FACTORS AFFECTING THE DISTRIBUTION OF BROOK TROUT (SALVELINUS FONTINALIS) IN THE WOOD-PAWCATUCK WATERSHED OF RHODE ISLAND

By

ERICA TEFFT

A MAJOR PAPER SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF MASTER OF ENVIRONMENTAL SCIENCE

AND MANAGEMENT

December 2013

UNIVERSITY OF RHODE ISLAND

MAJOR PAPER ADVISOR: P. V. August

MESM Track: Remote Sensing and Spatial Analysis

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Table of Contents

Abstract……………………………………………………………………………………………………………………………………..3

Acknowledgements……………………………………………………………………………………………………………………4

Introduction……………………………………………………………………………………………………………………………….5

Monitoring and Modeling of Brook Trout Populations……………………………………………………………….6

Objectives………………………………………………………………………………………………………………………………..10

Study Area………………..…………………………………………………………………………………………………………….10

Data…………………………………………………………………………………………………………………………………………12

Survey Data………………………………………………………………………………………………………………….12

Ancillary Data……………………………………………………………………………………………………………….13

Methods…………………………….……………………………………………………………………………………………………13

Results and Discussion……………………………………………………………………………………………………………..15

Conclusion……………………………………………………………………………………………………………………………….26

References……………………………………………………………………………………………………………………………….29

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List of Figures

Figure 1: Map of the study area……………………………………………………………………………………………….11

Figure 2: Species richness per sampling station……………………………………………………………………….20

Figure 3: Average length and number of trout per sampling station………………………………………..21

Figure 4: Predictive kriging surface of watershed for total count……………………………………………..22

Figure 5: Estimation surface for total count……………………………………………………………………………..23

Figure 6: Estimation surface for species richness……………………………………………………………………..24

Figure 7: Difference graphs between observed and estimated values……………………………………..25

List of Tables

Table 1: Summary of candidate metrics……………………………………………………………………………………11

Table 2: Correlation matrix for buffer testing…………………………………………………………………………..16

Table 3: Correlation matrix all metrics……………………………………………………………………………………..18

Table 4: Stepwise regression for core metrics………………………………………………………………………….19

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Abstract

This analysis seeks to identify the important landscape-scale factors affecting brook

trout (Salvelinus fontinalis) distribution and abundance throughout the Wood-Pawcatuck

Watershed of southeastern Rhode Island. To do this, nine landscape metrics are tested to

determine if a significant relationship exists between these variables and the total number of

brook trout (Tot_Count) and the total number of species (SpecPerSam) captured at each

sampling station. Five metrics had significant correlations with total brook trout - total

agriculture, total forest, total hydric soil, total pasture and total wetland area; two metrics had

significant correlations with species richness - total forest area and total road length. A stepwise

regression was computed for metrics relating to total number of brook trout and species

richness; the resulting estimates were then used to create a suitability model for brook trout in

the watershed. The accuracy of the predictive surfaces was determined using the root mean

square error (RMSE) of the difference between observed trout abundance and the predicted

trout abundance. The predictive surface for Tot_Count yielded a RMSE of 48.2 fish. The

predictive surface for SpecPerSam had an RMSE of 3.8 species. Both RMSE values are of

comparable magnitude with the population means for each (Tot_Count = 34.4 and SpecPerSam

= 5.68), however, I believe that the accuracy of both models can be further improved with an

increased number of sampling stations and additional landscape metrics. The inclusion of water

quality metrics such as temperature, dissolved oxygen or riffle quality, might improve the

accuracy of the model due to the sensitivity of brook trout to water quality. Overall, my

recommendations for fisheries biologists are to create more comprehensive management

regimes with monitoring programs that integrate traditional field sampling with GIS and other

modeling approaches; this will allow for more successful management and restoration

programs.

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Acknowledgements

I would like to thank the following people for their invaluable help throughout the

course of this project, without them it would not have been possible. First, I would like to say

thank you to Alan Libby, freshwater fisheries biologist of the Rhode Island Department of

Environmental Management Division of Fish and Wildlife, for allowing me to use the brook

trout data that he spent over 10 years collecting as the basis of this project. I hope that this will

provide insight into the distribution of brook trout throughout the Wood-Pawcatuck watershed

and provide some ideas for locations of new sampling stations in the watershed.

I would also like to thank my major paper advisor Dr. Peter August for all of his advice

and help during the course of my study here at the University of Rhode Island. I truly appreciate

the level of patience and assistance that you have provided me throughout my time in the

MESM program.

Finally I would like to thank Mike Miner for listening to my incessant talking about brook

trout and statistics throughout the past six months, and for always being there to support me.

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Introduction

The Eastern brook trout (Salvelinus fontinalis) represents an important native cold water

species which ranges along the Atlantic coast from Newfoundland and Labrador, to the

southern Appalachian Mountains in Georgia and South Carolina. This species of trout requires

cold, clean, well-oxygenated water, and has adapted to live in many different environments,

from tiny tributaries to lakes to estuary and ocean environments (Trout Unlimited; EBTJV,

2008). In many areas of the eastern United States, brook trout populations are declining due to

reductions in available high-quality habitat, and also due to an increase in anthropogenic

stresses such as pollutants as runoff from roadways (EBTJV, 2008; Hudy et al., 2008; Trout

Unlimited). As these problems arise, it is important to monitor populations of native brook

trout and attempt to determine perturbations that may be negatively impacting them. By

developing models and other long-term study protocols, it may be possible for biologists and

natural resource managers to discover what landscape-scale factors are negatively impacting

brook trout and develop management and restoration plans and goals to address local

population needs.

This issue is important for a multitude of reasons. Brook trout represent a native

species of fish that is an important cold water indicator species. When present in a stream

system, the overall health of the ecosystem is verified because of the very specific water

chemistry needs of brook trout (Trout Unlimited). These fish also represent a species of interest

to anglers due to their natural beauty. As a native fish in this area of the country, it is important

to make sure there is adequate habitat for brook trout to thrive.

One of the difficulties associated with determining the landscape scale factors that

affect the ability of brook trout to thrive, is that they may differ over the range of the species

(Hudy et al., 2008). While there may be some factors that are similar throughout the range of

the species, the tolerance to these perturbation levels vary. Fishery biologists and other natural

resource managers studying the species have to first determine what perturbations negatively

affect brook trout in their study area, and in what magnitudes. Another difficulty associated

with the conservation and restoration of brook trout involves the high level of human sprawl

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that occurs in many of the watersheds in the home range of this species. In some areas it may

not be possible to undertake the necessary restoration measures due to existing infrastructure.

The Eastern Brook Trout Joint Venture (now referred to as EBTJV), a conglomerate of

federal, state, public and non-profit organizations, has many management and restoration

initiatives in place to improve the standing of the eastern brook trout throughout its eastern

range. In a 2008 report published by the EBTJV, titled “Conserving the Eastern Brook Trout:

Action Strategies” each member state of the program provided a detailed list of long- and

short-term management goals. Rhode Island was not included in this report, but Massachusetts

(among many other states) provided a detailed, multipart conservation strategy. Listed as the

first priority goal is assessment; this includes continuing distribution assessments, annual

population monitoring and the creation of a comprehensive brook trout GIS data layer. The

second priority goal is listed as habitat protection; this includes both protection and

improvement of brook trout habitat throughout the state. The next goal is outreach; through

this goal, Massachusetts hopes to increase public awareness and increase the participation of

landowners in habitat restoration programs. The next two goals are to increase protection and

restoration of brook trout habitat, and also to increase recreational fishing opportunities

throughout the state. A comprehensive plan, such as the ones found in the EBTJV report (2008)

mentioned above, can provide a good model for the state of Rhode Island, and other states

seeking to improve brook trout populations.

Monitoring and Modeling of Brook Trout Populations

There are many examples of situations in which traditional monitoring techniques, such

as electric fishing, have been combined with modern geospatial analytical techniques to

determine potential suitable habitat for brook trout and other trout species. These studies

provide important guidelines for the creation of similar studies. In papers by Hudy et al. (2008)

and Dunham et al. (2002), management and monitoring strategies for brook trout are discussed

in detail. Dunham et al. (2002) discusses the traditional use of electric fishing as a study tool for

brook trout. They test the hypothesis that electric fishing does not result in an increase in

upstream movement, although other studies argue that electric fishing does increases fish,

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specifically brown trout (Salmo trutta), movement upstream (Nordwall, 1999; Gowan and

Fausch, 1996). Through the use of a control and a test stream, Dunham et al. (2002) were able

to determine that electric fishing does not increase the amount of upstream movement by

brook trout. These findings are important because they verify that electric fishing is an effective

way for sampling populations of trout and that electric fishing does not bias fish counts due to

fish moving out of the study area. Hudy et al. (2008) use electrofishing data from state agencies

throughout the eastern range of the brook trout to create a habitat suitability model; this

model was designed to be used at a regional scale, and the five metrics included may be slightly

less effective or responsive at a smaller study scale. The model used in this paper is briefly

described below, as it was a great influence in the creation of the model for use in the Wood-

Pawcatuck Watershed.

Hudy et al. (2008) describe the relationship between land use characteristics and the

status and distribution of brook trout throughout their eastern range in the United States. The

goals of this study were to classify each subwatershed within the study area based on

remaining self-sustaining brook trout populations, to develop a model that could be used to

predict the status of brook trout in areas where the status is not known, and to identify metrics

that indicate changes in the status of a specific population of brook trout. There were a total of

11,754 subwatersheds in 17 states included in the study area. Data from each state was

provided by state agencies; Rhode Island’s data were provided by Alan Libby of RI Fish and

Wildlife.

Originally, 63 “candidate metrics” for determining brook trout status were developed;

these were then put through four screening tests which helped test the completeness, range,

redundancy and responsiveness of each metric (Hudy et al., 2008). Five core metrics were then

chosen for the model; these included, total forest (deciduous, evergreen and mixed –

TOTAL_FOREST), percent agriculture (PERCENT_AG), mixed forest (MIXED_FOREST2), road

density (ROAD_DN), and deposition of sulfate and nitrate (DEPOSITION; Hudy et al., 2008).

After running the model, it was determined that 1,660 (31%) subwatersheds had intact habitat,

1,859 (35%) subwatersheds had reduced habitat, 1,482 (28%) subwatersheds had habitat from

which brook trout were extirpated, and 278 (5%) subwatersheds had an absence of brook trout

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with an unknown explanation for why (Hudy et al., 2008). The conclusion of the authors was

multifold. They suggested that natural resource managers watch certain levels of agriculture,

deposition and forest in watersheds, as these may be good indicators for potential concern.

They also stress that additional inventory and monitoring is necessary to gather more data on

the status of brook trout.

The creation of viable stream suitability indices for brook trout in the Mid-Atlantic

region of the United States was addressed by Smith and Sklarew (2012) in a paper that focused

on statistical modeling and multimetric indices for modeling. They discussed alternative indices

that exist, but do not cater directly to the habitat needs to brook trout. The authors developed

five metrics that are representative of suitable brook trout habitat; percentage of watershed in

agriculture, distance from the sample site to nearest road, riffle/run quality, dissolved oxygen

concentration, and water temperature (Smith and Sklarew, 2012). With these five metrics, the

authors developed what they termed the suitability statistic (S) which is calculated by the

equation:

S = -0.78 + -0.4(TEMP_FLD) + 0.3(DO_FLD) + 0.19(RIFFQUAL) + 0.02(%Ag) + 1.11(LOG_RD)

Through the use of a principal component analysis, Smith and Sklarew (2012) determined that

all of these variables (metrics) are independently valuable to assess stream suitability for brook

trout. When compared with the Mid-Atlantic brook trout stream index, the suitability statistic

seemed to verify the results of the stream index, proving its worth as a predictor of adequate

brook trout habitat; although the authors caution that this is the first time it has been used

(Smith and Sklarew, 2012).

Anthropogenic stresses and other human activities can influence brook trout

populations throughout their native range. Power and Power (2005) used a model-based

approach to determine how brook trout are affected by human-induced stresses. Power and

Power (2005) discussed how there is a need for a shift from the study of organismal-level

impacts, to population-level impacts in environmental risk assessment (Emlen, 1989). After

discussing the merits of various models for measuring population-level effects of anthropogenic

9

stresses, the authors proposed their own modeling framework to go beyond representing

population abundance, which can be inaccurate, to showing pre-cursors to environmental

stress (Munkittrick & Dixon, 1989) that can give biologists an indication that a population may

be experiencing some kind of stress. The goal of their paper was to create an individual-based

model for the population-level analysis of brook trout (Power and Power, 1995). The basic

model structure can be described as working through the birth to death process that all

individual trout experience. The authors make the argument that in instances of chronic stress,

abundance studies may under-estimate, or not detect harm being done to the population by

stressors (such as the toxic agricultural byproduct examined in this study), but that

reproduction and growth rates will more than likely suffer under the conditions imposed by

these stressors and provide an indication that a population is being stressed (Power and Power,

1995). This is important for biologists to consider when studying brook trout populations; the

data used in this study represents abundance data and may not accurately represent the status

of the brook trout population in the Wood-Pawcatuck watershed. Although the primary test of

the model in this instance used a toxic agricultural byproduct (toxaphene), the authors argue

for the versatility of the model, and that it could be successfully used to study other

anthropogenic stressors such as habitat loss or exploitation (Power and Power, 1995).

The response of populations of brook trout to habitat fragmentation, another

important factor to consider in the success of brook trout populations, was addressed in a

paper by Letcher et al. (2007). In this study, models were used to determine how populations of

brook trout in open and isolated systems would respond to reductions in connectivity and

decreased immigration from other populations (resulting in decreased genetic variability). By

studying the persistence of open and isolated populations, it may be possible for natural

resource managers to better understand why some populations are more successful than

others. This study was completed over a 6-year period (2001-2006) in Western Massachusetts;

during this time, three distinct cohorts of brook trout were sampled via electrofishing four

times per year (once per season; Letcher et al., 2007). It was determined that, as expected,

isolated populations were genetically different from open populations; it was also determined

that these isolated populations have a survival rate that is higher for smaller fish (Letcher et al.;

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2007). In open systems, when the mainstem was blocked, extinction typically occurred after a

period of ~2-6 generations (Letcher et al., 2007). In general, the results determined that habitat

fragmentation could increase the likelihood of extinction in a stream system and that this

occurred independently of reductions in habitat availability (Letcher et al., 2007). This study

provides natural resource managers and fishery biologists with a good idea of how habitat

fragmentation can affect brook trout populations.

Objectives

This paper will address factors that influence brook trout distribution and presence in

stream systems, specifically in the Wood-Pawcatuck watershed in southern Rhode Island. I will

attempt to determine potential locations in the watershed that may represent suitable brook

trout habitat, and potential future sampling locations. Native brook trout are an important cold

water species and serve as an indicator of a healthy stream environment in Rhode Island. The

goal of this paper is to provide Alan Libby, a fisheries biologist with Rhode Island Department of

Environmental Management, with information on the spatial distribution of brook trout based

on his electric fishing surveys, and to provide potential future sampling locations based on a

suitability analysis run in GIS.

Study Area

The Wood-Pawcatuck River watershed is one of the largest watersheds in the state, and

includes 10 Rhode Island towns (Charlestown, Coventry, East Greenwich, Exeter, Hopkinton,

North Kingstown, Richmond, South Kingstown, Westerly, and West Greenwich) and four

Connecticut towns (North Stonington, Sterling, Stonington and Voluntown; RIRC). This

watershed represents important habitat not only for brook trout, but also for many

anadromous species of fish such as the alewife (Alosa pseudoharengus) and blueback (Alosa

aestivalis) river herrings, and the American shad (Alosa sapidissima). Due to the many

important species that thrive in this watershed, it is the site of many restoration projects such

as dam removal and fish passage projects all along the Pawcatuck River. The Wood-Pawcatuck

11

watershed is also a candidate for addition to the National Wild and Scenic River System (The

Library of Congress THOMAS).

Figure 1: Map of the study area showing sampling locations.

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Data

Survey Data

In the state of Rhode Island, yearly stream surveys are undertaken to determine species

composition of different stream systems and to determine the presence or absence of brook

trout. State fisheries biologist Alan Libby has carried out his brook trout stream surveys since

1998. He determines the presence or absence of brook trout, collects information on the

number of trout captured, their total length (TL) and data on the number of other species

captured in the study stream. Data on water quality, such as dissolved oxygen, conductivity, pH

and temperature are also collected. To carry out these surveys, a Smith-Root, Inc. Model 12-A

backpack electrofisher is utilized. The voltage of the electrofisher is dependent on the

conductivity, as this affects the effectiveness of the electrofisher; however the voltage range is

typically between 200 and 700 volts. The data used for the analysis in this report are specific to

the Wood-Pawcatuck watershed.

These data were consolidated into an extensive Microsoft Excel file and were then

processed before being imported into ArcGIS. The first step was to select only those sample

dates that were obtained with a backpack electrofisher; all other data obtained through other

means (boat electrofishing, seining, diving, gill netting and fish traps) were excluded from this

study. It was also necessary to format the date scheme as this was not immediately intuitive

without explanation. The final step was to create one entry for each station. The original data

included multiple entries for one sampling date because each species caught was listed

separately; as this study focused on brook trout (how many and average size) and the total

number of species captured, extra entries not relevant to brook trout were removed. This was

done by recording the total number of species, deleting all data but brook trout data and

adding a new field titled “Btrout_Captured” which was populated with ‘y’ for yes brook trout

captured or ‘n’ for no brook trout captured. Figure 1 provides a map of the study area that

shows all sampling stations, and their designation as having successfully sampling brook trout

or not.

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Ancillary Data

Additional GIS data such as shapefiles of HUC 12 watersheds, streams, lakes and ponds,

land use, wetland, soils, TIGER roads, impervious surface (raster dataset) and the state line

were used; all of these datasets were obtained from Rhode Island Geographic Information

System (RIGIS) Data Distribution System. These data were clipped (and if necessary dissolved)

to the study area using ArcMap software (V10.2). Esri basemaps were also utilized in mapping

process.

Methods

Three potential buffer distances of 100 meters, 200 meters and 300 meters were

created around each sampling station. The 100 meter buffer was chosen because this buffer

distance was found to be most appropriate for large scale analyses (Hudy et al. 2008). A 300

meter buffer distance was chosen following this logic: I determined the average distance

between my sampling stations through the utilization of the Point Distance tool (Analysis

toolbox). The mean distance between points was 13.40 kilometers with a fairly large standard

deviation of 6.59 kilometers. Due to this large distance between sampling stations, I chose a

300 meter buffer distance because I believed that it had the potential to provide a more

accurate landscape-scale representation of land use practices around each sampling station.

The buffer distance of 200 meters was chosen as a mid-point between the buffer distance

taken from the Hudy et al. (2008) study and the value of 300 meters. I believe that it may bridge

the gap between including too much information (300 m) and not including enough information

(100 m) on surrounding land use practices.

The nine candidate metrics used in this study are summarized in Table 1 on the next

page. Some of these metrics were modeled after those used by Hudy and his colleagues

(2008), while others were selected on the basis of the potential for them to positively or

negatively affect brook trout abundance and distribution throughout a watershed in general.

To test which buffer distance provided the best analytical power, three metrics (Tot_Wet,

Tot_Imp, and Tot_Road) were clipped to each of the three buffer areas and through the use of

the Union and Dissolve tools (Analysis and Data Management toolboxes respectively), a new

14

layer was created in which the total hectares of the metric was provided for each buffered area.

This new layer was then temporarily joined to the original sample station location dataset and

through the utilization of the Field Calculator; a new field was permanently added to represent

the total amount of each metric that appeared at each station for all three buffer distances.

List of Candidate Metrics for Analysis

Metric Abbreviation Description Units Data Source

Total Forest Tot_Forest All forest area, including all deciduous, evergreen and mixed forest types.

Hectares (ha)

RIGIS Land Use dataset (1995)

Total Agriculture Tot_Ag All agriculture, including cropland, orchards and turf.

ha RIGIS Land Use dataset (1995)

Total Pasture Tot_Past All pasture, including active and inactive pasture areas.

ha RIGIS Land Use dataset (1995)

Total Residential Tot_Res

All residential areas, including single family, multi-family, apartments and condominiums.

ha RIGIS Land Use dataset (1995)

Total Wetland Tot_Wet Freshwater wetland. ha RIGIS Wetlands dataset (1993)

Total Hydric Tot_Hydric All soils classified as hydric. ha RIGIS SSURGO Soil Polygon dataset (2013)

Total Impervious Surfaces

Tot_Imp All impervious surface area (roads, parking lots and buildings).

ha RIGIS Impervious Surface dataset (2013)

Total Commercial and Industrial

Tot_Comm All commercial and industrial land.

ha RIGIS Land Use dataset (1995)

Total Road Tot_Road Total road length. meters

(m) RIGIS TIGER Roads dataset (2006)

Table 1: Summary of the nine candidate metrics tested for use in this study.

To determine the appropriate buffer distance, R (a free software environment for

statistical computations and graphing, also known as “The R Project for Statistical Computing”)

was used in conjunction with the ‘Rcmdr’ package to undertake a number of statistical tests.

First, each variable was tested for normality with the Shapiro – Wilk test for normality; for any

p-value less than 0.05 the variable deviated from a normal distribution. Since the data were

15

proven to be non-parametric (almost all variables were not normally distributed), a Spearman

Rank Correlation was used to test the correlation of total wetland, total impervious surface and

total roads with total count of brook trout and species richness. The result (Table 2) shows that

the coefficients of correlation are relatively similar and exhibited strong negative correlations

between wetlands and total count for all three buffer distances. Because of the similarity in

results among the three buffer distances, I used the 200 meter buffer for all subsequent

analyses.

All remaining metrics were sampled around each sampling station using the 200 meter

buffer distance. Further statistical testing was done to determine which metrics were

appropriate for the formulation of the suitability analysis. The Shapiro – Wilk test for normality

showed that all of the variables were not normally distributed, therefore, I used the non-

parametric Spearman Correlation test to evaluate relative importance of variables in

comparison to both Tot_Count and SpecPerSam.

The final step was to create predictive surfaces of each dependent variable (Tot_Count

and SpecPerSam), however before the predictive surfaces could be created, the layers for each

variable needed to be converted from polygons to rasters that were classified so that 1 was

equal to “yes” (variable present) and 0 was equal to “no” (variable not present). The Focal

Statistics tool (Spatial Analyst toolbox) was used to calculate the neighborhood around each

pixel based on the VALUE field. The neighborhood type was chosen to be circle with a radius of

656.168 feet (the map unit equivalent of the 200 meter buffer size), and the statistic type of

SUM was chosen so that the total number of “1’s,” or yes pixels in a neighborhood could be

added up. The resulting raster surface provided information on the number of pixels that had

the variable (i.e., agriculture) and the number of pixels that did not have the variable, and

created the basis for layers to be input into Raster Calculator for the calculation of both

predictive surfaces.

Results and Discussion

Five variables (Tot_Ag, Tot_Forest, Tot_Hydric, Tot_Past, and Tot_Wet) were highly

correlated with the total count of brook trout, and two variables (Tot_Forest and Tot_Road)

16

were highly correlated with species per sample (Table 3). Surprisingly, Tot_Count displayed a

strong negative correlation with the total amount of wetlands and also a strong negative

correlation was apparent between Tot_Count and the total amount of hydric soil. It was

surprising to see that there was a strong positive correlation between SpecPerSam and the total

length of roads.

Correlation and pairwise p-values for 100 meter buffer

Tot_Imp100 Tot_Road100 SpecPerSam Tot_Count Tot_Wet100

Tot_Imp100 0.70 0.10 0.11 -0.40 Tot_Road100 0.0000 0.07 0.15 -0.37

SpecPerSam 0.4038 0.5367 0.33 -0.10

Tot_Count 0.3596 0.1767 0.0028 -0.43

Tot_Wet100 0.0002 0.0008 0.3899 0.0000

Correlation and pairwise p-values for 200 meter buffer

Tot_Imp200 Tot_Road200 SpecPerSam Tot_Count Tot_Wet200 Tot_Imp200 0.68 0.15 0.06 -0.32 Tot_Road200 0.0000 0.25 0.10 -0.46

SpecPerSam 0.1767 0.0256 0.33 -0.15

Tot_Count 0.6105 0.3960 0.0028 -0.49

Tot_Wet200 0.0046 0.0000 0.1767 0.0000

Correlation and pairwise p-values for 300 meter buffer

Tot_Imp300 Tot_Road300 SpecPerSam Tot_Count Tot_Wet300 Tot_Imp300 0.63 0.13 0.02 -0.18 Tot_Road300 0.0000 0.21 0.08 -0.38

SpecPerSam 0.2579 0.0627 0.33 -0.17

Tot_Count 0.8530 0.4856 0.0028 -0.49

Tot_Wet300 0.1179 0.0006 0.1271 0.0000 Table 2: Result of the summary statistics for each buffer distance. Italicized values below the diagonal of the table represent pairwise p-values, while values above the diagonal in the table are the Spearman coefficients of correlation. Pairwise associations between fish metrics and landscape variables that show significant (or nearly so) correlations (P < 0.2) are outlined in red.

Based on the results of the Spearman Correlation matrix, a Stepwise Linear Regression

was completed twice, once with Tot_Count as the dependent (response) variable, and once

with SpecPerSam as the dependent variable (Table 4). Due to the fact that different metrics

corresponded with each response variable, the regression was completed twice so that more

actuate values could be obtained for both the intercept of each line and for each estimated

17

multiplier for each metric. For total number of brook trout, both Tot_Past and Tot_Forest

significantly predicted brook trout, R = 0.259, F(5, 75) = 5.039, p < 0.05. For species richness, the

result showed that Tot_Road was a very significant variable in relation to the prediction of how

many species are found at sampling sites, R = 0.127, F(2, 75) = 5.451, p < 0.05. The resulting

estimated value for the intercept and each metric was then used to create a predictive surface

for the total number of brook trout and species richness throughout the watershed using Raster

Calculator in ArcGIS.

The units of each independent variable was converted to hectares by multiplying by 900

square feet (determined from a pixel size of 30 feet by 30 feet) and divided by 107,639 square

feet, or the number of square feet in a hectare. In the case of roads, the variable is not an area

value, but one of length; to approximate the number of meters of roads in 1 pixel, the value of

9.144 was used. This represents 30 feet, or the length of one side of a pixel. The resulting

surfaces show estimate numbers of brook trout and number of species, and can help to show

expected regional trends in numbers throughout the Wood-Pawcatuck Watershed. The

following equations were used to create the predictive raster surfaces:

BTTotal = -36.685 + (12.373 * (("Ag_FocalStats" * 900) / 107639)) + (5.718 *

(("Forest_FocalStats" * 900) / 107639)) + (5.614 * (("Hydric_FocalStats" * 900) / 107639)) +

(22.913 * (("Pasture_FocalStats" * 900) / 107639)) + (-3.542 * (("Wetland_FocalStats" * 900) /

107639))

Where BTTotal is the total number of brook trout captured at a location.

SR = 2.109084 + (0.144730 * (("Forest_FocalStats" * 900) / 107639)) + (0.004033 *

("Road_FocalStats" * 9.144))

Where SR is the total species richness of fish captured at a location.

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19

Stepwise Regression: Tot_Count Overall F(5, 72) = 5.039, P < 0.001

Estimate Std. Error t-value Pr(>|t|)

(Intercept) -36.685 27.514 -1.333 0.186621

Tot_Ag 12.373 6.980 1.773 0.080540 . Tot_Forest 5.718 2.039 2.805 0.006467 **

Tot_Hydric 5.614 3.929 1.429 0.157354

Tot_Past 22.913 6.584 3.480 0.000855 ***

Tot_Wet -3.542 4.095 -0.865 0.390046

Stepwise Regression: SpecPerSam Overall F(2, 75) = 5.415, P < 0.01

Estimate Std. Error t-value Pr(>|t|)

(Intercept) 2.109084 1.127807 1.870 0.065400 . Forest200_Area 0.144730 0.118763 1.219 0.226800 Road200_Length 0.004033 0.001501 2.686 0.008900 **

*** < 0.0001, **= 0.001, and . < 0.1

Table 4: The results of the stepwise regressions for both total count and species richness as response variables are presented above. For the Tot_Count analysis, the p-value was < 0.001 and for the SpecPerSam analysis, the p-value was < 0.01

Finally, to determine what class each stream segment falls into, each raster surface was

converted to a polygon feature class and the reclassify tool (Spatial Analyst toolbox) was used

to create five classes for the brook trout predictive surface, and four classes for the species

richness predictive surface. The Intersect tool (Analysis toolbox) was then used to essentially

extract the values from the polygons to the stream segments (a line feature). By doing this, it is

possible to determine what class each stream segment falls into by symbolizing the layer by the

“gridcode” attribute field that details the expected amount of either brook trout or species in a

given area of the watershed.

20

For this study a total of 78

sampling stations were used; this includes

a double count of those stations at which

sampling occurred on more than one

occasion. Out of these 78 sampling

stations, brook trout have successfully

been captured at 60.3% of them, while

the remaining 39.7% of these stations

have been unsuccessful in capturing

brook trout. Species richness varied

greatly throughout the watershed; at

some stations, only 1 to 3 different

species were captured, while at other

stations 12-18 different species were

captured. Figure 2 displays the varied

species richness throughout the

watershed and demonstrates that the

majority (60.3%) of sampling stations only

captured 6 different species or less. There

are only 2 stations at which greater than 12 species were captured, and a small handful of

stations at which between 6 and 12 species were observed.

The average total length of brook trout represents the average total length for all of the

trout captured at that station. The overall (watershed-wide) average brook trout length was

128.1 millimeters; however, the standard deviation was fairly large at 40.2 millimeters. This

large standard deviation shows that there was a high variability in the sizes of trout being

captured throughout the watershed. Figure 3 displays both the distribution of average total

length throughout the watershed, and also the distribution of the number of trout captured at

each station throughout the watershed. Since the total number captured at each station will be

Figure 2: Species richness throughout the watershed can generally be described as fairly low as 60.3% of sampling stations only observed 6 species or less.

21

looked at more in depth later in the discussion, this map can provide some reference to how

brook trout are distributed throughout the watershed as a result of this electrofishing survey.

Prior to the creation of the estimation surfaces, one more analysis was completed. An

ordinary kriging surface was created using the values of total count as an input. This surface

was created because it can be visually compared with the output results of the estimation

surface for total count. While this method is less precise than the estimations made using raster

calculator (as described above in the Data Processing & Analysis section), the interpolation

should be moderately accurate due to the distribution of the sampling stations. While the

distribution is fairly even, there are a higher number of stations in the northern areas of the

watershed, while the southern areas are slightly barren. The results (Figure 4) show that there

are two areas in the northern parts of the watershed where more fish can be expected to be

captured, while the southern area of the watershed is not expected to yield as many fish. This

Figure 3: Average total length and total count of brook trout sampled in the watershed. Total length refers to the length of the fish from the tip of the snout to the end of the tail.

22

surface, although a good visual aid, is overly

descriptive as the majority of the areas

covered by the surface are land areas where

trout cannot live.

The estimation surfaces for both total

count and species richness provided some

very useful information visually before and

after the information extraction via the

Intersect tool took place. These patterns are

much easier to see on the complete surface

than the surface that has been extracted only

to the stream segments (Figure 5). Unlike the

trends displayed in the krigged surface, the

areas where more trout are more likely to

occur in abundance are in two areas along

the eastern portion of the watershed and also

in the southwest portion of the watershed.

This is even more unexpected when the

spatial pattern of the total count from Figure

3 is considered due to the fact that the entire

southern part of the watershed, both west

and east, have relatively low brook trout

catch at each station in that area. On the extracted portion of the estimation surface, spatial

patterns are harder to discern, but three distinct clusters of “hot streams,” or streams where

more trout may be captured, are fairly apparent. These are located in the northeast, southeast

and southwest portions of the watershed as mentioned before when discussing the complete

estimation surface. The estimated surface values were then compared with the original survey

data to determine the fit of the model. In general there was high variability between actual

values and estimated values; this variability was higher than actual values at some stations, and

Figure 4: A predictive kriging surface of the watershed based on the values of total count as interpolated by ArcGIS.

23

lower than actual values at other stations. The average difference between the estimated

surface value and observed survey value was 32.4 fish, with a standard deviation of 35.2 (see

figure 7). The root mean square error (RMSE) was determined to be 48.2 for total number of

trout; when compared to the mean value for the number of brook trout captured at each

sampling station (mean = 34.4), the RMSE value is fairly close to the population mean. Field

verification is necessary to determine the true accuracy of this model to field conditions.

The spatial patterns of the species richness estimation surface are harder to detect; on

appearance, Figure 6 appears to show that the majority of areas within the watershed fall into

Figure 5: Figure 5A displays the complete estimation surface for total count, while figure 5B displays the extraction of the estimate values to the Wood-Pawcatuck streams.

A B

24

one of two classes, “2 to 4” species or “4 to 6” species. The areas represented as “4 to 6”

species follow fairly linear patterns; these patterns represent the roads

throughout the watershed. Due to the importance of roads in the model, they appear distinctly

on the complete surface. In Figure 6B where the estimated values have been extracted to the

streams, it becomes more evident that the majority of stream area throughout the watershed

falls into the “2 to 4” species class. Very few stream segments are classed as “6 to 9” species

and even less are classed as potentially containing “9 to 18” different species. Only one small

hot spot can be found on this surface; in the extreme southwestern portion of the watershed

on the border with Connecticut, a small cluster of stream segments that appears to potentially

contain a higher number of species than other areas of the watershed. The average difference

between the estimated surface value and observed survey value was 3.1 species, with a

Figure 6: The map to the left displays the complete estimation surface for species richness, while the map to the right displays the extraction of these estimate values to the Wood-Pawcatuck streams.

A B

25

standard deviation of 2.2 (see figure 7). The root mean square error (RMSE) was determined to

be 3.8 species for species richness. When compared to the population mean for the number of

species captured at each sampling station (mean = 5.68 species) the RMSE value is of

comparable magnitude, a pattern observed with the number of brook trout captured at a site.

Like with the brook trout predictive surface, field testing of the model would be necessary to

determine if the model is in fact an accurate representation of field observed values.

Figure 7: The top graph depicts the vast differences in the observed number of brook trout captured at each station and the estimated values of trout that could potentially be captured at each station. The lower graph depicts the differences between the observed number of species at each station and the estimated number of species that could potentially be captured at each station.

26

Conclusions

The use of models, specifically habitat suitability models for different trout species, is

well documented in fisheries literature. Many of these models use electric fishing data as a

basis for the study (Hudy et al., 2008; Letcher et al., 2007). Authors of these studies also argue

that when creating a habitat suitability analysis, while there are many indices for brook trout

“out there,” many may be too “broad-based” to be of significant use in a specific geographic

area outside of the area of development (Binns and Eiserman, 1979; Schmitt, Lemly, & Winger,

1993). This stresses the need for unique and individualized models to be created specific to the

study area as habitat needs may vary by area.

My suitability model is specific to the Wood-Pawcatuck watershed. By statistically

analyzing nine candidate metrics, which were chosen based on metrics that were successfully

used in other studies (Hudy et al., 2008; Smith and Sklarew, 2012), and then determining how

these metrics correlated to both the total number of captured brook trout and the total

number of captured species, it was possible to create two unique estimation surfaces which

could potentially be used by state fisheries biologists in the selection of new brook trout

sampling stations. Once the final metrics were chosen for each analysis, two surfaces based on

equations gained through the stepwise regression analysis estimation values were created.

The final metrics for this study were somewhat expected, however there were some

surprises associated with them. Although total road length was expected to be an important

factor, it was surprising to see that species richness has a positive relationship with this metric,

and that the total number of brook trout had no relationship to road density. Intuitively, I

would have predicted that the greater the amount of roadways in a watershed, the less species

diversity there will be in that area; the results of the Spearman Rank Correlation show that this

is not the case. It was also surprising to see that there was no relationship between total

impervious surface area and either response variable. This suggests that the presence of roads

and other impervious surfaces does not affect fish abundance, and that runoff and potential

pollutants from this source are not significant.

Another important factor in the creation of accurate suitability models is the use of

accurate data. Hudy et al. (2008) noted that more inventory and monitoring is necessary to

27

gather more data on the status of brook trout; 33% of the subwatersheds included in their

study are did not have a sufficient amount of data to indicate if brook trout habitat was being

influenced by land use characteristics. The accuracy of the model created through this analysis

could potentially have been improved with more sampling stations spread more evenly

throughout the watershed. Model accuracy could also have been improved with more available

data layers. The metrics used throughout this study consisted of those readily available as GIS

layers on the RIGIS geospatial data download site. If more information was available to create

GIS data layers for dissolved oxygen, conductivity, riffle quality, deposition of nitrates and

sulfates, the accuracy of the model could have been increased. With more landscape scale data

available, it would have been possible to create a wider variety of candidate metrics. The

inclusion of metrics that directly addressed water quality could have vastly improved model

accuracy as brook trout are extremely sensitive to, and indicative of good water quality (Trout

Unlimited).

Seasonal variation in water temperature, precipitation levels, and air temperature may

have also affected the abundance of both brook trout and the number of species throughout

the sampling period of 1998 to 2012. The majority of sampling occurred from May to

September and can be considered (for the most part) to be the hot and dry summer months,

although some years, the summer months can be rainy and cool. This seasonal variation may

affect the abundance of both trout and other species; in hotter, drier years it may be expected

that less fish (of any kind) will be sampled. Another factor to consider is annual variation in

trout populations and annual variations in the abundance of other species. Not every year can

be considered a strong year, successful reproduction and growth of a year class naturally varies

year to year. These natural fluctuations in populations can also affect the sampling results. For a

more representative sample, it would be recommended to sample locations throughout the

watershed in all four seasons so that seasonal variability is accounted for. By also sampling the

same stations every year, in all four seasons it will be possible to create a more accurate model

for this watershed.

Although the average difference between observed and estimated values were high for

both the total count of trout and species richness (32.4 and 3.1 respectively), without further

28

field testing to verify the estimates made by these models, it is hard to know the true accuracy

of this model. Improvements in the number and variety of candidate metrics in this study could

provide state of Rhode Island fisheries biologists with a more accurate predictive surface. An

accurate surface could assist biologists in the selection of future electric fishing sampling sites

throughout the Wood-Pawcatuck watershed. This type of model could also be used as the basis

for the creation of a state-wide predictive surface that could be used to select sampling sites

statewide and also to select potential areas for protection or restoration efforts.

A more comprehensive management approach that integrates both traditional

management techniques with geospatial mapping and modeling could help to greatly improve

the effectiveness of monitoring and restoration programs. For example, in a study completed

by Letcher et al. (2007), models are used to determine how populations of brook trout in open

and isolated systems will respond to reductions in connectivity and decreased immigration

from other populations (resulting in decreased genetic variability). If used in conjunction with

the suitability study by Hudy et al. (2008), and traditional field sampling, conservation and

restoration goals may become more defined, and provide fisheries managers and biologists

with a framework to work within.

29

References

Binns, N. A., & Eiserman, F. M. (1979). Quantification of fluvial trout habitat in Wyoming. Transactions of the American Fisheries Society, 215-228.

Dunham, K. A., Stone, J., & Moring, J. R. (2002). Does electric fishing influence movement of fishes in streams? Experiments with brook trout, Salvelinus fontinalis. Fisheries Management and Ecology, 249-251.

Eastern Brook Trout Joint Venture. (2006). Eastern Brook Trout: Status and Threats. Eastern Brook Trout Joint Venture.

Eastern Brook Trout Joint Venture. (2008). Conserving the Eastern Brook Trout: Action Strategies. Eastern Brook Trout Joint Venture.

Emlen, J. M. (1989). Terrestrial population models for ecological rish assessment: a state-of-the-art review. Enviornmental Toxicology, 831-842.

Frank, B. M., Piccolo, J. J., & Baret, P. V. (2011). A review of ecological models for brown trout: towards a new demogenetic model . Ecology of Freshwater Fish, 167-198.

Gowan, C., & Fausch, K. D. (1996). Mobile brook trout in two high-elevation Colorado streams: re-evaluating the concept of restricted movement. Canadian Journal of Fisheries and Aquatic Sciences, 1370-1381.

Hudy, M., Thieling, T. M., Gillespie, N., & Smith, E. P. (2008). Distribution, Status, and Land Use Characteristics of Subwatersheds within the Native Range of Brook Trout in the Eastern United States. North American Journal of Fisheries Management, 1069-1085.

Letcher, B. H., Nislow, K. H., Coombs, J. A., O'Donnell, M. J., & Dubreuli, T. L. (2007). Population Response to Habitat Fragmentation in a Stream-Dwelling Brook Trout Population. PLOS ONE, 1-11.

Libby, A. D. (2013). Inland Fishes of Rhode Island. Kingston: Rhode Island Division of Fish and Wildlife.

Munkittrick, K. R., & Dixon, D. G. (1989). Use of white sucker (Catostomus commersoni) populations to assess the health of aquatic ecosystems exposed to low-level contaminant stress. Canadian Journal of Fisheries and Aquatic Science, 1455-1462.

Nordwall, F. (1999). Movements of brown trout in a small stream: effects of electrofishing and consequences for population estimates. North American Journal of Fisheries Management, 462-469.

30

Platts, W. S., & Nelson, R. L. (1988). Fluctuations in Trout Populations and Their Implications for Land-Use Evaluation. North American Journal of Fisheries Management, 333-345.

Power, M., & Power, G. (1995). A modellling framework for analyzing anthropogenic stresses on brook trout (Salvelinus fontinalis) populations. Ecological Modelling, 171-185.

Reid, S. M., Yunker, G., & E., J. N. (2009). Evaluation of single-pass backpack electric fishing for stream fish community monitoring. Fisheries Management and Ecology, 1-9.

RIGIS 1989. State Boundary; statline. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: December 2013). RIGIS 1993. Wetlands; wetlands93. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: November 2013). RIGIS 1998. Hydrolines; streams. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: December 2013). RIGIS 2005. Land Use 1995; rilu95c_shp. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: November 2013). RIGIS 2006. Roads - TIGER; tigerds05c. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: November 2013). RIGIS 2009. Watershed Boundary Dataset; HUC12_RI_09. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: December 2013). RIGIS 2011. Lakes and Ponds; lakes5k10. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: December 2013).

31

RIGIS 2013. Impervious Surfaces. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: October 2013). RIGIS 2013. Soil Survey Geographic (SSURGO) Soil Polygons; soils13. Rhode Island Geographic Information System (RIGIS) Data Distribution System, URL: http://www.edc.uri.edu/rigis, Environmental Data Center, University of Rhode Island, Kingston, Rhode Island (last date accessed: November 2013).

Rhode Island Rivers Council. (n.d.). Wood-Pawcatuck Watershed. Retrieved September 2013, from Rhode Island Rivers Council: http://www.ririvers.org/Watersheds/WoodPawcatuckWatershed.htm

Schmitt, C. J., Lemly, A. D., & Winger, P. V. (1993). Habitat Suitability Index Model for Brook Trout in Streams of the Southern Blue Ridge Province: Surrogate Variables, Model Evaluation, and Suggested Improvements. Biological Report 18.

Smith, A. K., & Sklarew, D. (2012). A stream suitability index for brook trout (Savelinus fontinalis) in the Mid-Atlantic United States of America. Ecological Indicators, 242-249.

The Library of Congress THOMAS. (n.d.). Daily Digest - Tuesday, June 11, 2013. Retrieved September 2013, from Congressional Record: http://thomas.loc.gov/cgi-bin/query/B?r113:@FIELD%28FLD003+d%29+@FIELD%28DDATE+20130611%29

Trout Unlimited. (n.d.). Biology & Habitat Needs. Retrieved September 2013, from Trout Unlimited: http://www.tu.org/conservation/eastern-conservation/brook-trout/education/biology-habitat-needs

Trout Unlimited. (n.d.). Brook Trout. Retrieved September 2013, from Trout Unlimited: http://www.tu.org/conservation/eastern-conservation/brook-trout

Trout Unlimited. (n.d.). The New England Brook Trout: Protecting a Fish, Restoring a Region. Arlington: Trout Unlimited.