Cross-scale forecasting of Everglades wading bird dynamics

University Of Florida, Gainesville FL

Investigators

Abstract

Ecological forecasting is a crucial emerging area of scientific research that attempts to predict changes in ecosystems over time. Accurately predicting future change is important for managing natural resources, conserving protected areas, and improving scientific understanding of the natural world. The behavior of ecosystems is affected by scale -- the size of the area and amount of time being studied -- because the importance of different ecological processes often changes as area or time increases. However, how this impacts ecological forecasts is currently unknown. This research will use long-term monitoring of wading birds in the Everglades to advance our understanding of how scale impacts ecological forecasting. In the Everglades, nature operates at a variety of distinct scales of space and time that this research will use to advance our understanding of how scale impacts ecological forecasting. The project will provide information on which scales allow for the most accurate forecasts, how this is influenced by changes in the accuracy of weather forecasts with scale, and whether forecast models developed at one scale can be used to make accurate predictions at other scales. This will produce improved forecasts to guide Everglades restoration and a broad understanding of how to incorporate the size of the area, and amount of time being predicted, into ecological forecasts in general. The project will also make data and forecasts for Everglades wading birds broadly available and facilitate their use for science and education. Leveraging the intensive monitoring of wading birds and hydrology in the Everglades, the research will address three aspects of the impact and integration of scale in ecological forecasting: 1) Quantify how forecastability, drivers, and uncertainty vary across spatial scales by comparing models fit to the entire Everglades, ecohydrological regions, and individual colonies; 2) Leverage cross-spatial scale drivers and interactions to understand cross-scale ecology and improve ecological forecasts by fitting two types of cross-scale model, comparing them to single scale models, and evaluating how the importance of cross-scale drivers is related to driver forecasts; and 3) Evaluate transferability of annual scale models to seasonal forecasting to understand if annual scale models can be used to improve seasonal forecasting by assessing how well annual models perform for seasonal forecasts and comparing them to seasonal models. To facilitate the use of the resulting data and forecasts for research, education, and management, the project will: 1) Make existing data on Everglades wading bird dynamics findable, accessible, interoperable, and reusable (FAIR); 2) Develop software for working with this data and associated ecohydrological drivers and using it to make and evaluate near-term iterative forecasts; 3) Produce a suite of educational resources designed for both individual use and incorporation into college and university courses including interactive forecasting websites, YouTube videos, and lesson material; and 4) Provide training and research experiences in ecological forecasting in the Everglades for both graduate students and undergraduates. 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.