AIM Conservation planning exercises increasingly rely on species distributions predicted either from one particular statistical model or, more recently, from an ensemble of models (i.e. ensemble forecasting). However, it has not yet been explored how different ways of summarizing ensemble predictions affect conservation planning outcomes. We evaluate these effects and compare commonplace consensus methods, applied before the conservation prioritization phase, to a novel method that applies consensus after reserve selection. LOCATION Europe. METHODS We used an ensemble of predicted distributions of 146 Western Palaearctic bird species in alternative ways: four different consensus methods, as well as distributions discounted with variability, were used to produce inputs for spatial conservation prioritization. In addition, we developed and tested a novel method, in which we built 100 datasets by sampling the ensemble of predicted distributions, ran a conservation prioritization analysis on each of them and averaged the resulting priority ranks. We evaluated the conservation outcome against three controls: (i) a null control, based on random ranking of cells; (2) the reference solution, based on an expert-refined dataset; and (3) the independent solution, based on an independent dataset. RESULTS Networks based on predicted distributions were more representative of rare species than randomly selected networks. Alternative methods to summarize ensemble predictions differed in representativeness of resulting reserve networks. Our novel method resulted in better representation of rare species than pre-selection consensus methods. MAIN CONCLUSIONS Retaining information about the variation in the predicted distributions throughout the conservation prioritization seems to provide better results than summarizing the predictions before conservation prioritization. Our results highlight the need to understand and consider model-based uncertainty when using predicted distribution data in conservation prioritization.

中文翻译：

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保护优先级中的集合分布模型：从共识预测到共识储备网络


AIM 保护规划工作越来越依赖于根据一个特定的统计模型或最近的一组模型（即集合预测）预测的物种分布。然而，尚未探索总结集合预测的不同方法如何影响保护规划结果。我们评估这些影响，并将在保护优先阶段之前应用的常见共识方法与在保护区选择后应用共识的新颖方法进行比较。地点欧洲。方法 我们以不同的方式使用了 146 种西古北界鸟类的预测分布集合：四种不同的共识方法以及随变异性折扣的分布，用于生成空间保护优先级的输入。此外，我们开发并测试了一种新颖的方法，其中我们通过对预测分布的集合进行采样来构建 100 个数据集，对每个数据集进行保护优先级分析，并对所得优先级排名进行平均。我们根据三个对照评估了保护结果：(i) 空对照，基于细胞的随机排序； (2) 基于专家精炼数据集的参考解决方案； (3) 基于独立数据集的独立解决方案。结果基于预测分布的网络比随机选择的网络更能代表稀有物种。总结集合预测的替代方法在所得储备网络的代表性方面有所不同。我们的新方法比预选共识方法更好地代表了稀有物种。 主要结论在整个保护优先排序过程中保留有关预测分布变化的信息似乎比在保护优先排序之前总结预测提供了更好的结果。我们的结果强调，在保护优先级中使用预测分布数据时，需要理解和考虑基于模型的不确定性。