Tag: marikok

Exploration of short-term predictions and long-term projections of Barents Sea cod biomass using statistical methods on data from dynamical models

Koseki M, Sandø AB, Ottersen G, Årthun M, Stiansen JE 2025: Exploration of short-term predictions and long-term projections of Barents Sea cod biomass using statistical methods on data from dynamical models. PLoS One. https://doi.org/10.1371/journal.pone.0328762

Summary: This study aims to explore how well simple statistical modeling can generate short-term predictions and long-term projections of the total biomass of the Northeast Arctic stock of Atlantic cod (Gadus Morhua) inhabiting the Barents Sea. We examine the predictability of statistical models only based on hydrographic and lower trophic level biological variables from dynamical modeling. Simple and multiple linear regression models are developed based on gridded variables from the regional ocean model NEMO-NAA10km and the ecosystem model NORWECOM.E2E. This includes the essential environmental variables temperature, salinity, sea ice concentration, primary production and secondary production. The regression models are statistically evaluated to find variables that can capture variability in Barents Sea cod biomass. Finally, future total cod stock biomass is projected by applying the best found regression models to the range of downscaled IPCC climate scenarios from the coupled Intercomparison Project Phase 6 (CMIP6 Shared Socioeconomic Pathways; SSP1–2.6, SSP2–4.5, SSP5–8.5). Our prediction models are based on variables that affect cod both directly and indirectly. We find that several regression models have high prediction skill and capture the variations in total stock biomass of the Northeast Arctic cod well. Our results suggest that increased ocean temperature and abundant zooplankton may lead to a large cod stock. However, even if total stock biomass has a positive trend with an increase in copepods in the highest warming scenario SSP5–8.5, we found that it has a negative trend in the low emission scenario SSP1–2.6 when the regional ocean and ecosystem models show weak cooling and reduced zooplankton. We show that variability in essential environmental variables can provide a remarkably good first approximation to cod dynamics. However, to resolve the full picture other factors like fishing and natural mortality also need to be addressed explicitly.

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Prediction of the Northeast Arctic cod biomass in the Barents Sea

Mariko Koseki joined BCPU as an intern in autumn 2021. Mariko has a Masters in Environmental Science which she obtained at Hokkaido University in Japan.

“I, Mariko Koseki, am an intern within BCPU, and I have been working with Dr. Anne Britt Sandø at the Institute of Marine Research since autumn of 2021.
During the internship, we have focused on the Northeast Arctic cod (NEA cod/Gadus Morhua) biomass in the Barents Sea and developed regression models to predict variations in cod biomass in the future.
The NEA cod is one of the most important species in the Barents Sea for both the ecosystem and as a commercial stock. Several earlier studies reported that the recent warming condition in the Barents Sea has led to high cod biomass.
To construct regression models for total stock biomass of the NEA cod, we used hydrographic and biological variables, such as temperature, salinity, sea ice fraction, primary- and secondary production as explanatory variables. These variables were obtained from hindcast simulations with regional ocean and ecosystem models. Finally, we used the same regression models with variables from downscaled climate scenarios to project future variations in the NEA cod.
We found that several of the regression models have high prediction skills and captured the variations in total stock biomass in the Barents Sea well. Moreover, based on downscaled climate projections, we made maps of spatial distributions of cod biomass in the future. However, errors between observations and predictions of cod biomass necessitate further improvement of the regression models. Now we are preparing to publish this study as a scientific article.
I would like to thank everyone who has supported my internship, and I hope to make use of my experience in my next steps.” – Mariko