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INFLUENCE RELATIVE DES FACTEURS ECOLOGIQUES ET DE LA PECHE SUR LA STRUCTURATION DES STOCKS DE POISSONS RECIFAUX DANS SIX PECHERIES DU ROYAUME DES TONGA (PACIFIQUE SUD)

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Two fishing grounds were selected in each of the three archipelagos of Tonga, according to fishing pressure (high and low). In each ground, socioeconomic surveys provided an evaluation of fishing pressure. Reef fish stocks were assessed through underwater visual censuses along 241 transects and their habitat was described with a novative method (Medium Scale A p p r o a c h ) , better adapted to their life territories. The analysis of ecological and fishing data showed a variation between global fishing pressure at the archipelago level, which was responsible, in combination with ecological factors, for more differences than between the pair of sites within each archipelago. The fishing factor explains globally less variance of fish populations (1.6 to 5.7%) than factors acting at micro-scale (depth, hard substrate and live coral coverages, heterogeneity and topographic complexity) and at meso-scale (oceanic influence), that explain 23.3 to 34.3% of variance. The study confirmed fishing effects already known (such as reduction of fish populations average size, compensatory increase of density in the small size classes) and showed, at least for scarids, a “shifting dominance” phenomenon, based on the decrease of large-size target species, which benefits to small size species, less vulnerable to fishing. Clustering of species according to diet or life history traits revealed gradual changes of density and biomass in specific groups according to fishing pressure gradient. This result provides potential for setting up indicators of stock status for better management of reef fish resources.
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... We assumed in our study that the habitat variables were a major structuring factor of consumed fish populations, as shown by several authors (e.g. Jennings et al., 1996), in particular if we compare it with fishing effects (Clua, 2004). Other structuring factors are known to have significant effects, such as recruitment (Sale, 1991;Hixon and Webster, 2002), inter-specific predation (Caley, 1993), fishing (Russ and Alcala, 1998;Jennings and Kaiser, 1998) or temporal variability (Galzin, 1987;Friedlander and Parrish, 1998b;Thompson and Mapstone, 2002). ...
... As far as we know, the literature does not mention these variables as usual structuring variables of fish populations, except for "branching coral" in a study conducted in Hawaii (Friedlander et al., 2003). On the other hand, many authors have already shown the structuring role of environmental variables such as "live coral" (Bell and Galzin, 1984;Legendre et al., 1997) or "hard bottom" and "sand" (Labrosse, 2000;Clua, 2004), as revealed by the LIT method in our study on biomass by size classes and trophic groups. ...
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Habitat characteristics play a critical role in structuring reef fish communities subjected to fishing pressure. The line intercept transect (LIT) method provides an accurate quantitative description of the habitat, but in a very narrow corridor less than 1 m wide. Such a scale is poorly adapted to the wide-ranging species that account for a significant part of these assemblages. We developed an easy-to-use medium scale approach (MSA), based on a semi-quantitative description of 20 quadrats of 25 m2 (500 m2 in total). We then simulated virtual reef landscapes of different complexities in a computer, on which we computed MSA using different methods of calculation. These simulations allowed us to select the best method of calculation, obtaining quantitative estimates with acceptable accuracy (comparison with the original simulated landscapes: R2 ranging from 0.986 to 0.997); they also showed that MSA is a more efficient estimator than LIT, generating percentage coverage estimates that are less variable. A mensurative experiment based on thirty 50-m transects, conducted by three teams of two divers, was used to empirically compare the two estimators and assess their ability to predict fish–habitat relationships. Three-factor multivariate ANOVAs (Teams, Reef, Methods) revealed again that LIT produced habitat composition estimates that were more variable than MSA. Canonical analyses conducted on fish biomass data successively aggregated by mobility patterns, trophic groups, and size classes, showed the higher predictive power of MSA habitat data over LIT. The MSA enriches the toolbox of methods available for reef habitat description at intermediate scale (< 1000 m2), between the scale where LIT is appropriate (< 100 m2) and the landscape approach (> 1000 m2).
... A study on fishing practices and household fish consumption was conducted for each fishing ground in parallel with the underwater censuses. This study used a qualitative approach which confirmed that there was no critical difference among the five fishing grounds in terms of gear types (hooked lines, spearfishing, and nets) and main target species (Clua 2007). This approach did not, however, allow an accurate assessment of the fishing pressure. ...
... Fish caught by Tongan fishers are home-consumed for one third, and bargained or sold in local markets for the other two thirds. Scarid species are, before Serranids and Acanthurids, the main targeted coral reef fishes in the three archipelagos of the Kingdom of Tonga, with no differential preference for any given species between archipelagos (Clua 2007). This allowed us to consider the ranking of villages, which was established by two independent approaches using all species, to be applicable to Scarids. ...
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... L'habitat a en effet une part importante dans la structuration des assemblages de poissons et celle-ci doit être prise en compte dans toute mesure de gestion spatialisée. De nombreuses méthodologies différentes existent pour déterminer des variables d'habitat (Charbonnel et al., 2002 ;Clua, 2004 ;García-Charton et al., 2004 ;Ferraris et al., 2005) (Harvey et al., 2001 ;Harvey et al., 2004 ;Tessier et al., 2005). Quelle que soit la méthode utilisée, il faut toujours s'assurer au préalable qu'elle n'induit pas d'effet sur la variabilité spatiale par l'introduction d'éventuels biais (e.g. ...
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