Realised niche and suitability index of toxic phytoplankton species. Dataset
|Temporal extent||1988 -2014|
|Author(s)||Guallar Carles1, 2, Chapelle Annie2, Bacher Cedric2|
|Affiliation(s)||1 : Universitat de Barcelona, Spain
2 : Ifremer, France
|Keyword(s)||Alexandrium minutum, habitat suitability model, harmful algal bloom, niche overlap, Pseudo-nitzschia australis, Pseudo-nitzschia fraudulenta, realised niche, kernel functions, REPHY, time-series, phytoplankton, France, multivariate analysis, principal component analysis, temperature, salinity, irradiance, river flow, turbidity, monitoring program|
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.
|Acknowledgments||The authors would like to thank the REPHY and SBR monitoring networks for providing phytoplankton and environmental data and all the members involved, especially to Elisabeth Nezan for providing expertise support. We also thank CDOCO for providing river flow data. Data were also successively collected in the framework of the following projects: FINAL, VELYGER, PARALEX and DAOULEX. The authors also wish to thank Francis Gohin and Philippe Bryère for providing satellite data.|