Potential application of artificial neural networks for analyzing the occurrences of fish larvae and juveniles in an estuary in northern Vietnam.

Do, Anh Ngoc Thi; Tran, Hau Duc

The early stages of fish during their life cycle, including larvae and juveniles, are sensitive to the environment. Determining the occurrences of fish larvae and juvenile relative to their associated environments is essential for conservation and fisheries management. Computer-based modeling has rarely been applied for forecasting the distribution patterns of the early fish stages in dynamic systems such as estuaries. In the present study, we applied novel modeling techniques to fish larval and juvenile samples collected in May, September, November, and December during 2019 along the Ba Lat estuary of the Red River, northern Vietnam. The results showed that the occurrences of freshwater and marine fish larvae and juveniles were inversely related to environmental factors (electrical conductivity, temperature, pH, depth, shore distance and turbidity) with a high square of multiple correlation coefficients. The occurrences of the two fish groups were strongly related to temporal and spatial changes in the estuary, and these correlations could be utilized for machine learning processing. Linear regression, Gaussian process models, ensemble regression, and artificial neural network (ANN) models were applied to elucidate the distributions of fish larvae and juveniles. It shows that ANN models obtained the highest R2 (> 0.63). In addition, the spatial distribution prediction of fish larvae and juveniles using ANN models was similar to the field measurement. Thus, we suggest utilizing ANN models to predict the occurrences of early fish stages in estuaries in tropical regions such as Vietnam. Recommendations for further applications of ANN models are also given in this study.

VIETNAM; FISH larvae; ESTUARIES; LIFE cycles (Biology); FISH conservation; ARTIFICIAL neural networks; GEOGRAPHICAL distribution of fishes; FISHING techniques

Aquatic Ecology, 2023, Vol 57, Issue 4, p813

1386-2588

Academic Journal

10.1007/s10452-022-09959-5