The specification of the data model part in the SAM model matters

This paper considers a general state-space stock assessment modeling framework that integrates a population model for a fish stock and a data model. This way observed data are linked to unobserved quantities in the population model. Using this framework, we suggest two modifications to improve accuracy in results obtained from the stock assessment model SAM and similar models. The first suggestion is to interpret the “process error” in these models as stochastic variation in natural mortality, and therefore include it in the data model. The second suggestion is to consider the observed catch as unbiased estimates of the true catch and modify the observation error accordingly. We demonstrate the efficacy of these modifications using empirical data from 14 fish stocks. Our results indicate that the modifications lead to improved fits to data and prediction performance, as well as reduced prediction bias.publishedVersio

Caveats with estimating natural mortality rates in stock assessment models using age aggregated catch data and abundance indices

We consider the challenge in estimating the natural mortality, M, in a standard statistical fish stock assessment model based on time series of catch- and abundance-at-age data. Though anecdotal evidence and empirical experience lend support to the fact that this parameter may be difficult to estimate, the current literature lacks a theoretical justification. We first discuss the estimatability of a time-invariant M theoretically and present necessary conditions for a constant M to be identifiable. We then investigate the practical usefulness of this by estimating M from simulated data based on models fitted to 19 fish stocks. Using the same data sets, we next explore several model formulations of time varying M, with a pre-specified mean value. Cross validation is used to assess the prediction performance of the candidate models. Our results show that a time-invariant M can be estimated with reasonable precision for a few stocks with long time series and typically high values of the true M. For most stocks, however, the estimation uncertainty of M is very large. For time-varying M, we find that accounting for variability across age and time using a simple model significantly improves the performance compared to a time-invariant M. No significant improvement is obtained by using complex models, such as, those with time dependencies in variability around mean values of M.publishedVersio

Sea lice as a density-dependent constraint to salmonid farming

Fisheries catches worldwide have shown no increase over the last two decades, while aquaculture has been booming. To cover the demand for fish in the growing human population, continued high growth rates in aquaculture are needed. A potential constraint to such growth is infectious diseases, as disease transmission rates are expected to increase with increasing densities of farmed fish. Using an extensive dataset from all farms growing salmonids along the Norwegian coast, we document that densities of farmed salmonids surrounding individual farms have a strong effect on farm levels of parasitic sea lice and efforts to control sea lice infections. Furthermore, increased intervention efforts have been unsuccessful in controlling elevated infection levels in high salmonid density areas in 2009–2010. Our results emphasize host density effects of farmed salmonids on the population dynamics of sea lice and suggest that parasitic sea lice represent a potent negative feedback mechanism that may limit sustainable spatial densities of farmed salmonids

Salmon lice (Lepeophtheirus salmonis) in the stomach contents of lumpfish (Cyclopterus lumpus) sampled from Norwegian fish farms: Relationship between lice grazing and operational conditions

publishedVersio

Final report for the REDUS project - Reduced Uncertainty in Stock Assessment

The REDUS project (2016-2020) has been a strategic project at the Institute of Marine Research (IMR) aimed at quantifying and reducing the uncertainty in data-rich and age-structured stock assessments (e.g., cod, herring, haddock, capelin). Work was organized in four topical work-packages: Fisheries-dependent (catch) surveys and assessment modeling (WP1), Fishery-independent (scientific) surveys (WP2), Evaluating and testing of long-term management strategies (WP3), and Communication of uncertainty, dissemination of project results and capacity building (WP4). The Norwegian Computing Center (NR) was contracted in as a strategic partner in statistical modeling and analysis, contributing mainly to WP1 and WP2, but found the research of fundamental interest therefore also allocating internal (NR) funding to develop the statistical science base of several of the methods.publishedVersio

Feiing og salting i Strømsås-tunnelen mars 2004: innledende analyse

I mars 2004 blei det gjennomført forsøk med vasking og salting i Strømsåstunnelen, med sikte på å redusere konsentrasjonen av svevestøv. Samtidig blei det gjort målinger av PM10, meteorologi og trafikkvolum

Estimating and decomposing total uncertainty for survey-based abundance estimates of Norwegian spring-spawning herring

We demonstrate how the total uncertainty of the abundance estimate and abundanceat- age estimate of Norwegian spring-spawning herring are affected by uncertainty in the parameters of vessel avoidance, shadowing and depth dependent target strength, and uncertainty from trawl hauls and spatial-temporal coverage. The total uncertainty is decomposed into the sum of the contribution from each source separately. In addition, we highlight the potentially dramatic combined effect of correcting for vessel avoidance, shadowing and depth dependent target strength on the abundance estimate. The main framework is believed to be a promising tool for focusing the effort for reducing the uncertainty in the abundance estimates. The method is applied to data from surveys on the over-wintering stock in the Vestfjord system and Vesteraalen in northern Norway in November/December in the years 2001–2004