Advanced fish stock assessment models

先进的鱼类种群评估模型

• 批准号: RGPIN-2016-04307
• 负责人: Cadigan, Noel
• 金额: $ 2.04万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682057
• 关键词: Advanced; fish; stock; assessment; models

Increasingly consumers want to buy fish that is caught sustainably - meaning that the fishery is not affecting the health of future fisheries. Although it can be difficult to decide if a fishery is sustainable, the United Nations Food and Agricultural Organization (FAO) and other organizations have developed detailed standards to help assess this. In Canada many fisheries have pursued certification with the International Marine Stewardship Council (MSC), which is a major organization involved in setting standards for sustainable fishing practices and certifying that fisheries meet these standards. MSC certified fish can result in better market access and improved prices to harvesters, leading to sustainable and more profitable fisheries in the long-term. ******This research program will focus on quantifying if existing fisheries harvest strategies (e.g. catch levels) are sustainable and proposing better alternatives when necessary. A related and important challenge in fish stock assessment is providing short (1-3 years) and medium-term (3-10 years) quantitative projections of the impacts of future management actions on changes in stock size. Mathematical models are used for these purposes and these models are based on the birth, growth, and death processes of the stock. Information available on these processes may come from large scale surveys or smaller but high intensity surveys. The complexity of the information and varying spatial scales is a problem for stock assessment. Best practice is evolving and case-specific.******The objectives of this program are to develop novel integrated and spatial stock assessments models. An integrated model strives to include all relevant data on stock productivity which is necessary to ensure that management decisions are based on the best possible information. Spatial models are required to include data sources from a range of spatial scales. Equally important, the productivity of a fish stock may depend on its spatial distribution. Initial applications will include Atlantic cod and snow crab. Integrated and spatial stock assessment models for such species do not exist because of the complexity of the species life-history processes and available data. Such models will be used to evaluate short to medium-term impacts of future harvest levels which will be important contributions to MSC certification initiatives for these stocks. These models will also be used to evaluate and optimize harvest strategies, with the objective of prosperous and sustainable fisheries.******This project will provide advanced training opportunities for students to develop skills for stock assessment and to assist governments and the fishing industry with their fisheries sustainability objectives and contribute to more prosperous fisheries in the future. Such skills are in high demand internationally and within Canada.

越来越多的消费者希望购买可持续捕捞的鱼类-这意味着渔业不会影响未来渔业的健康。虽然很难决定一个渔业是否可持续，但联合国粮食及农业组织（粮农组织）和其他组织已经制定了详细的标准来帮助评估这一点。在加拿大，许多渔业向国际海洋管理理事会（海安会）申请认证，该理事会是一个参与制定可持续捕捞方法标准并认证渔业符合这些标准的主要组织。 MSC认证的鱼类可以为捕捞者带来更好的市场准入和更高的价格，从而实现可持续和更有利可图的长期渔业。** 本研究计划将侧重于量化现有的渔业捕捞策略（如渔获量水平）是否可持续，并在必要时提出更好的替代方案。鱼类种群评估方面的一个相关和重要的挑战是对未来管理行动对种群规模变化的影响作出短期（1-3年）和中期（3-10年）的定量预测。数学模型用于这些目的，这些模型基于种群的出生，生长和死亡过程。关于这些过程的现有信息可能来自大规模调查或较小但高强度的调查。信息的复杂性和不同的空间尺度是种群评估的一个问题。最佳做法在不断发展，并因具体情况而异。该计划的目标是开发新的综合和空间资源评估模型。综合模型力求包括关于种群生产力的所有相关数据，这是确保管理决策以尽可能最佳的信息为基础所必需的。空间模型需要包括来自一系列空间尺度的数据源。同样重要的是，鱼类种群的生产力可能取决于其空间分布。最初的应用将包括大西洋鳕鱼和雪蟹。由于这些物种的生活史过程和现有数据的复杂性，目前还没有这些物种的综合和空间种群评估模型。这些模型将用于评估未来收获水平的短期至中期影响，这将是对海安会这些种群认证倡议的重要贡献。这些模型还将用于评估和优化捕捞策略，以实现繁荣和可持续的渔业目标。该项目将为学生提供高级培训机会，以发展种群评估技能，并协助政府和渔业实现其渔业可持续性目标，为未来更繁荣的渔业做出贡献。这些技能在国际和加拿大国内都有很高的需求。