ISSN: 1578-665X | e-ISSN: 2014-928X An international journal devoted to the study and conservation of animal biodiversity, open-access, free for authors, driven by a fast-paced editorial process that includes assessment by experts. It is published twice a year.

Machine learning as a successful approach for predicting complex spatio–temporal patterns in animal species abundance

Martín, B. González–Arias, J.  Vicente–Vírseda, J. A. 



Our aim was to identify an optimal analytical approach for accurately predicting complex spatio–temporal patterns in animal species distribution. We compared the performance of eight modelling techniques (generalized additive models, regression trees, bagged CART, k–nearest neighbors, stochastic gradient boosting, support vector machines, neural network, and random forest –enhanced form of bootstrap. We also performed extreme gradient boosting –an enhanced form of radiant boosting– to predict spatial patterns in abundance of migrating Balearic shearwaters based on data gathered within eBird. Derived from open–source datasets, proxies of frontal systems and ocean productivity domains that have been previously used to characterize the oceanographic habitats of seabirds were quantified, and then used as predictors in the models. The random forest model showed the best performance according to the parameters assessed (RMSE value and R2). The correlation between observed and predicted abundance with this model was also considerably high. This study shows that the combination of machine learning techniques and massive data provided by open data sources is a useful approach for identifying the long–term spatial–temporal distribution of species at regional spatial scales.

Key words

Balearic shearwater, Machine learning, Random forest, Chlorophyll, NAO index

Reception date: 27 V 21  |   Acceptation date: 24 VII 21  |   Publication date: 15 IX 21

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Índex de Volume 44.2 (2021)