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Successful management of natural resources requires local action that adapts to larger‐scale environmental changes in order to maintain populations within the safe operating space (SOS) of acceptable conditions. Here, we identify the boundaries of the SOS for a managed freshwater fishery in the first empirical test of the SOS concept applied to management of harvested resources. Walleye (Sander vitreus) are popular sport fish with declining populations in many North American lakes, and understanding the causes of and responding to these changes is a high priority for fisheries management. We evaluated the role of changing water clarity and temperature in the decline of a high‐profile walleye population in Mille Lacs, Minnesota, USA, and estimated safe harvest under changing conditions from 1987 to 2017. Thermal–optical habitat area (TOHA)—the proportion of lake area in which the optimal thermal and optical conditions for walleye overlap—was estimated using a thermodynamic simulation model of daily water temperatures and light conditions. We then used a SOS model to analyze how walleye carrying capacity and safe harvest relate to walleye thermal–optical habitat. Thermal–optical habitat area varied annually and declined over time due to increased water clarity, and maximum safe harvest estimated by the SOS model varied by nearly an order of magnitude. Maximum safe harvest levels of walleye declined with declining TOHA. Walleye harvest exceeded safe harvest estimated by the SOS model in 16 out of the 30 yr of our dataset, and walleye abundance declined following 14 of those years, suggesting that walleye harvest should be managed to accommodate changing habitat conditions. By quantifying harvest trade‐offs associated with loss of walleye habitat, this study provides a framework for managing walleye in the context of ecosystem change. [ABSTRACT FROM AUTHOR]

Abstract Many ecosystems have abruptly changed in the past and may again in the future, yet prediction and inference of mechanisms causing abrupt changes remains challenging. Critical transitions are one such mechanism, occurring when systems with alternative states cross a threshold. Such transitions are associated with a loss of resilience, often signaled by increasing variability or autocorrelation over time. However, critical transitions are difficult to distinguish from other causal mechanisms, and detection of resilience loss in sedimentary archives can be confounded by time-averaging and discontinuous sampling. Here, we simulate woodland-grassland regime shifts resulting from critical transitions and other mechanisms. We then test the diagnostic ability of two widely-used resilience indicators, standard deviation and autocorrelation time, after alterations common in sedimentary records: time averaging, discontinuous sampling, and varying sedimentation rates. Standard deviation—but not autocorrelation time—still distinguishes gradually forced critical transitions from other regime shifts when sedimentation rates are constant, and can be robust to abrupt changes in sedimentation rate. Unfortunately, shifts in standard deviation alone are rarely definitive evidence of critical transitions. Under exponential sedimentation regimes, which are common in younger upper-column sediments, neither resilience indicator is effective. Discontinuous sampling weakened the strength of resilience indicators. A demonstrative analysis of abrupt early Holocene deforestation recorded at Steel Lake, Minnesota showed signals consistent with resilience loss during early Holocene aridification. Hence, signals of resilience loss can be recovered from sedimentary archives, but efficacy varies among indicators and sedimentation regime. High-resolution and multi-proxy records remain essential to inferring causes, while process-based time series modeling such as this can be calibrated to systems of interest to explicitly test hypotheses about abrupt change causes. Highlights • Sedimentation and sampling can obscure signals of resilience loss in paleoecological records. • Standard deviation is a robust resilience indicator under most sedimentation and sampling scenarios. • Autocorrelation time is not an effective resilience indicator for sedimentary records. • Neither resilience indicator is effective when sedimentation is exponential. • Discontinuous sampling weakens or eliminates indicators of resilience loss. [ABSTRACT FROM AUTHOR]