Ensemble modelling of species distribution: the effects of geographical and environmental ranges
Ensemble modelling of species distribution: the effects of geographical and environmental ranges
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DOI：
10.1111/j.1600-0587.2010.06152.x
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发表时间：
2011-02-01
期刊：
影响因子：
5.9
通讯作者：
Lek, Sovan
中科院分区：
文献类型：
--
作者：
Grenouillet, Gael;Buisson, Laetitia;Lek, Sovan
本研究的目的是分析物种的地理和环境范围对物种分布模型（SDM）的预测性能的影响。我们探讨了集成建模方法的有用性，并测试了物种属性是否会影响这种方法的结果。八个SDM被用来模拟目前的分布在法国1110流段的35种鱼类。我们首先量化了每个鱼类的预测结果之间的共识。接下来，我们通过取单个模型预测的平均值来创建平均模型，并测试平均模型是否提高了单个SDM的预测性能。最后，我们描述了鱼类沿着四个梯度的范围：纬度，热，流梯度（即上游-下游）和海拔。在考虑系统发育相关性和物种流行率的影响后，这四个物种属性与观察到的SDMS之间的共识和预测性能的变化有关，通过使用广义估计方程。我们的研究结果突出了集成方法的实用性，用于确定预测之间的协议的地理区域。虽然物种的地理范围对SDM的性能没有影响，但我们证明，对于具有低热量和海拔范围的物种，可以获得更一致和准确的预测，验证了专家物种产量模型比通才模型具有更高准确性的假设。我们强调，通过使用平均模型可以显著提高SDM的准确性。此外，这些改进是较高的物种与较小的范围沿着四个梯度研究。地理范围和物种的范围沿着环境梯度提供了有前途的见解，我们的物种分布模型的不确定性的理解。
The aim of this study was to analyse the effects of species geographical and environmental ranges on the predictive performances of species distribution models (SDMs). We explored the usefulness of ensemble modelling approaches and tested whether species attributes influenced the outcomes of such approaches. Eight SDMs were used to model the current distribution of 35 fish species at 1110 stream sections in France. We first quantified the consensus among the resulting set of predictions for each fish species. Next, we created an average model by taking the average of the individual model predictions and tested whether the average model improved the predictive performances of single SDMs. Lastly, we described the ranges of fish species along four gradients: latitudinal, thermal, stream gradient (i.e. upstream-downstream) and elevation. After accounting for the effects of phylogenetic relatedness and species prevalence, these four species attributes were related to the observed variations in both consensus among SDMs and predictive performances by using generalized estimation equations. Our results highlight the usefulness of ensemble approaches for identifying geographical areas of agreement among predictions. Although the geographical extent of species had no effect on the performances of SDMs, we demonstrated that more consensual and accurate predictions were obtained for species with low thermal and elevation ranges, validating the hypothesis that specialist species yield models with higher accuracy than generalist ones. We emphasized that significant improvements in the accuracy of SDMs can be achieved by using an average model. Furthermore, these improvements were higher for species with smaller ranges along the four gradients studied. The geographical extent and ranges of species along environmental gradients provide promising insights into our understanding of uncertainties in species distribution modelling.