Philippa Kaur

1.Hughes, A. C. et al. Horizon scan of the belt and road initiative. Trends Ecol. Evol. 35(7), 583–593. https://doi.org/10.1016/j.tree.2020.02.005 (2020).Article 
PubMed 

Google Scholar 
2.Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13(6), 064006. https://doi.org/10.1088/1748-9326/aabd42 (2018).ADS 
Article 

Google Scholar 
3.DNIT. Sistema de Gerencia de Pavimentos. Relatório dos levantamentos funcionais das rodovias federais. Departamento Nacional de Infraenstrutura de Transporte (2013). www.dnit.gov.br.4.Teixeira, F. et al. The need to improve and integrate science and environmental licensing to mitigate wildlife mortality on roads in Brazil. Trop. Conserv. Sci. 9, 24–42. https://doi.org/10.1177/194008291600900104 (2016).Article 

Google Scholar 
5.Barber, C. P., Cochrane, M. A., Souza, C. M. & Laurance, W. F. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol. Conserv. 177, 203–209. https://doi.org/10.1016/j.biocon.2014.07.004 (2014).Article 

Google Scholar 
6.Reid, J. & Souza, W. C. Infrastructure and conservation policy in Brazil. Conserv. Biol. 19, 740–746. https://doi.org/10.1111/j.1523-1739.2005.00699.x (2005).Article 

Google Scholar 
7.Bowman, J., Ray, C. R., Magoun, A. J. & Johnson, D. F. N. Roads, logging, and the large-mammal community of an eastern Canadian boreal forest. Can. J. Zool. 88(5), 454–467. https://doi.org/10.1139/z10-019 (2010).Article 

Google Scholar 
8.Fahrig, L. & Rytwinski, T. Effects of roads on animal abundance: An empirical review and synthesis. Ecol. Soc. 14(1), 21 (2009).Article 

Google Scholar 
9.Laurance, W. F., Goosem, M. & Laurance, S. G. W. Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evol. 24, 659–669. https://doi.org/10.1016/j.tree.2009.06.009 (2009).Article 
PubMed 

Google Scholar 
10.Ruiz-Capillas, P., Mata, C. & Malo, J. E. Road verges are refuges for small mammal populations in extensively managed Mediterranean landscapes. Biol. Conserv. 158, 223–229. https://doi.org/10.1016/j.biocon.2012.09.025 (2013).Article 

Google Scholar 
11.Grilo, C. et al. Individual spatial responses towards roads: Implications for mortality risk. PLoS ONE 7(9), e43811. https://doi.org/10.1371/journal.pone.0043811 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Jacobson, S. L., Bliss-Ketchum, L. L., de Rivera, C. E. & Smith, W. P. A behavior-based framework for assessing barrier effects to wildlife from vehicle traffic volume. Ecosphere 7, e01345. https://doi.org/10.1002/ecs2.1345 (2016).Article 

Google Scholar 
13.Li, T. et al. Fragmentation of China’s landscape by roads and urban areas. Landsc. Ecol. 25, 839–853. https://doi.org/10.1007/s10980-010-9461-6 (2010).Article 

Google Scholar 
14.Walker, R. et al. Modeling spatial decisions with graph theory: Logging roads and forest fragmentation in the Brazilian Amazon. Ecol. Appl. 23, 239–254. https://doi.org/10.1890/11-1800.1 (2013).Article 
PubMed 

Google Scholar 
15.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515. https://doi.org/10.1146/annurev.ecolsys.34.011802.132419 (2003).Article 

Google Scholar 
16.Püttker, T. et al. Indirect effects of habitat loss via habitat fragmentation: A cross-taxa analysis of forest-dependent species. Biol. Conserv. 241, 108368. https://doi.org/10.1016/j.biocon.2019.108368 (2020).Article 

Google Scholar 
17.Signorelli, L., Bastos, R. P., De Marco, P. & With, K. A. Landscape context affects site occupancy of pond-breeding anurans across a disturbance gradient in the Brazilian Cerrado. Landsc. Ecol. 31, 1997. https://doi.org/10.1007/s10980-016-0376-8 (2016).Article 

Google Scholar 
18.Zimmermann, B., Nelson, L., Wabakken, P., Sand, H. & Liberg, O. Behavioral responses of wolves to roads: Scale-dependent ambivalence. Behav. Ecol. 25, 1353–1364. https://doi.org/10.1093/beheco/aru134 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
19.Jaeger, J. A. G. et al. Predicting when animal populations are at risk from roads: An interactive model of road avoidance behaviour. Ecol. Model. 185, 329–348. https://doi.org/10.1016/j.ecolmodel.2004.12.015 (2005).Article 

Google Scholar 
20.Jaeger, J. A. G., Fahrig, L., Ewald, K. C. Does the configuration of road networks influence the degree to which roads affect wildlife populations? In Proceedings of the 2005 International Conference on Ecology and Transportation (ICOET) (eds Irwin, C. L., Garrett, P. & McDermott, K. P.) 151–163 (Center for Transportation and the Environment, North Carolina State University, Raleigh, NC, 2006).21.Rytwinski, T. & Fahrig, L. Reproductive rates and body size predict road impacts on mammal abundance. Ecol. Appl. 21(2), 589–600. https://doi.org/10.1890/10-0968.1 (2011).Article 
PubMed 

Google Scholar 
22.Rytwinski, T. & Fahrig, L. Do species life history traits explain population responses to roads? A meta-analysis. Biol. Conserv. 147(1), 87–98. https://doi.org/10.1016/j.biocon.2011.11.023 (2012).Article 

Google Scholar 
23.Nowell, K. & Jackson, P. Wilds Cats: Status Survey e Conservation Action Plan (IUCN, 1996).
Google Scholar 
24.de La Torre, J., Zarza, H., Ceballos, G. & Medellin, R. The jaguars’ spots are darker than they appear: Assessing the global conservation status of the jaguar Panthera onca. Oryx 51, 1–16. https://doi.org/10.1017/S0030605316001046 (2017).Article 

Google Scholar 
25.Morrison, J. C., Sechrest, W., Dinerstein, E., Wilcove, D. S. & Lamoreux, J. F. Persistence of large mammal faunas as indicators of global human impacts. J. Mammal. 88(6), 1363–1380. https://doi.org/10.1644/06-MAMM-A-124R2.1 (2007).Article 

Google Scholar 
26.Alvarenga, G. C. et al. Multi-scape path-level analysis of jaguar habitat use in the Pantanal ecosystem. Biol. Conserv. 253, 108900. https://doi.org/10.1016/j.biocon.2020.108900 (2021).Article 

Google Scholar 
27.Espinosa, S., Celis, G. & Branch, L. C. When roads appear jaguars decline: Increased access to an Amazonian wilderness area reduces potential for jaguar conservation. PLoS ONE 13(1), e0189740. https://doi.org/10.1371/journal.pone.0189740 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Pallares, E., Manterolla, C., Conde, D. A. & Colchero, F. Case Study: roads and jaguars in the Mayan Forest. In Handbook of Road Ecology (eds Van der Ree, R. et al.) 313–136 (Wiley, 2015).
Google Scholar 
29.Zeilhofer, P., Cezar, A., Tôrres, N. M., Jácomo, A. T. A. & Silveira, L. Jaguar Panthera onca habitat modeling in landscapes facing high land-use transformation pressure—Findings from Mato Grosso, Brazil. Biotropica 46(1), 98–105. https://doi.org/10.1111/btp.12074 (2014).Article 

Google Scholar 
30.Colchero, F., Conde, D. A., Manterola, C., Rivera, A. & Ceballos, G. Jaguars on the move: Modeling movement to mitigate fragmentation from road expansion in the Mayan Forest. Anim. Conserv. 4, 158–166. https://doi.org/10.1111/j.1469-1795.2010.00406.x (2011).Article 

Google Scholar 
31.Conde, D. A. et al. Sex matters: Modeling male and female habitat differences for jaguar conservation. Biol. Conserv. 143(9), 1980–1988. https://doi.org/10.1016/j.biocon.2010.04.049 (2010).Article 

Google Scholar 
32.De Angelo, C., Paviolo, A., Wiegand, T., Kanagaraj, R. & Di Bitetti, M. S. Understanding species persistence for defining conservation actions: A management landscape for jaguars in the Atlantic Forest. Biol. Conserv. 159, 422–433. https://doi.org/10.1016/j.biocon.2012.12.021 (2013).Article 

Google Scholar 
33.Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, 2006).Book 

Google Scholar 
34.Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368. https://doi.org/10.1890/08-1034.1 (2009).Article 

Google Scholar 
35.Morato, R. G. et al. Jaguar movement database: A GPS-based movement dataset of an apex predator in the Neotropics. Ecology 99, 1691–1691. https://doi.org/10.1002/ecy.2379 (2018).Article 
PubMed 

Google Scholar 
36.Geofabrik. OpenStreetMap-Shapefiles (2019). http://download.geofabrik.de. Accessed 15 Aug 2019.37.Projeto MapBiomas – Coleção 2 da Série Anual de Mapas de Cobertura e Uso de Solo do Brasil, acessado em 12/04/2019 através do link. http://mapbiomas.org/pages/database/mapbiomas_collection_download.38.Morato, R. G. et al. Resource selection in an apex predator and variation in response to local landscape characteristics. Biol. Conserv. 228, 233–240. https://doi.org/10.1016/j.biocon.2018.10.022 (2018).Article 

Google Scholar 
39.ESRI. Environmental Systems Research Institute. ArcGIS. Geographic Information System for Desktop, version 10.3.1 (2015).40.RStudio Team. RStudio: Integrated Development for R (RStudio Inc, 2016).
Google Scholar 
41.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579. https://doi.org/10.1111/2041-210X.12512 (2016).Article 

Google Scholar 
42.Grace, J. B., Adler, P. B., Harpole, W. S., Borer, E. T. & Seabloom, E. W. Causal networks clarify productivity–richness interrelations, bivariate plots do not. Funct. Ecol. 28, 787–798. https://doi.org/10.1111/1365-2435.12269 (2014).Article 

Google Scholar 
43.Bollen, K. A. & Pearl, J. Eight myths about causality and structural equation models. In Handbook of Causal Analysis for Social Research (ed. Morgan, S. L.) 301–328 (Springer, 2013).Chapter 

Google Scholar 
44.Fan, Y. et al. Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecol. Process 5, 19. https://doi.org/10.1186/s13717-016-0063-3 (2016).Article 

Google Scholar 
45.Cressie, N. A. C. Statistics for Spatial Data. Wiley Series in Probability and Mathematical Statistics (Wiley, 1993).
Google Scholar 
46.Haining, R. Spatial Data Analysis: Theory and Practice (Cambridge University Press, 2003).Book 

Google Scholar 
47.Amrhein, V., Greenland, S. & McShane, B. Retire statistical significance. Nature 567(7748), 305–307. https://doi.org/10.1038/d41586-019-00857-9 (2019).ADS 
CAS 
Article 

Google Scholar 
48.Benítez-López, A., Alkemade, R. & Verweij, P. A. The impacts of roads and other infrastructure on mammal and bird populations: A meta-analysis. Biol. Conserv. 143, 1307–1316. https://doi.org/10.1016/j.biocon.2010.02.009 (2010).Article 

Google Scholar 
49.Torres, A., Jaeger, J. A. & Alonso, J. C. Assessing large-scale wildlife responses to human infrastructure development. Proc. Natl. Acad. Sci. USA 113(30), 8472–8477. https://doi.org/10.1073/pnas.1522488113 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Morato, R. G., Ferraz, K. M. P. M. B., Paula, R. C. & Campos, C. B. Identification of priority conservation areas and potential corridors for jaguars in the Caatinga biome, Brazil. PLoS ONE 9(4), e92950. https://doi.org/10.1371/journal.pone.0092950 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Rodríguez-Soto, C. et al. Predicting potential distribution of the jaguar (Panthera onca) in Mexico: Identification of priority areas for conservation. Divers. Distrib. 17, 350–361. https://doi.org/10.1111/j.1472-4642.2010.00740.x (2011).Article 

Google Scholar 
52.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343(6167), 1241484. https://doi.org/10.1126/science.1241484 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Rabinowitz, A. & Zeller, K. A. A range-wide model of landscape connectivity and conservation for the jaguar, Panthera onca. Biol. Conserv. 143(4), 939–945. https://doi.org/10.1016/j.biocon.2010.01.002 (2010).Article 

Google Scholar 
54.Jaeger, J. Landschaftszerschneidung. Eine transdisziplinäre Studie gemäß dem Konzept der Umweltgefährdung (Landscape Fragmentation. A Transdisciplinary Study According to the Concept of Environmental Threat) (Eugen Ulmer, 2002).
Google Scholar 
55.Torres, A., Jaeger, J. A. G. & Alonso, J. C. Multi-scale mismatches between urban sprawl and landscape fragmentation create windows of opportunity for conservation development. Landsc. Ecol. 31, 2291–2305. https://doi.org/10.1007/s10980-016-0400-z (2016).Article 

Google Scholar 
56.Morato, R. G. et al. space use and movement of a neotropical top predator: The endangered jaguar. PLoS ONE 11(12), e0168176. https://doi.org/10.1371/journal.pone.0168176 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Sollmann, R. et al. Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movement using spatial capture-recapture models for jaguars in central Brazil. Biol. Conserv. 144, 1017–1024. https://doi.org/10.1016/j.biocon.2010.12.011 (2011).Article 

Google Scholar 
58.Marchini, S. & Macdonald, D. W. Mind over matter: Perceptions behind the impact of jaguars on human livelihoods. Biol. Conserv. 224, 230–237. https://doi.org/10.1016/j.biocon.2018.06.001 (2018).Article 

Google Scholar 
59.González-Gallina, A. et al. Home range of a male jaguar spatially associated with the landfill of the city of Playa del Carmen, Mexico. Mammalia 82(1), 54–61. https://doi.org/10.1515/mammalia-2016-0065 (2017).Article 

Google Scholar 
60.Associação Onçafari. Enteda o caso da onça-pintada de Juiz de Fora (2019). https://oncafari.org/2019/06/03/entenda-o-caso-da-onca-pintada-de-juiz-de-fora/. Accessed 09 Oct 2020.61.Dickson, B. G., Jenness, J. S. & Beier, P. Influence of vegetation, topography, and roads on cougar movement in southern California. J. Wildl. Manag. 69, 264–276. https://doi.org/10.2193/0022-541X(2005)069%3c0264:IOVTAR%3e2.0.CO;2 (2005).Article 

Google Scholar 
62.Thatte, P., Joshi, A., Vaidyanathan, S., Landguth, E. & Ramakrishnan, U. Maintaining tiger connectivity and minimizing extinction into the next century: Insights from landscape genetics and spatially-explicit simulations. Biol. Conserv. 218, 181–191. https://doi.org/10.1016/j.biocon.2017.12.022 (2018).Article 

Google Scholar 
63.Thompson, J. J. et al. Environmental and anthropogenic factors synergistically affect space use of jaguars. Curr. Biol. 31(15), 3457–3466. https://doi.org/10.1016/j.cub.2021.06.029 (2021).CAS 
Article 
PubMed 

Google Scholar 
64.Dickson, B. G. & Beier, P. Home-range and habitat selection by adult cougars in southern California. J. Wildl. Manag. 66, 1235–1245. https://doi.org/10.2307/3802956 (2002).Article 

Google Scholar 
65.Poessel, S. A. et al. Roads influence movement and home ranges of a fragmentation-sensitive carnivore, the bobcat, in an urban landscape. Biol. Conserv. 180, 224–232. https://doi.org/10.1016/j.biocon.2014.10.010 (2014).Article 

Google Scholar 
66.Johnson, D. H. The comparison of usage and availability measurements for evaluation of resource preference. Ecology 61, 65–71. https://doi.org/10.2307/1937156 (1980).Article 

Google Scholar 
67.Silva, L. G., Cherem, J., Kasper, C., Trigo, T. & Eizirik, E. Mapping wild cat roadkills in southern Brazil: An assessment of baseline data for species conservation. Cat News (Bougy) 61, 04–07. https://doi.org/10.13140/RG.2.2.17640.88327 (2014).Article 

Google Scholar 
68.Srbek-Araujo, A. C., Mendes, S. L. & Chiarello, A. G. Jaguar (Panthera onca Linnaeus, 1758) roadkill in Brazilian Atlantic Forest and implications for species conservation. Braz. J. Biol. 75, 581–586. https://doi.org/10.1590/1519-6984.17613 (2015).CAS 
Article 
PubMed 

Google Scholar 
69.Kerley, L. L. et al. Effects of roads and human disturbance on Amur tigers. Conserv. Biol. 16, 97–108. https://doi.org/10.1046/j.1523-1739.2002.99290.x (2002).Article 

Google Scholar 
70.Teixeira, F. Z., Rytwinski, T. & Fahrig, L. Inference in road ecology: What we know versus what we think we know. Biol. Lett. https://doi.org/10.1098/rsbl.2020.0140 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
71.Laurance, W. F. The anthropocene. Curr. Biol. 29, 942–995. https://doi.org/10.1016/j.cub.2019.07.055 (2019).CAS 
Article 

Google Scholar 
72.Kanda, C. Z. et al. Spatiotemporal dynamics of conspecific movement explain a solitary carnivore’s space use. J. Zool. 308, 66–74. https://doi.org/10.1111/jzo.12655 (2019).Article 

Google Scholar 
73.Zarco-González, M. M., Monroy-Vilchis, O. & Alaníz, J. Spatial model of livestock predation by jaguar and puma in Mexico: Conservation planning. Biol. Conserv. 159, 80–87. https://doi.org/10.1016/j.biocon.2012.11.007 (2013).Article 

Google Scholar 
74.Bager, A., Borghi, C. E. & Secco, H. The influence of economics, politics, and environment on road ecology in South America. In Handbook of Road Ecology (eds Van der Ree, R. et al.) 407–413 (Wiley, 2015).
Google Scholar 
75.Kaszta, Z. et al. Simulating the impact of Belt and Road initiative and other major developments in Myanmar on an ambassador felid, the clouded leopard, Neofelis nebulosa. Landsc. Ecol. 35, 727–746. https://doi.org/10.1007/s10980-020-00976-z (2020).Article 

Google Scholar 
76.Cullen, L. Jr. et al. Implications of fine-grained habitat fragmentation and road mortality for jaguar conservation in the Atlantic Forest, Brazil. PLoS ONE 11(12), e0167372. https://doi.org/10.1371/journal.pone.0167372 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
77.Ceia-Hasse, A., Borda-de-Agua, L., Grilo, C. & Pereira, H. M. Global exposure of carnivores to roads. Glob. Ecol. Biogeogr. 26, 592–600. https://doi.org/10.1111/geb.12564 (2017).Article 

Google Scholar 
78.Grilo, C., Koroleva, E., Andrášik, R., Bíl, M. & González-Suárez, M. Roadkill risk and population vulnerability in European birds and mammals. Front. Ecol. Environ. 18(6), 323–328. https://doi.org/10.1002/fee.2216 (2020).Article 

Google Scholar 
79.Cerqueira, R. C. et al. Potential movement corridor and high road-kill likelihood do not spatially coincide for felids in Brazil: Implications for road mitigation. Environ. Manag. 67, 412–423 (2021).Article 

Google Scholar 
80.Clevenger, A. P. & Waltho, N. Performance indices to identify attributes of highway crossing structures facilitating movement of large mammals. Biol. Conserv. 121, 453–464. https://doi.org/10.1016/j.biocon.2004.04.025 (2005).Article 

Google Scholar 
81.Spanowicz, A. G., Teixeira, F. Z. & Jaeger, J. A. G. An adaptive plan for prioritizing road sections for fencing to reduce animal mortality. Conserv. Biol. 34(5), 1210–1220. https://doi.org/10.1111/cobi.13502 (2020).Article 
PubMed 

Google Scholar 
82.Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232. https://doi.org/10.1038/nature13717 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
83.Carter, N., Killion, A., Easter, T., Brandt, J. & Ford, A. Road development in Asia: Assessing the range-wide risks for tigers. Sci. Adv. 6(18), eaaz9619. https://doi.org/10.1126/sciadv.aaz9619 (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
84.Galetti, M. et al. Atlantic rainforest’s jaguars in decline. Science 342, 930. https://doi.org/10.1126/science.342.6161.930-a (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
85.Paviolo, A. et al. A biodiversity hotspot losing its top predator: The challenge of jaguar conservation in the Atlantic Forest of South America. Sci. Rep. 6, 37147. https://doi.org/10.1038/srep37147 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
86.Freitas, S. R., Hawbaker, T. J. & Metzger, J. P. Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest. For. Ecol. Manag. 259, 410–417. https://doi.org/10.1016/j.foreco.2009.10.036 (2010).Article 

Google Scholar  More

1.Rinkevich, B. & Weissman, I. L. Chimeras in colonial inverebrates: A synergistic symbiosis or somatic- and cell-germ parasitism? Symbiosis 4, 117–134 (1987).
Google Scholar 
2.Buss, L. W. Somatic cell parasitism and the evolution of somatic tissue compatibility. Proc. Natl. Acad. Sci. U.S.A. 79, 5337–5341. https://doi.org/10.1073/pnas.79.17.5337 (1982).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Foster, K. R., Fortunato, A., Strassmann, J. E. & Queller, D. C. The costs and benefits of being a chimera. Proc. R. Soc. Lond. B 269, 2357–2362. https://doi.org/10.1098/rspb.2002.2163 (2002).Article 

Google Scholar 
4.Money, N. P. Fungal get-together. Nature 405, 751. https://doi.org/10.1038/35015659 (2000).CAS 
Article 
PubMed 

Google Scholar 
5.Franks, T., Botta, R., Thomas, M. & Franks, J. Chimerism in grapevines: Implications for cultivar identity, ancestry and genetic improvement. Theor. Appl. Genet. 104, 192–199. https://doi.org/10.1007/s001220100683 (2002).CAS 
Article 
PubMed 

Google Scholar 
6.Casares, A. & Sylvain, F. F. Higher reproductive success for chimeras than solitary individuals in the kelp Lessonia spicata but no benefit for individual genotypes. Evol. Ecol. 30, 953–972. https://doi.org/10.1007/s10682-016-9849-0 (2016).Article 

Google Scholar 
7.Santelices, B., González, A. V., Beltrán, J. & Flores, V. Coalescing red algae exhibit noninvasive, reversible chimerism. J. Phycol. 53, 59–69. https://doi.org/10.1111/jpy.12476 (2017).CAS 
Article 
PubMed 

Google Scholar 
8.Gauthier, M. & Degnan, B. M. Partitioning of genetically distinct cell populations in chimeric juveniles of the sponge Amphimedon queenslandica. Dev. Comp. Immunol. 32, 1270–1280. https://doi.org/10.1016/j.dci.2008.04.002 (2008).CAS 
Article 
PubMed 

Google Scholar 
9.Fidler, A. E., Bacq-Labreuil, A., Rachmilovitz, E. & Rinkevich, B. Efficient dispersal and substrate acquisition traits in a marine invasive species via transient chimerism and colony mobility. PeerJ 2018, 1–23. https://doi.org/10.7717/peerj.5006 (2018).CAS 
Article 

Google Scholar 
10.Rinkevich, B. & Weissman, I. Chimeras in colonial invertebrates: A synergistic symbiosis or somatic-and germ-cell parasitism. Symbiosis 4, 117–134 (1987).
Google Scholar 
11.Amar, K.-O., Chadwick, N. E. & Rinkevich, B. Coral kin aggregations exhibit mixed allogeneic reactions and enhanced fitness during early ontogeny. BMC Evol. Biol. 8, 126–126. https://doi.org/10.1186/1471-2148-8-126 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Puill-Stephan, E., Willis, B., van Herwerden, L. & van Oppen, M. Chimerism in wild adult populations of the broadcast spawning coral Acropora millepora on the Great Barrier Reef. PLoS One 4, e7751. https://doi.org/10.1371/journal.pone.0007751 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Hoeg, J. T. & Lutzen, J. Life cycle and reproduction in the Cirripedia, Rhizocephala. Oceanogr. Mar. Biol. Annu. Rev. 33, 427–485 (1995).
Google Scholar 
14.Gianasi, B. L., Hamel, J. F. & Mercier, A. Full allogeneic fusion of embryos in a holothuroid echinoderm. Proc. R. Soc. Lond. B 285, 1–7. https://doi.org/10.1098/rspb.2018.0339 (2018).CAS 
Article 

Google Scholar 
15.Rinkevich, B. Human natural chimerism: An acquired character or a vestige of evolution?. Hum. Immunol. 62, 651–657. https://doi.org/10.1016/S0198-8859(01)00249-X (2001).CAS 
Article 
PubMed 

Google Scholar 
16.Gill, D. E., Chao, L., Perkins, S. L. & Wolf, J. B. Genetic mosaicism in plants and clonal animals. Annu. Rev. Ecol. Syst. 26, 423–444 (1995).Article 

Google Scholar 
17.Biesecker, L. G. & Spinner, N. B. A genomic view of mosaicism and human disease. Nat. Rev. Genet. 14, 307–320 (2013).CAS 
Article 

Google Scholar 
18.Devlin-Durante, M. K., Miller, M. W., Precht, W. F. & Baums, I. B. How old are you? Genet age estimates in a clonal animal. Mol. Ecol. 25, 5628–5646. https://doi.org/10.1111/mec.13865 (2016).CAS 
Article 
PubMed 

Google Scholar 
19.Dubé, C. E., Planes, S., Zhou, Y., Berteaux-Lecellier, V. & Boissin, E. On the occurrence of intracolonial genotypic variability in highly clonal populations of the hydrocoral Millepora platyphylla at Moorea (French Polynesia). Sci. Rep. 7, 1–10. https://doi.org/10.1038/s41598-017-14684-3 (2017).CAS 
Article 

Google Scholar 
20.Maier, E., Buckenmaier, A., Tollrian, R. & Nürnberger, B. Intracolonial genetic variation in the scleractinian coral Seriatopora hystrix. Coral Reefs 31, 505–517. https://doi.org/10.1007/s00338-011-0857-9 (2012).ADS 
Article 

Google Scholar 
21.Schweinsberg, M., Weiss, L. C., Striewski, S., Tollrian, R. & Lampert, K. P. More than one genotype: How common is intracolonial genetic variability in scleractinian corals? Mol. Ecol. 24, 2673–2685. https://doi.org/10.1111/mec.13200 (2015).Article 
PubMed 

Google Scholar 
22.van Oppen, M. J., Souter, P., Howells, E. J., Heyward, A. & Berkelmans, R. Novel genetic diversity through somatic mutations: Fuel for adaptation of reef corals? Diversity 3, 405–423 (2011).Article 

Google Scholar 
23.Rinkevich, B. A critical approach to the definition of Darwinian units of selection. Biol. Bull. 199, 231–240. https://doi.org/10.2307/1543179 (2000).CAS 
Article 
PubMed 

Google Scholar 
24.Rinkevich, B. The apex set-up for the major transitions in individuality. Evol. Biol. 46, 217–228. https://doi.org/10.1007/s11692-019-09481-x (2019).Article 

Google Scholar 
25.Santelices, B. How many kinds of individual are there? Trends Ecol. Evol. 14, 152–155. https://doi.org/10.1016/S0169-5347(98)01519-5 (1999).CAS 
Article 
PubMed 

Google Scholar 
26.Pineda-Krch, M. & Lehtilä, K. Costs and benefits of genetic heterogeneity within organisms. J. Evol. Biol. 17, 1167–1177. https://doi.org/10.1111/j.1420-9101.2004.00808.x (2004).CAS 
Article 
PubMed 

Google Scholar 
27.Rinkevich, B. Quo vadis chimerism? Chimerism 2, 1–5. https://doi.org/10.4161/chim.14725 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Rinkevich, B. & Yankelevich, I. Environmental split between germ cell parasitism and somatic cell synergism in chimeras of a colonial urochordate. J. Exp. Biol. 207, 3531–3536. https://doi.org/10.1242/jeb.01184 (2004).Article 
PubMed 

Google Scholar 
29.Raymundo, L. J. & Maypa, A. P. Getting bigger faster: Mediation of size-specific mortality via fusion in juvenile coral transplants. Ecol. Appl. 14, 281–295. https://doi.org/10.1890/02-5373 (2004).Article 

Google Scholar 
30.Rinkevich, B., Shaish, L., Douek, J. & Ben-Shlomo, R. Venturing in coral larval chimerism: A compact functional domain with fostered genotypic diversity. Sci. Rep. 6, 19493. https://doi.org/10.1038/srep19493 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Rinkevich, B. Coral chimerism as an evolutionary rescue mechanism to mitigate global climate change impacts. Glob. Chang Biol. 25, 1198–1206. https://doi.org/10.1111/gcb.14576 (2019).ADS 
Article 

Google Scholar 
32.Amar, K.-O., Chadwick, N. E. & Rinkevich, B. Coral planulae as dispersion vehicles: Biological properties of larvae released early and late in the season. Mar. Ecol. Prog. Ser. 350, 71–78. https://doi.org/10.3354/meps07125 (2007).ADS 
Article 

Google Scholar 
33.Rinkevich, B. Immunology of human implantation: From the invertebrate’s point of view. Hum. Reprod. 13, 455–459. https://doi.org/10.1093/humrep/13.2.503 (1998).CAS 
Article 
PubMed 

Google Scholar 
34.González, A. V. & Santelices, B. Frequency of chimerism in populations of the kelp Lessonia spicata in central Chile. PLoS One 12, 1–20. https://doi.org/10.1371/journal.pone.0169182 (2017).CAS 
Article 

Google Scholar 
35.Nozawa, Y. & Hirose, M. When does the window close? The onset of allogeneic fusion 2–3 years post-settlement in the scleractinian coral, Echinophyllia aspera. Zool. Stud. 50, 396 (2011).
Google Scholar 
36.Puill-Stephan, E., van Oppen, M. J. H., Pichavant-Rafini, K. & Willis, B. L. High potential for formation and persistence of chimeras following aggregated larval settlement in the broadcast spawning coral, Acropora millepora. Proc. R. Soc. Lond. B Biol. Sci. 279, 699–708. https://doi.org/10.1098/rspb.2011.1035 (2012).CAS 
Article 

Google Scholar 
37.Frank, U., Oren, U., Loya, Y. & Rinkevich, B. Alloimmune maturation in the coral Stylophora pistillata is achieved through three distinctive stages, 4 months post-metamorphosis. Proc. R. Soc. Lond. B Biol. Sci. 264, 99–104. https://doi.org/10.1098/rspb.1997.0015 (1997).ADS 
Article 

Google Scholar 
38.Rinkevich, B. The branching coral Stylophora pistillata: Contribution of genetics in shaping colony landscape. Isr. J. Zool. 48, 71–82. https://doi.org/10.1560/BCPA-UM3A-MKBP-HGL2 (2002).Article 

Google Scholar 
39.Highsmith, R. Reproduction by fragmentation in corals. Mar. Ecol. Prog. Ser. 7, 207–226. https://doi.org/10.3354/meps007207 (1982).ADS 
Article 

Google Scholar 
40.Barfield, S., Aglyamova, G. V. & Matz, M. V. Evolutionary origins of germline segregation in Metazoa: Evidence for a germ stem cell lineage in the coral Orbicella faveolata (Cnidaria, Anthozoa). Proc. R. Soc. Lond. B Biol. Sci. 283, 20152128. https://doi.org/10.1098/rspb.2015.2128 (2016).CAS 
Article 

Google Scholar 
41.Chang, E. S., Orive, M. E. & Cartwright, P. Nonclonal coloniality: Genetically chimeric colonies through fusion of sexually produced polyps in the hydrozoan Ectopleura larynx. Evol. Lett. 2, 442–455. https://doi.org/10.1002/evl3.68 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Hancock, J. P., Goulden, N. J., Oakhill, A. & Steward, C. G. Quantitative analysis of chimerism after allogeneic bone marrow transplantation using immunomagnetic selection and fluorescent microsatellite PCR. Leukemia 17, 247–251. https://doi.org/10.1038/sj.leu.2402759 (2003).CAS 
Article 
PubMed 

Google Scholar 
43.Broestl, L., Rubin, J. B. & Dahiya, S. Fetal microchimerism in human brain tumors. Brain Pathol. 28, 484–494. https://doi.org/10.1111/bpa.12557 (2018).CAS 
Article 
PubMed 

Google Scholar 
44.Olsen, K. C., Moscoso, J. A. & Levitan, D. R. Somatic mutation is a function of clone size and depth in orbicella reef-building corals. Biol. Bull. 236, 1–12. https://doi.org/10.1086/700261 (2019).CAS 
Article 
PubMed 

Google Scholar 
45.Schweinsberg, M., Tollrian, R. & Lampert, K. P. Inter- and intra-colonial genotypic diversity in hermatypic hydrozoans of the family Milleporidae. Mar. Ecol. 38, 1–11. https://doi.org/10.1111/maec.12388 (2017).Article 

Google Scholar 
46.Santelices, B., Alvarado, J. L. & Flores, V. Size increments due to interindividual fusions: How much and for how long? J. Phycol. 46, 685–692. https://doi.org/10.1111/j.1529-8817.2010.00864.x (2010).Article 

Google Scholar 
47.Rinkevich, B. & Weissman, I. L. Chimeras vs genetically homegeneous individuals: Potential fitness costs and benefits. Oikos 63, 119–124 (1992).Article 

Google Scholar 
48.Mizrahi, D., Navarrete, S. A. & Flores, A. A. V. Groups travel further: Pelagic metamorphosis and polyp clustering allow higher dispersal potential in sun coral propagules. Coral Reefs 33, 443–448. https://doi.org/10.1007/s00338-014-1135-4 (2014).ADS 
Article 

Google Scholar 
49.Lambert, N. C. et al. Quantification of maternal microchimerism by HLA-specific real-time polymerase chain reaction: Studies of healthy women and women with scleroderma. Arthritis Rheumatol. 50, 906–914. https://doi.org/10.1002/art.20200 (2004).CAS 
Article 

Google Scholar 
50.Magor, B. G., De Tomoso, A., Rinkevich, B. & Weissman, I. L. Allorecognition in colonial tunicates: Protection against predatory cell lineages? Immunol. Rev. 167, 69–79. https://doi.org/10.1111/j.1600-065x.1999.tb01383.x (1999).CAS 
Article 
PubMed 

Google Scholar 
51.Duerden, J. E. Aggregated colonies in madreporarian corals. Am. Nat. 34, 461–471 (1902).Article 

Google Scholar 
52.Barki, Y., Gateño, D., Graur, D. & Rinkevich, B. Soft-coral natural chimerism: A window in ontogeny allows the creation of entities comprised of incongruous parts. Mar. Ecol. Prog. Ser. 231, 91–99. https://doi.org/10.3354/meps231091 (2002).ADS 
Article 

Google Scholar 
53.Linden, B., Huisman, J. & Rinkevich, B. Circatrigintan instead of lunar periodicity of larval release in a brooding coral species. Sci. Rep. 8, 5668. https://doi.org/10.1038/s41598-018-23274-w (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Shefy, D., Shashar, N. & Rinkevich, B. The reproduction of the Red Sea coral Stylophora pistillata from Eilat: 4-decade perspective. Mar. Biol. 165, 27. https://doi.org/10.1007/s00227-017-3280-0 (2018).Article 

Google Scholar 
55.Shafir, S., Van Rijn, J. & Rinkevich, B. Steps in the construction of underwater coral nursery, an essential component in reef restoration acts. Mar. Biol. 149, 679–687. https://doi.org/10.1007/s00227-005-0236-6 (2006).Article 

Google Scholar 
56.Rinkevich, B. & Loya, Y. The reproduction of the Red Sea coral Stylophora pistillata. I. Gonads and planulae. Mar. Ecol. Prog. Ser. 1, 133–144 (1979).ADS 
Article 

Google Scholar 
57.Santelices, B. Mosaicism and chimerism as components of intraorganismal genetic heterogeneity. J. Evol. Biol. 17, 1187–1188. https://doi.org/10.1111/j.1420-9101.2004.00813.x (2004).CAS 
Article 
PubMed 

Google Scholar 
58.Graham, D. E. The isolation of high molecular weight DNA from whole organisms or large tissue masses. Anal. Biochem. 85, 609–613. https://doi.org/10.1016/0003-2697(78)90262-2 (1978).CAS 
Article 
PubMed 

Google Scholar 
59.Douek, J., Barki, Y., Gateño, D. & Rinkevich, B. Possible cryptic speciation within the sea anemone Actinia equina complex detected by AFLP markers. Zool. J. Linn. Soc. 136, 315–320. https://doi.org/10.1046/j.1096-3642.2002.00034.x (2002).Article 

Google Scholar 
60.Banguera-Hinestroza, E., Saenz-Agudelo, P., Bayer, T., Berumen, M. L. & Voolstra, C. R. Characterization of new microsatellite loci for population genetic studies in the smooth cauliflower coral (Stylophora sp.). Conserv. Genet. Resour. 5, 561–563. https://doi.org/10.1007/s12686-012-9852-x (2013).Article 

Google Scholar 
61.Diwan, N. & Cregan, P. B. Automated sizing of fluorescent-labeled simple sequence repeat (SSR) markers to assay genetic variation in soybean. Theor. Appl. Genet. 95, 723–733. https://doi.org/10.1007/s001220050618 (1997).CAS 
Article 

Google Scholar 
62.Hearne, C. M., Ghosh, S. & Todd, J. A. Microsatellites for linkage analysis of genetic traits. Trends Genet. 8, 288–294. https://doi.org/10.1016/0168-9525(92)90256-4 (1992).CAS 
Article 
PubMed 

Google Scholar  More

Study area and sampling locationsWe conducted this study in Kedarnath Wildlife Sanctuary (30° 25′–30° 41′ N, 78° 55′–79° 22′ E), located in Uttarakhand state in the western Himalayas of India. This sanctuary covers a broad elevational gradient from 1400 to 4000 m above sea level (asl) (Fig. 1), with corresponding changes in habitat types: from Himalayan moist temperate forests dominated by Quercus spp. at low elevations, to sub-alpine forests dominated by Rhododendron spp. and alpine meadows at high elevations34. This sanctuary is known to harbour 26 species of bats35.Figure 1Map of India showing the location of the study area, Kedarnath Wildlife Sanctuary, and the sampling locations within the study area. Elevation is in m asl. The map was created using QGIS (v 3.6.3-Noosa) (QGIS Geographical Information System, www.qgis.org). Please note that the geographical boundaries represented in the map may contain areas considered disputed.Full size imageWe sampled at four locations spanning an elevational gradient of 2200 m. Sampling points within each location were spread across the elevations mentioned in parentheses: Mandal (1500–1800 m), Ansuya (2000–2200 m), Chopta (2700–3000 m) and Tungnath (3300–3700 m) (Fig. 1). Sampling was conducted between late-March and mid-May in 2018 and 2019, starting at lower elevations and then moving to higher elevations. This sampling duration coincides with summer in the Himalaya. To comprehensively sample the bat diversity, we employed a combination of automated ultrasonic recorders and capture sampling using mist-netting. Fieldwork was approved by the Internal Committee for Ethics and Animal Welfare, Institute for Zoo and Wildlife Research (approval no. 2018-06-01), and conducted under a permit issued by the Uttarakhand State Forest Department, Government of India (permit no. 2261/5-6).Sampling strategyFor acoustic sampling, we placed full spectrum passive ultrasonic recorders (SongMeter SM4BAT, Wildlife Acoustics, Maynard, MA, USA) in different habitat types (open, forest edge, and forest) at each elevation (hereafter, “passive recordings”). The recorders were programmed to record bat calls for two consecutive nights at each sampling point, from dusk to dawn (9–10 h/night), using a sample rate of 500 kHz/s, an amplitude threshold of 16 dB and a frequency threshold of 5 kHz. The dominant habitats at Ansuya and Tungnath are montane evergreen forests and alpine meadows respectively, therefore only these habitats were sampled at these elevations. The exact number of sampling points per habitat for each elevation is given in Table S1. On separate days after completing acoustic sampling at a site, we set up nylon and monofilament mist nets of 4, 6 or 9 m length, 16 × 16 and 19 × 19 mesh sizes (Ecotone GOC, Sopot, Poland) for four hours following dusk (starting between 18.30 h in early summer and 19.30 h in late summer). The captured bats were handled and measured following the guidelines of the American Society of Mammalogists36. To further refine identification in light of the paucity of taxonomic knowledge in the region, we collected only one specimen each of taxonomically-challenging species in accordance with our field research permit. We measured body mass (accuracy 0.1 g) using a spring balance (Pesola, Schindellegi, Switzerland), and forearm length (accuracy 0.01 mm) with vernier calipers (Swiss Precision Instruments SPI Inc., Melville, NY, USA). Next, we gently stretched the left wing and placed the live animal perpendicular to the background of a graph sheet of 1 × 1 cm grids. We photographed the outstretched wing using a Nikon D3400 DSLR camera at 55 mm zoom from a distance of about 90 cm. Subsequently, we released the bats and recorded their echolocation calls at a distance of 5 to 10 m using a handheld ultrasonic detector (Anabat Walkabout, Titley Scientific, Brendale, QLD, Australia) and saved them as audio files of .wav format. These recordings (henceforth referred to as “reference recordings”) formed the dataset used to develop a call library for identification.Call classifier and analysis of passive recordingsReference recordings from 2018 and 2019 were labelled using Raven Pro 1.5 (Cornell Lab of Ornithology, Ithaca, NY, USA) to generate a dataset of acoustic parameters for identification. We visualized calls using a spectrogram with Hanning window, size 1024 samples with 95% overlap. From each recording, we selected 10 clear pulses and measured the following parameters: average peak frequency, maximum peak frequency, centre frequency, minimum peak frequency, peak frequency at the start and end of the call, bandwidth at 90% peak amplitude, average entropy, and call duration. All frequency variables were measured in Hz and time variables in ms. We used the peak frequency contour to determine start and end frequencies and also used bandwidth at 90% peak amplitude because higher frequencies attenuate quickly with distance from the emitting bat (causing changes to the bandwidth), and these measures are therefore more reliable in field circumstances. Using this labelled call library as a training dataset, we trained a fine K-nearest neighbours classifier using supervised learning within the ‘Classification Learner’ app in MATLAB (Mathworks, Inc., Natick, MA, USA). We further employed fivefold cross-validation to obtain estimates of the accuracy of each classifier in assigning calls to species. Using these pairwise values of relative accuracy (%), we generated confusion matrices for these classifiers where the species identities were represented in the columns and rows as ‘True’ and ‘Predicted’ classes, respectively. Any species with classification accuracy below 85% was clubbed with possible confusion species into a “sonotype”, to improve accuracy of the classifier in the most conservative way possible (Fig. S4). The complete list of sonotypes and their mean echolocation call parameters is presented in Table 1. The classifier identified these sonotypes with  > 80% accuracy, with the exception of Miniopterus and the Plecotus type B call (which, however, we could manually identify because of their call structures and frequencies). For all subsequent analyses on functional diversity and phylogenetic diversity, we used these sonotypes to ensure accurate identification.Table 1 Trait matrix of the sonotypes in our assemblage (FA in mm; fmaxe, pfc.min, and pfc.max in kHz; Duration in ms).Full size tableNext, we analysed the passive recordings manually in Raven Pro. We labelled calls in subsets of 15 min per hour of the passive recordings. For each hour, the 15-min subsets were in the time windows 0–5 min, 20–25 min and 40–45 min, so as to spread out our sampling window across the hour. Following labelling, we obtained sonotype IDs using the classifier, and then verified them manually by visual comparison to the call library to improve discrimination. For every 5-min interval, we made a presence-absence matrix where 1 indicated the presence of a sonotype and 0 indicated its absence. The number of 5-min intervals in which a sonotype was detected (hereby “acoustic detections”) was summed up for each sampling point. We measured the relative abundance of sonotypes as the proportion of its total number of acoustic detections relative to the total number of acoustic detections of all sonotypes in a given elevational location. The use of such a presence-absence framework is akin to ‘Acoustic Activity Index’37 which represents a relatively less biased index of activity that is less affected by differences in vocal behaviour and echolocation frequencies of different species of bats.Assessing detectabilityTo assess the completeness of our species inventory, we estimated the species richness of each sampling point using the first-order Jackknife Estimator (Jack 1)38. Jack 1 is a nonparametric procedure for estimating species richness using presence or absence of a species in a given plot rather than its abundance39. Mean species detectability was calculated as the ratio of the observed to estimated species richness for different sampling point-year combinations40,41. We then assessed whether this mean species detectability depended on the habitat type, year, and location by fitting a linear model with the above-mentioned variables as fixed factor predictors and the mean detectability as a response. We also determined species-level detectability by following the approach of Kéry and Plattner42. If a sonotype was detected by mistnetting or acoustic sampling in sampling event i, we modelled its probability to be detected in sampling event i + 1. For each sonotype, we fitted a generalized linear mixed-effects model (logit link and binomial error distribution) with detection/non-detection as the response variable, and habitat type, location, and year as the fixed factor predictors. Site and species were included as random intercepts. The significance of the fixed effects was assessed with the Likelihood Ratio Test. This test allows one to choose the best of two nested models by assessing the ratio of their likelihoods. The significance of the random effect (species) was assessed by applying a parametric bootstrap (number simulations = 100) to the model with and without the random effect, using the function bootMer of ‘lme4’ package. In short, a parametric bootstrap consists of fitting the model to the data and bootstrapping the obtained residuals. For these and other statistical analyses we used R version 4.0.2 (R Core Team 2020).Taxonomic diversityWe calculated rarefied incidence-based species richness (SR) and Simpson diversity extrapolated to 50 sampling events (the number of sampling events in Mandal) using the ‘iNEXT’ R package43. The calculations were performed on a sonotype-by-sampling point presence-absence matrix with detections from both acoustic sampling and mistnetting pooled together. In the matrix, columns represented sampling units (Night 1, Night 2 and so on) and rows represented sonotype. By using sonotypes instead of species, we likely underestimated the SR, but this underestimation was uniform across elevations and is unlikely to change the pattern of SR with elevation.Functional diversityOur functional trait matrix (Table 1) comprised seven morphological and acoustic traits involved in guild classification, foraging and micro-habitat preferences (abbreviation followed by units): forearm length (FA, mm), aspect ratio (AR), wing loading (WL, N/m2), tip-shape index (I), echolocation peak frequency/frequency of maximum energy (FmaxE, kHz), minimum and maximum frequencies of the peak frequency contour (pfc.min and pfc.max, kHz) and call duration (D, ms). FA was measured in the field using vernier calipers. We used ImageJ (National Institutes of Health, Bethesda, MD, USA)44 to measure total wing area, areas of hand and arm wings and the wingspan from the standardised wing photos that were taken in the field. We calculated AR, WL, and I from these measurements following the equations given in Norberg and Rayner45. AR and WL both represent parameters that are correlated with flight aerodynamics and behaviour. I is influenced by the shape of the wing tip where values of 1 and above indicate broad, triangular tips, while those below 1 indicate acute wing tips. The four acoustic traits represent the shape of the echolocation call and they were measured from the reference recordings using Raven Pro, as described above.We first calculated the means for each of the seven traits across all species within a sonotype (thus obtaining one average trait value for each sonotype) (Table 1) and then used those to compute four multivariate functional diversity (FD) indices: functional richness (FRic), divergence (FDiv), evenness (FEve)46, and dispersion (FDis)47, using the function dbFD() in the ‘FD’ R package47. Our FD measures are unlikely to be underestimated due to the pooling of species into sonotypes because these species were similar in acoustic and morphological traits. FRic is the convex hull volume of the traits of species present in a community, measured in the multidimensional trait space. This measurement is not weighted by abundance, relative abundance or biomass of the species in the community, but, it is standardised such that it ranges from 0 to 1. FDiv reflects the distribution of abundance across taxa (sonotypes in our case) in the functional space. High FDiv means the taxa with extreme trait values are more abundant in a community whereas low FDiv means that those with the trait values close to the centre of the functional space are more abundant48. FEve, on the other hand, measures the evenness in the abundance distribution of taxa in the functional space. FEve is high when all taxa have similar abundances, and it is low when some functional groups are abundant while others are rare48. Lastly, FDis is measured as the mean distance of all taxa to the abundance-weighted trait community centroid. We performed two sets of analyses: one using the number of mistnet captures as a proxy for relative abundance, and another using the number of detections of different sonotypes in 5-min intervals in the passive recordings as a proxy of relative abundance. We did not pool acoustic detections and mistnet captures as they have inherently different detection probabilities and measure different entities (relative number of detections vs. number of captured individuals). Owing to rhinolophid bats at lower elevations being taxonomically and functionally different from the remaining species pool, we performed another set of FD calculations, excluding the four rhinolophid species and using acoustic detections as relative abundance. One species, Tadarida teniotis was commonly detected at all elevations on acoustic recorders, but we were unable to capture it as it foraged high above the ground, and thus were unable to collect morphological trait data. Additionally, in using acoustic detections as a measure of relative abundance, we had to exclude the non-echolocating pteropodid bat Sphaerias blanfordi which was caught only once at Chopta. Therefore, our FD values are likely systematically underestimated across all elevational communities, which does not affect the comparison of community composition across elevations.Phylogenetic diversityUsing the nexus file of a published phylogeny49, we pruned the tree to represent species in the 14 sonotypes. For each of these types, we chose the species most commonly mist-netted as representative of its group. Published DNA sequences are lacking for some of the species in this region, so we chose their closest relatives from the phylogeny instead. Thus, we made the following replacements: (a) Nyctalus leisleri represented the AMN sonotype, (b) Eptesicus serotinus represented the EH sonotype, (c) Murina aurata for Murina sonotype, (d) Myotis longipes for MS sonotype, (e) Pipistrellus javanicus for MP sonotype, and (f) Plecotus turkmenicus for Plecotus sonotype. After pruning the tree, we calculated three indices of phylogenetic diversity using the ‘picante’ R package50: Faith’s phylogenetic diversity (PD), Mean pairwise distance (MPD) and Mean nearest-taxon distance (MNTD). Faith’s PD is a measure of phylogenetic richness which is obtained by summing the branch lengths of the tree connecting the species in the community. MPD and MNTD measure phylogenetic dispersion of communities; whereas MPD measures the average phylogenetic distance among all the taxa in a community, MNTD measures the same for the nearest neighbouring taxa51. We weighted MPD and MNTD by relative abundance of the sonotypes in each community (like FD, the number of detections in five-minute intervals in the passive recordings was used as a proxy of relative abundance).Null model testingAs FD and PD are strongly correlated to species richness52, we used a null model to assess whether the observed was significantly different than expected due to chance alone. We produced the null distribution of each FD and PD index by randomizing the community matrix 999 times using the ‘independent swap’ method53,54, so as to preserve the species richness at each site and the number of sites in which each species can be found. Our randomization was further constrained by elevation, so that the abundances were randomized among the sampling points within each elevation. The null model allows for calculation of an effect size (difference between the observed value and mean of the null distribution). Given the range of FD and PD values, the effect sizes are not comparable across communities with vastly different species richness55. Therefore, standardized effect sizes (SES) of each index were calculated at each site as the difference between the observed value and the mean of the null distribution, divided by the standard deviation of the null distribution. SES  > 1 and SES  More

Plants and insectsRice (Oryza sativa) cultivar Minghui63 was used in this study. Rice plants were grown in a greenhouse at 27 ± 3 °C with 75 ± 10% RH (relative humidity) and a photoperiod of 16:8 h L:D (light:dark). The cultivation of rice plants followed the same procedure as described previously27. Plants were used for experiments when they were at the tillering stage, which occurred about 44–49 days after sowing.C. suppressalis larvae were reared on an artificial diet as described70. Ten percent honey water solution was provided to supply nutrition for the adults. N. lugens were maintained on a BPH-susceptible rice variety Taichung Native 1 (TN1)38. T. japonicum were obtained from Keyun Industry Co., Ltd (Jiyuan, China). Newly emerged adult wasps were maintained in glass tubes (3.5 cm diameter, 20 cm height) and supplied with 10% honey water solution as a food source and were maintained for at least 6 h to ensure free mating, before females were used for the following experiments. All three species were maintained in climatic chambers at 27 ± 1 °C, 75 ± 5% RH, and a photoperiod of 16:8 h L:D.Performance of caterpillars on insect-infested rice plantsMultiple types of rice plants were prepared: (i) uninfested plants, meaning that potted rice plants remained intact without insect infestation; (ii) SSB-infested plants, each potted rice plant was artificially infested with one 3rd instar SSB larva that had been starved for >3 h for 48 h; (iii) BPH-infested plants, each potted rice plant was artificially infested with a mix of fifteen 3rd and 4th instars BPH nymphs for 48 h; (iv) SSB/BPH-infested plants, each potted rice plant was simultaneously infested with one SSB larva and 15 BPH nymphs for 48 h; (v) SSB → BPH-infested plants, each potted rice plant was artificially infested with one SSB larvae alone for the first 24 h, then 15 BPH nymphs were additionally introduced for another 24 h; (vi) BPH → SSB-infested plants, namely each potted rice plant was artificially infested with 15 BPH nymphs for the first 24 h, then one SSB larvae were additionally introduced for another 24 h. Plant treatments were conducted as described in detail in our previous study27. During herbivory treatment, the uninfested plants were placed in a separate room to avoid possible volatile-mediated interference. During the subsequent bioassays, both SSB caterpillar and BPH nymphs remained in or on the rice plants.Two bioassays were conducted to test the performance of C. suppressalis larvae feeding on differently treated rice plants. The first bioassay included the plant treatments i, ii, iii, and vi, and the second bioassay included the plant treatments i, ii, v, and vi. Three 2-day-old larvae of C. suppressalis were gently introduced onto the middle stem of each rice plant using a soft brush. The infested rice plants were then placed in climatic chambers at 27 ± 1 °C, 75 ± 5% relative humidity, and a photoperiod of 16:8 h L:D. The C. suppressalis larvae were retrieved from the rice plants after 7 days feeding, and they were weighed on a precision balance (CPA2250, Sartorius AG, Germany; readability = 0.01 mg). The mean weight of the three caterpillars on each plant was considered as one biological replicate. The experiment was repeated four times using different batches of plants and herbivores, resulting in a total of 30–46 biological replicates for each treatment.Oviposition-preferences of C. suppressalis females choosing among differently infested rice plantsGreenhouse experimentIn the greenhouse, seven choice tests were conducted with C. suppressalis females including (i) SSB-infested plants versus uninfested plants; (ii) BPH-infested plants versus uninfested plants; (iii) SSB/BPH-infested plants versus uninfested plants; (iv) SSB-infested plants versus BPH-infested plants; (v) SSB-infested plants versus SSB/BPH-infested plants; (vi) BPH-infested plants versus SSB/BPH-infested plants; and (vii) the test in which C. suppressalis females were exposed to all four types of rice plants. The experiments were performed as described in detail by Jiao et al.30. In brief, four potted plants were positioned in the four corners of a cage (80 × 80 × 100 cm) made of 80-mesh nylon nets for each test. For paired comparisons, two potted plants belonging to the same treatment were placed in opposite corners of each age, and in the test with four types of rice plants, each type of plant was positioned in one of the four corners of each cage. Five pairs of freshly emerged moths (less than 1 day) were released in each cage, and a clean Petri dish (9 cm diameter) containing a cotton ball soaked with a 10% honey solution was placed in the center of the cage as food source. After 72 h, the number of individual eggs on each plant were determined. The experiment was conducted in a greenhouse at 27 ± 3 °C, 65 ± 10% RH, and a photoperiod of 16:8 h L:D. Each choice test was repeated with 9–11 times (replicates).Field cage experimentThe oviposition preference of SSB females was further assessed in a field near Langfang City (39.58° N, 116.48° E), China. Four choice tests were conducted: (i) SSB-infested plants versus uninfested plants; (ii) BPH-infested plants versus uninfested plants; (iii) SSB/BPH-infested plants versus uninfested plants; and (iv) SSB/BPH-infested plants versus SSB-infested plants. The treated rice plants were prepared as described above and were transplanted into experimental plots (1.5 × 1.5 m). For each pairwise comparison, six plots of rice plants were covered with a screened cage (8 × 5 × 2.5 m) made of 80-mesh nylon net to prevent moths from entering or escaping. Each of the six plots contained nine rice plants of a particular treatment, with three plots per cage representing the same treatment. Plots were separated by a 1 m buffer and they were alternately distributed in a 3 × 2 grid arrangement in each cage (Supplementary Fig. 4). Approximately 50 mating pairs of newly emerged C. suppressalis adults ( More

Yak and Tibetan sheep thrive under a co-grazing system on the QTP and/or are fed with the same materials; this offers an excellent opportunity to compare the gut microbiota in different host species which share a similar diet. In addition, the grazing systems on the QTP undergo seasonal diets changes in terms of pasture location and forage composition, especially between winter and summer. This presents a good natural “treatment” which helps vary the diets of the yak and Tibetan sheep populations. In the current study, based on a more substantial sample size than the previous study1, we found that diet and environment (represented by seasons winter and summer) superseded host genetics to the family level. That is to say that the gut microbiota of the two animal species showed convergent adaptation to high altitude and harsh environment in QTP, but this convergence had seasonal diets characteristics. These findings may provide a cautionary note for ongoing efforts to link host genetics to gut microbiota composition and function and would provide some food for thought in the breeding of these two livestock groups.The mammalian gut microbiota is acquired from the environment starting at birth, and its assembly and composition is largely shaped by factors such as age, diet, lifestyle, hygiene, and disease state. Researchers subconsciously believe that host species play a greater role than environmental factors when it comes to shaping gut microbiota, especially when there is a large taxonomical difference between the host species. So far, the vast majority of research have focused on the ruminal ecosystem because the rumen is primary site of feed fermentation15,16,17. It is rare to find studies that directly compare the gut microbiota of different species. However, evidence showed that energetically-important microbial products, including VFA (10–13% of total GIT VFA) are produced in the ruminant distal gut3. Hence, it is important to study the composition of distal gut microbiota of ruminants.In this study, at the phylum level, the gut microbiota composition in both groups of livestock was dominated by Bacteroidetes and Firmicutes, which was in agreement with previous reports concerning the yak18. At the same time our result consistently with other study in dairy cows that two dominated phyla Bacteroidetes and Firmicutes found in fecal samples in different seasons were abundant19,20. Firmicutes and Bacteroidetes are responsible for digestion of carbohydrates and proteins, Members of Bacteroidetes having extremely stronger ability to degrade crystalline cellulose. The previous report showed that intestinal microbiome plays an important role in digestion and absorption of the food, and maintaining animals’ health21,22. Intestinal tracts of the ruminants are rich in symbiotic bacteria that helps the body digest plant fibers23,24. Glycans are processed by the distal gut microbiota, generating biologically significant short-chain fatty acids (SCFAs, predominantly acetate, butyrate, and propionate), which serve as the principal energy source for colonocytes25. Fibers may be involved in the regulation of food intake and energy balance via the SCFA-mediated modulation of the secretion of gut hormones26. The higher abundance of Firmicutes and Bacteroidetes in yak may be associated with high-energy consumption at high altitude18.It is worth noting that, at the family level, the dominant genera (Unclassified Ruminococcaceae, Bacteroidaceae, Unclassified BS11, Unclassified Prevotellaceae, Unclassified Christensenellaceae, CF231, Unclassified Mogibacteriaceae and Unclassified Paraprevotellaceae) in the intestines of yak and Tibetan sheep were more greatly influenced by season than genetics (Fig. 5). This has not previously been accurately identified, which may be because there have been few studies into the gut microbial communities of the yak and Tibetan sheep in QTP. So, to improve their husbandry, it is important in the future to study their microbiota profiles using more precise methods such as 16S full-length sequencing or metagenomic sequencing. Ruminococcaceae is a family of autochthonous and benignspecies that primarily inhabit in the caecum and the colon27. It is known that Ruminococcaceae are common in the rumen and hindgut of ruminants, capable of degrading cellulose and starch28. As a member of short chain fatty acid (SCFA) producers, Ruminococcaceae is considered to be the most important fiber and polysaccharides-degrading bacterium in the intestine of herbivores, and produces large amounts of cellulolytic enzymes, including exoglucanases, endoglucanase, glucosidases and hemicellulase29. The microbial community of Yak and Sheep is greatly influenced by alterations in dietary nutrition, Bacteroidaceae have the ability to degrade complex molecules (polysaccharides, proteins) in the intestine18, which can promote the Yak utilizes grasses as its major source of nutrition, due to shortage of grain and other nutrients. Prevotellaceae is responsible for hemicellulose, pectin and high carbohydrate food digestion30. The higher abundance of these microbes may contribute to gaining more energy, and play vital roles in the process of adaption of the hosts to the harsh natural environment15. Bacteroidales BS11 gut group are specialized to active hemicellulose monomeric sugars (e.g., xylose, fucose, mannose and rhamnose) fermentation and short-chain fatty acid (e.g., acetate and butyrate) production that are vital for ruminant energy31. The Bacteroidales BS11 was positively correlated with some metabolites that are involved in amino acid metabolism and biosynthesis, as well as the metabolism of energy sources, such as starch, sucrose, and galactose32.At the genus level, 5-7N15 was most abundant in winter in both animals, on the contrary, the Provotella was predominate. Here, our results indicated that seasonal diets change superseded variations derived from genetic differences between the host species, even though the yak and Tibetan sheep are very different, both taxonomically and in terms of body size. In summer, the forage grass on the Qinghai-Tibet Plateau is dominated by Agropyron cristatum, Elymus nutans, Festuca ovina, Kobresia humilis, Poa pratensis, Stipa aliena, Kobresia pygmaea, Oxytropis biflora, Saussurea hieracioides, Astragalus arnoldii Hemsl. In winter, the main forage was Brachypodium sylvaticum. Carex crebra, Trisetum spicatum and Bupleurum smithii. Stipa has both high palatability and nutritional value, with a high content of crude protein, crude fat, and nitrogen- free extract, and low levels of crude fiber33. The levels of crude protein, crude fat, and nitrogen-free extracts of Brachypodium sylvaticum. Carex crebra, Trisetum spicatum and Bupleurum smithii were lower than that of Stipa, whereas the content of crude fiber was higher than that of Stipa34. Crude protein is the main nutrient of herbage. Crude fat and nitrogen-free extracts provide heat and energy33.Lopes et al. reported that some OTUs known to be functionally relevant for fiber degradation and host development were shared across the entire gastrointestinal tract and present within the feces35. Microbial diversity increases in the distal segments of the gastrointestinal tract. Microbial fermentation appears to be reestablished in the large intestine, with the proportion of acetate, propionate and butyrate being similar to the rumen.Several explanations for this phenomenon are possible. Firstly, both the yak and Tibetan sheep are ruminants. In herbivores, the gut microbiota is dominated by Firmicutes and Bacteroides, the functions of which are related to cellulose digestion36. Therefore, ruminant microbes could possibly be more similar across species than gut microbes from elsewhere.Secondly, the yaks and Tibetan sheep in our study co-grazed from birth to death. As such, the initial gut microbiota source, responsible for populating the remainder of the gut in the months and years after the initial seeding at birth, would necessarily come from the same environment. It has been established that early life events are critical for gut microbiota development and for shaping the adult microbiota. Lifestyle and diet will further influence the composition and function of the gut microbiota. In our study, the investigated animals shared a very similar lifestyle and obtained their diets from the same source. The results revealed that sheep and yaks presented almost identical gut microbiota compositions in the winter, but by the date of collection of the summer samples they were quite different. The reason for this could be that during summer and summer there is pronounced pastoral grass growth, giving the animals more variety and choice in their diets; it is known, after all, that sheep have different diet preferences to yaks37. However, during the winter, the animals have no option but to eat the same food in order to survive until winter.Thirdly, there could be a convergent evolution of gut microbiomes in yaks and Tibetan sheep due to the extremely harsh environment in high-altitude regions1,38. When compared with their low-altitude relatives, cattle (Bos taurus) and ordinary sheep (Ovis aries), metagenomic analyses revealed significant enrichment in rumen microbial genes involving volatile fatty acid-yielding pathways in yaks and Tibetan sheep, whereas methanogenesis pathways were enriched in the cattle metagenome. Analyses of RNA transcriptomes revealed significant upregulation in 36 genes associated with volatile fatty acid transport and absorption in the ruminal epithelium of yaks and Tibetan sheep. This suggests that, aside from host genetics, long-term exposure to harsh environments has allowed the gut microbiome to adapt in order to boost health and survival. In other words, although yaks and Tibetan sheep are very different genetically, their gut microbiota could be similar due to the selection pressures of the high altitude at which they live. Meanwhile, from our data based on functional gene composition (Fig. S3), it is also worth noting that there were no groups clearly distinguished from one another, although the PERMANOVA results indicated both a host and season effect, with the interaction between them being statistically significant. Though factors such as environment and diet (represented by seasons) can trump host genetics, we could not ignore the interplay of these factors as gut microbes are a very complex community.Winter is the harshest period for the survival of yak and Tibetan sheep. To maintain the survival, it’s best to feed the animals with a high protein content. Furthermore, to get more detailed data in different seasons and various dietary habits of yak and sheep, more study should be assessed about intestinal microbiota by collecting feces. More

1.Letko, M., Seifert, S. N., Olival, K. J., Plowright, R. K. & Munster, V. J. Bat-borne virus diversity, spillover and emergence. Nat. Rev. Microbiol. 18, 461–471 (2020). This is a review of the overall diversity of bat-borne coronaviruses and research agenda for enhanced characterization of their zoonotic and pandemic potential.CAS 
PubMed 

Google Scholar 
2.Dobson, B. A. P. et al. Ecology and economics for pandemic prevention. Science 369, 379–381 (2020).CAS 
PubMed 

Google Scholar 
3.Plowright, R. K. et al. Land use-induced spillover: a call to action to safeguard environmental, animal, and human health. Lancet Planet. Heal. 5, e237–e245 (2021).
Google Scholar 
4.Shivaprakash, K. N., Sen, S., Paul, S., Kiesecker, J. M. & Bawa, K. S. Mammals, wildlife trade, and the next global pandemic. Curr. Biol. 31, 3671–3677.e3 (2021).CAS 
PubMed 

Google Scholar 
5.Huong, N. Q. et al. Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013–2014. PLoS ONE 15, e0237129 (2020). This study provides evidence that coronavirus detection increases along the supply chain of rodents destined for human consumption.CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Xiao, X., Newman, C., Buesching, C. D., Macdonald, D. W. & Zhou, Z.-M. Animal sales from Wuhan wet markets immediately prior to the COVID-19 pandemic. Sci. Rep. 11, 11898 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Chen, L., Liu, B., Yang, J. & Jin, Q. DBatVir: the database of bat-associated viruses. Database 2014, 1–7 (2014).
Google Scholar 
8.Tao, Y. et al. Surveillance of bat coronaviruses in Kenya identifies relatives of human coronaviruses NL63 and 229E and their recombination history. J. Virol. 91, 1–16 (2017).CAS 

Google Scholar 
9.Cui, J., Li, F. & Shi, Z. L. Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol. 17, 181–192 (2019).CAS 
PubMed 

Google Scholar 
10.Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020). This is one of the first studies to discover viruses related to SARS-CoV-2 in wild Rhinolophus spp. bats in China.CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Forni, D., Cagliani, R., Clerici, M. & Sironi, M. Molecular evolution of human coronavirus genomes. Trends Microbiol. 25, 35–48 (2017).CAS 
PubMed 

Google Scholar 
12.Zhou, P. et al. Fatal swine acute diarrhoea syndrome caused by an HKU2-related coronavirus of bat origin. Nature 556, 255–258 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Wang, N. et al. Serological evidence of bat SARS-related coronavirus infection in humans, China. Virol. Sin. 33, 104–107 (2018). This study provides evidence of potentially undetected spillovers of bat-associated coronaviruses in rural human populations in China.PubMed 
PubMed Central 

Google Scholar 
14.Li, H. et al. Human-animal interactions and bat coronavirus spillover potential among rural residents in southern China. Biosaf. Heal. 1, 84–90 (2019).
Google Scholar 
15.Woo, P. C. Y. et al. Discovery of seven novel mammalian and avian coronaviruses in the genus Deltacoronavirus supports bat coronaviruses as the gene source of Alphacoronavirus and Betacoronavirus and avian coronaviruses as the gene source of Gammacoronavirus and Deltacoronavirus. J. Virol. 86, 3995–4008 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Anthony, S. J. et al. Global patterns in coronavirus diversity. Virus Evol. 3, 1–15 (2017). This is a review of ecological patterns of associations between bats and coronaviruses with information up to 2014.
Google Scholar 
17.Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 310, 676–679 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Lau, S. K. P. et al. Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats. Proc. Natl Acad. Sci. USA 102, 14040–14045 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Anthony, S. et al. Coronaviruses in bats from Mexico. J. Gen. Virol. 94, 1028–1038 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Latinne, A. et al. Origin and cross-species transmission of bat coronaviruses in China. Nat. Commun. 11, 4235 (2020). Using 5 years of surveillance data on coronaviruses in bats in China, the authors show that host switching is common in bat coronaviruses, particularly in Rhinolophus spp.CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Ithete, N. L. et al. Close relative of human Middle East respiratory syndrome coronavirus in bat, South Africa. Emerg. Infect. Dis. 19, 1697–1699 (2013).PubMed 
PubMed Central 

Google Scholar 
22.Råberg, L., Graham, A. L. & Read, A. F. Decomposing health: tolerance and resistance to parasites in animals. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 37–49 (2009).PubMed 

Google Scholar 
23.Schlottau, K. et al. SARS-CoV-2 in fruit bats, ferrets, pigs, and chickens: an experimental transmission study. Lancet Microbe 1, e218–e225 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Munster, V. J. et al. Replication and shedding of MERS-CoV in Jamaican fruit bats (Artibeus jamaicensis). Sci. Rep. 6, 1–10 (2016).
Google Scholar 
25.van Doremalen, N. et al. SARS-like coronavirus WIV1-CoV does not replicate in Egyptian fruit bats (Rousettus aegyptiacus). Viruses 10, 727 (2018).PubMed Central 

Google Scholar 
26.Plowright, R. K. et al. Transmission or within-host dynamics driving pulses of zoonotic viruses in reservoir–host populations. PLoS Negl. Trop. Dis. 10, 1–21 (2016).
Google Scholar 
27.Jeong, J. et al. Persistent infections support maintenance of a coronavirus in a population of Australian bats (Myotis macropus). Epidemiol. Infect. 145, 2053–2061 (2017).CAS 
PubMed 

Google Scholar 
28.Watanabe, S. et al. Bat coronaviruses and experimental infection of bats, the Philippines. Emerg. Infect. Dis. 16, 1217–1223 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Subudhi, S. et al. A persistently infecting coronavirus in hibernating Myotis lucifugus, the North American little brown bat. J. Gen. Virol. 98, 2297–2309 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Widagdo, W. et al. Tissue distribution of the MERS-coronavirus receptor in bats. Sci. Rep. 7, 1–8 (2017).CAS 

Google Scholar 
31.Banerjee, A. et al. Selection of viral variants during persistent infection of insectivorous bat cells with Middle East respiratory syndrome coronavirus. Sci. Rep. 10, 7257 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Wang, M.-N. N. et al. Longitudinal surveillance of SARS-like coronaviruses in bats by quantitative real-time PCR. Virol. Sin. 31, 78–80 (2016).PubMed 
PubMed Central 

Google Scholar 
33.Ge, X. Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, 1–27 (2017).CAS 

Google Scholar 
35.Smith, C. Australian bat coronaviruses (The University of Queensland, 2015).36.Baldwin, H. J. Epidemiology and ecology of virus and host: bats and coronaviruses in Ghana, West Africa (Macquarie University & Ulm University, 2015).37.Joffrin, L. et al. Bat coronavirus phylogeography in the Western Indian Ocean. Sci. Rep. 10, 1–11 (2020).
Google Scholar 
38.Plowright, R. K. et al. Reproduction and nutritional stress are risk factors for Hendra virus infection in little red flying foxes (Pteropus scapulatus). Proc. R. Soc. B Biol. Sci. 275, 861–869 (2008).
Google Scholar 
39.Peel, A. J. et al. Synchronous shedding of multiple bat paramyxoviruses coincides with peak periods of Hendra virus spillover. Emerg. Microbes Infect. 8, 1314–1323 (2019). This study provides evidence of co-circulation of multiple viruses in single and multispecies roosts of flying foxes, with higher diversity of viruses in mixed-species roosts.PubMed 
PubMed Central 

Google Scholar 
40.Wacharapluesadee, S. et al. Longitudinal study of age-specific pattern of coronavirus infection in Lyle’s flying fox (Pteropus lylei) in Thailand. Virol. J. 15, 1–10 (2018).
Google Scholar 
41.Lau, S. K. P. et al. Ecoepidemiology and complete genome comparison of different strains of severe acute respiratory syndrome-related Rhinolophus bat coronavirus in China reveal bats as a reservoir for acute, self-limiting infection that allows recombination events. J. Virol. 84, 2808–2819 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Willoughby, A., Phelps, K. & Olival, K. A comparative analysis of viral richness and viral sharing in cave-roosting bats. Diversity 9, 35 (2017).
Google Scholar 
43.Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Lee, K. A. Linking immune defenses and life history at the levels of the individual and the species. Integr. Comp. Biol. 46, 1000–1015 (2006).CAS 
PubMed 

Google Scholar 
45.Robinson, D. P. & Klein, S. L. Pregnancy and pregnancy-associated hormones alter immune responses and disease pathogenesis. Horm. Behav. 62, 263–271 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Pauly, M. et al. Novel alphacoronaviruses and paramyxoviruses cocirculate with type 1 and severe acute respiratory system (SARS)-related betacoronaviruses in synanthropic bats of Luxembourg. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01326-17 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
47.Drexler, J. F. et al. Amplification of emerging viruses in a bat colony. Emerg. Infect. Dis. 17, 449–456 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Annan, A. et al. Human betacoronavirus 2c EMC/2012-related viruses in bats, Ghana and Europe. Emerg. Infect. Dis. 19, 456–459 (2013).PubMed 
PubMed Central 

Google Scholar 
49.Montecino-Latorre, D. et al. Reproduction of East-African bats may guide risk mitigation for coronavirus spillover. One Heal. Outlook 2, 2 (2020).
Google Scholar 
50.Plowright, R. K., Becker, D. J., McCallum, H. & Manlove, K. R. Sampling to elucidate the dynamics of infections in reservoir hosts. Philos. Trans. R. Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2018.0336 (2019).Article 

Google Scholar 
51.Vanalli, C. et al. Within-host mechanisms of immune regulation explain the contrasting dynamics of two helminth species in both single and dual infections. PLoS Comput. Biol. 16, 1–19 (2020).
Google Scholar 
52.Ge, X. Y. et al. Coexistence of multiple coronaviruses in several bat colonies in an abandoned mineshaft. Virol. Sin. 31, 31–40 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Chu, D. K. W., Peiris, J. S. M., Chen, H., Guan, Y. & Poon, L. L. M. Genomic characterizations of bat coronaviruses (1A, 1B and HKU8) and evidence for co-infections in Miniopterus bats. J. Gen. Virol. 89, 1282–1287 (2008).CAS 
PubMed 

Google Scholar 
54.Drexler, J. F. et al. Genomic characterization of severe acute respiratory syndrome-related coronavirus in European bats and classification of coronaviruses based on partial RNA-dependent RNA polymerase gene sequences. J. Virol. 84, 11336–11349 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Tong, S. et al. Detection of novel SARS-like and other coronaviruses in bats from Kenya. Emerg. Infect. Dis. 15, 482–485 (2009).PubMed 
PubMed Central 

Google Scholar 
56.Wacharapluesadee, S. et al. Diversity of coronavirus in bats from eastern Thailand emerging viruses. Virol. J. 12, 1–7 (2015).
Google Scholar 
57.Valitutto, M. T. et al. Detection of novel coronaviruses in bats in Myanmar. PLoS ONE 15, e0230802 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Anthony, S. J. et al. A strategy to estimate unknown viral diversity in mammals. mBio 4, 289 (2013).
Google Scholar 
59.Prada, D., Boyd, V., Baker, M. L., O’Dea, M. & Jackson, B. Viral diversity of microbats within the south west botanical province of Western Australia. Viruses 11, 1–21 (2019).
Google Scholar 
60.Seltmann, A. et al. Seasonal fluctuations of astrovirus, but not coronavirus shedding in bats inhabiting human-modified tropical forests. Ecohealth 14, 272–284 (2017).PubMed 
PubMed Central 

Google Scholar 
61.Chu, D. K. W., Poon, L. L. M., Guan, Y. & Peiris, J. S. M. Novel astroviruses in insectivorous Bats. J. Virol. 82, 9107–9114 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Kemenesi, G. et al. Molecular survey of RNA viruses in Hungarian bats: discovering novel astroviruses, coronaviruses, and caliciviruses. Vector Borne Zoonotic Dis. 14, 846–855 (2014).PubMed 

Google Scholar 
63.Rizzo, F. et al. Coronavirus and paramyxovirus in bats from northwest Italy. BMC Vet. Res. 13, 1–11 (2017).
Google Scholar 
64.Paskey, A. C. et al. The temporal RNA virome patterns of a lesser dawn bat (Eonycteris spelaea) colony revealed by deep sequencing. Virus Evol. 6, 1–14 (2020).
Google Scholar 
65.Davy, C. M. et al. White-nose syndrome is associated with increased replication of a naturally persisting coronaviruses in bats. Sci. Rep. 8, 15508 (2018).PubMed 
PubMed Central 

Google Scholar 
66.Woo, P. C. Y., Lau, S. K. P., Huang, Y. & Yuen, K.-Y. Y. Coronavirus diversity, phylogeny and interspecies jumping. Exp. Biol. Med. 234, 1117–1127 (2009).CAS 

Google Scholar 
67.Fehr, A. R. & Perlman, S. in Coronaviruses. Methods in Molecular Biology Vol 1282 (eds Maier, H., Bickerton, E. & Britton, P.) 1–23 (Humana Press, 2015).68.Duffy, S., Shackelton, L. A. & Holmes, E. C. Rates of evolutionary change in viruses: patterns and determinants. Nat. Rev. Genet. 9, 267–276 (2008).CAS 
PubMed 

Google Scholar 
69.Jenkins, G. M., Rambaut, A., Pybus, O. G. & Holmes, E. C. Rates of molecular evolution in RNA viruses: a quantitative phylogenetic analysis. J. Mol. Evol. 54, 156–165 (2002).CAS 
PubMed 

Google Scholar 
70.Eckerle, L. D. et al. Infidelity of SARS-CoV Nsp14-exonuclease mutant virus replication is revealed by complete genome sequencing. PLoS Pathog. 6, 1–15 (2010).
Google Scholar 
71.Ogando, N. S. et al. The curious case of the nidovirus exoribonuclease: its role in RNA synthesis and replication fidelity. Front. Microbiol. 10, 1–17 (2019).
Google Scholar 
72.Nga, P. T. et al. Discovery of the first insect nidovirus, a missing evolutionary link in the emergence of the largest RNA virus genomes. PLoS Pathog. 7, e1002215 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Smith, E., Blanc, H., Vignuzzi, M. & Denison, M. R. Coronaviruses lacking exoribonuclease activity are susceptible to lethal mutagenesis: evidence for proofreading and potential therapeutics. PLoS Pathog. 9, e1003565 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Martin, L. B. et al. Extreme competence: keystone hosts of infections. Trends Ecol. Evol. 34, 303–314 (2019).PubMed 
PubMed Central 

Google Scholar 
75.Graham, R. L. & Baric, R. S. Recombination, reservoirs, and the modular spike: mechanisms of coronavirus cross-species transmission. J. Virol. 84, 3134–3146 (2010).CAS 
PubMed 

Google Scholar 
76.Letko, M. et al. Adaptive evolution of MERS-CoV to species variation in DPP4. Cell Rep. 24, 1730–1737 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Ermonval, M., Baychelier, F. & Tordo, N. What do we know about how hantaviruses interact with their different hosts? Viruses 8, 223 (2016).PubMed Central 

Google Scholar 
78.Su, S. et al. Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol. 24, 490–502 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Lai, M. M. et al. Recombination between nonsegmented RNA genomes of murine coronaviruses. J. Virol. 56, 449–456 (1985).CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Tian, P.-F. et al. Evidence of recombinant strains of porcine epidemic diarrhea virus, United States, 2013. Emerg. Infect. Dis. 20, 1731–1734 (2014).
Google Scholar 
81.Terada, Y. et al. Emergence of pathogenic coronaviruses in cats by homologous recombination between feline and canine coronaviruses. PLoS ONE 9, e106534 (2014).PubMed 
PubMed Central 

Google Scholar 
82.Decaro, N. et al. Recombinant canine coronaviruses related to transmissible gastroenteritis virus of swine are circulating in dogs. J. Virol. 83, 1532–1537 (2009).CAS 
PubMed 

Google Scholar 
83.Zhang, Y. et al. Genotype shift in human coronavirus OC43 and emergence of a novel genotype by natural recombination. J. Infect. 70, 641–650 (2015).PubMed 

Google Scholar 
84.Pyrc, K., Berkhout, B. & van der hoek, L. in Recent Research in Development of Infection & Immunity 3rd edn 25–48 (Transworld Research Network, 2005).85.Woo, P. C. Y. et al. Phylogenetic and recombination analysis of coronavirus HKU1, a novel coronavirus from patients with pneumonia. Arch. Virol. 150, 2299–2311 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Zhang, X. W., Yap, Y. L. & Danchin, A. Testing the hypothesis of a recombinant origin of the SARS-associated coronavirus. Arch. Virol. 150, 1–20 (2005).CAS 
PubMed 

Google Scholar 
87.Stanhope, M. J., Brown, J. R. & Amrine-Madsen, H. Evidence from the evolutionary analysis of nucleotide sequences for a recombinant history of SARS-CoV. Infect. Genet. Evol. 4, 15–19 (2004).CAS 
PubMed 

Google Scholar 
88.Wang, Y. et al. Origin and possible genetic recombination of the middle east respiratory syndrome coronavirus from the first imported case in China: phylogenetics and coalescence analysis. MBio 6, 1–6 (2015).
Google Scholar 
89.Huang, C. et al. A bat-derived putative cross-family recombinant coronavirus with a reovirus gene. PLoS Pathog. 12, 1–25 (2016). This study provides evidence of cross-family recombination between coronaviruses and reoviruses.
Google Scholar 
90.Drexler, J. F., Corman, V. M. & Drosten, C. Ecology, evolution and classification of bat coronaviruses in the aftermath of SARS. Antivir. Res. 101, 45–56 (2014).CAS 
PubMed 

Google Scholar 
91.Cui, J. et al. Evolutionary relationships between bat coronaviruses and their hosts. Emerg. Infect. Dis. 13, 1526–1532 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Davies, N. et al. Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Sci 372, eabg3055 (2021).CAS 

Google Scholar 
93.Reguera, J., Mudgal, G., Santiago, C. & Casasnovas, J. M. A structural view of coronavirus-receptor interactions. Virus Res. 194, 3–15 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
94.Lim, Y., Ng, Y., Tam, J. & Liu, D. Human coronaviruses: a review of virus–host interactions. Diseases 4, 26 (2016).
Google Scholar 
95.Masters, P. S. & Perlman, S. Coronaviridae. Fields Virol. 1, 825–858 (2013).
Google Scholar 
96.Letko, M., Marzi, A. & Munster, V. Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat. Microbiol. 5, 562–569 (2020). This study uses a functional viromics platform to rapidly characterize the zoonotic potential of new coronaviruses on the basis of genome sequences.CAS 
PubMed 
PubMed Central 

Google Scholar 
97.van Doremalen, N. et al. Host species restriction of middle east respiratory syndrome coronavirus through its receptor, dipeptidyl peptidase 4. J. Virol. 88, 9220–9232 (2014).PubMed 
PubMed Central 

Google Scholar 
98.Conceicao, C. et al. The SARS-CoV-2 spike protein has a broad tropism for mammalian ACE2 proteins. PLoS Biol. 18, e3001016 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
99.Li, W. et al. Receptor and viral determinants of SARS-coronavirus adaptation to human ACE2. EMBO J. 24, 1634–1643 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
100.Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA 117, 22311–22322 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Shi, J. et al. Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS–coronavirus 2. Science 1020, 1016–1020 (2020).
Google Scholar 
102.Oude Munnink, B. B. et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 371, 172–177 (2021). The study provides evidence of spillback of SARS-CoV-2 into mink populations, rapid and widespread transmission, and seeding of mink-associated genetic variants back into humans.CAS 
PubMed 

Google Scholar 
103.Hoffmann, M. et al. Differential sensitivity of bat cells to infection by enveloped RNA viruses: coronaviruses, paramyxoviruses, filoviruses, and influenza viruses. PLoS ONE 8, e72942 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
104.Li, W. et al. Animal origins of the severe acute respiratory syndrome coronavirus: insight from ACE2-S-protein interactions. J. Virol. 80, 4211–4219 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
105.Hou, Y. et al. Angiotensin-converting enzyme 2 (ACE2) proteins of different bat species confer variable susceptibility to SARS-CoV entry. Arch. Virol. 155, 1563–1569 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
106.Boni, M. F. et al. Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nat. Microbiol. 5, 1408–1417 (2020). This study provids phylogenetic evidence that suggests that the ancestral lineages from which SARS-CoV-2 may have originated have circulated undetected in bats for decades.CAS 
PubMed 

Google Scholar 
107.Wells, H. L. et al. The evolutionary history of ACE2 usage within the coronavirus subgenus Sarbecovirus. Virus Evol. 7, 1–22 (2021).
Google Scholar 
108.Urbanowicz, R. A. et al. Human adaptation of Ebola virus during the West African outbreak. Cell 167, 1079–1087.e5 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
109.Gupta, A. et al. Extrapulmonary manifestations of COVID-19. Nat. Med. 26, 1017–1032 (2020).CAS 

Google Scholar 
110.Hamming, I. et al. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. 203, 631–637 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
111.Lam, T. T. Y. et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).CAS 
PubMed 

Google Scholar 
112.Martina, B. E. E. et al. SARS virus infection of cats and ferrets. Nature 425, 915 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
113.Munster, V. J. et al. Respiratory disease in rhesus macaques inoculated with SARS-CoV-2. Nature 585, 268–272 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
114.Hou, Y. J. et al. SARS-CoV-2 reverse genetics reveals a variable infection gradient in the respiratory tract. Cell 182, 429–446.e14 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
115.Menachery, V. D. et al. Trypsin treatment unlocks barrier for zoonotic bat coronavirus infection. J. Virol. 94, 1–15 (2019).
Google Scholar 
116.Qian, Z., Dominguez, S. R. & Holmes, K. V. Role of the spike glycoprotein of human Middle East respiratory syndrome coronavirus (MERS-CoV) in virus entry and syncytia formation. PLoS ONE 8, 1–12 (2013).
Google Scholar 
117.Belouzard, S., Chu, V. C. & Whittaker, G. R. Activation of the SARS coronavirus spike protein via sequential proteolytic cleavage at two distinct sites. Proc. Natl Acad. Sci. USA 106, 5871–5876 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
118.Barlan, A. et al. Receptor variation and susceptibility to middle east respiratory syndrome coronavirus infection. J. Virol. 88, 4953–4961 (2014).PubMed 
PubMed Central 

Google Scholar 
119.Zheng, Y. et al. Lysosomal proteases are a determinant of coronavirus tropism. J. Virol. 92, 1–14 (2018).
Google Scholar 
120.Bertram, S. et al. Cleavage and activation of the severe acute respiratory syndrome coronavirus spike protein by human airway trypsin-like protease. J. Virol. 85, 13363–13372 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
121.Matsuyama, S., Ujike, M., Morikawa, S., Tashiro, M. & Taguchi, F. Protease-mediated enhancement of severe acute respiratory syndrome coronavirus infection. Proc. Natl Acad. Sci. USA 102, 12543–12547 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
122.Yang, Y. et al. Receptor usage and cell entry of bat coronavirus HKU4 provide insight into bat-to-human transmission of MERS coronavirus. Proc. Natl Acad. Sci. USA 111, 12516–12521 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
123.Wacharapluesadee, S. et al. Group C betacoronavirus in bat guano fertilizer, Thailand. Emerg. Infect. Dis. 19, 1349–1351 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
124.Luk, H. K. H., Li, X., Fung, J., Lau, S. K. P. & Woo, P. C. Y. Molecular epidemiology, evolution and phylogeny of SARS coronavirus. Infect. Genet. Evol. 71, 21–30 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
125.Wu, K., Peng, G., Wilken, M., Geraghty, R. J. & Li, F. Mechanisms of host receptor adaptation by severe acute respiratory syndrome coronavirus. J. Biol. Chem. 287, 8904–8911 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
126.Daszak, P., Olival, K. J. & Li, H. A strategy to prevent future epidemics similar to the 2019-nCoV outbreak. Biosaf. Heal. 2, 6–8 (2020).
Google Scholar 
127.Tidemann, C. & Vardon, M. Pests, pestilence, pollen and pot roasts: the need for community based management of flying foxes in Australia. Aust. Biol. 10, 77–83 (1997).
Google Scholar 
128.Mickleburgh, S., Waylen, K. & Racey, P. Bats as bushmeat: a global review. Oryx 43, 217–234 (2009).
Google Scholar 
129.Tuttle, M. D. & Moreno, A. Cave-Dwelling Bats of Northern Mexico: Their Value and Conservation Needs (Bat Conservation International, 2005).130.Rulli, M. C., D’Odorico, P., Galli, N. & Hayman, D. T. S. Land-use change and the livestock revolution increase the risk of zoonotic coronavirus transmission from rhinolophid bats. Nat. Food https://doi.org/10.1038/s43016-021-00285-x (2021).Article 

Google Scholar 
131.Mckee, C. D., Islam, A., Luby, S. P., Salje, H. & Hudson, P. J. The ecology of Nipah virus in Bangladesh: a nexus of land use change and opportunistic feeding behavior in bats. Viruses 13, 169 (2020).
Google Scholar 
132.Kessler, M. K. et al. Changing resource landscapes and spillover of henipaviruses. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.13910 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
133.Dighe, A., Jombart, T., Van Kerkhove, M. D. & Ferguson, N. A systematic review of MERS-CoV seroprevalence and RNA prevalence in dromedary camels: implications for animal vaccination. Epidemics 29, 100350 (2019).PubMed 
PubMed Central 

Google Scholar 
134.Hegde, S. T. et al. Using healthcare-seeking behaviour to estimate the number of Nipah outbreaks missed by hospital-based surveillance in Bangladesh. Int. J. Epidemiol. 48, 1219–1227 (2019).PubMed 
PubMed Central 

Google Scholar 
135.Glennon, E. E., Jephcott, F. L., Restif, O. & Wood, J. L. N. Estimating undetected Ebola spillovers. PLoS Negl. Trop. Dis. 13, 1–10 (2019).
Google Scholar 
136.Matson, M. J., Chertow, D. S. & Munster, V. J. Delayed recognition of Ebola virus disease is associated with longer and larger outbreaks. Emerg. Microbes Infect. 9, 291–301 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
137.Zheng, B. J. et al. SARS-related virus predating SARS outbreak, Hong Kong. Emerg. Infect. Dis. 10, 176–178 (2004). This study provides serological evidence that populations in Hong Kong sampled in 2001 may have been exposed to SARS-CoV or related viruses in bats or other animals before the first SARS outbreaks.PubMed 
PubMed Central 

Google Scholar 
138.Yu, S. et al. Retrospective serological investigation of severe acute respiratory syndrome coronavirus antibodies in recruits from mainland China. Clin. Diagn. Lab. Immunol. 12, 552–554 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
139.Lloyd-Smith, J. O. et al. Epidemic dynamics at the human-animal interface. Science 326, 1362–1367 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
140.Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502–510 (2017). This study formulates a conceptual model for the multiple layers of ecological and cellular barriers that affect the likelihood of pathogen spillover from animals.CAS 
PubMed 
PubMed Central 

Google Scholar 
141.Klompus, S. et al. Cross-reactive antibodies against human coronaviruses and the animal coronavirome suggest diagnostics for future zoonotic spillovers. Sci. Immunol. 6, eabe9950 (2021).PubMed 

Google Scholar 
142.Field, H. E. Hendra virus ecology and transmission. Curr. Opin. Virol. 16, 120–125 (2016).PubMed 

Google Scholar 
143.Chua, K. B. Nipah virus outbreak in Malaysia. J. Clin. Virol. 26, 265–275 (2003).PubMed 

Google Scholar 
144.Azhar, E. I. et al. Evidence for camel-to-human transmission of MERS coronavirus. N. Engl. J. Med. 370, 2499–2505 (2014).CAS 
PubMed 

Google Scholar 
145.Memish, Z. A. et al. Respiratory tract samples, viral load, and genome fraction yield in patients with middle east respiratory syndrome. J. Infect. Dis. 210, 1590–1594 (2014).CAS 
PubMed 

Google Scholar 
146.Buchholz, U. et al. Contact investigation of a case of human novel coronavirus infection treated in a German hospital, October-November 2012. Eurosurveillance 18, 1–7 (2013).
Google Scholar 
147.Chu, D. K. W. et al. MERS coronaviruses in dromedary camels, Egypt. Emerg. Infect. Dis. 20, 1049–1053 (2014).PubMed 
PubMed Central 

Google Scholar 
148.Zhou, H. et al. Identification of novel bat coronaviruses sheds light on the evolutionary origins of SARS-CoV-2 and related viruses. Cell 184, 4380–4391.e14 (2021). This study reports the discovery of additional coronaviruses related to SARS-CoV-2 in Rhinolophus spp. and use of ecological modelling to highlight areas of southern China and South-East Asia as hotspots of Rhinolophus species diversity.CAS 
PubMed 
PubMed Central 

Google Scholar 
149.Larsen, H. D. et al. Preliminary report of an outbreak of SARS-CoV-2 in mink and mink farmers associated with community spread, Denmark, June to November 2020. Eur. Surveill. 26, 2100009 (2021).CAS 

Google Scholar 
150.Bertzbach, L. D. et al. SARS-CoV-2 infection of Chinese hamsters (Cricetulus griseus) reproduces COVID-19 pneumonia in a well-established small animal model. Transbound. Emerg. Dis. 68, 1075–1079 (2021).CAS 
PubMed 

Google Scholar 
151.Fagre, A. et al. SARS-CoV-2 infection, neuropathogenesis and transmission among deer mice: Implications for spillback to New World rodents. PLoS Pathog. 17, e1009585 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
152.Imai, M. et al. Syrian hamsters as a small animal model for SARS-CoV-2 infection and countermeasure development. Proc. Natl Acad. Sci. USA 117, 16587–16595 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
153.Sia, S. F. et al. Pathogenesis and transmission of SARS-CoV-2 in golden hamsters. Nature 583, 834–838 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
154.Halfmann, P. J. et al. Transmission of SARS-CoV-2 in domestic cats. N. Engl. J. Med. 383, 592–594 (2020).PubMed 

Google Scholar 
155.Griffin, B. D. et al. SARS-CoV-2 infection and transmission in the North American deer mouse. Nat. Commun. 12, 1–10 (2021).
Google Scholar 
156.Palmer, M. V. et al. Susceptibility of white-tailed deer (Odocoileus virginianus) to SARS-CoV-2. J. Virol. 95, e00083–21 (2021).CAS 
PubMed Central 

Google Scholar 
157.Plowright, R. K. & Hudson, P. J. From protein to pandemic: the transdisciplinary approach needed to prevent spillover and the next pandemic. Viruses 13, 1298 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
158.Obameso, J. O. et al. The persistent prevalence and evolution of cross-family recombinant coronavirus GCCDC1 among a bat population: a two-year follow-up. Sci. China Life Sci. 60, 1357–1363 (2017). This study provides evidence of coronavirus evolution in a longitudinally sampled population of bats.PubMed 
PubMed Central 

Google Scholar 
159.Lazov, C. et al. Detection and characterization of distinct alphacoronaviruses in five different bat species in Denmark. Viruses 10, 486 (2018).PubMed Central 

Google Scholar 
160.Hu, D. et al. Genomic characterization and infectivity of a novel SARS-like coronavirus in Chinese bats. Emerg. Microbes Infect. 7, 1–10 (2018).
Google Scholar 
161.Pepin, K. M., Lass, S., Pulliam, J. R. C., Read, A. F. & Lloyd-Smith, J. O. Identifying genetic markers of adaptation for surveillance of viral host jumps. Nat. Rev. Microbiol. 8, 802–813 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
162.Hemida, M. G. et al. Coronavirus infections in horses in Saudi Arabia and Oman. Transbound. Emerg. Dis. 64, 2093–2103 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
163.Zhuang, Q. et al. Surveillance and taxonomic analysis of the coronavirus dominant in pigeons in China. Transbound. Emerg. Dis. 67, 1981–1990 (2020).CAS 

Google Scholar 
164.O’Brien, S. J. et al. Genetic basis for species vulnerability in the cheetah. Science 227, 1428–1434 (1985).PubMed 

Google Scholar 
165.Herrewegh, A. A. P. M., Smeenk, I., Horzinek, M. C., Rottier, P. J. M. & de Groot, R. J. Feline coronavirus type II strains 79-1683 and 79-1146 originate from a double recombination between feline coronavirus type I and canine coronavirus. J. Virol. 72, 4508–4514 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Tuber yieldPotato tuber yield increased gradually under 0 to 150 kg ha−1 of applied nitrogen (Fig. 1). Compared with the yield in N0, the yields in N60, N120 and N150 were greater by 16.1%, 21.5% and 67.9%, respectively, in 2017 and 18.2%, 27.4% and 44.9%, respectively, in 2018. However, at nitrogen rates of more than 150 kg ha−1, yield did not significantly differ. Furthermore, the fitted parabolic equation of each dataset from 2017 and 2018 showed maximum tuber yields of 19.7 and 20.4 t ha−1, respectively, where the nitrogen rates were 191 and 227 kg ha−1, respectively.Figure 1Potato tuber yield in treatments with different nitrogen fertilizer rates. Different letters indicate significant differences (P  More

1.Milshteyn, A., Schneider, J. S. & Brady, S. F. Mining the metabiome: identifying novel natural products from microbial communities. Chem. Biol. 21, 1211–1223 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Rutledge, P. J. & Challis, G. L. Discovery of microbial natural products by activation of silent biosynthetic gene clusters. Nat. Rev. Microbiol. 13, 509–523 (2015).CAS 
PubMed 

Google Scholar 
3.Dittmann, E., Gugger, M., Sivonen, K. & Fewer, D. P. Natural product biosynthetic diversity and comparative genomics of the cyanobacteria. Trends Microbiol. 23, 642–652 (2015).CAS 
PubMed 

Google Scholar 
4.Cragg, G. M., Kingston, D. G. & Newman, D. J. Anticancer Agents from Natural Products. (CRC press, 2011).5.Cragg, G. M. & Newman, D. J. Natural products: a continuing source of novel drug leads. Biochimica et. Biophysica Acta 1830, 3670–3695 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Singh, R., Kumar, M., Mittal, A. & Mehta, P. K. Microbial metabolites in nutrition, healthcare and agriculture. 3 Biotech 7, 15 (2017).PubMed 
PubMed Central 

Google Scholar 
7.Bohlmann, J. & Keeling, C. I. Terpenoid biomaterials. Plant J. 54, 656–669 (2008).CAS 
PubMed 

Google Scholar 
8.Kang, A. & Lee, T. S. Biotechnology for Biofuel Production and Optimization 35–71 (Elsevier, 2016).9.Nowruzi, B., Sarvari, G. & Blanco, S. The cosmetic application of cyanobacterial secondary metabolites. Algal Res. 49, 101959 (2020).
Google Scholar 
10.Crits-Christoph, A., Diamond, S., Butterfield, C. N., Thomas, B. C. & Banfield, J. F. Novel soil bacteria possess diverse genes for secondary metabolite biosynthesis. Nature 558, 440–444 (2018).CAS 
PubMed 

Google Scholar 
11.Sharrar, A. M. et al. Bacterial secondary metabolite biosynthetic potential in soil varies with phylum, depth, and vegetation type. Mbio. 11, 1–17 (2020).
Google Scholar 
12.Libis, V. et al. Uncovering the biosynthetic potential of rare metagenomic DNA using co-occurrence network analysis of targeted sequences. Nat. Commun. 10, 1–9 (2019).CAS 

Google Scholar 
13.Haro-Moreno, J. M., López-Pérez, M. & Rodriguez-Valera, F. Enhanced recovery of microbial genes and genomes from a marine water column using long-read metagenomics. Front Microbiol. 2410, 1–15 (2021).
Google Scholar 
14.Navarro-Muñoz, J. C. et al. A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol. 16, 60–68 (2019).PubMed 
PubMed Central 

Google Scholar 
15.Sugimoto, Y. et al. A metagenomic strategy for harnessing the chemical repertoire of the human microbiome. Science 366, 1–11 (2019).
Google Scholar 
16.Katz, M., Hover, B. M. & Brady, S. F. Culture-independent discovery of natural products from soil metagenomes. J. Ind. Microbiol. Biotechnol. 43, 129–141 (2016).CAS 
PubMed 

Google Scholar 
17.Lynch, M. D. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).CAS 
PubMed 

Google Scholar 
18.Amos, G. C. et al. Comparative transcriptomics as a guide to natural product discovery and biosynthetic gene cluster functionality. Proc. Natl Acad. Sci. USA 114, E11121–E11130 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Rodriguez-Caballero, E. et al. Dryland photoautotrophic soil surface communities endangered by global change. Nat. Geosci. 11, 185–189 (2018).CAS 

Google Scholar 
20.Reddy, B. V. B. et al. Natural product biosynthetic gene diversity in geographically distinct soil microbiomes. Appl. Environ. Microbiol. 78, 3744–3752 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Starkenburg, S. R. et al. Genome of the cyanobacterium Microcoleus vaginatus FGP-2, a photosynthetic ecosystem engineer of arid land soil biocrusts worldwide. J. Bacteriol. 193, 4569–4570 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Belnap, J., Weber, B. & Büdel, B. Biological Soil Crusts: an Organizing Principle in Drylands 3–13 (Springer, 2016).23.Swenson, T. L., Karaoz, U., Swenson, J. M., Bowen, B. P. & Northen, T. R. Linking soil biology and chemistry in biological soil crust using isolate exometabolomics. Nat. Commun. 9, 1–10 (2018).CAS 

Google Scholar 
24.Adamek, M., Spohn, M., Stegmann, E. & Ziemert, N. Antibiotics 23–47 (Springer, 2017).25.Couradeau, E. et al. Bacteria increase arid-land soil surface temperature through the production of sunscreens. Nat. Commun. 7, 1–7 (2016).
Google Scholar 
26.Martins, T. P. et al. Chemistry, bioactivity and biosynthesis of cyanobacterial alkylresorcinols. Nat. Prod. Rep. 36, 1437–1461 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Luesch, H., Moore, R. E., Paul, V. J., Mooberry, S. L. & Corbett, T. H. Isolation of dolastatin 10 from the marine cyanobacterium Symploca species VP642 and total stereochemistry and biological evaluation of its analogue symplostatin 1. J. Nat. Products 64, 907–910 (2001).CAS 

Google Scholar 
28.Chrapusta, E. et al. Microcystins and anatoxin-a in Arctic biocrust cyanobacterial communities. Toxicon 101, 35–40 (2015).CAS 
PubMed 

Google Scholar 
29.Van Goethem, M. W., Swenson, T. L., Trubl, G., Roux, S. & Northen, T. R. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. Mbio. 10, 1–15 (2019).
Google Scholar 
30.Makhalanyane, T. P. et al. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39, 203–221 (2015).CAS 
PubMed 

Google Scholar 
31.Bowker, M. A., Reed, S. C., Maestre, F. T. & Eldridge, D. J. Biocrusts: the living skin of the earth. (Springer, 2018).32.Büdel, B., Dulić, T., Darienko, T., Rybalka, N. & Friedl, T. Biological Soil Crusts: an Organizing Principle in Drylands 55–80 (Springer, 2016).33.Giraldo‐Silva, A., Nelson, C., Barger, N. N. & Garcia‐Pichel, F. Nursing biocrusts: isolation, cultivation, and fitness test of indigenous cyanobacteria. Restor. Ecol. 27, 793–803 (2019).
Google Scholar 
34.Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 
PubMed 
PubMed Central 

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

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

Google Scholar 
37.Mikheenko, A., Saveliev, V. & Gurevich, A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 32, 1088–1090 (2016).CAS 
PubMed 

Google Scholar 
38.Howe, A. C. et al. Tackling soil diversity with the assembly of large, complex metagenomes. Proc. Natl Acad. Sci. USA 111, 4904–4909 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Frank, J. A. et al. Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data. Sci. Rep. 6, 1–10 (2016).
Google Scholar 
40.Hiraoka, S. et al. Metaepigenomic analysis reveals the unexplored diversity of DNA methylation in an environmental prokaryotic community. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
41.Blin, K. et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 47, W81–W87 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Garcia-Pichel, F., Loza, V., Marusenko, Y., Mateo, P. & Potrafka, R. M. Temperature drives the continental-scale distribution of key microbes in topsoil communities. Science 340, 1574–1577 (2013).CAS 
PubMed 

Google Scholar 
43.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 

Google Scholar 
44.Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 499–509 (2020).45.Fuqua, C. & Greenberg, E. P. Listening in on bacteria: acyl-homoserine lactone signalling. Nat. Rev. Mol. Cell Biol. 3, 685–695 (2002).CAS 
PubMed 

Google Scholar 
46.Ciemniecki, J. A. & Newman, D. K. The potential for redox-active metabolites to enhance or unlock anaerobic survival metabolisms in aerobes. J. Bacteriol. 202, 1–14 (2020).
Google Scholar 
47.Rajeev, L. et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J. 7, 2178–2191 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Nunes da Rocha, U. et al. Isolation of a significant fraction of non-phototroph diversity from a desert biological soil crust. Front. Microbiol. 6, 277 (2015).PubMed 
PubMed Central 

Google Scholar 
49.Felde, V. J. M. N. L., Peth, S., Uteau-Puschmann, D., Drahorad, S. & Felix-Henningsen, P. Soil microstructure as an under-explored feature of biological soil crust hydrological properties: case study from the NW Negev Desert. Biodivers. Conserv. 23, 1687–1708 (2014).
Google Scholar 
50.Bushnell, B. BBMap: a fast, accurate, splice-aware aligner. No. LBNL-7065E. (Lawrence Berkeley National Lab (LBNL), Berkeley, CA (United States), 2014).51.Donia, M. S., Ruffner, D. E., Cao, S. & Schmidt, E. W. Accessing the hidden majority of marine natural products through metagenomics. ChemBioChem. 12, 1230–1236 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Van Der Maaten, L. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).
Google Scholar 
53.Couradeau, E., Giraldo-Silva, A., De Martini, F. & Garcia-Pichel, F. Spatial segregation of the biological soil crust microbiome around its foundational cyanobacterium, Microcoleus vaginatus, and the formation of a nitrogen-fixing cyanosphere. Microbiome 7, 1–12 (2019).
Google Scholar 
54.Magne, F. et al. The firmicutes/bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients? Nutrients 12, 1474 (2020).CAS 
PubMed Central 

Google Scholar 
55.Baran, R. et al. Exometabolite niche partitioning among sympatric soil bacteria. Nat. Commun. 6, 8289 (2015).CAS 
PubMed 

Google Scholar 
56.Kupriyanova, E. V. et al. Extracellular β-class carbonic anhydrase of the alkaliphilic cyanobacterium Microcoleus chthonoplastes. J. Photochem. Photobiol. B: Biol. 103, 78–86 (2011).CAS 

Google Scholar 
57.Hernandez, M. & Newman, D. Extracellular electron transfer. Cell. Mol. Life Sci. 58, 1562–1571 (2001).CAS 
PubMed 

Google Scholar 
58.Karaoz, U. et al. Large blooms of Bacillales (Firmicutes) underlie the response to wetting of cyanobacterial biocrusts at various stages of maturity. MBio. 9, e01366–01316 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Rodriguez-R, L. M., Gunturu, S., Tiedje, J. M., Cole, J. R. & Konstantinidis, K. T. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. MSystems. 3, 1–9 (2018).
Google Scholar 
60.Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
PubMed 

Google Scholar 
61.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
PubMed Central 

Google Scholar 
63.Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Van der Walt, A. J. et al. Assembling metagenomes, one community at a time. BMC Genom. 18, 1–13 (2017).
Google Scholar 
65.Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010).
Google Scholar 
66.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 

Google Scholar 
67.Arkin, A. P. et al. KBase: the United States department of energy systems biology knowledgebase. Nat. Biotechnol. 36, 566 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Kautsar, S. A. et al. MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res. 48, D454–D458 (2020).PubMed 

Google Scholar 
69.von Meijenfeldt, F. B., Arkhipova, K., Cambuy, D. D., Coutinho, F. H. & Dutilh, B. E. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol. 20, 217 (2019).
Google Scholar 
70.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
71.Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 

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

Google Scholar  More