Conservation Biogeography Research Group, Institute of International Rivers and Ecosecurity, Yunnan University, Kunming, Yunnan, 650091, China; Yunnan Key Laboratory of International Rivers and Transboundary Ecosecurity, Yunnan University, Kunming, Yunnan, 650091, China; School of Life Sciences, Fudan University, Shanghai, 200438, China.
Conservation Biogeography Research Group, Institute of International Rivers and Ecosecurity, Yunnan University, Kunming, Yunnan, 650091, China; Yunnan Key Laboratory of International Rivers and Transboundary Ecosecurity, Yunnan University, Kunming, Yunnan, 650091, China.
J Environ Manage. 2021 Jul 15;290:112630. doi: 10.1016/j.jenvman.2021.112630. Epub 2021 Apr 19.
Threatened species are inadequately represented within protected areas (PAs) across the globe. Species conservation planning may be improved by using public species-occurrence databases, but empirical evidence is limited of how that may be accomplished at local scales. We used the Three Parallel Rivers Region of China as a case to investigate the utility of public species data in improvement in conservation planning. We mapped the distribution of each species as suitable habitat ranges using species distribution models (for 261 plants and 29 animals with ≥5 occurrences) or as point locations (for 591 plants and 328 animals with <5 occurrences). Systematic conservation planning was then applied to identify three optimized portfolios of priority conservation areas (PCAs) for achieving increasing targets of 17, 31, and 50% of the total study area. We then compared the distributions of PCAs in this study with those in two existing PCA datasets. PCAs in this study covered greater areas in the southeastern highly-disturbed regions and along valleys of great rivers than two existing datasets that had a focus on intact ecosystems in remote mountain areas. The three portfolios of PCAs had some overlap with two existing PCA datasets, with the overlapping area accounting for 26.4-39.0% of the total areas of our PCAs. Our PCAs could complement existing PCAs by identifying more priority areas in developed landscapes; this is critical for protecting biodiversity in such areas as they face greater pressures. PCAs in this study received a much lower PA coverage (32.9-43.1%) than existing PCAs (60.2-60.8%) because of biased PA distribution toward mountain areas. Our results suggest that conservation planning based on limited public species data could improve local-scale priority-setting practices. The analysis supports effective integration of species targets in China's new national park system by identifying optimized networks of PCAs.
受威胁物种在全球的保护区（PA）中代表性不足。通过使用公共物种出现数据库，可以改进物种保护规划，但在当地范围内如何实现这一目标的经验证据有限。我们以中国的三江并流地区为例，调查了公共物种数据在改善保护规划方面的效用。我们使用物种分布模型（对于 261 种植物和 29 种动物，出现次数≥5）或点位置（对于 591 种植物和 328 种动物，出现次数<5）来绘制每个物种的分布作为适宜栖息地范围。然后，我们应用系统保护规划来确定三个优化的重点保护区（PCA）组合，以实现 17%、31%和 50%的总研究区域的目标。然后，我们将本研究中的 PCA 分布与两个现有的 PCA 数据集进行了比较。本研究中的 PCA 覆盖了东南部高度干扰地区和大河河谷的更大区域，而两个现有的数据集则侧重于偏远山区的完整生态系统。三个 PCA 组合与两个现有的 PCA 数据集有一些重叠，重叠区域占我们 PCA 总面积的 26.4-39.0%。我们的 PCA 可以通过确定在发达景观中更多的优先区域来补充现有的 PCA，这对于保护这些地区的生物多样性至关重要，因为它们面临着更大的压力。由于保护区分布偏向山区，本研究中的 PCA 获得的保护区覆盖率（32.9-43.1%）远低于现有的 PCA（60.2-60.8%）。我们的结果表明，基于有限的公共物种数据的保护规划可以改进当地的优先事项设定实践。该分析通过确定 PCA 的优化网络，支持中国新的国家公园系统中物种目标的有效整合。
