Bioclimate envelope model predictions for natural resource management: dealing with uncertainty
Bioclimate envelope model predictions for natural resource management: dealing with uncertainty
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DOI：
10.1111/j.1365-2664.2010.01830.x
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发表时间：
2010-08-01
影响因子：
5.7
通讯作者：
Hamann, Andreas
中科院分区：
文献类型：
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作者：
Mbogga, Michael S.;Wang, Xianli;Hamann, Andreas
生物气候包络模型被广泛用于预测气候变化下物种的潜在分布，但在概念上也适用于使政策和做法与预期或观察到的气候变化相匹配，例如通过重新造林中的物种选择。然而，由于不同的气候变化情景，建模方法和其他因素，生物气候包络模型的预测具有很大的不确定性。2在本文中，我们提出了一种新的方法来评估基于模型的自然资源管理建议的不确定性。我们不是从整体上评估建模结果的可变性，而是从多个模型运行中提取特定的统计数据，例如物种对特定再造林地点的适用性。然后，对该统计量进行方差分析，旨在缩小从业者需要预测的范围。在加拿大西部的四个案例研究中，我们评估了五个来源的不确定性与两到五个治疗水平，包括建模方法，插值类型的气候数据，包括地形土壤变量，选择的大气环流模型，选择排放情景。作为因变量，我们评估了144个处理组合下树种生境和生态系统分布的变化。对于这些案例研究，我们发现，包括地形土壤变量作为预测因子减少了四分之一的预计栖息地的变化，和大气环流模型有重大的主要影响。我们的对比建模方法主要是通过与气候变化预测的相互作用项来增加不确定性，即方法对特定气候变化情景（例如温暖和潮湿情景）的表现不同，但对其他情景的表现相似。合成与应用。方差分量的划分有助于解释建模结果，并揭示如何最有效地改进模型。量化主要影响的方差分量和不确定性来源之间的相互作用，也为研究人员提供了机会，可以过滤掉生物学和统计学上不合理的建模结果，为从业人员提供更好的预测范围，以便进行基于气候的自然资源管理。
Bioclimate envelope models are widely used to predict the potential distribution of species under climate change, but they are conceptually also suitable to match policies and practices to anticipated or observed climate change, for example through species choice in reforestation. Projections of bioclimate envelope models, however, come with large uncertainties due to different climate change scenarios, modelling methods and other factors.2 In this paper we present a novel approach to evaluate uncertainty in model-based recommendations for natural resource management. Rather than evaluating variability in modelling results as a whole, we extract a particular statistic of interest from multiple model runs, e.g. species suitability for a particular reforestation site. Then, this statistic is subjected to analysis of variance, aiming to narrow the range of projections that practitioners need to consider.3. In four case studies for western Canada we evaluate five sources of uncertainty with two to five treatment levels, including modelling methods, interpolation type for climate data, inclusion of topo-edaphic variables, choice of general circulation models, and choice of emission scenarios. As dependent variables, we evaluate changes to tree species habitat and ecosystem distributions under 144 treatment combinations.4. For these case studies, we find that the inclusion of topo-edaphic variables as predictors reduces projected habitat shifts by a quarter, and general circulation models had major main effects. Our contrasting modelling approaches primarily contributed to uncertainty through interaction terms with climate change predictions, i.e. the methods behaved differently for particular climate change scenarios (e.g. warm & moist scenarios) but similar for others.5. Synthesis and applications. Partitioning of variance components helps with the interpretation of modelling results and reveals how models can most efficiently be improved. Quantifying variance components for main effects and interactions among sources of uncertainty also offers researchers the opportunity to filter out biologically and statistically unreasonable modelling results, providing practitioners with an improved range of predictions for climate-informed natural resource management.