LTREB Renewal: Ecological Dynamics in an Experimentally-Tractable Natural Ecosystem

LTREB 更新：可实验处理的自然生态系统中的生态动力学

• 批准号: 1556874
• 负责人: John Wootton
• 金额: $ 44.85万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-15 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1556874&HistoricalAwards=false
• 关键词: LTREB; Renewal; Ecological; Dynamics; Experimentally

A central goal for ecology is to document if and how the environment is changing, to determine the causes of these changes, and to predict what the consequences of these changes will be to ecological systems. This is a challenge because of the complex network of connections among the living organisms and the non-living parts of ecosystems. Mathematical models are essential tools to keep track of these ecological interactions and to predict how they will respond to environmental changes. However, models need to be linked to data from nature. Two major challenges in developing predictive models of environmental change are 1) collecting sufficient data on how interactions among a complete set of species and environmental factors change over time, and (2) rigorously testing model predictions with experiments. This study will combine a quarter-century long series of data on 100+ species and relevant environmental variables in the rocky shoreline of Tatoosh Island in Washington state, with a long-term field experiment that mimics the extinction of a key species, the California mussel. The long term data will be applied to several different modeling approaches and predictions from these models will subsequently be tested with the long-term field experiment. The research will identify the most promising modeling approaches for making ecological prediction, and make them available to ecosystem managers and policy makers interested in the consequences of environmental impacts such as species extinction and global change. The comprehensive data series also will be made available to other scientists to be used as a platform for additional studies. This project will also engage undergraduate students in field research, data management, mathematical modeling, and in communicating with the public, managers, and policy makers. Furthermore, because the challenge of understanding networks of species interactions is shared with other scientific disciplines that deal with complex networks, project results will be of general value in other disciplines. The researcher will conduct annual surveys of replicated permanent plots for plants and animals on the shoreline in two ways: 1) by documenting the species identities under 2,600 fixed points over a 5-year period and generating annual transition probabilities among species, and 2) by generating abundance estimates in permanent 60 x 60 cm census plots. Fifteen experimental plots will be maintained by selectively removing individuals of Mytilus californianus when they appear, leaving all other species undisturbed. Environmental data will be collected every 30 minutes using a submersible data logger and a land-based weather station. Water chemistry, including critical nutrients, will be monitored. These data will be analyzed in several ways, including 1) parameterizing transition-based models (Markov chain models, spatially-explicit cellular automata) with environmental dependencies, 2) parameterizing multi-species population dynamic models from plot counts, 3) applying multi-spatial cross-convergent mapping and testing whether it accurately detects key species known to have strong causal effects from independent experiments, 4) applying neural network models and testing their predictions about the consequences of species extinction, and 5) testing whether there is a relationship between the variability of a species' abundance through time and its importance to the ecosystem as assessed by independent experiments. The community modeling projects enabled by the rich long term data sets have a strong potential to advance our understanding of mechanisms underlying community dynamics and their response to environmental change.

生态学的一个核心目标是记录环境是否以及如何变化，确定这些变化的原因，并预测这些变化对生态系统的影响。这是一个挑战，因为生物体和生态系统的非生存部分之间建立了复杂的联系网络。 数学模型是跟踪这些生态互动并预测它们将如何应对环境变化的重要工具。但是，模型需要链接到来自自然的数据。开发环境变化的预测模型的两个主要挑战是1）收集足够的数据，以了解整个物种和环境因素之间如何随着时间的变化而变化，以及（2）通过实验严格测试模型预测。这项研究将结合华盛顿州塔托什岛岩石海岸线的100多种物种和相关环境变量的四分之一世纪长的数据，以及一个长期的野外实验，模仿了关键物种的灭绝加利福尼亚贻贝。长期数据将应用于几种不同的建模方法，这些模型的预测将随后通过长期现场实验进行测试。该研究将确定对生态预测的最有希望的建模方法，并使对生态系统经理和政策制定者对环境影响的后果（例如物种灭绝和全球变化）的后果感兴趣。综合数据系列还将提供给其他科学家，以作为其他研究的平台。 该项目还将吸引本科生参与现场研究，数据管理，数学建模，并与公众，经理和政策制定者进行沟通。此外，由于了解物种相互作用网络的挑战与处理复杂网络的其他科学学科共享，因此项目结果将在其他学科中具有一般价值。研究人员将通过两种方式对海岸线上的动植物进行复制的永久性地块的年度调查：1）通过记录在5年期间的物种身份以下的物种身份以下的物种身份，并在物种之间产生年度过渡概率，以及2）通过在永久60 x 60 x 60 cm Census图中产生丰度估计。 通过有选择地删除Mytilus Californianus的个人出现时，将维持15个实验地块，而所有其他物种不受干扰。 环境数据将使用潜水数据记录仪和陆基气象站每30分钟收集一次。将监测包括关键营养物质在内的水化学。 这些数据将以几种方式进行分析，包括1）具有环境依赖性的基于参数的基于过渡的模型（马尔可夫链模型，空间解释的蜂窝自动机），2）从图中进行参数化的多种种群动态模型，3）应用多个空间的跨跨物质映射和测试，以确定其准确的键值，以确定其准确的键值，以确定其是否准确地效果，是否具有独立的CAUS效应，是否具有独立的CAUS效应，是否具有独立的CAUS效应，是否具有独立的CAUS效应）是否具有效果测试他们对物种灭绝后果的预测，以及5）测试物种丰度的变化之间是否存在关系，其对生态系统的重要性是由独立实验所评估的。由丰富的长期数据集启用的社区建模项目具有强大的潜力，可以提高我们对社区动态基础机制及其对环境变化的反应的理解。