Major ecological realignments are already occurring in response to climate change. To be successful, conservation strategies now need to account for geographical patterns in traits sensitive to climate change, as well as climate threats to species-level diversity. As part of an effort to provide such information, we conducted a climate vulnerability assessment that included all anadromous Pacific salmon and steelhead ( Oncorhynchus spp.) population units listed under the U.S. Endangered Species Act. Using an expert-based scoring system, we ranked 20 attributes for the 28 listed units and 5 additional units. Attributes captured biological sensitivity, or the strength of linkages between each listing unit and the present climate; climate exposure, or the magnitude of projected change in local environmental conditions; and adaptive capacity, or the ability to modify phenotypes to cope with new climatic conditions. Each listing unit was then assigned one of four vulnerability categories. Units ranked most vulnerable overall were Chinook ( O . tshawytscha ) in the California Central Valley, coho ( O . kisutch ) in California and southern Oregon, sockeye ( O . nerka ) in the Snake River Basin, and spring-run Chinook in the interior Columbia and Willamette River Basins. We identified units with similar vulnerability profiles using a hierarchical cluster analysis. Life history characteristics, especially freshwater and estuary residence times, interplayed with gradations in exposure from south to north and from coastal to interior regions to generate landscape-level patterns within each species. Nearly all listing units faced high exposures to projected increases in stream temperature, sea surface temperature, and ocean acidification, but other aspects of exposure peaked in particular regions. Anthropogenic factors, especially migration barriers, habitat degradation, and hatchery influence, have reduced the adaptive capacity of most steelhead and salmon populations. Enhancing adaptive capacity is essential to mitigate for the increasing threat of climate change. Collectively, these results provide a framework to support recovery planning that considers climate impacts on the majority of West Coast anadromous salmonids.

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Reintroductions are commonly employed to preserve intraspecific biodiversity in fragmented landscapes. However, reintroduced populations are frequently smaller and more geographically isolated than native populations. Mixing genetically, divergent sources are often proposed to attenuate potentially low genetic diversity in reintroduced populations that may result from small effective population sizes. However, a possible negative tradeoff for mixing sources is outbreeding depression in hybrid offspring. We examined the consequences of mixed-source reintroductions on several fitness surrogates at nine slimy sculpin (Cottus cognatus) reintroduction sites in south-east Minnesota. We inferred the relative fitness of each crosstype in the reintroduced populations by comparing their growth rate, length, weight, body condition and persistence in reintroduced populations. Pure strain descendents from a single source population persisted in a greater proportion than expected in the reintroduced populations, whereas all other crosstypes occurred in a lesser proportion. Length, weight and growth rate were lower for second-generation intra-population hybrid descendents than for pure strain and first-generation hybrids. In the predominant pure strain, young-of the-year size was significantly greater than any other crosstype. Our results suggested that differences in fitness surrogates among crosstypes were consistent with disrupted co-adapted gene complexes associated with beneficial adaptations in these reintroduced populations. Future reintroductions may be improved by evaluating the potential for local adaptation in source populations or by avoiding the use of mixed sources by default when information on local adaptations or other genetic characteristics is lacking.

The field‐derived thermal niche of aquatic vertebrates is potentially useful in determining whether resource management plans are adequate to protect sensitive vertebrates. Our objective was to use field data to estimate the thermal niches of 16 species of aquatic vertebrates and to compare these values among five geographic regions in Oregon. Thermal niche values varied among regions; for example, the upper thermal limit for rainbow trout Oncorhynchus mykiss was 22.4°C in the Blue Mountains ecoregion and 16.9°C in the Cascades ecoregion. Nonmetric multidimensional scaling (NMS) analysis of aquatic vertebrate assemblages revealed that level‐three ecoregions grouped vertebrate assemblages more cohesively than the third‐order hydrologic unit code (basins). Analysis of similarities of Bray–Curtis distance measures supported NMS findings that the structures of aquatic vertebrate assemblages coincide more with ecoregions than with basins. The realized thermal niches calculated in this study are generally comparable to the maximum growth temperatures and upper thermal limits established by other field and laboratory techniques. This information is valuable for managers who devise water temperature criteria as well as fisheries ecologists interested in quantifying or delineating thermal habitat.

The roles of temperature and ultraviolet radiation (UVR) in determining the spawning success of yellow perch Perca flavescens were investigated in two Pennsylvania lakes with different dissolved organic carbon (DOC) concentrations. In situ incubation experiments were used to manipulate temperature and UVR and to examine hatching time and hatching success. Extensive scuba surveys were used to document actual spawning depths. Differences in the temperature and UVR profiles of the two lakes led to contrasting responses of incubated yellow perch eggs. Higher temperatures in the surface waters of the higher‐DOC lake led to hatching times that were 10–26 d shorter than those in the surface waters of the low‐DOC lake or in the deeper waters of the higher‐DOC lake. The high levels of UVR in the surface waters of the low‐DOC lake killed 100% of the eggs before hatching. Ultraviolet radiation had little effect on survival in the higher‐DOC lake or in deeper waters of the low‐DOC lake. Scuba surveys revealed that spawning in the low‐DOC lake occurred at greater depths than previously recognized. Ninety‐two percent of the eggs spawned in the low‐DOC lake were located at depths greater than 3 m, while 76% of eggs in the higher‐DOC lake were spawned in water less than 1 m deep. Temperature and UVR are both important in determining among‐lake differences in spawning depths of yellow perch. Yellow perch are able to spawn at shallow depths in higher‐DOC lakes, where warmer temperatures accelerate developmental rates and DOC blocks potentially damaging UVR. In low‐DOC lakes, yellow perch must spawn at greater depths to avoid UVR damage. Spawning at greater depths may be costly due to the substantially slower developmental rates at lower temperatures. Our data suggest that the conflicting selective pressures of UVR and temperature create an optimal spawning‐depth range for yellow perch that differs among lakes as a function of DOC concentration.

The Sacramento-San Joaquin Delta is a major survival bottleneck for imperiled California salmonid populations, which is partially due to a multitude of non-native fish predators that have proliferated there throughout the 20th century. Understanding the diets of salmonid predators is critical to understanding their individual impacts, role in the food web, and the implications for potential management actions. We collected the stomach contents of Striped Bass Morone saxatilis, Largemouth Bass Micropterus salmoides, Channel Catfish Ictalurus punctatus and White Catfish Ameiurus catus sampled from three 1-km reaches in the lower San Joaquin River in 2014 and 2015 during the peak juvenile salmon outmigration period. We tested each stomach (n = 582) for the presence of juvenile Chinook Salmon Oncorhynchus tshawytscha and other prey items using a genetic barcoding technique. Channel Catfish had significantly higher frequency of Chinook Salmon in their stomachs (27.8% of tested Channel Catfish contained Chinook Salmon DNA), compared to the other three predators (2.8% to 4.8%). However, non-native fish species occurred at greater frequencies in the diets of all four predator species than salmon. Using depletion estimation from electrofishing, we were able to generate population densities for Striped Bass and Largemouth Bass in our reaches. Largemouth Bass were evenly distributed throughout all three reaches, at a mean density of approximately 333 (± 195 SE) per km of river. Striped Bass were patchily distributed, ranging from 21 to 1,227 per km. Extrapolating the frequency of salmon detected in stomachs to the predator abundance estimates, we estimate that the population of Largemouth Bass we sampled consumed between 3 and 5 Chinook Salmon per day per 1-km study reach (consumption rate of 0.011 salmon per predator per day), whereas the Striped Bass population consumed between 0 and 24 Chinook Salmon per day (0.019 salmon per predator per day).

1. The predictive modelling approach to bioassessment estimates the macroinvertebrate assemblage expected at a stream site if it were in a minimally disturbed reference condition. The difference between expected and observed assemblages then measures the departure of the site from reference condition. 2. Most predictive models employ site classification, followed by discriminant function (DF) modelling, to predict the expected assemblage from a suite of environmental variables. Stepwise DF analysis is normally used to choose a single subset of DF predictor variables with a high accuracy for classifying sites. An alternative is to screen all possible combinations of predictor variables, in order to identify several 'best' subsets that yield good overall performance of the predictive model. 3. We applied best-subsets DF analysis to assemblage and environmental data from 199 reference sites in Oregon, U.S.A. Two sets of 66 best DF models containing between one and 14 predictor variables (that is, having model orders from one to 14) were developed, for five-group and 11-group site classifications. 4. Resubstitution classification accuracy of the DF models increased consistently with model order, but cross-validated classification accuracy did not improve beyond seventh or eighth-order models, suggesting that the larger models were overfitted. 5. Overall predictive model performance at model training sites, measured by the rootmean-squared error of the observed/expected species richness ratio, also improved steadily with DF model order. But high-order DF models usually performed poorly at an independent set of validation sites, another sign of model overfitting. 6. Models selected by stepwise DF analysis showed evidence of overfitting and were outperformed by several of the best-subsets models. 7. The group separation strength of a DF model, as measured by Wilks' K, was more strongly correlated with overall predictive model performance at training sites than was DF classification accuracy. 8. Our results suggest improved strategies for developing reliable, parsimonious predictive models. We emphasise the value of independent validation data for obtaining a 359 realistic picture of model performance. We also recommend assessing not just one or two, but several, candidate models based on their overall performance as well as the performance of their DF component. 9. We provide links to our free software for stepwise and best-subsets DF analysis.

Predator–prey dynamics can have landscape‐level impacts on ecosystems, and yet, spatial patterns and environmental predictors of predator–prey dynamics are often investigated at discrete locations, limiting our understanding of the broader impacts. At these broader scales, landscapes often contain multiple complex and heterogeneous habitats, requiring a spatially representative sampling design. This challenge is especially pronounced in California’s Sacramento–San Joaquin River Delta, where managers require information on the landscape‐scale impacts of non‐native fish predators on multiple imperiled native prey fish populations. We quantified relative predation risk in the southern half of the Delta (South Delta) in 2017 using floating baited tethers that record the exact time and location of predation events. We selected 20 study sites using a generalized random tessellation stratified survey design, which allowed us to infer relationships between key environmental covariates and predation across a broader spatial scale than previous studies. Covariates included distance‐to‐nearest predators, water temperature, turbidity, depth, bottom slope, bottom roughness, water velocity, and distance‐to‐nearest riverbank and nearest aquatic vegetation bed. Model selection determined the covariates that best predicted relative predation risk: water temperature, time of day, mean predator distance, and river bottom roughness. Using this model, we estimated predation risk for the South Delta landscape at a 1‐day and 1‐km resolution. This effort identified hot spots of predation risk and allowed us to generate predicted survival for migrating fish transiting the South Delta. This methodology can be applied to other systems to evaluate spatio‐temporal dynamics in predation risk, and their biotic and abiotic predictors.