Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology
Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology
复制标题
DOI：
10.1098/rstb.2013.0198
复制
发表时间：
2014-05-26
影响因子：
6.3
通讯作者：
Rondinini, Carlo
中科院分区：
文献类型：
--
作者：
Di Marco, Moreno;Buchanan, Graeme M.;Rondinini, Carlo
虽然保护干预已经扭转了一些物种的下降，但我们的成功被更多物种走向灭绝所抵消。灭绝风险模型可以确定风险和尚未被认为受到威胁的物种的相关性。在这里，我们使用机器学习模型来识别非洲陆地哺乳动物灭绝风险的相关性，使用一组属于四个类别的变量：物种分布状态，人类压力，保护响应和物种生物学。我们从卫星图像中获得了关于分布状态和人类压力的信息。所有四个类别中的变量都被确定为灭绝风险的重要预测因子，并且观察到不同类别中的变量之间的相互作用（例如保护水平，人类威胁，物种分布范围）。物种生物学在调节外部变量的影响方面发挥着关键作用。该模型在物种灭绝风险状态分类方面的准确率为90%，但在少数情况下，观察到的灭绝风险与模型的灭绝风险不匹配。在这种情况下，物种可能会遭受不正确的灭绝风险分类（因此需要重新评估）。增加卫星图像的供应，再加上所生成地图的分辨率和分类准确性的提高，将在养护监测方面发挥越来越大的作用。
Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.