v>EPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-92/167
October 1992
A Synoptic
Approach to
Cumulative Impact
Assessment
A Proposed
Methodology
-------
-------
EPA/600/R-92/167
October 1992
A Synoptic Approach to Cumulative Impact
Assessment
A Proposed Methodology
.
Scott G. Leibowitz *
Brooke Abbruzzese 2
Paul R. Adamus 2
Larry E.Hughes2
Jeffrey T.Irish2
Technical Editors:
Scott G.McCannell3
Ann R. Hairston 2
1 U.S. Environmental Protection Agency
USEPA Environmental Research Laboratory
200 SW 35th Street
Corvallis,OR 97333
2ManTech Environmental Technology, Inc.
USEPA Environmental Research Laboratory
200 SW 35th Street
Corvallis, OR 97333
3 Word Design
610 NW Van Buren Avenue
Corvallis7OR 97330
U.S. Environmental Protection Agency
Environmental Research Laboratory
200 SW 35th Street
Corvallis,OR 97333
Printed on Recycled Paper
-------
NOTICE
This document has been reviewed in accordance with
U.S. Environmental Protection Agency policy and
approved for publication. Mention of trade names
or commercial products does not constitute endorse-
ment or recommendation for use.
// Synoptic Approach
-------
%, ..-^
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
OFFICE OF
WATER
Dear Reader:
The following document is a product of the Environmental
Protection Agency Office of Research and Development's (ORD)
Wetlands Research Program developed at the request of our office
in response to the need for more information on cumulative impact
assessment. The proposed methodology was designed to assist
wetland regulators in assessing the cumulative effect of
individual wetland impacts within the landscape. Other potential
applications of the approach include prioritizing areas for
restoration and protection as part of nonppint source abatement
efforts implementing the Coastal Zone Act Reauthorization
Amendments guidance, supporting the development of State Wetland
Conservation Plans and wetland water quality standards, including
designating uses and identifying Outstanding National Resource
Waters, prioritizing acquisition and restoration efforts for
other water quality or habitat benefits, and conducting regional
risk assessments and watershed planning efforts such as Advance
Identifications or Special Area Management Plans.
The synoptic approach allows wetland managers to produce
statewide maps that rank portions of the landscape according to a
set of landscape variables, or synoptic indices. These maps and
xndices should enable permit reviewers to consider the landscape
condition of the area in. which a particular permit is proposed,
and, in so doing, allow them to better consider the cumulative
impact of a proposed activity.
The synoptic approach was specifically designed for
situations in which time, resources, and information are limited.
It is practical within this context because an assessment is
prepared for an entire state or region, and not on a case-by-case
basis. _ln addition, the approach is intended to augment the best
professional judgement used daily by wetland managers and
regulators. It is not intended to provide a precise,
quantitative assessment of the cumulative effects within a
particular area. Rather, it provides a mechanism to compare
potential cumulative impacts between areas.
The report describes the steps of conducting a synoptic
assessment, and illustrates the use of synoptic information
through four case studies, in the Pearl River, Louisiana case
study the potential use of the synoptic approach for assessing
cumulative impacts under the Clean Water Act Section 404
regulatory program is illustrated. In the Illinois case study,
Synoptic Approach Hi
-------
subwatersheds are ranked for restoration according to their
potential for water quality improvement. In the Washington State
case studies, the approach is used for regional comparisons to
support the development of a State Wetland Conservation Plan and
to demonstrate the feasibility of introducing the concepts of
value and future risk into the synoptic assessments. /
The report does not provide a specific, detailed procedure
for choosing the synoptic indices, nor does it supply a
scientifically tested list of landscape indicators with
confidence limits. This is not possible, given the strong
dependency of the synoptic indices and landscape indicators on
the specific management goals and the actual environmental
conditions of the assessment.
*
ORD has issued this report as a proposed, rather than
operational methodology to allow testing of the approach in
Regional and State applications. We ask anyone conducting a
synoptic assessment to provide the Wetlands Research Program or
our office with feedback so that EPA can evaluate the suitability
of the method and refine the approach.
Sincerely,
\
-------
TABLE OF CONTENTS
List of Figures „ . , w
List of Tables w/
pise/aimer \x
Acknowledgments x
Preface.. „ ...xi
Glossary .„ xii
Section 1: The Synoptic Approach 1
Chapter 1: Introduction 3
Cumulative Impacts 3
Regulatory Mandate 5
Regulatory Context ,.. 6
The Synoptic Approach 6
Chapter 2: Ecological Basis for the Synoptic Indices 9
Rationale for a Landscape Approach g
Landscape Model of Ecosystem Function 70
Effect of Impacts on Landscape Function 7 1
Synoptic Indices 72
Function.. , 73
Value 13
Functional Loss 74
Replacement Potential.... .- 74
Synoptic Index Evaluation 74
Chapter 3: Conducting a Synoptic Assessment 15
Step 1: Define Goals and Criteria 75
Step 1.1 - Define Assessment Objectives 75
Step 1.2 - Define Intended Use "........! 76
Step 1.3 -Assess Accuracy Needs 77
Step 1.4 - Identify Assessment Constraints 77
Step 2: Define Synoptic Indices 77
Step 2.1 - Identify Wetland Types 75
Step 2.2-Describe Natural Setting jg
Step 2.3 - Define Landscape Boundary 73
Step 2.4 - Define Wetland Functions 20
Step 2.5 - Define Wetland Values "....."'.....20
Step 2.6 - Identify Significant Impacts 20
Step 2.7-Select Landscape Subunits 20
Step 2.8 - Define Combination Rules 22
Step 3: Select Landscape Indicators 22
Step 3.1 - Survey Data and Existing Methods 23
Step 3.2 - Assess Data Adequacy 23
Step 3.3-Evaluate Costs of Better Data 23
Step 3.4 - Compare and Select Indicators 24
Step 3.5 - Describe Indicator Assumptions 24
Step 3.6 - Finalize Subunit Selection 25
Step 3.7 - Conduct Pre-Analysis Review 25
Synoptic Approach v
-------
Step 4: Conduct Assessment : 25
Step 4.1 - Plan Quality Assurance/Quality Control 25
Step 4.2-PerformMap Measurements > .,.., 25
Step 4.3 - Analyze Data ., 26
Step 4.4-Produce Maps 26
Step 4.5 - Assess Accuracy 28
Step 4.6 - Conduct Post-Analysis Review ..'. 29
Step 5: Prepare Synoptic Reports 29
Step 5.1-Prepare User's Guide ..'. 29
Step 5.2-Prepare Assessment Documentation .•'...:..'... 29
Chapter* Case Studies 31
Pearl River Basin 32
Management Goal :'. .'. 32
Wetland Types 32
Landscape Boundary and Subunits 32
Natural Setting .'. :..... 32
Wetland Functions ., 32
Significant Impacts , 32
Synoptic Indices 34
Landscape Indicators 34
Map Interpretation 35
State of Louisiana 35
Management Goal 38
Wetland Types 38
Landscape Boundary and Subunits, 38
Natural Setting .: 38
Wetland Functions 38
Significant Impacts , 38
Synoptic Indices , „.... 38
Landscape Indicators 39
Map Interpretation , .41
State of Washington 41
Management Goal 41
Wetland Types 41
Landscape Boundary and Subunits 49
Natural Setting ....,, 49
Wetland Functions 49
Significant Impacts 49
Synoptic Indices 50
Landscape Indicators 50
Map Interpretation 50
State of Illinois '. 55
Management Goal 55
Wetland Types 55
Landscape Boundary and Subunits 55
Natural Setting 55
Wetland Functions 55
Significant Impacts 56
Synoptic Indices 56
Landscape Indicators 56
Map Interpretation 57
Chapter 5: Section 1 Summary 63
Future Directions 65
vi Synoptic Approach
-------
Section 2: Background Information
67
Chapter 6: Ecological Response to Stress 69
Ecosystem Stability 69
Adaptations to Stress 70
Physiological Adaptations 70
Life History 70
Gene Banks 72
Stress-Adapted Ecosystems 72
Effect of Disturbance on Ecosystem Function 72
Mitigating Effects of Landscape on Disturbance 74
Landscape Elements as Conduits or Barriers to Disturbance 74
Landscape Pattern of Sources and Sinks 74
Recovery from Local Extinction .„ 74
Landscape Fragmentation.,... „ 75
Chapter 7: A Review of Wetland Functions and the Effect
of Wetland Impacts 77
Wetland Functions 77
Hydrologic Functions 77
Water Quality Functions 78
Habitat Functions 81
Wetland Degradation , 81
Wetland Conversion.. 83
Effects of Cumulative Wetland Loss on Landscape Functions 85
Loss of Hydrologic Functions '. 85
Loss of Water'Quality Functions 86
Loss of Habitat Functions 87
References 91
Appendices 707
Appendix A . 109
Appendix B no
Appendix C 113
Appendix D 774
Appendix E , 775
Appendix F 720
Appendix G 722
Appendix H 724
Appendix I '.....127
Synoptic Approach vii
-------
LIST OF FIGURES
Figure 1.1. "A Short History of America" 4
Figure 1.2. Improving best professional judgment 7
Figure 2.1. Generic model of ecosystem impacts 11
Figure 3.1. Illustration of maps using different class intervals to represent
thesamedata 27
. .
Figure 3.2. Different possible data distributions 28
Figure 4.1. The Pearl River Basin in south-central Mississippi and southeastern
Louisiana and the five subunits 33
Figure 42. Functional loss for the Pearl River Basin 36
Figure 4.3. Loss of hydrologic function for the Pearl River Basin 37
Figure 4.4. The State of Louisiana and the 124 subunits 42
Figure 4.5. Replacement potential with respect to soils for Louisiana 43
Figure 4.6. Replacement potential with respect to hydrologic integrity for Louisiana 44
Figure 4.7. Replacement potential with respect to water quality for Louisiana 45
Figure4.8. Hydrologic function for Louisiana 46
Figure 4.9. Water quality function for Louisiana 47
Figure4.10. Habitat function for Louisiana > 48
Figure 4.11. The State of Washington and the 62 subunits 51
Figure4.12. Habitat value for Washington 52
Figure4.13. Future risk for Washington 53
Figure4.14. Future risk of habitat loss for Washington 54
Figure 4.15. The State of Illinois and the 90 subunits 58
Figure 4.16. Water quality function for Illinois 59
Figure4.17. Water quality value with respect to humans for Illinois 60
Figure 4.18. Water quality with respect to valued fish communities for Illinois 61
Figure 4.19. Replacement potential with regard to soils for Illinois 62
Figure 5.1. Applications of synoptic assessments at various spatial scales 64
Figure 6.1. Example of a disturbance, stress, and response 70
Figure 6.2. Disturbance and biotic responses occur in many forms and at various spatial
and temporal scales 71
Figure 6.3. Ecosystem resistance and resilience 71
Figure 6.4. Simple steady-state ecosystem where excess capacity buffers function
from disturbance , 73
Figure6.5. Buffering of landscape effects in a disturbed ecosystem 73
Figure 6.6. Ecosystem fragmentation 75
Figure 7.1. Historical loss of wetland area and percent loss in the United States, by state ...84
Figure F.I. Subunit 4 from Illinois case study overlaid with county boundaries 120
Figure F.2. Subunit 815 from Louisiana case study overlaid with
precipitation zone boundaries 121
Figure G.I. Subunit 4 from the Illinois case study overlaid with dot grid 122
FigureG.2. Subunit 4 from the Illinois case study overlaid with denser dot grid 122
Figure G.3. Electronic planimeter .' 123
Figure H.I. The Pearl River Basin with subunit boundaries 124
viii Synoptic Approach
-------
LIST OF TABLES
Table 1.1. Major steps in conducting a synoptic assessment 7
Table 2.1. Typology of cumulative impacts and cumulative effects 12
. Table2.2. Model of ecosystem response to increasing stress 13
Table 3.1. Steps in conducting a synoptic assessment . 16
Table 3.2. Examples of landscape descriptions that can be used in selecting indices 18
Table 3.3. Examples of technical questions that could be used to describe
the natural factors determining wetland function 19
Table 3.4. Typical relationships expected between agricultural impacts and wetland
degradation , 21
Table 3.5. Effect of wetland degradation on hydrologic functions and degree
of expected association 22
Table 3.6. Example of objectives and related questions for defining
landscape indicators for synoptic indices 24
Table 4.1. Landscape indicators for the Pearl River Basin case study 34
Table 4.2. Landscape indicators for the Louisiana case study 40
Table 4.3. Landscape indicators for the Washington case study 49
Table 4.4. Landscape indicators for the Illinois case study 56
Table 7.1. Functions that wetlands may perform 78
Table 7.2. Multiwatershed studies in which wetland area or related
variables significantly predicted streamflow conditions 79
Table 7.3. Relationship between increased wetland area and peak and base flows
by geographic region 80
Table 7.4. Examples of area-sensitive wetland bird species 82
Table 7.5. Stresses and associated impacts that can degrade wetlands 83
Table 7.6. Wetlands whose functions may be more sensitive or less resistant to
particular types of stress 85
Table 7.7. Generally expected effects of various stresses on hydrologic
functions of wetlands 86
Table 7.8. Generally expected effects of various stresses on water quality
functions of wetlands 87
Table 7.9. Generally expected effects of various stresses on habitat
functions of wetlands 88
Table B.I. Typical relationships expected between resource extraction impacts
and wetland degradation ; 110
Table B.2. Typical relationships .expected between urbanization impacts and
wetland degradation Ill
Table B.3. Typical relationships expected between water management impacts
and wetland degradation 112
Table C.I. Effect of wetland degradation on water quality functions and
degree of expected association 113
Table C.2. Effect of wetland degradation on habitat functions and degree of
expected association 113
Table D.I. Potential sources of mapped and tabular data for landscape
indicators of synoptic indices 114
Table F.I. Prorating county data to Subunit 4 for the Illinois case study 120
Synoptic Approach ix
-------
Table F.2.
Table G.I.
Table G.2.
Table G.3.
Table G.4.
Table H.I.
Table H2.
Table H3.
Prorating precipitation data to Subunit 815 for the Louisiana case study 121
Area estimates using dot grid method 122
Area estimates using denser dot grid 122
Partial listing of land-use areas for polygons within Subunit 4 of the
Dlinois case study , 123
Area estimates using GIS package 123
Calculation of weighting factors for counties overlapping
Washington Subunit 26 125
Conversion of county census data into joint county-subunit values 125
Weighted percent annual population change and agricultural change
for joint county-subunit areas , -. 126
x Synoptic Approach
-------
DISCLAIMER
The research described in this report has been funded
by the U.S. Environmental Protection Agency. This
document has beenpreparedattheEPAEnvironmental
Research Laboratory in CorvaJlis, Oregon, throughCon-
tract No. 68-C8-0006 to ManTech Environmental
Technology, Inc., and Contract No. 2B0245NTSA to
Word Design. This document has been subjected to the
Agency's peer and administrative review process and
approved for publication. Mention of trade names or '
commercial products does not constitute endorsement
or recommendation for use.
Two earlier reports (Abbruzzese et al. 1990a, 1990b)
were produced during the development of the synoptic
approach. Although these reports are useful in illus-
trating applications of the approach, the procedures
contained in this document supersede those earlier ver-
sions and should be used in conducting a synoptic
assessment. As the approach is further tested and
evaluated, it may become necessary to update this
method again. A mail-in form is provided in the back of
the report for those wanting future updates or related
products.
Synoptic Approach xt
-------
ACKNOWLEDGMENTS
Tltis report is the culmination of a five-year effort on
cumulative impact assessment and involved many in-
dividuals over the years. We would like to acknowledge
all those who collected information, analyzed data, or
otherwise helped with the case studies, including Jack
Davis, Robert Hippie, JoEllen Honea, Colleen Johnson,
Harbans Lai, Daren Moore, Frances Morris, Barbara
Peniston, and Susan Ross. Debby Sundbaum-Somers
produced Figures 2.1, 6.1, 6.2, and 6.4-6.6, and Linda
Maygarth prepared Figures 4.1 and 5.1. Kristina Miller
generated the computer graphics for Figures 1.2,3.1,3.2,
63,7.1, and G.3. Brenda Huntiey assisted in producing
the synoptic maps, Figures 42-4.19. Thanks to Robert
Crumb for allowinguse of his car toon, "A Short History
of America" (Figure 1.1).
Susan Christie assisted, in some of the initial editing of
this document. Special thanks to Myrna Branam, Janet
Converse, Kelly Davis, and the rest of those at Word
Design for making the extra effort and sacrifices with-
out complaint and for their high quality work. We
would especially like to thank and recognize our techni-
cal editors, Ann Hairston, for her energy and support,
and forshowingus the light at theend of the tunnel; and
Scott McCannell, for his creative contribution to the
document's layout, his attention to the details, his dedi-
cation to the project, and for bearing the brunt of it all.
We thank the individuals and agencies that supplied us
with data for the case studies, including the Louisiana
DeparbnentofEnvironmental Quality, the Washington
Department of Ecology, the Illinois Natural History
Survey, and the State Heritage Programs of Mississippi,
Louisiana, Washington and Illinois. We also thank EPA
Regions 4,5,6, and 10 for their cooperation and input.
Drs. Louis Iverson (Mnois Natural History Survey),
Joseph Larson (University of Massachusetts-Amherst),
Arnold O'Brien (University of Massachusetts-Lowell),
and Paul Risser (University of New Mexico) served as
official reviewers and provided many useful comments
that improved this document. We also want to thank all
those others who read through the draft manuscript
and provided useful feedback. Of course, final responsi-
bility for this document and its contents lies with the
authors.
We would especially like to thank our colleagues in the
Wetlands Division within the Office of Water for their
early and enthusiastic support of the synoptic approach.
John Maxted, now with the State of Delaware, was an
early believer in the method and provided additional
funding for the Louisiana and Illinois case'studies.
Doreen Robb has served as Wetlands Division liaison to
the Wetlands Research Program (WRP), coordinating
review and comments from the Office of Water and
providing us with her own useful feedback and vision.
We especially appreciate the support we have received
on our landscape concepts from John Meagher, Wet-
lands Division Director, and David Davis, Deputy
Director of the Office of Wetlands, Oceans and Water-
sheds.
Dr. Mary E. Kentula, acting WRP Program Leader, has
been kind enough to provide administrative cover, al-
lowing us to focus on completing this document. We
appreciate her patience during this period.
Richard Sumner, WRP regional liaison, deserves special
mention for his enthusiastic and tireless campaigning to
promote the synoptic approach among the Regions. He
has been responsible for acting as a "reality check" to
make sure this work would be useful to the program.
Also, he has been the motivating force behind our ideas
on the use of best professional judgment.
Finally, we would like to acknowledge the earlier work
and contributions of Dr. Eric M. Preston, AssistantChief
of the Watershed Branch and former WRP Program
Leader, who started it all. In spite of the growth and
evolution of the synoptic approach, it still includes
many of his original ideas. Although we miss his direct
involvement in this area, we look forward to his con-
tinuing influence through his work on biodiversity and
habitat.
We were deeply saddened to learn of the death of Dr. Allan Hirsch as we were completing this document.
Allan Hirsch was a visionary and leader in the field of environmental management, excelling in both science
and policy arenas. He served as the first Director of Fish and Wildlife Service's Office of Biological Services,
where his leadership established that office. He was subsequently Director of EPA's Office of Federal
Activities, where he led the EPA Wetlands Program during an important period in its growth and
maturation, and later was instrumental in establishing the Office of Wetlands Protection. Dr. Hirsch
contributed to the Wetlands Research Program and to this report both directly and indirectly through his
published ideas, by participating in an early WRP workshop on cumulative impacts, by serving as chairman
Of a Science Advisory Board review of WRP's five year research plan, and most recently by reviewing and
commenting on the draft of this report. This document is dedicated to Allan's vision and legacy.
xil Synoptic Approach
-------
PREFACE
A1987 study conducted by the Environmental Protec-
tion Agency (EPA) found that problems considered by
experts to pose the most serious threat to the environ-
ment were not those targeted most aggressively by
Congress or EPA (EPA 1987), A follow-up study by
EPA's Science Advisory Board suggested ways in which
EPA could reduce environmental risk, including a rec-
ommendation that EPA develop methods to improve
our ability to assess and compare environmental risks
(SAB 1990). . . •
Given this challenge, the Wetlands Research Program
(WRP) within EPA's Office of Research and Develop-'
ment has proposed a hierarchical, risk-based approach
to wetland assessment that.would allow evaluation at
three different scales (Leibowitz et al. 1992): a site-
specific scale, at which the function of individual
wetlands is assessed; an intraregional scale, at which
relative comparisons are made between wetlands within
the same watershed (or similar landscape subunit); and
an interregional scale, at which relative comparisons are
made between landscape subunits by considering the
aggregate characteristics of wetlands within those sub-
units. WRP's Wetland Function Project and
Characterization and Restoration Project are primarily
responsible for developing the site-specific and
intraregional approaches, respectively. The Landscape
Function Project is developing a method for making
assessments at the interregional scale. The latter,
known as the synoptic approach, is the subject of this
document.
WRP originally developed the synoptic approach so
that regulators could include information on cumula-
tive impacts of wetland loss during review of permits
for proposed discharges under Section 404 of the Clean
Water Act. However, the approach also fits into the
larger framework of risk assessment by providing man-
agers a broad view of wetlands within a landscape
context, and it can be used to assign priority to wetland
protection or replacement efforts as part of a compre-
hensive wetland management program. Because the
synoptic approach has not been tested in real manage-
ment applications, it should be viewed as a proposed,
rather than operational, methodology.
Synoptic Approach xiii
-------
GLOSSARY
Active pool
The materials (including biota) or energy within
a landscape that are actively being transferred
between component ecosystems as opposed to
materials or energy that are cycled or stored
within an individual ecosystem.
Barrier
An ecosystem that inhibits material movement
by excluding imports.
Best professional judgement
MaMng decisions based on personal experience
when better information is unavailable. Best
professional judgement is often used in day-to-
day management decisions.
Capacity
The maximum amount of a particular material
that an ecosystem can remove from the active
pool were the material not limiting; also referred
toas"assimilativecapatity." Couldbeusedmore
specifically, e.g., decomposition capacity. Also
refers to one of the components of the function
index.
Combination rule
A rule that specifies how two or more compo-
nents of a synoptic index will be mathematically
or logically combined.
Conduit
An ecosystem thatassists themovement of mate-
rials through diffa-ent parts of the landscape by
transferring imports between ecosystems with-
out altering the amount of material,
Conversion
Transformation of an ecosystem into a different
ecosystem type or land use (e.gv conversion of a
wetland for construction of a mall). Causes com-
plete functional loss of the original ecosystem
functions.
Creation
Building a wetland on an upland site, i.e., in a
location where wetlands did notpreviously exist
(compare with restoration).
Cumulative effects
The sum of all environmental effects resulting
from cumulative impacts.
Cumulative impacts
The sum of all individual impacts occurring over
time and space, including those of the foreseeable
future. ...••.• , .'.•;••
Degradation .
Partial functional loss caused by impacts that act
onan ecosystem withoutcausingconversion(e.g.,
reductions in productivity because of inputs of
pesticides through nonpoint source pollution).
Disturbance
The action that causes ecosystem stress; includes
actions caused bynaturalagents(e.g.,hurricanes)'
and human impacts..
Drainage area
See "Watershed." : •
Ecological function
An aggregate behavior that arises from one or
more physical, chemical, or biological processes.
Effect , ,
A physical, chemical, or biological change in an
ecosystem that results from an impact. The effect
can be an immediate consequence of the'impact
(direct effect) or it can be removed in time and
: space (indirect effect).
Excess capacity
The difference between a sink ecosystem's capacss
ity (the maximum amount df a material that the
ecosystem can remove if. the material is not limit-
ing) and the actual amount of material removed.
Excess capacity represents additional material
that could be removed and is a form of redun-
dancy that buffers an ecosystem from impacts.
Existing data
Data that were previously collected, usually for
purposesunrelatedtothecurrentobjective. Exist-
ing data must be used when time or money
preclude the collection of new data. Also referred
to as "available data."
xiv Synoptic Approach
-------
Forcing functions
Materials and energy that drive an ecosystem.
These materials and energy originate outside the
ecosystem boundary, but over the long run drive
most ecosystem processes. In the broadest sense,
forcing functions can be natural or anthropogenic
in origin. Also referred to as "driving factors."
Function
One of the four synoptic indices; refers to the total
amount of some function provided, by one or
moreecosystemswithinalandscapewithoutcon-
sideration of benefits. Capacity and input are
components of function.
Functional loss
One of the four synoptic indices; refers to the
complete or partial loss of one or more ecological
functions as a result of impacts.
Fragmentation
The break-up of an extensive ecosystem into a
number of smaller patches.
Habitat function
Ecological processes that, when taken together,
provide support (food, shelter, breeding sites,
etc.) for different species.
Home range
The area around an organism's home typically
used for feeding.
Hydrologic function
Ecological processes that, when taken together,
somehowmoderatehydrology;e.g.,reduceflood
peaks, recharge aquifers, etc.
Impact
A human-generated action or activity that either
by design or by oversight alters the characteristics
of one or more ecosystems.
Index
See "Synoptic Index."
Indicator
See "Landscape Indicator."
Input
The total amount of material imported into sink
ecosystems from one or more sources. Also re-
ferred to as "landscape input." Can eilso refer to
one of the components of the function index.
Landscape ecology
The study of interactions between ecosystems.
Landscape indicator
The actual data or measurements used to estimate
a synoptic index; in the synoptic approach, a
landscape indicator is usually a first-order ap-
proximation based on existing data.
Landscape subunit
The basic subdivision of a landscape for which
synoptic indices are calculated; a synoptic assess-
ment provides a comparison of landscape
subunits. Landscape subunits could be defined
environmentally (e.g.,watershedsorecoregions),
politically(e.g.,countiesorconservationdistricts),
or by other criteria.
Landscape
"A heterogeneous land area composed of a dus-
ter of interacting ecosystems that is repeated in
similar form throughout" (Forman and Godron
1986). A landscape is normally defined by geo-
morphology or climate. The study boundary for
a synoptic assessment need not include the entire
landscape.
Landscape unit
The specific landscape or portion of a landscape
for which a synoptic assessment is conducted.
The landscape unit can be larger than the study
unit because it can contain forcing functions that
are outside of the study unit.
Metapopulation
Thecombined population of all ecosystempatches
that are connected by movement of individuals.
The metapopulation contributes to the redun-
dancy of a landscape.
Patch
An irregularly shaped ecosystem embedded
within a larger "matrix" ecosystem.
Patch distance
The distance between two patches or, more gen-
erally, theaverage distance between patches in an
area.
Process
A basic physical, chemical, or biological transfor-
mation within an ecosystem which, in aggregate,
defines ecosystem functions.
Project-specific application
The use of a synoptic assessment to provide a
landscape context for a subunit that has been
preselected based on independent criteria (com-
pare with regional comparison).
Synoptic Approach xv
-------
Redundancy
The ability of an ecosystem to perform functions
in more than one way, or an excess capacity or
structure beyond what is normally needed. Re-
dundancy buffers an ecosystem from impacts.
Regiotml comparison
The use of a synoptic assessment to determine
which subunits \vithin a region best meet Some
specific criteria (compare with project-specific
application).
Replacement potential
One of the four synoptic indices; refers to the
degree to which a wetland and its valued func-
tions can be replaced by creation or restoration.
Specifically refers to thelandscape characteristics
as opposed to on-site characteristics that control
replacement.
Resilience
The ability of an ecosystem to return to
predisturbance levels of function.
Resistance
The ability of an ecosystem to resist loss of func-
tion as a result of a disturbance.
Response
The long-term physical, chemical, and biological
changes that result indirectly from stress.
Restoration
Building a wetiand on a non-upland site in a
location where a wetland previously existed
(compare with creation).
Risk assessment
An evaluation of environmental risks associated
with human actions.
Section 404
The portion of the Clean Water Act that specifies
that a permit must be obtained to discharge
dredged or fill materials into waters of the United
States.
Sink ecosystem
An ecosystem that causes a net decrease in the
totelamountofamaterialbeingtransferred within
the landscape; this occurs if exports are less than
imports (compare with source ecosystem). The
status of an ecosystem as a source or sink depends
upon the particular material.
Source ecosystem
An ecosystem that causes a net increase in the
totalamountof amaterialbeing transferred within
the landscape; this occurs if exports are greater
thanimports (compare withsinkecosystem). The
status of an ecosystem as a source or sink depends
upon the particular material.
Stress
The immediate physical, chemical, and biological
changes that result from a disturbance.
Stressor
Same as a disturbance.
Structure
The collection of an ecosystem's physical, chemi-
cal, and biological characteristics. Structure is
built from energy and raw materials.
Study unit
The actual geographic boundary of a synoptic
assessment. May-be based on political (e.g., a
state) or environmental (e.g., a geological prov-
ince) criteria.
Synoptic approach
A five step approach to assessing cumulative
impacts or environmental risk, as described in
this document, that provides a broad overview of
environmental and landscape factors.
Synoptic assessment
The process of following the five steps of the
synoptic approach in order to produce a set of
maps, data, and reports that can be used to assess'
cumulative impacts or environmental risk.
Synoptic index
A landscape variable that is used in a synoptic
assessment as a basis for comparing landscape
subunits. There are four general synoptic indices
(function, value, functional loss, and replacement
potential); in an actual assessment, a specific in-
dex would be defined for one or more of the
general indices.
Systems ecology
The study of ecological systems (ecosystems),
including their response to stress.
Travel distance
The maximum distance an organism can travel in
order to reach suitable habitat. An organism
cannot travel to a different patch if the patch
distance is greater than the travel distance.
xvi Synoptic Approach
-------
Value
One of the four synoptic indices; refers to the
benefits obtained by individuals or society from
an ecological function. Could include benefits
received indirectly, i.e., when the function acts on
something of value (e.g., flood reduction is valu-
able because it reduces loss of Ijfe and loss of
valued property).
Water quality function
Ecological processes that, when taken together,
improve water quality; e.g., reducepollutant con-
centrations, contribute to nutrient cycling, etc.
Watershed
A natural drainage unit defined by topographic
high points within which the only input of water
is precipitation. Used analogously with drainage
area, although the latter is more properly defined
relative to some specific point; e.g., the drainage
area for some particular point on a river includes
all the area that collects precipitation that is ulti-
mately routed through that point on the river.
Wetland
Any ecosystem characterized by the presence of
water; unique soils compared with adjacent up-
lands; the presence of vegetation adapted to wet
conditions; and the absence of flood-intolerant
vegetation (Mitsch and Gosselink 1986). In a
more limited sense, used to specifically refer to
those wetlands that are included under Section
404 of the Clean Water Act ("jurisdictional wet-
lands").
Synoptic Approach xvii
-------
-------
The Synoptic
Approach
I*
-------
-------
T
Chapter 1
Introduction
his report provides resource managers and
technical staff with an approach for evaluating
the cumulative environmental effects of indi-
vidual human impacts on the environment, particularly
with respect to wetlands. This document is intended to
give the reader a general understanding of cumulative
impacts and to describe how a synoptic assessment is
produced. Although specifically designed for use in
wetland permit evaluation under the Clean Water Act
(CWA), this method can be applied to cumulative im-
pact assessment in general1. A second objective of this
report is to encourage resource managers responsible
.for wetland protection to consider and view wetlands
within a landscape context.
The synoptic approach, so named because it provides a
broad overview of the environment, was developed
specifically for cases in which time, resources, and infor-
mation are limited. The method is not intended to
provide a precise, quantitative assessment of cumula-
tive impacts within an area, nor can it be us°d to assess
the cumulative effects of specific impacts. Rather, it
provides a relative rating of cumulative impacts between
areas. The approach is intended to be easily applied so
it can augment the best professional judgment used
;daily by wetland managers and regulators.
•This report is divided into two sections. Section 1
describes the method and illustrates its use. It defines
cumulative impacts, reviews the regulatory basis for
cumulative impact assessment, and introduces the Wet-
land Research Program's (WRFs) synoptic approach
(Chapter 1). It also provides the ecological basis for the
synoptic indices (Chapter 2), describes in detail how to
conduct a synoptic assessment (Chapter 3), illustrates
the method's use and several possible applications
through four case studies (Chapter 4), and contains a
summary that discusses future directions (Chapter 5).
Section 2 contains detailed background material for
readers interested in additional information. It includes
a discussion of environmental stress (Chapter 6) and a
review of wetland functions and the effects of impacts
on these functions (Chapter 7).
Cumulative Impacts
Traditionally, impact assessment has evaluated the likely
effects of a single action on the environment. There has
been concern, however, that numerous activities con-
sidered insignificant by themselves could, when taken
together, cause significant degradation and damage to
1 Because of its general nature, the synoptic approach is not
limited to legally defined (i.e., "jurisdictional") wetlands. We
therefore define wetlands in the broadest sense, as those
ecosystems that are characterized by: the presence of water;
unique soils, compared to adjacent uplands; the presence of
vegetation adapted to wet conditions; and an absence of flood-
intolerant vegetation (Mitsch and Gosselink 1986).
Introduction 3
-------
A KBKT KCtOM Of JWOUOI—
«e«w«
Fiflurol.1, "AShort History of America," by thecartoonistR. Crumb, graphically illustratescumulative impacts overtime. Although
nono of tho individual impacts would have been expected to significantly damage the environment, the cumulative result is a major
loss of environmental functions (from CoEvolution Quarterly No. 23, Fall 1979, ® R. Crumb 1992).
the environment (Kahn 1966; Odum 1982). An analogy
provided by Ehrlich and Ehrlich (1981) illustrates this
concept. If a single rivet pops out of a jef s wing, no
serious threat exists, because no one rivet contributes
significantly to the plane's airworthiness. But if enough
rivets are lost the integrity of the plane's structure
gradually weakens until a failure occurs. In this anal-
ogy, the cumulative effect of the individually minor
impacts would be catastrophic. In the same manner, a
conventional impact analysis might conclude that a
single discharge into a wetland would not amount to
significant impact and would therefore be acceptable.
However, an assessment that ignores the combined
effect of these cumulative impacts could seriously un-
derestimate the extent of environmental damage (Figure
1.1), thereby frustrating policy and management goals
(Invin and Rodes 1992).
A major difference between traditional impact assess-
ment and cumulative impact assessment is that the
former is performed with respect to the proposed distur-
bance. Cumulative impact assessment is performed
with respect to valued environmental functions
(Beanlands and Duinker 1983; Preston and Bedford
1988). Cumulative impact assessment must therefore
take a holistic view of the environment. An excellent
overview of cumulative impacts and wetlands is given
in a special volume edited by Bedford and Preston
(1988a) that includes a review; of regulatory issues
and the status of scientific understanding of cumula-
tive impacts with respect to hydrology, water quality,
and wildlife. This volume is highly recommended for
readers interested in a more in-depth treatment of the
subject.
4 Synoptic Approach
-------
Regulatory Mandate
Regulations prepared by the Council on Environmental
Quality under the National Environmental Polity Act
require environmental impact statements to "anticipate
a cumulatively significant impact on the environment
from Federal action" 2 (38 CFR Sect. 1500.6). A cumula-
tive impact is defined as: .
"...the impact on the environment which
results from the incremental impact of the
action when added to other past, present,
and reasonably foreseeable future actions re-
gardless of what agency (Federal or
non-Federal) or person undertakes such other
actions. Cumulative impacts can result from
individually minor but collectively signifi-
cant actions taking place over a period of
time." (40 CFR Sect. 1508.7)
Under CWA Section 404, permits must be obtained to
discharge dredged or fill material into waters of the
United States, which include most wetlands. The CWA
Section 404(b)(l) guidelines contain the criteria that are
used in evaluating a permit for a proposed discharge.
These regulations, promulgated by the Environmental
Protection Agency (EPA) in conjunction with the Army
Corps of Engineers, call for consideration of cumulative
impacts (40 CFR 230.11):
"[1] Cumulative impacts are the changes in
an aquatic ecosystem that are attributable to
the collective effect of a number of individual
discharges of dredged or fill material. Al-
though the impact of a particular discharge
may constitute a minor change in itself, the
2 "Federal action" has been interpreted to include any action
regulated by the federal government.
Introduction 5
-------
cumulative effect of numerous such piece-
meal changes can result in a major
impairment of the water resources and inter-
fere with the productivity and water quality
of existing aquatic ecosystems.
[2] Cumulative effects attributable to the dis-
charge of dredged or fill material in waters of
the United States should be predicted to the
extent reasonable and practical. The permit-
ting authority shall collect information and
solicit information from other sources about
the cumulative impacts on the aquatic eco-
system. This informationshallbedocumented
and considered during the decision-making
processconcerning theevaluationof individual
permit applications, the issuance of a Gen-
eral Permit, and monitoring and enforcement
of existing permits."
Regulatory Context
If a proposed discharge involves a major or controver-
sial action, permit evaluation requires extensive
information and may include collection of field data
and even an Environmental Impact Statement (Hirsch
1988). However, most of the permit requests received
each year are for minor, routine actions. Because of the
large number of requests and the limited amount of
time and staff, a simpler environmental assessment
must be conducted, based upon existing information.
There are a number of methods for evaluating cumula-
tive impacts (Appendix A); however, none of these are
practical within the regulatory constraints of Section
404. Although the concept of cumulative impacts is
intuitive enough to have influenced the guidelines
for permit evaluation, the lack of an easily applied
method makes it difficult to consider cumulative
impacts as part of routine permit decisions (Preston and
Bedford 1988). Therefore, regulators must often rely on
best professional judgment in order to comply with the
404(b)(D guidelines. A major goal of EPA's Wetlands
Research Program has been to provide permit review-
ers with an easily applied technical approach for
assessing cumulative impacts.
Our current understanding of the environment and our
lack of data make it impossible to provide a precise,
quantitative evaluation of the effects that cumulative
wetland losses will have in a specific region or to
predicthowadditional wetland losses will add to those
effects. However, our understanding of ecological pro-
cesses in general, and wetlands in particular, should be
sufficient for us to make qualitative comparisons of
these effects between different areas. For example, we
may not be able to say that the cumulative loss of 100
hectares of wetland within a particular area caused a
10% reduction in water quality; however, we should be
able to say that a 100 hectare loss of wetland in area "A"
will more likely cause a reduction in water quality than
a similar loss in area "B". The synoptic approach is a
response to Hirsch's (1988) call for "simple protocols,
analytical procedures, or logic flows, and some do's and
don'ts or rules of thumb" that can augment the site-
specific permit review process and improve in best
professional judgment (Figure 1.2). Managers can use
this approach to evaluate cumulative impacts until more
rigorous research provides better alternatives.
The Synoptic Approach
The synoptic approach is an inexpensive, rapid assess-
ment method that can assist managers and regulators in
evaluating cumulative impacts within the regulatory
constraints of tight schedules and budgets. Although
research on the loss of wetland function is far from
complete, the synoptic approach can support develop-
ment of the best possible management strategies based
on current knowledge.
Using the synoptic approach, wetland managers will be
able to produce regional or statewide maps^ that rank
portions of the landscape according to synoptic indices.
These maps and indices will enable permit reviewers to
consider the landscape condition of the area in which a
particular permit is proposed compared with other
areas within their jurisdiction. By providing the envi-
ronmental context in which wetlands occur, the maps
also will allow wetland managers to examine wetland
issues more comprehensively. Further, because the
assessment is prepared at the same time for an entire
state or region and not on a permit-by-permit basis,
using this method will save time and money.
The synoptic approach consists of five steps (Table 1.1).
Two major steps are definition of synoptic indices and
selection of landscape indicators. The synoptic indices
represent the actual functions and values within the
particular environmental setting of interest. The land-
scape indicators are the actual data used to represent
these indices. Choosing indicators often requires mak-
ing simplifying assumptions because of limited
information, time, and money. For example, agricul-
tural area as measured from a land-use map could be a
landscape indicator for agricultural nonpoint source
nutrient loading, which would be the synoptic index for
that particular management concern. The synoptic
index and landscape indicator are defined separately to
3 The end product of a synoptic assessment need not be a set of
maps, but could consist solely of tabular data summaries.
However, we believe that presentation as maps is more
appropriate for the intended use, and gives a "big picture"
overview that tables cannot provide. •'
6 Synoptic Approach
-------
keep them distinct, so we remember that agricultural
area is not the management concern; it is only useful to
the extent to which it represents nonpoint source
nutrient loading.
The synoptic approach is flexible enough to cover a
broad spectrum of management objectives and con-
straints. The specific synoptic indices and landscape
indicators used in an application depend on the particu-
lar goals and constraints of the assessment. They also
depend on the actual environmental setting. However,
this handbook does not provide a specific, detailed procedure
for choosing thesynopticindices, nor does it supply a scientifi-
cally-tested list of landscape indicators having known
confidence limits. This is not possible, given our current
state of knowledge and the strong dependency of the
synoptic indices and landscape indicators on the par-
ticulars of the assessment. Instead, the approach relies
on the assessment team to make decisions, since they are
best qualified to know their particular needs and con-
straints. The synoptic approach provides the user
with an ecologically-based framework in which local
information and best professional judgment can be
combined to address cumulative impacts and other
landscape issues.
The synoptic approach is not a fixed procedure that
always uses the same data sources and provides a
standard end product. Rather, a synoptic assessment is
a creative process that requires the manager to weigh
the need for precision—as determined by management
objectives—againsttheconstraints: limited time,money,
and information. An initial synoptic assessment could
be conducted using the best available information
and then updated as better data become available.
(a)
(c)
50
Accuracy (percent)
—T-
100
Figure 1.2. Improving best professional judgment (BPJ). "a"
represents the hypothesized accuracy of BPJ under current
conditions; most professionals probably give correct answers
more than 50% of the time, arid the most experienced
professionals may be fairly accurate. However, the least
experienced professionals may do worse than the flip of a coin,
i.e., their answers may be wrong more often than right. A
precise, quantitative assessment would greatly improve the
accuracy of BPJ ("c"> and reduce variability. However, such an
assessment could be impractical within a regulatory context.
The synopticapproachisa compromise thatcan be implemented
within regulatory constraints and yet still improve the accuracy
of BPJ ("b").
Table 1.1. Major steps in conducting a synoptic
assessment.
Step 1.
Step 2.
Step 3.
Step 4.
Step 5.
Define Goals and Criteria
Define Synoptic Indices
Select Landscape Indicators
Conduct Assessment
Prepare Synoptic Reports
Introduction 7
-------
-------
Chapter 2
Ecological
Basis for the
Synoptic
Indices
The synoptic approach provides a framework for
making comparisons between landscape sub-
units1 so cumulative impacts can be considered
in management decisions. Comparisons are made by
evaluating one or more landscape variables, or synoptic
indices, for each subunit. Defining the proper synoptic
indices for a particular assessment is a critical step and
depends on the environmental setting and the specific
goals of the assessment. In this chapter, we provide an
overview and rationale for the synoptic indices, draw-
ing on concepts from three disciplines: systems ecology,
of the study of ecological systems (ecosystems), includ-
ing their response to stress; landscape ecology, which
examines the interactions between ecosystems; and risk
assessment, which evaluates environmental risks
associated with human actions.
Rationale for a Landscape Approach
The purpose of a cumulative impact assessment is to
evaluate the cumulative environmental response to vari-
ous impacts. Because no standard usage exists for the
term, we define impact as a human-generated action
or activity that either by design or by oversight alters
the characteristics of one or more ecosystems; cumu-
lative impacts are the sum of all individual impacts
occurring over time and space, including those of the
foreseeable future. We define effects as the physical,
chemical, and biological changes that result from an
impact, including direct and indirect changes that can
be removed in time and space. Cumulative effects,
then, are the sum of all these changes resulting from
cumulative impacts.
In conducting a cumulative impact assessment, we are
particularly concerned with the loss of valued func-
tions. These ecologicalfunctions are aggregate behaviors
that arise from the many physical, chemical, and bio-
logical processes that take place in the environment. For
example, whether a wetland reduces flood peaks de-
pends on the processes that determine the wetland's
hydrologic budget, e.g., precipitation, evapotranspira-
tion, surface and groundwater inflows and outflows,
and tidal input (Mitsch and Gosselink 1986).
Because an impact can affect more than one ecosystem
and because an ecosystem can be affected by activities
outside its boundaries, an assessment of cumulative
impacts cannot be limited to a single ecosystem. Also,
many ecological functions valued by society depend on
interactions between ecosystems; they are more prop-
erly viewed as landscape functions, rather than
ecosystem functions. For example, the water quality of
a river is not determined by any one ecosystem but by
Examples of possible subunits are counties, watersheds, and
ecoregions; selection of subunits as part of a synoptic assessment
is discussed in Chapter 3.
The Synoptic Indices 9
-------
the aggregate effect and interaction of all ecosystems
within its drainage area. The landscape is an appropri-
ate unit for considering cumulative impacts, especially
since landscape factors partially determine an
ecosystem's response to cumulative impacts. For ex-
ample, the survival of organisms following disturbance
can depend on landscape characteristics such as corri-
dor quality (Henein and Merriam 1990) and the degree
of habitat fragmentation (Merriam and Wegner 1992;
Stacey and Taper 1992).
Synoplic indices allow us to evaluate overall wetland
condition for a particular landscape subunit through
comparison with other suburtits. Because the approach
is not intended to provide a detailed landscape assess-
ment, we must simplify and generalize our view of the
landscape to ensure that relevant factors are included.
The synoptic indices are therefore based on a simple
model that describes ecosystem functions within the
landscape and includes the effect of impacts on these
functions. Because the focus of an assessment is valued
ecological functions, concepts of risk assessment are
also incorporated.
Landscape Model of Ecosystem
Function
Forman and Godron (1986) have defined a landscape as
"a heterogeneous land area composed of a cluster of
interacting ecosystems that is repeated in similar form
throughout." Wetlands, forests, lakes, and streams are
examples of suchecosys terns. Interactions occur through
transfers of energy and material—including nutrients,
minerals, and organisms — between ecosystems. A
landscape can be viewed as a portion of the environ-
ment composed of ecosystems within which materials
and energy are transferred as a result of various ecologi-
cal processes. To further simplify this view, we will
consider these ecosj'Stems only as they affect the
transfer of materials within and through the landscape.
At any time, a landscape contains a pool of materials 2
and energy being transferred between component eco-
systems (as opposed to being cycled or stored urithin
individual ecosystems). This dynamic state can be
described by the aggregate flow of these materials
\vithin and through the landscape; it also includes the
processes that drive or are controlled by these flows.
Landscape functionsresult from these interactions, as in
the earlier discussion of the effect of drainage area on
river water quality. Ecosystems contribute to landscape
functions by affecting (1) the quantity of transferred
material, i.e., either increasing or decreasing the active
pool; (2) the quality of the material, i.e., transforming
it into different forms; or (3) the timing of material
transfers, e.g., introducing a temporal lag in transfers or
altering transfer rates.
From the simplest perspective, each component ecosys-
tem can be considered to function as either a source, or, a
sink for a given material. An ecosystem is a source if it
'causes a net increase in the total amount of material
being transferred within the landscape (i.e., exports
from the ecosystem are greater than imports into it); it is
considered a sink if it causes a net reduction in the
material flux 3. We define these terms in the broadest
sense, without regard to the specific processes respon-
sible for the functions. For example, an ecosystem could
function as a sink through biochemical conversion, fil-
tration (e.g., removal of suspended materials from water
as it passes through clays), or trapping (e.g., settling out
of parriculates from water). In the case of biological
materials, an ecosystem would be a sink if emigration
were less than immigration, which could occur if the
death rate exceeded the birth rate (MacArthur and
Wilson 1967; Pulliam 1988).
Because our definition of a sink is independent of
causative processes, an ecosystem that induces a net
transfer of materials to on-site storage would also be
considered a sink since this would lead to a net reduc-
tion in the pool of materials. Conversely, an ecosystem
that removes material from storage and returns it to
the pool acts as a source. For example, a riparian
forest acts as a sink where stream velocities are low
and sediment storage increases through deposition;
however, it acts as a source if high current velocities
cause bank erosion, thereby removing sediment from
storage (Pinay et al. 1992).
A landscape model that describes an ecosystem as ei-
ther a source or a sink can easily account for the effect
ecosystems can have on the quantity of transferred
materials. When the status of the ecosystem as source or
sink is dynamic, the model can also account for qualita-
tive and timing effects. For example, an ecosystem that
converts nitrate to molecular nitrogen through deni-
trification (a qualitative effect) would be described as a
sink for nitrate and a source for molecular nitrogen. An
ecosystem that stores water below ground during spring
runoff functions as a sink at that time of year, then as a
source during summer and fall, when it slowly releases
the water from storage.
The ability and degree to which an ecosystem functions
as a source or a sink is controlled by on-site conditions,
such as local hydrology and geomorphology, soil and
vegetative characteristics, nutrient availability, and
population densities. However, an ecosystem with the
potential to reduce material flows could not function as
a sink if the particular material was unavailable. In
2 We define materials broadly to include biotic and abiotic
materials.
3 An ecosystem could be neither a source nor a sink if exports are
equal to imports. Such an ecosystem would be neutral with
respect to changes in the magnitude of landscape flows. However,
such an ecosystemcould still affect the distribution of materials;
see Chapter 6.
10 Synoptic Approach
-------
other words, an ecosystem can reduce the pool of active
landscape materials only if it is connected to at least one
source. Thus the ability of an ecosystem to function as a
sink depends on two factors: the assimilative capacity,
which is the amount of material the ecosystem could
remove, assuming it was available; and landscape in-
put, which is the amount of mated?' imported into the
ecosystem from source ecosystems'5. WMe capacity is
controlled by characteristics within the ecosystem, land-
scape input is determined by interactions between
ecosystems and depends on (1) the magnitude of the
various sources, (2) where these sources are located
relative to the target ecosystem, (3) the transport mecha-
nism of the particular material (e.g., passive diffusion,
wind-bomedispersion, gravity flow,ormigratoiymove-
ment in animals), and (4) the occurrence of any sinks
along the transfer pathway.
Phosphorus retention by a wetland is one example of
how capacity and landscape input control sink func-
tions. A wetland's capacity to retainphosphorus depends
on factors such as plant uptake; the concentrations of
minerals that precipitate phosphorus (e.g., ferric iron
and aluminum); soil pH, which affects phosphorus
solubility; and adsorption to soil constituents such as
clays and organic matter (Mitsch and Gosselink 1986).
The landscape input of phosphorus into the wetland
depends on the types of neighboring ecosystems, land-
use practices outside the wetland (e.g., fertilizer
application rates), and landscape characteristics that
control sedimentation rates into the wetland, such as
slope.
According to the model we have been describing, the
landscape is a collection of source and sink ecosystems
embedded within a matrix of neutral ecosystems. Al-
though this is somewhat simplistic and ignores actual
processes, simplifying the overwhelming complexity of
a real landscape is necessary if overall function is to
become understandable. This model allows us to visu-
alize the landscape as a dynamic network of interacting
ecosystems, each of which can affect the quantity, qual-
ity, and timing of the materials transferred within the
landscape. It also provides a framework that allows us
to consider the effect of impacts on landscape function.
Effect of Impacts on Landscape Function
It is important to differentiate between an activity (the
impact) and the ecological response to it (the effect),
because many environmental regulations target activi-
ties (e.g., discharge of dredge and fill materials under
CWA Section 404). Numerous ecosystem characteris-
tics could be altered by an impact. Lugo (1978)
developed a generic model that described five ways
in which an ecosystem could be stressed. We further
aggregate these to define three general types of im-
pact based on the type of characteristic being altered
(Figure 2.1):
4 As-defined here, the capacity is the net amount of material that
canbe removed, after accounting for removal of on-site material.
If gross capacity is preferred, landscape input would have to
include on-site production.
Figure 2.1 Generic model of ecosystem impacts. An impact can affect external driving factors (forcing functions) before they cross
the ecosystem boundary, e.g., hydrologic diversion (a); an impact can affect ecosystem processes, e.g., discharge of industrial
pollutants that alter productivity (b); and an impact can alter ecosystem structure, e.g., harvesting wildlife through hunting
-------
• Changes in forcing functions—Ecosystems are ulti-
mately driven by material and energy flows that
originate outside their boundaries. These driving
factors are referred: to as forcing functions. For ex-
ample, sunlight is the ultimate forcing function for
most ecosystems, and hydrologic input (in the form
of surface water, groundwater, or tides) is an impor-
tant driving factor for wetlands. Forcing functions
can be diverted or reduced in magnitude, or the
timing can be changed. New forcing functions to
which the system is not adapted can be introduced,
or the magnitude of an existing factor can be
increased beyond its natural range.
• Changes in ecosystem process — Processes such as
production or respiration can be stimulated or de-
pressed, and material or energy distribution within
the ecosystem can be altered.
• Changes in structure—Structure, built from energy
and raw materials, is the collection of an ecosystem's
physical, chemical, andbiological characteristics. Bio-
logical examples of ecosystem structure include the
various organisms, their complex behaviors, trophic
relationships between organisms, seed banks that
maintain biodiversity, and even dead matter. Physi-
calstructureincludesconcentrationsofrawmaterials,
such as lake water. Examples of structural impacts
include harvesting of organisms by hunting or farm-
ing, introduction of domestic species not naturally
present, reductions in water level through drainage,
and destruction of soil structure by compaction.
In general, ecosystems affected by stress exhibit the
following properties (Odum 1985): (1) internal material
cycling is reduced, (2) the community reverts to earlier
successional stages, (3) efficiency of resource use
declines, and (4) parasitism increases. In stressed eco-
systems, native speciescanbe replaced by opportunistic
species; this is especially significant in wetlands, where
invasion by weedy species such as purple loosestrife
can alter community structure (Wilcox 1989).
Not only does the environment respond to individual
impacts, it also responds to them cumulatively. Ex-
amples of cumulative impacts and cumulative effects
appear in Table 2.1. Bormann (1987) described seven
stages of ecosystem stress, ranging from insignificant
effects at low levels of pollution to complete ecosystem
collapse under continued, severe pollution (Table 2.2).
Although based on air pollution, these seven stages
could represent a general model of ecosystem response
to cumulative impacts. From a landscape perspective,
the ultimate consequence of these changes is a loss of
ecosystem function. This translates into a change in the
ability of an ecosystem to act as a source or a sink either
quantitatively (an increase or a decrease in the existing
level of function) or qualitatively (e.g., a change from
source to sink or vice versa).
The boundaries for cumulative impacts and cumula-
tive effects need not coincide. Some cumulative effects
could occur outside a cumulative impact boundary;
conversely, cumulative effects within an area could
partially result from impacts occurring outside the
boundary. If the objective is to determine the cumula-
tive effects within a specific area, a larger boundary
must be defined that includes impacts to external
forcing functions.
Synoptic Indices
Based on these principles, we define four synoptic indi-
ces for assessing cumulative impacts and relative risk:
function, value, functional loss, and replacement poten-
tial. These indices are landscape-level measures, so
each is evaluated for an entire landscape subunit, rather
than for an individual component ecosystem. Although
the indices are generic and could be applied to any
ecosystem type, we discuss each as it applies specifically
to wetlands. The hierarchical evaluation of these indi-
ces as part of a risk assessment can be found in
Leibowitzetal.(1992).
Table 2.1. Typology of cumulative impacts and cumulative effects (after Beanlands et al. 1986).
Cumulative Impact
Description
Time-crowded Perturbations
Space-crowded Perturbations
Disturbances that are so frequent in time that the ecosystem does not have the chance to
recover between disturbances ..',.
Disturbances that are so close in space that their effects overlap J'c
Cumulative Effect
Description
Synergisms
Indirect Effects
Nibbling
Interaction of different types of disturbance to produce a response that is qualitatively and
quantitatively different than the separate effects combined
Effects that are produced through a complex pathway and that are removed in time and/
or distance from the initial disturbance ,
Simple additive effects that result from cumulative impacts ' ;;
12 Synoptic Approach
-------
Table 2.2. Model of ecosystem response to increasing stress (adapted from Bormann 1987).
Stress Level Ecosystem Response
Insignificant
Low levels
Levels inimical to some species
Increased stress
Severe levels
Continued severe stress
Insignificant
Relatively unaffected; ecosystem may function as a sink
Changes in competitive ability of sensitive species; selection of resistant genotypes' little
effect on biotic regulation
Resistant species substitute for sensitive ones; some niches opened for lack of
substitutes; biotic regulation may be disrupted, but may return as system becomes
wholly populated by resistant species
Large plants, trees, shrubs of all species die off; ecosystem converted to open-small
shrubs, weedy herb system; biotic regulation severely diminished; increased runoff,
erosion, nutrient loss
Ecosystem collapse; completely degraded ecosystem; ecosystem seeks lower level of
stability with much less control over energy flow and little biotic regulation
Function
Wetlands are capable of performing various functions
as a result of physical, chemical, and biological pro-
cesses. These functions can be divided into three general
categories:
• Habitat functions—Providing support for wetland-
dependent species, including food, shelter, and
breeding sites;
• Water quality functions — Water quality improve-
ment, nutrient cycling and supply; and
• Hydrologic functions—Flood attenuation and mod-
eration of hydrologic flow.
The function index refers to the total amount of a
particular function a wetland provides within a land-
scape subunit without consideration of benefits. The index
is the rate at which material or energy is added to or
removed from the active landscape pool. In the case of
a sink function, the index is separated into two compo-
nents 5: capacity, which is the maximum net amount of
material that could be removed by a subunif s wet-
lands if the supply of material were unlimited; and
landscape input, or the total amount of the material
imported into wetlands from contributing sources.
Value
Environmental regulations such as the Clean Water Act
consider both ecosystem functions and their impact on
public welfare (Preston and Bedford 1988; Westman
1985); thus we identified valued ecological functions as
the target of a cumulative impact assessment. Wetlands
can be valued for the tangible benefits they provide,
such as clean water or hunting, or for intangible benefits
such as aesthetics. However, values are highly subjec-
tive, and a wetland characteristic valued by one
individual could be perceived as a liability to another.
Even when the wetland provides a service that benefits
the individual (such as improved water quality), the
service could be undervalued because of poor informa-
tion or conflicting goals.
Whether a particular ecological function is considered
valuable is not a technical issue, but must be determined
by the policy maker initiating the synoptic assessment.
Such a decision might be based on law or on agency
mandate. For example, by enacting the Endangered
Species Act, Congress has determined that endangered
species are valuable; similarly, an agency mandated
with protection of drinking water would value func-
tions that improve water quality. Policy makers could
determine values through public input, inferagency con-
sensus or both. Gosselink and Lee discuss policy
considerations and the importance of goal-setting as
part of a cumulative impact assessment (Gosselink and
Lee 1989; Lee and Gosselink 1988). A framework for
including the effects of cumulative impacts on program-
matic decisions is given in Irwin and Rodes (1992).
Once it is decided that a particular function is important,
the value index can be used to determine the relative
value of that function within each landscape subunit.
This ranking depends on two factors. First, value is
related to overall level of function, although this need
not be a linear relationship (e.g., there could be dimin-
ishing returns at higher functional levels). Second, a
function may be considered valuable not because of its
inherent value, but because it acts upon something else
valued by society. In such instances, the overall value
also depends on the occurrence of this valued object. For
example, flood reduction has no inherent value; it is
5 These two sub-components are similar to the terms
"effectiveness" and "opportunity" used in the Wetland
Evaluation Technique (Adamus 1983). However, the synoptic
terms and their meaning are derived from the previously
described landscape model.
The Synoptic Indices 13
-------
valued because itreduces property damage and human
injuries and deaths. Dams are not necessarily built
where the largest floods occur, but where floods threaten
human populations, valuableproperty, or both. Valued
objects can also include plants and animals; the value of
wetlands for habitat could increase with the number of
rare and endangered species supported by that habitat.
Thisindex can also includefuture values by considering
the future benefits of these functions. Finally, we note
that this index does not represent economic value, since
it does not consider market factors, etc. Instead, it
provides an estimate of the value provided by a func-
tion within a landscape subunit, relative to other
subunits.
Functional Loss
Functional loss represents the cumulative effects on a
particular valued function that have occurred within a
subunit. Functional loss caused by changes in forcing
functions/processes, and structure should all be consid-
ered. Theindex should includecompletelossof function
from cotwersion, where the ecosystem is changed into a
different ecosystem or land use (e.gv filling in a wetland
to build a home), and partial loss through degradation,
where the impact does not change the ecosystem type
but alters function (e.g., reduced production through
pesticide contamination). Future loss should also be
considered as called for by Council on Environmental
Quality regulations (40 CFR Sect. 1508.7).
Functional loss depends on the characteristics of the
impact, including the type of impact, its magnitude,
timing, and duration; and ecosystem resistance, or the
rela ti ve sensi ti vity of the ecosystem to the impact, based
on its robustness and overall health (see Chapter 6).
Replacement Potential
Replacement potential refers to the ability to replace a
wetland and its valued functions. In this case, we are
referring to functional replacement carried out by people;
however, natural recovery could also be considered.
Although not a component of a cumulative impact
assessrnentper se, replacement potential is included as a
synoptic index because it is a consideration within the
404 permit process and is also an important component
of risk assessment (Leibowitz et al. 1992). The ability to
offset the loss of valued functions and reduce ecological
risk is greater if replacement potential is high; con-
versely, protection is more critical for risk reduction if
replacement potential is low.
Replacement potential depends on many factors spe-
cific to the particular wetland, such as the type of wetland,
the function to be restored, and, in the case of restora-
tion, the kind of impact that altered the original wetland
(Kentula et al. 1992; Kusler and Kentula 1990). In a
synoptic assessment, however, we are more concerned
with the landscape factors that contribute to replace-
ment potential. Because it is more difficult to replace a
wetland if critical driving factors have been disrupted,
this index depends on the overall environmental condi-
tion of the subunit. For example, it would be difficult to
restore a swamp within a historical flood plain if a levee
had been constructed on the river. If restoration did take
place, the wetland probably would not be sustainable
because natural overbank flooding, which was a driv-
ing factor for the original swamp, would be disrupted.
Synoptic Index Evaluation
In conducting a synoptic assessment, it is necessary to
refine the general synoptic indices into a specific set of
indices that are most relevant to management concerns
within a particular landscape setting. For example, in
an application concerned with nonpoint source nitro-
gen pollution within an agricultural region, the specific
indices for capacity and landscape input might be the
maximum denitrin'cation rate and the nitrate loading
rate, respectively. However, quantifying the specific
indices accurately for large landscape subunits would
be difficult if not impossible. In order to evaluate the
indices, the synoptic approach uses landscape indica-
tors of actual functions, values, and effects. The indicators
are first-order approximations that represent some par-
ticular index, given certain assumptions (see discussion
in Chapter 3, Step 35). For example, data on agricul-
tural nonpoint source nitrate loadings might not be
available, in which case agricultural area could be used
as a first-order landscape indicator.
In addition, we often take a risk-based approach to
estimate specific indices. For example, we may not be
able to quantify the actual loss of hydrologic function
due to cumulative impacts, but we could assume that
the risk of actual loss is greater in areas with high
function and high cumulative impacts, compared with
areas having low function and low impacts. Such an
approach will undoubtedly make errors in assigning a
relative ranking to each landscape subunit. However, a
synoptic assessment need not provide a perfect evalu-
ation of cumulative effects. The goal is to provide
information that will improve permit evaluation and
• management decisions overall.
14 Synoptic Approach
-------
ChapterS
Conducting a
Synoptic
Assessment
The process of producing a synoptic assesment
involves five steps (Table 3.1). Although pre-
sented and discussed sequentially, it might be
necessary in an actual application to follow these steps
iteratively. We suggest that information resulting from
this process not be viewed as the ultimate end product,
but that synoptic assessments be updated periodically
to reflect changing objectives and environmental condi-
tions or to incorporate better data. Further, it may not be
possible to achieve the desired management objectives
in a one- or two-year period. By producing an initial
assessment and improving it over time, an agency can
obtain the desired results over the long run while gain-
ing useful short-run results. A synoptic assessment
should be an iterative process.
Preparation of a synoptic assessment requires the ef-
forts of a team of individuals having different
/ backgrounds and responsibilities (in an actual assess-
f ment, these roles need not literally be performed
separately by three individuals):
• The manager, who is in charge of the resource man-
agement program and who makes the decision to
conduct a synoptic assessment, is the individual
with primary responsibility for defining the overall
goals of the assessment.
• The resource specialist, who is the ultimate user of
the final maps (e.g., a permit reviewer) and who is
familiar with the area's wetland resources and their
ecological functions, has the primary responsibility
for defining the ecological relationships relevant to
the particular management objectives.
• The technical analyst who assembles the data, makes
measurements, calculates the index values, and then
maps them, should be familiar with database man-
agement and geographic information systems (GIS)
or computerized mapping.
Step 1: Define Goals and Criteria
The purpose of this step is to identify explicitly the
assessment objectives, intended use, required accuracy
level, and the constraints within which the assessment
will be conducted. Often the objectives call for more
accuracy and detail than constraints allow. This step
may require repetition until an acceptable combination
of objectives, accuracy, and resource allocation is agreed
upon.
Step 1.1 - Define Assessment Objectives
The general objectives of the assessment depend on the
overall mission and goals of the particular agency or
organization conducting it. If the manager works with-
in a Department of Environmental Quality, the focus
could be wetland water quality functions. A manager
Conducting a Synoptic Assessment 15
-------
Table 3.1. Steps in conducting a synoptic assessment.
Stops
Procedures
1. Define Goals and Criteria
2. Define Synoptic Indices
3. Select Landscape Indicators
4. Conduct Assessment
5. Prepare Synoptic Reports
1.1 Define Assessment Objectives
1.2 Define Intended Use
1.3 Assess Accuracy Needs
1.4 Identify Assessment Constraints
2.1 Identify Wetland Types
2.2 Describe Natural Setting
2.3 Define Landscape Boundary
2.4 Define Wetland Functions
2.5 Define Wetland Values
2.6 Identify Significant Impacts
2.7 Select Landscape Subunits
2.8 Define Combination Rules
3.1 Survey Data and Existing Methods
3.2 Assess Data Adequacy
3.3 Evaluate Costs of Better Data
3.4 Compare and Select Indicators
3.5 Describe Indicator Assumptions
3.6 Finalize Subunit Selection
3.7 Conduct Pre-Analysis Review
4.1 Plan Quality Assurance/Quality Control
4.2 Perform Map Measurements
4.3 Analyze Data
4.4 Produce Maps
4.5 Assess Accuracy
4.6 Conduct Post-Analysis Review
5.1 Prepare User's Guide
5.2 Prepare Assessment Documentation
for the Fish and Game Division might be particularly
interested in wetland habitat functions. A manager of a
wetland protection program, however, might be inter-
ested in not just one particular function but in several
functions or in wetland restoration. The management
objectives could be very specific, e.g., determination of
wetland degradation caused by superfund sites, protec-
tion of wetland habitat for sport fish, protection of
floodplain wetlands, etc.
During this step, the boundary for the study unit needs
to be defined explicitly. This would typically be either
a political boundary, based on the agency's jurisdiction
(a state or multi-county region) or a natural boundary,
e.g., a natural watershed or geomorphological prov-
ince. The study area could be of special interest to
management(oneforwhicha special area management
plan is being developed). It may be necessary to get
input from other agencies or interested parties before
finalizing the boundary.
Step 1.2 - Define Intended Use
The manager should define how assessment results will
be applied. The assessment could be used to support
very specific decisions, e.g., to support cumulative im-
pact assessment as part of Section 404 permit review, or
it could be used for general planning, e.g., to help
identify areas sensitive to future impacts as part
of a State Wetland Conservation Plan. The particular
use affects the level of accuracy required and the degree
of review the final products must undergo. In addition,
an assessment used as part of a regulatory program
might need to meet specific legal tests or require public
16 Synoptic Approach
-------
comment or interagency consensus. The manager
should also determine whether the assessment is to
be purely technical or whether political consider-
ations need to be included.
Step 1.3-Assess Accuracy Needs
The overall management objectives and intended use of
the information determine the level of uncertainty the
manager is willing to accept in decisions that make use
of a synoptic assessment. EPA guidelines on data
quality assurance refer to the process of selecting the
level of accuracy needed as defining the data quality
objectives. This process includes five steps (EPA 1989):
• Define the decision;
• Describe the information needed for the decision;
• Define the use of environmental data;
• Define the consequences of an incorrect decision
attributable to inadequate environmental data; and
• Estimate available resources.
The previous sectionscovered the first three steps of this
process. Since any analysis has a level of uncertainty,
and thus the chance of erroneous conclusions, the man-
ager must consider the repercussions of incorrect
decisions based on the level of uncertainty. If it could
lead to litigation, for example, an assessment devel-
oped for regulatory applications might require a high
confidence level. If the assessment is being con-
ducted for broad-scale planning using best
professional judgment, results might be sufficient as
long as they are "more right than wrong." In other
words, results need not be completely accurate; rather,
the data must be adequate for the stated purposes of the
assessment. The manager, in consultation with other
team members, must define the level of accuracy needed
for an assessment so the benefits outweigh the liabilities.
Estimating available resources is discussed in the
following section.
Step 1.4 - Identify Assessment Constraints
The manager must estimate the amount of time, money,
and personnel hours that can be committed to the project.
Regardless of the objectives and needs for accuracy, the
. effort will be limited by available resources.
As an example of possible assessment costs, the Louisi-
ana and Washington pilot projects that are discussed in
Chapter 4 each took a year and a half for completion and
required a half-time senior scientist and both a full-time
and half-time technical analyst (i.e., two full-time equiva-
lents per year for each project). Much of the technical
analysts' time was spent collecting data from various
agencies, conducting quality control checks, perform-
ing map calculations, digitizing, and creating various
databases. Other costs included approximately $20,000
for supplies and materials (excluding data, which mostly
were obtained from cooperating agencies), plus access
to a GIS. Although the purpose of the pilots was
methods and development, and not an actual applica-
tion, costs for a similar statewide analysis should be
comparable. At the opposite extreme, an application
requiring high precision and field verification could
easily require several years of effort and cost hundreds
of thousands of dollars for data collection, analysis, and
labor. Project costs depend on study area extent and
whether adequate data already exist (Steps 3.1-3.3).
The team should also consider other constraints that
influence the outcome of an assessment, such as legal
requirements, agency mandates, institutional con-
straints, and the need for public comment or interagency
coordination.
If the resources available for an assessment are much
less than what is deemed necessary based on best pro-
fessional judgment (Steps 1.1-1.3), then management
can change the objectives (e.g., assess a smaller area or
accept less accurate results), relax the constraints (find a
source of extra funding), or conclude that the assess-
ment is not feasible at that time.
Step 2: Define Synoptic Indices
Once the objectives have been deteimined, the resource
specialist must define a specific set of synoptic indices
that will meet the objectives and intended use of the
assessment. This involves replacing the four generic
indices (function, value, functional loss, and replace-
ment potential) with a set of indices specific to the
objectives.
Defining the specific indices and the factors they in-
clude requires an understanding of the interactions
between wetlands and regional landscapes. To summa-
rize this understanding, the resource specialist can
provide a landscape description that includes wetland
types, functions and related societal values, natural
factors sustaining the wetlands and major impacts
(Table 3.2).
The resource specialist can consult with regional ex-
perts for assistance in determining these interactipns,
for example:
• University or state Soil Conservation Service (SCS)
soil scientists are familiar with regional factors affect-
ing denitrification capacity and adsorption potential
(e.g., percent of organic matter);
Conducting a Synoptic Assessment 17
-------
Table 3.2. Examples of landscape descriptions that can be used in selecting indices.
Category
Example 1
Management Objective
Wetland Type
Natural Setting
Landscape Boundary
Significant Impacts
Specific Indices
Landscape Subunits
Develop risk assessment guidance for county planners to protect sparse wetland
populations of central Washington for waterfowl and other wildlife habitat.
Palustrine (emergent, scrub-scrub and forested) on floodplains; saline (scrub-scrub) in
playas and wind created depressions (Canning and Stevens 1989).
Basin, characterized by loess deposits and deep dry channels cut into basalt, surrounded
by mountain ranges which provide hydrologic inputs; arid climate (23-64 cm average
annual precipitation); streams predominantly influent, many go dry in dry years (Omernik
and Gallant 1988).
Columbia Basin in Central Washington.
Water withdrawal for irrigation; altered water quality and stream morphology from
grazing; high nutrient and suspended sediments from agriculture and mining.
Habitat support, low stream flow and hydrologic modification (water withdrawal); non-
point source pollution.
Subwatersheds and county boundaries.
Category
Example 2
Management Objective
Wetland Type
Natural Setting
Landscape Boundary
Significant Impacts
Specific Indices
Landscape Subunits
Include cumulative impacts as part of 404 permit review in Southern California.
Intertidal salt marshes.
Mediterranean climate, accretion and erosion of sediments, warm ocean current from
Mexico, tidal flushing. Natural perturbations include storm events and catastrophic
sedimentation; drought; lagoon closure (Zedler 1982).
Southern California coast including intertidal slopes in river valleys, from Point
Conception to the international border with Mexico.
Urban development (dredge and fill disposal); reduced circulation from anthropogenic
sedimentation; altered watershed hydrology (Zedler 1982).
Cumulative wetland loss, suspended sediment loading, peak discharge, hydrologic
modification.
Coastal watersheds.
• Hydrologists with universities or the state office of
the U.S. Geological Survey (USGS) can provide in-
sight into the hydrologic factors that form wetlands,
and can also provide information on hydrologic
modifications that may affect wetland functions;
• Biologists with the U.S. Fish and Wildlife Service
(USFWS),stateagendes,or theNature Conservancy/
Natural Heritage Program can provide expertise on
wetland habitatandwetland-dependentspecies;and
• Biologists with the SCS and other agencies will be
familiar with wetlands in agricultural settings, as
well as with opportunities for restoration.
Other valuable resources are USFWS "Community Pro-
file" reports. Each of these reports provides a wealth of
information on a regional wetland type and often in-
cludes discussions of geological/climatic setting, natural
forcing functions, ecological functions, ecosystem
structure, and degradation by human impacts.
Step 2.1- Identify Wetland Types
The first step in developing synoptic indices is to com-
pile a list of the major wetland types found in the
assessment area, e.g., specific wetland communities.
This list can be limited to a particular type of wetland if
management objectives are narrow, or it can include all
of the area's wetlands if objectives are broad. The
identification of these wetland types can be based on
popular classifications (e.g., marsh, bog, or pothole), a
functional classification (e.g., Novitzki 1979; O'Brien
and Motts 1980), or the more detailed system developed
by USFWS (Cowardin et al. 1979). The choice of classi-
fication should match the assessment objectives and
constraints. For example, if protection of wetlands for
flood control is the primary objective, the analyst could
focus on palustrine or floodplain wetlands as defined
by the Cowardin system or floodplain/river lower per-
ennial wetlands as defined by a hydrogeomorphic
classification (personal communication, M. Brinson, East
Carolina University, Greenville, North Carolina). If,
18 Synoptic Approach
-------
however, the objective is protection of wetlands for
environmental education, then unique or rare wetlands
near urban areas could be classified using a popular
system or one defined by the State Heritage Program.
Where the objective is to assess cumulative impacts, it
will be important to select a classification that is broad
and synthetic.
Selection of a particular wetland classification scheme
also depends upon the availability of information. For
example, if National Wetland Inventory (NWI) maps
are available for the region, the Cowardin classification
is a logical choice. At the minimum, the classification
should include or be cross-referenced with information
on geomorphic setting and source of water because
both are important components of the natural setting
(Step 2.2) and are useful for identifying significant
impacts (Step 2.6).
Step 2.2 - Describe Natural Setting
The analyst should understand the landscape driving
factors or forcing functions responsible for the forma-
tion and maintenance of wetlands because this
information is important for defining landscape bound-
aries (Step 23) and for evaluating the significance of
impacts (Step 2.6). The natural factors include natural
stresses, such as drought, and structural components,
such as soil and seed banks (see Chapter 6). The classi-
fication used to identify wetland types (Step 2.1) should
provide relevant information. Abroad-scaleor detailed
description of natural factors can be developed around
a series of questions such as those listed in Table 3.3.
Step 2.3 - Define Landscape Boundary
In Chapter 2 we noted that the boundaries for cumula-
tive impacts and cumulative effects need not be the
same; the cumulative effects occurring within a given
area could result partially from impacts that take place
outside the boundary. The resource specialist must
define the landscape boundary to include the appropri-
ate natural setting (Step 2.2) and impacts (Step 2.6) that
could be operating outside the study area. Even if the
actual analysis ignores this larger boundary, the bound-
ary must be defined so the resource specialist can
determine the degree to which the assessment might be
ignoring important factors.
Because hydrology is the single most important deter-
minant of wetland type and function, the landscape
boundary should include at least the entire drainage
area in which the study is located. For example, an
assessment of the state of Louisiana cannot stop at the
state boundary but must consider hydrologic input
from upstream segments of the Mississippi, Red, Sabine,
Ouachita, and Pearl rivers. The landscape boundary
for groundwater discharge wetlands might include
recharge areas hundreds of miles outside the study
area; likewise, the boundary for coastal wetlands will
probably include estuarine, nearshore, and even off-
shore waters. These hydrologic boundaries also delimit
many water quality processes, such as transport of
nutrients, sediments, and pollutants.
Defining the boundary for habitat processes is more
problematic than for the other functions. Biotic factors
operate on scales .defined by the ranges of wetland-
dependent species. Given the diversity of species, no
single spatial unit can encompass all species' ranges
for a particular study area. Many times, ecoregions
provide useful landscape units for habitat support
(Omernik 1987); research by Inkley and Anderson (1982)
and Larsen et al. (1986) demonstrates a correspondence
between ecoregions and wildlife and fish communities,
respectively. If habitat of wide-ranging migratory spe-
cies is an important element of the assessment, a broader
landscape boundary must be defined.
Table 3.3. Examples of technical questions that could be used to describe the natural factors determining wetland
function.
Technical Questions
Describing natural wetland
setting related to forcing
functions, ecosystem processes,
and structure:
What are the geological processes responsible for the wetlands' formation, e.g.,
deposition of marine or riverine sediments, glaciation?
What are the physiographic characteristics associated with the wetlands, e.g., large
depressions, river valleys, karst topography?
What are the hydrologic influences, e.g., tidal, riverine or lacustrine energy, or
groundwater influence?
What are the climatic influences, e.g., timing, type and amount of precipitation, length of
growing season?
What are the chemical characteristics and fluxes of the wetlands, e.g., salinity, organic
content, nutrient and mineral availability?
What are the natural perturbations that wetlands are either adapted to or dependent on,
e.g., fire dependent species, periodic inundation, seasonal drought?
Conducting a Synoptic Assessment 19
-------
Step 2.4-Define Wetland Functions
The resource specialist next defines the particular wet-
land functions to be addressed. Depending on
management objectives, the functions of interest could
be either specific or broad. Because it is impossible to
assess all functions, even when the objectives are gen-
eral, the specialist must determine a subset of functions
that best represents the broader class. For example,
consideration of hydrologic function in regions where
small, non-tidal wetlands prevail might include wet-
land influence on peak flow but not on storm surges,
which occur mainly in larger, tidal wetlands.
Habitat functions can be defined by determining the
various species (including birds, fish, and mammals)
that are dependent on or utilize the wetland communi-
ties identified in Step 2.1. For hydrologic and water
quality functions, wetlands often function as sinks.
Therefore it is useful to consider the hydrologic and
water quality sources that are found within the particu-
lar landscape setting, since the source is a component of
sink functions (Chapter 2). Natural and anthropogenic
sources should both be included. Chapter 7 provides a
detailed discussion of wetland functions that have been
reported in the literature and can serve as a source of
candidate functions that should be considered during
this step.
Step2.5-Define Wetland Values
As discussed in Chapter 2, whether a function is valued
isapolicydecisionrathermanatechnical consideration.
These valued functions could be a given, based on the
objectives. However, the manager might choose to map
the relative magnitude of many functions first, then use
this information to determine which wetland functions
are most valuable. If so, the manager has deferred the
valuation until after analysis. In either instance, the
value may also depend on the co-occurrence of the
function and "valued objects" such as property.
To define a synoptic index for value, the team must
determine who ultimately benefits from the various
wetland functions and whether other valued objects are
involved (see discussion on value, Chapter 2). For
example, they might decide that the value of flood
protection is low if it occurs mostly in uninhabited
regions or that the value of water quality improvement
is very high if it occurs in areas that supply drinking
water to large urban centers.
Functions and values are kept distinct by defining them
in separate steps. This allows the team to consider
whether important ecological functions, based on tech-
nical information, are being undervalued in terms of
social perceptions.
Step 2.6 - Identify Significant Impacts
In this step, the resource specialist determines the most
significant impacts on the functions of interest. If the
proportion of recent wetland conversion within a par-
ticular region is high, it may be the dominant cause of
functional loss, in which case other factors may be
assigned lower priority. In this case, the index for
functional loss would be loss of wetland area.
If conversion in the region is insignificant or if the
specialist thinks conversion is not the dominant cause of
functional loss, then the impacts most likely to cause
wetland degradation must be identified. Tables 3.4 and
3.5 are examples of how best professional judgment
could be organized to guide this process. Table 3.4
contains a list of impacts associated with agriculture
along with the type of degradation each is expected to
produce. Similar tables for other major classes of
wetland impacts (resource extraction, urbanization,
and water management) appear in Appendix B. Us-
ing Table 3.4 or a modification, the specialist can
identify significant types of degradation that would
result from commonly occurring impacts. Then the
specialist could use Table 3.5 to determine which
hydrologic functions would most likely be affected
by these impacts (similar tables for water quality and
habitat functions appear in Appendix C). The tables
can be used in reverse order to determine which
impacts would most likely degrade a given function.
As an example, in a state where livestock ranching is a
major agricultural activity, possible impacts include
fertilizers, harvesting, pesticides, species introduction,
trampling, and water consumption (Table 3.4). Based
on familiarity with the region, the specialist might de-
cide that harvesting and trampling are the two most
common impacts. Both have a high likelihood of
causing degradation through changes in behavior or
habits of wetland animals resulting from habitat al-
teration, and both have a medium likelihood of causing
denudation (Table 3.4). If the overall function of
interest is hydrology, Table 3.5 indicates that func-
tional loss from changes in animal behavior is not
likely.
These tables represent hypotheses about the mecha-
nistic linkages between impacts, degradation, and
functions; they are an example of how best profes-
sional judgment could be used to guide the selection
process. The resource specialist should consult regional
experts to ascertain whether these relationships hold
true in the specific study area.
Step 2.7- Select Landscape Subunits
At this time the resource specialist defines the landscape
subunits that will be the basis for making relative com-
parisons and reporting results. For now, the decision
20 Synoptic Approach
-------
Table 3.4. Typical relationships expected between agricultural impacts and wetland degradation based on best
professional judgment. Letter indicates degree of expected association and not the intensity or duration of impact
(H = high, M = medium, L = low).
Impact Acidification Altered Animal Behavior Compaction Contamination/Toxicity Denudation
Channelization3
Drainage3-4
Fertilizers1"5
Fill2-3
Harvesting or Burning1"5
Impoundment1
Irrigation/Flooding3
Pesticides1"5
Species Introduction1"5
Tillage3
Trampling1"5
Veh icIes/Boats/Plan es1"4
Water Consumption1"5
• ' L
L
L
M
L
L
* H L
H ' ' H
H1-3
H
M
H
L
H L
M M
M
M
L
M
M
H
L
M
H
M
H
M2
M
H
M
L
Impact
Dehydration Eutrophication/Enrichment Erosion
Inundation
Light Reduction
'Channelization3
Drainage3-4
Fertilizers1-5
Fill2-3
Harvesting and Burning1'5
Impoundment1
Irrigation/Flooding3
Pesticides1'5
Species Introduction1'5
Tillage3
Trampling1'5
Vehicles/Boats/Planes1-4
Water Consumption1'5
M
H
H
L
H
M
M
H
M
M
M
M
M
M
M
L
L
M
H
L
M
L
M
L
H
M2
H M
H M
L
L
L
Impact
Salinization
Sedimentation Surface Runoff Timing Thermal Warming
Channelization3 L
Drainage3-4 L
Fertilizers1'5 M
Fill2-3 L
Harvesting and Burning1'5
Impoundment1 M
Irrigation/Flooding3 H
Pesticides1'5
Species Introduction1'5
Tillage3 L
Trampling1'5
Vehicles/Boats/Planes1-4
Water Consumption1'5 M
4 .
L
M
H
M2
M
M
H
H '
H
M
M2
H
M
M
H
M
H2
L - • - . •
L
1 Aquaculture (e.g., cranberries, rice, crayfish)
2 Crops - No Till
3 Crops-Till
"Forestry
5 Livestock
Conducting a Synoptic Assessment 21
-------
Table 3.5. Effect of wetland degradation on hydrologic functions and degree of expected association based on
best professional judgment (H = high, M = medium, L = low).
Typ» of Peak Flow
Degradation Reduction
Acidification
Animal Behavior
Compaction L
Contamination/Toxicity
Denudation M
Dehydration H
Eutrophication/Enrichment
Erosion
Habitat Fragmentation
Inundation H
Light Reduction
Salintzation
Sedimentation M
Surface Runoff H
Thermal Warming
Storm Surge
Reduction
L
M
H
M
H
L
H
Water Conservation
M
H
L
H
L
M ;
H '
L
Groundwater
Exchange
'
M
H
H
L
M
H
L
M '
H
L
Hydrologic Input ,
M
M
H
H
H
should be based on management objectives and eco-
logical considerations; data availability will be
considered in Step 3. For assessments at the state or
regional level, the USGS cataloging unit or a similar
state unit might be most appropriate because it func-
tions as a natural drainage area. Ecoregion subunits
(see the previous section) or finer-resolution subunits,
e.g., soil-vegetation associations, may also be useful.
Selection of landscape subunits might also be based on
political criteria, e.g., county boundaries.
Step 2.8 - Define Combination Rules
A specific synoptic index is typically a mathematical
expression that includes several factors. Factors that
maybe combined inanindexinclude components of an
index (for example, capacity and landscape input could
be components of function, and degradation and con-
versioncouldbecomponentsoffunctionalloss)or other
indices (e.g., an index of value would include function).
Although a separate index could be defined for each of
these factors (e.g., separate indices of functional loss
through stormwater runoff and agricultural conver-
sion), it is often desirable to mathematically combine
them into a single index, in which case a set of combina-
tion rules needs to be defined. These combination rules
must address the following questions:
• Will the factors be combined by addition, multiplica-
tion, or some other operation?
• Will the data be normalized, that is, adjusted to a
common ordinal scale, prior to combination? If so,
by what procedure?
• Will all factors be considered to contribute equally, or
should weighting factors be applied to some?
• Will the same combination rules apply to all wetland
types and across the entire range of conditions within
the study area?
Decisions concerning combination rules are difficult
and often subjective, but deserve careful attention to
reduce error. Mathematical relationships between fac-
tors may be available from the literature or regional
models. It is often necessary, however, to assume that
factors have equal weight(i.e.,are added withoutweight-
ing factors) or that there is a first-order proportionality
between factors, i.e., that the factors are multiplicatively
combined. At the minimum, the resource specialist
should explicitly describe the combination rules and
any assumptions as part of the review (Step 3.7) and
documentation (Step 5.2). Combination rules are fur-
ther discussed in Hopkins (1977), 6'Banion (1980),
Skutch and Flowerdew (1976), Smith and Theberge
(1987), and USFWS (1981).
Step 3: Select Landscape Indicators
Landscape indicators are the actual measures used to
estimate the synoptic indices; either a single indicator or
combination of indicators can be used. Selecting indica-
tors requires balance between accuracy and cost. Major
considerations are discussed below.
Selection of landscape indicators, which depends on
data availability, should not begin until goals are de-
fined (Step 1) and the relevant environmental variables
are identified (Step 2). In order to evaluate the adequacy
of an assessment (Step 4-5), it is important to keep the
goals and environmental variables distinct from the
trade-offs that occur because of data limitations. If data
availability is considered too early on, real-world limita-
tions begin to dominate the process before the goals and
22 Synoptic Approach
-------
environmental variables are articulated. Goal setting,
defining synoptic indices, and selecting landscape indi-
cators should occur iteratively and not simultaneously.
Step 3.1 - Survey Data and Existing Methods
Contact various federal and state agencies having juris-
diction over the study area to determine what kind of
environmental data are available; for smaller study
areas, include county agencies. Other sources could be
university experts and state and university libraries.
The survey should include both mapped and tabular
information available for the entire assessment area.
(Examples of data that can be used for the various
synoptic indices appear in Appendix D; sources for the
data appear in Appendix E). As part of the survey, the
technical analyst should also note the following types of
information, which will be necessary for assessing data
adequacy (Step 3.2):
• The purpose of the database and the type of informa-
tion it contains;
• The methods used in collecting, measuring, and
analyzing the data;
• Examples of how the data have been used, especially
if reported as case studies;
• Known problems or limitations;
• Data format, e.g., hard copy or computer compatible;
• Availability of documentation, both for data collec-
tion and quality assurance procedures and, if
appropriate, file formats for computerized databases;
• Procedure needed to acquire data, including cost.
The survey need not be limited to databases. Various
existing methods and techniques can also be used to
estimate indices. For example, the USGS collects dis-
charge data at various sampling locations on many
streams and rivers. Annual water resources data re-
ports for each state provide summaries of these data;
they are also entered into the WATSTORE database (see
Appendix E). Unfortunately, monitoring stations are
not typically at the locations needed for the synoptic
assessment, e.g., at the lowest downstream point of the
subunit. The technical analyst would have to select an
indicator appropriate for estimating discharge at that
location.
One possibility is to use regression equations published
by most state USGS offices for estimating discharge
using watershed characteristics. For example, variables
for regression equations developed for eastern Missis-
sippi include watershed area, channel slope, and
mainstem channel length (Landers and Wilson 1991).
Alternatively, mathematical models can estimate many
variables; e.g., SCS's TR-55 (SCS 1986) and the USDA
Agricultural Research Service's AGNPS model (Young
et al. 1987) estimate peak discharge and agricultural
nonpoint source pollution, respectively, from factors
such as topography, precipitation, land use, and soils.
The technical analyst can determine whether appropri-
ate methods are available through a literature review,
by conferring with regional experts, or both.
Step 3.2 - Assess Data Adequacy
Adequacy of existing data depends on several factors,
including the degree to which an indicator based on the
data represents the index and the quality of the data
relative to the management objectives (Table 3.6). The
following example illustrates the difference between
these factors: For a synoptic index of peak discharge,
two possible indicators are runoff volume as calculated
by the "curve number" technique (SCS 1986) and dis-
charge estimates produced by the USGS regression
methods, discussed above. For the former, the physical
quantity being estimated (volume) is different from the
variable of interest (peak rate of discharge or volume/
time). There is a relationship between runoff and peak
discharge, but the two variables are not identical. How-
fever, the estimate of runoff could be accurate if based on
highqualitydata. Conversely, an indicator based on the
USGS regression represents the same physical quantity
defined by the index, yet it could be unacceptable if
calculated using poor quality data. Both of these issues
must be taken into account. If an indicator that is
physically different from the index is being considered,
the resource specialist or technical analyst must deter-
mine whether the indicator represents a reasonable
first-order approximation to the actual index and
whether the use of that indicator is contingent upon any
unreasonable assumptions (Step 35).
Potential indicator data should be evaluated according
to a set of criteria (e.g., Table 3.6). The technical analyst
must also consider extra effort required to translate the
data into the format needed for the assessment. For
example, data found in reports might require entry into
a database. It is especially important to consider the
extra effort required for processing mapped data. Do
not assume that more detail is better until you consider
the additional cost. For example, the use of 1:250,000
scale STATSGO soil maps, if available, may be much
more appropriate for statewide synoptic assessments
than 1:20,000 scale county soil survey maps because
greater effort would be required to analyze the more
detailed maps.
Step 3.3 - Evaluate Costs of Better Data
The technical analyst should assess the time and cost of
obtaining better data. Identifying the types of data
needed and the associated costs for producing results
of various confidence levels is useful. For example, how
much would the highest quality, most up-to-date infor-
mation cost? What would be the gain in accuracy if the
budget were increased by $10,000 or if two extra months
were available for the assessment? These consider-
ations would allow existing information to be compared.
Conducting a Synoptic Assessment 23
-------
Table 3.6. Example of objectives and related questions for defining landscape indicators for synoptic indices.
Objectives
Technical Questions
Determine how well the indicator
represents the index:
Assess the quality of existing data:
Determine level of confidence in
the data:
Do comparable data exist for the entire study area or are there gaps that would limit
intraregional comparison?
Do standardized data exist for the appropriate time period, e.g.', the past ten years, the
entire year, or by season?
Are data at the appropriate spatial scale or are there major scale differences between data
sources?
Are the classification systems used for wetlands and other landscape variables
compatible? For example, the USFWS National Wetland Inventory maps, SCS soils maps
and USGS Land Use/Land Cover maps classify wetlands according to different criteria.
What is the source of the data, e.g., agency or university?
Can the originator (person or agency responsible for data collection) be contacted?
When, where and how often were the data collected?
What methods were used for the data collection?
Was the data collection associated with a Quality Assurance program? If so, what
information is available on the precision, accuracy, representativeness, comparability and
completeness of the data?
Are there assumptions, limitations or caveats to consider in using the database?
What are the time, personnel and cost constraints of obtaining better data?
What are the common assumptions between indicators and indices?
What evidence would violate these assumptions?
How should the weighing of variables be adjusted to compensate?
Step 3.4 ~ Compart! and Select Indicators
Given the adequacy of available data (Step 3.2) and the
cost of obtaining better information (Step 3.3), the re-
source specialist and technical analyst can select a suite
of indicators that best balances the level of accuracy
needed to satisfy management objectives (Step 13) within
existing constraints (Step 1.4). These choices are an
optimal solution, given the existing opportunities and
constraints.
Step 3.5-Describe Indicator Assumptions
Once indicators have been selected, the resource spe-
cialist and the technical analyst should carefully
determine which assumptions must hold if the indica-
tor is to represent the synoptic index adequately (in this
case, "adequately" is defined relative to the need for
accuracy, as stated in Step 1.3). It is important for these
assumptions to be stated explicitly, so they can be revis-
ited later in the assessment to determine whether the
assumptions were violated (Step 4.5). This informa-
tion will also be included as part of the assessment
documentation (Step 5.2). Examples of assumptions
that can affect the outcome of an analysis are:
• The USGS regression estimates for peak discharge
are often developed using data from watersheds
that are not heavily urbanized, channelized, or
dammed (e.g., Landers and Wilson 1991); in other
words, these regressions are meant to represent "pris-
tine" conditions. Use of regressions developed in
this manner would include the implicit assumption
that none of the watersheds has undergone signifi-
cant hydrologic modification.
Use of area as an indicator for wetland function
assumes that function or capacity per unit area is
similar for all wetlands or, if it varies, that wetlands
having different unit area responses are similarly
distributed between landscape subunits. The use
of area as an indicator of a sink function further
assumes that all wetlands receive import from a
source or, if not, that the spatial relationship between
wetlands and sources is similar between landscape
subunits.
The use of hydric soil area as an indicator of historical
wetland area assumes that (a) wetland soil retains its
hydric characteristics after drainage or conversion,
(b) hydric soils are properly mapped, and (c) more
permanently flooded wetlands, which could ap-
pear on SCS maps as water and not hydric soils, are
either insignificant in an area or are distributed in
such a way that bias is uniform across all subunits.
24 Synoptic Approach
-------
Step 3.6- Finalize Subun'rt Selection
After selecting the final indicators, the resource analyst
should reconsider subunits in light of the type of data
available. For example, at first the analyst may select
watersheds for subunits in Step 2.7 but later find that
most data were based on county units. The analyst must
then decide whether to prorate the county data to water-
shed units (see Appendix F) or to use counties as
landscape subunits. This will depend on overall project
goals and on whether the assumptions necessary for
prorating hold true.
Step 3.7- Conduct Pre-Analysis Review
Before conducting the assessment, the analyst should
ask management and technical experts to review the
overall management objectives, the synoptic indices
that were defined, and the selected landscape indica-
tors. The experts should, in particular, consider the
appropriateness of the indicators with respect to objecv
lives and constraints, and also review indicator
assumptions for any evidence of violations. If violations
are found, data may need to be adjusted or discarded,
and alternate indicators considered.
Step 4: Conduct Assessment
Once landscape indicators have been defined and as-
sumptions have been explicitly identified, maps and
data can be obtained from the appropriate sources. The
technical analyst can begin the process of producing the
synoptic maps.
Step 4.1- Plan Quality Assurance/Quality
Control
Data for a synoptic assessment typically come from
multiple sources (e.g., state and federal agencies, uni-
versities, and non-profit organizations) and come in a
variety of formats, including mapped data, tabular data
from reports, and computerized databases. Because
reliability of the final product depends on quality con-
trol of data processing, a set of protocols should be
developed for determining and maintaining data qual-
ity. The technical analyst should begin this step even
before data are received, using information obtained
during the data survey phase (Step 3.1).
Protocols should be developed for designing the data-
base and for screening, archiving, and documenting the
data. For example, protocols developed for data screen-
ing should identify questionable data based on an
understanding of expected values and obvious outli-
ers: A value of 100 centimeters per year for average
precipitation would be questionable for a state in the
arid southwest, and a peak discharge of only 100
cubic meters per second would obviously be too low
for a major river. Percentages should add up to 100,
and areas for component land uses should add up to
total area. Protocols should also be developed for any
variables to be measured, e.g., map measurements,
and should include criteria for assessing accuracy,
precision, completeness, representativeness, and com-
parability (EPA 1989).
In addition to the initial information collected during
the data survey (Step 3.1), data documentation should
include descriptions of the protocols, database design,
and archiving formats. This information should be
included as part of the assessment documentation
(Step 5.2).
Step 4.2 - Perform Map Measurements
Much of the information used in a synoptic assessment
is derived from maps. Examples of information and
sources include: wetland area and number of wetland
types from NWI maps, hydric soil area from county soil
surveys, elevations and stream channel lengths from
USGS topographic maps, and non-wetland land use
from USGS Land Use/Land Cover (LULC) maps.
Two types of measurements are often made from maps:
area and length. If the map is in digital format, a GIS can
be used to generate these measurements. If a GIS is not
available, the features can be planimetered or estimated
using a dot grid. These three techniques are discussed
in Appendix G.
If data reported for one type of spatial unit are to be
prorated to another type of unit, joint areas must be
calculated to serve as weighting factors. For example, if
population data reported by county need to be adjusted
to watershed subunits, the percent of the county lying in
a particular watershed must be determined from an
overlay of the two different areas (see Appendix F).
Error or bias can be introduced in map measurement
through inadequate technician training, differences in
accuracy between analysts, and defects or improper
calibration of equipment. If maps are digitized for
analysis in a GIS, compare hard copies of the digitized
maps to the originals for accuracy. Also perform a
quality control check for all map measurements by
having a different analyst repeat 5% to 10% of the
measurements to establish an error level. A discrep-
ancy of more than 5% between analysts might be
considered unacceptable. If the target is not met, a
more comprehensive check is necessary.
The technical analyst must keep in mind the difference^
between accuracy of map measurement and overall map
accuracy. A map can be measured very accurately, but
still have unacceptable overall accuracy if the map
itself contains errors. For example, a map produced
through photo-interpretation of aerial photography
Conducting a Synoptic Assessment 25
-------
might contain significant classification errors if the
photo-interpreter is inexperienced. A good discussion
of data quality and errors in mapping is found in
Burrough (1986).
Step 4.3 - Analyze Data
A number of calculations could be required to produce
an index value for each landscape subunit from the
various data sources. Common analyses might include:
• Calculating Channel Slope- USGS discharge regres-
sions often include channel slope as a variable. This
slope is defined as the difference between the eleva-
tion of points located at 85% and 10% of the
mainstream channel length. This difference is di-
vided by the channel distancebetween the two points,
i.e., 75% of the channel length (Appendix H).
• Prorating Areas-As discussed inStep4.3, data must
be prorated if an indicator is to be calculated for one
type of unit based on data reported for a different
type of subunit. Many types of data are typically
reported by county, e.g., population statistics, agri-
cultural data,soilcharacteristicsdata,and endangered
species statistics; if the synoptic subunits are not
counties, these data must be prorated using the
weightings generated in Step 4.2.
• 305b Water Quality Summaries - Under Section
305b of the Clean Water Act, states are required to
report the extent to which their waters are meeting
water quality standards. These 305b reports list, by
stream segment or type of water body, whether a
sampled segment fully supports, partially supports,
or does not support (non-supporting) the "desig-
nated use" of that segment (for example, a stream can
be designated as swimmable or fishable). If the
segment is not fully supporting, the report lists the
categoryofpollutantimpacting the waters,e.g., point
or nonpoint. The percentage of assessed streams that
fully support state designated uses could be em-
ployed as an indicator of overall water quality. To
produce suchanindicator,thestream segments within
each subunit must be identified and the relevant data
summarized for that subunit. Note that the quality of
state 305b reports varies by state. The analyst should
also be aware of how the data were collected.
Final index estimates are produced by completing any
other necessary calculations and converting to standard
units, e.g., from English to metric. However, caution
must be exercised when using regression equations.
For example, the USGS regression equations for Missis-
sippi (Landersand Wilson 1991)estimatepeakdischarge
in ftVsec, using area (mi2), channel length (mi), and
slope (ft/mi); using metric units for area, channel
length, and slope would be incorrect, since the regres-
sion equation was based on those English units. If
metric units were desired, discharge should first be
calculated in ftVsec using the English units, and then
converted to m3/sec. This indicator of hydrologic input
could then be combined with an indicator of capacity to
produceanestimateofhydrologicfunction. Additional
examples of index estimation are provided in the case
studies (Chapter 4).
After index values are calculated for each subunit, the
subunits can be ranked by numerical values. For
example, in an assessment of 50 subunits, the subunit
with the highest value could be given a rank of 1 for that
index, and the subunit with the lowest value raven a
rank of 50. Statistical packages such as SAS® (SAS
Institute, Inc. 1988) can perform these calculations auto-
matically. Rankings for each index should be included
as part of the database.
The last step in analyzing the data is to perform a
complete data quality check on the final database. For
any calculations performed by computer, the analyst
should recalculate a sample by hand to assure that the
algorithms were programmed properly and that the
output is accurate.
Step 4.4 - Produce Maps
The final synoptic maps can be produced by a computer
mapping package, such as a GIS, or manually if re-
sources are extremely limited or if no automated system
is available. A GIS is recommended because it offers
easy storage and manipulation of data and allows in-
terim products to be used in later analyses. A GIS also
gives the technical analyst greater flexibility to experi-
ment with different display formats.
If a GIS is used, two different databases are typically
required: one of the digital boundaries of the study
area and its subunits and one of the index values that
will be assigned to the subunits. Boundaries for all
U.S. states, counties, and USGS accounting units have
been digitized and are available at low cost in various
formats (see LULC entry, Appendix E). If digital
boundary data are not available, hand digitization
may be necessary. This could be cost prohibitive if
the study area includes a large number of highly
detailed polygons, but the benefits of producing com-
puter-generated maps often outweigh the digitizing
costs. In some instances, sufficient accuracy may be
achieved at even lower cost by using electronic
scanners that digitize maps automatically.
The index values and rankings for each subunit must
also be entered into the GIS. The method of accomplish-
ing this and the amount of effort required will depend
on the particular database-GIS combination. Many GIS
packages provide routines for loadinginformation from
commonly used commercial databases.
Once the data are in the GIS, map production can begin.
We recommend that the technical analyst produce com-
ponent maps for each index if the index represents a
combination of data sources. For example, if the
26 Synoptic Approach
-------
66.0-100
(a)
Jb)
(c) . (d)
Figure 3.1. Illustration of maps using different class intervalsto representthe same data: (a) equal intervals based on the data range;
(b) intervals based on quartiles; (c) intervals increasing at constant rate; and (d) intervals based on the frequency distribution (adapted
from Robinson et al. 1984).
USGS regressions are being used in Mississippi for
peak discharge, then component maps of area, chan-
nel length, and slope should also be produced. This
would allow the technical analyst and resource special-
ist to examine the data and determine whether the
resulting spatial relationships are reasonable.
One of the most important decisions in the map produc-
tion phase is how to display the data. At a minimum,
the map should include the index value for each
subunit. However, to promote interpretation, the
data are typically aggregated into classes, or inter-
vals. Ideally, class boundaries should reflect actual
thresholds of function or value, e.g., patch sizes be-
low which wildlife use drops precipitously or stream
size above which local urban flooding is known to
occur. Because such technically specific information
is often unavailable, common alternatives are to di-
vide the range of numeric values into equal intervals,
or assign an equal number of subunits to each interval
based on rankings (e.g., quartiles). The visual appear-
ance of a given set of results can vary greatly,
depending on how intervals are selected (Figure 3.1).
The choice of class intervals is one of the more impor-
tant decisions in the entire process because the synoptic
maps will be the assessment's most visible outcome.
People can easily reach erroneous conclusions if the
map they are examining contains improperly dis-
played data. Perhaps the best way to design the
intervals for map display is to first create a histogram
or frequency curve showing the distribution of the
numerical data (Figure 3.2). This will allow the ana-
lyst to detect any natural dumpings and also reveal
Conducting a Synoptic Assessment 27
-------
(a)
(b)
c
-------
is similar in all subunits. Even if some subunits, are
much more dominated by deciduous forest than
others, the analyst might be able to derive a correc-
tion factor to adjust the subunits, based on the percent
of riparian land cover.
If the indices cannot be adjusted in such a fashion, the
analyst may need to discard the data for the landscape
subunits in which violations occurred. In some cases,
the analyst might determine that the indicator is unsuit-
able for the required level of accuracy.
Throughout the entire assessment process, the tech-
nical analyst must consider the quality and accuracy
of data sources to determine the overall quality of the
final products. Unfortunately, no formal process for
weighing the various factors exists. Ultimately, the
technical analyst and resource specialist must use
their own judgment and familiarity with the data to
determine whether the synoptic results meet the stated
needs (Step 1.3).
Step 4.6- Conduct Post-Analysis Review
The assessment team should again seek technical ex-
perts' review comments following completion of the
data analysis and synthesis. This information will assist
the team in deriving conclusions and suggesting ways
the results can be used. Because there is no method for
quantitatively assessing the accuracy of results, this step
and the pre-analysis review (Step 3.7) are essential to
assure that results are adequate for the intended use.
Step 5: Prepare Synoptic Reports
The last step in the assessment is to report how the
information was derived and how it can be used.
Two different documents are appropriate: a report
for the manager and resource specialist (a user's guide)
and a detailed reporting'of procedures to serve as a
record of the complete assessment process (assess-
ment documentation). Draft versions of these
documents could also be included as part of the post-
analysis review (Step 4.6).
Step 5.1- Prepare User's Guide
This report should focus on the results of the assessment
and how the results can be used to meet the original
objectives. It might include protocols and illustra-
tions of how the synoptic maps can be used in 404
permit reviews and should include any important
caveats and assumptions as well as the overall level of
accuracy. In particular, the user's guide should make
clear that final numeric values are relative rankings,
and should be treated as such. For example, if a
subunit is ranked lowest of six for habitat functions,
this does not necessarily mean the subunit lacks habi-
tat or that its habitat is insignificant. It means it has
lower habitat function, relative to the other subunits.
Similarly, a relatively high subunit ranking for wet-
land replacement potential does not necessarily mean
all wetland losses in that subunit can be easily replaced.
The intended audience for this report includes re-
source specialists who are involved in decision-making
or planning, as well as resource agencies, scientists, and
the public.
Step 5.2 - Prepare Assessment
Documentation
Each synoptic assessment should include, for internal
use or distribution to interested parties, complete
documentation of how the assessment was conducted,
including the objectives, constraints, rationale for in-
dex definition and indicator selection, assumptions
related to the indicators, and detailed descriptions of
the procedures used in measuring and analyzing the
data. Any problems encountered should also be
described. The report should carefully document the
sources and quality of the various data sets and de-
scribe where and how the data are archived. It also
should include an overall assessment of data quality
and recommendations on how the assessment could
be improved in the future. This document is a de-
tailed record of the assessment process, and could be
valuable if procedures are forgotten, challenged (e.g.,
through litigation), or if the assessment is updated.
Conducting a Synoptic Assessment 29
-------
-------
T
Chapter 4
Case Studies
his chapter presents four case studies as hypo-
thetical examples of how a synoptic assessment
could be used. It illustrates both project-specific
applications and regional comparisons.
The management question for project-specific applica-
tions focuses on a specific, preselected subunit, e.g., a
watershed or ecoregion. The objective could be to
determine whether the subunit meets selected criteria,
to develop broad goals for the subunit, or to see whether
any "red flags" exist for that subunit relevant to a
particular management objective. An example would
be using a synoptic assessment to determine whether a
proposed discharge of fill material is located within an
area already at risk when compared to other areas.
For regional comparisons, the management objective is
to determine which subunits within a region best meet
a specific criteria; for example, subunits could be
screened for their restoration potential. In this case
the management objective is given, but the geographic
locations meeting the criteria are unknown.
Each of the following case studies is designed to
(1) provide the reader with an illustration of how results
from a synoptic assessment could be used to support a
specific management objective, (2) give examples of the
kind of information that could be used as landscape
indicators, and (3) identify and discuss technical issues.
The first case study is purposely kept simple; complex-
ity is added in later examples.
We preface the casestudies with one major caveat: these
four examples are based on pilot studies conducted as
part of the development of the synoptic approach (e.g.,
Abbruzzese et al. 1990a, 1990b). We made no attempt to
focus on real managementproblems because the method
was developmental. Also, one of our specific objectives
was to demonstrate that a synoptic assessment could be
conducted using information available for most of the
country. Where possible, we used the simplest combi-
nation rules—no normalization or weighting—because
we were developing the method, not applying it. The
maps and data presented do not necessarily include the most
appropriate indices or indicators for the management issue
being illustrated. This is why we refer to these as hypo-
thetical examples.
In particular, we did not follow all five steps for con-
ductinga synoptic assessment (TableS.l); our experience
with these pilots led to the final development and
articulation of these five steps. The four examples
presented in this chapter are not true case studies and do
not document an actual application of the approach.
The reader should keep the hypothetical nature of these
examples in mind.
Case Studies 31
-------
Pearl River Basin
The subject of the first case study is the Pearl River
Basin, a 22,600 km? region in southern Mississippi and
Louisiana (Figure 4.1). The focus is a project-specific
management goal: the use of a synoptic assessment in
404 permit review. Functional loss and landscape inpu t
arc introduced in this example. We illustrate differ-
ences between landscape and subunit boundaries by
discussing the dependence of hydrologic function on
cumulative area.
Management Goal
The goal of this hypothetical application is to provide
404 permit reviewers with information about cumula-
tive impacts within the Pearl River Basin for inclusion
in the review process. Two management scenarios will
be considered: wetland loss from conversion and the
effects of that loss on hydrologic function.
Wetland Types1
Bottomland hardwood forests are the dominant type of
inland wetland within the basin. Freshwater, brackish,
and saline marshes are found within the coastal area.
Landscape Boundary and Subunits
The Pearl River Basin forms a natural watershed bound-
ary. Climate patterns produced by the Gulf of Mexico
are significant forcing functions for the southern coastal
area. Thebasin'sfive USGS catalogingunits(Figure4.1)
were used as subunits; they range in size from 3,160 to
6,450 km2 (Seaber et al. 1984).
Natural Setting
The prevailing climate of the study area is humid
subtropical with rain occurring throughout the year
(Trewartha 1957). The 130-150 cm of annual precipi-
tation is the only source of runoff in the basin;
discharge from the Pearl enters the Gulf of Mexico.
Naturally occurring environmental disturbances in-
clude hurricanes, tornados, and flooding.
The Pearl River is bordered by the Pascagoula,
Tombigbee, and Biloxi river basins to the east, by the
Gulf of Mexico to the south, and by the Mississippi
River Basin to the west and north. The Pearl River Basin
has low relief, with peak elevations of about 120 m
occurring in headwater areas. Valleys are steep and
narrow at the head, but they grade to level and wide
in lower reaches (USDA1983); streams meander con-
siderably in the lower valley. Loess or silt soils,
formed under forest vegetation, dominate the drain-
age except in the coastal area (USDA1983). Many of
the soils are subject to erosion when disturbed.
Southern mixed forest originally dominated thedrain-
age, with cordgrass prairie vegetation in the coastal
area and oak-savanna in the northwest edge (Kuchler
1985). Current vegetation patterns reflect land use:
oak-hickory-pine forests occur with a mixture of pas-
ture and hay cropping in the upland areas, and
oak-gum-cypress forests mixed with agricultural land
dominate the valley (USGS 1967).
Wetland Functions
Hydrologic, water quality, and habitat functions are all
important in the basin. Among the hydrologic func-
tions, the potential role of wetlands in attenuating peak
flow is the focus of the synoptic indices. This role is
particularly noteworthy because floodplains are popu-
lated and several major cities lie within the basin.
The basin's wetland forests, marshes, and lakes provide
habitat for many species of plants and animals. Mink,
muskrat, and beaver inhabit riparian and wetland ar-
eas. Wild turkey, whitetail deer, and raccoon use both
wetland and upland areas. Migrating ducks and geese
feed and rest in the region. Common fish species in-
clude largemouth bass, crappie, bluegill, and various
species of catfish (Lowe and Cooley 1981).
Significant Impacts
Conversion of wetlands for agriculture has been a
major economic activity in the basin. Pasture and hay
area is about twice that of croplands; soybeans are the
dominant crop. Agricultural activities contribute to
nonpoint source pollution in the form of suspended
sediments, nitrogen, and phosphorous (Gosselink et
al. 1990a). Softwood forestry has also been important
to the economy. Bottomland hardwood forests have
been converted to loblolly pine in conjunction with
"bedding," i.e., mounding soil in areas subject to
flooding to provide a drier environment for pine.
Sand and gravel mining occurs within current and
former river channels of the lower basin; this contrib-
utes to channel instability and water turbidity.
Although the basin has not been extensively modi-
fied hydrologically compared to neighboring river
basins in the Gulf Coastal Plain, at least 290 km of
streams have been channelized, and the river is im-
pounded above Jackson, below Bogalusa, and west of
Picayune (USFWS 1981).
1 Information on wetland types, natural setting, wetland functions,
and significant impacts was not usually used because the original
objective of the assessments was methods development. We
include this information as part of the four case studies to
illustrate the kind of information that could be incorporated into
an actual assessment.
32 Synoptic Approach
-------
Gulf of Mexico
Figure 4.1. The Pearl River Basin in south-central Mississippi and southeastern Louisiana and the five subunits. Subunits are USGS
cataloging units.
Case Studies 33
-------
Synoptic Indices
For the first scenario, we define the percentage of his-
torical wetland area that has been converted as our
specific index of functional loss:
%LOSS = l(AREAH - AREAC)/AREAH] x 100
Equation 4.1
where %LOSS is the percentage of lost wetland area,
AREAHis the historical weflandarea, and AHEA^is the
current wetland area.
In the second management scenario, we are specifi-
cally concerned with the cumulative effect this loss
may have had on the hydrologic function of wet-
lands. We assume that loss of hydrologic function
will be greatest in areas with high hydrologic input
and high rates of wetland loss. We use peak dis-
charge for a 50-year flood event as an estimate of
hydrologic input because flood control along the main
channel is an important hydrologic function of Pearl
River wetlands. Ourlossofhydrologicfunctionindex is
therefore defined as follows:
Qx%LOSS
50
Equation 4.2
where LOSSH is the index for loss of hydrologic func-
tion, Q-g is the peak discharge for a 50-year flood, and
%LOSS is defined in Equation 4.1. This is a simple index
and does not account for wetland influence attributable
to position within a subunit or to hydrologic regime.
Such factors can influence greatly the cumulative wet-
land capacity to moderate peak flows. Also, note that
we do not normalize or weight either variable; we
assume instead a first-order proportionality.
In a real application for cumulative impacts, the re-
source specialist conducting the assessment could
decide to focus specifically on impacts to bottomland
hardwood forests and could include degradation.
Indices would also be needed for loss of bottomland
hardwood function due to impacts of farming, timber
harvest, and sand and gravel mining. The analyst
could include indices for water quality and habitat
function as well as future risk. The latter is included
in regulatory definitions of cumulative impacts; see
Chapter 1. Illustrations of these indices appear in later
case studies. '•
Landscape Indicators
Table 4.1 summarizes the landscape indicators used for
the components of the synoptic indices defined in Equa-
tions 4.1 and 4.2. The use of the indicators for LOSS is
based on several assumptions: (1) USGS land-use class-
ification of wetlands and SCS classification of hydric
soils agree with generally accepted criteria, (2) 1:250,OQO
scale maps represent current wetland area adequately,
(3) Hydric soils can be used to estimate historical wet-
land extent, and (4) Hydrologic loss is proportional to
the loss of wetland area regardless of where in the
subunit the loss occurred.
These assumptions are violated in certain instances.
Some of the areas adjoining lakes and estuaries are
defined as wetlands by USGS, but are classified as open
water by SCS. In addition, coastal wetlands lost to open
water through subsidence are not accounted for using
this method. These sources of error result in an inaccu-
rate depiction of net wetland gain. On the other hand,
some areas commonly considered wetlands are not
classified as such by USGS maps; in particular, season-
ally flooded riverine wetlands are sometimes classified
as deciduous forests. In addition, 1:250,000 USGS
maps omit small wetland patches. These sources of
error would result in an underestimate of current
wetland area, causing an overestimate of historic loss.
However, this indicator of loss should be adequate for
relative comparisons as long as classification errors are
consistent between subunits.
Table 4.1. Landscape indicators for the Pearl River Basin case study.
Index Component
Indicator
AREA,! (historic wetland area)
AREAg (current wetland area)
Qsotpeak discharge for 50-yr flood)
A (watershed drainage area)
L (mainstem channel length)
S (channel slope}
Area of hydric soils, estimated with dot grid from county and parish soil surveys; hydric
soils identified from SCS (1987)
Area of wetland land cover, estimated with dot grid from 1:250,000 USGS LULC maps
Estimated from USGS regression equations (Landers and Wilson 1991), based on
watershed drainage area (A), mainstem channel length (L), and channel slope (S)
Defined for USGS cataloging units in Seaber et al. (1984)
Measured with planimeter from 1:250,000 USGS topography maps
Calculated as the slope between points that are 10% and 85% of the mainstem channel
length (Landers and Wilson 1991); mainstem channel length as above, and elevation
estimated from USGS 1:250,000 topography maps
34 Synoptic Approach
-------
Other indicators of loss could be used. These might
include percent change in bottomland forest types if
data from forest surveys (e.g., Me Williams and Rosson
1990) are considered adequate for the assessment
subunits. ' —
For LOSSj^, the use of Q 50 (the 50-year flood event) as an
indicator requires using USGS regression equations
(Landers and Wilson 1991). This adds the assumption
that watershed hydrology has not been significantly
altered. The Pearl River Basin does contain a major
structural modification, the Ross Barnett Dam near
Jackson. However, this dam functions primarily as a
reservoir and would have minimal impact on larger
floods (personal communication, P. Turnipseed, USGS,
Jackson, Miss.). We therefore chose a 50-year flood
event in order to minimize this effect. Use of the USGS
regression method also assumes that the area is unaf-
fected by tides, which would decrease the rate of
discharge but increase flood stage. Use of the regression
method further assumes that channelization has no
significant effect on discharge or that the effect is similar
between subunits. An alternative would have been to
use a hydrologic model such as TR-55 (SCS 1986) to
calculate peak discharge, which would take into
account damming and channelization.
Measurements of watershed drainage area, mainstem
channel length, and channel slope are required to
calculate discharge for the Pearl River subunits. How-
ever, it is important to differentiate between drainage
area and subunit (cataloging unit) area because dis-
charge is a cumulative phenomenon. Subunit 1 is a
closed hydrologic unit and receives no water input
except rain. The discharge from Subunit 1 is therefore
dependent on the area of Subunit 1 only. However,
Subunit 2 is not a closed watershed; besides local
precipitation, it receives downstream import from Sub-
unit 1. The combined area of Subunits 1 and 2 is used to
calculate discharge for Subunit 2. Similarly, the dis-
charge for Subunit 4 is dependent on the area of the
entire Pearl River Basin (Appendix H).
Mainstem channel length is also cumulative; it is
defined as the length of the main channel from the
point of discharge to the drainage divide. The chan-
nel length used to calculate discharge for Subunit 4 is
the combined lengths of Subunits 1, 2, 3, and 4; the
length of Subunit 5 would not be included in this par-
ticular calculation because it is not part of the main
channel (see Appendix H). In situations where a politi-
cal boundary defines the study area, the analysis must
similarly consider landscape factors outside of the study
area for such a cumulative phenomenon; this is further
discussed in the Louisiana case study and Appendix H.
Map Interpretation
The relative ranking of cataloging units in the Pearl
River Basin for cumulative wetland loss is shown in
Figure 4.2. Subunit 3 has the highest relative wetland
loss, followed by Subunits 2, 5,1, and 4. If a permit
were being reviewed for a project in Subunit 4, this
particular analysis would indicate that cumulative
impacts are of lesser concern. The permit decision
would be based solely on site-specific evaluation. If
the proposed discharge were located within Subunit
3, however, the high level of wetland loss would raise
an additional issue to be considered along with other
information. The assumption is that the cumulative
loss of wetland area within a subunit reduces valued
wetland functions such as flood control.
If a site assessment indicated that local impacts would
be significant, this plus the cumulative impacts could
provide sufficient reason for modifying or denying the
permit. Regardless of the local impact, additional com-
pensatory mitigation might be required for the project
because this subunit had already experienced a high
rate of wetland loss.
Given that the basin is a flood-prone area, the resource
manager might be most concerned with loss of hydro-
logic function. The subunit experiencing the greatest
wetland loss need not have experienced the greatest loss
of hydrologic function, since that subunit could have a
smaller flood potential. The second scenario incorpo-
rates hydrologic input as a weighting factor to focus on
this particular function. Consider a permit request for
gravel mining along the main channel in Subunit 2
(Figure 4.3). The reviewer might determine that addi-
tional wetland alteration would exacerbate flooding,
since this subunit has a high relative ranking for loss of
hydrologic function. This information could strengthen
the basis for negotiating on-site mitigation aimed spe-
cifically at reducing the risk of increased flooding as a
condition of the permit. At a minimum, the reviewer
could use this information to require the applicant to
demonstrate that increased flooding is not a relevant
consideration in the particular permit decision.
In this example, both Q^ and %LOSS had values that
varied by a factor of three (2,151 to 6,417m3/s for Q^
and 32 to 96% for %LOSS). Both would contribute
similarly to the range of LOSSH. For a landscape where
the mainstem varied from small streams to major rivers,
QJQ could vary by orders of magnitude and dominate
the trends in LOSSH. In such a case, weighting factors
could be used to give the wetland-dependent variable
%LOSS greater weight, or both variables could be nor-
malized.
State of Louisiana
The Louisiana case study provides a second example of
a project-specific application; in this instance, we use
synoptic results to help define restoration goals and to
determine whether any "red flags" exist for a restora-
tion project. To do this, we introduce restoration potential
and wetland function as synoptic indices. We also
Case Studies 35
-------
FUNCTIONAL
LOSS
SSLOSS
Hydric Soffl Area - Wefland Area
Hydric Soil Area
S u b u n i t
1
2
3
4
5
%LOSS
86.3
89.7
96.3
31.7
89.5
Rank
2
4
5
1
3
S u b u n i t
(Rank)
x 100
Figure 4.2. Functional loss for the Pearl River Basin. Within each subunit, the upper value is the subunit number and the lower
parenthetical value is the rank. The variables included in the equation for %LOSS represent the landscape indicators not the
components of the synoptic index (Equation 4.1).
36 Synoptic Approach
-------
FUNCTIONAL
LOSS -
HYDROLOGY
LOSSH=
LOS S
LOS S.,
H
327,284
425,872
484,067
203,380
192,592
Rank
3
4
5
2
1
S u b u n i t
(Rank)
Figure 4.3 Loss of hydrologic function for the Pearl River Basin. Within each subunit, the upper value is the subunit number
and the lower, parenthetical value is the rank. The variables included in the equation for LOSSH represent the landscape indicators,
not the components of the synoptic index (Equation 4.2).
Case Studies 37
-------
discuss difficulties associated with determining hydro-
logic input when a study area is defined by political,
rather than hydrologic, boundaries.
Management Goal*
The management goal is to produce synoptic maps that
can be used to identify limitations and set specific goals
for restoration projects being proposed for compensa-
tory mitigation. Thesustainability of arestored wetland
is dependent on landscape condition as well as on site
characteristics and wetland type (Leibowitz et al. 1992).
A synoptic assessment can provide landscape infor-
mation that allows subunits to be evaluated rapidly
for potential environmental problems, and it can help
identify landscape functions that would benefit from
restoration.
Wetland Types
Louisiana encompasses many wetlands; more than
12,000 km2 of inland wetlands (freshwater marshes
and bottomland hardwood swamps) and 12,000 km2
of coastal wetlands (swamps and freshwater, brack-
ish, and saline marshes) exist within the state (LDEQ
1988). Approximately 25% of the coastal wetlands in
the contiguous United States are found in Louisiana
(Alexander et al. 19:36).
Landscape Boundary and Subunits
The state is bordered by Arkansas to the north, the
Sabine River and Texas to the west, the Mississippi
River and the state of Mississippi to the northeast, the
Pearl River to the southeast, and the Gulf of Mexico to
the south. Because the Mississippi River drains a major-
ity of the United States, the state's hydrologic boundary
includes much of the nation.
Water Management Units defined by the Louisiana
DepartaentofEnviranmental Quality are used for land-
scape subunits. These are modifications of the USGS
cataloging units for tine state; 124 subunits are included
(Rgure4.4).
Natural Setting
Principal factors that influence the state's climate are
subtropical latitude, proximity to the Gulf of Mexico,
and northerly continental fronts (Gosselink 1984). As
much as 160 cm of precipitation falls annually (Conner
and Day 1987). Hurricanes and tropical storms occur
between July and December and are natural environ-
mental disturbances that cause coastal erosion (Boyd
andPenland 1981; Chabreck and Palmisano 1973). With
a maximum elevation of 160 meters in the northwest
hills, the state has little topographic relief; the landscape
gently slopes from the north to the southern coast.
The most important factor that has shaped Louisiana's
landscape is the combined Mississippi River system,
which drains two-thirds of the continental United
States. As a result of coastal deposition the river's
sediment supply has formed a broad plain of overlap-
ping deltas (Coleman 1988). Sediment deposition
through overbank flooding and erosional cutting by
the river has similarly built the Mississippi alluvial
valley. Sedimentation by the river and its shifting
between deposition sites over thousands of years are
critical processes for the construction and mainte-
nance of the state's coastal and alluvial (bottomland
hardwoods) wetlands.
Wetland Functions
The hydrologic, water quality, and habitat functions
of Louisiana wetlands are important for the entire
state. These wetlands constitute one of the nation's
most productive environments and they provide habi-
tat for hundreds of bird and mammal species. Two
migratory bird routes cross the state and provide
wintering grounds for a quarter of the nation's puddle
ducks and more than half of the geese found in the
Mississippi Fly way. Coastal marshes support a vari-
ety of furbearers, including nutria, coyote, muskrat,
racoon, mink, red and gray fox, otter, bobcat, opossum,
skunk, and beaver; this resource is valued at $25 million
annually. Commercial and sport fisheries important to
the state's economy are also wetland dependent: com-
mercial landings of fish and shellfish ranked first in the
nation in 1984.
Significant Impacts
Human alteration of the Mississippi River system has
been extensive and includes three major impacts: (1) a
51% reduction in the river's suspended sediment levels
between 1953 and 1962, primarily through construction
of upstream locks and dams (Kesel 1989); (2) construc-
tion of a control structure that limits flow down the
Atchafalaya River to 30% of total discharge, which
prevents the system from switching to this distributary;
and (3) the construction of a flood-control levee along
the lower Mississippi, which prevents overbank flood-
ing. Direct impacts to Louisiana wetlands include
conversion of coastal marsh to open water through
construction of oil and gas canals and pipelines and
conversion of bottomland hardwoods by logging and
agricultural drainage.
Synoptic Indices
Two management scenarios are presented here. The
first examines wetland restoration from the perspective
of landscape replacement potential, i.e., the ability of the
landscape to contribute to wetland maintenance. The
resulting indices can be used to evaluate the feasibility
38 Synoptic Approach
-------
or sustainability of planned restoration projects. For
this particular application, we chose three separate fac-
tors relevant to the state's inland wetlands: soils,
hydrologic integrity, and water quality.
The first index for replacement potential considers the
proportion of non-wetland hydric soils, e.g., soils in
former wetlands converted to agricultural land. Re-
placement potential should be greater for hydric soils
because they retain certain wetland characteristics and
are located where natural factors favor wetland forma-
tion. Thus non-wetland hydric soils are good candidates
for restoration. The specific index is given as
function
REPLACE s = (AREA H - AREA w) / AREA H
Equation 4.3
where REPLACEg is the replacement potential with
respect to soil conditions, AREAH is the area of hydric
soils, and AREAW is the area of current wetlands. Note
that this is similar to the index used for loss of wetland
area (%LOSS) in the Pearl River case study (Equation
4.1): the more wetlands that have been converted, the
greater the number of potential restoration sites.
Since hydrology is critical to wetlands, we assume that
long-term replacement of wetland functions will be
more difficult in an area where natural hydrology has
been altered; thus we include an index based on the
degree of hydrologic integrity:
REPLACE H=WATER N / (WATER N +WATER M)
Equation 4.4
where REPLACEH is the replacement potential with
respect to hydrologic integrity, WATERN is fhe.amount
of naturally occurring waters, and WATERM is the
amount of hydrologically modified waters.
Finally, restoration can be more difficult in an area that
is stressed by pollutant exposure; thus we include an
index that represents overall water quality:
REPLACE WQ = WATER u / (WATER u + WATER p)
Equation 4.5
where REPLACEWQ is the replacement potential with
respect to water quality, WATERy is the amount of
unpolluted waters, and WATERp is the amount of
polluted waters.
These indices do not account for several factors impor-
tant to estimating replacement potential, such as
presence of hazardous substances, local climate, and
land usage. If data on these or other important factors
are available, specific indices could be developed for
them.
To help determine restoration goals, the second sce-
nario providesindicesof wetland function for hydrology,
water quality, and habitat. The index for hydrologic
p) combines wetland capacity
with hydrologic input:
FUNCTION mD= CAPACITY
Equation 4.6
The variable for hydrologic input, 7Q1£), is defined as the
lowest 7-day mean discharge for a 10-year recurrence
interval;inotherwords, this represents a 10-year drought.
The contribution of wetlands to maintaining base flow
is assumed to be more critical in areas where 7Q10
valuesarelow. ......
The next index is a measure of relative wetland function
with respect to water quality. The index combines
wetland capacity (the ability of wetlands to promote
landscape function through processing of pollutants)
with pollutant input (the opportunity for wetlands to
contribute to landscape function):
FUNCTION WQ * CAPACITY WQ x INPUT WQ
Equation 4.7
where FUNCTIONWQ is an index of pollution reduc-
tion actually occurring, C AP ACITYWQ is the capacity of
the wetland to remove or otherwise transform pollut-
ants, and INPUTWQ is the pollutant loading rate.
The index for habitat function, FUNCTION^g, is a
measure of function relative to wetland-dependent spe-
cies. This function isnotdependenton landscape inputs
and is defined as the density of wetlands within a
subunit:
FUNCTION = AREA /A
W
- Equation 4.8
where FUNCTIONf^ is the habitat function, AREA w
is current wetland area, and A is subunit area.
Landscape Indicators
Table 4.2 contains a summary of the indicators for the
Louisiana case study. Below we discuss some of the
assumptions and issues related to these data.
In the first scenario, the indicators for the three replace-
ment potential indices are based on the following
assumptions: (1) Soils mapped as hydric are wetland
substrate and exist in landscapes with adequate and
appropriately timed sources of water that can sustain
wetland processes; (2) The major hydrologic impacts
that affect the sustainability of wedands are damming
and channelization; both have similar overall effects on
replacement potential, and both are adequately esti-
mated by dot counts; and (3) Water quality data from
state 305b reports represent an accurate and unbiased
sample of natural water quality as it pertains to wetland
stress. Because the indicators for replacement potential
Case Studies 39
-------
Table 4.2. Landscape indicators for the Louisiana case study.
Index Component
Indicator
AREA H (hydric soil area)
Area of hydric soils, estimated with dot grid from parish soil surveys; hydric soils
identified from SCS (1987)
AREA w (current wetland area) Area of wetland land cover, estimated by GIS from digital 1:250,000 USGS LULC maps
WATER N (naturally occurring waters) Number of dots on hydrologically unmodified waters from 1:250,000 USGS topographic
maps
WATER M (hydrologically modified) Number of dots on dammed or channelized waters from 1:250,000 USGS topographic
maps
WATER y (unpolluted waters)
WATER P (polluted waters)
Length of streams listed as "fully supporting" designated uses in 305b report (LDEQ
1988)
Length of streams listed as "partially supporting" or "non-supporting" designated uses in
305b reports (LDEQ 1988)
CAPACITY HYD (hydrologic capacity) Area of wetland cover, estimated by GIS from digital 1:250,000 USGS LULC maps
7Q 10 (7-day low discharge for Estimated using several different methods, based on Lee (1985a); see text
10-yr drought)
CAPACITY YYQ (water quality capacity) Area of wetland cover, estimated by GIS from digital 1:250,000 USGS LULC maps
INPUT WQ (loading rate of pollutants) Defined as the percent of polluted waters: WATERP/(WATERP + WATER^; indicators as
above
A (watershed area) Watershed area, estimated by GIS from digital 1:250,000 USGS LULC maps
with respect to soil include those used earlier for %LOSS
(compare Equation 4.1 with Equation 43 and Table 4.1
with Table 4.2), the earlier assumptions also hold for
REPLACE s.
For wetland functions, the 7-day low flow was esti-
mated using several methods based on Lee (1985a);
these are discussed in more detail below. Assumptions
for functional indicators are as follows: (1) Wetlands
contribute to baseflow and this contribution is more
significant in areas with smaller 10-year low flows, i.e.,
those more susceptible to drought; (2) The proportion
of streams classified as not fully supporting desig-
nated uses such as "public water supply" or "fish and
wildlife propagation" is indicative of pollutant load-
ings; and (3) Wetland function for hydrology, water
quality, and habitat is dependent on wetland area as
mapped by USGS land-use maps. Since the indicator
for landscape input of pollutants is the complement
of the indicator used for water quality replacement
potential (See Table 4.2 and Equation 4.4), those
assumptions also hold.
In this case study we introduce a technical problem
related to study area boundaries. Because regulatory
jurisdiction is rarely defined by environmental crite-
ria, the boundary for a study will typically not be a
natural watershed as it was for the Pearl River Basin.
In cases where portions of a subunit are outside of the
study area, the analyst must consider hydrologic in-
put from upstream tributaries. Louisiana is such a
case because most of the flow for the Mississippi and
Red rivers is derived from import into the state. The
USGS regression equations provide a relatively simple,
standardized technique for estimating discharge; how-
ever, these equations are not appropriate for rivers
with large watersheds, which are typically excluded
from statistical analyses. Even for smaller water-
sheds, it can be difficult to obtain appropriate and
comparable data for areas that lie outside of a state
boundary.
Given these limitations, we used several different meth-
ods for estimating 7Q1Q based on a USGS report (Lee
1985a). For subunits having a gage station on the
mainstem channel near the bottom of the subunit, we
used actual 7Q10 values if they were defined; 37 of the
124 subunits met the criterion. Values for two addi-
tional subunits were derived from graphs of 7Q10 versus
the drainage area of those two subunits (Lee 1985a).
For subunits without suitable gage stations, regres-
sion equations based on watershed area, precipitation,
and channel slope were used if total watershed area
was not more than 1360 km2 and if the watershed
was not within a region of the state for which 7Q10
was undefinable (Lee 1985a). The latter included the
entire coast, which is subject to tidal influence, and
portions of the Atchafalaya Basin, where channels
have been modified by man and are interconnected.
Ten additional subunits met these criteria, and low
flow values were calculated using the regressions.
Watershed area for these subunits was obtained by
GIS from 1:250,000 USGS LULC maps; note that fdr
40 Synoptic Approach
-------
subunits where a portion of the watershed is outside
the study area (e.g., a portion of the watershed for
Subunit 705 is in Mississippi), the area of this outside
portion must also be estimated. Precipitation was
calculated by digitizing precipitation contours and
prorating them to watershed units (Appendix F);
channel slope was calculated in the same manner as
in the Pearl River example.
Two additional subunits were located in a region of
the state dominated by low flow values of zero; thus
a zero value was assigned to these units. Low flow for
the remaining 72 subunits — more than half of the
state's subunits — is undefined, either because the
subunit was located in an undefined portion of the
state or because the subunit area was greater than
1,360 km2. This clearly illustrates the difficulty in
attempting to define discharge for study subunits.
Map Interpretation
Assume that a wetland restoration project has been
proposed for compensatory mitigation and that the site
is to be located on a parcel of land in Subunit 805 (Figure
4.5). To identify potential problems, the permit re-
viewer wouldfirstexaminethesynopticmaps to evaluate
the subunit's relative replacement potential.
The maps for hydrologic integrity (Figure 4.6) and
water quality (Figure 4.7) suggest that hydrology is
relatively unimpaired and that water quality prob-
lems are not likely to cause stress. However, the
relatively low proportion of non-wetland hydric soils
(Figure 4.5) raises a red flag; overall, this subunit
might not be suitable for wetland restoration projects.
The permit reviewer should scrutinize the proposed
site more carefully to determine the likelihood of
successful restoration. This information could also be
the basis for negotiating a project design that specifi-
cally addresses any soil problems, e.g., the applicant
might be required to supply an appropriate substrate
for the site.
If the decision is made to restore a wetland at the site,
the three wetland function maps (Figures 4.8-4.10)
can be used to help define restoration goals. For
example, a function rated as low might be naturally
unsuited to that area or unnecessary because of low
landscape input, while a function with a high rating is
already at an acceptable level; functions with inter-
mediate ratings might benefit most from restoration.
The map for hydrology (Figure 4.8) indicates inter-
mediate levels of that function for Subunit 805, which
suggests that wetlands might help alleviate low flows.
In comparison, the map for water quality (Figure 4.9)
shows low function; water quality improvement
would be unnecessary in this area because pollutant
loadings are low. Habitat function also ranks some-
what low (Figure 4.10)::possibly indicating naturally
low habitat function. Thus the reviewer might
initially focus on hydrologic function (base flow) as a
goal for the site. The information from the synoptic
maps can therefore be used as a screening tool; how-
ever, these initial findings should be confirmed with
a site-specific evaluation, especially to assure that the
restored wetland was designed in such a manner as to
reduce low flow. The degree to which wetlands
contribute to base flow is still unresolved (see Chap-
ter 7). We do not mean to imply that wetlands do
contribute to base flow in this region. This example
merely illustrates how this information would be
used if that were the case.
Unlike the Pearl River case study, the subunits in this
case study were ranked and mapped based on quartiles.
The Pearl River Basin has only five subunits, and an
ordinal ranking of the units is easily understood. In a
study area with as many units as Louisiana, grouping
by quartiles conveniently depicts the relative rankings.
State of Washington
In the next two case studies, we illustrate how synoptic
assessments can be used for regional comparisons. We
use results for the state of Washington to illustrate how
this kind of information could support the development
of a State Wetland Conservation Plan. We also intro-
duce value and future risk as synoptic indices and
demonstrate the use of weighting factors for combining
components of an index.
Management Goal
The purpose of the assessment is to provide information
on future risk of valued habitat loss to identify habitat
areas for protection as part of the development of a State
Wetland Conservation Plan. In particular, habitat that
supports rare, threatened, or endangered species is the
value of interest in this case.
Wetland Types
Washington containsadiversity of wetland types. These
can be grouped according to the four regions in which
they are found: the coastal plain, the Puget lowlands,
the mountains, and the Columbia Basin (Winter 1990).
Within the coastal region, estuaries and salt marshes
predominate. Freshwater emergent marshes, bogs,
and freshwater swamps oqcur in the Puget lowlands.
The primary wetland types in the northern moun-
tains are kettlehole depressions and wet meadows; in
other mountain regions, freshwater emergent and
riparian wetlands are more abundant. Vernal pools,
playas, and wet areas are found along intermittent
streams in the arid east (Canning and Stevens 1989).
Case Studies 41
-------
LOUISIANA SUBUNIT INDEX
WATER MANAGEMENT UNITS
Figure 4.4. The State of Louisiana and the 124 subunits. Subunits are Water Management Units as defined by the Louisiana
Department of Environmental Quality.
42 Synoptic Approach
-------
REPLACEMENT POTENTIAL - SOILS
Arkcnsos
-1.150 - 0.251
Hydric Soil Area - Wetland Area
Hydric Soil Area
304-0369
305 - 0.898
306-0.980
307-0.8%
308-0.966
309-0.968
310 - 0.679
311 - 0.406
401 -0577
402-0.846
403 -0.847
404 - 0.018
405 - 0.791
406-0.090
407 - 0.768
408-0.882
409 - 0.615
411 - 1.030
412- 1.020
413 - 0.948
605-0.779
606-0.602
607-0370
608-0.934
609-O273
611 - 1.010
701 - 0.795
702-0.447
703 - 0.897
704 -'0.362
705 -0.861
801 - 0.481
802-0.878
803 - 0510
804-0.487
805 -0.604
806-0308
807-0.780
808-0.863
809 -0544
810-0597
811 -0548
812-0504
813 -0580
814 - 0.790
815 - 0531
816 - 0.767
$01 - 1.140
902- 1.250
903 -0597
904-0577
$05 -0.854
1001 - 0.674
1002 - 0.644
1003 -0^56
1004 - 0.743
1005 -0342
1006 -0.870
1007 - 0.746
1008 -O843
Figure 4.5. Replacement potential with respectto soils for Louisiana. Darker hatching corresponds to higher replacement potential.
The variables included in the equation for REPLACEg represent the landscape indicators, not components of the synoptic index
(Equation 4.3).
Case Studies 43
-------
REPLACEMENT POTENTIAL - HYDROLOGY
Arkansas
REPLACEH
0.000 - 0321
0.323 - 0.571
0581 - 0.781
0.800 - 1.000
TfnpIIi
,TT =
Natural + Modified Stream Length
30* -0.071
305-0553
306-ljOOO
307-O600
30S-0.93S
309-0333
310 - 0.236
311 - 0.467
•401 - 1.000
402-0.571
403- LOOO
404 - 0391
•405-0.964
406 - 0.111
407-1:000
40S-O.S67
409-OJ>17
411 - 0.500
412 - 0375
413 - 0.125
1009-0.667
1010 - 0.667
1011 - 0.880
1012 - 0.750
1013 - 0.529
1014 - 0X00
1015 - 0.714
1016 - 0345
1101 - 0X90
1102 - 0.971
1103 -0.094
1104 - 0875
1105 - 0.714
1106 - 0250
1201 -0.4S8
1202 -0.197
1203
1204
1205
1206
1207
0.111
0333
0.286
0.429
0303
Figure 4.6. Replacement potential with respect to hydrologic integrity for Louisiana. Darker hatching corresponds to higher
replacement potential. The variables included in the equation for REPLACE., represent the landscape indicators, not components
of the synoptic index (Equation 4.4).
44 Synoptic Approach
-------
REPLACEMENT POTENTIAL - WATER QUALITY
Arkansas ' . -
EEHACEWQ
0.000
0.046-0.688
0.696 - 0544
™ Supporting + Non-Supporting Length
304- 1,000
305 -1.000
305- 1.000
307- 1.000
308- 1.000
309-0.000
310 - 0.444
311 - 1.000
401 - 0.813
402-0.000
403 -0.556
404- 1.000
405-0.915
406-0.000
407 - 0.491
408-0309
409 -0.046
411 - 0.000
412 -0.000
413 - 0.000
Figure 4.7. Replacement potential with respect to water quality for Louisiana. Darker hatching corresponds to higher replacement
potential. The variablesjncluded in the equation for REPLACEWQrepresentthe landscape indicators, not components of thesynoptic
index (Equation 4.5).
Case Studies 45
-------
HYDROLOGIC FUNCTION
101 - 710000
102- 105,000
KB-ufa&ai
10* . rofa&al
105 - suiafinal
105 - eofefned
undefined
0 - 31
32 - 1,110
1730 - 6,910,000
Siibumt -
201-OB.feCood
203 -soAeCnod
2M- 533
205 -BoeeGnod
206 - t
204 -
209-
210 -
XI -
302 -
303 -
304-radofined
305 - Bndefined
306- 6
307- 15
308- 3
309 - undefined
310 ~ TTTvV^TW^
311 - undefined
401- 33
402 — Ttfytft^rvui
403- 12,400
*M - natofitBd
«»- 2,720
414 -
416 - imfaCaed
17,500
X07 - 7,7X0
408- 527
409 - nafcfiood
411 - miefined
412 - noiofinal
413 -aodofincd
418 - nndoBned
419 -undefined
420-noddiaed
421 — tnvfcfiiryxl
501 -mdofioed
502 ~ nikidfiiud
503 - 61
504 -mdefined
505 - n~t.g~v<
506 - nafafinod
507 — T
50$ -
601 -
602-
603 -
604-ndcSned
574
8^20
£05 - undefined
606 - ™<**<™*A
607 - undefined
608 - undefined
609 " *tn<»
1204 - undefined
1205 -undefined
1206 - undefined
1207 - undefined
Figure 4.8. Hydrologic function for Louisiana. Darker hatching corresponds to higher hydrologic function. The variables included
in the equation for FUNCTIONHYD represent the landscape indicators, not components of the synoptic index (Equation 4.6).
46 Synoptic Approach
-------
WATER QUALITY FUNCTION
Arkcnsas
FUNCTION
WQ
z Wetland
Ares
Non-Supporting length
"Q Supporting + Non-Supporting length
304- 00
305- OS)
306- 0.0
307- 0.0
308- 0.0
309 - 22
310 - 89.6
311 - 0.0
401 - 0.6
401- 31.6
403-64.1
404- 0.0
405- 9.6
406- 0.0
407- 49.0
408 - 33.9
409 - 102.0
411 - 0.0
412- 83.2
413 - 6.0
605 - 35.1
606- 0.0
607-88.4
608-50.6
609 - 119.0
611 - 0.0
701 - 0.0
702- 0.0
703- 0.0
704 - 395.0
70S - 223
801 - 10.8
802- 0.0
803 - 0.0
804- OX)
805 - 0.0
806-34-5
807- 0.0
- OX)
809- 9.6
810- OX)
811 - OX)
812- 0.0
813 - 0.0
814 - 91.6
815-24.3
816 - 14.8
901 - 113.0
902-76.6
903 - 0.0
904- 0.0
905- 0.0
1001 - 0.0
1002- 46.1
1003 - 19.7
1004- 44.5
1005 - OX)
1006 -
1007 - 44.1
1008 - 32
Figure 4.9. Water quality function for Louisiana. Darker hatching corresponds to higher water quality function. The variables
included in the equation for FUNCTIONWQ represent the landscape indicators, not components of the synoptic index (Equation 4.7).
Case Studies 47
-------
HABITAT FUNCTION
Arkoreos
101-OH
Ktt-oio
103-041
104-032
105-O77
10S-OS9
107-O4S
10S-O94
201-O61
202-O66
KB -O&S
204-OOt
205 -O«l
206-O44
207-1X8
204-0.74
209-O92
210-059
301-au
302-O3S
303-O17
304-O4S
305-0X4
306-0.01
307-O07
303-O02
309-O03
310-036
311 - O31
401 -0.00
402-OOS
403-0.07
404-O36
405-0.03
405-054
407-O07
408-O04
409-016
411 - 0.70
412 - 071
413 - 003
FUNCTION^
EH 0.00 - 0.04
0.04 - 0.13
0.14 - 0.48
0.49 - 1.13
Wetland Area / Total Area
Submit - FUNCTION,
'HAB
414-066
415-0.00
416-O20
417 - 0.65
415 - 054
419 - O17
420-O.SS
421 - 0.75
501 - O14
502-0.04
503-O27
504-0.15
505-0.05
506-031
507-0.48
SOS -O94
601-0.06
602-0.08
603-0.00
604-OOO
605-0.21
606 - 0.17
607-0.13
608-0.04
609-0.42
611 -O93
701 - 0.12
702-O37
703-0.02
704-053
705-0X3
801 - 0.13
802-0.06
803 -0.03
804 - 0.01
805-0.07
806-0X9
807-0.06
808-0.05
809 -O01
810-OOO
811 - 001
812-0.04
813-0.01
S14-O04
815-O02
816-009
901-036
902-054
903-OOO
904-0.01
905 -O05
1001 - O09
1002 -O21
1003-0.03
1004-O07
1005 -0.08
1006 -O04
1007-O07
1008 -O05
1009 -0.03
1010 - 0X0
1011 - OX*
1012 - 0.02
1013 - 0X7
1014 - 0.01
1015 - 0.29
1016 - 021
1101 - 0.02
1102 - O24
1103 - 1.13
1104 -0.00
1105 - 0.01
1106 - 0.78
1201 - 0.13
1202-O59
1203 -0.49
1204-054
1205-0.66
1206-O79
1207-0.91
Figure 4.10. Habitat function for Louisiana. Darker hatching corresponds to higher habitat function. The variables included in the
equation for FUNCTIONHAB represent the landscape indicators, not components of the synoptic index (Equation 4.8).
48 Synoptic Approach
-------
Landscape Boundary and Subunits
Washington is bordered on the north by the Olympic
Mountains and Canada; on the northwest by Puget
Sound; on the west by the Pacific Ocean; on the east by
the Blue Mountains, the northern Rockies, and Idaho;
and on the south by the Columbia River and Oregon.
Subunits were denned using the state's 62 Water Re-
source Inventory Areas, which are based on natural
drainages (Figure 4.11).
Natural Setting
Climate and geomorphology are the most important
determinants of wetland location and type in the state.
Washington is divided by the Cascade Range into two
distinct climatic regions: The west has a mild, wet,
maritime climate, and the east has an arid continental
climate. Precipitation ranges from 18 cm east of the
Cascades to as much as 640 cm for the Olympic Moun-
tains (Cummans et al. 1975).
Coastal and northwestern wetlands are influenced by
high precipitation and cooler temperatures. Freeze and
thaw cycles contribute to wetland formation in most of
the alpine and subalpine regions.
In the Puget lowland, wetlands have developed on
underlying gravel, silts, and days deposited by Pleis-
tocene glaciers (Franklin and Dyrness 1984). The large
rivers of the lowlands periodically flood, creating wide
floodplains with numerous riparian wetlands
(Cummans et al. 1975).
Northern mountain wetlands were formed by receding
glaciers that created kettlehole depressions, moraines,
and outwash plains (Winter 1990).
Although low precipitation limits wetland density in
eastern Washington, damaging floods caused by brief,
intense thunderstorms occur during spring snow-
melt. Winds deposit loess soils from Canada in the
Columbia Basin and create blowout depressions where
playas and vernal pools form (Doling 1988).
Wetland Functions
An estimated 359 of 414 wildlife species found in
western Washington use wetland habitats during
some season or part of their life cycle (Oakley et al.
1985). Washington wetlands play a major role in
providing nesting and wintering grounds for the
ducks, geese, and swans that use the Pacific Water-
fowl Flyway. The ponds and potholes of central and
eastern Washington produce one-half million ducks
and geese annually and are essential for other wildlife
in times of drought. Coastal wetlands provide critical
habitat for millions of shorebirds, many species of
game, and commercial species of fish and shellfish,
which have an estimated value of $1.1 billion annually.
Significant Impacts
Loss of wetlands is the most important problem fac-
ing waterfowl and fur-bearing wildlife and is a limiting
factor in maintaining wild anadromous fish popula-
tions (Canning and Stevens 1989). The variety of
impacts that affects these wetlands corresponds to
the diversity of regional land use. Coastal impacts
include dredging for port development, filling for
road construction and urban and industrial develop-
ment, and drainage for agriculture.
Montane wetlands are lesssubject to conversion, but are
impacted by vegetation removal, soil compaction, and
sediment runoff from forestry, grazing, mining, and
recreation.
Forestry and agriculture practices, filling for urban de-
velopment, and pollution from increased urban
stormwater runoff impact the Puget lowland wetlands.
Table 4.3. Landscape indicators for the Washington case study.
Index Component
Indicator
AREA c (current wetland area)
AAGR (agricultural growth)
A (subunit area)
AURB (urban growth)
RF AGR (agricultural risk factor)
RF URB (urban risk factor)
RTE {number of rare, threatened,
and endangered wetland-
dependent species)
Area of wetland land cover, estimated with dot grid from 1:250,000 USGS LULC maps
The percent annual change in agricultural area between ,1972 and 1984, based on
agricultural census data (U.S. Bureau of Census 1974,1982a); prorated from county to
subunit areas, and set to zero if subunit showed negative growth
Calculated by GIS from digitized subunits
The percent annual change in human population between 1970 and 1980, based on the
U.S. Census (U.S. Bureau of Census 1972,1982b); prorated from county to subunit areas,
and set to zero if subunit showed negative growth
A factor of 87/95 is used, based on historical loss of national wetlands by agricultural
conversion (Tiner 1984)
A factor of 8/95 is used, based on historical loss of national wetlands by urban expansion
(Tiner 1984)
County RTIE data from Washington Department of Wildlife (1990) and Washington
Department of Natural Resources (1990), prorated to subunit areas (Appendix F)
Case Studies 49
-------
Within the Columbia Basin, primary impacts are veg-
etation removal, trampling, nutrient loading from
grazing, and excavation for energy development and
mining (Canning and Stevens 1989).
Synoptic Indices
The first index for this case study is habitat value.
Because the management objective specifically focuses
on rare, threatened, or endangered species, the index is
weighted for subunits where these species occur:
VALUE HAB - (AREA C/A) x RTE
Equation 4,9
where VALUE ^^ is (he index for habitat value, AREA^
is current wetlandarea, A is the subunit area, and RTE
is the number of rare, threatened, or endangered species
within that subunit. The proportion of the state's rare,
threatened, or endangered species occurring within the
subunitcould also be used as an index. RTE could have
been divided by wetland density (AREA^/A) rather
than multiplied; this is discussed further below.
The second index is future risk, which is based on a
weighted estimate of agricultural and urban growth:
RISK = (AAGR x RFAGR) + (AURB x RFURB)
.Equation 4-10
where RISKisthesyncpacriskindex,AAGRand AURB
are expected rates of agricultural and urban growth,
^respectively, and RFAgR and KFVKB are risk factors for
weighting the relative importance of these two impacts.
Finally, the third synoptic index is future loss of valued
habitat with respect to rare, threatened, or endangered
species (LOSSp). This index combines habitat value
wi th future risk-
LOSS = VALUE x RISK
Equation 4.11
Landscape Indicators
Synoptic indicators for the Washington case study ap-
pear in Table 43. The use of rare, threatened, and
endangered species assumes that the distribution of
such species is uniform and that census taking is unbi-
ased. These assumptions may not be entirely true
because (1) census taking can be biased by more intense
sampling of urban areas or accessible areas, e.g., near
roads, and (2) prorating county rare, threatened, or
endangered species data to subunit areas may be unre-
alistic, especially in counties with few species. For the
latter,a better approach ina real application would be to
map actual sighting data onto subunits, but these data
are not always available.
More importantly, the index for habitat value assumes
that it is dependent on the product of wetland density
with the number of rare, threatened, or endangered
species. This assumes that these species benefit from
greater wetland densities; however, the most important
wetlands for these species may be scarce wetlands (those
that occur at low densities), in which case the density
would be used as a divisor.
As an indicator of expected agricultural growth, we use
the change in agricultural area from the most recent
agricultural census data. Once this value was prorated,
we then set any negative subunit values to zero because
a loss of agricultural area would not necessarily equate
to a gain of wetland area. For urban growth, we use
human population as the indicator and calculate the
.value in a similar fashion.
The risk factors for weighting agricultural and urban
growth are based on figures of 87% and 8% for nation-
wide historical loss of wetlands by agricultural
conversion and urban expansion, respectively (Tiner
1984). Since we ignore the remaining 5%, the actual risk
factors we use are 87/95 and 8/95 because this makes
the sum of the factors one (Appendix H).
Use of the risk factor assumes that (1) agricultural and
urban growth in the recent past are good indicators of
their future growth, (2) future population growth rates
are a good indicator of wetland loss from urban expan-
sion; and (3) historical causes of national wetland loss
will also be the important causes of future wetland loss
in Washington. In addition, prorating county census
data to subunits assumes that agriculture and popula-
tion are uniformly distributed throughout the area. In
some instances this is violated, especially where the
populations of counties are clustered around large cities
like Seattle. In a real application, data must be adjusted
to account for this.
Map Interpretation
The objective for this assessment is to provide informa-
tion on future risk that can be used to identify habitat
protection areas as part of a State Wetland Conservation
Plan. "The component maps of habitat value and future
risk are shown in Figures 4.12 and 4.13 (class intervals
for all Washington maps were selected by visual inspec-
tionanddonotrepresentquartiles). Figure4.13 provides
planners with a quick overview of areas at risk from
combined primary causes of wetland loss. If necessary
for planning purposes, risk from agricultural conver-
sion and urban expansion could be separated into two
maps; this would indicate that risk from agricultural
conversion is ubiquitous throughout the state, but popu-
lation appears to be more of a threat in the Puget
lowlands and along the coast. Combining habitat value
and future risk, Figure 4.14 maps in darker hatching
areas where future loss of habitat value is predicted to
50 Synoptic Approach
-------
WASHINGTON SUBUNTT INDEX
(Water Resource Inventory Areas)
Figure 4.11. This State of Washington and the 62 subunits. Sufagnits are Water Resource Inventory Areas.
Case Studies 51
-------
HABITAT VALUE
Canada
1 - 0.031
2-0.000
3-0302
4-O.CC5
5-ttOJ7
6-O.OSS
7-0.060
S-O.CM3
9-0,000
10-0.000
11 - 0.071
VALUE
'HAB'
Wetland Area x Number RTE Species
Total Area
Subunh-VALUE
12-0.000
13 -0.046
H-aoso
15 - 0.013
IS - 0.061
17 - 0.000
IS - 0.009
19 - 0.013
20-0.032
21 - 0.137
22 - 0.145
23 -0.029
24 - 0.157
25- 0.000
26 -0.024
27-0.000
28 - 0526
29 -0.022
30 - 0.000
31 - 0.011
HAB
32 - 0.010
33 -0.000
34 - 0.010
35 - 0.000
36 - 0.011
37 - 0.130
38 - 0.013
39 - 0.052
40- 0.000
•41 - 0.120
0.022 - 0.057
0.060 - 0302
42 - 0.000
43 -0.000
44 - 0.009
45 - 0.035
46-0.000
47 - 0.000
48 - 0.025
49 - 0.108
50 - O.Olfi
51 - 0.000
52 - 0.008
53 - 0.000
54 - 0.000
55 - 0.015
56 - 0.000
57 - 0.000
58 - 0.029
59 - 0.000
60-0.030
61 - 0.000
62 - 0.000
W VAmrat ValUG f°r Wa.shr.nfl5'n- Da.rkei: hatching corresponds to higher habitat value. The variables included in the
for VALUEHA8 represent the landscape indicator, not components of the synoptic index (Equation 4.9).
52 Synoptic Approach
-------
FUTURE RISK
Canada
RISK (x 10"3)
RISK = % Weighted Annual Pop. Change +
% Weighted Annual Agr. Change
Subunit - RISK (x 10~3)
1-4.43
2-8.69
3 - 25.17
4 - 75.97
5 - 32.70
6 -29.90
7-27.43
8 -4339
9 - 17.84
10 - 33.81
11 -24.26
12 - 14.17
13 - 10.09
14 - 11.95
15 - 6734
16 - 33.70
17- 27.86
18- 27.86.
19 - 4.08
20- 27.86
21 - 33.70
22-48.99
23 - 36.84
24 - 15.89
25 - 27.47
26 - 34.14
27 - 27.67
28 - 17.93
29 - 7.14
30-9.09
31 - 10.42
32- 5.54
33 - 8.55
34- 631
35 - 137
36 - 11.98
37-937
38 - 16.71
39 - 51.72
40 -34.97
41 - 4.87
42 - 4.12
43 - 3.83
44-43.02
45 - 77.29
46 - 49.84
47 - 81.82
48 -5232
49 - 5.42
50 - 59.94
51 - 13.66
52- 9.18
53 - 1331
54 - 12.18
55 - 10.76
56- 2.08
57- 5.16
58 - 10.93
59 - 14.13
60- 12.11
61 - 14.13
62 - 9.16
Figure 4.13. Future risk for Washington. Darker hatching corresponds to lower future risk. The variables included in the equation
for RISK represent the landscape indicator, not components of the synoptic index (Equation 4.10).
Case Studies 53
-------
FUTURE MSK - HABITAT
Canada
LOSSF =
Subunh - LOSS
1 - 1.13
a- aoo
3 - 10.91
239
335
1.44
MS
199
9- 000
10-0.00
ix- aoo
IS- 033
H - 0.61
1J- 131
14- 3.27
17- 0.00
1!:- 0.48
I!'- 0.05
20-169
21 - 13.69
22-23.98
23 - 3.59
24- 6.13
25 - 0.00
26-534
27 - 0.00
28- 458
29- 035
30 - 0.00
31 - 0.46
32- 0.19
33 - 0.00
34- 0.46
35- 0.00
36 - 059
37- 9.15
38 - 0.63
39 - 14.90
40- 0.00
41 - 3.82
42 - 0.00
43 - 0.00
44- 121
45 - 9.78
46 - 0.00
47- 0.00
48 - 7.13
49 - 3.17
50- Z14
51 - 0.00
52 - 020
53 - 0.00
54 - 0.00
55 - 028
56 - 0.00
57 - 0.00
58 - 0.91
59 - 0.00
60 - 0.92
61 - 0.00
62 - 0.00
F!0ure4.14. Future risk of habitat loss for Washington. Darker hatching corresponds to lower future habitat loss. The variables
included in the equation for LOSSF represent the landscape indicator, not components of the synoptic index (Equation 4.11).
54 Synoptic Approach
-------
be low. This category includes subunits that have low
risk, low habitat value, or both; note that future loss of
value cannot occur in subunits that have low value.
As an alternative to defining future habitat loss as the
product of value and risk (Equation 4.11), subunits
could be numerically ranked and categories of future
loss could be defined by specific criteria. The following
example provides th-ee possible categories:
• High future loss - subunits where habitat value and
future risk are both ranked in the upper third (sub-
units 5,16,20-23,26,39,45, and 48);
a Low future loss - subunits where habitat value and
future risk are both ranked in the lower third (sub-
units 2,30,33,35,42,43,56,57,62);
• Intermediate future loss- the remaining 43 subunits.
Given these categories and the goal of identifying habi-
tat protection areas, priority areas for conservation
should be subunits in the first category because they
represent areas with the greatest amount of habitat
value subject to the greatest risk. Wetland managers
might want to adopt county- or basin-wide conserva-
tionplansin these areas. Wedandsin the second category
are of low function and are not at risk and therefore
would be the lowest priority for habitat protection.
Within the intermediate areas, protection is not an im-
mediate necessity because areas either are not at risk or
have low value; the state might adopt a "wait and see"
attitude for subunits in the intermediate category until
risks begin to change. However, conservation ap-
proaches might be important in these subunits on an
ad-hoc basis in cases where high habitat value occurs
locally.
State of Illinois
While the previous three studies made use of data
generally available throughout much of the United
States, the Illinois case study illustrates the use of
better information available in some areas. However,
the data were obtained from an ongoing study that
selected areas based on specific criteria; the entire
state is not included.
Management Goal
The objective in this example is to develop synoptic
maps that can be used to rank subunits for restoration
according to the potential for water quality improve-
ments. Specifically, the resource manager is interested
in identifying areas where riparian wetland restoration
would provide the greatest benefit from reduced nitro-
gen levels to human water supply and to non-degraded
fish communities.
Wetland Types
Forested wetlands and wet meadows are the most com-
mon wetland types in Illinois. They generally occur in
close association with river systems. Swamps occur in
the southernmost portion of the state.
Landscape Boundary and Subunits
Illinois is bordered by the Mississippi River to the west,
the Ohio River to the southeast, the Wabash and White
rivers to the east, and Lake Michigan to the northeast.
The state has an area of 144,120 km2 and encompasses
18 principal river basins, each with numerous tributar-
ies. However, this case study addresses a subset of 90
subunits included in an investigation into the landscape
function of Illinois wetlands that is being conducted by
the Wetlands Research Program. USGS stream gage
stations that met specific criteria on data availability and
lack of hydrologic modifications or pollutant discharges
were identified. Once the monitoring stations were
selected, the boundary of their drainage area was deter-
mined fromtopograpnicmaps. These subunitsrepresent
natural watersheds, each having a drainage area less
than 3,750 km2 (Figure 4.15).
Natural Setting
Geologic and climatic factors are driving forces affect-
ing wetlands that were formed by glaciers in northeast
and east central Illinois and by rivers throughout the
state (Bell 1981). Illinois is characterized by low relief,
with elevations ranging from 90 to 300 m. The climate is
humid continental with hot, moist summers and cold,
drier winters. Annual precipitation averages between
80 and 120 cm and is concentrated in the summer
months. Illinois is considered a "water excess" state: It
is surrounded by freshwater, supports an impressive
network of rivers, and has an abundance of ground wa-
ter (Neely and Heister 1987). Tornados, hailstorms, and
flooding are naturally occurring environmental distur-
bances.
Wetlands are found along the banks of glacially formed
lakes or depressions where water has accumulated.
Wetlands are also created and controlled by the mean-
dering and flooding of major river channels like the
upper Mississippi and the Ohio (Bell 1981). The natural
vegetation of the state is oak-savanna in the north,
which changes to a mixture of bluestem prairie and oak-
hickory forest in the central part of the state, and
oak-hickory and cypress swamps in the south.
Wetland Functions
The historic wetlands of Illinois provided habitat for
many wetland-dependent species. The remaining wet-
lands are used by waterfowl for feeding, breeding, and
resting areas. The swamps of southern Illinois support
Case Studies 55
-------
Table 4.4. Landscape indicators for the Illinois case study.
Index Component
Indicator
CAPACITY™^ (water quality
capacity of riparian wetlands)
1NPUTN (nitrogen loading rates
into riparian wetlands)
HUMAN (human benficiaries of
water quality)
FISH (valued fish populations)
AREA H (riparian hydric soil area)
AREA w (current riparian wetland
area)
Area of all wetlands within a stream buffer of 150 m (75 m per side); stream buffers
defined by GIS from 1:100,000 USGS Digital Line Graph data and overlaid with wetland
area from digital 1:24,000 National Wetland Inventory coverages
Mean nitrate+nitrite loadings (mg/s) at USGS water quality stations for 1978-1987;
calculated as the product of mean concentration (mg/l) and discharge (l/s) '
Human population in 1980, based on the U.S. Census (U.S. Bureau of Census 1982b); ' ;
prorated from county to subunit areas
Fish Index of Biotic Integrity values based on state surveys (IEPA 1990)
Area of hydric soils within 150 m stream buffer (as defined above), estimated by GIS from
digital soil association maps; associations classified as hydric based on whether
component soil series were predominantly hydric, as identified by SCS (1987)
Same as CAPACITY RPN above
stands of cypress and tupelo and a variety of endan-
gered or threatened plants and animals. Marshes and
wet prairies in northeastern Illinois provide habitat for
several endangered birds. Bald eagles and river otters
reside in wetlands along the Mississippi River. State-
wide, wetlands provide habitat for 40% of the state's
endangered plant and animal species.
Significant Impacts
i'
Activities associated withagricultureandlivestockpro-
duction, which account for more than 80% of the state's
land use, have had the greatest impact on wetlands.
Historically, 85% of Illinois wetlands have been de-
stroyed (Dahl 1990); most of this loss has resulted from
agricultural drainage. Conversion of wetlands to agri-
culture has the doubleeffectofaddinganutrient source
by replacing anutrientsink. Stream channelization and
trampling by livestock are other causes of degradation.
Urban expansion is an increasingly significant cause of
wetland loss, especially near the Chicago metropolitan
area.
Agricultural runoff containing fertilizers, herbicides,
and insecticides can degrade wetland water quality.
Crop cultivation on slopes can cause sedimentation and
increased water turbidity, especially in areas having
loessal soils (Omemiketal. 1981). Oil and gas extraction
and strip mining for coal also degrade water quality,
especially in southern and western Illinois.
Synoptic Indices
The first synoptic index defined for this case study is an
indexof water quality function. It specifically addresses
the role of riparian wetlands in reducing nitrogen
concentrations:
FUNCTION WQ = CAPACITY RPN x INPUTN
Equation 4.12
where FUNCTION ^Q is the water quality function of
riparian wetlands, CAPACrrYgp^ is the capacity of
riparian wetlands with respect to nitrogen reduction,
and INPUTN is the loading of nitrogen into these wet-
lands. Next, we define two indices of value based on the
benefits of this water quality function for human water
supplies, VALUE H, and for valued fish communities,
VALUE
VALUE H = FUNCTION WQ x HUMAN
Equation 4.13
VALUE F = FUNCTION WQ x FISH
Equation 4.14
where HUMAN represents the human beneficiaries of
this water quality function, and FISH represents popu-
lations of valued fish communities, i.e., those that have
not been degraded by impacts.
For replacement potential, use the same index used in
the Louisiana case study for soils (Equation 4.3) except
that it is limited to riparian wetlands.
REPLACE s = (AREA H - AREAw) /AREA H
Equation 4.15
Landscape Indicators
Table 4.4 contains a list of landscape indicators for the
Illinois case study. Wetland areas were measured
from 1:24,000 digital N WI maps instead of the 1 =250,000
LULC maps used in the previous case studies. The
NWI maps have a one-tenth hectare resolution and
represent the best existing statewide digital wetland
data. For the indicator of water quality function, we
assumed that reduction in nitrogen loadings by exist-
ing wetlands is related to the product of riparian
56 Synoptic Approach
-------
wetland area, i.e., wetlands occurring within a 150 m
river buffer (75 m per side), and stream nitrate+nitrite
levels, as derived from USGS water quality monitoring
data.
We estimated the value of water quality for human
water supply by multiplying water quality function
by 1980 population; this, therefore, assumes that the
entire population of a subunit benefits from water
quality improvement and that stream water is either
the major source of water or is an adequate indicator
of overall water quality. For fish, we used the Index of
Biotic Integrity (IBI) to define non-degraded fish com-
munities (IEPA 1990). The IBI is an index of fish
species richness, composition, abundance, condition,
and trophic composition (Karr 1981); high IBI values
are associated with less degraded conditions.
The indicator for replacement potential was similar to
that used in the Louisiana case study for determining
non-wetland hydric soils (REPLACES
• Areas were limited to the riparian buffer,
• Wetland area was estimated using 1:24,000 NWI
maps, and
• Area of hydric soils was based on a digital soil asso-
ciation map rather than on dot count estimates from
soil series maps.
Map Interpretation
Figure4.16shows water quality function with respect to
riparian wetlands and nitrate+nitrite levels (class inter-
vals for all Illinois maps were selected by visual
inspection, as in the Washington case study). A planner
could use this map to target restoration in subunits
having intermediate water quality function, since these
areas are already somewhat functional and could ben-
efit from additional wetlands. However, those subunits
might not be located where nitrogen reduction would
be of greatest value. Thus the maps of value with
respect to humans (Figure 4.17) and fish communities
(Figure 4.18) can be used to optimize for both function
and value. For example, human populations in the
greater Chicago metropolitan area could benefit most
from improved water quality (Figure 4.17).
The original management objective was to assign prior-
ity to subunits for wetland restoration; although the
preceding maps identify areas that could benefit most
from increased water quality function, the assessment
does not take into account whether restoration would
be successful. It is also important to screen the subunits
for replacement potential, i.e., large proportions of non-
wetland hydric soils (Figure 4.19). This map can then be
compared to the water quality value maps to focus
restoration efforts on watersheds where soil conditions
are most favorable for establishment of wetlands. Given
Figures 4.17-4.19, a planner could screen the state for
subunits that best meet the management objectives. For
example, units 1 and 58 would be good candidate areas
for locating restoration projects.
Case Studies 57
-------
ILLINOIS SUBTJNTT INDEX
(Natural Watersheds)
Figure 4.15. The Stale of Illinois and the 90 subunits. Subunits are natural watersheds defined around USGS gage stationsl
58 Synoptic Approach
-------
WATER QUALITY FUNCTION
FUNCnONWQ (x 1010 )
0.0 - 1.1
12 - 2.8
2.9 - 5.6
5.7 - 38.0
Subunit - FUNCTIONWQ (x 10
10
FUNCTION WQ = (NO5+NOj) x
NWI Riverine Wetland
1
2
3
4
5
6
7
8
9
10
11
- 12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
- 11.0
- 03
- 0.8
- 53
- 1.6
- 5.6
-27.0
- 19.0
- 2.8
- 33
- 12
- 43
- 5.0
- 42
- 1.4
- 23
- 0.1
- 0.0
- 1.6
- 2.8
- 8.4
- 0.8
- 11.0
- 5.7
- 3.7
- 1.9
- 92
- 7.6
- 3.0
- 1.4
- 0.1
- 02
- 1.4
- 1.8
- 52
- 73
- 9.1
- 0.6
- 1.4
- 27.0
- 8.8
- 23
- 38.0
- 1.0
- 21.0
46-
47-
48 -
49-
50-
51 -
52 -
53 -
54-
55-
56-
57-
58 -
59 -
60 -
61 -
62-
63-
64 -
65-
66 -
67-
68 -
69 -
70 -
71 -
72-
73 -
74-
75-
76-
77-
78 -
79 -
80 -
81 -
82-
83 -
84 -
85-
86 -
87-
88 -
89 -
90 -
0.6
0.0
53
24.0
62
6.1
83
23
4.0
0.8
5.0
2.0
11.0
23
23.0
20.0
23
33
4.4
5.0
2.9
99
0.0
12
0.7
03
1.1
1.1
1.8
3.7
2.9
52
19
5.0
10.0
23
0.8
0.6
1.0
1.8
4.1
03
32
02
4.7
Figure4.16. Waterquality function for Illinois. Darkerhatchingcorrespondsto higher waterquality function. The variables included
in the equation for FUNCTION WQ represent the landscape indicator, not components of the synoptic index (Equation 4.2).
Case Studies 59
-------
r
WATER QUALITY VALUE
HUMAN POPULATION
VALUEH (x 1015 )
0.0 - 0.4
05 - 1.4
1.5 - 4.7
5.1 - 45.0
VALUBH - (NCVNO,) x Fop x
NWI Riverine Wetland
Subunit - VALUEH (x 1015 )
1 - 7.8
2- 0.6
3 - 13
4-55
5-03
6- 7.1
7 - 15.0
8- 3.0
9-05
10- 05
11 - 0.4
12- 0.9
13 - 0.9
14- 03
15- 05
16 r 0.7
17- 0.1
18 - 0.0
19- 0.2
20- 0.6
21 - 14.0
22- 1.6
23-12,0
24- 3.4
25 - 1.6
26- 0.7
27- 3.7
28-23
29- 1.4
30-03
31 - 4.1
32- 0.7
33 - 41.0
34 - 39.0
35 - 30.0
36 - 45.0
37- 4.7
38-32.0
39 - 3.4
40 - 14.0
41 - 3.7
42- 0.9
43 -36.0
44-03
45 - 12.0
46-03
47 - 0.0
48 - 5.1
49 - 20.0
50- 2.9
51 - 1.7
52- 4.1
53 - 4.7
54-2.6
55 - 0.6
56 - 4.6 '
57- 1.6
58 - 13.0
59-05
60- 6.7
61 - 55
62-0.$
63 - 13
64- 2.0
65- 1.9
66 - 3.9
67- 7.6
68 - 0.0
69-03
70 - 0.2
71 - 0.1
72-03
73 - 0.4
74 - 0.8
75 - 1.4
76 - 0.9
77- 1.6
78 - 2.8
79 - 11.0
80-24.0
81 - 6.1
82-03
83-03
84-03
85 - 0.7
86-15
87 - 02
88 - 1.0
89- 0.1
90 - 1.1
Fifluro4.17. Water quality value with respect to humans for Illinois. Darker hatching corresponds to higher water quality value.
The variables included in the equation for VALUE H represent the landscape indicator, not components of the synoptic index
(Equation 4.13).
60 Synoptic Approach
-------
WATER QUALITY VALUE - FISH
VALUEp (x 1010)
VALUEp =
(NOj+NO,) x IBI x
NWI Riverine Wetland
1 - 47
48 - 180
200 - 1500
Siibunit-VALUEp (x 1010)
46 - 0
4V - 0
48- 0
49-680
50-200
51 - 0
52^250
53 - 0
54-180
55 - 0
56-170
57- 97
58- 410
»- 0
60- 0
61-920
62- 0
63 - 0
64 - 0
65 - 0
66 - 110
67-400
68 - 0
69- 0
70-32
71 - 14
72-47
73-33
74-48
75-120
76- 0
77-230
78-62
79 - 150
80-340
81 - 62
82- 0
83 - 0
84 - 0
85-43
86 - 150
87 - 0
88 - 0
89- 0
90 - 0
Figure 4.18. Water quality with respectto valued fish communities for Illinois. Darker hatching corresponds to higher water quality
value. The variables included in the equation for VALUE F represent the landscape indicator, not components of the synoptic Index
(Equation 4.14).
Case Studies 61
-------
REPLACEMENT POTENTIAL
SOILS
REPLACEo
(Riverine Hydric - NWI Riverine
Wetland) / Riverine Hydric
-3.17 - 0.48
0.48 - 0.66
0.67 - 0.84
0.84 - 1.00
Subunit - REPLACED
46--034
47 - -3.17
48 - 0.92
49 - 0.69
50-0.80
51 - 0.71
52 - 0.62
53 - 0.40
54- 0.87
55 - 0.97
56- 0.87
57- 0.85
58- 0.84
59 - 0.49
60- 057
61 - 0.49
62 - 0.84
63 - 0 JO
64- 0.74
65 - 0.79
66-0.66
67- 0.84
68- 1.00
69- 0.93
70- 0.62
71 - 0.79
72- 0.78
73- 0.65
74 - 0.67
75-058
76- 0.80
77- 0.66
78- 0.58
79 - 0.42
80 - 032
81 - 0.79
82-058
83 - 033
84-054
85 - 0.48
86-037
87 - 0.47
88 - 0.48
89 - 0.29
90- 0.44
Figuro 4.19. Replacement potential with regard to soils for Illinois. Darker hatching corresponds to higher replacement potential
'" eqUatfon for REPLACE s represent the landscape indicator, not components of the synoptic index
62 Synoptic Approach
-------
Chapters
Section 1
Summary
A synoptic assessment provides resource
managers with a landscape context for
both, project-specific decisions and regional
planning. Although designed for evaluating cumula-
tive impacts to wetlands for 404 permit review, the
approach has broader applications. For example, it
could be used to augment a regional risk assessment, to
help define regional priorities for protection and resto-
ration as part of a State Wetland Conservation Plan, and
to identify areas that would contribute most to water
quality improvement as part of a watershed approach
to reducing nonpoint source pollution. Although we
have illustrated the approach mostly with statewide
assessments, the synoptic approach could be applied to
issues at different geographic scales (Figure 5.1), for
example, to:
• Develop priorities for wetland protection and re-
search at the national scale;
• Provide a landscape context for advance planning at
regional or state scales; and
• Identifypotentialcandidateareasformitigationbank-
ing at the watershed scale.
The aspects of the synoptic approach that should make
it most useful for resource managers are (1) an assess-
ment can be completed within a year at relatively low
cost; (2) flexibility in selection of indices and indicators
allows an assessment to be customized to specific needs;
and (3) results are presented in mapped format to
facilitate their understanding and use.
The synoptic approach is not a fixed procedure that
always uses the same data sources and produces a
standard set of end products. Rather, a synoptic
assessment is a creative process that relies heavily on
the user to ensure that the final assessment is appro-
priate for the intended use. The two most critical
steps in conducting a synoptic assessment are defin-
ing the synoptic indices and selecting the landscape
indicators. The synoptic indices serve as the basis for
comparing the characteristics of landscape subunits;
they represent the actual functions, values, and im-
pacts of concern to the manager. The resource
specialist familiar with a particular landscape is re-
sponsible for defining the synoptic indices most
relevant to the specific objectives. The landscape
indicators used to estimate the synoptic indices are
also specific to the particular assessment and are
dependent on management objectives, the level of
confidence required, and on constraints.
We note, however, that this report does not provide a
specific, detailed procedure for chosing the indices, nor
does it provide a scientifically tested list of landscape
indicators with known confidence limits. Instead, the
approach relies on best professional judgment (BPJ) for
making these decisions. We often refer to the synoptic
as a framework, since it provides professionals with an
Summary 63
-------
National Scale
• Research prioritization
• Wetland protection prioritization
• Regional or state context
Regional or State Scale
• 404 permitting
• Mitigation planning
• Water quality standards
• Advanced identification
Watershed or Subwatershed Scale
• Advanced identification
• Goal setting
• Mitigation siting
Figure 5.1. Applications of synoptic assessments at various spatial scales.
64 Synoptic Approach
-------
ecologically-based structure that allows them to use BPJ
to address landscape issues such as cumulative impacts.
In fact, the simplest form of a synoptic assessment
would bea workshop where a group of regional experts
work through the steps in Chapter 3 using only BPJ,
without any data.
the accuracy of an assessment. Ultimately, accuracy
depends on (1) how well the indices reflect the actual
environmental conditions, (2) the quality of the data
being used, and (3) the degree to which assumptions
concerning the use of indicators are valid. Evaluating
the accuracy of an assessment is an important step in the
overall process because accuracy determines the degree
to which synoptic results can be incorporated into real
decision making. Results from a simple assessment
should be used only to provide broad background
information, to serve as an initial screening tool, or to
raise "red flags" requiring more intensive consider-
ation. Using such results for critical or controversial
decisions would be inappropriate unless the conclu-
sions were validated with more detailed information.
Management decisions can rely more heavily on the
conclusions if better data with higher confidence levels
are used.
The synoptic approach is a compromise between the
need for rigorous results and the need for timely
information. Ideally it should be an iterative ap-
proach, with analysts updating the completed
assessment when better indicators or more time to
gather data become available. The approach is not a
magic mirror that provides all the answers; it cannot
be accomplished without the creative input of spe-
cialists who must determine how to characterize the
landscape and select the appropriate indicators care-
fully. Common sense must be used when interpreting
and applying the results of an assessment. Assuring
that the results best fit needs and constraints is, there-
fore, the burden of the people conducting the
assessment. The usefulness of the information will
ultimately depend upon their knowledge of the envi-
ronmental processes relevant to particular management
questions.
Future Directions
In the future, we hope to see two improvements in the
synoptic approach. First, as part of the WRFs five-year
plan for 1992-1996 (Leibowitz et al. 1992), we will be
developing regional synoptic handbooks for the prairie
pothole region and for southeastern bottomland hard-
wood forests. These regional handbooks will follow
several years of research that will include landscape
studies; thus they will be based on validated models of
regional landscape function and will include tested
landscape indicators. These "second generation" prod-
ucts will take much of the BPJ out of the synoptic process
and replace it with more rigorous information. These
handbooks will also serve as prototypes for producing
regional analyses.
The second improvement we hope to see will rely on
users who conduct their own synoptic assessments.
We noted that the steps described in Chapter 3 re-
sulted, from the pilot studies that were the basis for
the Chapter 4 case studies. At this time we have no
real case study that explicitly followed these steps
and demonstrates their use. In the future, we hope
many people will produce their own synoptic assess-
ments using these procedures, and that these
assessments will act as true case studies to illustrate
the approach.. We also hope those who conduct
synoptic assessments will contact us and share their
successes and failures, so we can improve the method
in the future. We have provided an information form
(Appendix I) for any readers who want to be included
on a mailing list for future products and who want to
provide us with feedback about this report.
Summary 65
-------
-------
-------
-------
T
Chapters
Ecological
Response to
Stress
he synoptic approach allows information on
landscape condition to be included in decisions
based on available information and limited re-
sources. To achieve this, the synoptic indices were
based on a simple landscape model (Chapter 2). In this
chapter, we discuss additional information related to
cumulative impacts but which is too detailed to be
included in a synoptic assessment. This information
could still be useful in formulating the synoptic indices.
This chapter also introduces some of the relevant eco-
logical literature for those interested in additional
information.
We begin with several definitions. In Chapter 1, we
introduced the terms "impact" and "effect" because
they are found in regulations and literature on environ-
mental assessment. Within ecological literature,
however,asecond terminology is more commonly used
with reference to ecological stress; unfortunately, this
second vocabulary is also inconsistently applied. For
example, "stress" has been used to signify both cause
and effect (Odum 1985). Because there appears to be no
standard usage, we will adopt the following definitions
(Figure 6.1):
« Disturbance— The action that causes a stress. This
can also be referred to as a stressor.
• Stress — The immediate physical, chemical, and
biological changes that result from the disturbance.
• Response — The long-term physical, chemical, and
biological changes that indirectly result from a dis-
turbance.
This terminology is not limited to actions caused by
humans; stress caused by natural agents is also included
here, e.g., damage from hurricanes or fires. We can
redefine impacts as the subset of disturbances caused
by people; effects are a combination of stress (direct
effects) and response (indirect effects), and are similarly
limited to changes resulting from human actions.
Ecosystem Stability
One of the remarkable properties of natural ecosystems
is their ability to persist over time in spite of disturbance
and a changing environment. Disturbance occurs in
many forms and at various spatial and temporal scales
(Figure 6.2). Disturbances can affect individuals, groups
of individuals, populations, ecosystems, and entire land-
scapes. A collection of papers discussing the effect of
natural disturbance on various ecological communities
is found in Pickett and White (1985a). Natural distur-
bances such as fire, flooding, and volcanism can be
major factors in landscape development (Forman and
Godron 1986; Pickett and White 1985b).
A significant body of ecological literature has been
devoted to the subject of ecosystem stability in an at-
tempt to provide a theoretical explanation for this
Ecological Response to Stress 69
-------
(a)
Figure 6.1. Example of a disturbance, stress, and response. The disturbance is dredging by the excavator (a), and hydrologic
modification and compaction from spoil deposits are the stress (b). The response includes altered animal behavior (c) and other
indirect effects, such as reduced productivity.
property (e.g., Rolling 1973; Loucks 1970; MacArthur
1955; Margalef 1968; May 1974; Odum 1969; Odum et
al. 1979; Rutledge et al. 1976; Ulanowicz 1979). Early
ecological theory held that populations of competi-
tors or predators and prey would approach a stable
between species within a food web (MacArthur 1955).
The argument was that the more paths there were for
energy or nutrients to pass through a food web, the less
the impact if any one pathway were lost. Given a
disturbance, the community with the greatest diversity
would return to equilibrium population levels most
rapidly, thus minimizing variability. However, exten-
sive analyses of population models by May (1974) led
him to caution against the overly simplistic view that
stability is an automatic consequence of diversity (or
complexity, in his terminology).
Holling (1973) later argued that the emphasis placed on
stability amounts to a static view of nature based on the
concept of the stable equilibrium; in nature, populations
are dynamic and often occur in transitional states. Na-
ture as a stable equilibrium implies homogeneity.
Holling contended that variability over space and time
allows populations to respond to environmental distur-
bance because it permits a population to exist in low
numbersandtakeadvantageofopportunitiesforgrowth.
He therefore distinguished between the ability of a
system to resist disturbance and the ability of a system
to return to the equilibrium state, given some distur-
bance. Although Holling referred to these two
characteristics as resilience and stability, respectively,
we shall use the more current terms resistance and
resilience (Figure 6.3) (e.g., Forman and Godron 1986;
Odum et al. 1987). A recent paper by Fisher and
Grimm (1991) discusses resistance and resilience in
stream ecosystems.
Adaptations to Stress
Ecosystem stability is ultimately derived from adapta-
tions by resident species. Adaptation can occur through
many different mechanisms. We discuss below three
factors that contribute to ecosystem resistance and
resilience.
Physiological Adaptations
By developing physiological adaptations, organisms
can exist in a large number of stressful environments;
they can adapt to extremes of temperature, pressure,
oxidation, and desiccation. For example, pitch pine and
shortleaf pine have adapted to fire by developinga thick
bark and by evolving a mechanism that allows them to
sprout new shoots if stressed by fire; thus revegetation
can quickly take place after a fire (Robichaud and Buell
1983).
Halophytic ("salt loving") plants are another example of
physiological adaptation. Halophytes have evolved a
variety of mechanisms that allow them to avoid direct
toxicity and dehydration due to salinity (the latter oc-
curs because of the higher osmotic potential of the
saltwater relative to the cell). For example, some plants
are able to balance osmotic pressure while avoiding
sodium toxicity by increasing the amount of potassium
in the cell; other plants maintain osmotic pressure by
producing glycerol. Still other halophytic adaptations
include barriers that prevent salt entry, organs that
excrete salt, and mechanisms that reduce water loss
(and danger of dehydration) due to transpiration (Mitsch
and Gosselink 1986).
Life History
In a stable setting, the amount of variability in environ-
mental conditions is minimal, and a maximal amount of
70 Synoptic Approach-
-------
109 -
zf
S 106 -
1
CO
1
1 103 -
£
-
•
10°
K
ENVIRONMENTAL piaie
DISTURBANCE Tectonics
REGIMES |
IGlacial-
Interglacial
Climactic Cycles
-«— I — Climactic Fluctuations »-
« Human Artivitip*; ^
•X- Disturbance Events
I I I I I I I I I I I I | 1
3° 104 108 1012
CO
0)
_
CO
o
CO
2
o
a.
o>
109 -
106 -
_
103 -
"~*
—
m°
f
BIOTIC RESPONSES Evolution of
i
f Secondary
Succession
Gap-phase j
Replacement
I
Competition
I
Productivity
f
i i i I I I
the Biota
1
i
I
Ecosystem Change
Speciation
Extinction
1
Species Migration
\
1 1 1 1 1 1 1 1
10
,12
Spatial Scale (m2)
Spatial Scale (m2)
Figure 6.2. Disturbance (a) and biotic responses (b) occur in many forms and at various spatial and temporal scales (adapted from
Delcourt et al. 1983). *Disturbance event examples include: wildlife, wind damage, clear cut, flood, and earthquake.
predictability allows populations to approach their car-
rying capacity (the maximum number the environment
can support). Populations in climax ecosystems tend to
be adapted for competitive ability and increased re-
source utilization (Ricklefs 1979). Other general
characteristics of these populations, which are known
as K species *, are slow development, large size, special-
ized resource use, delayed reproducu'on,and production
of small numbers of seed. In an environment that
Time
Figure 6.3. Ecosystem resistance and resilience. Graph
shows the functional response of two ecosystems to a similar
disturbance. The ecosystem represented by the solid line has
higher resistance, since the same disturbance causes a smaller
change in function. However, the ecosystem represented by
the broken line has higher resilience, since the timefor recovery
to the original level of function is snorter.
experiences periodic disturbance, however, there is a
maximal amount of variability, and a minimal amount
of predictability; the population's ability to re-establish
itself quickly is more important than competitive abil-
ity. Thus populations in successional ecosystems,
known as rspecies 2, tend to develop rapidly, have small
body size, use a broader range of resources, reproduce
early, and produce large numbers of seed.
Competition dominates in the climax community, lead-
ing to specialization for limited resources; the result is
high interspecific diversity. Given the stability of this
environment, the main reproductive strategy is to de-
vote a maximum amount of energy per offspring by
minimizing the amount of seed produced, giving the
offspring a competitive advantage. In successional eco-
systems, however, environmental uncertainty dominates
over competition, creating the need to adapt to variabil-
ity. Because the probability of survival is lower, a large
number of offspring are produced, with minimal en-
ergy investment per offspring. High intraspecific
diversify allows the species to adapt to the uncertainty
that results from a wide range of conditions.
1 The "K" represents the term used in population models to
represent the carrying capacity of that population.
2 The "r" represents the term used in population models to
represent the intrinsic growth rate of the population, thus
signifying high growth.
Ecological Response to Stress 71
-------
Gene Banks
Development of physiological adaptations or r versus
K characteristics in a species takes place over evolution-
ary time through natural selection of new genotypes
produced by mutation or genetic recombination. Over
shorter ecological Jimes, this variability exists in the
form of a "gene bank," which is the total collection of
genes within an ecosystem or region. If a disturbance
decimates a particular population that uses a specific
resource, another population is often "waiting in the
wings" to exploit that resource. In the prairie pothole
region of North America, seed banks 3 containing veg-
etation adapted to various levels of flooding allow the
potholes to adapt to wet/dry cycles (Kantrud et al.
1989). In this case, the in-place seed bank provides a
source of biological diversity.
Gene banks represent a form of redundancy that con-
tributes to ecosystem stability. Redundancy, a formal
concept derived from information theory (Gatlin 1972;
Shannon 1949; Shannon and Weaver 1963), is defined
as the additional amount of information that must be
added to a signal to assure transmission over a noisy
channel without loss of information. In Chapter 1 we
alluded to redundancy in discussing the effect a lost
rivet would have on the airworthiness of a jet. In this
case, redundancy is introduced by adding more rivets
than would be necessary under perfect conditions. The
"transmittal" of the jet through the "noisy channel" (air
turbulence and wing stress) can occur without a loss of
information (a crash), despite the occasional loss of a
rivet.
In ecosystems, the signal transmission is the transfer of
genes through reproduction, and the noisy channel is
disturbance and other stresses that either cause mortal-
ity or interfere with reproduction (Margalef 1968).
Ecosystems contain redundancy when functions can be
performed in more than one way or when a reserve
capacityofstructure exists (Bormann 1987). Theamount
of ecosystem redundancy is one of the factors that
determines the degree to which a given disturbance
causes a loss of ecosystem function. For example, by
providing a source of new plant material, a seed bank
serves as a back-up system that buffers an ecosystem
from disturbance. However, the bank itself can be
altered by disturbance (Wisheu and Keddy 1991).
Stress-Adapted Ecosystems
Although a disturbance such as fire is stressful to or-
ganisms and even entire populations, the effect at the
ecosystem level need not be adverse if the ecosystem is
adapted to that stress. Adaptation can take place if
there has been sufficient time for adaptation to occur,
and if the magnitude and frequency of the stress justify
the physiological costs of adaptation.
Ecosystems can be so well adapted to stress that the
stress actually becomes critical to the sustainability of
the system. The classic example is the fire-adapted, or
fire-climax ecosystem. Fire is a natural component of
the New Jersey pine barrens, where pine species are
dominant and oak is subdominant (Robichaud and
Buell 1983). The pine is able to maintain dominance
because it is less easily damaged by fire and has evolved
mechanisms that allow it to revegetate quickly after-
wards. When people suppress fire, a layer of organic
materials (mostly pine needles) builds up on the forest
floor and eventually causes a shift in dominance from
pine to oak, because oak seedlings are better established
on this layer. This leads to the paradox that fire, which
would normally be considered a stress, is in fact a
forcing function necessary to the maintenance of the
community, from the perspective of the pine forest.
From this viewpoint, fire suppression is the stress.
As the preceding discussion demonstrates, stress can-
not be thought of in an absolute, context-free sense at the
level of the community or ecosystem; we cannot say
whether fire is a stress without first considering whether
it is a natural component of an ecosystem adapted to
that stress. Barrett et al. (1976) define stress as "a
perturbation applied to a system (a) which is foreign to
that system or (b) which is natural to that system but
applied at an excessive level." In general, we use the
term stress throughout this document only when an
adverse effect is implied.
Effect of Disturbance on Ecosystem
Function
Disturbance can affect processes and structure within
an ecosystem or the outside forcing functions driving
the ecosystem (Figure 2.1). Whether a disturbance
causes a loss of ecosystem function depends on the
degree of redundancy in the ecosystem. In particular, if
the amount of material being removed or retained by a
sink ecosystem is less than the assimilative capacity,
then the ecosystem has an excess capacity to remove
additional material; this is a form of redundancy that
buffers ecosystem function from disturbance. Consider
a simple steady-state ecosystem where import is equal
to 25 units and production is equal to 50 units, for a total
input of 75, and where decomposition capacity is equal
to 100 units (Figure 6.4). In this case, there is an excess
capacity of 25, since only 75 units are decomposed;
export is equal to zero, since all input can be removed
on-site. This excess capaci ty buffers the landscape from
certain impacts; for example an impact that increases
either import or production by as much as 25 units or
decreases capacity by up to 25 units would not
3 A seed bank represents one specific type of gene bank, i.e., a
collection of viable plant seed within the soil.
72 Synoptic Approach
-------
I Capacity Excess
C = 100 X = 25
Figure 6.4. Simple steady-state ecosystem where excess capacity buffers function from disturbance. Total input
(import plus production) is equal to 75 units.allofwhich is decomposed. Since decomposition capacity is 100, there
is an excess capacity of 25 units.
affect export, and thus landscape flows (Figure
6.5). For the original ecosystem (Figure 6.4), an
impact that reduced imports or production or
that increased capacity would similarly have no
effect outside the ecosystem, and would in-
crease the excess capacity. Next, consider an
ecosystem that could potentially function as a
sink, because decomposition capacity exceeds
production, but that receives no import. This
ecosystem also has excess capacity, and an im-
pact that decreases capacity or increases
production would have no effect on landscape
flows, as long as capacity remains greater than
on-site production. In these situations, the effect
of a disturbance is limited to changes within the
ecosystem because of excess capacity. From a
landscape perspective, such a disturbance has no
effect. However, loss of excess capacity does
Decomposition
0 = 35
I Capacity Excess
I C = 100 X = 5
Figure 6.5. Buffering of landscape effects in a disturbed ecosystem. Impacts to the original ecosystem (Figure
6.4) have increased import and production by 10 units each. Since there was an excess capacity of 25 units, the
additional 20 units of input has no effect on exports. However, excess capacity is reduced.
Ecological Response to Stress 73
-------
limit the amount of additional material that the ecosys-
tem could remove or retain in the future, if imports or
production were to increase later.
Mitigating Effects of Landscape on
Disturbance
Landscape pa ttem plays an important role in determin-
ing ecosystem response to disturbance. Recognizing
this role, Forman (1990) has proposed that management
efforts be focused on the landscape mosaic rather than
on the individual ecosystem. Below are several ex-
amples of how landscape characteristics can influence
the effects of disturbance.
Landscape Elements as Conduits or Barriers
to Disturbance
In Chapter 2, we based our definitions of source and
sink ecosystems upon the difference between imports
and exports. In cases where import is exactly equal to
export(indudingimportand export both equal to zero),
the ecosystem is neither a source nor a sink. Such an
ecosystem can stillplayanimportantroleasaconduitor
barrier (Forman and Godron 1981). A conduit is an
ecosystem thatassists the movementofmaterials through
different parts of the landscape by transferring imports
between ecosystems without altering the amount of
material. For example, hiking trails can function as
conduits for plant species in Rocky Mountain National
Park (Benrunger-Truax et al. 1992). A barrier is an
ecosystem that inhibits material movement by exclud-
ingimports froman ecosystem. Streams serveas barriers
to organisms that are unable to swim or fly.
A disturbance is not simply a static event that affects a
discrete area. Disturbances are often dynamic and can
move through the landscape; examples include fire,
floods, storms, and pest outbreaks. For each of these, a
landscape element may act as a conduit or barrier to the
movement of the disturbance through space. Stream
channels no t only act as conduits for floods, but they can
also serve as conduits for debris flows (Swanson et al.
1992). In upstate New York, the thruway can act as a
conduit for migration by the exotic purple loosestrife
(Lythrum salicaria L.), which then invades adjacent wet-
lands (Wilcox 1989). Aquatic habitats, ridges, valleys,
and patches of low flammability vegetation can all act as
barriers to fire (Knight 1987). The position of conduits
and barriers relative to a disturbance can determine the
degree to which a particular ecosystem is affected by that
disturbance.
Landscape Pattern of Sources and Sinks
At the landscape level, a change in ecosystem function
caused by disturbance has the effect of increasing or
decreasing the amount of one or more materials that are
transferred between ecosystems. The degree to which
this change affects material flows within the landscape
depends on the spatial distribution of sources and sinks.
We described earlier how an increase in import or
production or a decrease in capacity would have no
effect on landscape flows if sufficient excess capacity to
buffer this change existed (Figures 6.4 and 6.5). Simi-
larly, the effect of impacts to a source would be limited
if a source ecosystem were directly coupled to a sink
havinga large excess capacity; a small increase in source
flow would have no effect in this instance beyond the
changes within the two ecosystems. In general, the
tighter the coupling between sources and sinks and the
greater the excess capacity, the more the landscape is
buffered from such effects. As we noted previously,
however, this would affect an ecosystem's ability to
retain or remove additional materials in the future.
Recovery from Local Extinction
Off-site gene banks represent a form of redundancy that
can deter local population extinctions. Recolonization
from surrounding patches is in some ways analogous to
the colonization of newly created islands, and prin-
ciples from the theory of island biogeography have been
applied to the study of population maintenance within
the landscape (Gosselink and Lee 1989; Harris 1984;
MacArthur and Wilson 1967).
For plants, the amount of time it takes to recolonize an
area depends on seed characteristics and the amount of
seed produced. For example, seed weight, seasonal1
timing and duration of seed production, and method of
dispersal (wind, birds, or mammals) are all factors in
determining the re-establishment of woody plants fol-
lowing a disturbance (Canham and Marks 1985). In
particular, the dispersal mode and the spatial arrange-
ment of regional seed banks within the landscape can
affect recovery after disturbance. For example, wind-
dispersed seeds may tend to concentrate in the center of
forest gaps, while seeds dispersed by animals may be
more common at gap edges (Canham and Marks 1985).
This phenomenon can affect not only the spatial pattern
of the resulting vegetation but also whether a species
becomes dominant in the ecosystem. The dispersal
characteristics of a seed may themselves be partially
determined by the different selection pressures that can
result from landscape pattern (Manicacci et al. 1992).
Locally extinct animal populations similarly can be re-
established through emigration from regional gene
banks. For a particular species, a population can be
defined as the collection of individuals inhabiting a
particular patch (an irregularly shaped area embedded
within a matrix of different structure). The
metapopulation is the combined population of all the
patches connected by the movement of individuals
(Henderson etal. 1985; Merriarn and Wegner 1992). If a
local extinction occurs within one of those patches, the
74 Synoptic Approach
-------
metapopulation serves as a gene bank for recolonizing
the area. Whether and how quickly a patch is recolo-
nizedfollowinglocalextinctiondependson the dispersal
characteristics of the organism and the spatial arrange-
ment of donor patches. The travel distance for a
particular organism is the maximum distance it can
travel in order to reach suitable habitat. Travel distance
might be limited by the organism's storage of metabolic
energy or by the organism's need to remain close to its
breeding site. In general, the lower the body weight of
the animal, the smaller the range (Harris 1984). The
ability of a species to recolonize a patch that has experi-
enced local extinction dependson thepatck distance, or
the distance between ecosystem patches. Thelocation of
barriers within the landscape, such as mountains, can
further restrict recolonization from a regional genebank.
It is interesting to note that a population living in a
marginal ecosystem can be sustained over time, in spite
of a death rate greater than the birth rate, as long as
immigration from surrounding ecosystems is high
enough to offset deaths. An ecosystem in which this
occurs would be considered a population sink
(MacArthur and Wilson 1967; Pulliam 1988).
Landscape Fragmentation
Loss of landscape structure can affect ecosystem func-
tions and the ability of populations to survive
disturbance. Approaches to preserving biodiversity
that focus only on the species will have limited success
if the habitat supporting that species is destroyed. Rec-
ognition of this basic relationship has led to increased
focus on preservation of regional habitat and the land-
scape as a means of preserving biodiversity (CEQ1991;
Harris 1988).
From a spatial perspective, a landscape can be viewed
as a mosaic containing three different kinds of ecosys-
tem geometries: patches (defined earlier); corridors
(narrow strips of land, such as streams and hedgerows,
surrounded by matrix on each side); and the back-
ground matrix, which represents an extensive, or
continuous, resource.
If disturbance in a matrix ecosystem occurs with a
frequency lower than the amount of time required for
recovery (Figure 6.3), then the ecosystem is stable over
the long run, and a loss of ecosystem area will not occur.
In this case, the disturbance represents the "figure"
within the "background" of the matrix (Figure 6.6a).
If, however, disturbance becomes more frequent or
causes a permanent alteration (e.g., conversion to a
different land use), then eventually the matrix will be-
gin breaking up into smaller patches (Figure 6.6b).
Finally, if the frequency of disturbance is great enough,
the disturbed ecosystem becomes the dominant back-
ground matrix, with the original ecosystem occurring as
remnant patches (Figure 6.6c). This process whereby an
ecosystem is broken up into smaller pieces is referred to
as fragmentation.
Franklin and Forman (1987) have studied this figure/
background switch in matrix and patch using a model of
forest clearcutting. Within a fragmented landscape, five
different kinds of patches can be distinguished (Forman
and Godron 1981,1986):
• Environmental resource patches, which are normal
components of a heterogeneous environment;
• Ephemeral patches, which are transient patches that
occur from normal, short-term fluctuations;
• Spot disturbance patches, which result from a small
disturbance within the matrix;
• Introduced patches, which—asinthecaseof agricul-
tural areas — are brought about by people either by
accident or design; and
(a) IM
(b)
(0
Figure 6.6. Ecosystem fragmentation: (a) disturbance is the
"figure" within the "background" of the natural matrix; (b)
break-up of matrix into smaller patches; and (c) converted land
use becomes the dominant background matrix. Fragmentation
can effect ecosystem function.
Ecological Response to Stress 75
-------
• Remnant patches — the opposite of spot distur-
bances — which occur when a large disturbance
takes place, reducing the background matrix to a
patch.
Many organisms have home ranges, defined as the area
around their homes typically used for feeding (Forman
and Godron 1986; Harris 1984). Fragmentation reduces
patch area and therefore reduces the amount of habitat
within the home range. This can lead to local extinction,
especially in the later phases. Fragmentation also in-
creases thedrcumference-to-arearatioof patches, which
causes a change in species composition from interior
species to edge species (Merriam and Wegner 1992).
Although this increases species diversity, the result is a
general shift from native (interior) to opportunistic
(edge) species (Gosselink and Lee 1989). Fragmentation
can also cause a loss of interconnecting corridors and
lower the size of a metapopulation. This effect together
with increased distance between patches reduces the
likelihood that locally extinct populations can be re-
established.
76 Synoptic Approach
-------
Chapter 7
A Review of
Wetland
Functions and
the Effect of
Wetland
Impacts
In this chapter we review current research on
three major wetland functions, emphasizing the
landscape scale. We discuss the effects of wetland
degradation, which results in partial loss of function, as
well as the effects of wetland conversion, which results
in total loss of function. The closing section addresses
the effects of cumulative wetland loss on overall land-
scape function. This information can provide a starting
point for defining specific indices of function and func-
tional loss. This chapter can also serve as a resource for
those wanting a review of wetland functions or addi-
tional information on how these functions are affected
by impacts.
Wetland Functions
Over the past several decades, new information has
highlighted the functions wetlands perform through
various physical, chemical, and biological processes
(Table 7.1). These functions can be divided into three
major categories: hydrologic functions, water quality
functions, and habitat functions.
Hydrologic Functions
Wetlands can function as hydrologic sinks by removing
water from local surface flow systems; this occurs when
floodwaters are temporarily stored within a wetland,
when runoff infiltrates the wetland surface, or when
runoff is converted to water vapor through transpira-
tion. Wetlands can also act as hydrologic sources,
conserving water and sustaining local moisture. Wet-
lands function as sources by serving as conduits for
groundwater discharge or by increasing or conserving
hydrologic inputs through interception, snow deten-
tion, condensation, or reduced evaporation. The factors
that determine whether source or sink functions domi-
nate in a given setting include regional climate, season,
landscape geomorphology, and wetland type and posi-
tion. Adamus et al. (1991), Carter (1986), Duever (1988),
Kadlec (1987), LaBaugh (1986), Winter (1988,1990), and
Winter and Woo (1990) review literature on hydrologic
functions of wetlands.
Almost any wetland has the potential to stagger the
arrival of runoff to downstream areas, and in so doing
reduce flood peaks. Many studies have found inverse
correlations between streamflow and the percentage of
watershed area occupied by wetlands, or variables that
could be related to wetlands (Table 7.2). Most often,
these studies support the hypothesis that wetlands are
important for attenuating peak flows (e.g., Table 7.3).
Watersheds with a large proportion of wetlands have
qualitatively different streamflow response to precipi-
tation, both in urban settings (Brown 1988) and in vast
peatland watersheds (Schwartz and Milne-Home 1982).
A Review of Wetland Functions 77
-------
Other hydrologic functions are not as well documented
as flood control. For example, some seasonal or tempo-
rary wetlands in arid regions can recharge shallow
aquifers (e.gv Heath 1982; token 1991; Wood and
Osterkamp 1984), as can some unforested bogs and fens
in wet boreal regions (e.g., Siegel 1988). Yet from a
national perspective it seems unlikely that most wet-
lands would recharge groundwater on a net annual
basis (Carter 1986). Similarly, evidence of wetlands
performing as sources is limited. Some studies of resto-
ration projects in arid regions suggest that certain
headwater riparian wetlands can promote water con-
servation (Ponce and Lindquist 1990; Winegar 1977).
They do so partly by reducing channel erosion, increas-
ing shallow infiltration of runoff, and reducing stream
velocity. Riverine wetlands that accumulate coarse
sediments or that are formed by beaver dams also tend
to release water gradually throughout the growing sea-
son (Debano and Schmidt 1990; Van Haveren 1986;
Wilenetal.1975).
In the Sand Hills of Nebraska and in areas of volcanic
origin (e.gv parts of Idaho and Oregon), wetlands are
typically sites of groundwater discharge, which is the
primary source of stream base flow (Rogers and
Armbruster 1990). Ata landscape scale, empirical stud-
ies of multiple watersheds have sometimes found
non-peak stream flow to be positively correlated with
the proportion of wetlands or other storage areas in the
watersheds. For example, Thomas and Benson (1970)
detected aggregate effects of wetlands and other storage
areas on base-flow volume (Table 7.3). However, these
findings are not universal; Milne and Young (1989)
reported that the effect of Arizona stockponds having
an average storage capacity of 1,803 m3 on base flow was
virtually undetectable ata density of 0.2 ponds per km2.
The ability of wetlands to sustain streamflow has not
been documented conclusively on a broad basis.
Water Quality Functions
Just as wetlands can function as hydrologic sinks or
sources, they can also act as sinks or sources of chemi-
cals important to ecological communities and to^
society. Whether a wetland serves as a sink or a
source depends on hydrology, pollutant loading rates,
and the relative magnitudes of various water quality
processes. Sedimentation, sediment stabilization, deni-
trification, nitrification, biological uptake and processing,
adsorption, and photosynthesis are all important pro-
cesses that affect these water quality functions.
Numerous studies have examined these processes, as
discussed in reviews by Adamus and Stockwell (1983),
Johnston et al. (1990b), Johnston (1991), Kadlec (1987),
Phillips (1989a), and Richardson (1989). Most evidence
suggests that wetlands function within a landscape as a
•Sink dr transformer of suspended inorganic sediment,
inorganic phosphorus, nitrate, and sulfate; however,
wetlands generally function as a source or transformer
for carbon and perhaps for the organic forms of phos-'
phorus and nitrogen. A summary of several studies
follows.
Perhaps the first broad scale empirical study reporting
on the aggregate effect wetlands have on water quality
was by Jones et al. (1976). Examining 34 watersheds in
northwestern Iowa, researchers found that watersheds
with a larger proportion of wetlands had less nitrate in
streamflow. Studies in Minnesota funded by EPA's
Wetlands Research Program examined watersheds of
33 mostly eutrophic lakes (Detenbeck et al. 1988) and 15
headwater streams (Johnston et al. 1988,1990a). Lakes
with drainage areas containing a greater proportion of
wetlands tended to have more ammonia but had lower
levels of suspended solids, chloride, and -lead; these
lakes were also, less eutrophic. For streams, sampling
sites closer to wetlands had lower concentrations of
suspended solids, nitrate, chloride, lead, and fecal
Table 7.1 Functions that wetlands may perform (adapted from Conservation Foundation 1988; Mitsch and
Gosselink 1986; OWRS 1990).
Hydrologic Functions
Water Quality Functions
Habitat Functions
Convey flood waters
Act as' barriers to waves
Prevent erosion
Store flood waters
Maintain base flow
Replenish aquifers
Stabilize sediments
Retain sediments
Remove or transform hazardous chemicals
Remove or transform nutrients
Maintain water quality
Provide feeding areas
Provide breeding areas
Act as dispersal corridors
Provide watering areas
Provide staging areas
Provide shelter
78 Synoptic Approach
-------
Table 7.2 Multiwatershed studies in which wetland area or related variables1 significantly predicted streamflow
conditions.
Location
Source
Alabama
Alaska
Arkansas
California
Colorado
Delaware
Florida
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Minnesota
Mississippi
Missouri
Montana
Nebraska
New Jersey
New Mexico
New York
North Carolina
Ohio
Oklahoma
Oregon
Pennsylvania
Tennessee
Texas
Utah
Virginia
West Virginia
Wisconsin
Newfoundland
Yukon territory
New England states
Upper Mississippi and
Hudson Bay Basins
Selected urban areas
Mains 1973; Olin 1984 .
USDA 1984 (southeastern)2
Bedinger and Sniegocki 1976; Neely 1987; Patterson 1971
Thomas and Benson 1970 (Central Valley)
Benson 1964; Thomas and Benson 1970 (Arkansas River watershed)
Simmons and Carpenter 1978; Tice 1968
Bridges 1982; Lopez and Woodham 1983
Thomas et al. 1973 (east-central)
Allen and Bejcek 1979; Wetzel and Bettandorff 1986
Davis 1974; Glatfelter 1984; Wetzel and Bettandorff 1986 (western)
Lara 1973; Thomas and Benson 1970 (south-central)
Thomas and Benson 1970 (northern)
Choquette 1988; Wetzel and Bettandorff 1986 ' '''
Benson 1964; Lee 1985b; Lowe 1979; Neely 1976; Thomas and Benson 1970
Hayes and Morrill 1970
Armentrout and Bissell 1970; Carpenter 1983; Thomas and Benson 1970; Tice 1968;
Walker 1971 .
Guetzkow 1977; Jacques and Lorenz 1988; Moore and Larson 1979
Colson and Hudson 1976
Hauth 1974; Thoma's and Benson 1970 (northwestern)
Johnson and Omang 1976 •
Beckman 1976 (northern and southeastern); Thomas and Benson 1970 (southern)
Stankowski 1974; Tice 1968
Benson 1964; Hjel 1984; Scott 1971
Darmer 1970; Lumia 1,984; Tiee 1968 (southern); Zembrzuski and Dunn 1979
Edgerton 1973 ,
Koltun and Roberts 1990; Webber and Bartlett 1977; Wetzel and Bettandorff 1986 (eastern)
Sauer1974
Harris et al. 1979 (western); Laenen 1980 (Portland-Vancouver area)
Rippo 1977; Thomas and Benson 1970 (south-central); Tice 1968 (eastern); Wetzel and
Bettandorff 1986 (western)
Wetzel and Bettandorff 1986 (central)
Benson 1964
Christensen et al. 1986
Armentrout and Bissell 1970 (northern); Miller 1978; Nuckels 1970; Thomas and Benson
1970 (northern); Tice 1968 (eastern)
Tice 1968 (eastern); Wetzel and Bettandorff 1986
Conger 1971; Novitzki 1979
NEEC 1984; Panu and Smith 1989; Poulin 1971
Janowicz 1986
Benson 1962
Patterson and Gambel 1968 ,,
SaUeretal. 1983
1 Variables expected to be related to wetland area, such as area of lakes, ponds, swamps, and noncontributing storage areas,
and mainstem channel slope. These variables may not be related to wetland area in all situations and should be used with
caution.
2 Refers to study area within state. ' •
A Review of Wetland Functions 79
-------
coliformbacteria;thegreatest nitrate reduction occurred
during low flow. Other empirical analyses have found
generally positive correlations between wetland area
and water quality in central Minnesota (Oberts 1981),
meNortheast(lJegeletal.l991),andWisconsin(Hindall
1975). However, a study of 32 forested stream water-
sheds in central Ontario (Dillon et al. 1991) using eight
years of detailed hydrologic and loading data indicated
that wetlands cumulatively export total phosphorus and
total organicnitrogen. Wetland ability to act as a source
or sink may depend on loading rates as indicated by
watershed land cover or on other factors.
Findings that show a relationship between wetlands
and the export of organic carbon include studies by
Eckhardt and Moore (1990), Gorham et al. (1985),
Johnston et al. (1988, 1990a), Kerekes et al. (1986),
LaZerte and Dillon (1984), Mulholland and Kuenzler
(1979), Rapp et al. (1985), and Rasmussen et al. (1989).
Bogs and fens may export more carbon per unit area
than other temperate wetland types, despite limited
surface water connections to other waters (Urban et al
1989).
Wetlands can also affect the time at which substances
become available to biological communities. For ex-
ample, in watersheds with a large proportion of
wetlands, some nutrients tend to be delivered to
downstream rivers and estuaries relatively evenly
throughout the year (Eckhardt and Moore 1990;
Urban et al. 1989). This is important if the annual
productivity, biodiversity, or resilience (recovery time)
of biological communities in lowland rivers and estu-
aries are contingent on a steady or predictable flow of
pre-processed nutrients from upstream, as suggested
byDeAngelisetal.(1990),Meyer(1990),Naimanetal.
(1988), O'Neill et al. (1980), and Pringle et al. (1988).
From a landscape perspective, the biochemical trans-
formations that occur in wetlands may take place in a
spatial sequence; an upstream wetland serves as a
source of a particular substance to the next wetland
downstream, which transforms the substance and passes
it to other wetlands farther downstream (e.g., Elder 1985;
Newbold et al. 1982). Although some of the most effec-
tive waste water treatment facilities employ such coupled
transformations (Hammer 1992), few studies have docu-
mented this occurrence among natural wetlands.
Evidence for this kind of coupling is strongest for organic
forms of nutrients (e.g., Fisher et al. 1982; Jordan et al.
1986; Naiman et al. 1988).
The distance over which an individual wetland can
benefit downstream water quality is not yet clear. In
most cases, water quality improvement appears to ex-
tend only a short distance (e.g., Bianchi and Findlay 1990;
Johnston et al. 1990a; Jones and Smock 1991; Phillips
1989b), but a substance that decomposes slowly may
travel up to 800 km before it is completely oxidized
(Edwards and Meyer 1987). Questions also remain over
the best indicator of wetland water quality functions.
For example, Johnston et al. (1990a) found that water
quality was related to near channel wetland area,
while Omernik et al. (1981) suggested that the best
indicator was land cover averaged over the whole
watershed. Depending on the geomorphic character-
istics of the landscape and the duration and rate of
loading, either may be true. As Whigham et al. (1988)
note, predictions of watershed water quality using
simple indicators such as proportion of wetland area
are probably much less reliable than predictions that
consider spatial distribution of the wetland area, its
types, and the degree to which runoff comes into
contact with wetlands.
Table 7.3. Relationship1 between increased wetland area2 and peak and base flows by geographic region3 (from
Thomas and Benson 1970).
Region
East
South
Peak Flow
10-, 25-, and 50-year recurrence intervals
1-, 2-, and 5-year recurrence intervals
2-vear reeiirrf>rv:e inton/al (7-/-)o«, nooMi«..,\
Relationship
[-]
Base Flow
Average streamflow
(November to January and July)
Variability (January)
Relationship
M
West 2-, 10-, and 20-year recurrence intervals
(7-day peak flow)
10- and 20-year recurrence intervals (3-day peak flow)
Central Variability of peak flow
H
Average streamflow
(June to July and mean monthly)
Variability of base flow
1A [+] indicates increased flow is correlated with increased storage area; a [-] indicates decreased flow is correlated with
Increased area.
* Includes lake and alluvial area as well as wetland area.
Results for each region based on analyses of the following number of watersheds: east - 41 watersheds within the Potomac
River Basin; south -42 watersheds mostly within the Louisiana Pine Hill Region; west - 44 watersheds within California's Central
Valley; and central - 41 watersheds mostly within Kansas.
80 Synoptic Approach
-------
Habitat Functions
In many regions, more species are restricted to wetlands
than to any other habitat (Williams and Dodd 1978), and
among the various types of undeveloped lands, wet-
lands often make the largest contribution to regional
biodiversity (e.g., Brinson et al. 1981). They do so by
functioning as feeding, roosting, and staging sites; dis-
persal corridors; and shelters. Even in individual
wetlands with low species diversity/the species are
often found in few other habitats (Moore et al. 1989).
Further, a disproportionate number of endangered
and threatened species depend wholly or in part on
wetlands.
Several coastal studies have reported correlations be-
tween the number of nests in wading bird colonies and
the proportion of wetlands within the home range (e.g..
Burger 1981; Gibbs et al. 1987; Werschkul et al. 1976),
although a study by Erwin et al. (1986) found no such
relationship. The proportion of wetlands within the
home range has also been correlated with production of
many other organisms. One study of 332 wetlands
found that the occurrence of four amphibians was corre-
lated with thelocalnumberof wetland pools. In contrast,
altitude, cumulative pool area, and surrounding land
cover were either less important or insignificant (Mann
et al. 1991). Low wetland densities can even make
populations of wetland-dependent species more sus-
ceptible to outbreaks of disease; Smith and Higgins
(1990) found that the density of semipermanent wet-
lands was lower in areas where avian cholera epizootics
occurred than in areas where there were no epizootics.
For many animal species, no single wetland, regardless
of size, can provide for all their needs over an entire life
cycle. Therefore, for the species to survive, several
wetlands of various types must exist in the same area.
The proximity of at least one permanently or regularly
flooded wetland appears to be important to the habitat
potential of wetlands that are either drier or less regu-
larly or predictably flooded, especially during drought.
Conversely, the proximity of many drier wetlands to a
permanent or regularly flooded wetland is essential for
some species inhabiting more permanent wetlands, es-
pecially during wet years or high tides (e.g., Powell
1987). As a result, a complex of multiple wetland types
might be a more appropriate unit for assessing habitat
function, rather than an individual wetland. Determin-
ing the boundaries of such complexes requires
information on spatial characteristics of wetland dis-
tribution along with knowledge of the life cycle and
metabolic requirements of the species whose geo-
graphic ranges lie within the particular region.
The previous discussion is not meant to suggest that
many small wetlands are always preferable to one
large wetland of equivalent total area because this
could result in loss of some area-sensitive species
(Table 7.4). Some species cannot breed successfully in
small or narrow wetlands, particularly in wetlands
whose size results from hydrologic impermanence or
in which increased vulnerability to human disturbance
and contamination exists. Also, small wetlands or lakes
individually have fewer species of aquatic plants (Ebert
and Balko 1987; Rorslett 1991), invertebrates (Aho 1978;
Driver 1977), fish (Eadie et al. 1986; Leitman et al. 1991;
Tonn and Magnuson 1982), and birds (Bostrom and
Nilsson 1983; Brown and Dinsmore 1988; Croonquist
and Brooks 1991; Durham et al. 1987; Gibbsand Melvin
1990).
Wetland Degradation
Wetlands perform importanthydrologic, water quality,
and habitat functions, but those functions can be im-
paired by stress. Wetlands can be stressed in various
ways (Figure 2.1; Table 75), including changes to wet-
land forcing functions, e.g., alteration of wetland
hydrology or sediment budgets; changes to wetland
processes, such as increased surface runoff through
ditching or increased production through enrichment;
and loss of wetland structure, e.g., soil compaction or
harvesting of species. Degradation, or loss of function,
results from such stresses. The severity of functional
degradation depends on three factors (Adamus and
Stockwell 1983; Table 7.6):
• Impact Characteristics—The proximity, magnitude,
duration, extent, frequency, seasonality, and predict-
ability of the particular impact;
• Wetland Type — The particular hydrologic regime,
water chemistry, community structure, and land-
scape position of a wetland; the tolerance of its
organisms and nearness of conditions to a critical
geomorphic or physiological threshold;
• Landscape Characteristics—The climatic, chemical,
geomorphic, and land use characteristics of the land-
scape along with their spatial and temporal
configurations, particularly with regard to how they
may provide refuge from, buffer, or compensate for
effectsof particular wetland impacts (e.g., Sedell et al.
1990; Sparks etal. 1990).
The effects stresses generally have on hydrologic, water
quality, and habitat functions are listed in Tables 7.7-7.9,
respectively. Unfortunately, most examinations of wet-
land degradation have focused on structure, and not on
function; Tables 7.7-7.9 are based upon our general
understanding of the mechanistic linkages among ac-
tivities, impacts, and functions (e.g., Adamus and
Stockwell 1983;PattersonandWhillansl984; Williamson
etal. 1987).
A Review of Wetland Functions 81
-------
Table 7.4. Examples of area-sensitive wetland bird species1.
Species
Piod. billed grobo
Groat blue heron
Black-crowned night heron
American bittern
Least bittern
Canada gooso
Btus-wingod teal
Grcen-winQod teal
Mallard
Gadwall
Northern pintail
Northern ahovolor
Redhead
Ruddy duck
American coot
Virginia rail
Sora
Forstor'a torn
Swallow-tailed kilo
Rod shouldered hawk
Black torn
Piloatcd woodpecker
Acadian flycatcher
Voory
Marsh wren
Northern parula
Prothonotary warbler
Northern waterthrush
Louisiana waterthrush
Kentucky warbler
Swalnson's warbler
Swamp sparrow
Minimum Patch Size (ha)
5
A,B
[>20], C
C
D
[<1],C
nai
11, [5]
1-5
C
1-5
C
C
[51, C
5,C
11
A
[<1),C
A,B
A
C
115]
225, E
20, [5]
[25], B
112]
165.E
15. [0.2], E
C.E
C
[24], E
20, [9], E
[28], E
A, B
[<1],A
[12]
520,[10],E
[54], E
[24], E
A
200, [24], E
300,125], E
[42], E
C, E
C
17, [9], E
18], E
[2.3], E
A
1-5
Reference
Brown and Dinsmore 1986
Gibbs and Melvin 1990
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Gibbs and Melvin 1990
Brown and Dinsmore 1986
Tyser 1983
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Brown and Dinsmore 1986
Gibbs and Melvin 1990
Brown and Dinsmore 1986
Gibbs and Melvin 1990
Gibbs and Melvin 1990
Brown and Dinsmore 1986
O'Meara 1984
Robbinsetal. 1989
Brown and Dinsmore 1986
Gibbs and Melvin 1990
Tyser 1983
Robbinsetal. 1989
Robbins et al. 1989
Harris and Wallace 1984
Triquetetal. 1990
Blake and Karr 1987
Robbinsetal. 1989
Blake and Karr 1987
Gibbs and Melvin 1990
Brown and Dinsmore 1986
Tyser 1983
Robbins et al. 1989
Hayden et al. 1985
Blake and Karr 1987
Robbinsetal. 1989
Robbins et al. 1989
Robbinsetal. 1989
Hayden et al. 1985
Harris and Wallace 1984
Triquet et al. 1990
Robbins et al. 1989
Hayden et al. 1985
Blake and Karr 1987
Harris and Wallace 1984
Brown and Dinsmore 1986
VHVV ..»«, .nwtuMt* iiwn TVUKUIIU wpwwi^o iiiai a i c aiou ai ca-aciiimi vc aliu JJdl Llaliy oufJJJUl LcU Uy Well ail OS.
Numbers represent patch size at which probability of occurrence is 50% of the maximum as determined from a series of habitat
platen inventories; this level has been suggested as appropriate for conservation planning by Robbins et al. (1989). Bracketed
figures are sizes of smallest patches found to be occupied; it cannot be assumed that birds bred successfully in these areas
(Gibbs and Faaborg 1990).
A: The listed species' breeding occurrence appears to be influenced by wetland patch size.
B: The listed species' breeding occurrence appears to be influenced by local wetland density.
C: Results not statistically significant given sample size; however, distribution pattern suggests area
dependence.
D: The listed species' breeding occurrence appears to be influenced by proximity to other wetlands.
E: Area includes adjoining undeveloped forest.
82 Synoptic Approach
-------
Wetland Conversion
A wetland can be so severely stressed that it is com-
pletely transformed into adifferent typeof ecosystem or
land use; we refer to this process as conversion. Histori-
cally, most wetland conversion has been intentional,
e.g., to increase agricultural area. However, conversion
can also be caused inadvertently. For example, severe
long-term sedimentation in a shallow wetland can raise
the wetland substrate above the water table, accelerate
invasion by upland species, and eventually cause suc-
cession to upland.
A number of studies have assessed cumulative loss of
wetland area at various scales. Between the 1780s and
1980s, losses in the 50 states ranged from 0.1% for
Alaska to 91% for California (Figure 7.1). Historical loss
of wetland area for the entire United States was esti-
mated as 30% over the last two centuries; if Alaska is
excluded, this amount increases to 53% (Dahl 1990).
During the 1970s and 1980s, a net 1.1 million of the 41.8
million hectares of wetlands in the United States (2.5%)
were lost through conversion (Dahl et al. 1991). Fresh-
water wetlands accounted for most of this recent loss,
particularly Southeastern forested floodplain wetlands.
Table 7.5. Stresses and associated impacts that can degrade wetlands.
Stress
Impacts
Acidification
Biomass Removal
Compaction or Erosion
Contamination/Toxicity1
Dehydration
Eutroph ication/En rich ment
Habitat Fragmentation and
Exotic Species Invasion
Inundation
Light Reduction
Salinization
Sedimentation
Thermal Warming
Fossil fuel combustion
Agricultural/silviculture
Aquatic weed control
Channelization
Agriculture/silviculture
Mining and construction
Agricu Itu ral/silvicltu ra I pesticides
Aquatic weed control
Fossil fuel combustion
Hazardous waste sites
Industrial air pollution
Anthropogenic water withdrawals
Global climate change
Subsurface tile drainage
Artificial drainage
Fertilizer application
Fossil fuel combustion
Landfills
Livestock
Channelization/ditching
Land clearing
Road construction
Grazing
Excavation (deepening)
Impoundment
Agricultural runoff
Urban stormwater
Sediment resuspension by animals and wind
Ineffective wastewatertreatment plants
Domestic/industrial wastes
Irrigated soil
Agriculture
Deposition of dredged or other fill material
Disturbance of stream flow regimes
Global climate warming
Impoundments
Mineral extraction
Defoliation from airborne contaminants
Grazing, herbivory, disease, and fire
Urban development
Disturbance of stream flow regimes
Deposition of dredged or other fill material
Landfills
Mineral extraction
Urban stormwater
Wastewater treatment systems
Mosquito control pesticides
Invasion by highly transpirative plant species
Ditching/channelization of nearby streams
Surface ditching, drainage, and outlet widening
Mineral extraction
Peat extraction
Urban stormwater
Wastewater treatment systems
Silvicultural activities
Urban development
Impoundments
Artificial drainage
Flow blockage by road construction
Land use that increases runoff to wetlands
Placement of bridges and other structures
Disturbance of stream flow regimes
Erosion from mining and construction sites
Blooms of algae responding to excess nutrients
Road salt used for winter ice control
Saltwater intrusion from tidal or groundwater
Erosion from mining and construction sites
Ineffective wastewater treatment plants
Urban stormwater
Power plants and industrial facilities
Vegetation removal
1 From heavy metals.
A Review of Wetland Functions 83
-------
Wetland Loss (millions of hectares)
Percent Wetland Loss
I
Florida .
Taxes -
Louisiana -
Arkansas -
Illinois .
Minnesota .
Mississippi -
Michigan -
North Carolina -
Indiana -
California -
Ohio.
Wisconsin -
Missouri -
Alabama -
Iowa -
North Dakota •
Oklahoma •
South Carolina -
Georgia -
Now York -
Kentucky -
Maine -
Maryland -
Tennessee -
Nobrosko -
Colorado -
South Dakota -
Oregon -
Virginia -
Wyoming -
Pennsylvania -
Now Jersey -
Connecticut -
Idaho -
Washington -
Kansas -
Arizona -
Montana -
Delaware -
Nevada -
Utah-
Now Mexico -
Massachusetts -
Alaska -
Vermont -
Rhode Island -
West Virginia -
Now Hampshire -
Hawaii -
3 1.0 2.0 3.0 4
I J ' ' ' I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
a
J
I
I-
I
I
I
IZJ
m
m
m
Zl
ZJ
I]
5
D
a
a
n
3
3
]
]
]
]
]
(a)
.0
California -
Ohio.
Iowa -
Missouri -
Indiana -
Illinois -
Kentucky -
Connecticut •
Maryland -
Arkansas -
Oklahoma -
New York -
Tennessee -
Mississippi -
Idaho -
Pennsylvania -
Delaware -
Texas -
Nevada -
Michigan -
Colorado -
Alabama.
North Dakota •
North Carolina -
Kansas -
Louisiana •
Florida •
Wisconsin -
Minnesota -
Virginia -
New Jersey -
Oregon -
Wyoming •
Rhode Island •
Arizona -
Vermont -
South Dakota -
Nebraska -
New Mexico -
Washington -
Utah-
Massachusetts -
South Carolina -
Montana -
West Virginia -
Georgia -
Maine -
Hawaii -
New Hampshire -
Alaska -
O 20 4O 60 ' 8O . 10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
I
1
1
1
1
|
|
1
1
1
1
1
1
1
1
1
1' :
1
1
1
i
i
1
i
i
i
i
i
i
1
1
^ (b)
Figuro 7.1. Historical loss of wetland area (a) and percent loss (b) in the United States, by state (data from Dahl 1990).
84 Synoptic Approach
-------
Table 7.6. Wetlands whose functions may be more sensitive or less resistant to particular types of stress.
Sensitive Function
Type of Stress
Wetland Type
Hydrology
Water Quality
Habitat
Sedimentation
Vegetation removal
Dehydration
Inundation
Fragmentation
Enrichment and contamination
Organic loading
Acidification
Turbidity/shade and
vegetation removal
Dehydration
Inundation
All degradation types
Physically isolated wetlands
Forested wetlands
Permanently flooded wetlands
Wetland depressions overlying thin
impermeable strata
Wetlands not permanently flooded
Wetlands in flat landscapes
Wetlands that have been previously exposed
to large chemical loadings
Physically isolated wetlands with high annual
production
Wetlands with cation exchange capacity only
at the substrate surface
Wetlands where submerged plant uptake rather
than microbial metabolism or adsorption is the
major process controlling nutrient or
contaminant cycling
Permanently flooded wetlands
Wetlands not permanently flooded
Wetlands connected to others only by narrow
corridor
Wetlands near a size threshold for a species
Wetlands with a poor seed bank
While agricultural conversion has been the primary
cause of wetland loss since the 1950s, it has become less
dominant. Agricultural conversion, urbanization, and
other forms of development {conversion of wetlands to
non-agricultural, rural land uses) accounted for 87%,
8%, and 5% of loss from the 1950s to the 1970s, respec-
tively (Tiner 1984), compared with losses of 54%, 5%,
and 41 % for these same categories during the 1970s and
1980s (Dahletal. 1991).
Effects of Cumulative Wetland Loss
on Landscape Functions
Because few studies have examined degradation of
wetland function on a large scale, our understanding of
how conversion and degradation affect wetland func-
tions at the landscape level is even more limited. The
following three sections discuss the possible effects of
cumulative wetland loss on hydrologic, water quality,
and habitat functions. The findings are based upon
studies thatgenerallyfallinto three categories (Leibowitz
et al. 1992): empirical landscape analyses, case studies,
and landscape modeling. Adamus (1989) discusses
strengths and limitations of various approaches.
Loss of Hydrologic Functions
In only a limited number of studies have researchers
attempted to measure and compare hydrologic func-
tions before and after loss or alteration of wetlands.
Among them are studies of Mississippi River flooding
by Belt (1975), southwestern riparian areas by Burkham
(1976), diked Florida wetlands by Hammett et al. (1978),
and prairie pothole wetlands by Brun et al. (1981). Al-
though none of these investigations contradicts the notion
that loss of wetland area causes increased flow peaks,
the limited number of study sites makes it difficult to
distinguish effects of wetland loss from effects of other
land use changes within the watershed and from short-
term climate trends.
Nevertheless, results from spatially-based empirical
analyses can be used to explore the effects of wetland
loss. In Minnesota, Johnston et al. (1990a) suggested
and demonstrated the use of simple equations based
on spatial data (such as those found in the references
in Table 7.2) to estimate how past wetland loss may
have affected peak flow for any particular watershed
and to infer changes in discharge that might occur
from future wetland losses. The results were then
used to rank watersheds according to relative risk.
Andersson and Sivertun (1991) used a simulation
A Review of Wetland Functions 85
-------
model to estimate regional impacts to groundwater
recharge and discharge resulting from decades of
wetland drainage.
Computer simulations have also been used to exam-
ine wetland and floodplain behavior and, in most
cases, have supported the role of cumulative wetland
area or other forms of natural storage in attenuating
streamflow peaks. In what was perhaps the first such
study, Dewey and Kropper Engineers (1964) simu-
lated floodplain storage in the Connecticut River.
Their findings indicated that flood stage could in-
crease by 0.3,1.2, and 2.1 meters as the result of 10%,
20%, and 30% reductions in storage, respectively. Sub-
sequently, the Corps of Engineers conducted simulations
of the Charles River in Massachusetts, concluding that
downstream flooding can be reduced more cost-
effectively byprevenringfloodplain encroachments than
by constructing control structures (Childs 1970). Other
watershed simulations (e.g.,Dreheretal. 1989;Horeset
al. 1982; Haan and Johnson 1968; Moore and Larson
1979; Ogawa and Male 1983,1986,1990) have condition-
ally supported and quantified the cumulative effects of
wetlands as runoff dissipators. However, reductions in
floodplain storage and channel roughness appeared to
ha velittleeffectonpeakflows in watershed simulations
conducted by Johnson and Senter (1977). Hydrologic
modeling of wetlands and floodplains is addressed in
reviews by Corps of Engineers (1988), DeVries (1980),
Dreher et al. (1989), and Duever (1988).
The effect cumulative wetland loss can have on land-
scape hydrology depends on (a) the remaining
percentage of wetlands in the watershed, (b) the posi-
tions of other wetlands and storage areas, and
(c) whether the altered wetlands are located at a
hydrologic control point (a place where channel stor-
age or conveyance influences a much wider area
because flows are funneled by landforms). With
regard to the role of wetland area, limited evidence
from Wisconsin and Minnesota watersheds suggests
that loss of wetlands in watersheds having a small
proportion of wetland area will have greater effect
than the same loss in watersheds with a larger pro-
portion of wetland area. This is particularly true if the
new losses occur disproportionately in areas near
mainstem channels. Where conversion losses occur
mainly in headwater areas, watersheds with a large
proportion of wetland area (perhaps >10%) can partly
compensate for the associated loss of storage (Ogawa
and Male 1983). The influence of wetland position on
prediction of instantaneous streamflow probably in-
creases with increasing proportion of wetland area
(e.g., NEEC1984). The position of wetland area within
a watershed influences the nature of the cumulative
effect. Watersheds where wetland conversions are fo-
cused within mainstem floodplains (Ogawa and Male
1983), or where headwater wetlands are channelized
but wetlands downstream or at a control point are not,
may experience the greatest increase in flood peaks.
This is because the position of such conversions, or even
activities such as new wetland creation, can synchro-
nize the arrival of runoff and lead to higher flood peaks
(McCuen 1979). Finally, no evidence suggests that
maintaining wetland type or size diversity provides
greater support for streamflow-related values.
Loss of Water Quality Functions
Only a few published studies (e.g., Beasley and Granillo
1988; Mader etal. 1989) compare watershed water qual-
ity before and after alterations in wetland vegetation or
changes in a watershed's proportion of wetland area.
An analysis of Louisiana's Tensas Basin by Childers and
Gosselink (1990) found that turbidity, total phosphorus,
and total suspended solids were significantly related to
water level at three sites. Observing that these trends
were characteristic of cleared watersheds, the authors
suggested that stream enrichment in the Tensas could
Table 7.7. Generally expected effects of various stresses on hydrologic functions of wetlands1.
Sedimentation/Soil Compaction
Vegetation Removal
Dehydration
Inundation
Fragmentation
Reduction in storage, infiltration, and groundwater recharge causing an increase in
surface runoff
Reduction in interception, condensation, evapotranspiration, and surface roughness
(runoff resistance), and an increase in runoff velocity and groundwater discharge
Reduction in groundwater exchange (sometimes) and an increase in evapotranspiration
(during early vegetational succession); these effects are especially likely where
dehydration results from channelization or artificial drainage (Winter 1988)
Usually increases infiltration and recharge within the wetland, but may convert nearby
wetlands from recharge to discharge areas or vice-versa (Born et al. 1979)
Can reduce groundwater recharge and discharge in remaining wetlands (Winter 1988)
1 This is intended as a general guide, and effects may differ depending on wetland type and the timing, duration, extent, and
intensity of the stress.
86 Synoptic Approach
-------
Table 7.8. Generally expected effects of various stresses on water quality functions of wetlands1.
Increase in denitrification rate, sediment stabilization, and biological uptake and
processing; may depress the latter if extreme or chronic
Reduces biological uptake/processing, especially at high loadings or if associated with
acidification; increases sedimentation and denitrification rates under moderate loadings;
enhances mobilization of some substances through oxidation effects
Variable effects, depending on the specific contaminant and other factors; can depress
denitrification, biological uptake/processing, and photosynthesis
Usually depresses denitrification, biological uptake and processing, and perhaps
photosynthesis; effects on chemical adsorption depend on the chemical, but acidification
usually results in increased mobility of heavy metals
May depress denitrification, biological uptake, and photosynthesis and enhance
adsorption of some chemicals; response depends partly on the degree to which the
system is adapted to salinity
Depresses biological uptake, processing, and photosynthesis, and may reduce hydrologic
residence time; other effects are variable
Reduces photo-oxidation of some contaminants, and usually depresses denitrification,
photosynthesis, and perhaps biological uptake
Reduces sedimentation, sediment stabilization, photosynthesis, biological uptake/
processing, and perhaps denitrification. Sediment removal capacity of early successions!
forested wetlands may increase (Aust et al. 1991; Cooper et al. 1986}
Increases rates of most chemical and biological functions up to a point
Concentration of inorganic chemicals increases as dehydration proceeds; complete
drawdown temporarily remobilizes many substances, especially organics and
phosphorus, but may renew wetland adsorption capacity for some substances; effects on
other water quality functions are variable (e.g., Bourbonierre 1987; Moore 1987).
May increase sedimentation and decrease biological uptake and processing, and
photosynthesis; effects on other functions are variable
Increasing the distance between wetlands could reduce the effectiveness of coupled
functions important to water quality
1 This is intended as a general guide, and effects may differ depending on wetland type and the timing, duration, extent, and
intensity of the stress.
2 From heavy metals and pesticides.
Enrichment
Organic Loading
Contamination 2
Acidification
Salinization
Sedimentation/Soil Compaction
Turbidity/Shade
Vegetation Removal
Thermal Warming
Dehydration
Inundation
Fragmentation
have been caused by logging bottomland hardwoods.
The number of streams was not large enough to test
whether other factors might have caused these water
quality trends. However, considering this evidence
along with other findings (Gosselink et al. 1990b), the
investigators concluded that the water quality func-
tion of the Tensas declined as a result of forested
wetland loss. In Illinois, Osborne and Wiley (1988)
demonstrated the use of regression equations to esti-
mate the risk that a watershed would exceed water
quality limits if forested land were converted to
urban or agricultural uses.
Computer simulations have also been used to estimate
the cumulative effects of wetland loss on downstream
sedimentation and water quality. Examples of such
analyses are simulations by Auble et al. (1988), Bedient
et al. (1976, 1985), and Maristany and Bartel (1989).
Attempts to model water quality functions of wetlands
are restricted partly by the limited ability of existing
hydrologic models to account for biological functions
within wetlands and partly by uncertainty regarding
appropriate routing algorithms in complex situations
such as floodplain and peatland watersheds (Costanza
and Sklar 1985; Mitsch 1983).
Loss of Habitat Functions
Numerous anecdotal accounts of species loss are associ-
ated with cumulative wetland loss (e.g., Bellrose et al.
1979; Harris 1988; Hunter et al. 1987; Kushlan 1979;
Williams et al. 1989), but apparently only one study
(Burdick et al. 1989) has attempted to statistically link
reductions in regional biodiversity over time with loss
of wetlands. These authors compared trends in the
relative abundance of birds with reductions in bottom-
land hardwood areas and examined the relative
abundance of birds in areas with varying amounts of
forest. They found evidence that the declining number
of forest species and the densities of interior species
were related to cumulative loss of forest area. The
investigators also suggested that reduction in forested
A Review of Wetland Functions 87
-------
Table 7.9. Generally expected effects of various stresses on habitat functions of wetlands (from Adamus and
Brandt 1990J1.
Initial enrichment increases production and within-wetland biotic diversity, but prolonged
or extreme enrichment results in increased dominance of a few invasive species,
decreased species richness, diminished wetland structural diversity, decreased
production and, in some regions, succession to upland vegetation
All habitat functions are generally impaired
Results in diminished native biodiversity and production
In freshwater wetlands, usually results in diminished species richness (especially of
woody species), but surviving species may be relatively unique and thus contribute
disproportionately to overall regional diversity
Diminishes species richness as a result of reduced light, smothering, etc.; however,
moderate amounts of sediment can increase production of some woody plants in
floodplains and can increase habitat in deeper depressions by providing additional
shallow substrate for colonization
Variable effects; can diminish habitat suitability by reduced plant biomass, but can benefit
some species by providing shelter from predation and extreme heat
Diminishes habitat space; scattered thinning of dense stands can increase species
richness and spatial heterogeneity; selectively benefits some species but detrimental to
many others
Reduces species richness, but surviving species may be relatively unique and thus
contribute disproportionately to regional diversity if warming is local
Temporary dehydration, if infrequent and brief, can reinvigorate nutrient cycling in
wetlands and thus increase primary production; effects of partial drawdowns are variable;
drawdowns can result in invasion by undesirable weed species, such as common reed or
purple loosestrife; permanent dehydration results in conversion to upland habitat
Can increase habitat space for aquatic communities (particularly if the result is an
interspersion of wetland vegetation and open water), facilitate dispersal of isolated
aquatic populations, increase bank erosion, and dilute contaminants; contaminants,
suspended sediment, plant material, and nutrients can also be reintroduced from newly
flooded areas
Increasing the distances between wetlands usually reduces regional biodiversity,
although invasion by aggressive non-native species can be similarly reduced
1 This table is intended as a general guide, and effects may differ depending on wetland type and the timinq, duration extent
and intensity of the stress.
2 From heavy metals and pesticides.
Enrichment and Organic Loading
Contamination 2
Acidification
Salinization
Sedimentation/Soil Compaction
Turbidity/Shade
Vegetation Removal
Thermal Warming
Dehydration
Inundation
Fragmentation
wetlands might have caused the elimination of the red
wolf and Florida panther in parts of Louisiana and led to
reduction in the number of the black bears, no w listed as
a threatened species there. Comparing relatively undis-
turbed and disturbed watersheds inPennsylvania,Brooks
etal (1990) and Croonquist and Brooks (1991) reported
differences in avian, amphibian, and mammalian com-
munity structure. They attributed these differences to
multiple impacts associated with development in the
watershed, e.g., channelization, a reduction in natural
land cover types surrounding wetlands, and increased
human visitation. Continental waterfowl declines have
also been blamed on a combination of wetland habitat
loss, contamination, over-harvest, and disease. How-
ever, analyses of this sort encounter problems with a
scarcity of consistently collected long-term data and the
presence of major confounding variables (e.g., annual
variation in climate, interspecific competition, and other
land uses).
Because many vertebrate species require multiple wet-
lands or wetland types to meet their feeding and
reproductive needs (Cowardin 1969; Dzubin 1969; Hake
1979; Kantrud and Stewart 1984; Patterson 1976), di-
minished diversity of wetland types or increased
wetland isolation (i.e., increased patch distance; see
Chapter 6) can be detrimental. Similar isolation
effects have been described for communities of wet-
land microbes (McCormick et al. 1987) and
invertebrates (e.g., Jeffries 1989).
Habitat loss through wetland fragmentation is an area
of recent interest. Fragmentation increases vulnerabil-
ity of wetland species to predation, and causes some
species to expend so much energy traveling (and being
exposed to hazards) that the costs of using the nearest
wetland offset the gains. Data from semipermanent
Iowa wetlands (Brown and Dinsmore 1986) suggest
that, at least in that region, a wetland density of 1 to 5
88 Synoptic Approach
-------
hectares per square kilometer may be required to sup-
port a diverse aquatic avifauna. Richnessof aquatic bird
communities in individual Maine wetlands was also
correlated with local wetland density, but not with
distance to the nearest wetland (Gibbs and Melvin 1990).
From various studies (e.g., Cowardin et al. 1988;
Frederick 1983), it is apparent that wetland-dependent
bird species characteristically requiring multiple wet-
lands during a breeding season generally need the
wetlands to be located within 05 to 25 kilometers of
each other; the exact travel distance depends on the
species and other factors. Amphibians and wetland
reptiles need wetlands in even closer proximity (Brown
et al. 1990). If the increased patch distance is greater
than the usual distance an individual is able to safely
travel, population losses can potentially occur. Fac-
tors that could mitigate habitat fragmentation include:
• Suitability of intervening land cover for habitat;
• Suitable type, dimensions, and hydrologic perma-
nence of habitat corridors that form connections
among wetlands or between wetlands and other
ecosystems crucial to some species;
• Ecological integrity and type of dominant wet-
land;
• Diversity of local wetland types (e.g., as defined by
hydrology, vegetation, water quality and size).
Suppose many wetland-dependent species in a region
must, for energetic reasons, forage within 1 km of where
they breed (i.e., wetland patch distances <1 km re-
quired). If a mitigation banking arrangement or
evaluation technique allows for loss of many small (but
proximate) wetlands and protection of fewer large but
more isolated ones, then patch distances will probably
increase and cumulative effects on populations could be
adverse. The same could occur if a new regulation
exdudingsmallwetlandsindirectlyresulted in increased
patch distances among the remaining wetlands. Loss of
wetland area could also have a disproportionate effect
on wildlife if losses are focused on temporary concen-
tration areas (e.g., migratory staging areas, corridors, or
nodes within a wildlife dispersal network).
Wetland plant communities may be better able to
resist the effects of fragmentation than wetland ani-
mal populations. Field data from more than 400 lakes
surveyed by Rorslett (1991) indicate that the richness
of herbaceous plants was not strongly related to the
pool of aquatic plant species potentially available for
colonization in a region. However, simulations by
Hanson et al. (1990) predicted that fragmentation
would lead to reduced richness of woody plants in
remaining riparian areas.
It is important to reiterate that travel distance and patch
size are only two factors that affect habitat use. The
ecological integrity or suitability of within-patch habitat
quality is often at least as important (Kushlan 1979). For
example, the placement of dikes or pathways built on
fill within wetlands can decrease water bird nesting
success because of increased predator access (Peterson
and Cooper 1991). The point is not that a particular
wetland characteristic such as size is "better," but that
wetlands be assessed as whole complexes and that their
distribution patterns, condition, and actual wildlife use
be taken into consideration by resource managers in
wetland regulatory programs.
A Review of Wetland Functions 89
-------
I
-------
-------
-------
REFERENCES
Abbruzzesa, B., S.G. Loibowitz, and R. Sumner - 1990a
A Synoptic Approach to Wetland Designation: A Case Study in
Louisiana
EPA/600/3-90/066. U.S. Environmental Protection Agency,
Environmental Research Laboratory, Corvallis, Ore.
Abbruzzese, B., S.G. Leibowitz, and R. Sumner - 1990b
A Synoptic Approach to Wetland Designation: A Case Study in
Washington
EPA/600/3-90/072. U.S. Environmental Protection Agency,
Environmental Research Laboratory, Corvallis, Ore.
Adamus, P.R -1983
A Method for Wetland Functional Assessment; Volume H: FHWA
Assessment Method
FHA-IP-82-24. U.S. Department of Transportation, Federal
Highway Administration, Washington, D.C.
Adamus, P.R - 1989
Determining the cumulative effects afforested wetlands: EPA's
research program, and choices for research and monitoring designs
P. 36-47 in "Best Management Practices for Forest Wetlands:
Concerns, Assessment, Regulation, and Research." National
Council of the Paper Industry for Air and Stream
Improvement, Inc., Corvallis, Ore.
Adamus, P.R -1992
Data sources and evaluation methods for addressing wetland issues
P. 171-224 in "Statewide Wetlands Strategies: A Guide to
Managing the Resource" World Wildlife Fund and Island
Press, Washington, D.C
Adamus, P.R., and K. Brandt - 1990
Impacts on Quality of Inland Wetlands of the United States: A
Survey of Indicators, Techniques, and Applications of Community
Level Biomonitoring Data
EPA/600/3-90/073. U.S. Environmental Protection Agency,
Cincinnati, Ohio.
Adamus, P.R., and L.T. Stockwell - 1983
A Method for Wetland Functional Assessment, Volume I. Critical
Review and Evaluation Concepts
FHWA-IP-82-23. US. Department of Transportation, Federal
Highway Administration, Washington, D.C.
Adamus, P.R., L.T. Stockwell, E.J. Clairain, Jr., M.E.
Morrow, L.P. Rozas, and R.D. Smith - 1991
Wetland Evaluation Technique (WET)
Volume I. Literature Review and Evaluation Rationale.
Wetlands Research Program Technical Report WRP-DE-2.
Army Engineers Waterways Experiment Station, Vicksburg,
Miss.
Aho,J.M-1978
Freshwater snail populations and the equilibrium theory of island
biogeography II. Relative importance of chemical and spatial
variables
Annales Zoologici Fennici 15:155-164.
Alexander, C.E., M.A. Broutman, and D.W. Field - 1986
An Inventory of Coastal Wetlands of the USA
Strategic Assessment Branch, National Oceanic and
Atmospheric Administration, Rockville, Md.
Allen, H.E., Jr., and R.M. Bejcek - 1979
Effects of Urbanization on the Magnitude and Frequency of Floods
in Northeastern Illinois
Water-Resources Investigations 79-36. U.S. Geological
Survey, Denver, Colo.
Aller, L.,T. Bennett, J.H. Lehr, R.J. Petty, and G. Hackett
-1987
DRASTIC: A Standardized System for Evaluating Ground Water
Pollution Potential Using Hydrogeologk Settings
EPA/600/2-87/035. U.S. Environmental Protection Agency,
Kerr Environmental Research Laboratory, Ada, Okla.
Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E Witmer -
1976
A land use and land cover classification system for use with remote
sensor data
Geological Survey Professional Paper 964. U.S. Geological
Survey, Washington, D.C.
Andersson, L., and A. Sivartun - 1991
A CIS-supported method for detecting the hydrological mosaic and
the role of man as a hydrological factor
Landscape Ecology 5:107-124.
Armentrout, C.L., and R.B. Bissell - 1970
Channel slope effect on peak discharge of natural streams
American Society of Civil Engineers Journal of Hydraulics
%(HY2)307-315.
Armour, C.L., and S.C. Williamson - 1988
Guidance for Modeling Causes and Effects in Environmental
Problem Solving
Biological Report 89(4). U.S. Fish and Wildlife Service, Fort
Collins, Colo.
Auble, G.T., C.A. Segelquist, and L.S. Ischinger - 1988
Assessment of the Role of Bottomland Hardwoods in Sediment and
Erosion Control
NERC-88/11. U.S. Fish and Wildlife Service, Fort Collins,
Colo.
Aust, W.M., R. Lea, and J.D. Gregory- 1991
Removal offloodwater sediments by a clearcut tupelo-cypress
wetland
Water Resources Bulletin 27:111-116.
Bailey, R.G-1988
Problems with using overlay mapping for planning and their
implications for geographic information systems
Environmental Management 12:11-17.
Bain, M.B., J.S. Irving, R.D. Olsen, E.A. Stull, and G.E.
Witmer-1986
Cumulative impact assessment: Evaluating the environmental
effects of multiple human developments
Argonne National Laboratory, Argonne, fll.
Barrett, G.W., G.M. Van Dyne, and E.P. Odum - 1976
Stress ecology
BioScience 26(3):192-194.
Bastedo, J.D., J.G. Nelson, and J.B. Theberge - 1984
Ecological approach to resource survey and planning for
environmentally significant areas: The ABC method
Environmental Management 8:125-134.
References 93
-------
Beantands, G.E., and P.N. Duinkor- 1983
An ecological framework for environmental impact assessment in
Canada
Institute for Resource and Environmental Studies, Dalhousie
University, Halifax, Nova Scotia, and Federal Environmental
Assessment Review Office, Hull, Quebec.
Bennlands, G.E., W.J. Erckmann, G.H. Orians, J.
O'Riordan, D. Policimsky, M.H. Sadar, and B. Sadler
(ed«.)-1986
Cumulative Environmental Effects: A Binational Perspective
Canadian Environmental Assessment Research Council,
Ottawa, Ontario, and U.S. National Research Council,
Washington, D.C
Boasloy, R.S., and A.B. Granillo - 1988
Sediment and water yields from managed forests on flat coastal
plain sites
Water Resources Bulletin 24361-366.
Bockman, E.W -1976
Magnitude and frequency offloads in Nebraska
Water-Resources Investigations 76-109. US. Geological
Survey, Lincoln, Net>.
Bedford, B.L., and EM. Preston (eds.) - 1988a
Cumulative effects on landscape systems of wetlands: Scientific
status, prospects, and regulatory perspectives
Environmental Management 12(5) [special issue].
Bedford, B.L., and E.M. Preston - 1988b
Developing the scientific basis for assessing cumulative effects of
wetland loss and degradation on landscape functions: Status,
perspectives, and prosjKcls
Environmental Management 12:751-772.
Bodiont, P., A. Floras, S. Johnson, and P. Pappas -1985
FloodpJain storage and land use analysis at the Woodlands, Texas
Water Resources Bulletin 21543-547.
Bodiont, P.P., W.C. Huber, and J.P. Heaney - 1976
Modeling hydrologic-lmd use interactions in Florida
P. 362-366 in W.R. Oil (ed.), "Proceedings of the Conference
on Environmental Modeling and Simulation." EPA/600/9-
76/016. VS. Environmental Protection Agency,
Washington, D.C
Bodingor, M.S., and R.T. Sniegocki - 1976
Summary Appraisals of the Nations Ground-Water Resources —
Arkansas-Yihite-Red Region
Geological Survey Professional Paper 813-H. U.S. Geological
Survey, Rcston, Va.
Ball, H-1981
Illinois Wetlands: Their Value and Management
Illinois Institute of Natural Resources, Chicago, ffl.
Bollroio, F.C., F.L. Pavoglfo, and D.W. Steffeck - 1979
Waterfowl populations and the changing environment of the
Illinois River Valley
Illinois Natural History Survey Bulletin 32(l):l-54.
Bolt, C.B-1975
The 1973 flood and man's constriction of the Mississippi River
Sdence 189:681-684.
Bonningor-Truax, M., J.L. Vankat, and R.L. Schaefor -
1992
Trail corridors as habitat and conduits for movement of plant
species in Rocky Mountain National Park, Colorado, USA
Landscape Ecology 6(4):269-278.
Benson, M.A -1962
Factors influencing the occurrence offloads in a humid region of
diverse terrain
Water-Supply Paper 1580-B. U.S. Geological Survey,
Washington, D.C
Benson, M.A - 1964
Factors affecting the occurrence offloads in the southwest
Water-Supply Paper 1580-D. U.S. Geological Survey,
Washington, D.C
Bianchi, T.S., and S. Findlay - 1990
Plant pigments as tracers of emergent and submergent
macrophytes from the Hudson River
Canadian Journal of Fisheries and Aquatic Sciences 47:492-
494.
Blake, J.G., and J.R. Karr - 1987
Breeding birds of isolated woodlots: Area and habitat relationships
Ecology 68:1724-1734
Boling, M.S - 1988
General sott map of Washington Stale
Master of Science Thesis. Department of Agronomy and
Soils, Washington State University, Pullman, Wash.
Bormann, F.H - 1987
landscape ecology and air pollution
P. 37-57 in M.G. Turner (ed.), "Landscape Heterogeneity and
Disturbance." Ecological Studies, Volume 64. Springer-
Verlag, New York, N.Y.
Born, S.M., S.A. Smith, and D.A. Stephenson - 1979
Hydrology of glacial-terrain lakes, with management and planning
applkations
Journal of Hydrology 43:7-43
Bostrom, U., and S.G. Nilsson - 1983
Latitudinal gradients and local variations in species richness and
structure of bird communities on raised peat-bogs in Sweden
Ornis Scandanavica 14:213-226.
Bourbonniere, R.A - 1987
Organic geochemistry of bog drainage waters
P. 37-57 in CD.A. Rubec and R.D. Overend (compilers),
"Proceedings of the Symposium on Wetlands and
Peatlands." Contribution #87-133. National Water
Resources Institute, Environment Canada, Burlington,
Ontario.
Boyd, R., and S. Penland - 1981
Washover of deltaic barriers in the Louisiana coast
Transactions of the Gulf Coast Association of Geological
Societies 31:243-248.
Bridges, W.C- 1982
Technique for Estimating Magnitude and Frequency of Floods on
Natural-Flow Streams in Florida
Water-Resources Investigations 82-4012. U.S. Geological
Survey, Denver, Colo.
Brinson, M.M., B.L. Swift, R.C. Plantico, and J.S. Barclay
-1981 y
Riparian Ecosystems: Their Ecology and Status
FWS/OBS-81 /17. U.S. Fish and Wildlife Service,
Washington, D.C
94 Synoptic Approach
-------
Brooks, R.P., D.E. Arnold, E.D. Bellis, C.S. Keener, and
M.J. Croonquist- 1990
A Methodology for Biological Monitoring of Cumulative Impacts
on Wetland, Stream, and Rparian Components of Watersheds
Association of Wetland Managers, Inc., Berne, N.Y.
Brown, M., and J.J. Dinsmore - 1986
Implications of marsh size and isolation for marsh bird
management
Journal of Wildlife Management 50(3)392-397.
Brown, M., and J.J. Dinsmore - 1988
Habitat islands and the equilibrium theory of island biogeography:
testing some predictions
Oecologia 75:426-429.
Brown, M.T., J.M. Schaefer, and K.H. Brandt - 199O
Buffer Zones for Water, Wetlands, and Wildlife in East Central
Florida
CFW Publication #89-07. Center for Wetlands, University of
Florida, Gainesville, Fla.
Brown, R.G - 1988
Effects of precipitation and land use on storm runoff
Water Resources Bulletin 24:421-426.
Brun, L.J., J.L. Richardson, J.W. Enz, and J.K. Larson -
1981
Stream flow changes in the southern Red River Valley of North
Dakota
North Dakota Farm Research 38:11-16.
Burdick, D.M., D. Cushman, R. Hamilton, and J.G.
Gosseiink-1989
Faunal changes and bottomland hardwood forest loss in the Tensas
Watershed, Louisiana
Conservation Biology 3:282-291.
Burger, J- 1981
A model for the evolution of mixed-species colonies of
Ciconiiformes
Quarterly Review of Biology 56:143-167.
Burkham, D.E— 1976
Hydraulic effects of changes in bottomland vegetation on three
major foods, Gila River in southeastern Arizona
Geological Survey Professional Paper 655-J. U.S. Geological
Survey, Reston, Va.
Burrough, P.A - 1986
Principles of Geographical Information Systems for Land
Resources Assessment
Carendon Press, Oxford, England.
Canham, C.D., and P.L. Marks - 1985
The response of woody plants to disturbance: Patterns of
establishment and growth
P. 197-216 in S.T.A. Pickett and P.S. White (eds.), "The
Ecology of Natural Disturbance and Patch Dynamics."
Academic Press, Inc., New York, N.Y.
Canning, D.J., and M. Stevens - 1989
Wetlands of Washington: A Resource Characterization
Land and Wetland Resources Subcommittee, Environment
2010 Technical Advisory Committee, Washington
Department of Ecology, Olympia, Wash.
Canters, K.J., C.P. den Herder, A.A. de Veer, P.W.M.
Veelenturf, and R.W. de Waal - 1991
Landscape-ecological mapping of the Netherlands
Landscape Ecology 5:145-162.
Carpenter, D.H - 1983
Characteristics of Stream Flow in Man/land
Report of Investigations No. 35. Maryland Geological
Survey, Annapolis, Md.
Carter, V-1986
An overview of the hydrologk concerns related to wetlands in the
United States
Canadian Journal of Botany 64:364-374.
Chabreck, R.H., and A.W. Palmisano - 1973
The effects of Hurricane CamUle on the marshes of the Mississippi
River delta
Ecology 54:1118-1123.
Chatoian, J — 1988
Cumulative off-site watershed effects analysis
In "USFS Region V Soil and Water Conservation
Handbook." FSH 2509.22. USDA Forest Service, San
Francisco, Calif.
Childers, D.L., and J.G. Gosseiink - 1990
Assessment of cumulative impacts to water quality in a forested
wetland landscape
Journal of Environmental Quality 19:455-464.
Childs, E.F-1970
Upper Charles River Watershed Hydrology
Appendix E. New England Division, U.S. Army Corps of
Engineers, Waltham, Mass.
Choquette. A.F -1988
Regionalization of Peak Discharges from Streams in Kentucky
Water-Resources Investigations 87-4209. U.S. Geological
Survey, Denver, Colo.
Christensen, R.C., E.B. Johnson, and G.G. Plantz - 1986
Manual for Estimating Selected Streamflow Characteristics of
Natural-Flow Streams in the Colorado River Basin in Utah
Water-Resources Investigations 85-4297. U.S. Geological
Survey, Denver, Colo.
Cobourn, H - 1989
Is cumulative watershed effects analysis coming of age?
Journal of Soil and Water Conservation 44(4):267-270.
Coleman, J.M - 1988
Dynamic changes and processes in the Mississippi River delta
Geological Society of America Bulletin 100:999-1015.
Colson, B.E., and J.W. Hudson - 1976
Flood Frequency of Mississippi Streams
RO-76-014-PR. Mississippi Highway Department, Jackson,
Miss.
Conger, D.H-1971
Estimating Magnitude and frequency offloads in Wisconsin
Open-file Report. U.S. Geological Survey, Water Resources
Division, Madison, Wis.
Conner, W.H., and J.W. Day, Jr - 1987
The Ecology ofBarataria Basin, Louisiana: An Estuarine Profile
Biological Report 85(7.13). National Wetlands Research
Center, U.S. Fish and Wildlife Service, Slidell, La.
The Conservation Foundation - 1988
Protecting America's Wetlands: An Action Agenda. The Final
Technical Report of the National Wetlands Policy Forum
The Conservation Foundation, Washington, D.C.
References 95
-------
Contnnt, C.K., and I.. Ortolano - 1985
Evaluating a cumulitive impact assessment approach
Water Resources Research 21(9):1313-1318.
Coopor, J.R., J.W. Giilliam, and T.C. Jacobs - 1986
Riparian areas as a control of nonpoint pollutants
P. 166-192 in D.L Correll (ed), "Watershed Research
Perspectives." Smithsonian Institution Press, Washington,
D.C
Corps of Engineers— 1988
Comparison of Modeling Techniques for Wetland Areas
Project Report No. 88-4. Hydrologic Engineering Center,
US. Army Corps of Engineers, Davis, Calif.
Costanza, R., and F.H. Sklar - 1985
Articulation, accuracy and effectiveness of mathematical models:
A review of freshwater wetland applications
Ecological Modelling 27:45-68.
Council on Environmental Quality [CEO] - 1991
Uniting ecosystems and biodiversity
P. 135-187 in "Environmental Quality: 21st Annual Report."
VS. Government Printing Office, Washington, D.C
Cowardin, L.M — 1S69
Use offloaded timber by waterfowl at the Montezuma National
Wildlife Refuge
Journal of Wildlife Management 33:829-842.
Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoa -
1979
Classification of Wetlands and Deepwater Habitats of the United
States
FWS/OBS-79/31. Office of Biological Services, U.S. Fish and
Wildlife Service, Washington, D.C.
Cowardin, L.M., D.H. Johnson, T.L. Shaffer, and D.W.
Sparling-1988
Applications of a Simulation Model to Decisions in Mallard
Management
Technical Report 17. U.S. Fish and Wildlife Service,
Jamestown, N.D.
Croonquist, M.J., and R.P. Brooks - 1991
Use of avion and mammalian guilds as indicators of cumulative
impacts in riparian-wetland areas
Environmental Management 15:701-714.
Cummans, J.E., M.R. Ceilings, and E.G. Nassar- 1975
Magnitude and Frequency of Floods in Washington
Open-File Report 74-336. State of Washington Department
of Highways, Tacotna, Wash.
Dahl,T.E-1990
Wetlands Losses in the United States 1780s to 1980s
US. Fish and Wildlife Service, Washington, D.C
Dahl, T.E., C.E. Johnson, and W.E. Prayer - 1991
Wetlands Status and Trends in the Conterminous United States,
mid-1970's to mid-1980's
US. Fish and Wildlife Service, Washington, D.C
Demos and Moore, Iric — 1981
Methodology for the Analysis of Cumulative Impacts of Permit
Activities Regulated by tfie U.S. Army Corps of Engineers
Institute for Water Resources, Fort Belvoir, Va.
Darmor, K.I-1970
A Proposed Streamflatu Data Program for New York
Open-file Report. US. Geological Survey, Albany, N.Y.
Davis, L.G - 1974
Floods in Indiana: Technical Manual for Estimating Their
Magnitude and Frequency
Circular 710. U.S. Geological Survey, Reston, Va.
DeAngelis, D.L., P.J. Mulholland, J.W. Elwood, A.V.
Palumbo, and A.D. Steinman - 1990
Biogeochemical cycling constraints on stream ecosystem recovery
Environmental Management 14:685-697.
Debano, L.F., and L.J. Schmidt - 1990
Potential for enhancing riparian habitats in the southwestern
United States with watershed practices
Forest Ecology and Mangement 33/34:385-403.
Delcourt, H.R., P.A. Delcourt, and T. Webb, III - 1983
Dynamic plant ecology: The spectrum of vegetational change in
space and time
Quaternary Science Reviews 1:153-175.
Detenbeck, N.E., C.A. Johnston, and G.J. Niemi - 1988
The Effect of Wetlands on Lake Water Quality: A Landscape
Approach
Natural Resources Research Institute, University of
Minnesota, Duluth, Minn.
DeVries, J.J- 1980
Effects ofFloodplain Encroachments on Peak Flow
Water Resources Support Center, U.S. Army Corps of
Engineers, Davis, Calif.
Dewey and Kropper Engineers - 1964
Effect of Loss of Valley Storage Due to Encroachment —
Connecticut River
Connecticut Water Resources Commission, Hartford, Conn.
Dickert, T.G., and A.E. Tuttle — 1985
Cumulative impact assesment in environmental planning: A
coastal wetlands watershed example
Environmental Impact Assessment Review 5(1): 37-64.
Dillon, P.J., L.A. Molot, and W.A. Schneider- 1991
Phosphorus and nitrogen export from forested stream catchments
in central Ontario
Journal of Environmental Quality 20:857-864.
Dreher, D.W., G.C. Schaefer, and D.L. Hey - 1989
Evaluation ofStormwater Detention Effectiveness in Northeastern
Illinois
Northeastern Illinois Planning Commission, Chicago, 111.
Driver, E.A-1977
Chironomid communities in small prairie ponds: some
characteristics and controls
Freshwater Biology 7:121-133.
Duever, M.J - 1988
Hydrologic processes for models of freshwater wetlands
P. 9-39 in W.J. Mitsch, M. Straskraba, and S.E. Jorgensen
(eds.), "Wetland Modelling." Developments in
Environmental Modelling, 12. Elsevier Science Publishers,
New York, N.Y.
Durham, D., R. Abernethy, and P. Hamel - 1987
Development of the West Tennessee Bottomland Hardwood
Habitat Evaluation System Model
U.S. Army Corps of Engineers and Tennessee Department of
Conservation, Nashville, Tenn.
96 Synoptic Approach
-------
Dzubin, A- 1969
Comments on carrying capacity of small ponds for ducks and
possible effects of density on mallard production
P. 239-267 in J.T. Ratti, L.D. Hake, and W.A. Wentz (eds.),
"Waterfowl Ecology and Management: Selected Readings."
Allen Press, Lawrence, Kan.
Eadie, J.M., T.A. Hurly, R.D. Montgomerie, and K.L.
Teather-1986
Lakes and rivers as islands: Species-area relationships in the fish
faunas of Ontario
Environmental Biology of Fishes 15:81-89.
Ebert, T.A., and M.L. Balko - 1987
Temporary pools as islands in space and time: the biota of vernal
pools in San Diego, Southern California
Archiv fur Hydrobiologie 110:101-123.
Eckhardt, B.W., and T.R. Moore - 199O
Controls on dissolved organic carbon concentrations in streams,
southern Quebec
Canadian Journal of Fisheries and Aquatic Sciences 47:1537-
1544.
Edgerton, C.R -1973
Handbook of Design for Highway Surface Drainage Structures
Department of Transportation, North Carolina State
Highway Commission, Raleigh, N.G
Edwards, R.T., and J.L. Meyer - 1987
Metabolism of a subtropical low gradient blackwater river
Freshwater Biology 17251-263.
Ehrlich, P.R., and A.H. Ehrlich - 1981
Extinction: The Causes and Consequences of the Disappearance of
Species
Random House, New York, N.Y.
Elder, J.F-1985
Nitrogen and phosphorus speciation and flux in a large Florida
river wetland system
Water Resources Research 21:724-732.
Emery, R.M -1986
Impact interaction potential: A basinwide algorithm for assessing
cumulative impacts from hydropower projects
Journal of Environmental Management 23:341-360.
Environmental Protection Agency [EPA] - 1987
Unfinished Business: A Comparative Assessment of
Environmental Problems
Overview Technical Report. U.S. Environmental Protection
Agency, Washington, D.C.
Environmental Protection Agency [EPA] - 1989
Quality Assurance Program Plan For the Environmental Research
Laboratory—Corvallis
Revision 2. EPA Corvaffis No. 110.001. U.S. Environmental
Protection Agency, Environmental Research Laboratory,
Corvallis, Ore.
Erwin, R.M., J.A. Spendelow, P. Geissler, and B.K.
Williams- 1986
Relationships between nesting populations of wading birds and
habitat features along the Atlantic Coast
P. 55-69 in W.R. Whitman and W.H. Meredith (eds.),
"Proceedings of a Symposium on Waterfowl and Wetlands
in the Coastal Zone of the Atlantic Flyway." Delaware
Department of Natural Resources and Environmental
Control, Dover, Del.
Fisher, S.G., L.J. Gray, N.B. Grimm, and D.E. Busch -
1982
Temporal succession in a desert stream ecosystem following flash
flooding
Ecological Monographs 52:93-110.
Fisher, S.G., and N.B. Grimm- 1991
Streams and disturbance: Are cross-ecosystem comparisons
useful?
P. 196-221 in J. Cole, G. Lovett, and S. Findlay (eds.),
"Comparative Analyses of Ecosystems: Patterns,
Mechanisms, and Theories." Springer-Verlag, New York,
N.Y.
Flake, L.D - 1979
Wetland diversity and waterfowl
P. 312-319 in P.E. Greeson, J.R. dark, and J.E. Clark (eds.),
"Wetland Functions and Values: The State of Our
Understanding." American Water Resources Association,
Minneapolis, Minn.
Flippo, H.N., Jr - 1977
Floods in Pennsylvania
Water Resources Bulletin No. 13. Pennsylvania Department
Environmental Resources, Harrisburg, Pa.
Flores, A.C., P.B. Bedient, and L.W. Mays - 1982
Method for optimizing size and location of urban detention storage
P. 357-365 in "Proceedings of the International Symposium
on Urban Hydrology, Hydraulics, and Sediment Control,"
American Society of Civil Engineers, New York, N.Y.
Flowerdew, R., and M. Green - 1989
Statisical methods for interference between incompatibk zonal
systems
P. 239-247 in M.F. Goodchild and S. Gopal (eds.), "Accuracy
of Spatial Databases." Taylor and Francis Ltd., New York,
N.Y.
Forman, R.T.T - 1990
Ecologically sustainable landscapes: The role of spatial
configuration
P. 261-278 in LS. Zonneveld and R.T.T. Forman (eds.),
"Changing Landscapes: An Ecological Perspective."
Springer-Verlag, New York, N.Y.
Forman, R.T.T., and M. Godron - 1981
Patches and structural components for a landscape ecology
BioScience 31(10):733-740.
Forman, R.T.T., and M. Godron - 1986
Landscape Ecology
John Wiley and Sons, New York, N.Y.
Franklin, J.F., and C.T. Dyrness - 1984
Natural Vegetation of Oregon and Washington
Oregon State University Press, Corvallis, Ore.
Franklin, J.F.. and R.T.T. Forman - 1987
Creating landscape patterns by forest cutting: Ecological
consequences and principles
Landscape Ecology 1(1):5-18.
Frederick, R.B - 1983
Developing an energetics model for waterfowl refuge management
P. 253-257 in W.K. Lauenroth, G.V. Skogerboe, and M. Plug
(eds.), "Analysis of Ecological Systems: State-of-the-art in
Ecological Modeling." Hsevier Scientific Publishing
Company, New York, N.Y.
References 97
-------
Gatlin, L.L-1972
Information Theory and the Living System
Columbia University Press, New York, N.Y.
Gibb», J.P., and J. Fanborg - 1990
Estimating the viability ofovenbird and Kentucky warbler
populations in forest fragments
Conservation Biology 4:193-196.
Gibbs, J.P., and S.M. IMolvin - 1990
An Assessment of Wading Birds and Other Wetlands Avifauna
end Their Habitats in Maine
Maine Department Inland Fisheries and Wildlife, Augusta,
Maine.
Gibbc, J.P., S. Woodward, M.L. Hunter, and A.E.
Hutchin»on -1987
Determinants of Great Blue Heron colony distribution in coastal
Maine
Auk 1043847.
Glatfoltor, D.R-1984
Techniques for Estimating Magnitude and Frequency offloads on
Streams in Indiana
Water Resources Investigations 84-4134. US. Geological
Survey, Denver, Colo.
Gorham, E., SJ. Eisonroich, J. Ford, and M.V. Santelman
-1985
The chemistry of bog waters
P. 339-363 in W. Stumm (ed.), "Chemical Processes in
Lakes." John Wiley and Sons, New York, N.Y.
GoMoIink,J.G-1984
The Ecology of Delta Marshes of Coastal Louisiana: A Community
Profile
FWS/OBS-84/09. US. Fish and Wildlife Service,
Washington, D.C
GoMolink, J.G., and L.C. Lee - 1989
Cumulative impact assessment in bottomland hardwood forests
Wetlands 9:83-174.
Gossolink, J.G., C.E. Sassor, L.A. Creasman, S.C.
Hamilton, E.M. Swonson, and G.P. Shaffer - 1990a
Cumulative Impact Assessment in the Pearl River Basin,
Mississippi and Louisiana.
LSU-CE1-90-03. Coastal Ecology Institute, Louisiana State
University, Baton Rouge, La.
Gocsolink, J.G., G.P. Shaf or, L.C. Lee, D.M. Burdick, D.L.
Childors, N.C. Loibowitz, S.C. Hamilton, R. Boumans, D.
Cushman, S. Fields, M. Koch, and J.M. Vissor - 1990b
Landscape conservation in a forested wetland watershed: Can we
manage cumulative impacts?
BioScicnee 40:588-600.
Guotzkow, L.C -1977
Tcdmiques for Estimating Magnitude and Frequency of Floods in
Minnesota
Water Resources Investigations 77-31. U.S. .Geological
Survey, St. Paul, Minn.
Haan, C.T., and H.P. Johnson - 1968
Drainage Hydrology of Depressional Watersheds
American Society of Civil Engineers Paper No. 69.
Hains, C.F-1973
Floods in Alabama
Open-file Report. U.S. Geological Survey, Montgomery, Ala.
Hammer, D - 1992
Designing constructed wetlands systems to treat agricultural
nonpoint source pollution
Ecological Engineering 1(1/2): 49-82.
Hammett, K.M., J.F. Turner, and W.R. Murphy - 1978
Magnitude and Frequency of Flooding on the Mydkka River,
Southwest Florida
Open-File Report. U.S. Geological Survey, Tallahassee, Fla.
Hanson, J.S., G.P. Malanson, and M.P. Armstrong -1990
Landscape fragmentation and dispersal in a model of riparian forest
dynamics
Ecological Modelling 49:277-296.
Harris, D.D., L.L. Hubbard, and L.E. Hubbard - 1979
Magnitude and frequency of floods in western Oregon
Open-file Report 79-553. U.S. Geological Survey, Portland,
Ore.
Harris, L.D-1984
The Fragmented Forest: Island Biogeography Theory and the
Preservation ofBiotic Diversity
University of Chicago Press, Chicago, HI.
Harris, L.D-1988
The nature of cumulative impacts on biotic diversity of wetland
vertebrates
Environmental Management 12(5):675-693.
Harris, L.D., and R.D. Wallace - 1984
Breeding bird species in Florida forest fragments
Proceedings of the Annual Conference of the Southeastern
Association of Fish and Wildlife Agencies 38:87-96.
Hauth,L.D-1974
Technique for Estimating the Magnitude and Frequency of
Missouri Floods
Open-File Report. U.S. Geological Survey, Denver, Colo.
Hayden, T.J., J. Faaborg, and R.L. Clawson - 1985
Estimates of minimum area requirements for Missouri forest birds
Transactions of the Missouri Academy of Science 19:11-22.
Hayes, G.S., and R.A. Merrill - 1970
A Proposed Streamflow Data Program for Maine
Open-file Report. U.S. Geological Survey, Augusta, Maine.
Heath, R.C - 1982
Classification ofgroundwater systems of the United States
Ground Water 20:393-401.
Henderson, M., G. Merriam, and J. Wegner - 1985
Patchy environments and species survival: Chipmunks in an
agricultural mosaic
Biological Conservation 31:95-105.
Henein, K., and G. Merriam - 1990
The elements of connectivity where corridor quality is variable
Landscape Ecology 4(2/3):157-170.
Hindall, S.M -1975
Measurement and Prediction of Sediment Yields in Wisconsin
Streams
Water Resources Investigations 54-75. U. S. Geological
Survey, Madison, Wis.
Hirsch, A-1988
Regulatory context for cumulative impact research
Environmental Management 12(5):715-723.
98 Synoptic Approach
-------
Hjel, H.R-1984
Use of Selected Bank Characteristics to Estimate Mean Annual
Runoff and Peak Discharges for Ungaged Streams in Drainage
Basins Containing Strippable Coal Resources, Northwestern New
Mexico
Water-Resources Investigations Report 844260. U.S.
Geological Survey, Albequerque, N.M.
Moiling, C.S-1973
Resilience and stability of ecological systems
Annual Review of Ecology and Systematics 4:1-24.
Hopkins, L.D - 1977
Methods for generating land suitability maps: A comparative
evaluation
American Institute of Planners Journal 43:386-400.
Horak, G.C., E.C. Vlachos, and E.W. Cline - 1983
Methodological Guidance for Assessing Cumulative Impacts on
Fish and Wildlife
Contract No. 14-16-0009-81-058. Dynamac Corporation,
Enviro Control Division, Fort Collins, Colo.
Hunter, W.C., R.D. Ohmart, and B.W. Anderson - 1987
Status of breeding riparian-obligate birds in southwestern riverine
systems
Western Birds 18:10-18.
Illinois Environmental Protection Agency [IEPA3- 1990
Ittinens Water Quality Report 1988-1989
IEPA/WPC/90-160. Division of Water Pollution Control,
Springfield, HI.
Inkley, D.B, and S.H. Anderson - 1982
Wildlife Communities and Land Classification Systems.
Transactions of the 47th North American Wildlife and Natural
Resources Conference
Wildlife Management Institute, Washington, D.C.
Irwin, F., and B. Rodes - 1992
Making Decisions on Cumulative Environmental Impacts: A
Conceptual Framework
World Wildlife Fund, Washington, D.C.
Jacques, J.E., and D.L. Lorenz - 1988
Techniques for Estimating the Magnitude and Frequency of Floods
of Ungaged Streams in Minnesota
Water-Resources Investigations 87-4170. U.S. Geological
Survey, St. Paul, Minn.
Janowicz, J.R - 1986
A methodology for estimating design peak flows for Yukon
Territory
P. 313-320 in "Cold Regions Hydrology Symposium."
American Water Resources Association, Bethesda, Md.
Jeffries, M - 1989
Measuring Tailing's 'element of chance' in pond populations
Freshwater Biology 20383-393.
Johnson, B.H., and P.K. Senter- 1977
Effect of loss of Valley Storage in the Cannelton Pool on Ohio
River Flood Heights
Miscellaneous Paper H-77-7. U.S. Army Corps of Engineers,
Waterways Experiment Station, Vicksburg, Miss.
Johnson, M.V., and R.J. Omang - 1976
A Method for Estimating Magnitude and Frequency of Floods in
Montana
Open-File Report. U.S. Geological Survey, Denver, Colo.
Johnston, C.A - 1991
Sediment and nutrient retention by freshwater wetlands: Effects
on surface water quality '•
Critical Reviews in Environmental Control 21:491-565.
Johnston, C.A., N.E. Detenbeck, J.P. Bonde, and G.J.
Niemi-1988
Geographic Information Systems for cumulative impact assessment
Photogrammetric Engineering and Remote Sensing 54:1609-
1615.
Johnston, C.A., N.E. Detenbeck, J.P. Bonde, and G.J.
Niemi - 1990a
The cumulative effect of wetlands on stream water quality and
quantity: A landscape perspective
Biogeochemistry 10:105-141.
Johnston, C.A., T. Johnson, M. Kuehl, D. Taylor, and J.
Westman-1990B
The Effects of Freshwater Wetlands on Water Quality: A
Compilation of Literature Values
Natural Resources Research Institute, University of
Minnesota, Duluth, Minn.
Jones, J.B., Jr., and L.A. Smock - 1991
Transport and retention of part iculate organic matter in two low-
gradient headwater streams
Journal of the North American Benthological Society 10:115-
126.
Jones, J.R., B.R. Borofka, and R.W. Bachmann - 1976
Factors affecting nutrient loads in some Iowa streams
Water Research 10:117-122.
Jordan, T.E., D.L. Correll,W.T.Peterjohn, and D.E. Welter
—. 1986
Nutrient flux in a landscape: The Rhode River watershed and
receiving waters
P. 57-76 in D.L. CorreE (ed.), "Watershed Research
Perspectives." Smithsonian Institution Press, Washington,.
D.C
Kadlec, J.A - 1987
Nutrient dynamics in wetlands
P. 393-419 in K.R. Reddy and W.H. Smith (eds.), "Aquatic
Plants for Water Treatment and Resource Recovery."
Magnolia Publishing, Inc., Orlando, Fla.
Kahn, A.E-1966
The tyranny of small decisions: Market failures, imprefections, and
the limits of economics
Kyklos 19:2347.
Kantrud, H.A., J.B. Millar, and A.G. van der Valk - 1989
Vegetation of wetlands of the prairie pothole region
P. 132-187 in A. van der Valk (ed.), "Northern Prairie
Wetlands." Iowa State University Press, Ames, Iowa.
Kantrud, H.A., and R.F. Stewart - 1984
Ecological distribution and crude density of breeding birds on
prairie wetlands
Journal of Wildlife Management 48:426-437.
Karr, J.R-1981
Assessment ofbiotic integrity using fish communities
Fisheries 6(6):21-31.
Kentula, M.E., R.P. Brooks, S.E. Gwin, C.C. Holland, A.D.
Sherman, and J.C. Sifneos - 1992
An Approach to Improving Decision Making in Wetland
Restoration and Creation
EPA/600/R-92/150. U. S. Enviromental Protection Agency,
Environmental Research Laboratory, Corvallis, Ore.
References 99
-------
Korokoi, J., S. Boauchamp, R. Tordon, and T. Pollock -
1986
Sources of sulphate and acidity in wetlands and lakes in Nova
Scotia
Water, Air, and Soil Pollution 31207-214.
Ko»al, R.H-1989
The role of the Mississippi River in wetland loss in southeastern
Louisiana, U.S.A
Environmental Geology and Water Sciences 13:183-193.
Knight, D.H-1987
Parasites, lightning, and the vegetation mosaic in wilderness
landscapes
P. 59-83 in M.G. Turner (ed.), "Landscape Heterogeneity and
Disturbance." Ecological Studies, Volume 64. Springer-
Vcrlag, New York, N.Y.
Koltun, G., and J.W. Roberts - 1990
Techniques for Estitruting Flood-Peak Discharges of Rural,
Unregulated Streams in Ohio
Water-Resources Investigations 89-4126. U.S. Geological
Survey, Denver, Colo.
Kuchlor,A.W-1985
Potential Natural Vegetation
1:7,500,000 map. US. Geological Survey, Reston, Va.
Ku»hIan,J.A-1979
Uife reserves: lessons
Design and managetnent of continental wildlife
from the Everglades
Biological Conservation 15281-290.
Ku«lor, J.A., and M.I!. Kontula - 1990
Executive Summary
P. xvii-xxv in J.A. Kusler and M,E. Kentula (eds.), "Wetland
Creation and Restoration: The Status of the Science." Island
Press, Washington, D.C
LaBaugh. J.W-1986
Wetland ecosystem studies from a hydrologic perspective
Water Resources Bulletin 22(1):1-10.
£
Laonon, A-1980
Storm Runoff as Related to Urbanization in the Portland, Oregon
—Vancouver, Washington Area
Open-File Report 80-689. U.S. Geological Survey, Portland,
Ore.
Landor*, M.N., and IC.V. Wilson, Jr - 1991
Flood characteristics of Mississippi streams
Water-Resources Investigations 91-4037. U.S. Geological
Survey, Jackson, Miss.
Lane, P.A., and R.R. Wallace - 1988
A User's Guide to Cumulative Effects Assessment in Canada
Canadian Environmental Assessment Research Council,
Ottawa, Ontario.
Lara, O.G -1973
Hoods in Iowa: Technical Manual for Estimating Their
Magnitude and frequency
Open-File Report. US. Geological Survey, Denver, Colo.
Larson, D.P., J.M. Omornik, R.M. Hughes, C.M. Rohm,
T.R. Whittior, A.J. Kinnoy, A.L. Gallant, and D.R. Dudley
-1986
The correspondence between spatial patterns in fish assemblages in
Ohio streams and aquatic ecoregions
Environmental Management 10:815-828.
LaZerte, B.D., and P.J. Dillon - 1984
Relative importance of anthropogenic versus natural sources of
acidity in lakes and streams of central Ontario
Canadian Journal of Fisheries and Aquatic Sciences 41:1664-
1677.
Lee, F.N - 1985a
Analysis of the Low-Flow Characteristics of Streams in Louisiana
Water Resources Technical Report No. 35. Louisiana
Department of Transportation and Development, Baton
Rouge, La.
Lee, F.N-1985b
Floods in Louisiana, Magnitude and Frequency
Water Resources Technical Report No. 36. Louisiana
Department of Transportation and Development, Baton
Rouge, La.
Lee, L.C., and J.G. Gosselink - 1988
Cumulative impacts on wetlands: Linking scientific assessments
and regulatory alternatives
Environmental Management 12(5):591-602.
Leibowitz, S.G., E.M. Preston, L.Y. Arnaut, N.E.
Detenbeck, C.A. Hag ley, M.E. Kentula, R.K. Olson, W.D.
Sanville, and R.R. Sumnor - 1992
Wetland Research Plan FY92-96: An Integrated Risk-Based
Approach
EPA/600/R-92/060. U.S. Environmental Protection Agency,
Environmental Research Laboratory, Corvallis, Ore.
Lehman, H.M., M.R. Darst, and JJ. Nordhaus - 1991
Fishes in the Forested Flood Plain of the Ochlockonee River,
Florida, During Flood and Drought Conditions
Water-Resources Investigations 90-4202. U.S. Geological
Survey, Tallahassee, Ha.
Liegel, L., D. Cassell, D. Stevens, P. Shaffer, and R.
Church -1991
Regional characteristics of land use in northeast and southern Blue
Ridge Province: Associations with acid rain effects on surface-
water chemistry
Environmental Management 15:269-279:
Loken, L.G - 1991
Wetland soil characteristics of basins in closed groundwater
catchment systems
Masters of Science Thesis. North Dakota State University,
Fargo, N.D.
Lopez, M.A., and W.M. Woodham - 1983
Magnitude and Frequency of Flooding in Small Urban Watersheds
in the Tampa Bay Area, West-Central Florida
Water-Resources Investigations 82-42. U.S. Geological
Survey, Denver, Colo.
Loucks, O.L-1970
Evolution of diversity, efficiency, and community stability
American Zoologist 10:17-25.
Louisiana Department of Environmental Quality [LDEQ]
- 1988
State of Louisiana Water Quality Management Plan
Volume 6, Part A: Nonpoint source pollution assessment
report. Louisiana Department of Environmental Quality,
Baton Rouge, La.
Lowe, A.S-1979
Magnitude and Frequency of Floods for Small Watersheds in
Louisiana
Research Study 65-2H. Louisiana Department of
Transportation and Development, Baton Rouge, La.
100 Synoptic Approach
-------
Lowe, R.W.. and C.D. Cooley- 1981
A Resource Inventory of the Pearl River Basin, Mississippi and
Louisiana
U.S. Fish and Wildlife Service, Decatur, Ala.
Lugo, A.E-1978
Stress and ecosystems
P. 62-101 in "Energy and Environmental Stress in Aquatic
Ecosystems." U.S. Department ofEnergy Symposium Series
CONF-77114. National Technical Information Service,
Washington, D.C
Lumia, R-1984
Flood-Discharge Profiles of Selected Streams inRockland County,
New York
WaterrResources Investigations 84-4049. U.S. Geological
Survey, Denver, Colo.
MacArthur, R-1955
Fluctuations of animal populations, and a. measure of community
stability
Ecology 36(3):533-536.
MacArthur, R.H., and E.G. Wilson - 1967
The Theory of Island Biogeography
Monographs in Population Biology 1. Princeton University
Press, Princeton, N.J.
Mader, S.F., W.M. Aust, and R. Lea - 1989
Changes in functional values of a forested wetland following timber
harvesting practices
P. 149-154 in D.D. Hook and R. Lea (eds.), "Proceedings of
the Symposium: The Forested Wetlands of the Southern
United States." General Technical Report SE-SO. USDA
Forest Service, Asheville, N.C.
Manicacci, D., I. O livieri, V. Parrot, A. Atlan, P.-M. Gouyon,
J.-M. Prosper!, and D. Couvet-1992
Landscape ecology: Population genetics at the metapopulation
level
Landscape Ecology 6(3):147-159.
Mann, W., P. Dorn, and R. Brand I - 1991
Local distribution of amphibians: The importance of habitat
fragmentation
Global Ecological Biogeography Letters 1:36-41.
Margalef, R-1968
Perspectives in Ecological Theory
University of Chicago Press, Chicago.
Maristany, A.E., and R.L. Bartel - 1989
Wetlands and starmwater management: A case study of Lake
Munson. Part I: Long-term treatement efficieinceies
P. 215-230 in D.W. Fisk (ed.), "Wetlands Concerns and
Successes." American Water Resources Association,
Bethsda,Md.
May, R.M- 1974
Stability and Complexity in Model Ecosystems
Monographs in Population Biology 6. Princeton University
Press, Princeton, N.J.
McCormick, P.V., P.M. Stewart, and J. Cairns, Jr - 1987
Effect of.distance from a source pool on protozoan colonization of
isolated aquatic systems
Journal of Freshwater Ecology 4:1-15.
McCuen, R.H - 1979
Downstream effects of stormwater management basins
American Society of Civil Engineers Journal of Hydraulics
Division 105 (HY11):1343-1356.
McHarg, 1-1969
Design with Nature
Natural History Press, New York, N.Y.
McWiiiiama, W.H., and J.F. Rosson, Jr - 1990
Composition and vulnerability of bottomland hardwood forests of_
the Coastal Plain Province in the south central United States
Forest Ecology and Management 33/34:485-501.
Megahan, W.S-1992
Status of the NCASI cumulative watershed effects program and. •
methodology
National Council of the Paper Industry for Air and Stream
Improvement, Inc., Corvallis, Ore.
Merriam, G., and J. Wegner - 1992
Local extinctions, habitat fragmentation, andecotones
P. 150-169 in A.J. Hansen and F. di Castri (eds), "Landscape
Boundaries: Consequences for Biotic Diversity and
Ecological Flows." Ecological Studies, Volume 92. Springer-
Verlag, New York, N.Y.
Meyer, J.L-1990
A blackwater perspective on riverine ecosystems
BioScience 40:643-651.
Miller, E.M - 1978
Technique for Estimating Magnitude and Frequency of Floods in
Virginia
Water-Resources Investigation 78-5. U.S. Geological Survey,
Denver, Colo.
Milne, M.M., and D.W. Young - 1989
The impact of stockwatering ponds (stockponds) on runoff from
large Arizona watersheds '
Water Resources Bulletin 25:165-173. -
Mitsch, W.J-1983
Ecological models for management of freshwater wetlands
P. 283-310in S.E. Jorgensen and WJ.Mitsch (eds.),
"Application of Ecological Modeling in Environmental
Management, Part B." Elsevier Science Publishers,
Amsterdam, The Netherlands.
Mitsch, W.J., and J.G.Gosselink-1986
Wetlands
Van Nostrand Reinhold Co., New York, N.Y.
Moore, I.D., and D.L. Larson - 1979
Effects of Drainage Projects on Surface Runoff from Small
Depressional Watersheds in the North-central Region
Water Resources Research Center Bulletin 99. University of
Minnesota, Minneapolis, Minn.
Moore, J.R.J., P.A. Keddy, C.L. Gaudet, and I.C. Wisheu
-1989
Conservation of wetlands: Do infertile wetlands deserve a higher
priority?
Biological Conservation 47:203-217.
Moore. T.R-1987
A preliminary study of the effects of drainage and harvesting on
water quality in ombrotrophic bogs near Sept-lies, Quebec
Water Resources Bulletin 23:785-791. •
Muehrcke, P.C - 1978
Map Use, Reading, Analysis, and Interpretation
J.P. Publications, Madison, Wis.
Mulholland, P.J., and E.J. Kuenzler - 1979
Organic carbon export from upland and forested wetland
watersheds
Limnology and Oceanography 24:960-966.
References101
-------
Naiman, R.J., H. DaCamps, J. Pastor, and C.A. Johnson
— 1988
The potential importance of boundaries to fluvial ecosystems
Journal of the North American Benthological Society 7:289-
306.
Neoly, B.L-1976
floods m Louisiana, Magnitude and frequency
Open-File Report. LJ.S. Geological Survey, Denver, Colo.
NooJy,B.L-1987
Magnitude and Frequency offloads in Arhtnsas
Water-Resources Invesdgations 84-4191. U.S. Geological'
Survey, Denver, Colo.
Nooly, R.D., and C.G. Hoistor (compilers) - 1987
The Natural Resources of Illinois: Introduction and Guide
Illinois Natural History Survey Special Publication 6. Illinois
Natural History Survey, Department of Energy and Natural
Resources, Champaign, HI.
Nowbold, J.D., P.J. Mulholland, J.W. Elwood, and R.V.
O'Noill -1982
Organic carbon spiralling in stream ecosystems
Oikos 38266-272.
Newfoundland Environment and Environment Canada
INEEC1-1984
Regional Flood Frequency Analysis for the Island of Newfoundland
St. Johns, Newfoundland.
Novitzki, R.P -1979
Hydrologic characteristics of Wisconsin's wetlands and their
influence on floods, stream flow, and sediment
P. 377-388 in P.E Greeson, J.R. dark, and J.E. dark (eds.),
"Wetland FuncBons and Values: The State of Our
Understanding." American Water Resources Association,
Minneapolis, Minn,
Nuckols, E.H-1970
Virginia Streamflow Data Program Analysis
Open-File Report. US. Geological Survey, Richmond, Va.
Oakloy, A.L., J.A. Collins, L.B. Everson, D J\. Heller, J.C.
Howorton, and R.E. Vincent - 1985
Riparian Zones and Freshwater Wetlands
P. 57-113 in ER. Brown (ed.), "Management of Wildlife and
Fish Habitats in Forests of Western Oregon and Washington.
Part 1 —Chapter hfarratives." U.S. Forest Service, Pacific
Northwest Region, Portland, Ore
O'Banion, K-1980
Use of value functions in environmental decisions
Environmental Management 4-3-6.
Obort», G.L-1981
Impact of wetlands on watershed water quality
P. 213-227 in B. Richardson (ed.), "Selected Proceedings of
the Midwest Conference on Wetland Values and
Management." Fresh water Society, Navarre, Minn.
O'Brion, A.L., and W.S. Motts - 1980
HydrogeoJogic evaluation of wetland basins for land use planning
Water Resources Bulletin 16(5):785-789.
Odum, E.P -1969
Tlte strategy ofecosystem development
Science 164:262-270.
Odum, E.P- 1985
Trends expected in stressed ecosystems
BioScience 35<7):419-422.
Odum, E.P., J.T. Finn, and E.H. Franz - 1979
Perturbation theory and the subsidy-stress gradient
BioScience 29(6)349-352.
Odum, W.E- 1982
Environmental degradation and the tyranny of small decisions
BioScience 32(9):728-729.
Odum, W.E., T.J. Smith, III, and R. Dolan - 1987
Suppression of natural disturbance: long-term ecological change
on the outer banks of North Carolina
P. 123-135 in M.G. Turner (ed.), "Landscape Heterogeneity
and Disturbance." Ecological Studies, Volume^. Springer-
Verlag, New York, N.Y.
Office of Water Regulations and Standards [OWRS] -
199O
Water Quality Standards for Wetlands: National Guidance
EPA 440/S-90-011. U.S. Environmental Protection Agency,
Washington, D.C.
Ogawa, H., and J.W. Male - 1983
The Flood Mitigation Potential of Inland Wetlands
Water Resources Center, University of Massachusetts,
Amherst, Mass.
Ogawa, J., and J.W. Male - 1986
Simulating the flood mitigation role of wetlands
Journal of Water Resources Planning and Management.
112:114-128. ' "
Ogawa, J., and J.W. Male - 1990
Evaluation framework for wetland regulation
Journal of Environmental Management 30:95-109.
Olin, D.A-1984
Magnitude and Frequency of Floods in Alabama
Water-Resources Investigations 86-4335. U.S. Geological
Survey, Denver, Colo.
O'Meara, T.E - 1984
Habitat-island effects on the avian community in cypress ponds
Proceedings of the Annual Conference of the Southeastern
Association of Fish and Wildlife Agencies 38:97-110.
Omemik, J.M - 1987
Ecoregions of the conterminous United States
Annals of the Association of American Geographers
77(1):118-125.
Omernik, J.M., A.R. Abernathy, and L.M. Male - 1981
Stream nutrient levels and proximity of agricultural and forest
land to streams: Some relationships
Journal of Soil and Water Conservation 36(4):227-23l.
Omernik, J.M., and A.L. Gallant - 1988
Ecoregions of the Upper Midwest States
EPA/600/3-88/037. U.S. Environmental Protection Agency,
Corvallis, Ore.
O'Neil T.A., and G.W. Witmer - 1991
Assessing cumulative impacts to elk and mule deer in the Salmon
River Basin, Idaho
Applied Animal Behavior Science 29:225-238.
O'Neill, R.V., J.W. Elwood, and S.G. Hildebrand - 1980
Theoretical implications of spatial heterogeneity in stream
ecosystems
P. 79-101 in G.S. Innis and R.V. O'Neill (eds.), "Systems
Analysis of Ecosystems." International Co-operative
Publishing House, Fairland, Md.
102 Synoptic Approach
-------
Osborne, L.L., and M.J. Wiley - 1988
Empirical relationships between land use/ cover and stream water
quality in an agricultural watershed
Journal of Environmental Management 26:9-27.
Panu, U.S., and D.A. Smith - 1989
Instantaneous peak flow estimation procedures for Newfoundland
streams
Water Resources Bulletin 25:1151-1162.
Parrish, D., and C. Langston - 1991
Environmental risk based planning using CIS, Region 6
comparative risk project: Evaluating ecological risk
P. 427-436 in "Proceedings of the llth Annual ESRI User
Conference." ESRI, Redlands, Calif.
Patterson, J.H -1976
The role of environmental heterogeneity in the regulation of duck
populations
Journal of Wildlife Management 40:22-32.
Patterson, J.L - 1971
Floods in Arkansas, Magnitude and Frequency Characteristics
Through 1968
Water Resources Circular No. 11. U.S. Geological Survey,
Little Rock, Ark.
Patterson, J.L., and C.R. Gambel - 1968
Magnitude and Frequency of Floods in the United States. Part 5.
Hudson 'Bay and Upper Mississippi River Basin
Water-Supply Paper 1678. U.S. Geological Survey, Denver,
Colo.
Patterson, N.J., and T.H. Whillans - 1984
Human interference with natural water level regimes in the
context of other cultural stresses on Great Lakes wetlands
P. 209-239 in H.H. Prince and F.M. D'ltri (eds.), "Coastal
Wetlands." Lewis Publishers, Inc., Chelsea, Mich.
Peterson, T.L., and J.A. Cooper - 1991
Impacts of center pivot irrigation systems on birds in prairie
wetlands
Journal of Wildlife Management 51:238-247.
Phillips, J.D - 1989a
Fluvial sediment storage in wetlands
Water Resources Bulletin 25:867-873.
Phillips, J.D-1989b
Nonpoint source pollution risk assessment in a watershed context
Environmental Management 13:493-502.
Pickett, S.T.A., and P.S. White (eds.) - 1985m
The Ecology of Natural Disturbance and Patch Dynamics
Academic Press, New York, N.Y.
Pickett, S.T.A., and P.S. White - 1985b
Patch dynamics: A synthesis
P. 371-384 in S.T. A. Pickett and P.S. White (eds.), "The
Ecology of Natural Disturbance and Patch Dynamics."
Academic Press, New York, N.Y.
Pinay, G., A. Fabre, P. Vervier, and F. Gazelle - 1992
Control of C, N, P distribution in soils of riparian forests
Landscape Ecology 6(3):121-132.
Ponce, V.M., and D.S. Lindquist - 1990
Management of baseflow augmentation: A review
Water Resources Bulletin 26:259-286.
Poulin, R.Y-1971
Flood Frequency Analysis for Newfoundland Streams
Water Planning and Operations Branch, Environment
Canada, Ottawa, Ontario.
Powell, G.V.N - 1987
Habitat use by wading birds in a subtropical estuary: implications
of hydrography
Auk 104:740-749.
Preston, E.M., and B.L. Bedford - 1988
Evaluating cumulative effects on wetland functions: A conceptual
overview and generic framework
Environmental Management 12(5):565-583.
Pringle, CM., R.J. Naiman, G. Bretschko, J.R. Karr, M.W.
Oswood, J.R. Webster, R.L. Welcomme, and M.J.
Winterbouon - 1988
Patch dynamics in lotic systems: the stream as a mosaic
Journal of the North American Benthologjcal Society 7:503-
524.
Pulliam, R.H -1988
Sources, sinks, and population regulation
American Naturalist 132(5):652-661.
Radbruch-Hall, D.H., K. Edwards, and R.M. Batson -1987
Experimental Engineering-Geologic and Environmental-Geologic
Maps of the Conterminous United States
Bulletin 1610. U.S. Geological Survey, Reston, Va.
Rapp, G., J.D. Allert, B.W. Liukkonen, J.A. Use, O.L.
Loucks, and G.E. Glass - 1985
Acid deposition and watershed characteristics in relation to lake
chemistry in northeastern Minnesota
Environment International 11:425-440.
Rasmussen, J.B., L. Godbout, and M. Schallenberg -
1989
The humic content of Jake water and its relationship to watershed
and lake morphometry
Limnology and Oceanography 34:1336-1343.
Richardson, C.J - 1989
Freshwater wetlands: Transformers, filters, or sinks?
P. 25-46 in R.R. Sharitz and J.W. Gibbons (eds.), "Freshwater
Wetlands and Wildlife" CONF-8603101. Office of Science
and Technical Information, U.S. Department of Energy, Oak
Ridge, Tenn.
Ricklefs, R.E - 1979
Ecology
Chiron Press, Inc., New York, N.Y.
Robbins, C.S., D.K. Dawson, and B.A. Dowell - 1989
Habitat area requirements of breeding forest birds of the middle
Atlantic states
Wildlife Monographs No. 103. Journal of Wildlife
Management 53 (Supplement).
Robichaud, B., and M.F. Buell -1983
Vegetation of New Jersey: A Study of Landscape Diversity
Rutgers University Press, New Brunswick, N.J.
Robinson, A.H., R.D. Sale, J.L. Morrison, and P.C.
Muehrcke - 1984
Elements of Cartography
Fifth Edition. John Wiley and Sons, New York, N.Y.
References 103
-------
Rogers, J.D., and J.T. Armbruster - 1990
Lorn flaws and hydrotogic droughts
P. 121-129 in M.G. Wolman and H.C Riggs (eds.), "The
Geology of North America. Vol. 0-1, Surface Water
Hydrology." Geological Society of America, Boulder, Colo.
Rorsfett, B-1991
Principal determinants of aquatic macrophyte richness in northern
European lakes
Aquatic Botany 39:173-193.
Rutkadgo, R.W., B.L. Basoro, and R.J. Mulholland - 1976
Ecological stability: An information theory viewpoint
Journal of Theoretical Biology 57355-371.
SAS Institute, Inc - 1988
MS* Procedures Guide, Release 6,03 Edition
SAS Institute, Inc., Gary, N.G
Snuor. V.B-1974
Hood Characteristics of Oklahoma Streams
Water-Resources Investigations 52-73. US. Geological
Survey, Oklahoma City, Okla.
Sauor, V.B., W.O. Thomas, V.A. Strieker, and K.V. Wilson
-1983
flood Characteristics of Urban Watersheds in the United States
Water-Supply Paper 2207. US. Geological Survey, Reston,
Va.
Schwartz, F.W., and W.A. Milne-Home - 1982
Watersheds in muskeg terrain
Journal of Hydrology 57267-305.
Science Aduitory Board [SAB] - 1990
Reducing Risk Setting Priorities and Strategies for
Environmental Protection
SAB-EC-90-021. US. Environmental Protection Agency,
Science Advisory Board, Washington, D.C
Scott, A.G-1971
Preliminary Flood Frequency Relations and Summary of
Maximum Discharges in New Mexico
Open-file Report. US. Geological Survey, Albuquerque,
N.M.
Scott, J.M., B. Csuti, J.D. Jacobi, J.E. Estes - 1987
Species richness: A geographic approach to protecting future
biological diversity
BJoSdencc 37:782-788.
Sonbor, P.R., F.P. Kapinos, and G.L. Knapp - 1984
Slate Hydrologic Unit Maps
Open-File Report 84-708. US. Geological Survey, Denver,
Colo.
Sodoll, J.R., G.H. Reeves, F.R. Hauer, J.A. Stanford, and
C.P. Hawkins-1990
Roleofrefugia in recovery from disturbances: modern fragmented
and disconnected river systems '
Environmental Management 14:711-724.
Shannon, C.E- 1949
Communication theory of secrecy systems
Bell System Technical Journal 28(4):656-715.
Shannon, C.E., and W. Weaver - 1963
The Mathematical Theory of Communication
University of Illinois Press, Chicago, 111.
Siegel, D.I -1988
The Recharge-discharge function of wetlands near Juneau, Alaska:
Part II, Ceochemical investigations :
Ground Water 26:580-586.
Simmons, R.H., and D.H. Carpenter - 1978
Technique for Estimating Magnitude and Frequency offloads in
Delaware ' • '
Water-Resources Investigations 78-93. US. Geological
Survey, Denver, Colo.
Skutch, M.M., and R.T.N. Flowerdew - 1976
Measurement techniques in environmental impact assessment
Environmental Conservation 3:209-2171
Smith, B. J-, and K. F. Higgins - 1990
Avian cholera and temporal changes mzoetland numbers and
densities in Nebraska's rainwater basin area
WeUands 10(l):l-5.
Smith, P.G.R., and J.B. Thebergo - 1987
Evaluating natural areas using multiple critera: Theory and
practice
Environmental Management 11:447-460.
Soil Conservation Service [SCS]- 1986 <
Urban Hydrology for Small Watersheds
Technical Release 55,210-VI-TR-55. U.S. Government
Printing Office, Washington, D.C
Soil Conservation Service [SCS]-1987
Hydric Soils of the United States
USDA Soil Conservation Service in cooperation with the
National Technical Committee for Hydric Soils. U.S.
Department of Agriculture, Washington, D.C.
Sparks, R.E., P.B. Bayley, S.L. Kohler, and L.L. Osborne -
1990
Disturbance and recovery of large floodplain rivers
Environmental Management 14:699-709.
Stacey, P.B., and M. Taper- 1992
Environmental variation and the persistence of small populations
Ecological Applications 2(l):18-29.
Stankowski, S.J -1974
Magnitude and Frequency of Floods in New Jersey with Effects of
Urbanization
Open-File Report. U.S. Geological Survey, Denver, Colo.
Stull, E.A., K.E. Lagory, and W.S. Vihikour - 1987
Methodologies for Assessing the Cumulative Environmental
Effects of Hydroelectric Development on Fish and Wildlife in the
Columbia River Basin (2 vols)
Bonneville Power Administration, US. Department of
Energy, Portland, Ore.
Swanson, F.J., S.M. Wondzell, and G.E. Grant - 1992
Landforms, disturbances, and ecotones
P. 304-323 in A.J. Hansen and F. di Castri (eds.), "Landscape
Boundaries: Consequences for Biotic Diversity and
Ecological Flows." Ecological Studies, Volume 92. Springer-
Verlag,N.Y.
Thomas, C.A., W.A. Harenberg, and J.M. Anderson -
1973
Magnitude and Frequency of Floods in Small Drainage Basins in
Idaho
Water-Resources Investigations 7-73. U.S. Geological
Survey, Denver, Colo.
104 Synoptic Approach
-------
Thomas, D.M., and M.A. Benson - 197O
Generalization of Streamflato Characteristics From Drainage-Basin
Characteristics
Water-Supply Paper 1975. U.S. Geological Survey, Reston,
Va.
Tice, R.H - 1968
Magnitude and Frequency of Floods in the United States. Part 1-B,
North Atlantic Slope Basins, New York to York River
Water-Supply Paper 1672. U.S. Geological Survey,
Washington, D.C
Tiner. R.W., Jr-1984
Wetlands of the United States: Current status and recent trends
National Wetlands Inventory, U.S. Fish and Wildlife Service,
U.S. Government Printing Office, Washington, D.C
Tonn, W.M., and J.J. Magnuson - 1982
Patterns in the species composition and richness offish assemblages
in northern Wisconsin lakes
Ecology 63(4):1149-1165. -
Trewartha, G.T - 1957
Climatic regions map. 1:75,000,000 map
In E.B. Espenshane, Jr. (ed.), "Goode's World Atlas, 18th
edition (1990)." Rand McNally and Company, Chicago, HI. '
Triquet, A.M., G.A. McPeek, and W.C. McComb - 1990
Songbird diversity in clearcuts with and without a riparian buffer
strip
Journal of Soil and Water Conservation 45(4):SOO-503.
Tyser, R.W-1983
Species-area relations of cattail marsh avifauna
Passenger Pigeon 45:125-128.
Ulanowicz, R.E -1979
Complexity, stability, and self-organization in natural
communities
Oecologia 43:295-298.
Urban, N.R., S.E. Bayley, and S.J. Eisonroich - 1989
Export of dissolved organic carbon and acidity from peatlands
Water Resources Research 25:1619-1628.
U.S. Bureau of the Census - 1972
Census of Population and Housing: 1970
US. Government Printing Office, Washington, D.C
U.S. Bureau of the Census - 1974
Census of agriculture
Vol. 1, Part 47, Washington State and county data. U.S.
Government Printing Office, Washington, D.C
U.S. Bureau of the Census - 1982a
Census of Agriculture
Vol. 1, Part 47, Washington State and county data. U.S.
Government Printing Office, Washington, D.C
U.S. Bureau of the Census - 1982b
Census of population and housing: 1980
Part 49, summary characteristics for governmental units and
standard metropolitan statistical areas, Washington. U.S.
Government Printing Office, Washington, D.C
U.S. Department of Agriculture [USDA] - 1983
Agricultural Resources of the Pearl River Basin (Mississippi Part)
U.S. Department of Agriculture, Jackson, Miss.
U.S. Department of Agriculture [USDA] - 1984
R-10 Guidlines for Estimating Streamflow
USDA Forest Service, Juneau, Ala.
U.S. Fish and Wildlife Service [USFWS] - 1981
Standards for the Development of Habitat Suitability Index Models
103 ESM. U.S. Fish and Wildlife Service, Fort Coffins, Colo.
U.S. Geological Survey [USGS] - 1967
Major Forest Types. l:7J500flOOmap
U.S. Geological Survey, Reston, Va.
Van Haveren, B.P - 1986
Management of instream flows through runoff detention and
retention
Water Resources Bulletin 22:399-404.
Walker, P.N-1971
Flow characteristics of Maryland streams
Report of Investigations No. 16. Maryland Geological
Survey, Baltimore, Md.
Washington Department of Natural Resources - 1990
Wetland Associated Plant Species of Concern by Priority
Natural Heritage Information System, Washington
Department of Natural Resources, Olympia, Wash.
Washington Department of Wildlife - 1990
Number of Wetland Associated Species of Concern
Non-Game Program, Washington Department of Wildlife,
Olympia, Wash.
Webber, E.E., and W.P. Bartlett, Jr - 1977
Floods in Ohio: Magnitude and frequency
Bulletin 45. Division of Water, Ohio Department of Natural
Resources, Columbus, Ohio.
Werschkul, D.F., E. MacMahon, and M. Leitschut-1976
Some effects of human activities on the Great Blue Heron in
Oregon
Wilson Bulletin 88:660-662.
Westman,W.E-1985
Ecology, Impact Assessment, and Environmental Planning
Wiley-Interscience, New York, N. Y.
Wetzel, K.L., and J.M. Bettandorff - 1986
Techniques for Estimating Streamflow Characteristics in the
Eastern and Interior Coal Provinces of the United States
Water-Supply Paper 2276. U.S. Geological Survey, Denver,
Colo.
Whigham, D.F., C. Chitterling, and B. Palmer - 1988
Impacts of freshwater wetlands on water quality: A landscape
perspective
Environmental Management 12(5):663-671.
Wilcox, D.A-1989
Migration and control of purple loosestrife (Ly thrum salicaria L)
along highway corridors
Environmental Management 13(3): 365-370.
Wilen, B.O., W.P. McConnell, and D.L. Mader - 1975
The effects of beaver activity on water quality and water quantity
Proceedings of the Society of American Foresters 1975:235-
240.
Williams, J.D., and C.K. Dodd, Jr - 1978
Importance of wetlands to endangered and threatened species
P. 565-575 in P.E Greeson, J.R. dark, and J.E. dark (eds.),
"Wetland Functions and Values: The State of our
Understanding." American Water Resources Association,
Minneapolis, Minn.
References 105
-------
William*, J.E., J.E. Hohnson, DJV. Hendrickson - 1989
Fishes of North America: endangered, threatened, or of special
concern
fisheries 142-20.
Williamson, S.C., C.L. Armour, G.W. Kinser, S.L.
Fundorburk, and T.N, Hall - 1987
Gmutatioe impacts assessment: An application to Chesapeake
Bay
Transactions of the North American Wildlife Natural
Resources Conference 52377-388.
Winagar,H.H-1977
Camp Creek channel fencing: Paint, wildlife, son, and water
response
Rangemen's Journal 4:10-12
Winn, D.S., and K.R. Barber - 1985
Cartographic modelling: A method of cumulative effects appraisal
Proceedings of the Grizzly Bear Habitat Symposium, General
Techical Report INT-207. USDA Forest Service, Fort Collins,
Colo.
Winter, T.C-1988
A conceptual framework for assessing cumulative impacts on the
hydrology ofnonlidal wetlands
Environmental Management 12:605-620.
Winter, T.C -1990
A Physiographic and Climatic Framework for Hydrologic Studies
of Wetlands
Hydrology Research Institute Symposium Series No. 7.
Environment Canada.
Wintor, T.C., and M.K. Woo - 1990
Hydrology of lakes and wetlands
P. 159-187 in M.G. Woolman and H.C Riggs (eds.), "The
Geology of North America. Vol. 0-1, Surface Water
Hydrology." Geological Society of America, Boulder, Colo.
Wisheu, I.C., and P.A. Keddy - 1991
Seed banks of a rare wetland plant community: Distribution
patterns and effects of human-induced disturbance
Journal of Vegetation Science 2:181-188.
Witmer, G., and T.A. O'IMeil - 1991
Assessing cumulative impacts to wintering bald eagles in western
Washington
P. 144-150 in R.S. Mitchell, CJ. Sheviak, and D.J. Leopold
(eds.), "Ecosystem Management: Rare Species and
Significant Habitats." New York State Museum Bulletin No.
471. Albany, N.Y.
Wood, W.E., and W.R. Osterkamp - 1984
Recharge to the Ogallala aquifer from playa lake basins in the Uano
Estacado
P. 337-349 in G.A. Whetstone (ed.), "Proceedings of the
Ogallala Aquifer Symposium II." Texas Tech University,
Lubbock, Texas.
Young, R.A., C.A. Onstad, D.D. Bosch, and W.P. Anderson
- 1987
AGNPS, Agricultural Non-Point Source Pollution Model: A
watershed analysis tool
Conservation Research Report 35. Agricultural Research
Service, U.S. Department of Agriculture, Washington, D.C ,
Zadler, J.B-1982
The Ecology of Southern California Coastal Salt Marshes: A
Community Profile
FWS/OBS-81/54. Biological Services Program, U.S. Fish and
Wildlife Service, Washington, D.C.
Zembrzuski, T.J., Jr., and B. Dunn - 1979
Techniques for Estimating Magnitude and Frequency of Floods on
Rural Unregulated Streams in New York State, Excluding Long
Island
Water-Resources Investigations 79-83. U.S. Geological
Survey, Denver, Colo.
106 Synoptic Approach
-------
-------
-------
APPENDIX A
Review of Methods for Assessing
Cumulative Impacts
Although none deals specifically with wetlands, a num-
ber of methods have been devised for assessing the
effects of cumulative impacts (see reviews by Horak et
al. 1983; Lane and Wallace 1988). Each method was
examined prior to and during development of the syn-
optic approach. In addition to not addressing wetlands
specifically, their drawbacks include:
• Focus on individual ecosystems rather than land-
scape subunits such as watersheds;
• Focus on interactions of impacts rather than on the
influence of wetlands or other ecosystems on land-
scape function;
• Incompatibility with use of widely available data;
and
• Lack of speed and flexibility.
Existing methods can be grouped as follows:
Conceptual Frameworks
These methods provide general narrative procedural
guidance for incorporating cumulative impacts in deci-
sion-making. Products are not pre-specified, so they
can differ greatly, ranging from narrative descriptions
of cumulative impacts, to quantitative assessments. They
include reports by Bedford and Preston (1988b) for
EPA; Dames and Moore Inc. (1981) for the Army Corps
of Engineers; Horak et al. (1983) for the U.S. Fish and
Wildlife Service; Lane and Wallace (1988) for the Cana-
dian government; and Stull et al. (1987) for the U.S.
Department of Energy.
Descriptive Cause-Effect Methods
These methods are intended to describe mechanistically
(and in some cases, dynamically) the direct and indirect
effects of one or more disturbances. They assume that
cumulative effects to a resource can be estimated by
identifying individual effects and mathematically rep-
resenting the manner in which they interact and
accumulate. In most cases, products are ratings for
particular project alternatives or activities that describe
their relative potential for generating cumulative ef-
fects; they are organized as flow diagrams, matrices, or
networks. Bain et al. (1986) developed a matrix method
that was then used by Stull et al. (1987), CXNeil and
Witmer (1991), and Witmer and CWeil (1991) for as-
sessing multiple hydropower projects. Other examples
include Armour and Williamson (1988), Emery (1986),
and Patterson and Whillans (1984).
Map Overlay Methods
Perhaps closest to the synoptic approach, these meth-
ods are intended to identify areas most sensitive or
vulnerable to impacts and areas where consequences of
impacts are expected to be greatest, or both. Maps are
used as planning tools with the assumption that future
impacts will be of greatest concern where (for example)
sensitivity, value, and past losses have been most se-
vere. Map overlay methods employ thematic maps or
databases according to some aggregation scheme to rate
landscapes generally (e.g., Bastedo et al. 1984; Canters et
al. 1991; McHarg 1969; Radbruch-Hall et al. 1987) or to
rate water resources specifically (e.g., Aller et al. 1987).
Map overlay methods also (a) assess past impacts by
overlaying maps of land cover trends and erosion sensi-
tivity (Dickert and Turtle 1985), (b) assess relative
geographic risks by overlaying maps of impacts (e.g.,
Parrish and Langston 1991), or (c) prioritize individual
habitat patches at a landscape level using maps and
biogeographic theory (e.g., Gosselink and Lee 1989;
Gosselink et al. 1990b; Scott et al. 1987). Bailey (1988)
discusses methodological issues.
Methods Based on Statistical Data Analysis
or Simulation
These methods attempt to quantitatively assess or pre-
dict cumulative impacts based on analysis of historical
patterns of impacts by examining permit use (e.g.,
Contant and Ortolano 1985), airphotos, or field data
(e.g., Gosselink and Lee 1989; Gosselink et al. 1990a, b).
Products include tabulations, graphs, and interpreta-
tions of trends. In some cases, statistical models are
developed to specify landscape assimilative capacity, or
thresholds of degradation and loss, that if surpassed
result in unacceptable effects (e.g., Osborne and Wiley
1988). As summarized by Adamus (1989), these models
include statistical methods applied in Louisiana (Burdick
et al. 1989; Childers and Gosselink 1990; Gosselink and
Lee 1989;Gosselinketal. 1990b) and Minnesota (Johnston
et al. 1988), as well as models used by an Army Corps
District (Contant and Ortolano 1985) and forest manag-
ers (Chatoian 1988;Cobourn 1989; Megahan 1992). They
also include landscape simulations of hydrology (e.g.,
Bedient et al. 1985; Dreher et al. 1989; Flores et al. 1982),
water quality (e.g., Ziemer et al. 1991), and wildlife
habitat (e.g., Cowardin et al. 1988; Winn and Barber
1985).
Appendices 109
-------
APPENDIX B
Table B.1. Typical relationships expected between resource extraction impacts and wetland degradation based on
best professional judgment. Letter indicates degree of expected association and not the intensity or duration of
impact (H = high, M = medium, L = low).
Impact Acidification
Blasting/Drilling 3
Burning/Air pollution 2 H
Channelization 3
Drainage W L
Dredging/Excavation 3 M
Fertilizers 2 L
Harvesting
Pesticides 2
Solid Waste Disposal 3 H
Species Introduction 1<2
Structures/Pavement 3
Trampling 1-3
Vehicles/Boats/Planes 1'3
Water Consumption 3
1 Fishing/Hunting/Trapping
2 Forestry
3 Mining - Mineral and Peat
Impact Dehydration
Blasting/Drilling 3 L
Burning/Air pollution 2
Channelization 3 M
Drainage 2<3 H
Dredging/Excavation 3
Fertilizers 2
Harvesting
Pesticides 2
Solid Waste Disposal 3
Species Introduction 1<2 L
Structures/Pavement 3
Trampling 1-3
Vehicles/Boats/Planes 1-3
Water Consumption 3 H
Altered Animal Behavior Compaction
M
M
H L
H1-3
H
L H
H L
MM
1VI
——•—•—•»•— „
Eutrophication/Enrichment Erosion
H
M M
M M
M H
H
M L
l_
M L
M
M
Contamination/Toxicity
M
\-\
M
i
L.
.
M
Inundation
M
M2
Denudation
L
M1'3
t
L
Light Reduction
t
L
M1
i
L
1
l_
I
L
1
L
2 Forestry
3 Mining-Mineral and Peat
Impact Sa
Blasting/Drilling 3
Burning/Air pollution 2
Channelization 3
Drainage W
Dredging/Excavation 3
Fertilizers 2
Harvesting V3
Pesticides 2
Solid Waste Disposal3
Species Introduction 1-2
Structures/Pavement 3
Trampling 1-3
Vehicles/Boats/Planes 1'3
Water Consumption 3
—
.linization
L
L
L
L
M
L
M
=========
Sedimentation
L
' M
H
M2
M
— — — •——• —
Surface Runoff Timing
M
M2
M
H
=
Thermal Warming
H /
, '
1
L
H2
I
L.
L
2 Forestry
3Mining- Mineral and Peat
110 Synoptic Approach
-------
Table B.2. Typical relationships expected between urbanization impacts and wetland degradation based on best
professional judgment. Letter indicates the degree of expected association and not the intensity or duration of
impact (H = high, M = medium, L = low).
Impact Acidification
Blasting/Drilling
Burning/Air pollution
Channelization
Drainage
Dredging/Excavation
Fertilizers
Fill
Harvesting
Impoundment
Industry/Manufacturing
Pesticides
Sewage Treatment
Solid Waste Disposal
Species Introduction
Stormwater Runoff
Structures/Pavement
Trampling
Veh icIes/Boats/Plan es
Water Consumption
H
L
M
L
L
L
H
L
Impact Dehydration
Blasting/Drilling
Burning/Air pollution
Channelization
Drainage
Dredging/Excavation
Fertilizers
Fill
Harvesting
Impoundment
Industry/Manufacturing
Pesticides
Sewage Treatment
Solid Waste Disposal
Species Introduction
Stormwater Runoff
Structures/Pavement
Trampling
Vehicles/Boats/Planes
Water Consumption
Impact
Blasting/Drilling
Burning/Air pollution
Channelization
Drainage
Dredging/Excavation
Fertilizers
Fill
Harvesting
Impoundment
Industry/Manufacturing
Pesticides
Sewage Treatment
Solid Waste Disposal
Species Introduction
Stormwater Runoff
Structures/Pavement
Trampling
Veh icIes/Boats/Plan es
Water Consumption
L
M
H
H
L
H
Salinization
L
L
L
L
M
L
M
L
L
L
L
M
Altered Animal Behavior Compaction eontamination/Toxicity
M .,'.''.
M
H
H
H
L
H
L
H
M
Eutrophication/Enrichment
H
M
M
M
H
M
M
L
H
M
M
M
M
L
H
L
M
Erosion
M
M
H
L
L
L
L
L
L
M
Sedimentation Surface Runoff Timing
L
M
H
. H
M
M
M
H
H
M
M
M
H
M
M
M
H
L
H
M
M
H
M
M
H
H
H
M
L
M
Inundation
M
M
H
M
H
Thermal Warming
H
L
H
L
H
L
L
L
|VMMBaMBB^BB^^B^H^^^^^^^
Denudation
L
L
H
M
M
H
M
M
H
M
L
Light Reduction
L
M
M
L
H
M
L
L
H
L
L
^^^^^^•MM^^^HMMMH^KK^MMM
Appendices 111
-------
Table B.3. Typical relationships expected between water management impacts and wetland degradation based
on best professional judgment. Letter indicates degree of expected association and not the intensity or duration
of impact (H = high, M = medium, L = low).
Impact
Blasting/Drilling 1
Channelization ^
Drainage 1
Dredging/Excavation '
Fertilizers 1
Fill '
Harvesting 1
Impoundment 1<2
Irrigation/Flooding ^
Pesticides 1
Saltwater Intrusion 2
Water Consumption 2
1 Flood Management
2 Water Supply
======
Impact
Blasting/Drilling 1
Channelization i-2
Drainage 1
Dredging/Excavation ^
Fertilizers 1
Harvesting 1
Impoundment 1^
Irrigation/Flooding 1i2
Pesticides 1
Saltwater Intrusion 2
Water Consumption 2
1 Flood Management
2 Water Supply
- _— — —
Impact
Blasting/Drilling 1
Channelization 1<2
Drainage 1
Dredging/Excavation 1-2
Fertilizers T
Fill1
Harvesting
Impoundment 1>z
Irrigation/Hooding 1<2
Pesticides 1
Saltwater Intrusion 2
Water Consumption 2
1 Flood Management
2 Water Supply
Acidification
L
•2 M
L
L
L
L
=-•'
Dehydration
L
M
H
H
H
-•'"
Salinization
L
L
L
M
L
M
H
M
M
Altered Animal Behavior
M
M
H
H
H
M
M
.—
Eutrophication/Enrichment
M
M
M
M
M
M
M
=
Compaction
I
Erosion
M"
M
L
.
M
Sedimentation Surface Runoff Timing
L
M
H
H
M2
M
M
M
M2
u
M
H
Contamination/Toxicity
L
M
H
M
M
Inundation
M1 '
H
Thermal Warming
H2
1
L
Denudation
L
H-
M'
M ' '
H= ' '
•
M T - .
_ ;
Light Reduction
i
M
M-
L
H
M • . -
• 'i
112 Synoptic Approach
-------
APPENDIX C
Table C.1. Effect of wetland degradation on water quality functions and degree of expected association based on
best professional judgment (H = high, M = medium, L = low).
Input
Acidification
Animal Behavior
Compaction
Contamination/Toxicity
Denudation
Dehydration
Sediment
Detention
L
H
M
H
Eutrophication/Enrichment L
Erosion
Habitat Fragmentation
Inundation
Light Reduction
Salinization
Sedimentation
Surface Runoff
Thermal Warming
H
L
H
L
H
H
L
Sediment
Stabilization
M
H
L
H
L
L
H
L
L
H
L
Phosphorus
Detention
H
L
M
L
H
H
H
M
L
H
L
L
H
H
L
Nitrate Removal
M
L
M
M
H
H
H
M
L
H
L
L
M
H
M
Detoxification
H
L
M
H
M
H
M
M
L
H
L
M
M
H
M
Water Quality
M
M
H
H
M
M
H
H
M
M
L
H
H
M
M
Table C.2. Effect of wetland degradation on habitat functions and degree of expected association based on best
professional judgement (H = high, M = medium, L = low). '
Input
Biological Production Biodivei-sity'
Acidification L
Animal Behavior M
Compaction M
Contamination/Toxicity M
Denudation H
Dehydration H
Eutrophication/Enrichment H
Erosion M
Habitat Fragmentation L
Inundation H
Light Reduction H
Salinization L
Sedimentation M
Surface Runoff M
Thermal Warming H
M
H
M
H
H
H
H
M
H
H
H
M
H
H
H
1 Number of normally uncommon native species per unit area.
Appendices 113
-------
APPENDIX D
Table D.I. Potential sources of mapped and tabular data for landscape indicators of synoptic indices V
Mapped D.t« Resolution' Wetland Hydrology Water Quality
-
CCAP
CESCR
DEM
DIG
FEMAMaps
GAP
LULC
NATSGO
NHBCDS
NWI Maps
PNV
STATSGO
Stream N/P
uttem input capacity value Input Capacity Value
P X
p
SC X
SC X X
P X
p
P x x
ss x x x
p
P x x x
ss
sc x x x xx
SC Y v
A A
T.buUrData Resolution * Wetland Hvdroloav Water Quality
AgrCensus
BBS
CBC
CCAP
CRPL
DrainStat
DWSF
EMAP5
FEMA Data Files
FIA
ISS
Marine Fish
NADP
NASS
NAWQA
NHVCA
NPUD
NRCBR
NRI
NWI Trends
NWUDS
Precip
Priority
RCF
STORET
TIGER
TRI
WATSTORE
WBGS
WPF
WWS
305b
1Data from these ;
c*iem input capacity Value Input Capacity Value
c X
SC
SC
SC X
C v
c x
SC Y
R X x
ex x
SS X
p
R
SS v
c x
R X x
s
c x
p *
ss x
R
SC x
ss x
p
SC X X X
p x
SC x x
P X
SC X
ss
c
SC
sc xx
sources are believed to be of ootential USP fnr annir/-=t;/-.nc- nt »K« -,„
Habitat Loss3 Rep. Pot.*
function Value Conv. Degr.
X*r
X
X
XX X
X X
x
/\
XX X
x x
X
Habitat Loss3 Ren Pot*
Function Value Conv. Degr
X X X X
X X X X
X XXX
x x
X XX
X*/
X
X X
XXX
X
X
X
X X
X
X
X
X
X
X
X
X
X
X
XX X
X X
XX X
y
• • ---..__ j . j_ .._ Uf^f,. vuu, , . iviwkib
-------
APPENDIX E]
Contact Information and Examples
of Variables from National Maps and
Databases
AgrCensus - Census of Agriculture
Customer Service
Census of Agriculture
Agricultural Division
Bureau of the Census
US. Department of Commerce
Washington, DC 20233
(301) 763-4100
Example of derivable variables: "percent change in
number of cattle (or fertilized cropland, harvested wild
hay, irrigated land, etc.) in Fort County, 1982-1987."
BBS - Breeding Bird Survey
Coordinator
Breeding Bird Survey
US. Fish and Wildlife Service
Patuxent Wildlife Research Center
Laurel, MD 20708
(301)498-0330
Example of derivable variable: "percent of surveys in
southern Missouri in which wetland-dependent song-
birds declined, 1976-1985."
CBC - Christmas Bird Count
Christmas Bird Count Database Coordinator
U.S. Fish and Wildlife Service
Patuxent Wildlife Research Center
Laurel, MD 20708
(301)498-0490
Example of derivable variable: "numbers of egrets in
Mississippi delta counts reporting > 6 wading bird spe-
cies."
CCAP- Coastwatch Change Analysis Program
Program Manager
Coastwatch Change Analysis Program
Beaufort Fisheries Laboratory
National Marine Fisheries Services
101 Pivers Island Road
Beaufort, NC 28516
(919) 728-3595
1 Potential uses of these data as landscape indicators can be
found in Appendix D. "Derivable variable" is a variable that
could be derived from the map or database using a GIS or
statistical package, respectively.
CESCR - Commercially/Ecologically Signifi-
cant Coastal Resources
For coastal areas, contact regional office of the U.S. Fish
and Wildlife Service and request atlases of coastal
waterbird colonies, Coastal Ecological Inventory maps,
and Ecological Characterization Project maps.
CRPL - Conservation Reserve Program Land's
CRP Data Coordinator
Agricultural Stabilization and Conservation Service
P.O. Box 2415
Washington, DC 20013
(202)720-6303
Example of derivable variable: "percent of county with
highly erodible land idled from cultivation."
DEM-Digital Elevation Model
DEM Data Coordinator
National Cartographic Information Center
U.S. Geological Survey, Bldg. 3101
NSTL Station, MS 39529
(601)688-3541
Example of derivable variable: "percent of Muddy Creek
watershed with < 2% slope."
DLG-Digital Line Graphs
Reach File Coordinator
Monitoring Branch (WH553)
Assessment and Watershed Protection Division
U.S.EPA
401MStreetSW
Washington, DC 20460
(202) 260-7028
Example of derivable variable: "area of instream im-
poundments located < 20 km upriver of Smithville."
DrainStat - Drainage Statistics
Example of variable: "area of drainage in Sioux County
in 1960." See reports cited in: Pavelis, G.A. (ed.). 1987.
Farm drainage in the United States: FKstory, status, and
prospects. Misc. Pub. No. 1455, USDA Economic Re-
search Service, Washington, DC.
DWSF-Drinking Water Supply File
Drinking Water Supply File Coordinator
Monitoring Branch (WH553)
Assessment and Watershed Protection Division
U.S. EPA
401MStreetSW
Washington, DC 20460
(202)260-7028
Example of derivable variable: "number of drinking
water intakes located < 15 km downstream from
Marshville."
Appendices 115
-------
EMAP-EnvirontnetitalMonitoringandAssess-
ment Program
EMAP-Wetlands Technical Director
U5. EPA Environmental Research Laboratory
200 SW 35th Street
Corvallis,OR 97333
(503) 754-4457
Example of derivable variable: "percent change in per-
centcoverof regularly flooded tidal emergent wetlands,
southeastern region." Data are not currently available,
but will become available beginning in the mid to late
1990s.
FEMA (maps anddatafiles) -FederalEmergency
Management Agency
Flood Map Distribution Center
Federal Emergency Management Agency
6930 San Tomas Road
Baltimore, MD 21227-6227
(800)333-1363
Example of derivable variable: "number of residences
in the Fargo, ND, 100-yr floodplain."
FIA-ForestInventoryAnalysis
FIA Coordinator
USDA Forest Service
Washington, DC
(202)205-1343
Example of derivable variable: "percent change, 1967-
1982, in > 25-cm diameter oak-gum-cypress stands in
southeastern Georgia."' For specificdata, contact USDA
Forest Service experimental stations (Fort Collins, CO
Qgden, UT; St. Paul, MM; Broomall, PA; Portland, OR;
Berkeley, CA; Asheville, NC; New Orleans, LA).
GAP - Gap Analysis Projects
GAP Analysis Projects
Idaho Cooperative Fish & Wildlife Res. Unit
University of Idaho
Moscow, ID 83843
(208)885-6960
Exampleof derivable variable: "percentof Utah areas in
publicownershipthatareinhabitedbyuncommon wet-
land mammals."
ISS - International Shorebird Survey
Data Coordinator
International Shorebird Survey
Manomet Bird Observatory
P.O. Box 936
Manomet, MA 02345
(508)224-6521
Example of derivable variable: "numbers of shorebirds
at monitored sites in Region 4 having the largest concen-
trations of migratory shorebirds."
LULC - USGS Land Use/Land Cover
Earth Science Information Center
US. Geological Survey
507 National Center
Reston, VA 22092
(800) USA-MAPS
Example of derivable variable: "percent of forested
wetlands and open water in Green County."
Marine Fish-Marine Fisheries
Statistical Coordinator
Commercial and Recreational Fisheries Statistics Offices
National Marine Fisheries Service
Washington, DC
(301)713-2328
Example of derivable variable: "percent change in catch
of wetland-dependent fish species in southeast report-
ing region, 1980-85."
NADP - National Atmospheric Deposition
Program/National Trends Network
Data Manager
Natural Resource Ecology Laboratory
Colorado State University
Fort Collins, CO 80523
(303)491-1464
Example of derivable variable: "percent change in nitro-
gen deposition of Coastal Piedmont sites."
NASS -National Agriculture Statistics Service
Database Coordinator
Statistical Methods Branch
Estimates Division
National Agricultural Statistics Service
Washington, DC 20250
(202) 720-7590
Example of derivable variable: "percent change in soy-
bean area in Thomas County, 1989-1990."
NATSGO-SCSNATSGOMaps
National Cartographic and CIS Center
USDA Soil Conservation Service
P.O.Box6567
Fort Worth, TX 76115
(817) 334-5559
Example of derivable variable: "percent of region hav-
ing hydric soils with > 5% organic matter and slope
" ^
116 Synoptic Approach
-------
NAWQA
NAWQA Coordinator
Water Resources Division
US. Geological Survey
Reston,VA 22092
(703) 648-5114
Example of derivable variable: "percent change in ni-
trogen loading to the Sacramento River estuary." Data
are currently available only for selected areas.
NHBCDS - National Heritage/Nature
ConservancyBiological and ConservationData
System
Database Coordinator
Biological and Conservation Data System
The Nature Conservancy
201 Devonshire Street - 5th Floor
Boston, MA 02110
(617)542-1908
NHVCA - National Heritage/Nature
Conservancy Vertebrate Characterization
Abstracts
Database Coordinator
Vertebrate Characterization Abstracts
The Nature Conservancy
201 Devonshire Street - 5th Floor
Boston, MA 02110
(617)542-1908
Exampleof derivable variable: "numberof wetland types
in Michigan used by raptors vs. by songbirds."
NPUD - National Pesticide Use Database
Coordinator
National Pesticides Use Database
Resources for the Future
1616 P Street NW
Washington, DC 20036
(202)328-5025
Exampleof derivable variable: "area of corn treated with
atrazine in Jones County in 1988."
NRCBR- Nest Record and Colonial Bird
Registry
Coordinator
Nest Record Program
Cornell Laboratory of Ornithology
Sapsucker Woods Road
Ithaca, NY 14850 -'
(607) 254-2473
Example of derivable variable: "percent success of cen-
tral Iowa nests of wetland-dependent species,
1980-1990."
NRI- National Resource Inventory
Resources Inventory Division
USDA Soil Conservation Service
P.O; Box 2890
Washington, DC 20013
(202)720-5420 ,
Example of derivable variable: "percent wetland loss,
1982-87, in areas having > 10% highly erodible land."
Federal lands not included. For data specifically on
substate trends in wetlands, contact
Dr. Curtis Flather
USDA Forest Service
Fort Collins, CO
(303)498-1660
NWT - National Wetlands Inventory
US. Fish and Wildlife Service
c/o Earth Science Information Center
US. Geological Survey
507 National Center
Reston,VA 22092
(800) USA-MAPS
Example of derivable variable: "percent of Iowa wet-
lands > 5 ha that are seasonally flooded emergent
wetlands." Not available for all of the US.
NWI Trends - National Wetland Inventory
National Wetlands Inventory
US. Fish and Wildlife Service
9720 Executive Center
Monroe Bldg. - Suite 101
St. Petersburg, FL 33702
(813)893-3624
Example of derivable variable: "regional wetland loss
between 1950 and 1970."
NWUDS - National Water Use Data System
Coordinator
National Water Use Data System
Water Resources Division
US. Geological Survey
Reston,VA 22092
(703) 648-6815
Example of derivable variable: "percent of groundwa-*
ter withdrawals inKansas used for irrigation." Formerly
called the State Water Use Data System.
PNV- Potential Natural Vegetation
CIS Coordinator
US. EPA Environmental Research Laboratory
Corvallis,OR 97333
(503)754-4352
Example of derivable variable: "regions of California
potentially supporting rule wetland vegetation."
Appendices 117.
-------
Predp - Precipita lion Network
Precipitation Network Data Coordinator
National Weather Service
US. National Climatic Data Center
Federal Building
Asheville,NC 28801-2696
(704)259-0682
Priority - Listings of Priority Wetlands
Contact (a) the regional office of the US. Fish and Wild-
life Service, and (b) the State Conservation and Outdoor
Recreation Plan (SCORP) Coordinator and request the
"RegionaIWetlandsConceptP]an"foraparticular state
or region. Listings also available from other state and
federal resource agencies and frompiivateconservation
groups.
JRCF- Readi Characteristics File
Reach Characteristics File Coordinator
Monitoring Branch (WH553)
Assessment and Watershed Protection Division
US. EPA
401M Street SW
Washington, DC 20460
(202)260-7028
Exampleofderivablevariable:"percentofmainstemFish
River channelized, between Adams and Jefferson."
STATSGO - SCS STATSGO Maps
National Cartographic and CIS Center
USDA Soil Conservation Service
P.O. Box 6567
Fort Worth, TX 76115
(817)334-5559
Example of derivable variable: "percent of watershed
or region having hydric soils with > 5% organic matter
and slope <1%."
STORET-EPA STORET Database
STORET Coordinator
Monitoring Branch (WH553)
Assessment and Watershed Protection Division
US. EPA
401M Street SW
Washington, DC 20460
(202)260-7028
Example of derivable variable: "percent of sampling
stations in Rock Creek watershed that violated nitrate
criteria > 75% of the time, 1968-1988."
Stream N/P - Stream Nitrate/Phosphate
Regional Effects Program
US. EPA Environmental Research Laboratory
Corvallis,OR 97333
Send to above address for maps: Omemik,J.M. 1977.
Nutrient concentrations in streams from nonpoint
sources.
TIGER-U.S. Census
TIGER Database Coordinator
Data User Services Division
Customer Services
Bureau of the Census
Washington, DC 20233
(301) 7634100
Example of derivable variable: "percent change in rural
population of Jackson County, 1980-1990."
TRI - Toxic Release Inventory
Coordinator
Toxic Release Inventory
Information Management Division
Office of Pollution Prevention and Toxics (NEGOQ8)
US. EPA
401M Street SW
Washington, DC 20460
(202) 260-3938
Example of derivable variable: "kgs of cadmium re-
leased annually upstream from Eagle Wildlife Refuge."
WATSTORE - USGS WATSTORE Discharge
Files
Coordinator
WATSTORE Database
Water Resources Division
US. Geological Survey
Reston,VA 22092
(703) 648-5659
Example of derivable variable: "number of days annu-
ally at which discharge in the Black River was < 1 cms at
the gaging station below Marshton."
WBGS - Waterfowl Breeding Ground Surveys
Database Coordinator
Waterfowl Breeding Ground Surveys
US. Fish and Wildlife Service
Patuxent Wildlife Research Center
Laurel, MD 20708
(301)498-0404/0401
Exampleof derivable variable: "assessed wetlands hav-
ing > 4 nesting duck species."
WPF- Waterfowl Parts Files
Coordinator
Waterfowl Parts Database
US. Fish and Wildlife Service
Patuxent Wildlife Research Center
Laurel, MD 20708
(301)498-0404/0401
Example of derivable variable: "numbers of geese in 5
Arkansas counties with the largest annual harvest of
waterfowl."
118 Synoptic Approach
-------
WWS -Winter Waterfowl Survey
Database Coordinator
Winter Waterfowl Surveys
U.S. Fish and Wildlife Service
Patuxent Wildlife Research Center
Laurel, MD 20708
(301)498-0404/0401
Example of derivable variable: "number of waterfowl
wintering in three areas of Oregon reporting the highest
annual use by waterfowl."
305b -State 305b Reports
Waterbody Database Coordinator
Monitoring Branch (WH553)
Assessment and Watershed Protection Division
U.S.EPA
410 M Street SW
Washington, D.C. 20460
(202) 260-7028
Exampleofderivablevarible:"percentof assessed stream
segments in Mitchell County with riparian destruction
listed as a probable sourceof water quality degradation."
Available for all states, but coverage within states is
limited.
Appendices 119
-------
APPENDIX F
Area! Prorating
In conducting a synoptic assessment, data that are re-
ntlir*vi miop^Ffvkmrv-vrfn^T^T/>ct-*»ifi-5li«Mi*<* i-U^*. Jifr^-i^,^-^
tion data may be reported by county, but could be
needed by watershed In such instances, the reported
data are prorated to the needed subunits. The method
for prorating depends on the type of data. Two ex-
amples are discussed below. A more in-depth treatment
of thisproblem is given in Flowerdew and Green (1989).
Aggregate Data
With aggregate data, the value associated with the re-
ported unit represents the total number of objects found
in that unit Total number of people, number of rare,
threatened or endangered species, total income, and
farm area are examples of aggregate data. The following
equation is used to prorate aggregate data for the re-
ported units to the subunits needed for the assessment
exists in this method when the qbjects represented by
the aggregate data are clustered, as in population cen-
ters, or are isolated, such as a particular endangered
species. If this assumption is violated, it may still be
possible to adjust the data to account for bias (see for
example the discussion of "Landscape Indicators"
within the Washington case study). Generally this
error decreases as the size of the final subunit increases,
because random variations and local heterogeneities
are averaged out.
TOTALs=2TOTALrx(AREAfcs)/AREAr) ]
i «' Equation F.I
where TOTAL s is the value for the needed subunit s,
TOTAL r is the value for reported unit r, AREA, , is the
joint area of r and s (i.e., the intersection of r ands), and
AREA r is the total area of reported unit r.
Rgure F.I shows Subunit 4, which is a watershed from
the Illinois case study, overlaid with county boundaries.
The areas of each county and the joint county-watershed
areas are also shown (areas were determined by CIS, as
described in Appendix G). Table Rl shows how popu-
lation from the three counties was prorated to Subunit 4.
The validity of this approach depends on the assump-
tion that the aggregate data are distributed uniformly
throughout the reported unit. A possibility for error
Varmlllion County
Area = 2297.70
Champaign County
Area = 2544.48
Figure F.1. Subunit 4 from Illinois case study overlaid with
county boundaries.
Table F.I. Prorating county data to Subunit 4 for the Illinois case study.
County
Population by county1
TOTAL,
Joint area 2
AREA ,„.
County area 2
AREA,
i«»v f«v|*Miu«iwii tiv/ui vs.u. uuicau Ul Lllc l>t2l1oUS \ liJOZD/.
z Area in km2 derived by GIS; see Appendix G.
3The population within the joint county-subunit area, equal to TOTAL r x (AREA / AREA J
Partial sum 3
Champaign
Edgar
Vormillion
Subunit 4 Total
168392
21725
95222
65.29
22.29
401.93
2544.48
1613.49
2297.70
4321
300
16657
21278
120 Synoptic Approach
-------
Intensity Data
With intensity data, the reported value is not a total, but
instead is an average intensity or rate of some process;
the intensity represents the average value at every point
within the bounded area. For example, mean annual
precipitation is the average amount of precipitation
received in a year at each point within the reported unit,
ininchesorcentimeters. Otherexampleswouldinclude
mean elevation, mean insolation (solar energy), average
depth to groundwater, etc. Such data are prorated
according to the following equation:
INTENSITY f £ INTENSITY r X (AREA (r s) / AREA sj
Equation F.2
where INTENSITY,, is the value for the needed subunit s,
INTENSITY,, is the value for the recorded unit r, ARE A,.
is the total area of the needed subunit, and AREA,. ^ is
the joint area of r and s. Note that in this case area of the
needed subunit is used as the denominator, rather than
the area of the recorded unit.
Figure F.2 shows Subunit 815, which is a state Water
Management Unit from the Louisiana case study, over-
laid with precipitation zones. The precipitation zones
were derived by taking the average value between
adjacentcontoursof mean annual precipitation, in inches
(precipitation was required in inches for calculation of
7Q in values using USGS regression equations; see Ap- Figure F.2. Subunit 815 from Louisiana case study overlaid
pendixH). Table F.2 shows how precipitation data from with precipitation zone boundaries.
the four zones were prorated to Subunit 815.
Zone 1
Area = km2
Zone 4
Table F.2. Prorating
Zone
1
2
3
4
Subunit 815 Total
precipitation data to Subunit 815 for the Louisiana case study.
Precipitation by zone 1
INTENSITY,
51
53
55
57
Joint area 2
AREAfr.,
355.2
814.0
644.9
6296
Subunit area 2
AREA.
2441.5
2441.5
2441.5
2441.5
Partial sum 3
7.4
17.7
14.5
14.7
54.3
1 Mean annual precipitation in inches, derived by averaging the value of adjacent precipitation contours; precipitation contours
digitized from Lee (1985b).
2 Area in km2 derived by GIS; see Appendix G. , .
3The average annual precipitation within the joint zone-subunit area, equal to: INTENSITY fx (AREA (rs}l AREA s).
Appendices 121
-------
APPENDIX G
Areal Estimation Techniques
In this appendix we briefly discuss three methods for
estimating mapped areas. We also discuss quality as-
surance and quality control measures to be employed
when using the methods.
Dot Grid Method
Figure G.I shows a map of Subunit 4 from the Illinois
case study (Chapter 4) overlaid with a dot grid. The
proportion of the subunit area in a particular land use
was calculated by counting the dots falling into each
land-use category and dividing by the total number of
dots within the subunit.
land use = dot counVtotal dots
Equation G. 1
To arrive at the area of each land use, this proportion is
multiplied by subunit area:
area = land use x sufauhit area
Equation G.2
The results of estimating area using the dot grid method
for Subunit 4 appear in Table G.I.
I I Agricultural Land
I I Urban or Built-up Land
Forest Land
Figure G.1. Subunit4 from thelllinois case study overlaidwith
dot grid. Polygons represent different land-use classes.
Figure G.2 shows a higher grid density (four times as
many dots) imposed on the same map. Area estimation
results using this dot grid are given in Table G.2. Al-
though results are more accurate when using a denser
grid, the effort in counting the dots also increases
(Muehrcke 1978).
Geographic Information System (GIS}
A GIS is a valuable tool in the construction, manipula-
tion, and display of spatial data. The area estimates here
were generated using the ARC/INFO® GIS software.
Table G3 shows a partial list of polygon areas by land-
use type. The software automatically calculates the
AREA in ft2; SQKM is a user-defined conversion of
those values into km2. Table G.4 contains land-use
totals arrived at using the ARC/INFO® GIS package for
comparison with Tables G.I and G.2.
Note that 0.2 km2 of barren land is included in the GIS
estimate; neither of the dot grid estimates contained this
category because the grid density was too low for sam-.
pling small, rare polygons. If estimating the number or
area of such polygons is essential, a higher grid density
would need to be used.
Agricultural Land
'«:';*• : Urban or Built-up Land
Forest Land
Figure G.2. Subunit 4from the Illinoiscase study overlaid with
denser dot grid. Polygons represent different land use classes.
Table G.1. Area estimates using dot grid method.
"Land Use" represents the Level I land use class from
Anderson et al. (1976).
Lund DM C!«35
Agricultural Land
Urban or Built-up Land
Forest Land
Totals
Dot Count
28
1
2
31
Land Use
0.903
0.032
0.065
1.000
Area (km2)
442.1
15.8
31.6
489.5
Table G.2. Area estimates using denser dot grid.
"Land Use" represents the Level I land use class from
Anderson et al. (1976).
Land Use Class
Agricultural Land
Urban or Built-up Land
Forest Land
Totals
Dot Count
120
2
5
127
Land use
0.945
0.016
0.039
1.000
Area (km2)
462.5
7.7
19.3
489.5
722 Synoptic Approach
-------
Planimeter
Under certain conditions, a planimeter can be used to
calculate area. A polygon is planimetered by tracing its
perimeter with a pen-like tool. If a polygon contains a
smaller polygon, the smaller area must be subtracted
from the larger "donut" polygon. The overall size of the
various polygons within a subunit determines whether
this method is practical. Where polygons are mostly
large, measurements are fairly quick and accurate. As
the average polygon size decreases, however, the effort
increases and accuracy decreases. Figure G.3 shows an
electronic planimeter; manual versions are also
available.
Quality Assurance and Quality Control
It is important to check the data to ensure that the areal
measurements meet the requirements of an assessment.
Therefore, various checks must be performed depend-
ing on which estimation technique is being used.
When using a dot grid, several steps can be taken to
reduce error. First, position the dot grid and tally the
dots at least twice—three times if the dot counts differ
substantially between the first two counts. Second, use
a grease pencil or water-based marker and a tally meter
to eliminate confusion when counting large numbers of
dots. Third, check to ensure that the proportions of all
land-use types add to one. Finally, have another indi-
vidual repeat the process on 10% of the areas measured.
When using a GIS package, various data sets are en-
tered, manipulated, and displayed. Each step can lead
to errors. Maintain copies of the raw data sets to verify
how these data are displayed in the end product. If
digitizing is required, certain operating procedures
should be followed. When beginning the digitizing
session, establish the acceptable amount of error al-
lowed for the project, then make sure it is not exceeded
by comparing hard copies of the digitized maps against
the originals for accuracy. This is done by overlaying
the original and the digitized maps on a light table. If
boundaries do not match, the polygon data in the GIS
should be edited.
Table G.3. Partial listing of land-use areas for polygons
within Subunit 4 of the Illinois case study (see Figure
G.1). AREA is in ft* and is automatically generated by
ARC/INFO®; SQKM is a user-defined variable that
converts area to km2. LEVEL2 is the code for the
Anderson et al. (1976) Level II land use class.
LEVEL2
AREA
SQKM
12
11
41
17
21
13
41
11
1035258.0
2153979.0
60510968.0
1429273.0
5074137000.0
873996.8
11819784.0
29863508.0 '
0.10
0.20
, 5.62
0.13
471.40
0.08
1.10
2.77
Table G.4. Area estimates using GIS package. "Land
Use' represents the Level I land-use class from
Anderson et a!. (1976).
Land Use Class
Area (km2)
Agricultural Land
Urban or Built-up Land
Forest Land
Barren Land
Totals
471.4
7.0
10.9
0.2
489.5
If a planimeter is used, make sure it has been recently
calibrated. Use the scale bar on a quality map to deter-
mine whether the area registering on the machine
corresponds to a geographic area as represented on the
map. Be sure to enter the proper scaling factor on the
planimeter. As with the dot count, the average of two or
more readings should be used. Again, another person
should check 10% of the areas measured. Further infor-
mation on errors in mapping and geographic analysis
appears in Burrough (1986).
Figure G.3. Electronic planimeter.
Appendices 123
-------
APPENDIX H
Sample Calculations
Stream Discharge
Hydrology can be influenced by factors outside of sub-
unit boundaries if the subunit is not a dosed drainage
unit. Upstream characteristics such as slope, precipita-
tion, and land use are examples of potential influences
on hydrology within a particular subunit. Figure H.1
shows thePearlRiverBasinoverlaid with subunitbound-
aries; the basin contains two main channels. Subunitsl
and 5 are dosed drainage areas, meaning that precipita-
tionpro videstheonlyinputof water. However,Subunits
2,3,and4arenotdosed because they receivehydrologic
input from upstream subunits in addition to rainwater
(Figure H.1). Streamflow in these subunits is cumula-
tive, i.e., it is dependent on upstream subunits. We
illustrate how this can affect calculations by calculating
thepeakdischar^eforaSO-year flood (Q-) for Subunit
4. Qcp can be estimated using the following regression
equation developed by the USGS (Landers and Wilson
1991):
648 x AREA0-85 X SLOPE0-"" xLENGTH-0-31
/ Equation H.1
where Q
mainstem channel slope (ft/mi), and LENGTH is the
mainstem channel length (mi). Note that English units
must be used with the independent variables, as the
regression was developed to calculate Q^ in f t?/s.
Because AREA represents total watershed area, not
subunit area, AREA for Subunit 4 indudes the entire
basin and is equal to the sum of the five subunit areas:
AREA = 2478.88 mi2+1972.49 mi2 +1194.29mi+
1785.10 mi2* 1294.81 mi2 ' " ' "
« 8725.57 rni2
Equation H.2
Themainstem channel length is the length of the longest
channel and is therefore equal to the combined channel
lengths within Subunits 1 through 4:
LENGTH = 81,8 mi+ 98.5 mi+ 74.2 mi 4145.8 rrji ,
= 400.3 mi ' -•"--
. Equation H.3
Note that the channel length of SubunitS is notinduded
because it is not a part of the main stem. The mainstem
channel slope is similarly dependent on Subunits 1
through 4. For Equation H.1, slope must be calculated
Main Channels
Area = 1972.49
Length = 98.5
Area = 2478.88
Length = 81.8
Areas: 1194.29
Length = 74.2
Area = 1294.81
Length = 101.8
Area = 1785.10
Length = 145.8
Areas = mi2
Length = miles
Figure H.1. The Pearl River Basin with subunit boundaries.
Areas and lengths are for each individual subunit and are not
cumulative.
from points 10% and 85% up the mainstem channel
(Landers and Wilson 1991), which in this example is at
points 40.0 miles (0.10 x 4003 mi) and 340.2 miles (0.85 x
400.3 mi) up the channel length. The elevations at these
points are 35 and 340 feet, respectively, based on a
1:250,000 USGS topographic map. The length between
these points is 75% of the channel length (85% -10%),
and SLOPE for Subunit 4 is equal to:
SLOPE =(ELEVATION 8^ - ELEVATION
;;{LENGTHx0.75),
,
?i=(340ft - 35ft)/300.2 mi = 1.02 ft/mi
Equation tf.4
Substituting the valuesof AREA, LENGTH, and SLQPE
into Equation H.I gives the following peak discharge
for Subunit 4 (values rounded):"
124 Synoptic Approach
-------
QJ t= 648x8725.o70-85x1.02°-11x400.30-31
=; 226,602 ftVsec
Equation H.5
This value can be converted to m3/s using a conversion
factor of 0.02832 m3/f t3, giving a valr - of 6417 m3/s. A
similar approach was used to calcula, e 7Q10 values (7-
day mean discharge for a 10-year recur/ence interval for
the State of Louisiana (Lee 1985a).
The weighted percent annual change in population and
agriculture is then calculated for each joint area using
the following two equations:
[j_POP80-J_POP7(f|
WTPOPCHG =
L (J_POP70)
10
J 8
Equation H.8
Future Risk
The calculation of future risk for the State of Washing-
ton (Equation 4.10 and Table 4.3) is based on recent
urban and agricultural growth. Because population
and agricultural census data are reported by county,
weighting factors must be calculated first (Appendix F)
so county data can be prorated to the subunits needed
for the assessment; Weighting factors (WEIGHT) are
calculated by dividing the joint county-subunit area
(J_AREA) by total county area (AREA):
WEIGHT
Equation tj,6
Table H.I contains weighting factors calculated for the
five counties that overlap Washington Subunit 26. Us-
ing these data, joint population or agriculture area (e.g.,
the estimated population for the portion of the county
within the subunit) is calculated using the following
general equation:
J_VALUE = VALUE x WEIGHT
Equation H.7
where J_VALUE is the joint value, VALUE is the county
value, and WEIGHT is the weighting factor from Equa-
tion H.6. Joint values are calculated for 1970 population,
1980 population, 1974 agricultural land, and 1982 agri-
cultural land in Table H.2.
p_AGR82-J_AGR74~] - "
WTARGCHG =
(J_AGR74)
' 8 -'
J 87 - '
X 95"
< Equation H.9
where WTPOPCHGandWtAGRCHGare the weighted
percent annual change in population and agriculture,
respectively, and the other variables are as previously
defined. The terms are divided by the number of years
between the census dates (10 and 8 for population and
agriculture, respectively) to put the change on an an-
nual basis. Because 8 and 87% of national wetland loss
has been due to urban expansion and agricultural con-
version (Tiner 1984), these values are used as weights in
Equations H.8 and H.9 to account for the relative impor-
tance of the two impacts (8/95 and 87/95 are actually
Table H.1. Calculation of weighting factors for coun-
ties overlapping Washington Subunit 26.
County
J_AREA'
AREA2
WEIGHT 3
Cowlitz
Lewis .
Pierce
Skamania
Yakima
1757.11
3816.26
135.98
700.03
18.81
2964.12
6407.65
4299.84
4369.82
11104.69
0.5928
; ' 0.5956
0.0316
0.1602
0.0017
1 Joint county-subunit area in km2
2 County area in km2
3 Weighting factor from Equation H.6.
Table H.2. Conversion of county census data into joint county-subunit values for counties overlapping
Washington Subunit 26. >
County
WEIGHT1 POP702 J_POP703 POP802 J_POP80» AGR742 J.AGR743 AGR822' JAGR82
Cowlitz
Lewis
Pierce
Skamania
Yakima
0.5928
0.5956
0.0316
0.1602
0.0017
68616
45467
411027
5845
144971
40675
27079
12998
936
246
79548
56025
485643
7919
172508
47155
33367
15358
1269
292
153.93
554.97
251.33
33.72
7155.05
91.25
330.53
7.95
5.40
12.12
165.22
548.71
279.09
36.19
6942.55
97.94
326.80
8.83
5.80
11.76
1 Weighting factor from Equation H.6.
2 POP70, POP80, AGR74, and AGR82 are cou'nty values for 1970 population, 1980 population, 1974 agricultural Jand, and 1982
agricultural land, respectively; areas in km2. ,
3 J_POP70, J_POP80, J_AGR74, and J_AGR82 are joint county-subunit values for 1970 population, 1980 population, 1974
agricultural land, and 1982 agricultural land, respectively; areas in km2. •
Appendices 125
-------
used so that the factors sum to one; the remaining 5% of would not necessarily translate into a gain in wetlands).
t"»'S fortM^I I^\^.e ic* *f+*+r\**nj3\ TV^l*!^ TJ O AA.« !_«.I_ *. .J_i__ ^_ A J Jl *.1_ _ 1 •» •_•_!_ /• TI TnnT^^^T^y^^v T^-> •»
national loss is ignored). Table H.3 contains data for
WTPOPCHG and WTAGRCHG by joint area and gives
totals for Subunit 26 (if either of the two subunit sums
were less than zero, the value would have been set to
zero since a loss of population or agricultural area
Adding the subunit totals for WTPOPCHG and
WTAGRCHG gives theactual riskfactor for Subunit 26:
RISK =={9.41 +2473) X1(T3 =34.14xl.0'3 , , ,
" "' 'Equation H. W
Table H.3. Weighted percent annual population change and agricultural change for joint county-subunit areas of
counties overlapping Washington Subunit 26 (values rounded).
County
CowlitZ
Lewis
Pierce
Skamania
Yakima
Total
J_POP70'
40675
27079
12998
936
246
J.POP801
47155
33367
15358
1269
292
WTPOPCHG Z(x10's)
1.34
1.96
1.53
2.99
1.60
9.41
.LAGR743
91.25
330.53
7.95
5.40
12.12
JJW3RS2 3
97.94
326.80
8.83
5.80
11.76
WTAGRCHG «(x10's)
8.40
-1.29
12.64
8.38
-3.40
24.73
' J_POP70 and J_POP80 are Joint county-subunit populations for 1970 and 1980, respectively.
2 Weighted percent annual change in population (Equation H.8)
' .LAGR74 and J_AGR82 are joint county-subunit agricultural land areas (in km2) for 1974 and 1982, respectively.
4 Weighted percent annual change in agriculture (Equation H.9)
1*1
126 Synoptic Approach
-------
APPENDIX I
Information Form
TotheReader, ; - . :
We hope you have found A Synoptic \pproach to Cumulative Impact Assessment valuable. In the event that we update
this report, we'd appreciate having y our opinion about how it might be improved.
We'd like to get an idea of who our audience is, so please fill in the information below and mail it back to us. Feel
free to make any other comments as well. - ' .--.-.-.
Many thanks for helping., • • - • • . '* ' :
Wetlands Research Program
.-•-•••• U.S. Environmental Protection Agency
200 SW35TH Street •
1 '•• Corvallis, OR 97333 ./,'.'.
Nfme:
Affiliation'
Phone*
Fav:
FHiiraHonal Rarlcpronnd:
Jnh Position:
Is your primary responsibility policy, regulatory,
technical, or other?
What do you like least about A Synoptic Approach to
Cumulative Impact Assessment? > • •
What isn't in A Synoptic Approach to Cumulative Impact
Assessment that should be?
Primary reason you are interested in this approach:
Would you like to receive revisions of this document if
it is updated, or related reports as they become ^ you pian on conducting a cumulative impact
assessment?
Q YES Q NO
available?
Would you like to receive the WRP update?
Q YES Q NO
Q YES a NO
If so, do you plan on using the synoptic approach?
a YES a NO
What do you like best about A Synoptic Approach to If you answered no, could you please tell us why you
Cumulative Impact Assessment? felt the synoptic approach was inappropriate?
*U.S.GOVERNMENTPRINnNGOFFICE:1992 •fi'tS -003,60082
Appendices 127
-------
-------
ERL-COR-
M TECHNICAL REPORT DATA
ffleate rted Inanition* on tht nvme befart completing)
I. REPORT NO.
EPA/600/R-92/167
I. TITLI AND SUBTITLE
2.
A Synoptic Approach to Cumulative Impact
Assessment - A Proposed Methodology
1. RECIPIENT* ACCESSION NO.
PB 93-100147
>. REPORT DATE
October 1992
I. PERFORMING ORGANIZATION CODE
7. AUTMOR(S) •
Scott G. Leibowitz, Brooke Abbruzzese, Paul Adamus,
Larry Hughes, and Jeffrey Irish
I. PERFORMING ORGANIZATION REPORT NO
. PERFORMING ORGANIZATION NAME AND ADDRESS
ManTech Environmental Technology,
US EPA ERL-Corvallis, OR
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
US Environmental Protection Agency
Environmental Research Laboratory
200 SW 35th Street
Corvallis. OR 97333
13. TYPE OF REPORT AND PERIOD COVERED
Published Report
14. SPONSORING AGENCY CODE
EPA/600/02
S. SUPPLEMENTARY NOTES
1992. U.S. Environmental Protection Agency, Environmental Research Laboratory
Corvallis, OR. *
. ABSTRACT
This proposed methodology was designed for assessing cumulative impacts to wetlands
as part of permit review under Section 404 of the Clean Water Act. The synoptic
approach allows wetland managers to produce regional or statewide maps that rank
portions of the landscape according to a set of landscape variables, or synoptic
indices. The approach is intended to be easily applied so it can augment the best
professional judgement used daily by wetland managers and regulators. The report
describes the five steps of conducting a synoptic assessment, and illustrates the use
of synoptic information through four case studies.
7.
KEY WORM AND DOCUMENT ANALYSIS
DESCRIPTORS
cumulative impacts,
cumulative effects,
synoptic assessment
S. DISTRIBUTION STATEMENT
Release to Public
KPA r»rm UaO-1 1U-7J)
IDENTIFIERS/OPEN ENDED TERMS
»0. SECURITY CLASS (Thilf*tel
Unclassified
c. COS AT I Field/Group
21. NO. OF PAGES
137 15°
22. PRICE
129
-------
------- |