【摘 要】
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Background:The prediction of biogeographical patterns from a large number of driving factors with complex interactions,correlations and non-linear dependences require advanced analytical methods and modeling tools.This study compares different statistical
【机 构】
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DepartmentofCropandForestSciences,JointResearchUnitCTFC-AGROTECNIO-CERCACenter,ForestScienceandTechn
【基金项目】
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supported by the Secretariat for Universities and of the Ministry of Business,Knowledge of the Government of Catalonia and the European Social Fund,partially supported by the Spanish Ministry of Scien
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Background:The prediction of biogeographical patterns from a large number of driving factors with complex interactions,correlations and non-linear dependences require advanced analytical methods and modeling tools.This study compares different statistical
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