Spatio-temporal distribution of Konosirus punctatus spawning and nursing ground in the South Yellow Sea

Evaluation of the Effects of Environmental Factors on Seasonal Variations in Fish Diversity on a Coastal Island in Western Japan

“…For example, Konosirus punctatus and Hyporhamphus sajori are usually found near the sites where seagrass and seaweed are present. Females of these species often enter seaweed beds and attach their eggs to seaweeds or drifting seaweeds during their spawning season [30,80,82]. During the summer season, the seagrass meadows of Ikuchijima Island serve as the spawning and nursery grounds for some fish species, leading to a high relative abundance of these species.…”

“…Seasonal seawater temperature patterns impact the metabolism of fish species, the activities of larvae, and the distribution of fish eggs [20,[27][28][29]. The migration and spawning behaviors of fish species are likewise influenced by seasonal variations in temperature and salinity, leading to fluctuations in species number, biomass, and density [30][31][32][33]. Thus, to ensure sustainable use of fishery resources, it is necessary to obtain basic information on fish communities and their responses to changes in the seasonal variations in environmental factors.…”

Do Two Different Approaches to the Season in Modeling Affect the Predicted Distribution of Fish? A Case Study for Decapterus maruadsi in the Offshore Waters of Southern Zhejiang, China

“…The spatial distribution of fish plays an important role in understanding the dynamics of marine ecosystems [1][2][3]. The aggregation and distribution of fish are the result of a combination of influencing factors [4], so understanding the spatial and temporal dynamics of fish habitats and their relationships with environmental factors through effective methods is of great importance for the management and sustainable use of fisheries.…”

“…When predicting the distribution patterns of target species resources across seasons, calculations are often carried out in two ways. The first approach is to model the entire dataset and take into account the 'season' (or month) as an influencing factor [2,14] (Yearly-GAM), and then use the best model to predict the distribution of the target species in each season. The second approach is to build models in separate seasons [3,15] and then use the best models for different seasons to separately make predictions to derive the spatial distribution characteristics of the target species in the corresponding season.…”

“…It has the advantages of high accuracy and flexible application [17] and is useful in studying the relationship between fish resources and the environment. The GAM has been widely used in studying the relationship between fish stocks and environmental factors [9] and predicting spatial distribution [1,2,12]. In addition, related studies [18,19] have shown that the Tweedie distribution is superior to other methods in dealing with the zero-value problem and can explore the relationship between fish stock density and environmental factors more accurately to a certain extent, providing a more reasonable solution to the zero-value problem in fisheries.…”