GoalsOur objectives for this project are to develop models to detect and assess ecological regime shifts in space and time, to identify components of adaptive capacity, and to identify species and techniques that may serve as leading indicators of thresholds of changing ecological regimes.
We are utilizing monitoring and surveying data that are currently available in North America (e.g., Breeding Bird Surveys) with novel statistical tools and theory to assess long-term trends in the resilience of landscapes, changes in ecological regimes in both space and time, and species vulnerable to decline and extinction.
A growing concern with effects of climate change and globalization include rapid and undesirable shifts in the structure and functioning of ecological systems. Identifying and predicting these changes (or ‘regime shifts’, ‘state changes’ or ‘abrupt changes’) would be of great utility to ecological systems management. Numerous quantitative methods are proposed for detecting these changes, however, most have yet to be implemented by practitioners. We present a regime shift detection metric that tracks the trajectory of systems data and is simple to calculate, intuitive, and appears insensitive to variable selection and data availability. The metric, distance traveled (Figure, right), is simply calculated as a cumulative summation of changes in state variables over time. Using resampling techniques, we found evidence to suggest the distance traveled metric is more robust to sampling errors that are common in ecological data collection (e.g., unequal sampling, sampling only a subset of the community).
Principal Investigator(s)-Craig R. Allen
-David G. Angeler