U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2010–5079

Prepared in cooperation with the

New Jersey Pinelands Commission

Evaluating Effects of Potential Changes in Streamflow 

Regime on Fish and Aquatic-Invertebrate Assemblages 

in the New Jersey Pinelands

NEW

JERSEY

Pinelands

Flow-ecology response models

Framework Model

Top

E

Streamgaging

Aquatic-invertebrate 

sampling

Electrofishing

Assemblage characterization

Hydrologic variability

Seining

Evaluating Effects of Potential Changes 

in Streamflow Regime on Fish and 

Aquatic-Invertebrate Assemblages 

in the New Jersey Pinelands

By Jonathan G. Kennen and Melissa L. Riskin

Prepared in cooperation with the New Jersey Pinelands Commission

Scientific Investigations Report 2010–5079

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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reproduce any copyrighted materials contained within this report.

Suggested citation:

Kennen, J.G., and Riskin, M.L., 2010, Evaluating effects of potential changes in streamflow regime on fish and 

aquatic-invertebrate assemblages in the New Jersey Pinelands: U.S. Geological Survey Scientific Investigations 

Report 2010–5079, 34 p.
iii

Contents

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

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

Purpose and Scope ..............................................................................................................................3

Description of Study Area ...................................................................................................................3

Data Collection and Analysis.......................................................................................................................3

Site Selection.........................................................................................................................................3

Fish-Assemblage Sampling.................................................................................................................6

Aquatic-Invertebrate Assemblage Sampling...................................................................................6

Water-Quality Monitoring....................................................................................................................6

Habitat Assessment..............................................................................................................................7

Hydrologic Assessment.......................................................................................................................7

Cross-Section Calculations........................................................................................................7

Methods of Validation .................................................................................................................7

Hydrologic Attributes ..................................................................................................................8

Hydroecological Integrity Assessment Process ....................................................................8

Analytical Methods........................................................................................................................................8

Sampling Results..........................................................................................................................................10

Fish ..................................................................................................................................................10

Aquatic Invertebrates ........................................................................................................................12

Water Quality.......................................................................................................................................12

Effects of Potential Changes in Streamflow Regime on Fish and Aquatic-Invertebrate 

Assemblages ..................................................................................................................................16

Fish-Species Response......................................................................................................................16

Ordination Results .....................................................................................................................16

Land Use, Water Chemistry, Hydrology, and other Ancillary Data....................................16

Aquatic-Invertebrate Response.......................................................................................................16

Ordination Results .....................................................................................................................16

Land Use, Water Chemistry, Hydrology, and other Ancillary Data....................................18

Flow-Ecology Response Relations...................................................................................................18

Implications of Ecologically Relevant Flow Measures..........................................................................26

Summary........................................................................................................................................................28

Acknowledgments.......................................................................................................................................29

References Cited..........................................................................................................................................29

.........
iv

Figures

1. Map showing location of Pinelands biological assessment study areas, Atlantic, 

Burlington, and Camden Counties, New Jersey......................................................................4

2–6. Graphs showing—

2. Regression relation for fish ordination axis I scores between high- and 

low-flow sampling periods at 43 New Jersey Pinelands stream reaches...............11

3. Regression relation between MA1 and the observed mean annual daily flow 

at 15 New Jersey Pinelands stream sites .....................................................................20

4. Regression relation between MA1 and mean dissolved-oxygen concentration 

at 15 New Jersey Pinelands stream sites .....................................................................21

5. Invertebrate-assemblage flow-ecology response relations between the 

mean of the daily mean flow values for the entire flow record and 

(A) INTOL_ABUNp, (B) EPT_CH, (C) RICH, and (D) ABUNTOL ...................................21

6. Fish-assemblage flow-ecology response relations between the mean of the 

daily mean flow values for the entire flow record and (A) CPSPRich, 

(B) MM, and (C) R_MIGRA...............................................................................................22

Tables

1. Location and general watershed characteristics of streams used in analysis of 

fish and aquatic-invertebrate assemblages, New Jersey Pinelands..................................5

2. Taxonomic list and biogeographical classification of fish species collected 

in 43 New Jersey Pinelands stream reaches ........................................................................10

3. Relative abundance and frequency of occurrence of fish species collected 

at 43 New Jersey Pinelands stream reaches ........................................................................11

4. Relative abundance and frequency of occurrence of aquatic-invertebrate taxa 

for samples collected at 43 New Jersey Pinelands stream reaches ................................12

5. Median water-quality properties measured bimonthly at 15 New Jersey 

Pinelands stream sites...............................................................................................................15

6. Land-use, water-chemistry, and stream-reach characteristics that are 

significantly correlated with fish axis I scores for New Jersey Pinelands 

stream reaches ...........................................................................................................................17

7. Multiple-regression model relating fish NMS axis I scores to basin 

characteristics for New Jersey Pinelands stream reaches ...............................................17

8. Reduced set of low- and annual-flow hydrologic variables that met the screening

criteria and represent the hydrologic basis for developing fish-assemblage

flow-ecology response models for New Jersey Pinelands stream reaches ...................17

9. Land-use, water-chemistry and stream-reach characteristics that are 

significantly correlated with aquatic-invertebrate axis I scores for New Jersey 

Pinelands stream reaches ........................................................................................................18

10. Multiple-regression model relating invertebrate NMS axis I scores to basin

characteristics for New Jersey Pinelands stream reaches ...............................................18

11. Reduced set of low- and annual-flow hydrologic variables that met the screening

criteria and represent the hydrologic basis for developing invertebrateassemblage flow-ecology response models .........................................................................19
v

12. Regression relations (R-squared values) between mean monthly flow variables 

and the mean of all daily flows for the entire flow record (MA1) and drainage 

area for New Jersey Pinelands stream reaches ..................................................................19

13. Aquatic-invertebrate assemblage metrics that were significantly correlated with

the reduced set of hydrologic measures................................................................................24

14. Fish-assemblage metrics, species, and species traits that are strongly correlated

with the reduced set of hydrologic measures .......................................................................25

Conversion Factors and Datums

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

micrometer (μm) 0.00003937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2) 247.1 acre

square meter (m2) 10.76 square foot (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 0.2642 gallon (gal)

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

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

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

Altitude, as used in this report, refers to distance above the vertical datum.

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 

25°C).

Concentrations of chemical constituents in water are given in either milligrams per liter (mg/L) 

or micrograms per liter (μg/L).
vi

Abbreviations and Symbols Used in this Report

Abbreviation Description

ANOSIM Analysis of Similarities

DO Dissolved Oxygen

ELOHA Ecological Limits of Hydrologic Alteration

ERHIs Ecologically Relevant Hydrologic Indices

GIS Geographic Information System

HIP Hydroecological Integrity Assessment Process

HIT Hydrologic Indices Tool

IDAS Invertebrate Data Analysis System

NMS Non-metric Multidimensional Scaling

NWIS National Water Information System

PCA Principal Components Analysis

PRESS Predicted Residual Sum of Squares

SC Specific Conductance

USGS U.S. Geological Survey

VIF Variance Inflation Factor

> Greater than

< Less than

≥ Greater than or equal to

≤ Less than or equal to
Evaluating Effects of Potential Changes in Streamflow 

Regime on Fish and Aquatic-Invertebrate Assemblages 

in the New Jersey Pinelands

By Jonathan G. Kennen and Melissa L. Riskin

Abstract

Changes in water demand associated with population 

growth and changes in land-use practices in the Pinelands 

region of southern New Jersey will have a direct effect on 

stream hydrology. The most pronounced and measurable 

hydrologic effect is likely to be flow reductions associated 

with increasing water extraction. Because water-supply needs 

will continue to grow along with population in the Pinelands 

area, the goal of maintaining a sustainable balance between the 

availability of water to protect existing aquatic assemblages 

while conserving the surficial aquifer for long-term support of 

human water use needs to be addressed.

Although many aquatic fauna have shown resilience and 

resistance to short-term changes in flows associated with water 

withdrawals, sustained effects associated with ongoing waterdevelopment processes are not well understood. In this study, 

the U.S. Geological Survey sampled forty-three 100-meterlong stream reaches during high- and low-flow periods across 

a designed hydrologic gradient ranging from small- (4.1 square 

kilometers (1.6 square miles)) to medium- (66.3 square kilometers (25.6 square miles)) sized Pinelands stream basins. 

This design, which uses basin size as a surrogate for water 

availability, provided an opportunity to evaluate the possible 

effects of potential variation in stream hydrology on fish 

and aquatic-invertebrate assemblage response in New Jersey 

Pinelands streams where future water extraction is expected 

based on known build-out scenarios. Multiple-regression 

models derived from extracted non-metric multidimensional 

scaling axis scores of fish and aquatic invertebrates indicate 

that some variability in aquatic-assemblage composition across 

the hydrologic gradient is associated with anthropogenic 

disturbance, such as urbanization, changes in stream chemistry, and concomitant changes in high-flow runoff patterns. To 

account for such underlying effects in the study models, any 

flow parameter or assemblage attribute that was found to be 

significantly correlated (|rho| ≥ 0.5000) to known anthropogenic drivers (for example, the amount of urbanization in the 

basin) was eliminated from analysis. A reduced set of low- and 

annual-flow hydrologic variables, found to be unrelated to 

anthropogenic influences, was used to develop assemblageresponse models. Many linear (monotonic) and curvilinear 

bivariate flow-ecology response models were developed for 

fish and invertebrate assemblages. For example, the duration 

and magnitude of low-flow events were significant predictors 

of invertebrate-assemblage complexity (for example, invertebrate-species richness, Plecoptera richness, and Ephemeroptera abundance); however, response models between flow 

attributes and fish-assemblage structure were, in all cases, 

more poorly fit. Annual flow variability also was important, 

especially variability across mean minimum monthly flows 

and annual mean streamflow. In general, all response models 

followed upward or downward trends that would be expected 

given hydrologic changes in Pinelands streams. This study 

demonstrates that the structural and functional response of 

aquatic assemblages of the Pinelands ecosystem resulting from 

changes in water-use practices associated with population 

growth and increased water extraction may be predictable. 

Introduction

The Kirkwood-Cohansey aquifer system underlies most 

of the New Jersey Pinelands. The interaction between groundwater and surface waters associated with this aquifer is among 

the most important factors influencing the structure and function of the Pinelands ecosystem. An important feature of the 

region is the trillions of gallons of freshwater stored within 

the area’s streams and aquifer systems (Rhodehamel, 1970). 

Demand for water from the Kirkwood-Cohansey aquifer 

system is increasing as growth and development occur within 

the Pinelands and nearby metropolitan areas. The potential 

effects of these increased water demands are a concern for 

many State and municipal agencies. In addition to allowing 

enough water to satisfy population growth and development, 

State resource-management agencies must make certain that 

increased consumptive water use does not adversely affect 

the unique habitats and ecology in the Pinelands streams, 

wetlands, and surrounding areas. This protection of Pinelands 

ecology has been mandated by Public Law 2001, chapter 165, 
2 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

which directs named partners to “assess and prepare a report 

on the key hydrologic and ecological information necessary 

to determine how the current and future water supply needs 

within the Pinelands area may be met while protecting the 

Kirkwood-Cohansey aquifer system and while avoiding any 

adverse ecological impact on the Pinelands area.”

Increasing demand for water and the concomitant alteration of hydrologic processes has been identified as one of the 

most serious threats to the ecological sustainability of aquatic 

ecosystems (Ward and Stanford, 1989; Poff and others, 1997; 

Arthington and others, 2006). This issue is exemplified by the 

strong linkages commonly established between modification 

to hydrologic processes and ecosystem function (Richter and 

others, 1996; Ward and Stanford, 1989; Bunn and Arthington, 

2002; Townsend and others, 1997). The cumulative effects of 

such hydrologic alterations markedly affect the composition 

and structure of stream assemblages (Poff and Allan, 1995; 

Clausen and Biggs, 1997; Pusey and others, 2000; Konrad and 

Booth, 2005), most commonly by modifying natural complexity and simplifying intact systems by pushing them to a point 

beyond resiliency or sustainability (Baron and others, 2002). 

Many authors have stressed that to sustain biotic integrity, natural streamflow patterns need to be protected (Arthington and 

others, 1991; Sparks, 1992; Richter and others, 1996, 1997; 

Stanford and others, 1996). The natural flow regime paradigm 

(Poff and others, 1997) further emphasizes these hydroecological linkages and indicates that maintenance of inter- and 

intra-annual hydrologic variation is essential for sustaining the 

native biodiversity of aquatic ecosystems. 

Water withdrawals for agricultural, municipal, and other 

uses have been found to reduce in-stream flows, resulting 

in a loss of habitat for stream biota (Postel, 2000; McKay 

and King, 2006). This decrease in base flow resulting from 

changes in groundwater and surface-water use greatly affects 

the suitability of a stream to sustain many types of aquatic 

fauna (Klein, 1979). Changes in hydrology, including annual 

changes in water levels, have been found to cause measurable physical and biological changes in streams (Riley, 1998). 

Modifications to the hydrologic regime can alter the composition, structure, and function of aquatic ecosystems through 

their effects on environmental and habitat characteristics, 

including water temperature, oxygen content, and water chemistry (Richter and others, 1996; Ward and Stanford, 1989). 

In addition, structural and functional components of stream 

assemblages are strongly influenced by temporal variation in 

flow processes (Biggs and others, 2005), and many aquatic 

species have evolved specific life-history traits that allow 

them to take advantage of various characteristics of the flow 

regime (Poff and others, 1997; Vieira and others, 2006; Poff 

and others, 2006). Flow components including low flows 

(base flows), annual flow pulses, seasonality of flows, and 

annual variability provide the conditions necessary to support 

natural-assemblage complexity (Stanford and others, 1996; 

Poff and others, 1997; Richter and others, 1997; Mathews, 

2005). Alterations in the timing, duration, and magnitude of 

many of these flow processes can substantially affect sensitive 

aquatic fauna that embody less resilient or robust life histories. 

For example, flow is an important factor in the life histories of 

fish, as critical life events such as spawning behavior, larval 

survival, growth patterns, and recruitment are directly linked 

to natural annual patterns in the flow regime (Humphries and 

others, 1999). Reduced flow commonly leads to decreases 

in dissolved-oxygen content, which is critical to larval fish 

survival (Robinson and others, 2004). Many fish species 

display a preference for particular types of habitat, such as 

riffles, pools, or backwater areas, all of which are affected 

by flow (Pusey and others, 1993). The richness of species 

typically increases as instream habitat complexity increases, 

with depth, velocity, and cover being the most important 

variables (Pusey and others, 1995). Associations between fish 

and their habitats are influenced by flow variability at a range 

of spatial scales. Flow variability and habitat structure can 

have a clear influence on fish assemblages within a drainage 

network as well as on a regional scale. For example, changes 

in flow variability, such as those resulting from surface-flow 

dewatering and lowered groundwater levels, have been linked 

to losses of important instream habitat (for example, riffles 

and woody debris) that can affect fish, invertebrate, and native 

mussel species distribution. Studies in streams in the eastern 

United States (for example, Armstrong and others, 2001), 

identified fluvial-dependent fish species as being sensitive to 

hydrologic stress associated with reduced streamflow; abundances of habitat generalists, in contrast, have been found to 

increase (Roy and others, 2005). Prolonged periods of low and 

reduced-magnitude flows also can reduce habitat availability 

and quality, ultimately altering trophic dynamics (Power and 

others, 1996; Poff and others, 1997). In addition, non-native 

and invasive species are commonly favored by alteration of 

the flow regime. Long-term success of an invading fish species 

is much more likely in a permanently altered aquatic system 

than in a lightly disturbed system (Moyle and Light, 1996). 

The most successful invaders will be those that are adapted to 

the modified flow regime. 

Fluctuations in base flow and repeated exposure of areas 

along stream margins have been shown to result in slow recovery and decreased production of aquatic-invertebrate assemblages (Perry and Perry, 1986; Blinn and others, 1995). Stream 

velocity, substrate complexity, and dissolved-oxygen concentration are among the most important factors influencing the 

distribution and abundance of aquatic invertebrates. These 

three factors are highly interrelated, with stream velocity 

partly determining both sediment type and dissolved-oxygen 

levels (Allan, 1995). Rivers with unstable substrates tend to be 

characterized by low species diversity and abundance, and the 

existing biota typically have life-history or behavioral characteristics that reflect frequently disturbed environments. Maitland (1994) reported greater reductions in the fauna of sandy 

compared to stony areas during flooding, apparently as a result 

of the reduced stability of the sandy-bottomed substrate. The 

nature of the effects of changing water levels and alterations in 

natural variability on aquatic insect communities depends on 

the extent, duration, and rapidity of the fluctuations, the season 
Data Collection and Analysis 3

during which drawdown occurs, the trophic status of the water 

body, and the climate of the region. Rapid lowering of the 

water level may strand large numbers of benthic organisms. 

Immobile pupae of aquatic insects are especially vulnerable to 

changes in water level. Some aquatic insects are dependent on 

natural water-level fluctuations for the completion of their life 

cycles; without these changes, various stages of development 

can be prolonged for a year or more (Ward, 1992). 

In this study, the U.S. Geological Survey (USGS) evaluated fish and aquatic-invertebrate assemblages in streams 

that are likely to experience changes in streamflow regime 

in the New Jersey Pinelands and postulated that variation in 

streamflow characteristics (that is, the variation in at least one 

of the primary components of streamflow–magnitude, frequency, duration, timing, and rate of change) associated with 

increased water extraction would explain a significant portion 

of the variation in assemblage complexity along a hydrologic gradient. In order to test this hypothesis, variation in 

streamflow processes was evaluated by (1) deriving simulated 

hydrographs for all study sites based on data records from 

continuous gaging stations, (2) identifying a subset of significant hydrologic variables (for example, duration, magnitude, 

frequency, and seasonality of low- and annual-flow events), 

(3) evaluating the linkages between hydrologic indicators and 

variation in fish and aquatic-invertebrate assemblage structure and function in the absence of underlying anthropogenic 

disturbance, and (4) developing a set of predictive linear and 

nonlinear flow-ecology response models that identify the 

most significant hydrologic attributes that, if modified through 

changes in water-use practices, most reliably account for 

variation in the richness and composition of fish and invertebrate assemblages along a hydrologic-response profile. 

Purpose and Scope

This report describes the development of flow-ecology 

response models that can be used to predict the potential 

effects of streamflow reductions on fish and aquatic-invertebrate assemblages in New Jersey Pinelands streams. The 

relevance of the models to the management of the flow modifications is discussed.

Description of Study Area

The New Jersey Pinelands is a 1.1-million-acre natural 

reserve in southern New Jersey that has diverse land use, 

including recreational, agricultural, residential, and commercial (State of New Jersey, 2007). The Pinelands lies within the 

Northern Atlantic Coastal Plain physiographic province and is 

underlain by the Kirkwood-Cohansey aquifer system (fig. 1). 

The Kirkwood-Cohansey aquifer system is the uppermost 

hydrogeologic unit of a wedge-shaped sequence of Coastal 

Plain sediments. The aquifer is composed of sand and gravel 

aquifers separated by silt and clay confining layers that thicken 

and dip from the western limit of the Coastal Plain at the Fall 

Line to the southeast, reaching a thickness of more than 1,981 

m (6,500 ft) at Cape May, New Jersey (Farlekas and others, 

1976; Zapecza, 1989). The upland vegetation of the Pine 

Barrens is dominated by pitch pine and a variety of oaks. The 

wetland communities occupy about 25 percent of the region 

and are dominated by pitch pine, Atlantic white cedar, and red 

maple. Surface waters are fed primarily from groundwater 

discharge (Rhodehamel, 1979), and typically are nutrientpoor, stained brown by humic materials and iron, and acidic 

(Morgan, 1984). 

This study focused primarily on three basins in the 

Kirkwood-Cohansey aquifer system: the Batsto River, Pump 

Branch/Albertson Brook, and Morses Mill Stream Basins. 

Also included in the study were two other basins–the Bass 

River and Mt. Misery Brook Basins (fig. 1). The sites in these 

study basins represent a range in watershed land use consistent with existing development and agricultural practices 

within the Pinelands (table 1).

Data Collection and Analysis

This study was designed to apply fish and aquaticinvertebrate sampling techniques appropriate for Atlantic 

coastal streams to the development of a series of flow-ecology 

response models relating variation in assemblage structure 

and function to variation in streamflow regimes as described 

in the Kirkwood-Cohansey Project work plan, available at 

http://www.state.nj.us/pinelands/science/kirkwood/.

Site Selection

Fifteen stream sites representing a range of discharge 

regimes and basin areas less than 70 km2 (27 mi2) in size were 

selected in the Batsto River, Morses Mill Stream, Bass River, 

Mt. Misery Brook, and Pump Branch/Albertson Brook Basins 

(fig. 1). At all but one of the eight sites selected in the Batsto 

River Basin, four 100-m-long sampling reaches (only two at 

Batsto River near Tabernacle because of similarity in stream 

properties), representing different habitats and characterized by differing flow and velocity regimes, were selected. 

At seven stream sites in the Morses Mill Stream, Bass River, 

and Pump Branch/Albertson Brook Basins, two 100-m-long 

reaches were selected; at the North Branch Mt. Misery Brook 

site, however, only one suitable 100-m-long reach was identified, for a total of 43 reaches. This hydrologic gradient (that 

is, varying stream velocity, discharge, and basin area) was 

specifically incorporated into this study because of its influence on sediment size, temperature, dissolved-oxygen content, 

and aquatic-vegetation abundance, all of which directly influence habitat diversity, complexity, and distribution of aquatic 

assemblages. All 43 study reaches (table 1) were sampled 

during low- and high-flow periods to account for variability in 

assemblage structure and function.
4 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Figure 1. Location of Pinelands biological assessment study areas, Atlantic, Burlington, and Camden Counties, New Jersey.

Kirkwood Formation

outcrop

Study basin Ecological site and number

Cohansey Sand overlying 

the Kirkwood Formation

Boundary of New Jersey

Pinelands Area

OCEAN

COUNTY

ATLANTIC

COUNTY

BURLINGTO N

COUNTY

CUMBERLAND

COUNTY

CAMDEN

COUNTY

GLOUCESTER

COUNTY

Albertson

Brook

Morses Mill

Stream

NEW JERSE Y

206

30

GARDEN

STATE

PARKWAY

ATLANTIC CITY EXPRESSWAY

322

40

72

70

50

NEW JERSEY TURNPIKE

295

73

Batsto River

North Branch

Mt. Misery

Brook

West Branch

Bass River

01466080

01410225

01410200

01409480

01409460

01409455

01409444

01409440

01409439

01409431

01409425

014094102

014094097 014094082

014094060

75° 74°45' 74°30'

39°45'

39°30'

014094102

0 5 10 MILES

0 5 10 KILOMETERS EXPLANATION

Base from U.S. Geological Survey digital line graph files, 1:24,000, 

Universal Transverse Mercator projection, Zone 18, NAD 83

NEW YORK

DELAWARE

PENNSYLVANIA

0 20 MILES

0 20 KILOMETERS

DELAWARE 

BAY

75° 74°

39°

40°

41°

ATLANTIC OCEA

N

C O A S T A L P L A I N

FALL LINE

Area of detail
Data Collection and Analysis 

5

Table 1. Location and general watershed characteristics of streams used in analysis of fish and aquatic-invertebrate assemblages, New Jersey Pinelands. [USGS, U.S. Geological Survey; km2, square kilometers; %, percent of drainage area; Ag., agriculture; latitude and longitude are given in degrees (°), minutes (’), and seconds (”)]

USGS site name

USGS 

station 

number

Report 

abbreviation

Number of 

sampling 

reaches

Latitude Longitude

Drainage 

area 

(km2)

Altitude 

(meters)

Land use

Urban 

(%)

Wetland

(%)

Forest

(%)

Ag.

(%)

Water

(%)

Barren

(%)

Batsto River near Tabernacle, 

NJ

01409425 BatTab 2 39°50’16” 74°39’47” 4.1 26.0 0.0 54.1 43.4 0.3 2.0 0.2

Muskingum Brook at Oriental, 

NJ

01409444 Musk 4 39°49’05” 74°44’16” 7.8 24.4 14.8 15.2 29.5 40.2 0.1 0.2

Pump Branch at Cedar Brook, 

NJ

0140940607 PumpCed 2 39°42’57” 74°53’54” 8.8 36.7 17.6 4.6 61.1 15.8 0.4 0.5

Penn Swamp Branch near 

Batsto, NJ

01409480 PenSwmp 4 39°41’03” 74°39’01” 12.4 7.6 0.0 21.5 78.4 0.0 0.0 0.1

West Branch Bass River near 

New Gretna, NJ

01410200 WBBass 2 39°37’27” 74°26’47” 16.8 1.5 0.4 14.7 84.1 0.0 0.8 0.0

Morses Mill Stream at Port 

Republic, NJ

01410225 Morses 2 39°30’23” 74°30’20” 21.5 1.5 18.6 20.4 49 9.5 2.0 0.5

Skit Branch at Hampton 

Furnace, NJ

01409439 SkitHam 4 39°46’01” 74°40’40” 28.0 12.2 0.2 30.4 68.5 0.2 0.7 0.0

North Branch Mt. Misery Brook 

in Byrne State Forest, NJ

01466080 MtMis 1 39°55’21” 74°28’35” 28.2 4.6 2.2 10.3 85.3 1.5 0.6 0.1

Pump Branch above Blue 

Anchor near Elm, NJ

0140940820 PumpElm 2 39°41’37 74°48’59” 28.5 24.5 23.2 7.0 46.0 21.5 1.7 0.6

Batsto River near High 

Crossing, NJ

01409431 BatHigh 4 39°46’55” 74°40’21” 35.0 15.2 3.1 36.8 46.4 12.0 1.3 0.4

Albertson Brook below RR 

bridge near Elm, NJ

0140940972 AlbElm 2 39°41’42” 74°47’49” 45.3 21.3 24.0 7.4 42.6 23.7 1.8 0.5

Springers Brook near Hampton 

Furnace, NJ

01409455 SprHam 4 39°45’19” 74°41’47” 47.4 13.7 19.1 33.2 24.3 21.8 1.2 0.4

Albertson Brook above 

Great Swamp Branch near Hammonton, NJ

0140941020 AlbHam 2 39°41’06” 74°43’19” 53.3 12.2 21.0 11.1 42.6 23.0 1.9 0.4

Springers Brook near Atsion, NJ 01409460 SprAts 4 39°44’26” 74°41’02” 55.0 10.7 16.4 37.9 25.1 18.8 1.4 0.4Batsto River near Hampton 

Furnace, NJ

01409440 BatHam 4 39°45’19” 74°40’45” 66.3 10.7 2.0 36.4 52.5 7.8 1.1 0.2
6 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Fish-Assemblage Sampling

All fish-assemblage sampling was conducted in 2005 

during high-flow (June) and low-flow (September–October) 

periods at all forty-three 100-m-long reaches in the Batsto 

River, Morses Mill Stream, Bass River, Mt. Misery Brook, and 

Pump Branch/Albertson Brook Basins. In an effort to minimize seasonal variations in flow among sites, all fish surveys 

were performed within a short time period (< 5 weeks). 

Fish surveys were performed in an upstream direction following the protocols outlined by Moulton and others (2002), 

using nylon seines and a Smithroot® Model LR-24 pulsed DC 

backpack electrofishing unit. Voltage output typically ranged 

from 150–800 at 30 hertz, depending on specific conductance. 

Specific conductance (ability of water to carry an electrical 

current) was measured prior to electrofishing to determine the 

appropriate output voltage for effective fish capture (Reynolds, 

1996). Some interior Pinelands streams have, on average, 

very low specific conductance (for example, Skit Branch at 

Hampton Furnace (01409439), 33 μS/cm) and require more 

power to effectively capture fish, whereas streams nearer the 

periphery of the Pinelands have higher specific conductance 

(for example, Muskingum Brook at Oriental (01409444), 188 

μS/cm) and typically require less power. Some modifications 

to the Moulton and others (2002) protocols were implemented 

for proper fish capture in Pinelands streams typically stained 

brown by humic materials. In some instances, these modifications included setting up blocking seines at the upstream and 

(or) downstream ends of a stream reach to prevent emigration during sampling. This procedure is necessary in some 

Coastal Plain streams where no natural barriers (for example, 

riffles, bedrock terraces, or stone dams) can be established. In 

addition, a seine was positioned downstream from the electrofishing unit to capture fish that did not exhibit galvanotaxis 

(that is, fish that avoid rather than swim voluntarily toward the 

anode) or any cryptically colored fish that eluded initial capture (for example, pirate perch and yellow bullhead). During 

the sampling process, stunned fish were netted immediately 

(6.35-mm mesh) and placed in aerated holding containers. All 

major and minor habitats (for example, riffles, runs, pools, 

debris dams, back eddies, side channels, undercut banks, 

vegetation mats) in the stream reach were sampled thoroughly. 

All fish were identified to species, counted, weighed (grams), 

measured (millimeters total length), examined externally for 

disease and anomalies, recorded, and then returned to the 

stream reach. 

Aquatic-Invertebrate Assemblage Sampling

Multihabitat aquatic-invertebrate assemblage samples 

were collected within the same 100-m-long stream reaches that 

were sampled for fish following the protocols outlined by the 

Mid-Atlantic Coastal Stream Workgroup (U.S. Environmental 

Protection Agency, 1997), which were specifically selected 

to effectively sample aquatic invertebrates in mid-Atlantic 

Coastal Plain streams (Maxted and others, 2000). The 30 study 

reaches in the Batsto River Basin were sampled in 2004 under 

high-flow (June–July) and low-flow (October–November) 

conditions and the 13 study reaches in the Morses Mill Stream, 

Bass River, Mt. Misery Brook, and Pump Branch/Albertson 

Brook Basins were sampled under both high-flow (May–June) 

and low-flow (October–November) conditions during 2005.

At each of the 100-m-long reaches, 20 D-net jab samples, 

distributed proportionally throughout targeted habitats, including undercut banks, woody snags, submerged macrophytes, 

organic debris, sand, and muck, were collected. In addition to 

the 20 jab samples, five pieces of woody debris were collected at each of the reaches. The woody debris collected were 

submerged sections of wood (branch or log) having on average 

a minimum diameter of 1 cm and colonized by aquatic organisms. They were processed separately, and then composited 

with the 20 discrete D-net samples. These composites provide 

a fully integrated and representative aquatic-invertebrate sample for each 100-m-long reach. Once the sample was collected, 

large organic and inorganic material was inspected, rinsed, 

and removed. All materials in the samples, including small 

debris and loose material, were elutriated and sieved through a 

standard brass 500-μm mesh sieve (Moulton and others, 2002). 

The remaining material was placed into a 1-L container, preserved with 95-percent ethyl alcohol, and shipped to EcoAnalysts, Inc., in Moscow, Idaho, for processing.

In the laboratory, samples were sieved and rinsed with 

tap water to remove fine sediments and excess preservative. 

A quantitative fixed-count processing method was used to 

identify and estimate the abundance of each taxon sorted in 

the samples. Samples were placed on a gridded Caton tray 

(a 250-μm mesh, rectangular, stainless steel, gridded sieve 

that nests in a plastic tray) and distributed homogeneously 

throughout the tray. Sections of the gridded Caton tray 

(5.1 cm x 5.1 cm) were chosen randomly and a 300-organism 

subsample was removed systematically, sorted into gross taxonomic categories (for example, Chironomidae, Oligochaeta, 

and all other organisms), and placed in glass vials containing 

70-percent ethanol. This approach is similar to the methods 

described by Barbour and others (1999) and Plafkin and others 

(1989) and meets the quality-assurance/quality-control elements important for accurate evaluation of taxonomic results 

used in biological assessments (Stribling and others, 2003). 

Aquatic invertebrates were identified to the lowest possible 

taxonomic level, enumerated, and entered in a digital database 

for further analysis.

Water-Quality Monitoring

Water-quality samples were collected at or near continuous-record and staff gages colocated at all fish- and invertebrate-sampling reaches using a YSI 6920 multiparameter meter 

from July 2004 through August 2006. Water-temperature, 

air-temperature, pH, specific-conductance (SC), and dissolvedoxygen (DO) measurements were made bimonthly throughout 
Data Collection and Analysis 7

the duration of the study; however, degraded road conditions 

(that is, snow and ice covering the roads) during the winter 

months occasionally prevented access to some sampling sites 

(twice at BatHam and once at AlbHam). In addition, waterquality measurements were made prior to all fish- and invertebrate-sampling events.

Habitat Assessment

Instream habitat was assessed following a modification of the habitat-sampling protocol outlined by Fitzpatrick 

and others (1998). Measurements of instream habitat were 

made at each 100-m-long sampling reach, which was divided 

into 25-m sections, and habitat characteristics were assessed 

across each of these five transects perpendicular to streamflow. 

Along each transect, channel features such as wetted width, 

water-column velocity, altitude, substrate size, dominant 

substrate, geomorphic channel unit (run, riffle, pool), riparian and canopy conditions, streambank angle, height, and 

stability, and canopy opening and riparian width and stability 

were recorded. At each transect, three point-velocity measurements were made using a pygmy or Price AA standard flow 

meter (depending on depth). Instantaneous stream discharge 

(in cubic feet per second) was acquired from a colocated 

USGS streamflow-gaging station or assessed indirectly from 

staff gages installed at the sampling reaches. Stream width, 

depth, and water-surface altitude also were measured. Canopy 

openings were measured through the use of a handheld clinometer. Angular measurements were made from mid channel to the tallest objects to the left and right of each transect. 

The left and right angles were summed and subtracted from 

180 degrees to acquire the open-canopy angle.

Instream vegetation was measured along transects established perpendicular to flow at 5-m intervals using the lineintercept method. Transects begin and end at edge-of-water. 

At each transect, the length of contiguous cover of individual 

plant species, woody cover, and areas that did not have woody 

cover or plant cover was recorded. Submerged woody cover 

consisted of branches or logs that were ≥ 10 cm in diameter. 

In some instances, instream vegetation could not be sampled 

as a result of deep water (three transects total–the uppermost 

transect on Muskingum Brook, reach 3, and the two uppermost transects on Batsto River near Hampton Furnace, reach 

3). Percentage cover was calculated by dividing the length of 

cover for a species at a transect by the total length of the transect. Tree-canopy cover was measured with a spherical densiometer in the center of each 25-m section. Four measurements 

were made in the center of each 25-m section and averaged to 

obtain a final tree-canopy cover value. 

Hydrologic Assessment

Streamflow characteristics such as annual and seasonal 

high and low flows, monthly flows, and flow variability 

are essential for understanding and predicting the effect of 

alterations in the natural flow regime on stream biota (Olden 

and Poff, 2003). Therefore, the development of hydrographs 

and measurement of stream velocity at all sampling sites was 

vital to meeting the objectives of this study. The following 

sections describe the methods used to obtain the flow information necessary for developing the hydroecological models 

presented in this report.

Cross-Section Calculations

Velocity measurements were made at all of the ecological transects using a pygmy or Price AA standard flow meter 

(depending on depth) at three points across each transect–the 

thalweg and two points on either side of the thalweg midway 

to the edge of water (see Fitzpatrick and others, 1998). Using 

these three velocity measurements, water depth, and bankheight measurements, a simple model was developed to calculate the area of water available at any transect for a specific 

gage height. Water area was calculated as a rectangle centered 

over each depth measurement. Water level was lowered by 

subtracting a predetermined incremental drop from bankfull 

depth to near base flow. Bankfull depth is equal to the height 

of the staff at the time of habitat assessment plus the measured 

bank height. The width is then recalculated along the edges 

using proportional triangles. Given that velocity (meters per 

second) is equal to the discharge (cubic meters per second) 

divided by the area (square meters), velocity could be calculated using the area from the simple model and discharge from 

the USGS stage-discharge-relation rating tables (for a given 

staff-gage height). Velocities were then calculated for each of 

the five transects within a reach and averaged across the length 

of the stream reach to characterize the velocity profile for each 

sampling reach.

Methods of Validation

Several methods of validation were used to ensure the 

model was an accurate means of characterizing the variation in 

velocity throughout the sampling reaches. The model needed 

to be validated because USGS guidelines dictate that the ideal 

standard measurement is one in which no partial stream section contains more than 5 percent of the total discharge and, on 

average, that has 25 to 30 sections measured across a stream 

transect to accurately determine area, velocity, and discharge 

(Buchanan and Somers, 1969). Because that level of detail 

was not feasible for all of the 43 sampling reaches in this 

study, it was necessary to determine whether three depth and 

velocity measurements at a given transect were adequate to 

characterize the velocity profile of a stream reach. 

First, flow calculations based on the simple model were 

validated against the “complete” standard USGS discharge 

measurements that were made at a gage height when flow 

conditions were similar to the flow conditions when habitat 

assessments were made. Standard USGS discharge measurements were made many times throughout the study 
8 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

(approximately nine times per year) to establish a gage rating 

as part of the hydrologic component of the study (White and 

others, 2006). The comparability between the simple model 

flow estimates and those derived from a standard USGS 

approach for the subset of data points compared was within an 

acceptable 5 percent of the more intensive standard measurement values. The area values calculated from the standard 

measurement also were compared to the area values from the 

three measured points used in the model; these two measurements also were highly comparable and rarely differed by 

more than 3 percent. Model output also was directly validated against velocities calculated using discharges from the 

USGS rating tables and areas from the model using the actual 

instantaneous velocities measured in the field during the 

habitat assessments. These relations also were highly comparable. Modeled discharge and velocities based on the transect 

approach used in this study consistently came within a small 

percentage (about 5 percent) of the values calculated with the 

more intense standard cross-sectional assessment method. 

Subsequently, the simple modeling method was applied to all 

sites and transects in the study.

Hydrologic Attributes

Instantaneous-streamflow measurements were made 

approximately every 6 weeks at all of the ecological sampling sites from spring 2004 through fall 2006. Typically, 

9 to 12 flow measurements provide enough information to 

establish a significant regression between flow conditions at 

a continuous-record index gaging station and those at a site 

with no continuous record of streamflow–only instantaneousflow measurements (that is, a partial-record station) (Watson 

and others, 2005). Maintenance of Variance Extension Type 1 

(MOVE1) method of analysis (Hirsch, 1982) uses daily mean 

flows from at least three proximal continuous-record index 

gaging stations either on the same stream or at nearby streams 

with similar basin characteristics and then regresses them 

against flows measured at a partial-record station (in this 

study, all ecological sampling sites with installed staff gages). 

Continuous-record index gaging stations are those with a 

minimum of 20 years of continuous discharge record (Watson 

and others, 2005). 

MOVE1 generates regression equations that summarize 

the relation between the daily mean flows at a set of proximal USGS index sites and the partial-record sites of interest. 

For this study, the index site for which flows correlated best 

with those at a specific ecological sampling site was chosen 

based on the highest average regression R-squared value 

(all R-squared values used were > 0.98). Mullica River near 

Batsto, NJ (01409400), was the index station selected for all 

ecological sampling sites in the Batsto River Basin using the 

period of record 1957–2006. Great Egg Harbor River at Folsom, NJ (01411000), was used for the Pump Branch/Albertson 

Brook Basin, Morses Mill Stream, North Branch Mt. Misery 

Brook, and West Branch Bass River Basin ecological sampling 

sites using the period of record 1925–2006. Using these index 

stations and the equations derived from MOVE1, hydrographs 

were generated for all of the ecological sampling sites for the 

study period of record, 2004–06. 

Hydroecological Integrity Assessment Process

The Hydroecological Integrity Assessment Process (HIP) 

software is used to relate alterations in hydrologic processes as 

a result of water depletion or hydrologic alteration scenarios to 

aquatic-assemblage response (Kennen and others, 2007). The 

software generates a series of ecologically relevant hydrologic 

indices (ERHIs) to help characterize and classify streamflow 

regimes that are thought to be important in shaping ecological 

processes in streams (Olden and Poff, 2003; Henriksen and 

others, 2006; Kennen and others, 2007). The HIP software 

uses USGS daily mean and peak-flow discharges from the 

National Water Information System (NWIS) databases (available at http://waterdata.usgs.gov/nwis/sw). The Hydrologic 

Index Tool (HIT), a stand-alone program that is part of the 

HIP suite of software, generates 171 hydrologic indices by 

using daily mean and peak-flow discharge data (if they are 

available). If peak-flow data are not available, then eight HIT 

indices (that is, FH11, DH22, DH23, DH24, TA3, TH3, TL3, 

and TL4; see Kennen and others (2007) for definitions of all 

171 indices) are not calculated. The 171 indices are based on 

the five primary components of the flow regime–magnitude, 

frequency, duration, timing, and rate of change (Poff and 

others, 1997). Daily hydrologic data for all of the ecological 

sampling sites from May 2004 through May 2006 were run 

through HIT and a suite of ERHIs was generated. Peak-flow 

data were not available for the sampling sites (because the 

daily mean values were generated using a regression equation), so only 163 indices were calculated. 

Analytical Methods

Variation in hydrologic, land-use, and environmental attributes and fish and aquatic-invertebrate assemblage 

structure and function was assessed using a combination of 

correlation, regression, and multivariate analyses to identify 

potential linkages among these characteristics and to determine the most statistically significant hydrologic attributes 

driving assemblage response. Fish and aquatic-invertebrate 

assemblages were analyzed on the basis of species composition. Species composition was based on the total abundance 

of selected taxa. Aquatic-invertebrate site by species matrices 

were censured by eliminating rare species that accounted for 

less than 0.01 percent of overall abundance and that were 

present in less than 2 percent of the samples by using the 

Invertebrate Data Analysis System (IDAS) (Cuffney, 2003; 

Cuffney and others, 2007). Fish matrices were not censured 

as all taxa were readily identified to species in the field and 

no rare taxa were found. IDAS was also used to derive many 

aquatic-invertebrate structural and functional metrics for use 
Analytical Methods 9

in univariate and multivariate analyses and the development of 

flow-ecology response relations (Arthington and others, 2006).

Non-metric multidimensional scaling (NMS) was used to 

evaluate variation in fish and aquatic-invertebrate assemblage 

structure and function across the hydrologic gradient. Fish and 

aquatic-invertebrate data were first standardized by total abundance and square-root transformed. The distance measure used 

was Bray Curtis, and NMS procedures were performed using 

PRIMER v6 software (Clarke and Warwick, 2001; Clarke 

and Gorley, 2006). The NMS analysis was used primarily to 

explore whether environmental attributes other than hydrology were accounting for a proportion of the variability in the 

distribution of fish and invertebrate taxa in ordination space 

(for example, Roy and others, 2003; Walters and others, 2003; 

Kennen and others, 2005). It was also essential to the modelbuilding process that hydrologic attributes and assemblage 

metrics that were directly responding to anthropogenic alteration rather than to changes in annual streamflow processes be 

removed. This procedure was accomplished by deriving MLR 

models based on the extracted synthetic factors (NMS axis I 

scores) for fish and invertebrate assemblages and determining whether a significant amount of the variance across the 

hydrologic gradient was accounted for by known indicators 

of anthropogenic disturbance (that is, the percentage of urban 

and agricultural land in a basin, pH, and specific conductance) (Zampella and Bunnell, 1998), and then by running a 

series of Spearman correlations using a statistically reduced 

subset of significant hydrologic variables against indicators 

of anthropogenic degradation. Any hydrologic attribute or 

assemblage metric that was found to be significantly correlated (|rho| ≥ 0.5000) with the percent of basin urbanization or 

agriculture or indicators of diminished water quality (that is, 

high pH or SC) was eliminated from further analysis. 

A total of 448 environmental variables (that is, hydrologic, landscape, water-quality, and physical variables associated with each stream reach) was evaluated for this study. 

Principal components analysis (PCA) (SAS Institute Inc., 

1989) in combination with collinearity assessment was used 

to isolate a subset of variables for use in response models 

that accounted for the greatest proportion of variance while 

minimizing redundancy (for example, Olden and Poff, 2003). 

Distributions of all variables were evaluated for normality 

and transformed when necessary. We conducted PCA on the 

correlation matrix and evaluated the significance of principal 

components using the broken stick method (Jackson, 1993; 

McCune and Grace, 2002). By using the correlation matrix, 

we ensured that all the environmental variables contributed 

equally to the PCA and that the contributions were scaleindependent (Legendre and Legendre, 1998). Loadings of the 

environmental variables on each significant principal component were used to identify variables that extracted dominant 

patterns of variation. A Spearman rank correlation matrix 

(SAS Institute Inc., 1989) of the reduced set of environmental 

variables was examined to eliminate any remaining redundant variables with a Spearman’s rho > 0.75. This conservative data-reduction approach helped avoid the common 

pitfalls associated with establishing significant (p ≤ 0.05) 

correlations among a large suite of environmental variables 

simply by chance and introducing interdependencies among 

multiple explanatory variables (Van Sickle, 2003; King and 

others, 2005).

We used correlation, linear, and nonlinear (for example, 

polynomial) regression analysis to relate the reduced set of 

hydrologic variables to fish and aquatic-invertebrate assemblage response. Ordination results were incorporated into 

MLR analysis by using the NMS axis I scores as the response 

variable. Stepwise MLR analysis using forward selection and 

backward elimination procedures was then used to identify 

the minimum set of explanatory variables needed to account 

for the observed variation in the response variables—that 

is, MLR models were constructed that describe the relation 

between environmental variables and the distribution of fish 

and invertebrate assemblages along the hydrologic gradient. 

Conservative entry and removal criteria (p = 0.05) were used 

to ensure high predictive power. By using multiple explanatory variables to estimate values of a response variable, errors 

in prediction were limited while still accounting for a large 

proportion of the variance in the response variable. In addition, this approach provides diagnostic tools that allow us 

to explicitly account for the dependencies between multiple 

candidate explanatory variables (Van Sickle, 2003). In combination, the data-reduction approach used in this study helped 

reduce the number of environmental and assemblage metric 

variables available for modeling from 448 to 45.

Best-fit MLR models were derived from the reduced set 

of explanatory variables. The comparative performance of 

MLR models was evaluated using the coefficient of determination (R-squared value), which is the percentage of the 

variability of the dependent variable that is explained by the 

variation of the independent variables. The R-squared value 

ranges from 0 to 1, with 1 being a perfect fit; most MLR 

modeling procedures attempt to maximize this value. Models 

also were evaluated on the basis of Variance Inflation Factor (VIF) of the component variables; a higher VIF indicates 

that a variable is more closely related to one or more other 

variables in the model than to the model itself. A standard 

VIF cut-off criterion of 10 was used for evaluating whether 

there was any undue influence of one independent variable on 

another; however, this value is considered somewhat arbitrary 

as there are no formal criteria for determining the magnitude 

at which an inflation factor actually results in poorly estimated regression coefficients (SAS Institute Inc., 1991). MLR 

models were validated using an n-1 (that is, “leave-one-out”) 

cross-validation approach using the predicted residual sum 

of squares (PRESS) procedure (Weisberg, 1985). At each 

iteration a different observation is omitted from calibration 

to generate a predicted value. At the end of this process, a 

series of n predictions is assembled from the deleted observations and is compared with the observed values to produce 

the PRESS statistic, which is a validation statistic of model 

accuracy and error. As a validation technique, PRESS tests 

how well the current model would predict each of the points in 
10 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

the dataset (in turn) if they were not included in the regression. 

Low values of PRESS generally indicate that the model is not 

overly sensitive to any single data point. PRESS is considered 

comparable to tests of independent validation (Kozak and 

Kozak, 2003) and is often recommended over the split-sample 

validation approach, which has been shown to be less reliable, 

commonly providing highly biased estimates of prediction 

accuracy and performance (Olden and Jackson, 2000). Best-fit 

models were chosen based on simplicity, R-squared value, 

and PRESS values: a significant MLR model with the lowest 

PRESS value and the highest R-squared value was considered 

the “best”–that is, the most parsimonious model.

Sampling Results

This section presents a summary of the fish- and invertebrate-assemblage samples and describes the characteristics 

of water-quality samples collected throughout the New Jersey 

Pinelands study reaches.

Fish

Fourteen native and several non-native fish species were 

collected in New Jersey Pinelands streams during this study 

(also see Zampella and Bunnell, 1998). Hastings (1984) 

categorized the native Pinelands fish as being either restrictedcharacteristic or widespread-characteristic species (table 2). In 

New Jersey, those species identified as being restricted-characteristic are mostly limited to the interior Pinelands, whereas 

the widespread-characteristic species are distributed peripherally in the Pinelands and in many other streams throughout 

New Jersey. Native Pinelands fish species are adapted to 

the shallow and slow-moving waters that typify Pinelands 

streams; however, the conditions associated with some small 

headwaters streams (low pH and DO) may also limit the distribution of some species and influence species richness. Table 2 

shows the biogeographical classification of the fish species 

collected at all sampling reaches during this study.

Fish were collected during high- and low-flow periods to 

assess interannual variability in species distribution. Twentyone different species were collected, for a total of 6,100 fish 

Table 2. Taxonomic list and biogeographical classification of fish species collected in 43 New Jersey Pinelands 

stream reaches.

[NW, native widespread; I, introduced; P, peripheral; NR, native restricted]

Common name1 Latin name1 Abbreviation Distribution2

Tessellated darter Etheostoma olmstedi TD P

Largemouth bass Micropterus salmoides LMB I

Creek chubsucker Erimyzon oblongus CCS NW

Pumpkinseed Lepomis gibbosus PS P

Banded sunfish Enneacanthus obesus BDS NR

Blackbanded sunfish Enneacanthus chaetodon BBDS NR

Bluespotted sunfish Enneacanthus gloriosus BSS NW

Mud sunfish Acantharchus pomotis MSF NR

Bluegill Lepomis macrochirus BG I

American eel Anquilla rostrata EEL NW

Pirate perch Aphredoderus sayanus PP NR

Yellow bullhead Ameiurus natalis YBH NR

Brown bullhead Ameiurus nebulosus BBH P

Swamp darter Etheostoma fusiforme SWD NR

Eastern mudminnow Umbra pygmaea MM NW

Redfin pickerel Esox americanus americanus RFP NW

Chain pickerel Esox niger CP NW

Tadpole madtom Noturus gyrinus TPM NW

Golden shiner Notemigonus crysoleucas GSH P

Channel catfish Ictalurus punctatus CCF I

Black crappie Promoxis nigromaculatus BCR I

1

Nomenclature corresponds with that presented in Nelson and others (2004).

2

Fish-species distributions are based on classifications by Hastings (1984). 
Sampling Results 11

(table 3). The most common species captured throughout the 

43 stream reaches was the eastern mudminnow (MM), which 

accounted for 31 percent of the total fish abundance; banded 

sunfish (BDS) and American eel (EEL) represented 16 and 

14 percent of the total abundance, respectively (table 3). Eight 

species (MM, chain pickerel (CP), swamp darter (SWD), tessellated darter (TD), BDS, EEL, pirate perch (PP), and yellow 

bullhead (YBH)) together accounted for more than 90 percent 

of the total abundance. Three species (black crappie, channel 

catfish, and golden shiner) were captured only once. MM and 

EEL were the most frequently encountered species (79.1 percent) (table 3). CP (76.7 percent), SWD (59.3 percent), and 

BDS (58.1 percent) were also frequently captured throughout 

the study. Little difference in species abundance was observed 

between the high- and low-flow sampling periods and a regression relation of the axis I scores between these two sampling 

periods was near unity (fig. 2). This comparison was confirmed by analysis of similarities (ANOSIM: PRIMER-E v6), 

which showed no faunal difference between the high- and 

low-flow sampling periods (Global R = 0.005; P = 0.53). As 

a result, these two datasets were pooled and all univariate and 

multivariate analyses were based on the combined high- and 

low-flow samples (n = 43). 

Table 3. Relative abundance and frequency of occurrence 

of fish species collected at 43 New Jersey Pinelands stream 

reaches. 

Common name Species Total 

abundance

Relative 

abundance 

(percent)

Frequency of 

occurrence 

(percent)

Eastern mudminnow MM 1,884 31.0 79.1

Banded sunfish BDS 997 16.0 58.1

American eel EEL 849 14.0 79.1

Tessellated darter TD 520 8.5 46.5

Swamp darter SWD 491 8.0 59.3

Chain pickerel CP 288 4.7 76.7

Pirate perch PP 270 4.4 55.8

Yellow bullhead YBH 253 4.1 54.7

Creek chubsucker CCS 128 2.1 41.9

Redfin pickerel RFP 94 1.5 46.5

Tadpole madtom TPM 80 1.3 29.1

Pumpkinseed PS 70 1.1 23.3

Mud sunfish MSF 48 0.8 34.9

Bluespotted sunfish BSS 36 0.6 20.9

Bluegill BG 32 0.5 14.0

Blackbanded sunfish BBDS 23 0.4 11.6

Brown bullhead BBH 20 0.3 14.0

Largemouth bass LMB 14 0.2 12.8

Channel catfish CCF 1 0.0 1.2

Golden shiner GS 1 0.0 1.2

Black crappie BCR 1 0.0 1.2

Total 6,100

HIGH FLOW

LOW FLOW

2.0

1.0

0

-1.0

-2.0

-2.0 -1.0 0 1.0 2.0

y = 1.0343x - 0.0007

R2 = 0.8848

Figure 2. Regression relation for fish ordination axis I scores between high- and low-flow sampling periods at 43 New Jersey 

Pinelands stream reaches. 
12 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Aquatic Invertebrates

Compilation and processing of the aquatic-invertebrate 

assemblage data through IDAS for the 43 study sites produced an analysis dataset with 125 taxa (table 4). Number 

of taxa at a site ranged from 9 to 49 (median 37), and total 

abundance ranged from 84 to 432 (median 323). Tribelos sp.

was the most abundant benthic invertebrate taxon identified 

during this study, accounting for 8.1 percent of the aquaticinvertebrate assemblage and occurring at 72 percent of the 

sampling sites (table 4). Thienemannimyia sp. and Simulim sp.

accounted for 6.9 and 6.6 percent of the total abundance, 

respectively. Other taxa that were present in significant 

quantities included Caecidotea sp. (6.4 percent), Leuctra sp.

(5.73 percent), Hydropsyche sp. (4.0 percent), Stenelmis sp.

(3.9 percent), Polypedilum sp. (3.7 percent), and Ancyronyx sp. (3.1 percent). The most frequently occurring aquatic 

invertebrates included Thienemannimyia sp. (88 percent), 

Polypedilum sp. (85 percent), Bezzia/Palpomyia sp. (79 percent), Hydropsyche sp. (78 percent), and Enchytraeidae

(76 percent) (table 4). 

Analysis of similarities (ANOSIM: PRIMER-E v6) 

confirmed that no distinct faunal differences were present 

between the high- and low-flow sampling periods (Global 

R = 0.116; P = 0.10). Therefore, these two datasets, like the 

fish data, were pooled and all further univariate and multivariate analyses were based on the pooled samples. 

Water Quality

Variation in Pinelands streamwater quality is typically 

associated with the extent of land-use alteration in a watershed 

(Zampella and others, 2007). Pinelands streams with some 

upstream basin development tend to display slightly higher pH 

and SC values than those with little altered land. In general, 

average daily water temperatures increase with the percentage 

of urbanization in the basin, and these warmer temperatures 

help to explain decreases in concentrations of DO. Median 

values were calculated for all of the water-quality properties 

at each of the sampled streams (table 5). Values of pH for all 

study streams ranged from 4.1 to 6.4 and SC ranged from 

33 to 304 μS/cm (table 5). Median water temperature and DO 

concentration ranged from 12.3 to 19.3°C and 4.3 to 9.7 mg/L, 

respectively. SC was highest at PumpCed and lowest at 

SktHam, pH was lowest at PenSwmp, and DO was lowest 

at BatTab.

Table 4. Relative abundance and frequency of occurrence of aquatic-invertebrate taxa for samples collected at 43 New Jersey 

Pinelands stream reaches.—Continued

Order Species1 Species abbreviation Total abundance

Relative 

abundance 

(percent)

Frequency of 

occurrence 

(percent)

Ephemeroptera Caenis sp. Caen_sp 26 0.09 8

Eurylophella sp. Eury_sp 332 1.21 44

Leptophlebiidae Lept_fm 315 1.14 31

Baetidae Baet_fm 2 0.01 2

Pseudocloeon sp. Pseud_sp 21 0.08 8

Heptageniidae Hept_fm 218 0.79 23

Stenonema sp. Sten_sp 419 1.52 20

Odonata Aeshnidae Aesh_fm 43 0.16 22

Boyeria sp. Boy_sp 44 0.16 24

Cordulegaster sp. Cord_sp 32 0.12 21

Gomphidae Gomp_fm 42 0.15 26

Gomphus sp. Gomp_sp 19 0.07 6

Libellulidae Lib_fm 10 0.04 10

Macromia sp. Macro_sp 33 0.12 20

Calopteryx sp. Calop_sp 157 0.57 48

Coenagrionidae Coen_fm 15 0.05 10

Argia sp. Argia_sp 89 0.32 28

Plecoptera Leuctra sp. Leuct_sp 1,578 5.73 50

Taeniopteryx sp. Taen_sp 335 1.22 30

Perlidae Perl_fm 12 0.04 7

Acroneuria sp. Acron_sp 10 0.04 7

Perlesta sp. Perlo_sp 130 0.47 19

Perlodidae Perlo_sp 10 0.04 9
Sampling Results 13

Table 4. Relative abundance and frequency of occurrence of aquatic-invertebrate taxa for samples collected at 43 New Jersey 

Pinelands stream reaches.—Continued

Order Species1 Species abbreviation Total abundance

Relative 

abundance 

(percent)

Frequency of 

occurrence 

(percent)

Hemiptera Corixidae Corix_fm 10 0.04 7

Coleoptera Hydroporinae Hydr_fm 46 0.17 23

Neoporus sp. Neop_sp 40 0.15 20

Dineutus sp. Dine_sp 242 0.88 66

Gyrinus sp. Gyrin_sp 6 0.02 7

Peltodytes sp. Pelto_sp 7 0.03 6

Ancyronyx sp. Ancy_sp 842 3.06 67

Macronychus sp. Macrn_sp 506 1.84 33

Microcylloepus sp. Micro_sp 121 0.44 15

Stenelmis sp. Stene_sp 1,074 3.90 70

Sperchopsis sp. Sper_sp 17 0.06 10

Scirtidae Scirt_fm 13 0.05 5

Megaloptera Nigronia sp. Nigro_sp 224 0.81 69

Sialis sp. Sial_sp 203 0.74 67

Diptera Chironomus sp. Chiro_sp 89 0.32 13

Cryptochironomus sp. Crypt_sp 204 0.74 55

Demicryptochironomus sp. Demi_sp 21 0.08 15

Nilothauma sp. Nilo_sp 44 0.16 19

Paracladopelma sp. Parac_sp 15 0.05 8

Paratendipes sp. Parat_sp 22 0.08 12

Phaenopsectra sp. Phaen_sp 84 0.31 21

Polypedilum sp. Polyp_sp 1,005 3.65 85

Stelechomyia sp. Stele_sp 72 0.26 29

Stenochironomus sp. Steno_sp 334 1.21 67

Tribelos sp. Tribe-sp 2,227 8.09 72

Rheotanytarsus sp. Rheo_sp 668 2.43 49

Tanytarsus sp. Tanyt_sp 175 0.64 40

Potthastia sp. Pott_sp 11 0.04 7

Brillia sp. Brill_sp 33 0.12 15

Corynoneura sp. Coryn_sp 44 0.16 22

Cricotopus sp. Crico_sp 58 0.21 13

Heterotrissocladius sp. Heter_cp 45 0.16 22

Limnophyes sp. Limno_sp 28 0.10 16

Nanocladius sp. Nano_sp 13 0.05 8

Orthocladius sp. Ortho_sp 86 0.31 36

Parachaetocladius sp. Parch_sp 88 0.32 34

Parakiefferiella sp. Parki_sp 90 0.33 22

Parametriocnemus sp. Parm_sp 60 0.22 19

Psectrocladius sp. Psec_sp 73 0.27 19

Rheocricotopus sp. Rheoc_sp 95 0.35 38

Stilocladius sp. Stilo_sp 25 0.09 12

Thienemanniella sp. Thien_sp 48 0.17 21

Tvetenia sp. Tvet_sp 303 1.10 30

Unniella sp. Unni_sp 67 0.24 14

Xylotopus sp. Xylo_sp 163 0.59 31

Clinotanypus sp. Clino_sp 93 0.34 30
14 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Table 4. Relative abundance and frequency of occurrence of aquatic-invertebrate taxa for samples collected at 43 New Jersey 

Pinelands stream reaches.—Continued

Order Species1 Species abbreviation Total abundance

Relative 

abundance 

(percent)

Frequency of 

occurrence 

(percent)

Diptera Apsectrotanypus sp. Apsec_sp 188 0.68 20

Ablabesmyia sp. Abla_sp 539 1.96 86

Labrundinia sp. Labru_sp 118 0.43 44

Pentaneura sp. Pent_sp 15 0.05 8

Thienemannimyia sp. Thiem_sp 1,904 6.91 88

Zavrelimyia sp. Zavre_sp 23 0.08 8

Procladius sp. Procl_sp 288 1.05 45

Diptera Hemerodromia sp. Hemer_sp 245 0.89 57

Tabanidae Tab_fm 7 0.03 6

Chrysops sp. Chrys_sp 50 0.18 20

Ceratopogoninae Cerat_sp 1 0.00 1

Probezzia sp. Prob_sp 143 0.52 34

Bezzia/Palpomyia sp. Bezz_sp 499 1.81 79

Simulium sp. Simul_sp 1,816 6.59 69

Tipulidae Tipul_fm 6 0.02 7

Hexatoma sp. Hexat_sp 25 0.09 15

Tipula sp. Tipu_sp 29 0.11 19

Tricoptera Phylocentropus sp. Phylo_sp 16 0.06 15

Hydropsychidae Hydro_fm 7 0.03 2

Cheumatopsyche sp. Chem_sp 558 2.03 28

Hydropsyche sp. Hydro_sp 1,098 3.99 78

Macrostemum sp. Macst_sp 63 0.23 19

Chimarra sp. Chim_sp 319 1.16 31

Polycentropodidae Poly_fm 14 0.05 6

Paranyctiophylax sp. Para_sp 62 0.23 28

Polycentropus sp. Poly_sp 175 0.64 36

Brachycentrus sp. Brach_sp 758 2.75 49

Lepidostoma sp. Lepid_sp 31 0.11 13

Leptoceridae Lepto_fm 4 0.01 5

Ceraclea sp. Cera_sp 15 0.05 9

Oecetis sp. Oec_sp 479 1.74 63

Triaenodes sp. Trian_sp 246 0.89 50

Limnephilidae Limno_fm 67 0.24 16

Hydatophylax sp. Hydat_sp 30 0.11 8

Pycnopsyche sp. Pycno_sp 115 0.42 40

Phryganeidae Phry_fm 7 0.03 5

Ptilostomis sp. Ptilo_sp 11 0.04 8

Hydroptila sp. Hydrop_sp 73 0.27 13

Oxyethira sp. Oxy_sp 32 0.12 16

Gastropoda Micromenetus sp. Mic_sp 20 0.07 9

Bivalvia Sphaeriidae Sph_fm 71 0.26 35

Pisidium sp. Pisid 104 0.38 9
Sampling Results 15

Table 4. Relative abundance and frequency of occurrence of aquatic-invertebrate taxa for samples collected at 43 New Jersey 

Pinelands stream reaches.—Continued

Order Species1 Species abbreviation Total abundance

Relative 

abundance 

(percent)

Frequency of 

occurrence 

(percent)

Annelida Lumbriculidae Lumb 307 1.11 44

Nais sp. Nais_sp 7 0.03 7

Tubificidae Tubif 429 1.56 49

Enchytraeidae Ench_fm 442 1.60 76

Placobdella sp. Plac_sp 12 0.04 7

Erpobdellidae Erpo_sp 16 0.06 14

Acari Hygrobates sp. Hygro_sp 14 0.05 9

Mideopsis sp. Mid_sp 32 0.12 21

Crustacea Caecidotea sp. Caec_sp 1,761 6.40 65

Synurella sp. Syn_sp 248 0.90 38

Hyalella sp. Hya_sp 15 0.05 9

Other taxa Turbellaria Turb 57 0.21 17

Prostoma sp. Pros_sp 35 0.13 19

Nematoda Nema 265 0.96 71

1

All taxonomic identifications were verified using the Integrated Taxonomic Information System (ITIS) (http://www.itis.gov).

Table 5. Median water-quality properties measured bimonthly at 15 New Jersey 

Pinelands stream sites.

[μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; °C, degrees Celsius]

Site 

abbreviation pH

Specific 

conductance

(μS/cm)

Dissolved-oxygen 

concentration

(mg/L)

Water 

temperature 

(°C)

BatTab 4.22 47 4.34 14.2

BatHigh 5.25 52 9.21 12.3

SkitHam 4.41 33 8.67 12.7

BatHam 4.89 40 9.08 13.8

Musk 5.96 188 6.44 13.1

SprHam 5.98 117 8.06 12.9

SprAts 5.85 102 8.66 12.8

PenSwmp 4.07 44 7.92 12.8

PumpCed 6.22 304 6.53 16.4

PumpElm 6.27 85 8.31 19.3

AlbElm 6.40 86 8.95 14.4

AlbHam 6.29 77 9.68 18.4

WBBass 4.44 36 9.66 15.3

Morses 5.21 84 8.83 17.0

MtMis 4.30 38 5.18 15.6
16 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Effects of Potential Changes in 

Streamflow Regime on Fish and 

Aquatic-Invertebrate Assemblages 

Flow variables describing magnitude, frequency, duration, timing, and rate of change were generated using HIT. 

Highly correlated variables were filtered by principal component and correlation analysis (see Data Collection and 

Analysis section) to obtain a greatly reduced subset of variables that explain the majority of variation in the complete set 

(Olden and Poff, 2003) for fish and invertebrate assemblages. 

To reduce the influence of land use on model development, 

all hydrologic or assemblage attributes that were correlated 

(|rho| ≥ 0.5000) with anthropogenic disturbance variables (that 

is, urban and agricultural land use, pH, or SC) were eliminated 

from further analysis. Data on distribution and composition of 

fish and invertebrate assemblages, including dominant taxa, 

structural and functional metrics and indices, species traits 

(fish only), and NMS ordination axis I scores, were assembled. A series of univariate (correlation) and multivariate 

(MLR) response models using these hydrologic, assemblage, 

and other ancillary variables (for example, water-quality and 

land-use data) were developed to evaluate whether underlying 

anthropogenic processes are influencing the hydrologic gradient and to assess the relation between variability in the flow 

regime and fish and aquatic-invertebrate assemblage response 

in Pinelands streams.

Fish-Species Response

This section presents a summary of ordination, correlation, and multiple-regression analyses relating fish NMS 

ordination axis I scores to land use, water chemistry, and 

stream-reach characteristics, and identifies a reduced set of 

low- and annual-flow hydrologic variables for developing 

fish-assemblage flow-ecology response models for New Jersey 

Pinelands stream reaches.

Ordination Results

Results of 25 NMS iterations using the PRIMER 

software (Clarke and Gorley, 2006) indicated that the threedimensional solution was the best solution, with a final stress 

of 15.0. Higher dimensions did little to improve the model. 

Together, the three axes accounted for 76 percent of the variance in the analytical dataset. The first NMS axis accounted 

for the majority of the fish-assemblage variation (41 percent). The second and third axes accounted for a significant, 

but generally smaller, proportion of the overall variance 

(10 and 25 percent, respectively) and were not considered for 

further analysis. 

Land Use, Water Chemistry, Hydrology, and other 

Ancillary Data

 Percent urban land (rho = +0.7912) was the variable most 

highly correlated with fish axis I scores (table 6) and accounted 

for the greatest amount of variability in the fish MLR model 

(table 7). Other ancillary variables highly correlated with fish 

axis I scores were pH (rho = +0.7182), aspect (rho = -0.7115), 

percent agricultural land (rho = +0.7064), and floating vegetation in the stream reach (rho = -0.5269). 

Variation in the NMS axis 1 scores for fish was best 

explained by the interaction among percent urban land use and 

dissolved-oxygen concentration (R2 = 0.5742; table 7). This 

two-variable MLR model significantly predicts that components of the fish assemblage are modified along the hydrologic 

gradient, especially by land use. For example, the amount of 

urbanization in the catchment accounted for the majority of the 

overall variability (> 40 percent). Multiple-regression procedures indicated that estimates of model-prediction error and 

precision were not highly biased, and variance inflation factors 

were low (table 7). Correlation coefficients were not inflated 

and comparison between actual model values and those predicted through an n-1 validation approach (that is, the PRESS 

statistic) differed little. 

Of the 163 hydrologic variables aggregated for this study, 

only a subset met the conservative screening criteria and was 

retained for use in developing fish-assemblage flow-ecology 

response models. These variables represent components of 

mean annual flow, mean and median minimum annual flows, 

variability across minimum monthly flows, and duration of 

minimum flows (table 8).

Aquatic-Invertebrate Response

This section presents a summary of ordination, correlation, and multiple-regression analyses relating invertebrate 

NMS ordination axis I scores to land use, water chemistry, 

and stream-reach characteristics, and identifies a reduced set 

of low- and annual-flow hydrologic variables for developing 

invertebrate-assemblage flow-ecology response models for 

New Jersey Pinelands stream reaches.

Ordination Results

Results of 25 NMS iterations indicated that the threedimensional solution was the best solution, with a final stress 

of 14.0. Higher dimensions did little to improve the model. 

Together, the three axes accounted for 86 percent of the variance in the analytical dataset. The first NMS axis accounted for 

the majority of invertebrate-assemblage variation (50 percent). 

The second and third axes accounted for a significant but generally smaller proportion of the overall variance (11 and 25 percent, respectively) and were not considered for further analysis. 
Effects of Potential Changes in Streamflow Regime on Fish and Aquatic-Invertebrate Assemblages 17

Table 6. Land-use, water-chemistry, and stream-reach characteristics that are significantly correlated with fish axis I scores for New 

Jersey Pinelands stream reaches. 

[N, number of observations; rho, Spearman’s rho; mg/L, milligrams per liter; μS/cm, microsiemens per centimeter; H+, hydrogen ion; <, less than]

Ancillary variable Variable 

abbreviation Description (unit of measurement)

NMS axis I

N rho p-value

Percent urban UrbPer Amount of 2002 urban land in the basin (percent) 43 +0.7912 <0.0001

pH pH Measure of stream acidity (-log of H+ concentration) 43 +0.7182 <0.0001

Percent agriculture AgPer Amount of 2002 agricultural land in the basin (percent) 43 +0.7064 <0.0001

Float-leaved FL_Leav Amount of floating vegetation in the stream reach (percent) 43 -0.5269 0.0003

DO DO Dissolved-oxygen concentration (mg/L) 43 +0.4960 0.0007

SC SC Specific conductance (μS/cm) 43 +0.3370 0.0271

Aspect Asp Direction of streamflow (degrees) 43 -0.7115 <0.0001

Table 7. Multiple-regression model relating fish NMS axis I scores to basin characteristics for New Jersey Pinelands stream reaches.

[R2, R-squared; <, less than; VIF, Variable Inflation Factor; variable definitions are found in table 9]

Number in model Model R2 Model p-value Partial R2 p-value Variable abbreviation VIF

2 0.5742 <0.0001

+0.4388 <0.0001 UrbPer 1.07

+0.1354 0.0010 DO 1.07

Table 8. Reduced set of low- and annual-flow hydrologic variables that met the screening criteria and represent the hydrologic basis 

for developing fish-assemblage flow-ecology response models for New Jersey Pinelands stream reaches.

[m3/s, cubic meters per second; D, dimensionless; N, number of observations; variables were screened for use in model development using known indicators of 

anthropogenic degradation (that is, urban and agricultural land use, pH, and specific conductance). Refer to Kennen and others (2007) for a complete description of hydrologic-variable calculation. Hydrologic-variable calculations are based on the study period of record from May 2004 to May 2006]

Hydrologic 

index Description (unit of measurement) N Mean Minimum Maximum

MA1 Mean of the daily mean flow values for the entire flow record (m3/s) 43 0.51 0.01 1.09

MA16 Mean of all May flow values over the entire record (m3/s) 43 0.40 0.01 0.96

MA17 Mean of all June flow values over the entire record (m3/s) 43 0.29 0.01 0.73

MA19 Mean of all August flow values over the entire record (m3/s) 43 0.26 0.01 0.70

MA22 Mean of all November flow values over the entire record (m3/s) 43 0.32 0.01 0.75

ML5 Mean of the minimums of all May flow values over the entire record (m3/s) 43 0.25 0.01 0.65

ML9 Mean of the minimums of all September flow values over the entire record (m3/s) 43 0.19 0.00 0.51

ML13 Variability (coefficient of variation) across minimum monthly flow values (percent) 43 199.40 55.35 206.43

ML16 Median of annual minimum flows (D) 43 0.18 0.02 0.37

DL1 Mean of annual minimum of 1-day average flow (m3/s) 43 0.11 0.00 0.33

DL5 Mean of annual minimum of 90-day moving average flow (m3/s) 43 0.29 0.01 0.73
18 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Land Use, Water Chemistry, Hydrology, and other 

Ancillary Data

Percent urban land (rho = +0.8486) was the variable 

most highly correlated with invertebrate axis I scores (table 9). 

Other ancillary variables highly correlated with invertebrate 

axis I scores were pH (rho = +0.8207), aspect (rho = -0.6665), 

percent agricultural land (rho = +0.7452), and floating vegetation in the stream reach (rho = -0.5635). 

The NMS axis I score was best explained by the interaction among landscape configuration and water-quality characteristics (83 percent; table 10). Attempts to generate higher 

order models resulted in some undesirable collinearity among 

explanatory variables, as indicated by elevated VIFs (that 

is, > 10.0). Results of n-1 validation procedures showed that 

estimates of model-prediction error and precision were not 

highly biased, variance inflation factors were relatively low 

(table 10), and correlation coefficients of the actual model 

were not inflated. 

Only a subset of 163 hydrologic variables aggregated for 

this study met the screening criteria and was retained for use 

in developing invertebrate-assemblage flow-ecology response 

models. These variables represent components of mean annual 

flow, mean minimum annual flows, and duration of minimum 

flows (table 11). 

Flow-Ecology Response Relations

Results of this study indicate that many mean monthly 

flow variables (that is, MA12–23) are highly intercorrelated 

and account for nearly the same proportion of the hydrologic 

variability (table 12). The variance of these flow-magnitude 

attributes can be summarized, for the most part, by using the 

mean of the daily mean flow values for the entire flow record 

(MA1; table 12) as a surrogate. The regression relation was 

strongest between MA1 and MA21 (R2 = 0.9909). MA1, however, was calculated using the Hydrologic Indices Program 

(HIP) based on simulated hydrographs that were derived using 

the approach outlined in the Hydrologic Assessment section. 

For purposes of data accuracy and flow-metric validation, 

MA1 and the observed mean annual flow (acquired directly 

from the rated staff gages) were directly compared. Results of 

the regression relation indicate that the observed and calculated mean annual daily flow values are near unity (fig. 3). 

This result is consistent with metric-validation results previously published for all HIP metrics (see Henriksen and others, 

2006; Kennen and others, 2007). Linear-regression results also 

indicate a high level of correspondence between measures of 

mean annual daily flow and basin size (table 12), further supporting the applicability of the hydrologic gradient approach 

used in this study. 

Table 9. Land-use, water-chemistry and stream-reach characteristics that are significantly correlated with aquatic-invertebrate axis I 

scores for New Jersey Pinelands stream reaches.

[N, number of observations; rho, Spearman’s rho; mg/L, milligrams per liter; μS/cm, microsiemens per centimeter; H+, Hydrogen ion; <, less than]

Ancillary variables Variable 

abbreviation Description (unit of measurement)

NMS Axis I

N rho p-value

Percent urban UrbPer Amount of 2002 urban land in the basin (percent) 43 +0.8486 <0.0001

pH pH Measure of stream acidity (-log of H+ concentration) 43 +0.8207 <0.0001

Percent agriculture AgPer Amount of 2002 agricultural land in the basin (percent) 43 +0.7452 <0.0001

Float-leaved FL_Leav Amount of floating vegetation in the stream reach (percent) 43 -0.5635 <0.0001

SC SC Specific conductance (μS/cm) 43 +0.4926 0.0008

DO DO Dissolved-oxygen concentration (mg/L) 43 +0.3721 0.0140

Aspect Aspect Direction of streamflow (degrees) 43 -0.6665 <0.0001

Table 10. Multiple-regression model relating invertebrate NMS axis I scores to basin characteristics for New Jersey Pinelands stream 

reaches. 

[R2, R-squared; VIF, Variable Inflation Factor; variable definitions can be found in table 9]

Number in model Model

 R2

Model 

p-value Partial R2 p-value Variableabbreviation VIF

3 0.8295 <0.0001

+0.6990 0.0013 UrbPer 3.50

+0.1183 <0.0001 pH 4.56

+0.0122 0.0003 SC 1.35
Effects of Potential Changes in Streamflow Regime on Fish and Aquatic-Invertebrate Assemblages 19

Table 11. Reduced set of low- and annual-flow hydrologic variables that met the screening criteria and represent the hydrologic basis 

for developing invertebrate-assemblage flow-ecology response models. 

[m3/s, cubic meters per second; N, number of observations; variables were screened for use in model development using known indicators of anthropogenic 

degradation (that is, urban and agricultural land use, pH, and specific conductance). Refer to Kennen and others (2007) for a complete description of hydrologicvariable calculation. Hydrologic-variable calculations are based on the study period of record from May 2004 to May 2006]

Hydrologic 

index Description (unit of measurement) N Mean Minimum Maximum

MA1 Mean of the daily mean flow values for the entire flow record (m3/s) 43 0.51 0.01 1.09

MA12 Mean of all January flow values over the entire record (m3/s) 43 0.60 0.08 1.47

MA13 Mean of all February flow values over the entire record (m3/s) 43 0.45 0.01 0.97

MA14 Mean of all March flow values over the entire record (m3/s) 43 0.39 0.01 0.83

MA15 Mean of all April flow values over the entire record (m3/s) 43 0.53 0.01 1.26

MA17 Mean of all June flow values over the entire record (m3/s) 43 0.29 0.01 0.73

MA20 Mean of all September flow values over the entire record (m3/s) 43 0.44 0.01 0.99

MA21 Mean of all October flow values over the entire record (m3/s) 43 0.35 0.01 0.72

MA22 Mean of all November flow values over the entire record (m3/s) 43 0.33 0.01 0.75

MA23 Mean of all December flow values over the entire record (m3/s) 43 0.50 0.01 1.14

ML8 Mean of the minimums of all August flow values over the entire record (m3/s) 43 0.16 0.00 0.46

ML9 Mean of the minimums of all September flow values over the entire record (m3/s) 43 0.19 0.00 0.51

DL4 Mean of annual minimum of 30-day moving average flow (m3/s) 43 0.19 0.00 0.52

DL5 Mean of annual minimum of 90-day moving average flow (m3/s) 43 0.29 0.01 0.73

Table 12. Regression relations (R-squared values) between 

mean monthly flow variables and the mean of all daily flows for 

the entire flow record (MA1) and drainage area for New Jersey 

Pinelands stream reaches. 

[Variable definitions can be found in tables 8 and 11; significance for all 

regression relations was at the p < 0.0001 level; --, not applicable]

Hydrologic variable MA1 Drainage area

MA1 -- 0.9536

MA12 0.8940 0.8029

MA13 0.9812 0.9213

MA14 0.9870 0.9409

MA15 0.8912 0.8060

MA16 0.8627 0.8833

MA17 0.6798 0.7367

MA18 0.9194 0.9257

MA19 0.7672 0.8021

MA20 0.9572 0.8793

MA21 0.9909 0.9596

MA22 0.9700 0.9503

MA23 0.9687 0.8926
20 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Some environmental factors are also known to vary with 

streamflow (for example, DO), and many fish and invertebrate 

taxa in lotic systems depend on the availability of DO for survival (Hynes, 1970). DO concentrations in streams typically 

increase with decreasing water temperature, increasing turbulence, and increasing surface area (Allan, 1995). Few interior 

Pinelands streams, however, would be considered turbulent. 

Therefore, DO concentrations are likely more dependent on 

stream temperature and surface area (that is, stream size). In 

this study, DO concentrations generally appeared to increase 

with increasing MA1 values for Pinelands streams (fig. 4, 

R2 = 0.6075). This relation, although not surprising, may contribute to a more complete interpretation of the aquatic-assemblage response along the hydrologic gradient. For example, 

organisms that are considered to be intolerant of changes in 

flow conditions are also likely to be intolerant of other environmental stressors, such as low DO conditions (U.S. Environmental Protection Agency, 2006). This covariation may 

help to explain, in part, the strong flow-ecology response relations established between intolerant and tolerant invertebrate 

taxa and MA1 (figs. 5a and 5d, respectively), as well as the 

inverse relation established between MA1 and mudminnows 

(fig. 6c). Mudminnows are commonly associated with slowmoving backwater areas and are known to be highly tolerant 

of low levels of DO (Hastings, 1984; Jenkins and Burkhead, 

1993). Evaluating the covariation among habitat attributes 

and flow processes may help to avoid overestimating the 

importance of flow-ecology response relations (Snelder and 

Lamouroux, 2010). The broader effects of variation associated 

with habitat attributes in this study were investigated using 

correlation and MLR for fish and invertebrate assemblages. 

Only the percent of floating vegetation in the stream reach 

(FL_Leav) was found to be significantly correlated with the 

fish- and invertebrate-axis scores (tables 6 and 9, respectively), 

however; FL_Leav was not found to account for a significant 

portion of the variation in the MLR models (tables 7 and 10, 

respectively). Further analysis indicated that habitat attributes 

such as stream depth (rho = 0.5030) and stream-bank height 

(rho = 0.6318) were significantly correlated with MA1; however, this finding is not unexpected as stream size and streamflow are highly related (fig. 3).

The success of this project depended in part on basin size 

(that is, the hydrologic gradient) accounting for a high proportion of the hydrologic variability without being influenced 

Figure 3. Regression relation between MA1 (calculated using Hydrologic Indices Tool) and the observed mean annual daily flow at 15 

New Jersey Pinelands stream sites. (Refer to table 1 for detailed site information.)

0 0.2 0.4 0.6 0.8 1.0 1.2

0

0.2

0.4

0.6

0.8

1.0

1.2

SprAts AlbHam

SprHam

AlbElm

BatHigh

SkitHam

BatHam

PumpElm

WBBass

Morses

MtMis Musk

PumpCed

PenSwmp

BatTab

MA1 (CALCULATED)

y = 0.8919x + 0.0185

 R2 = 0.9385

OBSERVED MEAN ANNUAL DAILY FLOW,

IN CUBIC METERS PER SECOND
Effects of Potential Changes in Streamflow Regime on Fish and Aquatic-Invertebrate Assemblages 21

Figure 4. Regression relation between MA1 and mean dissolved-oxygen concentration at 15 New Jersey Pinelands 

stream sites. DISSOLVED-OXYGEN CONCENTRATION, IN MILLIGRAMS PER LITER

MA1 (CALCULATED)

0

2

4

6

8

10

12

y = 0.7944ln(x) + 9.5222

R2 = 0.6075

0 0.2 0.4 0.6 0.8 1.0 1.2

R2 = 0.6148

y = -23.352x2 + 41.86x + 22.623

MA1

RICH INTOL_ABUNp

Log EPT_CH AbundTOL

Log MA1

MA1 Log MA1

y = -36.386x2 + 79.094x + 3.7457

R2 = 0.7823 R2 = 0.6538

y = 1.4429x - 0.652

R2 = 0.5845

y = -5.8286x + 6.0485

A

C

B

D

70

60

50

40

30

20

10

0

0 0.2 0.4 0.6 0.8 1.0 1.2

0 0.2 0.4 0.6 0.8 1.0 1.2

50

40

30

20

10

0

8.0

6.0

4.0

2.0

0

0.8

0.6

0.4

0.2

0

0 0.05 0.10 0.15 0.20 0.25 0.30 0.35

0 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Figure 5. Invertebrate-assemblage flow-ecology response relations between the mean of the daily mean flow values for the entire 

flow record (MA1, in cubic meters per second) and (A) INTOL_ABUNp, (B) EPT_CH, (C) RICH, and (D) ABUNTOL. (Graphs A and C 

are fitted with a polynomial response curve. Definitions of assemblage metrics can be found in table 13. INTOL, intolerance; ABUN, 

abundance; EPT, Ephemeroptera, Plecoptera, Trichoptera; CH, Chironomidae; RICH, Richness; TOL, tolerance; p, percent; A full 

explanation of all invertebrate metrics can be found in Cuffney and others (2005). Tolerance and intolerance values are derived from U.S. 

Environmental Protection Agency, (1997).)
22 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey CPSPRich Log MM R_MIGRA

Log MA1

Log MA1 Log MA1

y = 13.222x + 5.0168

R2 = 0.3915

R2 = 0.2810

y = 93.404x + 7.2324

R2 = 0.2488

y = -3.2771x + 1.6193

0 0 0.05 0.10 0.15 0.20 0.25 0.30 .35

0 0 0.05 0.10 0.15 0.20 0.25 0.30 .35 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35

70

60

50

40

30

20

10

0

12

10

8

6

4

2

0

3.0

2.5

2.0

1.5

1.0

0.5

0

A B

C

Figure 6. Fish-assemblage flow-ecology response relations between the mean of the daily mean flow values for the entire flow record 

(MA1, in cubic meters per second) and (A) CPSPRich, (B) MM, and (C) R_MIGRA. (Definitions of assemblage metrics can be found in 

table 14. CPSP, Coastal Plain species; MM, mudminnow; MIGRA, migratory species; Species traits (for example, migratory species) 

evaluations are based on the approach presented in Goldstein and Meador (2005). Fish metrics (for example, CPSP) used in this study 

are generally based on the work of Crouse (2006).)
Effects of Potential Changes in Streamflow Regime on Fish and Aquatic-Invertebrate Assemblages 23

by known anthropogenic effects. Without more intensive 

hydrologic modeling (for example, Kennen and others, 2008), 

it would be difficult to derive hydrographs that represent the 

Pinelands streams under pre-development conditions. That is, 

we were unable to derive simulated hydrographs that represent 

unimpacted flow conditions (as suggested by Poff and others, 

2009) as part of this study. This limitation was overcome by 

using a variable-screening procedure of known anthropogenic drivers (that is, percent urban and agricultural land, SC, 

and pH) to identify a subset of hydrologic variables (tables 8 

and 11) for use in response-model development. This approach 

appeared to be highly successful in identifying hydrologic 

attributes with predictive power (for example, MA1 and 

MA12–23) that were not influenced by anthropogenic drivers. 

Flow-ecology response relations between a reduced set 

of individual flow attributes and ecological measures were 

evaluated using Spearman’s correlation and regression. Many 

hydrologic measures accounting for the duration and magnitude of low flows and the magnitude of average annual 

flows were significantly correlated with ecological response 

(tables 13 and 14). No frequency, timing, or rate-of-change 

attributes were retained for analysis as none met our screening criteria (that is, they are uncorrelated (|rho| < 0.5000) 

with anthropogenic indicators such as urban and agricultural 

land use, pH, and SC). Many invertebrate-assemblage metrics 

(n = 183) accounting for richness, abundance, and function 

of the aquatic-invertebrate assemblage were computed using 

the IDAS software (Cuffney and others, 2005); however, only 

a subset (n = 10 invertebrate metrics) that met the screening 

criteria was retained for further analysis (table 13). In addition, 

a smaller suite of metrics (n = 9), species (n = 11), and species 

traits (n = 22) was aggregated for the fish assemblage. A subset of these fish measures meeting the above screening criteria 

was also retained for development of flow-ecology response 

relations (table 14).

The percentage abundance of intolerant taxa 

(INTOL_ABUNDp) was the invertebrate metric most 

strongly correlated with flow (rho = 0.8628). All of the 

flow-ecology response relations between aquatic-invertebrate 

metrics and flow measures shown in table 13 were highly 

significant (p < 0.0001). Richness of Coastal Plain species 

(CPSPRich) was the fish metric most strongly correlated with 

flow (rho = 0.7105) and all flow-ecology response relations 

between fish metrics and flow measures were significant at the 

p < 0.002 level (table 14). In general, all fish and invertebrate 

response patterns followed positive or negative trends that 

would be expected given changes in stream hydrology or other 

environmental attributes, such as DO (fig. 4), that directly 

vary with streamflow. For example, as MA1 increased, there 

was an increase in the percent of intolerant invertebrate taxa 

(fig. 5A) and a concomitant decrease in tolerant taxa (fig. 5D). 

That is, as average annual flows of Pinelands streams increase, 

a higher proportion of intolerant and fewer tolerant taxa are 

present. In addition, as the average magnitude of monthly 

flows increased (MA1–average monthly flow), the total richness of taxa increased. The richness of EPT and Plecoptera 

taxa also was directly related to changes in the duration and 

magnitude of annual and low flows (table 13).

Abundance and richness of fish species appear to change 

in response to increases in the duration and magnitude of 

low-flow events (table 14). For example, as annual minimum 

flows for a 90-day moving average (that is, DL5) increased, 

total Coastal Plain richness increased. Higher minimum flows 

also appear to be highly significant in maintaining fish-community structure, especially spring and summer low flows (for 

example, ML5 and ML9–the mean of the minimum flows for 

May and September, respectively; table 14). Changes in functional response also were evident as the percentage of invertebrate gatherer-collectors (GC_Rich) and shredder abundance 

(SH_ABUND) increased with increasing average flows in 

May (MA16) and June (MA17), respectively (table 13). 

Generally, all flow-ecology response relations developed during this study represent linear or slightly curvilinear 

responses (for example, figs. 4 and 5). No discrete “thresholds” or break points in the relations between flow and ecological response were apparent; however, some curves appeared 

to be asymptotic. For example, the abundance of intolerant invertebrates and total richness appear to increase with 

increasing annual flow, but level off at or near 1 m3/s (fig. 5A 

and C). Even in a restricted study area where all streams were 

of the same stream type or class (that is, Pinelands streams, or 

streams referred to as Class B in Kennen and others (2007)), 

which typically are stable streams with high base flow in the 

Coastal Plain, the high amount of variability seen in these 

data, especially those for fish, may have obscured our ability 

to identify any discrete thresholds in the aquatic-assemblage 

response patterns. Establishing empirically based flow-ecology 

response relations such as these, however, does provide insight 

into those aspects of flow that help to maintain the structural 

and functional integrity of aquatic assemblages and can be 

used for targeting the maintenance, restoration, or remediation 

of natural streamflow processes. 
24 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Table 13. Aquatic-invertebrate assemblage metrics that were significantly correlated with the reduced set of hydrologic measures. 

[Refer to table 11 or Kennen and others (2007) for hydrologic variable definitions. A detailed explanation of all invertebrate metrics can be found in Cuffney 

and others (2005). Functional feeding groups (for example, gatherer-collectors (GC) and shredders (SH)) are derived from Barbour and others (1999). Tolerance 

(TOL) and intolerance (INTOL) values are derived from U.S. Environmental Protection Agency (1997). <, less than; EPT, Ephemeroptera, Plecoptera, Trichoptera. See the highlighted blue box on the following page for additional explanatory material]

Metric 

abbreviation Metric description Hydrologic variable rho p-value

INTOL_ABUNDp Percentage abundance of intolerant taxa 

MA20 0.8628 < 0.0001

MA1 0.8349 < 0.0001

MA13 0.8207 < 0.0001

RICH Total taxa richness

MA23 0.7218 < 0.0001

MA13 0.6876 < 0.0001

MA1 0.7302 < 0.0001

AbundTOL Abundance-weighted USEPA tolerance 

value for sample

MA22 -0.8507 < 0.0001

MA13 -0.8083 < 0.0001

MA1 -0.8453 < 0.0001

PLECOR Richness of Plecoptera taxa

MA17 0.7896 < 0.0001

DL5 -0.7858 < 0.0001

ML9 0.7439 < 0.0001

EPTR Total richness of EPT taxa

MA1 0.6666 < 0.0001

MA20 0.7015 < 0.0001

DL5 -0.6670 < 0.0001

EPEM Total abundance of Ephemeroptera taxa

DL5 -0.5490 < 0.0001

MA20 0.7642 < 0.0001

MA1 0.6857 < 0.0001

GC_Rich Richness composed of gatherer-collectors1

DL4 -0.7132 < 0.0001

MA16 0.6497 < 0.0001

ML8 0.7107 < 0.0001

Dom4 Total abundance of the four most dominant2

taxa

MA21 -0.6843 < 0.0001

MA1 -0.6645 < 0.0001

DL4 0.6494 < 0.0001

EPT_CH Ratio of EPT taxa to chironomid3 abundance

MA20 0.7812 < 0.0001

MA1 0.7520 < 0.0001

DL5 0.7176 < 0.0001

SH_ABUND Total abundance composed of shredders4

ML8 0.7692 < 0.0001

DL4 -0.7482 < 0.0001

MA17 0.6624 < 0.0001

1

 Gatherer-collectors (GC) are aquatic organisms that feed primarily on fine pieces of decomposing particulate organic matter (< 1 millimeter in diameter) 

deposited in streams (for example, Hydropsyche sp., Caecidotea sp., Tribelos sp.) (Vannote and others, 1980; Merritt and Cummins, 1996).

2

 Dom4 is the combined abundance of the four most dominant taxa in the sample; in Pinelands streams, Dom4 typically is a combination of the genera Hydropsyche sp., Caecidotea sp., Simulium sp., Tribelos sp., Rheotanytarsus sp., and Brachycentrus sp.

3

 Chironomidae (CH) are small, non-biting fly larvae commonly referred to as midges that can be found in most aquatic habitats (for example, Rheotanytarsus 

sp., Polypedilum sp., and Tribelos sp.).

4

 Shredders (SH) are aquatic organisms that chew primarily large pieces of decomposing vascular plant tissue (>1 millimeter diameter) along with its associated microflora and fauna (for example, Pycnopsyche sp., Cricotopus sp., Polypedilum sp., and Leuctra sp.) (Vannote and others, 1980; Merritt and Cummins, 

1996).
Effects of Potential Changes in Streamflow Regime on Fish and Aquatic-Invertebrate Assemblages 25

Table 14. Fish-assemblage metrics, species, and species traits that are strongly correlated with the reduced set of hydrologic 

measures. 

[Refer to tables 8 and 11 or Kennen and others (2007) for hydrologic variable definitions. Species traits (for example, migratory species) evaluations are based 

on the approach presented in Goldstein and Meador (2005). Fish metrics used in this study are generally based on the work of Crouse (2006). <, less than; MM, 

mudminnow; TPM, tadpole madtom; BBDS; black-banded sunfish; BSS, blue-spotted sunfish; CPSP, Coastal Plain species–species native to the Coastal Plain 

of NJ (for example, mud sunfish, mudminnow, creek chubsucker, pirate perch, and tadpole madtom); AcidTolSp, sunfish species considered to be acid tolerant 

(that is, bluespotted, banded, blackbanded, and mud sunfish); MIGRA, a reproductive category represented by migratory species–in this study, this trait is 

composed only of eel and creek chubsucker] 

Metric 

abbreviation Metric description Hydrologic variable rho p-value

CPSPRich Richness of Coastal Plain species 

DL5 0.7105 < 0.0001

MA17 0.7160 < 0.0001

ML5 0.6872 < 0.0001

AcidTolSp Richness of acid-tolerant sunfish species

DL1 0.5647 < 0.0001 

ML9 0.5171 0.0004 

ML16 0.5236 0.0003

TPM Total abundance of tadpole madtom

DL5 0.6405 < 0.0001 

MA16 0.6284 < 0.0001 

ML5 0.5926 < 0.0001 

MM Total abundance of mudminnow

ML13 0.6747 < 0.0001

MA16 0.4792 0.0012

ML5 0.4762 0.0012

BBDS_BSS Total abundance of black-banded and blue-spotted sunfish 

MA16 0.7361 < 0.0001

ML9 0.4979 0.0007

DL1 0.6298 < 0.0001

R_MIGRA Total abundance of fish species that exhibit migratory behavior for reproduction

MA16 0.6025 < 0.0001

DL5 0.5915 < 0.0001

MA1 0.5450 0.0002

Aquatic-Invertebrate-Assemblage Metrics: Tolerance and Richness

Tolerance values range from 0 (extremely sensitive organism) to 10 (tolerant organism). Tolerant taxa are considered 

to be tolerant of stream degradation (for example, Hydropsyche sp., Chironomus sp., Caecidotea sp., Tubificidae, Oecetis sp., and Pisidium sp.). A tolerant organism is one that is likely to be found in a site that has been altered by some type 

of environmental stressor (U.S. Environmental Protection Agency, 2006). Therefore, a sensitive taxon is one that tends to 

decline in abundance or occurrence probability along a defined stressor gradient (for example, streamflow modification, 

dissolved-oxygen concentration, etc.). In the Invertebrate Data Analysis System (IDAS) software, the tolerance values 

chosen for each taxon were based on values derived from the Mid-Atlantic Coastal Plain Work Group (U.S. Environmental Protection Agency, 1997), which included representatives from the states of New Jersey, Delaware, Maryland, North 

Carolina, and South Carolina. Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa are considered by most taxonomists 

to be highly sensitive to changes in stream condition (for example, E – Eurylophella sp., Stenonema sp., and Leptophlebiidae; P – Leuctra sp., Taeniopteryx sp., and Perlesta sp.; and T – Chimarra sp., Pycnopsyche sp., and Polycentropus sp.), 

and it is well established that the richness of EPT taxa varies proportionally with stream size, especially in smaller 

(1st- through 4th- order) streams (Paller and others, 2006). 
26 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

Implications of Ecologically Relevant 

Flow Measures

The flow-ecology response modeling effort presented 

in this report was used to identify hydrologic attributes that 

were directly related to differences in fish and aquatic-invertebrate assemblage complexity across a hydrologic gradient 

(tables 13 and 14). This effort was in direct response to the 

need to predict how increasing demand for water from the 

Kirkwood-Cohansey aquifer system (that is, water withdrawals for residential, commercial, and agricultural uses) resulting 

from increasing growth and development will affect aquatic 

assemblages in New Jersey Pinelands streams. Many of the 

hydrologic variables identified in this study accounted for a 

significant proportion of the variability and appeared to be a 

significant determinant of fish and invertebrate-assemblage 

structure and function—for example, the mean annual flow 

over the entire record (MA1) was highly correlated with 

many individual fish- and invertebrate-assemblage metrics 

(figs. 4 and 5). The annual minimum 90-day low flows (DL5) 

and the mean of the minimums of all September flow values 

over the entire record (ML9) were both significant predictors 

of changes in fish- and invertebrate-assemblage structure, 

respectively. The findings of this study are consistent with the 

results of other recent studies that point to changes in annual 

streamflow processes as being a significant driver of changes 

in assemblage structure and function (for example, Poff and 

Allan, 1995; Clausen and Biggs, 1997; Pusey and others, 

2000; Bunn and Arthington, 2002; Kennen and Ayers, 2002; 

Kennen and others, 2010). In this study, however, a series of 

flow-ecology response relations was developed that directly 

relates variability in hydrologic processes to assemblage 

structure and function (see Poff and others, 2009) using bivariate linear and curvilinear response models (figs. 4 and 5). 

This approach has been strongly advocated by Arthington and 

others (2006), who suggest that once the types and degrees of 

flow modification have been determined for a specific stream 

type (that is, Pinelands streams in this study), the next crucial 

step is to develop empirically based quantitative relations 

between indicators of assemblage structure and function and 

streamflow processes. 

It was postulated that variation in streamflow processes 

(that is, variation in one of the five main components of 

streamflow–magnitude, frequency, duration, timing, and rate 

of change) associated with continued water extraction would 

explain a significant portion of the variation in assemblage 

structure and function (that is, complexity) along a hydrologic gradient. The study findings provide evidence that many 

flow components, specifically duration and magnitude of low 

and annual flows, accounted for a significant portion of the 

variation in assemblage attributes. Even though recent studies 

have indicated that aquatic invertebrates appear to be resilient 

to stress associated with short-term reductions in streamflow 

(Miller and others, 2007; James and others, 2008), possibly 

utilizing the hyporheic zone as refugia (Dewson and others, 

2007), the current study results may indicate that changes in 

flow processes could potentially modify natural assemblage 

complexity and push the aquatic assemblages beyond their 

capacity for resistance or resilience.

Periods of low flow tend to favor taxa that prefer slower 

velocities (Jowett, 1997) or those taxa that are more tolerant 

of stressors (for example, O2 depletion and increased water 

temperatures) associated with more slowly flowing water. In 

this study, fish- and invertebrate-assemblage structure across 

the hydrologic gradient appeared to be related to decreasing 

low-flow magnitudes. In particular, minimum flows, especially 

spring low flows (that is, ML5–the mean of the minimum 

flows for May), appeared to be important for maintaining 

fish-assemblage structure. The maintenance of spring-flow 

magnitude is known to be important for species adapted to a 

particular flow regime, especially those fishes that rely on flow 

cues for spawning, and support of crucial life-cycle stages 

(Grossman, 1982; Poff and Ward, 1989). 

Timing, duration, and magnitude of annual and low-flow 

events are known to be important for the support of native 

stream communities (Poff and others, 1997) and are particularly relevant for synchronization of life-history processes. 

Lytle and Poff (2004) suggest that even though it is difficult to 

forecast annual flow events, it is likely that aquatic organisms 

adapt to the long-term averages, especially if such occurrences 

are in regions where there is some level of flow predictability 

(for example, annual spring flows or summer low flows in 

the northeastern United States). Synchronizing reproductive 

processes with annual or low-flow periods likely optimizes 

reproductive success and helps avoid high mortality rates 

during extreme events such as floods or droughts (Lytle, 2002; 

Boulton, 2003). In this study, annual flow variability–specifically variability across minimum monthly flows (for example, 

ML13)–was important for the fish assemblage (for example, 

mudminnows, table 14). This result may indicate that as the 

magnitude of low-flow processes is altered, some fish species with life-history and behavioral constraints that rely on 

annual flow patterns or fluctuations in flow for reproduction 

may become less abundant, whereas species with greater 

resilience to changes in natural stream variability will become 

more abundant. This response is exemplified by the significant 

relations established between taxa richness (RICH) and MA1 

(fig. 5C), which show that a decrease in annual flow from 1.13 

to 0.14 m3/s could reduce RICH by nearly half. Similarly, the 

abundance of intolerant taxa over the same range in annual 

flow could decrease by three-quarters (fig. 5A). Groundwater 

is the primary resource for support of streamflow in Pinelands 

systems (Rhodehamel, 1979; Zapecza, 1989) and likely buffers streamflow more during the spring than during summer 

low-flow periods, when aquifers and streamflows are most 

stressed. It can be postulated that exacerbation of low-flow 

periods in Pinelands streams due to increased water extraction 

may have a greater effect on the aquatic assemblages than it 

would during high-flow periods (also see Kennen and others, 2007), simply because less water is available in streams 

during low-flow periods. It is also possible that changes in 
Implications of Ecologically Relevant Flow Measures 27

average annual and monthly streamflow due to increased 

water extraction will result in a shift in aquatic-invertebrate 

species richness and abundance. This shift in assemblage 

structure along the hydrologic-response profile (for example, 

figs. 4 and 5) can be interpreted as an alteration of life-history 

or behavioral cues due to modified annual flow patterns. For 

example, emergence periods for more sensitive taxa (those 

taxa with a less plastic life histoy) like Ephemeroptera and 

Plecoptera (table 13) appeared to be affected by alterations 

in mean annual flow processes, whereas aquatic-invertebrate 

taxa that are more tolerant of changes in annual streamflow 

processes reflected a decrease in abundance in some response 

models (for example, AbundTOL; fig. 5D). DO concentrations in Pinelands streams appear to increase with increasing 

streamflow (fig. 4); thus, this covariation may also help to 

explain, in part, the strong relations seen between tolerance 

value and streamflow. 

Even though the modeling approach used in this study 

attempted to eliminate the effects of anthropogenic degradation across the hydrologic-response profile, changes in water 

use to support population growth will likely alter the natural flow regime and have a measurable effect on native and 

endemic species. For example, abundance of native sunfish 

species including BSS and BBDS (BBDS_BSS; table 14) 

are responding to variation in the hydrologic regime and 

were highly related to the magnitude and duration of low 

flows, indicating that, in general, as increases in water use 

reduce streamflow, the abundance of native sunfish will likely 

decrease. This finding has important management implications for future water development in Pinelands streams. If 

the goal is to protect native fish species while simultaneously 

providing additional extraction for water-supply purposes, 

the magnitude and the duration of low flows may need to be 

maintained to ensure continued success of these species. Not 

all fish relations followed that pattern, however; for example, 

the abundance of eastern mudminnow (Umbra pygmaea), 

another native fish species, responded inversely and tended 

to decrease with increases in mean annual flow (fig. 6B). A 

large amount of scatter in these data was found; however, the 

downward trend was evident and a similar response was seen 

for annual monthly flows (table 14). A possible explanation for 

this inverse relation is that MM are tolerant of low DO, low 

pH, and high temperatures, and tend to prefer areas of slowmoving backwater with vegetation and organic material. These 

types of habitats are common throughout small Coastal Plain 

streams as compared to larger streams with higher flow where 

MM are much less abundant and are often restricted primarily 

to the back-water areas. 

The findings presented in this report are subject to some 

important limitations. A confounding difficulty encountered 

in this study was the unintended consequences of a sampling 

design that ultimately included underlying anthropogenic disturbance effects. In general, most of the sites sampled as part 

of this assessment were chosen because their basins contained 

minimal amounts of urban land use (≤ 24 percent; see table 1). 

However, results of multiple-regression models demonstrate 

that there was an underlying effect of urbanization, indicating 

that, even at relatively low levels, anthropogenic change can 

modify the hydrologic gradient and potentially alter interpretation of the assemblage response. Using a fairly rigorous 

screening criterion, those hydrologic and assemblage attributes 

that were directly responding to anthropogenic degradation 

as a result of land-use change were eliminated. This approach 

greatly enhanced the interpretive value of these data and 

helped to produce stronger and more scientifically defensible 

flow-ecology response models with the potential for management application. 

Stronger predictive power was attained for development 

of invertebrate response models than fish models. Although 

significant, fish response models (fig. 6 A–C) tended to reflect 

more scatter and weaker R-squared values. This result may 

indicate that factors other than hydrology are influencing fishassemblage response and obfuscated the results. Availability 

of preferential habitats, such as emergent plants or undercut 

banks that provide cover and protection from predation, are 

likely key confounding factors (see Snelder and Lamouroux, 

2010). Another possible limitation is the short-term duration of 

this study. Fish and aquatic-invertebrate assemblages represent 

the cumulative effects of hydrologic changes over time (Poff 

and others, 1997). Therefore, adaptive longer term studies 

would be useful in evaluating whether changes in water-use 

practices will actually result in a cumulative effect on aquatic 

assemblages. Long-term studies, however, may be impractical and difficult to implement given monetary constraints. 

Although rare, some exceptions do exist (see Daufresne and 

others, 2003; Humphries and others, 2008). The sampling 

network used in this study, however, was designed to evaluate projected changes in hydrologic processes through time 

by using stream-basin size as a surrogate for changes in flow, 

and represents a viable alternative for predicting the effects 

of hydrologic alteration in a relatively short timeframe. The 

significant flow-ecology response models identified in this 

study appear to indicate that variation in fish- and invertebrateassemblage structure could occur as a result of additional 

water extraction in the Pinelands.

Assessing potential variation in fish- and invertebrateassemblage structure along a hydrologic gradient, such as in 

this study, provides an opportunity for scientists and resource 

managers to focus on the most relevant hydrologic attributes and develop a management strategy that could include 

remediative, restorative, or preventive approaches, depending 

on the extent or degree of assemblage change or steepness of 

the slope of the flow-ecology response line or curve. That is, 

streams that have undergone minimal alterations in flow may 

need less remediation and more protection to avoid adverse 

consequences on stream biota, whereas streams whose flow is 

more modified and that have a steeper response curve represent systems whose remediative or restorative needs may be 

more immediate. Predictive bivariate linear and curvilinear 

response models, such as those presented in this study (figs. 4 

and 5), can be used as a resource support tool to help managers and policy makers identify the point along the response 
28 Evaluating Effects of Potential Changes in Streamflow Regime on Aquatic Assemblages in New Jersey

function that fundamentally represents the “best-” or “worst-” 

case scenario for a particular level of water extraction and 

devise an approach for protecting, maintaining, or restoring stream hydrology and aquatic-assemblage structure and 

function. If sensitive or intolerant species were of interest, the 

point of maximum richness or abundance along the flowecology response curve or line (figs. 4 and 5) might be the 

basis for a management decision. For example, the abundance 

of intolerant invertebrates at 0.14 m3/s is approximately 10; 

however, at 1.0 m3/s abundance quadruples to more than 40, 

whereas the abundance of highly tolerant taxa decreases by 

more than a third over the same flow range. Arthington and 

others (2006) suggest that an approach such as that presented 

in this report is needed to develop predictive models that 

reflect the ecological consequences of flow alteration and to 

inform scientific debate about ecosystem responses to flow 

modification and climate change (see also Baron and others, 

2002; Naiman and others, 2002; Poff and others, 2003; Meyerson and others, 2005; Richter and others, 2006).

A recent paper by Poff and others (2009) has been 

instrumental in outlining a unified framework for developing 

regional environmental-flow standards called the Ecological 

Limits of Hydrologic Alteration (ELOHA). ELOHA builds 

directly upon the work of Arthington and others (2006), who 

challenged water scientists to establish and validate thresholds 

for flow measures using empirical biological data from natural 

or “reference” streams and flow-altered streams. The authors 

suggest that flow-ecology response relations be developed 

for a suite of ecological metrics across a gradient of flow 

regimes, similar to that used in this study. ELOHA, however, 

is designed to support comprehensive regional flow management and strives to synthesize available scientific information 

into ecologically based and socially acceptable goals and 

standards for management of environmental flows. Key steps 

are outlined to help environmental-flow practitioners develop 

relations between flow alteration and ecological response. 

These include (1) building a sound hydrologic foundation of 

baseline hydrographs for ungaged streams using a flow-modeling tool (for example, Kennen and others, 2008); (2) employing a set of ecologically relevant flow attributes to classify 

streams into distinctive flow-regime types (for example, Olden 

and Poff, 2003; Kennen and others, 2007; Armstrong and others, 2008); (3) determining the deviation of current-condition 

flows from baseline-condition flows (for example, Esralew 

and Baker, 2008); and (4) developing flow-ecology response 

relations. The approach presented in this report is consistent 

with ELOHA and incorporates most of the major steps. The 

final step outlined in ELOHA–directly establishing flowecology response relations is accomplished by using a class 

of Pinelands streams where USGS continuous-record gaging 

stations have been instrumented and (or) staff gages have 

been established (see Hydrologic Assessment section). These 

flow-ecology response relations can be further used to support 

flow-management strategies by providing stream-type-specific 

empirical results to guide the implementation of remediation 

efforts or to determine the point along a specific response 

curve representing the greatest loss of native Pinelands species 

or assemblage complexity, and subsequently manage projected 

development to minimize these changes. In addition, these 

empirical relations can be used to guide the development of 

State environmental-flow programs whether they are descriptive or based on ecologically relevant flow measures such as 

those presented in the Hydroecological Integrity Assessment 

Process (Kennen and others, 2007) or the Indicators of Hydrologic Alteration (Richter and others, 1997). Ultimately, such 

relations can inform water-resource managers, planners, and 

policy makers on the suite of hydrologic indices that may be 

most effective for use in setting environmental-flow standards 

in and near the Pinelands protection area.

Summary

Increases in water demand associated with population 

growth in the Pinelands region of southern New Jersey are 

likely to have a direct effect on stream hydrology, including reductions in streamflow associated with increased water 

extraction. State resource-management agencies are mandated 

by law to ensure that increased consumptive water use does 

not adversely affect the unique habitats and ecology of the 

Pinelands area. Although many aquatic fauna have shown 

resilience and resistance to short-term changes in flows 

associated with water withdrawals, sustained effects associated with ongoing water development are not well understood. 

Therefore, the U.S. Geological Survey sampled forty-three 

100-m-long stream reaches to evaluate whether changes in 

water demand associated with population growth and changes 

in land-use practices will have a measurable effect on the ecology of the Pinelands. Fourteen native and several non-native 

fish species and more than 125 invertebrate taxa were collected at the 43 New Jersey Pinelands streams. Additionally, 

more than 445 environmental variables (that is, hydrologic, 

water-quality, and physical variables associated with each 

stream reach) were summarized for this study. A combination 

of Spearman rank correlation, regression, and multivariate 

statistical methods were used to identify potential linkages 

among environmental factors and determine the significant 

hydrologic attributes accounting for fish- and invertebrateassemblage response.

The findings of this study indicate that variation in the 

composition of fish and invertebrate assemblages could occur 

as a consequence of alterations in streamflow processes resulting from increased water extraction from Pinelands basins. 

The hydrologic characteristics that were found to be most 

important in determining aquatic-assemblage composition 

included changes in annual and low flows. Many of these attributes were good predictors of differences in assemblage complexity. Flow-ecology response relations were different for fish 

and invertebrate assemblages. Even though the response relations developed for this study followed upward or downward 

trends that would be expected as a result of shifts in natural 
References Cited 29

stream hydrology, stronger and more significant flow-ecology 

response models were developed for the invertebrate assemblage. In general, the ecological-response models predict that 

variation in the magnitude and duration of average annual and 

low flows are related to the variation in aquatic-assemblage 

structure across the hydrologic-response profile. This result 

may indicate that as the Pinelands become more developed 

and water extraction increases to support population growth, 

the streams will have a reduced capacity to adequately buffer 

these effects and aquatic-assemblage structure and function 

could be compromised. Flow alterations, especially alteration 

of spring flows, which are necessary for spawning, recruitment, and emergence of many aquatic species, have been 

consistently found to directly affect stream assemblages. 

Reductions in streamflow also can greatly reduce available 

habitat and can directly affect water temperatures, oxygen 

levels, and primary productivity. In addition, alterations in the 

flow regime may affect aquatic species that have life-history 

strategies that are adapted to the natural flow patterns. Therefore, as water extraction along the periphery of the Pinelands 

protection area continues to increase, the resulting hydrologic 

alterations likely will have a measurable effect on fish- and 

invertebrate-assemblage structure and function. Implementation of longer term studies would facilitate the evaluation of 

ongoing adjustments in assemblage structure resulting from 

hydrologic alteration.

The many bivariate linear and curvilinear fish- and 

invertebrate-response models presented in this report could 

be used as the basis for setting flow maxima or minima that 

would protect biotic complexity and native Pinelands species 

and could be used as reference points for imposing or relaxing 

water-allocation constraints. In addition, results of hydrologic 

modeling could be used to develop a subset of streamflowchange scenarios that could be applied to the flow-ecology 

response models developed in this study to predict the effects 

of potential water extraction on the abundance and complexity of fish and aquatic-invertebrate assemblages throughout 

the Pinelands. 

Acknowledgments

The authors thank the many individuals of the New Jersey Pinelands Commission, especially Robert Zampella, John 

Bunnell, Kim Laidig, Nicholas Procopio, and Kimberly Spiegel, who dedicated staff resources and time to assist with field 

work, including the collection of fish samples, canopy and 

in-stream plant-cover measurements, and water-quality assessment, that was an integral part of this study. Additional field 

support from Pamela Reilly, Jason Lewis, and Holly Weyers 

of the USGS is immensely appreciated. We are indebted to the 

many individuals who carried out the continuous-record and 

staff-gage installation and monitoring throughout the duration of this study, including Blaine White, Ken Hayes, Rick 

Edwards, Andy Watson, Jason Shvanda, Tom Moffett, John 

Trainor, Brian Painter, and Aric Vanselous. Special thanks is 

due to Kara Watson of the USGS for her assistance with GIS 

data analysis and mapping. Thanks also to Gary Lester and 

his very talented staff at EcoAnalysts for processing all the 

Pinelands benthic samples and identifying the invertebrates to 

the lowest possible taxonomic level. Evan Hornig and Robert 

Reiser of the USGS and John Bunnell of the New Jersey 

Pinelands Commission, respectively, provided many helpful 

suggestions that greatly improved this report. 

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For additional information, write to:

Director, U.S. Geological Survey

New Jersey Water Science Center

810 Bear Tavern Road, Suite 206

West Trenton, NJ 08628

or visit our Web site at:

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Document prepared by the West Trenton Publishing Service Center
Kennen and Riskin—Evaluating Effects of Potential Changes in Streamflow Regime on Fish and Aquatic-Invertebrate Assemblages in New Jersey—SIR 2010–5079