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

了解海洋物种在生态和经济重要区域内的空间分布对于检验基本生态理论至关重要。这对于保护工作和预测气候变化的潜在影响也至关重要。生态学家越来越多地获得和负担得起的技术来采样生态群落的细尺度分布和栖息地条件。具体而言，成像系统可以在多个空间尺度上生成丰富且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和指导各种保护和管理行动。采用GSDM框架来利用成像系统固有的丰富的多物种，多尺度数据，并从正在进行的NSF资助的计划中为我们提供;并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能力，这些框架将克服将来自高空间分辨率采样平台的数据纳入计算方面的挑战。这些框架将用R计算语言编码，以访问基于图像的采样系统收集的越来越多的高分辨率多物种数据。这种方法，利用图形和贝叶斯网络模型的最新进展，将提高生产性海洋生态系统内的营养相互作用和生活史连接的理解。它还将进一步为研究、养护和管理界开发通用生态工具。目标受众包括马里兰州大学系统的毕业生和生态研究界，以及通过切萨皮克湾方案目标执行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、网络研讨会和专用网站（https：//esc.cbl.umces.edu/gsdm）参与共同开发过程。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.