Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding.

ABSTRACT: The way animals select their breeding habitat may have great impacts on individual fitness. This complex process depends on the integration of information on various environmental factors, over a wide range of spatiotemporal scales. For seabirds, breeding habitat selection integrates both land and sea features over several spatial scales. Seabirds explore these features prior to breeding, assessing habitats' quality. However, the information-gathering and decision-making process by seabirds when choosing a breeding habitat remains poorly understood. We compiled 49 historical records of larids colonies in Cuba from 1980 to 2020. Then, we predicted potentially suitable breeding sites for larids and assessed their breeding macrohabitat selection, using deep and machine learning algorithms res

SUBMITTER: Garcia-Quintas A 

PROVIDER: S-EPMC10505760 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature