总的来说,收集足够的经验数据来捕获定义复杂系统的整个过程是不可行的,本质上或从不同的地理或时间角度查看系统时。在这种情况下,另一种方法是考虑模型的可转移性,这是将为一个环境构建的模型转换为另一个不太为人所知的情况的行为。模型的可转移性和适应性可能是非常有益的-有助于模型的重用和适应性的方法,特别是对于数据有限的网站,将受益于广泛的模型吸收。除了开发模型所需的工作量减少之外,将模型转移到不同的应用程序上下文时,可以简化数据收集。本文提出的研究重点是一个案例研究,以确定和实施模型适应的指南。我们的研究将海草生态系统的一般动态贝叶斯网络(DBN)调整到节点相似的新位置,但是条件概率表有所不同。我们专注于位于Arcachon湾的两种海草(Zosteranoltei和Zosteramarina),法国。专家知识用于补充同行评审的文献,以确定哪些组件需要调整,包括模型和预期结果的参数化和量化。我们采用了语言标签和基于场景的启发,以从专家那里引出用于量化DBN的条件概率。根据拟议的准则,保留了一般DBN的模型结构,但是条件概率表适用于表征Zosteraspp中生长动态的节点。人口位于Arcachon湾,以及它们繁殖的季节性变化。特别注意光变量,因为它是海草生长和生理的关键驱动因素。我们的指南提供了一种使通用DBN适应特定生态系统的方法,以最大程度地提高模型重用性并最大程度地减少重新开发工作。从可转移性的角度来看,特别重要的是数据有限的生态系统指南,以及如何在这些情况下使用模拟和先前的预测方法。
In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well-known situation. Model transferability and adaptability may be extremely beneficial-approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement
guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a
seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of
seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our
guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re-development effort. Especially important from a transferability perspective are
guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.