Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology

摘要: Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number species moving towards extinction. Extinction risk modelling can identify correlates and not yet recognized to be threatened. Here, we use machine learning models extinction in African terrestrial mammals using set variables belonging four classes: distribution state, human pressures, response biology. We derived information on state pressure from satellite-borne imagery. Variables all classes were identified as important predictors risk, interactions observed among different (e.g. level protection, threats, ranges). Species biology had key role mediating effect external variables. The model was 90% accurate classifying status but few cases modelled mismatched. this condition might suffer an incorrect classification (hence require reassessment). An increased availability satellite imagery combined with improved resolution accuracy resulting maps will play progressively greater monitoring.