Main environmental variables influencing the abundance of plant species under risk category  

作　　者：Pablo Antúnez 

机构地区：[1]División de Estudios de Posgrado,Universidad de la Sierra Juárez,68725 Oax.,México

出　　处：《Journal of Forestry Research》2022年第4期1209-1217,共9页林业研究（英文版）

基　　金：the National Council of Science and Technology of Mexico for the postdoc fellowship awarded。

摘　　要：Determining climatic and physiographic variables in Mexico’s major ecoregions that are limiting to biodiversity and species of high conservation concern is essential for their conservation.Yet,at the national level to date,few studies have been performed with large data sets and crossconfirmation using multiple statistical analyses.Here,we used 25 endemic,rare and endangered species from 3610 sampling points throughout Mexico and 25 environmental attributes,including average precipitation for different seasons of the year,annual dryness index,slope of the terrain;and maximum,minimum and average temperatures to test our hypothesis that these species could be assessed with the same weight among all variables,showing similar indices of importance.Our results using principal component analysis,covariation analysis by permutations,and random forest regression showed that summer precipitation,length of the frost-free period,spring precipitation,winter precipitation and growing season precipitation all strongly influence the abundance of tropical species.In contrast,annual precipitation and the balance at different seasons(summer and growing season)were the most relevant variables on the temperate region species.For dry areas,the minimum temperature of the coldest month and the maximum temperature of the warmest month were the most significant variables.Using these different associations in different climatic regions could support a more precise management and conservation plan for the preservation of plant species diversity in forests under different global warming scenarios.

关 键 词：Correlative models Climate change Effect of topography Endemic species IUCN list Machine learning algorithms 

分 类 号：Q948[生物学—植物学]