It is notoriously difficult to predict abrupt changes involving critical transitions (a type of regime shift), and these changes include management-relevant events like fisheries collapses and harmful algal blooms. However, recent theoretical and experimental work suggests that even when ecosystem state changes abruptly, the resilience and dynamical behavior of the system changes steadily. These slow changes in resilience might provide early warnings of regime shifts.
As early warning theory has developed, many potential barriers toward its implementation have been identified. Among these challenges are issues concerning how to measure ecosystem state and calculating early warning statistics, how managers should respond to these statistics, and whether warnings will provide useful opportunities for intervention. In this talk, I will focus on algal blooms as a case study for discussing how temporal scale can present challenges to implementing resilience-based forecasts. Are forecasts sensitive to the frequency of field measurements and the temporal scale of statistical analyses? How does the time horizon of a forecast interact with the time scale of governance and politics to produce sustainable strategies for managing ecosystems prone to critical transitions?
Results/Conclusions
We used high-frequency observations from automated sensors during an experimental whole-lake fertilization to test for the sensitivity of early warning statistics (namely lag-1 autocorrelation) to time scale. We found that autocorrelation calculated at different time scales produced qualitatively different outcomes: some time scales showed weak or no warning, others showed strong warnings several days or weeks prior to the onset of an algal bloom. We then present alternative analytical approaches that consider information from all available time scales to result in a single comprehensive statistic that provides an early warning.
At the end of the talk, I will discuss a vision for how early warnings could be used as a management tool in the case of algal blooms. Short-term forecasts on the scale of days to weeks are unlikely to be long enough in advance to avoid a bloom via reduced nutrient input. Therefore, the utility of this forecast is likely to be mitigation. The advantages and disadvantages of mitigation-enhancing forecasts are discussed in context of social-ecological dynamics.
We conclude that early warning theory can provide useful information about upcoming state transitions, but that developing the theory of social-ecological dynamics is critical for sustaining ecosystem and human health.