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使生态模型更充分。

Making ecological models adequate.

机构信息

Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, 94720, USA.

Schools of Mathematical Sciences and Life Sciences, University of KwaZulu, Natal, South Africa.

出版信息

Ecol Lett. 2018 Feb;21(2):153-166. doi: 10.1111/ele.12893. Epub 2017 Dec 27.

Abstract

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

摘要

迫切需要对生态模型的充分性进行批判性评估,以提高其在发展理论中的效用,并使环境管理者和政策制定者能够做出明智的决策。支持不力的管理可能会对环境和社会造成有害、昂贵或不可逆转的影响。在这里,我们检查了生态建模中的常见问题,并提出了改进建模框架的标准。在给定可用数据和对生态结构的理解的情况下,适当的过程描述水平对于构建最佳模型至关重要。没有数据支持的模型细节通常会导致过度参数化和模型性能不佳。相反,缺乏机械细节可能会限制模型预测管理对生态系统响应的能力。使用模型的生态研究应遵循一套模型充分性评估协议,包括:提出一系列关于状态和控制变量选择、数据确定性以及分析的敏感性和有效性的关键问题。我们还需要改进模型阐述、细化和粗粒化程序,以更好地理解我们的模型的相关性和充分性,以及它们在推进理论、改善预测、解决问题和管理方面的作用。

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