OPUS: Recovery, resilience and the ecology of change

A diverse ecological community is composed of many species of organisms, all responding to environmental change as well as interacting with each other. In this complex web of nature, predicting how biodiversity will change over time is challenging. Because changes in one species abundance propagate through interactions in the community to affect other species, it is often not sufficient to understand only how organisms respond directly to environmental conditions.

This same challenge affects the utilization of monitoring information by conservation and restoration practitioners. Considerable time and resources are devoted to monitoring these complex changes, and it can be difficult to discern whether changes indicate a healthy self-sustaining system or one that is declining and on the verge of collapse. Other disciplines outside ecology also deal with analogous complex processes and have developed metrics and analytical frameworks to describe dynamics of multi-dimensional systems over time.

In the proposed work, these metrics will be applied to long-term ecological datasets to inform how managers can utilize monitoring datasets. Datasets in the proposed synthesis will also form the foundation for graduate student seminars and exchanges to train across subdisciplines and connect theory to applied work.

This project seeks to develop a new perspective on the temporal dynamics of multispecies communities by highlighting, through synthesis, ways to describe how complex ecological systems respond to environmental change and how species organization within a community may cause different dynamics in the propagation of environmental change. The project focuses on several innovative metrics that can describe dynamic stability of multispecies communities, using a series of eight long-term plant community datasets.

It then seeks to apply new methods that describe the organization of species interactions to connect dynamics with community organization. The last part of the project seeks to make this understanding more useable to decision-makers through collaborations with managers on analyses of long-term conservation monitoring datasets. Partnering with managers and training graduate students, the research will explore new ways to arrive at a better understanding of how to monitor and anticipate dynamic ecological responses to environmental 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.