Collaborative Research: Timescale-Dependent Effects of Transient Dynamics in Plant-Pollinator Networks

合作研究：植物传粉者网络中瞬态动力学的时间尺度依赖性效应

• 批准号: 2129758
• 负责人: Mark Novak
• 金额: $ 14.05万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129758&HistoricalAwards=false
• 关键词: Collaborative; Research; Timescale; Dependent; Effects

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Biological communities are comprised of multiple plant and animal species that interact to form dynamic networks that profoundly shape the coexistence of species. These networks are constantly confronted by both anthropogenic and natural perturbations such as drought, fire, and habitat loss. Predicting how these complex systems respond to perturbations is challenging because the perturbations can propagate through the interwoven networks in unexpected ways. A particular challenge is that perturbations create impacts at different timescales: some species can respond in a matter of hours via the behavioral changes of individuals, others in a matter of years via changes in population abundance, and still others over decades by going extinct. This project will combine mathematical models, computer simulations, and network analyses with extensive observations and field experiments to understand how the responses of plant-pollinator networks – whose functioning is vital to natural and human-managed agriculture– can be understood and predicted. As perturbations to ecological systems become increasingly frequent and extreme, society will need to develop the tools to predict how ecological systems will respond to perturbations and to develop appropriate solutions. In addition, integrating mathematical models with real world data is a broader impact challenge that this project addresses by offering a workshop for early career scientists with diverse backgrounds in years 2 and 3. This project also supports diversity initiatives through the Center for the Advancement of Multicultural Perspectives on Science.This project will advance understanding of the responses of plant-pollinator communities to perturbations by identifying which ecological processes (e.g., adaptive foraging, functional responses, benefit accrual) are relevant and necessary for prediction across time scales. More specifically, the goal of this work is to understand how short-term (hours, days) behavioral responses propagate to medium-term (years) responses in abundance of species, and how these short- and medium-term responses, in turn, propagate to the long-term (decades, centuries) persistence and functioning of plant-pollinator networks. We will develop models for each of these timescales—parameterized and validated with field data from the Rocky Mountain Biological Laboratory—and use scale transition theory to understand how responses from shorter timescales will propagate to longer timescale. This work will fill important gaps in understanding responses to perturbations in ecological networks; in timescale separation in ecological models; and in theory-data integration in mutualistic networks.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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。生物群落由多种动植物物种组成，它们相互作用以形成动态网络，从而深刻影响物种的共存。这些网络不断面对人为和自然扰动，例如干旱，火灾和栖息地丧失。预测这些复杂系统如何响应扰动是挑战，因为扰动可以以意外的方式通过交织的网络传播。一个特殊的挑战是，扰动在不同的时间范围内产生影响：某些物种可以在几个小时内通过个人的行为变化，其他几年来响应，而其他人的人口丰度的变化在几年内以及数十年来通过灭绝而响应。该项目将将数学模型，计算机模拟和网络分析与广泛的观察结果和现场实验相结合，以了解如何理解和预测其功能对自然和人为管理的农业至关重要的植物 - 授粉网络的响应。随着对生态系统的扰动变得越来越频繁，极为极端，社会将需要开发工具，以预测生态系统将如何响应扰动并开发适当的解决方案。此外，将数学模型与现实世界数据整合在一起是一个更广泛的影响挑战，该项目通过为在第2和3年度提供多样性背景的早期职业科学家提供研讨会。该项目还通过多元文化观点的发展中心支持多样性的倡议。功能响应，准确的福利是相关的，对于跨时间尺度的预测是必不可少的。更具体地说，这项工作的目的是了解短期（小时，天）的行为反应如何传播物种抽象中的中期（年）反应，以及这些短期和中期反应如何又如何传播到长期（十年，几个世纪）的持久性和植物收集器网络的功能。我们将为每个时间尺度的每个时间表开发模型，并通过落基山生物实验室的现场数据进行验证和验证 - 并使用量表过渡理论来了解如何从较短的时间表范围传播到更长的时间表。这项工作将填补重要的空白，以理解对生态网络中扰动的反应；在生态模型中的时间尺度分离中；并在互助网络中的理论数据集成中。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是珍贵的支持。