Editor recommendation: Predicting Animal Behaviour Using Deep Learning

Post provided by Jana McPherson

Common guillemots were one of the species used in this study. ©Richard Crossley

Common guillemots were one of the species used in this study. ©Richard Crossley

Understanding key habitat requirements is critical to the conservation of species at risk. For highly mobile species, discerning what is key habitat as opposed to areas that are simply being traversed (perhaps in the search for key habitats) can be challenging. For seabirds, in particular, it can be difficult to know which areas in the sea represent key foraging grounds. Devices that record birds’ diving behaviour can help shed light on this, but they’re expensive to deploy. In contrast, devices that record the birds’ geographic position are more commonly available and have been around for some time.

In their recent study entitled ‘Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds,’ Ella Browning and her colleagues made use of a rich dataset on 399 individual birds from three species, some equipped with both global positioning (GPS) and depth recorder devices, others with GPS only. The data allowed them to test whether deep learning methods can identify when the birds are diving (foraging) based on GPS data alone. Results were highly promising, with top models able to distinguish non-diving and diving behaviours with 94% and 80% accuracy.

What excites me about this study is that the methodology will allow older data on seabirds, which often are limited to GPS data only, to be recycled for new and deeper insights, and help provide a historic perspective on key seabird habitats and how they’ve changed over time.

To find out more, read the Open Access Methods in Ecology and Evolution article ‘Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds’ by Browning et al.

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