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

点击翻译按钮获取中文摘要

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.