RAPID: Quantifying the potential impacts of the BP Deepwater Horizon oil spill on carbon services of salt marshes along the northern Gulf Coast

多种空间尺度上的因素可以影响沼泽生境的光合作用，从单个植物的物种和条件，到非生物和生物资源的微站点变化，再到潮差、河流输入和石油泄漏的更广泛的景观尺度效应。该项目将研究盐沼植物光合作用的空间模式，这些植物已经并将潜在地受到墨西哥湾北部海岸英国石油公司深水地平线漏油事件的影响。调查人员将检验这样一种假设，即压力较大的盐沼对漏油干扰的适应能力较差。将采用随机建模方法(分层贝叶斯模型)来解决研究问题。它能够量化不确定性，并通过吸收新数据来减少不确定性，这使它成为一种强大的方法。研究人员将开发一种可扩展和可转移的方法，可用于全国以河流为主的盐沼生态系统。它将促进对生态系统动态的干扰影响的了解，并可用于指导未来的研究和管理工作，以评估受漏油影响的沼泽的长期影响和恢复。此外，该项目预计将促进墨西哥湾沿岸受影响地区更知情的湿地恢复计划。该项目将为两名研究生提供丰富和支持性的教育经验，他们将学习获取和管理实地环境数据所需的基本技术，以及最新的建模技术。

Factors at multiple spatial scales can influence photosynthesis in marsh habitats, from the species and conditions of individual plants, to the microsite variation in non-living and living resources, to the broader landscape-scale effects of tidal ranges, river inputs and oil spills. This project will study spatial patterns in photosynthesis of salt marsh plants which have been and will potentially be impacted by the BP Deepwater Horizon oil spill along the northern Gulf Coast. The investigators will test the hypothesis that salt marshes under higher stresses will be less resilient to oil spill disturbance. A stochastic modeling approach (Hierarchical Bayesian models) will be applied to address the research question. Its ability to quantify uncertainties and reduce uncertainties by assimilating new data makes it powerful approach. The investigators will develop a scalable and transferrable methodology that can be used in river-dominated salt marsh ecosystems around the nation. It will advance the understanding of disturbance effects on ecosystem dynamics, and can be used to guide future research and management efforts in assessing long-term impacts and recovery of marshes impacted by oil spills. In addition, the project is expected to facilitate more informed wetland restoration plans at the impacted areas on the Gulf coast. This project will provide enriching and supportive educational experiences for two graduate students who will learn the basic techniques necessary to obtain and manage field based environmental data, and updated modeling techniques.