Background Describing where distribution hotspots and coldspots are located is crucial for any science-based species management and governance.Thus,here we created the world's first Super Species Distribution Mode...Background Describing where distribution hotspots and coldspots are located is crucial for any science-based species management and governance.Thus,here we created the world's first Super Species Distribution Models(SDMs)including all described primate species and the best-available predictor set.These Super SDMs are conducted using an ensemble of modern Machine Learning algorithms,including Maxent,Tree Net,Random Forest,CART,CART Boosting and Bagging,and MARS with the utilization of cloud supercomputers(as an add-on option for more powerful models).For the global cold/hotspot models,we obtained global distribution data from www.GBIF.org(approx.420,000 raw occurrence records)and utilized the world's largest Open Access environmental predictor set of 201 layers.For this analysis,all occurrences have been merged into one multi-species(400+species)pixel-based analysis.Results We present the first quantified pixel-based global primate hotspot prediction for Central and Northern South America,West Africa,East Africa,Southeast Asia,Central Asia,and Southern Africa.The global primate coldspots are Antarctica,the Arctic,most temperate regions,and Oceania past the Wallace line.We additionally described all these modeled hotspots/coldspots and discussed reasons for a quantified understanding of where the world's non-human primates occur(or not).Conclusions This shows us where the focus for most future research and conservation management efforts should be,using state-of-the-art digital data indication tools with reasoning.Those areas should be considered of the highest conservation management priority,ideally following‘no killing zones'and sustainable land stewardship approaches if primates are to have a chance of survival.展开更多
文摘Background Describing where distribution hotspots and coldspots are located is crucial for any science-based species management and governance.Thus,here we created the world's first Super Species Distribution Models(SDMs)including all described primate species and the best-available predictor set.These Super SDMs are conducted using an ensemble of modern Machine Learning algorithms,including Maxent,Tree Net,Random Forest,CART,CART Boosting and Bagging,and MARS with the utilization of cloud supercomputers(as an add-on option for more powerful models).For the global cold/hotspot models,we obtained global distribution data from www.GBIF.org(approx.420,000 raw occurrence records)and utilized the world's largest Open Access environmental predictor set of 201 layers.For this analysis,all occurrences have been merged into one multi-species(400+species)pixel-based analysis.Results We present the first quantified pixel-based global primate hotspot prediction for Central and Northern South America,West Africa,East Africa,Southeast Asia,Central Asia,and Southern Africa.The global primate coldspots are Antarctica,the Arctic,most temperate regions,and Oceania past the Wallace line.We additionally described all these modeled hotspots/coldspots and discussed reasons for a quantified understanding of where the world's non-human primates occur(or not).Conclusions This shows us where the focus for most future research and conservation management efforts should be,using state-of-the-art digital data indication tools with reasoning.Those areas should be considered of the highest conservation management priority,ideally following‘no killing zones'and sustainable land stewardship approaches if primates are to have a chance of survival.
