missionatlantic/3D_SDMs_AtlanticFish: 0.0
Creators: name: Valle, Mireia
Publisher: name: Zenodo
Year: 2024

description: Full Changelog: https://github.com/missionatlantic/3D_SDMs_AtlanticFish/commits/v2
description: 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).

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. 
description: Fisheries have a crucial contribution, with animal protein supply and economic income, to the subsistence and blue economy of several human societies of the Atlantic Ocean, the second largest water body in the planet. However, an accurate distribution of commercial fish across the Atlantic and through the water column is still unknown. The wide use of Species Distribution Models (SDMs) for marine fish mapping generally faces two shortcomings: (i) ignoring the vertical dimension of the ocean; and (ii) ignoring the ecological niche theory in the model fitting. Our aim is to develop 3D habitat models of the main commercial fishes across the Atlantic Ocean, accounting for 67% of the total biomass catches, to provide an enhanced spatial representation of the environmental niche of the fish species. In particular, here we (1) explore the macroecological patterns testing if latitudinal-vertical distribution of main commercial fish species follows the isothermal distribution across the Atlantic ocean; (2) apply a novel 3D modelling approach incorporating depth dimension into the environmental data and based exclusively on public species occurrence data; (3) use Shape-Constrained Generalized Additive Models (SC-GAMs) to build SDMs in accordance with the ecological niche theory (GAM-NICHE model), avoiding potential model overfitting and hence allowing automatic model selection; and (4) estimate potential fish catch biomass in the 3D space based on the species probability of occurrence. Our results indicated that latitudinal-vertical distribution follows the prevailing isothermal distribution in the ocean, confirming that an accurate representation of stock distributions needs 3D modelling and incorporate explicitly depth dimension into the environmental data. The species response curves to 3D environmental gradients for the 30 main commercial fish species of the Atlantic yielded very good model accuracy performance (78-98%). The developed 3D models of fish occurrence probability have the capability to be improved with the updates of new data for data-poor species, and to be projected under climate change scenarios. The obtained 3D maps conform useful and new knowledge that may help policy makers to balance the need for environmental protection with sustainable marine resource exploitation of the Atlantic Ocean.