394
ARTICLE
Temperature-dependent performance as a driver of warm-water
fish species replacement along the river continuum
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Matthew J. Troia, Michael A. Denk, and Keith B. Gido
Abstract: Replacement of fish species by their congeners along gradients of stream size is common in warm-water streams, but
the causative environmental factors driving this turnover are not fully understood. We used laboratory experiments to test for
differences in temperature-dependent egg hatch success and age-0 food ration size for three congeneric cyprinids that differ in
abundance along temperature–stream size gradients. Headwater species (Pimephales promelas and Pimephales notatus) had lower
thermal optima and narrower thermal breadths for hatch success compared with a river mainstem species (Pimephales vigilax).
Temperature sensitivity of ration size was lowest for P. promelas, intermediate for P. notatus, and highest for P. vigilax. Using an
empirical stream temperature model, we predicted water temperatures and projected hatch success and ration size for 7974 stream
segments in Kansas, USA. Projected hatch success from May to July and ration size from July to September generally matched
abundance–stream size patterns, suggesting that increasing temperature along the river continuum may drive replacements
among Pimephales species. Our findings combined with evaluations of timing and duration of spawning seasons will improve
mechanistic understanding of species replacements along the river continuum.
Résumé : Si le remplacement d'espèces de poissons par des congénères le long de gradients de taille du cours d'eau est répandu
dans les cours d'eau chaude, les facteurs du milieu ambiant à l'origine de ces remplacements ne sont pas entièrement compris.
Nous avons utilisé des expériences en laboratoire pour vérifier s'il y avait des variations du succès d'éclosion des œufs dépendante
de la température et de la taille des rations de nourriture des individus de moins d'un an le long de gradients de températuretaille du cours d'eau. Deux espèces de cours supérieur (Pimephales promelas et Pimephales notatus) présentaient des températures
optimales plus faibles et des fourchettes de températures moins grandes pour un bon succès d'éclosion qu'une espèce de bras
principal (Pimephales vigilax). La sensibilité à la température de la taille des rations était la plus faible pour P. promelas, intermédiaire pour P. notatus et la plus élevée pour P. vigilax. En employant un modèle empirique de température du cours d'eau, nous
avons prédit les températures de l'eau, le succès d'éclosion et la taille des rations pour 7974 tronçons de cours d'eau au Kansas
(États-Unis). Le succès d'éclosion projeté de mai à juillet et la taille des rations de juillet à septembre concordaient généralement
avec les motifs d'abondance–taille du cours d'eau, donnant à penser que l'augmentation de la température le long du continuum
du cours d'eau pourrait être à l'origine des remplacements entre espèces de Pimephales. Nos constatations, combinées à des
évaluations du moment et de la durée des périodes de frai, amélioreront la compréhension mécaniste des remplacements
d'espèces le long du continuum du cours d'eau. [Traduit par la Rédaction]
Introduction
Longitudinal network position (i.e., stream size) is a key environmental correlate of fish community composition in stream systems
throughout the world (Huet 1958; Rahel and Hubert 1991; Edds 1993).
Several putative mechanisms driving community nestedness — the
addition of species along an environmental gradient — have been
documented. Greater habitat area, diversity, and temporal stability
promote lower extinction rates and higher colonization rates in river
main stems compared with headwaters (Poff and Allan 1995; Gotelli
and Taylor 1999; Roberts and Hitt 2010). By contrast, the mechanisms
that drive community turnover — the replacement of one species by
another along an environmental gradient — are not fully understood. Longitudinal changes in food resources correlate with trophic
traits and drive turnover in some, but not all, subsets of the fish
community (Vannote et al. 1980; Ibañez et al. 2009; but see Troia
and Gido 2014). Temperature is also an important abiotic factor
that limits the distributions of fish species along gradients of
stream size and elevation. Most notably, species turnover in the
downstream direction is associated with cold-water faunas (e.g.,
salmonids) being replaced by warm-water faunas (e.g., cyprinids)
(Huet 1958; Rahel and Hubert 1991; Edds 1993). Within strictly coldwater stream systems, subtle differences in thermal performance
among closely related species, particularly among congeneric salmonids, drive species replacements along gradients of stream
size and elevation (Taniguchi and Nakano 2000; Wenger et al. 2011).
Turnover among congeneric species is also common within
strictly warm-water stream systems. For example, in central and
eastern North America, congeneric minnow pairs belonging to
the genera Pimephales, Cyprinella, Notropis, and Lythrurus (Taylor
and Lienesch 1996; Troia and Gido 2014), as well as a pair of congeneric topminnows of the genus Fundulus (Braasch and Smith
1965), often replace one another from upstream to downstream.
However, the two previously documented mechanisms of community change along stream size gradients within strictly warmwater stream systems (i.e., food resources and hydrologic regimes)
do not adequately explain the contrasting stream size niches of
these congeneric minnows and topminnows. First, all of these
Received 16 February 2015. Accepted 11 September 2015.
Paper handled by Associate Editor Jordan Rosenfeld.
M.J. Troia,* M.A. Denk, and K.B. Gido. Division of Biology, 116 Ackert Hall, Kansas State University, Manhattan, KS 66506, USA.
Corresponding author: Matthew J. Troia (email: troiamj@gmail.com).
*Present address: Environmental Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008 MS6038, Oak Ridge National Laboratory, Oak Ridge,
TN 37830, USA.
Can. J. Fish. Aquat. Sci. 73: 394–405 (2016) dx.doi.org/10.1139/cjfas-2015-0094
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congeners either belong to the same trophic guild or possess
trophic traits that do not match the turnover expected from longitudinal changes in food resources (Vannote et al. 1980; Cross and
Collins 1995; Frimpong and Angermeier 2009). Second, headwaters are more hydrologically variable than river main stems and
differentially filter species that possess differing reproductive life
history strategies (Winemiller and Rose 1992; Poff and Allan 1995),
yet all of these congeners exhibit similar reproductive life history
strategies (Frimpong and Angermeier 2009; Troia and Gido 2015).
Indeed other environmental factors, including subtle temperature shifts, vary with stream size in warm-water stream systems.
Slight differences in thermal optima among these warm-water
congeners may function as a causative driver of species replacement along gradients of stream size as they do in some cold-water
communities (e.g., Taniguchi and Nakano 2000), yet surprisingly
few studies have compared thermal performance among closely
related warm-water species (but see Schaefer 2012). Studies aimed
at characterizing thermal niche dimensions of warm-water fishes
and comparing them among closely related species provide causal
tests of species replacements along gradients of temperature associated with stream size, elevation, or other spatial gradients
(Harvey and Pagel 1991; Troia and Gido 2014).
In the Great Plains of the central United States, Pimephales
promelas and Pimephales notatus attain higher abundances in headwater streams, whereas Pimephales vigilax is most abundant in river
main stems (Cross and Collins 1995; Troia and Gido 2014). The
objective of this study was to test summer temperature variation
along the river continuum as a causal driver of these congeneric
species replacements. This study objective involved a four-step
approach. First, we used field survey data to verify that these
congeneric species exhibit contrasting stream size niches. Second,
we developed an empirical water temperature model using air
temperature and landscape characteristics as predictors and then
projected monthly summer water temperatures to 7974 stream
segments in central Kansas, USA. Third, we carried out laboratory
experiments to test for interspecific variation in the temperature
dependence of two performance metrics associated with early life
history success: hatch success of fertilized eggs and food ration
size of age-0 individuals. We evaluated these two performance
metrics with the assumption that they were indicators of success
of early life stages — via their effects on survival, growth, and
recruitment — and thus would influence population vital rates of
small-bodied fishes typical of North American fresh waters (Post
and Evans 1989; Garvey et al. 1998; Vélez-Espino et al. 2006). We
hypothesized that if increasing temperature with stream size is a
causal driver of species replacement, then P. vigilax would perform
better at higher temperatures compared with P. promelas and
P. notatus. Fourth, we used the experimentally derived performance–
temperature relationships and modelled temperature–stream size
relationships to project hatch success and ration size across
stream segments in our study area and compared these projected
performance–stream size relationships among the three species.
We hypothesized that increasing temperature with stream size
would lead to increasing performance with stream size for P. vigilax
and decreasing performance with stream size for P. promelas
and P. notatus. By projecting laboratory-derived performance–
temperature relationships to summertime stream temperatures,
our goal was to (i) experimentally isolate the effect of temperature
from other environmental variables that change along the river
continuum and (ii) evaluate temperature-dependent performance
along realistic temperature gradients experienced by fishes throughout the summertime spawning and growing season.
Materials and methods
Stream size niche dimensions
We sampled fish communities from 40 reaches distributed
along a gradient of stream size in the Big Blue River basin during
395
Fig. 1. The extent of water temperature, egg hatch success, and
age-0 ration size predictions in the Flint Hills ecoregion (dashed
black line) and Big Blue River basin (solid black line) in Kansas, USA.
Locations of 40 community sampling reaches (open circles), 76 water
temperature recording sites (open and closed circles), and
15 meteorological stations (open squares) from which air temperature
data were compiled.
July and August of 2012 (Fig. 1). Eight to 12 seine hauls were made
within each 200 m sampling reach and were positioned within the
reach such that all mesohabitat types (i.e., pools, riffles, and runs)
were sampled in proportion to their prevalence. See Troia and
Gido (2015) for additional information on this sampling protocol.
Abundances (i.e., individuals per 10 m2) of P. promelas, P. notatus,
and P. vigilax were calculated for each reach. Stream size at each
reach was measured as upstream catchment area (km2) using an
existing GIS dataset (Gido et al. 2006).
Theoretical and empirical studies of environmental tolerance
suggest that abundance of a species typically exhibits a symmetric
unimodal response to an environmental gradient (MacArthur and
Levins 1967; Lynch and Gabriel 1987). Thus, we used Gaussian
functions (eq. 1) to model abundance–stream size relationships
for each species, separately.
(1)
2
2
Y ⫽ A · e[⫺(X⫺B) /2 · C ]
where Y is abundance, X is stream size, and the three parameters
describe an optimum stream size (A), a maximum abundance at
the optimum stream size (B), and the range of stream sizes occupied (i.e., stream size breadth) (C). We used quantile regression to
fit Gaussian functions to the 90th abundance quantile because we
were interested in quantifying the maximum abundance of each
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396
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Table 1. Thirty-two environmental variables used to predict water temperature in the Flint
Hills and Big Blue River basin.
Environmental variable
Variable type
Sampling resolution
Data source
Same day ATresidual
1-day ATresidual
2-day ATresidual
3-day ATresidual
4-day ATresidual
5-day ATresidual
6-day ATresidual
7-day ATresidual
8-day ATresidual
9-day ATresidual
10-day ATresidual
Percent urban land cover
Percent agriculture land cover
Percent shrubland cover
Percent grassland cover
Percent forest land cover
Percent wetland cover
Percent open water cover
Maximum elevation
Stream segment gradient
Catchment slope
Water table depth
Bedrock depth
Soil erodibility
Soil permeability
Soil water capacity
Soil bulk density
Soil organic matter
Soil loss tolerance
Soil wind erosion group
Catchment area
Latitude
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Landscape
Stream segment
Stream segment
Stream segment
Stream segment
Stream segment
Stream segment
Stream segment
Stream segment
Stream segment
Stream segment
Stream segment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Stream segment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Catchment
Stream segment
Weather stations
Weather stations
Weather stations
Weather stations
Weather stations
Weather stations
Weather stations
Weather stations
Weather stations
Weather stations
Weather stations
NLCD
NLCD
NLCD
NLCD
NLCD
NLCD
NLCD
NED
NED
NED
NRCS
NRCS
NRCS
NRCS
NRCS
NRCS
NRCS
NRCS
NRCS
NHD
ArcMap
Note: NLCD, National Land Cover Dataset; NED, National Elevation Dataset; NRCS, Natural Resources
Conservation Service; NHD, National Hydrology Dataset.
species at any given stream size. Quantile regression is an effective technique for identifying environmental limits to biological
responses and can elucidate causal relationships between individual predictor variables and biological responses when other environmental variables are not measured (Cade and Noon 2003). We
hypothesized that increasing temperature with stream size imposes a constraint on the upper limit of abundance, but other
environmental factors (e.g., biotic interactions) may further reduce abundance below what is attainable at any given stream size.
Selecting an appropriate quantile requires a trade-off between
approximating the maximum of the response variable (i.e., ideally
the 100th quantile) while minimizing the error in parameter estimation, which increases at the extremes of the response variable
distribution (Cade et al. 1999). Preliminary evaluation of upper
quantiles indicated that the 90th quantile provided the most robust parameter estimates for the three species, given the sample
size and response distribution of our dataset. A bootstrapping procedure was used to estimate confidence intervals of parameter estimates. Gaussian quantile fits were performed in the R programming
environment using the nlrq library (version 2.13.1; R Development
Core Team 2011).
Stream temperature model
We developed an empirical model to predict mean daily water
temperatures for 7974 confluence-to-confluence stream segments
(hereinafter “stream segments”) throughout the Flint Hills and
Big Blue River basin using air temperatures and GIS-derived land-
1
scape characteristics as predictors. We deployed temperature recorders (Onset Computer Corporation, Bourne, Massachusetts) at
76 sites representing streams ranging in size from second-order
headwater streams (mean catchment area = 13.7 km2) to eighthorder river main stems (mean catchment area = 133 597.2 km2;
Fig. 1). Hourly water temperatures were recorded for a duration of
25 months from February 2012 through March 2014 (hereinafter
the “calibration period”; refer to online supplementary data,
Fig. S11). Temperature recorders were placed in main channels to
ensure that water temperatures represented the well-mixed primary channel and not stagnant side channels. For each site, mean
daily water temperatures were calculated from the 24 hourly
measurements recorded from each day that we were confident
the recorder was submerged below the water surface (Fig. S11). We
separated the annual time series of mean daily water temperatures into a long-term seasonal component (WTseason) and a shortterm residual component (WTresidual) by fitting a generalized
additive model to the annual time series (Caissie et al. 1998; Jeong
et al. 2013). Daily residuals (WTresidual) were extracted from the
time series and modelled as a function of 32 predictor variables
(Table 1). We used 11 temporal predictor variables, which included
the residualized air temperature (ATresidual) at each site on the
same day (i.e., day 0) and the ATresidual averaged over the previous
1 to 10 days. ATresidual was also calculated by fitting generalized
additive models to the annual air temperature time series for each
site. We compiled mean daily air temperatures from 15 weather
Supplementary data are available with the article through the journal Web site at http://nrcresearchpress.com/doi/suppl/10.1139/cjfas-2015-0094.
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stations located throughout the study area (Fig. 1) and used ordinary kriging with an exponential semivariogram model to interpolate mean daily air temperatures at the 76 temperature recording
sites during the calibration period (Courault and Monestiez 1999).
Kriging was performed with the fields library in the R programming
environment (Furrer et al. 2009). In addition to the 11 ATresidual variables, we also used 21 GIS-derived predictor variables describing
catchment characteristics and latitude from an existing dataset
(Gido et al. 2006). These variables were derived from the National
Hydrology Dataset (USGS 1997), National Land Cover Dataset
(USGS 1992), and the State Soil Geographic Database (NRCS 1994)
(Table 1). Generalized additive models were fit using the mgcv
library in the R programming environment.
We used random forests to model WTresidual as a function of
ATresidual, catchment characteristics, and latitude. Random forests
is a regression tree technique in which many regression trees are
generated from random subsets of the observations and predictor
variables. The response variable is then predicted from the combination of all regression trees. This approach performs well when
modelling nonlinear relationships between predictors and the
response and can accommodate complex interactions among predictors. These model properties are important for modelling air–
water temperature relationships across space and time because
stream thermal assimilation of air temperatures frequently are
spatially and temporally heterogeneous (LeBlanc et al. 1997). As
such, interactions between spatial (e.g., stream size) and temporal
(e.g., air temperature) predictors can be effectively accounted for by
the modelling algorithm. We trained the model on a random 50%
of the temperature recording sites and withheld the remaining
50% to independently test model performance. This validation
procedure was repeated 10 times, and model performance was
characterized using the average root mean squared error from the
10 random validation datasets. Because a random subset of predictor variables is withheld during each iteration of the random
forests model, this algorithm allows for a test of variable importance as well as the construction of partial dependence plots to
visualize the response of WTresidual to each predictor variable
(Cutler et al. 2007). Random forests models were fit using the
randomForest library in the R programming environment.
The temperature model described above was used to project
WTresidual for each Julian day across the 7974 stream segments in
the study area. For this extrapolation, the model was trained on
the entire dataset as opposed to the 50% used for model validation.
Mean daily water temperatures were predicted based on the air
temperatures from the historical period (1951–2006) as opposed to
air temperatures during the model calibration period (2012–2014),
because we hypothesized that the physiological and behavioural
adaptations associated with the measured performance metrics
would correspond to long-term mean temperatures along the
river continuum. These historical air temperatures were obtained
as raster grids (4 km2 resolution) depicting mean monthly temperatures from the Climate Wizard tool (Girvetz et al. 2009). Mean
daily air temperatures were interpolated by fitting generalized
additive models to the monthly time series for each stream segment, treating each monthly mean as the temperature on the 15th
day of each month (Fig. S21). To predict mean daily water temperatures during the historical period, we followed the approach of
Jeong et al. (2013). Residualized air temperatures (ATresidual) were
calculated as the difference between ATseason during the calibration period and ATseason during the historical period. This difference in ATseason is represented by the vertical difference between
the two time series shown in Fig. S21. Because all predictor variables were acquired from GIS layers, we were able to project mean
daily water temperatures to all 7974 stream segments in our study
area. Mean monthly water temperatures for May, June, July, August,
and September were calculated from the modelled daily water
temperatures for all 7974 stream segments and used in performance projections described below.
397
Laboratory performance experiments
Hatch success
Two brood stocks each of P. promelas and P. vigilax were collected
from the Kansas River basin in June and July of 2013 by seining or
DC-pulsed backpack electrofishing. Two to three males and three
to five females of each species were stocked in separate field enclosures located in the Kansas River and were provided with ceramic spawning tiles. See Troia and Gido (2014) for additional
information on this spawning protocol. One brood stock of
P. notatus was collected from the Kansas River basin in September
of 2013 and stocked in a 208 L indoor aquarium (10 males and
10 females) maintained at 25 °C with a photoperiod of 16 h light :
8 h dark. Spawning tiles were checked for eggs daily at 0800.
When a clutch of eggs was present, 10 to 20 eggs each were transferred to hatching cups containing 50 ml of water collected from
the spawning location. Hatching cups were assigned to one of six
constant incubation temperatures (17, 21, 24, 27, 30, and 33 °C).
These temperature treatments were selected to represent the
range of temperatures available in streams of the Flint Hills and
Big Blue basin during summer. Although thermal regimes experienced by eggs in natural streams fluctuate throughout the day,
previous studies indicate that physiological responses often are
consistent across temperature treatments, whether thermal regimes are constant or fluctuating (Schaefer and Ryan 2006; Fischer
et al. 2011). Potential variation in hatch success due to parental
effects or clutch identity was controlled by dividing each clutch
into a multiple of six hatching cups and distributing these cups
equally across the six temperature treatments. The temperatures
of hatching cups assigned to 17, 30, and 33 °C treatments were
gradually changed over 4 h to limit egg mortality caused by rapid
and large temperature fluctuation. Hatching cups were checked
every 12 h (0800 and 2000) to remove nonviable eggs and hatched
larvae. A 50% water change was carried out every 24 h at 0800.
Temperatures in each cup were measured once per day and varied
by less than 1 °C for all treatment temperatures. Oxygen concentrations were measured at all treatment temperatures with a dissolved oxygen probe (Yellow Springs Instruments, Yellow Springs,
Ohio, USA) and remained at least 98% saturated for all treatments.
Hatch success was characterized as the proportion of larvae that
hatched without deformities (e.g., kyphosis) from each hatch cup.
Juvenile feeding
Age-0 individuals of each species were collected from the Kansas
River basin in March and April of 2014 by seining or DC-pulsed
backpack electrofishing. Ten to 20 individuals of each species
were housed in separate indoor 38 L aquaria and maintained at
23 °C with a photoperiod of 16 h light : 8 h dark. All fish were fed
an excess of dry flake food (Tetra, Inc., Blacksburg, Virginia) and
frozen Diptera larvae (San Francisco Bay Brand, Inc., Newark, California) once per day between 1000 and 1300. All individuals were
housed under these conditions for at least 20 days to acclimate
them to a temperature intermediate to the treatment temperatures used during feeding trials. Although in an ideal study design
all test individuals would be reared from the zygote stage in a
common garden to eliminate the potential for irreversible developmental plasticity affecting temperature-dependent feeding, other
investigators have successfully characterized temperature-dependent
physiological performance of wild-born fish acclimated in captivity to a common temperature for similar acclimation durations
(e.g., Lankford and Targett 2001). Moreover, other studies of cyprinid species have indicated that irreversible developmental plasticity has a limited effect on acute thermal performance metrics
relative to innate species-specific differences (Schaefer and Ryan
2006).
Laboratory feeding trials were carried out in April and May of
2014. Individuals of each species were transferred to aerated 2 L
aquaria (one individual per aquarium) that were incubated in
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Can. J. Fish. Aquat. Sci. Vol. 73, 2016
water baths (i.e., 75 L glass aquaria) maintained at constant temperatures ranging from 16 to 33 °C. Water baths were covered on
all sides and on the top with 1.5 cm thick Styrofoam insulation to
maintain temperatures and minimize visual stressors and distractions. An 8 cm × 8 cm square portion of Styrofoam was removed
from the front Styrofoam panel for direct observation of feeding
behaviour. Twelve water baths were used at a time, and each
contained three 2 L aquaria for a total of 18 test fish per experimental run. During each experimental run, individuals of each
species were assigned to an equal number of temperature treatments to control for variation in ration size among experimental
runs carried out at different times. Test fish were acclimated at
temperature treatments for 6 or 7 days prior to the feeding trials
and were fed an excess of Diptera larvae once per day. Food was
withheld 48 h prior to feeding trials. Temperatures in each 2 L
aquarium were measured once per day and varied by less than 1 °C
during the 6- or 7-day acclimation period for all temperature treatments and experimental replicates. Oxygen concentrations were
measured for all temperature treatments with a dissolved oxygen
probe and remained at least 98% saturated throughout the duration
of each 7-day acclimation period for all temperature treatments.
All feeding trials were carried out between 1000 and 1300. We
selected frozen Diptera larvae as prey because they are a common
food resource in prairie streams and are fed upon by all species in
their natural environment (Cross and Collins 1995; Pflieger 1997).
Larvae were cut in half to minimize handling time for the small
individuals (29.0–55.5 mm total length) used for the feeding trials.
Smaller food items also allowed for a larger number to be consumed within each feeding trial and provided a finer resolution of
ration size. Feeding trials were conducted one fish at a time by an
observer and a data recorder. At the start of each feeding trial,
60 larvae halves were introduced into the aquarium and the test
fish was allowed to feed for 5 min. Food consumption was quantified by direct observation through the viewing window, and the
number of larva halves consumed during 15 s intervals was tallied
for the duration of the 5 min trial. Following feeding trials, all fish
were euthanized with a lethal dose of MS-222 (tricaine methanosulfonate) and preserved in 5% buffered formalin. Preserved specimens were later eviscerated, padded dry with a paper towel, and
weighed to the nearest 0.1 mg.
Analysis of cumulative consumption curves using the 15 s intervals indicated that test fish became satiated in less than 5 min for
all temperature treatments, indicating that the number of food
items consumed in 5 min is an accurate estimate of ration size.
Ration sizes were divided by eviscerated wet mass to account for
variation in body size among fish. These standardized ration sizes
(i.e., items per 1 mg of wet mass) were multiplied by the mean
individual mass (548 mg) so that ration sizes were presented in
items per average-sized individual.
Experimental data analysis
Physiological and behavioural responses to laboratory temperature gradients can be linear, symmetric unimodal, or asymmetric unimodal, depending on the species, the performance metric,
and the range of temperature treatments (Angilletta 2009; Mordecai
et al. 2013). We explored these response shapes by fitting linear
(eq. 2), Gaussian (eq. 3), and Briere (eq. 4; Briere et al. 1999) functions to each performance metric for each species:
(2)
Y ⫽ (A · X) ⫹ B
where Y is performance, X is temperature, A is the slope, and B is
the y intercept.
(3)
2
2
Y ⫽ A · e[⫺(X⫺B) /2 · C ]
where Y is performance, X is temperature, A is the maximum
performance, B is the optimum temperature, and C is the thermal
performance breadth.
(4)
Y ⫽ A · X · (X ⫺ B) · (C ⫺ X)2
where Y is performance, X is temperature, A is a unitless coefficient, B is the thermal minimum, and C is the thermal maximum.
For hatch success, we fitted each equation to the mean of the
performance distribution using traditional least squares regression and calculated pseudo-r2 values for each species (NCSS 2015).
For ration size, we used quantile regression to fit each equation to
the 90th quantile. The upper quantiles are well-suited for characterizing the thermal sensitivity of behaviour-dependent response
variables such as ration size, because individuals of the same species exposed to the same environmental stimuli often exhibit
varying degrees of behavioural reactivity and aggressiveness (Sih
et al. 2004). Thus, model parameters of these upper quantiles provide estimates of temperature sensitivity while factoring out the
effect of individual fish that are less reactive and aggressive (Cade
and Noon 2003). A bootstrapping procedure was used to estimate
standard errors for all model parameters. We calculated Akaike’s
information criterion (AIC) values for each model and selected the
model with the lowest AIC value for each species and performance
metric (Angilletta 2009; Mordecai et al. 2013). These most parsimonious models were used to project performance–temperature
relationships to the 7974 stream segments in the Flint Hills and
Big Blue basin. All analyses were performed in the R programming
environment using the QuantReg library.
Projecting water temperatures and individual performance
Temperature-dependent hatch success was projected for the early
summer months (May, June, and July), which correspond to the
spawning seasons of the three species (Pflieger 1997). Temperaturedependent ration size was projected for the late summer months
(July, August, and September), which correspond to the time period when rapid somatic growth is necessary for winter survival
and recruitment (Post and Evans 1989; Garvey et al. 1998). We used
the parameter estimates of the most parsimonious performance–
temperature models for each species (from the laboratory performance experiments) and predicted mean monthly water temperature
(from the stream temperature model) to solve eqs. 2–4 for the
performance values of each species in each of the 7974 stream
segments for each month. These performance projections were
plotted as a function of stream size (catchment area) for each
species × performance metric × month combination (18 plots in
total).
Results
Stream size niche dimensions
Abundance across the 40 sampling reaches ranged from
0 individuals·10 m−2 for all species to a maximum of
58.6 individuals·10 m−2 for P. promelas, 80.4 individuals·10 m−2 for
P. notatus, and 22.5 individuals·10 m−2 for P. vigilax (Fig. 2). Gaussian
functions fit to the 90th quantile of abundance indicated that
P. promelas reached peak abundance in small headwater streams
draining areas of 40.8 km2, P. notatus in small to intermediate
streams draining areas of 105.7 km2, and P. vigilax in river main
stems draining areas of 1613.2 km2. Breadth parameters suggest
that the three species have similar stream size breadths.
Stream temperature model
We compiled 22 178 estimates of mean daily water temperature
across the 76 sites during the calibration period (Fig. S11). Root
mean squared error of the temperature model averaged across the
10 random validation datasets was 1.14 ± 0.01. Measures of air
temperature were the strongest predictors of WTresidual, particuPublished by NRC Research Press
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Fig. 2. Abundance as a function of stream size for two headwater
species, (a) P. promelas and (b) P. notatus, and one mainstem species,
(c) P. vigilax, at 40 sites in the Big Blue River basin. Three-parameter
Gaussian functions (see eq. 1) were fit to the 90th quantile of the
response distribution.
399
species (Table 2; Figs. 4a, 4c, 4e). Correlations between hatch success and incubation temperature were moderate for P. promelas
(pseudo-r2 = 0.55) and P. notatus (pseudo-r2 = 0.57) and weak for
P. vigilax (pseudo-r2 = 0.07). The Briere functions indicated that
P. promelas and P. vigilax exhibit lower thermal optima and narrower
thermal niche dimensions than P. vigilax. Optimum temperature
was 23.3 °C for P. promelas, 22.0 °C for P. notatus, and 26.8 °C for
P. vigilax, while maximum hatch success (i.e., the proportion of
eggs hatching at the optimum) was 0.85 for P. promelas, 0.90 for
P. notatus, and 0.79 for P. vigilax.
Juvenile feeding
The mean ration sizes across all temperature treatments was
10.5 items per individual for P. promelas, 18.5 items per individual
for P. notatus, and 20.0 items per individual for P. vigilax. AIC model
selection indicated that the linear function (eq. 2) was the most
parsimonious model for all three species (Table 2; Figs. 4b, 4d, 4f).
Thermal sensitivity of maximum ration size represented by the fitted slope estimate from eq. 2 was lowest for P. promelas (0.09 items
per 1 °C), intermediate for P. notatus (1.21 items per 1 °C), and
highest for P. vigilax (4.50 items per 1 °C).
larly the 1-day and 2-day mean ATresidual (Fig. S31). Modelled monthly
mean temperatures across all stream segments were 17.8, 22.7,
24.8, 23.6, and 19.3 °C for May, June, July, August, and September,
respectively (Fig. 3). Catchment area was the strongest spatial
predictor of WTresidual, and projected water temperatures increased with stream size (Figs. 3, S31). The mean eighth-order
river main stem was 2.5 to 2.7 °C warmer than the mean secondorder tributary. The warmest eighth-order river main stems
were as much as 3.5 to 4.1 °C warmer than the coldest of the
second-order tributaries.
Projecting individual performance
Hatch success and maximum ration size projected to stream
segments varied with stream size (i.e., catchment area), but direction of these performance–stream size relationships depended on
species-specific performance–temperature relationships and on
month. With regard to hatch success, mean stream temperatures
in May were lower than optimum for all three species across all
stream sizes. Consequently, projected hatch success increased
with catchment area for all three species because warmer mainstem segments were closer to the thermal optima than were cooler
headwater segments (Figs. 5a, 5d, 5g). Higher water temperatures
in June and July resulted in optimal temperatures in headwater
segments and suboptimal temperatures (i.e., too warm) in mainstem segments for P. promelas and P. notatus (Figs. 5b–5c, 5e–5f,
respectively). This stream size effect was more pronounced in July
compared with June for both species and was more pronounced
for P. notatus than for P. promelas. By contrast, June and July water
temperatures in mainstem segments were optimal for P. vigilax,
whereas headwater segments were suboptimal (i.e., too cold;
Figs. 5h–5i). This stream size effect was more pronounced in June
compared with July. When comparing hatch success among species, the rank order of performance was highest for P. notatus,
intermediate for P. promelas, and lowest for P. vigilax regardless of
stream size or month (Figs. 5j–5l).
Projected maximum ration was consistently low for P. promelas
across stream sizes and months (Figs. 6a–6c). Projected maximum
ration of P. notatus exhibited a slight increase with stream size for
all months (Figs. 6d–6f). Projected maximum ration of P. vigilax
was more strongly influenced by stream size and month compared
with the other two species (Figs. 6g–6i). When comparing maximum ration among species in July and August, the rank order of
performance was highest for P. vigilax, intermediate for P. notatus,
and lowest for P. vigilax regardless of stream size (Figs. 6j–6k). In
September, maximum ration sizes were similar across all three
species in headwater streams with catchment areas smaller than
⬃1000 km2; however, in river main stems with catchment areas
larger than ⬃1000 km2, the rank order of performance was highest
for P. vigilax, intermediate for P. notatus, and lowest for P. promelas
(Fig. 6l).
Individual performance experiments
Discussion
Hatch success
Mean hatch success across all temperature treatments were
0.71 for P. promelas, 0.69 for P. notatus, and 0.72 for P. vigilax. AIC
model selection indicated that the asymmetric unimodal Briere
function (eq. 4) was the most parsimonious model for all three
Compositional change in fish communities along the river continuum is a common pattern in stream systems throughout the
world (Huet 1958; Rahel and Hubert 1991; Edds 1993). Observational studies have documented numerous examples of congeneric fishes replacing one another along the river continuum in
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400
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Fig. 3. Model projections of mean monthly water temperatures based on historical (1951–2006) air temperatures during the summer for
7974 stream segments in the Flint Hills ecoregion and Big Blue River basin. Scatterplots show model projections of mean monthly water
temperatures as a function stream size. Grey dots show projections for individual stream segments, and the black line shows a fitted
generalized additive model function.
Table 2. Samples sizes, model parameters, and model performance criteria for three model types characterizing the relationship between two
performance metrics and incubation temperature for P. promelas, P. notatus, and P. vigilax.
Parameter estimate (standard error)
Performance
metric
Species
Sample
sizea
Mean body
size (range)b
Hatch success
P. promelas
65
NA
P. notatus
30
NA
P. vigilax
76
NA
P. promelas
24
43.2 (36.0–51.5)
P. notatus
40
42.1 (29.0–55.5)
P. vigilax
51
39.7 (31–52.5)
Ration size
Model
Quantile
A
B
C
AIC
Linear
Gaussian
Briere*
Linear
Gaussian
Briere*
Linear
Gaussian
Briere*
Linear*
Gaussian
Briere
Linear*
Gaussian
Briere
Linear*
Gaussian
Briere
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
Mean
90th
90th
90th
90th
90th
90th
90th
90th
90th
−0.02 (0.01)
0.87 (0.04)
9.50e–5 (0.00)
−0.03 (0.01)
0.97 (0.10)
7.41e–7 (0.00)
2.72e–3 (0.00)
0.79 (0.04)
1.10e–4 (0.00)
0.09 (0.17)
15.99 (43.13)
7.02e–4 (0.00)
1.21 (0.82)
38.63 (75.76)
0.02 (0.02)
4.50 (2.91)
52.64 (30.67)
0.06 (0.06)
1.29 (0.14)
21.89 (0.75)
−0.99 (0.01)
1.46 (0.24)
21.19 (1.15)
−1.66e4 (3.28e6)
0.65 (0.12)
25.82 (1.11)
−57.37 (9.08)
12.45 (4.32)
51.35 (913.82)
−211.21 (2345.87)
−4.58 (22.04)
41.27 (81.52)
8.32 (10.00)
−66.92 (65.67)
30.46 (16.98)
14.76 (5.41)
NA
6.14 (0.78)
33.09 (0.11)
NA
5.58 (1.12)
33.00 (0.02)
NA
8.93 (2.01)
37.00 (2.10)
NA
47.67 (772.15)
37.75 (13.29)
NA
12.78 (30.92)
33.96 (2.61)
NA
7.56 (9.54)
34.93 (8.21)
−0.26
−13.47
−33.31
19.35
15.21
6.43
−6.70
−9.73
−10.23
131.45
133.34
142.86
317.05
318.73
318.00
499.74
502.46
501.48
Note: See eqs. 2–4 in the Materials and methods section for a description of parameters A, B, and C for each model. The best supported model (lowest AIC) for each
species and performance metric is denoted with an asterisk and corresponds to the best-fit lines in Fig. 4.
aNumber of cups for hatch success; number of fish for ration size.
bTotal length in mm.
warm-water stream systems (Braasch and Smith 1965; Cross and
Collins 1995; Taylor and Lienesch 1996). Our field surveys corroborate the upstream to downstream replacement of P. notatus by
P. vigilax in prairie streams of Kansas documented recently by
Troia and Gido (2014). In addition, we quantitatively document
the narrow and more extreme headwater niche of P. promelas,
relative to P. notatus, in prairie streams of Kansas that was qualitatively described by Cross and Collins (1995). Identifying the causative environmental factors driving these replacements has remained
elusive because many environmental variables — both abiotic
and biotic — vary with stream size (Vannote et al. 1980), making
the task of experimentally isolating and independently testing
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Fig. 4. (a, c, e) Egg hatch success and (b, d, f) age-0 ration size as
functions of temperature for (a–b) P. promelas, (c–d) P. notatus, and
(e–f) P. vigilax. Three-parameter Briere functions (see eq. 4) were fit to
the mean response for panels a–c. Linear regression equations (see
eq. 2) were fit to the 90th quantile of the response distribution for
panels d–f. See Table 2 for parameter estimates and model selection
criteria.
these potentially causative environmental gradients difficult. For
example, Troia and Gido (2014) used field enclosures to document
an increase in spawning success and age-0 growth rates along a
stream size gradient for P. notatus and P. vigilax; however, covarying gradients of increasing water temperature and food abundance along the river continuum prevented them from isolating
the links between these individual performance metrics and
these two potentially causative environmental factors.
Laboratory experiments testing single environmental gradients
as causal drivers of species’ stream size niches provide controlled
tests and more clearly identify the adaptive differences between
species and how they relate to optimal performance along environmental gradients and fundamental niche dimensions (McGill
et al. 2006; Kearney and Porter 2009). Temperature is a key abiotic
factor that varies along the river continuum (Vannote et al. 1980)
and also drives physiological rates and behaviour of stream fishes
(Brett 1956; Taniguchi et al. 1998). Our experimental assessment of
temperature-dependent performance indicates that the Pimephales
species exhibit differing thermal niches that may reflect their
contrasting stream size niches. Specifically, at higher temperatures characteristic of river main stems, P. vigilax exhibited higher
hatch success and age-0 ration size compared with P. promelas and
P. notatus. Whether differences in these performance–temperature
relationships among the Pimephales species reflect a mechanism
driving species replacements along the river continuum depends on several factors, which we discuss in the following
sections.
Spatial and temporal variation in stream temperature
Characterizing spatial variation in stream temperatures and
identifying stream size as the primary environmental driver of
401
temperature is the first step in testing the hypothesis that
temperature-dependent performance is a mechanistic driver of
abundance–stream size relationships for the Pimephales species.
Our temperature model accurately predicted daily stream temperatures, and model performance was comparable to that of other
empirical stream temperature models developed at regional spatial extents (Caissie et al. 1998; Jeong et al. 2013), including those
using machine learning algorithms (Roberts et al. 2013; DeWeber
and Wagner 2014). Stream size was the most influential landscape
predictor of summertime stream temperature throughout central
Kansas and confirmed our hypothesis that temperature gradients
exist along the river continuum in strictly warm-water stream
systems. This temperature gradient is likely due to decreasing
groundwater influence and increasing canopy cover and solar radiation from upstream to downstream (Story et al. 2003; Johnson
2004; DeWeber and Wagner 2014). Air temperature was the strongest overall predictor of stream temperature in central Kansas.
This overriding effect of air temperature on stream water temperatures has been documented in other regional-scale temperature
models (e.g., DeWeber and Wagner 2014) and is a direct consequence of heat exchange at the air–water interface (Sinokrot and
Stefan 1993). Accordingly, month-to-month stream temperature
predictions tracked air temperatures as they increased from May
through July and decreased from July through September.
Extrapolating performance along the river continuum
Projecting temperature-dependent performance to realistic temperatures associated with the river continuum is a necessary step in
testing the temperature–stream size gradient as a causal driver of
abundance–stream size relationships among Pimephales species.
Although we documented substantial variation in performance
along a temperature gradient in the laboratory that spanned 16 to
33 °C, our stream temperature model suggests that summertime
temperature gradients along the river continuum in central Kansas span a narrower range of temperatures, ranging only 2.6 °C
along the mean stream gradient. Consequently, the variation in
temperature-dependent performance, when projected along the
river continuum, is subtle (Figs. 5–6). Still, the qualitative hatch–
stream size relationships for P. promelas in July, P. notatus in June
and July, and P. vigilax in May, June, and July matched the corresponding abundance–stream size relationship for each species.
With regard to age-0 feeding, ration–stream size relationships for
P. vigilax match the corresponding abundance–stream size relationships of this species in July, August, and September. By contrast, ration–stream size relationships for P. promelas and P. notatus
did not match the abundance–stream size relationships for these
two species, suggesting that ration size does not play a causative
role in their high abundance in headwater streams.
Another important consideration with ration size is that relative differences in this performance metric between species (as
opposed to absolute differences along the river continuum) may
govern the outcome of exploitative competition between congeners, because ration size is a direct measure of resource use and
depletion (Werner 1992), including temperature-dependent competitive outcomes (Taniguchi and Nakano 2000). Although maximum ration size relative to body size declines with ontogenetic
development (Rosenfeld et al. 2015), all three species were at similar ontogenetic stages during the feeding trials (Pflieger 1997). As
such, interspecific differences in ration size probably cannot be
attributed to the slight differences in developmental stage among
species. For July and August, our ration size projections suggest
that P. vigilax should be a superior exploitative competitor in all
stream sizes, but this competitive asymmetry would be greatest in
warmer river main stems. In September, the three species are
projected to consume similar-sized rations in headwater streams
that drain areas up to ⬃1000 km2. In larger river main stems
draining areas greater than ⬃1000 km2, P. notatus and to a greater
extent P. vigilax are projected to have larger rations sizes comPublished by NRC Research Press
402
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Fig. 5. Projected egg hatch success of (a–c) P. promelas, (d–f) P. notatus,
and (g–i) P. vigilax based on mean (a, d, g) May, (b, e, h) June, and
(c, f, i) July water temperatures modelled for 7974 stream segments in
the Flint Hills ecoregion and Big Blue River basin. Grey dots show
projections for individual stream segments, and black lines show fitted
generalized additive model functions. Generalized additive model
functions are superimposed for P. promelas (dotted line), P. notatus
(dashed line), and P. vigilax (solid line) for (j) May, (k) June, and (l) July to
facilitate interspecific comparisons of hatch success in each month.
pared with P. promelas and potentially be superior competitors if
food resources are limited.
It is clear that seasonal timing of spawning and age-0 recruitment will play a major role in performance of these early life
stages, because the variation in temperature from May through
September of the average historical year is greater than the upstream to downstream variation in temperature. Other freshwater
species with protracted spawning seasons also exhibit temporally
varying age-0 performance (Garvey et al. 2002). The identification
of environmental cues that influence spawning chronology will
be necessary to characterize season-long performance differences
among species and between headwater and mainstem populations.
Ecological relevance of performance metrics
Identifying performance metrics that respond to increasing
temperature in the same way that abundance responds to stream
size is an important requisite in validating temperature-dependent
performance as a mechanistic driver (and not simply a spurious
correlate) of abundance–stream size relationships. Performance
metrics must be causally linked to individual survival, growth,
and (or) reproduction to be relevant to population dynamics and
ultimately the abundance of a species observed along a natural
Can. J. Fish. Aquat. Sci. Vol. 73, 2016
Fig. 6. Projected ration size of age-0 (a–c) P. promelas, (d–f) P. notatus,
and (g–i) P. vigilax based on mean (a, d, g) July, (b, e, h) August, and
(c, f, i) September water temperatures modelled for 7974 stream
segments in the Flint Hills ecoregion and Big Blue River basin.
Ration size projections are based on the 90th quantile of the
response distribution and therefore represent the maximum ration
size for a given temperature. Grey dots show projections for
individual stream segments, and the black lines show fitted
generalized additive model functions. Generalized additive model
functions are superimposed for P. promelas (dotted line), P. notatus
(dashed line), and P. vigilax (solid line) for (j) July, (k) August, and
(l) September to facilitate interspecific comparisons of maximum
ration size in each month.
environmental gradient (Van Winkle et al. 1993; Kearney and
Porter 2009). For freshwater fishes, metrics associated with the
performance of early life history stages are likely to be informative because fecundity and age-0 survival have high elasticities in
stage-structured projection matrix models (Vélez-Espino et al. 2006),
including small-bodied cyprinid species that occupy streams and
rivers of the Great Plains (Wilde and Durham 2008). Egg hatch
success is an informative performance metric because it is related
to adult fecundity as well as to survival of the initial life stage.
Recently fertilized eggs also lack a thermal history so their
temperature-dependent survival is not influenced by thermal acclimation or irreversible developmental plasticity (Lankford and
Targett 2001; Schaefer and Ryan 2006), which can blur interspecific differences in temperature-dependent survival. Temperaturedependent egg development is also a useful proxy for metabolic
rate, which represents cellular physiology that scales up to affect
individual- and population-level rates (Brown et al. 2004).
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Troia et al.
Acquisition of food resources is linked directly to growth and
survival; however, food assimilation efficiency, metabolic rate,
and size at maturity must simultaneously be considered to infer
growth rates or survival to maturity (Jobling 1994), particularly
when considering the effect of temperature (Petersen and Paukert
2005). Bioenergetic models shed light on the trade-off between
increased ingestion (i.e., ration size) and increased metabolic costs
associated with foraging in high temperature habitats. Such models have been parameterized with empirical measurements for
many stream-dwelling cyprinid species and show that optimum
temperatures for maximizing food consumption and assimilation
efficiency and minimizing metabolic costs are typically correlated
across species (reviewed by Petersen and Paukert 2005). This proportional scaling of bioenergetic parameters across species combined with similar adult body sizes among Pimephales species
(Troia and Gido 2015) suggests that interspecific differences in
temperature-dependent ration size probably translates to similar
interspecific differences in temperature-dependent growth and
survival to maturity. This assumption should nevertheless be
tested for Pimephales species via experimental evaluation of
temperature-dependent assimilation efficiency and metabolic costs
associated with foraging behaviour. Lastly, ration size also can be
an informative measure of behavioural activity and interspecific
competitive potential in terms of resource exploitation or direct
interference of foraging behaviour (Werner 1992; Werner and
Anholt 1993). This mechanism of competitive dominance can play
an important role in the abundance of species along temperature
(Taniguchi and Nakano 2000) and other gradients (Torres-Dowdall
et al. 2013) associated with stream network position.
The fundamental niche of any organism is complex and multidimensional (Hutchinson 1957; Guisan and Zimmermann 2000).
Although temperature may, in part, drive species turnover along
the river continuum via its effect on egg hatch success and age-0
feeding, other environmental factors and performance metrics
likely play an accompanying role in the observed turnover along
the river continuum. For example, temperature may affect other
performance metrics (e.g., adult egg production), or environmental variables other than temperature (e.g., turbidity; Bonner and
Wilde 2000) may affect egg hatch success, age-0 feeding, or yet
another influential performance metric. Lastly, we have to this
point addressed the potential abiotic factors that differentially
affect the performance of headwater versus mainstem species. It
is possible that abundance–stream size patterns of Pimephales species reflect differences in the realized niche — a pattern documented in other studies of stream fish distributions. Such realized
niche differences could stem from interspecific differences in susceptibility to predation (Schlosser 1988) or competitive displacement by a congener (Taniguchi and Nakano 2000) or more distantly
related yet ecologically similar species (Winston 1995).
Broader implications
Species replacements are not restricted to fishes and gradients
of stream size; rather, species replacements are widespread along
most environmental gradients (MacArthur and Levins 1967). For
example, fish community turnover has been documented along
depth gradients in temperate-zone lakes (Brandt et al. 1980) and
along salinity gradients in arid-land streams (Echelle et al. 1972).
Moreover, congeneric mussels of the genus Dreissena segregate
along depth gradients in streams (Domm et al. 1993), and congeneric skinks of the genus Plestiodon segregate along thermal gradients in terrestrial habitats (Watson and Gough 2012). Thus,
identifying the mechanistic underpinnings of such species replacements is a broadly applicable and fundamental goal of community ecology (McGill et al. 2006; Kearney and Porter 2009).
Refined understanding of fundamental and realized niche dimensions is an essential first step in developing environmental
niche models for predicting species’ distributions in unsurveyed
regions or under future climate or environmental scenarios
403
(Guisan and Zimmermann 2000). Environmental niche modelling
can be carried out using a correlative or mechanistic approach,
each of which has advantages and disadvantages (reviewed by
Kearney and Porter 2009). Correlative models are used most frequently because records of species occurrence and environmental
datasets are abundant and accessible to investigators. A disadvantage of correlative models is that they do not distinguish realized
from fundamental niche dimensions and may not accurately predict distributions in novel environments (Jiménez-Valverde et al.
2009). Mechanistic models can effectively characterize fundamental niche dimensions, which provide insight into the causative
abiotic drivers of species distributions and indicate whether the
contemporary spatial distribution of a species is at equilibrium
with the fundamental niche (Monahan 2009). A disadvantage of
mechanistic models is the time-consuming and costly experimental assessments of performance–environment relationships
necessary to parameterize models, particularly when developing
models for many species. Ultimately, correlative and mechanistic
models may best be viewed as complementary rather than substitutable; the strength of correlative models is in their predictive
capability, whereas mechanistic models explain why species occur where they do along environmental gradients. At this time,
our incomplete understanding of performance–environment relationships for the Pimephales species likely precludes our ability
to develop mechanistic environmental niche models that predict
the distributions of these species more accurately than correlative
niche models. Nevertheless, the performance–environment relationships quantified in the current study, the projection to a realistic temperature gradient (i.e., a stream size gradient in central
Kansas), and comparison of performance–environment relationships among closely related species advances understanding of
the mechanisms that underlie replacements among Pimephales
species and, more generally, the assembly of fish communities in
lotic habitats.
Acknowledgements
The authors thank Nate Cathcart, Sky Hedden, Emily Johnson,
Kevin Kirkbride, Josh Perkin, Dustin Shaw, Trevor Starks, Allison
Veach, James Whitney, and Rebecca Zheng for field and laboratory assistance, as well as the Tallgrass Prairie National Preserve,
Kansas Department of Wildlife Parks and Tourism, Joe and Alison
Gerken, and many other private land owners for providing access
to streams. The authors also thank Jake Schaefer for advice on
experimental design and data analysis, as well as John Blair for use
of equipment and laboratory facilities. This research was funded
by the National Science Foundation (DEB No. 1311183), the Southwestern Association of Naturalists, Prairie Biotic Research Inc.,
and the Kansas Academy of Science. Brood stocks were collected
under the permission of the Kansas Department of Wildlife Parks
and Tourism (permit No. SC-089-2014) and housed and spawned
under the permission of the Kansas State University Institutional
Animal Care and Use Committee (permit No. 2996). This is publication 16-086-J from the Kansas Agricultural Experiment Station.
The authors have no conflicts of interest to declare.
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