Understanding the phytoplankton in aquaculture ponds is critical for proper pond management. Despite the importance, the relationships between phytoplankton composition, cultured fish type, season, and nutrients were not well understood. This study statistically investigated these relationships in aquaculture ponds. Data collected at 21 tilapia and 13 catfish ponds in September 2009 (wet season), December 2009 (cold season), and March 2010 (hot season) in northern Thailand were used for the analysis. The statistical analysis showed that PO4-P and NH4-N concentrations in catfish ponds were significantly higher than in tilapia ponds (p < 0.05, Wilcoxon test). The cyanobacterial abundance in catfish ponds was significantly greater than in tilapia ponds (p < 0.05, Wilcoxon test). In the hot season (March), green algae were abundant (p < 0.05), while cyanobacteria were depleted (p < 0.05). Multiple linear regression model was applied to determine important factors for statistically explaining cyanobacterial abundance. The result indicated that the best model selected by AICc included season and pond type as factors influencing cyanobacterial abundance but not nutrients. However, since the effect of nutrients was included in the difference in nutrient concentration due to the difference in fish species in the ponds, it was speculated that nutrients were insignificant as explanatory variables. Furthermore, it was hypothesized that cyanobacterial abundance was reduced in March (hot season) because the predation of cyanobacteria by tilapia may be encouraged at high temperature.
This study is a preliminary spatial-temporal assessment method of the ungauged catchment to determine the variation in water quality (WQ) and the land use influence on river basins’ health. The intermittent WQ data, the principal component analysis, and the redundancy analysis were used to evaluate the (dis)similarity among the 10 ungauged streams and their significance in the entire catchment. These revealed some similarities/differences in nutrient pollution and latent land-use influence on the streams’ health. There were similarities between R6-R7, R9-R10, among R1 to R4 basins, while R5 and R8 had distinct variances in their WQ dynamics. The intensive vegetable and rice production in R5, R7, R8, R9, and R10 basins were the major sources of high nutrient concentrations. The unique variations, especially in R5 and R8 basins could be attributed to other different pollution sources. Hence, it’s of great significance to carry out comprehensive research in the above 5 river basins. That is the efficiency of management practices, identification of pollution sources, and the extent to which the elevated nutrients in the streams interact with biota within the river regime. This research offers a method to evaluate WQ dynamics in relation to human interferences in river basins of a catchment with limited data under similar climatic conditions.
The blooming of toxic cyanobacteria Microcystis in eutrophicated reservoirs causes serious difficulties for water supply worldwide. For the appropriate management of such reservoirs, a prediction model of toxic cyanobacteria Microcystis can be a useful tool. Therefore, this study aims to develop a Bayesian hurdle Poisson model for statistical prediction of toxic Microcystis from only two predictors, air temperature and trophic state index (TSI) calculated from chlorophyll-a. The gene copy number of the mcyB gene was used as a surrogate of toxic Microcystis cell density. The data on mcyB gene and chlorophyll-a were collected from 20 reservoirs in Nagasaki Prefecture (Japan). The daily average air temperature was downloaded from the local meteorological stations and a mean for 30 days before sampling date was calculated. The results showed that higher temperature and larger TSI accelerate the growth of toxic Microcystis. Furthermore, this model successfully predicted mcyB gene copy number as a surrogate of toxic Microcystis cell density for different conditions of air temperature and TSI with sufficient accuracy. Therefore, the proposed model has the potential to be a useful prediction tool for toxic cyanobacteria Microcystis in the integrated management of reservoirs.
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