Indiana University – Purdue University Fort Wayne
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Masters' Theses
Graduate Student Research
5-2014
The Effects of Water and Habitat Quality on
Amphibian Assemblages in Agricultural Ditches
Abel Jesus Castaneda
Indiana University - Purdue University Fort Wayne
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Abel Jesus Castaneda (2014). The Effects of Water and Habitat Quality on Amphibian Assemblages in Agricultural Ditches.
http://opus.ipfw.edu/masters_theses/28
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The Effects of Water and Habitat Quality on Amphibian Assemblages in Agricultural Ditches
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Master of Science
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Mark A. Jordan
Bruce A. Kingsbury
Robert B. Gillespie
Peter C. Smiley
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THE EFFECTS OF WATER AND HABITAT QUALITY ON AMPHIBIAN
ASSEMBLAGES IN AGRICULTURAL DITCHES
A Thesis
Submitted to the Faculty
of
Purdue University
by
Abel Jesus Castaneda
In Partial Fulfillment of the
Requirements for the Degree
of
Master of Science
May 2014
Purdue University
Fort Wayne, Indiana
ii
For my parents who have always believed in me.
iii
ACKNOWLEDGMENTS
I would like to thank Dr. Mark Jordan and Dr. Robert Gillespie for allowing me to
work on this project and for all the guidance and advice given to me. I would also like to
thank Dr. Peter Smiley for all the help and advice regarding the statistical analyses.
Thanks to Katarina Aram, Jennifer Sposito, and Ben Judge for helping me with the
collection of amphibians. My parents have been quite supportive and I would like to
thank them for this and all that they have given me to get to this point.
iv
TABLE OF CONTENTS
Page
LIST OF TABLES ...............................................................................................................v
ABSTRACT....................................................................................................................... vi
INTRODUCTION ...............................................................................................................1
METHODS ..........................................................................................................................4
Study Area ...............................................................................................................4
Water Chemistry and Hydrology .............................................................................5
Substrate...................................................................................................................7
Amphibian Assemblages .........................................................................................7
Statistical Analyses ..................................................................................................8
RESULTS ..........................................................................................................................12
DISCUSSION ....................................................................................................................15
LIST OF REFERENCES ...................................................................................................19
APPENDIX ........................................................................................................................23
v
LIST OF TABLES
Appendix Table
Page
A1.
Substrate categories and particle sizes of substrate classifications ........................23
A2.
Instream habitat classifications and descriptions ...................................................23
A3.
Sampling periods for Cedar Creek, Indiana and Upper Big Walnut
Creek, Ohio. ...........................................................................................................24
A4.
Eigenvalues for the first ten axes of the Principal Component
Analysis of instream habitat from channelized agricultural headwater
streams in Cedar Creek, Indiana, and Upper Big Walnut Creek, Ohio,
2008 to 2009 ..........................................................................................................24
A5.
Eigenvalues for the first seven axes of the Principal Component Analysis
of water chemistry from channelized agricultural headwater streams in
Cedar Creek, Indiana, and Upper Big Walnut Creek, Ohio, 2008 to 2009 ...........25
A6.
Eigenvectors for habitat variables on the first four axes of the Principal
Component Analysis of instream habitat from channelized agricultural
headwater streams in Cedar Creek, Indiana, and Upper Big Walnut Creek,
Ohio, 2008 to 2009 ................................................................................................26
A7.
Eigenvectors for habitat variables on the first axis of the Principal
Component Analysis of water chemistry from channelized agricultural
headwater streams in Cedar Creek, Indiana, and Upper Big Walnut Creek,
Ohio, 2008 to 2009 ................................................................................................27
A8.
Standardized coefficients from general linear model regression tests
depicting the relative influence of instream habitat and water chemistry on
amphibian communities .........................................................................................28
vi
ABSTRACT
Castaneda, Abel Jesus. M.S., Purdue University, May 2014. The Effects of Water and
Habitat Quality on Amphibian Assemblages in Agricultural Ditches. Major Professor:
Mark A. Jordan.
Agricultural drainage ditches represent the headwaters of most watersheds in the
Midwest. Constructed to improve agricultural productivity, there has been little attention
given to their role in providing habitat to freshwater biota. The purpose of my study was
to examine the relationship between the composition of amphibian assemblages that use
ditch systems and water and habitat quality in a portion of the Cedar Creek watershed in
northeastern Indiana and the Upper Big Walnut Creek Watershed in Ohio. Instream
habitat, water chemistry, and amphibian assemblages were characterized at 14 sites
sampled three times per year for two years. Principal components analysis was used to
identify variables that contributed most to variation within habitat and water quality
categories. Axes identified were then regressed against measures of amphibian
abundance, diversity, and assemblage composition. Overall, amphibian assemblage
variables were most correlated with measures of instream habitat. Streams with high
velocity and discharge had lower abundance and species diversity. Although there was
identifiable variation among sites in water chemistry, axes were not associated with any
measure of assemblage structure. Parallel results from fish assemblages in the same
vii
system suggest that management for enhanced habitat quality should be prioritized when
applying conservation practices.
1
INTRODUCTION
Headwater streams have the potential to be negatively impacted by agricultural
land use through habitat alteration and agricultural contaminants. According to the
United States Department of Agriculture (USDA), 20% of land across the U.S. is used for
agricultural crop land (USDA/ERS, 2011). In Indiana in 2007, 51,400 km², 64% of the
total area of the state, was used for agricultural crops (USDA/NASS, 2009). It has been
estimated by the USDA that as much as 50% of the agricultural land in Indiana is
drained, and that Indiana ranks second in the nation for amount of land that uses drainage
systems in order to be farmed (Baker et al., 2006). Headwater streams often receive
surface and subsurface runoff originating from agricultural fields. In addition to being
used for drainage, these headwater streams support aquatic communities which have the
potential to be impacted by adjacent agricultural land use, either from subsurface
drainage tiles or surface runoff. Despite the presence of farming in Indiana for more
than 150 years, little is known about the impacts of agriculture on aquatic organisms in
the 56,300 km (McCall & Knox, 1979) of receiving streams in Indiana.
Amphibians have declined dramatically in the United States and many other areas
of the world. These declines seem to have worsened recently and amphibians are now
more threatened than many other vertebrate taxa (Stuart et al., 2004). Although loss of
habitat is known to have impacted amphibian populations for decades (Blaustein et al.,
2
1994), recent research has been focused on many other factors including emerging
diseases (Lips, 1999; Morell, 1999), the introduction of invasive species (Morgan and
Buttemer, 1996), environmental contaminants (Berrill et al., 1997; Bonin et al., 1997),
and climate change (Pounds et al., 1999; Kiesecker et al., 2001). Such factors may
interact with each other, and high levels of mortality may lead to population declines.
Exposure to pollutants from runoff is a possible cause of population decline in
amphibians. The effects of agricultural run-off have been identified in several studies
(Relyea, 2005: Werner et al, 2007), but investigations of the health of amphibian
populations in running water systems are minimal relative to ponds and wetlands. Due in
large part to their developmental process and permeable skin, amphibians are acutely
sensitive to many physical, chemical and biological paramters and can act as key
indicators of ecosystem health (Blaustein and Wake, 1995). Nitrogenous compounds
have been shown to reduce feeding and locomotion and are associated with
morphological abnormalities such as bent tails, body swelling, head deformities, and
digestive system abnormalities (Hecnar, 1995). Atrazine, the most commonly used
herbicide in the United States, has been reported to influence gonad differentiation during
larval development (Orton et al., 2006). These chemicals, and likely others, are
environmentally relevant due to coincidental timing of agricultural activity with the
amphibian breeding season and subsequent larval development.
Although there is strong evidence that water quality can affect viability of
populations and the structure of aquatic communities, variation in broader features of the
surrounding habitat also plays a role. Smiley et al. (2008) reported that instream habitat
had a greater positive influence on fish communities in agricultural drainage ditches than
3
either riparian habitat or water chemistry. However, this does not empirically show cause
and effect. Lau et al. (2006) reported that channelization in agricultural streams causes a
significant decrease in fish community diversity when compared to fish communities in
unchannelized agricultural streams. Other studies have also reported a negative
relationship between fish community structure and channelized streams (Scarnecchia,
1988; Sullivan et al., 2004). Results from published research suggest that the quality of
instream habitat is more important than water quality at explaining variation in fish
community response variables for fishes that inhabit streams in agricultural watersheds.
The goal of my thesis was to examine the relationship between the structure of
amphibian assemblages and its relationship to water chemistry and habitat quality in the
agricultural ditches of two Midwestern watersheds. Ecological assessments of instream
habitat and water chemistry in streams were performed in order to determine their
correlation with amphibian community response variables. The study takes advantage of
long term monitoring of water quality in the drainage ditches that has demonstrated
variation among sites in the levels of a range of agrochemicals (Smiley et al., 2008).
Sites impacted by elevated pesticide and nutrient loads are expected to show reduced
species diversity and smaller populations. Meanwhile, the effects of habitat variation will
also be assessed to determine its relative role in influencing the aforementioned
assemblage variables. In addition to assisting the evaluation of water quality, analysis of
habitat variation will contribute to the limited understanding of the ecology of
amphibians in agricultural ditches and streams in the Midwest.
4
METHODS
Study Area
Cedar Creek (CC) is a tributary of the St. Joseph’s River located in northeast
Indiana (latitudes 41°53’78’’ – 41°19’23’’, longitudes 85°31’88’’– 84°91’50’’).
Dominant land use in CC is cropland consisting of soybean or corn. The majority of
streams within the CC watershed have been channelized for agricultural drainage.
Additionally, increased loadings of nutrients and pesticides from agricultural fields and
bacteria from failed septic tanks are nonpoint source pollutants of concern within the
watershed (St. Joseph Watershed Initiative, 2005). Upper Big Walnut Creek (UBWC) is
located in central Ohio (latitudes 40°’60’’– 40°32’30’’, longitudes 82°56’00’’–
82°42’00’’) and is part of the Scioto River watershed. Dominant land use in the UBWC
watershed is cropland consisting of soybean, wheat, or corn. The majority of headwater
streams in the watershed are impaired by nutrient enrichment, pathogens, and habitat
degradation stemming from current agricultural management practices (Ohio EPA, 2003,
2004). These watersheds are also two of 14 benchmark watersheds within the
Agricultural Research Service’s Conservation Effects Assessment Project Watershed
Assessment Study.
5
Water Chemistry and Hydrology
Water chemistry, instream habitat, and amphibians were sampled at seven sites in
three channelized headwater streams within CC and seven sites in seven channelized
headwater streams within the UBWC. Measurements of all variables were from 2008 to
2009 in CC and 2009 in UBWC. The watershed size of channelized streams in CC
ranged from 13 to 43km2 and watershed size of study streams in the UBWC ranged from
0.7 to 10 km2. Each site was 125m long and located near automated water samplers or
location of grab sample collection sites. In the UBWC only four streams have automated
water samplers out of nine and in those four the UBWC sites are located downstream of
the automated sampler in one stream and upstream in three streams.
Water samplers from Cedar Creek collected composite samples on a daily basis
that were analyzed by the USDA-ARS National Soil Erosion Research Laboratory.
Water samples from the Upper Big Walnut Creek were collected by either automated
samplers or grab samples on a weekly basis and were analyzed by the USDA-ARS Soil
Drainage Research Unit. Water samples were analyzed for the concentration of soluble
reactive phosphorous, ammonia, total nitrogen, nitrate plus nitrite, total phosphorous,
simazine, atrazine, acetochlor, metolachlor, and alachlor.
Concentrations of nitrate plus nitrite, ammonium, and dissolved reactive
phosphorus were determined colorimetrically. Ammonium and nitrate plus nitrite were
determined by application of the copperized-cadmium or hydrazine sulfate reduction
method and dissolved reactive phosphorus was determined by the ascorbic acid reduction
method (Parsons et al., 1984). Total phosphorus analyses were performed on unfiltered
samples following alkaline persulfate oxidation (Koroleff, 1983) with subsequent
6
determination of nitrate plus nitrite and dissolved reactive phosphorus. Herbicide
concentrations of alachlor, atrazine, metolachlor, and simazine were determined using
gas chromatography following standard protocols for pesticide analyses (U.S. EPA,
1995a). Measurements of these herbicides were taken because they are more frequently
detected and often occur in greater concentrations than insecticides within agricultural
watersheds in the United States (Gilliom, 2007).
Median nutrient and herbicide concentrations used in my analyses were calculated
from selected measurements obtained during a 6-week period prior the sampling periods.
A 6-week time period was chosen to attain a more representative measure than what
might be collected during a spike due to rainfall or spraying of agrochemicals.
Measurements of dissolved oxygen, temperature, pH, and conductivity were
obtained with a multiparameter meter from each site three times a year concurrent with
amphibian sampling. These were taken in the months of May, June, and July.
Measurements of water depth, water velocity, and wet width were obtained on the same
sampling days. One measurement of wet width and four measurements of water velocity
and depth were obtained along six transects established at 25m intervals throughout each
site. Water velocity was measured with an electromagnetic velocity meter. Water depths
were measured with a meter stick and wet widths were measured with a tape measure.
Mean water depths, velocity, and wet width from each site during each sampling period
were calculated.
7
Substrate
Substrate data was taken from both CC and UBWC each season. Twenty four
samples were collected at each site using a grab sampling method along the six transects
used for wet width during hydrology sampling. Along each transect, measurements were
taken at 20%, 40%, 60% and 80% of the stream wet width. At each of the 24 data
collection points in the sample zone water depth, velocity, substrate and habitat were
characterized. Substrate categories included clay, silt, sand, gravel, cobble and boulder
(Table A1). Habitat classifications included terrestrial vegetation, aquatic plant, small
woody debris, large woody debris, leaf litter, and algae (Table A2). A percentage of each
substrate category was calculated for each site. This was done by taking the number of
samples present of a variable and dividing it by the total number of data collection points.
Amphibian Assemblages
Amphibians were collected three times a year through the spring and summer
(Table A3). Unbaited minnow traps were used to catch amphibians. Fourteen minnow
traps were set at each site and secured to the banks. Each minnow trap was spaced 9 m
apart and placed in an alternating bank pattern and left for 48 hours. This was done to
maintain an equal trapping effort at each site during each season. Minnow traps have
been used in another study to effectively catch tadpoles (Adams, 1998). Other methods
were considered including dip nets, seine nets, and electroshocking. Due to many of the
sites being choked with vegetation during the second and third sampling periods, dip nets
and seine nets could not be used as it would not have been possible to maintain equal
trapping effort. Electroshocking was ineffective during concurrent studies focused on
8
fish sampling due to morphology of the tadpoles and their lack of responsiveness to the
electric shocks.
Sixteen amphibian response variables were analyzed. The abundance of bullfrog
tadpoles, green frog tadpoles, bullfrog post-metamorphs, green frog post-metamorphs,
wood frog post-metamorphs, and leopard frog post-metamorphs were all used as counts
from each site. Overall species richness, tadpole richness and adult richness were derived
from the count data for each site. Total abundance of amphibians, tadpole abundance,
and adult abundance were also calculated and used for my analysis. Shannon’s index and
evenness were also calculated for amphibians, tadpoles and adults at each site.
Statistical Analyses
I first conducted principal component analyses (PCA) using PC-ORD on the
instream and water chemistry parameters to obtain site scores for the identification of the
most important environmental gradients. The main purpose of PCA is to condense the
information contained in a large number of original variables into a smaller set of new
composite dimensions, with a minimum loss of information. It reduces the original
dimensions of the data set, where each dimension is defined by one variable, into fewer
new dimensions, where each new dimension is defined by a linear combination of the
original variables. PCA provides a meaningful interpretation of each principal
component based on the variables that are most important in defining the dimension. The
relationships among sampling sites are evaluated by the sites’ relative positions on the
newly defined gradients. If the gradients have meaningful ecological interpretations, then
9
the ecological relationships among sites can be defined based on the sites’ relative
positions on the ordination axes.
Median nutrient and herbicide concentrations used in our analyses were calculated
from measurements obtained during a 6-week period prior the sampling periods. The
selected measurements were then transformed to have a multivariate normal distribution.
This is necessary because principal components analysis assumes that the underlying
structure of the data is multivariate normal. The measurements of dissolved oxygen,
temperature, pH, and conductivity were also transformed to have a multivariate normal
distribution. Multivariate normal distribution was applied to the measurements of water
depth, water velocity, mean water depth, mean velocity and wet width variables as well.
For this study, a correlation matrix was appropriate due to the unit of
measurement differing among variables (Noy-Meir et al., 1975). Eigenvalues represent
the variances of the corresponding principal components. They measure the extent of
variation among sampling sitess along the dimension specified by the principal
component. Each eigenvalue is associated with one principal component.
All of the eigenvalues are positive or zero, and the larger the eigenvalue is, the
greater the sample variation associated with a principal component. The component with
the largest eigenvalue captures the sample variance-covariance structure, while the
component with the smallest eigenvalue captures the least sample variance-covariance
structure. The first eigenvalue is the largest, thus the first principal component defines
the dimension or gradient with the single highest variance. The second eigenvalues
measures the variance along the second principal component. It represents the largest
variance in a dimension independent of the first dimension.
10
Once the eigenvalues were attained, a broken stick method was used to determine
which eigenvectors will be retained for further analyses. The broken stick model
assumes that if the total variation is randomly distributed among components, then a
scree plot exhibits a broken stick distribution (Frontier, 1976). Observed eigenvalues that
exceed the eigenvalues expected under the broken stick distribution are considered
meaningful and are retained for interpretation. Broken stick methods of estimating the
number of components to retain have produced the best results of any tests on simulated
data with known distributions (Jackson, 1993).
Once the eigenvalues that are considered meaningful are retained, the next step in
the analysis is to determine which principal component loadings are worth considering in
the ecological interpretation of each component. A ‘rule of thumb’ frequently employed
for this purpose is that principal component loadings greater than 0.3 or less than -0.3 are
considered significant (Hair, Anderson, and Tatham, 1987). The larger the absolute size
of the loading, the more significant the loading is in interpreting the principal component
structure.
I then performed a generalized linear model for further evaluation of the data to
determine the statistical significance, standardized coefficients, and types of relationships
that occurred between the environmental parameters and the amphibian data. I used the
site scores from the PCA of water chemistry and instream habitat data as independent
variables and the amphibian response variables as dependent variables in my analyses.
Generalized linear models are an extension of the linear modeling process that
allows models to be fit to data that follow probability distributions other than the normal
distribution such as Poisson, binomial and multinomial. This method was necessary due
11
to the number of sampling sessions with zero values for the dependent variables. The
data displayed a negative binomial distribution and was thus analyzed this way in SPSS
version 20.
12
RESULTS
The following species and life stages were observed: green frog (Lithobates
clamitans melanota) post-metamorph and tadpoles, bullfrog (Lithobates catesbeianus)
post-metamorph and tadpoles, wood frog (Lithobates sylvaticus) adults and tadpoles, and
northern leopard frog (Lithobates pipiens) adults. I calculated amphibian response
variables (i.e. species richness, evenness, abundance) for each site during each sampling
period. Species richness is the number of amphibian species captured and abundance is
the number of amphibians captured. Evenness is the reciprocal of the Simpson’s index
divided by species richness (Smith and Wilson, 1996).
Four instream habitat PCA axes and one water chemistry PCA axis were
identified as explaining sufficient variation according to the broken stick criterion and
thus retained for subsequent regression analyses (Table A4 and Table A5). Broken-stick
eigenvalues of axes 5-10 had higher values than their respective eigenvalues rendering
them not meaningful in explaining variation in habitat (Table A4). For water chemistry
only the first PCA axis exhibited eigenvalues greater than the broken stick eigenvalues
and as such was the only axis retained (Table A5).
Habitat PC1 had five variables with significant loading scores (Table A6). Mean
velocity, standard deviation of velocity, discharge, and percent gravel all had negative
loading scores and percent clay had a positive loading score, meaning that sites with
13
greater scores had greater velocity, greater discharge, greater amounts of gravel and less
clay than sites with lesser scores. Habitat PC 2 had four variables with significant
loading scores. Percent silt and percent sand had negative loading scores and percent
cobble and percent leaf litter had positive loading scores. Habitat PC 3 had four variables
with significant loading scores. Mean depth, standard deviation of depth and mean wet
width all had negative loading scores and temperature had a positive loading score.
Habitat PC 4 had four variables with significant loading scores. Discharge and percent
clay had negative loading scores and conductivity had a positive loading score. Water
chemistry PC 1 had five significant loading scores (Table A7). Atrazine, Metalachlor,
Simazine, NH 4 and PO 4 P all had positive loading scores.
Overall, the generalized linear models indicated that instream habitat influenced
amphibian response variables more than water chemistry (Table A8). Two of the
principle component axes were most often linked to variation in measures of the
amphibian assemblage. Habitat PC 1 produced the highest standardized coefficients in six
of the 16 amphibian response variables. Instream habitat site scores of PC 1 were
positively and significantly correlated with seven response variables. Habitat PC 3
produced the highest standardized coefficients in five response variables. Instream
habitat site scores of PC 3 were positively and significantly correlated with one of the 16
amphibian response variables and negatively and significantly correlated with nine of the
16 amphibian response variables.
The abundance of post- metamorphic green frogs and bullfrogs were positively
correlated (P< 0.05) with the first PCA axis of instream habitat. The abundance of postmetamorphic green frogs and bullfrogs increased with increasing amounts of clay,
14
decreased water velocity, decreased discharge and decreasing amounts of gravel. Postmetamorphic richness and abundance parameters were positively correlated with the first
PCA axis of instream habitat. It would appear that these environmental factors are the
most significant in affecting species and community parameters in post-metamorphic
frogs.
Green frog tadpole abundance was most positively influenced by PC 2. The
abundance of tadpoles increased with increasing amounts of cobble and leaf litter and
decreased with increasing amounts of silt and sand. Total amphibian abundance was also
most positively influenced by PC 2. Bullfrog tadpoles were most influenced by PC3.
This correlation is different than the others in that it is a negative correlation. Mean
depth, standard deviation of depth, and mean wet width are the environmental factors
with negative loading scores and temperature being the lone environmental factor with a
positive loading score.
15
DISCUSSION
Principal components analysis combined with multiple linear regressions suggest
that instream habitat better explains amphibian community response variables than water
quality. Below I explore this result in the context of related observations in fish
assemblages and amphibian biology.
The fact that instream habitat has a significant effect on amphibian community
diversity might be expected in the context of what is known about freshwater fishes.
Literature since as early as 1939 has reported fish community diversity decreasing after
dredging and channelization in two streams in Ohio (Trautman, 1939). Since then, there
have been many studies that have reported the negative impacts of agricultural land use
and stream channelization on associated biotic communities (Hortle and Lake, 1983;
Scarnecchia, 1988; Sullivan et al., 2004). Streams modified for drainage have exhibited
less fish community diversity then unmodified streams (Gorman and Karr, 1978).
Agricultural land use in the upper reaches of a watershed has been shown to significantly
increase sediment load within streams and decrease fish community species diversity in
the downstream reaches of the watershed (Walser and Bart, 1999). Sullivan et al. (2004)
found that as habitat quality increases, fish community diversity increases, and that fish
community composition was positively influenced by the pool, glide, riffle, and run
16
habitats within the stream. Lau et al. (2006) found that fish community diversity is
positively correlated with habitat quality in Indiana headwater streams.
The relationship of stream habitat and fish communities seems to be well
understood, but the role of stream habitat and amphibian populations is less so. It should
be no surprise that the abundance of post-metamorphic bullfrogs and green frogs and the
abundance of post-metamorphic frogs were negatively correlated with mean velocity,
standard deviation of velocity and discharge (Tables A6 and A8). Kupferberg et al
(2011) found that even low velocities can cause tadpoles and post-metamorphs to become
displaced. Their study showed that individuals exposed to repeated velocity stressors
grew significantly less and experienced greater predation than those in ambient velocities.
In another study, tadpoles were observed to occupy low velocity streams presumably to
avoid being washed downstream (Haramura, 2005).
The abundance of green frog tadpoles was most positively influenced by PC 2 in
my study (Table A8). Percent cobble and percent leaf litter were positively correlating
environmental factors. These findings are in accord with other studies that have
investigated amphibians in regard to substrate type. Fine sediments (particles <2.0 mm
diameter) comprise one of the most pervasive stressors of lotic systems worldwide
(Waters, 1995). There are several recent studies showing that fine sediments have a
significant negative impact on populations of headwater amphibians (Corn and Bury,
1989; Welsh and Ollivier, 1998). Tadpoles are limited by fine sediment accumulation in
larval habitats due to these sedimentary rock types being too soft and porous to provide
an adequate surface for attachment of the suctorial mouth (Wilkins and Peterson, 2000).
Tadpoles require a foraging substrate of smooth hard rocks, preferably >55 mm or leaf
17
litter (Bull and Carter, 1996). While green frog tadpoles were most influenced by PC 2,
tadpole abundance and overall amphibian abundance were also significantly correlated
probably because green frog tadpoles accounted for a majority of the tadpoles caught in
the study (Table A8).
The abundance of bullfrog tadpoles was most influenced by habitat PC 3, an axis
correlated with higher mean depths, wet widths and lower temperatures. Bullfrog
tadpoles have been known to select moderate temperatures for thermoregulation during
development (Dupre and Petranka, 1985). It was further observed that late premetamorphic tadpoles selected even lower temperatures presumably to optimize growth
and differentiation rates during developmental periods (Wollmuth, 1988). Bullfrog
tadpoles have been known to behaviorally thermoregulate by changing location and
alterations in posture (Lillywhite, 1970). Radiant energy is used as a heat source and
surrounding water is used either as a heat source or sink. This would support our
findings of bullfrog tadpoles in sites with lower temperatures. The sites with lower
temperatures tended to also be the sites with greater mean water depths and wet widths.
The results of this study are in accord with those of Smiley et al (2008) and
Sanders (2012), which looked at the relative contributions of water quality and habitat to
fish community diversity. It is possible that the variation in water quality is low among
sites. If every site is uniformly poor, relative to a pristine or reference site, they are all
affected the same way. It is possible that effects on the amphibian population have
already taken place, such that the uniformity among sites makes it difficult to detect
correlates of water quality.
18
The information gathered here could be of use for future studies on how
amphibian populations are affected by water quality and instream habitat variables in
headwater streams that serve as agricultural drainage systems or unchannelized streams.
It seems that amphibian community parameters are greatly influenced by instream habitat
factors and relatively unaffected by the water quality factors. An area to look into is if
amphibian populations are influenced more, less, or significantly differing in channelized
versus unchannelized streams. Additionally, how does the riparian habitat influence the
amphibian population? Hecnar (1996) found that the amount of woodland surrounding
ponds was the most important habitat factor determining anuran species richness in ponds
located in agricultural landscapes.
This study shows a method for determining the relative contributions of water
quality and habitat on amphibian community composition. Data from this study
indicated that exposure to the agricultural contaminants did not negatively affect the
amphibian community parameters. It is possible that exposure to the contaminants
present in the streams caused sub-lethal effects that were not detected in the results of the
study. It is also possible that the water quality is showing less of an effect on amphibian
populations because of the lower than acute and chronic toxicity benchmark contaminant
concentrations found in these streams. These lower concentrations could be due to the
agricultural conservation practices that are already in place in this CEAP study site. The
results of this study suggests that in order to improve amphibian community integrity in
channelized headwater streams surrounded by agricultural land use, it will be important
to put effort into improving the quality of instream habitat in addition to current efforts
focused on improving water quality.
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19
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APPENDIX
23
APPENDIX
Table A1. Substrate categories and particle sizes of substrate classifications.
Substrate
Clay
Silt
Sand
Gravel
Cobble
Boulder
Particle size (mm)
<0.004
0.004-0.06
0.06-2.0
2.0-64
64-256
>256
Table A2. Instream habitat classifications and descriptions.
Habitat
Terrestrial vegetation
Aquatic Plant
Algae
Small woody debris
Large woody debris
Leaf litter
Description
Living, includes root masses
Submerged or growing on surface
Small sticks
Large sticks or logs
24
Table A3. Sampling periods for Cedar Creek, Indiana and Upper Big Walnut Creek,
Ohio.
Watershed/Year
Cedar Creek 2008
Cedar Creek 2008
Cedar Creek 2008
Cedar Creek 2009
Cedar Creek 2009
Cedar Creek 2009
Upper Big Walnut Creek 2009
Upper Big Walnut Creek 2009
Upper Big Walnut Creek 2009
Start of sampling period
5/20/2008
6/24/2008
7/21/2008
5/30/2009
6/27/2009
7/29/2009
6/3/2009
6/30/2009
8/4/2009
End of sampling period
5/22/2008
6/26/2008
7/23/2008
6/2/2009
6/29/2009
7/31/2009
6/5/2009
7/2/2009
8/6/2009
Table A4. Eigenvalues for the first ten axes of the Principal Component Analysis of
instream habitat from channelized agricultural headwater streams in Cedar Creek,
Indiana, and Upper Big Walnut Creek, Ohio, 2008 to 2009. Bolded numbers are those
eigenvalues that were retained based on the broken-stick criteria (Jackson 1993).
-----------------------------------------------------------------------------------AXIS Eigenvalue % of Variance Cum.% of Var. Broken-stick
Eigenvalue
------------------------------------------------------------------------------------1
335.283
22.963
22.963
244.957
2
191.409
13.109
36.072
178.587
3
171.664
11.757
47.828
145.403
4
133.796
9.163
56.992
123.280
5
94.802
6.493
63.485
106.687
6
71.262
4.881
68.365
93.414
7
70.188
4.807
73.172
82.352
8
50.533
3.461
76.633
72.871
9
48.506
3.322
79.955
64.574
10
47.452
3.250
83.205
57.200
------------------------------------------------------------------------------------
25
Table A5. Eigenvalues for the first seven axes of the Principal Component Analysis of
water chemistry from channelized agricultural headwater streams in Cedar Creek,
Indiana, and Upper Big Walnut Creek, Ohio, 2008 to 2009. Bolded numbers are those
eigenvalues that were retained based on the broken-stick criteria (Jackson 1993).
----------------------------------------------------------------------------------AXIS Eigenvalue % of Variance Cum.% of Var. Broken-stick
Eigenvalue
-----------------------------------------------------------------------------------1
199.875
46.809
46.809
158.166
2
81.111
18.995
65.804
97.165
3
62.175
14.561
80.365
66.665
4
41.650
9.754
90.119
46.331
5
22.444
5.256
95.375
31.081
6
11.954
2.799
98.174
18.881
7
7.795
1.826
100.000
8.714
------------------------------------------------------------------------------------
26
Table A6. Eigenvectors for habitat variables on the first four axes of the Principal
Component Analysis of instream habitat from channelized agricultural headwater streams
in Cedar Creek, Indiana, and Upper Big Walnut Creek, Ohio, 2008 to 2009. Bolded
numbers are loadings that that are considered to be statistically significant (> 0.3 or < 0.3).
-----------------------------------------------------------------------------Eigenvector
Variable
1
2
3
4
-----------------------------------------------------------------------------Mean depth (m)
-0.0873 -0.0586 -0.4853 -0.1365
Std. dev. depth (m)
-0.0252 -0.0365 -0.4954 -0.0856
Mean velocity (m/s) -0.3539 -0.0979
0.0802 -0.2216
Std. dev. velocity (m/s) -0.3479 -0.0821
0.1420 -0.1854
Mean wet width (m) -0.1486 -0.0722 -0.4047
0.0492
Discharge
-0.3657 -0.1859 -0.0171 -0.4020
% algae
-0.0380 -0.0499 -0.0138
0.0343
% silt
0.1942 -0.3878 -0.1174
0.0716
% clay
0.4852 -0.2275
0.1445 -0.5582
% sand
-0.1483 -0.4215 -0.0819
0.1481
% gravel
-0.3282 -0.0001
0.1178
0.1476
% cobble
-0.0747
0.3014 -0.0176
0.1194
% boulder
-0.2289 -0.1895
0.0118 -0.2742
% terrestrial vegetation 0.2458 -0.2812 -0.1060 -0.0125
% leaf litter
0.0288
0.3208 -0.0490 -0.2139
% small woody debris -0.1126 -0.1644
0.0653
0.0482
% large woody debris -0.0299 -0.0610
0.1676 -0.1740
% aquatic vegetation -0.0586 -0.2069 -0.2033
0.2591
temperature (ºC)
-0.0212 -0.1848
0.3186
0.1761
Dissolved oxygen (mg) -0.2127
0.1008
0.1137
0.0234
Conductivity
0.0413 -0.2824
0.1212
0.3116
pH
-0.0402 -0.2247
0.2370
0.0358
------------------------------------------------------------------------------
27
Table A7. Eigenvectors for habitat variables on the first axis of the Principal Component
Analysis of water chemistry from channelized agricultural headwater streams in Cedar
Creek, Indiana, and Upper Big Walnut Creek, Ohio, 2008 to 2009. Bolded numbers are
loadings that that are considered to be statistically significant (> 0.3 or < -0.3).
---------------------------------------Eigenvector
Chemical
1
---------------------------------------Atrazine (ug/L)
0.4535
Metalachlor (ug/L) 0.4166
Simazine (ug/L)
0.4117
NH 4 (mg/L)
0.4236
PO 4 P (mg/L)
0.3721
TN (mg/L)
0.2512
TP (mg/L)
0.2651
----------------------------------------
28
Table A8. Standardized coefficients from general linear model regression tests depicting
the relative influence of instream habitat and water chemistry on amphibian communities.
PC 1 - principal components axis 1, PC 2 – principal components axis 2, PC 3 – principal
components axis 3 and PC 4 – principal components axis 4. Bold coefficients identify
the environmental factors with the greatest influence on a single amphibian community
parameter. Coefficients in shaded blocks indicate significant correlations between
amphibian community parameters and environmental factors (Pearson Correlation
Analysis: P<0.05).
Water chemistry
Instream habitat
Green frog tadpole
Bullfrog tadpole
Wood frog tadpole
Green frog post-metamorph
Bullfrog post-metamorph
Wood frog post-metamorph
Leopard frog tadpole
Richness
Richness (tadpole)
Richness (post-metamorph)
Abundance
Abundance (tadpole)
Abundance (post-metamorph)
Shannon's
Shannon's (tadpole)
Shannon's (post-metamorph)
PC 1
0.301
-0.161
1.095
0.383
0.348
1.526
0.946
0.101
0.058
0.401
0.232
0.135
0.466
-0.036
0.043
0.451
PC 2
0.531
0.606
0.756
0.181
0.328
-0.231
-0.196
0.137
0.198
0.144
0.369
0.510
0.184
0.253
0.066
0.264
PC 3
-0.338
-0.691
-0.582
-0.237
0.303
0.168
0.533
-0.221
-0.411
-0.019
-0.356
-0.443
-0.144
-0.379
-0.281
-0.239
PC 4
0.183
0.183
0.659
0.355
-0.127
1.663
0.878
0.072
0.032
0.350
0.077
0.107
0.306
0.150
-0.032
0.522
PC 1
0.044
0.369
0.046
-0.344
-0.251
-0.192
0.009
0.041
0.155
-0.131
-0.008
0.092
-0.258
0.122
0.141
-0.108