Ecology

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

European Commission Directorate General for Research and Innovation. A sustainable Bioeconomy for Europe: Strengthening the Connection Between Economy, Society and the Environment: Updated Bioeconomy Strategy (Directorate General for Research and Innovation, 2018).
Google Scholar 
Teitelbaum, L., Boldt, C. & Patermann, C. Global Bioeconomy Policy Report (IV): A Decade of Bioeconomy policy (International Advisory Council on Global Bioeconomy, 2020).
Google Scholar 
European Parliament; European Council. Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources (2018). (Online). http://data.europa.eu/eli/dir/2018/2001/oj.European Parliament; European Council. Directive 2009/28/EC on the Promotion of the Use of Energy from Renewable Sources (2009). (Online). http://data.europa.eu/eli/dir/2009/28/oj.Glasenapp, S., & McCusker, A. Wood energy data: the joint wood, in Wood Energy in the ECE Region: Data, Trends and Outlook in Europe, the Commonwealth of Independent States and North America, Geneva, United Nations’ Economic Commission for Europe: ECE/TIM/SP/42, 12–29 (2018).Eurostat. Wood Products—Production and Trade (2021). (Online). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Wood_products_-_production_and_trade#Wood-based_industries. Accessed 10 9 2021.Food and Agriculture Organization of the United Nations. FAOSTAT: Forestry Production and Trade (2021). (Online). http://www.fao.org/faostat/en/#data. Accessed 13 September 2021.The Intergovernmental Panel on Climate Change. Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (PCC Task Force on National Greenhouse Gas Inventories, 2019).
Google Scholar 
European Parliament; European Council. Commission Delegated Regulation (EU) 2019/807 of 13 March 2019 Supplementing Directive (EU) 2018/2001 of the European Parliament and of the Council as Regards the Determination of High Indirect Land-Use Change-Risk (2018) (Online). fttps://eur-lex.europa.eu/eli/reg_del/2019/807/oj.de Oliveira Garcia, W., Amann, T. & Hartmann, J. Increasing biomass demand enlarges negative forest nutrient budget areas in wood export regions. Sci. Rep. 8, 5280 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Searchinger, T. et al. Europe’s renewable energy directive poised to harm global forests. Nat. Commun. 9, 3741 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Galik, C. S. & Abt, R. C. Sustainability guidelines and forest market response: An assessment of European Union pellet demand in the southeastern United States. GCB Bioenergy 8, 658–669 (2016).
Google Scholar 
Favero, A. D. & Sohngen, B. Forests: Carbon sequestration, biomass energy, or both?. Sci. Adv. 6(13), eaay6792 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cowie, A. et al. Applying a science-based systems perspective to dispel misconceptions about climate effects of forest bioenergy. GCB-Bioenergy 13, 1210–1231 (2021).
Google Scholar 
Camia, A, Jonsson, G. J. R., Robert, N., Cazzaniga, N., Jasinevičius, G., Avitabile, V., Grassi, G., Barredo, J., & Mubareka, S. The Use of Woody Biomass for Energy Production in the EU (European Commission, Joint Research Center, 2021).Aguilar, F. X., Mirzaee, A., McGarvey, R., Shifley, S. & Burtraw, D. Expansion of US wood pellet industry points to positive trends but the need for continued monitoring. Sci. Rep. 10, 18607 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dale, V., Parish, E., Kline, K. & Tobin, E. How is wood-based pellet production affecting forest conditions in the southeastern United States?. For Ecol Manag 396, 143–14 (2017).
Google Scholar 
Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. Nature 583, 72–77 (2020).ADS 
CAS 
PubMed 

Google Scholar 
FORISK Consulting. U.S. Wood Bioenergy Database (2020). (Online). https://forisk.com/. Accessed 2020.Domke, G. et al. Toward inventory-based estimates of soil organic carbon in forests of the United States. Ecol. Appl. 27(4), 1223–1235 (2017).CAS 
PubMed 

Google Scholar 
Python Org. Python Programming Language (2022) (Online). https://www.python.org/. Accessed 1 January 2018.STATA. Stata: statistical software for data science (2022) (Online). https://www.stata.com/. Accessed 1 January 2018.QGIS. Free and Open Source Geographic Information System (2021). (Online). https://qgis.org/en/site/.US Department of Agriculture, Forest Service. Forest Inventory and Analysis National Program (2020). (Online). https://www.fia.fs.fed.us/.Burrill, E. A., Wilson, A. M., Turner, J. A., Pugh, S. A., Menlove, J., Christiansen, G., Conkling, B., & David, W. The Forest Inventory and Analysis Database: Database Description and User Guide Version 8.0 for Phase 2 (US Department of Agriculture, US Forest Service, 2018).Ahmed, M. et al. Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States. J. Environ. Manag. 199, 158–171 (2017).
Google Scholar 
Timilsina, N. et al. A framework for identifying carbon hotspots and forest management drivers. J. Environ. Manag. 114, 293–302 (2012).
Google Scholar 
Coulston, J., Ritters, K., McRoberts, R., Reams, G. & Smith, W. True versus perturbed forest inventory plot locations for modeling: A simulation study. Can. J. For. Res. 36, 801–807 (2006).
Google Scholar 
Anselin, L. Spatial effects in econometric practice in environmental and resource economics. Am. J. Agric. Econ. 83(3), 705–710 (2001).MathSciNet 

Google Scholar 
Strange-Olesen, A., Bager, S., Kittler, B., Price, W., & Aguilar, F. Environmental Implications of Increased Reliance of the EU on Biomass from the South East US (European Commission Report ENV.B.1/ETU/2014/0043, 2015).Spelter, H., & Toth, D. North America’s Wood Pellet Sector (U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2009).Goerndt, M., Aguilar, F. & Skog, K. Drivers of biomass co-firing in US coal-fired power plants. Biomass Bioenerg. 58, 158–167 (2013).
Google Scholar 
US Department of Agriculture, Forest Service. Forest Inventory and Analysis National Program: Timber Products Output Studies (2022). (Online). https://www.fia.fs.fed.us/program-features/tpo/. Accessed 2022.Sonter, L. et al. Mining drives extensive deforestation in the Brazilian Amazon. Nat. Commun. 8(1013), 66. https://doi.org/10.1038/s41467-017-00557-w (2017).CAS 

Google Scholar 
Mirzaee, A., McGarvey, R., Aguilar, F. & Schliep, E. Impact of biopower generation on eastern US forests. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-022-02235-4 (2022).
Google Scholar 
Brandeis, C., Taylor, M., Abt, K., & Alderman, D. Status and Trends for the U.S. Forest Products Sector: A Technical Document Supporting the Forest Service 2020 RPA Assessment (US Department of Agriculture, Forest Service Southern Research Station, Forest Inventory and Analysis, 2021).US Environmental Protection Agency. Emissions & Generation Resource Integrated Database (eGRID) (2021) (Online). https://www.epa.gov/egrid.US Department of Transportation. Ports: ArcGIS Online (2021) (Online). https://data-usdot.opendata.arcgis.com/datasets/usdot::ports/about.US Census Bureau. TIGER/Line Shapefiles (2021) (Online). https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html.US Census Bureau. Population and Housing Units Estimates Datasets (2021) (Online). https://www.census.gov/programs-surveys/popest/data/data-sets.html.McCann, P. The Economics of Industrial Location: A Logistics-Costs Approach (Springer, 1998).Singh, D., Cubbage, F., Gonzalez, R. & Abt, R. Locational determinants for wood pellet plants: A review and case study of North and South America. BioResources 11(3), 7928–7952 (2016).
Google Scholar 
Boukherroub, T., LeBel, L. & Lemieux, S. An integrated wood pellet supply chain development: Selecting among feedstock sources and a range of operating scales. Appl. Energy 198, 385–400 (2017).
Google Scholar 
Heckman, J., Ichimura, H. & Todd, P. Matching as an econometric evaluation estimator: Evidence from evaluating a JobTraining Programme. Rev. Econ. Stud. 64(4), 605–654 (1997).MATH 

Google Scholar 
Caliendo, M. & Kopeinig, S. Some practical guidance for the implementation of propensity score matching. J. Econ. Surv. 22(1), 31–72 (2008).
Google Scholar 
Woo, H., Eskelson, B. & Monleon, V. Matching methods to quantify wildfire effects on forest carbon mass in the U.S. Pacific Northwest. Ecol. Appl. 31(3), e02283 (2021).PubMed 

Google Scholar 
Morreale, L., Thompson, J., Tang, X., Reinmann, A. & Hutyra, L. Elevated growth and biomass along temperate forest edges. Nat. Commun. 12(7181), 66 (2021).
Google Scholar 
Isard, W. The general theory of location and space-economy. Q. J. Econ. 63(4), 476–506 (1949).
Google Scholar 
Aguilar, F. X. Spatial econometric analysis of location drivers in a renewable resource-based industry: The U.S. South Lumber Industry. For. Policy Econ. 11(3), 184–193 (2009).
Google Scholar 
Aguilar, F. X. Conjoint analysis of industry location preferences: evidence from the softwood lumber industry in the US. Appl. Econ. 66, 3265–3274 (2010).
Google Scholar 
Aguilar, F. X., Goerndt, M., Song, N. & Shifley, S. Internal, external and location factors influencing cofiring of biomass with coal in the US northern region. Energy Econ. 34, 1790–1798 (2012).
Google Scholar 
Ferraro, P. J. et al. Estimating the impacts of conservation on ecosystem services and poverty by integrating modeling and evaluation. Proc. Natl. Acad. Sci. 112(24), 7420–7425 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, D. & Pearse, P. Forest Economics 412 (UBC Press, 2011).
Google Scholar 
Villalobos, L., Coria, J. & Nordén, L. Has forest certification reduced forest degradation in Sweden?. Land Econ. 94, 220–238 (2018).
Google Scholar 
Wooldridge, J. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2010).Blackman, A., Corral, L., Lima, E. & Asner, G. Titling indigenous communities protects forests in the Peruvian Amazon. PNAS 114(16), 4123–4128 (2016).ADS 

Google Scholar 
Abt, K. L., Abt, R. C., Galik, C. S., & Skog, K. E. Effect of Policies on Pellet Production and Forests in the U.S. South: A Technical Document Supporting the Forest Service Update of the 2010 RPA Assessment USDA (Forest Service GTR Srs-202, 2014).Hardie, P. Parks, P. Gottleib and D. Wear, “Responsiveness of rural and urban land uses to land rent determinants in the U.S. South,” Land Economics, vol. 76, no. 4, pp. 659–673, 2000.Parish, E., Herzberger, A., Phifer, C. & Dale, V. Transatlantic wood pellet trade demonstrates telecoupled benefits. Ecol. Soc. 23(1), 28 (2018).
Google Scholar 
Titus, B. et al. Sustainable forest biomass: A review of current residue harvesting guidelines. Energy Sustain. Soc. 11, 66. https://doi.org/10.1186/s13705-021-00281-w (2021).
Google Scholar 
Jandl, R. et al. How strongly can forest management influence soil carbon sequestration?. Geoderma 137(3), 253–268 (2007).ADS 
CAS 

Google Scholar 
Nave, L., Vance, E., Swanston, C. & Cepas, P. S. Harvest impacts on soil carbon storage in temperate forests. For. Ecol. Manag. 259, 857–866 (2010).
Google Scholar 
Mayer, M. et al. Tamm review: Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis. For. Ecol. Manag. 466, 118127 (2020).
Google Scholar 
Berryman, E., Hatten, J., Page-Dumroese, D. S., Heckman, K. A., D’Amore, D. V., Puttere, J., & Domke, G. M. Soil carbon in Forest and Rangeland Soils of the United States Under Changing Conditions 9–31 (Springer, 2020).Nave, L. E. et al. Land use and management effects on soil carbon in US Lake States, with emphasis on forestry, fire, and reforestation. Ecol. Appl. 66, 2356 (2021).
Google Scholar 
Cao, B., Domke, G. M., Russell, M. B. & Walters, B. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States. Sci. Total Environ. 654, 94–106 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Coulston, J. & Wear, D. From sink to source: Regional variation in U.S. forest carbon futures. Sci. Rep. 5, 66. https://doi.org/10.1038/srep16518 (2015).
Google Scholar 
Röder, M., Whittaker, C. & Thornley, P. How certain are greenhouse gas reductions from bioenergy? Life cycle assessment and uncertainty analysis of wood pellet-to-electricity supply chains from forest residues. Biomass Bioenerg. 79, 50–63 (2015).
Google Scholar 
Hanssen, S., Duden, A., Junginger, M., Dale, D. & D. vander Hilst,. Wood pellets, what else? Greenhouse gas parity times of European electricity from wood pellets produced in the south-eastern United States using different softwood feedstocks. GC-Bioenergy 9(9), 1406–1422 (2017).CAS 

Google Scholar 
Picciano, P., Aguilar, F., Burtraw, D. & Mirzaee, A. Environmental and socio-economic implications of woody biomass co-firing at coal-fired power plants. Resour. Energy Econ. 6, 66 (2022).
Google Scholar 
Hetchner, S., Schelhas, J., & Brosius, J. Forests as Fuel: Energy, Landscape, Climate, and Race in the U.S. South (Lexington Books, 2022).Coulston, J., Wear, D. & Vose, J. Complex forest dynamics indicate potential for slowing carbon accumulation in the southeastern United States. Sci. Rep. 5, 8002 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Palahí, M. et al. Concerns about reported harvests in European forests. Nature 592, E15–E17 (2021).PubMed 

Google Scholar  More

Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).Article 
PubMed 
CAS 

Google Scholar 
Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).Article 
PubMed 
CAS 

Google Scholar 
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Tully, B. J. & Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography and lifestyle. Cell 176, 649–662 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509, https://doi.org/10.1038/s41587-020-0718-6 (2021).Article 
PubMed 
CAS 

Google Scholar 
Gilbert, J. A., Jansson, J. K. & Knight, R. The Earth Microbiome project: successes and aspirations. BMC Biol 12, 69 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Saheb Kashaf, S., Almeida, A., Segre, J. A. & Finn, R. D. Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data. Nat. Protoc. 16, 2520–2541 (2021).Article 
PubMed 
CAS 

Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).Article 
PubMed 
CAS 

Google Scholar 
Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 49, D10–D17 (2021).Article 
PubMed 
CAS 

Google Scholar 
Kluyver, T., et al. Jupyter Notebooks – a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and Power in Academic Publishing: Players, Agents and Agendas. p. 87–90 (2016).Banfield, J. Development of a Knowledgebase to Integrate, Analyze, Distribute, and Visualize Microbial Community Systems Biology Data. (2015). Report number: DOE-UCB-4918, OSTI ID: 1167269.Chen, I.-M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res 47, D666–D677 (2019).Article 
PubMed 
CAS 

Google Scholar 
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44, W3–W10 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Devisetty, U. K., Kennedy, K., Sarando, P., Merchant, N. & Lyons, E. Bringing your tools to CyVerse discovery environment using Docker. F1000Res. 5, 1442 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L., Lu, Z., Van Buren, P. & Ware, D. SciApps: a bioinformatics workflow platform powered by XSEDE and CyVerse. in Proceedings of the Practice and Experience on Advanced Research Computing 1–5 (Association for Computing Machinery, 2018).Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 45, D535–D542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).PubMed 
CAS 

Google Scholar 
Wu, Y.-W. et al. Ionic liquids impact the bioenergy feedstock-degrading microbiome and transcription of enzymes relevant to polysaccharide hydrolysis. mSystems 1, e00120–16 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Rajeev, L. et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J 7, 2178–2191 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Foster, I. Globus Online: accelerating and democratizing science through cloud-based services. IEEE Internet Comput 15, 70–73 (2011).Article 

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46, W95–W101 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

Google Scholar 
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma 10, 421 (2009).Article 

Google Scholar 
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res 42, D26–D31 (2014).Article 
PubMed 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Freitas, T. A. K., Li, P.-E., Scholz, M. B. & Chain, P. S. G. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res 43, e69 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).Article 
PubMed 
CAS 

Google Scholar 
Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 2014 (2019).Article 

Google Scholar 
Youngblut, N. D. & Ley, R. E. Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets. Peer J 9, e12198 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform 12, 385 (2011).Article 

Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
PubMed 
CAS 

Google Scholar 
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).Article 
PubMed 
CAS 

Google Scholar 
Orakov, A. et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 22, 178 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 
PubMed 
CAS 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).Article 
PubMed 
CAS 

Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25, 1043–1055 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Delcher, A. L., Salzberg, S. L. & Phillippy, A. M. Using MUMmer to identify similar regions in large sequence sets. Curr. Protoc. Bioinform. Chapter 10, Unit 10.3 (2003).
Google Scholar 
Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res 14, 1394–1403 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res 50, D785–D794 (2022).Article 
PubMed 
CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Brettin, T. et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Overbeek, R. et al. The SEED and the rapid annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res 42, D206–D214 (2014).Article 
PubMed 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
PubMed 
CAS 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform 11, 119 (2010).Article 

Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 
PubMed 
CAS 

Google Scholar 
Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res 46, D851–D860 (2018).Article 
PubMed 
CAS 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48, 8883–8900 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res 43, D261–D269 (2015). (Database Issue).Article 
PubMed 
CAS 

Google Scholar 
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res 47, D427–D432 (2019).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res 41, D387–D395 (2013). (Database issue).Article 
PubMed 
CAS 

Google Scholar 
Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42, D490–D495 (2014).Article 
PubMed 
CAS 

Google Scholar 
Chivian, D., Dehal, P. S., Keller, K. & Arkin, A. P. MetaMicrobesOnline: phylogenomic analysis of microbial communities. Nucleic Acids Res 41, D648–D654 (2013).Article 
PubMed 
CAS 

Google Scholar 
Karaoz, U. & Brodie, E. L. microTrait: a toolset for a trait-based representation of microbial genomes. Front. Bioinform. https://doi.org/10.3389/fbinf.2022.918853 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood-Charlson, E. M. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat. Rev. Microbiol. 18, 313–314 (2020).Article 
PubMed 
CAS 

Google Scholar 
Hofmeyr, S. et al. Terabase-scale metagenome coassembly with MetaHipMer. Sci. Rep. 10, 10689 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).Article 
PubMed 
CAS 

Google Scholar 
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27, 722–736 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Bertrand, D. et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat. Biotechnol. 37, 937–944 (2019).Article 
PubMed 
CAS 

Google Scholar 
Chen, L.-X. et al. Accurate and complete genomes from metagenomes. Genome Res 30, 315–333 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lui, L. M., Nielsen, T. N. & Arkin, A. P. A method for achieving complete microbial genomes and improving bins from metagenomics data. PLoS Comput Biol 17, e1008972 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Miller, C. S., Baker, B. J., Thomas, B. C., Singer, S. W. & Banfield, J. F. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol 12, R44 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chivian, D. et al. Genome extraction from shotgun metagenome sequence data. KBase n/33233/628 https://doi.org/10.25982/33233.606/1831502 (2022).Article 

Google Scholar 
Chivian, D., et al. Moab desert crust – sample 4E. KBase n/62384/334 (2022). https://doi.org/10.25982/62384.253/1831503Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform 11, 538 (2010).Article 

Google Scholar 
Benson, D. A. et al. GenBank. Nucleic Acids Res 46, D41–D47 (2018).Article 
PubMed 
CAS 

Google Scholar 
Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).Article 
PubMed 
CAS 

Google Scholar 
Teiling, C. BaseSpace: Simplifying metagenomic analysis. 26th European Congress of Clinical Microbiology and Infectious Diseases (2016) 10.26226/morressier.56d5ba2ed462b80296c9509dReich, M. et al. The GenePattern notebook environment. Cell Syst 5, 149–151.e1 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Karp, P. D. et al. A comparison of microbial genome web portals. Front. Microbiol. 10, 208 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Yue, Y. et al. Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets. BMC Bioinform 21, 334 (2020).Article 
CAS 

Google Scholar 
Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 11, 2864–2868 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Li, L., Stoeckert, C. J. Jr & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13, 2178–2189 (2003).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Kumari, S. et al. A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in sorghum. Curr. Plant Biol. 28, 100229 (2021).Article 
CAS 

Google Scholar 
Seaver, S. M. D. et al. The ModelSEED biochemistry database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 49, D575–D588 (2021).Article 
PubMed 
CAS 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar  More

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

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

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

Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 

Google Scholar 
Kaiser, M. I. & Müller, C. What is an animal personality?. Biol. Philos. 36, 1 (2021).
Google Scholar 
Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

Google Scholar 
Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).PubMed 

Google Scholar 
Biro, P. A. & Stamps, J. A. Are animal personality traits linked to life-history productivity?. Trends Ecol. Evol. 23, 361–368 (2008).PubMed 

Google Scholar 
Koolhaas, J. M. Coping style and immunity in animals: Making sense of individual variation. Brain Behav. Immun. 22, 662–667 (2008).PubMed 

Google Scholar 
Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).
Google Scholar 
Stamps, J. A. Growth-mortality tradeoffs and ‘personality traits’ in animals. Ecol. Lett. 10, 355–363 (2007).PubMed 

Google Scholar 
Finkemeier, M. A., Langbein, J. & Puppe, B. Personality research in mammalian farm animals: Concepts, measures, and relationship to welfare. Front Vet. Sci. 10(5), 355–363 (2018).
Google Scholar 
Murphy, E., Nordquist, R. E. & van der Staay, F. J. A review of behavioural methods to study emotion and mood in pigs. Sus. Scrofa. Appl. Anim. Behav. Sci 159, 9–28 (2014).
Google Scholar 
Lauber, M. C. Y., Hemsworth, P. H. & Barnett, J. L. The effects of age and experience on behavioural development in dairy calves. Appl. Anim. Behav. Sci. 99, 41–52 (2006).
Google Scholar 
Neave, H. W., Costa, J. H. C., Weary, D. M. & von Keyserlingk, M. A. G. Personality is associated with feeding behavior and performance in dairy calves. J. Dairy Sci. 101, 7437–7449 (2018).PubMed 

Google Scholar 
Foris, B., Zebunke, M., Langbein, J. & Melzer, N. Evaluating the temporal and situational consistency of personality traits in adult dairy cattle. Plos One 13, e0204619 (2018).PubMed 
PubMed Central 

Google Scholar 
Dingemanse, N. J. & Dochtermann, N. A. Quantifying individual variation in behaviour: Mixed-effect modelling approaches. J. Anim. Ecol. 82, 39–54 (2013).PubMed 

Google Scholar 
Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).PubMed 

Google Scholar 
Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biol. Rev. https://doi.org/10.1111/j.1469-185X.2010.00141.x (2010).Article 
PubMed 

Google Scholar 
Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 

Google Scholar 
Neave, H. W., Costa, J. H. C., Benetton, J. B., Weary, D. M. & von Keyserlingk, M. A. G. Individual characteristics in early life relate to variability in weaning age, feeding behavior, and weight gain of dairy calves automatically weaned based on solid feed intake. J. Dairy Sci. 102, 10250–10265 (2019).PubMed 

Google Scholar 
Berckmans, D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Tech. OIE 33, 189–196 (2014).
Google Scholar 
Carslake, C., Vázquez-Diosdado, J. A. & Kaler, J. Machine learning algorithms to classify and quantify multiple behaviours in dairy calves using a sensor: Moving beyond classification in precision livestock. Sensors 21, 88 (2020).ADS 
PubMed Central 

Google Scholar 
Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8(1), 1–18 (2020).
Google Scholar 
Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, 212019 (2022).ADS 
PubMed 
PubMed Central 

Google Scholar 
Carslake, C., Occhiuto, F., Vázquez-Diosdado, J. A. & Kaler, J. Repeatability and predictability of calf feeding behaviors—quantifying between- and within-individual variation for precision livestock farming. Front. Vet. Sci. https://doi.org/10.3389/fvets.2022.827124 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Tolkamp, B. J. & Kyriazakis, I. To split behaviour into bouts, log-transform the intervals. Anim. Behav. 57, 807–817 (1999).PubMed 

Google Scholar 
Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948 (2017).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R. Preprint at (2021).Bürkner, P.-C. Advanced bayesian multilevel modeling with the R package brms. R. J. 10, 395 (2018).
Google Scholar 
Dancey, C. P. & Reidy, J. Statistics without maths for psychology (Pearson education, 2007).
Google Scholar 
von Keyserlingk, M. A. G., Brusius, L. & Weary, D. M. Competition for teats and feeding behavior by group-housed dairy calves. J. Dairy Sci. 87, 4190–4194 (2004).
Google Scholar 
Fraley, R. C. & Roberts, B. W. Patterns of continuity: A dynamic model for conceptualizing the stability of individual differences in psychological constructs across the life course. Psychol. Rev. 112, 60–74 (2005).PubMed 

Google Scholar 
Ashcroft, J., Semmler, C., Carnell, S., van Jaarsveld, C. H. M. & Wardle, J. Continuity and stability of eating behaviour traits in children. Eur. J. Clin. Nutr. 62, 985–990 (2008).PubMed 

Google Scholar 
Neave, H. W., Costa, J. H. C., Weary, D. M. & von Keyserlingk, M. A. G. Long-term consistency of personality traits of cattle. R. Soc. Open Sci. 7, 191849 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Müller, R. & von Keyserlingk, M. A. G. Consistency of flight speed and its correlation to productivity and to personality in Bos taurus beef cattle. Appl. Anim. Behav. Sci. 99, 193–204 (2006).
Google Scholar 
Neja, W., Sawa, A., Jankowska, M., Bogucki, M. & Krężel-Czopek, S. Effect of the temperament of dairy cows on lifetime production efficiency. Arch. Anim. Breed 58, 193–197 (2015).
Google Scholar 
Haskell, M. J., Simm, G. & Turner, S. P. Genetic selection for temperament traits in dairy and beef cattle. Front Genet. https://doi.org/10.3389/fgene.2014.00368 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Whalin, L., Neave, H. W., Føske Johnsen, J., Mejdell, C. M. & Ellingsen-Dalskau, K. The influence of personality and weaning method on early feeding behavior and growth of Norwegian red calves. J. Dairy Sci. 105, 1369–1386 (2022).PubMed 

Google Scholar 
Dammhahn, M., Dingemanse, N. J., Niemelä, P. T. & Réale, D. Pace-of-life syndromes: A framework for the adaptive integration of behaviour, physiology and life history. Behav. Ecol. Sociobiol. 72(3), 1–8 (2018).
Google Scholar 
Kelly, D. N. et al. Large variability in feeding behavior among crossbred growing cattle. J. Anim. Sci. 98, 1–10 (2020).
Google Scholar 
Neave, H. W., Weary, D. M. & von Keyserlingk, M. A. G. Review: Individual variability in feeding behaviour of domesticated ruminants. Animal 12, S419–S430 (2018).PubMed 

Google Scholar 
DeVries, T. J., von Keyserlingk, M. A. G., Weary, D. M. & Beauchemin, K. A. Measuring the feeding behavior of lactating dairy cows in early to peak lactation. J. Dairy Sci. 86, 3354–3361 (2003).PubMed 

Google Scholar 
Kelly, D. N., Sleator, R. D., Murphy, C. P., Conroy, S. B. & Berry, D. P. Phenotypic and genetic associations between feeding behavior and carcass merit in crossbred growing cattle. J. Anim. Sci. 99, skab285 (2021).PubMed 

Google Scholar 
Weary, D. M., Huzzey, J. M. & von Keyserlingk, M. A. G. Board-invited review: Using behavior to predict and identify ill health in animals. J. Anim. Sci. 87, 770–777 (2009).PubMed 

Google Scholar 
Carter, A. J., Feeney, W. E., Marshall, H. H., Cowlishaw, G. & Heinsohn, R. Animal personality: What are behavioural ecologists measuring?. Biol. Rev. 88, 465–475 (2013).PubMed 

Google Scholar 
Biro, P. A. Do rapid assays predict repeatability in labile (behavioural) traits?. Anim Behav 83, 1295–1300 (2012).
Google Scholar 
Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 20. Plos Biol. 18, e3000411 (2020).PubMed 
PubMed Central 

Google Scholar  More

Chauhan, B. S. Grand challenges in weed management. Front. Agron. https://doi.org/10.3389/fagro.2019.00003 (2020).Article 

Google Scholar 
Oerke, E. C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).
Google Scholar 
Samejima, H. & Sugimoto, Y. Recent research progress in combatting root parasitic weeds. Biotechnol. Biotechnol. Equip. 32(2), 221–240 (2018).CAS 

Google Scholar 
Aly, R. Conventional and biotechnological approaches for control of parasitic weeds. In Vitro Cell. Dev. Biol. Plant 43(4), 304–317 (2007).
Google Scholar 
Fernández-Aparicio, M., Delavault, P. & Timko, M. P. Management of infection by parasitic weeds: A review. Plants 9(9), 1184 (2020).PubMed Central 

Google Scholar 
Rodenburg, J., Demont, M., Zwart, S. J. & Bastiaans, L. Parasitic weed incidence and related economic losses in rice in Africa. Agric. Ecosyst. Environ. 235, 306–317 (2016).
Google Scholar 
Weisberger, D., Nichols, V. & Liebman, M. Does diversifying crop rotations suppress weeds? A meta-analysis. PLoS One 14(7), e0219847 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ejeta, G. The Striga scourge in Africa: A growing pandemic. In Integrating New Technologies for Striga Control: Towards Ending the Witch-hunt 3–16 (World Scientific, 2007). https://doi.org/10.1142/9789812771506_0001.Chapter 

Google Scholar 
Netting, R. M. & Stone, M. P. Agro-diversity on a farming frontier: Kofyar smallholders on the Benue plains of central Nigeria. Africa 66(1), 52–70 (1996).
Google Scholar 
Pimentel, D. et al. Conserving biological diversity in agricultural and forestry systems. Bioscience 42, 354–362 (1992).
Google Scholar 
Khoshbakht, K. & Hammer, K. How many plant species are cultivated?. Genet. Resour. Crop Evol. 55(7), 925–928. https://doi.org/10.1007/s10722-008 (2008).Article 

Google Scholar 
Hajjar, R., Jarvis, D. I. & Gemmill-Herren, B. The utility of crop genetic diversity in maintaining ecosystem services. Agric. Ecosyst. Environ. 123(4), 261–270 (2008).
Google Scholar 
He, H. M. et al. Crop diversity and pest management in sustainable agriculture. J. Integr. Agric. 18(9), 1945–1952 (2019).
Google Scholar 
Ofori, F. & Stern, W. R. Cereal–legume intercropping systems. Adv. Agron. 41, 41–90 (1987).
Google Scholar 
Tanveer, M., Anjum, S. A., Hussain, S., Cerdà, A. & Ashraf, U. Relay cropping as a sustainable approach: Problems and opportunities for sustainable crop production. Environ. Sci. Pollut. Res. 24(8), 6973–6988 (2017).
Google Scholar 
Hartwig, N. L. & Ammon, H. U. Cover crops and living mulches. Weed Sci. 50(6), 688–699 (2002).CAS 

Google Scholar 
Raseduzzaman, M. D. & Jensen, E. S. Does intercropping enhance yield stability in arable crop production? A meta-analysis. Eur. J. Agron. 91, 25–33 (2017).
Google Scholar 
Davis, A. S., Hill, J. D., Chase, C. A., Johanns, A. M. & Liebman, M. Increasing cropping system diversity balances productivity, profitability and environmental health. PLoS One 7(10), e47149 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Himmelstein, J., Ares, A., Gallagher, D. & Myers, J. A meta-analysis of intercropping in Africa: Impacts on crop yield, farmer income, and integrated pest management effects. Int. J. Agric. Sustain. 15(1), 1–10 (2017).
Google Scholar 
Abson, D. J., Fraser, E. D. & Benton, T. G. Landscape diversity and the resilience of agricultural returns: A portfolio analysis of land-use patterns and economic returns from lowland agriculture. Agric. Food Secur. 2(1), 1–15 (2013).
Google Scholar 
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571(7764), 257–260 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Gaudin, A. C. et al. Increasing crop diversity mitigates weather variations and improves yield stability. PLoS One 10(2), e0113261 (2015).PubMed 
PubMed Central 

Google Scholar 
Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2(3), 284–293 (2020).ADS 

Google Scholar 
Chauhan, B. S., Singh, R. G. & Mahajan, G. Ecology and management of weeds under conservation agriculture: A review. Crop Prot. 38, 57–65 (2012).
Google Scholar 
Nichols, V., Verhulst, N., Cox, R. & Govaerts, B. Weed dynamics and conservation agriculture principles: A review. Field Crop Res. 183, 56–68 (2015).
Google Scholar 
Banik, P., Midya, A., Sarkar, B. K. & Ghose, S. S. Wheat and chickpea intercropping systems in an additive series experiment: Advantages and weed smothering. Eur. J. Agron. 24(4), 325–332 (2006).
Google Scholar 
Workayehu, T. & Wortmann, C. S. Maize–bean intercrop weed suppression and profitability in Southern Ethiopia. Agron. J. 103(4), 1058–1063 (2011).
Google Scholar 
Haugaard-Nielsen, H., Ambus, P. & Jensen, E. S. Interspecific competition, N use and interference with weeds in pea barley intercropping. Field Crop Res. 70, 101–109 (2001).
Google Scholar 
Jensen, E. S. Intercropping of Cereals and Grain Legumes for Increased Production, Weed Control, Improved Product Quality and Prevention of N-losses in European Organic Farming Systems, Final Report on Intercrop Project (QLK5-CT-2002-02352) (Risø National Laboratory, 2006).Arlauskienė, A., Šarūnaitė, L., Kadžiulienė, Ž, Deveikytė, I. & Maikštėnienė, S. Suppression of annual weeds in pea and cereal intercrops. Agron. J. 106(5), 1765–1774 (2014).
Google Scholar 
Szumigalski, A. & van Acker, R. Weed suppression and crop production in annual intercrops. Weed Sci. 53(6), 813–825 (2005).CAS 

Google Scholar 
Stoltz, E. & Nadeau, E. Effects of intercropping on yield, weed incidence, forage quality and soil residual N in organically grown forage maize (Zea mays L.) and faba bean (Vicia faba L.). Field Crop Res. 169, 21–29 (2014).
Google Scholar 
Sauerborn, J., Müller-Stöver, D. & Hershenhorn, J. The role of biological control in managing parasitic weeds. Crop Prot. 26(3), 246–254 (2007).
Google Scholar 
Jamil, M., Rodenburg, J., Charnikhova, T. & Bouwmeester, H. J. Pre-attachment Striga hermonthica resistance of New Rice for Africa (NERICA) cultivars based on low strigolactone production. New Phytol. 192(4), 964–975. https://doi.org/10.1111/j.1469-8137.2011.03850.x (2011).Article 
CAS 
PubMed 

Google Scholar 
Yoneyama, K. et al. Nitrogen deficiency as well as phosphorus deficiency in sorghum promotes the production and exudation of 5-deoxystrigol, the host recognition signal for arbuscular mycorrhizal fungi and root parasites. Planta 227(1), 125–132. https://doi.org/10.1007/s00425-007-0600-5 (2007).Article 
CAS 
PubMed 

Google Scholar 
Sauerborn, J. Legumes used for weed control in agroecosystems in the tropics. Plant Res. Dev. 50, 74–82 (1999).
Google Scholar 
Ejeta, G. & Butler, L. G. Host-parasite interactions throughout the Striga life cycle, and their contributions to Striga resistance. Afr. Crop Sci. J. 1(2), 75–80. https://doi.org/10.4314/acsj.v1i2.69889 (1993).Article 

Google Scholar 
Carsky, R. J., Singh, L. & Ndikawa, R. Suppression of Striga hermonthica on sorghum using a cowpea intercrop. Exp. Agric. 30(3), 349–358. https://doi.org/10.1017/s0014479700024467 (1994).Article 

Google Scholar 
Hsiao, A. I., Worsham, A. D. & Moreland, D. E. Effects of temperature and dl-strigol on seed conditioning and germination of witchweed (Striga asiatica). Ann. Bot. 61(1), 65–72. https://doi.org/10.1093/oxfordjournals.aob.a087528 (1988).Article 
CAS 

Google Scholar 
Patterson, D. T. Effects of Environment on Growth and Reproduction of Witchweed and the Ecological Range of Witchweed (Monograph Series of the Weed Science Society of America, 1990).Stewart, G. R. & Press, M. C. The physiology and biochemistry of parasitic angiosperms. Annu. Rev. Plant Biol. 41(1), 127–151. https://doi.org/10.1146/annurev.pp.41.060190.001015 (1990).Article 
CAS 

Google Scholar 
Anil, L., Park, R. H. P. & Miller, F. A. Temperate intercropping of cereals for forage: A review of the potential for growth and utilization with particular reference to the UK. Grass Forage Sci. 53, 301–317 (1998).
Google Scholar 
Mamolos, A. & Kalburtji, K. Significance of allelopathy in crop rotation. J. Crop Prod. 4, 197–218 (2001).
Google Scholar 
Khan, T. D., Chung, M. I., Xuan, T. D. & Tawata, S. The exploitation of crop allelopathy in sustainable agricultural production. J. Agron. Crop Sci. 191(3), 172–184 (2005).
Google Scholar 
Cissoko, M., Boisnard, A., Rodenburg, J., Press, M. C. & Scholes, J. D. New Rice for Africa (NERICA) cultivars exhibit different levels of post-attachment resistance against the parasitic weeds Striga hermonthica and Striga asiatica. New Phytol. 192(4), 952–963 (2011).CAS 
PubMed 

Google Scholar 
Rodenburg, J. et al. Do NERICA rice cultivars express resistance to Striga hermonthica (Del.) Benth. and Striga asiatica (L.) Kuntze under field conditions?. Field Crop Res. 170, 83–94 (2015).
Google Scholar 
Randrianjafizanaka, M. T., Autfray, P., Andrianaivo, A. P., Ramonta, I. R. & Rodenburg, J. Combined effects of cover crops, mulch, zero-tillage and resistant varieties on Striga asiatica (L.) Kuntze in rice-maize rotation systems. Agric. Ecosyst. Environ. 256, 23–33 (2018).
Google Scholar 
Rodenburg, J. et al. Genetic variation and host–parasite specificity of Striga resistance and tolerance in rice: The need for predictive breeding. New Phytol. 214(3), 1267–1280. https://doi.org/10.1111/nph.14451 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nickrent, D. L. & Musselman, L. J. Introduction to parasitic flowering plants. Plant Health Instr. 13(6), 300–315 (2004).
Google Scholar 
Parker, C. Parasitic weeds: A world challenge. Weed Sci. 60(2), 269–276 (2012).CAS 

Google Scholar 
Shai Vaingast 2014. im2graph. Retrieved from: https://www.im2graph.co.il/free-downloads/windows-3264bit/ (2014).Google Maps 2021. https://maps.google.com [Accessed February 2021–December 2022].Kambach, S. et al. Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis. Ecol. Evol. 10(20), 11699–11712 (2020).PubMed 
PubMed Central 

Google Scholar 
Nakagawa, S. & Freckleton, R. P. Missing inaction: The dangers of ignoring missing data. Trends Ecol. Evol. 23(11), 592–596 (2008).PubMed 

Google Scholar 
Idris, N. R. N. & Robertson, C. The effects of imputing the missing standard deviations on the standard error of meta analysis estimates. Commun. Stat. Simul. Comput. 38(3), 513–526. https://doi.org/10.1080/03610910802556106 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).
Google Scholar 
van Buuren, S. Flexible Imputation of Missing Data (CRC Press, 2018).MATH 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).Article 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Surv. Data Ser. 691(10), 4–9 (2012).
Google Scholar 
Reuter, H. I., Nelson, A. & Jarvis, A. An evaluation of void filling interpolation methods for SRTM data. Int. J. Geogr. Inf. Sci. 21(9), 983–1008 (2007).
Google Scholar 
CGIAR—Consortium for Spatial Information. http://srtm.csi.cgiar.org © 2004–2021. Accessed September 19, 2021, via: http://srtm.csi.cgiar.org/srtmdata/.QGIS Development Team. QGIS Geographic Information System http://qgis.osgeo.org (Open Source Geospatial Foundation Project, 2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82(13), 26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Song, C., Peacor, S. D., Osenberg, C. W. & Bence, J. R. An assessment of statistical methods for non-independent data in ecological meta-analyses. Ecology 101(12), e03184. https://doi.org/10.1002/ecy.3184 (2020).Article 
PubMed 

Google Scholar 
Del Rey, A. C. compute.es: Compute Effect Sizes. R package version 0.2-2. https://cran.r-project.org/package=compute.es (2013).R Core Team. R: A language and environment for statistical computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R package version 0.4. 3 (2015)Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Liebman, M. & Dyck, E. Crop rotation and intercropping strategies for weed management. Ecol. Appl. 3(1), 92–122 (1993).PubMed 

Google Scholar 
Pumariño, L. et al. Effects of agroforestry on pest, disease and weed control: A meta-analysis. Basic Appl. Ecol. 16(7), 573–582 (2015).
Google Scholar 
Kuyah, S., Whitney, C. W., Jonsson, M., Sileshi, G. W., Öborn, I., Muthuri, C. W. & Luedeling, E. Agroforestry delivers a win-win solution for ecosystem services in sub-Saharan Africa. A meta-analysis (2019).Evidente, A., Fernandez-Aparicio, M., Andolfi, A., Rubiales, D. & Motta, A. Trigoxazonane, a mono-substituted trioxazonane from Trigonella foenum-graecum root exudates, inhibits Orobanche crenata seed germination. Phytochemistry 68, 2487–2492 (2007).CAS 
PubMed 

Google Scholar 
Khan, Z. R. et al. Control of witchweed Striga hermonthica by intercropping with Desmodium spp., and the mechanism defined as allelopathic. J. Chem. Ecol. 28(9), 1871–1885 (2002).CAS 
PubMed 

Google Scholar 
Nakagawa, S. et al. Methods for testing publication bias in ecological and evolutionary meta-analyses. Methods Ecol. Evol. 13(1), 4–21 (2022).
Google Scholar 
Bakker, A. et al. Beyond small, medium, or large: Points of consideration when interpreting effect sizes. Educ. Stud. Math. 102(1), 1–8 (2019).
Google Scholar 
Scott, D. et al. Mapping the drivers of parasitic weed abundance at a national scale: A new approach applied to Striga asiatica in the mid-west of Madagascar. Weed Res. 60(5), 323–333 (2020).
Google Scholar 
Scott, D. et al. Identifying existing management practices in the control of Striga asiatica within rice–maize systems in mid-west Madagascar. Ecol. Evol. 11(19), 13579–13592 (2021).PubMed 
PubMed Central 

Google Scholar 
Rubiales, D. & Fernández-Aparicio, M. Innovations in parasitic weeds management in legume crops. A review. Agron. Sustain. Dev. 32(2), 433–449 (2012).CAS 

Google Scholar 
Bir, M. S. H. et al. Weed population dynamics under climatic change. Weed Turfgrass Sci. 3(3), 174–182 (2014).
Google Scholar 
Mohamed, K. I., Bolin, J. F., Musselman, L. J. & Townsend, P. A. Genetic diversity of Striga and implications for control and modelling future distributions. In Integrating New Technologies for Striga Control—Towards Ending the Witch-Hunt (eds Ejeta, G. & Gressel, J.) 71–84 (World Scientific, 2007).
Google Scholar 
Mandumbu, R., Mutengwa, C. S., Mabasa, S. & Mwenje, E. Predictions of the Striga scourge under new climate in southern Africa. J. Biol. Sci. 17, 192–201. https://doi.org/10.3923/jbs.2017.194.201 (2017).Article 

Google Scholar 
Mudereri, B. T. et al. Multi-source spatial data-based invasion risk modelling of Striga (Striga asiatica) in Zimbabwe. GIScience Remote Sens. 57(4), 553–571. https://doi.org/10.1080/15481603.2020.1744250 (2020).Article 

Google Scholar  More