Summary1. Management of highly migratory species is reliant on spatially and temporally explicit information on their distribution and abundance. Satellite telemetry provides time-series data on individual movements. However, these data are underutilized in management applications in part because they provide presence-only information rather than abundance information such as density. 2. Eastern North Pacific blue whales are listed as threatened, and ship strikes have been suggested as a key factor limiting their recovery. Here, we developed a satellite-telemetry-based habitat model in a case-control design for Eastern North Pacific blue whales Balaenoptera musculus that was combined with previously published abundance estimates to predict habitat preference and densities. Further, we operationalize an automated, near-real-time whale density prediction tool based on up-to-date environmental data for use by managers and other stakeholders. 3. A switching state-space movement model was applied to 104 blue whale satellite tracks from 1994 to 2008 to account for errors in the location estimates and provide daily positions (case points). We simulated positions using a correlated random walk model (control points) and sampled the environment at each case and control point. Generalized additive mixed models and boosted regression trees were applied to determine the probability of occurrence based on environmental covariates. Models were used to predict 8-day and monthly resolution, year-round density estimates scaled by population abundance estimates that provide a critical tool for understanding seasonal and interannual changes in habitat use. 4. The telemetry-based habitat model predicted known blue whale hot spots and had seasonal agreement with sightings data, highlighting the skill of the model for predicting blue whale habitat preference and density. We identified high interannual variability in occurrence emphasizing the benefit of dynamic models compared to multiyear averages. 5. Synthesis and applications. This near-real-time tool allows a more accurate examination of the year-round spatio-temporal overlap of blue whales with potentially harmful human activities, such as shipping. This approach should also be applicable to other species for which sufficient telemetry data are available. The dynamic predictive product developed here is an important tool that allows managers to consider finer-scale management areas that are more economically feasible and socially acceptable.

Fishery management measures to reduce interactions between fisheries and endangered or threatened species have typically relied on static time‐area closures. While these efforts have reduced interactions, they can be costly and inefficient for managing highly migratory species such as sea turtles. The NOAA TurtleWatch product was created in 2006 as a tool to reduce the rates of interactions of loggerhead sea turtles with shallow‐set longline gear deployed by the Hawaii‐based pelagic longline fishery targeting swordfish. TurtleWatch provides information on loggerhead habitat and can be used by managers and industry to make dynamic management decisions to potentially reduce incidentally capturing turtles during fishing operations. TurtleWatch is expanded here to include information on endangered leatherback turtles to help reduce incidental capture rates in the central North Pacific. Fishery‐dependent data were combined with fishing effort, bycatch and satellite tracking data of leatherbacks to characterize sea surface temperature (SST) relationships that identify habitat or interaction ‘hotspots’. Analysis of SST identified two zones, centered at 17.2° and 22.9°C, occupied by leatherbacks on fishing grounds of the Hawaii‐based swordfish fishery. This new information was used to expand the TurtleWatch product to provide managers and industry near real‐time habitat information for both loggerheads and leatherbacks. The updated TurtleWatch product provides a tool for dynamic management of the Hawaii‐based shallow‐set fishery to aid in the bycatch reduction of both species. Updating the management strategy to dynamically adapt to shifts in multi‐species habitat use through time is a step towards an ecosystem‐based approach to fisheries management in pelagic ecosystems.

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The utilization and capabilities of biotelemetry are expanding enormously as technology and access rapidly improve. These large, correlated datasets pose statistical challenges requiring advanced statistical techniques to appropriately interpret and model animal movement. We used satellite telemetry data of critically endangered Eastern Pacific leatherback turtles (Dermochelys coriacea) to develop a habitat‐based model of their motility (and conversely residence time) using a hierarchical Bayesian framework, which could be broadly applied across species. To account for the spatiotemporally auto‐correlated, unbalanced, and presence‐only telemetry observations, in combination with dynamic environmental variables, a novel modeling approach was applied. We expanded a Poisson generalized linear model in a continuous‐time discrete‐space (CTDS) model framework to predict individual leatherback movement based on environmental drivers, such as sea surface temperature. Population‐level movement estimates were then obtained with a Bayesian approach and used to create monthly, near real‐time predictions of Eastern Pacific leatherback movement in the South Pacific Ocean. This model framework will inform the development of a dynamic ocean management model, “South Pacific TurtleWatch (SPTW),” and could be applied to telemetry data from other populations and species to predict motility and residence times in dynamic environments, while accounting for statistical uncertainties arising at multiple stages of telemetry analysis.