Skip to main content
NCBI home page
Scientific Reports logoLink to Scientific Reports
. 2025 Oct 31;15:38256. doi: 10.1038/s41598-025-21997-1

Evaluation of non-indigenous biological assessment tools using benthic macroinvertebrates in a regulated river in the semi-arid region of Iran

Fakhrieh Mohseni 1, Mohammad Nemati Varnosfaderany 1,✉, Alireza Soffianian 1, Sima Fakheran 1
PMCID: PMC12578998  PMID: 41174057

Abstract

Benthic macroinvertebrates are widely used as key indicators in the biomonitoring of aquatic ecosystems, particularly in fragile ecosystems downstream of dams. Nevertheless, the use of non-indigenous biological tools requires regional evaluation, especially for developing countries and rivers with regulated flow in semi-arid regions. The Zayandehrud River is a perennial river in central Iran. Since 1953, it has been subject to inter-basin water transfer projects, and its flow is regulated by reservoirs and diversion dams. This river has recently become increasingly intermittent and fragile due to anthropogenic activities, highlighting the need for tailored assessment methods. This makes it a critical case study for evaluating benthic macroinvertebrate biological tools (BMBTs). Different BMBTs, including community composition, biotic indices (BMWP, ASPT, and LIFE), and the FFGs approach, were evaluated for the Zayandehrud River. According to the results, the macroinvertebrate communities downstream of the Zayandehrud Reservoir Dam (ZRD) showed homogenization of beta diversity due to river regulation, with significant spatial variation in Shannon diversity and community composition. Specifically, BMWP and ASPT indices effectively demonstrated the impact of flow interruptions and regulation. In contrast, the LIFE index, Shannon diversity index and FFGs approach do not accurately represent the environmental conditions, especially drying up of the Zayandehrud River downstream of the Cham-Aseman Diversion Dam (CDD). These methods likely failed because their assumptions (continuous flow and specific sensitivity traits) are inconsistent with the intermittent nature of semi-arid rivers and the desiccation tolerance of their taxa. Consequently, BMBTs must be applied cautiously to avoid misclassifications and misunderstandings in assessing regulated rivers. This underscores the urgent need for regionally adapted BMBTs to inform effective water management and policy decisions.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-21997-1.

Keywords: Zayandehrud river, Biomonitoring, Dam, Regulated river, Benthic macroinvertebrates

Subject terms: Predictive markers, Environmental sciences, Hydrology, Limnology

Introduction

Benthic macroinvertebrates are one of the most important freshwater groups and a key component of global biodiversity within aquatic ecosystems1. These organisms are significantly affected by anthropogenic disturbances and natural habitat fluctuations, resulting in a range of behavioral responses. Furthermore, their functional traits and life histories have also contributed to the development of biological monitoring tools to assess stressors and anthropogenic disturbances2. Benthic macroinvertebrates are very sensitive to variability and alteration in the flow regime and hydrology of the river3,4 as well as water quality5. Benthic macroinvertebrate biotic indices have been widely used as reliable tools for assessing the ecological status of freshwater ecosystems. These indices were primarily developed using data from indigenous rivers in the British. Several decades have passed since the use of biotic indices to evaluate water quality6, and there has been a growing variety of benthic macroinvertebrates biological tools (BMBTs)7. BMBTs are divided into several categories including, biotic indices5,9, richness and diversity indices10–12, and functional feeding groups (FFGs)13–16. BMBTs can provide comprehensive data on ecosystem conditions and improve the planning and decision-making process8. But most of them were developed for specific regions, and their application in other countries, where they are considered non-indigenous, may result in inaccurate ecological classification. Non-indigenous BMBTs in temporary or intermittent streams may lead to an inaccurate ecological categorization of these systems and a misunderstanding of the river’s state9. Some researches have highlighted the ability of different non-indigenous BMBTs in other rivers to improve environmental monitoring programs in specific regions, such as the downstream areas of dams10,11.

Therefore, selecting the most useful non-indigenous BMBTs for river health assessments in a basin is crucial for achieving accurate, reliable results and determining their applicability in other regions11–13.

The ecological condition of downstream areas of dams is currently deteriorating14. Flow alternation due to dam construction with compensation flows, characterized by a decrease in peak flows and an increase in low flows15 is one of the most significant anthropogenic impacts on a wide range of biotic and abiotic factors in aquatic ecosystems5. Several studies have been conducted to examine the impact of large dams16,17, small dams18, diversion dams19,20, and flow regulation21 on aquatic ecosystems. The impact of large dams has garnered more attention than the impact of diversion dams on the ecology of rivers22. Over the past decade, researchers have utilized flow-ecology correlations at the regional level for environmental flow monitoring. Nevertheless, access to comprehensive ecological and hydrological data has remained a significant challenge, especially in developing countries. Biomonitoring studies based on benthic macroinvertebrates have only spanned a brief period due to the limitations of available ecological data, particularly downstream of the large dam as a result of high flow variability5. Thus, further research is needed to validate and assess the applicability of different BMBTs in these regions, especially to ensure accurate and reliable river health assessment.

One of the most widely used biotic indices is the scoring system developed by the Biological Monitoring Working Party (BMWP) for assessing water quality in the UK. However, its development in a temperate climate presents technical challenges when applied to regions with different climatic and ecological conditions23. The BMWP index has been applied in many regions worldwide, including arid and semi-arid climates; however, there are notable limitations .Ecological differences, taxonomic compositions, physicochemical characteristics, and specific geographical factors in other regions can reduce the accuracy of the index scoring under local conditions24. Some sensitive or pollution-tolerant macroinvertebrate families may be absent from these ecosystems or respond differently than in reference environments, further decreasing the index’s accuracy. Moreover, natural stressors such as high salinity, pronounced flow fluctuations, and limited vegetation cover can alter index values even in the absence of anthropogenic pollution25.

Despite these challenges, the BMWP index remains one of the oldest and most widely utilized biotic index in developing countries and rivers facing issues such as irregular flow patterns and regulated dams (e.g., Zayandehrud River26, . A frequent lack of financial and evaluation plans for biomonitoring in developing countries has resulted in the neglect of several environmental issues, including the deterioration of streams, rivers, and watersheds27.

The Zayandehrud River, a heavily regulated river, serves as an example of how such factors impact water quality and aquatic life. In this study, we aim to compare different BMBTs to evaluate the reliability and applicability of selected BMBTs in a semi-arid, regulated river system.

Methods

Study area

The Zayandehrud River is the main perennial river in the Gavkhooni (Gavkhuni) Basin of Iran’s central plateau, spanning 460 km. This basin has arid and semi-arid areas; however, wet areas are located at the headwater source of the river, in the western areas of the basin. The overall precipitation in the basin ranges from 1,500 mm in the west to 50 mm in the east, with an average annual precipitation of 130 mm. The river originates from the Zard-Kuh Mountain, with an average natural flow of 900 MCM, and terminates in the Gavkhooni International Wetland. This basin provides crucial water for agricultural, industrial, urban, and environmental needs28. This basin exhibits unique hydrological and environmental conditions. Inter-basin water transfer projects have diverted water from the Karun and Dez basins to the Gavkhooni Basin since 195329,30. Isfahan, Shahre-Kord, Yazd, and Kashan cities, export water outside of the basin to meet the needs of their industrial and urban water demands31.

The Zayandehrud Reservoir Dam )ZRD(, built in 1970, has a capacity of 1470 MCM. Its purpose is to regulate water and manage the different needs of the basin. This dam has played a critical role in improving water supply and storage in the basin30. Hydropower production conditions and the management of the ZRD outlet, in addition to the climate, influence the flow regime of this river26. Industrial use of water from the river began in 1971 and has increased since 1997.

In 1989, the Cham-Aseman Diversion Dam (CDD), 100 km away from the ZRD, was established to direct water into sedimentation pools with a water intake capacity of 12.5 m3/s for drinking water supply. Recently, the CDD closed and discharged water specifically to support agricultural activities, industrial consumption, and the Yazd inter-basin water transfer project. On the other hand, water is present in 30% of the river from the ZRD to the CDD, and 70% of the Zayandehrud River has dried up. Major land uses in the Gavkhooni Basin include bare land, rangeland, agriculture, outcrop, forest, wetland areas, and urban regions, respectively 60.39%, 21.27%, 9.17%, 5.53%, 1.52%, 1.17%, and 0.96%32.

The Pole-Kalleh hydrometry station is located 7 km downstream of the CDD. The time series plot of the flow rate at the Pole-Kalleh hydrometry station depicts significant fluctuations in the hydrological conditions of the river, with both lack of water and low flow periods throughout the year. Over the past 40 years, the outflow discharge from the CDD has been regulated, and this station has also reported zero flow. River flow downstream of the CDD (Figures S1 and S2; S refers to supplementary materials) has decreased since 2015. In 2018, for 256 days, and in 2021, for approximately 201 days, the river remained dry for the majority of the year. Recent studies32 determined that the environmental flow for the Pole-Kalleh station is 10 m3/s. Consequently, the biotic communities at this station are experiencing drying and low-flow crises.

Benthic macroinvertebrate sampling

Sampling of benthic macroinvertebrates along the Zayandehrud River was conducted from the upstream and downstream of the ZRD, covering a stretch of 157 km in the late spring 2021. There are no financial and evaluation plans for biomonitoring of the river, and only a few biomonitoring studies have been conducted on benthic macroinvertebrates32–35. Sampling stations were selected based on previous studies32–35, location of hydrometry stations, recent land use changes, and spatial development. The average distance between the two selected stations was 13 km. A total of 48 samples were collected from 12 stations (Fig. 1). One station was selected upstream of the ZRD as the control station (S1, upstream segment), 9 stations within the 121-kilometer intermediate range between the ZRD and the CDD (S2-S10, midstream segment), and two stations downstream of the CDD (S11 and S12, downstream segment). We used a Surber sampler with a 25 × 25 cm frame for three replicate quantitative samples of benthic macroinvertebrates at each sampling station. Each sample was gathered using a standardized approach of kick-sampling for 3 min, followed by an extra 1-minute hand search28. Furthermore, one qualitative sample was collected at each station using a D-net.

Fig. 1.

Fig. 1

Open in a new tab

The location of the Zayandehrud River within Iran and the location of the 12 benthic macroinvertebrates monitoring stations including: S1: Ghale-Shahrokh; S2: Hojat-Abad, S3: Tanzimi-Dam; S4: Markade; S5: Hore; S6: Zamankhan; S7: Cham-Heydar; S8: Cham-Alishah; S9:Morgan; S10: Bagh-Bahadoran; S11: Cham-Aseman; S12: Pole-Kalleh )the map was created using QGIS software, version 3.28.3. https://qgis.org).

The samples were washed with a 60-mesh sieve and preserved with 4% formalin36 and transferred to the laboratory for analysis. Most benthic macroinvertebrates were identified to the lowest possible taxonomic level (genus or species), while some taxa were identified only to the family level due to morphological identification limitations43–46. In addition, all individuals were counted in each sample. This approach was applied to ensure uniform data quality across all stations and to facilitate comparison with previous studies, with identification and sampling levels consistent with those used in previous researches. Quantitative and qualitative data were used to determine the maximum number of benthic macroinvertebrate taxa to calculate biotic indices. Whereas, only quantitative data were used to calculate the Taxa Richness and Shannon diversity index .

Data analysis

The BMBTs, including community composition )Taxa richness and Shannon diversity index(, benthic macroinvertebrates biotic indices (BMWP and ASPT37, and LIFE38 ( and FFGs approach39, were calculated for this study and previous monitoring studies (200 samples) of benthic macroinvertebrates in the Zayandehrud River32–35. Biotic indices serve as essential tools for identifying the impacts of environmental stressors on the structure and dynamics of riverine communities. In this study, the ASPT and BMWP indices were selected due to their high sensitivity in assessing the ecological responses of macroinvertebrates to organic pollution, while the LIFE index was employed for its specificity in detecting alterations in flow regimes (e.g., reduced discharge or flow intermittency). The integration of these complementary indices enables the simultaneous evaluation of two key drivers (water quality and hydrological status) in the study area.

Scoring systems utilizing benthic macroinvertebrates have been developed for interpreting and quantifying large amounts of data resulting from biological monitoring. The BMWP and ASPT are crucial indices used to evaluate river conditions, considering the maximum taxa48. FFGs were quantified using two complementary approaches: (1) the percentage of taxa, which represents the taxonomic richness within each FFG, and (2) the relative abundance of organisms, which reflects the numerical dominance of individuals in each FFG. The simultaneous application of these methods provided a more comprehensive characterization of the macroinvertebrate community’s trophic structure, thereby enhancing the robustness of the analysis. Both metrics were evaluated concurrently to elucidate structural similarities and differences among the FFGs58. In accordance with the methodology proposed by Extence et al.38, benthic macroinvertebrates were classified based on their association with flow velocity to calculate the LIFE index. Then the final score was derived by synthesizing the abundance of each taxon across various flow categories, assigning higher scores to taxa indicative of faster flow velocities38. Moreover, these indices are applicable to previously collected data (200733, 201334, 201532, and 201735, enabling temporal comparisons across distinct monitoring periods.

We assessed the Taxa Richness, Shannon diversity index, and similarity between macroinvertebrate communities at stations using cluster analysis to determine community composition. Moreover, beta diversity was examined spatially using the Mantel test40–42. Because this examination could potentially enhance our understanding of community composition43. Generally, the species composition is likely to shift along spatial or environmental gradients, respectively44. The Mantel test determines whether the measured station distance can accurately predict shifts in the composition of macroinvertebrate species with 999 permutations45. To examine the composition of the benthic macroinvertebrate communities along the river, the sampling stations were categorized based on the longitudinal gradient along the river (upstream, midstream, and downstream), and multivariate statistical analyses were conducted. Community structure patterns were visualized using non-metric multidimensional scaling (NMDS). Accordingly, the homogeneity of group dispersions was first assessed using Permutational Analysis of Multivariate Dispersions (PERMDISP), followed by Permutational Multivariate Analysis of Variance (PERMANOVA) to determine the significance of differences in species composition across these river segments46 with 999 permutations. Similarity percentage analysis (SIMPER) was conducted to identify the key taxa contributing to within-group similarity and between-group dissimilarity47. All analyses were based on Bray–Curtis dissimilarity. One-way ANOVA was used to compare the diversity among the sampling stations, followed by Tukey’s post-hoc test for pairwise comparisons to identify significant differences between upstream, midstream, and downstream stations.

All calculations were performed using the vegan package in the R 4.2.2 software with the significance level set at 0.05, and Microsoft Excel was used.

Results and discussion

Community composition

A total of benthic macroinvertebrates were identified, including 4419 individuals, classified into 6 classes,11 orders, 18 families, and 24 genus/species. To ensure uniform data quality across all stations and compare to previous studies, we analyzed the benthic macroinvertebrate data at the genus level, excluding taxa identified at the family level that originated from morphological identification issues. The most abundant taxa belonged to the families Chironomidae and Gammaridae.

According to Fig. 2, based on the NMDS analysis (Stress = 0.101) the midstream stations exhibit more similar biological patterns and are separated from the upstream and downstream, indicating a variation in community composition along the river longitudinal gradient. The PERMDISP test revealed no significant differences in multivariate dispersion among the groups (p > 0.05), confirming the homogeneity of variances. In contrast, PERMANOVA results showed a statistically significant difference in macroinvertebrate community composition across river segments (p < 0.05). The results of PERMDISP and PERMANOVA suggest that although the overall level of diversity among sites was similar, species composition differed, which was mainly due to species turnover rather than a decline in diversity or species loss48. These differences, similar to the patterns observed between control and impacted sites, were mainly driven by species turnover, indicating that hydrological changes caused by anthropogenic activities such as dam construction have a significant impact on the composition of benthic macroinvertebrate communities49.

Fig. 2.

Fig. 2

Open in a new tab

Cluster analysis (A) and NMDS ordination (B) of the Zayandehrud River sampling stations based on the relative abundance of benthic macroinvertebrate communities.

The SIMPER analysis clearly illustrated patterns of similarity and dissimilarity across different river segments. In the comparison between upstream and midstream, Chironomus (22.19%, p = 0.002), Gammarus (18.93%, p = 0.003), and Rhithrogena robusta (8.23%, p = 0.005) contributed most to community dissimilarity. In upstream versus downstream comparisons, Rhithrogena robusta (8.51%, p = 0.008) emerged as the key distinguishing taxon, while Chironomus (20.28%, p = 0.031) had the highest contribution between midstream and downstream. The dominance of Chironomus in community differentiation can be explained by including multivoltine or short life cycles and the ability to adapt to variable environmental conditions50. In contrast, Rhithrogena robusta is highly sensitive to organic pollution, thermal stress, and reduced oxygen availability, making its presence or decrease a strong indicator of water quality and hydrological stability. A decline in the population of this species may indicate the negative impacts of anthropogenic activities such as dam construction and hydrological alterations on riverine habitat quality51.

The comparison of current taxa distribution with previous studies in the Zayandehrud River32–35 revealed that the macroinvertebrate community structure downstream of the Zayandehrud River has changed over time because of flow regulation. According to previous studies32–35, some families, such as Tipulidae and Leptophlebidae, were present downstream of the ZRD but were not identified in the current study at the sampling stations. On the other hand, our sampling results revealed the presence of the Callicorixa praeusta species downstream of the ZRD, which had not been reported in previous studies in this area (Table 1). The presence of Corixidae as non-indigenous taxa in some stations may be attributed to stressed habitats, which may be due to their tolerance, adaptability, and generalist feeding strategy52. Their ability to withstand elevated temperatures, low oxygen levels, desiccation, and other environmental stresses may allow these species to survive in challenging habitats53,54. The findings of this study show, that flow management has led to alterations in the ecosystem, such as the elimination of vulnerable species and the increase of drought-tolerant organisms, particularly those adapted to low-flow conditions. The overall change in the flow regime is responsible for the increase of resistance (such as the Corixidae family and Oligochaeta and Diptera orders) in the downstream stations of the river55. The lack of success of specialist species in completing their life cycles results in limited community diversity in sites where water flows for short periods or where flow regimes shift7.

Table 1.

List of benthic macroinvertebrates present at studied stations in the Zayandehrud River.

Class Order Family Genus/Species Upstream Downstream of the Zayandehrud Reservoir Dam Downstream of the Cham-Aseman Dam
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12
Insecta Ephemeroptera Heptageniidae Rhithrogena sp ✓
Rhithrogena robusta ✓ ✓ ✓ ✓ ✓
Epeorus ✓
Ephemerellidae Teloganopsis deficiens ✓ ✓
Caenidae Caenis
Baetidae Pseudocentroptiloides ✓ ✓ ✓
Diphetor hageni ✓
Baetis flavistriga ✓
Baetis sp ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Trichoptera Hydropsychidae Hydropsyche sp ✓ ✓ ✓ ✓ ✓
Psychomyiidae Psychomya ✓
Diptera Simuliidae Simulium ✓
Chironomidae Chironomus ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Coleoptera Elmidae Elmis ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Odonata Gomphidae Ophiogomphus ✓
Agridae Argia ✓
Hemiptera Corixidae Callicorixa praeusta ✓
Crustacea Amphipoda Gammaridae Gammarus ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Bivalvia Veneroida Sphaeridae ✓ ✓ ✓ ✓ ✓
Gastropoda Basommatophora Physidae Physella ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Heterostropha Valvatidae Valvata ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Hirudinae Arhynchobdellida Erpobdellidae Mooreobdella ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Erpobdella ✓ ✓ ✓ ✓ ✓ ✓
Oligochaeta Lumbriculida Lumbriculidae ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Open in a new tab

Community composition based on specific groups or clusters has the potential to represent more specific groups of species, which might be advantageous in applications that focus on smaller spatial scales .These clusters may be linked to river types that exhibit especially challenging conditions, such as regulated flow, fluctuating flow, or total stoppage of flow following the closure of a dam. According to the taxa clustering results (Fig. 2), the sampling stations based on the relative frequency of benthic macroinvertebrates are categorized into two primary groups and four subgroups. Figure 2 shows that the upstream of the ZRD (S1) and the downstream of the CDD (S12) belong to two distinct clusters. The control station (S1) is located upstream of the ZRD, while S12, located downstream of the diversion dam, only experiences water seepage and infiltration most of the time. Consequently, the classification into two distinct groups aligned with the anticipated results and accurately depicted the water scarcity resulting from the dam diversion process. The downstream stations of the ZRD are grouped based on the homogeneity in species type, the changes in community composition due to dam construction, and water flow regulation. The composition of species mainly changes due to variations in environmental conditions across different locations, while the impact of spatial factors is less significant56. Species similarity between sites decreases along spatial distances (e.g57. , , particularly if the spatial extent is sufficiently large58. The Mantel test assesses the homogeneity and similarity of the regulated flow at different stations. The test yielded a non-significant difference between stations (Sig. 0.187), indicating that there is no substantial variation in benthic macroinvertebrates among the stations as the geographical distance increases, and downstream uniformity is observed. Generally, the Mantel test and clustering indicate that environmental distance may not change along spatial or environmental gradients. The macroinvertebrate communities downstream of the ZRD are experiencing homogenization based on beta diversity as a result of river regulation. This finding fits into the studies done by Gillespie59 and Krajenbrink et al.5, which also emphasized a significant distinction between stations affected by regulated flow.

The taxonomic diversity (Fig. 3) has been declining both between upstream and downstream areas, as well as from previous studies to the present. Typically, the Shannon diversity index declines downstream of the dam60. The Shannon diversity index decreases downstream of the ZRD dam and has few fluctuations at stations S1–S8. Regulated rivers have reduced species diversity compared to unregulated rivers, and regulated rivers have a lower number of taxa compared to unregulated ones60. However, a cross-sectional increase in station diversity does not imply that dams are beneficial for river diversity; rather, regional richness decreases and beta diversity is lower16. There is a shift in the composition of the macroinvertebrate community, with a shift towards taxa that are more tolerant to pollution61. The indicators of diversity and richness partially reflect the regulation of the river and the interruption of its flow specifically downstream of the CDD. The results of the one-way ANOVA indicated significant differences in Shannon diversity index among the sampling stations (p < 0.0001).

Fig. 3.

Fig. 3

Open in a new tab

Comparing spatiotemporal variation of the Taxa richness and Shannon diversity index of macroinvertebrate at 2007 (Varnosfaderany et al., 2009), 2013 (Ebrahimi et al., 2017), 2015 (Department of Natural Resources., 2017), 2017 (Department of Natural Resources., 2019), and 2022 (the current study) in the Zayandehroud River.

Tukey’s pairwise comparisons revealed that the upstream station (S1) differed significantly from the downstream stations (S11 and S12, p < 0.0001 and p < 0.05, respectively). However, no significant differences were found between the some midstream and upstream stations (S1 vs. S4), nor between some midstream and downstream stations (S9 vs. S11 and S12).

Macroinvertebrate biotic indices

The LIFE index (Fig. 4) exhibits little variation from the upstream to the downstream of the ZRD and CDD. During the sampling period, the closure of the CDD decreased water flow at the both stations located downstream of the CDD (S11 and S12), and only leakage and seepage contributed a minor quantity of water to these stations. However, the LIFE index is increasing in the S11, and this index does not provide information about the scarcity of water or any alterations in the flow pattern at these two stations. England et al.21 and Krajenbrink et al.5 suggest that the LIFE Index, a metric used to assess the ecological state of rivers, is an unreliable indication when the loss in species variety is impacted by the river flow. One reason for this is that the index is less likely to be affected by river drying up, and it is calculated with attention to the flow preferences of the species that are already there38.

Fig. 4.

Fig. 4

Open in a new tab

Spatial and temporal variation of BMWP and ASPT indices in the Zayandehrud River; 2007 (Varnosfaderany et al., 2009), 2013 (Ebrahimi et al., 2017), 2015 (Department of Natural Resources., 2017), 2017 (Department of Natural Resources., 2019), and 2022 (the current study).

BMWP index for station S1, indicates a score between 20 and 40, a moderate condition in the poor category. The BMWP index declines from upstream to downstream of the ZRD, and the poor category spans from 5 to 56 km. The results of the ASPT index, along with those of other studies32–35, show that standardizing calculations is a good way to keep taxonomic richness, which changes in different places and times. The ASPT index offers a more accurate representation of river conditions when compared to other indices62. Based on previous studies in the study area, no specific pollution has been introduced. Instead, the regulatory flow has caused the score of the index to decrease.

The ASPT index indicates that the water is clean at S1, then decreases downstream of the ZRD. Until S11, the water maintains an average score condition between 4 and 5. At S12, it decreases to a score < 4, which is caused by the interrupted flow of the river, and there is no specific pollution. Belmar et al.3 expect the river to undergo self-purification after 11 km, leading to an improvement in the indices. At the downstream of the ZRD, its self-purification ability may be lost. This could be due to various inputs into the river resulting from hydrological conditions and changes in flow regime, possibly influenced by seasonal fluctuations and management decisions. In addition, closing the CDD (Fig. 4) creates unfavorable conditions, such as water scarcity or even water depletion. The BMWP and ASPT indices do not prominently reflect this aspect. They indicate a poor condition only at the S12 station, where the water reached its minimum level.

A cross-sectional comparison of previous studies reveals that the BMWP and ASPT indices have recently declined. It was found that these biotic indices were lower downstream of ZRD in 2022 than they were in 200733, 201334, 201532, and 201735. According to the investigations, the decrease in BMWP and ASPT indices in the stations is not due to the introduction of new sources of pollution into the river. Rather, the recent reduction in flow following the outflow of the ZRD may be a significant contributing factor. However, the control site (Ghale-Shahrokh, S1) in the upstream did not exhibit this change, suggesting that the regulatory flow did not significantly impact the BMWP and ASPT indices there. Although it is necessary to calibrate biotic indices for semi-arid regions63, the BMWP and ASPT indices can show some of the effects of the river drought conditions and regulatory flow. In contrast, the LIFE index does not accurately represent the environmental conditions following the closure of the CDD. Accordingly, the application of the LIFE index in flow-regulated rivers in semi-arid regions should be undertaken with caution and ideally supplemented by complementary metrics to ensure a more comprehensive ecological assessment.

The composition of the FFGs in Fig. 5 reveals that the C-G (gathering collectors) group is the most dominant in the river. The presence of this group is most likely a result of higher amounts of organic matter, which may indicate a rise in agricultural activity, Although autochthonous food sources are significant in C-G and C-F (filtering collectors)64. The SC (scrapers) group exhibits substantial variations among habitats. Scraper taxa are formed from the accumulation of organic debris and large particles in arid regions adjacent to the river. Szałkiewicz et al.65 found that the abundance of these organisms is higher in areas with dense vegetation along the riverbanks. The gatherers and filterers are more resilient to pollution that may affect the availability of specific food sources66. On the other hand, scrapers are considered to be more susceptible to environmental changes66. According to reports, during protracted high flows, the relative abundances of gatherers decreased due to the mobilization of fine sediment and scouring of benthic substrates. Inversely, scrapers increased as flows increased67.

Fig. 5.

Fig. 5

Open in a new tab

Composition function feeding groups (FFG) based on the relative abundance of taxa (a), and percentage number of taxa (b) C-G: gathering collectors, C-F: filtering collectors, SC: scrapers, and PRE: predators.

The presence of C-F in rivers may be attributed to their ability to consume a wider variety of food sources compared to specialist groups68. In the downstream of CDD, Hydropsychidae, a C-F group, are eliminated when flow stops at these sites, as they rely on water current for both food and oxygen. Conversely, Erpobdellidae as a PRE group, is present downstream but absent upstream of the ZRD. There was no correlation between predators and flow increases at any site; however, as the flow was regulated, according to the reports in the studies, the relative abundances of predators increased downstream of the dam67. In addition to the flow, in arid and semi-arid areas, the substrate type plays a significant role in shaping the structural variations of functional feeding groups. This factor may reduce the accuracy of standard indices, which are primarily developed for stable flow conditions and more humid environments, as these indices often fail to account for the specific traits of taxa adapted to dry conditions and intermittent flows. Therefore, local calibration of such indices is essential to accurately reflect ecological pressures in these unique ecosystems [58,78].

Conclusion

The findings of this study indicate that the BMWP and ASPT biotic indices successfully reflected the impact of flow interruptions and regulation by the ZRD and CDD dams. However, other tools, including the LIFE index, Shannon diversity index, and the FFGs approach, did not adequately capture environmental changes following the closure of the CDD.

Our findings indicate that employing BMBTs not specifically tailored to a region can lead to inaccurate river quality assessments. The construction of the dam has significantly altered the river’s flow and, consequently, its ecological condition. Therefore, adapting BMBTs to local conditions or developing more suitable alternatives is crucial for accurately monitoring the biological status of rivers, particularly in semi-arid regions. Such adjustments will significantly improve assessment precision and support more effective water management strategies.

Moving forward, recalibrating existing indices and developing new functional trait-based metrics that better reflect arid-region ecology, while directly integrating these tools into water management strategies, will ensure more accurate and effective river status assessments.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (374.9KB, docx)

Author contributions

F.M. conducted the laboratory analyses, analyzed the data, prepared the figures, and wrote the initial manuscript draft. M.N.V. acted as a supervisor and provided resources for the study. A.S. and S.F. designed the study. All authors reviewed the https://submission.springernature.com/new/submission/1fa25b17-69eb-4e73-9a6a-61457aba5b55/reviewmanuscript.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Raitif, J., Plantegenest, M. & Roussel, J. M. From Stream To Land: Ecosystem Services Provided by Stream Insects To Agriculture270p. 32–40 (Agriculture, ecosystems & environment, 2019).
  • 2.Bonada, N. et al. Developments in aquatic insect biomonitoring: a comparative analysis of recent approaches. Ann. Rev. Entomol.51 (1), 495–523 (2006). [DOI] [PubMed] [Google Scholar]
  • 3.Belmar, O. et al. Functional responses of aquatic macroinvertebrates to flow regulation are shaped by natural flow intermittence in mediterranean streams. Freshw. Biol.64 (5), 1064–1077 (2019). [Google Scholar]
  • 4.Doretto, A. et al. Effectiveness of artificial floods for benthic community recovery after sediment Flushing from a dam. Environ. Monit. Assess.191 (2), 88 (2019). [DOI] [PubMed] [Google Scholar]
  • 5.Krajenbrink, H. J. et al. Macroinvertebrate community responses to river impoundment at multiple Spatial scales. Sci. Total Environ.650, 2648–2656 (2019). [DOI] [PubMed] [Google Scholar]
  • 6.Metcalfe, J. L. Biological water quality assessment of running waters based on macroinvertebrate communities: history and present status in Europe. Environ. Pollut.60 (1–2), 101–139 (1989). [DOI] [PubMed] [Google Scholar]
  • 7.Laini, A. et al. Using invertebrate functional traits to improve flow variability assessment within European rivers. Sci. Total Environ.832, 155047 (2022). [DOI] [PubMed] [Google Scholar]
  • 8.Bendary, R. E. & Hegab, M. H. Applying diversity and pollution biotic indices based on macroinvertebrates to assess water quality of the Ismailia Canal, Egypt. Egypt. J. Aquat. Biology Fisheries. 28 (1), 1277–1291 (2024). [Google Scholar]
  • 9.Acuña, V., Hunter, M. & Ruhí, A. Managing temporary streams and rivers as unique rather than second-class ecosystems. Biol. Conserv.211, 12–19 (2017). [Google Scholar]
  • 10.Marzin, A., Verdonschot, P. F. & Pont, D. The relative influence of catchment, riparian corridor, and reach-scale anthropogenic pressures on fish and macroinvertebrate assemblages in French rivers. Hydrobiologia704, 375–388 (2013). [Google Scholar]
  • 11.Cox, B., Oeding, S. & Taffs, K. A comparison of macroinvertebrate-based indices for biological assessment of river health: A case example from the sub-tropical Richmond river catchment in Northeast new South Wales, Australia. Ecol. Ind.106, 105479 (2019). [Google Scholar]
  • 12.Rose, P. et al. Incorporating species losses and gains into a fish-based index for stream bioassessment increases the detection of anthropogenic disturbances. Ecol. Ind.69, 677–685 (2016). [Google Scholar]
  • 13.Stitz, L., Fabbro, L. & Kinnear, S. Response of macroinvertebrate communities to seasonal hydrologic changes in three sub-tropical Australian streams. Environ. Monit. Assess.189, 1–15 (2017). [DOI] [PubMed] [Google Scholar]
  • 14.Poff, N. L. & Zimmerman, J. K. Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshw. Biol.55 (1), 194–205 (2010). [Google Scholar]
  • 15.Chen, W. & Olden, J. D. Evaluating transferability of flow–ecology relationships across space, time and taxonomy. Freshw. Biol.63 (8), 817–830 (2018). [Google Scholar]
  • 16.Wang, J. et al. What explains the variation in dam impacts on riverine macroinvertebrates? A global quantitative synthesis. Environ. Res. Lett.15 (12), 124028 (2020). [Google Scholar]
  • 17.Schneider, S. C. & Petrin, Z. Effects of flow regime on benthic algae and macroinvertebrates-A comparison between regulated and unregulated rivers. Sci. Total Environ.579, 1059–1072 (2017). [DOI] [PubMed] [Google Scholar]
  • 18.Linares, M. S. et al. Small hydropower dam alters the taxonomic composition of benthic macroinvertebrate assemblages in a Neotropical river. River Res. Appl.35 (6), 725–735 (2019). [Google Scholar]
  • 19.Baker, D. et al. Downstream effects of diversion dams on sediment and hydraulic conditions of Rocky mountain streams. River Res. Appl.27 (3), 388–401 (2011). [Google Scholar]
  • 20.Dewson, Z. S., James, A. B. & Death, R. G. A review of the consequences of decreased flow for instream habitat and macroinvertebrates. J. North. Am. Benthological Soc.26 (3), 401–415 (2007). [Google Scholar]
  • 21.England, J. et al. An invertebrate-based index to characterize ecological responses to flow intermittence in rivers. Fundamental Appl. Limnol.193 (1), 93–117 (2019). [Google Scholar]
  • 22.Huang, J. et al. How do small dams alter river food webs? A food quality perspective along the aquatic food web continuum. J. Environ. Manage.355, 120501 (2024). [DOI] [PubMed] [Google Scholar]
  • 23.Ochieng, H., Odong, R. & Okot-Okumu, J. Comparison of temperate and tropical versions of biological monitoring working party (BMWP) index for assessing water quality of river Aturukuku in Eastern Uganda. Global Ecol. Conserv.23, e01183 (2020). [Google Scholar]
  • 24.Ruiz-Picos, R. et al. Assessing ecological impairments in Neotropical rivers of mexico: calibration and validation of the biomonitoring working party index. Int. J. Environ. Sci. Technol.14 (9), 1835–1852 (2017). [Google Scholar]
  • 25.Carrasco, C. et al. Macroinvertebrados acuáticos En Arroyos asociados Con Bofedales altoandinos, Ayacucho Perú. Revista De Biología Trop.68, 116–131 (2020). [Google Scholar]
  • 26.Varnosfaderany, M. N. et al. Biological assessment of the Zayandeh Rud River, Iran, using benthic macroinvertebrates. Limnologica-Ecology Manage. Inland. Waters. 40 (3), 226–232 (2010). [Google Scholar]
  • 27.Fathi, P. et al. Development of the Karun macroinvertebrate tolerance index (KMTI) for semi-arid mountainous streams in Iran. Environ. Monit. Assess.194 (6), 421 (2022). [DOI] [PubMed] [Google Scholar]
  • 28.Murray-Rust, H. et al. An Overview of the Hydrology of the Zayandeh Rud Basin3p. 52–54 (IAERI-IWMI Research Reports, 2000).
  • 29.Gohari, A. et al. Water transfer as a solution to water shortage: a fix that can backfire. J. Hydrol.491, 23–39 (2013). [Google Scholar]
  • 30.Isfahan, I. Integrated Water Resource Management in Isfahan, Iran, Zayandehrud River catchment Reports. (2014).
  • 31.Safavi, H. R., Golmohammadi, M. H. & Sandoval-Solis, S. Scenario analysis for integrated water resources planning and management under uncertainty in the Zayandehrud river basin. J. Hydrol.539, 625–639 (2016). [Google Scholar]
  • 32.Department of Natural Resources. I.U.o.T., Environmental flow requirement of Zayandehrud River and Gavkhooni International Wetland for sustainable ecological functions 2017.
  • 33.Varnosfaderany, M. N. et al. Water quality assessment in an arid region using a water quality index. Water Sci. Technol.60 (9), 2319–2327 (2009). [DOI] [PubMed] [Google Scholar]
  • 34.Ebrahimi Dastgerdi, H., Ebrahimi, E. & Fakheran, S. Impacts of Zayandehroud dam on the macro-benthic invertebrate and water quality of Zayandehroud river using BMWP and ASPT biological indices. Iran. J. Appl. Ecol.6 (2), 55–67 (2017). [Google Scholar]
  • 35.Department of Natural & Resources, I. U. T. Biodiversity monitoring studies of macrobenthos and macrophytes of Gavkhooni Basin in order to be used in The Compr. Manage. plan. Watershed Gavkhooni Int. Wetland (2019).
  • 36.ISO. Water quality - guidelines for the selection of sampling methods and devices for benthic macroinvertebrates in fresh waters (ISO 10870). (2012).
  • 37.Armitage, P. et al. The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. Water Res.17 (3), 333–347 (1983). [Google Scholar]
  • 38.Extence, C., Balbi, D. & Chadd, R. River flow indexing using British benthic macroinvertebrates: a framework for setting hydroecological objectives. Regulated Rivers: Res. Management: Int. J. Devoted River Res. Manage.15 (6), 545–574 (1999). [Google Scholar]
  • 39.Cummins, K. W. & Klug, M. J. Feeding ecology of stream invertebrates. Annu. Rev. Ecol. Syst.10, 147–172 (1979). [Google Scholar]
  • 40.Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res.27 (2_Part_1), 209–220 (1967). [PubMed] [Google Scholar]
  • 41.Bis, B., Zdanowicz, A. & Zalewski, M. Effects of catchment properties on hydrochemistry, habitat complexity and invertebrate community structure in a lowland river. In Assessing the Ecological Integrity of Running Waters: Proceedings of the International Conference, held in Vienna, Austria, 9–11 November. 2000. Springer. 2000. Springer. (1998).
  • 42.Villéger, S., Grenouillet, G. & Brosse, S. Decomposing functional β-diversity reveals that low functional β‐diversity is driven by low functional turnover in E uropean fish assemblages. Glob. Ecol. Biogeogr.22 (6), 671–681 (2013). [Google Scholar]
  • 43.Legendre, P. Interpreting the replacement and richness difference components of beta diversity. Glob. Ecol. Biogeogr.23 (11), 1324–1334 (2014). [Google Scholar]
  • 44.Cai, Y. et al. Composition, diversity, and environmental correlates of benthic macroinvertebrate communities in the five largest freshwater lakes of China. Hydrobiologia788, 85–98 (2017). [Google Scholar]
  • 45.Abati, S. et al. Response of macrophyte communities to flow regulation in mountain streams. Environ. Monit. Assess.188, 1–12 (2016). [DOI] [PubMed] [Google Scholar]
  • 46.Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr.69 (1), 1–24 (1999). [Google Scholar]
  • 47.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol.18 (1), 117–143 (1993). [Google Scholar]
  • 48.Keke, U. N. et al. Weak relationships among macroinvertebrates beta diversity (β), river status, and environmental correlates in a tropical biodiversity hotspot. Ecol. Ind.129, 107868 (2021). [Google Scholar]
  • 49.Marino, A. et al. Functional traits drive the changes in diversity and composition of benthic invertebrate communities in response to hydrological regulation. Water16 (7), 989 (2024). [Google Scholar]
  • 50.Serra, S. R. et al. Chironomidae traits and life history strategies as indicators of anthropogenic disturbance. Environ. Monit. Assess.189, 1–16 (2017). [DOI] [PubMed] [Google Scholar]
  • 51.Bredenhand, E. & Samways, M. J. Impact of a dam on benthic macroinvertebrates in a small river in a biodiversity hotspot: cape floristic Region, South Africa. J. Insect Conserv.13, 297–307 (2009). [Google Scholar]
  • 52.Gerth, W. J., Li, J. & Giannico, G. R. Agricultural land use and macroinvertebrate assemblages in lowland temporary streams of the Willamette Valley, Oregon, USA. Ecosyst. Environ.236, 154–165 (2017). Agriculture. [Google Scholar]
  • 53.Hershkovitz, Y. & Gasith, A. Resistance, resilience, and community dynamics in mediterranean-climate streams. Hydrobiologia719, 59–75 (2013). [Google Scholar]
  • 54.Rosser, Z. C. & Pearson, R. G. Hydrology, hydraulics and scale influence macroinvertebrate responses to disturbance in tropical streams. J. Freshw. Ecol.33 (1), 1–17 (2018). [Google Scholar]
  • 55.Ogbeibu, A. & Oribhabor, B. Ecological impact of river impoundment using benthic macro-invertebrates as indicators. Water Res.36 (10), 2427–2436 (2002). [DOI] [PubMed] [Google Scholar]
  • 56.Cottenie, K. Integrating environmental and Spatial processes in ecological community dynamics. Ecol. Lett.8 (11), 1175–1182 (2005). [DOI] [PubMed] [Google Scholar]
  • 57.Saito, V. S. et al. Dispersal traits drive the phylogenetic distance decay of similarity in Neotropical stream metacommunities. J. Biogeogr.42 (11), 2101–2111 (2015). [Google Scholar]
  • 58.Heino, O. A., Takala, A. J. & Katko, T. S. Challenges to Finnish water and wastewater services in the next 20–30 years. E-Water1, 1–20 (2011). [Google Scholar]
  • 59.Gillespie, B. R. et al. A critical analysis of regulated river ecosystem responses to managed environmental flows from reservoirs. Freshw. Biol.60 (2), 410–425 (2015). [Google Scholar]
  • 60.Nashaat, M. R. & Al-Bahathy, I. A. Impact of Hindiya Dam on Copepoda Diversity in Euphrates River, North of Babylon Province, Iraq6p. 121–143 (Biological and Applied Environmental Research, 2022). 2.
  • 61.Turak, E. & Waddell, N. Development of AUSRIVAS Models for New South Wales (Environment Australia, 2002).
  • 62.Chadd, R. Assessment of aquatic invertebrates. Conservation monitoring in freshwater habitats: A practical guide and case studies, : pp. 63–72. (2010).
  • 63.Arana Maestre, J. et al. Aquatic macroinvertebrates of arid and semi-arid ecosystems of Perú. Front. Environ. Sci.9, 658940 (2021). [Google Scholar]
  • 64.Torres-Ruiz, M., Wehr, J. D. & Perrone, A. A. Trophic relations in a stream food web: importance of fatty acids for macroinvertebrate consumers. J. North. Am. Benthological Soc.26 (3), 509–522 (2007). [Google Scholar]
  • 65.Szałkiewicz, E., Kałuża, T. & Grygoruk, M. Detailed analysis of habitat suitability curves for macroinvertebrates and functional feeding groups. Sci. Rep.12 (1), 10757 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Barbour, M. et al. A framework for biological criteria for Florida streams using benthic macroinvertebrates. J. North. Am. Benthological Soc.15 (2), 185–211 (1996). [Google Scholar]
  • 67.Brooks, A. J. et al. Long-term environmental flows restore benthic invertebrate communities in a highly regulated river. Restor. Ecol.32 (5), e14174 (2024). [Google Scholar]
  • 68.Merritt, R. W. et al. Development and application of a macroinvertebrate functional-group approach in the bioassessment of remnant river oxbows in Southwest Florida. J. North. Am. Benthological Soc.21 (2), 290–310 (2002). [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (374.9KB, docx)

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES