Developpement de la peche artisanale dans les eaux Burundaises du Lac Tanganyika

A light fishery for "Ndagala" (Strolothrissa tanganicae) has been practised for many years on Lake Tanganyika. Initially this had a low catch rate, but has since been developed by the introduction of an artisanal fishery unit based on the catamaran. A unit consists of a pair of metal canoes joined together. The fish are attracted by three lights mounted on the structure, and are caught with a pyramid-shaped lift net. Selected beaches have been reserved for the artisanal fishery and the numher of units operating has increased from 12 in 1957 to 538 in 1972. The mean annual catch per unit is 11,000 kg, which is not sufficient for the fishery to be economic. However, prediction of a possible mean catch as high as 40 tons year encouraged the Burundi Government to launch a project with help from the Freedom from Hunger Campaign. This was designed to develop the fishery by the creation of artisanal fishing centres, and to make available a large number of fully equipped catamarans which could be paid for by a system of hire-purchase. The success of the project has illustrated that the furnishing of adequate equipment can bring about a transformation of the traditional fishery.

Temporal Coherence of Chlorophyll a during a Spring Phytoplankton Bloom in Xiangxi Bay of Three-Gorges Reservoir, China

Algal bloom phenomenon was defined as "the rapid growth of one or more phytoplankton species which leads to a rapid increase in the biomass of phytoplankton", yet most estimates of temporal coherence are based on yearly or monthly sampling frequencies and little is known of how synchrony varies among phytoplankton or of the causes of temporal coherence during spring algal bloom. In this study, data of chlorophyll a and related environmental parameters were weekly gathered at 15 sampling sites in Xiangxi Bay of Three-Gorges Reservoir (TGR, China) to evaluate patterns of temporal coherence for phytoplankton during spring bloom and test if spatial heterogeneity of nutrient and inorganic suspended particles within a single ecosystem influences synchrony of spring phytoplankton dynamics. There is a clear spatial and temporal variation in chlorophyll a across Xiangxi Bay. The degree of temporal coherence for chlorophyll a between pairs of sites located in Xiangxi Bay ranged from -0.367 to 0.952 with mean and median values of 0.349 and 0.321, respectively. Low levels of temporal coherence were often detected among the three stretches of the bay (Down reach, middle reach and upper reach), while high levels of temporal coherence were often found within the same reach of the bay. The relative difference of DIN between pair sites was the strong predictor of temporal coherence for chlorophyll a in down and middle reach of the bay, while the relative difference in Anorganic Suspended Solids was the important factor regulating temporal coherence in middle and upper reach. Contrary to many studies, these results illustrate that, in a small geographic area (a single reservoir bay of approximately 25 km), spatial heterogeneity influence synchrony of phytoplankton dynamics during spring bloom and local processes may override the effects of regional processes or dispersal.

Variation in Reproductive Traits between Populations of Neosalanx taihuensis above and below the Three-Gorges Dam

Reproductive characteristics of the spring spawning stock of Neosalanx taihuensis varied significantly between the populations in the Three-Gorges Reservoir (TGR) and in the Tian-e-zhou Oxbow (TEO, below the dam). Larger body size, higher condition, higher fecundity, and larger oocyte diameter of the spawning stock in the TGR indicated faster individual growth and higher reproductive investment of the TGR population than the TEO population. With higher population abundance associated with higher reproductive investment of N. taihuensis in the TGR than in the TEO population, we suggest that reproductive investment is an important factor regulating resource fluctuation of N. taihuensis populations.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.