This paper explores the possibility of using the ensemble modelling paradigm to fully capture assessment uncertainty and improve the robustness of advice provision. We identify and discuss advantages and challenges of ensemble modelling approaches in the context of scientific advice. There are uncertainties associated with every phase in the stock assessment process: data collection, assessment model choice, model assumptions, interpretation of risk, up to the implementation of management advice. Additionally, the dynamics of fish populations are complex, and our incomplete understanding of those dynamics and limited observations of important mechanisms, necessitate that models are simpler than nature. The aim is for the model to capture enough of the dynamics to accurately estimate trends and abundance, and provide the basis for robust advice about sustainable harvests. The status quo approach to assessment modelling has been to identify the “best” model and generate advice from that model, mostly ignoring advice from other model configurations regardless of how closely they performed relative to the chosen model. We discuss and make suggestions about the utility of ensemble models, including revisions to the formal process of providing advice to management bodies, and recommend further research to evaluate potential gains in modelling and advice performance.

中文翻译：

---

为渔业管理提供科学建议的集合模型的运行

本文探讨了使用集成建模范式来充分捕捉评估不确定性并提高建议提供的稳健性的可能性。我们在科学建议的背景下确定并讨论集成建模方法的优势和挑战。存量评估过程的每个阶段都存在不确定性：数据收集、评估模型选择、模型假设、风险解释，直至管理建议的实施。此外，鱼类种群的动态是复杂的，我们对这些动态的不完全理解和对重要机制的有限观察，需要模型比自然简单。目的是让模型捕捉足够的动态来准确估计趋势和丰度，并为关于可持续收获的有力建议提供基础。评估建模的现状方法一直是识别“最佳”模型并从该模型中生成建议，大多数情况下忽略来自其他模型配置的建议，无论它们相对于所选模型执行得有多接近。我们讨论并就集成模型的效用提出建议，包括修改向管理机构提供建议的正式流程，并建议进一步研究以评估建模和建议性能的潜在收益。