Regime shifts (periods of rapid change punctuating longer periods of lower variability) are observed in a wide range of ecosystems, and effective fisheries management requires the ability to detect these shifts. Detecting shifts is straightforward in single-species time series when transitions are detectable as periods of rapid change. However, shifts in complex and spatially structured communities may be harder to detect. We propose an approach to characterize community regime shifts, using nonparametric spatiotemporal regression models to estimate three indicators of community change (the among-species mean rate of change, mean per-capita rate of change, and standard deviation of per-capita rate of change). These indicators can detect shifts that result in either changes in abundance or composition. We applied our approach to a 37-year community biomass time series that spans the Newfoundland Shelf groundfish collapse. Our method detected a community shift earlier than alternative single-indicator breakpoint approaches and gave additional insight into the spatiotemporal pattern of the change, including detecting three separate periods of change and characterizing the first locations to show signs of recovery. The indicators applied in this study provide novel insights into Newfoundland groundfish dynamics and should be useful in the characterization of other abrupt ecological changes. Read More: https://academic.oup.com/icesjms/advance-article-abstract/doi/10.1093/icesjms/fsaa056/5835266?redirectedFrom=fulltext