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.

海洋物种的前所未有的地理重新分布挑战了当前的海洋生物资源和生态系统的管理和保护方法。越来越多地要求采取更灵活的渔业管理措施来应对观察到的物种范围的变化。在几个时间尺度上（每天到十年）对物种分布的定量预测被广泛用于促进自适应渔业管理。但是，由于局限性和知识差距，通常会质疑这些预测的可靠性。局限性包括不包含对局部热度制度的人口适应，以及在环境条件和栖息地使用中的季节性变化很少。知识差距包括对驱动物种范围变化的机制的有限理解。该研究计划的长期目标是确定地面鱼类种群空间动力学中可变性的驱动因素，以改善在多个时间尺度上响应气候和捕鱼的物种分布的投影。本研究计划中提出的独特方法是将遥测技术的使用从个人的传统表征扩展到人口水平的空间过程的量化。遥测技术提供了不足的高分辨率渔业数据来源，以量化地面鱼空间动力学的驱动因素。重点关注西北大西洋的大西洋鳕鱼和大西洋大比目鱼，我的团队将解决四个免费的短期目标：（1）确定最小样本量以推断人口级别的深度和温度偏好； （2）评估大比目鱼密度依赖性栖息地选择的季节变化； （3）评估热偏好的人口间差异如何影响鳕鱼分布的投影； （4）制定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性和盈利对于加拿大大西洋沿海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一种动态方法来管理这一重要物种。从长远来看，对温度和密度依赖对海洋鱼类空间动力学的影响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决策者和渔业经理设计有效的海洋保护区，并更新渔业捕捞和努力以更好地反映海洋物种的范围变化。该研究计划将通过在统计分析，数据可视化，计算机编程和操纵大型数据集中培训学生来促进学生的数据素养。这些技能在加拿大众多行业的需求不断增长。