Advanced fish stock assessment models
先进的鱼类种群评估模型
基本信息
- 批准号:RGPIN-2016-04307
- 负责人:
- 金额:$ 2.04万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2018
- 资助国家:加拿大
- 起止时间:2018-01-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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--这意味着渔业不会影响未来渔业的健康。尽管很难确定渔业是否可持续,但联合国粮食及农业组织(FAO)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,许多渔业已向国际海洋管理理事会(海洋管理理事会)申请认证,该理事会是一个主要组织,参与制定可持续捕捞做法的标准,并证明渔业符合这些标准。经MSC认证的鱼可以带来更好的市场准入和更好的收割机价格,从而在长期内带来可持续和更有利可图的渔业。*本研究方案将侧重于量化现有渔业捕捞战略(例如渔获量)是否可持续,并在必要时提出更好的替代方案。鱼类种群评估的一个相关而重要的挑战是提供关于未来管理行动对种群规模变化的影响的短期(1-3年)和中期(3-10年)定量预测。数学模型用于这些目的,这些模型是基于股票的出生、生长和死亡过程。有关这些过程的信息可能来自大规模调查,也可能来自规模较小但强度较高的调查。信息的复杂性和变化的空间尺度是股票评估的一个问题。最佳实践是不断发展的,并因具体情况而异。*该计划的目标是开发新的综合和空间存量评估模型。一个综合模式力求包括所有有关库存生产力的数据,这对于确保管理决策以尽可能最佳的信息为基础是必要的。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。由于物种生活史过程和现有数据的复杂性,这类物种的综合和空间种群评估模型尚不存在。这些模型将被用来评估未来收成水平的短期和中期影响,这将是对这些种群的MSC认证倡议的重要贡献。这些模式还将用于评估和优化捕捞战略,以繁荣和可持续渔业为目标。*该项目将为学生提供高级培训机会,培养评估种群的技能,协助各国政府和捕鱼业实现其渔业可持续性目标,并促进未来更繁荣的渔业。这种技能在国际上和加拿大国内都很受欢迎。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Cadigan, Noel其他文献
Estimation of the Von Bertalanffy growth model when ages are measured with error
- DOI:
10.1111/rssc.12340 - 发表时间:
2019-08-01 - 期刊:
- 影响因子:1.6
- 作者:
Dey, Rajib;Cadigan, Noel;Zheng, Nan - 通讯作者:
Zheng, Nan
Detecting and correcting underreported catches in fish stock assessment: trial of a new method
- DOI:
10.1139/f10-051 - 发表时间:
2010-08-01 - 期刊:
- 影响因子:2.4
- 作者:
Bousquet, Nicolas;Cadigan, Noel;Rivest, Louis-Paul - 通讯作者:
Rivest, Louis-Paul
Cadigan, Noel的其他文献
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{{ truncateString('Cadigan, Noel', 18)}}的其他基金
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2019
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2017
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2016
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Improving the foundations of sequential population analysis
改善序贯总体分析的基础
- 批准号:
298365-2007 - 财政年份:2013
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Improving the foundations of sequential population analysis
改善序贯总体分析的基础
- 批准号:
298365-2007 - 财政年份:2010
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Improving the foundations of sequential population analysis
改善序贯总体分析的基础
- 批准号:
298365-2007 - 财政年份:2009
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Improving the foundations of sequential population analysis
改善序贯总体分析的基础
- 批准号:
298365-2007 - 财政年份:2008
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Improving the foundations of sequential population analysis
改善序贯总体分析的基础
- 批准号:
298365-2007 - 财政年份:2007
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
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相似海外基金
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2022
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2021
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2019
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Development of predictive models for fish stock dynamics by elucidating mechanisms underlying the variability in recruitment
通过阐明补充变异性背后的机制,开发鱼类种群动态预测模型
- 批准号:
17H07413 - 财政年份:2017
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$ 2.04万 - 项目类别:
Grant-in-Aid for Research Activity Start-up
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
RGPIN-2016-04307 - 财政年份:2017
- 资助金额:
$ 2.04万 - 项目类别:
Discovery Grants Program - Individual
Advanced fish stock assessment models
先进的鱼类种群评估模型
- 批准号:
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解释变异的渔业海洋学方法:补充、种群结构以及鱼类和鲸鱼摄食生态学
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183728-2004 - 财政年份:2008
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