Transmission Networks in Trait-Based Communities

基于特征的社区中的传输网络

• 批准号: 9241579
• 负责人: Lynn S. Adler
• 金额: $ 40.96万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-21 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9241579
• 关键词: Address; Agricultural Crops; Angiosperms; Bees; Collection; Communities; Complex; Coupling; Data; Disease; Disease Vectors; Ecosystem; Environment; Epidemiology; Flowers; Goals; Heterogeneity; Individual; Infection; Knowledge; Link; Machine Learning; Modeling; Observational Study; Organism; Parasites; Pattern; Plants; Population; Prevalence; Research; Resource Sharing; Resources; Role; Sampling; Services; Shapes; Structure; System; Techniques; Testing; Time; Work; base; disease transmission; economic implication; improved; infancy; insight; model building; network models; novel; research study; theories; tool; trait; transmission process; vector

The complexity of ecological communities creates challenges to understanding multi-host parasite transmission. Pronounced heterogeneity in transmission among individuals, species and across space is the rule rather than the exception. Community ecologists are beginning to make great strides in predicting multi-species interactions using a trait-based rather than taxonomic approach, identifying key functional attributes of organisms and environments that are important to understanding the system. At the same time, disease ecologists generally use network modeling to understand parasite transmission in complex communities. Yet the merging of a trait-based approach with network modeling to understand multi-host transmission across space and time is in its infancy. We will take advantage of a highly tractable system - diverse communities of bees that transmit parasites via networks of flowering plants - to merge trait-based theory with network modeling, introducing a novel theoretical framework for multi-host parasite transmission in complex communities. We will collect empirical contact pattern and trait data from plant-pollinator networks to identify aspects of network structure that contribute to disease spread. Through the collection of extensive data on bee traits, floral traits and parasite spread, we will use machine learning techniques to construct and parameterize trait-based models of disease transmission in order to make falsifiable predictions for further testing. We will then test model predictions via whole-community manipulations of bees, parasites and plants in mesocosms. Such whole-community manipulations will offer unparalleled insight into the specific network patterns and traits that shape transmission in multi-host communities.

生态群落的复杂性给理解多宿主寄生虫传播带来了挑战。个体、物种和跨空间传播的明显异质性是普遍现象，而不是例外。社区生态学家开始在使用基于性状而非分类学的方法来预测多物种相互作用方面取得了长足的进步，确定了对于理解系统很重要的生物体和环境的关键功能属性。与此同时，疾病生态学家通常使用网络模型来了解复杂群落中的寄生虫传播。然而，将基于特征的方法与网络建模相结合以了解跨空间和时间的多主机传输仍处于起步阶段。我们将利用一个高度易于处理的系统——通过开花植物网络传播寄生虫的多样化蜜蜂群落——将基于性状的理论与网络建模相结合，为复杂群落中的多宿主寄生虫传播引入一种新颖的理论框架。我们将从植物传粉媒介网络收集经验接触模式和性状数据，以确定有助于疾病传播的网络结构的各个方面。通过收集有关蜜蜂性状、花卉性状和寄生虫传播的大量数据，我们将使用机器学习技术来构建和参数化基于性状的疾病传播模型，以便为进一步测试做出可证伪的预测。然后，我们将通过对中生态系统中的蜜蜂、寄生虫和植物的整个群落操作来测试模型预测。这种对整个社区的操作将为塑造多主机社区中的传播的特定网络模式和特征提供无与伦比的洞察力。