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Ecosystem patterning can arise from environmental heterogeneity, biological feedbacks that produce multiple persistent ecological states, or their interaction. One source of feedbacks is density‐dependent changes in behaviour that regulate species interactions. By fitting state‐space models to large‐scale (~500 km) surveys on temperate rocky reefs, we find that behavioural feedbacks best explain why kelp and urchin barrens form either reef‐wide patches or local mosaics. Best‐supported models in California include feedbacks where starvation intensifies grazing across entire reefs create reef‐scale, alternatively stable kelp‐ and urchin‐dominated states (32% of reefs). Best‐fitting models in New Zealand include the feedback of urchins avoiding dense kelp stands that can increase abrasion and predation risk, which drives a transition from shallower urchin‐dominated to deeper kelp‐dominated zones, with patchiness at 3–8 m depths with intermediate wave stress. Connecting locally studied processes with region‐wide data, we highlight how behaviour can explain community patterning and why some systems exhibit community‐wide alternative stable states. [ABSTRACT FROM AUTHOR]

Distinguishing between human impacts and natural variation in abundance remains difficult because most species exhibit complex patterns of variation in space and time. When ecological monitoring data are available, a before‐after‐control‐impact (BACI) analysis can control natural spatial and temporal variation to better identify an impact and estimate its magnitude. However, populations with limited distributions and confounding spatial‐temporal dynamics can violate core assumptions of BACI‐type designs. In this study, we assessed how such properties affect the potential to identify impacts. Specifically, we quantified the conditions under which BACI analyses correctly (or incorrectly) identified simulated anthropogenic impacts in a spatially and temporally replicated data set of fish, macroalgal, and invertebrate species found on nearshore subtidal reefs in southern California, USA. We found BACI failed to assess very localized impacts, and had low power but high precision when assessing region‐wide impacts. Power was highest for severe impacts of moderate spatial scale, and impacts were most easily detected in species with stable, widely distributed populations. Serial autocorrelation in the data greatly inflated false impact detection rates, and could be partly controlled for statistically, while spatial synchrony in dynamics had no consistent effect on power or false detection rates. Unfortunately, species that offer high power to detect real impacts were also more likely to detect impacts where none had occurred. However, considering power and false detection rates together can identify promising indicator species, and collectively analyzing data for similar species improved the net ability to assess impacts. These insights set expectations for the sizes and severities of impacts that BACI analyses can detect in real systems, point to the importance of serial autocorrelation (but not of spatial synchrony), and indicate how to choose the species, and groups of species, that can best identify impacts. [ABSTRACT FROM AUTHOR]