NSF Award
2327030
Many ecosystems are exhibiting increased variability as a result of human activities. This environmental variability poses substantial challenges for managers and decision-makers, who can no longer use historical baselines to guide predictions of future ecosystem conditions. Consequently, advancing the capacity to predict the future for a range of physical, chemical, and biological ecosystem variables that influence water quality is paramount for improving resource management. In response to this need, this Long Term Research in Environmental Biology (LTREB) project will support a field monitoring and data-sharing program at two drinking water supply reservoirs. The ecosystem data that will be collected (which will include water temperature, clarity, chemistry, and phytoplankton, among other variables) will be used to generate and evaluate real-time forecasts (predictions of future ecosystem conditions, and the uncertainty associated with them) at daily to annual scales. Forecasting is a powerful approach for quantifying ecological predictability, as it requires using models that represent our best hypotheses about how ecosystems function to predict ecological conditions into the future. Iteratively evaluating these forecasts as new data are collected will reveal which models perform best in different environmental conditions and identify how far into the future different variables can be accurately predicted, from one day to one decade in advance. This project will enable the testing of fundamental hypotheses about the predictability of ecosystems; develop novel workflows for integrating environmental observations into real-time forecasting and data publishing; and broaden the participation of students from underrepresented groups in environmental data science. Moreover, all forecasts will be disseminated to water utilities in real time, enabling their immediate use as decision-making tools for water management.<br/> <br/>This LTREB project will represent one of the first systematic analyses of the predictability of ecosystem dynamics, thereby providing valuable information on the gradients and controls of predictability between contrasting ecosystems and among ecosystem variables. Importantly, researchers will be able to compare the performance of different forecast models with competing representations of ecosystem dynamics (e.g., varying driver variables, model structures) to test ecological hypotheses about predictability and examine the controls on ecosystem function. For example, forecast accuracy will be compared between two reservoirs that are similar in all characteristics except for oxygen availability to determine how anoxia, which is increasing in waterbodies globally, alters predictability. In addition, this LTREB project will develop novel FAIR (Findable, Accessible, Interoperable, Reusable) data-publishing workflows in collaboration with the Environmental Data Initiative (EDI) that advance reproducibility in ecology, support environmental data science education, and enable the scaling of ecological forecasting to other sites. Altogether, this project will result in data products for water reservoir physical, chemical, and biological ecosystem variables available in real-time for automated forecasting; a suite of different forecasting models and evaluated forecasts; forecasting and data-publishing workflows and software; and most critically, substantial ecosystem knowledge gained about the predictability of reservoir dynamics.<br/><br/>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.
