Zooplankton Biodiversity Along a Pollution-Salinity Gradient in Bahr Al-Najaf, Iraq: Implications for Biomonitoring and One Health

2.1 Study area and sampling design

Bahr Al-Najaf is a small, shallow basin with a surface area of about 24 square kilometers and an average depth of 1.5 to 3.0 meters. It is in central Iraq, 10 km southwest of Najaf City (31°59′ N, 44°18′ E). The basin gets its water from seasonal rain, drainage from nearby date palm farms, and mostly untreated municipal wastewater and runoff from solid waste from Old Najaf City. The system is very likely to become salty and polluted because it loses a lot of water (about 2,000 mm per year) and doesn't get much new water [1, 3].

Three sampling zones were created to capture the spatial gradient in environmental stress. These zones were chosen based on (i) visible signs of pollution (like water clarity, smell, and floating trash); (ii) how close the pollution sources are (like sewage outfalls and solid waste dumping sites); (iii) how easy it is to get to and how safe it is; and (iv) how well the depression covers the area (Figure 1):

• Zone 1 (Reference/Low Impact): Northern peripheral areas (31°59′45″ N, 44°17′30″ E) with very little direct sewage input, not a lot of people around, fairly stable water quality, and submerged macrophyte beds mostly made up of Potamogeton spp.

• Zone 2 (Moderate Impact): Central transitional areas (31°59′20″ N, 44°18′15″ E) that get wastewater discharge on occasion, mixed agricultural and urban runoff, and moderate turbidity with patchy invasive macrophyte (Eichhornia crassipes) coverage.

• Zone 3 (High Impact): Southern areas (31°58′50″ N, 44°18′50″ E) next to direct sewage inflows and active solid waste dumping sites. These areas are always hypoxic, have high turbidity, a bad smell, salinity stratification, and dense organic pollution.

Figure 1. Geographic location of Bahr Al-Najaf

2.2 Sampling protocol and temporal design

The sampling took place over 18 months (March 2023 to August 2024) to characterize major seasonal trends in water levels while also taking into account the limitations that are common in dry wetland systems. There were six sampling campaigns: March, April, May, June, September, and October 2023; and March, April, May, June, July, and August 2024. These months were chosen because (i) they are hydrologically stable, avoiding times of complete drying out (July-August) or very cold temperatures (December-February) that make it hard for living things to move around; (ii) they are when Mesopotamian wetlands have the most diverse zooplankton [26, 27]; and (iii) they are when flooding makes it hard to get around. To cut down on daily changes in physicochemical parameters and zooplankton vertical migration, all sampling was done between 08:00 and 10:00 hours local time.

Within-zone microhabitat heterogeneity was obtained by establishing three (replicate) sampling points approximately 100 m apart within each zone with similar environmental conditions. Within each zone, the sampling points were selected to have similar water depth, substrate type, hydrological connectivity, and macrophyte coverage to reduce local habitat variability. Before each sampling campaign, sites were visually inspected for consistency in physicochemical characteristics and absence of major habitat disturbances. To reduce the temporal and diel variability, all samples were taken between 08:00 and 10:00 h. All samples were collected under comparable weather conditions and using identical sampling procedures and equipment in all zones. During each sampling campaign, replicate points were located with a GPS and revisited to ensure that spatial consistency was maintained throughout the study period. Temporal replicates (n = 12 sampling events per zone) were pooled for statistical analysis after confirming no significant temporal heterogeneity among sampling periods (PERMANOVA, p > 0.05), thereby reducing pseudo-replication while maintaining spatial replication (3 zones × 3 replicate points = 9 spatial replicates per sampling event).

The study design was capable of capturing seasonal and spatial variability over an 18-month period, but the logistical scope of the present investigation would not have allowed for continuous high-frequency monitoring. Therefore, short-term pollution pulses and rapid hydrological variations may not have been fully resolved. However, repeated standardised sampling over several seasons provided a robust representation of long-term ecological trends within Bahr Al-Najaf.

2.3 Environmental analysis

In situ physicochemical parameters were measured at 0.5 m depth using a calibrated multiparameter probe (YSI ProPlus, USA), including water temperature (℃), pH, dissolved oxygen (DO, mg/L), electrical conductivity (EC, µS/cm), salinity (ppt), and total dissolved solids (TDS, mg/L). The YSI probe was calibrated daily prior to field measurements using manufacturer-recommended standard solutions, and calibration accuracy was verified after every sampling campaign. Detection limits for probe-based measurements were as follows: DO = 0.01 mg/L, EC = 1 µS/cm, salinity = 0.1 ppt, and TDS = 1 mg/L.

Water samples (2 L) were collected from the surface layer (0-0.3 m depth) for laboratory analyses of nutrients and organic pollution indicators. Nitrate (NO₃⁻), nitrite (NO₂⁻), ammonium (NH₄⁺), and phosphate (PO₄³⁻) concentrations were determined spectrophotometrically according to APHA (2017) standard methods (4500-NO₃⁻, 4500-NO₂⁻, 4500-NH₃, and 4500-P). Biochemical oxygen demand (BOD₅, mg/L) was determined using the 5-day incubation method (APHA, 2017; Method 5210B), while chemical oxygen demand (COD, mg/L) was measured using the closed-reflux colorimetric method (APHA, 2017; Method 5220D). Total organic carbon (TOC, mg/L) was analyzed by high-temperature combustion using a Shimadzu TOC-VCPH analyzer. Method detection limits (MDLs) for nutrient analyses were: NO₃⁻ = 0.01 mg/L, NO₂⁻ = 0.001 mg/L, NH₄⁺ = 0.01 mg/L, PO₄³⁻ = 0.005 mg/L, BOD₅ = 0.5 mg/L, COD = 1 mg/L, and TOC = 0.1 mg/L.

Surface sediment samples (0-5 cm depth) were collected using a stainless-steel Ekman grab sampler (225 cm²), air-dried at ambient temperature (25 ± 3 ℃) for 72 h, homogenized, and sieved through a 63 µm nylon mesh. Subsamples (0.5 g dry weight) were digested using an acid mixture of HCl: HNO₃ (3:1, v/v) following USEPA Method 3050B. Concentrations of lead (Pb), cadmium (Cd), copper (Cu), zinc (Zn), nickel (Ni), and chromium (Cr) were quantified using inductively coupled plasma optical emission spectrometry (ICP-OES; PerkinElmer Optima 8300). ICP-OES calibration was performed at the beginning of each analytical run and verified after every 10 samples using multi-element standard solutions to minimize instrumental drift. Instrumental limits of detection (LOD, mg/kg dry weight) were: Pb = 0.01, Cd = 0.002, Cu = 0.01, Zn = 0.02, Ni = 0.01, and Cr = 0.01.

Quality assurance and quality control (QA/QC) procedures included calibration using certified multi-element standards (R² > 0.995 for all calibration curves), procedural blanks (n = 2 per batch), duplicate analyses (n = 3 per batch; relative percent difference <10%), and certified reference materials (NIST SRM 1643e; recoveries ranged from 90-110%). MDLs were calculated as three times the standard deviation of blank measurements, and all measured concentrations exceeded their respective MDLs. Prior to statistical analyses, datasets were screened for potential outliers and analytical anomalies. Values exceeding ±3 standard deviations were rechecked against laboratory records, calibration performance, and field observations. Samples associated with instrument instability, contamination, or QA/QC failure were re-measured or excluded, whereas environmentally plausible extreme values were retained to preserve natural ecological variability.

The heavy metal pollution index (HPI) was calculated following [28] to integrate multiple metal contaminants into a single quantitative metric:

$HPI=\frac{\sum \left( {{Q}_{i}}\times {{W}_{i}} \right)}{\sum {{W}_{i}}}$

where, Q_i= C_i/S_i represents the sub-index for metal (i), C_i is the measured metal concentration (mg/kg), S_i is the corresponding sediment quality guideline value based on the Canadian Sediment Quality Guidelines (CCME, 2001), and W_i = 1/S_i is the unit weight. The sediment guideline values used were: Pb = 35 mg/kg, Cd = 0.6 mg/kg, Cu = 35.7 mg/kg, Zn = 123 mg/kg, Ni = 18 mg/kg, and Cr = 37.3 mg/kg. HPI values were classified as follows: <1.0 = low pollution, 1.0-2.5 = moderate pollution, 2.5–5.0 = high pollution, and >5.0 = severe pollution.

2.4 Zooplankton collection and processing

We used a 30-cm diameter conical plankton net with a 64-200 µm mesh to collect zooplankton. The net was suitable for catching small rotifers and juvenile cladocerans. A calibrated mechanical flowmeter (General Oceanics, Model 2030R) was attached to the net opening to measure the amount of water filtered (mean volume per tow: 2,450 ± 380 L). Tows were done at a speed of about 1 m/s over 100 m long, parallel to the coast. To stop predation and degradation, samples were quickly preserved in 4% buffered formalin (final concentration). We used an Olympus CX43 compound microscope (100-400× magnification) in the lab to count and concentrate the samples. The number of zooplankton was given in individuals per cubic meter (ind. m⁻³).

For morphometric analysis, 30 adult specimens per dominant taxon (determined by the presence of eggs, embryonic development, or fully differentiated morphological characteristics) were randomly selected from each zone and season. Using an Olympus DP27 digital camera with a calibrated scale bar, we took pictures of the specimens. We used ImageJ software (version 1.53k) [29] to measure the following: (i) for rotifers: lorica length and width; (ii) for cladocerans: body length (not including the spine), carapace length, and antennae length; and (iii) for copepods: prosome length and total body length. All measurements adhered to established protocols [30, 31].

2.5 Species identification and taxonomic analysis

We employed established identification keys to find the lowest possible taxonomic level for zooplankton taxa: Rotifera according to studies [32, 33]; Cladocera according to studies [30, 34]; Copepoda according to research [31, 35]. The genus Chydorus was given special attention because it has a lot of phenotypic plasticity and hidden diversity. Diagnostic features like valve sculpture, rostrum morphology, postabdominal claw structure, and setation patterns were checked in at least 10 individuals per zone to make sure they were correct [30].

Functional traits were ascribed to each identified taxon according to the Freshwater Ecology.info database [36] and regional literature [37], as shown in Figure 2.

The included traits were: (i) feeding mode (filter-feeder, predator, detritivore, or mixed); (ii) body size class (small: <200 µm, medium: 200-500 µm, large: >500 µm); (iii) reproductive strategy (mictic/amictic ratio for rotifers; parthenogenetic/sexual for cladocerans); (iv) salinity tolerance (stenohaline, mesohaline, euryhaline); and (v) organic pollution tolerance (sensitive, tolerant, highly tolerant) following research [3, 38].

Figure 2. Scientific schematic of representative zooplankton taxa based on taxonomic identification criteria

2.6 Statistical analysis

Biodiversity was quantified using: (i) species richness (S, total number of taxa); (ii) Shannon-Wiener diversity index (H′ = -Σp_i × ln(p_i)); and (iii) Simpson's index of diversity (1-D, where D = Σp_i²), calculated as 1 - Σp_i², where p_i = proportional abundance of species i [39]. This formulation (also known as the Gini-Simpson index) ranges from 0 (no diversity, single species dominance) to 1 (infinite diversity, all species equally abundant). This approach was selected to ensure an intuitive interpretation, in which higher values indicate greater diversity, consistent with the Shannon index.

To lessen the effect of species with high abundance and double zeros, zooplankton abundance data were Hellinger-transformed (y' = √ (y/row sum)) [40]. Detrended correspondence analysis (DCA) was used to compare linear redundancy analysis (RDA) and unimodal canonical correspondence analysis (CCA) ordination methods. A gradient length of 3.2 standard deviations on Axis 1 showed unimodal responses, which supported the choice of CCA. We used forward selection with 999 Monte Carlo permutations (p < 0.05) to choose the environmental variables (DO, TDS, BOD, COD, TOC, HPI) for CCA. We looked at variance inflation factors (VIF) to get rid of collinear variables (VIF threshold = 10). Permutation tests (999 iterations) were used to find out how important the first canonical axis and the overall analysis were.

Differences in community composition between zones were tested using permutational multivariate analysis of variance (PERMANOVA; 999 permutations), based on Bray-Curtis dissimilarity across all taxa identified. We conducted an Indicator Species Analysis (ISA) to identify taxa significantly associated with individual zones (indicator value > 0.25, p < 0.05). All statistical analyses were conducted in R version 4.2.2 [41]. Community ecology and multivariate analyses were performed using the vegan package [42], indicator species analyses were conducted using indicspecies, and graphical outputs were generated using ggplot2.

Previous taxonomic studies identified the need for detailed morphological differences to definitively identify Chydorus species, such as areas with cryptic diversity and poorly documented taxa [30].

4.1 Community restructuring under multi-stressor gradients

This study demonstrates that zooplankton communities in Bahr Al-Najaf are structured by interacting stressors, including salinity, organic enrichment, and metal contamination. The most prominent pattern is the decoupling of abundance and diversity: although total zooplankton density increased in the most impacted zone, both taxonomic and functional diversity declined significantly. This indicates a transition from diverse assemblages dominated by large-bodied cladocerans to simplified communities composed primarily of small, stress-tolerant taxa.

Elevated salinity (approximately 4.8 ppt) likely imposed substantial osmoregulatory costs on freshwater crustaceans, reducing fitness and excluding sensitive taxa such as Daphnia, Diaphanosoma, and Simocephalus. Simultaneously, increased metal bioavailability under saline conditions and high organic loading from sewage inputs likely intensified physiological stress. These combined pressures function as ecological filters, favoring opportunistic, r-selected species such as Brachionus, Rotaria, and Mesocyclops, which can exploit detrital and microbial pathways. Comparable patterns of community simplification and dominance by tolerant taxa have been widely documented in eutrophic and polluted wetlands, supporting the conclusion that multiple stressors operate synergistically rather than independently.

Observed shifts in zooplankton community composition are likely a product of both independent effects of salinity and pollution gradients and the synergistic interactions of multiple environmental stressors. Higher salinity could increase the bioavailability and physiological toxicity of heavy metals and organic pollutants, resulting in higher stress for stenohaline and pollution-sensitive taxa. These interacting stressors may explain the observed drastic decline in cladoceran diversity and concurrent dominance of stress-tolerant rotifers and cyclopoid copepods in Zone 3. Similar synergistic effects among salinity, heavy metal contamination and eutrophication have been reported in degraded inland wetlands and hypersaline transitional ecosystems, where combined environmental pressures drive biotic homogenisation and loss of sensitive zooplankton assemblages.

4.2 Body size reduction as an early ecological indicator

A pronounced reduction in cladoceran body size was observed along the pollution gradient, with decreases exceeding 50% in highly impacted areas. This trend reflects sublethal stress responses, in which energy is diverted from growth and reproduction to maintenance processes such as osmoregulation and detoxification. Additionally, metal exposure and organic pollution further disrupt moulting and metabolic processes, thereby reinforcing size suppression.

Size reduction was observed prior to local extinction, particularly in moderately impacted zones, highlighting its potential as an early-warning indicator. Shifts toward smaller body sizes have significant ecological consequences, including reduced grazing efficiency, altered nutrient cycling, and diminished energy transfer to higher trophic levels. The transition from large-bodied (>500 µm) to small-bodied (<300 µm) communities thus represents both taxonomic loss and functional degradation. These results support the integration of morphometric traits into biomonitoring frameworks. In comparison to behavioural indicators, body size offers a robust, quantifiable, and cost-effective proxy for chronic environmental stress, especially in arid wetland systems where monitoring resources are limited. The reduction in body size and simplification of community structure observed may be an adaptive ecological response to environmental stress. High salinity and pollution stress might divert a large part of the metabolic energy for osmotic pressure regulation and physiological functions other than for growth and reproduction. Such strategies of energy allocation can favour smaller, stress-tolerant organisms, while sensitive species gradually decline in the community. Therefore, the community structure becomes less complex and increasingly dominated by organisms that can survive unstable environmental conditions. These mechanisms could account for the observed decreasing of diversity and simplification of structure in the studied sites.

4.3 Ecological and One Health implications of stress-tolerant assemblages

The predominance of stress-tolerant taxa in degraded zones indicates advanced ecosystem disturbance and carries significant ecological and health implications. Rotifers, including Brachionus and bdelloids (Rotaria), along with cyclopoid copepods (Mesocyclops), are established indicators of eutrophic, hypoxic, and organically enriched environments. The increased abundance of these taxa suggests a transition toward microbial-based food webs, which diminishes trophic efficiency and compromises ecosystem stability.

Ecologically, the decline of large, lipid-rich cladocerans limits food resources for fish larvae and other consumers, which may hinder recruitment and reduce biodiversity. Concurrently, stress-tolerant taxa are frequently linked to elevated pathogen loads and contaminant accumulation, thereby increasing risks to water quality and food safety. These trends underscore the function of zooplankton as sensitive indicators of environmental change, with implications for ecosystem, animal, and human health.

4.4 From ecological signals to monitoring and management

The strong associations between zooplankton community structure and environmental gradients confirm their effectiveness as cost-efficient bioindicators. Shifts in community composition, reductions in organism size, and changes in dominance patterns reliably correspond to critical thresholds of ecosystem stress, such as low DO, high salinity, and elevated metal concentrations. These findings support the use of zooplankton metrics in early-warning monitoring systems. Integrating taxonomic, quantitative, and trait-based indicators yields a more ecologically relevant assessment than reliance on physicochemical parameters alone. In settings with limited resources, these approaches provide practical tools for detecting ecosystem degradation, informing management strategies, and promoting sustainable wetland utilization.

4.5 Comparison with literature

Shannon diversity values obtained in Bahr Al-Najaf were comparable to those reported from other stressed saline wetlands in the Middle East. Diversity values in the relatively less impacted Zone 1 (H′ = 2.45) were similar to those reported from moderately productive Iraqi marsh ecosystems, while the significantly reduced diversity in Zone 3 (H′ = 1.10) was comparable to conditions reported from heavily polluted hypersaline wetlands and eutrophic lagoons in arid regions of Iran and Egypt. These results support the interpretation that increased salinity and organic pollution strongly simplify the zooplankton community structure in transitional desert wetlands.

This study demonstrates that zooplankton communities in Bahr Al-Najaf are highly sensitive to combined salinity and pollution gradients, showing predictable changes in diversity, size structure, and functional composition. The shift toward low-diversity, small-bodied, stress-tolerant assemblages signals a significant loss of ecological function and indicates ecosystem degradation. By integrating abundance, diversity, and trait-based indicators, this research advances biomonitoring for arid wetlands and establishes zooplankton as effective indicators of environmental change. These results have direct implications for ecosystem management, biodiversity conservation, and the protection of ecosystem services in vulnerable freshwater systems. The results highlight the importance of long-term environmental monitoring programs and mitigating actions to prevent the slow deterioration of wetland ecosystems. Routine monitoring of physical, chemical and biological indicators can improve environmental evaluation and assist sustainable management decisions. In addition, wetland conservation methods and minimizing the inflow of untreated pollutants can be advocated to save the ecological community structure, ecological balance, and biodiversity of the impacted ecosystem.

We thank the field teams and laboratory technicians who assisted with sampling and analyses. Local authorities in Najaf Governorate facilitated access to the site.