Title :

Modeling of Beta Diversity in Tunisian Waters: Predictions Using Generalized Dissimilarity Modeling and Bioregionalisation Using Fuzzy Clustering

Author(s) :

Ben Rais Lasram, Frida [Auteur]
Ecosystèmes et Ressources Aquatiques [UR03AGRO1]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Hattab, Tarek [Auteur]
Ecosystèmes et Ressources Aquatiques [UR03AGRO1]
Laboratoire des Sciences de l'Environnement Marin (LEMAR) [LEMAR]
Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 UPJV [EDYSAN]
Halouani, Ghassen [Auteur]
Ecosystèmes et Ressources Aquatiques [UR03AGRO1]
Laboratoire des Sciences de l'Environnement Marin (LEMAR) [LEMAR]
Romdhane, Mohamed Salah [Auteur]
Ecosystèmes et Ressources Aquatiques [UR03AGRO1]
Le Loc’h, François [Auteur]
Laboratoire des Sciences de l'Environnement Marin (LEMAR) [LEMAR]

Journal title :

PLoS One

Pages :

e0131728

Publisher :

Public Library of Science

Publication date :

2015

ISSN :

1932-6203

English keyword(s) :

ACL
SPECIES DISTRIBUTION
CLIMATE-CHANGE
COASTAL
BIODIVERSITY
ASSEMBLAGES
INDICATOR
PATTERNS
REGIONS
GABES
SCALE

HAL domain(s) :

Sciences de l'environnement/Biodiversité et Ecologie

English abstract : [en]

Spatial patterns of beta diversity are a major focus of ecology. They can be especially valuable in conservation planning. In this study, we used a generalized dissimilarity modeling approach to analyze and predict the ...
Show more >Spatial patterns of beta diversity are a major focus of ecology. They can be especially valuable in conservation planning. In this study, we used a generalized dissimilarity modeling approach to analyze and predict the spatial patterns of beta diversity for commercially exploited, demersal marine species assemblages along the Tunisian coasts. For this study, we used a presence/absence dataset which included information on 174 species (invertebrates and fishes) and 9 environmental variables. We first performed the modeling analyses and assessed beta diversity using the turnover component of the Jaccard’s dissimilarity index. We then performed nonmetric multidimensional scaling to map predicted beta diversity. To delineate the biogeographical regions, we used fuzzy cluster analysis. Finally, we also identified a set of indicator species which characterized the species assemblages in each identified biogeographical region. The predicted beta diversity map revealed two patterns: an inshore-offshore gradient and a south-north latitudinal gradient. Three biogeographical regions were identified and 14 indicator species. These results constitute a first contribution of the bioregionalisation of the Tunisian waters and highlight the issues associated with current fisheries management zones and conservation strategies. Results could be useful to follow an Ecosystem Based Management approach by proposing an objective spatial partitioning of the Tunisian waters. This partitioning could be used to prioritize the adjustment of the actual fisheries management entities, identify current data gaps, inform future scientific surveys and improve current MPA network.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Laboratoire d'Océanologie et de Géosciences (LOG) - UMR 8187

Source :

Harvested from HAL