Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean Data.zip

• Main Authors: Manuel Martins (10730730), Jorge Assis (783330), David Abecasis (674712)
• Format: Dataset
• Language: unknown
• Published: 2021
• Subjects: Ecology; Climate Science; Conservation and Biodiversity; Environmental Management; Biologically meaningful predictors; Climate change; Demersal fish; North Atlantic fisheries; Paris Agreement; Species Distribution Modelling; North Atlantic
• Online Access: https://doi.org/10.6084/m9.figshare.14519589.v1

This data was used to demonstrate the benefits of complying to the Paris Agreement and limiting environmental change, by assessing future distributional shifts in 10 commercially important demersal fish species in the Northern Atlantic Ocean. Distributional shift analysis compared near present-day conditions (2000-2017) with two Representative Concentration Pathway (RCP) scenarios of future climate change. One following the Paris Agreement climate forcing (RCP2.6) and another without stringent mitigation measures (RCP8.5). We use machine learning distribution models coupled with biologically meaningful predictors to project future latitudinal and depth shifts. We show that limiting future climate changes by complying with the Paris Agreement can translate into reduced distributional shifts for demersal fish, supporting biodiversity conservation and marine resource management. Furthermore, including predictors beyond temperature in species distribution modelling can improve predictive performances.