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

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。生物群落由多种植物和动物物种组成，它们相互作用形成动态网络，深刻地塑造了物种的共存。这些网络不断面临着人为和自然的干扰，如干旱、火灾和栖息地丧失。预测这些复杂系统对扰动的反应是具有挑战性的，因为扰动可以以意想不到的方式在相互交织的网络中传播。一个特别的挑战是，扰动会在不同的时间尺度上产生影响：一些物种可以在几个小时内通过个体的行为变化做出反应，另一些物种可以在几年时间内通过种群数量的变化做出反应，还有一些物种需要几十年的时间才能灭绝。该项目将把数学模型、计算机模拟和网络分析与广泛的观察和实地实验相结合，以了解如何理解和预测植物传粉媒介网络的反应——其功能对自然和人为管理的农业至关重要。随着对生态系统的扰动变得越来越频繁和极端，社会将需要开发工具来预测生态系统将如何应对扰动并制定适当的解决方案。此外，将数学模型与现实世界数据相结合是一个更广泛的影响挑战，该项目通过在第2年和第3年为具有不同背景的早期职业科学家提供研讨会来解决这一挑战。该项目还通过促进科学多元文化视角中心支持多样性倡议。该项目将通过确定哪些生态过程（如适应性觅食、功能反应、收益累积）与跨时间尺度的预测相关和必要，促进对植物传粉者群落对扰动的响应的理解。更具体地说，这项工作的目标是了解短期（小时，天）的行为反应如何在丰富的物种中传播到中期（年）的反应，以及这些短期和中期的反应如何反过来传播到长期（几十年，几百年）的持久性和植物传粉者网络的功能。我们将为这些时间尺度中的每一个开发模型——参数化并使用落基山生物实验室的现场数据进行验证——并使用尺度转换理论来理解从较短时间尺度的响应如何传播到较长的时间尺度。这项工作将填补理解对生态网络扰动响应的重要空白；生态模型的时间尺度分离；从理论上讲，互惠网络中的数据整合。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.