Water Quality Influences Cerrado Odonata Larval Assemblages

Water Quality Influences Cerrado Odonata Larval Assemblages | 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 Research Article Water Quality Influences Cerrado Odonata Larval Assemblages Gabrielly Silva Melo, Victor Rennan Santos Ferreira, Leandro Juen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3692715/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 Changes in water quality and stream habitats can significantly impact Odonata larvae dynamics as a result of their specific ecological requirements. This study investigated the effects of water quality and microhabitats on the structure of Odonata larval communities. During the same period, substrate types, water variables, and Odonata larvae were sampled. Our findings reveal that substrate type had no impact on community structure. However, the presence of ammonia and oxygen levels, among other water quality, influenced the abundance of Odonata larvae. While Odonata organisms are sensitive to the local environment, the intermittent flow of the creek and seasonal changes may be responsible for the lack of substrate influence on the Odonata community. Abiotic factors also play a critical role since Odonata larvae have limits to which they can tolerate. Variations in these factors can have a profound impact on the persistence and survival of the larvae. Additionally, the larvae's physiological responses, such as respiration, are influenced by these factors. Zygoptera larvae, which have branchial respiration, necessitate a water environment with low temperatures and high levels of dissolved oxygen. Therefore, the abiotic variables of the stream significantly affect the Odonata larvae community. Incorporating natural environmental changes and variations into monitoring and conservation programs is essential. By doing so, we can enhance our understanding of biodiversity and its distribution patterns, leading to more effective preservation efforts. Anisoptera stream dragonfly substrate Zygoptera Figures Figure 1 Figure 2 Figure 3 Introduction Understanding how biodiversity relates to environmental conditions is essential in comprehending organism distribution patterns (Case and Gilpin 1974 ). The Niche Theory (Hutchinson 1957 ) predicts that species can solely exist in environmental conditions that meet their specific niche requirements. Environmental characteristics, such as water quality and substrate types, act as filters that regulate aquatic species presence and persistence, generating observed biodiversity patterns (Hutchinson e MacArthur 1959). Consequently, changes in these conditions can increase or decrease abundance (Oliveira-Junior endJuen 2019). Aquatic environments constitute an extensive network of interconnected drainage systems that are prone to being influenced by different factors, among them anthropogenic disturbances (e.g., deforestation, wildfires, pollution) and natural events, such as seasonality (Lake 2000 ). The transition from dry to wet periods can induce increased stream flows and dissolved oxygen and reduced temperatures (Wolda 1988 ). These modifications also affect stream habitat structure, as increased flows homogenize some habitats by removing leaf banks, branches, and sediments (Datry et al. 2018 ). These changes facilitate seasonal biological dynamics, including in the Odonata (Resende et al. 2021 ). The Odonata include two sub-orders, Anisoptera and Zygoptera, which differ morphologically. Anisoptera larvae possess an anal pyramid at the end of the abdomen, making them more sturdy than Zygoptera, which have a slender body and three large gills at the end of the abdomen (Neiss e Hamada 2014). Anisoptera larvae exhibit broader tolerance limits and can thrive in diverse habitats, such as lentic and lotic environments (Ribeiro et al. 2021 ; Juen et al. 2007 ). They are also able to tolerate higher temperatures, lower oxygen levels, and increased light exposure (Fulan et al. 2011 ; Silva et al. 2021 ; Silva et al. 2022 ). They prefer inorganic substrates (e.g., rocks and gravel) because of their creeping and burrowing tendencies (Carvalho e Nessimian 1998; Pires et al. 2020a ). Conversely, Zygoptera larvae show lower tolerance to environmental impacts (Ribeiro et al. 2021 ) and prefer organic substrates (e.g., leaf litter, macrophytes) over other microhabitats. This is because of their ability to climb and grasp organic substrates (Carvalho and Nessimian 1998 ) and their genera are usually linked to more pristine environments with higher oxygen concentrations (Fulan et al. 2011 ). Therefore, we assessed the impact of water quality and stream substrates on Odonata larval assemblage structure in Bacaba Park. We had five hypotheses. (i) Anisoptera would have greater abundance and richness on inorganic substrates because of their burrowing and creeping habits. (ii) Zygoptera will have greater abundance and genera richness in the presence of organic substrates, owing to their climbing and grasping habits. (iii) Anisoptera abundance and richness will be positively affected by poorer water quality because of their greater resistance to environmental changes. (iv) Zygoptera abundance and richness will be negatively affected by poorer water quality because of their greater sensitivity to environmental changes. (v) Anisoptera and Zygoptera assemblage composition will differ based on substrate types and water quality because of each suborder's distinct ecological requirements. Material and methods Study Area The Maria Viana Municipal Park (Bacaba Park) is a conservation area that spans approximately 500 hectares, situated in the municipal district of Nova Xavantina, in the state of Mato Grosso and the Cerrado biome. The main water source in the park is Bacaba stream, a second-order stream approximately five km long with of native riparian vegetation. The region falls within the Aw climate classification (Peel et al. 2007 ) and has two distinct seasons: a dry season from May to October and a rainy season from November to April (Almada et al. 2019 ). Air temperatures range from 19°C to 34°C, and the annual average precipitation is 1.200 mm (INMET 2018). The study was conducted in three distinct parts of Bacaba Creek (Fig. 1 ). Where the section closest to the source displays steep slopes containing rocky outcrops composed of quartzite and phyllite, which have small waterfalls. In the intermediate section, preserved gallery forest predominates, although a water collection dam was constructed over 40 years ago and has remained for the past 20 years. The section adjacent to the park boundary experiences constant cattle trampling (Giehl et al. 2015 ; Almada et al. 2019 ). Biotic Data Collection We sampled quarterly over a three-year period, from November 2012 to April 2016 at each site, except for two instances at the downstream site, where sampling was not possible when the site was dry. We collected Odonata larvae by using a kick-net (18 cm diameter, 15 cm depth, 250 µm mesh) along a 100-m transect, which was subdivided into 20 segments of 5 m each (Cabette et al. 2010 ). In the field, the material collected was pre-sorted by using white trays and forceps. The sorted specimens were immersed in 85% ethanol for later identification in the laboratory. Identification was conducted to genus level employing Costa, Souza, and Oldrini (2004) and Neiss e Hamada (2014). This approach has demonstrated efficacy in ecological studies for evaluating environmental concerns (Silva et al. 2021 ; Resende et al. 2021 ; Silva et al. 2022 ). Voucher specimens are deposited in the James Alexander Ratter Zoobotanical Collection (CZNX) at the Nova Xavantina Campus of the State University of Mato Grosso. Collection of Water Quality and Substrate Data Water quality data were collected concurrently with biological samples by using a HORIBA® U-5000 multiparameter probe to measure dissolved oxygen (mg/L) (DO), hydrogen ion potential (pH), conductivity (µS/cm), turbidity (NTU), water temperature (ºC), ammonia (ppm), and total dissolved solids (mg/L) (TDS). Substrates were measured following Peck et al. ( 2006 ). In each segment of the stream, five fixed points (0, 25, 50, 75, and 100 m) were selected with 25-m intervals between them. To determine the stream substrate type, we used an aluminum rod inserted vertically into the stream bed every 20 cm (across) and recorded the predominant substrate category. The substrate categories were silt/clay (< 0.064 mm), sand (0.064 mm to 2 mm), gravel (2 mm to 80 mm), boulders (large, loose rocks), slab (large, flat rock), leaf litter (leaves and small branches), and submerged roots (Table S1 ). The proportion of substrate was computed for each collection by ascertaining the total number of substrate observations and the number of observations for each substrate category. We then multiplied the quantity of substrates in each category by 100 and divided the total by the number of substrates. Data Analysis Each visit was considered a sampling unit, for a total of 43 samples. To minimize multicollinearity effects within both the abiotic variables, we performed a Pearson correlation test and eliminated variables with correlations > 70%. To assess the impact of substrate (i and ii) and water quality (iii and iv) on the abundance and richness of the Odonata suborders, we employed generalized linear mixed models (GLMM) (Zuur et al. 2017 ). We considered the sites (headstream, midstream and downstream) as a random factor for spatial dependence between them. We used the negative binomial and the Poisson distribution for abundance and richness of Anisoptera. For Zygoptera, we just employed the negative binomial for both abundance and richness. We verified the absence of overdispersion by analyzing the residuals of the analyses. We conducted a Redundancy Analysis (RDA) to evaluate the influence of water quality and micro-habitat on genera composition (v). The abundance of Anisoptera and Zygoptera genera was used as the response variable, transformed using the Hellinger method. In addition, the standardized matrix of water quality and the percentage of substrate categories were used as predictor variables. All analyses were executed with R software (R Core Team 2021 ) using the "glmer," "glmer.nb," "overdisp_fun," "decostand," and "rda" functions from the lme4 (Bates et al. 2015 ), and vegan (Oksanen et al. 2017 ) packages. Results General Community Pattern We collected a total of 2,201 specimens, comprised of eight families and 29 genera; 1,200 Anisoptera and 1,001 Zygoptera. The most abundant families, with representative genera, were Libellulidae (n = 1,066, 14 genera) and Coenagrionidae (n = 822, 6 genera). The most commonly found Zygoptera genera were Argia (n = 452) and Epipleoneura (n = 222), for Anisoptera, they were Elga (n = 340) and Macrothemis (n = 206) (Table S2 ). Substrate Types Contrary to our hypotheses (i) and (ii), the abundance and richness of Anisoptera and Zygoptera were not affected by substrate (Table S3). Similarly there was no significant effect of specific substrate types on genera composition for Anisoptera ( F = 1.28, p = 0.15) nor Zygoptera ( F = 1.17, p = 0.29). Water Quality Regarding the effect of water quality on Anisoptera and Zygoptera, hypotheses (iii and iv) received partial support. Richness was not affected by water quality, but abundance was influenced. Anisoptera abundance was adversely affected by ammonia (coef.= -0.48, p = 0.05), suggesting that higher concentrations of dissolved ammonia in the water were associated with fewer Anisoptera larvae. Zygoptera abundance exhibited a negative correlation with increased temperature (coef. = -0.21, p = 0.03) and a positive correlation with DO (coef. = 0.26, p = 0.01) (Table 1 ; Table S4). Table 1 Coefficients, p -values, and standard errors from GLMMs adjusted for mixed effects. Variable Estimate Std. Error z p Abundance Anisoptera (R²= 0.54) (Intercepto) 1.931 1.913 1.009 0.313 pH 0.101 0.141 0.711 0.477 Conductivity -0.064 0.037 -1.750 0.080 Turbidity -0.003 0.003 -0.802 0.422 TDS -0.038 0.045 -0.832 0.405 Temperature 0.050 0.074 0.673 0.501 DO -0.148 0.088 -1.682 0.093 Ammonia -0.476 0.247 -1.923 0.049* Abundance Zygoptera (R² = 0.54) (Intercepto) 9.041 2.468 3.663 < 0.001* pH -0.002 0.152 -0.010 0.992 Conductivity 0.010 0.038 0.264 0.792 Turbidity 0.000 0.003 0.002 0.999 TDS -0.119 0.070 -1.704 0.088 Temperature -0.205 0.094 -2.171 0.029* DO 0.262 0.105 2.485 0.013* Ammonia -0.221 0.701 -0.315 0.7 Water quality significantly affected Anisoptera genera composition. The first RDA axis accounted for 46% of the assemblage variation (R²adj = 0.14, F = 1.95, p = 0.002). Oligoclada demonstrated the most significant relationships, being positively correlated with temperature and ammonia, and negatively correlated with DO. Progomphus was positively related to conductivity and negatively to ammonia, and Elga was inversely related to conductivity, turbidity, pH and TDS (Fig. 2 ). For Zygoptera genera, the first RDA axis explained 65% of the variability (R²adj = 0.23, F = 2.6, p = 0.001). Epipleoneura was positively associated with TDS, whereas the relationship between Hetaerina/Mnesarete and pH and DO was positive. Conversely, Argia and Acanthagrion were negatively correlated with pH, turbidity, and conductivity (Fig. 3 ). Discussion We found that substrate had no effect on Anisoptera and Zygoptera assemblage structure, negating hypotheses one, two, and partially negating hypothesis five. However, water quality affected Odonata larval assemblage structure, with ammonia, temperature, and conductivity most affecting both abundance and composition, supporting hypotheses three, four, and partially negating hypothesis five. This highlights the importance of water conditions for the maintenance of Odonata assemblages. Although substrate has been shown to be an important factor in structuring Odonata larval assemblages in previous studies (Carvalho e Nessimian 1998; Pires et al. 2020a , b ), our results demonstrated that it was not significant in structuring larval assemblages. The absence of a substrate effect can be attributed to seasonality. Resende et al. ( 2021 ) also found that seasonality had a noticeable effect on the hydrodynamics of Bacaba stream. During the dry season, several locations became intermittent. However, during the initial rains and occasional floods of the wet season, extensive substrate transportation took place, leading to the depletion of microhabitats, as reported by Datry et al. ( 2018 ). We found that higher ammonia concentrations resulted in significantly decreased Anisoptera larval abundance. In addition, DO and conductivity reduced larval abundance, despite their greater capacities to persist in low-oxygen environments because of their rectal gills higher water exchange rates. Grazing near Bacaba stream is a common activity (Giehl et al. 2015 ), leading to decreased riparian vegetation and increased levels of contaminants. Some Anisoptera genera showed relationships with DO. Decreased riparian vegetation leads to increased water temperature, decreased DO, and increased macrophytes (Fares et al. 2020 ), which lead to increased Anisoptera ( Brito et al. 2021 ). In addition, Oligoclada can persist in open aquatic environments, thanks to thermoreceptors present in larval Libellulidae (Rebora et al. 2007 ). Zygoptera abundance was significantly affected by DO (positively) and temperature (negatively) and somewhat affected by TDS. Respiration takes place through gill-like structures located in the tail, thus requiring higher levels of DO (Corbet 1999 ). Lower temperature environments with relatively higher DO concentrations create suitable ecological conditions for larval survival (Jooste et al. 2020 ). Fulan et al. ( 2011 ) also found that Hetaerina/Mnesarete were positively associated with DO and negatively affected by temperature. Conclusion No substrate effect was observed on assemblage structure, but the abundance of Odonata larvae was affected by ammonia and oxygen. The absence of a substrate effect may have resulted from seasonality effects, which led to sediment transport that masked changes in the Odonata larval assemblages, and our small sample size. These findings underscore the importance of water quality for Odonata larval assemblages. Therefore, it is essential for biodiversity monitoring and conservation programs to include changes in water quality to protect Odonata assemblages. Declarations We declare that the manuscript reports unpublished work that it is not under active consideration for publication elsewhere, nor been accepted for publication, nor been published in full or in part. We assume the compromise of sending it for English correction at a professional service upon acceptance. All authors and relevant institutions have read the submitted version and approve of its submission and all persons entitled to authorship have been so included. The work conforms the legal requirements of the country in which it was carried out. Conflicts of interest The authors declare no conflict of interest. Availability of data and material. The data and material are available if needed. Please, contact the corresponding author. Acknowledgments We would like to acknowledge the PELD/CNPq project "Cerrado-Amazon Transition: Ecological and Socioenvironmental Foundations for Conservation (Phase I - Process No. 558069/2009-6, Phase II - Process No. 403725/2012-7, and Phase III - Process No. 441244/2016-5)" for financial support, as well as Universidade do Estado de Mato Grosso (UNEMAT) and the Laboratório de Entomologia de Nova Xavantina (LENX) for their logistical support. We are grateful to CNPq and the State of Mato Grosso Research Support Foundation (FAPEMAT) for supporting this research through undergraduate research scholarships. Special thanks to the "Special Topics in Ecology XI: Scientific Publication in Ecology (PGECOOO46)" course, taught by Professors Rafaella Teixeira Marciel Oliveira and Raquel Luiza de Carvalho, and to our classmate Beatriz da Luz Silva, who contributed to the development of this article with suggestions and revisions. 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13:44:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3692715/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3692715/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47733536,"identity":"52bb3132-67dd-49bc-a0c6-6e62b12c8911","added_by":"auto","created_at":"2023-12-06 17:29:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2161740,"visible":true,"origin":"","legend":"\u003cp\u003eBacaba stream sites. headstream (1), midstream (2), and downstream (3).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3692715/v1/6130582cc2ee04ab59ab88e3.png"},{"id":47733066,"identity":"ceb7fd4b-d16e-4a5e-bfc7-f102bb6b1bb6","added_by":"auto","created_at":"2023-12-06 17:21:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166385,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between water quality and Anisoptera genera in Bacaba Stream.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3692715/v1/34cf1f048f02055c627ab5cb.png"},{"id":47733538,"identity":"af6e5682-03c8-4675-803f-22bfa80b3923","added_by":"auto","created_at":"2023-12-06 17:29:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":207427,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between water quality and Zygoptera genera in Stream Bacaba.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3692715/v1/b2b71243fa1477a735defe37.png"},{"id":49353075,"identity":"71c35aa9-3aa5-43b0-8d20-8c219f9052e1","added_by":"auto","created_at":"2024-01-09 07:07:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2680115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3692715/v1/d2147e9d-5a6d-40fa-94cd-66c66943576f.pdf"},{"id":47733064,"identity":"baed0279-e74f-4737-9470-5503e69feba2","added_by":"auto","created_at":"2023-12-06 17:21:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27668,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemantaryManuscritoWATERQUALITY.docx","url":"https://assets-eu.researchsquare.com/files/rs-3692715/v1/60fe264a099da58562d4cc5c.docx"},{"id":47733537,"identity":"9007a779-db16-4ba3-9ef4-8bc1cc89874c","added_by":"auto","created_at":"2023-12-06 17:29:06","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4244,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.csv","url":"https://assets-eu.researchsquare.com/files/rs-3692715/v1/da9ca8750daca226a1fdc95e.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eWater Quality Influences Cerrado Odonata Larval Assemblages\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding how biodiversity relates to environmental conditions is essential in comprehending organism distribution patterns (Case and Gilpin \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). The Niche Theory (Hutchinson \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1957\u003c/span\u003e) predicts that species can solely exist in environmental conditions that meet their specific niche requirements. Environmental characteristics, such as water quality and substrate types, act as filters that regulate aquatic species presence and persistence, generating observed biodiversity patterns (Hutchinson e MacArthur 1959). Consequently, changes in these conditions can increase or decrease abundance (Oliveira-Junior endJuen 2019).\u003c/p\u003e \u003cp\u003eAquatic environments constitute an extensive network of interconnected drainage systems that are prone to being influenced by different factors, among them anthropogenic disturbances (e.g., deforestation, wildfires, pollution) and natural events, such as seasonality (Lake \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The transition from dry to wet periods can induce increased stream flows and dissolved oxygen and reduced temperatures (Wolda \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). These modifications also affect stream habitat structure, as increased flows homogenize some habitats by removing leaf banks, branches, and sediments (Datry et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These changes facilitate seasonal biological dynamics, including in the Odonata (Resende et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Odonata include two sub-orders, Anisoptera and Zygoptera, which differ morphologically. Anisoptera larvae possess an anal pyramid at the end of the abdomen, making them more sturdy than Zygoptera, which have a slender body and three large gills at the end of the abdomen (Neiss e Hamada 2014). Anisoptera larvae exhibit broader tolerance limits and can thrive in diverse habitats, such as lentic and lotic environments (Ribeiro et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Juen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). They are also able to tolerate higher temperatures, lower oxygen levels, and increased light exposure (Fulan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They prefer inorganic substrates (e.g., rocks and gravel) because of their creeping and burrowing tendencies (Carvalho e Nessimian 1998; Pires et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Conversely, Zygoptera larvae show lower tolerance to environmental impacts (Ribeiro et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and prefer organic substrates (e.g., leaf litter, macrophytes) over other microhabitats. This is because of their ability to climb and grasp organic substrates (Carvalho and Nessimian \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and their genera are usually linked to more pristine environments with higher oxygen concentrations (Fulan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, we assessed the impact of water quality and stream substrates on Odonata larval assemblage structure in Bacaba Park. We had five hypotheses. (i) Anisoptera would have greater abundance and richness on inorganic substrates because of their burrowing and creeping habits. (ii) Zygoptera will have greater abundance and genera richness in the presence of organic substrates, owing to their climbing and grasping habits. (iii) Anisoptera abundance and richness will be positively affected by poorer water quality because of their greater resistance to environmental changes. (iv) Zygoptera abundance and richness will be negatively affected by poorer water quality because of their greater sensitivity to environmental changes. (v) Anisoptera and Zygoptera assemblage composition will differ based on substrate types and water quality because of each suborder's distinct ecological requirements.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eStudy Area\u003c/p\u003e \u003cp\u003eThe Maria Viana Municipal Park (Bacaba Park) is a conservation area that spans approximately 500 hectares, situated in the municipal district of Nova Xavantina, in the state of Mato Grosso and the Cerrado biome. The main water source in the park is Bacaba stream, a second-order stream approximately five km long with of native riparian vegetation. The region falls within the Aw climate classification (Peel et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and has two distinct seasons: a dry season from May to October and a rainy season from November to April (Almada et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Air temperatures range from 19\u0026deg;C to 34\u0026deg;C, and the annual average precipitation is 1.200 mm (INMET 2018).\u003c/p\u003e \u003cp\u003eThe study was conducted in three distinct parts of Bacaba Creek (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Where the section closest to the source displays steep slopes containing rocky outcrops composed of quartzite and phyllite, which have small waterfalls. In the intermediate section, preserved gallery forest predominates, although a water collection dam was constructed over 40 years ago and has remained for the past 20 years. The section adjacent to the park boundary experiences constant cattle trampling (Giehl et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Almada et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBiotic Data Collection\u003c/p\u003e \u003cp\u003eWe sampled quarterly over a three-year period, from November 2012 to April 2016 at each site, except for two instances at the downstream site, where sampling was not possible when the site was dry. We collected Odonata larvae by using a kick-net (18 cm diameter, 15 cm depth, 250 \u0026micro;m mesh) along a 100-m transect, which was subdivided into 20 segments of 5 m each (Cabette et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the field, the material collected was pre-sorted by using white trays and forceps. The sorted specimens were immersed in 85% ethanol for later identification in the laboratory. Identification was conducted to genus level employing Costa, Souza, and Oldrini (2004) and Neiss e Hamada (2014). This approach has demonstrated efficacy in ecological studies for evaluating environmental concerns (Silva et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Resende et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Voucher specimens are deposited in the James Alexander Ratter Zoobotanical Collection (CZNX) at the Nova Xavantina Campus of the State University of Mato Grosso.\u003c/p\u003e \u003cp\u003eCollection of Water Quality and Substrate Data\u003c/p\u003e \u003cp\u003eWater quality data were collected concurrently with biological samples by using a HORIBA\u0026reg; U-5000 multiparameter probe to measure dissolved oxygen (mg/L) (DO), hydrogen ion potential (pH), conductivity (\u0026micro;S/cm), turbidity (NTU), water temperature (\u0026ordm;C), ammonia (ppm), and total dissolved solids (mg/L) (TDS).\u003c/p\u003e \u003cp\u003eSubstrates were measured following Peck et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In each segment of the stream, five fixed points (0, 25, 50, 75, and 100 m) were selected with 25-m intervals between them. To determine the stream substrate type, we used an aluminum rod inserted vertically into the stream bed every 20 cm (across) and recorded the predominant substrate category. The substrate categories were silt/clay (\u0026lt;\u0026thinsp;0.064 mm), sand (0.064 mm to 2 mm), gravel (2 mm to 80 mm), boulders (large, loose rocks), slab (large, flat rock), leaf litter (leaves and small branches), and submerged roots (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The proportion of substrate was computed for each collection by ascertaining the total number of substrate observations and the number of observations for each substrate category. We then multiplied the quantity of substrates in each category by 100 and divided the total by the number of substrates.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eEach visit was considered a sampling unit, for a total of 43 samples. To minimize multicollinearity effects within both the abiotic variables, we performed a Pearson correlation test and eliminated variables with correlations\u0026thinsp;\u0026gt;\u0026thinsp;70%. To assess the impact of substrate (i and ii) and water quality (iii and iv) on the abundance and richness of the Odonata suborders, we employed generalized linear mixed models (GLMM) (Zuur et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We considered the sites (headstream, midstream and downstream) as a random factor for spatial dependence between them. We used the negative binomial and the Poisson distribution for abundance and richness of Anisoptera. For Zygoptera, we just employed the negative binomial for both abundance and richness. We verified the absence of overdispersion by analyzing the residuals of the analyses.\u003c/p\u003e \u003cp\u003eWe conducted a Redundancy Analysis (RDA) to evaluate the influence of water quality and micro-habitat on genera composition (v). The abundance of Anisoptera and Zygoptera genera was used as the response variable, transformed using the Hellinger method. In addition, the standardized matrix of water quality and the percentage of substrate categories were used as predictor variables. All analyses were executed with R software (R Core Team \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) using the \"glmer,\" \"glmer.nb,\" \"overdisp_fun,\" \"decostand,\" and \"rda\" functions from the lme4 (Bates et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and vegan (Oksanen et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eGeneral Community Pattern\u003c/p\u003e \u003cp\u003eWe collected a total of 2,201 specimens, comprised of eight families and 29 genera; 1,200 Anisoptera and 1,001 Zygoptera. The most abundant families, with representative genera, were Libellulidae (n\u0026thinsp;=\u0026thinsp;1,066, 14 genera) and Coenagrionidae (n\u0026thinsp;=\u0026thinsp;822, 6 genera). The most commonly found Zygoptera genera were \u003cem\u003eArgia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;452) and \u003cem\u003eEpipleoneura\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;222), for Anisoptera, they were \u003cem\u003eElga\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;340) and \u003cem\u003eMacrothemis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;206) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubstrate Types\u003c/p\u003e \u003cp\u003eContrary to our hypotheses (i) and (ii), the abundance and richness of Anisoptera and Zygoptera were not affected by substrate (Table S3). Similarly there was no significant effect of specific substrate types on genera composition for Anisoptera (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15) nor Zygoptera (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29).\u003c/p\u003e \u003cp\u003eWater Quality\u003c/p\u003e \u003cp\u003eRegarding the effect of water quality on Anisoptera and Zygoptera, hypotheses (iii and iv) received partial support. Richness was not affected by water quality, but abundance was influenced. Anisoptera abundance was adversely affected by ammonia (coef.= -0.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05), suggesting that higher concentrations of dissolved ammonia in the water were associated with fewer Anisoptera larvae. Zygoptera abundance exhibited a negative correlation with increased temperature (coef. = -0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and a positive correlation with DO (coef. = 0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S4).\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\u003eCoefficients, \u003cem\u003ep\u003c/em\u003e-values, and standard errors from GLMMs adjusted for mixed effects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAbundance Anisoptera\u003c/p\u003e \u003cp\u003e(R\u0026sup2;= 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercepto)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\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\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAmmonia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.476\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.247\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.923\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.049*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAbundance Zygoptera\u003c/p\u003e \u003cp\u003e(R\u0026sup2; = 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercepto)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity\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\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTemperature\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.205\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.094\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.171\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.029*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.262\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.105\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.485\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.013*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\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\u003eWater quality significantly affected Anisoptera genera composition. The first RDA axis accounted for 46% of the assemblage variation (R\u0026sup2;adj\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). \u003cem\u003eOligoclada\u003c/em\u003e demonstrated the most significant relationships, being positively correlated with temperature and ammonia, and negatively correlated with DO. \u003cem\u003eProgomphus\u003c/em\u003e was positively related to conductivity and negatively to ammonia, and \u003cem\u003eElga\u003c/em\u003e was inversely related to conductivity, turbidity, pH and TDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor Zygoptera genera, the first RDA axis explained 65% of the variability (R\u0026sup2;adj\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). \u003cem\u003eEpipleoneura\u003c/em\u003e was positively associated with TDS, whereas the relationship between \u003cem\u003eHetaerina/Mnesarete\u003c/em\u003e and pH and DO was positive. Conversely, \u003cem\u003eArgia\u003c/em\u003e and \u003cem\u003eAcanthagrion\u003c/em\u003e were negatively correlated with pH, turbidity, and conductivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found that substrate had no effect on Anisoptera and Zygoptera assemblage structure, negating hypotheses one, two, and partially negating hypothesis five. However, water quality affected Odonata larval assemblage structure, with ammonia, temperature, and conductivity most affecting both abundance and composition, supporting hypotheses three, four, and partially negating hypothesis five. This highlights the importance of water conditions for the maintenance of Odonata assemblages.\u003c/p\u003e \u003cp\u003eAlthough substrate has been shown to be an important factor in structuring Odonata larval assemblages in previous studies (Carvalho e Nessimian 1998; Pires et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003eb\u003c/span\u003e), our results demonstrated that it was not significant in structuring larval assemblages. The absence of a substrate effect can be attributed to seasonality. Resende et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also found that seasonality had a noticeable effect on the hydrodynamics of Bacaba stream. During the dry season, several locations became intermittent. However, during the initial rains and occasional floods of the wet season, extensive substrate transportation took place, leading to the depletion of microhabitats, as reported by Datry et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that higher ammonia concentrations resulted in significantly decreased Anisoptera larval abundance. In addition, DO and conductivity reduced larval abundance, despite their greater capacities to persist in low-oxygen environments because of their rectal gills higher water exchange rates. Grazing near Bacaba stream is a common activity (Giehl et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), leading to decreased riparian vegetation and increased levels of contaminants. Some Anisoptera genera showed relationships with DO. Decreased riparian vegetation leads to increased water temperature, decreased DO, and increased macrophytes (Fares et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which lead to increased Anisoptera ( Brito et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, \u003cem\u003eOligoclada\u003c/em\u003e can persist in open aquatic environments, thanks to thermoreceptors present in larval Libellulidae (Rebora et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZygoptera abundance was significantly affected by DO (positively) and temperature (negatively) and somewhat affected by TDS. Respiration takes place through gill-like structures located in the tail, thus requiring higher levels of DO (Corbet \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Lower temperature environments with relatively higher DO concentrations create suitable ecological conditions for larval survival (Jooste et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fulan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) also found that \u003cem\u003eHetaerina/Mnesarete\u003c/em\u003e were positively associated with DO and negatively affected by temperature.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNo substrate effect was observed on assemblage structure, but the abundance of Odonata larvae was affected by ammonia and oxygen. The absence of a substrate effect may have resulted from seasonality effects, which led to sediment transport that masked changes in the Odonata larval assemblages, and our small sample size. These findings underscore the importance of water quality for Odonata larval assemblages. Therefore, it is essential for biodiversity monitoring and conservation programs to include changes in water quality to protect Odonata assemblages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWe declare that the manuscript reports unpublished work that it is not under active consideration for publication elsewhere, nor been accepted for publication, nor been published in full or in part.\u0026nbsp;We assume the compromise of sending it for English correction at a professional service upon acceptance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors and relevant institutions have read the submitted version and approve of its submission and all persons entitled to authorship have been so included. The work conforms the legal requirements of the country in which it was carried out.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflicts of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. Availability of data and material. The data and material are available if needed. Please, contact the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the PELD/CNPq project \u0026quot;Cerrado-Amazon Transition: Ecological and Socioenvironmental Foundations for Conservation (Phase I - Process No. 558069/2009-6, Phase II - Process No. 403725/2012-7, and Phase III - Process No. 441244/2016-5)\u0026quot; for financial support, as well as Universidade do Estado de Mato Grosso (UNEMAT) and the Laborat\u0026oacute;rio de Entomologia de Nova Xavantina (LENX) for their logistical support. We are grateful to CNPq and the State of Mato Grosso Research Support Foundation (FAPEMAT) for supporting this research through undergraduate research scholarships. Special thanks to the \u0026quot;Special Topics in Ecology XI: Scientific Publication in Ecology (PGECOOO46)\u0026quot; course, taught by Professors Rafaella Teixeira Marciel Oliveira and Raquel Luiza de Carvalho, and to our classmate Beatriz da Luz Silva, who contributed to the development of this article with suggestions and revisions. LJ (process 304710/2019-9) is funded continuously by Brazilian National Council for Scientific and Technological Development (CNPq) productivity grants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmada HKS, Silv\u0026eacute;rio DV, Macedo MN et al (2019) Effects of geomorphology and land use on stream water quality in southeastern Amazonia. Hydrological Sciences Journal 64:620-632. https://doi.org/10.1080/02626667.2019.1587563\u003c/li\u003e\n\u003cli\u003eBates D, Maechler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67:1-48. doi:10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eBrito JS, Michelan TS, Juen L (2021) Aquatic macrophytes are important substrates for Libellulidae (Odonata) larvae and adults. Limnology 22:139-149. https://doi.org/10.1007/s10201-020-00643-x\u003c/li\u003e\n\u003cli\u003eCabette HSR, Giehl NFS, Dias-Silva K, Juen L, Batista JB (2010) Distribui\u0026ccedil;\u0026atilde;o de Nepomorpha e Gerromorpha (Insecta: Heteroptera) da Bacia Hidrogr\u0026aacute;fica do Rio Sui\u0026aacute;-Mi\u0026ccedil;u, MT: riqueza relacionada \u0026agrave; qualidade da \u0026aacute;gua e do h\u0026aacute;bitat. In: Santos JE, Galbiati, C, Moschini LE (ed) Gest\u0026atilde;o e educa\u0026ccedil;\u0026atilde;o ambiental: \u0026aacute;gua, biodiversidade e cultura, 2rd.\u003cstrong\u003e \u003c/strong\u003eS\u0026atilde;o Carlos: Rima, pp 113-137\u003c/li\u003e\n\u003cli\u003eCarvalho AL, Nessimian JL (1998) Odonata do Estado do Rio de Janeiro, Brasil: h\u0026aacute;bitats e h\u0026aacute;bitos das larvas. Oecologia brasiliensis 5:1\u003c/li\u003e\n\u003cli\u003eCorbet PS (1999) Dragonflies: behavior and ecology of Odonata. International Review for the Sociology of Sport 36:230\u0026ndash;233 \u003c/li\u003e\n\u003cli\u003eCase TJ, Gilpin ME (1974) Interference competition and niche theory. Proceedings of the National Academy of Sciences 71:3073-3077\u003c/li\u003e\n\u003cli\u003eCosta JM, Santos TC, Oldrini BB (2004) Chave para identifica\u0026ccedil;\u0026atilde;o das fam\u0026iacute;lias e g\u0026ecirc;neros das larvas conhecidas de Odonata do Brasil: coment\u0026aacute;rios e registros bibliogr\u0026aacute;ficos (Insecta, Odonata). Rio de Janeiro: Museu Nacional\u003c/li\u003e\n\u003cli\u003eDatry T, Foulquier A, Corti R et al (2018) A global analysis of terrestrial plant litter dynamics in non-perennial waterways. Nature Geoscience 11:497-503. https://doi.org/10.1038/s41561-018-0134-4\u003c/li\u003e\n\u003cli\u003eFares ALB, Calvao LB, Torres NR, Gurgel ESC, Michelan TS (2020) Environmental factors affect macrophyte diversity on Amazonian aquatic ecosystems inserted in an anthropogenic landscape. 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Hydrobiologia 1-14. https://doi.org/10.1007/s10750-022-05097-z\u003c/li\u003e\n\u003cli\u003eSilva LF, Castro DM, Juen L, Callisto M, Hughes RM, Hermes MG (2021) A matter of suborder: are Zygoptera and Anisoptera larvae influenced by riparian vegetation in Neotropical Savanna streams?. Hydrobiologia 848:4433-4443. https://doi.org/10.1007/s10750-021-04642-6\u003c/li\u003e\n\u003cli\u003eWolda H (1988) Sazonalidade dos insetos: por qu\u0026ecirc;?. Revis\u0026atilde;o Anual de Ecologia e Sistem\u0026aacute;tica 19:1-18.\u003c/li\u003e\n\u003cli\u003eZuur AF, Ieno, EN, Saveliev, AA (2017) Spatial, temporal and spatial\u0026ndash;temporal ecological data analysis with R-INLA. Volume I: using GLM and GLMM. Newburgh, UK: Highland Statistics.\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":"Anisoptera, stream, dragonfly, substrate, Zygoptera","lastPublishedDoi":"10.21203/rs.3.rs-3692715/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3692715/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChanges in water quality and stream habitats can significantly impact Odonata larvae dynamics as a result of their specific ecological requirements. This study investigated the effects of water quality and microhabitats on the structure of Odonata larval communities. During the same period, substrate types, water variables, and Odonata larvae were sampled. Our findings reveal that substrate type had no impact on community structure. However, the presence of ammonia and oxygen levels, among other water quality, influenced the abundance of Odonata larvae. While Odonata organisms are sensitive to the local environment, the intermittent flow of the creek and seasonal changes may be responsible for the lack of substrate influence on the Odonata community. Abiotic factors also play a critical role since Odonata larvae have limits to which they can tolerate. Variations in these factors can have a profound impact on the persistence and survival of the larvae. Additionally, the larvae's physiological responses, such as respiration, are influenced by these factors. Zygoptera larvae, which have branchial respiration, necessitate a water environment with low temperatures and high levels of dissolved oxygen. Therefore, the abiotic variables of the stream significantly affect the Odonata larvae community. Incorporating natural environmental changes and variations into monitoring and conservation programs is essential. By doing so, we can enhance our understanding of biodiversity and its distribution patterns, leading to more effective preservation efforts.\u003c/p\u003e","manuscriptTitle":"Water Quality Influences Cerrado Odonata Larval Assemblages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-06 17:21:01","doi":"10.21203/rs.3.rs-3692715/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":"1ba53399-fd04-4f4c-a0c5-46db75fe721a","owner":[],"postedDate":"December 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-27T15:00:11+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-06 17:21:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3692715","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3692715","identity":"rs-3692715","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}