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<p>Climate change is altering vegetation patterns on the Qinghai-Tibet Plateau, where diverse Köppen climate zones reveal complex ecological responses. Here, we advance from a categorical view of climate, represented by Köppen-Geiger classifications, to a continuous characterization of the climate heterogeneity index to better capture the link between climate and vegetation across 1- and 10-km scales. We show that climate heterogeneity strengthens vegetation–climate relationships across spatial scales, with its influence increasing 1.2-fold at coarse resolution compared to fine scales. This enhancement stems from this index providing critical heterogeneity information absent in coarse macroclimate data, thereby refining suitability predictions in regions where fine-scale topographic variability is unresolved. At finer 1-km scales, topographic heterogeneity inherently dominates, reducing the relative impact of climate heterogeneity. Overall, incorporating climate heterogeneity compensates for coarse-scale data limitations, enhancing suitability predictions where topographic detail is lost, thereby bridging macroclimate biases and microscale dynamics, and advancing robust ecological forecasting.</p>

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Climate heterogeneity enhances macroclimate data-driven vegetation modeling on the Qinghai-Tibet Plateau

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Yanlong Guan,
  • Wenqing Wu,
  • Mengxi He,
  • Huaikai Weng,
  • Delong Li,
  • Xiaoxun Huang,
  • Deliang Chen,
  • Shungui Zhou,
  • Eduardo Eiji Maeda

摘要

Climate change is altering vegetation patterns on the Qinghai-Tibet Plateau, where diverse Köppen climate zones reveal complex ecological responses. Here, we advance from a categorical view of climate, represented by Köppen-Geiger classifications, to a continuous characterization of the climate heterogeneity index to better capture the link between climate and vegetation across 1- and 10-km scales. We show that climate heterogeneity strengthens vegetation–climate relationships across spatial scales, with its influence increasing 1.2-fold at coarse resolution compared to fine scales. This enhancement stems from this index providing critical heterogeneity information absent in coarse macroclimate data, thereby refining suitability predictions in regions where fine-scale topographic variability is unresolved. At finer 1-km scales, topographic heterogeneity inherently dominates, reducing the relative impact of climate heterogeneity. Overall, incorporating climate heterogeneity compensates for coarse-scale data limitations, enhancing suitability predictions where topographic detail is lost, thereby bridging macroclimate biases and microscale dynamics, and advancing robust ecological forecasting.