Script for performing the Mission Atlantic RISK Analysis. It has been modified from the ODEMM ('Options for Delivering Ecosystem-Based Marine Management') - approach. For details see https://doi.org/10.3389/fmars.2022.1037878

An R Package that provides supporting functions for conducting Integrated Ecosystem Assessments (IEA), developed in the framework of Mission Atlantic. The package includes methods for data exploration and assessment of the current ecosystem status. Forked repository in Mission Atlantic. For latest version, check the original repository.

Finding suitable ecological state indicators is challenging and cumbersome in stochastic and complex ecological systems. INDperform is an R package for validating the performance of ecological state indicators and assessing the ecological status based on a suite of indicators. The performance evaluation addresses the sensitivity and robustness of indicators.

The ecorisk package will be published under this repository soon. The ecorisk R package is designed to perform and analyse (ecosystem) risk assessments.

A marine end-to-end ecosystem model and R package. The aim is to represent the entire interconnected marine ecosystem (from physics and chemistry, to whales and fisheries in continental shelf regions) by exploring 'what if' experiments and explore uncertainty. View the application, the website or the latest publication.

Repository for code and generated data for " Pan-Atlantic 3D distribution model incorporating water column for commercial fish " by Mireia Valle, Eduardo Ramirez-Romero, Leire Ibaibarriaga, Leire Citores, Jose A. Fernandes-Salvador, and Guillem Chust, published in Ecological Modelling journal (2024). Valle, M., E. Ramírez-Romero, L. Ibaibarriaga, L. Citores, J. A. Fernandes-Salvador, and G. Chust. 2024. Pan-Atlantic 3D distribution model incorporating water column for commercial fish. Ecological Modelling 490:110632. https://doi.org/10.1016/j.ecolmodel.2024.110632 External data need to be downloaded to get the environmental data and be able to make predictions over the environmental space. To do so, run all scripts in the _scripts directory in numeric order from the root project directory. SC-GAMs generated in Valle et al. (2024) can be directly build running the script from _scripts/06_SCGAMs directory using the data uploaded to the _data directory of this repository.