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An evaluation of the relations between flow regime components, stream
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characteristics, and species traits and meta-demographic rates of warmwater
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streams fishes: Implications for aquatic resource management
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James T. Peterson1, U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research
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Unit, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA
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30602
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Colin P. Shea2, Warnell School of Forestry and Natural Resources, University of Georgia,
Athens, GA 30602
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104 Nash Hall, Corvallis, Oregon 97331 USA, E-mail address: jt.peterson@oregonstate.edu
Current address: US Geological Survey, Oregon Cooperative Fish and Wildlife Research Unit
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Tennessee Technological University, Cookeville, Tennessee 38505 USA
Current address: Tennessee Cooperative Fishery Research Unit, Department of Biology,
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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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ABSTRACT
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Fishery biologists are increasingly recognizing the importance of considering the dynamic nature
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of streams when developing streamflow policies. Such approaches require information on how
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flow regimes influence the physical environment and how those factors, in turn, affect species-
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specific demographic rates. A more cost effective alternative could be the use of dynamic
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occupancy models to predict how species are likely to respond to changes in flow. To appraise
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the efficacy of this approach, we evaluated relative support for hypothesized effects of seasonal
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stream flows, stream channel characteristics, and fish species traits on local, colonization, and
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recruitment (meta-demographic rates) of stream fishes. We used four years of seasonal fish
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collection data from 23 streams to fit multi-state, multi-season occupancy models for 42 fish
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species in the lower Flint River Basin, Georgia. Modeling results suggested that meta-
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demographic rates were influenced by streamflows, particularly short-term (10 day) flows. Flow
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effects on meta-demographic rates also varied with stream channel morphology and size and fish
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species traits. Small-bodied species with generalized life-history and reproductive characteristics
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were more resilient to flow variability than were large-bodied species with specialized
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reproductive and life-history characteristics. Using this approach, we simplified the modeling
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framework thereby facilitating the development of dynamic, spatially explicit evaluations of the
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ecological consequences of water resource development activities over broad geographic areas.
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INTRODUCTION
Recent decades have seen a rapid growth in human demand for the natural resources
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throughout the world. Such demand has resulted in the need for resource development strategies
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that consider both future human needs and the conservation needs of valued ecosystems
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(Arthington et al. 2006). As such, managers around the world are increasingly being asked to
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predict ecological outcomes of alternative resource-management decisions, typically in the
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context of changing climate and land uses (Clark et al. 2001, Araujo and Rahbek 2006).
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Management of water availability in streams and rivers provides a prominent example.
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Population growth and expanding agricultural irrigation are increasing the demands to divert,
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transfer and store water from flowing water ecosystems (Postel 2000, Postel and Richter 2003,
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Fitzhugh and Richter 2004). At the same time, the declining capacity of river systems to support
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native biota, including imperiled species and fisheries, is a primary concern for natural resource
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managers and conservationists (Pringle et al. 2000, Arthington and Pusey 2003, Postel and
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Richter 2003, Dudgeon et al. 2006). Both problems – increasing water demands relative to
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availability and declining aquatic species – will likely be exacerbated by future changes in land
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use, especially urbanization (Paul and Meyer 2001, Fitzhugh and Richter 2004), and climate
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(Milly et al. 2008, Palmer et al. 2008, Nelson et al. 2009).
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Current methods for assessing the stream flow requirements of aquatic biota are often
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resource intensive (time and money) and limited in their spatial, temporal, and ecological scope.
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Although more than 200 techniques have been developed for evaluating instream flow
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requirements of aquatic biota, habitat simulation methodologies are the most commonly used in
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North America (Tharme 2003). Habitat simulation methods employ hydraulic models to estimate
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the response of fishes to changes in amounts and types of habitats under differing stream
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discharge and habitat suitability (or use) criteria (Bovee et al. 1998). Habitat simulation methods
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require substantial data collection for quantifying flow-habitat relationships and because of their
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cost are often conducted only at very few locations. Habitat simulation methods also are often
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narrow in their ecological scope because they are often restricted to particular species or species
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groups. Hence, there often remains considerable uncertainty regarding how observed flow-
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habitat relationships transfer to other species or vary across space and time.
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Alternative methods for assessing the stream flow requirements of aquatic biota should
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possess several characteristics to be most useful for developing and evaluating management
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strategies (Arthington et al. 2006). First, assessment techniques should be cost effective and able
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to incorporate larger spatial and temporal extents (e.g., river basins over multiple years).
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Effective techniques also should enable quantification of the responses of multiple species to
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changes in streamflow within the context of stream and watershed-level environmental
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conditions (Freeman et al. 2013). Lastly, effective assessment techniques should consider the
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dynamic nature of stream systems; namely, that species are continually responding to changing
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streamflow conditions, and such dynamics should be explicitly accounted for in resource
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assessments (Arthington et al. 2006). This requires information of the dynamics of populations
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and how those dynamics vary in response to changes in flow regimes and stream habitats.
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Although the information needs for such an approach appear daunting, occupancy modeling
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approaches (McKenzie et al. 2006) may provide an effective and efficient means for modeling
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aquatic populations. Dynamic occupancy models track changes in the state of animal
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populations (e.g., present, absent, abundant, rare) through space and time can be used to evaluate
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the relations between state transitions (e.g., absent to present= colonization) and biotic and
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abiotic factors. In the context of modeling animal populations, we define these as meta-
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demographic rates for convenience.
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Previously, we developed and evaluated a geomorphic channel classification for
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estimating habitat availability and fish species presence and abundance and demonstrated that it
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is possible to bypass detailed habitat measurements (i.e., habitat simulation) to quantify stream
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fish species responses to changes in stream flow (Peterson et al. 2009). We then used that
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classification system to evaluate the influence of seasonal streamflows and stream
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geomorphology on the structure of fish assemblages (McCargo and Peterson 2010).We now
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intend to use that classification system to estimate the influence of seasonal streamflows, stream
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geomorphology, and stream channel characteristics on stream fish meta-demographic rates. Our
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goal was to develop a spatially-explicit, dynamic multi-state occupancy model to quantify stream
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fish response to changes in flow within the context of local geology, channel form, and species-
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specific life history traits. Thus, we studied Southeastern US, Coastal Plain stream fish
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assemblages with the following objectives: (1) to estimate site-level colonization, extinction, and
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reproduction as a function of streamflow and stream channel characteristics; (2) to identify the
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seasonal flow conditions that have the greatest influence on meta-demographic rates; (3) to
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identify the life history characteristics or species traits that are most predictive of how species
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will respond to changes in streamflow conditions; and (4) to demonstrate the potential usefulness
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of such an approach for managing stream fish populations over large spatial and temporal
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extents.
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METHODS
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Study area
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We evaluated the influence of seasonal flows, stream characteristics, and species traits on
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fish meta-demographic rates in 23 stream study sites in lower Flint River Basin in Southwestern
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Georgia (Figure 1). We selected the study sites based on stream size, surficial geology, and gross
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channel morphology and classified each as Fall Line Hills or Ocala Limestone and confined or
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unconfined channel morphology. Streams in the Fall Line Hills district were characterized by
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sandy-mud substrate and relatively high turbidity levels and streams in the Ocala Limestone
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district contained greater amounts of coarse substrates and lower turbidity (Peterson et al. 2009).
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Confined channels were single-threaded with high, well-defined banks and greater amounts of
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pool and riffle habitats compared to unconfined channels (Peterson et al. 2009). Unconfined
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channels had low and indistinct channel banks and were generally shallower with greater
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amounts of glide habitats compared to confined channels. The stream size of each study site was
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characterized using link magnitude (Shreve 1966) and the relative position of a study site using
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the link magnitude of the nearest downstream segment (Osborne and Wiley 1992).
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The study sites lengths varied from site to site but were sufficient to include all
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representative habitat types and minimize the effect of localized species-specific distribution
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patterns. Wadeable sample sites were approximately 100 m long, whereas the length of non-
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wadeable sites was approximately 150 m. However, two study sites were approximately 50 m
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long during sampling in the summer 2001.
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Fish and habitat sampling
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Fish sampling and habitat measurements were conducted seasonally from summer 2001
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to the summer 2004. Seasons were defined as: spring, April-June; and summer, July-September
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and all samples were collected during the latter third of each season. Because the physical
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characteristics of different streams varied widely, no single gear type could effectively sample
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fish assemblages in all study sites. Therefore, we used three standardized sampling methods that
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varied with the size of the stream.
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In narrow, wadeable streams (mean wetted width < 8 m and mean depth < 0.5 m), the
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upstream and downstream boundaries of a site were blocked with 7-mm mesh nets and sampled
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during three passes with a pulsed DC backpack electrofisher operating at approximately 2 A. The
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first pass was made in an upstream direction, followed by a downstream pass, and a final
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upstream pass.
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Wide (mean wetted width > 8 m), wadeable streams also were blocked with 7-mm mesh
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nets and sampling was conducted during three passes with a tote-barge mounted electrofishing
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unit and two anode probes powered by a 3000-W generator producing approximately 3 A pulsed
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DC. The sequence of passes was identical to the backpack electrofisher with first pass in an
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upstream direction, followed by a downstream pass, and a final upstream pass.
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Non-wadeable sites (mean depth > 0.5 m) were sampled with six passes of a boat
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electrofisher equipped with two Wisconsin rings and powered by a 3000-W generator at
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approximately 3 A pulsed DC. The first pass was made in an upstream direction in the middle of
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the stream; the second was in a downstream direction adjacent to the first, but along one of the
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banks; and the third pass was made in an upstream direction along the opposite bank. The next
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three passes were identical to the first three with the direction (upstream or downstream)
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reversed.
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All captured fish were identified to species and total length (TL) was measured to the
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nearest mm. Large fish (>100 mm) and all Centrarchidae and Catastomidae were identified,
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measured, and released; small fish (<100 mm) were preserved in 10% formalin and taken to the
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laboratory for identification and more accurate measurement. We considered the presence of
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young-of year fishes (YOY) at a site to be an indicator of stream fish reproduction. Thus, we
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grouped individual fish into two age classes: YOY and juvenile/adult (non-YOY) using seasonal,
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species-specific length-frequency histograms.
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Stream features that were used to estimate fish capture probability (discussed below)
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were measured at each site near the time of fish sampling. Using calibrated hand held meters,
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conductivity, temperature, and turbidity were measured in the middle of a site. Stream habitat
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characteristics were estimated by measuring depth and average current velocity at eight evenly-
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spaced points along 10 evenly-spaced transects. At each transect, wetted stream width (to the
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nearest 0.1 m) was measured perpendicular to flow. At each point along a transect, crews
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measured mean current velocity (to the nearest 0.01 m/s) with a Marsh-McBirney digital flow
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meter and depth (to the nearest 0.01 m) with a standard top-set wading rod. When water depth
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was less than 0.65 m, average velocity was measured at 0.6 of total depth; whereas average
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velocity at greater depths was measured as the mean of readings taken at 0.2 and 0.8 of total
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depth. For each site, mean current velocity and mean water depth were estimated by averaging
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each point measurement, mean wetted width by averaging the widths of each transect and stream
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discharge as the product of the average width, depth, and current velocity.
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Definitions and statistical analyses
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Streamflow estimation. – One of our objectives was to identify the stream flow
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components that had the greatest influence on fish meta-demographic rates. However, only 4 of
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the 23 sites were located near continuous discharge measurement gages. Therefore, we used
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existing study site-specific models relating discharge at the ungaged sites to discharge at long-
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term USGS stations located in the lower Flint River Basin (McCargo and Peterson 2010). These
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linear regression models were relatively precise with coefficients of determination that averaged
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0.87. The estimated and observed daily discharge at the ungaged and gaged sites, respectively
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were used to calculate seasonal flow statistics.
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To evaluate the influence of stream flows on fish local extinction, colonization, and
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reproduction, we calculated site-specific seasonal flow statistics for the seasonal period prior to
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fish sampling. Our primary hypotheses of interest focused on the evaluation of the relative
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influence of three components of the flow regime: high flows, low flows, and flow variability.
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Based on previous studies (Freeman et al. 2001, Craven et al. 2010, McCargo and Peterson
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2010), we also wanted to evaluate in the relative influence of short- and long-term discharge
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conditions on fishes. We characterized short-term low flows as the 10-day low discharge, which
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was calculated as the lowest average discharge for 10 consecutive days for the season prior to
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fish sampling. The short-term high flows were similarly calculated as highest average discharge
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for 10 consecutive days for the season prior to fish sampling. Long-term flow conditions were
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characterized as the median discharge and flow stability as the standard deviation (SD) in
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discharge during the season prior to fish sampling.
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Stream sizes varied substantially among our study sites, which would complicate the
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evaluation of the effects of flow components on fish meta-demographic rates. To facilitate the
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evaluation, we standardized the discharge statistics (described above) for each site by dividing
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each statistic by the median seasonal discharge for the period of record at gaged sites and the
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model-estimated median seasonal discharge at ungaged sites for the period of record of their
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reference gages (following McCargo and Peterson 2010).
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Occupancy modeling. - We estimated local extinction, colonization, and reproduction
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using multistate, multiseason occupancy models (MacKenzie et al. 2009). Multistate,
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multiseason occupancy models can be used to model changes in the states of animal populations
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at one or more locations through time. We considered three population states (m) for each
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species: unoccupied, occupied with no reproduction (i.e., adults and juveniles present but YOY
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absent), occupied with successful reproduction (i.e., YOY present). Here we used the conditional
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binomial parameterization of Nichols et al. (2007) and modeled the probability of successful
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reproduction, given that the site was occupied. The conditional probability of successful
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reproduction was fixed at zero for the spring season because YOY fishes were too small to be
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reliably collected with our methods. Local extinction, colonization, and conditional reproduction
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were modeled as a function of seasonal flow components, stream characteristics, and species
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traits using linear logistic hierarchical models, discussed below.
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To account for incomplete detection (i.e., false absences), we estimated species and size
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class-specific capture probability for each sample using capture-recapture models and used them
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in place of state-specific conditional detection probabilities normally used in multistate
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occupancy models (MacKenzie et al. 2009). The capture-recapture models estimated fish capture
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probability as a function of sampling method, species, fish body length, and the physiochemical
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characteristics of the study sites (McCargo and Peterson 2010). We estimated the capture
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probability separately for YOY and adult/juvenile fishes using the species-specific median body
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lengths of each size class collected during the study. Because capture probabilities were not
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known with certainty, the predicted probability distribution (PDF) was incorporated using a beta
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PDF during the model fitting.
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Fish sampling began in summer 2001 and coincided with initiation of a severe drought
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period in the region. We were concerned that the initial occupancy (t = 0) at each site would
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reflect a fish community that was already affected by the low flow conditions. Therefore, we
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used existing fish collection data collected during 1980-1999 from previous studies in the Flint
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River Basin by the Georgia Department of Natural Resources (GADNR), U.S. Geological
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Survey (USGS), and the University of Georgia personnel to predict initial occupancy (unaffected
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by drought) at the 23 study sites. The data were collected from 234 stream reaches in the Flint
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River Basin using standardized protocols that ensured site-specific species detection probabilities
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 70% (Ruiz and Peterson 2007). These data were used to estimate species-specific initial
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occupancy probabilities at each site as a function of link magnitude, downstream link magnitude,
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channel confinement, and surficial geology. The best approximating initial occupancy model was
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used for evaluating the relative support of all candidate multistate, multiseason occupancy
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models.
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We believed that the relation between flow components and fish meta-demographic rates
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was likely to vary among fish species and among streams. To account for the potentially varying
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response of fishes and among sites, we examined relationships between flow components and
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fish meta-demographic rates using hierarchical models (Royle and Dorazio 2008). Hierarchical
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models differ from more familiar linear modeling techniques in that they consist of upper and
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lower level models. In the lower level models, the values of parameters (e.g., slope and
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intercepts) can vary among subjects (Royle and Dorazio 2008), here species or sites. For our
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study, the lower level models treated the intercept and the effect of flow components and stream
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characteristics on meta-demographic rates as varying among species. We interpreted the fixed
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effects associated with the lower level intercept as the relation between the species traits, site-
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specific characteristics, and season on the overall probability of local extinction, colonization, or
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reproduction. The fixed effects associated with the flow components were expressed as
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interactions between flow components and species trait, site-specific characteristics, and season.
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For example, a model containing a link magnitude by flow interaction meant that the effect of
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the flow component was modeled as a function of link magnitude. We interpreted these fixed
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effects as the influence of these factors on the relation between the flow component and local
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extinction, colonization, or reproduction. In addition, we evaluated the support for an additional
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random effect corresponding to each study site to account for potential spatial autocorrelation.
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Model selection. - We used an information-theoretic approach (Burnham and Anderson
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2002) to evaluate the relative influence of flow components, stream characteristics, and species
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traits on the meta-demographic rates of stream fishes. Our primary hypotheses of interest were to
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evaluate the relative influence of short- and long-term seasonal flows on stream fish meta-
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demographic rates. Secondarily, we sought to determine the influence of species traits, stream
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characteristics, and season on the relation between flow components stream fish meta-
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demographic rates. Thus, we contrasted three sets of submodels with each corresponding to a
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meta-demographic parameter. Candidate model parameters were systematically entered and
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excluded from each submodel and only one flow component was included in each candidate
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submodel at a time to avoid multicolinearity. Local extinction submodels included one of three
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flow regime components (Table 1), stream size (link magnitude), stream channel confinement,
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and three species traits (Table 2). Colonization submodels included one of three flow regime
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components, stream size, size of nearest downstream tributary (downstream link magnitude),
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stream channel confinement, and season with three species traits (Table 2). Conditional
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reproduction included four flow regime components (Table 1), stream size, and channel
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confinement with three traits hypothesized to influence conditional reproduction (Table 2). As
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discussed above, conditional reproduction was fixed at zero for the spring season because YOY
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fishes were too small to be sampled reliably. To evaluate the influence of flow components on
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reproduction during the spring, the third model set included four flow regime components from
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the spring and the summer for a total of eight flow components. The candidate model set also
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included models without species traits, stream characteristics, and season.
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To accommodate a model structure that included random effects, we used Markov Chain
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Monte Carlo (MCMC) as implemented in BUGS software, version 1.4 (Lunn et al. 2000) to fit
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the initial occupancy and multistate multiseason occupancy models. All models were fit based on
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500,000 iterations with 50,000 burn in (i.e., the first 50,000 MCMC iterations were dropped) and
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diffuse priors. The number of iterations was determined by fitting the candidate model that
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modeled the meta-demographic parameters as a function of median discharge, all stream
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characteristics, and all species traits and running six parallel chains and testing for convergence
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using the Gelman- Rubin diagnostic (Gelman and Rubin 1992). The relative support of each
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candidate model was evaluated by calculating Akaike’s Information Criteria (AIC; Akaike 1973)
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with the small-sample bias adjustment (AICc; Hurvich and Tsai 1989). Because the MCMC
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methods produce a distribution of AICc values, we used the mean AICc for all inferences
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(Fonnesbeck and Conroy 2004). The number of parameters used to estimate AICc included the
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fixed effects and random effects (Burnham and Anderson 2002). We also calculated Akaike
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weights that range from zero to one with the most plausible candidate model having the highest
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weight (Burnham and Anderson 2002). We then constructed a confidence set of models as those
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candidate models that had Akaike weights of 0.10 (10 % of the highest importance weight) or
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higher, similar to the cut-off established by Royall (1997) as a basis for evaluating strength of
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evidence. All inferences were based on the candidate model set. The precision of each fixed and
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random effect in the best supported models was estimated by computing 95% credible intervals
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(Congdon 2001), which are analogous to 95% confidence intervals. Goodness-of-fit (GOF) was
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assessed for the global model for each flow component using a simple discrepancy measure and
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1000 simulated data points (Gelman et al. 1996).
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Prior to evaluating the fit of our candidate models, link magnitude and downstream link
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magnitude were natural log transformed to facilitate MCMC model fitting. We binary coded
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season with spring coded as 1 when the season was spring and 0 otherwise, channel confinement
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with unconfined channels coded as 1 and 0 otherwise, and surficial geology with Ocala
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limestone coded as 1 otherwise 0. Categorical species traits predictors (adult body size, adult
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habitat preference, locomotion morphology, and spawning behavior) also were binary coded.
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RESULTS
Stream flows varied considerably among sampling years and seasons (Table 1) with 10-
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day low discharge ranging from 0 to 2.0 times, and 10-day high discharge ranging from 0.5 to
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20.9 times, the long-term median discharge at a study site. The observed discharge at the long-
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term gage USGS at our Spring Creek study site was representative of temporal discharge patterns
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during the period of study (Figure 2). Daily discharge was similar to the long term average
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during the spring 2001, but was substantially below average from the summer 2001 through the
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fall 2002. Stream flows were much higher than the average long-term discharge during all of
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2003 and were similar to average long-term flows during 2004.
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We collected a total of 136 samples and captured 53 fish species during the study.
Eleven species were collected in less than 5% of the samples. Rare species generally have little
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influence on assemblage dynamics, and their inclusion in an analysis could significantly distort
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trends or relationships (Gauch 1982). Therefore, were restricted our evaluation of fish meta-
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demographic rates to the 42 species that occurred in more than 5% of the samples (Table 3).
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The best approximating model for estimating initial occupancy contained link magnitude,
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downstream link magnitude, stream channel confinement, and surficial geology and random
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effects corresponding to each fixed effect (Table 4). This model was used for each candidate
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multistate, multiseason occupancy model during model selection. Bayesian goodness-of-fit tests
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of each global flow component model estimated p-values that ranged from 0.28 - 0.73 suggesting
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that model fit was adequate.
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The best approximating multistate, multiseason occupancy model relating stream flows,
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stream characteristics, season, and fish species traits to fish meta-demographic rates included
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local extinction modeled as a function of 10-day low discharge, stream link magnitude, stream
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channel confinement, and the two-way interactions: 10-day low discharge by link magnitude, 10-
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day low discharge by unconfined stream channel, 10-day low discharge by adult body size;
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colonization as a function of 10-day high discharge, stream link magnitude, stream channel
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confinement, spring, and the two-way interactions: 10-day high discharge by link magnitude, 10-
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day high discharge by adult body size; and conditional reproduction as a function of summer
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discharge SD, stream channel confinement, and summer discharge SD by locomotion
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morphology interaction (Table 5). The Akaike importance weight of this model indicated that it
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was 1.8 times more likely than the next best approximating model, which was similar to the best
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model but included a 10-day high discharge by locomotion morphology interaction in place of
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the 10-day high discharge by adult body size interaction in the colonization model. The Akaike
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model weights indicated support for nine models and these comprised the confidence model set
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(Table 5). The remaining models in the confidence set were similar to the two best
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approximating and suggested that there was evidence that local extinction was related to species
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tolerance and median seasonal discharge. In addition, there was evidence that conditional
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reproduction was related to 10-day high discharge during the summer and fish locomotion
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morphology.
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Parameter estimates indicated that local extinction was negatively related to 10-day low
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discharge, but that the effects of discharge varied with stream size, channel confinement, and
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species traits (Table 6). Estimated local extinction decreased with increased 10-day low
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discharge and was generally lowest in large, confined channel streams and greatest in small,
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unconfined channels (Figure 3a). The effect of discharge also was greatest in large streams with
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estimated local extinction probabilities that were, on average, 15 times lower with each 0.1
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increase in standardized 10-day low discharge in medium streams compared to small streams
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(i.e., link magnitudes 100 vs. 10 respectively; Figure 3a). In contrast, the interaction between
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channel confinement and discharge suggested that 10-day low discharge had a smaller effect on
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the probability of local extinction in unconfined stream channels compared to confined channels
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(Table 6). The effect of discharge on local extinction also was lower for small bodied and
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tolerant species compared to larger sized and intolerant species, respectively. However,
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estimates suggest that effect of species traits on local extinction was smaller than that of stream
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size and channel characteristics (Figure 3).
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Colonization probability was positively related to 10-day high discharge, link magnitude,
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and downstream link magnitude (Table 7). Parameter estimates also suggested that colonization
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was, on average, 2 times greater in the spring compared to the summer and more than 3 times
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lower in unconfined stream channels relative to confined channels. Similar to local extinction,
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the effects of discharge on colonization varied with stream size and species traits (Table 7).
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Colonization increased with higher 10-day high discharges and was greater in larger and
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confined channel streams (Figure 4a). Colonization also was greater in streams with greater link
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magnitudes, but the estimated effect of link magnitude was much smaller than that of stream
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size, channel confinement, and season (Figure 4). Species traits had relatively strong influence
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on the relation between 10-day high discharge and colonization. We estimate that with each 1
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unit increase in standardized 10-day high discharge, colonization was almost 2 times greater for
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large sized fishes and more than 2 times lower for smaller fish relative to medium fishes (Figure
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5a). Locomotion morphology also influenced the relation between 10-day high discharge and
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colonization and was greatest for species with cruiser morphology and lowest for species with
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hugger morphology (Figure 5b). However, the parameter estimate for the relation between 10-
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day high discharge and hugger morphology was relatively imprecise (Table 7).
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Conditional reproduction was negatively related to summer discharge SD and positively
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related to 10-day high discharge during the spring (Table 8). In contrast to local extinction and
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colonization, there was no evidence that stream size and channel characteristics influenced the
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relation between discharge and conditional reproduction. However, the parameter estimates
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indicated that the probability of reproduction was, on average, 2 times lower in confined channel
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streams (Table 8). Of the species traits considered, locomotion morphology had the greatest
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influence on the relation between discharge and conditional reproduction. We estimate that
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species with cruiser morphology were most sensitive and hugger species, least sensitive to flow
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variability during the summer (Figure 6a). There also was evidence that spawning behavior
380
influenced the relation between reproduction and 10-day high discharge during the spring. We
381
estimate that species with broadcast and complex spawning behavior were most sensitive to 10-
17
382
day high discharge during the spring (Figure 6b). However, the parameter estimate for complex
383
spawning behavior was relatively imprecise and confidence limits spanned zero (Table 8).
384
385
DISCUSSION
386
We found strong evidence that local colonization, extinction, and reproduction rates of
387
stream fishes were related to flow regime components in the lower Flint River Basin, GA. The
388
effect of flows on these meta-demographic rates, however, varied substantially among species
389
and with stream channels characteristics. There also was evidence that the effect of flows was, in
390
part, mediated by behavioral and morphological traits of the resident species. Although our
391
measure of population state was relatively coarse (i.e., species presence and absence), we
392
postulate that the observed relations represent how local environmental conditions affect
393
individuals in a population. Thus, interpreting the relationships between meta-demographic rates
394
and site- and species characteristics requires understanding of mechanisms that influence fish
395
population dynamics.
396
Local extinction of fish in the study sites was strongly negatively related to 10-day low
397
flow. Habitat availability and dissolved oxygen in the study reaches were positively related to
398
stream flows, whereas water temperature was negatively related (Peterson et al. 2009). In
399
addition, the effect of flows on habitat availability and water quality were more pronounced in
400
smaller streams and unconfined stream channels (Peterson et al. 2009). Thus, the relations
401
between streamflows, channel characteristics, and local extinction likely represent the effect of
402
flows on resource availability and environmental suitability. This suggests that local extinction
403
was likely due to a combination of emigration and mortality. Previous studies suggest that some
404
fish move out of reaches as flows are reduced (Albanese et al 2004; Hodges and Magoulick
18
405
2011), while others report little to no evidence of mass emigration in response to severely
406
reduced flows (Larimore et al. 1959; Bayley and Osborne 1993; Matthews 1998). We believe
407
that extinction in the study reaches was primarily due to mortality associated with low flow
408
conditions. There was no support for colonization models that included low flow and stream link
409
magnitude, which would be expected had large numbers of fish emigrated to larger downstream
410
reaches. The strong support for 10 day low flows also suggest that local extinction was primarily
411
related to short term (i.e., acute) conditions. During these relatively short periods of low to no
412
flow, dissolved oxygen levels dropped below 3 mg/L and maximum temperatures reached 30 oC
413
in small streams (Peterson et al. 2009). These inhospitable conditions would have likely killed
414
species that were less tolerant, which is consistent with the evidence that local extinction was
415
greater for species with low to moderate tolerance during low flow periods. Similarly, large
416
bodied fishes would be more vulnerable to terrestrial predators in small streams with reduced
417
flows, which also was consistent with our observations that local extinction was greater for
418
larger-bodied fishes. The fate of the fishes in flow-impaired reaches has important implications
419
for modeling the response of fishes to changes in flows, which we discuss below.
420
Fish colonization was strongly positively related to 10 day high flows and was greater
421
during the spring, but was the relation was highly variable among species. This general pattern is
422
consistent with previous studies of warmwater stream fish that reported large-scale upstream
423
migrations of adult and larger juvenile fish associated with high flow events (Hall 1972; Bayley
424
and Osborne 1993; Peterson and Rabeni 2001; Albanese et al. 2004). Thus, the relationship
425
between spring discharge and local colonization likely reflects the influence that discharge has
426
on seasonal migrations. Observed variability in the effect of high discharge among species may
427
be due to differences in species-specific movement patterns (Albanese et al. 2004), but some of
19
428
the variation among species was related to body size and locomotion morphology, with lower
429
colonization rates for small bodied fishes and higher rates for species with cruiser morphology.
430
Large-bodied fishes are generally faster swimmers and likely have greater energy reserves for
431
sustained migration and species with cruiser morphology are streamlined and have shapes that
432
are associated with greater swimming efficiency (Goldstein and Meador 2004). Consequently,
433
we believe that these traits reflect the relative differences in swimming ability and possibly,
434
stamina. Contrary to our expectations, we found no evidence of a relation between spawning
435
duration and colonization rate. We expected species with shorter spawning duration to colonize
436
faster than species with protracted spawning seasons. The lack of evidence of a relation
437
combined with the overwhelming importance of short term high flows lends support to the
438
contention of Bayley and Osborne (1993) that most large scale stream colonization is due to
439
pulsed movement.
440
Local colonization also was related to stream channel morphology and position in the
441
watershed and was lower in unconfined stream channels and headwater streams. Unconfined
442
channel streams in the lower Flint River Basin tended to be wider and shallower with relatively
443
homogeneous habitat when compared to confined channel streams (Peterson et al. 2009). During
444
high flows, unconfined channels were relatively deep (> 1 m), so the relation probably does not
445
represent fish passage effects. In fact, there were unconfined channels downstream of two of our
446
sites, and fishes were able to colonize both sites. Rather, we believe that fish were able to access
447
and pass through unconfined reaches, but the species that successfully colonized the reaches
448
reflected the filtering effect of habitat structure (Peterson and Bayley 1993). The positive relation
449
between downstream link magnitude and colonization also suggests that the primary source of
450
colonists was likely from larger downstream reaches. However, the effect size of downstream
20
451
link magnitude was much smaller than we expected, as modeling results suggested that the
452
colonization probability of a small tributary (link magnitude 10) joining a large stream (link
453
magnitude 500) was less than 5% greater than that of a small tributary joining with another small
454
tributary. Based on these patterns and the observations of others (Larimore et al 1959; Bayley
455
and Osborne 1993), we hypothesize that fish colonization was primarily due to long-distance
456
migrations. Although we were unable to identify the colonization source and migration routes
457
with our data, this information is crucial for modeling the response of fishes to changes in flows
458
as we discuss below.
459
Successful reproduction, as evidenced by the presence of age-0 fish, was related to flow
460
variability during the summer, short term high flows during the spring, and channel morphology.
461
The probability of reproduction was negatively related to flow variability during the summer,
462
which included the spawning period for a limited number of species and the rearing period for all
463
species prior to late summer fish sampling. Additionally, the effects of flow variability on
464
reproductive success were greater for species with cruiser morphology. This is consistent with
465
numerous studies that reported negative effects of flow variability on reproductive success and
466
survival of age 0 streamfishes (Freeman et al. 2001; Weyers et al. 2003; Craven et al. 2010; and
467
others), particularly for fishes that are generally restricted to swimming in the water column
468
(Harvey 1987). The positive relation between short term high flows during the spring and
469
reproduction, however, suggests that a different (or additional) mechanism may affect
470
reproductive success. The spring time period used to calculate the flow statistic included the
471
spawning time period for more than half of the study species, so the relation likely represents the
472
effect of flows on conditions prior to or during fish spawning. Given the short term nature of the
473
flows (i.e., 10-day rather than median), we believe that the high flows during spring may have
21
474
affected habitat availability during spawning or the condition of habitat shortly after spawning.
475
For example, high flows reportedly remove fine sediment in spawning substrates, increasing egg
476
incubation rates and hence, reproductive success. Alternatively, the effect of short term high
477
flows in the spring could represent the influence of high flows on spawning migration as
478
discussed above.
479
480
481
Management implications
We demonstrated that the meta-demographic rates of multiple stream fish species can be
482
estimated using dynamic occupancy models. Importantly, these models were fit using field
483
sample data collected with relatively cost efficient methods in comparison with mark and
484
recapture studies. Peterson et al. (2009) demonstrated that the response of fish to changes in
485
streamflows can be modeled using inexpensive and readily available data on gross channel
486
morphology, stream size and stream position in the watershed, and streamflow, compared with
487
approaches that require calibrated flow habitat models. By combining these two approaches, we
488
developed a tool that can be used to evaluate the effects of water resource development activities
489
(Freeman et al. 2013), stream fragmentation, and other alterations to the hydrologic regime, such
490
as climate change, on aquatic biota. By using three states to describe fish population status (i.e.,
491
rather than tracking abundance) and coarse fish habitat surrogates (i.e., stream size,
492
morphology), we greatly simplified the modeling framework thereby facilitating the
493
development of dynamic, spatially explicit evaluations of the ecological consequences of water
494
resource development activities over broad geographic areas.
495
496
Despite the potential advantages of using meta-demographic models for evaluating the
response of fishes to streamflow alteration, there remains substantial uncertainty that would
22
497
likely complicate evaluations of how streamflow alteration may affect fish populations. As
498
discussed above, we could not determine the fate of fishes in reaches that experienced a local
499
extinction event, nor could we determine the source of colonists. Indeed, the predicted effects of
500
water development are heavily dependent on factors such as how far fish move (Freeman et al.
501
2013). Thus, identifying the specific mechanisms associated with colonization and extinction
502
events would improve the evaluations. Similarly, there was significant variation in species-
503
specific responses to flow components after accounting for the effects of species traits. Reducing
504
these uncertainties would also improve the evaluations and, in turn, water resource decision
505
making.
506
Adaptive resource management (ARM) allows managers to make resource decisions
507
while reducing important uncertainties through time using monitoring data (Conroy and Peterson
508
2013). In ARM, observed outcomes (monitoring data) are compared to model predictions and
509
used to update model parameters; hence, a key component of ARM is that monitoring must
510
match the predictions from management decision models. For example, if a management model
511
predicts abundance, monitoring should focus on estimating abundance. Thus, using meta-
512
demographic models for estimating the response of fishes to management actions can easily be
513
integrated into a relatively cost effective ARM framework due to the relative ease of estimating
514
fish occupancy.
515
516
517
518
ACKNOWLEDGEMENTS
23
519
We are indebted to many technicians, volunteers, and graduate students, including N.
520
Banish, B. Bowen, D. Carroll, S. Craven, S. Hawthorne, B. Henry, C. Holliday, D. McPherson,
521
J. McGee, P. O’Rouke, J. Ruiz, and D.Taylor. We also thank A. Wimberly for assisting with
522
obtaining GIS maps and figures. Funding and logistical support for this project was provided by
523
the U.S. Fish and Wildlife Service, the Georgia Department of Natural Resources, and US
524
Geological Survey. The manuscript was improved with suggestions from T. Kwak, M. Freeman,
525
and anonymous reviewers. This study was performed under the auspices of University of
526
Georgia animal use protocol IACUC# A2002-10080-0. The use of trade, product, industry or
527
firm names or products is for informative purposes only and does not constitute an endorsement
528
by the U.S. Government or the U.S. Geological Survey. The Georgia Cooperative Fish and
529
Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey, the U.S. Fish and
530
Wildlife Service, the Georgia Department of Natural Resources, the University of Georgia, and
531
the Wildlife Management Institute.
24
532
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666
ACKNOWLEDGEMENTS
We are indebted to many technicians, volunteers, and graduate students, including Nolan
667
Banish, Bryant Bowen, Denise Carroll, Scott Craven, Shane Hawthorne, Brent Henry, Chris
668
Holliday, Dale McPherson, Jason McGee, Patrick O’Rouke, John Ruiz, and Drew Taylor. We
669
also thank Daryl MacKenzie for providing us with example WinBugs code. Funding and
670
logistical support for this project was provided by the U.S. Fish and Wildlife Service, the
671
Georgia Department of Natural Resources, and the U.S. Geological Survey. The manuscript was
672
improved with suggestions from C. Moore,… and anonymous reviewers. The use of trade,
673
product, industry or firm names or products is for informative purposes only and does not
674
constitute an endorsement by the U.S. Government or the U.S. Geological Survey. The Georgia
675
Cooperative Fish and Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey,
676
the U.S. Fish and Wildlife Service, the Georgia Department of Natural Resources, the University
677
of Georgia, and the Wildlife Management Institute.
31
678
Table 1. Mean, standard deviation (SD), and range for stream characteristics and
stream flow components used in candidate models stream fish meta-demographic rates
in 23 study sites in the lower Flint River Basin, Georgia. Seasonal discharge is
expressed as a proportion of the site-specific median seasonal discharge.
Stream characteristics
Variable
Mean (SD)
Range
Site length (m)
92.0 (31.9)
53 - 165
Link magnitude
206.0 (230.5)
2 - 807
Downstream link magnitude1
793.5 (2060.5)
3 - 8497
10-day low discharge
0.506 (0.313)
0.00 - 1.20
Median discharge
1.120 (0.677)
0.00 - 3.00
10-day high discharge
2.444 (1.551)
0.78 - 7.83
Discharge SD2
0.954 (0.660)
0.24 – 2.83
10-day low discharge
0.540 (0.372)
0.00 - 1.98
Median discharge
1.551 (1.259)
0.00 - 11.01
10-day high discharge
4.946 (5.077)
0.53 - 20.93
Discharge SD2
1.683 (1.811)
0.08 - 6.73
Spring
Summer
679
680
681
1
2
Only included in candidate colonization models.
Only included in candidate conditional reproduction models.
32
Table 2. Species traits used in candidate models relating the stream fish extinction,
colonization, and reproduction to seasonal stream discharge.
Trait
Description
Biological interpretation
Extinction
Adult habitat
use
Primary adult habitat use:
deep > 1 m depth
fast current > 0.25 m/s
Body size
Adult body size (total length):
small ≤100 mm,
medium1 > 100 mm and < 200 mm,
large >200 mm
Tolerance
Tolerance to anthropogenic
alterations: low, moderate1, high
Local extinction is primarily due to
loss of habitats associated with
changing discharge.
Body size is positively related to
extinction during low flow periods
due to increased vulnerability to
terrestrial predators, loss of habitats,
and decreased water quality.
Local extinction is due to changes in
water quality (dissolved oxygen,
temperature) associated with
changing discharge.
Colonization
Locomotion
morphology2
Body size
Spawning
duration
cruiser: streamlined fishes that are
generally found swimming in the
water column,
hugger: fishes that are generally in
contact with the stream bottom,
other1
Adult body size (total length):
small ≤100 mm,
medium1 > 100mm and < 200 mm,
large >200 mm
The effect of discharge on
colonization is related to fish
swimming ability as indexed by
morphology.
The effect of discharge on
colonization is related to fish
swimming ability as indexed by
body size.
Number of months devoted to
spawning in a given year
Colonization is primarily due to
spawning migration so the effect of
discharge on colonization is related
to spawning duration.
complex spawning: species that
build and guard nests,
broadcast spawning: species that
broadcast eggs into the water
column or over substrate during
spawning,
other1
Complex spawners devote greater
physiological resources to spawning
activities (e.g., nest building) and are
more vulnerable to variable flows
Number of months devoted to
spawning in a given year
Species with protracted spawning
durations have greater spawning
opportunities and are less influenced
by discharge during the spawning
period (spring).
Reproduction
Spawning
behavior
Spawning
duration
33
682
683
684
cruiser: streamlined fishes that are
generally found swimming in the
Young-of-year fishes are vulnerable
Locomotion
water column,
to changing discharge conditions
2
morphology
hugger: fishes that are generally in
during juvenile rearing period
contact with the stream bottom,
(summer).
other1.
1
Category used as baseline in binary coding.
2
Terminology is from Goldstein and Meador (2004) and hugger is combined hugger and
creeper morphology of Goldstein and Meador (2004).
34
Table 3. Name and traits for fish species used to model extinction, colonization, and reproduction in study sites in the lower
Flint River Basin, Georgia. Species traits were determined using Fishes of Alabama (Boschung and Mayden 2004) and
Georgia Department of Natural Resources designations (GADNR 2005). The four habitat types listed below include: shallow
slow (SS), deep slow (DS), shallow fast (SF), and deep fast (DF).
Body
Scientific name
Common name
Habitat
Spawning
Locomotion
size
Tolerance
use
behavior
morphology
Lepisosteus oculatus
Spotted Gar
large
moderate
DS
broadcast
cruiser
Amia calva
Bowfin
large
high
DS
complex
cruiser
Cyprinella venusta
Blacktail Shiner
small
high
SS
broadcast
cruiser
Ericymba amplamala
Longjaw Minnow
small
high
SS
other
cruiser
Clear Chub
small
moderate
SS
other
cruiser
Notemigonus crysoleucas
Golden Shiner
small
high
SS
other
cruiser
Notropis chalybaeus
Ironcolor Shiner
small
low
SS
other
cruiser
Notropis harperi
Redeye Chub
small
low
SS
other
cruiser
Notropis hypsilepis
Highscale Shiner
small
low
SS
other
cruiser
Notropis longirostris
Longnose Shiner
small
high
SS
other
cruiser
Notropis petersoni
Coastal Shiner
small
high
SS
other
cruiser
Notropis texanus
Weed Shiner
small
low
SS
broadcast
cruiser
Opsopoeodus emiliae
Pugnose Minnow
small
high
SS
other
cruiser
Hybopsis sp. cf. H.
winchelli
35
Pteronotropis
Apalachee Shiner
small
high
SS
other
cruiser
Spotted Sucker
large
high
DS
broadcast
cruiser
Apalachicola Redhorse
large
high
DF
broadcast
cruiser
Moxostoma lachneri
Greater Jumprock
large
high
DF
broadcast
cruiser
Ameiurus brunneus
Snail Bullhead
medium
low
DS
complex
hugger
Ameiurus natalis
Yellow Bullhead
medium
high
DS
complex
hugger
Ameiurus nebulosus
Brown Bullhead
medium
high
DS
complex
hugger
Ictalurus punctatus
Channel Catfish
large
high
DS
complex
hugger
Noturus leptacanthus
Speckled Madtom
small
high
SF
complex
hugger
Pylodictis olivaris
Flathead Catfish
large
high
DF
complex
hugger
Esox americanus
Redfin Pickerel
large
moderate
SS
broadcast
cruiser
Aphredoderus sayanus
Pirate Perch
medium
high
SS
complex
other
Labidesthes sicculus
Brook Silverside
small
high
SS
broadcast
cruiser
Gambusia holbrooki
Mosquitofish
small
high
SS
other
cruiser
Ambloplites ariommus
Shadow Bass
medium
moderate
DS
complex
other
Lepomis auritus
Redbreast Sunfish
medium
moderate
DS
complex
other
Lepomis cyanellus
Green Sunfish
medium
high
DS
complex
other
Lepomis gulosus
Warmouth
medium
moderate
SS
complex
other
grandipinnis
Minytrema melanops
Moxostoma sp. cf. M.
poecilurum
36
Lepomis macrochirus
Bluegill
medium
high
DS
complex
other
Lepomis marginatus
Dollar Sunfish
small
high
SS
complex
other
Lepomis microlophus
Redear Sunfish
medium
moderate
DS
complex
other
Lepomis punctatus
Spotted Sunfish
medium
high
SS
complex
other
Micropterus cataractae
Shoal Bass
large
low
DF
complex
cruiser
Micropterus salmoides
Largemouth Bass
large
moderate
DS
complex
cruiser
Etheostoma edwini
Brown Darter
small
moderate
SF
other
hugger
Etheostoma fusiforme
Swamp Darter
small
high
SS
other
hugger
Etheostoma swaini
Gulf Darter
small
moderate
SF
broadcast
hugger
Percina nigrofasciata
Blackbanded Darter
small
high
SF
other
hugger
Elassoma zonatum
Banded Pygmy Sunfish
small
moderate
SS
complex
other
685
686
37
687
Table 4. Estimates of fixed and random effects, their standard deviation
(SD), and lower and upper 95% credible intervals for the best approximating
model of initial species occupancy.
Parameter
Estimate
SD
Lower
Upper
Fixed effects
Intercept
0.206
0.341
-0.459
0.885
Link magnitude
2.264
0.847
0.716
4.056
Downstream link magnitude
-0.025
0.052
-0.129
0.078
Unconfined channel
-0.202
0.237
-0.671
0.257
Ocala limestone
-0.323
0.291
-0.898
0.249
3.567
1.154
1.868
6.310
15.700
7.467
5.480
34.180
Downstream link magnitude
0.021
0.022
0.004
0.070
Unconfined channel
0.236
0.306
0.007
1.085
Ocala limestone
1.739
0.815
0.553
3.691
Random effects
Intercept
Link magnitude
688
38
689
Table 5. Predictor variables, number of parameters (K), mean AICc, AICc, and Akaike weights (w) for the confidence set
of candidate models (i) of fish species local extinction (), colonization (), and conditional reproduction (R). Akaike
weights are interpreted as relative plausibility of candidate models.
Candidate model1, 2
K
AICc
AICc
wi
38
3180.0
0.00
0.263
38
3181.2
1.24
0.142
38
3181.8
1.82
0.106
(Link, unconfined, 10-day low discharge, 10-day low discharge*link, 10-day low
discharge* unconfined, 10-day low discharge*body size), (Link, dlink, 10-day high
discharge, unconfined, spring, 10-day high discharge*link, 10-day high discharge* body
size), R(Unconfined, summer discharge SD, summer discharge SD* locomotion
morphology)
( Link, unconfined, 10-day low discharge, 10-day low discharge*link, 10-day low
discharge* unconfined, 10-day low discharge* body size),  (Link, dlink, 10-day high
discharge, unconfined, spring, 10-day high discharge*link, 10-day high discharge,
locomotion morphology), R(Unconfined, summer discharge SD, summer discharge SD*
locomotion morphology)
( Link, unconfined, 10-day low discharge, 10-day low discharge*link, 10-day low
discharge* unconfined, 10-day low discharge* tolerance),  (Link, dlink, 10-day high
discharge, unconfined, spring, 10-day high discharge*link, 10-day high discharge* body
size), R(Unconfined, summer discharge SD, summer discharge SD* locomotion
39
morphology)
(Link, unconfined, median discharge, median discharge*link, median discharge*
unconfined, median discharge* body size),  (Link, dlink, 10-day high discharge,
38
3181.9
1.89
0.102
38
3183.1
3.06
0.057
38
3183.1
3.13
0.055
38
3183.7
3.71
0.041
unconfined, spring, 10-day high discharge*link, 10-day high discharge* body size),
R(Unconfined, summer discharge SD, summer discharge SD* locomotion morphology)
( Link, unconfined, 10-day low discharge, 10-day low discharge*link, 10-day low
discharge* unconfined, 10-day low discharge* tolerance),  (Link, dlink, 10-day high
discharge, unconfined, spring, 10-day high discharge*link, 10-day high discharge,
locomotion morphology), R(Unconfined, summer discharge SD, summer discharge SD*
locomotion morphology)
( Link, unconfined, median discharge, median discharge*link, median discharge*
unconfined, median discharge* body size),  (Link, dlink, 10-day high discharge,
unconfined, spring, 10-day high discharge*link, 10-day high discharge, locomotion
morphology), R(Unconfined, summer discharge SD, summer discharge SD* locomotion
morphology)
(Link, unconfined, median discharge, median discharge*link, median discharge*
unconfined, median discharge*tolerance), (Link, dlink, 10-day high discharge,
unconfined, spring, 10-day high discharge*link, 10-day high discharge* body size),
40
R(Unconfined channel, summer discharge SD, summer discharge SD* locomotion
morphology)
(Link, unconfined, 10-day low discharge, 10-day low discharge*link, 10-day low
discharge* unconfined, 10-day low discharge* body size),  (Link, dlink, 10-day high
discharge, unconfined, spring, 10-day high discharge*link, seasonal 10-day high
38
3184.6
4.59
0.026
discharge*body size), R(Unconfined, spring 10-day high discharge, spring 10-day high
discharge*spawning behavior)
690
1
Initial occupancy model (not shown) was the same for each candidate model and contained 5 fixed and 5 random effects.
691
2
Link = link magnitude, dlink = downstream link magnitude, unconfined = unconfined stream channel
41
692
Table 6. Estimates of fixed and random effects, their standard deviation (SD), and
lower and upper 95% credible intervals for the two best approximating extinction
submodels contained in the confidence model set.
Parameter
Estimate
SD
Lower
Upper
2.726
0.932
0.841
4.606
10-day low discharge
-6.779
2.102
-11.015
-2.540
Link magnitude
-0.346
0.130
-0.609
-0.082
0.970
0.406
0.149
1.791
-0.598
0.273
-1.147
-0.046
1.305
0.629
0.040
2.576
1.265
0.378
0.500
2.029
-0.506
0.322
-1.156
0.145
Intercept
1.568
0.342
1.065
2.533
10-day low discharge
0.675
0.147
0.458
1.090
3.071
1.217
0.608
5.535
10-day low discharge
-6.394
2.366
-11.169
-1.604
Link magnitude
-0.402
0.168
-0.741
-0.062
1.099
0.537
0.013
2.187
-0.664
0.317
-1.301
-0.024
1.512
0.768
-0.032
3.063
0.558
0.333
-0.113
1.231
Best approximating model:
Fixed effects
Intercept
Unconfined stream channel
10-day low discharge* link magnitude
10-day low discharge* unconfined stream
channel
10-day low discharge* large adult body
size
10-day low discharge* small adult body
size
Random effects
Second best approximating model:
Fixed effects
Intercept
Unconfined stream channel
10-day low discharge* link magnitude
10-day low discharge* unconfined stream
channel
10-day low discharge* low tolerance
42
10-day low discharge* high tolerance
-1.371
0.289
-1.956
-0.787
Intercept
1.764
0.434
1.123
2.989
10-day low discharge
0.748
0.163
0.507
1.208
Random effects
693
694
43
695
Table 7. Estimates of fixed and random effects, their standard deviation (SD), and
lower and upper 95% credible intervals for the two best approximating colonization
submodels contained in the confidence model set.
Parameter
Estimate
SD
Lower
Upper
Best approximating model:
Fixed effects
Intercept
-5.773
2.515 -10.778
-0.717
10-day high discharge
2.271
0.909
0.472
4.097
Link magnitude
0.140
0.064
0.011
0.268
Downstream link magnitude
0.025
0.008
0.010
0.040
Unconfined stream channel
-1.174
0.302
-1.785
-0.564
0.653
0.249
0.152
1.156
-0.018
0.008
-0.033
-0.002
0.508
0.261
-0.020
1.035
-0.729
0.222
-1.178
-0.280
11.152
2.434
7.582
18.016
0.356
0.078
0.242
0.575
-6.673
0.680
-8.046
-5.300
10-day high discharge
1.697
0.696
0.305
3.102
Link magnitude
0.157
0.082
-0.008
0.322
Downstream link magnitude
0.028
0.010
0.009
0.047
Unconfined stream channel
-1.356
0.398
-2.159
-0.554
0.756
0.302
0.149
1.365
-0.021
0.720
0.010
0.259
-0.041
0.197
0.001
1.243
Spring
10-day high discharge*link magnitude
10-day high discharge *large adult body
size
10-day high discharge *small adult body
size
Random effects
Intercept
10-day high discharge
Second best approximating model:
Fixed effects
Intercept
Spring
10-day high discharge* link magnitude
10-day high discharge* cruiser locomotion
44
10-day high discharge* hugger locomotion
-0.266
0.302
-0.878
0.345
12.471
3.012
8.052
20.967
0.433
0.094
0.294
0.699
Random effects
Intercept
10-day high discharge
696
697
45
Table 8. Estimates of fixed and random effects, their standard deviation (SD), and
lower and upper 95% credible intervals for the two best approximating reproduction
submodels contained in the confidence model set.
Parameter
Estimate
SD
Lower
Upper
Best approximating model:
Fixed effects
Intercept
2.886
0.970
0.946
4.826
Summer discharge SD
-1.097
0.531
-2.168
-0.041
Unconfined stream channel
-0.703
0.313
-1.324
-0.074
Summer discharge SD* cruiser locomotion
-0.544
0.245
-1.036
-0.050
Summer discharge SD* hugger locomotion
0.182
0.060
0.061
0.302
Intercept
0.602
0.131
0.409
0.973
Summer discharge SD
0.008
0.002
0.006
0.014
-0.526
0.266
-1.058
0.001
0.707
0.330
0.044
1.374
-0.726
0.371
-1.464
0.018
0.462
0.227
0.006
0.922
0.647
0.330
-0.016
1.313
Intercept
1.160
0.253
0.788
1.873
Spring maximum 10-day discharge
0.050
0.011
0.034
0.081
Random effects
Second best approximating model:
Fixed effects
Intercept
Spring maximum 10-day discharge
Unconfined stream channel
Spawning maximum 10-day discharge*
broadcast spawning
Spawning maximum 10-day discharge*
complex spawning
Random effects
698
46
699
700
Figure captions
701
Figure 1. Locations of the 23 study sites in the lower Flint River Basin, Georgia, that
702
were sampled during 2001- 2004.
703
704
Figure 2. Daily discharge in the Spring Creek, Georgia at USGS gage number 02357000
705
for the period of this study (black line) and average daily discharge (gray line) for the
706
period of record, 73 years.
707
708
Figure 3. The estimated probability of extinction for (A) medium sized fishes in three
709
sizes of confined (solid line) and unconfined (broken line) stream channels and (B) three
710
body sizes of fish in medium (link magnitude= 100), confined channel streams. Estimates
711
were made using the best approximating extinction submodel relating extinction to 10-
712
day low discharge (expressed as a proportion of the long term median) and study site
713
characteristics.
714
715
Figure 4. The estimated probability of colonization for medium sized fish in (A) three
716
different sized confined (solid line) and unconfined (broken line) streams with
717
downstream link magnitude of 501 and (B) small (link magnitude = 10), confined
718
channel streams with two different downstream link magnitudes during the spring (solid
719
lines) and summer (broken lines) months. Estimates were made using the best
720
approximating colonization submodel relating colonization to 10-day high discharge
721
(expressed as a proportion of the long term median) and study site characteristics.
722
47
723
Figure 5. The estimated probability of colonization for (A) three fish body sizes and (B)
724
three locomotion morphologies in medium (link magnitude= 100), confined channel
725
streams with downstream link magnitude of 501. Estimates were made using the (A) best
726
and (B) second best approximating colonization submodels relating colonization to 10-
727
day high discharge (expressed as a proportion of the long term median) and study site
728
characteristics.
729
730
Figure 6. The estimated probability of reproduction for (A) three locomotion
731
morphologies under varying summer discharge standard deviation (SD) and (B) three
732
spawning behaviors under varying spring 10-day high discharge in confined (solid line)
733
and unconfined (broken line) channel streams. Estimates were made using the best
734
approximating models relating reproduction to (A) summer discharge SD and (B) spring
735
10-day high discharge (expressed as a proportion of the long term median) and study site
736
characteristics.
737
48
State of Georgia
Atlanta
0
738
49
50 km
Average daily discharge(m3/s)
200
150
100
50
0
2001
2002
2003
Year
739
50
2004
2005
740
A.
1.0
Probability of extinction
Link magnitude
0.8
500
100
10
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
10-day low discharge/ long-term median
B.
0.8
Probability of extinction
Adult body size
Large
0.6
Medium
Small
0.4
0.2
0.0
0.0
741
742
0.2
0.4
0.6
0.8
10-day low discharge/ long-term median
51
1.0
A.
Probability of colonization
1.0
0.8
Link magnitude
0.6
500
100
0.4
10
0.2
0.0
0.0
1.0
2.0
3.0
4.0
5.0
10-day high discharge/ long-term median
B.
Probability of colonization
1.0
0.8
Downstream
link magnitude
0.6
500
11
0.4
0.2
0.0
0.0
743
744
1.0
2.0
3.0
4.0
10-day high discharge/ long-term median
52
5.0
A.
Probability of colonization
1.0
0.8
Adult body size
0.6
Small
Medium
0.4
Large
0.2
0.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
10-day high discharge/ long-term median
B.
Probability of colonization
1.0
0.8
Locomotion
morphology
0.6
Cruiser
Other
0.4
Hugger
0.2
0.0
0.0
745
1.0
2.0
3.0
4.0
5.0
6.0
10-day high discharge/ long-term median
53
7.0
A.
Probability of reproduction
1.0
Locomotion
morphology
0.8
Cruiser
Other
Hugger
0.6
0.4
0.2
0.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
Summer discharge SD/ long-term median
B.
Probability of reproduction
1.0
0.8
Spawning behavior
0.6
Broadcast
Other
0.4
Complex
0.2
0.0
0.0
746
2.0
4.0
6.0
Spring 10-day high discharge/ long-term median
54
8.0