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Electronic monitoring (EM) is increasingly used to monitor catch and bycatch in wild capture fisheries. EM video data are still manually reviewed and adds to ongoing management costs. Computer vision, machine learning, and artificial intelligence-based systems are seen to be the next step in automating EM data workflows. Here we show some of the obstacles we have confronted and approaches taken as we develop a system to automatically identify and count target and bycatch species using cameras deployed to an industry vessel. A Convolutional Neural Network was trained to detect and classify target and bycatch species groups, and a visual tracking system was developed to produce counts. The multiclass detector achieved a mean average precision of 53.42%. Based on the detection results, the visual tracking system provided automatic fish counts for the test video data. Automatic counts were within two standard deviations of the manual counts for the target species and most times for the bycatch species. Unlike other recent attempts, weather and lighting conditions were largely controlled by mounting cameras under cover.

Simulation is used to explore the effect of spatial heterogeneity and spatial closures on the ability of feedback-control management strategies to achieve goals relating to conservation and utilization of fishery resources. The operating model underlying the projections is based on pink ling, Genypterus blacodes, off southern Australia and assumes that animals are sedentary following settlement. The management strategies are able to move the resource towards the target level in the absence of spatial closures even though assessment results are biased. The probability of reducing the stock below its limit reference point is higher when growth rates vary spatially, but the effect is small. The probability of the stock being above its target reference point is lower when one of the smaller spatial areas is closed. However, performance is markedly different when a larger fraction of the total area is closed, with stock size being substantially larger than the target at the end of the projection period.

Simulation is used to evaluate the ability of a two-region, age-structured assessment model to provide accurate and precise estimates of stock status (i.e., the ratio of female spawning biomass to unfished female spawning biomass) for coral trout (Plectropomus leopardus) on the Great Barrier Reef (GBR), Australia. The model used to generate the simulated data used by the assessment model is a spatially complex age- and sex-structured population dynamics model that captures the protogynous nature of coral trout. Stock status is underestimated (negatively biased), with the extent of negative bias related to mis-specification of the breeding strategy of the target fish stock, the impact of the amount of larval connectivity among reefs, the number of reefs closed to fishing, as well as exploitation rates. The estimates of stock status were less negatively biased when fishery-independent index and age- and length-composition data were available from closed areas. The results will inform the development of management strategies for coral trout in the GBR and highlight the importance of basing evaluations of estimation and management performance on operating models that capture ecologically important processes such as metapopulation dynamics and protogynous life history.

Three dominant recruitment patterns were identified across 30 stocks from Australia, New Zealand, Chile, South Africa, and the Falkland Islands using data from 1980 to 2010. Cluster and dynamic factor analysis provided similar groupings. Stocks exhibited a detectable degree of synchrony among species, in particular the hakes and lings from Australia, New Zealand, Chile, and South Africa. We tested three climate indices, the Interdecadal Pacific Oscillation (IPO), Southern Annular Mode (SAM), and Southern Oscillation Index (SOI), to explore their relationship with fish stock recruitment patterns. The time series of IPO and SOI showed the strongest correlation with New Zealand hoki (blue grenadier, Macruronus novaezelandiae) and Australian jackass morwong (Nemadactylus macropterus) (r = 0.50 and r = –0.50), and SAM was positively related to Australian Macquarie Island Patagonian toothfish (Dissostichus eleginoides) (r = 0.49). Potential linkages in recruitment patterns at sub-basin, basin, and multibasin scales and regional and global climate indices do account for some of the variation, playing an important role for several key Southern Hemisphere species.