Widespread habitat loss and redistribution of marine top predators in a changing ocean.

The Northwest Atlantic Ocean and Gulf of Mexico are among the fastest warming ocean regions, a trend that is expected to continue through this century with far-reaching implications for marine ecosystems. We examine the distribution of 12 highly migratory top predator species using predictive models and project expected habitat changes using downscaled climate models. Our models predict widespread losses of suitable habitat for most species, concurrent with substantial northward displacement of core habitats >500 km.

Building use-inspired species distribution models: Using multiple data types to examine and improve model performance.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags).

Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular).

To protect the most vulnerable marine species it is essential to have an understanding of their spatiotemporal distributions. In recent decades, Bayesian statistics have been successfully used to quantify uncertainty surrounding identified areas of interest for bycatch species. However, conventional simulation-based approaches are often computationally intensive.

Environmental characteristics associated with the presence of the Spinetail devil ray (Mobula mobular) in the eastern tropical Pacific.

In the eastern Pacific Ocean, the tropical tuna purse-seine fishery incidentally captures high numbers of five mobulid bycatch species; all of which are classified as mortalities by the Inter-American Tropical Tuna Commission due to uncertainties in post-release mortality rates. To date, the factors (operational or environmental) leading to the capture of these species by the fishery have not been well studied. Here, we developed Generalized Additive Models for fisheries observer data to analyze the relationships between the presence/absence of Mobula mobular bycatch and oceanographic conditions, the spatial and temporal variability in fishing location, and the set type (associated with dolphins, free-swimming tuna schools or floating objects).