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一个早期的生命阶段，在这个阶段，洋流可以将幼虫分散到很远的距离，并将地理上不同地区的种群联系起来。这种幼虫的连通性依赖于多种生物和环境因素的复杂相互作用，这使得可靠地预测一个种群对不断变化的气候和人类对海洋的利用的适应能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过程中变异性的原因和后果，并为决策者提供实用的量化建议。我还将培训下一代海洋生态系统的科学家和管理者，使他们能够利用建模来促进我们对生态的理解，并可持续地管理海洋生物资源。*工作将集中于西北大西洋扇贝，它包括加拿大东部和美国的高价值渔业，对其幼体的连接引入了跨界问题，并增加了对气候变化的敏感性，从而使管理变得复杂。我和我的学员们将建立以扇贝产卵为特征的模型，并模拟幼虫的生长、游泳、死亡和洋流的运输。我们将进行一系列战略模型实验，以改变不同的生物和环境因素以及捕捞努力和养护的变化。结果将被模拟为扇贝产卵量和幼虫在产卵季节之间、过去几年之间以及潜在未来情景中的空间模式的“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缺口。*我们的发现将提供关于该地区和其他地区影响扇贝种群的因素的新的基础知识。我们将生成可检验的假设，并优先安排未来需要的实地工作和实验，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，以及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。学生将在实践中获得具有科学价值和社会意义的职业技能。

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.***********