Combining Multiple Data Sources to Predict IUCN Conservation Status of Reptiles

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Soares, N (Autor) Outra; Gonçalves, JF (Autor) Outra; Vasconcelos, R (Autor) Outra; Rita Ribeiro (Autor) FCUP

Ata de Conferência Internacional

Título: Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings

Páginas: 302-314

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00W-FNC

DOI: 10.1007/978-3-031-01333-1_24

Abstract (EN): Biodiversity loss is a hot topic. We are losing species at a high rate, even before their extinction risk is assessed. The International Union for Conservation of Nature (IUCN) Red List is the most complete assessment of all species conservation status, yet it only covers a small part of the species identified so far. Additionally, many of the existing assessments are outdated, either due to the ever-evolving nature of taxonomy, or to the lack of reassessments. The assessment of the conservation status of a species is a long, mostly manual process that needs to be carefully done by experts. The conservation field would gain by having ways of automating this process, for instance, by prioritising the species where experts and financing should focus on. In this paper, we present a pipeline used to derive a conservation dataset out of openly available data and obtain predictions, through machine learning techniques, on which species are most likely to be threatened. We applied this pipeline to the different groups within the Reptilia class as a model of one of the most under-assessed taxonomic groups. Additionally, we compared the performance of models using datasets that include different sets of predictors describing species ecological requirements and geographical distributions such as IUCN's area and extent of occurrence. Our results show that most groups benefit from using ecological variables together with IUCN predictors. Random Forest appeared as the best method for most species groups, and feature selection was shown to improve results.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13