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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Zhang, S. et al. Sustained productivity and agronomic potential of perennial rice. Nat. Sustain. https://doi.org/10.1038/s41893-022-00997-3 (2022). More

Experiment results and regression modelThe simulation experiment results based on the design scheme are presented in Table 4, including 24 analysis factors and 7 zero-point experiments for estimating the errors. Quadratic multiple regression analysis of the results in Table 4 was performed using the Design-Expert software, and the regression models between the influencing factors and evaluation indices were established as follows:$$ Y_{{1}} = {1767.57} – {64.29}X_{{1}} + {117.46}X_{{2}} + {324.46}X_{{3}} + {107.87}X_{{4}} – {21.81}X_{{1}} X_{{2}} + {17.94}X_{{1}} X_{{3}} – {41.44}X_{{1}} X_{{4}} + {16.69}X_{{2}} X_{{3}} – {41.19}X_{{2}} X_{{4}} + {73.56}X_{{3}} X_{{4}} + {23.2}{X_{{1}}^{{2}}} – {82.42}{{X_{{2}}}^{{2}}} – {13.17}{{X_{{3}}}^{{2}}} – {53.67}{{X_{{4}}}^{{2}}} $$$$ Y_{{2}} = {1968.14} + {636.42}X_{1} + {34.42}X_{2} + {66}X_{3} + {115.17}X_{{4}} + {28.63}X_{{1}} X_{{2}} + {9.13}X_{{1}} X_{{3}} – { 45.87}X_{{1}} X_{{4}} + {1}0X_{{2}} X_{{3}} + {30.5}X_{{2}} X_{{4}} – {1.75}X_{{3}} X_{{4}} + {55.03}{X_{{1}}^{{2}}} – {8.1}{{X_{{2}}}^{{2}}} – {72.72}{{X_{{3}}}^{2}} + {61.03}{{X_{{4}}}^{{2}}} $$Table 4 Experiment schemes and results.Full size tableThe relationship between the actual values of the efficiency of conveying-soil and the distance of throwing-soil and the predicted values of the regression model is shown in Fig. 7. It can be seen from Fig. 7 that the actual values are basically distributed on the predicted curve, consistent with the trend of the predicted values, and linearly distributed.Figure 7Scatter plot. (a) Scatter plot of actual and predicted distance of throwing-soil. (b) Scatter plot of actual and predicted efficiency of conveying-soil.Full size imageVariance analysis and discussionThe F-test and analysis of variance (ANOVA) were performed on the regression coefficients in the regression models of the evaluation indices Y1 and Y2, and the results are shown in Table 5. According to the significance values P of the lack of fitting in the regression models of the objective functions Y1 and Y2 in Table 5, PL1 = 0.1485  > 0.05 and PL2 = 0.2337  > 0.05 (both were not significant), indicating that no loss factor existed in the regression analysis, and the regression model exhibited a high fitting degree.Table 5 ANOVA results of regression model.Full size tableAccording to the ANOVA, the significance values P of each influencing factor in the test could be determined28. For the evaluation index Y1, the factors X1, X2, X3, X4, X3X4, X22, X42 had extremely significant influences, while the factors X1X4, X2X4 had a significant influence. For the evaluation index Y2, the factors X1, X3, X4, X1X4, X12, X32, X42 had extremely significant influences, and the factors X2, X1X4 had a significant influence. Within the level range of the selected factors, according to the F value of each factor as shown in Table 5, the weight of the factors affecting the efficiency of conveying-soil is feeding speed  > helix angle of auger  > rotating speed of auger  > slope angle. And the weight of the factors affecting the distance of throwing-soil is slope auger  > rotating speed of auger  > feeding speed  > helix angle of auger.In addition, it is obvious that there are interactions between the feeding speed and rotating speed of the auger, slope auger and rotating speed of auger, helix angle of the auger and rotating speed of the auger on the efficiency of conveying-soil Y1. For the distance of throwing-soil Y2, there is an interaction between the slope angle and the rotating speed of the auger.Analysis of response surfaceThe fitting coefficient of the efficiency of conveying-soil is R2 = 0.9714, R2adjust = 0.9263, R2pred = 0.8082, the difference between R2adjust and R2pred is less than 0.2. The fitting coefficient of the distance of throwing-soil is R2 = 0.9873, R2adjust = 0.9742, R2pred = 0.9355, the difference between R2adjust and R2pred is smaller than 0.2. It is indicated that the response surfaces of the two models established have good consistency and predictability for the experimental results29.The response surface is created directly using the Design-Expert software. After entering the data, select “Analysis” module. In the “Model-Graph” menu bar, select “3D-surface” to switch to the 3D view. To express the interactive influence of each factor on the efficiency of conveying-soil Y1 and distance of the throwing-soil Y2, the above two quadratic regression equations of the evaluation indices were subjected to the dimensionality reduction treatment. Two of the factors was set to level 0, while the other two underwent interaction effect analysis to study the influence law on the evaluation indices Y1 and Y2, and the corresponding response surfaces were generated, as illustrated in Fig. 8.Figure 83D response diagram effect of evaluation indices. (a) Effect of interaction between X1 and X2 on efficiency of conveying-soil. (b) Effect of interaction between X2 and X4 on efficiency of conveying-soil. (c) Effect of interaction between X3 and X4 on efficiency of conveying-soil. (d) Effect of interaction between X3 and X4 on distance of throwing-soil.Full size imageIt can be seen in Fig. 8a, when the slope angle was constant, the efficiency of conveying-soil increased with the rotating speed of the auger to a certain value, then the efficiency increase changed more gently. The reasons for this phenomenon are described as follows. On the one hand, the greater the kinetic energy of the soil when leaving the original position, and the thinner the soil was cut, resulting in the smaller the probability of blockage in the spiral blade space. On the other hand, the centrifugal force of soil arriving at the pit mouth is greater, so it does not obstruct in the pit mouth. However, if the rotation speed of the auger was too high and the soil layer cut was too thin, the subsequent soil’s driving effect to the front would be weakened, or even the flow would be interrupted, so the vertical rising speed of the soil would be reduced. When the rotational speed of the auger was constant, the efficiency of conveying-soil decreased with the increase of slope and then slightly increased. With the increase of slope, the time of slope cutting process increased, and there was more soil backfilling on the side of high altitude, which leaded to the reduction of soil discharge efficiency. However, with the increase of slope, the amount of soil slide at the pit mouth was increased, improving the efficiency of soil discharge. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the rotating speed than in that of the slope angle, indicating that the rotating speed of auger X4 had a more significant influence than the slope angle X1.As can be seen in Fig. 8b, when the helix angle of the auger was fixed, the efficiency of conveying-soil continued to increase with the increase of the rotation speed. When the rotating speed of auger was fixed, the efficiency of conveying-soil increased with the increase of the helix angle and tends to decrease when it reached a certain value. The spiral blades space was the channel of soil movement. This phenomenon was caused by the increase of the gap between the two spiral blades with the increase of the helix angle of the auger, the soil was not easy to produce blockage. Meanwhile, the movement distance of soil was shorter, and the soil with higher kinetic energy was discharged more quickly from the pit. When reaching the pit mouth, the angle of soil throwing was larger and the soil backfilling rate was reduced. However, if the helix angle of auger was too large, the upward support ability and friction of the spiral blade surface to the soil would be reduced. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the helix angle than the rotating speed of the auger, indicating that the helix angle of the auger X2 had a more significant influence than the rotating speed of the auger X4.When the feeding speed was fixed, the efficiency of throwing-soil continued to increase with the increase of the rotating speed. When the rotating speed of auger was fixed, the efficiency of the throwing-soil with the increase of the feeding speed (see in Fig. 8c). The phenomenon was caused by the faster the feeding speed of the auger, the thickness of soil cut per unit time increased. Furthermore, the subsequent driving force of soil increased, and the soil kinetic energy increased. However, in the actual production, excessive feeding speed would cause soil blockage on the surface of spiral blades. The reason is due to in the simulation process, the soil would not stop moving because of blockage. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the rotating speed than in that of the feeding speed, indicating that the rotating speed of auger X4 had a more significant influence than the feeding speed X3.When the slope was fixed, the distance of the throwing-soil increased with the increase of rotation speed of the auger, and the increase amplitude increased gradually, as shown in Fig. 8d. The reason for this phenomenon was that the soil had more kinetic energy when it left its original position and the centrifugal force it received when it reaching the pit mouth is greater. When the rotation speed was too low, the soil layer was thin and the subsequent soil driving force was insufficient, resulting in the soil mass per unit area at the pit mouth was light and then the kinetic energy was small. When the rotating speed of auger was fixed, the distance of the throwing-soil increased continuously with the increase of the slope. As the slope increased, the time of soil swipe down process increased and then the rolling distance on the slope increased. Further analysis demonstrated that the response surface for Y2 changed more rapidly in the direction of the slope angle than in that of the rotating speed of auger, indicating that the slope angle X1 had a more significant influence than the rotating speed X3.Comprehensive optimal designAs relative importance and influencing rules of various experimental factors on evaluation indexes were different from each other, evaluation indexes should be taken into comprehensive consideration30. The optimization equation is obtained by the Design-Expert software multi-objective optimization method with Y1 and Y2 as the optimization objective function.$$25le {X}_{1}le 45$$$$10le {X}_{2}le 22$$$$0.04le {X}_{3}le 0.1$$$$30le {X}_{4}le 120$$$${{Y}_{1}}_{mathrm{max}}({X}_{1},{X}_{2},{X}_{3},{X}_{4})$$$${{Y}_{2}}_{min}({X}_{1},{X}_{2},{X}_{3},{X}_{4})$$In practice, the best combination of parameters needs to be selected according to the terrain slope. When the slope was fixed, the Design-Expert software was applied to optimize and solve the above mathematical model. The optimal combination of working parameters affecting the efficiency of conveying-soil Y1 and distance of throwing-soil Y2 for the auger were obtained and are shown in Table 6. If the ground preparation was required before the digging operation, the digging parameters can be designed according to values of Group 6 in Table 6.Table 6 Optimal parameter combinations of several terrain slopes.Full size tableDisturbance of soilA soil disturbance is defined as the loosening, movement and mixing of soil caused by an auger passing through the soil16. In the interface of the EDEM Analyst, add a “Clipping plane” to show the movement of the auger inside the pit. The kinetic energy, soil particle velocity vector, and velocity value of soil particles is observed when the auger in the middle of the soil bin31,32, as shown in Fig. 9.Figure 9The disturbance of the soil effect by spiral blade.Full size imageThe soil was lifted to the surface and then dropped to the lower side. In addition to the volume occupied by the spiral blades, the disturbed area also included the out-of-pit disturbed area caused by the compression of the cutting end of the spiral blade, as shown in the lower left corner of the auger.The kinetic energy and velocity of soil decreased firstly and then increased along the opposite direction of the auger feeding. The cutting end of the auger and the soil-throwing section occurred in the region with high kinetic energy and velocity. This was because the maximum kinetic energy was obtained at the cutting end of the auger, which was gradually consumed in the process of rising. After reaching the dumping end, the soil lost the restraint of the pit wall. When the centrifugal force of soil lost the reaction force, the kinetic energy of soil increased. Too much kinetic energy, however, can cause the soil to spread too far, causing subsequent trouble. The kinetic energy of the soil at the cutting end was related to the rotational speed of the auger. The spiral angle affected the angle between the force and gravity, and then the kinetic energy consumption in the process of soil increased.Verification experimentsTo verify the accuracy of the optimization model for auger working, as well as to evaluate the rationality of the working parameter combination optimized by the virtual experiment, performance verification tests were carried out on the EDEM software. According to the optimized process parameter setting test (as shown in Table 6), the relative error between the theoretical value and the experimental value was obtained. The verification test results are summarized in Table 7. The average relative errors of the efficiency of conveying-soil and the distance of throwing-soil between the Theoretical value and text value were only 4.4%, 9.1%. The simulation model is fairly accurate. The field performance verification experiments were carried out in slope. Figure 10 illustrates the field test and working conditions.Table 7 Results and comparison of validation test.Full size tableFigure 10Operation diagram at the experiment site.Full size image More

All methods were performed in accordance with the local relevant guidelines, regulations and legislation.InstrumentsLI-6400 photosynthesis system (LI-6400 Inc., Lincoln, NE, USA) and PAM-2500 portable chlorophyll fluorescence apparatus (PAM-2500, Walz, Germany) were used in the study.MaterialsAbout 90 living E. brevicornu plants were collected from Taihang Mountains in October 2018. The E. brevicornu was not in endangered or protected. The collection of these E. brevicornu plants was permitted by local government. These plants were averagely planted in nine plots of 2 m2. The roots of E. pubescens were planted 6–8 cm below ground. These plots were placed on farmland near Taihang Mountains and covered with sunshade net (about 70% light transmittance). These plants were timely irrigated after planting to ensure that they grew well but not fertilized.Determination of photosynthetic characteristicsThe photosynthetic characteristics of mature leaves on the E. brevicornu plants were determined between June 6–8, 2019 with the Li-6400 photosynthesis system. The diurnal variation of photosynthesis in three leaves of three plants was determined. When the light response curve was determined, the temperature of the leaf chamber was set at 28 °C, and the concentration of CO2 in the leaf chamber was set at 400 µmol mol−1. When determining the CO2 response curve, the light intensity in the leaf chamber was set at 1000 µmol m−2 s−1, and the temperature of the leaf chamber was set at 28 °C. The light response curve and CO2 response curve were determined three times in three leaves of three different plants.Determination of chlorophyll fluorescence characteristicsThe fluorescence characteristics of chlorophyll in E. brevicornu leaves were determined with PAM-2500 portable chlorophyll fluorescence apparatus between June 8–9, 2019. The leaves underwent dark adaptation for 30 min before determining slow kinetics of chlorophyll fluorescence. Then the light curves of chlorophyll fluorescence were determined. All of these determinations were repeated three times on three mature leaves of three plants.The data was analysed with SPSS (Statistical Product and Service Solutions, International Business Machines Corporation, USA). The light response curves were fitted with following modified rectangular hyperbola model11,12.$${text{Photo}}, = ,{text{E}}cdotleft( {{1} – {text{M}}cdot{text{PAR}}} right)cdotleft( {{text{PAR}} – {text{LCP}}} right)/({1}, + ,{text{N}}cdot{text{PAR}})$$PAR is the value of light intensity in leaf chamber of Li-6400 photosynthesis system. Photo is net photosynthetic rate. LCP is the light compensation point. E is the apparent quantum yield. M and N are parameters. The dark respiration rate under the LCP is calculated according to E·LCP. The light saturation point (LSP) is calculated according to (((M + N) ·(1 + N·LCP)/M)½)/−1)/N.The net photosynthetic rate under the light saturation point (LSP) can be calculated according to the above model.The CO2 response curves were fitted with below modified rectangular hyperbola model11,12.$${text{Photo}}, = ,{text{E}}cdotleft( {{1} – {text{M}}cdot{text{PAR}}} right)cdotleft( {{text{PAR}} – {text{CCP}}} right)/({1}, + ,{text{N}}cdot{text{PAR}})$$PAR is the value of light intensity in leaf chamber of Li-6400 photosynthesis system. Photo is net photosynthetic rate. CCP is CO2 compensation point. E is also the apparent quantum yield. M and N are parameters. The dark respiration rate under the CO2 calculated according to E·CCP. The CO2 saturation point (CSP) is calculated according to (((M + N) ·(1 + N·CCP)/M)½)/−1)/N.The net photosynthetic rate under the CO2 saturation point (CSP) can be alternatively calculated according to the above model.The light curves of chlorophyll fluorescence were fitted according to the below model of Eilers and Peeters12,13.$${text{ETR}}, = ,{text{PAR}}/({text{a}}cdot{text{PAR}}^{{2}} , + ,{text{b}}cdot{text{PAR}}, + ,{text{c}})$$ETR is the electron transport rate of photosynthetic system II. PAR is fluorescence intensity. The letters a, b and c are parameters. More

Study siteWe conducted laboratory studies at the field site in Finca Irlanda, which is a 300-hectare organic shaded coffee farm located at 1100-m altitude, in the municipality of Tapachula, the state of Chiapas in Southern Mexico (92° 20′ 29″ W and 15° 10′ 65″ N). For the laboratory experiments, all organisms were freshly collected from Finca Irlanda or reared in the lab from insects collected from the field close by. The lab and field work was performed with a permit from the farm owner the Peters family.Ant aggression testTo examine the effect of phorid flies (P. lascinosus) on the aggressivity of ants (A. sericeasur) towards the parasitoids of the beetle larvae (A. orbigera), we conducted an ant aggression test with two treatments: with and without phorids. In the first treatment, a small coffee branch containing two leaves with scale insects (C. viridis) and 20 ant workers were both introduced into a one-liter plastic container. This was done to mimic as much as possible field conditions where the ants are tending scale insects. After waiting for at least 15 min for the ants to calm down and start tending the scale insects, one third- or fourth-instar larva of the beetle was introduced. In the second treatment, all settings were the same except for the addition of 3–4 phorid flies. Once the two treatments were established, one female parasitoid wasp (H. shuvakhinae) was released into each container. During a forty-minute trial, each time that a parasitoid wasp encountered an ant worker, the response of the ant individual was recorded. Ant responses to parasitoids were classified into two categories: (1) the ant ignores the wasp; (2) the ant attacks the wasp. All insects were used for a single replicate and then discarded. A total of four replicates were completed for both the presence and absence of phorids. For each trial, we calculated the proportion of actions (either aggressive or none) by ants when encountering the parasitoid wasp in the treatments with and without phorid flies. We used R36 to conduct a two-sample Mann–Whitney U test on the proportion of ant actions.Parasitism experiments and analysesTo examine the parasitoid wasp’s host preference and the effect of the 1st degree and the 2nd degree HOIs on the beetle’s parasitism and sex ratio, we conducted a laboratory experiment in insect tents (60 cm × 60 cm × 60 cm) with three treatments: (1) no ants (no HOIs but only the wasp and the beetle larvae), (2) ants (1st degree HOI), and (3) ants and phorids (1st and 2nd degree HOIs) (Fig. 1-B). We randomly assigned insect tents to each treatment in each trial, and the tents for each treatment were also shuffled in each trial. All beetle larvae used for these experiments were reared in the laboratory for at least two generations from freshly collected beetle adults. In each tent we placed a coffee branch with 4–6 leaves infested with approximately 100 scale insects inside a plastic container at the center of an insect tent. The set up for the three treatments of species combinations were as follows: (1) 4–5 third or fourth instar beetle larvae and a parasitoid wasp; (2) 4–5 third or fourth instar beetle larvae, a parasitoid wasp, and about 60–80 ant workers; (3) 4–5 third or fourth instar beetle larvae, a parasitoid wasp, about 60–80 ant workers and 3–4 phorid flies. Organism densities in these treatments were close to those observed in the field. To allow for acclimation, we introduced organisms into the tents in the following order: first, we introduced the coffee branch containing scales, immediately followed by the ants (in treatments 2 and 3). After the ants settled down and started tending the scale insects, we introduced the beetle larvae. Once the larvae began moving on the coffee leaves, we introduced the phorids (in treatment 3). When the three treatments were established, and the organisms exhibit normal behavior, we released one lab-reared female parasitoid wasp (H. shuvakhinae) in each tent (treatments 1, 2, and 3). We allowed the organisms to interact for 24 h. After 24 h, we collected all beetle larvae in each treatment and reared them with sufficient scale insects as food, until beetle adults emerged or parasitism symptoms appeared (parasitized larvae turned into hardened black mummies). The treatments of no HOI and 1st + 2nd degree HOI were repeated for 10 consecutive times, and the treatment of 1st degree HOI was repeated for 11 consecutive times, with new individuals of each organism. We recorded parasitism instances and beetle sexes upon emergence. To estimate the sex ratio without parasitoid influence, 78 randomly selected beetle individuals were reared on coffee leaves with scale insects without any interaction with other organisms.To analyze the effect of the parasitoid, the ant and the phorid fly on the parasitism rate and the sex ratio of the beetle, we developed a nested model, starting from$$logitleft(widehat{P}(S)right)=a+bA$$where (widehat{P}(S)) is the probability of an individual being parasitized, A is a binary variable, standing for the absence (0) and presence (1) of ants, a is the baseline probability of parasitism, and b is the magnitude of parasitism altered by ants in the logistic function. We further hypothesized that phorid attacks modify the strength of the interaction modification that ants exert upon the host-parasitoid interaction. Therefore,$$b=g+hP$$where P is another binary variable, standing for the presence (1) and absence (0) of phorids. Substituting b, we obtain the following function,$$logitleft(widehat{P}(S)right)=a+gA+hAP$$where g represents the effect of ants on the parasitism rate of A. orbigera larvae, and h represents the effect of the fly’s facilitation, via interfering with the ant’s interference on the parasitism rate of A. orbigera larvae. We used binary responses (1: survival; 0: parasitized) of all available beetle individuals across the three treatments. We performed model selection based on the Akaike Information Criterion (AIC) and likelihood ratio tests. For the latter, we started model selection by fitting the full model and preceding each step by eliminating the term that had the least significance (the greatest p-value) on the explanation of the dependent variable. The analysis was performed with the application of the bbmle package in R. By doing this, we determined the maximum likelihood estimates of survival probability of the beetle, (widehat{P}(S)), in the three treatments: (1) A = 0, AP = 0 (no HOI); (2) A = 1, AP = 0 (one HOI: ant interference) and (3) A = 1, AP = 1 (interacting HOIs: phorid interference with ant interference), and errors associated with these estimates.The same idea applies to the sex ratio of the beetle under the influence of various organisms. We developed the following equation,$$logitleft(widehat{P}(F|S)right)= r+mA+nAP$$where (widehat{P}(F|S)) is the probability of a parasitism survivor being female. A and P are both binary variables. Respectively, they represent the ant and the phorid fly, and the numeric attributes, 0 and 1, denote their absence and presence. As before, model selection and parameter estimates were conducted with AIC. By doing this, we determined (widehat{P}(F|S)), the estimate of being a female beetle given survival, for the three treatments: (1) A = 0, AP = 0 (no HOI); (2) A = 1, AP = 0 (one HOI: ant interference) and (3) A = 1, AP = 1 (interacting HOIs: phorid interference with ant interference), and errors associated with these estimates. We employed the mle2 function in the bbmle package in R to estimate the female probability (1) in the absence of HOI (the beetle and the parasitoid alone), (2) in the presence of the 1st degree HOI (the beetle, the parasitoid and the ant), and (3) in the presence of the 1st and the 2nd degree HOIs (the beetle, the parasitoid, the ant and the phorid fly).Probabilities of per capita female and per capita male survival from parasitism under the influence of ant and the phorid flyTo test whether the sex ratio of beetle survivors’ population is due to sex-differential survival probability, Bayes’ theorem was employed. Per capita female survival probability from parasitism in each treatment of the parasitism experiment was derived based on (widehat{P}(F)), (widehat{P}left(F|Sright),) and (widehat{P}(S)), and per capita male survival probability was derived based on (widehat{P}(M)), (widehat{P}left(M|Sright),) and (widehat{P}(S)). According to the Central Limit Theorem, the estimates of proportions, (widehat{P}left(S|Fright)) and (widehat{P}left(S|Mright)), are approximately normally distributed,$$widehat{P}left(S|Fright)sim Nleft(widehat{P}left(S|Fright), sqrt{frac{widehat{P}(S|F)times left(1-widehat{P}left(S|Fright)right)}{{n}^{*}}}right)$$$$widehat{P}left(S|Mright)sim Nleft(widehat{P}left(S|Mright), sqrt{frac{widehat{P}(S|M)times left(1-widehat{P}left(S|Mright)right)}{{n}^{*}}}right)$$with means (widehat{P}left(S|Fright)) and (widehat{P}(S|M)), and standard deviations (sqrt{frac{widehat{P}left(S|Fright)times (1-widehat{P}left(S|Fright))}{{n}^{*}}}) and (sqrt{frac{widehat{P}left(S|Mright)times (1-widehat{P}left(S|Mright))}{{n}^{*}}}), where (widehat{P}(S|F)) and (widehat{P}(S|M)), respectively, are the population proportions of females and males. Here we employ n*, the smallest sample size among those of the three variables in the Bayesian formulas for males and females. Since the three variables have different sample sizes, n* guarantees a conservative estimate of standard error, and thus confidence interval, of each derived probability. More

The plots of Igeo, PERI, and PLI of HMs in the topsoil of the tourist area of Sayram Lake (Fig. 5) reveal the degree of HM pollution and eco-risk in this study area on the one hand and, on the other hand, indicate the direction for the relevant agencies to target soil environmental protection and HM pollution prevention and control measures. In this study, the Igeo results showed that Cd was the most highly enriched HM, and Pb, Zn, Cd, and Ni were slightly enriched in a few sample sites. The unnatural accumulation of these elements is usually closely associated with human activities in the area34. Tourism is the main economic activity in the district, and published studies have reported that tourism infrastructure construction (e.g., roads, buildings, etc.) and tourism wastes (e.g., plastic bags, batteries, hotel wastewater) release Cd into the soil35. Additionally, the accumulation of Pb, Zn, Cu and Ni in soils is usually associated with traffic emissions36. The PERI showed that the study area was at low risk overall, with only point ss04 exhibiting medium risk; however, this result was caused by the abnormally high Cd concentration value (Fig. 4) at point ss04 (Cd (concentration): 1.08 mg/kg, Cd (background): 0.34 mg/kg). This anomalous concentration value has a large influence on the PERI calculated based on the measured concentration, the background value and the toxicity coefficient. Therefore, references to this point can be appropriately removed when considering eco-risk. The PLI of each sampling point was greater than 1 and less than 2, which means that the area was in a moderately contaminated state. In general, the degree of soil HM contamination in this area was low; however, due to HM toxicity, bioaccumulation, and persistence37, the HM contamination of this area still requires sustained attention.Figure 5Contamination and ecological risk indices: (a) geoaccumulation index (Igeo) of HMs; (b) ecological risk of individual HMs; (c) potential ecological risk index (PERI) of HMs; (d) pollution load index (PLI) of HMs.Full size imageCorrelation analysis is an efficient way to reveal correlations among HMs through Pearson correlation coefficients, and HMs with significant correlations may originate from the same source38. As shown in Table S5, the elemental pairs Cd-Cu (p  More

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