Non-indigenous species distribution models for the French Natura 2000 MPA network area

Le résumé fourni par la source

This dataset contains 42 GeoTIFF (.tif) raster layers representing modelled probability of occurrence for 21 non-indigenous species in the French Natura 2000 MPA network area. For each species, two layers are provided: the mean predicted probability of occurrence and the associated standard error (SE), representing prediction uncertainty. The layers were developed within the Blue4All project for use in the Non-indigenous Species Effects Assessment Tool (NEAT). Species occurrence records were compiled from GBIF, OBIS, French national/OFB sources and participatory mapping. Because records were sparse within the initial French study area, the occurrence-data search was expanded to the Greater North Sea, Celtic Seas and Baltic Sea to capture broader environmental gradients. After temporal and spatial duplicates were removed, presence-only species distribution models were fitted using the Maxent framework through the R package maxnet for species with more than 20 occurrence records. Species-specific subsets of environmental and anthropogenic predictors were used, including tidal amplitude, nutrient variables and net primary production, depth, salinity, temperature, substrate type, vessel density and wind speed. Input layers were obtained mainly from Copernicus, EMODnet and AVISO. Species-specific environmental tolerance limits were applied to constrain predictions to ecologically suitable conditions. Additional OFB and participatory mapping data were incorporated to improve predictions within the Living Lab area. The maps were refined and validated in two rounds with local experts, after which 21 species were retained as suitable for the French Natura 2000 study area. The layers cover the English Channel and adjacent parts of the North Sea and Celtic Sea and use Coordinate Reference System (CRS) EPSG:3035.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.