CMG Collaborative Research: Reconstruction of Dispersal Strategies of Marine Organisms via Semiparametric Dynamic Spatial Regression

CMG 合作研究：通过半参数动态空间回归重建海洋生物的扩散策略

• 批准号: 0934961
• 负责人: Lorenzo Ciannelli
• 金额: $ 46.58万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934961&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Reconstruction; Dispersal

Marine organisms often undergo a long dispersion stage in order to migrate from a spawning ground (source) to favorable nursery grounds (sink), a process that is essential for their long-term survival. Various species may adopt different dispersal strategies to harness ocean circulation for successful dispersion. Yet much is unknown about such dispersion processes given the difficulty of direct observation and tracking minute marine organisms during their dispersion stage. This proposal is about using ocean circulation models coupled with field data of the organisms? distribution to both rebuild drift pathways and to predict the most likely source location and timing of drift initialization. The focus of the analysis is on few species of fish and jellyfish. These organisms represent opposite ecological spectrums. Fish disperse mostly during the early life stages (eggs and larvae) while jellyfish disperse during the adult stages. A new class of semiparametric dynamic spatial regression models is proposed and the development of new statistical tools for sink-source reconstruction, and assessment of various dispersal strategies. The proposed methods provide a novel framework for assessing the significance of dispersal duration, spawning time distribution, and the contribution of organisms from particular sources. Furthermore, a dispersal-strategy selection criterion will be developped that will pick the dispersal strategy most consistent with survey data.The research team consists of two marine ecologists, one statistician and one physical oceanographer. The proposed work will provide general tools for understanding the dispersal strategies of various species. Climate change may alter ocean circulation patterns, and our increased understanding of dispersal strategies can be useful for predicting the effects of climate change on population fluctuations of marine organisms. The methods may be extended for assessing dispersal strategies for organisms that use other media for their dispersal, e.g. wind. A computer package implementing the proposed methods will be freely available to the public. The team will continue to maintain strong record of training PhD students in cross-disciplinary research.

海洋生物通常会经历长期的分散阶段，以便从产卵区（来源）迁移到有利的托儿所（Sink），这对于它们的长期生存至关重要。各种物种可能采用不同的分散策略来利用海洋循环以成功分散。然而，由于在分散阶段的直接观察和跟踪分钟生物的困难，因此对于这种分散过程而言，这是未知的。该建议是关于使用海洋循环模型以及生物体的田间数据？分布到重建漂移途径，并预测最可能的源位置和漂移初始化的时机。分析的重点是几种鱼类和水母。这些生物代表相反的生态谱。鱼类主要在早期阶段（鸡蛋和幼虫）分散，而水母在成年阶段分散。提出了一类新的半参数动态空间回归模型，并开发了用于下沉源重建的新统计工具，并评估了各种分散策略。所提出的方法为评估分散持续时间，产卵时间分布以及来自特定来源的生物的贡献的重要性提供了一个新颖的框架。此外，将制定一个分散策略的选择标准，该标准将选择与调查数据最一致的分散策略。研究团队由两名海洋生态学家，一名统计学家和一名物理海洋学家组成。拟议的工作将为理解各种物种的分散策略提供一般工具。气候变化可能会改变海洋循环模式，我们对分散策略的了解增加对于预测气候变化对海洋生物种群波动的影响很有用。可以扩展这些方法，以评估使用其他培养基进行分散的生物体的分散策略，例如风。实施该方法的计算机软件包将免费提供给公众。该团队将继续保持跨学科研究的培训博士生的良好记录。