Project: LTREB: Ecological Dynamics in an Experimentally-Tractable Natural Ecosystems

NSF Award Abstract for DEB-0919420:

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

A core challenge for ecologists is to develop frameworks to predict how complex natural communities and ecosystems will respond to environmental impacts such as species extinction and global change. Because ecological interactions are comprised of complex networks, meeting this challenge requires an integration of mathematical frameworks with empirical data. In this project, the investigator will extend several long-term multi-species data sets for marine rocky intertidal organisms and environmental conditions, and use these data to estimate species interaction strengths in dynamic, multi-species models. These results will then be used to test for general patterns of interaction strength and to generate predictions for different potential environmental impacts. Long-term species manipulation experiments in this intertidal habitat will be used to validate predictions generated by multi-species models.

Results of this study will significantly enhance the ability to address an urgent societal need - the prediction of natural ecosystem responses to global change, including climate change. In the process, the study will increase collaboration and data sharing among university researchers and governmental management agencies (Tribal and NOAA National Marine Sanctuary staff), provide advanced training for Ph. D. students, and facilitate research experience for undergraduate in ecological science. Data associated with the project will be publicly available through the University of Chicago, the Knowledge Network for Biocomplexity, and through the Ecological Society of America's Ecological Archives.

NSF Award Abstract for Continuing Award DEB-1556874:

A central goal for ecology is to document if and how the environment is changing, to determine the causes of these changes, and to predict what the consequences of these changes will be to ecological systems. This is a challenge because of the complex network of connections among the living organisms and the non-living parts of ecosystems. Mathematical models are essential tools to keep track of these ecological interactions and to predict how they will respond to environmental changes. However, models need to be linked to data from nature. Two major challenges in developing predictive models of environmental change are 1) collecting sufficient data on how interactions among a complete set of species and environmental factors change over time, and (2) rigorously testing model predictions with experiments. This study will combine a quarter-century long series of data on 100+ species and relevant environmental variables in the rocky shoreline of Tatoosh Island in Washington state, with a long-term field experiment that mimics the extinction of a key species, the California mussel. The long term data will be applied to several different modeling approaches and predictions from these models will subsequently be tested with the long-term field experiment. The research will identify the most promising modeling approaches for making ecological prediction, and make them available to ecosystem managers and policy makers interested in the consequences of environmental impacts such as species extinction and global change. The comprehensive data series also will be made available to other scientists to be used as a platform for additional studies. This project will also engage undergraduate students in field research, data management, mathematical modeling, and in communicating with the public, managers, and policy makers. Furthermore, because the challenge of understanding networks of species interactions is shared with other scientific disciplines that deal with complex networks, project results will be of general value in other disciplines.

The researcher will conduct annual surveys of replicated permanent plots for plants and animals on the shoreline in two ways: 1) by documenting the species identities under 2,600 fixed points over a 5-year period and generating annual transition probabilities among species, and 2) by generating abundance estimates in permanent 60 x 60 cm census plots. Fifteen experimental plots will be maintained by selectively removing individuals of Mytilus californianus when they appear, leaving all other species undisturbed. Environmental data will be collected every 30 minutes using a submersible data logger and a land-based weather station. Water chemistry, including critical nutrients, will be monitored. These data will be analyzed in several ways, including 1) parameterizing transition-based models (Markov chain models, spatially-explicit cellular automata) with environmental dependencies, 2) parameterizing multi-species population dynamic models from plot counts, 3) applying multi-spatial cross-convergent mapping and testing whether it accurately detects key species known to have strong causal effects from independent experiments, 4) applying neural network models and testing their predictions about the consequences of species extinction, and 5) testing whether there is a relationship between the variability of a species' abundance through time and its importance to the ecosystem as assessed by independent experiments. The community modeling projects enabled by the rich long term data sets have a strong potential to advance our understanding of mechanisms underlying community dynamics and their response to environmental change.