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Spatial optimizations of multiple plant species for ecological restoration of the mountainous areas of North China

机译:华北山区生态恢复多种植物物种的空间优化

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Intensive human land use and climate change have led to widespread ecological degradation which requires optimizing species distributions to achieve ecological restoration. In this paper, we combine predictive species distribution models (SDMs) with field investigations in the mountainous areas of northern China to determine where ecological restoration should be implemented. Using three species distribution models, i.e., generalized additive models (GAMs), generalized linear models, and classification tree models, we predict optimal species algorithms for ecological restoration. The results show that GAMs have more accurate predictive power to detect relationships between biogeographic factors and species distributions than the other two models based on cross-validation. In addition, the regionalization schemes designed in this study provide scientific guidance for ecological restoration by combining simulation results of SDMs with field investigations. By considering the suitability of different land use/cover types, restoration scenarios could be used to guide ecological restoration. The methodology proposed here provides a scientific basis for the restoration of species diversity, improved ecosystem services provision, and can be adopted in regions with extensive human disturbance and environmental change.
机译:密集的人类土地利用和气候变化导致了广泛的生态降解,这需要优化物种分布,以实现生态修复。在本文中,我们将预测物种分布模型(SDMS)与中国北部山区的实地调查结合在一起,以确定应实施生态修复的地方。使用三种物种分布模型,即广义添加剂模型(Gams),广义线性模型和分类树模型,我们预测了用于生态恢复的最佳物种算法。结果表明,GAMS具有更准确的预测力,以检测生物地理因子和物种分布之间的关系,而不是基于交叉验证的其他两种模型。此外,本研究中设计的区域化方案通过将SDMS的模拟结果与现场调查组合,为生态恢复提供了科学指导。通过考虑不同土地使用/覆盖类型的适用性,可以使用恢复方案来指导生态修复。此处提出的方法为恢复物种多样性提供了科学依据,改善了生态系统服务提供,并且可以在具有广泛的人类干扰和环境变化的地区采用。

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