Using replicated empirical networks to understand drivers of ecosystem structure and stability

使用复制的经验网络来了解生态系统结构和稳定性的驱动因素

• 批准号: 1457371
• 负责人: Hillary Young
• 金额: $ 50.88万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1457371&HistoricalAwards=false
• 关键词: Using; replicated; empirical; networks; understand

Science still does not understand the basic drivers of ecosystem structure (e.g., complexity) or how an ecosystem's structure affects its ability to resist disturbance. Understanding these relationships is necessary for predicting the response of ecosystems to many kinds of disruption (e.g. species extinction, invasion, habitat loss). This conceptual shortcoming thus prevents the efficient use of scarce conservation resources by limiting our ability to identify and conserve systems most sensitive to human disturbance. By comparing ecosystem structure across a series of islands that vary in ecosystem size and productivity - this project will examine the extent to which these basic properties drive ecosystem structure and how that structure determines ecosystem stability. This work should lead to broad management insight on the importance of environmental characteristics in determining the impact of species removals and invasions. This project will train multiple graduate students, undergraduates, and a postdoctoral researcher, with a focus on underrepresented groups. Furthermore, through partnerships with the university Kids in Nature program, the researchers will also engage grade school children, and train elementary school teachers in ecology and invasive species. Ecological complexity makes it difficult to identify general patterns in nature, such as community stability. To understand what drives stability, one could measure changes in systems over time across environmental gradients. Another approach has been to consider how system structure affects community stability. However, it is unlikely that structure and environmental gradients are independent, because environmental factors might alter system structure as well as their stability. To understand how ecosystem size and productivity influences ecosystem stability, 23 high-resolution food-webs from a series of Pacific islets that vary independently in size and productivity will be assembled and compared. A suite of mathematical modeling approaches will then create predictions for how variation in food-web structure affects food-web stability. These model-generated predictions of stability will then be tested against observed changes in food-web structure before and after the removal of a common omnivore, the rat (Rattus rattus). These results can then be used to evaluate the relative importance of ecosystem size, productivity, and food-web structure, in predicting system stability. Cumulatively, these efforts will help identify not only how environmental characteristics structure communities but also the extent to which they drive system-level responses to perturbation.

科学仍然不了解生态系统结构的基本驱动因素（例如，复杂性）或生态系统的结构如何影响其抵抗干扰的能力。了解这些关系对于预测生态系统对多种破坏（例如物种灭绝、入侵、栖息地丧失）的反应是必要的。这种概念上的缺陷，从而防止有效地利用稀缺的保护资源，限制了我们的能力，以确定和保护系统最敏感的人为干扰。通过比较一系列生态系统规模和生产力不同的岛屿的生态系统结构，该项目将研究这些基本特性在多大程度上驱动生态系统结构，以及这种结构如何决定生态系统的稳定性。这项工作应该会让管理层对环境特征在确定物种清除和入侵影响方面的重要性有更广泛的了解。该项目将培训多名研究生，本科生和博士后研究员，重点关注代表性不足的群体。此外，通过与大学Kids in Nature项目的合作，研究人员还将让小学生参与进来，并对小学教师进行生态学和入侵物种方面的培训。生态的复杂性使得很难确定自然界的一般模式，如社区稳定性。为了理解是什么驱动了稳定性，人们可以测量系统在不同环境梯度下随时间的变化。另一种方法是考虑系统结构如何影响社区稳定。然而，这是不可能的，结构和环境梯度是独立的，因为环境因素可能会改变系统的结构以及它们的稳定性。为了了解生态系统的规模和生产力如何影响生态系统的稳定性，将收集和比较来自一系列太平洋岛屿的23个高分辨率食物网，这些岛屿的规模和生产力各不相同。然后，一套数学建模方法将预测食物网结构的变化如何影响食物网的稳定性。这些模型生成的预测的稳定性，然后将测试对观察到的食物网结构的变化之前和之后的一个共同的杂食动物，大鼠（Rattus rattus）。这些结果可以用来评估生态系统的规模，生产力和食物网结构的相对重要性，在预测系统的稳定性。累积起来，这些努力将不仅有助于确定环境特征如何构成社区，而且有助于确定它们在多大程度上推动系统一级对扰动作出反应。