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Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change—rather than accurately predict specific outcomes—it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change. [ABSTRACT FROM AUTHOR]

An abundance of studies in marine systems have documented species range shifts in response to climate change, and many more have used species distribution models to project species ranges under future conditions. However, there is increasing interest in moving beyond a single‐species focus to understand how species redistribution alters ecosystem dynamics via changes in trophic interactions. We employed spatiotemporal models to characterize decadal‐scale changes in spatial overlap between the distribution of juvenile walleye pollock Gadus chalcogrammus and the distributions of four of its groundfish predators: arrowtooth flounder Atheresthes stomias, Pacific cod Gadus macrocephalus, Pacific halibut Hippoglossus stenolepis and adult walleye pollock. These fishes represent ecologically and commercially important species in a rapidly changing sub‐Arctic ecosystem, the eastern Bering Sea, Alaska, USA. We then examined whether changes in spatial overlap corresponded to changes in predation, using spatiotemporal models of predator stomach contents. We found marked shifts in spatial overlap between juvenile pollock and two predators (arrowtooth flounder and Pacific halibut) over 34 years, with changes in overlap corresponding to increases in population‐scale predation pressure. By contrast, we did not find clear relationships between spatial overlap and predation for Pacific cod and adult pollock, the two predators for which juvenile pollock constitute a much smaller diet proportion. Our findings highlight the complexity of predicting predation dynamics for generalist marine species and suggest a need for better process‐based methods for understanding the potential future ecological impacts of coupled species range shifts. However, simple metrics of spatial overlap between relatively specialized predators and their prey offer promise as a means to integrate predictions from species distribution models into ecosystem‐based fisheries management. [ABSTRACT FROM AUTHOR]

Accurate forecasts of how animals respond to climate‐driven environmental change are needed to prepare for future redistributions, however, it is unclear which temporal scales of environmental variability give rise to predictability of species distributions. We examined the temporal scales of environmental variability that best predicted spatial abundance of a marine predator, swordfish Xiphias gladius, in the California Current. To understand which temporal scales of environmental variability provide biological predictability, we decomposed physical variables into three components: a monthly climatology (long‐term average), a low frequency component representing interannual variability, and a high frequency (sub‐annual) component that captures ephemeral features. We then assessed each component's contribution to predictive skill for spatially‐explicit swordfish catch. The monthly climatology was the primary source of predictability in swordfish spatial catch, reflecting the spatial distribution associated with seasonal movements in this region. Importantly, we found that the low frequency component (capturing interannual variability) provided significant skill in predicting anomalous swordfish distribution and catch, which the monthly climatology cannot. The addition of the high frequency component added only minor improvement in predictability. By examining models' ability to predict species distribution anomalies, we assess the models in a way that is consistent with the goal of distribution forecasts – to predict deviations of species distributions from their average historical locations. The critical importance of low frequency climate variability in describing anomalous swordfish distributions and catch matches the target timescales of physical climate forecasts, suggesting potential for skillful ecological forecasts of swordfish distributions across short (seasonal) and long (climate) timescales. Understanding sources of prediction skill for species environmental responses gives confidence in our ability to accurately predict species distributions and abundance, and to know which responses are likely less predictable, under future climate change. This is important as climate change continues to cause an unprecedented redistribution of life on Earth. [ABSTRACT FROM AUTHOR]

Background: Habitat suitability models give insight into the ecological drivers of species distributions and are increasingly common in management and conservation planning. Telemetry data can be used in habitat models to describe where animals were present, however this requires the use of presence-only modeling approaches or the generation of 'pseudo-absences' to simulate locations where animals did not go. To highlight considerations for generating pseudo-absences for telemetry-based habitat models, we explored how different methods of pseudo-absence generation affect model performance across species' movement strategies, model types, and environments. Methods: We built habitat models for marine and terrestrial case studies, Northeast Pacific blue whales (Balaenoptera musculus) and African elephants (Loxodonta africana). We tested four pseudo-absence generation methods commonly used in telemetry-based habitat models: (1) background sampling; (2) sampling within a buffer zone around presence locations; (3) correlated random walks beginning at the tag release location; (4) reverse correlated random walks beginning at the last tag location. Habitat models were built using generalised linear mixed models, generalised additive mixed models, and boosted regression trees. Results: We found that the separation in environmental niche space between presences and pseudo-absences was the single most important driver of model explanatory power and predictive skill. This result was consistent across marine and terrestrial habitats, two species with vastly different movement syndromes, and three different model types. The best-performing pseudo-absence method depended on which created the greatest environmental separation: background sampling for blue whales and reverse correlated random walks for elephants. However, despite the fact that models with greater environmental separation performed better according to traditional predictive skill metrics, they did not always produce biologically realistic spatial predictions relative to known distributions. Conclusions: Habitat model performance may be positively biased in cases where pseudo-absences are sampled from environments that are dissimilar to presences. This emphasizes the need to carefully consider spatial extent of the sampling domain and environmental heterogeneity of pseudo-absence samples when developing habitat models, and highlights the importance of scrutinizing spatial predictions to ensure that habitat models are biologically realistic and fit for modeling objectives. [ABSTRACT FROM AUTHOR]

Climate change is modifying species distributions around the world, forcing some species poleward, where they can alter trophic interactions. Many tropical herbivorous fishes have successfully expanded their ranges into temperate ecosystems, and while it is clear they drive increases in herbivory rates in specific localities, little is known about how they might affect the diversity of herbivory functions across large spatial scales, considering their interaction with assemblages of native herbivores in temperate habitats.We assessed the spatial overlap and habitat associations of native temperate and range‐expanding tropical herbivorous fishes in six subregions of south‐western Australia to determine how incursions of tropical species may have affected the diversity, redundancy (index of uniqueness) and the 'spatial extent' (addition of functions in new areas) and 'intensity' (increasing density of functional groups) of specific herbivory functions in recipient ecosystems.Tropical herbivores had high abundances in temperate ecosystems, forming schools from 40 (parrotfish) to 200 (rabbitfish) individuals strongly associated with seagrass meadows and reefs with high cover of turf algae. Overlap with temperate herbivores was highest in the northern subregions, forming unique assemblages, with no apparent species displacements. The addition of tropical species increased functional diversity and uniqueness (the complement of redundancy), introducing novel herbivory functions to many locations. Seagrass browsing increased in spatial extent (27%) and intensity (15×), while seaweed browsing and grazing increased in intensity by up to 2.5× in regions with high abundances of tropical herbivores.Our results suggest that the diversity, intensity and spatial extent of different herbivory functions can change as tropical species with different habitat affinities, behaviours and diets shift their distributions poleward. Changes in functional redundancy are likely to be heterogeneous in space and might not increase initially because the diversity of herbivory functions is relatively low in some temperate marine ecosystems. However, there is the potential for greater redundancy as further tropical species arrive, their abundances increase and the spatial and functional overlap of communities rises. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Fishing communities are increasingly required to adapt to environmentally driven changes in the availability of fish stocks. Here, we examined trends in the distribution and biomass of five commercial target species (dover sole, thornyheads, sablefish, lingcod, and petrale sole) on the US west coast to determine how their availability to fishing ports changed over 40 years. We show that the timing and magnitude of stock declines and recoveries are not experienced uniformly along the coast when they coincide with shifts in species distributions. For example, overall stock availability of sablefish was more stable in southern latitudes where a 40% regional decline in biomass was counterbalanced by a southward shift in distribution of >200 km since 2003. Greater vessel mobility and larger areal extent of fish habitat along the continental shelf buffered northerly ports from latitudinal changes in stock availability. Landings were not consistently related to stock availability, suggesting that social, economic, and regulatory factors likely constrain or facilitate the capacity for fishers to adapt to changes in fish availability. Coupled social–ecological analyses such as the one presented here are important for defining community vulnerability to current and future changes in the availability of important marine species. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are a common approach to describing species' space‐use and spatially‐explicit abundance. With a myriad of model types, methods and parameterization options available, it is challenging to make informed decisions about how to build robust SDMs appropriate for a given purpose. One key component of SDM development is the appropriate parameterization of covariates, such as the inclusion of covariates that reflect underlying processes (e.g. abiotic and biotic covariates) and covariates that act as proxies for unobserved processes (e.g. space and time covariates). It is unclear how different SDMs apportion variance among a suite of covariates, and how parameterization decisions influence model accuracy and performance. To examine trade‐offs in covariation parameterization in SDMs, we explore the attribution of spatiotemporal and environmental variation across a suite of SDMs. We first used simulated species distributions with known environmental preferences to compare three types of SDM: a machine learning model (boosted regression tree), a semi‐parametric model (generalized additive model) and a spatiotemporal mixed‐effects model (vector autoregressive spatiotemporal model, VAST). We then applied the same comparative framework to a case study with three fish species (arrowtooth flounder, pacific cod and walleye pollock) in the eastern Bering Sea, USA. Model type and covariate parameterization both had significant effects on model accuracy and performance. We found that including either spatiotemporal or environmental covariates typically reproduced patterns of species distribution and abundance across the three models tested, but model accuracy and performance was maximized when including both spatiotemporal and environmental covariates in the same model framework. Our results reveal trade‐offs in the current generation of SDM tools between accurately estimating species abundance, accurately estimating spatial patterns, and accurately quantifying underlying species–environment relationships. These comparisons between model types and parameterization options can help SDM users better understand sources of model bias and estimate error. [ABSTRACT FROM AUTHOR]

Background: Studies that attempt to measure shifts in species distributions often consider a single species in isolation. However, understanding changes in spatial overlap between predators and their prey might provide deeper insight into how species redistribution affects food web dynamics. Predator–prey overlap metrics: Here, we review a suite of 10 metrics [range overlap, area overlap, the local index of collocation (Pianka's O), Hurlbert's index, biomass‐weighted overlap, asymmetrical alpha, Schoener's D, Bhattacharyya's coefficient, the global index of collocation and the AB ratio] that describe how two species overlap in space, using concepts such as binary co‐occurrence, encounter rates, spatial niche similarity, spatial independence, geographical similarity and trophic transfer. We describe the specific ecological insights that can be gained using each overlap metric, in order to determine which is most appropriate for describing spatial predator–prey interactions for different applications. Simulation and case study: We use simulated predator and prey distributions to demonstrate how the 10 metrics respond to variation in three types of predator–prey interactions: changing spatial overlap between predator and prey, changing predator population size and changing patterns of predator aggregation in response to prey density. We also apply these overlap metrics to a case study of a predatory fish (arrowtooth flounder, Atheresthes stomias) and its prey (juvenile walleye pollock, Gadus chalcogrammus) in the Eastern Bering Sea, AK, USA. We show how the metrics can be applied to understand spatial and temporal variation in the overlap of species distributions in this rapidly changing Arctic ecosystem. Conclusions: Using both simulated and empirical data, we provide a roadmap for ecologists and other practitioners to select overlap metrics to describe particular aspects of spatial predator–prey interactions. We outline a range of research and management applications for which each metric may be suited. [ABSTRACT FROM AUTHOR]