Forecasting the effects of habitat fragmentation on the distribution of Sillago larvae and juveniles: a case study in Ka Long Estuary, Vietnam

Abstract

Abstract Habitat fragmentation potentially impacts animal diversity due to a decrease in ecological connectivity, especially in estuarine and mangrove environments, where many aquatic organisms spend their early developmental stages on. This study used collections of different species of Sillago larvae and juveniles from 2014 to 2015 in the Ka Long estuary, northern Vietnam to examine the above case based on deep learning models. The combined model, particle swarm optimization - gradient boosting regression, can be used to predict the spatial distribution of Sillago fish in the context of habitat fragmentation, one of the most important parameters to influence in the fish distribution. It reveals that larvae and juveniles of Sillago spp. are more abundant in areas next to mangroves. Based on deep convolutional neural network approach, land use/land cover changed considerably in 2013 and 2023 as mangrove forests and waterbody decreased due to the increase of urbanization. In addition, landscape indices additionally show that the habitat connectivity is decreasing and the number of edges is growing, which indirectly impact the fish larvae and juveniles inhabiting mangroves. Based on genetic algorithm analysis, the present work forecasts Sillago larval and juvenile distribution under the effect of landscape fragmentation in 2033 and 2043, when the Sillago density will have fallen dramatically. Yet, they will choose to reside in mangrove forests, indicating that conserving this environment is essential to maintaining this commercial fish in the study site as well as in adjacent mangrove forests in northern Vietnam.