Time series plot of mean temperature and discharge across site types.

<p>Time series plots are of year versus mean April_t-June_t maximum water temperature and mean March_t-June_t discharge (Q). Means were for the 2 large tributary sites and the 4 main stem sites.</p

Seven study sites within the upper Shavers Fork watershed in Pocahontas and Randolph counties, WV.

<p>Seven study sites within the upper Shavers Fork watershed in Pocahontas and Randolph counties, WV.</p

Brook trout density coefficient of variation (C.V.) as a function of stream drainage area.

<p>Brook trout density coefficient of variation (C.V.) as a function of stream drainage area.</p

Time series, best model, and residual plots for young-of-the-year brook trout analyses.

<p>Plots of YOY brook trout density time series, the highest Akaike weighted (<i>w_i</i>) model, and the residuals of the best model as a function of the best DI predictor variable for per capita rate of change in the YOY brook trout population (ryoy). The residual plot was selected based on the highest <i>R²</i> model in the multi-mechanism set that also contained the highest weighted predictor variable. Horizontal dotted lines represent the local carrying capacity for each site.</p

Results from candidate models using AIC_c for the 7 study sites.

<p>Only interpretable predictor variables in at least one site are provided (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091673#pone.0091673.s002" target="_blank">Table S2</a> for all models analyzed). The first value represents the Akaike's weight (<i>w_i</i>) given to each model in the candidate set followed by the <i>R²</i> statistic and the direction of the relationship. Bold values represent the best model in each candidate set. Abbreviations are as follows: rpop  =  per capita growth rate (r = ln(n_t/n_t−1) for the total brook trout population, radult  = r for adults, ryoy  = r for young-of-the-year, dtrout  =  density of all brook trout, dadult  =  density of adult brook trout, dyoy  =  density of young-of-the-year brook trout, sp_tT  =  mean April-June maximum temperature, su_t-1T  =  mean July maximum temperature, and sp_tQ  =  mean March-June discharge. Missing values represent predictor variables that were correlated with another predictor variable in the candidate set and therefore removed. No models were constructed for ryoy at main stem sites because few YOY were found in those sites. Models with an * were considered interpretable models using criteria from Grossman et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091673#pone.0091673-Grossman1" target="_blank">[14]</a>.</p

Multiple stepwise regression predicting density of YOY at Headwater 1.

<p>The final regression equation is y = 0.2956dadult_t-1 + 0.0561su_t-1T + 0.0005sp_tQ −0.9751.</p

Time series, best model, and residual plots for adult brook trout analyses.

<p>Plots of adult brook trout density time series, the highest Akaike weighted (<i>w_i</i>) model, and the residuals of the best model as a function of the best DI predictor variable for per capita rate of change in the adult brook trout population (radult). The residual plot was selected based on the highest <i>R²</i> model in the multi-mechanism set that also contained the highest weighted predictor variable. Horizontal dotted lines represent the local carrying capacity for each site.</p

Predictors of young-of-the-year recruitment in the core.

<p>Stock-recruitment curve for YOY brook trout density as a function of (A) adult brook trout density the previous year and (B) mean July maximum temperature at the headwater 1 site.</p

Brook trout pairwise correlation analysis among sites.

<p>a) Correlation of brook trout densities regressed against drainage area differences between sites (<i>p</i> = 0.01, R² = 0.27) and b) correlation of brook trout densities regressed against swim distances between sites (<i>p</i> = 0.93, R² = 0.00).</p

The role of density-dependent and –independent processes in spawning habitat selection by salmon in an Arctic riverscape

<div><p>Density-dependent (DD) and density-independent (DI) habitat selection is strongly linked to a species’ evolutionary history. Determining the relative importance of each is necessary because declining populations are not always the result of altered DI mechanisms but can often be the result of DD via a reduced carrying capacity. We developed spatially and temporally explicit models throughout the Chena River, Alaska to predict important DI mechanisms that influence Chinook salmon spawning success. We used resource-selection functions to predict suitable spawning habitat based on geomorphic characteristics, a semi-distributed water-and-energy balance hydrologic model to generate stream flow metrics, and modeled stream temperature as a function of climatic variables. Spawner counts were predicted throughout the core and periphery spawning sections of the Chena River from escapement estimates (DD) and DI variables. Additionally, we used isodar analysis to identify whether spawners actively defend spawning habitat or follow an ideal free distribution along the riverscape. Aerial counts were best explained by escapement and reference to the core or periphery, while no models with DI variables were supported in the candidate set. Furthermore, isodar plots indicated habitat selection was best explained by ideal free distributions, although there was strong evidence for active defense of core spawning habitat. Our results are surprising, given salmon commonly defend spawning resources, and are likely due to competition occurring at finer spatial scales than addressed in this study.</p></div