Calculation of macroinvertebrate MMI for water bodies in central China based on land use classification and topographic grouping

Calculation of macroinvertebrate MMI for water bodies in central China based on land use classification and topographic grouping | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Calculation of macroinvertebrate MMI for water bodies in central China based on land use classification and topographic grouping Biao Zheng, Shuang Tian, Bingjiao Sun, Xin Gao, Wennuo Han, Bin Wu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4272409/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Multi-Metric Index (MMI) is a comprehensive assessment index that has gained popularity in aquatic ecological assessment due to its integrated consideration of various factors such as community composition, diversity and function. However, when applied over large spatial areas, the MMI may not be effective in discriminating the level of human disturbance due to the influence of natural variability. To reduce the impact of natural variability on the assessment results, we collected macroinvertebrate community data from the water bodies in central China in 2023. We then grouped the data according to different topographies and constructed separate MMIs. We used the Catchment Disturbance Index (CDI) to categorise human disturbances into low-disturbance and strong-disturbance groups. In order to eliminate the influence of subjective factors on the selection of indicators, when the correlation of indicators was greater than 0.75, we considered all possible combinations of indicators and finally selected the best performance as the final MMI. The results showed that the point classification based on the Catchment Disturbance Index (CDI) could accurately reflect the changes of communities under different levels of human disturbance. The gradient of human disturbance levels in the mountain group was small, and the final MMI was not significantly correlated with the CDI. In contrast, the MMI results for the plains and hills were better than those for the entire catchment area where MMI was not grouped. Biological sciences/Ecology/Freshwater ecology Earth and environmental sciences/Environmental sciences/Environmental impact macroinvertebrates land use topography index of biological integrity human disturbance Figures Figure 6 Highlights 1. Classification of points based on land use for large-scale spatial data can effectively distinguish between low and high interference points. 2. Calculating MMI based on terrain grouping can improve accuracy to a certain extent. 3. The accuracy of MMI calculated with strong natural disturbances and a small human interference gradient in mountainous areas is not as good as the result of the whole catchment area. 4. Considering all possibilities when retaining indicators and removing the influence of subjective factors can effectively improve the accuracy of the results. 1. Introduction A river ecosystem is a component of a larger network of watersheds or catchments. Smaller headwater streams discharge into medium-sized streams that gradually flow into a larger network of rivers. The main area of a river ecosystem is determined by the gradient of the river bed or the velocity of the water flow 1 . As a whole, it has a large natural gradient, which supports greater biodiversity and biological integrity than lentic ecosystems 2 . However, the health of river ecosystems is steadily degrading due to rapid human economic and social development 3 . Previous studies have relied on measured water quality variables to reflect the health of rivers 4,5 . However, it is now evident that water quality is being restored while the biological composition is still dominated by resistant species. Using aquatic organisms that play a crucial role in the ecosystem as indicator organisms is a more effective approach to reflect the health of the aquatic ecosystem 6,7 . Scientists have developed various evaluation methods for rivers in different regions of the world 8-10 , with the Multi-Metric Index (MMI) being one of the most widely used. Its predecessor, the Index of Biological Integrity (IBI), was originally developed using fish monitoring data 11 . MMI has been widely applied in many countries, including the United States and the European Union, following the enactment of the Clean Water Act (CWA) and the European Water Framework Directive (WFD). The evaluation system has been trained using basin-scale or even national-scale data. Larger spatial scales tend to have greater gradients of natural and anthropogenic disturbance and 12,13 . From an ecological niche perspective, they support the establishment and persistence of a greater diversity of species 14 . Therefore, species composition should vary more between sites, further illustrating the importance of bioassessment of large catchments. However, most research in China still relies on small-scale data to develop the MMI 15,16 . This limits the model's applicability, making it imperative to use a large-scale dataset to train and construct the MMI model, facilitating subsequent monitoring. Recently, it has been found that the effects of natural disturbances on biological communities may outweigh the effects of human disturbances on them, especially at larger spatial scales 17,18 . In this context, the precise selection of indicators that accurately reflect human disturbances instead of the effects of natural disturbances or reducing the impact of natural variability on the indicators is especially crucial for the development of MMI models 19,20 . This is because the primary objective of environmental monitoring is still to mirror the influence of human activities on the health of the ecosystem, so that it can be safeguarded and redeemed. In addition to this, quantifying the true level of human interference is a crucial technical issue during the MMI development process. Water environmental factors are commonly used as indicators of localized human disturbance, while land use is used as an indication of catchment human disturbance. However, not all factors reflect human disturbance, and some may be concealed by other factors. This text will solely concentrate on land use variables as they are pertinent to human activities on a large scale. On the other hand, there is no strict methodology for the MMI calculation process and there may be some subjective factors in the screening of indicators, for example, when there are pairs of indicators with correlations greater than 0.75, there is usually no fixed answer as to which indicator to remove, which may lead to a lower performance of the MMI in the end 21 . In this case, retaining all possible combinations of indicators and selecting the best performing combination as the final MMI can effectively remove the subjective factor. In order to better quantify the effects of human disturbance on aquatic ecosystems, it is important to select organisms that are most sensitive to human disturbance. In this context, macroinvertebrates have proved to be one of the best indicator organisms for biomonitoring because of their short life cycle, sensitivity to environmental changes and wide distribution in rivers 22,23 . The study area stretches across central China from west to east and is of great importance for local economic and social development 24 . However, most previous studies have assessed main stem tributaries or sub-tributaries in the region and, to our knowledge, no studies have attempted to assess macroinvertebrate MMI for the study area. In view of this, the aim of our study is to identify an MMI evaluation system that is applicable to the study area, facilitates subsequent long-term monitoring of the area, and can be improved and then applied to other basins across the country. In order to reduce the influence of natural factors on the evaluation results, we refer to the results of other people's studies 20,25 to partition the the area according to different terrain, specifically three types: mountain, hill and plain. In this study, two hypotheses were tested using macroinvertebrate monitoring data from water bodies in central China. Firstly, the catchment human disturbance index (CDI) can distinguish between minimally disturbed and strongly disturbed sites. Secondly, the macroinvertebrate MMI, based on topographical zoning, is more precise than the ungrouped basin-wide MMI. 2. Materials and methods 2.1. Study area The study area shows a distribution trend of higher temperatures in the east and south and lower temperatures in the west and north. The temperature in the middle and lower reaches is higher than in the upper reaches, and the temperature in the south is higher than in the north. The temperature in the upper reaches is the lowest in the whole basin. The average annual rainfall in the region is 1067 mm. Due to the vastness of the area, the complexity of the terrain and the distinctive monsoon climate, the spatial and temporal distribution of rainfall and rainstorms is uneven 26 . 2.2. Macroinvertebrate data Field sampling was carried out in spring and autumn 2023 at 169 randomly selected stream sites in the study area (Figure 1), based on the spatial distribution data of 1 million landform types in China downloaded from the Institute of Geographic Sciences and Resources, Chinese Academy of Sciences (IGSR, CAS) 27 , the spatial connectivity analysis of ArcGis 10.8 was used to classify each point into mountains (40), hills (37) and plains (92) according to the terrain. To ensure consistency, three separate teams were assigned to conduct the sampling due to the large number of sites and the extended time frame. A consistent sampling strategy was employed to eliminate any potential influence of sampling errors on the data results. Before sampling, all sampling personnel underwent unified training and an examination. Only those who passed the exam were allowed to carry out sampling. Experts were also present to conduct on-site quality control during each sampling. Macroinvertebrate samples were collected at each site after preparation. For the wadeable streams, we set up five different habitats to be sampled separately by two people using a Surber-net. The samples were then combined into a single sample. Five habitat samples were collected from non-wadeable streams using Peterson's mud collector by two individuals and then combined into one sample. The collected substrate or mud samples were washed with a sieve and sorted on site for macroinvertebrate samples. Insect samples were placed in wide-mouth bottles, while larger samples such as snails and mussels were put into ziplock bags and preserved in a 75% alcohol solution. To ensure consistent classification of sample identification across different identifiers, a unified reference books 28-30 was used to identify samples at the family or genus level. Once all the samples had been identified, the identifiers were brought together for centralised analysis, starting with cross-validation to check the results of different people, discussing disputed species and seeking the help of taxonomists for those that could not be resolved. To calculate metrics that reflect the functional composition and diversity of macroinvertebrates, we selected nine functional traits from 32 categories. These traits describe the functional structure of macroinvertebrate communities and include life history, resistance or resilience, and basic biological characteristics. We categorised species traits primarily by reference to published studies 30-33 . 2.3 Effects of human and natural disturbances on MMI In order to quantify the extent of anthropogenic disturbance at different points and to distinguish between reference and degraded status, we extracted the land use types (forest, cropland, water body, grassland, impervious surface, wasteland) in the upstream catchment of each sampling point based on the 2022 Chinese 30m land cover data 34 and calculated the respective percentages. As there were no truly undisturbed points, we classified all the points as relatively less disturbed and strongly disturbed. The Catchment Disturbance Index (CDI) 35 was calculated for each point, and all points were classified based on the 25th percentile of the CDI index. Points less than or equal to the 25th percentile were considered less disturbed, whilst those above were considered strongly disturbed. To reduce the impact of natural variability on the MMI indicator values, we divided the points based on the topography of the study area. We also calculated all the points without grouping for comparison purposes. We constructed four sets of MMI evaluation systems for mountains, hills, plains, and the whole basin, respectively. 2.4 Construction of the MMI system for macroinvertebrates in the study area The MMI system was constructed using 23 years of aggregated spring and autumn data on macroinvertebrates. The construction process was divided into the following steps: (1) The study selected 87 candidate indicators (Appendix Table 1) that reflect various aspects of macroinvertebrate structure, function, tolerance, and biodiversity. (2) A range analysis was conducted on the candidate indicators, calculating their 15th and 75th quartiles and the difference between them. Indicators with small ranges were removed. (3) The study performed a Mann-Whitney U test on the indicators of the less disturbed points and the indicators of the strongly disturbed points. Indicators with a p-value < 0.05 were retained. (4) Correlation analysis of the remaining indicators. To eliminate the influence of subjective choices on MMI results. If any two indicators had a correlation ≥ 0.75, all possible combinations of indicator results were retained and it was ensured that no two variables in the combination had a correlation greater than 0.75. Finally, the combination of indicators with the highest accuracy was selected as the final MMI. (5) Referring to the methodology of previous studies 15 , the different indicators were normalised to a range of 0 to 1. The results of each set of schemes should be validated, and the MMI with the highest accuracy should be determined. This is done by conducting a Spearman's correlation analysis between the MMI and CDI indices. The best performance is achieved when the correlation coefficient is highest. We computed the entire MMI process using written code, and correlation analyses were computed using the cor.test function. We also examined differences in macroinvertebrate community composition across terrain using NMDS analyses and ANOSIM analyses based on the "metaMDS" and "anosim" functions of the vegan package, respectively. All data analyses in this study were performed in R 4.3.2 36 . 3. Results 3.1. Macroinvertebrate community composition In 2023, a total of 270 species of macroinvertebrates were found in the study area, belonging to 5 phyla, 30 orders and 127 families. Among them, there are 194 species in plain terrain, 130 species in hilly terrain and 171 species in mountainous terrain. Chironomidae had the highest number of species among all sites, with 64 species accounting for 23.7% of the total. Palaemonidae, Atyidae, Polypedilum, and Corophiidae were the dominant species during the survey period. The macroinvertebrate community composition differed significantly among mountains, hills, and plains (Stress = 0.1945, ANOSIM_R = 0.1094, p = 0.001) (Figure 2), and therefore the topography-based grouping effectively attenuated the effect of natural conditions on community composition. Specifically, macroinvertebrate community composition at mountain sites was dominated by Insecta (71%), followed by Gastropoda (12%), Oligochaeta (6%), with Heptageniidae, Cricotopus sp, Baetidae, Hydropsychidae, and Polypedilum sp as dominant taxa. The macroinvertebrate community composition at the hill sites was dominated by Insecta (63%), followed by Gastropoda (12%), Oligochaeta (67%), with Palaemonidae, Atyidae, Polypedilum sp, and Semisulcospira sp as dominant taxa. The macroinvertebrate community composition at the plain sites was dominated by Insecta (63%), followed by Gastropoda (12%), Bivalvia (67%), with Palaemonidae, Atyidae, Polypedilum sp, Corbicula sp, and Corophiidae as the dominant taxa. 3.2. Differences in land use variables The study area exhibits a high land-use gradient, as evidenced by the significant variation in land-use composition across different sites (Table 1), with agricultural land (51.87 ± 25.24) being the most common land use type in all sites, followed by forest (23.6 ± 27.62) and grassland (2.80 ± 11.16) being the least common. In terms of different terrains, forest (59.08 ± 22.28) was the most common land use type in hills, agricultural land (65.57 ± 21.08) in the hills and agricultural land (58.20 ± 19.96) in plains. In terms of different land use types, agricultural land (65.57 ± 21.08) was the most common in the hills, forest (59.08 ± 22.28) in the mountains, grassland (11.55 ± 20.81) in the mountains and water bodies (17.12 ± 15.57) and urban areas (15.40 ± 13.52) in the plains. Table 1 Differences in land-use composition of sites with different topography, give the p-value for the Kruskal-Wallis H-test Land use type All sites Mountain sites Hill sites Plain sites kruskal.test Mean SD Mean SD Mean SD Mean SD Farmland % 51.87 25.24 24.64 19.28 65.57 21.08 58.20 19.96 p < 0.001 Forest land % 23.36 27.62 59.08 22.28 19.89 22.59 9.22 15.05 p < 0.001 Grassland % 2.80 11.16 11.55 20.81 0.21 0.68 0.04 0.20 p < 0.001 Water area % 11.01 13.42 2.37 2.65 5.18 2.61 17.12 15.57 p < 0.001 Urban land % 10.76 12.63 1.64 2.56 9.09 11.20 15.40 13.52 p < 0.001 3.3 MMI results for macroinvertebrates in different terrains 3.3.1. Mountain results Of all 40 mountain sites, 29 were classified as impaired and 11 as reference sites. We began with 87 indicators and reduced the number to 67 after removing those with small distributions. Only 2 indicators, M59 (% number of filter feeders) and M79 (Dispersal), met the screening criteria for discriminability and the correlation between them did not exceed 0.75, so M59 and M79 were the final indicators retained for the mountain MMI calculation (Figure 3). Finally, the MMI score was calculated for each point and it was found that the score was not significantly correlated with the CDI ( R = -0.2, p = 0.21) (Appendix Figure 2). 3.3.2. Hilly results These points were categorised in the same way as above. Out of a total of 37 mound points, 29 are impaired and 8 are reference points. We started with 87 indicators and reduced this to 60 after removing indicators with small distributions. Only four indicators met the screening criteria for discriminability, so these were tested for correlation and, as before, variables with a final correlation of no more than 0.75 were retained. The final retained metrics were M17 (number of bivalve taxonomic units), M25 (percentage of individuals in dominant taxonomic units), M68 (Maglev species richness index) and M77 (Rao quadratic entropy index) (Figure 4). The final MMI score was significantly correlated with the CDI ( R = -0.4, p = 0.015) (Appendix Figure 3). 3.3.3. Plain results Of all 92 plain points, 69 are impaired and 23 are reference points. We started with 87 indicators and reduced them to 57 after removing indicators with small distributions. Nine indicators met the screening criteria for discriminatory ability, so these were tested for correlation, and as before, variables with a final correlation of no more than 0.75 were retained. As there were multiple indicator combination scenarios, the indicator combination scenario with the highest correlation coefficient was selected based on the correlation of MMI scores with CDI for each scenario, i.e. scenario b ( R = -0.4, p < 0.001) (Appendix Figure 4). The final metrics selected for final retention were M22 (number of taxonomic units of telopods and molluscs), M23 (number of total taxonomic units), M53 (percentage of the number of individuals of telopods and molluscs), M59 (percentage of the number of individuals of filter feeders), M60 (percentage of the number of individuals of collectors), and M71 (BI) (Figure 5). 3.3.4. All point results When the points were grouped without the use of topography, out of a total of 169 points, 135 were classified as impaired and 34 as reference points. We started with 87 indicators and reduced this to 60 after removing indicators with small distributions. Fifteen indicators met the screening criteria for discriminatory ability, so these were tested for correlation and, as before, variables with a final correlation of no more than 0.75 were retained. As there were multiple indicator combination scenarios, the indicator combination scenario with the highest correlation coefficient, scenario j, was selected based on the correlation of MMI scores with CDI for each scenario ( R = -0.34, p < 0.001) (Appendix Figure 5). The final metrics selected for final retention were M10 (number of taxonomic units of aquatic insects), M19 (number of taxonomic units of crustaceans), M21 (number of taxonomic units of crustaceans and molluscs), M24 (percentage of individuals in EPT), M25 (percentage of individuals in dominant taxonomic units), M63 (percentage of individuals in trappers), M71 (BI), M74 (FEve), M83 (Rheophily), M86 (Functional Feeding Group) (Figure 6). 4. Discussions There have been a number of stream or river assessments based on MMI, but the vast majority have focused on small-scale streams or rivers. However, different indicator screening methods make it difficult to directly compare different streams and rivers, which means that it is not possible to obtain consistent assessment results at large scales, and small scales generally have small environmental gradients, making it difficult to distinguish between low and high disturbance conditions. Therefore, for environmental managers and restoration practitioners, large-scale assessment results are more useful for prioritising protection and restoration, avoiding unnecessary costs and ensuring practicality. To this end, this study collected spring and autumn data from the study area in 2023 to construct the MMI, and finalised four sets of MMI results for the three terrains and the entire basin through a series of indicator screening processes. We found that hill and plain-based MMIs were more accurate than basin-wide MMIs, while mountain-based MMIs were not significantly correlated with CDI. Similar to our initial hypothesis, using the CDI to classify points enables a clear differentiation between reference and impaired status. However, it may be challenging to identify indicators with significant differences when considering the water environment factor. The significant variability observed in macrobenthic communities may be related to the higher land use gradient in the study area. In contrast, the gradient of water environmental factors in the study area was small, which made it difficult to reflect differences in macroinvertebrate communities. This is consistent with the general ecological theory that at small local scales, local environmental factors dominate the influence on macroinvertebrate communities. At larger regional scales, catchment variables and spatial factors tend to drive macroinvertebrate community variation 37 . The categorisation of points based on CDI reflects the direct impacts of human activities on riparian habitats and, therefore, the indirect impacts on riverine organisms 38 . Biological communities are most affected by human disturbance when the surrounding land use is predominantly agricultural or urban 39 . For instance, the use of organic fertilisers for irrigation can lead to increased nitrogen and phosphorus concentrations in rivers. Similarly, organic pollution from domestic waste in cities can also worsen the burden on river ecosystems. These conditions can make macroinvertebrate communities vulnerable, resulting in reduced diversity, community homogenisation, and dominance by tolerant species. In grassland-dominated habitats, continued grazing can lead to a decline in water quality and eutrophication due to animal faeces. Conversely, forested habitats have lower anthropogenic intensity. Most of these sites are located in the headwaters of streams, with more intact habitats and a higher proportion of cleaner species in the community composition. The variation in the community is mainly influenced by natural variability. Our second hypothesis is partially valid. We discovered that MMIs founded on topographic zoning are more precise than basin-wide MMIs lacking zoning as they weaken the gradient of natural variables. The outcomes for hills and plains upheld our hypothesis, and the grouping grounded on hills eventually identified four indicators to build the MMI system. The M17 index shows the number of non-tolerant bivalve species in the community. As most land-use types in the hills are farmland, non-tolerant bivalve species are common in the least disturbed sites. Therefore, as the percentage of agricultural land gradually increased, the intensity of anthropogenic disturbance also increased. This led to a decrease in the non-tolerant species in the community composition and an increase in the proportion of tolerant species, which eventually became the dominant species. The species richness index (M68) is a single-indicator evaluation method for species diversity. Its magnitude can be used to judge water quality. According to the moderate disturbance hypothesis 40 , biodiversity is highest when the biome is at a moderate level of disturbance. Biodiversity decreases above this threshold. Therefore, this index can distinguish well between low-disturbance and strong-disturbance sites in hilly areas. Ecological assessments can be challenging due to the need to distinguish between environmental and spatial influences on communities. Therefore, in this study, we introduced nine functional traits of macroinvertebrates to calculate functional diversity due to their high responsiveness to environmental change and small spatial extent of variation 41 . Our results clearly support the above, as multiple metrics of functional diversity served as one of the factors used to calculate the final MMI score. Recent studies have shown 42 that the quadratic entropy index in Rao reflects the overall distribution of functional diversity of species in the community. This reflects the variation in functional distance between species. This reflects the variation in functional distance between species and increases the variation in functional traits in the community. Therefore, when disturbance is enhanced, species with tolerant traits are retained, whilst species with sensitive traits are eliminated. The plain sites resulted in multiple schemes, unlike the hilly sites. We identified the scheme with the highest accuracy based on the correlation coefficients between the MMI scores and the CDI index. The number of taxonomic units for telopods and molluscs (M22) and the percentage of individuals for telopods and molluscs (M53) were identified as core metrics for the plains MMI system. This is due to the high level of anthropogenic disturbance on the plains. Even the less disturbed sites showed very few EPT insects and were mainly composed of telopods and molluscs. In contrast, the strongly disturbed sites were dominated by resistant species. The total number of taxonomic units (M23) has always been a commonly used indicator 43,44 , closely related to disturbance. The percentage of filter-feeder individuals (M59) significantly decreased with increasing disturbance, while the percentage of collector individuals (M60) significantly increased. The findings of most studies support this conclusion 32,33 : filter feeders are typically clean aquatic insects or telopods that reside in fast-flowing water bodies and filter their food through water currents. As human disturbance increases, the proportion of filter feeders decreases due to the destruction of their natural habitat and the increase of nutrients in the water. Conversely, the number of nutrient-loving collectors, which are mostly tolerant species, increases 45 . The BI (M71) is a core indicator that has been widely used in bioassessments due to its strong correlation with human disturbance. A higher score indicates a stronger disturbance, while a lower score indicates a weaker disturbance. The mountain MMI was not significantly correlated with the CDI, suggesting that the results may not be meaningful. Firstly, the number of mountain sites is small, which could lead to unrepresentative results. Secondly, most of the hill sites are located at high elevations with little human disturbance around them, making it difficult to distinguish differences in macroinvertebrate communities with small gradients of human disturbance. In addition, although grouping based on topography can attenuate some of the effects of natural factors, such effects are still present. The MMI results based on all points ultimately identified the greatest number of indicators as they had the greatest gradient of human disturbance, and the communities were distributed along this gradient. This reflects the conclusion that the MMI results are not as good as the overall results when the gradient of human disturbance is smaller when grouped by topography. And when the grouping still has a high disturbance gradient, the MMI accuracy is higher for the terrain-based grouping. However, it is worth noting that although we have grouped the points based on topography, natural factors are still an important factor that cannot be ignored, and even in the same group, there are some differences in elevation, geology, climate, and so on. It is therefore important to develop more effective methods to minimise the effects of natural variability in future studies. At the same time, the number of monitoring sites should be increased where conditions allow to find the gradient of community variation that truly reflects human disturbance, and our results should be further validated using data from many years of monitoring to eliminate the influence of time on the results. 5. Conclusions Large-scale biological integrity assessment has been a challenge for monitoring managers and researchers due to insurmountable sampling and observation difficulties. In this study, we proposed to address this issue using macroinvertebrate survey data from the spring and autumn of 2023 in the study area. We classified points based on the catchment disturbance index CDI and constructed several different MMI schemes based on topographic groupings. We found that the CDI-based categorisation allowed a good distinction between candidates for low and high disturbance sites, but there were some differences in grouping by topography. In particular, the accuracy of the MMI for hilly and plain points was better than the basin-wide ungrouped MMI, while the results of the mountain-based MMI were worse than the basin-wide ungrouped MMI. We recommend that for large-scale biomonitoring data, the catchment disturbance index CDI should continue to be used to differentiate between sites when evaluating results using the Index of Biotic Integrity (IBI) because the CDI has a large gradient of variability and describes the intensity of human activities well. In addition, grouping sites according to topography may improve the accuracy of results to some extent when developing the IBI. Declarations Declaration of competing interest The authors declare that they have no conflicts of interest to this work (Title: Calculation of macroinvertebrate MMI for water bodies in central China based on land use classification and topographic grouping). Acknowledgments This study was supported by the Yangtze River Joint Research Phase II Program (2022-LHYJ-02-0102) Authorship contribution statement B. Z. conceptualized the main framework, computed the data analysis, and wrote the main text.S.T. reviewed the data and organized it.B.J.S. reviewed the data and organized it.X.G. sampled collected and organized the data.W.N.H. organized the data. B.W. sampled to collect and organize data. S.H. sampled to collect and organize data. Z.L. conceptualized the main framework and reviewed it. s.d. conducted the review. All authors reviewed the manuscript. 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Land use and local environment affect macroinvertebrate metacommunity organization in Neotropical stream networks. Journal of Biogeography 48 , 479-491, doi:10.1111/jbi.14020 (2021). Le Gall, M., Palt, M., Kail, J., Hering, D. & Piffady, J. Woody riparian buffers have indirect effects on macroinvertebrate assemblages of French rivers, but land use effects are much stronger. Journal of Applied Ecology 59 , 526-536, doi:10.1111/1365-2664.14071 (2022). Mamun, M., Kim, J.-E. & An, K.-G. Land Cover and Human Disturbance Impact on Water Chemistry and Ecological Health in an Asian Temperate Lotic System. Land 11 , 1428, doi:10.3390/land11091428 (2022). Biswas, S. R. & Mallik, A. U. Disturbance effects on species diversity and functional diversity in riparian and upland plant communities. Ecology 91 , 28-35, doi:10.1890/08-0887.1 (2010). Li, Z. et al. The drivers of multiple dimensions of stream macroinvertebrate beta diversity across a large montane landscape. Limnology and Oceanography 66 , 226-236, doi:10.1002/lno.11599 (2021). Zhang, H. et al. Using functional trait diversity to evaluate the contribution of multiple ecological processes to community assembly during succession. Ecography 38 , 1176-1186, doi:10.1111/ecog.01123 (2015). Cai, K. et al. Application of a benthic index of biotic integrity for the ecosystem health assessment of Lake Taihu. J. Lake Sci 26 , 74-82 (2014). Wang, B., Yang, L., Hu, B. & Shan, L. A preliminary study on the assessment of stream ecosystem health in south of Anhui Province using Benthic-Index of Biotic Integrity. Acta Ecologica Sinica 25 , 1481-1490 (2005). AL‐SHAMI, S. A. et al. Drivers of beta diversity of macroinvertebrate communities in tropical forest streams. Freshwater Biology 58 , 1126-1137, doi:10.1111/fwb.12113 (2013). Additional Declarations No competing interests reported. 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Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYBACPmYGNiBlQ4IWNoiWNFK0gBHDYVK0sLM/e/Cj4ry8OXt34geGmjtEOSzdsOfMbcOdPWc3SzAce0aUlmMSvG23Ewxu5G5jYGwgwoVszIxtkn/bziUY3H9LtBZmNmnetgNAW3iJ1sLGJi1zJtlww5nczRIJx4jQws9//Jnkmwo7eYPjZzd++FBDSmiDQQKpGkbBKBgFo2AUYAcAQ9AzFW52w9MAAAAASUVORK5CYII=","orcid":"","institution":"China National Environmental Monitoring Centre","correspondingAuthor":true,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Li","suffix":""},{"id":292680562,"identity":"59885c27-d980-4647-8c3f-0bb2db38aeee","order_by":8,"name":"Seng Ding","email":"","orcid":"","institution":"Chinese Research Academy of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seng","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-04-16 01:42:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4272409/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4272409/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55187599,"identity":"f878f6a1-8fa3-42a6-87ef-5318838f2114","added_by":"auto","created_at":"2024-04-23 18:31:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":327043,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4272409/v1/ee43fa956709f5acc12f8323.png"},{"id":65641235,"identity":"b5bc2541-5dde-41e3-aec1-9783a355ac39","added_by":"auto","created_at":"2024-09-30 19:53:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":800986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4272409/v1/f53b301e-f175-4dc1-928d-65bb82eca4c0.pdf"},{"id":55187602,"identity":"cd157319-7fa0-47b9-a2ef-daf1bc17cdab","added_by":"auto","created_at":"2024-04-23 18:31:24","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":3701502,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4272409/v1/4eafbecc79fb4028ac2f7c10.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Calculation of macroinvertebrate MMI for water bodies in central China based on land use classification and topographic grouping","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. Classification of points based on land use for large-scale spatial data can effectively distinguish between low and high interference points.\u003c/p\u003e\n\u003cp\u003e2. Calculating MMI based on terrain grouping can improve accuracy to a certain extent.\u003c/p\u003e\n\u003cp\u003e3. The accuracy of MMI calculated with strong natural disturbances and a small human interference gradient in mountainous areas is not as good as the result of the whole catchment area.\u003c/p\u003e\n\u003cp\u003e4. Considering all possibilities when retaining indicators and removing the influence of subjective factors can effectively improve the accuracy of the results.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eA river ecosystem is a component of a larger network of watersheds or catchments. Smaller headwater streams discharge into medium-sized streams that gradually flow into a larger network of rivers. The main area of a river ecosystem is determined by the gradient of the river bed or the velocity of the water flow\u003csup\u003e1\u003c/sup\u003e. As a whole, it has a large natural gradient, which supports greater biodiversity and biological integrity than lentic ecosystems\u003csup\u003e2\u003c/sup\u003e. However, the health of river ecosystems is steadily degrading due to rapid human economic and social development\u003csup\u003e3\u003c/sup\u003e. Previous studies have relied on measured water quality variables to reflect the health of rivers\u003csup\u003e4,5\u003c/sup\u003e. However, it is now evident that water quality is being restored while the biological composition is still dominated by resistant species. Using aquatic organisms that play a crucial role in the ecosystem as indicator organisms is a more effective approach to reflect the health of the aquatic ecosystem\u003csup\u003e6,7\u003c/sup\u003e. Scientists have developed various evaluation methods for rivers in different regions of the world\u003csup\u003e8-10\u003c/sup\u003e, with the Multi-Metric Index (MMI) being one of the most widely used. Its predecessor, the Index of Biological Integrity (IBI), was originally developed using fish monitoring data\u003csup\u003e11\u003c/sup\u003e. MMI has been widely applied in many countries, including the United States and the European Union, following the enactment of the Clean Water Act (CWA) and the European Water Framework Directive (WFD). The evaluation system has been trained using basin-scale or even national-scale data. Larger spatial scales tend to have greater gradients of natural and anthropogenic disturbance and\u003csup\u003e12,13\u003c/sup\u003e. From an ecological niche perspective, they support the establishment and persistence of a greater diversity of species\u003csup\u003e14\u003c/sup\u003e. Therefore, species composition should vary more between sites, further illustrating the importance of bioassessment of large catchments. However, most research in China still relies on small-scale data to develop the MMI\u003csup\u003e15,16\u003c/sup\u003e. This limits the model\u0026apos;s applicability, making it imperative to use a large-scale dataset to train and construct the MMI model, facilitating subsequent monitoring.\u003c/p\u003e\n\u003cp\u003eRecently, it has been found that the effects of natural disturbances on biological communities may outweigh the effects of human disturbances on them, especially at larger spatial scales\u003csup\u003e17,18\u003c/sup\u003e. In this context, the precise selection of indicators that accurately reflect human disturbances instead of the effects of natural disturbances or reducing the impact of natural variability on the indicators is especially crucial for the development of MMI models\u003csup\u003e19,20\u003c/sup\u003e. This is because the primary objective of environmental monitoring is still to mirror the influence of human activities on the health of the ecosystem, so that it can be safeguarded and redeemed. In addition to this, quantifying the true level of human interference is a crucial technical issue during the MMI development process. Water environmental factors are commonly used as indicators of localized human disturbance, while land use is used as an indication of catchment human disturbance. However, not all factors reflect human disturbance, and some may be concealed by other factors. This text will solely concentrate on land use variables as they are pertinent to human activities on a large scale. On the other hand, there is no strict methodology for the MMI calculation process and there may be some subjective factors in the screening of indicators, for example, when there are pairs of indicators with correlations greater than 0.75, there is usually no fixed answer as to which indicator to remove, which may lead to a lower performance of the MMI in the end\u003csup\u003e21\u003c/sup\u003e. In this case, retaining all possible combinations of indicators and selecting the best performing combination as the final MMI can effectively remove the subjective factor.\u003c/p\u003e\n\u003cp\u003eIn order to better quantify the effects of human disturbance on aquatic ecosystems, it is important to select organisms that are most sensitive to human disturbance. In this context, macroinvertebrates have proved to be one of the best indicator organisms for biomonitoring because of their short life cycle, sensitivity to environmental changes and wide distribution in rivers\u003csup\u003e22,23\u003c/sup\u003e. The study area stretches across central China from west to east and is of great importance for local economic and social development\u003csup\u003e24\u003c/sup\u003e. However, most previous studies have assessed main stem tributaries or sub-tributaries in the region and, to our knowledge, no studies have attempted to assess macroinvertebrate MMI for the study area. In view of this, the aim of our study is to identify an MMI evaluation system that is applicable to the study area, facilitates subsequent long-term monitoring of the area, and can be improved and then applied to other basins across the country. In order to reduce the influence of natural factors on the evaluation results, we refer to the results of other people\u0026apos;s studies\u003csup\u003e20,25\u003c/sup\u003e to partition the the area according to different terrain, specifically three types: mountain, hill and plain. In this study, two hypotheses were tested using macroinvertebrate monitoring data from water bodies in central China. Firstly, the catchment human disturbance index (CDI) can distinguish between minimally disturbed and strongly disturbed sites. Secondly, the macroinvertebrate MMI, based on topographical zoning, is more precise than the ungrouped basin-wide MMI.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study area shows a distribution trend of higher temperatures in the east and south and lower temperatures in the west and north. The temperature in the middle and lower reaches is higher than in the upper reaches, and the temperature in the south is higher than in the north. The temperature in the upper reaches is the lowest in the whole basin. The average annual rainfall in the region is 1067 mm. Due to the vastness of the area, the complexity of the terrain and the distinctive monsoon climate, the spatial and temporal distribution of rainfall and rainstorms is uneven\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Macroinvertebrate data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eField sampling was carried out in spring and autumn 2023 at 169 randomly selected stream sites in the study area (Figure 1), based on the spatial distribution data of 1 million landform types in China downloaded from the Institute of Geographic Sciences and Resources, Chinese Academy of Sciences (IGSR, CAS)\u003csup\u003e27\u003c/sup\u003e, the spatial connectivity analysis of ArcGis 10.8 was used to classify each point into mountains (40), hills (37) and plains (92) according to the terrain. To ensure consistency, three separate teams were assigned to conduct the sampling due to the large number of sites and the extended time frame. A consistent sampling strategy was employed to eliminate any potential influence of sampling errors on the data results. Before sampling, all sampling personnel underwent unified training and an examination. Only those who passed the exam were allowed to carry out sampling. Experts were also present to conduct on-site quality control during each sampling. Macroinvertebrate samples were collected at each site after preparation. For the wadeable streams, we set up five different habitats to be sampled separately by two people using a\u0026nbsp;Surber-net. The samples were then combined into a single sample. Five habitat samples were collected from non-wadeable streams using Peterson\u0026apos;s mud collector by two individuals and then combined into one sample. The collected substrate or mud samples were washed with a sieve and sorted on site for macroinvertebrate samples. Insect samples were placed in wide-mouth bottles, while larger samples such as snails and mussels were put into ziplock bags and preserved in a 75% alcohol solution. To ensure consistent classification of sample identification across different identifiers, a unified reference books\u003csup\u003e28-30\u003c/sup\u003e was used to identify samples at the family or genus level. Once all the samples had been identified, the identifiers were brought together for centralised analysis, starting with cross-validation to check the results of different people, discussing disputed species and seeking the help of taxonomists for those that could not be resolved.\u003c/p\u003e\n\u003cp\u003eTo calculate metrics that reflect the functional composition and diversity of macroinvertebrates, we selected nine functional traits from 32 categories. These traits describe the functional structure of macroinvertebrate communities and include life history, resistance or resilience, and basic biological characteristics. We categorised species traits primarily by reference to published studies\u003csup\u003e30-33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Effects of human and natural disturbances on MMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to quantify the extent of anthropogenic disturbance at different points and to distinguish between reference and degraded status, we extracted the land use types (forest, cropland, water body, grassland, impervious surface, wasteland) in the upstream catchment of each sampling point based on the 2022 Chinese 30m land cover data\u003csup\u003e34\u003c/sup\u003e and calculated the respective percentages. As there were no truly undisturbed points, we classified all the points as relatively less disturbed and strongly disturbed. The Catchment Disturbance Index (CDI)\u003csup\u003e35\u003c/sup\u003e was calculated for each point, and all points were classified based on the 25th percentile of the CDI index. Points less than or equal to the 25th percentile were considered less disturbed, whilst those above were considered strongly disturbed. To reduce the impact of natural variability on the MMI indicator values, we divided the points based on the topography of the study area. We also calculated all the points without grouping for comparison purposes. We constructed four sets of MMI evaluation systems for mountains, hills, plains, and the whole basin, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Construction of the MMI system for macroinvertebrates in the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MMI system was constructed using 23 years of aggregated spring and autumn data on macroinvertebrates. The construction process was divided into the following steps: (1) The study selected 87 candidate indicators (Appendix Table 1) that reflect various aspects of macroinvertebrate structure, function, tolerance, and biodiversity. (2) A range analysis was conducted on the candidate indicators, calculating their 15th and 75th quartiles and the difference between them. Indicators with small ranges were removed. (3) The study performed a Mann-Whitney U test on the indicators of the less disturbed points and the indicators of the strongly disturbed points. Indicators with a p-value \u0026lt; 0.05 were retained. (4) Correlation analysis of the remaining indicators. To eliminate the influence of subjective choices on MMI results. If any two indicators had a correlation\u0026nbsp;\u0026ge;\u0026nbsp;0.75, all possible combinations of indicator results were retained and it was ensured that no two variables in the combination had a correlation greater than 0.75. Finally, the combination of indicators with the highest accuracy was selected as the final MMI. (5) Referring to the methodology of previous studies\u003csup\u003e15\u003c/sup\u003e, the different indicators were normalised to a range of 0 to 1. The results of each set of schemes should be validated, and the MMI with the highest accuracy should be determined. This is done by conducting a Spearman\u0026apos;s correlation analysis between the MMI and CDI indices. The best performance is achieved when the correlation coefficient is highest. We computed the entire MMI process using written code, and correlation analyses were computed using the cor.test function. We also examined differences in macroinvertebrate community composition across terrain using NMDS analyses and ANOSIM analyses based on the \u0026quot;metaMDS\u0026quot; and \u0026quot;anosim\u0026quot; functions of the vegan package, respectively. All data analyses in this study were performed in R 4.3.2\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Macroinvertebrate community composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2023, a total of 270 species of macroinvertebrates were found in the study area, belonging to 5 phyla, 30 orders and 127 families. Among them, there are 194 species in plain terrain, 130 species in hilly terrain and 171 species in mountainous terrain. Chironomidae had the highest number of species among all sites, with 64 species accounting for 23.7% of the total. Palaemonidae, Atyidae, Polypedilum, and Corophiidae were the dominant species during the survey period.\u003c/p\u003e\n\u003cp\u003eThe macroinvertebrate community composition differed significantly among mountains, hills, and plains (Stress = 0.1945, ANOSIM_R = 0.1094, \u003cem\u003ep\u003c/em\u003e = 0.001) (Figure 2), and therefore the topography-based grouping effectively attenuated the effect of natural conditions on community composition. Specifically, macroinvertebrate community composition at mountain sites was dominated by Insecta (71%), followed by Gastropoda (12%), Oligochaeta (6%), with Heptageniidae, \u003cem\u003eCricotopus\u003c/em\u003e sp, Baetidae, Hydropsychidae, and \u003cem\u003ePolypedilum\u003c/em\u003e sp as dominant taxa. The macroinvertebrate community composition at the hill sites was dominated by Insecta (63%), followed by Gastropoda (12%), Oligochaeta (67%), with Palaemonidae, Atyidae, \u003cem\u003ePolypedilum\u003c/em\u003e sp, and \u003cem\u003eSemisulcospira\u003c/em\u003e sp as dominant taxa. The macroinvertebrate community composition at the plain sites was dominated by Insecta (63%), followed by Gastropoda (12%), Bivalvia (67%), with Palaemonidae, Atyidae, \u003cem\u003ePolypedilum\u003c/em\u003e sp, \u003cem\u003eCorbicula\u003c/em\u003e sp, and Corophiidae as the dominant taxa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Differences in land use variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study area exhibits a high land-use gradient, as evidenced by the significant variation in land-use composition across different sites (Table 1), with agricultural land (51.87 \u0026plusmn; 25.24) being the most common land use type in all sites, followed by forest (23.6 \u0026plusmn; 27.62) and grassland (2.80 \u0026plusmn; 11.16) being the least common. In terms of different terrains, forest (59.08 \u0026plusmn; 22.28) was the most common land use type in hills, agricultural land (65.57 \u0026plusmn; 21.08) in the hills and agricultural land (58.20 \u0026plusmn; 19.96) in plains. In terms of different land use types, agricultural land (65.57 \u0026plusmn; 21.08) was the most common in the hills, forest (59.08 \u0026plusmn; 22.28) in the mountains, grassland (11.55 \u0026plusmn; 20.81) in the mountains and water bodies (17.12 \u0026plusmn; 15.57) and urban areas (15.40 \u0026plusmn; 13.52) in the plains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Differences in land-use composition of sites with different topography, give the p-value for the Kruskal-Wallis H-test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.665615141955836%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLand use type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.7192429022082%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAll sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.350157728706623%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMountain sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.350157728706623%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHill sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.034700315457414%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePlain sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.8801261829653%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ekruskal.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.731481481481481%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.962962962962964%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.194444444444445%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003eFarmland %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70253164556962%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.860759493670885%\" valign=\"bottom\"\u003e\n \u003cp\u003e65.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.018987341772151%\" valign=\"bottom\"\u003e\n \u003cp\u003e58.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003eForest land %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70253164556962%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.860759493670885%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.018987341772151%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003eGrassland %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70253164556962%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.860759493670885%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.018987341772151%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003eWater area %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70253164556962%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.860759493670885%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.018987341772151%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.72151898734177%\" valign=\"top\"\u003e\n \u003cp\u003eUrban land %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.70253164556962%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.860759493670885%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.018987341772151%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 MMI results for macroinvertebrates in different terrains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1. Mountain results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf all 40 mountain sites, 29 were classified as impaired and 11 as reference sites. We began with 87 indicators and reduced the number to 67 after removing those with small distributions. Only 2 indicators, M59 (% number of filter feeders) and M79 (Dispersal), met the screening criteria for discriminability and the correlation between them did not exceed 0.75, so M59 and M79 were the final indicators retained for the mountain MMI calculation (Figure 3). Finally, the MMI score was calculated for each point and it was found that the score was not significantly correlated with the CDI (\u003cem\u003eR\u003c/em\u003e = -0.2, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.21) (Appendix Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2. Hilly results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese points were categorised in the same way as above. Out of a total of 37 mound points, 29 are impaired and 8 are reference points. We started with 87 indicators and reduced this to 60 after removing indicators with small distributions. Only four indicators met the screening criteria for discriminability, so these were tested for correlation and, as before, variables with a final correlation of no more than 0.75 were retained. The final retained metrics were M17 (number of bivalve taxonomic units), M25 (percentage of individuals in dominant taxonomic units), M68 (Maglev species richness index) and M77 (Rao quadratic entropy index) (Figure 4). The final MMI score was significantly correlated with the CDI (\u003cem\u003eR\u003c/em\u003e = -0.4, \u003cem\u003ep\u003c/em\u003e = 0.015) (Appendix Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3. Plain results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf all 92 plain points, 69 are impaired and 23 are reference points. We started with 87 indicators and reduced them to 57 after removing indicators with small distributions. Nine indicators met the screening criteria for discriminatory ability, so these were tested for correlation, and as before, variables with a final correlation of no more than 0.75 were retained. As there were multiple indicator combination scenarios, the indicator combination scenario with the highest correlation coefficient was selected based on the correlation of MMI scores with CDI for each scenario, i.e. scenario b (\u003cem\u003eR\u003c/em\u003e = -0.4, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Appendix Figure 4). The final metrics selected for final retention were M22 (number of taxonomic units of telopods and molluscs), M23 (number of total taxonomic units), M53 (percentage of the number of individuals of telopods and molluscs), M59 (percentage of the number of individuals of filter feeders), M60 (percentage of the number of individuals of collectors), and M71 (BI) (Figure 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4. All point results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen the points were grouped without the use of topography, out of a total of 169 points, 135 were classified as impaired and 34 as reference points. We started with 87 indicators and reduced this to 60 after removing indicators with small distributions. Fifteen indicators met the screening criteria for discriminatory ability, so these were tested for correlation and, as before, variables with a final correlation of no more than 0.75 were retained. As there were multiple indicator combination scenarios, the indicator combination scenario with the highest correlation coefficient, scenario j, was selected based on the correlation of MMI scores with CDI for each scenario (\u003cem\u003eR\u003c/em\u003e = -0.34, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Appendix Figure 5). The final metrics selected for final retention were M10 (number of taxonomic units of aquatic insects), M19 (number of taxonomic units of crustaceans), M21 (number of taxonomic units of crustaceans and molluscs), M24 (percentage of individuals in EPT), M25 (percentage of individuals in dominant taxonomic units), M63 (percentage of individuals in trappers), M71 (BI), M74 (FEve), M83 (Rheophily), M86 (Functional Feeding Group) (Figure 6).\u003c/p\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThere have been a number of stream or river assessments based on MMI, but the vast majority have focused on small-scale streams or rivers. However, different indicator screening methods make it difficult to directly compare different streams and rivers, which means that it is not possible to obtain consistent assessment results at large scales, and small scales generally have small environmental gradients, making it difficult to distinguish between low and high disturbance conditions. Therefore, for environmental managers and restoration practitioners, large-scale assessment results are more useful for prioritising protection and restoration, avoiding unnecessary costs and ensuring practicality. To this end, this study collected spring and autumn data from the study area in 2023 to construct the MMI, and finalised four sets of MMI results for the three terrains and the entire basin through a series of indicator screening processes. We found that hill and plain-based MMIs were more accurate than basin-wide MMIs, while mountain-based MMIs were not significantly correlated with CDI.\u003c/p\u003e\n\u003cp\u003eSimilar to our initial hypothesis, using the CDI to classify points enables a clear differentiation between reference and impaired status. However, it may be challenging to identify indicators with significant differences when considering the water environment factor. The significant variability observed in macrobenthic communities may be related to the higher land use gradient in the study area. In contrast, the gradient of water environmental factors in the study area was small, which made it difficult to reflect differences in macroinvertebrate communities. This is consistent with the general ecological theory that at small local scales, local environmental factors dominate the influence on macroinvertebrate communities. At larger regional scales, catchment variables and spatial factors tend to drive macroinvertebrate community variation\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe categorisation of points based on CDI reflects the direct impacts of human activities on riparian habitats and, therefore, the indirect impacts on riverine organisms\u003csup\u003e38\u003c/sup\u003e. Biological communities are most affected by human disturbance when the surrounding land use is predominantly agricultural or urban\u003csup\u003e39\u003c/sup\u003e. For instance, the use of organic fertilisers for irrigation can lead to increased nitrogen and phosphorus concentrations in rivers. Similarly, organic pollution from domestic waste in cities can also worsen the burden on river ecosystems. These conditions can make macroinvertebrate communities vulnerable, resulting in reduced diversity, community homogenisation, and dominance by tolerant species. In grassland-dominated habitats, continued grazing can lead to a decline in water quality and eutrophication due to animal faeces. Conversely, forested habitats have lower anthropogenic intensity. Most of these sites are located in the headwaters of streams, with more intact habitats and a higher proportion of cleaner species in the community composition. The variation in the community is mainly influenced by natural variability.\u003c/p\u003e\n\u003cp\u003eOur second hypothesis is partially valid. We discovered that MMIs founded on topographic zoning are more precise than basin-wide MMIs lacking zoning as they weaken the gradient of natural variables. The outcomes for hills and plains upheld our hypothesis, and the grouping grounded on hills eventually identified four indicators to build the MMI system. The M17 index shows the number of non-tolerant bivalve species in the community. As most land-use types in the hills are farmland, non-tolerant bivalve species are common in the least disturbed sites. Therefore, as the percentage of agricultural land gradually increased, the intensity of anthropogenic disturbance also increased. This led to a decrease in the non-tolerant species in the community composition and an increase in the proportion of tolerant species, which eventually became the dominant species. The species richness index (M68) is a single-indicator evaluation method for species diversity. Its magnitude can be used to judge water quality. According to the moderate disturbance hypothesis\u003csup\u003e40\u003c/sup\u003e, biodiversity is highest when the biome is at a moderate level of disturbance. Biodiversity decreases above this threshold. Therefore, this index can distinguish well between low-disturbance and strong-disturbance sites in hilly areas. Ecological assessments can be challenging due to the need to distinguish between environmental and spatial influences on communities. Therefore, in this study, we introduced nine functional traits of macroinvertebrates to calculate functional diversity due to their high responsiveness to environmental change and small spatial extent of variation\u003csup\u003e41\u003c/sup\u003e. Our results clearly support the above, as multiple metrics of functional diversity served as one of the factors used to calculate the final MMI score. Recent studies have shown\u003csup\u003e42\u003c/sup\u003e that the quadratic entropy index in Rao reflects the overall distribution of functional diversity of species in the community. This reflects the variation in functional distance between species. This reflects the variation in functional distance between species and increases the variation in functional traits in the community. Therefore, when disturbance is enhanced, species with tolerant traits are retained, whilst species with sensitive traits are eliminated.\u003c/p\u003e\n\u003cp\u003eThe plain sites resulted in multiple schemes, unlike the hilly sites. We identified the scheme with the highest accuracy based on the correlation coefficients between the MMI scores and the CDI index. The number of taxonomic units for telopods and molluscs (M22) and the percentage of individuals for telopods and molluscs (M53) were identified as core metrics for the plains MMI system. This is due to the high level of anthropogenic disturbance on the plains. Even the less disturbed sites showed very few EPT insects and were mainly composed of telopods and molluscs. In contrast, the strongly disturbed sites were dominated by resistant species. The total number of taxonomic units (M23) has always been a commonly used indicator\u003csup\u003e43,44\u003c/sup\u003e, closely related to disturbance. The percentage of filter-feeder individuals (M59) significantly decreased with increasing disturbance, while the percentage of collector individuals (M60) significantly increased. The findings of most studies support this conclusion\u003csup\u003e32,33\u003c/sup\u003e: filter feeders are typically clean aquatic insects or telopods that reside in fast-flowing water bodies and filter their food through water currents. As human disturbance increases, the proportion of filter feeders decreases due to the destruction of their natural habitat and the increase of nutrients in the water. Conversely, the number of nutrient-loving collectors, which are mostly tolerant species, increases\u003csup\u003e45\u003c/sup\u003e. The BI (M71) is a core indicator that has been widely used in bioassessments due to its strong correlation with human disturbance. A higher score indicates a stronger disturbance, while a lower score indicates a weaker disturbance.\u003c/p\u003e\n\u003cp\u003eThe mountain MMI was not significantly correlated with the CDI, suggesting that the results may not be meaningful. Firstly, the number of mountain sites is small, which could lead to unrepresentative results. Secondly, most of the hill sites are located at high elevations with little human disturbance around them, making it difficult to distinguish differences in macroinvertebrate communities with small gradients of human disturbance. In addition, although grouping based on topography can attenuate some of the effects of natural factors, such effects are still present. The MMI results based on all points ultimately identified the greatest number of indicators as they had the greatest gradient of human disturbance, and the communities were distributed along this gradient. This reflects the conclusion that the MMI results are not as good as the overall results when the gradient of human disturbance is smaller when grouped by topography. And when the grouping still has a high disturbance gradient, the MMI accuracy is higher for the terrain-based grouping.\u003c/p\u003e\n\u003cp\u003eHowever, it is worth noting that although we have grouped the points based on topography, natural factors are still an important factor that cannot be ignored, and even in the same group, there are some differences in elevation, geology, climate, and so on. It is therefore important to develop more effective methods to minimise the effects of natural variability in future studies. At the same time, the number of monitoring sites should be increased where conditions allow to find the gradient of community variation that truly reflects human disturbance, and our results should be further validated using data from many years of monitoring to eliminate the influence of time on the results.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eLarge-scale biological integrity assessment has been a challenge for monitoring managers and researchers due to insurmountable sampling and observation difficulties. In this study, we proposed to address this issue using macroinvertebrate survey data from the spring and autumn of 2023 in the study area. We classified points based on the catchment disturbance index CDI and constructed several different MMI schemes based on topographic groupings. We found that the CDI-based categorisation allowed a good distinction between candidates for low and high disturbance sites, but there were some differences in grouping by topography. In particular, the accuracy of the MMI for hilly and plain points was better than the basin-wide ungrouped MMI, while the results of the mountain-based MMI were worse than the basin-wide ungrouped MMI. We recommend that for large-scale biomonitoring data, the catchment disturbance index CDI should continue to be used to differentiate between sites when evaluating results using the Index of Biotic Integrity (IBI) because the CDI has a large gradient of variability and describes the intensity of human activities well. In addition, grouping sites according to topography may improve the accuracy of results to some extent when developing the IBI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest to this work (Title: Calculation of macroinvertebrate MMI for water bodies in central China based on land use classification and topographic grouping).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Yangtze River Joint Research Phase II Program (2022-LHYJ-02-0102)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. Z. conceptualized the main framework, computed the data analysis, and wrote the main text.S.T. reviewed the data and organized it.B.J.S. reviewed the data and organized it.X.G. sampled collected and organized the data.W.N.H. organized the data. B.W. sampled to collect and organize data. S.H. sampled to collect and organize data. Z.L. conceptualized the main framework and reviewed it. s.d. conducted the review. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due data needs to be kept confidential but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCampbell, N. A., Reece, J. B., Taylor, M. R., Simon, E. 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A.\u003cem\u003e et al.\u003c/em\u003e Drivers of beta diversity of macroinvertebrate communities in tropical forest streams. \u003cem\u003eFreshwater Biology\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 1126-1137, doi:10.1111/fwb.12113 (2013).\u003c/li\u003e\n\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, land use, topography, index of biological integrity, human disturbance","lastPublishedDoi":"10.21203/rs.3.rs-4272409/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4272409/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Multi-Metric Index (MMI) is a comprehensive assessment index that has gained popularity in aquatic ecological assessment due to its integrated consideration of various factors such as community composition, diversity and function. However, when applied over large spatial areas, the MMI may not be effective in discriminating the level of human disturbance due to the influence of natural variability. To reduce the impact of natural variability on the assessment results, we collected macroinvertebrate community data from the water bodies in central China in 2023. We then grouped the data according to different topographies and constructed separate MMIs. We used the Catchment Disturbance Index (CDI) to categorise human disturbances into low-disturbance and strong-disturbance groups. In order to eliminate the influence of subjective factors on the selection of indicators, when the correlation of indicators was greater than 0.75, we considered all possible combinations of indicators and finally selected the best performance as the final MMI. The results showed that the point classification based on the Catchment Disturbance Index (CDI) could accurately reflect the changes of communities under different levels of human disturbance. The gradient of human disturbance levels in the mountain group was small, and the final MMI was not significantly correlated with the CDI. In contrast, the MMI results for the plains and hills were better than those for the entire catchment area where MMI was not grouped.","manuscriptTitle":"Calculation of macroinvertebrate MMI for water bodies in central China based on land use classification and topographic grouping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 18:31:18","doi":"10.21203/rs.3.rs-4272409/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":"7cc237ba-ec90-40a1-b559-fdffbff6b6c1","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30836488,"name":"Biological sciences/Ecology/Freshwater ecology"},{"id":30836489,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2024-09-30T19:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-23 18:31:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4272409","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4272409","identity":"rs-4272409","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}