Flood susceptibility mapping to improve models of species distributions

Abstract

As significant ecosystem disturbances flooding events are expected to increase in both frequency and severity due to climate change, underscoring the critical need to understand their impact on biodiversity. In this study, we employ advanced remote sensing and machine learning methodologies to investigate the effects of flooding on biodiversity, from individual species to broader ecological communities. Specifically, we utilized Sentinel-1 synthetic aperture radar (SAR) images and an ensemble of machine-learning algorithms to derive a flood susceptibility indicator. Our primary objective is to investigate the potential benefits of incorporating flood susceptibility, as a proxy for flood risk, into species distribution models (SDMs). By doing so, we aim to improve the performance of SDMs and gain deeper insights into the consequences of floods to biodiversity. Within the biodiverse landscape of the Zagros Mountains, a crucial Irano-Anatolian biodiversity hotspots, we examined the sensitivity of mammals, amphibians, and reptiles’ distributions to flooding. Our analysis compared the performance of models that combined flood susceptibility with climate variables against models relying solely on climate variables. The results indicate that the inclusion of flood susceptibility significantly improves the capacity of models to explain and map species distributions for 67% of the species in our study region. Notably, amphibians and mammals are more profoundly affected by flooding compared to reptiles. The study highlights the importance of incorporating flood susceptibility as a predictor variable in species distribution models to improve the baseline characterization of potential species distributions. The importance of this variable will obviously depend on the regional context and the species studied but its relevance is likely to increase with climate change. In summary, our research demonstrates the integration of remote sensing and machine learning as a potent approach to advance biodiversity data science, monitoring, and conservation in the face of climate-induced flooding.