Biodiversity benefits of China’s 20-year efforts in forest restoration

China has become the global leader in forest restoration, through unprecedented investment and various large-scale programs since 2000. Nevertheless, the overall biodiversity outcomes of these efforts remain to be resolved. Here, using 402 non-migratory forest bird species as indicators, we assessed biodiversity changes from 2000 to 2020 at a national scale, controlling for the effects of climate change by using an ecological niche modeling framework. We found substantial biodiversity benefits linked to the expansion of forest extent and the improvement in forest structure, with 73.6% of avian species experiencing habitat expansion and mean species richness increasing by 2.886 ± 8.418. Protection-focused and afforestation-dominated restoration programs both effectively enhanced biodiversity, with planted forests performing similarly to natural forests over this period. Key factors facilitating this enhancement included tree cover and canopy structural complexity. Our findings suggest that restoring planted forests in degraded landscapes can provide substantial biodiversity benefits, although this should incorporate mixed tree species silviculture practices to enhance the structural complexity of planted forests. This study highlights China’s achievements but also the potential risks involved in forest and biodiversity restoration, and provides general insights for the optimization of future restoration policy and management in China and beyond.
Raw data and R codes that can be used to reproduce the results of this paper are included in the dataset.

自2000年以来，中国凭借前所未有的投入与各类大规模修复项目，已成为全球森林修复领域的领军者。然而，这些修复举措的整体生物多样性成效仍有待明确。本研究以402种非迁徙性森林鸟类作为指示物种，借助生态位建模框架控制气候变化的影响，在国家尺度上评估了2000年至2020年间的生物多样性变化。研究发现，森林面积扩张与林分结构优化带来了显著的生物多样性增益：73.6%的鸟类物种种群栖息地得以扩张，物种平均丰富度提升了2.886±8.418。以保护为导向、以造林为主导的修复项目均有效提升了生物多样性，在此期间人工林与天然林的修复效果并无显著差异。助力该成效提升的关键因素包括林木覆盖率与林冠结构复杂度。本研究结果表明，在退化景观中修复人工林可带来可观的生物多样性增益，但需结合混交林造林实践以提升人工林的结构复杂度。本研究既肯定了中国在森林修复领域取得的成就，也指出了森林与生物多样性修复中潜藏的风险，同时为中国乃至全球未来的修复政策优化与管理实践提供了通用参考。本数据集包含可复现本文研究结果的原始数据与R代码。