United States Environmental

Protection Agency

Office of Water

Washington, DC 20460

EPA-822-R-02-019

March 2002

Methods for evaluating wetland condition

#9 Developing an Invertebrate Index

of Biological Integrity for Wetlands
United States Environmental

Protection Agency

Office of Water

Washington, DC 20460

EPA-822-R-02-019

March 2002

Methods for evaluating wetland condition

#9 Developing an Invertebrate Index

of Biological Integrity for Wetlands

Principal Contributor

Minnesota Pollution Control Agency

Judy Helgen, PhD

Prepared jointly by:

The U.S. Environmental Protection Agency

Health and Ecological Criteria Division (Office of Science and Technology)

and

Wetlands Division (Office of Wetlands, Oceans, and Watersheds)
United States Environmental

Protection Agency

Office of Water

Washington, DC 20460

EPA-822-R-02-019

March 2002

Notice

The material in this document has been subjected to U.S. Environmental Protection Agency (EPA)

technical review and has been approved for publication as an EPA document. The information

contained herein is offered to the reader as a review of the “state of the science” concerning wetland

bioassessment and nutrient e nrichment and is not intended to be prescriptive guidance or firm advice.

Mention of trade names, products or services does not convey, and should not be interpreted as

conveying official EPA approval, endorsement, or recommendation.

Appropriate Citation

U.S. EPA. 2002. Methods for Evaluating Wetland Condition: Developing an Invertebrate Index

of Biological Integrity for Wetlands. Office of Water, U.S. Environmental Protection Agency,

Washington, DC. EPA-822-R-02-019.

Acknowledgments

EPA acknowledges the contributions of the following people in the writing of this module:

Judy Helgen (Minnesota Pollution Control Agency), Jeanne DiFranco (Maine Department of Environmental Protection), Mike Gray (Ohio Environmental Protection Agency), Peter Lowe (United States

Geological Survey), Randy Apfelbeck (Montana Department of Environmental Quality), and Russ

Frydenborg (Florida Department of Environmental Protection).

This entire document can be downloaded from the following U.S. EPA websites:

http://www.epa.gov/ost/standards

http://www.epa.gov/owow/wetlands/bawwg

ii
iii

Contents

Foreword .............................................................................................................. v

List of “Methods for Evaluating Wetland

Condition” Modules...........................................................................................vi

Summary .................................................................................................................1

Purpose..................................................................................................................1

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

Process for Developing an Invertebrate IBI

for Wetlands .......................................................................................................3

Step 1. Select study sites ......................................................................5

Step 2. Plan the invertebrate sampling .............................................5

Step 3. Field sampling methods and decisions .................................8

Step 4. Sample processing procedures .......................................... 15

Step 5. Metric analysis ......................................................................... 18

A. Selection of attributes .................................................... 18

B. Forming the IBI...................................................................... 22

Conclusions and Recommendations .......................................................... 23

References ........................................................................................................ 25

Appendixes.......................................................................................................... 34

Glossary ............................................................................................................. 46

List of Figures

Figure 1: Invertebrate IBI Score Plotted Against an

Estimation of Human Disturbance on Large

Depressional Wetlands in Minnesota ....................................2

Figure 2: Flowchart for Developing an Invertebrate Index of

Biological Integrity .....................................................................4

Figure 3: Illustration of Sampling Sites in a Wetland With

Emergent Vegetation Shown as Symbols ..............................7
i v

Figure 4: Framed Screen for Separating Vegetation From

Invertebrates During Dipnetting........................................... 12

Figure 5: Activity Traps Used by Minnesota .......................................... 13

Figure 6: Total Number of Invertebrate Taxa Plotted Against

the Log of the Chloride in the Water (mg/L) From

Large Depressional Wetlands in Minnesota ..................... 19

Figure 7: Intolerant Taxa Plotted Against the Log of

Phosphorus (mg/L) in the Water of Large

Depressional Wetlands in Minnesota ................................. 20
v

Foreword

In 1999, the U.S. Environmental Protection Agency (EPA) began work on this series of reports entitled

Methods for Evaluating Wetland Condition. The purpose of these reports is to help States and

Tribes develop methods to evaluate (1) the overall ecological condition of wetlands using biological

assessments and (2) nutrient enrichment of wetlands, which is one of the primary stressors damaging

wetlands in many parts of the country. This information is intended to serve as a starting point for States

and Tribes to eventually establish biological and nutrient water quality criteria specifically refined for

wetland waterbodies.

This purpose was to be accomplished by providing a series of “state of the science” modules concerning

wetland bioassessment as well as the nutrient enrichment of wetlands. The individual module format

was used instead of one large publication to facilitate the addition of other reports as wetland science

progresses and wetlands are further incorporated into water quality programs. Also, this modular

approach allows EPA to revise reports without having to reprint them all. A list of the inaugural set of

20 modules can be found at the end of this section.

This series of reports is the product of a collaborative effort between EPA’s Health and Ecological

Criteria Division of the Office of Science and Technology (OST) and the Wetlands Division of the

Office of Wetlands, Oceans and Watersheds (OWOW). The reports were initiated with the support

and oversight of Thomas J. Danielson (OWOW), Amanda K. Parker and Susan K. Jackson (OST),

and seen to completion by Douglas G. Hoskins (OWOW) and Ifeyinwa F. Davis (OST). EPA relied

heavily on the input, recommendations, and energy of three panels of experts, which unfortunately have

too many members to list individually:

n Biological Assessment of Wetlands Workgroup

n New England Biological Assessment of Wetlands Workgroup

n Wetlands Nutrient Criteria Workgroup

More information about biological and nutrient criteria is available at the following EPA website:

http://www.epa.gov/ost/standards

More information about wetland biological assessments is available at the following EPA website:

http://www.epa.gov/owow/wetlands/bawwg
v i

List of “Methods for Evaluating Wetland

Condition” Modules

1 ................. Introduction to Wetland Biological Assessment

2 ................. Introduction to Wetland Nutrient Assessment

3 ................. The State of Wetland Science

4 ................. Study Design for Monitoring Wetlands

5 ................. Administrative Framework for the Implementation of a

Wetland Bioassessment Program

6 ................. Developing Metrics and Indexes of Biological Integrity

7 ................. Wetlands Classification

8 ................. Volunteers and Wetland Biomonitoring

9 ................. Developing an Invertebrate Index of Biological

Integrity for Wetlands

10............... Using Vegetation to Assess Environmental Conditions

in Wetlands

11 ............... Using Algae to Assess Environmental Conditions in

Wetlands

12 ............... Using amphibians in Bioassessments of Wetlands

13 ............... Biological Assessment Methods for Birds

14 ............... Wetland Bioassessment Case Studies

15 ............... Bioassessment Methods for Fish

16 ............... Vegetation-Based Indicators of Wetland Nutrient

Enrichment

17 ............... Land-Use Characterization for Nutrient and Sediment

Risk Assessment

18............... Biogeochemical Indicators

19 ............... Nutrient Load Estimation

20 .............. Sustainable Nutrient Loading

Module # Module Title
1

Summary

T

he invertebrate module gives guidance for

developing an aquatic invertebrate Index of

Biological Integrity (IBI) for assessing the condition of wetlands. In the module, details on each

phase of developing the IBI are given. First, in the

planning stage, invertebrate attributes are selected,

the wetland study sites are chosen, and decisions

are made about which stratum of the wetland to

sample and what is the optimal sampling period or

periods. Then, field sampling methods are chosen.

The module describes field methods used in several States, and gives recommendations. Laboratory sampling procedures are reviewed and discussed, such as whether and how to subsample,

and what taxonomic level to choose for identifications of the invertebrates. Specific categories of

attributes, such as taxa richness, tolerance, feeding

function, and individual health are discussed, with

examples. Appendices to the invertebrate module

give details about the advantages and disadvantages

of using invertebrates, of the different attributes, of

various field sampling methods, and of lab processing procedures as used by several State and Federal agencies. The module and appendices give a

detailed example of one State’s process for developing an invertebrate IBI, with a table of metrics

with scoring ranges, and a table of scores of individual metrics for 27 wetlands. A glossary of terms

is provided.

Purpose

T

he purpose of the invertebrate module is to

describe the advantages of using aquatic

invertebrates for assessing the condition of

wetlands, and to present approaches for developing IBIs for wetlands.

Introduction

T his module describes the advantages of usingaquatic invertebrates for assessing the condition of wetlands and presents approaches for developing an invertebrate IBI for wetlands. Processes, methods, and examples of invertebrate IBIs

for wetlands are presented, along with summaries

of approaches currently used in several States. The

module is based primarily on work done on ponded

freshwater wetlands, but the approaches described

can be modified for other types of wetlands. The

module describes development of the IBI, but other

indexes and approaches for assessing the condition

of wetlands, particularly multivariate techniques

(Davies et al. 1999, Reynoldson et al. 1997), can

be used. A glossary of terms is also provided.

Why use aquatic invertebrates to

assess wetlands condition?

n Because they respond to many kinds of

stressors to wetlands, as shown in Figure 1.

What are the advantages and

disadvantages of using aquatic

invertebrates?

Several advantages and disadvantages of using

aquatic invertebrates are reviewed in Appendix A.

Briefly, some of the advantages of using invertebrates for biological assessments of wetlands are:

n They are commonly and widely distributed in

many types of wetlands (Batzer et al. 1999).

n They respond with a range of sensitivities to

many kinds of stressors; they are commonly

used for toxicity testing and ecological

assessments in waterbodies (e.g., see Barbour

et al. 1999, Beck 1977, Cairns and

Niederlehner 1995, deFur et al. 1999, Euliss

and Mushet 1998, Hart and Fuller 1974,
2

Heliovaara and Vaisanen 1993, Helgen and

Gernes in press, Hellawell 1986, Kerans and

Karr 1994, Lewis et al. 1999, Morris et al.

1989, Rainbow 1996, Saether 1979, Servos

1999, Stuijfzand et al. 2000, Warwick 1980).

n Many aquatic invertebrates complete their life

cycles in wetlands; they are exposed directly to

physical, chemical, and biological stressors

within the wetland (Clarke 1981, Driver 1977,

Hanson and Swanson 1989, Fairchild 2000,

Klemm 1982, Larson 1987, Mackie, in press;

Merritt and Cummins 1996a; Walker 1953,

1958, Walker and Corbet 1975, Westfall and

May 1996, Wiggins 1996, Wiggins et al. 1980,

Wrubleski 1987).

n Aquatic invertebrates are important in wetland

food webs of wildlife (Batt et al. 1992, Colburn

1997, Deutschman 1984, Eldridge, J. 1990,

Fredrickson and Reid 1988, King and

Wrubleski 1998, Krapu and Reinecke 1992,

Swanson et al. 1977, Swanson et al. 1985,

Wissinger 1999).

n They have public appeal in citizen monitoring

programs (see Module 8, Volunteers and

Wetland Biomonitoring; Helgen and Gernes

1999).

Many wetland invertebrates complete the life cycle

from egg to adult in a wetland, and therefore are

directly exposed to wetland conditions and stressors. In infrequently flooded, seasonal, and temporary wetlands, invertebrates will have shorter life

cycles of days to weeks (Schneider and Frost 1996,

Wiggins et al. 1980). In more regularly flooded,

more permanent wetlands, invertebrates with longer

life cycles of weeks to months, such as dragonflies

or crayfish, will be present. Populations of invertebrates with shorter life cycles, such as fairy shrimp

and mosquitoes, will respond more quickly to human disturbances, but they may recover more

quickly, either from resting eggs or from

recolonization by adult insects. Invertebrates with

Figure 1: Invertebrate IBI score plotted against an estimation

of human disturbance on large depressional wetlands

in Minnesota.

 The IBI has 10 scored metrics; the gradient combines factors of chemical pollution and alterations in the

buffer zone and near wetland landscape.
3

longer life cycles, such as dragonflies, some fingernail clams, and snails, will experience longer exposure to wetlands conditions. In some cases, recovery from losses of juveniles may take longer, because of more limited seasons of egg laying by adults

and longer development times from egg to adult

(Corbet 1999).

The disadvantages of using aquatic invertebrates

for biological assessments are detailed in Appendix

A. The biggest challenge is the amount of staff time

and expertise that is needed for picking the organisms from the samples and for identifications. Techniques for reducing the amount of picking time, such

as using a screen under vegetation while dipnetting,

using activity traps, or by subsampling, will be described below. Some State agencies or organizations may lack the laboratory facilities to do the work

in-house and will need to contract out the work.

When work is contracted out, it is important for

the project managers to provide the contractor with

explicit protocols and procedures for identifications

and analysis in relation to the needs of the program.

An advantage of doing the work in-house is that

the staff involved can provide active input into the

development of the biological indexes and can participate in implementing the findings into the State’s

programs. Whether or not the identifications are

contracted out, it is vital to have scientists on staff

to provide active input into biocriteria development

and implementation.

In sum, disadvantages of using macroinvertebrates

are:

n Sample processing takes a lot of staff time or

resources.

n Organizations may lack facilities for processing

and identifying invertebrates.

Process for

Developing an

Invertebrate IBI for

Wetlands

A

 flow chart for developing an invertebrate

IBI for wetlands is shown in Figure 2. A similar flowchart showing an example of the process

for developing an invertebrate IBI in Minnesota is

shown in Appendix K. Overall, the process involves sampling invertebrate attributes of several

wetlands of similar class and region representing a

range of human disturbance or impairment, from

least to most disturbed. After sampling, the information on the degree of impairment is related to the

measures of various invertebrate attributes to see

which of the attributes show predictable responses

to impairment. These attributes will constitute the

metrics for the IBI. Scores are assigned to each

metric to indicate the level of response to human

disturbance (see Module 6: Developing Metrics and

Indexes of Biological Integrity). The metric scores

are summed for the total IBI score. Decisions are

made as to which range of IBI scores indicates a

poor condition, i.e., not attaining designated use (if

one exists), or a moderate or excellent condition.

This module will focus primarily on the steps in the

process that relate to developing invertebrate IBIs.

Additional detail for this process can be found in

Module 4: Study Design, Module 7: Wetlands Classification, Module 6: Developing Metrics and Indexes of Biological Integrity, and Module 17: Landscape Characterization for Wetlands Assessments.

Additional information on biological monitoring of

wetlands is also available in Rader et al. (in press).

It needs to be stated that other approaches are

successful in evaluating the condition of waterbodies

with invertebrates. Multivariate methods for ana-
4

l Select ecogeographic region

l Decide on wetland class

l Select sites within wetland classes that exhibit a

range of disturbances

l Select invertebrate attributes of sensitivity, richness, tolerance, trophic structure, or other attributes

likely to respond to human disturbance

l Gather literature about regional invertebrates for the

wetland class

l Determine which strata of wetland to sample

l Select optimal seasonal sampling period for maturity

of invertebrates

l Select appropriate sampling methods for objectives

of the program

l Pretest sampling methods to determine number of

samples to be taken per site, and to assure the

desired types of invertebrates are collected

l Decide if samples will be preserved and

processed in the lab, or sorted and identified

in the field; write Standard Operating Procedures

l Sample sites within the index period, at the same

time collect samples for chemical analysis and

assess the surrounding landscape

l Decide to pick entire sample or subsample

l Decide on taxonomic levels for identifications

establish database of taxa lists and ITIS codes

l Develop Standard Operating Procedures and set up

a Quality Assurance plan for repicking and for

independent verifications of identifications

l Establish a reference collection with several

specimens of each taxon

l Plot attribute data against human disturbance

gradient

l Select most responsive 8-12 metrics

l Score metrics by trisecting data or other method

l Sum the metric scores for IBI, plot IBI against

disturbance gradient

step 1:

select study

sites

step 2:

Plan invertebrate

sampling

step 3:

Field Sampling

step 4:

Sample Processing

step 5:

Metric Analysis

Figure 2: Flowchart for Developing an Invertebrate Index of

Biological Integrity.
5

lyzing invertebrate data are used for stream assessments in the Biological Monitoring Program in Maine

(Davies and Tsomides 1997, Davies et al. 1995,

1999). Discriminant analysis of invertebrate data

predicts the degree of impairment by comparison

with known biological characteristics of the state of

Maine’s four water quality management classes.

(See also Marchant et al. 1997, Norris 1995, Wright

1995, Hawkins and Carlisle in press, U.S. EPA

1998, Lake and Reservoir Bioassessment and

Biocriteria Appendix E). Reynoldson and others

(1997) found estimates of accuracy and precision

to be higher when multivariate techniques were used

for data analysis compared with multimetric methods. If multivariate analysis is used, it is important

to use a method that allows interpretation of proportional attributes rather than one that is limited to

data on the presence or absence of taxa (see

Reynoldson et al. 1997). A disadvantage of multivariate methods is the complexity of setting up the

data analysis.

Step 1. Select study sites

To reduce natural variability, an ecoregion or ecological region and a class of wetlands are chosen

for developing the IBI (see Module 7 on Classification). It is necessary to select specific wetland

classes, because invertebrate assemblages may differ among classes of wetlands (in Batzer et al. 1999;

see Huryn and Gibbs, Leslie et al., Marshall et al.,

Smock et al., and others; Carlisle et al. 1999,

Weisberg et al. 1997). It is important to include

several least impaired or reference wetlands along

with a range of wetlands that are affected by human

disturbances. Sufficient numbers of least-impaired

reference wetlands and wetlands experiencing a

range of impairments are needed to detect significant dose-response relationships between the invertebrate attributes (Y axis) and the measures of

human disturbances (X axis). See the Glossary for

definitions of disturbance and human disturbance.

The impaired wetlands can be selected to target

the major types of human-caused stressors to wetlands, those that are most likely to be causing impairment to the invertebrates within the region and

wetland class. Invertebrates exhibit a wide range

of sensitivities to human-induced stressors such as

pesticides, metals, siltation, acidification, loss of

vegetation or vegetation diversity, nutrient enrichment, and changes in the oxygen regime (Adamus

1996, Beck 1977, Eyre et al. 1993, Helawell 1986,

Resh and Rosenberg 1984, Saether 1979).

Macroinvertebrates respond to disturbances in

wetland vegetation because invertebrates are dependent on the vegetation as part of their food

source (Wissinger 1999), for attachment sites, refugia (Corbet 1999, p. 164, Orr and Resh 1989),

and egg laying. Some dragonflies and damselflies

lay eggs on specific types of aquatic vegetation

(Sawchyn and Gillott 1974, Corbet 1999, p. 591).

Step 2. Plan the invertebrate

sampling

Selection of invertebrate attributes

Attributes are measures of the invertebrate composition to be tested to see if they show a graded

response, or dose-response, to human disturbances

such as chemical pollution, siltation, or habitat alteration. If a response is seen, the attribute will be

selected as a metric and scored as part of the overall IBI score. See Module 6: Developing Metrics

and Indexes of Biological Integrity. More detailed

information on the selection of invertebrate attributes

is given near the end of this module in Step 5 on

Metric Analysis. Appendix F lists advantages and

disadvantages of different kinds of attributes, and

Appendix I shows metrics used by Minnesota for

large depressional wetlands. Appendix G shows

the attributes used or tested by several States.

Attributes are selected from major categories of

invertebrate composition (Barbour et al. 1996, Resh

et al. 1995), such as measures of taxonomic richness, measures of tolerance proportions and intolerant taxa, measures related to trophic structure and

functional feeding groups (see Merritt et al. 1996,
6

1999) and other measures related to longevity, introduced or exotic species, and invertebrate health

or condition (see Diggins and Stewart 1998,

Hudson and Ciborowski 1996, Warwick 1980).

Several invertebrate attributes are tested, and the

data for the attributes are related to measures of

human influence. From the attributes that show significant relationships with human disturbances, a set

of 8 to 12 are selected as metrics. An attribute that

does not show a significant response to human disturbances will likely be discarded. However, if there

is some reason to think the attribute might show a

response to different kinds of stressors that were

not included in the study design, it might be retained

for further testing.

It is important to plan to measure the types of invertebrates that would be expected to inhabit the

class of wetland in the ecological region of interest.

The taxonomic composition of the invertebrates will

likely differ in bogs, playa lakes, temporary ponds,

prairie potholes or riparian wetlands, for example

(see Batzer et al. 1999). Information can be sought

from regional scientific literature and from invertebrate biologists in the area. Whatever the class of

wetland might be, the major categories of attributes

will be represented, such as taxa richness and tolerant and intolerant taxa, even if the taxonomic composition differs. If there is little preexisting information on species composition for the particular wetland class, it may be helpful to do preliminary sampling in reference wetlands to develop a list of species and then select the attributes to be tested. This

could be done when the sampling methods are being tested.

Determine which stratum or zone(s) of the

wetland to sample

One decision is whether to attempt to sample the

entire wetland and all of its habitats, or to sample

defined zones or strata within the wetland. Wetlands have several zones that are related to the influence of hydrology and/or plant communities. For

example, freshwater marshes have shallow water,

emergent macrophyte, and floating-leafed and submersed aquatic plant zones. Bottomland forest

wetlands may have zones ranging from aquatic to

swamp to semipermanently flooded to seasonally

then temporarily flooded (Mitsch and Gosselink

1993). It may not be necessary to sample all the

zones in a wetland to assess its condition. Either

enough of the wetland habitat should be sampled to

assess its condition, or the area most sensitive to

impairment should be assessed. In a small wetland, sampling all the habitats may be possible, but

in larger wetlands the work effort may necessitate

choosing strata for the sampling and maintaining a

consistency among wetlands of the habitats that are

sampled. The choice can include the following considerations:

n Sample in the zone or stratum that is likely to

have the greatest variety and production of

macroinvertebrates,

n Sample in the zone or stratum that is considered

to be most vulnerable or most affected by human

disturbances, or

n Select a habitat type that is representative of

the wetland, as opposed to unique or minor in

extent; sample enough of the habitats to integrate

the conditions in the wetland.

See also Module 4: Study Design for Monitoring

Wetlands. Examples of the zones that were selected by different States involved in wetland monitoring are given in Appendix B.

Aquatic macroinvertebrates occur in association

with benthic sediments, emergent vegetation,

submergent vegetation, and open-water habitats.

They colonize hard substrates such as tree roots or

rocks. Some feed on the microflora that colonize

the surfaces of plants and hard substrates; some

are predators on the smaller herbivorous or

detritivorous invertebrates. Different invertebrate

assemblages are associated with these different
7

habitats, even with different kinds of wetland vegetation (Burton et al. 1999, Gathman et al. 1999,

King and Brazner 1999). In preliminary studies of

coastal wetlands, some invertebrate attributes differed among types of emergent vegetation zones.

Attributes were selected that gave consistent, rather

than contradictory, responses to human disturbance

among sites across four defined plant zones (Burton et al. 1999).

The vegetated areas of wetlands have been observed to have more taxa of chironomids (Driver

1977) and aquatic beetles (Aitkin 1991, Timms and

Hammer 1988) and other taxa (Dvork 1987).

Emergent vegetation areas had greater richness

when compared with open water areas that lacked

submersed vegetation (Olson et al. 1995, Voigts

1976). If fish are present, open-water areas may

have more predation by fish on invertebrates

(Hanson and Riggs 1995). Having more taxa in

vegetated areas may not reflect direct herbivory on

the plants, but the conversion of macrophytes into

detritus is an important source of nutrition for invertebrates (Euliss et al. 1999). Also, as stated previously, macrophytes provide refugia, substrates for

growth of algae and small organisms, and egg laying sites.

An example of sampling locations in a depressional wetland in Minnesota is given in Figure 3.

Select the optimal seasonal sampling period

The seasonal index period is the window of time

when the sampling of the wetland is optimal to obtain the most representative, mature invertebrate

community and the maximum number of identifiable taxa. The different and seasonal life cycle stratTwo standardized dipnetting samples are taken from the wetland about 20-30 meters apart (DN1 and DN2).

Each sample consists of three to five sweeps repeated in two efforts (represented by two DN1s and two

DN2s). Ten activity traps (BT) are placed at the edge and in representative areas of the near shore emergent

vegetation zone in water < 1 m deep. Wetland is not drawn to any scale. Method is used by Minnesota

(MPCA). See Appendixes B and C.

Figure 3: Illustration of sampling sites in a wetland with

emergent vegetation shown as symbols.

DN1

DN1

DN

DN

BT

BT

BT

BT

BT

BT

BT

BT

BT

BT

2

2
8

egies of invertebrates present a challenge as to when

to sample, especially if the sampling is to be done

just once in a season. If the wetland is sampled too

early in the year, the invertebrates may be less mature, making the identifications more difficult. If the

wetland is sampled later in the season, there may

be emergences of aquatic insects from the wetland

and immigration of adult insects that fly into the

wetland from other waterbodies. If the wetland is

sampled too late in a season, the smaller wetlands

may have dried down or become choked with vegetation.

The seasonal index period will differ across different regions of the United States and it will differ

for different types of wetlands. Invertebrates are

known to undergo seasonal changes in populations

and species that inhabit a waterbody. Ideally, the

wetland should be sampled in more than one season; however, this may not be practical for State

programs. In selecting an index period, the following should be considered:

n The invertebrates should have developed

sufficiently to be identified by biologists.

n The index period should bracket a time when

as many resident taxa as possible are present.

n The index period is not during a time that the

wetland is likely to dry down or become

choked with vegetation (so it is still sampleable).

n The index period could attempt to precede

maximum fish predation, if any, while still

encompassing the season of maximum richness

and development of invertebrates.

n The index period should be shifted somewhat

to account for unusually late or early seasons

(the main goal is to optimize the number of

invertebrate taxa present and mature).

See Appendix B for the approaches taken by different States and Appendix H for addresses of State

contacts. Maine samples in June, to obtain the most

mature of some of the invertebrates (Davies et al.

1999), Minnesota (Gernes and Helgen 1999,

Helgen and Gernes in press) samples during June

when there is greater maturity in the Odonata and

more water in the wetlands. Wetlands in the more

southern location of a study set are sampled earlier

in the index period. Montana (Apfelbeck 1999)

samples the Plains Ecoregion in April to mid-June,

the Intermountain Valley and Prairie Ecoregion from

June to August, and the Rocky Mountain Ecoregion

from mid-June to September. Rapid Bioassessment

Protocols for Use in Streams and Wadeable Rivers (Barbour et al. 1999) contains a discussion of

the sampling seasons for stream invertebrates.

Step 3. Field sampling methods

and decisions

Before the actual sampling for monitoring wetlands

takes place, several decisions need to be made.

These decisions will be based in a large part on the

program objectives or information needs that the

invertebrate IBI is addressing:

n What strata or zones of the wetland to sample

(see discussion of selecting sampling stratum

above and see Module 4 on Study Design)

n Whether to sample once or more than once

during the season (see discussion on selecting

seasonal index period above)

n Whether to sample all habitats in the stratum or

zone (multihabitat approach), or selected

habitats

n Whether the approach collects the range of

invertebrates needed for the attributes

n What sampling methods will be used to collect

the desired invertebrates efficiently

n How many samples can be processed in the

lab

Recommendations for field methods

Several methods for collecting invertebrates in

wetlands are described below and listed with ad-
9

vantages and disadvantages in Appendix C. The

following are recommended, but may not be applicable or suitable for researchers or State agencies

under certain circumstances.

n Sample a stratum of the wetland that contains

most of the macroinvertebrates: often the

shallow areas have emergent or submerged

vegetation; very small wetlands can be sampled

all around the edges.

n Sample once during the season, after

determining when the maximum development

of the invertebrates occurs, and take more than

one sample at that time; if resources permit,

sample in two seasons taking more than one

sample on each visit date.

n Sample all habitats within the stratum or zone,

or sample selected habitats if they are known

to have a wide representation of invertebrates.

n Use a dipnetting method with a standardized

and repeatable protocol; combine this with

activity traps to collect the motile predators (see

discussion of these and other methods below).

Determining the number of samples to take:

species richness and sampling effort

Once these decisions have been made, the sampling methods should be pretested in the field to

ensure they collect the invertebrates that are needed

for the attributes and to determine the numbers of

samples that will be needed. In practice, States,

Tribes, and researchers often have to balance the

limitations of resources against the requirements for

validation of data. It is useful to test methods in

reference wetlands to determine how many samples

are needed to obtain the desired representation of

the invertebrate fauna.

It is well known in ecological research that the

number of species or taxa collected frequently increases with the sampling effort (Magurran 1988).

A quantitative sampling method (e.g., cores or

Gerking samplers) will yield data on the density of

taxa or species per unit area sampled. A

semiquantitative sampling method (dipnetting, activity traps), as described below, will record the

number of taxa per total sample count. This is called

numerical taxa richness. In either case, the number

of species or taxa usually increases with sample size,

i.e., with the number of samples taken or the area

sampled. For the purposes of comparing sites, it is

essential that the number of samples and area

sampled be the same at all sites.

Several samples should be taken with consistent

methods at all sites to determine how many samples

are necessary to collect a desired percentage of the

total number of species collected. Ideally, enough

samples will be collected to achieve the plateau level

at which taking more samples does not increase the

number of species. However, this may not be practical, because a plateau may not be reached even

with many samples (see Mackey et al. 1984).

Sparling et al. (1996) collected multiple samples at

the same sites and recorded the increases in the

percent of total invertebrate taxa with the increase

in the number of samples analyzed. In 1, 2, 3, 4, 5,

and 6 samples there were, cumulatively collected,

40%, 56%, 60%, 64%, 68%, and 72% of the invertebrate taxa, respectively. In addition to comparing the taxa gained by added sampling effort,

the effects on the final evaluation of the wetland,

which lead to the IBI or other index score, should

be gauged for each level of effort. To reduce variability, a minimum of three samples is recommended,

although each method should be tested for its variability if possible.

It may not be possible to do a power analysis at

the beginning of IBI development. Power analysis

determines how many samples need to be taken to

obtain enough statistical power to detect differences

among sites using the IBI scores. To do this, several samples would be taken in each of several

wetlands that have a range of human disturbances.

For discussions of power analysis and the number

of samples to take, see Eckblad (1991), Allan
10

(1984), and Green (1989). Several papers may

be helpful (Davies et al. 1995, 1999, Davies and

Tsomides 1997, Fore et al. 1994, Rankin and Yoder

1990, Rathbun and Gerritsen in press). Variability

in the biological data may increase with greater impairment, as seen in biological monitoring in Ohio

streams (Rankin and Yoder 1990, Yoder 1991).

Select the appropriate sampling methods

The methods appropriate for sampling will depend

on the type of wetland and the goals of the sampling. Appendix B describes sampling protocols

used by several states with advantages and disadvantages. The emergent or submerged vegetation

in wetlands presents a challenge for sampling. Selected methods used in wetlands studies are summarized in Appendix C with advantages and disadvantages. More detail on the methods can be obtained from references cited in the text to follow.

See Batzer et al. (in Rader et al. in press) on sampling invertebrates in wetlands.

There is a need for comparisons among the most

commonly used sampling methods for sampling invertebrates, particularly for differences in invertebrate composition and attributes. Brinkman and

Duffy compared Gerking samplers, cores, activity

traps, and artificial substrates (1996) and found

Gerking samples collected significantly more taxa

than cores. Hyvonen and Nummi (2000) compared

activity traps with corers, finding fewer active invertebrates in core samples. Some of the articles

cited below have comparative studies of methods.

Is a quantitative sample necessary?

A quantitative sampling method collects invertebrates by trapping the column of water and/or the

bottom sediments from a known dimension of bottom area. This permits calculating the number of

invertebrates per unit area of wetland bottom.

Quantitative methods may be particularly useful in

assessing the productivity of wetlands in relation to

waterfowl production. They are not as necessary

for developing IBIs (Karr and Chu 1999). Examples of quantitative sampling methods for

macroinvertebrates are the Gerking box sampler

(Gerking 1957, Anderson and Smith 1996); the

stovepipe sampler (see Wilding sampler in Welch

1948, Turner and Trexler 1997); and various methods for collecting (Gates et al. 1987) or coring the

benthic sediments (Hyvonen and Nummi 2000,

Swanson 1978, 1983). Some have used an Ekman grab sampler mounted on a pole.

The Gerking box sampler—a quantitative

sampler

The Gerking box sampler is lowered into the water until the open bottom of the sampler (0.3 m2

) is

pushed into the sediments leaving the open top projecting above the water’s surface. Everything within

the sampler is collected from the sediments, vegetation, and water column. A flat 1 mm mesh plate

is slid across the bottom before the sampler is pulled

up and drained, and the sample is rinsed into a sieve.

The advantages of this device are the quantitative

estimates of the invertebrates and the fact that it

captures many of the organisms from the benthos,

vegetation, and water column. Gerking samplers

collect greater abundance and greater numbers of

invertebrate taxa than activity traps or artificial substrates (Brinkman and Duffy 1996). The chief disadvantage of the Gerking box is the labor involved

in processing the large samples that it collects. In

addition, the device is bulky, requiring two people

to carry it to the site and a crew of three to four to

operate. It is not useful if woody vegetation is present.

This method has been used in projects at the Patuxent

National Wildlife Refuge (see Appendix B).

Core sampler—a quantitative sampler

The advantage of using core samplers is the

quantitation per unit bottom area of wetland and

the shorter collection time. A disadvantage is the

fact that core samples contain fewer invertebrate

taxa than Gerking samplers or sweep nets (Hyvonen

and Nummi 2000, Cheal et al. 1993) and the organisms have to be processed from the mud. Cores
11

sample a smaller bottom area than the Gerking box

and they do not capture the motile taxa. The frequently anaerobic benthic sediments of some wetlands would not be expected to have the range of

taxa found in other habitats. Cores are appropriate

if the goal is to analyze the benthic invertebrates,

such as oligochaete worms, benthic molluscs, and

chironomids, or if the type of wetland has low water or saturated conditions (see Hershey et al. 1998).

Only 2 taxa of chironomids were collected from

cores pooled from benthic sediments in depressional

wetlands, whereas a mean of 12 taxa were collected by a dipnet method (Helgen et al. 1993).

In samples that include benthic sediments, such

as cores or stationary samplers, the organisms can

be floated from the sediments in dense (30%) sucrose or salt solutions (Anderson 1959, Ritchie and

Addison 1991). As much sediment as possible is

washed out of the sample with running water on a

No. 30 (600 mm) mesh sieve. After water is

drained from the residual debris and sediment, the

30% sucrose solution is added to float out the organisms from the residual. This technique is mostly

limited to muddy benthic sediment samples that are

very difficult to pick.

Semiquantitative sampling methods

Dipnet or sweep net samples. Dipnetting, also

referred to as sweep netting, is probably the most

common method for sampling invertebrates in shallow vegetated wetlands (see Appendix C). With

consistent, standardized protocols, dipnetting yields

semiquantitative data on invertebrate abundances

and taxa richness. Without a consistent effort,

dipnetting yields only qualitative results. See Appendix B for methods used by different States to

produce semiquantitative data with dipnetting. Repeatability is dependent on the standardization of

protocols and the training and skill of the field crews.

Ways to assure repeatability in dipnetting protocols include:

n Defining the number of sweeps (Minnesota,

Florida)

n Defining the amount of time for sweeps

(Montana, Ohio, Merritt et al. 1996, 1999)

n Defining the distance of sweeps and the number

(Florida, Maine)

n Doing consistently repeated efforts at each site

(Minnesota)

Dipnetting samples a large area and the range of

wetland habitats. Dipnets have been considered a

useful method because they capture a high richness

of species, comparable to that obtained with the

Gerking box sampler (Cheal et al. 1993, Kaminski

1981). Dipnets collect more taxa than are collected

by cores or artificial substrates (Mackey et al. 1984),

and collected more taxa of chironomids than corers, artificial substrates, or activity traps in Minnesota (Helgen et al. 1993). An advantage of

dipnetting is that experienced crews can collect

samples quickly over a wide range of habitats.

See Appendix C for more details of dipnet procedures. In Florida, 20 0.5-m sweep net efforts

are distributed in proportion to the representation

of the habitat type, with emphasis on the “productive habitats” (Florida DEP 1994, 1996, and FL

SOP). In Minnesota, 3 to 5 sweeps are done twice

for each sample, i.e., a total of 6 to 10 sweeps per

sample, and 2 samples are collected per wetland

(Gernes and Helgen 1999, Helgen and Gernes in

press). In Ohio, a 30-minute multihabitat dipnetting

is done and invertebrates are handpicked from substrates that could not be sampled by dipnet

(Fennessey et al. 1998). Montana samples all habitats in the wetland for 1 minute, 3 to 4 times, depending on the wetland size and complexity, or until

at least 300 organisms are collected into one

composited sample (Apfelbeck 1999). Merritt,

Cummins, and others have used 30-second sweeps

with D-frame nets to assess the status of vegetated

riparian systems (Merritt et al. 1996b, 1999,
12

Cummins and Merritt in press; Cummins et al.

1999). A time-constrained dipnetting protocol may

be difficult when wetlands are particularly mucky

or habitats are farther apart.

A disadvantage of dipnetting is the amount of vegetation and other debris that gets trapped in the net.

This adds greatly to the picking time needed in the

lab. Unless debris is removed in the field, it increases the amount of sample that must be preserved. Minnesota reduces the vegetation in

samples by laying the net contents on a framed 1⁄2”

12” x 16” hardware cloth screen that sits on a pan

of water contained in a floatable tray (see Figure 4

and Appendix C). The vegetation is gently teased

apart periodically over a 10-minute period and the

invertebrates are encouraged to drop into the water in the pan beneath the screen. This process is

done twice for one dipnet sample, then the pan of

water is poured through 4” cylindrical 200 micron

mesh sieves. Florida reduces the amount of vegetation in its multihabitat dipnet samples by washing

it in the net and removing the larger pieces of vegetation, as does Ohio.

Some habitats are not amenable to dipnetting, e.g.,

areas with a lot of woody debris or roots or very

dense vegetation, or water that is too shallow.

Coring methods may be necessary in very shallow,

saturated wetlands or during drought cycles

(Hershey et al. 1998). Also, dipnetting tends to miss

some of the very motile invertebrates, e.g., the large

predatory beetles and bugs. This problem is overcome by combining the use of dipnets and activity

traps.

Multihabitat dipnetting methods. The multihabitat dipnetting method takes samples from most

of the habitats within the wetland. The sampling

can be distributed among the habitats in proportion

to the habitat type or by other consistent protocols

such as time constraints. Training field crews to

assess the habitats and to sample consistently using

Figure 4: Framed screen for separating vegetation from

invertebrates during dipnetting.

 1⁄2” hardware cloth 12” x 16” screen sits on a pan of water contained in a larger tray. Vegetation is

placed on top of the screen and invertebrates are encouraged to drop into water in pan over a period of 10

minutes. Process is done two times for one sample.
13

standardized protocols is important. See Appendix C for the variations on the multihabitat method

used by Florida, Montana, and Ohio.

Advantages of the multihabitat method are that

the sample represents the complexity of the wetland and collects most of the invertebrate taxa, except the very motile taxa. Disadvantages of the

method are the time needed for processing the large

composite sample, or, alternatively, the need to

subsample or use a minimum count (e.g., 200 or

300) or organisms (see Appendix C). Multihabitat

samples tend to have a lot of vegetation and debris,

unless it is removed in the field. Also, wetlands

may differ in habitat types (Burton et al. 1999).

While it would be preferable to preserve separately the samples from each habitat, for efficiency,

the sample is usually a composite of collections from

all the habitat types. Separate preservation would

require added effort in the field. An advantage of

keeping the collections from the habitat types separate would be in the analysis of the data to determine which habitats show the most response to human disturbance.

Activity traps—a semiquantitative method. An

activity trap is a passive sampler usually containing

a funnel-shaped opening and an enclosed container

(jar or cylinder) that receives and traps organisms

that swim into the trap. With a funnel opening around

2.5cm diameter, macro-invertebrates can pass into

the trap. When placed horizontally in the wetland,

activity traps (AT) give semiquantitative data on the

motile wetland invertebrates, effectively trapping the

motile predators (e.g., the leeches, aquatic beetles,

and bugs) better than dipnets (Hilsenhoff 1987b,

1991, Turner and Trexler 1997). They are less suitable for collecting the nonmotile types of invertebrates (Hyvonen and Nummi 2000).

Activity traps are variously styled as funnel traps

(Swanson 1978, Fennessey et al. 1998, Hanson et

al. 2000, Gernes and Helgen 1999, Helgen and

Gernes in press, Murkin et al. 1983, Swanson

1978). They are left out at least one night, so the

night-active invertebrates swim or crawl into the

funnel openings. The style of trap that is used by

Minnesota is pictured in Figure 5. Traps used by

Minnesota and Ohio EPA are described in Appendix C.

Figure 5: Activity

traps used by

Minnesota.

 Ten activity traps are placed

horizontally in near shore area of

wetlands. a. Trap is held to 1⁄2” 4 ft

dowel by a sliding bracket of 3" thin

wall PVC. A wingnut unites this and

the larger bracket that holds the

bottle; b. funnel is cut from the top

end of a 2 liter beverage bottle. Four

grooves 1/8 x 2" are cut into rim of

funnel to snap into bottle, c. bottle is

held by a 4" PVC open bracket. (see

Wik D. in References)
14

Activity traps should be placed in shallow water

(<1 m to a few cm deep), because the active invertebrates, such as predaceous beetles and bugs, feed

there. If they are placed too deep, fewer invertebrates are collected. Typically, the traps are placed

horizontally, although vertical placement has been

used, primarily for zooplankton with smaller opening size in the funnel (Whiteside and Lindegarrd

1980). The traps are usually retrieved after 24 to

48 hours, depending on the water temperature, with

a longer period in colder water.

An advantage of activity traps is that they provide

semiquantitative data. Also, less training and time

are required to set out and collect several traps in a

repeatable way. Activity traps collect a sample that

is clean of most vegetation and requires less processing time. Minnesota averaged 2.3 hours of processing time for 10 activity traps per wetland.

A disadvantage of activity traps is the need to revisit the site to collect the traps after one or two

overnights. Activity traps do not collect the range

of macroinvertebrates needed for the IBI. They

must be used in conjunction with another sampling

method, such as dipnetting, if the goal is to have a

broad representation of the invertebrates for the IBI

attributes. There is also a concern that predation

within the trap might alter the invertebrate composition. One study (Elmberg et al. 1992) suggests

that fish, but not invertebrate predators, may affect

richness, but not abundance, of the invertebrates.

Another disadvantage of activity traps is the possible collection of large numbers of tadpoles so dense

they seem to exclude macroinvertebrates. Decomposition was advanced even after 24 hours in the

water (Peter Lowe, Patuxent Wildlife Research,

personal communication; Sparling et al. 1995).

Dead organisms in the traps might attract some

predators to the traps, although this has not been

evaluated.

More study is needed on the effectiveness of the

different designs for activity traps, particularly the

size of the funnel opening and how it might affect

the size of organisms, including vertebrate predators, that can enter the traps. The effect of trap

volume and whether the trap is enclosed (plastic or

glass) versus open (screen) should be reviewed for

predation impact that might be reduced by declining oxygen levels in the enclosed traps. Minnesota

excludes air bubbles in activity traps to reduce activity of predators inside the traps (Ralph Gunderson,

St. Cloud State U., MN, personal communication).

Finally, more study is needed on the relation between water temperature and efficiency of funnel

traps for active macroinvertebrates. Murkin et al.

(1983) suggest that water temperature was not significantly correlated with the abundance of invertebrates in activity traps, but temperature does affect

invertebrate activity (Henrikson and Oscarson

1978).

 Artificial substrates. Artificial substrates are

passive samplers that are made of hard substrates

(plates, tiles, or objects that mimic the natural substrates) that are placed in the water for a few weeks

to allow colonization on the substrates by certain

aquatic invertebrates. The substrates are colonized

by epiphytic fauna and have been used to collect

information on a number of attributes (King and

Richardson, in press; King and Richardson, submitted). They were useful in studies on the impact

of mosquito control agents on chironomids (Liber

et al. 1998, Ferrington 1994). More taxa of chironomids were found on artificial substrates on plates

that were placed up in the aquatic macrophytes than

on plates placed on the bottom (Liber et al. 1998,

Ferrington 1994). A disadvantage of artificial substrates is the lack of collection of actively swimming

invertebrates and consequent lower taxa than obtained with dipnets (Turner and Trexler 1997).
15

Advantages of using artificial substrates are:

n They yield a clean sample relatively free of

debris.

n The data are semiquantitative (based on area,

size and number of plates).

n They are easy to deploy in the field.

Disadvantages of artificial substrates are:

n They collect chironomids, oligochaetes, snails,

and other epiphytic taxa, but not Odonata or

other active invertebrates collected by other

methods.

n The need to put the samplers out and collect

them several weeks later, with the possibility of

loss or disturbance of samplers over time.

Step 4. Sample processing

procedures

Several decisions need to be made concerning

sample processing:

n Whether to preserve samples in alcohol in the

field or chill and bring back live

n Whether to pick samples in the lab or do much

of the picking in the field

n How to conduct the sample picking in the lab

n Whether to pick the entire sample or a

subsample

Appendix D lists some options for the above

choices, along with advantages and disadvantages.

If samples are preserved in alcohol or another preservative, there should be adequate ventilation for

sample processing staff even after the samples have

been rinsed and placed in water. The following are

recommended, but other options may be needed

depending on the circumstances of the researchers

or State agencies. Whatever the protocols are, they

must be used consistently.

n Preservation of the sample in the field is

preferable, so the processing of the sample takes

place in controlled conditions in a laboratory.

n Picking the samples should be done in the

laboratory, not under varying field conditions

of climate and lighting.

n Staff who pick the samples should have some

training in aquatic invertebrates, but they need

not be the taxonomists who do the

identifications.

n Picking the sample in the lab should be done

either under a microscope or in a glass tray over

a light box with a magnifying lamp.

n The sample should be picked into partly sorted

categories to expedite identifications.

n If the entire sample is picked, it is more efficient

to collect a sample that has reduced vegetation

and debris.

n Preservative should be rinsed off samples

picked in water; adequate ventilation should be

provided for staff doing the picking and the

identifications.

Subsampling or picking the entire sample?

It is recommended the entire sample be picked if

the sampling method and resources permit this. To

make this more efficient, it is helpful to collect

samples that are not overloaded with vegetation and

debris. When wetlands are sampled with a repeatable, consistent sampling effort, picking the entire

sample improves proportion metrics of total sample

count, taxa richness, and variability in metrics

(Doberstein et al. 2000, Ohio EPA 1988,

Courtemanch 1996) and allows better comparability among sites. However, with stationary samples

such as the Gerking box, stovepipe, or Wilding samplers, or with certain multihabitat dipnetting methods, there may be a lot of vegetation and debris. In

such cases, a subsampling process may be necessary. See Appendix C for some methods to reduce debris in these samples.
16

A sample can be subsampled by using a gridded

screen or a grid underlying a glass tray. Random

squares or rows are selected to ensure that a defined proportion (at least 20%) of the sample grid

is picked. There are sample splitters and other

methods, but these need to be tested and their use

may be difficult with debris-laden samples from

wetlands.

Alternatively, picking the sample until a minimum

number of macroinvertebrates, typically 100, 200

or 300, is found will reduce the picking effort. There

are disagreements on the validity of taking this kind

of subsample. For issues of subsampling see Sovell

and Vondracek (1999), Barbour and Gerritsen

(1996), Courtemanch (1996), Vinson and Hawkins

(1996), Somers et al. (1998), and Doberstein et al.

(2000).

Some investigators facilitate the sample picking

by staining the invertebrates with Rose Bengal stain

(100 mg Rose Bengal in 1,000 mL preservative,

see Lackey and May 1971).

Taxonomic resolution and identifications of

invertebrates

The level of taxonomic identifications, to order,

family, genus, or species, will depend on the attributes or metrics that are used, the degree of certainty needed in the wetland assessments, and the

ease or difficulty of identifying the particular groups

of invertebrates. More information about the condition of a waterbody is obtained when organisms

are identified to the genus or species level. Among

genera within a family there are often differences in

sensitivities to factors causing impairment, likewise

among the species within a particular genus. A very

rapid assessment approach or a citizen monitoring

program might identify to the family level.

n It is recommended to identify the invertebrates

to the lowest possible taxonomic level, at least

to genus and to species where possible, because

of the different sensitivities within some

taxonomic groups.

n It is recommended, where possible, to have the

identifications done by biologists who are on

staff and trained in taxonomy; these staff can

also participate in the development of biological

monitoring tools and help guide the biological

monitoring program.

n It is recommended that reference collections be

maintained with several specimens of each

organism that was identified, and to have these

identifications verified by outside taxonomists if

the taxonomist on staff lacks expertise in

particular groups (e.g., chironomids).

n It is recommended that a database management

staff person be dedicated to the biological

database and participate with the biological staff

in its development of the program.

n It is recommended that the taxonomic names

be coded in the database with the national

Integrated Taxonomic Information System

(ITIS) codes (www.itis.usda.gov); unique codes

will be needed for the taxa that are not yet

coded by ITIS.

n It is recommended that the known functional

feeding groups for each taxon be built into the

database so that functional feeding group

attributes can be tested.

Identifications at least to the genus level are recommended for biological assessments to be used

for resource agency decisions. A number of excellent keys and sources are available (for example,

Merritt and Cummins 1996a; Hilsenhoff 1995,

Thorpe and Covich 1991, Walker 1953, 1958,

1975, Needham and Westfall 1954, Klemm 1982,

Westfall et al. 1996, Clarke 1973, 1981, Mackie

in press, to name a few). Some taxa will be identified to the lowest practical level, depending on available taxonomic sources, staff expertise, and work

effort for identifications. For some groups or life

stages, reliable taxonomic keys may not be avail-
17

able. For other groups, such as the fingernail clams

and leeches, identifications are difficult because of

the time needed to process the shells to view the

hinge teeth, or to relax certain leeches and dissect

the reproductive structures. Some snails can be identified to species, others only to genus level.

Once the choices are made, they should be consistently applied in the assessment protocol by all

staff working on the identification of the samples.

For the total taxa metric, a clear definition of which

taxa to be included must be determined for the standard identification procedures in the lab. The staff

biologists need to keep track of changes in taxonomy and maintain literature sources used for the

identifications. The ITIS codes should be regularly

updated (see Appendix E).

Some agencies contract out the identifications of

certain special groups, such as chironomid larvae

to genus level. If chironomids are identified only to

the four subfamily/tribe levels, information about

condition is lost compared to identifications to the

genus level, where more than 12 to 16 genera may

be found in one wetland (Diggins and Stewart 1998,

Helgen and Gernes 1999, Gernes and Helgen in

press, Beck 1977).

For some of the functional feeding group metrics,

taxonomy is carried to the level needed to describe

the function (Merritt and Cummins 1996a). In this

case, all Odonata would immediately be classed as

predators, or all members of a family might be

classed in one functional feeding group. In other

cases, identifications to lower taxonomic levels are

needed to define the functional feeding groups (see

Mihuc 1997). Therefore it is prudent to identify the

macroinvertebrates to the lowest possible taxonomic

level, genus or species, where possible.

Immature specimens and morphospecies

Some specimens will be too immature to identify

to genus and will have to be left at the higher level

of identification, e.g., family. Others may be damaged and not identifiable. In some cases the experienced taxonomist may be able to extrapolate, with

caution, from other specimens in the collection.

Some specimens will be identified to genus but will

be distinctly different from others in the same genus

in the sample. These specimens must be recorded

so that they are recognized as an additional species

in the sample, i.e., as a “morphospecies” (Oliver

and Beattie 1996). If species are carefully identified as distinct morphologies, this level of identification can produce accurate measures of species

richness, but only when the experienced biologist is

certain the differences are not simply due to differing stages of development.

It is important to define standard protocols for

counting the number of different taxa when the identifications of different taxa are at different levels of

taxonomic resolution. For the counting rules used

by Maine, see Davies and Tsomides (1997).

Contracting out the identifications

of the invertebrates

Contracting the services of taxonomic experts is

an option when staffing levels or limitations in expertise and facilities prevent analyzing the invertebrates “in house,” especially for particular invertebrates that require specialists to identify. Contracting some invertebrate taxonomy can free the staff

to develop the indexes and analyze and report data.

This can be a cost-efficient way to obtain reliable

data, however it entails staff time in forming and

tracking contracts.

It is important to give the contractors clear guidance on the groups of invertebrates to identify, the

taxonomic levels, the protocols, and the attributes

desired, and not to let the contractor make these

decisions. It is important to have in-house staff with

expertise in macroinvertebrates give guidance for

the work of any outside consultants. It is recommended to have all of the macroinvertebrate groups
18

identified to allow calculation of new or improved

metrics that may not have been previously used.

Step 5. Metric analysis

a. Selection of attributes

Selecting invertebrate attributes

As stated in Step 2, attributes are candidate

metrics that are measures of the invertebrate community. They are tested to see if they show a doseresponse to increasing levels of impairment, such

as chemical pollution, unnatural hydrologic or habitat alterations, or siltation. If a response to impairment is seen, the metric will be scored and its score

will contribute to the overall IBI score for a wetland.

An IBI is more robust if it is composed of 8 to 12

metrics selected from different categories of attributes that represent patterns of responses to

changes in the physical, chemical, and biological

integrity of the wetland and its surrounding landscape. The major categories of attributes and their

expected responses to increases in impairment (in

parentheses) are:

n Measures of taxa richness (decrease)

n Measures of tolerance (increase) and

intolerance (decrease)

n Measures of trophic structure and functional

feeding groups (varies)

n Measures of life cycles, such as longevity and

reproduction (decrease)

n Measures of poor condition or poor health of

individuals (increase)

Advantages and disadvantages and expected responses to human disturbance of these and other

categories of attributes are reviewed in Appendix

F. Attributes in use by several States are given in

Appendix G. Some of the categories of attributes

are discussed below, with a rationale for the expected responses.

Taxa richness

Taxa richness is the count of the number of types

of invertebrates that inhabit an ecosystem. The taxa

richness, primarily genera or species, of a wetland

is enumerated by counting up all the types of invertebrates collected from the sampling effort. It is

important to use a standard protocol for counting

invertebrate taxa when the taxonomic level for the

identifications differs among taxa. The number of

taxa commonly declines as human disturbance increases (Barbour et al. 1995, Kerans and Karr

1994, Fore et al. 1996). This attribute shows a

high statistical power for detecting differences among

sites (Sandin and Johnson 2000). However, there

are exceptions, e.g., when low-nutrient wetlands

receive more nutrients (Rader and Richardson

1994), or forested canopy areas are opened up

over wetlands that were shaded and less productive before the forest clearing (King et al. 2000).

Plotting the taxa richness against the measure of disturbance will show the response curve. If there is a

unimodal response, with a peak of taxa richness at

the intermediate level of human disturbance, the metric may not be useful.

In Figure 6, the number of invertebrate taxa decreases as the concentration of chloride increases

in the water in the wetland. The increased chloride

may alter the active pumping systems of invertebrates for maintaining osmotic balance in body fluids. Urban wetlands (Urb) receiving stormwater

runoff are especially high in chloride.

Tolerance

Tolerant taxa inhabit a wide range of habitats and

tolerate a wide range of conditions. The number of

tolerant taxa may not change with impairment, but

the relative abundance of tolerant organisms tends

to increase as the amount of impairment to the site

increases. This might be measured by the proportion of known taxa, or by the proportion repre-
19

sented by the dominant two or three taxa to the

sample count.

Tolerance values assigned to stream invertebrates

are based largely on organic enrichment from data

sets of stream invertebrates (Hilsenhoff 1987a). The

Hilsenhoff Biotic Index for streams (HBI, see

Hilsenhoff 1987a, 1995) is calculated from tolerance values assigned to species of various stream

invertebrates in relation to their sensitivities to organic pollution. New England has a list of familylevel tolerance values derived primarily from EPA’s

listing (EA Mid-Atlantic Regional Operations 1990)

with some regional modifications. Hilsenhoff also

has developed a family-level HBI, the FBI, for a

rapid field assessment or organic pollution

(Hilsenhoff 1988).

Tolerance values assigned to stream invertebrates

may not be applicable to wetlands invertebrates

because many wetlands invertebrates are tolerant

of, or adapted to, the fluctuating oxygen conditions

in wetlands. In addition, there are many invertebrates in wetlands that are “wetland specialists”:

water boatmen; backswimmers; diving beetles and

marsh beetles; fairy shrimp, clam shrimp, and tadpole shrimp; mosquitoes; marsh flies; biting midges;

horse and deer flies; the snails in the families

Physidae, Lymnaeidae, and Planorbidae; and fingernail clams (see Wissinger 1999). For these and

other species that are predominantly wetland specialists, there is little or no existing information on

their tolerances to human caused impairments.

Attributes that relate to tolerances of

macroinvertebrates in wetlands need to be derived

20

30

40

50

60

70

80

90

0 0.5 1 1.5 2 2.5

Log Chloride in Water

Total # Invertebrate Taxa

Ref

Ag

Urb

Figure 6: Total number of invertebrate taxa plotted against

the log of the chloride in the water (mg/L) from large

depressional wetlands in Minnesota.

Total taxa included the number of genera of mayflies, caddisflies, dragonflies, damselflies, chironomids,

beetles, bugs, macrocrustaceans, plus the number of genera or species of snails and leeches and the

presence/absence of fingernail clams and Chaoborus. Chloride range 1–110 mg/L.
20

for the invertebrates of the wetland class and region. Decisions about which taxa are “tolerant” need

to be based on examination of data sets from a range

of high-quality and impaired wetlands. Tolerant taxa

might be present in the range of wetlands, but they

would show a proportionate increase in wetlands

with greater human disturbance. The designations

of tolerance then need to be tested on another data

set for validation. Karr and Chu (1999) suggest

that about 10% of the taxa, or 5% to 15%, be defined as either tolerant or intolerant. One caution

on designations of tolerance values, if done at the

genus level, is the possibility that the species within

a genus may differ in their tolerances (Hilsenhoff

1998). It is preferable, but not always possible, to

designate species rather than genera as tolerant or

intolerant.

Intolerant taxa, by definition, are more likely to

disappear under impaired conditions. Their presence indicates good conditions. Preliminary determinations of intolerant species would require the

examination of the data set to see which taxa tend

to disappear from the more impaired wetlands but

are found in reference sites. Again, designations of

intolerant taxa need to be tested with other data

sets. An example of intolerant taxa, originally derived from data on depressional wetlands and applied to a new data set on large depressions is shown

in Figure 7.

Trophic function and functional feeding

group attributes

Trophic function attributes relate to the type of

food eaten by the invertebrates: herbivores consume algae and plant material, predators consume

animals, omnivores eat both plant and animal material, and detrivores consume decomposed particulate material. The proportion of predators is expected to decline as impairment increases (Kerans

and Karr 1994). Many wetlands are lacking in fish

predators and are dominated by invertebrate predators such as leeches, dragonfly and damselfly larvae, and juvenile and adult aquatic beetles and bugs

0

1

2

3

4

5

6

7

8

-2 -1.5 -1 -0.5 0 0.5

Log Phosphorus in Water

Number of Intolerant Taxa

Ref

Ag

Urb

Figure 7: Intolerant taxa plotted against the log of phosphorus

(mg/L) in the water of large depressional wetlands in Minnesota.

The types of intolerant taxa were derived from a previous project on depressional wetlands. The intolerant taxa

were two genera of dragonflies(Leucorrhinia, Libellula), two of caddisflies (Triaenodes, Oecetis), two chironomid

genera (Tanytarsus, Procladius) and fingernail clams (Sphaeriidae). P range 0.015–1.38 mg/L.
21

(Fairchild et al. 1999, Wissinger 1999). In the fish

IBI, the proportion of individuals that are top carnivores (Simon and Lyons 1995) is expected to show

a decrease in response to human disturbance. More

work is needed in testing of attributes of trophic

function against gradients of human disturbance in

wetlands.

Attributes of functional feeding groups (FFGs)

merit further exploration. Such groups are based

on the mode of food acquisition rather than the type

of food eaten (Merritt and Cummins 1996b, Merritt

et al. 1996, 1999). Merritt’s work in defining functional feeding groups of many macroinvertebrates

has laid a foundation for the analysis of FFG attributes. The FFGs of each taxon, if defined, should

be included in databases to facilitate the testing of

FFG metrics.

In streams, the analysis of functional feeding groups

has been useful for understanding the changes in

stream ecology from losses of riparian vegetation

or conversion from a shaded, leafy stream to an

open system in which algae, rather than leaf litter,

become the primary food source for the invertebrates (Merritt et al. 1999, see also Rawer-Jost et

al. 2000, Hannaford and Resh 1995, Resh 1995).

Cummins and Merritt (2001) and Cummins et al.

(1999) have analyzed FFGs in wetland riparian

areas.

Functional feeding group attributes that decreased

with increasing human disturbance in streams were

the proportion of grazers and the proportion of

predators (Kerans and Karr 1994). An attribute

that tended to increase as human disturbance increased was the relative proportion of filterers

(Kerans and Karr 1994). Much more testing is

needed of functional feeding group attributes in relation to human disturbance in wetlands. See Merritt

et al (1996) for criteria levels for FFG ratios for

evaluating ecosystem parameters in streams.

Some functional feeding group attributes have

shown greater variability than taxa richness measures, partly because some of the FFG attributes

have been expressed as ratios (e.g., scrapers/gatherers) rather than proportions (Resh 1988, Resh

1994, Stan Szczytko, University of Wisconsin,

Stevens Point, WI, personal communication). Ratios are susceptible to variability if both of the variables are changing. Other FFG attributes are expressed as proportions and these may be more robust when tested in wetland systems. Taxonomic

levels needed for FFGs vary widely. Some groups,

e.g., Odonata, can be entirely classed as predators, whereas others (see Merritt and Cummins

1996a) will require identifications to genus or species level. It is recommended that identifications

be done routinely to the lowest taxonomic level.

Other invertebrate attributes to consider

Other attributes can be considered; some are in

Appendix F. Some can be found in the literature

(Barbour et al. 1996, Resh et al. 1995, Kerans and

Karr 1994). A few that might apply to wetlands in

the future are discussed below.

Condition or health of individual

invertebrates

In the fish IBI, there is a metric that records the

number of deformities, erosion, lesions, and tumors

in individual fish in the sample (Sanders et al. 1999).

This metric is most responsive in highly contaminated areas, as was found in Ohio. To date, malformations have been recorded in some aquatic invertebrates, i.e., chironomids and dragonflies. In

chironomids, malformations have been shown to be

more numerous in response to contaminants (Palmer

1997, Hudson and Ciborowski 1996a,b; Warwick

1980 1990). Minnesota has data on malformations in chironomids from 43 wetlands (done by

Leonard C. Ferrington), but has not yet developed

a metric. Malformations were found in the cast

exoskeletons (exuviae) of dragonflies in Minnesota,

first in a report from rivers and bog/fen areas

(Steffens and Smith 1999) and then from some large
22

depressional wetlands (Smith 2000). There is little

monitoring data on malformations in most invertebrates. Therefore, it is unknown whether malformations are increasing in invertebrates the way they

appear to have increased in frogs in the 1990s in

northern North America (Helgen et al. 2000, Hoppe

2000, Ouellet et al. 1997, Northeast Natural Resource Center 2000).

A possible attribute to indicate the health of invertebrates is the proportion of coverage of aquatic

insects by bacteria as an indicator of nutrient enrichment in wetlands, based on recent work by

Lemly and King (2000).

Introduced or exotic species

There are numerous exotic or introduced species

in freshwaters of the United States (see Cox 1999,

Mack et al. 2000). Some of these invade wetlands. As an attribute, the number of exotic taxa or

the proportion would be expected to increase as

human disturbances increase. Introduced fish will

alter the invertebrate composition (Hanson and

Riggs 1995). In Minnesota, the huge oriental mystery snail, Cipangopaludina chinensis, has been

found in urban wetlands, probably after being discarded from aquaria. It consumes submersed

aquatic plants and creates open areas in the vegetation. The rusty crayfish, Orconectes rusticus,

has been expanding its range and introduced into

lakes and wetlands (Helgen 1990), where it consumes the macrophytes.

Generalists and specialists

Attributes that address the proportion or richness

of generalists and specialists could be explored (see

Wissinger 1999, Mihuc 1997). A community with

a high proportion of wetland specialists is thought

to reflect more competition and evolution of specialists, whereas one with a high proportion of generalists may indicate less competition and more use

of the same resources. The number of specialist

taxa, or the proportion of specialist individuals, might

be expected to decrease as the human disturbance

increases in wetlands. In the Floristic Quality Assessment method (Fennessy 1998) for using plant

communities to assess wetlands, plant taxa that have

a higher fidelity for particular types of habitats, i.e.,

a higher coefficient of conservatism, will give the

Floristic Quality Assessment Index a higher score

(see Module 10, Using Vegetation to Assess Environmental Conditions in Wetlands).

b. Forming the IBI

To form the invertebrate IBI, the attributes are

plotted as a scatter plot against stressors or human

disturbance gradients (e.g., Figure 1, see Fore et

al. 1996, Karr and Chu 1999). Eight to 12 attributes that show a response are selected and

scored. Another method of visualizing metrics is to

plot the attribute data using box-and-whisker plots

to show the spread of values of an attribute in reference sites compared with the range in the impaired

sites (Barbour et al. 1995, Barbour et al. 1992).

Attributes that show very little overlap between reference sites and impaired sites are chosen as metrics

and scored.

There are various ways to score the metrics. The

examples given below are for assigning scores with

a 1, 3, 5 system for metric data that are showing a

linear or regular response to disturbance. Other

scoring ranges can be used. For metrics whose

data indicate a nonlinear response to the gradient

of human disturbance, other methods, such as assigning scores on either side of inflection points,

need to be used (Karr and Chu 1999). It is important that the study sites used to assign scores to

metrics include the full range of impairment and the

fullest range of biological response values for the

metrics.

n The range of the data values for the metric can

divided evenly into three parts from the lowest

data value to the highest data value. The low

sector is assigned a score of one, the middle is

given a three, the top third is given a five. This
23

method assumes the data ranges include a range

of values from the most impaired to the least impaired wetlands, and the data values respond

evenly to the human disturbances (Karr and Chu

1999, Helgen and Gernes in press, Gernes and

Helgen 1999).

n The data for a metric are plotted in rank order

for the sites. Then the sites are divided into thirds

and the associated data values are assigned scores

of 1, 3, or 5 for the low, middle, and top third of

ranked sites.

n The metric values for the reference sites are arranged in quartiles. The values for the top three

quartiles (top 75th percentile) are assigned a

score of 5. The range of values from the upper

range of the lowest quartile to the lowest data

value is divided in two and assigned a 3 or 1

(Barbour et al. 1996). This method assumes there

is an adequate number of reference sites including some with the least amount of human disturbance or no disturbance. See Module 6: Developing Metrics and Indexes of Biological Integrity. Once the metrics are scored, a matrix of

scores is made in a table for all the sites such as

the table in Appendix J. The scores are added

to the total IBI score and the IBI scores are divided to indicate three or more categories of

condition. Appendix I shows the scoring criteria

for the 10 metrics used by Minnesota for large

depressions. It indicates how many wetlands that

were designated as reference, agricultural or

stormwater-influenced received the scores of 5,

3, or 1.

Conclusions and

Recommendations

1

 Biological assessment of wetlands by

invertebrate metrics should be increased

because the invertebrate metrics are sensitive to a broad range of impairments to the physical, chemical, and biological integrity of wetlands.

This will require:

n More work on the development of

invertebrate IBIs for different classes of

wetlands

n More information on the tolerances and

sensitivities of invertebrates in wetlands

n Consistent funding dedicated to staff to work

on invertebrate biological assessments

2

 Biological assessment of wetlands using

macroinvertebrates will provide scientifically

sound data for States, Tribes, and organizations to gauge the degree of impairment to wetlands from a mix of stressors. This will enable

them to:

n Develop invertebrate biocriteria for determining

aquatic life use support

n Report on condition of wetlands in 305(b)

reports and in 303(d) (TMDL) lists and facilitate

prioritization

n Understand the condition of watersheds using

carefully designed studies

n Compare the results from biological condition

assessments with assessments from other

“rapid” physical assessment methods to validate

the latter

n Provide a solid foundation for citizen

biomonitoring programs using protocols derived

from those used by States, Tribes, and

organizations

n Provide a sound method to determine the

effectiveness and suitability of permitting

decisions

n Provide a sound method to assess the results

of restorations and mitigations in wetland

replacements
24

Appendix J shows an example of the metric scores

for a set of depressional wetlands in Minnesota.

The sites are sorted by the IBI scores, and tentative lines were drawn to indicate which of the sites

might be considered to be in excellent, moderate,

or poor condition. This was done by trisecting the

range of IBI scores (10 to 50 total points for 10

metrics). Although most of the sites that were considered to be reference, or least impaired, wetlands

had IBI scores in the excellent range, a few candidate reference sites scored as moderate. It is expected that some sites chosen to be reference sites

may not be in the highest condition, but will themselves have a range of conditions. Other approaches

may be used for determining the criteria lines.
25

References

Barbour MT, Plafkin JL, Bradley BP, Graves CG,

Wisseman RW. 1992. Evaluation of EPA’s rapid

bioassessment benthic metrics: metric redundancy and

variability among reference stream sites. Environ

Toxicol Chem 11:437-449.

Batt BDJ, Afton AD, Anderson MG, Ankney CD,

Johnson DH, Kadlec JA, Krapu GL, eds. 1992.

Ecology and management of breeding waterfowl.

University of Minnesota Press, Minneapolis, MN.

Batzer DP, Rader RB, Wissinger SA (eds). 1999.

Invertebrates in freshwater wetlands of North America

ecology and management. New York: John Wiley.

Batzer DP, Shurtleff AS, Rader RR. In press. Ch. 15,

Sampling invertebrates in wetlands. In: Rader, RB,

Batzer DP, Wissinger SA (eds). Bioassessment and

management of North American freshwater wetlands.

New York: John Wiley.

Beck WM Jr. 1977. Environmental requirements and

pollution tolerance of common freshwater

chironomidae. April 1977. U.S. EPA Office of Research and Development. Cincinnati, OH. 261 pp.

EPA-600/4-77-024.

Brinkman MA, Duffy WG. 1996. Evaluation of four

wetland aquatic invertebrate samplers and four sample

sorting methods. J Freshw Ecol 11:193-200.

Buckton ST, Ormerod SJ. 1997. Effects of liming on the

Coleoptera, Hemiptera, Araneae and Opiliones of

catchment wetlands in Wales. Biol Conserv 79:43-57.

Burton TM, Uzarski DG, Gathman JP, Genet JA, Keas

BE, Stricker CA. 1999. Development of a preliminary

invertebrate index of biotic integrity for Lake Huron

coastal wetlands. Wetlands 19(4):869-882.

Carlisle BK, Hicks AL, Smith JP, Garcia SR, Largay BG.

1999. Plants and aquatic invertebrates as indicators of

wetland biological integrity in Waquoit Bay Watershed, Cape Cod. Environment Cape Cod 2(2):30-60.

Cairns J Jr, Niederlehner BR. 1995. Ecological Toxicity

Testing. CRC Lewis Publishing.

Cheal F, Davis JA, Growns JE, Bradley JS, Whittles FH.

1993. The influence of sampling method on the

classification of wetlands macroinvertebrate communities. Hydrobiologia 257:47-56.

Adamus PR. 1996. Bioindicators for Assessing

Ecological Integrity of Prairie Wetlands. U.S. EPA

Office of Research and Development, Corvallis OR.

July 1996. EPA/600/R-96-082.

Aiken RB. 1991. Characterization and phenology of a

predaceous diving beetle community in a central

Alberta lake. Can Entomol 123:305-313.

Anderson RO. 1959. A modified flotation technique for

sorting bottom fauna samples. Limnol Oceanogr

4:223-224.

Anderson JT, Smith LM. 1996. A comparison of

methods for sampling epiphytic and nektonic aquatic

invertebrates in playa wetlands. J Freshwater Ecol

11(2):219-224.

Allan JD. 1984. Hypothesis testing. In: Resh VH,

Rosenberg DM (eds). The Ecology of Aquatic

Insects. New York: Praeger, pp. 484-507.

Apfelbeck RS. 1999. Development of Biocriteria for

Wetlands in Montana. Montana Department of

Environmental Quality, Helena, MT.

Barbour MT, Gerritsen J. 1996. Subsampling of benthic

samples: a defense of the fixed-count method. J North

Am Benthol Soc 15(3):386-391.

Barbour MT, Gerritsen J, Snyder BD, Stribling JB. 1999.

Rapid Bioassessment Protocols for Use in Streams and

Wadeable Rivers: Periphyton, Benthic

Macroinvertebrates and Fish. 2nd Ed. U.S. Environmental Protection Agency, Office of Water, Washington DC. EPA 841-B-99-002.

Barbour MT, Gerritsen J, Griffith GE, Frydenborg R,

McCarron E, White JS, Bastian ML. 1996. A framework for biological criteria for Florida streams using

benthic macroinvertebrates. J North Am Benthol Soc

15(2):185-211.

Barbour MT, Stribling JB, Karr JR. 1995. Multimetric

approach for establishing biocriteria and measuring

biological condition. In: Davis WS, Simon TP (eds).

Biological Assessment and Criteria. Boca Raton, FL:

CRC Press, pp. 63-77.
26

Clarke AH. 1981. The Freshwater Molluscs of Canada.

National Museum of Natural Sciences. National

Museums of Canada. Ottawa, Canada. 446 pp.

Clarke AH. 1973. The freshwater molluscs of the

Canadian Interior Basin. Malacologia Vol. 13, No. 1-2

(includes snails and fingernail clams and distribution

maps).

Colburn EA. 1997. Certified. A Citizen’s Step-by-Step

Guide to Protecting Vernal Pools. 7th edition. Lincoln,

MA: Massachusetts Audubon Society

Corbet, PS. 1999. Dragonflies: Behavior and Ecology of

Odonata. Comstock Publishing Associates. Ithaca,

NY: Cornell University Press.

Courtemanch DL. 1996. Commentary on the

subsampling procedures used for rapid

bioassessments. J North Am Benthol Soc 15(3):381-

385.

Cox GW. 1999. Alien Species in North America and

Hawaii: Impacts on Natural Ecosystems. Washington,

DC: Island Press.

Cummins KW, Merritt RW. In press. Application of

invertebrate functional feeding and habitat groups to

wetland ecosystem function and biomonitoring. In:

Rader RB, Batzer DP, Wissinger S (eds).

Biomonitoring and Management of North American

Freshwater Wetlands. New York: John Wiley.

Cummins KW, Wilzbach M, Merritt RW, Brock J. 1999.

Evaluation of the resotration of the Kissimmee River in

south Florida. In: Hamilton SW, White DS, Chester

EW, Finley MT, eds. The natural history of the lower

Tennessee and Cumberland River Valleys. 8th

Symposium Center for Field Biology. Clarksville, TN:

Austin Peay State University Press, pp. 1-20.

Davies SP, Tsomides L. 1997. Methods for biological

sampling and analysis of Maine’s inland waters.

Maine Department of Environmental Protection, DEPLW107-A97.

Davies SP, Tsomides L, Courtemanch DL, Drummond F.

1995. Maine biological monitoring and biocriteria

development program. DEP-LW 108. Maine Department of Environmental Protection. Augusta, ME.

61 pp.

Davies SP, Tsomides L, DiFranco JL, Courtemanch DL.

1999. Biomonitoring retrospective: fifteen year

summary for Maine Rivers and Streams. Maine

Department of Environmental Protection, December

1999. DEPLWq999-26. Augusta, ME. 190 pp. Web

site: http://janus.state.me.us/dep/blwq/docmonitoring/

biological/biorep2000.htm.

deFur PL, Crane M, Ingersoll CG, Tattersfield L. 1999.

Endocrine disruption in invertebrates: endocrinology,

testing, and assessment. Pensacola, FL: SETAC Press.

Deutschman MR. 1984. Secondary production, habitat

use and prey preference of the tiger salamander

(Ambystoma tigrinum) in south-central North Dakota.

Master’s Thesis, North Dakota State University,

Fargo, ND.

Diggins TP, Stewart KM. 1998. Chironomid deformities,

benthic community composition and trace elements in

the Buffalo River (New York) Area of Concern. J North

Am Benthol Soc 17(3):311-323.

Doberstein CP, Karr JR, Conquest LL. 2000. The effect

of fixed-count subsampling on macroinvertebrate

biomonitoring in small streams. Freshw Biol 44:355-

371.

Driver EA. 1977. Chironomid communities in small

prairie ponds: some characteristics and controls.

Freshw Biol 7:121-133.

Dvork J. 1987. Production-ecological relationships

between aquatic vascular plants and invertebrates in

shallow waters and wetlands—a review. Arch

Hydrobiol Beih Ergebn Limnol 27:181-194.

EA Mid-Atlantic Regional Operations. 1990. Freshwater Macroinvertebrate Species List Including Tolerance Values and Functional Feeding Group Designations for Use in Rapid Bioassessment Protocols.

Report No. 11075.05. U.S. Environmental Protection

Agency. Assessment and Watershed Protection

Division, Washington, DC.

Eckblad JW. 1991. How many samples should be

taken? BioScience 41(5):346-348.

Eldridge J. 1990. Aquatic invertebrates important for

waterfowl production. U.S. Fish and Wildlife Leaflet

13.3.3:1-7. Waterfowl Management Handbook. Fish

and Wildlife Service. Washington DC.
27

Elmberg J, Nummi P, Poysa H, Sjoberg K. 1992. Do

intruding predators and trap position affect the

reliability of catches in activity traps? Hydrobiologia

239:187-194.

Euliss NH, Mushet DM. 1999. Influence of agriculture

on aquatic invertebrate communities of temporary

wetlands in the prairie pothole region of North Dakota,

USA. Wetlands 19(3):578-583.

Euliss NH, Wrubleski DA, Mushet DM. 1999. Wetlands

of the prairie pothole region, invertebrate species

composition, ecology and management. In: Batzer DP,

Rader RB, Wissinger SA (eds). Invertebrates in

Freshwater Wetlands of North America. New York:

John Wiley, pp. 471-514.

Eyre MD, Foster GN, Young AG. 1993. Relationships

between water-beetle distributions and climatic

variables: a possible index for monitoring global

climate change. Arch Hydrobiol 127 (4):437-450.

Fairchild GW, Faulds AM, Saunders LL. 1999. Constructed marshes in Southeast Pennsylvania. Invertebrate foodweb structure. In: Batzer DP, Rader RB,

Wissinger SA (eds). Invertebrates in Freshwater

Wetlands of North America. New York: John Wiley,

pp. 423 446.

Fairchild GW, Faulds AM, Matta JF. 2000. Beetle

assemblages in ponds: effects of habitat and site age.

Freshw Biol 44(3) 523-534.

Fennessy S. 1998. Testing the Floristic Quality

Assessment Index as an Indicator of Riparian Wetland

Disturbance. Ohio EPA Wetlands Unit. Final Report

to U.S. Environmental Protection Agency. June 1998.

Fennessy S, Gray MA, Lopez RD. 1998. An Ecological

Assessment of Wetlands Using Reference Sites

Volume 1: Final Report. Ohio EPA Final Report to U.S.

Environmental Protection Agency. State of Ohio EPA.

Columbus, OH.

Ferrington LC. 1994. Characterization of Chironomidae

in five wetlands. Report prepared for the Metropolitan

Mosquito Control District, St. Paul, MN. 26 pp. +

appendix.

Florida Department of Environmental Protection. April

1994. The Biological Success of Created Marshes in

Central Florida. Biology Section. Division of Technical Services. Florida DEP.

Florida Department of Environmental Protection. May

1996. A Biological Study of Mitigation Efforts at

Selected [stream] Sites in North Florida. Biology

Section. Division of Administrative and Technical

Services. Florida DEP.

Florida DEP SOP Draft 11/21/00. FS 7000 General

Biological Community Sampling. Contact Russ

Frydenborg, FL DEP

Russel.Frydenborg@dep.state.fl.us or

www8.myflorida.com/environment/learn/science/

laboratories/reports/index.html.

Fore LS, Karr JR, Wisseman RW. 1996. Assessing

invertebrate responses to human activities: evaluating

alternative approaches. J North Am Benthol Soc

15:212-231.

Fore LS, Karr JR, Conquest LL. 1994. Statistical

properties of an index of biological integrity used to

evaluate water resources. Can J Fish Aquat Sci

51:1077-1087.

Fredrickson LH, Reid FA. 1988. Nutritional values of

waterfowl foods. U.S. Fish and Wildlife Leaflet

13.1.1:1-6. Waterfowl Management Handbook. Fish

and Wildlife Service. Washington DC.

Gates TE, Baird DJ, Wrona FJ, Davies RW. 1987. A

device for sampling macroinvertebrates in weedy

ponds. J North Am Benthol Soc 6(2):133-139.

Gathman JP, Burton TM, Armitage BJ. Coastal wetlands

of the Upper Great Lakes: distribution of invertebrate

communities in response to environmental variation.

In: Batzer DP, Rader RB, Wissinger SA (eds). Invertebrates in Freshwater Wetlands of North America. New

York: John Wiley, 949-994.

Gerking SD. 1957. A method for sampling the littoral

macrofauna and its application. Ecology 38:219-226.

Gernes MC, Helgen JC. 1999. Indexes of Biotic Integrity

(IBI) for Wetlands: Vegetation and Invertebrate IBIs.

Minnesota Pollution Control Agency, Environmental

Outcomes Division. Final Report to U.S. Environmental Protection Agency. Assistance #CD995525-01.

April 1999.

Green RH. 1989. Power analysis and practical strategies

for environmental monitoring. Environ Res 50:195-205.
28

Hannaford MJ, Resh VH. 1995. Variability in

macroinvertebrate rapid-bioassessment surveys and

habitat assessments in a northern California stream. J

North Am Benthol Soc 14(3):430-439.

Hanson BA, Swanson GA. 1989. Coleoptera species

inhabiting prairie wetlands of the Cottonwood Lake

area, Stutsman County, North Dakota. Prairie Naturalist 21(1):49-57.

Hanson MA, Riggs MR. 1995. Potential effects of fish

predation on wetland invertebrates: a comparison of

wetlands with and without fathead minnows. Wetlands 15(2):167-175.

Hanson MA, Roy CC, Euliss NH, Zimmer KD, Riggs

MR, Butler MG. 2000. A surface-associated activity

trap for capturing water-surface and aquatic invertebrates in wetlands. Wetlands 29(1):205-212.

Hart CW, Jr, Fuller SLH. 1974. Pollution Ecology of

Freshwater Invertebrates. New York: Academic Press.

Hawkins CP, Carlisle DM. In press. Use of predictive

models for assessing the biological integrity of

wetlands and other aquatic habitats. In: Rader RB,

Batzer DP, Wissinger SA (eds). Bioassessment and

Management of North American Freshwater Wetlands.

New York: John Wiley.

Helgen JC. 1990. The Distribution of Crayfishes in

Minnesota. Report No. 405. Section of Fisheries

Investigational Report. Minnesota Department of

Natural Resources, St. Paul, MN.

Helgen JC, Gernes MC. In press. Monitoring the

condition of wetlands: indexes of biotic integrity using

invertebrates and vegetation. In: Rader RB , Batzer

DP, Wissinger SA (eds). Bioassessment and Management of North American Freshwater Wetlands. New

York: John Wiley.

Helgen JC, Gernes MC. 1999. Wetland Evaluation

Project: developing methods of biological assessment

for citizen evaluations of wetlands. Minnesota

Pollution Control Agency, Environmental Outcomes

Division. Final Report to U.S. Environmental Protection Agency. Assistance #CD985060-01.

Helgen JC, Gernes MC, Kersten SM, Chirhart JW,

Canfield JT, Bowers D, Haferman J, McKinnell RG,

Hoppe DM. 2000. Field investigations in malformed

frogs in Minnesota 1993 1997. J Iowa Acad Sci

107(3):96-112.

Helgen JC, Thompson K, Gathman JP, Gernes MC,

Ferrington LC, Wright C. 1993. Developing an index of

biological integrity for 33 depressional wetlands in

Minnesota. Minnesota Pollution Control Agency

report.

Heliovaara K, Vaisanen R. 1993. Insects and Pollution.

Boca Raton, FL: CRC Press.

Hellawell JM. 1986. Biological Indicators of Freshwater

Pollution and Environmental Management. New York:

Elsevier Applied Science. 546 pp.

Henrikson L, Oscarson H. 1978. A quantitative sampler

for air-breathing aquatic insects. Freshw Biol 8:73-77.

Hershey AE, Lima AR, Niemi GJ, Regal RR. 1998.

Effects of Bacillus thuringiensis israelensis (Bti) and

methoprene on nontarget macroinvertebrates in

Minnesota wetlands. Ecol Appl 8(1):41-60.

Hilsenhoff WL. 1995. Aquatic Insects of Wisconsin.

Publication Number 3 of the Natural Museums

Council. Madison: University of Wisconsin. 79 pp.

Hilsenhoff WL. 1991. Comparison of bottle traps with

D-frame net for collecting adults and larvae of

Dytiscidae and Hydrophilidae (Coleoptera). Coleopterists Bull 45(2):143-146.

Hilsenhoff WL. 1987a. An improved biotic index of

organic stream pollution. Great Lakes Entomol 20:31-

39.

Hilsenhoff WL. 1987b. Effectiveness of bottle traps for

collecting Dytiscidae (Coleoptera). Coleopterists Bull

41(4):377-380.

Hilsenhoff WL. 1998. A modification of the biotic index

of organic stream pollution to remedy problems and

permit its use throughout the year. Great Lakes

Entomol 31(1):1-12.

Hilsenhoff WL. 1988. Rapid field assessment of organic

pollution with a family-level biotic index. J North Am

Benthol Soc 7(1):65-68.

Hoppe DM. 2000. History of Minnesota frog abnormalities: do recent findings represent a new phenomenon? J Iowa Acad Sci 107(3):86-89.

Hudson LA, Ciborowski JJH. 1996a. Spatial and

taxonomic variation in incidence of mouthpart

deformities in midge larvae (Diptera: Chironomidae:

Chironomini). Can J Fish Aquat Sci 53:297-304.
29

Hudson LA, Ciborowski JJH. 1996b. Teratogenic and

genotoxic responses of larval Chironomus to contaminated sediment. Environ Toxicol Chem 15:1375-1381.

Huryn AD, Gibbs KE. 1999. Riparian sedge meadows in

Maine a macroinvertebrate community structured by

river-floodplain interaction. In: Batzer DP, Rader RB,

Wissinger SA (eds). Invertebrates in Freshwater

Wetlands of North America. New York: John Wiley,

pp. 363-382.

Hyvonen T, Nummi P. 2000. Activity traps and the

corer: complementary methods for sampling aquatic

invertebrates. Hydrobiologia 432:121-125.

Kaminski RM. 1981. Evaluation of two devices for

sampling nektonic invertebrates. J Wildl Manage

45(2):493-496.

Karr JR, Chu EW. 1999. Restoring Life in Running

Waters. Better Biological Monitoring. Washington,

DC: Island Press.

Karr JR, Dudley DR. 1981. Ecological perspective on

water quality goals. Environ Manage 5:55-68.

Kerans BL, Karr JR. 1994. A benthic index of biotic

integrity (B-IBI) for rivers of the Tennessee Valley.

Ecol Appl 4:768-785.

King RS, Wrubleski DA. 1998. Spatial and diel availability of flying insects as potential duckling food in

prairie wetlands. Wetlands 18:100-114.

King RS, Brazner JC. 1999. Coastal wetland insect

communities along a trophic gradient in Green Bay,

Lake Michigan. Wetlands 19:426-437.

King RS, Nunnery KT, Richardson CJ. 2000.

Macroinvertebrate assemblage response to highway

crossings in forested wetlands: implications for

biological assessment. Wetlands Ecol Manage 8:81-

94.

King RS, Richardson CJ. In press. Macroinvertebrate

and fish responses to experimental P additions in

Everglades sloughs. In: Richardson CJ (ed). The

Everglades Experiments: Lessons for Ecosystem

Restoration. New York: Springer-Verlag.

King RS, Richardson CJ. Submitted. Detecting

changepoints in biological attributes: an approach for

developing numerical water-quality criteria. Duke

University, Durham, NC.

Klemm DJ. 1982. Leeches (Annelida: Hirudinea) of

North America. EPA 600/3-82-025. Environmental

Monitoring and Support Laboratory, Office of

Research and Development, U.S. Environmental

Protection Agency, Cincinnati, OH.

Krapu GL, Reinecke KJ. 1992. Foraging ecology and

nutrition. In: Batt BDJ, Afton AD, Anderson MG,

Ankney CD, Johnson DH, Kadles JA, Krapu GL (eds).

Ecology and Management of Breeding Waterfowl.

Minneapolis: University of Minnesota Press, pp.1-29.

Lackey RT, May BE. 1971. Use of sugar flotation and

dye to sort benthic samples. Trans Am Fish Soc 4:794-

797.

Larson DJ. 1987. Aquatic Coleoptera of peatlands and

marshes in Canada. In: Rosenberg DM, Danks HV

(eds). Aquatic Insects of Peatlands and Marshes in

Canada. Memoirs of the Entomological Society of

Canada No. 140:99-132. Ottawa, Canada.

Lemly AD, King RS. 2000. An insect-bacteria

bioindicator for assessing detrimental nutrient

enrichment in wetlands. Wetlands 20:91-100.

Leslie AJ, Prenger JP, Crisman TL. 1999. Cypress

domes in North Florida: invertebrate ecology and

response to human disturbance. In: Batzer DP, Rader

RB, Wissinger SA (eds). Invertebrates in Freshwater

Wetlands of North America. New York: John Wiley,

pp. 105-120.

Lewis MA, Mayer FL, Powell RL, Nelson MK, Klaine SJ,

Henry MG, Dickson GW (eds). 1999. Ecotoxicology

and Risk Assessment for Wetlands. Proceedings from

the SETAC Pellston Workshop on Ecotoxicology and

Risk Assessment for Wetlands (30 July-3 August

1995, Anaconda, MT). Pensacola, FL: SETAC Press.

Liber K, Schmude KL, Rau DM. 1998. Toxicity of

Bacillus thuriengensis var. israelensis to chironomids

in pond mesocosms. Ecotoxicology 7:343-354.

Mack RN, Simberloff D, Lonsdale WM, Evans H, Clout

M, Bazzaz FA. 2000. Biotic invasions: causes,

epidemiology, global consequences and control.

Issues in Ecology Technical Report. Ecol Appl

10(3):689-710.

Mackey AP, Cooling DA, Berrie AD. 1984. An evaluation of sampling strategies for qualitative surveys of

macroinvertebrates in rivers, using pond nets. J Appl

Ecol 21:515-534.
30

Mackie GL. In press. Biology of the Freshwater

Corbiculacea of North America.

Magurran AE. 1988. Ecological Diversity and Its

Measurement. Princeton, NJ: Princeton University

Press.

Marchant R, Hirst A, Norris RH, Butcher R, Metzeling L,

Tiller D. 1997. Classification and prediction of

macroinvertebrate assemblages from running waters in

Victoria, Australia. J North Am Benthol Soc 16(3):664-

681.

Marshall SA, Finnamore AT, Blades DCA. 1999.

Canadian peatlands: diversity and habitat specialization of the arthropod fauna. In: Batzer DP, Rader RB,

Wissinger SA (eds). Invertebrates in Freshwater

Wetlands of North America. New York: John Wiley,

pp. 383-400.

Merritt RW, Cummins KW. 1996a. An Introduction to

the Aquatic Insects of North America, 3rd ed.

Dubuque, IA: Kendall/Hunt Publishing. 862 pp.

Merritt RW, Cummins KW. 1996b. Trophic relations of

macroinvertebrates. In: Hauer FR, Lamberti GA (eds).

Methods in Stream Ecology. Boston: Academic Press,

pp. 453-474.

Merritt RW, Wallace JR, Higgins MJ, Alexander MK,

Berg MB, Morgan WT, Cummins KW, VandenEeden

B. 1996. Procedures for the functional analysis of

invertebrate communities of the Kissimmee Riverfloodplain ecosystem. Florida Sci 59:216-274.

Merritt RW, Higgins MJ, Cummins KW, VandenEeden B.

1999. The Kissimmee River-riparian marsh ecosystem,

Florida: seasonal differences in invertebrate functional

feeding group relationships. In: Batzer DP, Rader RB,

Wissinger SA (eds). Invertebrates in Freshwater

Wetlands of North America. New York: John Wiley,

pp. 55-80.

Mihuc TB. 1997. The functional trophic role of lotic

primary consumers: generalist versus specialist

strategies. Freshw Biol 37:455-462.

Mitsch WJ, Gosselink JG. 1993. Wetlands, 2nd ed.

New York: Van Nostrand Reinhold.

Morris R, Taylor EW, Brown DJA, Brown JA. 1989.

Acid Toxicity and Aquatic Animals. New York:

Cambridge University Press.

Murkin HR, Abbott PG, Kadlec JA. 1983. A comparison

of activity traps and sweep nets for sampling nektonic

invertebrates in wetlands. Freshw Invert Biol 2(2):99-

106.

Needham J, Minter G, Westfall J. 1954. The Dragonflies

of North America. Berkeley: University of California

Press.

Norris RH. 1995. Biological monitoring: the dilemma of

data analysis. J North Am Benthol Soc 14(3):440-450.

North Carolina Divison of Water Quality (NCDWQ).

1997. Standard operating procedures, biological

monitoring. North Carolina Department of Environment and Natural Resouces, Raleigh, NC, USA.

Northeast Natural Resource Center. 2000. Frogs in a Fix

II: Recommendations to Address Frog Abnormalities

in Vermont. Montpelier, VT.

Oliver I, Beattie AJ. 1996. Invertebrate morphospecies

as surrogates for species: a case study. Conserv Biol

10:99-109.

Olson EJ, Engstrom E, Doeringsfeld M, Bellig B. 1995.

Abundance and distribution of macroinvertebrates in

relation to macrophyte communities in a prairie marsh,

Swan Lake, Minnesota. J Freshw Ecol 10(4):325-335.

Orr BK, Resh VH. 1989. Experimental test of the

influence of aquatic macrophyte cover on the survival

of Anopheles larvae. J Am Mosquito Control Assoc

5(4):579-585.

Ouellet M, Bonin J, Rodrigue J, DesGranges J-L, Lair S.

1997. Hindlimb deformities (ectromelia, ectrodactyly)

in free-living anurans from agricultural habitats. J

Wildl Dis 33(1):95-104.

Ohio EPA. 1988. Biological Criteria for the Protection of

Aquatic Life, Vol 13. Ecological Assessment Section.

Division of Water Quality Monitoring and Assessment. Ohio Environmental Protection Agency,

Columbus, OH.

Palmer AT. 1977. Mandible and mentum deformities of

Chironomus tentans (Diptera, Chironomidae) exposed

to cadmium. MS Thesis. University of Wisconsin-La

Crosse, WI.

Picket STA, White PS. 1985. The Ecology of Natural

Disturbance and Patch Dynamics. Academic Press.

Orlando, FL.
31

Rader RB, Batzer DP, Wissinger S (eds). In press.

Biomonitoring and Management of North American

Freshwater Wetlands. New York: John Wiley.

Rader RB, Richardson CJ. 1994. Response of

macroinvertebrates and small fish to nutrient enrichment in the Everglades. Wetlands 14:134-146.

Rainbow PS. 1996. Heavy metals in aquatic invertebrates. In: Beyer WN, Heinz GH, Redmon-Norwood

AW (eds). Environmental Contaminants in Wildlife:

Interpreting Tissue Concentrations. Boca Raton, FL:

Lewis Publishers.

Rankin ET, Yoder CO. 1990. The nature of sampling

variability in the Index of Biotic Integrity in Ohio

streams. In: Davis WS (ed). Proceedings of the 1990

Midwest Pollution Control Biologists Meeting. U.S.

Environmental Protection Agency Region 5. Chicago,

IL. pp. 9-19. EPA 905-9-90-005.

Rathbun SL, Gerritsen J. In press. Statistical issues for

sampling wetlands. In: Rader RR, Batzer DP,

Wissinger SA (eds). Bioassessment and Management

of North American Freshwater Wetlands. New York:

John Wiley.

Rawer-Jost C, Bohmer J, Blank J, Rahmann H. 2000.

Macroinvertebrate functional feeding group methods

in ecological assessment. Hydrobiologia 422/433:225-

232.

Resh VH. 1988. Variability, accuracy, and taxonomic

costs of rapid assessment approaches in benthic

biomonitoring. In: The Role of Benthos in Impact

Assessment. North American Benthological Society

Technical Information Workshop, organized by JA

Gore and TW La Point. Tuscaloosa, AL.

Resh VH. 1995. Freshwater benthic macroinvertebrates

and rapid assessment procedures for water quality

monitoring in developing and newly industrialized

countries. In: Davis WS, Simon TP (eds). Biological

Assessment and Criteria. Boca Raton, FL: Lewis

Publishers.

Resh VH. 1994. Variability, accuracy and taxonomic

costs of rapid assessment approaches in benthic

macroinvertebrate monitoring. Boll Zool 61:375-383.

Resh VH, Rosenberg DM (eds). 1984. The Ecology of

Aquatic Insects. New York: Praeger.

Resh VH, R.H. Norris RH, Barbour MT. 1995. Design

and implementation of rapid assessment approaches

for water resource monitoring using benthic

macroinvertebrates. Austral J Ecol 20:108-121.

Reynoldson TB, Norris RH, Resh VH, Day KE,

Rosenberg DM. 1997. The reference condition: a

comparison of multimetric and multivariate approaches

to assess water-quality impairment using benthic

macroinvertebrates. J North Am Benthol Soc

16(4):833-852.

Ritchie SA, Addison DS. 1991. Collection and separation of Aedes taeniorhynchus eggshells from mangrove soil. J Am Mosquito Control Assoc 7(1):113-

115.

Rosenberg DM, Danks HV (eds). 1987. Aquatic Insects

of Peatlands and Marshes in Canada. Memoirs of

Entomological Society of Canada. No. 140. Ottawa,

Canada.

Saether OA. 1979. Chironomid communities as water

quality indicators. Holarctic Ecol 2:65-74.

Copenhagen.

Sanders RE, Miltner RJ, Yoder CO, Rankin ET. 1999.

The use of external deformities, erosion, lesions and

tumors (DELT anomalies) in fish. In: Simon T (ed).

Assessing the Sustainability and Biological Integrity

of Water Resources Using Fish Communities. Boca

Raton, FL: CRC Press, pp. 225-246.

Sandin L, Johnson RK. 2000. The statistical power of

selected indicator metrics using macroinvertebrates for

assessing acidification and eutrophication of running

waters. Hydrobiologia 422/433:233-243.

Sawchyn WW, Gillot C. 1974. The life histories of three

species of Lestes (Odonata: Zygoptera) in

Saskatchewan. Can Entomol 106:7-12.

Schneider DW, Frost TM. 1996. Habitat duration and

community structure in temporary ponds. J North Am

Benthol Soc 15:64-86.

Servos MR. 1999. Review of the aquatic toxicity,

estrogenic responses and bioaccumulation of

alkylphenols and alkylphenol polyethoxylates. Water

Qual Res J Can 34(1):123-177.
32

Simon TP, Lyons J. 1995. Application of the index of

biotic integrity to evaluate water resource integrity in

freshwater ecosystems. In: Davis WS, Simon TP (eds).

Biological Assessment and Criteria. Boca Raton, FL:

Lewis Publishers, pp. 245-262.

Smith WA. 2000. Examination of dragonfly exuviae for

malformations. Report to Minnesota Pollution Control

Agency (MPCA). Contract No. L00-023. Biological

Monitoring Unit, MPCA, St. Paul, MN.

Smock LA. 1999. Riverine floodplain forests of the

Southeastern United States: Invertebrates in an

aquatic-terrestrial ecotone. In: Batzer DP, Rader RB,

Wissinger SA (eds). Invertebrates in Freshwater

Wetlands of North America. New York: John Wiley,

pp. 137-166.

Somers KM, Reid RA, David DSM. 1998. Rapid

biological assessments: how many animals are

enough? J North Am Benthol Soc 17:348-358.

Sovell LA, Vondracek B. 1999. Evaluation of the fixedcount method for Rapid Bioassessment Protocol III

with benthic macroinvertebrate metrics. J North Am

Benthol Soc 18(3):420-426.

Sparling D, Teels B, Lowe P. 1996. Assessing reconstructed depressional wetlands in the mid-Atlantic

states. Study Plan. May 1996. USGS Patuxent

Wildlife Research Center and NRCS Wetland Science

Institute. Laurel, MD.

Sparling DW, Lowe TP, Day D, Dolan K. 1995. Responses of amphibian populations to water and soil

factors in experimentally-treated aquatic macrocosms.

Arch Environ Contam Toxicol 29:455-461.

Steffens WP, Smith WA. 1999. Status survey for

special concern and endangered dragonflies in

Minnesota: population status, inventory and monitoring recommendations. Report to Minnesota Department of Natural Resources Natural Heritage and

Nongame Research Program. St. Paul, MN.

Stuijfzand S, Poort L, Greve G, van der Geest HG, Kraak

M. 2000. Variables determining the impact of diazonin

on aquatic insects: taxon, developmental stage and

exposure time. Environ Toxicol Chem 19(3):582-587.

Swanson GA. 1983. Benthic sampling for waterfowl

foods in emergent vegetation. J Wildl Manage

47(3):821-823.

Swanson GA. 1978. A simple lightweight core sampler

for quantitating waterfowl foods. J Wildl Manage

42(2):426-428.

Swanson GA. 1978. Funnel trap for collecting littoral

aquatic invertebrates. Progr Fish-Culturist 40(2):73.

Swanson GA, Meyer MI, Adomaitus VA. 1985. Foods

consumed by breeding mallards on wetlands of southcentral North Dakota. J Wildl Manage 49:197-203.

Swanson GA, Krapu GL, Serie JR. 1977. Foods of

laying female dabbling ducks on the breeding

grounds. In: Bookout TA (ed). Waterfowl and Wetlands—An Integrated Review. Proceedings of the

1977 Symposium at the North Central Section, The

Wildlife Society, Madison, WI. pp. 47-57.

Thorpe JH, Covich AP. 1991. Ecology and Classification of North American Freshwater Invertebrates.

New York: Academic Press. 911 pp.

Timms V, Hammer UT. 1988. Water beetles of some

saline lakes in Saskatchewan. Can Field-Naturalist

102:246-250.

Turner AM, Trexler JC. 1997. Sampling aquatic

invertebrates from marshes: evaluating the options. J

North Am Benthol Soc 16:694-709.

U.S. Environmental Protection Agency. 1998. Lake and

Reservoir Bioassessment and Biocriteria Technical

Guidance Document. Appendix E, Statistical Analysis

Methods for Biological Assessment. EPA 841-B-98-

007.

Vinson MR, Hawkins CP. 1996. Effects of sampling area

and subsampling procedure on comparisons of taxa

richness among streams. J North Am Benthol Soc

15(3):392-399.

Voigts DK. 1976. Aquatic invertebrate abundance in

relation to changing marsh vegetation. Am Midland

Naturalist 95(2):313-322.

Walker EM. 1953. The Odonata of Canada and Alaska.

Vol. 1, Part I: General; Part II: The Zygoptera—the

Damselflies. Toronto: University of Toronto Press.

Walker EM. 1958. The Odonata of Canada and Alaska.

Vol. 2, Part III: The Anisoptera—Four Families.

Toronto: University of Toronto Press. Toronto.
33

Walker EM, Corbet PS. 1975. The Odonata of Canada

and Alaska. Vol. 3, Part III: The Anisoptera—Three

Families. Toronto: University of Toronto Press.

Warwick WF. 1990. Morphological deformities in

Chironomidae (Diptera) larvae from the Lac St. Louis

and Laprairie basins of the St. Lawrence River. J Great

Lakes Res 16(2)185-208.

Warwick WF. 1980. Chironomidae (Diptera) responses

to 2800 years of cultural influence: a

palaeolimnological study with special reference to

sedimentation, eutrophication and contamination

processes. Can Entomol 112:1193.

Weisberg SG, Ranashinghe JA, Dauer DM, Schaffner

LC, Diaz RJ, Frithsen JB. 1997. An estuarine benthic

index of biotic integrity (B-IBI) for Chesapeake Bay.

Estuaries 20(1):149-158.

Welch PS. 1948. Limnological Methods. New York:

McGraw-Hill.

Westfall MJ, Jr, May ML. 1996. Damselflies of North

America: 1996. Gainesville, FL: Scientific Publications.

Whiteside MC, Lindegarrd C. 1980. Complementary

procedures for sampling small benthic invertebrates.

Oikos 35(3):317-320.

Wiggins, Glenn B. 1996. Larvae of the North American

Caddisflies (Trichoptera), 2nd ed. Toronto: University

of Toronto Press.

Wiggins GB, Mackay RJ, Smith IM. 1980. Evolutionary

and ecological strategies of animals in annual temporary pools. Arch Hydrobiol (Suppl 58)1/2:97-206.

Wik D. (for activity traps) Prototype Fabrications. E.

4596 266th Ave, Menomonie, WI 54751.

dwik_protofab@hotmail.com.

Wissinger SA. 1999. Ecology of wetland invertebrates.

In: Batzer DP, Rader RB, Wissinger SA (eds). Invertebrates in Freshwater Wetlands of North America. New

York: John Wiley, pp. 1043-1086.

Wright JF. 1995. Development and use of a system for

predicting the macroinvertebrate fauna in flowing

waters. Austral J Ecol 20:181-197.

Wrubleski DA. 1987. Chironomidae (Diptera) of

peatlands and marshes in Canada. In: Rosenberg DM,

Danks HV (eds). Aquatic Insects of Peatlands and

Marshes in Canada. Memoirs of the Entomological

Society of Canada No. 140. Ottawa. pp. 141-171.

Yoder CO. 1991. Answering some questions about

biological criteria based on experiences in Ohio. In:

Flock GH (ed). Water Quality Standards for the 21st

Century, Proceedings of a National Conference in

Arlington, Virginia. U.S. Environmental Protection

Agency, Office of Water, Washington, DC. pp. 95-104.

EPA-440-5-91-005.
34

Appendix A. Advantages and disadvantages of using invertebrates for

 biological analysis of wetlands

Advantages Disadvantages/Comments

Invertebrates can be expected to respond to a wide array of stresses to

wetlands, such as pollutants in water and bottom sediments, nutrient

enrichment, increased turbidity, loss or simplification of vegetation,

siltation, rearing of bait or game fish, input of stormwater or wastewater

runoff, introductions of exotic species, or alterations of the landscape

around the wetland.

Because it is likely that multiple stressors are present, it may not be

possible to pinpoint the precise cause of a negative change in the

composition of invertebrates. However, data from major sources of

human disturbance, e.g., water and sediment chemistry, the nearby

wetland landscape features, sources of hydrologic alteration, and other

disturbance factors cam be assessed in relation to the invertebrate data

to see which factors have the greatest effects.

Life cycles of weeks to months allow integrated responses to both

chronic and episodic pollution, whereas algae recover rapidly from acute

sources, and vertebrates and macrophytes may take longer to respond to

chronic pollution

Information on short-term, pulse impairments (using algae,

zooplankton) or more long-term impairments (using macrophytes,

vertebrates) or more landscape-level (using birds, amphibians)

impairment may be desired.

Toxicological/laboratory based information is extensive. Invertebrates are

used for a large variety of experimental approaches.

Toxicological response data may not be available for all invertebrates;

data for some wetlands species are less extensive than for stream species.

There is an extensive history of analysis of aquatic invertebrates in

biological monitoring approaches for streams.

Using invertebrates to assess the condition of wetlands is now under

development in several States and organizations.

Invertebrates are used for testing bioaccumulation of contaminants to

analyze effects of pollutants in food webs. Tissue contaminant analyses are always costly. This is true for tissue

analysis of any group of organisms: vertebrate, invertebrate, or plant.

Invertebrates are important in food webs of fish, salamanders, birds,

waterfowl, and predatory invertebrates.

Aquatic invertebrates tend not to be valued by the public as much as

fish, amphibians, turtles, or birds. However, citizens do respond to

invertebrates.

Many invertebrates are ubiquitous in standing water habitats.

Invertebrate composition will differ in different wetland classes, as will

other groups of organisms (plants, birds) that might be used to assess

wetlands.

Many invertebrates are tightly linked to wetland conditions, completing

their life cycles within the wetlands. They are exposed to site-specific

conditions.

Some invertebrates migrate in from other waterbodies, these taxa are not

as tightly linked to the conditions in the specific wetland.

Many invertebrates depend on diverse wetland vegetation, some depend

on particular types of vegetation for reproduction.

Loss of invertebrates may be a secondary effect from the loss of wetland

vegetation, e.g., from herbicide treatments. Vegetation loss is an

impairment.

Invertebrates have short and long life cycles and they integrate stresses to

wetlands often within a 1-year time frame.

Many complete their life cycle within a year, they are not as "long-lived"

as birds, amphibians or perennial vegetation.

Invertebrates can be easily sampled with standardized methods. Picking invertebrate samples is labor-intensive.

Invertebrates can be sampled once during the year, if the best index

period is selected for optimal development of invertebrates.

Invertebrate composition of wetlands often varies within the seasons of

the yearly cycle. Invertebrates mature at different times. This necessitates

selecting an "index period" for sampling once, or alternatively, sampling

more than once in the season.

Invertebrates can be identified using available taxonomic keys within labs

of the entities doing the monitoring. Staff help develop biomonitoring

programs.

Expertise is required to perform identifications of invertebrates. Some

may choose to contract out some or all the identifications. There is a

cost involved.

High numbers of taxa and individual counts permits the use of statistical

ordination techniques that might be more diffucult with just a few

species, e.g. with amphibians.

Large numbers of taxa and individual counts make the sample

processing more labor intensive than other groups. Adequate training

and staff time are required. More lab time is needed than for some

other groups of organisms.

Citizens can be trained to identify wetlands invertebrates and become

interested and involved in wetlands assessment. Citizens are excited to

see the richness of wetland invertebrates.

Citizen monitoring requires training to learn many invertebrates in a

short time, a structured program, and a commitment by volunteers and

local governments; citizens may tend to underrate high quality wetlands.
35

, part yti vi tca = TA, tenpi d = ND. sei cnega dna setat Slareves ni sdnalte wf o si sylana etarbetrevni f oyrammuS. Bxi dnepp A

. Hxi dnepp A ni sesserdda tcatnoC. noi taci fi tnedi = DI

YCNEGA ROETATS ADI ROLF OI HO E NI A M ATOSE NNI M ANATNOM

EFI L DLI WTNEXUTAP

EGUFER

ssal c dnalte W

,t negre me,l anoisser pe D

detser of dna s uoecabreh

s dnalte w

,s dnalte wl anoisser pe D

- bur hs ;t negre me dna detser of

s dnalte wburcs

dna enirevir ,l anoisser pe D

egnirf enirts ucal

t negre mel anoisser pe D

s dnalte whsra m

dahtaht sepyt dnalte wll A

no s ucof( reta wecafr us

)l anoisser ped

,l anoisser pe D

t negre me detaerc/ der otser

sehsra m

r o dei duts noi ge R

noi ger oce

dna al us ni nep adir ol F

el dnahnap

dna s ni al Ptle B nr o Cnretsa E

ekaL dna tfir Doirat n Oeir E

s ni al P

gni dnapxe, dehsreta wya B ocsa C

eni a Mnreht uos f o saera reht o ot

doowdra Hl art ne Chtr o N

hguor ht noi ger oce tser oF

NMl art nec

s ucof , anat no Mf o etats erit nE

3;syell avanat no M WNno

s noi ger oce

ni al pl atsaoc cit nalt A- di M

del p mas atart S enoz t negre me er ohs rae N stati bahll a , peed m1 < r of "21 ot "6 morf aera egdE stati bahll a ND, TA m5. 0 <,enoz t negre me er ohs rae N tenpi dr of m1<,tekcubr of t negre me er ohs rae N peed m1 < enoz < enoz t negre me er ohs rae N peed m1 tsepeed,stces nart modna R er ohs raen ot t ops

sel p mas f o #;s dohte M

etis/etad/ nekat

s pee ws 02,stenpi D

eno ot detis op moc

tati bahitl u m,el p mas

dohte m

morf gni kci p dnah,tenpi D

;s part yti vitca dna ,setarts bus

s TA 01, NDetis op moc eno

01,tekcub mott ob nepo noll ag 5

;etis/ 3tekcub/s pee ws tendnah

dna s pee ws m1 3 gnitset ,stenpi d

gnil p mas etis op moc tati bahitl u m

,etis repsel p mas tenpi d 2

,str offe owt gni vah hcae

noitategev r of des u neercs

s part yti vitca 01,l avo mer

etis rep

gnis ustr offe tenpi d 5- 4

eno r of dohte mtati bahitl u m

dna del p mas bus ; hcae et uni m

eno; dleif ni detis op moc

etis rep el p mas

xob gni kre Gevitatit nau Q

etis/sel p mas 6;rel p mas

f o #,)s( doirep xednI

setad

,rebotc Ohguor ht lir pA

raey rep x1 el p mas

-enuJ dna ; ya M;lir pA- hcra M

setad 3,yl uJ etad el p mas eno,enuJ etad gnil p mas eno,enuJ etad ,ecno,.t peS-lir pA noi ger oce no s dneped el p mas ;reb met peS- enuJ setad 5- 4

bal r o dleif ni kci P bal ni kci P bal ni kci P tati bahitl u mr of t pecxe, bal ni kci P

sel p mas bal ni kci P bal ni kci P bal ni kci P

bal r o dleif ni DI bal ni DI bal ni DI )r otcart noc f o( bal ni DI bal ni DI )r otcart noc f o( bal ni DI bal ni DI

?s DI t uotcart no C o N o N s DI ll a t uotcart no C s di monori hc t pecxe, o N s DI ll a t uotcart no C o N

r o el p mas erit ne kci P

?el p mas bus

001 kci p dna el p mas buS

s msi nagr o 051 ot

pecxe,el p mas bus erit ne kci P

t nadnuba yle mert xe el p mas bus

axat

el p mas erit ne kci P

t pecxe el p mas erit ne kci P

yart ni det nuoc( aiel poe N

)xobt hgil no

kci p dna , dleif ni el p mas buS

bal ni s msi nagr o 003 ot 002

ot 1⁄2r o 1⁄4ot el p mas til pS

e mit gni kci p ecuder

f olevel ci monoxaT

s noitacifit nedi

,sei ceps r o s une G

syek eht no gni dneped

ot e mos ,s uneg ot ylts o M

e mos gni dul cni sei ceps

s di monori hc

fi s uneg( level l acitcar ptse woL

s uneg ot detagergga axat ;)el biss op

cirte mssenhcir r of level

ot e mos ,s uneg ylts o M

s mal c li anregnif ,sei ceps

yli maf ot

ot e mos ,s uneg ot ylts o M

sei ceps

,s uneg ylts o M

dna ebirt ot s di monori hc

yli maf bus

?t nei darg ecnabr utsi D t ne mpoleve Des udnaL ) nwor B kra M( xednI t ne mssess A di pa Roi h O 0. 5 v ) MARO( dohte M hti w,er ocs ecnabr utsi d epacs dnaL l acisyhp dna ,s noitaretl a ci gol or dyh

atadl aci mehc

s ul p epacs dnal er ocS

s ul p noitaretl a ci gol or dyh

eno ot ni atad yrtsi mehc

ecneulf ni na muhf o er ocs

ecnabr utsi d epacs dnal dessess A

s ul p noitaretl a ci gol or dyhs ul p

yrtsi mehc

t ne mpolevedredn U

ssessa ot des usexednI

s dnalte w t ne mpolevedrednuI BI t ne mpolevedrednusexednI rednu xedni dna scirte M t ne mpoleved net f o des op moc I BI scirte m I BI rednuI BI dna scirte M t ne mpoleved

gnis ufi dohte mgnir ocS

scirte m t ne mpolevedredn U t ne mpolevedredn U sisyl ana etairavitl u mgnir ol pxE s dohte m hcae r of atadtcesir T cirte m no desab det hgie were wser ocS sr osserts ot es nopser cirte m t ne mpolevedredn U
36

. tenpi D = ND. segatnavdasi d dna segatnavda, setarbetrevni dnalte wgni lpmas rof sdohte M. Cxi dnepp A

. Hxi dnepp A ni sesserdda tcatnoC. snoi taci fi tnedi = DI . part yti vi tca = TA

dohte M noit pircse D segat navdA t ne mmoc/segat navdasi D bal ni e mit gnissecor p el p maS

rel p mas xob gni kre G

l anoita Nt nexutaP(

)stcej or p egufe Refil dli W

1 gni dils hti wxobll at mc 16,esab mc 67 x mc 93

no nwod dehs upsi xob, mott ob no neercs hse m mm

si yart ; noitategev dna n mul oc reta whguor ht mott ob

. du mci ht nebf o mc pot hguor ht dils

m92. 0sel p mas ,evitatit nauqsI 2

reta w, ci ht nebser ut pa C. aera

detai cossa- noitategev dna n mul oc

setar betrevni

noitategev ,) noll ag 1( egral yrev si el p maS

kci p ot s uoir obal , gni kci p dna gnis nir s deen

,ff ats 4- 3seri uqer dleif ni es u,el p mas egral

del p mas bus yll aus u

yfit nedi dna tr os , kci p ot set uni m521- 08

1⁄4ot ( del p mas bus neeb evahsel p mas nehw

)el p mas f o 1⁄2r o

rel p mas el yts epi pevot S

)eni a M()rel p mas tekcub(

dehs up devo mer mott ob hti wli ap citsal p noll ag 5

eveis ni es nir dna noitategev evo mer ;t ne mi des ot ni

ten norci m005ll a ms hti ws pee ws 01 ekat ,tekcub

el p mas ot detis op moc era etis/ 3,tekcub ni hti w morf

reta wsel p mas ,evitatit nauqi meS

;st ne mi des dna noitategev , n mul oc

etis ot pirt eno; dleif ni ysae

ot r obal hcu m,s utirtedf o st nuo ma egraL

ot detcirtser si rel p mas t nerr uc ;sel p mas kci p

. peed mc 05- 04reta w

hti wseirav ,el p mas repsr uoh 8 ot p U

el p mas

l avo mer hti wgnittenpi D

neercs no noitategev f o

)at osenni M(

norci m006( ten e marf- Dhti wenods pee ws 5- 3

r of decal p era tenf o st net noc ,reta whguor ht ) hse m

naprevo neercs ht ol c era wdrah " 1⁄2"61 x "21 no' 01

rep eci wt enod, devo mer neht noitategev ,reta wf o

.eveis norci m002 hguor ht deretlif el p maS .el p mas

; axat etar betrevni f o reb mun egraL

,etis eht ta tfel si noitategev ts o m

eht t uo gni kci ptr offe ssel

erit ne kci p nac ,setar betrevni

el p mas

,t netsis noc a sa enodt ub,evitatit nauqi meS

tr offe detaeper

DI ,el p mas NDhcae sr h 6. 3 egareva kci P

2. 4,sel p mas TA 01s ul p NDowt r of e mit

sr h

hti wgnittenpi D

gnil p mas tati bahitl u m

)anat no M(

ll a morf gnitcell oc f o et uni meno, deni arts noc-e mi T

etis repse mit 4- 3 enod dnalte weht ni sepyt tati bah

gni ht yreve, des utenpi d hse m mm1, detis op moc dna

devreser psi

ll a morf axat f o reb mun egraL

dnalte w; dnalte wni stati bah

el p mas ni det neser per si yti xel p moc

evis net xe,t pek si noitategev dna sir bedt ol A

kci p ot dedeen,sel p mas eht gnitr os ni e mit

el p mas bus msi nagr o- 002 a

el p mas repsr uoh 81- 4 ot pu koot gni kci P

)r otcart noc yb enod(

hti wgnittenpi D

gnil p mas tati bahitl u m

)adir ol F(

fi ",stati bah evitcudor p" ni enods pee ws etercsi d 02

owt fi ,tati bahr oni mni 01s ul p 01t neser psi eno

ni 5,t neser p 3fi ,r oni mni 6 dna hcae ni 7,t neser p

degdolsi d era s msi nagr O. cte,r oni mni 5 dna hcae

.e mul ov el p mas ecuder ot sir bed morf

ssel ,etis f o egarevoc l aitaps rette B

retteb ;r orre gnil p mas

yti nu mmoc f o noitaziretcarahc

tati bahitl u mses u LF ;er utcurts

s maerts ni dohte m

gnissessa ni ht obt natr op mi si ffats gni ni ar T

2- 1sekat ; gnittenpi d eht ni dna tati bah eht

etis a el p mas ot sr uoh

a r of sr uoh 03- 51si gnissecor p el p maS

etis

hti wgnittenpi D

gnil p mas tati bahitl u m

) oi h O(

595( hse m03ral ugnairt hti wgnil p mas set uni m03

ni noitategev hguor ht kci p;stati bahll a ten) norci m

l auna m;setar betrevni evo mer ot reta wf o yart r oten

sleef werc lit nus pecr of hti wsetarts bus f o gni kci p

era setar betrevni ; dnuof gnieb axat wen on era ereht

. dleif eht ni sir bedf ot uo dekci p

yteirav a sel p mas ,tcell oc ot kci u Q

stati bahf o

ll a morf s noitcell oc ,evitatit nauqt o N

axat etai cossa t' nac os , del oop era stati bah

r o elit o mteg t' nod,tati bah cifi ceps hti w

ezi dradnats ot drah eb ya m, axat l anr utcon

s werc

eno DI ,sr uoh 5. 1- 5. 0 dleif ni ssecor P

fi ;s di monori hc hti wsr uoh 3- 2 ni el p mas

bal ni ssecor p ot sr h 8 ot pu,etis esrevi d

retil 2: part yti vitc A

lennuf citsal p des ol cne

)at osenni M( part

si pot os t uc eltt ob egareveb citsal prael c retil 2

,reta w woll ahs ni yll at nozir oh decal p,lennuf detrevni

ni sri ap ni st hgi n owt t hgi nrevot uotfel ,etis/s part 01

. peed m1 <reta wni enoz t negre me er ohs raen

.) mc 3. 2 ac( "8/ 7 gni nepolennuF

gni mmi ws ylevitca ser ut pa C

sgub dna selteeb,sehceel ,sr otader p

eht ni lle wsa t hguac t' nera taht

ni ei d ot dnet sr otader p;stenpi d

senil ced ODsa part

seri uqer ,st hgi n owt t uotfel eb ot dee N

ll uf er ut pac t' nseod,etis eht ot tisi ver

; dohte m NDhti wes u ot deen,ssenhcir

part ni setar betrev f o htaed

01 morf sel p mas del oop 5 kci p ot sr uoh 3. 2

,sel p mas TA 01 + ND 2r of e mit DI ;s part

sr h 2. 4

: part yti vitc A

eda mel yts part wonni m

wodni w munu mul a f o

) oi h O( neercs

"8 neercs wodni w muni mul a f o srednil yc "81

ssal grebif f o eda ms dne ht obta slennuf ,rete mai d

01,sgni nepo ) mc 5. 4 ac( "57. 1 hti wneercs wodni w

ni( dnalte wf o ret ne mirep dnuora decaps etis/s part

reta w

"21- "6 ni ecal p,)s dnalte wll a ms

ot ysae,tcell oc ot kci u Q

egnar daor bser ut pac ,ezi dradnats

, axat l anr utcon dna evitca egral f o

dna hsif teg,sir bedf o eerf

evitaler r of s nai bi hp ma

f o yteirav ni es u,secnadnuba

stati bah

m/atad ytis nedteg t' no D 2 tcell oc t' nod,

e mos f o yti vitca ,ylevitceffe axat yrat nedes

,er utarep met dna rehtae wot etaler ya maxat

etis ot tisi v dnoces a seri uqer

DI dna ssecor p ot TA hcae sr uoh 8. 0- 4. 0
37

segatnavdasi d dna segatnavda, serudecorp gni ssecorp yrotarobaL . Dxi dnepp A

ser udecor P s noit p O segat navdA t ne mmo C/segat navdasi D

noitavreser P

dleif ni l ohocl a %59 hti wevreser P

, %08- 07f o cnoc l anif ot

bal ni evreser per

AQr of sel p mas s peek; kr owretal r of sel p mas eht s ni at ni a M

eb nac el p mas ;el p mas ni noitader pf o ecnahc on;ser udecor p

s noitacifit nedi dna gni kci pr of reta wni t up

setar betrev fi el bati us t on,sel p mas yvaeh evreser per ts u M

f oor p e malf deen,l ohocl a f ots oc ,) nil a mr of %8 es u( t neser p

evil f o scitsiretcarahc r oi vaheb dna r ol oc es ol ,egar ots

s msi nagr o

, dleif ni el p mas lli hc dna tcell o C

bal ni detaregirfer er ots

f o es opsi d nac ,l ohocl a f ots oc sevas ,r ol oc ni ater s ne mi cepS

l ohocl a ni gni vreser p er ofebsir bedf o kl ub

ni noitader pf o ytili biss op,e mit tr ohs ni sel p mas ssecor pts u M

ecaps noitaregirfer seri uqer ,sel p mas

sel p mas gni kci P

bal ni setar betrevni t uo kci P

t hgil hti w,s noiti dnoc t netsis noc rednu dessecor p eblli wel p maS

eb nac gnil p mas bus ;epocs orci mr o s nel gni yfi nga m, xob

etauqeda si gnit hgil , dirg hti wdell ort noc

.el p mas eht ni si sir bed hcu mwoh no gni dneped,e mit sekaT

dleif ni setar betrevni kci P ot s deenl aireta mssel , bal ni gni kci p el p mas r of e mit secude R bal ot kcabt hguor b dna devreser p eb ycnetsis nocni r of eka mya mt hgil dna rehtae wf o s noiti dno C eht nwods wols , noitacifi nga mf o kcal , gni kci pf o ytil auq ni noit ul oser ci monoxat ecuder ya m, kr owdleif

sraj el p maS

citsal P dleif eht ni es ur of doog,el bakaer bnu,t hgie wt hgi L

refs nart ot deen ya m,egar ots mret gnol r of doog ebt on ya M

t hgit-l ohocl a hti wsraj epyt-enegl an gnis ussel nusraj ssal g ot

s dil

ssal G ees nac ,sl aes f oor pt nevl os hti w,egar ots mret- gnol r of doo G

el p mas

el bakaer b, kr owdleif r of yvaeh ooT

el p mas ot s di A

gni kci p

dna )"5. 1 x "21x"8( yart ssal G

xobt hgil

wodni w morf eka m, xobt hgil revo el p mas gni wei v r of doo G

, p mal gni yfi nga mhti wes u,segde pot eht epat r ol aes ,ssal g

rerael c el p mas seka mevoba dna woleb morf t hgil

yb e mocrevo eb nac si ht ,sir bedf ot ol a sah el p mas fi tl uciffi D

os reta whti wdet uli d el p mas f o s noitr oprell a ms gni od

enodlit nu yart ni t uo daer ps dna el bisi v era s msi nagr o

epocs orci mgnitcessi D s di monori hc sa hcus s msi nagr oll a ms gni kci pr of doo G e mit enota dessecor psi el p mas f ot nuo ma rell a mS

yart r o nap eti h W neziti c r of l ufes u, p mal gni yfi nga mhti wes u,evis nepxenI gnir oti no m woleb morf t hgil on, xobt hgil hti wyart ssal g sa doog sa t o N el p mas eht

der ogno C,l agneb es oR,s ni at S deni ats )s di monori hc . g.e( setar betrevni devreser p e mos seka M el bisi v er o mdna dleif ni el p mas evreser p ot des ul ohocl a eht ni ebts u mni at S ) 6991 yff u Ddna na mknir B, 1791 ya Mdna yekcaL(

taolf t nei darg ytis ne D ot setar betrevni ses uac t ne mi des deveis ot es orcus %03 gni ddA el p mas evil hti wrettebs kr ow, putaolf , putaolf s msi nagr o rehtr uf onlit nu enod eb ot s deen,ysse msI , 9591 nosrednA( sel p mas devreser p hti wlle wsa kr owt on ya m ) 1991 nosi ddA dna ei hcti R

f ot nuo mA

kci p ot el p mas

el p mas erit ne kci P

sei ceps no noita mr of ni mu mi xa msevi g el p mas erit ne gnit nuo C

ni secnadnuba evitaler f o noita mitse rettebs woll a dna el p mas ni

)t xet ees( el p mas

t ol a sah el p mas eht fi yll ai cepse,tr offe kr owretaerg seri uqe R

sir bedf o

el p mas buS sir bedf ot ol a nehwyll ai cepse el p mas gni kci p ni tr offe secude R t neser psi setar betrevni eht f o noitis op moc eht no noita mr of ni es ol ya M etis eht ta

t nuoc dexif a ot kci P f o mu mi ni ma ot enodsi ,el p mas gni kci p ni tr offe secude R

s msi nagr o 003r o 002, 001

axat re wol saht nuoc dexif ,t nuoc el p mas l at ot ot derap mo C

)t xet ees( scirte mr of atad ni ytili bairav er o mdna ssenhcir
38

Appendix E. Issues related to Quality Assurance for invertebrate

bioassessment work

Any biological monitoring program should have its own written Standard Operating Procedures

(SOP’s) for each assemblage that is used, and for all field and laboratory protocols and data analysis

methods. Detailed guidance for developing a Quality Assurance Project Plan is available from the

U.S. Environmental Protection Agency, Guidance for the Data Quality Objectives Process (EPA

QA/G-4, EPA/600/R-96/055, ORD) and EPA’s Content Requirements for Quality Assurance Project

Plans for Water Division Programs (EPA Region V, Environmental Sciences Division, August 1994).

For examples of SOPs see Florida DEP SOP Draft 2000, North Carolina DWQ 1997 SOP.

The following text describes some considerations specific to biological assessment using

macroinvertebrates.

1. At least 10% of the samples should be checked and verified for the accuracy of taxonomic

identifications by a second taxonomist and/or by comparison to the reference collection. Changes in

taxonomy must be tracked and continuously updated, using current taxonomic references and regional experts. At least 10% of the picked residuals of samples should be checked by the experienced taxonomist to assure all the macroinvertebrates were picked out.

2. Samples should be stored carefully for a defined period of time, if not indefinitely, for later verifications or quality assurance, especially if the assessments are part of a regulatory decision. They

should not be disposed of right after the identifications are completed. Taxonomists are revising

certain groups of invertebrates as new information is gathered. Archived samples can be reanalyzed

for the group in question if necessary.

3. The lab should have defined rules for counting of taxa of each invertebrate group to be identified

at different levels of taxonomic resolution, for identifying immature specimens, and for when it is

acceptable to identify “morphospecies.”

4. Taxonomic coding. In addition to having a lab list of taxa identified with unique codes, the

wetland invertebrates are coded in the database using the national Integrated Taxonomic Information

System (ITIS) codes (www.itis.usda.gov/). This is a partnership of Federal agencies in the United

States and Canada to improve and standardize biological nomenclature nationwide. The ITIS database includes taxonomic information with authority, synonyms, common names, a unique taxonomic

serial number (the code), publications, experts, and data quality indicators. There should be a routine

procedure for checking the ITIS database to update and change any codes in the invertebrate database.

One problem is that not all wetland invertebrates have received the codes or serial numbers in the

ITIS system. So the agency must, temporarily, create its own codes for uncoded taxa. Minnesota is

coding taxa not found in the ITIS database with unique negative numbers. These can be easily

flagged and have no possibility of interfering with ITIS codes. ITIS is requesting input from biologists, so states can notify them of uncoded taxa. The ITIS database should be searched periodically
39

to see if uncoded taxa have received codes so the in-house, temporary codes can be replaced with

ITIS codes.

5. Database and records management. It is important to have a database management staff

person assigned to ongoing work with the biological data and the associated physical and chemical

wetlands data. This is especially true during the time of development of the biological database and

the selection of metrics and indexes. But the work is never static; changes occur and new directions

arise as the biological assessments are made and applied to reports and decisions. Taxonomy is

updated, and new needs arise in linking the biological data to other data sets and in making assessments accessible to the public through agency web pages. Data should be backed up routinely.

6. Field data forms. Standardized field forms should be developed to ensure that methods and site

conditions are thoroughly recorded and all procedures are consistently documented. Backup photocopies should be stored separately from original data forms and field notebooks in case data are lost

or destroyed. It is important to reconcile issues of data comparability between old and new methods.

In addition to the standard locational, wetland habitat, and physical sampling information, the invertebrate sampling method, numbers of samples collected, and the depth and locations of sampling should

be included on a field sketch. GPS (Global Positioning System) locations of latitude and longitude of

sampling locations should be recorded.

7. Sample labeling and tracking. All sample containers should be clearly labeled with pencil or

India ink on labels of 100% cotton or alcohol-proof paper placed inside the jar and include the

following information: sampling method, sample number, station ID, date, collector, site name and

location (township, county), and sample jar number (e.g., jar 1 of 2, etc.) if more than one jar is used

for a single sample. Standard procedures for sample tracking and chain of custody should be established as part the monitoring program’s quality assurance plan.

8. Laboratory Procedures.

a. Coding and recordkeeping. In the laboratory, a unique identification code should be assigned to

each sample. This code should be included in all subsequent records associated with the sample (vial

or jar labels, tally sheets, databases, etc.). All laboratory data forms should be standardized and

should include the information described above (see Sample Labeling and Tracking) in addition to the

sample identification code and laboratory staff name(s). As with field data forms, backup photocopies should be made of all laboratory records.

b. Sample sorting. Macroinvertebrates should be sorted from detritus (picked) by trained personnel

working under the supervision of a professional biologist. Picking procedures should be included in

Standard Operating Procedures. It is recommended that a 10% random subset of all samples be

repicked by different experienced laboratory personnel working under the supervision of a professional biologist to determine sorting completeness. In all cases, initial sample sorting and quality

control repicking should be completed by different individuals.

c. The Standard Operating Procedures should clearly define which groups of macroinvertebrates

are to be picked, what if any methods can be used to subsample, and what level of taxonomic

resolution should be used for the identifications for each group of invertebrates.

Appendix E. (continued)
40

. segatnavdasi d dna segatnavda, sexedni laci gol oi b rof setubi rtta etarbetrevnI . F xi dnepp A

yr ogetac et ubirtt A segat navdA st ne mmo C/segat navdasi D

l at ot :ser usae mssenhci R

ssenhcir ci monoxat

sa ylt nacifi ngis senil ced netf ots o maxat f o reb munl at ot eht , hcir- noita mr of nI

tse wol r o ytili bairav tse wol eht evah netf o ser usae mssenhcir ;sesaercni t ne mri ap mi

noitairav f o st nei ciffeoc

ci monoxat seri uqer ,) gni nepo yponac ,. g.e( ecnabr utsi d etaredo mni esaercni yll autca ya M

sei ceps ot deifit nedi eb nac s puorgll a t on;esitrepxe

t narel ot f o noitr opor P

setar betrevni

ot s dnet t nuoc el p mas l at ot eht ot setar betrevni t narel ot f o noitr opor p ehT

ot el ba er o mdna tsil areneg er o mera yeht es uacebsesaercni t ne mri ap mi sa esaercni

yl daor b deef

e moceb axat hci hwgnitceted ybstesatad morf devired eb ot s deent narel ot era axat hci h W

stesatadreht o notseter ot deen;t ne mri ap mi retaerg rednu det neser per yletanoitr opor psi d

t narel ot ni f o reb mu N

axat

rednu enil ced ot ylekil ts o mera yeht ;st ne mri ap mi ot evitis nes era axat t narel ot nI

setis ecnerefer deri ap mi tsael ni r uccolli wyeht ;s dnalte wot secnabr utsi d na muh

stesatad morf devired eb ot s deent narel ot r ot narel ot ni era axat hci hwot sa noita mr of nI

stesatadreht o no detseter dna ssal c dnalte weht r of

er utcurts ci hpor T dna er utcurts yti nu mmoc xel p moc tcelfer ya msr otader pf o noitr opor p hgi h A r otader p eht yb netae sepyt yer pf o ecnadnuba na muhf o st nei darg ot er utcurts ci hport f o set ubirtta etaler ot dedeensi gnitset er o M dnopser set ubirtta eht lle w woh ees ot s dnalte wni ecneulf ni

gni deef l anoitcnuF

) GFF( s puorg

sa tfi hs ya ms puorg gni deef eht ,ygol ohpr o mgni deef dna gnirehtag doof no desa B

f o noitr opor p eht ni esaercni na ;egnahc yhport ot ua r o yhport oretehf o s noiti dnoc

eagl a dehcatta morf eagl a ci not knal p ot tfi hs a etaci dni ya msreht oll a ot sreretlif

ses nopser wohs ot s puorg gni deef l anoitcnuf f o set ubirtta etaler ot dedeensi gnitset er o M

s dnalte wni secnabr utsi d na muhf ot nei darg ot

axat f o yti vegnoL

dnalte wot er us opxe regnol ecneirepxe )atanod O. g.e( axat dnalte wdevil-regnoL

tcepxe;) p mir hs yri af dna seoti uqs o m. g.e( axat devil-tr ohs yrev naht s noiti dnoc

setis ot t ne mri ap mi ci nor hc yb decuder eb ot axat devil- gnol

evahlli ws dnalte wt nena mrep er o m;setar betrev e mos naht devil retr ohs era axat dnalte W

s dnalte wyfissal c ot deen;re wef evahlli ws dnalte wl anosaes er o m,setar betrevni devil regnol

I BI r of

sei ceps cit oxe evisavnI ;er utcurts yti nu mmoc etar betrevni eht retl a ylevitagen ya msei ceps cit oxe evisavnI ecneulf ni na muhretaerg hti wsei ceps evisavni ni esaercni na tcepxe s dnalte wyna mni dnuof ebt on ya msetar betrevni cit oxE

knab gge gnitser li oS yr d evi vr us ot li os ni sgge tis opedtaht sei ceps t nediser no noita mr of ni sedi vor P s dnalte wdednop ylirar op met er o mni l ufes u;sel cyc era sgge gnitser ot syek,sli os gnissecor pseri uqer ,s dnalte wt nena mrep er o mni l ufes ut o N s noitacifit nedi r of sgge raer ot deen,etauqedani

ssa moi br o ecnadnubA

ser usae m

na muh, dnalte weht f o noitcudor p yradnoces ll arevo gni dnatsrednur of tseret ni f O

axat ll a t ont ub yna mf o secnadnuba ecuder nac ecnabr utsi d

gnil p mas dna segnahc l anosaes ot tcej bus er o m,seir ogetac et ubirtta eht f o el bairav ts o M

ecnabr utsi d hti wesaercni ya msl audi vi dni t narel ot ;r orre

s noitr opor p.sv s oita R

yti nu mmoc erit ne eht ot noitaler ni gni gnahc si puorg eno nehwetaci dni na C

era s noitr opor p;) oitar( puorg etar betrevni reht ona ot noitaler ni r o ) noitr opor p(

dedne mmocer

r of detset eb ot deen)s msi nagr o 002,. g.e( t nuoc el p mas l aitrap no desabs noitr opor P

nehwr orre ot el barenl uv era s oitar ;el p mas erit ne eht gnit nuoc hti wderap moc ytili bairav

gni yrav era s puorg ht ob

htl aeh etar betrevnI ni s di monori hc ni nwohs neeb evah,st nani mat noc etaci dni s noita mr ofl a M

eai vuxe ylf nogar d ni derr ucco evah,st ne mi des detani mat noc

, gniti mil reve woh,esitrepxe seri uqer ;s noita mr ofl a mf olevel enilesabf o egdel wonk seri uqe R

esitrepxe ssel seri uqer )s di monori hc ni hteet art xe r o gnissi m( s noita mr ofl a mrael c ot
41

Appendix G. Examples of invertebrate attributes used as metrics in

different States for biological analysis of wetlands. Attributes being

tested are starred.

Metrics Maine Minnesota Montana Ohio Patuxent

Richness Total genera

richness* Total taxa Total taxa Total taxa* Total taxa*

EOT genera* Chironomid genera Chironomidae taxa Chironomid taxa* # Snail genera*

Odonata genera* Leech taxa Leech, sponge and clam taxa Mollusca taxa* # Odonata genera*

Ephemeroptera

genera* Odonata genera POET taxa # Intolerant taxa*

# Ephemeroptera and Trichoptera

gen.+ presence of Sphaeriidae and

dragonflies*

Trichoptera

genera* Snail taxa Mollusca+Crustacea taxa — —

Chironomid

genera* — Odonata + Trichoptera taxa* — —

Composition — — %Chironomidae* — —

— — %Mollusca+Crustacean taxa* — —

— —

%Orthocladiinae to total

Chironomidae* — —

— — %Diptera* — —

— — %Tanytarsini* — —

— — %Trichoptera* — —

— — %Odonata* — —

— —

Ratio POET to POET and

Chironomidae* — —

— — %Pelycypoda* — —

Tolerance/

intolerance

% of total count

for : ETSD metric %1, 2, 5 dominant taxa % dominant 3 taxa of total count —

EOT* # of intolerant taxa — Physella snail abundance —

Odonata* Tolerants % of total

count

—

% Tanypodinae and Tanytarsini of

Chironomidae —

Ephemeroptera* Erpobdella % of total

count

— — —

Trichoptera* Dominant 3 % of total

count

— — —

Gastropoda* — — — —

Isopoda* — — — —

Oligochaeta* — — — —

Amphipoda* — — — —

Trophic structure Corixidae % beetles +

bugs % shredders Corixidae % beetles + bugs*

— — — — % Total indiv as scrapers*

— — — — % total indiv as shredders*

— — — — —

Individual health —

Chironomid

malformations* — — —

Other metrics — — — —

% total abundance of 3 most

abundant taxa*

— — — —

Ratio Ephemeroptera +

Odonata+Trichoptera abundance to

chironomids*

— — — — Shannon-Weaver Index*
42

txet ni dna Bxi dnepp A ni sei cnega/setat S rof noi tamrofni tcatnoC. Hxi dnepp A

eni a M at osenni M anat no M adir ol F oi h O t nexutaP

e ma N ocnar Fi DennaeJ trahri hCleoJ , negle HyduJ kceblef pAydna R gr obnedyr Fss uR yar Geki M

retePr o gnilrapS dl ano D

e woL

ycnegA f ot ne mtrape Deni a M noitcet or Pl at ne mnori vnE l ort no Cnoit ull oP at osenni M ycneg A f o.t pe Danat no M ytil au Ql at ne mnori vnE PE Dadir ol F APE oi h O efil dli Wt nexutaP S GS U ret ne Chcraese R

margor P ti n Ugnir oti no Ml aci gol oi B ti n Ugnir oti no Ml aci gol oi B gnir oti no Mytil au Qreta W noitceS t ne mssess Al at ne mnori vnE noitceS WS D

sser ddA E M, dnaltr oP,. dRocna C213 30140 ,l uaP.t S,. dRetteyafaL 025 55155 NM , anele H,.evAxi neohP 9022 02605 T M eessahall aT. dRenot S ri al B 0062 99323 LF enaL oi h Ore mo H5764 52134 HOtr opevor G

yll o Hnacire mA 01511

- 80702 DM,ler uaL.r D

7104

li a m- E s u.e m.etats @ocnarfi d. L.ennaeJ -js u. n m.etats. acp @negleh.yduj s u. n m.etats. acp @trahri hc.leo s u.t m.etats @kceblef par s u.lf.etats. ped @gr obnedyrf.less ur s u. ho.etats. ape @yarg.eki m

- GS U@gnilrapS_dl ano D

- S U@e woL

_

retePVOG. S

VOG. S G

be W

sser dda ped/s u.e m.etats.s unaj//: ptt h s u. n m.etats. acp. www//: ptt h /s u.t m.etats. qed. www//: ptt h

-ri vne/ moc. adir olf y m. www//: ptt h

-eir otar obal/ecnei cs/ nrael/t ne mno

l mt h. xedni/str oper/s

-/s u. ho.etats. ape. www//: ptt h

-aoi b

_

dnalte w/s dnalte w/ ws d

l mt h.ssess

vog.sgs u. cr wp. www
43

Appendix I. Invertebrate index of biological integrity for depressional

wetlands in Minnesota.

Ten metrics with scoring criteria and data ranges for large depressions. The numbers of reference sites (ref) and

agriculture- (ag) and urban stormwater-influenced (urb) sites scoring in each range are given. n is # of sites

scoring in the ranges. Codes are given for each metric. These codes are used in Appendix J.

Metric Metric data range Ranges Score Ref Ag Urb n

>59-79 5 9 5 0 14

>41-59 3 5 8 8 21

1. Total invertebrate taxa

Code: TaxaTotal

23-29 taxa

<23-41 1 0 3 6 9

5-6 5 8 6 3 17

3-4 3 6 7 4 17

2. Odonata

Code: Odonata

1-7 general

0-2 1 0 3 7 10

14->20 5 11 4 3 18

7-13 3 3 9 5 17

3. Chironomid taxa

Code: ChirTaxa

0-21 general

0-6 1 0 3 6 9

6-9 5 6 2 0 8

3-5 3 3 8 6 17

4. Leech taxa

Code: LeechTaxa

0-9 taxa

0-2 1 5 6 8 19

7-9 5 6 3 2 11

4-6 3 8 8 5 21

5. Snail taxa

Code: SnailTaxa

1-9 taxa

0-3 1 0 5 7 12

7-10 5 8 2 1 11

4-6 3 3 9 9 21

6. ETSD*

Code: ETSD

1-10 

0-3 1 3 5 4 12

5-7 5 7 1 0 8

3-4 3 4 5 7 16

7. Number of intolerant taxa

Code: IntolTaxa

0-7 taxa

0-2 1 3 1 7 20

13-39% 5 6 2 2 10

>39-65% 3 5 5 6 16

8. Tolerant taxa proportion of 

sample count

Code: Toler%

13-92%

>65% 1 7 5 6 18

0-<1% 5 13 7 9 29

1-5% 3 1 5 3 3

9. Leech erpobdella

Code: Erpo%

0-14%

>5% 1 0 4 2 6

<33% 5 9 8 8 25

33-67% 3 4 4 3 11

10. Corixidae proportion of beetles 

and bugs in activity traps

Code: Corix%

14-87%

>67% 1 1 4 3 8

*ETSD metric is total of number of genera of mayflies (Ephemeroptera), caddisflies (Trichoptera) plus the 

presence of fingernail clams (Sphaeriidae) and dragonflies.
44

Site Name SiteType TaxaTotal Odonata ChirTaxa LeechTaxa SnailTaxa ETSDMetr IntolTaxa Toler% Erpo% Corix% IBI Score ConditionField Ref 5 5 5 5 5 5 5 5 5 5 50 ExcGlacial Ref 5 5 5 5 5 5 5 3 5 5 48 ExcLasher Ref 5 5 5 3 5 5 5 5 5 3 46 ExcMinnow N Ref 5 3 5 5 5 5 3 5 5 3 44 ExcBloom Ref 3 5 5 3 5 5 5 5 5 1 42 ExcOverby N. Ref 5 5 5 1 3 5 5 5 5 3 42 Exc

Sheets Big Ag 5 5 5 3 3 5 5 5 5 1 42 Exc

Kasma Ref 5 5 3 5 3 3 5 1 3 5 38 ExcLeman's Ref 5 3 5 5 3 5 1 1 5 5 38 ExcMalardi Ag 5 3 3 5 5 1 3 5 3 5 38 ExcMud Urb 3 3 5 3 3 3 3 5 5 5 38 ExcPrairie Ref 3 3 5 1 3 5 5 3 5 5 38 ExcSheets Small 1 Ag 5 5 5 3 3 3 3 1 3 5 36 ModTurtle Urb 3 1 5 3 3 3 3 5 5 5 36 ModZager Ref 3 5 5 1 3 1 3 5 5 5 36 Mod

Battle Urb 3 5 1 1 5 5 3 5 5 1 34 ModDonley Lg. N Ref 5 3 3 3 5 3 1 3 5 3 34 ModLake 21 Ref 3 5 5 1 3 1 3 3 5 5 34 ModLegion Urb 3 5 3 1 3 3 1 5 5 5 34 Mod

New London Ref 3 3 5 1 3 3 3 5 5 3 34 ModCataract Ref 5 3 3 5 3 1 1 1 5 5 32 ModLake Park Ag 5 3 5 1 5 1 1 3 3 5 32 Mod

Savage Urb 3 3 5 1 3 3 3 3 5 3 32 Mod

Seter Ag 5 5 3 1 3 3 1 3 5 3 32 Mod

Sethre Ag 3 5 3 3 5 3 1 3 3 3 32 ModSkarpness E Ag 3 3 3 3 3 3 3 1 5 5 32 ModTyrone Bean Ag 3 3 3 5 1 3 3 3 3 5 32 ModBunker Ag 1 5 5 1 1 3 3 3 5 3 30 ModRound Urb 3 3 3 1 3 3 1 3 5 5 30 ModSunset Urb 1 5 1 1 5 1 1 3 5 5 28 ModJones Urb 1 1 1 3 1 3 3 5 3 5 26 ModNew Prairie Ag 3 3 1 3 3 5 1 1 1 5 26 ModWood Lake Urb 3 1 1 3 1 3 3 5 1 5 26 ModBreen Ag 3 3 1 1 3 1 1 5 1 5 24 PoorDavis Ag 1 1 1 3 3 1 1 5 5 3 24 PoorGrass Urb 3 3 1 3 1 3 3 3 1 3 24 PoorSigler Ag 3 5 3 3 1 3 1 1 1 3 24 PoorTrappers Ag 3 1 3 3 3 3 1 1 5 1 24 PoorNey Ag 3 3 3 1 1 3 1 5 1 1 22 PoorCasey Urb 1 1 3 1 1 1 1 3 3 3 18 Poor

Lost1 Urb 1 1 3 3 1 1 1 3 3 1 18 PoorWakefield Urb 1 1 3 1 1 1 1 1 5 3 18 PoorRose Golf Urb 1 1 1 1 1 3 1 1 5 1 16 PoorSucker N Ag 1 1 3 1 1 1 1 1 5 1 16 Poor

Appendix J. Scores for invertebrate metrics for 43 large depressional

wetlands in Minnesota, condition is hypothetical.

Codes in Appendix I.
45

Appendix K

Case study flow chart for developing invertebrate IBI in Minnesota for large depressional

wetlands (see text, Helgen and Gernes in press, Gernes and Helgen 1999).

l Ecoregion: North Central Hardwood Forest (has abundant wetlands, is

central to the state, includes Twin Cities metro area)

l Wetland class: large depressions

l Range of disturbances: 15 least impaired , 15 agriculture-affected, 16

urban-affected wetlands

l Attribute selection: 10 attributes tested based on previous work,

additional attributes were tested (see Appendix J)

l Sampling strata: sampling to take place in near shore emergent vegetation zone from edge to less than 1 m deep (vegetated zone contains

greatest richness of invertebrates; nearshore area may be more exposed

to disturbances from the near wetland landscape)

l Sampling methods: two standardized dip netting procedures plus 10

activity traps; all samples taken within approx. 40-50 m distance along

shoreline

l Seasonal index period in June for optimal invertebrate development

l Sampling methods had been pretested in previous projects

l Samples were preserved in field and processed in lab; vegetation from

dip netting procedure was left at the site

l Water and sediment chemistry samples were collected (for total N, total

P, conductivity, chloride, chlorophyll a, metals)

l Entire sample was picked for macroinvertebrates (not for Ostracoda or

zooplankton); Neopleia was counted on a grid over lightbox and not

picked; chironomid identifications were contracted out, all others

identified at MPCA lab

l Most taxa were identified to genus with exceptions: snails to species

where possible; fingernail clams at family level

l SOP is written and reference collections made for verifcation

l Data were entered into ACCESS database using ITIS coding and MPCA

coding where necessary.

l Sites were analyzed for a human disturbance gradient composed of

estimates of disturbance in 50 m buffer and in near wetland landscape,

estimates of hydrologic alteration and rankings of water and sediment

chemistry data as compared to reference condition; each disturbance

was scored and summed to one score for human disturbances

l Attribute data was plotted against scores for human disturbance

gradient for all the sites, and against water and sediment chemistry data

(see Figure 6 and 7) for all sites

l Attribute data that showed a response to the human disturbance

gradient or to the chemistry data was trisected for a 1, 3 5 scoring as

metrics (see Appendix I)

l 10 metric scores were summed to IBI score and IBI scores were plotted

against the human disturbance gradient and chemistry data (see Figure 1)

l IBI scoring range (10 – 50) was trisected for a hypothetical ranking of

best, moderate and poor condition (see Appendix J)

step 1:

select study

sites

step 2:

Plan invertebrate

sampling

step 3:

Field Sampling

step 4:

Laboratory

Processing

step 5:

Metric Analysis
46

Glossary

condition or health of a place (e.g., a stream, wetland, or woodlot).

Biological integrity “The ability of an aquatic

ecosystem to support and maintain a balanced,

adaptive community of organisms having a species

composition, diversity, and functional organization

comparable to that of natural habitats within a region” (Karr and Dudley 1981).

Biological monitoring Sampling the biota of a

place (e.g., a stream, a woodlot, or a wetland).

Biota All the plants and animals inhabiting an

area.

Bottletraps A kind of activity trap that is constructed from a bottle with a funnel opening.

Box-and-whisker plots Plots of data values

made for individual attributes with percentile “boxes”

around the median value, e.g., 25% and 75% boxes.

The “whiskers” are lines extending beyond the percentile boxes for data value ranges that extend outside the percentile. These are used to compare the

amount of overlap in the data range for an attribute

between reference and impaired sites.

Community All the groups of organisms living

together in the same area, usually interacting or depending on each other for existence.

Detritivores Organisms that consume decomposed organic particulate matter.

Dipnet A sturdy, long-handled aquatic net for

sampling aquatic habitats, also called sweep net.

Mesh sizes range from 500 to 1000 microns.

Disturbance “Any discrete event in time that

disrupts ecosystems, communities, or population

structure and changes resources, substrate availAbundance The number or count of all individuals of one taxon or all taxa in a sample. When expressed per unit area or unit volume, it is called

density.

Activity trap (AT) A passive sampler, usually

containing a funnel-shaped opening and a container

that is enclosed, either as a bottle or jar, or with a

mesh screen. The organisms swim into the funnel

and are trapped in the container. The size of the

funnel opening determines the size of organisms that

can swim into the trap.

Aquatic life use support The ability of a

waterbody to support the native aquatic life that it

is capable of supporting when there is little or no

human disturbance, or the ability of the waterbody

to support aquatic life as designated for that type of

water in Water Quality Rules.

Assemblage An association of interacting populations of organisms in a wetland or other habitat.

Examples of assemblages used for biological assessments include algae, amphibians, birds, fish,

macroinvertebrates (insects, crayfish, clams, snails,

etc.), and vascular plants

Attribute A measurable component of a biological system. In the context of biological assessments,

attributes include the ecological processes or characteristics of an individual or assemblage of species

that are expected, but not empirically shown, to

respond to a gradient of human influence.

Benthic invertebrates Invertebrates that inhabit

the bottom or benthic area of a waterbody.

Benthos All the organisms that inhabit the bottom (benthic) area of a waterbody.

Biological assessment Using biomonitoring data

of samples of living organisms to evaluate the
47

ability or the physical environment” (Picket and

White 1985). Examples of natural disturbances are

fire, drought, and floods. Human-caused disturbances can be referred to as “human influence” and

tend to be more persistent over time, e.g., plowing,

clearcutting of forests, conducting urban stormwater

into wetlands.

Dominance The relative increase in the abundance

of one or more species in relation to the abundance

of other species in samples from a habitat.

Dose-response In toxicology, a graded response

by test organisms to increasing concentrations of a

toxicant. In biological assessment, dose response

indicates a graded response (up or down) of an

attribute to a gradient of human disturbance.

Ecosystem The community plus its habitat; the

connotation is of an interacting system.

Ecoregion A region defined by similarity of climate, landform, soil, potential natural vegetation,

hydrology, and other ecologically relevant variables.

Epiphytic A layer of periphyton located on or

attached to the surfaces of stems of macrophytes.

See periphyton.

Family A taxonomic category comprising one or

more genera or tribes of common evolutionary origin, and often clearly separated from other families.

Family is between the categories of order and tribe

(or genus).

Functional feeding groups (FFGs) Groupings

of different invertebrates based upon the mode of

food acquisition rather than the category of food

eaten. The groupings relate to the morphological

structures, behaviors and life history attributes that

determine the mode of feeding by invertebrates.

Examples of invertebrate FFGs are shredders, which

chew live plant tissue or plant litter, and scrapers,

which scrape periphyton and associated matter from

substrates (see Merritt and Cummins 1996a,b).

Funnel trap See Activity trap.

Genus (plural genera) A taxonomic category

of organisms composed of one or more species that

are related morphologically and evolutionarily, the

principal category between family and species.

Gradient A regularly increasing or decreasing

change in a factor (e.g., a single chemical) or combination of factors (as in the Human disturbance

gradient).

Gradient of human influence The relative ranking of sample sites within a regional wetland class

based on the estimation of degree of human influence (e.g., pollution and physical alteration of habitats).

Habitat The sum of the physical, chemical, and

biological environment occupied by individuals of a

particular species, population, or community.

Herbivore An organism that consumes plant or

algal material.

Human disturbance gradient A gradient or

range of perturbations or impairments (or humancaused disturbances) that alter the physical, chemical, or biological integrity of ecosystems or habitats. The effects may persist over periods of time.

Examples pertaining to wetlands are alterations in

the natural hydrology, in the near wetland landscape,

or in the wetland buffer; or chemical pollution, siltation, removal of aquatic vegetation, exotic species

introductions, cattle, fish-rearing, conducting of urban stormwater, or agricultural runoff. Compare

human disturbances with discrete events of natural

disturbances.

Impairment Adverse changes occurring to an

ecosystem or habitat. An impaired wetland has

some degree of human influence affecting it.
48

Index of biologic integrity (IBI) An integrative

expression of the biological condition that is composed of multiple metrics. Similar to economic indexes used for expressing the condition of the

economy.

Index period A defined interval of the season

that serves as the sampling period for biological

assessments. For invertebrates the index period

would be the interval of time when there would be

optimal development of invertebrates and optimal

presence of resident taxa that developed in the

waterbody.

Intolerant taxa Taxa that tend to decrease in wetlands or other habitats that have higher levels of

human disturbances, such as chemical pollution

or siltation.

ITIS The national Integrated Taxonomic Information System (ITIS) coding system for organisms

in the United States and Canada. The database

includes information on each organism with authority, synonyms, common names, and a unique taxonomic serial number (the ITIS code) for all taxonomic levels. It lists publications, experts, and data

quality indicators. ITIS accepts input from investigators regarding uncoded taxa. The database is

update periodically. Web site: www.itis.usda.gov/

Macroinvertebrates Animals without backbones

that are caught with a 500-800 micron mesh net.

Macroinvertebrates do not include zooplankton or

ostracods, which are generally smaller than 200

microns in size.

Macrophytes The visible aquatic plants that are

emergent, floating, or submersed under water. They

may be attached to the bottom or not. Distinguished

from algae, most of which are microscopic.

Metric An attribute with empirical change in the

value along a gradient of human influence. See Attribute. Metrics are scored individually, and each

score composes the total score for the IBI.

Morphology The structure and form of an organism, both external and internal. Taxonomic identifications are mostly derived from the examination

of external morphologies, with exceptions.

Morphospecies A taxon that has distinct morphologies from other similar taxa and is identified

as a distinct taxon based solely on the morphological differences (see Oliver and Beattie 1996). The

biologist must determine that the differences are not

from developmental stages or change in features

within the same taxon before the taxon can be

counted in the overall taxa richness.

Multihabitat method A sampling method in

which the sampling effort attempts to sample most

of the habitats with the zone of study. The effort is

variously distributed among the habitats; in some

methods it is distributed in proportion to the representation of the habitat in the zone of study, in other

methods it is distributed by giving greater effort to

habitats more likely to have the desired organisms.

Multivariate analysis Mathematical analysis

that examines numerous variables simultaneously.

Data from communities of organisms are multivariate because there are several species with differing

abundances responding to numerous environmental factors. Various statistical methods are used,

e.g., ordination or discriminant analysis, to analyze

several variables at once.

Omnivores Are organisms that consume both

plant and animal material.

Periphyton The layer of algae that coats substrates in aquatic systems such as plant stems, rocks,

logs, and benthic muds. This layer of algae coating

is often also colonized by bacteria, protozoans, and

rotifers and other microorganisms.

Population A group of individual organisms of

the same species living in the same area.
49

Predators Organisms that consume animals.

Proportion The mathematical relation of one

part to the whole, expressed as magnitude or degree, e.g., percent. An example of proportion attributes are the percent of tolerant individuals of the

whole sample count or the proportion of the abundance of the top two dominant taxa to the sample

abundance.

QA or QAPP The written plan for quality assurance, or the quality assurance program plan, that

provides written detail for quality assurance plans

for quality assurance checks for all aspects of field

and laboratory procedures.

Ratio The numerical quotient of two variables or

quantities. An example of an attribute that is a ratio

is the ratio of scrapers/collector-filterers (see proportion).

Reference site A minimally impaired site that is

representative of the expected ecological conditions

and integrity of other sites of the same type and

region.

Relative abundance Is the abundance of one

group or taxon of organisms in relation to the total

abundance of the sample.

Replicate A term usually reserved for the repetition of an experiment to obtain information for

estimating experimental error. It is sometimes used

informally to describe repeated consistent samples

taken at a site with the same method, in the same

strata and habitat, and on the same date.

Sample A representative part of a larger unit

used to study the properties of the whole.

SOP Acronym for Standard Operating Procedure. This is the detailed, written description of all

the methods to be used for field and laboratory procedures.

Species A taxonomic category below genus, the

fundamental biological unit, a population of organisms that share a gene pool and are able to reproduce successfully and produce young that are able

to reproduce.

Stress (or stressor) Any environmental factor

that impedes normal growth, reproduction, or survivorship of organisms, or causes adverse changes

in populations of organisms or in ecosystems.

Sweep net Another term for Dipnet.

Taxon (singular, taxa plural) A distinct taxonomic group of any level (e.g., family, genus or species); includes all subordinate groups. Taxon is any

group of organisms that is distinct enough from other

groups to be treated as a separate unit.

Taxonomic system The hierarchy of classification of organisms.

Taxonomist A biologist who specializes in the

identification of organisms.

Taxonomy The practice of describing, naming,

and classifying organisms.

Tolerance The biological ability of different species or populations to survive successfully within a

certain range of environmental conditions.

Tolerant taxa Taxa that tend to increase in wetlands or other habitats that have higher levels of

human disturbances, such as chemical pollution

 or siltation.

Trisecting The division into three parts of a

range of data for scoring a metric.

Trophic Feeding, thus pertaining to energy transfers.
50

Wetland(s) (1) Those areas that are inundated or

saturated by surface or groundwater at a frequency

and duration sufficient to support, and that under

normal circumstances do support, a prevalence of

vegetation typically adapted for life in saturated soil

conditions [EPA, 40 C.F.R.§ 230.3 (t) / USACE,

33 C.F.R. § 328.3 (b)]. (2) Wetlands are lands

transitional between terrestrial and aquatic systems

where the water table is usually at or near the surface or the land is covered by shallow water. For

the purposes of this classification, wetlands must

have one or more of the following three attributes:

(a) at least periodically, the land supports predominantly hydrophytes, (b) the substrate is predominantly undrained hydric soil, and (c) the substrate is

nonsoil and is saturated with water or covered by

shallow water at some time during the growing season of each year (Cowardin et al. 1979). (3) The

term “wetland,” except when such term is part of

the term “converted wetland,” means land that (a)

has a predominance of hydric soils, (b) is inundated

or saturated by surface or ground water at a frequency and duration sufficient to support a prevalence of hydrophytic vegetation typically adapted

for life in saturated soil conditions, and (c) under

normal circumstances does support a prevalence

of such vegetation. For purposes of this Act and

any other Act, this term shall not include lands in

Alaska identified as having a high potential for agricultural development which have a predominance

of permafrost soils [Food Security Act, 16 U.S.C.

801(a)(16)].