LTREB: Ecological Dynamics in an Experimentally-Tractable Natural Ecosystems

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)资助的。生态学家面临的一个核心挑战是制定框架，预测复杂的自然群落和生态系统将如何应对环境影响，如物种灭绝和全球变化。由于生态相互作用是由复杂的网络组成的，因此应对这一挑战需要将数学框架与经验数据相结合。在这个项目中，调查者将扩展几个关于海洋岩石潮间带生物和环境条件的长期多物种数据集，并使用这些数据来估计动态多物种模型中的物种相互作用强度。这些结果将被用来测试相互作用强度的一般模式，并产生对不同潜在环境影响的预测。在这个潮间带栖息地进行的长期物种操纵实验将被用来验证多物种模型产生的预测。这项研究的结果将大大提高解决紧迫社会需求的能力--预测自然生态系统对包括气候变化在内的全球变化的反应。在此过程中，这项研究将加强大学研究人员和政府管理机构(Tribal和NOAA国家海洋保护区工作人员)之间的合作和数据共享，为博士生提供高级培训，并促进本科生在生态科学方面的研究经验。与该项目相关的数据将通过芝加哥大学、生物复杂性知识网络和美国生态学会生态档案馆公开获得。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).A core challenge for ecologists is to develop frameworks to predict how complex natural communities and ecosystems will respond to environmental impacts such as species extinction and global change. Because ecological interactions are comprised of complex networks, meeting this challenge requires an integration of mathematical frameworks with empirical data. In this project, the investigator will extend several long-term multi-species data sets for marine rocky intertidal organisms and environmental conditions, and use these data to estimate species interaction strengths in dynamic, multi-species models. These results will then be used to test for general patterns of interaction strength and to generate predictions for different potential environmental impacts. Long-term species manipulation experiments in this intertidal habitat will be used to validate predictions generated by multi-species models. Results of this study will significantly enhance the ability to address an urgent societal need - the prediction of natural ecosystem responses to global change, including climate change. In the process, the study will increase collaboration and data sharing among university researchers and governmental management agencies (Tribal and NOAA National Marine Sanctuary staff), provide advanced training for Ph. D. students, and facilitate research experience for undergraduate in ecological science. Data associated with the project will be publicly available through the University of Chicago, the Knowledge Network for Biocomplexity, and through the Ecological Society of America's Ecological Archives.