Citizen science for predicting spatio-temporal patterns in seabird abundance during migration.

ABSTRACT: Pelagic seabirds are elusive species which are difficult to observe, thus determining their spatial distribution during the migration period is a difficult task. Here we undertook the first long-term study on the distribution of migrating shearwaters from data gathered within the framework of citizen science projects. Specifically, we collected daily abundance (only abundance given presence) of Balearic shearwaters from 2005 to 2017 from the online databases Trektellen and eBird. We applied machine-learning techniques, specifically Random Forest regression models, to predict shearwater abundance during migration using 15 environmental predictors. We built separated models for pre-breeding and post-breeding migration. When evaluated for the total data sample, the models explained more than

SUBMITTER: Martin B 

PROVIDER: S-EPMC7428152 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature