RESUMO
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.
RESUMO
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.