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  Biosensors for the monitoring of harmful algal blooms

  McPartlin, D. A.; Loftus, J. H.; Crawley, A. S.; Silke, J.; Murphy, C. S.; O’Kennedy, R. J. (Elsevier, 2017)

  Harmful algal blooms (HABs) are a major global concern due to their propensity to cause environmental damage, healthcare issues and economic losses. In particular, the presence of toxic phytoplankton is a cause for concern. Current HAB monitoring programs often involve laborious laboratory-based analysis at a high cost and with long turnaround times. The latter also hampers the potential to develop accurate and reliable models that can predict HAB occurrence. However, a promising solution for this issue may be in the form of remotely deployed biosensors, which can rapidly and continuously measure algal and toxin levels at the point-of-need (PON), at a low cost. This review summarises the issues HABs present, how they are difficult to monitor and recently developed biosensors that may improve HAB-monitoring challenges.
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  Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

  Dedman, Simon; Officer, Rick; Clarke, Maurice; Reid, David G.; Brophy, Deirdre (Public Library of Science (PLoS), 2017)

  Boosted Regression Trees. Excellent for data-poor spatial management but hard to use Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage. BRTs automated and simplified for accessible general use with rich feature set We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement. Gbm.auto for management in various settings By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios.
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  Evaluation of Non-destructive Molecular Diagnostics for the Detection of Neoparamoeba perurans

  Downes, Jamie K.; Rigby, Megan L.; Taylor, Richard S.; Maynard, Ben T.; MacCarthy, Eugene; O'Connor, Ian; Marcos-Lopez, Mar; Rodger, Hamish D.; Collins, Evelyn; Ruane, Neil M.; et al. (Frontiers Media SA, 2017)

  Amoebic gill disease (AGD) caused by Neoparamoeba perurans, has emerged in Europe as a significant problem for the Atlantic salmon farming industry. Gross gill score is the most widely used and practical method for determining AGD severity on farms and informing management decisions on disease mitigation strategies. As molecular diagnosis of AGD remains a high priority for much of the international salmon farming industry, there is a need to evaluate the suitability of currently available molecular assays in conjunction with the most appropriate non-destructive sampling methodology. The aims of this study were to assess a non-destructive sampling methodology (gill swabs) and to compare a range of currently available real-time polymerase chain-reaction (PCR) assays for the detection of N. perurans. Furthermore a comparison of the non-destructive molecular diagnostics with traditional screening methods of gill scoring and histopathology was also undertaken. The study found that all molecular protocols assessed performed well in cases of clinical AGD with high gill scores. A TaqMan based assay (protocol 1) was the optimal assay based on a range of parameters including % positive samples from a field trial performed on fish with gill scores ranging from 0 to 5. A higher proportion of gill swab samples tested positive by all protocols than gill filament biopsies and there was a strong correlation between gill swabs tested by protocol 1 and gross gill score and histology scores. Screening for N. perurans using protocol 1 in conjunction with non-destructive gill swab samples was shown to give the best results.
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  The GOSHIP A02 Survey 2017 Taking the Pulse and Temperature of the North Atlantic Ocean.

  McGovern, E.; Cusack, C.; Wallace, D.; Croot, P. (The Journal of Ocean Technology, 2017)
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  Fisher's preferences and trade-offs between management options

  Fitzpatrick, Mike; Maravelias, Christos D; Eigaard, Ole Ritzau; Hynes, Stephen; Reid, David (Wiley, 2017)

  Failure to understand the potential responses of fishers to management measures creates a significant risk of revisiting the familiar scenario of perverse and unintended consequences of those measures. This paper reports on a choice experiment survey to evaluate fisher's preferences for various management measures proposed under the EU Common Fisheries Policy (CFP) reform process, but the conclusions have wider relevance as similar measures are used by comparable fleets in fisheries globally. The survey was conducted with fishers involved in mixed pelagic and demersal fisheries in Ireland, pelagic fisheries in Denmark and demersal fisheries in Greece. Fisheries management policies were characterized by five attributes designed both to cover the principal CFP reform proposals and to integrate ecological, social, economic and institutional factors affecting fisher's decisions. The study uses a random utility modelling framework to reveal the preferences of the fishers across the alternative policy attributes. Results show that while there are generally preferences both for healthy stocks and for maintaining the importance of fishing to the local community, strong interfishery preference differences exist. These differences are most notable in relation to a discard ban and to the use of individual transferable fishing rights, favoured in Denmark, but not in Ireland for instance. The strength of these interfishery differences supports the assertion that there are no panaceas in fisheries management and that solutions should be tailored within the context of specific fisheries. Not doing so could create a significant risk of inappropriately managed fisheries that may lead to unsustainable outcomes.

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