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Species distribution models that predict species occurrence or density by quantifying relationships with environmental variables are used for a variety of scientific investigations and management applications. For endangered species, such as large whales, models help to understand the ecological factors influencing variability in distributions and to assess potential risk from shipping, fishing, and other human activities. Systematic surveys record species presence and absence, as well as the associated search effort, but are very expensive. Presence-only data consisting only of sightings can increase sample size, but may be biased in both geographical and niche space. We built generalized additive models (GAMs) using presence-absence sightings data and maximum entropy models (Maxent) using the same presence-absence sightings data, and also using presence-only sightings data, for four large whale species in the eastern tropical Pacific Ocean: humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), Bryde's (Balaenoptera edeni), and sperm whales (Physeter macrocephalus). Environmental variables were surface temperature, surface salinity, thermocline depth, stratification index, and seafloor depth. We compared predicted distributions from each of the two model types. Maxent and GAM model predictions based on systematic survey data are very similar, when Maxent absences are selected from the survey trackline data. However, we show that spatial bias in presence-only Maxent predictions can be caused by using pseudo-absences instead of observed absences and by the sampling biases of both opportunistic data and stratified systematic survey data with uneven coverage between strata. Predictions of uncommon large whale distributions from Maxent or other presence-only techniques may be useful for science or management, but only if spatial bias in the observations is addressed in the derivation and interpretation of model predictions.

The coastal waters of the Moray Firth in northeast Scotland (57º41'N 2º40'W) provide rich, inshore feeding grounds for minke whales (Balaenoptera acutorostrata) during the summer and autumnal months. In order to better understand the habitat selection, movements and feeding ecology of the animals utilising this North Sea region, distribution data from the southern coastline of the outer Moray Firth were subsequently examined with respect to the marine physiography of the area, specifically the environmental variables water depth, slope, aspect and sediment-type. A total of 305 minke whale encounterscollected from dedicated boat surveys conducted between May and October 2001 to 2006 inclusive -were used in the construction of a Geographic Information System (GIS) for the 860 square-km study site. The subsequent analysis revealed a strong spatial preference by whales in this location for water depths between 20 and 50 metres (mean 46.9 m, SD=30.9), steep slopes (mean 75.7 degrees, SD= 8.9), a northerly-facing aspect and sandy-gravel sediment type. Kruskal-Wallis tests for variance confirmed that the distribution of B. acutorostrata was significantly different across each of these physiographic features examined (P< 0.05). In particular, water depth and sediment type were shown to be highly correlated with the frequency of whales observed (Spearman's Rank Correlation P<0.05 for depth and sediment respectively). From these results, we conclude that sea bottom characteristics may be used to predict the fine-scale distribution of minke whales on their feeding grounds; the physiographic features identified providing valuable proxies for inferring prey distributions in the absence of fisheries data. However, an appreciation of both abiotic and biotic factors (using a combination of GIS and remote sensing outputs) is clearly desirable for ecosystembased management approaches for the coastal conservation of these whales. The application of GIS capacities to ecological studies based largely on field data of these marine mammals is highly recommended in the present study to cetologists, environmental modellers and conservation managers alike.

Area-based conservation is essential to safeguard declining biodiversity. Several approaches have been developed for identifying networks of globally important areas based on the delineation of sites or seascapes of importance for various elements of biodiversity (e.g., birds, marine mammals). Sharks, rays, and chimaeras are facing a biodiversity crisis with an estimated 37% of species threatened with extinction driven by overfishing. Yet spatial planning tools often fail to consider the habitat needs critical for their survival. The Important Shark and Ray Area (ISRA) approach is proposed as a response to the dire global status of sharks, rays, and chimaeras. A set of four globally standardized scientific criteria, with seven sub-criteria, was developed based on input collated during four shark, biodiversity, and policy expert workshops conducted in 2022. The ISRA Criteria provide a framework to identify discrete, three-dimensional portions of habitat important for one or more shark, ray, or chimaera species, that have the potential to be delineated and managed for conservation. The ISRA Criteria can be applied to all environments where sharks occur (marine, estuarine, and freshwater) and consider the diversity of species, their complex behaviors and ecology, and biological needs. The identification of ISRAs will guide the development, design, and application of area-based conservation initiatives for sharks, rays, and chimaeras, and contribute to their recovery.

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