Realised niche and suitability index of toxic phytoplankton species. Dataset

DOI

Understanding the spatial and temporal preferences of toxic phytoplankton species is of paramount importance in managing and predicting harmful events in aquatic ecosystems. In this study we address the realised niche of the species Alexandrium minutum, Pseudo-nitzschia fraudulenta and P. australis. We used them to highlight distribution patterns at different scales and determine possible drivers. To achieve this, we have developed original procedures coupling niche theory and habitat suitability modelling using abundance data in four consecutive steps: 1) Estimate the realised niche applying kernel functions. 2) Assess differences between the species’ niche as a whole and at the local level. 3) Develop habitat and temporal suitability models using niche overlap procedures. 4) Explore species temporal and spatial distributions to highlight possible drivers. Data used are species abundance and environmental variables collected over 27 years (1988-2014) and include 139 coastal water sampling sites along the French Atlantic coast. Results show that A. minutum and P. australis niches are very different, although both species have preference for warmer months. They both respond to decadal summer NAO but in the opposite way. P. fraudulenta realised niche lies in between the two other species niches. It also prefers warmer months but does not respond to decadal summer NAO. The Brittany peninsula is now classified as an area of prevalence for the three species. The methodology used here will allow to anticipate species distribution in the event of future environmental challenges resulting from climate change scenarios.

Identifier
DOI https://doi.org/10.17882/75706
Metadata Access http://www.seanoe.org/oai/OAIHandler?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:seanoe.org:75706
Provenance
Creator Guallar, Carles; Chapelle, Annie; Bacher, Cedric
Publisher SEANOE
Publication Year 2020
Rights CC-BY
OpenAccess true
Contact SEANOE
Representation
Resource Type Dataset
Discipline Marine Science