%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Jaonalison, H.
%A Durand, Jean-Dominique
%A Mahafina, J.
%A Demarcq, Hervé
%A Lagarde, R.
%A Ponton, Dominique
%T Spatial and interannual variability of presettlement tropical fish assemblages explained by remote sensing oceanic conditions
%D 2020
%L fdi:010079319
%G ENG
%J Marine Biodiversity
%@ 1867-1616
%K Fish post-larvae ; DNA barcoding ; Structure ; Environmental conditions ; Hierarchical classification ; Gradient forest
%K MADAGASCAR ; OCEAN INDIEN ; TULEAR ; ANAKAO ; ZONE TROPICALE
%M ISI:000545939800001
%N 4
%P art. 52 [15]
%R 10.1007/s12526-020-01068-6
%U https://www.documentation.ird.fr/hor/fdi:010079319
%> https://www.documentation.ird.fr/intranet/publi/2020/08/010079319.pdf
%V 50
%W Horizon (IRD)
%X Understanding the interannual effect of various environmental factors on biodiversity distribution is fundamental for developing biological monitoring tools. The interannual variability of environmental factors on presettlement fish assemblages (PFAs) has been so far under investigated, especially in Madagascar. Numerous explanatory variables including local hydro-dynamic conditions recorded during the sampling night, characteristics of the benthic substrate and remotely sensed oceanic conditions (RSOC) were used to explain the spatio-temporal variability of PFAs in southwestern Madagascar. Gradient forest analyses were used to hierarchically classify the effect of these explanatory variables on the PFAs for two sites and during two different recruitment seasons. RSOC variables appeared to better explain the PFAs than the local variable and the characteristics of the benthic substrate. The PFAs caught in water masses with coastal characteristics were better explained than those with open water characteristics. This spatial variability is hypothesised to be linked to differences in feeding conditions among water masses. The gradient forest analyses also highlighted the complexity of predicting PFAs as the species for which abundances were better explained by RSOC variables varied between years. This interannual variability was mainly explained by the interannual variation of chlorophylla(Chl a) concentration, wind and surface current, with better prediction obtained during the year with high Chl a values associated with high averaged sea surface temperature. These findings suggest the importance of forecasting Chl a concentrations, taking into account the impact of tropical storms and climate variability in order to predict PFAs in the future.
%$ 034 ; 126