Predictive capacity of nine algorithms and an ensemble model to determine the geographic distribution of tree species

iForest: Biogeosciences and Forestry

article

Juan Carlos Montoya-Jiménez¹  José René Valdez-Lazalde¹  Gregorio Ángeles-Perez¹  Héctor Manuel De Los Santos-Posadas¹  Gustavo Cruz-Cárdenas² 

[1] Postgrado en Ciencias Forestales. Colegio de Postgraduados. Campus Montecillo;Instituto Politécnico Nacional

关键词: TSS;    AUC;    BRT;    SVM;    MaxEnt;    Random Forests;    GAM;    Ensemble Model;   

DOI  :  10.3832/ifor4084-015

学科分类：社会科学、人文和艺术（综合）

来源: Societa Italiana di Selvicoltura ed Ecologia Forestale (S I S E F)

【 摘 要 】

0.72); the opposite was found in Bioclim and Domain (AUC<0.75 and <0.82; and TSS<0.5 and 0.055); on the contrary, Maxent and the ensemble model attained high predictive stability (CV<0.015). The ensemble model obtained greater performance and predictive stability in the predictions of the distribution of the 17 species of pines. The differences found in performance and predictive stability of the algorithms suggest that the ensemble model has the potential to model the distribution of tree species.

【 授权许可】

Unknown