The use of range size to assess risks to biodiversity from stochastic threats

AimStochastic threats such as disease outbreak, pollution events, fire, tsunami and drought can cause rapid species extinction and ecosystem collapse. The ability of a species or ecosystem to persist after a stochastic threat is strongly related to the extent and spatial pattern of its geographical distribution. Consequently, protocols for assessing risks to biodiversity typically include geographic range size criteria for assessing risks from stochastic threats. However, owing in part to the rarity of such events in nature, the metrics for assessing risk categories have never been tested. In this study, we investigate the performance of alternative range size metrics, including the two most widely used, extent of occurrence (EOO) and area of occupancy (AOO), as predictors of ecosystem collapse in landscapes subject to stochastic threats. MethodsWe developed a spatially explicit stochastic simulation model to investigate the impacts of four threat types on a dataset of 1350 simulated geographic distributions of varying pattern and size. We empirically estimated collapse probability in response to each threat type and evaluated the ability of a set of spatial predictors to predict risk. ResultsThe probability of ecosystem collapse increased rapidly as range size declined. While AOO and EOO were the most important predictors of collapse risk for the three spatially explicit threats included in our model (circle, swipe and cluster), core area, patch density and mean patch size were better predictors for edge effect threats. Main conclusionsOur study is the first to quantitatively assess the range size metrics employed in biodiversity risk assessment protocols. We show that the current methods for measuring range size are the best spatial metrics for estimating risks from stochastic threats. Our simulation framework delivers an objective assessment of the performance of hitherto untested but widely used measures of geographic range size for risk assessment.