Deep inference of seabird dives from GPS-only records: Performance and generalization properties.

ABSTRACT: At-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird trajectories are recorded through the deployment of GPS, and a variety of statistical approaches have been tested to infer probable behaviours from these location data. Recently, deep learning tools have shown promising results for the segmentation and classification of animal behaviour from trajectory data. Yet, these approaches have not been widely used and investigation is still needed to identify optimal network architecture and to demonstrate

SUBMITTER: Roy A 

PROVIDER: S-EPMC8942281 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature