Weight Prediction for Fishes in Setiu Wetland, Terengganu, using Machine Learning Regression Model

Predicting fish weight holds several essential implications in ecology, such as population assessment, trophic interactions within ecosystems, biodiversity studies of fish communities, ecosystem modelling, habitat evaluation for different fish species, climate change research, and support fisheries management practices. The objective of the studies is to analyse the prediction performance of machine learning (ML) regression models by applying different statistical analysis techniques. This study collected biometric measurements (total length and body weight) for 19 fish families from three locations in Setiu Wetland, Terengganu, captured between 2011 and 2012. The study adopts two regression types: Linear Regression (i.e., Multiple Linear, Lasso, and Ridge model) and Tree-based Regression (i.e., Decision Tree, Random Forest, and XGBoost model). Mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) were used to evaluate performance. The results showed that the proposed ML regression models successfully predicted fish weight in Setiu Wetlands, and the Tree-based Regression model provides more accurate prediction results than the Linear Regression model. As a result, Random Forest is the best predictive model out of the six suggested ML regressions, with the highest accuracy at 96.1% and the lowest RMSE and MAE scores at 3.352 and 0.880, respectively. In conclusion, the use of machine learning is crucial for rapid, precise, and cost-effective fish weight measurement. By incorporating weight prediction into ecological research and management practices, we may make informed decisions supporting the conservation and sustainable use of fish populations and their habitats

Food and Feeding Habits of Fishes in Brunei Bay, Malaysia

The study of the food and feeding habits of fishes is crucial in understanding their ecology. Food and feeding habits of the 30 fish species belonging to 22 families from Bukit Sari and Awat-awat of Lawas in the Bay of Brunei were studied on 11th February 2020 and 12th February 2020 respectively. Samples were collected using “Kabat” nets, casting nets, and seine nets. The dietary components of each species were studied and expressed as a percentage of numerical composition (N), percentage of weight composition (W), and percentage of frequency of occurrence (F). Diet compositions of the species were estimated using the Index of Relative Importance (%IRI) and trophic level (TROPHj). The major food and their Index of Relative Importance (%IRI) showed the highest was shrimps (64.25%) followed by crabs (11.78%), zooplankton (6.94%), fish (6.91%), algae (4.21%), plants (1.48%), mollusks (1.01%) and others below 1.0%. TROPHj value ranged from 2.0 to 4.2 and the trophic level value of 25 fish species was carnivorous, followed by 2 species (detritivorous and herbivorous) respectively, and 1 species (piscivorous). The findings of the study may offer important data for developing management plans for the region's fishing resources

Weight Prediction for Fishes in Setiu Wetland, Terengganu, using Machine Learning Regression Model

Predicting fish weight holds several essential implications in ecology, such as population assessment, trophic interactions within ecosystems, biodiversity studies of fish communities, ecosystem modelling, habitat evaluation for different fish species, climate change research, and support fisheries management practices. The objective of the studies is to analyse the prediction performance of machine learning (ML) regression models by applying different statistical analysis techniques. This study collected biometric measurements (total length and body weight) for 19 fish families from three locations in Setiu Wetland, Terengganu, captured between 2011 and 2012. The study adopts two regression types: Linear Regression (i.e., Multiple Linear, Lasso, and Ridge model) and Tree-based Regression (i.e., Decision Tree, Random Forest, and XGBoost model). Mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) were used to evaluate performance. The results showed that the proposed ML regression models successfully predicted fish weight in Setiu Wetlands, and the Tree-based Regression model provides more accurate prediction results than the Linear Regression model. As a result, Random Forest is the best predictive model out of the six suggested ML regressions, with the highest accuracy at 96.1% and the lowest RMSE and MAE scores at 3.352 and 0.880, respectively. In conclusion, the use of machine learning is crucial for rapid, precise, and cost-effective fish weight measurement. By incorporating weight prediction into ecological research and management practices, we may make informed decisions supporting the conservation and sustainable use of fish populations and their habitats

First records of the sole, Aseraggodes kobensis (Steindachner, 1896) (Pleuronectiformes, Soleidae), from Malaysia

Fifteen specimens (56.4–112.9 mm standard length) of Aseraggodes kobensis (Steindachner, 1896) (Pleuronectiformes, Soleidae), previously known from southern Japan to the Gulf of Thailand, were collected from Malaysia. A detailed description is given for the specimens, being the first collected from Malaysian waters and southernmost records of the species

Opistognathus nigromarginatus Rüppell, 1830 (Perciformes, Opistognathidae), Bridled Jawfish: a first record from Malaysia

A specimen (125.5 mm in standard length) of bridled jawfish, Opistognathus nigromarginatus Rüppell, 1830 was collected from the Pulau Bidong, Terengganu, Malaysia using research trawler. Opistognathus nigromarginatus previously has been recorded from Southern Africa to the Persian Gulf, India, Thailand, and Vietnam. We document the first record of this species in Malaysia and the southernmost occurrence in the South China Sea and Western Pacific Ocean. Detailed morphometric and meristic data are presented along with brief diagnostic characters

Food and feeding habits of fishes in Brunei Bay, Malaysia

The study of the food and feeding habits of fishes is crucial in understanding their ecology. Food and feeding habits of the 30 fish species belonging to 22 families from Bukit Sari and Awat-awat of Lawas in the Bay of Brunei were studied on 11th February 2020 and 12th February 2020 respectively. Samples were collected using “Kabat” nets, casting nets, and seine nets. The dietary components of each species were studied and expressed as a percentage of numerical composition (N), percentage of weight composition (W), and percentage of frequency of occurrence (F). Diet compositions of the species were estimated using the Index of Relative Importance (%IRI) and trophic level (TROPHj). The major food and their Index of Relative Importance (%IRI) showed the highest was shrimps (64.25%) followed by crabs (11.78%), zooplankton (6.94%), fish (6.91%), algae (4.21%), plants (1.48%), mollusks (1.01%) and others below 1.0%. TROPHj value ranged from 2.0 to 4.2 and the trophic level value of 25 fish species was carnivorous, followed by 2 species (detritivorous and herbivorous) respectively, and 1 species (piscivorous). The findings of the study may offer important data for developing management plans for the region's fishing resources