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Photo Credits – Cover Page

Top left: Emerging caddisfly larvae, Oregon State University 

Top right: Drought conditions in the Little Medicine Bow River at Boles Spring, R. Swanson, 

USGS 

Bottom left: Dimmer Road East Fork, West Branch, J. Grabarkiewicz 

Bottom right: Rainbow trout, W. Davis, http://www.epa.gov/bioindicators 
EPA/600/R-07/085 

March 2008 

Climate Change Effects on Stream and River Biological 

Indicators: A Preliminary Analysis 

Global Change Research Program 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Washington, DC 20460 
DISCLAIMER 

 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 endorsement or recommendation for use. 

ABSTRACT 

Climate change is projected to affect aquatic ecosystems through changes in water 

temperature, hydrological cycles, and degree days. These effects will manifest themselves 

through changes in community composition, phenology, number of reproductive cycles, 

evolutionary adaptations, and genetic selection. These changes also serve as indicators of 

climate change effects on ecosystems and could be used to document ecosystem condition. State 

and tribal water quality agencies use biological indicators to assess ecosystem condition as 

required by the Clean Water Act. These assessments rely on comparisons of reference and nonreference sites. Climate change, however, will affect organisms at both types of sites, unlike 

traditional stressors. Therefore, understanding how biological indicators respond to the effects of 

climate change, what novel indicators may be available to detect effects, how well current 

sampling schemes may detect climate-driven changes, and how likely it is that current sampling 

schemes will continue to detect impairment, are important issues in need of discussion. This 

report is meant to initiate this discussion by providing information on the potential effects of 

climate change on biological indicators, outlining initial strategies to modify assessment 

activities to account for climate change effects, and highlighting possible next steps. 

Preferred citation: 

U.S. Environmental Protection Agency (U.S. EPA). (2008) Climate change effects on stream and river biological 

indicators: A preliminary analysis. Global Change Research Program, National Center for Environmental 

Assessment, Washington, DC; EPA/600/R-07/085. Available from the National Technical Information Service, 

Springfield, VA, and online at http://www.epa.gov/ncea.

ii
CONTENTS 

Page

LIST OF TABLES.......................................................................................................................... v 

LIST OF FIGURES ....................................................................................................................... vi 

PREFACE....................................................................................................................................viii 

AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................................................. ix 

EXECUTIVE SUMMARY ............................................................................................................ x 

1. INTRODUCTION ........................................................................................................... 1-1 

1.1. BIOINDICATORS, BIOCRITERIA, AND THE CLEAN WATER ACT.........1-3 

1.2. CLIMATE CHANGE EFFECTS ON AQUATIC ORGANISMS AND 

ECOSYSTEMS.................................................................................................... 1-4 

1.2.1. Changes in Ranges, Distributions of Species, and Community 

Composition............................................................................................. 1-4 

1.2.2. Changes in Phenology.............................................................................. 1-7 

1.2.3. Evolutionary Effects ................................................................................ 1-7 

1.2.4. Ecosystem Effects.................................................................................... 1-8 

1.3. ORGANISMAL/BIOLOGICAL INDICATORS OF CLIMATE CHANGE......1-9 

2. STATE BIOASSESSMENT PROGRAMS—RIVERS AND STREAMS .....................2-1 

2.1. BIOASSESSMENTS OF RIVERS AND STREAMS ........................................ 2-1 

2.2. BIOINDICATORS USED IN STATE PROGRAMS—RIVERS AND STREAMS

.............................................................................................................................. 2-2 

3. SENSITIVITY TO CLIMATE CHANGE OF BIOLOGICAL INDICATORS USED IN 

STATE BIOCRITERIA PROGRAMS............................................................................ 3-1 

4. CASE STUDY 1—ASSESSING TRENDS: THE POWER OF BIOLOGICAL 

ASSESSMENTS TO DETECT CLIMATE CHANGE................................................... 4-1 

4.1. OBJECTIVES...................................................................................................... 4-1 

4.2. ANALYSIS APPROACH ................................................................................... 4-1 

4.2.1. Ability to Detect Change–Power Analysis.............................................. 4-2 

4.2.2. The Maryland Biological Stream Survey Data Set..................................4-3 

4.2.3. Information on Taxa Loss Rates.............................................................. 4-4 

4.2.4. Prediction of Expected Taxa Losses with Projected Temperature Increases

.................................................................................................................. 4-6 

4.3. KEY FINDINGS.................................................................................................. 4-7 

4.3.1. How Long Must Monitoring be Conducted to have a Fixed Probability of 

Detecting a Change in the Mean Native Taxon Richness of the Reference 

Site Population? ....................................................................................... 4-7 

4.3.2. How Long Must Monitoring be Conducted to have a Fixed Probability of 

Detecting a Change in the Mean Native Taxon Richness for a Particular 

Site?........................................................................................................ 4-10 

4.4. KEY CONCLUSIONS ...................................................................................... 4-14 

5. CASE STUDY 2—ACCOUNTING FOR TRENDS: BIOLOGICAL ASSESSMENT IN 

THE PRESENCE OF CLIMATE CHANGE .................................................................. 5-1 

5.1. OBJECTIVES...................................................................................................... 5-1 

5.2. ANALYSIS APPROACH ................................................................................... 5-1 

iii
5.2.1. Datasets Evaluated................................................................................... 5-2 

5.2.2. Metrics..................................................................................................... 5-2 

5.2.3. Regional Data Partitions.......................................................................... 5-3 

5.2.4. Climate Data ............................................................................................ 5-3 

5.2.5. Hydrologic Attributes.............................................................................. 5-6 

5.2.6. Specific Analyses..................................................................................... 5-6 

5.3. KEY FINDINGS.................................................................................................. 5-8 

5.3.1. Observed Stressor-Responses.................................................................. 5-8 

5.3.2. Estimates of Climate Change Effects ...................................................... 5-9 

5.4. DISCUSSION AND KEY CONCLUSIONS .................................................... 5-21 

5.4.1. Reference Conditions............................................................................. 5-22 

5.4.2. Importance of Monitoring...................................................................... 5-22 

5.4.3. Analytical Methods................................................................................ 5-23 

5.4.4. Stressor Identification ............................................................................ 5-23 

5.4.5. Biocriteria .............................................................................................. 5-24 

5.4.6. Universal Scale to Measure Biological Condition.................................5-24 

6. RECOMMENDATIONS FOR U.S. EPA TO IMPLEMENT A FOUNDATION FOR 

STATE/TRIBAL BIOASSESSMENT/BIOCRITERIA PROGRAMS TO CONSIDER 

CLIMATE CHANGE...................................................................................................... 6-1 

6.1. RECOMMENDATIONS FOR U.S. EPA ........................................................... 6-1 

6.2. RECOMMENDATIONS FOR STATES AND TRIBES....................................6-3 

7. POTENTIAL NEXT STEPS ........................................................................................... 7-1 

8. CONCLUSIONS.............................................................................................................. 8-1 

REFERENCES ............................................................................................................................... 1 

APPENDIX A: REGIONAL PATTERNS OF CLIMATE CHANGE PROJECTSION 

AND CONSEQUENCES FOR RIVERS AND STREAMS............................. A-1 

APPENDIX B: DESCRIPTIVE AND SUPPLEMENTAL RESULTS DETAILS FOR 

CASE STUDY 2: BIOLOGICAL ASSESSMENT IN THE PRESENCE 

OF CLIMATE CHANGE ..................................................................................B-1 

iv
LIST OF TABLES 

Page

2-1. Table of benthic macroinvertebrate metrics taken from the Rapid Bioassessment 

Protocols .......................................................................................................................... 2-4 

2-2. Fish metrics used in various bioassessment programs..................................................... 2-5 

3-1. Summary of expectations for responses of common categories of stream and 

river biological indicators to climate change influences on water temperature 

and hydrologic regime ..................................................................................................... 3-3 

3-2. Novel indicators that may be sensitive to climate change ............................................... 3-6 

4-1. Average annual temperature increases expected by region of the U.S............................4-6 

4-2. The time (years) to achieve a fixed probability of detecting a statistically 

significant effect of temperature increases on macroinvertebrate and fish taxa 

loss across different regions under maximum and minimum temperature 

projections...................................................................................................................... 4-12 

5-1. Discrimination efficiencies of IBIs and EPT taxa under 3 climatic conditions.............5-17 

v
LIST OF FIGURES 

Page

1-1. Conceptual diagram of how climate change affects aquatic ecosystems and the 

possible ecosystem responses that can be measured using biological indicators............1-5 

4-1. Effects of confidence level (α [a] and β [b]) on time to detect a climate effect on 

macroinvertebrate taxa loss due to climatic warming at high taxa loss rates in the 

Northeast/Mid-Atlantic U.S............................................................................................. 4-8 

4-2. Effects of confidence level (α [a] and β [b]) on time to detect a climate effect on 

macroinvertebrate taxa loss due to climatic warming at low taxa loss rates in the 

Northeast/Mid-Atlantic U.S............................................................................................. 4-9 

4-3. Effects of confidence level (α [a] and β [b]) on time to detect a climate effect on 

fish taxa loss due to climatic warming in the Northeast/Mid-Atlantic U.S...................4-11 

4-4. Effects of sample size on time to detect a climate effect on macroinvertebrate 

taxa loss due to climatic warming at high taxa loss rates in the Northeast/ 

Mid-Atlantic U.S............................................................................................................ 4-13 

4-5. Effects of sample size on time to detect a climate effect on macroinvertebrate 

taxa loss due to climatic warming at low macroinvertebrate taxa loss rates in 

the Northeast/Mid-Atlantic U.S..................................................................................... 4-15 

4-6. Effects of sample size on time to detect a climate effect on fish taxa loss due 

to climatic warming in the Northeast/Mid-Atlantic U.S................................................ 4-16 

5-1. Maryland MBSS sampling stations showing regional divisions ..................................... 5-4 

5-2. Monthly Palmer HDI for the 30-year period 1970-1999................................................. 5-5 

5-3. Fish richness vs. temperature in Highland reference streams........................................ 5-10 

5-4. (a) Macroinvertebrate richness vs. temperature; (b) EPT richness vs. 

temperature in Highland reference streams; (c) EPT vs. tempterature relation; 

and (d) fish richness vs. temperature relation in reference sites in Piedmont 

streams ........................................................................................................................... 5-11 

5-5. Benthic IBI performance and climatic condition........................................................... 5-13 

5-6. EPT performance and climatic condition ...................................................................... 5-14 

5-7. Fish IBI performance in three climatic conditions ........................................................ 5-16 

vi
LIST OF FIGURES continued 

Page

5-8. (a) Relationship of EPT richness to conductivity under drought (red), base 

(blue), and wet (black) conditions and (b) conditional probability in 

impairment for the same three relationships.................................................................. 5-18 

5-9. Conductivity CDFs—Piedmont..................................................................................... 5-19 

5-10. (a) Relationship of EPT richness to impervious surface under drought (red), 

base (blue), and wet (black) conditions, and (b) conditional probability of 

impairment for the same three relationships.................................................................. 5-20 

vii
PREFACE 

 This report was prepared by Tetra Tech, Inc. and the Global Change Research Program 

(GRCP) in the National Center for Environmental Assessment of the Office of Research and 

Development at the U.S. Environmental Protection Agency (U.S. EPA). It is intended for 

managers and scientists working on biological indicators, bioassessment, and biocriteria. The 

report will provide them with (1) information on the potential effects of climate change on 

indicator organisms used, (2) initial strategies for adapting their programs to accommodate these 

environmental changes, and (3) highlight possible next steps. The GCRP established a 

partnership with the Health and Ecological Criteria Division within the U.S. EPA’s Office of 

Water with State Water Quality Agencies, and with some Tribal Environmental Agencies in an 

effort to develop a foundation for linking climate change to their monitoring and assessment 

programs. The background information and research results in this report were presented at a 

workshop with state and tribal biocriteria managers and scientists from the U.S. EPA and the 

academic community. The “Introductory Workshop on climate Change Effects on Biological 

Indicators” was held in Baltimore, MD in March 2007 and focused on climate change effects on 

river and stream ecosystems. The Workshop provided state and tribal biocriteria managers with 

information on how climate change may affect their monitoring and assessment programs for 

water resource protection and restoration. The Workshop included keynote presentations on the 

current state of scientific understanding of climate change effects on aquatic ecosystems, 

particularly rivers and streams, climate change trends in the past, present, and future, and models 

and tools that managers can use to monitor and assess climate change effects. Workshop 

attendees also participated in breakout sessions in an effort to identify (1) current biological 

indicators of environmental condition, (2) vulnerabilities of biocriteria programs in water quality 

agencies, and (3) adaptations of program elements to recognize effects of climate change. Case 

studies were presented to aid in understanding the technical ramifications of adapting existing 

biocriteria programs. This report includes background information about climate change effects 

on rivers and streams and the initial elements of a framework that state and tribal biocriteria 

managers can use to modify their programs in response to these effects. The framework 

elements described in this report and presented at the workshop are (1) an approach for 

identifying biological indicators sensitive to climate change, (2) an analysis for detecting climate 

change effects, and (3) methods for continuing to detect impairment under climate change. 

Workshop participants made recommendations about research needs and information gaps. This 

information is presented in this report and was used in part to derive the proposed next steps that 

conclude this report. 

viii
AUTHORS, CONTRIBUTORS, AND REVIEWERS 

 The Global Change Research Program, within the National Center for Environmental 

Assessment, Office of Research and Development, is responsible for publishing this report. This 

document was prepared by Tetra Tech, Inc. under Contract No.GS-10F-0268K, U.S. EPA Order 

No. 1106. Dr. Britta Bierwagen served as the Technical Project Officer. Dr. Bierwagen 

provided overall direction and technical assistance, and she contributed as an author. 

AUTHORS

Center for Ecological Sciences, Tetra Tech, Inc., Owings Mills, MD

Anna Hamilton, Michael Barbour, Jeroen Gerritsen, Michael Paul 

U.S. EPA

Britta G. Bierwagen 

REVIEWERS 

This report benefited greatly from the comments and suggestions of the following 

reviewers. 

U.S. EPA Reviewers

Mark Bagley, Evan Hornig∗, Sue Norton, Lester Yuan 

Other Reviewers

Paul Kazyak (Maryland Department of Natural Resources, Steve Ormerod (Cardiff University, 

UK), Mark Southerland (Versar) 

ACKNOWLEDGMENTS 

 The authors thank the Global Change Research Program staff in NCEA, particularly 

Susan Julius, Tom Johnson, Chris Weaver, and Jordan West, for their input and advice 

throughout the development of this project. We also thank staff in the Office of Science and 

Technology in the U.S. EPA’s Office of Water for their input and assistance. We appreciate all 

of the comments from workshop participants. Their thoughts and ideas contributed greatly to 

this report and the formulation of further research. 

 

∗ Present affiliation with U.S. Geological Survey. 

ix
EXECUTIVE SUMMARY 

Climate change can have a variety of effects on aquatic species. Changes in air 

temperature and precipitation patterns are reflected in changes in water temperature, hydrological 

cycles, and degree days. These alterations, in turn, affect aquatic ecosystems, whose responses 

can be documented through changes in community composition, phenology, number of 

reproductive cycles, evolutionary adaptations, and genetic selection. One method for 

documenting changes in ecosystems is through indicators that are particularly sensitive to the 

changes or stressor of interest—in this case climate. Some potential indicators of climate change 

effects include the following: ratios of drought tolerant to intolerant mussel species; ratios of 

invertebrate response guilds that indicate hydrological status; and changes in community 

composition. Indicators of changes in composition include shifts from cold- or cool-water fishes 

to warm-water fishes; shifts from species associated with hydrologically stable to variable 

conditions; and declines in particularly sensitive species, such as salmon, brook trout, or darter 

species. The goals of this report are to provide managers and scientists working on biological 

indicators, bioassessment, and biocriteria with information on the potential effects of climate 

change on indicator organisms used and initial strategies for adapting their programs to 

accommodate these environmental changes, and to highlight possible next steps. 

 Biocriteria programs exist in state and tribal water quality agencies to assess the 

biological status and health of ecosystems as required by the Clean Water Act. The United 

States Environmental Protection Agency (U.S. EPA)’s Office of Water has developed guidance 

documents for states and tribes on bioassessment methods and biocriteria establishment. River 

and stream ecosystems were among the first for which methods were developed. The general 

approach of bioassessment includes defining reference conditions so that impaired sites can be 

defined by comparison with these natural or minimally impacted sites. Currently, there is no 

mandate or guidance for biocriteria programs to include climate change effects in the design of 

monitoring programs or assessment of impairment. However, as a stressor, climate change will 

impact both reference and non-reference sites, unlike the more conventional anthropogenic 

stressors currently considered in bioassessment. 

 Because climate change will affect both, reference and non-reference sites, bioassessment 

programs would benefit from the collection of data on climate change effects in their systems. 

These data may come from indicators that detect such effects. Biological indicators that are 

currently used in bioassessment programs have been selected for their sensitivity to certain 

environmental stressors. Knowledge about their sensitivities allows a general extrapolation of 

their response to other environmental variables related to climate change. Therefore, most 

indicators that are sensitive to the conventionally considered anthropogenic stressors are also 

x
expected to have some sensitivity to climate change: For example, changes in temperature and 

precipitation patterns that affect stream flow. Biological indicators that are only (or at least 

predominantly) sensitive to climate change may be possible to define, but are likely to be novel, 

at least in terms of their application in state bioassessment programs. Although review of the 

scientific literature may lead to the identification of novel indicators, these indicators will need to 

be easy to measure and practical to implement by state programs in order to be widely adopted. 

 Indicators that are specifically sensitive to climate change effects are only one approach 

that programs could use to detect effects. The case studies discussed in this report present 

additional methods and considerations to aid in the detection of climate change effects. The first 

case study discusses issues of sampling power needed to detect effects using one type of 

indicator of climate change and the second case study examines how climate change may affect 

the ability of current monitoring programs to detect impairment due to conventional stressors. 

 The first case study, Assessing Trends, focuses on sampling power. The power to detect 

effects depends on the effect size. In this case, that means the species’ loss rate due to increases 

in water temperature. The case study explores a low and high species loss rate and low versus 

high temperature change scenarios. Using data from one long-term data set and sampling 

scheme, it would take 15 years to detect effects due to climate change under the high loss rate 

and high temperature change scenario. The other extreme, low loss rate and low temperature 

change, would take more than 100 years to detect. As expected, an increasing number of 

samples will help detect effects sooner. 

 The second case study, Accounting for Trends, examines the ability of bioassessment 

programs to continue to detect impairment due to conventional stressors in the face of climate 

change. The analysis shows that climate change effects will decrease the ability of states to 

discriminate between reference and impaired sites, particularly if reference sites are already 

somewhat stressed. These results underscore the importance of monitoring sentinel sites, sites 

that are revisited during each sampling cycle, in order to detect deterioration of condition at 

reference sites due to climate change. 

 The results of these case studies, preliminary analyses of indicator sensitivities, and 

reviews of the literature of climate change effects on aquatic ecosystems were presented at the 

Workshop for state biocriteria managers of rivers and streams. Their responses to this 

information led to recommendations for additional research and a variety of mechanisms for 

assistance to states from U.S. EPA concerning climate change effects in these ecosystems. The 

large number of recommendations suggests that it is important to continue this dialogue by 

conducting further research and other activities leading to more specific recommendations and 

assistance for state programs. This information could then be used to modify state programs to 

account for climate change effects and to ensure that management goals continue to be reached. 

xi
State biocriteria managers also outlined a number of actions that they could take now in response 

to the information presented. In particular, their response reflects an understanding that climate 

change will affect the entire ecosystem, and, therefore, regular and repeated monitoring of 

reference and sentinel sites to collect biological, hydrological, and temperature information will 

be particularly valuable to detect and control for climate change effects. 

 The recommendations for further research also lead to potential next steps. These steps 

include (1) conducting another workshop for biocriteria managers of other aquatic ecosystems; 

(2) conducting more in-depth analyses of climate change effects on river and stream 

bioassessment programs in different regions of the U.S.; (3) disseminating information on 

regional climate change effects on biological indicators; and (4) coordinating information across 

U.S. EPA and state agencies to evaluate trends in bioassessment data. 

xii
1. INTRODUCTION 

Changes in climate affect ecosystems, and, therefore, also their management. State and 

tribal water quality (WQ) programs fulfilling Clean Water Act of 1972 CWA) requirements will 

need to take these climate change-induced effects into account. Currently, state and tribal 

assessment programs include biological monitoring in addition to physical and chemical 

parameters, because organisms, and the communities they comprise, integrate the effects of 

physico-chemical changes over time and space and provide any trends detected with ecological 

relevance. It is for this reason that understanding the biological and ecological responses to 

climate change, as well as interactions between climate change effects and other environmental 

stressors, is important for continued operation of bioassessment programs and interpretation of 

bioassessment results. 

Human activities and natural factors have already changed the climate; these trends are 

likely to continue into the future (IPCC, 2007). There is now high confidence that anthropogenic 

emissions of greenhouse gases and aerosols have resulted in warming, with evidence of globally 

increasing air and ocean temperatures, melting of snow and ice, and rising sea levels (IPCC, 

2007; Rahmstorf et al., 2007). Global air temperatures have increased about 0.6°C over the last 

30 years and 0.8°C over the last century, and global ocean temperatures are probably as warm 

now as they were during the Holocene maximum (about 5000 to 9000 years ago) (Hansen et al., 

2006). Observed increases are greater over land masses than over oceans, and they are greatest 

at high latitudes in the Northern Hemisphere (Hansen et al., 2006). Extreme cold days, cold 

nights, and frost have been less frequent; hot days, hot nights and heat waves have been more 

frequent (IPCC, 2007). The third Intergovernmental Panel on Climate Change (IPCC) report 

(2001) revealed that the diurnal temperature range was decreasing; however, evaluation of more 

extensive data in the fourth assessment report shows that daytime and nighttime temperatures are 

actually increasing at comparable rates (IPCC, 2007). An understanding of the potential 

consequences of these climatic changes for aquatic ecosystems is an initial step that will assist 

water resource managers in modifying their programs to ensure that they will continue to meet 

their management goals. 

The goals of this report are to provide managers and scientists working on biological 

indicators, bioassessment, and biocriteria with information on the potential effects of climate 

change on indicator organisms used, initial strategies for adapting their programs to 

accommodate these environmental changes, and highlight possible next steps. This report 

supports these goals by presenting background information about climate-change effects on 

rivers and streams (see Section 1), an overview of bioassessment programs (see Section 2), and 

the initial elements of a framework that state and tribal biocriteria managers can use to modify 

1-1
their programs in response to these effects. The framework elements described in this report are 

(1) a proposed approach for identifying biological indicators sensitive to climate change (see 

Section 3), and (2) two preliminary case studies evaluating selected aspects of biological 

assessment programs (see Sections 4 and 5). 

Biological assemblages integrate effects from all impinging sources of stress, including 

“conventional” anthropogenic stressors, which are commonly the focus of state programs 

assessing and regulating water quality, and any other significant source of environmental change, 

including climate change. This integrative characteristic makes biological assemblages effective 

monitoring tools, but it also means that the analyses must reasonably account for all major 

sources of stress in order to attribute observed responses to particular sources of stress in a 

reliable manner. This attribution allows for more effective regulation of the stressor and/or 

management of the resource. The ongoing success of biological monitoring and assessment 

programs will require an understanding of both an understanding of what climate-associated 

changes are occurring in monitored aquatic communities and how monitoring programs can 

account for them. Accounting for climate-change influences will support effective attainment of 

management goals using monitoring program results as a foundation. 

The two case studies included in this report examine how climate-change effects can be 

taken into account through program design and/or analytical approaches and how climate change 

may affect the ability of biological monitoring and assessment programs to meet key goals. 

Climate change can be viewed as a “global stressor” that affects both reference and nonreference locations monitored for the effects of more “conventional” stressors. The ability to 

account for climate change requires (1) an understanding of how vulnerable monitoring data are 

to climate-change effects, and (2) how effectively differences that are a result of climate change 

can be detected within existing monitoring programs. 

The first case study (see Section 4) describes several important temporal aspects of 

change detection. The second case study (see Section 5) examines methods for continuing to 

detect impairment under climate change. More detailed examinations of these and other 

important components of biological assessment programs are essential before comprehensive 

recommendations for adaptation of bioassessment programs to climate change can be made. 

Preliminary recommendations, derived from these analyses and the participants in the 

“Introductory Workshop on Climate Change Effects on Biological Indicators,” and a summary of 

proposed next steps conclude this report (see Sections 6 and 7). 

1-2
1.1. BIOINDICATORS, BIOCRITERIA, AND THE CLEAN WATER ACT 

The CWA of 1972 (Federal Water Pollution Control Act, Public Law (P.L.) 92-500) 

as amended in 2002 (P.L. 107-303, November 27, 2002) has as a stated goal and policy 

(Section 101(a)): 

“...to restore and maintain the chemical, physical, and biological integrity of the Nation’s 

waters.” 

The concept of biological integrity has received much attention since passage of the 

CWA. It is commonly defined as “the ability to support and maintain a balanced, integrated, and 

adaptive community with a biological diversity, composition, and functional organization 

comparable to those of natural aquatic ecosystems in the region” (Karr et al., 1986; Karr and 

Dudley, 1981; Frey, 1977). This wording highlights some key attributes of biological 

communities that are fundamental to preserving “integrity” (diversity, composition, and 

functional organization), and it alludes to a basic element of the approach used in bioassessment, 

which is comparison to existing natural communities, or reference conditions. 

The use of biological monitoring and assessment to establish criteria is mandated in 

Section 303(c)(2)(B) and 304(a)(8) of the CWA. Biological criteria (biocriteria), derived from 

biological monitoring and assessment, provide narrative and numeric targets that define the 

desired condition of communities of aquatic organisms inhabiting streams and rivers where water 

quality is subject to regulation. Biocriteria and biological assessment (bioassessment) thus 

provide a valuable and direct regulatory mechanism for protecting biological resources at risk 

from chemical, physical, and biological impacts. The CWA requires that states and tribes 

designate aquatic life uses (i.e., environmental goals) for their waters that appropriately address 

biological integrity and adopt biological criteria necessary to protect those uses (Barbour et al., 

2000). 

Biologists and other natural resource scientists develop biocriteria by using accepted 

scientific principles to characterize the regional reference conditions for the different water 

bodies found within a state or tribal nation (Barbour et al., 2000). Effects of climate change are 

broad and need to be considered in tracking trends in regional reference conditions that form the 

basis of assessing ecological condition. Biological assessment programs are now widespread 

throughout the states (U.S. EPA, 2002) and are best served by indicators that can be used for 

multiple purposes. Water resource agencies in the 50 states and several tribes are in various 

stages of development and implementation of bioassessment methods (Barbour et al., 2000). 

Essentially, the multiple purposes of bioassessment can be reduced to two basic questions: 

(1) asking whether a water body meets, or exceeds, an impairment threshold, and (2) asking 

whether the biological condition of a water body is degraded or improved compared to an earlier 

1-3
time, an upstream or a nearby site (Barbour and Gerritsen, 2006). Climate change influences 

both of these questions. 

1.2. CLIMATE CHANGE EFFECTS ON AQUATIC ORGANISMS AND 

ECOSYSTEMS 

There is a substantial weight of evidence, summarized in several reviews and metaanalyses, of ecological changes that are linked to existing climate change (Walther et al., 2002; 

Root et al., 2003; Parmesan, 2006). Figure 1-1 is a conceptual model of how climate changes 

affect aquatic ecosystems, and some possible ways in which ecological systems respond, which 

can be measured using indicators. These analyses identify several categories of ecological 

responses expected from climate change, including (1) changes in range and distribution of 

species; (2) changes in phenology; and (3) evolutionary effects on morphology, behavior, and 

genetic frequencies, due to altered selection regimes. These, in turn, are predicted to alter 

community composition and interactions, as well as ecosystem processes, including production 

and material cycling. A few of the more common examples are discussed here. 

1.2.1. Changes in Ranges, Distributions of Species, and Community Composition 

Water temperature drives many biological functions in aquatic invertebrates and fish, 

including growth and metabolic rates, reproduction, feeding, and survival. Many fish and insect 

species have fairly narrow temperature range tolerances, and these narrow ranges influence their 

distribution. Temperature regime determines distributions of species in relation to temperature 

tolerances and adaptations combined with competitive interactions, effects on food supply, and 

other factors (e.g., Sweeney and Vannote, 1978; Vannote and Sweeney, 1980; Matthews, 1998). 

 Changing thermal regimes are expected to shift species ranges to the north (and/or to 

higher elevations); species at the southern limits of their ranges will migrate or suffer local 

extinctions. However, in many areas northward or upstream migrations of certain aquatic 

species may be limited by barriers to dispersal such as habitat fragmentation due to dams and 

reservoirs, deforestation, and water diversions (Poff et al., 2002; Moore et al., 1997; Covich et 

al., 1997; Smith, 2004; Hawkins et al., 1997). There is some experimental evidence for dispersal 

of Ephemeroptera-Plecoptera-Trichoptera (EPT) taxa across watersheds in Wales, such that 

natural fragmentation of river basins is not directly an impediment to recovery from large-scale 

disturbance (Masters et al., 2007). However, northward migrations may be limited in regions 

including the southwest and southern Great Plains of the U.S. where most drainages flow east 

and west (Poff et al., 2002). Species that are already restricted to headwater streams may be 

displaced (Poff et al., 2002). In the U.S., from 36% (Mohseni et al., 2003) to 50% (Eaton and 

Scheller, 1996) of cold-water fish habitat, and up to 15% of cool-water habitat may disappear 

1-4
Figure 1-1. Conceptual diagram of how climate change affects aquatic 

ecosystems and the possible ecosystem responses that can be measured using 

biological indicators. 

1-5
(Mohseni et al., 2003) due to the warming projected for a doubling of atmospheric CO2

concentrations. Fish with the smallest geographic ranges are the most vulnerable. Rahel et al. 

(1996) estimate habitat losses for cold-water fish species in the Platte River, Wyoming ranging 

from 7–76% for temperature increases of 1–5oC. They anticipated potential population 

fragmentation as cold-water species were progressively limited to colder headwater stream 

reaches; fragmented populations (based on models for cut-throat trout) may be more susceptible 

to extirpation if populations are isolated with limited carrying capacity or interactions with other 

populations (Hilderbrand, 2003). In the Mid-Atlantic Appalachian mountains, cold-water brook 

trout are near the southern limit of their range, and suitable habitat is mainly found at higher 

elevations. Projected temperature increases could raise the elevation at which acceptable 

temperatures occur by 700 m, effectively eliminating most brook trout habitat in this region 

(Moore et al., 1997). 

Daufresne et al. (2003) documented species replacements, range shifts, and variations in 

community composition for both fish and macroinvertebrates in the Upper Rhone River in 

France associated with increasing water temperatures from atmospheric warming. Increased fish 

abundances were associated with increased temperatures and lower flows during the 

reproductive period (April–June). In moorland and forest streams in Wales, directional climate 

change (increasing temperatures ) decreased spring macroinvertebrate abundances over a 25 year 

period, yielding an estimated average of 21% reduction in abundance per 1°C of temperature 

increase, and in combination with the North American Oscillation (NAO) accounted for 70% of 

interannual variation (Durance and Ormerod, 2007). 

There are several other examples of community responses to climate variables in 

different regions of the U.S. In the Southeast, freshwater mussels are especially vulnerable to 

drought, along with the corresponding low flows and depressed dissolved oxygen (DO) levels, 

leading to increased mortalities and local extinctions (Golladay et al., 2004). In the Great Plains, 

where many fishes already exist at or near their thermal tolerance limits as a result of high 

temperatures and low flows typical of shallow water habitats, increasing temperatures due to 

climate change are expected to result in increased extinctions of endemic and local species 

populations (Covich et al., 1997). Finally, in the Southwest, the stream fauna is typically highly 

resilient and adapted to disturbance, but nonetheless is vulnerable to habitat losses that could 

accompany increased runoff variability (Grimm et al., 1997). Biological effects may be 

manifested as changes in relative abundance, species losses (local extinctions), and reduced 

diversity. 

In addition to temperature effects, projected changes in stream flow from climate change 

may alter community structure. When considering climate change alone, the Sacramento River 

could lose 10–18% (low and high climate change scenarios) of its fish species by 2080; the 

1-6
Colorado River 0–5% of fish species; the Rio Grande River 0–5%; and the Sabine River 11–13% 

(Xenopoulos et al., 2005). Xenopoulos et al. (2005) predict high risk of species extinctions for 

subtropical and tropical rivers with a rich endemic fauna and note the vulnerability of fish 

species that require seasonal floodplain connection for life cycle completion. Using a similar 

approach, Xenopoulos and Lodge (2006) estimated potential fish richness losses associated with 

20–90% reductions in discharge for several rivers in two regions of the U.S., and report a range 

of 2–30% of fish species lost in rivers of the Lower Ohio-Upper Mississippi Basin, and 3–38% 

of fish species lost in the Southeastern U.S. Changes in timing of spring flows resulting from 

climate change may have the greatest effects on spring spawning fishes in the Northeast and may 

alter the survival of Atlantic salmon by changing migration timing and coincidence with optimal 

conditions for survival (Hayhoe et al., 2007). It should be noted that climate-driven changes in 

hydrology are considered potentially difficult to define because inter-annual rainfall variability is 

large relative to trends predicted from climate change (see Wilby, 2006). 

1.2.2. Changes in Phenology 

Warmer water may increase the growth rates of aquatic invertebrates and result in earlier 

maturation (Poff et al., 2002). In a mesocosm experiment using the mayfly Cloeon dipterum, 

temperature increases alone had little effect on nymph abundance, and only small effects on 

body length, though emergence began earlier in the year (McKee and Atkinson, 2000). McKee 

and Atkinson (2000) also show that for treatments with both increased temperatures and 

nutrients, both nymph abundance and size increase. For a Japanese species of mayfly (Ephoron 

shigae), cumulative degree days and time of emergence are significantly correlated, explaining 

80–90% of the variation in emergence date, depending on whether the analysis is done for all 

individuals or separately by sex (Watanabe et al., 1999). For at least this species and most likely 

for related species, increasing water temperatures associated with climate change will likely 

result in earlier emergence of mayflies due to an earlier accumulation of degree days. 

1.2.3. Evolutionary Effects 

Evolutionary changes may play a small role in species’ responses to climate change 

through adaptation (Parmesan, 2006; Berteaux et al., 2004; Hogg and Williams, 1996). These 

include processes that have been documented for range shifts due to hybridization and novel 

adaptations, chromosomal inversions that allowed tolerance of warmer temperatures in southern 

range sub-populations, and body size responses to increasing temperatures due to genetic 

plasticity (Parmesan, 2006). However, capacity for evolutionary responses of species will be 

limited by range of genetic diversity and generation time, with species characterized by small, 

short-lived and abundant individuals more likely to respond adaptively (Bradshaw and 

1-7
Holzapfel, 2006; Berteaux et al., 2004; Hogg et al., 1995). Extinctions are still expected as a 

likely consequence of directional climate change even with evolutionary changes: in part 

because the mean phenotypes lag behind optimal phenotypes, and the rates of environmental 

change can outpace estimated maximum sustainable rates of evolution (Bradshaw and Holzapfel, 

2006; Berteaux et al., 2004; Burger and Lynch, 1995). 

Parmesan (2006) points out that while there are local examples of adaptations to 

changing environmental conditions, there is little evidence in the geologic record of the 

appearance of novel genotypes in species in response to the larger climate changes associated 

with glaciations and interglacial periods. It is expected that species’ responses to climate change 

will primarily be through range shifts and extinctions rather than through evolution. 

1.2.4. Ecosystem Effects 

There is evidence that projected increases in CO2 will reduce the nutritional quality of 

leaf litter to macroinvertebrate detritivores. Reduced litter quality would result in lower 

assimilation and slower growth (Tuchman et al., 2002). While seemingly a secondary climatechange effect, changes in these processes could have food web implications: altered stream 

productivity that impacts fish and other consumers. In contrast to this, Bale et al. (2002) found 

little evidence of the direct effects of CO2 on insect herbivores and instead discuss a range of 

temperature effects (including interactions with photoperiod cues) on various life history 

processes that affect ecological relationships. 

It is not clear whether changes in nutrient loading due to climate change will have any 

effects on streams and rivers. Effects of nutrient enrichment in streams are highly variable, due 

to questions about which primary nutrient (nitrogen or phosphorus) is limiting, shading (light 

availability), water clarity, flow regime, and available substrate for periphyton growth (e.g., 

Dodds and Welch, 2000). In general, nutrient enrichment leads to changes in the algal and 

diatom community composition of a stream, and sometimes, in some streams, to increased 

production and chlorophyll concentrations, leading to changes in primary invertebrate consumers 

(e.g., Gafner and Robinson, 2007) which could cascade through the community (Power, 1990; 

Rosemond et al., 1993). 

Changes in the distribution and intensity of precipitation may induce related changes in 

nutrient loading to streams from runoff. However, it is not clear if total nutrient loading to a 

stream will change with altered precipitation. For example, increased precipitation does not 

increase nitrogen available on the land surface to run off. However, changes in precipitation 

patterns combined with other changes in land use, for example, may affect nutrient loadings. 

1-8
1.3. ORGANISMAL/BIOLOGICAL INDICATORS OF CLIMATE CHANGE 

Since organisms respond to climatic variability and trends, some of these responses may 

be useful as indicators of climate change (Figure 1-1). This section describes several candidate 

indicators based on the current literature. Section 3 identifies novel indicators and species traits 

that may be sensitive to climate change. 

Golladay et al. (2004) surveyed mussel species during drought conditions, identifying 

several low flow-sensitive species (e.g., Lampsilis straminea claibornensis, Villosa villosa, and 

Lampsilis subangulata). Other species (Pleurobema pyriforme, Mediunidus penicillatus) show 

signs of drought intolerance due to decreased DO concentrations during low flows. Mussel 

species less affected by drought-induced low flow and low oxygen levels include Elliptio 

complanata/icterina, Villosa vibex, and Villosa lineosa (Golladay et al., 2004). A comparison of 

drought intolerant to drought tolerant mussel species may be an indicator of hydrologic 

variability or drought possibly due to climate change. 

Golladay et al. (2004) suggest that wetland invertebrates could be divided into four 

response guilds to indicate hydrologic status that may be adaptable to river/stream systems: 

(1) overwintering residents that disperse passively, including snails, mollusks, amphipods, and 

crayfish; (2) overwintering spring recruits that require water availability for reproduction, 

including midges and some beetles; (3) overwintering summer recruits that only need saturated 

sediment for reproduction, including dragonflies, mosquitoes, and phantom midges; and 

(4) non-wintering spring migrants that generally require surface water for overwintering, 

including most water bugs and some water beetles. Changes in density-weighted ratios of these 

response guilds could be used as indicators of climate driven changes in hydrologic conditions 

over time. 

Monitoring changes in community composition, including shifts from cold- and coolwater dominated systems to warm-water communities, may be another good indicator. It is 

expected that cool-water and warm-water fishes will be able to invade freshwater habitats at 

higher latitudes, while cold-water fish will disappear from low latitude limits of their distribution 

where summer temperatures already reach fish maximum thermal tolerances (Carpenter et al., 

1992; Tyedmers and Ward, 2001). In east-west drainages fish may not be able to find thermal 

refuge and may experience local extinctions (Carpenter et al., 1992). However, cold-water fish 

that do persist at higher altitudes and latitudes may not experience as many winter stresses, and 

their ranges at may expand with increased duration of optimal temperatures (Carpenter et al., 

1992; Melack et al., 1997). 

Salmon species are known to prefer cold water temperatures and a number of studies 

have investigated the impact of potential climate changes on these fish species. Pacific salmon 

may be particularly sensitive to climatic changes because suitable habitat is projected to decrease 

1-9
due to altered thermal regimes (Schindler et al., 2005). Research has linked increased river 

temperatures with increased mortality of sockeye salmon, particularly in species which migrate 

during the summer when river temperatures are at their highest (British Columbia Ministry of 

Water, Land and Air Protection, 2002). Warmer waters cause increased energy use and 

bacterial/fungal infections in salmon, decreasing the likelihood that they will survive their 

migration and be equipped to spawn (British Columbia Ministry of Water, Land and Air 

Protection, 2002). Melack et al. (1997) suggest that higher temperatures will lead to reduced 

growth and increased mortality of sockeye salmon in freshwater and marine waters. In 

freshwater, Melack et al. (1997) suggest that there will be greater inputs of nutrients during the 

winter season rather than in the spring as well as a longer period of thermal stratification, which 

would likely lead to lower planktonic productivity and smaller juvenile sockeye salmon. 

However, a study in southwestern Alaska by Schindler et al. (2005) shows increased juvenile 

growth rates, because the warmer water temperatures increase the length of the growing season 

due to earlier ice breakup and increase zooplankton densities, prey for juvenile salmon. In 

marine waters, Melack et al. (1997) note that all of the growth and gathering of excess energy 

reserves is done during the time that Fraser River sockeye salmon spend in the ocean. However, 

general circulation models (GCMs) forecast increases in sea surface temperatures and weaker 

north-south pressure gradients over the north-east Pacific Ocean, which could weaken ocean 

upwelling and reduce secondary productivity (Melack et al., 1997; IPCC, 2007). The higher 

temperatures and reduced zooplankton would likely lead to smaller adult sockeye with fewer and 

smaller eggs and less energy reserves (Melack et al., 1997). In addition, the Fraser River 

sockeye salmon that Melack et al. (1997) focused on in their analysis already live at the southern 

edge of their thermal range. Melack et al. (1997) also reviewed the potential impacts of climate 

change on salmon species spawning such that increased winter flows and spring peaks may 

reduce salmonid egg to fry survival. For example, higher spring peaks in flow and warmer water 

temperatures may cause earlier emergence of fry and migration of pink and chum salmon fry to 

estuaries at a time when their food sources have not developed adequately (Melack et al., 1997). 

Similarly, low summer flow could lead to a decrease in available spawning and rearing habitat 

(Melack et al., 1997). For species that spawn in the fall, including many salmonid species, an 

increase in scouring resulting from higher precipitation rates in winter could result in the reduced 

survival of eggs (Tyedmers and Ward, 2001). 

Some research has shown that fish species living in streams and rivers in semi-arid 

regions may be more susceptible to climate impacts than species living in streams and rivers in 

sub-humid regions. Milewski (2001) found that species richness, number of insectivorous 

cyprinid (minnow) species, and number of species intolerant of degraded water quality and 

habitat were lower in the semi-arid region of their study suggesting that fish species rebound 

1-10
from low and high water levels more easily in sub-humid regions than in semi-arid regions. Poff 

and Allan (1995) also investigated hydrologic variation in streams and the impact of hydrologic 

variability on fish species. For the sites in their study, fish assemblages that were associated with 

the hydrologically variable streams had the following characteristics: generalized feeding 

strategies, association with silt and general substrata, slow velocity, headwater affinity, and 

tolerance to silt. Fish species occurring at more than 50% of the hydrologically variable sites but 

less than 50% of the stable sites included Ameiurus melas (black bullhead), Perca flavescens

(yellow perch), Notemigonus crysoleucas (golden shiner), Ameiurus natalis (yellow bullhead), 

and Lepomis gibbosus (pumkinseed). Fish species occurring only at hydrologically variable sites 

(often only one or two sites total) include Fundulus notatus (blackstripe topminnow), 

Lepisosteus osseus (longnose gar), Lepisosteus platostomus (shortnose gar), Amia calva 

(bowfin), Anguilla rostrata (American eel), and Dorosoma cepedianum (gizzard shad) (Poff and 

Allan, 1995). Fish species occurring at more than 50% of the stable sites and less than 50% of 

the hydrologically variable sites include Moxostoma macrolepidotum (shorthead redhorse), 

Micropterus dolomieu (smallmouth bass), Hypentelium nigricans (northern hog sucker), 

Rhinichthys cataractae (longnose dace), and Notropis rubellus (rosyface shiner). 

Cold-water fish species, and salmon species in particular, may be good indicators of 

climate-change effects in streams and rivers. To use a salmon species or any fish species as an 

indicator, one must be sure not to count or include fish that may have been stocked rather than 

occur naturally in a particular stream or river. Native brook trout populations may be a useful 

climate-change indicator for streams and rivers for certain regions since they often live at the 

edge of their thermal tolerance; therefore a decline in brook trout numbers in a certain area may 

be a sign of climate impacts. A decline in brook trout numbers would not always necessarily 

indicate climate effects, however, because a decline in this species could also be due to other 

stressors or even species competition. Species with widespread ranges and high thermal 

tolerance such as largemouth bass, carp, channel catfish, and bluegills would generally not be 

good indicators of climate impacts since they are relatively insensitive and their ranges extend 

south into Mexico. Another possible effect of increased water temperatures is to reduce DO 

levels in stream waters. Darter species are sensitive to benthic oxygen depletion because they 

feed and reproduce in benthic habitats (U.S. EPA, 1999), making them another potential 

indicator of climate change. 

1-11
2. STATE BIOASSESSMENT PROGRAMS—RIVERS AND STREAMS 

Aquatic organisms integrate the effects of all sources of stress that impinge on them, 

including “conventional” anthropogenic stressors, which are commonly the focus of state 

programs assessing and regulating water quality (e.g., point and non-point sources of pollutants, 

habitat alterations, landscape-level changes), and any other significant source of environmental 

change, including climate change. Because organisms reflect all sources of environmental 

disturbance to which they are exposed over time, assessments of biological communities can 

provide information that may not be revealed by measurements of concentrations of chemical 

pollutants or toxicity tests (U.S. EPA, 1999; Rosenberg and Resh, 1993; Resh and Rosenberg, 

1984). Bioassessment thus provides a means of assessing not just biological condition or health 

but also overall ecological integrity of stream and river systems. 

Their integrative characteristic makes biological assemblages effective monitoring tools, 

but it also means that all major sources of stress must be reasonably accounted in order to 

reliably attribute observed responses to particular sources of stress and to effectively regulate the 

stress and/or manage the resource. The ongoing success of biological monitoring and assessment 

programs will require an understanding of what climate-associated changes are occurring in 

monitored aquatic communities and how monitoring programs can account for them. 

Accounting for climate-change influences will support effective attainment of management goals 

using monitoring program results as a foundation. 

2.1. BIOASSESSMENTS OF RIVERS AND STREAMS 

Since the mid-1980s, the U.S. EPA has worked interactively with national, regional, and 

state agency biologists and other nationally recognized experts to develop approaches and 

technical guidance for implementation of biological assessment. Resulting guidance included 

U.S. EPA’s Rapid Bioassessment Protocols (RBPs) (U.S. EPA, 1989), which provided a 

technical framework for using benthic macroinvertebrate and fish assemblage data as a direct 

indicator of ecological health. These were updated with the additional consideration of 

periphyton communities, in 1999 (U.S. EPA, 1999). As a complement to the bioassessment 

development, procedures for developing narrative biocriteria were published in 1992 (U.S. EPA, 

1992), and for developing biocriteria for streams and rivers in 1996 (U.S. EPA, 1996). 

Following this initial focus on streams and rivers, bioassessment technical guidance was 

developed for lakes and reservoirs (U.S. EPA, 1998), estuaries, and coastal marine waters 

(U.S. EPA, 2000), and wetlands (U.S. EPA, 2002). 

2-1
Any well designed monitoring and assessment program (in this case, bioassessment) is 

inherently anticipatory in that it will provide information for present needs and those not yet 

determined (Yoder and Rankin, 1995). Programs that are adaptable to immediate and future 

needs are also cost efficient (Barbour et al., 2000). Regardless of approach, all bioassessment 

programs adhere to some basic technical elements: (1) selection and calibration of appropriate 

biological indicators, (2) determination of reference condition or benchmarks for assessment, and 

(3) use of standardized protocols that maximize the information on the indicators, optimize gear 

efficiency, and minimize variability due to sampling error (Barbour et al., 2000). 

Biological indicators are considered the best overall measure of ecological integrity from 

multiple stressors, because of their continuous exposure to magnitude, frequency, and duration to 

the synergistic effects of chemical and non-chemical stressors; therefore, these indicators need to 

be calibrated on a regional basis and possess a range of sensitivity to the various stressors, 

including climate change. Section 2.2 addresses the more common and relevant components of 

bioindicators. 

Reference conditions are established in various ways (U.S. EPA, 1996). However, the 

use of actual reference sites in a regional population of minimally disturbed sites is ideal for 

calibrating a quantitative means of assessing ecological condition. The influence of climate 

change will affect the maintenance of stable reference conditions. A gradient of degradation of 

reference sites over time is plausible, and it is an important factor for establishing a credible 

bioassessment. Bioassessment programs throughout the U.S. have established viable reference 

conditions for assessment. Many programs also establish sentinel sites that are assessed during 

each monitoring cycle. The continued monitoring of sentinel sites within the reference 

population will be important to identify where on the condition gradient a set of reference sites 

may be for a state or tribal program. 

Standardized protocols are a feature of all bioassessment programs. However, these may 

vary among agencies, and they are not necessarily comparable between jurisdictions. As the 

effects of climate change upon bioassessment programs are better described, modification of 

protocols to capture more sensitive indicators or to collect specific attributes of established 

indicators may be necessary. 

2.2. BIOINDICATORS USED IN STATE PROGRAMS—RIVERS AND STREAMS 

The choice of bioindicators has some commonality throughout the U.S. Benthic 

macroinvertebrates are the most common assemblage used for bioassessment in streams and 

rivers among the states and tribes (U.S. EPA, 2002). Fish assemblages are the second most 

prevalent assemblage used to assess biological condition. The U.S. EPA recommends the use of 

multiple assemblages in programs to increase the robustness of the overall bioassessment 

2-2
(U.S. EPA 1996). Periphyton or algae is of interest to many states as an added assemblage for 

use in their monitoring and assessment program because of their sensitivities to stressors. 

Tables 2-1 and 2-2 list the common metrics, which are measures of change in features or 

attributes of the structure and/or function of the assemblage due to exposure to stressors, for both 

benthic macroinvertebrates and fish. These metrics generally respond to various stressors in 

different manners. The sensitivity to climate change is known, in a general sense, for some of 

these attributes. Further study is needed to characterize signature responses to climate change 

for specific use in bioassessment programs around the country. The aggregation of a series of 

metrics into a biological index provides the primary measure of overall attainment of the desired 

biological condition. However, certain bioassessment programs (e.g., Maine DEP, Oregon 

DEQ) use discriminant or predictive models as primary bioindicators, which may provide a 

different dimension of climate-change sensitivity. 

2-3
Table 2-1. Table of benthic macroinvertebrate metrics taken from the Rapid 

Bioassessment Protocols (U.S. EPA, 1999) 

Category Metric Definition

Predicted 

response to 

increasing 

perturbation

Total number of taxa Measures the overall variety of the 

macroinvertebrate assemblage 

Decrease 

Number of EPT taxa Number of taxa in the insect orders 

Ephemeroptera (mayflies), Plecoptera 

(stoneflies), and Trichoptera (caddisflies) 

Decrease 

Number of 

Ephemeroptera taxa 

Number of mayfly taxa (usually genus or 

species level) 

Decrease 

Number of Plecoptera 

taxa 

Number of stonefly taxa (usually genus of 

species level) 

Decrease 

Richness measures 

Number of Trichoptera 

taxa 

Number of caddisfly taxa (usually genus or 

species level) 

Decrease 

% EPT Percent of the composite of mayfly, stonefly, 

and caddisfly larvae 

Composition Decrease 

measures 

% Ephemeroptera Percent of mayfly nymphs Decrease 

Number of intolerant taxa Taxon richness of those organisms considered 

to be sensitive to perturbation 

Decrease 

% Tolerant organisms Percent of macrobenthos considered to be 

tolerant of various types of perturbation 

Increase 

Tolerance/ 

intolerance 

measures 

% Dominant taxon Measures the dominance of the single most 

abundant taxon. Can be calculated as dominant 

2, 3, 4, or 5 taxa. 

Increase 

% Filterers Percent of the macrobenthos that filter FPOM 

from either the water column or sediment 

Feeding measures Variable 

% Grazers and scrapers Percent of the macrobenthos that scrape or 

graze upon periphyton 

Decrease 

Habit measures Number of clinger taxa Number of taxa of insects Decrease 

% Clingers Percent of insects having fixed retreats or 

adaptations for attachment to surfaces in 

flowing water 

Decrease 

2-4
Table 2-2. Fish metrics used in various bioassessment programs.a

 Table 

adapted from the Rapid Bioassessment Protocols (U.S EPA, 1999) 

Category Metric Definition

Predicted 

response to 

increasing 

perturbation

Total number of species Measures the overall variety of the fish 

assemblage 

Decrease 

Number of native fish 

species 

Those species of fish that are indigenous Decrease 

Number of salmonid age 

classesb

Measures the life stage representation of 

particular top predators in coldwater systems 

Decrease 

Number of darter species Diversity of darters, which are typically in fast 

flowing waters and cobble substrate 

Decrease 

Number of sculpin species Normally coldwater bottom feeders Decrease 

Number of benthic 

insectivore species 

Those species that depend on aquatic insects for 

primary food source 

Decrease 

Number of darter and 

sculpin species 

Combination of clean-water forms, mostly in 

coldwater systems 

Decrease 

Number of darter, sculpin, 

and madtom species 

Combination of key taxa that represent 

important structure of fish assemblage in 

certain systems 

Decrease 

Number of salmonid 

juveniles (individuals)b

Density of juvenile salmon intended to evaluate 

nursery function 

Decrease 

% round-bodied suckers Warm-water species of suckers representative 

of good quality bottom feeders 

Decrease 

Number of benthic species Diversity of feeders of all benthic fauna, 

including insects and non-insects 

Decrease 

Number of sunfish species Warm-water pelagic species representative of 

good water quality and habitat 

Decrease 

Number of cyprinid 

species 

Diversity of minnows that include a range of 

tolerance 

Decrease 

Number of water column 

species 

Indicative of good quality pools and migration 

routes 

Decrease 

Number of sunfish and 

trout species 

Combination of species representing good 

water and habitat quality 

Decrease 

Number of salmonid 

species 

Diversity of salmon in coldwater systems able 

to accommodate a variety of top carnivores 

Decrease 

Richness 

measures 

Number of headwater 

species 

Diversity in generally depauperate systems Decrease 

2-5
Table 2-2. continued. 

Category Metric Definition

Predicted 

response to 

increasing 

perturbation

Number of sucker species Diversity of all suckers—round-bodied and 

other 

Richness Decrease 

measures 

(continued) Number of sucker and 

catfish species 

Combination of suckers and catfish in warmwater systems to be indicative of healthy 

systems 

Decrease 

Number of 

intolerant/sensitive 

species 

Diversity of sensitive fish species; may be 

stressor dependent 

Decrease 

Presence of brook trout Indigenous to many areas of the Midwest and 

threatened by competition of other species 

Decrease 

% stenothermal cool and 

cold water species 

Narrow temperature tolerance of coldwater taxa Decrease 

% of salmonid individuals 

as brook trout 

Compositional dominance of brook trout to 

other salmonids 

Decrease 

Number of green sunfish Tolerant of warm-water sunfish that becomes 

dominant as other taxa decline 

Increase 

% common carp Tolerant bottom feeder Increase 

% white sucker Tolerant bottom feeder Increase 

% tolerant species Compositional dominance of all tolerant 

species 

Increase 

% creek chub Tolerant minnow species Increase 

% dace species Tolerant minnow species Increase 

Tolerance/ 

intolerance 

measures 

% eastern mudminnow Tolerant minnow species Increase 

% omnivores No particular food preference Increase 

% generalist feeders Generalist feeders, able to deal with a variable 

diet 

Increase 

% insectivorous cyprinids Minnows that prefer aquatic insects as primary 

diet 

Decrease 

% insectivores All fish that prefer aquatic insects Decrease 

% specialized insectivores Highly specialized in food preference and 

easily affected by decrease in food availability 

Decrease 

% juvenile trout Indicative of food source able to support 

nursery function of juvenile trout 

Decrease 

% insectivorous species Composition of taxa with preference for aquatic 

insects 

Decrease 

Trophic measures 

% top carnivores Composition of taxa that prey on other fish and 

non-fish higher trophic levels 

Decrease 

2-6
Table 2-2. continued. 

Category Metric Definition

Predicted 

response to 

increasing 

perturbation

Trophic measures 

(continued) 

% pioneering species Those species that occur early in succession of 

an ecosystem, and are usually very tolerant 

Increase 

Number of individuals (or 

catch per effort) 

Relative measure of density of fish in 

ecosystem related to amount of effort to sample 

the fish assemblage 

Decrease 

Density of individuals Density regardless of effort Variable 

% abundance of dominant 

species 

Dominance versus evenness of taxa in fish 

assemblage 

Increase 

Effort measures 

Biomass (per m2

) Relative measure of ability to sustain healthy 

fish assemblage through food availability and 

good habitat 

Variable 

% hybrids Measures breakdown of distinct reproductive 

guilds usually due to habitat alteration 

Increase 

% introduced species Intentionally or non-intentionally taxa 

introduced into ecosystem and competitive or 

predatory upon native taxa 

Increase 

% simple lithophills Composition of individual fish that spawn in 

clean sand or gravel 

Decrease 

% simple lithophills 

species 

Composition of species as lithophills Decrease 

% native wild individuals Measure of relative reproductive success for 

native taxa 

Decrease 

Reproduction 

measures 

% silt-intolerant spawners Need for clean substrate of larger particles than 

silt; affected by sedimentation processes 

Decrease 

Disease measures % Diseased individuals 

(deformities, eroded fins, 

lesions, and tumors) 

Chronic exposure to stressors resulting in some 

form of disease or deformation that may result 

in lethal conditions 

Increase 

a

 Data from Karr et al. (1986), Leonard and Orth (1986), Moyle et al. (1986), Fausch and Schrader (1987), Hughes 

and Gammon (1987), Ohio EPA (1987), Miller et al. (1988), Steedman (1988), Simon (1991), Lyons (1992), 

Barbour et al. (1995), Simon and Lyons (1995), Hall et al. (1996), Lyons et al. (1996), Roth et al. (1997). b

 Metric suggested by Moyle et al. (1986) or Hughes and Gammon (1987) as a provisional replacement metric in 

small western salmonid streams. 

Note: X = metrics used in region. Many of these variations are applicable elsewhere. 

2-7
3. SENSITIVITY TO CLIMATE CHANGE OF BIOLOGICAL INDICATORS 

USED IN STATE BIOCRITERIA PROGRAMS 

The sensitivity of common biological indicators to climate change is not very well 

known. The review of relevant literature presented in Section 1.2, as well as in this section, 

helps establish expectations for the significant modes of effect and probable categories of 

responses. However, important details regarding (1) the sensitivity or robustness of specific 

metrics or ecological attributes to changing climate parameters over time and among different 

regions; (2) the mechanisms by which specific responses will interact with other stressors and 

impact interpretation of effects and their causes; and (3) how such responses might combine to 

alter biological index responses are recommended as components of needed research (see 

Section 7). 

To understand probable climate-change effects on stream/river biological indicators, the 

linkage between climate and stream/river ecology must be defined. Anthropogenic increases in 

greenhouse gases directly affect air temperature and precipitation (considered primary climate 

drivers). Climate-change projections for the year 2100 include global average air temperature 

increases of 1.1–2.9°C (for the lowest emissions scenario) to 2.4–6.4°C (for the highest 

emissions scenario) (IPCC, 2007). Increases in precipitation are predicted for many regions, 

with a higher percentage of total precipitation occurring in more frequent and intense storms. 

Other predictions include more precipitation in winter and less precipitation in summer; more 

winter precipitation as rain instead of snow; earlier snow-melt; earlier ice-off in rivers and lakes; 

and longer periods of low flow and more frequent droughts in summer (Hayhoe et al., 2007; 

IPCC, 2007; Barnett et al., 2005; Fisher et al., 1997). 

Changes in these primary climate drivers will affect stream/river water and aquatic-life 

resources mainly through direct and indirect alterations in hydrologic and thermal regimes. 

Changes in hydrologic regime (including magnitude, timing, duration and frequency of runoff 

events) will vary regionally (NAST, 2001), but they are expected to include changes in the 

magnitude of flow ranging from increases of 10–40% in the northeastern U.S. to decreases in 

annual flow of 10–30% in the South, Midwest, and West (Hayhoe et al., 2007; Milly et al., 2005; 

Magnuson et al., 1997). Changes in patterns of flow will likely include increases in stream flow 

occurring mainly in the winter and spring, lower stream flow in the summer and fall, and greater 

variability and “flashiness” of stream flows (Hayhoe et al., 2007; Moore et al., 1997). These 

projected alterations in stream flow dynamics are critical in structuring aquatic ecosystems 

through influence on sediment supply and transport, habitat stability, channel formation and 

maintenance, and water volume, which, in part, controls habitat availability and water quality 

(Poff et al., 2002; Richter et al., 1996; Poff et al., 1996; Poff and Allan, 1995). Seasonal patterns 

3-1
of flow and other flow dynamics strongly influence the types of species that can inhabit an area, 

defining the composition, structure, and functioning of aquatic assemblages (Poff et al., 2002; 

Richter et al., 1996; Poff and Allan, 1995). As a result, climate-associated changes in streamflow magnitude are expected to modify habitat, species composition and abundance, and 

ecological interactions over time. 

Stream/river water temperature regimes will be altered by air temperature increases and 

modified by other influences including variations in flow volume and snow melt, groundwater 

influence, riparian shading, presence of deep pools, meteorology, river conditions, and 

geographic setting (Cassie et al., 2006; Mohseni et al., 2003; Daufresne et al., 2003; 

Hawkins et al., 1997). Thermal regime influences the distribution and abundance of aquatic 

species in relation to temperature tolerances and evolutionary adaptations combined with 

competitive interactions, effects on food supply, and other factors; it also drives timing of life 

cycle events (phenology), biological productivity, and species interactions (e.g., Matthews, 1998; 

Hawkins et al., 1997; Vannote and Sweeney, 1980; Sweeney and Vannote, 1978). 

As discussed in Section 2.2, common metrics monitored as biological indicators in 

existing bioassessment programs are measures of change in features or attributes of the structure 

and/or function of the macroinvertebrate or fish assemblages; Tables 2-1 and 2-2 summarize 

many of the widely applied categories of biological indicators. Additional research will provide 

information on specific sensitivities of individual biological indicators to climate change. 

However, taken by category of metric, expectations for probable responses of various biological 

indicators to climate change can be summarized from literature information and projections of 

future climate changes (see Sections 1.2, 1.3, and this section). Table 3-1 summarizes expected 

climate change responses by category (Note: this summary of potential responses represents 

examples and is not considered comprehensive. Categorization as sensitive or tolerant refers 

generally to anticipated climate-change sensitivity, in particular to temperature and hydrologic 

changes). 

It is clear that many of the types of responses that can be expected for common categories 

of biological indicators in response to climate change can be similar to changes caused by other 

conventional stressors. For biological indicators that are sensitive to both conventional stressors 

and climate change, the confounding interactions of climate change and other stressor effects 

will influence the process of attributing cause to particular stressors. This interaction will require 

the development of an approach to partition observed responses between climate change and 

other stressors, so that the ability to manage resources and regulate water quality through the 

process of monitoring and assessing biological indicator data remains viable. 

Conceptually, this approach can include adaptations of monitoring methods in order to 

account for climate change. Preliminary aspects of this component are discussed in Section 4 

3-2
Table 3-1. Summary of expectations for responses of common categories of 

stream and river biological indicators to climate change influences on water 

temperature and hydrologic regime 

Category Expected climate change effects/sensitivities References 

Macroinvertebrates 

Richness and 

abundance 

measures 

Overall richness generally expected to decline due to 

temperature sensitivity and hydrologic stresses including 

increased flashiness, increased instances of summer low 

flows, drought, etc. However, replacements over time 

with tolerant forms may ameliorate this in some 

situations. Abundance or eurytolerant species may 

increase in some habitats. 

Durance and Ormerod, 

2007; Bradley and 

Ormerod, 2001 

Community 

composition, 

persistence 

measures 

Compositional changes resulting from reductions in 

temperature and/or flow sensitive taxa (examples 

potentially include Chloroperla, Protoneumura, 

Neumura, Rhyacophila munda, Agabus spp,, 

Hydrophilidae, and Drusus annulatus) and increases in 

less temperature and/or flow sensitive taxa (examples 

potentially include Athricops, Potamopyrgus, 

Lepidostoma, Baetis niger, Tabanidae, Hydropsyche 

instabilis, Helodes marginata, Caenis spp.), and/or from 

shifts in range ; patterns of persistence or community 

similarity that track climatic patterns; changes may also 

occur in functional roles of species. 

Daufresne et al., 2003; 

Durance and Ormerod, 

2007; Bradley and 

Ormerod, 2001; 

Burgmer et al., 2007; 

Golladay et al., 2004; 

Parmesan, 2006; 

Hawkins et al., 1997 

Tolerance/ 

intolerance 

measures 

Climate-change sensitivities related to temperature or 

flow regime may be documented as decreases 

(potentially resulting from local extinctions and/or range 

shifts) in richness (number of taxa) of temperature or 

flow-regime sensitive groups (see “Composition 

Measure” for examples). Dominance by tolerant taxa 

also may increase. 

Daufresne et al., 2003; 

Durance and Ormerod, 

2007; Burgmer et al., 

2007; Golladay et al., 

2004; Parmesan, 2006 

Feeding measures Variable responses expected, driven by interactions 

between temperature, which may increase phytoplankton 

and periphyton productivity and thus increase associated 

feeding type; hydrologic factors which may decrease 

periphyton if habitat stability is decreased or 

sedimentation is increased; CO2 concentrations, which 

can directly affect leaf litter composition and 

decomposition; and changes in riparian vegetation. 

Gafner and Robinson, 

2007; Dodds and 

Welch, 2000; Tuchman 

et al., 2002 

Habitat measures Number and percent composition of clingers likely to 

decrease if hydrologic changes decrease habitat stability, 

increase embeddedness, or decrease riparian inputs of 

woody vegetation. 

Johnson et al., 2003; 

Townsend et al., 1997 

3-3
Table 3-1. continued. 

Category Expected climate change effects/sensitivities References 

Fish 

Richness and 

abundance 

measures 

May have initial increase in diversity as more warmwater assemblages replace cool- or cold-water species. 

Habitat availability is expected to be diminished by 

altered flow regimes with an associated loss of diversity. 

If barriers to dispersal limit community replacements, 

richness also may decline. May also, for example, lose 

spring spawners (e.g., some salmon species) due to 

changes in timing of spring flows. Abundance of warmwater species may increase, while coldwater species may 

decrease. 

Xenopoulos and Lodge, 

2006; Xenopoulos et 

al., 2005; Poff et al., 

2002; Grimm et al., 

1997; Hayhoe et al., 

2007; Wehrly et al., 

2003 

Composition 

measures 

Expect fish community compositional changes resulting 

from losses of cold- and/or cool-water fishes (e.g., brook 

trout, dace and bleak), and increases in warm-water 

fishes (e.g., chub and barbell). 

Daufresne et al., 2003; 

Mohseni et al., 2003; 

Schindler, 2001; 

Covich et al., 1997; 

Moore et al., 1997; 

Rahel et al., 1996, 

Eaton and Scheller, 

1996 

Tolerance/ 

intolerance 

measures 

Loss of temperature-sensitive cold- and cool-water 

species will decrease intolerant measures, increase 

tolerant measures. 

Mohseni et al., 2003; 

Moore et al., 1997; 

Rahel et al., 1996; 

Eaton and Scheller, 

1996 

Feeding measures Shift in food sources through attrition of lower trophic 

levels will affect higher trophic levels, including top 

carnivores. 

Schindler et al., 2005; 

Melack et al., 1997 

Habitat measures Breakdown of habitat features and connectivity fosters 

hybridization and drift in species gene pool. 

Matsubara et al., 2001; 

Heggenes and Roed, 

2006 

3-4
(below), to the extent that they were addressed in this preliminary case study. Another aspect of 

program adaptation is restructuring the analytical approach used to evaluate biological 

monitoring data, detect impairment, and assess cause (see preliminary case study results 

discussed in Section 5). These monitoring components—sampling strategy and analytical 

approach—are clearly inter-related, and implicitly include components such as tracking changes 

at reference locations through both altered sampling design and appropriate analyses. 

Another component that is being considered for its potential contribution to tracking and 

differentiating climate-change effects from other stressors is the categorization of biological 

indicators based on differing sensitivities to these effects. These indicators include both 

community metrics and population measures of individual sensitive species (see Table 3-1). In 

concept, there would be analytical and interpretive advantages if at least some biological 

indicators could be identified that are especially sensitive to particular conventional stressors but 

insensitive to climate-change effects. Conversely, community metrics and individual taxa that 

are specifically sensitive to climate change would be valuable in identifying and defining trends 

at reference sites. These could be applied analytically to separate monitored biological responses 

into components related to long-term, climate-change effects and other stressors. Such 

separation is the major goal of efforts to adapt bioassessment programs to account for climate 

change. 

In practice, evidence gathered from the literature and the professional opinions of many 

state/tribal bioassessment managers1 suggests that few, if any, biological indicators currently 

used in bioassessment programs are likely to be insensitive to climate-change effects. This is 

largely because climate change affects aquatic communities through the critical ecological 

drivers of flow dynamics (hydrology) and water temperature. Thus, the modes of action of 

climate-change effects and effects of other stressors are similar in many cases, and taxa that are 

sensitive to conventional stressors are likely to be sensitive to climate change as well. Taxa 

identified in the Workshop as being “potentially insensitive to climate change” were mainly 

those species already characterized as being broadly tolerant, “weedy,” and/or generalist species. 

Beyond categorization of existing biological indicators as sensitive/insensitive to climatechange effects, there are biological metrics that could be considered for incorporation into 

bioassessment programs that are not currently measured on a routine basis in most existing 

programs. Such “novel” indicators are considered specifically because of their sensitivity to 

climate-change effects—most have been predicted or observed in the literature as biological 

responses to directional climate change, especially increases in water temperature. Table 3-2 

summarizes examples of such “novel” biological indicators. 

 

1

 Hamilton, A; Barbour, M; Gerritsen, J; et al. (2007) Introductory workshop on climate change effects on biological 

indicators: workshop summary report. 

3-5
Table 3-2. Novel indicators that may be sensitive to climate change 

Category Metric Comments References 

Early emergence of mayfly 

species (also stonefly and 

caddis species) 

Indirect effects on timing of 

salmonid feeding regime 

Harper and 

Peckarsky, 2006; 

Briers et al., 2004; 

Gregory et al., 2000; 

McKee and 

Atkinson, 2000 

Early trout spawning in 

warmer water 

Cooney et al., 2005 

Phenology 

Accelerated development and 

earlier breeding of the 

amphipod Hyallela azteca

Hogg et al., 1995 

Increased algal productivity In northern areas a response 

to decreased ice cover and 

increased light penetration 

Longer Flanagan et al., 2003 

growing 

season 

Additional reproductive 

periods of amphipod species 

Hogg et al., 1995 

Altered sex ratios for certain 

insects (e.g., trichopteran 

Lepidostoma) 

Hogg and Williams, 

1996 

Smaller size at maturity and 

reduced fecundity of 

plecopteran Nenoura 

trispinosa and amphipod

Hyallela azteca

From increased temperature Turner and Williams, 

2005; Hogg et al., 

1995 

Life stagespecific 

Decreased salmon egg to fry 

survival 

Increased turbidity from 

eroded sediment due to 

increased precipitation 

Melack et al., 1997 

Reduced size of sockeye 

salmon 

Reduced growth and 

increased mortality in higher 

temperatures as well as to 

lower plankton productivity 

Melack et al., 1997 

Increased growth rate of 

juvenile salmon in Alaska 

Schindler et al., 2005 

Temperature 

sensitivity 

Decreased growth rate of trout Jensen et al., 2000 

3-6
Table 3-2. continued. 

Category Metric Comments References 

Decreased survival of eggs of 

autumn-spawning salmon 

(e.g., dolly varden, brook 

trout, coho salmon) 

Results in decreased 

abundance of autumnspawning species, and/or 

change in relative 

composition between spring 

and autumn spawners 

Gibson et al., 2005 

Decreased fry survival of pink 

and chum salmon due to 

earlier (late winter to early 

spring) peak flows 

Earlier emergence and 

migration of pink and chum 

salmon fry to estuaries at a 

time when their food sources 

have not developed 

adequately 

Melack et al., 1997 

Hydrologic 

sensitivity 

Differential mortality of 

drought-intolerant mussel 

species (e.g., Lampsilis 

straminea claibornensis, 

Villosa villosa, Lampsilis 

subangulata)

Results in changes in relative 

abundance, extirpation of 

vulnerable species 

Golladay et al., 2004 

3-7
One consideration that must be taken into account in the ongoing evaluation of potential 

novel indicators and their role in adaptation of bioassessment programs is that many of these 

metrics are more difficult or time- and resource-consuming to measure, especially on a routine 

basis. Some of them also require sampling techniques and timing or frequency of sampling that 

are quite different from the commonly applied bioassessment approaches. For example, the 

process of measuring sizes of all individuals of one or more species of mayflies, stoneflies, or 

caddisflies (representative EPT taxa) to establish size-class composition and evaluate reduction 

in size of the last instars (i.e., the last nymphal stage just before emergence) and how this 

changes over time to define climate-change effects; or similarly, the sampling of emerging adult 

insects that would be needed to evaluate earlier emergence, are not commonly done. Another 

consideration for future evaluation of novel indicators is their potential sensitivity to other 

(conventional) stressors, in addition to their responsiveness to climate change. This will affect 

how they might be incorporated into a monitoring design and analysis approach. 

Having given a summary of climate-change effects, an overview of state bioassessment 

and biocriteria programs, and a framework for considering the sensitivities of established and 

novel biological indicators to climate change, preliminary consideration can be given to aspects 

of possible vulnerabilities of biological assessment programs to climate change. This report first 

addresses the sampling power needed to detect climate-change effects using current indicators. 

Secondly, the report describes how climate change may affect reference and non-reference sites 

differently. The report includes case studies using data from one program to illustrate these 

preliminary results obtained from implementation of the proposed framework. 

3-8
4. CASE STUDY 1—ASSESSING TRENDS: THE POWER OF BIOLOGICAL 

ASSESSMENTS TO DETECT CLIMATE CHANGE 

The ability to account for climate change requires an understanding of how vulnerable 

monitoring data are to climate change effects and how effectively differences that are a result of 

climate change can be detected within existing monitoring programs. This case study describes 

(1) how much sampling would be needed to distinguish expected levels of climate change 

effects, and (2) how long it would take to detect climate change effects with a specified 

probability of detection, given a particular monitoring framework. The information summarized 

in this section highlights the approach, the key results, and the main conclusions of Case Study 1. 

4.1. OBJECTIVES 

The main objective of this case study is to evaluate one aspect of the vulnerability of 

biological monitoring and biocriteria programs to climate change with respect to the effects on 

ecological communities. This case study focuses on the ability of a typical bioassessment 

program to detect expected climate change effects on one selected community component, taxon 

richness. The focus is on two questions: 

• How long must monitoring be conducted to have a fixed probability of detecting a change 

in the mean native taxon richness of the reference site population? 

• How long must monitoring be conducted to have a fixed probability of detecting a change 

in mean native taxon richness for a particular site? 

The first question is important because most states use reference populations as the basis 

for constructing indices and deriving biocriteria. The second is important because many 

individual sites are tracked for specific regulatory reasons (permitting, restoration, etc.). 

4.2. ANALYSIS APPROACH 

The questions in this study are approached by evaluating the ability, or power, of a 

typical biological monitoring program to detect expected levels of change in a particular 

biological attribute—in this case species richness. Statistical power, the ability to detect a real 

effect, is a critical issue in designing monitoring programs and is expressed as a probability. The 

more power a test has, the more likely one is to correctly infer that a real change has actually 

occurred. In this case study, the power analysis approach is used to evaluate how much of a 

change in taxon richness (the effect size or minimal detectable difference) can be detected in a 

typical biomonitoring program. This detectable difference can then be compared to expected 

4-1
taxa loss from climate change to see how long (in years) the program must monitor to ensure 

detection (at a set probability level) of a taxa loss signal resulting only from climate change. 

4.2.1. Ability to Detect Change–Power Analysis 

Power is defined as the probability of rejecting a false null hypothesis and is related to 

type II error (β), which is the probability of accepting a null hypothesis (no change) when it is 

false (there is change). Thus, power = 1-β. Power analysis requires several critical components: 

• sample size (N) 

• variability in an observed factor (taxon richness in this case) (σ) 

• effect size (how much of a change one wants to detect) (δ) 

• significance level (type I error, α) 

For this application of power analysis, the desired level of power (1 - β) must be fixed as well. 

This case study demonstrates how changing some of these components can increase or decrease 

the ability to detect a climate change effect. 

The equations for calculating effect size for comparing two paired population means can 

be found in many statistical textbooks. The basic formula, assuming normally distributed 

populations, is: 

n

ZZ σ

δ α β ×+ = − − ( ) 12/1

 (Eq. 1) 

where Zα and Zβ are the z-scores (probability levels) for the desired type I (α) and type II (β) 

error rates.2

 

For this case study, variance (σ

2

) is estimated using existing monitoring data for sites 

sampled repeatedly over several years during an index period. Such data give an estimate of the 

natural variability in biological condition through time, assuming minimal external changes. 

Knowledge about what taxa changes (effect size) might be expected in response to climate 

change is also needed. This value was derived from existing literature on taxa loss in relation to 

temperature changes (see Section 4.2.3). 

For each of the two assessment questions (see Section 4.1), different confidence levels 

were investigated (i.e., α and β were varied). The effects of sample size were also investigated 

 

2

 Source: Snedecor and Cochran (1980). 

4-2
for both questions. To address the first question of detecting change in a reference population, 

the number of sampling sites, N, was either fixed per year (at N = 40 for the reference 

population) or cumulative (N increasing by 40 each year for the reference population) and 

assumed a comparison of year one versus a cumulative pooled sample size through time. For the 

second question (detecting a change at a particular site), N was either fixed at one of three 

different levels (N = 5, 10, and 20) or N was cumulative, increasing by 5, 10, or 20 for the 

respective analysis runs. 

Variance estimates were derived from one particular state bioassessment data set (see 

Section 4.2.2 below) and were then used for each region evaluated. Z scores were varied by 

changing the type I and type II error rates to illustrate the effects of these choices on the ability to 

detect a climate change effect. The outputs from these analyses are time series of taxa loss rates 

predicted from climate change effects. These outputs are compared with minimal detectable 

effect sizes to illustrate the length of time required to detect a climate change effect on taxon 

richness under various conditions (taxa loss rates, temperature scenarios, and error rates). 

4.2.2. The Maryland Biological Stream Survey Data Set 

For this case study, variance (reflecting natural variability in biological condition over 

time) is estimated using existing monitoring data from the Maryland Biological Stream Survey 

(MBSS) (Boward et al., 1999; URL: http://www.dnr.state.md.us/streams/mbss/). The MBSS 

program includes statewide monitoring of all watersheds using a multi-stage probability based 

design with a 5-year rotating basin sampling approach. It also includes repeated annual sampling 

at a series of 28 fixed reference (“sentinel”) sites using the same sampling methods. It is this 

repeat sampling of benthic macroinvertabrates and fish at sentinel sites that was used to estimate 

population variance (σ

2

) in taxon richness. While this case study compares expected effects 

across various regions of the U.S., the MBSS derived variance was used for all regions as the 

estimate of variance associated with biomonitoring. 

Relative variability of taxon richness is assumed to be constant over time. However, this 

is likely not true as both the mean and variation in biological condition of sites may change with 

warming water temperatures. For simplicity, it is assumed that the variance in taxon richness 

associated with the MBSS program can be extrapolated in time and across different regions. 

MBSS data (Boward et al., 1999; URL: http://www.dnr.state.md.us/streams/mbss/) are 

available with uniform collection methods for the period 1994–2004, with sentinel site data 

available from 1998–2004. The 6-year period for which sentinel site data are available 

fortuitously includes both a dry and a wet climate cycle. Sampling for the MBSS is conducted 

during index periods—the spring reproduction/recruitment period for benthic macroinvertebrates 

(March–early May), and the summer-fall low-flow period for fish (June–September). A wide 

4-3
range of physical, chemical, and habitat variables are measured and/or calculated in association 

with the biological collections. These include water chemistry variables (e.g., temperature, pH, 

DO concentration, various nutrient concentrations, conductivity); numerous physical habitat 

variables (e.g., the Maryland physical habitat index (PHI), instream habitat condition, epifaunal 

substrate, water velocity, water depth, embeddedness, shading, distance to road, riffle quality, 

etc.); and land use characteristics (17 detailed categories aggregated to the larger categories of 

agriculture, urban, water, wetland, barren, and forest). 

Benthic macroinvertebrates are collected using D-nets, employing a multi-habitat 

approach over a 75-m reach. A 100-organism subsample is processed for each sample. 

Identifications are made to the genus level. Fish are collected using quantitative, double-pass 

electrofishing in 75-meter stream segments, with a blocking net at the end of the segment. Fish 

are identified to the species level (Boward et al., 1999). 

4.2.3. Information on Taxa Loss Rates 

This study focuses on climate change effects associated with temperature because (1) the 

goal is to demonstrate a process for calculating the capacity of a program to detect change and 

not to predict all the effects of climate change and (2) while few data exist on climate change 

effects on aquatic assemblages in general, there are more data on temperature effects than 

hydrologic effects. 

Predicted macroinvertebrate taxa loss rates due to temperature increases were derived 

from the literature based on observed changes in taxon richness associated with temperature 

increases. For this study, native or expected taxon richness is considered rather than total 

richness; species replacement is not considered. Total richness may not change if species 

replacement rates are high. For example, it is possible that stenothermal species (those with a 

narrow range of temperature tolerance) that are lost will be replaced over time by eurythermal 

taxa (those with wide temperature tolerances). However, native taxa are expected to be lost from 

many streams (e.g., Parmesan, 2006; Xenopolous et al., 2005; Moore et al., 1997), and native 

taxon richness based on current climate will decrease. 

Other ecological responses are expected but are not considered in this study: density 

changes, range shifts, timing changes in important life history stages and phenology, 

morphological changes, physiological changes, and behavioral changes, and gene frequencies 

changes (Parmesan, 2006; Root et al., 2003; Walther et al., 2002; Hogg et al., 1998; Schindler, 

1997). Taxon richness, a very common component metric evaluated in bioassessment programs 

and incorporated in multimetric indices, is evaluated for signs of bioassessment program 

vulnerability. 

4-4
Macroinvertebrate and fish taxa (i.e., genus or species, reflecting practical taxonomic 

limitations) loss rates were obtained from literature reporting on climate-change associated 

temperature effects on taxon richness (Daufresne et al., 2003), and on thermal discharge effects 

(Gammon, 1973; Lehigh University, 1960) using linear projections (estimating average species 

loss per unit of temperature change). Given the complexity of biotic interactions as well as 

temperature effects, this is probably not an accurate representation of reality. In reality, taxa loss 

rates are likely to occur episodically over time, for instance when particular species thresholds 

are reached. For example, loss of keystone taxa may precipitate abrupt and dramatic changes on 

stream communities as well as on stream processes (Flecker, 1996; Pringle et al., 1993; Power, 

1990; Power et al., 1985). In addition, the amount of decrease and range of variation around 

expected decreases may also be affected by differences in the size of the species pool, e.g., 

species-rich sites could lose more taxa, and/or show greater variation in loss rates. On the other 

hand, small losses from locations with a naturally poor fauna may be ecologically more 

significant. However, the simplifying assumption allows us to model changes into the future. 

Based on the limited information currently available in the literature, the high and low native 

taxa loss rates used are simply to bracket a range to estimate detection ability. The ecological 

implications and relative importance of being able to detect changes in taxa will still vary 

between sites. 

Daufresne et al. (2003) observed a loss of 7 macroinvertebrate taxa in streams associated 

with a 1.5oC increase over the period 1980–1999. This equals a loss rate of roughly 4.6 taxa per 

°C and is being considered the high taxa loss rate. A second estimate was derived from the 

literature associated with thermal discharge studies associated with the CWA 316 program. 

Most of the 316(a) studies focused on fish effects, and, out of those studies, many were 

physiological. One study (Lehigh University, 1960) included macroinvertebrate effects and this 

study found a loss rate of approximately 1 taxon per °C over the range 22–28°C; this is the low 

taxa loss rate. This represents a fairly high thermal range, but these studies were designed to 

investigate effects of thermal effluent, not effects of climate change. Nevertheless, the results 

are considered applicable. There are likely more 316(a) studies with invertebrate data, but these 

individual studies, for the most part, are not published in standard scientific citation databases 

and can be hard to locate (but see the Energy Citations Database 

[http://www.osti.gov/energycitations/index.jsp]). 

Predicted fish taxa loss rates were also considered from the thermal discharge literature. 

A study of thermal effluent on the Wabash River found a loss rate of 3.6 fish taxa per °C increase 

in temperature (Gammon, 1973). This may be on the high side for loss rates, but it was one of 

the few data-based values found within a temperature range comparable to climate change 

projections. 

4-5
4.2.4. Prediction of Expected Taxa Losses with Projected Temperature Increases 

Estimates of taxa loss rates were coupled with projected temperature increases to model 

the expected rate of taxa loss per year due to climate change. Projected temperature increases 

due to climate change for each region of the U.S. were taken from the National Assessment 

Synthesis Team (NAST) summary report (2001). NAST (2001) relied mainly on results from 

two coupled atmosphere/ocean general circulation models (AO-GCMs) that were used to 

estimate projected temperature increases for various regions of the U.S. (Table 4-1). Predicted 

temperature increases by the year 2100 ranged between 2.3°C and 6.5°C for the Hadley and 

Canadian models, respectively. Although the biological data are from the Mid-Atlantic region, 

we also investigated how projected climate changes in other regions affected taxa loss rates. 

Table 4-1. Average annual temperature increases expected by region of the 

U.S. (NAST, 2001) 

Average Annual Temperature (oC) 

Increases by 2100 

Region Min Max 

Northeast/Mid-Atlantic 2.6 (Hadley) 5 (Canadian) 

Southeast 2.3 (Hadley) 5.5 (Canadian) 

Midwest 3 (Hadley) 6 (Canadian) 

Great Plains 3 (Hadley) 6.5 (Canadian) 

West 4 (Hadley) 5.5 (Canadian) 

Pacific Northwest 2.7 (by 2050) (Hadley) 3.2 (by 2050) (Canadian) 

Reported rates of temperature increase were linked with estimated rates of taxa losses to 

model taxa losses per year due to climate change, incorporating both the low and high estimates 

of each. 

Linear projections of climate change effects are used in this case study as a basis for 

estimating ability to detect climate-induced changes after various monitoring periods. It is likely 

that climate will change in a non-linear fashion with periods of fast change followed by periods 

of slower changes (IPCC, 2007). There is little way to predict this course, however, so the linear 

assumption is the more conservative approach and is a common assumption used in the literature 

(e.g., Najjar et al., 2000). 

4-6
4.3. KEY FINDINGS 

4.3.1. How Long Must Monitoring be Conducted to have a Fixed Probability of Detecting 

a Change in the Mean Native Taxon Richness of the Reference Site Population? 

If a population of reference sites (N = 40) is sampled each year, an average 

macroinvertebrate taxon richness in reference streams can be calculated. For comparing any two 

samples of N = 40 sites, there is a fixed difference in mean taxon richness (effect size) at which 

significance can be detected with a specified power. For α = β = 0.05 (95% confidence, 95% 

power), and the Maryland data, the effect size is 4.5 taxa. Thus, to have a 95% probability of 

detecting a significant (p < 0.05) taxa loss between 2 samples of 40 sites, requires a mean 

difference of 4.5 taxa. At high taxa loss rates and under the higher estimate for warming in the 

Northeast/Mid-Atlantic region, it will take 15 years to achieve a mean loss of 4.5 taxa 

(Figure 4-1), assuming that (1) the same 40 sites are sampled each year; (2) samples from a site 

are not treated as cumulative through time; and (3) the analysis uses type I and type II error rates 

of 0.05. This value is derived by identifying the point where the effect size line (hatched) 

crosses the taxa loss rate line (solid) (Figure 4-1). 

Figure 4-1 illustrates a variety of scenarios representing different confidence levels and 

either fixed or cumulative sample sizes. For example, relaxing the confidence level decreases 

the time to detect a change. Increasing α and β from 0.05 to 0.20 (reducing both confidence and 

power), reduces the time to achieve 80% probability of detecting a significant climate change 

effect (p < 0.2) to approximately 8 years. If a 1 in 5 (rather than a 1 in 20) chance that 

statistically significant results are due to random chance alone is acceptable, a taxa change 

attributable to climate change could be detected in half the time. This is the type of trade-off that 

is important for programs to consider. 

Similarly, if samples taken across the reference population are treated as cumulative 

estimates of the population condition (replicates of the reference condition), then the projected 

climate change effect can be detected very quickly under the conditions of a high taxa loss rate 

and high temperature increase. If this replication is temporal, i.e., if samples from consecutive 

years are grouped, this also would increase the ability to detect the climate change effect. 

However, there is an associated assumption that interannual variation is constant (i.e., that 

successive years are comparable and can be grouped for analysis). This may be true on short 

time scales, but it might be faulty over longer periods given climate change, which is 

progressive. Combining samples into decadal (or shorter) groups (N = 400) may be more 

defensible and would also result in detecting the climate change effect more quickly. 

Using the same assumptions but altering the taxa loss rate to the lower taxa loss rate 

(1 taxon per °C), the time to detect a climate change effect increases dramatically (Figure 4-2). 

This is reasonable, given that subtle effects will be much harder to detect than strong effects. 

4-7
0.00

0.50

1.00

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2.50

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3.50

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0 20 40 60 80 100

Predicted Taxa Loss

(alpha = beta = 0.95) Fixed N

(alpha = beta = 0.95) Cumulative N

(alpha = beta = 0.90) Fixed N

(alpha = beta = 0.90) Cumulative N

(alpha = beta = 0.80) Fixed N

(alpha = beta = 0.80) Cumulative N

e.g., at fixed N=40 and a and b = 0.95 

it will take 15 years to significantly 

detect loss of taxa due to climate 

change

High Taxa Loss Rate (4.6 per degree C)

Years

Effect Size and Predicted Taxa Loss

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

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0 20 40 60 80 100

Predicted Taxa Loss

(alpha = beta = 0.95) Fixed N

(alpha = beta = 0.95) Cumulative N

(alpha = beta = 0.90) Fixed N

(alpha = beta = 0.90) Cumulative N

(alpha = beta = 0.80) Fixed N

(alpha = beta = 0.80) Cumulative N

e.g., at fixed N=40 and a and b = 0.95 

it will take 15 years to significantly 

detect loss of taxa due to climate 

change

High Taxa Loss Rate (4.6 per degree C)

Years

Effect Size and Predicted Taxa Loss

Figure 4-1. Effects of confidence level (α [a] andβ [b]) on time to detect a 

climate effect on macroinvertebrate taxa loss due to climatic warming at high 

taxa loss rates in the Northeast/Mid-Atlantic U.S. Sample size (N) is either 

fixed at 40 per year or is cumulative. This analysis was based on a high 

estimate of global warming (5oC by 2100). 

4-8
 

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

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0 20 40 60 80 100

Predicted Taxa Loss

(alpha = beta = 0.95) Fixed N

(alpha = beta = 0.95) Cumulative N

(alpha = beta = 0.90) Fixed N

(alpha = beta = 0.90) Cumulative N

(alpha = beta = 0.80) Fixed N

(alpha = beta = 0.80) Cumulative N

Low Taxa Loss Rate (1.0 per degree C)

Years

Effect Size and Predicted Taxa Loss

Figure 4-2. Effects of confidence level (α [a] and β [b]) on time to detect a 

climate effect on macroinvertebrate taxa loss due to climatic warming at low 

taxa loss rates in the Northeast/Mid-Atlantic U.S. Sample size (N) is either 

fixed at 40 per year or is cumulative. This analysis is based on a high 

estimate of global warming (5oC by 2100). 

4-9
Uncertainty about the effects of warming water temperatures on taxa loss also influences the 

ability to detect a climate change signal. The effects of confidence level and sample size are the 

same under the lower taxa loss rates as under the high taxa loss rates. For example, under the 

same fixed sample size (N = 40) and confidence level (95%), it would take approximately 70 

years to detect a climate effect under the low taxa loss rate as opposed to 15 years under the high 

taxa loss rate. 

Lastly, there was only one defensible rate for fish taxa loss (3.6 per °C). This fish taxa 

loss rate was used in the same models as those for the macroinvertebrate loss rate and indicates 

similar effects of sample size and confidence level on time to detect a change. Under these 

assumptions, it would take 10 to 20 years to achieve a fixed probability of detecting the loss of 

fish taxa due to climate change in the reference site population using confidence levels of 0.80 

and 0.95, respectively (see Figure 4-3). 

Similar analyses were run for the lower temperature increase scenario for this region 

(Table 4-1). Not surprisingly, if temperatures warm more slowly, they will have less of an effect 

on taxa loss and it will take comparatively longer to detect a loss in average taxon richness in the 

reference population (Table 4-2). 

4.3.2. How Long Must Monitoring be Conducted to have a Fixed Probability of Detecting 

a Change in the Mean Native Taxon Richness for a Particular Site? 

This second question focuses on the ability to detect these same effects at a single site, 

which could be a reach of stream or a watershed. In either case, the assumption is that replicate 

samples are apportioned probabilistically across the site. The analysis specifically defines the 

effect of increasing sample size. 

For this question, three different sample sizes were investigated (N = 5, 10, or 20) and 

were treated as either fixed (non-additive over time) or cumulative. Only results for the 

Northeast/Mid-Atlantic region under the maximum predicted temperature increase are shown, 

although results are similar across regions, shifting only due to differences in the projected 

temperature increases. 

Whether for a watershed or a specific reach, increasing the sample size will shorten the 

time required to detect an effect of climate change on taxon richness (see Figure 4-4). Many 

biomonitoring programs may collect only one sample at a site per year; a means comparison 

could be applied in this framework (use N = 1 in equation), but the differences would have to be 

quite large to be significant, and this is not likely over the short term (e.g., between consecutive 

years). Samples could be combined cumulatively over consecutive years to support testing, but 

the same problem exists in combining consecutive years over a long time period for analysis: the 

communities being sampled are probably changing over time due to climate change. 

4-10
0.00

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2.50

3.00

3.50

4.00

4.50

5.00

0 20 40 60 80 100

Predicted Taxa Loss

(alpha = beta = 0.95) Fixed N

(alpha = beta = 0.95) Cumulative N

(alpha = beta = 0.90) Fixed N

(alpha = beta = 0.90) Cumulative N

(alpha = beta = 0.80) Fixed N

(alpha = beta = 0.80) Cumulative N

Fish Taxa Loss Rate (3.6 per degree C)

Years

Effect Size and Predicted Taxa Loss

Figure 4-3. Effects of confidence level (α [a] and β [b]) on time to detect a 

climate effect on fish taxa loss due to climatic warming in the Northeast/MidAtlantic U.S. Sample size (N) is either fixed at 40 per year or is cumulative. 

This analysis is based on a high estimate of global warming (5oC by 2100). 

4-11
Table 4-2. The time (years) to achieve a fixed probability of detecting a 

statistically significant effect of temperature increases on macroinvertebrate 

and fish taxa loss across different regions under maximum and minimum 

temperature projections. These data are for question 1 and assume a fixed 

sample size of N = 40 reference sites sampled each year. Data are shown for 

different taxa loss rates and for different confidence levels. 

Regions 

Northeast/ 

Mid-Atlantic Southeast Midwest 

Great 

Plains West 

Pacific 

Northwest 

Maximum predicted temperature increase by 2100

Macroinvertebrates—high taxa loss rate 

(4.6 per °C) 

 α = β = 0.95 15 14 13 --- 13 --- 

 α = β = 0.8 8 7 7 --- 7 --- 

Macroinvertebrates—low taxa loss rate 

(1 per °C) 

 α = β = 0.95 70 64 58 --- 57 --- 

 α = β = 0.8 36 33 30 --- 29 --- 

Fish taxa loss rate 

(3.6 per °C) 

 α = β = 0.95 20 18 17 --- 16 --- 

 α = β = 0.8 10 9 9 --- 9 --- 

Minimum predicted temperature increase by 2100 

Macroinvertebrates—high taxa loss rate 

(4.6 per °C) 

 α = β = 0.95 29 33 38 19 17 14 

 α = β = 0.8 15 17 19 10 9 7 

Macroinvertebrates—low taxa loss rate 

(1 per °C) 

 α = β = 0.95 >100 >100 >100 88 79 64 

 α = β = 0.8 69 78 89 45 41 33 

Fish taxa loss rate (3.6 per °C) 

 α = β = 0.95 38 42 49 25 22 18 

 α = β = 0.8 19 22 25 13 12 9 

4-12
0.00

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Predicted Taxa Loss

(alpha=0.95) Fixed N=5

(alpha=0.95) Fixed N=10

(alpha=0.95) Fixed N=20

(alpha=0.95) Cumulative N=5

(alpha=0.95) Cumulative N=10

(alpha=0.95) Cumulative N=20

High Taxa Loss Rate (4.6 per degree C)

Years

Effect Size and Predicted Taxa Loss

Figure 4-4. Effects of sample size on time to detect a climate effect on 

macroinvertebrate taxa loss due to climatic warming at high taxa loss rates 

in the Northeast/Mid-Atlantic U.S. The confidence level is fixed at 0.95. This 

analysis is based on a high estimate of global warming (5oC by 2100) and the 

highest macroinvertebrate taxa loss rate. 

4-13
As before, relaxing assumptions about required power and confidence levels (e.g., 

decreasing α and β to 0.8) will decrease the duration of monitoring needed to be able to detect a 

climate change effect of taxa loss at a particular site. Similarly, if macroinvertebrate taxa loss 

rates are slower than the high taxa loss rate used for Figure 4-4, it would take much longer to 

detect an effect (see Figure 4-5), all else being equal. Effects of sample size on fish taxa loss 

rates are similar to those for macroinvertebrates (see Figure 4-6): Locations with higher rates of 

climate change-associated temperature increases and/or higher rates of taxa loss responses would 

require less monitoring time to detect (i.e., statistically demonstrate) an effect; the converse 

(lower ranges of temperature increase and/or taxa loss) would increase the required monitoring 

time. 

4.4. KEY CONCLUSIONS 

Results of this case study highlight considerations for monitoring programs in light of the 

need to account for climate change. Increasing sample size, either by increasing the number of 

reference sites sampled each year or increasing the number of samples taken per watershed, or 

per reach for targeted studies, will increase the ability to discern a climate change effect using 

biomonitoring data. Regions with lower rates of climate change and/or taxa loss rates will 

require either a longer monitoring period or a larger sampling effort to detect climate change taxa 

losses effectively. On the other hand, with lower rates of climate change, effects from other 

regulated sources of perturbation may be reliably detectable for longer, although increases in 

variability and degradation of signal-to-noise ratio will degrade ability to detect impairment to 

some extent (see Section 5). Since greater variability in the data decreases ability to detect 

differences in taxon richness due to climate change, region-specific estimates of data variance 

are important for an evaluation of a particular monitoring program. In addition, factors within a 

monitoring design that can control for predictable sources of variation, such as partitioning by 

watershed or ecoregion, become important, as they would reduce (account for) natural sources of 

variation and increase ability to reliably recognize climate change effects. 

The choice of a probabilistic or targeted sampling protocol is an important monitoring 

design issue, and will depend on the questions being asked. It also bears on the ability to detect 

climate change effects. Probabilistic designs are good for asking questions about, for instance, 

the average condition of streams or watersheds, including taxon richness, within a region. With 

regard to climate change effects, probabilistic sampling across reference sites would be ideal for 

defining condition but would require relatively large sample sizes to detect differences in 

biological attributes such as taxon richness because of the greater variation in the data. In the 

context of this case study, sample size and power are based on paired tests, which are much more 

4-14
0.00

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4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0 20 40 60 80 100

Predicted Taxa Loss

(alpha=0.95) Fixed N=5

(alpha=0.95) Fixed N=10

(alpha=0.95) Fixed N=20

(alpha=0.95) Cumulative N=5

(alpha=0.95) Cumulative N=10

(alpha=0.95) Cumulative N=20

Low Taxa Loss Rates (1 taxa per degree C)

Years

Effect Size and Predicted Taxa Loss

Figure 4-5. Effects of sample size on time to detect a climate effect on 

macroinvertebrate taxa loss due to climatic warming at low 

macroinvertebrate taxa loss rates in the Northeast/Mid-Atlantic U.S. The 

confidence level is fixed at 0.95. This analysis is based on a high estimate of 

global warming (5oC by 2100) and the highest macroinvertebrate taxa loss 

rate. 

4-15
0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0 20 40 60 80 100

Predicted Taxa Loss

(alpha=0.95) Fixed N=5

(alpha=0.95) Fixed N=10

(alpha=0.95) Fixed N=20

(alpha=0.95) Cumulative N=5

(alpha=0.95) Cumulative N=10

(alpha=0.95) Cumulative N=20

Fish Taxa Loss Rate (3.6 per degree C)

Years

Effect Size and Predicted Taxa Loss

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0 20 40 60 80 100

Predicted Taxa Loss

(alpha=0.95) Fixed N=5

(alpha=0.95) Fixed N=10

(alpha=0.95) Fixed N=20

(alpha=0.95) Cumulative N=5

(alpha=0.95) Cumulative N=10

(alpha=0.95) Cumulative N=20

Fish Taxa Loss Rate (3.6 per degree C)

Years

Effect Size and Predicted Taxa Loss

Figure 4-6. Effects of sample size on time to detect a climate effect on fish 

taxa loss due to climatic warming in the Northeast/Mid-Atlantic U.S. The 

confidence level is fixed at 0.95. This analysis is based on a high estimate of 

global warming (5oC by 2100) and the highest macroinvertebrate taxa loss 

rate. 

4-16
powerful than drawing new independent samples every year because site-to-site differences are 

removed from the variance term, leaving only differences over time between sites. 

Targeted site selection, however, is often needed to answer specific questions, including 

site-specific questions such as whether a site is meeting its designated use or permit 

requirements. Another question that benefits from targeted site selection is what the effect of a 

specific land use is on stream condition, because of the benefits of targeting sampling locations 

along a gradient of effects. This may be important for studying how land use will interact with 

climate change to affect stream condition. 

If a bioassessment program is going to encompass climate change monitoring, there are 

several points to consider: 

1. Protection of reference streams emerges as an important concept, especially considering 

that reference sites will be used to gauge climate change effects as well as the relative 

effects of climate change on other stressors. 

2. Ongoing monitoring of reference sites becomes an even more important aspect of 

program design, with more sampling sites in reference locations and/or greater frequency 

of sampling increasing the ability to detect change. 

3. The use of rotating designs (rotating sampling among basins over years so that a 

complete cycle of sampling may take 5 or more years) is often employed by state 

biomonitoring programs to optimize resources, because crews can stay within defined 

areas, travel can be limited, and total numbers of samples collected and processed each 

year is reduced by focusing on a subset of basins. This approach also means that 

reference sites within any one basin will only be sampled once every several years, 

increasing the time it will take to obtain replicate samples needed to define climate 

change-associated trends. 

4-17
5. CASE STUDY 2—ACCOUNTING FOR TRENDS: BIOLOGICAL 

ASSESSMENT IN THE PRESENCE OF CLIMATE CHANGE 

Detection of biological impairment and identification of its causes are two principal 

objectives of bioassessment. Climate change will affect these central objectives, especially the 

ability to discern impairment by comparison to reference locations. The second case study 

examines how to detect impairment under climate change, particularly the ability to differentiate 

between reference conditions and locations of reduced biological condition and the ability to 

assign cause to impaired conditions. This approach is a foundation for defining how monitoring 

may have to be modified to incorporate climate change and how data can be analyzed to account 

for climate change and remain viable. Appendix B provides some additional details of this case 

study, in particular descriptive and supplemental results of correlations examining potential 

relationships between environmental variables and stressors that might be used to reflect direct 

climate change effects. 

5.1. OBJECTIVES 

The case study examines the potential vulnerability of biomonitoring programs and 

assessment methods to biological changes that result from climate change. This case study 

addresses the following questions: 

• How do we detect impairment under climate change? 

• How does climate change affect our ability to identify causes of biological impairment? 

• Are there analytical or monitoring design approaches that will allow managers to 

effectively identify and manage stressors independently of climate change? 

Climate change effects will likely drive the attributes of reference sites toward greater 

similarity with impaired sites (i.e., decreased distance between the condition state of reference 

and impaired). This decrease in effect (signal) may also be accompanied by increases in 

variation (noise). A decrease in the signal-to-noise ratio would decrease the ability to detect 

impairment. In addition to direct effects on site assessment, climate change effects may interact 

with conventional stressors, further confounding the ability to discriminate stressor effects based 

on reference/impaired site comparisons. 

5.2. ANALYSIS APPROACH 

The case study uses existing data, and by examining the associations of biological 

attributes with proxy attributes of climate change, evaluates the potential effects and 

5-1
vulnerabilities of aquatic biomonitoring programs to climate change. Biological responses of 

streams to various stressors were examined—with particular emphasis on hydrologic parameters 

that may be influenced by climate change. 

Detectable biological responses to climate change effects in streams that are important in 

a bioassessment framework include the following: 

• Southern taxa expanding their range northward 

• Habitat change from increased winter/spring scour 

• Loss of taxa sensitive to summer drought periods or higher temperatures (including 

higher water temperature associated with drought) 

• Improved conditions for invasive species, including disturbance regimes favoring 

invasive species and warmer water temperatures allowing overwintering 

• Change in number of reproductive periods leading to changes in timing of peak 

abundance (possibly also tied to changes in phenology) 

• In addition to the direct effects of temperature change, streams are also subject to 

hydrologic changes from changed precipitation patterns and increased evapotranspiration 

(e.g., Moore et al., 1997). Extreme stream flows reshape the stream habitat, and summer 

low-flow events represent bottlenecks of both warm temperature and reduced habitat 

(Moore et al., 1997; Poff and Ward, 1989). This analysis is focused on changes that 

might occur in the Mid-Atlantic region, but the results may be generalized to similar 

changes occurring in other regions 

Data were partitioned into subsets defined by wet, normal, and dry periods, and 

biological indicators of reference and impaired sites were examined. Several stressor-response 

relationships were evaluated under the different climatic conditions. The intent was to estimate 

probable minimum and maximum changes. 

5.2.1. Datasets Evaluated 

The MBSS data set was used to evaluate biologic responses to stressors under different 

conditions. Section 4.2.2 describes attributes of this data set. 

 

5.2.2. Metrics 

Several invertebrate metrics were calculated in the Ecological Data Application System 

(EDAS) database for the 1320 randomly located benthic samples in the Piedmont and Highlands 

regions that were collected over the 10-year period (1994-2004) and analyzed as response 

variables. These included total taxa (taxon richness), number of taxa in the insect families of 

mayflies (Ephemeroptera), stoneflies (Plecoptera), and caddisflies (Trichoptera) (EPT taxa 

5-2
5-3

collectively), and the 2005 version of the Maryland benthic index of biotic integrity (B-IBI). For 

fish, response variables examined include the Maryland fish IBI, total number (abundance) of 

fish, and number of species of fish (taxon richness). The main analytical focus was on 2 fish 

indicators and 2 benthic macroinvertebrate indicators: the Maryland fish IBI score, and fish 

taxon richness; and the Maryland B-IBI score, and the EPT taxon richness. The selected 

indicators are all responsive, general indicators of stress but are not diagnostic of any particular 

stressor. 

The Maryland 305(b) evaluation of the status of waters of the state, which uses the MBSS 

data in addition to other data sources, uses benthic and fish Indices of Biotic Integrity (IBIs) to 

determine impairment status and attainment of uses (Maryland Department of the Environment, 

2004; http://www.mde.state.md.us/assets/document/AppndxC2004-303d_Final.pdf). For a 

single stream reach assessment, Maryland takes into account population-wide measurement 

error. The approximate result is that if both indexes are >3.3, the stream segment is considered 

unimpaired, and if either index is <2.7, the segment is impaired. Intermediate values are 

considered to be potentially impaired but are still listed as supporting aquatic life uses. 

5.2.3. Regional Data Partitions 

The MBSS data were partitioned based on Maryland’s classification into four ecoregions 

(Coastal Plain, Eastern Piedmont, Cold-water Highlands, and Warm-water Highlands; see 

Figure 5-1), to account for known sources of natural variation in both habitat (physical and 

chemical) and biological data. The heavily developed Eastern Piedmont region, with a high level 

of urbanization that represents an existing source of impairment, was targeted for evaluation. 

Due to the level of development, the Eastern Piedmont region has relatively few reference areas. 

In the original MBSS index development, the Piedmont and Highlands regions were deemed to 

be biologically similar (Roth et al., 1998; Stribling et al., 1998). Sampling of more reference 

sites showed that the Piedmont can be separated from the Highlands region (Southerland et al., 

2007, 2005). However, to have sufficient reference locations to support the analyses, the original 

classification was used, recombining the Piedmont and Highlands sites for analyses requiring 

identified reference sites. 

5.2.4. Climate Data 

The National Climatic Data Center (NCDC; www.ncdc.noaa.gov) makes available 

several average monthly parameters, organized by state climatic regions. Although climate does 

not follow state boundaries, it was convenient in this case because our biological data did follow 

the state boundaries. We used data from two NCDC regions of Maryland: the Northern Central 

Division (primarily Northern Piedmont ecoregion, and the Blue Ridge ecoregion within 
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Assessment Region Borders

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Figure 5-1. Maryland MBSS sampling stations showing regional divisions. 

 5-4
Maryland), and the Appalachian Mountain Division (Central Appalachian Ridge and Valley 

ecoregion). Maryland’s Piedmont and warm-water mountain streams occur primarily in these 

two climatic divisions. The Northern Central Division data were applied to the Eastern 

Piedmont streams, and the Appalachian Division data to Highlands streams. 

We estimated potential hydrologic effects of climate change by using the Palmer 

Hydrologic Drought Index (PHDI) as a proxy for estimates of hydrologic changes due to climate 

change. The PHDI is a monthly hydrological drought index used to assess long-term moisture 

supply to water bodies (Karl, 1986) and is described in detail on the NCDC website 

(http://www.ncdc.noaa.gov/oa/climate/onlineprod/drought/ readme.html). The index ranges 

from −7 to +7, with negative values indicating dry spells, and positive values indicating wet 

conditions. The PHDI takes into account water storage as soil and groundwater, and therefore is 

more applicable to stream flow than the PHDI, which uses only temperature and rainfall 

information (Karl, 1986). Figure 5-2 shows the 30-year distribution of the PHDI for the 

Maryland Northern Central Division, which includes the Piedmont. The range of the PHDI 

varies little from month to month, but the 30-year median value is positive during the 

spring/summer macroinvertebrate sampling index period (>0 in March–May) and markedly 

lower in late summer-early fall during the last half of the fish sampling index period (<−1 in 

September–October). 

MD Northern Central Division, 1970-1999

 Median 

 25%-75% 

 Non-Outlier Range 

 Outliers

Extremes 1 2 3 4 5 6 7 8 9 10 11 12

Month

-6

-4

-2

0

2

4

6

8

Palmer Hydrologic Drought Index

Figure 5-2. Monthly Palmer HDI for the 30-year period 1970-1999 (Source: 

NCDC; http://www.ncdc.noaa.gov). 

5-5
In addition to calculating monthly PHDI, a variety of alternative running averages were 

also calculated to account for possible lags in effects, and specifically for the time lag between 

when droughts occur and directly impact the biota (summer/early fall) and the sampling period 

(the spring index period): the previous 6-month average, the previous 12-month average, and the 

previous summer PHDI. These alternative PHDI averaging methods did not yield results (i.e., in 

developing wet/dry/average year comparison) different from those developed using the simple 

monthly average; therefore average monthly PHDI was used in all analyses. 

5.2.5. Hydrologic Attributes 

Baker’s flashiness index (Baker et al., 2004) was estimated for each stream. Flashiness is 

a component of the hydrologic regime of streams, and, in general, is related to the frequency of 

short-term changes in runoff associated with rainfall events, and how rapidly each event comes 

and goes. Flashiness is generally considered to increase with increases in impervious cover 

associated with urbanization and/or with land clearing for agriculture (Allan, 2004). It is both 

responsive to urbanization as an existing stressor, and it also is expected to change in the future 

in response to climate change projections of increased frequency and intensity of storms within 

many regions of the U.S. Baker’s index is calculated as the average of absolute values of daily 

mean flow change divided by mean flow for the 2-day period. The maximum range is from 0 

(absolutely constant flow) to 2 (alternating days of flow and no flow). 

Daily flows were simulated for each site using the Flow Time Series Estimation tool 

(FTSE; Tetra Tech, 2005). The model can be used to estimate daily flows for ungauged streams 

based on multiple regressions using a smaller set of gauged streams. The main criterion for 

proper functioning of the model is that there must be gauged stations relatively near to the 

ungauged streams (e.g., within the same ecoregion) so that a standard is available for calibrating 

the model. Estimates had been developed for a set of 764 streams in the Piedmont only (Barbour 

et al., 2006); no set of appropriate gauged streams was available for the Appalachians. 

5.2.6. Specific Analyses 

5.2.6.1. Sensitivity of the System to Climate Change 

As discussed in Sections 1 and 3, the primary hydrologic stressors associated with 

climate change are changes in precipitation patterns combined with changes in temperature 

regime, which will drive changes in hydrologic regime. The projected extent of changes in 

temperature and precipitation varies regionally in the U.S.; therefore, so too will changes in the 

magnitude, frequency, flashiness, and other patterns of runoff. The National Assessment of 

climate change in the U.S. provides regional summaries of projected changes in the temperature 

5-6
and precipitation regimes of the major regions of the U.S. (NAST, 2001). Table B-1 (in 

Appendix B) summarizes these projections by region. 

5.2.6.2. Stress-Responses 

We established the ranges and variability of system response variables to climate change, 

and described the response signatures to stressors. Due to the large number of parameters 

available in the MBSS database, correlation analyses were used to identify stressor variables that 

were most strongly related to response variables. Spearman Correlation coefficients were used 

for this analysis; as a non-parametric test, there is no need to make assumptions about or test for 

normal distributions for each variable. A locally weighted scatter plot smoothing (LOWESS) 

line was used to illustrate the pattern of any relationship between the variables being correlated. 

LOWESS smoothing was done in Systat10 by running along the x values and finding predicted 

values from a weighted average of nearby y values. The surface is allowed to flex locally to 

better fit the data. For the LOWESS, the degree to which the line or surface (tension) is allowed 

to flex locally to fit the data to 0.5 was specified, meaning that half the points are included in the 

running window. Graphs of key variables (scatter plots with LOWESS line) were used to 

illustrate the relationships defined by correlation analysis, and to confirm that all relationships 

reflected consistent data with no errors or false trends introduced by data entry errors, reporting 

unit errors, or other inconsistencies. The subset of parameters showing the strongest 

relationships were used for further exploration of stressor-response models. 

A conditional probability approach (Paul and MacDonald, 2005) was used to examine 

changes in the biological community along stressor gradients. A conditional probability 

statement provides the likelihood (probability) of a predefined response, if the value of a 

pollutant stressor (condition) is exceeded. Conditional probability is the probability of an event 

when it is known that some other event has occurred. To estimate conditional probability of 

impairment, we first define impairment as a specific value for a response variable (e.g., EPT <11 

genera). The analysis addresses the following question: for a given threshold of a stressor, what 

is the cumulative probability of impairment? For example, if total phosphorous concentration is 

greater than 0.2 mg/L, what is the probability of biological impairment for each site under 

consideration? All observed stressor values (in this example, all observed values of total 

phosphorous) are used to develop a curve of conditional probability (Paul and MacDonald, 

2005). 

5.2.6.3. Effects of Climate Change 

We used proxy estimates of climate (in the existing data) that are representative of 

projected climate change, and examined the ability to detect biological impairment and stressor5-7
response relationships. Our proxies of climate change were the estimates of wetter-than-normal 

and drier-than-normal conditions in the PHDI for each sampling event. The MBSS data were 

post-stratified into dry, normal, and wet conditions based on the index; selected stressor-response 

relationships were then reexamined under the wet and dry scenarios. This evaluation was done 

separately for reference sites (defined a priori in the MBSS), impaired sites (defined a priori in 

the MBSS plus sites with 10% or more impervious surface), and intermediate sites (sites not 

included in the impaired or reference groups). 

As part of the analysis, it can be assumed that future biological responses to altered 

hydrological conditions will be similar to responses to current natural variability, and that future 

hydrologic changes will be comparable to extremes observed in the past 10 years. The 

assumptions are probably reasonable in the near-term (i.e., 50 years), but become less reasonable 

farther into the future. 

5.3. KEY FINDINGS 

5.3.1. Observed Stressor-Responses 

Establishing definitive stressor-response relationships is a critical step in the Stressor 

Identification (SI) process, and it is fundamental to identifying probable causes of impairment. 

Numerous relationships were examined; Appendix B summarizes these results. Only a subset of 

results that show some correlation and/or those that were considered potentially important but 

showed no significant relationship are presented in this section. 

The fish and benthic invertebrate response variables that showed the strongest responses 

in these correlations were the fish index of biotic integrity (FIBI), fish taxon richness, total 

number of fish, the Maryland benthic IBI, total benthic taxon richness, and total EPT taxon 

richness (see Appendix B). From these, 2 fish indicators were selected and 2 benthic 

macroinvertebrate indicators were used to evaluate the Maryland Fish IBI score, and fish taxon 

richness; and the Maryland Benthic IBI score (B-IBI), and EPT taxon richness. 

 

5.3.1.1. Physical Habitat 

Both fish and benthic macroinvertebrate measures were correlated with overall physical 

habitat, as measured by the Maryland Physical Habitat Index (Paul et al., 2002) (Appendix B). 

Fish taxon richness was not correlated with the habitat index, but the fish IBI and both 

invertebrate indicators were strongly correlated, increasing with improved habitat score. Among 

habitat components, the EPT taxa were also positively correlated with the embeddedness score, 

reflecting a component of habitat (interstitial spaces in cobble substrate) utilized by these 

organisms. Fish taxon richness was also very strongly correlated with total flow, but this was a 

reflection of the effect of stream size. 

5-8
5.3.1.2. Hydrology 

Both the fish and the benthic macroinvertebrate indicators were negatively associated 

with Baker’s flashiness index (see Appendix B). Below a flashiness index value of 0.5, 

biological indicator values could be in the normal range, but above a flashiness of 0.6, most 

biological values indicated impairment. Flashiness is affected by impervious surface, which in 

the study area, indicates urban land use. The macroinvertebrate indicators declined with 

impervious surface in a catchment, but the fish indicators did not (see Appendix B). 

5.3.1.3. Water Quality 

The invertebrate indicator EPT was associated with dissolved organic carbon (DOC), 

total phosphorus (TP), and conductivity, with the number of EPT taxa declining as the stressors 

increased. The strongest association was with conductivity. No other chemical water quality 

measures were associated with either fish or benthos (DO was uniformly moderate to high in the 

dataset, and there were too few observations of low DO to show any relationship). 

5.3.1.4. Temperature 

The associations of both the fish and benthic macroinvertebrate communities to water 

temperature were examined. Fish observations in the data set had already been classified 

according to expected warm-water and cold-water communities, using current and likely 

sustainable distributions of brook trout to define cold-water streams in the region west of Evitts 

Creek in western Maryland (Southerland et al., 2005). It is important to note that temperature 

was measured in late summer and fall, at the same time that the fish assemblage was sampled. 

Macroinvertebrates were sampled in spring, and temperature was not measured at that time. 

Fish taxon richness increased with temperature in warm-water streams in both the 

Piedmont and in the Appalachians, but there was no detectable relationship in the cold-water 

streams (see Figure 5-3). EPT and total macroinvertebrate taxon richness (measured in early 

spring) were reduced in the cold-water Highland streams where late summer temperatures 

exceeded 18-20oC (see Figures 5-4a, b). There was no detectable relationship between 

temperature and benthic macroinvertebrates in Piedmont streams (see Figures 5-4c, d). 

5.3.2. Estimates of Climate Change Effects 

5.3.2.1. Temperature 

Increases in average regional temperature might have the result that some fraction of 

cold- or cool-water streams change to warm-water conditions and biota. Global average air 

temperatures are expected to increase by at least 2oC by 2100 (likely range 2oC to 4.5oC, likely 

5-9
Cold Water 

(Blue)

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Figure 5-3. Fish richness vs. temperature in Highland reference streams. 

Lines are LOWESS estimates. 

5-10
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Figure 5-4. (a) Macroinvertebrate richness vs. temperature; (b) EPT 

richness vs. temperature in Highland reference streams; (c) EPT vs. 

temperature relation; and (d) fish richness vs. temperature relation in 

reference sites in Piedmont streams. Lines are LOWESS estimates. 

5-11
5-12

average 3oC; IPCC, 2007). On the average, summertime air temperature increases are projected 

to be less than wintertime increases (MacCracken et al., 2001), and the late-summer stream water 

temperature increases are expected to be less than the average increase (Mohseni et al., 2003). 

Based on these results for fish and invertebrate taxa in Mid-Atlantic streams, a net increase in 

site-specific fish richness can be expected, as individual streams change from cold- or cool-water 

conditions to warm-water. Fish taxon richness has previously been found to be higher in warmwater habitats (Wehrly et al., 2003). In contrast, invertebrate taxa per site may decrease in 

Highland streams that exceed 18oC due to climate change, but with no change in streams that 

remain well below 18oC in late summer, suggesting that Highland streams macroinvertebrate 

communities may be sensitive to climate change according to (future) temperature regime. 

5.3.2.2. Hydrology 

To examine the potential effects of changed rainfall and evapotranspiration patterns, the 

existing data was divided into three groups: samples taken in relatively dry conditions, samples 

taken in approximately normal conditions, and samples taken in relatively wet conditions. 

“Dry,” “Normal,” and “Wet’ were defined according to the distribution of the PHDI in the data 

set, thus, the range of conditions from the recent past (from the month of sampling to the 

preceding year) was used to obtain some insight into consequences of climate change. The range 

of PHDI was from −4.24 to +4.75, with a median of +1.8. Although the total range was 

symmetrical from extreme drought (< −4) to extreme wetness (> +4), there were more wet 

months than dry months in the 10-year period. We defined 3 climatic groupings: Dry: PHDI < -

2.5 (N = 264); Normal: −1.1 < PHDI < 1 (N = 176); and Wet: PHDI > 3.5 (N = 353). These 

groupings were selected to get substantial differences between wet and dry conditions, i.e., to 

eliminate confounding effects of “moderately dry” and “moderately wet” conditions, and yet 

have sufficient sample size in each of the hydrologic groups. 

Figure 5-5 shows the Benthic IBI (B-IBI) scores of the three stream classes under the 

three climatic conditions. Dry conditions are associated with greater variability of reference 

sites, and a net degradation of median B-IBI score in both reference and intermediate sites. Wet 

conditions are similarly associated with increased variability and a net decline in median B-IBI 

score, but less so than in dry conditions. A comparison of reference sentinel site B-IBI (as well 

as F-IBI) scores for the period 2000–2004, which included a wet year (2003) and a drought 

period (2002–early 2003) did not show a notable variation between years among the Piedmont 

and Highland sentinel sites (Prochaska, 2005). However, fewer sites and years are included in 

that analysis, and variation around the mean is not evaluated. The EPT taxa metric showed the 

same overall pattern (Figure 5-6): a slight net loss of median number of taxa in reference and 

intermediate sites, and increased variability in reference sites. The macroinvertebrate 
 5-13Ref Intermed Deg

Dry

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Benthic BI I

Ref Intermed Deg

Normal

0123456

Benthic BI I

Ref I

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Figure 5-5. Benthic IBI performance and climatic condition. The dry, normal and wet designation

of the three graphs refers to categorizations based on the PHDI. 
Ref Intermed Deg

Dry

05

101520

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Figure 5-6. EPT performance and climatic condition. 

 5-14
5-15

communities at degraded sites were low in EPT taxa and IBI scores, so changes of hydrological 

condition did not affect them much. The Fish IBI was also similar (Figure 5-7) but showed 

slightly greater effects under wet conditions than did macroinvertebrates: larger decline in 

median reference score and larger reference variability. It should be noted that there are 

differences in sample sizes between categories of climate condition, and, in particular, there are 

fewer “dry” years represented. This is a consequence of limited (10 years) data combined with 

the rotating-basin sampling scheme (i.e., only a subset of basins are sampled each year). Having 

more data would improve these comparisons. 

A quantitative measure of the efficacy of an index in discriminating between reference 

and stressed sites is the Discrimination Efficiency (DE), which is calculated as the percent of 

stressed sites with scores less than the 25th percentile of the reference sites (U.S. EPA, 1999). 

DE is influenced both by the absolute difference between the reference and stressed site mean 

scores, and the variability or spread of the scores. DEs under the scenarios described above are 

given in Table 5-1. From this analysis, it appears that increased drought degrades reference sites 

enough to reduce the ability to discriminate impaired from reference conditions for both the 

benthic IBI and EPT taxon richness. Interestingly, the median value under both dry and wet 

conditions was reduced compared to normal conditions in the intermediate sites, indicating a net 

impairment from normal conditions. Also, the overall spread or variability of reference IBI 

scores increased in both the wet and dry scenarios. 

Benthic macroinvertebrates were sampled in spring, and fish were sampled in late 

summer and fall. In wet years, the fish IBI showed much higher variability in reference sites, 

reducing the discrimination efficiency (see Figure 5-7, Table 5-1). Late summer and fall are 

slightly drier than other times of the year: the 30-year median of the PHDI during the fish 

sampling index period is less than −1 (Figure 5-2). Thus, wet conditions during the fish 

sampling period may represent a greater departure from a median expectation than do dry 

conditions during the invertebrate sampling period. This may explain the increased variability of 

the reference site fish IBI values under wet conditions than under dry conditions, and the 

reduction of discrimination efficiency. 

The pattern of extreme (wet or dry) hydrologic conditions both decreasing mean index 

values at reference stations and increasing variability demonstrates a tendency for these surrogate 

estimates of hydrologic changes associated with climate change to drive reference locations to be 

more like impaired locations, and thus decrease the ability to discriminate between the two based 

on biological indicator data. 

The association of climatic condition on the relationships between EPT taxa and two 

environmental stressors, conductivity and impervious surface, which had shown good stressor- 
Ref Intermed Deg

Dry

0123456

Fish BI I

Ref Intermed Deg

Normal

0123456

Fish BI I

Ref I

0123456

Fish BI I

Figure 5-7. Fish IBI performance in three climatic conditions. The dry, normal and wet designatioeach of the three graphs refers to categorizations based on the PHDI. 

 5-16
Table 5-1. Discrimination efficiencies of IBIs and EPT taxa under 3 climatic 

conditions 

Climatic condition Benthic IBI EPT Taxa Fish IBI 

Base (current normal year) 100% 100% 69% 

Dry year 64% 78% 60% 

Wet year 98% 95% 16% 

response relationships, was examined further (Appendix Figure B-6). The full data set (both 

Piedmont and Highland warm-water streams) was included in these analyses. 

The plots of the stressor-response relationships from all three climate scenarios were 

overlaid to determine whether changes in the response curves might be associated with the 

climate change scenario, namely increasing drought or increasing storm events. Figure 5-8 

shows the stress-response relationships (with linear regressions) between EPT taxon richness and 

conductivity for the Piedmont region, and the conditional probability analysis. First, mean 

number of EPT taxa is generally higher in the base condition, and reduced under wet conditions, 

with little difference between base and dry conditions. 

The conditional probability analysis (see Figure 5-8b) examined the probability of 

impairment along the stressor gradient. EPT taxa <8 was defined as the threshold of impairment, 

consistent with the threshold used by Maryland DNR in the Piedmont (Southerland et al., 2005). 

Conditional probabilities of EPT impairment under base, wet and dry conditions, show that the 

probability of impairment is higher under the wet scenario than under baseline conditions This is 

not merely the result of reduced conductivity in wet years because the overall distribution of 

conductivity in wet and normal years is almost identical (see Figure 5-9; CDF of conductivity). 

Under dry conditions, the probability of impairment was greater at low conductivities, and less at 

high conductivities, though the actual difference in numbers of EPT taxa were small. 

The natural conductivities of streams in the region are generally low due to low buffering 

capacities of the parent rocks and soils, with the exception of limestone-influenced streams in the 

Great Valley, in smaller limestone valleys of the Ridge and Valley ecoregion, and marble 

formations in the Piedmont ecoregion (Woods et al., 1999). Increased conductivity is 

consistently and reliably associated with reduced stream biological condition throughout the 

Appalachian region (Gerritsen and Zheng, unpublished data). 

Figure 5-10 shows the relationship with impervious surface for the climate scenarios. 

One of the consequences of urbanization is an increase in impervious area from roads, parking 

5-17
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Figure 5-8. (a) Relationship of EPT richness to conductivity under drought 

(red), base (blue), and wet (black) conditions and (b) conditional probability 

in impairment for the same three relationships. 

5-18
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Figure 5-9. Conductivity CDFs—Piedmont. Drought—red, base—blue, 

wet—black. 

5-19
0.01 0.10 1.00 10.00

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Figure 5-10. (a) Relationship of EPT richness to impervious surface under 

drought (red), base (blue), and wet (black) conditions, and (b) conditional 

probability of impairment for the same three relationships. 

5-20
lots, and rooftops. Impervious surface increases the “flashiness” of streams, as well as being a 

conduit for urban contaminants and pollutants. Overall, the base group (i.e., average hydrologic 

conditions) had higher levels of EPT taxa than either the drought or the storm groups, but the 

differences were subtle, a difference of approximately 1–2 taxa, and the differences were not 

consistent. Drought conditions yield a higher risk of impairment with impervious surface, but 

the change is marginal. 

5.4. DISCUSSION AND KEY CONCLUSIONS 

Several biological indicators and their associations with stressors have been examined 

under scenarios of normal, relatively dry, and relatively wet conditions. These scenarios were 

derived by partitioning a long-term data set from the Mid-Atlantic Piedmont and Appalachians 

by moisture conditions estimated by the PHDI. Some caveats regarding the sampling design and 

the partitioning include the following: 

• Grab-sample temperature and water chemistry measurements did not coincide with the 

benthic macroinvertebrate samples. Macroinvertebrates as well as nutrients were 

sampled in spring (March–early May), coincident with the spring freshet; fish and water 

quality (temperature, DO, conductivity, habitat, etc.) were sampled in late summer (June–

September), coincident with annual low water. Although the MBSS deploys continuous 

temperature loggers during the summer index period (June 1–September 30), these data 

were not available at the time the analyses for this report were conducted, and the 

deployment still does not cover the benthic sampling index period. Different index 

periods for the organisms would have resulted in different drought index estimates. 

• The PHDI applied to the month and year a site was visited; all sites sampled in the same 

month (e.g., March 1999) and NCDC district (e.g., Piedmont) would have the same PHDI 

value. 

• The climatic conditions we examined are all recent, from the period 1995–2005. Future 

climate is expected to show a greater frequency of extreme conditions, but they have not 

been linked to the frequency and magnitude of climate projections and models. 

Differences in median values and distributions of several biological indicators associated 

with dry, normal, or wet conditions were not observed; however, the associations may have been 

due to an “unlucky” random sample and can not be ruled out—especially at the basin level. All 

samples from a particular basin-year sampling would fall in the same dry-normal-wet category, 

and there is no assurance that basins sampled in any one year are representative of the range of 

stressor conditions throughout the region, especially with respect to urbanization. 

In spite of these caveats, the results indicate the potential consequences of climate change 

on bioassessment indicators. In dry and wet years, indicator variability increased markedly in 

5-21
reference sites and there were slight reductions in median indicator values. Consequently, there 

was reduced ability to discriminate between reference and stressed sites under dry conditions 

(especially for macroinvertebrates) and under wet conditions (especially for fish). Associations 

of the indicators with stressors, which are used to develop stress-response relationships for SI 

(Suter et al., 2002; Norton et al., 2002), may also change as evidenced by the apparent response 

to conductivity; though there was little change in response to impervious surface. 

5.4.1. Reference Conditions 

These results illustrate the potential sensitivity of reference sites to climate change. 

Reference sites in many regions of the country are not pristine, but are merely the “best 

available” in the region. This is especially true for the eastern Piedmont ecoregion, which has 

been settled, farmed, and industrialized since Colonial times. It is unlikely that there are any 

sampled watersheds in the Piedmont of Maryland that are free of suburban development; the 

average population density of HUC-8 accounting units in the Maryland Piedmont ranges from 

111 to >400 persons per square kilometer (1990 census; U.S. EPA, 1997). 

Moderately stressed reference sites may be more sensitive to slight increases in additional 

stress due to climate change than truly minimally stressed reference sites (Stoddard et al., 2006). 

Therefore, it would be important to identify minimally stressed reference sites if they exist, to 

document reference site selection criteria, whether minimally stressed or not, and to monitor 

reference sites to document changes over time. 

5.4.2. Importance of Monitoring 

To be able to account for the effects of climate change on biological indicators and on 

stressor-response relationships, it will be necessary to monitor a set of fixed sites over time 

(“sentinel” sites), such that the same sites are revisited. Systematic changes in biological 

attributes can only be attributed to climate change if other potential causes are eliminated or 

accounted for, hence the need to have sentinel sites that span a wide range of other potential 

stressors, and not just least-stressed reference sites. 

Because climate change effects are pervasive, components of trends that are common to 

all sentinel sites can be assumed to reflect climate change effects. If no other degradation was 

occurring at reference sites, then the magnitude and variation in trends at reference sites could be 

used directly to characterize the climate change component and account for that component 

within trends observed at non-reference sites. However, assumptions of continued “pristine” (or 

even steady) conditions at reference sites are unlikely over time, given population growth, 

invasion of non-native species, expected encroachment of suburban and other land uses, 

increased water withdrawals for human use, and other landscape-scale effects. Even if 

5-22
recommendations to protect reference sites are adopted, lack of contribution from landscapescale stressors would have to be verified in the process of estimating climate change-associated 

trends. 

Once trends common to all sentinel monitoring sites are defined, different components of 

trends at non-reference sites can be considered potentially due to other stressors and evaluated 

through the SI approach. 

5.4.3. Analytical Methods 

A question that arises is whether there are more robust or more powerful analytical 

methods that can overcome the projected degradation in signal quality and discrimination ability. 

Unfortunately, it is the quality of the information (signal to noise) that will degrade, and not the 

analytical methods. If the information is degraded, then no amount of statistics can recover 

something that no longer exists. Nevertheless, tracking time trends at both reference and nonreference “sentinel” locations over time provides a framework for defining climate changeassociated trends and differentiating these from the effects of conventional stressors that are of 

regulatory interest. 

In view of the likelihood of ubiquitous biological degradation due to climate change, it 

becomes increasingly important to protect reference sites from degradation. Application of the 

Biological Condition Gradient (BCG) (a kind of universal measurement yardstick that will be 

explain in Section 5.4.6) and Tiered Aquatic Life Uses (TALU) would establish a framework for 

such protection (U.S. EPA, 2005) (see also Section 5.4.6). For example, one expected outcome 

of defining TALUs is that states would adopt “high” and “exceptional” quality use classes along 

the BCG, which would be above their current action threshold for “fishable/swimmable.” Each 

aquatic life use class would have biological criteria associated with it, which would allow 

detection of degradation at reference sites at a stage substantially before the reference site would 

be “impaired” under current definitions. Such a formalized process also provides for 

implementation of particular management actions, such as identification of the cause of 

impairment and implementation of corrective actions. 

5.4.4. Stressor Identification 

At least some associations of the indicators with stressors, which are used to develop 

stressor-response relationships for SI (Suter et al., 2002; Norton et al., 2002), are expected to 

change as hydrological conditions are altered by climate change. There was a marked change in 

the stressor-response relationship between macroinvertebrates and conductivity under wetter 

than usual conditions, which was associated with an increased probability of impairment. 

However, almost no response was observed for impervious surface. SI may be similarly 

5-23
hampered by pervasive degradation and increased variability of all sites. If the conductivity 

stressor-response in the wet condition is considered a typical scenario, then conductivity is 

implicated in a smaller fraction of impairment (because the baseline frequency of impairment is 

higher), yet the threshold water quality criterion for conductivity would also be lower. That is, 

protection from degradation by conductivity may need to be tighter and set at a lower 

conductivity than before the climate changed. 

5.4.5. Biocriteria 

Increased variability of reference sites as a consequence of climate change could decrease 

the ability of states to detect impairment, if impairment thresholds are determined by a statistical 

percentile of the indicator distribution in reference sites. Many states use a lower percentile of 

the reference distribution as a numerical biocriterion for 305(b) assessment, for example, the 25th

percentile (Ohio EPA), or the 10th percentile (Maryland), or the 5th percentile (West Virginia). If 

climate change causes the percentiles to drift downward, and the state reevaluates its water 

quality criteria with new data, then the new criteria may set a lower bar, i.e., permit more 

degradation to take place, before any kind of management is implemented (e.g., total maximum 

daily load (TMDL) calculations). The potential drift of reference site condition due to climate 

change illustrates the importance of establishing a universal measurement scale of biological 

condition (e.g., the BCG) so that reference site drift can be identified as such. 

5.4.6. Universal Scale to Measure Biological Condition 

Acceptable biological condition is determined in many states from statistical properties of 

a numerical index. Index values and criteria vary widely from state to state because of 

differences among data sets used to develop the respective indexes. Furthermore, the criteria 

“action level” often reflects substantial biological degradation from relatively undisturbed 

conditions, such that the highest quality waters are not adequately protected. Results of this case 

study demonstrate that biological responses to climate change may further confound assessment 

and criteria for water management. To resolve these issues, panels of state and academic aquatic 

biologists have proposed a conceptual model for a universal measurement scale of aquatic 

biological condition—the BCG (Davies and Jackson, 2006). 

The conceptual BCG model describes ecological changes that take place in flowing 

waters with increased anthropogenic degradation, from pristine to degraded (Davies and Jackson, 

2006). The BCG promotes consistency among agencies in the application of the CWA by 

identifying tiers, or condition classes, that can be operationally defined in a consistent manner. 

The model is intended to be broadly applicable to any kind of stream; the tiers are independent of 

actual monitoring methods. Although the model promotes conceptual unification, it recognizes 

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regional natural variability, and is not applied as a one-size-fits-all approach. The BCG is a 

general description of change in aquatic communities, it is consistent with ecological theory, and 

the BCG approach has been verified by aquatic biologists throughout the U.S. (Davies and 

Jackson, 2006). 

Calibration of the BCG to local conditions, and on a nationwide basis, would help 

establish two baselines that would reduce the effects of confounding by climate change. The 

first baseline is the description of pristine or nearly pristine conditions, Tier 1 of the BCG. In 

many regions, the description of Tier 1 must rely on historical descriptions of fauna and 

historical ranges of organisms (these may be available for fish, but rarely aquatic invertebrates), 

on modeling approaches, on best professional judgment, or on sites available across political 

boundaries (Stoddard et al., 2006). The second baseline is the description of the present-day 

reference, or least stressed condition, before large-scale effects of climate change have occurred. 

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6. RECOMMENDATIONS FOR U.S. EPA TO IMPLEMENT A FOUNDATION 

FOR STATE/TRIBAL BIOASSESSMENT/BIOCRITERIA PROGRAMS 

TO CONSIDER CLIMATE CHANGE 

6.1. RECOMMENDATIONS FOR U.S. EPA 

Results of the case study analyses to date, continued development and review of indicator 

sensitivity classification, and discussion and input from state/tribal biocriteria managers at the 

Workshop in March 2007 provide the basis for recommending the focus of ongoing and future 

efforts to continue development and implementation of a framework for biological assessment 

programs to account for climate change effects. Recommendations can be categorized as 

technical requirements and resource requirements. Technical requirements focus on information 

needed to better understand the interactions between expected effects of climate change and 

biomonitoring program endpoints, additional technological support, and general policy support. 

During the Workshop, some of these activities were identified as falling within the purview of 

the U.S. EPA’s ORD, and some in the purview of U.S. EPA’s Office of Water (OW). These 

identifications are made after each recommendation. 

The ORD can 

• Conduct further research through pilot studies (see Section 7) to determine the best 

hydrologic and biological response indicators, to define biologically sensitive measures 

to hydrologic changes, and to identify species traits responsive to climate change 

(temperature, flow, sediment). 

• Investigate how taxa replacement will affect biological indices used in state programs. 

Determine the extent of change in the biological indices if specific metrics are changed. 

• Develop and provide technical guidance regarding program adaptations and other 

approaches needed to account for climate change in biological assessment programs, 

including categorization of indicators (metrics), modification of monitoring designs, data 

analysis approaches, etc., through guidance documents and/or website support . 

• Fill gaps in knowledge and available modeling tools and outputs between regional 

climate, hydrologic, and ecological models. 

• Develop tools to make climate data available to other models (e.g., CADDIS). 

The OW can 

• Include language in U.S. EPA grants, policies, etc. on climate change as a stressor for 

monitoring and assessment programs, to establish climate change as an important 

program focus. 

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• Provide assistance to state bioassessment and resource management programs to integrate 

the concept of climate change as a significant issue that should be accounted for in 

assessing the condition of aquatic resources. 

• Evaluate Water Quality Standards to be protective in the face of a changing condition 

paradigm. 

• Provide funding support for state/tribal water quality programs to assist in adaptations to 

existing programs. 

• Provide support for identification and sampling of reference sites, re-sampling of 

reference sites, and more intensive characterization of reference and sentinel sites. 

Together, the ORD and the OW can 

• Conduct additional workshops to begin the process of evaluation and development of 

recommendations for other aquatic ecosystems (e.g., large rivers, lakes, wetlands, coral 

reefs, estuaries). 

• Develop a nationwide database of state biological monitoring and assessment data to 

support evaluation of national/ecoregional climate change trends and effects. 

• Transfer technology for use of equipment, such as in situ temperature monitors, that 

could be used to extend and enhance the value of monitoring data collected by state 

programs with limited resources, including incorporation of processes and guidance. 

• Provide technical support for data management tools (e.g., R code) to manage 

temperature logger data and reduce it to useable metrics. 

• Form partnerships across the U.S. EPA and other federal agencies on a comprehensive 

climate change strategy to address mandates of CWA. 

• Provide a summary of this meeting to U.S. EPA top management for information and 

support for making informed decision-making. 

States and tribes attending the Workshop also discussed resource limitations that impact 

their ability to implement new and/or additional efforts related to revising and adapting their 

existing programs to account for climate change. These limitations could be addressed through 

• funding support 

• personnel support 

• priority setting for management actions 

• assistance in developing and supporting a structure for sharing resources among agencies 

to expand capacity 

6-2
6.2. RECOMMENDATIONS FOR STATES AND TRIBES 

Even with constraints of limited resources, state and tribes participating in the Workshop 

identified several potential program adaptations they considered feasible with resource and 

technical assistance from the U.S. EPA. These actions include the following: 

• Conduct regular and repeat reference site sampling. 

• Consider strategies for maintenance and protection of reference sites and areas, including 

identification of water bodies in the best condition. 

• Evaluate the need to shift the sampling index period and/or expand sampling seasons. 

• Establish sentinel sites for trend monitoring. 

• Improve hydrological and temperature data collection. 

• Retrieve historical data records to establish a basis for evaluating climate change. 

• Incorporate traditional ecological knowledge, citizen monitoring, and phenological 

knowledge in assessment of biomonitoring data. 

• Continue the refinement of biocriteria programs to incorporate the Tiered Aquatic Life 

Use (TALU) strategy. 

• Accept moving target paradigm versus steady state model and adapt accordingly. 

• Perform critical elements reviews of individual programs to identify relevant refinements. 

• Engage in collaborative data and resource sharing to maximize limited resources. 

6-3
7. POTENTIAL NEXT STEPS 

Several components of work, with somewhat different time frames, should be 

contemplated to expand understanding of climate change effects on bioassessment programs and 

develop a toolbox of appropriate responses. 

1. Plan and conduct a national workshop for state and tribal WQ agency managers and 

biologists on the next most appropriate ecosystem. There would be benefits to 

developing and conducting a workshop for any of the remaining ecosystems of interest 

with regard to bioassessment and biocriteria programs (large rivers, lakes, freshwater 

wetlands, coastal wetlands, and estuaries). There are compelling reasons to consider 

lakes and freshwater wetlands, including: 

• Lakes have the next most well developed bioassessment and biocriteria programs, 

and, together with flowing systems, they have the most TMDL issues with 

biologically impaired waters; thus, integrating aspects of climate change with the 

TMDL process is important here. 

• Lakes are the next system of focus in the U.S. EPA’s national assessment of 

ecological condition of the Nation’s aquatic resources. 

• There is substantial overlap in state/tribal bioassessment/biocriteria scientists 

and/or managers dealing with streams/rivers and lakes (often the same 

individuals), providing an immediate opportunity to involve states and tribes that 

were unable to participate in the first stream/river-oriented workshop, while still 

expanding outreach to another ecosystem. 

• Climate change effects in lakes, and freshwater wetlands will probably be similar 

to effects in streams/rivers, but also would have different ecological responses 

with different levels of importance (e.g., wetlands may be particularly susceptible 

to droughts). Thus, this effort would build upon the experience and knowledge 

developed in the Workshop and expand both the knowledge base as well as 

consideration of ecosystem responses. 

• Combining lakes and freshwater wetlands offers some efficiencies in 

summarizing and discussing current information on climate change projections 

and evidences that are most pertinent to inland freshwater non-flowing 

ecosystems, despite ecological differences that certainly will have differences in 

ecological processes and in responses that are most important. 

• Inclusion of freshwater wetlands with lakes provides an opportunity to consider a 

system much less advanced in the process of bioassessment/biocriteria program 

development (wetlands) in an earlier time frame. 

2. Implement a more in-depth assessment of climate change effects on stream and river 

bioassessment programs in a detailed pilot study that would include selected states. It is 

recommended that states in different parts of the U.S. be targeted to serve as regionally 

distinct pilot studies. Ideally, as many as four states distributed regionally should be 

7-1
included, and, at minimum, two states should be included to account both for regional 

(ecological) variations and for differences between bioassessment programs to at least 

some extent. Some important considerations for including states in this pilot study are 

• Regional distribution, preferably representing a spectrum of very different 

geographical and ecological areas. 

• Continuity and temporal duration of data sets available (ideally at least 20 years) 

with comparable collection and analytical methods that would support rigorous 

long-term analyses. 

• Willingness of state personnel to be involved and interactive throughout the 

analysis process—to maximize effective consideration of state- and locationspecific issues. 

• Information from multiple basins or watersheds is typically needed to characterize 

the breadth of variation in stressors and responses. An analysis approach should 

be developed that includes several major aspects. 

• Evaluation of all the specific metrics and the composite indices used in the state. 

• Consideration and incorporation of the ecological traits of the species included in 

the state database (classification by ecological traits and sensitivities may already 

exist for the state databases likely to be utilized, especially if they completed 

development of biological indices). 

• Use of the long-term data sets to investigate and document existing evidence of 

climate change. 

• Compilation of thermal tolerance information for fish and invertebrates as a 

resource to support predictions of probable climate change effects. 

• Evaluation of the sensitivity of component metrics and biological indices to 

climate change effects, possibly including recalibration of indices (and/or the 

index development process) to identify components that may be more robust. 

Analyze how biological indices can be modified to detect or exclude climate 

change effects; investigate how taxa loss, replacement, and other predicted 

responses will affect multimetric and other biological indices. 

• Evaluation of index sampling periods, including the possible need to shift or 

expand recommended sampling periods to better account for climate change 

effects. 

• Incorporation of the BCG and TALU into the analysis framework, to evaluate, for 

example, how climate change degrades reference sites over time between tiers 

above the CWA “fishable/swimmable” threshold, how this progressively impacts 

detection of impairment and identification of stressors, and how reference 

locations can be classified and protected. 

• Use of ecological, habitat, and climatological data to characterize climate changes 

and resulting changes in biological structure and function, especially in reference 

sites or other benchmark for assessment of condition. May introduce targeted 

7-2
species/communities changes to the data to mimic climate change responses for 

“future” analyses, based on documented projections for local/regional climate 

effects and knowledge of species traits and sensitivities. Relate findings to state 

WQ standards and designated uses as an example of confounding factors for 

assessing and determining impairment. 

• If scope of effort allows, evaluation of some novel indicators/metrics identified in 

the framework based on extant research reported in the literature. Consideration 

should be given to the feasibility of long-term, spatially distributed measurements 

that could be made within the framework of a monitoring program; and to 

robustness and interpretability of results with regard to climate change effects and 

other stressors. 

• Beyond the physical and chemical habitat data and biological data typically 

collected in bioassessment programs, it will be very important to have 

comprehensive climatological data corresponding to the regions being analyzed. 

Projection of precipitation data to all sampling locations may be important. More 

specifically, it may be important to be able to develop site-specific hydrologic 

projections. In the preliminary case studies, the PHDI was used to project 

possible effects of dry years and wet years to establish a proxy for projected 

climate effects of increased summer droughts and increased precipitation. An 

alternative would be to develop site-specific hydrological estimates to correspond 

to sampled biological data. The calibrated FTSE model can be used to estimate 

high and low flow conditions for a specific site and a specific time period, to 

estimate hydrologic conditions associated with a given sampling event. Such 

hydrologic projections produced by the model could be informative in estimating 

the effects of dry periods, or of numerous storm events, and in projecting future 

climate changes. 

• Having sufficiently detailed climate change projections for the states that will be 

evaluated also is of great importance. It is clear from the Workshop just 

conducted that detailed regional downscaling from GCMs are possible, and that 

the technical approaches for developing these are improving. It was also clear 

that such regionally specific modeling is not accomplished for all areas. An effort 

will be needed to determine the nature of modeling results available for each 

state/region considered in the pilot study, and to interact with the appropriate 

climate modeling scientists to understand the status of these results and obtain 

needed outputs. 

3. Plan a special JNABS issue and special workshop/session at the ASLO/NABS 

conference in 2010 on the effects of climate change on biological indicators. This would 

provide a scientific forum to articulate the known science of the effects of climate change 

on biological indicators. This publication/special session is a follow-on to an earlier 

ASLO/NABS collaboration held in 1998. 

• Special publication series in the Journal of the North American Benthological 

Society would bring together international scientists working on the concept of 

climate change upon aquatic ecosystems, particularly biological indicators. 

7-3
Ideally, the papers would be published prior to the joint congress of the two 

societies in June 2010. 

• Special session devoted to climate change would be held at the joint congress and 

would be highlighted as a key theme of the congress. The international scientists 

in the publication series would be the featured speakers in the session at the 

ASLO/NABS conference to be held in Santa Fe, New Mexico in June 2010. 

4. Work across U.S. EPA and state programs to develop a national database compiling all 

available state/tribal bioassessment data to support regional and national-scale evaluation 

of climate change status and trends. To consider this strategy, it is suggested that 

development of a national database compiling all available state bioassessment data be 

considered to support regional and national-scale evaluation of climate change status and 

trends. At least two frameworks exist, which should be considered for adaptation to this 

purpose. 

• Oracle-based Ecological Data Application System (EDAS), an extension and 

improvement over Access-based EDAS that is already used by many states. This 

is a purpose-tailored database for bioassessment data, which accommodates 

physical, chemical and habitat data, and biological data for multiple assemblages 

including detailed taxonomic review and manipulation. In addition (and 

importantly), it includes built-in analyses that support all the steps in 

bioassessment, metric evaluation and index calculations and development. 

• WQX, the replacement for STORET, is being designed to accommodate existing 

state bioassessment data, but is not quite ready to house the volumes of state 

ecological data. The existing accessibility to all states is an advantage of this 

option. A disadvantage is the lack of associated bioassessment-specific analytical 

capability. This lack could be addressed relatively easily by developing an 

analysis front-end (from existing resources to a great extent). The handling of 

taxonomic data in WQX is potentially another disadvantage that may be more 

difficult to address. 

• The effort to establish a national data base with acceptable quality control, 

comparable data (considering taxonomy, reporting units, collection and analytical 

methods, sampling index periods, and many other factors) would be substantial. 

Analyses would be relatively simple once this was accomplished. It may be (and 

perhaps is likely) that not all state data would be adequate for inclusion, and 

certainly there will be large differences in spatial coverage, and especially in 

chronological longevity of the data sets. 

7-4
8. CONCLUSIONS 

The review of the literature on climate change effects on aquatic ecosystems shows that it 

is likely that changes are already occurring (see Section 1). Although current sampling schemes 

used by bioassessment programs are not explicitly designed to detect climate change effects, it is 

possible to use the data for this purpose. The case studies presented in this report demonstrate 

this capability. While the first case study focuses on the length of time it would take to detect a 

specific effect due to climate change under a variety of scenarios, it is important to remember 

that the aquatic systems being surveyed are probably already somewhere on the trajectory toward 

a detectable effect. Recent climate change reports underscore this point that systems are not at 

time zero with respect to effects (IPCC, 2007). 

Existing and ongoing climate change effects have impacts within bioassessment 

programs that affect how benchmarks are set and how expectations for acceptable conditions are 

anchored. Monitored reference conditions now reflect temporally changing conditions, and the 

results from case study two underscore the importance of monitoring reference sites. 

Characterizing climate change as an additional but global stressor must be accounted for within 

monitoring designs, analytical approaches, and assessment frameworks. Ultimately, efficacy of 

the current programmatic approach to definition of acceptable and/or desirable conditions and 

assessment of the need for regulatory intervention in the management of water resources requires 

an understanding of all significant influences on the systems being assessed and regulated. It is 

critically important to be able to distinguish between multiple stressors, and this is done through 

the acquisition of high-quality bioassessment and other ecological data. This, in part, guarantees 

the integrity of regulatory decisions through appropriate program adaptations. 

8-1
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APPENDIX A

REGIONAL PATTERNS OF CLIMATE CHANGE PROJECTIONS AND 

CONSEQUENCES FOR RIVERS AND STREAMS
A-2

A.1. CLIMATE CHANGE AND FUTURE PROJECTIONS

The rate of global warming has increased over the last century; the linear average over 

the last 50 years (0.13oC per decade) has almost doubled the linear rate over the last hundred 

years (0.074 oC per decade) (IPCC, 2007). The current rate is estimated at about 0.2oC per 

decade (IPCC, 2007; Rahmstorf et al., 2007; Hansen et al., 2006) and may increase in the future. 

There also have been widespread changes in precipitation. Frequency of heavy precipitation 

events is also predicted to increase over most areas, as is the frequency of droughts (IPCC,

2007). 

Climate change will continue and temperatures will potentially increase in the future 

(IPCC, 2007). General projections for the year 2100 include global average temperature

increases of 1.1–2.9oC (from the lowest emissions scenario) to 2.4–6.4oC (from the highest 

emissions scenario). Increases in precipitation are predicted, with a higher percentage of total 

precipitation occurring in more frequent and intense storms. Other projections include more 

precipitation in winter and less precipitation in summer; more winter precipitation as rain instead 

of snow; earlier snow-melt; earlier ice-off in rivers and lakes; and longer periods of low flow and 

more frequent droughts in summer (Hayhoe et al., 2007; IPCC, 2007; Barnett et al., 2005; Fisher 

et al., 1997). Changes in temperature and precipitation will have regional differences that will be 

important for assessing ecological effects.

A.1.1. REGIONAL PATTERNS

Several points must be considered to understand how the existing and future climate 

changes are likely to affect aquatic ecosystems, specifically streams and rivers. Ecosystems do 

not respond to global averages but to regional and local patterns (Walther et al., 2002). Regional 

patterns of climate change are affected by factors that include atmospheric circulation patterns, 

topography, land use, and region-specific feedbacks (Hayhoe et al., 2007), and they are more 

difficult to project because of these localized factors. Within the U.S., regional projections for 

future temperature increases are variable among models, but almost all regions project greater 

temperature increases in winter than summer (NAST, 2001). Table A-1 presents a summary of 

regional climate change projections from the National Assessment and Synthesis Team (NAST) 

(2001). Increased frequencies of extreme hot days (and decreases in extreme cold days) are also 

projected throughout the U.S., with the greatest increase projected for the Southwest (Seager et 

al., 2007). Other notable increases are projected for high-elevation areas of California, central 

Utah, central Idaho, and the Appalachian Mountains (Diffenbaugh et al., 2005). 
A-3

Table A-1. Summary of regional climate projections (NAST, 2001). Averages and/or ranges for Hadley (H) and 

Canadian (C) model projections to 2100 compared to 1961–90.

NortheastaSoutheastb Midwestc Great Plainsd Weste

Pacific 

Northwestf

Temperature Average 

annual

2.6–5

oC increase 2.3–5.5oC 

increase

2–6

oC increase 

(mins increase 

more than max)

Increase >3oC 

(both models)

4.5–6

oC 

increase

3

oC increase 

by 2050

Winter min 4 to 2–7

oC 5–7oC (winter 

average)

4.9–7.1oC 

(winter average)

4.7–5.9oC 

(winter 

average)

Winter 

max

1.6–3 to 3–6.6oC

Summer 

min

3

oC 4–5oC (summer 

average)

4.3–4.6oC 

(summer 

average)

4.1–4.6oC 

(summer 

average)

Summer 

max

1.3 to 4–6

oC

Precipitation Average 

annual

Small increase to 5–10% 

decrease (C); up to 25% increase 

(H)

10% decrease 

(C) to 20% 

increase (H)

Increase 20–40% 

by 2100 in upper 

Midwest, decrease 

up to 20% Ohio 

Valley (C ); 

average 

precipitation 

increases 

everywhere, up to 

20–40% (H)

13% increase 

(both models)

Double winter 

precipitation in 

CA, decrease in 

parts of Rockies 

(both models); 

summer 

precipitation no 

change (C ) to 

decrease (H).

Increase of a 

few to 20% 

(average 10%) 

(H); increase 

0–50% 

(average 30%) 

(C )

Extreme 

events

Small increase in count and 

strength of storms (H); decrease 

in count for most of region 

except mid–Atlantic (C)

Increase in mean 

associated with 

increase in 

frequency and 

intensity of heavy 

events

Likely increase, 

esp. in southern 

GPs

More extreme 

wet and dry

years (both 

models)

Winters wetter 

and warmer, 

increase in 

precipitation 

in heavy 

storms
A-4

Table A-1. continued.

NortheastaSoutheastb Midwestc Great Plainsd Weste

Pacific 

Northwestf

Droughts Less (H) to more (C) drought Slightly drier 

(C) to more 

precipitation 

in long term 

(H)

Small increase in 

soil moisture (H); 

small decrease (C)

Decrease in soil 

moisture in 

large part of 

region

Runoff Magnitude More high flow events in winter 

(+80% in northern areas under 

higher emissions).

Lowest weekly flow projected to 

decrease ~10% by 2100. 

For a 2.5oC increase in average 

temperature and 17.5% (17.5 

cm) increase in precipitation, 

flow at the mouth of the 

Susquehanna will increase 24% 

+ 13% (11.8 + 6.7 cm) (Najjar et 

al., 2000). Neff et al. (2000) 

projects decrease of 4% to 

increase of 24%. Moore et al.

(1997) estimates decrease of 

21–31% by 2100

Overall decrease 

in river levels (i.e., 

decrease in 

runoff?)

Great Lakes 

area runoff 

decrease up to 

32% (Magnuson 

et al., 1999)

Summer runoff 

less by 20% of 

annual total 

(Knowles and 

Cayan, 2002)

Timing Peak (spring) flows advancing 

10 to >14 days by 2100.

Low summer flows extended 

almost 1-month under higher 

emissions
A-5

There are substantial regional and model differences in projections for precipitation 

changes. The biggest average increases are projected for the Pacific Northwest and Midwest 

(10–30% by 2100) and the Northeast (up to 25% by 2100), with smaller increases in the Great

Plains (13% by 2100), and variable projections for the Southeast (10% decrease to 20% increase

by 2100) and the West (e.g., doubling of winter precipitation over California, but decreased 

precipitation over some parts of the Rockies) (NAST, 2001). Most of the increase in 

precipitation is projected to occur during the winter, with more frequent and/or more intense 

storm events, and with more winter precipitation as rain instead of snow (e.g., Polsky et al.,

2000; Magnuson et al., 1997). 

Most models project increases in evapotranspiration—due to increased temperature rather

than increased summer precipitation—leading to a net decrease in soil moisture and a greater 

likelihood of late-summer drought (NAST, 2001).

A.1.2. HYDROLOGY

There are also secondary drivers that are important in structuring aquatic ecosystems that 

will be altered by climatic changes, especially hydrologic regimes (e.g., Poff and Ward, 1989; 

Richter et al., 1996). Projected hydrologic changes, driven by climate-associated changes in 

temperature and precipitation, include changes in the magnitude, timing, frequency, and duration 

of various flow events. Together these projected hydrologic changes will result in redistributing 

stream flow (Hayhoe et al., 2007). 

In North America, projected changes in average stream flow range from an increase of 

10–40% (at high latitudes) to a decrease of about 10–30% (in mid-latitude western North 

America) by 2050 (Milly et al., 2005). Consistent with this large-scale pattern, Hayhoe et al. 

(2007) predict an increase in stream flow in the northeastern U.S., ranging from and increase of 

9–18% in the southwest part of the region to an increase of 11–27% in the northeast part of the 

region. In an earlier study, in the U.S. Northeast, however, average stream flow was projected to 

decrease by an average of 21–31%, reflecting a temperature-associated increase in 

evapotranspiration that will exceed small net changes in precipitation (increases in winter and 

spring and decreases in summer and fall) (Moore et al., 1997). Hayhoe et al. (2007) attributes 

these differences to their use of updated forcing scenarios that include projected increases in 

winter precipitation. Patterns of stream flow also are projected to change in the Northeast, with 

increases in stream flow occurring mainly in the winter and spring, but with lower stream flow in 

the summer and fall (Hayhoe et al., 2007). Climate changes in the Northeast may also include 

more intense thunderstorms, especially in the summer. If this happens, it could result in greater 

variability and ―flashiness‖ of stream flows (Moore et al., 1997). In the Great Lakes Basin, 
A-6

average basin runoff is projected mainly to decrease up to 32% in response to increased 

temperatures, despite precipitation increases (Magnuson et al., 1997). 

In snow-pack dominated regions, the combination of warming temperatures, a shift 

toward less winter precipitation as snow, and snow-melt occurring earlier will transition the peak 

runoff from spring to late-winter/early spring (Barnett et al., 2005). Predicted shifts in peak 

runoff (anywhere from about two weeks to one month earlier) by the end of the century are 

anticipated (Dettinger et al., 2004; Hayhoe et al., 2007). 

Rain-dominated streams are expected to be especially responsive to altered precipitation 

patterns, with runoff, flow variability, and flood frequency responding directly to changes in 

precipitation. These streams may also respond to increased variability in precipitation, impacting 

flood frequency and variation in flow; and to increasing temperatures, causing decreases in 

runoff (Poff at al., 1996). In a Mid-Atlantic perennial flow (rain-dominated) stream, with a 25% 

modeled increases in precipitation, Poff et al. (1996) predicts mean flow (runoff) to increase 

about 10–15%, flow variability to increase about 20–25%, and flood frequency to more than 

double (from 1.1 to 2.5 floods per year). Doubling the coefficient of variation of precipitation 

had an even more dramatic effect on these hydrologic characteristics.

In the Mid-Atlantic region, using the Susquehanna River as a model, Neff et al. (2000) 

estimated annual changes in stream flow by 2100. Estimated changes range from –4%, based on 

the Canadian Climate Center (CCC) model (Flato et al., 2000; McFarlane et al., 1992) to +24%,

based on the Hadley model (Johns et al., 1997). Both models project increased stream flows 

during the winter, with peak stream flow occurring up to 1 month earlier.

Even with net projected increases in annual precipitation for many regions of the U.S., 

increased durations of low flows and increased frequency of summer droughts are also projected. 

In California‘s Sacramento/San Joaquin Basin, Knowles and Cayan (2002) predict a 20% loss in 

the amount of annual stream flow occurring during the summer (April–July) by 2100. Dettinger 

et al. (2004) made a similar prediction for the Merced, American, and Carson Rivers in 

California, including a decrease in summertime low flows and reduced soil moisture.

Finally, the increased temperatures will lengthen the growing season, as well as increase 

evapotranspiration during the warm months. These could have the net effect of reducing 

groundwater recharge of streams, which would increase the severity of summer dry periods 

independent of rainfall. This could, however, be mediated to some extent by the increased CO2

concentrations, which reduce evapotranspiration (e.g., Gedney et al., 2006).

A.1.3. WATER TEMPERATURE

In addition to hydrologic alterations, changes in stream temperature are also expected. 

The IPCC report projects a continued increase in global air temperature at approximately 0.2oC 
A-7

each decade (IPCC, 2007). Although these are global averages, regional models also support 

these projections. For example, Mid-Atlantic regional models project that average air 

temperature will increase 2.6–5.0oC by 2100 (Polsky et al., 2000; Barron, 2001). These 

temperature changes will increase the maximum, average, and minimum stream temperatures as 

well as the number of degree days and the rate of degree day accrual (Note: degree days are the 

cumulative sum of average daily temperatures above a baseline. For example, if the baseline is 

10oC, then one day with an average temperature of 12oC contributes 2oC days).

Though a relationship between increasing air and water temperatures is expected, the 

magnitude and seasonal patterns of changes in stream and river water temperatures are likely to 

vary regionally. These regional differences will be due to water source influences (surface 

versus ground water), watershed characteristics, and season. Stephan and Preudhomme (1993) 

estimated weekly average water temperatures in oC (excluding the ice cover period) to be a factor 

of 0.86 times the weekly average air temperatures for 11 streams in the Mississippi River Basin.

Eaton and Scheller (1996) used this same linear relationship between air temperature and stream 

water temperature to estimate probable loss of fish habitat due to global warming projections. 

However, Mohseni et al. (2003) claim that the relationship between air and water temperatures is 

better explained by an S-curve such that at higher air temperatures, stream temperature increases 

level off due to evaporative cooling. In the Upper Rhone River, Daufresne et al. (2003) showed 

a clear, though non-linear, correspondence between long-term increases in annual average air 

temperature (increase of about 1.0oC, 1979–1999) and average annual water temperature 

(increase of about 0.6oC, 1979–1999). Annual patterns were similar, but average annual water 

temperature did not correspond with average air temperature perfectly, suggesting possible 

influences of other factors such as annual variations in flow conditions or snow melt. In a review 

of the thermal regime of rivers, Caissie (2006) showed that thermal regime is strongly influenced 

by meteorology, river conditions, and geographic setting.

A.1.4. HABITAT

Stream hydrologic patterns control habitat stability, channel formation and maintenance 

(Poff et al., 1996), and they define composition, structure, and functioning of aquatic 

assemblages (Richter et al., 1996, Poff and Allan, 1995). Changes in hydrologic pattern, 

especially flood frequency, episodic runoff events frequency and intensity (including 

―flashiness‖), peak runoff magnitude, and flow total, will alter stream habitat and its dynamics. 

Flow dynamics not only influence sediment supply and transport and, therefore, channel form, 

but water volume also influences the amount of available habitat and water quality. Seasonal 

patterns of flow magnitude, duration and frequency of runoff events, and other parameters

strongly influence the types of species that can inhabit an area (Poff et al., 2002). As a result, 
A-8

over time, regional changes in hydrologic regime are expected to modify habitat, species 

composition, and ecological interactions. 

In the Southwest, Grimm et al. (1997) concluded that neither hydrologic nor climate 

models were sufficiently developed at that time to predict magnitude and direction of future 

climate changes. Instead, they identified stream and river surface flows in the arid Southwest as 

being particularly vulnerable to even small changes in precipitation, with even modest decreases 

in precipitation potentially causing large decreases in stream flow. In addition, increased 

temperatures could increase the likelihood of winter/early spring precipitation as rain instead of 

snow and could increase the likelihood of severe episodic flooding, while increased temperatures 

may also be associated with increased drought conditions during the summer. Associated habitat 

changes could include changes in riparian vegetation, shifts from perennial to intermittent flow, 

loss of aquatic habitat, and alterations in nutrient retention and instream production (Grimm et 

al., 1997). More recent evaluation of the numerous climate model outputs available provide 

consistent projections for existing and future decreases in annual precipitation minus evaporation

(increased aridity), especially during the winter (Seager et al., 2007), which will have substantial 

negative impacts on availability and condition of stream and river aquatic habitat. 

A.1.5. POLLUTANT BEHAVIOR

Stream water quality is expected to respond to changes in runoff magnitude and timing. 

Reduced flow in summer combined with increased temperatures will likely decrease dissolved 

oxygen (DO) concentrations, while increased storm frequency and magnitude are likely to 

increase introduction of silt and pollutants (Poff et al., 2002). In the Mid-Atlantic region, 

increased stream flow in the winter and the spring is expected to degrade water quality due to 

increased inputs of nutrients, sediments, and toxicants (Neff et al., 2000; Rogers and McCarty,

2000). For example, nitrate loads have a high, positive correlation with stream flow in this area, 

r

2

= 0.8, (Neff et al., 2000). However, nitrate loads are projected to decrease in July and August 

associated with projected decreases in stream flow, which could, in part, ameliorate low (DO) 

conditions. In boreal streams in northwestern Ontario, Schindler et al. (1996) reported 

substantially reduced runoff during warm, dry periods in the 1970s and  ̳80s that coincided in 

magnitude with similar increases in temperature and decreases in precipitation that are projected 

for future climate change in that region. There was also a substantial reduction in stream export 

of phosphorus in association with these periods, though not in nitrogen export. In the Northeast, 

overall drier conditions and reduced stream flow are expected to increase watershed retention of 

non-point source nutrients, to reduce nutrient runoff, and to reduce erosion; increased 

thunderstorm intensity could increase episodic erosion and nutrient loading (Moore et al., 1997). 

These region-specific examples help define general expectations (i.e., for all regions) for 
A-9

pollutant loading and other water quality (e.g., DO) changes that should be expected in 

association with climate-driven changes in temperature and runoff. 
APPENDIX B

DESCRIPTIVE AND SUPPLEMENTAL RESULTS FOR CASE STUDY 2:

BIOLOGICAL ASSESSMENT IN THE PRESENCE OF CLIMATE CHANGE
B-2

B.1. RESULTS OF CORRELATION ANALYSES

The fish and benthic invertebrate response variables that showed the strongest responses 

in these correlations were the 2005 version of Maryland’s fish index of biotic integrity (IBI), fish 

taxa richness, total number of fish, the 2005 version of Maryland’s B-IBI, total benthic taxa 

richness, and total EPT taxa richness (Southerland et al., 2005). A series of these stressor 

response relationships are shown below, and are used for further exploration of stressor-response 

models.

Relationships between flashiness and a variety of environmental variables were 

investigated to identify variables that are potential stressors related to flashiness. Flashiness is a 

hydrologic characteristic that can be reflective of stream and surrounding watershed alterations, 

and might also be responsive to climate change. Figure B-1 shows the relationships between 

Baker’s flashiness index scores (Baker et al., 2004) and eight other environmental parameters 

tested (dissolved oxygen, phosphate concentration, physical habitat index, conductivity, 

embeddedness, percent urban, impervious surface, and total phosphorus). In this and subsequent 

figures, the solid line is the locally weighted scatter plot smoothing line (LOWESS). LOWESS 

smoothing was done in Systat10 by running along the x values and finding predicted values from 

a weighted average of nearby y values. The surface is allowed to flex locally to better fit the 

data. For the LOWESS, the degree to which the line or surface (tension) is allowed to flex 

locally to fit the data to 0.5 was specified, meaning that half the points are included in the 

running window. The strongest relationships were between percent urban land use and Baker’s 

flashiness index score, and between impervious surface and flashiness. Both of these are factors 

contribute to alterations in watershed runoff that result in greater “flashiness.” Some other 

variables that are closely associated with runoff, such as nutrient concentrations, had relatively 

weak relationships with flashiness (e.g., total phosphorus and total organic carbon).

Figure B-2 shows the relationships between physical habitat index (PHI) and 

macroinvertebrate and fish IBI scores in the MBSS data. Overall, as habitat condition improved 

(PHI increased), the fish and benthic IBIs increased.

Comparisons were made between three fish response variables and a suite of 

environmental parameters. Figure B-3 shows the relationships between fish IBI scores and 

dissolved oxygen (DO), instream habitat, temperature, channel flow, flashiness, and impervious 

surface. Fish IBI scores increased with increasing DO, habitat score, and channel flow. Fish IBI 

declined with increased impervious surface and flashiness. The lack of relationship between fish 

IBI and temperature is at least, in part, related to the factors that collections are made only during 

a seasonal (summer/fall) index period, and that these analyses are being conducted on a 

combination of only two very closely related ecoregions.
B-3

Figure B-1. Flashiness vs. environmental variables; the solid line is the locally weighted scatter plot smoothing 

line (LOWESS).

0.20.40.60.81.01.2

Baker's Flashiness Index

0

5

10

15

Dissolved Organic Carbon (mg/L)

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashiness Index

0.0001

0.0010

0.0100

0.1000

Phosphorus (mg/L)

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashiness Index

20

30

40

50

60

70

80

90

100

110

Physical habitat index

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashiness Index

10

100

1000

Conductivity (μS/cm)

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashiness Index

0

10

20

30

40

50

60

70

80

90

100

Embeddedness

0.01 0.10 1.00 10.00

% Urban

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Baker's Flashiness Index

0.01 0.10 1.00 10.00

Imperviousness surface

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Baker's Flashiness Index

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashiness Index

0.001

0.010

0.100

1.000

TP
B-4

Figure B-2. Index comparison; the solid line is the locally weighted scatter plot smoothing line 

(LOWESS).

20 30 40 50 60 70 80 90 100 110

Physical habitat index

0

1

2

3

4

5

6

Fish IBI

20 30 40 50 60 70 80 90 100 110

Physical habitat index

0

1

2

3

4

5

6

Fish IBI

20 30 40 50 60 70 80 90 100 110

Physical habitat index

0

1

2

3

4

5

6

Fish IBI

20 30 40 50 60 70 80 90 100 110

Physical habitat index

0

1

2

3

4

5

6

Fish IBI
B-5

Figure B-3. Fish vs. environment; the solid line is the locally weighted scatter plot smoothing line (LOWESS).

0 5 10 15

Dissolved Oxygen (mg/L)

0

1

2

3

4

5

6

Fish IBI

0 10 20 30 40

Temperature (°C)

0

1

2

3

4

5

6

Fish IBI

0.00100.01000.10001.0000

10.0000

100.0000

Flow

0

1

2

3

4

5

6

Fish IBI

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashness Index

0

1

2

3

4

5

6

Fish IBI

0.01 0.10 1.00 10.00

Impervious surface

0

1

2

3

4

5

6

Fish IBI

0 5 10 15

Dissolved Oxygen (mg/L)

0

1

2

3

4

5

6

Fish IBI

0 10 20 30 40

Temperature (°C)

0

1

2

3

4

5

6

Fish IBI

0.00100.01000.10001.0000

10.0000

100.0000

Flow

0

1

2

3

4

5

6

Fish IBI

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashness Index

0

1

2

3

4

5

6

Fish IBI

0.01 0.10 1.00 10.00

Impervious surface

0

1

2

3

4

5

6

Fish IBI
B-6

Figure B-4 shows the relationships between abundance of fish and the same suite of 

environmental variables (DO, habitat score, temperature, channel flow, flashiness, impervious 

surface). Fish abundance increased with increasing DO, though the relationship is very weak. 

Fish abundance also increased with increasing habitat score, PHI, and channel flow. Fish 

abundance declined with increasing impervious surface, though again in a very weak 

relationship. There was no meaningful relationship between fish abundance and flashiness. 

The number of fish species present (richness) also increased with increasing instream 

habitat score and with flow (Figure B-5). Fish richness increased with increasing DO, but with a 

weaker relationship. Fish species richness declined with increased impervious surface, but 

showed no real relationship with flashiness. 

For the benthic macroinvertebrate community, species richness increased very slightly 

with increasing PHI, and decreased slightly with increasing flashiness and impervious surface 

(Figure B-6). However, it appeared that total benthic taxa richness was not a strong response 

variable with most parameters. In comparison, EPT taxa richness showed reasonable stressorresponse relationship with several environmental parameters (Figure B-7). The number of EPT 

taxa increased with increasing PHI, and decreased with increasing DOC, phosphorus, 

conductivity, embeddedness, Baker’s flashiness index score, and impervious surface. EPT taxa 

had strong relationships with all these variables and were, therefore, selected as a primary 

response variable to examine potential climate change effects.

Table B-1 summarizes the hydrologic parameters of greatest interest in the analysis. Low 

flow events, high flow events, and Baker’s flashiness were estimated by the FTSE model. Low 

flow and high flow events are the number of events during a year below the 25th and above the 

75th percentiles, respectively, of the area-weighted mean discharge of all streams.
B-7

Figure B-4. Fish richness vs. others; the solid line is the locally weighted scatter plot smoothing line (LOWESS).

0 5 10 15

Dissolved Oxygen (mg/L)

0

10

20

30

40

Fish taxa richness

20 30 40 50 60 70 80 90 100 110

Physical Habitat Score

0

10

20

30

40

Fish taxa richness

0.00100.01000.10001.0000

10.0000

100.0000

Flow

0

10

20

30

40

Fish taxa richness

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashness Index

0

10

20

30

40

Fish taxa richness

0.01 0.10 1.00 10.00

Impervious surface (%)

0

10

20

30

40

Fish taxa richness

0 5 10 15

Dissolved Oxygen (mg/L)

0

10

20

30

40

Fish taxa richness

20 30 40 50 60 70 80 90 100 110

Physical Habitat Score

0

10

20

30

40

Fish taxa richness

0.00100.01000.10001.0000

10.0000

100.0000

Flow

0

10

20

30

40

Fish taxa richness

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker's Flashness Index

0

10

20

30

40

Fish taxa richness

0.01 0.10 1.00 10.00

Impervious surface (%)

0

10

20

30

40

Fish taxa richness
B-8

Figure B-5. EPT taxa vs. environmental 2; the solid line is the locally weighted scatter plot smoothing line 

(LOWESS).

0 5 10 15

Dissolved Organic Carbon (mg/L)

0

10

20

30

40

50

Total taxa richness

0.0001 0.0010 0.0100 0.1000

Phosphorus (mg/L)

0

10

20

30

40

50

Total taxa richness

20 30 40 50 60 70 80 90 100 110

Physical habitat index

0

10

20

30

40

50

Total taxa richness

5 15 25 35

Temperature (°C)

0

10

20

30

40

50

Total taxa richness

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker Flashness Index

0

10

20

30

40

50

Total taxa richness

0.01 0.10 1.00 10.00

Impervious surface (%)

0

10

20

30

40

50

Total taxa richness
B-9

Figure B-6. EPT and environmental variables; the solid line is the locally weighted scatter plot smoothing line 

(LOWESS).

0 5 10 15

Dissolved Organic Carbon (mg/L)

0

10

20

30

EPT taxa richness

0.0001 0.0010 0.0100 0.1000

Phosphorus (mg/L)

0

10

20

30

EPT taxa richness

20 30 40 50 60 70 80 90 100 110

Physical habitat index

0

10

20

30

EPT taxa richness

10 100 1000

Conductivity (μS/cm)

0

10

20

30

EPT taxa richness

0 10 20 30 40 50 60 70 80 90 100

Embeddedness

0

10

20

30

EPT taxa richness

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker Flashness Index

0

10

20

30

EPT taxa richness

0.01 0.10 1.00 10.00

Impervious surface (%)

0

10

20

30

EPT taxa richness

20 30 40 50 60 70 80 90 100 110

Physical Habitat Index

0

10

20

30

EPT taxa richness

0 5 10 15

Dissolved Organic Carbon (mg/L)

0

10

20

30

EPT taxa richness

0.0001 0.0010 0.0100 0.1000

Phosphorus (mg/L)

0

10

20

30

EPT taxa richness

20 30 40 50 60 70 80 90 100 110

Physical habitat index

0

10

20

30

EPT taxa richness

10 100 1000

Conductivity (μS/cm)

0

10

20

30

EPT taxa richness

0 10 20 30 40 50 60 70 80 90 100

Embeddedness

0

10

20

30

EPT taxa richness

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Baker Flashness Index

0

10

20

30

EPT taxa richness

0.01 0.10 1.00 10.00

Impervious surface (%)

0

10

20

30

EPT taxa richness

20 30 40 50 60 70 80 90 100 110

Physical Habitat Index

0

10

20

30

EPT taxa richness
B-10

Figure B-7. EPT and hydrology; the solid line is the locally weighted scatter 

plot smoothing line (LOWESS).

Table B-1. Summary of hydrologic parameters used in analyses

Baker’s Flashiness Index Palmer Hydrologic Drought Index

Number of cases 764 (streams) 15 (months)

Minimum 0.132 -4.24

Maximum 1.121 4.75

Median 0.362 1.02

Mean 0.385 0.819

Standard Deviation 0.179 2.56

-0.003 -0.002 -0.001 0.000

Fall

0

10

20

30

EPT taxa richness

0.000 0.001 0.002 0.003 0.004 0.005 0.006

Rise

0

10

20

30

EPT taxa richness

-0.003 -0.002 -0.001 0.000

Fall

0

10

20

30

EPT taxa richness

0.000 0.001 0.002 0.003 0.004 0.005 0.006

Rise

0

10

20

30

EPT taxa richness
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