Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Pinho, C (Autor) Outra; Kaliontzopoulou, A (Autor) Outra; Ferreira, CA (Autor) Outra; João Gama (Autor) FEP

Revista

Título: Zoological Journal of the Linnean Society

Vol. 198

Páginas: 184-201

ISSN: 0024-4082

Editora: Oxford University Press

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 10 Citações

Outras Informações

ID Authenticus: P-00X-FCS

DOI: 10.1093/zoolinnean/zlac087

Abstract (EN): Automated image classification is a thriving field of machine learning, and various successful applications dealing with biological images have recently emerged. In this work, we address the ability of these methods to identify species that are difficult to tell apart by humans due to their morphological similarity. We focus on distinguishing species of wall lizards, namely those belonging to the Podarcis hispanicus species complex, which constitutes a well-known example of cryptic morphological variation. We address two classification experiments: (1) assignment of images of the morphologically relatively distinct P. bocagei and P. lusitanicus; and (2) distinction between the overall more cryptic nine taxa that compose this complex. We used four datasets (two image perspectives and individuals of the two sexes) and three deep-learning models to address each problem. Our results suggest a high ability of the models to identify the correct species, especially when combining predictions from different perspectives and models (accuracy of 95.9% and 97.1% for females and males, respectively, in the two-class case; and of 91.2% to 93.5% for females and males, respectively, in the nine-class case). Overall, these results establish deep-learning models as an important tool for field identification and monitoring of cryptic species complexes, alleviating the burden of expert or genetic identification.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18