FN Archimer Export Format
PT J
TI Modeling of Beta Diversity in Tunisian Waters: Predictions Using Generalized Dissimilarity Modeling and Bioregionalisation Using Fuzzy Clustering
BT 
AF LASRAM, Frida Ben Rais
   HATTAB, Tarek
   HALOUANI, Ghassen
   ROMDHANE, Mohamed Salah
   LE LOC'H, Francois
AS 1:1;2:1,2,4;3:1,3;4:1;5:3;
FF 1:;2:;3:;4:;5:;
C1 Inst Natl Agron Tunisie, Unite Rech Ecosyst & Ressources Aquat UR03AGRO1, Tunis, Tunisia.
   Univ Picardie Jules Verne, Unite Rech Ecol & Dynam Syst Anthropises EDYSAN, CNRS, FRE 3498, Amiens, France.
   Inst Univ Europeen Mer, Lab Sci Environm Marin, UMR 6539, LEMAR,CNRS,UBO,IRD,Ifremer, Plouzane, France.
   Inst Univ Europeen Mer, Lab Sci Environm Marin, UMR 6539, LEMAR,CNRS,UBO,IRD,Ifremer, Plouzane, France.
C2 INAT, TUNISIA
   UNIV PICARDIE JULES VERNE, FRANCE
   IRD, FRANCE
   IFREMER, FRANCE
UM LEMAR
IN WOS Ifremer jusqu'en 2018
   DOAJ
   copubli-france
   copubli-p187
   copubli-univ-france
   copubli-int-hors-europe
   copubli-sud
IF 3.057
TC 17
UR https://archimer.ifremer.fr/doc/00275/38643/51762.pdf
   https://archimer.ifremer.fr/doc/00275/38643/51763.tif
   https://archimer.ifremer.fr/doc/00275/38643/51764.pdf
   https://archimer.ifremer.fr/doc/00275/38643/51765.tif
   https://archimer.ifremer.fr/doc/00275/38643/51766.pdf
LA English
DT Article
AB 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. 
PY 2015
PD JUN
SO Plos One
SN 1932-6203
PU Public Library Science
VL 10
IS 7
UT 000358157600136
DI 10.1371/journal.pone.0131728
ID 38643
ER

EF