Using Variance Structure As Statistical Indicators of Large Scale Ecological Change

Development of statistical indicators to detect and forecast large-scale change (i.e., regime shifts) in ecological systems could prove to be a valuable tool for the prediction of fish population dynamics, timely management and effective monitoring of fisheries resources. Switching between alternate basins of attraction in aquatic ecosystems can occur in response to natural processes as well as anthropogenic forcing.  Understanding how populations will respond to large-scale perturbations requires an understanding of how populations vary in space and time.  We analyzed long term fishery-independent survey data using a mixed modeling approach to partition total variability into spatial and temporal components. Variability, which is sometimes viewed as an impediment to understanding population response, may reveal important signals regarding critical thresholds, beyond which a system could transition into an alternate, perhaps less desirable, state. We explored the efficacy of this approach by comparing statistical shifts identified by the model to a known transition observed in the system, resulting largely from the establishment of zebra mussels. Improved understanding of spatiotemporal variance structure associated with ecological shifts will be an important contribution to further understanding the mechanisms underlying large-scale shifts, predicting population dynamics and adaptively managing fisheries resources.