School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia.
Finnish Museum of Natural History, University of Helsinki, Helsinki, FI-00140, Finland.
Conserv Biol. 2021 Aug;35(4):1309-1320. doi: 10.1111/cobi.13669. Epub 2021 Feb 9.
Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10-20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.
物种分布模型 (SDM) 越来越多地被用于保护和土地利用规划，作为描述生物多样性模式的输入。这些模型可以以不同的方式构建，关于数据准备、预测变量选择、模型拟合和评估的决策都会改变最终的预测结果。通常，物种的真实分布是未知的，并且缺乏独立的数据来验证选择哪种 SDM 变体。这种模型不确定性是规划者关注的问题。我们分析了在 25 个物种中，模型复杂度、预测因子、偏差处理和预测值阈值设置等 11 个常规决策如何改变保护优先级模式。使用 MaxEnt 创建模型，并通过 Zonation 运行以确定站点的优先级排名。尽管所有 SDM 变体的表现都很好（曲线下面积>0.7），但它们为物种和不同的保护优先级解决方案产生了空间上不同的预测结果。优先级受到不解决偏差或对预测值应用二进制阈值的决策的强烈影响；平均而言，分别有 40%和 35%的所有网格单元格得到了相反的优先级排名。强制使用高模型复杂度对保护解决方案的影响小于强制使用简单模型（分别有 14%和 24%的单元格具有相反的等级值）。使用较少的物种记录来构建模型或选择替代偏差处理方法具有中等影响（分别为 25%和 23%）。根据建模选择，优先级区域与基线解决方案的重叠程度低至 10-20%，对顶级和底部优先级的影响不同。我们的结果表明了基于模型的不确定性的程度，并量化了 SDM 构建决策的相对影响。当不确定最佳 SDM 方法和保护计划是什么时，解决不确定性或考虑替代方案对于那些改变计划最多的决策最为重要。
