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This year the group changed its name to Working Group on Acoustic and Egg Surveys for small pelagic fish in NE Atlantic (WGACEGG). WGACEGG coordinates, assesses and quality controls acoustic and daily egg production (DEPM) surveys of several pelagic stocks in ICES areas 6-9. During three 2020 WGACEGG meetings, results of three DEPM surveys, three spring/summer acoustic surveys and all six autumn surveys were presented. Implications of the cancellation of three surveys (SAREVA and PELACUS (Spain)) and PELGAS (France) were also discussed, as were possible mitigations to minimize the effects on the relevant stock assessments. No concerns were raised about the surveys that took place. The results from the DEPM survey for sardine in 9a could not be provided until December, as COVID-19 had restricted laboratory access. WGACEGG also met with members of the Working Group on Southern Horse Mackerel, Anchovy, and Sardine (WGHANSA) to present the results from the 2020 Portuguese (PELAGO) acoustic survey in advance of the June assessment meeting. The working groups discussed the implications of PELAGO using a different vessel and of the earlier timing (compared to the timeseries) on the sardine and anchovy indices. The joint meeting also identified which surveys were at risk of being cancelled because of COVID-19 and discussed possible mitigations. Biomass estimates for both sardine and anchovy in division 9a were higher than observed in recent years as confirmed by both acoustic and DEPM surveys; DEPM derived Bay of Biscay anchovy spawning stock biomass was the highest on record although the number of recruits in the Bay of Biscay observed later in autumn did not reflect this increase. However, this was at least partly due to a northwards expansion into the English Channel where Bay of Biscay juveniles were found for the first time; sardine biomass in the Bay of Biscay from all available surveys suggested slightly lower values than the long-term average; in the English Channel sardine biomass was the second highest in the time-series; sprat biomass in the English Channel was similar to those observed in the last few years; herring biomass in the Celtic Sea had increased from 2019 but was still very low; Horse mackerel south and west of Ireland continued a three-year declining trend in biomass whereas boarfish biomass in the same area more than doubled. The group updated the grid map database and the joint analyses of the ecological drivers of pelagic species distributions within their ecosystems. Two ICES TIMES reports will be published soon; the acoustic report has been accepted and the DEPM report is currently under revision. WGACEGG participants also contributed to several peer reviewed publications in 2020 and more are under revision. WGACEGG and the Working Group on Acoustic Trawl Data Portal Governance (WGAcousticGov) agreed to continue the move towards using the ICES trawl acoustic database as the primary survey data repository. Progress on other collaborative activities was discussed, including with WGSPF (Small Pelagic Fish) and, specifically, a WGACEGG-led theme session for the 2022 SPF Symposium.

Additional information on the model setup, performance and outputs.

Demersal fish stock biomass is usually estimated using commercial landings data, ‘tuned’ by various types of survey data, usually trawl. When collecting fish data by trawl it is only possible to take relatively small numbers of samples, but each component of the catch can be measured and resolved to species. Acoustic data, on the other hand, are easy to collect since continuous sampling can be maintained at speed (10 knots) but discerning species and size structure from data is difficult. Here we investigate whether these two different measurements of the same quantity (fish abundance/biomass) are actually related to each other. To do this, databases were built that consisted of simultaneous acoustic and catch data collected during a trawl haul. These are described as 'on station data'. Both trawl haul and acoustic data were found to have high variances and direct correlation between them tended to be weakly positive, although the spatial and temporal trajectories captured by both the trawl and acoustic data were similar. The reasons for this apparent inconsistency are explained. In many situations, however, direct positive correlation was found; relationships perhaps strong enough to be extended to acoustic data collected between trawl stations, ie. 'underway data'. Factors leading to such situations are also described. Given the number and characteristics of the favorable cases, we go on to comment whether such relationships can realistically be used in the construction of combined trawl and acoustic indices and under which conditions.

Groundfish have a wide and variable distribution making the use of trawling alone a highly inadequate sampling method. Trawl data provide species identification and numbers over a very small area and habitat type while acoustic data provide a wider coverage of the ecosystem, but fail to identify species. Both methods provide essential information required for assessing fish stock abundance and distribution, but no systematic method of combining these data has yet been identified. Acoustic and trawl data were collected for both the North and Barents Seas and their relationships investigated using artificial neural networks (ANNs). ANNs are information processing systems that were inspired by the structure of the brain. They can incorporate multiple variables and explore complex interactions between variables in far greater depth than many traditional statistical techniques. This modelling tool has had many successes in environmental modelling and is increasingly being applied in situations where the underlying relationships are poorly known. Network architectures, optimisation of connection weights (learning), model validation, and the reasons for the difference in performance between the North Sea and Barents Sea models are discussed.

Based on acoustic data and trawl catches collected during bottom trawl surveys, we compare the spatial structure of the biomass available in the first meters above the bottom in three different regions, the Barents Sea (1997-2002), the North Sea (1999-2003) and the Irish Sea (1997-2002). The objective was to compare areas with different levels of resource. Conclusions are conditioned to the number of samples available in each respective study area (large for Barents Sea, medium for the North Sea and small for the Irish Sea). This analysis combines acoustic and trawl data. The acoustic data were used to improve the knowledge of catch data spatial structure. Most of the time, hauls are too far to be correlated. Acoustic data could allow to take account spatial structures with shorter range since they are collected along the vessel track. The acoustic information can improve the accuracy in the catch spatial structures analysis. Two groups of species are distinguished: pelagic fish and demersal fish. Spatial structures are observed in the Barents Sea made of small scale heterogeneities and large scale structures of 300 nautical miles. Such a structure is consistent through time (i.e. from 1997 to 2002). Spatial patchiness is extremely important in the North Sea causing the classical geostatistical tools to be useless. Apart from the ecological interpretation of the density-dependency of spatial structures, we describe some possible ways to handle patchy distributions.

The Brandts’ bat (Myotis brandtii) and the whiskered bat (Myotis mystacinus) are cryptic species from distinct taxonomic clades whose ranges frequently overlap. What, if any, resource partitioning that exists between them is not well understood, especially at the northern extent of their range. The aim of this study is to compare the foraging ecology, roost ecology and diet of M. brandtii and M. mystacinus during the summer in southeastern Norway. Bats were captured using mist nets and adult females were radio-tagged. Tagged individuals were tracked for a week on average, during which time the entire period of foraging every night and roost activity were monitored. LiDAR (light detection and ranging) data describing habitat structure in observed foraging locations, and an equal number of random locations within each bat’s home range, were used to build resource selection functions (RSFs) to evaluate differences in foraging habitat use between the two species. Feces from captured bats were analyzed using molecular genetic analysis to verify in-hand identification of these cryptic species in addition to dietary analyses. I found that forests with varied canopy height were important foraging locations for both species but that M. mystacinus was predicted to use more diverse vegetation structure than M. brandtii, reinforcing previous research which has proposed that M. brandtii is more specialized to mature forests while M. mystacinus is more generalized in habitat selection. The home range of M. brandtii was over 3 times the size of M. mystacinus, further suggesting that M. brandtii is willing to travel farther distances to reach specific habitat than M. mystacinus that is adapted to using a variety of foraging habitat. Both species utilized similar roosts, with colonies only being found in the roofs of houses. This is the first study to compare the foraging ecology of M. brandtii and M. mystacinus using 3-dimensional continuous descriptions of habitat. Furthermore, it is one of few studies that has accomplished homing in on radio-tagged bats to collect precise foraging locations. The findings here can provide insights into monitoring techniques that can be applied to studying bats at northern latitudes as well as for studying resource selection of bats globally. M-ECOL