Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production System

Environmental factors influence the abundance of Vibrio species in shrimp culture systems. Prediction of the abundance of presumptive Vibrio species can help prevent the occurrence of bacterial diseases as this will provide insights about when and which environmental factors to manage. In t...

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Main Authors: ANGEL QUEENEE DAYTIC DEQUITO, VALERIANO JR CORRE, ELFRED JOHN ABACAN
Format: Article
Language:English
Published: Asian Fisheries Society 2022-04-01
Series:Asian Fisheries Science
Online Access:https://www.asianfisheriessociety.org/publication/downloadfile.php?id=1396&file=Y0dSbUx6QTFNRGc1TWpRd01ERTJOVEExTURFeE5UQXVjR1Jt
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author ANGEL QUEENEE DAYTIC DEQUITO
VALERIANO JR CORRE
ELFRED JOHN ABACAN
author_facet ANGEL QUEENEE DAYTIC DEQUITO
VALERIANO JR CORRE
ELFRED JOHN ABACAN
author_sort ANGEL QUEENEE DAYTIC DEQUITO
collection DOAJ
description Environmental factors influence the abundance of Vibrio species in shrimp culture systems. Prediction of the abundance of presumptive Vibrio species can help prevent the occurrence of bacterial diseases as this will provide insights about when and which environmental factors to manage. In this study, the parametric linear regression model (LRM) and negative binomial model (NBM), and semiparametric generalised additive model (GAM) were used to identify correlations and predict changes of Vibrio abundance with physicochemical and biological water parameters. Water parameters were recorded from three 300 m2 biofloc ponds stocked with Penaeus vannamei Boone, 1931, at 500 individuals.m-3 over four culture run periods. Each culture run lasted for 16 weeks. Imputed data were initially subjected to univariate analysis and Pearson’s correlation analysis. The abundance of presumptive Vibrio species was found to be highly correlated with alkalinity, pH, and phytoplankton density. GAM performed best among the three models based on Akaike’s information criterion (AIC), having the smallest value of 5,743.222 compared to 6,572.014 and 5,857.997 values for ordinary LRM and NBM, respectively. It also had the largest deviance explained statistic with 41.2 % of the deviance reduced by including the predictors compared with ordinary LRM and NBM with only 16.04 % and 14.5 % deviance reduced, respectively. GAM introduced flexibility that predicts the dependent variable better in terms of statistical significance than LRM and NBM. It is important to consider using a semiparametric modelling approach as a tool for aquaculture management.
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spelling doaj.art-c050e28bade94785a32612077be9ba5e2022-12-22T01:52:28ZengAsian Fisheries SocietyAsian Fisheries Science0116-65142073-37202022-04-0135210.33997/j.afs.2022.35.2.002Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production SystemANGEL QUEENEE DAYTIC DEQUITOVALERIANO JR CORREELFRED JOHN ABACAN Environmental factors influence the abundance of Vibrio species in shrimp culture systems. Prediction of the abundance of presumptive Vibrio species can help prevent the occurrence of bacterial diseases as this will provide insights about when and which environmental factors to manage. In this study, the parametric linear regression model (LRM) and negative binomial model (NBM), and semiparametric generalised additive model (GAM) were used to identify correlations and predict changes of Vibrio abundance with physicochemical and biological water parameters. Water parameters were recorded from three 300 m2 biofloc ponds stocked with Penaeus vannamei Boone, 1931, at 500 individuals.m-3 over four culture run periods. Each culture run lasted for 16 weeks. Imputed data were initially subjected to univariate analysis and Pearson’s correlation analysis. The abundance of presumptive Vibrio species was found to be highly correlated with alkalinity, pH, and phytoplankton density. GAM performed best among the three models based on Akaike’s information criterion (AIC), having the smallest value of 5,743.222 compared to 6,572.014 and 5,857.997 values for ordinary LRM and NBM, respectively. It also had the largest deviance explained statistic with 41.2 % of the deviance reduced by including the predictors compared with ordinary LRM and NBM with only 16.04 % and 14.5 % deviance reduced, respectively. GAM introduced flexibility that predicts the dependent variable better in terms of statistical significance than LRM and NBM. It is important to consider using a semiparametric modelling approach as a tool for aquaculture management.https://www.asianfisheriessociety.org/publication/downloadfile.php?id=1396&file=Y0dSbUx6QTFNRGc1TWpRd01ERTJOVEExTURFeE5UQXVjR1Jt
spellingShingle ANGEL QUEENEE DAYTIC DEQUITO
VALERIANO JR CORRE
ELFRED JOHN ABACAN
Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production System
Asian Fisheries Science
title Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production System
title_full Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production System
title_fullStr Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production System
title_full_unstemmed Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production System
title_short Generalised Additive Model Improves Estimates of Vibrio Species Abundance in Penaeus vannamei Boone, 1931 Biofloc Production System
title_sort generalised additive model improves estimates of vibrio species abundance in penaeus vannamei boone 1931 biofloc production system
url https://www.asianfisheriessociety.org/publication/downloadfile.php?id=1396&file=Y0dSbUx6QTFNRGc1TWpRd01ERTJOVEExTURFeE5UQXVjR1Jt
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