Abstract

Adding to the challenge of predicting fishery recruitment in a changing environment is downscaling predictions to capture locally divergent trends over a species’ range. In recent decades, the American lobster (Homarus americanus) fishery has shifted poleward along the northwest Atlantic coast, one of the most rapidly warming regions of the world's oceans. Building on evidence that early post‐settlement life stages predict future fishery recruitment, we describe enhancements to a forecasting model that predict landings using an annual larval settlement index from 62 fixed sites among 10 study areas from Rhode Island, USA to New Brunswick, Canada. The model is novel because it incorporates local bottom temperature and disease prevalence to scale spatial and temporal changes in growth and mortality. For nine of these areas, adding environmental predictors significantly improved model performance, capturing a landings surge in the eastern Gulf of Maine, and collapse in southern New England. On the strength of these analyses, we project landings within the next decade to decline to near historical levels in the Gulf of Maine and no recovery in the south. This approach is timely as downscaled ocean temperature projections enable decision makers to assess their options under future climate scenarios at finer spatial scales.

Highlights

  • Rapid climate change in coastal ecosystems increases the urgency to develop forecasting tools enabling fishing communities and resource managers to anticipate and adapt to distributional shifts in the abundance of target species (FAO 2016, Payne 2017)

  • For all study areas, we developed locally tuned area-specific models with (3) the logistic growth functions scaled to a Northeast Coastal Ocean Forecast System (NECOFS)-modeled fixed average temperature for the study area, (4) the growth function varying at each time step according to annually varying bottom temperature for the study area, (5) bottom temperature fixed + disease prevalence reported for the study area, and (6) bottom temperature variable + disease prevalence

  • York, Maine, we found no statistically significant relationship between YoY recruitment and landings, and this study area was excluded from further analysis

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Summary

Introduction

Rapid climate change in coastal ecosystems increases the urgency to develop forecasting tools enabling fishing communities and resource managers to anticipate and adapt to distributional shifts in the abundance of target species (FAO 2016, Payne 2017). Insufficient data are available on critical life stages or the environment, and over a large enough spatial scale, to give advance warning of geographic shifts in recruitment. A standard practice in fisheries science is to project future recruitment from assumed spawner–recruit relationships that are notoriously variable (Myers 1998, Wahle 2003). Our project is predicated on the assumption that, for relatively long-lived benthic species with pelagic larvae, Manuscript received 1 June 2019; revised 3 September 2019; accepted 10 September 2019.

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