Water Quality and Its Influence on Waterbird Habitat Distribution: A Study Along the Lieve River, Belgium

Abstract

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.

1. Introduction

Human activities are placing unprecedented pressures on global freshwater ecosystems, leading to significant declines in water quality and biodiversity [1]. Freshwater habitats are vital for supporting ecological functions, providing critical ecosystem services, and sustaining species dependent on specific environmental conditions [2]. Among these species, waterbirds can serve as bioindicators whose habitat preferences and distribution patterns are closely linked to water quality [3,4]. Environmental factors such as turbidity, nutrient concentrations, dissolved oxygen (DO), and pollutants create diverse habitat conditions within river systems, influencing where waterbirds forage, nest, and gather [5,6,7]. Conversely, waterbirds themselves can influence water quality through behaviors like foraging and excretion [8,9]. Understanding these interdependencies is critical for advancing ecological knowledge, informing effective conservation strategies, and identifying ecological drivers of species distribution, particularly in riparian zones where human pressures intersect with natural ecosystems [4,10,11].

The Lieve River in Belgium provides a valuable case study for examining the relationship between water quality and waterbird habitat distribution. Originally constructed in the 13th century as one of Europe’s earliest trade canals, it is now a small yet ecologically and historically significant freshwater system that has evolved into a biologically rich habitat and a recreational corridor on the outskirts of Ghent, Belgium’s third-largest city in the Flanders region [12]. This dual role as both a natural habitat for waterbirds and a public recreational area brings complex challenges, particularly in balancing habitat health with pressures from human use [13,14,15]. Urban runoff, agricultural drainage, and residential activities such as commuting, cycling, and horseback riding may influence waterbird distribution and water quality along the Lieve River [9,16,17]. Studying the local waterbird responses to water quality variations not only reveals insights specific to the Lieve River but also enhances our understanding of how biodiversity in urban freshwater systems is shaped by varying environmental conditions [18,19].

Water quality factors such as nutrient levels, organic matter, and chlorophyll-a concentrations play critical roles in shaping waterbird habitats. Previous studies have highlighted the importance of these parameters in influencing bird distribution patterns [17,20,21]. For instance, elevated chlorophyll-a levels, a proxy for trophic state, are often associated with eutrophication, creating productive habitats for species like the Eurasian coot and wild duck [22]. Similarly, DO is a key determinant of water health, with low levels often correlating with degraded environments that limit food availability for waterbirds [8]. Organic matter indicators, such as total organic carbon (TOC) and chemical oxygen demand (COD), also significantly influence habitat suitability by supporting food web productivity and vegetation growth [21,23]. Spatial variations in these water quality parameters along the Lieve River reflect the combined effects of agriculture, urban runoff, and direct discharge of domestic wastewater. These anthropogenic pressures not only create localized impacts but also cumulatively influence habitat conditions, making these parameters central to understanding bird distributions. Similar localized patterns have been documented in studies of ecological dynamics in freshwater systems [4,11,24].

In this study, we focused on the Lieve River, which traverses a landscape characterized by intensive agricultural use, small-town communities, and facilities such as animal feed factories that contribute to nutrient loading and organic pollution [12,25]. In overdeveloped and polluted river basins, habitat degradation has led to significant declines in waterbird populations, as evidenced by studies conducted in Europe and other regions with similar environmental conditions. Related studies include the impacts of organic micropollutants (OMPs) in Spain (which have driven waterbird population declines), river regulation in Poland (which reduced breeding-bird richness by 23% and abundance by 33%), and wetland restoration in Southern California (which improved water quality and boosted endangered bird populations), underscore the need for waterbird habitat protection and pollution control, as mandated by the EU Birds Directive 2009/147/EC, which designates IBAs as Special Protection Areas (SPAs) within the Natura 2000 network [26,27,28,29,30,31]. These findings underscore the critical need to investigate how water quality parameters affect waterbird habitat distribution along the Lieve River. Over an 11 km stretch, 48 monitoring locations were established to analyze key water quality parameters, such as dissolved oxygen, nutrient concentrations, and organic matter. Waterbird surveys targeted three principal species: the Eurasian coot (Fulica atra), the wild duck (Anas platyrhynchos), and the common moorhen (Gallinula chloropus), providing a comprehensive assessment of species abundance and distribution along the river. This study also considers the challenge of balancing ecological conservation with the preservation of historically significant freshwater systems like the Lieve River. Addressing these interconnected objectives helps bridge the gap between biodiversity conservation and cultural heritage management, offering insights into managing ecologically and culturally valuable systems [15].

The study addresses the following research questions: (1) What is the water quality along the Lieve River and are environmental limits exceeded? (2) How are birds distributed along the Lieve River? (3) Can correlations be found between water quality and bird distribution along the Lieve River? (4) What policy recommendations and future research directions can be proposed to enhance water quality management and the conservation of waterbird habitats along the Lieve River? The findings and the methodology of this study provide valuable insights into investigating similar river systems on the planet.

2. Materials and Methods

2.1. Study Area

This study was conducted along an 11 km stretch of the Lieve River, located in the municipality of Lievegem, near Ghent in Belgium’s Flanders region (51°03′ N, 3°43′ E) (Figure 1). It serves as a critical habitat for diverse waterbird species and remains a recreational corridor for the surrounding communities. Based on elevation data from Figure 1, the Lievegem area features low-lying terrain (30 m) and flat topography, which is particularly suitable for agricultural use [32]. The river flows through urban, agricultural, and peri-urban landscapes, creating a natural gradient of environmental pressures, such as nutrient runoff, urban inputs, and recreational use [25,33,34]. These anthropogenic factors contribute to spatial variations in water quality, influencing the habitat suitability of waterbirds.

To facilitate a detailed investigation of water quality and waterbird distributions, the study area was systematically divided into 24 monitoring regions, labeled F1, F2, F3, ... F24. Within each track, water samples were collected at 200 m intervals, resulting in 48 monitoring transects. Each transect is referred to as F1S1, F1S2, F2S1, F2S2, and so forth, where “S1” and “S2” indicate the first and second segments within each 200 m interval. A minor exception was made at F17, where F17S1 covers a 200 m segment, while F17S2 represents a slightly shorter distance of 150 m due to site-specific constraints. A visual representation of this division is provided in Supplementary Materials, Figure S1. This adjustment ensured that the overall monitoring design remained consistent and comprehensive.

In addition to water quality sampling, waterbird abundance was recorded on-site at each of the 48 monitoring transects during field surveys. This systematic field recording was conducted to align with the spatial resolution of the water quality monitoring, enabling detailed correlations between water quality parameters and bird distribution patterns. This structured sampling design ensured consistent and high-resolution coverage of both environmental and biological data across the entire study area [35]. The segmentation framework facilitated the identification of localized patterns in water quality and their associations with waterbird abundance and habitat preferences [13,36].

2.2. Water Quality Measurement

The water quality assessment for this study on the Lieve River was conducted during a comprehensive sampling campaign in March 2024, encompassing measurements across 48 designated monitoring 200 m transects along an 11 km stretch of the river. Water quality parameters were analyzed through both in situ field measurements and laboratory analyses to provide a detailed evaluation of spatial variability in key water quality indicators.

Field measurements: DO, water temperature, pH and electrical conductivity (EC), were measured using a handheld multiprobe (WTW Multi, WTW Xylem Inc., Weilheim, Bavaria, Germany). Turbidity and chlorophyll-a were measured using an AquaFluor handheld fluorometer (San Jose, CA, USA) from Turner Designs. Each parameter was measured 3 times per site to ensure consistency and accuracy, with average values recorded. To avoid cross-contamination, all probes were thoroughly rinsed with deionized water between measurements. In addition, weather conditions, water flow characteristics, and any notable human activities (e.g., recreational use or agricultural drainage) were documented onsite to supplement field observations. Equipment was checked before and after sampling to ensure reliable performance and data integrity.

In addition to in situ measurements, water samples were collected for further laboratory analysis. For water sample collection, a bucket was rinsed 3 times with stream water, with the sides and bottom hand-washed, and the rinsing water discarded onshore or downstream. From the bucket, unfiltered samples were transferred into leakproof, freeze-resistant recipients for COD, TOC, TN, and TP analysis, while filtered samples (0.45 µm pore PES filter) were transferred into separate recipients for the analysis of NO₂⁻, NO₃⁻, NH₄⁺, and PO₄³⁻.

Laboratory Analysis: Water samples from all 48 transects were transported at 4 °C in the dark to preserve their integrity and then stored in a refrigerator at −20 °C until analysis. Nutrient concentrations, including NH₄⁺-N, NO₃⁻-N, NO₂⁻-N, orthophosphate (PO₄³⁻-P), total nitrogen (TN), and total phosphorus (TP), were measured spectrophotometrically using low-range Merck test kits with a spectrophotometer (Hach^® DR6000^™, Hach Company, Loveland, Colorado, USA). The organic content, including total inorganic carbon (TIC) and TOC, was analyzed at the Laboratory of AECO at Ghent University using a TOC analyzer (Shimadzu TOC-L, Shimadzu Corporation, Kyoto, Japan). COD was also measured to evaluate organic matter content. All laboratory analyses were conducted with instruments regularly calibrated using certified standard solutions to ensure high analytical precision and accuracy. We adhered to the quality assured by the American Public Health Association (APHA) and the United States Environmental Protection Agency (US EPA) [37,38]. The detailed measurement accessories and kits are provided in Table S5 of Supplementary Materials.

2.3. Waterbird Survey

Waterbird surveys were conducted simultaneously with water quality measurements in March 2024 to provide integrated insights into the relationship between water quality and bird distribution. The survey focused on quantifying populations of three principal waterbird species: the Eurasian coot (Fulica atra), the wild duck (Anas platyrhynchos), and the common moorhen (Gallinula chloropus), all of which are ecologically significant indicators of freshwater habitat health [39,40,41]. The survey was conducted at every 50 m transect within these transects to ensure detailed and fine-scale data collection. Each transect was systematically marked to prevent overlap and ensure precise counts. Field observers followed standardized ornithological protocols, manually recording the number and species of waterbirds encountered at each interval. This systematic approach facilitated the accurate capture of bird abundance and species distribution across the study area. Ethical considerations were integral to the survey design. Observers maintained a sufficient distance from the birds to avoid disturbance and ensured minimal disruption to their natural behaviors. Field documentation also included notes on habitat conditions and potential human activities in the area, providing additional context for the recorded distributions [42,43].

2.4. Data Analysis

To evaluate the relationships between water quality parameters and waterbird abundance, both the Mantel test and Pearson correlation analysis were performed using R Studio (version 2022.12.0, Posit Software, Boston, MA, USA, 2022) [44,45]. The Mantel test was applied to identify spatial correlations between environmental factors (e.g., water quality parameters) and bird distributions by calculating the correlation between two distance matrices: one for environmental variables and the other for bird abundances across the 48 monitoring transects. The Pearson correlation analysis was conducted to examine pairwise relationships between water quality variables and bird abundance [46,47]. In this study, the terms NH₄-N, NO₃-N, NO₂-N, and PO₄-P are used to represent the weight of nitrogen (N) or phosphorus (P) atoms within their respective molecular forms (e.g., NH₄⁺, NO₃⁻, NO₂⁻, and PO₄³⁻), serving as proxies for the concentrations of these ions. Additionally, for the measurement of COD, a value of “<10” was recorded at site F1 S2; to facilitate calculations and maintain consistency in the analysis, the boundary value of 10 was used for this measurement.

To assess the degree of pollution in the Lieve River, the Basic Prati water quality index (Prati WQI; hereafter referred to as the Prati index), developed by Prati et al. (1971) [48], was utilized. This index aggregates multiple pollutants, more specifically dissolved oxygen saturation (DO_sat), COD, and NH₄-N, into a single score. Using transformation formulas, measured values are standardized and their average provides an index score that simplifies complex water quality data into a clear and logical format [49]. In this study, the Basic Prati index was calculated for each sampling point, with specific methods detailed in Supplementary Materials. Water quality was classified into five categories based on the Prati Index evaluation: Excellent (0–1), Acceptable (1–2), Polluted (2–4), Heavily Polluted (4–8), and Extremely Polluted (>8). Based on this, a standardized framework for identifying pollution gradients and hotspots in the Lieve River was used [50,51,52].

To evaluate the ecological health of the Lieve River, measured water quality parameters were compared against established environmental standards for freshwater systems. These standards (Supplementary Materials Table S6) were compiled from established international and regional sources, including the World Health Organization (WHO, 2011), the European Union Water Framework Directive (WFD, 2000/60/EC), the United States Environmental Protection Agency (US EPA), and VLAREM (the Flemish environmental legislation in Belgium), which classifies this section of the Lieve River as a “small river”, and supporting literature such as Williams (2001) and Kim (2021) [53,54,55,56,57,58].

3. Results and Discussion

3.1. Spatial Variability of Water Quality Parameters

The spatial variability of water quality parameters along the Lieve River was analyzed, focusing on environmental factors represented as in situ variables (Chlorophyll-a, DO, pH, EC), nitrogen species (TN, NO₃-N, NO₂-N, NH₄-N), phosphorus species (PO₄-N, TP), and organic matter (TIC, TOC, COD). Figure 2. illustrates the spatial distribution of these parameters, highlighting significant variations across monitoring locations. Other detailed values for those parameters are provided in Supplementary Materials Tables S1–S3.

Chlorophyll-a concentrations demonstrated a clear spatial gradient along the river. The values ranged from 41.1 ppb at F3 S2 to 126.1 ppb at F24 S1, the highest recorded value. Additional peaks were observed at F23 S2 (116.0 ppb) and F21 S2 (113.8 ppb). Areas closer to the F1 direction, such as F3 S2 (41.1 ppb) and F4 S1 (44.3 ppb), exhibited significantly lower chlorophyll-a levels. DO concentrations ranged from 5.30 mg L⁻¹ at F12 S2 to 12.07 mg L⁻¹ at F24 S1. The pH was very stable, with an average value hovering around 7.50. This spatial pattern indicates increased algal productivity towards the F24 direction (Figure 2a).

Nitrogen species displayed distinct spatial trends. TN concentrations ranged from 2.4 mg L⁻¹ at F15 S1 to 5.8 mg L⁻¹ at F22 S1. NO₃-N concentrations peaked at 2.7 mg L⁻¹ at F22 S1 and 2.6 mg L⁻¹ at F21 S1 and F18 S2. NH₄-N concentrations were highest at 0.31 mg L⁻¹ at F22 S1, while NO₂-N remained consistently low, ranging between 0.02 and 0.08 mg L⁻¹. Higher nitrogen levels in the F24 direction suggest localized nutrient enrichment, possibly from agricultural or urban runoff (Figure 2b).

Phosphorus species exhibited spatial variability. PO₄-P concentrations ranged from 0.03 mg L⁻¹ at F3 S1 and F3 S2 to 0.11 mg L⁻¹ at F12 S1, the highest value recorded. TP concentrations varied between 0.14 mg L⁻¹ at F17 S1 and 0.46 mg L⁻¹ at F22 S1. Elevated phosphorus levels at F12 S1 and F22 S1 indicate localized nutrient input, likely driven by human activities. Lower phosphorus levels were observed near the F1 direction, such as at F4 S1 (Figure 2c).

Organic matter indicators showed substantial spatial variability. TOC concentrations ranged from 9.8 mg C L⁻¹ at F1 S2 to 12.7 mg C L⁻¹ at F24 S1, while COD varied between 10 mg O₂ L⁻¹ at F1 S2 and mg O₂ L⁻¹ at F22 S1. TIC values also showed variability, with higher TIC often observed in regions of elevated TOC and COD. Elevated TOC and COD levels at F22 S1 and F24 S1 suggest organic matter accumulation, potentially linked to decomposition processes (Figure 2d).

3.2. Prati Index-Based Water Quality Assessment

The water quality of the Lieve River, assessed using the Prati index, displayed spatial and categorical variability across the 48 sampling points, as shown in Figure 3. Results indicated that 41.7% (20 locations) of the sampling locations were categorized as “Acceptable quality” (Prati index ≤ 2.0), while 58.3% (28 locations) were classified as “Polluted” (Prati index > 2.0). The distribution of water quality classifications reveals a trend, with cleaner sites predominantly located near the F1 direction and more polluted sites concentrated closer to the F24 direction. This spatial variability highlights potential cumulative anthropogenic impacts, such as urban runoff, agricultural discharge, and point-source pollution, contributing to water quality degradation. A detailed summary of Prati index results is provided in Supplementary Materials Table S4.

Spatial analysis of the Prati index values revealed distinct differences between sites. Locations near the F1 direction, including F1 S2 (Prati index = 1.40) and F4 S1 (Prati index = 1.93), were associated with higher DO_sat (≥80%), moderate COD (≤31 mg O₂ L⁻¹), and low NH₄-N (≤0.2 mg L⁻¹). These conditions reflect reduced levels of organic and nitrogenous pollution, which is indicative of favorable water quality. In contrast, sites near the F24 direction, such as F12 S2 (Prati index = 2.97) and F24 S1 (Prati index = 2.56), showed reduced DO_sat (e.g., F12 S2 = 49%) and elevated COD and NH₄-N levels. F24 S1, for instance, recorded high COD (47 mg O₂ L⁻¹) and NH₄-N (0.22 mg L⁻¹), suggesting significant organic and ammonia-related contamination at these locations. This spatial trend underscores the influence of cumulative pollution along the river.

Key water quality parameters strongly influenced the Prati index values. Dissolved oxygen saturation emerged as a critical factor, with higher DO_sat levels correlating with better water quality classifications. For example, F5 S1 (DO_sat = 85%, Prati index = 1.38) displayed favorable conditions, while reduced DO_sat in sites such as F13 S1 (DO_sat = 58.3%, Prati index = 2.90) was associated with poorer water quality. Similarly, elevated COD levels were a dominant contributor to higher Prati index values, with sites like F22 S1 (COD = 50 mg O₂ L⁻¹) and F24 S1 (COD = 47 mg O₂ L⁻¹) demonstrating substantial organic pollution. Ammonium concentrations (NH₄-N) also played a significant role, with locations like F23 S1 (NH₄-N = 0.27 mg L⁻¹) and F22 S1 (NH₄-N = 0.31 mg L⁻¹) corresponding to “Polluted” classifications.

The Prati index effectively captured the spatial variability and drivers of water quality along the Lieve River. Sites near the F1 direction exhibited better water quality conditions, likely due to natural purification processes and lower pollutant loads, while sites near the F24 direction reflected increasing anthropogenic pressures. These findings provide crucial insights into the spatial distribution of pollution hotspots and emphasize the need for targeted water quality management strategies in downstream (towards F24) sections of the river.

3.3. Waterbird Abundance and Distribution Patterns

The spatial distribution and abundance of waterbird species across the 48 monitoring transects along the Lieve River revealed distinct patterns in species composition and spatial variability (Figure 4). Three principal waterbird species were observed: Eurasian coot, common moorhen, and wild duck. Each species exhibited different abundance levels and spatial patterns, reflecting their habitat preferences and responses to localized environmental conditions. Strikingly, nearly in each section of 200 m, at least 2 individuals were found, in 16 sections there were more than 5 individuals recorded, and, on average, there were about 20 birds recorded per kilometer.

The Eurasian coot was the most abundant species, with a total of 105 individuals recorded across all transects. Peak coot counts were observed at F2 S2 and F9 S2, where 5 individuals were recorded. These locations may reflect the availability of abundant aquatic vegetation and calmer water conditions, which provide optimal foraging opportunities and shelter [59,60]. Interestingly, towards the F24 sections such as F23 S2 and F24 S1, which exhibited elevated chlorophyll-a concentrations and higher organic matter inputs in the earlier water quality analysis, they also recorded moderate coot abundances. This may suggest that nutrient-enriched environments, while often associated with reduced water quality, can support increased productivity of aquatic vegetation and phytoplankton, indirectly benefiting coots [60,61].

The common moorhen displayed a variable distribution, with a total of 71 individuals recorded. Moorhens reached their peak abundances at F9 S2 and F15 S2, where 5 individuals were observed. These locations may offer shallow water habitats characterized by emergent vegetation, which are commonly associated with moorhen foraging and nesting preferences, as suggested by previous studies [59,62]. Regions like F9 S2 exhibited moderate DO levels and elevated TOC concentrations, conditions that may indicate moorhens’ tolerance to localized organic matter accumulation. However, conclusions on their adaptability require further analysis across multiple sites.

The wild duck, the least abundant species, totaled 29 individuals and displayed a sporadic distribution. The highest number of 5 individuals was recorded at F21 S2, a towards-F24 section characterized by higher dissolved oxygen concentrations and moderate nutrient levels. The sporadic distribution of wild ducks, particularly their preference for regions with higher dissolved oxygen concentrations and moderate nutrient levels, may be attributed to the availability of submerged vegetation and aquatic invertebrates, critical food sources for this species [59,63]. However, variations in vegetation structure, predation pressure, and competition could explain their absence in other nutrient-rich, oxygenated areas [59,64,65].

The spatial distribution of waterbirds along the Lieve River reflects a combination of distinct patterns and relatively uniform trends influenced by species-specific habitat preferences and behavioral factors. While the dominance of Eurasian coots, localized peaks of common moorhens, and the sporadic presence of wild ducks indicate varying environmental tolerances and preferences, the overall distribution appears relatively homogeneous during the nesting season. This homogeneity can be partly attributed to territorial behavior, which influences how individuals spread across available habitats, and the absence of sites with extreme water quality conditions, either particularly favorable or unfavorable, which creates a more consistent environmental baseline. Regions with elevated chlorophyll-a concentrations and organic matter (TOC and COD) were associated with higher abundances of coots and ducks, demonstrating their affinity for nutrient-rich areas within this overall pattern.

3.4. Influence of Water Quality on Waterbird Distribution Patterns

The correlation analysis between water quality parameters and waterbird abundance revealed species-specific responses, providing insight into how water quality influences waterbird distribution along the Lieve River (Figure 5). Combined with the spatial patterns of water quality and waterbird abundance (3.3), these results highlight key drivers of habitat preference and species-specific ecological adaptations.

The Eurasian coot exhibited a weak but significant positive correlation with TP (r = 0.20, p = 0.048), indicating a clear association between changes in TP and the presence of coots. Among waterbirds, the Eurasian coot is known for producing a large TP mass, as highlighted by Scherer et al. (1995) [66]. This nutrient enrichment promotes the growth of aquatic vegetation, which in turn provides essential resources for coots, creating a feedback loop that can exacerbate localized eutrophication. This dynamic aligns with the findings of Scherer et al. (1995) and Clausen (2025), who emphasized that the excretion of omnivorous waterbirds like the Eurasian coot significantly alters phosphorus concentrations in aquatic systems [66,67]. Similarly, the study by Tóth et al. (2023) observed that, while the presence of waterbirds is not a decisive factor for overall water quality, it does have a substantial localized impact, particularly in nutrient-enriched habitats [9].

The common moorhen exhibits a weak but significant positive correlation with TOC (r = 0.10, p = 0.036), suggesting a preference for habitats with slightly elevated organic matter content. As an omnivorous bird, the common moorhen’s diet includes aquatic plants, seeds, insects, and small invertebrates, many of which thrive in environments with higher levels of organic matter [68]. Elevated TOC levels typically indicate the accumulation of organic material, creating microhabitats rich in detritus and microbial activity, which serve as a food base for invertebrates [69]. This finding supports the moorhen’s opportunistic foraging behavior, as it often feeds in shallow waters and along the edges of aquatic habitats rich in organic matter, aligning with observations from previous studies [68,70,71,72,73].

The wild duck exhibits weak but significant positive correlations with TP (r = 0.300, p = 0.046), suggesting a preference for habitats with slightly elevated concentrations of total phosphorus. Wild ducks feed on aquatic plants, seeds, insects, and small invertebrates, many of which thrive in environments rich in nutrients such as phosphorus and minerals [74]. Increased TP levels typically reflect nutrient enrichment [75,76]. This contributes to the growth of submerged vegetation and invertebrates, which are critical food resources for ducks. Additionally, studies have shown that wild ducks adapt to decreases in TP and increases in water clarity by altering their mating behaviors, which can influence breeding density and reproductive success [77,78]. These findings align with previous research, supporting the notion that wild ducks exhibit adaptive foraging behavior and reproductive strategies, as they frequently inhabit nutrient-rich aquatic environments that enhance food availability and facilitate their survival and reproduction [74,77,78,79].

By combining the observations from Figure 3 (Prati index distribution) in Section 3.2 and Figure 4 (spatial distribution of waterbirds) in Section 3.3, it was found that 71.4% of Eurasian coots were located in the yellow “polluted” areas. Wild ducks exhibited a similar trend, with 72.4% of their distribution also in polluted areas. In stark contrast, however, only 19.7% of common moorhens were found in polluted areas, with the majority of common moorhens choosing the green “acceptable quality” regions, indicating a preference for higher water quality. This observation aligns with the results of the correlation analysis, where Eurasian coots and wild ducks both showed correlations with TP, while common moorhens did not. Instead, common moorhens exhibited a weak relationship with TOC. Interestingly, the observed pollution is likely a result of water eutrophication, which is closely associated with phosphate concentrations.

These findings collectively point to the influence of eutrophication and organic matter enrichment and deposition. Such dynamics may be linked to land use practices in the monitored region, which include agricultural areas, animal feed factories, and densely populated residential zones along the Lieve River. The discharge of industrial and domestic wastewater, as well as agricultural activities, significantly contributes to water eutrophication and organic matter accumulation. Addressing these challenges requires targeted efforts to control wastewater discharge and manage the use of chemical fertilizers and organic manures in agriculture to mitigate their effects on water quality and aquatic ecosystems.

3.5. Water Quality Deviations from Environmental Standards and Ecological Implications

As shown in Figure 6, nutrient enrichment emerges as a significant driver of eutrophication in the Lieve River. TP levels peaked at 0.46 mg L⁻¹ (median: 0.18 mg L⁻¹), frequently exceeding the environmental threshold of 0.14 mg L⁻¹. Similarly, TN concentrations ranged from 2.7 to 5.6 mg L⁻¹ (median: 4.0 mg L⁻¹), with many locations meeting or surpassing the upper limit of 4.0 mg L⁻¹. These elevated nutrient levels correspond to chlorophyll-a concentrations (median: 70 ppb) far exceeding the upper threshold of 15 ppb. This nutrient accumulation promotes algal blooms and eutrophic conditions, providing fertile habitats for species such as the Eurasian coot. As previously noted, Eurasian coots are recognized for their role in increasing TP levels through excretion, reinforcing nutrient enrichment and contributing to feedback loops that exacerbate localized eutrophication [9,66].

Organic matter parameters, such as TOC and COD, also exhibit deviations from environmental standards. The median TOC value (11 mg C L⁻¹) exceeded the upper threshold of 10 mg C L⁻¹, while COD levels (median: 33 mg O₂ L⁻¹) consistently surpassed the limit of 30 mg O₂ L⁻¹. These results suggest significant organic matter accumulation, likely driven by agricultural runoff, wastewater discharges, and urban activities. Such conditions provide favorable environments for waterbirds, as the accumulation of organic material creates detritus-rich microhabitats that support microbial activity and invertebrate populations, which are critical resources for the opportunistic foraging behavior of waterbirds [74,75].

Overall, Figure 6 highlights severe deviations in TP, TN, chlorophyll-a, TOC, and COD from environmental standards, underscoring the combined impacts of nutrient enrichment and organic matter accumulation. These findings emphasize that agricultural runoff, untreated or insufficiently treated wastewater, and urban activities are the primary drivers of these deviations. Addressing these challenges requires targeted management strategies, including nutrient load reductions, improved wastewater treatment, and the implementation of riparian buffer zones, to restore water quality and support the ecological health of the Lieve River. These strategies are critical for balancing anthropogenic pressures with biodiversity conservation, ecosystem functionality, and sustainable development [80]. Future research should prioritize innovative monitoring technologies, such as drones and AI-assisted waterbird surveys, to strengthen conservation efforts and ensure long-term biodiversity sustainability [81].

4. Conclusions

This study provides an assessment of water quality and waterbird distribution along the Lieve River, highlighting the impacts of nutrient enrichment, organic matter accumulation, and anthropogenic activities on the river’s ecological health and its role as a multifunctional resource. The main findings are as follows:

Nutrient enrichment: The Lieve River exhibits significant nutrient enrichment, with elevated levels of TN, TP, and chlorophyll-a serving as key drivers of eutrophication.

Organic matter accumulation and habitat dynamics: TOC and COD levels frequently exceed environmental thresholds, particularly in areas influenced by agricultural runoff, urban wastewater discharge, and other human activities.

The Prati index analysis revealed that 58.3% of the Lieve River sampling points were classified as “Polluted”, with higher COD and NH₄-N levels and reduced DO_sat observed near the F24 direction. These results suggest that impacts from urban runoff, agricultural discharge, and point-source pollution influence water quality, emphasizing the need for targeted management in these areas.

The Eurasian coot was the most abundant species and showed a positive correlation with TP. The common moorhen correlated with TOC, while wild ducks showed weak correlations with TP. Total waterbird abundance was associated with chlorophyll-a, indicating nutrient dynamics as a key driver.

Management and monitoring recommendations: As one of Europe’s earliest artificial canals, the historical and cultural significance of the Lieve River enhances its value as an ecological and recreational resource. Protecting this dual role requires sustainable management practices that address both conservation and human utilization. Key actions include reducing nutrient loads through improved wastewater treatment, promoting sustainable agricultural practices, and implementing riparian buffer zones to minimize runoff. Furthermore, a similar approach as presented in this study is valuable for investigating similar river systems in the world.

Author Contributions

X.L.: Conceptualization, Investigation, Writing—original draft preparation; L.H.: Conceptualization, Investigation, writing—review and editing; A.D.C.: Conceptualization, Investigation, writing—review and editing; N.D.S.: Investigation; K.P.: Investigation; D.P.-C.: Investigation; M.A.E.F.: Investigation, writing—review and editing; P.L.M.G.: Conceptualization, Investigation, writing—review and editing, resources, supervision. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are contained within the article.

Acknowledgments

Marie Anne Eurie Forio is supported by the EU Horizon2020 projects OPTAIN and MERLIN and Horizon Europe project OneAquaHealth. Long Ho and Kim Pham are supported by the Horizon Europe project OneAquaHealth.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures

Figure 1. Study area. The blue line marks the Lieve River within Lievegem, where the sampling was conducted. Elevation data is sourced from NASA’s 30 m Digital Elevation Model (DEM) [32].

Figure 2. Spatial distribution of water quality parameters along the Lieve River. (a) In situ variables (Chlorophyll-a, ppb; pH; DO, mg L −1); (b) Nitrogen species (TN, NO3-N, NO2-N, NH4-N, mg L−1); (c) Phosphorus species (PO4-P, TP, mg L−1); (d) Organic matter indicators (TIC, TOC, mg C L−1; COD, mg O2 L−1).

Figure 3. Spatial distribution of Prati index and water quality classification along the Lieve River. Green bars represent “Acceptable quality” (Prati index ≤ 2.0); Yellow bars represent “Polluted” (Prati index [greater than] 2.0).

Figure 4. Spatial distribution of waterbird individuals (Eurasian coot, wild duck, and common moorhen) across the 48 monitoring locations along the Lieve River. The bar heights represent the total number of individuals recorded per species at each location.

Figure 5. Relationships between water quality parameters and waterbird abundance along the Lieve River. The figure combines Pearson correlation coefficients and Mantel test results to highlight associations between water quality parameters and waterbird species abundance. Positive correlations are shown in blue, while negative correlations are in red. The line thickness represents Mantel’s r values, with thicker lines indicating stronger correlations. Mantel’s r: A measure of the strength of correlation between two distance matrices, reflecting how closely the spatial variation of water quality matches bird abundance. Mantel’s p: The statistical significance of the Mantel test, with lines in orange representing significant relationships (p = 0.01–0.05) and lines in gray indicating non-significant relationships (p ≥ 0.05). (*) indicate the significance level of Pearson’s correlation coefficients: p [less than] 0.05 (*), p [less than] 0.01 (**), and p [less than] 0.001 (***).

Figure 6. Comparison of water quality parameters in the Lieve River against environmental thresholds defined by WHO (2011), WFD (2000/60/EC), US EPA, and VLAREM, as outlined in Supplementary Materials Table S6.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17040595/s1, Supplementary Materials provide a comprehensive overview of the water quality assessment conducted along the Lieve River, including detailed datasets, analysis methods, and a schematic representation of the monitoring design. The contents are as follows. Table S1: In situ parameters (Chlorophyll-a, DO, pH, EC) from the monitoring results of 48 transects along the Lieve River in March 2024. Table S2: Nutrient concentrations (NH₄-N, NO₃-N, NO₂-N, TN, PO₄-P, and TP) measured across 48 transects in March 2024. Table S3: Organic matter parameters, including TIC, TOC, and COD, derived from the same transects in March 2024. Table S4: Results of the Prati water quality index (Prati WQI) analysis, summarizing dissolved oxygen saturation (DO_sat), COD, NH₄-N levels, and corresponding water quality classifications for each transect in March 2024. Table S5: Summary of physicochemical water quality variables, measurement techniques, and associated standards. Table S6: Water quality standard thresholds for freshwater ecosystems. The Prati Index Methodology: A detailed explanation of the Prati index calculation, including its classification system and the transformation formulas used for key parameters (DO_sat, COD, NH₄-N). This section provides the basis for assessing pollution levels along the river. Figure S1: A schematic representation of the Lieve River monitoring design, illustrating the division of the river into 24 monitoring regions (F1–F24) and 48 transects (F1S1–F24S2) at 200 m intervals.

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