To understand the community structure dynamics of fish eggs and larvae in the Yellow River estuary, four trawl surveys were annually conducted between 2011 and 2014, including at 13 stations in 2011 and 18 stations between 2012 and 2014. A total of 2540 eggs and 143 larvae were collected during the four surveys, comprising 25 species, 8 orders, 16 families, and 20 genera. During the survey period, species numbers of eggs first decreased and then increased, while abundances first increased and then decreased. Larval species numbers exhibited the same trends as for eggs, while larval abundances fluctuated across the four years of the study. The primary species represented by the eggs were Konosirus punctatus, Sillago sihama, and Cynoglossus joyeri, while the larval species primarily comprised Amblychaeturichthys hexanema, Chaeturichthys stigmatias, and Engraulis japonicus. Cluster analysis of species compositions for eggs and larvae revealed the presence of four groups, wherein groups 1–3 comprised communities from 12, 3, and 2 stations, respectively, that were primarily distributed in the eastern part of the estuary. In contrast, group four only comprised communities from one station at the western part of the estuary near Bohai Bay. All biodiversity indices were lowest in 2012 and biodiversity of fish egg and larval communities were most correlated with the water depth among the parameters that were measured. Overall, interannual variation in the distribution and diversity of fish egg and larval communities in the Yellow River estuary reflected the responses of fish to environmental variability.

The coastal waters of the northern Shandong Peninsula have abundant fishery resources, which serve as a critical transitional fishing ground for economic fish migrating into the Bohai Sea for spawning and departing for overwintering habitats. However, anthropogenic pressures such as garbage dumping have led to severe degradation of local fishery resources and concomitant adverse effects on zooplankton communities. To assess these impacts, we analyzed the spatiotemporal distribution, community structure, dominant species, and diversity indices of zooplankton based on sampling data collected in spring from 2015 to 2018 in this region. A total of 24 zooplankton species and 11 larval classes were identified, with the highest species richness observed in 2016. Calanus sinicus and Centropages abdominalis were the primary dominant species, with C. sinicus consistently predominant across all four years. Notably, the dominant species exhibited marked annual variability. The abundance and biomass of zooplankton in the surveyed area exhibited significant annual variations, both showing a trend of first decreasing and then increasing. Peak abundance occurred in 2015 (594.36 ind/m³), while the lowest was recorded in 2017 (118.73 ind/m³). Spatially, abundance and biomass were heterogeneous, with coastal waters exhibiting higher concentrations than offshore areas. The overall low level of community diversity and its significant annual variations indicated that the zooplankton community structure in the surveyed sea area was unstable and showed a trend of degenerative succession. The community structure of zooplankton and larger-bodied dominant species showed stronger correlations with phytoplankton dynamics, whereas smaller-bodied species were more influenced by water temperature.

Accurately understanding the impact of Significant Wave Height (SWH) on mariculture productivity is crucial for developing a sustainable blue economy and mitigating the effects of increasing marine extreme events under climate change. However, a significant research gap exists in macroscale empirical tools capable of quantifying the complex, non-linear, and spatially non-stationary relationships between SWH and mariculture yield. Addressing this, our study focused on the Bohai and Yellow Seas, a critical mariculture region in China. We developed five novel SWH indices (LSDI, MSDI, HSDI, RSI, NDSI) to statistically link SWH with the Unit Area Yield (UAY) using buoy-calibrated ERA5 reanalysis data and regional fishery statistics. Geographically Weighted Regression (GWR) was further employed to uncover the spatial heterogeneity of this relationship. Results demonstrated that the Normalized Difference SWH Index (NDSI) most effectively captured the SWH-UAY relationship (r = 0.61, R² = 0.37), as its non-linear form integrates the positive effects of low SWH conditions and the negative effects of high SWH conditions. GWR analysis revealed significant spatial non-stationarity, with the SWH impact on yield being stronger in the eastern and southern open waters of the Yellow Sea and weaker in the northern semi-enclosed Bohai Sea. The index framework and spatial analysis method developed in this study provide a transferable tool for quantifying the impact of physical oceanographic processes on mariculture productivity at a macro scale, which can offer a scientific basis for climate-resilient mariculture zoning and adaptive management.