oEPA
United States
Environmental Protection
Agency
Quick Assessment Protocols for
Measuring Relative Ecological
Significance of Terrestrial
Ecosystems
Diversity
Parity
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EPA/600/R-08/061
May 2008
Quick Assessment Protocols
for Measuring Relative Ecological Significance
of Terrestrial Ecosystems
By
Audrey L. Mayer*, Allison H. Roy
Sustainable Technology Division
National Risk Management Research Laboratory
US Environmental Protection Agency
Cincinnati, OH 45268
and
Mary White
Office of Strategic Environmental Analysis
US Environmental Protection Agency Region 5
Chicago, IL 60604
and
Charles G. Maurice
Office of Research and Development—Office of Science Policy
and Region 5—Superfund Division
US Environmental Protection Agency
Chicago, IL 60604
and
Landon McKinney
ASC Group, Inc.
Columbus, OH 43214
National Risk Management Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
* Current Address: University of Helsinki, Faculty of Biosciences, P.O. Box 27, 00014 Helsinki FINLAND
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Notice
The U.S. Environmental Protection Agency through its Office of Research and Development funded and collaborated in
the research described here under the Regional Applied Research Effort (RARE) internal grant program with Region 5,
through a project called "Development of Methods to Evaluate Critical Ecosystems." Protocol development and planning
meetings were supported under work assignment 68-C-02-067 to Science Applications International Corporation (SAIC);
creation and formatting of draft protocols and the Quality Assurance Project Plan was accomplished under contract 68-W-02-
018 though the Great Lakes National Program Office to Booz Allen Hamilton; and field data collection was performed under
simplified acquisition GS-10F-0114M to ASC Group, Inc. It has been subjected to the Agency's peer and administrative
review and has been approved for publication as an EPA document.
Disclaimer
Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
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Foreword
The U. S. Environmental Protection Agency (USEPA) is charged by Congress with protecting the Nation's land, air, and
water resources. Under a mandate of national environmental laws, the Agency strives to formulate and implement actions
leading to a compatible balance between human activities and the ability of natural systems to support and nurture life. To
meet this mandate, USEPA's research program is providing data and technical support for solving environmental problems
today and building a science knowledge base necessary to manage our ecological resources wisely, understand how
pollutants affect our health, and prevent or reduce environmental risks in the future.
The National Risk Management Research Laboratory (NRMRL) is the Agency's center for investigation of technological
and management approaches for preventing and reducing risks from pollution that threaten human health and the
environment. The focus of the Laboratory's research program is on methods and their cost-effectiveness for prevention and
control of pollution to air, land, water, and subsurface resources; protection of water quality in public water systems;
remediation of contaminated sites, sediments and ground water; prevention and control of indoor air pollution; and
restoration of ecosystems. NRMRL collaborates with both public and private sector partners to foster technologies that
reduce the cost of compliance and to anticipate emerging problems. NRMRL's research provides solutions to environmental
problems by: developing and promoting technologies that protect and improve the environment; advancing scientific and
engineering information to support regulatory and policy decisions; and providing the technical support and information
transfer to ensure implementation of environmental regulations and strategies at the national, state, and community levels.
This publication has been produced as part of the Laboratory's strategic long-term research plan. It is published and
made available by USEPA's Office of Research and Development to assist the user community and to link researchers with
their clients.
Sally Gutierrez, Director
National Risk Management Research Laboratory
111
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Abstract
Land use change in USEPA's Region 5 (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin) is occurring
rapidly, particularly with the loss of agricultural land and gain in forest and urbanized land use. The risk of losing habitats
and ecosystems that are critical to the health of the Region is therefore very high; however, identifying high quality, critical
habitats remains a challenge. To address this issue, USEPA researchers developed a spatially-explicit, geographic
information system (GlS)-based model called the "Critical Ecosystem Assessment Model" or "CrEAM". The CrEAM
generated a relative ecological significance score for each undeveloped 300 m by 300 m cell within USEPA Region 5. This
report details protocols that were developed to gather field data to independently and quantitatively verify the CrEAM
generated score. The protocols prescribe data collection which capture measures of diversity, rarity, and persistence for
forested, nonforested, and wetland ecosystems. For each 300 mby 300 m site, data are collected in a 4-hour time period, by a
team of 4 people. Data collected using the protocols in field trials in 2005 and 2006 did not match well with the
corresponding CrEAM scores. However, particularly with respect to the plant communities, the protocol data did reflect
qualitative site assessments conducted by professional ecologists. The protocols were straight-forward to implement in the
field and may be useful for applications beyond this project.
IV
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Table of Contents
Notice ii
Disclaimer ii
Foreword iii
Abstract iv
Table of contents v
List of tables vi
List of figures vi
Acknowledgements vii
Chapter 1 Introduction 1
Chapter 2 Overview of the CrEAM Model 3
Landscape diversity criteria 8
Ecological persistence criteria 8
Landscape rarity 10
Composite scores (Diversity + Persistence + Rarity) 10
Model validation 11
Potential applications 13
Endnotes 13
Chapter 3 Protocol development and testing 15
Field data collection using the protocols 17
Issues in protocol use 19
Chapter 4 Data analysis 21
Methods 21
Results 23
Discussion 31
References 33
Appendix A List of meeting participants and affiliations A-l
Appendix B Forested terrestrial protocol and datasheets B-l
Appendix C Nonforested terrestrial protocol and datasheets C-1
Appendix D Wetlands protocol and datasheets D-l
Appendix E Quality Assurance Project Plan (QAPP) E-l
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Tables
Table 2.1. Land cover pixel aggregation of NLCD data 4
Table 2.2. Descriptions of CrEAM layers and scoring 5
Table 3.1. USEPA quick protocols versus other protocols 16
Table 3.2. Predicted versus observed site conditions 17
Table 4.1. Summary Kruskal-Wallis statistics for CrEAM predicted rank 24
Table 4.2. Summary Kruskal-Wallis statistics for qualitative site assessment rank 25
Table 4.3. Summary Kruskal-Wallis statistics for site characteristics by land cover class 29
Figures
Figure 2.1. Three composite CrEAM layers and combined layer 11
Figure 3.1. Location of field sites in 2005 and 2006 18
Figure 4.2. Qualitative site assessment rankings, diversity and richness variables 26
Figure 4.3. Qualitative site assessment rankings, diversity and richness for forests 27
Figure 4.4. Qualitative site assessment rankings, diversity and richness for nonforests 28
Figure 4.5. Qualitative site assessment rankings, diversity and richness for wetlands 28
Figure 4.6. Richness and diversity differences between forest, nonforest, and wetland sites 30
Figure 4.7. Richness and diversity differences between wetland subclasses 31
VI
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Acknowledgements
This work is the culmination of the effort of many people over many years, and we would like to acknowledge their
contribution here.
Dr. Heriberto Cabezas was chief of the Sustainable Environments Branch at the ORD-NRMRL lab in Cincinnati, Ohio
during the project, and we gratefully acknowledge his oversight, scientific advice, and support for this project from the initial
planning stages through its completion. Several USEPA-Region 5 staff members were critical to the project, and we wish to
extend a special thanks to Dr. David Macarus and Mr. John Perrecone. We also thank Dr. Matthew Hopton at USEPA-ORD-
Cincinnati for help with Figure 2.1, and thank Mr. John McCready at USEPA-ORD-Cincinnati for creating the report cover
design and CD jacket design. Pictures on the cover were taken by the contractor under simplified acquisition #GS-10F-
0114M. Dr. Audrey Mayer, Mr. Brian Westfall, and Dr. Allison Roy were project officers for various portions of the project.
Much of the protocol testing and data collection occurred on state or federally owned parks and preserves, and we thank
the staff in these areas for their invaluable service. These areas include: Midewin National Tallgrass Prairie (IL), Cook
County Forest Preserve (IL), Muscatatuck National Wildlife Refuge (IN), Big Oaks National Wildlife Refuge (IN), Hoosier
National Forest, Indiana Dunes State Park (IN), Hoosier Prairie State Nature Preserve (IN), Hartwick Pines State Park (MI),
Warren Dunes State Park (MI), Silver Lake State Park (MI), Lake Superior State Forest (MI), Huron-Manistee National
Forest (MI), Irwin Prairie State Nature Preserve (OH), Secor Metro Park (Toledo Metroparks, OH), along with sites owned
by the YMCA, The Nature Conservancy, and the University of Michigan. We are especially grateful for assistance from:
Sam Whiteleather (Minnehaha State Fish and Wildlife Preserve, IN); John Jaeger (Toledo Metroparks, OH); Steve Harvey
(Irwin Prairie State Preserve, OH); Cloyce Hedge, Ron Hellmich, and Jack Nelson (Indiana Department of Natural
Resources); and Greg Schneider and Rick Gardner (Ohio Department of Natural Resources).
We would also like to especially thank the participants of the protocol development meetings, whom we list in Appendix
A. These researchers were an invaluable part of the process, helping shape the protocols and boundaries for their use and
application. However, the final protocols presented here inevitably differ from the earlier drafts, and therefore
acknowledgement of these individuals should not be construed as acceptance of the final protocols and analyses.
Students at the University of Helsinki, Department of Biological and Environmental Sciences, participated in forest data
collection in Finland and northwestern Russia using the forest protocol. They provided invaluable comments and suggestions
which improved the protocol, and so we would like to thank Annukka Luomi, Noora Nieminen, Leena Nukari, and Leena
Vihermaa for their hard work and interest.
Finally, we would like to thank Dr. Denis White at the USEPA-ORD-Corvallis; Dr. Doug Boucher, Director of the
Tropical Forest and Climate Initiative, Union of Concerned Scientists; and an anonymous statistician for their thoughtful and
thorough reviews on earlier drafts of this report.
Dr. Audrey Mayer was supported by the Academy of Finland from 2006-2008.
vn
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CHAPTER 1
Introduction
Land use change in the USEPA Region 5 is occurring
rapidly, particularly the loss of agricultural land and gain in
forest and urbanized land use (Potts et al. 2004). The
USEPA Region 5 is the entity within the USEPA with
jurisdictional authority for the geographic region consisting
of Illinois, Indiana, Michigan, Minnesota, Ohio, and
Wisconsin. Henceforth in this report, it will be referred to
simply as Region 5. The USEPA is charged with protecting
human health and the environment, and the rapid rate of
land use change in Region 5 has increased the risk of losing
high quality habitats and ecosystems. Therefore, Region 5
senior management viewed protecting areas of relatively
high ecological significance as "critical" to the USEPA
mission. Identifying and delineating critical ecosystems
throughout the roughly 1 million km2 region is a difficult
task. Further, it is even more difficult to quantify the levels
of ecological significance over such a large region.
Identifying and delineating areas of high ecological
significance, so that they can be protected, is an important
but difficult task. It is even more difficult to quantify the
level of ecological significance of an area. Currently, the
level of ecological significance of an area is frequently
identified using best professional judgment. These
judgments are rarely verified through independent,
quantitative methods and they can be influenced by personal
and professional biases.
To meet the need to identify and delineate critical
ecosystems across Region 5, and to rate the relative
ecological significance of these undeveloped areas, USEPA
researchers developed a predictive model which used
remote sensing technology, spatially explicit data sets, and a
geographic information system (GIS). This GIS-based
predictive model is referred to as the "Critical Ecosystem
Assessment Model" or "CrEAM". The USEPA researchers
defined and estimated relative ecological significance by
applying three equally weighted criteria: ecological
diversity, rarity of land cover type and features, and
persistence of the habitat structure and community (i.e., the
inverse of physical and chemical perturbation). In turn, the
relative magnitudes of each of these criteria were estimated
by indicator measures based on spatially explicit data sets
and manipulations of those data sets. The CrEAM provides
two types of output maps or relative scores for each 300 m x
300 m area of undeveloped land. Results for each of the
three criteria can be accessed as well as the relative
cumulative ratings or scores.
Although this report is primarily intended to describe
three rapid ecological assessment protocols, we include in
this report an abbreviated description of the CrEAM,
including its methodology, data sources, and results, since
verification of this model was the primary reason for
developing the protocols. Because of this linkage with the
CrEAM, several characteristics of the protocols are a direct
consequence of the methods used in the CrEAM, as well as
the test data being collected throughout Region 5. Protocol
characteristics incorporated from the CrEAM include the
300 mby 300 m data collection area, the land cover classes
covered by the protocols, and the emphasis in the protocols
on collecting data to measure the diversity and rarity of, and
threats to, an area. In 2005, the USEPA Science Advisory
Board (SAB) reviewed CrEAM and offered a detailed
assessment on the model's methodology and appropriate
applications (Federal Register 2005, Science Advisory
Board 2005). Comments from this assessment are
incorporated into this description of CrEAM as footnotes
and a discussion at the end of Chapter 2.
Chapter 3 provides an explanation of how the protocols
were developed and tested, and highlights specific issues
that emerged during the field tests. The protocols are
designed for sampling forested (deciduous, evergreen,
mixed), nonforested (grassland, shrubland, bare
rock/sand/clay), and wetland (herbaceous, woody)
ecosystems. These protocols were developed and tested over
a 3 year period. Initial drafts were prepared by over 30
ecologists during a 2-day meeting held at the Region 5
offices in Chicago, Illinois. These draft protocols were first
tested in the field by Region 5 and USEPA Office of
Research & Development (ORD) personnel in the Chicago
area, and adjustments to the protocols were made after this
initial test. A full test of the protocols was performed during
a 3-day meeting in Bloomington Indiana; again, over 30
ecologists participated, including some from the first
protocol drafting meeting.
These final protocols were used to collect data at 26 sites
during the summer of 2005 and 2006. The data were then
used to assess the capability of these protocols to distinguish
between plots having relatively high, medium, and low
ecological significance as per CrEAM predictions. In 2005,
we chose field sites throughout Region 5 to get an even
number of sites in each of the 8 land cover classes
(deciduous, mixed, evergreen forest; grassland, shrubland,
dunes nonforested; forested and emergent wetland), and in
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each of three relative ecological significance categories
(high, medium, and low) predicted by the CrEAM, based on
the cumulative score of each 300 m by 300 m cell (Table
3.1). Due to the lack of areas exhibiting a high level of
ecological significance in the southern half of the Region,
the majority of these sites were in Minnesota, Wisconsin,
and Michigan. Between the 2005 and 2006 field seasons,
our focus changed from verifying the CrEAM to testing the
protocols themselves. In 2006, ASC Group, Inc. collected
data at an additional 10 sites throughout the southern half of
the Region, again representing all protocols. By the end of
the two summers, data had been collected for a total of 26
sites, with 5 sites for each of the following land cover types:
deciduous forest, mixed forest, forested wetlands, emergent
wetlands, and grasslands. In Chapter 4, we analyzed the data
collected with respect to 1) CrEAM predictions of the site,
and 2) qualitative assessments of site condition.
The protocols prescribe the collection of data on a 300 m
by 300 m site, in a 4 hour time period, by a team of 4
people. These data are used as measures of sample plot
characteristics, such as amount of human disturbance, soil
features, and flora and fauna community compositions,
which taken together can indicate the relative level of
ecological significance. In this respect, the protocols may
prove useful for other applications.
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CHAPTER 2
Overview of the CrEAM model
Although natural resource managers are responsible for
decisions which affect their jurisdictions at several scales,
information to support these decisions is rarely available for
all but the smallest areas. This is particularly true for the
significance of a small area to ecological sustainability goals
for larger assessment regions, regardless of how this
significance is measured (e.g., Jenson et al. 1996, Costanza
and Mageau 1999, O'Malley and Wing 2000, Xu et al.
2001, Campbell 2001). Collecting data that are consistent
and comparable over large areas is an additional challenge
to informed decision-making (Levin et al. 1997, Gaston
2000, Patil et al. 2001, Verburg et al. 2002). Landscape-
scale ecological assessment methods have been developed
for the Mid-Atlantic Region of the United States (the
Regional Vulnerability Assessment (REVA); Jones et al.
1997, Patil et al. 2002, Locantore et al. 2004, Smith et al.
2004), as well as the state of Maryland (the Green
Infrastructure Assessment; Weber and Wolf 2000), but the
unique landscape disturbances in the northern midwestern
United States, dominated by intense agriculture, suggested
that a different methodology would be prudent.
The Critical Ecosystems Team of USEPA Region 5 was
charged with the task of assessing ecological significance1
in the six Region 5 states. For this effort, ecologically
significant areas were considered to be distinct, unique
landscapes with high levels of biological diversity,
persistence, and rarity. Although this does not follow a
strict, ecological definition, this operational definition
focused the criteria on essential characteristics of robust
ecosystems, and could be used to identify the quality or
condition of habitat patches. In this report, the model was
validated by comparing GIS model predictions to field data
measuring the same characteristics (diversity, persistence,
rarity), rather than a broader definition of ecological
significance. The primary objective of the model was to
identify the most ecologically significant areas across the
Region so that Regional USEPA staff could use the
information to:
• guide internal USEPA resource allocations;
• track general landscape-scale conditions in the
Region;
• aid in reviewing grant proposals;
• identify and target protection and restoration efforts;
• aid in issuing and/or reviewing air and water quality
permits;
• inform National Environmental Policy Act (NEPA)
reviews;
• and help set compliance, enforcement or cleanup
targets2.
A GIS platform was used to allow investigators to
efficiently aggregate multiple geographically referenced
datasets, and can be used effectively to conduct landscape
scale analysis (van Horssen et al. 1999, Aspinall and
Pearson 2000, DellaSalla et al. 2001, Bojorquez-Tapia et al.
2002). The National Land Cover Database3 (NLCD,
Loveland and Shaw 1996) with a picture element ("pixel"4)
size of 30 m by 30 m was used as the base layer. This
database was generated by the Multi-Resolution Land
Characteristics (MRLC) program, begun as a cooperative
effort among four U.S. government agencies at the Earth
Resources Observation Systems (EROS) Data Center of the
US Geological Survey. The coverage is a mosaic of satellite
scenes taken between 1990 and 1992 in which the pixels
were classified into 23 land cover types in the continental
United States (Anderson et al. 1976).
In Region 5, three of 23 potential land cover categories
(perennial ice/snow, evergreen shrub land, and mixed shrub
land) were not present. Of the 20 land cover categories in
Region 5, nine are considered undeveloped and therefore
ecologically significant (Wade and Ebert 2005). These nine
(mixed forest, bare rock/sand/clay, evergreen forest,
deciduous forest, shrub land, woody wetlands, herbaceous
wetlands, grasslands/herbaceous vegetation) plus open
water (lakes and rivers, excluding the Great Lakes) were
used in further analyses. The original 30 m by 30 m pixels
from the NLCD were aggregated into 300 mby 300 m cells
to facilitate computer processing. These cells were assigned
the land cover classification possessed by the majority of the
10 by 10 pixels (Table 2.1). In forested areas where there
was no majority, deciduous and coniferous forest tallies
were summed and reclassified as mixed forest.
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Table 2.1. Percent of pixels by land cover for undeveloped data and number of cells after aggregation by median
and dominance.
NLCD land cover type
Open water
Sand/rock
Deciduous forest
Coniferous forest
Mixed forest
Shrubland
Grassland
Woody wetland
Herbaceous wetland
Original data
(% 30 m2
pixels)
7.39
0.05
52.42
7.02
6.94
0.32
1.79
18.58
5.50
Aggregated by
median
(% 300 m2 pixels)
7.44
0.05
52.51
6.93
6.89
0.32
1.78
18.60
5.48
Aggregated by
dominance
(% 300 m2 pixels)
8.20
0.03
56.08
6.36
4.24
0.22
0.64
20.21
4.03
Error rate for
aggregation by
dominance (%)
10.92
-48.84
6.98
-9.39
-38.87
-31.52
-64.30
8.76
-26.63
The CrEAM is a landscape scale assessment method
using GIS to compile a variety of spatially explicit data
available for the region, describing three broad categories:
1. Landscape diversity5: The presence of population,
community, and/or ecosystem diversity (Ehrlich
and Wilson 1991, Chapin et al. 2000);
2. Ecological persistence: The potential for an
ecosystem to persist without loss or decline,
preferably without external assistance or
management (Dale et al. 2000, Gunderson et al.
2002);
3. Landscape rarity: The occurrences of rare native
species, or communities and land cover types of
special ecological interest (Dobson et al. 1997,
Pimm and Lawton 1998).
Relevant existing datasets were used as indicators for the
three criteria. Datasets were spatially and temporally
consistent, covering the entire six state region, and
representative of conditions that existed in the early 1990's.
A total of 20 datasets were used as indicators for the three
criteria: 4 for landscape diversity, 12 for ecological
persistence, and 4 for landscape rarity (Table 2.2). In all of
the data layers and resultant criteria layers, scores were
scaled from 0 to 100, with zero indicating the lowest
quality, the greatest stress, or the least valuable observation.
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Table 2.2. Descriptions of the layers and scoring (see descriptions in text for more information).
Layer name
Layer description
Data source(s)
Extent
Resolution
Scoring
Landscape Diversity
Patch sizes of
undeveloped
land
Land cover
diversity
-evaluation of contiguous undeveloped areas
-based on principle that larger undeveloped areas favor
diversity
-only considered polygons >10 ha
-Shannon's diversity index on NLCD satellite imagery
-relative land cover diversity within Omernik Ecoregions
-NLCD satellite imagery
-Omernik Ecoregions
-NLCD satellite imagery
-Omernck Ecoregions
Omernik
Ecoregion
Omernik
Ecoregions
1 pixel
1 km by 1 km
squares
Continuum from 0 to 1 00 based on log
distribution of patches. The resultant
values spanned 7 orders of magnitude
in size, so to make comparisons
meaningful the patch areas were Iog10
transformed.
Continuum from 0 to 100 based on
ATtlLA diversity scores.
-considers both richness (# different categories) and
evenness (similarity of relative abundances)
-30m by 30m pixels aggregated into 1 km by 1 km
squares
-diversity "script" from ATtlLA tools (USEPA/ORD-LV)
were used
Temperature & -1990 to 1999 daily averages from Midwestern Regional
precipitation Climate Center (MRCC)
maxima -selection of areas having the highest temperature and
precipitation
-based on presumption that higher temperatures and
greater precipitation favor diversity
Temporal -comparison of NLCD land cover with Kuchler potential
continuity of natural vegetation
land cover type -evaluation of current (c. 1993) land cover type relative
to potential dominant native vegetation as an indicator of
potential to support diversity
-MRCC temperature
"bands"
-MRCC precipitation
"bands"
-Omernik Ecoregions
-NLCD satellite imagery
- Kuchler potential
natural vegetation
Omernik 12,500 ha or
Ecoregions 11 km by 11
km squares
Region 5
1 pixel
0 or 100, with 0 indicating minimum
temperature and precipitation.
Each cell assigned 0 (if incompatible
cover type with potential vegetation) or
100 (if compatible).
Ecological Persistence (continuity)
Perimeter to -evaluation of the boundary regularity of land cover
area ratio patches
-based on the principle that the least amount of
boundary results in the lowest amount of "edge effect"
thereby yielding the least disturbance or greatest
sustainability of the (interior) ecosystem(s)
-only considered polygons >10ha
Patch size by -evaluation of land cover patch sizes
land cover -based on principle that larger areas having similar
ecosystem types have greater sustainability
-only considered polygons >10 ha
Weighted road -evaluation of landscape fragmentation by roads
density -road density index applied to TIGER road dataset
segregated into 5 km by 5 km cells
-total road lengths weighted by a road classification
factor
-NLCD satellite imagery Omernik 1 pixel
-Omernik Ecoregions Ecoregion
-NLCD satellite imagery Omernik 1 pixel
-Omernik Ecoregions Ecoregion
-NLCD satellite imagery Regions 5 km by 5 km
-TIGER road data squares
Continuum from 0 to 100 based on log
of ratio of actual to ideal perimeter/area
(larger ratios resulted in higher scores).
Continuum from 0 to 100 based on log
distribution of polygons. The results
yielded a fragmentation indicator with a
range that spanned 7 orders of
magnitude, so to make the comparison
meaningful, a Iog10 transformation of the
area was used.
Continuum from 0 to 100 based on log
distribution of road densities (continuum
was a sum of all road types).
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Layer name
Layer description
Data source(s)
Extent
Resolution
Scoring
Waterway -identification of reservoirs for downgrading based on
impoundment dam locations
-dams and corresponding reservoirs interrupt the
continuities (fragmentation) of waterways
-intersection of NLCD open water and wetland patches
with Corps of Engineers dam locations
Land cover -comparison of NLCD land cover with Kuchler potential
suitability natural vegetation
-evaluation of current (c. 1993) land cover relative to
potential dominant native vegetation as an indicator of
the likelihood of sustainability of the corresponding
ecosystems
-NLCD satellite imagery Region 5 1 pixel
Corps of Engineers dam
data
-NLCD satellite imagery Regions <90haor<1
- Kuchler potential km by 1 km
natural vegetation
0 or 100, with 0 indicating that a dam or
other impoundment is present, 100 no
impoundments.
0 (current land cover not matching
potential vegetation) or 100 (current
cover matching potential).
Ecological Persistence (stressors)
Airport buffers -the zone of disturbance extents surrounding airports
are directly related to the sizes of the airplanes utilizing
them. Further, airplane sizes are directly related to
airport runway lengths. Therefore, the extents of the
zone of disturbance are directly related to the runway
lengths.
National Priority -unowned sites where hazardous waste was released to
List Superfund the environment and which were in the formal clean up
sites process during FY2000
-site property, plus a 300 m "disturbance zone" around
the periphery, is downgraded
RCRA owned sites where hazardous waste was released to
Corrective the environment and which were in the formal clean up
Action sites process during FY2000
-facility property, plus a 300 m "disturbance zone"
around the periphery, is downgraded
Water quality -ambient levels of total suspended solids, dissolved
summary oxygen, and nitrate/nitrite nitrogen based on summary of
1990 to 1994 NPDES permitted discharge levels
-using USEPA Office of Water BASINS model to
determine ambient levels
Watershed -dam density by watershed
obstruction -normalized for watershed area
Air quality -OPPT air risk model output for 85 pollutants
summary -human health toxicity used as a surrogate for
ecotoxicity
-scoring based on number of pollutants that exceeded a
chronic non-cancer threshold
Development -activities in urban and agricultural areas generate
disturbance disturbances to surrounding areas
buffer -300 m width buffer zone will surround >10 ha urban and
agricultural polygons
-takes into account stressors such as pesticides,
fertilizers, and noise
FAA runway length data Region 5
Region 5 CIRCLIS Regions
database
Region 5 RCRIS Region 5
database
STORE! water quality Omernik
data Ecoregion
Corps of Engineers dam Regions
data
TRI data Region 6
NLCD satellite imagery Region 5
0.5 cell 0 (at or within an airport buffer zone) or
100 (outside of buffer)6.
0.5 cell 0 (at or within 300 m of a site) or 100
(outside of buffer).
0.5 cell 0 (at or within 300 m of a site) or 100
(outside of buffer).
8-digit HUC Cells in HUCs which had no violations
of pollution thresholds received 100, if
one threshold exceeded the cell
received 66, if two thresholds 33, if all
three thresholds 0.
1 pixel Continuum depending upon the number
of dams in a HUC (from 0 to 209).
Census tract Cells in census tracts with no quality
violations or exceptions received 100,
cells with five or more received 0, and
the rest received a continuous score
between 0 and 100.
1 pixel 0 (at or within 300 m buffer) or 100
(outside of buffer)7.
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Layer name
Layer description
Data source(s)
Extent
Resolution
Scoring
Landscape rarity
Land cover
rarity
Species rarity
Rare species
abundance
Rare species
taxa
abundance
-NLCD data was summarized by Omernik Ecoregion
-each pixel was given a score based on the relative
rarity of the land cover type in the ecoregion
The highest species rarity (G1, G2, G3, G4, G5)
observed in a 7.5 minute quad
-NLCD satellite imagery
-Omernik Ecoregions
Natural Heritage
Database
The number of G1, G2, & G3 species occurrences per
7.5 minute quad
Natural Heritage
Database
The number of broad taxonomic groups of G1, G2, and
G3 species per 7.5 minute quad
Natural Heritage
Database
Omernik 1 pixel Cells of the more rare land cover type
Ecoregion (determined by # of cells) received 100,
cells in the most common type received
0, and other land cover types received
scores distributed logarithmic-ally
between 0 and 100.
Regions 7.5 minute If the highest observation in the quad
quads was G1, the whole quad received the
score of 100; if G2 through G5 the quad
scored 75, 50, 25, or 0, respectively. A
score from 100 to 0 was assigned to
each quad in the region, and each cell
was assigned the score of the quad in
which it was located.
Region 5 7.5 minute Rare species were those having GHRS
quads ranks of G1 through G3, so the number
of reported G1, G2, and G3 species
was summed for each quad in the
region. Quads with zero rare species
received a score of 0, those with 1-2
species received 25, 3-9 species
received 50, 10-15 received 75, and
quads with more than 15 rare species
received 1008.
Region 5 7.5 minute Quads with no presence of rare
quads taxonomic groups received a 0, quads
with 1 received a score of 25, quads
with 2-3 received 50, those with 4-6
received 75, and more than six received
1009.
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Landscape diversity criteria
Biological diversity typically refers to the number of
species (i.e., species richness) and distribution of
abundances of these species (i.e., evenness) within a defined
area. However, diversity has been measured at many scales,
from genes to communities to ecosystems, and has included
ecosystem processes, structures, and functions (Chapin et al.
2000, Dale et al. 2000, Convention on Biological Diversity
- Article 2). Indices of species and community diversity
require data that can be difficult and expensive to obtain,
especially at larger scales. The following four datasets were
used as indicators of relative landscape diversity. The four
ecological diversity layers were rasterized to the cell unit
and summed to produce a composite diversity layer.
1) Patch size of undeveloped land10 - Undeveloped patches
were defined as areas of undeveloped land cover surrounded
by developed11 land cover types. The size of undeveloped
land cover patches was used as an indicator of species
diversity, based on island biogeography theory which
correlates species richness with "island" (undeveloped
patch) size (MacArthur and Wilson 1967, Rosenzweig 1995,
Dale et al. 2000). For this layer, all pixels of undeveloped
land cover (irrespective of land cover type) were aggregated
into patches and the area of each patch was calculated.
Patches under 10 ha were omitted12.
2) Land cover diversity- The nine, undeveloped NLCD land
cover classes were used to calculate land cover diversity.
Diversity was calculated using the Shannon (H') index,
which was calculated for 1 km by 1 km13 squares using the
30 m by 30 m land cover (Magurran 1988). Each H' value
was then multiplied by the percent undeveloped area in each
respective 1 km by 1 km square in order to produce a
weighted or modified Shannon index.
3) Temperature and precipitation maxima14 - Areas having
the highest average temperature and precipitation were used
as an indicator of species diversity based on the ecological
principle that warmer, moister climate favors higher
numbers of species (Lugo and Brown 1991, Gaston 2000).
Sarkar et al. (2005) found that environmental data can be
used as surrogates for species diversity data, particularly
over large areas. Daily average temperature and daily total
precipitation data for the Midwest for 1990-1999 were
obtained in summary contours from the Midwestern
Regional Climate Center, Champaign IL. These data were
then georeferenced using 25 registration tie points
distributed on the state borders. Once georegistered, this
combined temperature and precipitation data layer was
superimposed onto the Omernik Level III Ecoregions, to
identify the portion of each ecoregion that was likely to
have the highest species diversity based on temperature and
moisture maxima (Omernik 1995, Omernik and Bailey
1997).
4) Temporal continuity of land cover type15 - Temporal
continuity was used as an indicator of species diversity since
long-term, established ecosystems tend to have more
complex communities with more species than younger
systems (Krohne 2001). For this calculation Ktichler
potential vegetation types based on climate and soils
(Ktichler 1964) were cross-referenced with the NLCD land
cover classifications, and classification correspondence was
used as an indicator of temporal continuity. Classification
correspondence was only considered if the land cover
classes were compatible. Compatibility was based on
whether the Ktichler classification could reasonably be
envisioned as existing within the NLCD classification. For
example, patches of oak hickory forest could exist in cells
classified by the NLCD as mixed forest, since tree species
are heterogeneously distributed in mixed forests and the
deciduous portion of the mixed forest could consist of oak
and hickory trees. NLCD cells that were deemed compatible
were assigned a score of 100, whereas cells with
incompatible vegetation were assigned a score of 0. Of the
nine undeveloped NLCD classifications, three
classifications (open water, bare rock/sand/clay, and
emergent herbaceous wetlands) were viewed as potentially
occurring anywhere in Region 5 and, thus, were treated as
universally compatible. The other six NLCD classifications
were viewed as being compatible with some of the Ktichler
potential vegetation types but not others.
Ecological persistence criteria
Ecological persistence was defined as the potential for
an ecosystem to persist for 100 years16, an arbitrary number
which may suggest a stable ecosystem, without external
assistance (e.g., management). Persistence was viewed as
being negatively impacted by two factors, landscape
fragmentation and presence of chemical, physical, and
biological stressors (Underwood 1989, Patil et al. 2001). A
data layer was included if it contributed information on
fragmentation or stressors, if consistent regional coverage
was available, and if it did not duplicate information in
another dataset within this criterion. The latter consideration
was subsequently verified through sensitivity analysis.
Landscape fragmentation was characterized by five datasets,
and stressors by seven datasets (Table 2.2). Although non-
indigenous invasive species are considered to be very
important stressors, they were not included due to the
unavailability of reliable, Region-wide datasets.
5) Patch perimeter to area analysis - NLCD pixel data were
aggregated into patches by land cover type, and the
perimeter of each patch was calculated (boundary
convolution was used as a measure of landscape
fragmentation; Gascon et al. 2000). Patches less than 10 ha
were eliminated. Low perimeter to area ratios translate into
patches impacted by lower edge effects (e.g., increased
exotic species invasions, microclimate changes), and these
patches received higher scores. Since shallower waters and
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shorelines tend to be the most active biologically, for open
water the perimeter-to-area ratio scores were inverted.
6) Patch size by land cover- The inverse of the size of a
patch of land was used as a direct measure of landscape
fragmentation; larger the patch of the same land cover type,
the higher the likely persistence of that patch (Dale et al.
2000, Gascon et al. 2000, Krohne 2001). Patch size was
calculated by aggregating the contiguous undeveloped
pixels of the same land cover type and calculating the area
(patches under 10 ha were omitted).
7) Weighted road density -Roads fragment undeveloped
areas, introduce corridors for invasive plants and animals,
modify hydrology and cause disturbance zones on both
sides of the road (Southerland 1994, Forman and Alexander
1998, Abbitt et al. 2000, Gascon et al. 2000, Lindenmayer
and Franklin 2002). Tiger/Line files from the U.S. Bureau
of the Census for 1990 were used to calculate road densities
in 5 km by 5 km squares across the region by summing the
linear lengths of roads. These road densities were then
weighted by road category (miscellaneous, local/rural,
secondary, primary) using multipliers of 1, 2, 2.67, and 3,
which correspond to the expected disturbance buffer of 600
m, 1200 m, 1600 m, and 1800 m for each road category,
respectively. The array of 5 km by 5 km squares, each
having a single weighted road density, was superimposed
onto the NLCD base map of undeveloped areas, and each
300 m by 300 m cell was assigned the weighted road
density score corresponding to the grid square in which it
was located.
8) Waterway impoundments17 - Dams, irrigation diversions,
and other water management structures can disrupt the
hydrology of streams and wetlands, and disturb critical
reproductive and foraging behavior for amphibious and
aquatic species, negatively impacting the species diversity
these habitats can support (Dougherty et al. 1995, Wilcove
et al. 1998). The location of the dams in the region was
obtained from the USGS, Reston VA, for the period ending
1996 (http://mapping.usgs.gov/esic/exic index.html). The
point data were superimposed onto the undeveloped NLCD
data which had been aggregated into patches. Any open
water, forested wetland or emergent wetland patch that was
within 500 m (an arbitrary distance) of a dam18 was
considered to be artificially impounded and thus
hydrologically fragmented. Cells located within the
impounded patches were given a score of 0 and the rest of
the cells were scored at 10019.
9) Airport buffers - The noise related to airports is a well-
known disturbance and stressor to wildlife (Manci et al.
1988). In general, the decibels of noise that an aircraft
produced was directly proportional to the size of the aircraft,
which is roughly proportional to the length of the runway
required by the aircraft (Dillingham and Martin 2000, FAA
2002). Public use airport data from the Bureau of
Transportation Statistics for 1996 was mapped and buffers
of various sizes were applied to the runways (from a buffer
of 610 m for very small airports with <500 m runways, to a
buffer of 7500 m around very large airports with 2000 m
runways). These two categories were based on runway
length only and no consideration was given to frequency of
use20. A buffer of 7500 m for very large airports was based
on a linear breakpoint of time vs. noise level data from
GAO(2000)21.
10) NPL Superfund sites - Superfund sites are areas with
high concentrations of contaminants which are known to
negatively impact the health of humans and the environment
(See http://www.epa.gov/superfund/sites/npl/npl hrs.htm
for an overview of the NPL listing process). The National
Priority List Superfund sites were mapped and buffered with
a 300 m radius. This buffer size was based on evidence that
the disturbance to forests due to edge effects can extend as
much as 300 m into the undeveloped area (Gascon et al.
2000).
11) RCRA corrective action sites22 - Resource Conservation
and Recovery Act (RCRA) sites that were identified as
"known or reasonably suspected to contain contamination at
unacceptable levels in groundwater other media to which
human exposures23 could occur" were used to indicate
disturbance. Human health exposure was used due to the
lack of environmental exposure information. The locations
of active corrective action sites which were regulated under
the RCRA and were in the Resource Conservation and
Recovery Information System database as of 2000 were
mapped (USEPA 2002). The cells with natural land cover
within the sites were considered to have been impacted by
the chemical stresses arising from hazardous waste releases
as well as the disruptive physical and chemical stresses
associated with cleanup and other activities at these sites.
These locations were buffered by 300 m (using the same
evidence used in the Superfund layer).
12) Watershed disturbance24 - Dissolved oxygen (DO),
nitrate and nitrite nitrogen (N), and total suspended solids
(TSS)25 are water quality parameters frequently associated
with impacts from agriculture and urban development.
These three parameters are the most widely available water
quality parameters recorded in the STORET (STOrage and
RETrieval) database for the Region 5 area. The STORET
database is an USEPA repository of water quality,
biological, and physical data collected from stream
monitoring programs throughout the United States
(http://www.epa.gov/STORETA. USEPA BASINS (Better
Assessment Science Integrating Point and Nonpoint
Sources) software was used to calculate the average DO, N,
and TSS in 8 digit USGS HUC from STORET data during
the years 1990-1994 (USEPA 2001). Threshold limits were
6 ppm for DO (Helsel 1993), 3 ppm for N from the federal
ambient water quality criteria (USEPA 1986), and 80 ppm
-------
for TSS. For each parameter, BASINS was used to identify
the HUCs for which parameter averages exceeded 85% of
the threshold limit.
13) Watershed obstructions - Data from the U.S. Army
Corps of Engineers26 was used for water impoundments in
this layer. To determine the intensity of hydrologic
alteration, the number of dams within each 8-digit HUC was
summed and assigned to all cells within the HUC.
14) Air quality summary - The USEPA air quality model,
Assessment System for Population Exposure Nationwide
(ASPEN), was used to obtain predicted ambient air
pollution concentrations (Rosenbaum et al. 1999). ASPEN
provides outdoor air concentrations for 148 of the 189
hazardous air pollutants listed in the 1990 Clean Air Act
Amendments. Concentrations of pollutants were predicted
by modeling air emissions from major stationary sources,
mobile sources, and area sources. Background was
estimated by considering residual air pollutants from
previous human activities, pollutants transferred from other
countries, and natural emission sources. In the current work,
we only included 85 of the air pollutants27, based on the
availability of robust human health, non-cancer chronic
health benchmarks (Caldwell et al. 1998). Human health
benchmarks28 were used in this study due to the lack of
widely available values for chronic stress on ecological
endpoints. A ratio was generated for each pollutant by
census tract29 by dividing the predicted ambient
concentration by the corresponding non-cancer chronic
health benchmark. Ratios greater or equal to one indicated
that the benchmark was exceeded.
15) Development disturbance buffer - The developed pixels
were aggregated into contiguous patches, and a 300 m
buffer zone was created outside each patch. Using the same
rationale as for the RCRA sites, these zones immediately
adjacent to the patches of development were presumed to be
stressed. While it is likely that different development types
will stress the environment by different amounts, there was
no quantitative evidence in the literature to support that
hypothesis, thus a single buffer of 300 m30 was used.
16) Land cover suitability - Land cover suitability provided
an indicator of the existing land cover viability. For this
layer, existing land cover types identified by the NLCD
were cross-referenced to the Ktichler potential vegetation
designations (Ktichler 1964) in the same manner that they
were for the temporal continuity of land cover type metric.
The cells of undeveloped land with land cover that
corresponded to the same types given in the Ktichler maps
were given the maximum score. Due to the lack of detail in
the Ktichler maps, some land cover categories such as open
water or bare land did not map into a potential vegetation
type. In order to not penalize these land cover cells, they
were given the maximum score (100).
Landscape rarity
Rarity is a measure of the abundance and/or the
distribution of an ecological unit, such as a species or
habitat type (Kuninand Gaston 1993, Gaston 1994). The
rarity composite used here is a combination of land cover
and biotic rarity. Land cover rarity is a measure of the
frequency distribution of NLCD land cover types within
Omernik ecoregions. Biotic rarity included both species
rarity and the rarity of higher taxonomic units. The biotic
rarity layers are based on rare species inventories of the six
states National Heritage Programs (NHP's). The Gl through
G5 Global Heritage Ranking System (GHRS) conservation
status ranks used by the NHP were adopted (Stein 2001):
Gl (critically imperiled); G2 (imperiled); G3 (vulnerable);
G4 (apparently secure); and G5 (secure). The NHPs of the
six Region 5 states provided these data to USEPA under
confidential business information (CBI) protection. Due to
the legal agreement, the data can only be summarized by
USGS 7.5 minute quadrangle (quad)31.
17) Land cover rarity - The cells of undeveloped land cover
were analyzed by ecoregion. Some ecoregions have as few
as three land cover types and some as many as six, but the
frequency distribution by land cover was always a
logarithmic distribution.
18) Species rarity -Within a quad, the rarest GHRS rank
determined the score for the entire quad.
19) Rare species abundance - The number of rare species
sighted in a quad was used as a measure of rare species
abundance.
20) Rare taxa abundance - The number of broad taxonomic
groups represented by the Gl, G2, and G3 species occurring
in a quad as rare species taxa abundance were reported. For
this indicator the broad taxonomic group designations
established by the NHP are amphibian, bird, bryophyte,
chelicerate, crustacean, dicot, fish, gymnosperm, insect,
lichen, mammal, mollusk, monocot, platyhelminth,
pteridophyte, reptile, and uniramian arthropod.
Composite scores (Diversity + Persistence + Rarity)
The 20 summary scores generated from the GIS data
layers were summed by criterion for each undeveloped cell
across Region 5 (Table 2.2). This resulted in 3 sets of raw
composite scores, one set each for Diversity, Persistence,
and Rarity. The model is linear and all of the data layers
were weighted equally. Unequal weighting is a value
judgment which, with little evidence, can introduce larger
artificial biases than the errors that they were intended to
alleviate (Dawes 1986)32.
The three sets of composite scores representing the three
criteria were weighted equally as well, based on the same
10
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logic applied to the 20 individual datasets. Each set of
composite scores was normalized from 1 to 100 so that each
criterion exerted an equal influence on the final scores. The
final scores for each cell were generated by summing the
three composite scores33. Thus, each undeveloped land
cover cell across Region 5 was assigned a relative rating
potentially ranging between 0 and 300 (Figure 2.1). This
data reduction approach has been not been subject to a
statistical evaluation, that is, it has not been evaluated
against competing data reduction methods. There is no
guarantee that this data reduction method is appropriate or
the "best" method (provides the optimal classification rule).
The purpose of this investigation is to provide a protocol for
assessing terrestrial ecosystem quality, based on the CrEAM
model that has been reported elsewhere. An in-depth
analysis of the individual category layers and competing
data reduction techniques is beyond the scope of this paper.
Diversity
(4 data layers)
Persistence
(12 data layers)
Rarity
(4 data layers)
Figure 2.1. Three composite layers and combined composite layer
Model validation34
Qualitative validation
Two types of qualitative validation have been performed
for CrEAM. First, CrEAM scores for cells in national or
state protected areas (which are areas of ecological
importance) were compared to cells outside of these areas.
Cells scored in the highest 1% of all the predicted scores in
the following locations: St. Croix River area in Minnesota,
Barabou Hills area in south-central Wisconsin, Shawnee
National Forest in southern Illinois, Indiana dunes along the
southern shore of Lake Michigan, and Sleeping Bear dunes
of the east shore of Lake Michigan in Michigan, Hoosier
National Forest in southern Indiana, and the Wayne
National Forest in southern Ohio. Second, CrEAM results
were compared to The Nature Conservancy's (TNC)
ecosystem conservation planning assessment (Poiani and
Richter 2000). The TNC has created a "portfolio" of sites
which consists of areas they believe are important to
preserve indigenous flora and fauna. The polygons of the
portfolio sites were converted into cells and developed cells
were removed from consideration. The portfolio sites
occupy 1,620,484 cells out of a total of 3,634,183
undeveloped cells, or 45% of the undeveloped area in the
study area. Of the highest scoring cells in the CrEAM
model, 56% of them were within TNC portfolio sites.
11
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Quantitative validation
The most direct and quantitative way to validate a GIS
effort is to field assess a number of randomly selected
undeveloped cells and compare the results to the
corresponding model predictions. Three quick assessment
protocols were developed to address broad land cover types,
including: terrestrial forest, terrestrial non-forest, and
wetlands. A fourth protocol for open water (lakes) was also
written and underwent an initial field test by USEPA staff;
however, the protocol is in a draft stage and not ready for
field implementation. The protocols (detailed in Chapter 3)
were designed to collect data relative to an area's diversity,
persistence, and rarity. They contain assessment measures
for the nine undeveloped land cover types occurring in the
model. Data were collected using these protocols at 26 sites
throughout Region 5, and the CrEAM model predictions
were assessed via correlation analyses between scores
generated by protocol data and CrEAM scores (Chapter 4).
Sensitivity and uncertainty analysis
In the CrEAM model the data layers are equally
weighted, and so there are no parameters or coefficients to
validate. A sensitivity analysis would first test how this
equal weighting of each layer affects the outcome of the
composite score. Sensitivity of the model predictions
depends on the quality of the data that are being included
and the number of data layers within a criterion (since each
criterion is scaled from 0 to 100 regardless of the number of
data layers). Sensitivity analysis investigating the effect of
unequal numbers of data layers, and the importance of each
variable in affecting the composite score, will hopefully be
completed in the future.
Duplication of data between the data layers within the
composite criterion was tested. If there were a high
correlation between two data layers, it would be equivalent
to applying a weight to the layer. Within the diversity
layers, the highest Kendall correlation was 0.41, and that
occurred between the layer 2 (land cover diversity) and
layer 1 (patch size of undeveloped land). Among the
persistence variables, the highest correlation was 0.45,
between layer 7 (weighted road density) and layer 15
(development disturbance buffers). And finally, within the
rarity layers, the highest correlation was 0.52, between layer
19 (rare species abundance) and layer 20 (rare taxa
abundance). None of these are exceptionally high
correlations (maximum variability explained less than 30%;
n=3,634,183; pO.OOOl) indicating that if any of the
individual data layers were omitted, information toward the
final scores would be lost. Factor analysis could not be
conducted to determine the individual contribution of each
layer on the final score because a number of the layers were
not continuous (that is, some were scored as either 0 or 100
rather than a continuous distribution between 0 and 100).
The most obvious omission is the lack of data layers that
inform ecological processes (as noted by the SAB). These
data are difficult to quantify on a small scale and even more
difficult on the landscape scale. Although it might be
possible to include groundwater recharge or carbon
sequestration data, the data would have to be available from
a consistent source across all six states, a common
limitation. Natural disturbance regimes are another essential
attribute that lack data. A future update of the model might
collect and quantify information about the history of large
scale storms and tornados. Other additions could include
genetic diversity (Bagley et al. 2003), light pollution
(Longcore and Rich 2004), and agricultural pesticide drift.
Aggregation errors
The resolution of the NLCD data was the 30 m by 30 m
pixel, and many of the layers were constructed using that
scale. The base layer for analysis was the land cover data
aggregated by dominant land cover type to the 300 m by
300 m cell. Moody and Woodcock (1994) found that
aggregation errors in satellite data tend to occur when the
data passes the 90 m threshold. Another effect, called
modifiable area! unit problem (MAUP; Plante et al. 2004),
results in errors depending upon the variable (such as land
cover class) and requires serious consideration when data of
different scales and geographic measures are compared
(Board on Earth Sciences and Resources 2002). Table 2.1
shows the percent land cover of the original data and the
data when aggregated by median and dominance. The
percent error for aggregation was calculated based on the
deviation from the one pixel percent coverage. The
categories with fewer pixels experience the highest percent
error, and the bias is toward reducing the number of cells,
not increasing them. For example, sand/rock covered 0.05 %
of the pixels, and 0.03 % of the aggregated cells, resulting in
a -48.84 % error rate. Conversely, deciduous forest covered
52.42 % of the pixels, and 56.08 % of the aggregated cells,
resulting in a 6.98 % error rate. Additionally, with
aggregation there is a loss of cells (A = 357728 cells, or 9%
less); if the majority of pixels in a cell was a developed land
cover type, the cell was eliminated. During analysis,
polygons and shapes less than 10 ha were omitted, creating
a bias against the most fragmented landscapes in developed
areas. Despite the error rate, we chose to aggregate by
dominance for two reasons. First, as stated previously, error
rates in identification of the land cover type of the NLCD
data are reduced when patch homogeneity increases.
Second, we were anticipating a ground validation exercise
and wanted to be sure that if a cell were picked for field
investigation, it would have a majority of the same land
cover type within it.
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Potential applications
Almost forty percent of the land area is undeveloped in
Region 5. However, most regulatory actions take place in
developed areas. This leaves a large portion of the region
without assessment or consideration, yet the agency is
charged with protecting all air, land and waters irrespective
of land ownership. USEPA is the only federal agency that
has the opportunity to protect the environment in such a
holistic manner.
In addition to the intended uses of CrEAM discussed at
the beginning of this chapter, the model could also provide a
trend analysis of ecosystem condition in the Region. The
data presented here represents the conditions in the early
1990's because the NLCD data that is the basis of much of
the analysis was collected from 1990-1992. If the same
analysis were rerun using the NLCD 2000 land cover and
corresponding data layers form 2000-2002, the results could
be compared to 1990. It would become possible to track
improvements due to restoration and protection efforts, as
well as document degradation in quality across the Region
at a landscape scale.
There are a number of programs which may benefit from
CrEAM (particularly if it is revised as per the SAB's
suggestions):
• The Assessment and Watershed Protection Division
of the Office of Water in USEPA Headquarters has
proposed using the data to create a "Stressor x
Quality" diagram to assist in their work. They are
considering prioritizing restoration efforts based on
recovery potential of watersheds (Norton 2004).
• In Region 5, the Underground Injection Control
Program for the state of Michigan is administered
by regional personnel. They have expressed an
interest in using these results to help them
prioritize well inspections.
• In other inspection, enforcement, or granting
activities, the sites near the highest scoring areas or
those most at risk could be used to help prioritize
workloads or grant awards.
• Analysts reviewing National Environmental Policy
Act (NEPA) Environmental Impact Statements
(EIS) could benefit from knowing the relative
ecological significance of various options being
proposed.
• A Supplemental Environmental Project (SEP) is part
of an enforcement settlement where a violator
voluntarily agrees to an environmental project. The
SEP must have a nexus (i.e., connection) to the
violation, and model results could help establish
that nexus and identify areas for restoration.
• The pesticide program has expressed an interest in
including this information in the training materials
that they provide to the states for training pesticide
applicators.
Aside from the numerous critiques the SAB provided
that are already listed in footnotes, the SAB supported the
concept and broad methodological approach of CrEAM, and
encouraged Region 5 to update and revise CrEAM with the
SAB's comments as a guide (Federal Register 2005, Science
Advisory Board 2005). The development of the rapid
assessment protocols, and their use to collect data in Region
5 for CrEAM validation, was a critical validation step
outlined by the SAB.
Endnotes
1 The SAB did not believe that the CrEAM methodology
reflected "ecological significance," because the
methodology lacks information on ecosystem processes,
functions, and ecosystem services.
2 The SAB stated that scientifically defensible uses of the
current version of CrEAM included: guidance for internal
USEPA resource allocations and grant reviews, and tracking
general conditions throughout the Region.
3 The SAB noted that in its current form, the NLCD has
poor accuracy, which could affect the accuracy of the
CrEAM. Also, NLCD classes may not be relevant for
NEPA reviews.
4
Throughout the rest of this chapter, the word "pixel" will
be used to refer to the original NLCD 30 m by 30 m data;
"cell" will refer to aggregated 300 m by 300 m land cover
data; "square" will be used when data are summarized into
other resolutions; "patch" will refer to pixels, cells or
squares that have been aggregated by a common
classification into irregular polygons; and "shape files" will
refer to GIS vector files.
5 Based on the data layers included in each category, the
SAB suggested a change in terminology from "ecological
diversity" to "landscape diversity", from "ecological
sustainability" to "ecological persistence", and from "rare
species and land cover" to "landscape rarity".
6 The SAB disagreed with this method of scoring.
7 The SAB found this scoring method to be problematic.
8 The SAB suggested that quads in this layer should be
scored continuously.
9 The SAB suggested that quads in this layer should be
scored continuously.
10 According to the SAB, the matrix of land cover type
surrounding habitat patches can also affect the diversity
within habitat patches. Categorizing all developed land
cover types as one type eliminates this information, however
in Region 5 only agriculture fell into this category.
11 The SAB stated that the reclassification of all
"developed" land cover types into one class ("developed")
could be problematic, since some developed land uses (such
as urban and residential areas) may have different (and
possibly greater) impacts on the "undeveloped" land cover
classes than other "developed" land uses (such as
13
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agriculture or silviculture).
12 The SAB stated that although the omission of patch sizes
less than 10 ha in this and other layers was due to the
aggregation of data into 300 m by 300 m cells, this omission
leaves out keystone habitats (such as ephemeral ponds)
which may be ecologically important.
13 The SAB noted that such a coarse resolution would
probably reduce the accuracy of species and habitat
diversity because it reduces habitat heterogeneity and
eliminates habitat types which naturally occur in patches
smaller than 1km by 1km. Also, this resolution is likely less
relevant for NEPA reviews.
14 The SAB was concerned that temperature and
precipitation measured at a large scale was unlikely to be
predictive of diversity at smaller scales, including Omernik
Ecoregions.
15 The SAB suggested that this layer could be omitted, since
it seemed to be identical to the "land cover suitability" layer.
16 The SAB pointed out that this criterion was unlikely to be
true for successional or transitional habitats which are
governed by natural disturbances such as fire.
17 The SAB pointed out that this layer may duplicate
information in the "watershed disturbances" layer and could
be eliminated.
18 The SAB noted that dam size may also be an important
factor.
19 The SAB disagreed with this scoring method.
20 The SAB stated that frequency of use is likely an
important factor influencing noise levels.
21 The SAB recommended that this layer be improved with
data from relevant NEPA/Environmental Impact Statement
reports for airports, and FAA data on noise at airports (e.g.,
FAA 1997, USEPA 1998, USEPA 2000, FAA 2003).
22 The SAB believed that the layer as it stands was of
limited utility, and possibly could be combined with the
Superfund layer. Furthermore, the layer does not incorporate
hydrologic linkages to the rest of the landscape (through
which pollutants can affect large areas).
23 The SAB pointed out that human effects may differ
qualitatively and quantitatively from ecological effects, and
therefore may be of limited utility. Data from ecological risk
assessments at RCPxA sites should be used to revise this
layer.
24 Upon the advice of the SAB, the name of this layer has
been changed from "water quality summary."
25 The SAB recommended adding data on phosphorus,
metals (e.g., mercury), and persistent organics (e.g., PCBs)
to this layer. However, these data did not exist for 1990.
26 The SAB pointed out that using the same data twice
double-counts the information.
27 The SAB suggested additional data for this layer,
including: atmospheric nitrogen deposition (wet),
tropospheric ozone concentration, and atmospheric mercury
inputs.
28 Again, the SAB indicated that human health thresholds
may not be well-correlated with ecological effects.
29 The SAB suggested the use of a more compatible
resolution, such as HUCs).
30 Again, the SAB states that different developed land cover
types will have different kinds and intensities of effects on
habitat, which requires a variety of buffer widths. At
minimum, the SAB suggests a wider buffer for urban land
uses.
31 Although the SAB acknowledged the legal reason for this
coarse resolution, it noted that the resolution made the data
much less useful than it otherwise would have been.
32 The SAB argued that equal weighting was likewise an
untested hypothesis.
33 The SAB stated that simply summing all three criteria
scores results in a metric that is not ecologically meaningful
without more information on how the weighting system
relates to real-world relationships.
34 The SAB pointed out that the resulting scores of CrEAM
are a unitless value, which complicates model validation.
The SAB also emphasized the need for validation and
explicit descriptions of model limitations before CrEAM is
put to use.
14
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CHAPTER 3
Protocol development and testing
The time and expense involved in repeated intensive
surveys are infeasible for most organizations. Instead,
similar information can be collected in two ways. Remote
sensing can be used to gather broad scale land cover
information and, paired with environmental variables such
as temperature and precipitation, can estimate or predict
areas of high biodiversity or other ecological characteristics.
These indicators or surrogates must be demonstrated to be
closely correlated to the ecological characteristics of
concern (Kurtz et al. 2001, OECD 2004, Carpenter et al.
2005, Simila et al. 2006). Alternatively, a team of experts
can visit an area which is known or assumed to support high
diversity or unique ecological features, and conduct a rapid
but exhaustive survey of all of the organisms and
environmental conditions they observe. This approach can
sometimes be done quantitatively, however it is usually
used for qualitative assessments (Sayre et al. 2000).
These quick assessment protocols are intended to
provide a rapid means of quantitative assessment which can
be used to assess similar habitats of various qualities. Such a
structured field data collection methodology is necessary if
the same sets of indicators are to be collected and compared
at many sites or over a long period of time (Fennessy et al.
2004). While several organizations have developed
protocols for rapidly assessing species diversity and habitat
quality in a variety of ecosystems, the assessment methods
in this project differ from other efforts in several key
respects (see Table 3.1). Few of the existing methods can be
completed by users representing a variety of expertise with
high accuracy and low cost (Innis et al. 2000). Most are
associated with developing lists of species occurrences in
particular areas which are of interest for biodiversity
conservation goals (e.g., Foster et al. 1994, Hayden 2007).
Further, the area surveyed in the other methods is not fixed
across sites, but varies due to ecological boundaries or
financial resources, reducing comparability across sites.
Although local-scale conservation efforts are necessary,
regional and national scale strategies are also critical for
coordinating policy actions which impact local conditions
(Pienkowski et al. 1995). It is this regional scale that the
protocols introduced here are meant to target. The terrestrial
protocols presented in this report mimic the USEPA's Rapid
Bioassessment Protocols for stream ecosystems in that they
require specific expertise (vegetation and birds), are for a
defined area, and can be applied and compared across large
geographical areas (Table 3.1).
These protocols were developed to assess diversity,
rarity, and persistence within 300 m by 300 m cells based on
the CrEAM methodology. While diversity and rarity can be
habitat-specific, our aim was to compare critical habitats
across the region (and thus have similar protocols). We
grouped the nine land cover times into three broad
categories: forested; nonforested; and wetlands,
encompassing both forested and nonforested wet areas.
Throughout this project, the accuracy of these strict
groupings as applied to the complexity of real habitats was
discussed. Ultimately, we decided to standardize the data
collection methods across all of the protocols as much as
possible, to ensure that the same data were collected in all
areas. In this way, habitat groupings can be revisited and
adjusted post-data collection, if necessary.
The protocols were developed by a volunteer group of
regional biologists and ecologists over several working
meetings (including field tests), tested by field crews over
two summers, and further adjusted. We describe the
protocol development process in part to explain the
reasoning behind the methodology and type of data
collected. The advantages of using a large group of experts
to develop these protocols include utilizing a wide range of
expert-level knowledge on ecosystem quality, field data
collection techniques and equipment, and other issues
central to collecting ecological data. In all of the meetings,
there was a general consensus reached on most of the major
issues, but of course disagreements remained on smaller
issues and details of the protocols. Therefore, these
protocols may not be suitable for every situation, and they
most certainly will not be agreeable to every ecologist or
natural resource manager. However, we hope that, by
incorporating as many voices and opinions as possible,
these protocols will be a robust tool for general use, to be
modified as needed by its future users for specific situations.
Meeting 1: Chicago IL, June 17-19 2003
The first working meeting of this research project was
held at the Region 5 headquarters in Chicago, Illinois. A
group of approximately 30 biologists and ecologists
(Appendix A) volunteered to participate at this first working
meeting. The group was tasked to develop three protocols,
one for each broad land cover type:
• Forested terrestrial: This includes three 1992 Level
IINLCD forest cover types, including deciduous,
evergreen, and mixed deciduous/evergreen forests
15
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Table 3.1. Comparison of the USEPA quick assessment protocol characteristics with other protocols.
Organization
USEPA
USEPA
The Nature
Conservancy
Conservation
International
Protocol name
Quick
Assessment
Protocols for
Terrestrial
Ecosystems
Rapid
Bioassessment
Protocols
Rapid Ecological
Assessment
(REA)
Rapid
Assessment
Program (RAP)
Purpose
Relative diversity,
persistence, and rarity of
an area
Stream quality
Identify areas of high
diversity, key threats to
important areas,
management requirements
of protected areas
Identify areas of high
diversity, develop
conservation
recommendations
Data collected
CIS layers of land cover
and human impacts form
base, data collected on the
ground
Data collected on the
ground (species inventory,
abundance, habitat
structure)
GIS layers of remote
sensing imagery form
base, data collected on the
ground
Data collected on the
ground (species inventory)
Area surveyed
300 m by 300 m
Length of reach
(variable) plus 18
m riparian buffer on
either side
Area of concern
(variable)
Area of concern
(variable)
References
This report
Barbouret al. 1999
Say re et al. 2000.
Roberts 1991,
Foster etal. 1994,
www.conservation.o
m*
The Field
Museum of
Natural History
(Chicago)
Rapid Biological
Inventory (RBI)
Identify areas of high
diversity, develop
conservation
recommendations
Data collected on the
ground (species inventory)
Area of concern
(variable)
Hayden 2007,
http://fm2.fieldmuse
um.org/rbi/what.asp
*http://www.biodiversitvscience.orq/xp/CABS/research/rap/methods/rapmethods.xml
(NLCD#41,42and43).
• Nonforested terrestrial: This includes three 1992
NLCD cover types; grassland, shrubland, dunes,
and barrens (#31, 51 and 71). These land cover
classes represent some of the most impacted habitat
types in the region, and therefore we included
grasslands reclaimed from mining and grazing in
our analysis to ensure an adequately large sample
size.
• Wetlands/Open water. The two 1992 NLCD cover
types in the wetlands category include emergent
and woody (forested) wetlands (#91 and 92). Open
water include streams and lakes.
Volunteers represented a full range of taxonomic
specialty (e.g., mammals, plants, aquatic invertebrates), and
grouped themselves according to their experience
concerning three land cover types: terrestrial forested,
terrestrial non-forested, and wetlands/open water. Early in
the session, the last group split into one wetlands and one
open water group, mainly due to the significant differences
in field methodology commonly used in these habitat types.
Meeting participants were faced with the following
charge: develop protocols which could be used to assess
ecosystem health for nine undeveloped land cover types.
Each protocol was to consist of a set of techniques that
could be conducted on a 300 m by 300 m plot, by a team of
four knowledgeable field researchers, in a four hour period.
The protocols were to consist of techniques that directly or
indirectly measure a) ecological diversity, b) ecological
persistence (or conversely, risk of deterioration from
disturbances), and c) rare or endangered species or features.
In addition, the three groups were asked to: determine the
required qualifications for each field team member; identify
supporting publications; construct lists of required
equipment; estimate approximate costs; and identify
seasonal considerations and any other significant factors that
would affect the protocols. Groups first met individually,
sketched out initial drafts, and then presented the drafts to
all of the participants for feedback. After this feedback
session, groups went back and revised their first drafts. As a
result of this meeting, four draft protocols were produced
that were somewhat similar to each other in terms of the
type of data collected, methodology, and required
qualifications for protocol users.
Protocol-testing at Midewin National Tallgrass
Prairie and Cook County Forest Preserve (IL),
September 19-23 2003
Three of the authors of this report (Dr. Charles Maurice,
Dr. Audrey Mayer, and Dr. Mary White) and Region 5
USEPA staff spent several days at field sites in Cook
County Forest Preserve (Maple Lake) and the Midewin
National Tallgrass Prairie, to field test the preliminary
protocols developed in the first meeting. From our
experiences with the protocols during this trip, we made a
few minor procedural and equipment adjustments. We also
developed datasheets and more detailed methodological
sections (especially with respect to equipment use) for the
protocols. As a result of this field work, a decision was
made to focus on the three terrestrial protocols that would
assess eight land cover types. Open water assessment was
dropped from further refinement at this time due to the
expense of conducting the necessary field work, and due to
already developed stream assessment methods (Barbour et
16
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al.1999).
Meeting 2: Bloomington IN, April 22-24 2004
The purpose of this meeting was to test the three
protocols in the field, to make sure that all potential
logistical problems had been identified, and to determine
whether the protocols were adequate to assess diversity,
persistence, and rarity. Thirty-four ecologists from
throughout Region 5 attended (some had also attended the
protocol development meeting in 2003), and two groups of
four ecologists were formed for each of the three protocols
(Appendix A). The nonforested terrestrial protocol was
tested four times (two sites visited on two mornings), while
the forested and wetland protocols were tested twice (two
sites visited on one morning). From these tests, minor
adjustments were made to the nonforested terrestrial
protocol (most notably changing species abundance
recording to species frequency), while more substantial
changes were made to the forested and wetland protocols.
Assessments on whether the protocols could accurately
gauge ecosystem health were not feasible due to several
factors, not least of which was the absence of true high-
quality sites of an adequate size (at least 300 m by 300 m)
for some habitat types, especially for grasslands.
Participants at this meeting were given the protocol prior
to the meeting. During the first afternoon session the day
before the first morning field trial, the groups familiarized
themselves with the methods and equipment, and identified
any obvious problems. After the first morning trial, groups
met individually to assess problems encountered in the
morning and modify the protocols as appropriate. A large
session with all participants was held the afternoon after the
second field trial day to discuss common problems with the
protocols.
Field data collection using the protocols
At the end of these meetings and early trials, the three
protocols (forested, Appendix B; nonforested, Appendix C;
wetlands, Appendix D) were standardized for formatting
and field data sheets were finalized. Using the final draft
protocols with data sheets, ASC Group, Inc. collected data
in the early summer of 2005 at 16 sites throughout
Minnesota, Michigan, and northern Indiana in Region 5.
These sites were selected at random by Region 5 staff to
represent a range of quality within each habitat cover type
as predicted by the CrEAM model (Table 3.2). The 16 sites
visited included habitat types for all three protocols: three
deciduous forests, three mixed forests, one evergreen forest,
three grasslands, three forested wetlands, and three
emergent wetlands. Shrub lands and dunes were not
sampled because we were unable to find enough sites of
sufficient size (300 m by 300 m) and quality where it was
logistically feasible to collect data. At the end of the field
Table 3.2. Site conditions as predicted by the CrEAM model versus conditions found by the field crews.
2006 sites are also listed along with their site assessments. Sites abbreviations: DF = deciduous forest;
MF = mixed forest; EF = evergreen forest; NG = nonforested grassland; FW= forested wetland; EW =
emergent wetland.
Site Year surveyed Qualitative site assessment
number (/OR AM score max 100)
DF1
DF2
DF3
DF4
DF5
MF6
MF7
MF8
MF9
EF10
EF11
NG12
NG13
NG14
NG15
NG16
FW17
FW18
FW19
FW20
FW21
EW22
EW23
EW24
EW25
EW26
2005
2005
2005
2006
2006
2005
2005
2006
2006
2005
2005
2005
2005
2005
2006
2006
2005
2005
2005
2006
2006
2005
2005
2005
2006
2006
Medium
Low
Low
High
High
Low
Low
Medium
Medium
Low
Low
High
High
High
Medium
Medium
High/61
High
High/65
High
Medium
High/64
Low/1 9
Low/9
High
High
CrEAM predicted condition
(using 1990 data layers)*
Low
Medium
Low
High
Medium
Low
Low
Low
Low
High
Medium
Low
Low
High
"CrEAM scores are not relevant for 2006 sites as these sites were selected from within protected areas
with known or suspected high condition, and not at random (as the 2005 sites were selected).
17
-------
season, some minor changes were made to the protocols,
most notably standardization of the datasheets across
protocols, plus methods for bird surveys and human
impacts.
In 2006, ASC Group, Inc. collected data at ten sites
throughout Ohio and southern Indiana in Region 5, again
representing all protocols. Two sites were visited in each of
five landcover types: deciduous forest, mixed forest,
grasslands, and forested wetlands, and emergent wetlands.
By the end of the two summers, data had been collected for
a total of 26 sites, with five sites for each of the following
land cover types: deciduous forest, mixed forest, forested
wetlands, emergent wetlands, and grasslands. Evergreen
forests were omitted from 2006 sampling because of the
difficulty in locating natural evergreen forests in 2005.
Figure 3.1 illustrates all 26 sites visited by ASC Group, Inc.
In addition to collecting the data as directed by the
protocols, the ASC Group, Inc. team also applied the Ohio
Rapid Assessment Methods (Mack 2001) for wetlands to
five wetland sites, and wrote short, qualitative narratives
about the condition of all sites. These narratives provide
some further insight into how well the protocols capture the
ecological conditions of the site, particularly in contrast to
the cumulative scores (Table 3.2).
Figure 3.1. Location of field sites in 2005 and 2006 in which protocols were used to collect data. DF = deciduous forest, MF = mixed
forest, EF = evergreen forest, NG = nonforested grassland, FW = forested wetland, EW = emergent wetland.
18
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In a separate project, the forest protocol was used to
collect data in known high-quality spruce and birch forests
in southern Finland and northwestern Russia (see
http://www.helsinki.fi/biosci/environment/boomerang.htm
for more information on that project). Data were collected
on 21 sites in Finland and 21 in Russia. The data on forest
composition and structure will be compared to data
collected by the National Forest Inventory programs in
Finland and Russia, to determine the completeness and
accuracy of the protocol with respect to forest conditions at
the sites. An initial comparison often Finnish sites
suggested that forest structure data such as mean height,
mean diameter at breast height, and basal area per ha, are
directly comparable to the data collected using the Finnish
National Forest Inventory methods.
Issues in protocol use
After extensive experience with the protocols in the
field, both the United States crew and the Finland crew had
opinions and suggestions that we feel are important to
include here. These opinions will help researchers and
natural resource managers determine whether these
protocols will be appropriate for the goals of the future
projects, or whether modifications to the protocols (or other
protocols) would be necessary.
Comments concerning all protocols
Size and Shape
After aggregating the GIS layers of the CrEAM model,
each land use pixel was 300 m by 300 m in size. Since the
entire pixel was given a predicted diversity, rarity, and
persistence score, the conditions on the ground needed to be
surveyed over this entire area. However, the size restriction
presents some difficulties. First, some of the most impacted
habitat types in Region 5 have been reduced to a size
smaller than 300 m by 300 m, which can prevent sampling
of sites across a range of disturbance. Other habitat types,
such as ephemeral wetlands, tend to occur in patches
smaller than 300 m by 300 m. Furthermore, the square
configuration eliminated large but long, linear habitats such
as shrubland ecotones, and remnant grasslands along
railroad tracks.
In small patches of habitat, and particularly for those
habitats which naturally occur in long, thin patches (such as
forested wetlands in riparian areas, or shrubland along
ecotones), the square 300 m by 300 m shape can prevent the
patch from being surveyed appropriately. Both the ASC
Group, Inc. team in the US and the Finnish team
successfully adjusted the shape to fit it into an irregular
patch in the field, while maintaining the overall survey area
of 9 hectares. As long as the same amount of area is
surveyed, the change should not be problematic. However,
for verification of the CrEAM model (which has a pixel size
of 300 m by 300 m), maintaining the square shape was
important (otherwise two pixels would be surveyed.)
Setting
The location of a site within a large habitat patch can
affect site quality. For example, sites located along the edge
of a habitat patch may be more disturbed than sites buffered
on all sides by the same habitat. Thus, site selection within a
habitat patch may affect the condition assessment of the
entire patch. Studies which use these protocols to compare
the quality of entire habitat patches should collect landscape
data related to patch size and amount of undeveloped buffer
surrounding the site.
Successional age
The successional stage of the habitat directly affects
field evaluation of community quality. However, succession
at a site can vary based on both natural and anthropogenic
disturbances. The size and extent of the disturbance will
determine whether or not the quality of the site is affected.
A determination of successional age and time since major
disturbance should be included in the field data collection.
This data could be used to separate quality differences due
to natural successional processes from anthropogenic
disturbances.
Seasonality
The protocols specify that the optimal sampling period
is during the growing season. Later weeks in the growing
season may be more suitable for some of the data collected,
particularly plants (when many are fruiting or flowering and
therefore more easily identified). However, due to the
seasonal life history patterns of many species of plants and
animals, data collection for comparison purposes should all
occur ideally within a two- week time span, and certainly
within no more than a four-week time span.
In the Finnish project, we collected data in Finland in
the last three weeks of May, and the Russian data in the first
three weeks of June. Although the field sites were all along
the same latitude, the changes over that time period were
especially pronounced, particularly with respect to
migratory birds and insects. When we began our surveys in
May, we found no insects, and about one-fourth of the bird
species expected to be observed on some of our sites had not
yet arrived in Finland. However, due to the number of sites
on which we needed to collect data, data collection spread
over six weeks was unavoidable. We would, however,
recommend that in northern zones, data collection not begin
until all of the migratory birds have arrived (which is also
typically when insects are emerging in great numbers). The
timing may be heavily dependent upon weather patterns
during the spring.
Seasonality also affects the amount of water in
wetlands, which can greatly impact the plant and animal
species are observed. Wetland inundation is also affected by
19
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inter-annual variation precipitation, regional precipitation
patterns, and depth of the groundwater table (location-
specific). Subsequent efforts should consider this natural
variation across sites when sampling, taking care not to
mistake natural differences in inundation for differences in
ecosystem quality.
Field logistics
The protocols have been designed to ensure a fixed
level of effort (four people for four hours), to ensure that
data can be compared across sites which are assumed to
represent a large variation in ecological characteristics.
However, sites supporting little ecological heterogeneity are
in practice easier and quicker to survey (e.g., less time
required to check identification, fewer structural features to
examine for fauna, etc.) than those sites which are more
heterogeneous. For this reason, very homogeneous sites are
more thoroughly sampled than very diverse sites. In our
experience, the level of effort allowed was adequate for all
the sites we visited, including those sites in relatively
undisturbed forests in Finland and northwestern Russia.
However, it is possible that for extremely diverse areas, the
level of effort allotted would be inadequate, and some data
would not be collected. Subsequent data analyses would
need to take this into consideration.
The four hour protocol was designed to allow two sites
to be sampled per day if necessary. However, bird behavior
varies considerably over the course of a day; they are most
active at dawn and dusk. For this reason, the two-person
animal crew should conduct point-count bird surveys at
these times, starting 30 minutes before sunrise if counting at
dawn, and ending 30 minutes after sunset if counting at
dusk. Although the two-person vegetation crew can
theoretically begin sampling 30 minutes after the point
counts begin, the low levels of light can affect sampling for
longer than these 30 minutes. Headlamps are recommended
equipment for the plant team, however we found in our field
experience that plant sampling is still impeded somewhat.
Thus, there is a small but unavoidable mismatch in the total
time that the animal team and the plant team actually spend
collecting data.
Forests, wetlands, and forested wetlands
In this project, we have assumed strict boundaries
between habitat types with respect to which protocol should
be used in which area. For this reason, we have attempted to
standardize the data collected and the datasheets to the
extent feasible. However, there are still some significant
differences between the protocols, due to the original
development process (in which specialists by habitat
recommended field methods they were most familiar with in
their habitat specialty). Therefore, the data collected, once
processed, may not be comparable across sites in different
broad land cover types.
We discussed whether forested wetlands should be
surveyed using the forested protocol or the wetlands
protocol. We decided to use the wetlands protocol because
the defining feature of the area would be its seasonally-
inundated nature, rather than the density of trees or other
forest characteristics on the site. Depending upon the goal of
the project, future users of these protocols should address
this issue explicitly when including forested wetlands in
surveyed sites.
After conducting their fieldwork in 2005 and 2006,
ASC Group, Inc. questioned whether the wetlands protocol
would adequately differentiate two distinctly different non-
forested wetlands, particularly between marsh communities
versus sedge or wet meadows. As it stands, the current
protocol may be better designed for marsh wetlands.
Comments for the forested protocol
In many states, natural heritage programs are charged
with monitoring species and natural communities which are
rare. As part of the monitoring of rare communities, these
programs are also charged with trying to identify the highest
quality remaining natural communities in their respective
state. Over the years, they have developed certain
characteristics that natural (vegetative) communities should
have in order to be considered high quality. Assessing a
natural (forested) community as high quality is
accomplished by looking at the following factors:
• Biodiversity
• Natural and anthropogenic disturbances
• Surrounding land use
• Invasive species
• Canopy age
• Stand size
Of these, the current protocol seems to address most of
these factors appropriately except for surrounding land use
and stand size.
Both the US and Finland teams were concerned about
the lack of adequate assessment of coarse woody debris,
which is an important habitat source for flora and fauna
communities, and can serve as an indicator of fungal and
invertebrate diversity. Lichen diversity and condition is a
useful indicator for air pollution, fire ecology, and forest
management effects. The Finnish group added two
additional survey methods to collect detailed information on
coarse woody debris, based on the methodology detailed in
Krankina et al. (2002), and lichen diversity and condition
based on the Finnish SFS5670 survey method. Due to the
additional work involved for these two methods, an
additional team member was added to aid the coarse woody
debris data collection. Future sampling efforts should
evaluate the costs of adding and additional field member
versus the benefits of the data collected.
20
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CHAPTER 4
Data analysis
The field data were analyzed according to three main
objectives: 1) to determine whether the ecological
characteristics of the sites supported the CrEAM model
quality classifications, 2) to assess whether the ecological
characteristics of diversity, persistence, and rarity reflected
qualitative perceptions of ecosystem quality, and 3) to
compare ecological characteristics across land cover types
(i.e., protocols). The first objective included only sites
sampled in 2005, since CrEAM quality scores were not
determined for 2006 sites, while the second and third
objectives included data from both years. For both the
CrEAM predictions and qualitative assessment, scores were
divided into categories of low, medium, and high quality.
Due to both the small sample size and the non-normal
distribution of several of the variables, we used a
nonparametric Kruskal-Wallis test to identify significant
differences between means of sites grouped by quality rank
or protocol used. Because of the low power in the tests, the
^-values were not corrected for the high number of
comparisons (47), but readers are cautioned regarding
increased risk of Type I error (erroneous rejections of the
null hypothesis).
Due to resource restraints and difficulties in the field, we
were unable to collect data on enough sites to quantitatively
compare on-the-ground conditions in each land cover and
condition category. Furthermore, some of the sites visited in
the 2005 field season were incorrectly classified by the 1992
NLCD land cover layer (e.g., NLCD predicted mixed forest
where there was evergreen forest). The combination of the
classification problems, plus the decreased administrative
support for the CrEAM model, prompted us to shift our
focus in 2006 from validation of the CrEAM model to
testing the protocols as field data collection tools. We
visited sites in 2006 which increased the sample size for
each protocol, irrespective of CrEAM site quality
predictions. While here we have maintained the "diversity,
rarity, persistence" categories in our data analysis for testing
ecological significance as defined by the CrEAM, users of
these protocols may need to analyze their data differently,
depending upon the goals of the project.
Methods
Diversity data
Diversity of ecosystems, species, organisms and their
genetic variance is considered to be an important property
of ecological systems (Wilson 1992; Rosenzweig 1995).
Richness is simply the number of species (or units of
interest). Diversity is calculated using the number of
different species (richness) and the equitability of the
abundance of those species, i.e., the distribution of
individuals among species in a given area. Communities
with many species of relatively equal dominance are more
quantitatively diverse than those with fewer species and/or
are dominated by one species. Although species are the
most common unit used to calculate diversity of ecological
systems, genotypes, functional groups, trophic levels, and
even morphological types have all been used (Magurran
1988; Rosenzweig 1995).
To quantify the ecological diversity of each site, we
calculated both richness and diversity (Table 3.2). For
richness, the number of native species observed on the site
was summed within each of the following taxonomic
groups: birds, mammals, plants, invertebrates, and
herpetofauna (amphibians and reptiles). These protocols
measure the richness of all taxonomic groups encountered,
although for some groups richness is measured at a higher
taxonomic grouping than species (such as genera or
family). The species richness of birds, mammals,
invertebrates, plants, and herpetofauna were recorded on
each site, with the exception of invertebrate richness on
wetlands sites in 2005. No amphibians or reptiles were
observed on the two nonforested sites in 2006.
Point counts and plots were used to collect
observations of birds and plants, respectively, and therefore
abundances of species were recorded, allowing diversity
calculations to be made for these two groups. Diversity was
calculated for birds and plants using two common diversity
indices:
Shannon's index (based on information theory):
s
and Simpson's index, using the following equation:
(4.1)
(42)
where/), is the proportional abundance of the /* species.
21
-------
While these indices account for both the species richness
and evenness of individuals among the species, the Shannon
index (Equation 4.1) is especially sensitive to the presence
of rare species (and therefore differences in species
richness), while the Simpson's index (Equation 4.2) is more
sensitive to evenness (in particular the presence of very
dominant species; Magurran 1988). Although both indices
behave similarly over very coarse scales, the different
sensitivities allow for detailed comparisons across sites.
Shannon and Simpson diversity were calculated for birds
and plants for all sites, with the exception of bird diversity
on the 2005 wetlands sites (bird census methods were added
to the wetlands protocol before the 2006 season).
While overall richness and diversity of taxonomic
groups may indicate more functionally intact ecosystems
(when compared with areas of similar ecosystem type),
these community variables are not always positively
correlated among taxonomic groups, regardless of
ecosystem functionality (Hopton and Mayer 2006).
Taxonomic differences in richness and diversity can indicate
important characteristics of a site; high bird diversity in a
site with low plant diversity, for example, may indicate an
area of complex vegetation structure and a beneficial
landscape, along with past human impacts which simplified
the plant community. Therefore, lumping species from all
taxonomic groups into single metrics of richness and
diversity is rarely advisable.
Persistence data
We interpret "persistence" here as the degree of
impairment evident on a site. This can be measured directly
by physical evidence of human activities, or by the presence
of invasive species. We assume that the higher the richness
or proportion of either, the greater the negative effects on
the ecological community at that site (Millennium
Ecosystem Assessment 2005). This view ignores less
obvious impacts, such as climate change, but may provide a
useful snapshot of the threats to persistence of a site. We
calculated two types of pressures: the number of kinds of
observable human activities, and the proportion of invasive
species within each taxonomic group.
1. Number of different kinds of observable human impacts
(e.g., trash, trails, noise). We assume that the greater the
number or "richness" of different types of impacts, the
lower the persistence of the site (through greater level of
threat). Although outright habitat destruction obviously
decreases persistence, other seemingly less destructive
activities may considerably degrade the ecological quality of
a site. Trash or trails by themselves may not have much
impact; however, these are an indication of human presence,
much like deer tracks indicate the presence of deer. Signs of
management, such as ditches or mowing, also indicate that
the habitat type or ecosystem function is not what it
otherwise would be without human activity. These data
were collected for all sites with the exception of wetlands in
2005 (human presence methods and bird point counts were
added to the wetlands protocol after the 2005 season). Since
a greater richness of human impacts is negatively related to
the persistence of the site, we multiplied each score by -1, so
that those sites with no impact observations (0) received the
highest score.
2. Proportion of number of invasive species to number of
native species (within each taxonomic group). Second to
outright habitat destruction, dominance by invasive species
is a significant cause of decline in native species, and can
lead to dramatic (and nearly irreversible) changes in habitat
conditions and ecological communities (Mooney and
Cleland 2001, Olden et al. 2004, Millennium Ecosystem
Assessment 2005). Similar to the rarity measures, this
measure compares the richness of known invasive/exotic
species to the richness of native species. The higher
proportion of invasive species relative to native species, the
greater the risk to the ecosystem. We recorded invasive
species richness for birds, mammals, and plants. However,
only one record of an invasive mammal was recorded (a
Norway rat, Rattus norvegicus, on a wetlands site in 2005),
so we excluded this variable from the analyses. Since a
larger proportion of invasive species is negatively related to
the persistence of the site, we multiplied each score by -1.
Rarity data
The rarity of a species depends on its geographic range,
habitat specificity, and local population size (Rabinowitz
1981). For example, species that are geographically
restricted, have very specialized habitat requirements, or
have a naturally sparse population size are considered
naturally rare. Naturally rare species can provide important
information about the characteristics about a site, in
particular the presence of unusual abiotic orbiotic
conditions. Therefore, these species are often referred to as
"indicator" species (Dale and Beyeler 2001). Using field
assessments to determine rarity may not be useful, because
species may be difficult to survey due to their small
population size (US Forest Service 2004), or a species may
be mistaken for a rare species because it is difficult to
observe or collect. Furthermore, some species may have
once been common, but are rare at a site due to disturbances
caused by human activity. Local inventories are necessary to
assess geographic range, habitat specificity, and local
population size, and this detailed information is often
difficult to collect. Thus, we calculated rarity based on
published, nationally available lists of threatened and
endangered species. In the United States, over 1000 species
have been listed at the Federal level as either endangered or
threatened, and the primary cause of endangerment in the
United States is habitat destruction (US Fish and Wildlife
Service 2006). Many more species are listed at the state
level. The presence of these threatened and endangered
22
-------
species on a site may indicate a unique habitat or a low
threat level, both which are important indicators of high
quality.
Proportion of number of rare species to number of native
species (within each taxonomic group). We used the
proportion of the total species richness which are included
on one or more rarity lists (e.g., federal and state threatened
and endangered lists, Gl and G2 ranked species, etc.). This
measure compares the richness of rare species to the
richness of all of the species on the site, within each
taxonomic group. A high proportion of rare to overall
species would indicate a site which provides a large variety
of specialized habitats or resources for native species, or
may indicate a site which may be particularly unaffected by
human activity. Simply using the number of rare species at
each site is not appropriate, since sites with naturally higher
species richness (such as those at lower latitudes) are more
likely to have more rare species than sites of equivalent
condition but in areas where fewer species are supported
(Rosenzweig 1995). Presence of particular indicators
species would also be important to consider with respect to
the total number of species observed. We calculated this
proportion of rare species or birds and plants, for all sites.
No listed mammals were recorded on any sites, so we
excluded this variable from the analyses.
Unused data
Not all of the data collected by the protocols were used
in this data analysis. However, it was the opinion of the
participants in the protocol development meetings that
collecting excess data was better than not collecting some
data and needing it later. Some of the data would be useful
in cases where a disturbance drastically changed the
character of a visited site. For example, a soil profile could
provide valuable information for future restoration efforts.
Other data, such as canopy cover, are a function of the age,
soil fertility, and disturbance dynamics of forested sites and
are expected to change over time with tree growth and
death. While cover is an important characteristic of a site, it
is a difficult measure to incorporate into a perspective which
ranks sites from high to low potential persistence.
Some of these variables could be used quantitatively,
while some will probably be restricted to qualitative
assessments. For example, the depth and color of the O
(organic, top) soil layer has important implications for the
productivity of the site. One could use these data to assess
the variability in species richness and diversity with
potential site productivity, in an investigation of the
theoretical relationship between diversity and productivity.
Some variables can be used to assess the accuracy of the
data collected. The data collected on the weather conditions
during the time of data collection provide qualitative but
valuable information on how complete the survey is likely
to have been. For example, flying animals tend to stay
sheltered during very windy days, and are therefore less
likely to be observed. The use of each variable and its
qualitative or quantitative contribution to a research project
will have to be decided on a case-by-case basis.
Results
CrEAM predicted scores and protocol data
Diversity, persistence, and rarity variables for seven
forest sites, two grassland sites, and five wetland sites were
compared to CrEAM quality scores (Table 3.2). CrEAM
pixel scores ranged from 13-260 (out of a possible 300)
within Region 5, and were divided into categories of low,
medium, and high condition based on breakpoints in the
distribution of pixels scores across the region. "Low"
condition had composite CrEAM scores of 13-73 (6% of
pixels), "medium" condition had scores of 12-156 (45% of
pixels), and "high" condition had scores of 183-260 (11% of
pixels).
Based on predicted CrEAM scores, we found no
significant differences for any protocol data variables
between sites in the low, medium or high categories for the
14 sites surveyed in 2005 (Table 4.1). For most variables,
the values of site characteristics such as diversity and rarity
did not increase with CrEAM quality score, as was
expected. The extremely low sample size precludes any
further differentiation within land cover types.
23
-------
Table 4.1. Summary statistics for Kruskal-Wallis analysis based on CrEAM predicted rank (low, medium, high quality).
N Mean StDev
N Mean StDev
Variable & CrEAM
rank
Bird species richness
Low
Medium
High
Shannon's bird diversity
Low
Medium
High
Simpson's bird diversity
Low
Medium
High
% Invasive bird species
Low
Medium
High
% Listed bird species
Low
Medium
High
8
3
3
6
2
1
15.4
15.7
13.0
2.2
2.6
2.3
K-Vtf
H p
6.8 0.22 0.894
8.5
5.6
0.5 1.42 0.491
0.2
Variable & CrEAM
rank
Herpetofauna richness
Low
Medium
High
Plant species richness
Low
Medium
High
8
3
3
8
3
3
1.38
2.00
2.00
19.6
25.0
30.7
0.92
1.00
1.00
18.3
30.3
34.6
K-W
H p
1 .51 0.469
0.85 0.652
Shannon's plant diversity
6
2
1
8
3
3
8
3
3
8.1
11.2
9.1
0.01
0.02
0.00
0.02
0.01
0.00
3.2 2.22 0.329
1.8
0.0 1.00 0.597
0.0
0.0
0.0 0.95 0.621
0.0
0.0
Mammal species richness
Low
Medium
High
Insect richness
Low
Medium
High
8
3
3
4.3
4.3
4.0
1.7 0.01 0.996
1.5
0.0
Low
Medium
High
Simpson's plant diversity
Low
Medium
High
% Invasive plant species
Low
Medium
High
% Listed plant species
Low
Medium
High
8
3
3
8
3
3
8
3
3
8
3
3
1.97
1.51
2.51
8.6
12.0
14.4
0.01
0.00
0.00
0.00
0.00
0.00
0.95
0.21
1.28
9.2
15.8
16.8
0.04
0.00
0.00
0.01
0.00
0.00
1 .22 0.544
1 .04 0.595
0.75 0.687
0.75 0.687
Human disturbance richness
6
2
1
6.5
4.0
5.0
2.7 2.27 0.322
1.4
Low
Medium
High
6
2
1
4.2
6.0
5.0
1.2
4.2
0.53 0.766
Qualitative site assessments and protocol data
After the field team left each site, they wrote a
qualitative narrative, which described the general conditions
of the site and the potential for long-term persistence of the
ecological conditions. Based on these narratives and the
overall perception of the team, all 26 sites were given a
ranking from high to low. High-ranked sites were those high
biodiversity and little or no evidence of recent disturbance,
or a particularly rare or unique community. A low ranking
reflected clear and recent signs of disturbance (e.g., logging,
invasives species, etc). A medium ranking would have
intermediate biodiversity and some evidence of disturbance.
The protocols were able to differentiate sites by the
quality rankings assessed by the field team. The qualitative
site assessment ranks (low, medium, and high) reflected
differences in the proportion of Shannon's and Simpson's
bird diversity, listed bird species, and herpetofauna richness,
plant species richness, Shannon's plant diversity, Simpson's
plant diversity, and number of human disturbances (Table
4.2; Figure 4.2). While bird diversity was highest on the
low-ranked sites, the proportion of bird species observed
which were listed as of concern, threatened or endangered
was highest on the high-ranked sites. Plant richness and
diversity variables, as well as human disturbance, followed
the expected pattern, where low-ranked sites had much
lower richness and diversity (and more evidence of human
disturbances) than the medium- or high-ranked sites (Figure
4.2). Variables describing mammal and insect communities
demonstrated no differences among qualitative site
assessment rank (Table 4.2).
24
-------
Table 4.2. Summary statistics for Kruskal-Wallis analysis based on qualitative site assessment ranks by ASC Group, Inc.
N Mean StDev
N Mean StDev
Variable &
ASC rank
Bird species richness
Low 8 15.4
Medium g 98
High 12 13.1
Shannon's bird diversity
Low e 2.42
Medium Q -\ Q-\
High s 1.88
Simpson's bird diversity
Low e 10.0
Medium Q 42
High s 6.0
% Invasive bird species
Low s 0.01
Medium Q o.OO
High 12 0.02
% Listed bird species
Low s 0.01
Medium 6 0.00
High 12 0.05
Mammal species richness
Low s 4.00
Medium Q 500
High 12 4.25
% Invasive mammal species
Low s 0.06
Medium 6 0.00
High 12 0.00
Insect richness
Low e 4.83
Medium Q 3. 17
H'9n 8 6.50
wibrv,v |\-W
H P
7.3 3.34 0.189
1.8
6.2
0.28 10.98 0.004
0.19
0.45
2.5 11.22 0.004
0.9
2.2
0.02 1 .97 0.373
0.00
0.05
0.02 6.74 0.034
0.00
0.07
1.41 1.88 0.392
1.10
1.66
Variable & ASC rank
Herpetofauna richness
Low
Medium
High
Plant species richness
Low
Medium
High
Shannon's plant diversity
Low
Medium
High
Simpson's plant diversity
Low
Medium
High
% Invasive plant species
Low
Medium
High
% Listed plant species
Low
Medium
High
8
6
12
8
6
12
8
6
12
8
6
12
3
8
3
3
8
3
1.63
0.67
2.08
7.6
36.8
39.6
1.42
2.54
2.78
3.2
18.8
17.5
0.00
0.01
0.00
0.00
0.00
0.00
0.74
0.82
1.51
4.2
34.0
18.1
0.38
1.32
0.79
1.3
17.9
11.0
0.00
0.04
0.00
0.00
0.01
0.00
K-W
H P
5.28 0.071
10.30 0.006
9.62 0.008
10.76 0.005
5.03 0.081
1 .46 0.483
Human disturbance richness
0.18 2.25 0.325
0.00
0.00
1.60 0.338 0.844
10.15
4.34
Low
Medium
High
6
6
8
5.00
2.00
3.13
2.10
1.10
2.10
6.96 0.031
25
-------
a)
c)
e)
9)
3.00
0.08
0.07 -
0.06 -
0.05 -
0.04 -
0.03 -
0.02 -
0.01 -
0.00
60.00
30.00
Shannon's bird diversity
Proportion of listed birds
^—
Plant species richness
Simpson's plant diversity
b)
d)
0.00
3.00
h)
7.00
Simpson's bird diversity
Herpetofauna richness
Shannon's plant diversity
Number of human disturbance types
Figure 4.2. Differences among all sites ranked as low, medium, and high in a) Shannon's bird diversity, b) Simpson's bird diversity,
c) proportion of listed bird species, d) herpetofauna species richness, e) plant species richness, f) Shannon's plant diversity,
g) Simpson's plant diversity, and h) number of human disturbance types (richness).
26
-------
Due to the low sample size, we were unable to
quantitatively analyze rankings within land cover types;
however, trends can be observed in bar graphs. For forests,
low and medium ranked sites tended to have lower plant
diversity and richness, and lower herpetofauna richness
(Figure 4.3). Interestingly, the medium ranked sites had the
lowest bird richness and diversity.
a)
c)
e)
Forest bird species richness
Forest Simpson's bird diversity
Forest plant species richness
Forest Simpson's plant diversity
Forest Shannon s bird diversity
d)
Forest herpetofauna species richness
Forest Shannon's plant diversity
9)
Figure 4.3. Means and standard errors of forest sites ranked as low, medium or high in qualitative site assessments in a) bird
richness, b) Shannon's bird diversity, c) Simpson's bird diversity, d) herpetofauna species richness, e) plant species richness,
plant diversity, and g) Simpson's plant diversity.
species
f) Shannon's
27
-------
All five nonforested sites were ranked as medium or
high quality by the qualitative site assessment. High quality
sites tended to support higher bird diversity (Figure 4.4).
Contrary to our expectations, high quality sites tended to
support much lower plant diversity than medium quality
sites. There were no other notable differences between
rankings for the other variables.
a)
Nonforest Simpson's bird diversity
Nonforest Simpson's plant diversity
b)
Nonforest Shannon's plant diversity
Figure 4.4. Means and standard errors of nonforested sites ranked as medium or high in qualitative site assessments in a) Simpson's
bird diversity, b) Shannon's plant diversity and c) Simpson's plant diversity. No sites were ranked as low quality.
Among the wetland sites, only one was ranked in the
qualitative assessment as medium quality (and only two
were ranked as low quality). High quality sites tended to
support much higher levels of plant species richness and
diversity (Figure 4.5). There were no notable differences
between rankings for the other diversity, rarity, and
persistence variables.
a)
Wetlands plant species richness
b)
Wetlands Simpson's plant diversity
30
25
20
15
10
5
0
^M
I
1
• low
Dhigh
Figure 4.5. Means and standard errors of wetland sites ranked as low or high in qualitative site assessments in a) plant species richness and
b) Simpson's plant diversity. Only one site was ranked as medium quality, and therefore it was excluded from the analysis.
28
-------
Comparisons among land cover types
Although a different protocol was used for each of the
three major land cover types, the methods were standardized
across protocols in such a way that the data analysis should
not result in differences due to the protocol. Thus, any
significant differences are considered differences among
land cover types (forested, nonforested, and wetland).
Several of the variables differed by major land cover types
(Table 4.3). Wetlands had lower bird richness and diversity
compared to forested and nonforested sites (Figure 4.6 a-c).
Nonforested areas supported the highest insect richness of
any of the land cover categories, yet the lowest herpetofauna
richness (Figure 4.6 d-e). Forested sites supported the
lowest plant richness and diversity (Figure 4.6 f-h), which
may be partially explained by the lack of spring ephemerals
in the groundcover layer after leaf-out. Finally, invasive
plant species composed higher proportions of the overall
community in nonforested areas, compared to forests and
wetlands.
Table 4.3. Summary Kruskal-Wallis statistics for site characteristics by land cover class.
Variable & N Mean
land cover
class
Bird species richness
Forest n 153
Nonforest 5 15.0
Wetland 10 91
Shannon's bird diversity
Forest n 2.11
Nonforest 5 2 05
Wetland 4 -\ 44
Simpson's bird diversity
Forest -\ -\ j QQ
Nonforest 5 g 48
Wetland 4 333
% Invasive bird species
Forest 1 1 rj.01
Nonforest 5 rj.01
Wetland 10 rj.02
% Listed bird species
Forest n rj.01
Nonforest 5 rj.03
Wetland 10 0.04
Mammal species richness
Forest ! ! 4.45
Nonforest 5 5 40
Wetland 10 3.70
% Invasive mammal species
Forest -\ -\ o.OO
Nonforest 5 o.OO
Wetland 10 0.05
Insect richness
Forest 1 1 44
Nonforest 5 149
Wetland 4 39
StDev
K-W
H p
5.8 6.865 0.032
6.3
4.3
0.44 6.093 0.048
0.35
0.28
3.16 7.566 0.023
2.07
1.02
0.02 0.652 0.722
0.02
0.06
0.02 1 .277 0.528
0.04
0.08
1 .04 2.936 0.230
1.82
1.49
0.00 1.6 0.449
0.00
0.16
1.7 10.735 0.005
7.6
4.1
Variable &
land cover class
Herpetofauna richness
Forest
Nonforest
Wetland
Plant species richness
Forest
Nonforest
Wetland
Shannon's plant diversity
Forest
Nonforest
Wetland
Simpson's plant diversity
Forest
Nonforest
Wetland
% Invasive plant species
Forest
Nonforest
Wetland
% Listed plant species
Forest
Nonforest
Wetland
Human disturbance richness
Forest
Nonforest
Wetland
N
11
5
10
11
5
10
11
5
10
11
5
10
11
5
10
11
5
10
11
5
4
Mean
1.82
0.20
2.10
7.5
47.8
43.6
1.43
2.82
3.02
3.3
18.9
21.8
0.00
0.11
0.02
0.00
0.00
0.01
3.55
3.60
2.50
StDev
1.33
0.45
0.99
3.7
23.5
19.8
0.31
0.83
0.90
0.9
16.9
10.6
0.00
0.09
0.02
0.00
0.01
0.02
2.34
1.52
2.52
K-W
H p
10.14 0.006
16.58 0.001
14.96 0.001
14.55 0.001
12.72 0.002
2.381 0.304
1 .205 0.547
29
-------
a)
c)
e)
g)
h
Bird species richness
18-
16-
14-
12-
10-
8-
4-
2
I
i
1
D Forest
D Nonforest
D Well and
Simpson's
10 -i
8-
7-
6-
5-
4 -
3
2
1 -
I
1
T
1
bird diversity
T
1
Q Forest
D Nonforest
D Wetland
3
2.5
2
1.5
1
0.5
0
Herpetofauna
ph
1
richness
r
J
D Forest
D Nonforest
D Well and
3.5
3
2.5
2
1.5
1
0.5
0
Shannon's plant diversity
1
J
[
L
I
1
O Forest
D Nonforest
D Well and
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
% invasive plant species
I
1
D Forest
O Nonforest
D Well and
h)
d)
f\
'/
h)
Shannon's bird
2-
1.5-
1 -
0.5-
0 -
diversity
J y
h
20
18
16
14
12
10
8
4
2
0
D Forest
D Nonforest
D Well and
Insect richness
f
1
T
1
D Forest
D Nonforest
D Well and
Plant species richness
60
50
40
30
20
10
I
1
I
D Forest
D Nonforest
D Wetland
30
25
20
15
10
5
0
Simpson's plant diversity
1
D Forest
D Nonforest
• Wetland
Figure 4.6. Characteristics which displayed significant differences between forest, nonforested, and wetland sites in a) bird species richness,
b) Shannon's bird diversity, c) Simpson's bird diversity, d) insect richness, e) herpetofauna richness, f) plant species richness, g) Shannon's
plant diversity, h) Simpson's plant diversity, and i) proportion of invasive plant species.
30
-------
There were very few noticeable distinctions between
the land cover subclasses (e.g., deciduous, mixed, and
evergreen forests), although this may be due to the
extremely small number of sites surveyed in each
subclass. There were no differences in forested subclasses
for any of the measured variables. Forested wetlands
tended to support higher species richness and diversity
than emergent wetlands for most taxonomic groups,
including birds, mammals, and plants (Figure 4.7).
Wetland subclasses
forested wetlands
emergent wetlands
Bird richness Mammal Plant richness Simpson's
richness plant diversity
Figure 4.7. Characteristics which displayed significant differences between wetland land cover subclass sites.
Discussion
We first evaluated the CrEAM predictions with
respect to the measured ecological conditions of the sites.
The CrEAM predictions were not accurate with respect to
the animal and plant community characteristics measured
by the protocols (Table 4.2) or the qualitative assessments
made by the field team upon leaving the site (based on
interpretation of Table 3.2). Although a larger sample size
may find better agreement, it is likely that the
inconsistencies between the 1992-era GIS data layers and
the on-the-ground conditions at the sites in 2005, as well
as other data issues as pointed out by the SAB,
contributed to the discrepancy. Updating the CrEAM
model with more current information might bring the
predicted condition scores in better agreement with the
field conditions. Additionally, if the site boundaries did
not match up to the correct CrEAM pixel, the pixel
actually surveyed on the ground may not have a similar
quality ranking (although presumably adjacent pixels
would not have wholly different predicted quality). While
the protocol did not match CrEAM predictions, this fact
should not detract from the value of these protocols at
assessing ecological health.
Overall, the data collected by the protocols were able
to differentiate among the qualitative assessment ranks,
and in particular differentiated plant communities on sites
judged to be low quality versus those of medium and high
quality. This result is somewhat expected, as the protocol
dictates the types of data collected by the field team, and
these data had at least some influence over their
qualitative assessment of a site upon leaving it. The
animal communities did not vary as expected; for
example, bird diversity was highest on the lowest ranked
sites. There are two possible explanations. First, the
qualitative site assessments may have preferentially used
plant community characteristics when making an
assessment. Unfortunately, we have no other independent
measure of site condition for which to compare data from
the protocols. Second, the protocols may be better
designed to collect data on plant communities which
represent ecosystem diversity and persistence qualities
than for animal communities. The fast, one-time visit may
be less suitable for highly mobile animal communities, as
many species which the site may usually support may not
be present at the time of the survey. Repeated visits might
build a more complete picture of these communities.
Alternatively, the animal communities may respond more
to structural features of the plant community, rather than
richness or diversity. If the protocols were designed to
collect more data on these structural features, they may be
more highly correlated with animal richness and diversity.
31
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While plant richness and diversity was higher in the
higher quality forest and wetland sites (as expected), the
nonforested grassland sites had higher plant richness and
diversity on the medium ranked rather than the high
ranked sites. High diversity in moderately disturbed sites
conforms to the intermediate disturbance hypothesis
(Connell 1978), whereby the highest quality grassland
sites may have lower plant diversity due to competitive
exclusion by the dominant species. However, we had the
fewest number of nonforested sites (5), so the lack of
patterns seen in the data may also be an artifact of the
small sample size.
Plant-based indicators were effective at
distinguishing site quality and have many logistical,
sampling advantages over animals. For example, plants
can be sampled at any time of day, and potentially in
larger time windows of the year compared to birds
(although some plants need to be flowering for accurate
identification). Moreover, a team of two can collect a
substantial amount of plant data in a relatively short time
frame, and documentation of species is easy. Given these
advantages, one might suggest limiting the assessments to
plant communities. However, the ecological health of an
area is dependent on all biotic and abiotic factors, and
cannot be determined by one taxonomic group. When
developing the protocols, we attempted to include as
much ecological information as possible given the time
and labor constraints. Nonetheless, it is important to
acknowledge that the protocols were more effective at
sampling plants than animals.
Very few invasive species and human disturbances
were found on the sites, so these characteristics were not
useful in differentiating site quality. The highest numbers
of invasive species were found in nonforested grasslands
and emergent wetlands (Figure 4.If), possibly reflecting
historical disturbances (e.g., tilling, tiling, and draining) in
the Upper Midwest. However, it should be remembered
that the sites visited in this project were those that were
known to be at least intact enough to classify as a
"natural" land cover class. Areas which are more highly
disturbed by human activities were less likely to meet the
CrEAM criteria for natural land cover classes, and were
therefore excluded from the site visits. We would expect
these areas to support much higher numbers of invasive
species, and we would expect many more observations (in
frequency and type) of human disturbances. Additionally,
if these protocols were repeated over time at the same
sites, it would be possible to determine the extent to
which these data are relevant to changes in quality in
these critical ecosystems.
Finally, it should be emphasized that the number of
sites visited was very small and the within treatment
variability was high, thus limiting the power to detect
differences among categories. While many of the
variables differed between sites, both in terms of quality
rank and land cover type, these differences did not meet a
0.05 significance level. Thus, we expect that our results
are conservative and additional data will likely result in
more ecological variables being significantly related to
site quality. Given a large enough number of sites for
each land cover subclass (e.g., deciduous, mixed, and
evergreen forest), it may be possible to also use these data
to distinguish between these subclasses.
The experience of the field teams with the three
protocols for forested, nonforested, and wetland land
cover types were generally positive. We found that the
data collection methods are straightforward, the list of
equipment adequately describes what is needed in the
field, and the data can be collected in the four hour time
period with four people. The protocols can be modified to
fit nine hectare areas which are not square, and the
complicated seasonal patterns of taxonomic groups could
be addressed by using the protocol repeatedly at the same
site, or confining sampling to a smaller time window.
While the protocols were designed with the specific
purpose of validating the CrEAM GIS model, they may
be suitable for other uses. However, further testing would
be required to make sure that the protocols collect the
necessary data. Alterations made to the protocols are
possible (such as the addition of lichen community
assessments in forests); however, they should also be
tested prior to use.
32
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APPENDIX A
List of meeting participants and affiliations
Meeting 1 Participants
Candice Bauer, US Environmental Protection Agency
Martin Berg, Loyola University
Douglas Boucher, Hood College
Richard Bradley, Ohio State University
Kim Brown, Ohio University
Kelly Burks-Copes, US Army Corps of Engineers
Guy Cameron, University of Cincinnati
George Estabrook, University of Michigan - Ann Arbor
Michael Gentleman, US Environmental Protection Agency
Jochen Gerber, The Field Museum
Edward Hammer, US Environmental Protection Agency
Edwin Herricks, University of Illinois - Chicago
George Host, Natural Resources Research Institute
Ricardo Lopez, US Environmental Protection Agency
Patrick Malone, Illinois Department of Natural Resources
Daniel Mazur, US Environmental Protection Agency
Barbara Mazur, US Environmental Protection Agency
Greg Mueller, The Field Museum
Darrel Murray, University of Illinois - Chicago
Diane Nelson, US Environmental Protection Agency
Gregory Nowacki, U S Forest Service
Dennis Nyberg, University of Illinois - Chicago
John Ritzenthaler, National Audubon Society
Robin Scribailo, Purdue University - North Central
Nancy Solomon, Miami University
Doug Stotz, The Field Museum
Phil Willink, The Field Museum
Kristopher Wright, University of Wisconsin - Platteville
Paul Zedler, University of Wisconsin - Madison
Meeting 2 Participants
Forested Terrestrial Protocol
Kim Brown, Ohio University
John Bruggink, Northern Michigan University
Gary Fewless, University of Wisconsin, Green Bay
Rick Gardner, Ohio Department of Natural Resources
Margaret Kuchenreuther, University of Minnesota - Morris
Jon Mendelson, Governors State University
Nancy Murray, Ohio Wesleyan University
Dennis Nyberg, University of Illinois - Chicago
Nancy Solomon, Miami University
Craig Wayson, Indiana University - Bloomington
Nonforested Terrestrial Protocol
Roger Anderson, Illinois State University
George Estabrook, University of Michigan - Ann Arbor
Tom Givnish, University of Wisconsin - Madison
Alice Heikens, Franklin College
Pat Malone, Illinois Department of Natural Resources
Darrel Murray, University of Illinois - Chicago
Daniel Pavuk, Bowling Green State University
Chris Stanton, Baldwin-Wallace College
Kathy Winnett-Murray, Hope College
Barbara Zom-Arnold, University of Illinois - Chicago
Wetlands Protocol
Tim Ellinger, University of Wisconsin - Milwaukee
Carl vol Ende, Northern Illinois University
Clark Garry, University of Wisconsin - River Falls
Jim Hodgson, St. Norbert College
David Lonzarich, University of Wisconsin - Eau Claire
Vicky Meretsky, Indiana University - Bloomington
Carl Richards, Sea Grant Minnesota
Greg Spyreas, Illinois Natural History Survey
Daniel Soluk, Illinois Natural History Survey
A-l
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APPENDIX B
Forested terrestrial protocol and datasheets
B-l
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STANDARD OPERATING PROCEDURE
FOR THE QUICK ASSESSMENT PROTOCOL:
FORESTED TERRESTRIAL
IN SUPPORT OF
U.S. ENVIRONMENTAL PROTECTION AGENCY
UNDER
RCRA ENFORCEMENT, PERMITTING, AND ASSISTANCE (REPA3)
ZONE 2 - REGION 5
CREATED FOR USE BY EPA REGION 5
FORESTED TERRESTRIAL SOP, REVISION NO. 3
EFFECTIVE DATE: January 2006
B-3
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Table of Contents
Table of Contents B-i
List of Tables and Figures B-ii
List of Appendices (Datasheets) B-ii
1.0 Scope and Application B-5
2.0 Method Summary B-5
3.0 Definitions B-5
4.0 Health and Safety B-6
5.0 Personnel Qualifications B-7
5.1 General Qualifications B-7
5.2 Fauna/Soil Crew B-7
5.2 Flora Crew B-7
6.0 Equipment and Supplies B-7
6.1 General Equipment Needed B-7
6.2 Additional Equipment Needed for Fauna/Soil Crew B-8
6.3 Additional Equipment Needed for Flora Crew B-8
7.0 Procedure B-8
7.1 Pre-Visit Preparations B-9
7.2 Fauna/Soil Crew Activities B-ll
7.2.1 Data Recording B-ll
7.2.2 Field Set-Up B-ll
7.2.3 Field Data Collection B-12
7.2.3.1 Bird Observations fromPoints B and C B-12
7.2.3.2 Fauna & Disturbance Observations from Transects CD, CB, and BA B-12
7.2.3.3 Soil & Earthworm Data Collection B-13
7.3 Flora Crew Activities B-14
7.3.1 Data Recording B-14
7.3.2 Field Set-Up B-15
7.3.3 Field Data Collection B-15
7.3.3.1 Survey Understory in the 10-mx 10-m Quadrats B-15
7.3.3.2 Survey Saplings in the 5-m x 5-m Quadrats B-16
7.3.3.3 Describe Canopy Cover, Vertical Foliar Structure,
Community Type, Successional Stage, and Presence of Water B-16
7.3.3.4 Survey Trees B-16
7.4 Exiting the Study Site B-17
7.5 Post-Visit Activities B-17
8.0 Data and Records Management B-17
9.0 Quality Assurance Procedures B-18
10.0 References B-18
B-i
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List of Tables and Figures
Table 1. Descriptions of Datasheets
Figure 1. Fauna/Soil Survey Scheme
Figure 2. Flora Survey Scheme
Figure 3. Illustration of Measurements Taken to Estimate Tree Height
List of Appendices (Datasheets)
Fl. Forest Bird Observation Data
F2. Fauna Transect Data for Vertebrates
F3. Fauna Transect Data for CWD, Snags, and Brush Piles
F4. Soil and Earthworm Data
F5. Photo Log
F6. Invasive Species/Human Impacts and Activities
F7. Understory Data
F8. Sapling Data
F9. Community Data
F10. Point Quarter Sampling Tree Data
B-ii
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1.0 Scope and Application
Knowledge of ecosystem health and quality is an important component of successful ecosystem
management. Ecological assessments are increasingly used in support of adaptive ecosystem management
and informed resource management. Rapid ecological assessment is a common technique that is most often
used to objectively assess the biological diversity of a relatively unknown ecological area. However, this
assessment technique can also be used to evaluate ecological characteristics other than diversity.
The purpose of this standard operating procedure (SOP) is to detail a rapid ecological assessment protocol
for evaluation of the condition of forested terrestrial ecosystems. This forested terrestrial protocol is one of
four protocols originally drafted as a product of a workshop held in June 2003. It was then revised after
being field tested by the participants of a second workshop held in spring 2004. When using this SOP, the
associated Quality Assurance Project Plan (QAPP) should be consulted. The QAPP serves as a generic plan
for all data collection activities conducted under the SOP and offers guidelines for ensuring that data are of
sufficient quality and quantity to support project objectives. Decisions regarding the application of data
collected while using this SOP should consider the precision, accuracy, and other statistical characteristics of
these data.
2.0 Method Summary
This SOP provides instructions for a rapid ecological assessment of forested terrestrial ecosystems, using
fauna surveys, flora surveys, and soil sampling of a 300-mby 300-m study plot. Generally, the optimal
season for implementing this protocol is during the growing season. Late spring offers the best opportunity
for sampling nesting birds in most temperate locations; however, this will not be the ideal time to identify all
plants. It is most important to sample all sites in a relatively small window (e.g., mid-May to mid-June,
depending on latitude and other climatic factors) to make comparisons across sites. The protocol is intended
to be completed in approximately four hours and requires a four-person team working together in pairs, as a
fauna/soil crew and a flora crew.
The fauna/soil pair conducts the fauna and soil portions of the protocol. Fauna surveys include: 1) 20
minute periods of bird observation from two different observation points within the plot; 2) traversal of three
transects, along which crew members look for fauna signs such as scat, and examine coarse woody debris for
invertebrates and other fauna; and 3) up to three holes excavated to determine earthworm presence or
absence. This field crew also looks for evidence of natural and human disturbances, and pushes soil cores to
determine depth, color, and composition of soil horizons.
The flora crew conducts flora surveys at nine flora nodes. During these surveys, 1) trees are surveyed by the
point quarter sampling method; 2) saplings are surveyed in 5-m x 5-m quadrats; and 3) understory vegetation
is surveyed in 10-m x 10-m quadrats. Detailed procedures are provided in Section 7.0.
3.0 Definitions
Coarse woody debris (CWD): Defined as: "sound and rotting logs and stumps, and coarse roots in all
stages of decay, that provide habitat for plants, animals and insects and a source of nutrients for soil
structure and development; material is generally greater than 7.5-cm in diameter." (Source: Stevens 1997).
Canopy class: Also referred to as "crown class"; the relative position of an individual tree or shrub crown
with respect to competing vegetation, and the amount of light received by the tree or shrub. (Modified from
USDA FS 1989). For the purposes of this protocol, canopy classes are dominant, co-dominant, or non-
dominant.
Co-dominant: Trees or shrubs with crowns receiving full light from above, but comparatively little from
the sides. Crowns usually form the general level of the canopy. (Modified from USDA FS 1989).
DBH: The diameter of a tree at breast height, or at 4.5 ft (1.37 m) above the forest floor on the uphill side
of the tree. For the purposes of determining breast height, the forest floor includes the duff layer that may
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be present, but does not include unincorporated woody debris that may rise above the ground line. (Source:
USDAFS1989).
Disturbance: A natural or human-induced (anthropogenic) environmental change that affects an
ecosystem's floral, faunal, or microbial communities. Disturbance may include, but is not limited to: roads,
gravel, asphalt, trails, berms, ruts in the soil, eroded areas, evidence of digging, hydrologic modifications
(e.g., ditches, weirs), evidence of mowing or tree felling, wind throw mounds, evidence of fire, litter, tires,
refrigerators, manure, and pig ruts.
Dominant: Trees or shrubs with crowns receiving full light from above and partial light from the sides;
usually larger than the average trees or shrubs in the stand, and with crowns that extend above the general
level of the canopy and that are well developed but possibly somewhat crowded on the sides. A dominant
tree is one which generally stands above all other trees in its vicinity, but the dominant canopy class could
also include a smaller tree that still receives full light from above and partial light from the sides. (Modified
from USDA FS 1989).
Fauna signs: Indications or signs that fauna are, or have recently been, present. May include, but are not
limited to: calls, tracks, mounds, burrows, holes, nutshells, scat (e.g., deer-pellet clumps), runways or trails,
browse lines, and tree rubbing.
Non-dominant: Trees or shrubs with crowns that may or may not reach the canopy level, but that receive
little or no direct light from above and none from the sides. (Modified from USDA FS 1989).
Saplings: Woody plants with diameters ranging from 2.5 cm to 10 cm.
Snags: Standing dead trees.
Stand: A standing growth of trees or plants.
Stand initiation: The first successional stage that occurs after a major disturbance. During this stage,
species are invading and the environment, growth pattern, and size of each plant are rapidly changing.
(Modified from Oliver and Larson 1996).
Steady state: A successional stage; a stand in the steady state stage is composed entirely of trees that have
developed in the absence of disturbance. (Modified definition of "true old growth" stands from Oliver and
Larson 1996).
Stem exclusion: A successional stage; a stand enters the stem exclusion stage when trees reoccupy all
growing space and exclude new woody plants from becoming established. (Modified from Oliver and
Larson 1996).
Successional stage: One of the sequence of communities that replace one another in a given area in the
orderly process of plant community change called ecological succession.
Trees: Woody plants with diameters >10 cm.
Understory: Vegetation including woody seedlings (diameters <2.5 cm), shrubs, and herbs.
Understory reinitiation: A successional stage that occurs several decades after stem exclusion begins and is
characterized by the development of a forest floor stratum of herbs and shrubs. (Modified from Oliver and
Larson 1996).
4.0 Health and Safety
Health and safety concerns for field workers in the forested terrestrial ecosystem include:
• slips, trips, and falls;
• thermal conditions such as excessive heat or cold;
• inclement weather, especially lightning;
• biological hazards, such as insects or other taxa that may bite and plants that may contain
substances causing allergic reactions; and
• hunting (depending upon land use designations).
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Field workers should wear closed shoes, long pants and long sleeves, but are individually responsible for
selecting footwear, clothing, gloves and outerwear as appropriate to the situation at hand. Field workers
should use insect repellant, as appropriate, and take care to avoid holes, fallen trees, and other obstacles that
may cause slips, trips, and falls. Workers should increase attentiveness to potential hazards during pre-dawn
activities. Workers should also determine potential hunting or other human activities that could put them at
increased risk, and take steps (such as wearing orange vests and notifying park rangers or other relevant law
enforcement agencies of their location) to mitigate these risks.
5.0 Personnel Qualifications
5.1 General Qualifications
This protocol requires a two-person fauna/soil field team, and a two-person flora field team, with one or
more of these team members performing pre-visit preparations. For both pairs, one of the crew members
must be an expert (bird and tree, respectively) whereas the other crew member can be a generalist. An expert
must be able to identify most or all of the common taxa found in the area to be surveyed and be able to
collect appropriate and sufficient field data on less common taxa to enable their later identification.
Regardless of their respective areas of specialization, all team members must have had some forest field
work experience. Before formally collecting data in the field, all team members should practice the protocol
at least once in a convenient forest habitat to make sure they are clear on how to collect the data and
complete each of the datasheets.
The individual(s) performing the pre-visit preparations should be generally familiar with the flora and fauna
found in the specific ecoregion. Preferably, this individual should have an educational background in biology
or ecology.
5.2 Fauna/Soil Crew
At least one of the two crew members must be an expert in field identification of birds, both by sightings and
by their behaviors (e.g., calls, flight). Also, at least one of the fauna/soil crew members must be generally
familiar (not necessarily at the species level) with other vertebrate and invertebrate fauna found in the
specific ecoregion, such as mammals, snakes, turtles, lizards, salamanders, frogs, toads, beetles, ants,
isopods, centipedes, and millipedes. Lastly, at least one of the fauna/soil crew members must have a
familiarity with the performance of basic soil sample collection survey techniques. One of the two fauna
crew members should also have experience with global positioning systems (GPS).
5.3 Flora Crew
At least one of the two flora crew members must have extensive knowledge of plant species, especially those
trees and understory species likely to be encountered in ecoregion-specific forest types. In addition, at least
one of the flora crew members must have training in conducting forest-specific surveys and data recording
methods. At a minimum, the other flora crew member must be familiar with botanical nomenclature, have
knowledge of common forest plant species, and have experience with surveying and data recording methods.
One of the two flora crew members should also have experience with global positioning systems (GPS).
6.0 Equipment and Supplies
6.1 General Equipment Needed
• Driving directions to the study plot
• Pens and markers with permanent, waterproof ink markers
• Insect repellant
• First aid kit
• Water
• Two digital cameras with zoom and panorama capabilities
• Two high capacity (at least 128MB) digital data cards appropriate for the digital cameras being used
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• Two global positioning system (GPS) units with 2-way radio capabilities and recharger for DC
power socket in automobile
• Two sets of red and blue flagging
• Two clipboards
• Two hand lenses
• Calculator
• Two thermometers
• Two measuring tapes (30 m each)
• Two hand-held compasses
• Two sets of aerial photos and maps with scale legends and printed on waterproof paper
• Two copies of this SOP, printed on waterproof paper
• Two accurate watches
• Two backpacks to carry equipment
6.2 Additional Equipment Needed for Fauna/Soil Crew
• Bird, mammal, reptile & amphibian, spider & insect, and animal tracks field guides
• Two pairs of binoculars
• Two flashlights
• Spray water bottle
• Soil probe and wet & dry probe extensions
• Munsell soil chart
• Hand trowel
• Stainless steel shovel
• Light colored tarp or cloth
• Fauna reference lists, printed on waterproof paper
• Fauna/soil crew datasheets (Fl to F6), printed on waterproof paper
6.3 Additional Equipment Needed for Flora Crew
Tree, shrub, forb, and grass and plant field guides
Metric DBH tape (5 m)
Convex spherical crown densiometer
Clinometer and case
Flora reference lists, printed on waterproof paper
Flora crew datasheets (F5 to F10), printed on waterproof paper
7.0 Procedure
This protocol requires a four-person field team who will work together in pairs to evaluate a 300-m x 300-m
plot. The protocol is intended to be completed in approximately 4 hours. One pair (the fauna/soil crew) will
conduct the fauna and soil portions of the protocol, while the other pair (the flora crew) will conduct the
flora portion of the protocol.
The first activity conducted at the plot is bird data collection, which is performed by the fauna/soil crew and
should begin half an hour before sunrise (or during the period when birds are typically the most vocal). In
order to create the least amount of disturbance prior to and during bird data collection, the flora crew should
not enter the study plot until after the fauna/soil crew has completed the first of the two 20-minute bird
observation periods. The two field crews will then work simultaneously for the remainder of the four-hour
evaluation period.
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7.1 Pre-Visit Preparations
Listed below are the steps that must be completed before the start of the field event.
Step 1. Obtain aerial photographs and maps of the site and surrounding area. These materials will
familiarize the field team members with the area and provide them with a context for the site.
Step 2. Determine the pre-assigned four-character site ID number for the study site. The first character
of the site ID number should be "F" for forested terrestrial ecosystem, the second character
identifies the state in which the site is located (i.e., 1 = Ohio, 2 = Michigan, 3 = Indiana, 4 =
Illinois, 5 = Wisconsin, and 6 = Minnesota); and the last two characters should be digits
assigned sequentially from 00 to 99.
Step 3. It may be necessary to obtain permission or a formal permit to access the study site. Check with
the land owner or manager to determine the need for such permission or permits. The minimal
sample collection envisioned for this protocol will not include vertebrate species and is not
expected to require a scientific collecting permit. This should be verified with fish and wildlife,
and other environmental agencies, as appropriate.
Step 4. Create reference lists of local flora and vertebrate fauna species from available databases and
resources. For all species: (1) categorize as native or non-native, (2) include state and federal
status, and (3) categorize as invasive or non-invasive. Fauna species should also be categorized
as regionally common or rare.
Useful information sources for determining species status include:
• Illinois Department of Natural Resources:
http://dnr.state.il.us/espb/datelist.htm - state list of threatened and endangered (T&E)
species.
• Illinois Natural History Survey:
http://www.inhs.uiuc.edu/cbd/ilspecies/ilsplist.html - list of flora and fauna occurring in
Illinois, including state and federal listing status.
• Indiana Department of Natural Resources:
http://www.in.gov/dnr/fishwild/endangered/e-list.htm - lists of T&E fauna;
http://www.in.gov/dnr/naturepr - lists of rare, threatened or endangered (RTE) species by
county and a list of Indiana's RTE vascular plants;
http://www.in.gov/dnr/fishwild/endangered/frogs.htm- list of frogs and toads in Indiana;
http://www.in. gov/dnr/invasivespecies/innatcom03 .pdf - lists of characteristic species
found in a variety of community types.
• Michigan Department of Natural Resources:
http://www.michigan.gOv/dnr/0.1607J-153-10370 12142—.OO.html - links to T&E lists
and a rare plant reference guide;
http://www.michigan.gOv/dnr/0.1607J-153-10370 12145—.OO.html - links to fauna
information;
http://www.michigan.gOv/dnr/0.1607.7-153-10370 12146—.OO.html - links to flora
information.
• Minnesota Department of Natural Resources:
http://www.dnr.state.mn.us/ets/index.html - Minnesota's list of endangered, threatened,
and special concern species.
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• Ohio Department of Natural Resources:
http://www.ohiodnr.com/wildlife/resources/default.htm - links to a variety of wildlife
resources, including T&E lists for fauna;
http://www.ohiodnr.com/forestry/Education/ohiotrees/treesindex.htm - list of Ohio's tree
species.
• Wisconsin Department of Natural Resources:
http://www.dnr.state.wi.us/org/land/er/ - includes lists of state and federal T&E species
occurring in Wisconsin, county maps that list known occurrences of T&E species, and a
searchable database of T&E species occurrences in Wisconsin.
• U.S. Fish and Wildlife Service:
http://www.fws.gov - links to a discussion of the endangered species program and a list
of Federally threatened and endangered species.
Useful sources of information on invasive species include:
• Indiana Department of Natural Resources: http://www.in.gov/dnr/invasivespecies
• Michigan Invasive Plant Council:
http://forestry.msu.edu/mipc
• Minnesota Department of Natural Resources:
http://www.dnr.state.mn.us/exotics/index.html
• Wisconsin Department of Natural Resources:
http://www.dnr.state.wi.us/org/land/er/invasive
• National Park Service, Alien Plant Working Group:
http://www.nps.gov/plants/alien/factmain. htm#pllists
• US Department of Agriculture: http://www.invasive.org
Step 5. Print reference lists developed in the previous three steps and the field datasheets on waterproof
paper. (Waterproof paper is commonly available from field equipment suppliers.)
Step 6. Assemble information needed for the field crew to efficiently and accurately determine the
correct location of the study plot. This will include developing driving directions to the study
plot vicinity; developing a strategy for convenient access to the study plot; determining the
study plot corner nearest to the access point (this will become fauna/soil survey point A);
recording the corresponding Universal Transverse Mercator (UTM) coordinates of this corner
on datasheets Fl and F7; and recording the UTM coordinates of fauna/soil survey points B, C,
and D on datasheet F3 and of flora survey nodes 1 to 9 on datasheet F7.
Step 7. Ensure that all geospatial coordinates on aerial photographs and maps are represented in
UTMs. Convert any latitude/longitude or other geospatial coordinates into their UTM
equivalents for ease of navigation and reference in the field.
Step 8. Assemble field equipment and check it against the list in Section 6.0.
Step 9. Measure standard walking pace of each team member and adjust to 1 m per pace so that team
members can consistently and accurately navigate within the study plot, since GPS readings
should be expected to be sporadic and unstable under most forest canopies. Additionally, team
members will find pacing off the longer transects and larger quadrats more convenient and
faster than trying to generate them with measuring tape, which is generally impractical in a
forest setting.
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Step 10. If not exceptionally proficient at estimating tree heights, the flora crew should spend time prior
to the sampling day, honing their skill at estimating tree heights by sight. This is to be
conducted by measuring a wide range of tree heights with the clinometer and visually and
mentally noting the respective heights. (Instructions regarding the use of a clinometer are in
section 7.3.3.4.) After several trials, the flora crew is to reverse the order by first estimating
various different tree heights and then verifying the accuracy of their tree height estimates with
the clinometer. Prior to conducting their work at the study plot, the flora crew must be able to
consistently achieve visual estimates with an accuracy of +/- 20% of the measured tree heights
(e.g., produce estimates between 24' and 36' for a 30' tree).
7.2 Fauna/Soil Crew Activities
This section describes the steps to be completed by the fauna/soil crew, including study plot set-up
activities, data recording, fauna surveys, and soil characterization.
7.2.1 Data Recording
• The fauna/soil crew should use datasheets Fl through F6. Table 1 describes each of the datasheets
and provides cross-references to SOP instructions. Use as many copies of each sheet as necessary
to record all fauna observations. Note that all fauna sightings during the 4 hour sampling period
should be recorded, regardless of the activity being performed at the time of the sighting.
• Before entering the study plot complete the header information on the datasheets, including
location, site ID number, UTM coordinates, date, and names of investigators.
• Do not fill out the species categorization columns (e.g., native or non-native, etc.) of the datasheets
until all other field activities are completed. If time permits, these columns should be completed in
the field using the fauna reference lists. Otherwise they can be completed back in the office.
• For datasheets F3 and F5, the number of sheets required will vary from plot to plot depending on
the numbers of observations and photographs. These sheets should be numbered in the spaces
provided at the tops of the sheets (i.e., Page of ) by the field crew members. Numbering the
pages will help keep sheets in order and allow verification that all sheets are present and accounted
for at the conclusion of field activities.
• Take photographs of any features (whether fauna, fauna signs or habitat, or disturbances) deemed
as being potentially meaningful. When in doubt, take the photograph.
• Before taking the first photograph, be sure that the time set in the camera is the same as the time
set on field team members' watches so that time can be used to associate the photographs with the
appropriate observation point.
• Keep a record of all photographs taken in the photo log on datasheet F5. Include the time the
photograph was taken, the subject of the photograph, the direction the photographer was facing
while taking the photograph, and a description of the location of the photograph.
• More specific data recording instructions are included in the sections that follow, and on the
datasheets themselves.
7.2.2 Field Set-Up
• Perform reconnaissance of the study plot the preceding afternoon, if possible, to ensure it can be
found in the dark and to select a parking area.
• Before arriving at the parking area near the study site coordinates, arrange materials in the car so
that vehicle can be exited quietly and quickly when it is parked.
• Synchronize the watches of all field team members and the time displayed on the two digital
cameras to make sure they all show the same time of day.
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• The access point should be located at or near one of the four study plot corners. The directional
identity of this corner is to be indicated on datasheet Fl by circling NW, NE, SE, or SW.
• Establish the corner of the study plot nearest to the access point (i.e., fauna/soil survey Point A)
using topographic features or other landmarks. If a GPS reading can be obtained, verify the UTM
coordinates of Point A as indicated on the aerial photographs, maps, and fauna/soil datasheets. If
the measured UTM coordinates agree with the projected UTM coordinates, place a check mark
next to the UTM coordinates listed on datasheet Fl. If a GPS reading cannot be made at or near
Point A, record "NS" for no signal next to the UTM coordinates listed on datasheet Fl.
• Mark this starting corner with red flagging and write "F/S Point A" on the flagging, using
permanent marker. Establish the direction of the plot boundary lines by placing blue flagging 5 m
north or south of the corner, and 5 m east or west of the corner (direction depending on the
orientation of plot with respect to the first corner).
• Regardless of which corner is nearest to the study plot access point, the combined fauna/soil
transect from Point A to Point D is to be oriented diagonally from the starting corner through the
center of the study plot (Figure 1). When proceeding from Point A to Point D, leave flagging
periodically to make the return traversal easier and faster.
• To establish the first bird observation point (i.e., Point B), the fauna/soil crew should pace 140 m
from Point A diagonally toward the study plot center, using a compass to navigate (Figure 1).
7.2.3 Field Data Collection
7.2.3.1 Bird Observations from Points B and C (adapted from Ralph et al. 1993)
• Commence bird data collection approximately half an hour before sunrise. Make as little noise
and other types of disturbance as possible before and during the observation period. Ideally, birds
will be surveyed at Point B just prior to sunrise and just after sunrise at Point C.
• Bird observation Points B and C should be located 140 m apart and 140 m and 280 m,
respectively, from Point A (Figure 1).
• Use datasheet Fl for data recording for this first exercise of the study plot assessment. Complete
the weather conditions section and note the start time immediately before bird observations begin.
• Working together at bird observation Point B, both fauna/soil crew members should observe birds
over a 20-minute observation period, using binoculars and bird reference lists as needed. The more
experienced crew member is responsible for identifying birds, while the other crew member should
record data. Record species that are seen or heard and the approximate number of individuals for
each observation of a species on a separate line in the form.
• Do not double count individuals that may be moving around their territory.
• Record the time at the end of the 20-minute observation period.
• Flag and label Point B in the same manner as Point A. To avoid disturbing birds before their
presence has been observed and recorded, flagging and labeling are to be conducted after the 20-
minute observation period.
• Using a compass for navigation, pace the 140 m from Point B to Point C.
• At Point C, repeat the bird observation procedures as described for Point B above.
7.2.3.2 Fauna & Disturbance Observations from Transects CD, CB, and BA
• Conduct fauna (including birds), habitat features, and disturbance observations along three 140-m
transect lines (i.e., Transects CD, CB, and BA as shown in Figure 1) according to the instructions
that follow.
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• Note the time and temperature at the beginning and at the end of the traversal of each transect, on
datasheet F3.
• Since the second bird observation period at Point C will just have been completed, survey fauna
and disturbances between Points C and D first, i.e., Transect CD. From Point D, reverse traversal
of Transect CD will be required in order to traverse Transect CB followed by Transect B A.
• Associate all features, whether fauna, fauna signs, fauna habitat (e.g., CWD, brush piles, snags), or
disturbances with a single transect (i.e., Transect CD, CB, or BA). Associate features visible from
more than one transect with whichever transect is closest. If no one transect is closest, then
associate the observation with the transect along which it was first observed.
• Enumerate and record on datasheet F2 any vertebrates or signs of vertebrates visible or audible
from each transect. Vertebrate signs include tracks, mounds, burrows, holes, nutshells, scat (e.g.,
deer-pellet clumps), runways, browse lines, and tree rubbing. Identify vertebrates as precisely as
feasible, to species level if possible. Also, record the means used to identify the presence of
vertebrates (e.g., individual, scat, track, skin, or mound).
• Enumerate and record on datasheet F3 the presence of habitat features such as CWD, brush piles,
and snags visible from each transect. Also, enumerate and record on F3 invertebrates observed
along the transects or associated with habitat features. Invertebrates should be identified to the
taxonomic level indicated on F3. If any habitat features or invertebrates are too numerous to count
(e.g., ants in an ant colony), estimate their numbers as orders of magnitude.
• Categorize CWD as logs having a maximum diameter of either <30 cm or >30 cm. Measure the
DBH of snags and categorize them as having a DBH of <30 cm or >30 cm.
• Examine the CWD, brush piles, snags, and any other noted features more closely to look for fauna
and fauna signs. Closer examination of CWD includes "log rolling" of up to four logs per transect
to search for snakes, lizards, salamanders, newts, frogs, beetles, ants, isopods, centipedes,
millipedes, and other invertebrates. Return any material that is disturbed to its previous position
and orientation after examination is complete.
• Record fauna presence and numbers for each rolled log and each closely examined brush pile and
snag. If possible, associate fauna signs with a particular type of animal.
• Record on datasheet F6 any evidence of anthropogenic or natural disturbances visible from each
transect. See F6 for examples of disturbances. Record the number and type of each disturbance per
transect. Also record any invasive or otherwise notable plant species on F6.
• At Points C and B, the digital camera should be used to take a full 360 degree panorama of
photographs, starting with the first shot toward the north. Record each photograph on datasheet F5.
7.2.3.3 Soil & Earthworm Data Collection
• Record soil and earthworm data on datasheet F4.
• Sample the soil at Points D, C, and B (Figure 1) by completing the following steps:
Step 1. Offset the soil sampling location 2 meters to the north of each Point so that the
location sampled will not be compacted by previous team activities.
Step 2. Collect data on soil sampled at each of the three points by driving the sampler
vertically into the ground until either refusal or a 4-foot depth is reached, whichever is
experienced first. Record the soil depth reached.
Step 3. Measure and record the soil core horizon depths (including the litter layer depth or
"O-horizon").
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Step 4. Compare soil horizon colors to a Munsell chart and record the hue, value, and chroma
of each horizon.
Step 5. Determine soil horizon composition by a combination of visual inspection and
moistening a small palm-full of soil with water from a spray bottle and rolling in into a
ball about the size of a large marble. Classify and record the soil as: "sandy" if the ball
will not hold together, "clay" if soil can be rolled into a ball and molded into a cube,
or "loam" if soil can be rolled into a ball but cannot be molded into a cube.
Step 6. Return the soil core to the ground in or near the hole from which it was taken.
• Test for earthworm presence at Points D, C, and B (Figure 1) by completing the following steps:
Step 1. Dig one 30.5-cm x 30.5-cm x 30.5-cm hole 1 m north of the soil sampling location
(i.e., 3 m north of the bird observation point). This results in the extraction of 1 cubic
foot of soil from an uncompacted location associated with each point.
Step 2. Deposit and spread out the soil on a light-colored tarp or cloth.
Step 3. Record the number of earthworms observed. Once earthworms are found, either by
digging holes or examining CWD, discontinue any searching for earthworms (if
earthworms are found anywhere in the study plot, they can be assumed to be present
throughout).
Step 4. Return the soil to the hole from which it was removed.
7.3 Flora Crew Activities
This section describes the steps to be completed by the flora crew, including plot set-up activities, data
recording, flora surveys using a variety of methods, and human impacts characterization.
7.3.1 Data Recording
• The flora crew should record observations from outside of the quadrats around the 9 flora nodes on
F6, and from within the quadrats on flora datasheets F7 through F10. Table 1 describes each of the
datasheets and provides cross-references to SOP instructions. Use as many sheets as necessary to
record all required data.
• Before entering the study plot complete the header information on the datasheets, including
location, site ID number, UTM coordinates, date, and names of investigators.
• Do not fill out the species categorization columns (e.g., native or non-native) of the datasheets
until all other field activities are completed. If time permits, these columns should be completed
using the flora reference lists.
• For some datasheets (e.g., datasheet F8), the number of sheets required will vary from study plot to
study plot depending on numbers of species observed. These sheets should be numbered in the
spaces provided at the tops of the sheets (i.e., Page of ) by the flora crew members.
Numbering the pages will help keep sheets in order and allow verification that all sheets are
present and accounted for at the conclusion of field activities.
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• Keep a record of all photographs taken in the photo log contained on datasheet F5. Include the
time the photograph was taken, the subject of the photograph, the direction the photographer was
facing while taking the photograph, and a description of the location of the photograph.
• More specific data recording instructions are included in the sections that follow, and on the
datasheets themselves.
7.3.2 Field Set-Up
• The two-person flora crew should begin after the fauna/soil crew has completed the first of its two
early morning bird surveys. This will enhance the likelihood of vertebrate fauna being observed by
the fauna team.
• Begin flora observations and measurements at flora node 1 and proceed sequentially to flora nodes
2 through 9 (Figure 2). Flag and survey each flora node according to instructions in Section 7.4.3.
• Navigation to the 9 flora nodes is to be accomplished by using a compass and pacing off distances.
The GPS unit is useful as a confirmatory navigational tool but, due to common interference of the
GPS signal by some forest canopies, it cannot be relied upon as the primary means of navigation.
• As illustrated in Figure 2, center the 10-m x 10-m quadrats for surveying the understory around
each flora node and nest the 5-m x 5-m quadrats for surveying saplings in the southeast corner of
each 10-m x 10-m quadrat. Consequently, the northwest corner of each 5-m x 5-m quadrat will be
anchored by the respective sampling node (Figure 2).
• Upon first arriving at flora nodes 1, 3, 5, 7, and 9, use the digital camera to take a full 360 degree
panorama of photographs, starting with the first photograph toward the north. It is crucial that
photography is the first activity conducted at these flora nodes in order to photo-document the
existing environmental condition prior to disturbance by surveying activities. Additional
photographs can be taken at any flora node whenever there is something deemed potentially
important to photo-document. Unidentifiable flora should also be photo-documented for potential
subsequent identification. Record the relevant information for each photograph on datasheet F5.
7.3.3 Field Data Collection
Collection of data on flora is done in a series of nested plots. At each flora node, it is important to survey
the flora in the following sequence: understory, saplings, canopy cover and associated measurements, and
lastly trees. This sequence, especially the collection of understory data before all other data, is important to
avoid trampling the understory before it has been characterized.
7.3.3.1 Survey Understory in the 10-m x 10-m Quadrats
• In each of the nine 10-m x 10-m understory quadrats, estimate percent cover separately for each
species of shrubs, seedlings, herbaceous groundcover, and bare ground. Experience has shown
that a 10-m x 10-m quadrat is too large to visually evaluate as a single unit. Therefore, percent
cover estimation is to be accomplished by visually inspecting five 2-m x 2-m subquadrats located
at the four corners and the center of the 10-m x 10-m quadrat.
• Later, in the office, the respective percentages of the three understory plant types and of the bare
ground can be averaged to estimate the relative percent cover of each type over the 10-m x 10-m
quadrat. Each of these averaged percent covers is then to be expressed as the matching Braun-
Blanquet cover class (see table of Braun-Blanquet cover classes and associated percent cover
ranges on the back side of datasheet F-9). Note the sum of the percent cover values at any given
location may be greater than 100 percent, because some cover types (e.g., herbaceous groundcover
and shrubs) may be overlapping.
• The respective Braun-Blanquet cover classes can be recorded on datasheet F7 at a later date.
B-15
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7.3.3.2 Survey Saplings in the 5-m x 5-m Quadrats
• Categorize saplings by species.
• Use datasheet F8 to record the DBH of every sapling stem occurring in each of the nine 5-m x 5-m
quadrats.
7.3.3.3 Describe Canopy Cover, Vertical Foliar Structure, Community Type, Successional Stage, and Presence
of Water
• On datasheet F9, describe and record the presence of water and water-related features such as
standing or flowing water and their marks (e.g., dry drainage channels and ephemeral pool
footprints) and riparian zones within or adjacent to the 9 flora nodes.
• Measure canopy cover at the 9 flora nodes using a convex spherical crown densiometer according
to the following 3 steps (modified from CDPR 2003a). Record the percent canopy cover on
datasheet F9.
Step 1. Hold the densiometer level (indicated by the round level in the lower left hand
corner), and far enough away from your body that your head is just outside the grid
(12 to 18 inches away).
Step 2. There are a total of 24 squares on the grid. Count and record the number of squares
showing open canopy. Partially filled squares can be added to make a complete
square.
Example: 4 completely open squares + 3 half-open squares + 5 quarter-open
squares = total of 6.75 open canopy squares.
Step 3. Calculate the percent overstory density with the following equation:
% Overstory density = 100 - (number of open canopy squares x 4.17)
Example: with 10 open squares, the overstory density is 58.3%
• Count the number of foliar layers at the 9 flora nodes, from the top canopy to and including
the herbaceous groundcover. Record the number of foliar layers on datasheet F9.
• Determine the forest community type at the 9 flora nodes, as the two to three most dominant
species in the canopy. List the species in order of decreasing dominance (e.g., Beech-Maple,
Maple-Beech-Birch). Record forest community types on datasheet F9.
• Characterize successional stage at the 9 flora nodes according to the Oliver and Larson (1996)
four-phase method. The successional stages are: stand initiation, stem exclusion, understory
reinitiation, and steady state (see definitions in Section 3.0). Record the successional stages
on datasheet F9.
7.3.3.4 Survey Trees
Conduct point quarter surveying on trees at each of the 9 flora nodes, by using the following three
steps. Record all data on datasheet F10.
Step 1. Establish a north-south and an east-west axis with the sampling node in the center.
Step 2. Select the tree in each of the four quadrants that is closest to the sampling node.
Step 3. Record the following data for each of the four trees per sampling node: species,
distance to the sampling node, DBH, tree height (see bulleted item immediately
below), and canopy class (i.e., dominant, co-dominant, or non-dominant).
The preferred method of determining tree heights is through trained visual inspection as discussed in
section 7.2. Use of the clinometer in a time limited assessment and with obstacles which potentially
will preclude a clear line of sight, is not recommended. The five-step instructions (modified from
B-16
-------
CDPR 2003b) appearing below, for determining tree height using a clinometer are primarily provided
for the practice sessions recommended in section 7.2. The clinometer can also be used to verify a tree
height that is questionable.
Step 1. Choose a location that is level with or up slope from the tree of interest.
Step 2. Look through the clinometer to sight the top of the tree (Figure 3). Read the angle
(% A) and record. Repeat, sighting the base of the tree and record (% B).
Step 3. Measure the distance from where the clinometer reading was taken to the base of the
tree.
Step 4. Calculate tree height according to the following equation:
Tree height = (% A + % B) x distance
Note that this equation uses angles measured as percentages, not degrees.
Step 5. If the top of the tree is not visible from the ground, but height can be roughly
estimated based on surrounding trees, record the height with an "E" qualifier to
signify that the height has been estimated. If height can not be estimated within a 20
percent margin of error, record "NM" on the datasheet, to indicate that the tree height
could not be measured.
7.4 Exiting the Study Site
• Remove all flagging, stakes, and other material transported to the study site by the field teams.
• Check all field equipment against the equipment list to ensure that no equipment is inadvertently
left at the study site.
7.5 Post-Visit Activities
The following activities should be completed back in the office after the field event.
• Using the reference lists developed during pre-visit preparations (Section 7.2), complete any of the
flora and fauna species categorization fields (e.g., native or non-native) that were left blank by the
field team. Using sources recommended in Section 7.2, determine and record appropriate
categories for species that were observed in the plot, but not included in the reference lists.
• Identify any "Bird Conservation Regions" designated avian species of concernand record on Fl.
• Where field team members have listed common names of species, add corresponding scientific
names to the datasheets.
• Complete any taxonomic verification by consulting taxonomic data sources as necessary.
• Verify overstory density and tree height calculations performed by the field crew on datasheet F10.
• On datasheet F7, calculate the average cover class for each of the three understory plant types and
bare ground at each of the flora nodes.
8.0 Data and Records Management
Data collected in this project will be made publicly available through an EPA centralized database.
Completed datasheets will be kept within ORD according to standard data and records management
protocols.
B-17
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9.0 Quality Assurance Procedures
A Quality Assurance Project Plan (QAPP) is associated with this SOP. It is hereby incorporated into this
document by reference. The QAPP should be referred to for details regarding quality assurance protocols
associated with this field program.
While analytical assessments conducted in the laboratory can be verified in a number of ways, the accuracy
of flora, fauna, and human impact assessments in the field cannot be objectively verified with the same
degree of precision. Nonetheless, the use of two-person field crews will allow each crew member to verify
the observations and documentation of the other. Photographs taken between fauna/soil points A and D and
at 5 of the flora nodes will provide additional verification of the data collected.
10.0 References
California Department of Pesticide Regulation (CDPR), 2003a. Standard Operating Procedure: Instructions
for the Calibration and Use of a Spherical Densiometer. SOP Number FSOT.002.00. Environmental
Monitoring Branch.
California Department of Pesticide Regulation (CDPR), 2003b. Standard Operating Procedure: Determining
Height and Slope Using the Brunton Clino Master®. SOP Number FSOT.003.00. Environmental
Monitoring Branch.
Mueller-Dombois, D. and H. Ellenberg, 1974. Aims and Methods of Vegetation Ecology. New York:
Wiley. 547 pp.
Oliver, C.D. and B.C. Larson, 1996. Forest Stand Dynamics. New York: Wiley. 520 pp.
Ralph, C.J., G.R. Geupel, P. Pyle, T.E. Martin, andD.F. DeSante. 1993. Handbook of Field Methods for
Monitoring Landbirds. Gen. Tech. Rep. PSW-GTR-144. U.S. Department of Agriculture, Forest
Service, Pacific Southwest Research Station, Albany, CA. pp. 30 - 35.
http://www.fs.fed.us/psw/publications/gtrs.shtml
Stevens, V., 1997. The ecological role of coarse woody debris: an overview of the ecological role of CWD in
B.C. forests. Res. Br, B.C. Min. For., Victoria, B.C. Working Paper 30/1997.
U.S. Department of Agriculture Forest Service (USDA FS),1989. Interim Resource Inventory Glossary.
June 14, 1989. File 1900. Washington, D.C.: U.S. Department of Agriculture, Forest Service. 96 pp.
B-18
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Table 1: Descriptions of Datasheets
Datasheet #
Fl
F2
F3
F4
F5
F6
F7
F8
F9
F10
Datasheet Title
Forest Bird Observation
Data
Fauna Transect Data for
Vertebrates
Fauna Transect Data for
CWD, Snags, and Brush
Piles
Soil and Earthworm
Data
Photo Log
Invasive
Species/Human Impacts
and Activities
Understory Data
Sapling Data
Community Data
Point Quarter Sampling
Tree Data
Description of Datasheet Items
Bird species, categorization (e.g., threatened, invasive),
numbers observed at observation points
Table 1 : Mammal species, categorization, numbers observed
in transects, behaviors observed, identification method (e.g.,
by tracks or scat)
Table 2: For each transect, numbers and characteristics of
faunal signs observed
Table 3 : Herpetofauna species, categorization, numbers
observed in transects, behaviors observed, identification
method (e.g., by tracks or scat)
Table 4: Bird species, categorization, numbers observed in
transects, behaviors observed
Numbers of herpetofauna and invertebrate taxa observed in
coarse woody debris (CWD), snags, and brush piles in the
fauna transects
Soil horizon depth, color, and composition; numbers of
earthworms
Descriptions of photos taken by both crews
Descriptions/numbers of invasive species and disturbances
observed in each transect, and plants observed outside of
quadrats.
Braun-Blanquet cover class for shrubs, seedlings, herbaceous
groundcover, and bare ground
Sapling species, categorization, numbers of stems, and DBH
of each stem
Canopy cover data, number of foliar layers, community type,
successional stage, and description of water and/or water-
related features. Braun-Blanquet scale.
Tree species, categorization, distance to node, DBH, height,
and canopy class, surveyed by point quarter sampling
method
Related SOP
Section Numbers
7.2.3.1
7.2.3.2
7.2.3.2
7.2.3.3
7.2, 7.3
7.2.3.2,7.3.1
7.3.3.1
7.3.3.2
7.3.3.3
7.3.3.4
B-19
-------
Figure 1. Fauna/Soil Survey Scheme. For purposes of this illustration, the study plot corner nearest the access point
is assumed to be the northwest (NW) corner; see section 7.3 for further explanation.
NW (Starting Corner)
sw
NE
360°
300m
300m
SE
observation point
transect
B-20
-------
Figure 2. Flora Survey Scheme. For purposes of this illustration, the study plot corner nearest the access point is
assumed to be the northwest (NW) corner; see section 7.4 for further explanation.
NW
(Sart)
•« 300m ».
1
75m
10m
fol I '1
360° 5m
1 2 3
_n jib Ji
6 5 360° 1 4
75m
rfei r 75m
7 360° 8 9
5mfo|
360°
360°
NE
i
30
i
i
Om
• tree sampling point
sapling sampling quadrat
understory sampling quadrat
i
sw
SE
Figure 3. Illustration of Measurements Taken to Estimate Tree Height
Distance to Tree (m)
B-21
-------
-------
F1: FOREST BIRD OBSERVATION DATA
Page of
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
Fauna and Soil Crew Names:
UTM Coordinates at four study plot corners (circle corner nearest to plot access point) and observation points:
NW Corner: NE Corner:
SW Corner: SE Corner:
Point B: Point C:
Weather Conditions At Start of Sampling
D storm (heavy rain) D % cloud cover
D rain (steady rain) D clear/sunny
D showers (intermittent) Air Temperature
°c
Weather
D storm
D rain (s
D showe
Air Temp
Conditions At End of £
(heavy rain)
teady rain
jrs (intermittent)
erature °C
Sampling
D % cloud cover
D clear/sunny
Comments:
Bird Species
Species Categorization
Native
(Yes/No)
Invasive
(Yes/No)
Regionally
Common
or Rare
(C/R)
T&E
Status*
Bird Numbers
Point B
Start Time
End Time
Point C
Start Time
End Time
B-23
-------
F1: FOREST BIRD OBSERVATION DATA
Page of
Bird Species
Species Categorization
Native
(Yes/No)
Invasive
(Yes/No)
Regionally
Common
or Rare
(C/R)
T&E
Status*
Bird Numbers
Point B
Start Time
End Time
Point C
Start Time
End Time
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
B-24
-------
F2: FAUNA TRANSECT DATA FOR VERTEBRATES
Faunal Signs
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Faunal Signs
Browse Line
Holes
Nutshells
Tree Rubbing
Other1
(describe/enumerate]
CD
D Present
D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
Associated Fauna:
Total Number
Tree Species:
Associated Fauna:
D Present
D Absent
Associated Fauna:
Notes:
Associated Fauna:
Transect
CB
D Present
D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
Associated Fauna:
Total Number
Tree Species:
Associated Fauna:
D Present
D Absent
Associated Fauna:
Notes:
Associated Fauna:
BA
D Present
D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
Associated Fauna:
Total Number
Tree Species:
Associated Fauna:
D Present
D Absent
Associated Fauna:
Notes:
Associated Fauna:
B-25
-------
F2: FAUNA TRANSECT DATA FOR VERTEBRATES
Mammals
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Mammal Species
Species Categorization
Native
(Yes/No)
Invasive
(Yes/No)
Common
or Rare
in Region
(C/R)
T&E
Status*
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Numbers Observed
CD
fransect
CB
BA
Other
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
B-26
-------
F2: FAUNA TRANSECT DATA FOR VERTEBRATES
Birds
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Bird Species
Species Characterization
Native
(Yes/No)
Invasive
(Yes/No)
Regionally
Common
or Rare
(C/R)
T&E
Status*
Numbers Observed
Transect
CD
CB
BA
Other
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
B-27
-------
F2: FAUNA TRANSECT DATA FOR VERTEBRATES
Herpetofauna
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Herpetofauna Species
Species Characterization
Native
(Yes/No)
Invasive
(Yes/No)
Common
or Rare
in Region
(C/R)
T&E
Status*
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Numbers Observed
Transect
CD
CB
BA
Other
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
B-28
-------
F3: FAUNA TRANSECT DATA FOR CWD, SNAGS, AND BRUSH PILES
Page of
Information to be filled in prior to site visit
Form Completed By:
Location Name: Location ID#:
Date F/S Crew Names:
UTM Coordinates: p0jnt A: Point B: Point C: Point D:
Comments:
Transect: D CD D CB DBA Note: Do not use this page for more than one transect.
Weather Conditions At Start of Sampling
D storm (heavy rain) D % cloud cover
D rain (steady rain) D clear/sunny
D showers (intermittent) Air Temperature °C
Feature
Characterize and enumerate (as appropriate) features:
DCWD
D Max DBH < 30 cm
D Max DBH > 30 cm
D Snag
D DBH < 30 cm
D DBH > 30 cm
D Brush Pile
Length cm Width cm
bNahW (de^ffibe)
Weather Conditions
D storm (heavy rain
n rain (steady rain>
D showers (intermit
At End of Sampling
D % cloud cover
D clear/sunny
:ent) Air Temperature °C
Invertebrates
Taxa
Beetles
Ants
Isopods
Centipedes
Millipedes
Snails and Slugs
Spiders and Ticks
Earthworms
Termites
Numbers
Start Time
End Time
;: DAM
DAM
DPM
DPM
Herpetofauna*
Taxa (list species)
Numbers (by ID method)
Ind
Scat
Track
Skin
Numbers recorded here should be added into herpetofauna totals for each transect recorded in F2.
-------
F4: SOIL AND EARTHWORM DATA
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
Fauna & Soil Crew Names:
Soil Horizon
O
A
E
B
C
Depth range from surface (cm)
Depth range from surface (cm)
Color (from Munsell chart)
Composition
Depth range from surface (cm)
Color (from Munsell chart)
Composition
Depth range from surface (cm)
Color (from Munsell chart)
Composition
Depth range from surface (cm)
Color (from Munsell chart)
Composition
Depth (m) reached by sampler*
Number of Earthworms
Observation Point
D
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
C
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
B
Hue
Value
Chroma
D Sand
D Loam
D Clay
Hue
Value
Chroma
D Sand
D Loam
D Clay
Hue
Value
Chroma
D Sand
D Loam
D Clay
Hue
Value
Chroma
D Sand
D Loam
D Clay
*As described in Section 7.2.3.3, Step 2.
B-30
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F5: PHOTO LOG
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Camera Type/Number
Comments
Time
Subject
Data
Sheet #
Location*
Direction
File
Namet
*For the Location field, record the observation point, transect, etc., where the photo was taken.
TFile name to be entered after returning from field and downloading pictures.
B-31
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F6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID# UTM-E UTM-N
Investigators
Form Completed By Date
Comments
Table 1. Invasive Plants
Plant Species*
In Designated Land Cover Type:
In Other Land Cover Types:
Tally
Total Number of
Occurrences
*Note: List each species on a separate line. Use as many sheets as necessary.
B-32
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F6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Table 2. Disturbance and Human Management Practices in the Designated Land Cover Type
Map ID
Number(s)*
Disturbance Indicator*
Paths
Car/Vehicle Tracks
Off-road vehicle tracks
not on well-worn paths
Loud noise
Bright, artificial lights
Evidence of human
management practices
Trash (appliances/tires)
Litter
(paper/plastic scraps)
Hydrologic modifica-
tions (e.g., ditch, weir)
Evidence of mowing,
tree felling
Oily or Soapy
Surface Water
Other*
Description*
Total Number
of Times
Encountered
Photo
Taken?
(Y/N)*
'Notes: Use as many sheets as necessary.
Map ID numbers should be assigned D1, D2, etc. Use these numbers to identify disturbances drawn on the plot sketch.
Other disturbance indicators are included in Section 3.0 of the Forested Terrestrial SOP.
Descriptions of disturbance indicators should include more detailed information about the disturbance, how frequently if was encountered in the
plot, and if appropriate, the size of the affected area.
List any photos taken on F5 (photo log); include the Map ID number in the Subject field of the photo log.
B-33
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F6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID# UTM-E UTM-N
Investigators
Form Completed By Date
Comments
Table 3. Plants observed outside of sample quadrats.
Plant Species*
In Designated Land Cover Type:
In Other Land Cover Types:
Tally
Total Number of
Occurrences
*Note: List each species on a separate line. Use as many sheets as necessary.
B-34
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F6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Comments
Table 4. Description of other special features in plot.
Feature
Description
Visual variation in
vegetation occurring
in the plot
Streams and riparian
zones
Water sample(s)
collected? D Y D N
How many?
(list sample ID numbers
in space at right)
Other surface water
Water sample(s)
collected? D Y D N
How many?
(list sample ID numbers
in space at right)
Fauna/Fauna
remains (list species
if known)
B-35
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F6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Comments
Figure 1. Sketch delineating areas of human disturbance, land cover types, surface water bodies, and other features in plot.
B-36
-------
F7: UNDERSTORY DATA
Page of
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Flora Crew Names:
UTM Coordinates:
NodeS
E:
N:
Model
E:
N:
Node6
E:
N:
Node 2
E:
N:
Node?
E:
N:
Location ID#:
Date:
NodeS
E:
N:
NodeS
E:
N:
Node 4
E:
N:
Node 9
E:
N:
Comments:
Understory
Vegetation
Bare Ground
Check one:
n Shrub
n Seedling
n Herbaceous
Species:
T/E? Invasive?
Visual Estimates of Relative Cover (%)
NE Corner
NW Corner
SW Corner
SE Corner
Average
Class*
NE Corner
NW Corner
SW Corner
SE Corner
Average
Class*
Model
Node 2
NodeS
Node 4
NodeS
Node6
Node?
NodeS
Node 9
-------
F7: UNDERSTORY DATA
Page of
Understory
Vegetation
Check one:
n Shrub
n Seedling
n Herbaceous
Species:
T/E? Invasive?
Check one:
n Shrub
n Seedling
n Herbaceous
Species:
T/E? Invasive?
Check one:
n Shrub
n Seedling
n Herbaceous
Species:
T/E? Invasive?
Check one:
n Shrub
n Seedling
n Herbaceous
Species:
T/E? Invasive?
Visual Estimates of Relative Cover (%)
NE Corner
NW Corner
SW Corner
SE Corner
Average
Class*
NE Corner
NW Corner
SW Corner
SE Corner
Average
Class*
NE Corner
NW Corner
SW Corner
SE Corner
Average
Class*
NE Corner
NW Corner
SW Corner
SE Corner
Average
Class*
Model
Node 2
NodeS
Node 4
NodeS
Node6
Node?
NodeS
Node 9
Cd
-------
F8: SAPLING DATA
Page of
Information to be filled in prior to site visit
Location Name:
Location ID#:
Date:
Form Completed By:
Flora Crew Names:
Comments:
Sapling Species
Native
(Yes/No)
Invasive
(Yes/No)
T&E
Status*
Saplings in Quadrats
Total* of Stems
DBH of each stem
(cm), separated by
commas
Total* of Stems
DBH of each stem
(cm), separated by
commas
Total* of Stems
DBH of each stem
(cm), separated by
commas
Total* of Stems
DBH of each stem
(cm), separated by
commas
Node 1
Node 2
Node3
Node 4
NodeS
td
* T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
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Cd
^
o
Comments:
Sapling Species
Native
(Yes/No)
Invasive
(Yes/No)
T&E
Status*
Saplings in Quadrats
Total* of Stems
DBH of each stem
(cm), separated by
commas
Total* of Stems
DBH of each stem
(cm), separated by
commas
Total* of Stems
DBH of each stem
(cm), separated by
commas
Total* of Stems
DBH of each stem
(cm), separated by
commas
NodeG
Node 7
NodeS
Node9
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F9: COMMUNITY DATA
Information to be filled in prior to site visit
Location Name:
Location ID#:
Date:
Form Completed By:
Flora Crew Names:
Comments:
Sampling
Node
1
2
3
4
5
6
7
8
9
Canopy Cover
Number
of Open
Squares
Overstory
Density
(%r
Number
of Foliar
Layers
Community Type*
Stage*
Slope (°) & Aspect*
Describe Presence of Water
and/or Water-Related Features
td
*Notes: Overstory density is to be calculated as described in Section 7.4.3.3, Step 3 of Forested Terrestrial SOP.
For community type, describe the community type present at each node as the two to three most dominant species in the canopy, listing the species in order of decreasing dominance
(e.g., Beech-Maple, Maple-Beech-Birch).
For stage, record successional stage: SI for stand initiation, SE for stem exclusion, UR for understory reinitiation, or SS for steady state.
For slope/aspect, record the approximate grade and facing aspect, for example, 20° N-NE.
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F9: COMMUNITY DATA
Information to be filled in prior to site visit
Location Name:
Location ID#:
Date:
Form Completed By:
Flora Crew Names:
Braun-Blanquet cover classes. From Aims and Methods of Vegetation Ecology, Muller-Dombois and Ellenberg, 1974.
Cd
Class
5
4
3
2
1
t
Range of Cover (%)
75-100
50-75
25-50
5-25
1-5
<1
Mean
87.5
62.5
37.5
15.0
2.5
-
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F10: POINT QUARTER SAMPLING TREE DATA
Information to be filled in prior to site visit
Location Name:
Location ID#:
Date:
Form Completed By:
Flora Crew Names:
Node
1
2
3
4
5
Tree
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
Tree Species (scientific name)
Species Categorization
Native
(Yes/No)
Invasive
(Yes/No)
T&E
Status*
Distance
to Node
(m)
DBH (cm)
Tree Height*
Angles
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
Distance to
Tree (m)
Height (m)
Canopy
Class*
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Node
6
7
8
9
Tree
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
Tree Species (scientific name)
Species Categorization
Native
(Yes/No)
Invasive
(Yes/No)
T&E
Status*
Distance
to Node
(m)
DBH (cm)
Tree Height*
Angles
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
%A %B
Distance to
Tree (m)
Height (m)
Canopy
Class*
td
* T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
Tree height should be determined visually as described in Section 7.1 (Step 10) and, if necessary, it can be measured with a clinometer as described in Section 7.3.3.4 of Forested Terrestrial SOP (only use first two columns if
necessary).
For Canopy Class field, record D for dominant, C for co-dominant, or N for non-dominant.
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APPENDIX C
Nonforested terrestrial protocol and datasheets
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STANDARD OPERATING PROCEDURE
FOR THE QUICK ASSESSMENT PROTOCOL:
NON-FORESTED TERRESTRIAL
IN SUPPORT OF
U.S. ENVIRONMENTAL PROTECTION AGENCY
UNDER
RCRA ENFORCEMENT, PERMITTING, AND ASSISTANCE (REPA3)
ZONE 2 - REGION 5
CREATED FOR USE BY EPA REGION 5
NONFORESTED TERRESTRIAL SOP, REVISION NO. 3
EFFECTIVE DATE: January 2006
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Table of Contents
Table of Contents C-i
List of Tables and Figures C-ii
List of Appendices (Datasheets) C-ii
1.0 Scope and Application C-5
2.0 Method Summary C-5
3.0 Definitions C-5
4.0 Health and Safety C-6
5.0 Personnel Qualifications C-6
5.1 Fauna Crew Member C-7
5.2 Flora and Soils Crew Members C-7
5.3 Human Impacts Crew Member C-7
6.0 Equipment and Supplies C-7
6.1 General Equipment Needed C-7
6.2 Additional Equipment Needed for Fauna Crew Member C-8
6.3 Additional Equipment Needed for Flora and Soils Crew Members C-8
6.4 Additional Equipment Needed for Human Impacts Crew Member C-8
7.0 Procedure C-8
7.1 Pre-visit Preparations C-9
7.2 Data Recording C-ll
7.3 Fauna Survey C-12
7.3.1 Field Set-up C-12
7.3.2 Field Data Collection C-12
7.3.2.1 Bird and Amphibian Data Collection from Observation Points C-12
7.3.2.2 Fauna Observations from Transects C-13
7.4 Flora and Soils Survey C-14
7.4.1 Field Set-Up C-14
7.4.2 Field Data Collection C-15
7.4.2.1 Point Surveys C-15
7.4.2.2 Quadrat Surveys C-15
7.4.2.3 Soil and Vegetation Stress C-16
7.4.2.4 Wandering Survey C-17
7.5 Human Impacts Survey C-17
7.5.1 Field Set-Up C-17
7.5.2 Wandering Survey C-17
7.6 Exiting the Field Study Site C-18
7.7 Post-Visit Activities C-18
8.0 Data and Records Management C-18
9.0 Quality Assurance Procedures C-19
10.0 References C-19
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List of Tables and Figures
Table 1. Descriptions of Datasheets.
Table 2. Bird Species of Special Interest.
Figure 1. Fauna and Human Impacts Observation Scheme.
Figure 2. Flora Survey Scheme.
List of Appendices (Datasheets)
Nl. Bird and Amphibian Point Counts
N2. Fauna Transect Data
N3. Photo Log
N4. Soil and Vegetation Stress Data
N5. Invasive Species/Human Impacts and Activities
N6. Point Survey Data
N7. Quadrat Survey Data
N8. Special Features
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1.0 Scope and Application
Knowledge of ecosystem health and quality is an important component of successful ecosystem
management. Ecological assessments are increasingly used in support of adaptive ecosystem management
and informed resource management. Rapid ecological assessment is a common technique that is most often
used to objectively assess the biological diversity of a relatively unknown ecological area. However, this
assessment technique can also be used to evaluate ecological characteristics other than diversity.
The purpose of this standard operating procedure (SOP) is to detail a rapid ecological assessment protocol
for evaluation of the health and quality of non-forested terrestrial ecosystems such as shrublands, prairies,
and sparsely vegetated areas. This non-forested terrestrial protocol is one of four protocols originally
drafted at a workshop held in June 2003. When using this SOP, the associated Quality Assurance Project
Plan (QAPP) should be consulted. The QAPP serves as a generic plan for all data collection activities
conducted under the SOPs and offers guidelines for ensuring that data are of sufficient quality and quantity
to support project objectives. Decisions regarding the application of data collected while using this SOP
should consider the precision, accuracy, and other statistical characteristics of these data.
2.0 Method Summary
This SOP provides instructions for a rapid ecological assessment of non-forested terrestrial ecosystems,
using fauna surveys, flora surveys, human impacts surveys, and soil sampling of a 300-m x 300-m plot.
Generally, the optimal season for implementing this protocol is during the growing season. Late spring
offers the best opportunity for sampling nesting birds in most temperate locations; however, this will not be
the ideal time to identify all plants. It is most important to sample all sites in a relatively small window
(e.g., mid-May to mid-June, depending on latitude and other climatic factors) to make comparisons across
sites.
The protocol requires a four person field team, and is intended to be completed in four hours. A fauna
expert conducts 10-minute periods of bird and amphibian observation from four different observation points
within the plot, and traverses four transects to look for fauna and fauna signs such as scat. Two of the field
team members, one of which is a flora expert, work as a pair to conduct flora surveys at 30 points and ten
0.5-m x 0.5-m quadrats along a 300-m transect which runs through the center of the plot, and takes four soil
samples along the transect to determine the depth, color, and composition of soil layers. These two then
systematically traverse the entire study plot to develop a complete plant species list for the plot. The fourth
field team member assists the fauna expert with the bird and amphibian point counts, and then
systematically traverses the entire study plot to record and map signs of human impacts and fauna
observations that the fauna expert may miss. Detailed procedures to are provided in Section 7.0.
3.0 Definitions
Canopy cover: Cover provided by tree crowns.
DBH: The diameter of a tree at breast height, or 4.5 ft (1.37 m) above the forest floor on the uphill side of
the tree. For the purposes of determining breast height, the forest floor includes the duff layer that may be
present, but does not include unincorporated woody debris that may rise above the ground line. (Source:
USDAFS 1989)
Designated land cover type: The land cover type that is the primary target of the assessment (i.e.,
shrublands, prairies, or sparsely vegetated areas for this SOP).
Disturbance indicator: Disturbance is a natural or human-induced (anthropogenic) environmental change
that affects an ecosystem's floral, faunal, or microbial communities. Disturbance may include, but is not
limited to: roads, gravel, asphalt, trails, berms, ruts in the soil, eroded areas, evidence of digging, wind
throw mounds, evidence of fire, litter, tires, refrigerators, manure, power lines, human tracks, off-road
vehicle tracks, hydrologic modifications (e.g., ditches, weirs), evidence of mowing or tree felling, loud
noise, and bright artificial lights.
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Generalist: A species that has a broad ecological niche, capable of utilizing a relatively wide variety of
resources.
Habitats of interest: Habitats that add diversity to such non-forested ecosystems as shrubland, prairie, and
sparsely vegetated area complexes (e.g., plantings left from prior human occupation) and/or that are
regionally scarce (e.g., wetlands).
Health/vigor indicators: For the purposes of this SOP, see description of vegetation stress indicators.
Landscape attributes: Landscape attributes include cliffs, water bodies, habitat types, relative height and
distribution of plants having different physiognomy, and other attributes that affect the general appearance
of the landscape.
Reproductive status: The flowering and/or fruiting status of a plant.
Soil probe: A 1 1A inch hollow pipe about 4 feet long that is cut on a 45-degree angle at one end and used
for sampling soils. It has closely-spaced holes along one side.
Specialist: A species that has a narrow ecological niche, utilizing a relatively limited set of resources.
Sporulation: Spore formation. Spores are small, usually single-celled, reproductive bodies produced
especially by certain bacteria, algae, fungi, and nonflowering plants. They may appear as spots, small sacs,
or on stalks and may become black as the spores ripen on surfaces of leaves that have become colonized by
fungi.
Vegetation stress indicators: Signs of vegetation stress include: leaves that are yellowing, wilting, or
dropping prematurely (for taxonomic groups in which this is uncommon); leaves that are spotted or
"burned," which may indicate herbicide drift from nearby agricultural fields; herbivory in excess; and
sporulation or other evidence of fungal infection. For this SOP, galls and brooms are not considered to be
signs of vegetation stress because they could represent a normal, healthy condition.
4.0 Health and Safety
Health and safety concerns for field workers in the non-forested terrestrial ecosystem include:
• slips, trips, and falls;
• thermal conditions such as excessive heat or cold;
• inclement weather, especially lightning;
• biological hazards, such as insects or other taxa that may bite and plants that may contain
substances causing allergic reactions; and
• hunting (depending upon land use designations).
Field workers should wear closed shoes, long pants and long sleeves, but are individually responsible for
selecting footwear, clothing, gloves and outerwear as appropriate to the situation at hand. Field workers
should use insect repellant, as appropriate, and take care to avoid holes and other obstacles that may cause
slips, trips, and falls. Workers should increase attentiveness to potential hazards during pre-dawn activities.
Workers should also determine potential hunting or other human activities that could put them at increased
risk, and take steps (such as wearing orange vests and notifying park rangers or other relevant law
enforcement agencies of their location) to mitigate these risks.
5.0 Personnel Qualifications
This protocol requires four personnel who comprise the "field team." One should be an expert in
identifying plants in non-forested terrestrial habitats, the second should have some botanical training, the
third should be an expert in animal identification, and the fourth can either have expertise similar to the
other three or be a relative novice at biological field work. More specific qualification requirements are
detailed below.
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5.1 Fauna Crew Member
This crew member should have familiarity with terrestrial vertebrate and invertebrate field measurement. In
particular:
• Birds: Bird identification skills must be at minimum to genus level, but preferably to species level for
all avian individuals observed.
• Mammals: Identification to genus, and preferably to species, by sight, tracks or scat, with help from a
field guide.
• Butterflies: Identification by sight of particular species of interest based on field guide or short field
sheets.
• Herpetofauna: Identification by sight of common species and of tracks that are obviously reptilian (e.g.
snake tracks). Identification of frogs and toads by calls is desirable but not necessary.
Knowledge concerning the use of Global Positioning System (GPS) equipment is also advantageous.
5.2 Flora and Soils Crew Members
At least one of the two flora crew members should be an expert with the ability to identify at least 200
species and with training to discriminate unknown flora. The second flora crew member will act as an
assistant, and should be familiar with botanical nomenclature, have knowledge of about 50 common
species, and have training in sampling and recording methods. One of the two crew members should also
have knowledge concerning the use of GPS equipment. (Note: This protocol assumes the flora crew will
be able to identify nearly all plant species in the field, but may occasionally have to collect a transitory
specimen for taxonomic verification.) In addition, one of the two crew members should have basic soils
knowledge, including differentiating horizons (e.g., organic, A, B, E, etc.) and estimating soil composition
(clay, loam, sand).
5.3 Human Impacts Crew Member
This crew member should preferably possess a minimal amount of elementary plant diversity knowledge.
He or she could be an entry-level participant or trainee. Knowledge concerning the use of GPS equipment
is also advantageous.
6.0 Equipment and Supplies
6.1 General Equipment Needed
• Three digital cameras (and 128 MB memory card) with zoom and panorama capabilities
• Two GPS units and recharger for DC power socket in automobile
• Flagging (three colors)
• Insect repellant
• Sunscreen
• Three clipboards
• Pens with permanent, waterproof ink (or mechanical pencils)
• Permanent ink markers
• First aid kit
• Three flashlights
• Three hand-held compasses
• Thermometer
• Aerial photos, maps, and driving directions to study sites
• Three copies of this SOP
• Three accurate watches
• Three backpacks to carry equipment.
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6.2 Additional Equipment Needed for Fauna Crew Member
• Bird binoculars
• Bird, mammal, herpetofauna, spider & insect, and animal scat/tracks field guides
• Butterfly identification field sheet
• Bird song CD
• Frog/toad song CD
• CD player with headphones
• Fauna reference lists, printed on waterproof paper (commonly available from field equipment
suppliers)
• Fauna datasheets (Nl through N3), printed on waterproof paper (commonly available from field
equipment suppliers).
6.3 Additional Equipment Needed for Flora and Soils Crew Members
• Tree, shrub, forb, and grass field guides
• Two measuring tapes (both 50 m)
• Walking stick or meter stick
• 3 -sided PVC (or wood) quadrat frame (0.5 m x 0.5 m)
• DBH tape (5 m)
• Plastic bags (3 to 5 quart size)
• Thin tape (for marking plants)
• 3 metal stakes (for anchoring ends of 300 m tape)
• Mallet to drive stakes (if necessary)
• Trowel (for plant collection)
• Clippers (for plant collection)
• Hand lens (with 10 X and 15 X magnification)
• Flora reference lists, printed on waterproof paper
• Flora datasheets (N3, N4, N6 through N8), printed on waterproof paper.
• Soil probe (with wet and dry core tips)
• Wooden rod (1.25-inch diameter for extracting soil from the soil probe)
• Munsell Chart
6.4 Additional Equipment Needed for Human Impacts Crew Member
• Spray water bottle
• Several 250-ml screw-top jars and labels (for surface water collection)
• Measuring tape (5 m)
• Shovel
• Gloves
• Flora and fauna reference lists, printed on waterproof paper (commonly available from field
equipment suppliers)
• Invasive species/human impacts datasheets (N5), plus copy of fauna datasheets (Nl through N3),
printed on waterproof paper (commonly available from field equipment suppliers).
7.0 Procedure
This protocol requires a field team of four people to evaluate a 300-m x 300-m plot, and is intended to be
completed in approximately four hours. Before formally collecting data in the field, all field team members
should practice the protocol at least once in a convenient shrubland, prairie, or sparsely vegetated habitat to
make sure they are clear on how to collect the data and complete each of the datasheets. One or more of the
team members will perform pre-visit preparations. Subsequently, one team member (the fauna crew) will
conduct the fauna portions of the protocol, with support from one of the flora crew members. A pair of
workers (the flora crew) will conduct the flora portion of the protocol, and the fourth team member (the
human impacts crew) will conduct the human impacts portion of the protocol.
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The first activity conducted at the plot is bird and amphibian data collection, which is performed by the
fauna crew member and the human impacts crew member, and should begin half an hour before sunrise. In
order to create the least amount of disturbance prior to and during this segment of the protocol, the other two
field team members should not enter the plot until bird data collection is complete. Once the bird and
amphibian data collection is complete, the flora survey should begin. All four field team members will then
work simultaneously for the remainder of the four-hour evaluation period.
7.1 Pre-visit Preparations
Listed below are the steps that must be completed before the start of the field event.
Step 1. Obtain aerial photographs and topographic maps of the site and surrounding area. These materials
will familiarize the field team members with the area and provide them with a context for the site.
Step 2. Determine the pre-assigned four-character site ID number for the study site. The first character of
the site ID number should be "N" for nonforested ecosystem, the second character identifies the
state in which the site is located (i.e., 1 = Ohio, 2 = Michigan, 3 = Indiana, 4 = Illinois, 5 =
Wisconsin, and 6 = Minnesota); and the last two characters should be digits assigned sequentially
from 00 to 99.
Step 3. It may be necessary to obtain permission or a formal permit to access the study site. Check with
the land owner or manager to determine the need for such permission or permits. The minimal
sample collection envisioned for this protocol will not include vertebrate species and is not
expected to require a scientific collecting permit. This should be verified with fish and wildlife,
and environmental agencies as appropriate.
Step 4. Determine corner points of the plot, the four bird observation point locations, and approximate
orientation of the 300-m flora transect using aerial photos, topographic maps, and/or Geographic
Information System (GIS) data. The transect should run through the plot's center and be oriented
along a major environmental gradient. Also, determine the approximate GPS coordinates of the
endpoints of the transect line. By using UTMs as a coordinate system, the corner points and
internal points can be determined from a single starting point (such as one corner), by adding and
subtracting meters from the UTMs of that first point. For example, to add a corner point that is due
north or due east of the starting corner point, add either 300 to the northing coordinate or 300 to
the easting coordinate. For points that are south or west, subtract 300 from the northing or easting.
When finding these points while on the site, walking in the direction of the point while watching
the GPS unit can tell you when you've reached the desired point (such as one of the bird
observation points). Once the coordinates on the GPS unit match the predetermined coordinates of
the point, you have arrived.
Step 5. Create reference lists of local flora and vertebrate fauna species from available databases and
resources. For all species: (1) categorize as native or non-native, (2) include state and Federal
status, and (3) categorize as invasive or non-invasive. Fauna species should also be categorized as
regionally common or rare, and as generalist or specialist.
Useful information sources for determining species status include:
• Illinois Department of Natural Resources:
http://dnr.state.il.us/espb/datelist.htm - state list of threatened and endangered (T&E)
species.
• Illinois Natural History Survey:
http://www.inhs.uiuc.edu/cbd/ilspecies/ilsplist.html - list of flora and fauna occurring in
Illinois, including state and federal listing status.
• Indiana Department of Natural Resources:
http://www.in.gov/dnr/fishwild/endangered/e-list.htm - lists of T&E fauna;
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http://www.in.gov/dnr/naturepr - lists of rare, threatened, or endangered (RTE) species
by county and a list of Indiana's RTE vascular plants;
http://www.in.gov/dnr/fishwild/endangered/frogs.htm - list of frogs and toads in
Indiana;
http://www.in. gov/dnr/invasivespecies/innatcom03 .pdf - lists of characteristic species
found in a variety of community types.
• Michigan Department of Natural Resources:
http://www.michigan.gOv/dnr/0.1607.7-153-10370 12142—.OO.html - links to T&E
lists and a rare plant reference guide;
http://www.michigan.gov/dnr/OJ607.7-153-10370 12145—.OO.html - links to fauna
information;
http://www.michigan.gOv/dnr/0.1607.7-153-10370 12146—.OO.html - links to flora
information.
• Minnesota Department of Natural Resources:
http://www.dnr.state.mn.us/ets/index.html - Minnesota's list of endangered, threatened,
and special concern species;
• Ohio Department of Natural Resources:
http://www.ohiodnr.com/wildlife/resources/default.htm - links to a variety of wildlife
resources, including T&E lists for fauna;
http://www.ohiodnr.com/forestry/Education/ohiotrees/treesindex.htm - list of Ohio's
tree species.
• Wisconsin Department of Natural Resources:
http://www.dnr.state.wi.us/org/land/er/ - includes lists of state and federal T&E species
occurring in Wisconsin, county maps that list known occurrences of T&E species, and a
searchable database of T&E species occurrences in Wisconsin.
• U.S. Fish and Wildlife Service:
www.fws.gov - links to a discussion of the endangered species program and a list of
Federally threatened and endangered species.
Useful sources of information on invasive species include:
• Indiana Department of Natural Resources: http://www.in.gov/dnr/invasivespecies
• Michigan Invasive Plant Council:
http://forestry.msu.edu/mipc
• Minnesota Department of Natural Resources:
http://www.dnr.state.mn.us/exotics/index.html
• Wisconsin Department of Natural Resources:
http://www.dnr.state.wi.us/org/land/er/invasive
• National Park Service, Alien Plant Working Group:
http://www.nps.gov/plants/alien/factmain.htotfpllists
• USDA: http://www.invasive.org
Step 6. Print reference lists developed in the previous step and field datasheets on waterproof paper
(commonly available from field equipment suppliers).
Step 7. The more experienced flora crew member should provide training to the human impacts crew
member in invasive plant identification that are likely to occur in the plot. The flora expert
C-10
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should also provide training, as necessary, on recognition of evidence of fungal infection in
plants.
Step 8. Assemble information needed for field crew to efficiently and accurately determine the correct
location of the study plot (e.g, develop driving directions, provide UTM coordinates).
Step 9. Assemble field equipment and check it against the list in Section 6.0.
Step 10. Measure standard walking pace of each field team member and adjust to 1 m per pace so that
team members can pace off the longer transects and larger quadrats rather than having to
measure them.
Step 11. Perform reconnaissance of the study site the preceding afternoon, if possible, to make sure it
can be found in the dark and to select a parking area.
Step 12. During the site reconnaissance, use the predetermined transect orientation along with
observations in the field to determine the exact location and orientation of the 300-m flora
transect. Diagonally opposite bird/amphibian survey locations will be along this transect line.
Flag the corner of the plot that is in line with the transect and is closest to the parking area
with red flagging. Place additional (differently colored, but not yellow) flags leading from the
parking area to the flagged plot corner if any difficulty in finding this corner in the dark is
anticipated.
Step 13. Synchronize the watches of all field team members and the time displayed on the digital
cameras to make sure they all show the same time of day.
7.2 Data Recording
Data collected in accordance with this SOP should be recorded as described below.
• The fauna observation data should be recorded on the fauna datasheets Nl and N2. Use as many
copies of each sheet as necessary to record all fauna observations. Note that all fauna sightings
during the four hour survey period should be recorded, regardless of the activity being performed at
the time of the sighting.
• Record flora survey data on the flora datasheets N4 and N6 through N8. Use as many sheets as
necessary to record all required information.
• Record human impacts survey data on the invasive species/human impact datasheets N5. Use as
many sheets as necessary to record all required information.
• Keep a record of all photos taken on the photo log datasheet N3. Include the time the photo was
taken, the subject of the photo, the direction the photographer was facing while taking the photo, a
description of the location of the photo, and if appropriate, a map identification number and
corresponding datasheet number (e.g., Nl). Map identification numbers can be assigned as the
photos are taken, and then recorded on the plot sketches at the approximate location of the subject
of the photo.
• Before entering the study plot complete the header information on the datasheets, including
location, site ID number, UTM coordinates, date, and names of investigators.
• Do not take time in the field to fill out the species categorization columns (e.g., native or non-
native) of the datasheets until all other field activities are completed. If time permits, these columns
should be completed in the field using the fauna reference lists. Otherwise they can be completed
back in the office.
• For some datasheets (e.g., Nl), the number of sheets required will vary from plot to plot depending
on numbers of species observed. These sheets should be numbered in the spaces provided at the
tops of the sheets (i.e., Page of ) by the field crew members. Numbering the pages will help
keep sheets in order and allow verification that all sheets are present and accounted for at the
conclusion of field activities.
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• More specific data recording instructions are included in the sections that follow, and on the
datasheets themselves. Table 1 describes each of the datasheets and provides cross-references to
SOP instructions.
7.3 Fauna Survey
The first hour of the fauna survey is conducted by the fauna crew member with the assistance of the human
impacts crew member, and is spent making bird and amphibian observations from four points in the study
plot. After this first hour, the flora crew members begin flora surveys, and the fauna crew member and
human impacts member work alone. The fauna crew member spends the next two hours walking four 100-
m transects looking for other fauna and their signs. After the transect surveys are completed, the fauna
crew member should spend any remaining time assisting with the flora survey or the human impacts survey.
Detailed instructions for completing the fauna survey are presented below.
7.3.1 Field Set-up
Step 1. Before arriving at the parking area near the study site coordinates, arrange materials in the car
so that the vehicle can be exited quietly and quickly when it is parked.
Step 2. The human impacts crew member should assist the fauna crew member with Steps 3 through 8
below.
Step 3. Document the corner of the 300-m x 300-m plot closest to the parking area using the GPS
unit. Record coordinates on Nl. If a GPS signal cannot be obtained, use topographic features
or other landmarks to document an appropriate location for this corner. Record the GPS
coordinates at the closest location where there is a signal, and note the approximate distance
and direction of the recorded coordinates from the plot corner.
Step 4. Verify or replace the red flagging at this corner.
Step 5. Use the GPS unit to find observation point A by walking in the direction of the point from the
corner until the GPS UTMs match the predetermined UTM coordinates for that point (Figure
1). If a GPS signal cannot be obtained, pace 100 m down one of the established boundaries of
the plot using a compass to continue the established direction of the boundary line. Then turn
90 degrees toward the interior of the plot square and pace another 100 m. At the end of this
100 m, the two field team members should both be at observation point A (Figure 1). Take a
GPS reading to document the location of observation point A and mark this point with
flagging. Record "NS" on the datasheets to indicate that no GPS signal was used to find the
point.
Step 6. Label observation point A on the flagging using a permanent ink marker.
Step 7. Note that, since the observation points should be located 100 m apart and 100 m from the
boundaries of the 300-m x 300-m plot (Figure 1), point A establishes the first corner of the
sampling square.
7.3.2 Field Data Collection
7.3.2.1 Bird and Amphibian Data Collection from Observation Points
Step 1. The point-count methodology detailed below is based on "extensive point counts"
procedures developed by Ralph et al. (1993).
Step 2. Commence bird and amphibian data collection at observation point A approximately half
an hour before sunrise. Make as little noise and other types of disturbance as possible
before and during the observation period.
Step 3. Use Nl for data recording. Complete the weather conditions section and note the start
time immediately before observations begin.
Step 4. Beginning at observation point A, the fauna crew member should observe birds over a 10-
min observation period, using binoculars, field guides, and the fauna reference lists as
needed. The human impacts crew member should be responsible for data recording.
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Record species that are seen or heard within 50 m of the observation point and the
approximate number of individuals per species. If necessary, the fauna crew member can
verify identification of bird calls after the 10-minute observation period is over using the
bird song CD. Bird species of special interest are listed for each type of habitat in Table
2.
Step 5. Record general behavior of each observation of a bird species (e.g., singing (S), flying
(F), perched (P), foraging (O)) and the approximate number of individuals if a number of
birds are observed at once. Do not double count individuals that may be moving around
their territory.
Step 6. During the 10-minute observation periods, frog and toad calls should also be identified to
species if possible, with the help of the CD. Record species and approximate numbers of
individuals for each species.
Step 7. Record the time at the end of the 10-minute observation period. Record the GPS
coordinates at the observation point.
Step 8. Repeat Steps 4 through 7 at each of the remaining three observation points, moving
clockwise around the four observation points and pacing 100 m in the appropriate
direction (Figure 1) between each pair of points. Flag and label observation points B, C,
and D. Ideally, two of the observation points should be visited during the half-hour
before sunrise and two should be visited during the half-hour after sunrise.
7.3.2.2 Fauna Observations from Transects
Step 1. Conduct observations of fauna (including birds) along four 100 m transect lines (i.e., AB,
BC, CD, and DA anchored by the four observation points as shown in Figure 1)
according to the instructions that follow. Use N2 for recording all fauna observations in
the transects. Identify vertebrate fauna as precisely as feasible, to species level if
possible. Invertebrates should be identified to the taxonomic level indicated on N2.
Count the numbers of individuals observed, or if too numerous to count (e.g., in an ant
colony), estimate numbers as orders of magnitude (e.g., ~10, -100, -1000).
Step 2. Record the time and weather conditions before beginning to walk the transect. Use the
digital camera to take a photo in the direction of the transect. Before taking the first
photo, be sure that the time set in the camera is the same as the time set on fauna crew
members' watches so that time can be used to associate the photographs with the
appropriate transects.
Step 3. Beginning at observation point A, pace 10 m toward point B, and record relevant
observations (described in Step 5 below) that occur within 5 m of the transect. Continue
to walk the transect, pacing off 10-m intervals for data recording. Spend approximately
30 minutes walking the transect.
Step 4. Record observations as follows:
• Mammals
> Individuals: if any individuals are seen or heard, identify to lowest possible
taxon and record number for each taxon.
> Scats: identify to lowest possible taxon and record number for each taxon.
> Tracks: identify to lowest possible taxon and record number for each taxon.
> Mole/gopher/ground squirrel mounds: identify to lowest possible taxon and
record number for each taxon.
> Browse line: record height(s) and lowest possible associated taxa of species
that may have created the browse line.
> Burrows: measure diameter of each burrow, list lowest possible associated
taxa, and note any extensive spider webbing. Extensive webbing would
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indicate the presence of a wolf spider, which is sensitive to excessive human
disturbance. It may also indicate that the burrow is not occupied by a larger
animal that would break through the webbing upon ingress/egress.
> Scent marks: record number and associated taxa.
> Other: describe any other fauna signs observed (e.g., tree rubbing, gnaw
marks), enumerate if appropriate, and record lowest possible associated taxa
• Birds: record any bird species seen or heard unless they were particularly
common during the point counts.
• Herpetofauna: identify to lowest possible taxon and record numbers for each
taxon identified by sight, calls, scats, tracks, or skins.
• Butterflies: record identification to lowest possible taxa and approximate
numbers per taxa. Species of special note are included on N2, Table 6; other
observed species should be added.
• Other invertebrates: record number of beetle species observed and note the
presence of invertebrates associated with disturbance, such as tent caterpillars,
snails, earthworms, spiders, and crickets/grasshoppers. Record approximate
numbers of individuals observed.
Step 5. If dead animals or their parts are encountered that cannot be identified in the field, put
appropriate reference material (e.g., skull, hair, feet, depending on the taxon) in a plastic
bag for later identification. A slip of paper with the site ID number, datasheet number,
and a temporary sequential taxon number (which is also recorded on the datasheet) should
be kept in the plastic bag until the reference material is identified and the correct taxon
added to the datasheet. This should be done immediately upon returning from the field
and the reference material should then be discarded. The collection of reference material
for taxonomic verification should be minimized. Photographs of any questionable taxa
should be used preferentially whenever appropriate.
Step 6. At the end of the transect, record the time again. Take another photo, again facing in the
direction of the transect just traversed.
Step 7. Repeat Steps 2 through 5 for each of the four transects. Move clockwise around the
square defined by observation points A through D. Associate all fauna and fauna signs
with a single transect (i.e., AB, BC, CD, or DA). Associate observations visible from
more than one transect with whichever transect is closest. If no one transect is closest,
then associate the observation with the transect along which it was first observed.
7.4 Flora and Soils Survey
Starting an hour after the bird counts begin, the flora crew works together for approximately two hours
surveying vegetation along a 300-m transect. The final two hours should be spent conducting a wandering
survey in order to amplify data collection. The optimal season for conducting the flora survey is mid-
growing season. However, it is important to note that early and late flowering species, especially grasses
and composites, will likely be missed or identified only to genus or family level. Detailed instructions for
completing the flora survey are presented below.
7.4.1 Field Set-Up
Step 1. The flora survey will occur along the diagonal of the 300-m x 300-m plot.
Step 2. To establish a 300-m transect line for the flora survey, begin at the corner of the plot that was
flagged during the previous day's reconnaissance. Use a compass or a GPS unit to determine
the orientation of the transect, which should be along a major environmental gradient. Note
that two of the four bird and amphibian observation points should fall along this line (Figures
1 and 2).
Step 3. Pace about 62 m along this line. Flag this point to mark the beginning of the 300-m transect.
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7.4.2 Field Data Collection
Step 1. Collection of flora data is done at 30 sampling points and 30 quadrats that are evenly spaced
along the 300-m transect that runs diagonally through the center of the study plot (Figure 2).
Flora crew members should work together at each sampling point and quadrat, with the less
experienced member recording data.
Step 2. Starting at the beginning of the transect marked in Section 7.4.2 above, run the 300 m tape
through the plot, using the GPS unit and compass as necessary. Anchor the beginning and the
end of the tape with stakes to prevent the tape from slipping or moving. Flora crew members
should record point survey data as instructed in Section 7.4.3.1 below.
Step 3. Starting at the 5 m mark on the tape and continuing every 10m, record point survey data as
instructed in Section 7.4.3.1 below.
Step 4. At each sample point (Step 3), place the 0.5-m x 0.5-m sampling quadrat on the ground,
positioning the sample point in the center of the quadrat and the sides of the square transect
perpendicular or parallel to the transect. Record data as instructed in Section 7.4.3.2 below.
Step 5. Continue to collect point data and quadrat data every 10 m.
Step 6. During traversal of the transect, take photos to document the plot's center, transect line
endpoints, and half of the sampling points in order to provide supplementary information for
post-process analysis. Photos should be taken at every other sampling point prior to any
disturbance created by survey activity.
Step 7. Once a total of 30 points and 30 quadrats has been surveyed, systematically traverse areas
outside of the 300-m transect, following the pattern in Figure 1, to develop as complete a
species list for the plot as possible. Record data as instructed in Section 7.4.3.4 below.
7.4.2.1 Point Surveys
Step 1. Record data at each of the 30 sample points on the flora datasheet N6.
Step 2. Place end of walking or meter stick on the ground at the sampling point (do this without
actually looking at the spot on the ground to avoid any potential bias). Record the following
at the spot where the stick lands
• Presence/absence of bare ground and canopy cover.
• Presence/absence of health/vigor indicators (defined in Section 3.0).
Step 3. Look up to determine whether canopy cover is present at the sampling point. If present,
identify species of canopy cover. For each species present, record: "P," if the plant is
present, but not flowering or fruiting; "FL," if the plant is flowering; or "FR," if the plant
is fruiting.
Step 4. Measure and record the distance to nearest tree that is within 5 m of the sampling point,
and has a DBH>10 cm. Also record the DBH of this tree. If no trees greater than 10 cm
in DBH occur within 5 m of the sampling point, then record "A" for absent.
Step 5. Record the presence/absence of disturbance indicators (defined in Section 3.0) within 5 m
of the sampling point.
7.4.2.2 Quadrat Surveys
Step 1. Record data at each of the 30 quadrats on flora datasheet N7.
Step 2. Record the presence/absence of disturbance indicators (defined in Section 3.0) occurring
within the quadrat itself.
Step 3. Record percent cover of bare ground and canopy cover.
Step 4. Identify to species level and record all rooted plants occurring in the quadrat. List species that
are present and make the following notations in the "Health/Repro" field. It may be necessary
to record multiple notations for some species; be sure to record all that apply to each species.
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• "P" if species is present and no reproductive or health status notations are
needed.
• "FL" if species is flowering.
• "FR" if species is fruiting.
• "E" if species exhibits early leaf fall.
• "W" if species exhibits wilting.
• "Y" if species exhibits yellowing.
• "H" if species exhibits evidence of herbivory.
Step 5. List each species on a separate line on N7, recording percent cover of each species.
Step 6. If any important rooted plants (i.e., those contributing at least 25 percent of the cover in the
quadrat) can not be identified to species, a small amount of appropriate reference material may
be taken from the field in a plastic bag, so long as the species is known not to be a species of
concern in the area. A slip of paper with the site ID number, quadrat number, datasheet
number, the taxonomic information that is known, and a temporary sequential taxon number
(which is also recorded on the datasheet) should be kept in the plastic bag. Reference material
should be identified immediately upon returning from the field, the correct taxon should be
added to the datasheet, and the reference material should then be discarded. The collection of
reference material for taxonomic verification should be minimized. Photographs of any
questionable taxa should be used preferentially whenever appropriate.
7.4.2.3 Soil and Vegetation Stress
Step 1. At 4 points along the 300 m transect (25 m, 100 m, 175 m, and 250 m) take a GPS reading and
record data on N4.
Step 2. Observe, record, and photograph direct signs of vegetation stress at each of the four sample
sites. Signs of vegetation stress include:
• Leaves yellowing, wilting, dropping prematurely (for taxonomic groups for
which this is uncommon).
• Leaves spotted or "burned" (may indicate herbicide drift from nearby
agricultural fields).
• Herbivory in excess.
• Sporulation or other evidence of fungal infection. Do not, however, record galls
or brooms because they could represent a normal, healthy condition.
Step 3. Take soil samples at each of these four locations according to the following steps.
• Drive a soil probe into the ground with feet or a mallet, if necessary.
• Measure and record the depth that the soil probe can be driven in. Remove the
soil probe.
• Measure and record the soil core layer depths (including the darker upper layer
or "O-horizon") by looking through the holes in the soil probe.
• Poke out the soil core with a wooden rod.
• Compare soil layer colors to a munsell chart and record the hue, value, and
chroma of each layer.
• Determine soil layer composition by a combination of visual inspection and by
moistening a small palm-full of soil with water and rolling it into a ball about the
size of a large marble. Classify and record the soil as: "sandy" if the ball will
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not hold together, "clay" if soil can be rolled into a ball and molded into a cube,
or "loam" if soil can be rolled into a ball but cannot be molded into a cube.
7.4.2.4 Wandering Survey
Step 1. Beginning at the plot corner closest to parking area, walk the plot in a systematic manner,
making six traversals at 50-m intervals as shown in Figure 1. Use a compass, if necessary, to
assist in walking relatively straight lines.
Record in N5 and photograph, as appropriate, the following features:
• Unique landscape attributes.
• Additions to the list of species present, being sure to include invasive and rare
species. If any plants that are important ecosystem components can not be
identified in the field, proceed as described in Section 7.4.3.2, step 6, above. Try
to develop as complete a species list for the plot as possible.
• Evidence of human management practices or disturbance.
• Habitats of interest such as those increasing the diversity of the shrubland,
prairie, and sparsely vegetated area complexes (e.g., plantings remaining from
prior human occupation) or regionally uncommon (e.g., wetlands).
Step 2. On the graph provided in N5, sketch the approximate locations of previously noted locations
of features on the plot sketch and label using the "Map ID Numbers" assigned when
completing Table 2 of N5. Use aerial photos and/or topographic maps as appropriate to
sketch locations as accurately as possible.
7.5 Human Impacts Survey
The human impacts survey is conducted by one field team member. During the first hour at the study site,
this person should assist the fauna crew member with bird and amphibian point count surveys. The next
two hours are spent systematically wandering the plot, noting evidence of human impacts. The remaining
hour is spent observing signs of vegetation stress and additional fauna information that the fauna person
may have missed. Detailed instructions for completing the human impacts survey are presented below.
7.5.1 Field Set-Up
The human impacts survey will use the same 300-m x 300-m plot established for fauna surveys. No
additional set-up is necessary.
7.5.2 Wandering Survey
Step 1. Beginning at the plot corner closest to parking area, walk the plot in a systematic manner, making
six traversals at 50-meter intervals as shown in Figure 1. Use a compass, if necessary, to assist in
walking relatively straight lines. Using this system, the total distance traveled is about 1.3 miles,
to be completed in about two hours. Complete Steps 2 through 8 below concurrently, not
sequentially, while walking over the plot.
Step 2. During the walk, stop every 50 m to look for and photograph the following signs of human
impacts, including but not limited to:
• Invasive species (from flora references list prepared in advance).
• Trash
• Paths or car tracks (excluding paved or improved dirt)
• Off-road vehicle tracks or damage
• Evidence of human management practices
Each time one of these signs is observed, tally it on N5 in the Designated Land Cover Type table if
observed in an area with the designated land cover type (i.e., shrublands, prairies, or sparsely
vegetated areas) or in the Other Land Cover Types table if observed in an area with land cover
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other than the designated type. For invasive plants, list each species observed and tally each
instance the species is observed at the 50-m stopping points. Once the wandering survey is
complete, record the total number of times each sign of human impact was encountered.
Step 3. Record the presence of any fauna encountered during the wandering survey. If any dead animals
or animal remains are present, inform the fauna crew member.
Step 4. Describe visual variation in vegetation that occurs in the plot.
Step 5. If surface water is encountered during the wandering survey, note whether the surface is oily or
soapy. If resources permit, take a water sample in a 250-ml screw-top jar for post-processing
analysis. Label the jar with the sample ID number, date, time, and the collector's initials. Sample
ID numbers should be assigned as eight-character combinations of numbers and letters: the first
four characters are the site ID number, the fifth and sixth characters are "WA" for water, and the
last two characters should be digits assigned sequentially (from 01 to 99). Record the sample ID
number on Table 4 of N5. Store the water sample in the dark and on ice until returning to the
laboratory.
Step 6. Flag any unusual plants and bring to the attention of the flora crew members.
Step 7. Briefly describe any streams and riparian zones that occur in the plot.
Step 8. Carry an aerial photo and/or topographic map of the plot during the survey. Use the map/photo in
combination with ground-truthing to sketch a map that delineates areas of human disturbance, the
designated cover type, other cover types, ponds, streams, roads, and any other notable features.
Note locations of features on the map using "Map ID Numbers" assigned when completing Tables
2 and 3 of N5.
7.6 Exiting the Field Study Site
Step 1. Remove all flagging, stakes, and other material transported to the study site by the field team.
Step 2. Check all field equipment against the equipment list to ensure that no equipment is
inadvertently left at the study site.
7.7 Post-Visit Activities
The following steps should be completed back in the office after the field event.
Step 1. Using the reference lists developed during pre-visit preparations (Section 7.1), complete any
of the flora and fauna species categorization fields (e.g., native or non-native) that were left
blank by the field crew. Using sources recommended in Section 7.1, determine and record
appropriate categories for species that were observed in the plot, but not included in the
reference lists.
Step 2. Identify any "Bird Conservation Regions" designated avian species of concern.
Step 3. Where field crew members have listed common names of species, add corresponding
scientific names to the datasheets.
Step 4. Immediately upon returning from the field, identify any reference material brought from the
field in plastic bags to the lowest reasonable taxon, record the taxon on the appropriate
datasheet, and discard the reference material.
Step 5. If water samples were collected, follow proper protocol for laboratory processing according to
analytical procedures. Depending on resources available, suggested analyses to detect human
disturbance include: nutrients (e.g., nitrogen, phosphorus), anions (e.g., chloride, bromide,
sulfate), metals (e.g., copper, iron, zinc), and suspended sediments.
8.0 Data and Records Management
Data collected in this project will be made publicly available through an EPA centralized database.
Completed datasheets will be kept within ORD according to standard data and records management
protocols.
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9.0 Quality Assurance Procedures
A Quality Assurance Project Plan (QAPP) is associated with this SOP. It is hereby incorporated into this
document by reference. The QAPP should be referred to for details regarding quality assurance protocols
associated with this field program.
While analytical assessments conducted in the laboratory can be verified in a number of ways, the accuracy
of flora, fauna, and human impact assessments in the field cannot be objectively verified with the same
degree of precision. Nonetheless, the use of a two-person team for the flora survey and portions of the
fauna survey will allow each team member to verify the observations and documentation of the other.
Photographs taken throughout the plot will provide additional verification of the data collected.
10.0 References
Ralph, C.J., G.R. Geupel, P. Pyle, T.E. Martin, andD.F. DeSante. 1993. Handbook of Field Methods for
Monitoring Landbirds. Gen. Tech. Rep. PSW-GTR-144. U.S. Department of Agriculture, Forest
Service, Pacific Southwest Research Station, Albany, CA. pp. 30 - 35.
http://www.fs.fed.us/psw/publications/gtrs.shtml
U.S. Department of Agriculture Forest Service (USDA FS). 1989. Interim Resource Inventory Glossary. June
14, 1989. File 1900. Washington, D.C.: U.S. Department of Agriculture, Forest Service. 96 pp.
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Table 1. Descriptions of Datasheetss
Datasheets #
Nl
N2
N3
N4
N5
N6
N7
N8
Datasheets Title
Bird and Amphibian Point
Counts
Fauna Transect Data
Photo Log
Soil and Vegetation Stress
Data
Invasive Species/Human
Impacts and Activities
Point Survey Data
Quadrat Survey Data
Special Features
Description of Datasheets Items
Table 1: GPS readings, weather conditions and times of bird
point counts
Table 2: Amphibian species, categorization (e.g., threatened,
invasive), numbers observed at observation points, behaviors
observed
Table 3: Bird species, categorization, numbers observed at
observation points, behaviors observed
Table 1 : Start and end times for transect surveys, weather
conditions
Table 2: Mammal species, categorization, numbers observed in
transects, behaviors observed, identification method (e.g., by
tracks or scat)
Table 3: For each transect, numbers and characteristics of other
mammal signs observed
Table 4: Herpetofauna species, categorization, numbers
observed in transects, behaviors observed, identification
method (e.g., by tracks or scat)
Table 5: Bird species, categorization, numbers observed in
transects, behaviors observed (for species not observed during
point counts)
Table 6: Butterfly species, categorization, numbers observed in
transects
Table 7: Other invertebrate taxa, numbers observed in transects
Descriptions of photos taken by all crews
Soil horizon depth, color, and composition; depth reached by
Soil probe; and signs of vegetation stress at four sampling
points
Table 1 : Invasive plant species and numbers in designated land
cover type and other land cover types
Table 2: Disturbance and human management practices in the
designated land cover type
Table 3: Disturbance and human management practices in other
land cover types
Table 4: Descriptions of other special features (e.g., visual
variation in vegetation, surface water, fauna)
Figure 1 : Sketch delineating areas of human disturbance, land
cover types, surface water bodies, and other features in plot
Disturbance indicators, presence or absence of bare ground,
plant health indicators, distance to and DBH of nearest tree,
presence or absence of canopy cover by species present at each
of 30 sampling points
Disturbance indicators, presence or absence of bare ground,
plant health indicators, distance to and DBH of nearest tree,
presence or absence of canopy cover by species present at each
of 30 quadrats
Data recorded during traversal of areas outside of the 300-m
transect:
Related SOP
Section Numbers
7.3.2.1
7.3.2.2
72 7322 742
7.4.2.3, 7.5.2
7.4.2.3
7.4.2.4, 7.5.2
7.4.2.1
7.4.2.2
7.4.2.4
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Datasheets #
Datasheets Title
Description of Datasheets Items
Related SOP
Section Numbers
Table 1: Plant species with categorization, relative abundance
in plot, and reproductive/health indicators
Table 2: Descriptions of special features
Figure 1: Sketch of landscape attributes, disturbance or human
management practices, and habitats in 300-m x 300-m plot
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Table 2. Bird Species of Special Interest
Habitat
Shrublands
Prairies
Sparsely
Vegetated
Areas
Generalists
blue jay
American crow
tufted titmouse
gray catbird
yellow warbler
field sparrow
song sparrow
white-throated sparrow
indigo bunting
northern cardinal
Brewer's blackbird
American kestrel
killdeer
horned lark
American crow
common yellowthroat
field sparrow
song sparrow
red-winged blackbird
Brewer's blackbird
American goldfinch
killdeer
horned lark
field sparrow
song sparrow
red-winged blackbird
Brewer's blackbird
Specialists
sharp-tailed grouse
eastern kingbird
loggerhead shrike
Bell's vireo
brown thrasher
blue-winged warbler
golden-winged warbler
chestnut-sided warbler
mourning warbler
yellow-breasted chat
rufous-sided towhee
clay-colored sparrow
sharp-tailed grouse
loggerhead shrike
sedge wren
vesper sparrow
savannah sparrow
Ammodramus sparrows (Henslow's, grasshopper,
dicksissel
bobolink
eastern meadowlark
western meadowlark
piping plover
blue-winged warbler
golden-winged warbler
prairie warbler
Invasives
European starling
brown-headed cowbird
house sparrow
European starling
house sparrow
rock dove
European starling
house sparrow
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Figure 1. Fauna and Human Impacts Observation Scheme. For the purposes of this illustration, the study plot corner
nearest the access point is assumed to be the northwest (NW) corner; see sections 7.3, 7.4.2.4, and 7.5 for further
explanation.
NW
(3art)
->—*-
sw
• 300m
-• <-•-
-• > •-
NE
• <
«B
->-*-
• Human Impxts Sampling Roint
^— Human Impacts Suvey Ftoute
• Birds andAmphibiansObservation Point
300m
SE
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Figure 2. Flora Survey Scheme; see section 7.4 for further explanation. Locations (25 m, 100 m, 175 m, and 250 m)
for characterizing soil and vegetation stress (section 7.4.2.3) are indicated.
NW
(Sart)
NE
A fauna observation point is
also located here
25m
photosat every
other
250 mT^feu V|_ quadrat
sw
• Flora Sampling Point
D Flora Sampling Quadrat (not drawn to scale)
• Fauna Observation Point, for reference
SE
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N 1: BIRD AND AMPHIBIAN POINT COUNTS
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Table 1: Miscellaneous Survey Information
GPS Readings and Point Count Times
Corner Closest to
Parking Area
N/A
UTM-E:
UTM-N:
Point A
Start
time AM
End
time AM
UTM-E:
UTM-N:
At Start of Point Counts
D storm (heavy rain I
D rain (steady rain) C
D showers (intermittent)
Air Temperature °
H % cloud cover
] clear/sunny
C
Point B
Start
time AM
End
time AM
UTM-E:
UTM-N:
Point C
Start
time AM
End
time AM
UTM-E:
UTM-N:
Point D
Start
time AM
End
time AM
UTM-E:
UTM-N:
Weather Conditions
At End of Point Counts
D storm (heavy rain)
D rain (steady rain)
D showers (intermittent
D % cloud cover
D clear/sun
Air Tempera
ny
ure °C
Table 2: Amphibian Point Count Data
Amphibian Species*
Species Categorization*
Native
Invasive
Common
or Rare
G/S
T&E
Status
Numbers at Observation Points
A
B
C
D
'Notes: List each species on a separate line. Use as many sheets as necessary.
For Native field, record Y if species is native or N if species is non-native. For Invasive field, record Y if species is invasive or N if
species is non-invasive. For Common or Rare field, record C if species is regionally common or R if species is regionally rare.
For G/S field, record G if the species is a generalist or S if the species is a specialist.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others here
as needed.
C-25
-------
N 1: BIRD AND AMPHIBIAN POINT COUNTS
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Table 3: Bird Point Count Data
Bird Species*
Species Categorization*
Native
Invasive
Common
or Rare
G/S
T&E
Status
Behavior*
Numbers at Observation Points
A
B
C
D
'Notes: List each species on a separate line. Use as many sheets as necessary.
For Native field, record Y if species is native or N if species is non-native. For Invasive field, record Y if species is invasive or N if
species is non-invasive. For Common or Rare field, record C if species is regionally common or R if species is regionally rare.
For G/S field, record G if the species is a generalist or S if the species is a specialist.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others here
as needed.
For Behavior, enter singing (S), flying (F), perched (P), or foraging (O).
C-26
-------
N2: FAUNA TRANSECT DATA
Page of _
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Table 1. Transect Information
Start Time
End Time
Weather
Conditions
Transect AB
D storm (heavy rain)
D rain (steady rain)
D showers (intermittent)
D % cloud cover
D sunny
Air Temperature °C
Transect BC
D storm (heavy rain)
D rain (steady rain)
D showers (intermittent)
D % cloud cover
D sunny
Air Temperature °C
Transect CD
D storm (heavy rain)
D rain (steady rain)
D showers (intermittent)
D % cloud cover
D sunny
Air Temperature °C
Transect DA
D storm (heavy rain)
D rain (steady rain)
D showers (intermittent)
D % cloud cover
D sunny
Air Temperature °C
Table 2. Mammals Identified by Individuals, Scats, Tracks, and Mounds (continued on next page)
Mammal Species
Species Categorization*
Native
Inva-
sive
OR
G/S
T&E
Status
Numbers Observed In Transect
Individuals
Scats
Tracks
Mounds
Individuals
Scats
Tracks
Mounds
Individuals
Scats
Tracks
Mounds
Individuals
Scats
Tracks
Mounds
Individuals
Scats
Tracks
Mounds
AB
BC
CD
DA
C-27
-------
N2: FAUNA TRANSECT DATA
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Mammal Species
Species Categorization*
Native
Inva-
sive
C/R
G/S
T&E
Status
Numbers Observed In Transect
AB
BC
CD
DA
Individuals
Scats
Tracks
Mounds
Individuals
Scats
Tracks
Mounds
Individuals
Scats
Tracks
Mounds
Individuals
Scats
Tracks
Comments (include physical descriptions of unidentified fauna, any other relevant observations)
*Notes: List each species on a separate line. Use as many sheets as necessary.
For Native field, record Y if species is native or N if species is non-native.
For Invasive field, record Y if species is invasive or N if species is non-invasive.
For Common or Rare field, record C if species is regionally common or R if species is regionally rare.
For G/S field, record G if the species is a generalist or S if the species is a specialist.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others
here as needed.
C-28
-------
N2: FAUNA TRANSECT DATA
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Comments:
Table 3. Other Mammal Signs
Fauna! Signs
Browse Line
Burrows
Scent Marks
Other:
(describe/
enumerate)
Other:
(describe/
enumerate)
Transect
AB
D Present D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
No. of burrows with ex-
tensive webbinq
Associated Fauna:
D Present D Absent
Associated Fauna:
Associated Fauna:
Associated Fauna:
BC
D Present D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
No. of burrows with ex-
tensive webbinq
Associated Fauna:
D Present D Absent
Associated Fauna:
Associated Fauna:
Associated Fauna:
CD
D Present D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
No. of burrows with ex-
tensive webbinq
Associated Fauna:
D Present D Absent
Associated Fauna:
Associated Fauna:
Associated Fauna:
DA
D Present D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
No. of burrows with ex-
tensive webbinq
Associated Fauna:
D Present D Absent
Associated Fauna:
Associated Fauna:
Associated Fauna:
C-29
-------
N2: FAUNA TRANSECT DATA
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Table 4. Herpetofauna Identified by Individuals, Scats, Tracks, and Skins
Herpetofauna Species *
Species Categorization*
Native
Inva-
sive
C/R
G/S
T&E
Status
Numbers Observed In Transect
Individuals
Scats
Tracks
Skins
Calls
Individuals
Scats
Tracks
Skins
Calls
Individuals
Scats
Tracks
Skins
Calls
Individuals
Scats
Tracks
Skins
Calls
Individuals
Scats
Tracks
Skins
Calls
AB
BC
CD
DA
'Notes: List each species on a separate line. Use as many sheets as necessary.
For Native field, record Y if species is native or N if species is non-native.
For Invasive field, record Y if species is invasive or N if species is non-invasive.
For C/R field, record C if species is regionally common or R if species is regionally rare.
For G/S field, record G if the species is a generalist or S if the species is a specialist.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others
here as needed.
C-30
-------
N2: FAUNA TRANSECT DATA
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Table 5. Bird Species Not Observed During Point Counts
Bird Species*
Species Categorization*
Native
Inva-
sive
C/R
G/S
T&E
Status
Numbers Observed In Transect
AB
BC
CD
DA
'Notes: List each species on a separate line. Use as many sheets as necessary.
For Native field, record Y if species is native or N if species is non-native. For Invasive field, record Y if species is invasive or N if
species is non-invasive. For Common or Rare field, record C if species is regionally common or R if species is regionally rare.
For G/S field, record G if the species is a generalist or S if the species is a specialist.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others
here as needed.
C-31
-------
N2: FAUNA TRANSECT DATA
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Table 6. Butterflies
Butterfly Species*
Monarch
Little sulphur
Leonard's skipper
Mottled duskywing
Wild indigo duskywing
Persius duskywing
Habitat(s)
where species
typically
occurs
P, S
P, S
P, S
P, S, D
P
S, D
Species
Categorization*
Inva-
sive
G/S
T&E
Status
Numbers in Transect
AB
BC
CD
DA
Comments:
Notes: Species of special interest are listed; list any other species in spaces provided. Use as many sheets as necessary.
P=Prairies, S=Shrublands, D=Dunes
For Invasive field, record Y if species is invasive or N if species is non-invasive.
For G/S field, record G if the species is a generalist or S if the species is a specialist.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others
here as needed.
C-32
-------
N2: FAUNA TRANSECT DATA
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments:
Table 7. Other Invertebrates
Other
Invertebrate Taxa
Beetles (no. of spp.)
Beetles (no. of
individuals)
Tent caterpillars
Snails and slugs
Spiders
Crickets and
grasshoppers
Other:
Numbers in Transect
AB
BC
CD
DA
Comments:
Notes: For beetles, record the number of species (or morphospecies) and total number of individuals observed in each transect.
For other taxa, record total numbers of individuals only. Iftaxa can be identified at a lower level, then list species/genera names under
"Other."
C-33
-------
N3: PHOTO LOG
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Camera Type/Number
Comments
Time
Subject
Data
Sheet #
Location*
Direction
File
Namet
*For the Location field, record the observation point, transect, etc., where the photo was taken.
TFile name to be entered after returning from field and downloading pictures.
C-34
-------
N4: SOIL AND VEGETATION STRESS DATA
Page 1 of 1
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Soil Horizon
GPS Coordinates : UTM-E
UTM-N
O
A
E
B
C
Depth (cm)
Depth (cm)
Color
(from Munsell chart)
Composition
Depth (cm)
Color
(from Munsell chart)
Composition
Depth (cm)
Color
(from Munsell chart)
Composition
Depth (cm)
Color
(from Munsell chart)
Composition
Depth (m) reached by soil probe*
Signs of Vegetation Stress*
Soil Sampling Point
1
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
2
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
3
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
4
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
*Notes: Record depth as described in Section 7.4.3.4, Step 3.
Record Signs of Vegetation Stress as follows: E for early leaf fall, W for wilting, Y for yellowing, S for leaves that are spotted or
burned, H for excess herbivory, and I for signs of fungal infection. List all that are observed at each sampling location.
C-35
-------
N5: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID# UTM-E UTM-N
Investigators
Form Completed By Date
Comments
Table 1. Invasive Plants
Plant Species*
In Designated Land Cover Type:
In Other Land Cover Types:
Tally
Total Number of
Occurrences
*Note: List each species on a separate line. Use as many sheets as necessary.
C-36
-------
N5: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Table 2. Disturbance and Human Management Practices in the Designated Land Cover Type
Map ID
Number(s)*
Disturbance Indicator*
Paths
Car/Vehicle Tracks
Off-road vehicle tracks
not on well-worn paths
Loud noise
Bright, artificial lights
Evidence of human
management practices
Trash (appliances/tires)
Litter
(paper/plastic scraps)
Hydrologic modifica-
tions (e.g., ditch, weir)
Evidence of mowing,
tree felling
Oily or Soapy
Surface Water
Other*
Description*
Total Number
of Times
Encountered
Photo
Taken?
(Y/N)*
'Notes: Use as many sheets as necessary.
Map ID numbers should be assigned D1, D2, etc. Use these numbers to identify disturbances drawn on the plot sketch.
Other disturbance indicators are included in Section 3.0.
Descriptions of disturbance indicators should include more detailed information about the disturbance, how frequently if was encountered in the
plot, and if appropriate, the size of the affected area.
List any photos taken in N3 (photo log); include the Map ID number in the Subject field of the photo log.
C-37
-------
N5: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Table 3. Disturbance and Human Management Practices in Other Land Cover Types
Map ID
Number(s)*
Disturbance Indicator*
Paths
Car/Vehicle Tracks
Off-road vehicle tracks
not on well-worn paths
Loud noise
Bright, artificial lights
Evidence of human
management practices
Trash (appliances/tires)
Litter
(paper/plastic scraps)
Hydrologic modifica-
tions (e.g., ditch, wier)
Evidence of mowing,
tree felling
Oily or Soapy
Surface Water
Other*
Description*
Total Number
of Times
Encountered
Photo
Taken?
(Y/N)*
C-38
-------
N5: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Comments
Table 4. Description of other special features in plot.
Feature
Description
Visual variation in
vegetation occurring
in the plot
Streams and riparian
zones
Water sample(s)
collected? D Y D N
How many?
(list sample ID numbers
in space at right)
Other surface water
Water sample(s)
collected? D Y D N
How many?
(list sample ID numbers
in space at right)
Fauna/Fauna
remains (list species
if known)
C-39
-------
N5: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Figure 1. Sketch delineating areas of human disturbance, land cover types, surface water bodies, and other features in plot.
C-40
-------
N6: POINT SURVEY DATA
Page 1 of 3
Location
Investigators
Site ID#
Date
UTM-E:
UTM-N:
Form Completed By
Comments
Table 1. Survey Data for Points 1 through 10
Disturbance Indicators (0 - 20, see codes*)
Bare Ground (P or A)*
Plant Health Indicators*
Distance to Nearest Tree >10cm DBH (m)*
DBH of Nearest Tree >10cm DBH (cm)*
Canopy Cover (P or A)*
Sampling Point
1
2
3
4
5
6
Canopy Cover Species* (list below-add N=native, l=invasive, T/E/S=threatened/endangered/threatened in () after all species names.
7
8
9
10
o
*Notes: Disturbance Indicators:
(list all that apply,
record 0 for none)
1-Roads 4-Asphalt 7-Power lines 10-Evidence of digging 13-Tracks (human, vehicle) 16-Loud noise
2-Trails 5-Ruts in soil 8-Berms 11-Wind throw mounds 14-Trash (appliances, tires) 17-Bright, artificial lights
3-Gravel 6-Erosion 9-Manure 12-Fire scars/charcoal 15-Litter (paper, plastic) 18-Hydrologic modifications (e.g., ditches, wiers)
19-Evidence of mowing, tree felling, etc. 20-Other:
For Bare Ground and Canopy Cover fields, record P for present or A for absent.
If there are no trees >10 cm DBH within 10 m ofthe sampling point, then record N/A in the Distance to nearest tree and DBH of nearest tree fields.
Plant Health Indicators: E for early leaf fall, W for wilting, Y for yellowing, S for leaves that are spotted or burned, H for excess herbivory, and I for signs of fungal infection (list all that apply).
For Distance to Nearest Tree and DBH of Nearest Tree: if no trees >10 cm DBH occur within 5 m ofthe sampling point, record "A" for absent.
List canopy cover species observed in blank rows, and at each point where the species occurs, record FL if flowering, FR if fruiting, or P if present, but not flowering or fruiting.
-------
N6: POINT SURVEY DATA
Page 2 of 3
Location
Investigators
Site ID#
Date
UTM-E:
UTM-N:
Form Completed By
Comments
Table 2. Survey Data for Points 11 through 20
Disturbance Indicators (0 - 20, see codes*)
Bare Ground (P or A)*
Plant Health Indicators*
Distance to Nearest Tree >10cm DBH (m)*
DBH of Nearest Tree >10cm DBH (cm)*
Canopy Cover (P or A)*
Sampling Point
11
12
13
14
15
16
Canopy Cover Species* (list below-add N=native, l=invasive, T/E/S=threatened/endangered/threatened in () after all species names.
17
18
19
20
o
-t.
Disturbance Indicators:
(list all that apply,
record 0 for none)
*Notes: Disturbance Indicators: 1-Roads 4-Asphalt 7-Power lines 10-Evidence of digging 13-Tracks (human, vehicle) 16-Loud noise
2-Trails 5-Ruts in soil 8-Berms 11-Wind throw mounds 14-Trash (appliances, tires) 17-Bright, artificial lights
3-Gravel 6-Erosion 9-Manure 12-Fire scars/charcoal 15-Litter (paper, plastic) 18-Hydrologic modifications (e.g., ditches, wiers)
19-Evidence of mowing, tree felling, etc. 20-Other:
For Bare Ground and Canopy Cover fields, record P for present or A for absent.
If there are no trees >10 cm DBH within 10 m of the sampling point, then record N/A in the Distance to nearest tree and DBH of nearest tree fields.
Plant Health Indicators: E for early leaf fall, W for wilting, Y for yellowing, S for leaves that are spotted or burned, H for excess herbivory, and I for signs of fungal infection (list all that apply).
For Distance to Nearest Tree and DBH of Nearest Tree: if no trees >10 cm DBH occur within 5 m of the sampling point, record "A" for absent.
List canopy cover species observed in blank rows, and at each point where the species occurs, record FL if flowering, FR if fruiting, or P if present, but not flowering or fruiting.
-------
N6: POINT SURVEY DATA
Page 3 of 3
Location
Investigators
Site ID#
Date
UTM-E:
UTM-N:
Form Completed By
Comments
Table 3. Survey Data for Points 21 through 30
Disturbance Indicators (0 - 20, see codes*)
Bare Ground (P or A)*
Plant Health Indicators*
Distance to Nearest Tree >10cm DBH (m)*
DBH of Nearest Tree >10cm DBH (cm)*
Canopy Cover (P or A)*
Sampling Point
21
22
23
24
25
26
Canopy Cover Species* (list below-add N=native, l=invasive, T/E/S=threatened/endangered/threatened in () after all species names.
27
28
29
30
o
Disturbance Indicators:
(list all that apply,
record 0 for none)
*Notes: Disturbance Indicators: 1-Roads 4-Asphalt 7-Power lines 10-Evidence of digging 13-Tracks (human, vehicle) 16-Loud noise
2-Trails 5-Ruts in soil 8-Berms 11-Wind throw mounds 14-Trash (appliances, tires) 17-Bright, artificial lights
3-Gravel 6-Erosion 9-Manure 12-Fire scars/charcoal 15-Litter (paper, plastic) 18-Hydrologic modifications (e.g., ditches, wiers)
19-Evidence of mowing, tree felling, etc. 20-Other:
For Bare Ground and Canopy Cover fields, record P for present or A for absent.
If there are no trees >10 cm DBH within 10 m of the sampling point, then record N/A in the Distance to nearest tree and DBH of nearest tree fields.
Plant Health Indicators: E for early leaf fall, W for wilting, Y for yellowing, S for leaves that are spotted or burned, H for excess herbivory, and I for signs of fungal infection (list all that apply).
For Distance to Nearest Tree and DBH of Nearest Tree: if no trees >10 cm DBH occur within 5 m of the sampling point, record "A" for absent.
List canopy cover species observed in blank rows, and at each point where the species occurs, record FL if flowering, FR if fruiting, or P if present, but not flowering or fruiting.
-------
N7: QUADRAT SURVEY DATA
Page 1 of 3
Location
Investigators
Site ID#
Date
UTM-E:
UTM-N:
Form Completed By
Comments
Table 1. Survey Data for Quadrats 1 through 10
Disturbance Indicators (0 - 20, see codes*)
Bare Ground (% cover)
Plant Health Indicators*
Distance to Nearest Tree >10cm DBH (m)*
DBH of Nearest Tree >10cm DBH (cm)*
Canopy Cover (% cover)
Sampling Point
1
2
3
4
5
6
Canopy Cover Species* (list below-add N=native, l=invasive, T/E/S=threatened/endangered/threatened in () after all species names.
7
8
9
10
o
'Notes: Disturbance Indicators:
(list all that apply,
record 0 for none)
1-Roads 4-Asphalt 7-Power lines 10-Evidence of digging 13-Tracks (human, vehicle) 16-Loud noise
2-Trails 5-Ruts in soil 8-Berms 11-Wind throw mounds 14-Trash (appliances, tires) 17-Bright, artificial lights
3-Gravel 6-Erosion 9-Manure 12-Fire scars/charcoal 15-Litter (paper, plastic) 18-Hydrologic modifications (e.g., ditches, wiers)
19-Evidence of mowing, tree felling, etc. 20-Other:
If there are no trees >10 cm DBH within 10 m ofthe sampling point, then record N/A in the Distance to nearest tree and DBH of nearest tree fields.
Plant Health Indicators: E for early leaf fall, W for wilting, Y for yellowing, S for leaves that are spotted or burned, H for excess herbivory, and I for signs of fungal infection (list all that apply).
For Distance to Nearest Tree and DBH of Nearest Tree: if no trees >10cm DBH occur within 5 m ofthe sampling point, record "A" for absent.
List canopy cover species observed in blank rows, and at each point where the species occurs, record FL if flowering, FR if fruiting, or P if present, but not flowering or fruiting.
-------
N7: QUADRAT SURVEY DATA
Page 2 of 3
Location
Investigators
Site ID#
Date
UTM-E:
UTM-N:
Form Completed By
Comments
Table 2. Survey Data for Quadrats 11 through 20
Disturbance Indicators (0 - 20, see codes*)
Bare Ground (% cover)
Plant Health Indicators*
Distance to Nearest Tree >10cm DBH (m)*
DBH of Nearest Tree >10cm DBH (cm)*
Canopy Cover (% cover)
Sampling Point
11
12
13
14
15
16
Canopy Cover Species* (list below-add N=native, l=invasive, T/E/S=threatened/endangered/threatened in () after all species names.
17
18
19
20
o
-k
(Jl
*Notes: Disturbance Indicators:
(list all that apply,
record 0 for none)
1-Roads 4-Asphalt 7-Power lines 10-Evidence of digging 13-Tracks (human, vehicle) 16-Loud noise
2-Trails 5-Ruts in soil 8-Berms 11-Wind throw mounds 14-Trash (appliances, tires) 17-Bright, artificial lights
3-Gravel 6-Erosion 9-Manure 12-Fire scars/charcoal 15-Litter (paper, plastic) 18-Hydrologic modifications (e.g., ditches, wiers)
19-Evidence of mowing, tree felling, etc. 20-Other:
If there are no trees >10 cm DBH within 10 m of the sampling point, then record N/A in the Distance to nearest tree and DBH of nearest tree fields.
Plant Health Indicators: E for early leaf fall, W for wilting, Y for yellowing, S for leaves that are spotted or burned, H for excess herbivory, and I for signs of fungal infection (list all that apply).
For Distance to Nearest Tree and DBH of Nearest Tree: if no trees >10 cm DBH occur within 5 m of the sampling point, record "A" for absent.
List canopy cover species observed in blank rows, and at each point where the species occurs, record FL if flowering, FR if fruiting, or P if present, but not flowering or fruiting.
-------
N7: QUADRAT SURVEY DATA
Page 3 of 3
Location
Investigators
Site ID#
Date
UTM-E:
UTM-N:
Form Completed By
Comments
Table 2. Survey Data for Quadrats 21 through 30
Disturbance Indicators (0 - 20, see codes*)
Bare Ground (% cover)
Plant Health Indicators*
Distance to Nearest Tree >10cm DBH (m)*
DBH of Nearest Tree >10cm DBH (cm)*
Canopy Cover (% cover)
Sampling Point
21
22
23
24
25
26
Canopy Cover Species* (list below-add N=native, l=invasive, T/E/S=threatened/endangered/threatened in () after all species names.
27
28
29
30
o
-k
ON
*Notes: Disturbance Indicators:
(list all that apply,
record 0 for none)
1-Roads 4-Asphalt 7-Power lines 10-Evidence of digging 13-Tracks (human, vehicle) 16-Loud noise
2-Trails 5-Ruts in soil 8-Berms 11-Wind throw mounds 14-Trash (appliances, tires) 17-Bright, artificial lights
3-Gravel 6-Erosion 9-Manure 12-Fire scars/charcoal 15-Litter (paper, plastic) 18-Hydrologic modifications (e.g., ditches, wiers)
19-Evidence of mowing, tree felling, etc. 20-Other:
If there are no trees >10 cm DBH within 10 m of the sampling point, then record N/A in the Distance to nearest tree and DBH of nearest tree fields.
Plant Health Indicators: E for early leaf fall, W for wilting, Y for yellowing, S for leaves that are spotted or burned, H for excess herbivory, and I for signs of fungal infection (list all that apply).
For Distance to Nearest Tree and DBH of Nearest Tree: if no trees >10 cm DBH occur within 5 m of the sampling point, record "A" for absent.
List canopy cover species observed in blank rows, and at each point where the species occurs, record FL if flowering, FR if fruiting, or P if present, but not flowering or fruiting.
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N8: SPECIAL FEATURES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Table 1. Additions to Species List
Plant Species
(List only those not already recorded in N6 & N7)
Species Categorization*
Native
Invasive
T&E
Status
Relative
Abundance
in Plot*
Health
Indicators*
Reproductive
Status*
'Notes: List each species on a separate line. Use as many sheets as necessary.
For Native field, record Y if species is native or N if species is non-native. For Invasive field, record Y if species is invasive or N if species is non-
invasive.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others here as
needed.
For Relative Abundance in Plot, record C for common, F for frequent, U for uncommon, or R for rare.
For Health Indicators, record E for early leaf fall, W for wilting, Y for yellowing, and H for herbivory. List all that apply.
For Reproductive Status, record FL for flowering or FR for fruiting. List all that apply.
C-47
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N8: SPECIAL FEATURES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Table 2. Special Features
Map ID
Number*
Description of Feature*
Magnitude*
Photo
Taken?
(Y/N)*
'Notes: Use as many sheets as necessary.
Map ID numbers should be assigned as follows: number landscape attributes as L1, L2, etc.; number disturbance and human management
practices as D1, D2, etc.; number habitats of interest as H1, H2, etc. Use these numbers to identify features drawn on the plot sketch.
In the Description of Features, list both dominant (D) and other (O) habitat or subhabitat types, being sure to include the D or O designator.
In the Magnitude field, give some description of the magnitude of the feature (e.g., approximate area of a particular habitat type, length and width
of a path).
List any photos taken on N3 (photo log); include the Map ID number in the Subject field of the photo log.
C-48
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N8: SPECIAL FEATURES
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Comments
Sketch of landscape attributes, disturbance or human management practices, and habitats in 300 m x 300 m plot.
C-49
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APPENDIX D
Wetlands protocol and datasheets
D-l
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STANDARD OPERATING PROCEDURE
FOR THE QUICK ASSESSMENT PROTOCOL:
FORESTED AND EMERGENT WETLANDS
IN SUPPORT OF
U.S. ENVIRONMENTAL PROTECTION AGENCY
UNDER
RCRA ENFORCEMENT, PERMITTING, AND ASSISTANCE (REPA3)
ZONE 2 - REGION 5
CREATED FOR USE BY EPA REGION 5
WETLANDS SOP, REVISION NO. 3
EFFECTIVE DATE: January 2006
D-3
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TABLE OF CONTENTS
Table of Contents D-i
List of Tables and Figures D-ii
List of Appendices (Datasheets) D-ii
1.0 Scope and Application D-5
2.0 Method Summary D-5
3.0 Definitions D-5
4.0 Health and Safety D-6
5.0 Personnel Qualifications D-6
5.1. General Qualifications D-6
5.2 Fauna/Soil Crew D-7
5.3 Flora Crew D-7
6.0 Equipment and Supplies D-7
6.1 General Equipment Needed D-7
6.2 Additional Equipment Needed for the Fauna/Soil Crew D-7
6.3 Additional Equipment Needed for the Flora Crew D-8
7.0 Procedure D-8
7.1 Pre-Visit Preparations D-8
7.2 Fauna/Soil Crew Activities D-10
7.2.1 Data Recording D-10
7.2.2 Field Set-up D-ll
7.2.3 Bird Observations From Points B andC D-12
7.2.4 Wandering Survey D-12
7.2.5 Soil Characterization D-13
7.3 Flora Crew Activities D-14
7.3.1 Data Recording D-14
7.3.2 Sample Plot and Subplot Surveys D-14
7.3.3 Percent Overstory Density Estimation D-15
7.4 Exiting the Field Study Site D-15
7.5 Post-Visit Activities D-16
8.0 Data and Records Management D-16
9.0 Quality Assurance Procedures D-16
10.0 References D-17
D-i
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List of Tables and Figures
Table 1. Descriptions of Datasheets.
Table 2. Braun-Blanquet Cover Classes.
Figure 1. Bird Observation Survey Sampling Scheme.
Figure 2. Vegetation and Wandering Survey Sampling Scheme.
List of Appendices (Datasheets)
Wl. Wetlands Bird Observation Data
W2. Aquatic Organism Data
W3. Fauna Transect Data for Vertebrates
W4. Soil Data
W5. Photo Log
W6. Invasive Species/Human Impacts and Activities
W7. Vegetation Data
W8. Canopy Cover Estimates and Macrophyte Identification
D-ii
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1.0 Scope and Application
Knowledge of ecosystem health and quality is an important component of successful ecosystem
management. Ecological assessments are increasingly used in support of adaptive ecosystem management
and informed resource management. Rapid ecological assessment is a common technique that is most often
used to objectively assess the biological diversity of a relatively unknown ecological area. However, this
assessment technique can also be used to evaluate ecological characteristics other than diversity.
The purpose of this standard operating procedure (SOP) is to detail a rapid ecological assessment protocol
for evaluation of the condition of wetland ecosystems. This emergent and forested wetlands protocol is one
of four protocols originally drafted as a product of a workshop held in June 2003. It was then revised after
being field tested by the participants of a second workshop held in spring 2004. When using this SOP, the
associated Quality Assurance Project Plan (QAPP) should be consulted. The QAPP serves as a generic
plan for all data collection activities conducted under the SOP and offers guidelines for ensuring that data
are of sufficient quality and quantity to support project objectives. Decisions regarding the application of
data collected while using this SOP should consider the precision, accuracy, and other statistical
characteristics of these data.
2.0 Method Summary
This SOP provides instructions for a rapid ecological assessment of forested and emergent wetland
ecosystems, using fauna surveys, flora surveys, and soil sampling of a 300-m by 300-m study plot.
Generally, the optimal season for implementing this protocol is during the growing season. Late spring
offers the best opportunity for sampling nesting birds in most temperate locations; however, this will not be
the ideal time to identify all plants. It is most important to sample all sites in a relatively small window
(e.g., mid-May to mid-June, depending on latitude and other climatic factors) to make comparisons across
sites. The protocol is intended to be completed in approximately four hours and requires a four-person team
working together in pairs, as a fauna/soil crew and a flora crew.
The fauna/soil crew conducts the fauna and soil portions of the protocol. Fauna surveys include: 1) 20
minute periods of bird observation from two different observation points within the plot; 2) traversal of
areas around four sample points, along which crew members look for fauna signs such as scat and use a D-
frame net to aid in animal identification; and 3) collection of soil cores to characterize the depth, color,
composition, and redoximorphic features of each soil horizon. This field crew also looks for evidence of
disturbance and human impacts.
The flora crew conducts flora surveys at four flora points. During these surveys, 1) plants and trees are
surveyed by the Braun-Blanquet sampling method; 2) water depth is measured; and 3) overstory vegetation
is estimated within 10 m of each point. Detailed procedures to implement this methodology are provided in
Section 7.0.
3.0 Definitions
Coefficient of conservatism (C): The estimated probability that a species is likely to occur in a landscape
relatively unaltered from what is believed to be a pre-settlement condition. Coefficients range from 0
(highly tolerant of disturbance, little fidelity to any natural community) to 10 (highly intolerant of
disturbance, restricted to pre-settlement remnants) (Bernthal 2003). Coefficients of conservatism are used
to calculate FQI values for study areas.
Disturbance: A natural or human-induced (anthropogenic) environmental change that affects an
ecosystem's floral, faunal, or microbial communities. Disturbance may include, but is not limited to: roads,
gravel, asphalt, trails, berms, ruts in the soil, eroded areas, evidence of digging, hydrologic modifications
(e.g., ditches, weirs), evidence of mowing or tree felling, wind throw mounds, evidence of fire, litter, tires,
refrigerators, manure, and pig ruts.
D-5
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Fauna signs: Indications or signs that fauna are, or have recently been, present. May include, but are not
limited to: calls, tracks, mounds, burrows, holes, nutshells, scat (e.g., deer-pellet clumps), runways and
trails, browse lines, and tree rubbing.
Floristic Quality Assessment (FQA): A tool to assist environmental consultants, scientists, natural resource
managers, land stewards, environmental decision-makers, and restorationists in assessing the floristic and,
implicitly, natural significance of any given area (Herman et al. 2001). FQA methodologies were originally
developed by Swink and Wilhelm (1994) as standardized, repeatable means of evaluating natural area
quality (Bernthal 2003).
Floristic Quality Index (FQI): A metric used in FQA that is sensitive to factors that increase species
richness. The FQI for a given study site is calculated as the sum of the coefficient of conservatism for each
species observed, divided by the square root of the total number of native species observed in the study area
(Bernthal 2003).
Hydrogeomorphic Classification (HGM): A wetland classification system based on type and direction of
hydrologic conditions, local geomorphology, and climate (Mitsch and Gosselink 2000). The seven
hydrogeomorphic classes defined by Smith et al. (1995) include: depression, lacustrine fringe, tidal fringe,
slope, riverine, mineral flat, and organic flat. The HGM classification methodology was originally
developed by Brinson (1993).
Redoximorphic features: Mineral soil features formed by the reduction, translocation, and/or oxidation of
iron and manganese oxides. Reduced soils can develop a black, gray, greenish-gray, or blue-gray color
(gleying). Spots of highly oxidized materials (mottles) appear orange/reddish-brown or dark reddish-
brown/black (Mitsch and Gosselink 2000).
4.0 Health and Safety
Health and safety concerns for field workers in the wetland ecosystem include:
• slips, trips, and falls;
• thermal conditions such as excessive heat or cold;
• inclement weather, especially lightning; and
• biological hazards, such as insects or other taxa that may bite and plants that may contain substances
causing allergic reactions.
Field workers should wear closed shoes, long pants and long sleeves, but are individually responsible for
selecting footwear, clothing, gloves and outerwear as appropriate to the situation at hand. Field workers
should use insect repellant, as appropriate, and take care to avoid holes, fallen trees and other obstacles that
may cause slips, trips, and falls. Workers should increase attentiveness to potential hazards during pre-dawn
activities.
5.0 Personnel Qualifications
5.1. General Qualifications
This protocol requires a two-person fauna/soil field team, and a two-person flora field team, with one or
more of these team members performing pre-visit preparations. For both pairs, one of the crew members
must be an expert (bird and plant, respectively) whereas the other crew member can be a generalist. An
expert must be able to identify most or all of the common taxa found in the area to be surveyed and be able
to collect appropriate and sufficient field data on less common taxa to enable their later identification.
Regardless of their respective areas of specialization, all team members must have had some wetland field
work experience. Before formally collecting data in the field, all team members should practice the
protocol at least once in a convenient wetland habitat to make sure they are clear on how to collect the data
and complete each of the datasheets.
The individual(s) performing the pre-visit preparations should be generally familiar with the flora and fauna
found in the specific ecoregion. Preferably, the individual(s) should have an educational background in
biology or ecology.
D-6
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5.2 Fauna/Soil Crew
At least one of the two crew members must be an expert in field identification of birds, both by sightings
and by their behaviors (e.g., calls, flight). At least one of the two crew members must have introductory
training in soil sampling and be able to identify redoximorphic features in soil samples. One of the crew
members must also be skilled in the identification of aquatic organisms such as benthic invertebrates and
amphibians. The other crew member should have general wetland familiarity and experience. In addition,
expertise in wildlife identification is preferred. One of the two fauna crew members should also have
experience with GPS.
5.3 Flora Crew
At least one of the two crew members must be an expert in wetland plant identification and be able to
identify at least 100 species appropriate to the wetland community being surveyed. This person must also
be trained in discriminating unknown flora. The second crew member should have general wetland
familiarity and experience, but no particular expertise is required. One of the two vegetation crew members
should also have experience with global positioning systems (GPS).
6.0 Equipment and Supplies
6.1 General Equipment Needed
• Two digital cameras with zoom and panorama capabilities
• Two high capacity (128MB) digital data cards appropriate for the digital cameras being used
• Two GPS units and recharger for DC power socket in automobile
• Flagging
• Flashlights
• Two clipboards
• Pens with permanent, waterproof ink
• Two hand lenses
• Two compasses
• Two 5 gal. plastic tubs (must be able to float), for transporting equipment and temporarily storing
vouchered plant and soil samples
• Scrub brushes
• Four frame backpacks (i.e., large backpacks)
• Four pairs of chest waders (or hip boots for sites with more shallow water) or breathable waders
• Life jackets if waders are used (i.e., manually inflating)
• Two accurate watches
• Sharpie markers
• Insect repellant
• First aid kit
• Aerial photos, maps, and driving directions to study sites
• Two copies of this SOP, printed on waterproof paper
6.2 Additional Equipment Needed for the Fauna/Soil Crew
• D-frame net with 1-mm mesh
• Photo developing trays
• Sorting trays
• Hand lens (10 power, large diameter)
• Soil probe with wetland soil bit
• Measuring tape (10 m)
• Thermometer to record air temperature
• Flexible forceps for invertebrates
• Munsell chart
D-7
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• Binoculars
• Fauna reference lists, printed on waterproof paper
• Bird, mammal, herpetofauna, fish, and invertebrate field guides.
• Datasheets W1 through W6, printed on waterproof paper
6.3 Additional Equipment Needed for the Flora Crew
• Measuring tape (10 m)
• B A prism or DBH tape
• Convex spherical crown densiometer
• PVC pipe square (0.5 m x 0.5 m)
• Water depth gauge (1.5 -m rope marked at 0.1 -m intervals and with weight attached at one end)
• Plastic bags (3 to 5 quart size)
• Flora reference lists, printed on waterproof paper
• Wetland vegetation field guides
• Pruning shears
• Hand saw
• Datasheets W5, W7, and W8, printed on waterproof paper
7.0 Procedure
This protocol requires a four-person field crew working together in pairs to evaluate a 300-m x 300-m site,
and is intended to be completed in approximately four hours. One pair will conduct the vegetation portion
of the protocol (i.e., flora crew) while the other pair will conduct the fauna and soil portions of the protocol
(i.e., fauna/soil crew).
One or more of these crew members will perform pre-visit preparations. To the degree possible, the fauna
and flora crews should work together in the same area to facilitate communication. All four crew members
should try to minimize trampling of vegetation. The fauna/soil crew field procedures are detailed in Section
7.2, and the flora crew field procedures are detailed in Section 7.3. If water in the study site is ephemeral,
careful consideration should be given to the timing of the field event to ensure that the wetland is not dry
during sampling.
7.1 Pre-Visit Preparations
Listed below are the steps that must be completed before the start of the field event.
Step 1. Obtain aerial photographs and maps of the site and surrounding area. These materials will
familiarize the field team members with the area and provide them with a context for the site.
Step 2. Acquire seasonal and decade hydrographs. Compare with weather data from this season to
determine how representative the hydrology is.
Step 3. If possible, determine the HGM classification for the study site, and obtain local wetland soil
series information.
Step 4. Consult the resources listed on pages 18 to 19 of Herman et al. (2001) to obtain any local
FQA information that may be available.
Step 5. Determine the pre-assigned four-character site ID number for the study site. The first
character of the site ID number should be "W" for wetland ecosystem, the second character
identifies the state in which the site is located (i.e., 1 = Ohio, 2 = Michigan, 3 = Indiana, 4 =
Illinois, 5 = Wisconsin, and 6 = Minnesota); and the last two characters should be digits
assigned sequentially from 00 to 99.
Step 6. It may be necessary to obtain permission or a formal permit to access the study site. Check
with the land owner or manager to determine the need for such permission or permits. The
minimal sample collection envisioned for this protocol will not include vertebrate species and
-------
is not expected to require a scientific collecting permit. This should be verified with
environmental and fish and wildlife agencies, as appropriate.
Step 7. Create reference lists of local flora and fauna species from available databases and resources.
For all species: (1) categorize as native or non-native, (2) list the state and Federal status, and
(3) categorize as invasive or non-invasive. For flora species, also list the coefficient of
conservatism. For fauna, also note whether each species is a generalist/specialist and whether
it is common/rare.
Useful information sources for determining species status include:
• Illinois Department of Natural Resources:
http://dnr.state.il.us/espb/datelist.htm - state list of threatened and endangered (T&E)
species.
• Illinois Natural History Survey:
http://www.inhs.uiuc.edu/cbd/ilspecies/ilsplist.html - lists of flora and fauna occurring in
Illinois, including state and Federal listing status.
• Indiana Department of Natural Resources:
http://www.in.gov/dnr/fishwild/endangered/e-list.htm - lists of T&E fauna;
http://www.in.gov/dnr/naturepr - lists of rare, threatened or endangered (RTE) species by
county and a list of Indiana's RTE vascular plants;
http://www.in.gov/dnr/fishwild/endangered/frogs.htm - list of frogs and toads in Indiana;
http://www.in.gov/dnr/invasivespecies/innatcom03 .pdf - lists of characteristic species
found in a variety of community types.
• Michigan Department of Natural Resources:
http://www.michigan.gOv/dnr/0.1607J-153-10370 12142—.OO.html - links to T&E lists
and a rare plant reference guide;
http://www.michigan.gov/dnr/0.1607.7-153-10370_12145—.OO.html - links to fauna
information;
http://www.michigan.gOv/dnr/0.1607J-153-10370 12146—.OO.html - links to flora
information.
• Minnesota Department of Natural Resources:
http://www.dnr.state.mn.us/ets/index.html - Minnesota's list of endangered, threatened,
and special concern species;
• Ohio Department of Natural Resources:
http://www.ohiodnr.com/wildlife/resources/default.htm - links to a variety of wildlife
resources, including T&E lists for fauna;
http://www.ohiodnr.com/forestrv/Education/ohiotrees/treesindex.htm - list of Ohio's tree
species.
• Wisconsin Department of Natural Resources:
http://www.dnr.state.wi.us/org/land/er/ - lists of state and Federal T&E species occurring
in Wisconsin, county maps that list known occurrences of T&E species, and a searchable
database of T&E species occurrences in Wisconsin.
D-9
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• U.S. Fish and Wildlife Service:
www.fws.gov - links to a discussion of the endangered species program and a list of
Federally threatened and endangered species.
Useful sources of information on invasive species include:
• Indiana Department of Natural Resources: http://www.in.gov/dnr/invasivespecies
• Michigan Invasive Plant Council:
http ://forestry. msu. edu/mipc
• Minnesota Department of Natural Resources: http://www.dnr.state.mn.us/exotics/index.html
• Wisconsin Department of Natural Resources: http://www.dnr.state.wi.us/org/land/er/invasive
• National Park Service, Alien Plant Working Group:
http://www.nps.gov/plants/alien/factmain.htmtfpllists
• US Department of Agriculture:
http://www.invasive.org
Step 8. Determine coefficients of conservatism for plant species using Bernthal (2003). For
additional information, consult FQA Web sites and points of contact listed by Herman et al.
(2001) on pages 18 to 19.
Step 9. Print reference lists developed in the previous step on waterproof paper. Field datasheets
should also be printed double-sided on waterproof paper.
Step 10. Assemble information needed for field crew to efficiently and accurately determine the correct
location of the study plot (e.g., develop driving directions, provide latitude/longitude
coordinates).
Step 11. Determine and record the UTM of the four corners of the 300 m cell. From an aerial
photograph, locate four points within the cell that characterize the diversity of the landcover.
These points should be spread as widely as possible while allowing for sampling as many
varied vegetation types visible on the photo. Record the UTM of the four locations and
number them one through four starting with the location closest to the entry location of the
cell. The area within a square of 20 m by 20 m around the sample point is known as a
"sample plot" (see Figure 2).
Step 12. Assemble field equipment and check it against the list in Section 6.0. Recharge all batteries,
inspect nets for tears, and ensure that the GPS unit and digital cameras are in good working
order. Ensure that all equipment and personal clothing, particularly footwear, has been
washed and dried to decontaminate it from previous field work.
Step 13. Familiarize all personnel with use of equipment including the GPS unit and digital cameras.
7.2 Fauna/Soil Crew Activities
This section describes the steps to be completed by the fauna/soil crew, including study plot set-up
activities, data recording, fauna surveys, and soil characterization.
7.2.1 Data Recording
• The fauna/soil crew should use datasheets Wl through W6. Table 1 describes each of the
datasheets and provides cross-references to SOP instructions. Use as many copies of each sheet
as necessary to record all fauna observations. Note that all fauna sightings during the four hour
sampling period should be recorded, regardless of the activity being performed at the time of the
sighting.
D-10
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Before entering the study plot complete the header information on the datasheets, including
location, site ID number, UTM coordinates, date, and names of investigators. Do not fill out the
species categorization columns (e.g., native or non-native, etc.) of the datasheets until all other
field activities are completed. If time permits, these columns should be completed in the field
using the fauna reference lists. Otherwise they can be completed back in the office.
For datasheets W3 and W5, the number of sheets required will vary from plot to plot
depending on the numbers of observations and photographs. These sheets should be numbered in
the spaces provided at the tops of the sheets (i.e., Page of ) by the field crew members.
Numbering the pages will help keep sheets in order and allow verification that all sheets are
present and accounted for at the conclusion of field activities.
Take photographs of any features (whether fauna, fauna signs or habitat, or disturbances)
deemed as being potentially meaningful. When in doubt, take the photograph.
Before taking the first photograph, be sure that the time set in the camera is the same as the
time set on field team members' watches so that time can be used to associate the photographs
with the appropriate observation point.
Keep a record of all photographs taken in the photo log on datasheet W5. Include the time the
photograph was taken, the subject of the photograph, the direction the photographer was facing
while taking the photograph, and a description of the location of the photograph.
More specific data recording instructions are included in the sections that follow, and on the
datasheets themselves.
7.2.2 Field Set-up
Inspect other field equipment to ensure all equipment was properly disinfected after the previous
sampling event. Use scrub brushes, as necessary, to disinfect.
Synchronize the watches of all field team members and the time displayed on the digital camera to
make sure they all show the same time of day.
Measure the standard walking pace of each team member and adjust to 1 meter per pace so that
team members can estimate distances rather than having to measure them. Note that pacing may
not be possible in many wetland environments.
Upon arrival at the site, spend about 10 minutes on reconnaissance and plot set-up activities, as
detailed below. When approaching the site location, be particularly aware of any herpetofauna,
birds, and mammals in the area that will be defined as the 300 m by 300 m site, because these
species will likely leave the area as the field crew approaches and likely will not return so long as
people are present in the site. See Section 7.2 for the protocol on data collection for these species.
Verify that the landcover is consistent with the predetermined wetland type and verify HGM
designation through observation. Establish the corner of the study plot nearest to the access point
(i.e., fauna/soil survey Point A) using topographic features or other landmarks. Document the
wetland with photos covering a 360 degree view. Conspicuously flag the entry corner of the site
and record UTM coordinates.
If a GPS reading can be obtained, verify the UTM coordinates of Point A as indicated on the aerial
photographs, maps, and fauna/soil datasheets. If a GPS reading cannot be made at or near Point A,
record "NS" for no signal next to the UTM coordinates listed on datasheet Wl.
Mark this starting corner with red flagging and write "F/S Point A" on the flagging, using
permanent marker. Establish the direction of the plot boundary lines by placing blue flagging 5 m
north or south of the corner, and 5 m east or west of the corner (direction depending on the
orientation of plot with respect to the first corner).
D-ll
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• Regardless of which corner is nearest to the study plot access point, the combined fauna/soil
transect from Point A to Point D is to be oriented diagonally from the starting corner through the
center of the study plot (Figure 1). When proceeding from Point A to Point D, leave flagging
periodically to make the return traversal easier and faster.
• To establish the first bird observation point (i.e., Point B), the fauna/soil crew should pace 140 m
from Point A diagonally toward the study plot center, using a compass to navigate (Figure 1).
7.2.3 Bird Observations from Points B and C (adapted from Ralph et al. 1993)
• Commence bird data collection approximately half an hour before sunrise. Make as little noise
and other types of disturbance as possible before and during the observation period. Ideally, birds
will be surveyed at Point B just prior to sunrise and just after sunrise at Point C.
• Bird observation Points B and C should be located 140 m apart and 140 m and 280 m,
respectively, from Point A (Figure 1).
• Use datasheet Wl for data recording for this first exercise of the study plot assessment. Complete
the weather conditions section and note the start time immediately before bird observations begin.
• Working together at bird observation Point B, both fauna/soil crew members should observe birds
over a 20-min observation period, using binoculars and bird reference lists as needed. The more
experienced crew member is responsible for identifying birds, while the other crew member should
record data. Record species that are seen or heard and the approximate number of individuals for
each observation of a species on a separate line in the form.
• Do not double count individuals that may be moving around their territory
• Record the time at the end of the 20-min observation period.
• Flag and label Point B in the same manner as Point A. To avoid disturbing birds before their
presence has been observed and recorded, flagging and labeling are to be conducted after the 20-
min observation period.
• Using a compass for navigation, pace the 140 m from Point B to Point C.
• At Point C, repeat the bird observation procedures as described for Point B above.
7.2.4 Wandering Survey
• Record weather conditions at the beginning of the first wandering survey and at the end of the
fourth wandering survey.
• Beginning on one side of the first sample plot, systematically wander over the sample plot,
avoiding the vegetation subplots while the vegetation crew is working. Be particularly alert to
frogs, turtles, birds, and mammals that are present and their locations. Since these species may
exit the survey site, not returning until the field crews are gone, they should be recorded if their
observed locations ultimately fall within the defined site. Take 20-25 minutes to wander each
sample plot.
• The expert in aquatic fauna taxonomy should periodically stop to use the D-frame net to collect
fish, benthic invertebrates, and amphibian larvae. Manually sort through the net contents,
identifying organisms to the lowest practicable taxonomic level, using a hand lens as needed. Call
out taxa names and numbers of individuals per taxa for the data recorder. The data recorder should
keep a running list on datasheet W2 of taxa and tallies of numbers of individuals per taxon. If
individuals of some taxa are too numerous to count, approximate numbers can be recorded. Keep
in mind that the purpose is to estimate the relative abundance of the various taxa observed. Return
all organisms to the water.
• Both fauna crew members should take note of any herpetofauna, birds, mammals, and fauna sign
observed throughout the field event, especially when first approaching the survey area. The data
recorder should record data on datasheet W3.
D-12
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• Record observations of all birds, mammals, and herpetofauna seen or heard within each sample
plot of the 300 m by 300 m site. As noted above, highly mobile herpetofauna, bird, and mammal
species and their locations relative to the sample plot boundaries should be recorded before they
exit the survey area for the duration of the survey. Use field guides as needed to aid species
identifications.
• Identify each bird species (or lowest possible taxon) and list all species observed for each sample
plot. For each species recorded in each sample plot, list the behavior(s) (if possible) observed as
singing (S), swimming (W), flying (F), perched (P), and/or foraging (O). Keep a tally of the
number of individuals per species observed in each sample plot.
• Identify each mammal species (or lowest possible taxon) and list all species observed for each
sample plot. For each species recorded in each sample plot, list the method(s) of identification as
sight (S), call (C), scat (A), track (T), and/or other (O). If O is recorded, provide a brief
description of the identification method. For each species recorded in each sample plot, list the
behavior(s) observed as swimming (W), calling (C), hiding/resting (H), and/or foraging (O). Keep
a tally of the number of individuals per species observed in each sample plot.
• Identify each herpetofaunal species (or lowest possible taxon) and list all species observed for each
sample point area. Note that larval amphibians collected using the D-frame net should be listed on
W2. For each species recorded in each sample point area, list the method(s) of identification as
sight (S), call (C), scat (A), track (T), skin (K) and/or other (O). If O is recorded, provide a brief
description of the identification method. Keep a tally of the number of individuals per species
observed in each sample plot.
• The data recorder should record on W6 and photograph evidence of human impacts as it is
encountered (see datasheet for examples of the types of disturbances that may be encountered),
and describe land uses that occur adjacent to the wetland area (e.g., residential, agricultural,
industrial). List invasive plant species on datasheet W6.
• At the conclusion of each sample plot's 20-25 minute wandering survey, total the numbers of
birds, mammals, and herpetofauna observed for that sample point area. Record this number and
circle it. If individuals of any taxa are too numerous to count, record approximate numbers and
note in the comments field of the datasheet that numbers are estimated.
• Repeat wandering survey for the remaining three sample plots.
7.2.5 Soil Characterization
• Take a soil sample from the center of the sample plot. Ensure that the wetland soil bit is screwed
into the soil probe. Push the soil probe to its fullest extent into the ground. Record the soil depth
reached on W5.
• Remove the probe. Measure and record the depth of each horizon. Compare soil horizon colors to
a Munsell chart and record the hue, value, and chroma of each horizon. If there are any
redoximorphic features in the sample, record the color and size of the feature on W5.
• Determine soil horizon composition by a combination of visual inspection and by moistening a
small palm-full of soil with water and rolling it into a ball about the size of a large marble.
Classify and record the soil as: "sandy" if the ball will not hold together, "clay" if soil can be
rolled into a ball and molded into a cube, or "loam" if soil can be rolled into a ball but cannot be
molded into a cube.
• If anything looks strange or questionable (e.g., oily sheen on the soil particles, an unnatural smell),
collect a sample for later analysis as follows: Place the soil of interest in an appropriate container
as defined by the QAPP. Label the container with the date, collectors' initials, the soil horizon (O,
E, A, B, or C) and the sample ID number. Sample ID numbers should be assigned as eight-
character combinations of numbers and letters: the first four characters are the site ID number, the
fifth character is "S" for soil, the sixth character is the plot number from which the sample was
D-13
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collected (1 - 8, corresponding to the numbers that set the location of the plot), and the last two
characters should be digits assigned sequentially (from 01 to 99). Record the sample ID number on
W5.
7.3 Flora Crew Activities
7.3.1 Data Recording
• The flora crew should use datasheets W5, W7, and W8. Table 1 describes each of the datasheets
and provides cross-references to SOP instructions. Use as many copies of each sheet as necessary
to record all fauna observations.
• Before beginning field activities, complete the header information on the datasheets, including
location, site ID number, approximate UTM coordinates of the sampling grid, date, and names of
investigators.
• Keep a record of all photos taken in the applicable photo log (W5). Include the time the photo was
taken, the subject of the photo, the direction the photographer was facing while taking the photo, a
description of the location of the photo, and if appropriate, a map identification number and
corresponding datasheet number.
• Do not fill out the species categorization fields (e.g., native or invasive/ introduced) of the
vegetation, wildlife, and aquatic organism datasheets; these fields will be completed back in the
office.
• For some datasheets, the number of sheets required will vary by study site. The field crew
members should number these sheets in the spaces provided at the tops of the sheets (i.e., Page
of ). Numbering the pages will help keep sheets in order and allow verification that all sheets
are present and accounted for at the conclusion of field activities.
• More specific data recording instructions are included in the sections that follow, and on the
datasheets themselves.
7.3.2 Sample Plot and Subplot Surveys
• Take a GPS reading at sample plot 1 and record on W7. Make every effort to minimize trampling
of vegetation while working in each sample plot. Minimize disturbance to vegetation and water in
the subplots before documenting them. Within each sample plot, there is aO.5m by 0.5m subplot.
Collect data on vegetation and water in the nested 0.5 m by 0.5 m subplot before proceeding to
collect data on the sample plot that contains it. Collect data on the vegetation in the sample plot
while moving toward the center of the area to collect the water data so the vegetation will not be
trampled before it is recorded.
• Proceed from the site corner to sample plot 1. The time spent at each sample plot should be about
30 minutes. Mark each sample point with flagging. Using a measuring tape, mark off four points
(north, south, east and west of the sample point) 10m from the center of the sample plot. Place the
0.5 m by 0.5 m PVC pipe square at or near the center, so that it is nested within the 20 m by 20 m
sample plot.
• Survey the 0.5 m by 0.5 m subplot by identifying all vegetation occurring within the PVC pipe
square and estimate the Braun-Blanquet Cover Classes (Table 2). Cover classes should be assigned
for both vegetated and unvegetated areas across the entire subplot. The total of the cover class
percentages may be greater than 100 percent because each cover class represents a range of values
and some plant species may overlap others. Plants include aquatic macrophytes and algae large
enough to be seen easily with an unaided eye. The plant expert should systematically inspect the
subplot from one side to the other, announcing species and percent coverage of each species. The
other vegetation crew member should list species observed on W7.
D-14
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• If any species cannot be fully identified in the field, a small amount of appropriate reference
material can be placed in a plastic bag for later taxonomic verification, so long as the species is
known not to be a species of concern in the area. A slip of paper with the site ID number,
datasheet number, and a temporary sequential taxon number (which is also recorded on the
datasheet) should be kept in the plastic bag until the reference material is identified and the correct
taxon added to the datasheet. This reference material should be identified immediately upon return
from the field and it should then be discarded. The collection of reference material for taxonomic
verification should be minimized. Photographs of any questionable taxa should be used
preferentially whenever appropriate.
• In the 0.5 m by 0.5 m subplot, measure the water depth using the calibrated line and weight and
record on W7. Record "0" as the depth if no water is present at the center of the subplot and, if the
subplot is not entirely dry, move to a location with water in the subplot to record water depth.
• Survey the 20 m by 20 m plot by identifying all plant species occurring within the plot and
estimating the Braun-Blanquet Cover Classes (Table 2) for each species. The plant expert should
systematically inspect from one side of the plot to the other announcing each species and the
corresponding cover class, while the other vegetation crew member should record data on W7.
Also record the Braun-Blanquet Cover Class for areas that are not vegetated (i.e., bare ground or
open water). As above, if any species can not be identified in the field, then representative
specimens should be vouchered.
• Estimate percent overstory density for the sample plot according to procedures outlined in Section
7.3.4 below and record data on datasheet W8.
• Repeat for each of the remaining three sample locations in the 300 m by 300 m site.
7.3.3 Percent Overstory Density Estimation
• Estimate the percent overstory density at four random locations within the 20 m by 20 m sample
plot using a convex spherical crown densiometer according to the following 3 steps. The same
person in each crew should make the estimate of overstory density throughout the whole field
protocol. Record data on W8.
Step 1. Hold the densiometer level (indicated by the round level in the lower left hand
corner), and far enough away from your body so that your head is just outside the
grid (12 - 18 inches away).
Step 2. There are a total of 24 squares on the grid. Count and record the number of squares
showing open canopy. Partially filled squares can be added to make a complete
square.
Example: 4 completely open squares + 3 half-open squares + 5 quarter-open squares
= total of 6.75 open canopy squares.
Step 3. Calculate the percent overstory density with the following equation:
% Overstory density = 100 - (number of open canopy squares x 4.17)
Example: with 10 open squares, the overstory density is 58.3%
• Calculate the average percent overstory density for each plot by summing the four measurements
and dividing by four.
7.4 Exiting the Field Study Site
• Remove all flagging and any other material transported to the study site.
• Check all field equipment against the equipment list to ensure that no equipment is inadvertently
left at the study site.
D-15
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7.5 Post-Visit Activities
The following steps should be completed back in the office after the field event.
Step 1. In a large wash basin, decontaminate field equipment including waders, D-frame nets, and
depth gauges. Use non-phosphate detergent and scrub brushes as needed.
Step 2. Examine any reference material or photographs for taxonomic verification immediately upon
returning from the field. Add any additional taxonomic information adjacent to the original
labeling of the species on the datasheet (e.g., "Unknown Species 1") without erasing or
crossing out the original information. .
Step 3. Submit the soil samples to the sample coordinator at the CRL. The sample coordinator will
arrange for the soil samples to be analyzed. More detail on the handling of soil samples is
provided in the QAPP.
Step 4. Using the flora and fauna reference lists prepared during the pre-visit activities, complete the
Species Categorization fields of datasheets W1-W3.
Step 5. Complete and/or verify calculations of overstory density on W8 according to instructions
given in Section 7.3.3.
Step 6. Complete the FQA on W7 as follows (equations from Bernthal 2003).
• Add up and record the total number of native species (N) for each of the sampled areas.
• Calculate the average coefficient of conservatism (Mean C) for each subplot according to
the following equation:
Mean C = E(d + c2 + c3 + ... cN)/N
where: c is the coefficient of conservatism for each native species (1 through N)
identified in the subplot; and
N is the total number of native species inventoried in the subplot.
• Calculate the FQI for each subplot according to the following equation:
FQI = Mean C * ^N
8.0 Data and Records Management
Data collected in this project will be made publicly available through an EPA centralized database.
Completed datasheets, plant specimens, and soil samples will be kept within the EPA Office of Research
and Development according to standard data and record management protocols.
9.0 Quality Assurance Procedures
A QAPP is associated with this SOP. It is hereby incorporated into this document by reference. The QAPP
should be referred to for details regarding sample handling and quality assurance protocols associated with
this field program.
While analytical assessments conducted in the laboratory can be verified in a number of ways, the accuracy
of flora, fauna, and human impact assessments in the field cannot be objectively verified with the same
degree of precision. Nonetheless, the use of two-person teams will allow each team member to verify the
observations and documentation of the other. Photographs taken will provide additional verification of the
data collected.
D-16
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10.0 References
Bernthal, T.W. 2003. Development of a Floristic Quality Assessment Methodology for the State of
Wisconsin, Final Report to the U.S. Environmental Protection Agency Region V, Wisconsin
Department of Natural Resources, Bureau of Fisheries Management and Habitat Protection, Madison,
WI. 18 pp. + Appendix. http://www.dnr.state.wi.us/org/es/science/publications/SS 986 2003.pdf
Brinson, M.M. 1993. A Hydrogeomorphic Classification for Wetlands. Technical Report WRP-DE-4,
U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS. NTIS No. AD A270 053.
http://www.wes.army.mil/el/wetlands/pdfs/wrpde4.pdf
California Department of Pesticide Regulation (CDPR). 2003. Standard Operating Procedure:
Instructions for the Calibration and Use of a Spherical Densiometer. SOP Number FSOT.002.00.
Environmental Monitoring Branch.
Herman, K.D., L.A. Masters, M.R. Penskar, A.A. Reznicek, G.S. Wilhelm, W.W. Brodovich, andK.P.
Gardiner. 2001. Floristic Quality Assessment with Wetland Categories and Examples of Computer
Applications for the State of Michigan - Revised, 2nd Edition. Michigan Department of Natural
Resources, Wildlife, Natural Heritage Program. Lansing, MI. 19 pp. + Appendices.
http://www.michigandnr.com/publications/pdfs/HuntingWildlifeHabitat/FQA text.pdf
Mitsch, W. J. and J.G. Gosselink. 2000. Wetlands. 3rd Edition. New York: John Wiley and Sons, Inc. 920
pp.
Mueller-Dombois, D. and H. Ellenberg. 974. Aims and Methods of Vegetation Ecology. New York: Wiley.
547 pp.
Ralph, C.J., G.R. Geupel, P. Pyle, T.E. Martin, andD.F. DeSante. 1993. Handbook of Field Methods for
Monitoring Landbirds. Gen. Tech. Rep. PSW-GTR-144. U.S. Department of Agriculture, Forest
Service, Pacific Southwest Research Station, Albany, CA. pp. 30 - 35.
http://www.fs.fed.us/psw/publications/gtrs.shtml
Smith, D. R., Ammann, A., Bartoldus, C., and Brinson, M. M. 1995. An Approach for Assessing Wetland
Functions Using Hydrogeomorphic Classification, Reference Wetlands, and Functional Indices.
Technical Report WRP-DE-9, U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS.
NTIS No. AD A307 121. http://www.wes.army.mil/el/wetlands/pdfs/wrpde9.pdf
Wetzel, R.G. 1983. Limnology. 2nd Edition. Fort Worth: Saunders College Publishing. 753 pp. +
Appendices.
D-17
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Table 1: Descriptions of Datasheets
Datasheet #
Wl
W2
W3
W4
W5
W6
W7
W8
Datasheet Title
Bird Observation Data
Aquatic Organism Data
Fauna Transect Data for
Vertebrates
Soil Data
Photo Log
Invasive Species/Human Impacts
and Activities
Vegetation Data
Canopy Cover Estimates and
Macrophyte Identification
Description of Datasheet Items
Bird species, categorization (e.g., threatened, invasive,
numbers observedat observation points)
In each sample point area: benthic invertebrate, fish, and
larval amphibian species, categorization, and total numbers
Table 1 : Mammal species, categorization, numbers observed
in transects, behaviors observed, identification method (e.g.,
by tracks or scat).
Table 2: For each transect, numbers and characteristics of
faunal signs observed.
Table 3 : Herpetofauna species, categorization, numbers
observed in transects, behaviors observed, identification
method (e.g by tracks or scat).
Table 4: Bird species, categorization, numbers observed in
transects, behaviors observed.
Soil horizon depth, color, and composition, based on one
sample from each of the four large subplots
Descriptions of photos taken by the fauna/soil and flora
crews
Descriptions/numbers of invasive species and disturbances
observed in each transect.
For each of the four sample areas: plant species,
categorization, Braun-Blanquet cover class, and voucher
sample ID numbers
For each sample point area: canopy cover data and
macrophyte sample ID numbers
Related SOP
Section Numbers
7.2.3
7.2.4
7.2.4
7.2.5
7.2, 7.3
7.2.4
7.3.2
7.3.3
Table 2. Braun-Blanquet Cover Classes. FmmAims and Methods of Vegetation
Ecology, Mueller-Dombois and Ellenberg, 1974.
Class
5
4
3
2
1
t
Range of Cover (%)
75 - 100
50-75
25-50
5-25
1 -5
<1
Mean
87.5
62.5
37.5
15
2.5
-
D-18
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Figure 1. Bird Observation Survey Sampling Scheme. For purposes of this illustration, the study plot corner
nearest the access point is assumed to be the northwest (NW) corner; see section 7.2.3 for further explanation.
NW (Starting Corner)
f~
SW
B fol
*^B3Vo°
NE
300m
observation point
transect
SE
D-19
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Figure 2. Vegetation and Wandering Survey Sampling Scheme. Scheme follows a stratified random sampling
design, with a 0.5-m x 0.5-m subplot nested within a 20-m x 20-m sample plot in each of the four quadrants of the
300-m x 300-m site. Subplots and plots are not drawn to scale.
NW
(start)
o
o
300m
NE
Nested 20-m x 20-m
plots and 0.5-m x 0.5-m
subplots
Wandering survey
systematic transects
sw
SE
D-20
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W1: WETLANDS BIRD OBSERVATION DATA
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
Fauna and Soil Crew Names:
UTM Coordinates at four study plot corners (circle corner nearest to plot access point) and observation points:
NW Corner: NE Corner:
SW Corner: SE Corner:
Point B: Point C:
Weather Conditions At Start of Sampling
D storm (heavy rain) D % cloud cover
D rain (steady rain) D clear/sunny
D showers (intermittent) Air Temperature
°c
Weather Conditions At End of S
D storm (heavy rain) [
D rain (steady rain) [
D showers (intermittent) fi
ampling
D % cloud cover
] clear/sunn
Ur Temperati
y
jre °C
Comments:
Bird Species
Species Categorization
Native
(Yes/No)
Invasive
(Yes/No)
Regionally
Common
or Rare
(C/R)
T&E
Status*
Bird Numbers
Point B
Start Time
End Time
Point C
Start Time
End Time
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
D-21
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W2: AQUATIC ORGANISM DATA
Page of
Location
Site ID#
UTM
Investigators
Form Completed By
Date/Time
Comments
Aquatic Organisms Observed at Quadrant Location
(Copy table for all 4 quadrants: NE, NW, SE, SW)
Benthic Invertebrate /
Larval Amphibian / Fish Species*
Species Categorization*
Native/
Invasive
T&E
Status
Generalist/
Specialist
Common/
Rare
Tally*
Total
Number
*Notes: List each species (or lowest practicable taxa) on a separate line. Use as many sheets as necessary.
Species Categorization: For Native/Invasive field, record N if species is native or I if species is invasive or introduced.
For Generalist/Specialist field, record G if species is a generalist or S if species is a specialist.
For Common/Rare field, record C if species is regionally common or R if species is regionally rare.
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened;
SE=State Endangered; list others here as needed.
For Tally field, keep a tally of numbers of individuals per species.
D-22
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W3: FAUNA TRANSECT DATA FOR VERTEBRATES
Faunal Signs
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Faunal Signs
Browse Line
Holes
Nutshells
Tree Rubbing
Other1
(describe/enumerate]
CD
D Present
D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
Associated Fauna:
Total Number
Tree Species:
Associated Fauna:
D Present
D Absent
Associated Fauna:
Notes:
Associated Fauna:
Transect
CB
D Present
D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
Associated Fauna:
Total Number
Tree Species:
Associated Fauna:
D Present
D Absent
Associated Fauna:
Notes:
Associated Fauna:
BA
D Present
D Absent
Height cm
Associated Fauna:
Diameter cm
Diameter cm
Diameter cm
Diameter cm
Associated Fauna:
Total Number
Tree Species:
Associated Fauna:
D Present
D Absent
Associated Fauna:
Notes:
Associated Fauna:
D-23
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W3: FAUNA TRANSECT DATA FOR VERTEBRATES
Mammals
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Mammal Species
Species Characterization
Native
(Yes/No)
Invasive
(Yes/No)
Regionally
Common
or Rare
(C/R)
T&E
Status*
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Individuals
Scats
Tracks
Mounds
Other
Numbers Observed
Transect
CD
CB
BA
Other
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
D-24
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W3: FAUNA TRANSECT DATA FOR VERTEBRATES
Birds
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Bird Species
Species Characterization
Native
(Yes/No)
Invasive
(Yes/No)
Regionally
Common
or Rare
(C/R)
T&E
Status*
Numbers Observed
Transect
CD
CB
BA
Other
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
D-25
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W3: FAUNA TRANSECT DATA FOR VERTEBRATES
Herpetofauna
Information to be filled in prior to site visit
Location Name:
Form Completed By:
Location ID#:
Date:
F/S Crew Names:
Comments:
Herpetofauna Species
Species Characterization
Native
(Yes/No)
Invasive
(Yes/No)
Regionally
Common
or Rare
(C/R)
T&E
Status*
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Individuals
Scats
Tracks
Skins
Numbers Observed
Transect
CD
CB
BA
Other
T&E Status Codes: FT= Federal Threatened; FE= Federal Endangered; ST=State Threatened; SE=State Endangered; list others as needed.
D-26
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W4: SOIL DATA
Page 1 of 2
Location
Site ID#
Starting UTM
Investigators
Form Completed By
Date
Comments
Soil Horizon
O
A
E
B
Depth range from surface (cm)
Depth range from surface (cm)
Color
(from Munsell chart)
Composition
Redoxomorphic Features (if
any, describe color and size)
Sample ID # (if applicable)
Depth range from surface (cm)
Color
(from Munsell chart)
Composition
Redoxomorphic Features (if
any, describe color and size)
Sample ID # (if applicable)
Depth range from surface (cm)
Color
(from Munsell chart)
Composition
Redoxomorphic Features (if
any, describe color and size)
Sample ID # (if applicable)
Sample point
1
Hue
Value
Chroma
D Sand
D Loam
D Clay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Sample point
2
Hue
Value
Chroma
D Sand
D Loam
D Clay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Sample point
3
Hue
Value
Chroma
D Sand
D Loam
D Clay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
Sample point
4
Hue
Value
Chroma
D Sand
D Loam
D Clay
Hue
Value
Chroma
D Sand
D Loam
DClay
Hue
Value
Chroma
D Sand
D Loam
DClay
D-27
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W4: SOIL DATA
Page 2 of 2
Location
Site ID#
Starting UTM
Investigators
Form Completed By
Date
Comments
Soil Horizon
Sample point
1
Sample point
2
Sample point
3
Sample point
4
Color
(from Munsell chart)
Hue
Value
Chroma
Hue
Value
Chroma
Hue
Value
Chroma
Hue
Value
Chroma
Composition
D Sand
D Loam
D Clay
D Sand
D Loam
D Clay
D Sand
D Loam
D Clay
D Sand
D Loam
D Clay
Redoxomorphic Features (if
any, describe color and size)
Sample ID # (if applicable)
Depth (m) reached by soil probe*
Comments
Note: See Section 7.3.2.3 for soil characterization procedures.
D-28
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W5: PHOTO LOG
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Camera Type/Number
Comments
Time
Subject
Data
Sheet #
Location*
Direction
File
Namet
*For the Location field, record the observation point, transect, etc., where the photo was taken.
TFile name to be entered after returning from field and downloading pictures.
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W6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID# UTM-E UTM-N
Investigators
Form Completed By Date
Comments
Table 1. Invasive Plants
Plant Species*
In Designated Land Cover Type:
In Other Land Cover Types:
Tally
Total Number of
Occurrences
*Note: List each species on a separate line. Use as many sheets as necessary.
D-30
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W6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Table 2. Disturbance and Human Management Practices in the Designated Land Cover Type
Map ID
Number(s)*
Disturbance Indicator*
Paths
Car/Vehicle Tracks
Off-road vehicle tracks
not on well-worn paths
Loud noise
Bright, artificial lights
Evidence of human
management practices
Trash (appliances/tires)
Litter
(paper/plastic scraps)
Hydrologic modifica-
tions (e.g., ditch, weir)
Evidence of mowing,
tree felling
Oily or Soapy
Surface Water
Other*
Description*
Total Number
of Times
Encountered
Photo
Taken?
(Y/N)*
*Notes: Use as many sheets as necessary.
Map ID numbers should be assigned D1, D2, etc. Use these numbers to identify disturbances drawn on the plot sketch.
Other disturbance indicators are included in Section 3.0.
Descriptions of disturbance indicators should include more detailed information about the disturbance, how frequently if was encountered in the
plot, and if appropriate, the size of the affected area.
List any photos taken in the photo log; include the Map ID number in the Subject field of the photo log.
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W6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID# UTM-E UTM-N
Investigators
Form Completed By Date
Comments
Table 3. Plants observed outside of sample quadrats.
Plant Species*
In Designated Land Cover Type:
In Other Land Cover Types:
Tally
Total Number of
Occurrences
*Note: List each species on a separate line. Use as many sheets as necessary.
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W6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E
UTM-N
Investigators
Form Completed By
Date
Comments
Table 4. Description of other special features in plot.
Feature
Description
Visual variation in
vegetation occurring
in the plot
Streams and riparian
zones
Water sample(s)
collected? D Y D N
How many?
(list sample ID numbers
in space at right)
Other surface water
Water sample(s)
collected? D Y D N
How many?
(list sample ID numbers
in space at right)
Fauna/Fauna
remains (list species
if known)
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W6: INVASIVE SPECIES/HUMAN IMPACTS AND ACTIVITIES
Page of
Location
Site ID#
UTM-E UTM-N
Investigators
Form Completed By
Date
Comments
Figure 1. Sketch delineating areas of human disturbance, land cover types, surface water bodies, and other features in plot.
D-34
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W7: VEGETATION DATA
Page of
Location
Investigators
Site ID#
Date/Time
Starting UTM
Form Completed By
Comments
Plant Species*
Species Categorization*
Native/
Invas-
ive
T&E
Status
General-
Special-
Comm
on/
Rare
Braun-Blanquet Cover Class*
UTME:
UTMN:
point 1
small
point 1
total
UTME:
UTMN:
point 2
small
point 2
total
UTME:
UTMN:
point 3
small
point 3
total
UTME:
UTMN:
point 4
small
point 4
total
a
i
Oi
-------
W7: VEGETATION DATA
Page of
Location
Investigators
Site ID#
Date/Time
Starting UTM
Form Completed By
Comments
Plant Species*
Species Categorization*
Native/
Invas-
ive
T&E
Status
General-
Special-
Comm
on/
Rare
Braun-Blanquet Cover Class*
UTME:
UTMN:
point 1
small
point 1
total
UTME:
UTMN:
point 2
small
point 2
total
UTME:
UTMN:
point 3
small
point 3
total
UTME:
UTMN:
point 4
small
point 4
total
a
L*J
ON
*Notes: List each species (or lowest practical taxa) on a separate line. Use as many sheets as necessary.
Species Categorization: For Native/Invasive field, record N if species is native or I if species is invasive or introduced.
For Generalist/Specialist field, record G if species is a generalist or S if species is a specialist.
For Common/Rare field, record C if species is regionally common or R if species is regionally rare.
T&E Status Codes: FT= Federal Threatened; FE= Federal Engangered; ST= State Threatened; SE= State
Endangered; list others here as needed.
For Braun-Blanquet Cover Class, record as t (for <1 % cover), 1 (for 1 - 5% cover), 2 (for 5 - 25% cover), 3 (for 25 - 50% cover), 4 (for 50 - 75% cover) or 5 (for 75 -100% cover). See Forested and
Emergent Wetland SOP, Section 7.3.2 and Table 2, for more information.
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W8: CANOPY COVER ESTIMATES AND MACROPHYTE IDENTIFICATION
Page 1 of 1
Location
Site ID#
Starting UTM
Investigators
Form Completed By
Date
Comments
Sample point
area
1
2
3
4
Canopy Cover*
location
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
Overstory Density
(%)
Subplot
Average (%)
Macrophyte ID and Comments
*Notes: See Section 7.3.3 for canopy cover estimation procedures.
D-37
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APPENDIX E
Quality Assurance Project Plan (QAPP)
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QUALITY ASSURANCE PROJECT PLAN
for Quick Assessment Evaluation
of In Situ Environmental Conditions in
Undeveloped Land Cover Types
IN SUPPORT OF
U.S. ENVIRONMENTAL PROTECTION AGENCY
UNDER
RCRA ENFORCEMENT, PERMITTING, AND ASSISTANCE (REPA3)
ZONE 2 - REGION 5
CREATED FOR USE BY EPA REGION 5
QAPP REVISION NO. 1.3
EFFECTIVE DATE: August 2005
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Table of Contents
Table of Contents E-i
List of Tables E-ii
List of Appendices E-ii
Distribution List E-ii
1.0 Introduction E-5
2.0 Project/Task Organization E-5
3.0 Problem Definition/Background E-6
4.0 Project/Task Description E-6
5.0 Quality Objectives and Criteria for Measurement Data E-6
6.0 Special Training/Certification E-7
7.0 Documents and Records E-8
7.1 Field Forms E-8
7.2 Photographs E-9
8.0 Data Collection/Sampling Process Design E-9
9.0 Data Collection/Sampling Methods E-9
10.0 Instrument/Equipment Testing, Inspections, and Maintenance E-9
10.1 Equipment Use and Management E-9
10.2 Inspection and Testing E-9
10.3 Preventive and Remedial Maintenance E-10
10.4 Storage and Disposal E-10
11.0 Inspection/Acceptance of Supplies and Consumables E-10
12.0 Non-Direct Measurements E-10
13.0 Data Management E-10
14.0 Assessments and Response Actions E-ll
15.0 Reports to Management E-ll
16.0 Data Review, Verification, and Validation E-ll
17.0 Verification and Validation Methods E-ll
18.0 Reconciliation with User Requirements E-ll
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List of Tables
Table 2-1. General Responsibilities E-5
Table 6-1. Field Team Personnel Qualifications E-8
List of Appendices
Appendix A. Site QA Form Template E-12
Appendix B. Data Types for Each SOP E-13
B-l. Forested Terrestrial Data Types E-13
B-2. Non-Forested Terrestrial Data Types E-14
B-3. Forested and Emergent Wetland Data Types E-15
Distribution List
EPA Region 5, Project Manager
EPA Region 5, OSEC QA Manager
EPA Region 5, Office of Research and Development - Cincinnati Laboratory
EPA Field Team Coordinator
Field Teams
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1.0 Introduction
This Quality Assurance Project Plan (QAPP) provides detailed quality assurance (QA) and quality control (QC)
procedures for data gathering activities performed in conjunction with the U.S. Environmental Protection Agency
(EPA) Region 5 Standard Operating Procedures (SOPs) for the Quick Assessment Protocol for three undeveloped
land cover types: forested terrestrial, non-forested terrestrial, and wetlands. In accordance with these SOPs, data
will be collected at a variety of undeveloped sites within EPA Region 5, and these data will be assessed to determine
the ecological condition of each site from the perspectives of biological diversity, rarity, and sustainability.
This QAPP serves as a generic plan for all data collection activities conducted under the SOPs and offers guidelines
for ensuring that data are of sufficient quality and quantity to support project objectives. The data collection effort
that this QAPP addresses is unique in that data will be collected by a variety of groups, from a variety of
backgrounds, but with common skill sets. Accordingly, there is a particular need for a stringent quality system in
order to ensure consistent results. The purpose of the QAPP is to provide that quality system by guiding how data
are collected and managed for this project. In addition, the QAPP serves as a means of communication between the
EPA office that is requesting the ecosystem assessment and the various groups of EPA employees, volunteers or
contractors who will perform the data collection activities.
During the planning process for data collection at each site, site-specific information will be documented in site-
specific QA forms. A template for the site QA form is provided in Appendix A. Information in the site QA forms
will not duplicate information provided in this QAPP; it will supplement this plan with information that is specific to
each site. Appendix B contains matrices explaining types of data to be collected under each SOP.
This QAPP was designed to be compliant with and support quality management policies of EPA Region 5. It was
also designed to be consistent with EPA Requirements for Quality Assurance Project Plans (QA/R-5), EPA
Guidance for Quality Assurance Project Plans (QA/G-5), EPA Quality Manual for Environmental Programs (EPA
5360), and Specifications and Guidelines for Quality Systems for Environmental Data Collection and Environmental
Technology Programs (ANSI/ASQC E4-1994). EPA Region 5 will maintain, review, approve, and control this
QAPP and all site QA forms developed under it. This QAPP, along with appropriate site QA forms, will be in the
possession of the field team.
2.0 Project/Task Organization
Table 2-1 identifies the individuals and organizations responsible for the planning and execution of field operations,
laboratory services, and data assessment, validation, and reporting.
Table 2-1. General Responsibilities
Entity
EPA Project Manager
Field Team
Coordinator
Field Team
Data Manager
OSEC QA Manager
Responsibility
Provide general program oversight. Ensure that all other entities understand their responsibilities.
Provide training to all entities on how to best perform their responsibilities.
Create field teams. Review qualifications of potential field team members and ensure that they meet
the minimum requirements listed in the SOPs. Assign field teams to sites. Create unique four-digit site
identification numbers and provide to field teams. Ensure the field team is trained and has filled out the
Site QA Form. Review the Site QA Form. Meet with the field team prior to the site visit to ensure
everything is ready to go. Obtain the completed Site QA Form and transfer to the EPA Data Manger.
Determine if equipment can be picked up by the field team, or if it must be shipped to the site. Arrange
for pickup or delivery of all equipment to the site. Maintain list of field equipment required for each SOP.
Maintain and calibrate field equipment, and provide to field teams.
Participate in training on how to collect data under the SOPs. Complete the Site QA Form. Pick up
equipment from the Field Team Coordinator. Travel to the site and gather data. (Note: The Field Team
Lead will be responsible for ensuring proper communication with the Field Team Coordinator.)
Obtain completed Site QA Forms from the Field Team Coordinator. Obtain completed data collection
forms and analytical data reports, along with digital photograph files, from the Field Team Coordinator.
Create a file for each site and file all completed forms along with the site photographs. Manage and
assess data, based on direction from the EPA Project Manager.
Perform data review, verification, and validation.
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3.0 Problem Definition/Background
Data collected under the SOPs associated with this QAPP will be used to assess the ecological condition of various
sites from the perspectives of biological diversity, rarity, and sustainability. Using the SOPs as a means of assessing
the ecological condition of sites will serve as a tool to help quantify the ecological condition of a site.
Currently, ecosystem condition is identified using best professional judgment, and this judgment is rarely verified
through other methods. Rapid ecological assessments are increasingly used for adaptive ecosystem management
and informed resource management decisions. A procedure to quantify ecosystem condition would benefit all
resource managers.
EPA determined that a set of procedures should be developed to provide consistency and structure to evaluate
ecological significance. Three Preliminary Quick Ecological Assessment Protocols were developed during an EPA
workshop held in June 2003. Subsequently, additional research and development was conducted, and a SOP was
prepared for each protocol. Each of the three SOPs covers a separate undeveloped land cover type:
• Forested Terrestrial
• Non-Forested Terrestrial
• Forested and Emergent Wetlands
4.0 Project/Task Description
The three SOPs involve the collection of various types of data, including:
• Flora and fauna identification and counts
• Minimum amounts of flora and fauna reference material to be used for taxonomic verification, then
discarded
• In situ soil measurements
• Characterization of human impacts
Each SOP includes a detailed discussion of the required procedures for data collection activities. In addition, each
SOP contains blank data collection forms, which are to be used in the field to record all data. The data collection
forms are comprehensive: field teams must fill in all the required information on each form to ensure a complete
data set for each site. Tables summarizing the types of data collected under each SOP are in Appendix B.
Prior to field activities, each team should work with the EPA Field Team Coordinator and complete a site QA form
to ensure proper field readiness. Appendix A includes a template for the site QA forms. The site QA forms will
guide the field teams through the planning process. By properly completing the site QA forms and participating in
training with the EPA Field Team Coordinator, the field teams can ensure they are adequately prepared for field
activities.
The EPA Field Team Coordinator will contact the necessary personnel to arrange a date for field equipment pickup
or shipment to the site, will retain lists of all required equipment for each SOP, and will maintained and calibrated
the required equipment. The EPA Field Team Coordinator will ensure that the required equipment is ready for
pickup by the Field Team Lead or shipment to the site.
During field activities, the field team members should carefully follow the procedures outlined in the SOP. The
Field Team Lead is responsible for ensuring that data collection forms are completed and returned to Field Team
Coordinator along with all equipment after field activities are complete. The Field Team Lead is also responsible
for ensuring that any samples for off-site identification are properly packaged and delivered to the field team
member responsible for the identification. The completed data collection forms will then be sent to the EPA Data
Manager.
5.0 Quality Objectives and Criteria for Measurement Data
Data Quality Objectives (DQOs) are qualitative and quantitative statements established prior to data collection that
specify the quality and quantity of data required to support the intended decision. DQOs provide statements of
acceptable limits of error. Applicable quality objective criteria include accuracy, precision, completeness,
representativeness, and comparability.
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At each site, the field teams will follow the SOPs to ensure that data conform to reasonable standards of accuracy,
precision, completeness, representativeness, and comparability. The three types of data to be collected under the
SOPs are:
• Species identification/counting
• Observations
• Measurements
Given the nature of the data collected under this program, most of the quality objective criteria are expressed
qualitatively, not quantitatively. Exceptions to this include the data collected that use measuring tape, DBH tape,
and clinometer which require reading of measurements to +/- 1A the smallest unit on the instrument. Examples of
species identification/counting data include identifying and counting bird, amphibian, mammal, or zooplankton
species. The quality objectives and criteria for this data type are accuracy, precision, completeness,
representativeness, and comparability, which can be achieved, to the extent practical, by following standardized
procedures (the SOPs), by ensuring that field personnel are properly qualified (as specified in the SOPs), and by
using reputable field guides. Lists of acceptable field guides are included in each SOP, however this list may be
supplemented by other guides that the field team scientists deem acceptable. The field team should list the
references used to identify species on the data collection forms. In addition, direction and guidance from the EPA
Field Team Coordinator will help ensure that the quality objectives are met by ensuring consistency amongst the
various field teams.
Examples of observation data include observing and recording fauna, fauna! signs, and evidence of human
visitation. The quality objectives and criteria for the observation data type, again, are accuracy, precision,
completeness, representativeness, and comparability. To achieve these objectives, field teams should use
standardized procedures (the SOPs), and the EPA Field Team Coordinator should ensure that field personnel are
properly qualified (as specified in the SOPs). In addition, field team members should consult with one another to
verify observations whenever possible, and remain alert and observant throughout the field effort.
For these SOPs, measurement data will be obtained in situ. Quality objectives and criteria include accuracy,
precision, completeness, representativeness, and comparability. Accuracy will be assessed by comparing the
measurements against standards of known values, such as species distributions, and data from similar sites.
Precision will be assessed through the use of field replicates for the measurements. Comparability, completeness,
and representativeness will be ensured through the use of standardized procedures and qualified personnel.
As part of the planning process for each site, the field team will meet with the EPA Field Team Coordinator to
receive training on how to implement the SOPs, and to walk through the site QA form to ensure field readiness.
Field team members will identify all required measurement data on the site QA form, along with parameters of
concern, media of concern, and number of samples. Any additions to or deviations from the procedures outlined in
this QAPP will be documented in the site QA form.
6.0 Special Training/Certification
Each of the three SOPs requires unique qualifications for implementing personnel. The field team coordinator will
evaluate the qualifications of each field team member and determine whether they meet the requirements. In
general, all field team members should have at least one year of field experience. Table 6-1 lists additional specific
requirements.
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Table 6-1. Field Team Personnel Qualifications
SOP
Forested Terrestrial
Non-Forested Terrestrial
Wetlands
Minimum # of Field
Personnel
4
4
4
Specific Requirements (Minimum)
1 expert in bird field identification
1 expert in plant species identification
2 additional team members experienced with forest-specific sampling
and recording methods
1 team member experienced with the use of GPS equipment
1 expert in plant identification
1 additional team member with botanical training
1 expert in bird and other animal identification
2 team members experienced with the use of GPS equipment
1 expert in wetland plant identification
1 team member trained in soil sampling, able to identify redoxomorphic
features in soil samples
1 team member skilled in identification of birds, aquatic organisms,
and other animals
2 team members experienced with the use of GPS equipment
*For the purposes of this QAPP,
at least five years experience in
an expert is defined as someone with formal training (undergraduate level education or higher) and
the discipline.
Prior to any site visit, the EPA Field Team Coordinator will provide training to all field team members. The training
will cover all phases of the data collection process, including planning, implementation, and post-site visit activities.
The training will explain the steps of the SOPs in detail, and relate how to obtain, operate, and return the field
equipment. The training will also describe each reference document (including this QAPP) and explain which field
guides are appropriate to use. Each piece of field equipment will be presented, and the field team members will be
instructed on how to operate the equipment to obtain reliable data. The training will explain the importance of
completely filling out the data collection forms. For example, photo logs should contain very detailed information
so that photographs can be correctly identified after completion of field activities. Finally, the training will address
any health and safety issues that the field team members might encounter at the sites.
7.0 Documents and Records
Upon completion of field activities, completed site QA forms, data collection sheets, and analytical reports will be
delivered to the EPA Data Manager, who will file and store the data at EPA Region 5. Project documents and
records will be prepared or generated, reviewed, approved, and controlled in accordance with EPA direction. Any
transfer of electronic data to EPA should be performed in accordance with applicable EPA guidelines and protocols.
7.1 Field Forms
Results of all field measurements will be recorded on separate data collection sheets. Templates for SOP-specific
data collection sheets are included at the end of each SOP. Field team members will take blank data collection
sheets to the field, along with clipboards and pens. On all data collection sheets, indelible black or blue ink should
be used. Changes should be crossed out with a single line so that the original text remains legible; the change
should be initialed and dated. Pages should be numbered as page x of y (where x is the page being looked at and y
is the total number of pages) so that pages may be kept in order, and to allow verification that all sheets are present
and accounted for at the conclusion of field activities. If a field notebook is used in the field, it should be secondary
to the data sheets; the field team should first ensure that all data required on the data collection sheets have been
recorded.
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7.2 Photographs
In support of each SOP, photographs will be taken to document field activities, as specified in the SOPs. Digital
cameras are included in the list of equipment to be provided to the field teams. Each set of data collection sheets
includes a photo log. In accordance with the SOPs, the following information will be recorded on the photo log data
collection sheet as photographs are taken:
• Site location, site ID number, and latitude and longitude of site
• Date
• Site investigators (field team members)
• Person completing photo log
• Camera type/film speed/pixel resolution
• Time, subject, location, and direction of each photograph
• Data sheet number to which each photograph relates
After completion of field activities, the field team will return the camera along with the field equipment. The digital
photograph files will be delivered to the EPA Data Manager, who will be responsible for storing the photographs
with the photo log in the project files.
8.0 Data Collection/Sampling Process Design
Detailed descriptions of the process designs for data and sample collection are included in each respective SOP.
9.0 Data Collection/Sampling Methods
Detailed descriptions of the data collection and sampling methods are included in each respective SOP. The purpose
of the SOPs is to lend consistency and reproducibility to quick ecological assessments in the field. Therefore, the
sampling methods for each SOP should be consistently applied at each site. To accomplish this, the field teams
should carefully adhere to the techniques specified in the SOPs.
10.0 Instrument/Equipment Testing, Inspections, and Maintenance
Field equipment used in the execution of work will be appropriate and approved for intended uses. The procurement
and handling of quality-affecting equipment will be overseen by the Office of Science, Ecosystems and
Communities (OSEC) QA Manager to ensure initial and continued conformance with applicable technical
requirements and acceptance criteria. Quality-affecting materials that are to be controlled include, but are not
limited to, field measurement and testing equipment, sampling equipment, and location finding devices such as GPS
units.
10.1 Equipment Use and Management
Equipment used in the execution of work will be appropriate and approved for its intended use, and will be operated,
handled, maintained, and stored in accordance with the manufacturer's specifications. Sample collection and
storage equipment will be cleaned, stored, and handled using the necessary precautions against cross-contamination,
corrosion, and damage.
10.2 Inspection and Testing
The Region 5 Central Regional Laboratory (CRL) and ORD-Cincinnati will be responsible for maintaining
laboratory and field equipment, respectively, according to the manufacturer's specifications. Field equipment will
be visually inspected by the ORD-Cincinnati before shipment to the field, and again by the field team before use.
The CRL, ORD-Cincinnati, and field team will clean, store, and handle the sample collection and storage equipment
using the necessary precautions against cross-contamination, corrosion, and damage. Equipment, parts, or
components that do not meet specifications (e.g., used sample container, dysfunctional pH meter) will be identified
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in a manner that is easily recognized. These items will be controlled so as to prevent their inadvertent use or
installation.
10.3 Preventive and Remedial Maintenance
Field and laboratory equipment will be maintained on routine preventive maintenance schedules by the ORD-
Cincinnati and CRL. Preventive and remedial maintenance will be performed and verified by qualified personnel at
the ORD-Cincinnati and CRL in accordance with approved procedures and manufacturer's recommendations.
Maintenance records will be generated, retained at the ORD-Cincinnati and CRL, and reviewed as part of the project
quality records.
Maintenance activities will be documented in instrument-specific or field logbooks. Entries should include the
following information:
• Equipment identification (e.g., type, model, serial number, and manufacturer)
• Procedure reference
• Date, description, and results of calibration/maintenance
• Name and affiliation of the person who performed maintenance
10.4 Storage and Disposal
The field team will be responsible for securing the appropriate storage and/or disposal of project equipment and
materials. After completion of field activities, re-useable equipment should be returned to the ORD-Cincinnati and
CRL, and disposable equipment and trash should be double-bagged and discarded.
11.0 Inspection/Acceptance of Supplies and Consumables
Materials used in the execution of field activities and laboratory analysis will be appropriate and approved for
intended uses. The ORD-Cincinnati or CRL will control the procurement and handling of quality-affecting
materials to ensure initial and continued conformance with applicable technical requirements and acceptance
criteria. These items will be visually inspected by the ORD-Cincinnati or CRL before shipment to the field, and
again by the field team before use. Inspection elements will include as appropriate, a review of expiration dates,
limitations of use, size and quantity. Quality-affecting materials that are to be controlled include, but are not limited
to disposable sampling supplies. Materials that do not meet performance specifications will be segregated and
labeled to preclude use.
12.0 Non-Direct Measurements
Data needs that will be met from non-measurement sources include aerial photos and site maps. The project team
should try to obtain the most accurate and up-to-date maps.
13.0 Data Management
The OSEC QA Manager should periodically audit the program information that is stored by the EPA Data Manager
to verify record integrity, retrievability, and security. The OSEC QA Manager should also conduct periodic record
audits to verify that the number of entries made equals the number of records logged and that data output correctly
corresponds to data input. Prior to "mixing" data sets or adding to an existing data set, the comparability of the data
should be verified and documented. For this purpose, comparability should be based on the type of data, the
comparability of the methods used to generate the data, the assessed quality of the data, and compatibility of the
electronic files. Rigid data management procedures should be implemented to ensure the integrity of stored project
data in terms of accuracy, completeness, and accountability. Data management procedures and controls should
provide appropriate security against unauthorized retrieval or modification of the information, whether intentional or
unintentional.
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14.0 Assessments and Response Actions
All aspects of the data collection activities conducted under this project should be regularly assessed; these aspects
include planning activities, field work, and laboratory work. The OSEC QA Manager should perform a periodic
assessment of the planning program (suggested to be done after each large field effort or after 20-25 field
assessments have been done) to document how it is working. The intent of the assessment would be to identify
opportunities for improvements for increased efficiencies. The assessments should be performed following EPA's
standard protocols for management reviews. In addition, the OSEC QA Manager should establish a schedule for
doing assessments of field activities. Corrective actions for identified non-conformities will be verified by the
OSEC QA Manager. The overall assessment process will allow the identification of ways to improve the program,
to find efficiencies, and to improve data quality. The OSEC QA Manager will report any recommendations for
improved data quality and efficiency to the EPA Project Manager.
15.0 Reports to Management
The OSEC QA Manager will provide a report to the EPA Project Manager providing recommendations from
assessment activities in Section 14.0.
16.0 Data Review, Verification, and Validation
The quality and usability of environmental data will be assessed and documented. The quality of data will be
assessed to establish usability for their intended purpose and to foster continuous improvement in data collection
efforts by identifying major or recurring sources of error. Data quality assessment will include data review.
Data review will be conducted for all completed data collection forms. EPA staff will perform a "reality check" as
soon as the forms are received to ensure that the information is sensible, legible, and complete. To the extent
practical, the Field Team Lead should retain copies of all completed data collection forms so that he/she can assist
EPA with answering any questions about the data.
Data verification and validation will be conducted for in situ measurements. Data reproducibility will be assessed in
water samples with replicate and duplicate samples. All other measurements will be assessed through close
examination of field notes.
17.0 Verification and Validation Methods
Data validation and verification will be similar to that required by the Contract Laboratory Program (CLP), with
certain modifications as noted below. Data will be evaluated as outlined in the CLP National Functional Guidelines
(NFGs)for Organic (EPA 540/R-94/012) Data Review, and as appropriate to the methods in this QAPP. Data
validation will also be performed in accordance with the appropriate EPA Region 5 procedures.
18.0 Reconciliation with User Requirements
The suitability of data for the intended use(s) will be determined by the OSEC QA Manager. Data usability involves
an evaluation of the quantity, type, and overall quality of generated data against the project objectives. The usability
of data that are associated with QC results outside established acceptance criteria is generally dependent on the
degree of the exceedance, whether the potential bias is high or low, and whether the uncertainty implied by the
exceedance is significant. Usability will be assessed after consultation with the Field Team Lead and the four-
member Field Team.
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APPENDIX A
Site QA Form Template
Site QA Form
Form Completed By:
Date:
Site Location:
Site ID# (4 digits, assigned by EPA):
Lat
" Long
0
Anticipated Date of Field Work:
Field Team Lead:
Other Field Team Members:
Land Cover Type (Check One): Forested Terrestrial
Wetlands Non-Forested Terrestrial
Has each field team member read the applicable SOP and QAPP ? (yes/no):
Has the field team reviewed aerial photographs and maps of the site and surrounding area? (yes/no):
Once the site has been assigned, and at least two weeks prior to field work (or as soon as possible), the Field Team
Lead should contact the EPA Field Team Coordinator to arrange for training. The EPA Field Team Coordinator will
work with the CRL to request equipment, and to place an order for sample analysis (if laboratory analyses are
required).
Date EPA Field Team Coordinator contacted:
Were equipment needs verified with the CRL POC? (yes/no):
Will the equipment be picked up or shipped to the site?
If equipment will be picked up, list verified equipment pick-up date:.
If equipment will be shipped to the site, list verified equipment shipment date:
List any anticipated changes to the SOP:
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APPENDIX B
Data Types for Each SOP
Appendix B-1. Forested Terrestrial Data Types
Sheet
F1
F2
F3
F4
F5
F6
F7
F8
F9
F10
Data
Bird Observation
Data
Fauna Transect Data
for Vertebrates
Fauna Transect Data
for CWD, Snags, and
Brush Piles
Soil and Earthworm
Data
Photo log
Invasive Species/
Human Impacts and
Activities
Understory Data
Sapling Data
Community Data
Point Quarter
Sampling Tree Data
Location
In Situ
In Situ
In Situ
In Situ
N/A
In Situ
In Situ
In Situ
In Situ
In Situ
Type*
Ct/ID
Ct/ID
0
M
Ct/ID
M
M
M
O
O
Ct/ID
0
0
Ct/ID
Ct/ID
M
0
M
Ct/ID
Ct/ID
0
Ct/ID
M
M
M
0
Description
Bird species and categorization
Fauna species and categorization
Faunal signs
Hole size, browse line height
Fauna species and categorization
Diameter at breast height (DBH)
Height, length, width of brush pile
Soil core layer depths
Soil layer colors (hue, value, chroma) using
Munsell chart
Soil layer composition
Number of earthworms
N/A
Disturbance type
Percent cover for shrubs, seedlings, and
herbaceous groundcover
Sapling species and categorization
DBH of each stem
Presence of water
Canopy cover
Number of foliar layers
Community type
Successional stage
Tree species and categorization
Distance to sampling node
DBH
Tree height
Canopy Class
Instrument
None
None
None
Measuring tape
None
DBH tape
Measuring tape
Soil probe
None
None
None
Camera
None
None
None
DBH tape
None
Spherical
densiometer
None
None
None
None
Measuring tape
DBH tape
Clinometer
None
' Ct/ID = Count/Identify; O = Observation; M = Measurement
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Appendix B-2. Non-Forested Terrestrial Data Types
Sheet
N1
N2
N3
N4
N5
N6
N7
N8
Data
Bird and Amphibian
Data
Fauna Transect Data
Photo log
Soil and Vegetation
Stress Data
Invasive Species/
Human Impacts and
Activities
Point Survey Data
Quadrat Survey Data
Special Features
Location
In Situ
In Situ
N/A
In Situ
In Situ
In Situ
In Situ
In Situ
Type*
Ct/ID
Ct/ID
M
M
M
Ct/ID
O
Ct/ID
Ct/ID
Ct/ID
Ct/ID
O
M
O
0
M
O
0
0
O
0
M
M
Ct/ID
Ct/ID
O
0
Ct/ID
0
Description
Bird species and categorization
Amphibian species and categorization
Point count start and stop times
GPS coordinates
Transect start and stop time
Mammal species and categorization
Mammal signs
Herpetofauna species and categorization
Bird species and categorization
Butterfly species and categorization
Other invertebrate taxa
N/A
Soil core layer depths
Soil layer colors (hue, value, chroma) using
Munsell chart
Soil layer composition
Depth reached
Signs of vegetative stress
Disturbance type
Disturbance indicators
Presence/absence of bare ground
Presence/absence of health/vigor indicators
Distance to nearest tree
DBH
Canopy cover species
Plant species and categorization
Disturbance indicators
Presence/absence of bare ground
Plant species and categorization
Sketch of landscape attributes, etc.
Instrument
None
None
Clock
GPS unit
Clock
None
None
None
None
None
None
Camera
Soil probe and
measuring tape
None
None
Tape measure
None
None
None
None
None
Measuring tape
DBH tape
None
None
None
None
None
None
*Ct/ID = Count/Identify; O = Observation; M = Measurement
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Appendix B-3. Forested and Emergent Wetland Data Types
Sheet
W1
W2
W3
W4
W5
W6
W7
W8
Data
Bird Observation Data
Aquatic Organism Data
Fauna Transect Data
for Vertebrates
Soil Data
Photo log
Invasive Species/
Human Impacts and
Activities
Vegetation Data
Canopy Cover
Estimates and
Macrophyte
Identification
Location
In Situ
In Situ
In Situ
In Situ
Ex Situ
N/A
In Situ
In Situ
In Situ
Type*
Ct/ID
Ct/ID
0, Ct/ID
0
M
O
O
M
Ct/ID
M
Ct/ID
Ct/ID
M
Description
Bird species and categorization
Aquatic species and categorization
Mammal, herpetofauna, and bird species
observed or tracked
Color, composition, and redoxomorphic
features
If necessary
N/A
Disturbance type and number
GPS Coordinates
Plant species and categorization
Water depth
Braun-Blanquet cover class
Macrophyte species
Percent overstory density
Instrument
None
D-frame net
None
None
Off-site analysis
Camera
None
GPS unit
None
Calibrated line and
weight
None
None
Spherical
densiometer
*Ct/ID = Count/Identify; O = Observation; M = Measurement
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