Reptile species classification using Swin Transformers for biodiversity conservation in the Canary Islands

Abstract

The Canary Islands, internationally recognized as a biodiversity hot spot, possess unique ecological characteristics including endemic reptile species that face substantial threats from invasive alien species. Particularly, concerning is the California kingsnake (Lampropeltis californiae), which exhibits remarkable adaptability and inflicts severe ecological damage on endangered endemic fauna. This proposal aims to address biodiversity conservation challenges in the Canary Islands ecosystem by advancing reptile species classification through the implementation of Swin Transformer architectures. The integration of these advanced neural network architectures with conservation biology provides an automated, precise tool for species identification across different taxonomic levels that can enhance monitoring and control strategies for biodiversity preservation worldwide. This technical approach addresses urgent conservation requirements in the Canary Islands while simultaneously establishing a methodological framework applicable to other ecological contexts facing comparable biodiversity threats. The methodology employed pre-trained Swin Transformer models, initially developing fine-grained reptile species discrimination capabilities through hyperparameter optimization on a limited-scale database (160 images, 40 per species) using nested cross-validation to ensure statistical rigor and independence. The optimized configuration was subsequently transferred to a substantially larger database (4,400 images) for binary classification distinguishing snake from non-snake reptiles, where training on only 400 samples achieved 99.38% accuracy on 4,000 independent test samples, directly addressing the invasive alien species monitoring challenge. The Swin-B Transformer model demonstrated robust performance, achieving up to 100% accuracy in multiclass species classification and 99.38% accuracy in the challenging, taxonomically broader binary snake versus non-snake classification. These results establish the transferability and scalability of the hyperparameter optimization methodology across datasets of substantially different magnitudes.