Estimating runoff in ungauged catchments remains a major challenge in hydrology, particularly in remote Andean headwaters where limited accessibility and budgetary constraints hinder the long-term operation of monitoring networks. This study integrates satellite-derived rainfall data, hydrological modeling, and benthic macroinvertebrate diversity analysis to explore how short-term antecedent flow conditions relate to temporal variation in community structure. The research was conducted in a pristine 0.26 km2 micro-catchment of the upper Collay basin (southern Ecuador). Daily simulated discharge was used to compute antecedent flow descriptors representing short-term variability and cumulative changes in stream conditions, which were related to taxonomic (i.e., H = Shannon diversity, E = Pielou evenness, and D = Simpson dominance) and functional indices (i.e., Rao = Rao's quadratic entropy, FAD1 = Functional Attribute Diversity, and wFDc = weighted functional dendrogram-based diversity) using Generalized Additive Models. Results showed progressively higher hydrology-biology associations with increasing antecedent flow integration length, suggesting that biological variability responds more strongly to cumulative than to instantaneous flow conditions. Among hydrological descriptors, the cumulative magnitude of negative flow changes was consistently associated with taxonomic diversity. H and E showed more coherent and robust patterns than functional metrics, indicating a faster response of community composition to short-term hydrological variability, whereas functional diversity integrates slower ecological processes. While based on modeled discharge under severe hydrometeorological data limitations, this study provides a practical ecohydrological starting point for identifying short-term hydrological memory signals potentially relevant to aquatic biodiversity in ungauged headwater systems.

Water resource competition has disrupted sustainable development in the Aral Sea Basin, necessitating integrated strategies for the water-food-energy-environment nexus to address challenges from ongoing climate change, ecological restoration, growing food demand, and potential hydropower projects impacting water stability. This study developed a multi-objective optimization model to address these issues. Results showed relatively equitable water allocation, with Gini coefficients consistently below 0.29 across all scenarios. Agricultural water use ranged from 71.71 to 80.53 × 109 m3, while seasonal pumped hydropower storage reservoirs increased upstream controllable water to 42.91-58.47 × 109 m3 (35%-44%). Hydropower remained stable owing to reservoir coordination. However, to ensure ecological flows (35.38-37.78 × 109 m3), crop areas should be reduced by 14.37%-21.05% under SSP2-4.5 and 16.16%-23.93% under SSP5-8.5. A trade-off emerged between benefits and water allocation equity, particularly in high-emission, low-inflow scenarios, alongside a positive correlation between benefits and greenhouse gas emissions. These findings emphasize the critical need for integrated management of the Aral Sea Basin's interconnected resource systems.

This study investigates the impact of two types of digital training programs (more personalized, MOOC++, and less personalized, MOOC+) on human performance, engagement level, and real-world application of technical competencies by operators in drinking water treatment plants in Ecuador. A completely randomized experimental design was conducted with two subgroups of 29 active operators assigned to each course. The results show that both training programs produced significant improvements within each group, but when comparing performance across all modules between the groups, the performance of MOOC++ is significantly higher than that of MOOC+. In addition, learner engagement was analyzed and results indicate that engagement in MOOC++ is significantly higher than in MOOC+. The findings highlight the importance of personalization in digital workforce training, particularly for practice-oriented operational tasks, and suggest that performance improvement is associated with the transfer of knowledge to real operational contexts. Furthermore, qualitative evidence indicates positive changes in operators’ technical performance and participation in daily work activities. In conclusion, the results demonstrate that personalized digital training can be an effective strategy to strengthen the technical competence of water treatment operators and support the safe and reliable operation of drinking water services, contributing to workforce development in essential public infrastructure systems.

Epigenetic modifications provide underlying mechanisms of organism responses to environmental stressors, yet their role in estuarine species that are exposed to combined natural and anthropogenic pressures remains poorly understood. Here, we quantified global 5-methylcytosine (5-mC) representing epigenetic alteration in the red mangrove crab Ucides occidentalis across 14 sites with varying salinity levels and contamination profiles in the Guayas Estuary (Ecuador). To obtain comprehensive view of crabs' environmental stressors, we measured concentrations of 10 metals and three common pesticides in both sediments and crab tissues using multi-compartment analysis. The metal panel (Zn, Cu, Fe, Cr, As, Cd, Ni, Al, Pb, Hg) and pesticides (diuron, azoxystrobin, cadusafos) were selected to reflect the documented multi-source contamination profile of the estuary, urban-industrial metal inputs and agricultural pesticide runoff, and their known or suspected relevance to epigenetic stress responses. Global DNA methylation strongly increased with salinity, with crabs from the high-salinity Salado sub-estuary showing up to 10-fold higher methylation levels than those from the low-salinity Churute sub-estuary. Among contaminants, cadmium and arsenic were consistently and positively associated with methylation across environmental compartments, whereas other metals and pesticides showed compartment-specific relationships. Random forest models identified salinity and tissue metals (Cu, As, Cd) as primary methylation predictors, while their sediment concentrations were of minor importance. These findings demonstrate that global 5-mC reflected the combined effects of natural salinity level and anthropogenic-driven bioaccumulated contaminants. The strong associations between DNA methylation and environmental conditions imply its potential as a biomarker in multi-stressor assessment.

Waterbirds function as sensitive indicators of ecological change in freshwater ecosystems. Conventional monitoring is labour-intensive, spatially limited, prone to observer bias and disturbance. Unmanned Aerial Vehicle (UAV) imagery combined with deep learning (DL) offers a promising alternative for efficient and scalable monitoring.This study evaluates a fully automated UAV-based workflow to detect, classify, and map waterbirds along a 12 km canal in Belgium. A two-stage pipeline combining YOLO-based detection and convolutional neural network (CNN) classification was developed and evaluated across varying flight altitudes and ground sampling distances (GSD). Model performance was further assessed using an independent seasonal dataset and analysed in relation to object size and habitat complexity. The detection model achieved a high precision of 0.91 and a recall of 0.81, while the classifier reached a weighted F1-score of 0.90. Detection performance was strongly dependent on ground sampling distance (GSD), with optimal results at ≤6.83 mm px−1 (≤25 m altitude) and marked declines in detection performance at higher GSD values. Additional analyses showed that smaller birds were more likely to be missed, while high-entropy environments such as reed beds may further reduce detection reliability. Comparisons between automated and manual counts revealed discrepancies, influenced by environmental conditions and survey timing. Georeferenced detections projected onto orthomosaics enabled spatially explicit mapping of waterbird distribution.Overall, UAV-based deep learning provides a promising and scalable approach for waterbird monitoring, while highlighting the importance of spatial resolution and habitat complexity in determining detection performance.

Submerged aquatic macrophytes play a key role in stream ecosystems, but their recovery in historically degraded Flemish streams is often limited. This study investigates whether sediment contamination constrains natural macrophyte germination and early seedling establishment. To address this knowledge gap, we combined a controlled mesocosm experiment with an analysis of long-term monitoring data from Flemish streams. The mesocosms showed that higher levels of sediment contamination reduced seedling emergence, indicating that sediment quality can directly inhibit germination and early establishment. In addition, historical monitoring data revealed only a weak association between sediment quality and macrophyte occurrence, pointing to the importance of interacting drivers such as hydrology, light availability, and habitat structure. Together, these findings highlight sediment contamination as a context-dependent but relevant barrier to macrophyte recruitment, underscoring the need to integrate sediment quality into broader restoration planning for streams in Flanders and abroad.

Riverine barriers are threatening freshwater fish migration, with major impacts on fish populations. Effective management requires understanding of fish movement and behaviour as they approach a barrier and fish pass, which can inform optimal mitigation options and barrier management. Here, the movements of upstream migrating barbel Barbus barbus and grayling Thymallus thymallus near a barrier were analysed and results used to develop predictive models. Fish were tracked via 2D acoustic telemetry. Hidden Markov models were used to distinguish behavioural states and step selection functions were applied to determine habitat selection by the fish in each state. Model results were explored to assess the benefits of including behavioural state and understand state-specific habitat preferences, then cross-validated and used to develop an individual based model to predict fish spatial usage. Little difference existed in habitat selection between states and individual variation was high, limiting general trends that could be described. Overall, barbel preferred deeper or faster water while for grayling, few trends could be described. Under the tested flow conditions, high spatial usage was predicted in the area directly downstream of the barrier. In addition, barbel usage was high in the area by and downstream of the fish pass entrance but not for grayling, which may indicate a need to improve pass attractiveness for grayling. The predictive model produced directed upstream movements of fish similar to those expected for upstream migrating freshwater fish, highlighting model potential for fish passage applications in future iterations. The high individual variability in fish behaviour drives the need for individual-based approaches for predicting fish movement.

Functional diversity (FD) calculations using benthic macroinvertebrates are useful for freshwater ecosystem evaluation. However, it is critical to determine the key traits and their categories that shape a community. This study (i) investigated the effect of fluvial habitat quality (characterised by a fluvial habitat index – FHI) on the trends of individual functional macroinvertebrate categories (FMaCs) and the rRao FD index; and (ii) evaluated the information provided by each FMaC for rRao index calculation along the FHI gradient. Macroinvertebrate samples were collected at 12 locations in Ecuador's Paute River Basin over six years. Families of macroinvertebrates were classified into eight traits and 42 FMaCs. A K-means cluster analysis produced three groups of sampling points based on their FHI values. For each FHI cluster, the percentage of each FMaC within its corresponding trait was calculated. The R2 coefficient was computed between the FHI cluster values and the previously obtained FMaC percentages. A second K-means clustering was performed on the R2 dataset, resulting in three groups of R2 values directly associated with FMaCs. We then assessed the sensitivity of the rRao index to the exclusion of specific trait categories by sequentially removing groups of FMaCs, ordered by decreasing R2 importance. This allowed us to evaluate the stability and robustness of functional diversity estimates when less informative traits were removed. Results indicated that certain FMaCs had a greater influence on rRao variation across habitat quality clusters, particularly those related to body form, locomotion, and exoskeleton hardness. In degraded habitats, certain FMaCs contributed little to rRao variation, suggesting limited functional differentiation within the multi-trait functional space and potentially lower monitoring value under such conditions. The most informative traits for rRao index calculation were body form, flexibility, and locomotion. These findings contribute to improved trait-based ecological modelling of macroinvertebrates and offer insights for river managers regarding potential ecohydrological stressors.

The extent of alien taxa impacts on river ecosystem health is unclear, but their frequency continues to rise. We investigated 1) the prevalence of including alien taxa in common bioindicators used in river bioassessment, 2) the effect of alien taxa on the richness and abundance of natives, and 3) whether including alien taxa in bioassessment tools increased their sensitivity to river degradation. In the 17 countries analyzed fish represented the greatest number of alien species (1726), followed by macrophytes (925), macroinvertebrates (556), and diatoms (7). Yet, alien species are only distinguished from natives in some fish and macrophyte indices. In addition, the analyses of 8 databases with fish, macroinvertebrate, or macrophyte data showed that abundance of alien taxa was associated with different stressors and pressures resulting in river degradation, and had a significant effect on native community composition. When alien species were accounted for, there was a strong negative correlation between the values of a fish index with alien richness and abundance while when alien taxa was not or only partially considered the results varied. Thus, we recommend: 1) Include specific metrics for alien species in biological quality indices. 2) Increase the investigation of alien taxa of small organisms (e.g. diatoms, small benthic invertebrates). 3) Eliminate sites with confirmed biological invasions for use as reference sites. 4) Remove alien from calculations of total richness and diversity. 5) Identify to the species level in biomonitoring programs. 6) Avoid legislation and management that protect alien species. 7) Encourage behaviors that prevent alien invasions of aquatic biota.

Tropical estuaries are increasingly subjected to pesticide contamination from agricultural and urban sources, yet the relationship between land use and pesticide contamination within these ecosystems remains insufficiently understood. This study investigated the occurrence and retention of pesticides in surface sediments and red mangrove crab (Ucides occidentalis) tissues across two sub-estuaries in Guayas Estuary (Ecuador) under the influence of various land-use patterns. Twelve pesticide compounds were detected in sediment samples, mainly from agriculture-influenced sites, while crab tissues contained 21 compounds, including 18 not detected in sediments, with more pesticide detections in crabs from urban-influenced sites. These divergent contamination profiles suggest that crabs may be exposed to pesticides through additional pathways beyond sediment contact, such as waterborne exposure and dietary uptake. Notably, the concentrations of diuron, a widely used herbicide, in agricultural sediments (0.43-5.7 μg kg-1), exceeded sediment quality guidelines (SQC EQSsed: 0.39 μg kg-1) but showed limited bioaccumulation. Conversely, azoxystrobin and cadusafos, commonly used fungicide and insecticide, respectively, were accumulated in crab tissues despite their negligible sediment levels. Land use analysis within a 1000 m buffer indicated that cropland dominated in pesticide detections in agriculture-influenced sub-estuary, while urban-influenced sub-estuary showed a balanced pattern of land use proportions across detected categories. These findings highlight the value of incorporating contaminant profiles from biota, alongside land-use context, into pesticide monitoring strategies to better characterize contamination pathways in tropical estuarine ecosystems.

The accurate monitoring of waterbird abundance and their habitat preferences is essential for effective ecological management and conservation planning in aquatic ecosystems. This study explores the efficacy of unmanned aerial vehicle (UAV)-based high-resolution orthomosaics for waterbird monitoring and mapping along the Lieve Canal, Belgium. We systematically classified habitats into residential, industrial, riparian tree, and herbaceous vegetation zones, examining their influence on the spatial distribution of three focal waterbird species: Eurasian coot (Fulica atra), common moorhen (Gallinula chloropus), and wild duck (Anas platyrhynchos). Herbaceous vegetation zones consistently supported the highest waterbird densities, attributed to abundant nesting substrates and minimal human disturbance. UAV-based waterbird counts correlated strongly with ground-based surveys (R2 = 0.668), though species-specific detectability varied significantly due to morphological visibility and ecological behaviors. Detection accuracy was highest for coots, intermediate for ducks, and lowest for moorhens, highlighting the crucial role of image resolution ground sampling distance (GSD) in aerial monitoring. Operational challenges, including image occlusion and habitat complexity, underline the need for tailored survey protocols and advanced sensing techniques. Our findings demonstrate that UAV imagery provides a reliable and scalable method for monitoring waterbird habitats, offering critical insights for biodiversity conservation and sustainable management practices in aquatic landscapes.

In Ecuador, only 4 out of 221 municipalities accredit a quality label for drinking water consumption. One of the causes affecting a low accreditation level is the deficiency of water treatment operators' technical skills, primarily due to limited access to fitting educational opportunities. To tackle this deficiency, we developed two Massive Open Online Courses (MOOCs) on physico-chemical water treatment in Ecuador, one highly personalized (MOOC++) and the other less personalized (MOOC+). The aim of this study is to implement an experimental design to assess the impact of personalization on learning performance and engagement in MOOC versions. We applied multivariate data analysis, multilevel models, and structural equation modeling using R (version 4.5.2). Overall, the results show the benefits of the higher degree of personalization regarding both engagement and performance of the trained professional. The results confirm the positive impact of investing in the personalization of MOOCs. Nevertheless, the findings also ask for further investigation into factors such as digital literacy, prior knowledge, and content type to optimize personalized learning for water operators.

Headwater streams in agricultural areas constitute significant sources of nitrous oxide (N2O) due to nutrient enrichment; however, their emissions are often overlooked in current environmental impact assessments. This scarcity highlights the importance of developing advanced decision tools to evaluate these contributions and create effective mitigation strategies. Our study establishes the first integrated modeling framework that combines a process-based model SWAT+ with a linear mixed model (LMM) to predict N2O emissions from a headwater agricultural river system in Belgium under diverse climate change and fertilization scenarios. In particular, the calibrated and validated SWAT+ model was used to simulate streamflow, nutrient transport, and crop yields under these scenarios, from which, together with biochemical data collected from sampling campaigns, riverine N2O emissions were predicted via LMM. Our results revealed hydrologically driven patterns in riverine N2O emissions, with peak emissions in winter and spring, driven by precipitations enhancing shallow subsurface flows, carrying leached nutrients from fields to the river, and fueling N2O emissions. These phenomena were intensified under climate change scenarios, especially during combined wetter and hotter winters and springs, which elevated headwater N2O emissions by 40%. Moreover, when coupling these conditions with a 20% increase in fertilizer rates, riverine N2O emissions would be boosted by 83%. These findings underscore the importance of integrating land-surface and river processes, to effectively quantify the feedback loop between river nutrient enrichment and climate change under the influence of agricultural practices, and to support comprehensive mitigation strategies under the warming climate.

Protected areas are a principal conservation tool for addressing biodiversity loss. Such protection is especially needed in freshwaters, given their greater biodiversity losses compared to terrestrial and marine ecosystems. However, broad-scale evaluations of protected area effectiveness for freshwater biodiversity are lacking. Here, we provide a continental-scale analysis of the relationship between protected areas and freshwater biodiversity using 1,754 river invertebrate community time series sampled between 1986 and 2022 across ten European countries. Protected areas primarily benefited poor-quality communities (indicative of higher human impacts) that were protected, or that gained protection, across a substantial proportion of their upstream catchment. Protection had little to no influence on moderate- and high-quality communities, although high-quality communities potentially provide less scope for effect. Our results reveal the overall limited effectiveness of current protected areas for freshwater biodiversity, likely because they are typically designed and managed to achieve terrestrial conservation goals. Broadly improving effectiveness for freshwater biodiversity requires catchment-scale management approaches involving larger and more continuous upstream protection, and efforts to address remaining stressors. These approaches would also benefit connected terrestrial and coastal ecosystems, thus generally helping bend the curve of global biodiversity loss.

Freshwater ecosystems face increasing pressures from human activities, leading to degraded water quality and altered habitats for aquatic species. This study investigates the relationship between water quality and waterbird distribution along the Lieve River, Belgium, based on manually conducted waterbird counts and water quality data collected from 48 transects in March 2024. Localized eutrophication was evident, with TN (2.7–5.6 mg L−1), TP (up to 0.46 mg L−1), and chlorophyll-a (median 70 ppb) exceeding environmental thresholds. Prati index analysis revealed that 58.3% of the sampling points along the Lieve River were categorized as “polluted”, reflecting extensive water quality degradation. Eurasian coots (71.4%) and wild ducks (72.4%) were predominantly found in polluted areas, thriving in nutrient-enriched habitats linked to high TP levels. In contrast, common moorhens (80.3%) preferred acceptable quality areas, indicating higher water quality requirements. These findings indicate that phosphate is a key driver of waterbody eutrophication, as evidenced by the TP concentrations measured on-site, which far exceed the thresholds set by environmental standards. Future research should explore advanced monitoring approaches to improve waterbird and water quality assessments, ensuring the conservation of the Lieve River as one of Europe’s oldest artificial canals, and the protection of its waterbird habitats.

Understanding the structural concordance between taxonomic and functional diversity (FD) metrics is essential for improving the ecological interpretation of community patterns in biomonitoring programs. This study evaluated the concordance between taxonomic and FD metrics of benthic macroinvertebrates along a fluvial habitat quality gradient in the Paute River Basin, Ecuador. Macroinvertebrate communities were sampled over six years at twelve sampling points and assessed using four taxonomic metrics: Shannon diversity (H), the Margalef index (DMg), family richness (N), and the Andean Biotic Index (ABI). Functional diversity was evaluated using four metrics: weighted functional dendrogram-based diversity (wFDc), Rao’s quadratic entropy (Rao), functional dispersion (FDis), and functional richness (FRic). The fluvial habitat index (FHI) was used as an environmental reference to evaluate diversity metric responses. K-means clustering was independently applied to each metric, and pairwise concordance was quantified using the Measure of Concordance (MoC) and overlap in sampling points groupings across replicates. Most metrics (except FRic and N) showed clear responsiveness to the FHI gradient, confirming their ecological relevance. Strong structural concordance was observed between H and DMg and the FD metrics Rao, FDis, and wFDc, showing that these metrics captured similar yet complementary aspects of community organization. In contrast, ABI showed marked sensitivity to the FHI gradient but low concordance with functional metrics, suggesting distinct dimensions of biological integrity not encompassed by trait-based metrics. These findings highlight the value of combining taxonomic and functional metrics to detect both broad and subtle ecological changes. Integrating metrics with differing structural properties and environmental sensitivities can enhance the robustness of freshwater biomonitoring frameworks, especially in systems undergoing ecological transition or habitat degradation.

Humans impact terrestrial, marine and freshwater ecosystems, yet many broad-scale studies have found no systematic, negative biodiversity changes (for example, decreasing abundance or taxon richness). Here we show that mixed biodiversity responses may arise because community metrics show variable responses to anthropogenic impacts across broad spatial scales. We first quantified temporal trends in anthropogenic impacts for 1,365 riverine invertebrate communities from 23 European countries, based on similarity to least-impacted reference communities. Reference comparisons provide necessary, but often missing, baselines for evaluating whether communities are negatively impacted or have improved (less or more similar, respectively). We then determined whether changing impacts were consistently reflected in metrics of community abundance, taxon richness, evenness and composition. Invertebrate communities improved, that is, became more similar to reference conditions, from 1992 until the 2010s, after which improvements plateaued. Improvements were generally reflected by higher taxon richness, providing evidence that certain community metrics can broadly indicate anthropogenic impacts. However, richness responses were highly variable among sites, and we found no consistent responses in community abundance, evenness or composition. These findings suggest that, without sufficient data and careful metric selection, many common community metrics cannot reliably reflect anthropogenic impacts, helping explain the prevalence of mixed biodiversity trends.

Inland navigation in Europe is proposed to increase in the coming years, being promoted as a low-carbon form of transport. However, we currently lack knowledge on how this would impact biodiversity at large scales and interact with existing stressors. Here we addressed this knowledge gap by analysing fish and macroinvertebrate community time series across large European rivers comprising 19,592 observations from 4,049 sampling sites spanning the past 32 years. We found ship traffic to be associated with biodiversity declines, that is, loss of fish and macroinvertebrate taxonomic richness, diversity and trait richness. Ship traffic was also associated with increases in taxonomic evenness, which, in concert with richness decreases, was attributed to losses in rare taxa. Ship traffic was especially harmful for benthic taxa and those preferring slow flows. These effects often depended on local land use and riparian degradation. In fish, negative impacts of shipping were highest in urban and agricultural landscapes. Regarding navigation infrastructure, the negative impact of channelization on macroinvertebrates was evident only when riparian degradation was also high. Our results demonstrate the risk of increasing inland navigation on freshwater biodiversity. Integrative waterway management accounting for riparian habitats and landscape characteristics could help to mitigate these impacts. An analysis of fish and macroinvertebrate communities in European rivers over 32 years shows that inland ship traffic is associated with declining taxonomic richness, diversity and trait richness and with increased taxonomic evenness.

Recent developments in fine-scale acoustic telemetry have resulted in large datasets containing highly detailed information on fish movement. A common tool in movement ecology is the application of hidden Markov models (HMMs) to uncover hidden behavioural states from telemetry data. Currently, the data collection can take place at a finer temporal scale than is typically used for HMMs. Although HMMs can still provide valuable insights into fish behaviour, the current fine-scale data can introduce some conceptual and practical challenges in model development. In this paper, we look at the potential of straightness index. This index retains fine-scale movement data while smoothing movement data, allowing for the development of HMMs. Using such an approach can be essential in finding behavioural responses of fish to the ecohydraulic environment, and might, in turn, inform fishway design.

Despite the recognized importance of flowing waters in global greenhouse gas (GHG) budgets, riverine GHG models remain oversimplified, consequently restraining the development of effective prediction for riverine GHG emissions feedbacks. Here we elucidate the state of the art of riverine GHG models by investigating 148 models from 122 papers published from 2010 to 2021. Our findings indicate that riverine GHG models have been mostly data-driven models (83%), while mechanistic and hybrid models were uncommonly applied (12% and 5%, respectively). Overall, riverine GHG models were mainly used to explain relationships between GHG emissions and biochemical factors, while the role of hydrological, geomorphic, land use and cover factors remains missing. The development of complex and advanced models has been limited by data scarcity issues; hence, efforts should focus on developing affordable automatic monitoring methods to improve data quality and quantity. For future research, we request for basin-scale studies explaining river and land-surface interactions for which hybrid models are recommended given their flexibility. Such a holistic understanding of GHG dynamics would facilitate scaling-up efforts, thereby reducing uncertainties in global GHG estimates. Lastly, we propose an application framework for model selection based on three main criteria, including model purpose, model scale and the spatiotemporal characteristics of GHG data, by which optimal models can be applied in various study conditions.