Department of Biometry and Environmental System Analysis, University of Freiburg, 79106, Freiburg, Germany.
School of Life Sciences, University of Nottingham, Nottingham, UK.
BMC Ecol. 2020 Jun 26;20(1):35. doi: 10.1186/s12898-020-00305-7.
Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion.
Focal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt's protected areas for conserving fauna was low.
For SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps.
空间保护优先化（SCP）是一组旨在支持有效分配保护行动重点区域的计算工具，但它受到许多不确定性源的影响，在优先化过程中应考虑这些不确定性源。我们量化了 SCP 应用程序（使用 Zonation 软件）对数据匮乏情况下可能的不确定性源的敏感性，包括使用不同的替代方案；纠正抽样偏差；如何整合连通性；物种分布模型（SDM）算法的选择；如何从景观中移除单元格；以及为物种分配权重的两种方法（红色名录地位或预测不确定性）。此外，我们评估了埃及保护区对保护的有效性，并在空间上分配了最优先的地点，以便进一步进行实地评估，作为保护区扩展的潜在区域。
焦点分类群（蝴蝶、爬行动物和哺乳动物）、抽样偏差、连通性和 SDM 算法选择是最敏感的参数；这些参数共同反映了数据质量问题。相比之下，单元格移除规则和物种权重对整体变异性的贡献要小得多。使用当前可用的物种数据，我们发现埃及保护区保护动物的现有效果很低。
为了使 SCP 有用，数据质量有一个下限，要求数据匮乏的国家改进抽样策略和数据质量，以尽可能获得无偏的多分类群数据。由于我们的敏感性分析可能不具有普遍性，保护规划者应更常规地使用敏感性分析，特别是依赖于多种 SDM 算法和替代组的组合，考虑纠正抽样偏差，并使用各种设置比较预测优先地点的空间模式。SCP 对连通性参数的敏感性意味着每个物种对栖息地丧失的反应是重要的知识空白。
