Rik. C. Buckworth

Commercial gillnet fisheries interact with a wide range of elasmobranch species, many of which are incidentally captured as bycatch. Deterrent technologies have emerged as a potential approach for reducing interactions with threatened elasmobranchs. In this exploratory study, captive Giant Shovelnose Rays ( Glaucostegus typus ) and Brownbanded Bamboo Sharks ( Chiloscyllium punctatum ) were exposed to a static green LED light and a pulsed electrosensory deterrent attached to an experimental gillnet. The light deterrent initially reduced interactions by Giant Shovelnose Rays; however, this response diminished over time. Over a 10‐min period, deterred Giant Shovelnose Rays showed a significantly greater distance of deterrence in both treatments compared to the control. There was also a significant difference in mean eye distance between the control and light treatments, but not in nose distance. In contrast, Brownbanded Bamboo Sharks exhibited no significant response to either deterrent. These contrasting responses may reflect differences in the sensory ecology of the two species, underscoring the importance of species‐specific considerations in developing elasmobranch bycatch mitigation technologies. Given the promising outcomes reported for these deterrents in other studies, and the apparent variability in responses among elasmobranch taxa, further evaluation across a broader range of bycatch species is warranted.

Understanding the effects of environmental conditions on fisheries resources is crucial for guiding the effective assessment and management of fish stocks. Previously assessed as depleted, the narrow‐barred Spanish mackerel fishery on the east coast of Queensland, Australia, is under management intervention to rebuild the stock. In this study, we use long‐term age composition data to reconstruct 24 years of historic year class strength and explore a broad range of potential environmental influences on recruitment variability in the fishery. Year class strength was extremely well captured (76%–84% deviance explained) by generalised additive models containing just four environmental indices. Recurring key environmental indices in top models included environmental conditions prior to spawning (large scouring flood events during the wet season before spawning and cool winter sea surface temperature immediately prior to spawning), during spawning (unseasonal flooding near the spawning grounds), during larval dispersal and proxies for food availability for juvenile Spanish mackerel. These results have since been used to hindcast recruitment variability in the fishery prior to the collection of the age‐composition data to better understand historic fluctuations in the fishery, and can also be incorporated into management strategies for rebuilding the stock.

Marine ecosystems evolve under many interconnected and area‐specific pressures. To fulfil society's intensifying and diversifying needs while ensuring ecologically sustainable development, more effective marine spatial planning and broader‐scope management of marine resources is necessary. Integrated ecological–economic fisheries models (IEEFMs) of marine systems are needed to evaluate impacts and sustainability of potential management actions and understand, and anticipate ecological, economic and social dynamics at a range of scales from local to national and regional. To make these models most effective, it is important to determine how model characteristics and methods of communicating results influence the model implementation, the nature of the advice that can be provided and the impact on decisions taken by managers. This article presents a global review and comparative evaluation of 35 IEEFMs applied to marine fisheries and marine ecosystem resources to identify the characteristics that determine their usefulness, effectiveness and implementation. The focus is on fully integrated models that allow for feedbacks between ecological and human processes although not all the models reviewed achieve that. Modellers must invest more time to make models user friendly and to participate in management fora where models and model results can be explained and discussed. Such involvement is beneficial to all parties, leading to improvement of mo‐dels and more effective implementation of advice, but demands substantial resources which must be built into the governance process. It takes time to develop effective processes for using IEEFMs requiring a long‐term commitment to integrating multidisciplinary modelling advice into management decision‐making.

Multivariate and machine‐learning methods were used to develop field identification techniques for two species of cryptic blacktip shark. From 112 specimens, precaudal vertebrae (PCV) counts and molecular analysis identified 95 Australian blacktip sharksCarcharhinus tilstoniand 17 common blacktip sharksCarcharhinus limbatus. Molecular analysis also revealed 27 of the 112 wereC. tilstoni × C. limbatushybrids, of which 23 hadC. tilstoniPCVcounts and four hadC. limbatusPCVcounts. In the absence of further information about hybrid phenotypes, hybrids were assigned as eitherC. limbatusorC. tilstonibased onPCVcounts. Discriminant analysis achieved 80% successful identification, but machine‐learning models were better, achieving 100% successful identification, using six key measurements (fork length, caudal‐fin peduncle height, interdorsal space, second dorsal‐fin height, pelvic‐fin length and pelvic‐fin midpoint to first dorsal‐fin insertion). Furthermore, pelvic‐fin markings could be used for identification:C. limbatushas a distinct black mark >3% of the total pelvic‐fin area, whileC. tilstonihas markings with diffuse edges, or has smaller or no markings. Machine learning and pelvic‐fin marking identification methods were field tested achieving 87 and 90% successful identification, respectively. With further refinement, the techniques developed here will form an important part of a multi‐faceted approach to identification ofC. tilstoniandC. limbatusand have a clear management and conservation application to these commercially important sharks. The methods developed here are broadly applicable and can be used to resolve species identities in many fisheries where cryptic species exist.

Sustainable exploitation of fisheries populations is challenging to achieve when the size of the population prior to exploitation and the actual numbers removed over time and across fishing zones are not clearly known. Quantitative fisheries' modeling is able to address this problem, but accurate and reliable model outcomes depend on high quality input data. Much of this information is obtained through the operation of the fishery under consideration, but while this seems appropriate, biases may occur. For example, poorly quantified changes in fishing methods that increase catch rates can erroneously suggest that the overall population size is increasing. Hence, the incorporation of estimates of abundance derived from independent data sources is preferable. We review and evaluate a fisheries‐independent method of indexing population size; inferring adult abundance from estimates of the genetic effective size of a population (N_e). Recent studies of elasmobranch species have shown correspondence between N_e and ecologically determined estimates of the population size (N). Simulation studies have flagged the possibility that the range of N_e/N ratios across species may be more restricted than previously thought, and also show that declines in N_e track declines in the abundance of model fisheries species. These key developments bring this new technology closer to implementation in fisheries science, particularly for data‐poor fisheries or species of conservation interest.

Developing bioeconomic models of sequential fisheries is complicated when the two (or more) fisheries overlap temporally. In such cases, each fishery will be affected by the other, so both cannot be properly modelled as separate activities. This has recently become an issue for the Northern Prawn Fishery, in which bioeconomic modelling is used to set management limits. Modelling has focused on the tiger prawn component of the fishery, which mostly occurs in the second half of the year. However, successful management leading to stock recovery has resulted in this fishery now overlapping temporally with the banana prawn fishery, which occurs in the first half of the year. Ideally, an integrated model covering the whole year would be developed, but this is not practical for a number of reasons. In this study, we model the factors driving the level of fishing effort in the banana prawn fishery, which affects, and is affected by, the tiger prawn fishery. As expected, banana prawn fishing effort is driven largely by stock size, fleet size, catch rates, prices, and fuel costs. Effort increases with fleet size, but at a decreasing rate. Further, we find evidence that opportunity cost, namely the ability to fish in the tiger prawn fishery and the price of tiger prawns, also affects the level of effort in the banana prawn fishery.

The complex multi-gear, multi-species tropical fisheries in developing countries are poorly understood and characterising the landings from these fisheries is often impossible using conventional approaches. A rapid assessment method for characterising landings at fish markets, using an index of abundance and estimated weight within taxonomic groups, is described. This approach was developed for contexts where there are no detailed data collection protocols, and where consistent data collection across a wide range of fisheries types and geographic areas is required, regardless of the size of the site and scale of the landings. This methodology, which was demonstrated at seven fish landing sites/fish markets in southern Indonesia between July 2008 and January 2011, provides a rapid assessment of the abundance and diversity in the wild catch over a wide variety of taxonomic groups. The approach has wider application for species-rich fisheries in developing countries where there is an urgent need for better data collection protocols, monitoring future changes in market demographics, and evaluating health of fisheries.