Title

Deep-sea seep mussels larval dispersal modelling experiment with FOCI +25Y, r...

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Abstract

A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.

Publication Date

March 21, 2024, 11:09 a.m.

Type

Vector Data

Keywords

0.975 , +25Y , Amazon fan (AM) , Atlantic , Bathymodiolus , Climate change predictions , Gigantidas , iAtlantic , Integrated Assessment of Atlantic Marine Ecosystems in Space and Time , larval dispersal modelling , 2043-12-01 , -1400.0 m a.s.l. , North Brazil margin , seep mussels , VIKING20X

Category

geoscientificInformation

Regions

Global

Owner

jollivet

More info

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Restrictions

CC-BY-4.0: Creative Commons Attribution 4.0 International

Language

English

Data Quality

The data set was checked for completeness, correctness, and consistency of metainformation. Validity of used methods was checked and - if applicable - precision and range of data.

Supplemental Information

All simulations are contained within one large (ca. 8.4 GB) netCDF file named: trajs_viking_with_FOCI_anomalies.nc
The netCDF file contains 5775 larval disp...

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