Causes and consequences of variability in larval connectivity

许多商业的、受保护的和入侵的海洋物种都有生命的早期阶段，在此期间，洋流可以将幼虫分散到很远的地方，并将地理上不同地区的种群联系起来。这种幼虫的连通性取决于多种生物和环境因素的复杂相互作用，这使得可靠地预测种群对气候变化和人类利用海洋的复原力或脆弱性具有挑战性。我的研究利用模型的力量来检验幼虫招募变化的原因和后果，并为决策者提供实用的定量建议。我还将培训下一代海洋生态系统科学家和管理人员，如何利用建模来提高我们对生态的理解，并可持续地管理海洋生物资源。******这项工作将集中在西北大西洋扇贝上，这是加拿大东部和美国非常有价值的渔业，对这些扇贝来说，幼虫的连通性通过引入跨境问题和对气候变化的敏感性增加，使管理复杂化。我的学员和我将建立模型来描述扇贝产卵的特征，并模拟幼虫的生长、游泳、死亡和洋流的运输。我们将进行一系列策略性模型试验，以改变不同的生物和环境因素，以及渔获量和养护的变化。结果将模拟扇贝产卵季节之间、过去几年以及潜在未来情景的空间模式和幼虫存活率的“数据”。我们将使用数学数据分析技术，如图像处理，来诊断那些影响最大的因素，并确定限制我们预测能力的关键知识差距。******我们的发现将提供有关影响该地区和其他地区扇贝种群的因素的新的基础知识。我们将产生可测试的假设，并优先考虑未来的实地工作和实验，以提高预测的可靠性。我们还将为渔业管理工作提供有用的建议，以量化风险。这项工作还将产生研究工具，可用于其他海洋物种的幼虫连通性研究，也可用于努力开发新方法来处理与模型相关的不确定性的数学生态学家。学生将在科学有用和社会重要的建模实践中获得就业技能。***********

Many commercial, protected, and invasive marine species have an early life stage during which ocean currents can disperse larvae long distances and link populations in geographically distinct areas. This larval connectivity depends on the complex interaction of multiple biological and environmental factors, which makes it challenging to reliably predict a population's resilience or vulnerability to changing climate and human use of the ocean. My research exploits the power of models to examine the causes and consequences of variability in larval recruitment, and to provide decision-makers with practical quantitative advice. I will also train the next generation of marine ecosystem scientists and managers in ways to use modeling to advance our ecological understanding and sustainably manage living marine resources.******The work will focus on Northwest Atlantic sea scallops, which comprise highly valuable fisheries in Eastern Canada and the USA, and for which larval connectivity complicates management by introducing trans-border issues and increased sensitivity to climate change. My trainees and I will build models that characterize scallop spawning, and simulate larval growth, swimming, death and transport by ocean currents. We will conduct a series of strategic model experiments that vary different biological and environmental factors as well as changes in fishing effort and conservation. Results will be modeled "data" for spatial patterns in scallop egg production and larval survivorship between spawning seasons, among past years, and for potential future scenarios. We will use mathematical data analysis techniques, such as image processing, to diagnose those factors with the greatest influence, and to determine critical knowledge gaps that limit our predictive capabilities. ******Our findings will deliver new fundamental knowledge about the factors affecting scallop populations in this and other regions. We will generate testable hypotheses, and prioritize future fieldwork and experiments needed to improve forecast reliability. We will also provide useful advice to fisheries management efforts to quantify risk. The work will also produce research tools that can be used for larval connectivity studies of other marine species, as well as for mathematical ecologists who are striving to develop new methods to deal with model-related uncertainty. Students will gain employable skills in the practice of scientifically-useful and societally-significant modeling.***********