Remotely-sensed vegetation and habitat structure can serve as suitable surrogates to predict the distribution of a micro-endemic bird species

Abstract

Biodiversity is increasingly threatened by human land use and climate change, making predictive modeling crucial for conservation planning and the identification of priority conservation areas. Large-scale species distribution projections can be improved by integrating remotely sensed vegetation indices, as they reflect important vegetation and habitat characteristics. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) indicate vegetation productivity, while texture metrics derived from these indices reveal habitat structure. When combined with climatic variables, these indicators can significantly improve the accuracy of species distribution models (SDMs). In this study, we evaluated the performance of SDMs using vegetation indices, texture metrics, and climatic variables to predict the distribution of the Violet-throated Metaltail (Metallura baroni), a microendemic hummingbird restricted to the environmentally complex high-altitude regions of the southern Ecuadorian Andes. Using a backward-selection and cross-validation approach for predictor selection and the Maximum Entropy (MaxEnt) algorithm, we compared model performance using AUC values. Our results demonstrate that incorporating habitat structure indicators (NDVI- and NDWI-derived texture metrics) together with climatic variables significantly improves SDM performance, allowing better discrimination of shrub ecosystems where M. baroni occurs. This approach highlights the importance of integrating key aspects of habitat structure as indicators of resource availability, which directly influence the distribution of bird species in complex landscapes.