Different Stages of Aquatic Vegetation Succession Driven by Environmental Disturbance in the Last 38 Years

Seasonal Variation and Spatial Heterogeneity of Water Quality Parameters in Lake Chenghai in Southwestern China

Seasonal dynamics and the vertical stratification of multiple parameters, including water temperature (WT), dissolved oxygen (DO), pH, and chlorophyll-a (Chl-a), were analyzed in Lake Chenghai, Northern Yunnan, based on monitoring data collected in 2015 (October), 2016 (March, May, July), 2017 (March, June, October), 2018 (August), and 2020 (June, November). The results indicate that the lake water was well mixed in winter and spring when the water quality was stable. However, when WT becomes stratified in summer and autumn, the Chl-a content and pH value changed substantially, along with the vertical movement of the thermocline. With rising temperature, the position of the stratified DO layer became higher than the thermocline, leading to a thickening of the water body with a low DO content. This process induced the release of nutrients from lake sediments and promoted eutrophication and cyanobacteria bloom. The thermal stratification structure had some influence on changes in DO, pH, and Chl-a, resulting in the obvious stratification of DO and pH. In summer, with an increase in temperature, thermal stratification was significant. DO and pH achieved peak values in the thermocline, and exhibited a decreasing trend from this peak, both upward and downward. The thermocline was anoxic and the pH value was low. Although Chl-a maintained a low level below the thermocline and was not high, there was a sudden increase in the surface layer, which should be urgently monitored to prevent large-scale algae reproduction and even local outbreaks in Lake Chenghai. Moreover, Lake Chenghai is deeper in the north and shallower in the south: this fact, together with the stronger wind–wave disturbance in the south, results in surface WT in the south being lower than that in the north year-round. This situation results in a gradual diminution of aquatic plants from north to south. Water quality in the lake’s southern extent is better than that in the north, exhibiting obvious spatial heterogeneity. It is recommended that lake water quality monitoring should be strengthened to more fully understand lake water quality and take steps to prevent further deterioration.

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

Estimating aquatic plant diversity and distribution in rivers from Jingjinji region, China, using environmental DNA metabarcoding and a traditional survey method

Traditional survey methods (TSMs) are difficult to use to perform a census of aquatic plant diversity completely in river ecosystems, and improved aquatic plant community monitoring programs are becoming increasingly crucial with a continuous decline in diversity. Although environmental DNA (eDNA) metabarcoding has been applied successfully to assess aquatic biodiversity, limited work has been reported regarding aquatic plant diversity in rivers. In this study, the efficiency of eDNA to estimate the aquatic plant diversity and spatial distribution of rivers from the Jingjinji (JJJ) region was evaluated by comparing results obtained by the TSM. Based on a combination of the two methods, 157 aquatic plant species, including 24 hydrophytes, 61 amphibious plants, and 72 mesophytes, were identified. The spatial patterns in species richness and abundance by eDNA exhibited agreement with the TSM results with a gradual decline from the mountain area (MA) to the agricultural area (AA) and then to the urban area (UA). Compared to the TSM, eDNA identified a significantly greater number of species per site (p < 0.01) and obtained a significantly higher abundance in hydrophytes (p < 0.01), supplementing the unavailable abundance data from the TSM. Furthermore, the aquatic plant assemblages from the different areas were discriminated well using eDNA (p < 0.05), but they were better discriminated by the TSM (p < 0.01). Thus, our study provides more detailed data on aquatic plant diversity in rivers from the JJJ region, which is essential for biodiversity conservation. Our findings also highlight that eDNA can be reliable for evaluating aquatic plant diversity and has the potential to respond to landscape heterogeneity in river ecosystems.

How the Local People Face the Rapid Change of the Tropical Freshwater Wetland Environment

Barito River crosses from the northeast of Central Kalimantan province, but almost two-third of 1000 km length lies in South Kalimantan. While water comes from Schwaner Mountain in the upper, this river receives water from Meratus pass through five big rivers, such as Tabalong, Batang Alai, Pagat, Amandit, and Riam Kiwa. Before the water flowed into Barito River, it retains for sometimes in the low land swampy area, forming huge lowland muddy into shallow lakes, simply named wetlands. How do people commit to sustainability living in extreme tropical wetlands for generations? The ecological investigation was conducted for 2016 – 2019, in-situ observation on the daily life-behavior of local people of 200 local people living Barito Kuala, Tapin, Hulu Sungai Selatan, Hulu Sungai Tengah, Hulu Sungai Utara residences, their technologies, their progress in sustainability development. Data was collected by interviewing directly to the local, face to face, meeting with family, observing and confirming nature with them, as well as studying scientific articles and grey literature from official reports. Statistical calculation helps to compare several data sets, as well as serial of photographs, which documented to support the evidence visually. In conclusion, the locals face the rapid change environments which influence their life, income, health, and their properties and prosperity. Mostly, they complain that life is much more difficult now than several decades ago.