Hydrologie et hydrochimie des eaux dans la zone de construction du chenal du port de pêche de Grand-Lahou, Côte d’Ivoire

Une caractérisation des eaux de l’estuaire de Grand-Lahou a été entreprise pour accompagner le projet de construction du chenal du port de pêche de Grand-Lahou qui vise à assurer la navigabilité de l’estuaire afin de redonner une vitalité à l’économie de la région. Ainsi, les variations spatio-temporelles de quelques paramètres physico-chimiques et des indicateurs de pollution chimique ont été étudiées en relation avec les variations saisonnières des paramètres hydrologiques et l’instabilité de la passe de Grand-Lahou. La variation des paramètres physico-chimiques de la lagune de Grand-Lahou est fortement influencée par la dynamique du fleuve Bandama, des petites rivières côtières et du degré d’ouverture ou de fermeture de la passe. Ainsi, la turbidité reste très élevée aux environs de la passe pendant la crue du fleuve Bandama (70 NTU < turbidité < 120 NTU). Par ailleurs, la salinité reste élevée (18‰ en moyenne) en périodes d’étiages où les eaux marines repoussent les eaux continentales. En revanche, en périodes de hautes eaux du fleuve Bandama et des rivières côtières, la salinité diminue pour atteindre des valeurs allant de 0,05‰ à 10‰. Les teneurs en sels nutritifs (CV > 15%) sont relativement élevées avec des baissent significatives en périodes d’étiage du fait de la prolifération des algues, de la photosynthèse, des activités bactériennes et de la sédimentation importante.Mots clés: Hydrologie, hydrochimie, estuaire, passe, lagune de Grand-Lahou

Effort de p&#234che et production poscicole au lac d'ayame i (Bassin de la bia; C&#244te d'Ivoire) apres le depart des p&3234cheurs

A monthly monitoring of indigenous fishing in three main landing sites of the man-made Lake Ayamé I, from August 2004 to July 2005, allowed to evaluate at 236.9 t the landed catches of these stations. The globalproduction of the lake, estimated to 381.8 t was below the 1060 t reported in 1996 before the closing of this reservoir to fishing activities. The catches were dominated by the Cichlid Sarotherodon melanotheron melanotheron which constituted yet 57 % of fish production. Nevertheless, these catches were characterised by the reappearance of tilapia, Oreochromis niloticus, reported as scarce from 1991 to 1996 in the landing, and the abundance of larger size specimens of the main exploited fishes. The fishing effort evaluated actually to 1 fisherman per kilometre-square, was lower than the one observed before the expulsion of the bozo fishermen, around 3 fishermen per kilometre-square. The population of fishermen mainly composed of natives, was dominated by the young men, who were less than 30 years old and who represent around 51 % of the population. Le suivi mensuel des pêches entre août 2004 et juillet 2005 dans les trois principaux débarcadères du lac d'Ayamé I a permis d'évaluer la production piscicole à 236,9 t de poisson dans ces stations. Cette productiona été estimée à 381,8 t, à l'échelle du lac ; ce qui demeure nettement inférieure aux 1060 t produites en 1996 avant la fermeture du lac aux activités de pêche. Les captures restent dominées par le Cichlidé Sarotherodon melanotheron melanotheron, avec une contribution de 57 %. Toutefois, cette production exploitée se caractérise, d'une part, par la résurgence du tilapia Oreochromis niloticus, une espèce considérée commerare dans les débarquements entre 1991 et 1996, et d'autre part, par l'abondance d'individus de grande taille pour les principales espèces pêchées. L'effort de pêche, estimé à 1 pêcheur au kilomètre carré, estnettement inférieur à celui observé avant le départ des "Bozos", soit une moyenne de 3 pêcheurs au kilomètre carré. La population actuelle de pêcheurs, essentiellement composée de nationaux, est dominée par lesjeunes de moins de 30 ans, qui constituent 51 % de l'ensemble des exploitants du plan d'eau

Comparison of the performances and validation of three methods for Yersinia spp. detection from animals stools samples in Abidjan: a non-endemic and a tropical area

Objective: Animals are the main reservoir for human enteropathogenics Yersinia. The aim of this survey was to compare the performances through three methods for detection of Yersinia enterocolitica 2/O: 9 and 1A/O: 14 bioserotypes in animals faecal samples contaminated in an artificial way and to validate the most efficient on animals samples from a tropical area.Methods and results: The compared methods were the direct plating of samples on selective agar CIN, (M1) the cold enrichment at +4°C) in PBS (Phosphate-buffered saline), (M2) and the prior-enrichment at 25°C in a BCC broth containing 2.5 mg / l of novobiocin followed by an enrichment modified PBS supplemented with 1% mannitol, 0.15% of bile salts, 0.5% of soy peptone (M3). These methods respectively had a sensitivity of 0.66, 0.73 and 0.88 on the non-pathogenic strain 1A/O: 14 and 0.54, 0.59 and 0.78 on the pathogenic strain 2/O: 9. The negative predictive value was 0.39, 0.45 and 0.65 respectively. The limit of detection of the two enrichment methods was 102 CFU ml-1 versus 103 CFU ml-1 for the direct plating. When detection rates of these methods were compared, it was observed that a significant difference on the one hand between the direct plating and the PBS modified and on the other hand between the enrichment in the cold weather and the PBS modified. No significant difference has been observed between the direct plating and the cold enrichment in Phosphate-buffered saline. It has been observed that no method encouraged significantly the detection of one of the two reference strains.The validation of the enrichment methods out of 496 samples showed that only the method of M3 enrichment permitted isolation of two strains of Yersinia intermedia, biotype 4, serotype 7,8-8-8,19 in piglets’ faeces.Conclusion: It appears from this study that the prior-enrichment at 25 ° C in a Brain Heart Infusion broth containing 2.5 mg / l novobiocin followed by a modified PBS (Phosphate-buffered saline) enrichment are appropriate methods for monitoring the epidemiology of enteropathogenics Yersinia in the animal faecal samples.Key words: Yersinia enterocolitica, Livestock animals, faecal samples, Methods of detection, sensitivity, limit of detectio

Microbiological risk infection assessment using QMRA in agriculture systems in Côte d’Ivoire, West Africa

Poor wastewater management that results from a lack of appropriate sanitation infrastructure contributes to increasing health risks in urban areas in Côte d’Ivoire. We assessed the health risks associated with the use of wastewater for watering salad destined for human consumption, to help local authorities in developing appropriate risk mitigation measures for Yamoussoukro, the political capital of Côte d’Ivoire. We applied a stochastic approach based on quantitative microbiological risk assessment (QMRA), focusing on wastewater for farming activities and salad consumption at the household level. Farming activities rely on a large degree on contaminated water and are conducted without any protection. The QMRA highlights that the poor quality of watering water increased the microbiological risk of the two assessed groups of urban farmers and individual households. The annual risk of infection due to watering wastewater in the city is estimated at 0.01 per person per year (pppy) for Giardia lamblia and 0.2 pppy for Escherichia coli O157:H7. The annual risk from salad consumption is 0.01 pppy for G. lamblia and 0.9 pppy for E. coli O157:H7. Both the annual risks from farming activities and salad consumption were higher than the tolerable standard of risk of 10−4 pppy as defined by the World Health Organization. There is a need to conduct a risk analysis and a cost-effectiveness study on intervention to improve public health and the livelihoods of the producers which are women in majority in Yamoussoukro

Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

Aim Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Location Tropical rain forest, West Africa and Atlantic Central Africa. Methods Alpha diversity estimates were compiled for trees with diameter at breast height ≥10cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone. Results The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances >50km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests. Main conclusions The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity. © 2011 Blackwell Publishing Ltd

Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

Aim: Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns.Location: Tropical rain forest, West Africa and Atlantic Central Africa.Methods: Alpha diversity estimates were compiled for trees with diameter at breast height ≥ 10 cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone.Results: The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances ≥ 50 km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests.Main conclusions: The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity.</p