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The Northern California Current is a highly productive marine upwelling ecosystem that is economically and ecologically important. It is home to both commercially harvested species and those that are federally listed under the U.S. Endangered Species Act. Recently, there has been a global shift from single-species fisheries management to ecosystem-based fisheries management, which acknowledges that more complex dynamics can reverberate through a food web. Here, we have integrated new research into an end-to-end ecosystem model (i.e., physics to fisheries) using data from long-term ocean surveys, phytoplankton satellite imagery paired with a vertically generalized production model, a recently assembled diet database, fishery catch information, species distribution models, and existing literature. This spatially-explicit model includes 90 living and detrital functional groups ranging from phytoplankton, krill, and forage fish to salmon, seabirds, and marine mammals, and nine fisheries that occur off the coast of Washington, Oregon, and Northern California. This model was updated from previous regional models to account for more recent changes in the Northern California Current (e.g., increases in market squid and some gelatinous zooplankton such as pyrosomes and salps), to expand the previous domain to increase the spatial resolution, to include data from previously unincorporated surveys, and to add improved characterization of endangered species, such as Chinook salmon (Oncorhynchus tshawytscha) and southern resident killer whales (Orcinus orca). Our model is mass-balanced, ecologically plausible, without extinctions, and stable over 100-year simulations. Ammonium and nitrate availability, total primary production rates, and model-derived phytoplankton time series are within realistic ranges. As we move towards holistic ecosystem-based fisheries management, we must continue to openly and collaboratively integrate our disparate datasets and collective knowledge to solve the intricate problems we face. As a tool for future research, we provide the data and code to use our ecosystem model.

The common practice of incorporating environmental indices into population models has greatly advanced our understanding of ecological systems. Unfortunately, we are increasingly seeing published correlations between population indicators and environmental indices breaking down when tested with new data. Examining how the correlations among indices change over time could help explain underlying causal mechanisms, which ultimately strengthen the basis for prediction of population indicators. For migratory animals such as anadromous salmon (Oncorhynchus spp.), the habitat conditions they experience can affect their lifetime fitness and population viability. We analyzed 43 freshwater, marine, and climate indices associated with 72 river sites and five coastal ecoregions inhabited by Chinook and coho salmon (O. tshawytscha and O. kisutch) in the western USA. Utilizing long time series (ranging from 32 to 124 years), we examined spatial and temporal patterns in correlations through hierarchical clustering across sites and non-stationarity across time. Individual river sites clustered into two Northwest and one Southwest groups. Northwest sites generally showed stronger correlations between freshwater and climate indices, while Southwest sites showed stronger correlations within freshwater or within marine/climate indices. For a closer examination at shorter periods, we parsed the time series into 10-year windows and showed how pairwise correlations changed over time with spring–summer Pacific Decadal Oscillation index in the Northwest and with spring flow in the Southwest. Stronger correlations across multiple indices tended to occur when large-scale climatic events (e.g., Oceanic Nino and Pacific Decadal Oscillation indices) were in-phase, and phase transitions (e.g., from positive to negative) occurred in the same 10-year window. In a third analysis, we assessed how well indices provided unique vs. confounding/complex information and had consistent vs. varying relationships based on the mean and variance of 10-year correlations. Across index types, the variance in correlations tended to be lowest in marine vs. climate indices, higher among freshwater indices, and highest for freshwater vs. marine/climate indices. Yet, the mean strength of correlations for freshwater vs. marine/climate indices was still comparable to those among freshwater ones. Overall, identifying time periods when correlations tend to change will help interpret historical and projected population indicators. Spatial trends in the strength of correlations also indicate that the level of confounding effects among indices can differ regionally. We emphasize the importance of knowing the strength and variability of correlations among indices, and their representativeness of ecological processes in the context of combined phases of multiple climatic indices.

The majority of Columbia River summer-run steelhead encounter high river temperatures (near or > 20°C) during their spawning migration. While some steelhead pass through the mid-Columbia River in a matter of days, others use tributary habitats as temperature refuges for periods that can last months. Using PIT tag detection data from adult return years 2004–2016, we fit 3-component mixture models to differentiate between “fast”, “slow”, and “overwintering” migration behaviors in five aggregated population groups. Fast fish migrated straight through the reach on average in ~7–9 days while slow fish delayed their migration for weeks to months, and overwintering fish generally took ~150–250 days. We then fit covariate models to examine what factors contributed to the probability of migration delay during summer months (slow or overwintering behaviors), and to explore how migration delay related to mortality. Finally, to account for the impact of extended residence times in the reach for fish that delayed, we compared patterns in estimated average daily rates of mortality between migration behaviors and across population groups. Results suggest that migration delay was primarily triggered by high river temperatures but temperature thresholds for delay were lowest just before the seasonal peak in river temperatures. While all populations groups demonstrated these general patterns, we documented substantial variability in temperature thresholds and length of average delays across population groups. Although migration delay was related to higher reach mortality, it was also related to lower average daily mortality rates due to the proportional increase in reach passage duration being larger than the associated increase in mortality. Lower daily mortality rates suggest that migration delay could help mitigate the impacts of harsh migration conditions, presumably through the use of thermal refuges, despite prolonged exposure to local fisheries. Future studies tracking individual populations from their migration through reproduction could help illuminate the full extent of the tradeoffs between different migration behaviors.

In 2015, the Pacific marine heat wave, low river flows, and record high water temperatures in the Columbia River Basin contributed to a near-complete failure of the adult migration of endangered Snake River sockeye salmon (Oncorhynchus nerka, NOAA Fisheries 2016). These extreme weather events may become the new normal due to anthropogenic climate change, with catastrophic consequences for endangered species. Existing anthropogenic pressures may amplify vulnerability to climate change, but these potential synergies have rarely been quantified. We examined factors affecting survival of endangered sockeye (Oncorhynchus nerka) and threatened Chinook salmon (O. tshawytscha) as they migrated upstream through eight dams and reservoirs to spawning areas in the Snake River Basin. Our extensive database included histories of 17,279 individual fish that migrated since 2004. A comparison between conditions in 2015 and daily temperatures and flows in a regulated basin forced by output from global climate models showed that 2015 did have many characteristics of projected future mean conditions. To evaluate potential salmon responses, we modeled migration timing and apparent survival under historical and future climate scenarios (2040s). For Chinook salmon, adult survival from the first dam encountered to spawning grounds dropped by 4-15%, depending on the climate scenario. For sockeye, survival dropped by ~80% from their already low levels. Through sensitivity analyses, we observed that the adult sockeye migration would need to shift more than 2 weeks earlier than predicted to maintain survival rates typical of those seen during 2008-2017. Overall, the greater impacts of climate change on adult sockeye compared with adult Chinook salmon reflected differences in life history and environmental sensitivities, which were compounded for sockeye by larger effect sizes from other anthropogenic factors. Compared with Chinook, sockeye was more negatively affected by a history of juvenile transportation and by similar temperatures and flows. The largest changes in temperature and flow were projected to be upstream from the hydrosystem, where direct mitigation through hydrosystem management is not an option. Unfortunately, Snake River sockeye have likely lost much of their adaptive capacity with the loss of the wild population. Further work exploring habitat restoration or additional mitigation actions is urgently needed.

Many migratory species traverse highly heterogeneous landscapes, often including habitats that have been altered by human activities. Modeling migration dynamics is challenging because individual variability in behavior at multiple spatial and temporal scales can produce complex, multi-modal distributions in migration travel times. Moreover, behavioral responses to conditions encountered en route can affect habitat-specific migration rates which then influence bioenergetic costs and mortality risk over the entire migration. To quantify impacts of conditions within migration corridors, refined analyses of behavior are needed. In this study, we developed a behavior-based simulation model that predicts individual adult salmon migration duration over 24 distinct river reaches totaling 922 km, including eight hydropower dams. The study population, threatened Snake River spring/summer Chinook salmon (Oncorhynchus tshawytscha), had observed migration durations ranging from 23 to 108 d. In a novel application of N-dimensional mixture models, which can account for subpopulations that behave differently, we simulated “fast” vs. “slow” travel through migration reaches. The proportion of migrants in each category was determined by diel, seasonal, and proximate river conditions, which captured the temporally shifting bimodal patterns in the data. We fit reach-specific models with data from 2188 tagged salmon migrating in 2000–2013 and validated the cumulative model with additional data through 2015. By accounting for multiple behaviors in this way, the model successfully recreated the breadth and variability in total travel times to within 3% of observed durations throughout the 5th–95th quantiles. En route mortality appeared to account for the loss of the slowest fish that encountered record-breaking high temperatures in 2015. For Chinook salmon, this combined reach and cumulative travel-time model provides an opportunity to link high-resolution behavioral data to individual fitness and population-level impacts on viability. More generally, the N-dimensional modeling approach offers a framework for assessing the cumulative impacts of alternative behaviors at small spatial and temporal scales. Improved accounting of changes in migration rate in response to local conditions will aid recovery efforts for species of concern traversing complex migration corridors.

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.

Aquatic species are threatened by climate change but have received comparatively less attention thanterrestrial species.Wegleanedkey strategiesforscientistsandmanagers seekingtoaddressclimatechange in aquatic conservation planning from the literature and existing knowledge. We address 3 categories of conservation effort that rely on scientific analysis and have particular application under the U.S. Endangered Species Act (ESA): assessment of overall risk to a species; long-term recovery planning; and evaluation of effects of specific actions or perturbations. Fewer data are available for aquatic species to support these analyses, and climate effects on aquatic systems are poorly characterized. Thus, we recommend scientists conducting analyses supporting ESA decisions develop a conceptual model that links climate, habitat, ecosystem, and species response to changing conditions and use this model to organize analyses and future research. We recommend that current climate conditions are not appropriate for projections used in ESA analyses and that long-term projections of climate-change effects provide temporal context as a species-wide assessment provides spatial context. In these projections, climate change should not be discounted solely because the magnitude of projected change at a particular time is uncertain when directionality of climate change is clear. Identifying likely future habitat at the species scale will indicate key refuges and potential range shifts. However, the risks and benefits associated with errors in modeling future habitat are not equivalent. The ESA offers mechanisms for increasing the overall resilience and resistance of species to climate changes, including establishing recovery goals requiring increased genetic and phenotypic diversity, specifying critical habitat in areas not currently occupied but likely to become important, and using adaptive management.

Environmental change can shift the phenotype of an organism through either evolutionary or nongenetic processes. Despite abundant evidence of phenotypic change in response to recent climate change, we typically lack sufficient genetic data to identify the role of evolution. We present a method of using phenotypic data to characterize the hypothesized role of natural selection and environmentally driven phenotypic shifts (plasticity). We modeled historical selection and environmental predictors of interannual variation in mean population phenotype using a multivariate state-space model framework. Through model comparisons, we assessed the extent to which an estimated selection differential explained observed variation better than environmental factors alone. We applied the method to a 60-year trend toward earlier migration in Columbia River sockeye salmon Oncorhynchus nerka, producing estimates of annual selection differentials, average realized heritability, and relative cumulative effects of selection and plasticity. We found that an evolutionary response to thermal selection was capable of explaining up to two-thirds of the phenotypic trend. Adaptive plastic responses to June river flow explain most of the remainder. This method is applicable to other populations with time series data if selection differentials are available or can be reconstructed. This method thus augments our toolbox for predicting responses to environmental change.

Although some organisms have moved to higher elevations and latitudes in response to recent climate change, there is little consensus regarding the capacity of different species to track rapid climate change via range shifts. Understanding species' abilities to shift ranges has important implications for assessing extinction risk and predicting future community structure. At an expanding front, colonization rates are determined jointly by rates of reproduction and dispersal. In addition, establishment of viable populations requires that individuals find suitable resources in novel habitats. Thus, species with greater dispersal ability, reproductive rate and ecological generalization should be more likely to expand into new regions under climate change. Here, we assess current evidence for the relationship between leading-edge range shifts and species' traits. We found expected relationships for several datasets, including diet breadth in North American Passeriformes and egg-laying habitat in British Odonata. However, models generally had low explanatory power. Thus, even statistically and biologically meaningful relationships are unlikely to be of predictive utility for conservation and management. Trait-based range shift forecasts face several challenges, including quantifying relevant natural history variation across large numbers of species and coupling these data with extrinsic factors such as habitat fragmentation and availability.

1. The size individuals attain reflects complex interactions between food availability and quality, environmental conditions and ecological interactions. A statistical interaction between temperature and the density of conspecifics is expected to arise from various ecological dynamics, including bioenergetic constraints, if population density affects mean consumption rate or activity level. Density effects on behaviour or size-selective predation could also generate this pattern. This interaction plays an important role in bioenergetic models, in particular, and yet has not been documented in natural populations. 2. The lengths of 131 286 juvenile wild Chinook salmon (Oncorhynchus tshawytscha) across 13 populations spread throughout the Salmon River Basin, Idaho, USA over 15 years were compared to test whether juvenile density alters the relationship between body size and temperature. 3. Strong evidence for a negative interaction between mean summer temperature and density emerged, despite the relatively cool temperatures in this high elevation habitat. Growth correlated positively with temperature at lower densities, but the correlation was negative at the highest densities. 4. This is the first study to document this interaction at such a large spatial and temporal scale, and suggests that warmer temperatures might intensify some density-dependent processes. How climate change will affect individual growth rates in these populations will depend intimately on ecological conditions, particularly food availability and population dynamics. More broadly, the conditions that led to the interactions observed in our study - limited food availability and temperatures that ranged above those optimal for growth - likely exist for many other natural populations, and warrant broader exploration.

Salmon life histories are finely tuned to local environmental conditions, which are intimately linked to climate. We summarize the likely impacts of climate change on the physical environment of salmon in the Pacific Northwest and discuss the potential evolutionary consequences of these changes, with particular reference to Columbia River Basin spring/summer Chinook (Oncorhynchus tshawytscha) and sockeye (Oncorhynchus nerka) salmon. We discuss the possible evolutionary responses in migration and spawning date egg and juvenile growth and development rates, thermal tolerance, and disease resistance. We know little about ocean migration pathways, so cannot confidently suggest the potential changes in this life stage. Climate change might produce conflicting selection pressures in different life stages, which will interact with plastic (i.e. nongenetic) changes in various ways. To clarify these interactions, we present a conceptual model of how changing environmental conditions shift phenotypic optima and, through plastic responses, phenotype distributions, affecting the force of selection. Our predictions are tentative because we lack data on the strength of selection, heritability, and ecological and genetic linkages among many of the traits discussed here. Despite the challenges involved in experimental manipulation of species with complex life histories, such research is essential for full appreciation of the biological effects of climate change.

Dams designed for hydropower and other purposes alter the environments of many economically important fishes, including Chinook salmon (Oncorhynchus tshawytscha). We estimated that dams on the Rogue River, the Willamette River, the Cowlitz River, and Fall Creek decreased water temperatures during summer and increased water temperatures during fall and winter. These thermal changes undoubtedly impact the behavior, physiology, and life histories of Chinook salmon. For example, relatively high temperatures during the fall and winter should speed growth and development, leading to early emergence of fry. Evolutionary theory provides tools to predict selective pressures and genetic responses caused by this environmental warming. Here, we illustrate this point by conducting a sensitivity analysis of the fitness consequences of thermal changes caused by dams, mediated by the thermal sensitivity of embryonic development. Based on our model, we predict Chinook salmon likely suffered a decrease in mean fitness after the construction of a dam in the Rogue River. Nevertheless, these demographic impacts might have resulted in strong selection for compensatory strategies, such as delayed spawning by adults or slowed development by embryos. Because the thermal effects of dams vary throughout the year, we predict dams impacted late spawners more than early spawners. Similar analyses could shed light on the evolutionary consequences of other environmental perturbations and their interactions.

Habitat conditions mediate the effects of climate, so neighboring populations with differing habitat conditions may differ in their responses to climate change. We have previously observed that juvenile survival in Snake River spring/summer Chinook salmon is strongly correlated with summer temperature in some populations and with fall streamflow in others. Here, we explore potential differential responses of the viability of four of these populations to changes in streamflow and temperature that might result from climate change. First, we linked predicted changes in air temperature and precipitation from several General Circulation Models to a local hydrological model to project streamflow and air temperature under two climate-change scenarios. Then, we developed a stochastic, density-dependent life-cycle model with independent environmental effects in juvenile and ocean stages, and parameterized the model for each population. We found that mean abundance decreased 20-50% and the probability of quasi-extinction increased dramatically (from 0.1-0.4 to 0.3-0.9) for all populations in both scenarios. Differences between populations were greater in the more moderate climate scenario than in the more extreme, hot/dry scenario. Model results were relatively robust to realistic uncertainty in freshwater survival parameters in all scenarios. Our results demonstrate that detailed population models can usefully incorporate climate-change predictions, and that global warming poses a direct threat to freshwater stages in these fish, increasing their risk of extinction. Because differences in habitat may contribute to the individualistic population responses we observed, we infer that maintaining habitat diversity will help buffer some species from the impacts of climate change.

The geographic ranges of most species are expected to shift to higher elevations and latitudes in response to global warming. But species react to specific environmental changes in individualistic ways, and we are far from a detailed understanding of range-shifts. Summer temperature often limits the ranges of insects and plants, so many range-shifts are expected to track summer warming. I explore this potential range-limiting factor in a case study of a northwardly expanding American butterfly, Atalopedes campestris (Lepidoptera, Hesperiidae). This species has recently colonized the Pacific Northwest, USA, where the mean annual temperature has risen 0.8-1.8 degrees C over the past 100 years. Using field transplant experiments across the current range edge, I measured development time, survivorship, fecundity and predation rates along a naturally occurring thermal gradient of 3 degrees C. Development time was significantly slower outside the current range in eastern Washington (WA), as expected because of cooler temperatures there. Slower development would reduce the number of generations possible per year outside the current range, dramatically lowering the probability that a population could survive there. Differences in survivorship, fecundity and predation rate across the range edge were not significant. The interaction between summer and winter temperature appears to be crucial in defining the current range limit. The estimated difference in temperature required to affect the number of generations is greater than the extent of summer warming observed over the past century, however, and thus historically winter temperature alone probably limited the range in southeastern WA. Nonetheless, extraordinarily warm summers may have improved colonization success, increasing the probability of a range expansion. These results suggest that extreme climatic events may influence rates of response to long-term climate change. They also demonstrate that range-limiting factors can change over time, and that the asymmetry in seasonal warming trends will have biological consequences.

As the climate warms, many species are moving to higher latitudes and elevations. However, range shifts can be caused by many factors. These factors are unknown in most cases. The specific role of climate in these dynamics needs study to better predict future consequences of global warming. This case study evaluates whether warming is driving the northward range expansion of a skipper butterfly (Atalopedes campestris). Recently colonized areas have warmed 2–4°C over the past 50 years. To assess the importance of climate change for population persistence in these areas, I compared population dynamics at two locations (at the current range edge and just inside the range) that differ by 2–3°C. Population growth rate at these two locations over two years was positively correlated with January mean and annual mean temperatures. To determine whether larval overwinter survivorship could explain this correlation, I transplanted larvae over winter to both sites. Larval survivorship was very low at both locations, ...

Two major approaches address the need to predict species distributions in response to environmental changes. Correlative models estimate parameters phenomenologically by relating current distributions to environmental conditions. By contrast, mechanistic models incorporate explicit relationships between environmental conditions and organismal performance, estimated independently of current distributions. Mechanistic approaches include models that translate environmental conditions into biologically relevant metrics (e.g. potential duration of activity), models that capture environmental sensitivities of survivorship and fecundity, and models that use energetics to link environmental conditions and demography. We compared how two correlative and three mechanistic models predicted the ranges of two species: a skipper butterfly (Atalopedes campestris) and a fence lizard (Sceloporus undulatus). Correlative and mechanistic models performed similarly in predicting current distributions, but mechanistic models predicted larger range shifts in response to climate change. Although mechanistic models theoretically should provide more accurate distribution predictions, there is much potential for improving their flexibility and performance.

Two major approaches address the need to predict species distributions in response to environmental changes. Correlative models estimate parameters phenomenologically by relating current distributions to environmental conditions. By contrast, mechanistic models incorporate explicit relationships between environmental conditions and organismal performance, estimated independently of current distributions. Mechanistic approaches include models that translate environmental conditions into biologically relevant metrics (e.g. potential duration of activity), models that capture environmental sensitivities of survivorship and fecundity, and models that use energetics to link environmental conditions and demography. We compared how two correlative and three mechanistic models predicted the ranges of two species: a skipper butterfly (Atalopedes campestris) and a fence lizard (Sceloporus undulatus). Correlative and mechanistic models performed similarly in predicting current distributions, but mechanistic models predicted larger range shifts in response to climate change. Although mechanistic models theoretically should provide more accurate distribution predictions, there is much potential for improving their flexibility and performance.

Hundreds of species are shifting their ranges in response to recent climate warming. To predict how continued climate warming will affect the potential, or “bioclimatic range,” of a skipper butterfly, we present a population‐dynamic model of range shift in which population growth is a function of temperature. We estimate the parameters of this model using previously published data for Atalopedes campestris. Summer and winter temperatures affect population growth rate independently in this species and therefore interact as potential range‐limiting factors. Our model predicts a two‐phase response to climate change; one range‐limiting factor gradually becomes dominant, even if warming occurs steadily along a thermally linear landscape. Whether the range shift accelerates or decelerates and whether the number of generations per year at the range edge increases or decreases depend on whether summer or winter warms faster. To estimate the uncertainty in our predictions of range shift, we use a parametric bootstrap of biological parameter values. Our results show that even modest amounts of data yield predictions with reasonably small confidence intervals, indicating that ecophysiological models can be useful in predicting range changes. Nevertheless, the confidence intervals are sensitive to regional differences in the underlying thermal landscape and the warming scenario.