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Biostimulation of ureolytic communitiesEnrichmentThe native communities of the soils were successfully grown in the enrichment media (NB5U). The cultivated communities after two subcultures were serially diluted (10–2 to 10–6) and were spread with a sterile loop over Nutrient agar plates supplemented with 2% urea. Later, 36 morphologically distinct single colonies were obtained on the urea agar base plates based on visual observation.Isolation, identification, and characterization of the ureolytic isolatesOut of 36 isolated bacteria, six isolates (BS1, BS2, BS3, BS4, LS1, and LS2) were selected after checking for the urease activity test on the urea agar base (UAB) plate. These selected isolates turned the color of the UAB plate from orange to pink within 12 h. The 16 s rRNA sequence revealed the isolates as relatives of Sporosarcina pasteurii (SP). The details of the identified isolates are provided by NCMR (details in Supplementary Table 1). The biochemical characterization (details in Supplementary Table 2) of the isolates revealed that all the isolates are Gram-positive. All the isolates were rod-shaped, endospore-forming, urease, and oxidase positive. All the isolates were not able to utilize the Lysine and ONPG, contrary to SP.Further investigation of the isolated sequence was done via the NCBI database. The sequences were submitted to the GenBank database of the NCBI (National Center for Biotechnology Information) under the accession number MW024144 to MW024149. The BLAST analysis suggested that these strains are close relatives and indicate the possibility of being novel strains of the Sporosarcina family. We found that the isolate BS1 and BS2 had 96.62% (coverage 100%) and 96.22% (coverage 99%) identity with Sporosarcina siberinisis (NCBI accession number NR 134188). BS3 had 98.8% identity (coverage 97%) with Sporosarcina pasteurii (NCBI accession number NR 104923). BS4 and LS1 had 97.4% (coverage 99%) and 97.37% identity (coverage 100%) with Sporosarcina soli (NCBI accession number NR 043527). Contrarily, LS2 was found to be related to the Pseudogracilibacillus family. LS2 was observed to be closely related to Pseudogracilibacillus auburnensis P-207 with 97.06% identity (96% coverage). Based on these findings, the Phylogenetic tree was constructed with bootstrap (1000 replicates) considering the reference sequences obtained from the BLAST analysis, as shown in Fig. 3. The threshold criteria to differentiate two species is defined as 98.65% similarity score with the reference culture from databank40, while another study has suggested that in case of similarity index is  > 99%, the unknown isolate should be assigned to a species, and if the unknown isolates have similarity score between 95 to 99% to a reference sequence, the isolate should be assigned to the genus41. However, further investigation is suggested to conclude if the reported strains are novel or merely mutants of the reference strains of the databank. Similar observations were made at Graddy et al.22, where the majority of the isolated strains (47 out of 57) from bio-stimulation soil tanks were found to be strains of the Sporosarcina genus. It is worth noting that the soil enrichment media for stimulation was rich in urea, similar to Gomez et al.42 and Graddy et al.22, which is conditional stress for selective stimulation of ureolytic microorganisms. Moreover, the isolated strains were screened based on morphology and qualitative urease activity.Figure 3Neighbor-joining phylogenetic tree based on the 16S rRNA sequence of the isolates and reference sequence from the GenBank database (NCBI).Full size imageEvaluation of biocementation potential of the isolated strainsGrowth and pHThe various parameters of the biocementation potential of the isolated strains in comparison with Sporosarcina pasteurii (SP) have been plotted in Fig. 4. The growth characteristics of the isolates in NBU media and pH during growth have been represented in Fig. 4a and b. The initial pH of the growth media is kept at 7.5. It was observed that the pH of the growth media rises to 9.5 within 24 h of growth, indicating that these strains favor an alkaline environment to grow similar to SP43. All the isolates start growing when the pH of the media rises to 8.5 or above. Isolate LS2 was observed to have slower growth when compared with other isolates. This can be explained as LS2 belongs to different genera (Pseudogracilibacillus).Figure 4(a) Growth characteristics, (b) pH, (c) specific urease activity, (d) calcium utilization rate, and (e) carbonate precipitation rate of the isolates and consortia.Full size imageSpecific urease activityThe specific urease activities of the isolates were found to be comparable with SP (shown in Fig. 4c). Based on the provided NBU media and growth condition, the specific urease activity of SP is found to be 173.44 mM urea hydrolyzed h−1 (OD600)−1, which is around 2.9 mM urea hydrolyzed min−1 (OD600)−1. The specific urease activity of the isolate BS3 was observed to be maximum as 186.6 mM urea hydrolyzed h−1 (OD600)−1 during a growth period of 24 h and pH  > 9. Consortia also demonstrated significant urease activity as 160 mM urea hydrolyzed h−1 (OD600)−1 at a growth period of 48 h. The maximum ureolytic activity in BS1 was observed after 72 h of growth with a value of 106.67 mM urea hydrolyzed h−1 (OD600)−1. Maximum specific urease activity of the isolate BS2, BS4, and LS1 was observed to be 160.2, 120, and 173.4 mM urea hydrolyzed h−1 (OD600)−1 respectively after a growth duration of 48 h. LS2 demonstrated the maximum specific urease activity of 146.4 mM urea hydrolyzed h−1 (OD600)−1. The observed order of specific urease activity at 24 h of growth period is BS3  > SP  > Consortia  > LS1  > BS2  > BS4  > LS2  > BS1. As the urease activity of the strains depends on the growth media, urea content, and environmental conditions such as pH and Temperature44, we considered the conditions at the riverbank at the time of isolation, and the pH and temperature of the growth media were set at 7.5 and 37 degrees Celsius. The specific urease measured by the electrical conductivity method is reported to be between 3 to 9.7 mM urea hydrolyzed min−1 (OD600)−1 in yeast-extract urea media at pH 7 and temperature 30 degrees Celsius43. It is reported around 5 mM urea hydrolyzed min−1 (OD600)−1 in the nutrient broth urea (2%) media at a temperature of 25 degrees Celsius44. The comparative analysis of the urease activity (measured by electrical conductivity method) was done considering SP as positive control in this study. The maximum specific urease activities of all isolates were found to be in a range of 106.67 to 186.67 mM urea hydrolyzed h−1 (OD600)−1 (1.78 to 3.11 mM urea hydrolyzed min−1 OD600–1), which indicates that all of the isolated strains are capable of biocementation43,45.Calcium utilization and carbonate precipitation potentialIt was experimentally observed that the depletion of the supplemented soluble calcium in the precipitation media (PM) was corresponding to the ureolytic activities of the isolated strains. Within 48 h of introducing 1% bacteria (OD600 = 1) in the precipitation media, the soluble calcium chloride (50 mM) was utilized to precipitate carbonate crystals, as illustrated in Fig. 4d. Within 12 h of the inoculation period, BS3 was able to utilize 75% of the supplied calcium, while SP was able to utilize only 62.5% of the soluble calcium. The order of the calcium utilization potential in the isolates was observed as BS3 ≥ LS2  > L.S.1  > Consortia  > SP  > BS4  > BS2  > BS1 during the inoculation period. Contrarily, LS2, despite being a comparatively slow urease-producing bacteria, was able to utilize calcium ions at par with other isolates. Negligible changes were observed in the soluble calcium concentration of the control group eliminating the possibility of abiotic precipitation.The carbonate precipitation rate for each isolate (1% at OD600 = 1) for the 50 mM cementation media is plotted in Fig. 4e. The isolate BS3 with maximum ureolytic activity (specific urease activity 186.6 mM urea hydrolyzed min−1 OD600–1) precipitated the highest carbonate crystals after 96 h of the incubation period. BS3 precipitated 438 mg/100 ml of carbonate crystals, which is around 87.66% precipitation from the total supplied CaCl2, while precipitation with SP was quantified as 389 mg/100 ml (78%). The precipitation in consortia was observed to be 407 mg/100 ml (81%), which is slightly higher than SP. Precipitation in other isolates was found to be significantly lower than isolate BS3. Isolate BS1and BS2 precipitated 334 mg/100 ml (67%) and 343 mg/100 ml (69%) of carbonate crystals respectively, whereas isolate LS1 and LS2 precipitated around 357 mg/100 (71%) ml of carbonate crystals each. Isolate BS4 precipitated minimum carbonate crystals 292 mg/100 ml (58%). No precipitation was observed in the negative control set. Low concentrations of bacterial cells (1%) were considered in this experiment to slow down the urea hydrolysis in order to differentiate the calcium utilization potential of the isolated strains. This approach was modified from Dhami et al.46, and our results show agreement with their finding where 1% of SP cells depletes the 25 mM of CaCl2 within 24 h. It was observed that all the isolates took approximately 48 h to deplete the 50 mM CaCl2. The depletion of soluble calcium concentration was rapid in the initial 24 h in all the isolates. After 48 h, the residual soluble calcium was observed to be in the range of 2.5–5 mM in all the isolates (except BS1 and BS2), which might be due to loss of super-saturation caused by the unavailability of nutrient for bacterial cells to continue urea hydrolysis in the precipitation media13,43,47. The mineralogy of precipitated carbonate polymorphs (calcite, aragonite, vaterite) and the residual calcium are also influenced by pH, temperature, saturation index, dissolved organic carbon concentration, and the Ca2+ /CO32−ratio along with the presence of metabolites in the precipitation media13,47,48. As maximum precipitation was recovered with the isolate BS3, the isolate BS3 was selected for further investigation on soil improvement.Microstructure analysis of the precipitatesThe FESEM images of the carbonate crystal precipitated from BS3 were investigated further. The shape of the precipitated crystals was observed to be rhombohedral and trigonal (Fig. 5a). The average size of the crystals was observed in a range of 25 to 50 microns. The entrapped bacteria and rod-shaped bacterial imprints were identified (Fig. 5b), indicating that the bacteria acted as a nucleation site14. The smaller crystals were observed to coagulate in layers to develop larger calcite crystals. The entrapped bacteria were noticed on the grown and coagulated calcite crystal in Fig. 5c. After taking the FESEM image (Fig. 5a) of the precipitate, EDX analysis was conducted, and the elemental composition suggested an abundance of calcium, carbon, and oxygen, which indicates the presence of calcium carbonate crystals (Supplementary Fig. 1). XRD analysis was conducted to confirm the mineralogy of the precipitates, and the majority of the observed peaks of the XRD plot belonged to calcite, which is consistent with the observation of rhombohedral crystal shapes in the FESEM image. The XRD analysis also suggested an insignificant presence of aragonite in the precipitates.Figure 5FESEM images of the calcite precipitated from BS3 (a) Coagulated crystals (b) Bacterial imprints, (c) Entrapped bacteria on the precipitates.Full size imageApplication of native communities on riverbank soil and its influence on soil strengthNeedle penetration resistance of treated soilThe average NPI (N/mm) for different cases has been shown in Fig. 6a. No notable resistance was observed in the loose untreated sand (control) against the needle penetration. With one bio cementation cycle treatment, the consortia-treated soil sample (Consortia-BC1) demonstrated a higher value of NPI (5.15 N/mm) than SP-BC1 (4.19 N/mm) and BS3-BC1 (4.64 N/mm). The increase in the biocementation cycle treatment significantly improved the needle penetration resistance. Sample BS3-BC2 showed 116% improvement with the NPI value of 10.03 N/mm when compared to one cycle treated sample BS3-BC1. A similar trend was observed in the sample BS3-BC3 (NPI = 16.12 N/mm), which showed around 347% improvement in NPI when compared to sample BS3-BC1. From the needle penetration test, it was evident that the penetration resistance of treated soil improves significantly with the increased level of biocementation cycles, indirectly indicating an improvement in the soil erodibility resilience. Since non-uniformity is one of the undesired traits of MICP, a contour was plotted corresponding to the 25 points NPI, as shown in Fig. 6b. The contrasting color difference in the contours of the samples BS3-BC1, BS3-BC2, and BS-BC3 clearly demonstrates the stark difference in the strength of treated samples. The non-uniformity in the strength of treated soil crust of sample BS3-BC2 and BS3-BC3 can also be realized with the contrasting color gradient of the NPI contours.Figure 6Comparison of the Needle penetration resistance (N/mm) of the treated soil specimen (a) average values and (b) the contours.Full size imageSince the rate of penetration has an insignificant influence on the test results, the needle penetration test is recommended by the International Society of Rock Mechanics (ISRM) for quick, non-destructive testing of the strength of the stabilized soils and soft rocks49. As a large number of tests can be conducted due to the small diameter of the needle without destroying the sample, the needle penetration test is a better alternative to evaluate the local grain bonding in the biocemented soil than bulk strength properties like unconfined compressive strength and calcite content. Another rationale for choosing needle penetration test over conventional soil strength evaluation tests was that a pocket type penetrometer could be developed with the configuration in the present study for non-destructive monitoring of the soil strength improvement with biocementation application in the field. The response of the needle penetration resistance in terms of nominal strain (ratio of penetration to rod diameter) also indicated that the measured responses are independent of needle diameter for a small range, i.e., 1 to 3 mm49,50. A portable penetrometer of Maruto. Co. ltd. (needle maximum diameter 0.84 mm at 12 mm from the tip) have been correlated with high confidence value to conventional physicochemical parameters such as unconfined compressive strength (UCS), elasticity modulus, and elastic wave velocity in several studies50. In our setup, we have utilized a similar configuration chenille 22 needle with (maximum diameter 0.86 mm at 9 mm from the tip) and a penetration rate of 15 mm/minute for measuring the strength properties of cemented soil. Adopting the UCS and NPI correlation suggested by Ulusay et al.51, the UCS of samples BS3-BC1, BS3-BC2, and BS3-BC3 are around 1.67 MPa, 3.4 Mpa, and 5.3 Mpa.It is worth noting that in the needle penetration resistance tests, the boundary of the Petri dish can influence the test results. Therefore, trials were conducted and based on the findings, all penetrations were conducted at points at least five times the diameter of the needle away from the boundary to negate the influence of boundary conditions. The maximum penetration was conducted only up to 50% of the depth of prepared biocemented soil samples in the Petri dishes to avoid inference from the bottom of the Petri dish.Erodibility test in the hydraulic flumeTo investigate the influence of hydraulic current on different levels of biocementation, all the treated samples were exposed to hydraulic current gradually varying from gentle flow (0.06 m/s) to five times the critical velocity (0.75 m/s) in a 45-min duration test and soil mass loss percentage by the initial dry mass of the treated sample is presented as a measure of soil erodibility in Fig. 7. As expected, with an increase of biocementation cycles, i.e., calcite content, the soil erodibility reduced substantially. The initial dry weight of the samples control, BC1, BC2, and BC3, were measured as 398, 403, 406, and 410 g, respectively. Approximately 7.3% of calcite content resulted in a drastic reduction in erodibility (12% mass loss), while 56% soil mass loss was recorded for control (untreated sand). One biocementation cycle treatment (sample BS3-BC1) produced an average of 2.5% of calcite, reducing the soil loss to 31%. Sample BS3-BC2 with 4.93% calcite content resulted in 22% soil mass loss during the hydraulic flume test. It is worth noting that higher precipitation in the soil pores may hinder the flow of water in the soil matrix and increase the pore water pressure resulting in catastrophic failures. However, the MICP technique is reported to be a great tool to improve soil strength, maintaining an adequate hydraulic conductivity to prevent the build-up of the excess pore water pressure11. Theoretically, the percentage pore volume filled with precipitates for samples BS3-BC1, BS3-BC2, and BS3-BC3 considering the observed calcite contents and pore volume (100 ml) is around 3.7, 7.14, and 11.08%. The influence of pore water pressure on erodibility has not been established in the present study, and it certainly is one of the exciting parameters to consider for future studies.Figure 7Weight of the eroded soil (%) after the hydraulic flume test.Full size imageFrom the visual observation of the soil specimen after the flume test, it was evident that the soil particles start bonding with an increased level of bio cementation. A tough crust was formed on the top of BS3-BC2 and BS3-BC3 the samples, which got eroded with the fluvial current. Insignificant aggregation was observed in the sample BS3-BC1. However, with two and three cycles of biocementation treatment (BS3-BC2 and BS3-BC3), the biocemented soil particles (BCS) were evidently noticed (photos are shown in Supplementary Fig. 4).Clarà Saracho et al.27 addressed the erosion due to tangential flow (similar to river current) by treating the soil specimen with ten pore volume of low concentration of cementation media (0.02 M to 0.1 M) by injection strategy and tested the specimen in the flow velocity ranging from 0.035 to 0.185 m/sec for 120 min in a modified erosion function apparatus (EFA). The study concluded that the treatment with 0.08 M cementation media (calcite content varying from 1.2 to 4%) resulted in negligible erosion in the stated test conditions, and with the increase in MICP treatment, a shift in the erosion mode from particulate mode to block failure was observed indicating that with the increase in calcite content and needle penetration resistance, there might be a threshold for biocemented soil, where the soil erosion might be catastrophic due to block failure. However, in this study, a consistent decrease in soil erodibility is observed with the increase in needle penetration resistance. We found that 7.3% of calcite content was required to control the soil erodibility substantially in the test flow range (0.06 m/s to 0.75 m/s). A similar trend was observed by Kou et al.52 and Chung et al.53, where consistent reduction in wave-induced erosion and rainfall-induced erosion was observed with an increase in needle penetration resistance for biocemented fine sand treated with the exogenous bacteria.Another aspect to note in the context of the applied treatment is the produced ammonia which can be toxic for riparian flora and fauna15. From stochiometric calculations, for each biocementation cycle, the produced ammonia is evaluated as 8.5 kg per metric ton of soil treated, and for the best performing treatment approach, i.e., BS3-BC3, the ammonia generated is evaluated as 2.63% by weight of the retained soil. The acceptable limit of ammonia in the surface water was recommended as 17 mg per liter for acute exposure and 1.9 mg/l for chronic exposure for protecting the aquatic life in the freshwater as per the environmental protection agency54. With the MICP technique, the threat of produced ammonia crossing the maximum acceptable quantity is highly plausible; however, for the field application, the ammonia generated can be reduced by reducing the quantity of reagents and increasing the period of applications. It is to be noted that the produced ammonia will also be subjected to dilution in the river stream. The average discharge of the Brahmaputra river is around 19.8 megaliters per second in the Assam valley33. Therefore, the area of the riverbank to be biocemented must be decided judiciously with the context of the produced ammonia quantity and its possible dilution to non-toxic levels.Microstructure and mineralogical analysis of the biocemented samplesTo investigate the influence of different biocementation levels on the erodibility of the treated sand grains, FESEM imaging was conducted for light biocemented samples (BS3-BC1) and heavy biocemented samples (BS3-BC3). While precipitated crystals were observed to be growing on the grooves of sand grains in the light biocemented sample (BS3-BC1), bridging of sand grains with rhombohedral crystals was observed in the heavy biocemented sample (BS3-BC3), as shown in Fig. 8. The effective calcium carbonate bridging between sand grains increases the frictional and cohesive property of sand grains55,56, leading to a substantial reduction in the erodibility of the soil. Bacterial imprints were observed in both cases, suggesting the precipitation to be biodgenic14. Further EDX analysis on a bridged sand grain (Supplementary Fig. 5) suggested an abundance of silicon and oxygen on the sand grains with a trace amount of chlorine and calcium. This indicates the presence of residual calcium chloride on silica grains. The EDX analysis on the grain bridge indicated the presence of calcium, carbon, and oxygen, suggesting CaCO3 precipitation. XRD analysis on treated and untreated sand confirmed the precipitation of calcite. Most of the peaks correspond to quartz (silica). In the biocemented sand sample, a visible peak of calcite was observed at around 29 degrees of 2Ɵ (Details in Supplementary Fig. 6). Therefore, the incorporation of microbial calcite as a binding agent for loose grain silica soil was found to reduce the soil erodibility.Figure 8FESEM images of the treated sand grains (a). Calcite crystal growing on the sand grains on BS3- BC1 sample (b). Calcite bridging in BS3-BC3 samples.Full size image More

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DataWe reviewed PPS reporting rates of AMR in healthy animals and animal food products in China between 2000 and 2019 (Supplementary Text S1). We focused on three common food animal species: chicken, pigs and cattle. Dairy cattle and meat cattle were pooled in this study, in consistency with the categorization adopted in the maps of livestock created by the Food and Agriculture Organization27. The review focused on four common foodborne bacteria: E. coli, non-typhoidal Salmonella, S. aureus and Campylobacter. We recorded resistance rates reported in PPSs, defined as the percentage of isolates tested resistant to an antimicrobial compound. In addition, we extracted the anatomical therapeutic chemical classification codes of the drugs tested, the year of publication, the guidelines used for susceptibility testing, the latitude and longitude of sampling sites, the number of samples collected and the host animals. We recorded sample types for each survey, including live animals, slaughtered animals, animal products and faecal samples. Each sample was taken from one animal or animal product. These sample types were pooled in the current analysis. In total, 10,747 rates of AMR were extracted from 446 surveys (Supplementary Fig. 8), including 318 surveys from China’s National Knowledge Infrastructure (CNKI), the leading Chinese-language academic search engine. All data extracted in the review are available at https://resistancebank.org.Two steps were taken to ensure comparability of the resistance rates extracted from the surveys. First, the panel of drug–bacteria combinations extracted from each survey was that recommended for susceptibility testing by the World Health Organization Advisory Group on Integrated Surveillance of Antimicrobial Resistance40. This resulted in the extraction of 6,295 resistance rates for 76 drug–bacteria combinations. Second, resistance rates were harmonized using a methodology4 accounting for potential variations in the clinical breakpoints used for antimicrobial susceptibility testing (Supplementary Text S1). There are two major families of methods used for susceptibility testing in this dataset: diffusion methods (for example, disc diffusion) and dilution methods (for example, broth dilution). Previous works have shown good agreement between the two approaches in measuring resistance in foodborne bacteria4,46. For each family of methods, variations of breakpoints may result from differences between laboratory guidelines systems (European Committee on Antimicrobial Susceptibility Testing vs Clinical and Laboratory Standards Institute), or from variations over time of clinical breakpoints within a laboratory guidelines system (Clinical and Laboratory Standards Institute or European Committee on Antimicrobial Susceptibility Testing). Here we accounted for both situations using distributions of minimum inhibitory concentrations and inhibition zones obtained from eucast.org (Supplementary Text S1).Trends in AMRWe defined a composite metric of AMR to summarize trends in resistance across multiple drugs and bacterial species. For each survey, we calculated the proportion of antimicrobial compounds with resistance higher than 50% (P50). For each animal–bacteria combination, we assessed the significance of the temporal trends of P50 between 2000 to 2019 using a logistic regression model, weighted by the log10-transformed number of samples in each survey.For each bacteria–drug (antimicrobial class) combination, we estimated prevalence of resistance by calculating a curve of the distribution of resistance rates across all surveys (Fig. 2). The analysis was conducted for surveys published between 2000 and 2009, and between 2010 and 2019, respectively. The distribution was estimated at 100 equally spaced intervals from resistance rates of 0% to 100%, using kernel density estimation. We used the centre of mass of the density distribution to estimate prevalence of resistance. The calculation was conducted for six animal–bacteria combinations. This included E. coli in chicken, pigs and cattle, Salmonella in chicken and pigs, and S. aureus in cattle. The remaining animal–bacteria combinations were excluded due to limited sample size, only represented in 32 out of 446 PPSs. The analysis was restricted to antimicrobial classes represented by at least 10 resistance rates. In addition, we estimated the association between resistance rates and the ease of obtaining antimicrobials from the market, using data from online stores (Supplementary Text S3).Geospatial modellingWe interpolated P50 values from the survey locations to create a map of P50 at a resolution of 10 × 10 km across China. The approach followed a two-step procedure47. In step 1, three ‘child models’ were trained using four-fold spatial cross-validation to quantify the relation between P50 and environmental and anthropogenic covariates (Supplementary Text S2 and Supplementary Table 1). In step 2, the predictions of the child models were stacked using universal kriging (Supplementary Text S2). This approach combined the ability of the child models to capture interactions and non-linear relationships between P50 and environmental and anthropogenic covariates, as well as the ability to account for spatial autocorrelation in the distribution of P50.The outputs of the two-step procedure were a map of P50 (Fig. 3) and a map of uncertainty on the P50 predictions (Supplementary Fig. 9 and Supplementary Text S2). The overall accuracy of the geospatial model was evaluated using the area under the AUC. The contribution of each covariate was evaluated by permuting sequentially all covariates, and calculating the reduction in AUC compared with a full model including all covariates (Supplementary Fig. 4). The administrative boundaries used in all maps were obtained from the Global Administrative Areas database (http://www.gadm.org).Identifying (optimal) locations for future surveys on AMRWe identified the locations of 50 hypothetical new surveys—the rounded average number of surveys conducted per year (54 surveys per year) between 2014 and 2019 in China. The location of each new survey was determined recursively such that it minimized the overall uncertainty levels on the geographical trends in AMR across the country. This process took into account the locations of existing surveys and the location of each additional hypothetical survey. The objective of this approach was to maximize gain in information about AMR given the resource invested in conducting surveys.The map of uncertainty consisted of the variance in the child model predictions Var(PBRT,PLASSO–GLM,PFFNN) (step 1) across 10 Monte Carlo simulations, where PBRT, PLASSO–GLM, and PFFNN were the predictions of P50 using boosted regression trees, logistic regression with LASSO regularization, and feed-forward neural network, and the kriging variance VarK (step 2):Vartotal = Var(PBRT,PLASSO–GLM,PFFNN) + VarKIn this study, the location of hypothetical surveys was solely based on VarK, instead of the sum of both terms. This approach was preferred because including both terms would have required to hypothesize P50 values associated with surveys to be conducted in the future, adding an additional source of uncertainty that cannot be quantified. In any case, the uncertainty attributable to VarK was 4.1 times the Var(PBRT,PLASSO–GLM,PFFNN) (Supplementary Text S2).The allocation of new surveys was based on a map of ‘necessity for additional surveillance’ (NS), defined as:NS = VarK × Wwhere VarK reflects the uncertainty of the spatial interpolation, and W is the log10-transformed population density of humans48, animals27 in total, and in chicken, pigs and cattle, separately, which reflected exposure (Supplementary Fig. 10). Animal population density was calculated here as the sum of population-corrected units of pigs, chicken and cattle, using methods described by Van Boeckel et al.7. We adjusted the values of W such that its density distribution equals that of VarK. Concretely, for each pixel i, we calculated the quantile of Wi on the map of W, and replaced the value by the corresponding value of VarK at the same quantile. VarK and W were both standardized to range [0,1], thus giving each term equal weight in the need for surveillance.Four approaches were used to distribute 50 surveys across China based on the map of NS. The reduction in uncertainty on AMR level associated with each of the four spatial configurations of the hypothetical surveys was evaluated by calculating the reduction in the mean values of NS across 7,857 possible pixels on the map of China.First, we used a ‘greedy’ approach where all possible locations for additional surveys were tested. Concretely, the first hypothetical survey was placed at each of the 7,857 possible pixel locations, and a revised map of NS(+1 survey) was calculated for each of the placements. The survey was eventually placed in the pixel that led to the largest reduction in NS(+1 survey). The map of NS was then revised to account for the reduction in uncertainty in the neighbourhood of the new survey. The process was repeated recursively for the next hypothetical surveys (2nd–50th). This approach, by definition, yields the optimal set of locations to reduce uncertainty, but it also bears a considerable computational burden, because every possible location is tested (Npixels = 7,857) by the geospatial model for each hypothetical survey.The second approach developed was a computational approximation to the greedy approach, hereafter referred to as the ‘overlap approach’. This approach exploits a key feature of the kriging procedure: the decrease of the kriging variance (VarK) with increasing proximity to existing survey locations. Each additional survey reduces the variance of the geospatial model at its own location, but also in its surrounding area (Supplementary Fig. 11). The ‘overlap approach’ selects an optimal set of locations that reflect a compromise between high local NS and distance to other surveys. It iteratively selects new locations based on the highest local NS penalized by the degree of overlap between the hypothetical new surveys and existing surveys (Supplementary Fig. 12). The first survey was placed at the location Xp,Yp with the highest local NS (Supplementary Fig. 12, part 1). Then the value of NS at each pixel location Xi,Yi was recalculated as ({mathrm{NS}}_{{(+1,{mathrm{survey}})}X_i,,Y_i}={mathrm{NS}}_{X_i,,Y_i}times(1-{mathrm{overlap}},{mathrm{area}}/{mathrm{neighborhood}},{mathrm{area}})) (Supplementary Fig. 12, Part 2), where the neighbourhood area was the circular area of decreased kriging variance around a new survey, and its radius was the distance until which NS decreased due to this new survey; the ‘overlap area’ is the shared area of the neighbourhoods of location Xp,Yp and of location Xi,Yi. The radius of the neighbourhood was determined using a sensitivity analysis, optimized by approximate Bayesian computation (sequential Monte Carlo)49 (Supplementary Text S5). The optimal neighbourhood radius was chosen such that it minimizes reduction in NS across all pixels. The procedure (Supplementary Fig. 12, parts 1 and 2) was repeated recursively for the hypothetical surveys (2nd–50th).The third approach tested consisted of distributing surveys equally between provinces to reflect a common approach to disease surveillance based on equal allocation of resources between administrative entities. Twenty-two provinces with the highest human population were assigned two surveys, and the remaining six provinces were assigned one survey per province. The exact location of each survey was randomly selected inside a province. Finally, all approaches were compared with the fourth approach (the random approach) as a ‘null model’, in which the 50 hypothetical surveys were located randomly across the country without any geographic weighting criteria. The reduction in NS associated with the third and fourth approaches, which was compared to the greedy approach and overlap approach, was the average over 50 simulations.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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