Horizontal distribution and overlap of planktivorous fish stocks in the Norwegian Sea during summers 1995–2006

The Norwegian Sea harbours several large pelagic fish stocks, which use the area for feeding during the summer. The period 1995–2006 had some of the highest biomass of pelagic fish feeding in the Norwegian Sea on record. Here we address the horizontal distribution and overlap between herring, blue whiting and mackerel in this period during the summers using a combination of acoustic, trawl and LIDAR data. A newly developed temperature atlas for the Norwegian Sea is used to present the horizontal fish distributions in relation to temperature. The centre of gravity of the herring distribution changed markedly several times during the investigated period. Blue whiting feeding habitat expanded in a northwestern direction until 2003, corresponding with an increase in abundance. Strong year classes of mackerel in 2001 and 2002 and increasing temperatures throughout the period resulted in an increased amount of mackerel in the Norwegian Sea. Mackerel was generally found in waters warmer than 8°C, while herring and blue whiting were mainly found in water masses between 2 and 8°C. The horizontal overlap between herring and mackerel was low, while blue whiting had a large horizontal overlap with both herring and mackerel. The changes in horizontal distribution and overlap between the species are explained by increasing stock sizes, increasing water temperature and spatially changing zooplankton densities in the Norwegian Sea

Report of the NAMMCO-ICES Workshop on Seal Modelling (WKSEALS 2020)

To support sustainable management of apex predator populations, it is important to estimate population size and understand the drivers of population trends to anticipate the consequences of human decisions. Robust population models are needed, which must be based on realistic biological principles and validated with the best available data. A team of international experts reviewed age-structured models of North Atlantic pinniped populations, including Grey seal (Halichoerus grypus), Harp seal (Pagophilus groenlandicus), and Hooded seal (Cystophora cristata). Statistical methods used to fit such models to data were compared and contrasted. Differences in biological assumptions and model equations were driven by the data available from separate studies, including observation methodology and pre-processing. Counts of pups during the breeding season were used in all models, with additional counts of adults and juveniles available in some. The regularity and frequency of data collection, including survey counts and vital rate estimates, varied. Important differences between the models concerned the nature and causes of variation in vital rates (age-dependent survival and fecundity). Parameterisation of age at maturity was detailed and time-dependent in some models and simplified in others. Methods for estimation of model parameters were reviewed and compared. They included Bayesian and maximum likelihood (ML) approaches, implemented via bespoke coding in C, C++, TMB or JAGS. Comparative model runs suggested that as expected, ML-based implementations were rapid and computationally efficient, while Bayesian approaches, which used MCMC or sequential importance sampling, required longer for inference. For grey seal populations in the Netherlands, where preliminary ML-based TMB results were compared with the outputs of a Bayesian JAGS implementation, some differences in parameter estimates were apparent. For these seal populations, further investigations are recommended to explore differences that might result from the modelling framework and model-fitting methodology, and their importance for inference and management advice. The group recommended building on the success of this workshop via continued collaboration with ICES and NAMMCO assessment groups, as well as other experts in the marine mammal modelling community. Specifically, for Northeast Atlantic harp and hooded seal populations, the workshop represents the initial step towards a full ICES benchmark process aimed at revising and evaluating new assessment models.Publisher PDFPeer reviewe