Trends in Pacific Canadian groundfish stock status

We assembled estimated biomass (B) time series from stock assessments for 24 Pacific Canadian groundfish stocks and modelled average and stock status through 2020 based on biomass relative to each stock's (1) Limit Reference Point (B/LRP), (2) Upper Stock Reference (B/USR), and (3) biomass at maximum sustainable yield (B/BMSY). The overall mean B/LRP in 2020 was 3.2 (95% credible interval [CI]: 2.6-3.9). The overall mean B/USR and B/BMSY in 2020 was 1.5 (95% CI: 1.3-1.9) and 1.4 (95% CI: 1.1-1.7), respectively. Average stock status declined from 1950 to around 2000 and has remained relatively stable since then. The change around 2000 followed the implementation of ITQs (individual transferable quotas) for the trawl fleet and the commencement of the synoptic trawl surveys. As of their last assessment, four stocks (Strait of Georgia Lingcod [Area 4B], coastwide Bocaccio, and inside and outside Quillback Rockfish) had a greater than 5% probability of being below their LRP (i.e., in the "critical zone"); Pacific Cod in Area 3CD had a 4.6% probability. Roughly one-third of stocks had a greater than 1 in 4 chance of being below their USR (i.e., in the "cautious zone"). Conversely, two-thirds of assessed groundfish stocks had a high (>75%) probability of being above the USR (i.e., in the "healthy zone").

Clustering and Classification in Fisheries

<p>This goal of this research is to investigate associations between presences of fish species, space, and time in a selected set of areas in New Zealand waters. In particular we use fish abundance indices on the Chatham Rise from scientific surveys in 2002, 2011, 2012, and 2013. The data are collected in annual bottom trawl surveys carried out by the National Institute of Water and Atmospheric Research (NIWA). This research applies clustering via finite mixture models that gives a likelihood-based foundation for the analysis. We use the methods developed by Pledger and Arnold (2014) to cluster species into common groups, conditional on the measured covariates (body size, depth, and water temperature). The project for the first time applies these methods incorporating covariates, and we use simple binary presence/absence data rather than abundances. The models are fitted using the Expectation-Maximization (EM) algorithm. The performance of the models is evaluated by a simulation study. We discuss the advantages and the disadvantages of the EM algorithm. We then introduce a newly developed function clustglm (Pledger et al., 2015) in R, which implements this clustering methodology, and perform our analysis using this function on the real-life presence/absence data. The results are analysed and interpreted from a biological point of view. We present a variety of visualisations of the models to assist in their interpretation. We found that depth is the most important factor to explain the data.</p>

Clustering and Classification in Fisheries

<p>This goal of this research is to investigate associations between presences of fish species, space, and time in a selected set of areas in New Zealand waters. In particular we use fish abundance indices on the Chatham Rise from scientific surveys in 2002, 2011, 2012, and 2013. The data are collected in annual bottom trawl surveys carried out by the National Institute of Water and Atmospheric Research (NIWA). This research applies clustering via finite mixture models that gives a likelihood-based foundation for the analysis. We use the methods developed by Pledger and Arnold (2014) to cluster species into common groups, conditional on the measured covariates (body size, depth, and water temperature). The project for the first time applies these methods incorporating covariates, and we use simple binary presence/absence data rather than abundances. The models are fitted using the Expectation-Maximization (EM) algorithm. The performance of the models is evaluated by a simulation study. We discuss the advantages and the disadvantages of the EM algorithm. We then introduce a newly developed function clustglm (Pledger et al., 2015) in R, which implements this clustering methodology, and perform our analysis using this function on the real-life presence/absence data. The results are analysed and interpreted from a biological point of view. We present a variety of visualisations of the models to assist in their interpretation. We found that depth is the most important factor to explain the data.</p>

Batoid Abundances, Spatial Distribution, and Life History Traits in the Strait of Sicily (Central Mediterranean Sea): Bridging a Knowledge Gap through Three Decades of Survey

Batoid species play a key role in marine ecosystems but unfortunately they have globally declined over the last decades. Given the paucity of information, abundance data and the main life history traits for batoids, obtained through about three decades of bottom trawl surveys, are presented and discussed. The surveys were carried out in two areas of the Central Mediterranean (South of Sicily and Malta Island), in a timeframe ranging from 1990 to 2018. Excluding some batoids, the abundance trends were stable or increasing. Only R. clavata, R. miraletus, and D. oxyrinchus showed occurrence and abundance indexes notable enough to carry out more detailed analysis. In particular, spatial distribution analysis of these species highlighted the presence of two main hotspots in Sicilian waters whereas they seem more widespread in Malta. The lengths at first maturity (L50) were 695 and 860, 635 and 574, and 364 and 349 mm total length (TL), respectively, for females and males of D. oxyrinchus, R. clavata, and R. miraletus. The asymptotic lengths (L∞) and the curvature coefficients (K) were 1365 and 1240 (K = 0.11 and 0.26), 1260 and 1100 (K = 0.16 and 0.26), and 840 and 800 mm TL (K = 0.36 and 0.41), respectively, for females and males of D. oxyrinchus, R. clavata, and R. miraletus. The lack of detailed quantitative historical information on batoids of Sicily and Malta does not allow to analytically judge the current status of the stocks, although the higher abundance of some species within Malta raises some concern for the Sicilian counterpart. In conclusion, suitable actions to protect batoids in the investigated area are recommended.

Evaluating acoustic-trawl survey strategies using an end-to-end ecosystem model

Fisheries independent surveys support science and fisheries assessments but are costly. Evaluating the efficacy of a survey before initiating it could save costs. We used the NORWECOM.E2E model to simulate Northeast Atlantic mackerel and Norwegian spring spawning herring distributions in the Norwegian Sea, and we ran vessel transects in silico to simulate acoustic-trawl surveys. The simulated data were processed using standard survey estimation software and compared to the stock abundances in the ecosystem model. Three existing real surveys were manipulated to demonstrate how the simulation framework can be used to investigate effects of changes in survey timing, direction, and coverage on survey estimates. The method picked up general sources of biases and variance, i.e. that surveys conducted during fish migrations are more vulnerable in terms of bias to timing and changes in survey direction than during more stationary situations and that increased effort reduced the sampling variance.

Abundance and spatial distribution of brown crab (Cancer pagurus) from fishery-independent dredge and trawl surveys in the North Sea

Brown crab (Cancer pagurus) is a widely distributed crustacean that occurs around the British coastline supporting important commercial fisheries. The habitat preferences of brown crab around Scotland are poorly documented and for the purposes of stock assessment, the species is considered data-poor. Based on an analysis of dredge and trawl surveys taking place in the North Sea (2008–2018), we describe the spatial distribution of brown crab and for the first time, develop abundance and recruitment indices for the species. We make use of geostatistical methods and apply generalized additive models to model catch rates in relation to a number of explanatory variables (depth, distance to the coast, sediment type and year). The dredge and trawl abundance indices were correlated showing a similar trend of increasing catch rates in the early years of the time series up to 2016 and a subsequent reduction. The recruitment index showed a gradual increase in captured juvenile crabs up to 2014 followed by a steep decrease with 2018 being the lowest value estimated. The derivation of robust indicators of stock abundance will contribute to the stock assessment of this species and enable the provision of improved fisheries management advice for brown crab around Scotland.