Cross-scale forecasting of Everglades wading bird dynamics

大沼泽地涉水鸟动态的跨尺度预测

• 批准号: 2326954
• 负责人: Ethan White
• 金额: $ 75万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326954&HistoricalAwards=false
• 关键词: Cross; scale; forecasting; Everglades; wading

Ecological forecasting is a crucial emerging area of scientific research that attempts to predict changes in ecosystems over time. Accurately predicting future change is important for managing natural resources, conserving protected areas, and improving scientific understanding of the natural world. The behavior of ecosystems is affected by scale -- the size of the area and amount of time being studied -- because the importance of different ecological processes often changes as area or time increases. However, how this impacts ecological forecasts is currently unknown. This research will use long-term monitoring of wading birds in the Everglades to advance our understanding of how scale impacts ecological forecasting. In the Everglades, nature operates at a variety of distinct scales of space and time that this research will use to advance our understanding of how scale impacts ecological forecasting. The project will provide information on which scales allow for the most accurate forecasts, how this is influenced by changes in the accuracy of weather forecasts with scale, and whether forecast models developed at one scale can be used to make accurate predictions at other scales. This will produce improved forecasts to guide Everglades restoration and a broad understanding of how to incorporate the size of the area, and amount of time being predicted, into ecological forecasts in general. The project will also make data and forecasts for Everglades wading birds broadly available and facilitate their use for science and education.Leveraging the intensive monitoring of wading birds and hydrology in the Everglades, the research will address three aspects of the impact and integration of scale in ecological forecasting: 1) Quantify how forecastability, drivers, and uncertainty vary across spatial scales by comparing models fit to the entire Everglades, ecohydrological regions, and individual colonies; 2) Leverage cross-spatial scale drivers and interactions to understand cross-scale ecology and improve ecological forecasts by fitting two types of cross-scale model, comparing them to single scale models, and evaluating how the importance of cross-scale drivers is related to driver forecasts; and 3) Evaluate transferability of annual scale models to seasonal forecasting to understand if annual scale models can be used to improve seasonal forecasting by assessing how well annual models perform for seasonal forecasts and comparing them to seasonal models. To facilitate the use of the resulting data and forecasts for research, education, and management, the project will: 1) Make existing data on Everglades wading bird dynamics findable, accessible, interoperable, and reusable (FAIR); 2) Develop software for working with this data and associated ecohydrological drivers and using it to make and evaluate near-term iterative forecasts; 3) Produce a suite of educational resources designed for both individual use and incorporation into college and university courses including interactive forecasting websites, YouTube videos, and lesson material; and 4) Provide training and research experiences in ecological forecasting in the Everglades for both graduate students and undergraduates.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.

生态预测是科学研究的一个重要的新兴领域，试图预测生态系统随时间的变化。准确预测未来的变化对于管理自然资源、保护保护区和提高对自然世界的科学认识非常重要。生态系统的行为受到规模（研究区域的大小和所研究的时间量）的影响，因为不同生态过程的重要性经常随着区域或时间的增加而变化。然而，这如何影响生态预测目前尚不清楚。这项研究将利用对大沼泽地涉水鸟类的长期监测来加深我们对规模如何影响生态预测的理解。在大沼泽地，大自然在各种不同的空间和时间尺度上运作，这项研究将利用这些尺度来加深我们对尺度如何影响生态预测的理解。该项目将提供以下信息：哪些尺度可以实现最准确的预测，不同尺度的天气预报准确性变化如何影响这种预测，以及在一种尺度上开发的预测模型是否可以用于在其他尺度上进行准确的预测。这将产生改进的预测，以指导大沼泽地的恢复，并广泛了解如何将区域大小和预测时间纳入一般生态预测中。该项目还将广泛提供大沼泽地涉水鸟类的数据和预测，并促进其在科学和教育中的应用。利用对大沼泽地涉水鸟类和水文的密集监测，该研究将解决大沼泽地影响和规模整合的三个方面。生态预测：1）通过比较适合整个大沼泽地、生态水文区域和单个群体的模型，量化可预测性、驱动因素和不确定性在空间尺度上的变化； 2）利用跨空间尺度驱动因素和相互作用来了解跨尺度生态并通过拟合两种类型的跨尺度模型、将它们与单尺度模型进行比较以及评估跨尺度驱动因素的重要性如何与驾驶员预测； 3) 评估年度规模模型到季节性预测的可移植性，通过评估年度模型在季节性预测方面的表现并将其与季节性模型进行比较，了解年度规模模型是否可用于改进季节性预测。为了促进将所得数据和预测用于研究、教育和管理，该项目将： 1) 使大沼泽地涉水鸟类动态的现有数据可查找、可访问、可互操作和可重复使用（公平）； 2) 开发软件来处理这些数据和相关的生态水文驱动因素，并用它来制定和评估近期迭代预测； 3) 制作一套供个人使用和纳入学院和大学课程的教育资源，包括交互式预测网站、YouTube 视频和课程材料； 4) 为研究生和本科生提供大沼泽地生态预测方面的培训和研究经验。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。