Applying a mechanistic model to predict interacting effects of chemical exposure and food availability on fish populations

Mintram, Kate; Maynard, Samuel K.; Brown, A. Ross; Boyd, Robin J.; Johnston, Alice S. A.; Sibly, Richard M.; Thorbek, Pernille; Tyler, Charles R.

doi:10.1016/j.aquatox.2020.105483

Cited by 6 publications

(3 citation statements)

References 84 publications

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“…This model structure also affects how chemical effects are implemented in these models. Energy‐budget models would allow prochloraz effects to be implemented on energy‐allocation decisions based on a known AOP (see Mintram et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Matrix models have been used to link vitellogenin (an estrogen-dependent yolk protein widely used as a biomarker of exposure to estrogenic EACs) and reductions in fecundity to changes in population abundance in the fathead minnow (Miller & Ankley, 2004) as well as to assess their vulnerability to pesticide exposure in 23 different European fish species (Ibrahim et al, 2014). More recently, individual-based models (IBMs) have been developed for zebrafish (Danio rerio; Beaudouin et al, 2015;Hazlerigg et al, 2014), three-spined stickleback (Gasterosteus aculeatus, David et al, 2019;Mintram et al, 2018Mintram et al, , 2020, and brown trout (Salmo trutta; Railsback et al, 2009) among others.…”

Section: Decision Frameworkmentioning

confidence: 99%

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Harnessing Modeling for Assessing the Population Relevance of Exposure to Endocrine‐Active Chemicals

Hazlerigg

Mintram

Tyler

et al. 2023

Enviro Toxic and Chemistry

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The presence of endocrine‐active chemicals (EACs) in the environment continues to cause concern for wildlife given their potential for adverse effects on organisms. However, there is a significant lack of understanding about the potential effects of EACs on populations. This has real‐world limitations for EAC management and regulation, where the aim in environmental risk assessment is to protect populations. We propose a methodological approach for the application of modeling in addressing the population relevance of EAC exposure in fish. We provide a case study with the fungicide prochloraz to illustrate how this approach could be applied. We used two population models, one for brown trout (Salmo trutta; inSTREAM) and the other for three‐spined stickleback (Gasterosteus aculeatus) that met regulatory requirements for development and validation. Effects data extracted from the literature were combined with environmentally realistic exposure profiles generated with the FOCUS SW software. Population‐level effects for prochloraz were observed in some modeling scenarios (hazard‐threshold [HT]) but not others (dose–response), demonstrating the repercussions of making different decisions on implementation of exposure and effects. The population responses, defined through changes in abundance and biomass, of both trout and stickleback exposed to prochloraz were similar, indicating that the use of conservative effects/exposure decisions in model parameterization may be of greater significance in determining population‐level adverse effects to EAC exposure than life‐history characteristics. Our study supports the use of models as an effective approach to evaluate the adverse effects of EACs on fish populations. In particular, our HT parameterization is proposed for the use of population modeling in a regulatory context in accordance with Commission Regulation (EU) 2018/605. Environ Toxicol Chem 2023;42:1624–1640. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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Section: Discussionmentioning

confidence: 99%

Section: Decision Frameworkmentioning

confidence: 99%

Harnessing Modeling for Assessing the Population Relevance of Exposure to Endocrine‐Active Chemicals

Hazlerigg

Mintram

Tyler

et al. 2023

Enviro Toxic and Chemistry

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“…In this respect, a good practice that should be encouraged is not only to calibrate, but also to validate a model alongside all of its sub-models, i.e., at all affected levels of biological organisation (as in Mintram et al, 2020). With this aim, a framework for the hierarchical assessment of aquatic ecosystem models has been proposed very recently (Hipsey et al, 2020).…”

Section: Adjusting Complexity Level To Current Knowledge Through Model Calibration Validation and Uncertainty Analysismentioning

confidence: 99%

Hitting the sweet spot of complexity: Reasons why the development of new custom-tailored models is still warranted and should be encouraged in aquatic sciences

2021

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Process-based aquatic ecosystem models are increasingly being developed and used in freshwater ecology and other aquatic sciences, as they are powerful tools to gain a mechanistic understanding of ecological processes and inform policy and decision making in environmental management. Over the last decades, not only have these models increased considerably in number, but also in their degree of complexity, which can improve predictive capacity. Nevertheless, it is also because of the higher degree of complexity of many models of current widespread use, that not all the hypotheses and assumptions upon which they have been built are always met by the relatively simple experiments that characterise fundamental ecological research. This is true for both laboratory experiments and those carried out outdoors, under semi-controlled conditions. Examples of the latter are the mesocosms experiments through which several novel questions are nowadays being addressed. In this article, we present our views on why the development of new custom-tailored aquatic ecosystem models of varying degrees of complexity is still very much warranted and should, therefore, be encouraged despite arguments in favour of always increasing complexity and against the creation of new models that are largely based on previously published ones (‘reinventing the wheel’). Deciding on the right complexity level should be linked to the biological organisation levels that are relevant to the specific research questions, and to how much knowledge on the subject is already available. Spatial and temporal scales are additional factors that a modeller should weigh in when deciding on the complexity of a model. To address these needs in the long term, the modelling community needs to grow. Training a new generation of model developers will not only benefit other scientists to better design future experiments but will also facilitate interdisciplinary research and teamwork, approaches such as ensemble modelling, as well as the communication of science to managers and many other stakeholders.

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Applied fish bioenergetics

et al. 2022

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Applying a mechanistic model to predict interacting effects of chemical exposure and food availability on fish populations

Cited by 6 publications

References 84 publications

Harnessing Modeling for Assessing the Population Relevance of Exposure to Endocrine‐Active Chemicals

Harnessing Modeling for Assessing the Population Relevance of Exposure to Endocrine‐Active Chemicals

Hitting the sweet spot of complexity: Reasons why the development of new custom-tailored models is still warranted and should be encouraged in aquatic sciences

Applied fish bioenergetics

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