Organophosphate ester in surface water of the Pearl River and South China Sea, China: Spatial variations and ecological risks.

This study focused on investigating the concentrations, compositional profiles, partitioning behaviors and spatial variations of organophosphate esters (OPEs) in the Pearl River (PR), South China Sea (SCS) region, to evaluate their environmental risks. ∑OPEs concentrations in the surface water of the PR ranged from 117.5 to 854.8 ng/L in the dissolved phase and from 0.5 to 13.3 ng/L in the suspended particulate matter. In the surface seawaters of the northern and western parts of the SCS, ∑OPEs concentrations were 1.3-17.6 ng/L (mean: 6.7 ± 5.2) and 2.3-24.4 ng/L (mean: 7.6 ± 5.5), respectively. The percentage of chlorinated OPEs in surface water samples from the PR to the SCS was 79 ± 15%. Tripentyl phosphate (TPeP) (average: 28.3%) and triphenylphosphate (TPhP) (average: 9.6%) exhibited significant particulate fraction. A significant negative correlation (p < 0.05) between salt concentration and OPE congeners in seawater suggested that river runoff predominantly introduced OPEs into the coastal waters of the SCS. The findings also showed higher levels of OPEs in the PR and estuary than in offshore waters. The OPE loading from the PR into the SCS was estimated to be ∼119 t y-1. The presence of TCEP (RQmax = 2.1), TnBP (RQmax = 0.48) and TPhP (RQmax = 0.3) in PR water samples pose a high risk to aquatic organisms, whereas OPEs (RQ < 0.1) in SCS water samples do not pose a threat to aquatic organisms. This research emphasizes the environmental fate and impact of OPEs on surface waters of the PR and SCS.

Assessment of Fish Diversity in the Ma'an Archipelago Special Protected Area Using Environmental DNA.

This study aimed to investigate the practical validity of the environmental DNA (eDNA) method for evaluating fish composition and diversity in different habitats. We evaluated the fish composition and diversity characteristics of seven different habitats in the Ma'an Archipelago Special Protected Area in April 2020. The results showed that a total of twenty-seven species of fishes belonging to six orders, eighteen families, and twenty-three genera of the Actinopterygii were detected in the marine waters of the Ma'an Archipelago Special Protected Area. The dominant species in each habitat were Larimichthys crocea, Paralichthys olivaceus, and Lateolabrax maculatus. The mussel culture area had the highest number of species, with 19 fish species, while the offshore bulk load shedding platform had the lowest number of species, with 12 fish species. The rest of the habitat was not significantly different. The results showed that the mussel culture area had the highest diversity index (average value of 2.352 ± 0.161), and the offshore bulk load shedding platform had the lowest diversity index (average value of 1.865 ± 0.127); the rest of the habitat diversity indices did not differ significantly. A comparison with historical surveys showed that the eDNA technique can detect species not collected by traditional methods such as gillnets and trawls. Our study demonstrates the role of eDNA technology in obtaining fish diversity in different habitats and provides a theoretical basis for the continuous monitoring and management of fish biodiversity in protected areas.

Determination of intertidal macroalgae community patterns using the power law model.

The spatial heterogeneity of macroalgae in intertidal zones affects the stability of marine ecosystem communities, contributes to the maintenance of coastal biodiversity, and has an essential role in ecosystem and habitat maintenance. We explored the feasibility of applying the power law model to analyze the spatial distribution of macroalgae on Lvhua Island (Zhejiang Province, China) and characterized the intertidal spatial heterogeneity of the macroalgae present. The results showed a strong association between the spatial distribution of macroalgae in the intertidal zone and the power law model (R2 = 0.98). There was a positive association between species occurrence frequency and the spatial heterogeneity index of macroalgae species. The model also indicated there was macroalgal habitat structure at the site as the spatial heterogeneity within the community was greater than that of random distribution. The power law model reported here provides a new method for macroalgae community ecology research and could be broadly utilized to analyze the spatial pattern of macroalgae in intertidal zones.

Variable Optimization of Seaweed Spectral Response Characteristics and Species Identification in Gouqi Island.

Probing the coverage and biomass of seaweed is necessary for achieving the sustainable utilization of nearshore seaweed resources. Remote sensing can realize dynamic monitoring on a large scale and the spectral characteristics of objects are the basis of remote sensing applications. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions from the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Sargassum fusifarme. The different seaweed spectra were identified and analyzed using a combination of one-way analysis of variance (ANOVA), support vector machines (SVM), and a fusion model comprising extreme gradient boosting (XGBoost) and SVM. In total, 14 common spectral variables were used as input variables, and the input variables were filtered by one-way ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a ratio of 3:1 for input into the SVM and fusion model. The results showed that when the input variables were the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), Vre, Abe, Rg, Lre, Lg, and Lr and the model parameters were g = 1.30 and c = 2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.99%, and the highest accuracy was 93.94% when distinguishing between the different seaweed phyla (g = 6.85 and c = 2.55). The classification of the fusion model also shows similar results: The overall accuracy is 73.98%, and the mean score of the different seaweed phyla is 97.211%. In this study, the spectral data of intertidal seaweed with different dry and wet states were classified to provide technical support for the monitoring of coastal zones via remote sensing and seaweed resource statistics.

Assessment of intertidal seaweed biomass based on RGB imagery.

The Above Ground Biomass (AGB) of seaweeds is the most fundamental ecological parameter as the material and energy basis of intertidal ecosystems. Therefore, there is a need to develop an efficient survey method that has less impact on the environment. With the advent of technology and the availability of popular filming devices such as smartphones and cameras, intertidal seaweed wet biomass can be surveyed by remote sensing using popular RGB imaging sensors. In this paper, 143 in situ sites of seaweed in the intertidal zone of GouQi Island, ShengSi County, Zhejiang Province, were sampled and biomass inversions were performed. The hyperspectral data of seaweed at different growth stages were analyzed, and it was found that the variation range was small (visible light range < 0.1). Through Principal Component Analysis (PCA), Most of the variance is explained in the first principal component, and the load allocated to the three kinds of seaweed is more than 90%. Through Pearson correlation analysis, 24 parameters of spectral features, 9 parameters of texture features (27 in total for the three RGB bands) and parameters of combined spectral and texture features of the images were selected for screening, and regression prediction was performed using two methods: Random Forest (RF), and Gradient Boosted Decision Tree (GBDT), combined with Pearson correlation coefficients. Compared with the other two models, GBDT has better fitting accuracy in the inversion of seaweed biomass, and the highest R2 was obtained when the top 17, 17 and 11 parameters with strong correlation were selected for the regression prediction by Pearson's correlation coefficient for Ulva australis, Sargassum thunbergii, and Sargassum fusiforme, and the R2 for Ulva australis was 0.784, RMSE 156.129, MAE 50.691 and MAPE 28.201, the R2 for Sargassum thunbergii was 0.854, RMSE 790.487, MAE 327.108 and MAPE 19.039, and the R2 for Sargassum fusiforme was 0.808, RMSE 445.067 and MAPE 28.822. MAE was 180.172 and MAPE was 28.822. The study combines in situ survey with machine learning methods, which has the advantages of being popular, efficient and environmentally friendly, and can provide technical support for intertidal seaweed surveys.