A Graphical Species Distribution Model of life history connectivity and multi-scale co-existence of marine species

了解生态和经济重要区域内海洋物种的空间分布，对于检验基本的生态学理论是至关重要的。这对保护工作和预测气候变化的潜在影响也至关重要。对于生态学家来说，越来越多的技术可以用来采样生态群落的精细分布和栖息地条件，而且这些技术也越来越负担得起。具体地说，成像系统可以在多个空间尺度上生成丰富和综合的生物和环境数据。然而，缺乏统计工具来分析这些多尺度数据，这限制了我们提取关键信息的能力。该项目将制定一个统计框架，涵盖海洋生态群落和空间尺度之间的复杂动态。该方法将提高我们对生物相互作用和栖息地因素如何塑造海洋群落空间格局的理解，但将足够简单，有助于广泛的生态研究并指导各种保护和管理行动。该项目包括以下活动：开发、评估和量化R环境中的图形物种分布建模(GSDM)的性能；利用GSDM框架来利用成像系统中固有的丰富的多物种、多尺度数据，并从正在进行的NSF资助项目中获得这些数据；以及促进海洋生态社区共同开发开源R包，为保护和管理决策提供信息。该项目将通过开发新的物种分布建模框架来建设海洋生物学方面的能力，这些框架克服了纳入高空间分辨率采样平台数据的计算挑战。这些框架将用R计算语言编码，以访问由基于图像的采样系统收集的日益可用的、高分辨率的多物种数据。这一方法利用了图形和贝叶斯网络模型的最新进展，将提高对生产性海洋生态系统内营养相互作用和生命历史连通性的理解。它还将为研究、保护和管理界进一步开发通用生态工具。目标受众包括毕业生和马里兰大学系统的生态研究社区，以及通过切萨皮克湾计划的目标实施小组的渔业管理人员和机构。来自社区成员的反馈将通过在线研讨会、会议、网络研讨会和专门网站参与共同发展进程(https://esc.cbl.umces.edu/gsdm).This奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

An understanding of the spatial distribution of marine species within ecologically and economically important regions is essential for testing fundamental ecological theory. It is also crucial for conservation efforts and to forecast potential impacts of climate change. Technologies are increasingly available and affordable for ecologists to sample the fine-scale distribution and habitat condition of ecological communities. Specifically, imaging systems can generate rich and integrated biological and environmental data at multiple spatial scales. However, there is a lack of statistical tools to analyze these multi-scale data, which limits our ability to extract critical information. This project will develop a statistical framework that encompasses the complex dynamics between marine ecological communities and spatial scales. The approach will improve our understanding of how biotic interactions and habitat factors shape the spatial patterns of marine communities, yet will be simple enough to contribute to broad ecological research and guide various actions of conservation and management.The project includes the following activities: develop, evaluate and quantify the performance of a graphical species distribution modeling (GSDM) within an R environment; employ the GSDM framework to leverage the rich multispecies, multiscale data inherent in imaging systems, and available to us from ongoing NSF-funded programs; and facilitate the co-development of an open-source R package by the marine ecological community to inform conservation and management decision making. This project will build capacity in marine biology through developing new species distribution modeling frameworks that overcome the computational challenge of incorporating data from sampling platforms with high spatial resolution. The frameworks will be coded in the R computing language to access the increasingly available, high resolution, multispecies data collected by image-based sampling systems. This approach, leveraging the most recent advances in Graphical and Bayesian network models, will improve the understanding of trophic interactions and life history connectivity within productive marine ecosystems. It will also further the development of generic ecological tools for the research, conservation, and management communities. Target audiences include graduates and the ecological research community of the University System of Maryland, and fisheries managers and agencies through the Goal Implementation Teams of the Chesapeake Bay Program. Feedback from community members will be engaged in a co-development process through online workshops, conference sessions, webinars, and dedicated websites (https://esc.cbl.umces.edu/gsdm).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.