Optimized Extrapolation Methods Enhance Prediction of Elsholtzia densa Distribution on the Tibetan Plateau

Zeyuan Liu, Youhai Wei (), Liang Cheng, Hongyu Chen and Hua Weng
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Zeyuan Liu: Institute of Plant Protection, College of Agricultural and Forestry Sciences, Qinghai University, Xining 810005, China
Youhai Wei: Institute of Plant Protection, College of Agricultural and Forestry Sciences, Qinghai University, Xining 810005, China
Liang Cheng: Institute of Plant Protection, College of Agricultural and Forestry Sciences, Qinghai University, Xining 810005, China
Hongyu Chen: Institute of Plant Protection, College of Agricultural and Forestry Sciences, Qinghai University, Xining 810005, China
Hua Weng: Institute of Plant Protection, College of Agricultural and Forestry Sciences, Qinghai University, Xining 810005, China

Sustainability, 2025, vol. 17, issue 18, 1-24

Abstract: Species distribution models (SDMs) grapple with uncertainty. To address this, a parameter-optimized MaxEnt model was used to predict habitat suitability for Elsholtzia densa , a predominant agricultural weed on the Tibetan Plateau. Through multiparameter optimization with 149 occurrence points and three climate variable sets, we systematically evaluated how the three MaxEnt extrapolation approaches (Free Extrapolation, Extrapolation with Clamping, No Extrapolation) influenced model outputs. The results showed the following: (1) Model optimization using the Kuenm R package version (1.1.10) identified seven critical bioclimatic variables (Feature Combinations = LQTH, Regularization Multipliers = 2.5), with optimized models demonstrating high accuracy (Area Under Curve > 0.9). (2) Extrapolation approaches exhibited negligible effects on variable selection, though four bioclimatic variables “bio1 (annual mean temperature)”, “bio12 (annual precipitation)”, “bio2 (mean diurnal range)”, and “bio7 (temperature annual range)” predominantly drove model predictions. (3) Current high-suitability areas are clustered in the eastern and southern regions of the Tibetan Plateau, and with Free Extrapolation yielding the broadest current distribution. Climate change projections suggest habitat expansion, particularly under conditions of No Extrapolation. (4) Multivariate Environmental Similarity Surface (MESS) and Most Dissimilar Variable (MoD) are not affected by the extrapolation method, and extrapolation risk analyses indicate that future climate anomalies are mainly concentrated in the western and southern parts of the Tibetan Plateau and that future warming will further increase the unsuitability of these regions. (5) Variance analysis showed that the extrapolation methods did not significantly affect the 10-replicate results but influenced the parameter and emission scenarios, with No Extrapolation methods showing minimal variance changes. Our findings validate that multiparameter optimization improves species distribution model robustness, systematically characterizes extrapolation impacts on distribution projections, and provides a conceptual framework and early warning systems for agricultural weed management on the Tibetan Plateau.

Keywords: climate change; maxent model; Elsholtzia densa; model extrapolation; the Tibetan Plateau; habitats (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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