A multi-scaled approach to evaluating the fish community ...€¦ · Web viewThe three land use...
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LRH: Kirsch and Peterson RRH: Factors affecting fish assemblage structure A multi-scaled approach to evaluating the fish assemblage structure within southern Appalachian streams USA Joseph E. Kirsch 1 Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, 30602 USA James T. Peterson 2 Georgia Cooperative Fish and Wildlife Research Unit, US Geological Survey, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602 USA 1 Current address: US Fish and Wildlife Service 850 S. Guild Ave., Suite 105 Lodi, CA 95240 USA E-mail address: [email protected] 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Transcript of A multi-scaled approach to evaluating the fish community ...€¦ · Web viewThe three land use...
LRH: Kirsch and Peterson
RRH: Factors affecting fish assemblage structure
A multi-scaled approach to evaluating the fish assemblage structure within
southern Appalachian streams USA
Joseph E. Kirsch1
Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia,
30602 USA
James T. Peterson2
Georgia Cooperative Fish and Wildlife Research Unit, US Geological Survey, Warnell School of
Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602 USA
1Current address: US Fish and Wildlife Service
850 S. Guild Ave., Suite 105
Lodi, CA 95240 USA
E-mail address: [email protected]
2To whom correspondence should be addressed.
Current address: Oregon Cooperative Fish and Wildlife Research Unit
US Geological Survey
104 Nash Hall, Corvallis, Oregon 97331 USA
E-mail address: [email protected]
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This draft manuscript is distributed solely for purposes of scientific peer review. Its content is
deliberative and predecisional, so it must not be disclosed or released by reviewers. Because the
manuscript has not yet been approved for publication by the U.S. Geological Survey (USGS), it
does not represent any official USGS finding or policy.
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<A>Abstract
There is considerable uncertainty regarding the relative roles of stream habitat, landscape
characteristics, and interspecific interactions in structuring stream fish assemblages. We
evaluated the relative importance of environmental characteristics and species interactions on
fish occupancy, at local and landscape scales, within the upper Little Tennessee River Basin,
Georgia and North Carolina. Using a quadrat sample design, fishes were collected at 525 channel
units within 48 study reaches during two consecutive years. We evaluated the relative support for
the influence of local- and landscape-scale factors on fish occupancy using multi-scaled,
hierarchical, multi-species occupancy models. Modeling results suggested that the fish
assemblage within the Little Tennessee River Basin was hierarchically structured and primarily
influenced by stream size and spatial context, urban land coverage, and
channel unit types. Landscape scale factors (i.e., urban land coverage,
stream size and spatial context) largely constrained the fish assemblage
structure at a stream reach and local scale factors (i.e., channel unit types)
influenced fish distribution within stream reaches. Our study demonstrates
the utility of a multi-scaled approach and the need to account for the inter-
scale interactions of factors influencing assemblage structure prior to monitoring
fish assemblages, developing biological management plans, or allocating management resources
throughout a stream system.
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North America possesses the richest diversity of temperate freshwater fishes within the
world (Jelks et al. 2008). Unfortunately, high fish endemism coupled with environmental
degradation has resulted in substantial imperilment among native fishes with approximately 46%
of the 1,187 described freshwater and diadromous fish taxa classified as vulnerable, threatened,
or endangered, a 92% increase from 1989 (Jelks et al. 2008). Assuming no future catastrophic
events, the extinction rate of North American freshwater fishes is projected to increase from
0.4% per decade (Ricciardi and Rasmussen 1999) to 3.2 % per decade (Burkhead 2012) due to
habitat deterioration associated with anthropogenic land and water development. Sedimentation,
nutrient loading, pollution, exotic species introduction, and altered hydrologic regimes are the
anthropogenic stressors that have been attributed to the precipitous increase in the freshwater fish
imperilment in North America (Richter et al. 1997; Ricciardi and Rasmussen 1999; Contreras-
Balderas et al. 2003; Dextrase and Mandrak 2006). To reduce these threats,
effective management strategies need to be developed at the most effective local and/or
landscape levels. Before these strategies are developed, it is essential to identify and quantify the
primary factors influencing lotic fish assemblage structure to appropriately allocate management
resources (Peterson and Dunham 2010).
The structure of a lotic fish assemblage is the result of a combination of biotic
interactions and environmental influences (Jackson et al. 2001). Biotic interactions (i.e.,
primarily predation and competition) have been shown to influence assemblage composition
through fish species deletions (Gilliam and Fraser 2001) and by altering fish behavior, such as
changing habitat use (Power et al. 1985; Taylor 1996). Environmental characteristics, such as in-
stream hydrogeomorphology (i.e., water depth, current velocity, and substrate composition),
influence fish assemblage composition by providing habitats that allow certain species to persist
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over others given species-specific morphological limitations and life history requirements
(Schlosser 1982; Moyle and Vondracek 1985; Jackson et al. 2001; Peterson and Rabeni 2001a).
Landscape charactersitics, such as topography and terrestrial landuse, can affect the structure of
local in-stream habitat characteristics, which in turn influence fish assemblage structure
(Schlosser 1991; Peterson and Kwak 1999; Paul and Meyer 2001). Despite the multitude of
studies demonstrating the effects of both biotic interactions and environmental characteristics on
fish assemblage structure, the percieved importance of mechanisms is not well understood and is
typically governed by their association with the scale of study (Jackson et al. 2001; Peterson and
Dunham 2010).
Scale is defined by the spatial and temporal resolution of the observational units (i.e.,
grain) and the dimensions of a study (i.e., extent; Peterson and Dunham 2010). All mechanisms
influencing fish assemblage structure, including biotic interactions and environmental
characteristics, potentially operate across multiple spatial and temporal scales (Figure 1; Frissell
et al. 1986; Tonn 1990; Poff 1997). The ability to observe or detect the effects of these factors,
however, depends on the within- and among-grain heterogeneity (Peterson and Dunham 2010).
Consequently, the perceived importance of factors influencing fish assemblage structure is
influenced by the spatial and temporal scale over which an investigation is conducted (Fausch et
al. 1994; Crook et al. 2001; Fausch et al. 2002; Peterson and Dunham 2010). For example,
studies conducted using large sample units in large spatial extents (e.g., stream reaches within
watersheds) often conclude that mechanisms operating over broad scales (e.g., topography) are
the primary processes affecting lotic fish assemblage structure (e.g., Fausch et al. 1994), whereas
studies conducted with smaller sample units and spatial extents (e.g., microhabitat use within a
stream reach) likely identify mechanisms operating at local levels (e.g., interspecific competition
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or hydrogeomorphic characteristics) as primary processes affecting lotic fish assemblage
structure (Jackson et al. 2001; Peterson and Dunham 2010). Though the association between
spatial scale of a study and the perceived importance of factors influencing fish assemblage
structure have been acknowledged (e.g., Jackson et al. 2001; Quist et al. 2005), few studies have
elucidated the importance of biotic interactions and environmental characteristics in structuring
lotic fish assemblages across spatial scales.
The need to understand the importance of factors influencing fish assemblages is
exemplified by the southeastern United States. The region possesses the richest diversity and
greatest number of endemic fishes within North America, north of Mexico (Mayden 1988;
Warren et. al. 2000, Jelks et al. 2008). The Southeast also has one of the highest proportions of
imperiled freshwater fish taxa, where approximately 28% of the 560 described species are
classified as imperiled or extirpated primarily due to a variety of anthropogenic stressors
(Warren and Burr 1994; Warren et al. 1997; Warren et al. 2000; Jelks et al. 2008).
Although lotic fish assemblage structure in the Southeast has been well studied, there remains
considerable uncertainty as to the importance of biotic and abiotic factors across spatial scales.
Therefore, we evaluated the factors influencing the fish assemblage structure within a Southeast
river basin with the following objectives: (1) to develop multi-scale framework for evaluating the
effects of biotic and abiotic factors operating at geomorphic channel unit and stream reach spatial
scales; (2) to determine the relative importance of scale-specific effects; and (3) to quantify the
scale-specific relations between the most influential factors and the occupancy of multiple fish
species.
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<A>Methods
Study area.- We investigated lotic fish assemblage structure and habitat use in the Little
Tennessee River Basin located in western North Carolina and northeast Georgia, USA. The
Little Tennessee River is a tributary of the upper Tennessee River and is located in the Blue-
Ridge province of the southern Appalachian Mountain range. The basin reportedly contains more
than 150 native freshwater fish species (Warren et al. 2000; Etnier and Starnes 2001). The
climate is classified as marine humid temperate based on its relatively mild air temperatures and
high moisture content (Swift et al. 1988). The Little Tennessee River Basin drains an area of
approximately 4,117km² and is dominated by second growth forests due to extensive
deforestation in 19th century (Scott et al. 2002). Anthropogenic land use within the basin
includes both agriculture and urban development, with agricultural land use declining and urban
development steadily increasing to support human immigration (Gragson and Bolstad 2006).
Site selection.- This study was conducted in collaboration with the Coweeta Long Term
Ecological Research Program's synoptic sampling and assessment project. A total of 48 sites
(henceforth, study reaches) were chosen to represent the range of land uses, stream sizes, stream
network positions, and elevations found within the Little Tennessee River Basin (Figure 2).
Forty-six study reaches were sampled during the pilot phase of the project in the summer (July –
September) of 2009. Based on the results of the pilot sampling, 8 of the 46 study reaches and two
additional study reaches (10 total) were selected to represent common land use (i.e., forest,
urban, and agriculture) and development types (i.e., valley development and hill-slope
development) within the Little Tennessee River Basin. The ten reaches then were sampled during
the spring (May) of 2010, summer (August) of 2010, and spring (May) of 2011.
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We obtained riparian buffer condition data for each study reach from Long and Jackson
(2013). We noted if a study reach had an intact riparian buffer which was defined as fully
forested cover extending 10m or more on both sides of the channel. All other landscape data for
each study reach were calculated using readily available Coweeta Long Term Ecological
Research geographic information system (GIS) data (CLTER 2011) and ArcGIS software
version 9.3. Land use data for each study reach were derived from 2006 land cover data
classified from Landsat Thematic Mapper satellite imagery with 30m resolution. Land cover
classes were grouped into four land use classes: urban, agriculture, forest, and other. Study reach
elevation, gradient, and hydrologic data were derived from a United States Geological Survey
seamless digital elevation model with 9.353 m resolution. For each study reach, the number of
contributing tributaries and watersheds boundaries were delineated using protocol described by
Jenson and Domingue (1988). Tributaries were identified using a minimum drainage area
threshold of 0.0875km2 to reflect observations during the summer of 2009. Link magnitude
(Link) and downstream link (D-Link) were calculated to represent study reach size and study
reach network positions, respectively. Link magnitude was defined as the number of contributing
tributaries headwater streams upstream of a study reach (Shreve 1966). Downstream link was
defined as the link magnitude of the next downstream confluence from a study reach (Osborne
and Wiley 1992).
Sampling design.- We used a multi-scale sample design in which samples were collected
from individual geomorphic channel units (henceforth, channel units) nested within study
reaches. At each study reach, the stream was stratified into distinct channel unit types and
sampled using a quadrat sample design (terminology follows Williams et al. 2002). All channel
units within a study reach were classified as either riffle, run, or pool following standardized
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definitions (Hawkins et al. 1993; Peterson and Rabeni 2001b). Riffle channel units were, on
average, shallow areas with swift current velocities, surface turbulence, and tended to have
coarser substrate than pools or runs. Conversely, pool channel units were, on average, deep areas
with slow current velocities, little to no surface turbulence, and tended to have finer substrate
than runs or riffles. Run channel units were, on average, areas with moderate depth, moderate to
swift current velocities, little to no surface turbulence, and mixed substrate.
Two to four replicates of each of the channel unit type present at each study reach were
randomly selected for fish sampling and habitat measurement. During the summer of 2009, the
randomly selected channel units were sampled during a single visit. During the spring and
summer of 2010 and the spring of 2011, each randomly selected sample unit was sampled twice
within each season to facilitate species detection probability estimation. When sampling occurred
twice within a season, the time between the two sampling occasions was 5-7 days to allow fish to
recolonize the channel units after sampling (Peterson and Bayley 1993).
Fishes in each channel unit at each study reach were sampled during daylight hours
with Smith-Root LR 24 pulsed DC backpack electrofishers operating between
0.2 to 0.3 amperes. Fish sampling began at the farthest downstream channel
unit at each study reach within a sampling occasion. In general, prior to
electrofishing, each channel unit had a 5mm mesh seine deployed across the
downstream end for riffles and runs or the upstream end for pools to
minimize fish escape and maximize detection during sampling. The seine
completely blocked off the end of the sample unit and was secured to the
streambed. Fishes then were then sampled via electrofishing using a single
pass starting from the opposite end of the channel unit from where the seine
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was deployed. In streams with average wetted widths ≤ 4 meters, one
backpack electrofisher was used by one crewmember with at least one
additional crewmember collecting stunned fish with a 5mm mesh dipnet. In
streams with average wetted widths > 4 meters, two backpack electrofishers
were used and at least two additional crewmembers collected stunned fish
with 5mm mesh dipnets. To maximize detection while sampling, all
crewmembers involved with electrofishing attempted to disturb the substrate
with their feet to limit stunned fishes from becoming lodged on the substrate
or woody debris to maximize detection. After the single pass was completed
for each channel unit, fishes were collected from the seine and all dipnets.
Immediately after sampling each channel unit, all captured fishes were
identified to species and released back into the stream. Fishes that could not
be accurately identified in the field were initially preserved in a 10% formalin
solution and brought back to the laboratory. Preserved fishes were later
transferred to a 70% ethanol solution and identified to species.
Physical habitat measurements.- Water quality and physical in-stream
habitat characteristics expected to influence fish occupancy or the ability to
detect fish were measured at each study reach and channel unit in
conjunction with fish sampling. Prior to fish sampling, water quality
characteristics were measured in flowing water using calibrated meters at
each study reach for each sampling occasion. Turbidity was measured using
an Oakton T-100 meter to the nearest 0.01 Nephelometric Turbidity Unit
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(NTU). Specific conductance was measured using an Oakton CON 400 Series
meter to the nearest 0.01 microsiemens per centimeter (μs·cm-1).
Immediately after fish sampling, physical in-stream habitat
characteristics were estimated within each channel unit at each study reach
for each sampling occasion. Mean wetted width, mean water velocity, and
mean water depth were estimated by averaging 3-5 randomly placed
measurements within each channel unit (following Peterson and Rabeni
2001b). Water depths were measured to the nearest centimeter using a two
meter top-set wading rod. Water velocities were measured at 0.6 depth
using a Marsh McBirney model 2000 Flo-mate meter. The length and wetted
widths of each unit were measured to the nearest centimeter using a
standard measuring tape. The mean cross-sectional area of each channel
unit was estimated by multiplying the channel unit mean width by the
channel unit mean depth. The surface area of each channel unit was
estimated by multiplying a channel unit mean width by length.
Substrate composition within a channel unit was visually estimated by
two or more crewmembers and averaged (Peterson and Rabeni 2001b).
Substrate composition was categorized based on particle diameter as fine
sediment (<5mm), gravel (5-50mm), and coarse sediment (>50mm; modified
from Dunne and Leopold 1978). Wood density was estimated by counting the
pieces of wood within the wetted channel unit that were at least 50cm in
length and 10cm in diameter or aggregates of smaller pieces of wood with
comparable volume and dividing by the channel unit area.
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Statistical analysis.- The probability of detecting lotic fish species is rarely 100% and is
often related to the factors that influence fish populations (Bayley and Peterson 2001; Peterson
and Paukert 2010). To minimize the effect of incomplete species detection, we evaluated the
relative importance of biotic interactions, in-stream habitat, and landscape characteristics on fish
assemblage structure using multi-species, multi-scale occupancy models (Mordecai et. al. 2011).
Multi-scale occupancy models consist of three submodels that estimate the probability that a
species occupies a study reach (ψ), the probability that the species uses a channel unit within an
occupied study reach (θ), and the probability of detecting a species given it occupies a channel
unit (p) in a single modeling framework. All of the multi-scale occupancy submodels (i.e., ψ, θ,
and p) were fit using logit linear functions of predictor variables (e.g., stream habitat
characteristics). The primary assumption of the occupancy estimator is that a species cannot
colonize or abandon a study reach between sample occasions (MacKenzie et al. 2006). We
believe that this study met this assumption because sample occasions were 5 -7 days apart within
a season and did not include periods of seasonal fish migration or seasonal changes in habitat use
(Peterson and Rabeni 2001a).
Fish samples that were collected from multiple channel units within individual study
reaches were likely statistically dependent (i.e., spatially autocorrelated). In addition, the
relations between physical in-stream habitat and terrestrial landscape characteristics and
occupancy were likely to vary among fish species. To account for dependence among repeated
samples at study reaches and to allow model parameters to vary among species, we used
hierarchical logit linear models to estimate reach and channel unit occupancy and detection
probability (Royle and Dorazio 2008). Hierarchical models differ from more familiar linear
models in that they can account for spatial dependence and variation in responses among species
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using fixed and randomly varying parameters (Appendix A). This provided a framework for
modeling the relation between scale-specific occupancy (i.e., study reach and channel unit within
reach) and habitat characteristics for multiple species with very different habitat use patterns
while accounting for incomplete detection using a single model and provided the means to
quantify the variability among species responses.
Based on the complexity of the modeling approach, we fit the occupancy models using
Markov Chain Monte Carlo (MCMC) implemented in WinBUGS version 1.4 (Lunn et al. 2000).
Models were fit using 700,000 iterations with a burn-in or discarding of the first 200,000
iterations. The number of required iterations was estimated using the global model (i.e.,
containing all predictor variables) and testing for convergence using the Gelman and Rubin
diagnostic test (Gelman and Rubin 1992). Non-informative priors were used for each parameter
(Kéry and Royle 2008).
We used an information theoretic approach (Burnham and Anderson 2002) to evaluate
the relative importance of biotic and abiotic factors on fish species occupancy in study reaches
and channel units within study reaches. Our primary hypotheses of interest were to evaluate the
relative influence biotic and abiotic factors on fish occupancy and habitat use. Secondarily, we
sought to determine the factors influencing species detection. Thus, we initially developed a
global model that contained all of the predictor variables corresponding to a priori hypotheses
regarding the relative effect of physical and biotic factors on fish occupancy at channel unit and
study reach scales (Table 1). Using the global model, we then evaluated the relative fit detection
models that related species-specific detection to variables that could affect capture efficiency
including: the number of backpack electrofishers, surface area and cross sectional area of the
channel unit sampled, conductance, turbidity, wood density, mean water velocity, and coarse
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substrate composition (Bayley and Peterson 2001; Peterson et al. 2004; Peterson and
Paukert 2009; Hense et al. 2010; Price and Peterson 2010). The best approximating detection
model was used to evaluate the relative support for the primary hypotheses of interest.
Based on previous investigations (Larson and Moore 1985; Freeman et al. 1988;
Grossman et al. 1998; Harding et al. 1998; Jones et al. 1999; Scott 2006; Burcher et al. 2008;
Hazelton and Grossman 2009), we hypothesized that species occupancy at the study reach level
was influenced by seasonal habitat availability, stream size and spatial context, land cover, and
species interactions operating at reach scales (Table 1). Within occupied reaches, we
hypothesized that species channel unit use was influenced by the characteristics of the channel
unit and the presence of potential competitors. The hypothesized biotic interactions focused on
previously documented interactions between nonnative or invasive species and native species in
the Little Tennessee River Basin under laboratory conditions (Fausch and White 1981; Larson
and Moore 1985; Hazelton and Grossman 2009). Native Brook Trout (Salvelinus fontinalis) were
hypothesized to alter their habitat use and distribution in the presence of a nonnative Rainbow
Trout (Oncorhynchus mykiss) and Brown Trout (Salmo trutta). Native Rosyside Dace
(Clinostomus funduloides) were hypothesized to alter their habitat use and distribution in the
presence of the invasive Yellowfin Shiner (Notropis lutippinis). To estimate the potential effects
of species interactions under incomplete species detection, we used the state space formulation of
the occupancy model (MacKenzie et al. 2006; Mordecai et. al. 2011). Here, we used the species-
specific latent variables to estimate three predictor variables: the presence of the potential
competitor in a study reach and a channel unit within a study reach and the proportion of specific
channel unit types in a reach that were occupied by the potential competitor (Table 1).
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Prior to constructing the candidate models, we estimated Pearson correlation coefficients
between all pairs of potential predictor variables. Only uncorrelated variables (r²<0.45) were
included in the same candidate occupancy models to avoid multicollinearity. All continuous
in-stream habitat and landscape data were standardized with a mean of zero
and standard deviation of one to facilitate model fitting. We also created
binary indicator variables for channel unit type, summer season, intact
riparian buffer, and for instances where two shockers were used to sample fish. We
determined the best variance structure (i.e., the parameters that needed to vary randomly among
species or study reaches) by evaluating the relative fit of the global model with each parameter
systematically treated as a fixed or randomly varying effect. The best approximating variance
structure was identified as that with the lowest Akiake Information Criteria with small sample
bias adjustment (AICc; Hurvich and Tsai 1989). The number of parameters used to calculate
AICc included both fixed and random effects. The best approximating variance structure then
was used during the evaluation of the relative plausibility of the candidate models.
The relative support of each candidate occupancy model was evaluated by calculating
AICc. Because MCMC methods produce a distribution of AICc values, we used the mean AICc
from MCMC iterations for inference (following Fonnesbeck and Conroy 2004). The relative fit
of candidate models were determined by calculating Akaike weights (wi; Burnham and Anderson
2002) that range from 0-1, with the greatest weight being the best fitting model. To assess the
amount of evidence one candidate model had over another, we calculated the ratios of Akaike
weights (following Burnham and Anderson 2002). Model selection uncertainty was expressed by
creating a confidence model set. Any candidate model with Akaike weights that were within
12% of the best-approximating candidate model’s Akaike weight was included within the
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confidence set of models (following Royall 1997). All inferences were based on the confidence
set of occupancy models.
To aid interpretation of parameter estimates, we calculated odds ratios (OR) for each
fixed effect parameter in the confidence set of occupancy models (Hosmer and Lemeshow 2000).
Because the continuous data were standardized, an OR corresponded to a one standard deviation
increase in the corresponding predictor variable. The precision of fixed effect parameter
estimates was assessed by calculating 95% credible intervals that are analogous to 95%
confidence intervals (Congdon 2001). In general, credible intervals that contained zero were
considered imprecise. We also plotted empirical Bayes estimates (Royle and Dorazio 2008) of
the relationship between environmental characteristics and occupancy for common species.
<A>Results
A total of 525 channel units were sampled in the 48 study reaches with 183 (35%)
channel units classified as a pool, 200 (38%) as riffles, and 142 (27%) channel units as runs. Half
of the study reaches (24) contained all three channel unit types and seven reaches consisted of a
single habitat type. The latter group was primarily small headwater streams with link magnitudes
less than 10. The study sites represented a wide range of stream sizes, physical characteristics,
and land uses typical of the region (Table 2). The three land use types, however, were relatively
strongly correlated (r2 > 0.6), which prevented their inclusion in the same occupancy model.
Therefore, the evaluation of the effect of land use on fish community structure included only
urban land use because land conversion to urban use was a management concern within the
basin.
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We captured 36 species during the study (Table 3). The most commonly detected species
were Mottled Sculpin (Cottus bairdii) and Creek Chub (Semotilus atromaculatus), which were
collected at more than half of the study reaches (Table 3). In general, few large bodied
piscivorous fishes (e.g., Smallmouth Bass Micropterus dolomieu) were collected. No species
were collected at all of the study reaches and no (zero) fishes were collected in five study
reaches. Eight species were collected in only 1-2 study reaches and less than 1% of samples
(Table 3). We were concerned that the occupancy model parameter estimates for these rare
species would be biased due to insufficient information and the effects of parameter shrinkage
that occurs in hierarchical models. Therefore, we eliminated these eight species from the dataset
and fit models using the remaining 28 species (Table 3).
Detection modeling.- The best approximating species detection model contained the
number of backpack electrofishers used, channel unit current velocity, surface area, and a
species-specific random effect. The Akaike weights indicated that the model was more than 33
times better supported than the next best approximating conditional detection model that
contained only current velocity, surface area, and a species-specific random effect. The
parameter estimates for the best fitting detection model indicated that the probability of detecting
a species was positively related to increases in channel unit surface area and negatively related to
the use of two backpack electrofishers and increases in current velocity (Table 4). The species-
specific random effect also indicated that detection varied among fish species and was, on
average, greatest for commonly collected species including Mottled Sculpin, Creek Chub, and
Blacknose Dace (Rhinichthys atratulus; Table 4; Figure 2). Therefore, the best fitting detection
model was used during model fit and evaluation of occupancy.
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Occupancy modeling.- The best approximating occupancy model (AICc
= 5055) contained season, stream size and spatial context (i.e., elevation,
gradient, link magnitude, and downstream link magnitude), and land cover
(i.e., urban cover and riparian buffer) for predicting study reach occupancy,
and channel unit type (i.e., riffle, run, and pool) and interactions between
channel unit type and seasons for predicting conditional channel unit
occupancy (Table 4). There was little or no support for any other candidate occupancy
model and thus there was little support for other predictors of occupancy. The best fitting model
was 12.2 times better supported than the next best approximating candidate model (AICc =
5060), which was similar but contained study reach interference competition (interactions
between the % of pools occupied by yellowfin shiner or non-native salmonids and occupancy of
rosyside dace or brook trout, respectively) for predicting study reach occupancy. As a result, we
based all inferences on the best model.
The best approximating model estimated that occupancy was greatest for all species in
larger and lower gradient streams (Table 5). In contrast, the effect and relative importance of
urban cover, elevation, and channel unit type on occupancy varied among species (Figures 3 and
4). Parameter estimates suggested that, on average, fish species were 2.26 times less likely to
occur in pool channel types relative to run channel types during the summer compared to the
spring (Table 5) and that the small benthic Mottled Sculpin, Central Stoneroller (Campostoma
anomalum), Gilt Darter (Percina evides) had affinities for riffle channel types whereas other
species generally had affinities for pool or run channel types (Figures 3 and 4). However, the
relative importance of channel type on the within-reach distribution was greater at more optimal
study reach conditions for each species (Figures 3 and 4). On average, fish occupancy was higher
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as urban cover increased within a watershed for Blacknose Dace and White Suckers (Catostomus
commersonnii) but was negatively related to high urban cover for all other species including the
highland endemic Longnose Dace (Rhinichythys cataractae), Tennessee Shiner (Notropis
leuciodes), and Warpaint Shiner (Luxilus coccogenis; Figure 3). On average, the occupancy of
the warmwater intolerant Longnose Dace, Brook Trout, and Brown Trout increased with
elevation, whereas site occupancy of all other species decreased with elevation (Figure 4). The
occupancy of all fish species were, on average, 13.9 times less likely when an intact riparian
buffer existed (Table 5) but the highland endemic Mottled Sculpin, Longnose Dace, and
Rosyside Dace were, on average, 5.56 times more likely to occur when an intact riparian buffer
existed.
<A>Discussion
We found that stream size and spatial context, land cover, and channel
unit types were the most important factors affecting fish occupancy within
the Little Tennessee River Basin. However, the relative importance of these
factors varied among species and spatial context. We surmise that environmental
stability, habitat quality, and the thermal limitations of fishes largely controls the fish assemblage
structure among stream reaches, but the behavior aimed at minimizing interspecific interactions
and exploiting food resources affects occupancy within stream reaches. Therefore, to
appropriately identify the primary mechanisms controlling a lotic fish assemblage requires an
understanding of the local and landscape scale processes and the recognition that the importance
of these processes on fish assemblage structure vary with scale.
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The structure of the fish assemblage in a stream reach is influenced, in
part, by thermal regimes (Jackson et al. 2001; Wehrly et al. 2003; Quist et al.
2005). The thermal regime of a stream reach often varies in response to
elevation, longitudinal position, and riparian cover within a stream network
(Scott et al. 2002; Martin and Petty 2009). In this study, the occupancy of
warm water intolerant fishes was positively related to elevation, whereas the
occupancy of warm water tolerant fishes (i.e., catastomids, centrarchids,
percinids, and most cyprinids) was negatively related to elevation. Scott et
al. (2002) demonstrated that stream reaches within the upper Tennessee
River Basin have generally cooler summer mean temperatures at higher
elevations and this is consistent with our observations. In general, warmer
streams have greater productivity relative to cooler streams and fishes have
greater metabolic rates in warmer water habitats (Vannote et al. 1980;
Schlosser 1982; Huryn and Wallace 2000). As a result, fishes that can
physiologically tolerant warm water often exploit the available resources in
streams at lower elevations (Rahel and Hubert 1991; Wehrly et al. 2003). However,
the distribution of fishes that cannot physiologically tolerate warm water for
sustained periods of time (e.g., Brook Trout) often are restricted to cooler
reaches at higher elevations (Swift and Messer 1971; Vannote et al. 1980;
Rahel and Hubert 1991; Jackson et al. 2001; Wehrly et al. 2003; Martin and Petty
2009). Therefore, we hypothesize that the differences in fish distribution
among stream reaches within the Little Tennessee River Basin were, in part,
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influenced by the physiological limitations of fishes in relation to thermal
regime.
Fish assemblage structure also is influenced, in part, by the size,
habitat complexity, and stability of stream reaches (Gorman and Karr 1978;
Schlosser 1982; Peterson and Rabeni 2001a; Shea and Peterson 2007). We
observed that fish occupancy was positively related to link magnitude and
negatively related to gradient within the Little Tennessee River Basin.
Headwater reaches typically possess relatively shallow and smaller habitats
with less structural diversity (e.g., substrate, depth, and current) compared
to reaches farther downstream (Gorman and Karr 1978; Vannote et al. 1980;
Schlosser 1982). In this study, we observed that headwater reaches were
dominated by either riffles, runs, or pools, whereas larger stream reaches
typically contained all of these habitats. Stream reaches with more structural
diversity often support more diverse fish assemblages based on possessing a
broad range of habitats that allows most species to encounter favorable or
optimal habitats within a reach to permit persistence (Gorman and Karr
1978; Vannote et al. 1980). In addition, headwater reaches often are
subjected to greater hydrologic variability and disturbance frequency (e.g.,
floods and droughts) compared to more downstream reaches, which can
exclude or eliminate intolerant fish species without adjacent source
populations (Whiteside and McNatt 1972; Gorman and Karr 1978; Schlosser 1982;
Resh et al. 1988; Reice et al. 1990; Osborne and Wiley 1992). The negative
relation between gradient and fish occupancy was likely due to higher
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gradient streams being dominated by habitats containing faster current
velocities including cascades and riffles. These habitats are often
energetically costly (Facey and Grossman 1990) and may inhibit movement
and thus immigration or recolonization (Taylor and Warren 2001; Grenouillet
et al. 2004). Therefore, we hypothesize that the fish distribution among
stream reaches was, in part, influenced by the variation in habitat
complexity and stability of a stream.
Anthropogenic land use activities occurring within a watershed can greatly modify the
processes that create and maintain the physical and chemical stream characteristics (Schlosser
1991; Allan 2004). Modeling results suggested that only fishes endemic to the southern
Appalachian highlands (i.e., those adapted to cool, sediment free, and nutrient-poor
environments) responded favorably to intact riparian buffers and all fishes endemic to the
southern Appalachian highlands were less likely to occur in watersheds with greater
urbanization. These results are generally consistent with previous studies of stream fishes (e.g.,
Jones et al. 1999; Rahel 2000; Scott and Helfman 2001; Sutherland et al. 2002; Miltner et al.
2004; Scott 2006). Many of these studies also demonstrated that urban land use can affect fish
assemblages through nutrient enrichment, the introduction of contaminants, increased
sedimentation, and altered flow and temperature regimes. Deforestation, the loss of a riparian
zone, and increases in impervious cover (e.g., compacted soil, roads, and buildings) associated
with urbanization can cause a reduction in evapotranspiration and infiltration rates and thus
increase the amount of surface runoff into streams during rainfall events resulting in lower base
flows and more frequent peak flows (Dunne and Leopold 1978; Webster et al. 1992; Paul and
Meyer 2001; Allen 2004; Alberti et al. 2007). In addition, streams with little or no shading from
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riparian cover receive greater amounts of solar radiation resulting in higher temperatures and
larger daily fluctuations in temperature (Swift and Messer 1971; Pusey and Arthington 2003;
Moore et al. 2005; Long and Jackson 2013). Urban land use practices and the loss of an intact
riparian buffer also can increase inputs of nutrients (e.g., industrial fertilizer and terrestrial plant
material), point sources (e.g., municipal discharge), and non-point sources (e.g., agricultural
pesticides) within streams, affecting water quality (Wiley et al. 1990; Webster et al. 1992; Paul
and Meyer 2001; Pusey and Arthington 2003; Miltner et al. 2004). Increased surface runoff
coupled with exposed soils created by the removal of terrestrial vegetation also increases surface
erosion and sediment inputs into streams (Webster et al. 1992; Harding et al. 1998; Jones et al.
1999; Paul and Meyer 2001; Sutherland et al. 2002). As sediment inputs increase within southern
Appalachian streams, aquatic habitats become more homogenous (Jones et al. 1999; Scott and
Helfman 2001; Sutherland et al. 2002). Overall, these anthropogenic stream modifications can
result in the loss and degradation of aquatic habitats, facilitating the loss of endemic fish species
and the homogenization of stream fish assemblages (Jones et al. 1999; Sutherland et al. 2002;
Miltner et al. 2004; Scott 2006). Although our study cannot identify the relative importance of or
differentiate between these mechanisms, the lack of evidence for an interaction between the
effect of urban land use and the presence of an intact riparian buffer on fish occupancy suggests
that intact riparian buffers alone were insufficient to mitigate the effects of large scale urban land
use.
Geomorphic channel unit types (e.g., riffle, run, or pool) represented unique, predictable
combinations of water depth, current velocity, and substrate composition. Because fish
distributions within a stream reach are known to be related to these habitat characteristics, fish
species may use one geomorphic channel unit type over another to fulfill one or more life history
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requirements (Peterson and Rabeni 2001a, 2001b). Presumably, the primary objective
of an individual fish is to maximize survival. Fish vulnerability to terrestrial
predation (e.g., birds and mammals) is a function of water depth and body
size with predation risk being greater for larger fish in shallower habitats,
whereas aquatic predation risk is generally higher for smaller fishes in
deeper habitats (Werner et al. 1983; Harvey and Stewart 1991). The habitat
use of most fishes in our study sites was greater in deeper channel units
(i.e., pools), which suggests that risk of aquatic predation may have been
lower than terrestrial predation. Indeed, few large bodied piscivorous fishes
were collected, so the risk of aquatic predation was likely very small
throughout our study sites. Therefore, we hypothesize that the distribution of
fish within the stream reaches was to minimize risk of terrestrial predation.
Fishes also should attempt to maximize net energy gain for growth
when selecting habitats within a stream reach (Hill and Grossman 1993). Fishes
should attempt to minimize their energy expenditures and maximize energy
inputs to maximize growth (Facey and Grossman 1990; Facey and Grossman
1992; Hill and Grossman 1993). High current velocity habitats contain relatively
high densities of macroinvertebrates (Huryn and Wallace 1987), but can be
energetically demanding for fishes that are not behaviorally,
morphologically, or physiologically adapted to high current velocity habitats
(Facey and Grossman 1990; Facey and Grossman 1992; Hill and Grossman 1993).
Consistent with previously cited studies, we observed that most small
benthic fishes (e.g., Mottled Sculpin) tended to use channel units with higher
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current velocities, whereas most pelagic fishes (e.g., sunfish) tended to use
habitats with lower current velocities. Small benthic fishes often possess the
hydrodynamic adaptations, including a depressed or streamlined body
profile, modified fins, and a reduced swim bladder to limit buoyancy (Etnier
and Starnes 2001), which reduce the energetic costs of occupying high
current habitats that have high prey densities (Facey and Grossman 1992;
Peterson and Rabeni 2001a). Therefore, we hypothesize that fish distribution
within a reach was, in part, influenced by morphological constraints and fish
behavioral adaptations intended to maximize survival and growth within stream
reaches.
In general, the strength of the relation between channel unit types and fish occupancy was
greater in larger and lower gradient streams and where fish species experienced less constraining
landscape elevations and urban coverage. As more species are added to a community, there can
be a corresponding increase in resource specialization (i.e., niche packing) to reduce niche
overlap and presumably, competition (Pianka 1974). Peterson and Rabeni (2001a) reported that
fish species used a smaller variety of channel unit types in Ozark stream reaches when the
assemblage contained more species and hypothesized that this was due to increased resource
specialization. Therefore, fishes may have occupied particular channel unit types
in larger and lower gradient streams, in part, to minimize competition. We also
observed that the relation between channel unit types and fish occupancy of southern
Appalachian highland endemic and warm water intolerant fishes was greatest as urban cover and
elevation decreased, respectively. We surmise that landscape scale factors controlled the fish
assemblage structure among our study reaches and that the distribution of fishes within reaches
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was the result of fish behavior (e.g., habitat use, species interactions) and channel unit
characteristics.
Management recommendations and application.- The allocation of fishery
management resources is generally based on the availability of resources and the perceived
importance of biotic and abiotic processes on fishes of concern. Fish resource managers typically
identify the relative importance of environmental variables and processes using data to relate the
differences within a fish assemblage to environmental factors. However, the spatial scale of fish
surveys used to collect fish data can vary considerably. Within lotic systems, surveys are often
conducted on stream reach or channel unit scales. If natural resource managers do not account
for hierarchal theory (Frissell et al. 1986; Poff 1997) and inter-scale interactions of factors
within their sample design and subsequent analyses, the prioritization of environmental variables
and their processes influencing fish communities can be inaccurate, which can have both
biological and economic consequences (Crook et al. 2001; Peterson and Dunham 2010). In this
study, our multi-scaled framework permitted us to take into account the incomplete detection of
fishes, the hierarchical nature of lotic fish assemblages, the effect of scale, and allowed for the
identification of the relative importance of biotic and abiotic processes on fish assemblage
structure at the stream reach and channel unit scales.
Our data indicate that the fish assemblage within the Little Tennessee River Basin was
hierarchically structured and primarily influenced by stream size and spatial context,
urbanization, and channel unit types. We found that landscape scale
processes (e.g., elevation) largely constrained fish distribution throughout
much of the stream network and that local scale processes (e.g., channel
unit types) constrained fish distribution when suitable landscape conditions
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existed for each species. These results demonstrate the utility of a multi-
scaled approach and the importance of accounting for inter-scale
interactions prior to monitoring fish assemblages, developing biological management plans,
or allocating management resources throughout a stream system (Frissell et al. 1986; Tonn
1990; Fausch et al. 1994; Poff 1997; Scheurer et al. 2003; Quist et al. 2005).
Currently, there are over seven species of concern that inhabit the Little Tennessee River
Basin (e.g., Brook Trout and Rosyside Dace; Warren et al. 2000; Etnier and Starnes 2001;
NCWRC 2005). Given that management resources are finite, we suggest that the management of
fishes within the Little Tennessee River Basin and other similar southern Appalachian
watersheds should initially focus on the landscape or watershed scale processes for treatment or
research to identify the optimal spatial extents or conditions to implement effective local scale
treatments (e.g., channel unit restoration) if larger scale treatments are not feasible.
As the need for more accurate, efficient, and economical management approaches
continues to rise in conjunction with the imperilment of our freshwater fish species, studies that
are able to robustly assess the relative importance of variables that operate at multiple spatial and
temporal scales across larger extents will become more necessary. The use of our multi-scaled
framework can provide fish managers with a more holistic approach to the prioritization of
environmental processes affecting lotic fish assemblages and thus natural resource management.
Future studies should continue to expand on both the resolution and extent of the multi-scaled
approach used here.
<A>Ackowledgements
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We are indebted to many technicians, volunteers, and graduate students, including
Camille Beasley, Kristen Cecala, Justin Dycus, Andrea Fritts, Tiffany Kay, Jason Meador,
Angela Romito, Colin Shea, and Brittany Trushel. We thank J. Hepinstall-Cymerman for
assistance in obtaining geographical information system data, C. R. Jackson for assistance in
obtaining riparian buffer information, and we are grateful to M. Freeman, B. Ratajczak, and
B. Albanese for assistance in identifying specimens. Funding and logistical support for this
project was provided by the Coweeta Long Term Ecological Research project and the National
Science Foundation. The manuscript was improved with suggestions from C. Shea and
anonymous reviewers. The use of trade, product, industry or firm names or products is for
informative purposes only and does not constitute an endorsement by the U.S. Government or
the U.S. Geological Survey. This study was performed under the auspices of the Coweeta LTER
master animal use protocol AUP #A2009 4-074. This research was supported by the Long Term
Ecological Research Program to the Coweeta LTER Program at the University of Georgia (DEB-
0823293). The Georgia Cooperative Fish and Wildlife Research Unit is jointly sponsored by the
U.S. Geological Survey, the U.S. Fish and Wildlife Service, the Georgia Department of Natural
Resources, the University of Georgia, and the Wildlife Management Institute.
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Table 1. Hypotheses and corresponding predictor variables by scale and hypothesis categories used to estimate the study reach and
conditional channel unit occupancy and detection probability of common fish species within the Little Tennessee River Basin, GA and
NC.
Category Predictor Hypothesis
a) Study reach scale
Habitat
availability
%Riffle a + %Pool a The percentage of total study reach area of riffles and pools affects occupancy
by influencing hydrogeomorphic habitat characteristics.
Stream size and
spatial context
Link a The link magnitude of a study reach affects occupancy by influencing aquatic
habitat characteristics, habitat volume, and hydraulic regimes.
D-Link a The downstream link of a study reach affects occupancy by influencing
immigration and colonization rates.
Elevation a The elevation of a study reach affects the occupancy of warmwater intolerant
species by influencing temperature regimes.
Gradient a The gradient of a study reach affects occupancy by influencing hydrodynamic
characteristics and colonization rates.
Land cover Urban a The percentage of urban cover within a study reach watershed affects
occupancy of highland endemic species by influencing hydraulic regimes and
aquatic habitat characteristics.
Riparian Buffer a The presence of an intact riparian buffer throughout the study reach affects
occupancy by influencing productivity, thermal regimes, and habitat diversity.
Study reach
interference
%Pool Yellowfin Shiner at
Reach* Rosyside Dace at Reach
The percent of pools occupied by invasive yellowfin shiners within a study
reach affects the occupancy of rosyside dace within a study reach based on
41
competition interference competition.
%Pool Non-native Salmonid at
Reach* Brook Trout at Reach
The percent of pools occupied by non-native salmonids within a study reach
affects the occupancy of brook trout within a study reach based on interference
competition.
Study reach
competitive
exclusion
Yellowfin Shiner at
Reach*Rosyside Dace at Reach
The presence of at least one invasive yellowfin shiner within a study reach
affects the occupancy of rosyside dace within a study reach based on
competitive exclusion.
Non-native Salmonid at Reach
*Brook Trout at Reach
The presence of at least one non-native salmonid affects the occupancy of
brook trout within a study reach based on competitive exclusion.
Seasonal
interactions
Summer*Link Summer affects occupancy by reducing habitat and precludes fish from
occupying small streams.
Summer*Elevation Summer affects occupancy by decreasing the ground water supply in higher
elevations and increasing water temperature.
Land cover
interactions
Riparian Buffer*Urban The relation between urban cover and occupancy varies when an intact riparian
buffer is present.
Land cover and
competitive
exclusion
interactions
Gradient* Non-native Salmonid
at Reach*Brook Trout at Reach
The relation between non-native salmonids and brook trout occupancy varies
with gradient.
Urban* Yellowfin Shiner at
Reach*Rosyside Dace at Reach
The relation between invasive yellowfin shiners and rosyside dace occupancy
varies with urban cover.
b) Channel unit scale
Channel unit type Pool a + Riffle a A pool or riffle, relative to a run, affects occupancy by influencing
hydrogeomorphic characteristics.
42
Channel unit
competition
Yellowfin Shiner*Rosyside
Dace*Pool + Yellowfin
Shiner*Rosyside Dace*Riffle
The presence of at least one invasive yellow shiner within a channel unit
affects the occupancy of rosyside dace within a channel units uniquely based
on competitive exclusion and resource partitioning.
Non-native Salmonid*Brook
Trout*Pool + Non-native
Salmonid*Brook Trout*Riffle
The presence of at least one non-native salmonid within a channel unit affects
the occupancy of brook trout within a channel units uniquely based on
competitive exclusion and resource partitioning.
c) Inter-scale
Inter-scale
competition
Yellowfin Shiner at
Reach*Rosyside Dace*Pool +
Yellowfin Shiner*Rosyside
Dace*Riffle
The presence of at least one invasive yellow shiner within a study reach affects
the occupancy of rosyside dace within channel units uniquely based on
competitive exclusion and resource partitioning.
Non-native Salmonid at
Reach*Brook Trout*Pool +
Non-native Salmonid*Brook
Trout*Riffle
The presence of at least one non-native salmonid within a study reach affects
the occupancy of brook trout within channel units uniquely based on
competitive exclusion and resource partitioning.
Channel unit and
landscape
interactions
Gradient*Pool +
Gradient*Riffle
The relation between channel unit types and occupancy is mediated by gradient
given changes in hydrodynamics.
Link*Pool + Link*Riffle The relation between channel unit types and occupancy varies with link based
on decreasing variability in hydrogeomorphic characteristics among channel
units as streams become larger.
Seasonal channel
unit interactions
Summer*Pool +
Summer*Riffle
The relation between channel unit types and occupancy varies with summer by
less flow being present and forcing fish to seek refugia in pools.
43
a The effect was modeled as varying among species.
44
977
Table 2. Mean, standard deviation (in parentheses), and range of
environmental conditions in sampled study reaches within the Little
Tennessee River Basin during the summer of 2009, spring and summer of
2010, and spring of 2011.
Variable Mean (SD) Range
Study reach characteristics
Elevation (m) 691.8 (88.21) 611 - 1060
Gradient (%) 4.6 (5.09) 0.5 - 23.7
Link magnitude 25.1 (28.31) 1 - 116
Downstream link magnitude 68.4 (174.86) 2 - 1112
Watershed area (km2) 7.3 (8.44) 0 - 36
Riparian buffer intact (%) 46.05(50.04) 0 - 100
Urban cover (% watershed) 5.6 (8.99) 0 - 48
Pool (% study reach area) 37.2 (23.54) 0 - 100
Riffle (% study reach area) 39.6 (19.39) 0 - 100
Run (% study reach area) 23.2 (24.04) 0 - 100
Channel unit characteristics
Conductivity (μs/cm) 26.5 (10.67) 8 - 56
Turbidity (NTU) 8.0 (4.93) 1 - 35
Surface area (m²) 39.8 (26.898) 1 - 196
Mean cross sectional area (m²) 0.94 (0.751) 0.02 - 4.9
Mean depth (m) 0.24 (0.120) 0.02 - 0.7
Mean velocity (m/s) 0.32 (0.173) 0.02 - 1.0
Coarse sediment (%) 33.72 (20.768) 0 - 90
Fine sediment (%) 38.65 (26.075) 5 - 95
Woody debris (no./m²) 0.03 (0.052) 0 - 0.4
45
978
Table 3. Fish species, the number and percentage (in parenthesis) of sites and samples they were collected, and the total length (TL) mean and range (in parenthesis) observed within the Little Tennessee River Basin.
Species Sites Samples Mean TL (mm)Mountain Brook Lamprey Ichthyomyzon greeleyi 10 (20.8) 40 (7.6) 117.6(52-154)Central Stoneroller Campostoma anomalum 19 (39.6) 94 (17.9) 94.8(34-237)Goldfish Carassius auratus 1 (2.1) 1 (0.2) 118.0(117-119)Rosyside Dace Clinostomus funduloides 18 (37.5) 87 (16.6) 65.5(28-109)Whitetail Shiner Cyprinella galactura 8 (16.7) 31 (5.9) 80.1(32-169)Warpaint Shiner Luxilus coccogenis 12 (25) 65 (12.4) 79.6(30-196)River Chub Nocomis micropogon 15 (31.2) 73 (13.9) 112.7(33-256)Golden Shiner Notemigonus crysoleucas 1 (2.1) 2 (0.4) 82.2(77-88)Tennessee Shiner Notropis leuciodes 12 (25) 50 (9.5) 64.2(33-84)Yellowfin Shiner Notropis lutipinnis 8 (16.7) 41 (7.8) 66.5(30-118)Mirror Shiner Notropis spectrunculus 2 (4.2) 3 (0.6) 68.0(64-72)Telescope Shiner Notropis telescopus 2 (4.2) 8 (1.5) 77.6(66-91)Fatlip Minnow Phenacobius crassilabrum 5 (10.4) 8 (1.5) 91.3(71-113)Fathead Minnow Pimephales promelas 1 (2.1) 1 (0.2) 64.0(N/A)Blacknose Dace Rhinichthys atratulus 17 (35.4) 85 (16.2) 55.5(23-161)Longnose Dace Rhinichythys cataractae 13 (27.1) 50 (9.5) 83.7(31-155)Creek Chub Semotilus atromaculatus 31 (64.6) 129 (24.6) 69.6(19-193)White Sucker Catostomus commersonnii 5 (10.4) 11 (2.1) 175.0(86-296)Northern Hog Sucker Hypentelium etowanum 15 (31.2) 75 (14.3) 120.0(27-335)Black Redhorse Moxostoma duquesnii 3 (6.2) 8 (1.5) 400.7(90-506)Golden Redhorse Moxostoma erythrurum 3 (6.2) 6 (1.1) 153.6(40-432)Rainbow Trout Oncorhynchus mykiss 20 (41.7) 82 (15.6) 115.1(30-405)Brown Trout Salmo trutta 7 (14.6) 31 (5.9) 138.9(31-505)Brook Trout Salvelinus fontinalis 7 (14.6) 17 (3.2) 143.3(52-362)Western Mosquitofish Gambusia affinis 1 (2.1) 1 (0.2) 43.0(N/A)Mottled Sculpin Cottus bairdii 38 (79.2) 243 (46.3) 62.5(18-121)Rock Bass Ambloplites rupestris 8 (16.7) 28 (5.3) 124.7(26-262)Redbreast Sunfish Lepomis auritus 15 (31.2) 39 (7.4) 98.6(34-183)Green Sunfish Lepomis cyanellus 5 (10.4) 13 (2.5) 70.4(34-134)Bluegill Sunfish Lepomis macrochirus 8 (16.7) 21 (4) 66.9(38-115)Smallmouth Bass Micropterus dolomieu 3 (6.2) 8 (1.5) 226.8(190-253)Spotted Bass Micropterus punctulatus 3 (6.2) 4 (0.8) 51.2(42-61)Largemouth Bass Micropterus salmoides 2 (4.2) 4 (0.8) 53.9(33-78)Greenside Darter Etheostoma blennoides 4 (8.3) 9 (1.7) 98.8(53-121)Greenfin Darter Etheostoma chlorobranchium 1 (2.1) 3 (0.6) 72.6(63-78)Gilt Darter Percina evides 3 (6.2) 19 (3.6) 57.8(44-77)
46
979980
Table 4. Standardized parameter estimates (standard deviation in parentheses), 95% credible intervals (CI), and odds ratios (OR) for the best fitting candidate occupancy model predicting study reach occupancy (ψ), conditional channel unit occupancy (θ│ψ), and conditional detection probability (p│ψθ) for common fish species within the Little Tennessee River Basin during the summer of 2009, spring and summer of 2010, and spring of 2011.
47
Parameter Estimate95% CI
ORLower Upper
Study Reach Occupancy (ψ)Fixed effects
Intercept -4.019 (1.021) -6.004 -2.000 0.018Link 2.673 (0.795) 1.114 4.214 14.483D-Link -0.712 (0.550) -1.841 0.333 0.491Elevation -2.035 (1.138) -4.301 0.165 0.131Gradient -5.096 (1.133) -7.323 -2.871 0.006Urban -1.035 (0.902) -2.833 0.733 0.355Riparian Buffer -2.625 (1.229) -4.990 -0.156 0.072Summer 0.192 (0.165) -0.126 0.518 1.212
Random effectsIntercepta 4.537 (0.713) 3.189 5.854Linka 2.739 (0.574) 1.834 4.074D-Linka 1.456 (0.533) 0.629 2.699Elevationa 5.008 (0.664) 3.548 5.953Gradienta 5.231 (0.564) 3.928 5.970Urbana 3.116 (0.655) 2.060 4.631Riparian Buffera 5.515 (0.399) 4.524 5.985Site 3.583 (0.635) 2.524 5.019
Conditional Channel Unit Occupancy (θ│ψ)Fixed effects
Intercept 1.514 (0.168) 1.209 1.865 4.545Riffle -0.581 (0.715) -1.905 0.920 0.560Pool 2.786 (0.850) 1.265 4.597 16.216Summer*Riffle -0.010 (0.260) -0.512 0.507 0.990Summer*Pool -0.817 (0.409) -1.636 -0.023 0.442
Random effectsRifflea 3.415 (0.822) 2.107 5.328Poola 3.294 (0.902) 1.869 5.383
Conditional Detection Probability (p│ψθ)Fixed effects
Intercept 0.122 (0.273) -0.421 0.654 1.13Velocity -0.149 (0.052) -0.25 -0.046 0.862Surface Area 0.119 (0.055) 0.012 0.229 1.1262 Shockers -0.242 (0.122) -0.478 -0.001 0.785
Random effectsIntercepta 1.371 (0.219) 1.012 1.868
48
a Random effects are expressed as variance components that varied among species.
49
981
Figure captions
Figure 1. Location of study reaches (n=48) within the Little Tennessee River Basin,
GA and NC.
Figure 2. Estimated mean conditional detection probabilities (р│ψθ) and their 95%
credible intervals for commonly observed fish species using a single shocker under
average channel unit conditions (current velocity = 0.32m/s, surface area = 39.8m2).
Species are listed in order of the percent of samples they were collected in.
Figure 3. Estimated mean occupancy probabilities (ψθ) for species within pool (solid line), riffle
(dotted line), and run (broken line) channel types in relation to urban cover (%) during the spring
assuming a non-intact riparian buffer, link = 87, D-link = 62, gradient = 1, and elevation = 674m
in the Little Tennessee River basin, GA and NC. Species with imprecise estimates of the effect
of urban cover on occupancy were excluded.
Figure 4. Estimated mean occupancy probabilities (ψθ) of species within pool (solid line), riffle
(dotted line), and run (broken line) channel types in relation to elevation (m) during the spring
assuming a non-intact riparian buffer, link = 87, D-link = 62, gradient = 1, and urban cover =
7.5% in the Little Tennessee River basin, GA and NC. Species with imprecise estimates of the
effect of urban cover on occupancy were excluded.
50
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
51
1003
Telescope ShinerGolden RedhorseSmallmouth Bass
Black RedhorseFatlip Minnow
Greenside DarterWhite Sucker
Green SunfishBrook Trout
Gilt DarterBluegill Sunfish
Rock BassBrown Trout
Whitetail ShinerRedbreast Sunfish
Mountain Brook LampreyYellowfin ShinerTennessee Shiner
Longnose DaceWarpaint Shiner
River ChubNorthern Hog Sucker
Rainbow TroutBlacknose DaceRosyside Dace
Central StonerollerCreek Chub
Mottled Sculpin
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Detection Probability
Spec
ies
52
1004
53
Occupancy Probability
Urban Cover (%)1005
54
Occupancy Probability
Elevation (m)1006
Appendix A. The structure of the hierarchical, multiscale occupancy models used to estimate
species presence and habitat use
The hierarchical models used to estimate study reach occupancy (ψ), the probability that the
species uses a channel unit within an occupied study reach (θ│ψ), and the probability of
detecting a species given it occupies a channel unit (p) as:
Logit(ψij) = α0j + α0i + α1iW1 + … + αriWr
Logit(θij│ψij) = γ 0 + γ1iX1 + γ2iX2 + … + γriXr
Logit(рijh│ψij, θij) = β0i + β 1Y1 + … + β rYr
where i indexes species, j indexes study reaches, h indexes replicated samples, Wr represents a
study reach-specific predictor variable (e.g., terrestrial landscape characteristic), α0j represents a
randomly varying intercept that varied among study reaches, α0i represents a randomly varying
intercept that varied among species, αri represents the effect of Wr on reach occupancy that varied
among species, Xr represents a channel unit-specific predictor variable (e.g., physical in-stream
habitat characteristic), γri represents the effect of Xr on channel unit occupancy that varied among
species, Yr represents an in-stream habitat predictor variable (e.g., water quality or physical in-
stream habitat characteristic), β0i represents a randomly varying intercept that varied among
species, and β r represents the effect of Yr on detection. Random effects (i.e., randomly varying
intercepts and slopes) were assumed to be normally distributed with a mean of zero and random
effect-specific variance (Royle and Dorazio 2008).
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