Ecology

Experimental designThis experiment was conducted at the Science and Technology experimental site (125° 27′ 5″ N, 49° 33′ 35″ E) of the North Corporation of Sinograin, Nenjiang, Heilongjiang, China. The soil in the tested area was classified as Black soil according to Chinese Soil Taxonomy (as Mollisol according to USDA classification system) with a thick humus layer and clay texture. The area has a mid-temperate continental monsoon climate with an average annual temperature of − 1.4 to 0.8 °C, precipitation of 450 mm, a frost-free period of 115 days, and an effective accumulated temperature of 2150 °C. The basic physicochemical properties at 0–20 cm plow layer of soil were shown in Table 2. The experiment included two modes: maize straw mulching (FG) and straw returning combined with rotary tillage. In the test plot, there were six 10-m ridges per treatment, and each treatment area was 39 m2. Each treatment has three replicate. The experiment started in 2012 under the continuous planting of maize, and the straw was mechanically crushed and returned to the field in the fall. In accordance with the adjustment in the C/N of the straw, the amount of applied fertilizer was N 150 kg hm−2, P2O5 75 kg hm−2, and K2O 75 kg hm−2. Five treatments were included: (1) stubble remaining in the field, which served as the control (CK); (2) full straw mulching (FG); (3) full straw returning combined with rotary tillage (1 XG) ; (4) 1/3 of full straw returning combined with rotary tillage (1/3 XG); and (5) half of full straw returning combined with rotary tillage (1/2 XG).Table 2 Basic physicochemical properties of surface soil.Full size tableSample collectionSoil samples were collected after the maize (Demeiya 2) was harvested in November 2019. Five soil cores (diameter 5 cm) were randomly taken from 0 to 20 cm depth in each plot, mixed thoroughly, and packed into cloth bags. After the crop roots and other debris were removed, the samples were air-dried for analyzing the content and chemical structure of SOC.Determination of total soil organic carbonThe content of total SOC was measured using a TOC (total organic carbon) analyzer (NC2100, Jena, Germany,) after air-dried soil samples were passed through a 100-mesh sieve.Purification of soil organic carbonFive grams of air-dried soil was added to a 100 mL plastic centrifuge tube, followed by the addition of 50 mL of hydrogen fluoride (HF) solution (10% v/v). After the tube was capped, the solution was shaken for 1 h and centrifuged for 10 min (3000 r min−1), and the supernatant was removed. Subsequently, the residue in the tube was treated with HF solution and then followed the above shaking and centrifuging steps. A total of eight repeats (according to the conditions of the actual samples) were performed with different duration of shaking (4 × 1 h, 3 × 12 h, and 1 × 24 h). Lastly, the residue in the tube was washed with double-distilled water four times, mainly to remove the residual HF in the soil sample. The detailed steps were as follows: 50 mL of double-distilled water was added into tube, shaken for 10 min and centrifuged (3000 r min−1) for 10 min, and then the supernatant was removed. The purified samples with free-HF were dried in an oven at 40 °C, ground through a 60-mesh sieve, and stored in a Zip-lock bag for NMR measurement.Determination of the chemical structure of soil organic carbonThe 13C solid-state NMR spectrum was collected on a Bruker AV400 NMR spectrometer (Switzerland). The cross-polarization magic-angle spinning (CPMAS) technique was used, the 13C NMR frequency was 400.18 MHz, the magic angle spinning frequency was 8 kHz, the contact time was 2 ms, the delay time was 3 s, and the number of data points was 3000. The chemical shift was calibrated based on the external standard sodium 2, 2-dimethyl-2-silapentane-5-sulfonate (DSS), the integrated area was automatically given by the instrument, and the relative content of organic C in each functional group of SOC was expressed as the percentage of the integrated area of a chemical shift interval to the total integrated area. The C structures corresponding to the chemical shift of the main 13C signal of SOC (Table 3) were as follows: alkyl C region (0–45 ppm), alkoxy C region (45–110 ppm), aromatic C region (110–160 ppm), and carbonyl C region (160–220 ppm)4,19.Table 3 13C solid-state NMR determination of organic carbon functional groups and corresponding high-molecular-weight compounds.Full size tableData analysisNMR spectra (CPMAS 13C-NMR) were analyzed using MestReNova professional software. After analyzing and extracting the source data, Microsoft Office Excel 2010 and Origin 8.0 software were used for data processing and plotting. The data in the “Available Data” were plotted using Origin by overlapping the fitted curve, and SPSS 17.0 (SPSS Inc., Chicago, USA) statistical analysis software was used to test for significant differences (Duncan’s method) and for correlation analysis. More

1.Costello, M. J., May, R. M. & Stork, N. E. Can we name Earth’s species before they go extinct? Science 339, 413–416 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Mora, C., Rollo, A. & Tittensor, D. P. Comment on ‘Can we name Earth’s species before they go extinct?’. Science 341, 237 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the Ocean? PLoS Biol. 9, e1001127 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.May, R. & Beverton, R. J. H. How many species? Phil. Trans. R. Soc. B 330, 293–304 (1990).Article 

Google Scholar 
5.Scheffers, B. R., Joppa, L. N., Pimm, S. L. & Laurance, W. F. What we know and don’t know about Earth’s missing biodiversity. Trends Ecol. Evol. 27, 501–510 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Raven, P. H. & Wilson, E. O. A fifty-year plan for biodiversity surveys. Science 258, 1099–1100 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Whittaker, R. J. et al. Conservation biogeography: assessment and prospect. Divers. Distrib. 11, 3–23 (2005).Article 

Google Scholar 
8.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

Google Scholar 
9.Guide to the Global Taxonomy Initiative (Secretariat of the Convention on Biological Diversity, 2010).10.Costello, M. J., May, R. M. & Stork, N. E. Response to comments on ‘Can we name Earth’s species before they go extinct?’. Science 341, 237 (2013).CAS 
PubMed 
Article 

Google Scholar 
11.Bebber, D. P., Marriott, F. H. C., Gaston, K. J., Harris, S. A. & Scotland, R. W. Predicting unknown species numbers using discovery curves. Proc. R. Soc. B 274, 1651–1658 (2007).PubMed 
Article 

Google Scholar 
12.Edie, S. M., Smits, P. D. & Jablonski, D. Probabilistic models of species discovery and biodiversity comparisons. Proc. Natl Acad. Sci. USA 114, 3666–3671 (2017).CAS 
PubMed 
Article 

Google Scholar 
13.Guenard, B., Weiser, M. D. & Dunn, R. R. Global models of ant diversity suggest regions where new discoveries are most likely are under disproportionate deforestation threat. Proc. Natl Acad. Sci. USA 109, 7368–7373 (2012).CAS 
PubMed 
Article 

Google Scholar 
14.Blackburn, T. M. & Gaston, K. J. What determines the probability of discovering a species – a study of South-American Oscine Passerine birds. J. Biogeogr. 22, 7–14 (1995).Article 

Google Scholar 
15.Costello, M. J., Lane, M., Wilson, S. & Houlding, B. Factors influencing when species are first named and estimating global species richness. Glob. Ecol. Conserv. 4, 243–254 (2015).Article 

Google Scholar 
16.Collen, B., Purvis, A. & Gittleman, J. L. Biological correlates of description date in carnivores and primates. Glob. Ecol. Biogeogr. 13, 459–467 (2004).Article 

Google Scholar 
17.Diniz-Filho, J. A. F. et al. Macroecological correlates and spatial patterns of anuran description dates in the Brazilian Cerrado. Glob. Ecol. Biogeogr. 14, 469–477 (2005).Article 

Google Scholar 
18.Costello, M. J., Houlding, B. & Joppa, L. N. Further evidence of more taxonomists discovering new species, and that most species have been named: response to Bebber et al. (2014). New Phytol. 202, 739–740 (2014).PubMed 
Article 

Google Scholar 
19.Meiri, S. Small, rare and trendy: traits and biogeography of lizards described in the 21st century. J. Zool. 299, 251–261 (2016).Article 

Google Scholar 
20.Klein, J. P. & Moeschberger, M. L. Survival Analysis: Techniques for Censored and Truncated Data.(Springer, 2003).21.Essl, F., Rabitsch, W., Dullinger, S., Moser, D. & Milasowszky, N. How well do we know species richness in a well-known continent? Temporal patterns of endemic and widespread species descriptions in the European fauna. Glob. Ecol. Biogeogr. 22, 29–39 (2013).Article 

Google Scholar 
22.Colli, G. R. et al. In the depths of obscurity: knowledge gaps and extinction risk of Brazilian worm lizards (Squamata, Amphisbaenidae). Biol. Conserv. 204, 51–62 (2016).Article 

Google Scholar 
23.Burgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there? J. Mammal. 99, 1–14 (2018).Article 

Google Scholar 
24.Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bellard, C. et al. Vulnerability of biodiversity hotspots to global change. Glob. Ecol. Biogeogr. 23, 1376–1386 (2014).Article 

Google Scholar 
26.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
PubMed 
Article 

Google Scholar 
27.Joppa, L. N., Roberts, D. L. & Pimm, S. L. How many species of flowering plants are there? Proc. R. Soc. B 278, 554–559 (2011).PubMed 
Article 

Google Scholar 
28.Giam, X. et al. Reservoirs of richness: least disturbed tropical forests are centres of undescribed species diversity. Proc. R. Soc. B 279, 67–76 (2012).PubMed 
Article 

Google Scholar 
29.Jetz, W. & Fine, P. V. A. Global gradients in vertebrate diversity predicted by historical area-productivity dynamics and contemporary environment. PLoS Biol. 10, e1001292 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Gouveia, S. F., Villalobos, F., Dobrovolski, R., Beltrão-Mendes, R. & Ferrari, S. F. Forest structure drives global diversity of primates. J. Anim. Ecol. 83, 1523–1530 (2014).PubMed 
Article 

Google Scholar 
31.Oliveira, B. F. & Scheffers, B. R. Vertical stratification influences global patterns of biodiversity. Ecography 42, 249–249 (2019).Article 

Google Scholar 
32.Oliveira, U. et al. The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers. Distrib. 22, 1232–1244 (2016).33.Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).PubMed 
Article 

Google Scholar 
34.Garnett, S. T. & Christidis, L. Taxonomy anarchy hampers conservation. Nature 546, 25–27 (2017).CAS 
PubMed 
Article 

Google Scholar 
35.Isaac, N. J. B., Mallet, J. & Mace, G. M. Taxonomic inflation: its influence on macroecology and conservation. Trends Ecol. Evol. 19, 464–469 (2004).PubMed 
Article 

Google Scholar 
36.Bremer, K., Bremer, B., Karis, P. & Källersjö, M. Time for change in taxonomy. Nature 343, 202 (1990).CAS 
PubMed 
Article 

Google Scholar 
37.Raposo, M. A. et al. What really hampers taxonomy and conservation? A riposte to Garnett and Christidis (2017). Zootaxa 4317, 179–184 (2017).Article 

Google Scholar 
38.Wake, D. B. Persistent plethodontid themes: species, phylogenies, and biogeography. Herpetologica 73, 242–251 (2017).Article 

Google Scholar 
39.Tedesco, P. A. et al. Estimating how many undescribed species have gone extinct. Conserv. Biol. 28, 1360–1370 (2014).CAS 
PubMed 
Article 

Google Scholar 
40.Jetz, W., McPherson, J. M. & Guralnick, R. P. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 27, 151–159 (2012).PubMed 
Article 

Google Scholar 
41.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 
Article 

Google Scholar 
43.Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.González-del-Pliego, P. et al. Phylogenetic and trait-based prediction of extinction risk for data-deficient amphibians. Curr. Biol. 29, 1557–1563.e3 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Moura, M. R. et al. Geographical and socioeconomic determinants of species discovery trends in a biodiversity hotspot. Biol. Conserv. 220, 237–244 (2018).Article 

Google Scholar 
46.Gaston, K. J., Blackburn, T. M. & Loder, N. Which species are described first? The case of North-American butterflies. Biodivers. Conserv. 4, 119–127 (1995).Article 

Google Scholar 
47.Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Sci. Data 4, 170123 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Feldman, A., Sabath, N., Pyron, R. A., Mayrose, I. & Meiri, S. Body sizes and diversification rates of lizards, snakes, amphisbaenians and the tuatara. Glob. Ecol. Biogeogr. 25, 187–197 (2016).Article 

Google Scholar 
49.Hallmann, K. & Griebeler, E. M. An exploration of differences in the scaling of life history traits with body mass within reptiles and between amniotes. Ecol. Evol. 8, 5480–5494 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Slavenko, A., Itescu, Y., Ihlow, F. & Meiri, S. Home is where the shell is: predicting turtle home range sizes. J. Anim. Ecol. 85, 106–114 (2016).PubMed 
Article 

Google Scholar 
51.Regis, K. W. & Meik, J. M. Allometry of sexual size dimorphism in turtles: a comparison of mass and length data. PeerJ 5, e2914 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Itescu, Y., Karraker, N. E., Raia, P., Pritchard, P. C. H. & Meiri, S. Is the island rule general? Turtles disagree. Glob. Ecol. Biogeogr. 23, 689–700 (2014).Article 

Google Scholar 
53.Faurby, S. & Svenning, J.-C. Resurrection of the island rule: human-driven extinctions have obscured a basic evolutionary pattern. Am. Nat. 187, 812–820 (2016).PubMed 
Article 

Google Scholar 
54.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).Article 

Google Scholar 
55.Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204A, 23–31 (2016).56.Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar 
57.Gaston, K. J., Blackburn, T. M. & Lawton, J. H. Interspecific abundance–range size relationships: an appraisal of mechanisms. J. Anim. Ecol. 66, 579–601 (1997).Article 

Google Scholar 
58.Borregaard, M. K. & Rahbek, C. Causality of the relationship between geographic distribution and species abundance. Q. Rev. Biol. 85, 3–25 (2010).PubMed 
Article 

Google Scholar 
59.IUCN Red List of Threatened Species. Version 2018 (IUCN, 2018).60.Freitag, S., Hobson, C., Biggs, H. C. & Jaarsveld, A. S. Testing for potential survey bias: the effect of roads, urban areas and nature reserves on a southern African mammal data set. Anim. Conserv. 1, 119–127 (1998).Article 

Google Scholar 
61.Kier, G. & Barthlott, W. Measuring and mapping endemism and species richness: a new methodological approach and its application on the flora of Africa. Biodivers. Conserv. 10, 1513–1529 (2001).Article 

Google Scholar 
62.Vilela, B. & Villalobos, F. letsR: a new R package for data handling and analysis in macroecology. Methods Ecol. Evol. 6, 1229–1234 (2015).Article 

Google Scholar 
63.Papavero, N. Essays on the History of Neotropical Dipterology: With Special Reference to Collectors: 1750–1905: Vol. I (Museu de Zoologia da Universidade de São Paulo, 1971).64.Baselga, A., Lobo, J. M., Hortal, J., Jiménez-Valverde, A. & Gómez, J. F. Assessing alpha and beta taxonomy in eupelmid wasps: determinants of the probability of describing good species and synonyms. J. Zool. Syst. Evol. Res. 48, 40–49 (2010).Article 

Google Scholar 
65.Yang, W., Ma, K. & Kreft, H. Environmental and socio-economic factors shaping the geography of floristic collections in China. Glob. Ecol. Biogeogr. 23, 1284–1292 (2014).Article 

Google Scholar 
66.Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
67.R Core Team R: A Language and Environment for Statistical Computing Version 3.5.3 (R Foundation for Statistical Computing, 2019).68.Hijmans, R. J. raster: Geographic Data Analysis and Modeling https://cran.r-project.org/package=raster (2015).69.Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
70.Klein Goldewijk, K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).Article 

Google Scholar 
71.Joppa, L. N., Roberts, D. L. & Pimm, S. L. The population ecology and social behaviour of taxonomists. Trends Ecol. Evol. 26, 551–553 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Wickham, H. stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.3.1 http://stringr.tidyverse.org (2018).73.Mahto, A. splitstackshape: Stack and Reshape Datasets After Splitting Concatenated Values. R package version 1.4.6 http://github.com/mrdwab/splitstackshape (2018).74.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
75.Kutner, M. H., Nachtsheim, C. J., Neter, J. & Li, W. Applied Linear Statistical Models (McGraw-Hill, 2004).
Google Scholar 
76.Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models https://cran.r-project.org/package=usdm (2017).77.von Linné, C. Systema Naturae https://doi.org/10.5962/bhl.title.542 (Impensis Direct Laurentii Salvii, 1758).78.Harrell, F. E. Regression Modeling Strategies (Springer, 2001).79.George, B., Seals, S. & Aban, I. Survival analysis and regression models. J. Nucl. Cardiol. 21, 686–694 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Jackson, C. flexsurv: a platform for parametric survival modeling in R. J. Stat. Softw. 70, 1–33 (2016).Article 

Google Scholar 
81.Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).82.Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
Article 

Google Scholar 
83.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.6 https://cran.r-project.org/package=MuMIn (2019).84.Alexander Pyron, R. & Wiens, J. J. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. 61, 543–583 (2011).PubMed 
Article 

Google Scholar 
85.Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
86.Fisher, D. O. & Blomberg, S. P. Correlates of rediscovery and the detectability of extinction in mammals. Proc. R. Soc. B 278, 1090–1097 (2011).PubMed 
Article 

Google Scholar 
87.Jetz, W., Sekercioglu, C. H. & Böhning-Gaese, K. The worldwide variation in avian clutch size across species and space. PLoS Biol. 6, e303 (2008).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
88.Jetz, W. & Rubenstein, D. R. Environmental uncertainty and the global biogeography of cooperative breeding in birds. Curr. Biol. 21, 72–78 (2011).CAS 
PubMed 
Article 

Google Scholar 
89.Jetz, W. & Rahbek, C. Geographic range size and determinants of avian species richness. Science 297, 1548–1551 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.12.4 https://cran.r-project.org/package=data.table (2019).91.Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: elements of a synthesis. J. Biogeogr. 35, 483–500 (2008).Article 

Google Scholar 
92.Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2014).CAS 
PubMed 
Article 

Google Scholar 
93.Database of Global Administrative Areas Version 3.6 (GADM, 2019); http://www.gadm.org More

1.Ji M, Greening C, Vanwonterghem I, Carere CR, Bay SK, Steen JA. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature. 2017;552:400–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Grostern A, Alvarez-Cohen L. RubisCO-based CO2 fixation and C1 metabolism in the actinobacterium Pseudonocardia dioxanivorans CB1190. Environ Microbiol. 2013;15:3040–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 2016;10:761–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Bay S, Ferrari BC, Greening C. Life without water: how do bacteria generate biomass in desert ecosystems? Microbiol Austral. 2018;39:28–32.Article 

Google Scholar 
5.Ray A, Zhang E, Terauds A, Ji M, Kong W, Ferrari BC. Soil microbiomes with the genetic capacity for atmospheric chemosynthesis are widespread across the poles and are associated with moisture, carbon and nitrogen limitation. Front Microbiol. 2020;11:1–13.Article 

Google Scholar 
6.Greening C, Berney M, Hards K, Cook GM, Conrad R. A soil actinobacterium scavenges atmospheric H2 using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. Proc Natl Acad Sci. 2014;111:4257–61.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Nogales B, Moore ERB, Llobet-Brossa E, Rossello-Mora R, Amann R, Timmis KN. Combined use of 16S ribosomal DNA and 16S rRNA to study the bacterial community of polychlorinated biphenyl-polluted soil. Appl Environ Microbiol. 2001;67:1874–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Nessner Kavamura V, Taketani RG, Lançoni MD, Andreote FD, Mendes R, Soares de Melo I. Water regime influences bulk soil and rhizosphere of Cereus jamacaru bacterial communities in the Brazilian Caatinga biome. PLoS ONE. 2013;8:e73606.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Serkebaeva YM, Kim Y, Liesack W, Dedysh SN. Pyrosequencing-based assessment of the bacteria diversity in surface and subsurface peat layers of a northern wetland, with focus on poorly studied phyla and candidate divisions. PLoS ONE. 2013;8:e63994.PubMed 
PubMed Central 
Article 

Google Scholar 
10.Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Holland-Moritz H, Stuart J, Lewis LR, Miller S, Mack MC, McDaniel SF, et al. Novel bacterial lineages associated with boreal moss species. Environ Microbiol. 2018;20:2625–38.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Ward LM, Cardona T, Holland-Moritz H. Evolutionary implications of anoxygenic phototrophy in the bacterial phylum Candidatus Eremiobacterota (WPS-2). Front Microbiol. 2019;10:1658.PubMed 
PubMed Central 
Article 

Google Scholar 
13.Sheremet A, Jones GM, Jarett J, Bowers RM, Bedard I, Culham C, et al. Ecological and genomic analyses of candidate phylum WPS-2 bacteria in an unvegetated soil. Environ Microbiol. 2020;22:3143–57.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Dewhirst FE, Klein EA, Thompson EC, Blanton JM, Chen T, Milella L, et al. The canine oral microbiome. PLoS ONE. 2012;7:e36067.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Camanocha A, Dewhirst FE. Host-associated bacterial taxa from Chlorobi, Chloroflexi, GN02, Synergistetes, SR1, TM7, and WPS-2 Phyla/candidate divisions. J Oral Microbiol. 2014;6. https://doi.org/10.3402/jom.v6.25468.16.Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Ji M, van Dorst J, Bissett A, Brown MV, Palmer AS, Snape I, et al. Microbial diversity at Mitchell Peninsula, Eastern Antarctica: a potential biodiversity “hotspot”. Pol Biol. 2015;39:237–49.Article 

Google Scholar 
18.Ferrari BC, Bissett A, Snape I, van Dorst J, Palmer AS, Ji M, et al. Geological connectivity drives microbial community structure and connectivity in polar, terrestrial ecosystems. Environ Microbiol. 2016;18:1834–49.PubMed 
Article 
PubMed Central 

Google Scholar 
19.Bissett A, Fitzgerald A, Meintjes T, Mele PM, Reith F, Dennis PG, et al. Introducing BASE: the biomes of Australian soil environments soil microbial diversity database. Gigascience. 2016;5:21.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Siciliano SD, Palmer AS, Winsley T, Lamb E, Bissett A, Brown MV, et al. Polar soil bacterial and fungal biodiversity survey, Ver. 1. Australian Antarctic Data Centre; 2014. https://doi.org/10.4225/15/526F42ADA05B1. Accessed 11 Feb 2021.21.Lane D. Nucleic acid techniques in bacterial systematics. In: Stackebrandt E, Goodfellow M, editors. Chichester NY: John Wiley and Sons; 1991. p. 115–75.22.Siciliano SD, Palmer A, Winsley T, Lamb E, Bissett A, Brown M, et al. Soil fertility is associated with fungal and bacterial richness, whereas pH is associated with community composition in polar soil microbial communities. Soil Biol Biochem. 2014;78:10–20.CAS 
Article 

Google Scholar 
23.Archer E. R package. 2016.24.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014. https://doi.org/10.1093/bioinformatics/btu170.25.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley, CA, United States: Lawrence Berkeley National Laboratory; 2014.27.Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P, Tyson GW. GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ. 2014;2:e603.PubMed 
PubMed Central 
Article 

Google Scholar 
28.Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.CAS 

Google Scholar 
34.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Murray AE, Freudenstein J, Gribaldo S, Hatzenpichler R, Hugenholtz P, Kämpfer P, et al. Roadmap for naming uncultivated Archaea and Bacteria. Nat Microbiol. 2020;5:987–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
Article 

Google Scholar 
37.Tabita FR, Hanson TE, Li H, Satagopan S, Singh J, Chan S. Function, structure, and evolution of the RubisCO-like proteins and their RubisCO homologs. Microbiol Mol Biol Rev. 2007;71:576–99.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Tabita FR, Hanson TE, Satagopan S, Witte BH, Kreel NE. Phylogenetic and evolutionary relationships of RubisCO and the RubisCO-like proteins and the functional lessons provided by diverse molecular forms. Philos Trans R Soc Lond B Biol Sci. 2008;363:2629–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Sondergaard D, Pedersen CN, Greening C. HydDB: a web tool for hydrogenase classification and analysis. Sci Rep. 2016;6:34212.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Fuchs BM, Wallner G, Beisker W, Schwippl I, Ludwig W, Amann R. Flow cytometric analysis of the in situ accessibility of Escherichia coli 16S rRNA for fluorescently labeled oligonucleotide probes. Appl Environ Microbiol. 1998;64:4973–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Schramm A, Fuchs BM, Nielsen JL, Tonolla M, Stahl DA. Fluorescence in situ hybridization of 16S rRNA gene clones (Clone-FISH) for probe validation and screening of clone libraries. Environ Microbiol. 2002;4:713–20.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Lindahl V. Improved soil dispersion procedures for total bacterial counts, extraction of sandy clayey silt soil bacteria and cell survival. J Microbiol Meth. 1996;25:279–86.Article 

Google Scholar 
46.Ferrari BC, Tujula N, Stoner K, Kjelleberg S. Catalysed reporter deposition-FISH allows for enrichment independent detection of microcolony forming soil bacteria. Appl Environ Microbiol. 2006;72:918–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Kim M, Lim HS, Hyun CU, Cho A, Noh HJ, Hong SG, et al. Local-scale variation of soil bacterial communities in ice-free regions of maritime Antarctica. Soil Biol Biochem. 2019;133:165–73.CAS 
Article 

Google Scholar 
48.Islam ZF, Welsh C, Bayly K, Grinter R, Southam G, Gagen EJ, et al. A widely distributed hydrogenase oxidises atmospheric H2 during bacterial growth. ISME J. 2020;14:2649–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Myers MR, King GM. Isolation and characterization of Acidobacterium ailaaui sp. nov., a novel member of Acidobacteria subdivision 1, from a geothermally heated Hawaiian microbial mat. Int J Syst Evol Microbiol. 2016;66:5328–35.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Cordero PRF, Bayly K, Leung PM, Huang C, Islam ZF, Schittenhelm RB, et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 2019;13:2868–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Tremblay PL, Lovley DR. Role of the NiFe hydrogenase Hya in oxidative stress defense in Geobacter sulfurreducens. J Bacteriol. 2012;194:2248–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Greening C, Cook GM. Integration of hydrogenase expression and hydrogen sensing in bacterial cell physiology. Curr Opin Microbiol. 2014;18:30–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.English RS, Lorbach SC, Qin X, Shively JM. Isolation and characterization of a carboxysome shell gene from Thiobacillus neapolitanus. Mol Microbiol. 1994;12:647–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Bonomi HR, Toum L, Sycz G, Sieira R, Toscani AM, Gudesblat GE, et al. Xanthomonas campestris attenuates virulence by sensing light through a bacteriophytochrome photoreceptor. EMBO Rep. 2016;17:1565–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Gamiz-Hernandez AP, Kaila VRI. Conversion of light-energy into molecular strain in the photocycle of the photoactive yellow protein. Phys Chem Chem Phys. 2016;18:2802–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Zhang E, Thibaut LM, Terauds A, Raven M, Tanaka MM, van Dorst J, et al. Lifting the veil on arid-to-hyperarid Antarctic soil microbiomes: a tale of two oases. Microbiome. 2020;8:37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Borisov VB, Gennis RB, Hemp J, Verkhovsky MI. The cytochrome bd respiratory oxygen reductases. Biochim Biophys Acta. 2011;1807:1398–413.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.McCrindle SL, Kappler U, McEwan AG. Microbial dimethylsulfoxide and trimethylamine-N-oxide respiration. Adv Micro Physiol. 2005;50:147–98.CAS 
Article 

Google Scholar 
59.Bogachev AV, Bertsova YV, Bloch DA, Verkhovsky MI. Urocanate reductase: identification of a novel anaerobic respiratory pathway in Shewanella oneidensis MR-1. Mol Microbiol. 2012;86:1452–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Hopper AC, Li Y, Cole JA. A critical role for the cccA gene product, cytochrome c2, in diverting electrons from aerobic respiration to denitrification in Neisseria gonorrhoeae. J Bacteriol. 2013;195:2518–29.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Nichols NN, Harwood CS. PcaK, a high-affinity permease for the aromatic compounds 4-hydroxybenzoate and protocatechuate from Pseudomonas putida. J Bacteriol. 1997;179:5056–61.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Fraga J, Maranha A, Mendes V, Pereira PJB, Empadinhas N, Macedo-Ribeiro S. Structure of mycobacterial maltokinase, the missing link in the essential GlgE-pathway. Sci Rep. 2015;5:8026.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Reina-Bueno M, Argandoña M, Nieto JJ, Hidalgo-García A, Iglesias-Guerra F, Delgado MJ, et al. Role of trehalose in heat and desiccation tolerance in the soil bacterium Rhizobium etli. BMC Microbiol. 2012;12:207.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Mougous JD, Petzold CJ, Senaratne RH, Lee DH, Akey DL, Lin FL, et al. Identification, function and structure of the mycobacterial sulfotransferase that initiates sulfolipid-1 biosynthesis. Nat Struct Mol Biol. 2004;11:721–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Oren A. Diversity of halophilic microorganisms: environments, phylogeny, physiology, and applications. J Ind Microbiol Biotechnol. 2002;28:56–63.CAS 
Article 

Google Scholar 
66.Cheggour A, Fanuel L, Duez C, Joris B, Bouillenne F, Devreese B, et al. The dppA gene of Bacillus subtilis encodes a new D-aminopeptidase. Mol Microbiol. 2000;38:504–13.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Geueke B, Heck T, Limbach M, Nesatyy V, Seebach D, Kohler HPE. Bacterial β-peptidyl aminopeptidases with unique substrate specificities for β-oligopeptides and mixed β,α-oligopeptides. FEBS J. 2006;273:5261–72.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Driessen AJM, van de Vossenberg JLM, Konings WN. Membrane composition and ion-permeability in extremophiles. FEMS Microbiol Rev. 1996;18:139–48.CAS 
Article 

Google Scholar 
69.Jones DS, Albrecht HL, Dawson KS, Schaperdoth I, Freeman KH, Pi Y, et al. Community genomic analysis of an extremely acidophilic sulfur-oxidizing biofilm. ISME J. 2012;6:158–70.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Nguyen NL, Yu WJ, Gwak JH, Kim SJ, Park SJ, Herbold CW, et al. Genomic insights into the acid adaptation of novel methanotrophs enriched from acidic forest soils. Front Microbiol. 2018;9:1982.PubMed 
PubMed Central 
Article 

Google Scholar 
71.Xue J, Ahring BK. Enhancing isoprene production by genetic modification of the 1-deoxy-d-xylulose-5-phosphate pathway in Bacillus subtilis. Appl Environ Microbiol. 2011;77:2399–405.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Baker-Austin C, Dopson M. Life in acid: pH homeostasis in acidophiles. Trends Microbiol. 2007;15:165–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Siebers A, Altendorf K. The K+-translocating Kdp-ATPase from Escherichia coli. Purification, enzymatic properties and production of complex- and subunit-specific antisera. Eur J Biochem. 1988;178:131–40.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Milkman R. An Escherichia coli homologue of eukaryotic potassium channel proteins. Proc Natl Acad Sci. 1994;91:3510–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Holtmann G, Bakker EP, Uozumi N, Bremer E. KtrAB and KtrCD: two K+ uptake systems in Bacillus subtilis and their role in adaptation to hypertonicity. J Bacteriol. 2003;185:1289–98.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Castanie-Cornet MP, Penfound TA, Smith D, Elliott JF, Foster JW. Control of acid resistance in Escherichia coli. J Bacteriol. 1999;181:3525–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Geisseler D, Horwath WR. Regulation of extracellular protease activity in soil in response to different sources and concentrations of nitrogen and carbon. Soil Biol Biochem. 2008;40:3040–8.CAS 
Article 

Google Scholar 
78.Einsle O, Messerschmidt A, Stach P, Bourenkov GP, Bartunik HD, Huber R, et al. Structure of cytochrome c nitrite reductase. Nature. 1999;400:476–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Simon J, Pisa R, Stein T, Eichler R, Klimmek O, Gross R. The tetraheme cytochrome c NrfH is required to anchor the cytochrome c nitrite reductase (NrfA) in the membrane of Wolinella succinogenes. Eur J Biochem. 2001;268:5776–82.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Nair RV, Bennett GN, Papoutsakis ET. Molecular characterization of an aldehyde/alcohol dehydrogenase gene from Clostridium acetobutylicum ATCC 824. J Bacteriol. 1994;176:871–85.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Axen SD, Erbilgin O, Kerfeld CA. A taxonomy of bacterial microcompartment loci constructed by a novel scoring method. PLoS Comput Biol. 2014;10:e1003898.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Erbilgin O, McDonald KL, Kerfeld CA. Characterization of a planctomycetal organelle: a novel bacterial microcompartment for the aerobic degradation of plant saccharides. Appl Environ Microbiol. 2014;80:2193–205.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.PubMed 
PubMed Central 
Article 

Google Scholar 
84.Srinivasan V, Morowitz HJ. The canonical network of autotrophic intermediary metabolism: minimal metabolome of a reductive chemoautotroph. Biol Bull. 2009;216:126–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Nunoura T, Chikaraishi Y, Izaki R, Suwa T, Sato T, Harada T, et al. A primordial and reversible TCA cycle in a facultatively chemolithoautotrophic thermophile. Science. 2018;359:559–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Bekal S, Van Beeumen J, Samyn B, Garmyn D, Henini S, Diviès C, et al. Purification of Leuconostoc mesenteroides citrate lyase and cloning and characterization of the citCDEFG gene cluster. J Bacteriol. 1998;180:647–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Dimroth P, Jockel P, Schmid M. Coupling mechanism of the oxaloacetate decarboxylase Na(+) pump. Biochim Biophys Acta. 2001;1505:1–14.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Mulkidjanian AY, Dibrov P, Galperin MY. The past and present of sodium energetics: may the sodium-motive force be with you. Biochim Biophys Acta. 2008;1777:985–92.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Lewis Smith RI. Plant community dynamics in Wilkes Land, Antarctica, vol. 3. Proceedings of the NIPR Symposium on Polar Biology. 1990. p. 229–44.90.Seppelt RD. Plant communities at Wilkes Land. In: Geoecology of Antarctic ice-free coastal landscapes. Ecological studies (Analysis and synthesis), vol. 154. Springer; 2002. p. 233–48. More

In this study, we gave a comprehensive description of the mating behavior of the greenhouse whitefly, T. vaporariorum. In particular, we defined the strict association between vibrational signals and behavioral steps of the pair formation process, from the male call to the final mating. We also described some social interactions between two or more individuals of both sexes, confined to a small portion of leaf, thus simulating a natural occurring aggregation. In this regard, we found that males tend to modify the quality of their vibrational signals, by changing some spectral features, according to either the social context or the behavioral step. For example, they tend to increase the fundamental frequency of their signals (i.e., chirps and PT) when in the presence of potential rivals. A possible explanation of this behavior could be associated with the male competition for food and/or mating. In fact, species that live in high population densities are subjected to strong male-male competitions and a male needs to show his quality to females but also to be clearly recognizable from the others24. The higher quality can be witnessed by the emission of specific aggressive calls which are characterized by lower frequencies, like in some anurans25 or in Chiropteran where the relative frequency of the social calls increases when more individuals compete for a food source. An example of individual recognition behavior is the change of frequency of the calling song to avoid signal overlapping thus allowing an individual to perceive the presence of more potential partners. Frequency overlapping, in general, can be noxious to animal communication, and male responsiveness can be reduced when background noise from conspecific signals obscure the species-specific temporal pattern of a female song26. In the southern green stink bug, Nezara viridula (Heteroptera, Pentatomidae), females were found to change their calling song frequency to let the males recognize them when exposed to a disturbance stimulus27. Even if small variations of the frequency pattern may potentially affect the partner responsiveness to a call28, overlapping frequencies can seriously compromise the signal reception29. In this way, the change to a different value of frequency in presence of other calling males seems to be a more desirable solution.Another signal variation that we observed in GW males, in the presence of another male (i.e., male duos trials), regarded the chirp duration. By increasing the duration of a mating signal, some species also increase the chances to elicit the female response at the earliest stage of the mating behavior30. In various acoustic insects, females prefer longer calls and males can vary their length by adding or subtracting call elements31. However, a limit of our study was that we could not associate the signal emissions to specific individuals, therefore we did not determine not only if one or both the males were actually singing but also whether this change of chirp duration involved one or both the individuals. A definitive explanation about male-male calling interactions and how males regulate their calling activities should be provided with additional experiments with the use of playbacks to stimulate single specimens.In general, we need to consider that the alteration of the signal features is a common strategy in animals with a complex mating behavior in which different stages can alternate in a non-linear sequence29,32,33. Such an intricate behavior is on the one hand, at the basis of a species-specific mate recognition system, on the other hand, is a result of the sexual selection that worked to shape signals with certain characteristics that are able to elicit the female acceptance to mate34. Despite the considerable knowledge about vibrational signal production in the family Aleyrodidae19, we still have little information about the importance of the courtship and of the female choice in driving the reproductive isolation and speciation in this family. Aleyrodid species are known to be morphologically similar and to form a species complex (i.e. Bemisia tabaci) with several biotypes35, where the characterization of the mating behavior can be an important tool to discriminate among them. For instance, variations in the courtship behavior between different B. tabaci biotypes demonstrated the presence of pre-copulation barriers36,37. Moreover, the analysis of male vibrational signals during the courtship, combined with genetic and morphological analysis, allowed to discriminate between the camellia spiny whitefly Aleurocanthus camelliae and the citrus spiny whitefly Aleurocanthus spiniferus18. In such a context, knowing the characteristics of the mating ritual may lead to distinguish, not only among different species, but also among different populations. For example, before this study, the GW mating behavior was described only from Japanese populations where the pair formation process started with the male approaching a female before emitting any vibrational signal (i.e. courtship stage)17. Instead, in our study with European populations of GW, we observed that the male, before starting the approach, emits calling signals which can elicit the female response from a certain distance. Such a difference between geographically distant GW populations seems to suggest a different strategies of mating behavior, likely associated to distinct populations or biotypes. On this regard, it would be interesting to test them with crossed mating trials (Japanese vs Europeans) to assess the effects of the observed differences on the mating success rate.In our study, we also measured a difference of male signal parameters between different behavioral phases of the pair formation process and in particular between the courtship stage and the call and alternated duet stages. We found a significant increase of signal duration, fundamental frequency and pulse repetition rate. The duration of the courtship stage was very variable in our trials, from zero (it was skipped when females replied immediately to the male signals) up to 78 min. This means, in first instance, that the role of the courtship is to elicit the female response and thus promoting her acceptance to mate. Indeed any single behavioral step is functional to elicit the female’s acceptance and in fact, whenever females showed high responsiveness since the early stage of the mating process, males could skip whole stages and even go directly from the call to the final precopula stage, the alternated duet. It also indicates that males are available to spend a remarkable amount of energy to perform the courtship38. The use of elaborated and energetic signals during the courtship is rather common in animals34. For example, the leafhopper S. titanus and the glassy-winged sharpshooter Homalodisca vitripennis have a mating strategy that reminds the GW’s, starting with a call which is followed by the location of the partner and by the courtship. While during call and location males make use of extremely simplified signals, during the courtship they emit the most elaborated (and energetically demanding) signals, through which they try to convince the female to accept the mating21,39. A study of Las (1980) demonstrated that the GW courtship persistence (i.e., duration) is an important trigger to address the female choice. A fast and prolonged male “cycling rate” (alternation of wing flicking and antennation) during the courtship is preferred by females who become even more selective after the first mating. On the other hand, in our tests, males showed a remarkable perseverance in courting the females. The ethogram showed that after a failed mating attempt, a male always restarted from the courtship. This means that the courtship phase is the key part of the mating process but also that the female choice drives the selection in favor of “stubborn” males that persist in courting the potential partner, performing a prolonged courtship, even if the first mating attempt fails. Stubbornness affects male’s survival for its energetic cost and risk of eavesdropping. Such character fits the handicap theory model, in which condition dependent and costly traits are honest indicators of male quality40,41. On the other hand, the option of an easy surrender, and the search for another available female, after investing so many energies in courting the first one, seems to be not convenient for the male in that it would mean to spend more energy in searching for/courting a new partner also risking the possibility of dealing with competitors42.In the GW, the male courtship can be considered successful when the mating moves to the overlapped duet stage in which the female emits the Female Responding Signal (FRS). The FRS is produced in synchronous with the courtship chirp and PT and, for this reason, it requires high degree of coordination between male and female. The presence of female acceptance signals synchronized with the male’s is known for the whitefly species Aleurothrixus floccosus (Maskell)43, in which the female signal can partially overlap the male’s one, but it was unknown in the GW, until now.Another signal that we found for the first time in the GW is the male rivalry signal (MRS). Males exhibit aggressiveness towards other males. A random encounter on the leaf is enough to trigger the expression of rivalry behavior in presence of a female. Such interaction has never been observed in duos, but only in groups with responsive females, thus suggesting that the presence of receptive/active females is required to trigger the MRS production and thus provoking a context of aggressiveness and competition between males. Another male rivalry behavior that we observed in the presence of a receptive female is the silent approach (satellite behavior) to intercept a female while duetting with another male44. This behavior is known in other aleyrodids like in B. tabaci. In this species, rival males interrupt the ongoing courtship of the duetting male by approaching the female from the opposite side. In response to the competitor, the first male spreads the wings and beats the rival on the head45. In GW, the rivalry behavior is associated with the continuous production of the MRS, which is the male signal at highest frequency. Such finding strengthens the hypothesis that the frequency shift has a role in competitor’s deterrence. The rivalry behavior of GW seems to be extraordinarily strong, as much to push females to abandon the interaction with both males. In our experiments, none of the females, even those that had already established a duet with a male, eventually mated. On the contrary, they left the arena before the end of the trial. Our findings are consistent with previous observations of GW behavior, in which the contended female always walked away when two males were competing15. Therefore, we can speculate that the adaptive advantage of the male rivalry behavior in GW is not immediate and the disruption of another male’s attempt could provide more chances in the future to the intruder, by leaving a receptive female unmated. Beside the effects of the male’s rivalry, we also observed females that refused to mate and rejected approaching males with the emission of specific vibrational signals. There are several reasons to refuse mating: immature females are not yet available to mate, and recently mated females must undergo to a refractory period before they accomplish other copulations15. On the other hand, a mature female can choose whether to accept or not a courting male depending on the level of his fitness which is, very likely, testified by the courtship performance. Females can evaluate the male’s quality based on the courtship persistence, so that they need to let males perform the whole ritual before choosing whether to mate or not46. In fact, we observed both females that rejected approaching males and females that rejected them at the end of the courtship performance. The latter, in particular, was associated to wing flicking and/or male’s aedeagus parrying with the legs. Similar behaviors were also observed in B. tabaci, in which the female can either walk or fly away from approaching males, flap the wings or push the male’s abdomen away with the middle pair of legs45. What seems to be a peculiar treat of GW is the use of a specific rejective signal (FRjS). The emission of FRjS seems to reinforce the motivation of the female to reject the male. However, it is not clear to us why the FRjS signal has been observed only in the group (males and females together) trials and never in pairs (one male and one female). Our hypothesis is that in case of groups, males can approach the “wrong” female, who was close the receptive one. This implies that males are not capable of precisely locating the responding female and that the emission of FRjS by an unreceptive female would help the males to not waste too much time (and energy) with them.To conclude, this study unveiled many aspects of the mating behavior of the GW that were previously overlooked and thus it contributes to fill several gaps of knowledge that will be important to start a program in the field of applied biotremology10. The question, from which originally arose this research study, was whether the use of vibrational signals could be suitable to manipulate the mating behavior of the GW. We can say that the vibrational communication is fundamental to accomplish mating and, in our trials, with pairs and groups, we never observed mating without the exchange of vibrational signals between male and female. This means that the interruption or the disruption of this communication could be potentially useful to reduce the rate of mating success. Manipulation of intraspecific communication by means of vibrational signals has been already developed for other insect species both in the lab and in the field10. For example, the male rivalry signal has been exploited for the development of a vibrational mating disruption strategy against the grapevine leafhopper Scaphoideus titanus29, while the female playback has been used to attract and trap males in the brown marmorated stink bug Halyomorpha halys47. The use of playbacks that cover the fundamental frequencies of the male and female signals could be used to mask their communication2. Another possible approach could be to use signals that mimic the natural signals of the species48. In the case of the GW, the FRS could be employed to disrupt males and induce them in courting unreceptive females. This would lead to a substantial reduction of the mating success rate but also to a considerable increase of wasted energy caused by the male persistence in courting unreceptive females. Another possible outcome could be a change of the gender balance in the population. GW females reproduce by arrhenotokous parthenogenesis in which unfertilized eggs develop into males49. Delays in mating could lead to a sex bias that could eventually mine the population structure. Another option is the use of the MRS to generate an aggressive and stressful environment. The transmission of MRS into the plant tissues in loop could eventually negatively affect the development of GW populations. All these approaches are potentially effective and could be in the future considered as tools for IPM and/or organic protection programs. Further applied research will provide a final answer to our question and will test the effectiveness of behavioural manipulation strategies for the control of the GW. Finally, considering that the GW uses a short range sexual pheromone emitted by females50 olfactory and vibratory cues could be potentially integrated to develop new pest control technologies10. More

Strains and growth conditionsSingle gene deletion strains were taken from the Keio collection [34] (Supplementary Table 1), which consists of all non-essential single gene deletions in E. coli K-12 strain BW25113. MreB and mrdA point mutant strains were from Ref. [35] (Supplementary Table 2). Plasmids pQY10 and pQY11 were created by Gibson assembly of Venus YFP A206K (for pQY10) or Venus CFP A206K (for pQY11) [31], and SpecR from pKDsgRNA-ack (gift from Kristala Prather, Addgene plasmid # 62654, http://n2t.net/addgene:62654; RRID:Addgene_62654) [36]. Plasmids pQY12 and pQY13 were created similarly but additionally with CmR from pACYC184.All E. coli experiments were performed in LB (Merck 110285, Kenilworth, New Jersey) with the appropriate antibiotics and experiments with S. cerevisae were performed in YPD [37]. All agar plates were prepared in OmniTrays (Nunc 242811, Roskilde, Denmark, 12.8 cm × 8.6 cm) or 12 cm × 12 cm square petri dishes (Greiner 688102, Kremsmuenster, Austria) filled with 70 mL media solidified with 2% Bacto Agar (BD 214010, Franklin Lakes, New Jersey). After solidifying, the plates were dried upside-down in the dark for 2 days and stored wrapped at 4 °C in the dark for 7–20 days before using.Tracking lineages with fluorescent tracer beadsIn order to track lineages, we spread fluorescent tracer beads with a similar size to the cells on the surface of an agar plate, allowed them to dry, then inoculated and grew a colony on top of the agar plate and imaged the tracer beads to track lineages. In this way, we are able to track lineages without genetic labels at low density (i.e. sparsely) in the colony so that we can distinguish individual lineages without needing high-resolution microscopy. We find that the bead trajectories track cell lineages over the course of one hour both at the colony front and behind the front (Figs. 1c, S1c, d, and S2). We chose to spread fluorescent tracer beads on the surface of the agar so that they could continue to be incorporated into the colony as it grew, which would allow us to track lineages even as existing beads and lineages get lost from the front. Even though behind the front many cells will be piled up on top of other cells rather than in contact with the agar, we don’t expect this to affect the ability of the beads to measure demographic noise, since lineages at the front (where cells are in a monolayer) are the most likely to contribute offspring to future generations [26].Fig. 1: Label-free method of measuring demographic noise in microbial colonies.a Schematic of bead-based sparse lineage tracing method for measuring demographic noise. b Schematic of existing method for measuring fraction of diversity preserved [26]. c (Top) The trajectory of a single bead (black) and the lineages of the cells neighboring it in the final-timepoint (colors) traced backwards in time in the Keio collection wild type strain. (Bottom) The deviation of the distance between the cell lineages and the bead from the final distance, backwards in time. Colors are the same as in the time series images. The gray shaded region shows a single cell width away or towards the bead. All cells that neighbor the bead in the final timepoint, except for one (orange), are neighbors of the bead in the first timepoint and stay within a single cell width of the final distance to the bead. d Example neutral mixtures of YFP and CFP tagged strains grown for 1 day and bead trajectories for strains highlighted in e. Black lines show the colony front at 12 and 23 hours. e Comparison of MSD at window size L = 50 µm to the fraction of diversity preserved for 3 E. coli strain backgrounds and 6 single gene deletions on the Keio collection wild type background (BW25113). Error bars in MSD represent the standard error of the weighted mean (N = 7–8, see Methods) and error bars in the fraction of diversity preserved represent the standard error of the weighted mean (N = 8) where weights come from uncertainties in counting the number of sectors.Full size imageFluorescent tracer beadsFor experiments with E. coli, 1 µm red fluorescent polystyrene beads from Magsphere (PSF-001UM, Pasadena, CA, USA) were diluted to 3 µg/mL in molecular grade water and 920 µL was spread on the surface of the prepared OmniTray agar plates with sterile glass beads. Excess bead solution was poured out, and the plates were dried under the flow of a class II biosafety cabinet (Nuaire, NU-425-300ES, Plymouth, MN, USA) for 45 min. The bead density was chosen to achieve ~250 beads in a 56x field of view. For experiments with S. cerervisiae, 2 µm dragon green fluorescent polystyrene beads from Bang’s labs (FSDG005, Fishers, IN, USA) were used at a similar surface density.Measurement of the distribution of demographic noiseWe randomly selected 352 single gene deletion strains from the Keio collection. For each experiment, cells were thawed from glycerol stock (see Supplementary Methods), mixed, and 5 µL was transferred into a 96-well flat bottom plate with 100 µL LB and the appropriate antibiotics. Plates were covered with Breathe-Easy sealing membrane (Diversified Biotech BEM-1, Doylestown, PA, USA) and grown for 12 h at 37 °C without shaking. A floating pin replicator (V&P Scientific, FP12, 2.36 mm pin diameter, San Diego, CA, USA) was used to inoculate a 2–3 mm droplet from each well of the liquid culture onto a prepared OmniTray covered with fluorescent tracer beads. Droplets were dried and the plates were incubated upside down at 37 °C for 12 h before timelapse imaging.To account for systematic differences between plates, we also put 8 wild type BW25113 wells in each 96-well plate in different positions on each plate. The mean squared displacement (MSD, see below) of each gene deletion colony was normalized to the weighted average MSD of the wild type BW25113 colonies on that plate, 〈MSD〉WT, and this “relative MSD” is reported. We performed three biological replicates for each strain (grown from the same glycerol stock, Fig. S3), and their measurements were averaged together weighted by the inverse of the square of their individual error in relative MSD. The reported error for the strain is the standard error of the mean. During the experiment, several experimental challenges impede our ability to measure demographic noise, including the appearance of beneficial sectors (identified as diverging bead trajectories that correspond to bulges at the colony front) either due to de novo beneficial mutations or standing variation from glycerol stock (see Supplementary Section 2.4, Figs. S4 and S5), slow growth rate leading to bead tracks that were too short for analysis, no cells transferred during inoculation with our pinning tool, inaccurate particle tracking due to beads being too close together, or out of focus images. In order to keep only the highest quality data points, we focused on the 191 strains that had at least 2 replicates free of such issues.Timelapse imaging of fluorescent beadsPlates were transferred to an ibidi stagetop incubator (Catalog number 10918, Gräfelfing, Germany) set to 37 °C for imaging. Evaporation was minimized by putting wet Kim wipes in the chamber and sealing the chamber with tape. The fluorescent tracer beads at the front of the colony were imaged with a Zeiss Axio Zoom.V16 (Oberkochen, Germany) at 56x magnification. A custom macro program written using the Open Application Development for Zen software was used to find the initial focal position for each colony and adjust for deterministic focus drift over time due to slight evaporation. Timelapse imaging was performed at an interval of 10 min for 12 h, during which time the colony grew about halfway across the field of view. Two z slices were taken for each colony and postprocessed to find the most in-focus image to adjust for additional focus drift. Subpixel-resolution particle tracking of the bead trajectory was achieved using a combination of particle image velocimetry and single particle tracking [38] and is described in detail in the Supplementary Methods.Measurement of bead trajectory mean squared displacementThe measurement of mean squared displacement (MSD) is adapted from [31] and is illustrated in Figs. 1a and S1a. Points in a trajectory that fall within a window of length L are fit to a line of best fit. The MSD is given by$$MSDleft( L right) = leftlangle {leftlangle {frac{1}{L}mathop {int}nolimits_l^{l + L} {left( {{Delta}wleft( {L^prime } right)} right)^2dL^prime } } rightrangle _{windows}} rightrangle _{trajectories}$$where Δw(L’) is the displacement of the bead trajectory from the line of best fit at each point, 〈〉windows is an average over all possible definitions of a window with length L along the trajectory (window definitions are overlapping), and 〈〉trajectories is a weighted average over all trajectories in a field of view, where the weight is the inverse squared standard error of the mean for each trajectory’s MSD(L) (Fig. S1a). We use 200 linearly spaced window sizes from L = 6 to 1152 μm. Window sizes that fit in fewer than 5 trajectories are dropped due to the noisiness in calculating the averaged MSD(L). The combined MSD(L) for all trajectories reflects that of bead trajectories at the colony front, which will have the largest contribution to the strength of demographic noise [26] (Fig. S6). Because we expect the trajectories to follow an anomalous random walk [31], the combined MSD(L) for all trajectories across the field of view is fit using weighted least squares to a power law, where the weight is the inverse square of the propagated standard error of the mean. Colonies with data in fewer than 5 window sizes are dropped due to the noisiness in fitting to a power law. The fit is extrapolated or interpolated to L = 50 µm to give a single summary statistic for each colony, and this quantity is reported as MSD(L = 50 µm) (see Supplementary Section 2.2, Figs. S7 and S8), and the error is calculated as half the difference in MSD (L = 50 µm) from using the upper and lower bounded coefficients to the fit. For calculating the distribution of demographic noise effects, only MSD values where the error is less than half of the value are kept.Measurement of phenotypic traitsFor the phenotypic trait measurements, in addition to the 191 single gene deletions, we also measured 41 additional strains of E. coli which included 4 strain backgrounds, 1 mreB knockout in the MC1000 background, 2 adhesin mutants, and 34 single gene knockouts from the Keio collection that we predicted may have large changes to demographic noise because of an altered biofilm forming ability in liquid culture [39] or altered cell shape from the wild type (using the classification on the Keio website, https://shigen.nig.ac.jp/ecoli/strain/resource/keioCollection/list). We normalized all phenotypic trait values to the average value measured from the wild type colonies on the same plate. The reported values for each strain are averages across 2–3 replicate colonies on different plates and the errors are the standard error of the mean. See the Supplementary Methods for more details of the specific phenotypic trait measurements.Measurement of neutral fraction of diversity preservedNeutral fluorescent pairs were created by transforming background strains with plasmids pQY10 (YFP, SpecR) or pQY11 (CFP, SpecR). Cells were streaked from glycerol stock and a single colony of each strain was inoculated into a 96 well plate with 600 µL LB and 120 µg/mL spectinomycin for plasmid retention. Plates were covered with Breathe-Easy sealing membrane and grown for 12 h at 37 °C without shaking. 50 µL of culture from each strain in a neutral pair were mixed and a floating pin replicator was used to inoculate a 2–3 mm droplet from the liquid culture onto a prepared OmniTray covered with fluorescent tracer beads. Droplets were dried and the plates were incubated at 37 °C.Colonies were imaged after 24 h with fluorescence microscopy using a Zeiss Axio Zoom.V16 and the number of sectors of each color was manually counted. The fraction of diversity preserved was calculated as in Ref. [26] by dividing the number of neutral sectors by one-half times the estimated initial number of cells at the inoculum front (see Fig. 1b). The factor of one-half accounts for the probability that two neighboring cells at the inoculum front share the same color label. The initial number of cells is estimated by measuring the inoculum size of each colony (manually measured by fitting a circle to a brightfield backlight image at the time of inoculation) divided by the effective cell size for E. coli (sqrt(length*width) taken to be 1.7 µm, Ref. [26]).Colony fitnessThe colony fitness coefficient between two strains was measured using a colony collision assay as described in Refs. [26, 40] by growing colonies next to one another and measuring the curvature of the intersecting arc upon collision. Cells were streaked from glycerol stock and a single colony for each strain was inoculated into LB with 120 µg/mL spectinomycin for plasmid retention and incubated at 37 °C for 15 h. The culture was back diluted 1:500 in 1 mL fresh LB with 120 µg/mL spectinomycin and grown at 37 °C for 4 h. 1 µL of the culture was then inoculated onto the prepared 12cmx12cm square petri dishes containing LB with different concentrations of chloramphenicol (0 µg/mL, 1 µg/mL, 2 µg/mL, 3 µg/mL) in pairs that were 5 mm apart, with 32 pairs per plate, then the colonies were incubated at 37 °C. After half of a day, bright field backlight images are taken and were used to fit circles to each colony to determine the distance between the two colonies. After 6 days, the colonies were imaged with fluorescence microscopy using a Zeiss Axio Zoom.V16. The radius of curvature of the intersecting arc between the two colonies was determined with image segmentation and was used to calculate the fitness coefficient between the two strains (Fig. S9a).Measurement of non-neutral establishment probabilityWe transformed 9 gene deletion strains from the Keio collection (gpmI, recB, pgm, tolQ, ychJ, lpcA, dsbA, rfaF, tatB) and 3 strain backgrounds (BW25113, MG1655, DH5α) with pQY11 (CFP, SpecR) or pQY12 (YFP, SpecR, CmR). Cells were streaked from glycerol stock and a single colony of each strain was inoculated into media with 120 µg/mL spectinomycin for plasmid retention, then incubated at 37 °C for 16 h. The culture was back-diluted 1:1000 in 1 mL fresh media with 120 µg/mL spectinomycin and grown at 37 °C for 4 h. YFP chloramphenicol-resistant and CFP chloramphenicol-sensitive cells from the same strain background were mixed respectively at approximately 1:500, 1:200, and 1:50 and distributed in a 96-well plate. A floating pin replicator was used to inoculate a 2–3 mm droplet from the liquid culture onto prepared OmniTrays with varying concentrations of chloramphenicol (0 µg/mL, 1 µg/mL, 2 µg/mL, 3 µg/mL). Droplets were dried and the plates were incubated at 37 °C for 3 days, then imaged by fluorescence microscopy using a Zeiss Axio Zoom.V16.The establishment probability of the resistant strain can be measured by counting the number of established resistant sectors normalized by the initial number of resistant cells at the inoculum front [26], which gives the probability that any given resistant cell in the inoculum escaped genetic drift and grew to a large enough size to create a sector. Briefly,$$p_{est} = N_{sectors}/N_0$$
(1)
where Nsectors is the number of resistant sectors after 3 days (counted by eye) and N0 is the estimated initial number of cells of the resistant type at the inoculum front. Because the establishment probability can only be accurately measured when the initial number of resistant cells is low enough that the resistant sectors do not interact with one another, we only keep colonies where neighboring resistant sectors are distinguishable at the colony front. In cases where we could see that a sector had coalesced from multiple sectors, we counted the number of sectors pre-coalescence. We also did not find a clear downward bias in the establishment probability as a function of initial mutant fraction (Fig. S10), suggesting that the probability of sector coalescence is low in the regime of these experimental parameters. The initial number N0 of cells of the resistant type is estimated by multiplying the initial number of cells at the inoculum front (see measurement of neutral fraction of diversity preserved) by the fraction of resistant cells in the inoculum (measured by plating and counting CFUs). More

1.Sharma VK. Adaptive significance of circadian clocks. Chronobiol Int. 2003;20:901–19.PubMed 
Article 

Google Scholar 
2.Paranjpe DA, Kumar Sharma V. Evolution of temporal order in living organisms. J Circadian Rhythms. 2005;3:1–13.Article 
CAS 

Google Scholar 
3.Nobs SP, Tuganbaev T, Elinav E. Microbiome diurnal rhythmicity and its impact on host physiology and disease risk. EMBO Rep. 2019;20:e47129.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Sartor F, Eelderink-Chen Z, Aronson B, Bosman J, Hibbert LE, Dodd AN, et al. Are there circadian clocks in non-photosynthetic bacteria? Biology. 2019;8:41.CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
5.Soriano M, Roibas B, Garcia A, Espinosa-Urgel M. Evidence of circadian rhythms in non-photosynthetic bacteria? J Circadian Rhythms. 2010;8:1–4.Article 

Google Scholar 
6.Thaiss CA, Zeevi D, Levy M, Zilberman-Schapira G, Suez J, Tengeler AC, et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell. 2014;159:514–29.CAS 
PubMed 
Article 

Google Scholar 
7.Ishiura M, Kutsuna S, Aoki S, Iwasaki H, Andersson CR, Tanabe A, et al. Expression of a gene cluster kaiABC as a circadian feedback process in cyanobacteria. Science. 1998;281:1519–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Aylward FO, Boeuf D, Mende DR, Wood-Charlson EM, Vislova A, Eppley JM, et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc Natl Acad Sci USA. 2017;114:11446–51.CAS 
PubMed 
Article 

Google Scholar 
9.Dvornyk V, Vinogradova O, Nevo E. Origin and evolution of circadian clock genes in prokaryotes. Proc Natl Acad Sci USA. 2003;100:2495–500.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Maniscalco M, Nannen J, Sodi V, Silver G, Lowrey PL, Bidle KA. Light-dependent expression of four cryptic archaeal circadian gene homologs. Front Microbiol. 2014;5:79.PubMed 
PubMed Central 
Article 

Google Scholar 
11.Bernal P, Allsopp LP, Filloux A, Llamas MA. The Pseudomonas putida T6SS is a plant warden against phytopathogens. ISME J. 2017;11:972–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Schmelling NM, Lehmann R, Chaudhury P, Beck C, Albers S-V, Axmann IM, et al. Minimal tool set for a prokaryotic circadian clock. BMC Evol Biol. 2017;17:1–20.Article 
CAS 

Google Scholar 
13.Hong L, Vani BP, Thiede EH, Rust MJ, Dinner AR. Molecular dynamics simulations of nucleotide release from the circadian clock protein KaiC reveal atomic-resolution functional insights. Proc Natl Acad Sci USA. 2018;115:11475–84.Article 
CAS 

Google Scholar 
14.Edgar RS, Green EW, Zhao Y, Ooijen Gvan, Olmedo M, Qin X, et al. Peroxiredoxins are conserved markers of circadian rhythms. Nature. 2012;485:459–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Harmer SL, Hogenesch JB, Straume M, Chang H-S, Han B, Zhu T, et al. Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science. 2000;290:2110–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Farré EM, Weise SE. The interactions between the circadian clock and primary metabolism. Curr Opin Plant Biol. 2012;15:293–300.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
17.Harmer SL. The circadian system in higher plants. Annu Rev Plant Biol. 2009;60:357–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Haydon MJ, Mielczarek O, Robertson FC, Hubbard KE, Webb AAR. Photosynthetic entrainment of the Arabidopsis circadian clock. Nature. 2013;502:689–92.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.DeAngelis KM, Brodie EL, DeSantis TZ, Andersen GL, Lindow SE, Firestone MK. Selective progressive response of soil microbial community to wild oat roots. ISME J. 2009;3:168–78.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Lundberg DS, Lebeis SL, Paredes SH, Yourstone S, Gehring J, Malfatti S, et al. Defining the core Arabidopsis thaliana root microbiome. Nature. 2012;488:86–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Wu G, Tang W, He Y, Hu J, Gong S, He Z, et al. Light exposure influences the diurnal oscillation of gut microbiota in mice. Biochem Biophys Res Commun. 2018;501:16–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Teichman EM, O’Riordan KJ, Gahan CGM, Dinan TG, Cryan JF. When rhythms meet the blues: circadian interactions with the microbiota-gut-brain axis. Cell Metab. 2020;31:448–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Zarrinpar A, Chaix A, Yooseph S, Panda S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 2014;20:1006–17.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Leone V, Gibbons SM, Martinez K, Hutchison AL, Huang EY, Cham CM, et al. Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe. 2015;17:681–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Liang X, Bushman FD, FitzGerald GA. Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Proc Natl Acad Sci USA. 2015;112:10479–84.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Kaczmarek JL, Musaad SM, Holscher HD. Time of day and eating behaviors are associated with the composition and function of the human gastrointestinal microbiota. Am J Clin Nutr. 2017;106:1220–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Deaver JA, Eum SY, Toborek M. Circadian disruption changes gut microbiome taxa and functional gene composition. Front Microbiol. 2018;9:737.PubMed 
PubMed Central 
Article 

Google Scholar 
29.Hubbard CJ, Brock MT, van Diepen LT, Maignien L, Ewers BE, Weinig C. The plant circadian clock influences rhizosphere community structure and function. ISME J. 2018;12:400–10.PubMed 
Article 
PubMed Central 

Google Scholar 
30.Staley C, Ferrieri AP, Tfaily MM, Cui Y, Chu RK, Wang P, et al. Diurnal cycling of rhizosphere bacterial communities is associated with shifts in carbon metabolism. Microbiome. 2017;5:1–13.Article 

Google Scholar 
31.Feng J, Xu Y, Ma B, Tang C, Brookes PC, He Y, et al. Assembly of root-associated microbiomes of typical rice cultivars in response to lindane pollution. Environ Int. 2019;131:104975.CAS 
PubMed 
Article 

Google Scholar 
32.Gremion F, Chatzinotas A, Harms H. Comparative 16S rDNA and 16S rRNA sequence analysis indicates that Actinobacteria might be a dominant part of the metabolically active bacteria in heavy metal-contaminated bulk and rhizosphere soil. Environ Microbiol. 2003;5:896–907.CAS 
PubMed 
Article 

Google Scholar 
33.Lavecchia A, Curci M, Jangid K, Whitman WB, Ricciuti P, Pascazio S, et al. Microbial 16S gene-based composition of a sorghum cropped rhizosphere soil under different fertilization managements. Biol Fertil Soils. 2015;51:661–72.CAS 
Article 

Google Scholar 
34.Wang B, Zhao J, Guo Z, Ma J, Xu H, Jia Z. Differential contributions of ammonia oxidizers and nitrite oxidizers to nitrification in four paddy soils. ISME J. 2015;9:1062–75.CAS 
PubMed 
Article 

Google Scholar 
35.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
36.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:633–42.Article 
CAS 

Google Scholar 
37.Edgar RC. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. 2016. https://www.biorxiv.org/content/10.1101/074161v1.38.Deng Y, Ruan Y, Ma B, Timmons MB, Lu H, Xu X, et al. Multi-omics analysis reveals niche and fitness differences in typical denitrification microbial aggregations. Environ Int. 2019;132:105085.CAS 
PubMed 
Article 

Google Scholar 
39.Yu M, Meng J, Yu L, Su W, Afzal M, Li Y, et al. Changes in nitrogen related functional genes along soil pH, C and nutrient gradients in the charosphere. Sci Total Environ. 2019;650:626–32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–12.Article 
CAS 

Google Scholar 
41.Hamady M, Lozupone C, Knight R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 2010;4:17–27.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Zhang J, Zhang N, Liu Y-X, Zhang X, Hu B, Qin Y, et al. Root microbiota shift in rice correlates with resident time in the field and developmental stage. Sci China Life Sci. 2018;61:613–21.PubMed 
Article 
PubMed Central 

Google Scholar 
43.Liaw A, Wiener M. Classification and regression by random Forest. R News. 2002;2:18–22.
Google Scholar 
44.Breiman L. Random forests. Mach Learn. 2001;45:5–32.Article 

Google Scholar 
45.Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, et al. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 2016;10:1891–901.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Wang B, Pourshafeie A, Zitnik M, Zhu J, Bustamante CD, Batzoglou S, et al. Network enhancement as a general method to denoise weighted biological networks. Nat Commun. 2018;9:1–8.Article 
CAS 

Google Scholar 
47.Luo F, Zhong J, Yang Y, Scheuermann RH, Zhou J. Application of random matrix theory to biological networks. Phys Lett A. 2006;357:420–3.CAS 
Article 

Google Scholar 
48.Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. Complex Syst. 2006;1695:1–9.
Google Scholar 
49.Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Icwsm. 2009;8:361–2.
Google Scholar 
50.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
51.Li H, Su J-Q, Yang X-R, Zhu Y-G. Distinct rhizosphere effect on active and total bacterial communities in paddy soils. Sci Total Environ. 2019;649:422–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Vieira S, Sikorski J, Dietz S, Herz K, Schrumpf M, Bruelheide H, et al. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 2019; 1–13.53.Yerushalmi S, Green RM. Evidence for the adaptive significance of circadian rhythms. Ecol Lett. 2009;12:970–81.PubMed 
Article 
PubMed Central 

Google Scholar 
54.Pedersen O, Sand‐Jensen K, Revsbech NP. Diel pulses of O2 and CO2 in sandy lake sediments inhabited by Lobelia dortmanna. Ecology. 1995;76:1536–45.Article 

Google Scholar 
55.Hernandez ME, Beck DAC, Lidstrom ME, Chistoserdova L. Oxygen availability is a major factor in determining the composition of microbial communities involved in methane oxidation. PeerJ. 2015;3:e801.PubMed 
PubMed Central 
Article 

Google Scholar 
56.Saifuddin M, Bhatnagar JM, Segrè D, Finzi AC. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat Commun. 2019;10:1–10.CAS 
Article 

Google Scholar 
57.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 
Article 
PubMed Central 

Google Scholar 
58.Saleem M, Hu J, Jousset A. More than the sum of its parts: microbiome biodiversity as a driver of plant growth and soil health. Annu Rev Ecol Evol Syst. 2019;50:145–68.Article 

Google Scholar 
59.Cozzi G, Broekhuis F, McNutt JW, Turnbull LA, Macdonald DW, Schmid B. Fear of the dark or dinner by moonlight? Reduced temporal partitioning among Africa’s large carnivores. Ecology. 2012;93:2590–9.PubMed 
Article 
PubMed Central 

Google Scholar 
60.Kohl MT, Ruth TK, Metz MC, Stahler DR, Smith DW, White PJ, et al. Do prey select for vacant hunting domains to minimize a multi-predator threat? Ecol Lett. 2019;22:1724–33.PubMed 
Article 
PubMed Central 

Google Scholar 
61.de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:1–12.Article 
CAS 

Google Scholar 
62.Schmidt JE, Kent AD, Brisson VL, Gaudin ACM. Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome. 2019;7:1–18.Article 

Google Scholar 
63.DeCoursey PJ, Walker JK, Smith SA. A circadian pacemaker in free-living chipmunks: essential for survival? J Comp Physiol A. 2000;186:169–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Worden BD, Skemp AK, Papaj DR. Learning in two contexts: the effects of interference and body size in bumblebees. J Exp Biol. 2005;208:2045–53.PubMed 
Article 
PubMed Central 

Google Scholar 
65.Yerushalmi S, Bodenhaimer S, Bloch G. Developmentally determined attenuation in circadian rhythms links chronobiology to social organization in bees. J Exp Biol. 2006;209:1044–51.PubMed 
Article 
PubMed Central 

Google Scholar 
66.Lone SR, Sharma VK. Exposure to light enhances pre-adult fitness in two dark-dwelling sympatric species of ants. BMC Dev Biol. 2008;8:1–11.Article 

Google Scholar 
67.Yadav P, Choudhury D, Sadanandappa MK, Sharma VK. Extent of mismatch between the period of circadian clocks and light/dark cycles determines time-to-emergence in fruit flies. Insect Sci. 2015;22:569–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Yadav P, Thandapani M, Sharma VK. Interaction of light regimes and circadian clocks modulate timing of pre-adult developmental events in Drosophila. BMC Dev Biol. 2014;14:1–12.Article 
CAS 

Google Scholar 
69.Woelfle MA, Ouyang Y, Phanvijhitsiri K, Johnson CH. The adaptive value of circadian clocks: an experimental assessment in cyanobacteria. Curr Biol. 2004;14:1481–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Lambert G, Chew J, Rust MJ. Costs of clock-environment misalignment in individual cyanobacterial cells. Biophys J. 2016;111:883–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Lai AG, Doherty CJ, Mueller-Roeber B, Kay SA, Schippers JHM, Dijkwel PP. CIRCADIAN CLOCK-ASSOCIATED 1 regulates ROS homeostasis and oxidative stress responses. Proc Natl Acad Sci USA. 2012;109:17129–34.CAS 
PubMed 
Article 

Google Scholar 
72.Tanaka K, Ishikawa M, Kaneko M, Kamiya K, Kato S, Nakanishi S. The endogenous redox rhythm is controlled by a central circadian oscillator in cyanobacterium Synechococcus elongatus PCC7942. Photosynth Res. 2019;142:203–10.CAS 
PubMed 
Article 

Google Scholar 
73.Tanaka K, Nakanishi S Time-of-day dependent responses of cyanobacterial cellular viability against oxidative stress. 2019. https://www.biorxiv.org/content/10.1101/851774v2.74.Krittika S, Yadav P. Circadian clocks: an overview on its adaptive significance. Biol Rhythm Res 2019;0:1–24.CAS 

Google Scholar 
75.Koilraj AJ, Sharma VK, Marimuthu G, Chandrashekaran MK. Presence of circadian rhythms in the locomotor activity of a cave-dwelling millipede Glyphiulus cavernicolus sulu (Cambalidae, Spirostreptida). Chronobiol Int. 2000;17:757–65.CAS 
PubMed 
Article 

Google Scholar 
76.Roenneberg T, Merrow M. Life before the clock: modeling circadian evolution. J Biol Rhythms. 2002;17:495–505.PubMed 
Article 

Google Scholar 
77.Espinasa L, Jeffery WR. Conservation of retinal circadian rhythms during cavefish eye degeneration. Evol Dev. 2006;8:16–22.PubMed 
Article 

Google Scholar 
78.Hubbard CJ, McMinn RL, Weinig C. Rhizosphere microbes influence host circadian clock function. 2018. https://www.biorxiv.org/content/10.1101/444539v1. More

The Wildfire Atlas of China (WFAC)The WFAC is based on the Forest Fire Prevention and Monitoring Information Center (FFPMIC) data product that combines satellite imagery and field observations and includes the location, date, and time of 135,246 fire occurrences in China (see “Methods”; Fig. 1a, Supplementary Data 1, and Supplementary Movie 1). We aggregated individual fire occurrences into their nearest gridpoints in a 2° × 2° network (number of fire occurrences per gridpoint at hourly resolution for 2005–2018) following a bilinear method to facilitate the analysis of diurnal patterns. In addition, we deleted 36 out of 219 gridpoints that had less than five fire occurrences in total (2005–2018). To study the influence of holidays and climate on fire occurrence, we then aggregated the hourly fire occurrences for each of the 183 remaining gridpoints into daily, monthly, and annual fire occurrence numbers. To develop a country-wide fire chronology from the monthly WFAC, we standardized annual fire occurrence time series for each gridpoint (to an average of zero and standardized deviation of 1) and then averaged all standardized gridpoint time series. In the same way, we developed regional WFAC fire chronologies for the ten fire regions (details below).Fig. 1: Spatiotemporal fire occurrences of the Wildfire Atlas of China (WFAC).a Total WFAC fire occurrences (2005–2018) in the 2 × 2° gridded network, the b time series of the annual percentage (percentage of all fires (sum of gridpoint-level standardized fire occurrences) in a given year) of the WFAC fire chronology, the c location of the Southwestern China (SWC) fire region, d the annual percentage of fires of the SWC fire chronology, the e location of Southeastern China (SEC), the Lower reaches of the Yangtze River (LYR), and Southern China (SOC), and f the annual percentage of fires of the LYR, SEC, and SOC fire chronologies. The other shaded fire regions are shown in the Supplementary Fig. 1.Full size imageSpatiotemporal wildfire characteristicsWe classified the gridded monthly WFAC network into ten distinct fire regions using rotated principal component analysis (RPCA, see “Methods” and Supplemental material for details) that resemble previous spatial classifications of climate33 and wildfire in China24,25,26,27. The WFAC fire chronologies for the whole country (Fig. 1a–b) and all subregions (Fig. 1c–f, Supplementary Fig. 1) show generally decreasing trends toward the present after reaching a peak in 2007, in contrast with the increasing fire occurrence numbers in many of the world’s tropics and high latitudes7,9,10,14. The ten fire regions combined include 90.9% (122,952 fires) of the total number of WFAC fires and show strong interannual variability. For example, the southwestern China (SWC) fire chronology showed four times more fires in 2010 compared to the following year 2011 (Fig. 1d).The vast majority (84%) of WFAC wildfires occurred in subtropical China (~20–30°N, 100–120°E) and this is also reflected in the results of the RPCA analysis: 90% of the fires in the ten regions (and thus more than 80% of all fires in China) occur in only four subtropical regions: SEC, SWC, SOC, and the lower reaches of Yangtze River (Fig. 1c–f). The predominance of wildfire in southern, subtropical China differs from the dominant fire patterns found in both southern and northern China in previous studies that were based on fire size rather than fire occurrence26,27. This combination of results suggests that fires in northern China can be of larger size to those in the south, but they occur less frequently26,27. The predominance of fire occurrences in subtropical China can be explained by three main factors related to fuel availability, climate, and ignition sources34,35. First, subtropical China is the most densely forested region in China and the world’s subtropics and is characterized by high fuel availability (Fig. 1a). For example, the top ten provinces with the highest forest cover ratios in China are in the subtropics (Supplementary Fig. 2). Second, subtropical China experiences seasonal drought stress in the non-monsoon season (approximately from October to April), during which the available fuels dry out and are flammable. Third, subtropical China is densely populated and rich in human-induced ignition sources. This subtropical fire-dominated pattern in China differs from spatial fire patterns in most other regions with a low ratio of subtropical fires32,35.Intra-annual and diurnal wildfire cyclesSeventy-one percent of fire occurrences in the four subtropical regions of China (68% of all ten subregions) occur in the winter season, from January to April (Fig. 2a, b). The fire season starts earliest, from January to March, in SEC (Fig. 2 and Supplementary Fig. 3), where the rainy season starts earliest in China, and fire occurrences decrease after March (Supplementary Fig. 4). In SWC, where the rainy season starts later, in May, the fire season also starts later and spans primarily from February to April. The fire season is delayed even more in northern China, from March to May, when the snow has largely melted, but the summer monsoon has not yet reached the north. For China as a whole, fire activity is lowest in summer (June to August), when the summer monsoon prevails and creates wet conditions. This monsoon related low number of fire occurrences in summer is in contrast to the peak summer fire season in boreal forests, which is driven by warm summer temperatures6,8,20, and the dry fall fire season in Mediterranean forests, such as in California11,15,36. Only in the northernmost region of China, which is one of the coldest areas and at the limits of the summer monsoon, does the peak fire season occur in summer and fall (Fig. 2).Fig. 2: Intra-annual and diurnal fire occurrences of the Wildfire Atlas of China (WFAC).a The peak fire season for each gridpoint containing more than 50% (mean of 73%) of the fire occurrences, b the monthly ratio (percentage of total fire occurrences in a given month) of the WFAC fire chronology, c the peak fire hours for each gridpoint containing more than 50% (mean of 77.4%) of the fire occurrences, and d the two-hourly ratio (percentage of total fire occurrences in a given 2-h time period) of the WFAC fire chronology.Full size imageMost wildfires occur during the daytime (Fig. 2c, d and Supplementary Fig. 5) when most human-induced ignition activities take place (e.g., agricultural burning). Vegetation and fuels are also relatively dry during the daytime due to moisture loss from transpiration. Sixty seven percent of the fires occur from 14:00 to 18:00, with fire occurrences peaking roughly 2 h earlier in northern China (12:00–17:00) compared to SOC (14:00–19:00) (Fig. 2c). In dry northern China, relative humidity tends to be low enough and vegetation tends to be dry enough to catch fire earlier in the daytime than in SOC, where relative humidity often is not low enough until later in the afternoon. Earlier peak fire activity in northern China, however, may also be related to lifestyle differences in northern versus SOC. For example, due to the colder climate in the north, people generally go to sleep earlier and tend to wake up earlier, resulting in earlier ignition activities relative to the south. In warmer SOC, people tend to nap in the early afternoon, which may limit ignition and thus fire activities from 12:00 to 14:00. A clear jump in fire occurrence after 10:00 suggests that vegetation flammability increases soon after this time of day, which supports the “10 o’clock” fire prevention policy that requires all fires to be put out by 10:00 in the morning after discovery37. Fire activity increases most sharply after 14:00, especially in SOC, and we suggest paying special attention to this time of day for fire prevention.In Europe and North America, fire occurrences are typically low during the weekend due to religious services and thus reduced outdoor activities38. This is not the case, however, in China. On the contrary, weekend fire occurrences are high in the Muslim region of northwestern China, where Friday, rather than Saturday and Sunday, is the religious service day38 (Supplementary Fig. 6). There is also no clear increase in fire activity in northwestern China on traditional Chinese holidays. For China as a whole, however, fires occur 5.7, 5.8, and 7.3 times more frequently on traditional Chinese holidays (Chinese New Year’s Eve (~February), Chinese New Year’s Day (~February), and Ching Ming Holiday (~April)) than the daily mean fires of the respective holiday months (Fig. 3). The fire numbers are particularly high in north central China on Chinese New Year’s Eve and in SOC on Chinese New Year’s Day, which may be related to a regional difference in the day that firework is typically lighted to scare away bad fortune. Fire activity on Ching Ming Holiday is especially high in eastern China (Fig. 3), reflecting a particularly longstanding tradition to burn paper money for dead relatives in that region. In contrast, the Torch Festival (~July) for the Yi and other minorities in SWC occurs in the low-fire monsoon season and has little impact on regional fire occurrence (Supplementary Fig. 7).Fig. 3: Fire occurrence ratios between the holidays and the 2005–2018 average.The ratio between the fire occurrences on holidays of a the New Year Day (January 1st), b Chinese New Year Eve (a day before the Chinese New Year, based on the lunar calendar, often in February), c Chinese New Year Day, and d Ching Ming Festival (based on the lunisolar calendar, in April) and the mean daily fire occurrences (2005–2018) in the month of the respective holiday.Full size imageFire–climate relationshipsWe analyzed the influence of climate on fire occurrence across the WFAC for the primary fire season (January to April), the monsoon season (May to September), the post-monsoon season (October to December), and the whole year. Fire–climate relationships are stronger for the first-differenced data than the original data (Fig. 4 and Supplementary Fig. 8), suggesting stronger fire–climate relationships on interannual than longer timescales. This may be because longer-term fire trends are related to fire suppression26. We found strong positive correlations between fire occurrence and fire season temperature in western and northern China (Fig. 4a and Supplementary Fig. 9). In these regions with limited fire season precipitation, high temperatures enhance evaporation, dry the fuel, and thus lead to more fire activity26.Fig. 4: Relationships between monthly fire occurrences and climate.Pointwise correlations between the first-differenced Wildfire Atlas of China (WFAC) fire occurrences (2005–2018) during the fire season (January to April) and average a temperature, b diurnal temperature range (DTR), c precipitation, and d Palmer Drought Severity Index (PDSI) for the same season.Full size imageIn SEC, on the other hand, fire occurrences are negatively correlated with fire season temperature (Fig. 4a) and precipitation (Fig. 4c), but strongly positively correlated with diurnal temperature ranges (DTR) (Fig. 4b). Fire occurrences further increase with warmer maximum temperatures in the fire season (Supplementary Fig. 9a), but decrease with warmer minimum temperature (Supplementary Fig. 9b) and increased cloud cover (Supplementary Fig. 9c). DTR and cloud cover are significantly anti-correlated in the fire season in SEC (Supplementary Fig. 10). Positive fire-DTR and maximum temperature and negative fire-cloud cover relationships indicate more fire occurrences on sunny days. High fire occurrence on sunny days may be due to intense solar irradiation that can enhance evapotranspiration and moisture loss39. Precipitation is relatively abundant from January to April in SEC and a lack of precipitation, rather than warm temperature, plays a more limiting role on fire activity relative to mean temperature in this region.The influence on fire occurrence of low precipitation and high DTRs in SEC and of warming in western and northern China point to drought-prone fire regimes, which is confirmed by generally negative correlations with the fire season Palmer Drought Severity Index (PDSI; Fig. 4d) and the Standard Precipitation-Evapotranspiration Index (SPEI; Supplementary Fig. 9d). Fire–climate relationships in China thus reflect fire–climate relationships in other drought-prone fire activities across the globe1,2,11. Fire–climate relationships are generally similar between the fire season and the entire year (Fig. 4 and Supplementary Fig. 11), largely due to the majority of yearly fire occurrences in the fire season.Fire–climate relationships are generally weak in the monsoon season (May to September) (Supplementary Figs. 12–15), particularly in SWC with a humid monsoon climate. Monsoon-season fire–climate relationships are stronger in SEC and northern China, where the monsoon season is relatively dry compared to SWC (Supplementary Figs. 12–15). Fire–climate relationships increase in strength in the post-monsoon season (Supplementary Figs. 16–19), when fire occurrences increase with warmer temperatures in the cold regions of central and northern China. In SEC, post-monsoon season fire occurrences, like fire season occurrences, are strongly influenced by DTR and cloud fraction.In addition to strong regional fire–climate relationships, we found a dipole pattern between wildfire occurrences in SEC and SWC, mainly modulated by the ENSO system (Fig. 5). The dipole pattern is indicated by the first singular value decomposition (SVD) mode between the first-differenced, fire season WFAC and global sea surface temperature (SST) fields, which accounts for 53.4% of their total covariance (Fig. 5). Results were similar for the entire year and for non-detrended time series (Supplementary Figs. 20 and 21). Positive ENSO (El Niño) phases, characterized by abnormally high SSTs in the eastern equatorial Pacific Ocean (Fig. 5b), resulted in an increase in wildfire activity in SWC, but a decrease in SEC and northern China (Fig. 5a). The reverse spatial wildfire pattern occurs during negative ENSO (La Niña) years. This ENSO-modulated fire dipole pattern is confirmed by a strong negative correlation (r = −0.97, p  More

Mosquito abundance, biting rate and morphological identificationsA total of 3920 Anopheles sp. females, 1167 and 2753 during the dry and rainy seasons respectively, were captured on a total of 60 collection days. Overall 81% (3187/3920) of the samples were collected in the cow odor-baited double net traps (CBNTs) and while this relative abundance was rather consistent between different collection sites for the CBNTs, 67% (490/733) of the Anopheles from the human odor-baited double net traps (HBNTs) were collected in the forest sites (Table S1).The biting rate (# of females/trap/day) for the HBNTs was consistently higher in the forest sites compared to all other locations during both the rainy and the dry seasons (Table 1). However, for the CBNTs, while the biting rate was the highest in the forest sites during the dry season, the tendency changed during the rainy season with a higher biting rate in the villages and the forests near the villages compared to the forest sites (Table 1).Table 1 Biting and infectious rates of Anopheles mosquitoes collected by HBNTs and CBNTs across sites and seasons.Full size tableA total of 3131 females were morphologically identified as 14 different Anopheles species or complexes of morphologically indistinguishable sibling species. Based on these morphological identifications, species thought to be primary vectors comprised only 10.2% of the collected mosquitoes: Anopheles dirus s.l. (8.1%, n = 319), A. minimus s.l. (0.4%, n = 15) and A. maculatus s.l. (1.7%, n = 67). The most abundant species (represented by more than a hundred individuals in our collection) constituted 75.8% of the collected Anopheles mosquitoes and were represented by 6 species complexes: A. barbirostris (21.2%, n = 831), A. philippinensis (14.6%, n = 571), A. hyrcanus (13.6%, n = 535), A. kochi (10.5%, n = 412), A. dirus (8.1%), A. aconitus (7.7%, n = 303).Molecular determination of mosquito speciesA total of 844 females were molecularly characterized for species in the random subset and represent 26 distinct Anopheles species as determined by ITS2 and CO1 (Table S2). The most abundant species (representing ≥ 5% of the samples; n ≥ 42) comprise 77.8% of the molecularly typed individuals and represent 8 species from 6 different species complexes. These most abundant species included A. dirus (13.2%, n = 112) from the Dirus complex. From the Barbirostris complex; A. dissidens (13.2%, n = 112), and A. campestris-wejchoochotei (8.1%, n = 69). From the Hyrcanus Group, A. peditaeniatus (12.8%, n = 108), and A. nitidus (5.7%, n = 48). The Annularis, Funestus, and Kochi Groups were each represented by a single species A. nivipes (9.2%, n = 78), A. aconitus (6.7%, n = 57), and A. kochi (8.7%, n = 74), respectively. The 18 less abundant species, represent by fewer than 42 samples and in many cases just a handful of samples included A. philippinensis (n = 17) and A. annularis (n = 1) from the Annularis Group, A. jamesii (n = 16), A. pseudojamesi (n = 1), and A. splendidus (n = 1) from the Jamesii Group and A. saeungae (n = 29) and A. barbirostris (n = 2) from the Barbirostris Group. From the Hyrcanus Group A. crawfordi (n = 40), A. argyropus (n = 1), An. nigerrimus (n = 28), and A. sinensis (n = 3) were sampled. Anopheles maculatus (n = 22), A. sawadwongporni (n = 4), and A. rampae (n = 2) from the Maculatus Group. Anopheles tessellatus from the Tessellatus Group and A. interruptus from the Asiaticus Group were each sampled once. A. vagus (n = 12) and A. karwari (n = 3) were also present. There were 2 mosquitoes that had 99.9% identical ITS2 and 99.4% identical CO1 sequences but matched no species in the NCBI database. In addition to the random subset, 79 Plasmodium sp. infected samples were molecularly characterized for species which resulted in a total of 29 Anopheles species as determined by ITS2 and CO1.Day biting rateOverall 20.2 ± 1.2% of the Anopheles females were captured during the daytime (between 06:00 and 18:00). Indeed, while the majority of Anopheles mosquitoes bite at night, an important proportion was active during the day (Fig. 2). Excluding species with extremely low sample sizes and unidentified samples, day biting behaviour was observed for all the Anopheles species and varied from 13 to 68% (Table S3).Figure 2Average number of Anopheles females collected per trap per hour in the different collection sites in the HBNTs and the CBNTs.Full size imageThe day biting rate in the HBNTs was not different across collection sites (19.6 ± 2.9%; χ2 = 3.6, df = 3, p = 0.3; Fig. 3a) but was higher during the dry season (25.9 ± 4.6%) compared to the rainy season (13.8 ± 3.5%; χ2 = 19.08, df = 1, p  More