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Aim Advances in ecological and environmental modelling offer new opportunities for estimating dynamic habitat suitability for highly mobile species and supporting management strategies at relevant spatiotemporal scales. We used an ensemble modelling approach to predict daily, year‐round habitat suitability for a migratory species, the blue whale (Balaenoptera musculus), and demonstrate an application for evaluating the spatiotemporal dynamics of their exposure to ship strike risk. Location The California Current Ecosystem (CCE) and the Southern California Bight (SCB), USA. Methods We integrated a long‐term (1994–2008) satellite tracking dataset on 104 blue whales with data‐assimilative ocean model output to assess year‐round habitat suitability. We evaluated the relative utility of ensembling multiple model types compared to using single models, and selected and validated candidate models using multiple cross‐validation metrics and independent observer data. We quantified the spatial and temporal distribution of exposure to ship strike risk within shipping lanes in the SCB. Results Multi‐model ensembles outperformed single‐model approaches. The final ensemble model had high predictive skill (AUC = 0.95), resulting in daily, year‐round predictions of blue whale habitat suitability in the CCE that accurately captured migratory behaviour. Risk exposure in shipping lanes was highly variable within and among years as a function of environmental conditions (e.g., marine heatwave). Main conclusions Daily information on three‐dimensional oceanic habitats was used to model the daily distribution of a highly migratory species with high predictive power and indicated that management strategies could benefit by incorporating dynamic environmental information. This approach is readily transferable to other species. Dynamic, high‐resolution species distribution models are valuable tools for assessing risk exposure and targeting management needs.

Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members. Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally. Forecasting ecology can support proactive decision-making in the face of uncertain environmental conditions. Using case studies on whale entanglement and sea turtle bycatch, this study showcases the capacity for existing management tools to transition to a forecast configuration and provide skilful forecasts up to 12 months in advance.

Marine heatwaves cause widespread environmental, biological, and socio-economic impacts, placing them at the forefront of 21st-century management challenges. However, heatwaves vary in intensity and evolution, and a paucity of information on how this variability impacts marine species limits our ability to proactively manage for these extreme events. Here, we model the effects of four recent heatwaves (2014, 2015, 2019, 2020) in the Northeastern Pacific on the distributions of 14 top predator species of ecological, cultural, and commercial importance. Predicted responses were highly variable across species and heatwaves, ranging from near total loss of habitat to a two-fold increase. Heatwaves rapidly altered political bio-geographies, with up to 10% of predicted habitat across all species shifting jurisdictions during individual heatwaves. The variability in predicted responses across species and heatwaves portends the need for novel management solutions that can rapidly respond to extreme climate events. As proof-of-concept, we developed an operational dynamic ocean management tool that predicts predator distributions and responses to extreme conditions in near real-time. This study examines the effect of four marine heatwaves in the Northeast Pacific on the distributions of 14 top predators, revealing a wide-array of predator responses both among and within heatwaves. Predator responses were highly predictable, demonstrating capacity for early warning systems of heatwave impacts, similar to weather forecasts.

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.

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.

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest. Species distribution models (SDMs) were developed for a taxonomically diverse suite of cetaceans using a robust set of systematic survey data within the California Current Ecosystem. Both boosted regression tress and generalized additive models were developed and their explanatory and predictive performance compared in light of features such as species distribution characteristics. Results have direct relevance for understanding the accuracy of SDMs and associated implications for marine management and conservation.

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modelling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks Isurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (<100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models.

Time-area closures are a valuable tool for mitigating fisheries bycatch. There is increasing recognition that dynamic closures, which have boundaries that vary across space and time, can be more effective than static closures at protecting mobile species in dynamic environments. We created a management strategy evaluation to compare static and dynamic closures in a simulated fishery based on the California drift gillnet swordfish fishery, with closures aimed at reducing bycatch of leatherback turtles. We tested eight operating models that varied swordfish and leatherback distributions, and within each evaluated the performance of three static and five dynamic closure strategies. We repeated this under 20 and 50% simulated observer coverage to alter the data available for closure creation. We found that static closures can be effective for reducing bycatch of species with more geographically associated distributions, but to avoid redistributing bycatch the static areas closed should be based on potential (not just observed) bycatch. Only dynamic closures were effective at reducing bycatch for more dynamic leatherback distributions, and they generally reduced bycatch risk more than they reduced target catch. Dynamic closures were less likely to redistribute fishing into rarely fished areas, by leaving open pockets of lower risk habitat, but these closures were often fragmented which would create practical challenges for fishers and managers and require a mobile fleet. Given our simulation’s catch rates, 20% observer coverage was sufficient to create useful closures and increasing coverage to 50% added only minor improvement in closure performance. Even strict static or dynamic closures reduced leatherback bycatch by only 30–50% per season, because the simulated leatherback distributions were broad and open areas contained considerable bycatch risk. Perfect knowledge of the leatherback distribution provided an additional 5–15% bycatch reduction over a dynamic closure with realistic predictive accuracy. This moderate level of bycatch reduction highlights the limitations of redistributing fishing effort to reduce bycatch of broadly distributed and rarely encountered species, and indicates that, for these species, spatial management may work best when used with other bycatch mitigation approaches. We recommend future research explores methods for considering model uncertainty in the spatial and temporal resolution of dynamic closures.

The mobile nature of migratory marine animals across jurisdictional boundaries can challenge the management of biodiversity, particularly under global environmental change. While projections of climate-driven habitat change can reveal whether marine species are predicted to gain or lose habitat in the future, geopolitical boundaries and differing governance regimes may influence animals’ abilities to thrive in new areas. Broad geographic movements and diverse governance approaches elicit the need for strong international collaboration to holistically manage and conserve these shared migratory species. In this study, we use data from the Tagging of Pacific Predators program to demonstrate the feasibility of using climate-driven habitat projections to assess species’ jurisdictional redistribution. Focusing on four species (shortfin mako shark, California sea lion, northern elephant seal, and sooty shearwater), we calculate the projected change in core habitat across jurisdictional boundaries throughout the century and highlight associated management implications. Using climate-driven habitat projections from the period of 2001 to 2010, and an RCP 8.5 climate scenario, we found that all four species are projected to face up to a 2.5-10% change in core habitat across jurisdictions in the Northeast Pacific, with the greatest gains of core habitat redistribution within the United States exclusive economic zone and in areas beyond national jurisdiction. Overall, our study demonstrates how efforts to understand the impacts of climate change on species’ habitat use should be expanded to consider how resulting shifts may provoke new management challenges in a legally bounded, yet physically borderless ocean. We discuss governance implications for transboundary habitat redistribution as highly migratory marine species potentially shift across legal jurisdictions, including new ocean areas beyond national judications, considerations which are applicable within and beyond this Pacific case study. Our study also highlights data needs and management strategies to inform high-level conservation strategies, as well as recommendations for using updated tagging data and climate models to build upon this approach in future work.

Agricultural contamination of groundwater in northwestern Mississippi, USA, has not been studied extensively, and subsurface fluxes of agricultural chemicals have been presumed minimal. To determine the factors controlling transport of nitrate-N into the Mississippi River Valley alluvial aquifer, a study was conducted from 2006 to 2008 to estimate fluxes of water and solutes for a site in the Bogue Phalia basin (1,250 km 2 ). Water-quality data were collected from a shallow water-table well, a vertical profile of temporary sampling points, and a nearby irrigation well. Nitrate was detected within 4.4 m of the water table but was absent in deeper waters with evidence of reducing conditions and denitrification. Recharge estimates from 6.2 to 10.9 cm/year were quantified using water-table fluctuations, a Cl – tracer method, and atmospheric age-tracers. A mathematical advection-reaction model predicted similar recharge to the aquifer, and also predicted that 15% of applied nitrogen is leached into the saturated zone. With current denitrification and application rates, the nitrate-N front is expected to remain in shallow groundwater, less than 6–9 m deep. Increasing application rates resulting from intensifying agricultural demands may advance the nitrate-N front to 16–23 m, within the zone of groundwater pumping.