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Aim: Species distribution modelling is a useful tool for determining important habitats. By accounting for specific animal behaviour in the model, it is possible to identify finer‐scale patterns of habitat use. Together with spatially explicit data on anthropogenic activities, models can be used to assess human impacts and inform conservation management. This study used observations of breeding behaviour to identify fine‐scale breeding habitats of humpback whales (Megaptera novaeangliae), as well as potential overlap of these habitats with cumulative anthropogenic impacts. Location: Eastern Tropical Atlantic, West Africa. Methods: Maxent was used to model humpback distribution using pertinent environmental predictors and an integrated dataset of humpback whale occurrences filtered for breeding‐specific behaviours. In conjunction with multiple anthropogenic activities, a subsequent cumulative utilization and impact analysis assessed the degree of overlap between predicted breeding habitat and potential anthropogenic impacts. Results: Greatest habitat suitability occurred in warm coastal waters of Gabon, and other highly suitable areas occurred off Equatorial Guinea (Bioko Island), Cameroon and Angola. Sea surface temperature and height contributed most to the model. Highest overlap between humpback whales and potential impacts from anthropogenic activities occurred off Gabon, Equatorial Guinea (Bioko Island), Cameroon and Angola. Impacts associated with oil and gas development (where oil and gas platforms serve as an indicator for industry activity) appeared to contribute most to potential cumulative impact. Main Conclusions: Depth and sea surface temperature of predicted breeding habitats were consistent with previous studies. However, lesser known characteristics such as sea surface height and wind speed, resulting in potentially more sheltered areas for breeding whales, may also be important in delineating finer‐scale habitat suitability. Identified areas of high potential cumulative impact occurred within exclusive economic zones of multiple countries and likely represent the minimum level of impact to humpback whales in the region, highlighting the need for additional research and effective management throughout the area. [ABSTRACT FROM AUTHOR]

Aim Fine-scale population structure is often unaccounted for in the delineation of conservation units, potentially compromising long-term species persistence. Identifying biogeographic and environmental drivers of population boundaries is therefore of key conservation concern. We aimed to explore barriers to dispersal for the harvested yellow anaconda ( Eunectes notaeus) using an ecological niche model. Our secondary aim was to test the relative geographic and environmental contributions of a multisource occurrence data set in species range predictions. Location Paraguay River drainage, central South America. Methods We developed an ecological niche model for the yellow anaconda using Maxent and a multisource species occurrence data set. Following nine iterations of model development, nine environmental variables were selected for model inclusion. We used the models to identify potential barriers to dispersal and employed jackknifing to identify the primary environmental variables that best explain barrier presence. We assessed the geographic and environmental overlap of models built with each data subset. Results Characterization of suitable habitat was found to be most powerful in northern Argentina and southern Paraguay. A persistent barrier to dispersal was identified in northern Argentina and corresponded to the presence of dry Cambisol soils. Data subsets were found to contribute different information to the final model in terms of geographic and environmental space. Main Conclusions Ecologically meaningful barriers to dispersal support recent genetic hypotheses of population subdivision. These barriers should be considered when delineating species management units to ensure sustainable harvest levels. Multisource data sets may produce more powerful niche predictions and represent a useful resource for data-poor species. Further, model results should be interpreted alongside complementary analyses for more effective conservation strategies. [ABSTRACT FROM AUTHOR]