Analytical and statistical review of procedures for collection and analysis of commercial fishery data used for management and assessment of groundfish stocks in the U.S. exclusive economic zone off Alaska

Effective management of multi-species fisheries requires reliable estimates of the amount and composition of catch and bycatch. In this report, we present the results of analyses of observer program data performed to compare several different estimators of catch and catch composition. Our analyses were applied to 1994 data collected in two fisheries selected based on data availability, representativeness, and economic importance: the walleye pollock and yellowfin sole fisheries in the USEEZ of the Eastern Bering Sea

Evaluation of sampling strategies for age determination of cod (Gadus morhua) sampled at the North Sea International Bottom Trawl Survey

The North Sea cod stock assessment is based on indices of abundance-at-age from fishery-independent bottom trawl surveys. The age structure of the catch is estimated by sampling fish for otoliths collection in a length-stratified manner from trawl hauls. Since age determination of fish is costly and time consuming, only a fraction of fish is sampled for age from a larger sample of the length distribution and an age–length key (ALK) is then used to obtain the age distribution. In this study, we evaluate ALK estimators for calculating the indices of abundance-at-age, with and without the assumption of constant age–length structures over relatively large areas. We show that the ALK estimators give similar point estimates of abundance-at-age and yield similar performance with respect to precision. We also quantify the uncertainty of indices of abundance and examine the effect of reducing the number of fish sampled for age determination on precision. For various subsampling strategies of otoliths collection, we show that one fish per 5-cm-length group width per trawl haul is sufficient and the total number of fish subsampled for age from trawl surveys could be reduced by at least half (50%) without appreciable loss in precision.publishedVersio

A simulation approach to assessing bias in a fisheries self-sampling programme

The hierarchical structure and non-probabilistic sampling in fisher self-sampling programmes makes it difficult to evaluate biases in total catch estimates. While so, it is possible to evaluate bias in the reported component of catches, which can then be used to infer likely bias in total catches. We assessed bias in the reported component of catches for 18 species in the Barents Sea trawl and longline fisheries by simulating 2000 realizations of the Norwegian Reference Fleet sampling programme using the mandatory catch reporting system, then for each realization we estimated fleet-wide catches using simple design-based estimators and quantified bias. We then inserted variations (e.g. simple random and systematic sampling) at different levels of the sampling design (sampling frame, vessel, and operation) to identify important factors and trends affecting bias in reported catches. We found that whilst current sampling procedures for fishing operations were not biased, non-probabilistic vessel sampling resulted in bias for some species. However, we concluded this was typically within the bounds of expected variation from probabilistic sampling. Our results highlight the risk of applying these simple estimators to all species. We recommend that future estimates of total catches consider alternative estimators and more conservative estimates of uncertainty where necessary.publishedVersio

A novel probabilistic survey method for at sea sampling in pelagic fisheries – the Norwegian catch sampling lottery

Reliable information on the age- and size-structure of the annual harvest of major commercial fish stocks is crucial input to analytical stock assessments. Such information is usually obtained from landing data (census of biomass) combined with biological sampling of selected landings. In this paper we describe a novel catch sampling method that we have developed and implemented for the major Norwegian pelagic fisheries that have annual landings around 1 million tonnes in recent years. The new sampling regime gradually implemented from 2018 is based on three pillars; probabilistic sampling of hauls, use of electronic logbook, and co-sampling. By a minor modification of the electronic logbook the vessels in the pelagic fishery now report the catch quantity at haul level immediately after each catch operation. This electronic report is automatically submitted to the Institute of Marine Research (IMR), where a random draw by computer in real time determines if a small sample of fish should be taken from that haul by the fishermen and be frozen and transported to IMR for analysis. Nearly 100% of the hauls annually goes through this “lottery”, and compliance (fraction of samples received) is currently around 60% and increasing. The sampling regime has been operationalized in cooperation with the fishing industry.publishedVersio

The Coastal Reference Fleet 2007-2019 - Fleet composition, fishing effort and contributions to science

Catch quotas are used for the sustainable management of fish stocks and are based on official catch statistics and research surveysSustainable management of the Norwegian fisheries depends on quota advice that is based on stock assessments, using scientific data from research surveys and official landings statistics. The official landings statistics are given in metric tons, and for vessels less than 15 meters only include rough information on fishing effort. The research cruises, on the other hand, only take place in certain areas and at certain times of the year. However, these methods provide only partial information, as the official catch data at best include rough measures of effort, and the survey data are only collected at certain times of the year. In 2005, the Norwegian Coastal Reference Fleet (CRF) was established by the Institute of Marine Research (IMR) ,to obtain data at-sea on in order to register catches in numbers per species including size- and age composition, discards, and bycatch, as well as data on fishing , gear, and effort. The vessels in the CRF were selected along the entire coast using criteria to ensure that they as represent able for the Norwegian coastal fishing fleet as closely as possible. These historic data and the development of the CRF are evaluated have been ccomprehensively visualisedin , and this report, using descriptive methods and provides a description and visual displaysanalysis of the CRF data. Between 2007-2019, a total of 64 fishing vessels particip ated in the CRF along the Norwegian coast collecting data from a cumulated total number of 287 taxa, and from 29 137 individual fishing operations. The most common species was cod (Gadus morhua), which occurred in 60.9% of the fishing operations. Throughout the study period gillnet was the most common gear in all years, but there were small shifts in the gear composition over time of the CRF shifted, however gillnet was the most common gear in all years. Catch per unit effort (CPUE) from fisheries standardises catch data for comparisons in both space and time and is often used as a proxy indices of for fish abundance, assuming constant catchability (the probability from 0 to 1 of a fish being caught per unit effort) over time. When collecting fisheries dependent data, however, fishers actively try to maximise catchability of their target species or species composition, by e.g., using different gears to handle natural variations in catchability dependent on season, and area. Broad coverage in space and time may reduce this human bias as well as providing information on the behaviour of the fishers and the effect of technological advancements. This report includes CPUE timeseries for cod, haddock (Melanogrammus aeglefinus), and golden redfish (Sebastes norvegicus) north of 62°N.publishedVersio

Spatial and temporal variations in seabird bycatch: Incidental bycatch in the Norwegian coastal gillnet-fishery

The general decline of seabird populations worldwide raises large concerns. Although multiple factors are interacting to cause the observed trends, increased mortality from incidental bycatch in fisheries has proven to be important for many species. However, the bulk of published knowledge is derived from longline fisheries, whereas bycatch in gillnet fisheries is less studied and even overlooked in some areas. We present seabird bycatch data from a 10-year time-series of fishery data from the large fleet of small-vessels fishing with gillnets along the Norwegian coast—a large area and fishery with no prior estimates of seabird bycatch. In general, we document high rates of incidental bycatch (averaging 0.0023 seabirds/ net, or approximately 0.08 seabirds/fishing trip). This results in an estimated annual bycatch between 1580 and 11500 (95% CI) birds in this fishery. There was a surprisingly high percentage (43%) of surface-feeding seabirds in the bycatch, with northern fulmar being the most common species. Among the diving seabirds caught, common guillemot was most numerous. Our findings suggest that coastal gillnet fisheries represent a more general threat to a wider range of seabird populations, as opposed to longline fisheries where surface-feeding seabird species seem to dominate the bycatch. The bycatch estimates for the Norwegian gillnet-fishery varied in time, between areas, and with fishing depth and distance from the coast, but we found no clear trends in relation to the type of gillnets used. The results enabled us to identify important spatio-temporal trends in the seabird bycatch, which can allow for the development and implementation of more specific mitigation measures. While specific time closures might be an efficient option to reduce bycatch for diving seabirds, measures such as gear modification and reduction in release of wastewater during fishing operation are probably a more effective mitigation approach for reducing bycatch of surface-feeding seabirds.publishedVersio

Assessing incidental bycatch of seabirds in Norwegian coastal commercial fisheries: Empirical and methodological lessons

-Unintentional bycatch, waterbirds, small vessel fishery, gillnetts, longlines, Northeast Atlanti

Integrating complementary survey methods to estimate catches in Norway’s complex marine recreational hook-and-line fishery

Marine recreational fishing is popular in Norway, but current estimates of the catches by resident and tourist anglers are lacking due to several challenges, in particular Norway’s long and intricate coastline with no defined access points and the large tourist fishery. To test methods for long-term monitoring of boat-based marine recreational anglers, estimate their catches, and characterize the fishery, we conducted a roving creel survey based on a novel spatial sampling frame and a survey of tourist fishing businesses in Troms and Hordaland County. These surveys showed that cod (Gadus morhua) and saithe (Pollachius virens) dominated the catches in Troms, while mackerel (Scomber scombrus) and saithe dominated the catches in Hordaland. The estimated total annual harvest of cod by all marine recreational anglers was 2 160 tonnes (relative standard error, or RSE 44%) in Troms and 73 tonnes (RSE 29%) in Hordaland, of which ∼40% (in weight) were landed in registered tourist fishing businesses, based on data from the tourist fishing survey. The results indicate that recreational anglers in Hordaland harvest more cod in coastal waters than commercial fishers. This study provides information for developing marine recreational fisheries monitoring in challenging survey situations to support science-based fisheries management.publishedVersio

The lobster fishery 2017 and 2018: Effort and catches

I 2017 ble det innført obligatorisk påmelding til hummerfisket for både fritidsfiskere og yrkesfiskere. Dette har lagt grunnlaget for muligheten til å beregne total innsats og fangst av hummer i Norge. Etter hummersesongen 2017 ble det gjennomført en etterundersøkelse av et tilfeldig utvalg av de påmeldte som en pilotstudie for datainnsamling. I 2018 ble et tilfeldig utvalg påmeldte fiskere kontaktet ved starten av hummerfisket og bedt om å rapportere alle sine fisketurer gjennom sesong. Resultatene fra 2017 og 2018 er ikke direkte sammenlignbare da det er benyttet to ulike metoder. I 2017 var det påmeldt 36 393 fiskere, mens det var totalt påmeldt 29 681 fiskere i 2018. En nedgang på 18 %. Årsaken til denne nedgangen er ikke kjent. I 2017 landet hver påmeldt fritidsfisker som deltok i hummerfisket i snitt 12 (95% CI=2,3) hummer med små regionale forskjeller. I 2018 ble det landet 10,9 (CI=1,5) hummer per påmeldte fritidsfiskersom deltok i fisket. Yrkesfiskerne landet i snitt 75 (95% CI=15) hummer hver i 2017 og 66 (95% CI=8) i 2018. Totalt for 2017 er det beregnet at fritidsfiskerne landet 321 000 hummer (95% CI; 260 000 - 381 000) og yrkesfiskerne landet 133 000 hummer (95% CI; 107 500 - 158 700). Totalt ble det da landet rundt 454 000 hummer i 2017. For 2018 er det beregnet at fritidsfiskerne landet totalt 227 000 (95% CI; 194 000 – 260 000), mens yrkesfiskerne landet 95 000 (95% CI; 61 000 – 128 000) hummer. Totalt ble det da landet rundt 322 000 hummer i 2018. I 2017 utgjorde fritidsfiskerne 71 % av landingene og i 2018 70 %. Fritidsfisket dominerer uttaket i alle tre regioner (Skagerrak, Vestlandet og Nord-Vestlandet).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