Scholarlink

<p>Land use history has significant impacts on forest biodiversity and ecosystem functioning during the process of forest conservation and reconstruction. However, persistent impacts of historical land use on the forest biodiversity-ecosystem function (BEF) relationship remain unclear. We assessed woody plant diversity and composition of a temperate forest in Northeast China, encompassing area with contrasting intensities of historical land use (high vs. low-intensity land use, ILU). We evaluated the structural and spectral characteristics using near-ground remote sensing data, used structural causal models which can make observational causal inference and considered spatial effects to test how the biodiversity-productivity relationship changes with land use history. Our findings revealed that productivity and woody plant diversity, encompassing taxonomic, functional and phylogenetic dimensions, were higher in high ILU areas, whereas spectral diversity and structural complexity were higher in low ILU areas. The explained variance in productivity increased from low to high ILU areas. In high ILU area, the explanatory power of woody plant composition (structural and spectral composition) and diversity (species diversity) for productivity was more pronounced, with woody plant composition dominating. Additionally, environmental conditions’ indirect influences, acting through structural composition and species diversity, were stronger in high ILU areas. Our study underscores the variation in biodiversity-productivity relationships under different land use histories and highlights the importance of near-ground remote sensing-derived metrics in explaining these patterns. Forest management and conservation strategies should be tailored to historical land-use intensity of specific areas, and integrating near-ground remote sensing data can provide more precise and comprehensive insights.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Historical land use has long-lasting effects on the biodiversity–productivity relationship in temperate forests

  • 期刊论文

  • 发表日期：

    2026年4月28日

查看全文
  • Pengcheng Jiang,
  • Han He,
  • Thomas Vanneste,
  • Zikun Mao,
  • Zhichao Xu,
  • Shuai Fang,
  • Fei Lin,
  • Ji Ye,
  • Xugao Wang

摘要

Land use history has significant impacts on forest biodiversity and ecosystem functioning during the process of forest conservation and reconstruction. However, persistent impacts of historical land use on the forest biodiversity-ecosystem function (BEF) relationship remain unclear. We assessed woody plant diversity and composition of a temperate forest in Northeast China, encompassing area with contrasting intensities of historical land use (high vs. low-intensity land use, ILU). We evaluated the structural and spectral characteristics using near-ground remote sensing data, used structural causal models which can make observational causal inference and considered spatial effects to test how the biodiversity-productivity relationship changes with land use history. Our findings revealed that productivity and woody plant diversity, encompassing taxonomic, functional and phylogenetic dimensions, were higher in high ILU areas, whereas spectral diversity and structural complexity were higher in low ILU areas. The explained variance in productivity increased from low to high ILU areas. In high ILU area, the explanatory power of woody plant composition (structural and spectral composition) and diversity (species diversity) for productivity was more pronounced, with woody plant composition dominating. Additionally, environmental conditions’ indirect influences, acting through structural composition and species diversity, were stronger in high ILU areas. Our study underscores the variation in biodiversity-productivity relationships under different land use histories and highlights the importance of near-ground remote sensing-derived metrics in explaining these patterns. Forest management and conservation strategies should be tailored to historical land-use intensity of specific areas, and integrating near-ground remote sensing data can provide more precise and comprehensive insights.