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Historical catch data represent a key source of information for fisheries stock assessment models. Historically, commercial fishery catch statistics were estimated from the portion of catch landed in ports. However, many species with relatively low economic value have not been recorded on a species basis but instead as a part of an aggregate category. Reconstructing component species catch from an aggregate category is a common challenge for fisheries stock assessment efforts around the world. Skates are one group of species with low economic value for which landed catch has not been commonly reported by species. In this paper, we present a novel approach to disaggregate the historical catch of the two most abundant skate species on the West Coast of the United States, longnose skate (Caliraja rhina) and big skate (Beringraja binoculata), landed within the aggregated skate category, in ports of Washington State. We used a combination of fishery-dependent and fishery-independent data sources to account for changes in the spatial extent of the fishery over time, and differences in the depth distribution of these two skate species. While developed to disentangle aggregate catch of longnose and big skates, the approach is not limited to skates on the West Coast of the United States, but can be adapted for any species which landings have been reported within an aggregated category elsewhere.

Collecting accurate information on catch levels from marine recreational fishing can be challenging and often results in highly uncertain estimates of landings and discards. Sparse spikes are a common characteristic of these data, with isolated values much higher (e.g., 2–4 times) the surrounding values in a time series of annual estimates. In many cases, it is unclear whether these spikes represent observation error or true, even if anomalous, values. Utilizing multiple data sources simultaneously, integrated stock assessment models may be able to distinguish truth from error in sparse-spike catch estimates, however this supposition has not been well tested. Here, we developed a simulation study to elucidate conditions under which an integrated stock assessment model might perform well (or poorly) when confronted with sparse-spike catch estimates. We found that unconstrained estimation could distinguish truth from error in anomalous values, but only under specific conditions of supporting data. If the anomaly is known to be biased high, estimated catches can get closer to the true value. If the error mechanism is unknown, our results suggest that the true underlying value of a sparse spike is only recoverable when other input data are highly accurate, primarily a reliable index of abundance, but also age composition data in the same year.

Blast fishing is an illegal, ecologically destructive fishing method, fatal for many fish species at large scales. Blast fishing using dynamite is preferred by the fishers, especially small scale fisheries in Turkey, as it requires minimal effort but still results in higher catches. In the current study, demonstration of 20 controlled real-time blasting results involves species composition, size of fish, catch rates, catch composition, commercial/discard ratios, CPUE estimates and clinical observations of external and internal abnormality symptoms of fish species. Blasting trials were carried out with special permission in limited numbers because of its harmful effect. The collected data was tested using PRIMER v6 and diversity indices were also analysed. For the similarity between collected species cluster analysis was used to examine the stability of the results. Multidimensional scaling (MDS) was also applied for the assessment of fish species in the catch. A total of 1014 individuals (63.8 kg) were collected and 18 fish species belonging to seven families were examined. According to laboratory examinations, it was found that blasting caused different external and internal abnormality symptoms in the sampled fish species during the study.

The unexpected success of China's PV industry in technology catch-up has been noted. However, existing research has overlooked the multidimensional nonlinear complexities in its underlying mechanisms. This study introduces the "Seven-Element Diamond Model" theoretical framework and employs machine learning (ML) methods to address this. Using observed data from 84 listed China's PV companies from 2000 to 2021, we systematically examine the intricate mechanisms of technology catch-up in China's PV industry. Key findings include: 1) The self-enhancing mechanism stemming from R&D effort, government subsidies, and domestic demand is pivotal for the successful technology catch-up of China's PV industry. 2) Key regulatory directions are identified from R&D effort to market competition, technology spillover, productivity, and government subsidies. 3) R&D effort forms the underlying logic of China's PV industry technology catch-up, and domestic market demand is an extremely important driver for China's PV technology catch-up. Government subsidies exhibit a "blowout" incentive effect, and the synergistic coupling between key variables is also significant in driving China's PV technology catch-up. This study offers valuable insights into the catch-up of clean energy or sustainable technologies in China and other emerging economies.

Joining a technical standards alliance is becoming increasingly crucial for latecomer enterprises’ innovation catch-up in today's fast-paced technological environment. This study investigates the influence of latecomers’ ambidextrous innovation on technical standards alliances while highlighting the importance of alliance routines. A structural equation model is used to analyse a sample of 118 latecomer members and 45 technical standards alliances in China's strategic emerging industries. Several novel findings are reported. First, latecomers’ two types of innovation catch-up and the three dimensions of alliance routines promote alliance performance, all of which positively impact alliance performance. Second, alliance routines have different effects on latecomers’ Ambidextrous innovation catch-up. All three dimensions of the alliance routine promote utilisation innovation catch-up, whereas behavioural logic and interactive consensus hinder exploration innovation catch-up. Third, utilisation innovation completely mediates between alliance practice and performance, whereas exploration innovation only plays a partial mediating role. This study presents specific recommendations for latecomer enterprises and technical standards alliances.

Highlights •Much of the variation in images of fish in bulk is caused by clutter.•Clutter variation can be effectively simulated using few 3D-models of each species.•Detailed 3D models of fish can be created and used to generate synthetic data sets.•Neural networks trained on simulated data perform well across real-life domains.