbial function. Stressed
microbial communities also sometimes have altered adenylate (ATP, ADP, AMP) energy charge
ratios. Microbial biomass can be indirectly monitored by comparing levels of adenosine
triphosphate (ATP) to ash-free dry weight (Meyer and Johnson 1983). The rates at which
microbial communities colonize sterile substrates introduced to a wetland, and the characteristics
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of the colonizing community, can also be used to indicate impacts from contaminant toxicity
(Caims et al. 1992).
2.5.3 Time-Integrating Methods
The nutrient status of a wetland during pre-settlement periods can sometimes be inferred by
examining photosynthetic pigments or structural remains found in wetland sediments. In
particular, levels of chlorophyll, chlorophyll derivatives, carotinoids, and myxoxanthin (a pigment
associated with eutrophic blue-green algae) can be used to infer nutrient status, at least in
permanent water bodies where past hydrological effects on species composition can be
presumed to be insignificant. Samples are collected with corers; pigments can then be extracted
with solvents and partitioned using chromatographic methods. Methods are described by
Leclercq and Maquet (1987) and Agbeti and Dickman (1989). A baseline was established using
such an approach in one prairie wetland (Begres 1971), and a project involving analyses of cores
from 50 prairie wetlands is ongoing (S. Fritz, personal communication, Limnological Research
Center, University of Minnesota, Minneapolis, MN).
2.5.4 Bioassay Methods
A review of laboratory, outdoor mesocosm, or in situ bioassay methods involving algae is beyond
the scope of this document. Use of bioassays to explore contaminant toxicity to algae in prairie
wetlands has been relatively limited. Examples include studies by Johnson (1986), Gurney and
Robinson (1989a), Wayland and Boag (1990), and Ruelle and Henry (1993). Impacts of phorate,
an organophosphate insecticide, on microbial populations were not detected using a culture test,
the Microtox test (Dieter et al. 1994).
2.6 Variability and Reference Points
The following subsections deal with spatial and temporal variability of algal and microbial
community characteristics in prairie wetlands.
2.6.1 Spatial Variability
Species Richness
One of the few studies to survey algal richness in prairie wetlands (Labaugh and Swanson 1988)
sampled pothole wetlands representing five different hydrochemical environments in the
Cottonwood Lakes area. When lists from all five wetlands and all six sampling dates (months)
were pooled, the species total was 306—clearly more species than are usually found in any
wetland's non-algal flora or fauna. Seventy-six algal species were found over the course of one
season on three species of plants in a single shallow lake in Manitoba (Pip and Robinson 1982).
In other regions, studies that have compared protozoan communities among wetlands include
Henebry et al. (1981) and Pratt et al. (1985). The former study, covering 13 Michigan wetlands
over a 5-year period, found a range of 93 to 365 protozoan species. The latter study, covering
28 Florida ponds, found a range of 112 to 410 species, with a mean of 338 species in non-
artificial ponds. Functional structure of the resident protozoan fauna changed slightly from year
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to year, but wetlands in the same geographic region and experiencing similar climatic patterns
had similar proportions of species in each functional group (Pratt et al. 1985).
Density, Biomass, and Production
Phytoplankton standing crop (biomass) is often expressed as chlorophyll-a, and can peak at
0.481 mg/L in some prairie lakes (Barica 1975, Barica et al. 1980). Phytoplankton averaged only
0.029 mg/L during the growing season in one Saskatchewan lake (Hickman and Jenkerson
1978), 0.002-0.006 mg/L in six prairie wetlands (Gloutney 1993), 0.001-0.380 mg/L in the
Cottonwood Lake semipermanent wetlands (Labaugh and Swanson 1993), and 0.010 mg/L in a
saline prairie lake (Campbell and Prepas 1986). Among 10 Northern Plains lakes and wetlands,
algal volume ranged over 4 orders of magnitude (Labaugh 1995). Metaphyton standing crop in a
prairie wetland that had been flooded for 2 years averaged 66 g/m2 over a growing season
(Hosseini and van der Valk 1989b) and peaked at 151 g/m2 dry weight. Metaphyton in another
prairie marsh was measured as 200 g/m2 (van der Valk 1986). Among six Saskatchewan prairie
wetlands, the biomass of epiphytic algae peaked at only 0.025 to 0.105 g/m2 (Gloutney 1993). In
Delta Marsh, epiphytic biomass was estimated to range from 2.3 to 32.3 g/m2 (Hooper and
Robinson 1976). Chlorophyll-a from Delta Marsh's epiphyton varied from < 0.01 to about 0.05
g/m2, and it was greatest within a cat-tail stand, 3 m from the edge with open water (Murkin et al.
1992). Chlorophyll-a from epiphyton in some saline prairie lakes in Alberta averaged less than
0.07 g/m2 (Campbell and Prepas 1986).
In shallow prairie lakes, phytoplankton densities can exceed 300,000 cells per ml (Hickman and
Jenkerson 1978); bacterial densities can exceed 10,000,000/mL (Campbell and Prepas 1986).
Phytoplankton primary productivity averaged 196.77 mg C(m3)hr1 and 196.77 mg C/hr/m2 in a
shallow prairie lake (Hickman and Jenkerson 1978), and in prairie wetlands the production of all
algae combined generally ranges up to a few hundred g C(m2)~1yr1 (Murkin and Batt 1987,
Crumpton 1989). The production of epiphytic algae is perhaps greater on emergent than
submersed vascular plants (Hooper and Robinson 1976) although it is difficult to standardize
estimates of available surface area of plants. Within emergent plant communities, the level of
epiphytic algal biomass varies largely with spatial and temporal variation in nutrient availability,
e.g., from 2.3 to 32.3 g C/m2 (Hooper and Robinson 1976, Hooper-Reid and Robinson 1978a).
Decomposition Rate
Few estimates are available to describe the variability of decomposition rates among and within
wetlands. The half-life of fallen emergent plant litter in two Iowa prairie lakes ranged from 128 to
1011 days, depending on plant species, season, and other factors (Davis and van der Valk
1978a,b; Neely and Davis 1985b; Neely and Baker 1989).
Denitrification Enzyme Activity (DEA)
Measurements of DEA within a wetland vary somewhat spatially. Variability (coefficient of
variation) of DEA measurements within wetlands ranges from 33%-89%, and the
coefficient of variation among true replicates is about 10% (P. Groffman, personal
communication, Institute for Ecosystem Studies, Millbrook, NY). Measurements of denitrification
using alternative methods are highly variable and difficult to compare (et al. 1994), but DEA
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measurements are believed to be generally less variable because they integrate conditions over
time.
2.6.2 Temporal Variability
Species Richness
In the Cottonwood Lakes area, data indicate considerable monthly variability in algal richness
(Table 1) (Labaugh and Swanson 1988). Species turnover rates have not been quantified for
prairie wetlands.
Density
In a study of five wetlands in the Cottonwood Lakes area, four relationships were noted:
Table 1. Species diversity by basin type and month in the Cottonwood Lakes area
(Labaugh and Swanson 1988).
Basin Type Month(s) with tins Most Month{s) with Sie Fewesi
Peaking Species Fsakirig Taxa
Seasonal
June (38 and 42 taxa)
May and July
(n = 2 basins)
Semipermanent
October (32 and 55 taxa) and
June and September
(n = 2 basins)
May (59 taxa)
Decomposition Rate
Interannual differences in decomposition rates and patterns of two species (Scolochloa and
Scirpus lacustris) in a flooded prairie wetland were negligible (Wrubleski et al. 1993).
Denitrification Enzyme Activity (DEA)
No published information was found on interannual variation in prairie wetlands of DEA or other
microbial functions.
2.7 Collection of Ancillary Data
It is easier to separate the anthropogenic from the natural causes of impairment of community
structure if data are estimated or inferred simultaneously on the following features of particular
importance to algae and microbes:
• age of the wetland and its successional status
• light penetration (water depth, turbidity, shade), temperature, sediment oxygen, general
chemistry of waters (particularly pH and conductivity)
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• leaf surface area and stand density of associated vascular plants
• density of grazing aquatic invertebrates
• moisture regime (e.g., time elapsed since last runoff, inundation, or desiccation event).
All of these features vary to a large degree naturally, as well as in response to human activities
such as soil tillage, compaction, and erosion; fertilizer and pesticide application; and water
regime modification.
2.8 Sampling Design and Required Level of Sampling Effort
For most algal communities, sample processing and species proportional counts (assuming 500
individuals) take 2-3 hours per sample (Stevenson and Lowe 1986).
2.9 Summary
The enormous diversity of algae (probably over 500 species in prairie wetlands) and the position
of algae and microbes at the base of the food chain suggests their considerable ecological
importance. It also highlights a need for monitoring key processes supported by algae and
microbes and continued research to associate various rates of these processes (e.g.,
decomposition) with the seasonal sequencing and occurrence of particular species compositions.
Published estimates of interwetland and interannual variability of algal and microbial taxonomic
composition in prairie wetlands are nearly nonexistent.
Most algal and microbial communities recover quickly from acute disturbances. Because of this
quick recovery, direct sampling of algal and microbial communities will fail to detect many acute
disturbances. Alternatively, indirect examination of the pigment from just the portion of the
diatom community that accumulates seasonally in sediment traps might provide some indication
of current wetland conditions.
Algal species composition, and to a lesser degree species richness, demonstrates diagnostic
responses to changes in vegetative cover, salinity, excessive nutrient loads, and sedimentation
or turbidity (Table 2). Algae also respond sensitively to changing water regime and pesticide or
heavy metal contamination, but existing information is too limited and confounding effects are too
prevalent to currently allow widespread use of algae to diagnose impairment of prairie wetlands
from these stressors.
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Table 2, Summary evaluations of possible algal and microbial indicators of stressors in prairie wetlands.
Evaluations are based on technical considerations, not cost or practicality. A rating of FAIR or POOR is
assigned when too few data (FD) suggest potential as an indicator or when confounding effects (CE) of
other variables often overshadow the effects of the listed stressor on the indicator.
EvaJuatior?
Hydrologic stressors
Species composition
FAIR (CE)
Richness
UNKNOWN
Density, biomass, productivity
FAIR (CE)
Decomposition
POOR
Denitrification
FAIR
Changes in vegetative cover
Species composition
GOOD
Richness
FAIR (FD)
Density, biomass, productivity
POOR (CE)
Decomposition
POOR (FD)
Salinity
Species composition
GOOD
Richness
GOOD
Density, biomass, productivity
FAIR
Decomposition
POOR (FD)
Sedimentation & turbidity
Species composition
FAIR (FD)
Richness
FAIR (FD)
Density, biomass, productivity
FAIR (CE, FD)
Decomposition
POOR (FD)
Excessive nutrients & anoxia
Species composition
GOOD
Richness
POOR
Density, biomass, productivity
GOOD
Decomposition
POOR (CE)
Denitrification
FAIR (CE)
Herbicides
Species composition
FAIR (FD)
Richness
POOR (FD)
Density, biomass, productivity
FAIR (CE)
Decomposition
POOR (FD)
Insecticides
Species composition
POOR
Richness
POOR
Density, biomass, productivity
POOR
Decomposition
POOR
Heavy Metals
Species composition
GOOD (CE)
Richness
POOR
Density, biomass, productivity
POOR
Decomposition
POOR
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3. Vascular Plants as Indicators of Prairie Wetland Integrity
3.1 Ecological Significance and Suitability as an Indicator
In prairie wetlands, three growth forms of vascular plants usually are defined: emergent, floating-
leaved, and submersed plants. To varying degrees, these form discrete zones within wetlands.
Wetlands containing all three forms (and many subforms and species) are generally those with a
core area that is flooded permanently, but with water levels that otherwise vary greatly from year
to year. In such wetlands, nutrients are more available and support substantial invertebrate
densities and waterbird use.
The foliage and/or seeds of several species of vascular plants are consumed regularly by
waterfowl (see Appendix A). The food values of wetland vascular plants are attributable both to
their being consumed directly (mainly by microinvertebrates and tadpoles) and to their serving as
an attachment surface and secondary energy source for algae, amphibian larvae, and
invertebrates.
Also, vascular plants as well as algae influence the fertility of prairie wetlands by harnessing
solar energy through photosynthesis. In contrast to algae which release the stored solar energy
almost immediately after their death, the energy from vascular plants is made available by
microbes slowly, over a period ranging from weeks to months. Thus, during seasons when algal
populations are at a minimum, the energy originally trapped by vascular plants could well be a
significant source of energy for invertebrates that later in the growing season are consumed by
waterbirds (Nelson and Kadlec 1984). Of particular importance is the value of vascular plants in
prairie wetlands as a substrate for growth of attached algae and microbes (Campeau et al. 1994)
and as habitat cover (shelter) that protects invertebrates, amphibians, and birds from predators
and severe weather (Murkin et al. 1992). Evidence from other regions (Hanson and Swanson
1989) suggests that the type of wetland plant can influence the size structure of invertebrate
communities (the ratio of large to small individuals), and thus perhaps influence the value of the
invertebrate community to waterbirds. Using isotope ratios to investigate food webs in Delta
Marsh, Neill and Cornwell (1992) found emergent vascular plants and the algae and microbes
attached to them, rather than submersed macrophytes or metaphyton, to be the most important
sources of organic matter to the invertebrate consumers that were most abundant during June.
Vascular plants are also important because they influence the amount, rate, and seasonal timing
of nutrient and contaminant cycling across the water column-sediment ecotone. Some plants
remove nutrients directly from the water column, thus seasonally tieing up some of the nutrients
that otherwise might support nuisance algal blooms. In the sediment, plant roots can take up
nutrients (and contaminants). In some cases, plant roots can transfer both nutrients and harmful
substances to plant foliage, making these substances more available to food chains. Wetland
plants also help maintain wetland water quality by stabilizing shorelines and reducing wind-driven
resuspension of sediments that otherwise impair light penetration and reduce primary
productivity.
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Characteristics of vascular plants usually considered advantages for indicating wetland integrity
include:
• immobility ( plants reflect site conditions and are practical for use in in situ exposure
assessments)
• interpretability of gross patterns of spatial distribution as indicators of condition
(patterns are interpretable from a distance without requiring permission for
access to private property, e.g., through interpretation of aerial imagery)
• sensitivity to a wide variety of stressors (especially hydrology, salinity, and changes in
vegetative cover); sensitivities of individual species are relatively well known; submersed
plants are especially sensitive (e.g., to turbidity, overenrichment)
• known taxonomy and straightforward identification to genera
• well-developed sampling techniques and community metrics.
Vascular plant characteristics usually considered disadvantages for indicating wetland integrity
include:
• lagged response to stressors (episodic stresses may not be reflected)
• relative insensitivity to insecticides and heavy metals (but can bioaccumulate these)
• difficulty sampling some assemblages (e.g., submersed species)
• laborious identification of some assemblages
• difficulty characterizing communities during the dormant season.
3.2 Potential Indicator Metrics
As applied to plant communities, the following measurements and metrics were considered for
characterizing conditions in reference wetlands, identifying the relative degree of past
disturbance of a prairie wetland, or assessing the current inhibition of key processes:
• richness of species, functional assemblages, and rare species (per unit area, or per
thousand randomly chosen individuals) in extant communities and in seed banks
• number and biomass of stems per unit area, or cumulative shoot length, or canopy cover
per unit area (when measured at the time of its annual maximum, this is commonly used
as a proxy for annual plant production)
• relative dominance and richness of species, particularly of species reputedly tolerant or
intolerant to a named stressor (measured both in extant communities and seed banks)
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• interannual variability in richness, density, and/or biomass
• germination rate of seeds in sediment samples
• bioaccumulation of contaminants.
The specific ways some of these metrics have been or could be interpreted as an indication of
stressed conditions are described in Section 3.4.1. Various in situ methods of measuring
macrophyte production (e.g., uptake rates of carbon radioisotopes, oxygen production,
photosynthetic rate, respiration) are not considered in this document. In some instances, whole-
system repiration rates, integrated over a 24-hour period, might usefully indicate wetland
functional integrity. I
3.3 Previous and Ongoing Monitoring in the Region
Virtually all studies of prairie wetlands mention plant species that are dominant in the studied
wetland. Studies focusing primarily on vascular plants number at least 59 and cover in excess of
2200 prairie wetlands (Appendix J). The metrics most commonly measured in plant surveys are
relative abundance and percent cover (canopy density). Although long-term monitoring of
mature vegetation has been conducted in the Cottonwood Lakes wetlands and perhaps other
areas, apparently only eight long-term studies (> 7 years of data) have been published.
No State agencies responsible for prairie wetlands currently monitor plants as indicators of
wetland ecological integrity. At a regional level, USEPA's EMAP has documented plant
communities in 30-40 wetlands that span gradients of water regime, probable disturbance, and
geography. Variables that are being measured include species richness, areal cover, cover ratio,
and amount of standing dead litter.
At more local levels, species composition and density of plants are being tested for possible use
as indicators of the success of wetland restoration efforts in Iowa (Galatowitch 1993a,b),
Minnesota (Madsen 1988, Sewell 1989), and perhaps elsewhere. Research on ecological
relationships affecting plant communities continues to be conducted at NPSC and at the State
universities. Personnel from The Nature Conservancy and/or State Natural Heritage programs
conduct botanical surveys of prairie wetlands in relatively undisturbed localities, and periodically
they check a few of the wetlands known to contain plant species of State, regional, or national
importance because of their rarity. In wetlands known to contain regionally rare species,
monitoring of these species can be used to indicate long-term integrity of the wetland. In prairie
pothole wetlands of the United States, examples of regionally rare species are Napaea dioica,
Carex formosa, Eleocharis wolfii, and Astragalus neglectus (interpreted from information provided
to USEPA in 1992 by The Nature Conservancy).
3.4 Response to Stressors
The following subsections describe responses of the vascular plant communities to hydrologic
stressors, vegetative cover conditions, salinity, sedimentation/turbidity, excessive nutrient
loads/anoxia, and pesticide and heavy metal contamination.
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3.4.1 Vascular Plants as Indicators of Hydrologic Stressors
Species Composition
The species composition of vascular plant communities, especially the submersed forms, is an
excellent indicator of hydrologic conditions that have occurred in prairie wetlands as recently as
within the past 2-4 years (Stewart and Kantrud 1972b, Millar 1973, Weller and Voigts 1983,
Weller et al. 1991, Squires and van der Valk 1992). As little as 2 cm of standing water in a
wetland can result in development of a plant community that differs by 75% from the one that
develops when surface water is absent (van der Valk and Davis 1978).
In general, a shift over a 2-3 year period toward increased dominance by submersed species
and a decline in emergent species (both adult plants and propagules) can indicate recent periods
of wetter-than-usual conditions (Weller and Spatcher 1965, Millar 1973, Weller and Fredrickson
1974, van der Valk 1981, van der Valk and Squires 1992, van der Valk et al. 1994). Conversely,
reduced dominance of submersed and other "obligate" wetland species (or species that typify
semipermanent wetlands, see Appendix F), in association with increased dominance of emergent
and "facultative" wetland species that are often annuals or biennials (or species that typify
temporary wetlands, see Appendix F), can be used to determine if relative drought conditions
have occurred recently (van der Valk and Davis 1978, Pederson 1981, Poiani and Johnson 1989,
Poiani et al. 1995). For a few species indexed in Appendix F, quantitative data on water-depth
ranges and related hydrologic variables also are available.
More specifically, van der Valk (1981) noted that recent hydrologic conditions of a prairie wetland
can often be surmised from current species composition, and less recent conditions can be
inferrred from the seed bank. Certain conditions can be deduced retrospectively by knowing the
proportion of extant species that characteristically 1) are annuals, perennials, or vegetative
reproducers (see Appendix A), 2) are mudflat-germinators vs. standing-water germinators, and 3)
have seeds that remain viable for long periods of time, vs. those with short-lived seeds which
depend highly on dispersal. For example, if few mud flat annuals (species that have long-lived
seeds and can only become established when there is no standing water) are found in seed
banks underlying open water, it suggests that standing water has probably persisted for many
years. Under ideal conditions, seed bank analysis can be used to infer conditions that existed up
to 70 years ago (Wienhold and van der Valk 1989). Unfortunately, autecological knowledge is
insufficient to assign many prairie wetland species to the predictive categories (especially 2 and
3 above) or to describe their typical seed longevity. This limits the utility of this deductive
approach.
If an attempt were made to define hydrologic criteria for protecting various kinds of prairie
wetland plant communities or assemblages of species, definitions of optimal heights and
durations of water levels (and their variability) would be two important components. Using data
on surrounding landscape factors (runoff potential) and anthropogenic uses, quantitative
estimates of expected water heights and durations could be made for wetlands within particular
landscapes, and from this, the types of plant communities that should be present could be
predicted (e.g., Poiani and Johnson 1993a,b,c; Poiani et al. 1995). However, a major limitation of
implementing this approach currently is that numeric estimates of adult plant and seed tolerances
for water depth and duration have been made for only a few prairie wetland species. Prairie
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wetland species with the most information on hydrologic tolerences include Typha spp. (>30
references), Scolochloa festucacea (21 references), and Phragmites australis (18 references).
See Appendix E for lists of references, by plant species, on water-depth tolerences in prairie
wetlands. Complicating this further is the fact that hydrologic tolerance thresholds vary
somewhat within species as a result of genetic variation and confounding influences of seasonal
(Meredino et al. 1990, 1991) and chemical conditions at the time that flooding or drawdown
occurred.
In general, flooding of prairie wetlands tends to have a greater effect on community composition
than does occasional drought (van der Valk and Squires 1992). The specific effects of
hydrologic alteration depend on flooding depth, season, frequency, duration, initial water levels,
sediment type, dominant plant species, and other factors. Nonetheless, vascular plants are
potent indicators of long-term hydrologic change in wetlands.
Community Zone Locations
The presence, position, and heterogeneity of vegetation zones, each representing a recognizable
plant community, can be used as an indicator of wetland integrity. For example, if a particular
zone is absent but was expected based on knowledge of a wetland's hydrology, then some
perturbation can cautiously be assumed to have occurred (van der Valk and Welling 1988).
When wet conditions continue for at least 2 years in wetlands that formerly had a temporary or
seasonal moisture regime (i.e., they become semipermanent or permanently inundated), the
shallower zones of emergent vegetation often die off first, leaving a central deep zone of
emergents surrounded by open water (Millar 1976). At least in situations where water level
increases are large and abrupt, emergent communities disappear rather than shift to shallower
areas (van der Valk 1994). During droughts, emergents can become re-established in the center
of a wetland basin. Overall, hydrologic changes usually cause only slight shifts in the position of
the submersed, emergent, and meadow zones (Harris and Marshall 1963, van der Valk and
Davis 1976, Johnson et al. 1987). Boundaries of vegetation zones in larger prairie wetlands tend
to be less distinct than in small wetlands (Johnson et al. 1987), so their use as an indicator can
be restricted in large wetlands.
Species Richness (of Mature Plants)
Plant species richness is a complex indicator of prior hydrologic conditions. Low richness can
indicate prior periods of drought (Driver 1977, Galatowitsch 1993a,b), partly because prolonged
dehydration facilitates the formation of homogeneous stands by a very few species. Perhaps
less often, low richness can indicate prior periods of inundation when floods drown many
emergent species. In some instances, prairie plant communities that are initially species-rich
tend to suffer less loss of productivity as a result of drought than communities that initially are
species-poor (Tilman and Downing 1994). Semipermanent wetlands generally have greater plant
species richness than temporary or seasonal wetlands of the same size. This is partly because
water persistence and depth is sufficient to allow submersed and floating-leaved plants, as well
as terrestrial forms, to exist and successfully reproduce.
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When increased species richness results from wetter conditions, it is often because increased
water levels tend to fragment the monotypic stands of emergent species (especially cat-tail,
bulrush, pickerelweed) and allow partial invasion by submersed and floating-leaved species that
add to diversity (Botts and Cowell 1988, Mclntyre et al. 1988). Wetlands that are more
persistently wet also can have higher usage by birds and other mobile animals that introduce
seeds, thus diversifying the wetland plant community (Pip 1987b).
In part of Delta Marsh, reflooding of various units to different depths after a drawdown that had
lasted 1 or 2 years had several effects (van der Valk and Squires 1992, van der Valk et al. 1994).
Reflooding to original water levels greatly increased the variety of vegetation forms (class
richness, and number of classes containing multiple species), whereas reflooding to water levels
that were 1 m above the original resulted in a halving of species richness. Reflooding to
intermediate water levels gave unclear results.
After a drawdown and gradual reflooding of an Iowa semipermanent wetland, the peak in total
species richness occurred 3 years after the drawdown, whereas the richness of just the
submersed and floating-leaved species did not peak until 4-5 years after drought. In their Delta
Marsh studies, van der Valk and Squires (1992) reported a lag time of at least 3 years before
small (< 1 m) changes in wetland water level could be detected using wetland plants.
Species Richness (of Seed Banks)
The species richness of seeds in wetland seed banks (seeds that lie dormant in soils and
sediments) can be used to crudely approximate the time elapsed since hydrologic alteration
occurred in wetlands that now are dry. This requires calibration to conditions currently present in
similar but hydrologically unaltered wetlands. For example, Wienhold and van der Valk (1989)
found that mean species richness in a series of unaltered wetlands was 12.3, but fell to 7.5, 5.4,
5.0, 3.2, and 2.1 species in potholes drained up to 5, 10, 20, 40, and 70 years ago, respectively.
After wetlands had been drained for more than 20 years, about 60% of the species disappeared
from the seed bank.
Biomass, Production, and Cover Ratio
For emergent plants, decreases in areal cover can signify recent inundation events (van der Valk
and Squires 1992), and increases in areal cover can indicate recent drought events. If periods of
dehydration are brief (a few hours or days), the areal extent of emergent plants, especially those
with rigid stems (e.g., cat-tail, common reed) is unlikely to change. The presence of "hemi-
marsh" conditions—relatively equal proportions of open water and emergent wetland—indicates
that a wetland has probably undergone a wet-dry cycle of about 2-7 years. During such a cycle,
droughts lasting more than 1 year (Welling et al. 1988a) and flooded conditions lasting at least
one winter (Millar 1973, McKee et al. 1989) can damage or impair the recruitment of many
wetland emergent plants. For submersed plants, flood conditions lasting at least 2 years are
required for establishment of some species (e.g., Potamogeton pusillus, Millar 1976). In Iowa,
prairie wetlands restored within the last year had significantly more floating-leaved plants than
those restored 2 years previously (Hemesath 1991). Emergent plant cover was less, and open
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water area was greater, on restored wetlands that had been drained > 30 years ago than on
restored wetlands that had been drained more recently.
Models are available for predicting the duration of floods and droughts within a wet-dry cycle that
likely produced a particular number and configuration of wetland types (Woo et al. 1993, Larson
1995) and associated species assemblages (e.g., Poiani and Johnson 1993a,b,c; Poiani et al.
1995). Similarly, models are available for predicting future plant species composition, given a
particular hydrologic change (Poiani and Johnson 1993a,b,c, van der Valk et al. 1989, FWS
1993, de Swart et al. 1994). A computer simulation by Poiani and Johnson (1993a,b,c) indicated
that for a particular depth class, the ratio of open water to wetland vegetation (the cover ratio)
was always greater when water levels were held constant for 5 years than when they fluctuated
during that time. Thus, a relatively large cover ratio might be considered potentially indicative of
a relatively sustained input of water to a wetland.
Areal cover of submersed and floating-leaved plants changes less with increased inundation than
does the areal cover of emergent vegetation. However, cover of non-emergent species is
usually reduced somewhat because of increased wave action and turbidity (van der Valk and
Davis 1978). For example, although flooding to 1 m above normal in Delta Marsh, allowed
bladderwort (Utricularia vulgaris) to invade stands of cat-tail, whitetop, and common reed, the
flooding completely eliminated beds of sago pondweed (Potamogeton pectinatus) (Murkin et al.
1991). At the opposite extreme, complete drawdown lasting for much of a growing season has
catastrophic effects on most submersed species. However, a few submersed and floating leaved
species can survive up to 1 year of dessication e.g., Callitriche palustris, Potamogeton
gramineus, Myriophyllum spicatum, Lemna minor, Spirodela, Naias, Potamogeton pectinatus,
Marsilea mucronata, and Ceratophyllum demersum (Stewart and Kantrud 1972b, van der Valk
and Davis 1978, Cooke 1980, Davis and Brinson 1980, Nichols et al. 1989).
Most studies of flooding effects in prairie wetlands have focused on semipermanent wetlands. A
study of seasonal wetlands found that production (especially aboveground production) of one
emergent species, Scholochloa festucacea, increased in response to 10 weeks of flooding to a
depth of 25 cm in the spring (Neill 1994).
Seed Density
The density of seeds in wetland seed banks can be used to crudely approximate the time
elapsed since hydrologic alteration occurred in wetlands that now are dry. This requires
calibration to conditions currently present in similar but hydrologically unaltered wetlands. For
example, Wienhold and van der Valk (1989) found that mean total seed density in a series of
unaltered or recently (within 5 years) altered wetlands was 3600-7000/m2, but declined to 1400,
1200, 600, 300, and 160 seeds/m2 for the 10, 20, 30, 40, and 70 years after drainage,
respectively.
Germination Rate
Seedlings of most emergent species in prairie wetlands fail to germinate from areas where
sediments are not exposed until after early July. Germination is particularly hindered if the
previous growing season has been especially wet, thus supporting exceptional biomass of
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submersed plants (as well as of emergent species) whose residual litter can smother emergent
seedlings (van der Valk 1986). Consequently, the presence of viable emergent plant seeds, but
not seedlings, sometimes can indicate that a drawdown, if it occurred, did not occur until late in
the preceding growing season (Welling et al. 1988b, Meredino et al, 1990); that the wetland was
overgrown with submersed plants and filamentous algae the preceding year; and/or that
conditions prior to the growing season dramatically affected microbial and invertebrate
communities that normally decompose most litter.
3.4.2 Vascular Plants as Indicators of Changes in Vegetative Cover Condition
Species Composition
Species composition can be used loosely to indicate the severity and type of process that has
removed vegetation in a prairie wetland. A dominance of relatively tall and robust, adventive
species or their hybrids, as opposed to shorter emergent species, is one possible sign of a lack
of periodic disturbance from livestock, burning, mowing, or cultivation (Stewart and Kantrud
1972a). A relative scarcity of highly palatable (to cattle) plant species can also signify that
intensified grazing has occurred during drier years. Plants that are annuals (see Appendix A)
tend to be the most affected by early-season mowing (Stewart and Kantrud 1972a). Other
emergent species tend to be affected differentially by herbicides and fire (Thompson and Shay
1985). Information on shifts in community composition that can result from grazing and other
removal processes is summarized by Kantrud et al. (1986a) and Kirby et al. (1992). Indicator
species that suggest the occurrence of previous grazing, tillage, or mowing in prairie wetlands
are shown in Appendix A, which was expanded from Stewart and Kantrud (1972), Millar (1973),
and Kantrud et al. (1989). However, both Walker and Coupland (1970) and Stewart and Kantrud
(1972 a,b) concluded that land use factors (e.g., intensity of grazing, mowing, tillage), as
compared with hydrologic and water chemistry influences, are less important determinants of
species composition, except during drought years.
Community Zone Locations
Cover removal differentially affects the outermost zones of prairie wetlands because these are
the most accessible to equipment and grazing livestock. Presence of a deep emergent zone
surrounded by open water can be a sign of intense grazing (Stewart and Kantrud 1972a).
Species Richness
Decreases in wetland plant richness are sometimes a reflection of reduced intensity of
disturbance from grazing, fire, or other removal processes in prairie wetlands. Conversely, long-
term increases in species richness can indicate periodic occurrence of these disturbances.
However, species richness is a poor indicator of vegetation removal because in some instances,
the thinning of stands of native vegetation allows new plant species to obtain a competitive
foothold. Although this can increase the species richness initially, species richness can decline
over the long term if new species, as is often the case, are ones that are highly aggressive and
tend to form homogeneous stands. Even at 3 years after being restored, several Iowa wetlands
had significantly lower species richness within each of their vegetative zones (except the
deepwater zone) than did natural wetlands (Galatowitsch 1993 a,b).
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Biomass, Area! Cover, and Cover Ratio
Decreases in areal cover and biomass of wetland vegetation, and increase in the cover ratio, are
a strong indicator of recent fire, haying, excessive grazing, or contamination with herbicides have
occurred recently or for a prolonged period. Conversely, long-term increases in areal cover can
indicate relative absence of these disturbances. A survey of restored prairie wetlands in Iowa
(Galatowitsch 1993 a,b) found that shoot densities of emergent vegetation were less in restored
wetlands than nearby natural wetlands.
3.4.3 Vascular Plants as Indicators of Wetland Salinity
Species Composition
The species composition of vascular plants is an excellent indicator of wetland salinity in prairie
wetlands (Stewart and Kantrud 1972b, Looman 1981). Most freshwater macrophytes cannot
tolerate more than 10 ppt dissolved salts (Reimold and Queen 1974, Ungar 1974). Of the 195
major plant species in prairie wetlands, the general categories of salinity that describe the
occurrence of 157 (79%) species are known (Appendix A), and quantified tolerance (or
preference) ranges are available for 120 species based on presence or absence in wetlands
spanning a salinity gradient (Appendix D). For many species, exact thresholds of salinity
tolerance vary by the type of salt (Mg, Na, etc.), life stage, genetic population, duration of
exposure, temperature, and other factors (Lieffers and Shay 1983).
Species Richness
Diminished species richness can be a sign of hypersaline conditions (Reynolds and Reynolds
1975).
Biomass and Cover Ratio
Compared to most prairie wetlands, hypersaline wetlands probably have lower areal cover and
biomass of emergent vegetation, but only a few data are available (e.g., Wali 1976). An analysis
of experimental data from the Delta Marsh suggested that, at least for some species, the
influence of salinity on vascular plant production can overshadow the effects of other factors
associated with water level change (Neill 1993). In Australia, saline lakes sometimes support
greater densities of submerged macrophytes (and consequently waterfowl) because salts
enhance the flocculation of suspended clay particles, thus increasing water clarity and light
penetration (Kingsford and Porter 1994). In contrast, in England increases in lake salinity may
cause shifts in plant community dominance from submerged plants to phytoplankton, at least at
intermediate levels of nutrient loading (Moss 1994). In Ohio, fertilization of salt-tolerant inland
wetland plants with nitrogen spurred their production (Loveland and Ungar 1983). In a study of
four prairie wetlands, Fulton et al. (1979) and Fulton and Barker (1981) found that "vegetation
density was a better indicator of soil properties than was the type of vegetation."
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Germination Rate
Seed germination rates are inhibited by high (> 2 mS/cm) salinities, as occurs most often during
drawdown events (Smith 1972, Galinato and van der Valk 1986).
Biomarkers
Preliminary experiments by Mendelssohn and McKee (1992) suggest that the concentration of
proline, an amino acid, in plant tissue can indicate the occurrence of salt stress that occurred
during the previous five days.
3.4.4 Vascular Plants as Indicators of Sedimentation and Turbidity
Species Composition and Community Zone Locations
A shift in community composition from submersed species to emergent and floating-leaved
species can signal recent increases in turbidity, which could be attributed to suspended sediment
and/or phytoplankton (Niemeier and Hubert 1986, Hough and Forwall 1988). Submersed plants
in northern latitudes often have only a brief period during which they must grow sufficiently to
reach the water surface. If turbidity or other factors inhibit their growth during this period, or if a
late winter shortens the growth period, submersed plants may not reach the water surface before
their growth is stunted by algal blooms that begin in early summer (Engel and Nichols 1994).
Submersed plants are generally less tolerant of increased turbidity than are benthic algae and
phytoplankton (Dennison et al. 1993).
Some of the submersed plant species of greatest value to waterfowl and invertebrates do not
persist when the Secchi disk depth is less than about 0.3 m (Chambers and Kalff 1985, Kantrud
1990). Secchi transparencies of 0.2-0.4 m are common for brief periods during algal blooms in
prairie wetlands (Barica 1975). Tolerances of submersed plants to turbidity are defined more
accurately by the light compensation point of each species, the point where its photosynthesis
equals its respiration (Dennison et al. 1993, Kahl 1993). In Wisconsin, submersed species that
are most tolerant of turbidity are characterized by rapid growth during the early spring, summer
leaf canopies, and winter tubers or rhizomes (Engel and Nichols 1994). Data on relative depth
maxima and ranges for many submersed species are compiled in Davis and Brinson (1980), and
a more refined database is currently being developed by USEPA's Wetlands Research Program
(N. Detenbeck, personal communication, USEPA Environmental Research Laboratory, Duluth,
MN). The more shade-tolerant submersed species can perhaps be identified from information on
turbidity in Appendix A.
The relative extent of submersed species also declines with increasing sediment input when the
sediment so fills a depression that water depths become too shallow and standing water fails to
persist through the growing season (Edwards 1969). Similarly, in very shallow, temporary
wetlands, sedimentation probably increases the dominance of non-wetland species.
The species composition of emergent species might also indicate relative degree of
sedimentation because mature plants, and perhaps their seeds, differ with respect to tolerance to
burial (van der Valk et al. 1981). Although stiff-stemmed emergents (e.g., cat-tail, common reed)
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seem least affected by sedimentation, comparative data for other prairie species are mostly
lacking.
Species Richness
Turbidity and sedimentation can reduce the species richness of wetland plants (e.g., Engel and
Nichols 1994), both directly and because of secondary hydrologic impacts. If the effects of
sediment on germinating seeds can be assumed to be similar to the effects of accumulating plant
litter, then the results of van der Valk's (1986) experiments in a prairie wetland suggest that the
species richness of adult emergent wetland plants would be reduced by increased sediment.
The viability of the seeds of most of these plants, however, is relatively unaffected when covered
by up to 35 cm of sediment (van der Valk and Davis 1979); removal of overlying sediment should
allow them to germinate.
Biomass and Cover Ratio
As described above, turbidity definitely decreases the areal cover of submersed macrophytes.
Decreases in areal cover or biomass of emergent species are probably not as strong an indicator
of sedimentation, but data are lacking. If the effects on germinating seeds of sediment can be
assumed similar to the effects of accumulating plant litter, then the results of van der Valk's
(1986) experiments in a prairie wetland suggest that the shoot density of emergent wetland
plants would be reduced. In deep permanent basins, sedimentation can increase the area of
substrate within the euphotic zone that can be colonized by wetland plants thereby supporting
increased areal cover of emergents.
Germination Rate
Repeated burial by as little as 5 cm of sediment per year can be detrimental to seedlings of some
emergent species (van der Valk et al. 1981). In Delta Marsh, a sediment layer of at least 1 cm
significantly reduced seed germination of many emergent species, and a layer of 4-5 cm
prevented germination of most of the species tested (Galinato and van der Valk 1986).
3.4.5 Vascular Plants as Indicators of Excessive Nutrient Loads and Anoxia
Species Composition and Community Zone Locations
Declines in submersed plants, and increases in emergent and especially floating-leaved plants
(e.g., Nuphar, Lemna, Wolffia), are one sign of increased inputs of nutrients to wetlands,
especially in wetlands that initially had been nutrient-poor. Although a few submersed species
Ceratophyllum demersum, Utn'cularia vulgaris) appear to tolerate moderate nutrient additions,
most submersed species decline. This is because algae respond more quickly than vascular
plants to nutrients; consequently they proliferate in open water areas in the form of light-
obscuring blooms that limit submersed macrophyte growth and reproduction (Mulligan et al.
1976, Phillips et al. 1978). Most emergent species (except those capable of forming floating
mats) increase less rapidly in response to waterbome nutrient inputs than do floating-leaved
species (Ozimek 1978, Shimoda 1984, Graneli and Solander 1989) because the emergent plants
obtain nutrients mainly from the sediment, whereas most floating-leaved plants obtain nutrients
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directly from the water column. Enrichment can also stress individual emergent plants, if it
causes algal blooms that, upon collapse, deprive sediments of oxygen for prolonged periods as
they decay (e.g., McDonald 1955, Barica and Mathias 1979, Barica 1984, Hartog et al. 1989).
Of the emergent species, various annual species, as well as cat-tail and common reed, often
dominate enriched wetlands (Kadlec 1979, Hartland-Rowe and Wright 1975, Finlayson et al.
1986, Kadlec 1990, Kadlec and Bevis 1990). Species that are most efficient in transferring
oxygen to their roots might flourish the most under highly enriched conditions, when sediment
oxygen levels decline (Barko and Smart 1983). Cat-tail, for example, effectively transfers oxygen
and also requires only trace amounts of dissolved oxygen for germination (Leek and Graveline
1979). Chemical conditions appear to have a greater influence on the presence of rarer,
perennial species than on the occurrence of aggressive, common species (Pip 1979). Many
reports, especially in the European literature, have categorized individual wetland plant species
according to their nutrient-level preferences, and thus as to their potential as indicators of
eutrophication (Moyle 1945, Swindale and Curtis 1957, Stewart and Kantrud 1972b, Pip 1979,
Wiegleb 1981, Zoltai and Johnson 1988, Husak et al. 1989). Plant species composition is less
effective in reflecting moderate enrichment than severe enrichment. This is because algal and
microbial communities are often initially more effective than vascular plants in assimilating inputs
of nutrients (Richardson and Marshall 1986).
Species Richness
Increasing species richness of herbaceous plants, particularly of emergent species, can signal
moderate increases in the fertility of a wetland (Pip 1987a,b; Graneli and Solander 1988).
However, severe enrichment can decrease species richness for reasons given above (Lind and
Cottam 1969, Lachavanne 1985, Tilman 1987, Hough et al. 1989, Toivonen and Back 1989).
Although plant richness is correlated positively with nutrient levels (especially phosphorus) in
prairie wetlands, water chemistry variables explain less than half the total variability in plant
richness (Pip 1987a).
Biomass and Cover Ratio
Increases in areal cover and biomass of non-submersed wetland plants are commonly one sign
of enrichment. Variables that describe plant characteristics having short turnover times, such as
aboveground biomass and leaf area of annual plants, can be relatively sensitive indicators of
enrichment. Cat-tail biomass and production respond to annual fluctuations in nitrate, making
cat-tail a possible indicator of erratic inputs of nutrients (Davis 1989). In one fertilization
experiment in a prairie wetland, the aboveground production of cat-tail (Typha glauca) increased
19% and that of burreed (Sparganium eurycarpum) increased 57% (Neely and Davis 1985a).
However, enrichment of prairie wetlands does not always increase plant biomass and production
in the long run. This is because the shading and smothering effect of litter that remains following
1 year's excessive production can inhibit the germination and growth of individuals in successive
years (Nelson and Anderson 1983, Neill 1990).
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Biomarkers
Experiments by Mendelssohn and McKee (1992) and others suggest that the concentration of
alcohol dehydrogenase in plant roots can indicate the occurrence of oxygen stress, perhaps from
overenrichment, within the previous five days.
3.4.6 Vascular Plants as Indicators of Pesticide and Heavy Metal Contamination
Limited data on the effects of pesticides and metals on prairie wetland plants are contained in
tables published by Sheehan et al. (1987), and in USEPA's PhytoTox database. A host of
factors associated with actual applications influence contaminant toxicity and plant mortality
(Doust et al. 1994), and these factors can include:
Environmental Factors: water temperature, organic content, pH, alkalinity,suspended
solids.
Dose Factors: concentration, the specific compound or formulation (inert ingredients),
frequency of application or exposure, duration of exposure.
Biotic Factors: the plant species, its life stage (season), degree of simultaneous stress
from other factors.
Species Composition
Wetland plant species differ in their tolerances of various heavy metals, selenium, and
herbicides. Consequently, changes in species composition can indicate past and ongoing
incidents of exposure to these contaminants.
Although few studies have compared relative sensitivities of various wetland plants to a particular
contaminant, data compiled from many single-species experiments involving heavy metals
suggest that emergent plants might be generally more tolerant of heavy metals than submersed
plants, which in turn might be more tolerant than algae (Outridge and Noller 1991). Duckweed
(Lemna) appears to be particularly sensitive to the heavy metals cadmium and nickel, and
chromium concentrations of 10 mg/L are inhibitory (Huffman and Ailaway 1973). Cat-tail can
tolerate lead, copper, and chromium accumulations of at least 10 jjg/g (dry weight) of
aboveground biomass; zinc accumulations in cat-tail can reach 25 (jg/g (dry weight) without
apparent ill effects (Mudroch and Capobianco 1979). Common reed can tolerate industrial
wastewater with high levels of heavy metals (e.g., up to 250 micrograms/g sediment copper
concentrations), as do bulrushes (Seidel 1966). In an Ontario river, submersed species (Elodea,
Ceratophyllum, and Myriophyilum) appeared to be less tolerant of industrial wastes than floating-
leaved and short, rooted aquatic plants (Potamogeton, Nuphar, and Nymphaea), which were in
turn less tolerant than cat-tail and common reed (Dickman et al. 1980, 1983; Dickman 1988).
By knowing the characteristic sensitivities of various plant species, plant community composition
can be used cautiously to infer past exposure to particular contaminants. For example,
application of one herbicide resulted in reduced dominance of Phragmites austraiis but increased
dominance of particular Lemna, Callitriche, and Potamogeton species. Floating-leaved
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herbaceous plants are sensitive to the physical effects of oil compounds associated with
herbicide applications, and growth of the duckweed Spirodela oligorhiza is affected by PCB
concentrations of 5 mg/L (Mahanty 1975). Cat-tail can tolerate petroleum oil concentrations of 1
g/L (Merezhko 1973) and, along with common reed (Phragmites), appeared to be the most
tolerant macrophyte downstream from an industrial effluent source in Ontario (Dickman 1988).
Bulrushes are killed by phenol concentrations of 100 mg/L and abnormalities occur at large
phenol concentrations, but new shoots form quickly (Seidel 1966).
Species Richness
Species richness of vascular plants in prairie wetlands would be expected to decline in response
to atypically high loadings of certain heavy metals and especially to chronic exposure to
herbicides. However, documenting data are lacking.
Biomass and Cover Ratio
Herbicides have an obviously direct effect in reducing the areal coverage of selected vascular
plants to which they are applied directly, especially early in the growing season. Indirect effects
also can occur. Emergent cat-tails can be killed by herbicides applied for control of submersed
plants (Newbold et al. 1974). Low concentrations of the popular herbicide, glyphosate
sometimes stimulated growth of sago pondweed, Potamogeton pectinatus, a major submersed
plant in prairie wetlands (Hartman and Martin 1985), but glyphosate is lethal to most emergent
wetland plants at typical application levels (Sheehan et al. 1987).
Relatively few chemical assays have been conducted in field mesocosms in the prairie region.
The commonly used herbicide, atrazine, has been examined the most. A review of published
literature on atrazine effects found a wide range of concentrations (0.050-1.310 mg/L)
associated with adverse effects on one prairie wetland species, Potamogeton perfoliatus
(Hofmann and Winkler 1990, Swanson et al. 1991). The concentration that would be expected to
occur immediately after a 0.5-ha prairie wetland is directly sprayed with atrazine would be about
0.413 mg/L (Sheehan et al. 1987), and chronic levels in prairie wetlands appear to be much lower
than this (Ruelle and Henry 1993). A concentration of 1 mg/L atrazine caused a 50% decline in
biomass of three macrophytes (Lemna, Ceratophyllum, and Elodea) over a 30 day period in a
prairie wetland mesocosm (Johnson 1986). The pesticides carbofuran, fonofos, phorate, treflan,
and triallate had no statistically significant effect on biomass of the plants mentioned above
(Johnson 1986). Effects of herbicides on flowering of plants and storage of energy reserves (as
seeds or tubers) has received little study in prairie wetlands, and could have important
implications for waterfowl that feed extensively on these items and their associated invertebrates
just prior to migration in late summer and early fall. Effects of heavy metals and selenium on
prairie wetland plants are mostly unstudied. Selenium is toxic to some wetland plants at
concentrations greater than 1.25 mg/L (Ornes et al. 1991).
Bioaccumulation
Wetland plants rapidly take up selenium (Ornes et al. 1991) and bioaccumulate many other
heavy metals (Freemark et al. 1990). Cat-tails are among the few plant species that have been
analyzed for bioaccumulation of contaminants in prairie wetlands (True and Dornbush 1984).
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Biomarkers
Activity of the enzyme, peroxidase, has demonstrated usefulness in aquatic plants as a marker
of previous exposure to several metals and organic contaminants (Byl 1994).
3.5 Monitoring Techniques
Many documents provide detailed guidance on sampling herbaceous vegetation (e.g., Higgins et
al. 1994). Some address wetlands specifically, for example, Phillips (1959), Schwoerbel (1970),
Mueller-Dombois and Ellenberg (1974), Woods (1975), Dennis and Isom (1983), Downing and
Anderson (1985), Moore and Chapman (1986), Fredrickson and Reid (1988a), van der Valk
(1989). Methods for measuring production of prairie species are described by Neill (1993).
3.5.1 Ground-based Sampling
If wetlands can be sampled only once, mid-growing season is usually the recommended time.
However, many plants are apparent and/or identifiable only for a few weeks of the growing
season. Thus, if the aim is to estimate annual production or quantify community composition
accurately, repetitive visits that account for the diverse phenologies of wetland species should be
implemented (Dickerman et al. 1986, Smith and Kadlec 1985). Ideally, annual visits should be
timed to coincide with year-specific weather conditions, rather than calendar dates. Trampling of
herbaceous vegetation and compaction of saturated soils during even a single site visit can
induce community changes detectable in subsequent visits. Thus, field crews should be as small
as possible and follow the same path in and out of a wetland. In deeper wetlands, use of
underwater SCUBA transects is sometimes appropriate (Schmid 1965). If herbaceous wetland
vegetation must be sampled destructively in order to obtain specimens for identification (e.g., in
very turbid or deep waters), then equipment such as dredges, oyster tongs, plant grappling
hooks, and steel garden rakes can be used (Britton and Greeson 1988). Equipment designed
specifically for sampling submersed macrophytes is described by Macan (1949), Woods (1975),
Dromgoole and Brown (1976), Satake (1987), and others. However, whenever possible, plants
should be identified in the field rather than collected.
3.5.2 Aerial Methods
The EMAP monitoring effort is using low-altitude aerial metric photographs (scale about 1:2400)
to measure vegetation cover annually on each of 48 wetlands. Low-altitude imagery has been
used successfully in several previous studies in the prairies (Kreil and Crawford 1986, Welling et
al. 1988b, van der Valk and Squires 1992, van der Valk et al. 1994) to differentiate emergent
plant communities, stem density classes, and in some cases, species. Filters and image-
processing techniques can also be used to highlight various spectra, such as those sensitive to
chlorophyll-a (Patience and Klemas 1993). Under ideal circumstances, such an approach might
be used to indicate the presence, relative biomass, and condition of particular wetland species.
When communities or species can be distinguished reliably from aerial images, the relative
spatial extent (percent cover) of the communities or species can be measured more accurately
and cost-effectively than from ground transects. In the van der Valk and Squires (1992) study,
false infrared imagery from an altitude of 610 m was used. Aerial videography is also being used
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more frequently for measuring wetland structure and determining wetland integrity (Cowardin and
Sklebar 1993), and can delineate cover types covering as little as 1 m2 in some instances (Olson
1992). As demonstrated by Welling et al. (1988b) and described by Caldwell and van der Valk
(1989), aerial imagery can be used to make maps which, when overlaid with bathymetric maps
derived from field transects, are useful for measuring the depth ranges of various plant species
and provide essential information that cannot be derived from quadrat data. Although aircraft are
commonly used for obtaining the photographs, tethered balloons with remotely triggered cameras
also show some potential for use in monitoring prairie wetlands (Edwards and Brown 1960), and
cost about $20 each, excluding the camera (Davis and Johnson 1991).
3.5.3 Potential or Historical Vegetation
Because the vegetation in prairie wetlands shows such tremendous interannual variability, seeds
lying in wetland soils or sediments (the "seed bank") are often sampled in lieu of or in addition to
mature plants. The assumption is that, because seeds decompose much more slowly than
foliage (over decades rather than months), they indicate conditions not necessarily shown by live
vegetation. Similarly, pollen from prairie wetlands remains intact in anaerobic sediments for long
periods, and under some circumstances can be identified to species under a microscope,
potentially yielding information on the nutrient status and water levels present historically in the
wetland (Watts and Bright 1968, Vance and Mattewes 1994).
The simplest and most economical method of analyzing the seed bank is known as the seedling
emergence (or seedling assay) method. Procedures are described by van der Valk and Davis
(1978) and Galatowitsch and van der Valk (1994). Each sample consists of about 1000 cm2 of
substrate removed to a depth of 5 cm. Large organic matter (leaves, etc.) is removed,
sometimes using a coarse sieve, and the sample is distributed in a shallow (
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often considerable inter- and intraspecific variation in seed set, seed viability, and seedling
competition. For these reasons, seed bank analysis techniques are inadvisable where the
objective is to inventory rare species. In particular, some seedbank assays tend to overestimate
annual "weedy" species (those with readily geminable seeds) while underestimating certain
species that are especially sensitive to competition and/or which inhabit the drier zones of
wetlands (S, Galatowitsch, personal communication, University Minnesota, St. Paul, MN).
Nonetheless, other evidence (Poiani and Johnson 1988) suggests that the influence of such
variation on the overall ability to characterize a wetland's past or potential vegetation is, at least
some of the time, probably minor.
3.5.4 Bioassay Methods
A review of laboratory, outdoor mesocosm, or in situ bioassay methods involving vascular plants
is beyond the scope of this document. Use of bioassays to explore contaminant toxicity to plants
in prairie wetlands has been relatively limited. Examples include studies by Johnson (1986),
Wayland and Boag (1990), and Ruelle and Henry (1993). Both mature plants and seeds have
been assayed. Test species and protocols for assaying the effects of pesticides or other
contaminants on wetland plants are proposed by Freemark et al. (1990), Smith (1991), Swanson
et al. (1991), and Doust et al. (1994).
3.5.5 Bioaccumulation
Methods for collecting wetland plants and assessing bioaccumulation of contaminants in plant
tissues are described in Moser and Rope (1993b).
3.6 Variability and Reference Points
The following subsections address spatial and temporal variability of vascular plant community
composition in prairie wetlands.
3.6.1 Spatial Variability
Spatial variability has been quantified for species richness, biomass and germination rate.
Species Richness
As a point of reference, approximately 922 herbaceous vascular plant species characteristic of
pothole or riverine wetlands have been recorded in the North Plains region, which includes the
Dakotas and eastern parts of Montana, Wyoming, and Colorado, but not parts of the prairie
region in Minnesota and Iowa (Reed 1988). In prairie counties of North Dakota, about 135 (15%)
of the North Plains region's wetland species are considered "rare" by the State's Natural Heritage
Inventory (Appendix I); this represents 55% of the 245 rare plant species occurring in any habitat
in North Dakota. Most of the rare wetland plants are associated with riverine, bog, or forested
wetlands rather than with typical prairie pothole basins.
About 191 (21%) of all the North Plains wetland species occur frequently as dominants in prairie
pothole wetlands (Appendix A). Among the dominant species are 17 species (9%) officially
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considered to be "introduced" (Reed 1988), of which five are annuals. These are: Acorus
calamus, Agropyron repens, Artemisia biennis, Bidens cemua, Cirsium arvense, Cirsium
floodmannii, Echinochloa crusgalli, Echinochloa muricata, Glaux maritima, Glyceria maxima,
Kochia scoparia, Lysimachia thyrsi flora, Lythrum salicaria, Plantago major, Sonchus arvensis,
Spirodela polyrhiza, and Stachys palustris. The total number of annuals that sometimes
dominate prairie wetlands is 33 (17% of all dominant species in prairie wetlands) (Appendix A).
In a survey of 112 prairie potholes in southern Manitoba, Pip (1979) found more than 47 vascular
plant species. In a subset of 39 of the sites, she found 32 species with a mean of 5.3 species
per wetland, and noted that even wetlands that were adjacent seldom had similar floras. In a
larger (n = 177) set of Manitoba wetlands, she found a mean of 4.9 species per wetland. In a
survey of 261 vegetation stands in 82 prairie potholes in northeastern Montana, Lesica (1993)
found a total of 173 species, of which 12 were exotics. Among seven wetland complexes
ranging in size from 7 to 15 ha, he found between 48 and 74 species per complex, and between
6 and 11 plant communities. Species richness was not strongly correlated with community
richness. From five natural wetlands in eastern North Dakota, Kreil and Crawford (1986)
reported a total of 64 vascular plant species. From 140 quadrats (each 1.0 x 0.5 m) in temporary
and seasonal wetlands of eastern North Dakota, Hubbard et al. (1988) collectively found 41, 38,
20, and 16 species (on Tetonka, Parnell, Worthing, and Southam soil types, respectively). In
part of the Delta Marsh, 65 plant species were found during four growing seasons, although no
more than 48 of these were present in any single year (Squires and van der Valk 1992).
In a survey of 20 semipermanent wetlands in prairie regions of Iowa, Galatowitsch (1993 a,b)
found a total of 158 species, of which 106 were species that typify wetlands. Collectively, there
were 133 species in ten natural wetlands (range 41-63 per wetland, mean = 45,8) and 83
species in ten restored wetlands (range 24-52 per wetland, mean = 26.9). There were 75
species (48 of them "wetland" species) that occurred only in the natural wetlands, and 25 species
(10 of them "wetland" species) that occurred only in the restored wetlands; most of the latter
species were submersed plants or species planted for erosion control. Between 1 and 22
species were in the driest parts of each wetland, 7 and 49 species in the sedge meadow zone,
and 7 and 19 species in the shallow emergent zone, Seedbanks of the natural wetlands
contained 15 species, as compared with eight species in the restored wetlands.
Number of species in the Delta Marsh seed bank varied from 8 to 20/m2, depending on the zone
and depth from which the samples were collected (van der Valk and Davis 1979, van der Valk
1986). Over 90% of the seeds in most seed banks consists of fewer species than this (Wienhold
and van der Valk 1989). However, germination of all seed bank samples from the Delta Marsh
resulted in a cumulative total of over 40 species. Up to 14 species were found in seed banks in
11 wetlands in Iowa (Wienhold and van der Valk 1989) but in Iowa's Eagle Lake wetlands, van
der Valk and Davis (1978) found 45 species. Cumulatively, from a sample of eight smaller Iowa
wetlands, van der Valk and Davis (1976) found 29 species. One seedbank survey of 35 Iowa
wetlands found an average of 16 species per wetland (Clambey 1975) and another survey of four
Iowa wetlands found 24 species per wetland (LaGrange and Dinsmore 1989b).
Within individual semipermanent wetlands, richness is generally less in the wetter marsh zones
than in drier meadow zones upslope (Nelson and Anderson 1983). From a survey of 246 stands
of wetland vegetation in southern Saskatchewan, Walker and Coupland (1970) found the
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greatest richness in the wet meadow and marsh meadow portions of wetlands that were slightly
saline and lightly grazed and mowed (43 species in nine wet meadow stands, 37 species in
seven marsh meadow stands). Across a gradient of moisture, salinity, and disturbance from
grazing and mowing, the stands that had the most unique floras were those that were the
wettest, the most saline, and the most disturbed.
Biomass, Density, Cover Ratio
Biomass also varies greatly by species and spatially. In the Canadian prairies, aboveground
biomass varied from 425 g/m2 for one emergent species (Scirpus lacustris) to 1750 g/m2 for
another (Typha latifolia) (Shay and Shay 1986). For just a single species (cat-tail), aboveground
biomass among several prairie wetlands can range 0 to 2106 g/m2 (van der Valk and Davis 1978,
Shay and Shay 1986), and up to 2400 g/m2 under conditions of artificial enrichment (Neill 1990).
Among seven communities of emergent wetland vegetation in eastern North Dakota, the net
production was reported to range from 0.30 to 0.97 g/m2/day (Hadley and Buccos 1967).
Submersed and floating-leaved species tend to be less productive than emergent species.
In describing their seed bank analyses, van der Valk and Davis (1978) noted, "Among replicate
samples within a vegetation type, there [is] a great deal of variation in the number of individuals
of a given species ... standard deviations are larger than the means in many cases." Moreover,
some of the species currently present in a wetland will be absent from seed banks, whereas
others may have seed densities in the sediment of several thousand per square meter. In the
Delta Marsh, total seed densities in 250 sediment samples averaged 4582/m2, and ranged from
140/m2 in open water areas, to 2230/m2 in common reed stands and 5810/m2 in cat-tail stands
(Pederson 1981). However, van der Valk and Davis (1979) found the number of viable seeds in
the upper 5 cm of the Delta Marsh seed bank to vary from 7363 to 56,289/m2, depending on the
zone and sediment depth from which the samples were collected (analyses of cores extending
from the surface down to 35 cm revealed a maximum of 255,000 seeds/m2). In a North Dakota
wetland, seed densities across wetland zones varied by a factor of about 8; a maximum of
9370/m2 was found (Poiani and Johnson 1989). In Iowa 10 natural (undrained, seasonally or
semipermanently flooded) wetlands had an average seed density of 7369/m2 whereas 10 nearby
restored wetlands (previously drained, now permanently flooded) wetlands had an average of
3019/m2 (Galatowitsch 1993 a,b). In contrast, in Minnesota a series of 30 undrained wetlands
had an average seed density of 3600/m2, whereas five recently drained wetlands had an average
as high as 8000/m2 (Wienhold and van der Valk 1989).
Cover ratio varies tremendously among prairie wetlands, but few estimates of spatial variability
are available.
Germination Rate
Even within a single species, germination rates can vary considerably, e.g., 14%—51 % for cat-tail
(Weller 1975).
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3.6.2 Temporal Variability
Species Richness
Over a 7-year period, species richness of plants in a single, 1.7-ha, semipermanent wetland in
Iowa varied from 7 to 19 species per year (Weller and Voigts 1983). Only one species was
present all 7 years of the wet-dry cycle; two of 23 common species occurred during only 1 or 2
years. Over an 85-year period, vascular plant richness in an Iowa prairie lake varied from about
11 to 29 taxa (Niemeier and Hubert 1986). Apparently only one of 52 species was found
throughout the period.
Biomass and Cover Ratio
Over a 5-year period, the biomass of one plant species (Scirpus validus) in an Iowa wetland
varied from 0 to 486 g/m2; that of another (Sparganium eurycarpum) varied from 271 to 543 g/m2;
and that of a third (Typha glauca) varied from 772 to 1075 g/m2 (van der Valk and Davis 1980).
Within a single year, the aboveground biomass of Carex rostrata in a single Minnesota wetland
varied from 114 to 852 g/m2, whereas the belowground biomass varied from 150 g/m2 to 328
g/m2. Production peaked at 11 g (m2)"1da1 (Bernard 1974). High interannual variation in the
cover ratio typifies prairie wetlands. In a 10-year study of 71 Manitoba wetlands, Millar (1973)
found that areal cover changed in 32 (46%) of the wetlands, and of these, a complete conversion
to open water occurred in 17 wetlands. In a North Dakota prairie wetland, seed densities in
sediments varied from 2840/m2 one year to 9370/m2 the next (Poiani and Johnson 1989).
3.7 Collection of Ancillary Data
It is easier to separate the anthropogenic from the natural causes of impairment of community
structure if data are collected or inferred simultaneously on the following variables of particular
importance to vascular plants:
• age of wetland and its successional status
• light penetration (particularly for submersed species)
• water or saturation depth
• conductivity and general chemistry of waters and sediments
• abundance of herbivores (particularly muskrat, geese, grazing cattle, crayfish)
• sediment type
• duration, frequency, and seasonal timing of regular inundation
• time elapsed since the last severe inundation, drought, or fire.
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All of these features vary to a large degree naturally as well as in response to human activities
such as soil tillage, compaction, and erosion; fertilizer and pesticide application; introduction of
exotic species; and water regime modification.
3.8 Sampling Design and Required Level of Sampling Effort
If the sole objective is to inventory the presence or absence of plant species within wetlands,
then a timed search method that covers all the obviously recognizable zones may be appropriate
(e.g., Pip 1979, 1987a,b,c), but if vegetative cover and dominance are to be quantified, decisions
must be made concerning the physical layout of transects and/or quadrats within a wetland. This
task can pose a particular challenge if the objective is to characterize a wetland as a whole unit
rather than describe ecological relationships within just one zone or biological community.
Choices involve deciding whether to place quadrats in a purely random manner, or randomly
along transects, or at regular intervals along transects, or randomly/regularly within delimited
vegetation zones. If transects are used, there are further choices regarding whether to locate the
transects randomly (van der Valk and Davis 1978, Gurney and Robinson 1988, LaGrange and
Dinsmore 1989b), evenly (gridded), or in relation to vegetation zones. Statistical methods are
available for defining zones somewhat objectively as demonstrated in prairie wetlands by
Johnson et al. (1987). The alignment of transects is usually perpendicular to shore (Wienhold
and van der Valk 1989), but can be parallel to the long axis of the wetland (Niemeier and Hubert
1986), or follow the four compass axes (e.g., Poiani and Johnson 1988). When transects are
used, vegetation can be enumerated using point counts (plants intercepted by the transect at
specified intervals are counted), intervals (simple presence/absence of plants intercepted by the
transect at specified intervals is noted), or intercepts (all plants intercepted are counted). Based
on data from one prairie wetland, Weller and Voigts (1983) concluded that the intercepts method
was least cost-effective, and the other two methods gave similar results. The EMAP effort has
characterized species composition, relative dominance, and richness of prairie wetlands by
randomly locating five 0.25-m2 quadrats within each vegetation zone of each wetland while
walking through the center of the zone parallel to its long axis. The rationale given for choice of
this method is that vegetation in prairie wetlands tends to occur as concentric zones, which
would not be well-sampled by transects perpendicular to the shore or center of the wetland.
Species not found by this method, but observed while walking between quadrats, are also
recorded.
3.8.1 General Considerations
To characterize vegetation at sampling points, investigators (e.g., Wienhold and van der Valk
1989) have used the Releve method (Mueller-Dombois and Ellenberg 1974), the Daubenmire
approach (Daubenmire 1959), or the Braun-Blanquet (1932) approach (e.g., LaGrange and
Dinsmore 1989a). In some situations the midpoint values of cover classes can be averaged to
obtain mean cover values for each species within a zone (vegetation community), and these
values can be weighted by area to obtain total cover values for species within zones.
If the sole purpose is to assess the habitat value of vegetation as shelter for wildlife (i.e., cover of
individual species does not need to be determined), then visual obstruction readings (Robel et al.
1970) and profile board methods (Jones 1968) can be used (Duebbert and Lokemoen 1976,
Nudds 1982, Higgins 1986, Barker et al. 1990). Cover estimates can be made once at the peak
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of vegetation development and/or during bird nesting, but additional estimates may be desirable
earlier in springtime (to estimate residual matter) and at other dates if livestock are grazing the
wetland. Cover estimations made by the EMAP effort featured the Daubenmire approach; cover
was estimated as the portion of water surface (for emergents) or bottom (for submersed plants)
shaded by each species.
One factor that affects sampling costs is the desired level of taxonomic identification. Identifying
plants to the species level usually allows investigators to make more refined statements about
the condition of a wetland, but this identification can increase the required time and requires
them to have advanced training and experience. There are no data to indicate whether, and
under what conditions, identification of plants only to the genus or family level would be sufficient
to define the ecological integrity of a prairie wetland.
Sampling costs are determined not only by the time required to identify plants, but also by the
number of quadrats examined. This number should depend on expected variability (coefficient of
variation), wetland size, and the desired precision. Larger wetlands require more transects or
quadrats, usually spaced farther apart, to accurately characterize overall community composition.
More linear wetlands (e.g., narrow fringe marshes along lakes) usually require more tightly
spaced sampling points, as do ecotone areas along transects. In a Wisconsin lake, the number
of samples needed to adequately quantify the biomass of the entire submersed plant community
within various plant community zones, given a goal of maintaining a probability of 95% of being
within 15% of the mean, ranged from seven to 200, depending on the zone (Nichols 1984).
3.8.2 Asymptotic Richness: Results of Analyses
If the goal is not just to quantify species richness within samples but for the whole wetland (or
complex), then considerably more samples are required. The number will be determined not
through examination of coefficients of variation but by plotting species accumulation curves (the
cumulative number of species vs. number of samples, or vs. wetland area). Based on their
Saskatchewan wetland data, Walker and Coupland (1968) reported that up to 30 quadrats (0.5 m
* 0.5 m) per vegetation zone were necessary to characterize the species composition of the
zone, i.e., to level off the species accumulation curve. Apparently no similar analyses have been
prepared for other prairie wetlands.
For this document, we analyzed plant taxonomic richness from two data sets from prairie
wetlands. The database descriptions that follow are generalized. For a detailed description of
monitoring design and data structure of each data set, see Appendix L. One was from quadrats
along transects in a series of 20 semipermanent wetlands in Iowa (Galatowitsch 1993 a,b). Data
from the quadrats and transects were pooled into a single list for each wetland. Our calculations
of asymptotic richness indicated that, for example, only 11 wetlands would need to be sampled to
capture 99% of the taxa present in all 20 wetlands (Appendix O). The statistical approach used
to determine this was described in Section 1.5.
The 20 Iowa wetlands consisted of 10 restored and 10 natural wetlands. When the restored
wetlands were compared with the natural wetlands, restored wetlands were found to accumulate
taxa at a slightly more rapid rate. This was likely due to the greater homogeneity of species
composition among the restored wetlands. Thus, for monitoring designs and conditions similar to
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those of Galatowitsch (1993 a,b), restored wetlands would not have to be sampled quite as
extensively as natural wetlands. About eight wetlands would be a sufficient number to detect
90% of the plants in the combined population of 20 wetlands.
The other data set consisted of vegetation quadrats from the Marsh Ecology Research Program
(MERP) experimental units of Delta Marsh, Manitoba (Squires 1991). Data involving multiple
quadrat collections per year were combined from two wetlands to create a single taxa list for
each of four treatments:
DD1LOW: One year of drawdown, followed by reflooding to the original depth (n = 385
quadrats covering four post-treatment years)
DD2LOW: Two years of drawdown, followed by reflooding to the original depth (n = 362
quadrats covering four post-treatment years)
DD2MED: Two years of drawdown, followed by reflooding to an intermediate depth (n =
387 quadrats covering four post-treatment years)
DD2HIGH: Two years of drawdown, followed by reflooding to a high (deep) depth (n =
351 quadrats covering four post-treatment years).
Our calculations of asymptotic richness revealed the following ordering of species accumulation
rates by treatment (Appendix O):
DD2MED (fastest) > DD1 LOW > DD2HIGH > DD2 LOW.
The differences in accumulation rate among treatments were not great, and the results suggest
species composition was most homogeneous among the quadrats in the DD2MED treatment. In
most cases an average of about 37 quadrats was adequate to detect 99% of the taxa that were
cumulatively present in the full 351-387 quadrats, although for any particular treatment, as few
as 20 and as many as 40 quadrats could be needed, depending on the sequence in which the
quadrats are collected. Had all 3281 quadrats been considered regardless of treatment (a
situation that more closely approximates sampling of natural wetlands with varied water regimes),
about 448 quadrats would be needed to detect 99% of the species found in all 3281 quadrats.
3.8.3 Power of Detection: Results of Analyses
The Components of Variance approach, as described in Section 1.5, was applied to just the
Squires data set. As tabulated in Appendix M, the mean species richness per quadrat levels off
at a sample size of about 20 quadrats in wetland situations and monitoring designs similar to
those of Squires. To distinguish interannual (or between-wetland) changes of (for example) two
plant species, data from a total of between 9 (minimal) and 12 (maximum) quadrats would need
to be collected. Conversely, if budget or other considerations limited sample size to five
quadrats per wetland, then one would be able to detect a mean difference of 2.9 to 3.6 species
between wetlands or years. In both instances, we are assuming there is an 80% certainty of
being correct at the 5% level. From the same data set, if the objective is to estimate mean
seedling density per quadrat, then more than about 12 quadrats are needed in wetland situations
and monitoring designs similar to those of Squires. To distinguish interannual (or between-
wetland) changes in seedling density of (say) 60 seedlings per quadrat, one would need to
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sample using only about four quadrats, but to detect a change of only 20 seedlings one would
need to use 25-30 quadrats.
3.9 Summary
The species composition of vascular plant communities, and to a lesser degree their species
richness, can indicate changes in prairie wetland salinity, water regime, and (among submersed
species) sedimentation or turbidity (Table 3). Thresholds for responses are well-documented
with extensive published field data from the region. Vascular plants also respond sensitively to
changing nutrient levels, grazing, and presence of some contaminants, but existing information is
too limited and confounding effects are too prevalent to currently allow widespread use of
vascular plants to diagnose impairment of prairie wetlands from these stressors.
Vascular plant communities are exceptionally valuable indicators of conditions in individual prairie
wetlands because they are immobile and because they integrate stresses that have occurred
intermittently or chronically over months and years. This is especially true when wetland seed
banks can be analyzed. Analysis of seed banks, although time-consuming and containing some
biases, also can provide one piece of data useful towards defining appropriate "reference"
conditions for a wetland resource. Compared with use of other indicators, monitoring of plant
species composition causes minimal disturbance, involves (for most taxa) relatively simple
identification procedures, and does not require frequent sampling or tight scheduling within a
season. Where access to private property is restricted, the gross spatial patterns of vascular
plants within a wetland can be characterized using widely available aerial photography, and then
used cautiously to infer wetland ecological condition.
Individual prairie wetlands generally contain about 40-60 species of vascular plants, whereas the
cumulative total of wetland plant species in the prairie region exceeds 900. There are relatively
few quantified, published estimates of interwetland and interannual variability of vascular plant
richness and biomass in prairie wetlands. Plant species composition varies considerably among
otherwise apparently similar wetlands and varies among years as well according to a distinctive
wet-dry cycle.
Additional research is needed to improve the potential for using vascular plants as indicators of
prairie wetland condition. Descriptive investigations of life histories of many species are needed
so that the daunting number of species can be grouped into fewer functional assemblages (such
as defined by van der Valk 1981 or Boutin and Keddy 1993). Responses to various stressors of
such streamlined groupings can then be investigated more efficiently (and the results can be
generalized more accurately) than if responses of purported indicator species were the sole
focus of research and extrapolation. Research is also particularly needed to document the
threshold responses of different vascular plant seeds, seedlings, and mature plants to
sedimentation.
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Table 3. Summary evaluations of vascular plant indicators of stressors in prairie wetlands. Evaluations are
based on technical considerations, not cost or practicality. A rating of FAIR or POOR is assigned when too
few data (FD) suggest potential as an indicator, or when confounding effects (CE) of other variables often
overshadow the effects of the listed stressor on the indicator.
.' :::.v ¦!.:=
Hydrologic stressors
Species composition
GOOD
Community zone locations
GOOD
Richness (mature plants)
FAIR (CE)
Richness (seed banks)
FAIR (CE)
Biomass, cover ratio
GOOD
Seed density
FAIR (CE)
Germination rate
POOR (CE)
Changes in vegetative cover
Species composition
GOOD
conditions
Community zone locations
GOOD
Richness
POOR (CE)
Biomass, cover ratio
GOOD
Salinity
Species composition
GOOD
Richness
FAIR (CE)
Biomass, cover ratio
FAIR (FD)
Germination rate
FAIR (CE)
Biomarkers
FAIR (FD)
Sedimentation & turbidity
Species composition
GOOD
Richness
POOR (FD)
Biomass, cover ratio
FAIR (CE)
Germination rate
POOR (CE)
Excessive nutrients & anoxia
Species composition
GOOD
Richness
POOR (CE)
Biomass, cover ratio
FAIR (CE)
Biomarkers
POOR (CE, FD)
Herbicides
Species composition
FAIR (FD)
Richness
POOR (FD)
Density, biomass, productivity
FAIR (CE)
Decomposition
POOR (FD)
Insecticides
Species composition
POOR
Richness
POOR
Density, biomass, productivity
POOR
Decomposition
POOR
Heavy metals
Species composition
FAIR (CE)
Richness
POOR (FD)
Density, biomass, productivity
POOR (FD)
Decomposition
POOR (FD)
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4. Invertebrates as Indicators of Prairie Wetland Integrity
4.1 Ecological Significance and Suitability as an Indicator
Invertebrates include aquatic insects, freshwater crustaceans (e.g., amphipods, crayfish), aquatic
annelids (worms), zooplankton, and immature stages of certain terrestrial insects (e.g.,
Lepidoptera) that occur mainly in wetlands. The term "macroinvertebrate" or "macrofauna" refers
to the larger organisms clearly visible to the unaided eye, as opposed to microinvertebrates,
which include most smaller zooplankton, such as rotifers. Although invertebrates occur in
wetlands everywhere, prairie wetlands support notably great numbers. This is because prairie
wetlands have especially rich soils, slow water turnover times, and seasonally fluctuating water
tables, all of which support the high levels of algal production and spatially complex vegetative
stands that are important to invertebrates.
Invertebrates are the vital link that makes algal production and emergent plant material available
as an energy source for waterbirds and other animals. Invertebrates do this by consuming algae
and decaying plant material and then by being consumed by higher order animals (Driver et al.
1974). Invertebrates represent grazing, filtering, detritivore, and predator trophic pathways of
energy flow, and thus should reflect the status of these fundamental processes in a wetland.
Planktonic invertebrates (e.g., cladocerans) are potentially able to consume more than an entire
day's production of algae (Porter 1977). In doing so, they considerably improve and maintain
light penetration of the water column during the growing season. This in turn gives submersed
aquatic plants a chance to flourish (Hanson and Butler 1990), and these macrophytes serve as a
substrate that supports an even greater density of invertebrates, as well as a food source for
many organisms.
In some cases, waterbirds appear to select wetlands having the greatest densities of
invertebrates (Talent et al. 1982). Even where they do not, waterbirds spend more time foraging
in wetlands that have greater abundance of macroinvertebrates (Kaminski and Prince 1981a,b,
1984). Whereas larvae are eaten mainly by ducks, emerging insects are consumed by many
songbird species. The nutritional requirements of growing ducklings and breeding hens can be
fulfilled only by an invertebrate diet (Swanson et al. 1974, 1977). However, the degree to which
food supply—as opposed to vegetative cover and predation—truly limits the breeding and
reproductive success of waterfowl populations at a regional scale is unknown. The variety
(species richness) of invertebrates might be at least as important as the quantity because
waterfowl require or use different invertebrate species from different parts of the wetland at
different seasons (Swanson and Meyer 1973, Kaminski and Prince 1981a,b, 1984). Invertebrate
richness supports elevated waterbird richness because different waterbirds use different
invertebrate assemblages. If changes in hydrologic regime or turbidity cause changes in a key
habitat component of invertebrates (e.g., submersed plants), the invertebrate species associated
with that habitat could be reduced or eliminated from the wetland, even if the wetland remains
well-vegetated with other types of plants. If the affected invertebrates are critical to waterbirds,
waterbird productivity could suffer.
Soil macroinvertebrates (especially earthworms and certain midge larvae) also dominate the diet
of several shorebird species that stop to feed in prairie wetlands during migration. Yet, soil
invertebrates have seldom been monitored in temporary and seasonal wetlands, especially
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during portions of the growing season when surface water is lacking. Nematodes are one
abundant, diverse, and sensitive invertebrate assemblage that has been found by many
European studies to be a useful indicator of soil condition (Bongers 1990, Freckman and Ettema
1993), and might find similar application here. Quantitative sampling methods are relatively well-
developed (Schouten and Arp 1991), and additional research could document the relative
importance of nematodes to ecosystem processes.
Invertebrates are also important because they influence the amount of contaminants that are
available to other components of the food web, and the rate of contaminant cycling across
several ecotones (e.g., sediment-water column, wetland-upland). In the sediment, burrowing
invertebrates can make more generally available the nutrients (or contaminants) contained in
decaying plant roots. Nutrients released to the water column by invertebrates help sustain algal
productivity.
Several characteristics of invertebrates usually considered advantages for monitoring ecosystem
integrity (Adamus and Brandt 1990) include:
• documented, characteristic responses to all major wetland stressors (hydroperiod,
sediment, nutrients, contaminants); many "indicator taxa" identified
• varied larval lifespans, ranging from short (e.g., cladocerans) to long (e.g., dragonflies),
allowing use of invertebrates as indicators of both chronic and acute disturbances
• noninsect invertebrates reflect the quality of the wetland itself
(they usually complete their entire life cycle within a single wetland)
• invertebrates can be confined for whole-effluent bioassays or in situ assessments
» decay-resistant remains (e.g., shells) provide a means for establishing historical
reference conditions in a wetland
¦ can be sampled with inexpensive equipment
» invertebrate sampling protocols available from USEPA.
Certain characteristics of invertebrates usually considered disadvantages for monitoring wetland
integrity include:
• requires excessive time to adequately isolate organisms from debris in many types of
samples
• laborious identification beyond the family level
• difficulty in interpreting the occurrence of a particular species in an individual prairie
pothole wetland (it is unknown whether occurrence is related to conditions within the
wetland, proximity to sources of effective colonizers, or ephemeral conditions such as
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favorable winds that facilitated colonization; this is true mostly of insects and is less true
of invertebrates that do not emerge from the wetland as adults)
» difficulty in measuring precisely the true densities in dense stands of vegetation
» requires many techniques and sampling tools to sample all important invertebrate
assemblages present in a wetland.
4.2 Potential Indicator Metrics
When applied to invertebrate communities, the following measurements and metrics can be used
to characterize conditions in reference wetlands, identify the relative degree of past disturbance
of a prairie wetland, or assess the current inhibition of key processes:
• richness of species and functional groups (per unit of area or volume, or per a specified
number of randomly chosen individuals)
• number and biomass of individuals per unit of area or volume
• relative dominance and richness of species reputedly tolerant to a named stressor
• interannual variability in richness, density, and/or biomass
• homogeneity of size or biomass classes within a species population
• levels of tissue contaminants (biaccumulation)
• density of dormant but viable life stages.
The specific ways some of these metrics have been or could be interpreted as an indication of
stressed conditions are described in Section 4.4.1 However, apparently no studies in prairie
wetlands have systematically examined correlations among these metrics or their merits relative
to one another. A few studies of this type that have been completed in streams (Barbour et al.
1992, Kerans et al. 1992, Resh and Jackson 1993, Niemi et al. 1993) might be used as a model.
4.3 Previous and Ongoing Monitoring in the Region
Aquatic invertebrates have been the focus of at least 35 published studies, covering over 200
prairie wetlands (Appendix J). All of these studies measured numbers of individuals (or density)
and at least four measured biomass. Apparently only Duffy and Birkelo (1993) have attempted to
measure annual production. In most studies the invertebrates were identified only to family.
Seven studies have sampled a wetland for more than 2 years, and only one-third of the studies
involved sampling of more than five wetlands.
Montana's water quality agency (Department of Health and Environmental Sciences) is currently
using benthic (bottom-dwelling) invertebrates on a trial basis as an indicator of the condition of
five prairie wetlands. USEPA's EMAP has not yet undertaken monitoring of prairie wetland
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invertebrates at a regional scale, but EMAP has investigated various sampling methods in
dozens of North Dakota wetlands. Variables that are being estimated include species richness
and relative abundance. At a localized level, invertebrates have been used as possible
indicators of the success of wetland restoration efforts in Iowa (Hemesath 1993) and Minnesota
(Sewell 1989, Sewell and Higgins 1991). Research on ecological relationships of invertebrates,
especially as affecting waterfowl, continues to be conducted by scientists at the NPSC, by the
Minnesota Department of Natural Resources, and by universities.
To draw conclusions about wetland integrity from samples of invertebrates, it is essential to have
species-specific information on their tolerances and life histories. Appendix B indexes
invertebrate taxa according to water regime, and more detailed databases of this type have been
assembled by Euliss (personal communication, NPSC, Jamestown, ND). Also, a database that
classifies prairie wetland invertebrates by feeding type and waterfowl food importance is
maintained at North Dakota State University (Overland et al. 1993). The recent book by
Rosenberg and Resh (1993) categorizes over 200 invertebrate species, many of them prairie
wetland species, according to their tolerances to acidic conditions and organic pollution.
4.4 Response to Stressors
The following subsections describe responses of the invertebrate communities to hydrologic
stressors, vegetative cover conditions, salinity, sedimentation/turbidity, excessive nutrient
loads/anoxia, and pesticide and heavy metal contamination.
4.4.1 Invertebrates as Indicators of Hydrologic Stressors
Species Composition
The usefulness of species composition for inferring hydrologic conditions of prairie wetlands has
been demonstrated with midges (Driver 1977, Euliss et al. 1993), water beetles (Hanson and
Swanson 1989), and macroinvertebrates generally (Neckles et al. 1990, Bataille and Baldassarre
1993). Species composition can indicate how long and in what seasons a wetland has contained
surface water. This requires that each species first be classified as to its hydrological
requirements, a relatively simple procedure using life history categories such as defined by
Hartland-Rowe (1966), McLachlan (1970, 1975, 1985), Wiggins et al. (1980), Jeffries (1989), and
Eyre et al. (1991). Appendix B contains such information for dominant prairie wetland
invertebrates.
In general, prairie wetlands can be cautiously deduced to be of greater hydrologic permanence
when they contain a higher density and richness of longer-lived and/or relatively immobile
species (e.g., snails, mollusks, amphipods, worms, leeches, crayfish), as compared with the
density and richness of short-lived species (e.g., anostracans, conchostracans), species that
survive the winter as drought-resistant eggs (e.g., Daphnia), and/or species that are relatively
mobile (e.g., chironomid midges). This is probably due to the likelihood that drought and
drawdown render the less mobile species more vulnerable to predation, as well as causing their
direct loss because of desiccation and saline toxicity. From season-long, weekly activity-trap
sampling of three pothole wetlands 80 miles west of the Delta Marsh, Bataille and Baldassarre
(1993) found that a permanent wetland was dominated by cladocerans, a semipermanent
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wetland by ostracods, and a seasonal wetland by copepods. Considering just the emerging
aquatic insect component, the permanent wetland was dominated by midges; the semipermanent
wetland by water beetles (early season) and midges and other fly species (mid- and late-
season); and the seasonal wetland by midges (mid-season) and other fly species (late season).
Some evidence (Neckles et al. 1990), however, suggests that wetland water regime in particular
situations has little effect on the dominance of several major taxa that characteristically
overwinter as adults or larvae (species of Dytiscidae, Corixidae, Ceratopogonidae, Ephydridae,
and some Chironomidae). Caution is required in interpreting data because anecdotal evidence
suggests that some species with supposedly minimal dispersal abilities are frequently carried
passively into new areas by mobile waterbirds (Swanson 1984).
A shift from herbivorous to detrivorous species of macroinvertebrates, and in the ratio of open-
water forms (e.g., zooplankton, water striders) to forms that characteristically dwell in vegetation
(e.g., some mayflies), can suggest that a prairie wetland has recently undergone inundation
(Murkin and Kadlec 1986 a,b; Murkin et al. 1991). In particular, densities of non-predatory
midges (Chironomidae) increase greatly during the first year after flooding, and within this family,
species characterized by the greatest tolerance for low oxygen levels increase the most (Murkin
and Kadlec 1986b). Densities of swimming (nektonic) and bottom-dwelling (benthic) predatory
invertebrates do not increase with flooding as much as do numbers of nektonic and benthic
herbivores and detritivores. Predatory species can even decrease after flooding (Murkin et al.
1991), and they often increase as drought or drawdown progresses (Table 4).
Long-term changes in wetland hydrology might be inferred by collecting decay-resistant remains
of invertebrates from sediment cores or settling traps, and determining if the species present are
ones that occur mostly in semipermanent, seasonal, or temporary wetlands.
Species Richness
Data from North Dakota indicate that even the wetlands that are flooded only temporarily have
many more species than non-wetland areas (Euliss et al. 1993). Within wetlands, flooding can
increase invertebrate richness somewhat, but perhaps only during the initial year of flooding
(Table 5). For example, flooding of Manitoba marshes containing cat-tail, hardstem bulrush, and
common reed to a level 1 m above normal increased the variety of both nektonic and benthic
invertebrates in vegetation but not in open water (Murkin et al. 1991). The increase in benthic
taxa persisted for only a short period after flooding (Murkin and Kadlec 1986b). A similar pattern
was noted for midge diversity in other Manitoba wetlands by Driver (1977). When inundation
persists for years with little fluctuation in water level, sediments often become anoxic and light
deficits can reduce the amount and variety of aquatic plants available as invertebrate habitats,
thus reducing invertebrate richness (Neckles et al. 1990). In drained wetlands whose water
regime is restored, richness increases during the first few years following restoration (Nilsson
and Danell 1981, Hemesath 1991).
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Table 4. Response of nektonic and benthic invertebrate herbivores/detritivores and predators to water
level manipulations in the Delta Marsh.
Unflooded = the normal water level of the experimental wetland. Flooded = the water level in an
otherwise similar wetland that had been raised about 1 m above normal at the beginning of the growing
season and maintained at that level throughout the season. The invertebrate response was measured
at the beginning of the growing season 1 year after the water level had been raised in the flooded
wetland (Murkin and Kadlec 1986b, Neckles etal. 1990, Murkin etal. 1991,1992).
Invertebrate
Component and lis Response
Habitat
ComJfSon
Nekton
(swimming
invertebrates)
Benthos
(bottom dueling invertebrates)
Open water
Unflooded
Herbivores/detritivores
surpass predators.
Herbivore/detritivore numbers are about
equal to those of predators.
Flooded
Herbivores/detritivores
greater than in
unflooded habitat.
Predators and herbivores/detritivores are
about equal, and both are about equal to
their numbers in unflooded habitat.
Emergent
vegetation
Unflooded
Predators surpass or
equal herbivores/
detritivores.
Herbivore/detritivore numbers are about
equal to those of predators.
Flooded
Predator and
herbivore/detritivore
numbers are about
equal to their numbers in
unflooded habitat.
Numbers of herbivores/detritivores are
greater than in unflooded habitat.
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Table 5. Response of taxonomic richness of nektonic and benthic invertebrates to water level
manipulations in the Delta Marsh.
Unflooded = the normal water level of the experimental wetland. Flooded = the water level in an
otherwise similar wetland that had been raised about 1 m above normal at the beginning of the growing
season and maintained at that level throughout the season. The invertebrate response was measured
at the beginning of the growing season 1 year after the water level had been raised in the flooded
wetland (Murkin and Kadlec 1986b, Neckles et al. 1990, Murkin et al. 1991,1992).
ttmrlsbrate Co
Condition
Nekton; Taxonomic Richness
^ T^oriot^lc=RicHness '
Unflooded
About equal in open water habitats to
levels in emergent vegetation.
About equal in open water habitats to levels in
emergent vegetation.
Flooded
About equal in flooded open water
habitat to levels in unflooded open
water, but in flooded emergent
vegetation is greater than in
unflooded emergent vegetation.
About equal in flooded open water to levels in
unflooded open water, but in flooded emergent
vegetation is greater than in unflooded emergent
vegetation (except for cat-tail stands of
emergents, where richness is unchanged from
unflooded condition).
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Species richness of midges tends to be greater in wetlands having longer durations of standing
water during the growing season (Driver 1977, Nelson and Butler 1987). This is partly because
wetlands with longer hydroperiods generally are deeper and more likely to contain submersed
and floating-leaved plants that diversify the range of habitats available to this assemblage of
invertebrates. Also, wetlands with longer durations of flooding are less likely to experience deep
freezing of sediments and types of human activities (e.g., soil compaction, cultivation) that
reduce habitat quality for invertebrates (Swanson et al. 1974). However, in a study of five
wetlands in the Cottonwood Lakes area, 47 species of water beetle were found in seasonal
wetlands whereas 38 were found in semipermanent wetlands (Hanson and Swanson 1989). The
seasonal wetlands had 18 exclusive species whereas the semipermanent wetlands had only 11.
Density and Biomass
Flooding generally increases invertebrate densities in wetlands, but perhaps only for about a year
after initiation of flooding (Table 6). For example, flooding of Manitoba marshes containing cat-
tail, hardstem bulrush, and common reed to a level 1 m above normal caused a major year-long
increase in numbers of nektonic invertebrates in both the vegetation and in open water areas.
Biomass of nektonic invertebrates increased only in the vegetated areas. Densities of benthic
invertebrates increased in flooded vegetation but not in open areas. On a year-round basis,
invertebrate biomass and production is probably greatest in semipermanent wetlands (Nelson
1983, 1989, Bataille and Baldassarre 1993, Duffy and Birkelo 1993), but sometimes can reach
greater seasonal peaks in temporary and permanent wetlands.
It has been suggested that the density and viability of dormant stages of some invertebrates
could be used to determine in advance whether (and how rapidly) the restoration of a drained
wetland will restore its functional characteristics, e.g., part of its invertebrate community (N.
Euliss, personal communication, NPSC, Jamestown, ND). The eggs or other dormant stages of
several invertebrate taxa—notably the cladocerans, anostracans, and conchostracans—may
hatch in response to certain conditions during an 8-week incubation in laboratory aquaria. If
incubated sediment samples from a farmed wetland fail to produce such hatchings, it could be
assumed that degradation has been so severe as to make full functional restoration impractical,
just as lack of seed viability of seed banks is sometimes interpreted.
4.4.2 Invertebrates as Indicators of Changes in Vegetative Cover
Species Composition
An increase in the ratio of algae-consuming species (e.g., certain mayflies) to detrivorous
species (e.g., certain worms, isopods, amphipods) and in the ratio of open-water forms (e.g.,
zooplankton, water striders, midges) to vegetation-dwelling forms (e.g., amphipods, snails, some
mayflies) is expected if a wetland has been exposed to herbicides, grazing, fire, flooding, or other
vegetation removal processes. This is because such disturbances, by thinning the canopy of
emergent plants, create open water areas where algae and submersed macrophytes proliferate
(Overland et al. 1993).
Data from the Delta Marsh (Murkin et al. 1991) suggest that the ratio of predatory to herbivorous-
detrivorous invertebrates might be used to indicate changes in cover conditions. Predatory
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Table 6. Response of density and biomass of nektonic and benthic invertebrates to water level
manipulations in in the Delta Marsh.
Unflooded = the normal water level of the experimental wetland. Flooded = the water level in an
otherwise similar wetland that had been raised about 1 m above normal at the beginning of the growing
season and maintained at that level throughout the season. The invertebrate response was measured at
the beginning of the growing season 1 year after the water level had been raised in the flooded wetland
(Murkin and Kadlec 1986b, Neckles etal. 1990, Murkin etal. 1991,1992).
Invertebrate Component and Its Response
: ;iS©9SQn
CQftditktti
Nekton: Density and
...••iicimass'- .3:"
Benthos: Density and Biomass
Spring
Unflooded
Greater in emergent than
open water habitats.
About equal in emergent and open water
habitats.
Spring
Flooded
Greater in emergent than
open water habitats. Density
in both habitats is greater
than it is in these habitats in
unflooded wetland, but only
density (not biomass) is
greater in flooded open water
than in unflooded open
water.
Greater in emergent than in open water
habitats, and in emergents is also greater than
in unflooded condition. Density and biomass in
flooded open water are no greater than in
unflooded open water.
Summer
Unflooded
Greater in open water than
emergent habitat.
About equal to levels in open water and
emergent habitat.
Summer
Flooded
(year 1)
Mostly greater than in
unflooded condition in both
habitats, but biomass in
flooded open water differs
little from biomass in
unflooded open water.
Greater in emergent than in open water
habitats.
Summer
Flooded
(year 2)
In emergent habitat, a
decline as compared to
levels in this habitat the first
year after flooding. Decline
results in levels similar to
those in unflooded emergent
habitat (except in stands of
Sclochloa).
Increased densities continue from first post-
flooding year in some emergent habitat
(common reed and hardstem bulrush), but
density changes only slightly in open water
habitat.
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species tend to dwell in emergent vegetation, but invade open water areas to some degree in
midsummer as submersed and floating-leaved plants develop. At that time, densities of
predatory species are similar to their densities in emergent stands in the springtime. Field
studies that have examined invertebrate responses to changes in vegetation cover are indexed
in Appendix F. Field studies that have examined invertebrate responses to changes in water
regime are indexed in Appendix G.
Species Richness
High invertebrate richness in prairie wetlands is associated with presence of vegetation. Beyond
some point, however, vegetation stands can become so dense that invertebrate richness
declines (e.g., Broschart and Under 1986, Kaminski and Prince 1981a,b), perhaps because of
developing anoxic conditions.
The variety of invertebrate families, especially of larger invertebrates, can be greater in wetlands
that have been mowed than in otherwise similar wetlands that have not, especially if the hay is
not removed (Kaminski and Prince 1981a,b, Beck et al. 1987). Surprisingly, a wetland whose
emergent cover had been rototilled had a greater variety of invertebrates than an otherwise
similar undisturbed wetland (Kaminski and Prince 1981b). This may have been a short-term
response attributable to more rapid warming of the disturbed soils in the spring (H. Murkin,
Institute for Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba).
Data from the Delta Marsh also suggest that during normal springtime conditions, open water and
emergent vegetation differ little with regard to their variety of nektonic and benthic invertebrates.
By midsummer, open water sites contain submersed plants, and consequently can support a
greater variety of nektonic invertebrates. If nearby emergent vegetation has been flooded within
the last 1-2 years, it can support as many or more nektonic and benthic species (Murkin et al.
1991). An exception might be stands of cat-tail. In these, flooding seems to have little effect on
the variety of benthic invertebrates (Murkin and Kadlec 1986b). Other data from the Delta Marsh
(Kaminski and Prince 1981a,b) suggest that invertebrate richness is not always greater in
wetland units that have relatively equal proportions of open water and emergent vegetation,
compared with those that do not.
Density and Biomass
Invertebrate biomass in prairie wetlands is strongly linked to plant biomass (McCrady et al. 1986)
and thus to areal cover of vegetation. This is because vegetation provides submersed habitat
space that macroinvertebrates colonize at a far greater density than open water areas (Engel
1990). Throughout the early growing season, an undisturbed seasonal wetland in North Dakota
had much greater densities of invertebrates than a flooded summer-fallow wetland (Swanson et
al. 1974). Invertebrates also were more abundant in the undisturbed wetland than in a flooded
grain-stubble wetland, except in late June.
Beyond some point, however, vegetation stands in prairie wetlands can become so dense that
invertebrate density and biomass decline (e.g., Kaminski and Prince 1981 a,b, Murkin et al. 1982,
Broschart and Under 1986, Murkin and Kadlec 1986b). At least in some prairie wetlands, the
paucity of some invertebrates in dense stands is due more to the lack of oxygen in sediments of
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these habitats, than to cooler temperatures or lack of algal foods in the stands, both of which are
due to shading (Wrubleski 1987, Murkin et al. 1992). A common amphipod in prairie wetlands,
Hyalella azteca, requires an oxygen concentration of at least 1.2 mg/L over the span of a month
to achieve full reproductive and growth potential (Nebeker et al. 1992).
Wetlands containing open-water areas interspersed with relatively equal areas of dense
vegetation often have the greatest invertebrate biomass (Kaminski and Prince 1981a,b).
However, a study in one prairie wetland (Murkin et al. 1992) indicated that peaks in the horizontal
distribution of invertebrates do not generally occur at the ecotone between the open water
patches and stands of macrophytes, as was commonly assumed (e.g., Voigts 1975). Rather,
peaks in invertebrate abundance seem to occur wherever the greatest variety of substrates
occurs, and this variety is not always greatest along the ecotone between open water and
vegetation.
Data from the Delta Marsh (Kaminski and Prince 1981 a,b) indicate that cover ratio has less
influence on invertebrate abundance and biomass than does the type of cover manipulation that
has occurred (mowing, rototilling, etc.). In particular, practices that allow large amounts of plant
litter to decay over the winter seem to support exceptional abundance and biomass of
invertebrates the following spring (Kaminski and Prince 1981 a,b; Ball and Nudds 1989). Thus,
the degree to which vegetation removal has a neutral or beneficial effect on invertebrates seems
to depend partly on the type of removal process (e.g., mechanical thinning, ditching, burning,
herbicides, herbivore introduction), the type of vegetation, and the particular spatial patterns that
are created (Nelson and Kadlec 1984).
Invertebrate association with open water or emergent vegetation varies by season. Data from
the Delta Marsh suggest that early in the growing season, numbers of nektonic and benthic
invertebrates are greater within emergent vegetation stands than in open water areas (Murkin et
al. 1992). By midsummer, open water sites containing submersed plants have more nektonic
invertebrates and about equal numbers of benthic invertebrates when compared to sites with
emergent vegetation. However, if the emergent vegetation has been recently flooded, densities
of benthic invertebrates (but not necessarily nektonic invertebrates) will be greater in emergent
vegetation than in open water areas (Murkin and Kadlec 1986b, Murkin et al. 1991). Also, data
from four North Dakota semipermanent wetlands show that stands of cat-tails supported greater
densities of invertebrates than open water patches, whether natural or created by herbicide
application (Solberg and Higgins 1993a).
Macroinvertebrate densities in a South Dakota prairie wetland (McCrady et al. 1986) were
greatest in Ceratophyllum demersum, and zooplankton densities were greatest in Lemna minor,
compared with other species of non-emergent plants. Higher summertime densities of
invertebrates in open water areas (containing submersed plants) than in stands of emergent
vegetation have also been documented in an Iowa marsh (Voigts 1975).
The occurrence of consistently high densities of invertebrates throughout a wetland is likely a
sign of hydrologic and vegetational conditions that are spatially diverse. This is because different
invertebrates (and different life stages of the same invertebrate species) require different cover
densities, hydroperiods, and types of vegetation at different seasons (Murkin et al. 1992).
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Interannual Variability of Density and Biomass
In the Delta Marsh, interannual changes in abundance and biomass of invertebrates were least in
areas that had been disturbed by mowing (Kaminski and Prince 1981b).
4.4.3 Invertebrates as Indicators of Wetland Salinity
Species Composition
Certain invertebrates are characteristic of hypersaline prairie wetlands. These include the
Anostracan brine shrimp (Artemia), brine flies (Ephydra), ostracods, and a few species of midges
and aquatic worms. Other taxa known to be relatively tolerant (up to < 30,000 mg/L salt) include
certain species of midges, mosquitoes, aquatic worms, dragonflies, water beetles (especially the
Dytiscidae and Hydrophilidae), and water bugs (Kreis and Johnson 1968, Swanson et al. 1974).
Although some of the salt-tolerant species in these groups also occur in less saline wetlands,
their abundance is typically greater in saline wetlands. Thresholds of 80 and 5000 pS/cm
specific conductance might be of ecophysiological significance for some wetland invertebrates
because these levels seem to represent disjunct points in the spatial distribution of water beetle
(Coleoptera) species-distribution in Canada (Lancaster and Scudder 1987). Above a salinity of
50 g/L, the usual numerical dominance of chironomid midges in prairie lakes shifts to dominance
by dolichopodids and ephydrids ("brine flies") (Timms et al. 1986). Composition of midge species
provides excellent evidence of salinity gradients in the 0-10 g/L range, but not above (Walker et
al. 1995). Among semipermanent wetlands, most gastropods occur only where specific
conductance is less than about 5000 pS/cm (Swanson et al. 1988) or 3 g/L (Timms et al. 1986).
Salinity ranges of dozens of prairie benthic invertebrates, as determined from their distribution
among many lakes, are reported by Larson (1975), Timms et al. (1986), Timms and Hammer
(1988), and Walker et al. (1995); these data have largely been incorporated into Appendix B.
Species Richness
The variety of invertebrate species within major taxonomic assemblages generally declines in
prairie wetlands with increasing salinity and/or with increasing specific conductance, in part
because the biomass of most submersed plants decreases (Hartland-Rowe 1966, Timms et al.
1986, Lancaster and Scudder 1987) and in part because fewer taxa are physiologically adapted
to higher salinity levels. However, the range of tolerances is likely to be wide, as evidenced by
studies of aquatic beetles, for which the correlation between salinity and species richness is not
strong (Timms and Hammer 1988).
Density and Biomass
The few species that tolerate highly saline conditions often occur at very great densities in prairie
wetlands. This is partly attributable to reduced pressures from competition and predation. Water
beetle populations in highly saline lakes also appear to have smaller body sizes and fewer class
sizes (Lancaster and Scudder 1987).
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4.4.4 Invertebrates as Indicators of Sedimentation and Turbidity
Species Composition
A shift from herbivorous and filter-feeding species (many midges, zooplankters, and mayflies) to
sediment-burrowing species (many aquatic worms) can indicate that major turbidity and
sedimentation incidents have occurred or are continuing. This is because a reduction in light
penetration kills submersed plants and attached algae, and these plants contain a characteristic
assemblage of herbivorous species. Burrowing species meanwhile can continue to exploit soft
sediments. Excessive sedimentation might be expected to have different effects on species that
overwinter in wetland sediments as eggs, as opposed to overwintering as diapausing adults, but
this apparently has not been investigated. Long-term changes in wetland turbidity and
sedimentation might be inferred by collecting decay-resistant remains of invertebrates from
sediment cores or settling traps, and determining if the historical species are ones that
characteristically prefer turbid, silty, anoxic environments (see Section 4.6.2).
Species Richness
A diminished variety of invertebrates is another sign that turbidity and sedimentation conditions
have been severe for the reasons just given. Species richness is particularly likely to decline in
semipermanent and permanent wetlands, where sediments are most likely to become anoxic.
Density and Biomass
Total density or biomass of invertebrates is a poor indicator of sedimentation because either
increases or decreases can occur in response to increased sedimentation. Increases often
occur when some species of burrowing aquatic worms that tolerate low oxygen conditions are
able to proliferate and in the absence of intense predation come to dominate the aquatic
community.
4.4.5 Invertebrates as Indicators of Excessive Nutrient Loads and Anoxia
Species Composition
Particular assemblages of invertebrate species have commonly been reported to be useful
indicators of lake trophic state [as categorized in the book by Rosenberg and Resh (1993)] and
might be similarly useful for signaling wetlands that have received excessive nutrients. In
general, the proportion of "scrapers" (species that characteristically graze on algae) increases
with eutrophication, at least during the early stages of enrichment. Specifically, increases in the
ratios of 1) tubificid worms to sedentary aquatic insects, 2) the midge subfamilies Tanypodinae
and/or Chironomini to the subfamily Orthocladiinae, 3) non-burrowing mayflies to burrowing
invertebrates, and/or 4) cladocerans to rotifers, have been reported to indicate excessive nutrient
loading of wetlands or other water bodies (Wiederholm 1980, Kansanen et al. 1984, Rosenberg
et al. 1984, Jones and Clark 1987, Ferrington and Crisp 1989, Radwan and Popiolek 1989).
Over 600 invertebrate species are categorized according to their association with a particular
water body's nutrient status in a literature-based table in Rosenberg and Resh (1993). Long-term
changes in nutrient status of a wetland might be inferred by collecting decay-resistant remains of
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invertebrates from sediment cores or settling traps, and using the Rosenberg and Resh (1993)
table to determine what proportion of the found species are ones that characteristically prefer
enriched environments.
Species Richness
Species richness of invertebrates can decrease (Wiederholm and Eriksson 1979, Sedana 1987)
or increase (Tucker 1958) in response to enrichment. In lakes, zooplankton richness initially
increases with increasing phytoplankton production, then it decreases as production continues to
rise (Dodson 1992).
Density and Biomass
Density and/or biomass of invertebrates, especially midges, increase with larger increases in
wetland fertility (Johnson and McNeil 1988, Ferrington and Crisp 1989, Murkin et al. 1991).
Indeed, the density of midges (as measured using emergence traps) has been recommended as
an efficient indicator in some situations of secondary production in lakes (Welch et al. 1988).
Although data from other regions show invertebrate density increasing in response to increased
nutrients (e.g., Tucker 1958, Sedana 1987, Belangerand Couture 1988, Cyrand Downing 1988),
substantial and chronic nutrient additions are needed to cause this response (Gabor et al. 1994,
Murkin et al. 1994b), and at some point the response of the whole invertebrate community
changes from an increase to a decrease in density. This occurs as plant litter accumulates faster
than it can be processed effectively and oxygen is depleted from the sediments and water
column (Almazan and Boyd 1978), causing a reduction in densities of many invertebrates
(Hartland-Rowe and Wright 1975, Schwartz and Gruendling 1985, Pezeshik 1987). Based on
results of two experimental nutrient loading studies in prairie wetlands, Murkin et al. (1994b)
suggest that "ideal nutrient addition levels" which balance positive fertilization effects against
adverse oxygen depletion are between 60 and 200 mg/L for phosphorus and between 1600 and
2400 mg/L for nitrogen, added biweekly during summer.
4.4.6 Invertebrates as Indicators of Pesticide and Heavy Metal Contamination
Species Composition
In general, herbicides are not as acutely lethal to invertebrates as are insecticides (e.g., Buhl and
Faerber 1990). Perhaps the most toxic herbicides are the triazines, including the commonly used
herbicide atrazine. Atrazine has been shown to cause shifts in community composition and
emergence times of aquatic insects at a concentration of 2 mg/L (Dewey 1986), and as little as
0.230 mg/L reduced the development of midges (Macek et al. 1976). The herbicide triallate is
also quite toxic to prairie invertebrates (Johnson 1986, Arts et al. 1995). Other herbicides used
in wetlands have been shown to increase the dominance of invertebrates (e.g., many aquatic
worms) that are tolerant of low dissolved oxygen, a result related to the large oxygen deficit
caused by decay of massive amounts of plants (Scorgie 1980). Herbicides can also increase the
dominance of open-water forms (e.g., cladocerans) as their algal food base blooms after the
reduction of shading aquatic vegetation.
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Among insecticides, the synthetic pyrethroids (especially deltamethrin) are generally more toxic
to invertebrates than the organochlorine, organophosphorus, and carbamate pesticides
(Sheehan et al. 1987). Mollusks are a possible exception to this ranking. Mayflies and
amphipods tend to be more sensitive to most insecticides than are midges and adult water
beetles. In one of the few biassays conducted in a prairie wetland, Johnson (1986) found the
insecticides carbofuran, fonofos, and phorate to be highly toxic to two invertebrates (Daphnia and
a midge species). Carbofuran's toxicity to aquatic invertebrates was corroborated in Wayland
and Boag's (1990, 1995) prairie wetland bioassays. Phorate's toxicity was corroborated in prairie
wetland mesocosms by Dieter et al. (1996). When applied to a Minnesota wetland at typical field
concentrations, the pesticides temephos, chlorpyrifos, and dursban killed copepods,
cladocerans, and phantom midges (Helgen et al. 1988).
A host of factors influence toxicity and mortality, and are sufficient to change the generic
rankings of insecticide toxicity as well as lethal thresholds. For example:
Environmental factors: water temperature, organic content, pH, alkalinity, suspended
solids.
Dose factors: concentration, the specific formulation (inert ingredients), frequency of
application, duration of exposure.
Biotic factors: the invertebrate species, its life stage, proximity of unexposed microhabitat
patches, degree of simultaneous stress from other factors that may be related (e.g.,
oxygen stress and enrichment from plant decomposition and photosynthetic inhibition for
1-2 weeks after herbicide application) or unrelated (e.g., drought).
The availability of vegetation can be particularly important to invertebrate survival in wetlands
having sediments that are contaminated chemically or that are persistently anoxic or saline. In
such situations vegetation provides a colonization surface isolated from the sediments, where
contaminants often are concentrated (Nebeker et al. 1988). Richness and abundance of
epiphytic and nektonic (swimming) invertebrate groups can thus remain high in some
contaminated but well-vegetated wetlands (McLachlan 1975).
In other regions and in sediments exposed to some herbicides or severely contaminated by
heavy metals, investigators have noted a shift from a community of aquatic insects and toward a
community dominated by certain oligochaetes (aquatic worms) (e.g., Howmiller and Scott 1977,
Wentsel et al. 1978, Winner et al. 1980). In non-wetland water bodies, areas that are at least
moderately contaminated often are dominated by chironomid midges (Winner et al. 1980,
Cushman and Goyert 1984, Rosas et al. 1985, Waterhouse and Farrell 1985, McCarthy and
Henry 1993) and other aquatic invertebrate species whose adults have wings and short life
cycles, e.g., water bugs (Hemiptera) and water beetles (Coleoptera) (Borthwick 1988,
Courtemanch and Gibbs 1979, Gibbs et al. 1981). Wetland amphipods (Gammarus, Hyalella ),
clam shrimp (Lynceus brachyurus), and many zooplankton species, appear to be very sensitive
to certain pesticides, whereas most aquatic snails and worms are less sensitive (Sheehan et al.
1987, McCarthy and Henry 1993). Amphipods are especially useful as indicators of
contamination because they are relatively stationary (i.e., because they do not emerge and fly
away like aquatic insects, their presence can be more indicative of the longer-term conditions of
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a wetland). Dosed populations may require at least a year to recover (Gibbs et al. 1981,
McCarthy and Henry 1993). Amphipods occur in most wetlands with relatively persistent
standing water, and their response to pesticides has been documented in prairie pothole
wetlands specifically (Borthwick 1988). Pesticide bioassays in prairie wetlands by Ruelle and
Henry (1993) indicated greater sensitivity of Daphnia magna than Hyalella azteca and greater
sensitivity among younger than older individuals of both.
In wetlands that lack surface water, nematodes can be particularly sensitive indicators of
contaminant toxicity; those of the subclass Adenophorea tend to be more sensitive than those of
the subclass Secernentea (Piatt et al. 1984, Zullini and Peretti 1986, Bongers 1990). The
nematode suborder Dorylaimina, oribatid mites, and many ground beetles (Carabiidae) are highly
sensitive. Apparently the least sensitive organisms in such habitats are the soft-bodied
invertebrates such as earthworms, terrestrial herbivores such as ants and weevils, and
invertebrates that inhabit the upper soil layers such as springtails (Collembola) (Bengtsson and
Tranvik 1989).
For protecting soil invertebrates, Bengtsson and Tranvik (1989) suggest maximum allowable
concentrations for lead of less than 100-200 mg/kg; less than 100 mg/kg for copper; less than
500 mg/kg for zinc, and less than 10-50 mg/kg for cadmium. Concentrations of metals,
pesticides, and other substances toxic to invertebrates are tabulated rather comprehensively in
USEPA's AQUIRE database.
Species Richness, Density, and Biomass
Depressed richness and density of aquatic invertebrates is sometimes suggestive of past or
ongoing exposure to pesticides, heavy metals, or other contaminants in permanently flooded
(Ferrington et al. 1988, Krueger et al. 1988, Winner et al. 1975, Marshall and Rutschsky 1974)
and drier (Bengtsson and Tranvik 1989) habitats. Richness of a pond invertebrate community
was reduced following application of the herbicide linuron (Stephenson and Kane 1984).
Richness and density of invertebrates can decline even at levels of phenols and oil-water ratios
not known to be toxic in laboratory studies (Cushman and Goyert 1984).
Bioaccumulation
Bioaccumulation of some substances appears to be greater among sand-dwelling invertebrates
than mud-dwelling invertebrates (Muir et al. 1983). Several aquatic invertebrate species
effectively accumulate certain heavy metals in lakes (e.g., Hare et al. 1991) and probably
wetlands, but few data are available specifically from prairie wetlands.
Physical and Genetic Deformities
Physical deformities of individuals often accompany severe pollution. For example, midges with
deformed mouth parts were noted in areas of synthetic-coal-derived oil pollution (Cushman and
Goyert 1984). This indicator is difficult to recognize objectively, and it has not been examined in
prairie wetlands. Perhaps more objective would be the application of electrophoresis techniques
in genetic analysis. Such an approach might be able to rapidly detect past exposure of an
invertebrate population to a pesticide, assuming that surviving organisms have a different gene
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frequency than the parent population (M. Brinkman and W. Duffy, personal communication,
South Dakota State University, Brookings, SD).
4.5 Monitoring Techniques
Methods and equipment for field-sampling of invertebrates, including wetland taxa, are reviewed
comprehensively by Murkin et al. (1994a). Other useful summaries are provided by Edmondson
and Winberg (1971), Downing and Rigler (1984), Isom (1986), Fredrickson and Reid (1988b),
Ross and Murkin (1989), Staley and Rope (1993), and Rosenberg and Resh (1993).
4.5.1 General Considerations
Larvae of most aquatic invertebrates can be found in wetlands throughout the year. However,
particular groups (e.g., midges, mayflies) are more evident from about May until September,
whereas others (e.g., Anostraca, some Trichoptera) are more abundant earlier in the spring and
can be found outside the usual growing season (Swanson et al. 1974). If wetlands can be
sampled only once, then the late wet season or beginning of the dry season is usually the
recommended time because density and richness in many wetlands tend to be greatest then
(Marchant 1982). Depending on the study objective, the sampling schedule may need to be
adjusted to coincide with phenologies of particular taxa (Resh 1979, Sklar 1985). For example,
one might want to avoid sampling immediately after a synchronous emergence of the usually
dominant species (i.e., a day or week when nearly all individuals of a species emerge at once).
Maximum information is often obtained when most invertebrates are within a size range (later,
larger instars) retained by nets used to sample them, and can be identified with greatest
confidence. Estimates of macroinvertebrate production or seasonal change in standing crop
generally require that samples be collected at least monthly and preferably biweekly.
4.5.2 Sampling Equipment
The choice of equipment depends largely on the wetland microhabitat to be sampled. Different
assemblages of wetland invertebrates inhabit sediments (benthos), rooted plants or algae
(phytomacrofauna), open water (nekton), and the surface film (neuston).
A significant problem in analyzing wetland invertebrate data arises from difficulties in determining
the spatial dimensions of the area from which a sample was drawn. Accurate estimates of
density (individuals per unit area) are difficult to achieve because of difficulties in accurately
measuring the complex wetland substrate (submersed plants, emergent plant stems, etc.). To
address this, some investigators have removed the substrate along with the collected sample,
measured both, and reported density as weight (or number of organisms) per unit of weight or
area of substrate (e.g., Mrachek 1966). In some cases regression coefficients have been
calculated to convert plant weights to plant area, which can be further converted to estimates of
invertebrate density using previously determined empirical relationships (Downing and Cyr 1985,
Downing 1986).
The most common method of sampling invertebrates in prairie wetlands has involved use of
sweep nets (or modified dip nets). These are the familiar long-handled, inexpensive insect nets.
They can be used in water or air, except in mostly robust or dense stands of vegetation. They
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are either swept through a standard length of vegetation, or placed on the bottom and hauled
vertically through the water column in a rapid stroke. User variability can be a concern, but
sweep nets are convenient to use and are particularly suited for capturing large (e.g., crayfish) or
quick-moving species such as adult dragonflies and water striders that are not collected by other
methods. Samples are not strictly quantitative because the unit of area swept is difficult to
determine accurately (Adamus 1984, Plafkin et at. 1989). Also, measured species composition is
strongly influenced by mesh size. In trial comparisons against a modified Gerking sampler (see
below), Kaminski and Murkin (1981) found sweep nets to be just as effective in sampling water-
column species. Other researchers who have described results from use of sweep nets in prairie
wetlands include Hanson (1952), Voigts (1975), Swanson (1984), McCrady et al. (1986), Kreil
and Crawford (1986), Lancaster and Scudder (1987), LaGrange and Dinsmore (1989b). Sweep
nets are one device being used to sample prairie wetlands in the EMAP effort as well as in the
effort conducted by the State of Montana.
A method suitable anywhere the water is at least a few inches deep involves use of activity
(funnel) traps. Most trap designs follow descriptions of Murkin et al. (1983) and Swanson
(1978b). Traps are positioned below the water level or on the bottom for hours or days, and
nektonic invertebrates that enter the funnel-shaped trap cannot escape. Traps can be positioned
vertically (more likely to capture emerging insects) or horizontally. Collections contain only
nektonic invertebrates or, if placed on the bottom, only non-sedentary invertebrates (e.g., adult
clams are usually missed). Non-insect invertebrates (e.g., Hyalella) as well as aquatic insects
are passively collected. Traps can be fitted with lights to increase their attraction to some insects
(Lancaster and Scudder 1987). Use is limited to wetlands with standing water, and traps are
probably more effective when placed in open water areas or at the edge of vegetation patches,
than if placed within dense stands. If traps are set for more than a few days, it sometimes is
necessary to move them as water levels recede. Samples are not strictly quantitative because it
is impossible to tell what size area the organisms came from and because some invertebrates
and fish caught in the trap prey on other captives during the holding time (Murkin et al. 1983).
However, the samplers are lightweight and inexpensive, and sample processing time is less than
for some other methods because samples are mostly free of plant material and sediment.
Because activity traps accumulate organisms over time, fewer species are missed as a result of
ephemeral factors that cause avoidance.
From two years of biweekly samples from 10 wetland sites, Murkin et al. (1983) found significant
correlation between the total numbers of invertebrates collected in activity traps and the number
collected using sweep nets, although species composition differed somewhat (e.g., traps attract
predatory invertebrates disproportionately). In a study of midges, Welch et al. (1988) found no
difference in total catch between 0.142-m2 and 0.283-m2 trap sizes. Traps with inverted funnels
inserted in the jar necks caught more pupae than traps without funnels, and total catch in the
traps without funnels was 58% of the catch in traps with funnels. Other researchers who
describe results of using activity or funnel traps in prairie wetlands include Armstrong and Nudds
(1985), Kreil and Crawford (1986), Helgen et al. (1988), Hanson and Swanson (1989), Neckles et
al. (1990), Murkin et al. (1991), and Bataille and Baldassarre (1993). Activity traps are also being
used in the EMAP effort.
If the objective is to sample invertebrates that inhabit mainly the water column, tube samplers
(e.g., "water column samplers," "Swanson samplers," "Gerking samplers", "stovepipes", "box
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samplers") can be used. These are plexiglass cylinders about 6 cm wide that enclose a standard
area of bottom substrate. Like corers (see below) they may sample some benthic organisms, but
they are not designed to effectively penetrate the sediment [the device described by Euliss et al.
(1993) is possibly an exception]. In some (e.g., the Gerking sampler), the bottom can be sealed
off with a sliding door, plug, or similar feature once the sampler is in place. Some have been
fitted with a reinforced cutting edge on the bottom. Tube samplers are not effective in dense
vegetation or for catching quick-moving organisms, burrowing species, very large organisms, or
many epiphytic species. A major advantage is that, by usually sampling the entire vertical extent
of the water column, they capture diurnally migrating species that are missed by samplers that
can sample only at a particular depth.
The most popular type of tube sampler for use in prairie wetlands seems to be the design of
Swanson (1978a, 1983), which has been used by Neckles et al. (1990), Broschart and Under
(1986), and LaBaugh and Swanson (1988). A refined and expanded version of this sampler is
described by Euliss et al. (1993). Also, Gates et al. (1987) described a type of tube sampler that
simultaneously collects invertebrates on plants and in the sediment. They found this to give
results for plant invertebrates at least as precise and sometimes more accurate than obtained by
clipping macrophytes (see below). Other designs are described by Gerking (1957), Korinkova
(1971), Mackay and Qadri (1971), Legner et al. (1975), Martin and Shireman (1976), Minto
(1977), Hiley et al. (1981), Freeman et al. (1984).
Emergence and areal light traps are another option. They consist of floating nets or funnels,
covering an area of 0.1 or 0.5 m2, that are anchored at and just above the water surface or
(rarely) are submerged. They are left in place for a specified period of time, during which they
are checked daily. They passively collect aquatic insects that are developing from larval to
winged adult stage (i.e., emerging from the water column), and thus do not collect non-emerging
invertebrates which sometimes are a dominant component of the invertebrate community. Use
of emergence traps is limited to wetlands containing open patches of surface water during the
growing season when most insects emerge. They can be placed over either open water or short
vegetation. If traps are set for more than a few days, they should be moved periodically to avoid
altering the sampled habitat by shading it (H. Murkin, personal communication, Institute for
Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba). Traps also must
sometimes be moved as water levels recede. A popular type of emergence trap appears to be
the design of LeSage and Harrison (1979), as modified by Wrubleski (1984). A design by M.
Butler, described by Nelson and Butler (1987) is also used. Results of using emergence traps in
prairie wetlands are described by Driver (1977), Nelson and Butler (1987), Wrubleski (1989),
Neckles et al. (1990), Bataille and Baldassarre (1993), and Ross and Murkin (1993 a,b).
Because emergence traps are left in place for up to several weeks, they reduce the problem
encountered by other samplers of missing key species because of an inappropriate time of visit.
Sample processing times are favorable because organisms do not have to be separated from
sediments and plant material. Because emerging insects come from a variety of
microenvironments, emergence and areal light traps can integrate well the extreme spatial
heterogeneity within many wetlands. On the other hand, the traps make it impossible to
standardize or determine the unit of area from which the organisms originated. Moreover, some
trapped organisms may prey upon each other, confounding any quantitative estimates, if traps
are not emptied often. Initial purchase and maintenance of traps can be costly, and vandalism
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can be a problem. If emergence traps are unavailable, many species of emerging aquatic
insects can be identified and densities grossly estimated from exuviae (shed remains) that are
easily sieved from the water surface (McGill et al. 1979).
Another choice for sampling invertebrates that normally attach to wetland plants involves use of
artificial substrates. Plants are not sampled directly, but rather, plastic plants or other sterile
surfaces (e.g., Hester-Dendy plate samplers) are totally submersed in the wetland water column
and allowed to be colonized over a period of at least a month (Macan and Kitching 1972, Cairns
1982). Because such substrates standardize the surface area and texture, collections from
substrate samplers are highly comparable to each other, making them attractive for use in
monitoring of water-column water quality. They also are lightweight, and sample processing is
relatively easy. However, disadvantages include the fact that a return trip to the wetland is
required, vandalism can be a problem, their use is limited to wetlands with surface water, they
sample only epiphytic species, and representativeness is sometimes uncertain (Adamus 1984).
In stands of submersed vegetation, Gerrish and Bristow (1979) used plastic mimics of the
pondweed, Potamogeton richardsonii, interspersed among live experimental plants. Although
this yielded no significantly different numbers of invertebrates or species per unit of surface area
than were found on real plants, aquatic worms were significantly more common on the artificial
substrates. Natural substrates initially devoid of organisms can also be used as colonization
substrates. For example, plant litter of measured area or volume can be placed in wetlands to
allow colonization by detrivorous species over a specified period of time.
If the objective is to sample invertebrate communities inhabiting relatively unvegetated wetland
sediments, then dredges—also called grab samplers (Ekman, Ponar, etc.)—are often used.
They consist of a box with jaws that is lowered onto the sediment. The jaws enclose a specified
area of bottom, and retrieve sediments and associated organisms to a sediment depth of about 5
cm. Dredges are used only where surface waters of at least 0.5 m in depth are present, and they
are not effective where there are aquatic plants that jam the jaws and prevent full closure.
Because an unknown number of organisms subsequently escape and the exact area and
sediment depth of the spot being sampled is never certain, estimates of density are only crudely
quantitative. Large organisms (e.g., crayfish), organisms in the water column, and fast-moving
species in particular are sampled poorly. Dredges are cumbersome and relatively expensive,
and their samples are time-consuming to sort, but they have been used in prairie studies by
Driver (1977) and Timms et al. (1986).
Another option for sampling sediments is core samplers. Unlike grabs, corers do not have jaws,
and instead rely on compactive force or suction to retrieve sediments, sometimes to a depth of
about 15 cm. They suffer the same disadvantages as dredges. Samples usually are more
precisely quantitative, but the mean size of organisms effectively captured is often smaller
because of the narrow dimensions of corers. Core samplers are sometimes the only option for
quantitatively sampling sediment organisms in wetlands that lack surface water. Where aquatic
plants interfere with core sampler operation, some investigators have suggested welding a saw
blade to the leading edge of the corer, for clipping heavy roots and stems (Murkin and Kadlec
1986b). The corer design that seems to have been used the most in prairie wetlands is that of
Swanson (1978c); a similar design is proposed by Bay and Caton (1969). Results of using
corers in prairie wetlands are described by Murkin et al. (1982), Talent et al. (1982), Murkin and
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Kadlec (1985a), Broschart and Under (1986), Kreil and Crawford (1986), Murkin and Kadlec
(1986b) Nelson and Butler (1987), Neckles et al. (1990).
During periods when sediments or soils are not covered by water, pitfall traps and soil extraction
techniques can be used, and sometimes yield the highest densities and species richness
(Coulson and Butterfield 1985). If only plant-dwelling invertebrates need to be sampled, another
approach involves directly clipping the vegetation while confining it in an enclosed box. Clipped
vegetation is then carefully examined for invertebrates in the laboratory. This might provide more
precise quantification than does use of sweep nets, although nektonic invertebrates are seldom
captured. Downing and Cyr (1985) found the most cost-effective quadrat size for clipping to be
500 cm2. Plants were enclosed in a 6-liter plastic box. Clipping aquatic macrophtyes in quadrats
of varying sizes yielded five times higher populations of invertebrates than did sampling with
some tube samplers (Gerking, Macan, or Minto samplers). Vacuum suction also can be used to
help remove small invertebrates from foliage in the field (Southwood 1981).
4.5.3 Time-Integrating Methods
The above methods are used primarily to sample living organisms. Sometimes, the sclerotized,
decay-resistant remains of particular invertebrate groups (chironomid head capsules, and
exoskeletons and eggs of snails, ostracods, daphnids, and conchostracans) persist in an
undecomposed condition in wetland sediments for months, years, or even decades and
centuries. Settled remains can be collected by a variety of devices (e.g., "sediment traps") and
sieved to separate the remains. This, along with identification and enumeration of body parts,
can be a difficult, laborious, and somewhat subjective process, but the resulting data on species
composition provide clues to the wetland's previous long-term environmental conditions.
Examples are demonstrated generally by Walker et al. (1991), Walker (1993), and Streever and
Crisman (1993), and in prairie lakes specifically by Synerholm (1979) and Euliss et al. (1993). In
wetlands exposed to mixing winds, the resulting resuspension of decay-resistant remains from
many time periods can complicate data interpretation, unless samples are collected for
comparison simultaneously using other methods (N. Euliss, personal communication, NPSC,
Jamestown, ND). As part of 1992-1994 pilot studies in the prairie region, EMAP sponsored the
development and testing of methods for accurately sampling decay-resistant remains of
invertebrates.
4.5.4 Bioassay Methods
A review of laboratory, outdoor mesocosm, or in situ bioassay methods involving invertebrates is
beyond the scope of this report. Use of bioassays to explore toxicity in prairie wetlands has been
relatively limited. Examples include studies by Johnson (1986), Borthwick (1988), Helgen et al.
(1988), Wayland and Boag (1990), and Ruelle and Henry (1993).
4.5.5 Bioaccumulation
Methods for collecting wetland invertebrates and assessing bioaccumulation of contaminants in
their tissues are described in Staley and Rope (1993).
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4,6 Variability and Reference Points
Numerical estimates cited in the following sections are difficult or impossible to compare with one
another because they are based on samples collected with a variety of equipment and methods,
in different wetlands, and for different time periods. They are cited only to provide order-of-
magnitude illustration of levels of various parameters that have been encountered in prairie
wetlands. For a specific listing of the methods behind each cited value and study, see
Appendix J.
4.6.1 Spatial Variability
Species Richness
The true species richness of invertebrates in prairie wetlands is generally unknown because
taxonomic knowledge and resources have nearly always been insufficient to make species-level
determinations of specimens (Table 7). This is suggested by the fact that a collection from four
prairie wetlands of 2594 individuals representing just a single invertebrate assemblage—the
aquatic Coleoptera—yielded 57 species. Remarkably, these four wetlands contained almost half
the aquatic Coleopteran species ever found in North Dakota (Hanson and Swanson 1989).
Similarly, weekly collections of midges emerging from a single pond within the Delta Marsh
yielded a total of 84 species (Wrubleski and Rosenberg 1990).
Although comparisons among studies are hindered by the fact that levels of resolution in
taxonomic determinations have varied greatly, some results are presented (Table 7).
Richness varies significantly within wetlands as well. When Delta Marsh samples from various
periods and years were pooled, the zone with cumulatively the most families was the hardstem
bulrush (Scirpus acutus) zone (47 families), followed by the open water zone (44 families), and
cat-tail zone (36 families). When manipulated parts of the Delta Marsh were included as well, the
zone with cumulatively the most families was the whitetop (Scolochloa festucacea) zone (50
families), followed by cat-tail (48 families), hardstem bulrush and red goosefoot (47 families),
softstem bulrush (Scirpus validus) (46 families), rayless aster (Aster brachyactis) (45 families),
and the open water zone (44 families) (H. Murkin, personal communication, Institute for Wetland
and Waterfowl Research, Oak Hammock Marsh, Manitoba). However, in both manipulated and
unmanipulated wetlands, the relative rankings of zones based on their species richness varied by
season and year.
Species Composition
Species that are most numerous or constitute the greatest biomass are often the most
ecologically influential. The species of invertebrates that dominate prairie wetlands vary from
wetland to wetland; those invetebrates reported in the literature to dominate at least one prairie
wetland are shown in Appendix B. Species that are most sensitive to environmental change are
often those with the narrowest habitat requirements, and species with narrow habitat
requirements can often be identified as those with locally restricted distributions. Of the
cumulative total of 74 taxa found by biweekly core sampling of four South Dakota semipermanent
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Table 7. Comparison of studies on species richness.
Location* citation ....
Bksin type
Sampling approach..
Number of taxa
Nebraska,
Rainwater Basin
(Gordon et al. 1990)
Seasonal
(n = 8),
1 field season
One field season
39 (cumulative)
South Dakota
(Duffy & Birkelo 1993)
Semipermanent
Cores, biweekly
74
(mean = 44/basin,
range = 31-58)
South Dakota
(Broschart &
Under 1986)
Lakeside marsh
Tube samples and cores,
one field season
Benthos; 5.7 families/basin
Nekton; 7.0
families/basin
Iowa
(Voigts 1975)
Lakeside marsh
No information
20-32 families
Iowa
(LaGrange &
Dinsmore 1989a, b)
Restored semipermanent
Sweep net samples,
single visit
4-16 families/basin
Iowa
(Hemesath 1991)
Restored
semipermanent
(n = 17)
Sweep net samples,
3/basin in June
7-17 families/basin, cumulative richness of
32 families
North Dakota
(Euliss, pers. comm., NPSC,
Jamestown, ND)
Semipermanent
(n = 18)
Sweep net samples,
2 field seasons
29 families cumulatively,
median = 18/basin (range, 9-25)
North Dakota, Cottonwood
Lakes
(LaBaugh &
Swanson 1988)
Semipermanent & Seasonal
(n = 5)
No information
9-19 species/basin
(rotifers, copepods, and cladocerans only)
Manitoba,
Delta Marsh
(Neckles et al. 1990, Murkin et
al. 1991)
Lakeside, unmanipuiated
Funnel trap (24-hr sets, 5
years)
54 families;
max,/sample = 18;
median = 8
Manitoba,
Delta Marsh
(Kaminski & Prince 1981a,b)
Lakeside
No information
1,2 families/m® (1 year);
1.9 families/m3
(another year)
Manitoba
(Bataille &
Baldassarre 1993)
Semipermanent
(n = 3)
Activity traps, weekly
26 families/500 samples
Emergence traps, weekly
50 families/500 samples
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wetlands, 33 taxa were found in only a single wetland (W. Duffy, personal communication, South
Dakota St. University, Brookings, SD). Of 32 families found collectively in the 17 restored prairie
wetlands in Iowa, five families were found only in one wetland (Hemesath 1991, Hemesath and
Dinsmore 1993), Of 54 families found in funnel traps in four differently manipulated units of the
Delta Marsh over a 5-year period, 19 were present In only one unit. The hydrologically
unmanipulated unit had the most taxa (8) that occurred nowhere else; these included several
mayflies (H. Murkin, personal communication, Institute for Wetland and Waterfowl Research, Oak
Hammock Marsh, Manitoba). In the survey of 18 semipermanent wetlands in North Dakota which
found 29 invertebrate groups, the groups with the most restricted distribution (as sampled with a
sweep net) were fairy shrimp, amphipods (2 wetlands each), and broad-shouldered water striders
(1 wetland) (N. Euliss, personal communication, NPSC, Jamestown, ND).
Density and Biomass
Macroinvertebrate densities as high as 36,000/m2 are reported from the Delta Marsh by Neckles
et al. (1990), although densities of just the benthic invertebrates reached a seasonal peak of
about 1200/m2 (Murkin et al. 1982, Murkin and Kadlec 1986b). In a lakeside prairie marsh in
South Dakota, Broschart and Under (1986) reported summertime means of 3534 and 7898/m2 in
ditched and unditched areas respectively. Another lakeside marsh in Iowa had a maximum of
about 15,000/m2, mainly associated with submersed plants (Voigts 1975). Midge larvae alone
were present at densities of up to 10,092/m2 in prairie wetlands used by mallards with broods,
whereas samples from 16 randomly selected wetlands had a maximum of 5337/m2 (Talent et al.
1982). When a corer was used, the mean densities from eight seasonal wetlands in the
Rainwater Basin of Nebraska ranged from only 28/m2 in one wetland to 86/m2 in the most
productive wetland (Gordon et al. 1990).
In sweep net samples from undisturbed seasonal wetlands in North Dakota, Swanson et al.
(1974) reported a mean of about 6500 individuals per m3, whereas densities in water column
samples from eight seasonal wetlands in the Rainwater Basin of Nebraska ranged from
106,000/m3 in one wetland to 1,636,000/m3 in another (Gordon et al. 1990).
After core-sampling five natural wetlands of eastern North Dakota bimonthly in summer for 2
years, Kreil and Crawford (1986) reported a mean of 2686 individuals/m3 in the poorest wetland
and a mean of 89,460 individuals/m3 per wetland. Using a core-type sampler in the Delta Marsh,
Kaminski and Prince (1981a,b) reported mean densities of benthic invertebrates of 6993 and
18,906/m3 (depending on year), whereas Neckles et al. (1990), using vertical activity traps,
reported a mean of 210,000/m3 from the Delta Marsh. The following year, the mean invertebrate
density was only 6993/m3. Mean density of core-sampled invertebrates among four South
Dakota semipermanent wetlands ranged from 4782/m2 to 20,063/m2 (Duffy and Birkelo 1993).
Using a corer elsewhere in North Dakota, Nelson (1989) found densities of 78,000/m2 in
semipermanent wetlands and 2400/m2 in seasonal wetlands.
In tube samples from the lakeside prairie marsh in South Dakota, Broschart and Linder (1986)
reported means of macroinvertebrates from tube samples of 9687 and 15,194/m3 in unditched
and ditched areas respectively. Zooplankton densities averaged about 700,000/m3. In a shallow
prairie lake in Minnesota, zooplankton densities peaked at over 450,000/m3 in early autumn
(Hanson and Butler 1990). In seasonal wetlands in North Dakota, they peaked at > 2,000,000/m3
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but peaked at < 1,000,000/m3 in semipermanent wetlands in the same area (LaBaugh and
Swanson 1993). Zooplankton densities also exceed 1,000,000/m3 in the Delta Marsh (Collias
and Collias 1963) and in seasonal wetlands of the Rainwater Basin of Nebraska (Gordon et al.
1990).
In five natural wetlands in eastern North Dakota, Kreil and Crawford (1986) found an average of
between 53 and > 3000 individuals per trap. Activity traps in three prairie wetlands in western
Minnesota caught an average of between 156 and 1323 individuals, with seasonal peaks of
between 738 and 4448 individuals (per sample per wetland per sampling period). Wetlands that
had the largest numbers 1 year did not necessarily have similar rank among the three wetlands
the following year (M. Hanson, personal communication, Minnesota Department of Natural
Resources, Bemidji, MN).
Numbers of nektonic invertebrates found in part of the Delta Marsh during 24-hr sets of each
underwater funnel trap ranged up to about 180 (Murkin et al. 1991), whereas in nearby potholes,
Bataille and Baldassarre (1993) found up to 2094 per trap. In unmanipulated parts of the Delta
Marsh, when samples from various periods and years were pooled, the zone with the greatest
numbers of individuals per activity trap was the cat-tail zone, followed distantly by the hardstem
bulrush and open water zones. When manipulated parts of the Delta Marsh were considered
instead, the zone with the greatest numbers was the red goosefoot (Chenopodium rubrum) zone,
followed by the whitetop, cat-tail, rayless aster, softstem bulrush, and open water zones (H.
Murkin, personal communication, Institute for Wetland and Waterfowl Research, Oak Hammock
Marsh, Manitoba). However, in both manipulated and unmanipulated wetlands, the relative
rankings of zones based on their invertebrate densities varied by season and year.
Published data from emergence traps are more limited. From 0.5-m2 emergence traps left in the
Delta Marsh for 29 days in late summer, the mean density of the 10 commonest midges was 179
per trap, and the maximum was 640/m2 (Wrubleski 1989). Emergence sampling of a permanent
wetland 80 miles west of Delta Marsh found a maximum density of 977/m2 (Bataille and
Baldassarre 1993). On a total annual basis, emergence of midges in the part of Delta Marsh
studied by Wrubleski varied from 2322 to 15,400 individuals/m2, depending on vegetation type
and cover ratio (Wrubleski 1989, Wrubleski and Rosenberg 1990).
Biomass estimates are influenced by mesh size, inclusion/exclusion of snail and clam shells, and
sampling equipment. From part of the Delta Marsh, Kaminski and Prince (1981a,b) reported a
mean biomass of invertebrates of 11,161 mg/m3 during 1 year and 2843 mg/m3 during another.
Based on sweep net samples, the median invertebrate biomass of 18 semipermanent wetlands in
North Dakota was about 1300 mg/m3 (N. Euliss, personal communication, NPSC, Jamestown,
ND). In tube samples from the lakeside prairie marsh in South Dakota, Broschart and Under
(1986) reported means of 8524 and 6564 mg/m3 from ditched and unditched areas respectively.
On a per-area basis, mean biomass of core-sampled invertebrates in four South Dakota
semipermanent wetlands ranged from 1543 to 5428 mg/m2, and production ranged from 4604 to
21,800 mg/m2 (Duffy and Birkelo 1993). In unmanipulated parts of the Delta Marsh, benthic
biomass peaked at about 8000 mg/m2 (Murkin et al. 1982, Murkin and Kadlec 1986b). Activity
trap samples from four Minnesota semipermanent wetlands yielded a biomass per sample of
from 0.38 g in one wetland to 3.23 g in another; seasonal peak biomass ranged from 0.89 g in
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one wetland to 7.48 g in another (M. Hanson, personal communication, Minnesota Department of
Natural Resources, Bemidji, MN). In a lakeside prairie marsh in South Dakota, Broschart and
Under (1986) reported biomass means of 1746 and 1314 mg/m2 from ditched and unditched
areas respectively. Data from submersed vegetation beds in 11 eastern Canadian lakes showed
a range of invertebrate densities of 1000-2900 mg/m2 (LaLonde and Downing 1992).
In unmanipulated parts of the Delta Marsh, when samples from various periods and years were
pooled, the zone with the greatest biomass per activity trap was the cat-tail zone, followed by the
softstem bulrush and open water zones (i.e., the same as when based on number of individuals).
Samples from the open water zone weighed only one-quarter the weight of those from the cat-tail
zone. When manipulated parts of the Delta Marsh were considered instead, the zone with the
greatest biomass was the red goosefoot (Chenopodium rubrum) zone, followed by the softstem
bulrush, whitetop, rayless aster, cat-tail, and hardstem bulrush zones; mean invertebrate
biomass in the last of these was about half that in the first (H. Murkin, personal communication,
Institute for Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba). However, as
noted earlier, in both manipulated and unmanipulated wetlands the relative rankings of zones
based on their invertebrate densities varied by season and year. Considering just the midge
component of the invertebrate community, numbers and biomass of emerging individuals were
greater in beds of pondweed (Potamogeton pectinatus) than in cat-tail or bulrush stands during a
2-year study in an unmanipulated part of the Delta Marsh (Wrubleski and Rosenberg 1990).
4.6.2 Temporal Variability
Species Composition
Within a season, species composition of invertebrates changes markedly. Water bugs
(Hemiptera), water beetles (Coleoptera), and snails (Gastropoda) seem to be more evident later
in the growing season in some prairie wetlands (Bartonek and Hickey 1969, Swanson et al.
1974). In unmanipulated parts of the Delta Marsh, data from activity traps and artificial
substrates showed that seasonal peaks were attained mostly in late spring or early summer by
mosquitoes, ostracods, and water mites; in mid-summer by lymnaeid snails and non-predacious
midges; and in late summer by planorbid and physid snails, cladocerans, copepods, and
amphipods (H. Murkin, personal communication, Institute for Wetland and Waterfowl Research,
Oak Hammock Marsh, Manitoba). In three western Minnesota semipermanent wetlands,
seasonal peaks were attained mostly in late spring or early summer by clam shrimps
(Conchostraca), water beetles, dragonflies, and cladocerans; in mid-summer by mayflies, snails,
leeches, and water mites; and in late summer by water bugs (Hemiptera), amphipods, and
copepods. However, the same taxa did not necessarily show the same seasonal patterns in all
wetlands, or even in the same wetland between years (M. Hanson, personal communication,
Minnesota Dept. Natural Resources, Bemidji, MN).
Species Richness
In the Delta Marsh, Murkin et al. (1991) documented variation in taxonomic richness within a
year. Richness in activity traps ranged from an average of about 11 families in late summer to
near 0 during October sampling (the latest sampling of the year). Similarly, when samples from
all years and zones within the unmanipulated wetlands were pooled, the data show relatively
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constant richness until early to mid-September, at which time richness drops. Richness in the
unmanipulated wetland ranged from a high of 33 families per seasonal period to a low of 11.
None of the manipulated units had more than 31 families per seasonal period, and most had no
more than 16. Some manipulated units had only 2 or 3 families per seasonal period (H. Murkin,
personal communication, Institute for Wetland and Waterfowl Research, Oak Hammock Marsh,
Manitoba).
Interannual variation also can be extreme. Of 54 families collected by activity traps in
unmanipulated parts of Delta Marsh over a 5-year period, no more than 51 were collected in any
single year, and in 1 year only 38 were found (H. Murkin, personal communication, Institute for
Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba). In one of 18
semipermanent wetlands in North Dakota, the number of invertebrate families changed from 4 to
16 families between just 2 years, whereas in several other wetlands the number of families
remained stable or decreased between years (N. Euliss, personal communication, NPSC,
Jamestown, ND). In six prairie wetlands in southern Saskatchewan, the number of midge
species over a 3-year period ranged from 3-9 in one wetland to 20-24 in another (Driver 1977).
Annual species extinction rates varied from 0% to 44% (of all midge species present during the
period), immigration rates varied from 0% to 58%, and turnover rates (the difference between
immigration and extinction rates) varied from 11% to 100%. As expected, immigration and
species replacement rates were greater in temporary wetlands than in semipermanent or
seasonal wetlands.
Density and Biomass
The seasonal variation in availability of invertebrates in prairie wetlands is a critical factor
affecting waterbird use. Although temporary wetlands (as noted above) harbor generally fewer
species of invertebrates than do semipermanent wetlands, the species that are present typically
occur in enormous quantities at a season when invertebrate biomass in more permanently
flooded wetlands is relatively small or unavailable because of persisting ice cover. In sweep net
samples from 3 undisturbed seasonal wetlands in North Dakota, invertebrates varied seasonally
within the growing period from a maximum of about 18,964/m3 in one wetland in April to about
1000/m3 in another wetland in June (Swanson et al. 1974). In South Dakota, the density and
biomass of benthic invertebrates in 3 seasonal wetlands was greatest in late June just before the
wetlands dried up, whereas in a semipermanent wetland, density and biomass increased as the
growing season progressed, reaching highest levels during the last sampling on September 21
(W. Duffy, personal communication, South Dakota State University, Brookings, SD). In 3 of 4
semipermanent wetlands in western Minnesota, peak biomass in activity trap samples occurred
in late spring to early summer (M. Hanson, personal communication, Minnesota Department
Natural Resources, Bemidji, MN).
In semipermanent wetlands of Delta Marsh, Murkin et al. (1991) and in an area 80 miles west,
Bataille and Baldassarre (1993) documented considerable variation in density and biomass by
season. Total numbers of individuals in activity traps ranged from about 2094 in early summer
(Bataille and Baldassarre 1993) and 300 in late summer (Murkin et al. 1991) to near 0 during
October sampling (the latest sampling of the year). Biomass ranged from about 150 mg in
summer to near 0 mg in October. Peaks in invertebrate abundance and biomass generally
occurred in spring, often coinciding with the period when waterfowl were laying eggs (Bataille and
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Baldassarre 1993), A second peak, especially of zooplankton, sometimes occurs in late summer
or early fall when inputs of plant litter to the water column peaked (Murkin et al, 1991). There is
some interannual variability in the timing of seasonal peaks; a late summer peak was noticeable
in some years and habitats but not in others, and the spring peak occurred earlier in some years
than in others (Wrubleski and Rosenberg 1990; H. Murkin, personal communication, Institute for
Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba). Cladocerans in particular
experienced a midsummer depression in numbers, coincident with an increase in cover of
submersed plants (Murkin et al. 1991).
Interannual variation can be tremendous. Between just 2 years, the number of individuals in
each of several semipermanent wetlands in North Dakota varied by orders of magnitude, but in
others it varied only slightly (N. Euliss, personal communication, NPSC, Jamestown, ND). The
invertebrate density (mean number of individuals per sample per period) in two semipermanent
wetlands of western Minnesota varied three- and five-fold between years, but density did not
change significantly in a third (M. Hanson, personal communication, Minnesota Department of
Natural Resources, Bemidji, MN). In unmanipulated parts of the Delta Marsh, numbers of
invertebrates caught in activity traps varied sevenfold over a 5-year period, and biomass varied
by a factor of 3. In emergence traps from the same general area, the mean abundance of
midges varied from 21,499/m2 one year to 29,627/m2 another, while biomass changed little
(Wrubleski and Rosenberg 1990). In manipulated parts of the Marsh, total invertebrate
abundance varied tenfold over the 5 years, and biomass varied twofold (H. Murkin, personal
communication, Institute for Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba).
In one part of the Delta Marsh, interannual changes in abundance and biomass of invertebrates
were least in areas that had been disturbed by mowing (Kaminski and Prince 1981b).
Interannual fluctuations in the amphipod, Hyalella, appear to be particularly great (Voigts 1975),
in one instance ranging between 0% and 71% of all invertebrates sampled in a wetland,
depending on the year (Bartonek and Hickey 1969). Interannual changes are probably a
reflection of changing vegetation and water conditions.
The part of the season during which the peak occurs in a particular wetland's invertebrate
population is not necessarily consistent between years (Wrubleski and Rosenberg 1990). For
example, in the same set of Minnesota wetlands, invertebrates in two of the wetlands appeared
to peak in late summer one year but early summer the next, but in a third wetland, the peaks
both years occurred in early summer,
Bioaccumulation
In an eastern Canadian lake, bioaccumulation of heavy metals in aquatic insects followed a
seasonal pattern, and in no case varied more than sixfold over the course of a year (Hare and
Campbell 1992). Bioaccumulation depends on characteristics of the particular contaminant, the
abiotic environment, and characteristics of the invertebrate species, e.g., its feeding habits, body
size, and microhabitat preferences (Krantzberg 1989, van Hattum et al. 1991, Hare et al. 1991).
4.6.3 Spatial vs. Temporal Variability
In a 4-year study of diving beetle communities in an Alberta lake, Aiken (1991) found that species
composition varied less between years than between zones within the lake (e.g., sedge, cat-tail,
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willow, willow-cat-tail, mixed, and open water). In a 2-year study of midges emerging from 3
habitats (pondweed, cat-tail, bulrush) of the Delta Marsh, Wrubleski and Rosenberg (1990) found
that numbers and biomass varied less between years than among these habitats.
4.7 Collection of Ancillary Data
It is easier to separate the anthropogenic from the natural causes of impairment of community
structure if data are collected or inferred simultaneously on the following variables of particular
importance to wetland invertebrates:
• age of wetland and its successional status
• water or saturation depth
• conductivity and baseline chemistry of waters and sediments (especially pH, alkalinity or
calcium, and organic carbon)
• sediment type
• presence of fish and salamanders
• density, type, and form of vegetation (particularly, total surface area)
• cover ratio
• duration, frequency, and seasonal timing of regular inundation
• time elapsed since the last severe inundation or drought.
All of these features vary to a large degree naturally as well as in response to human activities
such as soil tillage, compaction, and erosion; fertilizer and pesticide application; and water-
regime modification.
4.8 Sampling Design and Required Level of Sampling Effort
Locations within a wetland from which invertebrate samples are collected can be chosen
according to many of the designs described for sampling wetland vegetation (Section 3.8). One
EMAP sampling design is to collect invertebrate samples randomly from transects radiating in
four compass directions from the center of each wetland.
4.8.1 General Considerations
The time required to collect an invertebrate sample varies somewhat among the sampling
methods (sweep net, corers, etc.), and is about 1-3 minutes per sample, not including travel time
to the sampling site. The largest collection-time differences among methods relate to the number
of samples each method requires to achieve a prespecified level of precision and the sorting
times required to separate invertebrates from debris in the collected samples. If sorting is done
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at the wetland site, a screen such as designed by Swanson (1977b) and modified by Euliss and
Swanson (1989) can expedite the process. More often, samples are sorted in a laboratory under
high-intensity lights or in direct sunlight. Sorting time is about 15-30 minutes for samples from
emergence and activity traps and artificial substrates, about 15-45 minutes for sweep net and
tube samples, and at least 3 hours for some core and dredge samples (Murkin et al. 1983, Resh
et al. 1985). These estimates depend, of course, on how numerous and cryptic the sampled
organisms are, how large and "dirty" the sample is, and how completely one wishes to process
the sample. Samples collected with a fine-mesh net (e.g., < 80 microns) will naturally contain
many more individuals than samples collected with coarser nets.
Many investigators have used sugar flotation methods to separate invertebrates from debris;
apparently only a few have used rose bengal stain to increase the detection of individuals in
samples. Core samples are routinely sieved before sorting. Except for very small samples, live-
sorting (sorting of samples in the field prior to preservation) is unlikely to succeed in removing
more than a small proportion of individuals, which are usually the largest and most active forms
and thus not necessarily representative of the invertebrate community. Because the time that
would be required to find every individual in a dirty sample seems almost limitless, some
investigators have used a single, standard sorting time for all samples, but most have simply
exercised best professional judgement as to when they believe they have found nearly all
specimens. If species richness is to be determined, then sorting only a fixed number of
individuals is inadvisable because of a bias toward selecting larger and less cryptic individuals.
More preferable is the complete counting of all individuals within randomly selected subsamples;
subsamples may be delineated by drawing a grid on the bottom of the sorting pan. Once all
individuals have been sorted, tallies of species richness are justifiably based on a fixed number
of individuals (generally 100-1000) that are chosen randomly from within a sample.
If biomass determinations will be made, samples are typically dried in a drying oven to a constant
weight. In some cases, snail and clam shells are first dissolved with acid and caddisflies are
removed from their cases, which are not weighed.
An important consideration that affects sample costs is the desired level of taxonomic
identification. Identifying aquatic invertebrates to the species level usually allows the investigator
to make more refined statements about the condition of a wetland, but species-level identification
can increase sample processing time at least fourfold and requires advanced training,
experience, and the availability of region-specific taxonomic keys. There are no data to indicate
whether, and under what conditions, identification of organisms only to the family level would be
sufficient to define the ecological integrity of a prairie wetland. Determination of invertebrates to
genus or species takes at least three times longer than when identification is made only to the
family level (assuming the identifier is familiar with keys of all taxa). Processing samples
(sorting, identification to family, data entry) from sweep nets and activity traps can be
accomplished in 2-3 hours, but about 4 hours are required for processing samples collected
using corers and tube samplers (N. Euliss, personal communication, NPSC, Jamestown, ND).
Sampling costs are determined not only by sample collection, sorting, and identification times,
but also by the number of samples collected. This should depend on expected variability
(coefficient of variation) and the desired precision. Although ecologists commonly consider
acceptable a confidence level of 90% and standard errors less than 20% of the mean, Downing's
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(1979) review of the literature on benthic sampling in lakes revealed that fewer than 3% of
published studies attained this goal. For their work in a prairie wetland, Murkin and Kadlec
(1986a) stated beforehand that they would accept estimates within ± 30% of a mean, and as a
result they were able to specify the number of samples needed to achieve this precision. Among
prairie wetland researchers, they are apparently alone in using and reporting such relevant
information.
Interpreting the collected data poses many other methodological challenges that cannot be
addressed in this report. In particular, separating the component of variability that is due to
natural causes (weather, vegetation, etc.) from the component that is due to anthropogenic
causes is always challenging. In an analysis of stream invertebrate data from one California
stream, Resh and McElravy (1993) found that the chance of detecting a significant interannual
difference because of natural variability alone was at least 22% when species richness is used,
23%-35% when invertebrate density is used, at least 5% when a similarity index (Simpson's) is
used, and 20%-35% when a purported "indicator species" was used. They also found, for that
stream, the sampling regime in which five replicates were collected at a single site would result in
an ability to detect a 56% interannual (2-year) change in species richness and a 73% change in
the similarity index, whereas the same regime would only be sensitive enough to detect a
> 200% change in invertebrate density and a > 300% change in the indicator species (with a 95%
chance of being correct at the 5% level of significance).
From a review of 46 studies that monitored benthic invertebrates in lakes, Voshell et al. (1989)
found that 3 was the usual number of replicates collected. Review of the prairie wetland
literature indicates that previous studies (those intended to characterize the invertebrates or
algae) usually collected, in each wetland and at each point in time, 2-4 samples per zone (and in
a few cases, 2-3 replicates of each sample). For invertebrate biomass estimates in Minnesota
lakes, Hanson and Butler (1990) reported that samples collected at 4- and 6-week intervals were
very similar to those based on nine biweekly collections.
4.8.2 Asymptotic Richness: Results of Analysis
For this report, we analyzed invertebrate taxonomic richness from three data sets gaathered from
prairie wetlands. One data set consisted of replicates collected from four semipermanent
wetlands during a single growing season (Duffy, unpublished data). Data from four corer
replicates collected within a wetland were first combined into a single list containing all taxa for
each wetland-date combination. Two of the wetlands were sampled four times before they dried
up, and analysis of data from each showed that half the total number of species (from all four
dates) could have been detected in collections from any two dates, but that to detect 95% of the
species, sampling on all four dates was required (Appendix O). For a wetland that was sampled
on six dates, analysis suggested that half the species could have been found in samples from
any two dates, but that to detect 95% of the species, sampling on all six dates was required.
Finally, for a more persistently flooded wetland that was sampled on nine dates, the analysis
indicated that half the species could have been found (as before) in samples from any two dates,
but that to detect 95% of the species, sampling on all nine dates was required.
A second data set (Euliss, unpublished data) consisted of 381 invertebrate sweep-net samples
collected from multiple transects in 19 prairie wetlands during a 2-year period. Without pooling
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any of the samples, our analysis indicated that half the 29 species that were present collectively
could have been detected with only five samples, but to detect 90% of the taxa, at least 178
samples would be required (Appendix O). We then examined data just from the wetland that had
the greatest richness (wetland 28-III), and determined that any two samples would produce half
the 25 taxa found in all 26 samples, but 21 samples would be required to find 95% of the
species.
Finally, we examined a very large data set gathered from invertebrate activity traps used in the
MERP research effort on Delta Marsh, Manitoba (Murkin, unpublished data). Samples had been
collected at various depths in various zones and during various weeks over a 5-year period. See
Appendix L for a full description of the monitoring design and data structure. We conducted the
following analyses.
Number of Seasonal Periods
Just the data for the 2-zone-year combinations that had the greatest richness in the
unmanipulated "reference" unit of the Delta Marsh were compiled. These combinations were
zone 4 (Scirpus acutus zone) in 1985 (31 taxa) and zone 5 (cat-tail zone) in the same year (30
taxa). In the first instance, half the taxa that were present in the samples from 10 periods could
have been detected if only 2 periods (sampling weeks) had been covered, and 95% of the taxa
could have been detected from 9 periods. In the second instance, half the taxa present in the
samples from 20 periods could have been detected if only 3 periods had been sampled, and 95%
taxa could have been detected from 15 periods.
Number of Years
Just the data for the two zone-year combinations that had the greatest richness in the
unmanipulated "reference" unit of the Delta Marsh were compiled. These combinations were
zone 4 during period 4 (last week of May) and the same zone during period 7 (late June). In both
instances, half the 25 taxa that were present in the samples from all 5 years could have been
detected if only 2 years had been sampled, but to detect 95% of the taxa, the full 5 years are
required. Species accumulated at a slightly more rapid rate (i.e., interannual conditions were
more similar) in late May than in late June.
Number of "Replicates"
Data from all 246 sampled combinations of year, period, and zone were examined, from the
unmanipulated "reference" unit. Half the 53 taxa that were present collectively in the 246
samples could have been detected if only 13 samples had been collected, and 225 samples
would be needed to detect 95% of the taxa.
4.8.3 Power of Detection: Results of Analysis
The Components of Variance approach, as described in Section 1.5, was applied to invertebrate
data from four data sets. These data sets (Duffy, Euliss, Hanson, and MERP) are described in
Appendix L.
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Taxonomic Richness
Core sampling, as implemented by the Duffy study, was better able to detect interwetland
differences in the total sampled number of individuals than in the total sampled number of taxa.
The Hanson, MERP, and Euliss studies showed the converse. The apparent results of these
comparisons might be due less to the type of sampler used than to the relative intensities of
sampling. Analysis of the Duffy core data suggests that a sample size of 10 wetlands would
allow detection of interwetland differences of seven taxa, whereas if activity traps or sweep nets
were used, sampling the same number of wetlands would allow detection of interwetland
differences of two and 3 taxa, respectively. These taxa would likely be different because
different sampler types capture different species. For wetland types, sampler types, and
experimental designs similar to those of these studies, sampling additional wetlands beyond
6-13 wetlands has little effect on increasing the precision of the richness estimates.
Total Number of Sampled Individuals
Analysis of the data suggests that a sample size of 10 wetlands would allow detection of
interwetland differences of 9200 individual organisms per core sample. Sampling the same
number of wetlands with activity traps would allow detection of interwetland differences of 1600
individuals (Hanson data) or 6200 individuals (MERP data), whereas sampling with sweep nets or
sediment traps would allow detection of differences of 1300 or 120 individuals, respectively. The
sediment trap data also show that 10 transects would allow detection of intertransect differences
of 67 total individuals. For wetland types and experimental designs similar to those of these four
studies, sampling additional wetlands beyond 5-10 wetlands has little effect on increasing the
precision of the density estimates. For the sediment trap approach, sampling more than 14
transects brings diminishing returns with regard to precision of estimates of the number of
individuals.
Biomass
The data suggest a sample size of 10 wetlands allows detection of differences between total
biomass means of 3 g (using corer samples or activity traps), 2.1 g (using a sediment sampler),
0.7 g (sweep net sampling), or 0.180 g (more intensive activity trap sampling). Data also indicate
that beyond a sample size of about 6-12 wetlands, adding additional wetlands has little effect on
increasing the precision of estimates (i.e., the ability to distinguish between means of the total
sample biomass of any two of the region's wetlands). For the sediment trap approach, sampling
more than 10 wetlands or 12 transects brings diminishing returns with regard to precision of
biomass estimates.
Numbers of Individuals: Specific Taxa
Core sampling, as implemented by the Duffy study, was best able to detect interwetland
differences in the total sampled number of individuals of (in decreasing order of power of
detection) Chironomidae, Anostraca, Conchostraca, Amphipoda, Ostracoda, and total. The data
suggested that a corer sample size of 10 wetlands would allow detection of interwetland
differences of between 11 (Chironomidae) and 6000 (Ostracoda) individuals. For wetland types
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and experimental designs similar to those of the Duffy study, core-sampling additional wetlands
beyond about 9 wetlands has little effect on increasing the precision of estimates of most taxa.
Activity trap sampling, as implemented by the MERP project, was best able to detect interwetland
differences in the total sampled number of individuals of (in decreasing order of power of
detection) Tanytarsini (a Chironomid group), Amphipoda, Ostracoda, Physidae, Cladocera, and
total. In comparison, Hanson's activity trap data were best able to detect interwetland
differences in the sampled number of individuals of (in decreasing order of power of detection):
Hirudinea, Amphipoda, Conchostraca, Ostracoda, Copepoda, and Cladocera, The MERP and
Hanson data suggest that sampling 10 wetlands with activity traps would allow detection of
interwetland differences of between 2 (Tanytarsini) and 2100 (Cladocera) individuals. For
wetland types and experimental designs similar to those of the MERP and Hanson studies,
placing activity traps in additional wetlands beyond 5-12 wetlands has little effect on increasing
the precision of estimates of total sampled numbers of most taxa.
Sweep net sampling, as represented by the Euliss data, was best able to detect interwetland
differences in the total sampled number of individuals of (in decreasing order of power of
detection): Ephemeroptera, Physidae, Conchostraca, Lymnaeidae, and Chironomidae. The data
suggest that sampling 10 wetlands with sweep nets in the manner of Euliss' study would allow
detection of interwetland differences of between 3 (Ephemeroptera) and 500 (Chironomidae)
individuals. For wetland types and experimental designs similar to those of the Euliss study,
conducting sweep net sampling of additional wetlands beyond about six wetlands has little effect
on increasing the precision of estimates of total sampled numbers of individuals of a particular
taxon.
The sediment trap approach, as used by Euliss, was best able to detect interwetland differences
in the total sampled number of individuals of (in decreasing order of power of detection)
Lymnaeidae, Cladocera, and Ostracoda. The data suggest that sampling 10 wetlands with
sediment traps in the manner of Euliss' study would allow detection of interwetland differences of
between 1.6 (Lymnaeidae) and 100 (Ostracoda) individuals.
Biomass: Specific Taxa
Activity trap sampling, as implemented by the MERP project, was best able to detect interwetland
differences in the total sampled biomass of individuals of (in decreasing order of power of
detection) Amphipoda, Ostracoda, and Cladocera. The data suggested that sampling 10
wetlands with activity traps would allow detection of interwetland biomass differences of, at best,
between 0.011 g (Amphipoda) and 0.066 g (Cladocera). For wetland types and experimental
designs similar to those of the MERP and Hanson studies, placing activity traps in more than
5-12 wetlands has little effect on increasing the precision of estimates of the sampled biomass of
most taxa. Similarly, for designs similar to the Euliss study, placing sediment samplers in more
than 8-13 wetlands or 11-14 transects per wetland does little to increase the precision of
estimates of the sampled biomass of most taxa.
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4.9 Summary
The species composition of invertebrate communities, and to a lesser degree their species
richness, demonstrates diagnostic responses to changes in prairie wetland salinity, water regime,
and sedimentation/turbidity (Table 8). Invertebrates also respond sensitively to changing
vegetative cover, nutrient levels, and presence of some contaminants, but existing information is
too limited and confounding effects are too prevalent to currently allow widespread use of
invertebrates to diagnose impairment of prairie wetlands from these stressors. Even for the
better-known responses, few thresholds have been documented consistently, and the ability to
use invertebrates to distinguish natural from anthropogenic levels of stressors is currently limited.
Invertebrate communities are being monitored with increasing frequency in prairie wetlands partly
because of their recognized importance as food for waterbirds. Invertebrates that appear to be
sensitive to the widest variety of stressors include amphipods, mayflies, clam shrimp, and fairy
shrimp. Because of their high dispersal abilities and reproductive capacity, prairie wetland
invertebrate communities appear to recover quickly (within weeks or months) from the direct
effects of acute nonpersistent stressors. Because of this, they are poor temporal integrators of
prairie wetland condition, unless the expense of frequent sampling is acceptable, or a systematic
analysis of decay-resistant remains found in sediments is implemented. Results of such an
analysis of decay-resistant remains can help establish "reference conditions" for development of
regional water quality standards, but further information is first required on the tolerance
thresholds of the taxa most commonly found decay-resistantly.
Individual prairie wetlands that are semipermanently flooded generally contain about 20-40
invertebrate families, at densities of 1-20,000 organisms/m2. Estimates of species composition,
richness, and density are strongly influenced by the type of sampling gear and by sampling
design. Several studies have quantified the interwetland and interannual variability of
invertebrate communities in prairie wetlands. Variability spanning several orders of magnitude is
often strongly linked to long-term wet-dry cycles and associated vegetation changes in individual
wetlands.
Additional research is needed to document invertebrate response thresholds to all stressors, but
particularly to sedimentation and water level change. Before biocriteria can be fully developed,
information is also needed on the potential loss or gain of data resulting from various levels of
specimen identification and use of various sampling protocols.
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Table 8, Summary evaluations of possible Invertebrate indicators of stressors in prairie wetlands.
Evaluations are based on technical considerations, not cost or practicality. A rating of FAIR or POOR
is assigned when too few data (FD) suggest potential as an indicator, or when confounding effects
(CE) of other variables often overshadow the effects of the listed stressor on the indicator.
.''Stressors5
Possible Indicators ;
' Evaluation
Hydrologic stressors
Species composition
GOOD
Richness
GOOD
Density, biomass
FAIR (CE)
Changes in vegetative cover
Species composition
GOOD
conditions
Richness
FAIR (CE)
Density, biomass
GOOD
Salinity
Species composition
GOOD
Richness
FAIR (CE)
Density, biomass
POOR
Sedimentation & turbidity
Species composition
FAIR (FD)
Richness
FAIR (FD)
Density, biomass
POOR
Excessive nutrients & anoxia
Species composition
GOOD
Richness
POOR (CE)
Density, biomass
FAIR (CE)
Herbicides
Species composition
FAIR (FD)
Richness
FAIR (FD)
Density, biomass
FAIR (FD)
Insecticides
Species composition
GOOD
Richness
FAIR (FD)
Density, biomass
GOOD
Heavy metals
Species composition
FAIR (CE)
Richness
POOR (FD)
Density, biomass
POOR (FD)
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5. Amphibians as Indicators of Prairie Wetland Integrity
5.1 Ecological Significance
There is considerable concern among scientists about possible worldwide declines in amphibians
(Barinaga 1990, Wyman 1990), yet relatively little is known about the relative sensitivities or
ecological significance of amphibians (frogs, salamanders) in prairie wetlands. Larval stages of
most species feed largely on algae, but adults are mostly insectivorous and are consumed by
birds (e.g., bitterns, egrets, pelicans). Populations and biomass of amphibians can reach
relatively high levels under some conditions, but species richness is lower than for other groups
discussed in this report.
5.2 Potential Indicator Metrics
The following measurements and metrics deserve consideration when amphibian communities
are used to characterize conditions in reference wetlands, identify the relative degree of past
disturbance to a prairie wetland complex, or assess the current inhibition of key processes:
• number of individuals per unit area, by season
• reproductive success
• interannual variability in density
• bioaccumulation.
5.3 Previous and Ongoing Monitoring
Apparently only two broadscale attempts have been made to survey amphibians in prairie
wetlands. One is a county-wide survey conducted in Iowa (Lannoo et al. 1993). The other was
sponsored by EMAP, and it involved surveys in 11 North Dakota wetlands in summer 1993.
Productivity of salamanders in some prairie wetlands is reported by Deutschmann and Peterka
(1988).
5.4 Response to Stressors
Few studies examine amphibians in prairie wetlands. Species are too few to allow amphibian
richness to be used meaningfully at the individual site level as an indicator of impaired wetland
integrity. Nonetheless, at a regional level the distribution, abundance, and perhaps richness of
amphibians might be a sensitive indicator of overall changes in wetland water regimes,
sedimentation, eutrophication, and other stressors. Amphibians appear to be particularly
sensitive to pesticides because they absorb many chemical substances directly through their
skins (Harfenist et al. 1989). At a site level, various biomarkers might be used as indicators
(e.g., Leboulenger et al. 1982, Licht et al. 1983, Moore and Miller 1984), as well as amphibian
fecundity, incidence of deformities, and bioaccumulation.
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5.5 Monitoring Techniques
Amphibians can be sampled using methods and equipment described by Scott (1982), Vogt and
Hine (1982), Halvorson (1984), Jones (1986), Bury and Raphael (1988), Moseret al. (1993),
Heyer et al. (1994), and others.
5.5.1 General Considerations
Sampling amphibians effectively normally requires several repeated visits to a wetland or to a
series of wetlands along a survey route. Amphibians are best sampled during the mid- to late
growing season when maximum numbers of developing juveniles (e.g., tadpoles) are present.
However, many species are easily found only after the first few days of rain following a drought,
during late-summer thunderstorms, during the first spring thaw in northern areas, during mid-day
basking hours, or at night (Kaplan 1981). Occasionally, traditional winter hibernation areas can
be located and used to count individuals representing a larger (but undefinable) area.
5.5.2 Equipment and Methods
Amphibians are sampled using pitfall and funnel traps (often with drift fences and bait), visual belt
transects, direct capture methods, and vocalization recording. EMAP-sponsored efforts to collect
amphibians in prairie wetlands have used a setup involving drift fences and funnel traps. Fence
and funnel methods can provide relatively quantitative data, when arranged systematically and
level-of-effort (e.g., "trap-hours") is standardized. Efficiency can be increased by channeling
movements of amphibians in the direction of the fence or funnel. This is commonly done with
"drift fences" (Gibbons and Bennett 1974). These are fences constructed of wire screen or
polyethylene plastic, with lengths upwards of 15 m. Traps are placed at both ends of the drift
fence, along the fence at various points, or at the junction of several intersecting fences. The
bottom edge of the fence is implanted in the ground, or at least no space is provided for
amphibians to crawl under the fence.
Pitfall and funnel traps are perhaps the methods most widely used (Jones 1986). Pitfall traps
involve implanting a container in the soil, either on the periphery of the wetland or within it (if
surface water is absent), with the lip of the container placed flush with the ground surface.
Amphibians stumble in and cannot climb the steep sides to escape. Because some species can
drown if the container fills with rainwater, Jones (1986) recommends placing floatable material
(e.g., styrofoam) in the container to reduce mortality. Pitfall traps are impractical in all but the
most temporary parts of wetlands because otherwise the water table is so close to the land
surface that pits fill rapidly with water.
Pitfall and funnel traps often produce more species per sampling effort than direct capture
methods (Jones 1986). With funnel traps, animals enter a screened area and cannot find the
opening to escape. They are subsequently identified, counted, measured, and released. To
reduce loss of trapped animals to predation, traps and funnels are checked regularly (at least
every other day) and can be shaded, and/or filled with sufficient moist plant litter to minimize
physiologic stress to animals. Funnel openings are usually oriented toward land for greatest
effectiveness. The size of the trap, baits used, and trap placement can affect the species that
are caught.
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Drift fences and pit traps can be more effective and less biased in capturing amphibians than
walking transects, electroshocking, or searching and digging through litter. However, drift fences
are expensive; time and cost estimates for drift fence trapping are provided by Gibbons and
Semlitsch (1982). Drift fence/pitfall trap methods are less effective for quantifying populations of
frogs, toads, large snakes, terrestrial turtles, and salamanders than for quantifying populations of
small snakes (Jones 1986). Sizes and shapes of containers and associated drift fences and
their configurations vary greatly, depending partly on target species and wetland type. Various
designs are described by Stockwell 1985, Vickers et al. 1985, and others. For sampling
seasonal and semipermanent prairie wetlands, Euliss (personal communication, NPSC,
Jamestown, ND) has designed and used a particularly effective drift- fence-and-trap array.
The above methods require many visits to a wetland, first to set up and later to check traps.
Amphibians can also be monitored directly, that is, during a single visit, or without having to wait
for traps to catch individuals. However, direct methods usually do not provide accurate
quantitative data on abundance. Unless frequent visits are made and the correct microhabitats
are searched at the proper times of year, direct methods are also unlikely to yield good estimates
of species dominance or richness. However, they can provide a useful complement to trap
methods, locating species that are not easily trapped.
The simplest type of direct search involves scanning a wetland with binoculars to observe the
more obviously visible forms such as basking frogs. In some cases, floating egg masses of
amphibians can also be detected visually and identified to species. Observational methods can
be done formally along defined transects. Searches on foot, perhaps employing many people
shoulder-to-shoulder (e.g., Marshall and Buell 1955) have been used, but these searches could
be impractical and destroy habitat in many wetlands. To enhance opportunities for encountering
amphibians during direct searches, electrofishing can be used, at least for retrieving larger
salamanders and frogs. Frogs can sometimes be located more easily at night because their
eyes reflect light in the beam of a flashlight. Vocalizations of many frogs and toads are easily
identified (commercially available recordings are available to learn these) and can be used to
augment observations along survey routes. Frogs and toads can sometimes be induced to
vocalize by introducing sharp, loud sounds or by playing back tape recordings of vocalizations.
5.5.3 Bioaccumulation
Methods for collecting wetland amphibians and assessing bioaccumulation of contaminants in
their tissues are described in Moser et al. (1993).
5.6 Variability and Reference Points
Species Richness
Although 40 species of amphibians and reptiles have been found in South Dakota and 25 in
North Dakota, perhaps only 3—the tiger salamander, leopard frog, and chorus frog—appear to
be widespread and intimately associated specifically with prairie wetlands (Hubbard et al. 1988).
Biweekly sampling of 17 seasonal and semipermanent wetlands in North Dakota captured 6
species of reptiles and amphibians (N. Euliss, personal communication, NPSC, Jamestown, ND).
A survey of prairie wetlands in Iowa found 7 amphibian species; 2 species present in the 1920's
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(mudpuppy, Necturus maculosus; Blanchard's cricket frog, Acris crepitans blanchardi) apparently
were no longer present (Lannoo et al, 1993). Minnesota prairie wetlands support at least 4 toad
species (Oldfield and Moriarty 1994).
Density, Biomass, Production
A 2-year study of 3 prairie lakes in North Dakota revealed the maximum density of larval
salamanders to be 5000/ha. Maximum biomass was 180 kg/ha and maximum annual production
was 565 kg/ha (Deutschman and Peterka 1988).
5.7 Collection of Ancillary Data
It is easier to separate the anthropogenic from the natural causes of impairment of community
structure if data are collected or inferred simultaneously on the following variables of particular
importance to wetland amphibians:
• water depth
• temperature (site elevation, aspect)
• conductivity and baseline chemistry of waters and sediments (especially pH, DO, and
suspended sediment)
• shade
• amount and distribution of cover (logs, muskrat houses, etc.)
• cover ratio
• extent of plant litter
• vegetation type
• duration, frequency, seasonal timing of regular inundation
• time elapsed since the last severe inundation or drought.
All of these features vary to a large degree naturally, as well as in response to human activities
such as soil tillage, compaction, and erosion; fertilizer and pesticide application; and water
regime modification.
5.8 Sampling Design and Required Level of Sampling Effort
No quantified estimates of interwetland or interannual variability were found in the published
literature from the region, and no data sets were obtained for analysis, so requisite sample sizes
cannot be estimated.
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5.9 Summary
Although apparently constituting a large portion of the annual animal production of some
semipermanent wetlands, amphibian communities have seldom been investigated in prairie
wetlands. This is due in part to their relatively low species richness and spotty spatial and
temporal distribution. Limited information suggests amphibians in prairie potholes may be highly
sensitive to some chemical contaminants and to landscape-level fragmentation of wetland
resources. Considerably more research is required before amphibian species composition,
richness, and biomass can be used as unambiguous indicators of prairie wetland condition.
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6. Birds as Indicators of Prairie Wetland Integrity
6.1 Ecological Significance
Birds are an obvious feature of prairie wetlands during the growing season. Birds inhabiting
prairie potholes include waterfowl, shorebirds, large wading birds, and songbirds. The ecological
significance of birds in prairie wetlands stems from at least two characteristics:
• They are highly mobile, moving frequently among as many as 20 prairie potholes during a
growing season and between prairie potholes and wetlands in other regions during
migrations. In the course of these movements, they often passively carry with them
various invertebrates and seeds, which subsequently become established in new areas.
• Their movements and feeding within a wetland can alter vegetation structure (especially
submersed plants), invertebrate densities, and the mixing of sediments (which in turn can
affect wetland fertility).
The usefulness of birds as indicators of ecosystem integrity has been widely discussed (e.g.,
Reichholf 1976, Morrison 1986, Temple and Wiens 1989). Specific factors that make birds
attractive as indicators of wetland integrity include:
• ease of monitoring (usually no samples to process); simple identification, and willingness
of capable non-scientists to assist with surveys
• availably of established survey protocols
• tendency of some species (e.g., many raptors and wading birds) to accumulate toxic
substances because of their position at the end of food chains
• longer life spans than other bioindicators (this may make them more sensitive to some
cumulative impacts and more able than other groups to integrate the effects of episodic
events)
• usefulness for in situ assessments (confined or behaviorally imprinted individuals)
• availability of the only relatively extensive nationwide databases on trends, habitat needs,
and distribution
• availability of moderately extensive bioassay databases.
Certain characteristics usually considered disadvantages for using birds as indicators of wetland
integrity include:
• absence from most prairie wetlands in winter
• mobility makes it difficult to locate site-specific causes of mortality (could be factors that
operate thousands of miles away)
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• mobility makes it difficult to assume that wetlands used by birds also support other
organisms (birds may only be resting rather than feeding on these wetlands)
• no opportunities for routine analysis of decay-resistant remains (as with diatoms and
some invertebrates), to establish historical reference conditions in a wetland
nonbreeding individuals present in the breeding season (their presence in a wetland then
denotes little about the condition of the species' population)
• survey protocols well-established, with nonsystematic biases (e.g., some species are
much easier to detect than others)
• bird community structure highly controlled by physical habitat, predation, and perhaps
mortality as a result of being hunted by humans rather than by contaminants.
In summary, birds are likely to be poor indicators of the integrity of a specific wetland, but their
trends in species composition and relative abundance when measured throughout a region can
integrate changes occurring in wetlands across the region. Given the current availability of data
and tested protocols, birds are the only taxonomic group capable of serving this purpose.
6.2 Potential Indicator Metrics
The following measurements and metrics deserve consideration when bird communities are used
for characterizing conditions in reference wetlands, identifying the relative degree of past
disturbance to a prairie wetland complex, or assessing the current inhibition of key processes:
• richness of species and functional groups (per unit area, or per number of randomly
chosen individuals)
• number of individuals per unit area by season
• relative dominance and richness of species that are characteristically associated with a
particular habitat condition (e.g., grazing-sensitive species)
• reproductive success, including (see Sheehan et al. 1987 for definitions) numbers of
breeding pairs, nest density, clutch size, nest success, hatch success, number of broods
produced, brood size at fledging, broods per pair, and recruitment
• daily duration of specific activities (e.g., feeding, roosting) in the wetland complex, i.e.,
time budget analysis
• interannual variability in richness, density, and reproductive success
• bioaccumulation.
The specific ways some of these metrics have been or could be interpreted as an indication of
stressed conditions are described in Section 6.4.1.
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6.3 Previous and Ongoing Monitoring
Of the more than 80 publications describing field studies of birds in prairie wetlands, only 14
(18%) involved species other than waterfowl. The parameters most commonly measured in
waterfowl studies are the frequency of nests and broods. An impressive 16 surveys covered
more than 100 wetlands, but 15 studies were based on only a single year's data.
Few studies have systematically surveyed non-waterfowl species outside the breeding period.
Collection of data on habitat use by migrant shorebirds in particular has been limited (Eldridge
1992, Eldridge and Krapu 1993), despite the fact that available evidence suggests that prairie
wetlands are used extensively. In some instances, counts of shorebirds in the northern prairies
exceed those known from any other location on the Central Flyway of North America (G. Krapu,
personal communication, NPSC, Jamestown, ND). Compared to other mid-continent
populations, populations of migratory shorebirds that occur in Dakota wetlands contain a higher
proportion (55%) of long-distance migrants which depend most heavily on wetlands to replenish
their energy supplies during migration (Skagen and Knopf 1993).
No State agencies are currently monitoring birds for the specific purpose of using the data to
estimate the ecological integrity of prairie wetlands. At a regional level, USEPA's EMAP
investigated the use of estimates of four breeding waterfowl species as indicators of landscape
quality. Other ongoing regional efforts (Appendix K) include 1) the FWS's annual breeding
waterfowl surveys (18-mile long aerial and ground transect surveys), 2) the FWS's annual
Breeding Bird Survey (25-mile long roadside transects; an average of 69 routes have been run in
US and Canadian parts of the prairie region and contain an average of 14 years of data); and 3)
Breeding Bird Censuses (plot-based intensive surveys). At more localized levels, birds are being
tested for possible use as indicators of the success of wetland restoration efforts in Iowa
(Dinsmore et al. 1993, Zenner and LaGrange 1993) and cover management practices of the
Conservation Reserve Program. Research on ecological relationships affecting waterfowl in
particular continues to be conducted by NPSC and universities.
6.4 Response to Stressors
The following subsections describe responses of the bird communities to hydrologic stressors,
vegetative cover conditions, salinity, sedimentation/turbidity, excessive nutrient loads/anoxia, and
pesticide and heavy metal contamination.
6.4.1 Birds as Indicators of Hydrologic Stressors
Species Composition
Birds are affected both directly and indirectly by hydrologic changes. The assemblage of
breeding birds that have established territories in a particular prairie wetland can generally
indicate the present water depths of the wetland. For example, the regular presence of western
grebes and certain diving ducks can indicate relatively deep water (> 2 m) and consequently, the
likely seasonal persistence of water in an individual wetland. Much of the information on depth
requirements is summarized by Fredrickson and Taylor (1982), Fredrickson and Reid (1986), and
Short (1989). Species that are likely to be the most sensitive indicators of water levels might be
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those that 1) nest along water edges, 2) feed on mudflats (e.g., shorebirds), 3) require a
particular combination of wetland hydroperiod types in a region (e.g., Kantrud and Stewart 1984,
Maxsson and Riggs 1996). In contrast, species (e.g., marsh wren, some diving ducks) that
characteristically nest well above the water level might be less directly vulnerable, and thus are
probably weaker indicators.
Only when data are combined at a regional level is it likely that trends in bird community
composition will reflect trends in the hydrologic integrity of wetlands overall. Species composition
of the bird community in a single prairie wetland is a poor indicator of past hydrologic stresses to
that particular wetland because most birds can move freely among wetlands and among regions
(although this can reduce reproductive success). As documented by radiotelemetric and
modeling studies, many species appear to require wetland complexes, a particular combination
of wetland hydrologic types at a particular density on the landscape or in close proximity to each
other (Cowardin 1969, Welter 1975, Patterson 1976, Flake 1979, Talent et al. 1982, Kantrud and
Stewart 1984, Rotella and Ratti 1992a,b). Years of regional drought temporarily reduce the
number and perhaps the variety of wetland types, and subsequently cause drastic changes in
species composition for an indefinite number of years thereafter (Hammond and Johnson 1984).
Interannual fluctuations in bird numbers are likely to be smaller in landscapes containing intact
wetland complexes because the complexes support a "shifting mosaic" of water depths that
provides at least minimally suitable habitat regardless of regional drought or flood conditions
(Skagen and Knopf 1994).
Single-species Indicators
Mallards and other waterfowl species are widely monitored throughout the prairie region. It is not
apparent, however, that simple presence of a single species, its nest, and/or broods in a
particular wetland is evidence of good hydrologic integrity. Mallards, for example, seem to
inhabit a wide range of wetland types (as defined by hydrologic permanence). There are likely to
be many situations where hydrologic conditions in wetland complexes are sufficient to support
one or a few species such as mallard, but are too degraded (e.g., through drainage) to support
many other species, including some plants, invertebrates, and other vertebrates that are crucial
contributors to regional biodiversity because of their narrower habitat preferences.
Species Richness
At an individual wetland level, avian species richness is often greater in semipermanent and
permanent wetlands than in temporary and perhaps seasonal wetlands (Faanes 1982, Weber et
al. 1982). By reducing the number and perhaps the variety of wetland types, sustained regional
drought diminishes species richness in many individual wetlands and wetland complexes.
Among six restored prairie wetlands that were sampled in Iowa for 2 years, avian richness was
greater during the wetter year in all but one wetland, where it did not change (Hemesath 1991).
Richness in wetlands restored after being drained for > 30 years did not differ significantly from
richness in wetlands drained more recently. Birds colonized formerly drained wetlands within 1
year of restoration; other prairie wetland studies report similar results (LaGrange and Dinsmore
1989 a,b, Sewell 1989, Zennerand LaGrange 1993).
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Density and Bio mass
Among waterfowl, pair density during the early summer is usually greater in temporary and
seasonal wetlands that have ponded water, than in semipermanent and permanent wetlands
(Krapu and Duebbert 1974, Kantrud and Stewart 1977, Ruwaldt et al. 1979). When
presence/absence of ponded water is not considered, seasonal wetlands have the highest pair
densities. However, later during the summer and perhaps during dry years, the more permanent
wetland types support the greatest number of individuals per wetland area (Stewart and Kantrud
1971, 1973; Duebbert and Frank 1984; Talent et al. 1982). In contrast, for birds as a whole (all
species combined), breeding densities are greatest in semipermanent wetlands (Faanes 1982).
By reducing the number and perhaps the variety of wetland types, sustained regional drought
diminishes density of birds in many individual wetlands and wetland complexes (Greenwood et
al. 1995, Bethke and Nudds 1995).
Reproductive Success
Reproductive success of waterfowl and undoubtedly other prairie birds is diminished during
drought years (Higgins et al. 1992, Greenwood et al. 1995, Bethke and Nudds 1995).
6.4.2 Birds as Indicators of Changes in Vegetative Cover
Species Composition
Birds in prairie wetlands respond strongly to changes in vegetation density and type, both within
wetlands (Weller and Spatcher 1965, Lokemoen 1973) and in the surrounding landscape
(Duebbert and Kantrud 1974, Huber and Steuter 1984). Many species, primarily waterfowl and
shorebirds, benefit from (or tolerate) reduced ground cover and increased openings in dense
stands of vegetation (Keith 1961; Weller and Spatcher 1965; Weller and Fredrickson 1974; Krapu
et al. 1979; Kaminski and Prince 1981b, 1984; Blixt 1993; McMurl et al. 1993), For example,
breeding waterfowl in four semipermanent wetlands responded positively to thinning of dense
cat-tail stands for at least 4 years after the stands had been thinned by herbicides (Solberg and
Higgins 1993a). The waterfowl also used the treated wetlands to a greater degree than they
used untreated wetlands that had natural openings in the vegetation. However, effects of
increased open water on waterfowl as a result of another experimental application of herbicides
(Blixt 1993) were equivocal.
Several species, including sora (Fannucchi et al. 1986), other rails (Weller et al. 1991), northern
harrier, short-eared owl, and ring-necked pheasant (USDA Soil Conservation Service 1985,
Homan et al. 1993) do not necessarily benefit from reduced cover density. One North Dakota
study that used herbicides to reduce vegetation cover found a reduction in densities of marsh
wren, red-winged blackbird, yellow-headed blackbird, and common yellowthroat up to 2 years
after application (Blixt 1993, Linz et al. 1993, 1995, 1996). A Minnesota study found no positive
correlation between cover ratio and numbers of yellow-headed blackbird, song sparrow, or sora
(Olson 1992). Limited surveys of restored wetlands in Iowa seldom found certain species that
occurred only in natural (vs. restored) wetlands: least bittern, American bittern, sora, Virginia
rail—or more abundantly—common yellowthroat, red-winged blackbird, swamp sparrow (Delphey
and Dinsmore 1993, Dinsmore et al. 1993). Among waterfowl species, the northern pintail and
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northern shoveler appear to tolerate or benefit from partial removal of cover in surrounding
landscapes (e.g., from grazing) to a greater degree than do teal, gadwall, and American wigeon
(Stewart and Kantrud 1973).
Impacts of cover removal might be most evident among 1) species that nest in uplands, 2)
species with relatively large territories, and/or 3) species that nest early in the growing season,
before there is appreciable new growth by crops or pasture grasses (Batt et al. 1989). Species in
prairie wetlands that appear to benefit from light-intensity grazing (or mowing during the prior
autumn) include Wilson's phalarope, common yellowthroat, and red-winged blackbird; species
most sensitive to heavy grazing include LeConte's sparrow and sedge wren (Kantrud 1981). At a
regional level, changes in the frequency or range sizes of the species cited above (and others)
might indicate changes in the overall condition of vegetative cover. Literature on bird response
to vegetation removal in wetlands is summarized by Skovlin (1984) and Kantrud (1986a). Since
Kantrud's 1986 synopsis was published, an additional 15 research studies on the topic have
been published (Appendix J). Based on the literature, Short (1989) categorized 88 species of
birds that breed in the prairie region according to the vegetative cover types they prefer for
nesting and foraging.
Single-species Indicators
It is likely that conditions of vegetative cover that are suitable for nesting mallards and other
waterfowl are suitable for sustaining relative high levels of avian richness generally.
Nonetheless, cover needs vary among waterfowl species, and there are some bird species (e.g.,
piping plover) that do not generally occur in wetlands that are optimal for waterfowl. Definitions
and measurements of wetland integrity must be broad enough to account for needs of such
species.
Species Richness
High species richness within prairie wetlands typically occurs where there is a mix of vegetation
types, and/or a mixture of about 30%-50% open water with 50%-70% vegetation (Weller and
Spatcher 1965, Weller and Fredrickson 1974, Kaminski and Prince 1981b, 1984, Hemesath
1991, Olson 1992). However, avian richness in prairie wetlands cannot always be predicted by
structural diversity of vegetation (Olson 1992).
As dense stands of vegetation are thinned, the diversity of bird species using a wetland typically
increases or remains stable (Blixt et al. 1993), especially if open water begins to occupy spaces
cleared in the vegetation (Kaminski and Prince 1981b, 1984; Harris et al. 1983). Thus,
"moderate" levels of grazing, herbicide application, mowing, and/or tillage, if occurring at a time
of year that does not disturb nests, either have no effect (Kaminski and Prince 1981a,b, 1984) or
increase wetland bird species richness (Kantrud 1981). However, severe grazing, mowing, fire,
or herbicide application at inappropriate times is detrimental to waterfowl (Kantrud and Stewart
1984, Higgins 1977, Higgins et al. 1992). Also, wetlands that are tilled during the breeding
season tend to support fewer non-waterfowl wetland bird species than do unfilled wetlands
(Weber et al. 1982). Ongoing studies of Conservation Reserve Program (CRP) lands by the
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NPSC are intended to determine relationships of avian richness to patch sizes of unfarmed land.
The sizes, types, and distribution of wetlands on the study plots are being recorded incidentally.
Density and Biomass
Field data show that density of waterbirds tends to be greatest in prairie wetlands with the most
even balance between open water and vegetation (Stewart and Kantrud 1971, Weller and
Spatcher 1965, Weller and Fredrickson 1974, Kaminski and Prince 1981a,b, 1984, McMurl et al.
1993). In an Iowa lakeside marsh, open water patches that were 0.01 ha in size (about 10x10
m) were little-used by waterbirds, but patches > 0.02 ha were used by several species, especially
if they exceeded 100 m in their longest dimension (Weller 1975). Between years, changes in
cover density may also mediate the response of breeding waterfowl to limnological factors (Lillie
and Evrard 1994).
Waterfowl pair densities in tilled wetlands (especially wetlands with little crop debris) are only
20% of densities in untilled wetlands (Kantrud and Stewart 1977), and are lower than in grazed
wetlands (Barker et al. 1990). However, Kantrud (1981) found the total number of individual
birds (of all species) to increase with grazing intensity in North Dakota. In South Dakota, Bue et
al. (1952) found virtually no duck nests in areas grazed by cattle for more than 15 days per acre
per year.
Reproductive Success
Many studies have shown that reduced reproductive success in waterfowl can be a strong
indicator of loss of cover in a wetland or surrounding landscape because of grazing, herbicides,
cultivation, or other factors (Dwernychuk and Boag 1973, Higgins 1977, Duebbert and Frank
1984, Cowardin et al. 1995). Analysis of data from Canadian prairie wetlands indicates that
waterfowl populations might decrease once cropland occupies more than 56% of a landscape (a
rectangular 25.6-km2 area), and average nest success might decrease four percentage points for
every 10 percentage points increase in cropland (Greenwood et al. 1995). Refinement of
research study designs is needed (Clark and Nudds 1991).
6.4.3 Birds as Indicators of Wetland Salinity
Species Composition
Many waterfowl avoid hypersaline or alkali prairie wetlands unless freshwater wetlands are
located nearby (Kantrud and Stewart 1977, Lokemoen and Woodward 1992). However, a few
other waterbird species occur regularly in alkali wetlands during the breeding season (e.g.,
American avocet, phalaropes, killdeer) or migration (e.g., tundra swan; white-rumped,
semipalmated and Baird's sandpipers)(see Faanes 1982, Kantrud 1986b, Eldridge and Krapu
1993, and Earnst 1994). These relatively salt-tolerant species also occur in less saline wetlands,
but their abundance often is greatest in hypersaline wetlands and is related to sharp seasonal
peaks in the abundance of brine shrimp and other salt-tolerant invertebrates. Although changes
in the frequency or range sizes of these species at a regional level might indicate changes in the
occurrence of hypersaline wetlands, birds generally do not appear to be sensitive indicators of
less extreme variations in salinity, especially at a site level.
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Single-Species Indicators
It is likely that saline conditions that are suitable for nesting mallards and other waterfowl are
suitable for sustaining relatively high levels of biodiversity in general. Nonetheless, there are
some bird species (e.g., American avocet) and many plants that do not generally occur in
wetlands whose salinity is optimal for waterfowl. Definitions and measurements of wetland
integrity must be broad enough to account for needs of such species.
Species Richness
Avian richness is predicted reliably by salinity only among wetlands that are the most saline.
Avian richness is generally low in hypersaline or alkali wetlands of the prairie region (Faanes
1982).
Density and Biomass
The density of birds nesting in saline wetlands is generally low (Faanes 1982); this is particularly
true of waterfowl (Savard et al. 1994). Pair densities of breeding waterfowl in alkali (highly
saline) prairie wetlands are only one-tenth the densities in fresher wetlands (Kantrud and Stewart
1977). However, densities of some migrating waterbirds can be high in saline wetlands,
sometimes exceeding densities in many fresher wetlands (Kingsford and Porter 1994).
Reproductive Success
Although moderately saline wetlands can be highly productive, reproductive success of some
waterfowl species is limited in highly saline wetlands if they cannot gain access to fresh water.
For example, mallard ducklings are generally not present (or experience reduced growth) in
wetlands with salt concentrations greater than 10-20 pS/cm unless freshwater springs are
present (Swanson et al. 1983, Swanson et al. 1988). In a survey of part of the Canadian prairie,
2% of the wetlands were found to be potentially too saline to support waterfowl reproduction
(Leighton and Wobeser 1994).
6.4.4 Birds as Indicators of Sedimentation and Turbidity
Species Composition
Bird species (e.g., redhead) that feed on submersed plants and their associated invertebrates
can be defined, and they are likely to be affected the most by turbid conditions in prairie
wetlands. At a regional level, changes in the occurrence, frequency, or range sizes of such
species might indicate overall trends in turbidity and sedimentation. Using statistical regression
analysis, Flake et al. (1977) reported that turbidity negatively influenced numbers of mallard pairs
in stock ponds in western North Dakota, whereas a regression analysis in British Columbia
(Savard et al. 1994) found positive correlation between wetland turbidity and dabbling duck
densities.
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Species Richness
Changes in avian richness in response to increased turbidity and sedimentation are being
investigated by ongoing work sponsored by the NPSC and USEPA.
Density and Biomass
Changes in avian density and biomass in response to increased turbidity and sedimentation are
being investigated by ongoing work sponsored by the NPSC and USEPA.
Reproductive Success
Changes in avian reproductive condition in response to increased turbidity and sedimentation are
being investigated by ongoing work sponsored by the NPSC and USEPA.
6.4.5 Birds as Indicators of Excessive Nutrient Loads and Anoxia
Species Composition
No documentation exists for birds being influenced directly or measurably by the nutrient status
of prairie wetlands, and accordingly they are probably unsuitable indicators of this stressor.
Effects of nutrient enrichment are likely to be expressed as increases in density of vegetation
cover or turbidity (from algal blooms), to which birds respond mostly negatively (see Sections
6.4.2 and 6.4.4). In wetlands in other regions, the abundance and/or on-site diversity of
songbirds (Brightman 1976, Hanowski and Niemi 1987) and sometimes waterfowl (Piest and
Sowls 1985, Belanger and Couture 1988) have tended to increase with increased abundance of
aquatic invertebrates as in the case of wetland enrichment. However, some anecdotal
observations in prairie wetlands suggest that wetlands experiencing prolonged anaerobic
conditions following major runoff-induced algal blooms tend to support lower densities of birds
(G. Krapu, personal commiunication, NPSC, Jamestown, ND).
Single-species Indicators
Simple presence of a single species, its nest, and/or broods in a particular wetland is insufficient
evidence that the wetland is relatively enriched.
Species Richness
Migrant shorebirds and gulls often appear to concentrate at nutrient-enriched sites, e.g.,
wastewater lagoons, both in other regions (Fuller and Glue 1980, Campbell 1984) and in the
prairies (Swanson 1977a, Maxson 1981). Thus, overall avian diversity might be greater in
moderately enriched prairie wetlands than in unenriched ones. However, definitive data are
lacking.
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Density and Biomass
Large numbers of waterbirds congregate at all seasons in wastewater wetlands where the cover
of emergent vegetation is at least partly controlled (e.g., Swanson 1977a, Maxson 1981, Brady
and Giron-Pendleton 1983). This high level of use is attributable largely to the ready availability
of high densities of invertebrate foods in these areas. Nonetheless, little is known about waterbird
responses in prairie wetlands subject to eutrophication from fertilizer runoff.
Reproductive Success
Data are lacking to describe the effects of enrichment of prairie wetlands on waterbird
reproductive success.
6.4.6 Birds as Indicators of Pesticide and Heavy Metal Contamination
Species Composition
Few if any pesticides appear to be acutely toxic to waterbirds in prairie wetlands when applied as
prescribed, many indirect effects, i.e., mortality of foods upon which waterbirds depend and loss
of nesting cover, can be significant (Grue et al. 1986, 1988, 1989, Mineau 1987, Sheehan et al.
1987, Tome et al. 1990, 1991). Some studies from other regions (Hunter et al. 1986) and more
recently in prairie wetlands (Martin and Solomon 1990, McCarthy and Henry 1993) have
demonstrated indirect impacts to individual birds as a result of pesticide-induced loss of foods.
Whether loss of plant and/or invertebrate foods reduces bird populations depends partly on the
degree to which birds in a particular situation can avoid contaminated foods or shift (without
physiologic damage) their preferences from impacted foods to non-impacted foods.
Foods chosen by birds that inhabit prairie wetlands are relatively species-specific. Thus, the
exposure of wetlands to a pesticide or other contaminant that kills only a particular insect or plant
species or group might be reflected by the absence or widespread decline of just those bird
species that are associated with the target organism/group. For example, a local decline in
populations or breeding success of American wigeon and gadwall—ducks that rely most heavily
on plant foods—could signify impacts from herbicides. A local decline in northern shoveler could
indicate impacts to nektonic invertebrates that are its primary food. Invertebrate food choices of
various other prairie waterbirds are summarized from the literature in Appendix C. As noted
above, however, the effects on any species of loss of a particular food will depend on the
likelihood of unimpacted foods being selected and meeting the physiologic needs of birds.
Seasonal timing is also important.
Direct toxicity levels and descriptions of the effects of several heavy metals, selenium, and
synthetic organics are given in Hudson et al. (1984), USEPA's "TERRETOX" database, and in
the FWS's "Contaminant Hazard Reviews" series that summarizes data on arsenic, cadmium,
chromium, lead, mercury, selenium, mirex, carbofuran, toxaphene, PCBs, and chlorpyrifos.
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Single-species Indicators
Contaminant levels or population declines of a single species is seldom sufficient to indicate that
a particular wetland has been exposed to the contaminant.
Species Richness
Declines in avian richness would be expected at wetland complexes or regions heavily
contaminated by pesticides or heavy metals. However, data from prairie wetlands are lacking.
Density and Biomass
It is likely that declines in avian density and biomass should be expected at wetland complexes
or regions heavily contaminated by pesticides or heavy metals. However, data from prairie
wetlands are lacking.
Reproductive Success, Fledgling Growth, Population Demographics
Many studies have documented birds failing to reproduce or grow successfully in wetlands
severely contaminated with heavy metals (e.g., Scheuhammer 1987, Kraus 1989) and particular
pesticides, e.g., phorate (Dieter et al. 1995).
Bioaccumuiation
Selenium levels of > 0.050 mg/L, or > 0.030 mg/g of body weight, pose a potential risk to many
waterbird species because selenium is rapidly accumulated in food chains and body tissues
(Welsh et al. 1993). Analysis of waterbirds collected from > 24 wetlands in the prairie region,
mostly on National Wildlife Refuges, 1986-1992, revealed problems with selenium accumulation
in only a few localities (Ludden 1990, Welsh and Olson 1991). Selenium was not detected in
water samples from any of 238 wetlands in a part of the Canadian prairie with selenium-rich soils
(Leighton and Wobeser 1994). Incidences of organochlorines, PCB's, and mercury accumulating
in prairie birds, especially raptorial and fish-eating species, have been reported (Jackson 1986,
FWS 1989, DeSmet and Shoesmith 1990, Larson 1990, Welsh and Olson 1991).
Physical Condition, Deformities, Behavior
Eggshell thinning, physical deformities of embryos and hatching birds, and feather loss in adult
birds, are symptoms of severe contamination of wetland food chains with certain chemicals, such
as selenium (Scheuhammer 1987, Ohlendorf et al. 1990). Drooping wings and abnormal neck
posture can indicate poisoning by carbamate or organophosphate insecticides (Facemire 1991).
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Biomarkers
The FWS's Biomonitoring of Environmental Status and Trends (BEST) program has proposed
use of several biomarkers, including the following relatively well-established ones:
delta-aminolevulinic acid dehydratase (ALAD). Elevated concentrations of this
enzyme in birds and perhaps amphibians can indicate sublethal exposure to lead from
highway runoff or birdshot within the previous month.
acetylcholinesterase (AChE). Depressed concentrations of this enzyme in birds,
amphibians, and invertebrates can indicate exposure generally within a few hours or days
to organophosphorus and carbamate insecticides (Ludke et al. 1975), and perhaps to
some heavy metals.
cytochrome P450 mono-oxygenase system (MO). Elevated concentrations of this
enzyme in birds can indicate exposure, within the previous few days or weeks, to various
organic hydrocarbons.
hexacarboxylic acid porphyrin (HCP). Elevated concentrations of this enzyme in birds
can indicate ongoing exposure to various organic hydrocarbons.
retinol (vitamin A). Depressed concentrations of this enzyme can indicate reduced
viability of individual birds (Wobeser and Kost 1992).
thyroid hormones. Depressed concentrations of various thyroid hormones in birds can
indicate ongoing exposure to various organic hydrocarbons.
Laboratory costs for analysis of any of the above biomarkers generally range from $15 to $75 per
sample, processed at a rate of about 20 to 30 samples per day. Other potential biomarkers for
use with terrestrial vertebrates are described in Harder and Kirkpatrick (1994).
6.5 Monitoring Techniques
Methods for censusing of waterfowl in prairie wetlands are summarized by Hammond (1969),
Klett and Johnson (1981), Klett et al. (1986), Ball et al. (1988), and Higgins et al. (1992).
Although not specific to prairie wetlands, Kirby (1980) and Eng (1986) also discuss waterfowl
censusing. Methods for censusing marsh and shorebirds are discussed by Connors (1986),
Weller (1986), and Clark and Murkin (1989). Methods for surveying entire bird communities
within individual habitats are described by Burnham et al. (1980), Ralph and Scott (1981),
Halvorson (1984), Verner (1985), Verner and Ritter (1985), and others.
6.5.1 General Surveys
Observations that are part of a survey covering several wetlands should occur simultaneously or
be made within consecutive days unless severe weather conditions intervene. If the objective is
to compare between-year trends in a species, total species, or species richness, then simple
count methods (e.g., transects) are probably appropriate. However, if the objective is to rank
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wetland types or relative abundance of species, more time-consuming censusing techniques are
required to develop estimates of density (Steele et al. 1984). Determination of indices of relative
annual abundance, rather than an exhaustive census of the population, is suitable for most
purposes (Emlen 1981).
Although most common songbirds will not be disturbed by frequent visits by monitoring
personnel, raptors, waterfowl, other large or colonial species, and ground-nesting species can be
susceptible. Wetland songbird surveys are commonly conducted during May through July, when
breeding birds are most detectable by song. Species detection (especially of most songbirds) is
greatest during early morning hours. However, in winter some species are active at mid-day.
Night-time coverage is sometimes warranted, not only for typically nocturnal species such as
owls, but also for waterfowl and wading birds which sometimes use different prairie wetland types
for roosting and for feeding (e.g., Swanson and Sargeant 1972). Secretive species (e.g., rails,
some passerines) have sometimes been surveyed more effectively by playing back tape
recorded calls, using predator decoys, using dogs, and dragging ropes or chains through
wetlands (e.g., Glahn 1974, Ralph and Scott 1981, Gibbs and Melvin 1993).
Surveys can be conducted from ground level, from elevated observation posts, or aerially.
Ground-level, visual techniques cannot be used effectively in wetlands with tall vegetation.
Boats are typically used for surveys of wetlands wider than about 100 m, as visibility from shore,
even using a spotting scope, becomes restricted.
6.5.2 Reproductive Success
Where birds that colonize bird boxes are present, boxes provide a convenient means of
monitoring reproductive success with minimal disturbance and without the labor of having to find
nests. In other regions, boxes have been used successfully to monitor impacts from heavy
metals (Kraus 1989, Peterson and McEwan 1990) and acid precipitation (St. Louis and Barlow
1993).
6.5.3 Time Budget Analysis
Studies on prairie wetlands (Murkin and Kadlec 1986a, Eldridge and Krapu 1993) have
demonstrated that estimates of bird density are not necessarily sufficient to indicate a degraded
wetland condition (i.e., a wetland with diminished invertebrate densities), yet documenting the
hours of use of the wetland by various species can successfully indicate such a condition. Such
a time-budget approach usually requires purchase and installation of video equipment that
automatically photographs portions of the wetland at specified intervals. From viewing the tapes,
the duration of each activity (e.g. feeding) of visible birds in each photographed zone can be
determined. This method would be costly to implement for studies intended to survey more than
a few wetlands.
6.5.4 Bioassay Methods
A review of laboratory, outdoor mesocosm, or in situ bioassay methods involving birds or other
wildlife is beyond the scope of this report. Use of bioassays to explore direct contaminant
toxicity to birds has been relatively limited in prairie wetlands.
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6.5.5 Bioaccumulation
Methods for assessing bioaccumulation of contaminants in bird tissues are described in Moser
and Rope (1993a).
6.6 Variability and Reference Points
The following subsections describe spatial and temporal variability in avian community
composition of prairie wetlands.
6.6.1 Spatial Variability
Species Richness
As a point of reference, of the 302 bird species that occur regularly at some season in the prairie
region (Faanes and Stewart 1982), about 104 occur regularly in wetlands, 35 solely as migrants,
and 69 additionally as nesters (interpretion based on published literature and personal
experience, see Appendix C). During the nesting season, this wetland component of the
avifauna represents 78% of all 88 nesting species that Short (1989) notes are found regularly in
any habitat in the prairies.
In Iowa, a breeding-season survey of 17 restored prairie wetlands found 2-18 species per
wetland (Hemesath 1991), No species occurred in all wetlands and four species occurred only in
one. A survey of another 11 Iowa wetlands, both natural and restored, reported a cumulative
total of 22 species (Delphey and Dinsmore 1993). In yet another survey in Iowa, covering 30
natural wetlands ranging in size from 0.2 to 182 ha, Brown and Dinsmore (1988, 1991) found a
range of 2-17 species per wetland, similar to the 7.2 species per wetland found by Dinsmore et
al. (1993),
In North Dakota, a 2-year survey of 452 North Dakota prairie wetlands, in which each wetland
was visited 1-2 times annually to include the breeding period, found an average of three species
per wetland (range 0-45 species) (Igl and Johnson, unpublished data, NPSC, Jamestown, ND).
Richness varied among six subregions of the prairie region, from 1.38 species per wetland in one
subregion to 3.74 species per wetland in another. Cumulatively (among all the wetlands), 101
species were found the first year and 113 the second. In a single-visit survey of 95 randomly
selected plots in North Dakota wetland subregions, Kantrud (1981) found an average of about
seven breeding species per 31.5-ha plot. Avian richness was less variable among landforms
than was avian density. About 75Q acres of one North Dakota wetland (Kraft Slough) supported
29 breeding species (Krapu and Duebbert 1974). In a 1-year survey of breeding species in the
Cheyenne Lake area of North Dakota, Faanes (1982) found 36 species in a cumulative area of
76 ha of permanent wetland, 25 species in 24 ha of seasonal wetland, 22 species in 20 ha of
semipermanent wetland, and 17 species in 44 ha of alkali (saline) wetlands.
A major source of information on the species composition, distribution, and relative abundance of
prairie birds is the FWS's Breeding Bird Survey (BBS). This is a database containing data
collected, in some instances, as far back as 1966. Birds seen or heard at each of 50, 3-minute
stops along 25-mile roadside routes are recorded. Both waterfowl and nonwaterfowl are
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surveyed, as well as both wetland and nonwetland habitat (which are not identified specifically by
the survey). At a regional level, the FWS has calculated trends in abundance of each species;
trends of wetland species are shown in Appendix C ("Priority" column). For Appendix C of this
report, data from 160 routes in BBS strata 37, 38, and 40 were tabulated for the years
1966-1993. Each of these strata is located predominantly within the region commonly
considered to be the prairie pothole wetland region. Along these 160 BBS routes, a cumulative
total of 74 wetland species (71% of all wetland species that occur regularly at any season in the
region) have been recorded at least once during 27 years. During any year, the median number
of wetland species per route is 15 (14% of the region's wetland avifauna) and ranges from 46
species (44%) on the richest route during its highest-count year to three (3%) on the poorest
route during its lowest year. Among years, the richest route averages 37 wetland species, and
the poorest route averages five wetland species.
Fewer data are available to describe bird richness during migration periods. In a 28-visit
springtime survey of 13 varied wetlands in Brookings County, South Dakota, the cumulative
species total ranged from 0 to 21 species per wetland (Brady and Giron-Pendleton 1983). A
cumulative species total of 137 species (60 of them breeding) was found during a year-round, 2-
year study of a 176-ha restored prairie wetland in Minnesota (Svedarsky et al. 1993).
Density
Many investigators have documented waterfowl pair densities, both in wetlands and in adjoining
habitat, during the breeding season. Density estimates of waterfowl are difficult to compare, and
habitat relationships are difficult to define because unit of area can be measured in numerous
ways (Savard et al. 1994). Densities or regional population estimates based on probabilistic
sampling of hundreds of wetlands of all types are reported by Stewart and Kantrud (1974),
Brewster et al. (1976), Kantrud and Stewart (1977), Higgins (1977), Ruwaldt et al. (1979), Krapu
et al. (1983), and Duebbert and Frank (1984). Densities from smaller numbers of wetlands are
reported by Mundinger (1976), Krapu and Green (1978), Higgins et al. (1992), and many others.
Pair densities and/or regional abundance estimates of non-waterfowl species (as well) are
reported from multiple wetlands by Stewart and Kantrud (1972b), Kantrud (1981), Faanes (1982),
Weber et al. (1982), Kantrud and Stewart (1984), Brown and Dinsmore (1986), and Igl and
Johnson (unpublished data, NPSC, Jamestown, ND). The Igl and Johnson study (described in
Appendix L) found an average of 5.43 pairs of all species per wetland in the prairie region of
North Dakota (median = 0, range = 0-744 pairs). The number of pairs believed to actually be
breeding averaged 3.12 per wetland (median = 0, range = 0-397 breeding pairs). Mean density
of breeding pairs varied spatially (among six subregions of the prairie region) from 2.04 to 13.92
pairs per wetland. The maximum pairs per wetland also varied among subregions, from 27 pairs
in a Agassiz Lake Plain wetland to 397 pairs in a Northwestern Drift Plain wetland. About 750
acres of one North Dakota wetland (Kraft Slough) supported 2934 breeding pairs (Krapu and
Duebbert 1974). Similar data from other areas of the prairie region (including some non-wetland
habitat) are reported as part of the Breeding Bird Censuses published in the journals American
Birds and Audubon Field Notes.
Considering just the 1967-1987 period, there were only 76 instances in which any of the 7255
ten-mile route segments (that constitute BBS routes in the prairie region) failed to contain a
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single wetland species, and no segments have been devoid of wetland species during every year
that they have been visited. During any year, wetland species are typically found at greater than
9 (18%) of the 50 stops along each prairie BBS route (range = 1-50), and the median number of
individuals (all wetland species combined) per route is 159, The number of individuals of wetland
species varies spatially from 1133 on the richest route to 30 on the poorest during years when
numbers vary the least among routes. During years when numbers vary the greatest among
routes, the number of individuals varies from 1247 on the richest route to 6 on the poorest.
Species Composition
Of the 67 wetland species ever recorded from prairie BBS routes, a majority have been recorded
on at least 62% of the routes. However, of the 128 species found in prairie wetlands by Igl and
Johnson (unpublished data, NPSC, Jamestown, ND), none were found in more than 41% of the
individual wetlands, and a majority were present only in less than 1% of the wetlands surveyed.
Bioaccumulation
No published data pertaining to spatial variability of bioaccumulation in prairie wetlands were
found.
Reproductive Success
Dozens of studies in prairie wetlands and adjoining grasslands have documented reproductive
success rates of waterfowl (e.g., Sargeant et al. 1995), Nest success rates of about 50% are
typical for many species (Solberg and Higgins 1993b), but spatial and annual variability is great.
6.6.2 Temporal Variability
Species Richness
Between years, the variety of breeding birds on a single prairie wetland can range from near 0
species to over 20, depending largely on water conditions. Among 6 restored prairie wetlands
that were sampled in Iowa for 2 years, richness changed dramatically between years in 2 of the
wetlands (from 1 to 5-6 species, as the wetland matured during its first post-restoration year),
moderately in 3, and not at all in 1 (Hemesath 1991). In another survey in Iowa, the species
richness in each of 30 natural wetlands did not change between two consecutive years in 14
(47%) of the wetlands, and there was no statistically significant difference in species richness
between the years (Brown and Dinsmore 1988).
Igl and Johnson's 2 years of data from North Dakota prairie wetlands show that the number of
species per wetland changed from 2.71 in 1992 (a dry year) to 3.22 in 1993 (a wet year). The
greatest interannual variation was in the Agassiz Lake Plain subregion, where richness per
wetland changed from 0.88 in 1992 to 1.77 in 1993.
Along most BBS routes in the prairie region, the number of wetland species has varied by a
factor of less than 2.6 between years, but in one extreme instance changed from 1 to 11 species
between years. This was calculated as the number of species on the route during the year
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having the most species, minus the number of species on the
route during the year having the fewest. Bird richness in prairie wetlands is lowest in winter, but
the few birds present at that season are highly dependent on the vegetative cover of the
wetlands.
Density
In a wetland complex near Woodworth, North Dakota, that was surveyed annually for 17 years,
the density of waterfowl pairs varied annually from 19 to 56/km2 and averaged 40/km2 {Higgins et
al. 1992). Brood densities ranged from 10 to 63/km2 and averaged 12/km2. Mallard densities
over a 20-year period in another part of eastern North Dakota varied at least fourfold over a
multiyear period (Krapu et al. 1983). Among waterfowl species, the northern pintail, green-
winged teal, northern shoveler, and American wigeon appear to have the greatest interannual
variability (Stewart and Kantrud 1974).
Igl and Johnson's 2 years of data from North Dakota prairie wetlands show that the mean number
of pairs changed from 5.62 in 1992 (a dry year) to 5.64 in 1993 (a wet year). As was true of
species richness, the greatest interannual variation was in the Agassiz Lake Plain subregion,
where mean number of pairs per wetland changed from 1.48 in 1992 to 2.48 in 1993, indicating a
rapid response to improved water conditions. Among wetlands that contained birds both years,
the largest changes in numbers of pairs occurred in a wetland that experienced an interannual
increase of 22 pairs (30% increase) and the largest decrease occurred in a wetland that
experienced an interannual decrease of 49 pairs (12% decrease). Among wetlands that
contained birds both years, the species that showed the greatest interannual change (averaged
among all wetlands) were ruddy duck, eared grebe, bank swallow, black tern, American wigeon,
gadwall, redhead (declined between years), and Forster's tern, green-winged teal, American
coot, double-crested cormorant, and Franklin's gull (increased between years). Such species
might be good candidates as indicators of environmental change. However, in some situations
the annual fluctuations in waterfowl densities are caused by different environmental factors in
different wetlands (Lillie and Evrard 1994).
Data for prairie BBS routes during 1967-1987 indicate that the number of individuals of wetland
species can vary interannually by a factor of 49 (the most temporally dynamic route), but on most
routes varied by a factor of less than 3.58. This was based on 15-21 years of data from the 12
BBS routes having the most years of coverage. Along the routes, interannual trends in
nonwaterfowl wetland birds weakly mimic trends in waterfowl. Specifically, the number of
nonwaterfowl individuals (summed across routes) is correlated (r= 0.38, p < 0.09, n = 21) with
waterfowl individuals summed across routes, and the frequency of BBS stops at which waterfowl
were present is correlated (r- 0.51, p < 0.02, n = 21) with frequency of stops at which
nonwaterfowl were present. The mean richness per route of all wetland species hit lows in 1971
and 1981, despite coverage of a normal number of routes during those years.
Among 60 wetland species for which there are sufficient BBS data to calculate long-term trends
by subregion within the prairie region (see Appendix C), 49 species (82%) have declined in one
or more of the three subregions (the number is 44 species if only the trends that are statistically
significant are included). By subregion, the eastern and central subregions appear to have a
larger percentage (67%) of decreasing species than the western subregion (44%).
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Reproductive Success
Based on analysis of data from over 3000 nests during an 18-year period in the prairie region,
Klett et al. (1988) concluded that average nest success has changed little for most waterfowl
species in most subregions. Duck production in wetlands of the Woodworth complex ranged
from 15 to 61 broods per 100 pairs over a 17-year period and averaged 30 broods per 100 pairs
(Higgins et al. 1992).
Bioaccumulation
Based on 7 years of data from North Dakota wetlands, Welsh et al. (1993) speculated that
bioaccumulation of selenium might be greater during drought years.
6.7 Collection of Ancillary Data
It is easier to separate the anthropogenic from the natural causes of impairment of community
structure if data are collected or inferred simultaneously on the following variables of particular
importance to wetland birds:
• distribution of water depth classes
• vegetation (type, and vertical and horizontal diversity and arrangement)
• conductivity and baseline chemistry of waters and sediments (especially conductivity)
• distance and connectedness to other wetlands of similar or different type
• surrounding land cover (particularly within 500 feet of wetland perimeter)
• shoreline slope
• wetland size
• cover ratio
• spatial interspersion among vegetation classes
• duration, frequency, and seasonal timing of regular inundation
• time elapsed since the last severe inundation or drought.
All of these features vary to a large degree naturally, as well as in response to human activities
such as soil tillage, compaction, and erosion; fertilizer and pesticide application; and water
regime modification. In addition, disturbance from the presence of humans visiting wetlands can
directly alter the bird-community composition of the wetlands.
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6.8 Sampling Design and Required Level of Sampling Effort
"Scale" is an important issue in monitoring the birds of prairie wetlands. Methods used to
determine bird density and richness within a wetland become impractical and even inaccurate
when the objective is to make comparisons among many wetlands or wetland complexes,
especially at regional scales. Likewise, regional-scale methods are often too coarse for
application to individual wetlands.
When monitoring birds within prairie wetlands, point count methods have been used most often.
In Iowa, Brown and Dinsmore (1986) and Delphey and Dinsmore (1993) used fixed-radius (18 m)
circular plots; the first plot was placed randomly in a wetland and the rest were placed
equidistantly around the wetland until the investigators could not locate a plot at least 60 m from
another (or when a total of five were established). Alternatively, when surveying wetlands with
particularly tall, dense vegetation and limited access, the survey points might be placed such that
the largest portion of the wetland is visible from the fewest points. A third option would be to
allocate points in proportion to the sizes of various vegetation zones and water depths, if these
strata can be delimited beforehand. Where quantitative estimates of populations are not needed,
less formal survey methods can be used. For example, in bird surveys of quarter-sections (e.g.,
Kantrud and Stewart 1984), observers have simply walked in as straight a line as possible
through all habitats they recognize within a fixed area.
Costs of surveying birds depend on the number of visits that need to be made per wetland, the
number of points to be visited, and the duration of observations at each point. As with other
taxa, if numbers of individuals are to be estimated, the intensity of effort should reflect the
expected variability (coefficient of variation) and the desired precision. If the objective is to
assess species composition and biodiversity, species accumulation curves should also be
plotted, as described below (Section 6.8.1) and in Section 1.5.
Some biologists in other regions suggest that, for reasonably accurate estimates of breeding bird
richness in a wetland, three visits spread over the breeding season is usually desirable (Brooks
et al. 1989, Weller 1986). This is advisable because some waterfowl species breed in May, most
songbirds breed in June, and the remaining songbirds breed in July and August. In studies of
breeding birds in Iowa wetlands, the number of visits per wetland ranged from two (LaGrange
and Dinsmore 1989b) to five (Delphey and Dinsmore 1993). The time spent per observation
point ranged from six to eight minutes. Only a single visit was made to most of the 128 areas
covered by a survey of nongame birds conducted by the NPSC.
The foregoing discussion has described sampling of individual wetlands or quarter-sections. If
the objective is to estimate species composition, richness, and numbers at only a regional level,
then a different design can be used. For example, the Breeding Bird Survey bases estimates of
avian distribution, relative abundance, and trends on just a single 3-minute visit annually to
hundreds of points in a region. To conduct a regional survey of prairie avifauna, Stewart and
Kantrud (1972a, 1973) and Kantrud and Stewart (1984) selected quarter-sections (64.7 ha) as
plots. The plots were randomly selected by a cluster sampling (without replacement) process, in
which 120-130 quarter sections were grouped as 30 clusters, with clusters reflecting the major
landforms of the prairie region. A stratified random and cluster sampling design was also
implemented, in two stages, in regional avian surveys by Brewster et al. (1976) and Ruwaldt et
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al. (1979). They found that cluster sampling reduced the number of zero observations and travel
time, and thus increased the number of wetlands that could be visited. In just one field season,
they were able to do an avian survey involving two visits to 500 quarter-sections (64.7 ha each).
Four of these quarter-sections were selected, one in each of the four compass directions, from a
corner of each of 125 townships which had been selected randomly, for a total of 500.
Two hours were spent surveying birds in each quarter-section (about two minutes per ha). A
two-person survey of 128 quarter-sections was been able to cover about 160 acres in 1-2 hours
(about three minutes per observer per ha) (L. Igl, personal communication, NPSC, Jamestown,
ND). Because he was surveying a much smaller region and had somewhat different objectives,
Faanes (1982) used smaller (16.2 ha) plots which he was able to visit for longer duration.
If not only richness, but density, must be determined, then at least eight visits are probably
needed (Ralph and Scott 1981).
6.8.1 Asymptotic Richness: Results of Analysis
For this report, we used two data sets to estimate species accumulation and asymptotic
richness. The monitoring design and data structure of the BBS data are detailed in Appendix L.
The Igl and Johnson data are detailed in Appendix O. The BBS data, 1967-1993, were from all
routes in the prairie region, and the Igl and Johnson study covered 452 individual wetlands during
1992-1993. For the BBS data set, we examined only the assemblage of species that are most
characteristic of wetlands (see introduction to Appendix C), whereas for the Igl and Johnson
data, we examined all species.
Analysis of the Breeding Bird Survey (BBS) Data
Several issues were considered in the analysis of the BBS data:
Number of years. We analyzed data from the two routes having the greatest species
richness in each of the three subregions of the prairie (strata 37, 38, and 40). On these
routes, half the wetland species found during the entire interannual period of the route
could generally be found during any 2 years. To find 90% of the wetland species
collectively present during the entire period of coverage required a number of years
equivalent to 37%-83% of the route's total years of coverage. Of the six species-rich
routes, the one with the longest coverage required 15 years (range, 9-22 years) to detect
90% of the 49 species that were found collectively during the entire 27 years of that
count.
Number of routes. In each of the three subregions, we analyzed data from the 2 years
in which the most species were detected. Half the wetland species found collectively
among all 29-40 routes run per subregion during these years could generally be found on
any 2-3 routes. To find 90% of the wetland species collectively present on all these
routes during a given year required between 15 and 19 routes (or 44%-59% of the total
routes run during those years in the subregion). During the year (1993) in which the most
BBS routes were run, 16 routes (range, 5-32) were needed to detect 90% of the species
in each of two of the subregions.
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Another subset of the data also was examined to estimate the requisite number of routes.
This subset included just 11 routes that had been run during the same 26 or 27 years.
This analysis indicated that half the wetland species present collectively among all these
routes during the entire 26-27 years could be detected on just two routes at least
sometime during that period. Finding 90% of the species would require four routes
(range, 2-7).
Number of route segments. The objective of the analyses described above was to
estimate the requisite number of routes. Each route contains 50 point counts whose
totals are aggregated into five subtotals, each representing 10 point counts conducted
over a 5-mile segment. To analyze these segment subtotals, we considered just three
routes, selected on the basis of their being the richest routes run during any year in their
subregion. This analysis indicated that two segments were sufficient to detect half the
wetland species found collectively with all five segments, and four segments (range, 2-5)
were needed to detect 90% of the species total.
Analysis of the Igl and Johnson data
Species accumulation among wetlands was determined separately for 1992 and 1993 as well as
for breeding individuals vs. total individuals (presumed non-breeding as well as breeding).
Species accumulation among wetlands rose and began to level off sooner for breeding
individuals than for total individuals, and accumulation was slightly faster in 1993 than in 1992
(Appendix O). If only half the number of wetlands had been sampled, the cumulative species list
would have been about 75%-80% as large.
6.8.2 Power of Detection: Results of Analysis
Analysis of the BBS data
The BBS monitoring program was better able to detect inter-route differences in the total
sampled number of wetland species than in the total number of stops containing wetland species
or in the total number of individuals of wetland species. The data suggested that, for the prairie
pothole region as a whole, a sample size of 10 BBS routes would allow detection of inter-route
differences of six wetland species, 29 stops containing wetland species, or 140 individuals of
wetland species. When variability is reduced by analyzing data from just the routes that have
had the most consistent coverage over the years, the respective detection limits are two species,
25 stops, and 120 individuals. The data suggest that conducting additional BBS routes in the
prairie region, beyond about six routes, has diminishing effects on increasing the precision of the
richness-per-route estimates. Increasing beyond 10 routes has diminishing effects on the
increase in precision of the other two variables (stops with wetland species, total individuals of
wetland species).
Analysis of the Igl and Johnson data
If wetlands similar to those examined by Igl and Johnson are visited twice annually for 2 years,
approximately 10 wetlands would need to be visited to detect a difference of two species
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between any two wetlands. To detect a difference of 10 breeding pairs, about 35 wetlands would
need to be visited.
6.9 Summary
The species composition of bird communities, and to a lesser degree their species richness,
demonstrates diagnostic responses to changes in water levels and duration, and to vegetative
cover conditions, within a prairie wetland complex (Table 9). Birds also may respond over the
long term to changing wetland nutrient levels, sedimentation, and contaminant levels, but existing
information is too limited and confounding effects are too prevalent to currently allow widespread
use of birds to diagnose impairment of prairie wetlands from these stressors. Even for the
responses to water regime and vegetation change, the ability to use birds to distinguish natural
from anthropogenic levels of wetland disturbance is currently limited.
Bird communities are practical to monitor because sampling is nondestructive, and identification
is relatively simple. Although their high mobility confounds attempts to use birds as indicators of
the condition of individual wetlands, changes in species composition within a wetland complex or
subregion can demonstrate impacts to wetlands that are occurring at such broad scales. Birds
are the only group suitable and practical for indicating such impacts.
Individual prairie wetlands that are semipermanently flooded generally contain about three pairs
of breeding birds, representing three species. Some wetland complexes and larger individual
wetlands can support at least 400 breeding pairs representing 40 species. Between years, the
variety of breeding birds on a single prairie wetland can range from near 0 species to over 20
species, depending mainly on water conditions.
Additional data collected and applied primarily at a landscape or regional scale are needed to
support hydrologic and water quality criteria for nesting waterfowl and migratory shorebirds.
Related information is needed on the degree to which surrounding condition of upland habitats
influences the hydrologic and water quality requirements of wetland birds. Data are clearly
needed on factors that influence use of prairie wetlands by migratory shorebirds and on the
impacts of sedimentation and nutrient enrichment on the sustainability of wetlands as habitat for
waterbirds.
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Table 9. Summary evaluations of possible invertebrate indicators of stressors in prairie wetlands. Evaluations are based on
technical considerations, not cost or practicality. A rating of FAIR or POOR is assigned when too few data (FD) suggest potential as
an indicator, or when confounding effects (CE) of other variables often overshadow the effects of the listed stressor on the indicator
. Stressors •.
Possible Indteators (viifHfen jrieasured at a.:.
Evaluation^
mgfonal or wettenll^ofapfex sca)#.:
Hydrologic stressors
Species composition
FAIR (CE)
Single-species indicators
POOR
Richness
FAIR (CE)
Density, biomass
GOOD
Reproductive success
GOOD
Changes in vegetative cover
Species composition
GOOD
Single-species indicators
GOOD
Richness
FAIR (CE)
Density, biomass
FAIR (CE)
Reproductive success
GOOD
Salinity
Species composition
FAIR (CE)
Single-species indicators
FAIR (CE)
Richness
POOR
Density, biomass
POOR
Reproductive success
FAIR (CE)
Sedimentation & turbidity
Species composition
FAIR (FD)
Richness
FAIR (FD)
Density, biomass
POOR (FD)
Reproductive success
POOR (FD)
Excessive nutrients & anoxia
Species composition
POOR
Single-species indicators
POOR (CE)
Richness
FAIR (CE)
Density, biomass
FAIR (CE)
Reproductive success
POOR (FD)
Herbicides
Species composition
FAIR (FD)
Single-species indicators
POOR (FD)
Richness
POOR (FD)
Density, biomass
POOR (FD)
Reproductive success
POOR (CE)
Insecticides
Species composition
FAIR (FD)
Single-species indicators
POOR (FD)
Richness
POOR (FD)
Density, biomass
POOR (FD)
Reproductive success
GOOD
Bioaccumulation
FAIR (CE)
Physical condition, behavior
FAIR (CE)
Biomarkers
GOOD
Heavy Metals
Species composition
POOR
Single-species indicators
POOR (FD)
Richness
POOR (FD)
Density, biomass
POOR (FD)
Reproductive success
GOOD
Bioaccumulation
GOOD
Physical condition, behavior
FAIR (CE)
Biomarkers
GOOD
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7. Synthesis and Recommendations for Indicators
Determining the ecological integrity of a prairie wetland and diagnosing possible causes of
impairment should involve monitoring multiple indicators. In most prairie wetlands the possibility
of ongoing or recent past exposure to excessive sedimentation is probably best indicated by
species composition of algae and invertebrates, with emphasis on the epibenthic forms (taxa that
live on the top surfaces of the sediment). Epibenthic and epiphytic algae and invertebrates are
also useful indicators of excessive enrichment, removal of vegetative cover, and turbidity that is
occurring either currently or during past years as determined by analysis of decay-resistant
remains. Ongoing or recent past changes of water regime and salinity, as well as overgrazing, in
individual wetlands are perhaps best indicated by vascular plant species composition. Longer-
term changes in these factors can be inferred by examining seed banks and decay-resistant
remains of invertebrates. Exposure to pesticides and heavy metal contaminants can sometimes
be inferred from species composition of invertebrates and from various biomarkers in amphibians
and birds. For bioaccumulative contaminants, tissues of individual plants and birds can be
examined. Birds are also uniquely valuable for spatially integrating information on the hydrologic
stresses to wetlands across entire regions.
Although the choice of indicators and sampling methods is vital to establishing monitoring
programs, equally important are questions of how to interpret the collected data. Information
provided throughout this document is intended to support data interpretation, and potentially
diagnostic symptoms of the ecological integrity of prairie wetlands are summarized in Table 10.
Responses of wetland communities to environmental change are extremely variable and difficult
to interpret or predict. Following are some examples of symptoms that sometimes are
associated with particular causes.
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Table 10. Representative, sample symptoms of changes in the ecological integrity of prairie wetlands and
examples of possible causes (Because of a generally poor understanding of prairie wetland variability,
these examples are anecdotal, not definitive.)
Syrttptom r Possible Cause(s)
Plant species richness or number of functional
groups (Boutin and Keddy 1993) is declining or is
low relative to reference areas.
Wetland is being exposed (or recently has been
exposed) to contaminants, increasing or decreasing
water levels, excessive nutrient or sediment inputs, or
intense grazing.
Plant species richness or number of functional
groups is increasing or is high relative to
reference areas.
Wetland is being exposed (or recently has been
exposed) to increasing or decreasing water levels,
moderate nutrient inputs, or moderate levels of grazing
or other vegetation-thinning activities.
Blooms of algae occur more often, for longer
periods, and/or at atypical times of year.
Nutrient loading of the wetland from external sources
has increased recently; and/or enriched sediment or
decaying vegetation is being reflooded following a
drier period; and/or grazing, mowing, herbicides, or
other factors have removed vegetation that formerly
shaded the water column; and/or contaminants or
other factors have reduced populations of zooplankton
and other organisms that otherwise control algae by
grazing.
Percent cover and stem density of emergents
and associated epiphytic algae is relatively small
or declining, coinciding with larger or increasing
cover of submersed and floating-leaved species
and (perhaps) phytoplankton and benthic
(epipelic) algae.
Recent years have been wetter than normal and/or
wetland has recently been burned, mowed, tilled,
treated with herbicides, or intensely grazed
Percent cover and stem density of emergents is
extensive or increasing, coinciding with small or
declining cover of submersed and floating-leaved
species and/or phytoplankton and benthic
(epipelic) algae.
Recent years have been drier than normal; and/or
wetland has not been disturbed by fire, tillage, or
similar disturbance for many years; and/or water is
very turbid because of sediment runoff, wind
resuspension of bottom sediments, or previous algal
blooms triggered by excessive nutrient inputs.
The number of species and dominance of algae,
vascular plants, and/or invertebrates known to be
characteristically tolerant of turbidity and
sediment deposition (Appendices A, B) is
increasing relative to species that are not or is
high relative to reference areas.
Wetland is being exposed (or recently has been
exposed) to increasing runoff of sediment, shoreline
erosion, resuspension of bottom sediments by wind or
livestock.
The number of species and dominance of algae,
vascular plants, and/or invertebrates known to be
characteristically tolerant of high salinity
(Appendices A, B, D) is increasing relative to
freshwater species or is high relative to reference
areas.
Wetland is being exposed (or recently has been
exposed) to increasing salt concentrations as a result
of increased evapotranspiration, discharge of
groundwater into the wetland, or other factors.
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Table 10 (continued.)
Symptom
Possible Cause{s)
The number of species and dominance of
vascular plants know to be characteristically
tolerant of tillage is increasing or is high relative
to reference areas.
Wetland soils have recently been tilled.
Emergent plants are concentrated in middle of
wetland, surrounded by a ring of open water.
Recent years have been wetter than normal and/or
margins of the wetland have been intensely grazed.
Tall robust plant species are dominant or
increasing in dominance.
Wetland is exposed to relatively large or increasing
loadings or nutrients and/or sediment and/or wetland
has not been disturbed by fire, tillage, or similar
disturbance for many years.
Robust plants that are unpalatable to cattle are
dominant or increasing in dominance.
Wetland has been intensely grazed, perhaps because
prior years have been dry, allowing livestock better
access to wetland vegetation.
There is poor germination of emergent plants
after drawdown.
Drawdown occurred too late in the season (after July)
and/or recently deposited sediment or plant litter
(especially from submersed plants and filamentous
algae) is reducing light and/or oxygen needed by
seeds.
Few annual plant species are dominant, or
annuals are declining in dominance.
Wetland is intensely grazed or mowed early in the
growing season.
Seed bank is relatively devoid of wetland
species, and soil samples wetted and incubated
in the laboratory produce few
macroinvertebrates.
Wetland was mostly dry during previous decades.
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APPENDICES
The content and abbreviations used in electronic appendices (Appendix A-O) are described on
the following pages. The appendices themselves are contained on the computer diskette
accompanying this report. To read the appendices, copy ail the files on the diskette to a hard
drive directory on an IBM compatible computer. At the DOS prompt type browse and you will see
a list of the appendices, from which you can select the one you want to view. Press the esc key
to exit the appendix at any time and return to the browse menu. If you have a commercial
database program such as dBase of Paradox, you can import files of the appendices and use the
program to sort, link, or tally information.
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Appendix A, Taxonomic Index: Plant Species Tolerances and
Responses to Water Regime, Drainage, Land Use, Salinity, and
Turbidity; Food Value to Waterfowl; Toxicity Data Availability
This database primarily includes vascular plant species that the literature reports to be dominant
or abundant in at least one prairie wetland; it does not include all prairie wetland plant species.
Three aquatic mosses/liverworts and one alga are also included because of their large size.
Blanks in this database indicate a lack of information for the species. Codes in the database
mean:
Form [from Reed (1988)]
A
annual,
B
biennial,
P
perennial
N
native,
I
introduced
E
emergent
F
forb
G
grass
GL
grasslike
H
partly woody
H2
horsetail
J
algae
M
aquatic moss
P3
pepperwort
S
shrub
z =
submersed
$
succulent
I =
floating
Dependence [from Hubbard et al. (1988), Reed (1988)]
Reed (1988) notes that the dependence category should not be equated strictly to degrees of
wetness because many obligate species occur in temporary or seasonal wetlands (although most
occur in permanent or semipermanent wetlands).
OBL
FACW
FAC
FACU
Obligate wetland species that under natural conditions occur
almost always (> 99% probability) in wetlands.
Facultative wetland species that usually occur in wetlands
(67%-99% probability) but occasionally are found in
nonwetland upland (terrestrial) habitats.
Facultative species equally likely to occur in wetlands or
nonwetland upland habitats.
Facultative upland species that usually occur in nonwetlands
(67%-99% probability) but occasionally are found in wetlands.
173
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+, - = Slightly wetter (+) or drier (-) than indicated by one of the four
acronyms above.
WaterType [from Kantrud et al. (1989)]
SS = seasonally flooded wetlands
SP = semipermanently flooded wetlands
P = permanently flooded wetlands
T = temporarily flooded wetlands
CAPs = dominant in this habitat
lower case = less dominant but occurs frequently
Drainage
I = Seeds are relatively intolerant of sustained drainage; these
are the species that Galatowitsch (1993a, b) found
in natural wetlands but not in restored (reflooded) wetlands
that had been drained for long periods.
t = Seeds are relatively tolerant of sustained drainage; these are the
species whose seeds Wienhold and van der (1989) found to be
viable even after 30 years of drainage.
Land Use [from Kantrud et al. (1989)]
g = Species typically invades grazed wetlands, or it is
disproportionately unaffected by grazing.
h = Species typically invades hayed wetlands, or it is disproportionately
unaffected by haying activities.
p = Species typically invades plowed wetlands, or it is
disproportionately unaffected by soil tillage.
r = Species typically occurs mainly in relatively undisturbed
(reference) wetlands.
Salinity [mainly from Kantrud et al. (1989)]
f = fresh (< 800 |jS/cm conductivity, or < 3 g/L salt),
o = oligohaline (800-8,000 |jS/cm conductivity, or 4-20 g/L salt),
m = mesohaline (8,000-30,000 |jS/cm conductivity).
174
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p
polyhaline (30,000-45,000 pS/cm conductivity).
e = euhaline or hyperhaline (> 45,000 pS/cm conductivity, or > 70 g/L
salt).
* = documented specifically by literature [other entries were based on
judgement of Kantrud et al. (1989).
CAPS = salinities where it is dominant (not merely occurring)
according to Kantrud et al. (1989).
Turbidity [mainly from Kadlec and Wentz (1974), Davis and Brinson (1980), Nichols (1984),
Chambers and Kalff (1985)]
t = Species typically invades wetlands with highly turbid water
(minimal light penetration) or is disproportionately unaffected
by turbidity increases.
x = Species typically occurs in wetlands with the least turbid
water (great light penetration) or is highly sensitive to
turbidity increases.
Duck Food [mainly from Kadlec and Wentz (1974)]
s = Seeds are frequently consumed by waterfowl.
f = Foliage and/or tubers are frequently consumed by waterfowl.
* = Highly preferred by some species of waterfowl.
RecsPhyto
The number of records for this species in USEPA's PHYTOTOX database as of March 1994.
Each record represents the response of the species to one substance during one investigator's
experiment. G = number of records for the genus (not necessarily this species). The database
is presently in the process of being joined with USEPA's AQUIRE database.
RecsAquire
The number of records for this species in USEPA's AQUIRE database as of October 1993. Each
record represents the response of the species to one substance during one investigator's
experiment. The database can be publicly accessed, and it provides a quantitative report of
each experiment and the citation to the source literature.
175
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Appendix B. Taxonomic Index: Invertebrate Tolerances and Responses to
Water Regime, Oxygen, Salinity, and Sediment; Food Value to Waterfowl
For each of > 160 invertebrate taxa, this database describes what is known about preference or
tolerance with regard to water regime, oxygen, salinity, and turbidity. The next-to-last column
indicates taxa known to be consumed frequently by waterfowl. This database does not include
all invertebrate taxa found in prairie wetlands. Taxa are listed in phylogenetic order (as indicated
by the Sequence field), and were included in this database if 1) by numbers or weight they
constituted a large portion of samples collected for a published study or for an unpublished
database that was made available and/or 2) literature indicated a strong association with the
particular environmental variable. As is evidenced by the many blanks in the database, much
information is unavailable for many of the included taxa; however, certain abbreviations and
sources of information were used:
Reproduction (Repro) [mainly from Wiggins et al. (1980)]
1 = Group 1—Overwintering Residents. Capable of passive
dispersal only. Aestivate and overwinter in the dry basin either as
drought-resistant cysts/eggs or as juveniles and adults.
2 = Group 2—Overwintering Spring Recruits. Reproduce in
springtime surface water before it disappears because egg-laying
depends on water. Aestivate and overwinter in the dry basin
mainly as eggs or larvae (or for a few beetles, as adults).
3 = Group 3—Overwintering Summer Recruits. Can reproduce
even when basin is dry because egg-laying does not require
presence of surface water. Overwinter as eggs or larvae within the
egg matrix.
4 = Group 4—Nonwintering Spring Migrants. Reproduce in
springtime surface water before it disappears because egg-laying
depends on water. Adults of the subsequent generation(s) leave
the wetland as it dries and overwinter in permanent wetlands.
Water Regime [mainly from Swanson et al. (1974), Driver (1977), LaBaugh and Swanson (1988),
Neckles et al. (1990), Batailie and Baldassarre (1993)]
t = Occurs regularly in temporary and seasonal wetlands, as well as
(usually) in semipermanent and permanent wetlands.
sp = Occurs predominantly in semipermanent and permanent wetlands,
but may also be present in temporary and seasonal wetlands.
Oxygen [mainly from Beck (1977), Hilsenhoff (1982), Rosenberg and Resh (1993)]
1 = Most tolerant of oxygen deficits, as commonly occurs with severe
eutrophication.
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4 = Least tolerant of oxygen deficits.
a = Numeric rating to which this code is appended refers to a broader or
narrower taxonomic level to which this taxon belongs, and its applicability
to this particular taxon is unkftown.
b = Numeric rating to which this code is appended refers to taxa that appear to
be closely related, and its applicability to this particular taxon is unknown.
Salinity [mainly from Timms and Hammer (1986), Timms et al. (1986), Lancaster and Scudder
(1987), Walker etal. (1995)]
f = fresh (< 800 pS/cm conductivity, or < 3 g/L salt)
o = oligohaline (800-8,000 fjS/cm conductivity, or 4-20 g/L salt)
m = mesohaline (8,000-30,000 pS/cm conductivity)
p = polyhaline (30,000—45,000 (jS/cm conductivity)
e = euhaline or hyperhaline (> 45,000 ^iS/cm conductivity, or > 70 g/L salt)
Sediment
t = Species typically invades wetlands with highly turbid water (minimal
light penetration) or is disproportionately unaffected by turbidity
increases.
Duck Food
Species typically occurs in wetlands with the least turbid water (great light
penetration) or is highly sensitive to turbidity increases.
Literature from prairie wetlands, cited in Sheehan et al. (1987), indicates major use by the
following waterfowl species (H = hens, Y = young): American wigeon (AmWi), blue-winged teal
(BwTe), canvasback (Canv), gadwall (Gadw), lesser scaup (LeSc), mallard (Mall), northern pintail
(Pint), northern shoveler (Shov), redhead (Redh), ruddy duck (RuDu)
Sequence
This code is used to place the taxa in their approximate phylogenetic sequence. The first digit to
the left of the decimal distinguishes among taxa in different phyla or orders, whereas the first
digit to right distinguishes among orders or families, the second one to the right among families
or genera, and so on.
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Appendix C. Taxonomic Index: Bird Wetland Type
Associations, Relative Abundance, and Trends
This database includes bird species that occur regularly—generally annually—at some season in
multiple prairie wetlands and seem to use wetlands to a greater degree than nonwetland upland
or deepwater habitats. This judgement was the author's, and it is based on reviewed literature
[e.g., Duebbert (1981), Faanes (1982), Kantrud and Stewart (1984), Short (1989)] and knowledge
of the life history of species.1
The database contains these columns:
AOU
A numeric code assigned by the American Ornithological Union, used to sort species into their
approximate phylogenetic sequence.
Status (Migration, Breeding) (Status_mig, Status_br)
Information in this column on relative abundance is from Faanes and Stewart (1982) and applies
to North Dakota; it is generally but not exactly applicable to other areas of the prairie region.
Abundance of some species varies between spring vs. fall migration; in this database, only the
larger abundance term was used. Terms are as defined by Faanes and Stewart (1982):
abundant
common
fairly common
uncommon
rare
very large numbers and easily observed
large numbers
fair to moderate numbers
low numbers
very low numbers, but occurs somewhere at least annually
locally more numerous than indicated by the term
Type of Wetland (Wet_type)
The type of wetland (as defined by water regime) in which the species occurs most often. Inform
ation in this database was interpreted from Provost (1947), Faanes (1982), Weber et al. (1982),
Kantrud and Stewart (1984), Hop et al. (1989), Colwell and Oring (1990), and Short (1989).
Although nearly all of the listed species occur in nearly all wetland types, only the wetland types
used most often are noted. Seasonal/interannual variation, geographic variation, differences
1Note that although the following species (which were not included) are usually believed to depend
more on uplands than on wetlands, they were found in more than 5 (10%) of the North Dakota prairie areas
classified as wetlands by Igl and Johnson (unpublished data, NPSC, Jamestown, ND): brown-headed
cowbird, western meadowlark, eastern kingbird, morning dove, common grackle, horned lark, western
kingbird, American robin, clay-colored sparrow, upland sandpiper, gray partridge, vesper sparrow, lark
bunting.
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between foraging and nesting preferences, and needs for multiple types are not accounted for.
All species listed as occurring in temporary wetlands are also found along the margins of
seasonal, semipermanent, and permanent wetlands. Where information allows, wetland types
used to a greater degree are denoted by upper case.
a
= alkali basins
P
= permanently flooded basins
sp
= semipermanently flooded basins
ss
= seasonally flooded basins
t
= temporarily flooded basins
Layers
The general layers of habitat in which the species generally is found. Information in this database
was interpreted from Provost (1947), Faanes (1982), Weber et al. (1982), Colwell and Oring
(1990), and Short (1989). Although it is recognized that nearly all of the listed species can occur
in nearly all layers, only the layers used most often are noted. Seasonal/interannual variation,
geographic variation, and differences between foraging and nesting preferences, are not
accounted for.
es = inhabits emergent vegetation growing out of hydric soil (surface water
generally is absent)
ew = inhabits emergent vegetation whose stems are immersed in standing
water
ow = inhabits patches of open water that are unvegetated except for presence
of submersed aquatic plants
m = inhabits generally unvegetated mud, sand, bare soil, and gravel
t = inhabits trees or shrubs surrounding wetlands
Phenology
Actual peak breeding times vary somewhat depending on weather during a particular year. A
numeric code indicates the usual peak breeding period for the species in North Dakota:
1 =24 April to 7 June
2 =14 May to 10 July
3 = 22 May to 19 July
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Total Pairs (Pairs_tot)
The sum of breeding pairs found in 1993 in 416 North Dakota wetlands visited by Igl and
Johnson (unpublished data, NPSC, Jamestown, ND). All wetlands included in this tally are in the
Prairie Pothole Region.
Number of Wetlands (Num_Wets)
The number of wetlands where the species was present, based on visits by Igl and Johnson
(unpublished data, NPSC, Jamestown, ND). Numbers are just for 1992-1993 in North Dakota
wetlands within the Prairie Pothole Region. Although data were collected primarily during the
breeding season, these frequencies were calculated for migrant and breeding individuals
combined.
Frequency in Wetlands (Freqwets)
The percentage of the wetlands where the species was present, based on visits by Igl and
Johnson (unpublished data, NPSC, Jamestown, ND). Frequencies are just for 1992-1993 in
North Dakota wetlands within the Prairie Pothole Region. Although data were collected primarily
during the breeding season, these frequencies were calculated for migrant and breeding
individuals combined.
Maximum per Wetland (Max_per_w)
The largest number of breeding pairs counted in any single one of the 452 North Dakota prairie
wetlands visited by Igl and Johnson (unpublished data, NPSC, Jamestown, ND).
Region of Widest Distribution (Reg_frqma)
The North Dakota subregion in whose wetlands in 1993 the species was most widely distributed,
based on Igl and Johnson (unpublished data, NPSC, Jamestown, ND). Frequencies were
calculated for migrant and breeding individuals combined. See Stewart and Kantrud (1972a) for
a map of subregions.
Region of Greatest Mean Abundance (Reg_abumax)
The North Dakota subregion in whose wetlands the species had the greatest mean abundance
(on a per wetland basis) during 1993, and based on Igl and Johnson (unpublished data, NPSC,
Jamestown, ND). The mean abundances were calculated for migrant and breeding individuals
combined.
Region (Bbs_region)
This is a marker for data in the columns that follow to the right.
E = Data for the eastern part of the prairie region, including the Red River Valley
in eastern North Dakota and extending north slightly into Manitoba, all of
western Minnesota and nearly all of southern Minnesota (including some
nonprairie areas), and north-central Iowa [corresponds to Breeding Bird Survey (BBS)
stratum #40].
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C = Data for the central part of the prairie region, including much of eastern South
Dakota, eastern and central North Dakota, a small part of southern Manitoba, and
southcentral Saskatchewan and Alberta (corresponds to BBS stratum #37).
W = Data for the western part of the prairie region, including central South Dakota,
central and northwestern North Dakota, northern Montana, and southern
Saskatchewan and Alberta (corresponds to BBS Stratum #38).
# of Routes (Bbs_numrts)
The number of BBS routes in the region on which the species has ever been found.
Approximately 135 routes were run irrthe region at least once during the period 1966-1991,
which is the period from which the data in this database came. Each route consists of 50,
3-minute stops along a 25-mile roadside route. The routes encompass all habitats, not just
wetlands.
Pet of Routes (Bbs_rts)
The percentage of BBS routes on which the species has ever been found in the specified region.
# per Route (Bbs_avg_rt)
For the specified region, this is the number of individuals of the species found on the average
along a BBS route.
Maximum Frequency (Bbs_maxrt)
For the specified region, this is the greatest frequency of occurrence (% of 50 stops) found along
any BBS route.
Priority (Priortybb)
For the specified region, this is the trend in frequency of occurrence amongst BBS routes during
the period 1966-1991. Because of uneven coverage among routes and years during the period,
a bootstrapping technique (Sauer and Droege 1990) was used by the FWS in calculating the
trends. The author identified categories with these priorities:
1 = The frequency with which the species was encountered declined on more routes than
it increased during the period, and the difference was statistically significant.
2 = The frequency with which the species was encountered declined on more routes than
it increased during the period, but the difference was not statistically significant.
3 = The frequency with which the species was encountered increased on more routes
than it decreased during the period, but the difference was not statistically significant.
4 = The frequency with which the species was encountered increased on more routes
than it decreased during the period, and the difference was statistically significant.
? = The species may breed in the subregion but has not been detected along BBS routes.
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Appendix D. Taxonomic Index; Published Field Studies of
Plant-Salinity Relationships in Prairie Wetlands
This database indexes studies that have reported on plant associations in prairie wetlands with
salinity. It primarily includes species that the literature reports to be dominant or abundant in at
least one prairie wetland; it does not include all prairie wetland plant species. A few of the
included species are normally "upland" species (Reed 1988) even though they were found in a
wetland.
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Appendix E. Taxonomic Index: Published Field Studies of
Plant-Water Regime Relationships in Prairie Wetlands
This database indexes studies that have reported on plant associations in prairie wetlands with
various water regimes. It primarily includes species that the literature reports to be dominant or
abundant in at I?ast one prairie wetland; it does not include all prairie wetland plant species. A
few of the included species are normally "upland" species (Reed 1988) even though they were
found in a wetland.
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Appendix F. Taxonomic Index: Published Field Studies of
Invertebrate-Vegetation Cover Relationships in Prairie Wetlands
This database indexes studies that have reported on invertebrate associations with various
vegetation types, densities, and patterns in prairie wetlands. It primarily includes invertebrates
that the literature reports to be dominant or abundant in at least one prairie wetland; it does not
include all prairie wetland invertebrate species.
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Appendix G. Taxonomic Index; Published Field Studies of
Invertebrate-Water Regime Relationships in Prairie Wetlands
This database indexes studies that have reported on invertebrate associations with various water
regimes in prairie wetlands. It primarily includes invertebrates that the literature reports to be
dominant or abundant in at least one prairie wetland; it does not include all prairie wetland
invertebrate species.
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Appendix H. Dominant Algae in Prairie Wetlands
This database includes algae (arid a few Protista) that the literature reports to be dominant or
abundant in at least one prairie wetland; it does not include all prairie wetland algal species.
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Appendix I. Rare Wetland Plants Reported From Prairie Counties of North Dakota
The list of rare plants with county distributions was provided by Douglas Eiken, North
Dakota Natural Heritage Inventory in April 1991; it has been edited to include only species
officially considered to be wetland-dependent according to Reed (1988). These codes are
assigned to the data:
Form [from Reed (1988)]
A
annual
B
biennial
P =
perennial
N
native
I
introduced
E
emergent
F
forb
G
grass
GL
grasslike
H2
horsetail
Z
submersed
$
succulent
/
floating
Dependence [from Reed (1988)]
Reed (1988) notes that the dependence category should not be equated strictly to degrees of
wetness because many obligate species occur in temporary or seasonal wetlands (although most
occur in permanent or semipermanent wetlands). These abbreviations have been used:
OBL = Obligate wetland species that under natural conditions occur
almost always (> 99% probability) in wetlands.
FACW = Facultative wetland species that usually occur in wetlands
(67%-99% probability) but occasionally are found in nonwetland
upland (terrestrial) habitats.
FAC = Facultative species equally likely to occur in wetlands or
nonwetland upland habitats.
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Facultative upland species that usually occur in nonwetlands
(67%-99% probability) but occasionally are found in wetlands
Slightly wetter (+) or drier (-) than indicated by the acronym.
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Appendix J. Catalog of Published Biological Studies of Prairie Wetlands:
Locations, Sampling Regimes, Key Variables, and Related Descriptors
This database lists virtually all published studies since about 1970 that reported collecting
multispecies biological data from at least one prairie wetland. Abbreviations are interpreted as
follows:
IndepVar
A list of the major independent variables that were measured by the study. Some variables (e.g.,
temperature, season) that are not primarily anthropogenic were not included in this table. The
abbreviations are as follows:
h
= hydrologic gradient or fragmentation (among sites or within a site)
H
= hydrologic manipulation (depth, duration, etc.)
n =
= nutrient gradient (N, P)
N
= nutrient manipulation/dosing
P
= pesticide or heavy metal gradient
P
= pesticide or heavy metal dosing
s =
: salinity gradient (or alkalinity, Ca, Mg, SAR)
S
= salinity manipulated
t
= turbidity, light penetration, or sedimentation—gradient
T
= turbidity, light penetration, or sedimentation—manipulated
c =
= cover density or cover ratio—gradient
C
= cover density or cover ratio—manipulated
#Yrs
The maximum number of years covered by the study; some treatments or collections that are a
part of the study may have covered fewer years.
LocState1...3
This column indexes the State(s) or Province(s) where data were collected.
LocCo1...10
This column indexes the counties where data were collected; a few studies covered more than
10 counties.
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LocName
This column provides more specific information describing the location. The succeeding columns
first provide information on bird studies, then invertebrate studies, vascular plant studies, and
amphibian studies, as follows:
BirdGrp
The group of birds surveyed:
B = all birds
f = waterfowl (ducks, geese, swans)
F = one species of waterfowl
G = one species of gamebird
s = shorebirds (sandpipers, plovers, etc.)
w = waterbirds (mostly nonpasserine aquatic species)
BirdVars
The measurements reported:
b = biomass, weight, or standing crop
f = frequency, numbers, or density (of individuals, by species)
j = duration of use
k = home range size
m = mortality
p = production or growth
r = richness
t = trace or heavy metal content
v = behavior
BirdWets
The maximum number of major sample units (e.g., wetlands, fields) surveyed.
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BirdSeas
Season(s) covered by the survey:
p = spring
s = summer
f = fall
w = winter
BirdFreq
Average frequency of surveys;
d = daily
w = weekly (< w means more often than weekly but not daily)
bw = biweekly
t = triweekly
m = monthly
2 = (the number of visits per year)
InvVars
The measurements reported:
b = biomass or weight or standing crop (by taxon)
B = biomass or weight or standing crop (all taxa combined)
f = frequency or numbers or density (of individuals, by taxon)
m = mortality
n = nutrient or caloric content
r = richness
InvWets
The maximum number of major sample units (e.g., wetlands) surveyed.
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I rivRe ps/Wet
The maximum number of samples or transects per wetland.
InvSeas
Season(s) covered by the survey;
p = spring
s = summer
f = fail
w = winter
InvFreq
Maximum frequency of sample collections:
w = weekly (< w means more often than weekly but not daily)
bw = biweekly
m = monthly
2 = (the number of visits per year)
InvlDlevel
Most specific level to which most organisms were identified:
f = family (fc = only Coleoptera, fm = only midges)
o = order
s = species
VPIantVars
Same codes as for InvVars, plus:
a = alkaline (mono)phosphatase activity
b = biomass or weight or standing crop
c = percent cover by species (C= not broken out by species)
d = decomposition rate
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e =
f
F
g
h
m =
n =
P
r =
s =
t
u =
X =
VPIantWets
See InvWets above.
VPIantReps/Wet
See InvReps/Wet above.
VplantSeas
See InvSeas above.
Algae
Similar to VPIant columns
Amph
Similar to VPIant columns
electronic transfer system (ETS) flux
frequency, numbers, or stem density (of individuals, by
taxon)
total number or density (not broken out by taxon)
germination rate by species (G= all species combined)
height or length
mortality
nutrient or calorific content
production or growth
richness
seed production
trace or heavy metal content
glucose mineralization or metabolism
oxygen consumption or respiration
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Appendix K. Catalog of Ongoing Biological Studies of Prairie Wetlands:
Locations, Sampling Regimes, Key Variables, and Related Descriptors
This database lists many, but surely not all, studies that are currently underway in the prairie
region and involve collection of multispecies biological data from at least one prairie wetland.
Included are funded studies whose field work has not been completed, recent studies whose field
work has been completed but whose data have not been analyzed completely for publication,
and recent studies whose results have been reported so far only as abstracts at symposia.
Information fields include the investigator's name, phone number, bibliographic reference if any,
starting year (YRSTART), projected completion year (YREND), number of wetlands studied
(WETS), major variables examined (see Appendix J abbreviations), and location.
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Appendix L. Descriptions of Data Sets Analyzed For This Report
Below are described the unpublished data sets that were analyzed and used to derive estimates
of variance reported in Appendices M and N as well as the species accumulation reported in
Appendix O.
I. Invertebrates
A. Hanson Activity Traps
Records are indexed by year, period (month), wetland, sample, and vegetation. Each record
represents a unique combination of year-period-wetland-sample-vegetation for certain metrics;
number of species, number of individuals (all species combined), biomass (all species
combined). Only the data from wetlands that lacked high densities of fathead minnows were
included. Data are from activity traps and cover 2 years. In year one, there were 5 sample
periods, 3 wetlands sampled per period, and 8 random samples per wetland. In year two, there
were 4 sample periods (3 identical to year 1), 4 sampled wetlands (the same 3 wetlands in year
one plus one other), and 10 random samples collected per wetland. Vegetation condition
(present/absent) of each sample was recorded incidentally and was not part of the sampling
design. In both years, data were collected using the same equipment and mostly the same
protocol. The total number of unique records is 320. The data are from wetlands in east Polk
County in west-central Minnesota; they were provided by Dr. Mark Hanson (Minnesota
Department of Natural Resources, Bemidji, MN).
B. Euliss Sweep Nets
Records are indexed by year, wetland, transect, and sample. Each record represents a unique
combination of year-wetland-transect-sample for certain metrics: number of species and number
of individuals (all species combined). Samples were collected monthly with a sweep net, but
data are not broken out by month; only season totals were available. Data are from 2 years. In
year one, 16 wetlands were sampled; there were 2-8 transects per wetland with 1-2 samples per
transect for a total of 118 unique records. In year two, 18 wetlands were sampled (the same 16
wetlands plus two new ones), using 5 transects per wetland with 2-3 samples per transect for a
total of 265 unique records. The data were provided by Dr. Ned Euliss (NPSC, Jamestown, ND).
C. Euliss Sediment Traps
Records are indexed by region, plot2, wetland3, transect, wetland class, and health class. Data
are from 36 wetlands, all sampled once in 1993. Each record represents a unique combination
of region-plot-wetland-transect-wetlandclass-healthclass, for certain metrics; number of
individuals (all species combined) and biomass (all species combined). There are 2 regions, 35
wetlands, 3 wetland classes (semipermanent, seasonal, temporary), 10 plots, 5 transects, and 2
health classes. There are 18 wetlands in each of the two regions, 8-10 plots per wetland class,
and 5 transects per plot. For each of the two health classes ("healthy" and "unhealthy"), there
2 A "plot" is a set of 1 to 6 wetlands, generally of diverse water regime types and all located within a
4-mi2 area,
3Called "polygon" in the Euliss data set.
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are approximately equal numbers of plots, wetlands, transects, wetland classes, and regions.
Total number of unique records is 180. Data represent an entire growing season's collection of
settled, decay-resistant remains of snails, cladocerans, ostracods, and clam shrimp, as
measured using a standard protocol. The data were provided by Dr. Ned Euliss (NPSC,
Jamestown, ND).
D. MERP Substrate Samplers
Records are indexed by year, period (month), zone (cover type), and depth class. Data are from
2-6 wetlands, but unfortunately the wetland identifier was lost and all the data from individual
wetlands were combined. Each record represents a unique combination of year-period-zone-
depth class, for certain metrics: number of species, number of individuals (all species combined),
biomass (all species combined). Data cover 5 years. There are 21-24 periods per year, 6-7
zones per period, and 2 depth classes per zone. There are 7 zones per year and 18-25 periods
per zone. Total unique records is 790. In all years, data were collected using the same
equipment and a standardized protocol. The data are from experimentally manipulated wetland
cells (mesocosms) of the Marsh Ecology Research Program (MERP) located in the Delta Marsh,
Manitoba. The data were provided by Dr. Henry Murkin (Institute for Wetland and Waterfowl
Research, Oak Hammock Marsh,
Manitoba).
E. MERP Activity Traps
Records are indexed by year, period (month), wetland ("cell"), treatment, and zone. Each record
represents a unique combination of year-period-wetland-treatment-zone, for the following
metrics: number of species, number of individuals (all species combined), biomass (all species
combined). Data cover 5 years (except biomass data, which exclude 1988). There are 23-24
periods per year, 1-6 wetlands per period, 1 treatment per wetland, and 2-6 zones per
treatment. There are 20-25 periods per zone and 17-23 periods per wetland. There are 2-6
wetlands per zone and the same number per treatment. There are 1-4 zones per wetland and
6-7 zones per treatment. Total unique records is 1200. In all years, data were collected using
the same equipment and a standardized protocol. Data are highly variable because conditions
among wetlands were intentionally and dramatically manipulated as part of experiments. The
data are from experimentally manipulated wetland cells (mesocosms) of the Marsh Ecology
Research Program (MERP) located in the Delta Marsh, Manitoba. The data were provided by Dr.
Henry Murkin (Institute for Wetland and Waterfowl Research, Oak Hammock Marsh, Manitoba).
F. Duffy Data Set
Records are indexed by wetland and period (sampling date). Each record is the mean of four
samples per wetland per sample period, and it represents a unique combination of wetland-
period for certain metrics: number of species, number of individuals (all species combined),
biomass (all species combined). Data are all from a single year, representing four wetlands: 2
wetlands were sampled for four periods, 1 wetland for six periods (including the same four
periods), and one for nine periods. Total unique records is 23. In all periods and wetlands, data
were collected using the same equipment (a 3.5-inch PVC corer) and a standardized protocol.
The data are from Deuel County, South Dakota, and they were provided by Dr. Walter Duffy
(South Dakota Cooperative Fish and Wildlife Research Unit, Brookings, SD).
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II. Breeding Birds
A. FWS Breeding Bird Survey
Records are indexed by year, State, and route. Each record represents a unique combination of
year-State-route, for these metrics; number of species, number of individuals (all species
combined), number of sample points containing wetland species). Routes are surveyed
repetitively (but often noncontinously) among years, so that a partly different set of routes
constitutes each year's data. All data were collected using a standardized protocol. There are
21 years, 5 States (and provinces), and 51 routes. When routes are combined, there are 95
unique year-State combinations. Other characteristics are:
years per route: 1-21
routes per year: 19-30
routes per State: 2-16
routes per State per year: 1-14
years per State: 14-21
States per year: 5
The total number of unique records is 543; the data were provided by Sam Droege (National
Biological Service, Washington, DC).
B. Igl & Johnson Data Set
Records are indexed by year, wetland, and visit. Each record represents one wetland, described
in term of these metrics: number of breeding species, number of breeding + nonbreeding
species, number of breeding pairs, number of breeding plus nonbreeding individuals. The
maximum value from both visits and both years was used. Visits were made to 480 wetlands,
330 in 1992, and 416 in 1993. Each was visited 1-2 times per year, and visits were timed to
cover both early and late breeders. The sampling design is as described by Stewart and Kantrud
(1972 a & b). The study covered nearly all of North Dakota, but only the data from sites in the
Prairie Pothole Region were used in this analysis (i.e., Missouri Slope and Little Missouri Slope
subregional data were excluded).
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Appendix M. Results of Power of Detection Analyses of Existing Prairie Data Sets
This table describes the results of the components of variance approach that was applied to
estimate sample power of detection. Methods of calculation are described on Section 1.5.
Results are reported under these column headings:
Taxon
The major group for which variability was determined.
Metric
A measured characteristic of the biological community that was reported by the original
investigator.
Units
The units of measurement (e.g., grams) in which the metric is expressed.
Equipment
Sampling equipment used by the collector of the original data.
RandomVar
The variable for which the number of requisite samples and the associated level of precision
were calculated.
Data set
The source of the data; the experimental design associated with the analyzed data is detailed in
Appendix L.
Pr_forS10
The specified metric's precision that would be obtainable if 10 samples of the type described
were collected, e.g., a sample size of 10 would allow the user to distinguish a difference between
two means of (specified) grams, given the assumptions of the analysis. This figure is based on
the "optimistic" equation, but in most instances the value from the "conservative" equation
differed only slightly (see Section 1.5 for discussion of the equations). "Ten samples" was an
arbitrary number selected so that relative levels of resultant precision could be roughly compared
among taxa, sampling methods, and sampling designs.
BreakPt
The number of samples beyond which increasing the number of samples results in relatively little
increase in the level of precision. This number was estimated visually and subjectively from
plotted curves, so the true value could be plus or minus about two samples.
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DetectDifl
An example of the detectable difference in two means. This example is for the smallest
difference that can be predicted from the data we used (i.e., the "optimistic" estimate, as
described in Section 1.5).
SSizeMinl
The smallest number of samples that would be able to detect the specified difference, based on
the "optimistic" calculation method defined in Section 1.5.
SSizeMaxI
The smallest number of samples that would be able to detect the specified difference, based on
the "conservative" calculation method defined in Section 1.5.
DetectDif2
A second example of the detectable difference in two means. This example is for the largest
difference that can be predicted from the data we usea (i.e., the "conservative" estimated as
described in Section 1.5.
SSizeMin2, Max2
As above, but for the second data set.
PooledVars
Variables that were subsumed in defining the samples (records) that were used for the analysis.
199
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Appendix N. Library of Coefficients of Variation from Prairie Wetlands
This database quantifies the biological variability of prairie wetlands at multiple spatial and
temporal scales using a standard statistic—the coefficient of variation—which is defined as:
CV=(100 * SD) / x ,
where SD is the standard deviation of the data, x is the mean, and CV is the coefficient of
variation.
The 412 coefficients were calculated from 15 prairie wetland data sets, encompassing both
published literature and raw data provided to the author by investigators in the region. The data's
quality and representativeness of prairie wetlands in general are unknown. Data were obtained
on a purely opportunistic basis, and it is not known how much of the variability described by any
CV can be attributed to ecological variation as opposed to human-related effects.
This database was developed to illustrate the relative degree of spatial and temporal variability
that can be expected in a given situation, recognizing that each situation is unique; therefore,
the CVs must be interpreted with caution. Comparisons among CVs are confounded by the fact
that the sampling designs that resulted in each CV were not necessarily well-balanced (e.g., for a
given study, not all habitats were sampled equally, at the same times of year, etc.). More
significantly, and especially when values were extracted from the literature, it was seldom clear
what data might have been previously combined (e.g., replicates composited) in arriving at a
particular mean, standard deviation, or CV. When it was apparent to what a particular mean was
referring (i.e., which variables had been combined and/or averaged), data then were referenced
accordingly in the database. CVs were always reported at the finest level of detail (least amount
of pooling) possible as well as for pooled samples. The least-pooled sample CVs are indicated
by presence of an "x" in the AllSamp column, coinciding with absence of an "x" or "a" from all
remaining columns.
These abbreviations are used in the database:
Group
The broad taxonomic group that was the primary focus of the study, namely,
b = birds
i = invertebrates
p = plants
SampType
The type of equipment or protocol used:
emtrap = emergence trap
actrap = activity trap
200
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quadrat depth
seeds collected from sediment depths of 0-5 cm
Metric
biomass = collective weight of all individuals
numindiv = the number of individuals
numtaxa = the number of taxa (richness)
seed den = density of seedlings
shoots/m2 = density of plant shoots/m2
Regions
Samples from different subregions of the prairie region:
x = The CV was calculated after pooling data from multiple regions.
a, A = The CV represents the among-region variability (the number following this
letter indicates the number of pooled samples).
37 = The CV is only for the central part of the prairie region.
38 = The CV is only for the western part of the prairie region
40 = The CV is only for the eastern part of the prairie region
HealthCI
Samples from the two "wetland health" classes defined by the cited study:
x = The CV was calculated after pooling data from both health classes,
a, A = The CV represents the variability between the two health classes.
States
Prairie States or provinces covered by the BBS data:
x = The CV was calculated after pooling data from multiple States.
a, A = The CV represents the among-States variability after all routes and
years within a State were pooled.
201
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Routes
The BBS route data (each route is 25 mi long and contains 50 stops where data are collected):
x = The CV was calculated after pooling data from multiple routes.
a, A = The CV represents the among-route variability after each route's years
were pooled.
WetTypes
Data by wetland types:
x = The CV was calculated after pooling data from multiple wetland types,
a, A = The CV represents the among-type variability.
When the CV is just for the specified wetland type:
p = permanently flooded
ss = seasonal
sp = semipermanent
t = temporary
Wets
Data by discrete wetland:
x = The CV was calculated after pooling data from multiple wetlands.
a, A - The CV represents the among-wetland variability.
When the CV represents variability just within a single wetland, a number has been arbitrarily
assigned a reference number (1,2, etc. are arbitrarily assigned reference numbers).
Treat
Data by wetland treatment regime;
x = The CV was calculated after pooling data from multiple wetlands, each
having been hydrologically manipulated in a different manner.
a, A = The CV represents the among-treatment variability.
202
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When the CV represents variability Just within a single treatment regime:
d = Wetlands were subjected to drawdown,
f = Wetlands were subjected to flooding,
h = Wetlands in which emergent plants were harvested.
t1, t2 = Two different treatment regimes.
Trans
Data by transects within wetlands:
x = The CV was calculated after pooling data from multiple transects within the
wetlands.
a, A = The CV represents the among-transect variability.
Zones
Data by vegetation zones within wetlands:
x = The CV was calculated after pooling data from multiple zones within a
wetland.
a, A = The CV represents the among-zone variability.
When the CV represents variability just within the specified zone, a zonal term has been
included: wet meadow, shallow, etc. (zonal terms are mostly those of the originator of the data).
Depths
Data by depth class within wetlands:
x = The CV was calculated after pooling data from multiple depth zones within
a wetland.
a, A = The CV represents the among-depth zone variability; the two zones were
< 30 cm and > 30 cm.
Veg
Data by vegetated condition within wetlands:
x = The CV was calculated after pooling data from both vegetated and
unvegetated (open water) areas of the wetland.
203
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a, A = The CV represents the between-area variability.
AllSamps
x = The CV was calculated after pooling data from multiple wetlands,
transects, zones, months, years, etc., unless other columns are
blank (see explanation in second example query below).
Yrs
Data grouped by year:
x = The CV was calculated after pooling data from multiple years.
a, A = The CV represents the among-year variability.
When the CV represents variability just within a single year, a number has been to distinguish
discrete years (1, 2, etc. are arbitrarily assigned numbers).
Months
Data grouped by month or biweekly sampling period:
x = The CV was calculated after pooling data from multiple sampling
periods.
a, A = The CV represents the among-sampling period variability,
When the CV represents variability just within a single sampling period, numbers or a season
abbreviation were assigned arbitrarily to distinguish discrete periods (1,2, etc.):
es = early summer
Is = late summer
p = spring
CV
The coefficient of variation calculated for samples grouped in the manner defined by the
preceding columns.
CVmin
The smallest of several coefficients of variation calculated for samples grouped in the manner
defined by the preceding columns.
204
-------
Cvmax
The largest of several coefficients of variation calculated for samples grouped in the manner
defined by the preceding columns.
The organization and structure of the database are best illustrated by two example queries, as
follows:
Query #1
How much does species richness (number of taxa) vary among wetlands?
Approach:
Find (sort4) all records that have an entry "numtaxa" (number of taxa) in the column "Metric" AND
have an entry preceded by a lower-case "a" in the column "Wets" (among wetlands). In this
example, there are 12 records meeting these criteria. Inter-wetland variability is quantified by the
corresponding values in the column "CV." Different CV values for different records reflect other
influencing conditions. In this example, one CV was calculated for each of six sampling periods
indexed in the Months column. Four wetlands (indicated by "a4" in the Wets column) were
sampled on May 9 and 31, June 14, and June 29 (indicated in the Months column) and the
among-wetland CVs calculated for each date. An "x" in the AllSamps column indicates that
individual samples from within each wetland had been pooled prior to calculating the CV. In
addition, another "a4" record lacks any date in the Months column, but rather has an "x." This
indicates a situation where, for each wetland in the data set, the data from all the months were
pooled before calculating the CV. The CVmin column then reports the smallest among-month CV
from the four wetlands and the CVmax column reports the maximum (i.e., duplicating information
already in the CV column under the four separate dates).
These CVs have all been calculated from one study's data (the Duffy data set, as denoted in
column 1). Among-wetland estimates of variation are available from three other data sets. The
"Hanson activity traps" data set has a single entry meeting the query criteria. The "a4" in the
Wets column indicates that the CV estimate is from a comparison of four wetlands, and an "x" in
the Yrs, Months, Veg, and AllSamps columns indicates that samples from an unspecified number
of years, months, vegetation types, and replicates were pooled to represent each of the four
wetlands, prior to calculating the CV. The CVmin column reports the smallest CV that any of the
four wetlands had from the pooling of these other variables; CVmax reports the largest.
The "Euliss sweep net" data set has two entries that meet the selection criteria. One is a
comparison of richness among 18 wetlands (a18 in the Wets column) and the other, a similar
comparison among 16 wetlands (a16 in the Wets column). The difference is that each inter-
wetland CV is from a different year. As indicated in the Years column, the 18-wetland
comparison is from 1993 and the 16-wetland comparison is from 1992. The "x" in the Trans and
AllSamps columns indicates that for each among-wetland comparison, data from an unspecified
number of transects and replicates were pooled prior to calculating the CV.
4Software for sorting the values is not provided. Users must either sort manually or import the file
into a program such as dBase or Paradox.
205
-------
Finally, the "Driver 1977" data set has two entries that meet the criteria. Both involve a
comparison of 11 wetlands (a11 in the Wets column), in which data from an unspecified number
of months and years had been previously aggregated (indicated by an "x" in the Months and Yrs
columns). The difference between the two entries is apparent from viewing the WetTypes
column: one CV estimate is from a comparison of 11 semipermanent (sp) wetlands; the other is
from 13 temporary (t) wetlands. Also note, from the "Specific Taxon" column, that these richness
estimates pertain only to the Chironomidae (midges). Taken as a whole, these CV estimates
tentatively suggest that variability in midge species richness, as sampled by emergence traps, is
greater among temporary wetlands (CV = 39.00) than among semipermanent wetlands (CV =
31.46).
Conceptually, the same approach as described in the above paragraphs can be used to scan the
database for estimates of other types of variability. For example:
• To review estimates of interannual variability, retrieve records that have a lower-case
"a" in the Yrs column and proceed as described above.
• To review estimates of variation among zones within a wetland, retrieve all "a" records
in the Zones column and do likewise.
• To compare two metrics (e.g., numindiv and biomass) with regard to their variability,
retrieve all records for these as indicated in the Metrics column, and then match
records according to other characteristics to make the comparison of CVs as fair as
possible.
• To compare two major groups (e.g., invertebrates and plants) with regard to their
variability, retrieve all records for these as indicated in the Group column, and then
match records according to other characteristics to make the comparison as fair as
possible.
Query #2
Does precision appear to increase the most when samples from a particular study are grouped
by year, by season, by wetland, by zone, or some combination of these?
Approach:
First, go to the AllSamps column and for the particular study/data set and metric of interest, find
all entries consisting of a number preceded by an "x". For example, for the data set on the
Hanson activity traps and the metric "numiindiv," locate the entry "x320." This indicates that the
associated CV estimate is based on 320 samples. The simultaneous presence of an "x" in the
columns for Wets, Veg, Yrs, and Months indicates that the 320 samples constitute all the
samples from varied wetlands, vegetation conditions, years, and months.
This CV (281) can then be used as a baseline against which to compare the CVs based on
various subsets of the 320 samples. For example, when data are grouped by month (a5 in the
Months column), the CV drops from 281 to 92. The value in the CVmin column of the same row
indicates that under one vegetation condition in one wetland during 1 year, the variability among
206
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months was as low as 78. Likewise, the value in the CVmax column indicates that under one
vegetation condition in one wetland during 1 year, the variability among months was as high as
223.
If instead of month, the data are grouped by year (a2 in the Yrs column), the CV drops more
significantly from 281 to 80. The value in the CVmin column of the same row
indicates that under one vegetation condition in one wetland during one month, the variability
between years was as low as 119. Likewise, the value in the CVmax column indicates that under
one vegetation condition in one wetland during 1 year, the variability between years was as high
as 254.
A third option is to group the data by both year and month. This is indicated by a number (10, in
this case) preceded by an upper-case "A" in both the Yrs and Months columns. The value in the
CVmin column of the same row indicates that under one vegetation condition in one wetland, the
variability among the ten possible combinations of year and month was as low as 61. Likewise,
the value in the CVmax column indicates that under one vegetation condition in one (probably
another) wetland, the variability among the 10 year-month combinations was as high as 206.
Finally, note the few records where the AllSamps column has an entry consisting of a number
preceded by an "x" (e.g., x320) and none of the other columns have an entry preceeded by an
"x." The CVs for these records represent the variability among what are apparently true replicate
samples, i.e., samples collected at the exact same location at the same time with the same
equipment.
207
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Appendix O. Results of Asymptotic Richness
Calculations Using Existing Prairie Data Sets
This table documents the results of the asymptotic richness calculations. Methods were
described in Section 1.5. Column headings are as follows:
Data Set
The source of the data; the experimental design associated with the analyzed data is detailed in
Appendix L.
Equipment
Sampling equipment used by the collector of the original data.
Taxa
The category of organisms for which species accumulation rates were determined.
Samples
The type of samples (e.g., wetlands, quadrats, years) among which the taxa were accumulated.
Location
General location of the sampling; for BBS data:
stratum 37 = central prairie region
stratum 38 = western prairie
stratum 40 = eastern prairie
Total Taxa
Cumulative number of taxa in all samples of the analyzed data set.
Tot_NumSa
Total number of samples (wetlands, quadrats, years, etc.) among which species lists were
combined and accumulated.
Num_for50.„.etc.
Number of samples (of the type specified) that are required to detect 50, 75, 90, 95, and 99% of
the total taxa cumulatively present in the data set. The values given are the median for that
threshold, based on the 100 random runs.
208
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CurveTurn
A value of 0 indicates a curve terminating in a plateau (i.e., no slope). The larger the value, the
farther from an asymptotic condition is the near-final part of the curve and the more likely that the
sample was insufficient to estimate richness. Values were calculated as:
CurveTurn Value = 100 * (b-a) / (c-b),
where a = median for 90th percentile (Num_for 90),
b = median for 95th percentile,
c = median for 99th percentile.
CurveEnd
A value of 100 indicates that less than the full number of samples would have been required to
sufficiently estimate richness; the smaller the value, the likelier is it that the sample size was
more than sufficient, i.e., oversampling occurred. Values were calculated by dividing Num_for 99
by Tot_NumSam and multiplying by 100.
Qualifiers
Supporting information that describes the exact type of data used in the analysis.
PooledVars
Variables that were subsumed in defining the samples (records) that were used for the analysis.
209
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GENUS
SPECIES
FORM
DEPENDENCE
WATERTYPE
DRAINAGE
LANDUSE
SALINITY
TURBIDITY
DUCKFOOD
RECSPHYTO
RECSAQUIRE
Acorus
calamus
PIEF
OBL
1
0
Agropyron
repens
PIG
FAC
T
P
F
G43
Agropyron
repens
PIG
FAC
T
P
0
G43
Agropyron
trachycaulum
PNG
FACU
G43
Agrostis
stolonifera
PNG
FAC+
t
F*
15
Agrostis
stolonifera
PNG
FAC+
t
F*
89
Alisma
gramineum
PNEF
OBL
SS
g
0*
0
Alisma
gramineum
PNEF
OBL
ss
f*
0
Alisma
plantago-aquatica
PNEF
OBL
s
0
1
Alisma
plantago-aquatica
PNEF
OBL
SS
g
F
s
0
1
Alisma
plantago-aquatica
PNEF
OBL
SS
P
F*
s
0
1
Alisma
plantago-aquatica
PNEF
OBL
ss
m*
s
0
1
Alisma
plantago-aquatica
PNEF
OBL
SS
0*
s
0
1
Alisma
subcordatum
PNEF
OBL
0
Alopecurus
aequalis
PNG
OBL
SS
P
F*
G121
Alopecurus
aequalis
PNG
OBL
SS
0*
G121
Ambrosia
psilostachya
PNF
FAC
102
Ammannia
coccinea
ANF
OBL
t
0
Andropogon
gerardii
PNG
FACU
24
Anemone
canadensis
PNF
FACW
1
0
Apocynum
sibiricum
PNF
FACW
t
F*
G66
Apocynum
sibiricum
PNF
FACW
t
0*
G66
Artemisia
biennis
AIF
FAC
T
P
0
G105
Asclepias
incarnata
PNF
OBL
sa
1
F*
G12
Asclepias
incarnata
PNF
OBL
sa
1
0
G12
Aster
brachyactis
ANF
FACW
G18
Aster
ericoides
PNF
FACU
t
1
Aster
hesperius
PNF
OBL
t
f*
G18
Aster
hesperius
PNF
OBL
t
m*
G18
Aster
hesperius
PNF
OBL
t
0*
G18
Aster
junciformis
PNF
OBL
sa
0
G18
Aster
simplex
PNF
FACW
T
g
F*
G18
Aster
simplex
PNF
FACW
t
m*
G18
Aster
simplex
PNF
FACW
t
0*
G18
Atriplex
patula
ANF
FACW
T
r
M*
sf
5
Atriplex
patula
ANF
FACW
t
e*
sf
5
Atriplex
patula
ANF
FACW
t
h*
sf
5
Atriplex
patula
ANF
FACW
t
0*
sf
5
-------
Atriplex
patula
ANF
FACW
t
P*
sf
5
Bacopa
rotundifolia
PNF
OBL
SS
P
F*
0
Beckmannia
syzigachne
ANG
OBL
SS
g
F*
s
0
Beckmannia
syzigachne
ANG
OBL
SS
g
0*
s
0
Beckmannia
syzigachne
ANG
OBL
SS
P
F*
s
0
Beckmannia
syzigachne
ANG
OBL
SS
P
0*
s
0
Beckmannia
syzigachne
ANG
OBL
SS
m*
s
0
Bidens
cernua
AIF
OBL
t
0*
s
G19
Boltonia
asteroides
PNF
FACW
0
Boltonia
asteroides
PNF
FACW
t
F*
0
Boltonia
asteroides
PNF
FACW
t
0*
0
Bromus
inermis
G
FACU
155
Calamagrostis
canadensis
PNG
FACW+
T
1
r
F*
0
Calamagrostis
canadensis
PNG
FACW+
t
1
0*
0
Calamagrostis
inexpansa
PNG
FACW
T
h
0*
0
Calamagrostis
inexpansa
PNG
FACW
T
h
f*
0
Calamagrostis
inexpansa
PNG
FACW
T
r
0
0
Calamagrostis
inexpansa
PNG
FACW
sa
0
0
Calamagrostis
inexpansa
PNG
FACW
t
m*
0
Callitriche
hermaphroditica
PNZF
OBL
sp
0*
0
Callitriche
hermaphroditica
PNZF
OBL
sp
f*
0
Callitriche
verna
PNZ/F
OBL
SS
r
F
0
Carex
aquatilis
PNEGL
OBL
sa
0*
s
0
Carex
aquatilis
PNEGL
OBL
sa
f*
s
0
Carex
atherodes
PNEGL
OBL
SS
1
h
F*
s
0
Carex
atherodes
PNEGL
OBL
SS
1
h
0*
s
0
Carex
atherodes
PNEGL
OBL
SS
1
r
F*
s
0
Carex
atherodes
PNEGL
OBL
SS
1
r
0*
s
0
Carex
atherodes
PNEGL
OBL
SS
1
m*
s
0
Carex
aurea
PNGL
FACW
sa
0
s
0
Carex
buxbaumii
PNEGL
OBL
t
F*
s
0
Carex
interior
PNGL
OBL
sa
0
s
0
Carex
lacustris
PNEGL
OBL
sa
1
0*
s
0
Carex
laeviconica
PNEGL
OBL
t
f*
s
0
Carex
laeviconica
PNEGL
OBL
t
0*
s
0
Carex
lanuginosa
PNGL
OBL
sa
1
0
s
0
Carex
lanuginosa
PNGL
OBL
t
1
m*
s
0
Carex
lanuginosa
PNGL
OBL
t
1
0*
s
0
Carex
lanuginosa
PNGL
OBL
t
1
P*
s
0
-------
Carex
lanuginosa
PNGL
OBL
t
1
h
F*
s
0
Carex
lasiocarpa
PNEGL
OBL
s
0
Carex
praegracilis
PNGL
FACW
T
1
h
F*
s
0
Carex
rostrata
PNEGL
OBL
sa
1
0*
s
0
Carex
rostrata
PNEGL
OBL
sa
1
f*
s
0
Carex
sartwellii
PNGL
FACW
T
1
r
F*
s
0
Carex
sartwellii
PNGL
FACW
sa
1
0
s
0
Carex
sartwellii
PNGL
FACW
1
0*
s
0
Carex
stricta
PNEGL
OBL
1
F*
s
0
Carex
stricta
PNEGL
OBL
1
m*
s
0
Carex
stricta
PNEGL
OBL
1
0*
s
0
Carex
tetanica
PNGL
FAC
0*
s
0
Carex
vulpinoidea
PNEGL
OBL
f*
s
0
Carex
vulpinoidea
PNEGL
OBL
0*
s
0
Ceratophyllum
demersum
PN/F
OBL
sp
F*
t
f
0
26
Ceratophyllum
demersum
PN/F
OBL
sp
0*
t
f
0
26
Chenopodium
rubrum
ANF
OBL
1
57
Cicuta
maculata
PNF
OBL
sa
f*
0
Cicuta
maculata
PNF
OBL
sa
0*
0
Cirsium
arvense
PIF
FAC
t
0*
321
Cirsium
floodmannii
PIF
FACU
G322
Deschampsia
cespitosa
PNG
FACW
sa
0
0
Distichlis
spicata
PNG
FACW
t
e*
0
Distichlis
spicata
PNG
FACW
t
f*
0
Distichlis
spicata
PNG
FACW
t
h*
0
Distichlis
spicata
PNG
FACW
t
0*
0
Distichlis
spicata
PNG
FACW
t
P*
0
Echinochloa
crusgalli
AIG
FACW
T
t
P
F*
s*
210
Echinochloa
crusgalli
AIG
FACW
T
t
P
F*
s*
1002
Echinochloa
crusgalli
AIG
FACW
t
t
0*
s*
210
Echinochloa
crusgalli
AIG
FACW
t
t
0*
s*
1002
Echinochloa
muricata
AIG
FACW
G1270
Elatine
triandra
ANE/F
OBL
t
0
Eleocharis
acicularis
PNEGL
OBL
SS
g
F
t
0
Eleocharis
acicularis
PNEGL
OBL
ss
P
F
t
0
Eleocharis
acicularis
PNEGL
OBL
SS
P
0
t
0
Eleocharis
calva
PNGL
OBL?
sa
0
0
Eleocharis
compressa
PNEGL
FACW
t
F*
0
Eleocharis
compressa
PNEGL
FACW
t
0*
0
-------
Eleocharis
macrostachya
PNEGL
OBL
ss
f*
0
2
Eleocharis
macrostachya
PNEGL
OBL
ss
m*
0
2
Eleocharis
macrostachya
PNEGL
OBL
ss
0*
0
2
Eleocharis
macrostachya
PNEGL
OBL
ss
P*
0
2
Eleocharis
ovata
ANEGL
OBL
0
Eleocharis
palustris
PNEGL
OBL
SS
1
g
M
0
Eleocharis
palustris
PNEGL
OBL
SS
1
g
0
0
Eleocharis
smallii
PNGL
OBL
0
Elodea
canadensis
PNZF
OBL
sp
F*
t
0
Elodea
longivaginata
PNZF
OBL
sp
F
0
Epilobium
ciliatum
PNF
FACW
sa
0
G2
Epilobium
ciliatum
PNF
FACW
t
0*
G2
Epilobium
ciliatum
PNF
FACW
t
f*
G2
Equisetum
arvense
PNH2
FAC
t
F*
0
Equisetum
fluviatile
PNH2
OBL
ss
F*
0
Eriophorum
angustifolium
PNGL
OBL
sa
f*
0
Eriophorum
angustifolium
PNGL
OBL
sa
0*
0
Eupatoriadelphus
maculatus
PNF
FACW+
sa
F*
0
Eupatoriadelphus
maculatus
PNF
FACW+
sa
0
0
Euthamia
graminifolia
PNF
FACW
sa
0*
0
Euthamia
graminifolia
PNF
FACW
sa
f
0
Galium
trifidum
PNF
OBL
sa
F*
G41
Glaux
maritima
PI$F
OBL
0
Glyceria
borealis
PNEG
OBL
ss
0*
0
Glyceria
maxima
PIG
OBL
SS
1
g
F
0
Glyceria
maxima
PIG
OBL
ss
1
f*
0
Glyceria
maxima
PIG
OBL
ss
1
0*
0
Glyceria
striata
PNEG
OBL
sa
0*
s
0
Gratiola
neglecta
ANEF
OBL
t
0
Helenium
autumnale
PNF
FACW
t
f*
G1
Helenium
autumnale
PNF
FACW
t
0*
G1
Helianthus
nuttallii
PNF
FACW
sa
0
G528
Hippuris
vulgaris
PNZF
OBL
sp
0*
0
Hippuris
vulgaris
PNZF
OBL
sp
f*
0
Hordeum
jubatum
PNG
FACW
T
g
F*
5
Hordeum
jubatum
PNG
FACW
T
g
M*
5
Hordeum
jubatum
PNG
FACW
T
g
0*
5
Hordeum
jubatum
PNG
FACW
T
P
F*
5
Hordeum
jubatum
PNG
FACW
T
P
0*
5
-------
Hordeum
jubatum
PNG
FACW
t
e*
5
Hordeum
jubatum
PNG
FACW
t
P*
5
Impatiens
capensis
ANF
FACW
sa
F*
G75
Juncus
alpinus
PNGL
OBL
G6
Juncus
balticus
PNGL
FACW
T
g
F*
G6
Juncus
balticus
PNGL
FACW
T
h
F*
G6
Juncus
balticus
PNGL
FACW
T
h
0*
G6
Juncus
balticus
PNGL
FACW
t
m*
G6
Juncus
bufonius
ANGL
OBL
t
0*
G6
Juncus
interior
PNGL
FACW
t
F*
G6
Juncus
longistylis
PNGL
FACW
t
G6
Juncus
tenuis
PNGL
FAC
t
F*
G6
Juncus
torreyi
PNGL
FACW
sa
0
G6
Juncus
torreyi
PNGL
FACW
t
f
G6
Juncus
torreyi
PNGL
FACW
t
m
G6
Juncus
torreyi
PNGL
FACW
t
0
G6
Kochia
scoparia
AIF
FAC
t
35
Leersia
oryzoides
PNG
OBL
sf
3
Lemna
minor
PN/F
OBL
SS
g
F*
0
242
Lemna
minor
PN/F
OBL
SS
g
0*
0
242
Lemna
minor
PN/F
OBL
SS
h
F*
0
242
Lemna
minor
PN/F
OBL
SS
r
F*
0
242
Lemna
minor
PN/F
OBL
SS
r
0*
0
242
Lemna
minor
PN/F
OBL
sp
F*
0
242
Lemna
minor
PN/F
OBL
sp
0*
0
242
Lemna
minor
PN/F
OBL
SS
m*
0
242
Lemna
trisulca
PN/F
OBL
SS
g
F*
0
Lemna
trisulca
PN/F
OBL
SS
g
0*
0
Lemna
trisulca
PN/F
OBL
SS
h
F*
0
Lemna
trisulca
PN/F
OBL
SS
r
F*
0
Lemna
trisulca
PN/F
OBL
SS
r
0*
0
Lemna
trisulca
PN/F
OBL
sp
F*
0
Lemna
trisulca
PN/F
OBL
sp
0*
0
Lemna
trisulca
PN/F
OBL
SS
m*
0
Limosella
aquatica
APNEF
OBL
0
Lindernia
dubia
ANF
OBL
t
0
Lobelia
kalmii
PNF
OBL
sa
0
0
Lycopus
americanus
PNF
OBL
t
1
F*
0
Lycopus
asper
PNEF
OBL
t
1
0*
0
-------
Lycopus
asper
PNEF
OBL
1
f*
0
Lycopus
asper
PNEF
OBL
1
m*
0
Lycopus
asper
PNEF
OBL
1
P*
0
Lysimachia
hybrida
PNF
OBL
f*
0
Lysimachia
hybrida
PNF
OBL
0*
0
Lysimachia
thyrsiflora
PIF
OBL
sa
f*
0
Lysimachia
thyrsiflora
PIF
OBL
sa
0*
0
Lythrum
salicaria
PIF
OBL
0
Marsilea
vestita
PNEP3
OBL
SS
P
F
s
0
Mentha
arvensis
PNF
FACW
t
1
F*
54
Mentha
arvensis
PNF
FACW
t
1
0*
54
Mimulus
ringens
PNF
OBL
sa
F*
0
Muhlenbergia
asperifolia
PNG
FACW
T
r
M*
G19
Muhlenbergia
asperifolia
PNG
FACW
t
f*
G19
Muhlenbergia
asperifolia
PNG
FACW
t
0*
G19
Muhlenbergia
asperifolia
PNG
FACW
t
P*
G19
Muhlenbergia
glomerata
PNG
FACW+
sa
0
G19
Myriophyllum
pinnatum
PNEZF
OBL
sp
F*
sf
0
Myriophyllum
spicatum
PNZF
OBL
sp
F*
X
sf
0
131
Myriophyllum
spicatum
PNZF
OBL
sp
0*
X
sf
0
131
Myriophyllum
verticillatum
PNZF
OBL
sp
f*
X
sf
0
Myriophyllum
verticillatum
PNZF
OBL
sp
0*
X
sf
0
Najas
flexilis
ANZF
OBL
sp
F*
X
sf*
0
5
Nuphar
luteum
PNZF
OBL
sp
F*
X
s
0
Nymphaea
tuberosa
PNZF
OBL
sp
s
0
Panicum
capillare
ANG
FAC
15
Panicum
virgatum
PNG
FAC
51
Parnassia
glauca
PNF
OBL
sa
0*
0
Parnassia
palustris
PNF
OBL
sa
0
0
Penthorum
sedoides
PNF
OBL
0
Phalaris
arundinacea
PNG
FACW+
SS
h
F*
68
Phalaris
arundinacea
PNG
FACW+
SS
r
F*
68
Phalaris
arundinacea
PNG
FACW+
SS
0*
68
Phragmites
australis
PNEG
FACW
sa
f*
0
3
Phragmites
australis
PNEG
FACW
sa
m*
0
3
Phragmites
australis
PNEG
FACW
sa
0*
0
3
Phragmites
australis
PNEG
FACW
sa
P*
0
3
Plantago
eriopoda
PNF
FAC
t
m*
G257
Plantago
eriopoda
PNF
FAC
t
0*
G257
-------
Plantago
major
PIF
FAC
t
0*
39
Poa
palustris
PNG
FACW
T
1
r
F*
G1012
Poa
palustris
PNG
FACW
T
1
r
0*
G1012
Poa
pratensis
PNG
FAC
1
836
Polygonum
amphibium
PNE/F
OBL
SS
P
F*
sf
G304
Polygonum
amphibium
PNE/F
OBL
ss
P
0*
sf
G304
Polygonum
amphibium
PNE/F
OBL
SS
r
F*
sf
G304
Polygonum
amphibium
PNE/F
OBL
ss
r
0*
sf
G304
Polygonum
amphibium
PNE/F
OBL
ss
F*
sf*
G304
Polygonum
amphibium
PNE/F
OBL
ss
0*
sf*
G304
Polygonum
lapathifolium
ANF
OBL
T
P
F
t
sf*
G304
Polygonum
pensylvanicum
ANEF
FACW
t
t
sf*
G304
Potamogeton
alpinus
PN/F
OBL
sf
0
2
Potamogeton
amplifolius
PN/F
OBL
sf
0
Potamogeton
foliosus
PNZF
OBL
SS
g
F
t
sf
0
3
Potamogeton
foliosus
PNZF
OBL
SS
P
F
t
sf
0
3
Potamogeton
friesii
PNZF
OBL
sp
f*
sf
0
Potamogeton
friesii
PNZF
OBL
sp
0*
sf
0
Potamogeton
gramineus
PNZF
OBL
SS
g
F*
sf
0
Potamogeton
gramineus
PNZF
OBL
ss
m*
sf
0
Potamogeton
natans
PN/F
OBL
sf
0
2
Potamogeton
nodosus
PN/F
OBL
t
sf
0
1
Potamogeton
pectinatus
PNZF
OBL
sp
0*
t
sf
0
8
Potamogeton
pectinatus
PNZF
OBL
sp
f*
sf
0
8
Potamogeton
pectinatus
PNZF
OBL
sp
m*
t
sf
0
8
Potamogeton
pectinatus
PNZF
OBL
sp
P*
sf
0
8
Potamogeton
praelongus
PNZF
OBL
sp
X
sf
0
Potamogeton
pusillus
PNZF
OBL
sp
0*
t
sf
0
Potamogeton
pusillus
PNZF
OBL
sp
f*
t
sf
0
Potamogeton
richardsonii
PNZF
OBL
sp
F*
t
sf
0
Potamogeton
richardsonii
PNZF
OBL
sp
0*
t
sf
0
Potamogeton
vaginatus
PNZF
OBL
sp
0*
sf
0
Potamogeton
zosteriformis
PNZF
OBL
sp
0*
X
sf
0
Potamogeton
zosteriformis
PNZF
OBL
sp
f*
X
sf
0
Potentilla
anserina
PNF
OBL
T
g
0*
G3
Potentilla
anserina
PNF
OBL
t
f*
G3
Potentilla
norvegica
ABPNF
FAC
t
F*
1
Potentilla
norvegica
ABPNF
FAC
t
0*
1
Potentilla
rivalis
ANF
OBL
t
1
F*
G3
-------
Puccinellia
nuttalliana
PNG
OBL
SS
r
M*
0
Puccinellia
nuttalliana
PNG
OBL
ss
f*
0
Puccinellia
nuttalliana
PNG
OBL
SS
h*
0
Puccinellia
nuttalliana
PNG
OBL
ss
0*
0
Puccinellia
nuttalliana
PNG
OBL
ss
P*
0
Ranunculus
aquatilis
PNZF
OBL
sf
0
Ranunculus
cymbalaria
PNEF
OBL
SS
g
F*
sf
0
Ranunculus
cymbalaria
PNEF
OBL
SS
g
0*
sf
0
Ranunculus
cymbalaria
PNEF
OBL
ss
m*
sf
0
Ranunculus
flabellaris
PNEF
OBL
sp
f*
sf
0
Ranunculus
flabellaris
PNEF
OBL
sp
0*
sf
0
Ranunculus
gmelinii
PNEF
FACW+
sa
F*
sf
0
Ranunculus
gmelinii
PNEF
FACW+
sp
0
sf
0
Ranunculus
macounii
PNF
OBL
t
f*
sf
0
Ranunculus
macounii
PNF
OBL
t
0*
sf
0
Ranunculus
sceleratus
APNEF
OBL
SS
t
P
F
sf
4
Ranunculus
sceleratus
APNEF
OBL
SS
t
r
F*
sf
4
Ranunculus
sceleratus
APNEF
OBL
ss
t
m*
sf
4
Ranunculus
sceleratus
APNEF
OBL
ss
t
0*
sf
4
Ranunculus
septentrionalis
PNF
OBL
sa
0
sf
G15
Ranunculus
subrigidus
PNZ/F
OBL
SS
g
F*
sf
G15
Ranunculus
subrigidus
PNZ/F
OBL
SS
P
F*
sf
G15
Ranunculus
subrigidus
PNZ/F
OBL
SS
P
0*
sf
G15
Ranunculus
subrigidus
PNZ/F
OBL
sp
F*
sf
G15
Ranunculus
subrigidus
PNZ/F
OBL
sp
0*
sf
G15
Riccia
fluitans
M
OBL
SS
1
g
F*
0
Riccia
fluitans
M
OBL
SS
1
h
F*
0
Riccia
fluitans
M
OBL
SS
1
r
F*
0
Riccia
fluitans
M
OBL
SS
1
r
0*
0
Riccia
fluitans
M
OBL
sp
1
F*
0
Ricciocarpus
natans
M
OBL
SS
g
F
0
Ricciocarpus
natans
M
OBL
SS
g
0
0
Ricciocarpus
natans
M
OBL
SS
r
F
0
Ricciocarpus
natans
M
OBL
SS
r
0
0
Ricciocarpus
natans
M
OBL
sp
F*
0
Ricciocarpus
natans
M
OBL
sp
0*
0
Rorippa
palustris
ANEF
OBL
t
f*
G4
Rorippa
palustris
ANEF
OBL
t
0*
G4
Rosa
arkansana
NSH
FACU
G300
-------
Rumex
maritimus
ABNF
FACW+
t
s
G396
Rumex
mexicanus
PNF
FACW
t
f*
s
G396
Rumex
mexicanus
PNF
FACW
t
0*
s
G396
Ruppia
maritima
PNZF
OBL
sp
M*
sf*
0
1
Ruppia
maritima
PNZF
OBL
sp
0*
sf*
0
1
Ruppia
maritima
PNZF
OBL
sp
P*
sf*
0
1
Ruppia
maritima
PNZF
OBL
sp
f*
sf*
0
1
Ruppia
maritima
PNZF
OBL
sp
h
sf*
0
1
Ruppia
maritima
PNZF
OBL
sp
m*
sf*
0
1
Sagittaria
cuneata
PNEF
OBL
SS
r
0*
sf
G4
G3
Sagittaria
cuneata
PNEF
OBL
ss
F*
sf
G4
G3
Sagittaria
latifolia
PNEF
OBL
t
sf
G4
G3
Salicornia
rubra
AN$F
OBL
SS
g
M
0
Scirpus
acutus
PNEGL
OBL
SP
g
M*
s*
0
Scirpus
acutus
PNEGL
OBL
SP
g
0*
s
0
Scirpus
acutus
PNEGL
OBL
SP
r
M*
s
0
Scirpus
acutus
PNEGL
OBL
SP
r
0*
s
0
Scirpus
acutus
PNEGL
OBL
sp
f*
s
0
Scirpus
acutus
PNEGL
OBL
sp
P*
s
0
Scirpus
americanus
PNEGL
OBL
SS
g
M
s
0
Scirpus
americanus
PNEGL
OBL
SS
r
M
s
0
Scirpus
atrovirens
PNEGL
OBL
sa
0*
s
0
Scirpus
atrovirens
PNEGL
OBL
sa
f*
s
0
Scirpus
fluviatilis
PNEGL
OBL
SP
t
g
F*
s
0
Scirpus
fluviatilis
PNEGL
OBL
SP
t
P
F*
s
0
Scirpus
fluviatilis
PNEGL
OBL
SP
t
P
0*
s
0
Scirpus
heterochaetus
PNEGL
OBL
SP
g
F*
s
0
Scirpus
heterochaetus
PNEGL
OBL
sp
0*
s
0
Scirpus
maritimus
PNEGL
OBL
s
0
Scirpus
maritimus
PNEGL
OBL
SP
g
M
s
0
Scirpus
maritimus
PNEGL
OBL
SP
g
0*
s
0
Scirpus
maritimus
PNEGL
OBL
SP
r
M*
s
0
Scirpus
maritimus
PNEGL
OBL
sp
f*
s
0
Scirpus
maritimus
PNEGL
OBL
sp
h*
s
0
Scirpus
maritimus
PNEGL
OBL
sp
P*
s
0
Scirpus
microcarpus
PNGL
OBL
sa
f*
s
0
Scirpus
nevadensis
PNEGL
OBL
ss
m
s
0
Scirpus
pungens
PNEGL
OBL
ss
f*
s
0
Scirpus
pungens
PNEGL
OBL
ss
h*
s
0
-------
Scirpus
pungens
PNEGL
OBL
ss
m*
s
0
Scirpus
pungens
PNEGL
OBL
ss
0*
s
0
Scirpus
pungens
PNEGL
OBL
ss
P*
s
0
Scirpus
validus
PNEGL
OBL
SP
g
F*
X
s
0
Scirpus
validus
PNEGL
OBL
SP
p
F*
X
s
0
Scirpus
validus
PNEGL
OBL
sa
F*
X
s
0
Scirpus
validus
PNEGL
OBL
sa
0*
X
s
0
Scolochloa
festucacea
PNEG
OBL
SS
1
h
0*
s
0
Scolochloa
festucacea
PNEG
OBL
ss
1
f*
s
0
Scolochloa
festucacea
PNEG
OBL
ss
1
m*
s
0
Scutellaria
galericulata
PNF
OBL
sa
0
0
Scutellaria
galericulata
PNF
OBL
sa
1
F*
0
Sium
suave
PNEF
OBL
SS
g
F*
0
Sium
suave
PNEF
OBL
SS
g
0*
0
Sium
suave
PNEF
OBL
SS
P
0*
0
Solidago
altissima
PNF
FACU
0
Sonchus
arvensis
PIF
FAC
t
f*
62
Sonchus
arvensis
PIF
FAC
t
m*
62
Sonchus
arvensis
PIF
FAC
t
0*
62
Sparganium
eurycarpum
PNEF
OBL
SS
g
F
t
s
0
Sparganium
eurycarpum
PNEF
OBL
SS
r
F*
t
s
0
Sparganium
eurycarpum
PNEF
OBL
ss
0*
t
s
0
Spartina
gracilis
PNG
FACW
T
r
M*
G26
Spartina
gracilis
PNG
FACW
t
f*
G26
Spartina
gracilis
PNG
FACW
t
0*
G26
Spartina
pectinata
PNG
FACW
T
1
g
F*
G26
Spartina
pectinata
PNG
FACW
T
1
r
0*
G26
Spartina
pectinata
PNG
FACW
t
1
m*
G26
Spartina
pectinata
PNG
FACW
t
1
P*
G26
Spirodela
polyrhiza
Pl/F
OBL
sp
F*
t
f
0
Spirodela
polyrhiza
Pl/F
OBL
sp
0*
f
0
Stachys
palustris
PIF
OBL
t
1
f*
0
Stachys
palustris
PIF
OBL
t
1
0*
0
Suaeda
depressa
APNF
FACW
SS
g
M*
0
Suaeda
depressa
APNF
FACW
ss
0*
0
Suaeda
depressa
APNF
FACW
ss
P*
0
Teucrium
canadense
PNEF
FACW
t
F*
0
Teucrium
canadense
PNEF
FACW
t
m*
0
Teucrium
canadense
PNEF
FACW
t
0*
0
-------
Triglochin
maritimum
PNF
OBL
sa
m
s
0
Triglochin
maritimum
PNF
OBL
sa
0
s
0
Triglochin
maritimum
PNF
OBL
sa
P
s
0
Triglochin
maritimum
PNF
OBL
t
e*
s
0
Triglochin
maritimum
PNF
OBL
t
0*
s
0
Triglochin
maritimum
PNF
OBL
t
P*
s
0
Triglochin
maritimum
PNF
OBL
t
r
M*
s
0
Typha
angustifolia
PNEF
OBL
SP
t
r
F*
G1
Typha
angustifolia
PNEF
OBL
SP
t
r
0*
G1
Typha
angustifolia
PNEF
OBL
sp
t
m*
G1
Typha
latifolia
PNEF
OBL
SP
r
F*
G1
Typha
latifolia
PNEF
OBL
SP
r
0*
G1
Typha
latifolia
PNEF
OBL
sa
F*
G1
Typha
latifolia
PNEF
OBL
sa
0*
G1
Typha
latifolia
PNEF
OBL
sa
m*
G1
Typha
latifolia
PNEF
OBL
sp
m*
G1
Typha
X
glauca
PNEF
OBL
SP
r
F*
G1
Typha
X
glauca
PNEF
OBL
SP
r
0*
G1
Utricularia
intermedia
ANZF
OBL
0
Utricularia
macrorhiza
PN/F
OBL
SS
g
F*
0
Utricularia
macrorhiza
PN/F
OBL
SS
g
0*
0
Utricularia
macrorhiza
PN/F
OBL
SS
r
F*
0
Utricularia
macrorhiza
PN/F
OBL
SS
r
0*
0
Utricularia
macrorhiza
PN/F
OBL
sp
F*
0
Utricularia
macrorhiza
PN/F
OBL
sp
0*
0
Utricularia
macrorhiza
PN/F
OBL
SS
m*
0
Utricularia
minor
PNZF
OBL
0
Vernonia
fasciculata
PNF
FACW
t
F*
G16
Viola
nephrophylla
PNF
FACW
sa
0
G53
Wolffia
punctata
PN/F
OBL
t
f
0
Zannichellia
palustris
PNZF
OBL
SS
g
0*
t
sf
0
Zannichellia
palustris
PNZF
OBL
SS
r
0*
t
sf
0
Zannichellia
palustris
PNZF
OBL
sp
0*
t
sf
0
Zannichellia
palustris
PNZF
OBL
sp
f*
sf
0
Zannichellia
palustris
PNZF
OBL
sp
m*
t
sf
0
Chara
J
OBL
SS
g
0*
X
f
Chara
J
OBL
SS
r
0*
X
f
Chara
J
OBL
sp
0*
X
f
Chara
J
OBL
sp
f*
X
f
-------
Chara
J
OBL
sp
m*
X
f
Drepanocladus
M
OBL
SS
g
F*
Drepanocladus
M
OBL
SS
g
0*
Drepanocladus
M
OBL
SS
r
F*
Drepanocladus
M
OBL
SS
r
0*
Drepanocladus
M
OBL
sp
F*
Drepanocladus
M
OBL
sp
0*
-------
NCE
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.1000
.1000
.1010
.1100
.1100
.1100
.2000
.2100
.0000
.1000
.1100
.1110
.1110
.1200
.1210
.2000
.3100
.3110
.3110
.3110
REPRO WATERREGIM
OXYGEN
SALINITY SEDIMENT DUCKFOOD
t
sp
sp
sp
t
sp
sp
t_
t
t_
sp
t
MallH, PintH
3b
3b
3b
2-3
2a
LeScH
MallH, PintH, ShovH
MallH, PintH, GadwH, ShovH
PintY
-------
.3110
.3110
.3110
.3110
.3110
.3110
.3200
.3210
.3210
.3210
.3300
.3310
.3400
.3410
.4000
.4100
.4110
.4110
.4110
.4110
.4200
.4210
.4210
.4210
.4210
.5000
.5100
.5100
.5100
.5100
.5100
.6000
.7000
.7100
.7100
.9000
.9100
.0000
.0100
.0110
.1000
.1100
.1200
.1210
ShovH
Gadw
sp
sp
sp
BwTeH, LeScHY, RuDuHY
SP
sp
3b
3-4
o
o-m
sp
sp
sp
sp
CanvHY
3-4
2-3
2-3
-------
.2000
.2010
.2020
.2100
.2200
.2300
.2310
.2310
.2310
.2310
.2310
.3000
.3100
.3110
.3110
.3200
.3200
.3200
.3200
.3200
.3200
.3200
.5000
.5100
.5110
.5110
.5200
.5210
.6000
.6100
.6100
.6100
.6100
.6100
.6200
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
sp
MallHY, GadwH, LeScY, RedhY
sp
sp
LeScY
sp
1-3
sp
3a
sp
sp
2-4
3
SP
sp
2-4
SP
SP
3a
3a
3b
3b
m
~T~
MallHY, GadwHY, AmWiY
3b
MallHY,BeTeH, GadwH, LeScHY, CanvHY, RedhHY, RuDuY
2-4
LeScY
2b
2
1-2
MallY, RuDuY
PintH
GadwY, LeScY
MallHY, GadwHY, LeScY
sp
t
t
3b
3b
3b
3b
3b
-------
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6210
.6300
.6310
.6310
.6310
.6310
.6400
.6410
.6410
.6410
.7000
.7100
.7110
.7120
.7121
.7200
.7210
.7211
.7211
.7211
.7211
.7211
.7211
m-p
m
f-o
m
sp
2a
2tT
3b
3b
MallY
2b
2-3
2tT
2b
2
m-p
m
MallH, BwTeH
2
1-4
sp
1-4
3
MallHY, PintHY, BwTeH, GadwHY, LeScHY, CanvHY, RedhY, RuDuHY, AmWiY
-------
.7211
.7211
.7211
.7211
.7211
.7211
.7211
.7211
.7211
.7211
.7211
.7220
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7221
.7230
.7231
.7231
.7231
.7231
.7231
.7231
.7300
.7400
.7500
.7600
.7610
.7610
.0000
.1000
.1100
1-4
4b
4a
2-4
2-4
3-4
4a
3b
3b
3a
3a
2a
2a
2-4
2-4
2
2b
2b
2-3
3tT
2a
BwTeH
2a
2-4
3
sp
2-4
2-4
3a
RuDuH
MallHY, PintHY, BwTeH, ShovH, LeScHY, CanvHY, AmWiY
-------
.1100
.2000
.2100
.2100
.2100
.2100
.2100
.3000
.3100
.3100
.3100
.3100
.3100
.3100
.4000
.4100
BwTeH, PintY, CanvY
3b
3b
2-4
MallH, BwTeH, ShovH
sp
sp
3a
3tT
3b
3a
3b
2-4
2-4
-------
AOU
SPECIES
STATUS MIG
STATUS BR
WET TYPE
LAYERS
PHENOLOGY
PAIRS TOT
NUM WETS
FREQWETS
MAX PER W
REG FRQMA
REG ABUMAX
BBS REGION
BBS NUMRTS
BBS RTS
BBS AVG RT
BBS MAXRT
PRIORITYBB
10.0000
WESTERN GREBE
fairly common
common*
sp,P
ew,ow
3
1.0000
2.0000
0.4425
Missouri Coteau
Missouri Coteau
C
10.0000
19.6100
3.7400
2.0000
?
10.0000
WESTERN GREBE
sp,P
ew,ow
E
2.0000
5.7100
1.5000
2.0000
?
10.0000
WESTERN GREBE
sp,P
ew,ow
W
8.0000
19.5100
6.6800
2.0000
?
20.0000
RED-NECKED GREBE
uncommon
fairly common*
sp,P
ow
3
1.0000
1.0000
0.2212
NE Drift Plain
NE Drift Plain
C
5.0000
9.4340
0.0300
2.0000
30.0000
HORNED GREBE
fairly common
fairly common
ss,sp,P
ew,ow
0.0000
2.0000
0.4425
Missouri Coteau
Missouri Coteau
C
25.0000
47.1698
0.5500
26.0000
1
30.0000
HORNED GREBE
ss,sp,P
ew,ow
W
19.0000
42.2222
0.7900
8.0000
2
40.0000
EARED GREBE
common
common
ss,SP,P
ew,ow
3
22.0000
3.0000
0.6637
Missouri Coteau
NW Drift Plain
C
25.0000
47.1698
0.9400
24.0000
2
40.0000
EARED GREBE
ss,SP,P
ew,ow
W
24.0000
53.3333
2.0100
16.0000
2
60.0000
PIED-BILLED GREBE
fairly common
fairly common
ss,SP,P
ew,ow
3
7.0000
9.0000
1.9912
Coteau Slope
Missouri Coteau
C
45.0000
84.9057
1.2000
32.0000
1
60.0000
PIED-BILLED GREBE
ss,SP,P
ew,ow
E
25.0000
67.5676
0.6000
16.0000
2
60.0000
PIED-BILLED GREBE
ss,SP,P
ew,ow
W
18.0000
40.0000
0.4500
42.0000
3
510.0000
HERRING GULL
fairly common
P
m,ow
0.0000
0.0000
530.0000
CALIFORNIA GULL
fairly common
fairly common
sp,P
m,ow
2
1.0000
1.0000
0.2212
Missouri Coteau
Missouri Coteau
C
13.0000
24.5283
0.3300
8.0000
2
530.0000
CALIFORNIA GULL
sp,P
m,ow
W
29.0000
64.4444
1.3900
22.0000
3
540.0000
RING-BILLED GULL
common
common*
sp,P
m,ow
2
11.0000
13.0000
2.8761
Coteau Slope
Missouri Coteau
C
34.0000
64.1509
4.0500
34.0000
1
540.0000
RING-BILLED GULL
sp,P
m,ow
E
18.0000
48.6486
1.0800
26.0000
3
540.0000
RING-BILLED GULL
sp,P
m,ow
W
36.0000
80.0000
14.2900
80.0000
3
590.0000
FRANKLIN'S GULL
abundant
common*
sp,P
es,ew,ow,m
2
57.0000
7.0000
1.5487
NW Drift Plain
NW Drift Plain
C
46.0000
86.7925
16.5600
70.0000
1
590.0000
FRANKLIN'S GULL
sp,P
es,ew,ow,m
E
20.0000
54.0541
4.8700
62.0000
2
590.0000
FRANKLIN'S GULL
sp,P
es,ew,ow,m
W
34.0000
75.5556
9.6300
54.0000
2
600.0000
BONAPARTE'S GULL
fairly common
P
ew,ow,m
0.0000
0.0000
690.0000
FORSTER'STERN
uncommon
fairly common*
ss,sp,p,a
ew,ow
2
4.0000
3.0000
0.6637
NW Drift Plain
NW Drift Plain
C
10.0000
18.8679
0.0600
6.0000
3
690.0000
FORSTER'STERN
ss,sp,p,a
ew,ow
E
10.0000
27.0270
0.0500
4.0000
3
690.0000
FORSTER'STERN
ss,sp,p,a
ew,ow
W
6.0000
13.3333
0.1500
4.0000
?
700.0000
COMMON TERN
uncommon
fairly common*
sp,P
ew,ow
2
1.0000
3.0000
0.6637
Coteau Slope
Missouri Coteau
C
9.0000
16.9811
0.2300
10.0000
?
700.0000
COMMON TERN
sp,P
ew,ow
E
4.0000
10.8108
0.0200
2.0000
?
700.0000
COMMON TERN
sp,P
ew,ow
W
17.0000
37.7778
0.8000
20.0000
3
770.0000
BLACK TERN
common
common*
ss,SP,P
ew,ow
3
40.0000
27.0000
5.9735
NE Drift Plain
Missouri Coteau
C
50.0000
94.3396
6.9000
44.0000
1
770.0000
BLACK TERN
ss,SP,P
ew,ow
E
30.0000
81.0811
1.6200
16.0000
2
770.0000
BLACK TERN
ss,SP,P
ew,ow
W
33.0000
73.3333
2.8700
34.0000
2
1200.0000
DOUBLE-CREST. CORMORANT
fairly common
fairly common*
sp,P
ow,t
2
10.0000
4.0000
0.8850
Coteau Slope
Missouri Coteau
C
24.0000
45.2830
0.7000
12.0000
3
1200.0000
DOUBLE-CREST. CORMORANT
sp,P
ow,t
E
15.0000
40.5405
1.2400
10.0000
3
1200.0000
DOUBLE-CREST. CORMORANT
sp,P
ow,t
W
25.0000
55.5556
0.9900
34.0000
2
1250.0000
AMERICAN WHITE PELICAN
uncommon
common*
sp,P
ow
2
2.0000
0.4425
C
14.0000
26.4151
1.2300
8.0000
3
1250.0000
AMERICAN WHITE PELICAN
sp,P
ow
E
8.0000
21.6216
0.7800
14.0000
?
1250.0000
AMERICAN WHITE PELICAN
sp,P
ow
W
15.0000
33.3333
1.1200
10.0000
2
1251.0000
WHITE-FACED IBIS
ra re
ra re*
sp
es,ew
0.0000
0.0000
1252.0000
TUNDRA SWAN
common
t,ss,sp,p,A
es,ew,ow
0.0000
0.0000
1253.0000
SNOW GOOSE
abundant
t,ss,sp,p
es,ew,ow
0.0000
0.0000
1254.0000
GR. WHITE-FRONTED GOOSE
fairly common
t,ss,sp,p
es,ew,ow
0.0000
0.0000
1290.0000
COMMON MERGANSER
fairly common
P
ow
0.0000
0.0000
W
11.1111
2.0000
1310.0000
HOODED MERGANSER
uncommon
ss,sp,p
ow
1
1.0000
1.0000
0.2212
NW Drift Plain
NW Drift Plain
C
9.4340
2.0000
1311.0000
RED-BREASTED MERGANSER
ra re
sp,p
ow
0.0000
0.0000
1320.0000
MALLARD
abundant
common
t,ss,SP
es,ew,ow
1
152.0000
125.0000
27.6549
NE Drift Plain
NW Drift Plain
C
53.0000
100.0000
33.2100
70.0000
3
1320.0000
MALLARD
t,ss,SP
es,ew,ow
E
37.0000
100.0000
7.9200
40.0000
3
1320.0000
MALLARD
t,ss,SP
es,ew,ow
W
45.0000
100.0000
28.3500
60.0000
3
1330.0000
AMERICAN BLACK DUCK
uncommon
sp,P
ew,ow
0.0000
0.0000
1350.0000
GADWALL
common
common
t,ss,sp,p,A
es,ew
2
117.0000
69.0000
15.2655
Missouri Coteau
Missouri Coteau
C
51.0000
96.2264
5.5200
48.0000
2
1350.0000
GADWALL
t,ss,sp,p,A
es,ew
E
10.0000
27.0270
0.2700
8.0000
2
1350.0000
GADWALL
t,ss,sp,p,A
es,ew
W
44.0000
97.7778
6.4300
40.0000
3
1370.0000
AMERICAN WIGEON
common
uncommon
t,ss,sp,P,A
es,ew,ow
2
19.0000
4.2035
NW Drift Plain
Missouri Coteau
C
38.0000
71.6981
2.4100
32.0000
1
1370.0000
AMERICAN WIGEON
t,ss,sp,P,A
es,ew,ow
E
4.0000
10.8108
0.1100
4.0000
?
1370.0000
AMERICAN WIGEON
t,ss,sp,P,A
es,ew,ow
31.0000
15.0000
W
37.0000
82.2222
4.1600
22.0000
3
1390.0000
GREEN-WINGED TEAL
common
uncommon
t,ss,SP,p
es,ew
2
44.0000
31.0000
6.8584
NW Drift Plain
Missouri Coteau
C
40.0000
75.4717
1.1200
14.0000
1
1390.0000
GREEN-WINGED TEAL
t,ss,SP,p
es,ew
E
15.0000
40.5405
0.0800
6.0000
2
1390.0000
GREEN-WINGED TEAL
t,ss,SP,p
es,ew
W
29.0000
64.4444
0.7400
12.0000
4
1400.0000
BLUE-WINGED TEAL
abundant
abundant
t,ss,sp,P
es,ew
2
143.0000
104.0000
23.0088
S. Drift Plain
Missouri Coteau
C
51.0000
96.2264
11.3100
70.0000
2
1400.0000
BLUE-WINGED TEAL
t,ss,sp,P
es,ew
E
34.0000
91.8919
1.9900
20.0000
1
1400.0000
BLUE-WINGED TEAL
t,ss,sp,P
es,ew
W
43.0000
95.5556
8.0000
42.0000
2
1410.0000
CINNAMON TEAL
ra re
ra re
t,ss,sp
es,ew
1.0000
0.2212
C
6.0000
11.3208
0.0100
4.0000
?
1410.0000
CINNAMON TEAL
t,ss,sp
es,ew
W
8.0000
17.7778
0.1000
6.0000
?
1420.0000
NORTHERN SHOVELER
common
common
t,ss,SP,p,A
es,ew
2
51.0000
51.0000
11.2832
Missouri Coteau
Missouri Coteau
C
50.0000
94.3396
4.9700
42.0000
1
1420.0000
NORTHERN SHOVELER
t,ss,SP,p,A
es,ew
E
15.0000
40.5405
0.0700
4.0000
1
1420.0000
NORTHERN SHOVELER
t,ss,SP,p,A
es,ew
W
42.0000
93.3333
5.0400
38.0000
2
1430.0000
NORTHERN PINTAIL
abundant
common
t,ss,SP,A
es,ew
1
51.0000
50.0000
11.0619
Coteau Slope
S. Drift Plain
C
48.0000
90.5660
10.4900
60.0000
1
1430.0000
NORTHERN PINTAIL
t,ss,SP,A
es,ew
E
22.0000
59.4595
0.4900
16.0000
2
1430.0000
NORTHERN PINTAIL
t,ss,SP,A
es,ew
W
45.0000
100.0000
15.7400
52.0000
1
1460.0000
REDHEAD
common
common
t,ss,SP,a
es,ew,ow
3
44.0000
26.0000
5.7522
Missouri Coteau
NW Drift Plain
C
41.0000
77.3585
3.3300
22.0000
2
-------
1460.0000
REDHEAD
t,ss,SP,a
es,ew,ow
E
13.0000
35.1351
0.5900
6.0000
1
1460.0000
REDHEAD
t,ss,SP,a
es,ew,ow
W
31.0000
68.8889
1.6300
22.0000
3
1470.0000
CANVAS BACK
common
fairly common
sp,p,a
es,ew
2
5.0000
8.0000
1.7699
NW Drift Plain
NW Drift Plain
C
32.0000
60.3774
1.7400
16.0000
2
1470.0000
CANVAS BACK
sp,p,a
es,ew
E
6.0000
16.2162
0.1200
6.0000
?
1470.0000
CANVAS BACK
sp,p,a
es,ew
W
25.0000
55.5556
1.6200
8.0000
2
1471.0000
GREATER SCAUP
uncommon
t,ss,sp,p
es,ew,ow
0.0000
0.0000
1490.0000
LESSER SCAUP
abundant
uncommon
t,ss,sp,P
es,ew,ow
3
4.0000
7.0000
1.5487
Missouri Coteau
NW Drift Plain
C
34.0000
64.1509
2.7200
18.0000
1
1490.0000
LESSER SCAUP
t,ss,sp,P
es,ew,ow
E
10.0000
27.0270
0.0900
4.0000
1
1490.0000
LESSER SCAUP
t,ss,sp,P
es,ew,ow
W
33.0000
73.3333
4.3500
16.0000
3
1500.0000
RING-NECKED DUCK
uncommon
fairly common*
ss,sp,P
es,ew,ow
2
1.0000
4.0000
0.8850
Coteau Slope
NW Drift Plain
C
8.0000
15.0943
0.0800
6.0000
?
1500.0000
RING-NECKED DUCK
ss,sp,P
es,ew,ow
E
4.0000
10.8108
0.0600
4.0000
?
1510.0000
COMMON GOLDENEYE
uncommon
ss,sp,p
ew,ow
0.0000
0.0000
C
5.6604
2.0000
1530.0000
BUFFLEHEAD
fairly common
ra re*
ss,sp,p
ew,ow
3
1.0000
1.0000
0.2212
NW Drift Plain
NW Drift Plain
C
11.3208
4.0000
1670.0000
RUDDY DUCK
common
common
ss,SP,p
ew,ow
3
23.0000
7.0000
1.5487
Missouri Coteau
NW Drift Plain
C
39.0000
73.5849
1.9300
22.0000
2
1670.0000
RUDDY DUCK
ss,SP,p
ew,ow
E
10.0000
27.0270
0.8700
6.0000
2
1670.0000
RUDDY DUCK
ss,SP,p
ew,ow
W
24.0000
53.3333
1.6400
12.0000
3
1720.0000
CANADA GOOSE
abundant
fairly common*
t,ss,sp,P
es,ew,ow,m
1
5.0000
6.0000
1.3274
NW Drift Plain
NW Drift Plain
C
32.0000
60.3774
3.2700
34.0000
4
1720.0000
CANADA GOOSE
t,ss,sp,P
es,ew,ow,m
E
30.0000
81.0811
3.2500
20.0000
4
1720.0000
CANADA GOOSE
t,ss,sp,P
es,ew,ow,m
W
33.0000
73.3333
3.2800
38.0000
4
1900.0000
AMERICAN BITTERN
fairly common
fairly common
ss,SP,p
es,ew
3
8.0000
10.0000
2.2124
2.0000
S. Drift Plain
NW Drift Plain
C
44.0000
83.0189
1.0600
18.0000
1
1900.0000
AMERICAN BITTERN
ss,SP,p
es,ew
E
20.0000
54.0541
0.3600
12.0000
2
1900.0000
AMERICAN BITTERN
ss,SP,p
es,ew
W
23.0000
51.1111
0.4700
38.0000
2
1910.0000
LEAST BITTERN
ra re
ra re*
sp,p
es,ew
0.0000
0.0000
E
3.0000
8.1081
0.0100
2.0000
?
1940.0000
GREAT BLUE HERON
fairly common
fairly common*
ss,SP,P
ew,ow,m,t
2
2.0000
3.0000
0.6637
Coteau Slope
Coteau Slope
C
30.0000
56.6038
0.2800
8.0000
4
1940.0000
GREAT BLUE HERON
ss,SP,P
ew,ow,m,t
E
29.0000
78.3784
1.1600
18.0000
3
1940.0000
GREAT BLUE HERON
ss,SP,P
ew,ow,m,t
W
27.0000
60.0000
0.6100
8.0000
3
1960.0000
GREAT EGRET
ra re
ra re
ss,sp,p
ew,ow,m
0.0000
0.0000
C
5.0000
9.4340
0.0500
4.0000
?
1960.0000
GREAT EGRET
ss,sp,p
ew,ow,m
E
11.0000
29.7297
0.5500
14.0000
4
2020.0000
BLACK-CRN. NIGHT HERON
fairly common
common*
ss,SP,P
ew,ow,m,t
3
5.0000
5.0000
1.1062
NW Drift Plain
NW Drift Plain
C
26.0000
49.0566
0.7200
10.0000
1
2020.0000
BLACK-CRN. NIGHT HERON
ss,SP,P
ew,ow,m,t
E
17.0000
45.9459
0.2400
4.0000
3
2020.0000
BLACK-CRN. NIGHT HERON
ss,SP,P
ew,ow,m,t
W
19.0000
42.2222
0.2700
6.0000
2
2060.0000
SANDHILL CRANE
abundant
ra re*
t,ss,sp
es,ew,m
1.0000
0.2212
NW Drift Plain
NW Drift Plain
C
7.0000
13.2075
0.0600
10.0000
?
2120.0000
VIRGINIA RAIL
fairly common
fairly common
ss,SP,P
es
3
2.0000
4.0000
0.8850
S. Drift Plain
S. Drift Plain
C
11.0000
20.7547
0.0500
4.0000
1
2120.0000
VIRGINIA RAIL
ss,SP,P
es
E
8.0000
21.6216
0.0300
4.0000
?
2120.0000
VIRGINIA RAIL
ss,SP,P
es
W
3.0000
6.6667
0.1000
2.0000
?
2140.0000
SORA
common
common
t,ss,SP,P,a
es
3
78.0000
65.0000
14.3805
Coteau Slope
NW Drift Plain
C
46.0000
86.7925
2.5800
52.0000
1
2140.0000
SORA
t,ss,SP,P,a
es
E
25.0000
67.5676
0.4700
20.0000
2
2140.0000
SORA
t,ss,SP,P,a
es
W
33.0000
73.3333
0.9900
34.0000
2
2150.0000
YELLOW RAIL
ra re
ra re*
t,ss,sp
es
3
1.0000
0.2212
W
2.0000
4.4444
0.0400
2.0000
?
2210.0000
AMERICAN COOT
abundant
abundant
t,ss,SP,p
ew,ow
2
122.0000
29.0000
6.4159
50.0000
Coteau Slope
Missouri Coteau
C
46.0000
86.7925
7.8500
64.0000
1
2210.0000
AMERICAN COOT
t,ss,SP,p
ew,ow
E
26.0000
70.2703
2.5100
22.0000
2
2210.0000
AMERICAN COOT
t,ss,SP,p
ew,ow
W
36.0000
80.0000
5.5900
48.0000
3
2240.0000
WILSON'S PHALAROPE
common
common
t,ss,sp,A
ew,ow,m
2
31.0000
25.0000
5.5310
Missouri Coteau
Missouri Coteau
C
42.0000
79.2453
2.2700
30.0000
1
2240.0000
WILSON'S PHALAROPE
t,ss,sp,A
ew,ow,m
E
8.0000
21.6216
0.0500
6.0000
?
2240.0000
WILSON'S PHALAROPE
t,ss,sp,A
ew,ow,m
W
38.0000
84.4444
3.8700
24.0000
1
2241.0000
BLACK-NECKED STILT
ra re
t,ss
m
0.0000
0.0000
2250.0000
AMERICAN AVOCET
common
common
t,ss,sp,A
m
2
14.0000
9.0000
1.9912
7.0000
NW Drift Plain
NW Drift Plain
C
34.0000
64.1509
1.5200
24.0000
3
2250.0000
AMERICAN AVOCET
t,ss,sp,A
m
W
38.0000
84.4444
2.6300
20.0000
2
2300.0000
COMMON SNIPE
common
uncommon
t,SS,sp,P
es,m
2
93.0000
14.0000
3.0973
Missouri Coteau
NW Drift Plain
C
33.0000
62.2642
1.7800
62.0000
2
2300.0000
COMMON SNIPE
t,SS,sp,P
es,m
E
22.0000
59.4595
0.2100
30.0000
2
2300.0000
COMMON SNIPE
t,SS,sp,P
es,m
W
31.0000
68.8889
1.0300
26.0000
3
2301.0000
HUDSONIAN GODWIT
fairly common
t,ss,sp,p
m
1.0000
0.2212
2490.0000
MARBLED GODWIT
fairly common
fairly common
T,ss,sp,P,A
es,m
1
7.0000
9.0000
1.9912
Missouri Coteau
NW Drift Plain
C
45.0000
84.9057
3.2900
30.0000
2
2490.0000
MARBLED GODWIT
T,ss,sp,P,A
es,m
E
13.0000
35.1351
0.4500
12.0000
2
2490.0000
MARBLED GODWIT
T,ss,sp,P,A
es,m
W
43.0000
95.5556
6.1000
66.0000
4
2491.0000
RUDDY TURNSTONE
uncommon
t,ss,a
m
1.0000
0.2212
2492.0000
RED KNOT
ra re
t,ss,a
m
0.0000
0.0000
2493.0000
SANDERLING
uncommon
t,ss,A
m
2
1.0000
0.2212
2494.0000
GREATER YELLOWLEGS
fairly common
t,ss,a
m
12.0000
2.6549
NW Drift Plain
NW Drift Plain
2550.0000
LESSER YELLOWLEGS
common
T,ss,a
m
28.0000
6.1947
S. Drift Plain
NW Drift Plain
2551.0000
SEMIPALMATED SANDPIPER
abundant
t,ss,sp,A
m
3.0000
0.6637
NE Drift Plain
NE Drift Plain
2553.0000
WESTERN SANDPIPER
ra re
t,ss,a
m
1.0000
0.2212
Agassiz L.PIain
Agassiz L.PIain
2554.0000
LEAST SANDPIPER
common
t,SS,a
m
14.0000
3.0973
Agassiz L.PIain
Agassiz L.PIain
2555.0000
WHITE-RUMPED SANDPIPER
abundant
t,ss,sp,A
m
3.0000
0.6637
NE Drift Plain
NE Drift Plain
2556.0000
BAIRD'S SANDPIPER
common
t,SS,A
m
4.0000
0.8850
2557.0000
PECTORAL SANDPIPER
common
t,SS,sp,a
es,m
14.0000
3.0973
Agassiz LPIain
S. Drift Plain
2558.0000
DUNLIN
uncommon
t,ss,A
m
2.0000
0.4425
2559.0000
STILT SANDPIPER
common
t,SS,a
m
1.0000
0.2212
NW Drift Plain
NW Drift Plain
2560.0000
SOLITARY SANDPIPER
uncommon
t,ss
m
24.0000
5.3097
Agassiz LPIain
Agassiz L.PIain
-------
2561.0000
SHORT-BILLED DOWITCHER
uncommon
t,ss,a
m
2.0000
0.4425
2562.0000
LONG-BILLED DOWITCHER
common
t,ss,A
m
2.0000
0.4425
NW Drift Plain
NW Drift Plain
2580.0000
WILLET
fairly common
fairly common
t,ss,sp,P,A
m,es
2
25.0000
19.0000
4.2035
NW Drift Plain
Missouri Coteau
C
42.0000
79.2453
2.9700
30.0000
2
2580.0000
WILLET
t,ss,sp,P,A
m,es
E
7.0000
18.9189
0.0300
4.0000
?
2580.0000
WILLET
t,ss,sp,P,A
m,es
W
42.0000
93.3333
4.7400
42.0000
2
2630.0000
SPOTTED SANDPIPER
uncommon
uncommon
t,ss,A
m,es
3
6.0000
13.0000
2.8761
Coteau Slope
NE Drift Plain
C
25.0000
47.1698
0.1200
8.0000
3
2630.0000
SPOTTED SANDPIPER
t,ss,A
m,es
E
28.0000
75.6757
0.1600
4.0000
2
2630.0000
SPOTTED SANDPIPER
t,ss,A
m,es
W
16.0000
35.5556
0.2000
16.0000
3
2730.0000
KILLDEER
common
common
t,SS,sp,p,A
m
1
53.0000
75.0000
16.5929
Missouri Coteau
Coteau Slope
C
53.0000
100.0000
15.1700
80.0000
2
2730.0000
KILLDEER
t,SS,sp,p,A
m
E
37.0000
100.0000
13.9500
72.0000
3
2730.0000
KILLDEER
t,SS,sp,p,A
m
W
45.0000
100.0000
11.2500
56.0000
2
2731.0000
RED-NECKED PHALAROPE
abundant
t,ss,A
ew,ow
1.0000
0.2212
2732.0000
LESSER GOLDEN PLOVER
fairly common
t,SS,a
m,es
2.0000
0.4425
Missouri Coteau
Missouri Coteau
2733.0000
SEMIPALMATED PLOVER
fairly common
t,ss,A
m
3.0000
0.6637
2734.0000
PIPING PLOVER
uncommon*
uncommon*
A
m
2
1.0000
1.0000
0.2212
Missouri Coteau
Missouri Coteau
2735.0000
BLACK-BELLIED PLOVER
uncommon
t,SS,a
m
0.0000
0.0000
3091.0000
RING-NECKED PHEASANT
common
common
t
es
1
4.0000
6.0000
1.3274
Missouri Coteau
Missouri Coteau
C
39.0000
73.5849
9.9100
80.0000
4
3091.0000
RING-NECKED PHEASANT
t
es
E
30.0000
81.0811
19.0000
82.0000
2
3091.0000
RING-NECKED PHEASANT
t
es
W
38.0000
84.4444
6.8700
80.0000
2
3310.0000
NORTHERN HARRIER
common
fairly common
t,ss,sp
es
2
1.0000
3.0000
0.6637
NW Drift Plain
Missouri Coteau
C
53.0000
100.0000
1.3700
16.0000
1
3310.0000
NORTHERN HARRIER
t,ss,sp
es
E
25.0000
67.5676
0.2000
12.0000
2
3310.0000
NORTHERN HARRIER
t,ss,sp
es
W
44.0000
97.7778
2.9500
22.0000
2
3670.0000
SHORT-EARED OWL
fairly common
uncommon
t,ss
es
0.0000
0.0000
C
29.0000
54.7170
0.1900
20.0000
2
3670.0000
SHORT-EARED OWL
t,ss
es
E
6.0000
16.2162
0.0300
12.0000
?
3670.0000
SHORT-EARED OWL
t,ss
es
W
35.0000
77.7778
0.6900
18.0000
2
3900.0000
BELTED KINGFISHER
fairly common
fairly common
sp,p
ow
2
1.0000
1.0000
0.2212
Coteau Slope
Coteau Slope
C
15.0000
28.3019
0.0400
4.0000
3
3900.0000
BELTED KINGFISHER
sp,p
ow
E
27.0000
72.9730
0.3700
8.0000
3
3900.0000
BELTED KINGFISHER
sp,p
ow
W
6.0000
13.3333
0.0200
2.0000
?
4664.0000
WILLOW FLYCATCHER
fairly common
t
3
0.0000
0.0000
E
24.0000
64.8649
0.2300
10.0000
2
4664.0000
WILLOW FLYCATCHER
fairly common
t
W
12.0000
26.6667
1.0100
16.0000
3
4940.0000
BOBOLINK
fairly common
fairly common
t,ss
es
3
9.0000
15.0000
3.3186
NE Drift Plain
NE Drift Plain
C
50.0000
94.3396
7.7300
62.0000
3
4940.0000
BOBOLINK
t,ss
es
E
37.0000
100.0000
16.2100
98.0000
2
4940.0000
BOBOLINK
t,ss
es
W
25.0000
55.5556
2.9900
60.0000
2
4970.0000
YELLOW-HEADED BLACKBIRD
abundant
abundant
t,ss,SP,p
ew
2
164.0000
49.0000
10.8407
NE Drift Plain
NW Drift Plain
C
53.0000
100.0000
47.6800
84.0000
3
4970.0000
YELLOW-HEADED BLACKBIRD
t,ss,SP,p
ew
E
34.0000
91.8919
22.8000
60.0000
3
4970.0000
YELLOW-HEADED BLACKBIRD
t,ss,SP,p
ew
W
42.0000
93.3333
29.4400
94.0000
3
4980.0000
RED-WINGED BLACKBIRD
abundant
abundant
t,ss,SP,P,a
es,ew,t
2
456.0000
189.0000
41.8142
Missouri Coteau
Agassiz L.PIain
C
53.0000
100.0000
140.4900
100.0000
2
4980.0000
RED-WINGED BLACKBIRD
t,ss,SP,P,a
es,ew,t
E
37.0000
100.0000
124.3200
100.0000
2
4980.0000
RED-WINGED BLACKBIRD
t,ss,SP,P,a
es,ew,t
W
45.0000
100.0000
76.3300
96.0000
1
5420.0000
SAVANNAH SPARROW
abundant
common
t,ss,A
es
2
50.0000
89.0000
19.6903
NW Drift Plain
Missouri Coteau
C
51.0000
96.2264
15.1100
80.0000
3
5420.0000
SAVANNAH SPARROW
t,ss,A
es
E
36.0000
97.2973
9.5300
88.0000
2
5420.0000
SAVANNAH SPARROW
t,ss,A
es
W
45.0000
100.0000
15.2300
90.0000
3
5480.0000
LE CONTE'S SPARROW
uncommon
fairly common
t,ss,A
es
3
5.0000
7.0000
1.5487
Missouri Coteau
NE Drift Plain
C
23.0000
43.3962
0.5700
34.0000
2
5480.0000
LE CONTE'S SPARROW
t,ss,A
es
E
6.0000
16.2162
0.0800
8.0000
?
5480.0000
LE CONTE'S SPARROW
t,ss,A
es
W
7.0000
15.5556
0.0600
6.0000
?
5490.0000
SHARP-TAILED SPARROW
uncommon
fairly common
ss,sp,A
es
3
10.0000
11.0000
2.4336
NW Drift Plain
NW Drift Plain
C
19.0000
35.8491
0.0800
6.0000
2
5490.0000
SHARP-TAILED SPARROW
ss,sp,A
es
E
6.0000
16.2162
0.0200
4.0000
?
5490.0000
SHARP-TAILED SPARROW
ss,sp,A
es
W
6.0000
13.3333
0.2100
4.0000
?
5810.0000
SONG SPARROW
common
common
t,ss,sp,p
es,t
2
42.0000
40.0000
8.8496
NE Drift Plain
NW Drift Plain
C
51.0000
96.2264
4.8800
50.0000
2
5810.0000
SONG SPARROW
t,ss,sp,p
es,t
E
37.0000
100.0000
13.8600
82.0000
2
5810.0000
SONG SPARROW
t,ss,sp,p
es,t
W
31.0000
68.8889
1.1400
18.0000
2
5840.0000
SWAMP SPARROW
uncommon
uncommon*
t,ss
es
2
0.0000
3.0000
0.6637
NE Drift Plain
NE Drift Plain
C
10.0000
18.8679
0.1000
8.0000
2
6110.0000
PURPLE MARTIN
fairly common
fairly common
t,ss,sp,p
es,ew,ow,m
0.0000
0.0000
C
25.0000
47.1698
0.3500
94.0000
3
6110.0000
PURPLE MARTIN
t,ss,sp,p
es,ew,ow,m
E
35.0000
94.5946
2.5900
16.0000
2
6110.0000
PURPLE MARTIN
t,ss,sp,p
es,ew,ow,m
W
3.0000
6.6667
0.0800
?
6120.0000
CLIFF SWALLOW
abundant
abundant
t,ss,sp,p
es,ew,ow,m
3
6.0000
7.0000
1.5487
Coteau Slope
NE Drift Plain
C
42.0000
79.2453
22.9800
18.0000
4
6120.0000
CLIFF SWALLOW
t,ss,sp,p
es,ew,ow,m
E
35.0000
94.5946
15.1100
22.0000
3
6120.0000
CLIFF SWALLOW
t,ss,sp,p
es,ew,ow,m
W
32.0000
71.1111
18.5300
24.0000
3
6130.0000
BARN SWALLOW
abundant
abundant
t,ss,sp,p
es,ew,ow,m
3
16.0000
35.0000
7.7434
Agassiz L.PIain
NE Drift Plain
C
53.0000
100.0000
24.9700
54.0000
4
6130.0000
BARN SWALLOW
t,ss,sp,p
es,ew,ow,m
E
37.0000
100.0000
38.6400
56.0000
4
6130.0000
BARN SWALLOW
t,ss,sp,p
es,ew,ow,m
W
45.0000
100.0000
12.6800
38.0000
3
6140.0000
TREE SWALLOW
common
fairly common
t,ss,sp,p
es,ew,ow,m
3
4.0000
7.0000
1.5487
NW Drift Plain
NW Drift Plain
C
43.0000
81.1321
1.8300
26.0000
3
6140.0000
TREE SWALLOW
t,ss,sp,p
es,ew,ow,m
E
32.0000
86.4865
1.9300
20.0000
4
6140.0000
TREE SWALLOW
t,ss,sp,p
es,ew,ow,m
W
27.0000
60.0000
0.5600
6.0000
3
6160.0000
BANK SWALLOW
abundant
common
t,ss,sp,p
es,ew,ow,m
3
14.0000
11.0000
2.4336
31.0000
Coteau Slope
Missouri Coteau
C
43.0000
81.1321
3.2400
10.0000
2
6160.0000
BANK SWALLOW
t,ss,sp,p
es,ew,ow,m
E
34.0000
91.8919
3.8200
12.0000
2
6160.0000
BANK SWALLOW
t,ss,sp,p
es,ew,ow,m
W
28.0000
62.2222
2.9000
16.0000
2
6170.0000
N. ROUGH-WINGED SWALLOW
fairly common
fairly common
t,ss,sp,p
es,ew,ow,m
3
0.0000
1.0000
0.2212
NW Drift Plain
NW Drift Plain
C
29.0000
54.7170
0.7500
22.0000
2
6170.0000
N. ROUGH-WINGED SWALLOW
t,ss,sp,p
es,ew,ow,m
E
32.0000
86.4865
1.1300
10.0000
2
-------
6170.0000
N. ROUGH-WINGED SWALLOW
t,ss,sp,p
es,ew,ow,m
W
21.0000
46.6667
0.2900
4.0000
2
6520.0000
YELLOW WARBLER
common
common
t
B
3.0000
0.6637
C
51.0000
96.2264
4.3100
50.0000
3
6520.0000
YELLOW WARBLER
t
E
36.0000
97.2973
2.2300
34.0000
1
6520.0000
YELLOW WARBLER
t
W
37.0000
82.2222
1.7100
28.0000
2
6810.0000
COMMON YELLOWTHROAT
common
common
t,ss,SP,p
es
B
97.0000
61.0000
13.4956
NE Drift Plain
NW Drift Plain
C
51.0000
96.2264
9.0200
70.0000
3
6810.0000
COMMON YELLOWTHROAT
t,ss,SP,p
es
E
37.0000
100.0000
17.5300
88.0000
2
6810.0000
COMMON YELLOWTHROAT
t,ss,SP,p
es
W
35.0000
77.7778
2.8800
42.0000
2
7001.0000
AMERICAN PIPIT
fairly common
t,SS,A
m
1.0000
0.2212
Missouri Coteau
Missouri Coteau
7240.0000
SEDGE WREN
uncommon
fairly common
t,SS
es
3
17.0000
10.0000
2.2124
Missouri Coteau
S. Drift Plain
C
26.0000
49.0566
0.5900
20.0000
2
7240.0000
SEDGE WREN
t,SS
es
E
30.0000
81.0811
1.9200
22.0000
3
7240.0000
SEDGE WREN
t,SS
es
W
9.0000
20.0000
0.3200
12.0000
?
7250.0000
MARSH WREN
fairly common
common
ss,SP,p
es,ew
3
152.0000
31.0000
6.8584
S. Drift Plain
NW Drift Plain
C
35.0000
66.0377
1.8800
32.0000
3
7250.0000
MARSH WREN
ss,SP,p
es,ew
E
23.0000
62.1622
1.2400
22.0000
2
7250.0000
MARSH WREN
ss,SP,p
es,ew
W
14.0000
31.1111
1.6100
16.0000
3
-------
VASCPLANTS
AUTHORS
PUBYEAR
REF_APX_J
Agropyron smithii
Hubbard et al.
1988
315.0000
Agropyron trachycaulum
Stewart & Kantrud
1972a
198.0000
Alisma gramineum
Stewart & Kantrud
1972a
198.0000
Alisma plantago-aquatica
Shay & Shay
1986
189.0000
Alisma plantago-aquatica
Walker & Coupland
1970
297.0000
Alisma plantago-aquatica
Millar
1973
144.0000
Alisma plantago-aquatica
Stewart & Kantrud
1972a
198.0000
Alopecurus aequalis
Millar
1973
144.0000
Alopecurus aequalis
Walker & Coupland
1970
297.0000
Amaranthus albus
Hubbard et al.
1988
315.0000
Ambrosia artemisiifolia
Hubbard et al.
1988
315.0000
Ambrosia psilostachya
Stewart & Kantrud
1972a
198.0000
Andropogon gerardii
Hubbard et al.
1988
315.0000
Anemone canadensis
Stewart & Kantrud
1972a
198.0000
Apocynum cannabinum
Hubbard et al.
1988
315.0000
Aster brachyactis
Stewart & Kantrud
1972a
198.0000
Aster ericoides
Stewart & Kantrud
1972a
198.0000
Aster ericoides
Hubbard et al.
1988
315.0000
Aster hesperius
Walker & Coupland
1970
297.0000
Aster hesperius
Hubbard et al.
1988
315.0000
Aster laurentianus
Galinato & van der Valk
1986
68.0000
Aster simplex
Stewart & Kantrud
1972a
198.0000
Atriplex patula
Galinato & van der Valk
1986
68.0000
Atriplex patula
Walker & Coupland
1970
297.0000
Beckmannia syzigachne
Millar
1973
144.0000
Beckmannia syzigachne
Shay & Shay
1986
189.0000
Beckmannia syzigachne
Stewart & Kantrud
1972a
198.0000
Beckmannia syzigachne
Walker & Coupland
1970
297.0000
Bidens frondosa
Hubbard et al.
1988
315.0000
Boltonia latisquama
Stewart & Kantrud
1972a
198.0000
Calamagrostis canadensis
Shay & Shay
1986
189.0000
Calamagrostis inexpansa
Shay & Shay
1986
189.0000
Calamagrostis inexpansa
Stewart & Kantrud
1972a
198.0000
Calamagrostis inexpansa
Walker & Coupland
1968
296.0000
Calamagrostis inexpansa
Walker & Coupland
1970
297.0000
Calamagrostis neglecta
Hubbard et al.
1988
315.0000
Callitriche hermaphroditica
Walker & Coupland
1970
297.0000
Calystegia sepium
Hubbard et al.
1988
315.0000
Carex atherodes
Driver
1977
46.0000
Carex atherodes
Millar
1973
144.0000
Carex atherodes
Shay & Shay
1986
189.0000
Carex atherodes
Stewart & Kantrud
1972a
198.0000
Carex atherodes
Walker & Coupland
1968
296.0000
Carex atherodes
Walker & Coupland
1970
297.0000
Carex atherodes
Hubbard et al.
1988
315.0000
Carex athrostachya
Walker & Coupland
1970
297.0000
-------
Carex lanuginosa
Walker & Coupland
1968
296.0000
Carex lanuginosa
Walker & Coupland
1970
297.0000
Carex lanuginosa
Hubbard et al.
1988
315.0000
Carex praegracilis
Stewart & Kantrud
1972a
198.0000
Carex rostrata
Driver
1977
46.0000
Carex rostrata
Walker & Coupland
1970
297.0000
Carex sartwellii
Walker & Coupland
1970
297.0000
Carex tetanica
Hubbard et al.
1988
315.0000
Ceratophyllum demersum
Shay & Shay
1986
189.0000
Ceratophyllum demersum
Walker & Coupland
1970
297.0000
Chenopodium album
Walker & Coupland
1970
297.0000
Chenopodium rubrum
Galinato & van der Valk
1986
68.0000
Chenopodium rubrum
Stewart & Kantrud
1972a
198.0000
Chenopodium salinum
Stewart & Kantrud
1972a
198.0000
Cirsium arvense
Shay & Shay
1986
189.0000
Cirsium arvense
Walker & Coupland
1970
297.0000
Dicanthelium leibergii
Hubbard et al.
1988
315.0000
Distichlis stricta
Shay & Shay
1986
189.0000
Distichlis stricta
Stewart & Kantrud
1972a
198.0000
Eleocharis acicularis
Stewart & Kantrud
1972a
198.0000
Eleocharis acicularis
Walker & Coupland
1970
297.0000
Eleocharis acicularis
Hubbard et al.
1988
315.0000
Eleocharis compressa
Hubbard et al.
1988
315.0000
Eleocharis palustris
Millar
1973
144.0000
Eleocharis palustris
Shay & Shay
1986
189.0000
Eleocharis palustris
Stewart & Kantrud
1972a
198.0000
Eleocharis palustris
Walker & Coupland
1970
297.0000
Eleocharis palustris
Hubbard et al.
1988
315.0000
Equisetum fluviatile
Millar
1973
144.0000
Glaux maritima
Walker & Coupland
1970
297.0000
Glyceria borealis
Walker & Coupland
1970
297.0000
Glyceria grandis
Millar
1973
144.0000
Glyceria grandis
Stewart & Kantrud
1972a
198.0000
Glyceria grandis
Walker & Coupland
1968
296.0000
Glyceria grandis
Walker & Coupland
1970
297.0000
Glyceria pulchella
Walker & Coupland
1970
297.0000
Gratiola neglecta
Walker & Coupland
1970
297.0000
Helenium autumnale
Walker & Coupland
1970
297.0000
Helianthus maximiliani
Hubbard et al.
1988
315.0000
Hippuris vulgaris
Walker & Coupland
1970
297.0000
Hordeum jubatum
Galinato & van der Valk
1986
68.0000
Hordeum jubatum
Millar
1973
144.0000
Hordeum jubatum
Shay & Shay
1986
189.0000
Hordeum jubatum
Stewart & Kantrud
1972a
198.0000
Hordeum jubatum
Walker & Coupland
1970
297.0000
Hordeum jubatum
Hubbard et al.
1988
315.0000
Juncus
Walker & Coupland
1968
296.0000
-------
Juncus balticus
Shay & Shay
1986
189.0000
Juncus balticus
Stewart & Kantrud
1972a
198.0000
Juncus balticus
Walker & Coupland
1970
297.0000
Juncus tenuis
Walker & Coupland
1970
297.0000
Kochia scoparia
Stewart & Kantrud
1972a
198.0000
Lactuca serriola
Hubbard et al.
1988
315.0000
Lemna
Walker & Coupland
1968
296.0000
Lemna minor
Walker & Coupland
1970
297.0000
Lemna minor
Hubbard et al.
1988
315.0000
Lemna trisulca
Walker & Coupland
1970
297.0000
Lycopus asper
Walker & Coupland
1970
297.0000
Lysimachia ciliata
Walker & Coupland
1970
297.0000
Mentha arvensis
Walker & Coupland
1970
297.0000
Myriophyllum spicatum
Driver
1977
46.0000
Myriophyllum spicatum
Walker & Coupland
1968
296.0000
Myriophyllum spicatum
Walker & Coupland
1970
297.0000
Myriophyllum spicatum
Shay & Shay
1986
189.0000
Panicum virgatum
Hubbard et al.
1988
315.0000
Phalaris arundinacea
Walker & Coupland
1970
297.0000
Phragmites australis
Galinato & van der Valk
1986
68.0000
Phragmites australis
Shay & Shay
1986
189.0000
Poa palustris
Stewart & Kantrud
1972a
198.0000
Poa palustris
Walker & Coupland
1968
296.0000
Poa palustris
Walker & Coupland
1970
297.0000
Poa pratensis
Stewart & Kantrud
1972a
198.0000
Poa pratensis
Walker & Coupland
1970
297.0000
Poa pratensis
Hubbard et al.
1988
315.0000
Polygonum
Walker & Coupland
1968
296.0000
Polygonum amphibium
Walker & Coupland
1970
297.0000
Polygonum amphibium
Hubbard et al.
1988
315.0000
Polygonum amphibium
Driver
1977
46.0000
Polygonum amphibium
Millar
1973
144.0000
Polygonum amphibium
Shay & Shay
1986
189.0000
Polygonum amphibium
Stewart & Kantrud
1972a
198.0000
Polygonum amphibium
Walker & Coupland
1970
297.0000
Polygonum lapathifolium
Walker & Coupland
1970
297.0000
Potamogeton
Walker & Coupland
1968
296.0000
Potamogeton gramineus
Shay & Shay
1986
189.0000
Potamogeton gramineus
Walker & Coupland
1970
297.0000
Potamogeton pectinatus
Shay & Shay
1986
189.0000
Potamogeton pectinatus
Walker & Coupland
1970
297.0000
Potamogeton pusillus
Shay & Shay
1986
189.0000
Potamogeton pusillus
Walker & Coupland
1970
297.0000
Potamogeton richardsonii
Driver
1977
46.0000
Potamogeton richardsonii
Shay & Shay
1986
189.0000
Potamogeton richardsonii
Walker & Coupland
1970
297.0000
Potentilla anserina
Walker & Coupland
1970
297.0000
-------
Potentilla norvegica
Walker & Coupland
1970
297.0000
Puccinellia nuttalliana
Shay & Shay
1986
189.0000
Puccinellia nuttalliana
Stewart & Kantrud
1972a
198.0000
Puccinellia nuttalliana
Walker & Coupland
1970
297.0000
Ranunculus circinatus
Walker & Coupland
1968
296.0000
Ranunculus circinatus
Walker & Coupland
1970
297.0000
Ranunculus macounii
Walker & Coupland
1970
297.0000
Riccia fluitans
Walker & Coupland
1970
297.0000
Riccia fluitans
Hubbard et al.
1988
315.0000
Ricciocarpus natans
Walker & Coupland
1970
297.0000
Rorippa islandica
Walker & Coupland
1970
297.0000
Rumex crispus
Walker & Coupland
1970
297.0000
Rumex maritimus
Stewart & Kantrud
1972a
198.0000
Rumex maritimus
Walker & Coupland
1970
297.0000
Rumex mexicanus
Walker & Coupland
1970
297.0000
Rumex mexicanus
Hubbard et al.
1988
315.0000
Rumex stenophyllus
Walker & Coupland
1970
297.0000
Rumex stenophyllus
Hubbard et al.
1988
315.0000
Ruppia maritima
Shay & Shay
1986
189.0000
Sagittaria cuneata
Shay & Shay
1986
189.0000
Sagittaria cuneata
Walker & Coupland
1970
297.0000
Sagittaria latifolia
Walker & Coupland
1970
297.0000
Salicornia rubra
Shay & Shay
1986
189.0000
Salicornia rubra
Stewart & Kantrud
1972a
198.0000
Salicornia rubra
Walker & Coupland
1970
297.0000
Schizachyrium scoparium
Hubbard et al.
1988
315.0000
Sc
rpus acutus
Stewart & Kantrud
1972a
198.0000
Sc
rpus acutus
Walker & Coupland
1968
296.0000
Sc
rpus acutus
Walker & Coupland
1970
297.0000
Sc
rpus acutus
Hubbard et al.
1988
315.0000
Sc
rpus americanus
Driver
1977
46.0000
Sc
rpus americanus
Stewart & Kantrud
1972a
198.0000
Sc
rpus americanus
Walker & Coupland
1970
297.0000
Sc
rpus fluviatilis
Stewart & Kantrud
1972a
198.0000
Sc
rpus heterochaetus
Stewart & Kantrud
1972a
198.0000
Sc
rpus maritimus
Shay & Shay
1986
189.0000
Sc
rpus maritimus
Lieffers & Shay
1982a
274.0000
Sc
rpus maritimus
Lieffers & Shay
1982b
288.0000
Sc
rpus maritimus
Shay & Shay
1986
189.0000
Sc
rpus maritimus
Stewart & Kantrud
1972a
198.0000
Sc
rpus maritimus
Lieffers & Shay
1982a
274.0000
Sc
rpus maritimus
Lieffers & Shay
1982b
288.0000
Sc
rpus maritimus
Walker & Coupland
1970
297.0000
Sc
rpus validus
Shay & Shay
1986
189.0000
Sc
rpus validus
Walker & Coupland
1968
296.0000
Scolochloa festucacea
Driver
1977
46.0000
Scolochloa festucacea
Galinato & van der Valk
1986
68.0000
-------
Scolochloa festucacea
Millar
1973
144.0000
Scolochloa festucacea
Shay & Shay
1986
189.0000
Scolochloa festucacea
Stewart & Kantrud
1972a
198.0000
Scolochloa festucacea
Walker & Coupland
1968
296.0000
Scolochloa festucacea
Walker & Coupland
1970
297.0000
Senecio congestus
Stewart & Kantrud
1972a
198.0000
Senecio congestus
Walker & Coupland
1970
297.0000
Setaria glauca
Hubbard et al.
1988
315.0000
Sium suave
Walker & Coupland
1968
296.0000
Sium suave
Walker & Coupland
1970
297.0000
Solidago altissima
Stewart & Kantrud
1972a
198.0000
Solidago canadensis
Hubbard et al.
1988
315.0000
Solidago rigida
Hubbard et al.
1988
315.0000
Sonchus arvensis
Shay & Shay
1986
189.0000
Sonchus arvensis
Walker & Coupland
1970
297.0000
Sonchus arvensis
Hubbard et al.
1988
315.0000
Sparganium chlorocarpum
Walker & Coupland
1970
297.0000
Sparganium eurycarpum
Stewart & Kantrud
1972a
198.0000
Sparganium eurycarpum
Walker & Coupland
1970
297.0000
Spartina pectinata
Stewart & Kantrud
1972a
198.0000
Spartina pectinata
Hubbard et al.
1988
315.0000
Stachys palustris
Walker & Coupland
1970
297.0000
Suaeda depressa
Shay & Shay
1986
189.0000
Symphoricarpos occidentalis
Stewart & Kantrud
1972a
198.0000
Triglochin maritima
Walker & Coupland
1970
297.0000
Typha glauca
Galinato & van der Valk
1986
68.0000
Typha glauca
Stewart & Kantrud
1972a
198.0000
Typha latifolia
Shay & Shay
1986
189.0000
Typha latifolia
Walker & Coupland
1968
296.0000
Typha latifolia
Walker & Coupland
1970
297.0000
Utricularia vulgaris
Shay & Shay
1986
189.0000
Utricularia vulgaris
Walker & Coupland
1970
297.0000
Veronia scutellata
Walker & Coupland
1970
297.0000
Zanichellia palustris
Shay & Shay
1986
189.0000
Zanichellia palustris
Walker & Coupland
1970
297.0000
-------
TAX A
AUTHORS
PUBYEAR
REF_APX_J
Acorus calamus
Krapu et al.
1970
119.0000
Acorus calamus
Weller et al.
1991
264.0000
Agropyron smithii
Hubbard et al.
1988
315.0000
Agropyron trachycaulum
Stewart & Kantrud
1972a
198.0000
Alisma gramineum
Stewart & Kantrud
1972a
198.0000
Alisma plantago-aquatica
Millar
1973
144.0000
Alisma plantago-aquatica
Shay & Shay
1986
189.0000
Alisma plantago-aquatica
Stewart & Kantrud
1972a
198.0000
Alisma plantago-aquatica
Wienhold & van der Valk
1989
231.0000
Alisma plantago-aquatica
Walker & Coupland
1970
297.0000
Alisma subcordatum
Weller & Voigts
1983
234.0000
Alopecurus aequalis
Millar
1973
144.0000
Alopecurus aequalis
Walker & Coupland
1970
297.0000
Amaranthus albus
Hubbard et al.
1988
315.0000
Ambrosia artemisiifolia
Hubbard et al.
1988
315.0000
Ambrosia psilostachya
Stewart & Kantrud
1972a
198.0000
Ammannia coccinea
Wienhold & van der Valk
1989
231.0000
Andropogon gerardii
Johnson
1987
100.0000
Andropogon gerardii
Hubbard et al.
1988
315.0000
Anemone canadensis
Stewart & Kantrud
1972a
198.0000
Apocynum cannabinum
Hubbard et al.
1988
315.0000
Apocynum sibiricum
Smeins & Olson
1970
194.0000
Aster brachyactis
Stewart & Kantrud
1972a
198.0000
Aster brachyactis
Merendino et al.
1991
308.0000
Aster ericoides
Stewart & Kantrud
1972a
198.0000
Aster ericoides
Hubbard et al.
1988
315.0000
Aster hesperius
Walker & Coupland
1970
297.0000
Aster hesperius
Hubbard et al.
1988
315.0000
Aster laurentianus
Galinato & van der Valk
1986
68.0000
Aster laurentianus
Welling et al.
1988a
236.0000
Aster simplex
Stewart & Kantrud
1972a
198.0000
Atriplex patula
Galinato & van der Valk
1986
68.0000
Atriplex patula
Welling et al.
1988a
236.0000
Atriplex patula
Pederson
1981
269.0000
Atriplex patula
van der Valk
1986
295.0000
Atriplex patula
Walker & Coupland
1970
297.0000
Beckmannia syzigachne
Millar
1973
144.0000
Beckmannia syzigachne
Shay & Shay
1986
189.0000
Beckmannia syzigachne
Stewart & Kantrud
1972a
198.0000
Beckmannia syzigachne
Wienhold & van der Valk
1989
231.0000
Beckmannia syzigachne
Walker & Coupland
1970
297.0000
Bidens cernua
van der Valk
1978
256.0000
Bidens cernua
Welling
1988b
258.0000
Bidens frondosa
Hubbard et al.
1988
315.0000
Boltonia latisquama
Stewart & Kantrud
1972a
198.0000
Calamagrostis canadensis
Shay & Shay
1986
189.0000
-------
Calamagrostis canadensis
Weller et al.
1991
264.0000
Calamagrostis inexpansa
Shay & Shay
1986
189.0000
Calamagrostis inexpansa
Smeins & Olson
1970
194.0000
Calamagrostis inexpansa
Stewart & Kantrud
1972a
198.0000
Calamagrostis inexpansa
Walker & Coupland
1968
296.0000
Calamagrostis inexpansa
Walker & Coupland
1970
297.0000
Calamagrostis neglecta
Hubbard et al.
1988
315.0000
Callitriche hermaphroditica
Walker & Coupland
1970
297.0000
Calystegia sepium
Hubbard et al.
1988
315.0000
Carex
Krapu et al.
1970
119.0000
Carex
Wienhold & van der Valk
1989
231.0000
Carex atherodes
Driver
1977
46.0000
Carex atherodes
Millar
1973
144.0000
Carex atherodes
Shay & Shay
1986
189.0000
Carex atherodes
Squires & van der Valk
1992
195.0000
Carex atherodes
Stewart & Kantrud
1972a
198.0000
Carex atherodes
van der Valk
1976
245.0000
Carex atherodes
Murkin & Kadlec
1986b
259.0000
Carex atherodes
Pederson
1981
269.0000
Carex atherodes
Walker & Coupland
1968
296.0000
Carex atherodes
Walker & Coupland
1970
297.0000
Carex atherodes
Hubbard et al.
1988
315.0000
Carex athrostachya
Walker & Coupland
1970
297.0000
Carex lacustris
Weller & Voigts
1983
234.0000
Carex lanuginosa
Smeins & Olson
1970
194.0000
Carex lanuginosa
Walker & Coupland
1968
296.0000
Carex lanuginosa
Walker & Coupland
1970
297.0000
Carex lanuginosa
Hubbard et al.
1988
315.0000
Carex lasiocarpa
Johnson
1987
100.0000
Carex praegracilis
Stewart & Kantrud
1972a
198.0000
Carex rostrata
Driver
1977
46.0000
Carex rostrata
van der Valk
1976
245.0000
Carex rostrata
Walker & Coupland
1970
297.0000
Carex sartwellii
Smeins & Olson
1970
194.0000
Carex sartwellii
Walker & Coupland
1970
297.0000
Carex stricta
Weller et al.
1991
264.0000
Carex tetanica
Hubbard et al.
1988
315.0000
Ceratophyllum demersum
Shay & Shay
1986
189.0000
Ceratophyllum demersum
Weller & Voigts
1983
234.0000
Ceratophyllum demersum
van der Valk
1976
245.0000
Ceratophyllum demersum
Armstrong & Nudds
1985
249.0000
Ceratophyllum demersum
van der Valk
1978
256.0000
Ceratophyllum demersum
Walker & Coupland
1970
297.0000
Chenopodium album
Walker & Coupland
1970
297.0000
Chenopodium rubrum
Galinato & van der Valk
1986
68.0000
Chenopodium rubrum
Poiani & Johnson
1989
172.0000
Chenopodium rubrum
Stewart & Kantrud
1972a
198.0000
-------
Chenopodium rubrum
Welling et al.
1988a
236.0000
Chenopodium rubrum
Poiani & Johnson
1988
257.0000
Chenopodium rubrum
Pederson
1981
269.0000
Chenopodium rubrum
Merendino et al.
1991
308.0000
Chenopodium salinum
Stewart & Kantrud
1972a
198.0000
Cirsium arvense
Johnson
1987
100.0000
Cirsium arvense
Shay & Shay
1986
189.0000
Cirsium arvense
Walker & Coupland
1970
297.0000
Cyperus
van der Valk
1978
256.0000
Cyperus
Welling
1988b
258.0000
Dicanthelium leibergii
Hubbard et al.
1988
315.0000
Distichlis spicata
Shay & Shay
1986
189.0000
Distichlis spicata
Stewart & Kantrud
1972a
198.0000
Echinochloa crusgalli
Wienhold & van der Valk
1989
231.0000
Elatine triandra
Wienhold & van der Valk
1989
231.0000
Eleocharis
Weller et al.
1991
264.0000
Eleocharis acicularis
Stewart & Kantrud
1972a
198.0000
Eleocharis acicularis
Wienhold & van der Valk
1989
231.0000
Eleocharis acicularis
Walker & Coupland
1970
297.0000
Eleocharis acicularis
Hubbard et al.
1988
315.0000
Eleocharis compressa
Hubbard et al.
1988
315.0000
Eleocharis ovata
Wienhold & van der Valk
1989
231.0000
Eleocharis palustris
Millar
1973
144.0000
Eleocharis palustris
Shay & Shay
1986
189.0000
Eleocharis palustris
Stewart & Kantrud
1972a
198.0000
Eleocharis palustris
Weller & Voigts
1983
234.0000
Eleocharis palustris
Walker & Coupland
1970
297.0000
Eleocharis palustris
Hubbard et al.
1988
315.0000
Equisetum fluviatile
Millar
1973
144.0000
Glaux maritima
Walker & Coupland
1970
297.0000
Glyceria borealis
Walker & Coupland
1970
297.0000
Glyceria maxima
Millar
1973
144.0000
Glyceria maxima
Stewart & Kantrud
1972a
198.0000
Glyceria maxima
van der Valk
1976
245.0000
Glyceria maxima
Walker & Coupland
1968
296.0000
Glyceria maxima
Walker & Coupland
1970
297.0000
Glyceria pulchella
Walker & Coupland
1970
297.0000
Gratiola neglecta
Wienhold & van der Valk
1989
231.0000
Gratiola neglecta
Walker & Coupland
1970
297.0000
Helenium autumnale
Walker & Coupland
1970
297.0000
Helianthus maximiliani
Hubbard et al.
1988
315.0000
Hippuris vulgaris
Walker & Coupland
1970
297.0000
Hordeum jubatum
Galinato & van der Valk
1986
68.0000
Hordeum jubatum
Millar
1973
144.0000
Hordeum jubatum
Shay & Shay
1986
189.0000
Hordeum jubatum
Stewart & Kantrud
1972a
198.0000
Hordeum jubatum
Walker & Coupland
1970
297.0000
-------
Hordeum jubatum
Hubbard et al.
1988
315.0000
Juncus
Walker & Coupland
1968
296.0000
Juncus balticus
Shay & Shay
1986
189.0000
Juncus balticus
Smeins & Olson
1970
194.0000
Juncus balticus
Stewart & Kantrud
1972a
198.0000
Juncus balticus
Walker & Coupland
1970
297.0000
Juncus bufonius
Wienhold & van der Valk
1989
231.0000
Juncus longistylis
Wienhold & van der Valk
1989
231.0000
Juncus tenuis
Walker & Coupland
1970
297.0000
Kochia scoparia
Stewart & Kantrud
1972a
198.0000
Lactuca serriola
Hubbard et al.
1988
315.0000
Leersia oryzoides
Wienhold & van der Valk
1989
231.0000
Leersia oryzoides
Weller et al.
1991
264.0000
Lemna
van der Valk
1978
256.0000
Lemna
Walker & Coupland
1968
296.0000
Lemna minor
Wienhold & van der Valk
1989
231.0000
Lemna minor
Weller & Voigts
1983
234.0000
Lemna minor
Walker & Coupland
1970
297.0000
Lemna minor
Hubbard et al.
1988
315.0000
Lemna trisulca
Wienhold & van der Valk
1989
231.0000
Lemna trisulca
Weller & Voigts
1983
234.0000
Lemna trisulca
Walker & Coupland
1970
297.0000
Limosella aquatica
Wienhold & van der Valk
1989
231.0000
Lindernia dubia
Wienhold & van der Valk
1989
231.0000
Lycopus americanus
Wienhold & van der Valk
1989
231.0000
Lycopus asper
Walker & Coupland
1970
297.0000
Lysimachia ciliata
Walker & Coupland
1970
297.0000
Lythrum salicaria
Merendino et al.
1990
143.0000
Mentha arvensis
Wienhold & van der Valk
1989
231.0000
Mentha arvensis
Walker & Coupland
1970
297.0000
Myr
ophyllum
Weller et al.
1991
264.0000
Myr
ophyllum
Bataille & Baldassarre
1993
327.0000
Myr
ophyllum spicatum
Driver
1977
46.0000
Myr
ophyllum spicatum
Shay & Shay
1986
189.0000
Myr
ophyllum spicatum
Weller & Voigts
1983
234.0000
Myr
ophyllum spicatum
Walker & Coupland
1968
296.0000
Myr
ophyllum spicatum
Walker & Coupland
1970
297.0000
Najas flexilis
van der Valk
1978
256.0000
Najas flexilis
Welling
1988b
258.0000
Panicum capillare
Wienhold & van der Valk
1989
231.0000
Panicum virgatum
Johnson
1987
100.0000
Panicum virgatum
Hubbard et al.
1988
315.0000
Penthorum sedoides
Wienhold & van der Valk
1989
231.0000
Phalaris arundinacea
Weller et al.
1991
264.0000
Phalaris arundinacea
Walker & Coupland
1970
297.0000
Phragmites australis
Bishop et al.
1979
18.0000
Phragmites australis
Galinato & van der Valk
1986
68.0000
-------
Phragmites australis
Murkin et al.
1991
151.0000
Phragmites australis
Shay & Shay
1986
189.0000
Phragmites australis
Squires & van der Valk
1992
195.0000
Phragmites australis
Welling et al.
1988a
236.0000
Phragmites australis
van der Valk & Squires
1992
248.0000
Phragmites australis
Welling
1988b
258.0000
Phragmites australis
Murkin & Kadlec
1986b
259.0000
Phragmites australis
van der Valk
1986
295.0000
Phragmites australis
Merendino et al.
1991
308.0000
Poa palustris
Stewart & Kantrud
1972a
198.0000
Poa palustris
Walker & Coupland
1968
296.0000
Poa palustris
Walker & Coupland
1970
297.0000
Poa pratensis
Johnson
1987
100.0000
Poa pratensis
Stewart & Kantrud
1972a
198.0000
Poa pratensis
Weller et al.
1991
264.0000
Poa pratensis
Walker & Coupland
1970
297.0000
Poa pratensis
Hubbard et al.
1988
315.0000
Polygonum
Weller & Voigts
1983
234.0000
Polygonum
Weller et al.
1991
264.0000
Polygonum
Walker & Coupland
1968
296.0000
Polygonum
Hemesath
1991
310.0000
Polygonum amphibium
Driver
1977
46.0000
Polygonum amphibium
Millar
1973
144.0000
Polygonum amphibium
Shay & Shay
1986
189.0000
Polygonum amphibium
Stewart & Kantrud
1972a
198.0000
Polygonum amphibium
Walker & Coupland
1970
297.0000
Polygonum amphibium
Hubbard et al.
1988
315.0000
Polygonum lapathifolium
Wienhold & van der Valk
1989
231.0000
Polygonum lapathifolium
van der Valk
1978
256.0000
Polygonum lapathifolium
Welling
1988b
258.0000
Polygonum lapathifolium
Walker & Coupland
1970
297.0000
Polygonum pensylvanicum
Wienhold & van der Valk
1989
231.0000
Potamogeton
Walker & Coupland
1968
296.0000
Potamogeton foliosus
Weller & Voigts
1983
234.0000
Potamogeton gramineus
Shay & Shay
1986
189.0000
Potamogeton gramineus
Walker & Coupland
1970
297.0000
Potamogeton pectinatus
Murkin et al.
1991
151.0000
Potamogeton pectinatus
Shay & Shay
1986
189.0000
Potamogeton pectinatus
Weller & Voigts
1983
234.0000
Potamogeton pectinatus
van der Valk
1978
256.0000
Potamogeton pectinatus
Pederson
1981
269.0000
Potamogeton pectinatus
Walker & Coupland
1970
297.0000
Potamogeton pusillus
Shay & Shay
1986
189.0000
Potamogeton pusillus
van der Valk
1976
245.0000
Potamogeton pusillus
Walker & Coupland
1970
297.0000
Potamogeton richardsonii
Driver
1977
46.0000
Potamogeton richardsonii
Shay & Shay
1986
189.0000
-------
Potamogeton richardsonii
Walker & Coupland
1970
297.0000
Potentilla anserina
Walker & Coupland
1970
297.0000
Potentilla norvegica
Walker & Coupland
1970
297.0000
Puccinellia nuttalliana
Shay & Shay
1986
189.0000
Puccinellia nuttalliana
Stewart & Kantrud
1972a
198.0000
Puccinellia nuttalliana
Walker & Coupland
1970
297.0000
Ranunculus
Weller & Voigts
1983
234.0000
Ranunculus aquatilis
Bataille & Baldassarre
1993
327.0000
Ranunculus circinatus
Walker & Coupland
1968
296.0000
Ranunculus circinatus
Walker & Coupland
1970
297.0000
Ranunculus macounii
Walker & Coupland
1970
297.0000
Ranunculus sceleratus
Poiani & Johnson
1989
172.0000
Ranunculus sceleratus
Wienhold & van der Valk
1989
231.0000
Ranunculus sceleratus
Poiani & Johnson
1988
257.0000
Ranunculus sceleratus
Pederson
1981
269.0000
Riccia fluitans
Wienhold & van der Valk
1989
231.0000
Riccia fluitans
Weller & Voigts
1983
234.0000
Riccia fluitans
Walker & Coupland
1970
297.0000
Riccia fluitans
Hubbard et al.
1988
315.0000
Ricciocarpus natans
Wienhold & van der Valk
1989
231.0000
Ricciocarpus natans
Weller & Voigts
1983
234.0000
Ricciocarpus natans
Walker & Coupland
1970
297.0000
Rorippa palustris
Wienhold & van der Valk
1989
231.0000
Rorippa palustris
Welling
1988b
258.0000
Rorippa palustris
Walker & Coupland
1970
297.0000
Rumex
van der Valk
1978
256.0000
Rumex crispus
Walker & Coupland
1970
297.0000
Rumex crispus
Merendino et al.
1991
308.0000
Rumex maritimus
Poiani & Johnson
1989
172.0000
Rumex maritimus
Stewart & Kantrud
1972a
198.0000
Rumex maritimus
Wienhold & van der Valk
1989
231.0000
Rumex maritimus
Poiani & Johnson
1988
257.0000
Rumex maritimus
Welling
1988b
258.0000
Rumex maritimus
Pederson
1981
269.0000
Rumex maritimus
Walker & Coupland
1970
297.0000
Rumex mexicanus
Walker & Coupland
1970
297.0000
Rumex mexicanus
Hubbard et al.
1988
315.0000
Rumex stenophyllus
Walker & Coupland
1970
297.0000
Rumex stenophyllus
Hubbard et al.
1988
315.0000
Ruppia maritima
Shay & Shay
1986
189.0000
Sagittaria
Bishop et al.
1979
18.0000
Sagittaria
Krapu et al.
1970
119.0000
Sagittaria
Weller et al.
1991
264.0000
Sagittaria cuneata
Shay & Shay
1986
189.0000
Sagittaria cuneata
Wienhold & van der Valk
1989
231.0000
Sagittaria cuneata
Weller & Voigts
1983
234.0000
Sagittaria cuneata
Walker & Coupland
1970
297.0000
-------
Sagittaria latifolia
van der Valk
1976
245.0000
Sagittaria latifolia
van der Valk
1978
256.0000
Sagittaria latifolia
Walker & Coupland
1970
297.0000
Salicornia rubra
Shay & Shay
1986
189.0000
Salicornia rubra
Stewart & Kantrud
1972a
198.0000
Salicornia rubra
Walker & Coupland
1970
297.0000
Schizachyrium scoparium
Hubbard et al.
1988
315.0000
Sc
rpus
Poiani & Johnson
1988
257.0000
Sc
rpus
Hemesath
1991
310.0000
Sc
rpus
Bataille & Baldassarre
1993
327.0000
Sc
rpus acutus
Cowardin et al.
1985
36.0000
Sc
rpus acutus
Murkin et al.
1991
151.0000
Sc
rpus acutus
Poiani & Johnson
1989
172.0000
Sc
rpus acutus
Stewart & Kantrud
1972a
198.0000
Sc
rpus acutus
Wienhold & van der Valk
1989
231.0000
Sc
rpus acutus
Weller & Voigts
1983
234.0000
Sc
rpus acutus
van der Valk & Squires
1992
248.0000
Sc
rpus acutus
Walker & Coupland
1968
296.0000
Sc
rpus acutus
Walker & Coupland
1970
297.0000
Sc
rpus acutus
Hubbard et al.
1988
315.0000
Sc
rpus americanus
Driver
1977
46.0000
Sc
rpus americanus
Stewart & Kantrud
1972a
198.0000
Sc
rpus americanus
Wienhold & van der Valk
1989
231.0000
Sc
rpus americanus
Walker & Coupland
1970
297.0000
Sc
rpus atrovirens
Weller et al.
1991
264.0000
Sc
rpus fluviatilis
Bishop et al.
1979
18.0000
Sc
rpus fluviatilis
Cowardin et al.
1985
36.0000
Sc
rpus fluviatilis
Krapu et al.
1970
119.0000
Sc
rpus fluviatilis
Stewart & Kantrud
1972a
198.0000
Sc
rpus fluviatilis
Wienhold & van der Valk
1989
231.0000
Sc
rpus fluviatilis
Weller & Voigts
1983
234.0000
Sc
rpus fluviatilis
van der Valk
1980
246.0000
Sc
rpus fluviatilis
van der Valk
1978
256.0000
Sc
rpus fluviatilis
Welling
1988b
258.0000
Sc
rpus glaucus
Squires & van der Valk
1992
195.0000
Sc
rpus heterochaetus
Krapu et al.
1970
119.0000
Sc
rpus heterochaetus
Stewart & Kantrud
1972a
198.0000
Sc
rpus heterochaetus
van der Valk
1976
245.0000
Sc
rpus maritimus
Lieffers & Shay
1981
127.0000
Sc
rpus maritimus
Merendino et al.
1990
143.0000
Sc
rpus maritimus
Shay & Shay
1986
189.0000
Sc
rpus maritimus
Squires & van der Valk
1992
195.0000
Sc
rpus maritimus
Stewart & Kantrud
1972a
198.0000
Sc
rpus maritimus
Pederson
1981
269.0000
Sc
rpus maritimus
Lieffers & Shay
1982a
274.0000
Sc
rpus maritimus
Lieffers & Shay
1982b
288.0000
Sc
rpus maritimus
Walker & Coupland
1970
297.0000
-------
Scirpus validus
Johnson
1987
100.0000
Scirpus validus
Merendino et al.
1990
143.0000
Scirpus validus
Poiani & Johnson
1989
172.0000
Scirpus validus
Shay & Shay
1986
189.0000
Scirpus validus
Squires & van der Valk
1992
195.0000
Scirpus validus
Wienhold & van der Valk
1989
231.0000
Scirpus validus
Weller & Voigts
1983
234.0000
Scirpus validus
Welling et al.
1988a
236.0000
Scirpus validus
van der Valk
1980
246.0000
Scirpus validus
van der Valk & Squires
1992
248.0000
Scirpus validus
van der Valk
1978
256.0000
Scirpus validus
Welling
1988b
258.0000
Scirpus validus
Murkin & Kadlec
1986b
259.0000
Scirpus validus
Weller et al.
1991
264.0000
Scirpus validus
Pederson
1981
269.0000
Scirpus validus
Walker & Coupland
1968
296.0000
Scolochloa festucacea
Driver
1977
46.0000
Scolochloa festucacea
Galinato & van der Valk
1986
68.0000
Scolochloa festucacea
Johnson
1987
100.0000
Scolochloa festucacea
Merendino et al.
1990
143.0000
Scolochloa festucacea
Millar
1973
144.0000
Scolochloa festucacea
Murkin et al.
1991
151.0000
Scolochloa festucacea
Neill
1990
156.0000
Scolochloa festucacea
Poiani & Johnson
1989
172.0000
Scolochloa festucacea
Shay & Shay
1986
189.0000
Scolochloa festucacea
Squires & van der Valk
1992
195.0000
Scolochloa festucacea
Stewart & Kantrud
1972a
198.0000
Scolochloa festucacea
Welling et al.
1988a
236.0000
Scolochloa festucacea
van der Valk & Squires
1992
248.0000
Scolochloa festucacea
Armstrong & Nudds
1985
249.0000
Scolochloa festucacea
Murkin & Kadlec
1986b
259.0000
Scolochloa festucacea
Walker & Coupland
1968
296.0000
Scolochloa festucacea
Walker & Coupland
1970
297.0000
Scolochloa festucacea
Bataille & Baldassarre
1993
327.0000
Senecio congestus
Stewart & Kantrud
1972a
198.0000
Senecio congestus
Walker & Coupland
1970
297.0000
Setaria glauca
Hubbard et al.
1988
315.0000
Sium suave
Wienhold & van der Valk
1989
231.0000
Sium suave
Walker & Coupland
1968
296.0000
Sium suave
Walker & Coupland
1970
297.0000
Solidago altissima
Stewart & Kantrud
1972a
198.0000
Solidago canadensis
Hubbard et al.
1988
315.0000
Solidago rigida
Hubbard et al.
1988
315.0000
Sonchus arvensis
Johnson
1987
100.0000
Sonchus arvensis
Shay & Shay
1986
189.0000
Sonchus arvensis
Walker & Coupland
1970
297.0000
Sonchus arvensis
Hubbard et al.
1988
315.0000
-------
Sparganium
van der Valk
1978
256.0000
Sparganium chlorocarpum
Walker & Coupland
1970
297.0000
Sparganium eurycarpum
Johnson
1987
100.0000
Sparganium eurycarpum
Krapu et al.
1970
119.0000
Sparganium eurycarpum
Stewart & Kantrud
1972a
198.0000
Sparganium eurycarpum
Wienhold & van der Valk
1989
231.0000
Sparganium eurycarpum
Weller & Voigts
1983
234.0000
Sparganium eurycarpum
van der Valk
1980
246.0000
Sparganium eurycarpum
Welling
1988b
258.0000
Sparganium eurycarpum
Walker & Coupland
1970
297.0000
Spartina pectinata
Smeins & Olson
1970
194.0000
Spartina pectinata
Stewart & Kantrud
1972a
198.0000
Spartina pectinata
Weller et al.
1991
264.0000
Spartina pectinata
Hubbard et al.
1988
315.0000
Spirodela polyrhiza
Wienhold & van der Valk
1989
231.0000
Spirodela polyrhiza
van der Valk
1978
256.0000
Stachys palustris
Walker & Coupland
1970
297.0000
Suaeda depressa
Shay & Shay
1986
189.0000
Symphoricarpos occidentalis
Stewart & Kantrud
1972a
198.0000
Teucrium occidentale
Smeins & Olson
1970
194.0000
Triglochin maritimum
Walker & Coupland
1970
297.0000
Typha
Bishop et al.
1979
18.0000
Typha
Cowardin et al.
1985
36.0000
Typha
Merendino et al.
1990
143.0000
Typha
Peterson & Cooper
1991
166.0000
Typha
Poiani & Johnson
1989
172.0000
Typha
Weller & Voigts
1983
234.0000
Typha
Welling et al.
1988a
236.0000
Typha
Poiani & Johnson
1988
257.0000
Typha
Murkin & Kadlec
1986b
259.0000
Typha
Weller et al.
1991
264.0000
Typha
Pederson
1981
269.0000
Typha
Hemesath
1991
310.0000
Typha
Bataille & Baldassarre
1993
327.0000
Typha angustifolia
Johnson
1987
100.0000
Typha angustifolia
Krapu et al.
1970
119.0000
Typha angustifolia
Wienhold & van der Valk
1989
231.0000
Typha x glauca
Galinato & van der Valk
1986
68.0000
Typha x glauca
Neill
1990
156.0000
Typha x glauca
Squires & van der Valk
1992
195.0000
Typha x glauca
Stewart & Kantrud
1972a
198.0000
Typha x glauca
Weller
1975
232.0000
Typha x glauca
van der Valk
1980
246.0000
Typha x glauca
van der Valk & Squires
1992
248.0000
Typha x glauca
van der Valk
1978
256.0000
Typha x glauca
Welling
1988b
258.0000
Typha x glauca
van der Valk
1986
295.0000
-------
Typha latifolia
Shay & Shay
1986
189.0000
Typha latifolia
Weller
1975
232.0000
Typha latifolia
Armstrong & Nudds
1985
249.0000
Typha latifolia
Walker & Coupland
1968
296.0000
Typha latifolia
Walker & Coupland
1970
297.0000
Utricularia macrorhiza
Murkin et al.
1991
151.0000
Utricularia macrorhiza
Shay & Shay
1986
189.0000
Utricularia macrorhiza
Weller & Voigts
1983
234.0000
Utricularia macrorhiza
Poiani & Johnson
1988
257.0000
Utricularia macrorhiza
Pederson
1981
269.0000
Utricularia macrorhiza
Walker & Coupland
1970
297.0000
Veronia scutellata
Walker & Coupland
1970
297.0000
Wolffia punctata
Weller & Voigts
1983
234.0000
Zannichellia palustris
Shay & Shay
1986
189.0000
Zannichellia palustris
Walker & Coupland
1970
297.0000
-------
TAX A
AUTHORS
PUBYEAR
REF_APX_J
Acari
Solberg & Higgins
1993
329.0000
Aeshnidae
LaGrange & Dinsmore
1989
287.0000
Amphipoda
McCrady et al.
1986
138.0000
Amphipoda
Voigts
1975
221.0000
Caenis
Mrachek
1966
148.0000
Calanoida
McCrady et al.
1986
138.0000
Ceratopogonidae
Solberg & Higgins
1993
329.0000
Chaeoboridae
Solberg & Higgins
1993
329.0000
Chironomidae
Broschart & Linder
1986
25.0000
Chironomidae
Kaminski & Prince
1981a
104.0000
Chironomidae
McCrady et al.
1986
138.0000
Chironomidae
Murkin et al.
1982
152.0000
Chironomidae
Talent et al.
1982
211.0000
Chironomidae
Voigts
1975
221.0000
Chironomidae
Solberg & Higgins
1993
329.0000
Chironomus riparius
Johnson
1986
263.0000
Cladocera
McCrady et al.
1986
138.0000
Cladocera
Voigts
1975
221.0000
Cladocera
Murkin et al.
1992
265.0000
Conchostraca
Solberg & Higgins
1993
329.0000
Copepoda
Voigts
1975
221.0000
Copepoda
Murkin et al.
1992
265.0000
Corixidae
Broschart & Linder
1986
25.0000
Corixidae
Murkin et al.
1982
152.0000
Corixidae
Murkin et al.
1992
265.0000
Corixidae
Solberg & Higgins
1993
329.0000
Culicidae
Kaminski & Prince
1981a
104.0000
Culicidae
McCrady et al.
1986
138.0000
Cyclopoida
McCrady et al.
1986
138.0000
Daphnia magna
Johnson
1986
263.0000
Daphnidae
Kaminski & Prince
1981a
104.0000
Daphnidae
Murkin et al.
1982
152.0000
Dytiscidae
Kaminski & Prince
1981a
104.0000
Dytiscidae
Solberg & Higgins
1993
329.0000
Enalagma
Mrachek
1966
148.0000
Erpobdellidae
Solberg & Higgins
1993
329.0000
Gammarus lacustris
Talent et al.
211.0000
Gastropoda
McCrady et al.
1986
138.0000
Haliplidae
Solberg & Higgins
1993
329.0000
Helisoma
Mrachek
1966
148.0000
Hyalella azteca
Mrachek
1966
148.0000
Hyallela azteca
Talent et al.
211.0000
Hydrachnidae
Kaminski & Prince
1981a
104.0000
Hydrachnidae
Murkin et al.
1982
152.0000
Hydrocarina
Mrachek
1966
148.0000
Hydrophilidae
Solberg & Higgins
1993
329.0000
-------
Ischnura
Mrachek
1966
148.0000
Isopoda
Voigts
1975
221.0000
Isotomidae
Murkin et al.
1982
152.0000
Libellulidae
Solberg & Higgins
1993
329.0000
Lymnaea
Mrachek
1966
148.0000
Lymnaeidae
LaGrange & Dinsmore
1989
287.0000
Lymnaeidae
Solberg & Higgins
1993
329.0000
Mystacides longicornis
Mrachek
1966
148.0000
Nematoda
Broschart & Under
1986
25.0000
Notonectidae
LaGrange & Dinsmore
1989
287.0000
Notonectidae
Solberg & Higgins
1993
329.0000
Oligochaeta
Broschart & Under
1986
25.0000
Oligochaeta
Solberg & Higgins
1993
329.0000
Ostracoda
Murkin et al.
1992
265.0000
Physa
Mrachek
1966
148.0000
Physidae
Broschart & Under
1986
25.0000
Physidae
Murkin et al.
1982
152.0000
Physidae
Voigts
1975
221.0000
Planorbidae
Kaminski & Prince
1981a
104.0000
Planorbidae
Murkin et al.
1982
152.0000
Planorbidae
Talent et al.
211.0000
Planorbidae
Voigts
1975
221.0000
Planorbidae
Solberg & Higgins
1993
329.0000
Stratomyiidae
Kaminski & Prince
1981a
104.0000
Stylaria fossularis
Mrachek
1966
148.0000
Stylaria lacustris
Mrachek
1966
148.0000
Tabanidae
Kaminski & Prince
1981a
104.0000
-------
TAX A
AUTHORS
PUBYEAR
REF_APX_J
Ablabesmyia pulchripennis
Driver
1977
46.0000
Accomorpha
LaBaugh & Swanson
1988
275.0000
Acricotopus nitidellus
Driver
1977
46.0000
Alona guttata
LaBaugh & Swanson
1988
275.0000
Anostraca
LaBaugh & Swanson
1988
275.0000
Asplanchna
LaBaugh & Swanson
1988
275.0000
Brachionus
LaBaugh & Swanson
1988
275.0000
Canthocamptus
LaBaugh & Swanson
1988
275.0000
Ceratopogonidae
Bataille & Baldassarre
1993
327.0000
Ceriodaphnia quadrangula
LaBaugh & Swanson
1988
275.0000
Ceriodaphnia reticulata
LaBaugh & Swanson
1988
275.0000
Chaoboridae
Bataille & Baldassarre
1993
327.0000
Chironomidae
Kaminski & Prince
1981a
104.0000
Chironomidae
Neckles et al.
1990
155.0000
Chironomidae
Talent et al.
1982
211.0000
Chironomidae
Hemesath
1991
310.0000
Chironomidae
Bataille & Baldassarre
1993
327.0000
Chironomus attenuatus
Driver
1977
46.0000
Chironomus riparius
Driver
1977
46.0000
Chironomus staegeri
Driver
1977
46.0000
Chironomus tentans
Driver
1977
46.0000
Chironomus tentans
Murkin et al.
1986b
259.0000
Chydorus sphaericus
LaBaugh & Swanson
1988
275.0000
Cladocera
Murkin et al.
1991
151.0000
Cladocera
Neckles et al.
1990
155.0000
Cladocera
Bataille & Baldassarre
1993
327.0000
Conochilus
LaBaugh & Swanson
1988
275.0000
Copepoda
Bataille & Baldassarre
1993
327.0000
Corixidae
Hemesath
1991
310.0000
Culicidae
Kaminski & Prince
1981a
104.0000
Culicidae
Neckles et al.
1990
155.0000
Culicidae
Bataille & Baldassarre
1993
327.0000
Daphnia pulex
LaBaugh & Swanson
1988
275.0000
Daphnia rosea
LaBaugh & Swanson
1988
275.0000
Daphnia similis
LaBaugh & Swanson
1988
275.0000
Daphnidae
Kaminski & Prince
1981a
104.0000
Diacyclops bicuspidatus
LaBaugh & Swanson
1988
275.0000
Diaptomus clavipes
LaBaugh & Swanson
1988
275.0000
Diaptomus sicilis
LaBaugh & Swanson
1988
275.0000
Dytiscidae
Kaminski & Prince
1981a
104.0000
Dytiscidae
Hemesath
1991
310.0000
Dytiscidae
Bataille & Baldassarre
1993
327.0000
Euchlanis
LaBaugh & Swanson
1988
275.0000
Gammarus lacustris
Talent et al.
211.0000
Gastropoda
Neckles et al.
1990
155.0000
Glyptotendipes barbipes
Murkin et al.
1986b
259.0000
-------
Glyptotendipes barpipes
Driver
1977
46.0000
Hyalella azteca
Murkin et al.
1991
151.0000
Hyalella azteca
LaBaugh & Swanson
1988
275.0000
Hyallela azteca
Talent et al.
211.0000
Hydrachnidae
Kaminski & Prince
1981a
104.0000
Hydrophilidae
Hemesath
1991
310.0000
Hydrophilidae
Bataille & Baldassarre
1993
327.0000
Keratella quadrata
LaBaugh & Swanson
1988
275.0000
Keratella serrulata
LaBaugh & Swanson
1988
275.0000
Lecane
LaBaugh & Swanson
1988
275.0000
Leptoceridae
Bataille & Baldassarre
1993
327.0000
Limnophytes vunalis
Driver
1977
46.0000
Lymnaeidae
Bataille & Baldassarre
1993
327.0000
Macrocyclops fuscus
LaBaugh & Swanson
1988
275.0000
Monostyla
LaBaugh & Swanson
1988
275.0000
Notholca accuminata
LaBaugh & Swanson
1988
275.0000
Ostracoda
Neckles et al.
1990
155.0000
Ostracoda
Bataille & Baldassarre
1993
327.0000
Paracyclops fimbriatus
LaBaugh & Swanson
1988
275.0000
Physidae
Bataille & Baldassarre
1993
327.0000
Planorbidae
Kaminski & Prince
1981a
104.0000
Planorbidae
Talent et al.
211.0000
Planorbidae
Bataille & Baldassarre
1993
327.0000
Platyias
LaBaugh & Swanson
1988
275.0000
Pleuroxus procurvatus
LaBaugh & Swanson
1988
275.0000
Polyarthra
LaBaugh & Swanson
1988
275.0000
Procladius bellus
Driver
1977
46.0000
Psectrocladius barbimanus
Driver
1977
46.0000
Psectrotanypus guttularis
Driver
1977
46.0000
Scapholeberis quritus
LaBaugh & Swanson
1988
275.0000
Simocephalus vetulus
LaBaugh & Swanson
1988
275.0000
Stratomyiidae
Kaminski & Prince
1981a
104.0000
Synchaeta
LaBaugh & Swanson
1988
275.0000
Tabanidae
Kaminski & Prince
1981a
104.0000
Tanytarsus sp.2
Driver
1977
46.0000
Trichocera
LaBaugh & Swanson
1988
275.0000
-------
TAX A
AUTHORS
PUBYEAR
REF_APX_J
Aphanizomenon flos-aquae
Barica et al.
1980.
10.0000
Microcystis aeruginosa
Barica et al.
1980.
10.0000
Oocystis
Barica et al.
1980.
10.0000
Scenedesmus
Barica et al.
1980.
10.0000
Spirogyra
Mrachek
1966.
148.0000
Aphanizomenon flos-aquae
Robarts et al.
1992.
177.0000
Cercobodo varians
Robarts et al.
1992.
177.0000
Chaetoceros elmorei
Robarts et al.
1992.
177.0000
Chrysidalis peritaphrena
Robarts et al.
1992.
177.0000
Coccomyxa minor
Robarts et al.
1992.
177.0000
Gleothece rupestris
Robarts et al.
1992.
177.0000
Ochromonas polychrysis
Robarts et al.
1992.
177.0000
Oocystis borgei
Robarts et al.
1992.
177.0000
Rhodomonas lens
Robarts et al.
1992.
177.0000
Sphaerocystis schroteri
Robarts et al.
1992.
177.0000
Stephanodiscus niagarae
Robarts et al.
1992.
177.0000
Selanastrum capricornutum
Johnson
1986
263.0000
Anabaena elachista
LaBaugh & Swanson
1988
275.0000
Anabaena planctonica
LaBaugh & Swanson
1988
275.0000
Aphanothece
LaBaugh & Swanson
1988
275.0000
Chlamydomonas
LaBaugh & Swanson
1988
275.0000
Chroococcus
LaBaugh & Swanson
1988
275.0000
Chroococcus dispersus
LaBaugh & Swanson
1988
275.0000
Chroococcus pallidus
LaBaugh & Swanson
1988
275.0000
Chroomonas nordstedtii
LaBaugh & Swanson
1988
275.0000
Coelosphaerium collinsii
LaBaugh & Swanson
1988
275.0000
Cryptomonas
LaBaugh & Swanson
1988
275.0000
Gloeocapsa
LaBaugh & Swanson
1988
275.0000
Nodularia
LaBaugh & Swanson
1988
275.0000
Ochromonas
LaBaugh & Swanson
1988
275.0000
Oscillatoria angustissima
LaBaugh & Swanson
1988
275.0000
Pedimonas rotunda
LaBaugh & Swanson
1988
275.0000
Phormidium
LaBaugh & Swanson
1988
275.0000
Pleurocapsa
LaBaugh & Swanson
1988
275.0000
Pseudoanabaena
LaBaugh & Swanson
1988
275.0000
Rhabdoderma irregulare
LaBaugh & Swanson
1988
275.0000
Rhabdoderma sigmoidea
LaBaugh & Swanson
1988
275.0000
Rhodomonas minuta
LaBaugh & Swanson
1988
275.0000
Synechococcus elongatus
LaBaugh & Swanson
1988
275.0000
Synura
LaBaugh & Swanson
1988
275.0000
Anabaena
Barica
1975
278.0000
Aphanizomenon flos-aquae
Barica
1975
278.0000
Chlamydomonas
Barica
1975
278.0000
Coccomyxa
Barica
1975
278.0000
Cyclotella
Barica
1975
278.0000
Lauterborniella
Barica
1975
278.0000
-------
Merismopedia
Barica
1975
278.0000
Microcystis aeruginosa
Barica
1975
278.0000
Nitzschia
Barica
1975
278.0000
Synedra
Barica
1975
278.0000
Anabaena circinalis
Hickman &Jenkerson
1978
285.0000
Chlamydomonas globosa
Hickman &Jenkerson
1978
285.0000
Cryptomonas ovata
Hickman &Jenkerson
1978
285.0000
Dictyosphaerum pulchellum
Hickman &Jenkerson
1978
285.0000
Gomphosphaeria lacustris
Hickman &Jenkerson
1978
285.0000
Gonium sociale
Hickman &Jenkerson
1978
285.0000
Kirchneriella contorta
Hickman &Jenkerson
1978
285.0000
Microcystis aeruginosa
Hickman &Jenkerson
1978
285.0000
Oocystis parva
Hickman &Jenkerson
1978
285.0000
Oscillatoria
Hickman &Jenkerson
1978
285.0000
Rhodomonas minuta
Hickman &Jenkerson
1978
285.0000
Selanastrum minutum
Hickman &Jenkerson
1978
285.0000
Aphanizomenon flos-aquae
Shamess et al.
1985
290.0000
Aphanocapsa delicatissima
Shamess et al.
1985
290.0000
Calothrix epiphytica
Shamess et al.
1985
290.0000
Ceratium hirundinella
Shamess et al.
1985
290.0000
Chromulina frieburgensis
Shamess et al.
1985
290.0000
Cocconeis placentula
Shamess et al.
1985
290.0000
Cryptomonas erosa
Shamess et al.
1985
290.0000
Cyclotella meneghiniana
Shamess et al.
1985
290.0000
Epithemia turgida
Shamess et al.
1985
290.0000
Gomphonema angustatum
Shamess et al.
1985
290.0000
Gomphonema olivaceum
Shamess et al.
1985
290.0000
Lyngbya limnetica
Shamess et al.
1985
290.0000
Microcystis aeruginosa
Shamess et al.
1985
290.0000
Nitzschia denticula
Shamess et al.
1985
290.0000
Oscillatoria amphibia
Shamess et al.
1985
290.0000
Pleurosigma delicatulum
Shamess et al.
1985
290.0000
Stigoclonium nanum
Shamess et al.
1985
290.0000
Surirella ovata
Shamess et al.
1985
290.0000
Synedra acus
Shamess et al.
1985
290.0000
Trachelomonas robusta
Shamess et al.
1985
290.0000
Trachelomonas volvocina
Shamess et al.
1985
290.0000
Anabaena flos-aquae
Kl
ng
1975
312.0000
Ankyra judai
Kl
ng
1975
312.0000
Aphanizomenon flos-aquae
Kl
ng
1975
312.0000
Aphanothece clathrata
Kl
ng
1975
312.0000
Ceratium hirundinella
Kl
ng
1975
312.0000
Chlamydomonas triciliatum
Kl
ng
1975
312.0000
Chromulina erkensis
Kl
ng
1975
312.0000
Chroococcus limneticus
Kl
ng
1975
312.0000
Chroomonas breviciliata
Kl
ng
1975
312.0000
Chrysochromulina
Kl
ng
1975
312.0000
-------
Cryptomonas obovata
Kling
1975
312.0000
Cyclotella meneghiniana
Kling
1975
312.0000
Euglena
Kling
1975
312.0000
Gloeococcus schroeteri
Kling
1975
312.0000
Gymnodinium
Kling
1975
312.0000
Katablepharis ovalis
Kling
1975
312.0000
Lyngbya endophytica
Kling
1975
312.0000
Merismopedia tenuissima
Kling
1975
312.0000
Microcystis aeruginosa
Kling
1975
312.0000
Monoraphidium contortum
Kling
1975
312.0000
Nitzschia holsatica
Kling
1975
312.0000
Ochromonas verrucosa
Kling
1975
312.0000
Oocystis lacustris
Kling
1975
312.0000
Pediastrum boryanum
Kling
1975
312.0000
Pseudoanabaena constricta
Kling
1975
312.0000
Rhodomonas minuta
Kling
1975
312.0000
Scenedesmus
Kling
1975
312.0000
Schroederia setigera
Kling
1975
312.0000
Selenastrum bibraianum
Kling
1975
312.0000
Synedra acus
Kling
1975
312.0000
Cryptomonas
Campbell & Prepas
1986
313.0000
Fragilaria
Campbell & Prepas
1986
313.0000
Lyngbya birgei
Campbell & Prepas
1986
313.0000
Microcystis aeruginosa
Campbell & Prepas
1986
313.0000
Navicula
Campbell & Prepas
1986
313.0000
Pithophora
Campbell & Prepas
1986
313.0000
Rhizoclonium hieroglyphicum
Campbell & Prepas
1986
313.0000
Achnanthes minutissima
Pip & Robinson
1982
318.0000
Anabaena affinis
Pip & Robinson
1982
318.0000
Ankistrodesmus falcatus
Pip & Robinson
1982
318.0000
Chroococcus prescottii
Pip & Robinson
1982
318.0000
Cocconeis placentula
Pip & Robinson
1982
318.0000
Coleochaete scutata
Pip & Robinson
1982
318.0000
Cosmarium circulare
Pip & Robinson
1982
318.0000
Cymbella
Pip & Robinson
1982
318.0000
Dispora crucingeniodes
Pip & Robinson
1982
318.0000
Epithemia
Pip & Robinson
1982
318.0000
Eunotia pectinalis
Pip & Robinson
1982
318.0000
Fragilaria
Pip & Robinson
1982
318.0000
Gloeotrichia pisum
Pip & Robinson
1982
318.0000
Gomphonema acuminatum
Pip & Robinson
1982
318.0000
Merismopedia punctata
Pip & Robinson
1982
318.0000
Mougeotia
Pip & Robinson
1982
318.0000
Navicula
Pip & Robinson
1982
318.0000
Nitzschia
Pip & Robinson
1982
318.0000
Oedogonium
Pip & Robinson
1982
318.0000
Oscillatoria agardhii
Pip & Robinson
1982
318.0000
-------
Oscillatoria limnetica
Pip & Robinson
1982
318.0000
Oscillatoria minima
Pip & Robinson
1982
318.0000
Pediastrum boryanum
Pip & Robinson
1982
318.0000
Scenedesmus dimorphus
Pip & Robinson
1982
318.0000
Scenedesmus quadricauda
Pip & Robinson
1982
318.0000
Stigeoclonium
Pip & Robinson
1982
318.0000
Synedra
Pip & Robinson
1982
318.0000
Tabellaria fenestrata
Pip & Robinson
1982
318.0000
Anabaena
Hanson & Butler
1990
319.0000
Chroococcus
Hanson & Butler
1990
319.0000
Fragilaria
Hanson & Butler
1990
319.0000
Melosira
Hanson & Butler
1990
319.0000
Microcystis
Hanson & Butler
1990
319.0000
Oscillatoria
Hanson & Butler
1990
319.0000
Scenedesmus
Hanson & Butler
1990
319.0000
-------
SCI NAME
FORM
DEPENDENCE
BARNES
BENSON
BOTTINEAU
BURKE
BURLEIGH
CASS
CAVALIER
DIVIDE
EDDY
EMMONS
GRANDFORK
GRIGGS
KIDDER
LAMOURE
LOGAN
MCHENRY
MOUNTRAIL
NELSON
RAMSEY
RANSOM
RICHLAND
ROLETTE
SARGENT
STUTSMAN
TOWNER
WALSH
WARD
WELLS
WILLIAMS
ACORUS AMERICANUS
PIEF
OBL
X
X
X
AGRIMONIA GRYPOSEPALA
PNF
FACU
X
X
X
X
X
X
X
ALLIUM CANADENSE
PNF
FACU
X
APIOS AMERICANA
PNF
FACW
X
ATHYRIUM FILIX-FEMINA
PNF3
FAC
X
X
X
X
BOTRYCHIUM MATRICARIIFOLIUM
PNF3
FACU
X
X
BOTRYCHIUM MULTIFIDUM
PNF3
FAC
X
BROMUS KALMII
PNG
FACU+
X
CALLA PALUSTRIS
PNEF
OBL
X
CAMPANULA APARINOIDES
PNF
OBL
X
X
CARDAMINE BULBOSA
PNF
OBL
X
CAREX ALOPECOIDEA
PNGL
OBL
X
X
X
X
X
CAREXATHROSTACHYA
PNGL
FACW
X
X
X
X
CAREX BRUNNESCENS
PNGL
FAC
X
CAREX BUXBAUMII
PNEGL
OBL
X
X
CAREX CAPILLARIS
PNGL
FACW
X
X
CAREX CHORDORRHIZA
PNGL
OBL
X
CAREX DIANDRA
PNGL
OBL
X
X
X
X
CAREX DISPERMA
PNGL
FACW
X
X
X
X
X
X
CAREX FESTUCACEA
PNGL
FACW
X
CAREX GARBERI
PNGL
FACW
X
X
X
CAREX GYNOCRATES
PNGL
OBL
X
CAREX LASIOCARPAvar. AMERICANA
PNEGL
OBL
X
X
X
X
CAREX LEPTALEA
PNGL
OBL
X
X
X
X
CAREX LIMOSA
PNGL
OBL
X
X
CAREX NEBRASCENSIS
PNGL
OBL
X
X
CAREX PSEUDOCYPERUS
PNEGL
OBL
X
X
X
X
X
X
X
X
X
CAREX RICHARDSONII
PNGL
FAC-
X
X
X
CAREX SCOPARIA
PNGL
FACW
X
X
X
X
CAREX SIMULATA
PNGL
OBL
X
X
X
CAREX STERILIS
PNGL
OBL
X
CIRSIUM MUTICUM
BNF
OBL
X
X
X
X
CORALLORRHIZA STRIATA
PN-F
FACU+
X
X
CORALLORRHIZATRIFIDA
PN-F
FAC
X
X
X
CYPERUS DIANDRUS
ANGL
FACW
X
X
CYPERUS ENGELMANNII
ANGL
OBL
X
CYPERUS RIVULARIS
ANGL
FACW
X
X
X
CYPRIPEDIUM CALCEOLUS
PNF
FACW
X
X
X
X
X
X
X
X
CYPRIPEDIUM CANDIDUM
PNF
OBL
X
X
X
X
X
X
X
X
X
CYPRIPEDIUM REGINAE
PNF
FACW
X
X
X
X
CYPRIPEDIUM X AN DREWS II
PNF
FACW
X
DESMANTHUS ILLINOENSIS
PNF
FACU
X
X
DROSERAROTUNDI FOLIA
PNEF
OBL
X
DRYOPTERIS CRISTATA
PNEF3
OBL
X
X
X
X
X
DRYOPTERIS SPINULOSA
F3
FACW
X
X
X
ELATINETRIANDRA
ANE/F
OBL
X
X
X
X
ELEOCHARIS PARVULA
PNGL
OBL
X
X
X
X
ELEOCHARIS PAUCIFLORA
PNGL
OBL
X
X
X
X
X
X
ELEOCHARIS WOLFII
PNEGL
OBL
X
ELYMUSGLAUCUS
PNG
FACU
X
X
EPILOBIUM COLORATUM
PNF
OBL
X
X
X
EQUISETUM PALUSTRE
PNH2
FACW
X
X
EQUISETUM PRATENSE
PNH2
FACW
X
X
X
EQUISETUM SYLVATICUM
PNH2
FACW
X
EQUISETUM VARIEGATUM
PNH2
FACW
X
ERIOPHORUM CHAMISSONIS
PNGL
OBL
X
X
X
X
ERIOPHORUM GRACILE
PNEGL
OBL
X
ERIOPHORUM VIRIDICARINATUM
PNEGL
OBL
X
X
FESTUCA RUBRA
PNG
FACU
X
X
GALIUM LABRADORICUM
PNF
OBL
X
X
X
GENTIANOPSIS CRINITA
ABF
OBL
X
X
X
X
GERANIUM MACULATUM
PNF
FACU
X
GEUM MACROPHYLLUM
PNF
FACW
X
X
X
X
X
X
X
X
GYMNOCARPIUM DRYOPTERIS
PNF3
FACU
X
X
HALENIA DEFLEXA
ANF
FAC
X
HELIANTHUS GROSSESERRATUS
PNF
FACW
X
HEMICARPHA MICRANTHA
ANGL
OBL
X
HORDEUM PUSILLUM
ANG
FACU
X
HYPERICUM MUTILUM ssp. BOREALE
PNF
FACW
X
IRIS MISSOURIENSIS
PNF
FACW+
X
X
X
X
X
IVAANNUA
AIF
FAC
X
JUNCUS BRACHYCEPHALUS
PNGL
OBL
X
X
JUNCUS BREVICAUDATUS
PNGL
OBL
X
X
JUNCUS GERARDII
PNGL
FAC
X
X
JUNCUS VASEYI
PNGL
FACW
X
LEERS IA VIRGINICA
PNG
FACW
X
LIPARIS LOESELII
PNF
OBL
X
X
X
X
MADIA GLOMERATA
ANF
FACU
X
X
MENYANTHES TRIFOLIATA
PNEF
OBL
X
X
X
MIMULUSGUTTATUS
ANF
OBL
X
MITELLANUDA
PNF
OBL
X
X
MONOTROPA UNIFLORA
PN-SF
FACU
X
X
X
MUHLENBERGIA FILIFORMIS
ANG
FACW
X
MYOSURUS ARISTATUS
ANF
OBL
X
X
MYRIOPHYLLUM HETEROPHYLLUM
PNZF
OBL
X
X
-------
MYRIOPHYLLUM PINNATUM
PNEZF
OBL
X
X
X
X
X
NAJAS FLEXILIS
ANZF
OBL
X
X
X
X
X
NAJAS GUADALUPENSIS
ANZF
OBL
X
NAJAS MARINA
ANZF
OBL
X
X
OENOTHERA LACINIATA
ANF
FACU
X
OENOTHERA RHOMBIPETALA
ABNF
FACU
X
X
ONOCLEA SENSIBILIS
PNEF3
FACW
X
X
X
OPHIOGLOSSUM VULGATUM
PNF3
FACW
X
X
OROBANCHE UNIFLORA
AN-F
FACU
X
OXYTROPIS DEFLEXA
PNF
FACU
X
X
X
PARNASSIA PARVIFLORA
PNF
OBL
X
PENSTEMON PROCERUS
PNF
FAC
X
PETASITES FRIGIDUS
PNF
FAC
X
X
PI LEA FONT ANA
ANF
OBL
X
X
X
X
X
X
X
POGONIA OPHIOGLOSSOIDES
PNEF
OBL
X
POLYGONUM DOUGLASII
ANF
FAC
X
X
POLYGONUM PUNCTATUM
PNEF
OBL
X
X
X
X
POLYGONUM SAGITTATUM
APNF
OBL
X
POTAMOGETON AMPLIFOLIUS
PN/F
OBL
X
X
X
POTAMOGETON DIVERSIFOLIUS
PN/F
OBL
X
X
POTAMOGETON FILIFORMIS
PNZF
OBL
X
X
X
POTAMOGETON NATANS
PN/F
OBL
X
X
X
POTAMOGETON PRAELONGUS
PNZF
OBL
X
X
POTAMOGETON STRICTIFOLIUS
PNZF
OBL
X
X
POTAMOGETON VAGI NATUS
PNZF
OBL
X
X
X
X
X
POTENTILLA PALUSTRIS
PNF
OBL
X
X
X
PRIMULA INCANA
PNF
FACW
X
X
X
PTERIDIUM AQUILINUM
PNF3
FACU
X
PYCNANTHEMUM VIRGINIANUM
PNF
FAC
X
X
X
RANUNCULUS CARDIOPHYLLUS
PNF
FACW
X
RANUNCULUS FLAMMULA
PNEF
FACW
X
RANUNCULUS RECURVATUS
PNF
FAC
X
X
RHYNCHOSPORA CAPILLACEA
PNGL
OBL
X
X
X
X
X
SANICULA GREGARIA
PNF
FAC
X
SCHEUCHZERIA PALUSTRIS
PNEF
OBL
X
SENECIO EREMOPHILUS
PNF
FAC
X
X
SICYOSANGULATUS
ANF
FAC
X
SITANION HYSTRIX
PNG
FACU
X
X
SOLIDAGO FLEXICAULIS
PNF
FACU
X
X
X
X
SOLIDAGO RIDDELLII
PNF
OBL
X
SPIRANTHES CERNUA
PNF
FACW
X
X
X
SPIRANTHES ROMANZOFFIANA
PNF
OBL
X
X
X
SPOROBOLUS AIROIDES
PNG
FAC
X
SUCKLEYASUCKLEYANA
ANF
OBL
X
TOFIELDIA GLUTINOSA
PNF
FACW
X
UTRICULARIA INTERMEDIA
ANZF
OBL
X
X
UTRICULARIA MINOR
PNZF
OBL
X
X
X
X
X
X
UVULARIA SESSI LI FOLIA
PNF
FACU
X
X
VIOLA CONSPERSA
PNF
FACW
X
X
X
X
WOLFFIA COLUMBIANA
PN/F
OBL
X
X
X
-------
AUTHORS
southcentral
northwestern
1«m,
•liggmsetal
>
H
>ntf
jig Katie
)fr
j
)eltaMarsh
cfr
near Saskatoon
jdn
Cottonwood Lade
southwestern
JeltaMarsh
j
ieffers&Sha/
-------
-------
BIRDGRP BIRDVAR INVERTVAR PLANTVAR ALGAEVAR AMPHIBVAR TREATMENT STATE1 STATE2 STATE3 COUNTY1 COUNTY2 COUNTY3 COUNTY4 COUNTY5 ADDRESS
Montana Dept. Health & Envir. Sciences
Water Quality Bureau
Minnesota Dept.
Natural Heritage Program
Bo* 7, 500 Lafayette Rd.
University of North Dakota
701-777-4673
North Dakota Dept. Transportation
Iowa State University
US Environmental Protection Agency
Iowa State University
State University
Dept. of Botany
Environmental Research Laboratory
Dept. Animal Ecology
Coop. Fish & Wildlife Research Unit
218-720-5617 D
515-294-7669 C
605-688-6121 Duffy S Birkelo 1993
6201 Congdon Blvd.
POB22Q6
50011
55804
50011
57007
ogical Survey
701-252-5363
cal Survey
701-252-5363
cal Survey
701-252-5363
Gloutney
ogical Survey
a State University
Minnesota
ogical Survey
701-252-5363
SD MN McHenry Botineau Towner
306-975-4791 Gloutney 1993
ogical Survey
Rt. 1, Bo*26C
3 Hanson 1993
Pollution Control Agency
Minnesota
a State University
Water Quality Dr
P.O. Box 2206
Bemidji
Brookings
University
ogical Survey
Iowa State University
Biological Survey
Biological Survey
U.S. Geological Survey
Coop. Fish & Wildlife Research Ur
Water Resources Division
515-294-7990
701-252-5363
701-252-5363 E
303-236-4989 L
400.0000
5.0000
Wisconsin Dept.
1350 Femrite Dr.
U.S. Dept. Agriculture
U.S. Dept. Agr
U.S. Dept. Agriculture
U.S. Department of Agriculture
National Biological Survey
University of Minnesota
Denver Wildlife Research Station
Coop. Fish & Wildlife Research Unit
Blixt et al. 1993
3
J McCarthy & Henry 1993
12.0000
1.0000
Rt. 1, Box 26C
58105-5517
58401
55107
Institute for Wetland & Waterfowl Res.
204-467-3000
701-250-4572
Cornell University
Poiani & Johnson 1993
National Biological Survey
701-252-5363
Rt. 1, Box 26C
University of Minnesota
Institute for Wetland & Waterfowl Res.
Northwest Experimen t Station
Ecological Services
Ecological Services
il. 1993
3 Svedarsky et al. 1993
Welsh & Olson 1993
Institute for Wetland & Waterfowl Res.
204467-3000
Iowa State University
Fish and Wildlife Station
Dept. of Botany - Bessey Hall
515-357-3517 Zenner&LaGrange 1993
515-294-4374
-------
TAXON
METRIC
UNITS
EQUIPMENT
RANDOMVAR
DATASET
PR FOR S10
BREAKPT
DETECTDIF1
SSIZEMIN1
SSIZEMAX1
DETECTDIF2
SSIZEMIN2
SSIZEMAX2
POOLEDVARS
b
rds
numindiv: wetspp
BBS route
BBS
140.0000
10.0000
100.0000
19.0000
27.0000
300.0000
2.0000
3.0000
year
b
rds
numstops: wetspp
BBS route
BBS
29.0000
10.0000
20.0000
21.0000
30.0000
60.0000
3.0000
3.0000
year
b
rds
numtaxa: wetspp
BBS route
BBS
6.0000
6.0000
5.0000
10.0000
16.0000
40.0000
2.0000
2.0000
year
b
rds
numindiv: wetspp
BBS route
BBS15yr
150.0000
9.0000
100.0000
22.0000
48.0000
300.0000
3.0000
5.0000
year
b
rds
numstops: wetspp
BBS route
BBS15yr
35.0000
10.0000
25.0000
20.0000
46.0000
80.0000
2.0000
5.0000
year
b
rds
numtaxa: wetspp
BBS route
BBS15yr
5.0000
9.0000
3.0000
28.0000
57.0000
12.0000
2.0000
4.0000
year
b
rds
numindiv: wetspp
BBS route
BBS20yr
120.0000
6.0000
200.0000
4.0000
27.0000
300.0000
2.0000
13.0000
year
b
rds
numstops: wetspp
BBS route
BBS20yr
25.0000
7.0000
40.0000
4.0000
30.0000
60.0000
2.0000
13.0000
year
b
rds
numtaxa: wetspp
BBS route
BBS20yr
1.5000
9.0000
1.0000
25.0000
92.0000
3.0000
3.0000
10.0000
year
b
rds
numindiv: all
wetland
Igl & Johnson
25.0000
12.0000
10.0000
52.0000
60.0000
45.0000
2.0000
3.0000
year, visit
b
rds
numindiv: breeding pairs
wetland
Igl & Johnson
9.0000
14.0000
4.0000
43.0000
47.0000
20.0000
3.0000
3.0000
year, visit
b
rds
numtaxa: all
wetland
Igl & Johnson
2.0000
12.0000
1.0000
40.0000
41.0000
4.0000
3.0000
3.0000
year, visit
b
rds
numtaxa: breeding pairs
wetland
Igl & Johnson
2.0000
12.0000
1.0000
40.0000
41.0000
4.0000
3.0000
3.0000
year, visit
invertebrates
biomass: total
mg
corer
wetland
Duffy
2600.0000
8.0000
2000.0000
15.0000
60.0000
5000.0000
2.0000
10.0000
date
invertebrates
numindiv: Amphipoda
corer
wetland
Duffy
900.0000
7.0000
1000.0000
9.0000
25.0000
2000.0000
2.0000
6.0000
date
invertebrates
numindiv: Anostraca
corer
wetland
Duffy
15.0000
9.0000
10.0000
21.0000
36.0000
30.0000
2.0000
5.0000
date
invertebrates
numindiv: Chironomidae
corer
wetland
Duffy
11.0000
9.0000
10.0000
12.0000
29.0000
25.0000
2.0000
4.0000
date
invertebrates
numindiv: Conchostraca
corer
wetland
Duffy
900.0000
9.0000
1000.0000
9.0000
25.0000
2000.0000
3.0000
6.0000
date
invertebrates
numindiv: Ostracoda
corer
wetland
Duffy
6000.0000
9.0000
5000.0000
12.0000
64.0000
10000.0000
4.0000
15.0000
date
invertebrates
numindiv: total
corer
wetland
Duffy
9200.0000
6.0000
10000.0000
9.0000
42.0000
20000.0000
3.0000
11.0000
date
invertebrates
numtaxa
corer
wetland
Duffy
7.0000
7.0000
6.0000
14.0000
76.0000
16.0000
2.0000
9.0000
date
invertebrates
biomass: Cladocera
g
sedtraps
transect
Eul
ss
0.3600
11.0000
0.2000
29.0000
36.0000
0.6000
3.0000
4.0000
plot, polygon, region, class, health, cattle
invertebrates
biomass: Conchostraca
g
sedtraps
transect
Eul
ss
0.3400
12.0000
0.2000
9.0000
13.0000
0.4000
3.0000
4.0000
plot, polygon, region, class, health, cattle
invertebrates
biomass: Lymnaeidae
g
sedtraps
transect
Eul
ss
0.3400
11.0000
0.2000
28.0000
34.0000
0.6000
3.0000
4.0000
plot, polygon, region, class, health, cattle
invertebrates
biomass: Ostracoda
g
sedtraps
transect
Eul
ss
0.3400
13.0000
0.2000
30.0000
38.0000
0.7000
3.0000
3.0000
plot, polygon, region, class, health, cattle
invertebrates
biomass: Physidae
g
sedtraps
transect
Eul
ss
0.3400
14.0000
0.2000
27.0000
34.0000
0.7000
3.0000
3.0000
plot, polygon, region, class, health, cattle
invertebrates
biomass: Planorbidae
g
sedtraps
transect
Eul
ss
0.3400
13.0000
0.2000
28.0000
35.0000
0.8000
3.0000
3.0000
plot, polygon, region, class, health, cattle
invertebrates
biomass: total
g
sedtraps
transect
Eul
ss
2.1000
12.0000
1.0000
43.0000
53.0000
4.0000
2.0000
3.0000
plot, polygon, region, class, health, cattle
invertebrates
numindiv: Cladocera
sedtraps
transect
Eul
ss
3.6000
12.0000
2.0000
31.0000
39.0000
5.0000
5.0000
6.0000
plot, polygon, region, class, health, cattle
invertebrates
numindiv: Lymnaeidae
sedtraps
transect
Eul
ss
1.6000
13.0000
1.0000
23.0000
28.0000
3.0000
3.0000
4.0000
plot, polygon, region, class, health, cattle
invertebrates
numindiv: Ostracoda
sedtraps
transect
Eul
ss
75.0000
12.0000
40.0000
35.0000
44.0000
160.0000
3.0000
3.0000
plot, polygon, region, class, health, cattle
invertebrates
numindiv: Physidae
sedtraps
transect
Eul
ss
0.9000
12.0000
1.0000
8.0000
10.0000
2.0000
2.0000
2.0000
plot, polygon, region, class, health, cattle
invertebrates
numindiv: Planorbidae
sedtraps
transect
Eul
ss
1.1000
12.0000
1.0000
1.0000
12.0000
15.0000
3.0000
3.0000
plot, polygon, region, class, health, cattle
invertebrates
numindiv: total
sedtraps
transect
Eul
ss
67.0000
14.0000
40.0000
32.0000
40.0000
160.0000
2.0000
2.0000
plot, polygon, region, class, health, cattle
invertebrates
biomass: Cladocera
g
sedtraps
wetland
Eul
ss
0.3600
11.0000
0.2000
32.0000
54.0000
0.8000
2.0000
3.0000
plot, region, class, transect, health, cattle
invertebrates
biomass: Conchostraca
g
sedtraps
wetland
Eul
ss
0.3600
12.0000
0.2000
31.0000
51.0000
0.7000
3.0000
5.0000
plot, region, class, transect, health, cattle
invertebrates
biomass: Lymnaeidae
g
sedtraps
wetland
Eul
ss
0.3600
8.0000
0.2000
30.0000
48.0000
0.8000
1.0000
2.0000
plot, region, class, transect, health, cattle
invertebrates
biomass: Ostracoda
g
sedtraps
wetland
Eul
ss
0.3600
13.0000
0.2000
33.0000
54.0000
0.8000
2.0000
3.0000
plot, region, class, transect, health, cattle
invertebrates
biomass: Physidae
g
sedtraps
wetland
Eul
ss
0.3600
12.0000
0.2000
28.0000
38.0000
0.8000
1.0000
3.0000
plot, region, class, transect, health, cattle
invertebrates
biomass: Planorbidae
g
sedtraps
wetland
Eul
ss
0.3600
10.0000
0.2000
30.0000
51.0000
0.8000
2.0000
3.0000
plot, region, class, transect, health, cattle
invertebrates
biomass: total
g
sedtraps
wetland
Eul
ss
2.1000
10.0000
1.0000
43.0000
70.0000
5.0000
2.0000
3.0000
plot, region, class, transect, health, cattle
invertebrates
numindiv: Cladocera
sedtraps
wetland
Eul
ss
5.0000
10.0000
4.0000
16.0000
37.0000
10.0000
2.0000
6.0000
plot, region, class, transect, health, cattle
invertebrates
numindiv: Lymnaeidae
sedtraps
wetland
Eul
ss
1.6000
10.0000
1.0000
24.0000
49.0000
3.0000
3.0000
5.0000
plot, region, class, transect, health, cattle
invertebrates
numindiv: Ostracoda
sedtraps
wetland
Eul
ss
100.0000
10.0000
100.0000
11.0000
30.0000
200.0000
3.0000
7.0000
plot, region, class, transect, health, cattle
invertebrates
numindiv: total
sedtraps
wetland
Eul
ss
120.0000
7.0000
100.0000
10.0000
29.0000
200.0000
2.0000
8.0000
plot, region, class, transect, health, cattle
invertebrates
numindiv: Conchostraca
sweep nets
sample
Eul
ss
18.0000
11.0000
20.0000
9.0000
33.0000
40.0000
3.0000
8.0000
year, wetland, transect
invertebrates
numindiv: Lymnaeidae
sweep nets
sample
Eul
ss
30.0000
11.0000
20.0000
22.0000
49.0000
60.0000
3.0000
6.0000
year, wetland, transect
invertebrates
numindiv: Physidae
sweep nets
sample
Eul
ss
20.0000
11.0000
10.0000
34.0000
58.0000
40.0000
3.0000
4.0000
year, wetland, transect
invertebrates
biomass: Conchostraca
g
sweep nets
transect
Eul
ss
0.1800
11.0000
0.2000
8.0000
12.0000
0.4000
3.0000
4.0000
year, wetland, sample
invertebrates
numindiv: Conchostraca
sweep nets
transect
Eul
ss
50.0000
11.0000
25.0000
27.0000
56.0000
100.0000
4.0000
6.0000
year, wetland, sample
invertebrates
biomass: total
g
sweep nets
wetland
Eul
ss
0.7000
6.0000
1.0000
6.0000
9.0000
5.0000
2.0000
2.0000
year, transect, sample
invertebrates
numindiv: Chironomidae
sweep nets
wetland
Eul
ss
500.0000
6.0000
500.0000
10.0000
18.0000
3000.0000
3.0000
3.0000
year, transect, sample
invertebrates
numindiv: Conchostraca
sweep nets
wetland
Eul
ss
23.0000
7.0000
40.0000
5.0000
12.0000
100.0000
3.0000
4.0000
year, transect, sample
invertebrates
numindiv: Ephemeroptera
sweep nets
wetland
Eul
ss
3.0000
6.0000
4.0000
7.0000
13.0000
20.0000
2.0000
2.0000
year, transect, sample
invertebrates
numindiv: Lymnaeidae
sweep nets
wetland
Eul
ss
30.0000
6.0000
4.0000
8.0000
17.0000
250.0000
3.0000
3.0000
year, transect, sample
invertebrates
numindiv: Physidae
sweep nets
wetland
Eul
ss
23.0000
6.0000
25.0000
7.0000
13.0000
100.0000
3.0000
3.0000
year, transect, sample
-------
invertebrates
numindiv: total
sweep nets
wetland
Euliss
1300.0000
6.0000
500.0000
16.0000
25.0000
10000.0000
1.0000
2.0000
year, transect, sample
invertebrates
numtaxa
sweep nets
wetland
Euliss
3.0000
6.0000
4.0000
5.0000
8.0000
10.0000
3.0000
4.0000
year, transect, sample
invertebrates
biomass: Copepoda
g
activtraps
sample
Hanson
1.6000
10.0000
1.0000
27.0000
33.0000
3.0000
3.0000
4.0000
year, per
od, wetland, emergents
invertebrates
numindiv: Hirudinea
activtraps
sample
Hanson
16.0000
8.0000
10.0000
23.0000
30.0000
40.0000
2.0000
3.0000
year, per
od, wetland, emergents
invertebrates
numtaxa
activtraps
sample
Hanson
1.5000
9.0000
1.0000
20.0000
26.0000
3.0000
2.0000
4.0000
year, per
od, wetland, emergents
invertebrates
biomass: Cladocera
g
activtraps
wetland
Hanson
2.0000
5.0000
2.0000
8.0000
16.0000
10.0000
2.0000
2.0000
year, per
od, sample, emergents
invertebrates
biomass: Copepoda
g
activtraps
wetland
Hanson
2.3000
5.0000
2.0000
10.0000
20.0000
8.0000
3.0000
4.0000
year, per
od, sample, emergents
invertebrates
biomass: total
g
activtraps
wetland
Hanson
3.0000
6.0000
2.0000
20.0000
40.0000
20.0000
2.0000
3.0000
year, per
od, sample, emergents
invertebrates
numindiv: Amphipoda
activtraps
wetland
Hanson
25.0000
5.0000
40.0000
4.0000
12.0000
140.0000
3.0000
3.0000
year, per
od, sample, emergents
invertebrates
numindiv: Cladocera
activtraps
wetland
Hanson
1500.0000
5.0000
2000.0000
8.0000
20.0000
10000.0000
2.0000
2.0000
year, per
od, sample, emergents
invertebrates
numindiv: Conchostraca
activtraps
wetland
Hanson
80.0000
9.0000
100.0000
6.0000
28.0000
400.0000
2.0000
3.0000
year, per
od, sample, emergents
invertebrates
numindiv: Copepoda
activtraps
wetland
Hanson
275.0000
5.0000
200.0000
5.0000
21.0000
1000.0000
2.0000
5.0000
year, per
od, sample, emergents
invertebrates
numindiv: Hirudinea
activtraps
wetland
Hanson
20.0000
8.0000
20.0000
7.0000
13.0000
100.0000
2.0000
2.0000
year, per
od, sample, emergents
invertebrates
numindiv: Ostracoda
activtraps
wetland
Hanson
90.0000
5.0000
100.0000
7.0000
25.0000
600.0000
2.0000
2.0000
year, per
od, sample, emergents
invertebrates
numindiv: total
activtraps
wetland
Hanson
1600.0000
5.0000
2000.0000
7.0000
23.0000
10000.0000
1.0000
2.0000
year, per
od, sample, emergents
invertebrates
numtaxa
activtraps
wetland
Hanson
2.0000
7.0000
2.0000
6.0000
15.0000
10.0000
2.0000
2.0000
year, per
od, sample, emergents
invertebrates
biomass: Amphipoda
mg
activtraps
wetland
MERP
11.0000
8.0000
6.0000
31.0000
34.0000
20.0000
2.0000
2.0000
year, per
od,zone
invertebrates
biomass: Cladocera
mg
activtraps
wetland
MERP
66.0000
12.0000
30.0000
48.0000
52.0000
100.0000
4.0000
5.0000
year, per
od,zone
invertebrates
biomass: Ostracoda
mg
activtraps
wetland
MERP
58.0000
10.0000
30.0000
41.0000
44.0000
100.0000
3.0000
3.0000
year, per
od,zone
invertebrates
biomass: Tanytarsini
mg
activtraps
wetland
MERP
0.4200
11.0000
0.4000
23.0000
37.0000
1.4000
3.0000
4.0000
year, per
od,zone
invertebrates
biomass: total
mg
activtraps
wetland
MERP
180.0000
12.0000
100.0000
35.0000
36.0000
400.0000
2.0000
2.0000
year, per
od,zone
invertebrates
numindiv: Amphipoda
activtraps
wetland
MERP
12.0000
12.0000
6.0000
40.0000
43.0000
25.0000
2.0000
2.0000
year, per
od,zone
invertebrates
numindiv: Cladocera
activtraps
wetland
MERP
2100.0000
10.0000
1000.0000
43.0000
47.0000
4000.0000
3.0000
3.0000
year, per
od,zone
invertebrates
numindiv: Ostracoda
activtraps
wetland
MERP
160.0000
12.0000
50.0000
90.0000
100.0000
300.0000
3.0000
3.0000
year, per
od,zone
invertebrates
numindiv: Physidae
activtraps
wetland
MERP
1600.0000
11.0000
1.0000
9.0000
11.0000
2.0000
2.0000
2.0000
year, per
od,zone
invertebrates
numindiv: Tanytarsini
activtraps
wetland
MERP
2.2000
12.0000
2.0000
13.0000
22.0000
5.0000
2.0000
3.0000
year, per
od,zone
invertebrates
numindiv: total
activtraps
wetland
MERP
6200.0000
10.0000
3000.0000
41.0000
44.0000
10000.0000
3.0000
4.0000
year, per
od,zone
invertebrates
numtaxa
activtraps
wetland
MERP
2.0000
13.0000
1.0000
42.0000
44.0000
4.0000
3.0000
3.0000
year, per
od,zone
plants
numindiv: seedlings
quadrats
quadrat
Squires
32.0000
9.0000
20.0000
24.0000
30.0000
80.0000
2.0000
3.0000
treatment, year, period
plants
numtaxa
quadrats
quadrat
Squires
2.0000
8.0000
1.0000
32.0000
40.0000
5.0000
2.0000
3.0000
treatment, year, period
-------
STUDYDATA
GROUP
TAXON
SAMPTYPE
METRIC
REGIONS
POLYGONS
HEALTHCL
STATES
ROUTES
WETTYPES
WETS
TREAT
TRANS
ZONES
DEPTHS
VEG
ALLSAMPS
YRS
MONTHS
CV
CVMIN
CVMAX
Bataille & Baldassarre 1993
emtrap
numindiv
ss
x5
es
14.8889
Bataille & Baldassarre 1993
emtrap
numindiv
sp
deep
x5
P
16.7500
Bataille & Baldassarre 1993
emtrap
numindiv
sp
open water
x5
P
16.7500
Bataille & Baldassarre 1993
emtrap
numindiv
sp
deep
x5
es
19.8276
Bataille & Baldassarre 1993
emtrap
numindiv
sp
open water
x5
Is
21.5686
Bataille & Baldassarre 1993
emtrap
numindiv
ss
x5
P
22.2500
Bataille & Baldassarre 1993
actrap
numindiv
sp
open water
x5
es
23.9247
Bataille & Baldassarre 1993
emtrap
numindiv
sp
shallow
x5
Is
29.4931
Bataille & Baldassarre 1993
emtrap
numindiv
sp
shallow
x5
P
29.8333
Bataille & Baldassarre 1993
emtrap
numindiv
p
x5
P
33.7838
Bataille & Baldassarre 1993
actrap
numindiv
ss
x5
P
34.1463
Bataille & Baldassarre 1993
actrap
numindiv
p
x5
P
37.6623
Bataille & Baldassarre 1993
actrap
numindiv
sp
deep
x5
P
38.4615
Bataille & Baldassarre 1993
actrap
numindiv
p
x5
Is
42.3529
Bataille & Baldassarre 1993
actrap
numindiv
sp
shallow
x5
P
42.9245
Bataille & Baldassarre 1993
actrap
numindiv
p
x5
es
46.4720
Bataille & Baldassarre 1993
actrap
numindiv
sp
open water
x5
Is
48.6667
Bataille & Baldassarre 1993
emtrap
numindiv
sp
open water
x5
es
49.2000
Bataille & Baldassarre 1993
emtrap
numindiv
p
x5
Is
53.6562
Bataille & Baldassarre 1993
actrap
numindiv
sp
deep
x5
es
54.7278
Bataille & Baldassarre 1993
actrap
numindiv
ss
x5
es
55.4502
Bataille & Baldassarre 1993
actrap
numindiv
sp
shallow
x5
es
56.1303
Bataille & Baldassarre 1993
actrap
numindiv
ss
x5
Is
58.5366
Bataille & Baldassarre 1993
emtrap
numindiv
p
x5
es
58.9666
Bataille & Baldassarre 1993
emtrap
numindiv
ss
x5
Is
58.9744
Bataille & Baldassarre 1993
actrap
numindiv
sp
deep
x5
Is
59.0349
Bataille & Baldassarre 1993
emtrap
numindiv
sp
deep
x5
Is
59.5376
Bataille & Baldassarre 1993
emtrap
numindiv
sp
shallow
x5
es
62.9630
Bataille & Baldassarre 1993
actrap
numindiv
sp
a2
X
Is
49.0000
59.0000
Bataille & Baldassarre 1993
emtrap
numindiv
sp
a3
X
P
16.0000
30.0000
Bataille & Baldassarre 1993
emtrap
numindiv
sp
a3
X
Is
22.0000
60.0000
Bataille & Baldassarre 1993
emtrap
numindiv
sp
a3
X
es
20.0000
63.0000
Bataille & Baldassarre 1993
actrap
numindiv
sp
a3
X
es
24.0000
56.0000
Bataille & Baldassarre 1993
actrap
numindiv
sp
a3
X
P
38.0000
59.0000
Bataille & Baldassarre 1993
emtrap
numindiv
a3
X
X
P
21.0000
34.0000
Bataille & Baldassarre 1993
emtrap
numindiv
a3
X
X
es
25.0000
60.0000
Bataille & Baldassarre 1993
actrap
numindiv
a3
X
X
P
34.0000
46.0000
Bataille & Baldassarre 1993
actrap
numindiv
a3
X
X
Is
42.0000
54.0000
Bataille & Baldassarre 1993
actrap
numindiv
a3
X
X
es
45.0000
55.0000
Bataille & Baldassarre 1993
emtrap
numindiv
a3
X
X
Is
37.0000
59.0000
Bataille & Baldassarre 1993
emtrap
numindiv
ss
X
X
X
a3
15.0000
59.0000
Bataille & Baldassarre 1993
actrap
numindiv
sp
X
X
X
a3
21.0000
44.0000
Bataille & Baldassarre 1993
actrap
numindiv
p
X
X
X
a3
38.0000
46.0000
Bataille & Baldassarre 1993
emtrap
numindiv
sp
X
X
X
a3
45.0000
54.0000
Bataille & Baldassarre 1993
actrap
numindiv
ss
X
X
X
a3
34.0000
59.0000
Bataille & Baldassarre 1993
emtrap
numindiv
p
X
X
X
a3
34.0000
59.0000
Bataille & Baldassarre 1993
actrap
numindiv
sp
open water
x5
P
58.9041
Driver 1977
Chironomidae
emtrap
numtaxa
sp
all
X
X
X
31.4573
Driver 1977
Chironomidae
emtrap
numtaxa
t
al3
X
X
X
39.0000
Duffy
numindiv
sp
a2
open water
X
Julyl4
24.0000
Duffy
numindiv
sp
a2
open water
X
July28
31.0000
Duffy
numindiv
sp
a4
open water
X
Junel4
60.0000
Duffy
numindiv
sp
a4
open water
X
June29
7.0000
Duffy
numindiv
sp
a4
open water
X
May31
32.0000
Duffy
numindiv
sp
a4
open water
X
May9
65.0000
Duffy
numindiv
sp
a4
open water
X
X
14.0000
83.0000
Duffy
numindiv
sp
1
open water
X
a4
47.0000
Duffy
numindiv
sp
2
open water
X
a6
33.0000
Duffy
numindiv
sp
3
open water
X
a9
83.0000
Duffy
numindiv
sp
4
open water
X
a4
14.0000
Duffy
numindiv
sp
X
open water
X
a6
7.0000
65.0000
Duffy
numindiv
sp
X
open water
X
X
Duffy
numtaxa
sp
a2
open water
X
Julyl4
36.0000
-------
Duffy
numtaxa
sp
a2
open water
X
July28
9.0000
Duffy
numtaxa
sp
a4
open water
X
Junel4
15.0000
Duffy
numtaxa
sp
a4
open water
X
June29
22.0000
Duffy
numtaxa
sp
a4
open water
X
May31
37.0000
Duffy
numtaxa
sp
a4
open water
X
May9
23.0000
Duffy
numtaxa
sp
a4
open water
X
X
23.0000
42.0000
Duffy
numtaxa
sp
1
open water
X
a4
42.0000
Duffy
numtaxa
sp
2
open water
X
a6
30.0000
Duffy
numtaxa
sp
3
open water
X
a9
35.0000
Duffy
numtaxa
sp
4
open water
X
a4
23.0000
Duffy
numtaxa
sp
X
open water
X
a6
9.0000
37.0000
Duffy
biomass
sp
a4
open water
X
May9
48.0000
Duffy
biomass
sp
a4
open water
X
May31
20.0000
Duffy
biomass
sp
a4
open water
X
Junel4
43.0000
Duffy
biomass
sp
a4
open water
X
June29
54.0000
Duffy
biomass
sp
a2
open water
X
Julyl4
79.0000
Duffy
biomass
sp
a2
open water
X
July28
38.0000
Duffy
biomass
sp
a4
open water
X
X
20.0000
79.0000
Duffy
biomass
sp
1
open water
X
a4
56.0000
Duffy
biomass
sp
2
open water
X
a6
67.0000
Duffy
biomass
sp
3
open water
X
a9
69.0000
Duffy
biomass
sp
4
open water
X
a4
86.0000
Duffy
biomass
sp
X
open water
X
a6
56.0000
86.0000
Euliss sediment traps
biomass
X
X
X
sp
X
X
open water
xl80
184.0000
Euliss sediment traps
biomass
a2
X
X
X
X
X
X
162.0000
215.0000
Euliss sediment traps
biomass
X
X
X
a3
X
X
X
29.0000
167.0000
237.0000
Euliss sediment traps
biomass
X
X
a2
X
X
X
X
176.0000
193.0000
Euliss sediment traps
biomass
X
a35
X
X
X
X
X
106.0000
2.0000
224.0000
Euliss sediment traps
biomass
A6
X
X
A6
X
X
X
143.0000
253.0000
Euliss sediment traps
biomass
A12
X
A12
A12
X
X
X
126.0000
383.0000
Euliss sediment traps
biomass
A3 6
A3 6
A3 6
A3 6
A3 6
X
X
2.0000
224.0000
Euliss sediment traps
biomass
X
X
X
X
A180
A180
X
0.0000
245.0000
Euliss sediment traps
biomass
X
X
X
X
A180
X
X
73.0000
198.0000
Euliss sediment traps
biomass
X
X
X
ss
X
X
X
167.0000
Euliss sediment traps
biomass
X
X
X
sp
X
X
X
237.0000
Euliss sediment traps
biomass
X
X
X
t
X
X
X
167.0000
Euliss sediment traps
numindiv
X
X
X
ss
X
X
X
229.0000
Euliss sediment traps
numindiv
X
X
X
sp
X
X
X
180.0000
Euliss sediment traps
numindiv
X
X
X
t
X
X
X
309.0000
Euliss sediment traps
numindiv
X
X
X
X
X
X
xl80
228.0000
Euliss sediment traps
numindiv
a2
X
X
X
X
X
X
199.0000
255.0000
Euliss sediment traps
numindiv
X
X
X
a3
X
X
X
60.0000
180.0000
309.0000
Euliss sediment traps
numindiv
X
X
a2
X
X
X
X
194.0000
262.0000
Euliss sediment traps
numindiv
X
a35
X
X
X
X
X
157.0000
2.0000
224.0000
Euliss sediment traps
numindiv
A6
X
X
A6
X
X
X
109.0000
286.0000
Euliss sediment traps
numindiv
A12
X
A12
A12
X
X
X
83.0000
280.0000
Euliss sediment traps
numindiv
A3 6
A3 6
A3 6
A3 6
A3 6
X
X
2.0000
224.0000
Euliss sediment traps
numindiv
X
X
X
X
A180
A180
X
0.0000
200.0000
Euliss sediment traps
numindiv
X
X
X
X
A180
X
X
73.0000
198.0000
Euliss sweep nets
biomass
sp
A68
A68
X
92
1.0000
141.0000
Euliss sweep nets
biomass
sp
A90
A90
X
93
18.0000
170.0000
Euliss sweep nets
biomass
sp
al6
X
X
92
160.0000
57.0000
269.0000
Euliss sweep nets
biomass
sp
al8
X
X
93
161.0000
48.0000
360.0000
Euliss sweep nets
biomass
sp
X
X
xl67
92
287.0000
Euliss sweep nets
biomass
sp
X
X
x265
93
616.0000
Euliss sweep nets
numindiv
sp
A69
A69
X
92
1.0000
141.0000
Euliss sweep nets
numindiv
sp
A90
A90
X
93
5.0000
170.0000
Euliss sweep nets
numindiv
sp
al6
X
X
92
173.0000
41.0000
272.0000
Euliss sweep nets
numindiv
sp
al8
X
X
93
66.0000
65.0000
342.0000
Euliss sweep nets
numindiv
sp
X
X
xl67
92
282.0000
Euliss sweep nets
numindiv
sp
X
X
x265
93
154.0000
Euliss sweep nets
numtaxa
sp
A3 8
A38
X
92
0.0000
106.0000
Euliss sweep nets
numtaxa
sp
A90
A90
X
93
5.0000
120.0000
Euliss sweep nets
numtaxa
sp
al6
X
X
92
44.0000
4.0000
58.0000
-------
Euliss sweep nets
numtaxa
sp
al8
X
X
93
29.0000
20.0000
62.0000
Euliss sweep nets
numtaxa
sp
X
X
xll6
92
53.0000
Euliss sweep nets
numtaxa
sp
X
X
x265
93
42.0000
Fulton et al. 1981
P
0.25 m quad
biomass
sp
1
wet meadow
xlO
13.3838
Fulton et al. 1981
P
0.25 m quad
biomass
sp
3
wet meadow
xlO
23.5043
Fulton et al. 1981
P
0.25 m quad
biomass
sp
4
Scolochloa
xlO
32.2802
Fulton et al. 1981
P
0.25 m quad
biomass
sp
3
Typha glauca
xlO
32.5758
Fulton et al. 1981
P
0.25 m quad
biomass
sp
1
Typha glauca
xlO
35.6125
Fulton et al. 1981
P
0.25 m quad
biomass
sp
4
Typha latifolia
xlO
36.1559
Fulton et al. 1981
P
0.25 m quad
biomass
sp
2
wet meadow
xlO
39.6694
Fulton et al. 1981
P
0.25 m quad
biomass
sp
4
wet meadow
xlO
39.8649
Fulton et al. 1981
P
0.25 m quad
biomass
sp
2
Scolochloa
xlO
42.5729
Fulton et al. 1981
P
0.25 m quad
biomass
sp
1
Scirpus
xlO
46.5201
Fulton et al. 1981
P
0.25 m quad
biomass
sp
3
Scolochloa
xlO
48.0469
Fulton et al. 1981
P
0.25 m quad
biomass
sp
3
Scirpus
xlO
49.2126
Fulton et al. 1981
P
0.25 m quad
biomass
sp
4
Scirpus
xlO
50.1938
Fulton et al. 1981
P
0.25 m quad
biomass
sp
2
Typha latifolia
xlO
51.4577
Fulton et al. 1981
P
0.25 m quad
biomass
sp
2
Scirpus acutus
xlO
64.0097
Fulton et al. 1981
P
0.25 m quad
biomass
sp
1
Scolochloa
xlO
70.4545
Fulton et al. 1981
P
biomass
sp
a4
X
24.0000
49.0000
Fulton et al. 1981
P
biomass
sp
a4
X
32.0000
50.0000
Fulton et al. 1981
P
biomass
sp
a4
X
13.0000
70.0000
Fulton et al. 1981
P
biomass
sp
a4
X
40.0000
64.0000
Fulton et al. 1981
P
biomass
sp
a4
X
13.0000
40.0000
Fulton et al. 1981
P
biomass
sp
a4
X
32.0000
51.0000
Fulton et al. 1981
P
biomass
sp
a4
X
32.0000
70.0000
Fulton et al. 1981
P
biomass
sp
a4
X
47.0000
64.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
2
wet meadow
xlO
14.2466
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
3
Typha glauca
xlO
20.6897
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
4
Typha latifolia
xlO
21.4286
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
1
wet meadow
xlO
22.2561
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
3
Scirpus acutus
xlO
27.0115
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
2
Scolochloa
xlO
27.8997
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
4
Scolochloa
xlO
28.2862
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
4
wet meadow
xlO
28.4422
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
1
Typha glauca
xlO
33.8028
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
3
wet meadow
xlO
38.0313
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
3
Scolochloa
xlO
42.5887
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
4
Scirpus acutus
xlO
42.7027
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
2
Scirpus acutus
xlO
43.2584
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
1
Scirpus acutus
xlO
43.6261
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
2
Typha latifolia
xlO
52.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
1
Scolochloa
xlO
69.3069
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
all4
X
21.0000
43.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
all4
X
21.0000
43.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
all4
X
14.0000
52.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
all4
X
22.0000
69.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
a4
X
14.0000
38.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
a4
X
21.0000
52.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
a4
X
27.0000
44.0000
Fulton et al. 1981
P
0.25 m quad
shoots/m2
sp
a4
X
28.0000
69.0000
Hanson activity traps
numindiv
A3 5
X
X
A3 5
A35
23.0000
167.0000
Hanson activity traps
numindiv
a4
X
X
X
X
57.0000
100.0000
273.0000
Hanson activity traps
numindiv
X
a2
X
X
X
14.0000
239.0000
331.0000
Hanson activity traps
numindiv
X
X
X
A10
A10
61.0000
206.0000
Hanson activity traps
numindiv
X
X
X
a2
X
80.0000
119.0000
254.0000
Hanson activity traps
numindiv
X
X
X
X
a5
92.0000
78.0000
223.0000
Hanson activity traps
numindiv
X
X
x320
X
X
281.0000
Hanson activity traps
Diptera
numindiv
X
X
x440
X
X
273.0000
Hanson activity traps
Coleoptera
numindiv
X
X
x440
X
X
294.0000
Hanson activity traps
Ephemeroptera
numindiv
X
X
x440
X
X
264.0000
Hanson activity traps
Hemiptera
numindiv
X
X
x440
X
X
636.0000
Hanson activity traps
Odonata
numindiv
X
X
x440
X
X
723.0000
Hanson activity traps
Amphipoda
numindiv
X
X
x440
X
X
327.0000
-------
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
Cladocera
Copepoda
Conchostraca
Ostracoda
Hirudinea
Hydracarina
actrap
actrap
numindiv
numindiv
numtaxa
numtaxa
numtaxa
numtaxa
biomass
biomass
biomass
biomass
biomass
biomass
numindiv
numindiv
numindiv
numindiv
numtaxa
numtaxa
numtaxa
numtaxa
biomass
biomass
biomass
biomass
numindiv
numindiv
numindiv
numindiv
numtaxa
numtaxa
numtaxa
numtaxa
A3 5
a4
A935
all
A616
all
-------
Neckles et al. 1990
actrap
numtaxa
ss
x2
9
0.0000
Neckles et al. 1990
actrap
numtaxa
ss
x3
8
50.7937
Neckles et al. 1990
actrap
numtaxa
ss
x4
5
7.3529
Neckles et al. 1990
actrap
numtaxa
ss
x4
6
29.2308
Neckles et al. 1990
actrap
numtaxa
ss
x4
7
30.0000
Neckles et al. 1990
actrap
numtaxa
ss
x5
3
26.7857
Neckles et al. 1990
actrap
numtaxa
ss
x5
4
32.8125
Neckles et al. 1990
actrap
numtaxa
ss
x5
2
34.2105
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
10.6383
Neckles et al. 1990
actrap
numtaxa
sp
x6
5
16.1290
Neckles et al. 1990
actrap
numtaxa
sp
x6
7
16.9231
Neckles et al. 1990
actrap
numtaxa
sp
x6
6
20.8333
Neckles et al. 1990
actrap
numtaxa
sp
x6
1
22.8571
Neckles et al. 1990
actrap
numtaxa
sp
x6
4
23.0769
Neckles et al. 1990
actrap
numtaxa
sp
x6
9
27.1429
Neckles et al. 1990
actrap
numtaxa
sp
x6
8
28.0000
Neckles et al. 1990
actrap
numtaxa
ss
x6
1
32.5000
Neckles et al. 1990
actrap
numtaxa
sp
x6
3
40.6250
Neckles et al. 1990
actrap
numtaxa
ss
X
2
alO
14.0000
49.0000
Neckles et al. 1990
actrap
numtaxa
ss
X
2
alO
29.0000
56.0000
Neckles et al. 1990
actrap
numtaxa
ss
h
X
2
aS
16.0000
48.0000
Neckles et al. 1990
actrap
numtaxa
ss
h
x3
2
7
18.8889
Neckles et al. 1990
actrap
numtaxa
ss
x3
2
10
25.3012
Neckles et al. 1990
actrap
numtaxa
ss
h
x3
2
8
34.2466
Neckles et al. 1990
actrap
numtaxa
ss
x3
2
8
45.0000
Neckles et al. 1990
actrap
numtaxa
ss
x3
2
9
48.8372
Neckles et al. 1990
actrap
numtaxa
ss
x4
2
7
23.8636
Neckles et al. 1990
actrap
numtaxa
ss
x5
2
5
13.8298
Neckles et al. 1990
actrap
numtaxa
ss
x5
2
6
17.8571
Neckles et al. 1990
actrap
numtaxa
ss
h
x6
2
4
15.7480
Neckles et al. 1990
actrap
numtaxa
ss
x6
2
4
16.6667
Neckles et al. 1990
actrap
numtaxa
ss
x6
2
1
19.0476
Neckles et al. 1990
actrap
numtaxa
ss
h
x6
2
6
19.3548
Neckles et al. 1990
actrap
numtaxa
ss
x6
2
2
21.0526
Neckles et al. 1990
actrap
numtaxa
ss
h
x6
2
1
21.3333
Neckles et al. 1990
actrap
numtaxa
ss
h
x6
2
2
23.0769
Neckles et al. 1990
actrap
numtaxa
ss
x6
2
3
25.3012
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
9
28.5714
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
4
31.2500
Neckles et al. 1990
actrap
numtaxa
ss
h
x6
2
3
34.1176
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
10
36.3636
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
7
36.9863
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
6
43.5484
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
5
44.7368
Neckles et al. 1990
actrap
numtaxa
ss
h
x6
2
5
47.6190
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
2
48.8889
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
8
52.5641
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
3
53.3333
Neckles et al. 1990
actrap
numtaxa
sp
x6
2
1
56.2500
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
shallow emergent
X
a2
48.0000
86.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
deep emergent
X
a2
48.0000
114.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
open water
X
a2
68.0000
113.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
open water
xl2
67.7617
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
2
deep emergent
xl8
158.9418
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
2
openwater
x2
78.0242
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
shallow emergent
x4
86.7280
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
2
shallow emergent
x4
129.4385
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
deep emergent
x8
48.4155
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
a3
X
48.0000
87.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
2
a3
X
78.0000
159.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
a2
open water
X
68.0000
78.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
a2
deep emergent
X
48.0000
159.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
a2
shallow emergent
X
87.0000
129.0000
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
deep emergent
xl6
2
114.1942
-------
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
open water
x24
2
112.6686
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
shallow emergent
x8
2
48.7535
Poiani &Johnson 1989
P
depth 0-5 cm
seed dens
sp
1
all3
X
2
49.0000
114.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
37
A45
A45
X
6.0000
98.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
37
A86
X
A86
0.0000
130.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
37
a5
X
X
39.0000
99.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
37
X
a27
X
42.0000
10.0000
75.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
37
X
X
a21
42.0000
37.0000
81.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
37
X
x539
X
63.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
38
A41
A41
X
4.0000
60.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
38
a5
X
X
33.0000
65.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
38
X
a25
X
39.0000
4.0000
72.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
38
X
X
a21
13.0000
32.0000
102.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
38
X
x384
X
51.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
40
A35
A35
X
1.0000
41.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
40
A82
X
A82
4.0000
65.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
40
a4
X
X
24.0000
41.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
40
X
a28
X
33.0000
0.0000
42.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
40
X
X
a21
6.0000
30.0000
46.0000
USFWS Breeding B
rd Survey
b
wetland species
freq
40
X
x528
X
37.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
37
A45
A45
X
6.0000
41.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
37
A86
X
A86
8.0000
154.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
37
a5
X
X
38.0000
114.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
37
X
a27
X
117.0000
10.0000
57.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
37
X
X
a21
29.0000
66.0000
161.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
37
X
x539
X
119.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
38
A40
A40
X
1.0000
153.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
38
A86
X
A86
12.0000
113.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
38
a5
X
X
48.0000
81.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
38
X
a25
X
52.0000
1.0000
152.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
38
X
X
a21
12.0000
57.0000
131.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
38
X
x384
X
76.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
40
A35
A35
X
9.0000
73.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
40
A82
X
A82
3.0000
90.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
40
a4
X
X
36.0000
64.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
40
X
a28
X
70.0000
16.0000
73.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
40
X
X
a21
12.0000
35.0000
91.0000
USFWS Breeding B
rd Survey
b
wetland species
numindiv
40
X
x528
X
61.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
37
A45
A45
X
6.0000
41.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
37
A86
X
A86
3.0000
55.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
37
a5
X
X
28.0000
40.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
37
X
a32
X
27.0000
10.0000
57.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
37
X
X
a21
10.0000
26.0000
61.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
37
X
x537
X
39.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
38
A
x86
A86
8.0000
73.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
38
A40
A40
X
6.0000
64.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
38
a5
X
X
25.0000
55.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
38
X
a25
X
30.0000
7.0000
62.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
38
X
X
a21
11.0000
27.0000
64.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
38
X
x384
X
41.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
40
A35
A35
X
7.0000
42.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
40
A82
X
A82
7.0000
63.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
40
a4
X
X
28.0000
40.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
40
X
a28
X
43.0000
7.0000
47.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
40
X
X
A21
10.0000
37.0000
57.0000
USFWS Breeding B
rd Survey
b
wetland species
numtaxa
40
X
x528
X
47.0000
van der Valk & Davis 1976
P
1 m2 quad
numtaxa
sp
t2
x7
20.3390
van der Valk & Davis 1976
P
1 m2 quad
numtaxa
sp
t2
x7
28.7879
van der Valk & Davis 1976
P
1 m2 quad
numtaxa
sp
t2
x7
53.8462
van der Valk & Davis 1976
P
1 m2 quad
numtaxa
sp
tl
x8
17.2414
van der Valk & Davis 1976
P
1 m2 quad
numtaxa
sp
tl
x8
21.2766
van der Valk & Davis 1976
P
1 m2 quad
numtaxa
sp
tl
x8
33.3333
van der Valk & Davis 1976
P
1 m2 quad
numtaxa
sp
X
a3
X
17.0000
54.0000
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
f
Scirpus validus
x4
9.5764
-------
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
d
Carex
x4
25.9066
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
d
Typha
x4
30.2001
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
d
Scirpus validus
x4
31.6588
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
f
Sparganium
x4
31.9759
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
f
Carex
x4
32.0225
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
d
S.fluviatilis
x4
49.6662
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
d
Sparganium
x4
55.3103
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
f
S.fluviatilis
x4
64.9102
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
f
Typha
x4
66.7124
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
f
open water
x4
86.9206
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
d
open water
x4
101.4088
van der Valk & Davis 1978
P
1 m2 quad
seed dens
sp
X
all3
X
10.0000
101.0000
van der Valk & Davis 1980
P
Scirpus validus
1 m2 quad
biomass
sp
xll
1
28.5714
van der Valk & Davis 1980
P
Scirpus validus
1 m2 quad
biomass
sp
xl7
2
24.2857
van der Valk & Davis 1980
P
Scirpus validus
1 m2 quad
biomass
sp
x4
3
34.0153
van der Valk & Davis 1980
P
Scirpus validus
1 m2 quad
biomass
sp
x7
4
13.9918
van der Valk & Davis 1980
P
Sparganium eury
1 m2 quad
biomass
sp
xll
1
31.2500
van der Valk & Davis 1980
P
Sparganium eury
1 m2 quad
biomass
sp
xl4
2
14.5488
van der Valk & Davis 1980
P
Sparganium eury
1 m2 quad
biomass
sp
xl4
3
60.1476
van der Valk & Davis 1980
P
Sparganium eury
1 m2 quad
biomass
sp
xl2
4
33.5249
van der Valk & Davis 1980
P
Sparganium eury
1 m2 quad
biomass
sp
xll
5
22.0657
van der Valk & Davis 1980
P
Typha glauca
1 m2 quad
biomass
sp
x20
1
25.4197
van der Valk & Davis 1980
P
Typha glauca
1 m2 quad
biomass
sp
x21
2
29.6744
van der Valk & Davis 1980
P
Typha glauca
1 m2 quad
biomass
sp
x20
3
35.9833
van der Valk & Davis 1980
P
Typha glauca
1 m2 quad
biomass
sp
x23
4
24.6114
van der Valk & Davis 1980
P
Typha glauca
1 m2 quad
biomass
sp
xl9
5
17.1739
van der Valk & Davis 1980
P
Scirpus validus
1 m2 quad
biomass
sp
X
a4
14.0000
34.0000
van der Valk & Davis 1980
P
Sparganium eury
1 m2 quad
biomass
sp
X
a5
15.0000
60.0000
van der Valk & Davis 1980
P
Typha glauca
1 m2 quad
biomass
sp
X
a5
17.0000
36.0000
-------
DATASET
EQUIPMENT
TAXA
SAMPLES
LOCATION
TOTALTAXA
TOT NUMSA
NUM FOR50
NUM FOR75
NUM FOR90
NUM FOR95
NUM FOR99
CURVETURN
CURVEEND
QUALIFIERS
POOLE DVARS
BBS
birds: wetland spp
5-mi route segment
stratum 37
43.0000
5.0000
2.0000
3.0000
4.0000
4.0000
5.0000
0.0000
100.0000
1989, route 64x42
BBS
birds: wetland spp
5-mi route segment
stratum 38
46.0000
5.0000
2.0000
3.0000
4.0000
5.0000
5.0000
0.0000
100.0000
1990, route 64x11
BBS
birds: wetland spp
5-mi route segment
stratum 40
36.0000
5.0000
2.0000
2.0000
4.0000
5.0000
5.0000
0.0000
100.0000
1993, route 50x65
BBS
birds: wetland spp
routes
all ppstrata
66.0000
11.0000
2.0000
2.0000
4.0000
5.0000
9.5000
22.2222
86.3636
longest simultaneously running routes
years
BBS
birds: wetland spp
routes
stratum 37
65.0000
97.0000
3.0000
7.0000
18.5000
24.5000
36.0000
52.1739
37.1134
rich year 1989
routes
BBS
birds: wetland spp
routes
stratum 37
64.0000
100.0000
2.0000
6.0000
17.0000
23.5000
33.5000
65.0000
33.5000
rich year 1992
routes
BBS
birds: wetland spp
routes
stratum 38
59.0000
106.0000
3.0000
8.0000
15.0000
21.5000
29.5000
81.2500
27.8302
rich year 1992
routes
BBS
birds: wetland spp
routes
stratum 38
62.0000
104.0000
2.0000
7.0000
16.0000
24.0000
34.0000
80.0000
32.6923
rich year 1993
routes
BBS
birds: wetland spp
routes
stratum 40
51.0000
85.0000
3.0000
9.0000
17.0000
23.0000
25.0000
300.0000
29.4118
rich year 1991
routes
BBS
birds: wetland spp
routes
stratum 40
52.0000
104.0000
3.0000
7.0000
16.0000
23.5000
30.0000
115.3846
28.8462
rich year 1993
BBS
birds: wetland spp
years
stratum 37
56.0000
20.0000
2.0000
4.0000
9.0000
14.0000
18.5000
111.1111
92.5000
rich route 64x12
years
BBS
birds: wetland spp
years
stratum 37
56.0000
11.0000
2.0000
2.0000
4.0000
7.0000
9.0000
150.0000
81.8182
rich route 64x42
years
BBS
birds: wetland spp
years
stratum 38
57.0000
10.0000
2.0000
2.0000
4.0000
6.0000
9.0000
66.6667
90.0000
rich route 64x11
years
BBS
birds: wetland spp
years
stratum 38
49.0000
27.0000
3.0000
6.0000
15.0000
19.0000
25.0000
66.6667
92.5926
rich route 64x26
years
BBS
birds: wetland spp
years
stratum 40
47.0000
6.0000
2.0000
3.0000
5.0000
5.0000
6.0000
0.0000
100.0000
rich route 50x30
BBS
birds: wetland spp
years
stratum 40
46.0000
19.0000
2.0000
6.0000
12.0000
15.0000
18.0000
100.0000
94.7368
rich route 64x8
years
Duffy
corer
invertebrates
sampling dates
wetland 1
32.0000
4.0000
2.0000
3.0000
4.0000
4.0000
4.0000
0.0000
100.0000
replicates
Duffy
corer
invertebrates
sampling dates
wetland 2
57.0000
6.0000
2.0000
4.0000
5.0000
6.0000
6.0000
0.0000
100.0000
Duffy
corer
invertebrates
sampling dates
wetland 3
58.0000
9.0000
2.0000
5.0000
7.0000
9.0000
9.0000
0.0000
100.0000
Duffy
corer
invertebrates
sampling dates
wetland 4
35.0000
4.0000
2.0000
3.0000
4.0000
4.0000
4.0000
0.0000
100.0000
Euliss
sweep nets
invertebrates
samples
ND
29.0000
381.0000
5.0000
28.0000
178.0000
239.0000
319.0000
76.2500
83.7270
all
year, wetland, transect
Euliss
sweep nets
invertebrates
transects
ND
25.0000
26.0000
2.0000
6.0000
17.0000
21.0000
24.0000
133.3333
92.3077
transect
year, wetland, sample
Galatowitsch
quadrats
vascplants: all sp
wetlands
Iowa
133.0000
10.0000
2.0000
4.0000
7.0000
9.0000
10.0000
200.0000
100.0000
10 natural wetlands
Galatowitsch
quadrats
vascplants: all sp
wetlands
Iowa
158.0000
20.0000
2.0000
4.0000
8.0000
10.0000
11.0000
200.0000
55.0000
10 restored + 10 natural wetlands
Galatowitsch
quadrats
vascplants: all sp
wetlands
Iowa
83.0000
10.0000
2.0000
4.0000
7.0000
8.0000
10.0000
50.0000
100.0000
10 restored wetlands
Igl & Johnson
birds: all species
wetlands
ND prair
es
101.0000
175.0000
31.5000
73.5000
122.5000
142.0000
163.0000
92.8571
93.1429
1992; likely breeders + nonbreeders
visits
Igl & Johnson
birds: all species
wetlands
ND prair
es
61.0000
151.0000
29.0000
70.5000
105.5000
126.5000
145.5000
110.5263
96.3576
1992; likely breeders only
visits
Igl & Johnson
birds: all species
wetlands
ND prair
es
113.0000
302.0000
48.0000
126.0000
209.0000
251.0000
282.0000
135.4839
93.3775
1993; likely breeders + non breeders
visits
Igl & Johnson
birds: all species
wetlands
ND prair
es
61.0000
254.0000
32.0000
96.0000
171.0000
212.0000
241.0000
141.3793
94.8819
1993; likely breeders only
visits
MERP
activtraps
invertebrates
samples
Delta Marsh
53.0000
246.0000
13.0000
50.5000
132.0000
185.0000
225.0000
132.5000
91.4634
year, period, zone
MERP
activtraps
invertebrates
sampling periods
Delta Marsh
30.0000
10.0000
2.0000
5.0000
7.0000
9.0000
10.0000
200.0000
100.0000
year, zone
MERP
activtraps
invertebrates
sampling periods
Delta Marsh
31.0000
20.0000
3.0000
7.0000
12.0000
15.0000
18.0000
100.0000
90.0000
year, zone
MERP
activtraps
invertebrates
wetland types
Delta Marsh
32.0000
4.0000
2.0000
2.0000
3.0000
3.5000
4.0000
100.0000
100.0000
year, period, zone
MERP
activtraps
invertebrates
years
Delta Marsh
25.0000
5.0000
2.0000
3.0000
4.0000
5.0000
5.0000
0.0000
100.0000
period, zone
MERP
activtraps
invertebrates
years
Delta Marsh
25.0000
5.0000
2.0000
3.0000
5.0000
5.0000
5.0000
0.0000
100.0000
period, zone
Squires
quadrats
seedling plants
quadrats
Delta Marsh
65.0000
479.0000
30.0000
142.0000
292.0000
373.0000
459.0000
94.1860
95.8246
all treatments
treatment, year, period
Squires
quadrats
seedling plants
quadrats
Delta Marsh
40.0000
40.0000
5.0000
13.0000
23.0000
29.0000
37.0000
75.0000
92.5000
yrl low
year, period
Squires
quadrats
seedling plants
quadrats
Delta Marsh
34.0000
40.0000
3.0000
13.0000
26.0000
33.0000
37.0000
175.0000
92.5000
yr2 high
year, period
Squires
quadrats
seedling plants
quadrats
Delta Marsh
47.0000
40.0000
5.0000
13.0000
27.0000
32.0000
38.5000
76.9231
96.2500
yr2 low
year, period
Squires
quadrats
seedling plants
quadrats
Delta Marsh
50.0000
40.0000
6.0000
16.0000
26.0000
31.0000
36.0000
100.0000
90.0000
yr2 med
year, period
------- |