Prepared in cooperation with the Mississippi Department of Environmental Quality

Stressor Identification Framework of Biological Impairment 

in Mississippi Streams to Support Watershed Restoration 

and TMDL Development

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2020–5098
Cover.

Background, Foster Creek, Amite County, Mississippi. 

Top left, Chunky River, Lauderdale County, Mississippi.

Bottom left, Riverdale Creek, Grenada County, Mississippi. 

Map showing Mississippi Benthic Index of Stream Quality bioregions (see fig. 7). 

Photographs by Matt Hicks, U.S. Geological Survey.
Stressor Identification Framework of 

Biological Impairment in Mississippi 

Streams to Support Watershed Restoration 

and TMDL Development

By Matthew B. Hicks and Jennifer M. Cartwright

Prepared in cooperation with the Mississippi Department of 

Environmental Quality

Scientific Investigations Report 2020–5098

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

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Suggested citation:

Hicks, M.B., and Cartwright, J.M., 2020, Stressor identification framework of biological impairment in Mississippi 

streams to support watershed restoration and TMDL development: U.S. Geological Survey Scientific Investigations 

Report 2020–5098, 42 p., https://doi.org/10.3133/sir20205098. 

ISSN 2328-0328 (online)
iii

Contents

Abstract ..........................................................................................................................................................1

Introduction ....................................................................................................................................................1

Goal and Objectives of This Document ............................................................................................2

General Overview of Stressor Identification in Stream Ecosystems ...................................................2

Biological Impairment .........................................................................................................................2

Causal Assessment and Stressor Identification ............................................................................2

General Steps of a Stressor Identification Process ......................................................................4

Getting Started ............................................................................................................................4

Step 1. List Candidate Causes .........................................................................................4

Step 2. Analyze Evidence by Using Four Categories of Association .........................6

Step 3. Characterize Causes by Using Elimination, Diagnosis, and Weight of 

Evidence ................................................................................................................8

Common Complications and Options for Addressing Them .........................................................8

Insufficient Data ..........................................................................................................................8

Confounding Environmental Factors .......................................................................................9

Causal Masking ...........................................................................................................................9

Other Important Considerations ........................................................................................................9

Definitions of Impairment and Candidate Causes .................................................................9

Regional Clustering of Impairment ........................................................................................10

The Role of Statistical Hypothesis Testing ...........................................................................10

Reporting Level of Confidence ...............................................................................................10

Data Types for Stressor Identification in Stream Ecosystems ...................................................10

Analytical Approaches for Stressor Identification in Stream Ecosystems ..............................11

Tests of Significant Difference ...............................................................................................11

Uses and Advantages .....................................................................................................12

Constraints and Limitations ............................................................................................12

Multiple Regression .................................................................................................................12

Uses and Advantages .....................................................................................................12

Constraints and Limitations ............................................................................................12

Quantile Regression .................................................................................................................12

Uses and Advantages .....................................................................................................12

Constraints and Limitations ............................................................................................13

Ordination Methods ..................................................................................................................13

Uses and Advantages .....................................................................................................13

Constraints and Limitations ............................................................................................13

Discriminant Analysis ...............................................................................................................15

Uses and Advantages .....................................................................................................15

Constraints and Limitations ............................................................................................15

Classification and Regression Trees .....................................................................................15

Uses and Advantages .....................................................................................................15

Constraints and Limitations ............................................................................................15
iv

Random Forests .........................................................................................................................15

Uses and Advantages .....................................................................................................16

Constraints and Limitations ............................................................................................17

Boosted Regression Trees ......................................................................................................17

Uses and Advantages .....................................................................................................17

Constraints and Limitations ............................................................................................17

Stressor Identification of Biologically Impaired Streams in Mississippi ..........................................17

Biological Impairment in Mississippi: Data Collection and Assessment ..................................17

Components of the Stressor Identification Framework for Biologically Impaired 

Streams in Mississippi ........................................................................................................19

Step 1. Define the Impairment ................................................................................................19

Step 2. List the Candidate Causes of Impairment and Develop a Conceptual 

Model .............................................................................................................................19

Step 3. Compile All Data Relevant to the Impairment and Conceptual Model ...............21

Sources of Data ...............................................................................................................21

M-BISQ Data and Organization .....................................................................................22

Step 4. Evaluate the Data ........................................................................................................22

Co-occurrence and Antecedence ................................................................................25

Sufficiency ........................................................................................................................25

Hypothesis for Evaluation: High Specific Conductance at Site A Is a Possible 

Cause of Biological Impairment ................................................................................28

Step 5. Identify the Probable Causes of Impairment by Using a Weight-ofEvidence Approach .....................................................................................................30

Consistency of Evidence ................................................................................................30

Step 6. Generate a Report of the Results ..............................................................................30

Options for Future Enhancement of This Framework ...........................................................................32

Development of Advanced Analytical Approaches .....................................................................32

Predicting Environmental Conditions from Biological Observations ...............................32

Propensity Score Analysis ......................................................................................................32

Evidence of Alteration ..............................................................................................................32

Concept of Stressor Syndromes ............................................................................................32

Monitoring for Additional Stressor and Response Indicators ...................................................33

Summary .......................................................................................................................................................33

References Cited .........................................................................................................................................33

Appendix 1. Field Forms Used During Reconnaissance in Stressor Identification .........................39

Appendix 2. Tools for M-BISQ Data Compilation and Evaluation .......................................................42
v

Figures

1. Overview of the U.S. Environmental Protection Agency stressor identification 

process ..........................................................................................................................................3

2. Conceptual model of sources and stressors in the Coal River watershed, West 

Virginia ...........................................................................................................................................5

3. Diagram illustrating the process used to identify a candidate cause during the 

stressor identification process ..................................................................................................7

4. Graphs showing quantile regression relations between relative abundance of 

brook trout and blacknose dace and percent alteration of August median 

flow from groundwater withdrawals ......................................................................................13

5. Plot series illustrating nonmetric multidimensional scaling ordinations of 

Delmarva Peninsula algal community composition from Bray-Curtis similarity 

matrices .......................................................................................................................................14

6. Example of a regression tree identifying predictor variables that best explain 

observed variations in median annual yields of total phosphorus ....................................16

7. Map showing Mississippi Benthic Index of Stream Quality bioregions used to 

stratify analysis of ecological response to stressors in biologically impaired 

streams in Mississippi ..............................................................................................................18

8. Diagram showing examples of sources of stress and linkages to intermediate 

causes of impairment ................................................................................................................21

9. Diagram showing examples of intermediate causes and linkages to proximate 

causes of impairment ................................................................................................................22

10. Diagram showing an example of a conceptual model linking potential sources 

of stress within a watershed to intermediate causes and proximate stressors 

which produce observed biological responses ...................................................................23

11. Example comparison report to document and evaluate co-occurrence of 

stressors and responses from data collected at the time of the original sampling, 

comparison to East bioregion least disturbed and nonimpaired conditions, and 

comparison to site-specific comparators .............................................................................26

12. Example of a scatterplot divided into quadrants by using the 75th percentile 

of reference values of a physical variable and the 25th percentile of the 

biological response ...................................................................................................................28

13. Example weight-of-evidence worksheet for decrease in suitable habitat ......................31

Tables

1. Analytical approaches and associated case studies and references for stressor 

identification ...............................................................................................................................11

2. Common potential sources of stress in biologically impaired streams ............................20

3. Examples of common physical and chemical water-quality indicators of 

proximate causes and the typical availability of the indicator ..........................................24

4. Examples of types of evidence based on data from within a case ...................................24

5. Scoring system for co-occurrence, antecedence, and sufficiency from within a 

case ..............................................................................................................................................29
vi

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F = (1.8 × °C) + 32.

Datum

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Supplemental Information

Specific conductance is given in micromhos per centimeter (μmho/cm).
vii

Abbreviations

ρ Spearman’s rank correlation coefficient

ANOVA analysis of variance

BRT boosted regression trees

CADDIS Causal Analysis/Diagnosis Decision Information System (EPA)

CART classification and regression tree

CWA Clean Water Act

DA discriminant analysis

DO dissolved oxygen

EPA U.S. Environmental Protection Agency

EPT Ephemeroptera, Plecoptera, and Trichoptera

HUC hydrologic unit code

IBI index of biological integrity

LD least disturbed

M-BISQ Mississippi Benthic Index of Stream Quality

MDEQ Mississippi Department of Environmental Quality

NI nonimpaired

RF random forests

SI stressor identification

SSC site-specific comparator

TMDL total maximum daily load

USGS U.S. Geological Survey

WOE weight of evidence

Stressor Identification Framework of Biological 

Impairment in Mississippi Streams to Support Watershed 

Restoration and TMDL Development

By Matthew B. Hicks and Jennifer M. Cartwright

Abstract

The Clean Water Act (CWA) requires States to identify 

waters that are impaired for designated uses. These waters 

are published through a State’s §303(d) list. The CWA 

also requires that a total maximum daily load (TMDL) be 

completed for each water body to calculate the maximum 

amount of contaminants that can be present in that water 

body and still meet water-quality standards. The Mississippi 

Department of Environmental Quality (MDEQ) uses a 

statewide monitoring and assessment strategy to collect 

benthic macroinvertebrate community data to assess the health 

of streams and rivers and to identify impaired waters. Waters 

that are found to be impaired based on the macroinvertebrate 

community data are listed on the Mississippi §303(d) list, and 

the cause of impairment is listed as “biological impairment.” 

Although the CWA requires TMDLs to be developed for 

applicable contaminants identified in the §303(d) list, TMDLs 

cannot be computed for stream reaches in Mississippi listed 

for biological impairment because the actual stressors causing 

the impairment have not yet been determined. The MDEQ 

and other water-resource managers in Mississippi require a 

framework for stressor identification in biologically impaired 

streams and rivers. This report is organized to (1) provide 

a general overview of biological impairment and stressor 

identification in stream ecosystems and (2) provide a detailed 

framework for stressor identification of Mississippi streams 

that are biologically impaired. The intent is for the framework 

to reduce subjectivity, provide consistency, and allow for 

adaptation as the science evolves. The stressor identification 

framework for Mississippi involves six key steps:

1. Define the impairment,

2. List the candidate causes of impairment and develop a 

conceptual model,

3. Compile all relevant data,

4. Evaluate the data,

5. Identify probable causes of impairment by using a 

weight-of-evidence approach, and

6. Generate a report of results.

Introduction

The Clean Water Act (CWA), §305(b), requires each 

State to describe the quality of their surface-water and 

groundwater resources in a report for the U.S. Environmental 

Protection Agency (EPA), Congress, and the public on a 

biennial basis. The Mississippi Department of Environmental 

Quality (MDEQ), as the lead agency for environmental 

protection in Mississippi, is the State agency responsible 

for generating this report. To support this reporting process, 

statewide data are routinely collected through several 

monitoring activities associated with surface-water-quality 

programs. To assess the health of waters, MDEQ uses a 

modified process from EPA guidance (EPA, 1997, 2002). 

Waters assessed as not having the quality to attain their 

designated uses in the §305(b) assessment process become 

candidates for listing on Mississippi’s §303(d) list (Stribling 

and others, 2016).

Most of the data used to assess the overall health of 

streams and rivers in Mississippi and generate the §305(b) 

report and the §303(d) list are from statewide monitoring 

efforts of biological assemblages. Biological assemblage 

data, based on taxonomic identification and enumeration of 

benthic macroinvertebrates, are used to calculate a regionally 

calibrated index of biological integrity (IBI), the Mississippi 

Benthic Index of Stream Quality (M-BISQ). For a given site, 

the M-BISQ score is compared to a least-disturbed condition 

score for the region of the State in which the site is located. 

If the M-BISQ score is below the least disturbed condition 

score, the stream segment is placed on the §303(d) list, 

with the cause listed as “biological impairment.” The CWA 

requires action to be taken to restore the health of these listed 

impaired waters. Efforts to address biological impairment in 

streams and rivers—such as watershed restoration projects or 

development of total maximum daily loads (TMDLs)—require 

accurate identification of the specific stressors causing the 

impairment.

In many cases, stressor identification (SI) is complicated 

because multiple stressors may interact to produce biological 

impairment. Since the early 2000s, several scientific studies 

have focused on characterizing stressor-response relations in 
2 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

the context of natural and human-induced landscape variables 

and changes (Downes, 2010; Wagenhoff and others, 2011; 

Allan and others, 2013). However, these studies have been 

conducted in Australia, Europe, and in the upper Midwest 

and Northeast regions of the United States, and are either 

broad in scope, or are focused on a single stressor or a single 

biological response indicator. The need exists for improved 

understanding of connections and responses of biological 

communities to agents of stress at smaller, regional scales 

and to specific stressors in ecosystems of varying geology, 

topography, and land use.

In 2003, MDEQ developed and implemented an SI 

process to identify specific causes of impairment for waters 

that are on the §303(d) list for biological impairment, so 

that actions could be taken to improve water quality. It has 

been more than a decade since the original SI process was 

developed, and it has not been updated to reflect recent 

scientific advancements in characterization of stressorresponse relations. Several analytical and statistical 

frameworks have recently been applied to the problem of SI 

in other States (Griggs and Buchanan, 2012). Although each 

State has approached the problem somewhat differently, these 

frameworks commonly rely on a weight-of-evidence (WOE) 

approach to characterize and compare candidate stressors for 

biological impairment.

Goal and Objectives of This Document

The goal of this document is to provide a framework 

suitable for conducting identification of stressors causing 

impairment in biologically impaired streams in Mississippi. 

The intent is for the framework to (1) reduce subjectivity 

and provide consistency in the SI process, (2) allow for 

well-documented and clearly understood steps taken in the 

process, and (3) result in a sustainable approach for future use, 

allowing for adaptation as the science evolves. To accomplish 

this goal, the following objectives are described in detail in 

this document:

• Provide a general overview of biological impairment 

and SI in stream ecosystems generally and in Mississippi specifically,

• Describe the general framework that was developed,

• Describe common complications and shortcomings 

encountered in the SI process and provide options for 

addressing them, and

• Provide options for future improvement of the SI 

framework.

General Overview of Stressor 

Identification in Stream Ecosystems

Biological Impairment

Biological impairment of streams is a common ecological 

problem, especially in watersheds that are heavily affected 

by intensive land uses such as agriculture, development, and 

resource extraction. Improvements in biological integrity, 

water quality, and physical habitat are key goals of the 

CWA and State-level water-resource management activities. 

Efforts to address biological impairment in streams—such as 

watershed restoration projects or development of TMDLs for 

contaminants—require accurate identification of the specific 

stressors causing the impairment. In this context, a stressor can 

be defined as “a variable that, as a result of human activity, 

exceeds its range of normal variation and adversely affects 

individual taxa or community composition” (Townsend and 

others, 2008).

Biological responses to stressors can occur at various 

taxonomic levels and can manifest in a variety of ways. 

Biological data indicating impairment can be derived from 

individual organisms (for example, deformities, tumors, or 

mortality), from populations of a given species (for example, 

reductions in local abundance or changing demographic 

structure), or from assemblages or communities of multiple 

species (for example, changes in species richness or shifts 

in community structure based on taxon-level characteristics) 

(Suter and others, 2002). Common responses of biological 

communities to stressors include (1) decreased species 

richness, (2) loss of sensitive species with increased 

dominance of a small number of tolerant species, (3) increase 

in generalists and decrease in specialists, (4) reduced overall 

biomass, and (5) smaller populations of each species, even 

with no discernible change in community structure (Klein, 

1979; Karr and others, 1986; Onorato and others, 1998; 

Cormier and others, 2000).

Causal Assessment and Stressor Identification

A formal method of SI has several advantages over 

informal or ad hoc approaches to determining causes of 

biological impairment. In many cases, definitive causes of 

biological impairment in streams will not be immediately 

obvious, and a structured process of SI will be required 

to accurately identify likely causes. In addition, informal 

methods of causal inference are inherently subjective and 
General Overview of Stressor Identification in Stream Ecosystems 3

prone to problems (for example, bias or lapses of logic) that 

can only be revealed by a formal method (Suter and others, 

2002). For these reasons, informal methods may fail to 

convince skeptical stakeholders and may be vulnerable to legal 

challenge. In cases where remediation of identified stressors 

has the potential to be costly, time-consuming, politically 

challenging, or economically disruptive, it is essential that 

remediation efforts be appropriately targeted to achieve 

optimal results, thus strengthening the need for a structured 

and formal SI process. In environmental law, general causation 

concerns the question of whether an environmental cause 

(in this case, a candidate stressor) is capable of producing 

a deleterious result (in this case, biological impairment), 

whereas specific causation concerns the narrower question 

of whether, at a given site, the stressor in question actually 

did produce biological impairment (Schleiter, 2009). In a 

formal SI process, general causation may be a necessary but 

insufficient basis for establishing specific causation. In many 

cases, specific causation cannot be decisively proven; thus, 

the SI process involves the compilation and consideration of 

evidence for or against specific causation.

The EPA issued official guidance on the SI process in 

2000 to provide a consistent and actionable framework for 

causal inference in biologically impaired stream ecosystems 

(EPA, 2000). This SI framework has since been adapted and 

applied in multiple State and regional contexts (Griggs and 

Buchanan, 2012; Minnesota Pollution Control Agency, 2013; 

Virginia Department of Environmental Quality, 2014). The 

EPA also has provided an online resource to support SI efforts: 

the Causal Analysis/Diagnosis Decision Information System 

(CADDIS) (EPA, 2010a). CADDIS provides background 

information on many common types of stressors, illustrative 

examples of the SI process from case studies, guidance on the 

use of analytical methods to support causal inference, and a 

literature database from which to derive evidence for causal 

assessments.

Once an impairment is detected or suspected, the SI 

process involves three main steps: (1) listing candidate 

causes of impairment, (2) analysis of available evidence, 

and (3) causal characterization to draw conclusions (EPA, 

2000; Suter and others, 2002; Cormier and others, 2003). 

As illustrated in figure 1, the SI process is conceptually 

separate from, but also directly informs, two subsequent 

steps in ecological remediation: apportionment of sources of 

the identified stressors and management actions to directly 

address the identified stressors. The SI process is designed 

to be iterative if needed, such that results of management 

actions informed by a preliminary SI process can provide 

Define the case

List candidate causes

Evaluate data from the case

Evaluate data from elsewhere

Identify probable cause

Identify and apportion sources

Management action:

eliminate or control sources, monitor results

Biological condition restored or protected

Decision-maker 

and 

stakeholder

 involvement 

As necessary:

acquire data

and

iterate process

Figure 1. Overview of the U.S. Environmental Protection Agency (EPA) stressor identification 

process (from EPA, 2000).
4 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

new evidence for subsequent rounds of SI (EPA, 2000; Suter 

and others, 2002; Cormier and others, 2003). Decision-maker 

and stakeholder engagement is encouraged at each step in the 

SI process (EPA, 2000; Suter and others, 2002; Cormier and 

others, 2003).

General Steps of a Stressor Identification 

Process

Getting Started

Two important tasks are required before initiating the 

SI process. First, the scope of the SI process needs to be 

clearly defined, which involves delineating a geographic 

area for analysis, defining a project timeframe, and itemizing 

available data types for analysis. Key considerations to inform 

these decisions include the regulatory context, resource 

availability (for example, data availability, personnel costs, 

and available budget), and stakeholder interests and concerns 

(EPA, 2000).

Second, the nature of the biological impairment should 

be described using specific quantitative characterizations, 

such as counts or continuous variables, when possible. 

Multiple forms of impairment should be described separately 

rather than aggregating into an overall metric (Cormier and 

others, 2003). For example, a decline in fish species richness 

should be described separately from a decrease in mean fish 

body size. This is especially important when biological data 

are combined into a multimetric index. Defining biological 

impairment should be described with individual traits and 

(or) taxa presence, absence, or relative abundance and 

not the overall multimetric index score. This helps ensure 

that stressors can be accurately identified for each type of 

biological impairment. Each impairment should be described 

in terms of its nature, magnitude, and spatial and temporal 

extents (Suter and others, 2002). In general, exceedance 

of regulatory criteria is not a sufficient description of 

biological impairment because (1) not all stressors have such 

criteria, (2) numeric criteria may not accurately represent 

true thresholds that cause impairment, and (3) criteria for 

individual contaminants do not allow for synergistic effects or 

effects of sporadic spikes in concentration (EPA, 2000; Morris 

and others, 2006). As opposed to relying on a single point 

estimate, such as criterion, the original species sensitivity 

distribution models used to develop the criteria may be more 

useful.

Step 1. List Candidate Causes

The goal of the first step is to develop a comprehensive 

list of candidate stressors within the spatial and temporal scope 

of the SI project. Initially, an exhaustive list of all possible 

causes of impairment (stressors) is ideal. Identification 

of candidate stressors may derive from expert opinion, 

existing data, qualitative observations, and stakeholder input. 

Whenever possible, characterization of likely stressor sources 

can be helpful. This includes, for example, differentiating 

point-source from nonpoint-source contaminants and 

georeferencing possible point sources, such as wastewater 

effluent discharge sites. Stressors that are deemed highly 

improbable may be removed from the final list of candidates, 

accompanied by clear written explanations for the reasons 

justifying removal (EPA, 2000).

Conceptual models linking candidate stressors to 

observed metrics of impairment are an important component 

of the listing process. Figure 2 gives an example conceptual 

model from EPA (2010b) that shows sources, stressors, 

and causes with linkages in the Coal River watershed in 

West Virginia. Furthermore, including sources or activities 

associated with the proximate stressor is helpful because it 

sets the stage for load allocation in the process of TMDL 

development. Typically, a causal chain is required to link a 

candidate stressor to an observed impairment. For a given 

candidate stressor, the entire causal scenario should be 

articulated rather than listing each link in the chain as a 

separate stressor. For example, a causal scenario could involve 

agricultural runoff entering a stream, which increases nutrient 

levels, which promotes algal growth, which depletes dissolved 

oxygen (DO), which produces a fish kill (the observed 

biological impairment). In this example, algal growth itself is 

not a stressor; it is an intermediate link in a causal chain for 

which agricultural runoff is the ultimate stressor. Commonly, 

multiple competing hypotheses may exist for the pathways 

linking a stressor to the observed impairment. In such cases, 

each hypothetical conceptual model should be outlined 

separately. Ideally, these hypotheses should be refutable based 

on existing quantitative data. Additionally, each hypothetical 

causal pathway should be linked to a prediction that is unique 

to that pathway (Downes, 2010).
Mining Logging

Urbanization/ 

development

Point sources

(nonmining)

CSOs

Oil and gas

development

Chemical

spills

Metals

contamination

Increased

TSS/erosion

Nutrient

enrichment

AMD

Toxicity

Acidity

(low pH)

or high pH

Altered hydrology,

riparian impacts,

channelization, etc. H

High sulfates/ 

ionic strength

Shift in macroinvertebrate community

Increased sedimentation

and/or turbidity

Higher water

temperature

Reduced DO

Algal

growth

Organic 

enrichment / 

increased BOD

Food s

shi

Incre

p

Habitat alterations,

reduced interstitial spacing,

smothering, reduced

complexity, behavioral

changes, etc.

Figure 2. Conceptual model of sources and stressors in the Coal River watershed, West Virginia (from U.S. Environmental Protectio2010b). [AMD, acid and metalliferous drainage; CSOs, combined sewer overflows; TSS, total suspended solids; BOD, biological oxygedissolved oxygen]
6 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Step 2. Analyze Evidence by Using Four Categories of 

Association

The goal of the second step is to perform quantitative 

and qualitative analyses to determine strength of association 

between candidate stressors and observed biological 

impairments. EPA guidance on SI processes suggests four 

categories of association by which evidence may be evaluated 

(EPA, 2000; Suter and others, 2002; Cormier and others, 

2003):

1. Associations between measurements of candidate causes 

and effects. This type of association can be demonstrated 

by co-occurrence of a candidate cause and an observed 

effect at the same time and place. For this co-occurrence 

to be valid, the effect should be observed at the same 

time and place as the candidate cause, and the effect 

should not be observed when and where the candidate 

cause is not present. An example would be a case in 

which algal, chlorophyll a measurements and nutrient 

concentrations at a study site were greater than those at 

the reference site. Where spatial or temporal gradients 

exist in candidate causes and biological impairments, the 

intensity of the candidate cause and its proposed effect 

should be positively associated.

2. Association between candidate causes and effects at the 

site with causes and observed effects elsewhere. This 

type of association combines measures of exposure to 

a stressor at a study site with lab results or field studies 

from other sites which demonstrate effects of exposure. If lab tests, field tests, or observational studies 

from other sites indicate exposure-response relations 

between a stressor and a biological outcome, an important consideration is whether the stressor is present at 

the study site in sufficient quantity and frequency for the 

anticipated result to occur. An additional consideration 

is that lab conditions and organisms may not accurately 

represent field conditions. If no lab or field data exist for 

a candidate cause, analogous or surrogate data may be 

used (see Insufficient Data section), although this will 

increase the uncertainty of the association. An example 

of this association would be a situation in which 19 out 

of 20 streams in the same ecoregion with similar levels 

of organic enrichment had depressed DO.

3. Measurements at the site associated with the candidate causal mechanism. This type of association can 

increase confidence in the proposed causal mechanism 

by documenting intermediate links in a causal chain. For 

example, a causal scenario by which agricultural runoff 

produces fish kills by means of nutrient enrichment and 

hypoxia is more plausible if increases in algal biomass 

are measured. This type of association is particularly 

helpful in evaluating multiple competing causal mechanistic explanations that produce similar biological 

outcomes (Suter and others, 2002).

4. Association of effects of mitigation and manipulation of 

causes. This type of association may involve reduction 

or elimination of candidate stressors from a study site by 

manipulating key sources (for example, cattle exclusion 

from streams or effluent absence during shutdowns at 

a water-treatment plant). These “natural experiments” 

provide opportunities to obtain strong causal evidence if 

biological impairment is diminished following elimination of a candidate stressor. Lab experiments may 

also provide causal evidence by artificially eliminating 

(under controlled lab conditions) a stressor that exists in 

field conditions at a study site. Field experiments that 

control exposure can also provide causal evidence, such 

as caging previously unexposed organisms at contaminated locations (Crane and others, 2007). An example 

of this association would be a case in which stocked 

trout populations failed to survive over a 2-year period 

when fields were in alfalfa rotation with no pesticide 

application.

In summary, this step seeks to use a process that 

eliminates candidate causes early in the SI process if there is 

evidence of an interrupted causal pathway or evidence of lack 

of co-occurrence of a response with the candidate cause. If a 

candidate cause cannot be eliminated, then the process entails 

using strength of associations by using thresholds of effects 

from either the laboratory or the field (fig. 3).
General Overview of Stressor Identification in Stream Ecosystems 7

Evidence of an interrupted causal 

pathway?

Evidence of lack of co-occurrence 

with the candidate cause?

Evidence of sufficient intensity of

the candidate cause based on 

thresholds of effects?

Evidence that assemblage is similar to 

other sites impaired by that cause?

Explanation for any

discrepancies?

No

Yes

Yes

Below 

In 

weight of

evidence, discount

candidate

cause

Weigh

as strong or 

plausible

In 

weight of

evidence, discount

candidate

cause

In 

weight of

evidence, support

candidate

cause

Eliminate 

as a 

cause

No Yes

Identify

likelihood of cause

by weighing

evidence

Sufficiency

Alteration

Co-occurrence

Preceding causation

Above 

No

From stonecoal 

From region

Figure 3. Diagram illustrating the process used to identify a candidate cause during the stressor identification process (from U.S. 

Environmental Protection Agency, 2010b).
8 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Step 3. Characterize Causes by Using Elimination, 

Diagnosis, and Weight of Evidence

The goal of the third step is to characterize candidate 

stressors to draw conclusions about probable cause(s) of 

biological impairment at a study site. This step may involve 

elimination of candidate causes, diagnosis of causes, or 

causal evaluation by using a WOE approach. It is important 

to remember that the SI process involves building a case for 

and against alternative candidate cause. Evidence against a 

candidate cause is just as important as evidence for a candidate 

cause.

Where available evidence contradicts a candidate causal 

scenario, the candidate cause may be removed from further 

consideration. In general, only evidence from the study site in 

question (not from other related study sites) should be used for 

elimination. Elimination of candidate causes may be subject to 

several problems depending on the relations between stressors 

at a site. For example, ancillary causes may be masked by 

primary causes, thus leading ancillary causes to be erroneously 

eliminated. For more examples of complications by which one 

type of stressor may mask another, see the Causal Masking 

section in this report and table 4-1 in EPA (2000).

For certain stressors, causal diagnosis may be possible 

following techniques adapted from medical practice. For 

example, some pathogens and toxins produce unique and 

characteristic symptoms that may enable diagnosis, and 

similarly, hypoxia may be diagnosable based on characteristic 

fish behavior (gasping for air at the water surface). For many 

other stressors and for invertebrate impairment, definitive 

diagnostics are generally not available.

Following elimination or diagnosis of candidate causes 

(if applicable), any remaining candidate causes may be 

evaluated by using a WOE approach. EPA (2010b) present six 

characteristics of causation to organize arguments that directly 

show how types of evidence support the inference, which 

may simplify the organization of evidence and the reason for 

identifying some causes over others. The six characteristics of 

causation are the following:

1. Co-occurrence—Cause and effect co-occur at the same 

place in a study site,

2. Sufficiency—Cause is at levels known to cause observed 

effect,

3. Time order—Cause temporally precedes effect,

4. Alteration—The entity is changed by the interaction with 

the cause,

5. Antecedence—Each causal relation is a result of a larger 

web of cause and effect relations, and

6. Interaction—The cause physically interacts with the 

entity in a way that induces the effect.

By using these causal characteristics that are relevant 

to the study site, type of biological impairment observed, 

and datasets available, the SI process concludes with an 

identification of probable causes of impairment, accompanied 

by a detailed description of the conceptual, analytic, and 

logical bases for the conclusions drawn. In addition, an 

evaluation of uncertainty relating to the SI process should be 

documented.

Common Complications and Options for 

Addressing Them

SI efforts are commonly made more challenging by 

one or more complicating factors. These factors include 

insufficient data, presence of confounding environmental 

factors, and the possibility for one or more causes to mask 

another cause.

Insufficient Data

Limitations on data quality and spatial and temporal 

completeness are common in SI projects. Where resources 

permit acquisition of additional data, a listing of candidate 

causes (the first step in the SI process) may support 

prioritization of locations and data types for future data 

collection efforts.

If resource limitations preclude additional data collection, 

use of surrogate variables may be considered. For example, 

electrical conductivity can be used as a surrogate for total 

dissolved solids, and turbidity can be used as a surrogate for 

total suspended solids (Miguntanna and others, 2010; Vander 

Laan, 2012). Water-column chlorophyll a concentration can be 

a surrogate for algal biomass (Dodds and Welch, 2000; EPA, 

2000). Additional surrogates may be identified by searching 

the CADDIS literature database. In some cases, information 

on locations and attributes of stressor sources may be used 

as surrogates. For example, locations and times of effluent 

release for wastewater treatment plants might be used as 

surrogates for chemical analysis of effluent (EPA, 2000; Suter 

and others, 2002). Similarly, Vander Laan and others (2013) 

used total upstream reservoir volume and volume of the largest 

upstream reservoir from the National Inventory of Dams (U.S. 

Army Corps of Engineers, 2009) as surrogates for hydrologic 

alteration resulting from impoundments upstream from a study 

site.

Notably, use of surrogate data increases uncertainty 

associated with causal identification (EPA, 2000; Suter and 

others, 2002). Thus, it is important that surrogate variables 

be clearly identified as such in descriptions of the SI 

process. Use of surrogate variables involves assumptions 

that one variable (for which data exist) effectively represents 

another variable (for which data are absent or of insufficient 

quality) (EPA, 2000; Suter and others, 2002). The degree 

to which the use of surrogates is justified depends upon 

the establishment of a clear, direct, and ideally quantitative 

relation between the surrogate variable and the variable it is 

intended to represent.
General Overview of Stressor Identification in Stream Ecosystems 9

Confounding Environmental Factors

One of the most common challenges in the SI process 

involves differentiation of stressor effects on biological 

outcomes from natural variability in biological states and 

processes. Several confounding factors have been identified 

in the course of SI projects and have been addressed by 

using various analytical approaches (EPA, 2000; Suter and 

others, 2002). For example, natural landscape gradients 

commonly covary with anthropogenic influences (Allan and 

others, 2012). Clements (1994) used multivariate regression 

to address natural seasonal and longitudinal (upstream to 

downstream) changes in community composition, which 

complicate the discernment of community shifts in response 

to metal contamination by comparison of upstream reference 

sites to sites downstream of effluent discharge points. Norton 

and others (2000) accounted for correlations among stream 

size and stressor variables and biological response variables 

by performing regression analysis on variables with drainage 

area and using the regression residuals in subsequent analysis. 

Similarly, Vander Laan and others (2013) addressed the 

issue of natural gradients in stressor variables by modeling 

expected values and calculating differences between observed 

and expected values. Morris and others (2006) used siteselection criteria to address the issue of low fish abundance in 

intermittent headwater streams. As these examples illustrate, 

analytical approaches to deal with confounding variables 

should be chosen based on knowledge of the types of 

correlations present in the data at hand, as well as correlations 

with unmeasured environmental factors (Downes, 2010).

Typically, multiple stressors interact at any given study 

site to produce an observed biological outcome (EPA, 2000; 

Morris and others, 2006; Downes, 2010). For example, 

nutrient enrichment may interact with increased stream 

temperature to promote hypoxia. The interactive effects 

of multiple stressors can be difficult to predict based only 

on knowledge of individual stressor effects (Townsend 

and others, 2008; Matthaei and others, 2010). However, 

modeling these interactions can be accomplished by using a 

variety of analytical and statistical approaches. For example, 

Townsend and others (2008) used interaction terms in multiple 

linear regression models (for example sediment × nutrient 

concentrations). Matthaei and others (2010) addressed 

stressor interactions by experimentally manipulating three 

common stressors (nutrient loads, sediment additions, and 

water withdrawals) to quantify their individual and interactive 

effects. Several machine-learning approaches and variancepartitioning models provide robust modeling options in 

situations where multiple explanatory variables are correlated 

(Cutler and others, 2007; Elith and others, 2008; Smucker and 

others, 2013).

Rather than relying solely on statistical methods to 

model complex interactions, it can be helpful to incorporate 

possible interactions into the causal pathways identified in the 

SI process because the nature of stressor interactions depends 

in part on modes of action of the individual stressors. For 

example, multiple toxicants have similar modes of action in 

crustaceans such that their effects are multiplicative, whereas 

functional synergy occurs between copper toxicity and low 

DO, whereby increased gill movement increases copper 

uptake in mayfly nymphs (Townsend and others, 2008). The 

nature and magnitude of stressor interaction may also depend 

on whether the stressors involved directly affect physiology or 

behavior, or indirectly affect food webs.

Causal Masking

Because streams are complex systems, one or more 

causes of impairment may mask yet another cause for 

impairment, thus making its detection difficult or impossible. 

For example, causes of impairment located upstream may 

mask downstream causes, or severe causes of impairment 

(principal causes) may mask more subtle ones (ancillary 

causes). Table 4-1 in EPA (2000) presents several other 

examples of masking considerations. When the goals of SI 

include ecosystem restoration, the objective is to identify 

all causes that are able to impair the stream biology. To do 

this, each candidate cause can be eliminated iteratively with 

continuous monitoring of stream biological indicators. Each 

candidate cause eliminated during the first round of ecosystem 

restoration should be reexamined to determine whether it had 

been masking secondary causes (Suter and others, 2002). 

Notably, if a principal cause persists, removal of ancillary 

causes will produce little to no improvement in stream 

biological indicators. In addition, unresolved ancillary causes 

of biological impairment can restrict improvement even after 

the principal cause is removed (EPA, 2000). Any action (for 

example, regulatory, remediation, or ecosystem restoration) 

should be followed by monitoring to assess effects (Suter 

and others, 2002), with a goal of identifying any previously 

unexamined ancillary causes.

In addition, geographic movement of organisms may 

mask impairment and complicate the detection of causes. For 

example, local reproductive impairment may be masked if 

organisms can migrate from adjacent habitat to fill a niche left 

vacant because of the reproductive impairment (Cormier and 

others, 2000). This form of impairment is more likely to be an 

issue for mobile species, such as fish, than for sessile species, 

such as mussels.

Other Important Considerations

Definitions of Impairment and Candidate Causes

The outcome of an SI process may be sensitive to 

the manner in which impairment and candidate causes are 

defined. In general, biological impairments need to be defined 

precisely, which often requires defining multiple interrelated 

impairments (Cormier and others, 2003). To accurately 

target causes of impairment, aggregate metrics—such as an 

IBI—should be avoided because they conflate many distinct 

biological variables and hamper the process of identifying 
10 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

mechanistic causes of individual biological impairments. 

Instead, aggregate metrics can be broken down into their 

component variables. In addition, although taxonomic 

aggregation (for example, from family or order) can lower 

data collection costs, it can mask impairment of sensitive 

species within taxonomic groups (Clements and others, 2000), 

indicating that taxonomic detail can be important for accurate 

SI results (Downes, 2010).

Similarly, candidate causes need to be defined as 

precisely as possible; for example, use “riffle siltation” rather 

than “poor habitat” (Suter and others, 2002). In general, 

aggregate causes may be difficult to address, and so, are less 

helpful to identify than their specific component causes. For 

example, Wang and others (2000) examined relations between 

urbanization (an aggregate cause of impairment) and fish 

community structure, which is of limited value in identifying 

which specific stressors were at work (for example, 

contaminants, suspended sediment, or impervious surface). 

Remediation may be possible for certain individual stressors 

or sets of stressors, but urbanization itself is unlikely to be 

reversible.

Regional Clustering of Impairment

Certain regions may have particular sets of stressors 

that are widespread and common to many streams, owing to 

particular land-use practices that are regionally common, such 

as metal contamination from mining in the southern Rocky 

Mountains in Colorado (Clements, 1994; Clements and others, 

2000) and degraded channel morphology from dredging in 

agricultural streams in Ohio (Norton and others, 2000; Morris 

and others, 2006). Known stressors established in the literature 

for a given region are important candidate causes in new SI 

investigations but should not be assumed to be the only (or 

even most important) stressor at a given site without thorough 

investigation of other candidate stressors.

The Role of Statistical Hypothesis Testing

Statistical procedures are very useful in stressor 

identification to develop evidence and models of association 

and to assess strength and credibility of the evidence. 

Statistical analyses are also useful for assigning scores to the 

evidence based on qualities such as strength of association 

using the coefficient of determination (r2 value), frequency of 

occurrence, number of occurrences, and so forth. Statistical 

hypothesis tests address the likelihood of the data given 

the null (no-effect) hypothesis. They do not test whether 

the association is causal. Statistical tests are more useful 

for determining either the logical implication (direction) or 

strength (for example, r2 value) of a modeled causal relation. 

Using r-values rather than r2 is better because it also defines 

direction of the relation.

Summary statistics and correlations or regressions are 

encouraged, with caveats not to draw causal conclusions from 

statistical tests such as statistically significant correlations 

because correlation does not necessarily indicate causation. 

Suter and others (2002) suggested linear or nonlinear 

regression and (for categorical data) categorical regression 

to analyze relations. Correlation coefficients, r-values, and 

probability values can be reported but should not be used to 

definitively select or eliminate a candidate cause. Rather, they 

are a measure of qualities of the evidence to infer the logical 

implication of the evidence by the direction (sign) of the 

association and the strength by the magnitude of the r-value.

Reporting Level of Confidence

Many analytical tests are expressed in a manner that 

emphasizes confidence in the results, such as using confidence 

limits, probabilities, and likelihoods, and can be helpful in 

providing a level of confidence to the overall assessment. 

Confidence is also negatively affected by multiple sources 

of uncertainty regarding analysis and evaluation of candidate 

stressors and should be documented.

Possible sources of uncertainty include the following:

1. Data quality: age or incompleteness of data, possible 

measurement errors or biases, specificity of methods, 

quality of metadata;

2. Use of surrogates, analogies, or extrapolation of data 

beyond what was originally measured;

3. Masking considerations, including possible ancillary 

causes that could not be identified because they were 

masked by a primary cause; and

4. Confounding factors that may cause correlation between 

two variables to be a poor indicator of causation.

Data Types for Stressor Identification in Stream 

Ecosystems

A variety of biological, hydrological, physical, and 

chemical data types can support SI efforts. Biological data 

include aggregate metrics for biological communities, as 

well as variables at the level of individuals, population 

demographics, community structure, and data representing 

ecological and biological stressors. Water chemistry includes 

variables representing concentrations of nutrients and DO, 

potentially toxic organic compounds or metals, water pH, 

specific conductance, and total dissolved solids, among other 

indicators. Water physical properties, such as temperature, 

suspended sediment, and turbidity, can produce direct 

effects as stressors on biological communities and may also 

interact with water chemical properties to generate stressful 

conditions. Physical habitat characteristics include the 

structure and diversity of habitat types (such as riffles and 

pools), degree of shading, presence of woody debris, substrate 

particle size, and temporal stability of habitat. Channel 

characteristics may include natural geomorphic properties of 

stream channels (for example, stream size, stream gradient, 
General Overview of Stressor Identification in Stream Ecosystems 11

bed or bank erosion, and density of riparian vegetation) as 

well as human modifications to stream channels (for example, 

channelization, dredging, and infrastructure features). 

Streamflow characteristics include several hydrologic 

properties reflecting high-flow conditions, low- or base-flow 

conditions, temporal variability in flow, and frequency and 

seasonality of particular flow conditions. For a partial list of 

ecologically relevant streamflow characteristics that may be 

useful in SI efforts, see Table II in Knight and others (2014). 

Watershed characteristics, such as land-use type, impervious 

surface coverage, use of tile drainage, use of agricultural 

best-management practices, crop types, and locations and 

numbers of point sources (such as wastewater treatment plants 

or industrial effluent discharges) may produce direct effects 

and may regulate many of the previously described variables 

relevant to SI efforts.

Analytical Approaches for Stressor 

Identification in Stream Ecosystems

Analysis of State and regional monitoring data can help 

to determine whether environmental conditions or biological 

characteristics at a site differ from desired characteristics and 

to understand the relation between a stressor and a response 

in a particular region. Many analytical techniques have been 

used successfully for causal analysis (EPA, 2010a). Several 

are briefly described here, and case studies and references for 

each are listed in table 1.

Tests of Significant Difference

Tests of significant difference can be useful in 

determining whether the value(s) for a variable at a test site 

are significantly outside the range of values observed at 

reference sites. These tests evaluate a null hypothesis stating 

that the observed value(s) at the test site are not significantly 

different than those at the reference site against an alternative 

hypothesis that the test site value(s) are significantly different 

than those of the reference site. Traditionally, these tests use 

a 0.05 test of significance; however, for the use in stressor 

identification, it is recommended to consider limits of 

significance greater than 0.05 because they may indicate the 

potential for difference, albeit supported by less power of 

significance (EPA, 2010a).

Because of the assumptions underlying parametric tests 

(see the Constraints and Limitations subsection), several 

nonparametric approaches may be helpful:

• The Anderson-Darling test can be used to determine 

whether a set of observations could be derived from a 

probability distribution (for example, normal distribution). The K-sample Anderson-Darling test allows 

testing of whether several sets of observations can be 

modeled as derived from a single population.

• The Kendall rank correlation coefficient (also known 

as Kendall’s tau) can be used to determine the similarity of ordering of data based on rank variables.

• The Kolmogorov-Smirnov test can be used to compare 

two samples or to compare a sample to a reference 

probability distribution, based on distance between 

an empirical distribution function and a cumulative 

distribution function.

• The Mann-Whitney U test (or Wilcox test) can be used 

to test whether two samples derive from the same 

population; as such, it is a nonparametric alternative to 

a Student’s t-test.

Table 1. Analytical approaches and associated case studies and references for stressor identification.

Analytical approach Case studies and references

Tests of significant difference Clements (1994), Townsend and others (2008).

Multiple regression Clements and others (2000), Norton and others (2000), Townsend and others (2008), Black and others 

(2011), Vander Laan (2012).

Quantile regression Cade and Noon (2003), Cade and others (2005, 2008), Cade (2011), Schmidt and others (2012), Knight 

and others (2014).

Ordination methods Norton and others (2000), Morris and others (2006), Matthaei and others (2010), U.S. Environmental 

Protection Agency (2010a), Allan and others (2013), Smucker and others (2013), Palmer (2016).

Discriminant analysis Clements (1994), Norton and others (2000).

Classification and regression trees De’ath and Fabricius (2000), Prasad and others (2006), Usio and others (2006), Steen and others 

(2008).

Random forests Breiman (2001), Prasad and others (2006), Cutler and others (2007), Maloney and others (2009, 2013), 

Carlisle and others (2010), Black and others (2011), Vander Laan and others (2013), Liaw and 

Wiener (2014), Gregorutti and others (2017).

Boosted regression trees De’ath (2007), Elith and others (2008), Smucker and others (2013).
12 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Uses and Advantages

Where parametric assumptions can be met, analysis of 

variance (ANOVA) or t-tests can be used to test for significant 

differences between or among stations or reaches (Clements, 

1994; EPA, 2010a). ANOVA can also be used to support SI 

through controlled experiments (Townsend and others, 2008). 

Nonparametric methods have the advantage of not requiring 

normally distributed data; however, certain nonparametric 

tests may have their own assumptions or constraints.

Constraints and Limitations

Parametric approaches (such as t-tests or ANOVA) 

assume normality of data distribution, adequate sample size, 

and random sampling. In some practical cases of SI, these 

assumptions may not be valid.

Multiple Regression

Linear and nonlinear regression models can be useful 

in SI to model the relations between one or more candidate 

stressors and a biological response. If multiple candidate 

stressors are modeled (as is often the case), the regression 

analysis is referred to as multiple regression. Regression 

models can be evaluated based on goodness-of-fit statistics 

that describe how well the model fits the data, as well as by 

examining regression coefficients.

Uses and Advantages

Regression analysis allows the evaluation of a functional 

form between a candidate stressor and biological response. In 

addition, the difference between observed values and expected 

values based on regression equations (in other words, the 

regression model residuals) can be useful in investigating other 

variables that were not included in the original regression 

model. Where identification of stressor-response relations 

is made more difficult because of confounding correlations 

based on natural variation along gradients, regression residuals 

may be more appropriate than raw biological data for use in 

subsequent analysis. For example, because some explanatory 

and response variables in streams naturally correlate with 

stream size (such as drainage area), Norton and others (2000) 

first regressed these variables on drainage area and then used 

drainage-area residuals in subsequent analysis. Similarly, 

Vander Laan (2012) defined potential stress as a site-specific 

difference between an observed level of a physical or chemical 

variable and its expected value based on natural conditions.

A common issue in stream ecology is that two or more 

variables produce interactive effects on biological responses. 

To address this common issue, interaction terms can be fitted 

into regression models. For example, Townsend and others 

(2008) fitted interaction terms (such as sediment × nutrients) 

to model interactive effects on biological response. Another 

common issue in regression models for stream ecology is the 

challenge of selecting the appropriate explanatory variables, 

such that important predictors are not left out but also that 

the model is not overfitted. Stepwise multiple regression can 

be used to select explanatory variables, either with forward 

selection (starting with no variables and systematically adding 

variables based on a statistical criterion for entry into the 

model) or backward selection (starting with all candidate 

variables and removing variables systematically based on a 

criterion for removal). For example, using forward selection, 

Clements and others (2000) constructed regression models 

for 16 biological response variables and found that heavymetal concentration was the most important predictor, with 

other physical and chemical characteristics explaining smaller 

amounts of variation in community structure.

Constraints and Limitations

Linear regression, although simple in application and 

interpretation, requires an assumption of a constant relation 

between explanatory and response variables across all values 

of both variables. In stream ecology, nonlinear relations are 

common. The appropriateness of a linear regression model can 

be assessed by a quantile-quantile plot of the model residuals 

(EPA, 2010a). Other assumptions that should be checked prior 

to acceptance of linear regression results include assumptions 

of constant sampling variance and of independent samples (for 

example, lack of spatial autocorrelation).

Quantile Regression

Quantile regression estimates the conditional quantiles 

of a biological response variable in a linear model. Quantile 

regression can be useful in cases where there is a weak or no 

predictive relation between the mean value of the biological 

response variable and the explanatory variables, but relations 

do exist with other parts of the biological response variable 

distribution (Cade and Noon, 2003; Armstrong and others, 

2011) (fig. 4).

Uses and Advantages

Quantile regression is particularly applicable in cases 

of multiple measured and unmeasured limiting factors (in 

other words, physical or chemical variables that constrain 

a biological response such as species richness). In stream 

ecology, including SI processes, multiple unmeasured limiting 

factors are commonly present. In such cases, examination of 

quantile regression parameters for multiple quantile levels 

can be useful in discerning “ecological limits” set by the 

explanatory variable in question. Quantile process plots show 

the slope and intercept parameter estimates for all quantile 

levels and can include confidence intervals (for example, 

95-percent confidence) that can be compared to zero to test 

whether (at the given level of confidence) the parameter 

estimate is different from zero.
General Overview of Stressor Identification in Stream Ecosystems 13

EXPLANATION

90th regression quantile

95−percent CI

Site

n = 586 n = 528

0 20 40 60 80 100

0

200

400

600

800

Percent alteration of August median flow

from groundwater withdrawals

Relative abundance, in counts per hour

0 20 40 60 80 100

0

200

400

600

800

Relative abundance, in counts per hour

A. Brook trout B. Blacknose dace

Percent alteration of August median flow

from groundwater withdrawals

Figure 4. Quantile regression relations between relative abundance of A, brook trout and B, blacknose dace and percent 

alteration of August median flow from groundwater withdrawals (from Armstrong and others, 2011). [CI, confidence interval; n, 

number of sites]

Constraints and Limitations

If the number of model parameters is large relative to the 

sample size, parameter estimate precision may be poor (Cade 

and others, 2008).

Ordination Methods

Ordination methods are a family of statistical 

procedures that share a common goal: to synthesize highly 

multidimensional data into a smaller number of dimensions. 

This allows data to be plotted in two-dimensional ordination 

space (fig. 5), such that similar observations are plotted 

closer together and dissimilar observations are plotted 

farther apart. Commonly used ordination methods include 

principal component analysis, cluster analysis, principal factor 

analysis, detrended correspondence analysis, and nonmetric 

multidimensional scaling.

Uses and Advantages

Ordination methods are useful in finding patterns in 

datasets that are otherwise too complicated (in other words, 

too highly dimensional) to interpret. Environmental variables 

may be highly correlated or “redundant” with one another. For 

example, soil pH, calcium, magnesium, and cation exchange 

capacity are usually very tightly correlated. If so, any one 

of these variables could potentially be used as a proxy for 

all the others. Alternatively, an ordination method such as 

principal components analysis could be used to distill and 

summarize the dataset, such that a principal component might 

represent the combined effect of soil pH, calcium, magnesium, 

and cation exchange capacity, rather than representing each 

variable individually.

Constraints and Limitations

Ordination methods are generally considered exploratory 

tools and are less well suited to statistical hypothesis testing, 

meaning that they do not support rejection of a null hypothesis 

that no significant relations exist in a dataset. When applying 

ordination approaches to community composition data along 

a gradient (for example, species counts along a DO gradient), 

the beta diversity (how different the community composition 

is among samples along the gradient) should be considered 

in selecting the appropriate method. For example, principal 

components analysis functions best with low beta diversity, 

whereas detrended correspondence analysis and canonical 

correlation analysis perform better with high beta diversity 

(Palmer, 2016).
14 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

30 120 210 300 1 4 7 10

6 24 42 60

Wooded vegetation in 25-meter-reach buffer < 80%

Crops in 50-meter-segment buffer > 15%

Dissolved organic carbon > 4 mg/L

Dissolved inorganic nitrogen > 4.2 mg/L

Dissolved inorganic nitrogen < 3.2 mg/L

Dissolved inorganic nitrogen,

milligrams per liter

Total nitrogen to

total phosphorus ratio

Percent basin forested

Linktree grouping

A

EXPLANATION FOR LINKTREE GROUPING

B

C

D

E

2D Stress: 0.17

4.4 5.6 6.8 8

Abundance of intolerant

invertebrates

Average tolerance of

invertebrate abundance

4 16 28 40

Percent omnivore abundance

800 3,200 5,600 8,000

1 2

3

4

5

7

8

9

10

11

12

15

16

17

18

19

20

21

22

23

24 25 26

27

Figure 5. Plot series illustrating nonmetric multidimensional scaling (MDS) ordinations of Delmarva 

Peninsula algal community composition from Bray-Curtis similarity matrices (from Calhoun and others, 

2008). Top graphs are environmental variables, and bottom graphs are autecological algal indicators. [mg/L, 

milligram per liter; >, greater than; %, percent; <, less than]
General Overview of Stressor Identification in Stream Ecosystems 15

Discriminant Analysis

Discriminant analysis (DA) describes statistical methods 

for predictive models to classify observations into a set of 

classes based on a set of continuous response variables. The 

dataset used for DA includes a set of observations with known 

classification and values for the continuous variables. For 

example, a set of N sites that are already classified as “good,” 

“medium,” and “poor” for water quality, and which have 

data on Y continuous variables (such as nitrate, temperature, 

DO, and turbidity), could be a dataset used for DA. The 

discriminant model would use the Y continuous variables to 

assign each water-quality site as being “good,” “medium,” 

or “poor.” This output is used to generate a misclassification 

summary that provides an indicator of the goodness-of-fit of 

the model. The model can then be used predictively to classify 

future sites as “good,” “medium,” or “poor” based on their 

values for the Y continuous variables.

Uses and Advantages

DA can be used for predictive classification. In other 

words, DA can be used to assign “new” observations to 

predetermined categories based on continuous variables. 

DA can also detect which variables are most useful for 

differentiating between or among categories.

Constraints and Limitations

Linear DA assumes that the independent variables are 

normally distributed and that the variance/covariance matrices 

of variables are homogenous across classes. In addition, 

significance tests for linear DA may not be valid if the means 

for variables across classes are correlated with the variances.

Classification and Regression Trees

Classification and regression tree (CART) analysis is a 

machine-learning technique that involves recursive splitting 

of a dataset into progressively smaller and more homogenous 

groups by using combinations of explanatory variables. The 

output is a tree diagram with branches determined by the 

splitting rules derived from the input data (fig. 6).

Uses and Advantages

CART and ensemble tree-based methods can model 

nonlinear relations and interactions between explanatory 

variables. These approaches do not assume normality of data 

distributions and are able to handle missing values both for 

response and explanatory variables. In addition, CART and 

ensemble tree-based methods can be performed on a variety 

of data types, including continuous, binomial, and categorical 

data. Unlike ensemble tree-based methods, CART models 

produce a single decision tree, which makes results relatively 

easy to interpret. As such, CART models may be more “user 

friendly” to nonstatisticians than ensemble methods. For 

example, Usio and others (2006) used CART to identify 

thresholds of pH and water temperature (6.5 and 14.3 degrees 

Celsius [°C], respectively) associated with stream habitat 

for an invasive crayfish species in Japan. Similarly, Steen 

and others (2008) identified a water-temperature threshold 

of 18.66 °C associated with brown bullhead (Ameiurus 

nebulosus) habitat in Michigan streams. In each case, the 

decision trees provide clearly organized information on 

the relative importance of predictor variables in explaining 

patterns of the dependent variable, along with thresholds on 

predictor variables and an overall metric of the prediction 

accuracy of the CART model. CART and similar tree-based 

methods could be useful in the SI process for demonstrating 

evidence for or against several characteristics of causation, 

including co-occurrence and sufficiency. For example, if 

a CART analysis demonstrates a threshold response for a 

particular candidate stressor, and that stressor co-occurs at 

the study site at levels well exceeding that threshold, then the 

CART analysis could be used to help illustrate sufficiency of 

the candidate cause.

Constraints and Limitations

Depending on the size of the dataset, and the number 

and nature of the explanatory variables, individual trees 

from CART analysis may be unstable. For example, if a 

small subset of the original dataset is randomly selected and 

withheld from analysis, the resulting tree may have a different 

first split (indicating a different explanatory variable as being 

of primary importance) than if that subset was included. To 

deal with this issue of instability, ensemble tree-based methods 

were developed as described in the report sections Random 

Forests and Boosted Regression Trees.

Random Forests

Random forests (RF) is an ensemble tree-based method, 

meaning that it fits many (classification or regression) decision 

trees to an input dataset, unlike CART which produces only 

a single tree for each analysis run. The use of many “trees” 

produced the “forests” part of the method name. RF uses 

bootstrap aggregation (bagging); in other words, uniform 

samples are taken from the input dataset and used to create 

trees. Each decision tree is grown using a randomized subset 

of explanatory variables.

Useful outputs of RF include variable-importance plots 

and partial-dependence plots. Variable importance represents 

the magnitude of contribution of each of the explanatory 

variables to the recursive split algorithm (in other words, 

an estimate of how much worse the prediction would be for 

the dataset if that explanatory variable were removed from 

analysis). Partial dependence represents the relation of an 

individual explanatory variable with the response variable if 

all other explanatory variables were held constant.
16 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

< 132.91 ≥ 132.91

< 307.42 StrFlwYld ≥ 307.42

< 1.1 ≥ 1.1

≥ 53.5 < 53.5

< 27.2 ≥ 27.2

0.02

N=4

0.04

N=5

0.08

N=20

0.14

N=12

0.28

N=5

0.70

N=2

ForWt-W

%PSFlw

StrFlwYld

Agr-B

Percentage of agricultural land in stream buffer

Median annual streamflow yield, in million 

gallons per square mile

Percentage of forested land plus wetlands

in watershed

Median annual percentage point-source flow

contribution to stream

Terminal node with average median annual

 constituent yield in tons per square mile.

 N indicates the number of observations

 at the node.

Agr-B

StrFlwYld

ForWt-W

%PSFlw

0.08

N=20

EXPLANATION

Figure 6. Example of a regression tree identifying predictor variables that best 

explain observed variations in median annual yields of total phosphorus (from Harden 

and others, 2013).

Uses and Advantages

RF can be used for classification and for regression. 

When used for classification, RF classification accuracy is 

generally high. Like other tree-based methods, RF can model 

nonlinear relations and interactions among explanatory 

variables and is not adversely affected by missing values. 

Because it is an ensemble method, meaning that it averages 

results across many trees, results are generally more stable 

than from single-tree approaches such as CART. A common 

application of RF methods is to produce species-distribution 

models that predict occurrence (presence and absence) for 

species of interest based on a suite of habitat metrics. For 

example, Maloney and others (2013) used conditional RF 

models to predict the probability of presence for 30 common 

fish species in streams of the Chesapeake Bay watershed. 

Such predictive models can then be used to generate predictive 

maps for types of information such as species occurrence 

or stream biological condition for locations where these 

variables have not been measured directly. For example, using 

a conditional random forest model, Maloney and others (2009) 

predicted that approximately 34 percent of small nontidal 

stream reaches in the Maryland portion of the Chesapeake 

Bay watershed were in “fair” condition, with 30, 23, and 

13 percent in “good,” “poor,” and “very poor” biological 

condition, respectively. Similar to CART and other treebased approaches, RF could be useful in the SI process for 

demonstrating evidence for or against co-occurrence and (or) 

sufficiency of candidate causes. For example, an RF model 

could be used to predict that a site with a given set of physical 

parameters, such as water-quality and habitat metrics, would 

be biologically impaired. If a study site has those physical 

parameters in the ranges specified in the RF model and also 

demonstrates biological impairment, this could constitute 

evidence in favor of the candidate cause(s) included in the RF 

model.
Stressor Identification of Biologically Impaired Streams in Mississippi 17

Constraints and Limitations

When used for regression, RF cannot predict beyond 

the range in the training dataset. Additionally, RF may 

result in overfitting, especially when applied to datasets 

with a low signal-to-noise ratio. Strong correlation among 

explanatory variables may produce bias in the variableimportance plots produced in RF, such that variable 

importance is inflated for highly correlated variables. Bias in 

variable importance due to correlated explanatory variables 

may be addressed by using recursive feature elimination. 

RF and other ensemble tree methods have been referred to as 

“black box” approaches because interpretation of their results 

is less straightforward than for single decision trees, such as 

CART.

Boosted Regression Trees

Like RF, boosted regression trees (BRT) is an ensemble 

tree-based method which fits many decision trees to an input 

dataset, using recursive partitioning based on explanatory 

variables. In BRT, trees are created to handle residuals leftover 

from previously constructed trees; in other words, each tree 

attempts to explain remaining variation in data that was not 

explained by previous trees. As each new tree is built, the data 

are re-weighted to emphasize cases that were poorly predicted 

by previous trees.

BRT produces variable-importance plots and partialdependence plots that are similar to those from RF.

Uses and Advantages

Like other tree-based methods, BRT can model 

nonlinear relations and interactions among explanatory 

variables and is not adversely affected by missing values. 

BRTs are generally robust to extreme outliers and to the 

inclusion of irrelevant explanatory variables. Because it is 

an ensemble method, meaning that it averages results across 

many trees, results are generally more stable than those 

from single-tree approaches, such as CART. De’ath (2007) 

asserted that BRTs “consistently outperform [RF], particularly 

for regression.” Similar to both CART and RF, BRTs could 

be useful for evaluating the co-occurrence and sufficiency 

characteristics of causation.

Constraints and Limitations

Like other ensemble tree approaches (for example, RF), 

BRT has been referred to as a “black box” approach because 

interpretation of results is less straightforward than for single 

decision trees, such as CART.

Stressor Identification of Biologically 

Impaired Streams in Mississippi

Biological Impairment in Mississippi: Data 

Collection and Assessment

MDEQ annually collects benthic macroinvertebrate 

samples from streams statewide as part of their ambient 

surface-water monitoring program. Biological samples are 

processed by using taxonomic identification and counting of 

individual macroinvertebrates to produce biological metrics, 

which are then used to assess the health of wadable streams 

and to identify impaired streams based on differences in 

taxa richness and percent composition, feeding and habitat 

richness, and composition and tolerance traits. This statewide 

monitoring also involves assessment of habitat condition, 

sediment particle size, water-quality characteristics, and 

remotely sensed land-use and land-cover percentages. 

Calculation of an M-BISQ value for each sampling site 

involves comparing selected biological metrics for the site to 

their respective impairment-threshold values. The biological 

metrics used, and the stream reaches chosen to represent the 

impairment threshold, vary according to bioregions of the 

State. Bioregions represent geographically distinct natural 

assemblages of benthic macroinvertebrates (Stribling and 

others, 2016) and were determined by modifying level IV 

ecoregions (Omernik, 1987) with biological data to produce 

bioregions with relative biological homogeneity (fig. 7). For 

each bioregion, the impairment threshold is calculated as the 

25th percentile of the M-BISQ scores for a set of streams 

considered to be least disturbed by human activities. If the 

M-BISQ score for a sampling site falls below the impairment 

threshold for its bioregion, the site and its associated 

stream reach are listed as biologically impaired. Biological 

sample collection, processing, and subsequent data analysis 

procedures are explained in detail in the Quality Assurance 

Project Plan for the §303(d) List Assessment and Calibration 

of the Index of Biological Integrity for Wadable Streams in 

Mississippi (Stribling and others, 2016).

The M-BISQ assessment process identifies biologically 

impaired streams but does not seek to explain the causes 

of impairment. For many stream reaches, the causes of 

impairment are unknown, thus requiring an SI process to 

clarify the specific potential causes of impairment. Stream 

reaches that are listed as impaired based on an M-BISQ score 

are generally similar in the type, amount, and nature of data 

available for SI. Therefore, a standardized process for SI 

in these impaired reaches of Mississippi was developed by 

adapting the EPA framework for stressor identification (EPA, 

2000; Cormier and others, 2003). The goals of the Mississippi 

SI framework are to (1) provide a common guide for users 

to maintain consistency and transparency in assessment 

and documentation for impaired streams, and (2) serve as 

a foundation for sustainable and adaptive management of 

aquatic resources.
18 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

M-BISQ Bioregions (Stribling 

and others, 2016)

East

South Bluff

South East

West

Not designated

EXPLANATION

Base modified from U.S. Geological Survey digital data, 1:150,000

Albers Equal-Area Conic projection 

North American Datum of 1983

91° 90° 89° 88°

35°

34°

33°

32°

31°

30°

Study area

MISSISSIPPI

0 1,600 3,200 KILOMETERS

0 1,200 2,400 MILES

Figure 7. Mississippi Benthic Index of Stream Quality (M-BISQ) bioregions used to stratify analysis of ecological 

response to stressors in biologically impaired streams in Mississippi.
Stressor Identification of Biologically Impaired Streams in Mississippi 19

Components of the Stressor Identification 

Framework for Biologically Impaired Streams in 

Mississippi

The Mississippi stressor identification framework follows 

six general steps:

1. Define the impairment,

2. List the candidate causes of impairment and develop a 

conceptual model,

3. Compile all data relevant to the impairment and conceptual model,

4. Evaluate the data,

5. Identify the probable causes of impairment by using a 

WOE approach, and

6. Generate a report of results.

Step 1. Define the Impairment

The first step is to define the geographic and temporal 

scope of the biological impairment for the impaired stream 

reach. Basic information includes the reach name, a general 

location description, bioregion (including geographic 

details of any site spanning multiple bioregions), date the 

original biological sample was collected that resulted in the 

listing for biological impairment, dates of any subsequent 

biological sampling, drainage area, dominant current land 

uses and land cover, and historical land uses and landscape 

changes. Defining impairment for a given stream reach 

requires documentation of the biological metrics from the 

sample that was used to list the site as impaired, such as low 

numbers of sensitive taxa, low overall richness of taxa, or 

low diversity of functional feeding groups. To document the 

impairment, each biological metric value for the site should 

be listed along with the corresponding impairment-threshold 

value for the bioregion in which the impaired site is located. 

Other relevant biological information can be compiled in 

this step, such as M-BISQ scores and metrics from samples 

obtained at the same location or within the same watershed 

on different dates, or aquatic community data for taxonomic 

groups other than benthic macroinvertebrates. Additional 

documentation to define the impairment may come from field 

notes, information obtained during reconnaissance of the 

drainage area and site locations, §303(d) listing information, 

and remote-sensing data. In rare cases, stream reaches may 

have additional information that predates the use of M-BISQ 

to confirm impairment, such as TMDLs developed and actions 

taken to remediate the impairment. In addition, data from 

other local, State, Federal, nonprofit, and private sector entities 

may be considered for use.

Step 2. List the Candidate Causes of Impairment 

and Develop a Conceptual Model

This step includes the development of a list of candidate 

causes of stress (stressors) and a conceptual model to explain 

the potential linkages between stressors and the nature of 

biological impairment documented in step 1. The conceptual 

model can also suggest linkages among stressors and potential 

sources of stress, which can include a variety of human 

activities and artifacts within the watershed. A template list 

of common potential sources of stress (table 2) may be used 

as a guide and may be modified or expanded based on a 

site’s individual history or landscape context. Some potential 

sources of stress may be identified or eliminated based on 

land-use and land-cover characteristics of the watershed and 

field reconnaissance information. Geospatial analysis of 

mapped locations of point sources (for example, wastewater 

effluent discharge sites), septic tanks, and miscellaneous 

sources of contaminants may also help identify or eliminate 

potential sources of stress.

Once a list of potential sources of stress within the 

watershed has been compiled, associated proximate and 

intermediate causes can be identified. Proximate causes are 

defined as the immediate and effective causes of the biological 

impairment. MDEQ considers (Shawn Clark, MDEQ, personal 

communication, September 4, 2017) five main categories of 

proximate causes of impairment:

1. Decrease in suitable habitat,

2. Alteration to thermal regime,

3. Decrease in DO due to nutrient enrichment,

4. Decrease in DO due to organic enrichment, and

5. Increased toxicity, including ionic strength and other 

contaminants.

Intermediate causes are part of the causal pathway but 

do not directly cause the impairment. For example, sources of 

stress within a watershed, including water withdrawals, urban 

areas, agriculture, and channel alterations, could produce 

intermediate causes such as flow-regime alterations, changes 

in channel morphology, decreases in riparian canopy cover 

and instream habitat, and increases in solar input, suspended 

sediment, and erosion (fig. 8). These intermediate causes do 

not produce the biological impairment in and of themselves, 

but rather contribute to the proximate causes of impairment 

which could include decreased suitable habitat, altered thermal 

regime, and increased toxicity (fig. 9).
20 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Table 2. Common potential sources of stress in biologically impaired streams.

Source of stress Examples of more 

specific sources References for example studies

Resource extraction Sand and gravel mine

Oil field

Coal mine

Water withdrawal

Clements (1994), Clements and others (2000), Carlisle and others (2011), Merriam 

and others (2011), Bernhardt and others (2012), Lavoie and others (2018).

Silviculture Clear cutting

Specific trees on the farm

Allan and others (2012).

Urbanization Urban construction

Dumping

Fertilizer application 

Impervious surfaces

Onorato and others (1998), Wang and others (2000), Walsh and others (2005), 

DeGasperi and others (2009), Miguntanna and others (2010), Merriam 

and others (2011), Porter and others (2015), Maryland Department of the 

Environment (2016), Lavoie and others (2018).

Wastewater discharge Specific size or permitted 

discharge pollutants

Blazer and others (2012), Lavoie and others (2018).

Unsewered residential Septic tanks

Landfill

Lapointe and others (2012).

Agriculture Specific crops

Specific animal operations

Cormier and others (2000), Norton and others (2000), Matthaei and others (2010), 

Black and others (2011), Blazer and others (2012), Lange and others (2014), 

Maryland Department of the Environment (2016), Pearson and others (2016), 

Munn and others (2018).

Channel alteration Channelization

Dredging

Ditch construction

Road crossings

Bank erosion

Cormier and others (2000), Carlisle and others (2009), Doll (2011), Allan and 

others (2012), Voss and others (2012), Porter and others (2015).

Impoundments Dams and other barriers Ohio Environmental Protection Agency (2010), Carlisle and others (2011).

Roads Unpaved roads

Paved roads, highways

None.

A conceptual model can then be developed to show 

linkages between potential sources of stress, intermediate 

causes/pathways, proximate stressors, and biological 

responses (fig. 10). The conceptual model represents the 

plausible relations among possible sources of stress and the 

biological response variables, including the intermediate 

pathways. A conceptual model can consist of a single diagram 

or multiple diagrams that each represent a proximate cause of 

impairment. The process of conceptual model development 

may help eliminate some candidate stressors because of the 

lack of a source and (or) pathway. For example, a candidate 

stressor might be eliminated if a causal agent or source of 

stress were believed to be either mechanistically implausible 

or absent from the watershed in question.

Lists of causes and templates in figures 8 through 10

can help guide assessments but should be tailored to sitespecific watershed histories and characteristics as needed. 

In some cases, sources of impairment may be present in 

a watershed that are not included in the preliminary list. 

These may be revealed by field investigations, geospatial 

analysis of the watershed, or other sources of information. 

Such additional candidate sources of impairment should be 

evaluated in the relevant scientific literature to determine 

the causal pathway linking intermediate causes to proximate 

causes to the response indicator. Although it is practical to 

consider intermediate causes that are commonly measured 

and available, caution should be taken by also considering 

variables that may not have been measured. For example, 

pesticides, invasive species, pathogens, endocrine disruptors, 

pharmaceuticals, and polycyclic aromatic hydrocarbons are 

not routinely measured, but may be important in the actual 

causal-chain scenario. It is important to identify all the 

potential sources of stress and the potential causes of stress 

that are associated, because even though the data may not be 

available, these sources and causes may need to be explored. 

Future data collection, analyses, and refinement of linkages 

may allow for more specific, direct, and comprehensive 

proximate causes to be developed over time.
Stressor Identification of Biologically Impaired Streams in Mississippi 21

Intermediate Causes

Sources

Alteration of photosynthesis process

Increase in algal biomass

Increase in organic enrichment

Disruption of nutrient cycles

Change in food source characteristics

Increase in salts, minerals, and inorganic substances

Increase in toxic substance concentrations

Increase or decrease in pH

Alteration to natural flow regime

Decrease in suitable instream habitat

Alteration to channel morphology

Decrease in riparian canopy cover

Increase in suspended and deposited sediment

Decrease in suitable flood-plain habitat

Alteration to groundwater interaction

Increase in solar input

Increase in bed scouring

Increase in bank erosion

Wastewater discharge

Urban areas

Silviculture

Unsewered residential

Agriculture—crops

Agriculture—animals

Sand and gravel mining

Water withdrawal

Oil fields

Channel alteration

Impoundments

Roads

Change in food source characteristics

Alteration of respiration balance

Figure 8. Examples of sources of stress and linkages to intermediate causes of impairment.

Step 3. Compile All Data Relevant to the 

Impairment and Conceptual Model

By using the conceptual model as a guide, data 

compilation should focus mainly on data that may characterize 

proximate and intermediate causes linked to the potential 

sources of stress identified within the watershed.

Sources of Data

A reconnaissance survey of the stream reach and its 

watershed is typically conducted as part of this step to 

begin assembling data relevant to causes of impairment. 

This survey will provide a rapid geomorphic assessment of 

the stream reach, field measurements of water-quality and 

physical characteristics of the stream, and observations from 

upland locations throughout the watershed. Examples of the 

field forms that can be used for such surveys are given in 

appendix 1.

Following reconnaissance, all available water-quality, 

biological, physical, and hydrologic data available for 

the stream reach, watershed, and bioregion need to be 

gathered and organized. Data sources include information 

collected during preliminary site visits, MDEQ databases, 

the EPA Legacy Storage and Retrieval (STORET) database, 

USGS databases, land-cover geospatial datasets, and other 

government agency databases and scientific literature. 

Supporting information about the data is also compiled at 

this stage, including data sources, data collection dates and 

locations, data collection methods, and quality assurance 

protocols.
22 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Intermediate Causes

Proximate Causes

Alteration to thermal regulation

Decrease in dissolved oxygen

due to organic enrichment

Decrease in dissolved oxygen

due to nutrient enrichment

Decrease in suitable habitat

Increased toxicity

including ionic strength

Alteration of photosynthesis process

Increase in algal biomass

Increase in organic enrichment

Disruption of nutrient cycles

Change in food source characteristics

Increase in salts, minerals, and inorganic substances

Increase in toxic substances and concentrations

Increase or decrease in pH

Alteration to natural flow regime

Decrease in suitable instream habitat

Alteration to channel morphology

Decrease in riparian canopy cover

Increase in suspended and deposited sediment

Decrease in suitable flood-plain habitat

Alteration to groundwater interaction

Increase in solar input

Increase in bed scouring

Increase in bank erosion

Change in food source characteristics

Alteration of respiration balance

Figure 9. Examples of intermediate causes and linkages to proximate causes of impairment.

M-BISQ Data and Organization

Common data collected by MDEQ during biological 

stream monitoring that may be compiled in this step include 

stressor indicators of habitat, substrate particle size, physical 

and chemical water-quality indicators (table 3); response 

indicators of benthic macroinvertebrate community metrics; 

and taxonomic information. In addition to data from the 

impaired site, statewide data from a master database of 

stations monitored during 2001–14 may be used to compare 

the impaired site to other local sites that are not biologically 

impaired, termed “site-specific comparators” (SSCs), and a 

range of conditions from least disturbed (LD) and nonimpaired 

(NI) stream reaches. The statewide master database includes 

all physical, chemical, biological, and watershed data collected 

during 2001–14 and is used to define LD and NI conditions 

and to select SSCs. LD and NI conditions may be defined as 

the interquartile (25th to 75th percentile) ranges of LD and 

NI streams within the same bioregion. Appendix 2 contains 

details on the selection of SSCs.

Step 4. Evaluate the Data

The goal of this step is to perform quantitative and 

qualitative analyses to determine strengths of association 

between candidate stressors and observed biological 

impairments. Evaluation of data and information is performed 

to support a WOE approach that considers all relevant 

evidence from diverse sources. These sources include 

those described in step 3, in addition to interpretation 

of observations, summary statistics, and statistical and 

mathematical modeling, and weighing of evidence based 

on categorization of the types of data. CADDIS includes a 

scoring system that can be used to summarize the degree 

to which each type of available information strengthens or 

weakens the case for a candidate cause. The specifics of the 

scoring system for the Mississippi framework, a modification 

of the CADDIS scoring system, are detailed in step 5. In 

step 4, it is important to document (1) the data analysis 

methods used, (2) the scoring results, and (3) a summary 

of the ways in which each type of available evidence either 

strengthens or weakens the case for a candidate cause.

The objective of step 4 is to evaluate whether 

fundamental characteristics of a causal relation are present by 

using various types of evidence (table 4) (EPA, 2000; Suter 

and others, 2002; Cormier and others, 2003). Confidence in 

conclusions generally increases as more types of evidence are 

evaluated.

In most cases, data available for evaluation are those 

collected as part of the MDEQ statewide biological monitoring 

program. Therefore, the types of evidence typically available 

in practice relate to three primary types of causal inference: 

co-occurrence, antecedence, and sufficiency.
Decrease in dissolved oxygen due to organic enrichment Increase in toxic substances

# EPT taxa,

% Plecoptera, HBI,

% Amphipoda, # Dipteran taxa

Increased nutrients

(NN, TKN, TP)

Increased organic matter

(TOC, BOD, COD)

Increased 

algal biomass

Increased microbial

respiration 

% Clingers, % Sprawlers,

% Predators

Decrease in suitable

habitat

Alteration to

thermal regulation

Increased 

suspended and 

deposited sediment

Decreased

allocthonous

organic matter

Decrease in

dissolved oxygen

due to nutrients

Flow

alteration

Increased 

solar input

Increased 

bed scouring

Increased 

bank erosion

Decreased

riparian vegetation

Increased ionic

strength 

Increased salts

and minerals

Altered water

temperature input

Roads Urban areas

Water withdrawals Wastewater discharge

Figure 10. An example of a conceptual model linking potential sources of stress within a watershed to intermediate causes and proxwhich produce observed biological responses. [TOC, total organic carbon; BOD, biological oxygen demand; COD, chemical oxygen de+ Nitrite; TKN, total Kjeldahl nitrogen; TP, total phosphorus; %, percent; #, number; EPT, Ephemeroptera, Plecoptera, and Trichoptera; HBiotic Index]
24 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Table 3. Examples of common physical and chemical water-quality indicators of proximate causes and the typical availability of the 

indicator.

[RBP, rapid bioassessment protocol; Q, streamflow; 7Q10, lowest 7-day average flow that occurs (on average) once every 10 years; N, nitrogen]

Proximate cause Indicator Availability

Decrease in suitable habitat RBP habitat assessment usually

Quantified habitat measures rarely (site specific)

Turbidity rarely (site specific)

Suspended-sediment concentration rarely (site specific)

Particle-size distribution usually

Continuous Q rarely (site specific)

Regional ratings 7Q10

Alteration to thermal regime Temperature usually (not seasonal or diel)

Decrease in dissolved oxygen due to nutrient enrichment Dissolved oxygen usually (not diel)

Total nitrogen usually

Ammonia usually

Inorganic N usually

Total phosphorus usually

Dissolved inorganic phosphorus rarely (site specific)

Decrease in dissolved oxygen due to organic enrichment Dissolved oxygen usually (not diel)

Total organic carbon usually

Chemical oxygen demand usually

Biological oxygen demand rarely (site specific)

Increased toxicity including ionic strength Specific conductance usually

Total chlorides usually

pH usually

Alkalinity usually

Herbicides rarely (site specific)

Insecticides rarely (site specific)

Metals rarely (site specific)

Table 4. Examples of types of evidence based on data from within a case.

Causal inference Type of evidence General concept

Co-occurrence Spatial/temporal co-occurrence The biological effect is observed at the same time and place as the cause, or the 

stressor is greater compared to other places where the effect is not observed.

Time order Temporal sequence The cause precedes the biological effect in the same location.

Antecedence Causal pathway A pathway exists from the potential source of stress to the affected organism, their 

food source, or some entity that can affect them.

Interaction Evidence of exposure Measurements of the biota exhibit signs that the causal agent has occurred and the 

biota was exposed.

Alteration 

interaction

Laboratory tests of site media Laboratory tests using media (such as water) from the site in a controlled 

environment to mimic effects observed in the field.

Sufficiency Stressor-response relations 

from the site

The candidate cause from the impaired site is of sufficient magnitude to cause the 

observed biological effects based on an association of nearby sites (typically a 

strong r-value on a regression plot).
Stressor Identification of Biologically Impaired Streams in Mississippi 25

Co-occurrence and Antecedence

When biological impairment is identified by an M-BISQ 

score below the impairment threshold, evidence for causal 

inference may take the form of co-occurrence or antecedence. 

Co-occurrence is a type of evidence wherein the biological 

effect is observed at the same time and place as the cause 

and is not observed when and where the cause is absent. 

Antecedence is a form of evidence that suggests that the 

cause and effect are likely to have co-occurred, such as when 

evidence of the stressor (or a surrogate for the stressor) exists, 

but not necessarily at the same time as the observed biological 

effect.

The original process of documenting the observed 

biological effect—leading to an M-BISQ score below 

the impairment threshold—may have produced helpful 

information such as individual community metrics and 

taxonomic data associated with a candidate. Co-occurrence 

considers indicators of proximate causes collected at the same 

time and place as the biological sample. A cause is considered 

present based on MDEQ water-quality standards, LD/NI 

conditions, and SSC conditions. Figure 11 is an example 

of a comparison report used to document these results. 

Antecedence may consider information gathered during a 

reconnaissance visit, from point-source discharge records, 

or from other data from the site or watershed not necessarily 

collected at the same time as the biological sample.

Sufficiency

Evidence for a candidate cause may also derive from 

comparison of measures of exposure to a stressor at a study 

site with lab results or field studies from other sites that 

demonstrate biological effects of the exposure. If lab tests, 

field tests, or observational studies from other sites indicate 

exposure-response relations between a candidate stressor and 

a biological outcome, an important consideration is whether 

the stressor is present at the study site in sufficient quantity 

and frequency for the anticipated result to occur. An additional 

consideration is that lab conditions and organisms may not 

accurately represent field conditions. If no lab or field data 

exist for a candidate cause, analogous or surrogate data 

may be used, although use of a surrogate will increase the 

uncertainty of the association.

Typically, when biological impairment is identified by 

an M-BISQ score below the impairment threshold, analysis 

of data from other places takes the form of scatterplots of 

regional data showing relations between specific stressors 

(causes) and biological community metrics (effects).

Scatterplots depict biological community metrics 

(typically on the vertical y-axis) plotted against abiotic 

variables (candidate causal physical/chemical indicators, 

typically on the horizontal x-axis), with points representing 

pairs of observed variables from throughout a given 

region (fig. 12). A regression line—such as from quantile 

regression—may also be plotted to show the overall regional 

relation between the candidate stressor and the biological 

metric. The study site is plotted as a single point on the 

scatterplot, showing how the study site relates to other 

sites in the region. Summary statistics from reference sites 

throughout the region can be added as well, for evaluating 

how the study site compares to reference sites in terms of the 

candidate stressor and biological metric. Following similar 

thresholds used by Knight and others (2014), the interquartile 

ranges (values between the 25th and 75th percentiles) of 

the candidate stressor and biological metric for reference 

sites in a study region can be used to establish reference 

profiles for the region; in other words, ranges of values for 

the candidate stressor and biological response are considered 

generally similar to values observed at reference sites. We 

then propose that each scatterplot can be scored based on the 

location of the study site on the scatterplot, with the following 

scoring options: strong evidence (++), some evidence (+), 

incompatible (–), and uncertain (0).

Figure 12 illustrates the scoring process. In this example, 

there is a negative relation at an ecoregional scale between 

specific conductance and the number of Ephemeroptera, 

Plecoptera, and Trichoptera (EPT) taxa observed. The upper 

(90th quantile) limit of this relation is illustrated by quantile 

regression. For any specific conductance value (X value), 

the line depicting the 90th quantile shows the Y value for 

which 90 percent of sites in the region have equal or lower 

Y values. For example, at a specific conductance value of 

100 micromhos per centimeter, approximately 90 percent of 

sites have 12 or fewer EPT taxa. In the example scatterplot 

(fig. 12), the 25th percentile of the biological metric across 

reference sites is eight EPT taxa, meaning that 75 percent of 

reference sites have at least eight EPT taxa. Thus, any site that 

plots above that horizontal line (EPT taxa=8; in other words, 

in the upper left or upper right portions of the plot) could 

be considered to have the biological metric largely within 

the reference range observed for the region. The example 

scatterplot also includes a vertical line that represents the 75th 

percentile of specific conductance (the candidate stressor in 

this case). Any site that plots to the left of this vertical line 

has specific conductance less than this value and thus within 

the general range of reference sites of the region. A study site 

that plots in this left region of the plot therefore is unlikely 

to have specific conductance as a strong candidate stressor, 

because specific conductance values are actually observed 

to be generally within the reference range for the region. An 

additional vertical line is plotted based on the intersection 

point between the 90th quantile regression (diagonal) line and 

the 25th percentile of the biological metric (horizonal line). In 

figure 12, this vertical line represents a specific conductance 

value at which 90 percent of sites in the region have eight 

or fewer EPT taxa, in other words, fewer EPT taxa than the 

lower bound of the reference range for EPT taxa. Any study 

site that plots in the lower right portion of the scatterplot (in 

figure 12, having fewer EPT taxa than the reference range and 

having specific conductance greater than the value at which 

90 percent of sites have fewer EPT taxa than the reference 

range) would be considered to have relatively strong evidence 

in favor of the candidate cause.
26 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

A

Parameter Stressed 

site

Difference from LD condition Least disturbed condition

Percentile 

East used for LD

Bioregion LD

East 

nonimpaired East LD East nonimpaired

Chemical parameters

Specific conductance (μmho/cm) 0.00 lower lower 61.40 50.00 75th

Dissolved oxygen (% saturation) 0.00 lower lower 93.20 93.30 25th

Ammonia (mg/L) 0.00 lower lower 0.10 0.10 75th

Nitrate + nitrite (mg/L) 0.00 lower lower 0.19 0.23 75th

Total Kjeldahl nitrogen (mg/L) 0.00 lower lower 0.44 0.43 75th

Total nitrogen (mg/L) 0.00 lower lower 0.56 0.62 75th

pH 0.00 lower lower 6.80 6.83 75th

Total phosphorus (mg/L) 0.00 lower lower 0.04 0.04 75th

Temperature (°C) 0.00 lower lower 8.10 8.47 50th

Total organic carbon (mg/L) 0.00 lower lower 4.00 5.00 75th

Chemical oxygen demand (mg/L) 0.00 lower lower 13.00 12.00 75th

Total chlorides (mg/L) 0.00 lower lower 4.50 4.50 75th

Alkalinity (mg/L) 0.00 lower lower 11.73 11.80 75th

Turbidity (NTU) 0.00 lower lower 18.75 18.00 75th

Physical parameters

Basin area (mi2) 0.00 lower lower 11,046.16 14,422.42 50th

Total habitat score 0.00 lower lower 122.00 119.00 25th

Instream cover habitat score 0.00 lower lower 36.00 34.00 25th

Channel habitat score 0.00 lower lower 52.75 49.00 25th

Bank habitat score 0.00 lower lower 32.00 30.00 25th

Hydrohab 0.00 lower lower 39.75 35.00 25th

% Silt/clay 0.00 lower lower 18.00 13.00 50th

% Sand 0.00 lower lower 66.00 72.00 50th

% Gravel 0.00 comparable comparable 0.00 0.00 50th

% Cobble 0.00 comparable comparable 0.00 0.00 50th

% Boulder 0.00 comparable comparable 0.00 0.00 50th

% Bedrock 0.00 comparable comparable 0.00 0.00 50th

% Hardpan clay 0.00 comparable comparable 0.00 0.00 50th

Whole watershed natural LU/LC 0.00 lower lower 83.39 76.00 25th

Whole watershed disturbed 

LU/LC

0.00 lower lower 16.10 24.00 75th

Whole watershed, buffered 

natural LU/LC

0.00 lower lower 83.37 79.13 25th

Whole watershed, buffered 

disturbed LU/LC

0.00 lower lower 16.63 20.87 75th

1-km radius, 50,000-km buffered 

natural LU/LC

0.00 lower lower 88.74 79.78 25th

1-km radius, 50,000-km buffered 

disturbed LU/LC

0.00 lower lower 11.26 20.22 75th

Figure 11. Example comparison report to document and evaluate co-occurrence of stressors and responses from data collected at the 

time of the original sampling, A, comparison to East bioregion least disturbed and nonimpaired conditions, and B, comparison to sitespecific comparators. [LD, least disturbed; %, percent; LU/LC, land use/land cover; km, kilometer; μmho/cm, micromhos per centimeter; 

mg/L, milligrams per liter; °C, degrees Celsius]
Stressor Identification of Biologically Impaired Streams in Mississippi 27

B

Parameter Stressed 

site

Site-specific comparators Percentile 

used for LD Creek A Creek B Creek C Creek D

Chemical parameters

Specific conductance (μmho/cm) 0.00 0.00 0.00 0.00 0.00 75th

Dissolved oxygen (% saturation) 0.00 0.00 0.00 0.00 0.00 25th

Ammonia (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Nitrate + nitrite (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Total Kjeldahl nitrogen (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Total nitrogen (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

pH 0.00 0.00 0.00 0.00 0.00 75th

Total phosphorus (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Temperature (°C) 0.00 0.00 0.00 0.00 0.00 50th

Total organic carbon (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Chemical oxygen demand (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Total chlorides (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Alkalinity (mg/L) 0.00 0.00 0.00 0.00 0.00 75th

Turbidity (NTU) 0.00 0.00 0.00 0.00 0.00 75th

Physical parameters

Basin area (mi2) 0.00 0.00 0.00 0.00 0.00 50th

Total habitat score 0.00 0.00 0.00 0.00 0.00 25th

Instream cover habitat score 0.00 0.00 0.00 0.00 0.00 25th

Channel habitat score 0.00 0.00 0.00 0.00 0.00 25th

Bank habitat score 0.00 0.00 0.00 0.00 0.00 25th

Hydrohab 0.00 0.00 0.00 0.00 0.00 25th

% Silt/clay 0.00 0.00 0.00 0.00 0.00 50th

% Sand 0.00 0.00 0.00 0.00 0.00 50th

% Gravel 0.00 0.00 0.00 0.00 0.00 50th

% Cobble 0.00 0.00 0.00 0.00 0.00 50th

% Boulder 0.00 0.00 0.00 0.00 0.00 50th

% Bedrock 0.00 0.00 0.00 0.00 0.00 50th

% Hardpan clay 0.00 0.00 0.00 0.00 0.00 50th

Whole watershed natural LU/LC 0.00 0.00 0.00 0.00 0.00 25th

Whole watershed disturbed 

LU/LC

0.00 0.00 0.00 0.00 0.00 75th

Whole watershed, buffered 

natural LU/LC

0.00 0.00 0.00 0.00 0.00 25th

Whole watershed, buffered 

disturbed LU/LC

0.00 0.00 0.00 0.00 0.00 75th

1-km radius, 50,000-km buffered 

natural LU/LC

0.00 0.00 0.00 0.00 0.00 25th

1-km radius, 50,000-km buffered 

disturbed LU/LC

0.00 0.00 0.00 0.00 0.00 75th

Figure 11. Example comparison report to document and evaluate co-occurrence of stressors and responses from data collected at the 

time of the original sampling, A, comparison to East bioregion least disturbed and nonimpaired conditions, and B, comparison to sitespecific comparators. [LD, least disturbed; %, percent; LU/LC, land use/land cover; km, kilometer; μmho/cm, micromhos per centimeter; 

mg/L, milligrams per liter; °C, degrees Celsius]—Continued
28 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

10 100 1,000 10,000

Number of EPT taxa

Specific conductance (μmho/cm)

90th quantile regression

Intercept of 25th percentile 

of reference EPT and 

90th quantile regression 

25th percentile of reference 

EPT = 8

75th percentile of reference 

specific conductance = 

60 μmho/cm 

0

5

10

15

20

25

− 0

+ − + + 

Figure 12. Example of a scatterplot divided into quadrants by using the 75th percentile of reference values of a physical variable 

(the candidate stressor being evaluated, in this example specific conductance) and the 25th percentile of the biological response (in 

this example, number of Ephemeroptera, Plecoptera and Trichoptera [EPT] taxa). Color-coded quadrants are interpreted below with 

corresponding colored text. The blue dashed line represents the 90th percentile quantile regression line. The intercept of the 25th 

percentile of the reference response variable and the 90th percentile quantile regression distinguishes between some evidence (+) 

and strong evidence (++). [μmho/cm, micromhos per centimeter]

Hypothesis for Evaluation: High Specific 

Conductance at Site A Is a Possible Cause of 

Biological Impairment

Upper left quadrant—If site A falls in this quadrant 

of the regional distribution, the scatterplot contradicts 

the hypothesis and is scored as incompatible (–). In this 

example, specific conductance is lower than the 75th 

Upper right quadrant—If site A falls in this 

quadrant, the scatterplot does not support or contradict the 

hypothesis and is scored as uncertain (0). In the example, 

specific conductance is greater than the 75th percentile 

of specific conductance values from reference sites in 

percentile of specific conductance values from reference sites 

in the East Bioregion (fig. 7), indicating specific conductance 

that is generally in accordance with the reference range of 

specific conductance, and the number of EPT taxa is greater 

than the reference 25th percentile (generally in accordance 

with the range of numbers of EPT taxa observed at reference 

sites), suggesting that specific conductance is not causing 

biological impairment at site A.

the East Bioregion, suggesting that it may be a stressor, 

but the number of EPT taxa is greater than the reference 

25th percentile for the region, which does not support a 

conclusion of specific conductance causing biological 

impairment.

Lower left quadrant—If site A falls in this quadrant, 

the scatterplot contradicts the hypothesis, and is scored as 

incompatible (–). In this example, specific conductance 

is lower than the 75th percentile of specific conductance 

values from reference sites in the East Bioregion (suggesting 

that specific conductance is generally in accordance with 

reference conditions), but the number of EPT taxa is less 

than the reference 25th percentile, suggesting biological 

impairment. If site A falls in this quadrant, evidence from 

the scatterplot suggests that some physical or chemical 

variable(s) other than the one plotted are likely causing the 

biological impairment. In other words, for this example, 

biological impairment is illustrated by low numbers of EPT 

taxa, but this biological impairment is apparently being 

caused by factors other than specific conductance.
Stressor Identification of Biologically Impaired Streams in Mississippi 29

Lower right quadrant—This quadrant is divided 

into two parts by using the intercept of the 25th percentile 

of the response reference condition and the 90th quantile 

regression. If site A falls in this quadrant, the scatterplot 

supports the hypothesis, and is scored as some evidence 

(+) if the specific conductance value from a site is less 

than the intercept value of the reference percentile and the 

quantile regression, or strong evidence (++) if the specific 

conductance value is greater than that value.

Each of the three types of causal inference—

co-occurrence, antecedence, and sufficiency—are scored 

enabling an overall summary score to be assigned for each 

causal inference:

1. Co-occurrence—To evaluate co-occurrence, comparison 

reports may be constructed to document and evaluate 

physical, chemical, and biological data collected at the 

time of the original sampling. Figure 11 is an example 

of a comparison report. Based on an overall evaluation 

of these comparisons, this line of evidence will result 

in one of six scores (table 5). Stressor and response 

variables are compared to SSC values and to bioregional background conditions. There may be different 

causes for different effects. For example, a low number 

of Ephemeroptera taxa may be the result of a different 

cause than a low percentage of dominant filter feeders. 

It is useful to deconstruct the index or even a metric to 

find the most relevant effect signal. 

2. Antecedence—Five components need to be scored 

for this line of evidence: (A) causal pathway for indirect stressors relative to comparison sites (fig. 11B); 

(B) causal pathway for indirect stressors relative to 

bioregion background (fig. 11A); (C) reconnaissance 

data from the stream reach; (D) watershed data (from 

remote sensing and [or] reconnaissance information); 

and (E) data from any point sources (such as from 

compliance reports) that may be influencing the site, 

and data from other locations in the watershed that drain 

to the site, obtained from other water-quality databases 

(non-MDEQ data) and not collected during the original 

sampling. For any component, if insufficient data are 

available for evaluation, then the score can be assigned 

“ND” for no data (table 5). Finally, a single summary 

score is given for the antecedence causal inference, 

based on holistic consideration of the five individual 

scores (or the ones that are available if some are “ND”).

3. Sufficiency—This line of evidence is evaluated by 

using scatterplots of biological and stressor data from 

Mississippi stratified by bioregions (fig. 7). A single 

stressor variable may have multiple scatterplots. If the 

preponderance of plots provides strong evidence for or 

against the cause, the summary score would be ++ or 

–, respectively. If some plots provide evidence for the 

cause while others are uncertain, then a summary score 

of + may be appropriate, and conversely a summary of – 

may be appropriate where some plots provide evidence 

against the cause while others are uncertain. If all plots 

are uncertain or a roughly equivalent number of plots 

appear to provide evidence supporting and contradicting the cause, then a 0 score may be assigned (table 5). 

Sufficiency may also be evaluated by comparing site 

data to biological and stressor thresholds published 

by other States, in the literature, or from laboratory 

experiments.

Table 5. Scoring system for co-occurrence, antecedence, and sufficiency from within a case. 

[The number of plusses (+) and minuses (−) increases with the degree to which the evidence either supports or weakens the argument for a candidate cause, and 

a “0” is used when it is uncertain. Alternatively, a score for ND means “no data.”]

Finding Interpretation Score

Strong evidence All of the data support the case for the candidate cause ++

Some evidence Some of the data support the case for the candidate cause; no data weaken the case for the candidate cause +

Uncertain The data neither support nor weaken the case for the candidate cause; an equal amount of data supports and 

weakens the case, evidence is ambiguous 

0

Less likely Some of the data weakens the case for the candidate cause; no data supports the case −

Not supported All of the data weakens the case for the candidate cause − −

No data Data do not exist to evaluate this case for the candidate cause ND
30 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Step 5. Identify the Probable Causes of 

Impairment by Using a Weight-of-Evidence 

Approach

The goal of this step is to integrate the assessments of 

candidate stressors undertaken in previous steps to draw 

conclusions about probable causes of biological impairment 

at a study site. In this step, evidence and scores developed in 

step 4 are compared across all the candidate causes. Evidence 

for each candidate cause is weighed based upon the quality 

and quantity of data that were used for evaluation in an effort 

to inform overall confidence in conclusions. In the case of 

contrasting evidence, conclusions may be uncertain or may 

be tentative and accompanied by caveats regarding the level 

of confidence in the conclusion. Regardless of the degree of 

confidence, all available evidence should be evaluated before 

drawing a conclusion. At a minimum, this step can be used to 

eliminate certain candidate causes from further consideration 

and to identify data needs for future data collection efforts.

Consistency of Evidence

In the WOE worksheet (fig. 13), scores from 

co-occurrence, antecedence, and sufficiency are used to make 

an evaluation for the “consistency of evidence” conclusion. 

The scores for a candidate cause are not added, but rather are 

used to inform an overall subjective evaluation of the quality, 

quantity, consistency, and credibility of the total body of 

evidence available. The conclusion of consistency of evidence 

will fall into one of these six categories:

1. “All support,”

2. “Some support” (some are uncertain, but none are 

incompatible),

3. “Uncertain” (all are inconclusive),

4. “None support,”

5. “Inconsistent” (some support, some do not support), or

6. “No data” (there is no information to evaluate the cause).

Evaluation for this step produces a summary scoring 

table for each candidate cause and a consistency-of-evidence 

assessment across multiple lines of evidence. In some cases, 

the analysis may point clearly to a probable cause or causes. 

In most cases, it is possible to reduce the number of candidate 

causes by eliminating some causes from consideration 

(for example, those evaluated as “none support” based on 

the consistency-of-evidence assessment). Based on the 

consistency-of-evidence assessment, each candidate cause can 

then be assigned to one of five categories of probability of 

cause resulting in effect:

1. Probable primary cause of biological impairment,

2. Probable secondary cause of biological impairment,

3. Less probable cause of biological impairment,

4. Unlikely cause of biological impairment, or

5. Insufficient evidence to determine causality of biological 

impairment.

Figure 13 is an example of the WOE worksheet scored 

for each causal inference, consistency of evidence, and an 

overall assignment of probability of cause for decrease in 

suitable habitat as a proximate cause.

Step 6. Generate a Report of the Results

The results of the previous steps are compiled into a 

report in the last step to present to stakeholders and decision 

makers. The report of results will generally include an 

identification of probable causes of impairment, accompanied 

by a detailed description of the conceptual, analytic, and 

logical bases for the conclusions drawn. In addition, an 

evaluation of uncertainty relating to the SI process should be 

documented. Results in the summary report may include the 

following:

1. The reason for the SI,

2. A list of the candidate causes and the information used to 

support their selection,

3. The sources of data used in the analysis,

4. Conceptual models of causal pathways,

5. Tables of evidence derived from the data,

6. Key evidence that strengthens the probable cause(s) and 

weakens the others,

7. Confidence in conclusions (data quality, data quantity, 

consistency, and credibility), and

8. Next steps and recommendations (for example, to 

address a lack of data through future data-collection 

efforts).
Stressor Identification of Biologically Impaired Streams in Mississippi 31

Decrease in suitable habitat

Causal 

inference Type of evidence Evidence Score

Co-occurrence

Stressor level relative to site-specific comparators 

(see fig. 11B)

Document the habitat scores here 

and specific taxa that support 

this causal inference

Stressor level relative to bioregional background 

(see fig. 11A)

Document the habitat scores here 

and specific taxa that support 

this causal inference

Summary for evidence of co-occurrence Summary score for above two

Antecedence

Causal pathway for indirect stressors relative to 

comparison sites (see fig. 11B)

Causal pathway for indirect stressors relative to 

bioregion background (see fig. 11A)

Reconnaissance of station

Reconnaissance of watershed 

Point sources, enSPIRE (MDEQ’s water-quality 

database), other data sources

Summary for evidence of antecedence Summary score for above five

Sufficiency

Biological data and stressor data compared to 

position on bioregion scatterplot and threshold 

(fig. 12)

Biological data and stressor data compared to 

thresholds published by other States or in 

literature 

Biological data and stressor data compared 

to thresholds from laboratory, mesocosm, 

experiments

Summary for evidence of sufficiency Summary score for above three

Consistency of evidence

Scored based on the consistency 

of the above three summary 

scores

Probability of cause resulting in effect One of the five categories of 

probability

Figure 13. Example weight-of-evidence worksheet for decrease in suitable habitat.
32 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Options for Future Enhancement of This 

Framework

The framework presented here for impaired streams 

based on biological community assessment in Mississippi 

allows future enhancements to improve the quality and 

efficiency and reduce the cost and uncertainty of the SI 

process. Potential options for improvement fall into two 

categories: development of advanced analytical approaches 

and monitoring for additional stressor and response indicators.

Development of Advanced Analytical 

Approaches

CADDIS outlines several advanced analytical approaches 

for causal analysis, which could be incorporated as added 

WOE tools. The presence of a large statewide database of 

benthic macroinvertebrate community data coupled with 

physical and chemical environmental data enables a number 

of analytical approaches, which are discussed in the following 

sections, that could strengthen and streamline the SI process.

Predicting Environmental Conditions from 

Biological Observations

This approach is based on the theory that different 

taxa generally require different environmental conditions 

to persist. The environmental requirements of different 

taxa can be represented with taxon-environment relation. A 

taxon-environment relation quantifies the relation between 

the probability of observing a particular taxon and the value 

of one or more environmental variables. Existing taxonenvironment relations can be used (EPA, 2010a) or can be 

estimated by using regional datasets. A variety of statistical 

methods can be used to infer environmental conditions at a site 

by using study-site biological samples and taxon-environment 

relations, including weighted averaging, maximum likelihood, 

and categorical tolerance (Yuan, 2007). Given the extent of 

Mississippi’s statewide database, with hundreds of field sites 

sampled across a wide range of disturbances, opportunities 

exist to develop taxon-stressor relations for many different 

combinations of taxonomic groups and stressors. Such an 

approach could be especially beneficial for nonchemical 

stressors where dose-response studies are difficult to conduct 

in the laboratory.

Propensity Score Analysis

Analysis of the role of a given stressor may be inaccurate 

if confounding variables are not considered. One method 

for developing a deconfounded analysis of observational 

data involves the calculation of a propensity score. First, 

environmental data, such as land cover, habitat metrics, 

topographic metrics, and soil or geologic variables, are used 

to model a variable representing a candidate stressor (for 

example, total nitrogen; Yuan, 2010). Modeled values for 

the candidate stressor (for example, predicted total nitrogen) 

become propensity scores that are then used to stratify the 

dataset into bins based on propensity score. Finally, relations 

between propensity scores and biological metrics of interest 

are modeled. This approach has the advantage of minimizing 

the confounding effects of environmental variables other than 

the candidate stressor of interest because they are accounted 

for in calculating the propensity scores, which reduces bias 

and allows variations in biological metrics to be attributed 

more directly to the candidate stressors of interest (Pearson 

and others, 2016). Propensity scores are only useful with very 

large and complete datasets.

Evidence of Alteration

Evidence of alteration is becoming more common with 

larger datasets. Similar to symptoms and signs of disease 

used for human and veterinary diagnostics, differential 

occurrence and proportion of taxa can be indicative of a 

particular cause (Coffey and others, 2014). Evidence of 

alteration can be derived from regional models of different 

communities associated with different stressors. The genera 

at the test site are used to predict the probable cause. This 

could involve use of the probability of a stressor from a 

nonmetric multidimensional scaling model of the taxon from 

impaired sites as evidence of alteration (Zheng and others, 

2015). Models of specific alteration using taxon data can be 

very compelling. If there are not enough impaired sites of a 

particular type, data from Mississippi may be combined with 

other available data.

Concept of Stressor Syndromes

Groups of stressors are not randomly assigned across 

the landscape, but instead typically result from humandevelopment activities and land-use and resource-management 

practices. As a result, stream sites with common land-use 

patterns in their watersheds may share similar physical 

and water-quality conditions. For example, “urban stream 

syndrome” is an increasingly used term to describe a set 

of commonly observed parameters across streams with 

high levels of urban development in their watersheds, 

including increasing “flashiness” of streamflow, warmer 

water temperatures, nutrient enrichment, decreased channel 

complexity, and loss of sensitive species (Walsh and others, 

2005).

In Mississippi, relatively few watersheds are heavily 

urban, but many are dominated by agricultural land use, 

especially row-crop agriculture. Thus, exploratory work 

into a “row-crop syndrome” framework could be useful 

in Mississippi, to the degree that large numbers of sites in 

watersheds dominated by row-crop agriculture appear to 
References Cited 33

share common physical parameters reflecting water quality 

and habitat. For example, Munn and others (2018) examined 

a suite of related stressors common to agricultural streams in 

the midwestern United States, including nutrients, herbicides, 

fungicides, sediment loads, and altered streamflow. Such an 

approach could be paired with propensity score analysis to 

explore row-crop agriculture (for example, as a proportion 

of land use within each watershed) as a driver of multiple 

candidate stressors such as nutrients (total nitrogen, total 

phosphorous, etc.), DO, and other water-quality indicators. 

Propensity score analysis could allow other potentially 

confounding factors such as watershed geology, topography, 

and soil type (Carlisle and others, 2009) to be accounted 

for explicitly to minimize bias in evaluating the effects of 

row-crop agriculture on candidate stressors. Multivariate 

machine-learning approaches such as boosted regression trees 

or random forest models (Munn and others, 2018; Waite and 

others, 2019) could also be employed to model the interactive 

effects of many physical parameters simultaneously and 

to enable quantification of the relative importance of each 

parameter to various biological metrics related to stream 

impairment.

Monitoring for Additional Stressor and 

Response Indicators

To support future SI efforts, MDEQ may wish to consider 

investment in developing new datasets and tools to better 

evaluate potential intermediate and proximate causes not 

currently represented in MDEQ’s routine monitoring program. 

Nonroutine data could include indicators of hydrologic and 

geomorphic alteration from reference conditions, instream 

temperature monitoring data (historically, most temperature 

measurements occur in winter as a point sample and hence 

do not represent temperature cycles diurnally or across 

seasons), DO (similarly, most sampling historically occurs 

in winter as a point sample), alteration of food sources for 

aquatic organisms, and prevalence of toxic substances such 

as pesticides, herbicides, fungicides, and other organic 

contaminants. Furthermore, to complement MDEQ’s extensive 

database of macroinvertebrate data, monitoring protocols and 

datasets could be developed to evaluate other trophic-level 

communities and taxonomic groups (such as fish and algae) 

which may also be influenced by similar sets of stressors.

Summary

In order to calculate total maximum daily loadings for 

Mississippi streams listed as biologically impaired on the 

§303(d) list, the actual stressor(s) causing the impairment 

must be known. The U.S. Geological Survey, in cooperation 

with the Mississippi Department of Environmental Quality, 

developed a framework to identify stressors in biologically 

impaired streams and rivers of Mississippi. This framework 

was adapted from the existing U.S. Environmental Protection 

Agency framework for stressor identification and involves 

six general steps: (1) define the impairment, (2) list the 

candidate causes of impairment and develop a conceptual 

model, (3) compile all data relevant to the impairment and 

conceptual model, (4) evaluate the data, (5) identify the 

probable causes of impairment by using a weight-of-evidence 

approach, and (6) generate a report of results. The intent is for 

the Mississippi stressor identification framework to reduce 

subjectivity, provide consistency, and allow for adaptation as 

the science evolves.

References Cited

Allan, J.D., McIntyre, P.B., Smith, S.D.P., Halpern, B.S., 

Boyer, G.L., Buchsbaum, A., Burton, G.A., Campbell, L.M., 

Chadderton, W.L., Ciborowski, J.J.H., Doran, P.J., Eder, 

T., Infante, D.M., Johnson, L.B., Joseph, C.A., Marino, 

A.L., Prusevich, A., Read, J.G., Rose, J.B., Rutherford, 

E.S., Sowa, S.P., and Steinman, A.D., 2013, Joint analysis 

of stressors and ecosystem services to enhance restoration 

effectiveness: Proceedings of the National Academy of 

Sciences of the United States of America, v. 110, no. 1, 

p. 372–377. [Also available at https://doi.org/10.1073/

pnas.1213841110.]

Allan, J.D., Yuan, L., Black, P., Stockton, T., Davies, P., 

Magierowski, R., and Read, S., 2012, Investigating the 

relationships between environmental stressors and stream 

condition using Bayesian belief networks: Freshwater 

Biology, v. 57, p. 58–73. [Also available at https://doi.

org/10.1111/j.1365-2427.2011.02683.x.]

Armstrong, D.S., Richards, T.A., and Levin, S.B., 

2011, Factors influencing riverine fish assemblages 

in Massachusetts: U.S. Geological Survey Scientific 

Investigations Report 2011–5193, 58 p. [Also available at 

https://doi.org/10.3133/sir20115193.]

Bernhardt, E.S., Lutz, B.D., King, R.S., Fay, J.P., Carter, 

C.E., Helton, A.M., Campagna, D., and Amos, J., 2012, 

How many mountains can we mine? Assessing the regional 

degradation of central Appalachian rivers by surface coal 

mining: Environmental Science & Technology, v. 46, 

no. 15, p. 8115–8122. [Also available at https://doi.

org/10.1021/es301144q.]

Black, R.W., Moran, P.W., and Frankforter, J.D., 2011, 

Response of algal metrics to nutrients and physical factors 

and identification of nutrient thresholds in agricultura

streams: Environmental Monitoring and Assessment, v. 175, 

p. 397–417. [Also available at https://doi.org/10.1007/

s10661-010-1539-8.]
34 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Blazer, V.S., Iwanowicz, L.R., Henderson, H., Mazik, P.M., 

Jenkins, J.A., Alvarez, D.A., and Young, J.A., 2012, 

Reproductive endocrine disruption in smallmouth bass 

(Micropterus dolomieu) in the Potomac River basin—

Spatial and temporal comparisons of biological effects: 

Environmental Monitoring and Assessment, v. 184, no. 7, 

p. 4309–4334. [Also available at https://doi.org/10.1007/

s10661-011-2266-5.]

Breiman, L., 2001, Random forests: Machine Learning, 

v. 45, no. 1, p. 5–32. [Also available at https://doi.

org/10.1023/A:1010933404324.]

Cade, B.S., 2011, Estimating equivalence with quantile 

regression: Ecological Applications, v. 21, no. 1, p. 281–

289. [Also available at https://doi.org/10.1890/09-2060.1.]

Cade, B.S., and Noon, B.R., 2003, A gentle introduction to 

quantile regression for ecologists: Frontiers in Ecology and 

the Environment, v. 1, no. 8, p. 412–420. [Also available at 

https://doi.org/10.1890/1540-9295(2003)001[0412:AGITQ

R]2.0.CO;2.]

Cade, B.S., Noon, B.R., and Flather, C.H., 2005, Quantile 

regression reveals hidden bias and uncertainty in habitat 

models: Ecology, v. 86, no. 3, p. 786–800. [Also available at 

https://doi.org/10.1890/04-0785.]

Cade, B.S., Terrell, J.W., and Porath, M.T., 2008, Estimating 

fish body condition with quantile regression: Nort

American Journal of Fisheries Management, v. 28, no. 2, 

p. 349–359. [Also available at https://doi.org/10.1577/M07-

048.1.]

Calhoun, D.L., Gregory, M.B., and Weyers, H.S., 2008, Algal 

and invertebrate community composition along agricultural 

gradients—A comparative study from two regions of the 

eastern United States: U.S. Geological Survey Scientifi

Investigations Report 2008–5046. [Also available at https://

pubs.usgs.gov/sir/2008/5046/.]

Carlisle, D.M., Falcone, J., and Meador, M.R., 2009, 

Predicting the biological condition of streams—Use 

of geospatial indicators of natural and anthropogenic 

characteristics of watersheds: Environmental Monitoring 

and Assessment, v. 151, p. 143–160. [Also available at 

https://doi.org/10.1007/s10661-008-0256-z.]

Carlisle, D.M., Falcone, J., Wolock, D.M., Meador, M.R., and 

Norris, R.H., 2010, Predicting the natural flow regim

Models for assessing hydrologic alteration in streams: River 

Research and Applications, v. 2, p. 118–136. [Also available 

at https://doi.org/10.1002/rra.1247.]

Carlisle, D.M., Wolock, D.M., and Meador, M.R., 2011, 

Alteration of streamflow magnitudes and potentia

ecological consequences—A multiregional assessment: 

Frontiers in Ecology and the Environment, v. 9, 

no. 5, p. 264–270. [Also available at https://doi.

org/10.1890/100053.]

Clements, W.H., 1994, Benthic invertebrate community 

responses to heavy metals in the Upper Arkansas 

River Basin, Colorado: Journal of the North American 

Benthological Society, v. 13, no. 1, p. 30–44. [Also 

available at https://doi.org/10.2307/1467263.]

Clements, W.H., Carlisle, D.M., Lazorchak, J.M., and 

Johnson, P.C., 2000, Heavy metals structure benthic 

communities in Colorado mountain streams: Ecological 

Applications, v. 10, no. 2, p. 626–638. [Also available at 

https://doi.org/10.1890/1051-0761(2000)010[0626:HMSBC

I]2.0.CO;2.]

Coffey, D.B., Cormier, S., and Harwood, J., 2014, Using 

field-based species sensitivity distributions to infer multipl

causes: Human and Ecological Risk Assessment, v. 20, 

no. 2, p. 402–432. [Also available at https://doi.org/10.1080

/10807039.2013.767071.]

Cormier, S.M., Norton, S.B., and Suter, G.W., 2003, The U.S. 

Environmental Protection Agency’s stressor identificatio

guidance—A process for determining the probable causes 

of biological impairments: Human and Ecological Risk 

Assessment, v. 9, no. 6, p. 1431–1443. [Also available at 

https://doi.org/10.1080/10807030390250930.]

Cormier, S.M., Smith, M., Norton, S., and Neiheisel, T., 2000, 

Assessing ecological risk in watersheds—A case study of 

problem formulation in the Big Darby Creek watershed, 

Ohio, USA: Environmental Toxicology and Chemistry, 

v. 19, no. 4, p. 1082–1096. [Also available at https://doi.

org/10.1002/etc.5620190439.]

Crane, M., Burton, G.A., Culp, J.M., Greenberg, M.S., 

Munkittrick, K.R., Ribeiro, R., Salazar, M.H., and St-Jean, 

S.D., 2007, Review of aquatic in situ approaches for 

stressor and effect diagnosis: Integrated Environmental 

Assessment and Management, v. 3, no. 2, p. 234–245. [Also 

available at https://doi.org/10.1897/IEAM_2006-027.1.]

Cutler, D.R., Edwards, T.C., Jr., Beard, K.H., Cutler, A., Hess, 

K., Gibson, J., and Lawler, J.J., 2007, Random forests for 

classification in ecology: Ecology, v. 88, no. 11, p. 278

2792. [Also available at https://doi.org/10.1890/07-0539.1.]

De’ath, G., 2007, Boosted regression trees for ecological 

modeling and prediction: Ecology, v. 88, no. 1, p. 243–

251. [Also available at https://doi.org/10.1890/0012-

9658(2007)88[243:BTFEMA]2.0.CO;2.]
References Cited 35

De’ath, G., and Fabricius, K.E., 2000, Classification an

regression trees—A powerful yet simple technique for 

ecological data analysis: Ecology, v. 81, no. 11, p. 3178–

3192. [Also available at https://doi.org/10.1890/0012-

9658(2000)081[3178:CARTAP]2.0.CO;2.]

DeGasperi, C.L., Berge, H.B., Whiting, K.R., Burkey, 

J.J., Cassin, J.L., and Fuerstenberg, R.R., 2009, Linking 

hydrologic alteration to biological impairment in urbanizing 

streams of the Puget Lowland, Washington, USA: Journal 

of the American Water Resources Association, v. 45, no. 2, 

p. 512–533. [Also available at https://doi.org/10.1111/

j.1752-1688.2009.00306.x.]

Dodds, W., and Welch, E., 2000, Establishing nutrient criteria 

in streams: Journal of the North American Benthological 

Society, v. 19, no. 1, p. 186–196. [Also available at https://

doi.org/10.2307/1468291.]

Doll, J.C., 2011, Predicting biological impairment from 

habitat assessments: Environmental Monitoring and 

Assessment, v. 182, p. 259–277. [Also available at https://

doi.org/10.1007/s10661-011-1874-4.]

Downes, B.J., 2010, Back to the future—Little-used tools and 

principles of scientific inference can help disentangle effect

of multiple stressors on freshwater ecosystems: Freshwater 

Biology, v. 55, p. 60–79. [Also available at https://doi.

org/10.1111/j.1365-2427.2009.02377.x.]

Elith, J., Leathwick, J.R., and Hastie, T., 2008, A working 

guide to boosted regression trees: Journal of Animal 

Ecology, v. 77, no. 4, p. 802–813. [Also available at https://

doi.org/10.1111/j.1365-2656.2008.01390.x.]

Gregorutti, B., Michel, B., and Saint-Pierre, P., 2017, 

Correlation and variable importance in random forests: 

Statistics and Computing, v. 27, p. 659–678. [Also available 

at https://doi.org/10.1007/s11222-016-9646-1.]

Griggs, A., and Buchanan, C., 2012, Stressor identificatio

methods used for impaired waters in the Chesapeake Bay 

watershed States, DRAFT Report to the Chesapeake Bay 

Program Non‐Tidal Water Quality Workgroup: Interstate 

Commission on the Potomac River Basin Report 12‐05.

Harden, S.L., Cuffney, T.F., Terziotti, S., and Kolb, K.R., 

2013, Relation of watershed setting and stream nutrient 

yields at selected sites in central and eastern North 

Carolina, 1997–2008: U.S. Geological Survey Scientifi

Investigations Report 2013–5007. [Also available at https://

doi.org/10.3133/sir20135007.]

Karr, J.R., Fausch, K.D., Angermeier, P.L., Yant, P.R., and 

Schlosser, I.J., 1986, Assessing biological integrity in 

running waters—A method and its rationale: Illinois Natural 

History Survey, Special Publication 5, 28 p. [Also available 

at https://semspub.epa.gov/work/01/554353.pdf.]

Klein, R.D., 1979, Urbanization and stream quality 

impairment: Water Resources Bulletin, v. 15, 

no. 4, p. 948–963. [Also available at https://doi.

org/10.1111/j.1752-1688.1979.tb01074.x.]

Knight, R.R., Murphy, J.C., Wolfe, W.J., Saylor, C.F., and 

Wales, A.K., 2014, Ecological limit functions relating 

fish community response to hydrologic departures of th

ecological flow regime in the Tennessee River basin, Unite

States: Ecohydrology, v. 7, p. 1262–1280. [Also available at 

https://doi.org/10.1002/eco.1460.]

Lange, K., Townsend, C.R., and Matthaei, C.D., 2014, Can 

biological traits of stream invertebrates help disentangle the 

effects of multiple stressors in an agricultural catchment?: 

Freshwater Biology, v. 59, no. 12, p. 2431–2446. [Also 

available at https://doi.org/10.1111/fwb.12437.]

Lapointe, B.E., Herren, L.W., and Bedford, B.J., 2012, 

Effects of hurricanes, land use, and water management 

on nutrient and microbial pollution—St. Lucie Estuary, 

southeast Florida: Journal of Coastal Research, v. 28, 

p. 1345–1361. [Also available at https://doi.org/10.2112/

JCOASTRES-D-12-00070.1.]

Lavoie, I., Morin, S., Laderriere, V., and Fortin, C., 2018, 

Freshwater diatoms as indicators of combined long-term 

mining and urban stressors in Junction creek (Ontario, 

Canada): Environments, v. 5, no. 2. [Also available at 

https://doi.org/10.3390/environments5020030.]

Liaw, W., and Wiener, M., 2014, RandomForest: Breiman and 

Cutler’s random forests for classifications and regression

accessed May 2020 at http://cran.r-project.org/web/

packages/randomForest/.

Maloney, K.O., Weller, D.E., Michaelson, D.E., and Ciccotto, 

P.J., 2013, Species distribution models of freshwater stream 

fishes in Maryland and their implications for management

Environmental Modeling and Assessment, v. 18, no. 1, 

p. 1–12. [Also available at https://doi.org/10.1007/s10666-

012-9325-3.]

Maloney, K.O., Weller, D.E., Russell, M.J., and Hothorn, 

T., 2009, Classifying the biological condition of small 

streams—An example using benthic macroinvertebrates: 

Freshwater Science, v. 28, p. 869–884. [Also available at 

https://doi.org/10.1899/08-142.1.]

Maryland Department of the Environment, 2016, Watershed 

report for biological impairment of the non-tidal 

Potomac River Frederick County watershed, Frederick 

and Washington Counties Maryland biological stressor 

identification analysis results and interpretation: Baltimore

Maryland, Maryland Department of the Environment.
36 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Matthaei, C.D., Piggott, J.J., and Townsend, C.R., 2010, 

Multiple stressors in agricultural streams—Interactions 

among sediment addition, nutrient enrichment and water 

abstraction: Journal of Applied Ecology, v. 47, no. 3, 

p. 639–649. [Also available at https://doi.org/10.1111/

j.1365-2664.2010.01809.x.]

Merriam, E.R., Petty, J.T., Merovich, G.T., Jr., Fulton, J.B., 

and Strager, M.P., 2011, Additive effects of mining and 

residential development on stream conditions in a central 

Appalachian watershed: Journal of the North American 

Benthological Society, v. 30, no. 2, p. 399–418. [Also 

available at https://doi.org/10.1899/10-079.1.]

Miguntanna, N.S., Egodawatta, P., Kokot, S., and 

Goonetilleke, A., 2010, Determination of a set of 

surrogate parameters to assess urban stormwater quality: 

The Science of the Total Environment, v. 408, no. 24, 

p. 6251–6259. [Also available at https://doi.org/10.1016/j.

scitotenv.2010.09.015.]

Minnesota Pollution Control Agency, 2013, St. Louis River 

watershed stressor identification report: Saint Paul, Minn.

Minnesota Pollution Control Agency.

Morris, C.C., Simon, T.P., and Newhouse, S.A., 2006, A 

local-scale in situ approach for stressor identificatio

of biologically impaired aquatic systems: Archives of 

Environmental Contamination and Toxicology, v. 50, no. 3, 

p. 325–334. [Also available at https://doi.org/10.1007/

s00244-005-1066-8.]

Munn, M.D., Waite, I.R., and Konrad, C.P., 2018, Assessing 

the influence of multiple stressors on stream diatom metric

in the upper Midwest, USA: Ecological Indicators, v. 85, 

p. 1239–1248. [Also available at https://doi.org/10.1016/j.

ecolind.2017.09.005.]

Norton, S.B., Cormier, S.M., Smith, M., and Jones, R.C., 

2000, Can biological assessments discriminate among types 

of stress? A case study from the eastern corn belt plains 

ecoregion: Environmental Toxicology and Chemistry, 

v. 19, no. 4, p. 1113–1119. [Also available at https://doi.

org/10.1002/etc.5620190442.]

Ohio Environmental Protection Agency, 2010, Biological 

and habitat study of the Olentangy River, 2005 and 2009: 

Groveport, Ohio, Ohio Environmental Protection Agency.

Omernik, J.M., 1987, Ecoregions of the conterminous United 

States: Annals of the Association of American Geographers, 

v. 77, no. 1, p. 118–125. [Also available at https://doi.

org/10.1111/j.1467-8306.1987.tb00149.x.]

Onorato, D., Marion, K.R., and Angus, R.A., 1998, 

Longitudinal variations in the ichthyofaunal assemblages of 

the upper Cahaba River—Possible effects of urbanization 

in a watershed: Journal of Freshwater Ecology, v. 13, no. 2, 

p. 139–154. [Also available at https://doi.org/10.1080/0270

5060.1998.9663602.]

Palmer, M., 2016, Ordination methods for ecologists: 

Stillwater, Okla., Oklahoma State University, accessed May 

2020 at http://ordination.okstate.edu/.

Pearson, C.E., Ormerod, S.J., Symondson, W.O., and Vaughan, 

I.P., 2016, Resolving large-scale pressures on species and 

ecosystems—Propensity modelling identifies agricultura

effects on streams: Journal of Applied Ecology, v. 53, no. 2, 

p. 408–417. [Also available at https://doi.org/10.1111/1365-

2664.12586.]

Porter, C.M., Butler, D.R., and Janz, D.M., 2015, Central 

Oklahoma bioassessment study—Evaluation of stream 

health by using fish and macroinvertebrate communitie

as biological indicators: Proceedings of the Oklahoma 

Academy of Science, v. 80, p. 61–70.

Prasad, A.M., Iverson, L.R., and Liaw, A., 2006, Newer 

classification and regression tree techniques—Baggin

and random forests for ecological prediction: Ecosystems 

(New York, N.Y.), v. 9, no. 2, p. 181–199. [Also available at 

https://doi.org/10.1007/s10021-005-0054-1.]

Schleiter, K.E., 2009, Proving causation in environmental 

litigation: AMA Journal of Ethics, v. 11, no. 6, p. 456–460. 

[Also available at https://doi.org/10.1001/virtualmentor.200

9.11.6.hlaw1-0906.]

Schmidt, T.S., Clements, W.H., and Cade, B.S., 2012, 

Estimating risks to aquatic life using quantile regression: 

Freshwater Science, v. 31, no. 3, p. 709–723. [Also 

available at https://doi.org/10.1899/11-133.1.]

Smucker, N.J., Becker, M., Detenbeck, N.E., and Morrison, 

A.C., 2013, Using algal metrics and biomass to evaluate 

multiple ways of defining concentration-based nutrien

criteria in streams and their ecological relevance: Ecological 

Indicators, v. 32, p. 51–61. [Also available at https://doi.

org/10.1016/j.ecolind.2013.03.018.]

Steen, P.J., Zorn, T.G., Seelbach, P.W., and Schaeffer, J.S., 

2008, Classification tree models for predicting distribution

of Michigan stream fish from landscape variables

Transactions of the American Fisheries Society, v. 137, 

no. 4, p. 976–996. [Also available at https://doi.org/10.1577/

T07-119.1.]
References Cited 37

Stribling, J.B., Jessup, B.K., and Leppo, E.W., 2016, The 

Mississippi-Benthic Index of Stream Quality (M-BISQ)—

Recalibration and testing: Prepared for Mississippi 

Department of Environmental Quality, Office of Pollution 

Control, Jackson, Mississippi. [Also available at https://

www.mdeq.ms.gov/wp-content/uploads/2017/05/

MBISQ2015-with-appendixes-FINAL-20160203c.pdf.]

Suter, G.W., II, Norton, S.B., and Cormier, S.M., 2002, 

A methodology for inferring the causes of observed 

impairments in aquatic ecosystems: Environmental 

Toxicology and Chemistry, v. 21, p. 1101–1111.

Townsend, C.R., Uhlmann, S.S., and Matthaei, C.D., 2008, 

Individual and combined responses of stream ecosystems to 

multiple stressors: Journal of Applied Ecology, v. 45, no. 6, 

p. 1810–1819. [Also available at https://doi.org/10.1111/

j.1365-2664.2008.01548.x.]

U.S. Army Corps of Engineers, 2009, National inventory of 

dams, accessed May 2020 at http://nid.usace.army.mil/

cm_apex/f?p=838:12.

U.S. Environmental Protection Agency [EPA], 1997, 

Guidelines for preparation of the comprehensive State 

water quality assessments (305(b) Reports) and electronic 

updates—Report contents: Washington, D.C., U.S. 

Environmental Protection Agency, Office of Water, EPA841-B-97-0002A.

U.S. Environmental Protection Agency [EPA], 2000, Stressor 

identification guidance document: Washington, D.C., U.S

Environmental Protection Agency, EPA-822-B00-025, 

228 p.

U.S. Environmental Protection Agency [EPA], 2002, 

Consolidated assessment and listing methodology toward 

a compendium of best practices: Washington, D.C., U.S. 

Environmental Protection Agency, Office of Wetlands, 

Oceans, and Watersheds.

U.S. Environmental Protection Agency [EPA], 2010a, 

Causal Analysis/Diagnosis Decision Information System 

(CADDIS), accessed May 2020 at http://www.epa.gov/

caddis.

U.S. Environmental Protection Agency [EPA], 2010b, 

Inferring causes of biological impairment in the Clear Fork 

watershed, West Virginia (Final): Washington, D.C., U.S. 

Environmental Protection Agency, EPA/600/R-08/146.

U.S. Geological Survey, 2020, National Hydrography 

Dataset, accessed May 11, 2020, at https://www.usgs.gov/

core-science-systems/ngp/national-hydrography/nationalhydrography-dataset?qt-science_support_page_related_

con=0#qt-science_support_page_related_con.

Usio, N., Nakajima, H., Kamiyama, R., Wakana, I., Hiruta, 

S., and Takamura, N., 2006, Predicting the distribution of 

invasive crayfish (Pacifastacus leniusculus) in a Kusir

Moor marsh (Japan) using classification and regressio

trees: Ecological Research, v. 21, no. 2, p. 271–277. [Also 

available at https://doi.org/10.1007/s11284-005-0120-3.]

Vander Laan, J.J., 2012, Environmental assessment of 

streams—Linking land use, instream stressors, and 

biological indices to infer likely causes of ecological 

impairment: Utah State University.

Vander Laan, J.J., Hawkins, C.P., Olson, J.R., and Hill, R.A., 

2013, Linking land use, in-stream stressors, and biological 

condition to infer causes of regional ecological impairment 

in streams: Freshwater Science, v. 32, no. 3, p. 801–820. 

[Also available at https://doi.org/10.1899/12-186.1.]

Virginia Department of Environmental Quality, 2014, Benthic 

TMDL development stressor analysis report, Happy 

Creek, Warren County: Virginia, Virginia Department of 

Environmental Quality.

Voss, K.A., Pohlman, A., Viswanathan, S., Gibson, D., and 

Purohit, J., 2012, A study of the effect of physical and 

chemical stressors on biological integrity within the San 

Diego hydrologic region: Environmental Monitoring and 

Assessment, v. 184, no. 3, p. 1603–1616. [Also available at 

https://doi.org/10.1007/s10661-011-2064-0.]

Wagenhoff, A., Townsend, C.R., Phillips, N., and Matthaei, 

C.D., 2011, Subsidy-stress and multiple-stressor effects 

along gradients of deposited fine sediment and dissolve

nutrients in a regional set of streams and rivers: Freshwater 

Ecology, v. 56, no. 9, p. 1916–1936. [Also available at 

https://doi.org/10.1111/j.1365-2427.2011.02619.x.]

Waite, I.R., Munn, M.D., Moran, P.W., Konrad, C.P., Nowell, 

L.H., Meador, M.R., Van Metre, P.C., and Carlisle, D.M., 

2019, Effects of urban multi-stressors on three stream biotic 

assemblages: The Science of the Total Environment, v. 660, 

p. 1472–1485. [Also available at https://doi.org/10.1016/j.

scitotenv.2018.12.240.]

Walsh, C.J., Roy, A.H., Feminella, J.W., Cottingham, P.D., 

Groffman, P.M., and Morgan, R.P., II, 2005, The urban 

stream syndrome—Current knowledge and the search for a 

cure: Journal of the North American Benthological Society, 

v. 24, no. 3, p. 706–723. [Also available at https://doi.

org/10.1899/04-028.1.]

Wang, L., Lyons, J., Kanehi, P., Bannerman, R., and 

Emmons, E., 2000, Watershed urbanization and changes 

in fish communities in southeastern Wisconsin streams

Journal of the American Water Resources Association, 

v. 36, no. 5, p. 1173–1189. [Also available at https://doi.

org/10.1111/j.1752-1688.2000.tb05719.x.]
38 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Yuan, L.L., 2007, Maximum likelihood method for predicting 

environmental conditions from assemblage composition—

The R package bio.infer: Journal of Statistical Software, 

v. 22, no. 3, p. 1–20. [Also available at https://doi.

org/10.18637/jss.v022.i03.]

Yuan, L.L., 2010, Estimating the effects of excess nutrients on 

stream invertebrates from observational data: Ecological 

Applications, v. 20, no. 1, p. 110–125. [Also available at 

https://doi.org/10.1890/08-1750.1.]

Zheng, L., Gerritsen, J., and Cormier, S.M., 2015, The Clear 

Fork watershed case study—The value of State monitoring 

programs, chapter 4 of Norton, S.B., Cormier, S.M., and 

Suter, G.W., II, eds., Ecological causal assessment: Boca 

Raton, Fla. CRC Press, Taylor and Francis Group, p. 47–64.
Appendix 1. Field Forms Used During Reconnaissance in Stressor Identification

Appendixes
40 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Reconnaissance Field Form for Physical, Chemical, and Other Data and 

Observations

CA Sample Site Data Form_______________________Creek at _________________________(Road)

Time___________________Date___________________IBI #_____________ Basin ______________

Weather Conditions _____________________________ GPS N______◦_______’______.______”

Taken By___________________________ W______◦_______’______.______”

Temp 

(◦C)

Cond. 

(mg/l)

TDS 

(mg/l)

D.O. 

(mg/l)

%D.O. pH Sample

Depth (feet)

Bottom

Depth (feet)

Photo File #’s Upsteam____________Downstream__________Other_____________

Flow Status FLOWING PONDED DRY

Water Clarity/Turbidity CLEAR SLIGHT MODERATE SEVERE

Flow Alteration YES / NO Channel Alteration YES / NO

Morphology B C D E F G Channel Evolution I II III IV V VI

Entrenchment LOW MODERATE HIGH HEAD CUT/Nick Point?___

Bank Erodibility HIGH BNKS LEANING TREES LOW ROOT MASS OTHER________________

Canopy OPEN FILTERED OTHER___________________

Riparian ________Meters Age_______TREES SHRUBS GRASS LU_________________

Bed Material Consolidated YES / NO CLAY SILT SAND GRAVEL OTHER______________

Channel Structure RIFFLE POOL RUN POINT BAR

Siltation NONE SLIGHT MODERATE SEVERE

ALGAE FILM TRASH OTHER_______________________

Habitat CUT BANKS LEAF PACK WOODY BOTTOM

NOTES :_________________________________________________________________________________

Figure 1.1. Reconnaissance field form for physical, chemical, and other data and observations. [CA, causal analysis]
Appendixes 41

1. Primary bed material

Hardpan Boulder/Cobble Gravel Sand Silt/Clay

0 1 2 3 4

2. Bed/bank protection (rip rap, concrete, sheetpile, etc.)

No Bed (with) 1 bank protected 2 banks protected

0 1 2 3

3. Degree of incision (relative elevation of "normal" low water if flood plain/terrace is 100%)

0-10% 11-25% 26-50% 51-75% 76-100%

4 3 2 1 0

4. Degree of constriction (relative decrease in top-bank width from upstream to downstream)

0-10% 11-25% 26-50% 51-75% 76-100%

0 1 2 3 4

5. Streambank erosion (dominant process each bank)

None Fluvial mass wasting (failures)

Left 0 1 2

Right 0 1 2

6. Streambank instability (percent of each bank failing)

0-10% 11-25% 26-50% 51-75% 76-100%

Left 0 0.5 1 1.5 2

Right 0 0.5 1 1.5 2

7. Established riparian vegetative cover (woody or stabilizing perennial grasses each bank)

0-10% 11-25% 26-50% 51-75% 76-100%

Left 2 1.5 1 0.5 0

Right 2 1.5 1 0.5 0

8. Occurrence of bank accretion (percent of each bank with fluvial deposition)

0-10% 11-25% 26-50% 51-75% 76-100%

Left 2 1.5 1 0.5 0

Right 2 1.5 1 0.5 0

9. Stage of channel evolution (I and VI are generally stable < 11 total score, III and IV least stable generally > 15)

I II III IV V VI

0 1 2 4 3 1.5

10. TOTAL (35 Max)

enSPIRE# RAPID GEOMORPHIC ASSESSMENT (RGA) FORM CHANNEL 

STABILITY RANKING SCHEME

Project:

Station Name: Lat:

Station Location: Long:

Date and time: Team Basin

County Photo (US, DS, others:) Pattern (Meander/Straight/Braided):

Figure 1.2. Rapid geomorphic assessment field form.
42 Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration

Appendix 2. Tools for M-BISQ Data Compilation and Evaluation

Comparison reports (for physical and chemical data and 

for biological data) are documented for each bioregion by 

using spreadsheets. The purpose of the comparison reports is 

to organize parameter and indicator data from the impaired 

site to enable comparison against corresponding data from 

least disturbed (LD) and nonimpaired (NI) stream reaches and 

site-specific comparator (SSC) sites. Physical and chemical 

parameters included are those commonly collected as part of 

Mississippi Department of Environmental Quality’s (MDEQ’s) 

routine data collection and assessment program. Biological 

data to be included are typically the overall Mississippi 

Benthic Index of Stream Quality (M-BISQ) score, biological 

metrics used in the calculation of the M-BISQ score, and the 

metrics found to be of use in scatterplot evaluations for the 

bioregion.

Conditions for LD and NI are calculated by using the 

interquartile range of values for all streams listed as LD or 

assessed as NI from the master database and located in the 

bioregion of the study site. Offsite stream-reach SSCs are 

sites located nearby, within the same 8-digit hydrologic unit 

code (HUC) and the same bioregion, with relatively similar 

drainage area, and with an M-BISQ score above the bioregion 

impairment threshold (Stribling and others, 2016).

Selection of Site-Specific Comparators (SSCs)

Several guidelines can support selection of a useful set 

of SSCs. First, no more than four SSCs are typically selected. 

A comparator site does not necessarily have to be a “highquality” reference site. If a comparator site is not a part of 

the same aquatic system, it is important to ensure that, aside 

from the influence of anthropogenic stressors, the comparator 

and test sites are as similar as possible in terms of natural 

environmental factors (for example, elevation, stream size, 

drainage area, climate, slope, and geology).

The process of SSC selection involves using geospatial 

analysis (for example ArcGIS Esri software) with the 

following data layers: 8-digit HUC, bioregion, points 

representing all potential SSC sites (with associated data in 

the table), county, and National Hydrography Dataset stream 

coverage maps (U.S. Geological Survey, 2020). Starting with 

NI sites that are nearest the impaired site, perform a quick 

scan of similarly sized drainage areas to select three or ideally 

four SSCs that are relatively in the same size stream class 

as the impaired site. If more than four potential SSCs are in 

close proximity and of similar stream size, then other abiotic 

data (for example, substrate particle size or habitat assessment 

score) may be used to select a final set of four SSCs that are 

fairly typical for the given bioregion. Sometimes the four 

potential SSCs may be located relatively distantly from the 

impaired site, in which case effort should be made to select 

SSCs in the same major river basin, the same physiographic 

province, and most importantly, the same bioregion. The 

process of selecting SSCs is inherently subjective, and in 

many cases, there is no one “optimal” set of SSCs.

Stressor-Response Relations from Regional 

Data

Data from MDEQ’s master database of biological data 

are used to derive stressor-response relations in the form of 

scatterplots. For sites with multiple samples obtained over 

time, only the most recent sample should be used.

Spearman’s rank correlation coefficients (ρ) are 

calculated for relations between stressors (physical and 

chemical indicators) and responses (biological community 

metrics) and are stratified by bioregion. The 12 stressorresponse relations with the highest absolute values of 

correlation coefficients are visually examined as scatterplots. 

Scatterplots with clearly discernible positive or negative 

relations are retained. In cases where biological metrics 

are highly autocorrelated (ρ > 0.75 for relations between 

biological metrics), the metric with the strongest correlation to 

the stressor is retained. For the retained group of scatterplots, 

the quantile regression curve (90th or 10th percentile, for 

positive and negative relations, respectively) is added to 

better visualize the response. The scatterplots are divided 

into quadrants by using the 25th or 75th percentile of a 

physical variable (the candidate stressor being evaluated) from 

reference sites and by using the 25th or 75th percentile of a 

biological response from reference sites, with the quadrants 

color-coded. The lower right quadrant is further divided by 

the intercept of the quantile regression and the 25th or 75th 

percentile of a biological response from reference sites. The 

result is a set number of scatterplots for each of the four 

bioregions in Mississippi (fig. 7), grouped by major category 

of proximate cause.
For more information about this publication, contact

Director, Lower Mississippi-Gulf Water Science Center

U.S. Geological Survey

640 Grassmere Park, Suite 100

Nashville, TN 37211 

For additional information visit 

https://www.usgs.gov/centers/lmg-water/

Publishing support provided by

Lafayette Publishing Service Center
Hicks and Cartwright—Stressor Identification Framework of Biological Impairment in Mississippi Streams to Support Watershed Restoration—SIR 2020–5098

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20205098