Abstract
Changing ecosystem conditions present a challenge for the monitoring and management of living marine resources, where decisions often require lead-times of weeks to months. Consistent improvement in the skill of regional ocean models to predict physical ocean states at seasonal time scales provides opportunities to forecast biological responses to changing ecosystem conditions that impact fishery management practices. In this study, we used 8-month lead-time predictions of temperature at 250 m depth from the J-SCOPE regional ocean model, along with stationary habitat conditions (e.g., distance to shelf break), to forecast Pacific hake (Merluccius productus) distribution in the northern California Current Ecosystem. Using retrospective skill assessments, we found strong agreement between hake distribution forecasts and historical observations. The top performing models (based on out-of-sample skill assessments using the area-under-the-curve (AUC) skill metric) were a generalized additive model (GAM) that included shelf-break distance (i.e., distance to the 200 m isobath) (AUC = 0.813) and a boosted regression tree (BRT) that included temperature at 250 m depth and shelf-break distance (AUC = 0.830). An ensemble forecast of the top performing GAM and BRT models only improved out-of-sample forecast skill slightly (AUC = 0.838) due to strongly correlated forecast errors between models (r = 0.88). Collectively, our results demonstrate that seasonal lead-time ocean predictions have predictive skill for important ecological processes in the northern California Current Ecosystem and can be used to provide early detection of impending distribution shifts of ecologically and economically important marine species.
Highlights
Anticipating ecological change is an important component of living marine resource management where decisions often require lead-times of weeks to months
We found that: (1) the J-SCOPE model had considerable predictive skill of subsurface temperatures throughout the study domain, (2) distance to the 200 m shelf break was a strong predictor of historical hake occurrence and temperature at depth had a spatially varying effect on the probability of occurrence; and (3) the boosted regression tree (BRT) model had moderately higher forecast skill than the generalized additive model (GAM) and a multi-model ensemble forecast had slightly better out-ofsample forecast skill compared to the individual GAM and BRT models
Our results suggest that comparatively simple models can forecast hake distribution using seasonal projections of subsurface ocean temperature and distance to the 200 m shelf break
Summary
Anticipating ecological change is an important component of living marine resource management where decisions often require lead-times of weeks to months. Seasonal ecological forecasts provide a means to reduce related uncertainties and play a key role in supporting management of living marine resources into the future (Hobday et al, 2016; Payne et al, 2017; Tommasi et al, 2017). Increases in the predictive skill of physical ocean states has partially driven the increased availability of seasonal ecological forecasts and has resulted in the availability of skillful ocean forecasts with seasonal lead-times for many of the world’s large marine ecosystems (Stock et al, 2015; Tommasi et al, 2017; Jacox et al, 2020). In the northern California Current Ecosystem (CCE), the J-SCOPE (JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem) model provides forecasts of physical, chemical, and biological ocean states with seasonal lead times (e.g., 6–9 months) (Siedlecki et al, 2016). J-SCOPE seasonal forecasts of ocean conditions can be used to drive ecological forecasts, such as sardine distribution in the CCE (Kaplan et al, 2016)
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