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LRH: Kirsch and Peterson
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RRH: Factors affecting fish assemblage structure
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A multi-scaled approach to evaluating the fish assemblage structure within
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southern Appalachian streams USA
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Joseph E. Kirsch1
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Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia,
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30602 USA
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James T. Peterson2
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Georgia Cooperative Fish and Wildlife Research Unit, US Geological Survey, Warnell School of
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Forestry and Natural Resources, University of Georgia, Athens, Georgia 30602 USA
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Current address: US Fish and Wildlife Service
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850 S. Guild Ave., Suite 105
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Lodi, CA 95240 USA
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E-mail address: joseph_kirsch@fws.gov
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Current address: Oregon Cooperative Fish and Wildlife Research Unit
To whom correspondence should be addressed.
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US Geological Survey
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104 Nash Hall, Corvallis, Oregon 97331 USA
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E-mail address: jt.peterson@oregonstate.edu
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This draft manuscript is distributed solely for purposes of scientific peer review. Its content is
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deliberative and predecisional, so it must not be disclosed or released by reviewers. Because the
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manuscript has not yet been approved for publication by the U.S. Geological Survey (USGS), it
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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
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characteristics, and interspecific interactions in structuring stream fish assemblages. We
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evaluated the relative importance of environmental characteristics and species interactions on
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fish occupancy, at local and landscape scales, within the upper Little Tennessee River Basin,
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Georgia and North Carolina. Using a quadrat sample design, fishes were collected at 525 channel
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units within 48 study reaches during two consecutive years. We evaluated the relative support for
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the influence of local- and landscape-scale factors on fish occupancy using multi-scaled,
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hierarchical, multi-species occupancy models. Modeling results suggested that the fish
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assemblage within the Little Tennessee River Basin was hierarchically structured and primarily
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influenced by stream size and spatial context, urban land coverage, and channel unit types.
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Landscape scale factors (i.e., urban land coverage, stream size and spatial context) largely
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constrained the fish assemblage structure at a stream reach and local scale factors (i.e., channel
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unit types) influenced fish distribution within stream reaches. Our study demonstrates the utility
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of a multi-scaled approach and the need to account for the inter-scale interactions of factors
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influencing assemblage structure prior to monitoring fish assemblages, developing biological
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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
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world (Jelks et al. 2008). Unfortunately, high fish endemism coupled with environmental
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degradation has resulted in substantial imperilment among native fishes with approximately 46%
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of the 1,187 described freshwater and diadromous fish taxa classified as vulnerable, threatened,
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or endangered, a 92% increase from 1989 (Jelks et al. 2008). Assuming no future catastrophic
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events, the extinction rate of North American freshwater fishes is projected to increase from
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0.4% per decade (Ricciardi and Rasmussen 1999) to 3.2 % per decade (Burkhead 2012) due to
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habitat deterioration associated with anthropogenic land and water development. Sedimentation,
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nutrient loading, pollution, exotic species introduction, and altered hydrologic regimes are the
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anthropogenic stressors that have been attributed to the precipitous increase in the freshwater fish
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imperilment in North America (Richter et al. 1997; Ricciardi and Rasmussen 1999; Contreras-
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Balderas et al. 2003; Dextrase and Mandrak 2006). To reduce these threats, effective
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management strategies need to be developed at the most effective local and/or landscape levels.
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Before these strategies are developed, it is essential to identify and quantify the primary factors
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influencing lotic fish assemblage structure to appropriately allocate management resources
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(Peterson and Dunham 2010).
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The structure of a lotic fish assemblage is the result of a combination of biotic
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interactions and environmental influences (Jackson et al. 2001). Biotic interactions (i.e.,
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primarily predation and competition) have been shown to influence assemblage composition
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through fish species deletions (Gilliam and Fraser 2001) and by altering fish behavior, such as
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changing habitat use (Power et al. 1985; Taylor 1996). Environmental characteristics, such as in-
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stream hydrogeomorphology (i.e., water depth, current velocity, and substrate composition),
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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
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(Schlosser 1982; Moyle and Vondracek 1985; Jackson et al. 2001; Peterson and Rabeni 2001a).
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Landscape charactersitics, such as topography and terrestrial landuse, can affect the structure of
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local in-stream habitat characteristics, which in turn influence fish assemblage structure
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(Schlosser 1991; Peterson and Kwak 1999; Paul and Meyer 2001). Despite the multitude of
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studies demonstrating the effects of both biotic interactions and environmental characteristics on
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fish assemblage structure, the percieved importance of mechanisms is not well understood and is
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typically governed by their association with the scale of study (Jackson et al. 2001; Peterson and
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Dunham 2010).
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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
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influencing fish assemblage structure, including biotic interactions and environmental
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characteristics, potentially operate across multiple spatial and temporal scales (Figure 1; Frissell
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et al. 1986; Tonn 1990; Poff 1997). The ability to observe or detect the effects of these factors,
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however, depends on the within- and among-grain heterogeneity (Peterson and Dunham 2010).
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Consequently, the perceived importance of factors influencing fish assemblage structure is
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influenced by the spatial and temporal scale over which an investigation is conducted (Fausch et
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al. 1994; Crook et al. 2001; Fausch et al. 2002; Peterson and Dunham 2010). For example,
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studies conducted using large sample units in large spatial extents (e.g., stream reaches within
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watersheds) often conclude that mechanisms operating over broad scales (e.g., topography) are
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the primary processes affecting lotic fish assemblage structure (e.g., Fausch et al. 1994), whereas
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studies conducted with smaller sample units and spatial extents (e.g., microhabitat use within a
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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
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structure (Jackson et al. 2001; Peterson and Dunham 2010). Though the association between
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spatial scale of a study and the perceived importance of factors influencing fish assemblage
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structure have been acknowledged (e.g., Jackson et al. 2001; Quist et al. 2005), few studies have
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elucidated the importance of biotic interactions and environmental characteristics in structuring
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lotic fish assemblages across spatial scales.
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The need to understand the importance of factors influencing fish assemblages is
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exemplified by the southeastern United States. The region possesses the richest diversity and
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greatest number of endemic fishes within North America, north of Mexico (Mayden 1988;
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Warren et. al. 2000, Jelks et al. 2008). The Southeast also has one of the highest proportions of
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imperiled freshwater fish taxa, where approximately 28% of the 560 described species are
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classified as imperiled or extirpated primarily due to a variety of anthropogenic stressors
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(Warren and Burr 1994; Warren et al. 1997; Warren et al. 2000; Jelks et al. 2008). Although lotic
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fish assemblage structure in the Southeast has been well studied, there remains considerable
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uncertainty as to the importance of biotic and abiotic factors across spatial scales. Therefore, we
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evaluated the factors influencing the fish assemblage structure within a Southeast river basin
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with the following objectives: (1) to develop multi-scale framework for evaluating the effects of
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biotic and abiotic factors operating at geomorphic channel unit and stream reach spatial scales;
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(2) to determine the relative importance of scale-specific effects; and (3) to quantify the scale-
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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
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Tennessee River Basin located in western North Carolina and northeast Georgia, USA. The
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Little Tennessee River is a tributary of the upper Tennessee River and is located in the Blue-
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Ridge province of the southern Appalachian Mountain range. The basin reportedly contains more
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than 150 native freshwater fish species (Warren et al. 2000; Etnier and Starnes 2001). The
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climate is classified as marine humid temperate based on its relatively mild air temperatures and
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high moisture content (Swift et al. 1988). The Little Tennessee River Basin drains an area of
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approximately 4,117km² and is dominated by second growth forests due to extensive
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deforestation in 19th century (Scott et al. 2002). Anthropogenic land use within the basin
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includes both agriculture and urban development, with agricultural land use declining and urban
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development steadily increasing to support human immigration (Gragson and Bolstad 2006).
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Site selection.- This study was conducted in collaboration with the Coweeta Long Term
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Ecological Research Program's synoptic sampling and assessment project. A total of 48 sites
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(henceforth, study reaches) were chosen to represent the range of land uses, stream sizes, stream
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network positions, and elevations found within the Little Tennessee River Basin (Figure 2).
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Forty-six study reaches were sampled during the pilot phase of the project in the summer (July –
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September) of 2009. Based on the results of the pilot sampling, 8 of the 46 study reaches and two
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additional study reaches (10 total) were selected to represent common land use (i.e., forest,
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urban, and agriculture) and development types (i.e., valley development and hill-slope
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development) within the Little Tennessee River Basin. The ten reaches then were sampled during
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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
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(2013). We noted if a study reach had an intact riparian buffer which was defined as fully
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forested cover extending 10m or more on both sides of the channel. All other landscape data for
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each study reach were calculated using readily available Coweeta Long Term Ecological
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Research geographic information system (GIS) data (CLTER 2011) and ArcGIS software
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version 9.3. Land use data for each study reach were derived from 2006 land cover data
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classified from Landsat Thematic Mapper satellite imagery with 30m resolution. Land cover
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classes were grouped into four land use classes: urban, agriculture, forest, and other. Study reach
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elevation, gradient, and hydrologic data were derived from a United States Geological Survey
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seamless digital elevation model with 9.353 m resolution. For each study reach, the number of
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contributing tributaries and watersheds boundaries were delineated using protocol described by
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Jenson and Domingue (1988). Tributaries were identified using a minimum drainage area
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threshold of 0.0875km2 to reflect observations during the summer of 2009. Link magnitude
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(Link) and downstream link (D-Link) were calculated to represent study reach size and study
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reach network positions, respectively. Link magnitude was defined as the number of contributing
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tributaries headwater streams upstream of a study reach (Shreve 1966). Downstream link was
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defined as the link magnitude of the next downstream confluence from a study reach (Osborne
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and Wiley 1992).
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Sampling design.- We used a multi-scale sample design in which samples were collected
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from individual geomorphic channel units (henceforth, channel units) nested within study
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reaches. At each study reach, the stream was stratified into distinct channel unit types and
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sampled using a quadrat sample design (terminology follows Williams et al. 2002). All channel
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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
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average, shallow areas with swift current velocities, surface turbulence, and tended to have
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coarser substrate than pools or runs. Conversely, pool channel units were, on average, deep areas
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with slow current velocities, little to no surface turbulence, and tended to have finer substrate
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than runs or riffles. Run channel units were, on average, areas with moderate depth, moderate to
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swift current velocities, little to no surface turbulence, and mixed substrate.
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Two to four replicates of each of the channel unit type present at each study reach were
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randomly selected for fish sampling and habitat measurement. During the summer of 2009, the
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randomly selected channel units were sampled during a single visit. During the spring and
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summer of 2010 and the spring of 2011, each randomly selected sample unit was sampled twice
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within each season to facilitate species detection probability estimation. When sampling occurred
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twice within a season, the time between the two sampling occasions was 5-7 days to allow fish to
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recolonize the channel units after sampling (Peterson and Bayley 1993).
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Fishes in each channel unit at each study reach were sampled during daylight hours with
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Smith-Root LR 24 pulsed DC backpack electrofishers operating between 0.2 to 0.3 amperes.
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Fish sampling began at the farthest downstream channel unit at each study reach within a
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sampling occasion. In general, prior to electrofishing, each channel unit had a 5mm mesh seine
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deployed across the downstream end for riffles and runs or the upstream end for pools to
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minimize fish escape and maximize detection during sampling. The seine completely blocked off
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the end of the sample unit and was secured to the streambed. Fishes then were then sampled via
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electrofishing using a single pass starting from the opposite end of the channel unit from where
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the seine was deployed. In streams with average wetted widths ≤ 4 meters, one backpack
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electrofisher was used by one crewmember with at least one additional crewmember collecting
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stunned fish with a 5mm mesh dipnet. In streams with average wetted widths > 4 meters, two
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backpack electrofishers were used and at least two additional crewmembers collected stunned
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fish with 5mm mesh dipnets. To maximize detection while sampling, all crewmembers involved
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with electrofishing attempted to disturb the substrate with their feet to limit stunned fishes from
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becoming lodged on the substrate or woody debris to maximize detection. After the single pass
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was completed for each channel unit, fishes were collected from the seine and all dipnets.
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Immediately after sampling each channel unit, all captured fishes were identified to
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species and released back into the stream. Fishes that could not be accurately identified in the
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field were initially preserved in a 10% formalin solution and brought back to the laboratory.
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Preserved fishes were later transferred to a 70% ethanol solution and identified to species.
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Physical habitat measurements.- Water quality and physical in-stream habitat
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characteristics expected to influence fish occupancy or the ability to detect fish were measured at
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each study reach and channel unit in conjunction with fish sampling. Prior to fish sampling,
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water quality characteristics were measured in flowing water using calibrated meters at each
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study reach for each sampling occasion. Turbidity was measured using an Oakton T-100 meter to
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the nearest 0.01 Nephelometric Turbidity Unit (NTU). Specific conductance was measured using
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an Oakton CON 400 Series meter to the nearest 0.01 microsiemens per centimeter (μs·cm-1).
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Immediately after fish sampling, physical in-stream habitat characteristics were estimated
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within each channel unit at each study reach for each sampling occasion. Mean wetted width,
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mean water velocity, and mean water depth were estimated by averaging 3-5 randomly placed
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measurements within each channel unit (following Peterson and Rabeni 2001b). Water depths
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were measured to the nearest centimeter using a two meter top-set wading rod. Water velocities
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were measured at 0.6 depth using a Marsh McBirney model 2000 Flo-mate meter. The length
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and wetted widths of each unit were measured to the nearest centimeter using a standard
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measuring tape. The mean cross-sectional area of each channel unit was estimated by
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multiplying the channel unit mean width by the channel unit mean depth. The surface area of
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each channel unit was estimated by multiplying a channel unit mean width by length.
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Substrate composition within a channel unit was visually estimated by two or more
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crewmembers and averaged (Peterson and Rabeni 2001b). Substrate composition was
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categorized based on particle diameter as fine sediment (<5mm), gravel (5-50mm), and coarse
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sediment (>50mm; modified from Dunne and Leopold 1978). Wood density was estimated by
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counting the pieces of wood within the wetted channel unit that were at least 50cm in length and
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10cm in diameter or aggregates of smaller pieces of wood with comparable volume and dividing
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by the channel unit area.
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Statistical analysis.- The probability of detecting lotic fish species is rarely 100% and is
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often related to the factors that influence fish populations (Bayley and Peterson 2001; Peterson
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and Paukert 2010). To minimize the effect of incomplete species detection, we evaluated the
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relative importance of biotic interactions, in-stream habitat, and landscape characteristics on fish
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assemblage structure using multi-species, multi-scale occupancy models (Mordecai et. al. 2011).
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Multi-scale occupancy models consist of three submodels that estimate the probability that a
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species occupies a study reach (ψ), the probability that the species uses a channel unit within an
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occupied study reach (θ), and the probability of detecting a species given it occupies a channel
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unit (p) in a single modeling framework. All of the multi-scale occupancy submodels (i.e., ψ, θ,
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and p) were fit using logit linear functions of predictor variables (e.g., stream habitat
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characteristics). The primary assumption of the occupancy estimator is that a species cannot
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colonize or abandon a study reach between sample occasions (MacKenzie et al. 2006). We
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believe that this study met this assumption because sample occasions were 5 -7 days apart within
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a season and did not include periods of seasonal fish migration or seasonal changes in habitat use
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(Peterson and Rabeni 2001a).
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Fish samples that were collected from multiple channel units within individual study
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reaches were likely statistically dependent (i.e., spatially autocorrelated). In addition, the
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relations between physical in-stream habitat and terrestrial landscape characteristics and
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occupancy were likely to vary among fish species. To account for dependence among repeated
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samples at study reaches and to allow model parameters to vary among species, we used
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hierarchical logit linear models to estimate reach and channel unit occupancy and detection
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probability (Royle and Dorazio 2008). Hierarchical models differ from more familiar linear
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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
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modeling the relation between scale-specific occupancy (i.e., study reach and channel unit within
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reach) and habitat characteristics for multiple species with very different habitat use patterns
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while accounting for incomplete detection using a single model and provided the means to
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quantify the variability among species responses.
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Based on the complexity of the modeling approach, we fit the occupancy models using
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Markov Chain Monte Carlo (MCMC) implemented in WinBUGS version 1.4 (Lunn et al. 2000).
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Models were fit using 700,000 iterations with a burn-in or discarding of the first 200,000
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iterations. The number of required iterations was estimated using the global model (i.e.,
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containing all predictor variables) and testing for convergence using the Gelman and Rubin
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diagnostic test (Gelman and Rubin 1992). Non-informative priors were used for each parameter
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(Kéry and Royle 2008).
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We used an information theoretic approach (Burnham and Anderson 2002) to evaluate
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the relative importance of biotic and abiotic factors on fish species occupancy in study reaches
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and channel units within study reaches. Our primary hypotheses of interest were to evaluate the
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relative influence biotic and abiotic factors on fish occupancy and habitat use. Secondarily, we
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sought to determine the factors influencing species detection. Thus, we initially developed a
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global model that contained all of the predictor variables corresponding to a priori hypotheses
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regarding the relative effect of physical and biotic factors on fish occupancy at channel unit and
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study reach scales (Table 1). Using the global model, we then evaluated the relative fit detection
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models that related species-specific detection to variables that could affect capture efficiency
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including: the number of backpack electrofishers, surface area and cross sectional area of the
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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
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2009; Hense et al. 2010; Price and Peterson 2010). The best approximating detection model was
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used to evaluate the relative support for the primary hypotheses of interest.
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Based on previous investigations (Larson and Moore 1985; Freeman et al. 1988;
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Grossman et al. 1998; Harding et al. 1998; Jones et al. 1999; Scott 2006; Burcher et al. 2008;
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Hazelton and Grossman 2009), we hypothesized that species occupancy at the study reach level
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was influenced by seasonal habitat availability, stream size and spatial context, land cover, and
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species interactions operating at reach scales (Table 1). Within occupied reaches, we
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hypothesized that species channel unit use was influenced by the characteristics of the channel
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unit and the presence of potential competitors. The hypothesized biotic interactions focused on
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previously documented interactions between nonnative or invasive species and native species in
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the Little Tennessee River Basin under laboratory conditions (Fausch and White 1981; Larson
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and Moore 1985; Hazelton and Grossman 2009). Native Brook Trout (Salvelinus fontinalis) were
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hypothesized to alter their habitat use and distribution in the presence of a nonnative Rainbow
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Trout (Oncorhynchus mykiss) and Brown Trout (Salmo trutta). Native Rosyside Dace
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(Clinostomus funduloides) were hypothesized to alter their habitat use and distribution in the
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presence of the invasive Yellowfin Shiner (Notropis lutippinis). To estimate the potential effects
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of species interactions under incomplete species detection, we used the state space formulation of
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the occupancy model (MacKenzie et al. 2006; Mordecai et. al. 2011). Here, we used the species-
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specific latent variables to estimate three predictor variables: the presence of the potential
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competitor in a study reach and a channel unit within a study reach and the proportion of specific
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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
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between all pairs of potential predictor variables. Only uncorrelated variables (r²<0.45) were
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included in the same candidate occupancy models to avoid multicollinearity. All continuous in-
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stream habitat and landscape data were standardized with a mean of zero and standard deviation
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of one to facilitate model fitting. We also created binary indicator variables for channel unit type,
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summer season, intact riparian buffer, and for instances where two shockers were used to sample
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fish. We determined the best variance structure (i.e., the parameters that needed to vary
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randomly among species or study reaches) by evaluating the relative fit of the global model with
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each parameter systematically treated as a fixed or randomly varying effect. The best
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approximating variance structure was identified as that with the lowest Akiake Information
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Criteria with small sample bias adjustment (AICc; Hurvich and Tsai 1989). The number of
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parameters used to calculate AICc included both fixed and random effects. The best
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approximating variance structure then was used during the evaluation of the relative plausibility
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of the candidate models.
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The relative support of each candidate occupancy model was evaluated by calculating
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AICc. Because MCMC methods produce a distribution of AICc values, we used the mean AICc
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from MCMC iterations for inference (following Fonnesbeck and Conroy 2004). The relative fit
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of candidate models were determined by calculating Akaike weights (wi; Burnham and Anderson
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2002) that range from 0-1, with the greatest weight being the best fitting model. To assess the
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amount of evidence one candidate model had over another, we calculated the ratios of Akaike
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weights (following Burnham and Anderson 2002). Model selection uncertainty was expressed by
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creating a confidence model set. Any candidate model with Akaike weights that were within
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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
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set of occupancy models.
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To aid interpretation of parameter estimates, we calculated odds ratios (OR) for each
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fixed effect parameter in the confidence set of occupancy models (Hosmer and Lemeshow 2000).
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Because the continuous data were standardized, an OR corresponded to a one standard deviation
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increase in the corresponding predictor variable. The precision of fixed effect parameter
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estimates was assessed by calculating 95% credible intervals that are analogous to 95%
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confidence intervals (Congdon 2001). In general, credible intervals that contained zero were
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considered imprecise. We also plotted empirical Bayes estimates (Royle and Dorazio 2008) of
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the relationship between environmental characteristics and occupancy for common species.
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<A>Results
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A total of 525 channel units were sampled in the 48 study reaches with 183 (35%)
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channel units classified as a pool, 200 (38%) as riffles, and 142 (27%) channel units as runs. Half
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of the study reaches (24) contained all three channel unit types and seven reaches consisted of a
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single habitat type. The latter group was primarily small headwater streams with link magnitudes
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less than 10. The study sites represented a wide range of stream sizes, physical characteristics,
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and land uses typical of the region (Table 2). The three land use types, however, were relatively
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strongly correlated (r2 > 0.6), which prevented their inclusion in the same occupancy model.
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Therefore, the evaluation of the effect of land use on fish community structure included only
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urban land use because land conversion to urban use was a management concern within the
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basin.
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We captured 36 species during the study (Table 3). The most commonly detected species
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were Mottled Sculpin (Cottus bairdii) and Creek Chub (Semotilus atromaculatus), which were
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collected at more than half of the study reaches (Table 3). In general, few large bodied
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piscivorous fishes (e.g., Smallmouth Bass Micropterus dolomieu) were collected. No species
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were collected at all of the study reaches and no (zero) fishes were collected in five study
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reaches. Eight species were collected in only 1-2 study reaches and less than 1% of samples
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(Table 3). We were concerned that the occupancy model parameter estimates for these rare
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species would be biased due to insufficient information and the effects of parameter shrinkage
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that occurs in hierarchical models. Therefore, we eliminated these eight species from the dataset
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and fit models using the remaining 28 species (Table 3).
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Detection modeling.- The best approximating species detection model contained the
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number of backpack electrofishers used, channel unit current velocity, surface area, and a
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species-specific random effect. The Akaike weights indicated that the model was more than 33
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times better supported than the next best approximating conditional detection model that
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contained only current velocity, surface area, and a species-specific random effect. The
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parameter estimates for the best fitting detection model indicated that the probability of detecting
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a species was positively related to increases in channel unit surface area and negatively related to
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the use of two backpack electrofishers and increases in current velocity (Table 4). The species-
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specific random effect also indicated that detection varied among fish species and was, on
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average, greatest for commonly collected species including Mottled Sculpin, Creek Chub, and
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Blacknose Dace (Rhinichthys atratulus; Table 4; Figure 2). Therefore, the best fitting detection
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model was used during model fit and evaluation of occupancy.
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Occupancy modeling.- The best approximating occupancy model (AICc = 5055)
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contained season, stream size and spatial context (i.e., elevation, gradient, link magnitude, and
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downstream link magnitude), and land cover (i.e., urban cover and riparian buffer) for predicting
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study reach occupancy, and channel unit type (i.e., riffle, run, and pool) and interactions between
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channel unit type and seasons for predicting conditional channel unit occupancy (Table 4). There
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was little or no support for any other candidate occupancy model and thus there was little support
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for other predictors of occupancy. The best fitting model was 12.2 times better supported than
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the next best approximating candidate model (AICc = 5060), which was similar but contained
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study reach interference competition (interactions between the % of pools occupied by yellowfin
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shiner or non-native salmonids and occupancy of rosyside dace or brook trout, respectively) for
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predicting study reach occupancy. As a result, we based all inferences on the best model.
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The best approximating model estimated that occupancy was greatest for all species in
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larger and lower gradient streams (Table 5). In contrast, the effect and relative importance of
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urban cover, elevation, and channel unit type on occupancy varied among species (Figures 3 and
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4). Parameter estimates suggested that, on average, fish species were 2.26 times less likely to
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occur in pool channel types relative to run channel types during the summer compared to the
387
spring (Table 5) and that the small benthic Mottled Sculpin, Central Stoneroller (Campostoma
388
anomalum), Gilt Darter (Percina evides) had affinities for riffle channel types whereas other
389
species generally had affinities for pool or run channel types (Figures 3 and 4). However, the
390
relative importance of channel type on the within-reach distribution was greater at more optimal
391
study reach conditions for each species (Figures 3 and 4). On average, fish occupancy was higher
392
as urban cover increased within a watershed for Blacknose Dace and White Suckers (Catostomus
393
commersonnii) but was negatively related to high urban cover for all other species including the
394
highland endemic Longnose Dace (Rhinichythys cataractae), Tennessee Shiner (Notropis
395
leuciodes), and Warpaint Shiner (Luxilus coccogenis; Figure 3). On average, the occupancy of
396
the warmwater intolerant Longnose Dace, Brook Trout, and Brown Trout increased with
397
elevation, whereas site occupancy of all other species decreased with elevation (Figure 4). The
398
occupancy of all fish species were, on average, 13.9 times less likely when an intact riparian
399
buffer existed (Table 5) but the highland endemic Mottled Sculpin, Longnose Dace, and
400
Rosyside Dace were, on average, 5.56 times more likely to occur when an intact riparian buffer
401
existed.
402
403
404
<A>Discussion
We found that stream size and spatial context, land cover, and channel unit types were
405
the most important factors affecting fish occupancy within the Little Tennessee River Basin.
406
However, the relative importance of these factors varied among species and spatial context. We
407
surmise that environmental stability, habitat quality, and the thermal limitations of fishes largely
18
408
controls the fish assemblage structure among stream reaches, but the behavior aimed at
409
minimizing interspecific interactions and exploiting food resources affects occupancy within
410
stream reaches. Therefore, to appropriately identify the primary mechanisms controlling a lotic
411
fish assemblage requires an understanding of the local and landscape scale processes and the
412
recognition that the importance of these processes on fish assemblage structure vary with scale.
413
The structure of the fish assemblage in a stream reach is influenced, in part, by thermal
414
regimes (Jackson et al. 2001; Wehrly et al. 2003; Quist et al. 2005). The thermal regime of a
415
stream reach often varies in response to elevation, longitudinal position, and riparian cover
416
within a stream network (Scott et al. 2002; Martin and Petty 2009). In this study, the occupancy
417
of warm water intolerant fishes was positively related to elevation, whereas the occupancy of
418
warm water tolerant fishes (i.e., catastomids, centrarchids, percinids, and most cyprinids) was
419
negatively related to elevation. Scott et al. (2002) demonstrated that stream reaches within the
420
upper Tennessee River Basin have generally cooler summer mean temperatures at higher
421
elevations and this is consistent with our observations. In general, warmer streams have greater
422
productivity relative to cooler streams and fishes have greater metabolic rates in warmer water
423
habitats (Vannote et al. 1980; Schlosser 1982; Huryn and Wallace 2000). As a result, fishes that
424
can physiologically tolerant warm water often exploit the available resources in streams at lower
425
elevations (Rahel and Hubert 1991; Wehrly et al. 2003). However, the distribution of fishes that
426
cannot physiologically tolerate warm water for sustained periods of time (e.g., Brook Trout)
427
often are restricted to cooler reaches at higher elevations (Swift and Messer 1971; Vannote et al.
428
1980; Rahel and Hubert 1991; Jackson et al. 2001; Wehrly et al. 2003; Martin and Petty 2009).
429
Therefore, we hypothesize that the differences in fish distribution among stream reaches within
19
430
the Little Tennessee River Basin were, in part, influenced by the physiological limitations of
431
fishes in relation to thermal regime.
432
Fish assemblage structure also is influenced, in part, by the size, habitat complexity, and
433
stability of stream reaches (Gorman and Karr 1978; Schlosser 1982; Peterson and Rabeni 2001a;
434
Shea and Peterson 2007). We observed that fish occupancy was positively related to link
435
magnitude and negatively related to gradient within the Little Tennessee River Basin. Headwater
436
reaches typically possess relatively shallow and smaller habitats with less structural diversity
437
(e.g., substrate, depth, and current) compared to reaches farther downstream (Gorman and Karr
438
1978; Vannote et al. 1980; Schlosser 1982). In this study, we observed that headwater reaches
439
were dominated by either riffles, runs, or pools, whereas larger stream reaches typically
440
contained all of these habitats. Stream reaches with more structural diversity often support more
441
diverse fish assemblages based on possessing a broad range of habitats that allows most species
442
to encounter favorable or optimal habitats within a reach to permit persistence (Gorman and Karr
443
1978; Vannote et al. 1980). In addition, headwater reaches often are subjected to greater
444
hydrologic variability and disturbance frequency (e.g., floods and droughts) compared to more
445
downstream reaches, which can exclude or eliminate intolerant fish species without adjacent
446
source populations (Whiteside and McNatt 1972; Gorman and Karr 1978; Schlosser 1982; Resh
447
et al. 1988; Reice et al. 1990; Osborne and Wiley 1992). The negative relation between gradient
448
and fish occupancy was likely due to higher gradient streams being dominated by habitats
449
containing faster current velocities including cascades and riffles. These habitats are often
450
energetically costly (Facey and Grossman 1990) and may inhibit movement and thus
451
immigration or recolonization (Taylor and Warren 2001; Grenouillet et al. 2004). Therefore, we
20
452
hypothesize that the fish distribution among stream reaches was, in part, influenced by the
453
variation in habitat complexity and stability of a stream.
454
Anthropogenic land use activities occurring within a watershed can greatly modify the
455
processes that create and maintain the physical and chemical stream characteristics (Schlosser
456
1991; Allan 2004). Modeling results suggested that only fishes endemic to the southern
457
Appalachian highlands (i.e., those adapted to cool, sediment free, and nutrient-poor
458
environments) responded favorably to intact riparian buffers and all fishes endemic to the
459
southern Appalachian highlands were less likely to occur in watersheds with greater
460
urbanization. These results are generally consistent with previous studies of stream fishes (e.g.,
461
Jones et al. 1999; Rahel 2000; Scott and Helfman 2001; Sutherland et al. 2002; Miltner et al.
462
2004; Scott 2006). Many of these studies also demonstrated that urban land use can affect fish
463
assemblages through nutrient enrichment, the introduction of contaminants, increased
464
sedimentation, and altered flow and temperature regimes. Deforestation, the loss of a riparian
465
zone, and increases in impervious cover (e.g., compacted soil, roads, and buildings) associated
466
with urbanization can cause a reduction in evapotranspiration and infiltration rates and thus
467
increase the amount of surface runoff into streams during rainfall events resulting in lower base
468
flows and more frequent peak flows (Dunne and Leopold 1978; Webster et al. 1992; Paul and
469
Meyer 2001; Allen 2004; Alberti et al. 2007). In addition, streams with little or no shading from
470
riparian cover receive greater amounts of solar radiation resulting in higher temperatures and
471
larger daily fluctuations in temperature (Swift and Messer 1971; Pusey and Arthington 2003;
472
Moore et al. 2005; Long and Jackson 2013). Urban land use practices and the loss of an intact
473
riparian buffer also can increase inputs of nutrients (e.g., industrial fertilizer and terrestrial plant
474
material), point sources (e.g., municipal discharge), and non-point sources (e.g., agricultural
21
475
pesticides) within streams, affecting water quality (Wiley et al. 1990; Webster et al. 1992; Paul
476
and Meyer 2001; Pusey and Arthington 2003; Miltner et al. 2004). Increased surface runoff
477
coupled with exposed soils created by the removal of terrestrial vegetation also increases surface
478
erosion and sediment inputs into streams (Webster et al. 1992; Harding et al. 1998; Jones et al.
479
1999; Paul and Meyer 2001; Sutherland et al. 2002). As sediment inputs increase within southern
480
Appalachian streams, aquatic habitats become more homogenous (Jones et al. 1999; Scott and
481
Helfman 2001; Sutherland et al. 2002). Overall, these anthropogenic stream modifications can
482
result in the loss and degradation of aquatic habitats, facilitating the loss of endemic fish species
483
and the homogenization of stream fish assemblages (Jones et al. 1999; Sutherland et al. 2002;
484
Miltner et al. 2004; Scott 2006). Although our study cannot identify the relative importance of or
485
differentiate between these mechanisms, the lack of evidence for an interaction between the
486
effect of urban land use and the presence of an intact riparian buffer on fish occupancy suggests
487
that intact riparian buffers alone were insufficient to mitigate the effects of large scale urban land
488
use.
489
Geomorphic channel unit types (e.g., riffle, run, or pool) represented unique, predictable
490
combinations of water depth, current velocity, and substrate composition. Because fish
491
distributions within a stream reach are known to be related to these habitat characteristics, fish
492
species may use one geomorphic channel unit type over another to fulfill one or more life history
493
requirements (Peterson and Rabeni 2001a, 2001b). Presumably, the primary objective of an
494
individual fish is to maximize survival. Fish vulnerability to terrestrial predation (e.g., birds and
495
mammals) is a function of water depth and body size with predation risk being greater for larger
496
fish in shallower habitats, whereas aquatic predation risk is generally higher for smaller fishes in
497
deeper habitats (Werner et al. 1983; Harvey and Stewart 1991). The habitat use of most fishes in
22
498
our study sites was greater in deeper channel units (i.e., pools), which suggests that risk of
499
aquatic predation may have been lower than terrestrial predation. Indeed, few large bodied
500
piscivorous fishes were collected, so the risk of aquatic predation was likely very small
501
throughout our study sites. Therefore, we hypothesize that the distribution of fish within the
502
stream reaches was to minimize risk of terrestrial predation.
503
Fishes also should attempt to maximize net energy gain for growth when selecting
504
habitats within a stream reach (Hill and Grossman 1993). Fishes should attempt to minimize
505
their energy expenditures and maximize energy inputs to maximize growth (Facey and Grossman
506
1990; Facey and Grossman 1992; Hill and Grossman 1993). High current velocity habitats
507
contain relatively high densities of macroinvertebrates (Huryn and Wallace 1987), but can be
508
energetically demanding for fishes that are not behaviorally, morphologically, or physiologically
509
adapted to high current velocity habitats (Facey and Grossman 1990; Facey and Grossman 1992;
510
Hill and Grossman 1993). Consistent with previously cited studies, we observed that most small
511
benthic fishes (e.g., Mottled Sculpin) tended to use channel units with higher current velocities,
512
whereas most pelagic fishes (e.g., sunfish) tended to use habitats with lower current velocities.
513
Small benthic fishes often possess the hydrodynamic adaptations, including a depressed or
514
streamlined body profile, modified fins, and a reduced swim bladder to limit buoyancy (Etnier
515
and Starnes 2001), which reduce the energetic costs of occupying high current habitats that have
516
high prey densities (Facey and Grossman 1992; Peterson and Rabeni 2001a). Therefore, we
517
hypothesize that fish distribution within a reach was, in part, influenced by morphological
518
constraints and fish behavioral adaptations intended to maximize survival and growth within
519
stream reaches.
23
520
In general, the strength of the relation between channel unit types and fish occupancy was
521
greater in larger and lower gradient streams and where fish species experienced less constraining
522
landscape elevations and urban coverage. As more species are added to a community, there can
523
be a corresponding increase in resource specialization (i.e., niche packing) to reduce niche
524
overlap and presumably, competition (Pianka 1974). Peterson and Rabeni (2001a) reported that
525
fish species used a smaller variety of channel unit types in Ozark stream reaches when the
526
assemblage contained more species and hypothesized that this was due to increased resource
527
specialization. Therefore, fishes may have occupied particular channel unit types in larger and
528
lower gradient streams, in part, to minimize competition. We also observed that the relation
529
between channel unit types and fish occupancy of southern Appalachian highland endemic and
530
warm water intolerant fishes was greatest as urban cover and elevation decreased, respectively.
531
We surmise that landscape scale factors controlled the fish assemblage structure among our
532
study reaches and that the distribution of fishes within reaches was the result of fish behavior
533
(e.g., habitat use, species interactions) and channel unit characteristics.
534
Management recommendations and application.- The allocation of fishery management
535
resources is generally based on the availability of resources and the perceived importance of
536
biotic and abiotic processes on fishes of concern. Fish resource managers typically identify the
537
relative importance of environmental variables and processes using data to relate the differences
538
within a fish assemblage to environmental factors. However, the spatial scale of fish surveys
539
used to collect fish data can vary considerably. Within lotic systems, surveys are often conducted
540
on stream reach or channel unit scales. If natural resource managers do not account for hierarchal
541
theory (Frissell et al. 1986; Poff 1997) and inter-scale interactions of factors within their sample
542
design and subsequent analyses, the prioritization of environmental variables and their processes
24
543
influencing fish communities can be inaccurate, which can have both biological and economic
544
consequences (Crook et al. 2001; Peterson and Dunham 2010). In this study, our multi-scaled
545
framework permitted us to take into account the incomplete detection of fishes, the hierarchical
546
nature of lotic fish assemblages, the effect of scale, and allowed for the identification of the
547
relative importance of biotic and abiotic processes on fish assemblage structure at the stream
548
reach and channel unit scales.
549
Our data indicate that the fish assemblage within the Little Tennessee River Basin was
550
hierarchically structured and primarily influenced by stream size and spatial context,
551
urbanization, and channel unit types. We found that landscape scale processes (e.g., elevation)
552
largely constrained fish distribution throughout much of the stream network and that local scale
553
processes (e.g., channel unit types) constrained fish distribution when suitable landscape
554
conditions existed for each species. These results demonstrate the utility of a multi-scaled
555
approach and the importance of accounting for inter-scale interactions prior to monitoring fish
556
assemblages, developing biological management plans, or allocating management resources
557
throughout a stream system (Frissell et al. 1986; Tonn 1990; Fausch et al. 1994; Poff 1997;
558
Scheurer et al. 2003; Quist et al. 2005).
559
Currently, there are over seven species of concern that inhabit the Little Tennessee River
560
Basin (e.g., Brook Trout and Rosyside Dace; Warren et al. 2000; Etnier and Starnes 2001;
561
NCWRC 2005). Given that management resources are finite, we suggest that the management of
562
fishes within the Little Tennessee River Basin and other similar southern Appalachian
563
watersheds should initially focus on the landscape or watershed scale processes for treatment or
564
research to identify the optimal spatial extents or conditions to implement effective local scale
565
treatments (e.g., channel unit restoration) if larger scale treatments are not feasible.
25
566
As the need for more accurate, efficient, and economical management approaches
567
continues to rise in conjunction with the imperilment of our freshwater fish species, studies that
568
are able to robustly assess the relative importance of variables that operate at multiple spatial and
569
temporal scales across larger extents will become more necessary. The use of our multi-scaled
570
framework can provide fish managers with a more holistic approach to the prioritization of
571
environmental processes affecting lotic fish assemblages and thus natural resource management.
572
Future studies should continue to expand on both the resolution and extent of the multi-scaled
573
approach used here.
574
575
576
<A>Ackowledgements
We are indebted to many technicians, volunteers, and graduate students, including
577
Camille Beasley, Kristen Cecala, Justin Dycus, Andrea Fritts, Tiffany Kay, Jason Meador,
578
Angela Romito, Colin Shea, and Brittany Trushel. We thank J. Hepinstall-Cymerman for
579
assistance in obtaining geographical information system data, C. R. Jackson for assistance in
580
obtaining riparian buffer information, and we are grateful to M. Freeman, B. Ratajczak, and B.
581
Albanese for assistance in identifying specimens. Funding and logistical support for this project
582
was provided by the Coweeta Long Term Ecological Research project and the National Science
583
Foundation. The manuscript was improved with suggestions from C. Shea and anonymous
584
reviewers. The use of trade, product, industry or firm names or products is for informative
585
purposes only and does not constitute an endorsement by the U.S. Government or the U.S.
586
Geological Survey. This study was performed under the auspices of the Coweeta LTER master
587
animal use protocol AUP #A2009 4-074. This research was supported by the Long Term
588
Ecological Research Program to the Coweeta LTER Program at the University of Georgia (DEB-
26
589
0823293). The Georgia Cooperative Fish and Wildlife Research Unit is jointly sponsored by the
590
U.S. Geological Survey, the U.S. Fish and Wildlife Service, the Georgia Department of Natural
591
Resources, the University of Georgia, and the Wildlife Management Institute.
592
593
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38
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
%Riffle a + %Pool a
availability
Stream size and
The percentage of total study reach area of riffles and pools affects occupancy
by influencing hydrogeomorphic habitat characteristics.
Link a
spatial context
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
%Pool Yellowfin Shiner at
The percent of pools occupied by invasive yellowfin shiners within a study
interference
Reach* Rosyside Dace at Reach reach affects the occupancy of rosyside dace within a study reach based on
39
competition
interference competition.
%Pool Non-native Salmonid at
The percent of pools occupied by non-native salmonids within a study reach
Reach* Brook Trout at Reach
affects the occupancy of brook trout within a study reach based on interference
competition.
Study reach
Yellowfin Shiner at
The presence of at least one invasive yellowfin shiner within a study reach
competitive
Reach*Rosyside Dace at Reach
affects the occupancy of rosyside dace within a study reach based on
exclusion
Seasonal
competitive exclusion.
Non-native Salmonid at Reach
The presence of at least one non-native salmonid affects the occupancy of
*Brook Trout at Reach
brook trout within a study reach based on competitive exclusion.
Summer*Link
Summer affects occupancy by reducing habitat and precludes fish from
interactions
occupying small streams.
Summer*Elevation
Summer affects occupancy by decreasing the ground water supply in higher
elevations and increasing water temperature.
Land cover
Riparian Buffer*Urban
interactions
The relation between urban cover and occupancy varies when an intact riparian
buffer is present.
Land cover and
Gradient* Non-native Salmonid
The relation between non-native salmonids and brook trout occupancy varies
competitive
at Reach*Brook Trout at Reach
with gradient.
exclusion
Urban* Yellowfin Shiner at
The relation between invasive yellowfin shiners and rosyside dace occupancy
interactions
Reach*Rosyside Dace at Reach
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.
40
Channel unit
Yellowfin Shiner*Rosyside
The presence of at least one invasive yellow shiner within a channel unit
competition
Dace*Pool + Yellowfin
affects the occupancy of rosyside dace within a channel units uniquely based
Shiner*Rosyside Dace*Riffle
on competitive exclusion and resource partitioning.
Non-native Salmonid*Brook
The presence of at least one non-native salmonid within a channel unit affects
Trout*Pool + Non-native
the occupancy of brook trout within a channel units uniquely based on
Salmonid*Brook Trout*Riffle
competitive exclusion and resource partitioning.
c) Inter-scale
Inter-scale
Yellowfin Shiner at
competition
Reach*Rosyside Dace*Pool +
Yellowfin Shiner*Rosyside
Dace*Riffle
Non-native Salmonid at
Reach*Brook Trout*Pool +
Non-native Salmonid*Brook
Trout*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.
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
Gradient*Pool +
The relation between channel unit types and occupancy is mediated by gradient
landscape
Gradient*Riffle
given changes in hydrodynamics.
interactions
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
Summer*Pool +
The relation between channel unit types and occupancy varies with summer by
unit interactions
Summer*Riffle
less flow being present and forcing fish to seek refugia in pools.
41
a
The effect was modeled as varying among species.
42
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
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
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
0.03 (0.052)
0 - 0.4
Study reach characteristics
Channel unit characteristics
Conductivity (μs/cm)
Woody debris (no./m²)
941
43
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)
942
44
943
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.
95% CI
Parameter
Estimate
OR
Lower
Upper
Study Reach Occupancy (ψ)
Fixed effects
Intercept
-4.019 (1.021)
-6.004
-2.000
0.018
Link
2.673 (0.795)
1.114
4.214
14.483
D-Link
-0.712 (0.550)
-1.841
0.333
0.491
Elevation
-2.035 (1.138)
-4.301
0.165
0.131
Gradient
-5.096 (1.133)
-7.323
-2.871
0.006
Urban
-1.035 (0.902)
-2.833
0.733
0.355
Riparian Buffer
-2.625 (1.229)
-4.990
-0.156
0.072
Summer
0.192 (0.165)
-0.126
0.518
1.212
Random effects
Intercepta
4.537 (0.713)
3.189
5.854
Linka
2.739 (0.574)
1.834
4.074
1.456 (0.533)
0.629
2.699
5.008 (0.664)
3.548
5.953
5.231 (0.564)
3.928
5.970
3.116 (0.655)
2.060
4.631
D-Link
a
Elevation
a
Gradienta
a
Urban
a
Riparian Buffer
Site
Fixed effects
Intercept
Riffle
Pool
Summer*Riffle
Summer*Pool
Random effects
Rifflea
Poola
5.515 (0.399)
4.524
5.985
3.583 (0.635)
2.524
5.019
Conditional Channel Unit Occupancy (θ│ψ)
1.514 (0.168)
-0.581 (0.715)
2.786 (0.850)
-0.010 (0.260)
-0.817 (0.409)
1.209
-1.905
1.265
-0.512
-1.636
1.865
0.920
4.597
0.507
-0.023
3.415 (0.822)
2.107
5.328
3.294 (0.902)
1.869
5.383
Conditional Detection Probability (p│ψθ)
Fixed effects
45
4.545
0.560
16.216
0.990
0.442
Intercept
Velocity
Surface Area
2 Shockers
Random effects
0.122 (0.273)
-0.149 (0.052)
0.119 (0.055)
-0.242 (0.122)
-0.421
-0.25
0.012
-0.478
0.654
-0.046
0.229
-0.001
Intercepta
1.371 (0.219)
1.012
1.868
a
1.13
0.862
1.126
0.785
Random effects are expressed as variance components that varied among species.
944
46
945
Figure captions
946
947
Figure 1. Location of study reaches (n=48) within the Little Tennessee River Basin,
948
GA and NC.
949
950
Figure 2. Estimated mean conditional detection probabilities (р│ψθ) and their 95%
951
credible intervals for commonly observed fish species using a single shocker under
952
average channel unit conditions (current velocity = 0.32m/s, surface area = 39.8m2).
953
Species are listed in order of the percent of samples they were collected in.
954
955
Figure 3. Estimated mean occupancy probabilities (ψθ) for species within pool (solid line), riffle
956
(dotted line), and run (broken line) channel types in relation to urban cover (%) during the spring
957
assuming a non-intact riparian buffer, link = 87, D-link = 62, gradient = 1, and elevation = 674m
958
in the Little Tennessee River basin, GA and NC. Species with imprecise estimates of the effect
959
of urban cover on occupancy were excluded.
960
961
Figure 4. Estimated mean occupancy probabilities (ψθ) of species within pool (solid line), riffle
962
(dotted line), and run (broken line) channel types in relation to elevation (m) during the spring
963
assuming a non-intact riparian buffer, link = 87, D-link = 62, gradient = 1, and urban cover =
964
7.5% in the Little Tennessee River basin, GA and NC. Species with imprecise estimates of the
965
effect of urban cover on occupancy were excluded.
47
966
48
Species
Mottled Sculpin
Creek Chub
Central Stoneroller
Rosyside Dace
Blacknose Dace
Rainbow Trout
Northern Hog Sucker
River Chub
Warpaint Shiner
Longnose Dace
Tennessee Shiner
Yellowfin Shiner
Mountain Brook…
Redbreast Sunfish
Whitetail Shiner
Brown Trout
Rock Bass
Bluegill Sunfish
Gilt Darter
Brook Trout
Green Sunfish
White Sucker
Greenside Darter
Fatlip Minnow
Black Redhorse
Smallmouth Bass
Golden Redhorse
Telescope Shiner
0
0.2
0.4
0.6
Detection Probability
967
49
0.8
1
1
1
0.8
0.8
0.6
A) Blacknose Dace 0.6
0.4
0.4
0.2
0.2
0
0
0
0.001
12
24
36
48
0
0.8
0.0006
0.6
0.0004
0.4
0.0002
0.2
0
0
Occupancy Probability
0.0008
0
1
12
24
12
1
B) Bluegill Sunfish
36
48
1
C) Green Sunfish
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
24
36
48
G) Warpaint Shiner
0
0
12
24
36
48
36
48
36
48
36
48
H) White Sucker
0
0
1
12
24
36
48
0
0.8
D) Longnose Dace
0.8
12
24
I) Whitetail Shiner
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
0.05
12
24
36
E) Mountain Brook Lamprey
0
48
0.005
0.04
0.004
0.03
0.003
0.02
0.002
0.01
0.001
0
0
0
968
F) Tennessee Shiner
12
24
36
12
J) Yellowfin Shiner
0
12
48
Urban Cover (%)
50
24
24
0.001
0.0008
0.0006
0.0004
0.0002
0
650
1
0.8
0.6
0.4
0.2
0
Occupancy Probability
610
1
0.8
0.6
0.4
0.2
0
610
1
0.8
0.6
0.4
0.2
0
610
0.375
0.3
0.225
0.15
0.075
0
610
1
0.8
0.6
0.4
0.2
0
610
969
1
0.8
0.6
0.4
0.2
0
A) Bluegill Sunfish
G) Longnose Dace
650
750
850
950
1050
1
H) Mottled Sculpin
0.8
0.6
B) Brook Trout
0.4
0.2
0
610 685 760 835 910 985 1060
685 760 835 910 985 1060
0.5
I) Mountain Brook Lamprey
0.4
0.3
0.2
C) Brown Trout
0.1
0
610 685 760 835 910 985 1060
685 760 835 910 985 1060
1
D) Central Stoneroller
0.8
0.6
0.4
J) River Chub
0.2
0
685 760 835 910 985 1060
610 685 760 835 910 985 1060
0.03
K) Rosyside Dace
E) Creek Chub
0.025
0.02
0.015
0.01
0.005
0
610 685 760 835 910 985 1060
685 760 835 910 985 1060
0.75
F) Gilt Darter
L) Yellowfin Shiner
0.6
0.45
0.3
0.15
0
685 760 835 910 985 1060
610 685 760 835 910 985 1060
Elevation (m)
750
850
950
1050
51
970
Appendix A. The structure of the hierarchical, multiscale occupancy models used to estimate
971
species presence and habitat use
972
973
The hierarchical models used to estimate study reach occupancy (ψ), the probability that the
974
species uses a channel unit within an occupied study reach (θ│ψ), and the probability of
975
detecting a species given it occupies a channel unit (p) as:
976
Logit(ψij) = α0j + α0i + α1iW1 + … + αriWr
977
Logit(θij│ψij) = γ 0 + γ1iX1 + γ2iX2 + … + γriXr
978
Logit(рijh│ψij, θij) = β0i + β 1Y1 + … + β rYr
979
where i indexes species, j indexes study reaches, h indexes replicated samples, Wr represents a
980
study reach-specific predictor variable (e.g., terrestrial landscape characteristic), α0j represents a
981
randomly varying intercept that varied among study reaches, α0i represents a randomly varying
982
intercept that varied among species, αri represents the effect of Wr on reach occupancy that varied
983
among species, Xr represents a channel unit-specific predictor variable (e.g., physical in-stream
984
habitat characteristic), γri represents the effect of Xr on channel unit occupancy that varied among
985
species, Yr represents an in-stream habitat predictor variable (e.g., water quality or physical in-
986
stream habitat characteristic), β0i represents a randomly varying intercept that varied among
987
species, and β r represents the effect of Yr on detection. Random effects (i.e., randomly varying
988
intercepts and slopes) were assumed to be normally distributed with a mean of zero and random
989
effect-specific variance (Royle and Dorazio 2008).
52