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These authors contributed equally: Zhongru Gu, Shengkai Pan, Zhenzhen LinKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long & Xiangjiang ZhanCardiff University–Institute of Zoology Joint Laboratory for Biocomplexity Research, Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Shengkai Pan, Zhenzhen Lin, Li Hu, Han Su, Juan Long, Michael W. Bruford, Andrew Dixon & Xiangjiang ZhanUniversity of the Chinese Academy of Sciences, Beijing, ChinaZhongru Gu, Li Hu, Han Su, Juan Long, Mengru Sun & Xiangjiang ZhanSchool of Biological Sciences, University of Bristol, Bristol, UKXiaoyang DaiState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaJiang ChangKey Laboratory of RNA Biology, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, ChinaYuanchao Xue & Mengru SunWild Animal Rescue Centre, Moscow, RussiaSergey GanusevichInstitute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Ekaterinburg, RussiaVasiliy SokolovArctic Research Station of the Institute of Plant and Animal Ecology, Ural Division Russian Academy of Sciences, Labytnangi, RussiaAleksandr Sokolov & Ivan PokrovskyDepartment of Migration, Max Planck Institute of Animal Behavior, Radolfzell, GermanyIvan PokrovskyLaboratory of Ornithology, Institute of Biological Problems of the North FEB RAS, Magadan, RussiaIvan PokrovskyState Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, ChinaFen JiSchool of Biosciences and Sustainable Places Institute, Cardiff University, Cardiff, UKMichael W. BrufordEmirates Falconers’ Club, Abu Dhabi, United Arab EmiratesAndrew DixonReneco International Wildlife Consultants, Abu Dhabi, United Arab EmiratesAndrew DixonInternational Wildlife Consultants, Carmarthen, UKAndrew DixonCenter for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, ChinaXiangjiang Zhan More

Modelling microbenthic O2 exportWe explored how microbial processes and export fluxes of their metabolic substrates and products from ancient benthic photosynthetic ecosystems were influenced by daylength, environmental conditions and various regulatory mechanisms of photosynthetic production and respiration using an in silico microbenthic model. Model scenarios were constructed and simulated using MicroBenthos software12. MicroBenthos model definitions and parameters for the described scenarios are provided with this article. The software and usage instructions are available at https://microbenthos.readthedocs.io.The modelling framework is an adaptation of de Wit et al.61. Briefly, benthic systems are constructed as a diffusive–reactive system in a 1D computational domain, with discrete cells used to represent the spatial distribution of the state and parameter variables. While the study by de Wit et al.61 focused on biomass growth running over long simulation times, our interest was to study the dynamics of process rates and solute fluxes over diel timescales. Therefore, we set a fixed biomass for the microbial groups, added a water subdomain on top of the sediment as a diffusive boundary layer and ran simulations until a diel steady state was reached (5 days). Our model domain used 5 µm cells, with an 8 mm sedimentary subdomain and 1 mm diffusive boundary layer of water on top. O2 and sulfide concentrations were the state variables that we solved for. Photosynthetically active radiation (PAR) was expressed as a percent of the maximum intensity at the diel zenith, and followed a cosinusoidal pattern similar to that of diel insolation dynamics.Raero and SOX were formulated to occur throughout the sediment. Microbial groups (cyanobacteria and SRB) were represented as biomass distributions in the sediment subdomain, and biomass-dependent metabolism kinetics were expressed as multiplications of the response functions of salient environmental and state variables. Coupled partial differential equations of the state variables (O2 and H2S) were composed from the reaction terms that accounted for sediment porosity and were solved with finite-volume numerical approximations62.Our in silico mat allowed us to explore how diffusive mass transfer shapes the interplay between illumination dynamics, gross production and consumption rates, and diel O2 export. The effect of daylength was studied by varying the period of the illumination from 12 h to 24 h, the range of estimated daylengths over Earth’s history after the earliest estimates for the origin of OP63. We report the calculated average diel net export and process rates in units of mmol m−2 h−1 because the hour is the largest temporal unit unaffected by changes in the Earth’s rotation and thus allows for comparison across daylengths.First, we explored the simplest case of O2 production, which is with light availability. Two microbial processes were considered: OP performed by cyanobacteria and Raero. The parameters for the biotic reactions were re-expressed as a biomass-specific maximal yield (Qmax). A fundamental assumption is that the photosynthesis rate is strictly correlated to the instantaneous photon flux:$${rm{OP}} = Q_{{rm{max}}}times {rm{biomass}}times {rm{sat}}left( {{rm{PAR}},,K_{{rm{PAR}}}} right),$$
(1)
where sat is a Michaelis–Menten function with KPAR = 15% and the cyanobacterial biomass with a log-normal distribution with a peak value of 12 mg cm−3 at 0.5 mm depth (Supplementary Video 1). The only source of O2 is OP, and the sinks are aerobic (sedimentary) respiration (Raero). For the production and consumption rates of Corg, we assumed a stoichiometry of:$${mathrm{H}}_2{mathrm{O}} + {mathrm{CO}}_2 to {mathrm{O}}_2 + {mathrm{CH}}_2{mathrm{O}}$$
(2)
with respect to O2 cycling rates, where CH2O refers to one Corg equivalent. Assuming that Corg is predominantly particulate, with negligible diffusional transport, diel Corg burial was thus calculated as:$${mathrm{C}}_{{mathrm{org}}} {mathrm{buried}} = smallint {mathrm{OP}}-smallint {mathrm{R}}_{{mathrm{aero}}},$$
(3)
where ∫OP and ∫Raero are the diel depth-integrated rates of O2 production and consumption and are equivalent to Corg production and consumption according to equation (2). Thus, diel burial can also be represented through the export flux of O2 at the top and bottom interfaces of the sedimentary domain:$${mathrm{C}}_{{mathrm{org}}} {mathrm{buried}} = {mathrm{O}}_{2} {mathrm{export}} = smallint {mathrm{OP}}-smallint {mathrm{R}}_{{mathrm{aero}}},$$
(4)
which allowed us to assess the dynamic steady state of the diel model when the average diel depth-integrated rates equalled the export fluxes.To calibrate the O2 productivity for unitless PAR intensities, we determined the Qmax that caused a maximum O2 export that corresponded to the median maximal flux from illuminated benthic photosynthetic systems13. A Qmax of 4.0022 mmol g−1 h−1 produced the target export flux of 5.76 mmol m−2 h−1 under a sedimentary respiration load of 0.1 mM h−1. Note that by calibrating the productivity to the maximum diel illumination, the model represents a ‘mean solar day’ of a given Earth year59. This allowed us to disentangle the effect of daylength from geological-scale changes in the insolation intensity, such as in the ‘faint young Sun’ paradigm reviewed thoroughly by Feulner23, or changes in the solar spectrum related to atmospheric composition64.Next, we explored the effect of Ranaero on the daylength dependency of the process rates and export fluxes. We used the example of sulfate reduction performed by SRB with a log-normal biomass distribution with a peak value of 2 mg cm−3 (Supplementary Video 1). The Ranaero rate was either defined as a constant rate process for the scenario ‘OP SRB constant’ as:$${mathrm{R}}_{{rm{anaero}}} = Q_{{rm{max}}}times{rm{biomass}}$$
(5)
or as an O2- and H2S-sensitive process as:$$begin{array}{rcl}{mathrm{R}}_{{rm{anaero}}} & = & Q_{{rm{max}}} times {rm{biomass}}times {rm{inhibition}}([{rm{O}}_2],,K_{{rm{max}},{{rm{O}}_2}},,K{_{{rm{half}},{{rm{O}}_2}})}\ && times {rm{inhibition}}left( [{{rm{H}}_2{rm{S}}],,K_{{rm{max}},{rm{H}}_2{rm{S}}},,K_{{rm{half}},{rm{H}}_2{rm{S}}}} right)end{array}$$
(6)
where inhibition is a function of the local H2S and O2 concentration (x) of the form:$$frac{{K_{{rm{max}}} – x}}{{2times K_{{rm{max}}} – K_{{rm{half}}} – x}}$$
(7)
when x  More

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The volatiles from fresh feces of Przewalski’s horse at the pre-oviposition, oviposition, and post-oviposition stages of G. pecorum
Throughout the stages of pre-oviposition (PREO), oviposition (OVIP), and post-oviposition (POSO) of G. pecorum, 70 volatiles were identified in fresh feces of Przewalski’s horse. Among them, 46, 48, and 52 volatiles were identified at PREO, OVIP, and POSO, respectively, and 29 volatiles were common at all three stages. In addition, 4, 5, and 9 volatiles were common between PREO and OVIP, OVIP and POSO, as well as PREO and POSO, whereas 4, 10, and 9 volatiles were unique at the single stage of PREO, OVIP, and POSO, respectively (Table 1; Fig. S1). According to relative content, the two main chemical classes of volatiles were aromatic hydrocarbons and alkenes, that is, their respective contents in a sample were both more than 25% of the total content. Except alcohols which exhibited significant difference between PREO and POSO (One-way ANOVA, F = 8.400, df = 2, P = 0.018), there was no significant difference in all other pairwise comparisons among the nine chemical classes at three stages (One-way ANOVA or Kruskal–Wallis test: P  > 0.05) (Fig. 1). Non-metric multidimensional scaling (NMDS) analysis revealed certain extent of overlap (Fig. 2), while one-way analysis of similarity (ANOSIM) indicated that there were significant differences among the three stages (R = 0.5391, P = 0.008).Table 1 The volatiles from fresh feces of Przewalski’s horse at the stages of PREO, OVIP, and POSO of Gasterophilus pecorum.Full size tableFigure 1Volatile classes detected from fresh feces of Przewalski’s horse at the stages of PREO, OVIP, and POSO of Gasterophilus pecorum. PREO, OVIP, and POSO represent fresh feces at the stages of pre-oviposition, oviposition, and post-oviposition of Gasterophilus pecorum, respectively. Data are mean (n = 3) ± SE. Different letters indicate significant differences at P  0.05). Furthermore, acetic acid was common to PREO and POSO, but no difference was observed between them (Independent t test, t = 0.137, df = 4, P = 0.897) (Table 1).Of particular concern among the eight volatiles mentioned above, ammonium acetate and butanoic acid were unique to OVIP, the critical stage of oviposition. Although not one of the five most abundant volatiles, another nine volatiles were also specific to OVIP, of which hexanoic acid, cyclopentasiloxane,decamethyl- and cyclohexene,3-methyl-6-(1-methylethyl)- were higher than 1% in relative content (Table 1).Among the 47 volatiles common to two or three stages, only six volatiles were significantly different in relative contents. Of which, D-limonene was higher at PREO than at OVIP (One-way ANOVA: F = 11.936, df = 2, P = 0.012) or POSO (P = 0.012), and 1-butanol was higher at OVIP than at PREO (One-way ANOVA: F = 8.175, df = 2, P = 0.024) or POSO (P = 0.04). Relative contents of the other four volatiles were less than 1% (Table 1).The volatiles from feces of Przewalski’s horse with different freshness states at the OVIP stage of G. pecorum
Totally, 83 volatiles were detected from fresh feces (Fresh), semi-fresh feces (Semi-fresh), and dry feces (Dry) at the OVIP stage of G. pecorum. Of which, 48, 41 and 28 volatiles were identified in Fresh, Semi-fresh and Dry, and 7 volatiles were common to all three feces with different freshness states. In addition, 14, 3 and 3, were common between Fresh and Semi-fresh, Semi-fresh and Dry, as well as Fresh and Dry, whereas 24, 17, and 15 were unique to Fresh, Semi-fresh, and Dry, respectively (Table 2; Fig. S2). Aromatic hydrocarbons and alkenes, acids and ketones, as well as alcohols and aldehydes were the two main chemical classes of Fresh, Semi-fresh, and Dry in respective. Except esters and ‘others’ which showed no significant difference in the feces, there were significant differences among other seven classes in at least one pairwise comparison of the three freshness states (One-way ANOVA, Independent t-test or Kruskal–Wallis test: P  More

Physiochemical characteristics of water in the blowout regionThe physiochemical properties of water were measured in the laboratory. The analyzed properties are shown in Table 3.Table 3 The analyzed physiochemical properties of water.Full size tableThe average value of pH is 6.529 indicates water of the study area is slightly acidic in nature. The average value of CO2 (6.5) complied with the lowering tendency of pH. The average ORP value 36 also reflecting the sign of acidic water in the study region. According to WHO standards (2011), the value of conductivity within range 0–800, Total dissolved solids less than 500 ppm, alkalinity 120 ppm, and total hardness less than 300 mg/L are allowable for drinking and domestic purpose37. The average value of conductivity 76.7 µs/cm, total dissolved solids 44.2 ppm, alkalinity 109.1 concurred with the dirking water standard by WHO (2011). The average value of TH is 49 ppm points out that the properties of water are soft.Spatial distribution of trace elements derived from water bodies around the blowout areaThe primary purpose of this study is to understand the concentration level of different Trace metals in the area. In this study, Pb, Ni, Cu, Cd, and Zn were examined (Fig. 3). Besides the toxic metal spatial distribution map is constructed using the inverse distance weighting (IWD) method in Arc GIS (version 10.5). The map (Fig. 4) shows common patterns of hotspots near the Syl-1 blowout area for every metal. This scenario indicates that these metals are originated from the same source46.The contiguous area near Syl-4 well also exhibited a similar pattern to Syl-1 for all metals except Cd. A high concentration of Cd was found closed to the Syl-1 area. The elevated concentration of toxic metals like Ni, Pb, and Cd are found in the adjacent areas of blowout points (Fig. 4). Continuous gas escaping from these abandoned wells might stimulate the trace metal accumulation, especially Pb would be more toxic when it will come to a contact with gasoline (Syl-1 and Syl-4)7. The non-essential toxic metals like Ni, Cd, and Pb in water can pose a serious health threat inthesite47. In addition, these toxic elements can contribute to acute or chronic health issues like high blood pressure, kidney failure, headache, abdominal pain, cancer, nerve damage, and so on for the long-term consumption of such water48.Standard value of Pb in water is 0.01 mg/L, Ni is 0.02 mg/L, Cu is 2 mg/L, Cd is 0.003 mg/L in water37. In this analysis, the average value of Pb = 0.04, Cd = 0.05, Ni = 0.16, Cu = 0.03 mg/L, respectively. The TMs like Zn concentration is about zero or below the detection level for water samples in the study location. The values of Pb, Cd and Ni were higher than the standards level indicates that the water should not be used for any purpose49.Figure 3The concentration of trace elements in the study area.Full size imageFigure 4The spatial distribution map of toxic metals in the area.Full size imageCorrelation coefficient (R) matrix of water quality parameter presented in the blowout areaA Correlation matrix represents the relationship among several variables. It is generated based on the correlation coefficient, which ranges from − 1 to 1. The value of correlation coefficient (1, − 1) indicates perfect correlation, (− 0.9 to − 0.7 or 0.9–0.7) shows strong correlation, (0.4–0.6 either positive or negative) represents moderate correlation, (0.1–0.3 or − 0.1 to − 0.3) displays as weak and 0 indicates no relationship between variables50. The mathematical expressions are described in the article by MacMillan et al.51 to evaluate the correlation coefficient (r).The correlation matrix is shown in Table 4. From the Table 4, it is clear that the pH shows a moderate to strong correlation with CO2 (0.63) and alkalinity (0.69). Whereas, it shows a very strong positive correlation with Total Hardness (TH) and Ca2+ (0.88), respectively. The moderately positive correlation reflects with EC (0.41) and trace elements Ni (0.62). The rest of parameters show a negative correlation. The CO2 exhibits a good correlation with EC (0.72), TH and Ca2+ (0.52). Alkalinity states a good correlation with TH and Calcium ions (0.76). EC shows a maximum correlation with TDS (1.00); maximum correlation also found in the case of TH and Ca2+. Turbidity has a positive correlation for all of the parameters except EC and TDS. TDS shows a strong positive correlation with all of the trace elements, in the case of Pb (0.54), Cd (0.88), Ni (0.68) and for Cu the value is 0.64. All trace elements have a strong correlation with each other. Pb represents a good correlation with Cd (0.65), Ni (0.54) and Cd (0.35). Ni has a strong correlation with Cd (0.61), Cu (0.43). Cu also implies a good correlation with Cd (0.58). In the end, it can be mentioned that a strong positive correlation can be detected among all of the trace elements and also for most of the relative parameters. CO2 established the equilibrium state in the water with ions might be lowering the oxidation. The trace metals Cu and Cd were positively correlated with the turbidity. The washed turbid water from the blow out areas might stimulate these trace metals. The inverse association with oxidation and total hardness indicates the less vegetated areas have higher influx rate of soil materials. It implies the result of the correlation matrix indicated that all of the trace elements and also relevant ions presented in the water of blowout area resultant from the same source46.Table 4 Correlation coefficient matrix of water parameters.Full size tableFactor loading of water parametersThe interrelationship within a set of variables or objects is represented by factor analysis. The factors contain all of the basic information about a wider set of variables or observed objects. It shows how the variables are strongly correlated with the determined factor. Factor analysis is also known as a multivariate approach to reducing data33. Among different types of factor analysis, Principal component analysis account for the maximum variance of observed variables. So, it can be called variance-oriented33. Factor loading shows how certain variables strongly correlate for a given factor. Factor loading varies from − 1 to + 1 where the value of factor loading below − 0.5 or above 0.5 suggested good correlations and value closed to − 1 or + 1, suggesting a more robust correlation32. The Table 5 represented the principal component analysis result of factor solution.Table 5 Principal components analysis results of water parameters.Full size tableFrom analysis (Table 5), it can be realized that the water quality parameters such as Turbidity, TH, Ca2+, Cd, Ni and Cu have a stronger correlation with each other’s reflecting their source of origin might be from the same area14. Factor loading also suggested that more robust interconnection exists among CO2, EC and TDS. In this analysis, the two-factor solution explained approximately 80.6% of the variance. The eigenvalue, total variance explained are represented in Supplementary Table S1. That percentage is high enough to accept the results. It can also be added that the red and yellow colored loading represented strong correlation with each other46,52.Water quality index (WQI)The WQI is one of the best tools for monitoring the surface-groundwater contamination and can be used for water quality improvement programs. The WQI is determined from various  physicochemical parameters like pH, EC, TDS, TH, EC, and so forth. Higher estimation of WQI indicates poor water quality and lower estimation of WQI shows better water quality. During this examination, WQI esteems a range from 0.02633 to 5144.37 and are characterized into five water types shown in Table 6. The noteworthy WQI is recorded in case of (sample-1) which demonstrates an elevated level of contamination. Water sample 2, 5, 8 and 10 are grouped under class-1 which demonstrates there is a lower degree of pollution in water. In addition, WQI calculation for sample 2, 5, 8 and 10 excluded trace elements value and WQI evaluation for sample 1, 3, 4, 6, 7 and 9 included the heavy metals value in water. These results also clarify the association of heavy metals on water quality degradation of the study area.Table 6 Classification of the water quality index for individual parameter of water.Full size tableThe situation of contamination in the areaThe level of contamination has been demonstrated in terms of the CFi, PLI, and also PI analysis of water samples around the blowout area. The values of CFi are indicated the degree of contamination. The intensity of CFi has been determined with some numerical values like 1, 3, and 6. The CFi value is less than 1, which implies low contamination, as the value is > 6 indicated a high degree of contamination36. The Table 7 elucidates that the degree of contamination in the case of trace elements Pb, Cd, and Ni are very high for most of the locations of the research sides. Besides Cu and Zn exhibit that level contamination is low in the area. In other cases, the PLI can be evaluated by using the CFi value. The value of PLI greater than 1 symbolizes polluted and less than 1 represents the unpolluted status36,39. The pollution load index rate of Pb, Cd, and Ni are 2.3, 2.87, and 2.56, respectively (Fig. 5).This result indicates the pollution of water bodies in the sampling sites. The other elements such as Cu and Zn are within the allowable limit are shown in Fig. 5. Moreover, the PI indicates similar results as CFi and PLI.Table 7 Contamination factor of water samples.Full size tableFigure 5Pollution load index of the study area.Full size imageThe state of potential ecological threat in the areaThe ecological potential risk index has been appealed to detect the possible threat to the ecological system in the adjoining area. The calculated RI value provided the risk factor of water for understanding the ecological threat. When the RI value is more than 600, it is considered a polluted case11,14,36. The computed RI value of the study for Pb, Cd, Ni, and Cu are 123.5, 2770, 235, and 0.23, respectively (Table 8). The value of Cd is high enough (RI  > 600). So, the Cd values indicated that the potential threat to the ecological system. Besides, the TMs like Ni and Cu are specified medium to low ecological pollution in the area are shown in Table 8. Moreover, the spatial distribution has been presented to outlook the potential ecological threats around the blow out location of the gas field is shown in Fig. 6.Table 8 Ecological risk index (RI) of the study area.Full size tableFigure 6A map of the spatial distribution of potential ecological risk threats in the study area.Full size imageThe spatial distribution map of RI also pointed out the high ecological risk closed to the blowout areas (Fig. 6). From these results, it can be implied that the use of  this water for domestic or drinking purposes, can be harmful for living beings. Moreover, it can be distressed the ecological system in the site. Hence, the use of the water from this site should be avoided by dwellers near the blowout areas of the gas field.Assessment of noncarcinogenic health risksNoncarcinogenic risk is one of the vital categories of human health risk assessment. It is known that a polluted environment is highly liable for causing a health risk. Toxic metal presents in water also very harmful for public health, including child and adult both. The health risks may be extended through ingestion and skin absorption of water. To know the harmful impacts of trace elements of water on the human body, noncarcinogenic risk evaluation is more important. For that, the value of CDI for ingestion and dermal absorption was evaluated at the beginning to identify such risk index (Supplementary Table S2 and S3). Then the CDI has been divided with the RfD value. From where, the HQ can be acquired separately for ingestion and dermal absorption. The summation of HQingestion and HQdermal expressed the HQtotal. And the HQtotal entirety was used to achieve the HI are shown in Table 9.Table 9 The HQ and hazard index (HI) value of noncarcinogenic analysis of the area.Full size tableThe results elucidate that case of adult, the mean value of CDITotal for Pb is 1.29E-03, Cd is 1.45E-03, Ni is 4.93E-03 and Cu is 4.83E-04, respectively. For the child, the mean value of CDITotal in the case of Pb, Cd, Ni, and CU is4.7544E-03, 5.33E-03, 1.81E-03 and 1.77E-04, correspondingly. Additionally, the order of CDITotal for adults are Ni  > Cd  > Pb  > Cu (Supplementary Table S2) whereas, for child, it is quite different. In the case of children, the order  are Cd  > Pb  > Ni  > Cu are presented in Supplementary Table S3.The mean values of HQTotal of Pb, Cd, Cu, and Ni are ranging from 4.46E−05 to 1.45E−02 for child. Besides, these values for adults are extending from 1.21E−05 to 4.66E−03. These values suggest that the trace elements in the water of the study area are quite harmful to the child than an adult. The children’s HQTotal has been ordered as Cd  > Pb  > Ni  > Cu and for the adult Cu  More

Effects on intra-event-based flood runoff and sediment characteristicsBetween the 1960s and 1990s, there was no significant change in rainfall in the Chabagou watershed35. The mean values of runoff and sediment transport in the baseline period and measurement period were calculated. Regardless of rainfall influence, the effect of conservation measures was assessed by the time series contrasting method25.Table 1 shows the statistics of the characteristics of event-based flood flows and sediment in 1961–1990 (excluding 1970). Compared with those in the baseline period, T and Tr in the measurement period increased by 16.54% and 29.21%, respectively; however, Tp decreased by 55.52% in the measurement period, which showed that the soil and water conservation measures extended the flood duration while reducing the time of increased discharge. Under identical rainfall conditions, long-duration runoff with less time for increased discharge could cause less erosion than short-duration runoff with more time for increased discharge36. Hence, the conservation measures reduced soil erosion by prolonging the flood duration and reducing the time to peak. In addition, the hydrodynamic indices qp, H and qm were 75.2%, 56.0% and 68.0% lower, respectively, in the measurement period than in the baseline period. Moreover, E in the measurement period was only 10.2% that in the baseline period. The results showed that the conservation measures greatly reduced the hydrodynamic energy and thus soil erosion. In addition, the relative erosion indicators SSY, SCE and MSCE, decreased 69.2%, 33.3%, and 11.9%, respectively, in the measurement period compared with the baseline period, which indicated that the conservation measures significantly reduced soil erosion and decreased the mean sediment concentration, although the reduction in the maximum sediment concentration was relatively small. The conservation measures, especially the engineering measures, reduced the runoff velocity, extended the flood duration, and reduced the peak discharge, which sharply reduced the runoff erosion power37,38. As a consequence of the decrease in erosive energy, soil erosion was diminished.Table 1 Descriptive statistics of the characteristics of event-based flood flows and sediment in 1961–1990 (excluding 1970).Full size tableInfluence on intra-event-based flood regimesClassification of flood events and the characteristics of baseline period flood regimesFigure 2 shows the clustering results of the flood events at the Caoping hydrological station in 1961–1969. The flood events were divided into 4 regimes with a significance level of p  More

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