Wetland Types Research Articles

PH-Related Changes in Soil Bacterial Communities in the Sanjiang Plain, Northeast China.

Soil bacteria are crucial components of terrestrial ecosystems, playing an important role in soil biogeochemical cycles. Although bacterial community diversity and composition are regulated by many abiotic and biotic factors, how soil physiochemical properties impact the soil bacteria community diversity and composition in wetland ecosystems remains largely unknown. In this study, we used high-throughput sequencing technology to investigate the diversity and composition of a soil bacterial community, as well as used the structural equation modeling (SEM) method to investigate the relationships of the soil's physicochemical properties (i.e., soil pH, soil organic carbon (SOC), total nitrogen (TN), ammonium nitrogen (NH4+N), electrical conductivity (EC) and nitrate nitrogen (NO3-N)), and soil bacterial community structures in three typical wetland sites in the Sanjiang Plain wetland. Our results showed that the soil physicochemical properties significantly changed the α and β-diversity of the soil bacteria communities, e.g., soil TN, NH4+N, NO3-N, and SOC were the main soil factors affecting the soil bacterial α-diversity. The soil TN and pH were the key soil factors affecting the soil bacterial community. Our results suggest that changes in soil pH indirectly affect soil bacterial communities by altering the soil nitrogenous nutrient content.

Dynamic Response of the cbbL Carbon Sequestration Microbial Community to Wetland Type in Qinghai Lake.

The soil carbon storage in the Qinghai-Tibet Plateau wetlands is affected by microbiota and wetland types, but the response mechanisms of carbon sequestration microorganisms on the Qinghai-Tibet Plateau to different wetland types are still poorly described. To explore the differences in carbon sequestration microbial communities in different wetlands and the main influencing factors, this study took a marsh wetland, river source wetland and lakeside wetland of Qinghai Lake as the research objects and used high-throughput sequencing to study the functional gene, cbbL, of carbon sequestration microorganisms. The results showed that the dominant bacterial group of carbon sequestration microorganisms in marsh and river source wetlands was Proteobacteria, and the dominant bacterial group in the lakeside wetland was Cyanobacteria. The alpha diversity, relative abundance of Proteobacteria and total carbon content were the highest in the marsh wetland, followed by the river source wetland, and they were the lowest in the lakeside wetland. In addition, the physical and chemical characteristics of the three wetland types were significantly different, and the soil temperature and moisture and total carbon content were the most important factors affecting the community structures of carbon-sequestering microorganisms. There was little difference in the total nitrogen contents between the marsh wetland and river source wetland. However, the total nitrogen content was also an important factor affecting the diversity of the carbon sequestration microbial community. In summary, the wetland type significantly affects the process of soil carbon sequestration. Compared with the riverhead and lakeside wetlands, the marsh wetland has the highest carbon storage.

Triclosan disturbs nitrogen removal in constructed wetlands: Responses of microbial structure and functions

This study investigated the influence of wetland types (vertical and tidal flow constructed wetlands [CWs] [VFCW and TFCW, respectively]) and concentrations of triclosan (TCS) on the removal of pollutants (TCS and nitrogen) and microbial characteristics. The efficiency of TCS removal was significantly higher with 5 μg/L TCS (Phase B) than with 30 μg/L (Phase C) in the two CWs. The efficiencies of removal of NH4+-N and NO3−-N were significantly inhibited in Phase C. Compared with the VFCW, the TFCW removed more NH4+-N at the same concentration of TCS, whereas less NO3−-N was removed, and it even accumulated. Saccharimondales, an important functional genus with the highest abundance and more node connections with other genera, had a sharp decrease in relative abundance as the increasing concentrations of TCS of the two CWs conformed with its relative abundance and significantly negatively correlated with the concentration of TCS. Differentiated Roseobacter_Clade_CHAB-I-5_Lineage and Sphaerotilus were enriched in the VFCW and TFCW, respectively. The abundance of enzymes that catalyzed nitritation was significantly inhibited by TCS, whereas nitrate reductase (EC 1.7.99.4) catalyzed both denitrification and dissimilatory nitrate reduction to ammonium (DNRA), and nitrite reductase (NADH) (EC 1.7.1.15) that catalyzed DNRA comprised a larger proportion in the two CWs. Simultaneously, the abundances of two enzymes were higher in the TFCW than in the VFCW. The network analysis indicated that the main genera were promoted more by TCS in the VFCW, while inhibited in the TFCW. Moreover, the concentrations of nitrogen (NH4+-N, NO3−-N, and TN) significantly positively correlated with TCS-resistant bacteria, and negatively correlated with most nitrogen-transforming bacteria with species that varied between the VFCW and TFCW. The results of this study provide a reference for the molecular biological mechanism of the simultaneous removal of nitrogen and TCS in the CWs.

Retrieval of chlorophyll content for vegetation communities under different inundation frequencies using UAV images and field measurements

Karst wetlands are an important type of wetland among the global karst areas that suffer from serious ecosystem degradation. Chlorophyll content, an important parameter of vegetation functional traits, can indicate the photosynthetic activity, mutation and stress level, and nutritional state of vegetation. However, quantitative estimation of canopy chlorophyll content (CCC) of calcium-loving and alkali-resistant vegetation in karst wetlands using remote sensing technology for clarifying the relationship between canopy spectral reflectance and the CCC of vegetation communities remains a challenge. In this study, we propose a retrieval approach for estimating the CCC of seven typical vegetation communities in the largest karst wetland in China, using a combination of parameter/non– parameter estimation model, and unmanned aerial vehicle (UAV) multispectral images. We further evaluated the sensitivity of various spectral indices to the CCC of the vegetation communities and explored the influence of image feature distribution on CCC estimation accuracy. We revealed the variation patterns of leaf chlorophyll content (LCC) and CCC under different inundation frequencies in the karst wetland. We confirmed that the UAV-based inversion method proposed in this study achieved the CCC estimation of seven vegetation communities (R2 = 0.57 ∼ 0.93, RMSE = 8.80 SPAD ∼ 60.68 SPAD), and obtained high estimation accuracy (R2 = 0.77, RMSE = 22.04 SPAD) of Cladium chinense Nees (CCN, endemic vegetation in karst wetland). We found that the optimal reflectance ranges for estimating the CCC of the seven vegetation communities were 714 nm ∼ 746 nm and 814 nm ∼ 866 nm, and that the ChIrededge and MSR indices negatively influenced the CCC estimation of CCN and Carex baccans Nees (CBN), respectively. We demonstrated that vegetation communities with well-defined canopy structure (Triadica sebifera (Linnaeus) Small) achieved higher estimation accuracy with moderate uncertainty (ε = 10.80 % ∼ 15.49 %) than the herbaceous vegetation communities (CCN, CBN, and Cynodon dactylon (L.) Pers.). The bias of the CCC estimation based on linear regression and partial least squares regression models in different vegetation communities ranged from 1.61 % to 49.41 %. We confirmed that the magnitude of variations in LCC and CCC for these vegetation communities presented a decreasing pattern from that in high-frequency inundation communities to that in the low-frequency ones.

The competitive exclusion-tolerance rule explains habitat partitioning among co-occurring species of burying beetles.

Habitat partitioning among co-occurring, ecologically similar species is widespread in nature and thought to be an important mechanism for coexistence. The factors that cause habitat partitioning, however, are unknown for most species. We experimentally tested among three alternative hypotheses to explain habitat partitioning among two species of co-occurring burying beetle (Nicrophorus) that occupy forest (Nicrophorus orbicollis) and wetland (Nicrophorus hebes) habitats. Captive experiments revealed that the larger N. orbicollis (forest) was consistently dominant to N. hebes (wetland) in competitive interactions for carcasses that they require for reproduction. Transplant enclosure experiments in nature revealed that N. hebes had poor reproductive success whenever the dominant N. orbicollis was present. In the absence of N. orbicollis, N. hebes performed as well, or better, in forest versus its typical wetland habitat. In contrast, N. orbicollis performed poorly in wetlands regardless of the presence of N. hebes. These results support the competitive exclusion-tolerance rule where the competitively dominant N. orbicollis excludes the subordinate N. hebes from otherwise suitable or preferable forest habitat, while the subordinate N. hebes is uniquely able to tolerate the challenges of breeding in wetlands. Transplant experiments further showed that carcass burial depth-an important trait thought to enhance the competitive ability of the dominant N. orbicollis-is costly in wetland habitats. In the presence of N. hebes, N. orbicollis buried carcasses deeper; deeper burial is thought to provide a competitive advantage in forests but further compromised the reproductive success of N. orbicollis in wetlands. Overall, results provide evidence that the competitive exclusion-tolerance rule underlies habitat partitioning among ecologically similar species and that the traits important for competitive dominance in relatively benign environments are costly in more challenging environments, consistent with a trade-off.

Mapping wetlands in Northeast China by using knowledge-based algorithms and microwave (PALSAR-2, Sentinel-1), optical (Sentinel-2, Landsat), and thermal (MODIS) images

Wetlands are rich in biodiversity, provide habitats for many wildlife species, and play a vital role in the transmission of bird-borne infectious diseases (e.g., highly pathogenic avian influenza). However, wetlands worldwide have been degraded or even disappeared due to natural and anthropogenic activities over the past two centuries. At present, major data products of wetlands have large uncertainties, low to moderate accuracies, and lack regular updates. Therefore, accurate and updated wetlands maps are needed for the sustainable management and conservation of wetlands. Here, we consider the remote sensing capability and define wetland types in terms of plant growth form (tree, shrub, grass), life cycle (perennial, annual), leaf seasonality (evergreen, deciduous), and canopy type (open, closed). We identify unique and stable features of individual wetland types and develop knowledge-based algorithms to map them in Northeast China at 10 m spatial resolution by using microwave (PALSAR-2, Sentinel-1), optical (Landsat (ETM+/OLI), Sentinel-2), and thermal (MODIS land surface temperature, LST) imagery in 2020. The resultant wetland map has a high overall accuracy of >95%. There were a total 154,254 km2 of wetlands in Northeast China in 2020, which included 27,219 km2 of seasonal open-canopy marsh, 69,158 km2 of yearlong closed-canopy marsh, and 57,878 km2 of deciduous forest swamp. Our results demonstrate the potential of knowledge-based algorithms and integrated multi-source image data for wetlands mapping and monitoring, which could provide improved data for the planning of wetland conservation and restoration.

Turnover of phytoplankton and zooplankton communities driven by human-induced disturbances and climate changes in a small urban coastal wetland

Urbanization causes immense eutrophication pressure on small urban wetlands, which play a vital role in urban ecosystems. However, our comprehension of the prolonged impacts of eutrophication on these types of wetlands continues to be significantly limited. To address this knowledge gap, we analyzed monthly physical–chemical variables and seasonal phytoplankton and zooplankton communities at the Taipa-Coloane wetland in Macau, China, from 2013 to 2019. Through the generalized additive model (GAM) and principal component analysis (PCA), we found that the aquatic system mainly exhibited interannual variations instead of seasonal variations, with nutrient loads and river discharge (turbidity and salinity) being the most prominent features affected by anthropogenic activities and climate change. Over the years, the increasing turbidity and phytoplankton showed that bottom-up effects predominated the aquatic systems. Under the bottom-up effects of eutrophication and turbidity, the phytoplankton communities showed high adaptation that MP and S1 occupied more dominance after 2016, with higher abundance but lower diversity. Moreover, the bottom-up effects further affected the structure of zooplankton communities. The dominance of rotifers filter feeders (RF) decreased over the years while middle copepods and cladocerans filter feeders (MCF) increased, implying a trend of zooplankton toward large size over time. Overall, our result reveals the bottom-up control pattern in the aquatic systems of small urban wetlands. This research provides valuable insights into the dynamics and responses of plankton communities to environmental changes in small urban wetlands, providing valuable information for future conservation efforts.

Patterns of bacterial communities in the rhizosphere and rhizoplane of alpine wet meadows

Wet meadows, a type of wetland, are vulnerable to climate change and human activity, impacting soil properties and microorganisms that are crucial to the ecosystem processes of wet meadows. To decipher the ecological mechanisms and processes involved in wet meadows, it is necessary to examine the bacterial communities associated with plant roots. To gain valuable insight into the microbial dynamics of alpine wet meadows, we used Illumina MiSeq sequencing to investigate how environmental factors shape the bacterial communities thriving in the rhizosphere and rhizoplane of three plant species: Cremanthodium ellisii, Caltha scaposa, and Cremanthodium lineare. The most abundant bacterial phyla in rhizosphere and rhizoplane were Proteobacteria > Firmicutes > Actinobacteria, while Macrococcus, Lactococcus, and Exiguobacterium were the most abundant bacterial genera between rhizosphere and rhizoplane. The mantel test, network, and structure equation models revealed that bacterial communities of rhizosphere were shaped by total nitrogen (TN), soil water content (SWC), soil organic carbon (SOC), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), pH, however, rhizoplane bacterial communities exhibited varying results. The bacterial communities exhibited significant heterogeneity, with stochastic process predominating in both the rhizosphere and rhizoplane. PICRUSt2 and FAPROTAX analysis revealed substantial differences in key biogeochemical cycles and metabolic functional predictions. It was concluded that root compartments significantly influenced the bacterial communities, although plant species and elevation asserted varying effects. This study portrays how physicochemical properties, plant species, and elevations can shift the overall structure and functional repertoire of bacterial communities in alpine wet meadows.

A comprehensive evaluation of biochar for enhancing nitrogen removal from secondary effluent in constructed wetlands

Biochar has been used to improve nitrogen removal in constructed wetland (CW) treating secondary effluent from wastewater treatment plants (WWTPs). Nevertheless, the mechanism on enhancing nitrogen removal is unclear. Herein, biochar derived from three typical wetland plants, Typha latifolia (TLB), Phragmites australis (PAB), and Cyperus alternifolius (CAB) at 300°C, were used as substrates in CWs to uncover the gap. Results indicated that the total nitrogen removal efficiencies of TLB-CWs (59.84%), PAB-CWs (65.08%), and CAB-CWs (78.61%) were significantly higher than the gravel control (39.20%). Biochar enhanced plant growth for nitrogen uptake and boosted the secretion of root exudates for fueling microbial denitrification. Meanwhile, biochar notably increased the activities of dehydrogenase, electron transport system, and denitrifying enzymes and enriched the corresponding genes. Mass balance calculation showed that microbial denitrification was dominated in nitrogen removal in CWs. Pearson correlation analysis revealed that the proportion of C=O and pyrrolic N in biochar’s surface, biochar’s electron accepting capacity and electron donating capacity, and the lignin proportion of biochar feedstock, were significantly correlated with the removal efficiency of nitrogen. PLS-SEM demonstrated that biochar boosted nitrogen removal in CWs by enhancing plant growth, which promoted microbial activities. Overall, our results showed the enhancing interactive mechanisms among biochar properties, plant growth and microbial enzyme activity.

Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model

Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have significant consequences on the socio-economic life of the local population. In this paper, erosion, flood risk, and greenhouse gas sequestration resulting from sea-level rise in the coastal zone of the Benin coast were assessed with the Sea Level Affecting Marshes Model (SLAMM) using ArcGIS Pro 3.1 tools. The input features used were the Digital Elevation Map (DEM), the National Wetland Inventory (NWI) categories, and the slope of each cell. National Wetland Inventory (NWI) categories were then created using Support Vector Machines (SVMs), a supervised machine learning technique. The research simulated the effects of a 1.468 m sea-level rise in the study area from 2021 to 2090, considering wetland types, marsh accretion, wave erosion, and surface elevation changes. The largest land cover increases were observed in Estuarine Open Water and Open Ocean, expanding by approximately 106.2 hectares across different sea-level rise scenarios (RCP 8.5_Upper Limit). These gains were counterbalanced by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamp, Undeveloped, and Developed Dryland. Notably, Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) experienced the most significant expansion, indicating submergence and saltwater intrusion by 2090 due to sea-level rise. The largest reductions occurred in less tidally influenced categories like Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). As the sea-level rises by 1.468 m, these categories are expected to be notably diminished, with Estuarine Open Water and Open Ocean becoming dominant. Erosion and flooding in the coastal zone are projected to have severe adverse impacts, including a gradual decline in greenhouse gas sequestration capacity. The outputs of this research will aid coastal management organizations in evaluating the consequences of sea-level rise and identifying areas with high mitigation requirements.

Climate warming enhances microbial network complexity by increasing bacterial diversity and fungal interaction strength in litter decomposition

Microbial communities are important drivers of plant litter decomposition; however, the mechanisms of microbial co-occurrence networks and their network interaction dynamics in response to climate warming in wetlands remain unclear. Here, we conducted a 1.5-year warming experiment on the bacterial and fungal communities involved in litter decomposition in a typical wetland. The results showed that warming accelerated the decomposition of litter and had a greater effect on the diversity of bacteria than on that of fungi. Dominant bacterial communities, such as Bacteroidia, Alphaproteobacteria, and Actinobacteria, and dominant fungal communities, such as Leotiomycetes and Sordariomycetes, showed significant positive correlations with lignin and cellulose. Co-occurrence networks revealed that the average path length and betweenness centrality under warming conditions increased in the bacterial community but decreased in the fungal community. Both bacterial and fungal networks in the 2.0 °C warming treatment had the highest ratio of positive links (58.53 % and 98.14 %), indicating that moderate warming can promote the positive correlations and symbiotic relationships observed in the microbial community. This also suggests that small-world characteristics and weak-link advantages accelerate diffusion, and scale-free features facilitate propagation in microbial communities in response to climate warming. Logistic growth and Lotka-Volterra competition models revealed that climate warming enhances microbial network complexity mainly by increasing bacterial diversity and fungal interaction strength in litter decomposition. However, the symbiotic relationship decreased slightly under 4.0 °C warming, indicating that climate warming is a random attack rather than a targeted attack, and the microbial network has strong resistance to random attack, as shown by the highly robust dynamic performance of the microbial network in litter decomposition. Overall, the microbial community in litter decomposition responded to climate warming and shifted its network interactions, leading to further changes in emergent network topology and dynamics, thus accelerating litter decomposition in wetlands.

Distribution and sources of surface soil pollen in different coastal wetlands and their relationship with land cover: A case study of northwest Bohai Bay, north China

Understanding the relationship between modern pollen and vegetation in coastal wetlands is essential for reconstructing long-term land cover changes, and for determining the response of coastal wetland ecosystems to global changes. We selected 68 sampling sites in different types of coastal wetland in northwest Bohai Bay, North China, for palynological analysis, and hence to determine the relationships between pollen and environmental factors, such as soil grain size, salinity, and pH. A back-trajectory model and remote sensing LUCC data were used to reveal the pollen source area and the pollen representation for different land cover types. The results show that the pollen assemblages of these coastal wetlands are dominated by Chenopodiaceae and Poaceae. The pollen assemblages are significantly different between silty and sandy coastal wetlands, and they also reflect different vegetation landscapes. Chenopodiaceae pollen is a potential indicator of saline-alkali soil environments in coastal areas. In addition to the vegetation coverage and sedimentary environment, pollen concentrations are negatively correlated with the grain size of the surface soil. The pollen diversity is higher in areas with strong human influences (e.g., cultivated land, villages, roads), but lower in natural wetlands with a high vegetation coverage. The arboreal pollen is generally sourced from the northern and northwestern mountains, while the major dispersal area of halophyte and hygrophyte pollen is on a local scale, constrained by the local vegetation canopy and its settlement characteristics. Fern spores are mainly waterborne, carried by rivers and surface runoff from mountain areas. The arboreal and upland herbaceous pollen is over-represented for forest and grassland landscapes. Halophyte and hygrophyte pollen show a good correlation with the area of coastal wetland, and they can be used as an indicator of the extent of coastal wetland influenced by climate change and human activities.

Comparing the use of all data or specific subsets for training machine learning models in hydrology: A case study of evapotranspiration prediction

Machine learning has been widely used in hydrological modeling. However, the question of whether to use all data for modeling or only a specific subset for modeling and its implications are rarely investigated explicitly. As a case study, combining evapotranspiration (ET) observations from 168 flux stations, meteorological and biophysical variables, we used Random Forests to separately construct an 'All data' model trained with all data and 6 'plant functional type (PFT) specific' models trained with specific PFT data (i.e., Forest, Grassland, Cropland, Shrubland‚ Savannah, Wetland). We found ET simulations between different specific PFTs are transferable. The 'All data' model captured better ET and had a higher R-squared at 94 of 168 sites, especially in Wetland, Shrubland, Cropland, and Grassland types. Compared to using the 'All data' model, the 'PFT specific' model can further improve the accuracy in high R-squared grassland sites by reducing the effect of confusion of other PFTs and constraining the variance of the training data. When shifting from the 'All data' model to the 'PFT specific' model, the increase in the degree of encapsulation of the training set into the prediction set leads to a decrease in the R-squared. Accuracy pre-evaluation may be necessary before applying models trained from either all data or subset data.

Methane emissions partially offset carbon sink function in global wetlands: An analysis based on global data.

Wetlands serve as atmospheric carbon dioxide (CO2) sinks, as well as atmospheric methane (CH4) source due to the anaerobic soil environment. Although some studies report that the CH4 emission from wetlands partially offset their net CO2 uptake, there is no global data analysis on the offset of net ecosystem exchange of CO2 (NEE) by CH4 emission in wetland ecosystems. In this study, we collected the data sets of NEE and CH4 flux which were simultaneously measured in the inland wetlands (peatland and non-peatland wetland) and coastal wetlands (seagrass beds, salt marshes and mangroves) around the world. The results showed that all types of wetlands were atmospheric CO2 sink, with the NEE values ranking as follows: mangrove (-2011.0 g CO2·m-2·a-1) < salt marsh (-1636.6 g CO2·m-2·a-1) < non-peatland wetland (-870.8 g CO2·m-2·a-1) < peatland (-510.7 g CO2·m-2·a-1) < seagrass bed (-61.6 g CO2·m-2·a-1). When CH4 flux being converted into CO2-equivalent flux (CO2-eq flux) based on the 100-year scale global warming potentials, we found that the CH4 emissions partially offset 19.4%, 14.0%, 36.1%, 64.9% and 60.1% of the net CO2 uptake in seagrass beds, salt marshes, mangroves, non-peatland wetland and peatland, respectively. Over the 20-year scale, CH4 emissions partially offset 57.3%, 41.4%, 107.0%, 192.0% and 177.3% of the net CO2 uptake, respectively. Some mangroves, peatlands, and non-peatland wetlands acted as net CO2 equivalent source. Over the 100-year scale, the net greenhouse gas balance of each wetland ecosystem was negative value, which indicated that even accounting CH4 emission, wetland ecosystem was still an atmospheric carbon sink. Our results indicated that clarifying the main regulation mechanism of CH4 emission from wetland ecosystems and proposing reasonable CH4 reduction measures are crucial to maintain the carbon sink function in wetland ecosystems, and to mitigate the trend of climate warming.

Wetland water quality assessment of eco-engineered landscaping practices: a case study of constructed wetland parks in Hangzhou

Abstract Urban constructed water quality treatment (WQT) wetlands are often applied to be integrated with ecological engineered landscaping (EEL) measures for wastewater treatments. This study aims to identify the interrelationships between the hydrological factors and pollutant removal contributions of typical WQT wetlands at each WQT stage, i.e., subsurface flow wetlands (SSF), vertical flow wetlands (VF), free surface flow wetland (FSF), floating wetland island (FWI), aeration ponds (APs), and ornamental ponds (OPs) in four typical wetland parks in Hangzhou, China. Water quality indices (WQIs) of wetland parks have been monitored. Interactive comparisons and correlations between hydrological indicators and WQIs (i.e., pH, DO, NH3-N, CODCr, and TP) are developed, while the removal contribution of each WQI was explained. It is found that each stage had heterogeneous effects on wastewater treatment due to various geo-ecological factors, including hydraulic conditions, plant type, and microbial microenvironment, whereas the temperature of waterbodies affected WQT performances at full sites in all seasons. Three corresponding EEL guiding principles were derived, i.e., optimising the EEL measures, adapting planting methods, and incorporating multifunctional design and adaptive management. The findings will be helpful for improving the efficacy of WQT stages as one of the potential ecosystem services provided by wetland parks.

Contrasting patterns of biodiversity across wetland habitats using single‐time‐point environmental DNA surveys

Abstract Wildlife surveys are central to the conservation and restoration management of wetland habitats; however, the often laborious and costly nature of traditional survey methods can constrain the spatial and temporal extent and replication of survey efforts. Environmental DNA (eDNA) technologies now provide the opportunity to reduce some of these limitations, but applications in temporary and permanent freshwater marshes and meadows (herein referred to as ‘wetland habitats’) remain limited. This study investigates the performance of single‐time‐point eDNA surveys for characterizing fish, amphibian and bird assemblages of wetland habitats in south‐eastern Australia. Comparisons were made between coastal and inland wetlands, between wetlands of varying condition and between eDNA and traditional surveys. Findings show that eDNA surveys can provide comprehensive descriptions of faunal assemblages associated with wetland habitats, with resolution similar to that provided by traditional survey approaches. Analyses also revealed significant differences in patterns of biodiversity between wetland types (coastal vs. inland) but not between wetland habitats varying in condition. Overall, this study confirms that eDNA surveys provide a reliable option for undertaking wetland biodiversity assessments aimed at improving biodiversity data coverage for otherwise data deficient wetlands.

Classification of Potential Wetlands using the Random Forest in Google Earth Engine in Geomorphological Units - Rio Grande do Sul, Brazil

Wetlands are important and valuable ecosystems in the landscape of the extreme south of Brazil. However, they are among the ecosystems threatened by human pressures and climate change. Monitoring and managing these environments is a challenge due to their high spatial and temporal dynamics. The use of remote sensing techniques, supervised classification, and machine learning algorithms offers a promising opportunity to map and monitor wetlands. The objective of this work is to develop a method to map Potential Wetlands (PW) from the integration of images from different satellites, sensor systems, spectral, topographic, climatological and hydrological features, made available on the Google Earth Engine (GEE) cloud user platform. Supervised classification was performed on the geomorphological units Coastal Plain (CP) and Central Depression (CD) considering two classes: Potential Wetlands, which encompasses all types of wetlands, and No Wetlands, for the remaining land use and occupation classes. The supervised pixel-by-pixel classification, individual for each geomorphological unit, allowed more accurate results consistent with previous, consolidated mapping. The PW were mapped with a global accuracy higher than 88% and consumer and producer accuracy higher than 81% in the Coastal Plain and Central Depression. These results allow us to affirm that the proposed methodology enabled the identification of 22% to 24% increase in potential wetlands in geomorphological regions, from spectral signatures, and pixel-by-pixel supervised classification of PW using different image collections and sensor systems.

Research Progress on Removing Antibiotic Resistance Genes in Constructed Wetlands

Recently, the issue of environmental pollution and emerging antibiotic resistance genes (ARGs) has gradually gained attention. Antibiotic resistant bacteria (ARB) can be effectively reduced via the conventional water treatment processes. Although the ARB are completely inactivated during the disinfection process, the free ARGs can be incorporated into other microorganisms through transformation or transduction, allowing the ARGs to spread and propagate. Therefore, ARGs in wastewater must be handled by a specific process. It has been demonstrated in several studies that treatment using constructed wetland is an effective, economical, and environmentally friendly method of removing antibiotics and resistance genes from wastewater. Here, the research progress on the removal effect of constructed wetland water treatment systems on ARGs at home and abroad was reviewed. The results revealed that the removal efficiency of ARGs in subsurface flow constructed wetland was higher than that in surface flow-constructed wetlands. The composite-constructed wetland had significantly improved removal efficiency of ARGs compared to that in the ordinarily constructed wetland; however, the parameter setting of the composite process still requires further research. Several studies have reported that the removal efficiency of ARGs using constructed wetlands varies depending on the type of constructed wetland enhancement, plant, temperature, pH, and other factors. The results of the current study revealed that cross-mixing was the best way to combine plants, whereas the selection of plant species has not yet shown a clear dominant species. Temperature and pH affected the removal of ARGs by altering the microbial community in constructed wetlands. Although longer hydraulic residence time could increase the removal efficiency of ARGs, it also increased the enrichment risk of ARGs. The selection of constructed wetland substrate type should focus on fillers with a high specific surface area; the flow direction of the up-flow type was generally more efficient than the down-flow type in removing ARGs. In conclusion, the various factors (such as, the constructed wetland type, substrate type, hydraulic retention time, ambient temperature, and plant species) need to be integrated into the design of the constructed wetland system parameters to achieve the most effective treatment effect. The application of constructed wetlands in removing ARGs from the environment has broad prospects but also faces challenges.

Divergent response of blue carbon components to wetland types and hydrological effects in typical estuarine wetlands of Jiaozhou Bay, China

As ecosystems subject to periodic tides, estuarine wetlands have a significant capacity to sequester carbon over time. Understanding the distribution patterns of soil carbon components and identifying the key factors influencing these patterns are key to gaining insight into the function of “blue carbon” in coastal wetlands. To clarify the response of soil carbon components to wetland types and hydrological effects in estuarine wetlands, the typical estuarine wetlands in Jiaozhou Bay, China were selected as the study area, and the soil organic carbon (SOC), dissolved organic carbon (DOC), microbial biomass carbon (MBC), soil inorganic carbon (SIC) and dissolved inorganic carbon (DIC) under different wetland types and hydrological effects were investigated. The results showed that the SOC, SIC, and MBC contents were significantly influenced by the wetland types. The SOC and MBC contents were as follows: mudflat (GT) > Phragmites australis wetland (PA) > Suaeda salsa wetland (SS). The overall content of SIC was highest in PA, followed by GT and SS. Hydrological effects had significant influence on the soil MBC, DOC and DIC contents. With the increase hydrological effects, the soil MBC content decreased by 38.89%–72.22%, while the DOC and DIC contents increased by 15.13%–19.89% and 13.41%–86.70%, respectively. The results of the correlation analysis and structural equation model indicated that wetland types and hydrological effects directly or indirectly (through changes in soil pH, bulk density, water content, and salinity) drove the changes in soil carbon contents in estuarine wetlands. Altogether, our findings implied that the alterations of wetland types and hydrological effects will affect the blue carbon function of estuarine wetlands. In the future, for accurate assessment of a blue carbon budget for estuarine wetlands, the differences in wetland types and hydrological effects of different areas should be considered.

Trait-based approach of aquatic insects to track recovery of wetland ecosystems in Northeast China

Wetland restoration has received increasing attention to compensate for the continuing loss of natural wetlands and revive biodiversity and associated ecological functions. The development of methods for assessing the effectiveness of restoration measures is important for wetland management. However, most studies assessing restoration success in freshwater ecosystems are based on taxonomic biodiversity; trait-based approaches remain limited, particularly in wetland ecosystems. Here, we assess the effectiveness of wetland restoration in the Sanjiang Plain, Northeast China, based on the trait composition and functional diversity of aquatic insect assemblages. We found that functional richness in restored wetlands was significantly higher than impacted wetlands but lower than natural wetlands. Compared to natural wetlands, functional beta diversity of impacted wetlands was higher. In terms of dissimilarity between wetland types, trait composition of aquatic insects in restored wetlands showed a higher similarity to natural wetlands compared to those in impacted wetlands. Trait compositions of aquatic insect assemblages in restored wetlands were more similar to impacted wetlands in May but showed a greater similarity to natural wetlands in October. Our study shows that restoration measures have facilitated the partial recovery of functional diversity and trait composition of aquatic insect assemblages in the Sanjiang Plain wetlands. Our study confirms the potential of using trait-based approach to assess the effectiveness of wetland restoration. Given that assessments based on taxonomic and trait data can provide complementary information, we recommend incorporating both taxonomic and functional metrics and considering seasonal dynamics of wetlands in post-restoration monitoring and assessment in wetlands.