气候变化对物种分布影响模拟中的不确定性组分分割与制图--以油松为例  

Partitioning and mapping the sources of variations in the ensemble forecasting of species distribution under climate change: a case study of Pinus tabulaeformis

作　　者：张雷[1] 刘世荣[1] 孙鹏森[1] 王同立 

机构地区：[1]中国林业科学研究院森林生态环境与保护研究所,国家林业局森林生态环境重点实验室,北京100091 [2]Department of Forest Sciences, University of British Columbia, B.C. Canada V6T 1Z4

出　　处：《生态学报》2011年第19期5749-5761,共13页Acta Ecologica Sinica

基　　金：国家自然科学基金重大项目课题(30590383);林业公益性行业重大科研专项(200804001,201104006);中国林业科学研究院院所基金海外人才专项(CAFYBB2008007);国家科技部国际科技合作项目(2008DFA32070);“十一五”国家科技攻关项目(2006BAD03A04)

摘　　要：物种分布模型是预测评估气候变化对物种分布影响的主要工具。为了降低物种分布模型在预测过程中的不确定性,近期有学者提出了采用组合预测的新方法,即采用多套建模数据、模型技术,模型参数,以及环境情景数据对物种分布进行预测,构成物种分布预测集合。但是,组合预测中各组分对变异的贡献还知之甚少,因此有必要把变异组分来源进行分割,以更有效地利用组合预测方法来降低模型预测中的不确定性。以油松为例,采用8个生态位模型,9套模型训练数据,3个GCM模型和一个SRES(A2)排放情景,模型分析了油松当前(1961—1990年)和未来气候条件下3个时间段(2010—2039年,2040—2069年,2070—2099年)的潜在分布。共计得到当前分布预测数据72套,未来每个时间段分布数据216套。采用开发的ClimateChina软件进行当前和未来气候数据的降尺度处理。采用Kappa、真实技巧统计方法(TSS)和接收机工作特征曲线下的面积(AUC)对模型预测能力进行评估。结果表明,随机森林(RF)、广义线性模型(GLM),广义加法模型(GAM)、多元自适应样条函数(MARS)以及助推法(GBM)预测效果较好,几乎不受建模数据之间差异的影响。混合判别分析模型(MDA)对建模数据之间的差异非常敏感,甚至出现建模失败现象。采用三因素方差分析方法对组合预测中的不确定性来源进行变异分割,结果表明,模型之间的差异对模拟预测结果不确定性的贡献最大且所占比例极高,而建模数据之间的差异贡献最小,GCM贡献居中。研究将有助于加深对物种分布模拟预测中不确定性的认识。Niche-based models（NM）,which are based on the correlation between species occurrence records and environmental predictors,have been generally used to project and assess the effects of climate change on species distribution range.However,most of NMs do not produce consistent prediction results of species distribution,with significant projection differences in either magnitude or direction of a species range change in response to climate change.One of the recommended solutions is to use ensembles of forecasts by simulating from more than one set of model training data,model techniques,model parameters and environmental scenarios combinations to identify a range of uncertainties.However,ensemble forecasting is in its infancy in assessing the effects of climate change on species distribution,as little is known about the sources of variations and its contributions to uncertainties in ensemble forecasting.The objective of this research is to partition and map uncertainties in ensemble forecasting of species range change under climate change.In our study,the eight niche-based models（Random forest,RF;Generalized boosted method,GBT;Generalized linear models,GLM;Generalized additive models,GAM;Classification tree analysis,CTA;Artificial neural network,ANN;Mixture discriminant analysis,MDA;multivariate adaptive regression splines,MARS） and the nine sets of model training dataset（initial data were randomly divided into two parts： mode training data and testing data）,three global circulation models（GCM）（MIROC32_medres,JP;CCCMA_CGCM3;CA;BCCR-BCM2.0,NW） and one pessimistic SRES emissions scenarios（A2） were used to simulate current potential distribution（Baseline,1961—1990） and to project future potential distributions at three time-slices（2010—2039,2040—2069,2070—2099） of Chinese pine（Pinus tabulaeformis）.Totally,we obtained 72 predictions for the current distribution and 216 projections for the future distribution（herein termed ensemble forecasting）.The area under the curve（A

关 键 词：物种分布模拟 不确定性 变异分割 气候变化 油松 

分 类 号：Q948[生物学—植物学] S791.254[农业科学—林木遗传育种]