Dormann Carsten F, Schweiger Oliver, Arens P, Augenstein I, Aviron St, Bailey Debra, Baudry J, Billeter R, Bugter R, Bukácek R, Burel F, Cerny M, Cock Raphaël De, De Blust Geert, DeFilippi R, Diekötter Tim, Dirksen J, Durka W, Edwards P J, Frenzel M, Hamersky R, Hendrickx Frederik, Herzog F, Klotz St, Koolstra B, Lausch A, Le Coeur D, Liira J, Maelfait J P, Opdam P, Roubalova M, Schermann-Legionnet Agnes, Schermann N, Schmidt T, Smulders M J M, Speelmans M, Simova P, Verboom J, van Wingerden Walter, Zobel M
Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany.
Ecol Lett. 2008 Mar;11(3):235-44. doi: 10.1111/j.1461-0248.2007.01142.x. Epub 2007 Dec 7.
Observed patterns of species richness at landscape scale (gamma diversity) cannot always be attributed to a specific set of explanatory variables, but rather different alternative explanatory statistical models of similar quality may exist. Therefore predictions of the effects of environmental change (such as in climate or land cover) on biodiversity may differ considerably, depending on the chosen set of explanatory variables. Here we use multimodel prediction to evaluate effects of climate, land-use intensity and landscape structure on species richness in each of seven groups of organisms (plants, birds, spiders, wild bees, ground beetles, true bugs and hoverflies) in temperate Europe. We contrast this approach with traditional best-model predictions, which we show, using cross-validation, to have inferior prediction accuracy. Multimodel inference changed the importance of some environmental variables in comparison with the best model, and accordingly gave deviating predictions for environmental change effects. Overall, prediction uncertainty for the multimodel approach was only slightly higher than that of the best model, and absolute changes in predicted species richness were also comparable. Richness predictions varied generally more for the impact of climate change than for land-use change at the coarse scale of our study. Overall, our study indicates that the uncertainty introduced to environmental change predictions through uncertainty in model selection both qualitatively and quantitatively affects species richness projections.
在景观尺度上观察到的物种丰富度模式(γ多样性)并不总是能归因于一组特定的解释变量,而是可能存在质量相似的不同替代解释统计模型。因此,根据所选的解释变量集,对环境变化(如气候或土地覆盖变化)对生物多样性影响的预测可能会有很大差异。在这里,我们使用多模型预测来评估气候、土地利用强度和景观结构对欧洲温带地区七组生物(植物、鸟类、蜘蛛、野生蜜蜂、步甲、椿象和食蚜蝇)中每组物种丰富度的影响。我们将这种方法与传统的最佳模型预测进行对比,通过交叉验证表明,传统最佳模型预测的准确性较低。与最佳模型相比,多模型推断改变了一些环境变量的重要性,因此对环境变化影响给出了不同的预测。总体而言,多模型方法的预测不确定性仅略高于最佳模型,预测物种丰富度的绝对变化也相当。在我们研究的粗略尺度上,气候变化影响的丰富度预测通常比土地利用变化的预测变化更大。总体而言,我们的研究表明,通过模型选择的不确定性引入到环境变化预测中的不确定性,在定性和定量上都会影响物种丰富度预测。