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. Continue reading →
To truly understand how species’ distributions vary through space and time, biogeographers often have to make use of analytical techniques from a wide array of disciplines. As such, these papers cover advances in fields such as evolutionary analysis, biodiversity definitions, species distribution modelling, remote sensing and more. They also reflect the growing understanding that biogeography can include experiments and highlight the increasing number of software packages focused towards biogeography.
This Virtual Issue was compiled by Methods in Ecology and Evolution Associate Editors Pedro Peres-Neto and Will Pearse (both of whom are involved in the conference). All of the articles in this Virtual Issue are free for a limited time and we have a little bit more information about each of the papers included here: Continue reading →