Deep Learning Prediction of Reptile Diversity in Semi-Arid Ecosystems

Abstract

Introduction: Semi-arid environments host an unusually diverse range of specialized reptiles that thrive in environments with extreme temperatures, vegetation and variable precipitation, but quantifying this biodiversity remains a challenging task due to the elusive nature of reptiles, uneven distributions and high cost associated with sampling on the arid environment. Methodology: In this paper, the utilization of deep learning (DL) for predicting reptile biodiversity in semi-arid environments is theoretically synthesized. DL models based on image-based convolutional neural networks, deep regression for species richness prediction using bioclimatic variables and species distribution modeling approaches are discussed, and an analytical framework is proposed. Conclusion: Deep learning has provided a conceptual framework that can be used for reptile diversity prediction across semi-arid ecosystems, provided that occurrence data are sufficiently available, geographical bias is addressed, and appropriate aridity-related covariates are included in the model. Results: From the analysis, we note that deep learning models that have been trained on camera trap and citizen science images have the potential to provide high accuracy species identification for snakes and lizards, that non-linear richness gradients can be captured by deep regression and convolution models when used with bioclimatic and remote sensing covariates, and that model performance is heavily dependent on aridity-related covariates