Macroecological Predictions of Global Biodiversity from Remote Sensing Metrics

• Main Author: Leduc, Marie-Bé
• Other Authors: Kerr, Jeremy Thomas
• Format: Others
• Language: en
• Published: Université d'Ottawa / University of Ottawa 2019
• Subjects: Essential Biodiversity Variables; Climate; Remote Sensing; Prediction; Species richness
• Online Access: http://hdl.handle.net/10393/38630; http://dx.doi.org/10.20381/ruor-22882

Rapid biodiversity change at a global scale requires enhanced monitoring tools to predict how shifting environmental conditions might alter species’ extinction risk. Emerging remote sensing tools are essential to these efforts and provide the sole mechanism to detect environmental changes and their potential consequences for biodiversity rapidly. Here, I assess the extent to which remote sensing measurements predict species richness globally and within regions, facilitating the establishment of a single framework for monitoring diversity worldwide. I assembled global remote sensing metrics and data on diversity gradients to construct and cross-validate models predicting species richness of birds and mammals within and among the world’s biogeographic zones. Enhanced vegetation Index (EVI), land surface temperature (LST), the first principal component of habitat heterogeneity, and an interaction between energy and habitat heterogeneity are important remotely-sensed environmental measurements for predicting trends of species richness of birds and mammals at all scales, although the intensity of the relationship differs between groups and grain sizes. However, a global model does not explain differences in species richness of birds between distinct zoogeographical realms, indicating a possible threshold in biodiversity change prediction before onset of novel environmental conditions. Measuring potential nonlinear changes in species richness is a useful application of the essential biodiversity variables (EBV) framework for operational monitoring of global and regional biodiversity. The continued production of reliable and consistent remote sensing will facilitate further exploration of current and upcoming drivers of biodiversity change and will help improve macroecological models.