Using the updated and newly develop assessment models from PANDORA together with monthly to annual environmental predictions, the project provides improved short-term forecasts. Moreover, PANDORA pioneers in the use of a risk-based framework to evaluate trade-offs between stocks and fleets, and between catch yields and the risks of stock depletions in a context of varying sustainable yield. This augmentsmore traditional approaches and add value for decision-makers and other stakeholders.When it comes to the socioeconomic impacts on and of fishing in European seas, a series of bio-economic simulations explores longer-term scenarios for ensuring stable positive economic performance and employment opportunities. To describe empirically the direct drivers of fisheries in Europe these scenarios focus on two key economic aspects: profitability and employment.
This tool (located at https://apps.socib.es/MSET/) allows exploring various ocean variables providing information on the sea surface of the Western Mediterranean Sea. These variables include five key Essential Ocean Variables, temperature, salinity, sea level, chlorophyll-a and currents, and two additional variables, temperature and salinity fronts, derived from the EOVs. The information is obtained from the SOCIB Western Mediterranean Operational system and from satellite data provided by Copernicus Marine Service (CMEMS). This tool is aimed for a wide range of end users in the field fisheries sustainability, conservation and education. All data handled by the tool is publically available from the SOCIB and CMEMS data servers
Simulate a plankton ecosystem in the upper part of a watercolumn (http://oceanlife.dtuaqua.dk/Plankton/). Cell size is the only trait characterizing each plankton group. All groups are able to perform photoharvesting, taking up dissolve nutrients and carbon, and do phagotrophy. The trophic strategy is an emergent property.
This applet (https://www.stockassessment.org/spectrum/) simulates the size distribution of a single species (blue) subjected to fishing (red). The filled area is the mature individuals and the colour whether recruitment is high (green) or low (red). The species is characterized by the asymptotic (maximum) size (W∞) and a set of non-dimensional parameters.
This ensemble application (https://rconnect.cefas.co.uk/content/22/) uses the novel ensemble methods of Spence et al. (2018) to combine four mechanistic multispecies fish community models to produce, with quantifiable uncertainty, predictions of equilibrium response of 9 key commercial North Sea species to different rates of fishing, in terms of SSB and risk of depletion.
The ATLANTIS model was used to investigate scenarios of the combined effect of climate change and harvest control rules (in this application fishing effort) on the physiology and ecological, socio-economic fisheries profitability of the coastal and deep-water demersal fisheries in the Strait of Sicily targeting red mullet and Norway lobster.
Is a collection of tools to help utilize machine learning methods in marine science. The package contains various functions to handle spatiotemporal grids with the help of the "raster"-package and perform EOF (empirical orthogonal functions) and SOM-analysis (Self-organising Maps) for feature extraction. The derived features can then be used in a model fitting procedure e.g. in a Random Forest model. Feature selection can be performed via a multi-objective genetic algorithm wrapper-method (NSGA-II), which helps choosing a parsimonious model with build-in Cross-Validation. For details on the methodology see Kühn et al. (2021): “Using machine learning to link spatiotemporal information to biological processes in the ocean: a case study for North Sea cod recruitment.” Mar Ecol Prog Ser 664:1-22