当前位置: X-MOL 学术Ecol Modell › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
DEB-tox and Data Gaps: Consequences for individual-level outputs
Ecological Modelling ( IF 3.1 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.ecolmodel.2020.109107
Chiara Accolla , Maxime Vaugeois , Pamela Rueda-Cediel , Adrian Moore , Gonçalo M. Marques , Purvaja Marella , Valery E. Forbes

Abstract In this paper, we analyze the impact of data gaps in the context of ecotoxicology when parameterizing a species using a widely recognized theory, the Dynamic Energy Budget (DEB) theory. DEB-based models are used in many ecological domains, and have been recognized as particularly useful in ecotoxicology. However, available datasets are often insufficient to accurately estimate all model parameters. We utilized a data-rich, parameterized species that is widely used in laboratory tests, Danio rerio (zebrafish). We compared two versions (“old” and “new”) of DEB parametrization methods when (i) removing datasets one-by-one, to understand if one dataset was particularly relevant for parameter estimation; and (ii) removing datasets cumulatively, to test how many datasets were necessary to achieve a meaningful parametrization. Using the results of the newer version of the parametrization routine, we checked how differences in parameter estimations could affect the modeled length and egg production of zebrafish. Finally, we assessed the relevance of these differences in an ecotoxicological context. For this purpose, we applied five hypothetical stressors, with different Physiological Modes of Action, to understand the impact of data gaps on estimating stressor effects on individual fish length and egg production. Our work shows that the new parametrization method is robust and efficient even when used with the minimum amount of data suggested by DEB theory. The parameters that are affected the most by data gaps are the maturity threshold parameters. At the individual level, data gaps affect mostly egg production. However, the link between data gaps and individual egg production is not always straightforward. Stressor effects can amplify or decrease differences between data-rich and data-poor scenarios, but usually their effects are consistent within each scenario, confirming DEB models as a powerful tool in ecological and ecotoxicological studies. We suggest careful consideration of the potential effects of data gaps when implementing DEB-based population models.

中文翻译:

DEB-tox 和数据差距:个人级别输出的后果

摘要 在本文中,我们在使用广泛认可的理论动态能量收支 (DEB) 理论对物种进行参数化时,分析了生态毒理学背景下数据差距的影响。基于 DEB 的模型用于许多生态领域,并被认为在生态毒理学中特别有用。然而,可用的数据集通常不足以准确估计所有模型参数。我们利用了一种广泛用于实验室测试的数据丰富、参数化的物种 Danio rerio(斑马鱼)。我们比较了 DEB 参数化方法的两个版本(“旧”和“新”),当(i)一个一个删除数据集时,以了解一个数据集是否与参数估计特别相关;(ii) 累积删除数据集,以测试实现有意义的参数化需要多少数据集。使用较新版本的参数化程序的结果,我们检查了参数估计的差异如何影响斑马鱼的建模长度和产蛋量。最后,我们评估了这些差异在生态毒理学背景下的相关性。为此,我们应用了五种具有不同生理作用模式的假设压力源,以了解数据差距对估计压力源对个体鱼长和产蛋量的影响的影响。我们的工作表明,即使在使用 DEB 理论建议的最少数据量时,新的参数化方法也是稳健有效的。受数据差距影响最大的参数是成熟度阈值参数。在个人层面,数据差距主要影响鸡蛋产量。然而,数据差距与个体鸡蛋产量之间的联系并不总是那么简单。压力效应可以放大或减少数据丰富和数据贫乏的情景之间的差异,但通常它们的影响在每个情景中是一致的,证实 DEB 模型是生态和生态毒理学研究的有力工具。我们建议在实施基于 DEB 的人口模型时仔细考虑数据差距的潜在影响。
更新日期:2020-09-01
down
wechat
bug