CMG: Collaborative Research: Nonlinear Spatio-Temporal Dynamics and Source-Sink Reconstruction in Marine Species

CMG：合作研究：海洋物种的非线性时空动力学和源汇重建

• 批准号: 0621153
• 负责人: Lorenzo Ciannelli
• 金额: $ 27.91万
• 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: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0621153&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Nonlinear; Spatio

One of the primary goals of ecological studies is to develop the understanding and means to predict how the abundance and distribution of aquatic organisms respond to changing environmental conditions. After decades of monitoring large marine ecosystems, rich spatial and temporal datasets are beginning to emerge, yet, the statistical methods to analyze these complex systems have either not been developed or are not accessible to ecologists. By employing novel statistical approaches, the research team uses the scyphomedusa Chrysaora melanaster in the Bering Sea as a model system to examine processes that control the spatial and temporal patterns of marine organisms with complex life cycles involving a sessile (source) and a pelagic (sink) phase. Scyphomedusa (a.k.a., jellyfish) blooms are common occurrences in many marine habitats and are important events controlling plankton dynamics in these systems. Evidence has shown increases in jellyfish populations in various locations and so their impacts on zooplankton and fish populations probably are increasing. However, scientific knowledge on factors affecting jellyfish spatial and temporal dynamics in the field is very limited. This is in part due to the complex life cycle of these species, which alternates between a pelagic (medusa) and a benthic (polyp) stage. Most of the current knowledge of jellyfish dynamics comes from the study of the pelagic medusae, while little is known of polyp distributions and their interannual dynamics. This is a critical information gap as the benthic polyps are clearly the source of the pelagic medusae. Moreover, medusa distribution data are typically characterized by a number of undesirable statistical features (i.e., excess of zero counts and spatial autocorrelation) that hamper their study in relation with co-located and co-occurring environmental variables. In this study the research team proposes to analytically reconstruct the interannual distribution of C. melanaster benthic polyps, by statistically merging medusa distributional data and predictions from an ocean circulation model. Furthermore, the team proposes to identify the factors affecting the spatio-temporal dynamics of medusae by implementing a nonlinear and nonadditive regression framework that can simultaneously account for zero inflation and spatial autocorrelation. The statistical methods so developed could be applied broadly to study the distribution and dynamics of both aquatic and terrestrial species. The proposed approach is particularly relevant for rare species (which are often characterized by zero inflation and autocorrelation) and for species that disperse from specific source locations. For example, the proposed approach could be used to understand the movement of larval fish away from spawning grounds, the spread of herbivorous insects through forests, dispersal of non-indigenous species away from points of introduction, and the proliferation of infectious diseases from epicenters. The proposed research is motivated by the needs for developing new methodologies for understanding and predicting how the abundance and distribution of aquatic organisms respond to changing environmental conditions, e.g. global changes in climate. The research team uses Bering Sea jellyfish as a model system to examine processes that control the spatial and temporal patterns of marine organisms with complex life cycles. Jellyfish blooms are common occurrences in many marine habitats, which may affect the abundance and distribution of other fish species of commercial values through their trophic effects on plankton. The research team develops new statistical methods for (i) reconstructing the spatial distribution of jellyfish at various life stages, partly based on predictions from an ocean circulation model, and (ii) identifying the factors affecting the spatial and temporal variations of jellyfish. The statistical methods so developed could be applied broadly to study the impact of environmental changes on the distribution and dynamics of both aquatic and terrestrial species, especially for rare species and those that disperse from specific source locations (e.g., the proliferation of infectious diseases from epicenters).

生态研究的主要目标之一是加深理解和方法来预测水生生物的丰度和分布如何响应不断变化的环境条件。 经过数十年对大型海洋生态系统的监测，丰富的时空数据集开始出现，然而，分析这些复杂系统的统计方法要么尚未开发出来，要么生态学家无法使用。 通过采用新颖的统计方法，研究小组使用白令海的 scyphomedusa Chrysaora melanaster 作为模型系统，研究控制具有复杂生命周期的海洋生物的空间和时间模式的过程，涉及无柄（源）和中上层（汇） ） 阶段。水母（又名水母）的大量繁殖在许多海洋栖息地中很常见，并且是控制这些系统中浮游生物动态的重要事件。有证据表明，不同地点的水母数量有所增加，因此它们对浮游动物和鱼类数量的影响可能正在增加。然而，该领域关于影响水母时空动态的因素的科学知识非常有限。部分原因是这些物种的生命周期复杂，在中上层（水母）和底栖（息肉）阶段之间交替。目前对水母动力学的了解大部分来自于对远洋水母的研究，而对水螅体的分布及其年际动力学知之甚少。这是一个关键的信息差距，因为底栖息肉显然是远洋水母的来源。此外，水母分布数据通常具有许多不良的统计特征（即过多的零计数和空间自相关），这些特征阻碍了它们与同一地点和同时发生的环境变量相关的研究。在这项研究中，研究小组建议通过统计合并水母分布数据和海洋环流模型的预测来分析重建黑腹水母的年际分布。此外，该团队建议通过实施非线性和非加性回归框架来确定影响水母时空动态的因素，该框架可以同时解释零膨胀和空间自相关。如此开发的统计方法可广泛应用于研究水生和陆生物种的分布和动态。所提出的方法对于稀有物种（通常以零膨胀和自相关为特征）和从特定源位置分散的物种特别相关。 例如，所提出的方法可用于了解幼鱼远离产卵地的运动、草食性昆虫在森林中的传播、非本土物种远离引入点的扩散以及传染病从震中的扩散。 拟议研究的动机是需要开发新的方法来理解和预测水生生物的丰度和分布如何响应不断变化的环境条件，例如。全球气候变化。研究小组使用白令海水母作为模型系统来研究控制具有复杂生命周期的海洋生物的空间和时间模式的过程。水母大量繁殖在许多海洋生境中很常见，这可能通过对浮游生物的营养作用影响其他具有商业价值的鱼类的丰度和分布。研究小组开发了新的统计方法，用于（i）重建水母在不同生命阶段的空间分布，部分基于海洋环流模型的预测，以及（ii）识别影响水母时空变化的因素。如此开发的统计方法可广泛应用于研究环境变化对水生和陆生物种的分布和动态的影响，特别是对于稀有物种和从特定来源地点扩散的物种（例如，传染病从震中扩散） ）。