Cultures of (In)Security in comparison: From Comparative Literature to Critical Approaches to Security Studies

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Gadget for anchovy 9a South: Model description and results to provide catch advice and reference points (WGHANSA-1 2021)

The model speci fications presented below correspond to those benchmarked in WKPELA 2018. The main difference is that results are presented now for the end of the second quarter of each year instead of being presented at the end of the fourth quarter. This responds to practical modi cations in the de nition of the assessment year, now it goes from July 1st to June 30th of the next year. Model speci fications for this year are presented in section 2.2 and ??, as well as estimated parameters after optimization in Table 2

Bayesian spatio-temporal CPUE standardization: Case study of European sardine (Sardina pilchardus) along the western coast of Portugal

Understanding the key factors influencing population dynamics of fish stocks requires knowledge of their spatial distribution and seasonal habitat selection, but these spatio-temporal dynamics are often not explicitly included in ecological studies and stock assessment models. This study standardized the data of sardine fishery-dependent catch-per- unit- effort (CPUE) from the west coast of Portugal using Bayesian hierarchical spatio-temporal models (BHSTM) with the integrated nested Laplace approximation (INLA). Sardine CPUE was best explained by length of the vessel, vessel ID, month, year, and location (latitude, longitude). In terms of spatio-temporal distribution, sardine biomass prediction maps showed a constant pattern that changed every quarter of the year. In addition, sardine CPUE index showed a cyclical trend along the year with minimum values in July and maximum peak in November. This approach provided insights on variables and corresponding modelling effects that may be relevant in spatio-temporal fishery-dependent data standardization, and that could be applied to other fish species and areas.En prens

Bayesian spatio-temporal CPUE standardization: case study of European sardine (Sardina pilchardus) along the western coast of Portugal

Fishery data is one of the most accessible sources of information currently used for ecological studies and stock assessments. Unlike scientific surveys that are usually restricted to a given time of the year, fisheries dependent data is almost continuously available in time. Moreover, the information collected from the fisheries is less expensive and time consuming. However, for use as a relative abundance index, fishery-dependent data requires standardization as catch-per-unit-effort (CPUE) in order to remove the impact of vessel-specific differences and fishing behavior. Understanding the key factors that influence the population dynamics of fish species implies assessment of their spatial distribution and seasonal habitat selection but, spatio-temporal dependence issues are often not explicitly included in the modeling process. This study standardizes sardine fishery-dependent data obtained from the west coast of Portugal as CPUE by means of a Bayesian hierarchical spatio-temporal model using integrated nested Laplace approximation (INLA). This is one of the first studies of the region to provide maps of the relative abundance of this species for all months of the year. The best model included length of the vessel, vessel ID, month, year and location (latitude, longitude), while none of the five environmental covariates (Chl-a, SST, bathymetry, current velocity and direction) were relevant. In terms of spatial distribution, sardines were more abundant in the northern area, especially during the last quarter of the year. The applied methodology has contributed to improve our knowledge of European sardine distribution throughout the year, providing accurate predictive maps and insights into the standardization process of fishery-dependent data that could also be applied to other fish species and areas

Gadget for anchovy 9a South: Model description and results to provide catch advice and reference points (WGHANSA-1 2022).

The model speci fications presented below correspond to those benchmarked in WKPELA 2018. The main difference is that results are presented now for the end of the second quarter of each year instead of be presented at the end of the fourth quarter. This responds to practical modi cations in the defi nition of the assessment year, now it goes from July 1st to June 30th of the next year. Specifi c model assumptions for this year are presented in section 2.2 and 3, as well as estimated parameters after optimization in Table 2