Revision of the biological monitoring working party score system: Evidence from the subtropical urban river in China

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Reliable and affordable bioassessment tools are fundamental for managing and restoring urban river ecosystems. This study collected macroinvertebrates from a typical urban river of the Pearl River Delta region in the dry season (December 2021 to January 2022) and the wet season (May to June 2022). Family sensitivity values (FSVs) were revised based on local biotic and abiotic data, and then used to adapt the Biological Monitoring Working Party (BMWP) and Average Score per Taxon (ASPT) indices. The study employed Shapiro-Wilk normality test and linear regression model to analyze the fitting relationship between bio-indices and Water Quality Index (WQI), and compared their differences between using the origin FSVs and revised FSVs. The results indicated that the revised FSVs for urban rivers decreased. Furthermore, due to differences in macroinvertebrate taxa composition and water quality conditions between dry and wet seasons, the revised FSVs differed between the two seasons, and the lower FSV of the specific family were recommended, reflecting the lower limit of pollution tolerance. The adapted BMWP and ASPT indices provide more accurate water quality assessment results and are reliable indicators in urban rivers. Thus, the adapted macroinvertebrate indicator is a suitable bioassessment tool for subtropical urban rivers in this region, allowing the identification of priority areas for management and a recovery plan. Earth and environmental sciences/Ecology/Freshwater ecology Earth and environmental sciences/Ecology/Urban ecology Macroinvertebrates Bioindication Biomonitoring BMWP ASPT Urban river Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Accurate and effective monitoring and assessment of water quality is essential for the protection of aquatic ecosystems 1 . Water physicochemical monitoring, e.g. nutrient or oxygen-depleting substance content testing, provides an indication of the instantaneous state of water quality 2 . In contrast, bioindicators provide a non-transient approach to water quality assessment. Biotic indices take into account the sensitivity or tolerance of individual species or groups of species to pollution and are therefore suitable for assessing the health of aquatic ecosystems 3–4 . Macroinvertebrates are widely used as bioindicator in water quality monitoring and assessment due to their weak motility and sensitivity to environmental disturbances 5 . Among macroinvertebrates rapid bioassessment indices, the Biological Monitoring Working Party (BMWP) is one of the most widely used indices 6 . The BMWP index only requires identification to the family level and is therefore commonly used in rapid bioassessment programs in rivers around the world 7–12 . There are differences in the taxonomic composition of macroinvertebrates in different countries and regions. Moreover, the sensitivity of macroinvertebrates to organic pollution varies under different environmental conditions, e.g., Baetidae in different regions exhibit different organic pollution sensitivity characteristics 13 . The BMWP index needs to be adapted according to the local environment conditions to ensure the accuracy of the assessment. As a result, a number of regional indices have been developed, including Spain 14 , Thailand 15 , Brazil 16 , Poland 17 , Ethiopia 18 , South Korea 19 and Mexico 7 , among others. The assignment of family sensitivity values (FSVs) to the BMWP has evolved from qualitative or semi-quantitative approach to a quantitative framework that incorporates both biotic and abiotic data. Initially, the sensitivity values for 82 family-level taxa were derived based on empirical assessments by UK expert 6 . However, empirical values that have not been field-tested are often biased. Therefore, Walley and Hawkes 20 used a quantitative approach based on 17,000 samples covering 85 families to calibrate FSVs according to habitat classification and individual abundance classes. The method has since been applied in other regions 21 . However, there is a growing realization that FSVs need to be revised in conjunction with environmental pollution data to improve the accuracy of assessment 22–23 . For example, Ruiz-Picos et al. 24 calibrated macroinvertebrates FSVs in Neotropical rivers based on river-specific environmental characteristics and validated them in other rivers in the same ecoregion. Urban rivers play a vital role as essential water sources and transportation routes in the urban landscapes. Meanwhile, it is important to recognize that functions such as recreation and flood control are prioritized in the management of urban rivers. However, rapid urbanization poses a significant threat to the ecological conditions of these waterways 25 , including increased water pollution, altered hydro-morphological characteristics, and reduced riparian vegetation cover 26–27 . Despite the development of many effective management measures for urban rivers, water quality management in urban rivers remains a challenge in many countries. Among others, bioassessment is an important basis for restoring the ecological potential of urban rivers 27 . There are more reports on the calibration of macroinvertebrate BMWP index, but little attention has been paid to urban rivers. Urban rivers have unique contamination and disturbance history, so it is scientifically important to calibrate the BMWP by incorporating the water quality characteristics of urban rivers. Since the 1980s, the Pearl River Delta (PRD) region has experienced rapid urban expansion. By 2022, the urbanization rate in the region had soared to 87.5% 28 , which has seriously affected the health of regional river ecosystem. Consequently, subtropical urban rivers in the PRD region were selected for this study with the aim of ( 1 ) revising macroinvertebrates FSVs in context of the river environment characteristics, and ( 2 ) verifying the suitability of the adapted BMWP index for assessing the water quality of subtropical urban rivers in the region. We hypothesized that long-term intensive anthropogenic disturbance would alter the environmental adaptive capacity of macroinvertebrates in urban rivers, making them less sensitive to the environment. 2. Material and methods 2.1. Study area Dongguan is located in the south-central part of Guangdong Province, China, at the lower reaches of the Dongjiang River in the PRD (E 113°31′-114°15′, N 22°39′-23°09′). Dongguan has a dense river network, and the southeastern part is mountainous with undulating terrain ranging from 200 m to 600 m above sea level. The northwestern part is an alluvial plain with flat terrain and river channel. It has a subtropical monsoon climate with slight temperature changes and abundant rainfall. Rainfall is mainly concentrated from April to October. 2.2. Sampling and analyses Data from the local water resources department shows that the average monthly precipitation for the past three years is 134.05 mm. The maximum average precipitation was recorded in the months of May (271.70 mm) and June (300.47 mm), while the minimum average precipitation was recorded in the months of December (27.77 mm) and January (12.37 mm). Therefore, this study monitored macroinvertebrates and water quality at 35 urban river sites during the dry season (from December 2021 to January 2022) and the wet season (from May to June 2022, Fig. 1 ). Macroinvertebrates were collected according to the requirements in the Chinese industry standard 29 . In wadable reaches, macroinvertebrates were collected using D-net with a quantitative sampling length of 1 m, repeated 3 times. In nonwadable reaches, macroinvertebrates were collected using Petersen grab sampler (1/16 m 2 opening) for a total of 8 collections. Qualitative samples were collected in different habitats using D-net for 15 min. Samples were preserved in a 75% ethanol solution. Specimens were sorted using a stereomicroscope and identified to the lowest taxonomic level possible. Water quality parameters, such as water temperature (WT, ℃), pH, dissolved oxygen (DO, mg/L), electrical conductivity (EC, µS/cm), and total dissolved solids (TDS, mg/L) were measured by a portable multiparameter analyzers. Other parameters including total nitrogen (TN, mg/L), total phosphorus (TP, mg/L), ammonia nitrogen (NH 4 + -N, mg/L), nitrite nitrogen (NO 2 -N, mg/L), nitrate nitrogen (NO 3 -N, mg/L), five-days biochemical oxygen demand (BOD 5 , mg/L), chemical oxygen demand (COD cr , mg/L), and potassium permanganate index (COD Mn , mg/L) were determined in the laboratory after water sample collection. 2.3. Family sensitive values (FSVs) revision Macroinvertebrates FSVs were revised according to the method proposed by Ruiz-Picos et al. 24 , which has been validated in other study 7 . This revision consisted of five steps: ( 1 ) screening of water quality parameters (Table S1 , S2, S3), ( 2 ) standardization of water quality parameters according to Chinese surface water quality standards, ( 3 ) calculation of physicochemical quality index (Pcq), ( 4 ) classification of macroinvertebrate individual abundance, and ( 5 ) calculation of FSVs using Pcq and individual abundance classes. Given that sensitivity values for some macroinvertebrates may fluctuate over time 30 , this study revised FSVs for the dry and wet seasons to account for species composition and physicochemical conditions during each period. 2.4. Index Calculations 2.4.1. BMWP and ASPT The BMWP and ASPT indices were utilized to assess the water quality in urban rivers and were calculated as described below. In addition, separate calculations were made using the original FSVs and the revised FSVs to compare the accuracy of these indices in assessing water quality. $$BMWP={\Sigma }{t}_{i}$$ 1 $$ASPT=\frac{\sum _{i=1}^{n}{t}_{i}}{n}$$ 2 where t i represents the FSV of species i observed at the site, while n denotes the total number of taxa at the family level at the site. 2.4.2. Water Quality Index (WQI) The Water Quality Index (WQI) provides an objective assessment of river health. Comparison of the BMWP and ASPT indices with the WQI will determine whether the calibrated indices are more appropriate for assessing the water quality of urban rivers. The WQI is calculated as follow, and the normalized values and weights of each parameter were showed in Table S4 31 . $$WQI={\sum }_{i=1}^{n}{C}_{i}{P}_{i}/{\sum }_{i=1}^{n}{P}_{i}$$ 3 where, n represents the total number of water quality parameters, C i indicates the normalized value assigned to each parameter, and P i signifies the relative weight allocated to each parameter. 2.5. Statistical Analysis The Shapiro-Wilk normality test 32 was utilized to compare FSVs in the dry and wet seasons before and after the revision. It assessed the normality ( p > 0.05) of the original and revised FSVs as well as the distribution of species in each season. Moreover, linear regression model was used to explore the relationship between biological and water quality indices. The original BMWP and ASPT and the calibrated BMWP and ASPT were fitted to the WQI, respectively, and the significance of the fitted equations was examined ( p < 0.05). Additionally, the fitting relationships between WQI and BMWP, ASPT values before and after revision were compared, with higher r-squares indicating a better fit. All analyses were performed using Origin Pro 2021. 3. Results 3.1. Community composition of macroinvertebrates A total of 47 taxa of macroinvertebrates were collected in the two urban river surveys, including 8 phyla, 14 orders and 28 families. Among them, 23 taxa of Arthropoda, accounting for 48.9%, while 18 taxa of Mollusca accounting for 38.3%, and 6 taxa of Annelida accounting for 12.8%. In the dry season, a total of 38 taxa of macroinvertebrates were found, including 14 orders and 26 families, of which 16 taxa of Arthropoda (42.1%), 16 taxa of Mollusca (42.1%), and 6 taxa of Annelida (15.8%). In the wet season, 28 taxa of macroinvertebrates were collected, including 12 orders and 22 families. There were 13 taxa (46.4%) of Arthropoda, 12 taxa (42.9%) of Mollusca, and 3 taxa (10.7%) of Annelida (Table S5). 3.2. Changes in FSVs before and after the revision of dry and wet seasons The original and revised FSVs for the dry and wet seasons were compared in Fig. 2 . For the wet season, the revised FSVs of 17 family declined, and those of 3 family increased, but Hydropsychidae and Tubificidae remained unchanged. For the dry season, the revised FSVs of 24 family decreased, and only Muscidae and Tubificidae remained unchanged. Among the 20 shared taxa in the dry and wet seasons, the revised FSVs of 16 family showed the same changing trend, decreasing or remaining constant in both seasons. However, the revised FSVs ​​of Lymnaeidae, Simuliidae, and Glossiphoniidae exhibited the different changing trend, increasing in the wet season and decreasing in the dry season. Only Hydropsychidae maintained its FSV in the wet season but declined in the dry season (Table 1 ). Table 1 Original and revised FSVs for macroinvertebrates of 30 families. The revised FSVs are based on observations in the wet and dry seasons, with dashes (-) indicating the absence of macroinvertebrates in the corresponding hydrological period. Family Original FSVs Revised FSVs Wet season Dry season Viviparidae 6 1 1 Bithyniidae 6 1.45 1 Ampullariidae 6 1.4 1.6 Semisulcospiridae 7 - 2 Thiaridae 7 - 1 Physidae 3 1.8 1.4 Planorbidae 3 1.8 1 Lymnaeidae 3 3.6 1 Mytilidae 7 5.35 1 Unionidae 6 3 1 Corbiculidae 6 2.25 1.5 Solecurtidae 6 - 3.15 Hydropsychidae 6 6 1 Baetidae 7 5 1 Libellulidae 8 6 3 Coenagrionidae 6 4.2 1 Chironomidae 3 1 1 Muscidae 1 - 1 Simuliidae 5 6 1 Palaemonidae 6 1 1 Gammaridae 7 5 3 Grapsidae 6 - 1 Cambaridae 6 5 - Varunidae 6 1 - Tubificidae 1 1 1 Nephtyidae 6 1 1.9 Erpobdellidae 3 - 1 Glossiphoniidae 3 5.1 1.4 The histogram showed the differences in FSVs between the wet and dry seasons before and after the revision (Fig. 3 ). The Shapiro-Wilk test showed that FSVs were normally distributed in the wet season ( p > 0.05) but not in the dry season ( p < 0.05). In the wet season, the changes in the revised FSVs compared to the original varied between − 5 and 3. Four families exhibited a change in FSVs of less than 1, while the rest of families had a change in FSVs of more than 1. Eight families had changes in FSVs between − 2 to -1, and six families had changes in FSVs between − 5 to -4. In the dry season, the changes in the revised FSVs varied between − 6 to 1. Among these, twelve families had FSVs that varied between − 5 to -4, six families had FSVs that varied between − 2 to -1, and only two families exhibited changes in FSVs less than 1. The Shapiro-Wilk test results indicated that neither the original nor revised FSVs followed a normal distribution ( p < 0.05, Fig. 4 ). The distribution patterns of original FSVs were comparable in both wet and dry seasons. There were no family-level taxa with FSVs scoring 2, 4, 9 and 10, while the highest number of family-level taxa with FSVs scoring 6, and a similar number of family-level taxa with other scores. The revised FSVs in the wet season were clustered between 1 to 2 and included 10 family-level taxa, whereas 22 family-level taxa fell within this range in the dry season. Furthermore, the number of family-level taxa in the middle and high range of scores was obviously lower in both periods compared to the low range of scores. No family-level taxa with FSVs scoring between 7 to 10 in the wet season, while between 4 to 10 in the dry season. Compared to the wet season, the dry season had a similar range of FSVs for most family-level taxa, mostly between − 5 to -4, while the wet season had a range mainly between − 2 to -1 (Fig. 3 ). This difference in distribution resulted in a slope of less than 1 when comparing the both seasons in Fig. 5 . Overall, the variability in FSVs was higher in the dry season than in the wet season. Fifteen family-level taxa fell in the third quadrant, indicating their FSVs change showed a consistent trend in both seasons, with the majority of family-level taxa (16 out of 20 shared taxa between dry and wet seasons) showing FSVs variability significantly greater than 1 (Fig. 5 ). 3.3. Comparison of BMWP and ASPT Results of the linear regression indicated that the WQI exhibited a significant correlation with the revised ASPT ( p 0.05) in the wet season (Table 2 ). Moreover, the WQI showed significant correlation with the original and revised BMWP ( p < 0.05). It was noteworthy that the p-value of the revised BMWP was lower than the original BMWP. For the dry season, the WQI was also significantly correlated with the revised ASPT values ( p 0.05). However, the WQI lacked significant correlation with both the original and revised BMWP (p > 0.05). The revised BMWP and ASPT demonstrated better linear fitting than their original versions in both seasons, as indicated by their higher coefficients of determination (R 2 ). The above suggested that the revised BMWP and ASPT indices could offer a more accurate assessment of water quality than before the revisions. Additionally, the revised BMWP and ASPT exhibited higher t-values and lower p-values for their slope coefficients when incorporated into the WQI model. Table 2 Results of linear regression analyses of the original and the revised ASPT, BMWP, against WQI, in the wet and dry seasons. Data from the wet seasons: original ASPT (NO.1), revised ASPT (NO.2), original BMWP (NO.3), revised BMWP (NO.4);Data from the dry seasons: original ASPT (NO.5), revised ASPT (NO.6), original BMWP (NO.7), revised BMWP (N0.8). No. Model R 2 F df Variable Estimate SE t value p value 1 Wet season: Original ASPT ~ WQI 0.044 1.518 33 Intercept 2.546 1.660 1.534 0.135 Slope 0.037 0.030 1.232 0.227 2 Wet season: Revised ASPT ~ WQI 0.290 13.484 33 Intercept -1.267 0.834 -1.519 0.138 Slope 0.055 0.015 3.672 < 0.001 3 Wet season: Original BMWP ~ WQI 0.111 4.130 33 Intercept -2.787 8.335 -0.334 0.740 Slope 0.304 0.150 2.032 0.050 4 Wet season: Revised BMWP ~ WQI 0.175 7.016 33 Intercept -8.609 5.535 -1.555 0.129 Slope 0.263 0.099 2.649 0.012 5 Dry season: Original ASPT ~ WQI 0.010 0.347 33 Intercept 4.235 1.609 2.632 0.013 Slope 0.017 0.028 0.589 0.560 6 Dry season: Revised ASPT ~ WQI 0.247 10.837 33 Intercept 0.249 0.324 0.768 0.448 Slope 0.019 0.006 3.292 0.002 7 Dry season: Original BMWP ~ WQI 0.026 0.359 33 Intercept 6.020 17.248 0.349 0.729 Slope 0.280 0.301 0.931 0.359 8 Dry season: Revised BMWP ~ WQI 0.093 0.074 33 Intercept -3.973 5.400 -0.736 0.467 Slope 0.174 0.094 1.842 0.074 Figure 6 illustrated that both the revised ASPT and BMWP distributions for both seasons were narrower and more concentrated, with values lower than the original ASPT and BMWP. Generally, the revised ASPT distribution appeared to be more concentrated than the revised BMWP distribution, although the latter contained more points within the 95% confidence interval. In the dry season, both revised ASPT and BMWP were more concentrated compared to the wet season, with more points within the 95% confidence interval, albeit with lower values. These findings further confirmed the revised BMWP and ASPT indices were more accurate than their original versions, emphasizing the importance of combining the two indices for water quality assessment. The revised ASPT ranged from 1.00 to 4.09 in the wet seasons and from 1.00 to 2.00 in the dry season, whereas the revised BMWP fluctuated between 1.00 and 28.60 in the wet season and between 1.00 and 14.00 in the dry season (Fig. 6 ). Overall, the results of the revised ASPT and BMWP assessment showed that water quality in the wet season was prevails over that in the dry season. The water quality assessed by the revised ASPT was better than that assessed by the revised BMWP in the wet season, whereas water quality assessed by the revised ASPT was poor than that assessed by the revised BMWP in the dry season. 4. Discussion Three distinct patterns of revised FSVs were observed between the dry and wet seasons. These differences stem from varying species composition between seasons, as well as changes in water quality conditions in urban rivers. The first pattern is that the revised FSVs has the same trend (either decrease or increase) in both seasons. For example, the original FSV of Baetidae was 7, revised to 5 in the wet season and 1 in the dry season (Table 1 ). A decrease in revised FSV was associated with species richness and ecological resilience of this family. There were two genera (i.e., Baetis and Baetiella) in the dry season and only Baetiella in the wet season. In the Ethiopian highland rivers, Lakew and Moog 18 assigned three FSVs to Baetidae based on species richness, i.e., 9 for more than two species, 6 for two species, and 4 for one species, indicating that the family is tolerant to moderate or strong pollution when there are fewer than two species. Moreover, Ruiz-Picos et al. 24 assigned FSV of 1 to Baetidae in Mexico rivers, suggesting that the family had a high pollution tolerance. These findings are in general agreement with the results of the present study, suggesting that the revision method is applicable to urban rivers. The second pattern is that the revised FSVs increase in one season and decrease in the other. For example, the revised FSV of Glossiphoniidae increased in the wet season (5.1) and decreased in the dry season (1.4) compared to the original FSV of 3 (Table 1 ). This is related to the number of individuals of Glossiphoniidae in both seasons, with higher individual abundance weighing more heavily on the revision results. In this study, the individual abundance of Glossiphoniidae was higher in the dry season than in the wet season, suggesting that the family is more adapted to polluted environments, which is consistent with the findings of Luo et al. 33 that Glossiphoniidae showed higher abundance in the more urbanized and polluted rivers. The third pattern is that revised FSVs fluctuates in one season but remains constant in the other. For Hydropsychidae, the revised FSV remained 6 in the wet season but decreased to 1 in the dry season (Table 1 ). The revised FSV for Hydropsychidae in tropical rivers in Mexico was 4 24 . Mao et al. 34 revised the sensitivity value of Hydropsychidae in the Chishui River to 4.6 ~ 5.7. Despite Hydropsychidae species are categorized as environment sensitive indicator (EPT), they are distributed across different pollution gradients and display some degree of pollution tolerance in the larval stage 35 . The lower FSV for Hydropsychidae in this study may be related to the high level of pollution in urban rivers during the dry season, and reflects the lower limit of the family’s tolerance to pollution. Typically, pollution loads, especially organic pollution, are heavier in urban rivers, resulting in enhanced macroinvertebrate tolerant taxa and reduced sensitive taxa 9,33,36 . We found that revised FSVs in both seasons were skewed towards lower value distributions, indicating that macroinvertebrates in urban rivers generally show stronger pollution tolerance (Fig. 4 ). Previous studies on natural rivers have demonstrated that revised FSVs tend to be normally distributed, with most FSVs at intermediate ranges 18,23,34 , consistent with the distribution of species toxicity response 37 . Therefore, it is hypothesized that this long-term adaptation of macroinvertebrates in urban rivers to highly polluted environments increased their tolerance to pollution 33 , leading to a decreasing trend in most revised FSVs. The results showed that the revised FSVs were more variable in the dry season. This can be attributed to the decrease in water volume in this season, which exhibits an increase in pollutant loads concentrations in urban rivers 38 , highlighting that macroinvertebrates are more resilient to pollution 5,9,39 . The increased water volume in the wet season can effectively dilute organic pollutant concentrations 40 and nutrients levels 23 , resulting in improved water quality conditions 41 . Moreover, differences in the individual abundance or species composition of macroinvertebrates during different seasons may also account for differences in FSV revisions. For example, Baetidae, discussed earlier, have lower individual abundance and species composition in the wet season due to less habitat caused by the higher water level, resulting in less changes in FSV revisions in this season. Most studies have found that the adapted BMWP to be more advantageous in water quality assessment 7,18,34 . For example, Romero et al. 23 reported that a modified BMWP improved water quality classification accuracy in the Embalse del Guájaro River, Colombia. Ruiz-Picos et al. 24 found that the adapted BMWP distinguished the effects of agriculture and urban on water quality. The similar results were showed in this study where the revised BMWP and ASPT had better correlations with WQI (Fig. 6 ). Therefore, the adapted indices can be used as the tool for biomonitoring water quality in urban rivers in PRD. It is worth noting that the application of adapted indices for water quality assessment in other regions may fail. Ochieng et al. 5 reported that the adapted BMWP indices from Costa Rica failed to separate sites along river pollution gradient in Eastern Uganda. Additionally, the indicative performance of the adapted BMWP and ASTP for water quality is controversial. The adapted BMWP shows a better response to the pollution than the adapted ASPT 40 . da Silva-Santos et al. 42 also reported that BMWP was more able than ASPT to detect differences in habitat quality. Moreover, Deemool and Prommi 43 found that the adapted BMWP and ASPT responded to different water quality factors, respectively. Consequently, combining both indices for river water quality assessment would reduce the bias generated by one index. 5. Conclusion This study revised the FSVs of macroinvertebrates in subtropical urban rivers in China and analyzed the accuracy of BMWP and ASPT indices in water quality assessment. It was found that most of the revised FSVs were low, and FSVs were lower in the dry season than in the wet season, which was related to the high level of pollution due to the small amount of water in the dry season, and also reflected the minimum tolerance of macroinvertebrates to the environment. Therefore, it is recommended to use the revised results of lower FSVs. In addition, the higher correlation between the revised BMWP and ASPT indices and WQI index could improve the accuracy of water quality assessment in urban rivers, and the simultaneous use of the BMWP and ASPT indices could provide more reliable assessment results. The results of this study provide a set of FSVs for macroinvertebrates in urban rivers in the Pearl River Delta region, and an effective management tool for water quality assessment in subtropical urban rivers. Declarations Competing interests The authors declare no competing interests. Author Contribution Mengyue Zhang: Data curation, Methodology, Software, Validation, Writing-original draft and Writing-review, editing. Mingqiao Yu: Data curation and Investigation. Sen Ding: Funding acquisition, Supervision and Writing—review, editing. Zhao Li: Conceptualization. Data Availability The datasets used and analyzed during the current study available from the corresponding author on reasonable request. References Park, Y.-S. & Hwang, S.-J. Ecological Monitoring, Assessment, and Management in Freshwater Systems. Water 8, 324. https://doi.org/10.3390/w8080324 (2016). Dirican, S. Assessment of Water Quality Using Physico-chemical Parameters of Çamlıgöze Dam Lake in Sivas, Turkey. 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Development of Benthic Macroinvertebrates Family-Level Biotic Index for Biological Assessment on Korean Stream Environment. Journal of Korean Society on Water Environment 35, 152–164. https://doi.org/10.15681/KSWE.2019.35.2.152 (2019). Walley, W.J. & Hawkes, H.A. A computer-based reappraisal of the Biological Monitoring Working Party scores using data from the 1990 river quality survey of England and Wales. Water Res. 30, 2086–2094. https://doi.org/10.1016/0043-1354(96)00013-9 (1996). SNIFFER. Revision and testing of bmwp scores, Engineering and Technology. The Scoland and Northern Ireland Forum for Environmental Research (2007). Paisley, M.F., Trigg, D.J. & Walley, W.J. Revision of the Biological Monitoring Working Party (bmwp) Score System: Derivation of Present-Only and Abundance-Related Scores from Field Data. River Research and Applications 30, 887–904. https://doi.org/10.1002/rra.2686 (2014). Romero, K.C. et al . Lentic water quality characterization using macroinvertebrates as bioindicators: An adapted BMWP index. Ecol. Indic. 72, 53–66. https://doi.org/10.1016/j.ecolind.2016.07.023 (2017). Ruiz-Picos, R.A., Sedeño-Díaz, J.E., López-López, E., Ruiz-Picos, R.A., Sedeño-Díaz, J.E. & López-López, E. Calibrating and Validating the Biomonitoring Working Party (BMWP) Index for the Bioassessment of Water Quality in Neotropical Streams, in: Water Quality. IntechOpen. https://doi.org/10.5772/66221 (2017). King, R.S., Scoggins, M. & Porras, A. Stream biodiversity is disproportionately lost to urbanization when flow permanence declines: evidence from southwestern North America. Freshwater Science 35, 340–352. https://doi.org/10.1086/684943 (2016). Wu, W., Xu, Z., Zhan, C., Yin, X. & Yu, S. A new framework to evaluate ecosystem health: a case study in the Wei River basin, China. Environ. Monit. Assess. 187, 460. https://doi.org/10.1007/s10661-015-4596-1 (2015). Erba, S., Terranova, L., Cazzola, M., Cason, M. & Buffagni, A. Defining Maximum Ecological Potential for heavily modified lowland streams of Northern Italy. Sci. Total Environ. 684, 196–206. https://doi.org/10.1016/j.scitotenv.2019.05.348 (2019). Xu, S., Sun, Y. & Zhao, S. Contemporary urban expansion in the first fastest growing metropolitan region of China: A multicity study in the Pearl River Delta urban agglomeration from 1980 to 2015. Urban Science 5, 11. https://doi.org/10.3390/urbansci5010011 (2021). MEE. Technical guidelines for biodiversity monitoring — freshwater benthic macroinvertebrates. Ministry of Ecology and Environment of the People’s Republic of China , HJ 710.8–2014 (2014). Monaghan, K.A. Four Reasons to Question the Accuracy of a Biotic Index; the Risk of Metric Bias and the Scope to Improve Accuracy. PLOS ONE 11, e0158383. https://doi.org/10.1371/journal.pone.0158383 (2016). Zhao, K., Dong, A., Wang, S. & Yu, X. Ecological Health Status of the Yitong River, China, Assessed with the Planktonic Index of Biotic Integrity. Water 14, 3191. https://doi.org/10.3390/w14193191 (2022). Thode, H.C. Testing For Normality. CRC Press, Boca Raton. https://doi.org/10.1201/9780203910894 (2002). Luo, K. et al . Impacts of rapid urbanization on the water quality and macroinvertebrate communities of streams: A case study in Liangjiang New Area, China. Sci. Total Environ. 621, 1601–1614. https://doi.org/10.1016/j.scitotenv.2017.10.068 (2018). Mao, F. et al . Revision of biological indices for aquatic systems: A ridge-regression solution. Ecol. Indic. 106, 105478. https://doi.org/10.1016/j.ecolind.2019.105478 (2019). Geraci, C.J., Zhou, X., Morse, J.C. & Kjer, K.M., 2010. Defining the genus Hydropsyche (Trichoptera:Hydropsychidae) based on DNA and morphological evidence. Journal of the North American Benthological Society 29, 918–933. https://doi.org/10.1899/09-031.1 (2010). Nichols, J., Hubbart, J.A. & Poulton, B.C. Using macroinvertebrate assemblages and multiple stressors to infer urban stream system condition: a case study in the central US. Urban Ecosyst 19, 679–704. https://doi.org/10.1007/s11252-016-0534-4 (2016). Wheeler, J.R., Grist, E.P.M., Leung, K.M.Y., Morritt, D. & Crane, M. Species sensitivity distributions: data and model choice. Mar. Pollut. Bull. 45, 192–202. https://doi.org/10.1016/S0025-326X(01)00327-7 (2002). Hu, X. et al . Response of macroinvertebrate community to water quality factors and aquatic ecosystem health assessment in a typical river in Beijing, China. Environ. Res. 212, 113474. https://doi.org/10.1016/j.envres.2022.113474 (2022). Mahazar, A., Shuhaimi-Othman, M., Abas, A. & Desa, M. Monitoring Urban River Water Quality Using Macroinvertebrate and Physico-Chemical Parameters: Case study of Penchala River, Malaysia. Journal of Biological Sciences 13, 474–482. https://doi.org/10.3923/jbs.2013.474.482 (2013). Roche, K.F., Queiroz, E.P., Righi, K.O. & Souza, G.M.D. Use of the BMWP and ASPT indexes for monitoring environmental quality in a neotropical stream. Acta Limnol. Bras. 22, 105–108. https://doi.org/10.4322/actalb.02201010 (2010). Jerves-Cobo, R. et al. Biological impact assessment of sewage outfalls in the urbanized area of the Cuenca River basin (Ecuador) in two different seasons. Limnologica 71, 8–28. https://doi.org/10.1016/j.limno.2018.05.003 (2018). da Silva-Santos, J.V.A. et al . Assessing physical habitat structure and biological condition in eastern Amazonia stream sites. Water Biol. Secur. 2, 110132. https://doi.org/10.1016/j.watbs.2022.100132 (2023). Deemool, M. & Prommi, T.O. The use of biotic indices for evaluation of water quality in the streams, Western Thailand. Int. J. Pharm. Sci. Allied Res. 6, 89–98. https://api.semanticscholar.org/CorpusID :222260346 (2017). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4612128","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326771513,"identity":"d7d4c421-321a-4b09-9a5d-bc80d63e08c2","order_by":0,"name":"Mengyue Zhang","email":"","orcid":"","institution":"Chinese Research Academy of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mengyue","middleName":"","lastName":"Zhang","suffix":""},{"id":326771514,"identity":"5357f0e2-90da-465e-a6d9-c568d4fccc8d","order_by":1,"name":"Mingqiao Yu","email":"","orcid":"","institution":"Chinese Research Academy of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mingqiao","middleName":"","lastName":"Yu","suffix":""},{"id":326771516,"identity":"b14f41e7-c0ce-40e2-8c50-82021180fc81","order_by":2,"name":"Sen Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAwglwcDA3tj48ANpWngONxtLkKAFpCu9TYCHGC3m7M3PPhfUWNjzSz5sA1pmJ6fbQECLZc8x49kzjkkkzpyd2PaggCHZ2OwAIYfdSDBm5mGTSDC4ndhuIMFwIHEbQS33n39m5vknYW9/82CbBA9RWm7wGDPztkkwbpBgJFKLZU9OMTNvn0TijDOJwEA2IMIv5uzHNzPzfKuz528//vDhhwo7OYJa0N1JmvJRMApGwSgYBTgAAKEdPRuZ/rayAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Research Academy of Environmental Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sen","middleName":"","lastName":"Ding","suffix":""},{"id":326771517,"identity":"0a7281a8-b0bb-499e-9403-2c15aac40c13","order_by":3,"name":"Zhao Li","email":"","orcid":"","institution":"China National Environmental Monitoring Centre","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-20 13:37:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4612128/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4612128/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60479109,"identity":"52067b04-9fe4-455b-b905-bfdb03896e49","added_by":"auto","created_at":"2024-07-17 08:22:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":357626,"visible":true,"origin":"","legend":"\u003cp\u003eMap of sampling sites of macroinvertebrates in urban river of Dongguan\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/4b3f7b3f83419a2052c889f9.png"},{"id":60479111,"identity":"b1f72e82-44a7-4767-9643-260e0e26b65b","added_by":"auto","created_at":"2024-07-17 08:22:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31436,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the revised FSVs against the original FSVs. (a) data from the wet season，(b) data from the dry season. The x-axis represents the original FSVs, while the y-axis denotes the revised FSVs, the dashed diagonal lines denote equal values of x and y.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/aefa173a68582fe25347167b.png"},{"id":60479112,"identity":"0b3e45a1-2aac-4e55-ae89-e44c797d8298","added_by":"auto","created_at":"2024-07-17 08:22:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25249,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of FSVs change. (a) Data from the wet season, (b) data from the dry season. The x-axis represents the FSVs change (the difference between the revised and the original FSV for each macroinvertebrate family), while the y-axis denotes the number (frequency) of macroinvertebrate families in each FSV change interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/f64f36bc45081b3d2faea549.png"},{"id":60479702,"identity":"a1176229-b225-4915-8a7d-8050a5f29885","added_by":"auto","created_at":"2024-07-17 08:30:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57613,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of FSVs for families. (a) Original FSVs of the wet season, (b) Revised FSVs of the wet season, (c) Original FSVs of the dry season, (d) Revised FSVs of the dry season. The x-axis represents the FSVs while the y-axis denotes the number (frequency) of macroinvertebrate families in each FSV interval.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/380bc64c5d6c92fcca37d249.png"},{"id":60479114,"identity":"5808d007-36c0-46ed-b1a9-40eaa63d8a55","added_by":"auto","created_at":"2024-07-17 08:22:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26898,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between FSVs changes in the wet and dry seasons. Each point represents a macroinvertebrate family. Pink indicates consistent changes across both seasons, while green represents divergent alterations. The diagonal lines denote equal x and y values. The closer the point is to the diagonal line, the more similar the compared FSVs changes are.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/66b6e0fbaf77501521e4bf57.png"},{"id":60479113,"identity":"00659e09-d59e-4664-8459-96e9a94afc1c","added_by":"auto","created_at":"2024-07-17 08:22:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":127839,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression of the ASPT and BMWP scores against the WQI in the wet and dry seasons. (a) ASPT against WQI of the wet season, (b) ASPT against WQI of the dry season, (c) BMWP against WQI of the wet season, (d) BMWP against WQI of the dry season. The x-axis denotes the WQI while the y-axis denotes the index value. The violet circle represents the original index value, and the crimson triangle denotes the revised index value; the solid line depicts the fitted trend line between the evaluation index and the WQI, while the dashed line illustrates the upper and lower bounds of the 95% confidence interval.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/26bdc4985e9c7a4932250971.png"},{"id":69756639,"identity":"ba3df6c0-e054-4caf-9d37-fdfbf109fe31","added_by":"auto","created_at":"2024-11-25 03:32:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1270324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/2c1f4473-85bb-4aa1-bf58-bcaa95a417c5.pdf"},{"id":60479703,"identity":"d359ebed-0c86-4ce6-96fa-7664682c7283","added_by":"auto","created_at":"2024-07-17 08:30:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40855,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4612128/v1/86d88366f87a1d5324b501e0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revision of the biological monitoring working party score system: Evidence from the subtropical urban river in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccurate and effective monitoring and assessment of water quality is essential for the protection of aquatic ecosystems\u003csup\u003e1\u003c/sup\u003e. Water physicochemical monitoring, e.g. nutrient or oxygen-depleting substance content testing, provides an indication of the instantaneous state of water quality\u003csup\u003e2\u003c/sup\u003e. In contrast, bioindicators provide a non-transient approach to water quality assessment. Biotic indices take into account the sensitivity or tolerance of individual species or groups of species to pollution and are therefore suitable for assessing the health of aquatic ecosystems\u003csup\u003e3\u0026ndash;4\u003c/sup\u003e. Macroinvertebrates are widely used as bioindicator in water quality monitoring and assessment due to their weak motility and sensitivity to environmental disturbances\u003csup\u003e5\u003c/sup\u003e. Among macroinvertebrates rapid bioassessment indices, the Biological Monitoring Working Party (BMWP) is one of the most widely used indices\u003csup\u003e6\u003c/sup\u003e. The BMWP index only requires identification to the family level and is therefore commonly used in rapid bioassessment programs in rivers around the world\u003csup\u003e7\u0026ndash;12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are differences in the taxonomic composition of macroinvertebrates in different countries and regions. Moreover, the sensitivity of macroinvertebrates to organic pollution varies under different environmental conditions, e.g., Baetidae in different regions exhibit different organic pollution sensitivity characteristics\u003csup\u003e13\u003c/sup\u003e. The BMWP index needs to be adapted according to the local environment conditions to ensure the accuracy of the assessment. As a result, a number of regional indices have been developed, including Spain\u003csup\u003e14\u003c/sup\u003e, Thailand\u003csup\u003e15\u003c/sup\u003e, Brazil\u003csup\u003e16\u003c/sup\u003e, Poland\u003csup\u003e17\u003c/sup\u003e, Ethiopia\u003csup\u003e18\u003c/sup\u003e, South Korea\u003csup\u003e19\u003c/sup\u003e and Mexico\u003csup\u003e7\u003c/sup\u003e, among others.\u003c/p\u003e \u003cp\u003eThe assignment of family sensitivity values (FSVs) to the BMWP has evolved from qualitative or semi-quantitative approach to a quantitative framework that incorporates both biotic and abiotic data. Initially, the sensitivity values for 82 family-level taxa were derived based on empirical assessments by UK expert\u003csup\u003e6\u003c/sup\u003e. However, empirical values that have not been field-tested are often biased. Therefore, Walley and Hawkes\u003csup\u003e20\u003c/sup\u003e used a quantitative approach based on 17,000 samples covering 85 families to calibrate FSVs according to habitat classification and individual abundance classes. The method has since been applied in other regions\u003csup\u003e21\u003c/sup\u003e. However, there is a growing realization that FSVs need to be revised in conjunction with environmental pollution data to improve the accuracy of assessment\u003csup\u003e22\u0026ndash;23\u003c/sup\u003e. For example, Ruiz-Picos et al.\u003csup\u003e24\u003c/sup\u003e calibrated macroinvertebrates FSVs in Neotropical rivers based on river-specific environmental characteristics and validated them in other rivers in the same ecoregion.\u003c/p\u003e \u003cp\u003eUrban rivers play a vital role as essential water sources and transportation routes in the urban landscapes. Meanwhile, it is important to recognize that functions such as recreation and flood control are prioritized in the management of urban rivers. However, rapid urbanization poses a significant threat to the ecological conditions of these waterways\u003csup\u003e25\u003c/sup\u003e, including increased water pollution, altered hydro-morphological characteristics, and reduced riparian vegetation cover\u003csup\u003e26\u0026ndash;27\u003c/sup\u003e. Despite the development of many effective management measures for urban rivers, water quality management in urban rivers remains a challenge in many countries. Among others, bioassessment is an important basis for restoring the ecological potential of urban rivers\u003csup\u003e27\u003c/sup\u003e. There are more reports on the calibration of macroinvertebrate BMWP index, but little attention has been paid to urban rivers. Urban rivers have unique contamination and disturbance history, so it is scientifically important to calibrate the BMWP by incorporating the water quality characteristics of urban rivers.\u003c/p\u003e \u003cp\u003eSince the 1980s, the Pearl River Delta (PRD) region has experienced rapid urban expansion. By 2022, the urbanization rate in the region had soared to 87.5%\u003csup\u003e28\u003c/sup\u003e, which has seriously affected the health of regional river ecosystem. Consequently, subtropical urban rivers in the PRD region were selected for this study with the aim of (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) revising macroinvertebrates FSVs in context of the river environment characteristics, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) verifying the suitability of the adapted BMWP index for assessing the water quality of subtropical urban rivers in the region. We hypothesized that long-term intensive anthropogenic disturbance would alter the environmental adaptive capacity of macroinvertebrates in urban rivers, making them less sensitive to the environment.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eDongguan is located in the south-central part of Guangdong Province, China, at the lower reaches of the Dongjiang River in the PRD (E 113\u0026deg;31\u0026prime;-114\u0026deg;15\u0026prime;, N 22\u0026deg;39\u0026prime;-23\u0026deg;09\u0026prime;). Dongguan has a dense river network, and the southeastern part is mountainous with undulating terrain ranging from 200 m to 600 m above sea level. The northwestern part is an alluvial plain with flat terrain and river channel. It has a subtropical monsoon climate with slight temperature changes and abundant rainfall. Rainfall is mainly concentrated from April to October.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling and analyses\u003c/h2\u003e \u003cp\u003eData from the local water resources department shows that the average monthly precipitation for the past three years is 134.05 mm. The maximum average precipitation was recorded in the months of May (271.70 mm) and June (300.47 mm), while the minimum average precipitation was recorded in the months of December (27.77 mm) and January (12.37 mm). Therefore, this study monitored macroinvertebrates and water quality at 35 urban river sites during the dry season (from December 2021 to January 2022) and the wet season (from May to June 2022, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMacroinvertebrates were collected according to the requirements in the Chinese industry standard\u003csup\u003e29\u003c/sup\u003e. In wadable reaches, macroinvertebrates were collected using D-net with a quantitative sampling length of 1 m, repeated 3 times. In nonwadable reaches, macroinvertebrates were collected using Petersen grab sampler (1/16 m\u003csup\u003e2\u003c/sup\u003e opening) for a total of 8 collections. Qualitative samples were collected in different habitats using D-net for 15 min. Samples were preserved in a 75% ethanol solution. Specimens were sorted using a stereomicroscope and identified to the lowest taxonomic level possible.\u003c/p\u003e \u003cp\u003eWater quality parameters, such as water temperature (WT, ℃), pH, dissolved oxygen (DO, mg/L), electrical conductivity (EC, \u0026micro;S/cm), and total dissolved solids (TDS, mg/L) were measured by a portable multiparameter analyzers. Other parameters including total nitrogen (TN, mg/L), total phosphorus (TP, mg/L), ammonia nitrogen (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, mg/L), nitrite nitrogen (NO\u003csub\u003e2\u003c/sub\u003e-N, mg/L), nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e-N, mg/L), five-days biochemical oxygen demand (BOD\u003csub\u003e5\u003c/sub\u003e, mg/L), chemical oxygen demand (COD\u003csub\u003ecr\u003c/sub\u003e, mg/L), and potassium permanganate index (COD\u003csub\u003eMn\u003c/sub\u003e, mg/L) were determined in the laboratory after water sample collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Family sensitive values (FSVs) revision\u003c/h2\u003e \u003cp\u003eMacroinvertebrates FSVs were revised according to the method proposed by Ruiz-Picos et al.\u003csup\u003e24\u003c/sup\u003e, which has been validated in other study\u003csup\u003e7\u003c/sup\u003e. This revision consisted of five steps: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) screening of water quality parameters (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2, S3), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) standardization of water quality parameters according to Chinese surface water quality standards, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) calculation of physicochemical quality index (Pcq), (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) classification of macroinvertebrate individual abundance, and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) calculation of FSVs using Pcq and individual abundance classes. Given that sensitivity values for some macroinvertebrates may fluctuate over time\u003csup\u003e30\u003c/sup\u003e, this study revised FSVs for the dry and wet seasons to account for species composition and physicochemical conditions during each period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Index Calculations\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. BMWP and ASPT\u003c/h2\u003e \u003cp\u003eThe BMWP and ASPT indices were utilized to assess the water quality in urban rivers and were calculated as described below. In addition, separate calculations were made using the original FSVs and the revised FSVs to compare the accuracy of these indices in assessing water quality.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$BMWP={\\Sigma }{t}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$ASPT=\\frac{\\sum _{i=1}^{n}{t}_{i}}{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the FSV of species \u003cem\u003ei\u003c/em\u003e observed at the site, while \u003cem\u003en\u003c/em\u003e denotes the total number of taxa at the family level at the site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Water Quality Index (WQI)\u003c/h2\u003e \u003cp\u003eThe Water Quality Index (WQI) provides an objective assessment of river health. Comparison of the BMWP and ASPT indices with the WQI will determine whether the calibrated indices are more appropriate for assessing the water quality of urban rivers. The WQI is calculated as follow, and the normalized values and weights of each parameter were showed in Table S4\u003csup\u003e31\u003c/sup\u003e.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$WQI={\\sum }_{i=1}^{n}{C}_{i}{P}_{i}/{\\sum }_{i=1}^{n}{P}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cem\u003en\u003c/em\u003e represents the total number of water quality parameters, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e indicates the normalized value assigned to each parameter, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e signifies the relative weight allocated to each parameter.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk normality test\u003csup\u003e32\u003c/sup\u003e was utilized to compare FSVs in the dry and wet seasons before and after the revision. It assessed the normality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) of the original and revised FSVs as well as the distribution of species in each season. Moreover, linear regression model was used to explore the relationship between biological and water quality indices. The original BMWP and ASPT and the calibrated BMWP and ASPT were fitted to the WQI, respectively, and the significance of the fitted equations was examined (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the fitting relationships between WQI and BMWP, ASPT values before and after revision were compared, with higher r-squares indicating a better fit. All analyses were performed using Origin Pro 2021.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Community composition of macroinvertebrates\u003c/h2\u003e \u003cp\u003eA total of 47 taxa of macroinvertebrates were collected in the two urban river surveys, including 8 phyla, 14 orders and 28 families. Among them, 23 taxa of Arthropoda, accounting for 48.9%, while 18 taxa of Mollusca accounting for 38.3%, and 6 taxa of Annelida accounting for 12.8%. In the dry season, a total of 38 taxa of macroinvertebrates were found, including 14 orders and 26 families, of which 16 taxa of Arthropoda (42.1%), 16 taxa of Mollusca (42.1%), and 6 taxa of Annelida (15.8%). In the wet season, 28 taxa of macroinvertebrates were collected, including 12 orders and 22 families. There were 13 taxa (46.4%) of Arthropoda, 12 taxa (42.9%) of Mollusca, and 3 taxa (10.7%) of Annelida (Table S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Changes in FSVs before and after the revision of dry and wet seasons\u003c/h2\u003e \u003cp\u003eThe original and revised FSVs for the dry and wet seasons were compared in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For the wet season, the revised FSVs of 17 family declined, and those of 3 family increased, but Hydropsychidae and Tubificidae remained unchanged. For the dry season, the revised FSVs of 24 family decreased, and only Muscidae and Tubificidae remained unchanged. Among the 20 shared taxa in the dry and wet seasons, the revised FSVs of 16 family showed the same changing trend, decreasing or remaining constant in both seasons. However, the revised FSVs ​​of Lymnaeidae, Simuliidae, and Glossiphoniidae exhibited the different changing trend, increasing in the wet season and decreasing in the dry season. Only Hydropsychidae maintained its FSV in the wet season but declined in the dry season (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOriginal and revised FSVs for macroinvertebrates of 30 families. The revised FSVs are based on observations in the wet and dry seasons, with dashes (-) indicating the absence of macroinvertebrates in the corresponding hydrological period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOriginal FSVs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRevised FSVs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet season\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDry season\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eViviparidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBithyniidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAmpullariidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSemisulcospiridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eThiaridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhysidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlanorbidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLymnaeidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMytilidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUnionidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCorbiculidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSolecurtidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHydropsychidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaetidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLibellulidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCoenagrionidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChironomidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMuscidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSimuliidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePalaemonidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGammaridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrapsidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCambaridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVarunidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTubificidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNephtyidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eErpobdellidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGlossiphoniidae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe histogram showed the differences in FSVs between the wet and dry seasons before and after the revision (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Shapiro-Wilk test showed that FSVs were normally distributed in the wet season (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) but not in the dry season (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the wet season, the changes in the revised FSVs compared to the original varied between \u0026minus;\u0026thinsp;5 and 3. Four families exhibited a change in FSVs of less than 1, while the rest of families had a change in FSVs of more than 1. Eight families had changes in FSVs between \u0026minus;\u0026thinsp;2 to -1, and six families had changes in FSVs between \u0026minus;\u0026thinsp;5 to -4. In the dry season, the changes in the revised FSVs varied between \u0026minus;\u0026thinsp;6 to 1. Among these, twelve families had FSVs that varied between \u0026minus;\u0026thinsp;5 to -4, six families had FSVs that varied between \u0026minus;\u0026thinsp;2 to -1, and only two families exhibited changes in FSVs less than 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Shapiro-Wilk test results indicated that neither the original nor revised FSVs followed a normal distribution (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The distribution patterns of original FSVs were comparable in both wet and dry seasons. There were no family-level taxa with FSVs scoring 2, 4, 9 and 10, while the highest number of family-level taxa with FSVs scoring 6, and a similar number of family-level taxa with other scores. The revised FSVs in the wet season were clustered between 1 to 2 and included 10 family-level taxa, whereas 22 family-level taxa fell within this range in the dry season. Furthermore, the number of family-level taxa in the middle and high range of scores was obviously lower in both periods compared to the low range of scores. No family-level taxa with FSVs scoring between 7 to 10 in the wet season, while between 4 to 10 in the dry season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared to the wet season, the dry season had a similar range of FSVs for most family-level taxa, mostly between \u0026minus;\u0026thinsp;5 to -4, while the wet season had a range mainly between \u0026minus;\u0026thinsp;2 to -1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This difference in distribution resulted in a slope of less than 1 when comparing the both seasons in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Overall, the variability in FSVs was higher in the dry season than in the wet season. Fifteen family-level taxa fell in the third quadrant, indicating their FSVs change showed a consistent trend in both seasons, with the majority of family-level taxa (16 out of 20 shared taxa between dry and wet seasons) showing FSVs variability significantly greater than 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Comparison of BMWP and ASPT\u003c/h2\u003e \u003cp\u003eResults of the linear regression indicated that the WQI exhibited a significant correlation with the revised ASPT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but not with original ASPT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the wet season (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, the WQI showed significant correlation with the original and revised BMWP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It was noteworthy that the p-value of the revised BMWP was lower than the original BMWP. For the dry season, the WQI was also significantly correlated with the revised ASPT values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but not with the original ASPT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the WQI lacked significant correlation with both the original and revised BMWP (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The revised BMWP and ASPT demonstrated better linear fitting than their original versions in both seasons, as indicated by their higher coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e). The above suggested that the revised BMWP and ASPT indices could offer a more accurate assessment of water quality than before the revisions. Additionally, the revised BMWP and ASPT exhibited higher t-values and lower p-values for their slope coefficients when incorporated into the WQI model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of linear regression analyses of the original and the revised ASPT, BMWP, against WQI, in the wet and dry seasons. Data from the wet seasons: original ASPT (NO.1), revised ASPT (NO.2), original BMWP (NO.3), revised BMWP (NO.4);Data from the dry seasons: original ASPT (NO.5), revised ASPT (NO.6), original BMWP (NO.7), revised BMWP (N0.8).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet season: Original ASPT\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet season: Revised ASPT\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet season: Original BMWP\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet season: Revised BMWP\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry season: Original ASPT\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry season: Revised ASPT\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry season: Original BMWP\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry season: Revised BMWP\u0026thinsp;~\u0026thinsp;WQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrated that both the revised ASPT and BMWP distributions for both seasons were narrower and more concentrated, with values lower than the original ASPT and BMWP. Generally, the revised ASPT distribution appeared to be more concentrated than the revised BMWP distribution, although the latter contained more points within the 95% confidence interval. In the dry season, both revised ASPT and BMWP were more concentrated compared to the wet season, with more points within the 95% confidence interval, albeit with lower values. These findings further confirmed the revised BMWP and ASPT indices were more accurate than their original versions, emphasizing the importance of combining the two indices for water quality assessment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe revised ASPT ranged from 1.00 to 4.09 in the wet seasons and from 1.00 to 2.00 in the dry season, whereas the revised BMWP fluctuated between 1.00 and 28.60 in the wet season and between 1.00 and 14.00 in the dry season (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Overall, the results of the revised ASPT and BMWP assessment showed that water quality in the wet season was prevails over that in the dry season. The water quality assessed by the revised ASPT was better than that assessed by the revised BMWP in the wet season, whereas water quality assessed by the revised ASPT was poor than that assessed by the revised BMWP in the dry season.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThree distinct patterns of revised FSVs were observed between the dry and wet seasons. These differences stem from varying species composition between seasons, as well as changes in water quality conditions in urban rivers. The first pattern is that the revised FSVs has the same trend (either decrease or increase) in both seasons. For example, the original FSV of Baetidae was 7, revised to 5 in the wet season and 1 in the dry season (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A decrease in revised FSV was associated with species richness and ecological resilience of this family. There were two genera (i.e., Baetis and Baetiella) in the dry season and only Baetiella in the wet season. In the Ethiopian highland rivers, Lakew and Moog\u003csup\u003e18\u003c/sup\u003e assigned three FSVs to Baetidae based on species richness, i.e., 9 for more than two species, 6 for two species, and 4 for one species, indicating that the family is tolerant to moderate or strong pollution when there are fewer than two species. Moreover, Ruiz-Picos et al.\u003csup\u003e24\u003c/sup\u003e assigned FSV of 1 to Baetidae in Mexico rivers, suggesting that the family had a high pollution tolerance. These findings are in general agreement with the results of the present study, suggesting that the revision method is applicable to urban rivers.\u003c/p\u003e \u003cp\u003eThe second pattern is that the revised FSVs increase in one season and decrease in the other. For example, the revised FSV of Glossiphoniidae increased in the wet season (5.1) and decreased in the dry season (1.4) compared to the original FSV of 3 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This is related to the number of individuals of Glossiphoniidae in both seasons, with higher individual abundance weighing more heavily on the revision results. In this study, the individual abundance of Glossiphoniidae was higher in the dry season than in the wet season, suggesting that the family is more adapted to polluted environments, which is consistent with the findings of Luo et al.\u003csup\u003e33\u003c/sup\u003e that Glossiphoniidae showed higher abundance in the more urbanized and polluted rivers.\u003c/p\u003e \u003cp\u003eThe third pattern is that revised FSVs fluctuates in one season but remains constant in the other. For Hydropsychidae, the revised FSV remained 6 in the wet season but decreased to 1 in the dry season (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The revised FSV for Hydropsychidae in tropical rivers in Mexico was 4\u003csup\u003e24\u003c/sup\u003e. Mao et al.\u003csup\u003e34\u003c/sup\u003e revised the sensitivity value of Hydropsychidae in the Chishui River to 4.6\u0026thinsp;~\u0026thinsp;5.7. Despite Hydropsychidae species are categorized as environment sensitive indicator (EPT), they are distributed across different pollution gradients and display some degree of pollution tolerance in the larval stage\u003csup\u003e35\u003c/sup\u003e. The lower FSV for Hydropsychidae in this study may be related to the high level of pollution in urban rivers during the dry season, and reflects the lower limit of the family\u0026rsquo;s tolerance to pollution.\u003c/p\u003e \u003cp\u003eTypically, pollution loads, especially organic pollution, are heavier in urban rivers, resulting in enhanced macroinvertebrate tolerant taxa and reduced sensitive taxa\u003csup\u003e9,33,36\u003c/sup\u003e. We found that revised FSVs in both seasons were skewed towards lower value distributions, indicating that macroinvertebrates in urban rivers generally show stronger pollution tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Previous studies on natural rivers have demonstrated that revised FSVs tend to be normally distributed, with most FSVs at intermediate ranges\u003csup\u003e18,23,34\u003c/sup\u003e, consistent with the distribution of species toxicity response\u003csup\u003e37\u003c/sup\u003e. Therefore, it is hypothesized that this long-term adaptation of macroinvertebrates in urban rivers to highly polluted environments increased their tolerance to pollution\u003csup\u003e33\u003c/sup\u003e, leading to a decreasing trend in most revised FSVs.\u003c/p\u003e \u003cp\u003eThe results showed that the revised FSVs were more variable in the dry season. This can be attributed to the decrease in water volume in this season, which exhibits an increase in pollutant loads concentrations in urban rivers\u003csup\u003e38\u003c/sup\u003e, highlighting that macroinvertebrates are more resilient to pollution\u003csup\u003e5,9,39\u003c/sup\u003e. The increased water volume in the wet season can effectively dilute organic pollutant concentrations\u003csup\u003e40\u003c/sup\u003e and nutrients levels\u003csup\u003e23\u003c/sup\u003e, resulting in improved water quality conditions\u003csup\u003e41\u003c/sup\u003e. Moreover, differences in the individual abundance or species composition of macroinvertebrates during different seasons may also account for differences in FSV revisions. For example, Baetidae, discussed earlier, have lower individual abundance and species composition in the wet season due to less habitat caused by the higher water level, resulting in less changes in FSV revisions in this season.\u003c/p\u003e \u003cp\u003eMost studies have found that the adapted BMWP to be more advantageous in water quality assessment\u003csup\u003e7,18,34\u003c/sup\u003e. For example, Romero et al.\u003csup\u003e23\u003c/sup\u003e reported that a modified BMWP improved water quality classification accuracy in the Embalse del Gu\u0026aacute;jaro River, Colombia. Ruiz-Picos et al.\u003csup\u003e24\u003c/sup\u003e found that the adapted BMWP distinguished the effects of agriculture and urban on water quality. The similar results were showed in this study where the revised BMWP and ASPT had better correlations with WQI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Therefore, the adapted indices can be used as the tool for biomonitoring water quality in urban rivers in PRD. It is worth noting that the application of adapted indices for water quality assessment in other regions may fail. Ochieng et al.\u003csup\u003e5\u003c/sup\u003e reported that the adapted BMWP indices from Costa Rica failed to separate sites along river pollution gradient in Eastern Uganda. Additionally, the indicative performance of the adapted BMWP and ASTP for water quality is controversial. The adapted BMWP shows a better response to the pollution than the adapted ASPT\u003csup\u003e40\u003c/sup\u003e. da Silva-Santos et al.\u003csup\u003e42\u003c/sup\u003e also reported that BMWP was more able than ASPT to detect differences in habitat quality. Moreover, Deemool and Prommi\u003csup\u003e43\u003c/sup\u003e found that the adapted BMWP and ASPT responded to different water quality factors, respectively. Consequently, combining both indices for river water quality assessment would reduce the bias generated by one index.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study revised the FSVs of macroinvertebrates in subtropical urban rivers in China and analyzed the accuracy of BMWP and ASPT indices in water quality assessment. It was found that most of the revised FSVs were low, and FSVs were lower in the dry season than in the wet season, which was related to the high level of pollution due to the small amount of water in the dry season, and also reflected the minimum tolerance of macroinvertebrates to the environment. Therefore, it is recommended to use the revised results of lower FSVs. In addition, the higher correlation between the revised BMWP and ASPT indices and WQI index could improve the accuracy of water quality assessment in urban rivers, and the simultaneous use of the BMWP and ASPT indices could provide more reliable assessment results. The results of this study provide a set of FSVs for macroinvertebrates in urban rivers in the Pearl River Delta region, and an effective management tool for water quality assessment in subtropical urban rivers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMengyue Zhang: Data curation, Methodology, Software, Validation, Writing-original draft and Writing-review, editing. Mingqiao Yu: Data curation and Investigation. Sen Ding: Funding acquisition, Supervision and Writing\u0026mdash;review, editing. Zhao Li: Conceptualization.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePark, Y.-S. \u0026amp; Hwang, S.-J. Ecological Monitoring, Assessment, and Management in Freshwater Systems. Water 8, 324. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w8080324\u003c/span\u003e\u003cspan address=\"10.3390/w8080324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirican, S. 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Allied Res. 6, 89\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://api.semanticscholar.org/CorpusID\u003c/span\u003e\u003cspan address=\"https://api.semanticscholar.org/CorpusID\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e:222260346 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Macroinvertebrates, Bioindication, Biomonitoring, BMWP, ASPT, Urban river","lastPublishedDoi":"10.21203/rs.3.rs-4612128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4612128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite their socio-economic and ecological functions, urban rivers are among the most endangered and threatened ecosystems, especially in developing countries, where the impact of population growth, urbanization, etc., on urban river ecosystem is more pronounced. Reliable and affordable bioassessment tools are fundamental for managing and restoring urban river ecosystems. This study collected macroinvertebrates from a typical urban river of the Pearl River Delta region in the dry season (December 2021 to January 2022) and the wet season (May to June 2022). Family sensitivity values (FSVs) were revised based on local biotic and abiotic data, and then used to adapt the Biological Monitoring Working Party (BMWP) and Average Score per Taxon (ASPT) indices. The study employed Shapiro-Wilk normality test and linear regression model to analyze the fitting relationship between bio-indices and Water Quality Index (WQI), and compared their differences between using the origin FSVs and revised FSVs. The results indicated that the revised FSVs for urban rivers decreased. Furthermore, due to differences in macroinvertebrate taxa composition and water quality conditions between dry and wet seasons, the revised FSVs differed between the two seasons, and the lower FSV of the specific family were recommended, reflecting the lower limit of pollution tolerance. The adapted BMWP and ASPT indices provide more accurate water quality assessment results and are reliable indicators in urban rivers. Thus, the adapted macroinvertebrate indicator is a suitable bioassessment tool for subtropical urban rivers in this region, allowing the identification of priority areas for management and a recovery plan.\u003c/p\u003e","manuscriptTitle":"Revision of the biological monitoring working party score system: Evidence from the subtropical urban river in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 08:22:00","doi":"10.21203/rs.3.rs-4612128/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"256f682a-2c6d-47b0-8ea6-721f6750ba8b","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34574846,"name":"Earth and environmental sciences/Ecology/Freshwater ecology"},{"id":34574847,"name":"Earth and environmental sciences/Ecology/Urban ecology"}],"tags":[],"updatedAt":"2024-11-25T03:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-17 08:22:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4612128","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4612128","identity":"rs-4612128","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}