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LTREB: Using forecasting and long-term experiments to understand ecological dynamics under novel conditions

LTREB：利用预测和长期实验来了解新条件下的生态动态

基本信息

批准号：
1929730
负责人：
Morgan Ernest
金额：
$ 63.72万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-12-01 至 2024-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929730&HistoricalAwards=false
关键词：
LTREB Using forecasting long term

项目摘要

Ecosystems and the services they provide are changing. This makes predictions for how systems will change crucial for decision making by land managers and policy makers. However, current capabilities for making ecological forecasts are limited. Making forecasts requires understanding how ecosystems will respond to changing conditions. Because ecosystems are governed by complex interactions among species and their environment, our knowledge from the past may provide limited information about the future as conditions change. Thus, it is critical to develop and assess our ability to make forecasts when novel conditions occur. For over 40 years, the Portal Project has been collecting data on mammals and plants as part of a long-term experiment in southeastern Arizona. Continuing data collection at this site provides a unique opportunity to (1) assess how the occurrence of novel conditions impact the ability to forecast the population sizes of plants and mammals and (2) determine the best methods to forecast changes in ecological systems. This project will support the growing field of ecological forecasting by providing a high-quality, openly available data source for other researchers. The research team will also develop forecasting competitions to engage the broader scientific community in forecasting efforts and produce online educational materials to support classes to teach the next generation of ecological forecasters.This research project will use the unique strengths of the Portal Project to improve ecological forecasting under novel conditions. Comparing the performance of forecasting approaches under novel conditions requires long-term data and novel environments. Over the past two decades, the climate at the Portal Project has become warmer and drier. This creates novel environmental conditions for species. Additionally, experiments at the site create novel combinations of species. Ongoing data collection will be used to assess: (1) if models with more ecological complexity perform better, (2) if data from experiments can improve forecasts, and (3) if forecasting models can handle rapid changes. This research will use an automated forecasting system that serves as a model for ecological forecasting. The research requires ongoing data collection to test forecasts and to provide information on ecological changes as species and the environment change.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生态系统及其提供的服务正在发生变化。这使得预测系统将如何变化对土地管理者和政策制定者的决策至关重要。然而，目前进行生态预测的能力有限。做出预测需要了解生态系统将如何应对不断变化的条件。由于生态系统是由物种及其环境之间复杂的相互作用所控制的，随着条件的变化，我们过去的知识可能提供的关于未来的信息有限。因此，发展和评估我们在新情况发生时做出预测的能力至关重要。40多年来，门户项目一直在收集哺乳动物和植物的数据，作为亚利桑那州东南部长期实验的一部分。在这个网站上继续收集数据提供了一个独特的机会，（1）评估新情况的发生如何影响预测植物和哺乳动物种群规模的能力，（2）确定预测生态系统变化的最佳方法。该项目将通过为其他研究人员提供高质量、公开可用的数据源来支持不断增长的生态预测领域。该研究团队还将开发预测竞赛，以吸引更广泛的科学界参与预测工作，并制作在线教育材料，以支持下一代生态预报员的课程。该研究项目将利用门户项目的独特优势，在新的条件下改进生态预报。在新条件下比较预测方法的性能需要长期数据和新环境。在过去的二十年里，门户项目的气候变得越来越温暖和干燥。这为物种创造了新的环境条件。此外，该网站的实验创造了新的物种组合。正在进行的数据收集将用于评估：（1）具有更高生态复杂性的模型是否表现更好，（2）来自实验的数据是否可以改善预测，以及（3）预测模型是否可以处理快速变化。这项研究将使用一个自动预测系统，作为生态预测的模型。该研究需要持续的数据收集，以测试预测和提供有关物种和环境变化的生态变化的信息。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Shifts in competitive structures can drive variation in species' phenology

竞争结构的变化可能导致物种物候的变化

DOI：
10.1002/ecy.4160
发表时间：
2023
期刊：
Ecology
影响因子：
4.8
作者：
Dumandan, Patricia Kaye T.;Yenni, Glenda M.;Ernest, S. K. Morgan
通讯作者：
Ernest, S. K. Morgan

Declines in rodent abundance and diversity track regional climate variability in North American drylands

DOI：
10.1111/gcb.15672
发表时间：
2021-06-10
期刊：
GLOBAL CHANGE BIOLOGY
影响因子：
11.6
作者：
Cardenas, Pablo A.;Christensen, Erica;Rudgers, Jennifer A.
通讯作者：
Rudgers, Jennifer A.

Evaluating probabilistic ecological forecasts

评估概率生态预测

DOI：
10.1002/ecy.3431
发表时间：
2021
期刊：
Ecology
影响因子：
4.8
作者：
Simonis, Juniper L.;White, Ethan P.;Ernest, S. K. Morgan
通讯作者：
Ernest, S. K. Morgan

portalcasting: Supporting automated forecasting ofrodent populations

门户广播：支持啮齿动物种群的自动预测

DOI：
10.21105/joss.03220
发表时间：
2022
期刊：
Journal of Open Source Software
影响因子：
0
作者：
Simonis, Juniper L.;Yenni, Glenda M.;Bledsoe, Ellen K.;Christensen, Erica M.;Senyondo, Henry;Taylor, Shawn D.;Ye, Hao;White, Ethan P.;Ernest, S. K.
通讯作者：
Ernest, S. K.

Ecological Forecasting and Dynamics: A graduate courseon the fundamentals of time series and forecasting in ecology

生态预测和动力学：生态学时间序列和预测基础知识的研究生课程