Disentangling the Formation of Contrasting Tree-Line Physiognomies Combining Model Selection and Bayesian Parameterization for Simulation Models
Disentangling the Formation of Contrasting Tree-Line Physiognomies Combining Model Selection and Bayesian Parameterization for Simulation Models
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结合仿真模型的模型选择和贝叶斯参数化来解开对比树线面貌的形成
DOI：
10.1086/659623
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
E. Gutiérrez
中科院分区：
文献类型：
--
作者：
I. Martínez;T. Wiegand;J. Camarero;E. Batllori;E. Gutiérrez
高山树线交错带的特点是在小的空间尺度上发生显著的变化，可能导致各种地貌。一组替代的个人为基础的模型进行了测试，从四个对比鲜明的钩松交错带在西班牙比利牛斯山脉中部的数据，揭示树线形成所需的过程的最小子集。采用贝叶斯方法结合马尔可夫链蒙特卡罗方法获得模型参数的后验分布，允许使用模型选择程序。真实的林木线的主要特征只出现在考虑个体生长率或死亡率对海拔梯度的非线性响应的模型中。树线地貌的变化主要反映了这些非线性响应的相对重要性的变化，而其他过程，如扩散限制和促进，发挥了次要作用。不同的非线性响应也决定了krummholz的存在或不存在，与最近的研究结果一致，突出了扩散和突然或krummholz树线对气候变化的不同响应。该方法可广泛应用于基于个体的仿真模型中，使这类模型中的模型选择和评估变得更加透明、有效和高效。
Alpine tree-line ecotones are characterized by marked changes at small spatial scales that may result in a variety of physiognomies. A set of alternative individual-based models was tested with data from four contrasting Pinus uncinata ecotones in the central Spanish Pyrenees to reveal the minimal subset of processes required for tree-line formation. A Bayesian approach combined with Markov chain Monte Carlo methods was employed to obtain the posterior distribution of model parameters, allowing the use of model selection procedures. The main features of real tree lines emerged only in models considering nonlinear responses in individual rates of growth or mortality with respect to the altitudinal gradient. Variation in tree-line physiognomy reflected mainly changes in the relative importance of these nonlinear responses, while other processes, such as dispersal limitation and facilitation, played a secondary role. Different nonlinear responses also determined the presence or absence of krummholz, in agreement with recent findings highlighting a different response of diffuse and abrupt or krummholz tree lines to climate change. The method presented here can be widely applied in individual-based simulation models and will turn model selection and evaluation in this type of models into a more transparent, effective, and efficient exercise.