The code and files contained in this repository support replication of a broad-scale benthic habitat classification of the South Atlantic produced by McQuaid et al. (2023). We used statistical clustering algorithms to classify broad-scale (10km2) environmental data into distinct habitat classes, which reflect variation in physical conditions and we assume support distinct biological communities. We request that any use of the input data is referenced as per the table below, and that classification outputs are referenced as: McQuaid K. A. Bridges A. E. H., Howell K. L., Gandra T. B. R., de Souza V., Currie J. C., Hogg O. T., Pearman T. R. R., Bell J. B. B., Atkinson L. J., Baum D., Bonetti J., Carranza A., Defeo O., Furey T., Gasalla M. A., Golding N, Hampton S. L., Horta S., Jones D. O. B., Lombard A. T., Manca E., Marin Y., Martin S., Mortensen P., Passdore C., Piechaud N., Sink K. J. & Yool A. 2023. Broad-scale benthic habitat classification of the South Atlantic. Progress in Oceanography. DOI: https://doi.org/10.1016/j.pocean.2023.103016

The Plankton Lifeform Extraction Tool brings together disparate European plankton datasets into a central database from which it extracts abundance time series of plankton functional groups, called “lifeforms”, according to shared biological traits. This tool has been designed to make complex plankton datasets accessible and meaningful for policy, public interest, and scientific discovery. The Plankton Lifeform Extraction Tool currently integrates 155 000 samples, containing over 44 million plankton records, from nine different plankton datasets within UK and European seas, collected between 1924 and 2017. Additional datasets can be added, and time series can be updated.

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

Modelled density of the seapen Kophobelemnon stelliferum in the North East Atlantic. The Random Forest density model trained on data collected by an ROV was constrained by an ensemble of Maxent and Random Forest presence-absence model trained on a larger dataset also collected by an ROV. This species provides structural complexity in an environment where it is lacking and, thus, promotes higher biodiversity where they settle. They are vulnerable to mechanical disturbance of the sediment by fishing gear and a better understanding of their distribution will lead to better management of their population. This work was performed at the University of Plymouth in 2021.

Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Syringammina fragilissima fields assemblage in the North East Atlantic. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021.

Species distribution models (GAM, Maxent, and Random Forest ensemble) predicting the distribution of Sea pens and burrowing megafauna assemblages in the Northeast Atlantic. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image samples. Modeling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021.

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.

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.

Maps of potential biomass catches (tons/year) per surface unit (0.25º latitude x 0.25º longitude) based on 3-D probability of occurrence for the main commercial fish species of the Atlantic. To map potential catches, first, mean catches (tons/year) were calculated according to Watson (2020) Global fisheries landings (V4) database for period 2010-2015 and then the total mean catch value for each species was redistributed according to the occurrence probability value that was modelled in 3-D using Shape-Constrained Generalized Additive Models (SC-GAMs). Potential catch value of each cell integrates the catches along the water column (from surface until 1000 m depth). See Valle et al. (2024) in Ecological Modelling 490:110632 ( https://doi.org/10.1016/j.ecolmodel.2024.110632 ), for more details.