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Aim Changes in abundance and shifts in distribution as a result of a warming climate have been documented for many marine species, but opportunities to test our ability to forecast such changes have been limited. This study evaluates the ability of habitat‐based density models to accurately forecast cetacean abundance and distribution during a novel year with unprecedented warm ocean temperatures caused by a sustained marine heatwave. Location California Current Ecosystem, USA. Methods We constructed generalized additive models based on cetacean sighting and environmental data from 1991 to 2009 for eight species with a diverse range of habitat associations. Models were built with three different sets of predictor variables to compare performance. Models were then used to forecast species abundance and distribution patterns during 2014, a year with anomalously warm ocean temperatures. Cetacean sighting data collected during 2014 were used to assess model forecasts. Results Ratios of model‐predicted abundance to observed abundance were close to 1:1 for all but one species and accurately captured changes in the number of animals in the study area during the anomalous year. Predicted distribution patterns also showed good concordance with the 2014 survey observations. Our results indicate that habitat relationships were captured sufficiently to predict both changes in abundance and shifts in distribution when conditions warmed, for both cool‐ and warm‐temperate species. Main conclusions Models built with multidecadal datasets were able to forecast abundance and distribution in a novel warm year for a diverse set of cetacean species. Models with the best explanatory power did not necessarily have the best predictive power. Also, they revealed species‐specific responses to warming ocean waters. Results have implications for modelling effects of climate change on cetaceans and other marine predators.

Connectivity among marine populations is critical for persistence of metapopulations, coping with climate change, and determining the geographic distribution of species. The influence of pelagic larval duration (PLD) on connectivity has been studied extensively, but relatively little is known about the influence of other biological parameters, such as the survival and behavior of larvae, and the fecundity of adults, on population connectivity. Furthermore, the interaction between the seascape (habitat structure and currents) and these biological parameters is unclear. We explore these interactions using a biophysical model of larval dispersal across the Indo-Pacific. We describe an approach that quantifies geographic patterns of connectivity from demographically relevant to evolutionarily significant levels across a range of species. We predict that at least 95% of larval settlement occurs within 155 km of the source population and within 13 days irrespective of the species' life history, yet long-distant connections remain likely. Self-recruitment is primarily driven by the local oceanography, larval mortality, and the larval precompetency period, whereas broad-scale connectivity is strongly influenced by reproductive output (abundance and fecundity of adults) and the length of PLD. The networks we have created are geographically explicit models of marine connectivity that define dispersal corridors, barriers, and the emergent structure of marine populations. These models provide hypotheses for empirical testing.

We integrated coral reef connectivity data for the Caribbean and Gulf of Mexico into a conservation decision-making framework for designing a regional scale marine protected area (MPA) network that provides insight into ecological and political contexts. We used an ocean circulation model and regional coral reef data to simulate eight spawning events from 2008-2011, applying a maximum 30-day pelagic larval duration and 20% mortality rate. Coral larval dispersal patterns were analyzed between coral reefs across jurisdictional marine zones to identify spatial relationships between larval sources and destinations within countries and territories across the region. We applied our results in Marxan, a conservation planning software tool, to identify a regional coral reef MPA network design that meets conservation goals, minimizes underlying threats, and maintains coral reef connectivity. Our results suggest that approximately 77% of coral reefs identified as having a high regional connectivity value are not included in the existing MPA network. This research is unique because we quantify and report coral larval connectivity data by marine ecoregions and Exclusive Economic Zones (EZZ) and use this information to identify gaps in the current Caribbean-wide MPA network by integrating asymmetric connectivity information in Marxan to design a regional MPA network that includes important reef network connections. The identification of important reef connectivity metrics guides the selection of priority conservation areas and supports resilience at the whole system level into the future.

Vessel strikes are a critical threat to endangered North Atlantic right whales ( Eubalaena glacialis ), significantly contributing to their elevated mortality. Accurate estimates of these mortality rates are essential for developing effective management strategies to aid in the species’ recovery. This study enhances existing vessel strike models by incorporating detailed regional data on vessel traffic characteristics as well as whale distribution and behavior. Our model assesses the spatial and temporal variability in vessel strike risk along the U.S. east coast apportioned into three vessel length classes (26–65 feet, 65–350 feet, > 350 feet). By including regional right whale depth distributions and parameterizing potential whale avoidance based on factors such as descent rate, bottom depth, and vessel speed and size, the model provides a refined estimation of mortality risk. We also address the underrepresentation of smaller vessel activity via a correction factor, offering a more accurate annual mortality risk estimate for each vessel size class. These findings highlight that vessels > 350 feet in length pose the greatest risk to right whales. Simulations of reduced vessel speeds indicate that speed measures can mitigate mortality rates; however, residual risk remains even at speeds of 10 knots or less suggesting limitations to this mitigation approach.

In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels distribution and by extension, of marine top predator distributions. More proximal variables, such as potential prey fields, could refine relationships between top predator distributions and their environment. In situ data on prey distributions are not available over large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM), provides simulations of the biomass and production of zooplankton and six functional groups of micronekton at the global scale. Here, we explored whether generalised additive models fitted to simulated prey distribution data better predicted deep-diver densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus ) than models fitted to environmental variables. We assessed whether the combination of environmental and prey distribution data would further improve model fit by comparing their explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would simulate larger prey beyond 1,000 m would probably better characterise the prey of deep-diving cetaceans.

Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises (Phocoena phocoena), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions. Incorporating more than 18 months of harbour porpoise detection data from passive acoustic monitoring, generalized auto-regressive moving average and generalized additive models were used to investigate harbour porpoise occurrence within and around the Maryland WEA in relation to temporal and environmental variables. Acoustic detection metrics were compared to habitat-based density estimates derived from aerial and boat-based sightings to validate the model predictions. Harbour porpoises occurred significantly more frequently during January to May, and foraged significantly more often in the evenings to early mornings at sites within and outside the Maryland WEA. Harbour porpoise occurrence peaked at sea surface temperatures of 5°C and chlorophyll a concentrations of 4.5 to 7.4 mg m-3. The acoustic detections were significantly correlated with the predicted densities, except at the most inshore site. This study provides insight into previously unknown fine-scale spatial and temporal patterns in distribution of harbour porpoises offshore of Maryland. The results can be used to help inform future monitoring and mitigate the impacts of windfarm construction and other human activities.

The purpose of many wildlife population studies is to estimate density, movement, or demographic parameters. Linking these parameters to covariates, such as habitat features, provides additional ecological insight and can be used to make predictions for management purposes. Line‐transect surveys, combined with distance sampling methods, are often used to estimate density at discrete points in time, whereas capture–recapture methods are used to estimate movement and other demographic parameters. Recently, open population spatial capture–recapture models have been developed, which simultaneously estimate density and demographic parameters, but have been made available only for data collected from a fixed array of detectors and have not incorporated the effects of habitat covariates. We developed a spatial capture–recapture model that can be applied to line‐transect survey data by modeling detection probability in a manner analogous to distance sampling. We extend this model to a) estimate demographic parameters using an open population framework and b) model variation in density and space use as a function of habitat covariates. The model is illustrated using simulated data and aerial line‐transect survey data for North Atlantic right whales in the southeastern United States, which also demonstrates the ability to integrate data from multiple survey platforms and accommodate differences between strata or demographic groups. When individuals detected from line‐transect surveys can be uniquely identified, our model can be used to simultaneously make inference on factors that influence spatial and temporal variation in density, movement, and population dynamics. We developed an open spatial capture–recapture model that can be applied to line‐transect survey data. The model estimates demographic parameters while simultaneously providing inference on factors that influence spatial and temporal variation in density and space use. We illustrate its use with aerial survey data for North Atlantic right whales (Eubalaena glacialis) in the southeastern United States.

Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.

Fisheries bycatch is a recognized threat to marine megafauna. Addressing bycatch of pelagic species however is challenging owing to the dynamic nature of marine environments and vagility of these organisms. In order to assess the potential for species to overlap with fisheries, we propose applying dynamic habitat models to determine relative probabilities of species occurrence for specific oceanographic conditions. We demonstrate this approach by modelling habitats for Laysan (Phoebastria immutabilis) and black-footed albatrosses (Phoebastria nigripes) using telemetry data and relating their occurrence probabilities to observations of Hawaii-based longline fisheries in 1997–2000. We found that modelled habitat preference probabilities of black-footed albatrosses were high within some areas of the fishing range of the Hawaiian fleet and such preferences were important in explaining bycatch occurrence. Conversely, modelled habitats of Laysan albatrosses overlapped little with Hawaii-based longline fisheries and did little to explain the bycatch of this species. Estimated patterns of albatross habitat overlap with the Hawaiian fleet corresponded to bycatch observations: black-footed albatrosses were more frequently caught in this fishery despite being 10 times less abundant than Laysan albatrosses. This case study demonstrates that dynamic habitat models based on telemetry data may help to project interactions with pelagic animals relative to environmental features and that such an approach can serve as a tool to guide conservation and management decisions.

As human activities expand beyond national jurisdictions to the high seas, there is an increasing need to consider anthropogenic impacts to species inhabiting these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population-level impacts of these activities. We developed plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extended from a well-surveyed region within the U.S. Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa with available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models to reduce the extent of extrapolation beyond covariate ranges, and constrained them to model simple and generalizable relationships. To evaluate confidence in the predictions, we mapped where predictions were made outside sampled covariate ranges, examined alternate models, and compared predicted densities with maps of sightings from sources that could not be integrated into our models. Confidence levels in model results depended on the taxon and geographic area and highlighted the need for additional surveying in environmentally distinct areas. With application of necessary caution, our density estimates can inform management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, and deep-sea mining and be used to delineate areas of special biological significance in international waters. Our approach is generally applicable to other marine taxa and geographic regions for which management will be implemented but data are sparse. Conforme las actividades humanas se expanden mán allá de las jurisdicciones nacionales hacia alta mar, existe una necesidad creciente de considerar los impactos antropogénicos sobre las especies que habitan estas aguas. La carencia de observaciones científicas de cetáceos en alta mar impide la evaluación de los impactos a nivel poblacional de estas actividades. Desarrollamos estimaciones plausibles de densidad para facilitar una evaluación cuantitativa de los impactos antropogénicos sobre las poblaciones de cetáceos en estas aguas. Nuestra región de estudio se extendió desde una región bien estudiada dentro de la Zona Económica Exclusiva de los E.U.A. hasta una región en el oeste del Atlántico Norte con pocos censos sobre cetáceos. Modelamos las densidades de 15 taxones de cetáceos con datos de censos con transecto de línea disponible y covariables de hábitat, y extrapolamos las predicciones a regiones poco estudiadas. Formulamos los modelos para reducir la extensión de la extrapolación más allá de los rangos covariados y los restringimos para modelar relaciones simples y generalizables. Para evaluar la confianza de las predicciones mapeamos dónde las predicciones se hicieron fuera de las extensiones covariadas muestreadas, examinamos los modelos alternativos, y comparamos las densidades pronosticadas con los mapas de los avistamientos a partir de fuentes que no podían ser integradas a nuestro modelo. Los niveles de confianza en los resultados de los modelos dependieron del taxón y el área geográfica y resaltaron la necesidad de censos adicionales en áreas distintas ambientalmente. Con la aplicación de la cautela necesaria, nuestras estimaciones de densidad pueden informar a las necesidades de manejo en alta mar, como la cuantificación de las interacciones potenciales de cetáceos con los ejercicios de entrenamiento militar, embarcaciones, pesquerías, y la minería de aguas profundas; también puede usarse para delinear las áreas de importancia biológica especial en las aguas internacionales. Nuestra estrategia es aplicable generalmente a otros taxones marinos y regiones geográficas para las cuales el manejo va a ser implementado pero los datos son escasos.