WildGuard Conservation Analytics Model for Predicting Species Vulnerability Through Integrated Ecological Indicators

Abstract

The vulnerability assessment of species has become a key component of biodiversity conservation; however, currently used traditional approaches are hardly capable of coping with the fast pace of ecological changes. Most models based on periodic, criterion-dependent evaluation fail to capture the complexity of species' interactions in the changing habitat, climate, and presence of humans. This paper presents WildGuard, a new conservation analytics model which integrates multiple ecological indicators, such as habitat destruction, species' population dynamics, climate vulnerability, human interference, and genetic variation, to provide a comprehensive assessment of species' vulnerability. With the help of the structured ecological data and a weighted analytics engine, WildGuard is able to produce a vulnerability index which allows timely identification of vulnerable species and development of conservation strategies. Our approach is compared to the existing species vulnerability assessment models and shows high correlation with actual conservation results; what is more, it is much more adaptive to the multidimensional changes in ecology than traditional single-indicator models. Analysis of indicators shows that habitat destruction and population dynamics have the most significant effect on vulnerability scores.