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TRANSCRIPT
An evaluation of the relations between flow regime components, stream
characteristics, and species traits and meta-demographic rates of warmwater
streams fishes: Implications for aquatic resource management
James T. Peterson1, U.S. Geological Survey, Georgia Cooperative Fish and Wildlife Research
Unit, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA
30602
Colin P. Shea2, Warnell School of Forestry and Natural Resources, University of Georgia,
Athens, GA 30602
1Current address: US Geological Survey, Oregon Cooperative Fish and Wildlife Research Unit
104 Nash Hall, Corvallis, Oregon 97331 USA, E-mail address: [email protected]
2 Current address: Tennessee Cooperative Fishery Research Unit, Department of Biology,
Tennessee Technological University, Cookeville, Tennessee 38505 USA
This draft manuscript is distributed solely for purposes of scientific peer review. Its content is
deliberative and predecisional, so it must not be disclosed or released by reviewers. Because the
manuscript has not yet been approved for publication by the U.S. Geological Survey (USGS), it
does not represent any official USGS finding or policy.
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ABSTRACT
Fishery biologists are increasingly recognizing the importance of considering the dynamic nature
of streams when developing streamflow policies. Such approaches require information on how
flow regimes influence the physical environment and how those factors, in turn, affect species-
specific demographic rates. A more cost effective alternative could be the use of dynamic
occupancy models to predict how species are likely to respond to changes in flow. To appraise
the efficacy of this approach, we evaluated relative support for hypothesized effects of seasonal
stream flows, stream channel characteristics, and fish species traits on local, colonization, and
recruitment (meta-demographic rates) of stream fishes. We used four years of seasonal fish
collection data from 23 streams to fit multi-state, multi-season occupancy models for 42 fish
species in the lower Flint River Basin, Georgia. Modeling results suggested that meta-
demographic rates were influenced by streamflows, particularly short-term (10 day) flows. Flow
effects on meta-demographic rates also varied with stream channel morphology and size and fish
species traits. Small-bodied species with generalized life-history and reproductive characteristics
were more resilient to flow variability than were large-bodied species with specialized
reproductive and life-history characteristics. Using this approach, we simplified the modeling
framework thereby facilitating the development of dynamic, spatially explicit evaluations of the
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
throughout the world. Such demand has resulted in the need for resource development strategies
that consider both future human needs and the conservation needs of valued ecosystems
(Arthington et al. 2006). As such, managers around the world are increasingly being asked to
predict ecological outcomes of alternative resource-management decisions, typically in the
context of changing climate and land uses (Clark et al. 2001, Araujo and Rahbek 2006).
Management of water availability in streams and rivers provides a prominent example.
Population growth and expanding agricultural irrigation are increasing the demands to divert,
transfer and store water from flowing water ecosystems (Postel 2000, Postel and Richter 2003,
Fitzhugh and Richter 2004). At the same time, the declining capacity of river systems to support
native biota, including imperiled species and fisheries, is a primary concern for natural resource
managers and conservationists (Pringle et al. 2000, Arthington and Pusey 2003, Postel and
Richter 2003, Dudgeon et al. 2006). Both problems – increasing water demands relative to
availability and declining aquatic species – will likely be exacerbated by future changes in land
use, especially urbanization (Paul and Meyer 2001, Fitzhugh and Richter 2004), and climate
(Milly et al. 2008, Palmer et al. 2008, Nelson et al. 2009).
Current methods for assessing the stream flow requirements of aquatic biota are often
resource intensive (time and money) and limited in their spatial, temporal, and ecological scope.
Although more than 200 techniques have been developed for evaluating instream flow
requirements of aquatic biota, habitat simulation methodologies are the most commonly used in
North America (Tharme 2003). Habitat simulation methods employ hydraulic models to estimate
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
require substantial data collection for quantifying flow-habitat relationships and because of their
cost are often conducted only at very few locations. Habitat simulation methods also are often
narrow in their ecological scope because they are often restricted to particular species or species
groups. Hence, there often remains considerable uncertainty regarding how observed flow-
habitat relationships transfer to other species or vary across space and time.
Alternative methods for assessing the stream flow requirements of aquatic biota should
possess several characteristics to be most useful for developing and evaluating management
strategies (Arthington et al. 2006). First, assessment techniques should be cost effective and able
to incorporate larger spatial and temporal extents (e.g., river basins over multiple years).
Effective techniques also should enable quantification of the responses of multiple species to
changes in streamflow within the context of stream and watershed-level environmental
conditions (Freeman et al. 2013). Lastly, effective assessment techniques should consider the
dynamic nature of stream systems; namely, that species are continually responding to changing
streamflow conditions, and such dynamics should be explicitly accounted for in resource
assessments (Arthington et al. 2006). This requires information of the dynamics of populations
and how those dynamics vary in response to changes in flow regimes and stream habitats.
Although the information needs for such an approach appear daunting, occupancy modeling
approaches (McKenzie et al. 2006) may provide an effective and efficient means for modeling
aquatic populations. Dynamic occupancy models track changes in the state of animal
populations (e.g., present, absent, abundant, rare) through space and time can be used to evaluate
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-
demographic rates for convenience.
Previously, we developed and evaluated a geomorphic channel classification for
estimating habitat availability and fish species presence and abundance and demonstrated that it
is possible to bypass detailed habitat measurements (i.e., habitat simulation) to quantify stream
fish species responses to changes in stream flow (Peterson et al. 2009). We then used that
classification system to evaluate the influence of seasonal streamflows and stream
geomorphology on the structure of fish assemblages (McCargo and Peterson 2010).We now
intend to use that classification system to estimate the influence of seasonal streamflows, stream
geomorphology, and stream channel characteristics on stream fish meta-demographic rates. Our
goal was to develop a spatially-explicit, dynamic multi-state occupancy model to quantify stream
fish response to changes in flow within the context of local geology, channel form, and species-
specific life history traits. Thus, we studied Southeastern US, Coastal Plain stream fish
assemblages with the following objectives: (1) to estimate site-level colonization, extinction, and
reproduction as a function of streamflow and stream channel characteristics; (2) to identify the
seasonal flow conditions that have the greatest influence on meta-demographic rates; (3) to
identify the life history characteristics or species traits that are most predictive of how species
will respond to changes in streamflow conditions; and (4) to demonstrate the potential usefulness
of such an approach for managing stream fish populations over large spatial and temporal
extents.
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METHODS
Study area
We evaluated the influence of seasonal flows, stream characteristics, and species traits on
fish meta-demographic rates in 23 stream study sites in lower Flint River Basin in Southwestern
Georgia (Figure 1). We selected the study sites based on stream size, surficial geology, and gross
channel morphology and classified each as Fall Line Hills or Ocala Limestone and confined or
unconfined channel morphology. Streams in the Fall Line Hills district were characterized by
sandy-mud substrate and relatively high turbidity levels and streams in the Ocala Limestone
district contained greater amounts of coarse substrates and lower turbidity (Peterson et al. 2009).
Confined channels were single-threaded with high, well-defined banks and greater amounts of
pool and riffle habitats compared to unconfined channels (Peterson et al. 2009). Unconfined
channels had low and indistinct channel banks and were generally shallower with greater
amounts of glide habitats compared to confined channels. The stream size of each study site was
characterized using link magnitude (Shreve 1966) and the relative position of a study site using
the link magnitude of the nearest downstream segment (Osborne and Wiley 1992).
The study sites lengths varied from site to site but were sufficient to include all
representative habitat types and minimize the effect of localized species-specific distribution
patterns. Wadeable sample sites were approximately 100 m long, whereas the length of non-
wadeable sites was approximately 150 m. However, two study sites were approximately 50 m
long during sampling in the summer 2001.
Fish and habitat sampling
Fish sampling and habitat measurements were conducted seasonally from summer 2001
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
characteristics of different streams varied widely, no single gear type could effectively sample
fish assemblages in all study sites. Therefore, we used three standardized sampling methods that
varied with the size of the stream.
In narrow, wadeable streams (mean wetted width < 8 m and mean depth < 0.5 m), the
upstream and downstream boundaries of a site were blocked with 7-mm mesh nets and sampled
during three passes with a pulsed DC backpack electrofisher operating at approximately 2 A. The
first pass was made in an upstream direction, followed by a downstream pass, and a final
upstream pass.
Wide (mean wetted width > 8 m), wadeable streams also were blocked with 7-mm mesh
nets and sampling was conducted during three passes with a tote-barge mounted electrofishing
unit and two anode probes powered by a 3000-W generator producing approximately 3 A pulsed
DC. The sequence of passes was identical to the backpack electrofisher with first pass in an
upstream direction, followed by a downstream pass, and a final upstream pass.
Non-wadeable sites (mean depth > 0.5 m) were sampled with six passes of a boat
electrofisher equipped with two Wisconsin rings and powered by a 3000-W generator at
approximately 3 A pulsed DC. The first pass was made in an upstream direction in the middle of
the stream; the second was in a downstream direction adjacent to the first, but along one of the
banks; and the third pass was made in an upstream direction along the opposite bank. The next
three passes were identical to the first three with the direction (upstream or downstream)
reversed.
All captured fish were identified to species and total length (TL) was measured to the
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
laboratory for identification and more accurate measurement. We considered the presence of
young-of year fishes (YOY) at a site to be an indicator of stream fish reproduction. Thus, we
grouped individual fish into two age classes: YOY and juvenile/adult (non-YOY) using seasonal,
species-specific length-frequency histograms.
Stream features that were used to estimate fish capture probability (discussed below)
were measured at each site near the time of fish sampling. Using calibrated hand held meters,
conductivity, temperature, and turbidity were measured in the middle of a site. Stream habitat
characteristics were estimated by measuring depth and average current velocity at eight evenly-
spaced points along 10 evenly-spaced transects. At each transect, wetted stream width (to the
nearest 0.1 m) was measured perpendicular to flow. At each point along a transect, crews
measured mean current velocity (to the nearest 0.01 m/s) with a Marsh-McBirney digital flow
meter and depth (to the nearest 0.01 m) with a standard top-set wading rod. When water depth
was less than 0.65 m, average velocity was measured at 0.6 of total depth; whereas average
velocity at greater depths was measured as the mean of readings taken at 0.2 and 0.8 of total
depth. For each site, mean current velocity and mean water depth were estimated by averaging
each point measurement, mean wetted width by averaging the widths of each transect and stream
discharge as the product of the average width, depth, and current velocity.
Definitions and statistical analyses
Streamflow estimation. – One of our objectives was to identify the stream flow
components that had the greatest influence on fish meta-demographic rates. However, only 4 of
the 23 sites were located near continuous discharge measurement gages. Therefore, we used
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
linear regression models were relatively precise with coefficients of determination that averaged
0.87. The estimated and observed daily discharge at the ungaged and gaged sites, respectively
were used to calculate seasonal flow statistics.
To evaluate the influence of stream flows on fish local extinction, colonization, and
reproduction, we calculated site-specific seasonal flow statistics for the seasonal period prior to
fish sampling. Our primary hypotheses of interest focused on the evaluation of the relative
influence of three components of the flow regime: high flows, low flows, and flow variability.
Based on previous studies (Freeman et al. 2001, Craven et al. 2010, McCargo and Peterson
2010), we also wanted to evaluate in the relative influence of short- and long-term discharge
conditions on fishes. We characterized short-term low flows as the 10-day low discharge, which
was calculated as the lowest average discharge for 10 consecutive days for the season prior to
fish sampling. The short-term high flows were similarly calculated as highest average discharge
for 10 consecutive days for the season prior to fish sampling. Long-term flow conditions were
characterized as the median discharge and flow stability as the standard deviation (SD) in
discharge during the season prior to fish sampling.
Stream sizes varied substantially among our study sites, which would complicate the
evaluation of the effects of flow components on fish meta-demographic rates. To facilitate the
evaluation, we standardized the discharge statistics (described above) for each site by dividing
each statistic by the median seasonal discharge for the period of record at gaged sites and the
model-estimated median seasonal discharge at ungaged sites for the period of record of their
reference gages (following McCargo and Peterson 2010).
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Occupancy modeling. - We estimated local extinction, colonization, and reproduction
using multistate, multiseason occupancy models (MacKenzie et al. 2009). Multistate,
multiseason occupancy models can be used to model changes in the states of animal populations
at one or more locations through time. We considered three population states (m) for each
species: unoccupied, occupied with no reproduction (i.e., adults and juveniles present but YOY
absent), occupied with successful reproduction (i.e., YOY present). Here we used the conditional
binomial parameterization of Nichols et al. (2007) and modeled the probability of successful
reproduction, given that the site was occupied. The conditional probability of successful
reproduction was fixed at zero for the spring season because YOY fishes were too small to be
reliably collected with our methods. Local extinction, colonization, and conditional reproduction
were modeled as a function of seasonal flow components, stream characteristics, and species
traits using linear logistic hierarchical models, discussed below.
To account for incomplete detection (i.e., false absences), we estimated species and size
class-specific capture probability for each sample using capture-recapture models and used them
in place of state-specific conditional detection probabilities normally used in multistate
occupancy models (MacKenzie et al. 2009). The capture-recapture models estimated fish capture
probability as a function of sampling method, species, fish body length, and the physiochemical
characteristics of the study sites (McCargo and Peterson 2010). We estimated the capture
probability separately for YOY and adult/juvenile fishes using the species-specific median body
lengths of each size class collected during the study. Because capture probabilities were not
known with certainty, the predicted probability distribution (PDF) was incorporated using a beta
PDF during the model fitting.
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Fish sampling began in summer 2001 and coincided with initiation of a severe drought
period in the region. We were concerned that the initial occupancy (t = 0) at each site would
reflect a fish community that was already affected by the low flow conditions. Therefore, we
used existing fish collection data collected during 1980-1999 from previous studies in the Flint
River Basin by the Georgia Department of Natural Resources (GADNR), U.S. Geological
Survey (USGS), and the University of Georgia personnel to predict initial occupancy (unaffected
by drought) at the 23 study sites. The data were collected from 234 stream reaches in the Flint
River Basin using standardized protocols that ensured site-specific species detection probabilities
70% (Ruiz and Peterson 2007). These data were used to estimate species-specific initial
occupancy probabilities at each site as a function of link magnitude, downstream link magnitude,
channel confinement, and surficial geology. The best approximating initial occupancy model was
used for evaluating the relative support of all candidate multistate, multiseason occupancy
models.
We believed that the relation between flow components and fish meta-demographic rates
was likely to vary among fish species and among streams. To account for the potentially varying
response of fishes and among sites, we examined relationships between flow components and
fish meta-demographic rates using hierarchical models (Royle and Dorazio 2008). Hierarchical
models differ from more familiar linear modeling techniques in that they consist of upper and
lower level models. In the lower level models, the values of parameters (e.g., slope and
intercepts) can vary among subjects (Royle and Dorazio 2008), here species or sites. For our
study, the lower level models treated the intercept and the effect of flow components and stream
characteristics on meta-demographic rates as varying among species. We interpreted the fixed
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
reproduction. The fixed effects associated with the flow components were expressed as
interactions between flow components and species trait, site-specific characteristics, and season.
For example, a model containing a link magnitude by flow interaction meant that the effect of
the flow component was modeled as a function of link magnitude. We interpreted these fixed
effects as the influence of these factors on the relation between the flow component and local
extinction, colonization, or reproduction. In addition, we evaluated the support for an additional
random effect corresponding to each study site to account for potential spatial autocorrelation.
Model selection. - We used an information-theoretic approach (Burnham and Anderson
2002) to evaluate the relative influence of flow components, stream characteristics, and species
traits on the meta-demographic rates of stream fishes. Our primary hypotheses of interest were to
evaluate the relative influence of short- and long-term seasonal flows on stream fish meta-
demographic rates. Secondarily, we sought to determine the influence of species traits, stream
characteristics, and season on the relation between flow components stream fish meta-
demographic rates. Thus, we contrasted three sets of submodels with each corresponding to a
meta-demographic parameter. Candidate model parameters were systematically entered and
excluded from each submodel and only one flow component was included in each candidate
submodel at a time to avoid multicolinearity. Local extinction submodels included one of three
flow regime components (Table 1), stream size (link magnitude), stream channel confinement,
and three species traits (Table 2). Colonization submodels included one of three flow regime
components, stream size, size of nearest downstream tributary (downstream link magnitude),
stream channel confinement, and season with three species traits (Table 2). Conditional
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
discussed above, conditional reproduction was fixed at zero for the spring season because YOY
fishes were too small to be sampled reliably. To evaluate the influence of flow components on
reproduction during the spring, the third model set included four flow regime components from
the spring and the summer for a total of eight flow components. The candidate model set also
included models without species traits, stream characteristics, and season.
To accommodate a model structure that included random effects, we used Markov Chain
Monte Carlo (MCMC) as implemented in BUGS software, version 1.4 (Lunn et al. 2000) to fit
the initial occupancy and multistate multiseason occupancy models. All models were fit based on
500,000 iterations with 50,000 burn in (i.e., the first 50,000 MCMC iterations were dropped) and
diffuse priors. The number of iterations was determined by fitting the candidate model that
modeled the meta-demographic parameters as a function of median discharge, all stream
characteristics, and all species traits and running six parallel chains and testing for convergence
using the Gelman- Rubin diagnostic (Gelman and Rubin 1992). The relative support of each
candidate model was evaluated by calculating Akaike’s Information Criteria (AIC; Akaike 1973)
with the small-sample bias adjustment (AICc; Hurvich and Tsai 1989). Because the MCMC
methods produce a distribution of AICc values, we used the mean AICc for all inferences
(Fonnesbeck and Conroy 2004). The number of parameters used to estimate AICc included the
fixed effects and random effects (Burnham and Anderson 2002). We also calculated Akaike
weights that range from zero to one with the most plausible candidate model having the highest
weight (Burnham and Anderson 2002). We then constructed a confidence set of models as those
candidate models that had Akaike weights of 0.10 (10 % of the highest importance weight) or
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
random effect in the best supported models was estimated by computing 95% credible intervals
(Congdon 2001), which are analogous to 95% confidence intervals. Goodness-of-fit (GOF) was
assessed for the global model for each flow component using a simple discrepancy measure and
1000 simulated data points (Gelman et al. 1996).
Prior to evaluating the fit of our candidate models, link magnitude and downstream link
magnitude were natural log transformed to facilitate MCMC model fitting. We binary coded
season with spring coded as 1 when the season was spring and 0 otherwise, channel confinement
with unconfined channels coded as 1 and 0 otherwise, and surficial geology with Ocala
limestone coded as 1 otherwise 0. Categorical species traits predictors (adult body size, adult
habitat preference, locomotion morphology, and spawning behavior) also were binary coded.
RESULTS
Stream flows varied considerably among sampling years and seasons (Table 1) with 10-
day low discharge ranging from 0 to 2.0 times, and 10-day high discharge ranging from 0.5 to
20.9 times, the long-term median discharge at a study site. The observed discharge at the long-
term gage USGS at our Spring Creek study site was representative of temporal discharge patterns
during the period of study (Figure 2). Daily discharge was similar to the long term average
during the spring 2001, but was substantially below average from the summer 2001 through the
fall 2002. Stream flows were much higher than the average long-term discharge during all of
2003 and were similar to average long-term flows during 2004.
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
trends or relationships (Gauch 1982). Therefore, were restricted our evaluation of fish meta-
demographic rates to the 42 species that occurred in more than 5% of the samples (Table 3).
The best approximating model for estimating initial occupancy contained link magnitude,
downstream link magnitude, stream channel confinement, and surficial geology and random
effects corresponding to each fixed effect (Table 4). This model was used for each candidate
multistate, multiseason occupancy model during model selection. Bayesian goodness-of-fit tests
of each global flow component model estimated p-values that ranged from 0.28 - 0.73 suggesting
that model fit was adequate.
The best approximating multistate, multiseason occupancy model relating stream flows,
stream characteristics, season, and fish species traits to fish meta-demographic rates included
local extinction modeled as a function of 10-day low discharge, stream link magnitude, stream
channel confinement, and the two-way interactions: 10-day low discharge by link magnitude, 10-
day low discharge by unconfined stream channel, 10-day low discharge by adult body size;
colonization as a function of 10-day high discharge, stream link magnitude, stream channel
confinement, spring, and the two-way interactions: 10-day high discharge by link magnitude, 10-
day high discharge by adult body size; and conditional reproduction as a function of summer
discharge SD, stream channel confinement, and summer discharge SD by locomotion
morphology interaction (Table 5). The Akaike importance weight of this model indicated that it
was 1.8 times more likely than the next best approximating model, which was similar to the best
model but included a 10-day high discharge by locomotion morphology interaction in place of
the 10-day high discharge by adult body size interaction in the colonization model. The Akaike
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
approximating and suggested that there was evidence that local extinction was related to species
tolerance and median seasonal discharge. In addition, there was evidence that conditional
reproduction was related to 10-day high discharge during the summer and fish locomotion
morphology.
Parameter estimates indicated that local extinction was negatively related to 10-day low
discharge, but that the effects of discharge varied with stream size, channel confinement, and
species traits (Table 6). Estimated local extinction decreased with increased 10-day low
discharge and was generally lowest in large, confined channel streams and greatest in small,
unconfined channels (Figure 3a). The effect of discharge also was greatest in large streams with
estimated local extinction probabilities that were, on average, 15 times lower with each 0.1
increase in standardized 10-day low discharge in medium streams compared to small streams
(i.e., link magnitudes 100 vs. 10 respectively; Figure 3a). In contrast, the interaction between
channel confinement and discharge suggested that 10-day low discharge had a smaller effect on
the probability of local extinction in unconfined stream channels compared to confined channels
(Table 6). The effect of discharge on local extinction also was lower for small bodied and
tolerant species compared to larger sized and intolerant species, respectively. However,
estimates suggest that effect of species traits on local extinction was smaller than that of stream
size and channel characteristics (Figure 3).
Colonization probability was positively related to 10-day high discharge, link magnitude,
and downstream link magnitude (Table 7). Parameter estimates also suggested that colonization
was, on average, 2 times greater in the spring compared to the summer and more than 3 times
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).
Colonization increased with higher 10-day high discharges and was greater in larger and
confined channel streams (Figure 4a). Colonization also was greater in streams with greater link
magnitudes, but the estimated effect of link magnitude was much smaller than that of stream
size, channel confinement, and season (Figure 4). Species traits had relatively strong influence
on the relation between 10-day high discharge and colonization. We estimate that with each 1
unit increase in standardized 10-day high discharge, colonization was almost 2 times greater for
large sized fishes and more than 2 times lower for smaller fish relative to medium fishes (Figure
5a). Locomotion morphology also influenced the relation between 10-day high discharge and
colonization and was greatest for species with cruiser morphology and lowest for species with
hugger morphology (Figure 5b). However, the parameter estimate for the relation between 10-
day high discharge and hugger morphology was relatively imprecise (Table 7).
Conditional reproduction was negatively related to summer discharge SD and positively
related to 10-day high discharge during the spring (Table 8). In contrast to local extinction and
colonization, there was no evidence that stream size and channel characteristics influenced the
relation between discharge and conditional reproduction. However, the parameter estimates
indicated that the probability of reproduction was, on average, 2 times lower in confined channel
streams (Table 8). Of the species traits considered, locomotion morphology had the greatest
influence on the relation between discharge and conditional reproduction. We estimate that
species with cruiser morphology were most sensitive and hugger species, least sensitive to flow
variability during the summer (Figure 6a). There also was evidence that spawning behavior
influenced the relation between reproduction and 10-day high discharge during the spring. We
estimate that species with broadcast and complex spawning behavior were most sensitive to 10-
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day high discharge during the spring (Figure 6b). However, the parameter estimate for complex
spawning behavior was relatively imprecise and confidence limits spanned zero (Table 8).
DISCUSSION
We found strong evidence that local colonization, extinction, and reproduction rates of
stream fishes were related to flow regime components in the lower Flint River Basin, GA. The
effect of flows on these meta-demographic rates, however, varied substantially among species
and with stream channels characteristics. There also was evidence that the effect of flows was, in
part, mediated by behavioral and morphological traits of the resident species. Although our
measure of population state was relatively coarse (i.e., species presence and absence), we
postulate that the observed relations represent how local environmental conditions affect
individuals in a population. Thus, interpreting the relationships between meta-demographic rates
and site- and species characteristics requires understanding of mechanisms that influence fish
population dynamics.
Local extinction of fish in the study sites was strongly negatively related to 10-day low
flow. Habitat availability and dissolved oxygen in the study reaches were positively related to
stream flows, whereas water temperature was negatively related (Peterson et al. 2009). In
addition, the effect of flows on habitat availability and water quality were more pronounced in
smaller streams and unconfined stream channels (Peterson et al. 2009). Thus, the relations
between streamflows, channel characteristics, and local extinction likely represent the effect of
flows on resource availability and environmental suitability. This suggests that local extinction
was likely due to a combination of emigration and mortality. Previous studies suggest that some
fish move out of reaches as flows are reduced (Albanese et al 2004; Hodges and Magoulick
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2011), while others report little to no evidence of mass emigration in response to severely
reduced flows (Larimore et al. 1959; Bayley and Osborne 1993; Matthews 1998). We believe
that extinction in the study reaches was primarily due to mortality associated with low flow
conditions. There was no support for colonization models that included low flow and stream link
magnitude, which would be expected had large numbers of fish emigrated to larger downstream
reaches. The strong support for 10 day low flows also suggest that local extinction was primarily
related to short term (i.e., acute) conditions. During these relatively short periods of low to no
flow, dissolved oxygen levels dropped below 3 mg/L and maximum temperatures reached 30 oC
in small streams (Peterson et al. 2009). These inhospitable conditions would have likely killed
species that were less tolerant, which is consistent with the evidence that local extinction was
greater for species with low to moderate tolerance during low flow periods. Similarly, large
bodied fishes would be more vulnerable to terrestrial predators in small streams with reduced
flows, which also was consistent with our observations that local extinction was greater for
larger-bodied fishes. The fate of the fishes in flow-impaired reaches has important implications
for modeling the response of fishes to changes in flows, which we discuss below.
Fish colonization was strongly positively related to 10 day high flows and was greater
during the spring, but was the relation was highly variable among species. This general pattern is
consistent with previous studies of warmwater stream fish that reported large-scale upstream
migrations of adult and larger juvenile fish associated with high flow events (Hall 1972; Bayley
and Osborne 1993; Peterson and Rabeni 2001; Albanese et al. 2004). Thus, the relationship
between spring discharge and local colonization likely reflects the influence that discharge has
on seasonal migrations. Observed variability in the effect of high discharge among species may
be due to differences in species-specific movement patterns (Albanese et al. 2004), but some of
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the variation among species was related to body size and locomotion morphology, with lower
colonization rates for small bodied fishes and higher rates for species with cruiser morphology.
Large-bodied fishes are generally faster swimmers and likely have greater energy reserves for
sustained migration and species with cruiser morphology are streamlined and have shapes that
are associated with greater swimming efficiency (Goldstein and Meador 2004). Consequently,
we believe that these traits reflect the relative differences in swimming ability and possibly,
stamina. Contrary to our expectations, we found no evidence of a relation between spawning
duration and colonization rate. We expected species with shorter spawning duration to colonize
faster than species with protracted spawning seasons. The lack of evidence of a relation
combined with the overwhelming importance of short term high flows lends support to the
contention of Bayley and Osborne (1993) that most large scale stream colonization is due to
pulsed movement.
Local colonization also was related to stream channel morphology and position in the
watershed and was lower in unconfined stream channels and headwater streams. Unconfined
channel streams in the lower Flint River Basin tended to be wider and shallower with relatively
homogeneous habitat when compared to confined channel streams (Peterson et al. 2009). During
high flows, unconfined channels were relatively deep (> 1 m), so the relation probably does not
represent fish passage effects. In fact, there were unconfined channels downstream of two of our
sites, and fishes were able to colonize both sites. Rather, we believe that fish were able to access
and pass through unconfined reaches, but the species that successfully colonized the reaches
reflected the filtering effect of habitat structure (Peterson and Bayley 1993). The positive relation
between downstream link magnitude and colonization also suggests that the primary source of
colonists was likely from larger downstream reaches. However, the effect size of downstream
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link magnitude was much smaller than we expected, as modeling results suggested that the
colonization probability of a small tributary (link magnitude 10) joining a large stream (link
magnitude 500) was less than 5% greater than that of a small tributary joining with another small
tributary. Based on these patterns and the observations of others (Larimore et al 1959; Bayley
and Osborne 1993), we hypothesize that fish colonization was primarily due to long-distance
migrations. Although we were unable to identify the colonization source and migration routes
with our data, this information is crucial for modeling the response of fishes to changes in flows
as we discuss below.
Successful reproduction, as evidenced by the presence of age-0 fish, was related to flow
variability during the summer, short term high flows during the spring, and channel morphology.
The probability of reproduction was negatively related to flow variability during the summer,
which included the spawning period for a limited number of species and the rearing period for all
species prior to late summer fish sampling. Additionally, the effects of flow variability on
reproductive success were greater for species with cruiser morphology. This is consistent with
numerous studies that reported negative effects of flow variability on reproductive success and
survival of age 0 streamfishes (Freeman et al. 2001; Weyers et al. 2003; Craven et al. 2010; and
others), particularly for fishes that are generally restricted to swimming in the water column
(Harvey 1987). The positive relation between short term high flows during the spring and
reproduction, however, suggests that a different (or additional) mechanism may affect
reproductive success. The spring time period used to calculate the flow statistic included the
spawning time period for more than half of the study species, so the relation likely represents the
effect of flows on conditions prior to or during fish spawning. Given the short term nature of the
flows (i.e., 10-day rather than median), we believe that the high flows during spring may have
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affected habitat availability during spawning or the condition of habitat shortly after spawning.
For example, high flows reportedly remove fine sediment in spawning substrates, increasing egg
incubation rates and hence, reproductive success. Alternatively, the effect of short term high
flows in the spring could represent the influence of high flows on spawning migration as
discussed above.
Management implications
We demonstrated that the meta-demographic rates of multiple stream fish species can be
estimated using dynamic occupancy models. Importantly, these models were fit using field
sample data collected with relatively cost efficient methods in comparison with mark and
recapture studies. Peterson et al. (2009) demonstrated that the response of fish to changes in
streamflows can be modeled using inexpensive and readily available data on gross channel
morphology, stream size and stream position in the watershed, and streamflow, compared with
approaches that require calibrated flow habitat models. By combining these two approaches, we
developed a tool that can be used to evaluate the effects of water resource development activities
(Freeman et al. 2013), stream fragmentation, and other alterations to the hydrologic regime, such
as climate change, on aquatic biota. By using three states to describe fish population status (i.e.,
rather than tracking abundance) and coarse fish habitat surrogates (i.e., stream size,
morphology), we greatly simplified the modeling framework thereby facilitating the
development of dynamic, spatially explicit evaluations of the ecological consequences of water
resource development activities over broad geographic areas.
Despite the potential advantages of using meta-demographic models for evaluating the
response of fishes to streamflow alteration, there remains substantial uncertainty that would
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likely complicate evaluations of how streamflow alteration may affect fish populations. As
discussed above, we could not determine the fate of fishes in reaches that experienced a local
extinction event, nor could we determine the source of colonists. Indeed, the predicted effects of
water development are heavily dependent on factors such as how far fish move (Freeman et al.
2013). Thus, identifying the specific mechanisms associated with colonization and extinction
events would improve the evaluations. Similarly, there was significant variation in species-
specific responses to flow components after accounting for the effects of species traits. Reducing
these uncertainties would also improve the evaluations and, in turn, water resource decision
making.
Adaptive resource management (ARM) allows managers to make resource decisions
while reducing important uncertainties through time using monitoring data (Conroy and Peterson
2013). In ARM, observed outcomes (monitoring data) are compared to model predictions and
used to update model parameters; hence, a key component of ARM is that monitoring must
match the predictions from management decision models. For example, if a management model
predicts abundance, monitoring should focus on estimating abundance. Thus, using meta-
demographic models for estimating the response of fishes to management actions can easily be
integrated into a relatively cost effective ARM framework due to the relative ease of estimating
fish occupancy.
ACKNOWLEDGEMENTS
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We are indebted to many technicians, volunteers, and graduate students, including N.
Banish, B. Bowen, D. Carroll, S. Craven, S. Hawthorne, B. Henry, C. Holliday, D. McPherson,
J. McGee, P. O’Rouke, J. Ruiz, and D.Taylor. We also thank A. Wimberly for assisting with
obtaining GIS maps and figures. Funding and logistical support for this project was provided by
the U.S. Fish and Wildlife Service, the Georgia Department of Natural Resources, and US
Geological Survey. The manuscript was improved with suggestions from T. Kwak, M. Freeman,
and anonymous reviewers. This study was performed under the auspices of University of
Georgia animal use protocol IACUC# A2002-10080-0. The use of trade, product, industry or
firm names or products is for informative purposes only and does not constitute an endorsement
by the U.S. Government or the U.S. Geological Survey. The Georgia Cooperative Fish and
Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey, the U.S. Fish and
Wildlife Service, the Georgia Department of Natural Resources, the University of Georgia, and
the Wildlife Management Institute.
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ACKNOWLEDGEMENTS
We are indebted to many technicians, volunteers, and graduate students, including Nolan
Banish, Bryant Bowen, Denise Carroll, Scott Craven, Shane Hawthorne, Brent Henry, Chris
Holliday, Dale McPherson, Jason McGee, Patrick O’Rouke, John Ruiz, and Drew Taylor. We
also thank Daryl MacKenzie for providing us with example WinBugs code. Funding and
logistical support for this project was provided by the U.S. Fish and Wildlife Service, the
Georgia Department of Natural Resources, and the U.S. Geological Survey. The manuscript was
improved with suggestions from C. Moore,… and anonymous reviewers. The use of trade,
product, industry or firm names or products is for informative purposes only and does not
constitute an endorsement by the U.S. Government or the U.S. Geological Survey. The Georgia
Cooperative Fish and Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey,
the U.S. Fish and Wildlife Service, the Georgia Department of Natural Resources, the University
of Georgia, and the Wildlife Management Institute.
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Table 1. 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
Spring
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
Summer
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 - 6.731Only included in candidate colonization models. 2Only included in candidate conditional reproduction models.
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Table 2. Species traits used in candidate models relating the stream fish extinction,
colonization, and reproduction to seasonal stream discharge.
Trait Description Biological interpretationExtinction
Adult habitat use
Primary adult habitat use:deep > 1 m depthfast current > 0.25 m/s
Local extinction is primarily due to loss of habitats associated with changing discharge.
Body size
Adult body size (total length):small ≤100 mm,medium1 > 100 mm and < 200 mm,large >200 mm
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.
Tolerance Tolerance to anthropogenic alterations: low, moderate1, high
Local extinction is due to changes in water quality (dissolved oxygen, temperature) associated with changing discharge.
Colonization
Locomotion morphology2
cruiser: streamlined fishes that are generally found swimming in the water column,hugger: fishes that are generally in contact with the stream bottom,other1
The effect of discharge on colonization is related to fish swimming ability as indexed by morphology.
Body sizeAdult 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 body size.
Spawning duration
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.
Reproduction
Spawning behavior
complex spawning: species that build and guard nests, Complex spawners devote greater
physiological resources to spawning activities (e.g., nest building) and are more vulnerable to variable flows
broadcast spawning: species that broadcast eggs into the water column or over substrate during spawning,other1
Spawning duration
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).
33
Locomotion morphology2
cruiser: streamlined fishes that are generally found swimming in the water column,hugger: fishes that are generally in contact with the stream bottom,other1.
Young-of-year fishes are vulnerable to changing discharge conditions during juvenile rearing period (summer).
1Category used as baseline in binary coding.2Terminology is from Goldstein and Meador (2004) and hugger is combined hugger and creeper morphology of Goldstein and Meador (2004).
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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).
Scientific name Common name
Body
size Tolerance
Habita
t use
Spawning
behavior
Locomotion
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
Hybopsis sp. cf. H.
winchelliClear 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
Pteronotropis Apalachee Shiner small high SS other cruiser
35
grandipinnis
Minytrema melanops Spotted Sucker large high DS broadcast cruiser
Moxostoma sp. cf. M.
poecilurumApalachicola Redhorse large high DF broadcast cruiser
Moxostoma lachneri Greater Jumprock large high DF broadcast cruiser
Ameiurus brunneus Snail Bullheadmediu
mlow DS complex hugger
Ameiurus natalis Yellow Bullheadmediu
mhigh DS complex hugger
Ameiurus nebulosus Brown Bullheadmediu
mhigh 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 Perchmediu
mhigh SS complex other
Labidesthes sicculus Brook Silverside small high SS broadcast cruiser
Gambusia holbrooki Mosquitofish small high SS other cruiser
Ambloplites ariommus Shadow Bass mediu moderate DS complex other
36
m
Lepomis auritus Redbreast Sunfishmediu
mmoderate DS complex other
Lepomis cyanellus Green Sunfishmediu
mhigh DS complex other
Lepomis gulosus Warmouthmediu
mmoderate SS complex other
Lepomis macrochirus Bluegillmediu
mhigh DS complex other
Lepomis marginatus Dollar Sunfish small high SS complex other
Lepomis microlophus Redear Sunfishmediu
mmoderate DS complex other
Lepomis punctatus Spotted Sunfishmediu
mhigh 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
37
38
685
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
Random effects
Intercept 3.567 1.154 1.868 6.310
Link magnitude 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
39
686
687
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
(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)
38 3180.0 0.00 0.263
( 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)
38 3181.2 1.24 0.142
( 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
morphology)
38 3181.8 1.82 0.106
(Link, unconfined, median discharge, median discharge*link, median discharge* 38 3181.9 1.89 0.102
40
688
unconfined, median 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* 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)
38 3183.1 3.06 0.057
( 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)
38 3183.1 3.13 0.055
(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),
R(Unconfined channel, summer discharge SD, summer discharge SD* locomotion
morphology)
38 3183.7 3.71 0.041
(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
41
discharge*body size), R(Unconfined, spring 10-day high discharge, spring 10-day high
discharge*spawning behavior)1Initial occupancy model (not shown) was the same for each candidate model and contained 5 fixed and 5 random effects.2Link = link magnitude, dlink = downstream link magnitude, unconfined = unconfined stream channel
42
689
690
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
Best approximating model:
Fixed effects
Intercept 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
Unconfined stream channel 0.970 0.406 0.149 1.791
10-day low discharge* link magnitude -0.598 0.273 -1.147 -0.04610-day low discharge* unconfined stream
channel 1.305 0.629 0.040 2.576
10-day low discharge* large adult body
size1.265 0.378 0.500 2.029
10-day low discharge* small adult body
size-0.506 0.322 -1.156 0.145
Random effects
Intercept 1.568 0.342 1.065 2.533
10-day low discharge 0.675 0.147 0.458 1.090
Second best approximating model:
Fixed effects
Intercept 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
Unconfined stream channel 1.099 0.537 0.013 2.187
10-day low discharge* link magnitude -0.664 0.317 -1.301 -0.02410-day low discharge* unconfined stream
channel 1.512 0.768 -0.032 3.063
10-day low discharge* low tolerance 0.558 0.333 -0.113 1.231
43
691
10-day low discharge* high tolerance -1.371 0.289 -1.956 -0.787
Random effects
Intercept 1.764 0.434 1.123 2.989
10-day low discharge 0.748 0.163 0.507 1.208
44
692693
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
Estimat
e 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
Spring 0.653 0.249 0.152 1.15610-day high discharge*link magnitude -0.018 0.008 -0.033 -0.00210-day high discharge *large adult body
size 0.508 0.261 -0.020 1.035
10-day high discharge *small adult body
size -0.729 0.222 -1.178 -0.280
Random effects
Intercept 11.152 2.434 7.582 18.016
10-day high discharge 0.356 0.078 0.242 0.575
Second best approximating model:
Fixed effects
Intercept -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
Spring 0.756 0.302 0.149 1.36510-day high discharge* link magnitude -0.021 0.010 -0.041 0.001
45
694
10-day high discharge* cruiser locomotion 0.720 0.259 0.197 1.243
10-day high discharge* hugger locomotion -0.266 0.302 -0.878 0.345
Random effects
Intercept 12.471 3.012 8.052 20.967
10-day high discharge 0.433 0.094 0.294 0.699
46
695696
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 -0.050Summer discharge SD* hugger locomotion 0.182 0.060 0.061 0.302
Random effects
Intercept 0.602 0.131 0.409 0.973
Summer discharge SD 0.008 0.002 0.006 0.014
Second best approximating model:
Fixed effects
Intercept -0.526 0.266 -1.058 0.001
Spring maximum 10-day discharge 0.707 0.330 0.044 1.374
Unconfined stream channel -0.726 0.371 -1.464 0.018
Spawning maximum 10-day discharge*
broadcast spawning0.462 0.227 0.006 0.922
Spawning maximum 10-day discharge*
complex spawning0.647 0.330 -0.016 1.313
Random effects
Intercept 1.160 0.253 0.788 1.873
Spring maximum 10-day discharge 0.050 0.011 0.034 0.081
47
Figure captions
Figure 1. Locations of the 23 study sites in the lower Flint River Basin, Georgia, that
were sampled during 2001- 2004.
Figure 2. Daily discharge in the Spring Creek, Georgia at USGS gage number 02357000
for the period of this study (black line) and average daily discharge (gray line) for the
period of record, 73 years.
Figure 3. The estimated probability of extinction for (A) medium sized fishes in three
sizes of confined (solid line) and unconfined (broken line) stream channels and (B) three
body sizes of fish in medium (link magnitude= 100), confined channel streams. Estimates
were made using the best approximating extinction submodel relating extinction to 10-
day low discharge (expressed as a proportion of the long term median) and study site
characteristics.
Figure 4. The estimated probability of colonization for medium sized fish in (A) three
different sized confined (solid line) and unconfined (broken line) streams with
downstream link magnitude of 501 and (B) small (link magnitude = 10), confined
channel streams with two different downstream link magnitudes during the spring (solid
lines) and summer (broken lines) months. Estimates were made using the best
approximating colonization submodel relating colonization to 10-day high discharge
(expressed as a proportion of the long term median) and study site characteristics.
48
697698
699
700
701702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
Figure 5. The estimated probability of colonization for (A) three fish body sizes and (B)
three locomotion morphologies in medium (link magnitude= 100), confined channel
streams with downstream link magnitude of 501. Estimates were made using the (A) best
and (B) second best approximating colonization submodels relating colonization to 10-
day high discharge (expressed as a proportion of the long term median) and study site
characteristics.
Figure 6. The estimated probability of reproduction for (A) three locomotion
morphologies under varying summer discharge standard deviation (SD) and (B) three
spawning behaviors under varying spring 10-day high discharge in confined (solid line)
and unconfined (broken line) channel streams. Estimates were made using the best
approximating models relating reproduction to (A) summer discharge SD and (B) spring
10-day high discharge (expressed as a proportion of the long term median) and study site
characteristics.
49
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
0 50 km
State of Georgia
Atlanta
0 50 km0 50 km
State of Georgia
Atlanta
50
0
50
100
150
200
2001 2002 2003 2004 2005
Ave
rage
dai
ly d
isch
arge
(m3 /s
)
Year
0
50
100
150
200
2001 2002 2003 2004 2005
Ave
rage
dai
ly d
isch
arge
(m3 /s
)
Year
51
737
10-day low discharge/ long-term median
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Pro
babi
lity
of e
xtin
ctio
n 500
100
10
Link magnitude
10-day low discharge/ long-term median
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Pro
babi
lity
of e
xtin
ctio
n 500
100
10
Link magnitude
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Pro
babi
lity
of e
xtin
ctio
n 500
100
10
Link magnitude
A.
B.
0.0
0.2
0.4
0.6
0.8
0.0 0.2 0.4 0.6 0.8 1.0
10-day low discharge/ long-term median
Pro
babi
lity
of e
xtin
ctio
n Large
Medium
Small
Adult body size
0.0
0.2
0.4
0.6
0.8
0.0 0.2 0.4 0.6 0.8 1.0
10-day low discharge/ long-term median
Pro
babi
lity
of e
xtin
ctio
n Large
Medium
Small
Adult body size
Large
Medium
Small
Adult body size
52
738
739
0.0
0.2
0.4
0.6
0.8
1.0
0.0 1.0 2.0 3.0 4.0 5.0
10-day high discharge/ long-term median
Pro
babi
lity
of c
olon
izat
ion
0.0
0.2
0.4
0.6
0.8
1.0
0.0 1.0 2.0 3.0 4.0 5.0
10-day high discharge/ long-term median
Pro
babi
lity
of c
olon
izat
ion
500
100
10
Link magnitude
500
11
Downstreamlink magnitude
A.
B.
53
740
0.0
0.2
0.4
0.6
0.8
1.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
10-day high discharge/ long-term median
Pro
babi
lity
of c
olon
izat
ion
Small
Medium
Large
Adult body size
0.0
0.2
0.4
0.6
0.8
1.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
10-day high discharge/ long-term median
Pro
babi
lity
of c
olon
izat
ion
Cruiser
Other
Hugger
Locomotion morphology
0.0
0.2
0.4
0.6
0.8
1.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
10-day high discharge/ long-term median
Pro
babi
lity
of c
olon
izat
ion
Cruiser
Other
Hugger
Locomotion morphology
A.
B.
54
741
