Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change
Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change
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DOI：
10.1111/j.1600-0587.2013.00138.x
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发表时间：
2013-09-01
期刊：
影响因子：
5.9
通讯作者：
Bollmann, Kurt
中科院分区：
文献类型：
--
作者：
Braunisch, Veronika;Coppes, Joy;Bollmann, Kurt
相关物种分布模型常用于预测气候变化下物种范围的变化。然而，气候变量通常表现出高度共线性，大多数统计方法需要在强相关变量中选择一个。当物种存在与气候参数之间的因果关系未知时，变量选择往往是任意的，或基于当前条件下的预测性能。虽然这只会轻微影响目前的范围预测，但当气候参数不同步变化时，未来的分布可能会有很大变化。为了预测中欧四种山鸟的当前（2010年）和未来（2050年）分布，我们使用了四个高度相关的气候变量和一组恒定的景观变量来研究这种不确定性的来源。模拟不同的参数化决策，我们生成了a)四个模型，包括每个气候变量；b)一个模型同时利用所有变量；c) a)预测的非加权平均值。我们比较了模型在当前条件下的准确性，预测了四种气候变化情景下的分布，并利用历史发生数据评估了一个物种的反预测。尽管当前和未来的变量相关性保持不变，而且模式在当前条件下的准确性也没有差异，但在所有气候变化情景中，对未来范围的预测变化很大。平均模式和包含所有气候变量的模式同时产生中间预测；然而，后者在反向预测中表现最好。这一模式在不同的建模方法中是一致的，表明在模棱两可的情况下包括多种气候预测因子是有益的。变量选择被证明是未来范围预测的不确定性的重要来源，很难用当代信息来控制。气候变量的微小但不同的变化，被恒定的整体相关模式所掩盖，可能导致未来范围预测之间的实质性差异，这些差异需要加以考虑，特别是当结果用于保护决策时。
Correlative species distribution models are frequently used to predict species' range shifts under climate change. However, climate variables often show high collinearity and most statistical approaches require the selection of one among strongly correlated variables. When causal relationships between species presence and climate parameters are unknown, variable selection is often arbitrary, or based on predictive performance under current conditions. While this should only marginally affect current range predictions, future distributions may vary considerably when climate parameters do not change in concert. We investigated this source of uncertainty using four highly correlated climate variables together with a constant set of landscape variables in order to predict current (2010) and future (2050) distributions of four mountain bird species in central Europe. Simulating different parameterization decisions, we generated a) four models including each of the climate variables singly, b) a model taking advantage of all variables simultaneously and c) an un-weighted average of the predictions of a). We compared model accuracy under current conditions, predicted distributions under four scenarios of climate change, and - for one species - evaluated back-projections using historical occurrence data. Although current and future variable-correlations remained constant, and the models' accuracy under contemporary conditions did not differ, future range predictions varied considerably in all climate change scenarios. Averaged models and models containing all climate variables simultaneously produced intermediate predictions; the latter, however, performed best in back-projections. This pattern, consistent across different modelling methods, indicates a benefit from including multiple climate predictors in ambiguous situations. Variable selection proved to be an important source of uncertainty for future range predictions, difficult to control using contemporary information. Small, but diverging changes of climate variables, masked by constant overall correlation patterns, can cause substantial differences between future range predictions which need to be accounted for, particularly when outcomes are intended for conservation decisions.