||
Given the intercorrelation between defoliation, elevation, and forest composition that was previously observed in these watersheds (Sidhu et al., 2024), we employed a hierarchical partitioning (HP) modeling approach for variables with significant relationships to defoliation (flow, temperature, and SUVA) to compare the relative importance of cumulative defoliation to other potential landscape drivers. Briefly, HP involves the calculation and partitioning of goodness-of-fit for all potential models (2k for k predictors) within a multiple regression, enabling the interpretation of relative explanatory contributions of colinear variables (whereas traditional modeling cannot) (Lai et al., 2022; MacNally, 2002). HP was conducted using the rdacca.hp (1.0-8) R package, and determined both the shared and unique variance explained for cumulative defoliation and the landscape variables in all possible model combinations (Lai et al., 2022).
Archiver|手机版|科学网 ( 京ICP备07017567号-12 )
GMT+8, 2024-9-1 08:19
Powered by ScienceNet.cn
Copyright © 2007- 中国科学报社