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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
Published on: June 13, 2020
Environmental predictors of chaetognaths assemblages near Dajin Island: Insights from correlation analysis, random
Zhimin Song1, Jiping Zheng1, Yaquan Li2
1Zhuhai Marine Center, Ministry of Natural Resources, Zhuhai, 519015, China.
Abstract:
Chaetognaths play fundamental roles in marine ecosystems because of their critical trophic position and environmental sensitivity. Based on 18 cruises (2022-2024) conducted around Dajin Island in the South China Sea, this study identified the primary factors influencing the abundance dynamics of two chaetognath species (Flaccisagitta enflata and Zonosagitta bedoti) using an integrated framework combining correlation analysis, random forest (RF) modeling and generalized additive models (GAMs). Both species exhibited winter abundance maxima, showing significant positive responses to salinity (Sal), dissolved inorganic phosphorus (DIP), total suspended solids (TSS) and eastward wind (U), whereas negative responses to sea surface temperature (SST), chemical oxygen demand (COD) and dissolved inorganic nitrogen (DIN). The optimal generalized additive model (GAM) for F. enflata (U + SST + COD; AICc-minimized) explained 57.54 % of the deviance (adjusted R2 = 0.55, p < 0.001), indicating thermal (SST >24.9 °C) and organic pollution (COD >1.4 mg/L) constraints. For Z. bedoti, a habitat-dependent dual-driving pattern was observed: the GAM of U, COD and DIP explained 38.55 % of the deviance (adjusted R2 = 0.33, p < 0.001) in nearshore waters, while the GAM of SST, DIP and TSS explained 30.75 % (adjusted R2 = 0.28, p < 0.001) in offshore areas. The pronounced negative responses of both species to COD (ρ = -0.53 for F. enflata and ρ = -0.61 for Z. bedoti) strongly support their utility as bioindicators for organic pollution. The proposed tiered framework (statistical screening → machine learning validation → nonlinear modeling) effectively resolved the dominant environmental predictors governing chaetognath spatiotemporal dynamics, providing a transferable paradigm for studying marine organism-environment interactions. These GAMs, coupled with environmental scenario projections, enable reliable forecasting of chaetognath population dynamics, offering critical tools for ecosystem-based management.
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