Pandora integrates knowledge to build more robust tools to assess the future status of fish stocks, and improve science-based advice to fisheries management for maximizing the long-term, sustainable exploitation and Blue Growth of Europe’s fisheries resources. The biological information generated and collected in the project is integrated into stock assessment models. Examples include additional state variables or quality-improved catch data from industrial self-sampling in Kalman-filter based, retrospective estimation models (assessment sensu stricto) as well as short-term predictions including improved estimates of natural mortality and growth.
In addition, PANDORA will re-visit the concept of sustainability in the face of, from a biological perspective, species interactions, climate change and resulting distributional changes, and changes in carrying capacity that decouple spawning stock biomass from recruitment.
Through this web application the user can implement two fisheries stock assessment models/approaches:• the stochastic surplus production model in continuous time (SPiCT) • the statistical catch-at-age stock assessment model developed as part of the assessmentfor all (a4a)
This package contains input data, TMB (C++) and R code for close-kin mark recapture (CKMR) abundance estimation for two subpopulations of thornback ray (Raja clavata) in the central Bay of Biscay (offshore and Gironde estuary). Samples were taken between 2015 to 2020. Parent-offspring pairs were identified using SNP genotype information. Birth years of individuals were estimated using length information and growth curves by sex.
This package fits a Von Bertalanffy growth curve with varying asymptotic size on samples with age-size structure data. The population structure assumes individuals follow a Von Bertalanffy growth curve in which the asymptotic size constant over time or varies between timepoints. The population structure is fitted using a maximum likelihood method.
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