Supplement 1. OpenBUGS code for conditional access and abundance of species in stream networks.

<h2>File List</h2><div> <p><a href="ConditionalAccessAndAbundance.odc">ConditionalAccessAndAbundance.odc</a> (MD5: 2e72cba15bee987bf62307701a82ef94) </p> </div><h2>Description</h2><div> <p>ConditionalAccessAndAbundance.odc – Documented OpenBUGS code for fitting the conditional access and abundance model from capture–mark–recapture data in stream networks. </p> </div

Appendix A. Sensitivity of posterior summaries of model coefficients to the choice of prior distribution for the intercept μ1 associated with probability of access, and to the random coefficients (of species) arrangement for conditional access and abundance.

Sensitivity of posterior summaries of model coefficients to the choice of prior distribution for the intercept μ1 associated with probability of access, and to the random coefficients (of species) arrangement for conditional access and abundance

A Simple Prioritization Tool to Diagnose Impairment of Stream Temperature for Coldwater Fishes in the Great Basin

<p>We provide a simple framework for diagnosing the impairment of stream water temperature for coldwater fishes across broad spatial extents based on a weight-of-evidence approach that integrates biological criteria, species distribution models, and geostatistical models of stream temperature. As a test case, we applied our approach to identify stream reaches most likely to be thermally impaired for Lahontan Cutthroat Trout <i>Oncorhynchus clarkii henshawi</i> in the upper Reese River, located in the northern Great Basin, Nevada. We first evaluated the capability of stream thermal regime descriptors to explain variation across 170 sites, and we found that the 7-d moving average of daily maximum stream temperatures (7DADM) provided minimal among-descriptor redundancy and, based on an upper threshold of 20°C, was also a good indicator of acute and chronic thermal stress. Next, we quantified the range of Lahontan Cutthroat Trout within our study area using a geographic distribution model. Finally, we used a geostatistical model to assess spatial variation in 7DADM and predict potential thermal impairment at the stream reach scale. We found that whereas 38% of reaches in our study area exceeded a 7DADM of 20°C and 35% were significantly warmer than predicted, only 17% both exceeded the biological criterion and were significantly warmer than predicted. This filtering allowed us to identify locations where physical <i>and</i> biological impairment were most likely within the network and that would represent the highest management priorities. Although our approach lacks the precision of more comprehensive approaches, it provides a broader context for diagnosing impairment and is a useful means of identifying priorities for more detailed evaluations across broad and heterogeneous stream networks.</p> <p>Received July 8, 2014; accepted October 22, 2015</p

Thermal Regimes, Nonnative Trout, and Their Influences on Native Bull Trout in the Upper Klamath River Basin, Oregon

<p>The occurrence of fish species may be strongly influenced by a stream’s thermal regime (magnitude, frequency, variation, and timing). For instance, magnitude and frequency provide information about sublethal temperatures, variability in temperature can affect behavioral thermoregulation and bioenergetics, and timing of thermal events may cue life history events, such as spawning and migration. We explored the relationship between thermal regimes and the occurrences of native Bull Trout <i>Salvelinus confluentus</i> and nonnative Brook Trout <i>Salvelinus fontinalis</i> and Brown Trout <i>Salmo trutta</i> across 87 sites in the upper Klamath River basin, Oregon. Our objectives were to associate descriptors of the thermal regime with trout occurrence, predict the probability of Bull Trout occurrence, and estimate upper thermal tolerances of the trout species. We found that each species was associated with a different suite of thermal regime descriptors. Bull Trout were present at sites that were cooler, had fewer high-temperature events, had less variability, and took longer to warm. Brook Trout were also observed at cooler sites with fewer high-temperature events, but the sites were more variable and Brook Trout occurrence was not associated with a timing descriptor. In contrast, Brown Trout were present at sites that were warmer and reached higher temperatures faster, but they were not associated with frequency or variability descriptors. Among the descriptors considered, magnitude (specifically June degree-days) was the most important in predicting the probability of Bull Trout occurrence, and model predictions were strengthened by including Brook Trout occurrence. Last, all three trout species exhibited contrasting patterns of tolerating longer exposures to lower temperatures. Tolerance limits for Bull Trout were lower than those for Brook Trout and Brown Trout, with contrasts especially evident for thermal maxima. Our results confirm the value of exploring a suite of thermal regime descriptors for understanding the distribution and occurrence of fishes. Moreover, these descriptors and their relationships to fish should be considered with future changes in land use, water use, or climate.</p> <p>Received March 4, 2016; accepted July 27, 2016 Published online October 11, 2016 </p

Model selection metrics for hurdle count regression models fit to occurrence and abundance data for steelhead redds at 209 sites in the John Day River basin, Oregon.

1<p>Model results are ranked by AIC_c from best to worst, and Akaike weights (<i>w_i</i>,)>0.05 are also shown.</p>2<p><i>K</i> is the number of estimated parameters, L-L is the log-likelihood, and ΔAIC_c is the difference in AIC_c relative to the best model (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079232#pone.0079232-terBraak1" target="_blank">[41]</a> for details).</p

Spatial Ecological Processes and Local Factors Predict the Distribution and Abundance of Spawning by Steelhead (<i>Oncorhynchus mykiss</i>) across a Complex Riverscape

<div><p>Processes that influence habitat selection in landscapes involve the interaction of habitat composition and configuration and are particularly important for species with complex life cycles. We assessed the relative influence of landscape spatial processes and local habitat characteristics on patterns in the distribution and abundance of spawning steelhead (<i>Oncorhynchus mykiss</i>), a threatened salmonid fish, across ∼15,000 stream km in the John Day River basin, Oregon, USA. We used hurdle regression and a multi-model information theoretic approach to identify the relative importance of covariates representing key aspects of the steelhead life cycle (e.g., site access, spawning habitat quality, juvenile survival) at two spatial scales: within 2-km long survey reaches (local sites) and ecological neighborhoods (5 km) surrounding the local sites. Based on Akaike’s Information Criterion, models that included covariates describing ecological neighborhoods provided the best description of the distribution and abundance of steelhead spawning given the data. Among these covariates, our representation of offspring survival (growing-season-degree-days, °C) had the strongest effect size (7x) relative to other predictors. Predictive performances of model-averaged composite and neighborhood-only models were better than a site-only model based on both occurrence (percentage of sites correctly classified = 0.80±0.03 SD, 0.78±0.02 vs. 0.62±0.05, respectively) and counts (root mean square error = 3.37, 3.93 vs. 5.57, respectively). The importance of both temperature and stream flow for steelhead spawning suggest this species may be highly sensitive to impacts of land and water uses, and to projected climate impacts in the region and that landscape context, complementation, and connectivity will drive how this species responds to future environments.</p></div

Probability of steelhead redd occurrence as a function of the proportion of a spawning survey site with suitable spawning substrates (D50_SITE), in two neighborhood types : 1) Good has high amounts of suitable substrate in nearby reaches; and 2) Poor has low amounts.

<p>Estimates are from a hurdle count regression model. Dashed lines are 95% confidence intervals.</p

Conceptual model of hypothesized effects of landscape complementation on the occurrence of steelhead redds in stream networks.

<p>Likelihood of steelhead redd occurrence is indicated by black ( = high likelihood) or white ( = low likelihood) fill inside of the focal reach located within the center of each network. The likelihood of steelhead redd occurrence is hypothesized to increase when habitats utilized by juveniles (points) are abundant and located closer (along the stream network) to spawning reaches. Grey shading shows that the probability of juvenile movement exponentially declines with increasing stream distance from their natal reach (i.e., dispersal kernel).</p

Standardized model-averaged parameter estimates, unconditional SE values, and 95% confidence limits (CLs) for covariates predicting the occurrence (binomial model) and abundance (count model) of steelhead redds in the John Day River basin, Oregon.

1<p>Results are based on the top five hurdle count regression models, which were responsible for 96% of the collective model weight (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079232#pone-0079232-t002" target="_blank">Table 2</a>).</p>2<p>Levels for the TRT covariate are Lower Mainstem = Intercept, Middle Fork = TRTMF, North Fork = TRTNF, South Fork = TRTSF, and Upper Mainstem = TRTUM.</p>3<p>Log(θ) is the dispersion parameter.</p

Candidate hurdle count regression models used to estimate occurrence and abundance of steelhead redds in the John Day River basin, Oregon.

1<p>Covariates were applied to the occurrence (a) and/or abundance (b) model components.</p>2<p>Models were formulated to address hypotheses at two scales (Site and Neighborhood) and in combinations (Mixture).</p