Global sensitivity analysis for complex ecological models: a case study of riparian cottonwood population dynamics
Global sensitivity analysis for complex ecological models: a case study of riparian cottonwood population dynamics
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DOI：
10.1890/10-0506.1
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发表时间：
2011-06-01
影响因子：
5
通讯作者：
Fremier, Alexander K.
中科院分区：
文献类型：
--
作者：
Harper, Elizabeth B.;Stella, John C.;Fremier, Alexander K.
基于机制的生态模型是理解复杂生态系统驱动因素和做出明智的资源管理决策的宝贵工具。然而，如果忽略不确定性，则可以从围绕多个参数估计值具有很大程度的不确定性的模型中得出不准确的结论。这在具有多个相互作用变量的非线性系统中尤其如此。我们解决了这些问题的一个机制为基础的，人口模型的弗里蒙特杨（弗里蒙特棉白杨），占主导地位的河岸树种沿着美国西南部河流。许多棉白杨种群在大范围的洪泛区改造和流量调节之后已经下降。因此，需要准确的预测模型来分析未来气候变化和水资源管理决策的影响。为了量化参数不确定性的影响，我们开发了一种分析方法，将全局敏感性分析（GSA）与分类和回归树（CART）和随机森林（一种自举CART方法）相结合。我们使用GSA来量化所有参数估计值的全范围不确定性的相互作用，随机森林根据参数对模型预测的总影响对参数进行排名，CART用于识别高阶相互作用。GSA模拟产生了广泛的预测，包括10- 100%的年度发芽频率，0- 50%的年度第一年生存频率，0- 100%的补丁占用。这种变化主要是由复杂的非生物参数之间的相互作用，包括毛细管边缘高度，水位流量关系，和河漫滩淤积率，与生物因素相互作用，影响生存的解释。模型精密度主要受相关不确定性最小的经过充分研究的参数估计值的影响，并且几乎不受参数估计值的影响，因为没有可用的经验数据，因此存在很大程度的不确定性。因此，改进模型预测的研究不应总是集中在研究最少的参数上，而应集中在模型预测最敏感的参数上。我们提倡结合使用全局敏感性分析、CART和随机森林来：（1）通过对变量重要性进行排序来确定研究工作的优先级;（2）通过关注最重要的参数来有效地改进模型;（3）阐明复杂的模型属性，包括非线性相互作用。我们提出了一个分析框架，可以适用于任何模型与多个不确定参数估计。
Mechanism-based ecological models are a valuable tool for understanding the drivers of complex ecological systems and for making informed resource-management decisions. However, inaccurate conclusions can be drawn from models with a large degree of uncertainty around multiple parameter estimates if uncertainty is ignored. This is especially true in nonlinear systems with multiple interacting variables. We addressed these issues for a mechanism-based, demographic model of Populus fremontii (Fremont cottonwood), the dominant riparian tree species along southwestern U.S. rivers. Many cottonwood populations have declined following widespread floodplain conversion and flow regulation. As a result, accurate predictive models are needed to analyze effects of future climate change and water management decisions. To quantify effects of parameter uncertainty, we developed an analytical approach that combines global sensitivity analysis (GSA) with classification and regression trees (CART) and Random Forest, a bootstrapping CART method. We used GSA to quantify the interacting effects of the full range of uncertainty around all parameter estimates, Random Forest to rank parameters according to their total effect on model predictions, and CART to identify higher-order interactions. GSA simulations yielded a wide range of predictions, including annual germination frequency of 10-100%, annual first-year survival frequency of 0-50%, and patch occupancy of 0-100%. This variance was explained primarily by complex interactions among abiotic parameters including capillary fringe height, stage-discharge relationship, and floodplain accretion rate, which interacted with biotic factors to affect survival. Model precision was primarily influenced by well-studied parameter estimates with minimal associated uncertainty and was virtually unaffected by parameter estimates for which there are no available empirical data and thus a large degree of uncertainty. Therefore, research to improve model predictions should not always focus on the least-studied parameters, but rather those to which model predictions are most sensitive. We advocate the combined use of global sensitivity analysis, CART, and Random Forest to: (1) prioritize research efforts by ranking variable importance; (2) efficiently improve models by focusing on the most important parameters; and (3) illuminate complex model properties including nonlinear interactions. We present an analytical framework that can be applied to any model with multiple uncertain parameter estimates.