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 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.
Fish stock assessment models are quite complex systems. Stockassessment.org is a website that allows stock assessments to be conducted using a state-space approach that accounts for observational noise. This approach fully exploits the information in the data by exploiting the correlation between neighbouring unobserved quantities. Finally, the state-space approach allows the quantification of uncertainties in the estimated values such as fishing mortality rate and stock size. The state-space approach to fish stock assessment was developed by Anders Nielsen and Casper Berg at DTU Aqua and further developed within PANDORA.
Atlantic bluefin tuna spawn early to avoid metabolic meltdown in larvae. Code available on Zenodo, and data and model description can be downloaded from resource links.
Spawning site distribution of a bluefin tuna reduces jellyfish predation on early life stages. Code available on Github, and publication here.
Testing the hypothesis that recovery of the commercially exploited Atlantic bluefin tuna can affect early survival of the Mediterranean albacore through predator-prey interactions of their early life stages. We find that when the predator species is present, they have a large predatory capacity on the prey species, but their patchy distribution may limit their total effect. Code and Data available on Github.
credit: Flavia Gargiulo for Planet Tuna