Department of Forest and Natural Resources Management, SUNY College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York 13210, USA.
Ecol Appl. 2011 Jun;21(4):1225-40. doi: 10.1890/10-0506.1.
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.
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资源管理决策的有价值的工具。但是，如果忽略不确定性，那么对于具有多个参数估计不确定性的模型，可能会得出不准确的结论。对于具有多个相互作用变量的非线性系统来说，尤其如此。我们针对西南美国河流沿岸的主要河岸树种 Populus fremontii（弗雷斯诺棉白杨）的基于机制的人口模型解决了这些问题。在洪泛区转换和水流调节广泛进行之后，许多棉白杨种群已经减少。因此，需要准确的预测模型来分析未来气候变化和水管理决策的影响。为了量化参数不确定性的影响，我们开发了一种结合全局敏感性分析（GSA）与分类和回归树（CART）和随机森林（一种自举 CART 方法）的分析方法。我们使用 GSA 来量化所有参数估计不确定性的全范围的相互作用影响，使用随机森林根据其对模型预测的总影响对参数进行排名，并使用 CART 来识别高阶相互作用。GSA 模拟产生了广泛的预测结果，包括年发芽频率为 10-100％，年首次生存频率为 0-50％，和斑块占有率为 0-100％。这种变化主要是由包括毛细带高度、水位流量关系和洪泛区淤积率在内的非生物参数之间的复杂相互作用引起的，这些相互作用与生物因素相互作用，影响了生存。模型精度主要受研究较好的参数估计值的影响，这些参数估计值的不确定性最小，几乎不受没有可用经验数据的参数估计值的影响，因此具有很大的不确定性。因此，提高模型预测精度的研究不应总是侧重于研究最少的参数，而应侧重于对模型预测最敏感的参数。我们提倡结合使用全局敏感性分析、CART 和随机森林：（1）通过对变量重要性进行排名来优先考虑研究工作；（2）通过专注于最重要的参数来有效地改进模型；（3）阐明包括非线性相互作用在内的复杂模型属性。我们提出了一个可应用于具有多个不确定参数估计值的任何模型的分析框架。
