The Effects of Different Water Quality Parameters on Prawn (Macrobrachium Rosenbergii) Yield, Phytoplankton Abundance and Phytoplankton Diversity at New Zealand Prawns Limited, Wairakei, New Zealand

<p>Aquaculture is the fastest growing industry in the food sector and demand for aquaculture products is continuing to grow as many wild stocks from capture fisheries continue to decline. It is imperative that water quality in an aquaculture system is closely controlled in order to maintain the health of the species under culture and maximize production. New Zealand Prawns Limited (NZPL) is an aquaculture facility in Wairakei, New Zealand that cultures the freshwater prawn Macrobrachium rosenbergii. Dramatically reduced yields of prawns have been observed in ponds following periodic blooms of benthic algae. In this study, water quality variables were measured in grow out ponds at 9-11 day intervals. I measured temperature, phytoplankton abundance, phytoplankton diversity, turbidity, and concentrations of ammonia, nitrate, orthophosphate, dissolved oxygen and chlorophyll a. This data was combined with information on pond depth and prawn yield in order to investigate what variables influence the abundance and diversity of phytoplankton, benthic algal blooms and prawn yield. The difficulty of combining scientific endeavour with commercial enterprises resulted in only a small data set being available for analysis but it appears that benthic algal blooms at NZPL may be due to excessive light penetration to the benthos due to shallow pond depths, and reduced shading of the benthos when phytoplankton abundance is low. Low phytoplankton abundance may possibly be a result of low orthophosphate. There was insufficient data to determine what impacts, if any, the variables investigated have on prawn yield or how water quality variables change with time. Future studies and experiments are recommended in order to increase knowledge of farming M. rosenbergii; a valuable crustacean that has been shown to have a lower social and environmental impact than many other more common aquaculture species.</p>

The Effects of Different Water Quality Parameters on Prawn (Macrobrachium Rosenbergii) Yield, Phytoplankton Abundance and Phytoplankton Diversity at New Zealand Prawns Limited, Wairakei, New Zealand

<p>Aquaculture is the fastest growing industry in the food sector and demand for aquaculture products is continuing to grow as many wild stocks from capture fisheries continue to decline. It is imperative that water quality in an aquaculture system is closely controlled in order to maintain the health of the species under culture and maximize production. New Zealand Prawns Limited (NZPL) is an aquaculture facility in Wairakei, New Zealand that cultures the freshwater prawn Macrobrachium rosenbergii. Dramatically reduced yields of prawns have been observed in ponds following periodic blooms of benthic algae. In this study, water quality variables were measured in grow out ponds at 9-11 day intervals. I measured temperature, phytoplankton abundance, phytoplankton diversity, turbidity, and concentrations of ammonia, nitrate, orthophosphate, dissolved oxygen and chlorophyll a. This data was combined with information on pond depth and prawn yield in order to investigate what variables influence the abundance and diversity of phytoplankton, benthic algal blooms and prawn yield. The difficulty of combining scientific endeavour with commercial enterprises resulted in only a small data set being available for analysis but it appears that benthic algal blooms at NZPL may be due to excessive light penetration to the benthos due to shallow pond depths, and reduced shading of the benthos when phytoplankton abundance is low. Low phytoplankton abundance may possibly be a result of low orthophosphate. There was insufficient data to determine what impacts, if any, the variables investigated have on prawn yield or how water quality variables change with time. Future studies and experiments are recommended in order to increase knowledge of farming M. rosenbergii; a valuable crustacean that has been shown to have a lower social and environmental impact than many other more common aquaculture species.</p>

Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter.

The Seasonal Microbial Ecology of Plankton and Plankton-Associated Vibrio parahaemolyticus in the Northeast US

Microbial ecology studies have proven to be important resources for improving infectious disease response and outbreak prevention. Vibrio parahaemolyticus is an ongoing source of shellfish-borne food illness in the Northeast, United States and there is keen interest in understanding the environmental conditions that coincide with V. parahaemolyticus disease risk to aid harvest management and prevent further illness. Zooplankton and chitinous phytoplankton associate with V. parahaemolyticus dynamics elsewhere, however, this relationship is undetermined for the Great Bay estuary (GBE), an important emerging shellfish growing region in the Northeast, US. A comprehensive evaluation of the microbial ecology of V. parahaemolyticus associated with plankton was conducted in the GBE using three years of plankton community, nutrient concentration, water quality and V. parahaemolyticus concentration in plankton data. The concentrations of V. parahaemolyticus associated with plankton were highly seasonal and the highest concentrations of V. parahaemolyticus cultured from zooplankton occurred approximately one month before the highest concentrations of V. parahaemolyticus from phytoplankton. The two V. parahaemolyticus peaks corresponded with different water quality variables and a few highly seasonal plankton taxa. Importantly, V. parahaemolyticus concentrations and plankton community dynamics were poorly associated with nutrient concentrations and chlorophyll-a, commonly applied proxy variables for assessing ecological health risks, and human health risks from harmful plankton and V. parahaemolyticus elsewhere. Together, these statistical associations (or lack thereof) provide valuable insights to characterize the plankton-V. parahaemolyticus dynamic and inform approaches for understanding the potential contribution of plankton to human health risks from V. parahaemolyticus for the Northeast US. IMPORTANCE The Vibrio-plankton interaction is a focal relationship in Vibrio disease research; however, little is known about this dynamic in the Northeast, US where V. parahaemolyticus is an established public health issue. We integrated phototactic plankton separation with seasonality analysis to determine the dynamics of the plankton community, water quality and V. parahaemolyticus concentrations. Distinct bimodal peaks in the seasonal timing of V. parahaemolyticus abundance from phyto- vs zooplankton and differing associations with water quality variables and plankton taxa highlight that monitoring and forecasting approaches should consider the source of exposure when designing predictive methods for V. parahaemolyticus. Heliotheca tamensis has not been previously reported in the GBE. Its detection during this study provides evidence of the changes occurring in the ecology of regional estuaries and potential mechanisms for changes in V. parahaemolyticus populations. The Vibrio monitoring approaches can be translated to aid other areas facing similar public health challenges.

Water-Quality Data Imputation With High Percentage of Missing Values: A Machine Learning Approach

The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Luc&iacute;a Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered satisfactory (NSE &gt; 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than the ones positioned along the mainstream. IDW was the most chosen model for data imputation.