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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
1
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
2
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
3
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.
4
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.
5
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
6
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,
7
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
8
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.;
10
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.
12
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.
13
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.
18 Ta
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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
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