Browsing by Author "Schuchert, Pia"
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ItemThe accumulation of microplastic pollution in a commercially important fishing ground.(Nature Research, 2022-03-10) Cunningham, Eoghan M.; Ehlers, Sonja M.; Kiriakoulakis, Konstadinos; Schuchert, Pia; Jones, Nia H.; Kregting, Louise; Woodall, Lucy C.; Dick, Jaimie T.A.The Irish Sea is an important area for Norway Lobster Nephrops norvegicus fisheries, which are the most valuable fishing resource in the UK. Norway lobster are known to ingest microplastic pollution present in the sediment and have displayed reduced body mass when exposed to microplastic pollution. Here, we identified microplastic pollution in the Irish Sea fishing grounds through analysis of 24 sediment samples from four sites of differing proximity to the Western Irish Sea Gyre in both 2016 and 2019. We used µFTIR spectroscopy to identify seven polymer types, and a total of 77 microplastics consisting of fibres and fragments. The mean microplastics per gram of sediment ranged from 0.13 to 0.49 and 0 to 1.17 MP/g in 2016 and 2019, respectively. There were no differences in the microplastic counts across years, and there was no correlation of microplastic counts with proximity to the Western Irish Sea Gyre. Considering the consistently high microplastic abundance found in the Irish Sea, and the propensity of N. norvegicus to ingest and be negatively impacted by them, we suggest microplastic pollution levels in the Irish Sea may have adverse impacts on N. norvegicus and negative implications for fishery sustainability in the future. ItemMachine learning in marine ecology: an overview of techniques and applications(Oxford Univerity Press, 2023-08-03) Rubbens, Peter; Brodie, Stephanie; Cordier, Tristan; Destro Barcellos, Diogo; Devos, Paul; Fernandes-Salvador, Jose A.; Fincham, Jennifer I.; Gomes, Alessandra; Handegard, Nils Olav; Howell, Kerry; Jamet, Cédric; Kartveit, Kyrre Heldal; Moustahfid, Hassan; Parcerisas, Clea; Politikos, Dimitris; Sauzède, Raphaëlle; Sokolova, Maria; Uusitalo, Laura; Van den Bulcke, Laure; van Helmond, Aloysius T. M.; Watson, Jordan T.; Welch, Heather; Beltran-Perez, Oscar; Chaffron, Samuel; Greenberg, David S.; Kühn, Bernhard; Kiko, Rainer; Lo, Madiop; Lopes, Rubens M.; Möller, Klas Ove; Michaels, William; Pala, Ahmet; Romagnan, Jean-Baptiste; Schuchert, Pia; Seydi, Vahid; Villasante, Sebastian; Malde, Ketil; Irisson, Jean-Olivier; Fisheries and Aquatic EcosystemsMachine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets. ItemRefining Fisheries Advice With Stock-Specific Ecosystem Information(Frontiers Media, 2021-04-09) Bentley, Jacob W.; Lundy, Mathieu G.; Howell, Daniel; Beggs, Steven; Bundy, Alida; de Castro, Francisco; Fox, Clive J.; Heymans, Johanna J.; Lynam, Christopher P.; Pedreschi, Debbi; Schuchert, Pia; Serpetti, Natalia; Woodlock, Johnny; Reid, David G.Although frequently suggested as a goal for ecosystem-based fisheries management, incorporating ecosystem information into fisheries stock assessments has proven challenging. The uncertainty of input data, coupled with the structural uncertainty of complex multi-species models, currently makes the use of absolute values from such models contentious for short-term single-species fisheries management advice. Here, we propose a different approach where the standard assessment methodologies can be enhanced using ecosystem model derived information. Using a case study of the Irish Sea, we illustrate how stock-specific ecosystem indicators can be used to set an ecosystem-based fishing mortality reference point (FECO) within the “Pretty Good Yield” ranges for fishing mortality which form the present precautionary approach adopted in Europe by the International Council for the Exploration of the Sea (ICES). We propose that this new target, FECO, can be used to scale fishing mortality down when the ecosystem conditions for the stock are poor and up when conditions are good. This approach provides a streamlined quantitative way of incorporating ecosystem information into catch advice and provides an opportunity to operationalize ecosystem models and empirical indicators, while retaining the integrity of current assessment models and the FMSY -based advice process. ItemUsing Coupled Hydrodynamic Biogeochemical Models to Predict the Effects of Tidal Turbine Arrays on Phytoplankton Dynamics(MDPI, 2018-05-22) Schuchert, Pia; Kregting, Louise; Pritchard, Daniel; Savidge, Graham; Elsasser, BjornThe effects of large scale tidal energy device (TED) arrays on phytoplankton processes owing to the changes in hydrodynamic flows are unknown. Coupled two-dimensional biogeochemical and hydrodynamic models offer the opportunity to predict potential effects of large scale TED arrays on the local and regional phytoplankton dynamics in coastal and inshore environments. Using MIKE 21 Software by DHI (https://www.dhigroup.com), coupled two-dimensional biogeochemical and hydrodynamic models were developed with simulations including no turbines or an array of 55 turbines with four solar radiation scenarios to assess the temporal and spatial changes of phytoplankton dynamics in an idealised domain. Results suggest that the effect of TEDs on phytoplankton dynamics accounted for up to 25% of the variability in phytoplankton concentrations, most likely associated with an increased residence time in an inshore basin. However, natural variation, such as the intensity of photosynthetically active radiation, had a larger effect on phytoplankton dynamics than an array of TEDs