Search Results (202)

The present study examined fish biodiversity, livelihood dependence, cultural importance, and genetic connectivity in two ecologically linked habitats of the Sylhet region, Bangladesh: Hakaluki Haor and the Surma River. Surveys documented 60 fish species with distinct assemblage patterns between sites. Hakaluki Haor was dominated by floodplain spawners and small indigenous species that contribute to year-round subsistence harvests, whereas the Surma River supported a greater proportion of migratory and pelagic species, most notably Tenualosa ilisha. These ecological contrasts reflected differences in hydrology, habitat diversity, and fishing intensity. Household surveys confirmed the central role of fisheries in sustaining income and food security, while cultural practices surrounding hilsa consumption reinforced local stewardship norms. Mitochondrial cytochrome b sequence analysis of T. ilisha revealed low genetic differentiation between sites, indicating a single, well-connected stock maintained by seasonal flooding and the absence of major migration barriers. This convergence of ecological and genetic evidence supports treating the two sites as an integrated management unit. Effective conservation will require protecting hydrological connectivity, safeguarding dry season refugia, coordinating seasonal fishing restrictions across habitats, and incorporating cultural values into policy frameworks. The findings strengthen the scientific basis for national and regional conservation strategies and demonstrate the value of combining biological, socio-economic, and cultural dimensions in managing connected wetland–river systems. This approach can serve as a transferable model for other tropical floodplain–river complexes facing similar ecological and livelihood challenges. Full article

Riverine systems in tropical deltaic environments are increasingly exposed to hydrological variability driven by climate change, sea level rise, and extreme precipitation. In Nigeria’s Niger Delta, recurrent flooding and environmental degradation are intensifying pressures on freshwater ecosystems and dependent communities. This study examines hydrological stressors in riverine settlements of Bayelsa State and explores associated socio-ecological responses. Using an exploratory qualitative design, data were collected from 51 women residing in highly vulnerable riverine communities through 24 in-depth interviews and three focus group discussions. Thematic analysis identified prolonged flooding, riverbank erosion, salinity intrusion, water quality deterioration, and oil pollution, as key drivers of declining fisheries, reduced agricultural productivity, and household water insecurity. These stressors have prompted relocation, livelihood diversification, and reliance on indigenous adaptation practices. The study recommends: (1) installation of community-based flood early warning systems; (2) routine monitoring of surface water quality and salinity; (3) enforcement of oil spill remediation and pollution control measures; (4) rehabilitation of wetlands and natural drainage channels; and (5) targeted support for climate-resilient livelihoods such as aquaculture and elevated farming systems. These measures are critical for sustaining freshwater ecosystems and strengthening resilience in vulnerable deltaic communities. Full article

Framed within data-centric artificial intelligence, this study integrates statistics, geotechnologies and AI to improve water quality prediction. The primary objective was to identify the minimum sample size required to train robust and accurate machine learning models. Based on 30 sampling points in a tropical wetland in northern Colombia, ammonium concentration was selected as the target variable, and total dissolved solids, suspended solids, phosphate, dissolved oxygen, nitrate and chemical oxygen demand were chosen as predictors. Because 30 observations are insufficient to train robust models, data augmentation was performed using ordinary kriging (OK) and empirical Bayesian kriging (EBK). From the kriging-interpolated surfaces, 1000 synthetic points (randomly and spatially distributed while preserving the estimated spatial structure) were sampled; from this expanded dataset, subsamples of varying sizes were drawn to train six algorithms: multiple linear regression (MLR), random forest (RF), k-nearest neighbours (k-NN), gradient boosting machines (GBM), multilayer perceptron (MLP) and radial basis function neural network (RBF-NN). The RF, k-NN, MLP, RBF-NN and GBM models trained on the interpolated data exhibited excellent performance: in the testing phase, they achieved adjusted coefficients of determination > 0.95 and symmetric mean absolute percentage errors (SMAPEs) < 10%, and the resulting predictive surfaces showed comparable performance under external validation. According to the criteria of stability, goodness of fit, and external validation, the optimal minimum sample size for most algorithms was 104 observations. These results represent a significant advance in mitigating data scarcity in water quality modelling. The identification of effective data augmentation methods and the determination of appropriate sample sizes, as demonstrated here, support the robust application of AI techniques in water quality prediction. The proposed strategy is transferable to other quantitative, spatially continuous environmental variables and thus contributes to the development of the emerging subdiscipline of geospatial artificial intelligence (GeoAI). Full article

Methane (CH₄) is a potent greenhouse gas, and accurately estimating its global budget is essential for climate change mitigation. This review provides a comparative synthesis of top-down, bottom-up, and integrated approaches for quantifying methane emissions and sinks, with a particular focus on the role of remote sensing. Top-down methods, leveraging satellite observations from instruments like GOSAT and TROPOMI within atmospheric inversion frameworks (Bayesian, 4D-Var), provide observationally constrained, spatially integrated fluxes, reducing global budget uncertainty to ±5–10%. However, they face challenges in source attribution and rely heavily on transport model accuracy. Conversely, bottom-up approaches, including process-based models (e.g., CLM, DNDC) and emission inventories (e.g., EDGAR), offer detailed, sector-specific insights but are prone to underestimating emissions from super-emitters and diffuse sources like wetlands, with uncertainties often exceeding ±20–40% for individual sectors. Key persistent discrepancies between the two approaches are largest for natural sources (e.g., a 20–40 Tg yr⁻¹ gap for tropical wetlands). Integrated approaches, which synergize top-down atmospheric constraints with bottom-up inventory data, are emerging as the most robust methodology, effectively narrowing the global budget gap and improving confidence. Recent advancements in satellite missions (e.g., MethaneSAT), machine learning algorithms for plume detection, and high-resolution inversion models are transforming monitoring capabilities. However, challenges remain in harmonizing datasets, representing complex microbial processes in models, and expanding observational coverage in data-scarce tropical regions. This review concludes by outlining a future path centered on hybrid inversion frameworks, AI-driven source attribution, and cross-disciplinary collaboration to deliver the actionable methane budgets needed for effective climate policy. Full article

The once-extensive coastal forested wetlands (CFWs) of the Mississippi River Delta (MRD) are declining under the combined pressures of pervasive hydrologic change, unregulated harvesting, relative water level rise (due to the combination of geological subsidence and sea-level rise—SLR), and climate change. We synthesize here over 50 years of research conducted in the MRD to examine the history of the CFWs and their management, their ecosystem functions and services, and the nature, extent, and severity of ongoing changes. Seedling recruitment failure and increasing salinity levels are the most immediate threats to forest persistence, necessitating management that restores hydrologic function and sediment and nutrient supply to allow seedling survival and minimizes saltwater intrusion. Collectively, the evidence indicates that managed inflows can bolster accretion and sustain forest function, and long-term resilience requires hydrologic restoration at landscape scales coupled with site-level actions that secure recruitment and address local degradation trajectories. These include freshwater and sediment introduction, protection from herbivory, and, in some cases, planting. Our research findings have important implications for worldwide CFWs, and tidal freshwater ecosystems in general, which occur mainly in tropical deltas. Full article

Wetlands are the largest natural source of atmospheric methane (CH₄), and their emissions are projected to increase during the 21st century in response to climate change. However, how extreme climate events such as extreme heat, extreme precipitation, and their compound occurrences modulate future wetland methane emissions, remains poorly constrained. Here, we quantify the impacts of extreme temperature, precipitation, and compound hot–wet events on global wetland methane emissions (eCH₄) using simulations from the dynamic global vegetation model LPJ-wsl driven by four CMIP5 climate models under a high-emission scenario (RCP8.5) for the period 2006–2099. Our results show that extreme heat events intensify and become substantially more frequent, with global occurrence increasing by more than 303% by the end of the century. Correspondingly, their contribution to global wetland methane emissions rises from ~26–28% in 2006 to ~73–83% by 2099, making extreme heat the dominant driver of future eCH₄ increases. Extreme precipitation events exhibit relatively modest changes in frequency and mixed intensity. In contrast, compound hot–wet events, despite their low baseline frequency, increase by more than 600% and are associated with disproportionately strong methane responses, driven by the combined effects of elevated temperatures and enhanced anaerobic conditions. Across all event types, tropical wetlands account for 75–90% of global methane emissions, while contributions from mid-latitudes increase modestly and high-latitude contributions remain comparatively small. These findings highlight the emerging importance of climate extremes—particularly extreme heat and compound hot–wet events—in shaping future wetland methane emissions. Explicit consideration of extreme-event dynamics is therefore essential for improving projections of methane–climate feedback under continued global warming. Full article

Seasonal tropical wetlands such as the Brazilian Pantanal are increasingly threatened by climate variability and extreme hydrological events, creating a need for robust monitoring tools that capture flood dynamics at high spatial and temporal resolution. This study used Sentinel-1 Synthetic Aperture Radar (SAR) imagery to map and monitor flooding in the northern Pantanal, a Ramsar site renowned for its wildlife, between 2017 and 2020. Ground Range Detected (GRD) VV-polarized scenes were preprocessed using radiometric terrain normalization and speckle filtering (Lee filter, 5 × 5 window) to improve the separability of water and non-water surfaces. Flooded areas were initially extracted with Otsu’s histogram thresholding and validated using high-resolution optical imagery (PlanetScope and Landsat-8). A supervised Random Forest classifier then refined land-cover discrimination into three classes (open water/flood, open land/vegetation, and others), achieving an overall accuracy of 97.70% on the independent testing dataset (n = 6622), while temporal consistency was supported by Cuiabá River hydrological data. The results revealed strong interannual variability in flood extent, with inundation covering 34.7% of the reserve in March 2017 compared with 0.75% in March 2020 and reaching a peak of 79.9% in April 2017. Overall, Sentinel-1 SAR effectively delineated open water and flood-affected surfaces under persistent cloud cover, demonstrating its value for complementing existing products such as MapBiomas, strengthening wetland management, and supporting scalable flood monitoring in other tropical flood-prone Ramsar sites. Full article

Water buffaloes in northern Australia occupy tropical wetlands where conditions favour the proliferation of arthropod vectors and the transmission of vector-borne livestock diseases. However, their role in maintaining economically important arboviruses such as Akabane virus (AKAV), bluetongue virus (BTV), and bovine ephemeral fever virus (BEFV) remains poorly understood. These three viruses cause significant production losses in cattle and pose ongoing surveillance challenges in remote areas. To assess exposure to these viruses, a convenience sample of feral water buffaloes from the Northern Territory, Australia, was collected. Commercial enzyme-linked immunosorbent assays (ELISAs) were used to detect antibodies against AKAV, BTV, and BEFV in 119 samples stored as dried blood on filter paper. Seroprevalence was 18.5% for AKAV, 66.4% for BTV, and 15.1% for BEFV. These results are consistent with previous serological studies in northern Australian cattle, confirming the circulation of these pathogens in the region. Our findings demonstrate that water buffaloes are exposed to these economically important arboviruses and may contribute to their maintenance, highlighting the need to consider feral buffalo populations in regional arbovirus surveillance strategies and livestock disease management. Full article

This study investigates the cooling effects of coastal wetland systems in Hue City, Vietnam. The analysis focuses on their riparian buffer zones, defined here as areas within 600 m of the wetland boundary. Landsat 8 imagery was used to derive land surface temperature (LST) from 1 March to 31 July 2025—a recent period marked by multiple heatwaves across the region. To assess the cooling performance of wetlands, data samples were collected within the buffer zones. A Light Gradient Boosting Machine was trained to characterize the relationship between cooling intensity and a set of influencing factors (e.g., distance to wetland boundary, land use/land cover, built-up density, and green space density). The model explains approximately 91% of the variation in cooling intensity around wetlands. Notably, a machine-learning-based simulation framework was proposed to attain insights into the cooling characteristics of the riparian zone. The result indicates a mean cooling effect of about 2 °C and an effective cooling distance of 210 m from the wetland boundary. Partial dependence analysis further reveals that increasing built-up density substantially weakens cooling performance and implies that, for the conditions observed in Hue City, maintaining built-up density near wetlands below roughly 45% is favorable for sustaining effective cooling of the blue space, as indicated by the model-based partial dependence analysis. Overall, the research findings provide a data-driven basis for informing urban planning and wetland management in Hue City to mitigate heat stress. Full article

Rapid urbanisation in tropical megacities intensifies urban heat islands, especially during summer. Peri-urban wetlands help combat surface thermal stress through evapotranspiration, thermal inertia, and hydrological connectivity. However, their cooling effects are often oversimplified. This study assesses the complex cooling role of peri-urban wetlands, using a geospatial framework with Landsat imagery. We analyse land surface temperature (LST) variability and cooling patterns across the East Kolkata Wetlands (EKW). Results show a sharp thermal gradient, with waterbodies as the coolest surfaces (mean 25.4 °C) and dumping grounds as intense hotspots (mean 35.75 °C). Built-up areas adjacent to water are significantly cooler than urban cores. Cooling exhibits non-linear distance-decay and directional asymmetry, extending several kilometres but attenuated by dense western urban development. Internal thermal disruptions from dumping grounds create localised heat plumes. The findings demonstrate that wetland cooling is governed by hydrological connectivity and landscape permeability. Thus, conserving waterbody networks and mitigating thermally disruptive land uses are therefore critical. This positions peri-urban wetlands as dynamic climate-regulating infrastructure, offering a nature-based solution for urban heat adaptation that aligns with the sustainable development goals (SDGs). Full article

Mangrove ecosystems are recognized as highly efficient blue-carbon reservoirs, yet their monitoring requires scalable, transparent methods suitable for climate-finance and greenhouse-gas accounting applications. This study quantifies interannual carbon-stock dynamics and derives a carbon-market valuation indicator for Ecuador’s Churute Mangrove Ecological Reserve (2015–2021) using publicly available remote-sensing land-cover products. Annual activity data were derived from Copernicus Global Land Service LC100 (100 m, 2015–2019) and ESA WorldCover (10 m, 2020–2021), harmonized to a common reporting scheme, and combined with IPCC Tier 1 default coefficients for biomass and soil organic carbon in tropical wetlands. Total carbon stocks averaged 1.67 million t C across the period, remaining stable within the internally consistent LC100 phase (2015–2019), with trend statistics treated as descriptive given the short annual series, while a pronounced drop in 2020 primarily reflected methodological discontinuities between products rather than ecological change. Converted to CO₂e equivalents (mean 6.1 million t CO₂e), illustrative market values fluctuated between USD 18 and 123 million annually, driven predominantly by carbon-price variability. This remote-sensing-based, MRV-aligned approach provides a conservative baseline for protected-area blue-carbon accounting, highlighting the need for homogeneous high-resolution time series to distinguish real dynamics from classification artifacts in future assessments. Full article

The degradation of wetlands and forests is still a threat to the supply and recovery of ecosystem services in the tropics. Studies comparing restoration measures and ecosystem service recoveries are fragmented. This study investigated the spatial extent and drivers of wetland/forest degradation, and assessed the effects of restoration measures on the recovery of ecosystem services and resilient livelihoods. A cross-sectional household survey was conducted targeting households adjacent to restored and unrestored wetland/forest ecosystems. The data was analyzed using a Binary Logistic regression to characterize earlier and recovered ecosystem services between forest and wetland ecosystems. High spatial-resolution optical satellite imagery from the Airbus constellation was obtained and analyzed to examine wetland and forest degradation. Our findings revealed that the spatial extent of degraded land under wetlands and forests decreased between 2023 and 2025. Ecosystem service degradation was primarily driven by chronic poverty, excessive water abstraction, population growth, burning practices, overharvesting of resources, overgrazing, cultivation, infrastructure development, and the invasion of alien species (p < 0.05). The counteractive ecosystem restoration activities undertaken included mobilization and sensitization of communities on wetland restoration, wetland demarcation, revegetation, establishment of flood control measures, and provision of alternative livelihoods (p ≤ 0.05). The multiple direct and indirect ecosystem service recoveries reported were provisioning services (increases in pasture, enhanced livestock production, increased soil productivity, health-related benefits from crops and livestock products) and regulating services (improved water quality/quantity). The ecosystem service recoveries were more significant in the restored wetlands than the forests. The indicators of enhanced ecosystem-based resilient livelihoods included increased household incomes, higher livestock yields, increased crop productivity, improved health from crop/livestock products, improved water quality/quantity, and enhanced scenic beauty and tourism (p < 0.05). The restoration activities in degraded wetland systems had more potential to facilitate full recovery of the wetland ecosystem compared to the absence of interventions. This evidence highlights the need to restore high-ecological-sensitive ecosystems to sustain the delivery of ecosystem services for community and environmental resilience. Full article

African grasses deliberately introduced for cattle forage have become among the most destructive invaders of tropical wetlands globally, yet invasion mechanisms and management strategies remain poorly understood. We conducted field experiments examining competition dynamics between the invasive African grass Echinochloa pyramidalis and native wetland species in La Mancha, Mexico—a Ramsar site of international importance. Experiment 1 tested invasion potential within native Sagittaria lancifolia zones using four treatments: control, herbicide removal, E. pyramidalis transplant, and combined removal + transplant. Repeated-measures ANOVA showed significant treatment and time effects on invasion success, with vegetation removal facilitating invasion (relative importance value increasing from 0 to 149.4 ± 26.6 after 18 months) while transplants alone failed to establish (RIV < 7.0). Sagittaria maintained 35–48% biomass across treatments, demonstrating coexistence capacity. Experiment 2 examined natural invasion of the vegetation ecotone over 49 months. Mixed-effects models revealed that E. pyramidalis increased dominance in its zone (β = 9.98, z = 4.77, p < 0.001) but showed minimal expansion into the adjacent Sagittaria habitat, indicating propagule limitation rather than competitive exclusion as the invasion constraint. Sagittaria removal within E. pyramidalis zones significantly reduced invasion temporal increase (β = −6.44, z = −2.18, p = 0.030), suggesting biotic resistance. Results demonstrate that E. pyramidalis possesses invasion potential but requires disturbance to overcome establishment barriers. These findings support prevention-based management prioritizing disturbance limitation in intact wetlands and demonstrate that hydrological management maintaining permanent flooding (>30 cm depth) can effectively control established invasions by exploiting C4 photosynthetic limitations. Conservation implications for Mexican coastal wetlands—which lack legal protection equivalent to mangroves despite comparable ecosystem services—are discussed. These findings inform evidence-based management of African grass invasions in tropical wetlands worldwide. Full article

Dynamic Habitat Indices (DHIs) are crucial for understanding species richness patterns and provide a powerful tool for large-scale biodiversity conservation research. DHIs summarize three key aspects of vegetation productivity: (a) cumulative annual productivity, (b) minimum productivity, and (c) intra-annual variability. While DHIs have demonstrated potential for predicting richness patterns globally and in tropical regions, their predictive ability across climate zones and using multiple sources of species richness data remains untested. We assess the feasibility of using DHIs to predict the richness of resident birds in China and explore approaches to improve model performance. We used (a) IUCN range maps of terrestrial resident birds in China, and (b) species distribution models (SDMs) to delineate bird richness patterns, classifying species into six habitat guilds: forest, shrubland, grassland, cropland, wetland, and all resident birds. We linked DHIs to richness in each guild and quantified their predictive power using three modeling approaches: linear (GLM) and non-linear models (GAM, Random Forest). We also recorded the relative importance of each DHI component in the models. Our results show that DHIs best predicted cropland bird richness (SDM-based richness, adjusted R² = 0.73), while for IUCN-based guilds, adjusted R² ranged from 0.68 to 0.71. The Random Forest model achieved the highest performance and interpretability. Among DHI components, cumulative DHI consistently played the most dominant role in predicting richness from both SDM and IUCN sources. DHIs effectively capture the link between energy availability and resident bird richness in China, demonstrating considerable potential for biodiversity assessment and conservation planning. Full article

The meat industry generates wastewater with high organic matter loads, posing a significant environmental risk if not properly treated. The present study evaluated the performance of a horizontal subsurface flow constructed wetland (HSSF-CW) treating slaughterhouse effluents characterized by high-strength influent concentrations of 3570.51 ± 153.82 mg/L COD, 2114.33 ± 104.58 mg/L BOD₅, and 1173.77 ± 96.95 mg/L TOC. Furthermore, Response Surface Methodology (RSM) was employed to model and optimize the operational parameters. The independent variables considered were hydraulic retention time (HRT: 3, 5, and 10 days) and vegetation type (Heliconia latispatha, Typha latifolia, and polyculture). The results demonstrated a statistically significant improvement in treatment efficiency, achieving maximum removal efficiencies of 86.5% for COD, 89.4% for BOD₅, and 91.5% for TOC. The statistical models exhibited high accuracy (R² ≥ 0.996, p < 0.001). Adjusted response surface equations identified the polyculture with a 5-day HRT as the most favorable operational scenario. These findings confirm that properly designed and operated constructed wetlands represent a viable and sustainable alternative for treating high-load agro-industrial effluents, contributing to the protection of receiving water bodies. Future research should focus on full-scale studies and the inclusion of critical parameters such as nutrients and pathogens for a more comprehensive system characterization. Full article