Modelling groundfish spatial dynamics in the Northwest Atlantic

西北大西洋底栖鱼空间动态建模

• 批准号: RGPIN-2019-05203
• 负责人: LeBris, Arnault
• 金额: $ 2.4万
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738121
• 关键词: Modelling; groundfish; spatial; dynamics; Northwest

Unprecedented geographical redistribution of marine species challenges current approaches to the management and conservation of marine living resources and ecosystems. More flexible fisheries management measures are increasingly requested to cope with observed and predicted species range shifts. Quantitative forecasting of species distributions at several time scales (daily to decadal) is widely used to promote adaptive fisheries management. However, the reliability of these forecasts is often questioned due to limitations and knowledge gaps. Limitations include the non-inclusion of population adaptation to local thermal regimes and little incorporation of seasonal variations in environmental conditions and habitat use. Knowledge gaps include limited understanding of the mechanisms driving species range shifts. The long-term goal of this research program is to identify the drivers of variability in the spatial dynamics of groundfish populations to improve projections of species distributions in response to climate and fishing at multiple temporal scales. The unique approach proposed in this research program is to extend the use of telemetry technologies from the traditional characterization of the migratory behaviour of individuals to the quantification of population-level spatial processes. Telemetry technologies provide an underutilized source of high-resolution fishery independent data to quantify drivers of groundfish spatial dynamics. Focusing on Atlantic cod and Atlantic halibut in the Northwest Atlantic, my team will address four complimentary short-term objectives: (1) determining a minimum sample size to infer population-level depth and temperature preferences; (2) evaluating seasonal variations in halibut density-dependent habitat selection; (3) assessing how inter-population differences in thermal preferences affect projections of cod distribution; and (4) developing real time forecasts of halibut seasonal distributions. Ensuring that fisheries remain sustainable and profitable in a dynamic environment is critical for the livelihood and food security of coastal communities in Atlantic Canada. In the short-term, the real-time projections of Atlantic halibut distribution will facilitate the implementation of a dynamic approach to manage this commercially important species. In the long-term, a better understanding of the effects of temperature and density dependence on marine fish spatial dynamics will improve the reliability of species distribution forecasts. More reliable forecasts will assist policy makers and fisheries managers in designing effective marine protected areas and updating allocations of fishery catch and effort to better reflect range shifts in marine species. This research program will promote data literacy for students by training them in statistical analysis, data visualization, computer programming and manipulating large data sets. These skills are in growing demand across numerous industry sectors in Canada.

海洋物种前所未有的地理重新分布对当前海洋生物资源和生态系统的管理和保护方法提出了挑战。人们越来越需要采取更灵活的渔业管理措施，以应对观察到和预测的物种范围变化。多个时间尺度（每日到十年）的物种分布定量预测被广泛用于促进适应性渔业管理。然而，由于局限性和知识差距，这些预测的可靠性经常受到质疑。局限性包括不包括人口对当地热状况的适应以及很少考虑环境条件和栖息地利用的季节变化。知识差距包括对驱动物种范围变化的机制的了解有限。该研究计划的长期目标是确定底栖鱼类种群空间动态变化的驱动因素，以改进对多个时间尺度上气候和捕捞的物种分布预测。该研究计划提出的独特方法是将遥测技术的使用从个体迁徙行为的传统表征扩展到种群水平空间过程的量化。遥测技术提供了未充分利用的高分辨率渔业独立数据源，以量化底栖鱼类空间动态的驱动因素。我的团队将重点关注西北大西洋的大西洋鳕鱼和大西洋大比目鱼，他们将实现四个免费的短期目标：（1）确定最小样本量以推断种群深度和温度偏好； (2) 评估大比目鱼密度依赖性栖息地选择的季节变化； (3) 评估种群间热偏好差异如何影响鳕鱼分布的预测； (4) 开发大比目鱼季节性分布的实时预测。确保渔业在动态环境中保持可持续和盈利对于加拿大大西洋沿岸社区的生计和粮食安全至关重要。在短期内，大西洋大比目鱼分布的实时预测将有助于实施动态方法来管理这一具有重要商业价值的物种。从长远来看，更好地了解温度和密度依赖性对海洋鱼类空间动态的影响将提高物种分布预测的可靠性。更可靠的预测将有助于政策制定者和渔业管理者设计有效的海洋保护区并更新渔获量和努力的分配，以更好地反映海洋物种范围的变化。该研究项目将通过统计分析、数据可视化、计算机编程和操作大型数据集方面的培训来提高学生的数据素养。加拿大众多行业对这些技能的需求不断增长。