Hatum Paula Sobenko, McMahon Kathryn, Mengersen Kerrie, Wu Paul Pao-Yen
School of Mathematical Sciences, Science and Engineering Faculty Queensland University of Technology Brisbane Queensland Australia.
Centre for Marine Ecosystems Research, School of Science Edith Cowan University Joondalup Western Australia Australia.
Ecol Evol. 2022 Aug 4;12(8):e9172. doi: 10.1002/ece3.9172. eCollection 2022 Aug.
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 ( and ) 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 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.
一般来说，收集足够的实证数据以涵盖定义复杂系统的全部过程是不可行的，无论是从本质上，还是从不同的地理或时间角度审视该系统时。在这种情况下，一种替代方法是考虑模型可转移性，即将为一种环境构建的模型应用于另一个不太知名的情况的行为。模型可转移性和适应性可能极其有益——有助于模型重用和调整的方法，特别是对于数据有限的地点，将受益于广泛的模型采用。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背景时，数据收集也可以简化。本文提出的研究聚焦于一个案例研究，以确定并实施模型调整指南。我们的研究将海草生态系统的通用动态贝叶斯网络（DBN）应用于一个新地点，该地点的节点相似，但条件概率表不同。我们关注法国阿卡雄湾的两种海草（和）。利用专家知识补充同行评审文献，以确定哪些组件需要调整，包括模型的参数化和量化以及期望的结果。我们采用语言标签和基于场景的启发方式，从专家那里获取用于量化DBN的条件概率。按照提出的指南，保留了通用DBN的模型结构，但针对表征阿卡雄湾种群生长动态及其繁殖季节变化的节点，调整了条件概率表。特别关注了光照变量，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将通用DBN应用于特定生态系统的方法，以最大限度地提高模型重用率并最小化重新开发的工作量。从可转移性角度来看，对于数据有限的生态系统以及如何在这些背景下使用模拟和先验预测方法的指南尤为重要。
