U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2010–1139

Prepared in cooperation with the

Massachusetts Department of Conservation and Recreation, the

Massachusetts Department of Environmental Protection, and the 

Massachusetts Department of Fish and Game

Preliminary Assessment of Factors Influencing 

Riverine Fish Communities in Massachusetts

Preliminary Assessment of Factors 

Influencing Riverine Fish Communities 

in Massachusetts

By David S. Armstrong, Todd A. Richards, and Sara L. Brandt

Prepared in cooperation with the

Massachusetts Department of Conservation and Recreation, the

Massachusetts Department of Environmental Protection, and the 

Massachusetts Department of Fish and Game

Open-File Report 2010–1139

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Armstrong, D.S., Richards, T.A., and Brandt, S.L., 2010, Preliminary assessment of factors influencing riverine fish 

communities in Massachusetts: U.S. Geological Survey Open-File Report 2010–1139, 43 p.
iii

Acknowledgments

The authors are grateful to the MDCR, MDEP, MDFG, EOEEA, members of the Sustainable Water 

Management Initiative, and other public agencies, private organizations and individuals for 

supporting this study. The authors would like to thank M. Freeman (USGS), S. Wenger (Trout 

Unlimited/U.S. Forest Service), and N. Detenbeck (USEPA) for technical review; and L. Hutchins 

(MDCR), T. Lamonte and W. Kimball (MDEP), K. Baskin (EOEEA), A. Paul (Alberta FWD), J. Magee 

(NHFG), and others who provided review comments. Thank you also to L. Ostiguy and P. Steeves 

(USGS), who provided GIS assistance; the West Trenton Publishing Service Center, who provided 

editorial review and report layout; and R.Vogel (Tufts University), J. Kennen, J. Coles, B. Cade 

(USGS), and M. Baker (UMBC) who provided guidance and assistance during the project. The 

authors would also like to thank the MDFW and MDEP crews who collected the fish samples. 

The cooperation of public and private landowners who granted permission to access the rivers is 

appreciated. 
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v

Contents

Acknowledgments........................................................................................................................................iii

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

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

Purpose and Scope .............................................................................................................................2

Description of Study Area ..................................................................................................................2

Previous Studies ...................................................................................................................................3

Factors Limiting Fish Communities..............................................................................................................4

Methods...........................................................................................................................................................5

Streamflow Data and Measures of Flow Alteration ......................................................................5

Fish-Community Data and Fish Metrics ............................................................................................6

Environmental Characteristics ...........................................................................................................8

Statistical Analysis .............................................................................................................................10

Quantile Regression .................................................................................................................10

Generalized Linear Models and Extensions..........................................................................10

Model Fitting and Validation ....................................................................................................12

Relations Among Flow Alteration, Impervious Cover, and Fish Communities ...................................13

Data Exploration..................................................................................................................................13

Streamflow Alteration ..............................................................................................................13

Fish-Community Data ...............................................................................................................14

Environmental Characteristics ...............................................................................................17

Quantile Regression ...........................................................................................................................22

Generalized Linear Models ..............................................................................................................27

Fluvial-Fish Species-Richness Model ....................................................................................29

Fluvial-Fish Relative-Abundance Model................................................................................29

Brook Trout Relative-Abundance Model ...............................................................................30

Model Validation and Limitations............................................................................................30

Fish-Community Response to Flow Alteration and Impervious Cover..............................31

Suggestions for Further Study ..................................................................................................................32

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

References Cited..........................................................................................................................................34
vi

Figures

1. Map showing Massachusetts Division of Fisheries and Wildlife fish-sampling 

sites used to assess environmental and anthropogenic factors influencing fish 

comunities in Massachusetts.....................................................................................................3

2. Graph showing cumulative frequency in relation to percent alteration of 

August median flow for 756 Massachusetts Division of Fisheries and Wildlife 

fish-sampling sites and 1,429 subbasins and groundwater contributing areas 

across Massachusetts ..............................................................................................................13

3. Map showing percent alteration of August median flows at 756 Massachusetts 

Division of Fisheries and Wildlife fish-sampling sites, 1998–2008......................................14

4. Graphs showing fish relative abundance in counts per hour of (A) fluvial fish, (B) 

cold-water fish, (C) brook trout, and (D) white sucker in relation to percent of 

selected sites in Massachusetts, 1998–2008 .........................................................................16

5-6. Box plots showing—

5. Environmental characteristics for contributing areas to 756 fish-sampling 

sites in Massachusetts (A) wetland area, (B) mean basin slope, (C) channel 

slope, (D) drainage area, (E) elevation, (F) open-water area, and (G) sand and 

gravel area..........................................................................................................................18

6. Anthropogenic characteristics for contributing areas to 756 fish-sampling 

sites in Massachusetts (A) impervious cover, (B) 240-meter-buffer impervious 

cover, (C) dam density per square mile, (D) dam density per stream mile...............19

7. Map showing impervious cover for contributing areas to 756 Massachusetts 

Division of Fisheries and Wildlife fish-sampling sites in Massachusetts, 

1998–2008 .....................................................................................................................................20

8. Graph showing frequency distributions of impervious cover for contributing areas 

to 756 Massachusetts Division of Fisheries and Wildlife fish-sampling sites and 

1,429 subbasins and groundwater contributing areas across Massachusetts ...............20

9. Pairplots of environmental characteristics............................................................................21

10-22. Graphs showing—

10. Relative abundance of indicator fish species in relation to percent alteration 

of August median flow at selected net-depleted sites for (A) brook trout, (B) 

blacknose dace, (C) fallfish, and (D) white sucker, in Massachusetts streams, 

1998–2008 ............................................................................................................................23

11. Fish-community metrics in relation to percent alteration of August median 

flow at selected net-depleted sites for (A) fluvial-fish relative abundance and 

(B) fluvial-fish richness, in Massachusetts, 1998–2008...............................................23

12. Relative abundance of indicator fish species in relation to percent alteration 

of August median flow for selected net-surcharged sites for (A) brook trout, 

(B) blacknose dace, (C) fallfish, and (D) redfin pickerel, in Massachusetts 

streams, 1998–2008............................................................................................................24

13. Fish-community metrics in relation to percent alteration of August median 

flow at selected net-surcharged sites (A) fluvial-fish relative abundance, and 

(B) fluvial-fish richness, in Massachusetts streams, 1998–2008 ...............................24

14. Relative abundance of indicator-fish-species metrics in relation to impervious 

cover for (A) brook trout, (B) blacknose dace, and (C) fallfish, in selected 

Massachusetts streams, 1998–2008...............................................................................25

15. Fish-community metrics in relation to impervious cover for (A) fluvial-fish 

relative abundance, and (B) fluvial-fish richness, in selected Massachusetts 

streams, 1998–2008............................................................................................................25
vii

16. Relative abundance of indicator-fish-species metrics in relation to open 

water for (A) fluvial fish, (B) brook trout, and (C) blacknose dace, in selected 

Massachusetts streams, 1998–2008...............................................................................26

17. Generalized linear model output for relations among fluvial-fish richness, 

percent impervious cover, and channel slope, in selected Massachusetts 

streams, 1998–2008............................................................................................................29

18. Generalized linear model output for relations between fluvial-fish relative

abundance and percent alteration of August median flow for selected 

net-depleted and net-surcharged streams in Massachusetts, 1998–2008..............29

19. Generalized linear model output for relations among brook trout relative 

abundance, percent impervious cover, and percent area sand and gravel, 

in selected Massachusetts streams, 1998–2008 ..........................................................30

20. Generalized linear model output for relations among fluvial-fish richness, 

percent impervious cover, and (A) drainage area, (B) wetland area, (C) channel 

slope, and (D) outlet X for selected Massachusetts streams, 1998–2008................40

21. Generalized linear model output for relations among relative abundance of 

fluvial fish, percent impervious cover, or percent net August depletion or 

surcharge, and (A) drainage area, (B) channel slope, (C) wetland area, 

(D) outlet X, (E) drainage area, (F) drainage area, (G) channel slope, 

(H) channel slope, (I) wetland area, (J) wetland area, (K) outlet X, and

(L) outlet X for selectd Massachusetts streams, 1998–2008 ......................................41

22. Generalized linear model output for relations among relative abundance of 

brook trout, percent impervious cover, and (A) drainage area, (B) sand and 

gravel, (C) open water, (D) wetland area, (E) outlet X, and (F) channel slope 

for selected Massachusetts streams, 1998–2008 ........................................................43

Tables

1. Common and scientific names of fishes, species codes, and habitat use 

classifications for each species ................................................................................................7

2. Common names of fish, and number and percentage of sites sorted by the number 

of sites at which each species was captured, and number and percentage of 

individuals sorted by the number of individuals captured at sites used for the 

preliminary assessment of fish communities in Massachusetts........................................15

3. Descriptions, reference codes, units, number of sites, and ranges of values for 

environmental and anthropogenic variables .........................................................................18

4. Significant variables and coefficients for generalized linear model equations used 

to determine relations between natural and anthropogenic factors and riverine

fish communities in Massachusetts........................................................................................28

5. Measures of goodness of fit for comparisons of observed and predicted values 

for generalized linear model equations for fluvial-fish richness, fluvial-fish relative 

abundance, and brook trout relative abundance..................................................................31
viii

Conversion Factors and other Abbreviations

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2

)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2

) 

Volume

gallon (gal) 3.785 liter (L) 

gallon (gal) 0.003785 cubic meter (m3

) 

gallon (gal) 3.785 cubic decimeter (dm3

) 

million gallons (Mgal) 3,785 cubic meter (m3

)

cubic foot (ft3) 0.02832 cubic meter (m3

) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3

/s)

cubic foot per second per square mile 

[(ft3/s)/mi2] 0.01093 cubic meter per second per square kilometer [(m3/s)/km2]

gallon per minute (gal/min) 0.06309 liter per second (L/s)

gallon per day (gal/d) 0.003785 cubic meter per day (m3

/d)

gallon per day per square mile 

[(gal/d)/mi2] 0.001461 cubic meter per day per squarekilometer [(m3/d)/km2]

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3

/s)

million gallons per day per square mile 

[(Mgal/d)/mi2] 1,461 cubic meter per day per squarekilometer [(m3/d)/km2]

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

°F=(1.8×°C)+32

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

°C=(°F-32)/1.8
ix

Acronyms and Abbreviations

AIC Akaike’s information criterion

DEM Digital elevation model

EOEEA Executive Office of Energy and Environmental Affairs

FD Fluvial dependent

FS Fluvial specialist

GLM Generalized linear model

HUC Habitat Use Classification

IC Impervious cover

MDCR Massachusetts Department of Conservation and Recreation

MDEP Massachusetts Department of Environmental Protection

MDFG Massachusetts Department of Fish and Game

MDFW Massachusetts Division of Fisheries and Wildlife

MG Macrohabitat generalist

NHD National Hydrography Dataset

NPDES National Pollutant Discharge Elimination System

OLS Ordinary least squares

SYE Sustainable Yield Estimator

TITAN Threshold Indicator Taxa ANalysis

USGS U. S. Geological Survey

VIF Variance inflation factor

WUI Water-use intensity

YOY Young-of-the-year

ZINB Zero-inflated negative binomial
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Abstract

The U.S. Geological Survey, in cooperation with the 

Massachusetts Department of Conservation and Recreation 

(MDCR), Massachusetts Department of Environmental Protection (MDEP), and the Massachusetts Department of Fish 

and Game (MDFG), conducted a preliminary investigation of 

fish communities in small- to medium-sized Massachusetts 

streams. The objective of this investigation was to determine 

relations between fish-community characteristics and anthropogenic alteration, including flow alteration and impervious 

cover, relative to the effect of physical basin and land-cover 

(environmental) characteristics. Fish data were obtained for 

756 fish-sampling sites from the Massachusetts Division of 

Fisheries and Wildlife fish-community database. A review of 

the literature was used to select a set of fish metrics responsive 

to flow alteration. Fish metrics tested include two fish-community metrics (fluvial-fish relative abundance and fluvial-fish 

species richness), and five indicator species metrics (relative 

abundance of brook trout, blacknose dace, fallfish, white 

sucker, and redfin pickerel). Streamflows were simulated for 

each fish-sampling site using the Sustainable Yield Estimator 

application (SYE). Daily streamflows and the SYE water-use 

database were used to determine a set of indicators of flow 

alteration, including percent alteration of August median flow, 

water-use intensity, and withdrawal and return-flow fraction. 

The contributing areas to the fish-sampling sites were delineated and used with a Geographic Information System (GIS) 

to determine a set of environmental characteristics, including 

elevation, basin slope, percent sand and gravel, percent wetland, and percent open water, and a set of anthropogenic-alteration variables, including impervious cover and dam density. 

Two analytical techniques, quantile regression and generalized linear modeling, were applied to determine the association between fish-response variables and the selected environmental and anthropogenic explanatory variables. Quantile 

regression indicated that flow alteration and impervious cover 

were negatively associated with both fluvial-fish relative 

abundance and fluvial-fish species richness. Three generalized linear models (GLMs) were developed to quantify the 

response of fish communities to multiple environmental and 

anthropogenic variables. Flow-alteration variables are statistically significant for the fluvial-fish relative-abundance model. 

Impervious cover is statistically significant for the fluvial-fish 

relative-abundance, fluvial-fish species richness, and brook 

trout relative-abundance models. The variables in the equations were demonstrated to be significant, and the variability 

explained by the models, as measured by the correlation 

between observed and predicted values, ranges from 42 to 

65 percent. The GLM models indicated that, keeping all other 

variables the same, a one-unit (1 percent) increase in the percent depletion or percent surcharging of August median flow 

would result in a 0.4-percent decrease in the relative abundance (in counts per hour) of fluvial fish and that the relative 

abundance of fluvial fish was expected to be about 55 percent 

lower in net-depleted streams than in net-surcharged streams. 

The GLM models also indicated that a unit increase in impervious cover resulted in a 5.5-percent decrease in the relative 

abundance of fluvial fish and a 2.5-percent decrease in fluvialfish species richness. 

Introduction

Flow alteration and urban development have been associated with stream ecosystem degradation in flowing waters 

as measured through fish communities (Karr and Chu, 1999; 

Freeman and Marcinek, 2006; Zorn, 2008; Poff and others, 

2010) and macroinvertebrate communities (Coles and others, 

2004; Konrad and others, 2008; Kennen and others, 2009). 

Streamflow is one of many factors that influence the abundance and distribution of fish, and has been called the master 

variable because it influences habitat availability, channel 

geomorphology, and other factors that also influence habitat 

quality such as water quality and water temperature (Wilding 

and Poff, 2008; Poff and Zimmerman, 2010). The likelihood 

that flow alteration and urbanization will degrade aquatic communities is generally acknowledged by the scientific community and by water managers (Poff and Zimmerman, 2010), but 

the response of aquatic communities to flow alteration relative 

to the influence of physical basin and land-cover (environmental) characteristics and other anthropogenic factors is poorly 

understood. 

Understanding species-stressor and species-environment 

relations is an important step toward the conservation of 

aquatic communities. Several recent studies of fish-community 

Preliminary Assessment of Factors Influencing Riverine 

Fish Communities in Massachusetts

By David S. Armstrong, Todd A. Richards, and Sara L. Brandt
2 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

response to flow alteration in southern New England streams 

have determined that as flow alteration increases, the composition of the fish community shifts as fish dependent upon flow 

for various stages of their life cycle (fluvial fish) decrease in 

number (abundance) and richness (number of species) (Armstrong and others, 2001, 2004; Parasiewicz, 2004; Kanno and 

Vokoun, 2010). Although a decline in fluvial fish metrics is 

an expected response to increased flow depletion, the level of 

flow alteration at which responses occur, and the magnitude, 

rate, and shape of the response curve (for example, linear or 

nonlinear) are less well understood. Development of quantitative relations among fish-community structure, environmental 

characteristics, and anthropogenic-alteration variables would 

benefit water-resource managers in Massachusetts by helping 

to reduce the uncertainty associated with making management 

decisions designed to balance human uses with the requirements of fish and wildlife.

Until 2009, streamflow-simulation tools that could be 

used to estimate streamflows under existing water-use conditions and natural unaltered conditions were unavailable 

except for basins where streamflow had been simulated by 

a hydrologic model. However, the U.S. Geological Survey 

(USGS), in cooperation with the MDEP, recently developed a 

computer application–the Sustainable Yield Estimator (SYE, 

version 1.0) (Archfield and others, 2010)–that can be used to 

estimate altered and unaltered daily streamflows at ungaged 

sites. The USGS has also developed a series of indicators to 

characterize flow alteration and other anthropogenic effects 

for 1,429 subbasins in Massachusetts (Weiskel and others, 

2010). These new capabilities, together with new GIS datalayers of percent impervious cover and a comprehensive fishcommunity database developed by the Massachusetts Division 

of Fisheries and Wildlife (MDFW), have made possible this 

statewide assessment of the relations among fish-community 

response, environmental characteristics, and anthropogenic 

alteration.

Massachusetts State agencies are interested in determining the effects of flow alterations on fish communities in 

Massachusetts streams and rivers relative to other measures 

of anthropogenic alteration, such as impervious surface and 

dams, and the influence of physical basin characteristics and 

land-cover characteristics. A 3-year cooperative study was 

initiated for this purpose in 2009 by the USGS in cooperation 

with the MDCR. In the fall of 2009, however, Massachusetts 

State agencies convened the Sustainable Water Management 

Initiative to begin the process of determining safe-yield values 

to be used in accordance with the State’s Water Management 

Act to develop a statewide water-allocation program. The Sustainable Water Management Initiative prompted the MDCR, 

MDEP, and MDFG to fund the USGS to accelerate aspects of 

the project to help inform development of streamflow criteria. 

This accelerated investigation necessitated limiting the scope 

of data collection and data analysis so that a report could be 

completed within a deadline for safe-yield determinations. 

After the short-term project has been completed, the USGS 

plans to continue the investigation using a larger suite of 

fish-community and explanatory variables and a wider range 

of multivariate statistical tools. The results of the accelerated 

and ongoing studies are expected to provide a scientific basis 

for the Sustainable Water Management Initiative and will 

enable water-resource managers to make more informed decisions about managing streamflows and water withdrawals in 

Massachusetts.

Purpose and Scope 

This report describes the results of a preliminary study 

of the response of stream fish communities in Massachusetts 

to flow alteration and other measures of anthropogenic stress, 

such as impervious cover, relative to the effects of environmental characteristics. The report provides bivariate plots 

among selected fish metrics and measures of flow alteration, impervious cover, and area of open water, with quantile 

regression curves indicating fish response. The report also 

presents generalized linear model (GLM) equations and plots 

that indicate relations between selected fish-response variables 

and multiple environmental and anthropogenic explanatory 

variables.

The scope of the study included small to medium streams 

across Massachusetts but not streams on Cape Cod, the Islands 

(Martha’s Vineyard, Nantucket), and portions of southeastern 

Massachusetts where information on simulated streamflows 

was unavailable. Data on fish samples from the MDFW database collected during 1998–2008 were used in the analysis of 

fish communities; new fish-sampling data were not collected 

for this study. Analysis focused on riverine fish communities in free-flowing reaches of streams and rivers. Variables 

analyzed for the study were restricted to a subset of response 

and explanatory variables selected from a larger set of data 

collected for the ongoing study. A review of the literature was 

used to select fish-community variables, measures of anthropogenic stress, and environmental characteristics considered 

important in determining fish abundance and distribution. 

Altered and unaltered flows used in the study were simulated 

for fish-sampling sites using the Sustainable Yield Estimator. 

The statistical methods employed for analysis were reduced 

from a larger suite of analytical techniques in order to meet 

a request by Massachusetts State Agencies for a report on an 

accelerated timeline. 

Description of Study Area 

The study sites are on flowing reaches of small- to 

medium-sized (wadeable) freshwater streams in Massachusetts that were selected from the MDFW database (fig. 1). The 

MDFW fish-community database contains information on 

fish-sampling activities conducted in lakes, ponds, streams, 

and rivers. For the purposes of this analysis, fish-community 

samples were limited to collections made in free-flowing 

(non-impounded) reaches of streams because riverine fish 

are expected to respond to flow alterations in these settings 
Introduction 3

(Poff and others, 2010; Poff and Zimmerman, 2010). Fishcommunity samples from lakes, ponds, and reservoirs were 

not included because fish in these settings were not expected 

to demonstrate a response to flow alteration. The fish-sampling 

sites include multiple sites nested within larger drainage 

basins. The contributing areas to some fish-sampling sites in 

the study extend into adjacent states. Water use, physical basin 

characteristics, and land-cover data for the portion of contributing drainage areas outside of Massachusetts are included in 

the analysis.

The rivers and landscape in Massachusetts have been 

altered for hundreds of years, to the extent that almost all 

streams and rivers in the State reflect some aspect of human 

alteration. Alterations to rivers include physical alterations 

created by dams, impoundments, road crossings, channelization, and riparian (riverbank) and floodplain development; 

flow alterations from water withdrawals, diversions, return 

flows, hydropower, industrial regulation, and urban runoff; and 

water-quality and sediment alterations caused by wastewater 

and septic discharges, highway runoff, industrial contaminants, and runoff from urban, suburban, and agricultural land 

uses. Alterations to rivers are coupled with land-use disturbances that are ubiquitous across the landscape. Land use in 

Massachusetts is a mosaic of forest, wetland, and open water 

interspersed with urban, industrial, suburban, and agricultural 

land, the distribution of which reflects the history of settlement in the region. The distribution of land-use and land-cover 

characteristics retains close links to geology, topography, river 

corridors, and transportation routes (Kennen and others, 2009). 

Even areas classified as mostly forested have some roads, lowdensity housing, or remnants of historic alterations to the land 

and waterways. Although large areas of contiguous forest still 

exist at higher elevations in the Berkshire Mountains in western Massachusetts, many areas in eastern and central Massachusetts are experiencing rapid conversion of forested and 

agricultural lands to residential and commercial uses (Kennen 

and others 2009). As a consequence, natural patterns of fish 

distribution and abundance have been disturbed in many drainage areas in Massachusetts (Hartel and others, 2002).

Previous Studies

The USGS began a series of studies in 1995 to determine the spatial distribution and correlation among characteristics related to aquatic habitats and flow conditions of 

Massachusetts streams. These studies, done in cooperation 

with the MDCR, Office of Water Resources (formerly the 

1 - HUDSON

 a. Hoosic

 b. Kinderhook

 c. Bashbish

2 - HOUSATONIC

3 - DEERFIELD

4 - WESTFIELD

5 - FARMINGTON

6 - CONNECTICUT

7 - MILLERS

8 - CHICOPEE

9 - QUINEBAUG

10 - FRENCH

11 - NASHUA

12 - BLACKSTONE

13 - MERRIMACK

14 - CONCORD

 a. Assabet

 b. Concord and Sudbury

15 - SHAWSHEEN

16 - PARKER

17 - IPSWICH

18 - NORTH COASTAL

19 - BOSTON HARBOR

 a. Mystic

 b. Neponset

 c. Weymouth & Weir

20 - CHARLES

21 - SOUTH COASTAL

 a. North and South Rivers

 b. South Coastal Shore

22 - CAPE COD

23 - ISLANDS

24 - BUZZARDS BAY

25 - TAUNTON

26 - NARRAGANSETT BAY & 

MT. HOPE BAY SHORE

27 - TENMILE

12

19a

24

22

20 6 8

3

5 10

2

17 13 7

26

11

18

16

9

15

21a

14a

27 25

4

26

23

23

14b

19b 19c

21b

1a

5 1c

1b

13

18

9

23

73° 72° 71° 70° 

43°

42°

EXPLANATION

Massachusetts Planning Basins

Fish sampling site

0 10 20 30 40 50 MILES

0 10 20 30 40 50 KILOMETERS

Figure 1. Massachusetts Division of Fisheries and Wildlife fish-sampling sites used to assess environmental and anthropogenic 

factors influencing fish communities in Massachusetts. Fish samples were collected from 1998–2008.
4 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

Massachusetts Department of Environmental Management), 

and the MDEP, evaluated several aspects of streamflow 

requirements and aquatic habitat. Streamflow requirements 

determined from median daily mean flows for August, and 

from wetted-perimeter calculations determined from discharge 

measurements are described by Ries (1997) and Mackey and 

others (1998), respectively. Relations among stream habitat, 

fish communities, and hydrologic conditions in the Ipswich 

River Basin were investigated by Armstrong and others 

(2001), in the Charles and Assabet River Basins by Parker and 

others (2004), and in the Sudbury River Basin by Zarriello 

and others (in press). Streamflows and methods for determining streamflow requirements for aquatic-habitat protection 

at index stations in southern New England were studied by 

Armstrong and others (2004) and Parker and others (2004). 

Armstrong and others (2008) characterized and classified 

least-altered streamflows in Massachusetts. The effects of flow 

alteration and increased urbanization on fish and macroinvertebrate communities in southern New England streams are 

described in Coles and others (2004, 2010), Kennen and others 

(2009), Meador and others (2008), and Kanno and Vokoun 

(2010).

Fish species in Massachusetts are described in Hartel and 

others (2002). Freshwater fish in the southern New England 

states are characterized by low-species diversity in comparison 

to the diversity in Midwestern and southeastern United States 

(Whitworth, 1996; Meador and Carlisle, 2009). Consequently, 

there are relatively few species in Massachusetts that can be 

used to detect responses to anthropogenic stressors. The composition of fish communities from relatively unaltered rivers 

in southern New England and the expected composition of fish 

communities appropriate for restoration of mainstem rivers 

(Target Fish Communities) are described in Bain and Meixler 

(2000; 2008), Parasiewicz (2004), Legros and Parasiewicz 

(2007), University of New Hampshire and others (2008), and 

Kashiwagi and Richards (2009). 

Factors Limiting Fish Communities

Fish communities are considered to be good indicators 

of the condition of the habitat in which they live (Karr and 

Chu, 1999; Poff and Zimmerman, 2010). The distribution and 

abundance of fish are determined by both environmental and 

anthropogenic variables at multiple scales (Gido and others, 

2006). Many environmental variables play a role in structuring 

stream fish communities, including flow regime, water quality, 

stream temperature, habitat availability and connectivity, physical basin characteristics, land cover, and biotic interactions. 

Anthropogenic changes, such as flow and water-quality alterations, dams and impoundments, and urbanization of land also 

have large influences on aquatic habitat and fish communities.

Ecological responses to flow alterations include loss of 

sensitive species, reduced diversity, altered assemblages and 

dominant taxa, reduced abundance, and increases in nonnative species (Poff and Zimmerman, 2010). More than three 

decades of research have illustrated the effects of withdrawals 

and return flows (water quantity), the damming and channelization of streams (physical alteration of stream habitats), and 

urbanization (physical alteration of the landscape) on aquatic 

communities including fish (Freeman and Marcinek, 2006; 

Walsh and others, 2005; Wenger and others, 2009; Poff and 

Zimmerman, 2010). The result has been a widespread call for 

streamflows that mimic the magnitude, frequency, duration, 

timing, and rate of change of natural streamflows (the natural 

flow regime; Poff and others, 1997, 2010; Richter, 1997; Bunn 

and Arthington, 2002; Annear and others, 2004). Streamflows 

in Massachusetts are typically highest in spring and lowest in 

late summer and early fall. Low flows in summer, which maintain critical habitat that sustains fish communities, including 

rearing and growth of young fish, correspond with the period 

of highest human water use and tend to receive the most attention. Flows in other seasons are also important for maintaining 

stream fish-community integrity. Flood flows in early spring 

maintain habitat quality by scouring streambeds, modifying 

channel geomorphology and importing woody debris into 

stream channels. Moderately high flows in late spring and 

fall provide opportunities for species to migrate and provide 

spawning habitat critical for many species. Moderate flows in 

winter provide stable habitat during harsh winter conditions. 

Increased urbanization is associated with declines in 

aquatic communities (Wang and others, 2001a, 2001b; Meador 

and others, 2005; Roy and others, 2005; Walsh and others, 

2005; Wenger and others, 2008, 2009; Brown and others, 

2009b; Cuffney and others, 2010; Baker and King, 2010). 

These declines are caused by multiple mechanisms, including 

altered streamflow through increased stormwater runoff and 

reduced recharge, and altered stream geomorphology through 

changes in sediment supply, erosion, and filling and piping of 

headwater channels. Urbanization also degrades water quality 

by increasing ionic concentrations, nutrients, toxicants, and 

water temperatures, and by decreasing dissolved oxygen. It 

degrades fish habitat by removing riparian vegetation, decreasing shade, reducing input of woody debris, and introducing 

barriers to movement (Wenger and others, 2009). Impervious 

surface is often used as an indicator of urbanization. Although 

impervious surface can directly alter streamflow through 

acceleration of the timing and magnitude of stormwater runoff 

and alteration of the rates of recharge and evapotranspiration, 

it is not exclusively a measure of flow alteration but instead 

represents an accumulation of many factors that together 

impair the integrity of aquatic communities. 

The response of fish to multiple factors confounds the 

ability to discern the relative importance of individual stressors, such as flow alteration alone (Paul and Meyer, 2001; Brabec and others, 2002; Walsh and others, 2005). For example, 

poor water quality might limit or reduce the suitability of 

habitat for fish intolerant to water contamination but might not 

affect species susceptible to flow alteration, and vice versa. 

Consequently, it can be difficult to determine which factors or 

stressors may negatively affect fish habitat at any one place or 

time or for any given species or fish community.
Methods 5

Methods

Site-specific fish-community information was evaluated 

in relation to sub-regional and regional-scale environmental 

characteristics and anthropogenic-alteration variables including impervious cover and streamflow alteration. Information on fisheries was analyzed for 756 stream sampling sites 

documented in the MDFW fish-community database. Fish 

metrics were developed for each site using the fish-sampling 

data collected at the site. Fish metrics analyzed include two 

fish-community metrics (fluvial-fish relative abundance 

and fluvial-fish species richness) and five indicator-species 

metrics (relative abundance of brook trout, blacknose dace, 

fallfish, white sucker, redfin pickerel). Correlations between 

flow alterations, impervious cover, and ecological response 

were determined by use of a space-for-time substitution; that 

is, ecological conditions are characterized at sites along a 

gradient of alteration (space) rather than tracking changes in 

individual species or fish-community composition at a given 

site as alteration increases over multiple years (time) (Pickett, 

1989; Wickham and others, 1997; Carter and others, 2009; 

Brown and others, 2009a). Two statistical techniques, quantile 

regression and generalized linear modeling, were applied to 

determine the association between fish-response variables and 

the selected environmental characteristics and anthropogenic 

alteration-explanatory variables.

Streamflow Data and Measures of Flow 

Alteration 

Daily streamflows for altered and unaltered flows were 

simulated for fish-sampling sites using the Sustainable Yield 

Estimator (SYE, version 1.0; Archfield and others, 2010). To 

estimate streamflows at an ungaged site, the SYE first estimates selected streamflow quantiles for an unregulated, daily 

flow-duration curve by solving regression equations that are a 

function of physical basin and climate characteristics. A continuous daily flow-duration curve is determined by interpolating streamflow between the estimated quantiles. A time series 

of unregulated daily streamflow for the 1961–2004 was then 

created by transferring the timing of the daily streamflow at a 

selected minimally altered index gage to the ungaged site by 

equating exceedence probabilities of contemporaneous flow at 

the two locations. (Archfield and others, 2010). Altered daily 

streamflows were then estimated using a spatially referenced 

database of surface-water and groundwater withdrawals and 

discharges for 2000–2004, using techniques described in Archfield and others (2010) and Weiskel and others (2010). Sampling sites were only included for analysis if they met drainage-area criteria required by the SYE. Thus sites were limited 

to those having drainage areas greater than 2 mi2. Streamflows 

in the mainstems of the Merrimack and Connecticut Rivers 

were not determined because the SYE application does not 

provide flow estimates for streamflows in these areas. Streamflows in portions of southeastern Massachusetts, including 

Cape Cod and the Islands, were not determined because the 

SYE application does not provide streamflow estimates for 

streams in these areas unless the groundwater contributing 

areas to the fish-sampling sites are delineated, which was not 

done for this preliminary study.

Flow alteration often involves simultaneous modification of several components of the flow regime in combination 

with the effects of altered land use. Flow alterations that are 

accounted for in the SYE water-use database include surfacewater and groundwater withdrawals for municipal and nonmunicipal water supply, wastewater returns from sewage-treatment plants, and return flows from point sources permitted by 

the MDEP and the National Pollutant Discharge Elimination 

System (NPDES) and from septic systems. Surface-water 

withdrawals from reservoirs are included only in annual flowalteration statistics. Details on the withdrawal and discharge 

data used by the SYE to determine altered flows have been 

reported previously in the Massachusetts Water Indicators 

report (Weiskel and others, 2010). Streamflows simulated by 

SYE do not directly include non-consumptive flow alterations caused by industry, hydroelectric facilities, manipulation 

of water-level releases by reservoirs, or alterations to flow 

regimes caused by altered land use or impervious surfaces. 

However, some of these flow alterations may be present to 

varying degrees in the streamflow records of the index gages 

used by SYE to determine the timing of daily flows.

The SYE produces screening-level estimates of daily 

streamflows by applying current (2000–2004) monthly median 

water use estimates to over 40 years of simulated unaltered 

streamflow conditions (1960–2004). Comprehensive water use 

data for 1960–2004 are not available. Consequently, although 

annual and monthly variations of flows are well represented, 

daily streamflow values likely differ from those actually experienced by fish sampled in the streams. 

Streamflow statistics used as indicators of streamflow 

alteration in this study were selected from those used by 

Weiskel and others (2010). These statistics include percent 

alteration of August median flow, percent alteration of annual 

flow, and water-use intensity (WUI). The percent alteration 

flow statistics were calculated as 1 minus the ratio of altered 

flow to unaltered flow, multiplied by 100. Percent alteration 

of August median flow was selected because in Massachusetts it represents the seasonal period of lowest flows, highest 

temperatures, and highest human water demand. The signs 

for the percent alteration of August median flow and percent 

alteration of annual-flow statistics represent depleted and surcharged flows; a negative percent alteration represents a depletion, and a positive percent alteration represents a surcharge. 

The percent alteration of August median flow was highly 

correlated with percent alteration of other low-flow months, 

precluding their use together in the same analyses. The WUI 

indicator is a ratio defined as the sum of the absolute value of 

withdrawals and return flows relative to the long-term average 

unaltered streamflow from a basin (Weiskel and others, 2007; 

2010). The WUI indicator is used to identify “churned” basins, 

where human flows (withdrawals and return flows) are similar 
6 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

in magnitude to each other, yet large relative to unaltered 

streamflows from the basin. Four additional indicators were 

tested as explanatory variables: the ratio of annual return flows 

to annual unaltered flows (return-flow fraction), the ratio of 

withdrawals to unaltered flows (withdrawal fraction), and 

mean annual and August median flows normalized to cubic 

feet per second per square mile. 

Fish-Community Data and Fish Metrics

Fish samples used in this analysis were limited to those 

in the MDFW database. The fish samples were collected 

during the summer base-flow season (typically mid-June to 

mid-September) using MDFW protocols designed to gather 

a cross section of the fish community in wadeable streams 

(Appendix 1 of Armstrong and others, 2008). The fishcommunity data in the MDFW fish database were collected, 

with few exceptions, by the MDFW for statewide monitoring 

purposes and not specifically to address the potential effects 

of flow alteration or impervious surface on fish-community 

structure. 

The MDFW fish database required screening to select the 

fish sample data used in this study. Fish samples were limited 

to those collected by use of backpack and barge electrofishing gear (pulsed direct current) during a single upstream pass. 

Single-pass backpack electrofishing has been determined to be 

an effective and reliable approach for obtaining communitywide estimates (species richness) (Price and Peterson, 2010). 

Data were screened for efficiency, method parity, and interdependence. Efficiency screening was done to improve sample 

consistency and fish-capture efficiency, and to exclude sites 

where sampling conditions precluded capture of a representative cross section of the fish community. Sites were removed 

for reasons including high water, turbid water, and equipment 

failure. Stream reaches were evaluated for sample length and 

length-to-width ratio. Stream reaches of greater than 91 meters 

(100 yards) were included. Reaches less than 91 meters were 

scrutinized for length-to-width ratio to ensure that the habitat was sufficiently sampled. Samples as short as 30 meters 

were included only if the average stream width was 1 meter 

or less. Samples with multiple-pass-removal methods were 

corrected to reflect the sampling effort conducted at singlepass sites. Any fish captured in passes other than the first pass 

were removed, and the data were corrected to include only the 

shocking time from the first pass. 

Fish metrics were developed for each site by use of fishsampling data collected at each sample site. Fish samples from 

multiple sites were not combined for use in this study. The 

MDFW database included many sites that were sampled multiple times over nearly a decade. To reduce pseudoreplication 

(samples that are not independent) and spatial autocorrelation 

of environmental variables (similarity among variables based 

on the proximity of sample sites), sites used in this study were 

restricted to those that were greater than 0.5 km from each 

other (Wenger and others, 2008). Sites that were closer than 

0.5 km were identified as a family of sites. The sample from 

each family with the largest watershed area was included in 

this study. In the event of a tie, a site was selected randomly.

Family identification was retained as a variable in the dataset 

with the intent of examining temporal variability at a later 

date. 

Only year-round-resident freshwater fish species were 

included in the fish analysis (table1). The occurrence of nonresident individuals can be unpredictable and not representative of site-specific environmental conditions (Angermeier 

and Karr, 1986). Anadromous fish (alewife, blueback herring, 

American shad, striped bass, sea lamprey) and estuarine fish 

(mummichogs) were excluded. These species are uncommon in the MDFW samples and all life stages are not in the 

river systems for the entire sampling period. American eel, a 

catadromous species, was included in the analysis, as the species enters freshwater when juvenile and spends the majority 

of its adult life (potentially 15 years) in freshwater systems. 

Trout greater than 200 mm were excluded to avoid including 

trout stocked as adults in the analysis. All Atlantic salmon 

were excluded as these fish were stocked as fry as part of the 

Atlantic salmon restoration effort. Landlocked salmon were 

not removed, however, as they are produced from natural 

reproduction. Fish smaller than 40 mm, representing early 

life stage young-of-the-year (YOY), were excluded because 

of difficulties in sampling, identification, patchy distribution, 

and exceedingly high year-to-year variability in production 

that can occur from natural causes (such as a period of lower 

than average air temperatures or exceedingly high or low 

flows) or by anthropogenic disturbances. Removal of YOY 

is a recommended practice in the development of Indexes of 

Biotic Integrity (IBIs) (Halliwell and others, 1999; Angermeier 

and Karr, 1986) because their numbers can be highly variable through the summer season and because they can occur 

in large numbers seasonally, potentially skewing a dataset 

(Angermeir and Karr, 1986). To exclude YOY fish, all fish less 

than 40 mm were removed. 

Fish-community response variables selected for testing in 

the study included metrics determined from habitat-use classifications and temperature classifications. These classifications 

were determined to be those most likely to indicate a range of 

response and have been acknowledged to provide insight into 

anthropogenic alteration in terms of both water quantity and 

quality. 

Fish were assigned a habitat-use classification on the 

basis of published life-history traits (Bain and Meixler, 2000, 

2008; Halliwell and others, 1999; Kashiwagi and Richards, 

2009). The classification includes fluvial specialists (FS), 

fluvial dependents (FD), and macrohabitat generalists (MG) 

(table 1). Fluvial specialists, such as blacknose dace and 

fallfish, require flowing-water habitats throughout their life 

cycle (Bain and Meixler, 2008). Fluvial dependents, such as 

common shiners and white sucker, require access to streams 

or flowing-water habitats for a specific life stage but otherwise 

can be found in lakes, reservoirs, and rivers. Macrohabitat 

generalists, such as pumpkinseed and redfin pickerel, use a 
Methods 7

Table 1. Common and scientific names of fishes, species codes, and habitat use classifications for each species.—Continued 

[Kashiwagi and Richards (2009) Species Code: Massachusetts Division of Fisheries and Wildlife species abbreviaton. HUC (Habitat Use Classification): FD, 

fluvial ; FS, fluvial specialist; MG, macrohabitat generalist; -- species not categorized.]

Common name Species Code Scientific name

(Genus and species) HUC

Petromyzontidae (lampreys)

American brook lamprey BL Lampetra appendix --

Anguillidae (freshwater eels)

American eel AE Anguilla rostrata MG

Cyprinidae (carp and minnows)

Goldfish G Carassius auratus MG

Lake chub LC Couesius plumbeus MG

Common carp C Cyprinus carpio MG

Cutlips minnow CLM Exoglossum maxillingua FS

Common shiner CS Luxillus cornutus FD

Golden shiner GS Notemigonus crysoleucas MG

Bridle shiner BM Notropis bifrenatus MG

Spottail shiner SS Notropis hudsonius MG

Bluntnose minnow BNM Pimephales notatus MG

Blacknose dace BND Rhinichthys atratulus FS

Longnose dace LND Rhinicthys cataractae FS

Creek chub CRC Semotilus atromaculatus FS

Fallfish F Semotilus corporalis FS

Catostomidae (suckers)

Longnose sucker LNS Catostomus catostomus FD

White sucker WS Catostomus commersoni FD

Creek chubsucker CCS Erimyzon oblongus FS

Ictaluridae (bullhead catfishes)

White catfish WC Ameiurus catus MG

Yellow bullhead YB Ameiurus natalis MG

Brown bullhead BB Ameiurus nebulosus MG

Tadpole madtom TMT Noturus gyrinus FS

Esocidae (pike and pickerels)

Redfin pickerel RP Esox americanus americanus MG

Northern pike NP Esox lucius MG

Chain pickerel CP Esox niger MG

Umbridae (mudminnows)

Central mudminnow CM Umbra limi --

Salmonidae (salmon, chars, and trout)

Rainbow trout RT Oncorhynchus mykiss FS

Landlocked salmon LLS Salmo salar FD

Brown trout BT Salmo trutta FS

Brook trout EBT Salvelinus fontinalis FS

Fundulidae (killifishes)

Banded killifish K Fundulus diaphanus MG

Cottidae (sculpins)

Slimy sculpin SC Cottus cognatus FS
8 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

broad range of habitat; they include species commonly found 

in lakes, reservoirs, and rivers and can complete their life 

cycle in any one of these systems. For the purposes of this 

preliminary report, the fluvial-specialist and fluvial-dependent 

species were combined into a single fluvial classification. 

Fish metrics were developed on the basis of counts of 

the various taxa and were expressed in terms of fish relativeabundance (in counts per hour of electrofishing), proportionalabundance, and species richness. The relative-abundance, 

proportional abundance, and species richness metrics each 

represent different aspects of a fish community. For example, 

the fluvial-fish relative-abundance metric reflects the overall 

abundance of fluvial fish, regardless of species, and is a useful 

metric for Massachusetts because not all fluvial-fish species 

are distributed statewide. The fluvial-fish species-richness 

metric is a measure of the diversity of fluvial-fish species, 

but does not reflect the abundance of individual species. The 

fluvial fish proportional abundance metric is a measure of the 

fluvial composition of the fish community relative to species 

classified as macrohabitat generalists.

A set of relative-abundance metrics were determined for 

five indicator species (brook trout, blacknose dace, fallfish, white sucker, and redfin pickerel). These species were 

selected to represent a range of sensitivities to anthropogenic 

stress (pollution tolerant and intolerant species), mesohabitat 

preference (fluvial and generalist species), and temperature 

tolerances. The species also represent a range of biological attributes (maximum body size, migration habits, and

home-range sizes) which together provide information on fish 

species statewide. 

Environmental Characteristics

The environmental factors selected for testing as explanatory variables in this study were primarily sub-regional to 

regional-scale physical basin characteristics and natural landcover characteristics. These environmental characteristics are 

not expected to change substantially over short-term periods 

unless altered by anthropogenic activities. Land-use variables, 

such as the developed, high-, medium-, and low-intensity 

landcover classes determined from the National Land-Cover 

Database (NLCD) (U.S. Geological Survey, 2000), were not 

included in this preliminary report. Environmental variables used in the study were selected because they had been 

indicated in the literature to be related to fish-community 

attributes. Contributing drainage basins to the fish-sampling 

sites were delineated from a 10-meter digital elevation model 

(DEM) in ESRI ArcGIS using an automated batch procedure 

from Arc Hydro Tools. Stream networks were obtained from 

the high-resolution National Hydrography Dataset (NHD) 

(U.S. Geological Survey, 1999b). Environmental characteristics were determined for the contributing areas to the fishsampling sites using a Geographic Information System (GIS) 

and were compiled from a variety of GIS data sources and 

clearinghouses (table 2 in Armstrong and others, 2008). GIS 

Table 1. Common and scientific names of fishes, species codes, and habitat use classifications for each species.—Continued 

[Kashiwagi and Richards (2009) Species Code: Massachusetts Division of Fisheries and Wildlife species abbreviaton. HUC (Habitat Use Classification): FD, 

fluvial ; FS, fluvial specialist; MG, macrohabitat generalist; -- species not categorized.]

Common name Species Code Scientific name

(Genus and species) HUC

Moronidae (striped basses)

White perch WP Morone americana MG

Centrarchidae (sunfishes and black basses)

Rock bass RB Ambloplites rupestris MG

Banded sunfish BS Enneacanthus obesus MG

Redbreast sunfish RBS Lepomis auritus MG

Green sunfish GSF Lepomis cyanellus MG

Pumpkinseed P Lepomis gibbosus MG

Bluegill B Lepomis macrochirus MG

Smallmouth bass SMB Micropterus dolomieu MG

Largemouth bass LMB Micropterus salmoides MG

Black crappie BC Pomoxis nigromaculatus MG

Percidae (perches and darters)

Swamp darter SD Etheostoma fusiforme MG

Tesselated darter TD Etheostoma olmstedi FS

Yellow perch YP Perca flavescens MG
Methods 9

layers used were all projected in a NAD 83 State Plane Massachusetts Mainland, FIPS zone 2001 GIS projection.

Landscape-scale factors are known to structure fish 

assemblages through their association with finer-scale habitat 

features and variables that can have a physiological effect 

on organisms (Creque and others, 2005; Gido and others, 

2006; Austin, 2007). Environmental characteristics used in 

this study include contributing drainage area, elevation, basin 

slope, channel gradient, percent area of sand and gravel, and 

percent area of wetland and open water. Some of the landcover characteristics, such as percent area of open water, are 

also influenced by human activities (most lakes and ponds in 

Massachusetts are augmented by or formed entirely by dams) 

and are discussed as both environmental characteristics and 

anthropogenic alterations in this report. 

The contributing drainage area to a site can be used as 

an indicator of stream size. Fish-species composition in largeand medium-sized rivers differs from that in small streams and 

brooks; larger streams are expected to include a more diverse 

fish community (Goldstein and Meader, 2004). Fish-species 

richness also tends to vary with elevation; fish communities at 

higher elevations typically contain fewer species than those at 

lower elevations (Wickham and others, 1997). Stream gradient generally covaries with velocity and substrate, and these 

variables can influence fish-community composition; some 

fish species or life stages prefer high-gradient stream habitats 

but others occupy low-gradient reaches (Argent and others, 

2003). High-gradient streams typically have a higher prevalence of gravel, cobble, boulder, or rock substrate, whereas 

sand and silt substrates and aquatic vegetation are more 

common in low-gradient streams. In the glaciated Northeast, 

a high percent area of sand and gravel in a basin generally 

indicates aquifers that discharge groundwater to streams (Randall, 2001). Streamflow during prolonged dry periods between 

storms (base flow) is maintained primarily by groundwater 

discharge to streams; and streams that have a higher percent 

area of sand and gravel generally have higher base flows 

(Ries, 1999). Ground-water discharge also contributes to 

cool summer water temperatures that are critical for supporting coldwater fish communities. Large areas of wetland and 

open water in a drainage basin can moderate the variability 

of streamflow and alter water temperature and quality. Large 

areas of open water, which include areas of impoundments as 

well as that of lakes and ponds, also provide increased habitat 

for spawning and recruitment of macrohabitat generalist species into stream systems. 

Physical basin characteristics and land-cover characteristics at the basin scale are not expected to explain large 

amounts of the variation in fish communities. Basin-scale 

characteristics likely explain the variation in fish communities only to the extent that the basin-scale characteristics are 

correlated to finer-scale habitat features and physical conditions that are directly relevant to fish, such as stream velocity, 

water quality, water temperature, and habitat space available 

for different life stages, or other important factors, such as 

environmental stability and ecosystem productivity (Poff 

and others, 1997; Cereghino and others, 2005). Local habitat 

data (such as stream width, substrate, stream velocity, water 

depth) and mesohabitat information (riffle, run, pool) were 

not available for analysis. Consequently, local channel slope, 

which often covaries with these features, was estimated using 

GIS for each fish-sampling site. To calculate channel slope, 

elevations were first determined along the centerlines using 

the LongestFlowPath tool in Arc Hydro (ESRI) and then 

extracted by intersecting a 300-meter buffered circle, centered 

at the fish-sampling site, with the stream centerline. Channel 

slope was then calculated for the reach upstream from the fish 

sampling point by first determining the difference in elevation 

between the elevation of the upstream point on the centerline 

at the intersection of the 300-meter circle and the elevation 

of the downstream sampling point, and then dividing by the 

length of the centerline between these points. 

The location of each fish-sampling site (Outlet X, 

Outlet Y) was also included in the analysis. Outlet X and Outlet Y were calculated as the X and Y coordinates (in meters) 

divided by 10,000, respectively. Coordinates were determined 

from a NAD 83 State Plane Massachusetts Mainland FIPS 

zone 2001 GIS projection. Outlet X and Outlet Y increase in 

value from west to east, and south to north, respectively. Location variables co-vary with a number of variables that vary 

across Massachusetts, including climate, topography, geology, and land use. Because some anthropogenic stresses also 

follow an east-west gradient (for example, impervious cover), 

it is difficult to determine whether some species are present or 

absent as a result of post-glacial distribution or as a result of 

localized extirpations caused by past or current land and water 

use.

Indicators of land-cover alteration in the contributing 

areas were summarized by use of 1-meter resolution impervious-cover (IC) data (Massachusetts Office of Geographic 

and Environmental Information, 2007). The impervious-cover 

data for contributing basin areas outside of Massachusetts 

were determined using methods described in Weiskel and 

others (2010). Dam locations were obtained from a database 

maintained by the MDFG Division of Ecological Restoration Program. The presence of dams and impoundments in a 

basin were indicated primarily by measures of dam density 

per square mile and dam density per stream mile, and secondarily by area of open water. Open-water area includes areas of 

impoundments, natural lakes, and natural lakes augmented by 

dams.

Land use in a narrow corridor extending upstream 

throughout an entire stream network has been shown to be 

an effective predictor of fish communities (Van Sickle and 

Johnson, 2008). Consequently, the percent area of impervious 

cover within a 240-m buffer adjacent to the stream (BFIC) was 

also included as a variable in the study. A 240-m buffer width 

(120-m outward in both directions from the stream centerline) 

was used to be consistent with values published in Coles and 

others (2004).
10 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

Statistical Analysis

Two analytical techniques, quantile regression and generalized linear modeling, were applied to determine the association between fish-response variables and environmental characteristics and anthropogenic-alteration explanatory variables. 

Both quantile regression and generalized linear modeling are 

linear modeling techniques. Nonlinear models were not tested 

for this preliminary report.

Quantile Regression 

The ecological concept of limiting factors indicates that 

species will be abundant if no other factors are limiting, but 

if one or more factors are limiting, the species will be constrained to lower abundance than expected (Cade and others, 

2005). For example, even if flow alterations are nonexistent 

or minimal, fish habitat may be poor for some species if other 

requirements such as water-quality conditions conducive to 

fish survival are not met. Bivariate scatter plots of species 

and stressor variables illustrate the concept of limiting factors 

when data display a right-skewed, wedge-shaped relation 

distributed below an upper bound. The declining upper bound 

indicates that the explanatory variable can act as a constraint on the given response variable and also illustrates the 

maximum abundance of a species given ideal environmental 

conditions. The wedge shape of scatter plots indicates that the 

relations between the plotted variables exhibit high variability. This high variability arises when other environmental or 

biologic variables function as active limiting factors and keep 

the response variable from reaching the upper constraint line 

(Thomson and others, 1996; Cade and Noon, 2003; Schooley 

and Wiens, 2005; Anderson, 2008). 

A correlation analysis of a dataset that displays a wedgeshaped relation may be an insufficient tool for identifying 

relations between variables. Likewise, measures of central 

tendency such as lines indicating mean and median values, 

may not represent the response of all portions of the data. For 

example, a high density of points near the origin can make a 

correlation coefficient positive even though the upper edge of 

the data has a negative slope. 

Quantile regression is valuable for illustrating the upper 

limits of fish-response variables in relation to a stressor gradient and for indicating the direction of that association. Quantile regression can be used to estimate functional relations 

between variables for all portions of a probability distribution 

(Cade and Noon, 2003; Koenker, 2005), but it is used in this 

report only to estimate rates of change for functions along 

or near the upper boundary of the conditional distribution of 

responses. Viewing variables as constraints rather than as correlates can show that changes near the maximum response better represent effects when the measured factor is the limiting 

constraint (Cade and others, 2005). Regression quantiles near 

the upper boundary of a wedge-shaped relation describe potential rather than actual patterns of a species relative abundance 

and distribution (Vaz and others, 2008).

Quantile regression fits a regression line such that a 

defined proportion of the data points fall above the line and the 

remainder of the points fall below the line (Cade and Noon, 

2003). For example, for the 0.90 quantile (90th quantile), 

90 percent of the observations are below the fitted regression 

line. The 90th quantile has been used as a robust quantile to 

describe the upper bounds of wedge-shaped relations (Scharf 

and others, 1998; Wang and others, 2001a, 2001b ) and was 

selected over larger quantiles, such as the 95th quantile, to 

ensure sufficient points were used to estimate of the slope of 

the regression line (Konrad and others, 2008). 

Quantile regression models for this study were fit in 

the software program R (R Development Core Team, 2008), 

using the rq package of Koenker (2005). The rq package fits a 

linear model. Nonlinear quantile regression functions also can 

be estimated but were not tested for this preliminary report. 

Estimates of linear quantile regression were determined for the 

logs of the response variable (y). A small constant was added 

before determining the logarithms because there were some 

zero counts. These linear quantile regressions were returned to 

a nonlinear form by back-transformation (exponentiating). A 

back-transformation was possible because regression quantiles 

retain their statistical properties under any monotonic transformation of y (Cade and Noon, 2003; Cade and Guo, 2000; 

Koenker and Machado, 1999). That is, the 90th quantile of 

the transformed data is equivalent to the 90th quantile of the 

original data. The resulting regression line plots as a curve 

on a scatter plot of the data, the shape of which is appropriate 

for modeling relations with a wedge-shaped form (Wang and 

others, 2001a).

A bootstrap procedure, by which the dataset is resampled 

with replacement within the range of observed and predicted 

values, was used to obtain standard errors and confidence 

intervals. Confidence intervals can indicate that the ecological response is relatively imprecise if confidence intervals are 

wide and can indicate reliability if the 95-percent confidence 

interval excludes zero. Quantile regression models are insensitive to extreme values of response variables and can deal with 

zero counts, which are common in species-distribution models 

of abundance data (Cade and others, 1999; Vaz and others, 

2008).

Quantile regression is a univariate technique useful for 

determining the association between two variables (Creque 

and others, 2005). However, multiple environmental and 

anthropogenic factors interact to influence the local abundance 

and distribution of fish. If additional uncontrolled factors are 

measured and analyzed in a multivariable analysis (such as 

with a GLM model), some of the multiple factors that interact 

to produce the wedge-shaped scatter plots can be revealed.

Generalized Linear Models and Extensions

Ordinary Least Squares (OLS) multiple linear regression is commonly used to model multivariable relations in 

a dataset but is not appropriate for the dataset used for this 

study. OLS multiple linear regression requires the response 
Methods 11

variable to be normally distributed and model residuals to 

be normally distributed with a constant variance across all 

values of the explanatory variables. Scatter plots of many 

of the fish-response variables and anthropogenic-alteration 

variables tested in this study exhibit a wedge shape, violating 

the variance homogeneity assumption. Although it is common 

for fish-community data to have non-normal distributions that 

are highly skewed, many of the fish-response variables also 

contain a high number of zero values. Response variables that 

are highly skewed with many zero observations cannot be 

made normal by any transformation. Finally, fish-community 

metrics are typically expressed in units such as abundance 

(counts), ratios (percent), or presence-absence (binary) for 

which linear regression may not be appropriate. Rather than 

force data to fit linear regression models, generalized linear 

models (GLMs) and their extensions are increasingly used to 

develop regression equations for data with these characteristics (Zuur and others, 2007, 2009; Bolker, 2008; Wenger and 

Freeman, 2008).

A GLM extends linear models in two ways: it allows the 

response variable to have a distribution other than normal, 

and it allows for a transformation of the response variable 

(Ahmadi-Nedushan and others, 2006). A GLM enables models 

to be developed for response variables that are bounded (as 

in proportions), cannot be negative (as in counts) or have 

highly skewed distributions, and allows a more flexible variance structure in the fitted model. These characteristics allow 

GLMs to be applied to a wider range of data than traditional 

linear models, and to include linear regression models as a 

special case.

A GLM has many analogies to an OLS multiple linear 

regression model in that it predicts or models a response variable (dependent variable) from one or more explanatory variables (independent variables), and the right side of the GLM 

equation remains a linear function of explanatory variables. A 

GLM differs from an OLS multiple linear regression model, 

however, because the assumption that the relation between the 

explanatory variables and the response variable is linear is not 

incorporated into a GLM. A GLM specifies a nonlinear link 

function that equates the linear combination of explanatory 

variables with a function of the response variable (McCullagh 

and Nelder, 1989). This is similar to a data transformation of 

the response variable in OLS regression. Selection of the link 

function is dictated by the nature of the response variable. 

GLMs can accommodate response variables from the exponential distribution family. For example, a log link is used for 

count data that have a Poisson distribution, and a logit link 

is used for response variables that are proportions and have 

a binomial distribution. Once the model is fitted, the predicted values of the response variable (the mean value of Y) 

are obtained by applying the inverse of the link function (for 

example, exponentiating if a log link is used). 

GLMs also differ from OLS multiple linear regression by using maximum likelihood techniques to determine 

GLM equation coefficients and minimizing residuals of the 

fitted model rather than minimizing the sums-of-squares 

(McCullagh and Nelder, 1989). Consequently, although there 

are tools available for GLMs that can be used for model 

selection, and summary statistics such as parameter estimates, 

standard errors, and goodness-of-fit statistics that can be used 

to evaluate fitted GLMs, these tools are different than the 

familiar statistics used for this purpose in OLS models, such 

as the coefficient of determination (R2) and variance-inflation 

factors (VIF). 

A GLM with a negative binomial distribution can be used 

to model count data such as species abundance or richness. 

By definition, the negative binomial distribution consists of 

positive integers with a mean (μ) and a variance (μ + [μ/k]). 

The parameter k is called a dispersion parameter and allows 

the variance to be larger than the mean. Negative binomial 

distributions with small values of k are positively skewed with 

a high number of observations equal to zero. The probability 

of observing a value y = 0, 1, 2...n in a variable that has a 

negative binomial distribution is given by equation 1 (Zuur 

and others, 2009):

Γ(y + 1)Γ(1/k) (1 + μk) y + 1/k

ΡrNB (y) = Γ(y +1/k) (μk) y

 (1)

where

PrNB(y) = probability of observing y from a negative 

binomial distribution,

μ = mean of negative binomial distribution,

k = dispersion parameter, and

Γ(y) = gamma function: (y-1)! 

When y is a rate, μ is replaced by aμ , where μ is the count and 

a is the unit time. Models for rates such as catch per unit effort 

are fit with an offset to account for differing sampling times.

A high number of excess zeros, as observed in this fishsampling dataset, can lead to a characteristic that is called 

overdispersion. Overdispersion occurs when there is greater 

variability in a dataset than would be expected on the basis 

of the probability distribution assumptions of the statistical 

model. Although a negative binomial distribution can accommodate very skewed datasets, the presence of a large number 

of zeros can cause a negative binomial model to be overdispersed. This overdispersion violates the statistical assumptions 

of many analytical methods that are commonly used to predict 

species-environment relations and may lead to increased 

uncertainty regarding parameter estimates, cause the p-values 

of model coefficient estimates to be unreliable, and lead to 

poor model selection (Lewin and others, 2010). When the 

number of zeros is so large that the data do not fit standard distributions (for example, normal, Poisson, negative binomial), 

the dataset is referred to as “zero inflated” (Martin and others, 

2005; Brainwood and others, 2008).

Analysis of zero-inflated data requires application of 

techniques that are flexible concerning the distributional 

properties of the data. Zero-inflated negative binomial (ZINB) 
12 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

models are an extension of GLMs designed to accommodate 

discrete datasets that are overdispersed as a result of excessive 

zeros. Multivariable ZINB models are used in ecological studies to model species abundance and richness (Arab and others, 

2008; Wenger and Freeman, 2008; Martin and others, 2005; 

Zuur and others, 2009). ZINB models consider two types of 

zeros in the data. Structural zeros occur when a species is 

absent from a site owing to unsuitable habitat or habitat degradation and represent real ecological effects on a population. 

They may also be a result of a species that does not saturate 

its entire suitable habitat or has a low frequency of occurrence 

(rare species). Sampling zeros refer to zeros that occur when a 

species was present but undetected, or when a site was suitable 

for a species but the species was not present at the time of 

sampling owing to other, unknown stochastic processes (Cunningham and Lindenmayer, 2005; Martin and others, 2005).

ZINB models use a mixture distribution to account for 

sampling zeros separately from structural zeros and non-zero 

observations. The models are composed of two terms. The 

first term determines the probability of a sample containing 

zero counts, and the second term governs the magnitude of the 

count. Because the count portion of the model determines the 

probability for cases where positive catches may occur, but 

are not certain, the count term also determines a portion of the 

zero values. Sampling zeros follow a Bernoulli distribution 

(that is, they can take a value of only 0 or 1) and are modeled 

by a logistic regression where π is the probability of a sampling zero. Nonzero counts and structural zeros are modeled 

by a negative binomial regression and have a detection probability of (1-π). The probability of observing y = 0, 1, 2...n for 

the mixed distribution is given by equation 2 (Zuur and others, 

2009),

ΡrZINB (y) = { � + (1 - �) ΡrNB (y) for y = 0

(1 - �) ΡrNB (y) for y > 0 (2)

where

PrZINB(y) = probability of observing y from the mixed 

distribution,

π = probability of zero from the Bernoulli 

process, and

PrNB(y) = probability of observing y from the 

negative binomial process.

For a particular set of covariates, the zero-inflated negative 

binomial equation is

E(Y) = μ(1 - �) (3)

where

E(Y) = expected mean value of relative abundance 

or species richness and where

π = probability of a zero from the logistic 

portion of the equation, calculated as

� = 

eγo + γ1z1 + γ2z2 +∙∙∙+ γі

zі

1 + eγo + γ1z1 + γ2z2 +∙∙∙γі

zі

and where

μ = mean value of relative abundance or 

species richness from the negative 

binomial count portion of the equation, 

calculated as 

μ = e ßo + ß1x1 + ß2x2 +∙∙∙+ ßj

xj

and where

γi = coefficient of logistic component, 

βj = coefficient of negative binomial 

component,

zi = covariates for the logistic model, and

xj = covariates for the negative binomial model.

Only one outcome E(Y) is predicted by the ZINB model. 

However, some insight into factors determining the distribution and abundance of response variables can be gained 

by examining the different explanatory variables or covariates that are determined for the logistic and count parts of 

the model. The ZINB mixture models differentiate between 

explanatory variables that predict the absence of a species, and 

explanatory variables that primarily predict the abundance of a 

species for sites where the species occur (Wenger and Freeman, 2008). For example, for a species with a limited geographic range, presence/absence of a species may be predicted 

by the logistic portion of the model by use of geographic 

location as a covariate, whereas, in areas where the species is 

likely to occur geographically, abundance may be predicted 

by the count portion of the model by covariates that describe 

habitat suitability. 

Model Fitting and Validation

ZINB models were fit in the R statistical software 

program (R Core Development Team, 2008) using the pscl 

package (Zeileis and others, 2008). Stepwise model selection 

was performed from a pool of potential explanatory variables 

including physical basin characteristics, land cover, percent 

impervious cover, and water-use indicators. Several criteria 

were used to compare models. Log likelihood ratio tests were 

used to compare the selected regression model to the null 

model containing only the intercept. Akaike’s information criterion (AIC) (Akaike, 1974) was used to compare nested models. The AIC employs the log likelihood to measure the lack of 

a model fit relative to the number of explanatory variables in 

the model; as the number of variables in the model increases, 

the lack of fit decreases, and the penalty for having too many 

variables increases (Burnham and Anderson, 2002; Kennen 

and others, 2009). Models with a lower AIC have a better fit. 
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 13

Vuong’s test (Vuong, 1989) was used to compare the goodness 

of fit between non-nested models. 

There is no widely accepted measure of predictive power 

for GLMs, such as the coefficient of determineation (R2) used 

for OLS regression. AIC and likelihood ratio tests are measures of relative goodness-of-fit between two nested models 

and are appropriate for selecting between potential fitted models, but they do not indicate the predictive ability or amount of 

variance explained by a particular model. The predictive ability of a GLM is often examined by comparing observed and 

model-predicted values. Because of the distributional properties of a ZINB model, the correlation coefficient between 

predicted and observed values cannot be used to calculate the 

R2 of a model, such as in OLS linear regression; however, it 

does give a relative indication of the amount of variation in the 

data explained by the model (Zheng and Agresti, 2000). Alternatively, a simple linear regression between the observed and 

predicted values provides information about the bias of the fitted GLM (Potts and Elith, 2006; Sileshi and others, 2009). For 

a simple linear model of the form: Observedi = β0 + β1Fittedi, 

an intercept of 0 and a slope of 1 for the simple linear regression indicates an unbiased GLM fit. An intercept significantly 

different from zero indicates a prediction bias, and a slope 

significantly different from one indicates an inconsistent bias 

across the range of predictions. Taken together, the coefficients 

of a linear model for predicted versus observed, along with the 

correlation coefficient, give a complete picture of the goodness-of-fit and predictive ability of a GLM.

Relations Among Flow Alteration, 

Impervious Cover, and Fish 

Communities

The following sections describe streamflow alteration, 

fish-community composition, and environmental characteristics for the 756 fish-sampling sites. Results are summarized 

from the application of two analytical techniques, quantile 

regression and generalized linear modeling, to evaluate the 

association between fish-response variables and selected 

environmental characteristics and anthropogenic-alteration 

explanatory variables. Results are not shown for all explanatory variables tested during the study. Relations between some 

of the indicator species and stressor gradients were determined 

to be nonlinear and were not appropriate for application of the 

quantile regression models fit for this preliminary report; likewise, GLM models for some fish-response metrics were not 

significantly different than the null model or indicated extreme 

bias, and so are not shown. These issues are discussed further 

in the various sections.

Data Exploration

The following sections discuss the streamflow alteration, 

fish-community characteristics, and environmental characteristics represented by the 756 fish-sampling sites used in this 

study.

Streamflow Alteration 

The estimated (altered) August median flows for the 

study sites, normalized by drainage area, ranged from 0.0 

to 1.6 ft3/s/mi2, with a median value of 0.21 ft3/s/mi2. Mean 

annual flows, normalized by drainage area, ranged from 0.53 

to 2.7 ft3/s/mi2, with a median value of 2.0 ft3/s/mi2. 

The degree and range of percent alteration of August 

median flow represented by the 756 fish-sampling sites used 

in this study were compared to those reported in Weiskel and 

others (2010) for 1,429 subbasins and groundwater contributing areas in Massachusetts (fig. 2). The comparison indicated a 

similar range of percent flow alteration and distribution shape; 

however, the fish-sampling sites were weighted slightly more 

toward less-altered conditions for flow-depleted sites and 

toward slightly more-altered conditions for flow-surcharged 

sites. The percent alteration of August median flows at the 

fish-sampling sites ranged from 100 percent depleted to 373 

percent surcharged. Most sites exhibited only a small amount 

of flow alteration. The interquartile range (which includes 

the middle 50 percent of the data) ranged from -0.03 to 0.02 

percent alteration of August median flow. 

Of 756 fish-sampling sites used in the analysis, 67 percent were in net-depleted streams and 33 percent were in netsurcharged streams, under median August conditions. Thirteen 

0.01 0.5 2 10 30 50 70 90 98 99.5 99.99

-100

0

100

200

300

400

Percent flow alteration of August median flow

Cumulative frequency, in percent

Percent alteration of August median flow, 

756 fish sampling sites. 

Percent alteration of August median flow,

1,429 subbasins, Massachusetts

Water Indicators study

 EXPLANATION

Figure 2. Cumulative frequency in relation to percent alteration 

of August median flow for 756 Massachusetts Division of 

Fisheries and Wildlife fish-sampling sites and 1,429 subbasins and 

groundwater contributing areas across Massachusetts (Weiskel 

and others, 2010).
14 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

percent of the sites were indicated to have greater than 10-percent depletion under median August conditions, and twelve 

percent of the sites had greater than 10-percent surcharging 

under these conditions. A map of the percent alteration of 

August median flow for the fish-sampling sites used for this 

study (fig. 3) indicates that the majority of sites that have a net 

surcharge or net depletion of greater than 10-percent alteration of August median flows are in the highly populated areas 

just outside of the Massachusetts Water Resources Authority 

(MWRA) Boston-MetroWest water and sewer service area. 

The WUI indicator ranged from 0 percent to 282 percent 

of unaffected flows. Under long-term average conditions, 

74 percent of the fish-sampling sites were indicated to have 

water-use intensities of less than 5 percent, and only 8 percent 

of the fish-sampling sites had greater than 20 percent WUI, 

whereas statewide values were 64 percent and 12 percent, 

respectively (Weiskel and others, 2010). 

Fish-Community Data 

The fish-community data analyzed included 92,222 

individual fish of 45 species from 756 fish-sampling sites. 

However, only 23 of these species occurred at greater than 

5 percent of the sites. The five most common species in terms 

of species occurrence among sites were white sucker, blacknose dace, brook trout, pumpkinseed, and longnose dace 

(table 2A). These five species were each present at about 52, 

49, 42, 37, and 35 percent of the sites, respectively. The maximum observed species richness at a site was 15, (range 0–15), 

and the mean and median values for the observed species 

richness were 6 species. In terms of total abundance, 20 species accounted for greater than 95 percent of the total number 

of fish sampled. The five most common species in terms of 

abundance were blacknose dace, white sucker, longnose dace, 

brook trout, and fallfish (table 2B). Fish relative abundance, 

measured as the number of fish sampled per hour of electrofishing, ranged from 0 to 2,500.

Fish-species and fish-community metrics were assessed 

graphically for outliers, and underlying assumptions were 

assessed for statistical tests. As expected, histograms of the 

73° 72° 71° 70°

43°

42°

EXPLANATION

Percent alteration of August median flow

<, less than; >, greater than

-100 to <-15

-15 to <-10

-10 to >-5

-5 to 0

0 to 5

>5 to 10

>10 to 15

>15

Net depletion

Net surcharge 0 10 20 30 40 50 MILES

0 10 20 30 40 50 KILOMETERS

Massachusetts Planning

Basins

Figure 3. Percent alteration of August median flows at 756 Massachusetts Division of Fisheries and Wildlife fish-sampling sites, 

1998–2008.
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 15

Table 2. Common names of fish, and number and percentage of sites sorted by the number of sites at which each species was 

captured, and number and percentage of individuals sorted by the number of individuals captured at sites used for the preliminary 

assessment of fish communities in Massachusetts.

Common name Number of sites Percent of sites Common name Number of individuals Percent of individuals

White sucker 389 51.87 Blacknose dace 25,254 27.38

Blacknose dace 369 49.20 White sucker 8,086 8.77

Brook trout 318 42.40 Longnose dace 8,015 8.69

Pumpkinseed 277 36.93 Brook trout 7,316 7.93

Longnose dace 260 34.67 Fallfish 5,532 6.00

Bluegill 220 29.37 Slimy sculpin 5,466 5.93

Fallfish 194 25.87 Common shiner 5,132 5.56

Largemouth bass 186 24.80 Redfin pickerel 3,424 3.71

Chain pickerel 183 24.40 American eel 2,948 3.20

American eel 180 24.00 Pumpkinseed 2,733 2.96

Yellow bullhead 177 23.60 Bluegill 2,417 2.62

Redfin pickerel 175 23.33 Creek chub 1,940 2.10

Common shiner 172 22.93 Brown trout 1,818 1.97

Tesselated darter 154 20.53 Tesselated darter 1,815 1.97

Brown trout 135 18.00 Yellow bullhead 1,007 1.09

Brown bullhead 134 17.87 Largemouth bass 979 1.06

Slimy sculpin 134 17.87 Redbreast sunfish 972 1.05

Golden shiner 130 17.33 Rock bass 967 1.05

Yellow perch 114 15.20 Golden shiner 889 0.96

Creek chub 108 14.40 Yellow perch 841 0.91

Redbreast sunfish 56 7.47 Smallmouth bass 745 0.81

Banded sunfish 54 7.20 Chain pickerel 714 0.77

Creek chubsucker 45 6.00 Spottail shiner 658 0.71

Smallmouth bass 37 4.93 Bluntnose minnow 561 0.61

Rock bass 35 4.67 Brown bullhead 481 0.52

Swamp darter 30 4.00 Banded sunfish 381 0.41

Longnose sucker 25 3.33 Landlocked salmon 258 0.28

Spottail shiner 19 2.53 Longnose Sucker 237 0.26

Bluntnose minnow 13 1.73 Creek chubsucker 156 0.17

Black crappie 12 1.60 American brook lamprey 79 0.09

Green sunfish 11 1.47 Swamp darter 63 0.07

Landlocked salmon 9 1.20 Lake chub 63 0.07

Lake chub 8 1.07 Rainbow trout 46 0.05

Rainbow trout 8 1.07 Common carp 46 0.05

American brook lamprey 6 0.80 Cutlips minnow 40 0.04

Banded killifish 5 0.67 Green sunfish 32 0.03

Bridle shiner 4 0.53 Central mudminnow 26 0.03

Common carp 3 0.40 Bridle shiner 22 0.02

Central mudminnow 3 0.40 Black crappie 21 0.02

Northern pike 3 0.40 White perch 13 0.01

White perch 3 0.40 Banded killifish 10 0.01

Goldfish 2 0.27 Tadpole madtom 7 0.01

Cutlips minnow 1 0.13 Northern pike 5 0.01

Tadpole madtom 1 0.13 White catfish 4 0.00

White catfish 1 0.13 Goldfish 3 0.00
16 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

D White sucker

500 400 300 200 100 0

100

908070605040302010

0

Relative abundance, in count per hour

Percent of sample sites

C Brook trout

500 400 300 200 100 0

100

9080

70

6050

40

30

20

10

0

Relative abundance, in count per hour

Percent of sample sites

500 450 400 350 300 250 200 150 100 50 0

100

9080

7060

5040

3020

10

0

B Cold-water fish

Relative abundance, in count per hour

50

4030

2010

0 900 1,000 800 700 600 500 400 300 200 100 0

Percent of sample sites

Percent of sample sites

A Fluvial fish

Relative abundance, in count per hour

Figure 4. Fish relative abundance in counts per hour of (

A) fluvial fish, (

B) cold-water fish, (

C) brook trout, and (

D) white sucker in relation to percent of selected sites in 

Massachusetts, 1998–2008.
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 17

fish metrics indicated that many distributions were highly 

non-normal and had a positive or right skew, with a majority 

of observations at or near zero values (fig. 4). For example, 

redbreast sunfish occurred at only 7 percent of the sites, and 

24 other species occurred at even lower percentages of sites. 

Even the species that was present at the highest number of 

sites (white sucker), was absent at 48 percent of the sites. 

Pooling several species into fish-community metrics reduced 

the number of zero values. For example, fluvial fish occurred 

at 85 percent of the sampled sites.

Environmental Characteristics 

Boxplots illustrate the range of environmental characteristics of the fish-sampling sites used in the study (fig. 5). The 

mean and median drainage areas were 28 and 7.4 mi2, respectively, and drainage areas ranged from 2 to 395 mi2

. About 

60 percent of the sample sites were in small basins less than 

10 mi2 in size, and roughly another 30 percent of sites were in 

basins of 10 to 50 mi2. Less than 10-percent of the sites were 

in rivers with drainage areas greater than 50 mi2. Summaries 

of environmental characteristics for the contributing areas 

to 756 fish-sampling sites in Massachusetts are presented in 

table 3. The sites represent a wide range of topographic, geologic, and land-cover conditions. 

Boxplots illustrate the range of impervious cover and 

dam density at the fish-sampling sites used in the study 

(fig. 6). Dam density values ranged from 0 to 2.63 with a mean 

of 0.33 dams per square mile. Impervious-cover values ranged 

from 0.29 to 46 percent with mean and median values of 6.4 

and 3.9 percent, respectively. A map of impervious cover for 

the fish-sampling sites used for this study indicates that the 

majority of sites that have an impervious cover greater than 

12 percent are located in eastern and central Massachusetts 

(fig. 7). The degree and range of the percent impervious cover 

represented in the contributing areas to the 756 fish-sampling 

sites used in this study were compared to those reported in 

Weiskel and others (2010) for 1,429 subbasins and groundwater contributing areas in Massachusetts (fig. 8). The comparison indicated that the range and distribution of impervious 

cover for the sites used in this study are representative of 

impervious-cover values across Massachusetts or are only 

slightly underestimated. 

Variables that are linearly related and highly correlated 

cannot be used together in a multivariable regression equation. Prior to analysis, all environmental characteristics and 

anthropogenic variables were evaluated for associations 

among variables through graphing and Pearson correlation 

analysis. A pairplot of environmental characteristics indicates 

that few variables have clear linear relations (fig. 9). A number 

of explanatory variables exhibit wedge-shaped scatter plots, 

indicating that there are clear limits for certain combinations 

of environmental characteristics for stream basins in Massachusetts. For example, few basins have both high basin slope 

and large areas of sand and gravel or wetlands. 

Intercorrelations were expected and found among a few 

variables. Variables that exhibited a high degree of concordance (Pearson correlations, r > 0.70) were omitted or were 

not included in the same candidate models. For example, 

elevation was correlated with a number of variables including basin slope (0.72), sand and gravel (-0.72), and Outlet X 

(-0.88), and was omitted from further analysis. Outlet X was 

also correlated to basin slope (-0.75) but was retained for 

further evaluation during modeling. When a variable such as 

Outlet X is significant in regression equations, it can serve 

as a caution that correlation between response variables and 

explanatory variables is not equivalent to causation. For 

example, for fish species within their range of distribution, 

Outlet X does not in itself directly influence fish-community 

composition but likely covaries with multiple factors, such as 

gradient, climate, and land use. These factors, in turn, covary 

with unmeasured factors such as flow regime and velocity, 

water quality, and substrate conditions that directly influence 

fish distribution.
18 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

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Outlier data value more than 3 times the interquartile range outside the quartileOutlier data value less than or equal to 3 and more than 1.5 times the interquartile range outside the quartile75th percentileMedian25th percentile

EXPLANATION

o

x

Figure 5. Environmental characteristics for contributing areas to 756 fish-sampling sites in Massachusetts (

A) wetland area, (

B) mean basin slope, (

C) channel slope, 

(D) drainage area, (

E) elevation, (

F) open-water area, and (

G) sand and gravel area.

Table 3. Descriptions, reference codes, units, number of sites, and ranges of values for environmental and anthropogenic variables.

Variable name Code Units Number of sites Mean Minimum 25th percentile Median 75th percentile Maximum

Drainage area DA Square miles 756 27.7 2.0 3.7 7.4 22.7 394.5Longitude OutletX State Plane meters/10000 756 1.4953 0.3547 0.9517 1.5310 1.9886 2.5880Latitude OutletY State Plane meters/10000 756 9.0384 8.1431 8.8164 9.0873 9.2717 9.5800Elevation ELEV Meters 756 832.8 41.0 316.5 839.3 1211.6 2272.1Sand and gravel SG Percent area 756 20.6 0.0 3.5 15.4 28.9 100.0Open water OPENW Percent area 756 2.2 0.0 0.2 1.1 2.9 20.1Wetland WET Percent area 756 8.3 0.0 4.0 6.8 11.1 37.2Channel slope CHSLP Percent rise 756 1.1 0.0 0.0 0.6 1.5 10.6Basin slope BASSLP Percent rise 756 9.7 1.7 6.3 8.7 12.4 42.7Impervious cover IC Percent area 756 6.4 0.3 1.9 3.9 8.9 46.2Buffer impervious cover BFIC Percent area 749 5.2 0.0 2.1 3.8 6.6 39.8Dam density DamDsqmi Count/square mile 749 0.33 0.00 0.00 0.26 0.49 2.63Dam density DamDstmi Count/square mile 749 0.13 0.00 0.00 0.08 0.19 0.84Percent alteration of August median for net Surcharged streams AugPAsur Percent 253 24.4 0.0 1.5 5.8 22.1 372.9Percent alteration of August median for net Depleted streams AugPAdep Percent 503 -9.8 -0.01 -0.4 -0.9 -5.4 -100.0
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 19

x

x

x

x

x

x

x

x

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x

x

x

x

x

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0.0

0.2

0.4

0.6

0.8

1.0

Dam density per stream

mile

x

xx

x

x

x

x

xx

x

x

x

x

o

o

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Dam density per square mile

xx

xx

x

x

x

x

x

x

x

x

x

x

xx

x

x

xxx

x

x

x

x

x

x

x

x

x

x

o

o

o

o

o

o

o

o

10

0

20

30

40

50

Buffer impervious cover, as percent of buffer area

x

xx

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

xx

x

x

x

o

oo

o

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0

10

20

30

40

50

60

Impervious cover, as percent of basin area

25th percentile

Median

75th percentile

interquartile range outside the quartile

and more than 1.5 times the

Outlier data value less than or equal to 3

interquartile range outside the quartile

Outlier data value more than 3 times the

EXPLANATION

o

x

AA B C D

Figure 6. Anthropogenic characteristics for contributing areas to 756 fish-sampling sites in Massachusetts (A) impervious cover, (B) 

240-meter-buffer impervious cover, (C) dam density per square mile, (D) dam density per stream mile.
20 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

73° 72° 71° 70°

43°

42°

0 10 20 30 40 50 MILES

0 10 20 30 40 50 KILOMETERS

EXPLANATION

Percent impervious cover

>, greater than

0 to 4

>4 to 8

>8 to 12

>12 to 16

>16

Massachusetts Planning Basins

Figure 7. Impervious cover for contributing areas to 756 Massachusetts Division of Fisheries and Wildlife fish-sampling sites in 

Massachusetts, 1998–2008.

0.01 0.5 2 10 30 50 70 90 98 99.5 99.99

0

10

20

30

40

50

60

70

80

Cumulative frequency, in percent

Average impervious cover over basin, in percent

Cumulative impervious cover, 

1,429 subbasins, Massachusetts 

Water Indicators Study

EXPLANATION

Cumulative impervious cover

 for 756 fish sampling sites

Figure 8. Frequency distributions of impervious cover for contributing areas to 756 Massachusetts Division of Fisheries and Wildlife 

fish-sampling sites and 1,429 subbasins and groundwater contributing areas across Massachusetts (Weiskel and 

others, 2010).
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 21

Figure 9. Pairplots of environmental characteristics. The lower diagonal panels show correlation values. The upper diagonal panels 

show pair-wise scatterplots. Names for environmental characteristics are given in table 3.

0.055

0.11 0.72 ELEV

-0.086 -0.75 -0.88

-0.011 -0.56 -0.72 0.63

-0.22 0.33 0.43 -0.41 -0.38

-0.05 -0.69 -0.61 0.67 0.55 -0.32

0.017 -0.23 -0.14 0.18 0.13 -0.09 0.098

BASINSLP

OutletX

CHSLP

SG

WET

OPENW

DA

0.031 -0.54 -0.64 0.62 0.58 -0.33 0.35 0.05 IC

0.052 -0.42 -0.53 0.51 0.48 -0.28 0.21 0.04 0.94 BFIC

10 30 8 4 0 20 10 0 40 20 0

10 30 50,000 250,000 0 4 8 0 10 20 0 20 40

0 200 0 1,500 0 40 100 0 20 0 20

50,000

0 200 100 40 0 20 0 20 0 0 1,500
22 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

Quantile Regression

Quantile regression plots illustrate several trends in the 

limiting-factor relations between the fish-response variables 

and anthropogenic-alteration variables. For the plots shown, 

the direction of the slope of the 90th quantile indicates the 

nature of the relation. A quantile that decreases from left to 

right indicates a decrease in the fish-response variable in 

response to an increase in the stressor variable, and a quantile 

that increases from left to right indicates an increase in the 

fish variable in response to an increase in the stressor variable. 

The slope of the 90th-quantile line indicates the strength of 

the relation between the upper limit of the response variable 

and the stressor variable. A steeper line indicates a stronger 

relation between variables, and a flatter line indicates a weaker 

relation. The 95-percent confidence intervals around the 90th 

regression quantile lines indicate the uncertainty in the slope 

of the regression line. A narrow interval indicates a high 

degree of certainty; a wide interval indicates a lower degree of 

certainty. Confidence intervals that can encompass a line with 

a zero slope indicate that the relation between the two variables is insignificant.

Increases in the percent alteration of the August median 

flow for net-depleted sites were associated with large 

decreases in the 90th quantile for brook trout and blacknose 

dace relative abundance, slight declines in fallfish relative 

abundance, and little change in white sucker relative abundance (fig. 10). These trends are consistent with life history 

traits for the various species. Brook trout and blacknose dace 

are both fluvial specialists; the factor ceilings depicted by the 

90th quantile support their sensitivity to flow alteration. The 

steep slope of the 90th quantile for brook trout and blacknose 

dace relative to other fluvial-fish species indicates that these 

species may be more sensitive to flow alteration than the other 

fluvial species tested. The low slope and wide confidence 

intervals of the regression lines for fallfish and white sucker 

indicate that, for this dataset, there is little to no relation 

between relative abundance of these species and percent 

alteration of August median flow for net-depleted sites.

Increases in percent alteration of August median flow for 

net-depleted sites were also associated with moderate declines 

for fluvial-fish relative abundance and species richness 

(fig. 11). The fluvial-fish-species-richness plot illustrates that 

for the 90th quantile, fluvial-fish species richness is reduced 

roughly one fluvial species by approximately a 16 percent 

reduction in August median flow.

Increases in the percent alteration of the August median 

flow for net-surcharged sites were associated with declines in 

the 90th quantile for relative abundance of blacknose dace, 

fallfish, and redfin pickerel (fig. 12). The wide confidence 

interval for brook trout indicates that, for this dataset, there is 

little to no relation between relative abundance of brook trout 

and percent alteration of August median flow at net-surcharged 

sites. There are relatively few sites with net-surcharge where 

brook trout were present, however, and the sample size may 

be too small to indicate the relation between net-surcharge and 

brook trout relative abundance. Increases in the percent alteration of the August median flow for net-surcharged sites were 

associated with declines in the 90th quantile for the relative 

abundance of fluvial fish (fig. 13). The confidence intervals of 

the regression lines for fluvial-fish species richness indicate 

that, for this dataset, there is no relation between the number 

of fluvial fish species and percent alteration of August median 

flow for net-surcharged sites.

Increases in percent impervious cover were associated 

with large decreases in the 90th quantile for brook trout and 

blacknose dace, relatively little decline for fallfish, (fig. 14), 

and large decreases in the 90th quantile for fluvial-fish relative abundance and species richness (fig. 15). The declining 

relations shown in most of these plots illustrate the critical 

nature of the relations between impervious surface and aquatic 

condition. The narrow 95-percent confidence interval around 

the strongly declining relation between fluvial-fish species 

richness and impervious cover indicates a high degree of 

confidence in this relation. The effect of impervious cover has 

been hypothesized by Trebitz and others (2009) to be strong 

enough to mask other fish-habitat associations. 

Relations between fish-response variables and impoundments were tested by use of a variable that reflects the areal 

size of impoundments, lakes, and ponds in the contributing 

area. Although some water bodies represented in this variable 

are natural lakes or ponds that do not have dams, many lakes 

and ponds in Massachusetts do have dams or outlet structures 

that control water levels. Increases in percent open water were 

associated with decreases in the 90th quantile for the relative 

abundance of fluvial fish, brook trout, and blacknose dace 

(fig. 16).

The quantile regression models are useful for modeling 

species responses to stressor gradients when those responses 

are linear, such as for the upper bound of a wedge-shaped 

relation. Although many of the relations between species 

responses and stressor gradients examined in this study exhibited a wedge shape, some scatter plots indicated a different 

form of relation. For example, relations between the relative 

abundance of redfin pickerel and percent alteration of August 

median flow, redfin pickerel and impervious cover, and white 

sucker and impervious cover, indicated nonlinear, unimodal 

responses that approximated a Gaussian (normal) curve 

(Gauch, 1982). Quantile regression models were not developed for these nonlinear relations for this preliminary study.
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 23

Figure 10. Relative abundance of indicator fish species in relation to percent alteration of August median flow at selected netdepleted sites for (A) brook trout, (B) blacknose dace, (C) fallfish, and (D) white sucker, in Massachusetts streams, 1998–2008.

0 20 40 60 80 100

0

200

400

600

800

Relative abundance of white sucker,

in count per hour

D White sucker

Percent alteration of August median flow at

net-depleted sites

0 20 40 60 80 100

0

200

400

600

800

Relative abundance of fallfish,

in count per hour

C Fallfish

Percent alteration of August median flow at

net-depleted sites

0 20 40 60 80 100

0

200

400

600

800

Relative abundance of blacknose dace,

in count per hour

B Blacknose dace

0 20 40 60 80 100

0

200

400

600

800

Relative abundance of brook trout,

in count per hour

A Brook trout EXPLANATION

90th regression quantile

95-percent confidence interval

EXPLANATION

90th regression quantile

95-percent confidence interval

EXPLANATION

90th regression quantile

95-percent confidence interval

EXPLANATION

90th regression quantile

95-percent confidence interval

0 20 40 60 80 100

0

2

4

6

8

10

12

Number of fluvial fish species

B

Percent alteration of August median flow at

net-depleted sites

EXPLANATION

90th regression quantile

95-percent confidence interval

A Fluvial-fish relative abundance Fluvial-fish richness

1,000

1,500

2,000

2,500

0

500

0 20 40 60 80 100

Percent alteration of August median flow at

net-depleted sites

Relative abundance of fluvial fish, in count per hour

EXPLANATION

90th regression quantile

95-percent confidence interval

Figure 11. Fish-community metrics in relation to percent alteration of August median flow at selected net-depleted sites for (A) fluvialfish relative abundance and (B) fluvial-fish species richness, in Massachusetts, 1998–2008.
24 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

0 100 200 300 400

0

100

200

300

400

500

Relative abundance of redfin pickerel,

in count per hour

D

Percent alteration of August median flow at

net-surcharged sites

Redfin pickerel EXPLANATION

90th regression quantile

95-percent confidence interval

Percent alteration of August median flow at

net-surcharged sites

0 100 200 300 400

0

100

200

300

400

500

600

Relative abundance of fallfish,

in count per hour

C Fallfish EXPLANATION

90th regression quantile

95-percent confidence interval

0 100 200 300 400

0

200

400

600

800

1,000

Relative abundance of blacknose dace,

in count per hour

B Blacknose dace

0 100 200 300 400

0

50

100

150

200

250

300

Relative abundance of brook trout,

in count per hour

A Brook trout EXPLANATION

90th regression quantile

95-percent confidence interval

EXPLANATION

90th regression quantile

95-percent confidence interval

Figure 12. Relative abundance of indicator fish species in relation to percent alteration of August median flow for selected netsurcharged sites for (A) brook trout, (B) blacknose dace, (C) fallfish, and (D) redfin pickerel, in Massachusetts streams, 1998–2008.

0 100 200 300 400

0

2

4

6

8

10

12

Percent alteration of August median flow at

net surcharged sites

Number of fluvial fish species

B

EXPLANATION

90th regression quantile

95-percent confidence interval

Fluvial-fish richness

0 100 200 300 400

0

500

1,000

1,500

2,000

2,500

Percent alteration of August median flow at

net surcharged sites

Relative abundance of fluvial fish, in count per hour

A Fluvial-fish relative abundance

EXPLANATION

90th regression quantile

95-percent confidence interval

Figure 13. Fish-community metrics in relation to percent alteration of August median flow at selected net-surcharged sites (A) fluvialfish relative abundance, and (B) fluvial-fish species richness, in Massachusetts streams, 1998–2008.
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 25

0 10 20 30 40 50

0

100

200

300

400

500

Impervious cover, in percent area

Fallfish

EXPLANATION

90th regression quantile

95-percent confidence interval

0 10 20 30 40 50

0

200

400

600

800

1,000

Impervious cover, in percent area

Blacknose dace

EXPLANATION

90th regression quantile

95-percent confidence interval

Relative abundance of blacknose dace,

in count per hour

0 10 20 30 40 50

0

100

200

300

400

500

Impervious cover, in percent area

Relative abundance of fallfish,

in count per hour

C

A Brook trout B

EXPLANATION

90th regression quantile

95-percent confidence interval

Relative abundance of brook trout,

in count per hour

Figure 14. Relative abundance of indicator-fish-species metrics in relation to impervious cover for (A) brook trout, (B) blacknose dace, 

and (C) fallfish, in selected Massachusetts streams, 1998–2008.

0 10 20 30 40 50

0

2

4

6

8

10

12

Impervious cover, in percent area

Number of fluvial fish species

B

EXPLANATION

90th regression quantile

95-percent confidence interval

0 10 20 30 40 50

0

500

1,000

1,500

2,000

2,500

Impervious cover, in percent

Relative abundance of fluvial fish, in count per hour

A Fluvial-fish relative abundance Fluvial-fish richness

EXPLANATION

90th regression quantile

95-percent confidence interval

Figure 15. Fish-community metrics in relation to impervious cover for (A) fluvial-fish relative abundance, and (B) fluvial-fish species 

richness, in selected Massachusetts streams, 1998–2008.
26 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

0 5 10 15 20 25 30

0

200

400

600

800

1,000

Blacknose dace

EXPLANATION

90th regression quantile

95-percent confidence interval

Open water, in percent area

0 5 10 15 20 25 30

0

100

200

300

400

500

Open water, in percent area

Brook trout

EXPLANATION

90th regression quantile

95-percent confidence interval

Relative abundance of brook trout,

in count per hour

0 5 10 15 20 25 30

0

500

1,000

1,500

2,000

2,500

Open water, in percent area

Relative abundance of blacknose dace,

in count per hour

C

A Fluvial fish B

EXPLANATION

90th regression quantile

95-percent confidence interval

Relative abundance of fluvial fish,

in count per hour

Figure 16. Relative abundance of indicator-fish-species metrics in relation to open water for (A) fluvial fish, (B) brook trout, and (C) 

blacknose dace, in selected Massachusetts streams, 1998–2008.
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 27

Generalized Linear Models 

Three GLM regression models were developed for this 

study—two for fish-community metrics (fluvial-fish relative 

abundance and fluvial-fish species richness) and one for an 

indicator-species metric (brook trout). Each of the models 

are described in the following sections, together with an 

example showing relations between variables. Additional plots 

depicting relations between all variables in the equations are 

presented later in the report. 

The best-supported model for each of the fish-response 

variables was selected from the pool of potential explanatory 

variables on the basis of the candidate model with the smallest AIC. Significant variables and their coefficients for each 

regression equation are listed in table 4. The most commonly 

occurring variables are similar in all equations. Drainage area, 

channel slope, wetland area, impervious cover, and east-west 

location (Outlet X) occur in all three of the equations. The 

percent area of sand and gravel and the percent area of open 

water occur in the brook trout equation; two flow-alteration 

variables, percent alteration of August median flow and a variable indicating whether the site has either net-flow depletion 

or net-flow surcharge in August (DEPLAUG), each occur in 

the fluvial-fish relative-abundance equation.

Coefficients in the count and logistic portions of the 

GLM models are interpreted differently. Variable coefficients 

in the count portion of the model can be interpreted in the 

same way as coefficients from a multilinear OLS log-normal 

regression, provided those variables are not also present in 

the logistic part of the equation. For example, negative coefficients in the count portion of the fluvial-fish models indicate 

that an increase in that variable is associated with a decrease 

in fish abundance or species richness, and positive coefficients 

in the count portion of the models indicate that an increase in 

that variable is associated with an increase in fish abundance 

or species richness. The magnitude of the coefficient can be 

used to estimate the percent change in the response variable 

that is associated with a unit change in one covariate, keeping 

all other variables the same. The logistic portion of the equation models the probability of obtaining a zero in excess of 

any zeros expected from the count portion. This differs from 

a typical logistic regression that models the probability of 

occurrence. Positive coefficients in the logistic part of a ZINB 

model mean that an increase in that variable increases the 

probability that fish relative abundance or species richness will 

be zero. Because zeros can be generated by either the logistic 

process or the count process, interpretation of the magnitude 

of logistic coefficients is not straightforward. A graphical 

approach was used to examine the effect of a change in magnitude of a particular covariate on the response variable. Care 

should be taken in inferring cause and effect relations between 

response and explanatory variables. The equations should only 

be used to describe the current dataset.

The use of ZINB models is inappropriate for response 

variables that consist of percentages, such as percent fluvial 

fish. Response variables consisting of percentages can be fit 

with a GLM using a logit link. Logit GLMs fit with percentfluvial fish data from this study were highly overdispersed 

because of a high number of points that contained 100-percent 

fluvial fish. Standard errors of individual coefficients were 

corrected to account for this overdispersion; however, the 

resulting models were still biased and deviance residuals were 

irregular, indicating a poor model fit. Nonparametric models 

or other multivariable statistical tools may be more appropriate for the percentage data, but time constraints limited testing 

their use for this study. The GLM regression models tested 

for coldwater fish and four additional indicator species (white 

sucker, blacknose dace, fallfish, and redfin pickerel) were 

either not significant at the 0.05 level compared to the null 

model, or exhibited extreme bias, and so were not included in 

this preliminary report. 
28 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts Table 4. Significant variables and coefficients for generalized linear model equations used to determine relations between natural and anthropogenic factors and riverine- fish communities in Massachusetts. Independent variables and coefficients are for equation 3. Abbreviations for independent variables are given in table 3. [SE, standard error; <, less than]. Model Independent Variable for count process Count process Coefficient 

(βi)

SE p-val Independent Variable for logistic process Logistic process Coefficient 

(γi)

SE p-value

Fluvial-fish relative abundance Intercept 7.7647 0.1929 <0.001 Intercept -6.4089 0.8546 <0.001

WET -0.099 0.01 <0.001 DA -0.1726 0.058 0.0029CHSLP -0.096 0.0345 0.005 OutletX 3.2342 0.4218 <0.001DA -0.0026 0.0009 0.0071OutletX -0.3228 0.1117 0.0038IC -0.057 0.0122 <0.001AbsAugPA -0.0044 0.0015 0.0035DEPLAUG -0.799 0.1206 <0.001

Fluvial-fish species richness Intercept 2.2092 0.0641 <0.001 Intercept -5.0871 1.0201 <0.001

WET -0.0316 0.006 <0.001 DA -0.4592 0.1507 0.0023CHSLP -0.0974 0.0164 <0.001 OutletX 2.9988 0.4527 <0.001OutletX -0.3955 0.0565 <0.001IC -0.0256 0.0059 <0.001

Brook trout relative abundance Intercept 4.561 0.0998 <0.001 Intercept -4.0177 0.708 <0.001

DA -0.0744 0.006 <0.001 SG -0.0309 0.0102 0.0025

OPENW 0.2321 0.0571 <0.001WET 0.2399 0.0451 <0.001CHSLP -0.5241 0.2093 0.0123OutletX 1.123 0.4265 0.0085IC 0.1306 0.0379 0.0006
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 29

Fluvial-Fish Species-Richness Model

Fluvial-fish species richness was related to percent 

wetland, channel slope, Outlet X, impervious surface, and 

drainage area. Species richness is negatively associated with 

percent wetland, channel slope, Outlet X, and impervious 

surface in the count part of the equation. Increases in these 

variables were associated with decreases in fluvial fish-species 

richness. The coefficients of the logistic part of the equation 

indicate that the probability of sampling fluvial-fish species 

increases for basins with larger drainage area and for basins in 

western MA. The coefficients for the fluvial fish-species richness model (table 4) can be used in equation 3 to determine the 

effect of changes in impervious cover on fluvial-fish species 

richness. Keeping all other variables static, a unit (1 percent) 

increase in impervious surface is associated with a 2.5 percent 

decrease in fluvial-fish species richness.

To illustrate relations between variables, the association 

between fluvial-fish species richness and impervious cover is 

depicted in figure 17 for four different levels of channel slope. 

These relations were determined with the fluvial-fish-speciesrichness equation, using the median values from the dataset 

for the environmental characteristics in the equation (drainage 

area, percent area of wetland, channel slope, Outlet X). On the 

plot, fluvial-fish species richness is shown to decrease with 

increasing impervious cover, and, for sites with low impervious cover, a site with a higher channel slope is indicated 

to have a lower expectation for fluvial-fish species richness 

(by one species) than a site with a lower channel slope. 

Fluvial-Fish Relative-Abundance Model

Fluvial-fish relative abundance was related to percent 

wetland, channel slope, Outlet X, percent impervious cover, 

percent alteration of August median streamflow, and drainage 

area. The count portion of the equation indicated that fluvialfish relative abundance is negatively associated with percent 

wetland, channel slope, Outlet X, percent impervious cover, 

and percent alteration of August median streamflow. The coefficients in the logistic portion of the model indicate that the 

probability of sampling fluvial fish increases for basins with 

larger drainage areas and for basins in western MA. The coefficients for the fluvial-fish-relative-abundance model (table 4) 

can be used in equation 3 to determine the effect of changes 

in flow alteration on fluvial-fish relative abundance. Keeping 

all other variables static, a unit increase in impervious surface is associated with a 5.5-percent decrease in fluvial-fish 

relative abundance. Keeping all other variables static, a unit 

increase in percent alteration of August median streamflow is 

associated with a 0.4-percent decrease in fluvial-fish relative 

abundance for net-depleted or for net-surcharged streams. The 

equation also included a variable (DEPLAUG) that indicates 

there is a difference in the relative abundance of fluvial fish 

between streams that have net-depleted and those that have 

net-surcharged conditions. Streams with net-depleted conditions have about 55-percent fewer fish than streams with netsurcharged stream conditions. 

The association between fluvial-fish relative abundance 

and flow alteration is depicted in figure 18 for net-depleted and 

Figure 17. Generalized linear model output for relations among 

fluvial-fish species richness, percent impervious cover, and 

channel slope, in selected Massachusetts streams, 1998–2008.

0

100

200

300

400

500

600

700

800

0 50 100 150 200 250 300

Relative abundance of fluvial fish, in count per hour 

Percent August flow alteration

Net-depleted stream

EXPLANATION

Net-surcharged stream

Figure 18. Generalized linear model output for relations between 

fluvial-fish relative abundance and percent alteration of August 

median flow for selected net-depleted and net-surcharged 

streams in Massachusetts, 1998–2008.

0 20 40 60 80 100

0

2

4

6

8

10

Number of fluvial fish species

Percent imprevious cover

0

1

2

3

CHANNEL SLOPE, IN PERCENT

EXPLANATION
30 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

net-surcharged streams under median August conditions. For 

illustration, these relations were determined with the fluvialfish relative-abundance equation, using the median values 

from the dataset for the environmental characteristics, and setting the percent impervious cover value to zero.

Brook Trout Relative-Abundance Model

Brook trout relative abundance was related to impervious cover, channel slope, Outlet X, percent area of sand and 

gravel, open water, and wetland. The count part of the equation indicates that brook trout relative abundance decreases 

with increasing drainage area. Coefficients from the logistic 

portion of the equation indicate that brook trout are more 

likely to occupy basins with a high percent area of sand and 

gravel, low percent area of open water, low percent area of 

wetland, high channel slopes, low percent impervious cover, 

and basins in western Massachusetts (table 4). The coefficients 

of the logistic part of the brook trout relative-abundance equation indicate that increasing impervious cover decreases the 

probability of brook trout in a stream. 

Although the brook trout equation did not contain a 

flow-alteration variable, this does not necessarily indicate 

that brook trout are unaffected by flow alterations. Three 

other variables in the brook trout equation—percent sand 

and gravel, percent open water, and percent impervious 

cover—can each be considered to encompass a streamflow 

aspect. For example, increases in impervious cover have 

been demonstrated to be associated with increased flashiness 

and increased temperatures, and increases in impoundments 

(percent open water) may also result in flow alterations and 

increased temperatures. The percent area of sand and gravel 

tends to be positively correlated with baseflow; the presence 

of sand and gravel in the brook trout equation supports the 

assumption that high baseflows and associated cool stream 

temperatures are important factors for brook trout. The association between brook trout relative abundance and impervious cover is depicted in figure 19 for different percent areas 

of sand and gravel. These relations were determined with the 

brook trout relative-abundance equation, using the median values from the dataset for the environmental characteristics. The 

relations shown on the plot indicate that brook trout relative 

abundance does not initially decline as quickly (in response to 

increased impervious cover) in basins with a high percent area 

of sand and gravel as it does in basins with a low percent area 

of sand and gravel, indicating the importance of maintaining 

groundwater discharge to support cold-water fish communities 

in streams in developing areas.

Model Validation and Limitations

Predicted values from a remove-one cross validation 

were compared to observed values for each fitted GLM 

(table 5). Correlation coefficients for the fitted models ranged 

from 0.42 to 0.65, indicating a low-to-moderate correlation 

between observed and predicted values of fluvial-fish relative 

abundance and species richness. These values were within 

reported ranges for ecological models (Potts and Elith, 2006; 

Pearce and Ferrier, 2001). The equations for fluvial-fish species richness and fluvial-fish relative abundance had the best 

fit, with correlation coefficients of 0.65 and 0.54, respectively. 

In addition, for the fluvial-fish species-richness model, the 

intercept of the linear regression line between observed and 

predicted values was not significantly different from 0, and 

the slope was not different from 1, indicating that predictions 

from the fluvial-fish-species-richness equation are relatively 

unbiased. The fluvial-fish relative-abundance model had a 

lower proportion of the variability explained than the fluvialfish species-richness model, and a plot of the model-predicted 

versus observed data indicated that the fluvial-fish relativeabundance equation slightly overpredicts the relative abundance of fluvial fish for sites with high abundance. All three of 

the models were significant at the 0.05 level compared to the 

null model (intercept only). 

Statistical models of species abundance often fail to 

produce consistently reliable predictions (Pearce and Ferrier, 

2001). The measures of model validation in table 5 indicate 

that the models in this study have a low-to-moderate predictive capability, although they are within the range of reported 

values for ecological models (Pearce and Ferrier, 2001; Potts 

and Elith, 2006; Meador and Carlisle, 2009; Roy and others, 

2009; Snelder and Lamouroux, 2010). Poor model performance may be attributed to many causes, including unmeasured factors, the choice of environmental characteristics 

and anthropogenic covariates tested, location of sample sites 

Figure 19. Generalized linear model output for relations among 

brook trout relative abundance, percent impervious cover, and 

percent area sand and gravel, in selected Massachusetts 

streams, 1998–2008.

0

30

60

90

SAND AND GRAVEL AREA, IN PERCENT

EXPLANATION

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Percent impervious cover

Relative abundance of brook trout, in count per hour
Relations Among Flow Alteration, Impervious Cover, and Fish Communities 31

relative to alterations, errors in determining environmental 

predictors from a GIS, fish-capture efficiency, and the use 

of modeled rather than measured flow data. For example, 

although there may be significant relations between fish 

abundance and species richness and basin-scale characteristics 

such as drainage area and percent wetland, fish do not occupy 

an entire basin with equal frequency. Use of data concerning 

local-scale variables, such as site-specific habitat, streamflow, and stream characteristics, may help to improve model 

performance. Without accounting for many other factors that 

influence fish communities and that were unmeasured for this 

study, such as stream temperature, water quality, and site-specific streamflow measurements, it may be difficult to develop 

equations that have substantially higher model performance. 

Several issues need to be addressed to assist with interpretation of the models. The first is that fish-response variables 

do not necessarily drop to zero when simulated August median 

flows reach 100-percent depletion. One confounding factor 

is that fish-community sampling does not take place in dry 

streambeds, and so one extreme end of the spectrum of environmental alteration is missing from the dataset. A corollary 

to this is that streams were sampled during a period, month, 

or year when the flow was not zero. In addition, the condition could arise where the simulated SYE streamflows, which 

reflect overall water use, may not necessarily represent instantaneous streamflows. The SYE streamflows reflect daily flows 

modeled over a 45-year period, using water-use values that are 

determined from annual water-use data that is averaged over 

a 5-year period, disaggregated into monthly water use by use 

of a simulated demand curve, and further disaggregated into 

daily water use (Weiskel and others, 2010). In contrast, the 

fish-sample information came from a single sample collected 

in a single day. In the model, 100-percent depletion of August 

median flow would only correspond to dry streambeds for 

times when the median (or less) flow was present in the stream 

channel. If flows are higher than the August median, water 

withdrawals would still leave some (reduced) streamflow. 

Although fluvial-fish relative abundance does not, on average, 

drop to zero for simulated 100-percent depletion of August 

median flow, the likelihood of zero values for fluvial fish does 

increase. For example, the 100 samples with the lowest August 

depletion included only 6 zero values for fluvial fish relative abundance, and the 100 samples with the highest August 

alteration included 24 zero values. 

A second issue to consider when interpreting the models 

is that the range over which a fish variable responds should be 

considered the sensitivity of this variable to the explanatory 

variables. For example, if the fluvial-fish relative abundance at 

a given level of watershed size, wetland area, slope, impervious cover and range of August median flow depletion ranges 

from a relative abundance of 275 to 125, the variable is only 

responsive over 150 units. In other words, an alteration of 

150 units of fluvial-fish relative abundance in this example 

would be equivalent to 100 percent of the response.

 Each of the GLM equations do not necessarily contain 

all of the anthropogenic-stressor variables that were tested. 

For example, the fluvial-fish species-richness and brook trout 

relative-abundance models do not contain flow alteration 

variables, and the fluvial-fish species-richness and fluvialfish relative-abundance models do not contain a measure of 

impoundments or dam density. Although this indicates that for 

this dataset, a strong relation between these explanatory variables and the mean values for the fish community measures 

was not detected, it does not necessarily indicate that there is 

no relation between these factors and fish communities. For 

some variables, the quantile regression plots indicate relations 

between fish community measures and explanatory variables 

that were not depicted by the GLM equations. For example 

figure 10B indicates a declining relation between fluvial-fish 

species richness and percent alteration of August median flow, 

and figure 16A indicates a declining relation between the 

relative abundance of fluvial fish and the percent area of open 

water. 

Fish-Community Response to Flow Alteration and 

Impervious Cover

The multivariable relations between any two explanatory variables and a fish-response variable can be examined 

by generating a plot from the GLM equations showing a 

series of lines (or family of curves). Figures 20–22 (back of 

report) show families of curves for each of the three GLM 

equations. In these figures, fish response is plotted against the 

anthropogenic-alteration variables that are in the various equations, such as impervious cover or flow alteration. By varying 

a second, environmental characteristic variable in the equation 

in conjunction with the stressor variable, a family of curves 

Figure 5. Measures of goodness of fit for comparisons of observed and predicted values for generalized linear model equations for 

fluvial-fish richness, fluvial-fish relative abundance, and brook trout relative abundance.

[Pearson’s r, Pearson’s correlation coefficient; CI, confidence interval]

Model Pearson’s r intercept 95-percent CI 

for intercept Slope 95-percent CIfor slope

Fluvial-fish relative abundance 0.54 33.07 22.63 to 43.49 0.55 0.48 to 0.61

Fluvial-fish richness 0.65 0.00 -0.28 to 0.28 1 0.92 to 1.08

Brook trout relative abundance 0.42 -3.78 -11.20 to 3.63 1.22 1.02 to 1.40
32 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

can be drawn depicting the fish-metric response. For example, 

figures 21A-D show families of curves indicating the response 

of the relative abundance of fluvial fish to impervious cover, 

using the median values from the dataset for the environmental characteristics in the equation. For the fluvial-fish relativeabundance equation, a unit increase in impervious surface is 

associated with a 5.5-percent decrease in fluvial-fish relative 

abundance, keeping other variables constant. This association is held across all values of other explanatory variables. 

However, the maximum expected value for fluvial-fish relative 

abundance decreases when the channel slope or percent wetland is increased, producing a slightly different curve. Other 

variables on the fluvial-fish relative-abundance plots indicate 

less of a response. For example, a unit increase in drainage 

area does not result in large changes in the relative abundance 

of fluvial fish. 

The GLM equations could be used to develop families of 

curves for any characteristics for which appropriate information is available and used to illustrate the relative response 

of fluvial-fish species richness, and the relative abundance of 

fluvial fish and brook trout, to alteration while setting different 

initial expectations using environmental characteristics. For 

example, the equations could be used to assess the drainage 

area-size classes or gradient classes of various classification 

frameworks, such as the Northeast Aquatic Habitat Classification (Gawler and others, 2008; Olivero and others, 2008), 

for the particular variables that are in the equations. Another 

potential application could be to apply the regression equation statewide, for example, to each of the 1,429 sub basins 

delineated in the Massachusetts Water Indicators project 

(Weiskel and others, 2010). All variables for the GLM equations are available from the Massachusetts Water Indicators 

project, with the exception of local channel slope, which could 

be calculated. Once the equation has been run with the existing conditions (current impervious cover and flow alteration), 

expected reference conditions could be developed by setting the impervious cover and flow alteration metrics to zero 

or some least-altered value, and holding all other variables 

constant. 

The GLM model predictions represent the mean response 

at a given site. Stochastic processes make it difficult to predict 

abundance at a site with a high degree of precision, however. 

For a given reach, there is a high degree of temporal variability in abundance data. For any application of the models it is 

important to note that the various models only account for 42 

to 65 percent of the variability, as measured by the correlation 

between observed and predicted values, and particular caution 

is needed when extrapolating to the extremes of any one variable. For example, because very few fish-community measurements were made at sites having zero or over 20 percent 

impervious cover, the model is less robust at these ends of the 

impervious-cover spectrum. Consequently, these models are 

best employed to compare fish-community response among 

a set of sites or to look at the change in the mean response 

associated with a change in a particular covariate. Although 

the predictive ability is low to moderate in these models, the 

relations characterized by the coefficients in the model are 

highly significant. In a model with relatively little bias, these 

relations can be used to examine how fish-response variables 

change along a gradient of values for a particular explanatory 

variable.

Suggestions for Further Study 

The explanatory power of the GLM models can potentially be improved by identifying additional attributes to test 

while developing new equations. For example, the damdensity measure used to represent the effect of impoundments 

was determined not to be a significant variable for this dataset, 

even though research has indicated a link to this variable in the 

literature (Wenger and others, 2008). Although dam density 

in a watershed is easy to calculate and might be highly correlated to pooled fisheries information (for example, all the 

fish sites in a watershed for which dam density is calculated), 

other measures, such as counts of dams, distance to a dam, 

or length of river free of dams, may be better correlated to 

changes in fish-response variables at a given site (Wang and 

others, 2010). Stream temperature has also been determined to 

be a important variable for determining fish distribution and 

abundance (Zorn and others, 2009). These additional variables 

could be tested.

The diversity of the responses of individual fluvial-fish 

species to flow alteration and impervious cover highlights 

the importance of a species-by-species analysis in addition 

to evaluating fish-community metrics. Though a number of 

species can be classified in a similar fashion (for example, 

fluvial), this does not necessarily indicate that each species 

will respond in the same way to environmental variables or 

anthropogenic stresses. An a priori classification of fish, such 

as fluvial specialist or fluvial dependent, although intuitive, 

might homogenize responses and reduce the ability to detect 

the magnitude and direction of response. Assignments of 

each species to the habitat-use classifications used in this 

report were made on the basis of life-history traits and best 

professional judgment (Bain and Meixler, 2008; Kashiwagi 

and Richards, 2009). Additional tools like Threshold Indicator Taxa ANalysis (TITAN) (Baker and King, 2010; King 

and Baker, 2010) could be used to determine the nature of 

response of each species to various stressor gradients. TITAN 

analyses describe how individual species respond along a 

particular gradient (for example, impervious cover) and allow 

direct comparisons of the direction and extent of the response. 

Species that respond similarly could potentially be combined 

into community classes by grouping species that respond similarly (increasers or decreasers) to different stressor variables. 
Summary 33

Summary

Fish-sampling data for 756 Massachusetts Fisheries and 

Wildlife fish-sampling sites were used to determine a set of 

fish-community and indicator-species variables. A review of 

the literature was used to select a set of fish-community variables thought to be responsive to flow alteration, along with a 

set of environmental and anthropogenic variables considered 

to be important for determining fish abundance and distribution. Fish-community metrics selected for testing as response 

variables include relative abundance, species richness, and 

percent of fluvial fish and cold-water fish. Individual fishspecies-response variables tested included relative abundance 

of brook trout, blacknose dace, fallfish, redfin pickerel, and 

white sucker. Contributing areas to the fish-sampling sites 

were determined using a Geographic Information System 

(GIS), and a selected set of environmental characteristics and 

anthropogenic-alteration variables were determined for each 

fish-sampling site for use as explanatory variables. Environmental characteristics tested included basin slope, channel 

slope, elevation, percent sand and gravel, percent wetland, 

percent open water, and site location. Anthropogenic variables tested included flow alteration, impervious cover, and 

dam density. Flow-alteration metrics used in the study were 

determined from streamflows simulated for each fish-sampling 

site using the USGS Sustainable Yield Estimator (Archfield 

and others, 2010). Indicators of flow alteration tested were 

consistent with those used for the USGS Massachusetts Water 

Indicators report (Weiskel and others, 2010), and included 

percent alteration of August median flow, water use intensity, 

and the withdrawal and return-flow fraction of streamflow. 

Bivariate scatter plots of many relations between species and 

anthropogenic alterations have a wedge shape, indicating a 

heterogeneous variance and a strong right skew. The declining upper edge of a wedge-shaped relation can indicate that 

an explanatory variable can act as a constraint on organisms. 

Quantile regression was used to fit 90th regression quantiles 

to characterize this upper edge for selected fish-response 

variables. Results of quantile regression indicate that flow 

alteration and impervious cover are negatively associated with 

both fluvial-fish relative abundance and fluvial-fish species 

richness and that percent area of open water is negatively associated with fluvial-fish relative abundance. The strength of the 

response varied by species.

Generalized linear models (GLMs) were used to relate 

a suite of explanatory variables to the selected fish-response 

variables. GLMs are the appropriate analytical tool for 

non-normally distributed data, count data, and datasets with 

large numbers of zero values. Numerous models were tested. 

The three strongest models included those for fluvial-fish 

species richness, fluvial-fish relative abundance, and brook 

trout relative abundance. The variables in the equations were 

demonstrated to be significant (p < 0.05, with most < 0.01). 

The amount of variability in fish-response variables that could 

be attributed to anthropogenic alterations and environmental 

characteristics by the equations was low to moderate. Correlation coefficients between observed and predicted values of the 

fitted models range from 0.42 to 0.65, but these values are in 

line with those reported in the literature for similar studies.

Impervious cover and flow alteration both were demonstrated to influence fish communities, and the equations were 

used to quantify the change in fish-community metrics associated with a unit change in flow alteration and imperviousness. 

The fluvial-fish-species-richness equation indicated that, keeping all other variables the same, a unit increase in impervious 

surface is associated with a 2.5-percent decrease in fluvial-fish 

species richness. The fluvial-fish relative-abundance equation indicated that, keeping all other variables the same, a unit 

increase in percent alteration of August median streamflow in 

net-depleted streams or net-surcharged streams is associated 

with a 0.4-percent decrease in relative abundance of fluvial 

fish. Streams with net-depleted conditions have about 55-percent fewer fish than streams with net-surcharged stream conditions. Keeping all other variables the same, a unit increase in 

impervious surface is associated with a 5.5-percent decrease in 

fluvial-fish relative abundance. The brook trout relative-abundance equation indicated that an increase in impervious cover 

decreases the probability of brook trout presence in a stream. 

Brook trout relative abundance does not initially decline as 

quickly (in response to increased impervious cover) in basins 

with a high percent area of sand and gravel as it does in basins 

with a low percent area of sand and gravel, indicating the 

importance of maintaining groundwater discharge to support 

cold-water fish communities in streams in developing areas.

The GLM equations were used to illustrate multivariable 

relations between fish-response variables and explanatory variables by developing a family of curves for the model equations 

by changing the value for one variable and holding all other 

variables constant. The relations and equations can be used as 

a tool to help assess the potential fish responses to different 

water- and land-management decisions for small- to mediumsized streams in Massachusetts. The models can be used to 

illustrate statewide relations between fish-community metrics 

and anthropogenic and environmental variables. 
34 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

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40 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

EXPLANATION

OUTLET X

0

2

4

6

8

10

0 20 40 60 80 100

Western Massachusetts

Central Massachusetts

Eastern Massachusetts

Percent impervious cover

0

1

2

3

0

2

4

6

8

10

0 20 40 60 80 100

Percent impervious cover

CHANNEL SLOPE, IN PERCENT

EXPLANATION

Number of fluvial fish species

Number of fluvial fish species

0

2

4

6

8

10

0 20 40 60 80 100

Percent impervious cover

EXPLANATION

WETLAND AREA, IN PERCENT

0

10

20

30

Number of fluvial fish species

0

2

4

6

8

10

0 20 40 60 80 100

2 

DRAINAGE AREA, IN SQUARE MILES

5 

200 

Percent impervious cover

Number of fluvial fish species

EXPLANATION

C D

A B

Figure 20. Generalized linear model output for relations among fluvial-fish species richness, percent impervious cover, and (A) 

drainage area, (B) wetland area, (C) channel slope, and (D) outlet X for selected Massachusetts streams, 1998–2008.
 41

0

200

400

600

800

1,000

1,200

0 100 200 300

Percent net August surcharge

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

800

1,000

1,200

0 20 40 60 80 100

Percent net August depletion

Relative abundance of fluvial fish,

in count per hour

EXPLANATION

2

100

200

DRAINAGE AREA, IN SQUARE MILES

EXPLANATION

2

100

200

DRAINAGE AREA, IN SQUARE MILES

0

200

400

600

800

1,200

1,000

0 20 40 60 80 100

Western Massachusetts

Central Massachusetts

Eastern Massachusetts

EXPLANATION

OUTLET X

Percent impervious cover

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

800

1,000

1,200

0 20 40 60 80 100

0

WETLAND AREA, IN PERCENT

EXPLANATION

10

20

30

Percent impervious cover

Percent impervious cover Percent impervious cover

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

800

1,000

1,200

0 20 40 60 80 100

0

CHANNEL SLOPE, IN PERCENT

EXPLANATION

1

2

3

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

800

1,000

1,200

0 20 40 60 80 100

EXPLANATION

2

5

200

DRAINAGE AREA, IN SQUARE MILES

Relative abundance of fluvial fish,

in count per hour

D C

A B

E F

Figure 21. Generalized linear model output for relations among relative abundance of fluvial fish, percent impervious cover, or 

percent net August depletion or surcharge, and (A) drainage area, (B) channel slope, (C) wetland area, (D) outlet X, (E) drainage area, 

(F) drainage area, (G) channel slope, (H) channel slope, (I) wetland area, (J) wetland area, (K) outlet X, and (L) outlet X for selectd 

Massachusetts streams, 1998–2008.
42 Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts

0

200

400

600

800

1,000

1,200

0 100 200 300

Western Massachusetts

Central Massachusetts

Eastern Massachusetts

EXPLANATION

OUTLET X

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

800

1,000

1,200

0 20 40 60 80 100

Percent net August depletion Percent net August surcharge

Western Massachusetts

Central Massachusetts

Eastern Massachusetts

EXPLANATION

OUTLET X

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

 800

1,000

1,200

0 100 200 300

WETLAND AREA, IN PERCENT

EXPLANATION

0

10

20

30

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

800

1,000

1,200

0 20 40 60 80 100

WETLAND AREA, IN PERCENT

EXPLANATION

0

10

20

30

Relative abundance of fluvial fish,

in count per hour

0

200

400

600

800

1,000

1,200

0 100 200 300

0

CHANNEL SLOPE, IN PERCENT

1

2

3

EXPLANATION

Relative abundance of fluvial fish,

in count per hour

0

200

400

400

800

1,000

1,200

0

CHANNEL SLOPE, IN PERCENT

1

2

3

EXPLANATION

0 20 40 60 80 100

I J

H G

K L

Percent net August depletion Percent net August surcharge

Percent net August depletion Percent net August surcharge

Relative abundance of fluvial fish,

in count per hour

Figure 21. Generalized linear model output for relations among relative abundance of fluvial fish, percent impervious cover, or 

percent net August depletion or surcharge, and (A) drainage area, (B) channel slope, (C) wetland area, (D) outlet X, (E) drainage area, 

(F) drainage area, (G) channel slope, (H) channel slope, (I) wetland area, (J) wetland area, (K) outlet X, and (L) outlet X for selected 

Massachusetts streams, 1998–2008.—Continued.
 43

0

10

20

30

40

50

60

0 20 40 60 80 100

0

1

2

3

Percent impervious cover

EXPLANATION

CHANNEL SLOPE, IN PERCENT

0

10

20

30

40

50

60

0 20 40 60 80 100

Percent impervious cover

EXPLANATION

OUTLET X

Western Massachusetts

Central Massachusetts

Eastern Massachusetts

0

10

20

30

EXPLANATION

WETLAND AREA, IN PERCENT

0

10

20

30

40

50

60

0 20 40 60 80 100

0

5

10

15

0

10

20

30

40

50

60

0 20 40 60 80 100

EXPLANATION

OPEN WATER, IN PERCENT

0

30

60

90

0

10

20

30

40

50

60

0 20 40 60 80 100

EXPLANATION

DRAINAGE AREA, IN SQUARE MILES SAND AND GRAVEL, IN PERCENT

2

10

20

30

0

10

20

30

40

50

60

0 20 40 60 80 100

EXPLANATION

Relative abundance of brook trout,

in count per hour

C D

E

A B

F

Relative abundance of brook trout,

in count per hour

Relative abundance of brook trout,

in count per hour

Relative abundance of brook trout,

in count per hour

Relative abundance of brook trout,

in count per hour

Relative abundance of brook trout,

in count per hour

Figure 22. Generalized linear model output for relations among relative abundance of brook trout, percent impervious cover, and (A) 

drainage area, (B) sand and gravel, (C) open water, (D) wetland area, (E) outlet X, and (F) channel slope for selected Massachusetts 

streams, 1998–2008.
Prepared by the West Trenton Publishing Service 

Center. For more information concerning the 

research in this report, contact: 

Director 

USGS Massachusetts-Rhode Island 

Water Science Center 10 Bearfoot Road Northborough, 

MA 01532

or visit our Web site at:

http://ma.water.usgs.gov
Armstrong and others—Preliminary Assessment of Factors Influencing Riverine Fish Communities in Massachusetts—Open-File Report 2010–1139