Integrating Long-Term Ecological Data with Remote Sensing to Predict Wildlife Habitat Shifts Under Climate Change

Abstract

Climate change is rapidly altering ecosystems worldwide, leading to significant shifts in wildlife habitats. By creating efficient conservation policies, it is essential to predict these changes, particularly in the context of global biodiversity loss. This paper examines the possibility of using long-term ecological data and remote sensors to model and predict changes in wildlife habitats under different climatic conditions. Through remote sensing, this study can map and analyze habitat changes over time, providing a spatially explicit picture of the effects of climate change on wildlife by accessing historical information on species distribution, vegetation cover, and climatic variables. The predictive models produced in this study provide information on future habitat conditions that can help inform conservation activities, identifying areas that require protection and restoration. The main lessons learned are that it is possible to identify regions with the greatest potential for habitat loss under climate change, that machine learning algorithms such as MaxEnt are effective at making better habitat predictions, and that remote sensing complements ecological monitoring. The study findings imply that combining conventional ecological monitoring methods with state-of-the-art remote sensing to optimize their accuracy and reliability in predicting the impacts of climate change will contribute to the formulation of sustainable wildlife management policies and the proactive adoption of conservation strategies.