Philippa Kaur

Selection of surveys and extraction of dataWe selected published surveys that produced estimates of sperm whale population size or density (see Supplementary Information for methodology; surveys listed in Table 1). We extracted: the type of survey (ship, aerial; acoustic, visual), the years of data collection; the coordinates of the boundary of the study area; the estimates of g(0) and CV (g(0)) used to correct for availability bias, if given; and an estimate of sperm whale population or density in study area with CV. From these we calculated for each survey the survey area with waters greater than 1000 m deep (typical shallow depth limit of sperm whales3). When no value of g(0) was used (8 ship visual surveys) we corrected the population/density estimate using an assumed generic value of g(0) and recalculated the CV to include uncertainty in g(0) (as in Eq. 1 of8). Three ship visual surveys did calculate a single g(0) estimate: 0.62 (CV 0.35)32; 0.57 (CV 0.28)35; 0.61 (CV 0.25)37. These are consistent and suggest a generic g(0) = 0.60 (CV 0.29), also agreeing with g(0) = 0.60 estimated from pooled surveys in the California Current10.Global habitat of sperm whalesTo extrapolate sperm whale densities from surveyed study areas to the sperm whales’ global habitat, we created a one-degree latitude by one-degree longitude grid. We removed the following grid points as not being prime sperm whale habitat1,3,40: points on land or with central depths less than 1000 m; largely ice-covered points in the Beaufort Sea, and the waters north of Svalbard and Russia; the Black Sea and Red Sea both of which have shallow entrances that appear not to be traversable by sperm whales.Generally, food abundance is a good predictor of species distribution. However, this is not possible for sperm whales as we have no good measures of the abundance or distribution of most of their prey, deep-water squid57. Instead, oceanographic measures have been used to describe sperm whale distributions over various spatial scales with a moderate level of success13,14. We follow this approach. Measures that might predict sperm whale density were collected for each grid point, some at just the surface, others at the surface, 500 m depth, 1000 m depth or an average of the measures at the different depths (Supplementary Table S2). Water depth was the strongest predictor in Mediterranean encounters, when compared to slope and distance to shore13. Temperature and salinity have been used as predictors for the distribution of fish and larger marine animals, which could translate into prey availability and thus density for sperm whales58,59. Primary productivity and dissolved oxygen generally dictate the biomass of wildlife in an area, while nitrate and phosphate levels limit the amount of primary productivity in an area60. Eddy kinetic energy is a measure of the dynamism of physical oceanography which is becoming a commonly used predictor of cetacean habitat61. We did not use: latitude and longitude as these primarily describe the general geographic distribution of the study areas, and geographic aggregates of sperm whale catches62 as these proved to have no predictive power. The mean values of the 14 predictor measures were calculated over calendar months for each grid point, and then over the grid points in each study area.To obtain predictors of the sperm whale density at each grid point, we then made quadratic regressions of the density of sperm whales in each study area (i), d(i), on the mean values of the predictor measures, weighting each study area by its surface area. Because the surveys were conducted over different time periods, the densities were corrected based on the estimated trajectory of global sperm whale populations by multiplying d(i) by the ratio of the global population in 1993 over that in the mid-year of the survey (as in Fig. 4). Predictor variables were selected using forward stepwise selection based upon reduction in AIC.Sperm whale population sizeThe population of sperm whales globally, N, was then calculated as follows:$$N=sum_{k}dleft(kright)cdot aleft(kright),$$
(1)
where a{k} are the parameters of the regression; the summation is over k, the grid points; d(k) is the estimated sperm whale density at grid point k from the habitat suitability model; and a(k) is the area of the 1° cell centred on grid point k. Population estimates for other ocean areas (North Atlantic, North Pacific, Southern Hemisphere) were calculated similarly.The CVs of these population estimates were calculated following the methodology in8, (although there is an error in Eq. (3) of8 such that the squareroot symbol covers both the numerator and denominator rather than just the numerator). The error due to uncertain density estimates for the different surveys is:$$CVleft({D}_{T}right)=frac{sqrt{sum_{i}{left(CV({n}_{i})cdot {n}_{i}right)}^{2}}}{sum_{i}{n}_{i}}.$$
(2)
This is combined with the uncertainty in the extrapolation process (output from the linear models), CV(extrap.), to give an overall CV for the population estimate:$$CVleft(Nright)=sqrt{{CV({D}_{T})}^{2}+{CV(mathrm{extrap}.)}^{2}.}$$
(3)
Post-whaling trend in population sizeWe compiled a database of series of surveys producing population estimates of the same study area during the period 1978 (by which time most commercial sperm whaling had ceased) and 2022. Each series had to span at least 10 years, and all of the surveys in the series had to be comparable in terms of area covered throughout the time span. There also had to have been at least 3 surveys for a data set to be included.The data consisted of the survey area, A, the estimated population in area A in year y (for multi-year surveys, y would be the midpoint of the data collection years), nE(A,y), and the provided CV of that estimate, CV(nE(A,y)). The data series used for these analyses are summarized in Table 3.For each survey area, A, we calculated the trend in logarithmic population size, r(A), over time using weighted linear regression:$${text{Log}}left( {n_{E} left( {A,y} right)} right) , sim {text{ constant}}left( A right) , + rleft( A right) cdot y. left[ {{text{weight }} = { 1}/left( {{1} + {text{ CV}}left( {n_{E} left( {A,y} right)} right)} right)^{{2}} } right]$$
(4)
Table 3 also includes other published estimates of sperm whale population trends, from sighting rates or mark-recapture analyses of photoidentification data, with these estimates also having to span at least 10 years of data collection, and include data collected in three or more different years.Population trajectoryTo examine possible trajectories of the global sperm whale population following the start of commercial whaling in 1712, we used a variant of the theta-logistic, a population model that has been employed in other recent analyses of the population trajectories of large cetaceans45,63. The theta-logistic model is:$$nleft(y+1right)=nleft(yright)+rcdot nleft(yright)left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-fleft(yright)cdot cleft(yright).$$
(5)

Here, n(y) is the population of sperm whales in year y, r is the maximum potential rate of increase of a sperm whale population, and θ describes how the rate of increase varies with population size relative to its basal level before whaling in 1711, n(1711). The recorded catch in year y is c(y) and f(y) is a correction for bias in recorded catches.Whaling reduced the proportion of large breeding males64, likely disrupted the social cohesion of the females3, and may have had other lingering effects which reduced pregnancy or survival, and thus the rate of increase. Poaching has been found to reduce the reproductive output of African elephants, Loxodonta Africana, which have a similar social system to the sperm whales3, and this effect lingers well beyond the effective cessation of poaching46. There is some evidence for these effects of what we call “social disruption” on sperm whale population dynamics20,46,65. We added a term to the theta-logistic to account for such effects:$$nleft(y+1right)=nleft(yright)left[1+rcdot left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}right]-f(y)cdot c(y).$$
(6)

Here, (frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}) is the proportion of the population killed over the last T years, and q is the reduction in the rate of increase when almost all the whales have been killed. This reduction is modelled to fall linearly as the proportion killed declines to zero.The global sperm whale population has some geographic structure18. Females appear to rarely move between ocean basins, and males seem to largely stay within one basin. Furthermore, sperm whaling was progressive, moving from ocean area to ocean area as numbers were depleted4. We model this by assuming K largely separate sperm whale subpopulations of equal size. Exploitation in 1712 starts in subpopulation 1 and moves to subpopulations 1 and 2 when the population 1 falls to α% of its initial value, and so on for the other ocean areas. The catch in each year in each area being exploited is pro-rated by the sizes of the different subpopulations being exploited. The population model for subpopulation k, which is one of the KE subpopulations being exploited in year y, is:$$nleft(k,y+1right)=nleft(k,yright)left[1+rcdot left(1-{left(frac{nleft(k,yright)}{nleft(k,1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}C(k,t)}{nleft(k,y-Tright)}right]-Cleft(k,yright),$$
(7)
where the estimated catch in year y in subpopulation k is given by: (Cleft(k,yright)=f(y)cdot c(y)cdot n(k,y)/sum_{{k}^{mathrm{^{prime}}}= More

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Sample collection and isolation of V. cholerae O1 possessing the CT geneTwenty-three patients (patient numbers 9 to 31) who were diagnosed with cholera were examined in this study. The diagnosis was confirmed by the isolation of V. cholerae O1 from the stool of each patient. The age of patients, date of hospital admission, stool sampling date, pathogen isolated and medicines administered to the patients as treatments are described in Supplementary Table S1. Twenty-one of the stool samples were collected on the first day of hospitalization, while the remaining two stool samples were collected on the second day (patient number 29) and fourth day (patient number 10) of hospitalization. All patients had not been given any antibiotics and the samples of diarrheal stool were taken during severe diarrhea.To confirm the presence of the CT gene (ctx) in these 23 isolates, we examined the presence of ctxA in these isolates by PCR. The PCR to detect ctxA was performed as reported by Keasler and Hall6. In this PCR, amplification was performed in 30 cycles. The size of the amplified ctxA fragment was 302 bp. The target fragment was amplified from each of the V. cholerae O1 isolates. This indicated that all of the V. cholerae O1 isolates from the 23 cholera patients possessed ctxA.CT production from the isolatesThe production of CT from these 23 isolates was examined by detecting secreted CT in the medium. The 23 isolates were cultured statically in AKI medium7, and the secreted CT in the culture supernatants was measured using the GM1-ganglioside enzyme-linked immunosorbent assay (ELISA) method8. The detection limit of CT by the ELISA method used is 1.0 ng ml−1. All the samples examined were found to have CT above this concentration (Fig. 1). This shows that all isolates examined are toxigenic V. cholerae O1.Figure 1Amount of cholera toxin produced by V. cholerae O1 isolated from patients with diarrhea. Twenty-three strains of V. cholerae O1 were isolated from 23 patients with diarrhea. These isolates were cultured statically in AKI medium7 at 37 °C for 24 h. After removing the cells by centrifugation, the CT in the culture supernatants was measured using a GM1-ganglioside ELISA method8. The samples indicated by blue circle are isolates obtained by bacterial culture from two patients (patient 12 and patient 18), who are focused on in this study.Full size imageAnalysis of the stool samples of patients diagnosed with cholera diseaseMetagenomic sequencing analysisThe primary objective of this metagenomic analysis is to show the proportion of V. cholerae living in the diarrhea stool. Subsequently, if the number of V. cholerae infected in the intestinal tract is small, it is required to clarify the etiological microorganisms that cause diarrhea in that patient. For this analysis, it is necessary to investigate the presence of pathogenic microorganisms other than V. cholerae in the stool. To do this, we need to analyze the gene reads obtained by metagenomic analysis with a comprehensive manner. Therefore, we planned to obtain reads with the Burrows-Wheeler Alignment tool (BWA) with default parameters, a matching software with the ability to fulfill these objectives9 (http://bio-bwa.sourceforge.net).However, we were concerned that the genes derived from organisms other than V. cholerae in the stool were counted mistakenly as genes derived from V. cholerae in the analysis using BWA. We therefore first examined genes in stool from people unrelated to cholera disease to ensure that the analysis method we planned to use in this study would correctly detect genes from V. cholerae in stool. For this analysis we used DNA sequences reported by the NIH Human Microbiome Project (https://www.hmpdacc.org/hmp/hmp/hmasm2/). The genes we have analyzed are DNA derived from feces of 20 healthy individuals (10 males and 10 females). The results are shown in Supplementary Table S2.The number of reads analyzed in this analysis varied from sample to sample. The largest number obtained after quality filtering was 60,975,797. The lowest number was 10,301,809. However, the number of reads detected as originating from V. cholerae was very small (12 reads or less) in all samples, and none of them were detected in 7 samples. This very small number shows that the analytical method used is suitable for detecting the genes from V. cholerae in these samples.Therefore, we analyzed DNA and RNA samples from prepared diarrheal stool by the method using BWA. All raw sequencing data obtained were deposited into the DDBJ Sequence Read Archive under the accession code PRJDB10675. This number can be searched not only from DDBJ but also from EMBL and GenBank.Diarrheal stools are mostly composed of liquid, and their properties are very different from those of normal stools. The origin of the nucleic acids in diarrheal stools varies from patient to patient and is not constant. One sample may contain many genes derived from human cells, while another sample may contain many genes derived from microorganisms. To clarify the nature of the reads we obtained, we determined the proportion of reads of bacterial origin to the total number of reads in the samples analyzed, and presented this proportion in order of patient age (Fig. 2a). The ratios were not consistent, indicating that the cells of eukaryotic origin and microorganisms existing in the stool of patients with diarrhea varied from person to person.Figure 2Age of patient and the ratio of the number of read detected by metagenomic sequencing analysis of their stools. The DNA in the stool samples from 23 patients who were diagnosed with cholera disease were extracted using a commercially available kit. Patient ages are listed in Supplementary Table S1. The extracted DNA were investigated by a metagenomic sequencing analysis to clarify the origin of individual DNA. The origin of the DNA sequences was assigned by mapping to a database that included human and microorganism sequences. The obtained numbers of total reads, total bacterial reads, reads originating from V. cholerae, reads originating from ctxA in each sample are shown in Supplementary Table S3 (the data from DNA sample). The age of each patient and the ratio of the number of reads from all bacteria to the total number of reads after filtering (a) and the ratio of the number of reads from V. cholerae to the number of reads from all bacteria (b) were calculated. The horizontal axis of these figures shows the age of each patient and is the same arrangement in both (a) and (b). The numbers in parentheses indicate the sample numbers. This sample number is also the patient’s number.Full size imageThis result implied that it was difficult to detect V. cholerae in a sample with a small number of read derived from bacteria. Therefore, it was unclear whether the data obtained by the analysis was suitable for the detection of V. cholerae. In order to examine whether the data shown in Fig. 2a can be used to clarify the infection status of V. cholerae, the ratio of the reads from V. cholerae to the reads of all bacteria in the sample was calculated (Fig. 2b). As a result, the reads from V. cholerae were detected even in samples with a low ratio of bacterial genes, as seen in patients 13, 25, and 29. Conversely, some patients, such as patients 10, 18, and 17, had a high proportion of bacterial genes but a low detection rate of the read from V. cholerae (Fig. 2a,b). From these results, we thought that the data obtained are useful for analyzing the infection status of V. cholerae in the intestinal tract of the examined patients. The data also showed that patient age did not affect the intestinal retention of V. cholerae.In order to more clearly illustrate the presence of V. cholerae in the diarrheal stools of the patients examined, the ratio of reads from V. cholerae to total reads for each sample which was determined in Fig. 2a was sorted in descending order. The results are shown in Fig. 3a. The ratio (percentage) in each patient is indicated by the blue bar in the figure. The numbers in parentheses after the sample number, with D as the first letter, indicate the order from lowest to highest percentage obtained. As shown in Fig. 3a, the percentage of V. cholerae that the patients carried in their stools varied from 0.003% (sample 12(D1)) to 38.337% (sample 28(D23)).Figure 3The ratios of DNA and RNA derived from V. cholerae in stool samples. The DNA and RNA in the stool samples from 23 patients who were diagnosed with cholera disease were extracted using a commercially available kit. Subsequently, the RNA samples were treated with DNase I to remove DNA from the samples. Reverse-transcribed DNA was prepared from these RNA samples using random primers and reverse transcriptase. The extracted DNA and reverse-transcribed DNA were investigated by a metagenomic sequencing analysis to clarify the origin of individual DNA and RNA. The origin of the DNA sequences was assigned by mapping to a database that included human and microorganism sequences. The obtained numbers of total reads, total bacterial reads, reads originating from V. cholerae, reads originating from ctxA in each sample are shown in Supplementary Tables S3 (the data from DNA) and S4 (the data from RNA). The percentages of reads of DNA from V. cholerae and from ctxA relative to the total reads are presented by blue bar and red bar in panel a, respectively. The percentages of reads of DNA from V. cholerae relative to the total bacterial reads are presented in panels b. Samples are arranged in ascending order of the ratio of reads from V. cholerae to the total reads in the DNA analysis. The ranking of each sample is presented by the numbers in parentheses starting with the letter D. The samples in these panels are arranged in the order of the D number. Similarly, the ratio of reads from V. cholerae to the total RNA reads and the total bacterial RNA are presented in panels c and d, respectively. The samples indicated by red circle are samples from the diarrheal stools of a patients who are focused on in this analysis.Full size imageHowever, what we want to reveal in this study is the presence of toxigenic V. cholerae producing CT. The genes presented by blue bar in Fig. 3a appear to contain the genes derived from toxigenic V. cholerae, but it cannot be concluded that they are. It is highly possible that other genes derived from such as V. cholerae not possessing ctx or bacteria having the same gene sequence as V. cholerae, are included. So, in order to examine the existence of V. cholerae possessing ctx, we examined the number of reads derived from ctxA (Supplementary Table S3). In the samples with D number 8 or more, the gene derived from ctxA was detected in all the samples except one sample (D9). The ratio of read from ctxA to the number of reads from total DNA is shown by the red bar in Fig. 3a. The ratio of the number of reads derived from ctxA to the total DNA was correlated with the ratio of the number of reads derived from the V. cholerae gene to the total DNA (Fig. 3a). From these results, it seems that most of the genes of V. cholerae detected in Fig. 3a are derived from V. cholerae possessing ctx.Furthermore, the ratio of the number of reads of V. cholerae to the number of reads derived from total bacteria which was obtained in Fig. 2a, was arranged in the order used for the array in Fig. 3a (the order of the ratio of the number of reads from V. cholerae to the number of reads from total DNA) (Fig. 3b). From this arrangement of Fig. 3b, it can be seen that the sample with a large D head number has a large proportion of V. cholerae in the bacteria. The highest value was obtained from sample 24 (D22). The sample showed that 95.917% of the bacteria was V. cholerae.On the other hand, in many samples with small D numbers, this ratio is small, but there are exceptions. For example, in samples 25 (D3), 29 (D8) and 13 (D11), the presence of V. cholerae is clear. Although not as clear as these three samples, the presence of V. cholerae in other samples such as 22 (D4), 21 (D5), 9 (D10) and 11(D12) is evident, although in small quantities (Fig. 3b). Therefore, it was considered that these patients were infected with V. cholerae. These results seem to accurately reflect the actual state of V. cholerae in the stool. Therefore, it was considered that the infection status of V. cholerae in the patient could be inferred from the obtained data.As shown in Fig. 3b, in the samples of 18 (D2), 12 (D1), 17 (D7), 10 (D9)) and 23 (D6), the ratio of the read from V. cholerae to the read from total bacteria is very low at 0.032%, 0.118%, 0.225%, 0.244% and 0.285%, respectively. It was unknown whether these patients were infected with V. cholerae and developed diarrhea due to the infection with V. cholerae. Therefore, further examination was needed to determine if these patients were infected with V. cholerae. These five samples are marked by red circles in Fig. 3a,b.Subsequently, we examined the ratio of the reads of RNA of V. cholerae to clarify the expression of the genes of V. cholerae in the intestinal lumen of these patients. RNA samples were prepared by different methods from the patient’s stool and the RNA in these samples was analyzed by metagenomic sequencing analysis. The ratio of the number of reads derived from the RNA of V. cholerae to the number of reads derived from total RNA and to the number of reads derived from total bacterial RNA in the sample was determined. The results are shown in Fig. 3c,d, respectively. Samples that had fewer reads for genes derived from V. cholerae in the previous analysis of DNA reads (Fig. 3a,b)were also indicated with a red circle in Figs. 3c,d. These samples also had low amounts of RNA read from V. cholerae. In particular, the ratio of RNA read from V. cholerae to total bacterial RNA in samples 12 (D1) and 18 (D2) was low, 0.038% and 0.236%, respectively (Supplementary Table S4, Fig. 3d). Judging from these low values, it is doubtful that these two patients, patients 12 and 18, had diarrhea due to infection with V. cholerae.Detection of ctxA by PCRSubsequently, we amplified ctxA in the DNA samples extracted from the stool samples by PCR, in order to reconfirm the presence of ctx in stool samples. The PCR was performed using the same conditions used for the detection of ctxA in the isolates as described above in the “Sample collection and isolation of V. cholerae O1 possessing the CT gene” section of the “Results”. Amplification in this PCR was also done for 30 cycles.From the results of metagenomic sequencing shown in Fig. 3, we found that the samples from patient 12 (D1) and patient 18 (D2) contained few genes derived from V. cholerae O1. The results obtained by PCR are shown in Fig. 4. The samples from the two patients, 12 (D1) and 18 (D2), are indicated by blue circle. No distinct bands corresponding to ctxA were detected in the lanes analyzed sample 12(D1). Meanwhile, a very faint band was visible in the lane where the sample from 18(D2) was analyzed. However, it often happens that small amounts of sample are mixed into adjacent lanes when adding the sample to be analyzed in agar electrophoresis. Hence, we concluded that the amount of ctxA in these two samples amplified by PCR was very low. This supports our inference that the diarrhea in these two patients was not caused by the infection with V. cholerae O1.Figure 4PCR to detect ctxA in the stool samples of diarrhea patients. DNA was extracted from the stool samples of 23 patients who were diagnosed with cholera disease. PCR to amplify ctxA in these DNA samples was performed using the specific primers ctcagacgggatttgttaggcacg and tctatctctgtagcccctattacg6, and the products were analyzed by agarose gel electrophoresis. The sample numbers are the same as the numbers shown in the footnotes of Fig. 3. Numbers beginning with D in parentheses show the order of the content of DNA from V. cholerae among these samples. The samples indicated by blue circle are samples from the diarrheal stools of patients (patients 12 and 18), who are focused on in this study. S: the size marker for gel electrophoresis; N: the negative control in which DNA was not added to the reaction mixture; P: the positive control in which DNA prepared from V. cholerae O1 N1696128 was added to the reaction mixture.Full size imageSimilarly, clear bands were not detected in samples 9(D10), 10(D9), 13(D11), 22(D4), and 25(D3). The results of metagenomic analysis of these samples showed that the number of read from V. cholerae was low and ctxA was either not detected (samples 10(D9), 22(D4) and 25(D3)) or was detected but in small amounts (samples 9(D10) and 13(D11) (Supplementary Table S3, Fig. 3a).The amount of sample added to the reaction solution in the PCR reaction was as small as 5 µl, and it is not clear whether this small volume of solution contained the necessary amount of ctxA for the amplification in PCR. It is also possible that the sample contained substances that would inhibit amplification by PCR. For these reasons, we believe that no clear band corresponding to ctxA appeared in this PCR. However, it is clear from the results of Fig. 3b,d that these samples, (9(D10), 10(D9), 13(D11), 22(D4), and 25(D3)) contain the gene derived from V. cholerae (ctx). Therefore, we considered these four patients to be patients infected with V. cholerae.The levels of CT and proteolytic activity in the stool samplesFrom the genetic studies in Figs. 3 and 4, it was inferred that V. cholerae O1 was not involved in the onset of the diarrhea in two patients (12(D1) and 18(D2)). However, this inference was based on amplification and analysis of genetic sample prepared from diarrhea stool of patients. There is no proof that the sample procurement and the analysis of sample was done reliably with high probability. Hence, we thought that it was necessary to analyze samples adjusted from different perspectives by different means.Then, we challenged to measure the amount of CT. CT is the toxin responsible for the diarrhea caused by V. cholerae O1. CT is released into the intestinal lumen, where it acts on the intestinal cells of patients to induce diarrhea. Thus, we measured the CT content in the stool samples. In addition, we also measured the proteolytic activity in the stool samples, because CT is sensitive to proteolytic activity, and we were concerned that the CT would be degraded by proteases during storage outside of the body.The CT content and the proteolytic activity in the stool samples of the 23 cholera patients were measured by the GM1-ganglioside ELISA method and the lysis of casein, respectively8,10, and the results are presented in Fig. 5a,b, respectively.Figure 5The levels of CT and proteolytic activity in the stool samples. Twenty-three stool samples of patients who were diagnosed with cholera disease were centrifuged at 10,000×g for 10 min. The CT content of the supernatants was determined using a GM1-ganglioside ELISA method8 (a). The proteolytic activity of the supernatants was determined by the lysis of casein10 (b). The sample numbers are the same as the numbers shown in the footnotes of Fig. 3. Numbers beginning with D in parentheses show the order of the content of DNA from V. cholerae among these samples. The samples in this figure are arranged in the order of the D numbers. A bar indicating the amount of CT is not drawn in the figure for the sample whose CT amount was below the detection limit. From the tests shown in Figs. 2, 3 and 4, samples of two patients who are unlikely to have diarrhea caused by the infection with V. cholerae are marked with a blue circle. O.D.: optical density.Full size imageProteolytic activity was detected in all samples, although there were differences in the strengths of the activity. It was also found that high protease activity was not associated with decreased levels of CT in the samples, e.g., sample 11(D12) showed the highest protease activity among the samples examined, and the amount of CT in that sample was also high. Therefore, we considered that the proteolytic activity had almost no influence on the amount of CT in this study. Furthermore, the fact that protease activity was found in all samples indicated that these samples were collected and stored without any significant denaturation.The ELISA method used in this assay can accurately detect CT at concentrations above 1.0 ng ml−1, but it is impossible to accurately determine the concentration of CT at concentrations below 1.0 ng ml−1. Therefore, we treated samples containing less than 1.0 ng ml−1 of CT as containing no CT.As described above, we considered that the diarrhea in the two patients (12(D1) and 18(D2)) was not due to the infection with V. cholerae O1 from the genetic analysis. The analysis of CT in stool samples showed that the CT concentrations of these two samples were below the detection limit (Fig. 5a). This indicates that the number of V. cholerae O1 in the intestinal lumen of these patients, (12(D1) and 18(D2)), was extremely low at the time of sampling.Investigation of diarrheagenic microorganisms in diarrheal stoolIt was shown that diarrhea in patients 12 (D1) and 18 (D2) may have been caused by infection with microorganisms other than V. cholerae. Then we examined the data of metagenomic sequencing of these two patients to reveal the infected diarrhea-causing microorganisms (DDBJ Sequence Read Archive under the accession code PRJDB10675). As a result, we found that that DNA from the two bacteria, Streptococcus pneumoniae and Salmonella enterica was abundant in the stools of patients 12(D1) and 18(D2), respectively.The ratios of DNA read of St. pneumoniae in DNA samples of patient 12(D1) to the total DNA and to the total bacterial DNA are 0.095% and 3.988%, respectively. These ratios of V. cholerae in this patient, 12 (D1), are 0.003% and 0.118%, respectively. And those of S. enterica in the stools of patients 18(D2) are 0.536% and 1.118%, respectively. And these ratios of V. cholerae in this patient, 18 (D2), are 0.015% and 0.032%, respectively (Supplementary Table 2).These two bacteria, St. pneumoniae and S. enterica, are bacteria that are not detected as normal intestinal bacteria. As shown, these ratios of DNA of each bacteria in diarrheal stool are much higher than these of V. cholerae. Therefore, these two bacteria are considered to be related to these patients’ symptom, respectively.Nonetheless, toxigenic V. cholerae O1 was also isolated from these two patients in laboratory bacteriology tests. It is likely that some of the very few V. cholerae O1 in the intestinal tract were extruded with the diarrhea and were subsequently detected by the enrichment culture for V. cholerae. This indicated that V. cholerae O1 may cause subclinical infections in residents of the Kolkata region of India. With this subclinical infection, the number of V. cholerae O1 inhabiting the intestinal tract might be small.Surveillance of patient samples where no diarrhea-causing microorganisms were detectedTo detect people with a subclinical infection of V. cholerae O1, we further analyzed the specific-pathogen-free stool samples of diarrhea patients. “Specific-pathogen-free stool sample” refers to the stool samples in which no etiological agent of diarrhea, including V. cholerae, was detected by our bacterial examination in the laboratory.The number of samples examined in this analysis was 22 (samples number 1001 to 1022). All 22 diarrhea patients examined were inpatients at ID hospital, Kolkata. From the 22 patients, 20 patient stool samples were collected on the 1st day of hospitalization, and the stools of the remaining two patients (patients 1004 and 2022) were collected on the 2nd day of hospitalization. Antibiotics were used in a limited manner in these patients. Ofloxacin was the only antibiotic administered, and only four patients (patients 1001, 1011, 1012, and 1021) were administered with it (Supplementary Table S1).DNA and RNA were extracted from the stool samples, and the DNA and RNA were analyzed by a metagenomic sequencing analysis using the same method used in the analysis of diarrheal stools from cholera patients.Reads of the genes from V. cholerae were detected in every sample, although the value varied from sample to sample (Supplementary Tables S5 and S6). Although reads of the genes from V. cholerae were detected in every sample, we do not believe that every stool sample examined contained V. cholerae. In the metagenomic analysis, if the base sequence of a read was common to multiple bacteria, the read was recognized as being derived from those multiple bacteria. Therefore, even if a bacterium is not present in the sample, the reads in common with other bacteria are counted as the reads of those bacteria, i.e., if a read from bacteria other than V. cholerae is homologous to a corresponding gene of V. cholerae, its detection indicates that one gene derived from V. cholerae was found in the sample. The total number of such reads is finally counted as the number of reads of V. cholerae. Therefore, it is unclear whether bacteria presenting a low read count are present in the sample. In order to solve these problems, not only the reads derived from V. cholerae but also the reads derived from ctxA were searched for in the sample.In addition, as described above, other DNA present in diarrheal stool, such as food-derived DNA, might hinder the analysis of the bacteria in the stool. As such, we determined four relative values of the number of reads from the genes of V. cholerae: the ratio of DNA reads of V. cholerae to the total DNA; the ratio of the DNA reads of V. cholerae to the total bacterial DNA; the ratio of the RNA reads of V. cholerae to the total RNA; and the ratio of the RNA reads of V. cholerae to the total bacterial RNA. Furthermore, we determined the relative value of the number of reads from ctxA to the total DNA (Supplementary Tables S5 and S6). These ratios are also shown in Fig. 6a–d.Figure 6The ratio of DNA and RNA derived from V. cholerae in stool samples of the specific-pathogen-free patients. The stool samples from 22 diarrheal patients in which no etiological agent of diarrhea, including V. cholerae, was detected by our bacterial examination in the laboratory were analyzed in this examination. The extraction of DNA and RNA, and the preparation of reverse-transcribed DNA samples from the RNA samples were performed in the same manner as in Fig. 2. The origin of the reads obtained in this analysis was assigned by mapping to a database that included human and microorganism sequences. The obtained numbers of total reads, total bacterial reads, reads originating from V. cholerae, reads from ctxA in each sample are shown in Supplementary Tables S5 (the data from DNA) and S6 (the data from RNA). The percentages of reads of DNA from V. cholerae (blue bar in a) and of reads of DNA from ctxA (red bar in a) relative to the total DNA reads, and the percentages of reads of DNA from V. cholerae relative to the total bacterial DNA reads (b) are presented. Similarly, the results obtained from the RNA samples are presented in (c) and (d). The (c) and (d) show the percentages of reads of RNA from V. cholerae relative to the total RNA reads and to the reads of total bacterial RNA, respectively. The samples indicated by green circles are the samples of interest in this manuscript, as described in the text.Full size imageThe ratios of the number of reads derived from DNA of V. cholerae and the number of reads derived from ctxA to the number of reads of total DNA genes in these samples are shown by the blue and red bars in Fig. 6a, respectively. Reads from ctxA were detected in samples 1004, 1006, 1010, 1017 and 1018. This indicates that V. cholerae possessing ctx were alive in these samples; 1004, 1006, 1010, 1017 and 1018.The ratio of V. cholerae to total bacterial DNA in these samples was examined. The results are shown in Fig. 6b. The proportion of DNA of V. cholerae to total bacteria DNA in the stool of patients 1004, 1006, 1010, 1017, and 1018 is 28.633%, 0.234%, 73.068%, 2.282%, and 2.774%, respectively (Fig. 6b).In addition, the read of RNA from V. cholerae was examined. The ratio of the RNA to total RNA and to total bacterial RNA was calculated. RNA derived from V. cholerae was reliably detected in 4 of the 5 samples (1004, 1010, 1017, 1018). The ratio of the remaining one sample (1006) were low (Fig. 6c,d). However, it has been shown that the sample (1006) contains the read from DNA of ctxA (Supplementary Table S5). Therefore, we considered these five samples to be those containing toxigenic V. cholerae.As antibiotics were not administered to these five patients, the effects of antibacterial agents could be disregarded in our examination of the bacterial species in the stools. Among these 5 samples, the ratio of samples 1004 and 1010 examined in this examination was high and comparable to those of the samples of the cholera patients (Figs. 3 and 6). We considered that the diarrhea of the patients 1004 and 1010, might have been caused by the infection with V. cholerae O1.On the other hand, the samples of patients 1006, 1017 and 1018 did not show high values that could indicate that the diarrhea was caused by the infection with V. cholerae. It is probable that the diarrhea of these three patients (1006, 1017 and 1018) was caused by the actions of factors other than V. cholerae O1, and that a small number of V. cholerae inhabits the intestinal tract as a form of subclinical infection; this would explain why a gene derived from V. cholerae was detected by the metagenomic sequencing analysis. These results support the hypothesis that subclinical infections of V. cholerae occur in Kolkata. More

Dataset acquisitionPlant-frugivore network data were obtained through different online sources and publications (Supplementary Table 1). Only networks that met the following criteria were retrieved: (i) the network contains quantitative data (a measure of interaction frequency) from a location, pooling through time if necessary; (ii) the network includes avian frugivores. Importantly, we removed non-avian frugivores from our analyses because only 28 out of 196 raw networks (before data cleaning) sampled non-avian frugivores, and not removing non-avian frugivores would generate spurious apparent turnover between networks that did vs. did not sample those taxa. In addition, the removal of non-avian frugivores did not strongly decrease the number of frugivores in our dataset (Supplementary Fig. 20a) or the total number of links in the global network of frugivory (Supplementary Fig. 20b). Furthermore, non-avian frugivores, as well as their interactions, were not shared across ecoregions and biomes (Supplementary Fig. 21), so their inclusion would only strengthen the results we found (though as noted above, we believe that this would be spurious because they are not as well sampled); (iii) the network (after removal of non-avian frugivores) contains greater than two species in each trophic level. Because this size threshold was somewhat arbitrary, we used a sensitivity analysis to assess the effect of our network size threshold on the reported patterns (see Sensitivity analysis section in the Supplementary Methods and Supplementary Figs. 22–24); and (iv) network sampling was not taxonomically restricted, that is, sampling was not focused on a specific taxonomic group, such as a given plant or bird family. Note, however, that authors often select focal plants or frugivorous birds to be sampled, but this was not considered as a taxonomic restriction if plants and birds were not selected based on their taxonomy (e.g., focal plants were selected based on the availability of fruits at the time of sampling, or focal birds were selected based on previous studies of bird diet in the study site). The first source for network data was the Web of Life database42, which contains 33 georeferenced plant-frugivore networks from 28 published studies, of which 12 networks met our criteria.We also accessed the Scopus database on 04 May 2020 using the following keyword combination: (“plant-frugivore*” OR “plant-bird*” OR “frugivorous bird*” OR “avian frugivore*” OR “seed dispers*”) AND (“network*” OR “web*”) to search for papers that include data on avian frugivory networks. The search returned a total of 532 studies, from which 62 networks that met the above criteria were retrieved. We also contacted authors to obtain plant-frugivore networks that were not publicly available, which provided us a further 110 networks. The remaining networks (N = 12) were obtained by checking the database from a recently published study12. In total, 196 quantitative avian frugivory networks were used in our analyses.Generating the distance matrices to serve as predictor and response variablesEcoregion and biome distancesWe used the most up-to-date (2017) map of ecoregions and biomes3, which divides the globe into 846 terrestrial ecoregions nested within 14 biomes, to generate our ecoregion and biome distance matrices. Of these, 67 ecoregions and 11 biomes are represented in our dataset (Supplementary Figs. 1 and 2). We constructed two alternative versions of both the ecoregion and biome distance matrices. In the first, binary version, if two ecological networks were from localities within the same ecoregion/biome, a dissimilarity of zero was given to this pair of networks, whereas a dissimilarity of one was given to a pair of networks from distinct ecoregions/biomes (this is the same as calculating the Euclidean distance on a presence–absence matrix with networks in rows and ecoregion/biomes in columns).In the second, quantitative version, we estimated the pairwise environmental dissimilarity between our ecoregions and biomes using six environmental variables recently demonstrated to be relevant in predicting ecoregion distinctness, namely mean annual temperature, temperature seasonality, mean annual rainfall, rainfall seasonality, slope and human footprint38. We obtained climatic and elevation data from WorldClim 2.143 at a spatial resolution of 1-km2. We transformed the elevation raster into a slope raster using the terrain function from the raster package44 in R45. As a measure of human disturbance, we used human footprint—a metric that combines eight variables associated with human disturbances of the environment: the extent of built environments, crop land, pasture land, human population density, night-time lights, railways, roads and navigable waterways26. The human footprint raster was downloaded at a 1-km2 resolution26. Because human footprint data were not available for one of our ecoregions (Galápagos Islands xeric scrub), we estimated human footprint for this ecoregion by converting visually interpreted scores into the human footprint index. We did this by analyzing satellite images of the region and following a visual score criterion26. Given the previously demonstrated strong agreement between visual score and human footprint values26, we fitted a linear model using the visual score and human footprint data from 676 validation plots located within the Deserts and xeric shrublands biome – the biome in which the Galápagos Islands xeric scrub ecoregion is located – and estimated the human footprint values for our own visual scores using the predict function in R45.We used 1-km2 resolution rasters and the extract function from the raster package44 to calculate the mean value of each of our six environmental variables for each ecoregion in our dataset. Because biomes are considerably larger than ecoregions (which makes obtaining environmental data for biomes more computationally expensive) we used a coarser spatial resolution of 5-km2 for calculating the mean values of environmental variables for each biome. Since a 5-km2 resolution raster was not available for human footprint, we transformed the 1-km2 resolution raster into a 5-km2 raster using the resample function from the same package.To combine these six environmental variables into quantitative matrices of ecoregion and biome environmental dissimilarity, we ran a Principal Component Analysis (PCA) on our scaled multivariate data matrix (where rows are ecoregions or biomes and columns are environmental variables). From this PCA, we selected the scores of the four and three principal components, which represented 89.6% and 88.7% of the variance for ecoregions and biomes, respectively, and converted it into a distance matrix by calculating the Euclidean distance between pairs of ecoregions/biomes using the vegdist function from the vegan package46. Finally, we transformed the ecoregion or biome distance matrix into a N × N matrix where N is the number of local networks. In this matrix, cell values represent the pairwise environmental dissimilarity between the ecoregions/biomes where the networks are located. The main advantage of using this quantitative approach is that, instead of simply evaluating whether avian frugivory networks located in distinct ecoregions or biomes are different from each other in terms of network composition and structure (as in our binary approach), we were also able to determine whether the extent of network dissimilarity depended on how environmentally different the ecoregions or biomes are from one another.Local-scale human disturbance distanceTo generate our local human disturbance distance matrix, we extracted human footprint data at a 1-km2 spatial resolution26 and calculated the mean human footprint values within a 5-km buffer zone around each network site. For the networks located within the Galápagos Islands xeric scrub ecoregion (N = 4), we estimated the human footprint index using the same method described in the previous section for ecoregion- or biome-scale human footprint. We then calculated the pairwise Euclidean distance between human footprint values from our network sites. Thus, low cell values in the local human disturbance distance matrix indicate pairs of network sites with a similar level of human disturbance, while high values represent pairs of network sites with very different levels of human disturbance.Spatial distanceThe spatial distance matrix was generated using the Haversine (i.e., great circle) distance between all pairwise combinations of network coordinates. In this matrix, cell values represent the geographical distance between network sites.Elevational differenceWe calculated the Euclidean distance between pairwise elevation values (estimated as meters above sea level) of network sites to generate our elevational difference matrix. Elevation values were obtained from the original sources when available or using Google Earth47. In the elevational difference matrix, low cell values represent pairs of network sites within similar elevations, whereas high values represent pairs of network sites within very different elevations.Network sampling dissimilarityWe used the metadata retrieved from each of our 196 local networks to generate our network sampling dissimilarity matrices, which aim to control statistically for differences in network sampling. There are many ways in which sampling effort could be quantified, so we began by calculating a variety of metrics, then narrowed our options by assessing which of these was most related to network metrics. We divided the sampling metrics into two categories: time span-related metrics (i.e., sampling hours and months) and empirical metrics of sampling completeness (i.e., sampling completeness and sampling intensity), which aim to account for how complete network sampling was in terms of species interactions (Supplementary Table 2).We selected the quantitative sampling metrics to be included in our models based on (i) the fit of generalized linear models evaluating the relationship between number of sampling hours and sampling months of the study and network-level metrics (i.e., bird richness, plant richness and number of links), and (ii) how well time span-related metrics, sampling completeness and sampling intensity predicted the proportion of known interactions that were sampled in each local network (hereafter, ratio of interactions) for a subset of the data. This latter metric, defined as the ratio between the number of interactions in the local network and the number of known possible interactions in the region involving the species in the local network, captures raw sampling completeness. Therefore, ratio of interactions estimates, for a given set of species, the proportion of all their interactions known for a region that are found to occur among those same species in the local network. To calculate this metric, we needed high-resolution information on the possible interactions, so we used a subset of 14 networks sampled in Aotearoa New Zealand, since there is an extensive compilation of frugivory events recorded for this country48. After this process, we selected number of sampling hours, number of sampling months and sampling intensity for inclusion in our statistical models (Supplementary Figs. 7 and 8; Supplementary Table 2). We generated the corresponding distance matrices by calculating the Euclidean distance between metric values. Similarly, we generated a Euclidean distance matrix for differences in sampling year between pairs of networks, which aims to account for long-term changes in the environment, species composition and network sampling methods. We obtained the sampling year of our local networks from the original sources and calculated the mean sampling year value for those networks sampled across multiple years.Because sampling methods, such as sampling design, focus (i.e., focal taxa, which determines whether a zoocentric or phytocentric method was used), interaction frequency type (i.e., how interaction frequency was measured) and coverage (total or partial) might also affect the observed plant-frugivore interactions49, we combined these variables into a single distance matrix to estimate the overall differences in sampling methods between networks. Because most of these variables were categorical with multiple levels (Supplementary Table 3), we generated our method’s dissimilarity matrix by using a generalization of Gower’s distance method50, which allows the treatment of different types of variables when calculating distances. For this, we used the dist.ktab function from the ade4 package51. We ran a Principal Coordinates Analysis (PCoA) on this distance matrix, selected the first four axes, which explained 81.2% of the variation in method’s dissimilarity, and calculated the Euclidean distance between pairs of networks using the vegdist function from the vegan package46 in R45.Network dissimilarityWe generated three network dissimilarity matrices to be our response variables in the statistical models. In the first, cell values represent the pairwise dissimilarity in species composition between networks (beta diversity of species; βS)27. Second, we measured interaction dissimilarity (beta diversity of interactions; βWN), which represents the pairwise dissimilarity in the identity of interactions between networks27. Importantly, we did not include interaction rewiring (βOS) in our main analysis because this metric can only be calculated for networks that share interaction partners (i.e., it estimates whether shared species interact differently)27, which limited the number and the spatial distribution of networks available for analysis (but see the Rewiring analysis section for an analysis on the subset of our dataset for which this was possible). Metrics were calculated using the network_betadiversity function from the betalink package52 in R45.Finally, we calculated a third dissimilarity matrix to capture overall differences in network structure. We recognize that there are many potential metrics of network structure, and that many of these are strongly correlated with one another53,54,55,56. We therefore chose a range of metrics that captured the number of links, their relative weightings (including across trophic levels), and their arrangement among species, then combined these into a single distance matrix. Specifically, we quantified network structural dissimilarity using the following metrics: weighted connectance, weighted nestedness, interaction evenness, PDI and modularity.Weighted connectance represents the number of links relative to the number of possible links, weighted by the frequency of each interaction55, and is therefore a measure of network-level specialization (higher values of weighted connectance indicate lower specialization). Importantly, it has been suggested that connectance affects persistence in mutualistic systems54. We measured nestedness (i.e., the pattern in which specialist species interact with proper subsets of the species that generalist species interact with) using the weighted version of nestedness based on overlap and decreasing fill (wNODF)57. Notably, nested structures have been commonly reported in plant-frugivore networks33. Interaction evenness is Shannon’s evenness index applied for species interactions and represents how evenly distributed the interactions are in the network21,58. This metric has been previously demonstrated to decline with habitat modification as a consequence of some interactions being favored over others in high-disturbance environments21. PDI (Paired Difference Index) is a measure of species-level specialization on resources and a reliable indicator not only of specialization, but also of absolute generalism59. Thus, this metric contributes to understanding of the ecological processes that drive the prevalence of specialists or generalists in ecological networks59. In order to obtain a network-level PDI, we calculated the weighted mean PDI for each local network. Finally, we calculated modularity (i.e., the level of compartmentalization within networks) using the DIRTPLAwb+ algorithm60. Modularity estimates the extent to which species within modules interact more with each other than with species from other modules61, and it has been demonstrated to affect the persistence and resilience of mutualistic networks54. All the selected network metrics are based on weighted (quantitative) interaction data, as these have been suggested to be less biased by sampling incompleteness62 and to better reflect environmental changes21. All network metrics were calculated using the bipartite package63 in R45.We ran a Principal Component Analysis (PCA) on our scaled multivariate data matrix (N × M where N is the number of local networks in our dataset and M is the number of network metrics), selected the scores of the three principal components, which represented 89.9% of the variance in network metrics, and converted it into a network structural dissimilarity matrix by calculating the Euclidean distance between networks. In this distance matrix, cell values represent differences in the overall architecture of networks (over all the network metrics calculated), and therefore provide a complementary approach for evaluating how species interaction patterns vary across large-scale environmental gradients.Statistical analysisWe employed a two-tailed statistical test that combines Generalized Additive Models (GAM)29 and Multiple Regression on distance Matrices (MRM)30 to evaluate the effect of each of our predictor distance matrices on our response matrix. With this approach, we were able to fit GAMs where the predictor and responsible variables are distance matrices, while accounting for the non-independence of distances from each local network by permuting the response matrix30. The main advantage of using GAMs is their flexibility in modeling non-linear relationships through smooth functions, which are represented by a sum of simpler, fixed basis functions that determine their complexity29. Using GAM-based MRM models allowed us to obtain F values for each of the smooth terms (i.e., smooth functions of the predictor variables in our model), and test statistical significance at the level of individual variables. The binary versions of ecoregion and biome distance matrices (with two levels, “same” or “distinct”) were treated as categorical variables in the models, and t values were used for determining statistical significance. We fitted GAMs with thin plate regression splines64 using the gam function from the mgcv package29 in R45. Smoothing parameters were estimated using restricted maximum likelihood (REML)29. Our GAM-based MRM models were calculated using a modified version of the MRM function from the ecodist package65, which allowed us to combine GAMs with the permutation approach from the original MRM function (see Code availability). All the models were performed with 1000 permutations (i.e., shuffling) of the response matrix.We explored the unique and shared contributions of our predictor variables to network dissimilarity using deviance partitioning analyses. These were performed by fitting reduced models (i.e., GAMs where one or more predictor variables of interest were removed) using the same smoothing parameters as in the full model and comparing the explained deviance. We fixed smoothing parameters for comparisons in this way because these parameters tend to vary substantially (to compensate) if one of two correlated predictors is dropped from a GAM.Assessing the influence of individual studies on the reported patternsBecause our dataset comprises 196 local frugivory networks obtained from 93 different studies, and some of these studies contained multiple networks, we needed to evaluate whether our results were strongly biased by individual studies. To do this, we followed the approach from a previous study66 and tested whether F values of smooth terms and t values of categorical variables (binary version of ecoregion and biome distances) changed significantly when jackknifing across studies. We did this by dropping one study from the dataset and re-fitting the models, and then repeating this same process for all the studies in our dataset.We found a number of consistent patterns within different subsets of the data (Supplementary Figs. 15 and 16); however, some of the patterns we observed appear to be driven by individual studies with multiple networks, and hence are less representative. For instance, the study with the greatest number of networks in our dataset (study ID = 76), which contains 35 plant-frugivore networks sampled across an elevation gradient in Mt. Kilimanjaro, Tanzania67, had an overall high influence on the results when compared with the other studies. By re-running our GAM-based MRM models after removing this study from our dataset, we found that the effect of biome boundaries on interaction dissimilarity is no longer significant, whereas the effects of ecoregion boundaries, human disturbance distance, spatial distance and elevational differences remained consistent with those from the full dataset (Supplementary Table 33). Nevertheless, all the results were qualitatively similar to those obtained for the entire dataset when using network structural dissimilarity as the response variable (Supplementary Table 34).Rewiring analysisInteraction rewiring (βOS) estimates the extent to which shared species interact differently27. Because this metric can only be calculated for networks that share species from both trophic levels, we selected a subset of network pairs that shared plants and frugivorous birds (N = 1314) to test whether interaction rewiring increases across large-scale environmental gradients. Importantly, since not all possible combinations of network pairs contained values of interaction rewiring (i.e., not all pairs of networks shared species), a pairwise distance matrix could not be generated for this metric. Thus, we were not able to use the same statistical approach used in our main analysis, which is based on distance matrices (see Statistical analysis section). Instead, we performed a Generalized Additive Mixed-effects Model (GAMM) using ecoregion, biome, human disturbance, spatial, elevational, and sampling-related distance metrics as fixed effects and network IDs as random effects (to account for the non-independence of distances) (Supplementary Table 35). We also performed a reduced model with only ecoregion and biome distance metrics as predictor variables (Supplementary Table 36). The binary version of ecoregion and biome distance metrics (with two levels, “same” or “distinct”) were used as categorical variables in both models. Interaction rewiring (βOS) was calculated using the network_betadiversity function from the betalink package52 in R45. Although it has been recently argued that this metric may overestimate the importance of rewiring for network dissimilarity68, our main focus was not the partitioning of network dissimilarity into species turnover and rewiring components, but rather simply detecting whether the sub-web of shared species interacted differently. In this case, βOS (as developed by ref. 27) is an adequate and useful metric68. We fitted our models using the gamm4 function from the gamm4 package69 in R45. Smoothing parameters were estimated using restricted maximum likelihood (REML)29.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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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