The substrate is the key environmental factor that affects the growth, survival, population and distribution of dwelling mollusks in mudflat settings. To clarify the effect of the substrate grain size on soft substrate preference, burrowing ability and behavior during the selection process of juvenile Meretrix meretrix, four different grain size substrates (coarse sand, medium sand, fine sand, and natural substrate) were set up for comparison. The results indicated that: (1) the burrowing ability of juvenile specimens in fine sand was the strongest; (2) the degree (from high to low) of the juvenile’s preference for the four substrates was in the order of fine sand > natural substrate > medium sand > coarse sand; and (3) the selection process of the substrate by the juveniles could be divided into four stages: preparation, selection, burrowing and end stages. These stages showed the behavioral characteristics of a longer selection time and higher percentage of movement in coarse sand. Therefore, our results demonstrated that sea areas or ponds with fine sand as the main component are more suitable for stock enhancement with M. meretrix. These results provide basic data for habitat selection and suitability evaluations for the aquaculture of M. meretrix.
Since macrobenthos play an important role in the energy flow and material circulation of marine systems, they can act as an indicator of ecosystem health. Because there are generally complex relationships between macrobenthos and environmental factors, the optimal model for simulating macrobenthos habitat is a nonlinear, nonparametric model with a relatively flexible structure. This study applied canonical correlation analysis (CCA) to identify the key ecological factors affecting the community characteristics of macrobenthos in the bivalve farming area near Xiaoqing estuary. Responses of species richness to environmental factors were studied using the generalized additive model (GAM), and the Margalef index (dM) was used instead of individual indicator species to indicate diversity variation. Six factors were selected in the optimal model by stepwise regression: salinity (Sal), sediment organic matter (SOM), ammonium nitrogen (NH4-N), phosphate in interstitial water (PO4-Psoil), ammonium nitrogen in interstitial water (NH4-Nsoil), and nitrate nitrogen in interstitial water (NO3-Nsoil) in the substrates. The response curves generated by the GAM showed a unimodal relationship between taxa diversity and Sal and SOM, dM was positively correlated with NH4-N, and was negatively correlated with PO4-Psoil. The model optimized by forward stepwise optimization explained 92.6% of biomass variation, with a small residual (2.67). The measured dM was strongly correlated with the predicted dM (Pearson R2 = 0.845, p < 0.05). This study can increase understanding of the relationships between the macrobenthic community and aquaculture activities in the bivalve farming area near Xiaoqing estuary.
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