United States

Department of

Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report

RMRS-GTR-70

January 2001

Monitoring

Wilderness Stream

Ecosystems

Jeffrey C. Davis

G. Wayne Minshall

Christopher T. Robinson

Peter Landres
Abstract

Davis, Jeffrey C.; Minshall, G. Wayne; Robinson, Christopher T.; Landres,

Peter. 2001. Monitoring wilderness stream ecosystems. Gen. Tech. Rep.

RMRS-GTR-70. Ogden, UT: U.S. Department of Agriculture, Forest

Service, Rocky Mountain Research Station. 137 p.

A protocol and methods for monitoring the major physical, chemical, and

biological components of stream ecosystems are presented. The monitoring protocol is organized into four stages. At stage 1 information is

obtained on a basic set of parameters that describe stream ecosystems.

Each following stage builds upon stage 1 by increasing the number of

parameters and the detail and frequency of the measurements. Stage 4

supplements analyses of stream biotic structure with measurements of

stream function: carbon and nutrient processes. Standard methods are

presented that were selected or modified through extensive field application for use in remote settings.

Keywords: bioassessment, methods, sampling, macroinvertebrates,

production

The Authors

Jeffrey C. Davis is an aquatic ecologist currently working in Coastal Management for the State of Alaska. He

received his B.S. from the University

of Alaska, Anchorage, and his M.S.

from Idaho State University. His research has focused on nutrient dynamics and primary production in

freshwater streams.

G. Wayne Minshall is Professor of

Ecology at Idaho State University. He

received his B.S. in fisheries management from Montana State University

and his Ph.D. in zoology from the

University of Louisville. He later

served as a NATO Postdoctoral

Fellow at the Freshwater Biological

Association Laboratory in England.

Dr. Minshall is an internationally recognized expert on the ecology of flowing waters. His research interests

emphasize aquatic benthic invertebrates, community dynamics, and

stream ecosystem structure and function. For the past 19 years he has

been conducting research on the

long-term effects of wildfires on

stream ecosystems. He has authored

over 100 peer-reviewed journal articles and 85 technical reports. He has

served on advisory panels for the National Science Foundation (Environmental Biology, Long Term Ecological Research, NATO Postdoctoral

Fellowships) and the National Research Council (Graduate Fellowships in Biology, Committee on Inland

Aquatic Ecosystems).

Christopher T. Robinson is a Stream

Ecologist with the Swiss Federal Institute for Environmental Science

and Technology (EAWAG/ETH) working on the ecology of alpine streams.

He received his Ph.D. from Idaho
Rocky Mountain Research Station

324 25th Street

Ogden, UT 84401

come from the National Science

Foundation, USDA Forest Service,

Idaho State University, and personal

contributions of the participants. We

are indebted to the participants who

were involved in the many back country sampling expeditions during that

time, especially Douglas A. Andrews,

James T. Brock, and Dale A. Bruns.

Their curiosity and inventiveness

aided in the evolution of these methods. Michael T. Monaghan provided

insightful comments on the

penultimate draft of this document.

Review and comments on this document by Bert Cushing, Peter Bowler,

and Luna Leopold were greatly appreciated. Comments from Larry

Schmidt and John Potyondy of the

Rocky Mountain Research Station

Stream Systems Technology Center

helped ensure the use of standard

methods in the “Discharge” and

“Stream and Substratum Morphology” chapters. Preparation of this

manual, development of protocols,

and development and testing of advanced procedures (especially

stages 3 and 4) was funded by the

Aldo Leopold Wilderness Research

Institute, Rocky Mountain Research

Station, USDA Forest Service.

State University in 1992. While in

Idaho, he studied the effects of wildfire on stream ecosystems and on

the bioassessment of wilderness

streams. He has conducted ecological investigations on freshwater systems in Latvia and Russia.

Peter Landres is Research Ecologist

at the Rocky Mountain Research

Station’s Aldo Leopold Wilderness

Research Institute in Missoula,

Montana. He received his B.S. in

natural science from Lewis and Clark

College and his Ph.D. from Utah

State University. His research is

broadly concerned with developing

the ecological knowledge to improve

wilderness management nationwide

and, specifically on the landscapescale, understanding of fire and its

restoration as a natural process in

wilderness.

Acknowledgments

Most of the methods described

here were developed, tested, and refined for wilderness use over the past

17 years by members of the Stream

Ecology Center at Idaho State University. Support for the research in

which these methods were used has

Federal Recycling Program Printed on Recycled Paper

The use of trade or firm names in this publication is for

reader information and does not imply endorsement by the

U.S. Department of Agriculture of any product or service.
Contents

Page

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

Scope and Organization ............... 2

Goals and Objectives .................... 2

Selecting Appropriate

Measurements ...................... 3

Stage 1...................................... 7

Stage 2...................................... 9

Stages 3 and 4 .......................... 9

Selecting Sampling Locations ..... 13

Selecting Sampling

Reaches .............................. 13

Selecting Sampling Locations

Within a Reach ................... 16

Sampling Frequency ................... 20

Spatial Scale and Sampling

Frequency ........................... 20

Sampling Frequency and

Investigated Parameters ..... 21

Evaluating Differences ................ 22

Temperature ................................. 25

Methods: Stage 1, Stage 2,

Stage 3.................................... 25

Discharge ...................................... 28

Methods: Stage 2 ........................ 28

Methods: Stage 3, Stage 4 ......... 29

Solar Radiation ............................. 33

Methods: Stage 1 ........................ 34

Methods: Stage 2 ........................ 35

Methods: Stage 3 ........................ 35

Methods: Stage 4 ........................ 36

Stream and Substratum

Morphology ................................... 37

Methods: Stage 1 ........................ 38

Methods: Stage 2 ........................ 38

Methods: Stage 3 ........................ 40

Water Quality ................................ 41

Methods: Stage 1 ........................ 42

Specific Conductance/Total

Dissolved Solids ................. 42

pH ........................................... 43

Turbidity .................................. 43

Alkalinity .................................. 43

Hardness ................................. 46

Estimation of Major Ions ......... 47

Methods: Stage 2 ........................ 47

Calcium ................................... 47

Nitrate Nitrogen ....................... 48

Orthophosphorus .................... 49

Methods: Stage 3 ........................ 50

Nitrogen: Ammonia ................. 50

Nitrogen: Nitrate ...................... 50

Dissolved

Orthophosphorus ................ 50

Nutrient Flux............................ 50

Macroinvertebrates ...................... 52

Methods ...................................... 52

Methods: Stage 1 ........................ 55

Methods: Stage 2 ........................ 59

Methods: Stage 3 ........................ 61

Fish ................................................ 65

Methods: Stage 1 ........................ 65

Algae/Periphyton .......................... 67

Methods: Stage 2 ........................ 68

Methods: Stage 3 ........................ 72

Large Woody Debris .................... 73

Methods: Stage 1 ........................ 73

Methods: Stage 2 ........................ 73

Benthic Organic Matter ............... 78

Methods: Stage 2 ........................ 78

Methods: Stage 3 ........................ 78

Transported Organic Matter ........ 80

Methods: Stage 3 ........................ 80

Organic Matter Decomposition ... 82

Methods: Stage 4 ........................ 82

Primary Production ...................... 85

Methods: Stage 3 ........................ 86

Methods: Stage 4 ........................ 86

Carbon Turnover Length ......... 89

Nutrient Dynamics ....................... 90

Methods: Stage 3 ........................ 91

Nutrient Limitation: N:P

Ratios .................................. 91

Testing Potential Nutrient

Limitation ............................ 92

Ecosystem Uptake

Parameters: Open

System Methods ................. 94

Stage 4: Component Uptake

Parameters ....................... 100

References .................................. 102

Additional General

References........................ 108

Appendix A: Wilderness

Monitoring Equipment List .... 109

Stage 1 ...................................... 109

Stage 2 ...................................... 110

Stage 3 ...................................... 111

Stage 4 ...................................... 111

Appendix B: Vendor List .............. 113

Appendix C: Macroinvertebrate

List ........................................ 117

Page
Monitoring Wilderness

Stream Ecosystems

Authors:

Jeffrey C. Davis

G. Wayne Minshall

Christopher T. Robinson

Peter Landres
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 1

Wilderness streams are a unique and valued resource, offering many of

the “enduring benefits” envisioned by passage of the Wilderness Act of

1964. These benefits include fresh water and places to fish, relax, and enjoy

nature; unique habitats for plants and animals; reference sites to judge

direct and indirect impacts to our natural environment; and perhaps a

place where we can learn how to be stewards of the land and water.

Wilderness streams, because they are relatively unaffected by people

compared to most other streams, present one of the best opportunities for

learning about stream ecosystems and how they function. The value of

wilderness streams as a place to learn and as an ecological benchmark to

judge impacts is growing daily.

Myriad impacts threaten wilderness streams. Because of human and

physical nature, most threats inexorably move toward streams. People who

visit wilderness concentrate around streams and lakes, causing many

types of problems, including:

• Removal of surrounding vegetation in turn causing increased erosion, sediment deposition, and turbidity;

• Introduction of human and other animal wastes, and chemicals

such as fuel, soaps, and skin lotions;

• Trampling of bed material within streams and on stream margins

thereby disrupting fish spawning and rearing areas, amphibian

reproduction, and macroinvertebrates.

Other impacts include leachate from abandoned or active mines and

atmospheric deposition of acids and other pollutants that eventually wash

into streams and lakes. Cattle and other livestock spend much of their

time close to water, especially in the drier wilderness areas of the western

United States. Furthermore, compared to the total land area of most

wildernesses, streams are rare and therefore impacts to them are of greater

relative importance and significance.

Despite important social and biological values of wilderness streams and

recognition of the many threats to them, our understanding of these

relatively pristine aquatic ecosystems is meager. There are several reasons

for this lack of knowledge. First, there are no roads in wilderness and roads

have become the primary means of access for most scientists. The logistical

and practical hurdles of hauling sampling gear on foot or horse deters most

scientists. Second, there are no electrical outlets in the backcountry and

Introduction
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

scientific equipment is increasingly dependent on electricity. And third,

ecosystem-level understanding often requires manipulating the environment, and wilderness is the one place where such manipulation usually is

not allowed. Also, understanding the functional parameters of ecosystems

typically requires large amounts of expensive and bulky equipment that is

costly and difficult to transport.

This manual provides information to overcome most or all of these

challenges by demonstrating how to monitor streams in the backcountry

wilderness using equipment that is lightweight, portable, and rugged. Our

overall goal and purpose in developing this manual is to provide guidance

to biologists and wilderness managers who are interested in developing

baseline information and in evaluating known or likely impacts to wilderness streams.

Scope and Organization _________________

This manual provides detailed guidance on how to acquire data on

wilderness streams. We offer instruction on monitoring the entire range of

structural and functional stream parameters in a staged monitoring

system that provides increasing detail and rigor at each successive stage.

This staged system offers maximum flexibility allowing modification for

particular situations, goals, and needs. It is organized in a manner that,

while ensuring the analysis of key factors, allows for modification to

address particular objectives.

We begin, through the remainder of this introduction, by addressing the

basic questions that occur when initiating a monitoring program. What

stream components or factors should be measured? From where should

samples be taken? How often should samples be collected? How are

differences between or among locations and streams detected? Following

the introduction, detailed discussions are presented of the methods that

have been proven effective in evaluating the physical and biotic components in wilderness streams. The knowledge gained by the users of this

manual will help to fill the information gap on wilderness streams.

Goals and Objectives ___________________

Clearly outlining the goals and objectives of a monitoring program will

focus effort in the proper direction and thereby eliminate the needless costs

associated with collecting irrelevant data. Monitoring goals generally fall

into two main categories: obtaining baseline information or evaluating

potential impacts. Wilderness areas often contain the only unimpacted

streams within a region. Obtaining baseline data from within a wilderness

area can provide important information on the structure and function of

unimpacted stream ecosystems. These data then can be used to determine

the extent of impact in streams subjected to various degrees or types of

influence. Obtaining baseline data within wilderness areas also is beneficial for the evaluation of potential, unforeseen impacts. The monitoring
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 3

goal of obtaining baseline data can be further refined to a specific objective.

For example, the effects of livestock grazing on small upland Forest Service

streams may be a regional concern. The objective of the monitoring program

would then be refined to obtain data on similar small upland wilderness

streams. By defining goals and objectives, we have reduced potential

sampling sites from all wilderness streams to small upland wilderness

streams. More detailed stream classification (discussed below) can further

reduce the number of potential sampling sites.

The same logic applies for the goal of evaluating impacts. For example,

camp sites generally are concentrated within the stream/riparian corridor,

particularly where trails approach or cross streams. This concentrated use

could result in the compaction of soil, removal of riparian vegetation,

increased streambank erosion, and clearing of downed timber for firewood.

All these factors could negatively impact stream systems. Therefore, the

monitoring objective may be to determine whether these camp sites are

impacting the stream. Initial observations and stream classification could

confirm such negative impact or demonstrate that most of the problem sites

are on streams that have a low slope, are not confined, and have a relatively

large floodplain. This information could help to further refine monitoring

objectives and sampling locations.

Selecting Appropriate Measurements ______

The stream factors measured at each sampling location are outlined in

table 1 (Minshall 1994). The physical and biotic factors in table 1 are

organized into four different stages. Each increase in stage increases the

level of analysis and the number of factors measured. Stage 1 is considered

the minimum level of analysis required. Each subsequent stage incorporates the measures of the previous stage. The procedures consist of a nested

series of measurements grouped in units or “subsets” and arranged to

progressively increase the information available for management decisions, and permit adjustments for specific types of problems. A nested

arrangement assures that a basic set of comparable measurements will be

made in all cases but also permits further tailoring of the program for

specific needs and available resources. That is, the monitoring plan ensures

measurements of basic ecosystem factors at stage 1, and provides flexibility

through incorporation of additional levels (stages) of analysis for certain

factors, or through higher levels of analysis.

The monitoring objective, type of problem (for example, nutrients versus

toxic metals), and use of information (for example, a local management

question versus legal litigation) will determine the necessary stage of

analysis. However, selecting the appropriate stage of analysis will require

a management decision based on monitoring objectives and a basic understanding of stream ecosystems. For example, if the monitoring objective is

to obtain baseline information for comparison with potential future impacts to small upland streams, then stage 1 analysis could be conducted at

most sites, with stage 3 or 4 analysis conducted at 1 or 2 long-term reference

locations. If the monitoring objective is to evaluate potential changes in
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Table 1—Hierarchical sequencing of measurements of stream environmental conditions suitable for application at the stream segment level and lower (excluding habitat features addressed in table 2), arranged in order of increasing detail, with each subsequent stage intended to be cumulative. Stage 1 Measurement/feature Purpose Environmental factors: Temperature 24-hour maximum and minimum during Estimate of annual maximum and diel change warmest month of the year Solar radiation Yearly estimates using Solar Pathfinder Relative shading by vegetation and topographic features Substratum Mean and coefficient of variability (CV) Mean particle size distribution and of b-axis for ≥100 randomly selected heterogeneity particles Alkalinity Basic water chemistry analyzed using General water quality Hardness standard methods pHSpecific conductance Turbidity Biotic factors: Large woody debris Total count within reach Abundance of structural component Macroinvertebrates Rapid bioassessment protocol III Biotic condition indicators and community structure indices Fish (If specifically desired) Appropriate metrics, density and biomass Biotic condition indicators and community estimates structure indices Stage 2 Measurement/feature Purpose Environmental factors: Solar radiation Point incoming solar radiation reaching Measurement of daily solar energy input stream surface at 9, 12, 3, and 6 on a clear day in summer Temperature Seasonal 30-day thermograph records Improved characterization of thermal regime and heat budget (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 5 Discharge Summer baseflow Characterize stream size; permit calculation of fluxes Substratum Embeddedness and stability Estimate of suitability of streambed for fish (egg) and invertebrate survival Calcium Filtered sample Delineation of main cations and principal Magnesium Colorimetric field procedure plant nutrients Nitrate-N Phosphorus (ortho) Sulfate Biotic factors: Large woody debris Abundance and ranked score based on Quantification of an important component of importance streams Algae Periphyton chlorophylla and biomass Quantification of an important food source and biotic indicator Benthic organic matter Total Quantification of an important food source Invertebrates Total density, biomass, and analysis by Estimates of 2° consumer production functional feeding group Stage 3 Measurement/feature Purpose Environmental Factors: Solar radiation Stream surface, standard, depth and bottom Estimate of solar input PAR seasonally on clear days Temperature Annual thermograph records Improved information content Discharge Placement of stream stage height gauges; Improved characterization of flow regime 5 seasonal instantaneous measurements Current velocity and depth Measured at random locations throughout Characterization of stream habitat suitability; study area. Determine mean current determination of hydraulic stress velocity Table 1 (Con.) Stage 2 Measurement/feature Purpose (con.)
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Ammonia-N Laboratory analysis of filtered samples Further detail regarding nitrogen dynamics Nutrient flux Concentration X discharge (with Measure of resource availability (Fisher 1990) concentration determinations upgraded to laboratory quality) Biotic factors: Algae Diatom community metrics Biotic condition indicator Benthic organic matter Partitioned into coarse and fine sizes and Refined food resource analysis main sources Transported organic Same as for benthic organic matter Estimate of exported organic matter and food matter/invertebrate drift available for filter feeders and fish Ecosystem production/ Total-system metabolism using open- Measure of ecosystem function, productivity, respiration system methods and trophic state Nutrient spiraling/limitation Open system nutrient spiraling parameters/ Measure of ecosystem behavior and response to standard nutrient additions utilization/retention efficiencies/plant nutrient-growth status Stage 4 Measurement/feature Purpose Environmental factors: Solar radiation Annual solar radiation Determine solar radiation regime and energy input Discharge Annual hydrograph records Improved information content Biotic factors: Organic matter decomposition Leaf pack decay rates Estimate of decomposition by microbial and invertebrate detritivores Ecosystem production/ respiration Activity rates of colonized trays of native Measure of ecosystem function, productivity, substrata measured in recirculating and trophic state for each component chambers Nutrient spiraling Uptake rate of components measured in Uptake efficiencies of each component recirculating chambers Secondary production Monthly measurements of invertebrate Measure of impacts on fish-food producing standing crops capability of streams Table 1 (Con.) Stage 3 Measurement/feature Purpose
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 7

water chemistry near camp sites, then stage 1 analysis should be enhanced

by incorporating stage 4 analysis of water chemistry and stage 3 analysis

of nutrient limitation.

Included are measurements of physical and chemical factors that address the known key stream ecosystem parameters (Minshall 1994). Established (standard) procedures are used, where possible, in order to permit

rapid deployment and to assure comparability among studies and technical

personnel. The recommended procedures are sufficiently robust to be

applicable over a wide variety of situations.

Stage 1

Stage 1 procedures are based on the Environmental Protection Agency’s

Rapid Bioassessment Protocols (RBP) (Plafkin and others 1989) for both

habitat and biotic (macroinvertebrates, fish) components (MacDonald and

others 1991). The combination of RBP III and V are used in the ecosystem

assessments addressed in this study. We have modified the original RBP III

protocol to involve the analysis of 300 or more specimens, and use of 250

μm-mesh Surber net or comparable quantitative sampling device. Included

in this stage is a basic evaluation of physical habitat (temperature,

discharge, substratum) and diagnostic water quality conditions (alkalinity,

hardness, pH, specific conductance, turbidity).

Stage 1 protocols assume that all of the data needed at this level of

analysis will be obtained at the time the stream is visited and that may be

only once a year or less. Consequently, this stage provides only the minimal

information required to broadly characterize conditions. Maximum and

minimum temperature measurements over 24 hours provides a measure of

the range of values (both absolute and range) to which the organisms are

exposed during any particular time of the year. Measurements during the

warmest month provide information for one of the most stressful periods

and, when combined with an estimate of the annual minimum temperature

(often near 0 °C), can be used to estimate the annual range. Measurement

of the intermediate axis of 100 or more randomly selected pieces of

substratum (popularly known as the pebble count procedure) provides a

good characterization of inorganic materials covering the streambed, and

facilitates determination of a bed-stability index. Collectively, the suggested chemical measures can provide a good general characterization of

water quality conditions (see Water Quality section).

In addition to the factors specified in table 1, a habitat characterization,

as described by Plafkin and others (1989), and detailed site classification

(table 2, 3) should be conducted as a means of adequately describing and

classifying the study site and providing additional measures of physical

conditions. Photographs supplement the site characterization and, along

with global positioning systems, can be used to identify sampling locations

in subsequent years.
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Table 2

—Spatial hierarchical classification of three Big Creek wilderness streams (from Monaghan and Minshall 1996).

Stream habitat

(linear spatial scale) Defining measures Stream characteristics

Biogeoclimatic region Regional climate Northern Rocky Mountains Ecoregion, semi-arid steppe;

(105 m) hot dry summers, cold snowy winters (Bailey 1989;

Robinson and Minshall 1995)

Regional geology Central Idaho northern Rocky Mountains (Alt and Hyndman 1989)

Regional topography Narrow steep-sided canyons; forested mountain tops

Regional terrestrial vegetation Semi-arid steppe forest and grassland

Flow regime High snowmelt discharge, constant summer baseflow, rare

summer spates

Stream system Local climate 74 cm precipitation annually, 54 percent between November

(103-104 m) and March

Local geology Precambrian metamorphic schists and gneisses with Cretaceous

and Eocene granitic intrusions of the Atlanta (Idaho) batholith

(Alt and Hyndman 1989)

Local topography Cliff Creek—southern aspect; Pioneer

—northern aspect; Rush

—

northern aspect

Local terrestrial vegetation Douglas-fir and ponderosa pine; extensive areas of bare rock;

open areas of sagebrush and grass

Thermal regime Summer min/max of 9/20 °C

Segment system Tributary junctions Rush

—between Lewis Creek tributary and confluence with

(102-103 m) Big Creek

Major geologic Cliff

—change from granite to schist/gneiss bedrock occurs above

discontinuities study reach; Pioneer

—none noted; Rush

—none noted

Reach system Channel slope Cliff

—0.18; Pioneer

—0.25; Rush

—0.01

(101 - 102 m) Valley form Cliff

—narrow type A2 Rosgen (1994) classification; Pioneer

—

narrow type A3; Rush

—less confined type B3

Bed material Eroded cobble and gravel

Riparian vegetation Birch, alder, mountain maple, serviceberry
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 9

Stage 2

Stage 2 provides a more complete measure of environmental conditions

and an analysis of the food resources available to the heterotrophs.

Thermograph records are used for identifying and quantifying important

aspects of the thermal regime (Vannote and Sweeney 1980). They are

equally important for quantifying thermal budgets (for example, cumulative degree-days) that are important in explaining aquatic invertebrate and

litter-processing responses (Cummins and others 1989). The benthic

invertebrate analysis is expanded beyond stage 1 to include total density

(abundance per unit area), biomass (which require accounting for all

organisms in a sample), and partitioning of the results by functional feeding

group (Cummins 1973; Merritt and Cummins 1996). For this stage, habitat

features are quantified using procedures such as those described by

MacDonald and others (1991) and Platts and others (1983, 1987). However,

a standard quantified protocol for habitat analysis comparable to the

subjective protocols presented by Plafkin and others (1989) and Petersen

(1992) has yet to be developed.

Stages 3 and 4

These two stages differ primarily in the level of detail involved and the

incorporation of measurements of ecosystem function. Stages 3 and 4

supply additional environmental details and address some of the most

important aspects of stream ecosystem function: decomposition rates,

energy metabolism, and nutrient cycling. An ecosystem is at least a dual

entity: structural and functional (MacMahon and others 1978; O’Neill and

others 1986). Figure 1 is a simple model of a stream ecosystem. Quantification of each box, in other words, macroinvertebrate, fish, and algal

community composition and biomass, would be a description of biotic

structure. Biotic function is depicted in figure 1 by the arrows. Quantification of the flux of energy and elements among biotic and abiotic components

would contribute to a description of stream ecosystem function. For example, primary production, the transfer rate of energy (solar radiation)

and an element (carbon) to primary producers is a functional process. The

structural (population-community) dimension is organized according to

constraints involving organism interaction, natural selection (for example,

competition) and the physical habitat. The functional dimension is established according to constraints that involve mass balance and thermodynamics. Only in unusual circumstances can one be considered in isolation

from the other. That is, complete understanding (and monitoring) of stream

ecosystems requires a quantification of biotic components (boxes) and

the flux of energy and elements (arrows) among the different components.

The description of the state (status) of an ecosystem or determination of

changes in state must consider both biotic structural and functional

attributes. Therefore, a sound bioassessment program must incorporate

both structural and functional attributes of ecosystems. However, virtually
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Table 3

—Hierarchical classification of stream/riparian habitats (after Frissell and others 1986).

Stream habitat Defining Boundaries Procedure/guidelines

(linear spatial scale) measures Longitudinal Lateral Application Source of information references

Biogeoclimatic region Regional climate Region; State; Forest Topographic maps (15') Omernik 1987

(106 m) Regional geology District Geologic maps (15')

Regional topography Landsat photos

Regional terrestrial Annual discharge records Poff and Ward 1989

 vegetation

Flow regime

Stream system Local climate Drainage divides, Drainage divides Basin-wide surveys; Topographic maps (7.5') Omernik and

(103-104m) Local geology and seacoast, or bedrock faults, joints Cumulative impacts; Geologic maps Gallant 1986

Local topography catchment area controlling ridge Integration of sites Vegetation maps

Local terrestrial valley development within watersheds Aerial photos

 vegetation Annual temperature Chorley and others

Thermal regime records 1984; Gregory and

Walling 1973;

Vannote and

Sweeney 1980

Segment system Tributary junctions Tributary junctions Valley sideslopes Paired watersheds Topographic maps (7.5')

(102-103 m) Major geologic major falls; bedrock or bedrock out- Segment classes Ground reconnaissance

 discontinuities lithologic or crops controlling (for example uplands Low level aerial photos

 structural lateral migration versus lowlands)

 discontinuities

(con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 11 Reach system Channel slope Slope breaks: Local sideslopes Local effects; Ground survey/mapping Frissell and others (101-102 m) Valley form structures capable or erosion-resistant grazing allotments; 1986; MacDonald Bed material of withstanding banks; 50-year dredging and others 1991; Riparian vegetation <50-year flood floodplain margins Minshall 1984; Minshall and others 1989; Petersen 1992; Plafkin and others 1983, 1987; Platts and others 1989; Rosgen 1994 Pool/riffle system Bed form and material Water surface and Mean annual flood Aquatic habitat Ground survey/mapping Bisson and others (100-101 m) Origin bed profile slope channel; midchannel inventories; fisheries 1981; Frissell and Persistence breaks; location of bars; other flow- censuses others 1986; McCain Mean depth and genetic structures splitting obstructions and others 1990 velocity Microhabitat system Surface particle Zones differing Same as Characterizeation Direct measurement (101-100 m) size; underlying substratum type; longitudinal of local spatial particle size; size arrangement heterogeneity water depth; and effects (for velocity; example wading overhead cover by fisherman) (type) Table 3 (Con.) Stream habitat Defining Boundaries Procedure/guidelines (linear spatial scale) measures Longitudinal Lateral Application Source of information references
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

all schemes to date have focused almost exclusively on structural features

in spite of early admonitions by some aquatic ecologists (Cairns 1977) to

include functional aspects as well.

Selecting Sampling Locations ____________

Selecting sampling locations involves two different processes. First,

sampling reaches must be selected. This involves choosing reaches that will

Figure 1—Model of stream ecosystem identifying major biotic

and abiotic components. The acronym BOM, refers to benthic

organic matter, TOM, transported organic matter, TSS, total

suspended solids, and LWD, large woody debris. The biotic

components have both physical (circles) and biotic (rectangles)

characteristics. That is, LWD provides both cover for fish

(physical) and food for macroinvertebrates (biotic). Ecosystem

structure is described by a quantification of the physical and

biotic components (ovals, rectangles, and circles). Ecosystem

function is described by the relationship between components

(arrows). For example, the transfer of energy from the sun to

fish is one description of ecosystem function.

Fish

Macroinvertebrates

Autochthonous/Allochthonous 

Organic Matter

Hydrology

Discharge/Water Vel.

Habitat/Up and Down Migration

PHYSICAL CHARACTERISTICS

Substratum Stability Spatial Distribution

Spawning habitat

Habitat/Living Space

Periphyton Surface Area

Retention of BOM

Water Quality Nutrients

Light Penetration

Energy Energy

Nutrients

Oxygen

Toxins

Temperature

Oxygen

Toxins

Temperature

TSS

Solar Radiation Temperature

Energy

BIOTIC CHARACTERISTICS

BOM

TOM

LWD

Habitat

Resources

Habitat

Refuge

Riparian/

Wetlands

Nutrient and Organic Matter Exchange

Habitat

Habitat Resources

Habitat/Resource Aquisition
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 13

be representative of the spatial scale of inference and that conform to the

statistical design. Second, the exact location within the reach where

measurements will be taken or samples obtained must be determined.

These locations will depend both on the statistical design and the particular

factor being measured, but usually are established in a random or stratified-random fashion.

Selecting Sampling Reaches

As noted previously, monitoring of stream ecosystems usually is conducted to provide baseline data or to determine if some impact has

significantly altered the integrity of the stream or site in question. For

either of these monitoring goals, the scale of inference will influence the

selection of appropriate sites. For example, if the objective is to describe the

physical and biotic components within the ecoregion, then sample sites

should represent the types of streams occurring within that spatial scale.

Sites could be selected randomly among any sized stream (1st to 4th order)

and any segment of these streams (confined high slope to unconfined

shallow slope). In this case the variability in the data will be high and, while

providing a means to distinguish differences among ecoregions, differences

among locations within the ecoregion cannot be evaluated. On the other

hand, sampling only sites located on steep sloped 1st order streams cannot

provide data that is representative of all streams within the ecoregion.

Stream classification provides a means of stratifying streams and identifying sampling locations that addresses the spatial scale of inference and

objectives of the monitoring program.

A spatially nested hierarchical framework for classifying stream systems

(table 2), allows managers to identify the spatial scale of inference (Frissell

and others 1986; Hawkins and others 1993; Maxwell and others 1994). In

a hierarchical system, lower levels are modified and constrained by factors

operating at higher levels. Therefore, in an attempt to focus on factors

influencing stream ecosystems on a small scale one must be aware of

factors operating at larger scales. That is, one cannot evaluate and manage

to alleviate the effects of intense recreational use at a stream crossing when

similar or other impacts are occurring throughout the watershed. In

addition, comparisons between stream reaches cannot be made if they are

contained within different kinds of stream segments, systems, or ecoregions.

In other words, one would not compare physical and biotic data obtained

from a large river with similar data from a small headwater stream.

Therefore, effective management of local ecosystems (for example, stream

reaches or watersheds) requires attention to the landscape in which they

are embedded (Agee and Johnson 1988; Jensen and Bourgeron 1993).

In this approach, the ecoregion is set at the upper level of the hierarchy

(Minshall 1994). Stream systems, at successively lower levels of watersheds, consist of stream segments, reaches, pool/riffle complexes, and

microhabitat subsystems. The pool/riffle complex (in other words, channel

form) level can be further refined for more precise classification (Hawkins

and others 1993). Initial classification according to ecoregion is based on

Omernik (1987) and Gallant and others (1989). Inclusion of flow regime,
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

using the procedure of Poff and Ward (1989), further refines the

biogeoclimatic aspects and makes the classification more directly related to

flow: a major environmental driver of stream/riparian ecosystems. Classification of watersheds within an ecoregion is accomplished operationally by

distinguishing between “regional” versus “local” climate, geology, and

terrestrial vegetation. Proper classification at the watershed level requires

the availability of long-term records of atmospheric temperature, precipitation, and stream discharge. Environmental data will, in many cases, be

available from regional weather and stream-gauging stations (Finklin

1988; Mosko and others 1990). Snow cover and duration should be included

when describing the local climate. Terrestrial plant records can be obtained

from published sources such as Franklin and Dyrness (1973), Hall (1973),

and Steele and others (1981). Incorporation of thermal regime, as recommended by Vannote and Sweeney (1980), permits stratification by catchment-level differences. Catchments may be similar in external or regional

biogeoclimatic controls but differ in their thermal environments because of

different make-up combinations of ground and surface water or different

aspect of orientation to the sun.

Classification of stream segments is accomplished by conventional geomorphology practices which employ stream orders (Strahler 1957) or links

(Shreve 1966), based on either tributary junctions, or major geologic

discontinuities or both. Frissell and others (1986) and Rosgen (1994)

provide criteria for distinguishing stream reach classes. Important driving factors at the stream reach level include substratum particle size and

heterogeneity (Minshall 1984; Poff and Ward 1990) and woody debris

accumulations (Cushing and others 1995; Elwood and others 1983; Marston

1982; Platts and others 1987; Sedell and others 1988; Trotter 1990). Several

valley and channel features (Rosgen 1994) serve to further characterize the

physical environment, and are obtained through the classification of the

sampling sites. Channel slope (gradient), measured as the energy slope of

the water surface, exerts a major control on current velocity, turbulence,

and substratum composition. Valley form is expressed as the degree of

entrenchment: the ratio of flood prone width divided by bankfull width. Bed

form indicates whether the channel is straight, braided, or meandering.

Sinuosity, the ratio of channel length to valley length, indicates the extent

of meandering by the stream. Width/depth ratio, width at bankfull stage

divided by bankfull depth, measures the distribution of energy within

channels. The use of valley form (Minshall and others 1989; Rosgen 1994)

in place of side-slope gradient is better for characterizing features likely to

be important to riparian as well as stream dynamics at this classification

level. Classification of pool/riffle systems is an important description of

the templet on which patterns of biological diversity and production

appear.

When monitoring to provide baseline data, maps should be used to

classify the streams by habitat type within the ecoregion. From the maps,

basin area, stream order, and estimates of stream slope and confinement

can be determined. Site selection can then be stratified (see below for

discussion of stratified sampling) or refined based on objectives. For

example, if the focus is on the stream system scale (table 3), one 3rd order,

three 2nd order, and seven 1st order streams reaches could be randomly
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 15

selected and monitored (stratification should be proportional to the frequency of a stream type). This manner of site selection will increase the

level of resolution down to stream order, while still providing information

relating to the ecoregion. Further classification and stratification can be

extended to lower hierarchical levels (table 2) or by the reach classification

of Rosgen (1994) but obviously will increase the cost and effort required to

obtain data. Management objectives also can refine the spatial scale of

monitoring efforts. For example, it may be that a large portion of wilderness

use occurs at high elevations surrounding small 1st order streams. In this

case, monitoring could include only streams in this category.

When monitoring to obtain baseline data, it is important to provide

detailed classification of the sites monitored and to provide complete

descriptions of the sampling methods and results. This allows for confident

comparisons of the data with other sites or future studies.

Monitoring to determine possible impacts involves comparing impacted

sites with reference sites. Reference sites are the field ecologist’s equivalent

of the experimentalist’s more rigorously defined “control” condition. Reference sites can be of three types: a similar location upstream of the

disturbance (for small scale impacts), the same location prior to disturbance, or a similar site(s) located on a different stream or streams (either

historic or contemporary data). The selection of impacted and control sites

will vary with the spatial scale of the disturbance. If the disturbance affects

an entire basin, comparisons must be made with historic data (same

location or different location within the ecoregion) or data from other

streams in similar basins. Under ideal conditions, streams within the basin

(impacted and reference) are classified, and sampling sites are stratified

and selected randomly within each strata. Alternately, one representative

impacted sampling reach is selected and compared to a reference site. If one

sampling reach is used, it should be upstream of the mouth of the highest

order stream in the basin. This allows for the integration of multiple

impacts throughout the basin (fig. 2).

If impacts are confined to a stream segment, then multiple sampling

reaches or a representative sampling reach should be monitored. These

reaches can be selected randomly or by the investigator’s judgment. A

stream sampling reach is an arbitrary unit and is often defined as 20 times

bankfull width. For small streams, however, a minimum reach length of

50 to 100 m is established. Stream reaches also can be based on regular

patterns of morphology (Gordon and others 1992). For example, a reach

could be a section of stream containing two pools and two riffles. If a

representative reach is selected by the investigator, obvious biases should

be avoided. A reach should not be selected based on access if it is not

representative of the stream segment under investigation. Sampling locations should avoid modified sites, such as trail crossings, bridges, or

campsites, unless assessing their effects. Sampling reaches also should

avoid tributary inputs and be at least one reach upstream from a stream

confluence or mouth.

No matter what spatial scale the disturbance is impacting, reference

sites should have as similar a classification to impacted sites as possible. In

many cases, the best reference sites will not be those which are immediately

adjacent (or even in close proximity) to impacted sites. Proper and similar

classification of impact and reference reaches ensures viable comparisons.
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

For this reason, obtaining prior baseline data, particularly when future

impacts are expected, is preferred.

Selecting Sampling Locations Within a Reach

Once the sampling reaches are determined, the exact locations within the

reach where data will be collected must be identified. These decisions will

depend on the study design and whether statistical comparisons will be

made. Detailed explanation of research design can be obtained by referring

to statistics texts (Green 1979; Sokal and Rohlf 1969; Zar 1974) and will be

outlined only briefly here. The type of statistical or comparative analysis

for each of the physical and biotic components is outlined in table 4 and

described in more detail in their respective chapters. For comparative

data, the sampling location is selected to provide the best measurement of

Basin A

Basin B

1

1

1

1

1

1

1

1 1

1

1

1 1

1

1

1

1

1

1

1

1 1

1

1

2 2

2

2

2 2

3

3

Figure 2—Streams within two basins are classified by stream order. For

basin wide comparisons, sampling can be stratified based on the

classification. Potential reaches are determined within the 1st order,

2nd order (shaded ovals), and 3rd order (shaded rectangles) segments.

Dark rectangles represent potential sampling segments when only one

site in each basin can be monitored.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 17 Table 4

—Outline of site selection, sampling frequency, and type of data analysis for each monitoring component.

Site selection/

 Factor/component sampling locations Sampling frequency Data analysis

Temperature One representative location. Varies with stage of analysis: Comparative or statistical

Avoid slack water: sloughs or seasonal, monthly, continuous.

side channels.

Discharge One location where flows are Varies with stage of analysis Comparative or statistical

concentrated and channel and objectives.

uniform.

Solar radiation In small (1st and 2nd order) Stages 2 and 3, seasonally Comparative or statistical

streams, mid-channel at and 4 times daily. Stage 4,

5 randomly selected transects. continuous at representative

In larger streams stratified into location (location determined

margins and mid-channel. during early stage analysis).

Water chemistry One transect within sampling Varies with stage of analysis. Comparative and statistical

reach. In stage 4, stratified with flows.

Morphology/substratum Morphology: 5 randomly Annual or greater unless bankfull- Statistical: contingency

selected transects within reach. flows occur more often. table

Substratum: systematic

sampling.

Macroinvertebrates Random, stratified random, or Varies with stage of analysis: Comparative (metrics) or

systematic sampling. annual, seasonal, monthly. statistical

(con.)
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Algae/periphyton Five or more stones selected Varies with stage of analysis, Statistical but with caution haphazardly within reach. annual, seasonal, monthly. due to potential bias Large woody debris (LWD) Total population within reach. Annual Comparative (metrics) Benthic organic matter (BOM) Random, stratified random, or Varies with stage of analysis: Statistical systematic sampling. annual, seasonal, monthly. Transported organic matter (TOM) Three or more replicates at one Varies with stage of analysis. Statistical representative location. Drift In stage 4, stratified with flows. should be stratified by time of day. Organic matter decomposition Three or more randomly selected Annual Statistical locations. Can be stratified. Primary production Three or more replicates selected Annual or seasonal Statistical or comparative randomly within reach. Nutrient dynamics Nutrient limitation, one Annual or seasonal Statistical representative location or open and shaded sites. Table 4 (Con.) Site selection/ Factor/component sampling locations Sampling frequency Data analysis
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 19

the parameter. For statistical comparisons all suitable locations within the

reach should have an equal probability for being selected as sampling sites.

Four types of sampling are used in this monitoring manual: random,

systematic, stratified random, and haphazard sampling. For random

sampling, each location within the reach has an equal chance of being

sampled. This is accomplished by dividing the stream reach into discrete

sections (the area of each section equals the area of the sampler in use), each

section is then numbered, and numbered sections are chosen by referring

to a random numbers table. For example, the area of a Surber sampler

(most common invert sampler) is 0.12 m2

. For a stream that is 2 m wide,

reach length might be 40 m, and total area 80 m2. Therefore, there are over

600 potential sampling locations. Five randomly selected sampling locations are selected from the 600 potential sampling sites. Random sampling

is designed for homogeneous environments. Potentially all or most of the

samples could end up being collected in one area rather than throughout the

study reach. One way to spread out the potential sampling locations is to

divide the stream reach into transects. For the 2 m wide stream, potential

transects are spaced at 2 m intervals. There are 21 potential transects in

the reach. Five of these transects are selected randomly. Each transect is

then divided into 10 equal sections, one of which is randomly selected as a

sampling location. In heterogeneous environments (most streams), more

representative sampling may be obtained by the systematic or stratified

random approaches.

There often is a large degree of variation in biotic characteristics among

the different stream macrohabitats. Invertebrate community composition

of pools may be very different from those residing in riffles. This large

variability reduces the probability of determining differences between

impacted and reference sites. Stratified random sampling divides the

stream reach based on these distinct habitats or strata. A random sample

is then drawn from each strata. The number of samples taken within each

strata should be proportional to the area of each strata. That is, if 20 percent

of the stream reach is classified as pools, then 20 percent of the samples

should be taken within this habitat type. Further stratification might

involve selecting a single strata, for example, riffles.

In systematic sampling, the initial sampling location is selected randomly and subsequent sampling locations or transects are selected at fixed

intervals from this point. This method of sampling is used for determining

substratum size distribution.

Haphazard sampling is occasionally used when completely random

sampling is not practical. Haphazard sampling depends on the investigator

obtaining random samples based on his/her judgment. Sampling locations

are chosen by the investigator. For example, the area of the periphyton

sampler described in this manual is 3.54 x 10–4 m2

. For the 80 m2 sampling

reach there would be over 225,000 potential sampling locations. Dividing

a stream reach into this many sections would be impractical, and so rocks

sampled are best selected haphazardly or in association with established

transects. Similarly, limitations imposed by the sampling gear may preclude strict random sampling.
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Sampling Frequency ____________________

Sampling frequency can be broken or subdivided into two different

temporal scales. The larger temporal view addresses scale of inference and

is determined by the sampling objectives and the spatial level of disturbance or interest. The smaller temporal scale addresses how often samples

must be taken to adequately characterize the factor being measured. This

depends on the factor and stage of analysis.

Spatial Scale and Sampling Frequency

Natural landscape disturbances of a given frequency often are associated

with a particular spatial scale (O’Neill and others 1986; Urban and others

1987). In general, the longer the recurrence interval of a disturbance, the

larger the spatial scale and the higher the organizational level of the

system that must be considered (O’Neill and others 1986). For example,

small forest fires occur frequently but over small areas, and fires that

occur over larger areas have much longer recurrence intervals (fig. 3). The

River

Basins

Watershed

Reach/Segment

Habitat Type

Substratum Patch

Cobble

Pebbles

Small Wood

Gravel

Sand Grains

Algal

Patch Treefalls Wildfire

Tectonic

Events

Wildfire

Algal

Sloughing

Fish/Insect

Movements

10 6

10 4

10 2

10 0

10 -2

10 -4

10 10 10 10 10 10 -5 -3 -1 1 3 5

PATCH SIZE (m)

DISTURBANCE FREQUENCY (Years)

Figure 3—Relationship between time and spatial scales of

natural disturbances in reference to stream ecosystems

(Minshall 1994).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 21

relationship between natural spatial and temporal scales of disturbance

can help in determining sampling frequency. If the objective is to obtain

background or reference data, then the scale of inference (spatial scale) can

be used to establish sampling frequency. For example, if the scale of

inference is the ecoregion and sites are stratified by stream order, then one

may want to sample annually at the first-order sites, every other year at

third-order sites, and every 5 years at sites greater than fifth order. Smallorder sites drain a smaller area than large-order sites. Therefore, stream

conditions likely will vary on a shorter temporal scale and should be

sampled more frequently to document natural variability.

The relationship between spatial and temporal scales also can be used for

evaluating impacts. For example, atmospheric deposition of toxins or

nutrients likely will operate at the spatial scale of a watershed or ecoregion.

Impacts at this spatial scale (depending on intensity) will influence stream

systems at a temporal scale from 10 to 100 years. In this case, monitoring

every few years would be more appropriate than a monthly monitoring

frequency. However, in the case of intense recreational use of streamside

locations, an annual monitoring regime would be warranted with monthly

sampling during the summer months to evaluate the influence of altered

riparian cover on factors such as water temperature, algal abundance, and

macroinvertebrate community composition.

Selection of the appropriate temporal scale of operation will facilitate

the selection of the optimal sampling frequency to identify deviations in

stream structure and function. However, long-term monitoring will be

required to determine if deviations are outside the normal variability seen

in stream ecosystems. That is, when monitoring to determine the potential

effects of concentrated recreational use, differences observed between

impact and control sites may confirm suspected problems. However, annual sampling for multiple years or comparison to long-term sampling

locations may be required to determine if differences are outside the range

of natural variability.

Sampling Frequency and Investigated Parameters

How often must samples be taken to adequately describe the investigated

parameter? As shown in table 4, this depends on the parameter and the

stage of analysis. Some parameters are adequately described through

annual sampling. For example, both large woody debris and substratum

size distribution largely are influenced by bankfull flows. For streams in

the western United States, bankfull flows generally occur during annual

snowmelt. Therefore, more frequent measurements of these parameters is

not warranted. Most of the parameters measured vary throughout the year

and sampling frequency increases with the stage of analysis to better

characterize these changes. Stage 1 and stage 2 sampling can be completed

in a single day. Stage 3 requires several visits a year. Stage 4 was designed

for extensive analysis and will require frequent sampling. At stage 1,

midsummer daily temperature range is determined. This gives some

information toward the physical characteristics of the stream. At stage 2,
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

this information is increased to obtain monthly energy budgets, furthering

an understanding of this parameter. At stage 3, annual temperature data

are obtained thus completing the analysis of the variable on an annual

basis. Therefore, for most variables, selection of the stage of analysis will

determine sampling frequency.

Evaluating Differences __________________

As stated previously, the objective of the monitoring program often is to

determine whether impacted sites are different from reference sites. How

does one assess whether conditions are different at an impacted site in

comparison to a reference site? This will depend on the impact under

investigation and often will require statistical comparisons. When monitoring the biotic and physical characteristics of stream ecosystems, the entire

group of elements, or the total population, rarely are collected. Sampling is

a way to obtain a portion of the total population from which inferences

about the total population can be made. The characteristics of the total

populations are called parameters. An estimate of the population parameter is called a statistic and is obtained from the sample. That is, the

arithmetic mean obtained from the samples is a statistic and is used to

estimate the population mean. The more samples obtained, the closer the

sample statistics are to the population parameters.

If the total population were sampled, differences could be determined by

comparing parameters. However, because samples of the population are

being compared, statistical analyses are used to determine the probability

that the samples from the reference and impacted sites are from the same

population. This question is stated formally as a null hypothesis: there is

no difference between impacted and reference sites. There are two possible

errors associated with answering this question. First, one could conclude

that the samples are from different populations when in fact they are not.

This is a type I error. Second, one could conclude that the samples are from

the same population when they are not. This is a type II error. Since

increasing the number of samples causes sample statistics to approach

population parameters, increasing sample size can reduce the probability

of committing type II errors.

Increasing the number of samples increases sampling and processing

time and associated costs. Therefore, in selecting the number of samples

taken, one attempts to increase confidence in statistical analysis while

reducing time and costs. We recommend that at least 5 samples be taken

when statistical analysis are to be performed. There is a proportionally

larger increase in statistical confidence (statistical confidence per sample

size) when increasing the sample size from 3 to 5 than can be obtained by

increasing the sample size from 5 to 60 (Platts and others 1983, p. 37). The

exact number of samples required to obtain a certain level of confidence

in the statistical analysis can be calculated based on the magnitude of

difference in populations to be determined and the variability among

samples (refer to statistical texts).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 23

Performed statistical analyses can be either parametric or nonparametric. Parametric tests require that certain assumptions be met. These

assumptions are that samples are selected randomly, that samples come

from a normal population, and that variances are equal. There are a

number of different ways to transform the data if the assumptions of a

normal distribution and equality of variance are not met (Zar 1974). If

these assumptions cannot be met, nonparametric alternatives should be

considered.

When comparing reference and impacted sites there are only two populations: factors at reference sites and those at impacted sites. Therefore,

statistical tests generally are t-tests or some other nonparametric alternative for continuous data, and chi-square tests for discrete data. An exception is testing for nutrient limitation when the investigator instigates four

different treatments (dependent variables for each factor and stage are

presented in their respective chapters). When only one reference and one

impact site are compared, some of the factors outlined in this document can

only be used comparatively. Data variation when only two sites are sampled

from within each reach and sample size is the number of replicate samples

obtained.

When multiple reference and treatment sites are compared there are still

only two populations: impacted and reference. However, variance in this

case is from a number of different replicate streams. Because the variance

is from a number of different streams, it is important to make sure that both

reference and impact sites are of similar classification. Many of the factors

measured vary considerably among differently classified stream reaches.

For example, substratum particle size will be larger in small upland

confined streams than in larger floodplain streams. This inherent variability will mask impact effects, increasing the chance of committing type II

errors. If impacts occur at discrete locations, then a paired t-test can be used

as the statistical design. For example, multiple sites may be potentially

impacted by trail crossings. Impacted sites are selected below the crossing

and reference sites above. These two sites are paired and the sampling

statistic is the difference in factors between these two sites at multiple

locations. This reduces the among stream variability and reduces the

probability of committing a type II error.

Analogous to multiple reference and impacted sites is the situation where

multiple years of data are available at both locations. In this case data

variance is from the same stream over time. If each site were sampled over

the same time interval, then each year could be compared individually.

This may be beneficial when the impact is of short duration or management

has altered the conditions. For example, if significant differences were

determined between sites above and below a particular stream crossing, a

bridge could be constructed. If sampling continued for a number of years

after constructing the bridge, one may want to compare each year of data

independently.

Multivariate analyses also are applicable in some situations. Impacted or treatment sites may vary in intensity. Treatment intensity

may vary directly or over time. Using the previous example, ANOVA (or a
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

nonparametric alternative) could be used to determine the effectiveness of

bridge construction with each year representing a separate factor. Likewise, correlation between stream condition and years since bridge construction could be used to evaluate management actions. In this case,

treatment intensity changes with time. If one were evaluating the effect of

stream crossings on stream ecosystems, multiple reference and treatment

sites may be selected. However, some stream crossings may be used more

often than others. Treatment sites, could be subdivided into low and high

impact sites and significant differences determined with multivariate

statistics.

There are many different statistical designs depending on the monitoring

objectives and impact under consideration. Therefore each situation must

be evaluated independently. Once the sampling objectives are determined,

it is beneficial to consult with a biometrician to determine the appropriate

sampling and statistical design.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 25

Stream water temperature is an important environmental factor because

it affects many biotic processes. Stream temperature results from a combination of factors: source of water (snowmelt, groundwater, rain), air

temperature, solar energy input, and surface to volume ratio. In turn, water

temperature influences decomposition processes, primary production, invertebrate larval development, fish embryo development, and salmonid

survival.

Snow- and rain-derived stream water is generally colder or warmer than

groundwater sources and exhibits greater diel ranges. These relationships

are demonstrated in seasonal graphs of stream water temperatures obtained at three different Idaho wilderness streams in 1994 using a continuous recording device. The graph of mean daily values in Cliff Creek (fig. 4),

shows a decrease in temperature consistent with a loss of surface-fed

discharge. At this time, diel temperature range dropped from 6° to 2-3 °C

per day. The effect of solar input is demonstrated by the increase in mean

temperatures in all three streams through the season, and the difference in

mean temperature among the three streams during midsummer. The

temperature variation among the three streams represents differences in

solar energy input caused by drainage aspect and light attenuation by the

riparian canopy.

Methods: Stage 1, Stage 2, Stage 3 ________

Stream water temperature is measured at one representative location.

Water temperature should not be measured in backwater areas or sloughs

unless these habitats comprise a significant portion of the total habitats;

water mixing in these areas is reduced and temperatures can exceed those

in flowing water. Daily maximum and minimum temperature during the

warmest month of the year is obtained at stage 1. Sampling frequency

increases to obtain 30-day thermograph and annual thermograph records

at stages 2 and 3, respectively. The following tabulation outlines this

process:

Dependent variables Analyses

Stage 1 Maximum daily temperature, Comparative or statistical if multiple

 minimum daily temperature, years or multiple sites are sampled

 daily temperature range

Stage 2 Maximum seasonal temperature, Comparative or statistical if multiple

 minimum seasonal temperature, years or multiple sites are sampled

 seasonal temperature range

Stage 3 Annual (or seasonal) cumulative Comparative or statistical if multiple

 degree days years or multiple sites are sampled

Temperature
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001Figure 4

—Mean daily temperature and cumulative degree days for three streams in the Frank Church Wilderness Area.

0

750

500

250

1,750

1,500

1,250

1,000

5

7.5

10

12.5

15

17.5

Date

Degree Days

Degree Days

Degree Days

Cliff Temp.

Pioneer Temp.

Rush Temp.

10-May-94

C° Mean Daily Temperature 

Cumulative Degree Days

Date
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 27

Minimum and maximum stream temperatures demonstrate the variability of stream water with solar input and air temperature. Maximum

temperatures indicate the suitability of the system for cold water fish.

Maximum/minimum-recording thermometers (photograph 1), are relatively inexpensive, and can be placed within the stream during summer

baseflow and retrieved at a later date. The thermometer should be protected from physical damage by PVC casing. The thermometer casing

should be firmly attached to a stationary object, such as a large root, with

plastic-coated steel cable to keep it from being swept away during high flow.

Placement of the thermometer should be in an inconspicuous location

buried in the streambed and should ensure coverage of the thermometer by

water at baseflow. Before final placement within the stream, the thermometer should be equilibrated with the stream water temperature and

indicators shaken down to rest on top of the mercury column.

Temperature-data loggers are capable of recording daily, seasonal, and

annual temperature information. Though more expensive than maximum/

minimum thermometers, the continuous data obtained often warrants

their use. Temperature loggers, such as those manufactured by the Onset

Corporation (HOBO Temp and Stowaway models) are small (3 x 4 cm) and

light (2.06 g); and therefore particularly suited for wilderness use (photograph 1). These loggers are capable of recording temperatures every 4.8

hours for 360 days. Waterproof cases are needed to prevent water and

physical damage. Placement within the stream is the same as described for

maximum/minimum recorders. Alternatively, temperature data loggers

may be fastened to a stationary object, such as a metal rod, using stainless

steel hose clamps. Figure 4 displays data obtained from HOBO temperature loggers through the summer of 1994.

Photograph 1—Maximum/minimum thermometer and

HOBO temperature data logger. HOBO loggers available

from Onset Instruments Corporation, Pocasset, MA.
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Discharge

Discharge, at summer base flow, is a measure of minimum stream size

and an indicator of potential habitat for fish and aquatic invertebrates.

Discharge (Q) or flow is the product of mean water velocity (v) and cross

sectional area (width (w) x depth (d)) 

( ) Q wdv = . Water velocity varies with

slope, stream depth, hydraulic head, bed roughness, and viscosity. Water

velocity is important biologically by transporting food to filter feeders, and

by influencing the ability of organisms to obtain nutrients, meet respiratory

and photosynthetic requirements, avoid competitors and predators, and

leave unfavorable locations. Some of the methods described in this chapter

differ from standard methods used by stream physical scientists. The

primary purpose of this book is to understand biological systems in

streams, and the methods we describe for monitoring stream discharge are

sufficiently accurate for this purpose. If more comprehensive hydrogeomorphological methods are desired, the reader should consult the

National Handbook of Recommended Methods for Water Data Acquisition

(U.S. Geological Survey 1977) and Stream Channel Reference Sites: An

Illustrated Guide to Field Techniques (Harrelson and others 1994) for clear

and detailed directions.

Methods: Stage 2 ______________________

A summer baseflow discharge measurement is obtained at this stage. A

crude measurement of stream discharge in a wilderness setting may be

obtained by determining mean velocity using the average time it takes five

water-filled fishing bubbles to float a given distance and determining area

as the product of stream width times mean depth. More accurate measurements of discharge require dividing the stream into segments, calculating

discharge for each segment, and summing all segments to obtain total

discharge.

Total flow, as the sum of individual component flows, can be calculated

through the following equation (Platts and others 1983; Rantz and others

1982) (fig. 5):

Q vd w w

i i

i i

i

k

=  −



 



 + −

=

∑ () () 1 1

1 2 (1)

where

wi = horizontal distance from the initial point,

di = water depth for each section,

vi = measured velocity for each section.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 29

The number of sections measured varies with stream size, but no more

than 10 percent of total stream flow should pass through each section.

Water velocity is measured at 0.6 times depth (0.6d) from the surface at

most locations. However, if water depth is below 0.1 m then velocity is

measured at 0.5d, and if depth is greater than 0.76 m, velocity should be

measured at 0.2d and 0.8d and averaged.

Single, uniform stream channels should be used for discharge transect

locations. Confined channels with underlying bedrock direct most of the

flow into the open channel and allow for better discharge measurements.

Stream width is measured with a fiberglass tape stretched from bank to

bank and secured at or above the high water mark. Depth is measured

with a meter stick. Many different water-velocity meters are available

including propeller (Ott meters) and electronic- (Marsh-McBirney) based

equipment. The USGS recommends Price Type AA meters for use in

large streams and Price Pygmy meters in small streams. All velocity

meters should be calibrated prior to use. Top-set rods are desirable but

cumbersome in backcountry conditions.

Methods: Stage 3, Stage 4 _______________

Annual discharge can be monitored by obtaining a relationship between

discharge and water depth (stage). Water depth in remote areas generally

is evaluated by placement of an enamel-coated steel staff gauge. However,

staff gauges must be observed directly each time a measurement is desired,

thereby severely restricting the frequency and timing of measurements.

Continuous records can be obtained from clock or battery driven stage

height recorders or battery operated pressure transducers.

The staff gauge is firmly held within the stream by attachment to a

stationary object. For temporary placement, attachment can be made to a

w

w

w

w

Initial

Point

1

2

3

4

ddd

1

234

2

3

4

w

w

n-1

n

d d n-1 n

Figure 5—Schematic diagram of measurements taken for stream

discharge calculations (Platts and others 1983).
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

post driven into the streambed or a tree, rock face, or bridge abutment on

the stream edge. The gauge is placed out of the main channel to avoid

obstruction of floating debris. The lower edge of the gauge must remain

under water during low flow and the upper edge must be above the high

water mark. Water depth must be read off the gauge and recorded each time

a measurement is obtained.

A high flow gauge, for determining the maximum height of flows for a

given period, can be made by drilling a series of downward angled holes

along a board or pole and inserting plastic test tubes in the holes. The height

of the highest tube containing water is determined and measured. Then the

tubes are emptied and reset for the next period.

In some cases the use of staff and/or high-flow gauges may be aesthetically inappropriate. Some alternatives may be employed under these

circumstances. In wilderness streams, wooden gauges could be constructed

from natural materials and placed under bridges at stream crossings.

Another way of obtaining consistent water depth may be to drive a large

spike, or scribe a mark into the base of bridges at stream crossings or

permanent trees on the stream edge. Location of the spike or mark must be

carefully documented. A measuring tape could then be packed in and gauge

height monitored from this fixed location. Other methods include marks

scribed onto rock faces or large boulders. Additionally, one could use a pair

of bearing trees, one on each side of the stream, or other off-stream markers,

and a tightly stretched line. One would then measure the distance from the

line to the water surface.

Stage height also can be determined from changes in pressure. Pressure

transducers are available from a number of vendors including the Water

Log from H,OFX and the Accustage Level Recorder from Yellow Springs

Instrument (see appendix B). These transducers can be programmed to

obtain readings at desired intervals and the data transmitted by telemetry

from remote locations.

The gauge height/discharge relationship or rating curve is established

through multiple measurements of both variables (minimum 3). Discharge

(see above) is measured along with staff height, and both are plotted on a

log-to-log scale (fig. 6). A best fit, or regression line is then drawn through

the data points. Discharge can be determined directly from the graph or

calculated with the regression equation (example 1).

Estimates of annual peak flows can be obtained using the slope-area

method and Manning’s equation. Manning’s equation is:

 

Q n = AR S 1 23 12 / / (2)

where Q = discharge (m3

/s), n = Manning’s n, A = cross-sectional area (m2),

R = hydraulic radius (m), S = slope. Manning’s n is an indication of

streambed roughness. As bed roughness increases, turbulence and friction

cause a decrease in water velocity. Therefore, as Manning’s n increases,

discharge decreases. Manning’s n can be calculated from previous discharge measurements by solving the equation above for n. This value will

remain valid if the streambed composition remains similar with increasing

flows. In many cases high flows inundate the riparian vegetation, greatly

decreasing water velocity. In this case, a different n value is determined for

this portion of the channel and total discharge is obtained from the sum of
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 31

100

1000

10000

Discharge (L/s)

1 10 100

Gauge Height (cm)

Figure 6—Plot of discharge as a function of

gauge height on a log-log scale. R2

 = 0.990.

Discharge (L/s) = 10(2.166+0.966*Log10(Gauge height cm)).

Data from Rush Creek (1994) in the Frank Church

Wilderness Area, Idaho. Many more data points

than those presented here should be obtained

before applying this procedure.

Example 1—Regression relationship between the log of discharge in L/s, and the log of gauge height

in cm for a straight line. From: y = b + m(x); Log discharge = 2.166 + 0.966(log gauge

height). Discharge values can easily be converted to other units: (L/s) = 0.001(m3/s)

and 0.0353 (cfs).

y = Log x = Log y = yi- x = xi-

(flow L/s) (gauge cm) mean(y) mean(x) x2 xy

3.454 1.342 0.133 0.146 0.021 0.019

3.409 1.322 0.088 0.126 0.016 0.011

3.537 1.380 0.216 0.184 0.034 0.040

2.884 0.740 –0.437 –0.456 0.208 0.199

Mean Mean Sum Sum

3.321 1.196 0.279 0.270

Slope (m)= ∑xy/∑x2 = 0.270/0.279 = 0.966

Y intercept = Mean y – m(Mean x) = 2.166

each separate estimate (Gordon and others 1992). For peak flows, crosssectional area is measured from the seasonal high flow line. This line

usually is marked by the deposition of organic matter (twigs and leaves)

along the stream margin. Hydraulic radius and stream slope are described

further in the chapter titled “Stream and Substratum Morphology.”
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Estimates of long-term discharge data can be obtained through comparisons with local U.S. Geological Survey gauging stations. The regression

relationship between measured discharge data and data from the gauging

station is determined. Historic data from the gauging station then can be

used to estimate discharge at the sampling location.

In stage 4 of the monitoring design, sampling frequency will vary with

objectives. Important discharge characteristics include, maximum and

minimum flows, timing of peak discharge, total yield, and the change in

hydrograph with storm events. With multiple years of data, these characteristics can be used to determine important physical flow variables that

modify the biotic community: flood frequency, flood predictability, and flow

variability (Poff and Ward 1989). All of these characteristics can be

obtained with continuous stage height monitoring. If stage height is

recorded manually, then sampling frequency should increase when there

are rapid changes in flow such as during spring runoff and storm events.

More intensive sampling during high flows or storm events will provide a

better measure of the annual hydrograph (fig. 7). The following tabulation

outlines this process:

Dependent variables Analyses

Stage 2 Seasonal base flow Comparative or statistical if multiple

 years or multiple sites are sampled

Stage 3 Seasonal or 30-day range Comparative or statistical if multiple

Seasonal or 30-day yield years or multiple sites are sampled

Stage 4 Annual yield Comparative or statistical if multiple

Annual range years or multiple sites are sampled

Flood frequency

Flow duration analysis

Jan

Feb

Mar

Apr

May

June

July

Aug

Sept

Oct

Nov

Dec

Discharge

Figure 7—Increased sampling during the changing hydrograph or event

sampling (filled squares) is demonstrated in relationship to monthly or

fixed sampling (open circles). An identical number of samples is shown

in both cases but event sampling provides more information during times

when dissolved and suspended matter are likely to vary markedly.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 33

Measuring solar radiation is important because of its primary and

secondary effects on instream processes. Solar radiation can directly

control rates of instream photosynthesis, and has secondary effects on

stream temperature and flow regime. The amount of solar radiation reaching

a stream surface each day is influenced primarily by stream aspect, latitude,

time of year, and degree of shading. The first three factors affect the amount

of radiation contacting a given surface area. For example, as latitude increases, the portion of incoming light energy is spread over a greater surface

area. By similar means, a southern aspect (in the northern hemisphere)

concentrates solar energy on a reduced surface area. Time of year also affects

solar angle. Secondarily, the amount of solar radiation reaching a stream

surface is influenced by land forms, (for example, canyon or open), clouds,

and vegetative cover that intercept part of the available solar radiation.

Measurements of solar radiation are reported in distinct units based on

two theories of light properties: wave and photon. Radiant energy is

reported in the SI energy unit of Joules. Radiant flux is the energy per unit

time (J/s) and is recorded as Watts. Pyranometers measure radiant flux

over a unit of area and therefore the results are recorded as W/m2

.

Photosynthetically active radiation (PAR) is the light energy between the

wavelength of 400 and 700 nanometers (nm) that is used for photosynthesis. PAR is measured with a quantum meter and is reported in terms of

photons. The units used in PAR measurements are moles or Einsteins (E),

and flux per unit area is related as μmoles/m2

/s or μE/m2/s. PAR values can

be converted to energy units by multiplying by 0.2174; however, this still

represents only energy within the 400 to 700 nm wavelength and is not

comparable with pyranometer measurements.

In small streams (2 to 3 m width), solar radiation is measured at mid

channel at five randomly selected transects in addition to one representative open site. In larger streams solar radiation sample sites, at each

transect, should be stratified with right and left stream margin measurements taken at half the distance from mid channel to bank. Measurements

taken continuously or at least hourly from sunrise to sunset are desirable;

however, sampling every 4 hours can be adequate. The following tabulation

outlines this process:

Dependent variables Analyses

Stage 1 Annual solar input Comparative

Stage 2 Mean daily solar radiation Statistical

Mean daily percent of total

Stage 3 Mean seasonal solar radiation Statistical

Mean seasonal percent of total

Mean extinction coefficient for each season

Stage 4 Mean annual solar radiation Statistical

Solar Radiation
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Methods: Stage 1 ______________________

A rough estimate of actual solar energy reaching the surface of a stream

can be determined with a Solar Pathfinder instrument (appendix B). This

instrument was designed to estimate the PH2 energy available for photovoltaic panels (photo 2) but also has found application to ecological topics

(Tait and others 1994).

Photograph 2—Solar Pathfinder (above) showing, reflecting dome,

tripod and carrying case. A lighter carrying case can be constructed for

wilderness use. PAR sensor and LI-1000 data recorder below.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 35

The Solar Pathfinder estimates energy input based on location and the

portion of total available energy reaching the site (Platts and others 1987),

that is, total energy minus that intercepted by trees, mountains, or other

obstructions. The Solar Pathfinder is set up in the middle of the test stream,

leveled, and oriented to face south. Obstructions that would block solar

input are reflected on the domed surface. The reflection is then outlined on

a solar chart. Values representing the percent of total daily input for each

month are calculated to get independent percent per day values for each

month. These monthly values are then multiplied by a published energy

value for the closest permanent climatological site to obtain energy units

per day for each month. Energy per day for each month is then multiplied

by the number of days in the month, to get a total monthly value (energy per

day x days per month = energy per month), and total monthly values are

summed to obtain an annual value in BTU per ft2. This value can be

multiplied by 0.01136 to convert units to Megajoules per m2

. Estimations

are fairly accurate when few obstructions are reflected on the domed

surface; however, outlining the dense riparian canopy of a small stream is

difficult and the measurements are correspondingly rough.

Methods: Stage 2 ______________________

Stage 2 measurements of solar radiation give an estimate of the portion

of total solar radiation reaching the steam surface. Solar radiation, with a

quantum probe and meter, is measured hourly (at least 0900,1200, 1500,

and 1800 hour) throughout the day at selected stream transects and at a

location that receives direct sunlight. Solar radiation for both sites is

plotted as a function of time; both curves are then integrated to give daily

values. Percent PAR is then calculated as the ratio of these two integrated

values times 100.

Methods: Stage 3 ______________________

Solar radiation varies seasonally due to the changing angle of the sun and

the presence of deciduous leaves. In addition, the amount of radiation

reaching the stream bottom is attenuated by the water column. Absorption

of light by the water column will vary seasonally with turbidity and depth

of the water. Therefore, a more comprehensive measurement of available

light energy is obtained by seasonal measurements of surface and depthintegrated PAR.

Seasonal measurements of surface PAR are obtained by the methods

outlined in stage 2, repeated in the spring, summer, and autumn. Depthintegrated PAR is obtained by taking instantaneous light measurements at

multiple depths. This requires a submersible PAR probe. For example, PAR

is measured at the surface of the water, and at depths of 10 cm, 20 cm, 30

cm, and so forth until the stream bottom is reached. Estimation at depth,

and comparisons between seasons and streams can then be made by
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

calculation and comparison of extinction coefficients. Extinction coefficients are calculated by solving the equation:

Iz = Ioe–kz

where Iz = light at depth z, Io = light at the surface, k = the extinction

coefficient, and z = depth. The extinction coefficient is calculated by plotting

the natural log of Iz/Io as a function of depth. The negative slope of this line

is k.

Methods: Stage 4 ______________________

Stage 4 solar radiation measurements expand upon those outlined in

stage 3 by obtaining continuous measurements. Solar radiation is continuously monitored by using a PAR probe and data logger. The probe is fixed

in a location characteristic of local riparian cover. Solar radiation is

measured throughout the year. The actual amount of radiation reaching

the stream surface, and at various water depths, can be calculated from

monthly estimates of percent of total radiation at the stream surface and

extinction coefficients (as explained under stage 3).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 37

Stream and Substratum

Morphology

The stream substratum is the site of most biotic activity, particularly in

stream sizes most often found in wilderness areas. The composition and

diversity of aquatic insects is often the result of the substratum present

(Minshall 1984). The substratum is the site of algal growth, insect growth

and development, and fish egg incubation. Substratum is determined by

parent geology, but is modified by catchment-level and local processes. That

is, the substratum is affected by inputs from terrestrial sources, and the

forces of water flow. A stable channel has reached an equilibrium point,

balancing inputs with outputs. Monitoring substratum provides a means of

determining stream stability and evaluation of catchment level activities.

As in the chapter on Discharge, some of the methods described in this

chapter differ from standard methods used by stream physical scientists.

The primary purpose of this book is to understand biological systems in

streams, and the methods we describe for monitoring stream and substratum morphology are sufficiently accurate for this purpose. If more comprehensive hydro-geomorphological methods are desired, the reader should

consult the National Handbook of Recommended Methods for Water Data

Acquisition (U.S. Geological Survey 1977) and Stream Channel Reference

Sites: An Illustrated Guide to Field Techniques (Harrelson and others 1994)

for clear and detailed directions.

Quantification of surface substrata size distribution is accomplished by

conducting pebble counts (Wolman 1954). The intermediate (b) axis of

100 randomly selected stones is measured. The substratum size distribution is plotted as cumulative percent finer as a function of particle size

class. This distribution is then used for within and among stream comparisons and estimates of bed stability. Streambed stability is determined by

relating substratum particle size distribution to the kinetic energy of water

at bankfull discharge.

Measurements of channel morphology and substratum size distribution

usually are taken once a year. More frequent measurements are required only

if high flows occur more frequently. The following tabulation outlines this process:

Dependent variables Analyses

Stage 1 Mean stream width Statistical

Mean stream depth

Mean width/depth ratio

Mean and CV of particle size Comparative or statistical if multiple

 sites or years are available

Stage 2 Size distribution Chi-square

Mean percent embeddedness Statistical

Stage 3 Mean and CV of water velocity Statistical

Mean and CV of shear stress Statistical
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Methods: Stage 1 ______________________

The mean and coefficient of variation (CV) in streambed particle sizes

and five measurements of stream cross-sectional morphology are obtained in stage 1. Beginning at the downstream end of the sampling

reach, the intermediate axis of rocks is measured at roughly one meter

intervals as the investigator moves upstream, continually moving at an

angle from bank to bank (see Bevenger and King 1995). A meter stick

(the multipurpose backcountry measuring tool) may be used to measure

these rocks. For greater measuring accuracy and consistency, a lightweight aluminum measuring template may be used. The Hand Held Size

Analyzer (US SAH-97) is available from the Federal Interagency Sedimentation Project at http://fisp.wes.army.mil, and the Gravel Sizing Template is available from Hydro Scientific Ltd at http://members.aol.com/

HydroSci. Mean particle size (or the more commonly used 50 percent

median particle diameter size class) and the coefficient of variation are

used to derive a general impression of the stream particles and should not

be used to statistically compare different sampling reaches or streams.

Substratum particle size is inherently variable, a condition that reduces

the power of statistical comparisons at this stage. The CV is a measure

of habitat variability, and is used as a dependent variable in statistical

comparisons.

Cross-sectional morphology is measured for at least five systematically

selected transects, and may be combined with discharge measurements. A

tape is fixed to the right bank above high water mark, stretched level across

the stream, and secured to the left bank. The distance from the right,

vertical distance to the streambed, and vertical distance to the water

surface is measured at a minimum of 10 points covering the stream

channel. The frequency of measurements should increase with rapid

changes in the channel cross-sectional profile, and measurements should

be taken at all points of significant change in channel form. It is important

to make sure the tape is level; this can be accomplished by ensuring equal

distance from the tape to stream surface at both stream margins. Be certain

to record the point of bankfull width. Data analysis consists of calculating

the mean width, depth, and width/depth ratio for the sampling reach.

In streams that are outside of wilderness, cross-section locations may be

permanently marked with rebar stakes allowing long-term monitoring of

streambed morphology. Inside wilderness, however, there are significant

ethical concerns about permanently marking these locations, as well as

logistical concerns about transporting rebar or wooden stakes. The manager of each wilderness needs to be consulted for allowable practices. Where

long-term monitoring is deemed necessary, some managers may allow

rebar stakes to be driven all the way into the ground so they can be relocated

with a metal detector. Use of a survey-grade Global Positioning System

would allow relocating cross-sections without the use of stakes.

Methods: Stage 2 ______________________

Stage 2 analysis includes two additional field measurements: embeddedness and slope, and estimation of streambed stability. Embeddedness is
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 39

Figure 8—Diagram showing the calculation of slope by hydrostatic leveling.

h h2 1

L

the filling-in of the interstitial spaces surrounding rocks on the streambed by silt or fine sand. This is different than armoring, which is the

protection or covering of fine material by a layer of larger cobbles and

boulders. Embeddedness can reduce streambed surface area and living

space, the flow of oxygen and nutrients to developing fish eggs and aquatic

invertebrates, storage of organic carbon, and entrance to and movement

within the streambed by invertebrates.

Embeddedness is a qualitative estimate of the percent of the substratum

particles covered by fine materials. For each stone-intermediate axis

measurement, the percent of the particle embedded, in 25 percent increments, is recorded. Values are reported with simple statistics: mean,

standard deviation, and coefficient of variation.

Stream slope can be calculated by hydrostatic leveling, hand level and

rod, or with a clinometer. For hydrostatic leveling, two meter sticks and a

20-m length of 10-mm (3

⁄

8 inch) inside diameter tubing are required (fig. 8).

The hose is filled with water and extended along the streambed. When the

water within the tubing stabilizes, the change in height is determined by

the difference in water column height between the upstream and downstream end. The slope is the height (m) difference divided by length (m). The

resulting ratio is unitless but often is multiplied by 100 and reported as a

percentage. To use a hand level and rod, the level is supported on a stick cut

in the field to a known length. A second person supports a rod 25- to 50-m

downstream (folding or pocket rods are suitable for use in remote locations,

available through forestry suppliers, appendix B). By sighting through the

hand level, the height is determined from the rod. The difference between

the level support length and the sighted height on the rod over the distance

between level and rod is the slope. To determine slope with a clinometer, one

person supports a rod marking eye-level while a second person walks

upstream. Looking back downstream through the clinometer the crosshairs are lined up with eye-level on the rod. Slope in degrees, or as a

percentage, is read off of the meter. Slope, as a ratio, is equal to the tangent

of slope in degrees. Clinometers are convenient for wilderness use but

estimating slope by this method is difficult when visibility is limited and

hydrostatic leveling provides a more accurate measurement. With all

these methods every attempt should be made to measure slope between

consistent stream features, such as from the top of one riffle to the top

of the next riffle, or from the bottom of one pool to the bottom of the next

pool. If this is not possible, take several measurements, for example with

the 20 m hydrostatic level, and average them.
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Methods: Stage 3 ______________________

Stage 3 measurements of water velocity and shear stress further characterize the physical habitat. The hydraulic habitat variability as indicated

by the CV of water velocity and shear stress could influence invertebrate

species diversity up to some maximum value above which species diversity

declines (Monaghan and Minshall 1996).

Water velocity and stream water depth are measured at 20 random

locations within the sampling reach. Water velocity is measured at 0.6 to

0.8 times stream depth to characterize the streambed invertebrate habitat.

Shear stress (τ) is determined using the equation:

τ = gSdρ (3)

(Statzner and others 1988), where g is acceleration due to gravity (980 cm/s2),

S is the slope of the water surface, d is water depth (cm), and ρ is the density

of water (1 g/cm3).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 41

Analysis of stream water chemistry provides an understanding of the

environment to which biota are subject and the availability of macronutrients required for growth and reproduction. Stage I analysis provides an

initial evaluation of the general chemical environment. Turbidity is a

measure of light absorbance by water. Turbidity is altered by the amount

of particulate matter in the water column and is an important measurement when waters are subject to potential sediment inputs. Stream water

pH is a measure of hydrogen ion activity which affects many cellular and

biogeochemical processes. The pH of water is affected by the dissolution of

carbonate rocks and biological processes. In addition, the pH of natural

waters can be affected by dissolved nitrogen, phosphorus, and sulfur

compounds in precipitation. Alkalinity is the ability of stream water to

accept hydrogen ions, thereby buffering changes in pH. In natural waters,

alkalinity is due to carbonate and bicarbonate salts. Hardness is a measure

of calcium and magnesium ions that usually are the principal cations in

solution and are required for biotic growth. Specific conductance is the

reciprocal of electrical resistance; in other words, the ability of water to

conduct an electrical current. The conductance of water is affected by

temperature, therefore, specific conductance is standardized by temperature, usually 20 or 25 °C. In addition to temperature, specific conductance

is controlled by the concentration of dissolved salts in water. Specific

conductance can therefore be used to estimate the total dissolved solids in

water. Together, total dissolved solids, hardness, and alkalinity can provide valuable insights concerning the main components dissolved in the

water (Methods: Stage 1).

Stage 2 analysis of water chemistry is a direct measurement of the major

constituents dissolved in water which have biological significance: calcium,

magnesium, sulfate, nitrate-nitrogen, and dissolved orthophosphorus. Stage

1 analysis gives an indirect estimate of calcium- and magnesium- and a

direct measure of carbonate-concentrations. Stage 2 analysis partitions

hardness into its two main components, calcium and magnesium. Sulfate

is the most common anion dissolved in water after carbonate. Sulfate can

contribute to total dissolved solids and high levels may have adverse effects

on stream fauna (for example, Winget and Magnum 1979). Nitrogen and

phosphorus are the main nutrients regulating production and decomposition in streams. At stage 2, chemical analysis is conducted either in the field

or in the laboratory, using prepackaged reagents (Hach Chemical Co., see

appendix B) and spectrophotometry. Field analysis requires a batterypowered spectrophotometer produced by the Hach Chemical Company

Water Quality
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

(appendix B). The DR 700 model weighs 487 g (add 1.7 kg for rechargeable

battery) and is 10 x 22 x 7 cm in size. These methods provide only a rough

estimate for chemicals occurring in low concentrations, particularly nitrogen and phosphorus. Therefore, more accurate (stage 3) evaluation of these

important elements is obtained by field preservation of water samples and

laboratory analysis. The American Public Health Association (APHA)

describes the water sampling, preservation, and analysis methods outlined

in stage 3 (see APHA [1995] for invertebrate and algal preservation and

analysis).

Water samples are taken at one representative location within the

sampling reach where the stream water is well mixed. Always take water

samples upstream from where you are standing and avoid touching the

inside of the sample container or lid. All water samples should be collected

in clean polyethylene bottles. Bottles should be filled and rinsed three times

before the sample is retained. Water samples should be depth integrated.

Depth integrated samples can be taken by inverting the sample bottle,

trapping air within, and then submerging to the bottom of the stream. The

bottle is then allowed to fill as it is slowly moved to the surface. The

following tabulation outlines this process:

Dependent variables Analyses

Stage 1 Water chemistry values (WCV) Comparative or statistical if multiple

 sites or years are available

Stage 2 WCV for each season Comparative or statistical if multiple

 independently sites or years are available

Mean seasonal WCV Statistical

Maximum season WCV Comparative or statistical if multiple

Season range of WCV sites or years are available

Stage 3 Mean annual WCV Statistical

 nutrient flux

Methods: Stage 1 ______________________

Stage 1 water samples are taken once a year usually at baseflow. The

following methods are based on materials in Lind (1985) and APHA (1995).

Specific Conductance/Total Dissolved Solids

Specific conductance is determined using a conductivity probe and meter.

Specific conductance meters usually standardize for a particular temperature, generally 20 or 25 °C, or manual compensation can be made (see

instruction manual for particular instrument). If temperature compensation is unavailable, temperature adjustments can be estimated as conductivity increases from 2 to 3 percent for each degree Celsius. Specific

conductivity (sc) is based on the distance between electrodes, which is

usually 1 centimeter, however, check the probe being used to determine the

cell constant. The cell constant is multiplied by specific conductance

reading to give the final value. Specific conductance is reported in mhos

(reciprocal of ohms) or Sems (1 Sem = 1 mho) per centimeter. Total dissolved

solids (TDS) can be estimated by multiplying specific conductance by
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 43

0.65 (Rainwater and Thatcher 1960). For more accurate work, the conversion factor (k) for TDS should be determined directly for each stream,

region, or geologic type by measuring specific conductance, evaporating the

sample until dry, and determining the weight of the precipitate. The

conversion factor is proportional to the ratio of TDS (mg/L) to specific

conductance (μS/cm):

 

k TDS

SC ∝ . (4)

pH

The pH of water is measured using a hydrogen ion probe and meter. Most

modern pH probes have internal temperature compensation for variable

temperatures (see specific manual). The pH meter should be calibrated

prior to use. Calibration buffer solutions should bracket the expected pH.

In addition, buffer temperatures should be within 10 °C of the stream

water. Laboratory quality pH measurements can be obtained using some

portable field meters that are suitable for wilderness use and available from

a number of suppliers (appendix B).

Turbidity

Turbidity is measured with a nephelometer. This instrument measures

the light reflected at a 90° angle. Nephelometers are available from Hach

Chemical Company. Methods require following the manufacturer’s instructions. Turbidity is recorded in nephelometric turbidity units (NTU).

Alkalinity

The alkalinity of water is subject to change with time, so measurements

should be done in the field when possible. However, high alkalinity (>50 mg/

L CaCO3) samples may be stable for a week or so if not exposed to harsh

conditions.

Equipment and Materials—

1. pH meter with buffer solutions (same as for pH described above)

2. 60-ml plastic syringe or 100-ml graduated plastic cylinder

3. 0.02-N sulfuric acid solution. Dilute 200 ml of 0.1 N sulfuric acid

into 1 liter of carbon dioxide free water (need about 5 ml of solution for each

water sample at alkalinities of 50 mg/L CaCO3)

4. Calibrated dispenser (fig. 9)

5. 250-ml Erlenmeyer flask

Reagents—

1. Use 60-ml syringe or plastic graduated cylinder to dispense 100 ml

of stream water into the flask.

2. Stir gently with calibrated pH probe.

3. Fill dispenser with 0.02-N sulfuric acid solution.

4. Titrate water to pH of 8.3, and record ml of titrant. Titrate water

to pH of 4.5 and record ml of titrant.
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

5. Calculate carbonate and total alkalinity by the following formulas:

Carbonate alkalinity as mg CaCO3 per liter = A x N x 50,000/ml sample

and total alkalinity as mg CaCO3 per liter = B x N x 50,000/ml sample,

where A = ml titration to pH 8.3, B = ml total titration from start to pH 4.5,

and N = Normality of acid.

Under conditions of low pH, the Gran titration method should be used for

alkalinity analysis. This method is based on the rate of pH change with the

addition of acid and provides more precise measurements.

Procedure—

1. Using 60-ml syringe or graduated cylinder, fill titration flask with

100 ml of sample. More precise estimates of sample volume can be obtained

by weighing the titration vessel and the vessel with sample. Sample volume

is computed from sample mass and the density of water.

2. While maintaining continuous stirring, the initial pH is measured

with a rinsed and calibrated pH probe and meter. If the pH drifts, read after

60 seconds.

3. Using a Gilmont Syringe burette, or other calibrated dispenser,

add 0.1 N (or 0.02 N) HCl to sample until pH is less than 4.3. Allow 30-60

seconds for the pH to stabilize before recording. Record pH and volume of

added titrant.

4. Make two further acid additions between pH of 4.3 and 3.7. Record

volume of titrant added and pH after stabilization for the second and third

additions.

10-ml plastic

Pipet

5-mm ID polyethelene

tubing

Glass bead

Plastic automatic pipet tip

Figure 9—Portable calibrated solution

dispenser. The glass bead is forced inside

the polyethylene tubing which is fitted over a

10-ml disposable-plastic pipette. The tip of a

plastic automatic pipette tip is fit into the other

end. The pipette can then be filled with

solution. Drops of the solution then can be

released by pinching the tubing at the glass

ball.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 45

5. Total alkalinity milliquivalents (meq/L) is calculated from the

equation:

 

CaCO meq L V N Vs

3 2

1000 () , / = (5)

where Vs is the sample volume (ml), N is the acid normality, and V2 is the

Y-axis intercept of the regression relationship between Vt (titrant volume)

as a function of F2 (fig. 10). F2 is calculated for each titration as follows:

F2 = 10(5–pH)

 (Vs + Vt) (6)

CaCO3 (meq/L) = 50.04 CaCO3 (mg/ L)

1.55

1.56

1.57

1.58

1.59

1.6

Titrant Volume (ml)

500

2500200015001000

F2

y = 0.000x + 1.542

Figure 10—Calculation of V2 from the regression relationship

between F2 and Vt. From the regression equation the V2 (y-axis)

intercept is 1.542. Sample volume was 119.0 ml, and acid

normality was 0.10; therefore, total alkalinity as CaCO3 = 1.30

(meq/L) or 64.8 (mg/L).
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Hardness

Unpreserved water samples can be returned to the laboratory or analyzed in the field.

Equipment and Materials—

1. 60-ml plastic syringe or 100-ml graduated plastic cylinder

2. Calibrated dispenser

3. 250-ml Erlenmeyer flask

3. Stirring rod

4. White paper (we used reverse side of photocopied methods)

5. Distilled water (25 ml for each sample)

Reagents—These may be made up in the laboratory as indicated below

or purchased in prepared form from major chemical supply houses (appendix B).

1. Buffer solution. Dissolve 16.9 g ammonium chloride (NH4Cl) in

143 ml concentrated ammonium hydroxide (NH4OH). Add 1.25 g magnesium salt of ethylenediaminetetraacetic acid (EDTA) and dilute to 250 ml

with distilled water.

2. Indicator. Mix 0.8 g Eriochrome Black T dye and 100 g NaCl to

prepare a dry powder mix.

3. Standard EDTA titrant (0.01 M). Dissolve 0.3723 g Na2EDTAdihydrate in distilled water and dilute to 100 ml. Check by titrating against

a standard calcium solution: 1.00 ml = 1.00 mg CaCO3 = 0.4008 mg Ca.

4. Standard calcium solution. Weigh 1.000 g anhydrous calcium

carbonate powder, primary standard grade, into a 500 ml Erlenmeyer

flask. Add slowly one volume HCl diluted with an equal volume of distilled

water until all the CaCO3 has dissolved. Add 200 ml distilled water and

boil for a few minutes to expel CO2. Cool and adjust to pH 5.0 with either

NH4OH or 1 + 1 HCl. Transfer to a 1-liter volumetric flask, washing out the

Erlenmeyer flask several times with distilled water and adding to volumetric flask. Then dilute to mark with distilled water.

Procedure—

1. Dilute 25 ml of sample to about 50 ml with distilled water in

titration flask.

2. Add 1 to 2 ml of buffer solution to bring pH to 10.0 or 10.1.

3. Add approximately 0.1 g indicator powder.

4. Titrate with EDTA over a white surface with daylight or white

light. Stir continuously until the last red tinge disappears. Add the last

drops slowly, allowing about 5 seconds between drops. The entire duration

of titration should not exceed 5 minutes and should not require more than

15 ml of titrant. If more titrant than this is used, take a smaller aliquot and

repeat titration. An indistinct end point suggests interference and calls for

an inhibitor after step 2. Old indicator powder also produces an indistinct

end point.

Hardness as mg CaCO3/L = A x B x 1,000/ml of Sample (7)

where A = ml titration, and B = mg CaCO3 equivalent to 1.00 ml EDTA

titrant.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 47

Estimation of Major Ions

Estimates of the major cations and anions in water are possible using

measurements of total dissolved solids, alkalinity, and hardness. The

major cations in water are Ca+, Mg2+, and Na+. The major anions in

water are HCO3

–

, SO4

2–

, and Cl–. For example, if total dissolved solids are

200 mg/L, hardness is 150 mg/L, and alkalinity is 100 mg/L, then calcium

and magnesium carbonates constitute 100 mg/L. Therefore 50 mg/L are

calcium or magnesium sulfates or chlorides (difference between hardness

and alkalinity). The remainder of the total dissolved solids, 50 mg/L

(difference between hardness and total dissolved solids) are sodium sulfates or chlorides.

Methods: Stage 2 ______________________

Calcium

Equipment and Materials—

1. 60-ml plastic syringe or 100-ml graduated plastic cylinder

2. Calibrated dispenser

3. 250-ml Erlenmeyer flask

3. Stirring rod

4. White paper (plastic laminated 3 x 5 card)

Reagents—

1. Sodium hydroxide, 1 N. Dissolve 4 g NaOH in distilled water and,

when cool, dilute to 100 ml.

2. Murexide indicator. Grind together in a mortar 0.2 g powdered dye

and 100 g NaCl. Store in tightly stoppered bottle.

3. Standard EDTA titrant, 0.01 M. Same as in hardness determination. (1.00 ml = 0.4008 mg Ca).

Procedure—

1. Take a sample that contains less than 10 mg calcium. Usually a 50

ml water sample is correct but, if total alkalinity is greater than 250 mg/L,

it probably will be better to take a smaller aliquot and dilute to 50 ml with

distilled water.

2. Add 1 to 2 ml NaOH solution to produce a pH of 13 to 14. Stir.

3. Add about 0.2 g indicator powder. The color change is from pink to

purple on titration.

4. With continuous stirring, titrate slowly over a white surface with

the EDTA titrant. Since this is a gradual color change, the end point

recognition is facilitated by preparing a reference end point by adding

NaOH, indicator, and 1 or 2 ml EDTA to 50 ml distilled water.

mg Ca/L = A x B x 400.8/ml of sample,

where A = ml titration for sample and B = mg CaCO3 equivalent to 1.00 ml

EDTA titrant.

Magnesium—If both calcium concentration and hardness are known,

magnesium concentration can be calculated by difference (Rainwater and
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Thatcher 1960). Milliquivalents of hardness per liter are calculated from

milligrams of hardness per liter. The milliquivalents of calcium per liter are

subtracted from this, and the difference is multiplied by the equivalent

weight of magnesium to express magnesium in milligrams per liter.

milliquivalent hardness/L = mg hardness/L x 0.01998

milliquivalent Ca+2/L = mg Ca+2/L x 0.0499

mg Mg+2/L = 12.16 x (meq hardness/L – milliquivalent Ca+2/L)

Alternatively, Standard Methods (APHA 1995) states: hardness, mg

equivalent CaCO3/L = 2.497 (Ca, mg/L) + 4.118 (Mg, mg/L).

Sulfate—The following methods are from Hach Chemical Company

Equipment and Materials—

1. Portable spectrophotometer

2. 2.54-cm test tubes or cuvettes

3. 125-ml Erlenmeyer flask

4. 60-ml syringe

Reagents—

1. Standard sulfate solution (1.00 ml = 0.10 mg SO4). Using a

microburette, measure 10.41 ml standard 0.02-N H2SO4 titrant (from

alkalinity procedure) into a 100 ml volumetric flask and dilute to mark with

distilled water.

2. SulfaVer powder. From Hach Chemical Company

Procedure—

1. To a 25 ml sample in a 125 ml flask add 1.0 g SulfaVer powder, and

swirl evenly for 1 minute. A suspension of barium sulfate forms.

2. Pour entire sample into one of a pair of 2.54-cm test tubes that are

matched for spectral qualities, and let stand for 3 minutes.

3. Read absorbance produced by this suspended turbidity at a wavelength of 420 nm on a spectrophotometer. Estimate milligrams of sulfate by

comparing with a standard curve prepared by applying the same procedure

to a series of known standard concentrations. The highest standard should

not exceed 40 mg/L (1 mg/25 ml sample), since this method fails above that

concentration.

Nitrate Nitrogen

Equipment and Materials—

1. Portable spectrophotometer

2. 2.54-cm test tubes or cuvettes

3. 125-ml Erlenmeyer flask

4. 60-ml syringe

Reagents—

1. Hach NitraVer VI powder pillows (Hach Chemical Company)

2. Hach NitriVer III powder pillows (Hach Chemical Company)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 49

3. Stock nitrate solution (1 ml = 100 μg NO3-N). Dissolve 0.7218 g

anhydrous potassium nitrate (KNO3) and dilute to 1,000 ml with demineralized water.

4. Standard nitrate solution (1.00 ml = 2.5 μg NO3-N). Dilute 25 ml

stock solution to 1000 ml with demineralized water. Prepare fresh weekly.

5. Standard curve for nitrate nitrogen concentration in the original

water sample.

Procedure—

1. Add the contents on one NitriVer III powder pillow to a 25 ml

sample in an Erlenmeyer flask. Shake for 30 seconds. If a pink color

develops within 10 minutes, nitrite nitrogen is present. This may be

quantified by starting with step 5 below.

2. Add the contents of one NitraVer VI powder pillow to a 30 ml water

sample (or standard) in the glass bottle or flask. Stopper and shake

vigorously for at least 3 minutes. Be sure standards and samples are

shaken in exactly the same manner.

3. Wait 30 seconds to allow the cadmium metal to settle, then decant

25 ml into a clean flask.

4. Add the contents of one NitriVer III powder pillow and shake for

30 seconds.

5. If nitrate (or nitrite) nitrogen is present, a pink color will develop.

Allow the color to develop. After 10 minutes, but before 20 minutes,

measure the absorbance using the 2.54-cm test tubes and the spectrophotometer set at 500 nm. Determine the concentration of nitrogen from the

standard curve. If nitrite was detected in step 1 but not quantified, report

the results as combined nitrate and nitrite nitrogen.

Orthophosphorus

Equipment and Materials—

1. Portable spectrophotometer

2. 2.54-cm test tubes or cuvettes

3. 125-ml Erlenmeyer flask

4. 60-ml syringe

Reagents—

1. PhosVer 3 (Hach Chemical Company)

2. Stock phosphorus solution. A stock solution in which 1.00 ml

equals 0.05 mg phosphorus is prepared by dissolving 0.2197 g potassium

dihydrogen phosphate in distilled water. Dilute this to 1.0 liter. Add 1 ml

chloroform and store in the dark under refrigeration. The solution is stable

for several months.

3. Standard solution. Dilute 10.0 ml of phosphorus solution to 1.0 liter

with glass-distilled water. Should not be stored for more than a few days.

Procedure—

1. Fill an Erlenmeyer flask with 25 ml of sample water.

2. Add contents of PhosVer 3 powder pillow. Swirl and allow to react

for 2 minutes.
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

3. Pour the solution into cuvette and read absorbance at 890 nm then

calculate concentration from standard curve.

Methods: Stage 3 ______________________

At stage 3, sampling frequency is adjusted to obtain an estimate of

seasonal to annual changes in water chemistry and an estimate of nutrient

flux. Therefore sampling frequency increases and should reflect changes in

discharge, with more frequent sampling during the rising hydrograph

(fig. 7). Stage 3 water analysis provides more accurate evaluation of

element concentrations, particularly when concentrations are low. Stage 3

water analysis requires collecting and preserving samples in the field.

Sample preservation per Standard Methods (APHA 1995) is as follows.

However, preservation may vary with the laboratory conducting the

analysis.

Nitrogen: Ammonia

A 100-ml water sample is required and should be filtered immediately

after collection, using a 0.45 μm pore size filter. Filter holders that attach

to leur-lock syringes are available from supply companies and are suitable

for wilderness use. Preserve filtered samples with about 0.8 ml concentrated H2SO4/L to a pH between 1.5 and 2 and store at 4°C. The pH of the

acid-preserved sample should be between 1.5 and 2.

Nitrogen: Nitrate

Samples (100 ml) should be filtered as above and frozen or stored at 4 °C.

If nitrite analysis is not required, samples can be acidified with 2 ml

concentrated H2SO4/L.

Dissolved Orthophosphorus

Filtered samples (100 ml) are stored acidified with 1 ml concentrated

HCl/L and frozen. Do not store samples containing low concentrations of

phosphorus in plastic bottles unless they are frozen, because phosphates

can adsorb onto the walls of the bottles and be lost from solution. Rinse all

glassware for storage and analysis in hot dilute (0.1 molar) HCl, then rinse

several times in distilled water. Never use commercial detergents containing phosphate for cleaning glassware used in phosphate analysis.

Nutrient Flux

Nutrient flux is a measure of the total quantity of an element passing a

given point. Nutrient flux is a measure of nutrient availability (Fisher

1990) and has been used to evaluate changes in catchment level processes.

Nutrient flux is the product of element concentration and discharge. Total

yield for any given time interval can be determined by graphing nutrient

flux over time and integrating the area under the curve (fig. 11). Standardization by basin area allows for comparable measurements.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 51

0

200

400

600

800

1000

NO

3 Flux (g/day)

24-Apr 3-June 2-Aug 12-Oct

Date

Figure 11—Nitrate flux from April through October 1994 for

Pioneer Creek within the Frank Church Wilderness. Total yield,

calculated by determining the area under the curve, was 82.9 kg

NO3-N. Standardized by basin area (17 km2) is 4.9 g/m2.
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Macroinvertebrates are important indicators of water quality and the

primary food-base for fish in wilderness areas of the western U.S.A.

Macroinvertebrates are good indicators of stream quality because of their

relative lack of mobility and most have life spans of a few months to a few

years (Plafkin and others 1989; Platts and others 1983). The limited

mobility allows monitoring of local conditions, in addition to the integration

of watershed-level disturbances. Their short life span makes them characteristic of conditions in the recent past (Platts and others 1983).

Multiple metrics are used because it is unlikely that any has sufficient

sensitivity to be useful under all circumstances (Karr 1991). For the same

reason, the values for each measure should be kept separate, in addition to

summing them to produce a single index value. Separating the values gives

additional information and avoids defeating the purpose for multiple

analyses by preventing an inappropriate or insensitive index component

from obscuring the “signal” from a component that is appropriate or

sensitive or both (Steedman and Regier 1990). Graphing individual metric

values from reference and impact sites and visually evaluating differences

(Fore and others 1996) is the recommended method for determining the

valuable metrics for a given impact. The following tabulation outlines this

process:

Dependent variables Analyses

Stage 1 Mean total metric score Statistical

Mean value for each metric

Stage 2 Mean biomass Statistical

Stage 3 Total production Statistical

Production for each species

 and feeding group

Methods ______________________________

The methods for invertebrate sampling are the same for the different

stages of monitoring presented in this manual. A Surber sampler is

recommended because of its portability and widespread use by Federal and

State agencies. For wilderness use, it is recommended that repair equipment (for example, needle and thread, hot-glue stick) and a spare sampler

be included as standard equipment. Progressing to a higher stage of

analysis requires increased sampling frequency and level of data analysis.

1. The sampling location should be approached from downstream and the

frame of the Surber sampler (250-μm mesh net) placed into position as

Macroinvertebrates
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 53

quickly as possible to reduce the potential for escape by highly mobile

macroinvertebrates. Try to keep the bottom square part of the frame flush

with the substratum, and the bottom front edge of the net tight against the

streambed.

2. The larger rocks within the perimeter of the open quadrant frame

should be lifted by hand, rubbed and rinsed off at the mouth of the net

opening, and removed from the sampler. The remaining substratum should

be thoroughly disturbed to a depth of 10 cm by repeatedly digging and

stirring with a probe (for example, a large nail or a railroad spike), included

in the benthic monitoring kit (see photo 3). The invertebrates and lighter

debris will be carried into the net by the force of the current.

1

2 3 4 5

6

7

Photograph 3—Benthic sampling kit in canvas carrying

case (above) containing the following equipment (below):

(1) plastic pan, (2) squirt bottle, (3) grease pencil and

pencil, (4) legs of ring stand, (5) railroad spike, (6) ring

stand, and (7) cone shaped net (100 μm mesh).
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

3. When sampling is completed, the top of the net should be tipped

downstream until a 45° angle is formed with the streambed and the sampler

quickly removed from the water with a simultaneous forward and upward

motion. The net should be dipped several times in the stream to wash the

contents to the bottom, being careful not to submerge the net opening.

4. Grasp the net firmly with your thumb and forefinger just above the

contents and invert the net into a shallow pan (a white enamel pan or plastic

container approximately 40 cm long, 25 cm wide, and 5 cm deep is good)

partially filled with water. It may be necessary to partition the contents into

segments if it looks like they will fill the pan to overflowing. When the bulk

of the contents have been removed from the net, re-invert the net, re-dip it

in the stream, and again remove the contents. Carefully examine the

interior of the net, especially the seams, for any adhering material and

remove. A stream of water from a wash bottle is helpful at this stage.

5. Gently slosh the contents of the pan back and forth to suspend the

invertebrates and other organic matter and quickly pour the suspended

material into a cone-shaped net (a 3-legged ring stand makes a good holder

for the net) (photo 4). Repeat the process until all organic matter is removed

from the pan. As a final step, again partially fill the pan with water, spread

the inorganic sediments in a thin layer evenly over the bottom of the pan,

and examine the contents for any organisms remaining (photo 4) (for

example, stone-cased caddisflies, planarians). Remove these with fingers

or a forceps and place with the portion of the sample to be retained. When

finished, discard the inorganic sediments.

6. Transfer the contents of the cone-shaped net into a sample container

(for example, a whirl-pak bag) using a minimal amount of water (a wash

bottle is helpful here), label with location and date, add sufficient water to

cover the contents, and preserve to a final concentration of 5 percent

Photograph 4—Field processing benthic samples.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 55

formalin (2 percent formaldehyde) (= 5 ml concentrated formalin (40

percent formaldehyde) per 100 ml water) or other preservative. For safety’s

sake all pouring and transfers should be done over the opening of the net

or an empty pan. If a substantial amount of the sample is lost (an amount

of material the size of a pea may contain a 1,000 or more organisms or

several mg of organic matter on a per square meter basis), the entire sample

should be discarded and a new sample taken. The properly packaged and

preserved sample can then be transported to the laboratory for sorting and

identification of macroinvertebrates.

7. Prior to identification, the sample should be coarse-sorted into major

taxonomic groups. A small portion of the sample, no larger than a large

teaspoon, should be placed into a clear petri dish containing a small amount

of water. Invertebrates then are handpicked, using forceps, under a

dissecting microscope, and placed in leakproof containers containing preservative (10-ml glass vials with plastic caps work well). All vials from a

sample should be kept together and labeled appropriately. For large

samples, subsampling facilitates the process and may be aided by the use

of mechanical devices. If the sample is subsampled, a minimum of 300

individuals should be sorted (Plafkin and others 1989). The portion of the

total sample examined must be recorded.

8. The invertebrates are then identified to the lowest taxonomic level

feasible, given the goal of the particular study. Species is the preferred level

of identification, because many species look alike but behave differently

ecologically, however, in many cases genus is satisfactory for initial

bioassessment purposes. The dipterans, Chironomidae, and Simuliidae

commonly are identified only to subfamily due to the difficulty of more

detailed identification. This usually can be accomplished with a dissecting

microscope, but in some cases a compound microscope will be required. The

number of individuals in a taxonomic group is recorded.

9. If biomass values are needed (see stage 2), organisms should be

returned to storage vials after identification and counting. Each species

group is placed in a separate vial. Each vial should be properly identified

by sample location, date, and replicate.

Methods: Stage 1 ______________________

Stage 1 data analysis and sampling frequency follows the procedures

from Rapid Bioassessment Protocol III (RBP III) (Plafkin and others 1989),

modified by the use of additional metrics. The calculation of some biotic

metrics requires classification of aquatic insects by functional feeding

group (Cummins 1973, 1974). Functional feeding groups provide information concerning resource utilization by invertebrates in streams. A shift in

the relative abundance of the different functional feeding groups can

therefore indicate a shift in the resource base. Initial placement of an

aquatic insect into a particular functional feeding group can be accomplished by consulting “An Introduction to the Aquatic Insects of North

America” (Merritt and Cummins 1996) However, direct analysis of gut

contents is the preferred method for functional feeding group classification.

A general outline of the functional feeding groups is given in table 5 (also

see appendix C). Once organisms are identified to species, and classified by
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Table 5

—General functional feeding group divisions showing food resource, particle size and representative orders (after Merritt and Cummins 1996).

Functional group (based Subdivision of functional group Food particle size Representative

on feeding mechanism) Dominant food Feeding mechanism (micron) orders

Living Vascular hydrophyte tissue Herbivores-chewers and Trichoptera

 miners Lepidoptera

Coleoptera

Diptera

Shredders Decomposing Plant tissue (CPOM) Detritivores-chewers >103 Plecoptera

 and wood borers Trichoptera

Coleoptera

Diptera

Detritivores-filterers or Ephemeroptera

 suspension feeders Trichoptera

Lepidoptera

Diptera

Collectors Decomposing fine particulate Detritivores-gatherers or <103 Collembola

 organic matter (FPOM) deposit feeders Ephemeroptera

Hemiptera

Trichoptera

Coleoptera

Diptera

(con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 57 Collectors Periphyton-attached algae and Herbivores-grazing scrapers <103 Ephemeroptera associated material of mineral and organic surfaces Hemiptera Trichoptera Lepidoptera Scrapers Coleoptera Diptera Living vascular hydrophyte cell Herbivores-pierce tissues >102-103 and tissue fluid or cells Neuroptera Piercers-Living animal tissue Carnivores-attack prey and Megaloptera pierce tissues and cells and suck fluids Predators Engulfers-Living animal tissue Carnivores-whole animals or >103 Plecoptera parts Odonata Hemiptera Neuroptera Trichoptera Coleoptera Table 5 (Con.) Functional group (based Subdivision of functional group Food particle size Representative on feeding mechanism) Dominant food Feeding mechanism (micron) orders
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

functional feeding group, the following metrics are calculated (after Robinson

and Minshall 1995).

1. EPT/Chironomidae + Oligochaeta Ratio (EPT/C+O)—Based on the

relative abundance of Ephemeroptera, Plecoptera, Trichoptera to

Chironomidae and Oligochaeta to assess community health. A disproportionate number of the relatively pollution tolerant Chironomidae and

Oligochaeta suggests degraded habitat conditions.

2. Species Richness (Sp. Rich)—This metric reflects health of the community through a measure of the number of distinct species (or taxa) present.

Typically, a higher number of taxa suggests good habitat quality.

3. EPT Richness (EPT Rich.)—The total number of distinct taxa in the

orders Ephemeroptera, Plecoptera, Trichoptera. These groups are generally sensitive to pollution, with a low EPT Richness indicating degraded

habitat quality.

4. Hilsenhoff’s Biotic Index (HBI) detects organic pollution stress in

communities inhabiting stream riffles. HBI summarizes the pollution

tolerance of each taxon in the community, based on the abundance of

respective taxa, into a single value. Higher values typically indicate greater

levels of organic pollution. HBI is calculated as:

 

HBI x t

n

i i = ∑ (8)

where, xi = number of individuals within a species, ti = tolerance value of a

species, n = total number of organisms in the sample. Tolerance values are

available for the Western United States in Water Quality Monitoring

Protocols Report No. 5 (Clark and Maret 1993) and are reprinted in

appendix C.

5. EPT/Chironomidae Ratio—Uses the relative abundance of these

indicator groups to assess community balance. A high number of

Chironomidae indicates degraded habitat conditions.

6. Percent Dominance—A simple measure of a community’s redundancy

and evenness. The measure assumes that a highly redundant community

is impaired. Percent dominance is the number of individuals in the

dominant taxa (or 2 to 3 dominant taxa) to the total number of individuals

times 100.

7. Simpson’s Index (C)—A diversity index that reflects dominance or

evenness of an assemblage. Simpson’s index is:

 

C pi = ∑( )2 (9)

where, pi is the proportion of individuals in the ith species.

8. Percent Shredders—Measures the relative abundance of the shredding functional feeding group. A low number of shredders reflects poor or

altered riparian conditions.

9. Density—The number of macroinvertebrates in a given area. Low

benthic densities reflect degraded habitat conditions.

10. Percent Scrapers—A relative measure of the abundance of the

scraping functional feeding group. A greater percentage of scrapers suggests good habitat quality.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 59

11. Percent Filterers—A relative measure of the abundance of the

filtering functional feeding group. A large percentage of filterers may

indicate excessive sediment/organic load and consequently poor habitat

quality for most of the community.

12. Percent EPT—The relative abundance of Ephemeroptera, Plecoptera,

and Trichoptera in a stream. These groups are generally intolerant to

pollution and used as indicator taxa.

13. Percent Chironomidae + Oligochaeta—Measure of the relative abundance of the generally pollution tolerant groups. A community with a high

percentage of these organisms may indicate excessive erosion and/or

sediment/organic load in the stream.

14. Percent Chironomidae—A measure of the relative abundance of the

generally pollution tolerant group Chironomidae. A community with a high

percentage of Chironomidae may indicate excessive erosion and sediment/

organic load in the stream.

15. Percent Ephemeroptera, Percent Plecoptera, and Percent

Trichoptera— Measure of relative abundance of these pollution intolerant

groups.

In wilderness streams, the confidence interval for each metric obtained

from multiple samples of similarly classified stream locations can be used

to determine rank scores for each metric. Disturbance can then be evaluated by comparing metric scores from control and impacted sites. That is,

how far does the stream in question vary from the confidence interval

obtained from unimpacted sites. Alternatively, single stream trends can be

monitored on an annual basis, or one control and impact site can be

compared statistically. The first method however, is probably most consistent with the needs of wilderness stream managers and will be outlined in

more detail.

The mean (5 replicates) metric values for each stream are recorded

(example 2). The mean and 90 percent confidence interval for each column

is calculated. Each metric is then given a rank score (SC): 5 if metric value

better than upper confidence limit, 3 if within confidence limit, and 1 if

below confidence limit. Each metric is then interpreted individually, along

with the sum of all metric scores.

Methods: Stage 2 ______________________

Stage 2 increases the level of analysis beyond the indices outlined in

stage 1. In addition to the modified Rapid Bioassessment Protocol III, total

invertebrate biomass is calculated. For biomass measurements, invertebrates are dried (60 °C for 24 hours). If the biomass for each individual taxa

is required for secondary production calculations (see stage 4), each taxonomic group and each size class is dried and weighed separately. This

requires a balance with the ability to measure to 10–5

 grams, that is, 0.1 to

0.01 mg. Alternatively, the entire invertebrate sample can be combined,

dried, weighed, and standardized by surface area (cross sectional area of

the sampler). For AFDM values, the sample is then ashed (550 °C for 2

hours) rewetted, dried, cooled to ambient temperature in a desiccator, and
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Example 2

—Raw data for selected metrics. Below each column is the mean, upper confidence interval, and lower confidence

interval. Rank scores were based on the relationship to the raw score distribution. Total rank for each row in

addition to individual metric scores are used to evaluate each stream.

Stream EPT/C + O Score Sp. Rich Score EPT Rich Score HBI Score

1 2.7 3 22 3 12 3 3.57 3

2 1.2 3 24 5 16 5 3.94 3

3 1.1 3 24 5 17 5 3.19 5

4 0.8 3 24 5 18 5 3.6 3

5 1.2 3 27 5 16 5 4.07 3

6 8.3 5 17 1 10 3 3.16 5

7 1.3 3 12 1 5 1 3.08 5

8 0.3 1 21 3 13 3 4.46 1

9 0.5 3 17 1 9 1 4.37 1

10 0.4 1 18 3 7 1 4.45 1

Mean 1.79 20.6 12.3 3.79

St. Error 0.72 1.36 1.34 0.16

Upper 90%CI 3.10 23.09 14.76 4.09

Lower 90%CI 0.47 18.11 9.84 3.49

Score 5 >3.1 >23 >14.8 <3.49

3 0.47-3.1 18-23 9.8-14.8 3.49-4.09

1 <0.47 <18 <9.8 >4.09
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 61

reweighed and AFDM is obtained by difference. Invertebrate density,

richness, and biomass values can then be used to compare different streams

or to monitor streams over time. Additionally, AFDM values can be used to

construct biomass pyramids and quantify food webs which can be partitioned by taxon or functional feeding group.

Methods: Stage 3 ______________________

At this stage of analysis, secondary production is calculated. Annual

estimates are desirable but interval production for one or more seasons are

valuable. Annual and interval estimates require at least monthly sampling. For annual estimates, sampling must continue throughout the year.

Secondary production is a measure of the amount of energy transferred to

primary consumers and predatory insects. Secondary production is important in quantifying the flow of energy through an ecosystem (Benke 1984).

Secondary production also is an estimate of the energy available to fish,

which are an important food and recreational resource. Evaluation of

secondary production for functional feeding groups also gives a better

understanding of the relative importance of various food resources.

Secondary production is calculated at the level of a population. Total

community production, or production of functional feeding groups, can be

obtained only by summing all population secondary production estimates.

There are two general methods used to calculate secondary production

(Benke 1993). The method used depends on whether or not individual

cohorts can be followed. A cohort is a group of individuals of the same

species that have similar hatching times and developmental rates; that is,

a group of individuals that hatch on or near the same date and obtain

similar sizes at similar times (fig. 12). If cohort production occurs, an

invertebrate sample, on any given date, should contain individuals (within

a population) of similar size. Non-cohort production occurs when hatching

and development are distributed over time or when individuals from more

than one life cycle are present at a time. Samples of a non-cohort population

would produce individuals of many different sizes.

For cohort production, the instantaneous growth or increment-summation method can be used. Both of these methods are explained in Benke

(1984). For non-cohort production, the size frequency method produces the

best estimate. Because non-cohort production is common, and because the

size-frequency method also can be used for cohort production, the sizefrequency method is most generally applicable and will be described here.

The size-frequency method assumes that the size-frequency distribution,

at any given time, will be similar. That is, that the density of individuals of

a given size class should be similar across multiple sampling dates.

However, this assumption does not have to be met. This method also

assumes that the number of size classes present reflects the number of

cohorts. That is, if species size distribution can be distributed into 11

different 1-mm size classes then 11 different cohorts are present at this time

(fig. 12).
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Secondary production is calculated, by integrating the area under the

size-frequency distribution (Benke 1993), using means similar to removal

summation methods for cohort production. However, the size frequencydistribution is constructed from the mean numbers of individuals from each

size class throughout the sampling period (fig. 13) and the individual

weights of the different size classes (table 6). This integrated value is

multiplied by the number of size classes (representative of the number of

cohorts) and corrected by cohort production interval (CPI). That is,

 

P

CPI = i NW ∑ 365 ∆ , (10)

where i = the number of size classes, N = the mean number of individuals

in that size class (individuals/ m2

), and W equals weight (mg/individual)

(example 3). Integration of the area under the size-frequency distribution

results in interval production (IP) (table 6) which is used to calculate the

units used to describe secondary production.

TIME

Individual Cohorts

LENGTH OR MASS

Sampling Times

Size Classes

Cohort Production Interval

Figure 12—Representation of an invertebrate population over time.

Each curved line represents growth of an individual cohort. The vertical

lines indicate five different sampling periods. The horizontal rectangles

represent six different size classes. Each sampling period transects six

different cohorts but size class distribution is similar for each sampling

period. The number of size classes equals the number of cohorts.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 63

Table 6—Parameters used to describe secondary production (after Benke 1993).

Symbol Definition Units Description

W Individual weight mg Individual weight of animals

N Density No./m2 Density of individuals

B Biomass g/m2 Biomass of individuals

P Annual production g/m2/yr Biomass produced over a year

IP Interval production g/m2 Biomass produced over an

arbitrary time

IPc Cohort production g/m2 Biomass produced over the

cohort production interval

IPc/B Cohort P/B Relationship between cohort

production and biomass

usually ranges from 2 to 8

P/B Annual production /yr Relationship between annual

production and biomass

1-2 

2-3 

3-4

4-5

5-6

6-7

7-8

1

2

3

4

5

6

7

Mean Number/m 2

Mean Mass or Length/Individual

Size Classes (mm)

∆N/m = N -N 2

i i+1

Weight at Loss = w +w

2

i i+1

High

Low

Low High

Figure 13—Size-frequency distribution for macroinvertebrate

secondary production estimates. The mean number of individuals

over the sampling period are plotted against the mean mass for

individuals in that size class. Interval production is calculated by

integrating the area under the curve. Integration is accomplished by

summing all numbered areas.
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Example 3

—Calculation of secondary production for Drunella doddsi in Cliff Creek from Surber samples taken (five replicates) over a 7 month

interval beginning in April 1994. The population is enumerated and each individual length is measured. Individuals are separated

into 1-mm size classes, dried, and weighed. Column A is the length of size class. Column B is the total number of individuals in that

size class collected over the entire sampling period. Column C is the mean number of individuals per sample, column B divided by 35

(7 months x 5 replicates). Column D is corrected by the area of the Surber sampler. Column E is the mean weight per individual in the

size class and column F is weight per area (column D x C). The sum of column F is biomass (B). Column G is the difference in numbers

(column D) between size classes, generally the number of individuals lost in moving to the next highest size class. Column H is the

mean individual weight between adjacent size classes. Column I is the product of G and H. Interval production is the sum of I multiplied

by the number of size classes.

A BC D E FG H I

Length (mm) Number Number/ Number/ m2 Weight Weight

∆ N/m2 Weight at Loss Weight Loss

Sample (mg/Indiv.) (mg/m2) (mg/Indiv.) (mg/m2)

B/35 D x E G x H

1 0.8 - 2.0 148 4.229 45.517 0.036 1.635

2 2.0 - 3.0 145 4.143 44.595 0.083 3.707 0.923 0.060 0.055

3 3.0 - 4.0 52 1.486 15.993 0.142 2.273 28.602 0.113 3.221

4 5.0 - 6.0 9 0.257 2.768 0.266 0.737 13.225 0.204 2.701

5 6.0 - 7.0 6 0.171 1.845 0.889 1.641 0.923 0.578 0.533

6 7.0 - 8.0 3 0.086 0.923 1.455 1.343 0.923 1.172 1.082

7 8.0 - 9.0 9 0.257 2.768 3.182 8.809 –1.845 2.319 –4.279

8 9.0 -10.0 33 0.943 10.149 5.142 52.187 –7.381 4.162 –30.722

9 10.0 -11.0 38 1.086 11.687 8.758 102.352 –1.538 6.950 –10.687

10 11.0 -12.0 20 0.571 6.151 14.416 88.672 5.536 11.587 64.144

11 >12.0 5 0.143 1.538 27.749 42.670 4.613 21.082 97.258

1.538 13.874 21.335

306.0 144.6

IP = 11 x 144.6 mg/m2 = 1,591.0 mg/m2

or 1.59 g/m2

B = 306.0 mg/m2

or 0.306 g/m2

IP/B = 5.2/7 months
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 65

Fish

Sampling the fish community at stage 1 is optional but is included as a

means to evaluate potential impacts and because fish are often of prime

importance due their recreational and commercial value. Analysis of fish

community data for the evaluation of impacts is limited due to planting of

sport fish, fishing pressure, and in the West, low diversity. In anadromous

fish streams, juvenile salmonids can vary with the number of returning

adults and with different commercial and sport fish management plans.

This variability makes statistical comparisons difficult.

Methods: Stage 1 ______________________

Snorkeling is recommended as the preferred method for sampling fish in

wilderness streams. This method requires little equipment, is cost effective, and fish are not handled, reducing potential mortality. This is

particularly important in wilderness streams and in areas where protected

species are present. The methods described in Thurow (1994) are briefly

outlined below; however, this publication should be referenced for additional details.

In small streams, an individual snorkeler begins at the downstream end

of the reach and moves slowly upstream. The snorkeler should move from

side to side making sure that all habitat types, pools, eddies, and undercut

banks are investigated. In larger streams, two observers move upstream

with shoulders touching and count all fish passed between themselves and

the bank. In some cases, stream depth is too great for upstream movement

and the snorkeler must float downstream remaining as motionless as

possible. All fish are identified, counted, and fish length is estimated in a

single pass through the sampling reach. With training, the accuracy of

species identification and estimates of fish length can be improved. Published relationships between fish length and fish weight can be used to

estimate biomass.

The fish community is evaluated using the metrics from RBP V (Plafkin

and others 1989). Additional metrics for Idaho coldwater streams have

been developed by Chandler and others (1993) and Robinson and Minshall

(1995). These metrics are as follows:

1. Number of native species

2. Number of sculpin species

3. Number of native minnow species

4. Number of sucker species
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

5. Number of intolerant species

6. Percent of common carp

7. Percent omnivores

8. Percent insectivores

9. Percent catchable salmonids

10. Number individuals per kilometer

11. Percent introduced species

12. Percent anomalies

13. Total biomass (g/m2

)

14. Salmonid biomass (g/m2

)

15. Percent young of the year

16. Salmonid density (m–1), and

17. Salmonid biomass (g/m2

)

Each metric can be used as a dependent variable for statistical comparisons between reference and impacted sites. Alternately, each metric is

scored, based on the 90 or 95 percent confidence interval (example 3).

Metric scores also can be determined based on visual evaluation of the

range of data values (Fore and others 1996). The sum of all metric scores

is then used for comparisons. The following tabulation outlines this process:

Dependent variables Analyses

Stage 1 Mean metric values Statistical

Mean total metric score
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 67

Algae/Periphyton

Benthic algae, along with benthic organic matter and organic matter in

transport, represent the primary energy source for herbivory and detrital

food webs. The relative importance of these energy sources varies along a

continuum from headwater streams to larger rivers (Vannote and others

1980). Three main groups comprise the majority of the benthic periphyton

found in wilderness streams; Cyanophyta, Chlorophyta, and Chrysophyta.

The Cyanophyta, or blue-greens, lack a nucleus and contain pigments

within the cell membrane. The Chlorophyta are the green algae that are

characterized by containing chloroplasts in which chlorophyll is the predominant pigment and energy is stored as starch. The diatoms

(Bacillariophyceae) are the predominant class of organisms in the Chrysophyta division. Diatoms are generally unicellular, store food as oils, and are

surrounded by a thick siliceous cell wall. Algae occur in association with,

and are often embedded within the exudates of, heterotrophic bacteria and

fungi; collectively, these constitute periphyton. For convenience, the algae

and associated heterotrophic organisms and other organic matter are

sampled and analyzed as a unit. Additional morphological (for example,

counts of diatom frustules) or biochemical techniques (for example, chlorophyll or ATP analysis) may be employed to provide further information

about the sample in general and the algae in particular. Algal production

is directly affected by light, nutrients, water velocity, temperature, and

indirectly by primary and secondary consumers. Therefore, alterations of

theses variables can result in different levels of algal biomass or changes in

community composition. The following tabulation outlines this process:

Dependent variables Analyses

Stage 2 Mean AFDM Statistical

Mean Chl-a

Chl-a/ AFDM

Stage 3 Diatom community metrics Statistical

It is useful to divide incoming light into two components: light reaching

the stream surface and light penetrating to the stream bottom. In many

headwater streams in wilderness areas of the western U.S.A., benthic algae

can be limited by the amount of light reaching the stream surface (Hill and

Knight 1988; Shortreed and Stockner 1983). Alterations in the height and

density of riparian plants can therefore be transmitted to changes in algal

biomass. Community composition also can change with changes in light

intensity, as diatoms are known to drift depending on light availability

(Bothwell and others 1989). The amount of light penetrating to the bottom
68 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

of the stream can be altered by the turbidity of the water. Increasing

instream sediment can alter light availability and reduce algal biomass

(Davies-Colley and others 1992; Lloyd and others 1987; Quinn and others

1992).

The availability of limiting nutrients can alter algal biomass. Therefore,

alterations in nutrient input to a stream can be monitored by changes in

algal biomass. Algal community structure also can be affected by changes

in nutrient concentrations. For example: the presence of the nitrogen-fixing

cyanobacteria, Nostoc, is often an indication of low concentrations of

nitrogen.

Water velocity can either enhance or degrade accumulation of algal

biomass. Increasing stream velocities can facilitate the uptake of nutrients

and the removal of metabolic waste products. As water velocity increases,

the force of the turbulent water can remove dead or dying cells from the

periphyton matrix or patches of living organisms.

Algal growth rates often are positively associated with increasing temperatures. Higher stream temperatures often are accompanied by enhanced algal biomass. Community structure also can change due to differential growth responses to different temperatures.

Algal biomass can be affected secondarily by herbivorous invertebrates

(Hill and Knight 1987; Hill and others 1992; Lamberti and Resh 1983 ),

which in turn can be altered by the presence of insectivorous fish or

amphibians. For example, high levels of grazing insects can maintain a low

level of algal biomass; however, if fish reduce the density of grazing insects,

algal biomass can increase.

Methods: Stage 2 ______________________

Within the sampling area, algae from a known surface area of five

randomly chosen rocks is removed, filtered, and preserved in the field.

Removal and filtration of attached algae requires the use of tools contained

within the periphyton sampling kit (photo 5). Contents of the sampling

kit are described below.

1. Periphyton sampler constructed from the barrel of a 30-cc plastic

syringe which has been cut off 4 cm from the open end (the end that has

protrusions for fingers). The bore of the syringe barrel is used to delineate

the area for removal of periphyton. Neoprene foam (4-mm thick wetsuit

material) is glued, using a combination of epoxy and RTV-silicone neoprene

cement, to the flat surface of the syringe barrel to provide a water seal when

the sampler is placed snugly against the rock surface.

A heated cork borer provides a convenient means for making a hole in the

neoprene. The actual area circumscribed on a rock by the sampler can be

determined by using the sampler as a “rubber stamp” to transfer an

impression (using an ink pad) repeatedly onto paper. The area contained

within the donut-shaped impression is determined. The area of a sampler

fabricated as above is approximately 3.45 cm2

.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 69

4

5

6

1

2

3

Photograph 5—Periphyton sampling kit in canvas case (above) and

containing, (1) sampler, (2) plastic brush, (3) hand suction, (4) medicine

dropper, (5) forceps, and (6) filter holder.
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

2. A small brush constructed by gluing a 8 x 8 mm portion of a hardbristled toothbrush onto the end of a handle such that the bristles are

parallel with the handle.

3. Large plastic medicine dropper or plastic volumetric pipette with

suction bulb for aspirating periphyton from sampler. The pipette tip should

have an opening with a diameter of about 4 mm to facilitate suction of large

particles (a 5-ml medicine dropper is ideal).

4. Forceps, for handling filters and for use in removal of filaments or

invertebrates which otherwise would interfere with determination of

periphyton biomass.

5. Portable 47-mm filter holder with base and trap for filtrate.

6. Hose and suction device for filter. Hand-operated pump can be used for

suction.

7. Glass fiber filters (Whatman GF/F, 47 mm or equivalent with a

nominal pore size of 0.7 μm) pre-combusted in a muffle furnace for 1 h at 475

°C. Filters should be pre-weighed and stored in individual dust-free

containers if an estimate of periphyton dry weight or percent organic is

desired. Storage containers for filters can take various forms including

plastic scintillation or other vials, cryotubes, or 49 mm-diameter plastic

petri dishes.

8. Labels for filter containers. PolyPaper labels (Nalgene No. 6309 or

6315, 19 x 38 cm) are good since they are waterproof and can be removed

easily after laboratory analysis.

Algal or periphyton sampling follows the procedure outlined below.

1. The five rocks are brought to a central sampling location and placed

under water maintaining the original orientation. A rock is removed from

the water and a representative sampling location is identified on the upper

surface.

2. The periphyton sampler is held onto the rock surface with adequate

force to retain water within the plastic cylinder. With the medicine dropper

or wash bottle, add sufficient particle-free water into the cylinder to fill it

to a depth of 1-2 cm, brush vigorously for about 10 seconds to dislodge

periphyton, aspirate the contents with the dropper or pipette, and expel the

contents into filter head (photo 6). Repeat this process three times; more if

necessary to remove particularly large accumulations. The final rinse

should be particle-free. It may be necessary to repeat this procedure several

times for each rock in order to fully load the filter with material. The

objective is to collect sufficient material to minimize errors during gravimetric analysis (no less than 10 mg dry weight).

3. After filtration, the filter is placed within a labeled storage container,

and placed in a cool, dark place. If analysis of chlorophyll-a is desired, the

filtered sample must be stored in the dark at temperatures below 4 °C until

analysis (APHA 1995). A liquid-nitrogen cooled 3DS Dry Shipper (Union

Carbide Corporation: height 478 mm, diameter 194 mm, weight 6.8 kg) or

packing in dry ice can be used to freeze samples. Small (1.8 ml) cryogenic

vials work well for storage of filters in conjunction with Dry Shipper. If

values of ash free dry mass only are desired, the filtered algae can be
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 71

preserved in formalin. In the absence of a portable Dry Shipper, the

difficulty of freezing samples in remote wilderness streams may limit

analysis to AFDM.

4. Laboratory analysis of chlorophyll-a (corrected for pheophytin) and

AFDM is done following methods in the current Standard Methods (APHA

1995). We have found that accurate chlorophyll-a and AFDM values can be

obtained from each filtered sample.

a. Place thawed sample into a tissue grinder and cover with 3 ml of 90

percent acetone (or methanol). Grind sample for 1 minute.

b. Transfer sample to centrifuge tube and add an additional 7 ml of

acetone. Be sure to rinse all residual material from the grinder.

c. Place the centrifuge tube into a refrigerator (4 °C) for at least 2 hours.

d. Clarify sample by centrifuging for 20 minutes at 500 g.

e. Transfer 3 ml of the extract into a 1-cm cuvette and measure

absorbance at 664 and 750 nm.

f. Acidify with 2 drops of 0.1N HCl. After 90 seconds, measure absorbance at 665 and 750 nm.

 

Chlorophyll a mg m V

A L

bb aa − = −−−

×

(/) . (( ) ( )) 2 26 7 664 750 665 750

(11)

where V is the volume of extract (Liters), A is the area of the sampler

(m2

), L is the light path length (cm), and the subscripts b and a denote

before and after acidification, respectively.

Photograph 6—Field sampling of periphyton.
72 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

g. Return extract to centrifuge tube and transfer contents to crucible.

h. Place crucible under an exhaust fan until all the acetone has

evaporated.

i. Dry sample in drying oven (60 °C) for 24 hours, remove and cool to

room temperature in a desiccator, and obtain dry weight.

j. Ash samples in muffle furnace (550 °C) for 2 hours. Rewet samples

with distilled water and return to drying oven for 24 hours.

k. Place samples in desiccator and allow to cool to room temperature.

Obtain final dry weight.

 

AFDMg m W W

A

/ 2 1 2 = − (12)

where W1 is initial dry weight (g) and W2 is final ash weight (g).

Methods: Stage 3 ______________________

Stage 3 analysis is increased to include the preservation and identification of diatoms. These data are used to calculate diatom community metrics

using Montana Water Quality Bureau Protocol II after Bahls (1993) or

regionally refined metrics where available.

Diatom algae are collected from randomly selected rock substrates

comprising a mix of habitats representative of a particular study site.

Samples are brushed or scraped into a container, preserved in a 5 percent

formalin solution, labeled, and returned to the laboratory. Samples may be

processed and analyzed by the investigator or sent to a specialist. For the

investigator, generic keys include Barber and Haworth (1981), and Prescott

(1970). Patrick and Reimer (1966) provide a key to most North American

species. A number of laboratories and/or individuals identify diatoms for

biological monitoring projects, several of which are listed (appendix B). This

list is not comprehensive, and is included here only to provide managers

that require diatom identification with a starting point in their search.

For analysis, the composite sample is boiled in concentrated nitric acid,

rinsed, mounted in Naphrax mountant, and examined under 1000X oil

immersion. Analysis of the diatom community metrics requires identification of genera and, where possible, species. Counts of 600 to 1000 diatom

valves are made from each slide to determine relative density. Diatoms are

analyzed in terms of species richness, Simpson’s Index, Shannon diversity,

pollution tolerance index, siltation index, and a similarity index. These

values are calculated using relative abundance data for each site (Bahls

1993; Minshall 1996; Robinson and others 1994).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 73

Large Woody Debris

Large woody debris (LWD) plays an important role in lotic ecosystems.

It serves to stabilize the stream channel, retard the export of organic matter

and nutrients, and provide protection and habitat for invertebrates and

fish. Quantification of LWD often is ignored in ecological assessments

because it is regarded as difficult and time consuming. Here we propose a

relatively simple and straightforward technique for determining the amount

of LWD in and immediately adjacent to the active stream channel, and

evaluating several characteristics indicative of the contribution the material is likely to make in terms of channel/substratum stability, organic

matter retention, and habitat for fish. The following tabulation outlines

this process:

Dependent variables Analyses

Stage 1 Total piece count Comparative or statistical if multiple

Total debris dam count years or sites are available

Stage 2 LWDI

Total piece by size class Comparative or statistical if multiple

Total pieces in zones 1 and 2 years or sites are available

Total piece volume

Methods: Stage 1 ______________________

Large woody debris is described as the organic matter over 1 m in length

and at least 10 cm in diameter at one end (sticks to logs). When multiple

pieces of debris accumulate in the stream channel and retard water flow,

a debris dam is formed. Stage 1 LWD analysis is an inventory of all LWD

and debris dams over the entire sampling reach. All woody debris and

debris dams within the bankfull channel are counted and recorded. Total

counts are standardized by reach length or reach area. Large woody debris

sampling is conducted once a year or longer.

Methods: Stage 2 ______________________

The functional influence of LWD on stream ecosystems varies with many

factors, in addition to total counts of pieces and debris dams. The size

relative to stream size, position in channel, and stability of LWD will

determine its influence on streams. At stage 2 analysis, these factors are

quantified to provide a score for each piece and debris dam, which will

reflect their relative importance (table 7). The total score for LWD pieces

and debris dams over the sampling reach is summed to provide a large

woody debris index (LWDI) (example 4). The LWDI is standardized by

reach length or area.
74 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Table 7

—Rank scores for pieces and dams of large woody debris (LWD) based on their potential to influence stream morphology, hydrology, and

organic matter retention.

Score

 Pieces 1 2 3 4 5

Length/bankfull width 0.2 to 0.4 0.4 to 0.6 0.6 to 0.8 0.8 to 1.0 >1.0

Diameter 10-20 cm 20-30 cm 30-40 cm 40-50 cm ≥50 cm

Location Zone 4 Zone 3 Zone 2 Zone 1

Type Bridge Ramp Submersed Buried

Structure Plain Intermediate Sticky

Stability Moveable Intermediate Secured

Orientation 0-20° 20-40° 40-60° 60-80° 80-90°

Debris dams

Length (% of bankfull width) 0 to 20 20 to 40 40 to 60 60 to 80 80 to 100

Height (% of bankfull depth) 0 to 20 20 to 40 40 to 60 60 to 80 80 to 100

Structure Coarse Intermediate Fine

Location Partially in high In high flow Partially in low In mid low In low flow

 flow flow flow

Channel Channel Channel Channel Channel against

 bank

Stability Moveable Intermediate Secured
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 75 Pieces Length/Bankfull Width Diameter Location Type Structure Stability Orientation Total Debris Dams Length Height Structure Stability Total Score

1 2 3 4 5 Total

31

37

40

35

36

38

38

255

5

5

5

3

3 20 23 

54 10 66 20 105

5 Location

Example 4

—Data sheet for determining a large woody debris index (LWDI). Each piece of large woody debris (LWD) and one debris dam were ranked from

the sampling reach. For example, 16 pieces were counted (number of marks in row). Eight of these pieces had a length/ bankfull width ratio

of 0.2 to 0.4, 5 with a ratio of 0.4 to 0.6, and so forth. The far right column totals are the sum of the number of marks times the rank score. For

example, length to bankfull width ratio, 31 = (8)(1) + (5)(2) + (2)(4) + (1)(5). Total piece score (PS) is 255 and total debris dam score (DDS) is

23. LWDI = 

ΣPS + 5

ΣDDS = 255 + 5(23) = 370.
76 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

The size of individual pieces is determined by measuring the length and

diameter of the largest end. Longer, larger pieces should have a greater

influence, are less likely to be moved, and are given a higher score. The

location score is based on the portion of time a piece is likely to be in the

active channel. Pieces that are in the active channel only at bankfull flows

are given a lower score than pieces that will be in the channel at all times.

Score is based on the predominant location in one of the four stream zones

(Robison and Beschta 1990) (fig. 14). The different types of debris are shown

Ramp

Bridge

Submerged

Buried

Water Surface

at Low Flow

Water Surface

at Bankfull Flow

Zone 1

Zone 2

Bankfull Flow

Zone 4 Zone 3 Zone 4

Figure 14—Different “types” of large woody debris (LWD)

pieces and four stream zones.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 77

in figure 16. Scores for piece type are based on stability and their relative

influence on morphology, flow, and organic matter retention. Structure

score is based on the potential to retain organic matter. LWD with a “sticky”

structure has numerous branches or roots over its entire length. LWD

orientation is determined by the angle between the piece and the stream

bank. Pieces perpendicular to stream flow are more likely to create dam and

plunge pools, increasing habitat complexity and organic matter retention.

Pieces oriented 60 to 80° from the bank often divert flow and cause scour

pools.

Debris dam scores rank the length (across the channel), height, structure, and stability of the object. Length is relative to bankfull width. A

debris dam extending all the way across a stream will have a greater

influence on morphology, hydrology, and organic matter retention than one

that only partially disrupts flow. Debris dam height is relative to bankfull

depth and reflects the portion of the stream influenced. Location scores

reflect the position of the debris dam in relation to the active channel at low

flows. Structure relates to the retention capacity of the debris dam. A debris

dam with a fine structure will filter out more organic matter than a coarse

structured dam and is given a higher score. Stability scores are based on the

likelihood that the dam will be retained over variable flows.
78 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Benthic Organic Matter

Benthic organic matter (BOM) is the non-living organic matter deposited

on stream bottoms and can provide an important energy source for heterotrophic bacteria, fungi, invertebrates, and fish. In heavily shaded

streams, most of this material originates from the leaves, needles, and

associated litter of terrestrial plant and can be a major organic energy

source. Quantification of this resource is therefore important in determining the maximum biomass expected at upper trophic levels. Stage 2

analysis provides a measurement of this food base. Further subdivision of

BOM in stage 3 provides a measure of annual variation and the size

fractions of this resource. Size fractionation provides information in

understanding invertebrate distribution, particularly with respect to functional feeding groups, in relation to the condition of the riparian habitat and

the adjacent forest (Cummins and others 1989).

Methods: Stage 2 ______________________

Benthic organic matter can be obtained from the sample collected for

aquatic invertebrates. After all invertebrates are removed from the sample,

the remaining organic matter is rinsed and placed within a large crucible

or other suitable container that is stable at temperatures up to 600 °C.

AFDM is determined by methods outlined previously for periphyton. The

sample is placed in a drying oven (60 °C) for 24 hours or until weight

stabilizes. The sample is cooled to room temperature in a desiccator,

weighed, and placed within a muffle furnace (550 °C) for 2 hours or until

all of the organic matter is reduced to ash. Upon removal, the sample is

rewetted with distilled water, dried, cooled, and reweighed. The rewetting

process rehydrates all inorganic clays within the sample. The difference

between the initial and final dry weight is the AFDM. Resulting AFDM

values are standardized by sampler area and expressed as g AFDM/m2

. If

the benthic sample was subsampled, the value should be multiplied by the

inverse of the portion sampled to obtain a mass/sample value.

Methods: Stage 3 ______________________

Annual measurements of benthic organic matter can be obtained by

following the above procedure on a monthly or more frequent basis. At a

minimum spring, summer, and autumn values should be obtained to

represent the main periods of input and utilization.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 79

Fractionation of benthic organic matter requires sieving of samples,

which is most conveniently done in the laboratory. Three commonly used

size fractions are: coarse particulate organic matter (CPOM) (1 mm to 16

mm), fine particulate organic matter (FPOM) (0.05 mm to 1 mm), and ultra

fine particulate organic matter (UPOM) (0.45 μm to 0.05 mm). The use of

these size fractions will be based on the type of mesh used for invertebrate

analyses. There is a trade-off in the size of mesh used for sampling. Smaller

mesh size allows for the collection of smaller invertebrates, and the lower

organic matter fractions, but reduces the flow of water which can cause the

loss of sample integrity due to part of the sample being flushed out of the

open end of the Surber net. We have found a 250 μm mesh size to be the

minimum size for use in conjunction with invertebrate collection that does

not result in a loss of sample. If this mesh size is used, passing the sample

through a sieve with a mesh size of 1 mm will provide coarse and fine

fractions. The two fractions are then processed separately for AFDM as

above.

Where more specific information is desired regarding specific size classes

of BOM and smaller particles, further refinement can be obtained by

sampling solely for BOM and adding a 52 μm net to collect an additional

FPOM fraction. Sampling of UPOM would require that the material

passing through the 52 μm-mesh net be subsampled and collected on a 0.45

μm glass fiber filter (see Minshall and others 1983 for details).

In addition to size fractionation, identification of the types of plants and

algae that contribute organic matter to the benthos is useful for characterizing food quality. Direct observation through a dissecting microscope is

used to identify the organic matter. Identification only is possible for the

larger size fractions (>1 mm). Genus or species identification is preferred;

however, percent woody, autochthonus versus allochthonus, and deciduous

versus evergreen are adequate distinctions.
80 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Transported Organic Matter

Transported organic matter (TOM) and invertebrate drift samples

provide further quantification of the organic food base and a direct

measurement of the food base for fish. Many aquatic insects are adapted to

filtering organic matter from the water column. Measurements of transported organic matter allow a better understanding of the distribution of

aquatic invertebrates based on functional feeding group analysis. Many

fish, including salmonids, feed mainly on aquatic insects drifting in the

water column; therefore, quantification of this resource is important in

estimating the potential food base for these fishes. If the resource is to be

evaluated for fish, the following sampling regime should be expanded to

include dawn and dusk sampling, as aquatic invertebrate drift is usually

greater at these times. The following tabulation outlines this process:

Dependent variables Analyses

Stage 3 Mean TOM, total and for each Statistical

 size class, and TOM flux.

Methods: Stage 3 ______________________

The sampling regime should coincide with benthic organic matter collection so that the relative importance of each resource can be determined.

However, for more detailed measurements, TOM sampling should increase

with changes in discharge as described for stage 3 water chemistry.

Transport should be collected at 0.6 x depth, and at the surface, as a large

portion of the coarse fraction is transported along the surface (fig. 15). The

transport net frame should be constructed so that it can be supported at

different depths within the water column. Nets of different mesh size can

be nested so that multiple size fractions are collected simultaneously.

1. Rebar or steel spikes are passed through sleeves or collars on the

side of the net frame and driven into the streambed. The net frame, with

nested nets, is slid to the desired height and held in place by thumb screws

passing through the sleeves.

2. Initial time is recorded. Water velocity into the net is measured

and recorded by placing a velocity meter in front of the net opening.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 81

Side view of nested transport nets

Velcro

Top view

Thumb 

screw

Collecting Bucket

Openings of 

sleeve for 

support rod

Surface Transport

Transport at 0.6d

Support rod

Figure 15—Diagram of nested transport nets, frame, and stream placement.

3. Nets should be removed prior to sustained reduction of flow

resulting from accumulation of materials in the net. Interruption of flow

will result in underestimates of TOM, unless measured continuously. Time

of removal is recorded.

4. Once the net is removed from the water column, the inner coarse

net is slid up partially and all fine organic matter is rinsed into the apex of

the fine net. This should be accomplished without submerging the opening

of either net. The contents of the net are then emptied into a prelabelled

whirl-pak bag and preserved with formalin (5 percent by volume). The

content of the coarse net is treated similarly. Forceps may be useful in

removing leaves and twigs from the net.

5. Upon returning to the laboratory, the sample is rinsed free of

formalin, the aquatic invertebrates are removed and sorted into categories

of similar appearance, identified to appropriate taxonomic level, counted,

and weighed.

7. Each particulate organic matter size fraction is analyzed for

AFDM as outlined previously for periphyton and BOM. The results are

presented on a per-volume basis. Therefore, the AFDM value is standardized by the volume of water passing through the net in terms of g/m3

.

Volume (m3

) is calculated as the product of water velocity into the net (m/

s), area of net frame opening (m2), and total time the net was in place (s).

Comparable units with benthic samples (m2

) can be obtained by dividing

the volume by mean stream depth (m).
82 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Organic Matter Decomposition

Decomposition rates are important indicators of the quality of benthic

organic matter either as a food resource or microbial (bacterial and fungal)

activity or both. Decomposition rates are influenced by many biotic and

abiotic factors. These influences are summarized by Webster and Benfield

(1986), from which the following discussion is derived. Decomposition rates

are a function of temperature, invertebrate detrivores, the structural

quality of the detritus, and the nutrient quality of the detritus and

surrounding water. Decomposition generally is increased by elevated

temperatures, as microbial enzymatic activity is enhanced. The structural

quality of the litter also will influence breakdown rates, as fibrous cellular

material is more resistant to decay. The nutrient quality of the litter also

affects breakdown rates. In terrestrial systems, decay rates can be estimated from the C:N ratio of leaf litter. In aquatic systems, the nutrient

content of litter can be augmented by dissolved elements. Generally, a

higher nutrient content of the detritus and surrounding water results in

faster breakdown rates of detritus. Invertebrate shredders act to mechanically fractionate the detritus and convert it into small fecal residue and food

crumbs which are utilized directly by collectors or transported downstream. The pattern of detrital decomposition follows three stages. Initially, all soluble components of the cell are leached out. This results in a

rapid weight loss in the first 24 hours. Next, decomposition is carried out

by microbial and fungal breakdown. Finally, this conditioned detritus is

fractionated by the combined effects of invertebrate shredders and physical

processes.

Organic matter processing rates traditionally, as in this manual, have

been determined by the mass loss of CPOM over time. These methods are

an index of decomposition rates but provide little information concerning

the breakdown of the smaller organic matter size fractions. The presence

of extracellular enzymes has been used to estimate breakdown rates of

FPOM and UPOM (Sinsabaugh and others 1994) and could be used to

augment the methods outlined in this section.

Methods: Stage 4 ______________________

The breakdown of CPOM is determined by containing leaves in a mesh

bag or as a leaf pack, securing the leaves to the streambed, and measuring

weight loss over time. Mesh bags may reduce the flow of dissolved nutrients

and exclude invertebrates, thereby underestimating breakdown rates

(Cummins and others 1980). However, the use of large mesh size alleviates
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 83

these problems (Benfield and Webster 1985). Leaf packs are constructed by

binding leaves together with monofiliment line and may be more representative of stream conditions by providing flow of nutrients and access to

invertebrates. However, mechanical breakdown and loss of the smaller

organic matter size fractions can increase decomposition estimates.

1. Leaf litter, that is representative of the riparian vegetation surrounding the stream in question, is collected from the forest floor. It is

important to collect leaves after abscission because of the altered nutrient

status of abscised leaves. Alternatively, a tarp may be spread out and trees

or bushes shaken vigorously to dislodge dead leaves.

2. Leaves are dried at 60 °C until weight is stabilized; 5.0 to 10.0 g of

dried leaves are placed within a mesh bag (mesh pore size of 2.5 cm2

) or

bound into a leaf pack.

3. The dry weight of individually labeled mesh litter bags is recorded.

The number of mesh litter bags required is the product of replicates and

sampling dates. That is, if three replicates are to be collected on six separate

dates (day 1, 3, 10, 20, 30, and 60), then 18 litter bags are required.

4. Litter bags or packs are secured to the streambed at random

locations within the dominant flow type (riffle, run, or pool) by securing the

litter bag to a metal stake driven into the streambed or other stationary

object such as a root.

5. Replicate-litter bags (three or more) are collected at predetermined sampling dates, emptied into whirl-pak bags, and preserved with

formalin.

6. Upon returning to the laboratory, invertebrates are removed from

the sample and identified. The remaining organic matter is rinsed thoroughly and dried to a stable weight at 60 °C Dry weight and litter bag label

information are recorded.

7. Where inorganic sedimentation may interfere with weight loss,

initial and final AFDM values may be more representative of organic

matter decomposition. In this case, the initial AFDM must be estimated

from the relationship between dry weight and AFDM. At a minimum, thirty

5 g dry weight litter samples are ashed (550 °C for 2-3 hours), rewetted,

dried and weighed. Regression analysis can be used to estimate AFDM as

a function of dry weight.

Decomposition rates are obtained by fitting the data to a mathematical

model. Many different models are available. A review and comparison of

these models is provided by Wieder and Lang (1982). Generally, the single

exponential decay model is used to determine the decay rate constant k.

This constant can then be compared with other studies. The exponential

decay model is:

Xt = Xoe–kt (13)

where Xt is mass at time t (days), Xo is the initial mass, and t is time in days.

The weight of the organic matter collected on day 1 is used as the initial

weight to correct for material lost in transport and through leaching. The

decay rate constant, k, is determined by graphing the natural log of Xt/Xo

as a function of t (fig. 16). The negative slope of this line is k. The slope is

calculated through least-squares regression (example 1). Due to the effect
84 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

of temperature on decomposition rates, this value can be standardized by

degree days (Minshall and others 1983; Paul and others 1983), which allows

comparison of different streams or values obtained at one site at different

seasons. This is accomplished by regressing ln Xt/Xo as a function of degree

days rather than days. For sites that are difficult to access, single 30-day

removals from several streams may be a viable alternative. Single removals

from several streams may result in less precise measurements but would

at least allow for comparative 30-day organic matter losses. The typical

values were as follows:

Method k/day Reference

First order stream in Frank Church Bags 0.018 Unpublished

Wilderness Area, Idaho

Second order stream, Caribou Packs 0.0016 La Point (1980)

National Forest, Idaho

First order stream, Oregon Packs 0.0035 Minshall and others (1983)

(Carya tomentosa)

First order stream, Idaho Packs 0.0037 Minshall and others (1983)

(Carya tomentosa)

Third order stream, Michigan Packs 0.0105 Irons and others (1994)

(Salix alaxensis)

Second order stream, Virginia Bags 0.0486 Benfield & Webster (1985)

(Cornus florida)

Second order stream, Virginia Bags 0.022 Benfield & Webster (1985)

(Acer rubrum)

Second order stream, Alaska Packs 0.026 Irons and others (1994)

(Alnus crispa)

Second order stream, Alaska Packs 0.016 Irons and others (1994)

(Salix alaxensis)

-0.4

-0.3

-0.2

-0.1

0

0.1

Natural log (x t/xo)

0 5 10 15 20

Day

y = -0.018x r2 = 0.967

Figure 16—Calculation of the decay rate constant, k, by plotting

ln Xt/Xo versus time. Slope of line is –0.018, so k = 0.018.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 85

Primary Production

Primary production is a measure of within-stream or autochthonous

carbon fixation. This production may constitute a substantial carbon source

for herbivory and detrital based food webs (Minshall 1978). The relative

importance of primary production varies with stream size, increasing at

mid-order streams as the filtration of light by the riparian canopy diminishes, and then decreasing in larger rivers as light attenuation through the

water column increases (Bott and others 1985; Minshall and others 1983;

Minshall and others 1992; Naiman and Sedell 1980). Primary production

is described by three parameters: Gross Primary Production (GPP), Net

Primary Production (NPP), and Respiration (R). These three parameters

are related, because NPP is the total amount of carbon fixed (GPP) minus

that respired (R). Primary production can be described in terms of the

autotrophic component or at the community/ecosystem level. Attached

algae reside in a matrix composed not only of algae but also associated

bacteria and fungi. Therefore, autotrophic GPP consists of carbon fixed by

algae, minus algal, bacterial, and fungal respiration. However, large

portions of carbon are respired outside of this association. Therefore,

ecosystem-level measurements, in addition to autotrophic processes, include respiration by animals, and that associated with the microbial

breakdown of organic matter in transport, on the streambed, and beneath

and lateral to the streambed.

Measurements of primary production provide information that is not

available by evaluation of standing stocks of periphyton biomass or the

change in biomass over time. Biomass measurements are the result of NPP

minus the amount lost through herbivory and sloughing. Therefore, measurements of biomass underestimate the importance of autotrophic production as an energy source. Because of this underestimate, ratios of algal to

benthic biomass do not reflect the relative importance of these two energy

components. A more realistic evaluation is obtained by the ratio of Gross

Community Production to Gross Community Respiration, or a P/R ratio

(see Rosenfeld and Mackay 1987, and Meyer 1989 for a discussion of P/R

ratios and their interpretation). The resulting typical values were as

follows:

Method GPP (mg/O2/m2/hr) Reference

First order, Tennessee Open system 72.6 Marzolf and others (1994)

First order, Tennessee Chamber 67.3 Marzolf and others (1994)

Second order, New York Chamber 15.8 (NPP) Fuller and Bucher (1991)

First order, Alaska, Chamber 12.2-260.2 Duncan and Brusven (1985)

Second order, Idaho Chamber 26.9-74.0 Davis (1995)

Second order, Idaho Chamber 63 Minshall and others (1992)

First order, Oregon Chamber 16 Naiman and Sedell (1980)
86 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Methods: Stage 3 ______________________

Ecosystem measures of metabolism using open-system methods is suggested for stage 3 level of analysis where flow conditions permit. The open

system method (Odum 1956) involves measuring the change in oxygen or

carbon dioxide from upstream to the downstream end of a stream segment.

The changes in oxygen concentration must be corrected for oxygen accrual,

through tributaries or groundwater, and atmospheric diffusion. Careful

site selection can usually reduce non-photosynthetic oxygen accrual. Estimates of diffusion however, are difficult to obtain and the difficulty is

accentuated in highly turbulent streams (Marzolf and others 1994). In

rapid-headwater streams upstream-to-downstream changes in oxygen can

be dominated by diffusion rather than biotic processes, and open system

measurements are not recommended in these situations (Bott and others

1978). The use of streamside channels reduces diffusion and accrual

problems and has been used as an alternative to true open system measurements (Guasch and others 1995; Triska and others 1983).

Methods: Stage 4 ______________________

The second method used to measure primary production is through the

isolation of a portion of the streambed within a closed microcosm. This

method involves the use of recirculating chambers. Ecosystem-level measurements can be obtained by using microcosms that encompass most

components of production, or by measuring each component separately and

summing individual components. Periphyton productivity can be evaluated through the following chamber method using artificial or natural

substrata. Artificial substrata such as unglazed ceramic tiles, have the

advantage of easier determination of surface area, homogeneous colonization, and more simple algal biomass, but are disadvantageous because they

may not reflect natural biomass and community composition (Cattaneo and

Amireault 1992).

1. If artificial substrata are to be used, the material should be placed

in the stream at least 1 month prior to production measurements. Algalcolonized tiles, or randomly selected rocks are placed within the chamber

(fig. 17) (Bott and others 1978; Bowden and others 1992; Duff and others

1984). The chamber is sealed and placed in the stream to maintain ambient

stream temperatures within the chamber. Placement location should

reflect dominant light levels and light reaching chambers should be

recorded during productivity measurements (see the Solar Radiation

section).

2. Dissolved oxygen (D.O.), time, and water temperature are monitored at 15 to 30-minute intervals or recorded continuously with a data

logger. Duration of incubations will depend on the productivity within the

chamber and available power supply. Highly productive colonies will

produce oxygen supersaturation within the chambers, leading to diffusion

of oxygen out of the water. Therefore, chamber water should be renewed

periodically to avoid supersaturation.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 87

3. Night measurements or opaque coverings can be used to determine respiration rates.

4. After day and night measurements, the colonized substrata are

removed from the chamber. Periphyton biomass and chlorophyll-a is

evaluated by scrubbing all attached algae into a known volume of water.

Subsamples of the algal slurry are removed and filtered, preserved, and

returned to the laboratory for analysis (see the Algae/Periphyton section).

Slurry and subsample volume must be recorded in order to calculate the

total chamber chlorophyll-a and AFDM values.

5. Surface area of tiles can be determined by standard geometric

formulas. The surface area of rocks can be determined by weight/area

relationships. The area of the rock with attached algae is covered with

Port for D. O. Probe

Pump

PVC tubing

17 cm

17 cm

D.O Probe

Pump

To 12VDC Battery

To D.O. Meter

28 cm

Colonized Substratum

Side View of Chamber

Front View of Chamber

Figure 17—Diagram of photosynthesis chamber designed by

Aliquot (Appendix B). Each chamber, exclusive of pump and

probe, weighs 2.5 kg.

To Dissolved Oxygen (D.O.) Meter

D.O. Probe
88 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

aluminum foil, being careful not to overlap the foil. The foil is then weighed.

This weight is multiplied by the ratio of a known area of foil to the weight

of that area.

6. Primary productivity parameters are calculated as follows: NPP

(mg/h) = Final D.O. - Initial D.O. (mg/L) x Chamber volume (L) / Time (h).

A better estimate is obtained by regressing dissolved oxygen as a function

of time. The slope of this regression line (mg-L/h) multiplied by chamber

volume (L) equals productivity (mg/hr). Respiration is calculated in the

same manner using dark chamber data. GPP = NPP + Respiration.

7. Total daily production values can be obtained by 24 hour incubations, summing all incubations over a 24 hour period, or by estimation from

productivity rates. Estimated NPP (mg O2) = NPP (mg/h) x photoperiod (h);

Respiration (mg O2) = Respiration (mg/hr) x 24 hours; GPP24 = NPPDL +

Respiration24.

8. Productivity rates or production values are standardized by area,

chlorophyll-a, or biomass.

Ecosystem-level measurements can be obtained by summing individual

components, or by enclosing all components within the microcosm. To

encompass all or most components of ecosystem productivity, trays containing native substrata can be submerged into the streambed. After at

least 1 month colonization time, the tray can be removed and placed within

a recirculating-photosynthesis chamber and productivity values can be

determined as above (Bott and others 1985). Productivity or production is

then standardized by the area of the colonization tray which is representative of streambed area. Total algal biomass and chlorophyll-a can be

determined by scrubbing all rocks within the tray and washing all organic

matter and periphyton through a 1 mm sieve into a calibrated bucket.

Subsamples are then removed, and preserved for chlorophyll-a and AFDM

analysis. Alternatively, frames can be placed directly over the tray which

has been colonized in the stream. The frame is equipped with circulating

pumps and opaque or translucent tops for light and dark incubations

(Pennak and Lavelle 1979; Sumner and Fisher 1979). In this case, benthic

organic matter, chlorophyll-a, and algal biomass can be estimated from

instream values.

Summing individual components requires separate productivity measurements with chambers containing algae, benthic organic matter, and, in

some cases, transported organic matter (Minshall and others 1983; Minshall

and others 1992; Naiman 1983; Naiman and Sedell 1980). Algal metabolism is evaluated as above. Benthic organic matter respiration is determined by collecting BOM passively in trays placed within the streambed,

or through collection of organic matter in depositional areas, and placing

this organic matter in mesh bags. Values are expressed on a weight basis

(for example, g O2/g AFDM) and then extrapolated to an areal measure

based on the mean standing crop of BOM. TOM can be evaluated by

collecting FPOM in transport. A slurry of TOM is made and a subsample

removed and injected into the chamber. Light and dark metabolism

measurements are made. Ecosystem-level metabolism is the sum of all

individual components.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 89

Carbon Turnover Length

Using the BOM, TOM, and metabolism data, estimates of carbon turnover length can be calculated. Carbon turnover length is the average

distance a fixed carbon atom travels before it is respired. Carbon spiraling

length is a measure of the retention and utilization of available energy

sources (Minshall and others 1992). Carbon turnover length, S (m), is

calculated by the following equation (Elwood and others 1982; Newbold and

others 1981, 1983):

 

S v

k = , (14)

where, v (m/s), is the downstream velocity of carbon and is the product of

TOM (g/m3) and discharge (m3/day) divided by BOM (g/m2) and stream

width (m); and, k (m/s), is the portion of benthic organic carbon respired in

a year and is the ratio of benthic respiration to BOM. Respiration values

measured as the change in oxygen must be multiplied by 0.375 to convert

values to carbon, and TOM and BOM values are multiplied by 0.454 to

convert AFDM values to carbon.
90 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Nutrient Dynamics

Primary production in pristine streams often is limited by low levels of

macronutrients required for algal growth and reproduction. Nutrient

limitation in many situations is the result of low levels of nitrogen or

phosphorus, or a combination of these two elements. Stream phosphorus

concentrations are the result of the weathering of phosphorus-containing

minerals and atmospheric deposition throughout the stream catchment,

and their subsequent transformations through upland and riparian systems. Nitrogen in streams is the result of biological fixation and atmospheric deposition within the catchment. Organic nitrogen within the

catchment is mineralized and nitrified to nitrates which are mobile within

the groundwater. Nitrates are transformed by biogeochemical processes

within the catchment and riparian areas before entering stream water.

Once these macronutrients enter the stream, their concentrations and

forms are modified further by in-stream processes. These processes include

biological uptake and adsorption to organic and inorganic particles, and are

affected by many variables, including water velocity, benthic organic

matter, and retention in transient storage areas. Transient storage areas

include the portion of the stream flowing within and below the bed

(hyporheic zone) but distinct from the groundwater, slow water areas along

the stream margins, and backwater areas behind debris dams and other

obstructions.

Wilderness-stream nutrient concentrations and nutrient limitation can

be altered by disrupting natural biogeochemical processes. Monitoring

nutrient dynamics can provide historic data for undisturbed streams that

can be used for future comparisons. Although wilderness areas are protected from many disturbances, they are not completely isolated. For

example, alterations in global temperatures can change precipitation

events and mineralization rates and their role in nutrient cycling. Atmospheric inputs of nitrogen and phosphorus compounds from industrial

processes can increase inputs and alter stream water pH. On a smaller

scale, recreation and grazing in riparian areas can influence nutrient

concentrations directly (in other words, metabolic wastes or detergents) or

indirectly by altering biological and microbial processes differentially

affecting specific macronutrient inputs. Therefore, understanding and

monitoring of nutrient dynamics in streams can alert management agencies to potential problems and provide insight to management alternatives.

Evaluating nutrient limitation can provide information that will assist

in wilderness management. For example, managers may need to determine

why excessive algal accumulations are occurring around popular camping
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 91

sites and how this problem should be addressed. If previous nutrient

limitation experiments had demonstrated phosphorus limitation, then the

changes in periphyton abundance hypothetically could be the result of

phosphorus inputs from the use of detergents. This hypothesis could be

tested and, if confirmed, appropriate action could be taken.

Nutrient uptake rates are influenced by many biotic and abiotic components including the amount, type, and retention of benthic organic matter,

instream nutrient concentrations, and the hyporheic and lateral movement

of water. Therefore, nutrient uptake rates and retention indices provide

information concerning the interrelationships between biotic and abiotic

processes. For example, excessive silting of the streambed could disrupt the

connection between the stream and the hyporheic/groundwater zone. This

could affect stream microbial processes and the survival of organisms

dependent upon the movement of water through the streambed (macroinvertebrates and salmonid eggs) and could be demonstrated by ecosystemlevel measurements of nutrient uptake rates. The process is as follows:

Mass transfer

Uptake Uptake rate coeff.

length (m) (μg/m2/min) (x 10–5 m/s) Reference

Phosphorus

Second order, Idaho 370 33.6 11.2 Davis (1995)

Second order, Idaho 370 84.0 11.3 Davis (1995)

First order, North 85 18.6 31.1 Munn and Meyer (1990)

 Carolina

First order, Oregon 697 1.54 0.51 Munn and Meyer (1990)

First order, Tennessee 22-97 1.3-15.5 2.2-5.2 Mulholland and others

 (1985)

Nitrogen

Second order, Idaho 549 246 8.0 Davis (1995)

Second order, Idaho 1,839 449 2.27 Davis (1995)

First order, North 689 3.9 1.08 Munn and Meyer (1990)

 Carolina

First order, Oregon 42 11.9 9.88 Munn and Meyer (1990)

Methods: Stage 3 ______________________

Nutrient limitation can be estimated or evaluated by a number of

different methods. Estimations can be made based on the relative amounts

of elements in comparison to amounts required by biota. These estimations

can then be confirmed through nutrient amendments and measurements

of the resulting biotic effects. Nutrient amendments can be direct or

indirect through nutrient diffusing substrata. The estimation of nutrient

limitation based on nitrogen:phosphorus (N:P) ratios and enrichment

through nutrient diffusers is described below.

Nutrient Limitation: N:P Ratios

An initial method for evaluating nutrient limitation uses stream water

nitrogen to phosphorus ratios. This concept is based on the “Law of the

Limiting Factor” which states that at any given time only one resource can
92 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

limit production. The N:P ratio is the pivotal point at which either

nitrogen or phosphorus becomes the limiting agent. A high N:P ratio

denotes phosphorus limitation and a low N:P ratio is indicative of nitrogen

limitation.

The N:P ratio is a molar ratio of species and therefore requires conversion

of nutrient analysis results (often given in mg/L) to moles. Due to the many

forms of nitrogen and phosphorus found in stream waters it is important to

indicate which forms are used to construct N:P ratios. Nitrogen is found as

nitrate, nitrite, ammonia, and organic nitrogen, and phosphorus as orthopyro- meta- and organic-phosphorus either in a dissolved or particulate

form. N:P ratios will differ with the forms of nitrogen or phosphorus used.

Most N:P ratios are in the form of total inorganic nitrogen (sum of nitrate,

nitrite, and ammonia) to dissolved orthophosphorus, dissolved total, or

total phosphorus.

N:P ratios are limited in their use as a predictor of nutrient limitation

because optimal ratios are species specific. In a community of many

different species therefore, there may be a large range of values that signify

neither nitrogen nor phosphorus limitation. In addition, intraspecific

optimal N:P ratios can shift with water velocity (Borchardt 1994), light

(Wynne and Rhee 1986), and temperature (van Donk and Kilham 1990).

Regardless of these problems, N:P ratios can provide insight toward

potential nutrient limitation. Morris and Lewis (1988) concluded that the

best indicators of nutrient limitation were total dissolved inorganic nitrogen (DIN) to total phosphorus (TP) or total dissolved phosphorus (TDP). In

their study phosphorus was found limiting in lake waters at ratios above 12

and 20 for DIN:TP and DIN:TDP respectively. Nitrogen limitation occurred

at ratios below 2, for both ratios (DIN:TP and DIN:TDP), and co-limitation

or nonlimitation occurred at values within these ranges. In streams,

nitrogen has been found to limit primary production at and below 18 while

phosphorus has been found limiting at ratios at or above 18 (table 8).

Testing Potential Nutrient Limitation

Evaluation of potential nutrient limitation can be tested through enrichment of stream water and monitoring the response of primary producers.

Nutrient enrichment can be obtained through direct application of dissolved nutrients to stream water (Grimm and Fisher 1986; Hill and others

1992; Lohman and others 1991) or through nutrient diffusing substrata

(Bushong and Bachmann 1989; Chessman and others 1992; Coleman and

Dahm 1990; Fairchild and Everett 1988; Fairchild and Lowe 1984; Fairchild

and others 1985; Gibeau and Miller 1989; Grimm and Fisher 1986; Hill and

Knight 1988). The method used by Gibeau and Miller (1989), described

below, is particularly suited for wilderness streams due to the small size

and low weight of the diffusing substrata and the small amount of nutrients

released.

1. Soak porous porcelain or fused silica crucible covers (2.6 cm

diameter disc, Leco Corporation #528-042) in 10 percent HCl solution for 48

hours. Rinse copiously in deionized water.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 93

2. Fill a 10-dram plastic vial (Dynalab Corporation #2636-0010) with

30 ml of 2 percent nutrient enriched or unenriched agar. Enriched agar is

made by dissolving sodium nitrate (NaNO3) or potassium dibasic phosphate (KH2PO4) or both into a nutrient-free 2 percent agar solution. The

agar is then heated to boiling and poured into the diffusers while still hot.

The mass of chemicals added will vary with the enrichment concentrations

required. The majority of studies have used 0.1 molar concentrations,

which should be suitable for most wilderness streams. For 0.1 molar

concentrations, 8.5 g of NaNO3 and 13.6 g of KH2PO4 per liter of agar are

used. Treatments should include at least three replicates of control,

phosphorus, nitrogen, and nitrogen plus phosphorus diffusers.

3. Once the vials are filled, heated crucible covers are melted into the

top of the plastic vials, which are then turned upside down before the agar

solidifies.

4. The vials are glued into 3-cm holes drilled into 5 x 5 cm (2 x 2 inch)

lumber strips 70 to 100 cm long. Multiple strips can be combined to

construct a rack which is then secured within the stream (fig. 18).

Table 8—Summary of stream nitrogen:phosphorus (N:P) ratios and nutrients determined limiting

TDN = total dissolved nitrogen; TDP = total dissolved phosphorus; TN = total nitrogen;

TIN = total inorganic nitrogen.

Location N-limit N:P P-limit N:P Species Reference

Rhine River <10 >20 NO3-N:PO4-P Schanz and Joun (1983)

Michigan 40 NO3-N:PO4-P Pringle and Bowers (1984)

Alaska 60 TIN:TP Peterson and others (1983)

Arizona 1.6-2.6 NO3-N:PO4-P Grimm and Fisher (1986)

Missouri <18 >19 TN:TP Lohman and others (1991)

California <2 NA Hill and Knight (1988)

Australia 2 TIN:PO4-P Chessman and others (1992)

Australia >44 TIN:PO4-P Chessman and others (1992)

Australia 6 TIN:PO4-P Chessman and others (1992)

Australia 18 TIN:PO4-P Chessman and others (1992)

Crucible Cover

5 x 5 cm board 10-dram plastic vial

Support rod

10 cm

Figure 18—Nutrient diffuser frame showing vial placement

within wooden crossmembers.
94 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

5. The nutrient-diffusing vials are left in the stream long enough for

algal biomass to develop, but are removed before algal sloughing occurs. For

most sites this will be from 10 to 30 days. After incubation, the vials are

removed from the frame and the algal-colonized-crucible covers carefully

lifted from the vial tops. The attached periphyton is scraped into a 250-ml

graduated cylinder (or other suitable container) filled with 100 ml of water.

Subsamples of this algal slurry can be removed for algal species identification prior to filtering. The filtered algae can then be analyzed for chlorophyll-a and AFDM (see Algae/Periphyton section). Surface area is calculated from the area of exposed crucible covers and area-specific chlorophyll-a,

or AFDM values can be used to test for significant differences among

treatments.

In some cases, neither nitrogen, phosphorus, or nitrogen and phosphorus

enrichment results in any differential algal response. This implies that

some other factor is limiting algal accumulation such as micronutrients

(Pringle and others 1986), or light (Hill and Knight 1988; Triska and others

1983), or that differences are masked by grazing macroinvertebrates (Hill

and others 1992). Evaluation of light limitation can be tested by placing sets

of diffusers in locations within a stream that vary in light intensity. In this

case greater care should be taken to insure that other factors are similar

between sites, in particular current velocity. Testing for micronutrient

limitation involves modification of elements dissolved within the agar

matrix.

Ecosystem Uptake Parameters: Open System

Methods

Under conditions of nutrient limitation, the retention of elements is

essential for the productivity of the system. Uptake parameters also are a

measure of the “intactness” and proper functioning of stream ecosystems.

The ability of a stream to retain nutrients is best described by the nutrient

spiraling concept (Newbold and others 1981). Essentially, spiraling length

is the distance a nutrient atom travels in dissolved form (uptake length)

plus the distance traveled in particulate form (turnover length). Under base

flow conditions, uptake length dominates total spiraling length, due to the

rapid movement of nutrients in the water column. Uptake length is a

function of uptake rate, streamwater nutrient concentrations, and water

velocity. Therefore uptake length can be calculated by measuring these

parameters.

The uptake of nutrients from the water column occurs through autotrophic and heterotrophic processes. Nutrients are removed from the

water column by algae and incorporated into algal biomass, and by bacteria

and fungi which remove nutrients from the water column to augment the

breakdown of organic matter. The relative importance of these two uptake

processes will vary with the stream in question. In many headwater

streams, phosphorus uptake has been shown to be a function of the amount

of benthic organic matter available (Mulholland and others 1985; Newbold

and others 1983); however, in streams where autotrophic processes
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 95

dominate, algal uptake may dominate (Grimm 1987). The relative importance of these two processes is related to P/R ratios from productivity

measurements.

The retention of nutrients is a measure of stream channel stability or the

efficient use of available elements. Where organic carbon is the major site

of nutrient uptake, the ability of the system to hold this organic matter will

be important in nutrient retention. Undisturbed headwater streams,

typical of wilderness areas, have been shown to be effective in organic

matter retention (Minshall and others 1983). Retention of organic matter

is the result of physical and biotic processes. Physical processes include

debris dams, pools, and large woody debris in the stream channel. Biotic

processes may include filtering of organic matter in transport by filter

feeding invertebrates. Autotrophic uptake may be enhanced by the rapid

regeneration of algal biomass as a result of invertebrate grazing. Loss of

these biotic and abiotic processes, therefore, will lead to the inability of a

stream to utilize process-limiting nutrients.

Nutrient uptake rate and uptake length, from whole stream nutrient

releases, can be determined through two different methods. Both methods

require the release of a conservative tracer in addition to the biologically

active element under consideration. These two methods and their advantages and disadvantages were described by the Stream Solute Workshop

(1990). The first method requires fitting the data obtained from the change

in tracer and nutrient concentrations over distance to a mathematical

model describing the dispersion of elements in the water column and

uptake. The second method uses data obtained from the injection to directly

estimate uptake rates and length (Munn and Meyer 1990). This second

method will be described below, and entails injection of a NO3-N-PO4-Pchloride solution, and measurement of the resulting concentration at

successive locations downstream. The solution is injected at a constant rate

at an upstream location. The injection continues until constant elevated

stream water nutrient concentrations (plateau concentrations) are obtained throughout the study reach. Replicate samples of the plateau

concentrations are taken at multiple transects throughout the study reach.

These water samples are then analyzed for NO3-N, PO4-P, and chloride.

The change in nutrient concentrations over distance, corrected by the

change in chloride concentrations, is used to determine uptake.

1. The first step is the determination of the concentration of solutes

in the injectate. This requires prior knowledge of stream water nitrogen,

phosphorus, and chloride concentrations and stream discharge. Plateau

concentrations should not exceed stream water concentrations by a large

amount (usually 3 to 4 times background concentrations) and stream water

N:P ratios should be maintained (Stream Solute Workshop 1990). Once

plateau concentrations are determined, solution concentration and injection rate can be determined by the following formula (example 5):

 

Q Q C

C C

i i

p b

= − , (15)
96 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

where Q is discharge, C is concentration, and the subscript i, stands for

injectate, p, for plateau, and b, for background. The limits of solute

concentration are set by their saturation values, and the limits of the

injection rate are determined by the metering pump or other means of

nutrient injection being used. Saturation values are variable among sites

and difficult to determine. However, as a general rule, stream water

concentrations should be at or below 0.10 mg/L-N and 0.005 mg/L-P.

2. Once injectate concentrations are calculated, the total amount of

nitrogen, phosphorus, and chloride salts needed should be determined,

weighed out, and packaged in the laboratory in zip-lock bags or whirl-paks.

3. New water-sample bottles should be obtained with a separate

bottle for each element, sample time, and transect. For example, if samples

of the three elements are to be taken at seven transects, at eight different

times (multiple samples of plateau concentrations) then 168 sample bottles

are required. Sample bottles should be prelabelled.

4. The reach length and transect location should be determined

before beginning the injection. Reach lengths should be long enough to

ensure depletion of nutrient concentrations, but short enough to reduce the

accrual of groundwater. In small streams (1-4 L/s discharge) 20-m reaches

may be adequate whereas reaches of 300 m or longer will be required in

larger streams (100-200 L/s). Five to seven transects are spaced evenly

throughout the stream reach. The exact distance from the injection point to

each transect is measured, and each transect identified with flagging or

other marker.

5. The nutrient salts, 1-L graduated cylinder, 100-ml graduated

cylinder, mixing bucket (4-6 L), metering pump, and 12-VDC battery are

then carried to the upstream end of the reach. Stream water is used to

dissolve the nutrients in the mixing bucket. The metering pump is used to

drip the solution into the stream at the predetermined injection rate

(Qi) and roughly 10 m above the first sampling transect. The injection

rate should be determined manually prior to and after the injection, or

Example 5—Calculation of nutrient concentrations for uptake length experiments.

Stream water nutrient concentrations are 0.046 mg/L NO3-N, 0.005 mg/

L PO4-P, and 0.22 mg/L Cl. Stream discharge is 170 L/s. Plateau

concentrations desired are 0.1 mg/L NO3-N, 0.011 mg/L PO4-P, and

1.00 mg/L Cl. Injection rate will be 50 ml/min or 8.3 x 10–4 L/s.

Solving the formula for NO3-N:

Ci

: Ci=(Cp-Cb)Q/Qi=

(0.10-0.046)(170/8.3 x 10–4)=

11,016 mg/L or 11.02 g/L.

For a two hour injection at 50 ml/min, the total volume required (50 x 120) will be 6.0

L. Therefore 66.12 g (6 x 11.02) of NO3-N will be required. The total amount of nitrate

salt as NaNO3 will be 66.12 g NO3-N times the molecular weight of NaNO3 divided by

the molecular weight of N.

g NaNO3 = 66.12(85/14.01)= 401.2

The same computations are used for phosphorus and chloride.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 97

continuously with an in-line meter. The solution is dispensed upstream of

a turbulent area to allow complete mixing by the first sampling transect.

6. The nutrients are allowed to drip into the stream until plateau

concentrations are reached. The time required to reach plateau will

increase as transient storage areas increase. However, an hour generally is

enough time to reach plateau. If based on stream morphology, an extensive

hyporheic area is expected, initial injections of a NaCl solution could be

used to determine the time required to reach plateau.

7. Once plateau concentrations are reached, water samples are

taken roughly every 10 minutes at each transect. The total number of

samples or duration of sampling is variable. Multiple samples provide a

better measurement of plateau concentrations but require longer injection

times. Measurements of conductivity can be used to replace chloride

sampling and analysis.

8. After the sampling regime is completed, water samples are filtered

and preserved for analysis (see section on water chemistry).

The results from the water chemical analysis are then used to determine

uptake rates and uptake length. Uptake lengths are calculated by solution

of the following formula:

 

A e x

x Sw = – , (16)

where Ax = the ratio of observed to predicted concentrations at distance ‘x’,

x = distance downstream, and Sw = uptake length. Uptake length is then

calculated by the same methods used to determine decay rate constants;

that is, the ln of Ax is plotted as a function of distance downstream. The slope

of this line is 1/Sw, so the inverse of the slope is uptake length (example 6

and fig. 19).

Predicted concentrations are based on the dilution of the conservative

tracer and are calculated by the formula (Hart and others 1992):

 

C C Cl

Cl p o

x

o

= , (17)

Where Cp = the predicted concentration, Co = concentration at transect 1,

Clo = chloride concentration (or conductivity) at transect 1, and Clx =

chloride concentration at transect x.

The uptake parameters, uptake rate and mass transfer coefficient (U/C),

can be calculated from their relationship to uptake length, water velocity,

and mean depth (Stream Solute Workshop 1990). This relationship is

shown in the following equation:

 

S vh

U C

w = , (18)

where U = uptake rate (mg/m2/s), C = concentration (mg/m3) v and h are

mean water velocity (m/s) and mean depth (m), respectively.

The uptake rate calculated above can be corrected for background

streamwater concentrations. This correction is based on the assumption

that at below limiting levels of nutrients, uptake increases proportionally

with stream water concentrations. That is, the mass transfer coefficient
98 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Example 6—Water samples were analyzed for chloride and phosphorus at 6 transects extending 280 m downstream. Expected phosphorus concentrations remained constant because plateau chloride concentrations did not change throughout the reach. The natural log of observed to expected concentrations is plotted against downstream distance (fig. 21). Uptake length was 333 m. Velocity was 0.28 m/s, mean depth 0.157 m, background PO4-P concentrations were 5 mg/m3, and plateau concentrations 10 mg/m3. From equation 22, uptake rate, at plateau, was 0.00132 mg/m2/s or 79.2 μg/m2/min. Using equation 23, uptake rate was 39.6 μg/m2/min at background stream water concentrations. Transect Distance Chloride Observed PO4-P Expected PO4-P ln (observed/expected) m mg/L mg/L mg/L 1 25.6 2.38 0.005 0.005 0.00 2 60.0 2.38 0.0055 0.005 –0.10 3 133.5 2.38 0.004 0.005 –0.16 4 175.5 2.38 0.0026 0.005 –0.65 5 220.3 2.38 0.0035 0.005 –0.35 6 282.9 2.38 0.00225 0.005 –0.88
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 99

(uptake/concentration) is a constant under increasing concentrations (at a

given time and location) below saturation. Therefore uptake at stream

water concentrations is equal to:

 

U C

U

C c b

p

p

= (19)

where Uc = corrected uptake rate, Up = uptake at plateau concentrations,

Cp = plateau concentration, and Cb = background concentration.

Uptake length is the average distance an element will travel before being

taken up by the biota. Equation 17 demonstrates that uptake length is a

combination of physical factors, such as water velocity and stream depth,

and biotic factors, such as uptake rate per concentration or mass transfer

coefficient. Uptake length should, therefore, increase with stream order

and the associated increase in velocity and depth. In streams of similar size

and slope, uptake length will increase as physical complexity of the channel

decreases. The mass transfer coefficient will decrease as a result of factors

influencing biotic activity and the total area available for biotic uptake.

Siltation of the streambed will reduce the active area for periphyton

-1

-0.75

-0.5

-0.25

0

0.25

0.5

ln (Observed/Predicted)

Phosphorus

0 50 100 150 200 250 300

Distance (m)

y = -0.003x, r2 = 0.731

Figure 19—The natural log of the ratio of observed to expected

phosphorus concentrations is plotted against downstream

distance. Uptake length, Sw, is the negative inverse of the slope

of the regression relationship. Uptake Length = 1/0.003 or 333 m.
100 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

production and storage of allochthonous organic matter, decreasing nutrient uptake rates and causing uptake length to increase. Impacts including

disruption of riparian nutrient dynamics, alterations of organic matter

input and storage, alterations in litter quality, nutrient loading,

channelization, and loss of retention devices, can potentially alter the

functional integrity of streams and can be monitored through measurements of uptake parameters in stream ecosystems.

Stage 4: Component Uptake Parameters ___

Measuring nutrient uptake in streams is analogous to measuring primary production. That is, individual components or intact systems can be

evaluated. Chambers can be used for individual components or intact

micro/mesocosm measurements, while nutrient injections (stage 3) can be

used for whole-system measurements. Like productivity measurements,

individual component measurements allow the separation and identification of active areas of uptake but are susceptible to the compounding minor

errors during addition of components and extrapolation to whole stream

values. The enclosure of intact systems within chambers reduces the

magnification of errors but does not provide a means to identify active areas

and still requires extrapolation to whole stream values. Both chamber

methods likely exclude uptake within the hyporheic zone. Nutrient injections provide the most precise measurement of ecosystem level uptake

parameters but must be combined with chamber studies to isolate and

determine the relative importance of different components.

Measuring nutrient uptake rates in chambers (Duff and others 1984;

Grimm 1987) can be accomplished simultaneously with chamber productivity measurements (see Primary Production section). Once the component in question, either algae, or detritus, or a tray containing both, is

placed within the chamber, initial water samples are taken to determine

nutrient concentrations (see Water Quality section). After each productivity run, or prior to flushing the chambers, a second water sample is taken.

Water samples are analyzed for nitrate nitrogen, ammonia, and dissolved

orthophosphorus.

Net uptake (U) is calculated as the initial concentration (C1) minus the

final concentration (C2), times chamber volume (V), and divided by time (t).

That is:

 

U C C

t t = V −− ( ) . 1 2

2 1

(20)

This value is standardized by area, chlorophyll-a, or AFDM. These values

can then be converted to values relative to the abundance of the particular

component present in the test stream. For example, if uptake associated

with BOM was 0.1 mg-P/g-AFDM/hr and stream BOM was 10 g-AFDM/m2

,

then instream uptake of BOM would be 0.1 x 10 or 1 mg-P/m2

/hr. This same

procedure is then used for each of the components measured. Total area

uptake rates would be the sum of rates for each individual component.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 101

Once total area uptake rates are known, uptake lengths can be calculated

from the following equation (Stream Solute Workshop 1990).

 

S vd

U C

w = (21)

Where Sw = uptake length (m), v = mean stream water velocity (m/s),

d = mean depth (m), U = uptake rate (mg/m2/s), and C = stream water

element concentration (mg/m3).
102 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Agee, J. K., and D. R. Johnson (editors). 1988. Ecosystem management for parks and

wilderness. University of Washington Press, Seattle. 237 p.

Alt, D. D., and D. W. Hyndman. 1989. Roadside geology of Idaho. Mountain Press

Missoula, MT. 393 p.

APHA. 1995. Standard methods for the examination of water and wastewater. A. E.

Greenberg, L. S. Clesceri, and A. D. Eaton (editors). American Public Health

Association, Washington, DC.

Bahls, L. L. 1993. Periphyton bioassessment methods for Montana streams.

Montana Department of Health and Environmental Services, Water Quality

Bureau, Helena. 23 p.

Baily, R. G. 1989. Ecoregions of the continents (map). USDA Forest Service.

Barber, H. G., and E. Y. Haworth. 1981. A guide to the morphology of the diatom

frustule. Freshwater Biological Association Scientific Publication No. 44. Cumbria,

England.

Benfield, E. F., and J. R. Webster. 1985. Shredder abundance and leaf breakdown in

an Appalachian mountain stream. Freshwater Biology 15:113-120.

Benke, A. C. 1984. Secondary production of aquatic insects. Pages 289-323 in V. H.

Resh and D. M Rosenberg (editors). The Ecology of Aquatic Insects. Praeger

Scientific. New York.

Benke, A. C. 1993. Edgardo Baldi Memorial Lecture: Concepts and patterns of

invertebrate production in running waters. Verh. Internat. Verein. Limnol.

25:15-38.

Bevenger, G. S., and R. M. King. 1995. A pebble count procedure for assessing

watershed cumulative effects. USDA Forest Service. Rocky Mountain Forest and

Range Experiment Station. Fort Collins, CO. Research Paper RM-RP-319. 17 p.

Bisson, P. A., J. L. Nielsen, R. A. Palmason, and L. E. Grove. 1981. A system of

naming habitat in small streams, with examples of habitat utilization by salmonids during low streamflow. Pagen 62-73 in N. B. Armantrout (editor). Acquisition

and utilization of aquatic habitat inventory information. Proceedings of a

symposium, Oct. 28-30, 1981, Portland, Oregon. Hagen Publishing Co., Billings,

Montana.

Borchardt, M. A. 1994. Effects of flowing water on nitrogen and phosphorus-limited

photosynthesis and optimum N:P ratios by Spirogyra fluviatilis (Charophyceae).

Journal of Phycology 30:418-430.

Bothwell, M. L., K. E. Suzuki, N. K. Bolin, and F. J. Hardy. 1989. Evidence of dark

avoidance by phototrophic periphytic diatoms in lotic systems. Journal of Phycology 25:85-94.

Bott, T. L., J. T. Brock, C. E. Cushing, S. V. Gregory, D. King, and R. C. Petersen.

1978. A comparison of methods for measuring primary productivity and community respiration in streams. Hydrobiologia 60:3-12.

Bott, T. L., J. T. Brock, C. S. Dunn, R. J. Naiman, R. W. Ovink and R. C. Petersen.

1985. Benthic community metabolism in four temperate stream systems: An

inter-biome comparison and evaluation of the river continuum concept.

Hydrobiologia 123:3-45.

Bowden, W. B., B. J. Peterson, J. C. Finlay, and J. Tucker. 1992. Epilithic chlorophyll

a, photosynthesis, and respiration in control and fertilized reaches of a tundra

stream. Hydrobiologia 240: 121-131.

References
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 103

Bushong S. J., and R. W. Bachmann. 1989. In situ nutrient enrichment experiments

with periphyton in agricultural streams. Hydrobiologia 178:1-19.

Cairns, J. 1977. Quantification of biological integrity. Pages 171-185 in R. K.

Ballentine and L. J. Guarraia (editors). The integrity of water. U. S. Environmental Protection Agency, Office of Hazardous Materials, Washington, D. C.

Cattaneo, A., and M. C. Amireault. 1992. How artificial are artificial substrata for

periphyton? Journal of the North American Benthological Society. 11:244-256.

Chandler, G. L., T. R. Maret, and D. W. Zaroban. 1993. Protocols for assessment of

biotic integrity (fish) in Idaho streams. Water Quality Monitoring Protocols

Report No. 6: IDHW-300, 480440803, 3/93. Idaho Department of Health and

Welfare, Division of Environmental Quality, Boise, ID 83706-1253. 40 p.

Chessman, B. C., P. E. Hutton, and J. M. Burch. 1992. Limiting nutrients for

periphyton growth in sub-alpine, forest, agricultural and urban streams. Freshwater Biology 28:349-361.

Chorley, R. J., S. A. Schumm, and D. E. Sugden. 1984. Geomorphology. Methuen,

New York. 605 p.

Clark, W. H., and T. R. Maret. 1993. Protocols for assessment of biotic integrity

(macroinvertebrates) in Idaho streams. Water Quality Monitoring Protocols -

Report 5. Idaho Department of Health and Welfare, Division of Environmental

Quality, Boise, Idaho. 54 p.

Coleman, R. L., and C. N. Dahm. 1990. Stream geomorphology: effects on periphyton standing crop and primary production. Journal of the North American

Benthological Society. 9:293-302.

Cummins, K. W. 1973. Trophic relations of aquatic insects. Annual Review of

Entomology. 18:183-206.

Cummins, K. W. 1974. Structure and function of stream ecosystems. BioScience

24:631-641.

Cummins, K. W., G. L. Spengler, G. M. Ward, R. M. Speaker, R. W. Ovink, D. C.

Mahan, and R. L. Mattingly. 1980. Processing of confined and naturally entrained

leaf litter in a woodland stream ecosystem. Limnology and Oceanography

25:952-957.

Cummins, K. W., M. A. Wilzbach, D. M. Gates, J. B. Perry, and W. B. Taliafero. 1989.

Shredders and riparian vegetation. BioScience 39:24-30.

Davis, J. C. 1995. Functional processes in three wilderness streams. M.S. Thesis.

Idaho State University, Pocatello, Idaho. 112pp.

Davies-Colley, R. J., C. W. Hickey, J. M. Quinn, and P. A. Ryan. 1992. Effects of clay

discharges on streams 1. Optical properties and epilithon. Hydrobiologia

248:215-234.

Duff, J. H., K. C. Stanley, F. J. Triska and R. J. Avanzino. 1984 The use of

photosynthesis-respiration chambers to measure nitrogen flux in epilithic algal

communities. Verh. Internat. Verein. Limnol. 22:1436-1443.

Duncan, W. F. A. and M. A. Brusven. 1985. Energy dynamics of three low-order

Southeast Alaskan Streams: autochthonous production. Journal of Freshwater

Ecology 3:155-166.

Fairchild G. W., and R. L. Lowe. 1984. Artificial substrates which release nutrients:

effects on periphyton and invertebrate succession. Hydrobiologia 114:29-37.

Fairchild, G. W., R. L. Lowe, and W. B. Richardson. 1985. Algal periphyton growth

on nutrient-diffusing substrates: an in situ bioassay. Ecology 66:465-472.

Fairchild, G. W., and A. C. Everett. 1988. Effects of nutrient (N, P, C) enrichment

upon periphyton standing crop, species composition and primary production in an

oligotrophic softwater lake. Freshwater Biology 19:57-70.

Finklin, A. I. 1988. Climate of the Frank Church - River of No Return Wilderness,

Central Idaho. U. S. Forest Service, Ogden, UT, General Technical Report INT-240.

221 p.

Fisher, S. G. 1990. Recovery processes in lotic ecosystems: limits of successional

theory. Environmental Management 14:725-736.

Fore, L. S., J. R. Karr, and R. W. Wisseman. 1996. Assessing invertebrate responses

to human activities: evaluating alternative approaches. Journal of the North

American Benthological Society 15:212-231.
104 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Franklin, J. F., and T. Dyrness. 1973. Natural vegetation of Oregon and Washington.

U. S. Forest Service General Technical Report PNW-8. 417 p.

Frissell, C. A., W. J. Liss, C. E. Warren, and M. C. Hurley. 1986. A hierarchical

framework for stream habitat classification: viewing streams in a watershed

context. Environmental Management 10:199-214.

Fuller, R. L., and J. B. Bucher. 1991. A portable chamber for measuring algal primary

production in streams. Hydrobiologia 209:155-159.

Gallant, A. L., T. R. Whittier, D. P. Larsen, J. M. Omernik, and R. M. Hughes.

1989. Regionalization as a tool for managing environmental resources. U. S.

Environmental Protection Agency EPA/600/3-89/060. 152 p.

Gibeau, G. G., and M. C. Miller. 1989. A micro-bioassay for epilithon using nutrientdiffusing artificial substrata. Journal of Freshwater Ecology 5:171-175.

Gordon, N. D., T. A. McMahon, and B. L. Finlayson. 1992. Stream hydrology: an

introduction for ecologists. J. Wiley & Sons Inc, New York, New York. 526 p.

Green, R. H. 1979. Sampling design and statistical methods for experimental

biologists. J. Wiley and Sons, Inc., New York. 257 p.

Gregory, K. J., and D. E. Walling. 1973. Drainage basin form and process. J. Wiley

and Sons, New York. 458p.

Grimm, N. B., and S. G. Fisher. 1986. Nitrogen limitation in a Sonoran Desert

stream. Journal of the North American Benthological Society 5:2-15.

Grimm, N.B. 1987. Nitrogen dynamics during succession in a desert stream. Ecology

68:1157-1170.

Guasch, H., E. Marti, and S. Sabater. 1995. Nutrient enrichment effects on biofilm

metabolism in a Mediterranean stream. Freshwater Biology 33:373-383.

Hall, F. C. 1973. Plant communities of the Blue Mountains in eastern Oregon and

southwestern Washington. U. S. Forest Service Region VI Area Guide 3-1.

Harrelson, C. C., C. L. Rawlins, and J. P. Potyondy. 1994. Stream channel reference

sites: an illustrated guide to field technique. USDA Forest Service, General

Technical Report RM-245, Fort Collins, CO. 61 p.

Hart, B. T., Freeman, P., and McKeivie, I. D. 1992. Whole-stream phosphorus release

studies: variation in uptake length with initial phosphorus concentration. Hydrobiology 235/236: 573-584.

Hawkins, C. P., J. L. Kershner, P. A. Bisson, M. D. Bryant, L. M. Decker, S. V.

Gregory. D. A., McCullough, C. K. Overton, G. H. Reeves, R. J. Steedman, and M.

K. Young. 1993. A hierarchical approach to classifying stream habitat features.

Fisheries 18: 3-10.

Hill, W. R., and A. W. Knight. 1987. Experimental analysis of the grazing interaction

between mayfly and stream algae. Ecology 68: 1955-1965.

Hill, W. R., and A. W. Knight. 1988. Nutrient and light limitation of algae in two

northern California streams. Journal of Phycology 24:125-132.

Hill, W. R., H. L. Boston, and A. D. Steinman. 1992. Grazers and Nutrients

simultaneously limit lotic primary productivity. Canadian Journal of Fisheries

and Aquatic Sciences 49:504-512.

Irons, J. G. III, M. W. Oswood, R. J. Stout, and C. M. Pringle. 1994. Latitudinal

patterns in leaf litter breakdown: is temperature really important? Freshwater

Biology 32:401-411.

Jensen, M. E., and P. S. Bourgeron. 1993. Eastside forest ecosystem health assessment. Volume II. Ecosystem management: principles and applications. U. S.

Forest Service, Portland, OR.

Karr, J. R., and D. R. Dudley. 1981. Ecological perspective on water quality goals.

Environmental Management 5:55-68.

Karr, J. R. 1991. Biological integrity: a long-neglected aspect of water resource

management. Ecological Applications 1:66-84.

Lamberti, G. A., and V. H. Resh. 1983. Stream periphyton and insect herbivores: an

experimental study of grazing by a caddisfly population. Ecology 64:1124-1135.

La Point, T. W. 1980. The role of ciliated protozoa and bacteria in stream benthic

organic matter decomposition. Unpublished PhD. Dissertation. Idaho State University, Pocatello, Idaho. 134p.

Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73:1943-1967.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 105

Lind, O. T. 1985. Handbook of common methods in Limnology. Kendall/Hunt

publishing Company Dubuque, Iowa. 199 p.

Lloyd, D. S., J. P. Koenings, and J. D. LaPerriere. 1987. Effects of turbidity in fresh

waters of Alaska. North American Journal of Fisheries Management 7:18-33.

Lohman, D., J. R. Jones, and C. Baysinger-Daniel. 1991. Experimental evidence for

nitrogen limitation in a northern Ozark stream. Journal of the North American

Benthological Society 10:14-23.

MacDonald, L. H., A. W. Smart, and R. C. Wissmar. 1991. Monitoring guidelines to

evaluate effects of forestry activities on streams in the Pacific Northwest and

Alaska. Environmental Protection Agency EPA 910/9-91-001.

MacMahon, J. A., D. L. Phillips, J. V. Robinson, and D. J. Schimpf. 1978. Levels of

biological organization: an organism-centered approach. BioScience 28:700-704.

Marzolf, E. R., P. J. Mulholland, and A. D. Steinman. 1994. Improvements to the

diurnal upstream-downstream dissolved oxygen change technique for determining whole-stream metabolism in small streams. Canadian Journal of Fish and

Aquatic Sciences 51:1591-1599.

Maxwell, J., C. Deacon-Williams, L. Decker, C. Edwards, M. Jensen, H. Parrot,

S. Paustian, and K. Stein. 1994. A hierarchical framework for the classification

and mapping of aquatic ecological units in North America. ECOMAP, USDA

Forest Service, Lakewood, CO.

McCain, M., D. Fuller, L. Decker, and K. Overton. 1990. Stream habitat classification

and inventory procedures for Northern California. U. S. Forest Service Region 5

Fish Habitat Relationship Technical Bulletin Number 1.

Merritt, R. W., and K. W. Cummins. 1996. An introduction to the aquatic insects of

North America. Kendall/Hunt Publishing company. Dubuque, Iowa. 441 p.

Meyer, J. L. 1989. Can P/R ratio be used to assess the food base of stream ecosystems?

Oikos 54: 119-121.

Minshall, G. W. 1978. Autotrophy in stream ecosystems. BioScience 28:767-771.

Minshall, G. W. 1984. Aquatic insect-substrate relationships. Pages 358-400 in V. H.

Resh and D. M. Rosenberg (editors). The ecology of aquatic insects. Praeger

Publishers, New York. 625 p.

Minshall, G. W. 1994. Stream-riparian ecosystems: rationale and methods for basinlevel assessments of management effects. Pages 143-167 in M. E. Jensen and P.

S. Bourgeron (editors). Eastside forest ecosystem health assessment. Volume II:

Ecosystem Management: Principles and Applications. USDA Forest Service,

Pacific Northwest Research Station, Portland, Oregon 97208-3890.

Minshall, G. W. 1996. Bringing biology back into water quality assessments. Pages

289-324, In: Freshwater ecosystems: revitalizing educational programs in limnology. Water Science and Technology Board, Commission on Geosciences, Environment, and Resources, National Research Council, Washington D.C.

Minshall, G. W., S. E. Jensen, and W. S. Platts. 1989. The ecology of stream and

riparian habitats of the Great Basin Region: a community profile. National

Wetlands Research Center, Washington, DC. US Fish and Wildlife Service

Biological Report 85(7.24). 142 p.

Minshall, G. W., R. C. Petersen, K. W. Cummins, R. L. Bott, J. R. Sedell, C. E.

Cushing, R. L. Vannote. 1983. Interbiome comparison of stream ecosystem

dynamics. Ecological Monographs 53: 1-24.

Minshall, G. W., R. C. Petersen, T. L. Bott, C. E. Cushing, K. W. Cummins, R. L.

Vannote, and J. R. Sedell. 1992. Stream ecosystem dynamics of the Salmon River,

Idaho: an 8th-order system. Journal of the North American Benthological Society

11:111-137.

Monaghan, M. T., and G. W. Minshall. 1996. Development and testing of methods for

assessing the habitat, biota, and functions of natural and human-impacted

wilderness stream ecosystems. Idaho State University, Pocatello, ID 83209-8007.

Prepared for: USDA Forest Service, Aldo Leopold Wilderness Research Institute,

Missoula, MT 59807.

Morris, D. P., and W. M. Lewis. 1988. Phytoplankton nutrient limitation in Colorado

mountain lakes. Freshwater Biology 20: 315-327.
106 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Mosko, T. L., B. L. Jeffers, J. G. King, and W. F. Megahan. 1990. Streamflow data for

undisturbed, forested watersheds in Central Idaho. U. S. Forest Service General

Technical Report INT-272. 334 p.

Mulholland, P. J., J. D. Newbold, J. W. Elwood, and L. A. Ferren. 1985. Phosphorus

spiralling in a woodland stream: seasonal variations. Ecology 66:1012-1023.

Munn, N. S., and J. L. Meyer. 1990. Habitat-specific solute retention in two small

streams: an intersite comparison. Ecology 71:2069-2082.

Naiman, R. J., and J. R. Sedell. 1980. Relationships between metabolic parameters

and stream order in Oregon. Canadian Journal of Fisheries and Aquatic Sciences

37: 834-847.

Naiman, R. J. 1983. The annual pattern and spatial distribution of aquatic oxygen

metabolism in boreal forest watersheds. Ecological Monographs 53:73-94.

Newbold, J. D., J. W. Elwood, R. V. Van Winkle, 1981. Measuring nutrient spiralling

in streams. Canadian Journal of Fisheries and Aquatic Sciences 38:860-863.

Newbold J. D., J. W. Elwood, R. V. O’Neill, and A. L. Sheldon. 1983. Phosphorus

Dynamics in a woodland stream ecosystem: a study of nutrient spiralling. Ecology

64:1249-1265.

O’Neill, R. V., D. L. DeAngelis, J. B. Waide, and T. F. H. Allen. 1986. A hierarchical

concept of ecosystems. Princeton University Press, Princeton, New Jersey. 253 p.

Odum, H. T. 1956. Primary production in flowing waters. Limnology and Oceanography 1:102-117.

Omernik, J. A., and A. L. Gallant. 1986. Ecoregions of the Pacific Northwest. Corvalis

Environmental Research Laboratory, U.S. Environmental Protection Agency

EPA/600/3-86/033. 39 p.

Omernik, J. A. 1987. Ecoregions of the conterminous United States. Annals of the

Association of American Geographers 77:118-125.

Patrick, R., and C. W. Reimer. 1966. The diatoms of the United States. Monograph

No. 13 of the Academy of Natural Sciences of Philadelphia.

Paul, R. W. Jr., E. F. Benfield, J. Cairns Jr. 1983. Dynamics of leaf processing in a

medium-sized river. pg. 403-424 in T. D. Fontaine and S. M. Bartell (editors).

Dynamics of Lotic Ecosystems. Ann Arbor Science, Michigan. 494 p.

Pennak, R. W., and J. W. Lavelle. 1979. In situ measurements of net primary

production in a Colorado mountain stream. Hydrobiologia 66:227-235.

Peterson, B. J., J. E. Hobbie, T. L. Corliss, and K. Kriet. 1983. A continuous-flow

periphyton bioassay: tests of nutrient limitation in a tundra stream. Limnology

and Oceanography 28:583-591.

Petersen, R. C. 1992. The RCE: a riparian, channel, and environmental inventory for

small streams in the agricultural landscape. Freshwater Biology 27:295-306.

Plafkin, J. L., M. T. Barbour, K. D. Porter, S. K. Gross, and R. M. Hughes. 1989. Rapid

bioassessment protocols for use in streams and rivers: benthic macroinvertebrates

and fish. U. S. Environmental Protection Agency EPA/444/4-89-001.

Platts, W. S., W. F. Megahan, and G. W. Minshall. 1983. Methods for evaluating

stream, riparian, and biotic conditions. U.S. Forest Service General Technical

Report INT-138. 70 p.

Platts, W. S., and 12 others. 1987. Methods for evaluating riparian habitats with

applications to management. U.S. Forest Service General Technical Report

INT-221. 177 p.

Poff, N. L., and J. V. Ward. 1989. Implications of streamflow variability and

predictability for lotic community structure: a regional analysis. Canadian Journal of Fisheries and Aquatic Sciences 46:1805-1818.

Poff, N. L., and J. V. Ward. 1990. Physical habitat template of lotic systems:

recovery in the context of historical pattern of spatiotemporal heterogeneity.

Environmental Management 14:629-645.

Prescott, G. W. 1970. How to know the freshwater algae. Wm. C. Brown Debuque.

Pringle, C. M., and J. A. Bowers. 1984. An in situ substratum fertilization technique:

diatom colonization on nutrient-enriched sand substrata. Canadian Journal of

Fisheries and Aquatic Sciences 41: 1247-1251.

Pringle C. M., P. Paaby-Hansen, P. D. Vaux, and C. R. Goldman. 1986. In situ

nutrient assays of periphyton growth in a lowland Costa Rican stream.

Hydrobiologia 134:207-213.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 107

Quinn, J. M., R. J. Davies-Colley, C. W. Hickey, M. L. Vickers, and P. A. Ryan. 1992.

Effects of clay discharges on streams 2. Benthic invertebrates. Hydrobiologia

248:235-247.

Rainwater, F. H., and L. L. Thatcher. 1960. Methods for collection and analysis of

water samples. U.S. Geological Water Supply Paper 1954. U.S. Government

Printing Office, Washington, DC. 301 p.

Rantz, S. E., and others. 1982. Measurement and computation of streamflow—

Volume 1. Measurement of stage and discharge: U.S. Geological Survey WaterSupply Paper 2175, U.S. Government Printing Office, Washington, DC. 284 p.

Robinson, C. T., and G. W. Minshall. 1995. Biological metrics for regional biomonitoring

and assessment of small streams in Idaho, Final Report. State of Idaho, Department of Environmental Quality.

Robison, E. G., and R. L. Beschta. 1990. Characteristics of coarse woody debris for

several coastal streams of southeast Alaska, USA. Canadian Journal of Fisheries

and Aquatic Sciences 47: 1684-1693.

Robinson, C. T., S. R. Rushforth, and G. W. Minshall. 1994. Diatom assemblages of

streams influenced by wildfire. Journal of Phycology 30:209-216.

Rosenfield, J. S., and R. J. Mackay. 1987. Assessing the food base of stream

ecosystems: alternatives to the P/R ratio. Oikos 50:141-147.

Rosgen, D. L. 1994. A classification of natural rivers. Elseiver Publications,

Amsterdam, Netherlands. 50 pages plus figures and tables.

Schanz, F. and H. Juon. 1983. Two different methods of evaluating nutrient

limitations of periphyton bioassays, using water from the River Rhine and eight

of its tributaries. Hydrobiologia 102: 187-195.

Sedell, J. R., P. Q. Bisson, F. J. Swanson, and S. V. Gregory. 1988. What we know

about large trees that fall into streams and rivers. Pages 47-81 in C. Maser, R. F.

Tarrant, J. M. Trappe, and J. F. Franklin (technical editors). From the forest to

the sea: a story of fallen trees. USDA Forest Service General Technical Report

PNW-229. 153 p.

Shortreed, K. S., and J. G. Stockner. 1983. Periphyton biomass and species composition in a coastal rainforest stream in British Columbia: effects of environmental

changes caused by logging. Canadian Journal of Fisheries and Aquatic Sciences 40: 1887-1895.

Shreve, R. L. 1966. Statistical law of stream numbers. Journal of Geology 74:17-37.

Sinsabaugh, R. L. M. P. Osgood, and S. Findlay. 1994. Enzymatic models for

estimating decomposition rates of particulate detritus. Journal of the North

American Benthological Society 13: 160-169.

Sokal, R. R., and Rohlf, F. J. 1969. Biometry, W. H. Freeman, San Francisco. 776 p.

Southwood, T. R. E. 1988. Tactics, strategies and templets. Oikos 52: 3-18.

Stazner, B., J. A. Gore, and V. H. Resh. 1988. Hydraulic stream ecoloty: observed

patters and potential applications. Journal of the North American Benthological

Society 7:307-360.

Steedman, R. J., and H. A. Regier. 1990. Ecological bases for an understanding of

ecosystem integrity in the Great Lakes Basin. Great Lakes Fishery Commission

and International Joint Commission.

Steele, R., R. D. Pfister, R. A. Ryker, and J. A. Kittams. 1981. Forest habitat types

of Central Idaho. U. S. Forest Service General Technical Report INT-114. 138 p.

Strahler, H. N. 1957. Quantitative analysis of watershed geomorphology. American

Geophysical Union Transactions 33:913-920.

Stream Solute Workshop, 1990. Concepts and methods for assessing solute dynamics in stream ecosystems. Journal of the North American Benthological Society

9: 95-119.

Sumner, W. T., and S. G. Fisher. 1979. Periphyton production in Fort River,

Massachusetts. Freshwater Biology 9:205-212.

Tait, C. K., J. L. Li, G. A. Lamberti, T. N. Pearsons, H. W. Li. 1994. Relationships

between riparian cover and the community structure of high desert streams.

Journal of the North American Benthological Society 13:45-56.

Thurow, R. F. 1994. Underwater methods for study of salmonids in the Intermountain West. General Technical Report INT-GTR-307. USDA Forest Service,

Intermountain Research Station, Ogden, UT 84401. 28 p.
108 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Triska, F. J., V. C. Kennedy, R. J. Avanzino, and B. N. Reilly. 1983. Effect of

simulated canopy cover on regulation of nitrate uptake and primary production by

natural periphyton assemblages. Pages 129-159 in T. F. Fontaine, II, and S. M.

Bartell (editors). Dynamics of Lotic Ecosystems. Ann Arbor Science, Ann Arbor,

Michigan.

Trotter, E. H. 1990. Woody debris, forest-stream succession, and catchment morphology. Journal of the North American Benthological Society 9:141-156.

Urban, D. L., R. V. O’Neill, and H. H. Shugart. 1987. Landscape ecology. BioScience

37:119-127.

U.S. Geological Survey. 1977. National handbook of recommended methods for water

data acquisition. USDI Office of Water Data Coordination, Chapter 1. Surface

Water, updated August 1980. 130 p.

vanDonk, E., and S. S. Kilham. 1990. Temperature effects on silicon- and phosphorus-limited growth and competitive interactions among three diatoms. Journal of

Phycology 26:40-50.

Vannote, R. L. and B. W. Sweeney. 1980. Geographic analysis of thermal equilibrium: a conceptual model for evaluating the effect of natural and modified thermal

regimes on aquatic insect communities. American Naturalist 115:667-695.

Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell, and C. E. Cushing. 1980.

The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences

37:30-37.

Webster, J. R., and E. F. Benfield. 1986. Vascular plant breakdown in freshwater

ecosystems. Annual Review of Ecological Systems 17:567-594.

Wieder, R. K., and G. E. Lang. 1982. A critique of the analytical methods used in

examining decomposition data obtained from litter bags. Ecology 63:1636-1642.

Winget, R. N., and F. A. Magnum. 1979. Biotic condition index: integrated biological,

physical, and chemical stream parameters for management. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Intermountain Region. 51 p.

Wolman, M. G. 1954. A method of sampling coarse river bed material. Transactions

of the American Geophysical Union 35: 951-956.

Wynne, D., and G. Y. Rhee. 1986. Effects of light intensity and quality on the relative

N and P requirement (the optimum N:P ratio) of marine planktonic algae. Journal

of Plankton Research 8:91-103.

Zar, J. H. 1974. Biostatistical Analysis. Prentice-Hall, Englewood Cliffs, New Jersey.

620 p.

Additional General References ___________

Buchanan, T. J., and W. P. Somers. 1969. Discharge measurements at gauging

stations. U.S. Geological Survey Techniques, Water-Resource Investigations,

Book 3, Chapter A8. 65 p.

Cushing, C.E., K.W. Cummins, and G. W. Minshall. 1995. River and Stream

Ecosystems. Ecosystems of the World 22. Elsevier, Amsterdam, The Netherlands.

817 p.

Cusimano, R. F. 1994. Technical Guidance for Assessing the Quality of Aquatic

Environments. Washington State Department of Ecology, Olympia, Washington.

Dunne, T., and L. B. Leopold. 1978. Water in environmental planning. W. H.

Freeman, New York. 796 p.

Hauer, F. R., and G. A. Lamberti (editors). 1996. Methods in Stream Ecology.

Academic Press Inc., San Diego, California. 674 p.

Leopold, L. B., M. G. Wolman, and J. P. Miller. 1964. Fluvial processes in geomorphology. W. H. Freeman. San Francisco, CA. 522 p.

Stednick, J. D. 1991. Wildland Water Quality Sampling and Analysis. Academic

Press, Inc. Harcourt Brace Jovanovich, Publishers. San Diego, California. 217 p.

Wetzel, R.G., and G. E. Likens. 1990. Limnological Analyses. (Second Edition).

Springer-Verlag, New York, New York. 395 p.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 109

Appendix A: Wilderness

Monitoring Equipment List

Stage 1 _______________________________

Temperature

Maximum/Minimum thermometer or Hobo temperature data

logger

Protective PVC case

Plastic coated steel cable

U clamps

Pliers

Substratum

Data sheet

Meter sticks

Water Quality

pH meter and probe with buffer solutions (pH 10 and pH 4)

(thermometer if not available with probe)

Conductivity meter and probe

Turbidity meter and probe

Water analysis kit packed in Rubbermaid or other sealable

container containing:

60-ml plastic syringe or 100-ml plastic graduated cylinder

0.02-N H2SO4, 5-ml per sample

Distilled water, 25-ml per sample

250-ml Erlenmeyer flask

Calibrated dispenser

Stirring rod

Buffer solution

Indicator (hardness)

Standard 0.01 M EDTA titrant

Fish

Neoprene wetsuit

hood

gloves

mask

snorkel

Macroinvertebrates

Surber or Hess nets

Whirl-pak bags, 5 for each site
110 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

500 ml bottle of formalin

Shoulder length gauntlets (optional)

Glue, needles, thread, glue stick (repair)

Benthic sampling kit packed in canvas bag

Labels

Plastic pan

Cone shaped bag

Ring stand

Forceps

Spatula

Pencils

Marking pens

RR spike

250 ml Nalgene wash bottle

Stage 2 _______________________________

In addition to items contained under Stage 1 add:

Solar Radiation

Pyranometer or PAR probe and meter

Discharge

Data sheets

Teflon tape 50-100 meter

Meter stick

Substratum

20-to-30 meters of polyethylene tubing or clinometer

Water Quality

Portable Spectrophotometer with cuvettes

Add to water analysis kit

125-ml Erlenmeyer flask

SulfaVer powder

NitraVer VI

NitriVer III

PhosVer 3

Periphyton

Pre-fired filters, 5 for each site

Dewar’s flask or suitable alternative

Sampling kit packed in canvas bag

Cushing samplers

Plastic brush

Filter manifold and funnel assembly

25 ml Nalgene pipettes with bulb

Forceps

Pencils

Marking pen

Nunc tubes
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 111

Stage 3 _______________________________

In addition to items listed under Stage 1 and Stage 2

Discharge

Staff gauge, pressure transducer, or other alternatives

Water velocity meter

Water Quality

Dropper with sulfuric acid

0.45 micron filters stored in distilled water

60 ml sterile syringes with filter caps

Marking tape and permanent pen

Cooler for storing water samples

250-ml plastic storage containers

Transported Organic Matter

Transport frames (20 x 35 mm)

Transport nets (100 micrometer mesh)

9.5-mm diameter Rebar (50 cm length)

Whirl-pak bags

500-ml bottle formalin

250-ml Nalgene wash bottle

Forceps

Stopwatch

Digital flow meter

Whole System Nutrient Release

Preweighed nutrient salts

Metering pump

Sample vials-acid washed, but not with HCl.

Marking tape

Mixing bucket

Stop watches, one for each transect

Additional filters and sulfuric acid preservative

Nutrient Limitation

Nutrient diffusers

Extra filters and nunc tubes

Stage 4 _______________________________

In addition to items contained in Stages 1 through 3

Primary Production (will vary with type of chamber and method

used)

Chambers

Extra tubing and fittings

Extra stopcocks

Pumps and circuit box

Extra fuses, 1A250V

Extra pump

9 volt battery for volt meter

Two rechargeable 12 VDC batteries
112 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Battery charger (optional)

Power source for charger, solar or water power (optional)

D.O. probe and meter

Data logger (optional)

Substrate tiles or Trays

Decomposition-Leaf Packs

Pre-weighed (10 g dry weight each) marked packs. 20 for each site

Additional whirl-pak bags

An additional 500-ml bottle of formalin

Metal stakes (16 cm nails), 20 for each site

Miscellaneous

Clinometer

Topographic maps

Camera- film, polarized filter.

Data book

Global Positioning System
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 113

Addie Sewing

531 S. Charles Street

Salmon, ID 83467

(208) 756-2291

Aldrich Chemical Company

1001 W. Street Paul Avenue

Milwaukee, WI 53201-9358

(800) 558-9160

Aliquot

P.O. Box 2616

Boise, ID 83701

(208) 322-8950

Alpkem Corporation

P.O. Box 1260

Clackamas, AZ 97015

(800) 547-6275

Aquacare Environment Incorporated

P.O. Box 4315

Bellingham, WA 98227

(368) 734-7964

Aquaculture Research Association

P.O. Box 1303

Homestead, FL 33090

(305) 248-4205

Aquatic Ecosystem Incorporated

2056 Apopka Blvd.

Apopka, FL 32703

(407) 886-3939

Bausch & Lomb

635 St. Paul Street

Rochester, NY 14602

(716) 338-6000

Beckman Instruments Incorporated

Diagnostic Division

250 S. Krasmen Blvd.

Le Brea, CA 92621

(800) 526-5821

Appendix B: Vendor List

BelArt Products

Pequannock, NJ 07440-1992

(201) 694-0500

Ben Meadows Company

3589 Broad Street

Atlanta, GA 30341

(800)241-6401

Benz Microscope Optics

749 Airport Blvd. S1A

Ann Arbor, MI 48107

(313) 994-3880

Campbell Scientific Incorporated

815 W. 1800 N.

Logan, UT 84321-1784

(801) 753-1342

Coffelt Manufacturing

1311 E. Butter Avenue BDGB

Flagstaff, AZ 86001

(602) 774-8829

Cole Palmer

625 E. Bunker Court

Vernon Hills, IL 60061

(800) 323-4340

Cryogenics Northwest

4401 Airport Way South

Seattle, WA 98108

(206) 224-0430

Desert Research Institute

7110 Dandini Blvd.

Reno, NV 89512

(702) 673-7300

Difco Laboratories

P.O. Box 331058

Detroit, MI 48232-7058

(313) 462-8500
114 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Duraframe Airport

Route 2, Box 166

Viola, WI 54664

(608) 538-3140

Dynalab Corporation

Box 112

Rochester, NY 14601

(888)345-6040

Dynatech Laboratories

14340 Sullyfield Circle

Chantilly, VA 22021

(800) 336-4543

Epic Incroporated

654 Madison Avenue

Suite 1706

New York, NY 10021-8404

Fisher Scientific

2170 Martin Avenue

Santa Clara, CA 95050-2780

(603) 929-2650

Floy Tag & MFC, Incorporated

4616 Union Bay Place, NE

Seattle, WA 98105

(206) 524-2700

Forest Densiometer

5333 SE Cornel Drive

Bartlerville, OK 74006

Freshwater Ecosystems

2056 Apopha Boulevard

Apopha, FL 32703-9950

(800) 422-3939

Forestry Suppliers Incorporated

205 W. Rankor St.

P.O. Box 8397

Jackson, MS 39284-8397

(800) 647-5368

Frigid Units Incorporated

3214 Sylvania Avenue

Toledo, OH 43613

(419) 474-6971

Gelman Sciences

600 S. Wagner Rd.

Ann Arbor, MI 48106-1448

(313) 665-0651

Hach Chemical Co.

P.O. Box 589

Loveland, CO 80537

(800) 227-4224

H,OFX

75 W. 100 S.

Logan, UT 84321

(801) 753-2212

Kahl Scientific Instruments

P.O. Box 1166

El Cajon, CA 92022-1166

(619) 444-2158

Lab-line Instruments Incorporated

15th and Bloomindale Avenue

Melrose Park, IL 60160-1491

(800) 523-0257

Leco Corporation

3000 Lakeview Avenue

St. Joseph, MI 49085

(800) 292-6141

Li-Cor Incorporated

P.O. Box 4425

Lincoln, NE 68504

(800) 447-3576

Markson Sciences Incorporated

P.O. Box 1359

Hillsboro, OR 97123

(800) 528-5114

Marsh McBirney

4539 Metropolitan Center

Fredrick, MD 21701

(800) 368-2723

Martek Instruments

P.O.Box 97067

Raleigh, NC 27624

(800)628-8834

Onset Instruments Corporation

P.O. Box 3450

Pocasset, MA 02559

(508) 563-9000

Orion Research

529 Main Street

Boston, MA 02129

(800) 225-1480
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 115

Philips Electronic Instruments

P.O. Box 5370

Arvada, CO 80005-5370

(303) 467-9970

Real Goods

966 Mazzoni Street

Ukiah, CA 95482-3471

(707) 468-9292

Royce Instruments Corporation

13555 Gentilly Road

New Orleans, LA 70129

(800) 347-3505

S & M Microscopes Incorporated

4815 List Drive, Suite 118

Colorado Springs, CO 80919

(719) 894-0123

Sargent Welch Scientific

911 Commerce Court

Buffalo Grove, IL 60089-2362

(800) 727-4368

Sigma Chemical Company

P.O. Box 14508

St. Louis, MO 63178

(800) 325-3010

So-Low Environment Equipment

10310 Spartan Drive

Cincinnati, OH 45215-1279

(503) 772-9110

Solar Pathfinder

25720 465th Avenue

Hartford, SD 57033-6428

(605) 528-6473

Tetho

333 South Highland Avenue

Briarcliff Manor, NY 10510

(914) 941-7767

Thomas Scientific

P.O. Box 99

Swedesboro, NJ 08085-0099

(800) 345-2100

Union Carbide Corporation

Cryogenic Equipment

4801 W. 16th St.

Indianapolis, IN 46224

(203)794-2000

USA Chemical Company

South Highway

Idaho Falls, ID 83401

(208) 523-5816

Weathermeasue Corporation

P.O. Box 41257

Sacromento, CA 95841

(209) 824-6577

Whatman Lab Sales

P.O. Box 1359

Hillsboro, OR 97123-9981

(800) 942-8626

Wheaton Scientific

1000 North 10th Street

Millsville, NY 08332

(609) 825-1100

Wildfire Materials Incorporated

Route 1, Box 427A

Carbondale, IL 62901

(618) 549-6330

Wildlife Supply Company

301 Cass Street

Saginaw, MI 48602

(517) 799-8100

Yellow Springs Instrument

P.O. Box 279

Yellow Springs, OH 45387

(800) 865-4974

Diatom Identification Laboratories:

United States Geological Survey

National Water Quality LaboratoryBiological Unit

5293 Ward Road MS 426

Arvada, CO 80002

Contact: John C. Kingston

Rex Lowe

Department of Biology

Bowling Green State University

Bowling Green, OH 43043

(419) 372-8562

Stephen Main

Biology Department

Wartburg College

Waverly, IA 50677

(319) 352-8386
116 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001

Jeffrey R. Johansen

Department of Biology

John Carrol University

University Heights, OH 44118

(216) 397-1886

Ann St. Amand

PhycoTech

520 Pleasant Street Suite 210

St. Joseph, MI

(616) 983-3654

Michael D. Agbeti

Bio-Limno Research and Consulting

8210-109 Street P.O. 52197

Edmonton, Alberta

Canada T6G 2T5

Michael Hein

Water and Air Research

6821 SW Archer Road

Gainesville, FL 32608

Dr. R. Jan Stevenson

Center for Environmental Sciences

Department of Biology

University of Louisville

Louisville, KY 40292

(502) 852-5938

Barry H. Rosen

Alganomics

20916 Spinnaker Way

Boca Raton, FL 33428

(561) 477-8275

Michele De Seve Consultants

74 Outremont #4

Montreal, Quebec

Canada H2V 3N1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 117

Appendix C:

Macroinvertebrate List

Taxon name Taxon level Order Family TV1 FFG2

ANNELLIDA Phylum 5 CG

BRANCHIOBDELLIDA Class

Branchiobdellidae Family Branchiobdellidae Branchiobdellidae 6 CG

HIRUDINEA Class 10 PR

OLIGOCHAETA Class CG

Tubificidae Family Tubificida Tubificidae 10 CG

Tubifex Genus Tubificida Tubificidae 10 CG

ARTHROPODA Phylum

ARACHNOIDEA Class

Acari Order Acari PR

CRUSTACEA Class 8 CG

Amphipoda Order Amphipoda 4 CG

Gammaridae Family Amphipoda Gammaridae

Gammarus Genus Amphipoda Gammaridae 4 CG

Anisogammarus Genus Amphipoda Gammaridae 4 CG

Talitridae Family Amphipoda Talitridae 8 CG

Hyallela azteca Species Amphipoda Talitridae 8 CG

Cladocera Order Cladocera 8 CF

Copepoda Order Copepoda 8 CG

Decapoda Order Decapoda 8 SH

Astacidae Family Decapoda Astacidae 8 SC

Pacifasticus connectens Species Decapoda Astacidae 6 OM

Pacifasticus lentusculus Species Decapoda Astacidae 6 OM

Pacifastacus gambelii Species Decapoda Astracidae 6 OM

Eubranchiopoda Order Eubranchiopoda 8 CF

Ostracoda Order Ostracoda 8 CG

INSECTA Class

Coleoptera Order Coleoptera PR

Amphizoidae Family

(con.)
118 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Amphizoa Genus Coleoptera Amphizoidae 1 PR Carabidae Family Coleoptera Carabidae PR Dryopidae Family Coleoptera Dryopidae 5 SH Helichus Genus Coleoptera Dryopidae 5 SH Helichus striatus foveatus Species Coleoptera Dryopidae 5 SH Dytiscidae Family Coleoptera Dytiscidae 5 PR Oreodytes Genus Coleoptera Dytiscidae 5 PR Elmidae Family Coleoptera Elmidae 4 CG Ampumixis dispar Species Coleoptera Elmidae 4 CG Atractelmis Genus Coleoptera Elmidae 4 CG Cleptelmis Genus Coleoptera Elmidae 4 CG Cleptelmis ornata Species Coleoptera Elmidae 4 CG Dubiraphia Genus Coleoptera Elmidae 4 CG Gonielmis Genus Coleoptera Elmidae 5 CG Heterlimnius Genus Coleoptera Elmidae 4 CG Heterlimnius corpulentus Species Coleoptera Elmidae 4 CG Lara avara Species Coleoptera Elmidae 4 SH Microcylloepus Genus Coleoptera Elmidae 2 CG Microcylloepus similis Species Coleoptera Elmidae 2 CG Narpus Genus Coleoptera Elmidae 4 CG Narpus concolor Species Coleoptera Elmidae 4 CG Obdobrevia nubrifera Species Coleoptera Elmidae 4 CG Optioservus Genus Coleoptera Elmidae 4 SC Optioservus castanipennis Genus Coleoptera Elmidae 4 SC Optioservus divergens Species Coleoptera Elmidae 4 SC Optioservus quadrimaculatus Species Coleoptera Elmidae 4 SC Optioservus seriatus Species Coleoptera Elmidae 4 SC Rhizelmis Genuss Coleoptera Elmidae 7 SC Taxon name Taxon level Order Family TV1 FFG2 Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 119 Taxon name Taxon level Order Family TV1 FFG2 Stenelmis Genus Coleoptera Elmidae 7 SC Zaitzevia Genus Coleoptera Elmidae 4 CG Zaitzevia milleri Species Coleoptera Elmidae 4 CG Zaizevia parvula Species Coleoptera Elmidae 4 CG Gyrinus Genus Coleoptera Gyrinidae 5 PR Haliplidae Family Coleoptera Haliplidae 7 MH Brychius Genus Coleoptera Haliplidae SC Hydrophilidae Family Coleoptera Hydrophilidae 5 PR Crenitis Genus Coleoptera Hydrophilidae 5 PR Psephenidae Family Coleoptera Psephenidae 4 SC Eubrianix edwardsi Species Coleoptera Psephenidae 4 SC Psephenus falli Species Coleoptera Psephenidae 4 SC Diptera Order Diptera 7 UN Atherix Genus Diptera Athericidae 2 PR Atherix variagata Species Diptera Athericidae 2 PR Blephariceridae Family Diptera Blephariceridae 0 SC Ceratopogonidae Family Diptera Ceratopogonidae 6 PR Chironomidae Family Diptera Chironomidae 6 OM Bezzia Geunus Diptera Chironomidae 6 CG Boreochlus Genus Diptera Chironomidae 6 CG Boreoheptagyia Genus Diptera Chironomidae 6 CG Brillia Genus Diptera Chironomidae 5 SH Brillia flavifrons Species Diptera Chironomidae 5 SH Brillia retifinis Species Diptera Chironomidae 5 SH Brundiniella Genus Diptera Chironomidae 6 PR Cardiocladius Genus Diptera Chironomidae 5 PR Chaetocloadius Genus Diptera Chironomidae 6 CG Chironomus Genus Diptera Chironomidae 10 CG Cladotanytarsus Genus Diptera Chironomidae 7 CG Appendix C (Con.) (con.)
120 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Taxon name Taxon level Order Family TV1 FFG2 Conchapelopia Genus Diptera Chironomidae 6 PR Constempellina Genus Diptera Chironomidae 6 CG Corynoneura Genus Diptera Chironomidae 7 CG Cricotopus Genus Diptera Chironomidae 7 OM Cricotopus bicinctus Species Diptera Chironomidae 7 OM Cricotopus festivellus Species Diptera Chironomidae 7 OM Cricotopus isocladius Species Diptera Chironomidae 7 OM Cricotopus nostococladius Species Diptera Chironomidae 7 OM Cricotopus tremulus Species Diptera Chironomidae 7 OM Cricotopus trifasciata Species Diptera Chironomidae 7 OM Cryptochironomus Genus Diptera Chironomidae 8 PR Diamesa Genus Diptera Chironomidae 5 CG Dicrotendipes Genus Diptera Chironomidae 8 CG Einfeldia Genus Diptera Chironomidae 9 CG Endochironomus Genus Diptera Chironomidae 10 OM Eukiefferiella Genus Diptera Chironomidae 8 OM Eukiefferiella brehmi Species Diptera Chironomidae 8 OM Eukiefferiella brevicalcar Species Diptera Chironomidae 8 OM Eukiefferiella claripennis Species Diptera Chironomidae 8 OM Eukiefferiella devonica Species Diptera Chironomidae 8 OM Eukiefferiella gracei Species Diptera Chironomidae 8 OM Eukiefferiella pseudomontana Species Diptera Chironomidae 8 OM Heleniella Genus Diptera Chironomidae 6 UN Heterotrissocladius subpilosus Species Diptera Chironomidae 0 CG Hydrobainus Genus Diptera Chironomidae 8 SC Larsia Genus Diptera Chironomidae 6 PR Limnophyes Genus Diptera Chironomidae 8 CG Lopescladius Genus Diptera Chironomidae 6 CG Macropelopia Genus Diptera Chironomidae 6 PR Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 121 Taxon name Taxon level Order Family TV1 FFG2 Micropsectra Genus Diptera Chironomidae 7 CG Microtendipes Genus Diptera Chironomidae 6 CF Monodiamesa Gennus Diptera Chironomidae 7 C Monopelopia Genus Diptera Chironomidae 6 PR Nanocladius Genus Diptera Chironomidae 3 C Nilotanypus Genus Diptera Chironomidae 6 PR Nimbocera Genus Diptera Chironomidae 6 C Odontomesa Genus Diptera Chironomidae 4 C Oliveridia Genus Diptera Chironomidae 6 C Orthocladius Genus Diptera Chironomidae 6 CG Orthocladius complex Species Diptera Chironomidae 6 CG Orthocladius eudactylocladius Species Diptera Chironomidae 6 CG Orthocladius euorthocladius Species Diptera Chironomidae 6 CG Orthocladius pogonocladius Species Diptera Chironomidae 6 CG Pagastia Genus Diptera Chironomidae 1 CG Parachaetocladius Genus Diptera Chironomidae 6 CG Parakiefferiella Genus Diptera Chironomidae 6 CG Paramerina Genus Diptera Chironomidae 6 PR Parametriocnemus Genus Diptera Chironomidae 5 CG Paraphaenocladius Genus Diptera Chironomidae 5 CG Paratanytarsus Genus Diptera Chironomidae 6 CG Paratendipes Genus Diptera Chironomidae 8 CG Paratrichocladius Genus Diptera Chironomidae 6 CG Parorthocladius Genus Diptera Chironomidae 6 CG Pentaneura Genus Diptera Chironomidae 6 PR Phaenopsectra Genus Diptera Chironomidae 7 SC Polypedilum Genus Diptera Chironomidae 6 OM Polypedilum pentapedilum Species Diptera Chironomidae 6 OM Potthastia gaedii Species Diptera Chironomidae 6 OM Appendix C (Con.) (con.)
122 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Taxon name Taxon level Order Family TV1 FFG2 Potthastia longimana Species Diptera Chironomidae 2 CG Procladius Genus Diptera Chironomidae 9 PR Prodiamesa Genus Diptera Chironomidae 3 CG Psectrocladius Genus Diptera Chironomidae 8 CG Psectrocladius allopsectroclad Species Diptera Chironomidae 8 CG Psectrocladius limbatellus Species Diptera Chironomidae 8 CG Psectrocladius sordidellus Species Diptera Chironomidae 8 CG Psectrotanypus Genus Diptera Chironomidae 10 PR Pseudochitonomus Genus Diptera Chironomidae 5 CG Pseudodiamesa Genus Diptera Chironomidae 6 CG Pseudorthocladius Genus Diptera Chironomidae 0 CG Rheocricotopus Genus Diptera Chironomidae 6 CG Rheotanytarsus Genus Diptera Chironomidae 6 CF Stempellina Genus Diptera Chironomidae 2 CG Stempellinella Genus Diptera Chironomidae 4 CG Subletta Genus Diptera Chironomidae 6 UN Symbiocladius Genus Diptera Chironomidae 6 PA Sympotthastia Genus Diptera Chironomidae 2 CG Synorthocladius Genus Diptera Chironomidae 2 CG Tanytarsini Sup-Genus Diptera Chironomidae 6 CF Tanytarsus Genus Diptera Chironomidae 6 CF Thienemannimyia Genus Diptera Chironomidae 6 PR Thienemanniola Genus Diptera Chironomidae 6 CG Tvetenia Genus Diptera Chironomidae 5 CG Tvetenia bavarica Species Diptera Chironomidae 5 CG Tvetenia discoloripes Species Diptera Chironomidae 5 CG Zavrelia Genus Diptera Chironomidae 8 CG Zavrelimyia Genus Diptera Chironomidae 8 PR Appendix C Culicidae Family Diptera Culicidae 8 CG (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 123 Taxon name Taxon level Order Family TV1 FFG2 Deuterophlebia Genus Diptera Deuterophlebiidae 0 SC Dixidae Family Diptera Dixidae 1 CG Dixa Genus Diptera Dixidae 1 CG Empididae Family Diptera Empididae 6 PR Chelifera Genus Diptera Empididae 6 PR Clinocera Genus Diptera Empididae 6 PR Hemerodromia Genus Diptera Empididae 6 PR Oreothalia Genus Diptera Empididae 6 PR Wiedemannia Genus Diptera Empididae 6 PR Ephydridae Family Diptera Ephydridae 6 CG Muscidae Family Diptera Muscidae 6 Pelecorhynchidae Family Diptera Pelecorhynchidae 3 PR Glutops Genus Diptera Pelecorhynchidae 3 PR Psychodidae Family Diptera Psychodidae 10 CG Maruina Genus Diptera Psychodidae 1 SC Ptychopteridae Family Diptera Ptychopterydae 7 CG Simuliidae Family Diptera Simuliidae 6 CF Simulium bivattatum Species Diptera Simuliidae 6 FC Prosimulium Genus Diptera Simuliidae 3 CF Simulium Genus Diptera Simuliidae 6 CF Simulium vittatum Species Diptera Simuliidae 6 CF Twinnia Genus Diptera Simuliidae 6 CF Stratiomyidae Family Diptera Stratiomyidae 8 CG Euparyphus Genus Diptera Stratiomyidae CG Pericoma Genus Diptera Sychodidae 4 CG Tabanidae Family Diptera Tabanidae 8 PR Tipulidae Family Diptera Tipulidae 3 OM Antocha Genus Diptera Tipulidae 3 CG Dicranota Genus Diptera Tipulidae 3 PR Appendix C (Con.) (con.)
124 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Hesperoconopa Genus Diptera Tipulidae 1 OM Hexatoma Genus Diptera Tipulidae 2 PR Limnophila Genus Diptera Tipulidae 4 PR Limonia Genus Diptera Tipulidae 6 OM Pedicia Genus Diptera Tipulidae 6 PR Tipula Genus Diptera Tipulidae 4 OM Ephemeroptera Order Ephemeroptera Baetidae Family Ephemeroptera Baetidae 4 CG Baetis Genus Ephemeroptera Baetidae 5 OM Baetis bicaudatus Species Ephemeroptera Baetidae 2 OM Baetis insignificans Species Ephemeroptera Baetidae 6 CG Baetis intermedius Species Ephemeroptera Baetidae 6 CG Baetis tricaudatus Species Ephemeroptera Baetidae 5 OM Callibaetis Genus Ephemeroptera Baetidae 9 CG Centroptilum Genus Ephemeroptera Baetidae 2 CG Pseudocloeon Genus Ephemeroptera Baetidae 4 OM Caenidae Family Ephemeroptera Caenidae 7 CG Caenis Genus Ephemeroptera Caenidae 7 CG Ephemerellidae Family Ephemeroptera Ephemerellidae 1 CG Attenella Genus Ephemeroptera Ephemerellidae 3 CG Attenella delantala Species Ephemeroptera Ephemerellidae 3 CG Caudatella Genus Ephemeroptera Ephemerellidae 1 CG Caudatella edmundsi Species Ephemeroptera Ephemerellidae 1 CG Caudatella heterocaudata Species Ephemeroptera Ephemerellidae 1 CG Caudatella hystrix Species Ephemeroptera Ephemerellidae 1 CG Drunella Genus Ephemeroptera Ephemerellidae 0 SC Drunella coloradensis Species Ephemeroptera Ephemerellidae 0 PR Drunella doddsi Species Ephemerroptera Ephemerellidae 0 PR Drunella flavilinea Species Ephemeroptera Ephemerellidae 1 SC Taxon name Taxon level Order Family TV1 FFG2 Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 125 Taxon name Taxon level Order Family TV1 FFG2 Drunella pelosa Species Ephemeroptera Ephemerellidae 0 SC Drunella spinifera Species Ephemeroptera Ephemerellidae 0 SC Ephemerella Genus Ephemeroptera Ephemerellidae 1 CG Ephemerella aurivilli Species Ephemeroptera Ephemerellidae 0 CG Ephemerella grandis Species Ephemeroptera Ephemerellidae 1 CG Ephemerella inermis Species Ephemeroptera Ephemerellidae 1 SH Serratella Genus Ephemeroptera Ephemerellidae 2 CG Serratella tibialis Species Ephemeroptera Ephemerellidae 2 CG Timpanoga hecuba Species Ephemeroptera Ephemerellidae 7 CG Heptageniidae Family Ephemeroptera Heptageniidae 4 SC Cinygma Genus Ephemeroptera Heptageniidae 4 SC Cinygmula Genus Ephemeroptera Heptageniidae 4 SC Epeorus Genus Ephemeroptera Heptageniidae 0 SC Epeorus albertae Species Ephemeroptera Heptageniidae 0 SC Epeorus deceptivus Species Ephemeroptera Heptageniidae 0 SC Epeorus grandis Species Ephemeroptera Heptageniidae 0 SC Epeorus iron Species Ephemeroptera Heptageniidae 0 SC Epeorus longimanus Species Ephemeroptera Heptageniidae 0 SC Heptagenia Genus Ephemeroptera Heptageniidae 4 SC Heptagenia elegantula Species Ephemeroptera Heptageniidae 4 SC Ironodes Genus Ephemeroptera Heptageniidae 4 SC Nixe criddlei Species Ephemeroptera Heptageniidae 2 SC Nixe simplicioides Species Ephemeroptera Heptageniidae 2 SC Rhithrogena Genus Ephemeroptera Heptageniidae 0 SC Rhithrogena hageni Species Ephemeroptera Heptageniidae 0 CG Leptophlebiidae Family Ephemeroptera Leptophlebiidae 2 CG Leptophlebia Genus Ephemeroptera Leptophlebiidae 2 CG Paraleptophlebia Genus Ephemeroptera Leptophlebiidae 1 OM Paraleptophlebia bicornuta Species Ephemeroptera Leptophlebiidae 4 CG Appendix C (Con.) (con.)
126 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Paraleptophlebia heteronea Species Ephemeroptera Leptophlebiidae 2 CG Polymitarcidae Family Ephemeroptera Polymitarcyidae 2 CG Ephoron album Species Ephemeroptera Polymitarcyidae 2 CG Siphlonuridae Family Ephemeroptera Siphlonuridae 7 CG Ameletus Genus Ephemeroptera Siphlonuridae 0 CG Ameletus velox Species Ephemeroptera Siphlonuridae 0 CG Siphlonurus Genus Ephemeroptera Siphlonuridae 7 OM Tricorythidae Family Ephemeroptera Tricorythidae 4 CG Tricorythides Genus Ephemeroptera Tricorythidae 5 CG Tricorythodes minutus Species Ephemeroptera Tricorythidae 4 CG Hirudinidae Family Gnathobdellida Hirudinidae 7 PR Naididae Family Haplotaxida Naididae C Rhyacodrilus sodalis Species Haplotaxida Tubificidae 10 CG Hemiptera Order Hemiptera Lethocerus Genus Hemiptera Belostomatidae PR Corixidae Family Hemiptera Corixidae OM Callicorixa Genus Hemiptera Corixidae PR Cenocorixa Genus Hemiptera Corixidae OM Cenocorixa bifida hungerfordi Species Hemiptera Corixidae PR Corisella Genus Hemiptera Corixidae PR Graptocorixa Genus Hemiptera Corixidae PR perocorixa Genus Hemiptera Corixidae PH Sigara Genus Hemiptera Corixidae PH Sigara alternata Species Hemiptera Corixidae PH Gelastocoridae Family Hemiptera Gelastocoridae PR Gelastocoris Genus Hemiptera Gelastocoridae PR Gerridae Family Hemiptera Gerridae 5 PR Gerris Genus Hemiptera Gerridae PR Taxon name Taxon level Order Family TV1 FFG2 Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 127 Taxon name Taxon level Order Family TV1 FFG2 Gerris buenoi Species Hemiptera Gerridae 5 PR Gerris remigis Species Hemiptera Gerridae 5 PR Naucoridae Family Hemiptera Naucoridae 5 PR Microvelia Genus Hemiptera Veliidae PR Hydracarina Order Hydracarina 8 PR Hygrobatidae Family Hydracarina Hygrobatidae 8 PR Hygrobates Species Hydracarina Hygrobatidae 8 PR Lebertiidae Family Hydracarina Lebertiidae 8 PR Lebertia Genus Hydracarina Lebertiidae 8 PR Piersigiidae Family Hydracarina Piersigiidae 8 PR Protzia californensis Species Hydracarina Piersigiidae 8 PR Sperchonidae Family Hydracarina Sperchonidae 8 PR Sperchon pseudoplumifer Species Hydracarina Sperchonidae 8 PR Hymenoptera Order Hymenoptera 8 PA Isopoda Order Isopoda 8 CG Asellidea Family Isopoda Asellidae 6 CG Asellus Genus Isopoda Asellidae 8 CG Asellus occidentalis Species Isopoda Asellidae 8 CG Caecidotea communis Species Isopoda Asellidae 6 CG Lepidoptera Order Lepidoptera Pyralidae Family Lepidoptera Pyralidae 5 SH Petrophila Genus Lepidoptera Pyralidae 5 SC Limnophila Order Limnophila Lymnaeidae Family Limnophila Lymnaeidae Fossaria Genus Limnophila Lymnaeidae 8 SC Lymnaea Genus Limnophila Lymnaeidae 8 SC Vorticifex Genus Limnophila Planorbidae 8 SC Appendix C Megaloptera Order Megaloptera Corydalidae Family Megaloptera Corydalidae 0 PR (Con.) (con.)
128 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Orohermes Genus Megaloptera Corydalidae 0 PR Sialis Genus Megaloptera Sialidae 4 PR Mesogastropoda Order Mesogastropoda Fluminicola Genus Mesogastropoda Bithyniidae 5 SC Flumincola Genus Mesogastropoda Hydrobiidae 8 SC Hydrobiidae Family Mesogastropoda Hydrobiidae SC Fontelicella Genus Mesogastropoda Hydrobiidae 8 SC Odonata Order Odonata Aeshnidae Family Odonata Aeshnidae 3 PR Anax Genus Odonata Aeshinidae 8 PR Coenagrionidae Family Odonata Coenagrionidae 9 PR Calopteryx Genus Odonata Calopterygidae 6 PR Argia Genus Odonata Coenagrionidae 7 PR Enallagma Genus Odonata Coenagrionidae 9 PR Ishnura Genus Odonata Coenagrionidae 9 PR Zoniagrion Genus Odonata Coenagrionidae 9 PR Gomphidae Family Odonata Gomphidae 1 PR Octogomphus Genus Odonata Gomphidae 1 PR Ophiogomphus Genus Odonata Gomphidae 1 PR Amphiagrion Genus Odonata Proteneuridae 5 PR Erpobdellidae Family Pharyngodellida Erpobdellidae 8 PR Dina parva Species Pharyngodellida Eropobdellidae 8 PR Plecoptera Order Plecoptera UN Capniidae Family Plecoptera Capniidae 1 SH Capnia Genus Plecoptera Capniidae 1 SH Eucapnopsis brevicauda Species Plecoptera Capniidae 1 SH Paracapnia Genus Plecoptera Capniidae 1 SH Chloroperlidae Family Plecoptera Chloroperlidae 1 PR Alloperla Genus Plecoptera Chloroperlidae 0 PR Taxon name Taxon level Order Family TV1 FFG2 Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 129 Taxon name Taxon level Order Family TV1 FFG2 Kathroperla perdita Species Plecoptera Chloroperlidae 1 OM Paraperla Genus Plecoptera Chloroperlidae 1 PR Sweltsa complex Species Plecoptera Chloroperlidae 1 PR Leuctridae Family Plecoptera Leuctridae 0 SH Despaxia augusta Species Plecoptera Leuctridae 0 SH Megaleuctra Genus Plecoptera Leuctridae 0 SH Mosellia infuscata Species Plecoptera Leuctridae 0 SH Paraleuctra Genus Plecoptera Leuctridae 0 SH Paraleuctra occidentalis Species Plecoptera Leuctridae 0 SH Perlomyia Genus Plecoptera Leuctridae 0 SH Nemouridae Family Plecoptera Nemouridae 2 Amphinemura Genus Plecoptera Nemouridae 2 SH Malenka Genus Plecoptera Nemouridae 2 SH Malenka Genus Plecoptera Nemouridae 5 PR Podmosta Genus Plecoptera Nemouridae 2 SH Prostola besametsa Species Plecoptera Nemouridae 2 SH Soyedina Genus Plecoptera Nemouridae 2 SH Visoka cataractae Species Plecoptera Nemouridae 1 SH Zapada Genus Plecoptera Nemouridae 2 SH Zapada cinctipes Species Plecoptera Nemouridae 2 SH Zapada columbiana Species Plecoptera Nemouridae 2 SH Zapada frigida Species Plecoptera Nemouridae 2 SH Zapada oregonensis Species Plecoptera Nemouridae 2 SH Peltoperlidae Family Plecoptera Peltoperlidae 2 SH Soliperla Genus Plecoptera Peltoperlidae 2 SH Yoraperla Genus Plecoptera Peltoperlidae 2 SH Yoraperla brevis Species Plecoptera Peltoperlidae 2 SH Yoraperla mariana Species Plecoptera Peltoperlidae 2 SH Appendix C Perlidae Family Plecoptera Perlidae 1 PR (Con.) (con.)
130 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Beloneuria Genus Plecoptera Perlidae 3 PR Calineuria Genus Plecoptera Perlidae 3 PR Calineuria californica Species Plecoptera Perlidae 1 PR Claasenia Genus Plecoptera Perlidae 3 PR Claassenia sabulosa Species Plecoptera Perlidae 4 PR Doroneuria Genus Plecoptera Perlidae 1 PR Doroneuria baumanni Species Plecoptera Perlidae 1 PR Doroneuria theodora Species Plecoptera Perlidae 1 PR Hesperoperla pacifica Species Plecoptera Perlidae 1 PR Cascadoperla Genus Plecoptera Perlodidae 2 PR Perlodidae Family Plecoptera Perlodidae 2 PR Cultus Genus Plecoptera Perlodidae 2 PR Diura knowltoni Species Plecoptera Perlodidae 2 OM Frisonia picticeps Species Plecoptera Perlodidae 2 PR Isogenus Genus Plecoptera Perlodidae 2 PR Isoperla Genus Plecoptera Perlodidae 2 PR Isoperla fluva Species Plecoptera Perlodidae 2 PR Isoperla fusca Species Plecoptera Perlodidae 2 PR Kogotus Genus Plecoptera Perlodidae 2 PR Megarcys Genus Plecoptera Perlodidae 2 PR Oroperla Genus Plecoptera Perlodidae 2 PR Perlinodes aurea Species Plecoptera Perlodidae 2 PR Pictetiella expansa Species Plecoptera Perlodidae 2 PR Setvena bradleyi Species Plecoptera Perlodidae 2 PR Skwala Genus Plecoptera Perlodidae 2 PR Yugus Genus Plecoptera Perlodidae 2 PR Pteronarcydae Family Plecoptera Pteronarcydae 0 OM Pteronarcella Genus Plecoptera Pteronarcydae 0 OM Pteronarcella badia Species Plecoptera Pteronarcydae 0 OM Taxon name Taxon level Order Family TV1 FFG2 Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 131 Taxon name Taxon level Order Family TV1 FFG2 Pteronarcella regularis Species Plecoptera Pteronarcydae 0 OM Pteronarcys Genus Plecoptera Pteronarcydae 0 OM Pteronarcys californica Species Plecoptera Pteronarcydae 0 OM Pteronarcys princeps Species Plecoptera Pteronarcydae 0 OM Taeniopterygidae Family Plecoptera Taeniopterygidae 2 OM Doddsia occidentalis Species Plecoptera Taeniopterygidae 2 UN Taenionema Genus Plecoptera Taeniopterygidae 2 SC Taenionema pallidum Species Plecoptera Taeniopterygidae 2 SC Entocytheridae Family Podocopa Entocytheridae UN Glossiphoniidae Family Rhynchobdellida Glossiphoniidae 8 PR Glossiphonia complanta Species Rhynchobdellida Glossiphoniidae 8 PR Helobdella stagnalis Species Rhynchobdellida Glossiphoniidae 10 PR Piscicola salmositica Species Rhynchobdellida Piscicolidae 7 PR **Corticacarus delicatus Species Hygrobatidae 8 PR Trichoptera Order Trichoptera UN Brachycentridae Family Trichoptera Brachycentridae 1 CF Amiocentrus Genus Trichoptera Brachycentridae 1 CG Amiocentrus aspilus Species Trichoptera Brachycentridae 2 CG Brachycentrus Genus Trichoptera Brachycentridae 1 OM Brachycentrus americanus Species Trichoptera Brachycentridae 1 OM Brachycentrus occidentalis Species Trichoptera Brachycentridae 1 OM Micrasema Genus Trichoptera Brachycentridae 1 MH Oligoplectrum Genus Trichoptera Brachycentridae 1 C Calarnocreatidae Family Trichoptera Heteroplectron califormicum Species Trichoptera Calamocreatidae 1 SH Glossosomatidae Family Trichoptera Glossosomatidae 0 SC Agapetus Genus Trichoptera Glossosomatidae 0 SC Anagapetus Genus Trichoptera Glossosomatidae 0 SC Culoptila cantha Species Trichoptera Glossosomatidae 0 SC Appendix C (Con.) (con.)
132 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Glossosoma Genus Trichoptera Glossosomatidae 0 SC Glossosoma alascense Species Trichoptera Glossosomatidae 0 SC Glossosoma intermedium Species Trichoptera Glossosomatidae 0 SC Glossosoma montana Species Trichoptera Glossosomatidae 0 SC Glossosoma oregonense Species Trichoptera Glossosomatidae 0 SC Glossosoma penitum Species Trichoptera Glossosomatidae 0 SC Glossosoma wenatchee Species Trichoptera Glossosomatidae 0 SC Protopitla Genus Trichoptera Glossosomatidae 1 SC Protoptila coloma Species Trichoptera Glossosomatidae 1 SC Protoptila tenebrosa Species Trichoptera Glossosomatidae 1 SC Helicopsychidae Family Trichoptera Helicopsychidae 3 SC Elicopsyche borealis Species Trichoptera Helicopsychidae 3 SC Helicopsyche Genus Trichoptera Helicopsychidae 3 SC Hydropsychidae Family Trichoptera Hydropsychidae 4 CF Apataniinae Sub-Family Trichoptera Hydropsychidae 2 CF Cheumatopsyche Genus Trichoptera Hydropsychidae 5 CF Cheumatopsyche campyla Species Trichoptera Hydropsychidae 6 CF Cheumatopsyche enonis Species Trichoptera Hydropsychidae 6 CF Cheumatopsyche pettiti Species Trichoptera Hydropsychidae 6 CF Hydropsyche Genus Trichoptera Hydropsychidae 4 CF Hydropsyche californica Species Trichoptera Hydropsychidae 4 CF Hydropsyche occidentalis Species Trichoptera Hydropsychidae 4 CF hydropsyche oslari Species Trichoptera Hydropsychidae 4 CF Macronema Genus Trichoptera Hydropsychidae 3 CF Parapsyche Genus Trichoptera Hydropsychidae 1 PR Parapsyche almota Species Trichoptera Hydropsychidae 3 PR Parapsyche elsis Species Trichoptera Hydropsychidae 1 PR Hydroptilidae Family Trichoptera Hydroptilidae 4 PH Taxon name Taxon level Order Family TV1 FFG2 Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 133 Taxon name Taxon level Order Family TV1 FFG2 Agraylea Genus Trichoptera Hydroptilidae 8 PH Hydropitla ajax Species Trichoptera Hydroptilidae 6 SC Hydroptila Genus Trichoptera Hydroptilidae 6 PH Hydroptila arctia Species Trichoptera Hydroptilidae 6 SC Hydroptila argosa Species Trichoptera Hydroptilidae 6 SC Leucotrichia Genus Trichoptera Hydroptilidae 6 SC Neothrichia halia Species Trichoptera Hydroptilidae 4 S Ochrotrichia Genus Trichoptera Hydroptilidae 4 C Orthotrichia Genus Trichoptera Hydroptilidae 6 PR Stactobiella Genus Trichoptera Hydroptilidae 2 SH Lepidostomatidae Family Trichoptera Lepidostomatidae 1 SH Lepidostoma Genus Trichoptera Lepidostomatidae 1 SH Lepidostoma cinereum Species Trichoptera Lepidostomatidae 3 SH Leptoceridae Family Trichoptera Leptoceridae 4 CG Mystacides Genus Trichoptera Leptoceridae 4 C Nectopsyche gracilis Species Trichoptera Leptoceridae 3 S Nectopsyche halia Species Trichoptera Leptoceridae 3 S Nectopsyche lahontanensis Species Trichoptera Leptoceridae 3 S Nectopsyche stigmatica Species Trichoptera Leptoceridae 3 S Oecetis Genus Trichoptera Leptoceridae 8 PR Triaenodes Genus Trichoptera Leptoceridae 6 MH Limnephilidae Family Trichoptera Limnephilidae 4 OM Allocosmoecus partitus Species Trichoptera Limnephilidae 0 SC Apatania Genus Trichoptera Limnephilidae 1 SC Chyranda Genus Trichoptera Limnephilidae 1 SH Chyranda centralis Species Trichoptera Limnephilidae 1 SH Cryptochia Genus Trichoptera Limnephilidae 0 SH Dicosmoecinae Sub-Family Trichoptera Limnephilidae 1 OM Dicosmoecus Genus Trichoptera Limnephilidae 1 SH Appendix C (Con.) (con.)
134 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Dicosmoecus atripes Species Trichoptera Limnephilidae 1 PR Dicosmoecus gilvipes Species Trichoptera Limnephilidae 2 SC Ecclisocosmoecus scylla Species Trichoptera Limnephilidae 0 SH Ecclisomyia Genus Trichoptera Limnephilidae 2 OM Goerinae Sub-Family Trichoptera Limnephilidae 1 SC Goera archaon Species Trichoptera Limnephilidae 1 SC Grensia Genus Trichoptera Limnephilidae 6 SH Hesperophylax Genus Trichoptera Limnephilidae 5 OM Homophylax Genus Trichoptera Limnephilidae 0 SH Hydatophylax Genus Trichoptera Limnephilidae 1 SH Limnephilinae Sub-Family Trichoptera Limnephilidae 4 OM Limnephilus Genus Trichoptera Limnephilidae 5 OM Moselyana Genus Trichoptera Limnephilidae 4 C Neophylax Genus Trichoptera Limnephilidae 3 S Neophylax occidentalis Species Trichoptera Limnephilidae 3 S Neophylax rickeri Species Trichoptera Limnephilidae 3 S Neophylax splendens Species Trichoptera Limnephilidae 3 S Oligophlebodes Genus Trichoptera Limnephilidae 1 S Onocosmoecus Genus Trichoptera Limnephilidae 1 SH Onocosmoecus unicolor Species Trichoptera Limnephilidae 2 SH Pedomoecus sierra Species Trichoptera Limnephilidae 0 SC Psychoglypha Genus Trichoptera Limnephilidae 1 OM Psychoglypha bella Species Trichoptera Limnephilidae 2 OM Psychoglypha subborealsis Species Trichoptera Limnephilidae 2 OM Philopotamidae Family Trichoptera Philopotamidae 3 CF Dolophilodes Genus Trichoptera Philopotamidae 3 CF Wormaldia Genus Trichoptera Philopotamidae 3 CF Polycentropidae Family Trichoptera Polycentropodidae 6 CF Polycentropus Genus Trichoptera Polycentropodidae 6 PR Taxon name Taxon level Order Family TV1 FFG2 Appendix C (Con.) (con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 135 Taxon name Taxon level Order Family TV1 FFG2 Paychomyiidae Family Trichoptera Psychomyiidae 6 CG Psychomyia lumina Species Trichoptera Psychomyiidae 2 SC Tinodes Genus Trichoptera Psychomyiidae 6 SC Rhyacophilidae Family Trichoptera Rhyacophilidae 0 PR Rhyacophila Genus Trichoptera Rhyacophilidae 0 PR Rhyacophila acropedes Species Trichoptera Rhyacophilidae 1 PR Rhyacophila alberta Species Trichoptera Rhyacophilidae 0 PR Rhyacophila angelita Species Trichoptera Rhyacophilidae 0 PR Rhyacophila arnaudi Species Trichoptera Rhyacophilidae 0 PR Rhyacophila betteni Species Trichoptera Rhyacophilidae 0 PR Rhyacophila blarina Species Trichoptera Rhyacophilidae 0 PR Rhyacophila brunnea Species Trichoptera Rhyacophilidae 0 PR Rhyacophila coloradensis Species Trichoptera Rhyacophilidae 0 PR Rhyacophila hyalinata Species Trichoptera Rhyacophilidae 0 PR Rhyacophila iranda Species Trichoptera Rhyacophilidae 0 PR Rhyacophila narvae Species Trichoptera Rhyacophilidae 0 PR Rhyacophila pellisa Species Trichoptera Rhyacophilidae 0 PR Rhyacophila rotunda Species Trichoptera Rhyacophilidae 0 PR Rhyacophila sibirica Species Trichoptera Rhyacophilidae 0 PR Rhyacophila vagrita Species Trichoptera Rhyacophilidae 0 PR Rhyacophila verrula Species Trichoptera Rhyacophilidae 0 MH Wormaldia gabriella Species Trichoptera Rhyacophilidae 3 CF Sericostomatidae Family Trichoptera Sericostomatidae Grumaga Genus Trichoptera Sericostomatidae 3 SH Uenoidae Family Trichoptera Uenoidae Neothremma alicia Species Trichoptera Uenoidae 0 S Neothremma Genus Trichoptera Uenoidae 0 S MOLLUSKA Phylum S

GASTROPODA Class 7 SC

Appendix C (Con.)

(con.)
136 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 Taxon name Taxon level Order Family TV1 FFG2 Limnophila Order Limnophila Ancylidae Family Limnophila Ancylidae 6 SC Ferrissia Genus Limnophila Ancylidae 6 SC Lymnaeidae Family Limnophila Lymnaeidae 6 SC Physidae Family Limnophila Physidae 8 SC Physa Genus Limnophila Physidae 8 SC Physella Genus Limnophila Physidae 8 SC Planorbidae Family Limnophila Planorbidae 7 SC Gyraulus Genus Limnophila Planorbidae 8 SC Promentus Genus Limnophila Planorbidae 6 CG Mesogastropoda Order Mesogastropoda Juga Genus Mesogastropoda Thiaridae 7 OM

PELECYPODA Class 8 CF

Margaritifera Genus Pelecypoda Margaritiferidae 4 CF

Margaritifera margaritifera falcata Speicies Pelecypoda Margaritiferidae 8 CF

Sphaeriidae Family Pelecypoda Sphaeriidae 8 CF

Pisidium Genus Pelecypoda Sphaeriidae 8 CF

Pisidium casertanum Species Pelecypoda Sphaeriidae 8 SC

Pisidium compressum Species Pelecypoda Sphaeriidae 8 CF

Pisidium idahoenses Species Pelecypoda Sphaeriidae 8 CF

Sphaerium patella Species Pelecypoda Sphaeriidae 8 CF

Sphaerium striatum Species Pelecypoda Sphaeriidae 8 CF

Unionidae Family Pelecypoda Unionidae

Gonidea Genus Pelecypoda Unionidae 4 CF

Appendix C (Con.)

(con.)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-70. 2001 137 Anodonta nuttalliana idahoens Species Pelecypoda Unionidae 8 CF Gonidea angulata Species Pelecypoda Unionidae 8 CF NEMATODA Phylum 5 F PLATYHELMINTHES Phylum

TUBELLARIA Class 4 PR

Tricladida Order Tricladida UN

Planariidae Family Tricladida Planariidae OM

1Tolerance values (TV) range from 0 (low tolerance) to 10(high tolerance) from Clark and Maret (1993)

2Functional Feeding Group (FFG) Designations: CF = Collector-Filterer; PH = Piercer Herbivore; CG = Collector-Gatherer; PR =

Predator; MH = Macrophyte Herbivore; SC = Scraper; OM = Omnivore; SH = Shredder; PA = Parasite; UN = Unknown

Taxon name Taxon level Order Family TV1 FFG2

Appendix C (Con.)
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