Predictive power and risk assessment for marine fisheries in Canada

加拿大海洋渔业的预测能力和风险评估

• 批准号: RGPIN-2014-03934
• 负责人: Cox, Sean
• 金额: $ 1.75万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587503
• 关键词: Predictive; power; risk; assessment; marine

All fisheries exist within feedback systems that link harvest decisions to highly uncertain environment-fish stock dynamics. Failure rates of historical fishery management procedures, combined with the possibility that environment-stock interactions are changing faster than we can learn about them, raises concerns about our ability to accurately predict risks of future fishery failure. I propose research aimed at improving fishery models used to represent environment-stock interactions in fishery management strategy research. I will use (i) empirical analyses to test fishery model predictive power, (ii) empirical and simulation models to evaluate mechanistic hypotheses to explain changes in key population dynamics processes, and (iii) simulation of fishery management systems to identify how perceptions of risk depend on underlying model assumptions. Fishery system simulations integrating realistic ecological processes into fishery management planning will help identify critical knowledge gaps that affect risks in Canadian fisheries. Our broad selection of species will increase both the power of specific case studies and the broader relevance to management strategy evaluation research.

所有渔业都存在于将捕捞决定与高度不确定的环境--鱼类种群动态联系起来的反馈系统中。历史渔业管理程序的失败率，再加上环境-种群相互作用的变化速度可能快于我们对它们的了解，引发了人们对我们准确预测未来渔业失败风险的能力的担忧。我提出的研究旨在改进渔业管理战略研究中用于表示环境-种群相互作用的渔业模型。我将使用(I)经验分析来测试渔业模型的预测能力，(Ii)经验和模拟模型来评估解释关键种群动态过程变化的机制假设，以及(Iii)对渔业管理系统的模拟，以确定对风险的感知如何依赖于基本的模型假设。渔业系统模拟将现实的生态过程纳入渔业管理规划，将有助于查明影响加拿大渔业风险的关键知识差距。我们广泛的物种选择将增加特定案例研究的力量，并与管理战略评估研究具有更广泛的相关性。