Estimating consensus and associated uncertainty between inherently different species distribution models
Estimating consensus and associated uncertainty between inherently different species distribution models
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DOI：
10.1111/2041-210x.12032
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发表时间：
2013-05-01
影响因子：
6.6
通讯作者：
Chuine, Isabelle
中科院分区：
文献类型：
--
作者：
Gritti, Emmanuel S.;Duputie, Anne;Chuine, Isabelle
在当前全球变化的背景下，预测生物群和物种分布的变化是至关重要的。到目前为止，大多数植被分布预测依赖于相关物种分布模型(SDMS)。然而，基于过程的或基于明确的生理描述的混合模型在未来的气候条件下可能对外推更稳健。模型预测之间的差异可能很大，导致环境利益攸关方持怀疑态度。在这里，我们建议组合几个基于生理反应的分布模型的输出，以产生一致认为的发生地图和相关的不确定性地图。共识地图依赖于每个SDM的条件预测。由于使用的模型是以过程为基础的，它们的误差可能会随气候变化而一致，因为在一组给定的气候条件下，模型中没有实施的一些过程可能是重要的。因此，共识模型的不确定性通过与当前气候条件有关的偏差图的多模型回归来评估，并可外推到气候预报。我们使用三个SDMS，在三种广泛分布的欧洲树木(Fagus sylvatica L.，Quercus robur L.和Pinus cervestris L.)上说明了这种方法，并预测了它们在两种情景下的分布。在预测当前事件方面，条件共识优于经典的模型共识方法(即使用单个SDM输出的平均值、中位数或加权平均值)。与个别SDMS的结果一致，有条件的共识预测，西里木和罗布的适生区将向东北欧扩展，而西里木的适生区将收缩。对未来发生的预测最不确定的是分布的边缘(特别是后缘)。我们的方法可以帮助建模师识别每个SDM的局限性，并帮助利益相关者确定模型、协议和最高确定性的区域。
Forecasting shifts in biome and species distribution is crucially needed in the current context of global change. So far, most projections of vegetation distribution rely on correlative species distribution models (SDMs). Yet, process-based or hybrid models based on explicit physiological description may be more robust to extrapolation under future climatic conditions. Differences between model projections may be wide, leading to scepticism among environmental stakeholders. Here, we propose to combine outputs of several distribution models based on physiological responses, to produce both consensual maps of occurrences and maps of associated uncertainty. The consensus map relies on the conditional projections of each SDM. Because the models used are based on processes, their errors are likely to vary consistently with climate as some processes not implemented in a model might be important under a given set of climatic conditions. Uncertainty of the consensus model is thus assessed through multimodel regression of deviance maps with respect to current climatic conditions, and can be extrapolated to forecast climates. We illustrate this approach using three SDMs, on three widely distributed European trees (Fagus sylvatica L., Quercus robur L. and Pinus sylvestris L.), and project their distributions under two scenarios. The conditional consensus outperforms classical methods of model consensus (i.e. to use the mean, the median or a weighted average of individual SDM outputs) in projecting current occurrences. Consistently, with the results of individual SDMs, the conditional consensus projects that the suitable areas for F. sylvatica and Q. robur will expand towards north-eastern Europe, while that of P. sylvestris will contract. Projections of future occurrence are most uncertain towards the margins of the distribution (particularly the trailing edge). Our approach can help modellers identify the limitations of each SDM and stakeholders pinpoint the regions of models agreement and highest certainty.