Ecology

1.Ripple, W. J. et al. World Scientists’ Warning of a Climate Emergency 2021. BioScience. https://doi.org/10.1093/biosci/biab079 (2021).2.Liu, P. R. & Raftery, A. E. Country-based rate of emissions reductions should increase by 80% beyond nationally determined contributions to meet the 2 C target. Commun. Earth Environ. 2, 1–10 (2021).
Google Scholar 
3.IPBES. (eds Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T.) 56 (IPBES, 2019).4.CBD Secretariat. The Strategic Plan for Biodiversity 2011-2020 and the Aichi Biodiversity Targets Vol. Document UNEP/CBD/COP/DEC/X/2 (Secretariat of the Convention on Biological Diversity, 2010).5.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 

Google Scholar 
6.United State of America. The United States of America Nationally Determined Contribution- Reducing Greenhouse Gases in the United States: A 2030 Emissions Target. 24 (Submitted to the UNFCCC Secretariat under the Paris Agreement; https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/United%20States%20of%20America%20First/United%20States%20NDC%20April%2021%202021%20Final.pdf, 2021).7.Nelson, M. D. et al. Defining the United States land base: a technical document supporting the USDA Forest Service 2020 RPA assessment. Gen. Tech. Rep. NRS-191. 191, 1–70 (2020).
Google Scholar 
8.Pörtner, H. O. & et al. IPBES-IPCC co-sponsored workshop report on biodiversity and climate change. (IPBES and IPCC, https://doi.org/10.5281/zenodo.4782538, 2021).9.Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. 6, eaay0814 (2020).
Google Scholar 
10.Dinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).
Google Scholar 
11.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
Google Scholar 
12.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. 114, 11645–11650 (2017).CAS 

Google Scholar 
13.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).
Google Scholar 
14.Sexton, J. O. et al. Conservation policy and the measurement of forests. Nat. Clim. Chang. 6, 192–196 (2016).
Google Scholar 
15.Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).CAS 

Google Scholar 
16.Houghton, R. A., Hall, F. & Goetz, S. J. Importance of biomass in the global carbon cycle. J. Geophys. Res. 114, G00E03 (2009).
Google Scholar 
17.Mackey, B. et al. Understanding the importance of primary tropical forest protection as a mitigation strategy. Mitig. Adapt. Strateg. Glob. Chang. 25, 763–787 (2020).
Google Scholar 
18.Buotte, P. C., Law, B. E., Ripple, W. J. & Berner, L. T. Carbon sequestration and biodiversity co‐benefits of preserving forests in the western United States. Ecol. Appl.30, e02039 (2020).
Google Scholar 
19.Ruefenacht, B. et al. Conterminous US and Alaska forest type mapping using forest inventory and analysis data. Photogramm. Eng. Remote Sensing 74, 1379–1388 (2008).
Google Scholar 
20.USGS GAP. Protected Areas Database of the United States (PAD-US) 2.1: U.S. Geological Survey data release, https://doi.org/10.5066/P92QM3NT (2020).21.USGS. Gap Analysis Project Species Habitat Maps CONUS_2001. U.S. Geological Survey, https://doi.org/10.5066/F7V122T2 (2018).22.Wilson, B. T., Lister, A. J., Riemann, R. I. & Griffith, D. M. Live tree species basal area of the contiguous United States (2000-2009). (USDA Forest Service, Rocky Mountain Research Station, 2013).23.Wilson, B. T., Woodall, C. & Griffith, D. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance Management 8, 1–15 (2013).
Google Scholar 
24.Oleson, K. et al. Technical Descriptioin of Version 4.5 of the Community Land Model (CLM) (National Center for Atmospheric Research, 2013).25.Buotte, P. C. et al. Near‐future forest vulnerability to drought and fire varies across the western United States. Glob. Chang. Biol. 25, 290–303 (2019).
Google Scholar 
26.Noss, R. F. et al. Bolder thinking for conservation. Conserv. Biol. 26, 1–4 (2012).
Google Scholar 
27.Allen, C. D. & Breshears, D. D. Drought-induced shift of a forest–woodland ecotone: rapid landscape response to climate variation. Proc. Natl Acad. Sci. 95, 14839–14842 (1998).CAS 

Google Scholar 
28.Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
Google Scholar 
29.Lecina‐Diaz, J. et al. The positive carbon stocks–biodiversity relationship in forests: co‐occurrence and drivers across five subclimates. Ecol. Appl. 28, 1481–1493 (2018).
Google Scholar 
30.Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).
Google Scholar 
31.Glaser, C., Romaniello, C. & Moskowitz, K. Costs and consequences: the real price of livestock grazing on America’s public lands. Tucson, AZ: Center for Biological Diversity (2015).32.Flather, C. H. Species endangerment patterns in the United States. Vol. 241 (US Department of Agriculture, Forest Service, Rocky Mountain Forest and …, 1994).33.Beschta, R. L. et al. Adapting to climate change on western public lands: addressing the ecological effects of domestic, wild, and feral ungulates. Environ. Manag. 51, 474–491 (2013).
Google Scholar 
34.Betts, M. G., Gutiérrez Illán, J., Yang, Z., Shirley, S. M. & Thomas, C. D. Synergistic effects of climate and land-cover change on long-term bird population trends of the western USA: a test of modeled predictions. Front. Ecol. Evol. 7, https://doi.org/10.3389/fevo.2019.00186 (2019).35.Berner, L. T., Law, B. E., Meddens, A. J. & Hicke, J. A. Tree mortality from fires, bark beetles, and timber harvest during a hot and dry decade in the western United States (2003–2012). Environ. Res. Lett. 12, 065005 (2017).
Google Scholar 
36.Law, B. E. et al. Land use strategies to mitigate climate change in carbon dense temperate forests. Proc. Natl Acad. Sci. 115, 3663 (2018).CAS 

Google Scholar 
37.Ouren, D. S. et al. Environmental effects of off-highway vehicles on Bureau of land management lands: a literature synthesis, annotated bibliographies, extensive bibliographies, and internet resources. US Geol. Survey Open-File Rep. 1353, 225 (2007).
Google Scholar 
38.Talty, M. J., Mott Lacroix, K., Aplet, G. H. & Belote, R. T. Conservation value of national forest roadless areas. Conserv. Sci. Pract. 2, e288 (2020).
Google Scholar 
39.Belote, R. T. & Wilson, M. B. Delineating greater ecosystems around protected areas to guide conservation. Conserv. Sci. Pract. 2, e196 (2020).
Google Scholar 
40.DellaSala, D. A., Karr, J. R. & Olson, D. M. Roadless areas and clean water. J. Soil Water Conserv. 66, 78–84 (2011).
Google Scholar 
41.McLaren, D. P., Tyfield, D. P., Willis, R., Szerszynski, B. & Markusson, N. O. Beyond “net-zero”: a case for separate targets for emissions reduction and negative emissions. Front. Clim. 1, 4 (2019).
Google Scholar 
42.Mildrexler, D. J., Berner, L. T., Law, B. E., Birdsey, R. A. & Moomaw, W. R. Large Trees Dominate Carbon Storage in Forests East of the Cascade Crest in the United States Pacific Northwest. Front. For. Glob. Chang. 3, https://doi.org/10.3389/ffgc.2020.594274 (2020).43.Hudiburg, T. W., Luyssaert, S., Thornton, P. E. & Law, B. E. Interactive effects of environmental change and management strategies on regional forest carbon emissions. Environ. Sci. Tech. 47, 13132–13140 (2013).CAS 

Google Scholar 
44.Noss, R. F. & Daly, K. M. In Connectivity Conservation (eds K. Crooks & M. Sanjayan) 587–619 (Cambridge Univ. Press, 2010).45.Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
Google Scholar 
46.Omernik, J. M. Perspectives on the nature and definition of ecological regions. Environ. Manag. 34, S27–S38 (2004).
Google Scholar 
47.Hudiburg, T. et al. Carbon dynamics of Oregon and Northern California forests and potential land-based carbon storage. Ecol. Appl. 19, 163–180 (2009).
Google Scholar 
48.Leu, M., Hanser, S. E. & Knick, S. T. The human footprint in the west: a large‐scale analysis of anthropogenic impacts. Ecol. Appl. 18, 1119–1139 (2008).
Google Scholar 
49.Haight, J. & Hammill, E. Protected areas as potential refugia for biodiversity under climatic change. Biol. Conserv. 241, 108258 (2020).
Google Scholar 
50.Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Chang. Biol. 17, 1022–1035 (2011).
Google Scholar 
51.Jantz, P., Goetz, S. & Laporte, N. Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics. Nat. Clim. Chang. 4, 138–142 (2014).CAS 

Google Scholar 
52.McMenamin, S. K., Hadly, E. A. & Wright, C. K. Climatic change and wetland desiccation cause amphibian decline in Yellowstone National Park. Proc. Natl Acad. Sci. 105, 16988–16993 (2008).CAS 

Google Scholar 
53.Scott, J. M. et al. Recovery of imperiled species under the Endangered Species Act: the need for a new approach. Front. Ecol. Environ. 3, 383–389 (2005).
Google Scholar 
54.Miller, S. L. et al. Recent population decline of the Marbled Murrelet in the Pacific Northwest. Condor 114, 771–781 (2012).
Google Scholar 
55.Noon, B. R. & McKelvey, K. S. Management of the spotted owl: a case history in conservation biology. Annu. Rev. Ecol. System. 27, 135–162 (1996).
Google Scholar 
56.Ripple, W. J. et al. Ruminants, climate change and climate policy. Nat. Clim. Chang. 4, 2–5 (2014).CAS 

Google Scholar 
57.King, T. W. et al. Will Lynx lose their edge? Canada Lynx occupancy in Washington. J. Wildl. Manag. 84, 705–725 (2020).
Google Scholar 
58.Cayan, D. R. et al. Future dryness in the southwest US and the hydrology of the early 21st century drought. Proc. Natl Acad. Sci. 107, 21271–21276 (2010).CAS 

Google Scholar 
59.Rhoades, A. M., Ullrich, P. A. & Zarzycki, C. M. Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM. Clim. Dyn. 50, 261–288 (2018).
Google Scholar 
60.Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314 (2020).CAS 

Google Scholar 
61.Mote, P. W., Hamlet, A. F., Clark, M. P. & Lettenmaier, D. P. Declining mountain snowpack in western north America. Bull. Am. Meteorol. Soc. 86, 39–49 (2005).
Google Scholar 
62.Cook, B. et al. Twenty‐first century drought projections in the CMIP6 forcing scenarios. Earth’s Futur. 8, e2019EF001461 (2020).
Google Scholar 
63.Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
Google Scholar 
64.Johnson, Z. C., Leibowitz, S. G. & Hill, R. A. Revising the index of watershed integrity national maps. Sci. Total Environ. 651, 2615–2630 (2019).CAS 

Google Scholar 
65.Anderegg, W. R. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 

Google Scholar 
66.Buotte, P., Levis, S. & Law, B. E. NACP forest carbon stocks, fluxes, and productivity estimates, Western USA, 1979-2099. ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/1662 (2019).67.Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2012).
Google Scholar 
68.McDowell, N. G. et al. Multi-scale predictions of massive conifer mortality due to chronic temperature rise. Nat. Clim. Chang. 6, 295–300 (2015).
Google Scholar 
69.Williams, A. P. et al. Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States. Int. J. Wildland Fire 24, 14–26 (2014).
Google Scholar 
70.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. 113, 11770–11775 (2016).CAS 

Google Scholar 
71.Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).
Google Scholar 
72.Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. 114, 2946–2951 (2017).CAS 

Google Scholar 
73.Schoennagel, T. et al. Adapt to more wildfire in western North American forests as climate changes. Proc. Natl Acad. Sci. 114, 4582–4590 (2017).CAS 

Google Scholar 
74.Law, B. E. & Waring, R. H. Carbon implications of current and future effects of drought, fire and management on Pacific Northwest forests. For. Ecol. Management 355, 4–14 (2015).
Google Scholar 
75.Donato, D. C., Campbell, J. L. & Franklin, J. F. Multiple successional pathways and precocity in forest development: can some forests be born complex? J. Veg. Sci. 23, 576–584 (2012).
Google Scholar 
76.Campbell, J. L., Harmon, M. E. & Mitchell, S. R. Can fuel‐reduction treatments really increase forest carbon storage in the western US by reducing future fire emissions? Front. Ecol. Environ. 10, 83–90 (2012).
Google Scholar 
77.Harris, N. et al. Attribution of net carbon change by disturbance type across forest lands of the conterminous United States. Carbon Balanc. Management 11, 24 (2016).CAS 

Google Scholar 
78.Ghimire, B. et al. Large carbon release legacy from bark beetle outbreaks across Western United States. Glob. Chang. Biol. 21, 3087–3101 (2015).
Google Scholar 
79.Mitchell, S. R., Harmon, M. E. & O’connell, K. E. Forest fuel reduction alters fire severity and long‐term carbon storage in three Pacific Northwest ecosystems. Ecol. Appl. 19, 643–655 (2009).
Google Scholar 
80.Rhodes, J. J. & Baker, W. L. Fire probability, fuel treatment effectiveness and ecological tradeoffs in western US public forests. Open For. Sci. J. 1, 1–7 (2008).
Google Scholar 
81.Law, B. E. & Harmon, M. E. Forest sector carbon management, measurement and verification, and discussion of policy related to climate change. Carbon Management 2, 73–84 (2011).
Google Scholar 
82.Hudiburg, T. W., Law, B. E., Wirth, C. & Luyssaert, S. Regional carbon dioxide implications of forest bioenergy production. Nat. Clim. Chang. 1, 419–423 (2011).CAS 

Google Scholar 
83.Bonan, G. B. & Doney, S. C. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models. Science 359, eaam8328 (2018).
Google Scholar 
84.Law, B. E. Regional analysis of drought and heat impacts on forests: current and future science directions. Glob. Chang. Biol. 20, 3595–3599 (2014).
Google Scholar 
85.Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data 7, 1–22 (2020).
Google Scholar 
86.Kullberg, P. & Moilanen, A. How do recent spatial biodiversity analyses support the convention on biological diversity in the expansion of the global conservation area network? Natureza Conservacao 12, 3–10 (2014).
Google Scholar 
87.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).88.Hijmans, R. J. raster: Geographic Analysis and Modeling. R package version 3.0-12. http://CRAN.R-project.org/package=raster (2019).89.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-8. https://CRAN.R-project.org/package=rgdal (2019).90.O’Brien, J. gdalUtilities: Wrappers for ‘GDAL’ Utilities Executables. R package version 1. https://CRAN.R-project.org/package=gdalUtilities (2019).91.Dawle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.12.8. https://CRAN.R-project.org/package=data.table. (2019).92.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlang New York, 2016).93.Hurrell, J. W. et al. The community earth system model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).
Google Scholar 
94.Conservation Biology Institute. Protected Areas Database of the United States, CBI Edition Version 2. http://consbio.org/products/projects/pad-us-cbi-edition (2012).95.USDA Forest Service. Forests to Faucets 2.0 [spatial data set]. Retrieved from https://usfs-public.app.box.com/v/Forests2Faucets[Sept 21, 2021] (2019). More

1.Remaudiere, G., & Remaudiere, M. Catalogue of the World’s Aphididae: Homoptera Aphidoidea. 473–1275. (Institut National de la Recherche Agronomique (INRA), 1997).2.Fereres, A., Irwin, M.E., & Kamppeier, G.E. Aphid movement: Process and consequences. in (van Emden H.F.R.H. ed.) Aphids as Crop Pests. 2nd edn. 196–200. (CABI, 2017).3.Ng, J. C. K. & Perry, K. L. Transmission of plant viruses by aphid vectors. Mol. Plant Pathol. 5(5), 505–511. https://doi.org/10.1111/j.1364-3703.2004.00240.x (2004).Article 
PubMed 

Google Scholar 
4.Whitfield, A. E., Falk, B. W. & Rotenberg, D. Insect vector-mediated transmission of plant viruses. Virology 479–480, 278–289. https://doi.org/10.1016/j.virol.2015.03.026 (2015).CAS 
Article 
PubMed 

Google Scholar 
5.Elena, S. F., Bernet, G. P. & Carrasco, J. L. The games plant viruses play. Curr. Opin. Virol. 8, 62–67. https://doi.org/10.1016/j.coviro.2014.07.003 (2014).CAS 
Article 
PubMed 

Google Scholar 
6.Casteel, C.L., & Falk, B.W. Plant virus-vector interactions: More than just for virus transmission. in (Wang, A., & Zhou, X. eds.) Current Research Topics in Plant Virology. 2016. 217–240. https://doi.org/10.1007/978-3-319-32919-2_9 (2016).7.Eigenbrode, S. D., Bosque-Pérez, N. A. & Davis, T. S. Insect-borne plant pathogens and their vectors: Ecology, evolution, and complex interactions. Annu. Rev. Entomol. 63, 169–191. https://doi.org/10.1146/annurev-ento-020117-043119 (2018).CAS 
Article 
PubMed 

Google Scholar 
8.Blanc, S. & Michalakis, Y. Manipulation of hosts and vectors by plant viruses and impact of the environment. Curr. Opin. Insect Sci. 16, 36–43. https://doi.org/10.1016/j.cois.2016.05.007 (2016).Article 
PubMed 

Google Scholar 
9.Ingwell, L. L., Eigenbrode, S. D. & Bosque-Pérez, N. A. Plant viruses alter insect behavior to enhance their spread. Sci. Rep. 2(1), 578. https://doi.org/10.1038/srep00578 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Stafford, C. A., Yang, L. H., Mcmunn, M. S. & Ullman, D. E. Virus infection alters the predatory behavior of an omnivorous vector. Oikos 123, 1384–1390. https://doi.org/10.1111/oik.01148 (2014).Article 

Google Scholar 
11.Wang, Q. et al. Rice dwarf virus infection alters green rice leafhopper host preference and feeding behavior. PLoS ONE 13(9), 1–16. https://doi.org/10.1371/journal.pone.0203364 (2018).CAS 
Article 

Google Scholar 
12.Stafford, C. A., Walker, G. P. & Ullman, D. E. Infection with a plant virus modifies vector feeding behavior. Proc. Natl. Acad. Sci. 108(23), 9350–9355. https://doi.org/10.1073/pnas.1100773108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Zhang, Y. C., Cao, W. J., Zhong, L. R., Godfray, H. C. J. & Liu, X. D. Host plant determines the population size of an obligate symbiont (Buchnera aphidicola) in aphids. Appl. Environ. Microbiol. 82(8), 2336–2346. https://doi.org/10.1128/AEM.04131-15 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Hansen, A. K. & Moran, N. A. Aphid genome expression reveals host-symbiont cooperation in the production of amino acids. Proc. Natl. Acad. Sci. 108(7), 2849–2854. https://doi.org/10.1073/pnas.1013465108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
15.Nakabachi, A. et al. Transcriptome analysis of the aphid bacteriocyte, the symbiotic host cell that harbors an endocellular mutualistic bacterium, Buchnera. Proc. Natl. Acad. Sci. 102(15), 5477–5482. https://doi.org/10.1073/pnas.1013465108 (2005).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Wernegreen, J. J. Strategies of genomic integration within insect-bacterial mutualisms. Biol Bull. 223(1), 112–122. https://doi.org/10.1086/BBLv223n1p112 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Zhang, Y. et al. Genetic structure of the bacterial endosymbiont, Buchnera aphidicola, from its host aphid, Schlechtendalia chinensis, and evolutionary implications. Curr. Microbiol. 75(3), 309–315. https://doi.org/10.1007/s00284-017-1381-0 (2018).CAS 
Article 
PubMed 

Google Scholar 
18.Zhang, F. et al. Bacterial symbionts, Buchnera, and starvation on wing dimorphism in English grain aphid, Sitobion avenae (F) (Homoptera: Aphididae). Front. Physiol. 6, 155. https://doi.org/10.3389/fphys.2015.00155 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Machado-Assefh, C. R., Lopez-Isasmendi, G., Tjallingii, W. F., Jander, G. & Alvarez, A. E. Disrupting Buchnera aphidicola, the endosymbiotic bacteria of Myzus persicae, delays host plant acceptance. Arthropod. Plant Interact. 9(5), 529–541. https://doi.org/10.1007/s11829-015-9394-8 (2015).Article 

Google Scholar 
20.Douglas, A. E. Nutritional interactions in insect-microbial symbioses: Aphids and their symbiotic bacteria Buchnera. Annu. Rev. Entomol. 43(1), 17–37. https://doi.org/10.1146/annurev.ento.43.1.17 (1998).CAS 
Article 
PubMed 

Google Scholar 
21.Tamas, I. et al. 50 million years of genomic stasis in endosymbiotic bacteria. Science 296(5577), 2376–2379. https://doi.org/10.1126/science.1071278 (2002).ADS 
CAS 
Article 
PubMed 

Google Scholar 
22.Van Ham, R. C. H. J. et al. Reductive genome evolution in Buchnera aphidicola. Proc. Natl. Acad. Sci. 100(2), 581–586. https://doi.org/10.1073/pnas.0235981100 (2003).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Bouvaine, S., Boonham, N. & Douglas, A. E. Interactions between a Luteovirus and the GroEL chaperonin protein of the symbiotic bacterium Buchnera aphidicola of aphids. J. Gen. Virol. 92(6), 1467–1474. https://doi.org/10.1099/vir.0.029355-0 (2011).CAS 
Article 
PubMed 

Google Scholar 
24.Rana, V. S., Singh, S. T., Priya, N. G., Kumar, J. & Rajagopal, R. Arsenophonus GroEL interacts with CLCuV and is localized in midgut and salivary gland of whitefly B. tabaci. PLoS ONE 7(8), e42168. https://doi.org/10.1371/journal.pone.0042168 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Kliot, A. & Ghanim, M. The role of bacterial chaperones in the circulative transmission of plant viruses by insect vectors. Viruses 5(6), 1516–1535. https://doi.org/10.3390/v5061516 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Filichkin, S. A., Brumfield, S., Filichkin, T. P. & Young, M. J. In vitro interactions of the aphid endosymbiotic SymL chaperonin with Barley yellow dwarf virus. J. Virol. 71(1), 569–577. https://doi.org/10.1128/JVI.71.1.569-577.1997 (1997).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.van den Heuvel, J. F., Verbeek, M. & van der Wilk, F. Endosymbiotic bacteria associated with circulative transmission of Potato leafroll virus by Myzus persicae. J. Gen. Virol. 75(Pt 10), 2559–2565. https://doi.org/10.1099/0022-1317-75-10-2559 (1994).Article 
PubMed 

Google Scholar 
28.Gray, S. M. & Gildow, F. E. Luteovirus-aphid interactions. Annu. Rev. Phytopathol. 41(1), 539–566. https://doi.org/10.1146/annurev.phyto.41.012203.105815 (2003).CAS 
Article 
PubMed 

Google Scholar 
29.Li, C., Cox-Foster, D., Gray, S. M. & Gildow, F. Vector specificity of Barley yellow dwarf virus (BYDV) transmission: Identification of potential cellular receptors binding BYDV-MAV in the aphid, Sitobion avenae. Virology 286(1), 125–133. https://doi.org/10.1006/viro.2001.0929 (2001).CAS 
Article 
PubMed 

Google Scholar 
30.Dombrovsky, A., Gollop, N., Chen, S., Chejanovsky, N. & Raccah, B. In vitro association between the helper component-proteinase of Zucchini yellow mosaic virus and cuticle proteins of Myzus persicae. J. Gen. Virol. 88(5), 1602–1610. https://doi.org/10.1099/vir.0.82769-0 (2007).CAS 
Article 
PubMed 

Google Scholar 
31.van den Heuvel, J. F. et al. The N-terminal region of the luteovirus readthrough domain determines virus binding to Buchnera GroEL and is essential for virus persistence in the aphid. J. Virol. 71(10), 7258–7265. https://doi.org/10.1128/JVI.71.10.7258-7265.1997 (1997).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Morin, S. et al. A GroEL homologue from endosymbiotic bacteria of the whitefly Bemisia tabaci is implicated in the circulative transmission of Tomato yellow leaf curl virus. Virology 256(1), 75–84. https://doi.org/10.1006/viro.1999.9631 (1999).CAS 
Article 
PubMed 

Google Scholar 
33.Chaudhary, R., Atamian, H. S., Shen, Z., Briggs, S. P. & Kaloshian, I. GroEL from the endosymbiont Buchnera aphidicola betrays the aphid by triggering plant defense. Proc. Natl. Acad. Sci. 111(24), 8919–8924. https://doi.org/10.1073/pnas.1407687111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Vandermoten, S. et al. Comparative analyses of salivary proteins from three aphid species. Insect Mol. Biol. 23(1), 67–77. https://doi.org/10.1111/imb.12061 (2014).CAS 
Article 
PubMed 

Google Scholar 
35.Gray, S. M., Cilia, M. & Ghanim, M. Circulative, “nonpropagative” virus transmission: An orchestra of virus-, insect-, and plant-derived instruments. Adv. Virus Res. 2014, 89. https://doi.org/10.1016/B978-0-12-800172-1.00004-5 (2014).Article 

Google Scholar 
36.Eigenbrode, S. D., Ding, H., Shiel, P. & Berger, P. H. Volatiles from potato plants infected with Potato leafroll virus attract and arrest the virus vector, Myzus persicae (Homoptera: Aphididae). Proc. Biol. Sci. 269(1490), 455–460. https://doi.org/10.1098/rspb.2001.1909 (2002).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Rajabaskar, D., Wu, Y., Bosque-Pérez, N. A. & Eigenbrode, S. D. Dynamics of Myzus persicae arrestment by volatiles from Potato leafroll virus-infected potato plants during disease progression. Entomol. Exp. Appl. 148(2), 2. https://doi.org/10.1111/eea.12087 (2013).Article 

Google Scholar 
38.Patton, M. F., Bak, A., Sayre, J. M., Heck, M. L. & Casteel, C. L. A polerovirus, Potato leafroll virus, alters plant–vector interactions using three viral proteins. Plant Cell Environ. 43(2), 387–399. https://doi.org/10.1111/pce.13684 (2020).CAS 
Article 
PubMed 

Google Scholar 
39.Sadowy, E., Juszczuk, M., David, C., Gronenborn, B. & Danuta Hulanicka, M. D. Mutational analysis of the proteinase function of Potato leafroll virus. J. Gen. Virol. 82(Pt 6), 1517–1527. https://doi.org/10.1099/0022-1317-82-6-1517 (2001).CAS 
Article 
PubMed 

Google Scholar 
40.DeBlasio, S. L. et al. Insights into the polerovirus– plant interactome revealed by coimmunoprecipitation and mass spectrometry. Mol. Plant-Microbe Interact. 28(4), 467–481. https://doi.org/10.1094/MPMI-11-14-0363-R (2015).CAS 
Article 
PubMed 

Google Scholar 
41.Zhong, S. et al. High-throughput illumina strand-specific RNA sequencing library preparation. Cold Spring Harb. Protoc. 2011(8), 940–949. https://doi.org/10.1101/pdb.prot5652 (2011).Article 

Google Scholar 
42.Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and bioconductor. Nat. Protoc. 8(9), 1765–1786. https://doi.org/10.1038/nprot.2013.099 (2013).CAS 
Article 
PubMed 

Google Scholar 
43.Morgan, M. et al. ShortRead: A bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25(19), 2607–2608. https://doi.org/10.1093/bioinformatics/btp450 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
44.Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. (2010).45.Gauthier, J. P., Legeai, F., Zasadzinski, A., Rispe, C. & Tagu, D. AphidBase: A database for aphid genomic resources. Bioinformatics 23(6), 783–784. https://doi.org/10.1093/bioinformatics/btl682 (2007).CAS 
Article 
PubMed 

Google Scholar 
46.Kim, D. et al. TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36. https://doi.org/10.1186/gb-2013-14-4-r36 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Anders, S., Pyl, P. T. & Huber, W. HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics 31(2), 166–169. https://doi.org/10.1093/bioinformatics/btu638 (2015).CAS 
Article 

Google Scholar 
48.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Conesa, A. et al. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18), 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
Article 
PubMed 

Google Scholar 
50.Patton, M. F., Arena, G. D., Salminen, J. P., Steinbauer, M. J. & Casteel, C. L. Transcriptome and defence response in Eucalyptus camaldulensis leaves to feeding by Glycaspis brimblecombei Moore (Hemiptera: Aphalaridae): A stealthy psyllid does not go unnoticed. Austral. Entomol. 57(2), 247–254. https://doi.org/10.1111/aen.12319 (2017).Article 

Google Scholar 
51.Casteel, C. L. et al. Disruption of ethylene responses by Turnip mosaic virus mediates suppression of plant defense against the green peach aphid vector. Plant Physiol. 169(1), 209–218. https://doi.org/10.1104/pp.15.00332 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Nikoh, N. et al. Bacterial genes in the aphid genome: Absence of functional gene transfer from Buchnera to its host. PLoS Genet. 6(2), e1000827. https://doi.org/10.1371/journal.pgen.1000827 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Hansen, A. K. & Degnan, P. H. Widespread expression of conserved small RNAs in small symbiont genomes. ISME J. 8(12), 2490–2502. https://doi.org/10.1038/ismej.2014.121 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Hogenhout, S. A., van der Wilk, F., Verbeek, M., Goldbach, R. W. & van den Heuvel, J. F. Potato leafroll virus binds to the equatorial domain of the aphid endosymbiotic GroEL homolog. J. Virol. 72(1), 358–365. https://doi.org/10.1128/JVI.72.1.358-365.1998 (1998).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Camberg, J.L., Doyle, S.M., Johnston, D.M., & Wickner, S. Molecular Chaperones. in Brenner’s Encyclopedia of Genetics. 2nd Edn. 456–60. (Elsevier, 2013). https://doi.org/10.1016/B978-0-12-809633-8.06723-6.56.Segal, G. & Ron, E. Z. Regulation and organization of the groE and dnaK operons in Eubacteria. FEMS Microbiol. Lett. 138(1), 1–10. https://doi.org/10.1111/j.1574-6968.1996.tb08126.x (1996).CAS 
Article 
PubMed 

Google Scholar 
57.Zhang, L., Pelech, S. & Uitto, V. J. Bacterial GroEL-like heat shock protein 60 protects epithelial cells from stress-induced death through activation of ERK and inhibition of caspase 3. Exp. Cell Res. 292(1), 231–240. https://doi.org/10.1016/j.yexcr.2003.08.012 (2004).CAS 
Article 
PubMed 

Google Scholar 
58.Shigenobu, S., Watanabe, H., Hattori, M., Sakaki, Y. & Ishikawa, H. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS Nat. 407(6800), 81–86. https://doi.org/10.1038/35024074 (2000).CAS 
Article 

Google Scholar 
59.Dombrovsky, A., Sobolev, I., Chejanovsky, N. & Raccah, B. Characterization of RR-1 and RR-2 cuticular proteins from Myzus persicae. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 146(2), 256–264. https://doi.org/10.1016/j.cbpb.2006.11.013 (2007).CAS 
Article 
PubMed 

Google Scholar 
60.Dombrovsky, A., Huet, H., Zhang, H., Chejanovsky, N. & Raccah, B. Comparison of newly isolated cuticular protein genes from six aphid species. Insect Biochem. Mol. Biol. 33(7), 709–715. https://doi.org/10.1016/s0965-1748(03)00065-1 (2003).CAS 
Article 
PubMed 

Google Scholar 
61.Liang, Y. & Gao, X. W. The cuticle protein gene MPCP4 of Myzus persicae (Homoptera: Aphididae) plays a critical role in cucumber mosaic virus acquisition. J. Econ. Entomol. 110(3), 848–853. https://doi.org/10.1093/jee/tox025 (2017).CAS 
Article 
PubMed 

Google Scholar 
62.Silva, A. X., Jander, G., Samaniego, H., Ramsey, J. S. & Figueroa, C. C. Insecticide resistance mechanisms in the green peach aphid Myzus persicae (Hemiptera: Aphididae) I: A transcriptomic survey. PLoS ONE 7(6), e36366. https://doi.org/10.1371/journal.pone.0036366 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Deshoux, M., Monsion, B. & Uzest, M. Insect cuticular proteins and their role in transmission of phytoviruses. Curr. Opin. Virol. 33, 137–143. https://doi.org/10.1016/j.coviro.2018.07.015 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Gallot, A. et al. Cuticular proteins and seasonal photoperiodism in aphids. Insect Biochem. Mol. Biol. 40(3), 235–240. https://doi.org/10.1016/j.ibmb.2009.12.001 (2010).CAS 
Article 
PubMed 

Google Scholar 
65.Cilia, M. et al. Genetics coupled to quantitative intact proteomics links heritable aphid and endosymbiont protein expression to circulative polerovirus transmission. J. Virol. 85(5), 2148–2166. https://doi.org/10.1128/JVI.01504-10 (2011).CAS 
Article 
PubMed 

Google Scholar 
66.Wang, H., Wu, K., Liu, Y., Wu, Y. & Wang, X. Integrative proteomics to understand the transmission mechanism of Barley yellow dwarf virus-GPV by its insect vector Rhopalosiphum padi. Sci. Rep. 5, 10971. https://doi.org/10.1038/srep10971 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Seddas, P. et al. Rack-1, GAPDH3, and actin: proteins of Myzus persicae potentially involved in the transcytosis of Beet western yellows virus particles in the aphid. Virology 325(2), 399–412. https://doi.org/10.1016/j.virol.2004.05.014 (2004).CAS 
Article 
PubMed 

Google Scholar 
68.Yang, Z., Zhang, F., Zhu, L. & He, G. Identification of differentially expressed genes in brown planthopper Nilaparvata lugens (Hemiptera: Delphacidae) responding to host plant resistance. Bull. Entomol. Res. 96(1), 53–59. https://doi.org/10.1079/ber2005400 (2006).CAS 
Article 
PubMed 

Google Scholar 
69.Bass, C. et al. Gene amplification and microsatellite polymorphism underlie a recent insect host shift. Proc. Natl. Acad. Sci. 110(48), 19460–19465. https://doi.org/10.1073/pnas.1314122110 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.Ramsey, J. S. et al. Adaptation to nicotine feeding in Myzus persicae. J. Chem. Ecol. 40(8), 869–877. https://doi.org/10.1007/s10886-014-0482-5 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.Casteel, C. L. & Jander, G. New synthesis: Investigating mutualisms in virus-vector interactions. J. Chem. Ecol. 39(7), 809. https://doi.org/10.1007/s10886-013-0305-0 (2013).CAS 
Article 
PubMed 

Google Scholar 
72.Götz, M. et al. Implication of Bemisia tabaci HEAT SHOCK PROTEIN 70 in Begomovirus-whitefly interactions. J. Virol. 86(24), 13241–13252. https://doi.org/10.1128/JVI.00880-12 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Porras, M. F. et al. Enhanced heat tolerance of viral-infected aphids leads to niche expansion and reduced interspecific competition. Nat. Commun. 11(1), 1184. https://doi.org/10.1038/s41467-020-14953-2 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
74.Syller, J. The influence of temperature on transmission of potato leaf roll virus by Myzus persicae Sulz. Potato Res. 30(1), 47–58. https://doi.org/10.1007/BF02357683 (1987).Article 

Google Scholar 
75.Syller, J. The effects of temperature on the susceptibility of potato plants to infection and accumulation of Potato Leafroll Virus. J. Phytopathol. 133(3), 216–224. https://doi.org/10.1111/j.1439-0434.1991.tb00156.x (1991).Article 

Google Scholar 
76.Chung, B. N. et al. The effects of high temperature on infection by Potato virus Y, Potato virus A, and Potato leafroll virus. Plant Pathol. J. 32(4), 321–328. https://doi.org/10.5423/PPJ.OA.12.2015.0259 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
77.Hansen, A. K. & Moran, N. A. The impact of microbial symbionts on host plant utilization by herbivorous insects. Mol. Ecol. 23(6), 1473–96 (2014).Article 

Google Scholar 
78.Jiang, Z. et al. Comparative analysis of genome sequences from four strains of the Buchnera aphidicola Mp endosymbion of the green peach aphid, Myzus persicae. BMC Genom. 14(1), 917. https://doi.org/10.1186/1471-2164-14-917 (2013).CAS 
Article 

Google Scholar 
79.Enders, L. S. et al. Abiotic and biotic stressors causing equivalent mortality induce highly variable transcriptional responses in the soybean aphid. G3 (Bethesda) 5(2), 261–270. https://doi.org/10.1534/g3.114.015149 (2014).Article 

Google Scholar 
80.Wilcox, J. L., Dunbar, H. E., Wolfinger, R. D. & Moran, N. A. Consequences of reductive evolution for gene expression in an obligate endosymbiont. Mol. Microbiol. 48(6), 1491–1500. https://doi.org/10.1046/j.1365-2958.2003.03522.x (2003).CAS 
Article 
PubMed 

Google Scholar 
81.Karp, P. D. et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform. 20(4), 1085–1093. https://doi.org/10.1093/bib/bbx085 (2019).CAS 
Article 
PubMed 

Google Scholar 
82.Zhang, B., Leonard, S. P., Li, Y. & Moran, N. A. Obligate bacterial endosymbionts limit thermal tolerance of insect host species. Proc. Natl. Acad. Sci. 116(49), 24712–24718. https://doi.org/10.1073/pnas.1915307116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Chong, R. A. & Moran, N. A. Intraspecific genetic variation in hosts affects regulation of obligate heritable symbionts. PNAS 113(46),13114–13119. https://doi.org/10.1073/pnas.1610749113 (2016).ADS 
CAS 
Article 

Google Scholar 
84.Pers, D. & Hansen, A. K. The boom and bust of the aphid’s essential amino acid metabolism across nymphal development. G3 (Bethesda). 11(9), jkab115. https://doi.org/10.1093/g3journal/jkab115 (2021).Article 

Google Scholar 
85.Dunbar, H. E., Wilson, A. C. C., Ferguson, N. R. & Moran, N. A. Aphid thermal tolerance is governed by a point mutation in bacterial symbionts. PLoS Biol. 5(5), e96. https://doi.org/10.1371/journal.pbio.0050096 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
86.Moran, N. A. & Yun, Y. Experimental replacement of an obligate insect symbiont. Proc. Natl. Acad. Sci. 112(7), 2093–2096. https://doi.org/10.1073/pnas.1420037112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
87.Fares, M. A., Barrio, E., Sabater-Muñoz, B. & Moya, A. The evolution of the heat-shock protein GroEL from Buchnera, the primary endosymbiont of aphids, is governed by positive selection. Mol. Biol. Evol. 19(7), 1162–1170. https://doi.org/10.1093/oxfordjournals.molbev.a004174 (2002).CAS 
Article 
PubMed 

Google Scholar 
88.Kliot, A., Cilia, M., Czosnek, H., & Ghanim, M. Implication of the bacterial endosymbiont Rickettsia spp. in interactions of the whitefly Bemisia tabaci with Tomato yellow leaf curl virus. J. Virol. 88(10), 5652–5660. https://doi.org/10.1128/JVI.00071-14 (2014).89.Dheilly, N. M. et al. Who is the puppet master? Replication of a parasitic wasp-associated virus correlates with host behaviour manipulation. Proc. R. Soc. B Biol. Sci. 2015(282), 20142773 (1803).
Google Scholar 
90.Mohan, P. & Sinu, P. A. Does the solitary parasitoid Microplitis pennatulae use a combinatorial approach to manipulate its host?. Entomol. Exp. Appl. 168(4), 295–303 (2020).CAS 
Article 

Google Scholar 
91.Smith, T. E. & Moran, N. A. Coordination of host and symbiont gene expression reveals a metabolic tug-of-war between aphids and Buchnera. Proc. Natl. Acad. Sci. 117(4), 2113–2121. https://doi.org/10.1073/pnas.1916748117 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B 274(1608), 303–313 (2007).Article 

Google Scholar 
2.Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19, 915–918 (2009).CAS 
Article 

Google Scholar 
3.Aizen, M. A. et al. Global agricultural productivity is threatened by increasing pollinator dependence without a parallel increase in crop diversification. Glob. Change Biol. 25, 3516–3527 (2019).ADS 
Article 

Google Scholar 
4.Ribbands, C. R. The scent perception of the honey bee. Proc. R. Soc. Lond. B 143(912), 367–379 (1955).ADS 
Article 

Google Scholar 
5.von Frisch, K. The Dance Language and Orientation of Bees (Harvard University Press, 1967).
Google Scholar 
6.Reinhard, J., Srinivasan, M. V., Guez, D. & Zhang, S. W. Floral scents induce recall of navigational and visual memories in honeybees. J. Exp. Biol. 207(25), 4371–4381 (2004).Article 

Google Scholar 
7.Arenas, A., Fernández, V. M. & Farina, W. M. Floral odor learning within the hive affects honeybees’ foraging decisions. Naturwissenschaften 94, 218–222 (2007).ADS 
CAS 
Article 

Google Scholar 
8.Farina, W. M., Grüter, C. & Díaz, P. C. Social learning of floral odours inside the honeybee hive. Proc. R. Soc. Lond. B. 272(1575), 1923–1928 (2005).
Google Scholar 
9.Farina, W. M., Grüter, C., Acosta, L. & Mc Cabe, S. Honeybees learn floral odors while receiving nectar from foragers within the hive. Naturwissenschaften 94(1), 55–60 (2007).ADS 
CAS 
Article 

Google Scholar 
10.Grüter, C., Acosta, L. E. & Farina, W. M. Propagation of olfactory information within the honeybee hive. Behav. Ecol. Sociobiol. 60(5), 707–715 (2006).Article 

Google Scholar 
11.Arenas, A., Fernández, V. M. & Farina, W. M. Floral scents experienced within the colony affect long-term foraging preferences in honeybees. Apidologie 39, 714–722 (2008).Article 

Google Scholar 
12.Balbuena, M. S., Arenas, A. & Farina, W. M. Floral scents learned inside the honeybee hive have a long-lasting effect on recruitment. Anim. Behav. 84, 77–83 (2012).Article 

Google Scholar 
13.Farina, W. M., Arenas, A., Díaz, P. C., Susic Martin, C. & Estravis Barcala, M. C. Learning of a mimic odor within beehives improves pollination service efficiency in a commercial crop. Curr. Biol. 30, 4284–4290. https://doi.org/10.1016/j.cub.2020.08.018 (2020).CAS 
Article 
PubMed 

Google Scholar 
14.Stevenson, P. C., Nicolson, S. W. & Wright, G. A. Plant secondary metabolites in nectar: Impacts on pollinators and ecological functions. Funct. Ecol. 31(1), 65–75 (2017).Article 

Google Scholar 
15.Baker, H. G. Non-sugar chemical constituents of nectar. Apidologie 8(4), 349–356 (1977).Article 

Google Scholar 
16.Chalisova, N. I. et al. Effect of encoding amino acids on associative learning of honeybee Apis mellifera. J. Evol. Biochem. Fisiol. 47(6), 607 (2011).Article 

Google Scholar 
17.Strauss, S. Y. & Whittall, J. B. Non-pollinator agents of selection on floral traits. In Ecology and Evolution of flowers (eds. Harder, L. D. & Barret, S. C. H.) 120–138 (Oxford University Press, 2006).18.McArt, S. H., Koch, H., Irwin, R. E. & Adler, L. S. Arranging the bouquet of disease: Floral traits and the transmission of plant and animal pathogens. Ecol. Lett. 17, 624–636 (2014).Article 

Google Scholar 
19.Gatica Hernández, I., Palottini, F., Macri, I., Galmarini, C. R. & Farina, W. M. Appetitive behavior of the honey bee Apis mellifera in response to phenolic compounds naturally found in nectars. J. Exp. Biol. https://doi.org/10.1242/jeb.189910 (2019).Article 

Google Scholar 
20.Stevenson, P. C. For antagonists and mutualists: The paradox of insect toxic secondary metabolites in nectar and pollen. Phytochem. Rev. 19(3), 603–614 (2020).CAS 
Article 

Google Scholar 
21.Carlesso, D., Smargiassi, S., Pasquini, E., Bertelli, G. & Baracchi, D. Nectar non-protein amino acids (NPAAs) do not change nectar palatability but enhance learning and memory in honey bees. Sci. Rep. 11(1), 1–14 (2021).Article 

Google Scholar 
22.Kretschmar, J. A. & Baumann, T. W. Caffeine in Citrus flowers. Phytochemistry 52(1), 19–23 (1999).CAS 
Article 

Google Scholar 
23.Wright, G. A. et al. Caffeine in floral nectar enhances a pollinator’s memory of reward. Science 339(6124), 1202–1204 (2013).ADS 
CAS 
Article 

Google Scholar 
24.Couvillon, M. J. et al. Caffeinated forage tricks honeybees into increasing foraging and recruitment behaviors. Curr. Biol. 25, 2815–2818 (2015).CAS 
Article 

Google Scholar 
25.Arnold, S. E. et al. Bumble bees show an induced preference for flowers when primed with caffeinated nectar and a target floral odor. Curr. Biol. 31, 1–5 (2021).Article 

Google Scholar 
26.Gardener, M. C. & Gillman, M. P. Analyzing variability in nectar amino acids: Composition is less variable than concentration. J. Chem. Ecol. 27(12), 2545–2558 (2001).CAS 
Article 

Google Scholar 
27.Power, E. F., Stabler, D., Borland, A. M., Barnes, J. & Wright, G. A. Analysis of nectar from low-volume flowers: A comparison of collection methods for free amino acids. Methods Ecol. Evol. 9, 734–743 (2018).Article 

Google Scholar 
28.Terrab, A. et al. Analysis of amino acids in nectar from Silene colorata Poiret (Caryophyllaceae). Bot. J. Linn. Soc. 155, 49–56 (2007).Article 

Google Scholar 
29.Taha, E. K. A., Al-Kahtani, S. & Taha, R. Protein content and amino acids composition of bee-pollens from major floral sources in Al-Ahsa, eastern Saudi Arabia. Saudi J. Biol. Sci. 26, 232–237 (2019).CAS 
Article 

Google Scholar 
30.Müller, U. Inhibition of nitric oxide synthase impairs a distinct form of long-term memory in the honeybee, Apis mellifera. Neuron 16, 541–549 (1996).Article 

Google Scholar 
31.Müller, U. The nitric oxide system in insects. Prog. Neurobiol. 51, 363–381 (1997).Article 

Google Scholar 
32.Lopatina, N. G., Zachepilo, T. H., Kamyshev, N. G. & Chalisova, N. I. The influence of combinations of encoded amino acids on associative learning in the honeybee Apis mellifera L. J. Evol. Biochem. Physiol. 53(2), 123–128 (2017).CAS 
Article 

Google Scholar 
33.Marchi, I. L., Palottini, F. & Farina, W. M. Combined secondary compounds naturally found in nectars enhance honeybee cognition and survival. J. Exp. Biol. 224(6), jeb239616 (2021).Article 

Google Scholar 
34.Müller, U. Prolonged activation of cAMP-dependent protein kinase during conditioning induces long-term memory in honeybees. Neuron 27(1), 159–168 (2000).Article 

Google Scholar 
35.Negri, P. et al. Nitric oxide participates at the first steps of Apis mellifera cellular immune activation in response to non-self recognition. Apidologie 44(5), 575–585 (2013).CAS 
Article 

Google Scholar 
36.Lu, Y. H. et al. Identification of immune regulatory genes in Apis mellifera through caffeine treatment. Insects 11(8), 516 (2020).Article 

Google Scholar 
37.Delaplane, K. S. & Mayer, D. F. Crop Pollination by Bees (CAB International, 2000).
Google Scholar 
38.Cabrera, A. L. & Willink, A. Biogeografía de América Latina (Organización de Estados Americanos, 1973).
Google Scholar 
39.Gabai, A. et al. Protocol for Using Pollinators in Hybrid Seed Production: An Outline for Improving Pollinator Effectiveness. (International Seed Federation, 2018).40.Torretta, J. P., Medan, D., Roig Alsina, A. H. & Montaldo, N. H. Visitantes florales diurnos del girasol (Helianthus annuus L., Asterales: Asteraceae) en la Argentina. Rev. Soc. Entomol. Argent. 69(1–2), 17–32 (2010).
Google Scholar 
41.Sáez, A., Sabatino, M. & Aizen, M. A. Interactive effects of large-and small-scale sources of feral honeybees for sunflower in the Argentine Pampas. PLoS One 7(1), e30968 (2012).ADS 
Article 

Google Scholar 
42.Farina, W. M., Díaz, P. C. & Arenas, A. Patent AR082846B1: Una Formulación que Promueve la Polinización Dirigida de Abejas Melíferas Hacia Cultivos de Girasol (Instituto Nacional de Propiedad Intelectual, 2017).43.Noetzel, D. M. Insect Pollination Results on Sunflower 108–112 (Department of Entomology North Dakota State University Fargo USA, 1968).44.Johannsmeier, M. F. & Mostert, J. N. Crop pollination. In Beekeeping in South Africa, 3rd ed. 235–250 (ed. Johannsmeier, M. F.) (Plant Protection Research Institute handbook 14. Agricultural Research Council of South Africa, 2001).45.R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing). https://www.R-project.org/ (2021).46.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).Article 

Google Scholar 
47.Crawley, M. J. The R Book (Wiley, 2013).MATH 

Google Scholar 
48.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R Package Version 0.2.7. https://CRAN.R-project.org/package=DHARMa (2021).49.Chambers, J. M. & Hastie, T. J. Statistical Models in S (Chapman and Hall, 1992).MATH 

Google Scholar 
50.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R Package Version 1.4.4., https://CRAN.R-project.org/package=emmeans (2021). More

1.Hebert, P. D. N., Muncaster, B. W. & Mackie, G. L. Ecological and genetic studies on Dreissena polymorpha (Pallas): A new mollusc in the Great Lakes. Can. J. Fish. Aquat. Sci. 46, 1587–1591 (1989).
Google Scholar 
2.May, B. & Marsden, J. E. Genetic identification and implications of another invasive species of dreissenid mussel in the Great Lakes. Can. J. Fish. Aquat. Sci. 49, 1501–1506 (1992).
Google Scholar 
3.Ackerman, J. D., Cottrell, C. M., Ethier, C. R., Allen, D. G. & Spelt, J. K. Attachment strength of zebra mussels on natural, polymeric, and metallic materials. J. Environ. Eng. ASCE 122, 141–148 (1996).CAS 

Google Scholar 
4.Kobak, J. Attachment strength of Dreissena polymorph on artificial substrates. In The Zebra Mussel in Europe (eds van der Velde, G. et al.) 349–354 (Margraf Publishers, 2010).
Google Scholar 
5.Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Zebra versus quagga mussels: A review of their spread, population dynamics, and ecosystem impacts. Hydrobiologia 746, 97–112 (2015).CAS 

Google Scholar 
6.Karatayev, V. A., Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Lakewide dominance does not predict the potential for spread of dreissenids. J. Great Lakes Res. https://doi.org/10.1016/j.jglr.2013.09.007 (2013).Article 

Google Scholar 
7.Peyer, S. M., McCarthy, A. J. & Lee, C. E. Zebra mussels anchor byssal threads faster and tighter than quagga mussels in flow. J. Exp. Biol. https://doi.org/10.1242/jeb.028688 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Amini, S. et al. Preventing mussel adhesion using lubricant-infused materials. Science 357, 668–673 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Matsui, Y. et al. Attachment strength of Limnoperna fortunei on substrates, and their surface properties. Biofouling 17, 29–39 (2001).
Google Scholar 
10.Marsden, J. E. & Lansky, D. M. Substrate selection by settling zebra mussels, Dreissena polymorpha, relative to material, texture, orientation, and sunlight. Can. J. Zool. 78, 787–793 (2000).
Google Scholar 
11.Kobak, J. Factors influencing the attachment strength of Dreissena polymorpha (Bivalvia). Biofouling 22, 141–150 (2006).
Google Scholar 
12.Ackerman, J. D., Ethier, C. R., Allen, D. G. & Spelt, J. K. Investigation of zebra mussel adhesion strength using rotating disks. J. Environ. Eng. 118, 708–724 (1992).
Google Scholar 
13.Ackerman, J. D., Ethier, C. R., Spelt, J. K., Allen, D. G. & Cottrell, C. M. A wall jet to measure the attachment strength of zebra mussels. Can. J. Fish. Aquat. Sci. 52, 126–135 (1995).
Google Scholar 
14.Balogh, C., Serfőző, Z., bij de Vaate, A., Noordhuis, R. & Kobak, J. Biometry, shell resistance and attachment of zebra and quagga mussels at the beginning of their co-existence in large European lakes. J. Great Lakes Res. 45, 777–787 (2019).
Google Scholar 
15.Grutters, B. M. C., Verhofstad, M. J. J. M., van der Velde, G., Rajagopal, S. & Leuven, R. S. E. W. A comparative study of byssogenesis on zebra and quagga mussels: The effects of water temperature, salinity and light–dark cycle. Biofouling 28, 121–129 (2012).PubMed 

Google Scholar 
16.Naddafi, R. & Rudstam, L. G. Predator-induced behavioural defences in two competitive invasive species: The zebra mussel and the quagga mussel. Anim. Behav. 86, 1275–1284 (2013).
Google Scholar 
17.Bell, E. C. & Gosline, J. M. Mechanical design of mussel byssus: Material yield enhances attachment strength. J. Exp. Biol. 199, 1005–1017 (1996).CAS 
PubMed 

Google Scholar 
18.Brazee, S. L. & Carrington, E. Interspecific comparison of the mechanical properties of mussel byssus. Biol. Bull. 211, 263–274 (2006).PubMed 
PubMed Central 

Google Scholar 
19.Burkett, J. R., Wojtas, J. L., Cloud, J. L. & Wilker, J. J. A method for measuring the adhesion strength of marine mussels. J. Adhes. 85, 601–615 (2009).CAS 

Google Scholar 
20.Desmond, K. W., Zacchia, N. A., Waite, J. H. & Valentine, M. T. Dynamics of mussel plaque detachment. Soft Matter 11, 6832–6839 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Hamada, N., Roman, V., Howell, S. & Wilker, J. Examining potential active tempering of adhesive curing by marine mussels. Biomimetics 2, 16 (2017).
Google Scholar 
22.Farsad, N. & Sone, E. D. Zebra mussel adhesion: Structure of the byssal adhesive apparatus in the freshwater mussel, Dreissena polymorpha. J. Struct. Biol. 177, 613–620 (2012).PubMed 
PubMed Central 

Google Scholar 
23.Stalder, A. F. et al. Low-bond axisymmetric drop shape analysis for surface tension and contact angle measurements of sessile drops. Colloids Surf. A Physicochem. Eng. Asp. 364, 72–81 (2010).CAS 

Google Scholar 
24.Claxton, W. T., Wilson, A. B., Mackie, G. L. & Boulding, E. G. A genetic and morphological comparison of shallow- and deep-water populations of the introduced dreissenid bivalve Dreissena bugensis. Can. J. Zool. 76, 1269–1276 (1998).
Google Scholar 
25.Peyer, S. M., Hermanson, J. C. & Lee, C. E. Developmental plasticity of shell morphology of quagga mussels from shallow and deep-water habitats of the Great Lakes. J. Exp. Biol. 213, 2602–2609 (2010).PubMed 
PubMed Central 

Google Scholar 
26.Sprung, M. Field and laboratory observations of Dreissena polymorpha larvae: Abundance, growth, mortality and food demands. Arch. Hydrobiol. 115, 537–561 (1989).
Google Scholar 
27.Nichols, S. J. Maintenance of the zebra mussel (Dreissena polymorpha) under laboratory conditions. In Zebra Mussels: Biology, Impacts, and Control (eds Nalepa, T. F. & Schloesser, D. W.) 733–747 (Lewis Publishers, 1992).
Google Scholar 
28.Porter, A. E. & Marsden, J. E. Adult zebra mussels (Dreissena polymorpha) avoid attachment to mesh materials. Northeast. Nat. 15, 589–594 (2008).
Google Scholar 
29.Kimmins, K. M., James, B. D., Nguyen, M. T., Hatton, B. D. & Sone, E. D. Oil-infused silicone prevents zebra mussel adhesion. ACS Appl. Bio Mater. https://doi.org/10.1021/acsabm.9b00832 (2019).Article 

Google Scholar 
30.Peyer, S. M., Hermanson, J. C. & Lee, C. E. Effects of shell morphology on mechanics of zebra and quagga mussel locomotion. J. Exp. Biol. 214, 2226–2236 (2011).PubMed 
PubMed Central 

Google Scholar 
31.Berkman, P. A., Garton, D. W., Haltuch, M. A., Kennedy, G. W. & Febo, L. R. Habitat shift in invading species: Zebra and quagga mussel population characteristics on shallow soft substrates. Biol. Invasions https://doi.org/10.1023/A:1010088925713 (2000).Article 

Google Scholar 
32.Skaja, A., Tordonato, D. & Merten, B. Coatings for invasive mussel control: Colorado river field study. In Biol. Manag. Invasive Quagga Zebra Mussels West. United States 451–466 (2015) https://doi.org/10.1201/b18447-37https://doi.org/10.1201/b18447-37.33.Zhao, H., Robertson, N. B., Jewhurst, S. A. & Waite, J. H. Probing the adhesive footprints of Mytilus californianus byssus. J. Biol. Chem. https://doi.org/10.1074/jbc.M510792200 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Kimmins, K. Freshwater Mussel Adhesion: Interfacial Structures & Antifouling Surfaces (Univesity of Toronto, 2020).
Google Scholar 
35.Kobak, J. Behavior of juvenile and adult zebra mussels (Dreissena polymorpha). In Quagga Zebra Mussel Biol. Impacts, Control 331–344 (2013) https://doi.org/10.1201/b15437-28.36.Waite, J. H. Adhesion in byssally attached bivalves. Biol. Rev. 58, 209–231 (1983).CAS 

Google Scholar 
37.Lachance, A. A., Myrand, B., Tremblay, R., Koutitonsky, V. & Carrington, E. Biotic and abiotic factors influencing attachment strength of blue mussels Mytilus edulis in suspended culture. Aquat. Biol. 2, 119–129 (2008).
Google Scholar 
38.Lee, H., Scherer, N. F. & Messersmith, P. B. Single-molecule mechanics of mussel adhesion. Proc. Natl. Acad. Sci. 103, 12999–13003 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Rzepecki, L. M. & Waite, J. H. The byssus of the zebra mussel, Dreissena polymorpha. I: Morphology and in situ protein processing during maturation. Mol. Mar. Biol. Biotechnol. 2, 255–266 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Waite, J. H. & Qin, X. Polyphosphoprotein from the adhesive pads of Mytilus edulis. Biochemistry 40, 2887–2893 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Zhao, H. & Waite, J. H. Linking adhesive and structural proteins in the attachment plaque of Mytilus californianus. J. Biol. Chem. 281, 26150–26158 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Petrone, L. et al. Mussel adhesion is dictated by time-regulated secretion and molecular conformation of mussel adhesive proteins. Nat. Commun. 6, 8737 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Waite, J. H. Mussel adhesion—Essential footwork. J. Exp. Biol. 220, 517–530 (2017).PubMed 
PubMed Central 

Google Scholar 
44.Lee, B. P., Messersmith, P. B., Israelachvili, J. N. & Waite, J. H. Mussel-inspired adhesives and coatings. Annu. Rev. Mater. Res. 41, 99–132 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Ou, X. et al. Structure and sequence features of mussel adhesive protein lead to its salt-tolerant adhesion ability. Sci. Adv. 6, eabb7620 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Maier, G. P., Rapp, M. V., Waite, J. H., Israelachvili, J. N. & Butler, A. Adaptive synergy between catechol and lysine promotes wet adhesion by surface salt displacement. Science 349, 628–632 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Bilotto, P. et al. Adhesive properties of adsorbed layers of two recombinant mussel foot proteins with different levels of DOPA and tyrosine. Langmuir 35, 15481–15490 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Kim, S. et al. Cation–π interaction in DOPA-deficient mussel adhesive protein mfp-1. J. Mater. Chem. B 3, 738–743 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Hall EK, Bernhardt ES, Bier RL, Bradford MA, Boot CM, Cotner JB, et al. Understanding how microbiomes influence the systems they inhabit. Nature Microbiol. 2018;3:977–82.CAS 

Google Scholar 
2.Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat. Med. 2018;24:392–400.3.Suttle CA. Marine viruses-major players in the global ecosystem. Nat Rev Microbiol. 2007;5:801–12.CAS 
PubMed 

Google Scholar 
4.Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2020;18:21–34.5.Howard-Varona C, Lindback MM, Bastien GE, Solonenko N, Zayed AA, Jang HB, et al. Phage-specific metabolic reprogramming of virocells. ISME J. 2020;14:881–95.PubMed 
PubMed Central 

Google Scholar 
6.Sullivan MB, Lindell D, Lee JA, Thompson LR, Bielawski JP, Chisholm SW. Prevalence and evolution of core photosystem II genes in marine cyanobacterial viruses and their hosts. PLoS Biol. 2006;4:1344–57.CAS 

Google Scholar 
7.Lindell D, Jaffe JD, Johnson ZI, Church GM, Chisholm SW. Photosynthesis genes in marine viruses yield proteins during host infection. Nature. 2005;438:86–9.CAS 
PubMed 

Google Scholar 
8.Hurwitz BL, Hallam SJ, Sullivan MB. Metabolic reprogramming by viruses in the sunlit and dark ocean. Genome Biol. 2013;14:R123.PubMed 
PubMed Central 

Google Scholar 
9.Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci. 2011;108:E757–E764.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Gazitúa MC, Vik DR, Roux S, Gregory AC, Bolduc B, Widner B, et al. Potential virus-mediated nitrogen cycling in oxygen-depleted oceanic waters. ISME J. 2021;15:981–98.PubMed 

Google Scholar 
11.Vik D, Gazitúa MC, Sun CL, Zayed AA, Aldunate M, Mulholland MR, et al. Genome-resolved viral ecology in a marine oxygen minimum zone. Environ Microbiol. 2021;23:2858–74.CAS 
PubMed 

Google Scholar 
12.Rosenwasser S, Ziv C, Creveld SG, van, Vardi A. Virocell metabolism: metabolic innovations during host–virus interactions in the ocean. Trends Microbiol. 2016;24:821–32.CAS 
PubMed 

Google Scholar 
13.Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HB, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Trubl G, Jang HB, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:1–21.
Google Scholar 
15.Zhong Z-P, Tian F, Roux S, Gazitúa MC, Solonenko NE, Li Y-F, et al. Glacier ice archives nearly 15,000-year-old microbes and phages. Microbiome. 2021;9:160.PubMed 
PubMed Central 

Google Scholar 
16.Zhong Z-P, Rapp JZ, Wainaina JM, Solonenko NE, Maughan H, Carpenter SD, et al. Viral ecogenomics of arctic cryopeg brine and sea ice. mSystems. 2020;5:e00246–20.17.Anantharaman K, Duhaime MB, Breier JA, Wendt KA, Toner BM, Dick GJ. Sulfur oxidation genes in diverse deep-sea viruses. Science. 2014;344:757–60.CAS 
PubMed 

Google Scholar 
18.Gao S-M, Schippers A, Chen N, Yuan Y, Zhang M-M, Li Q, et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome. 2020;8:89.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Correa AMS, Howard-Varona C, Coy SR, Buchan A, Sullivan MB, Weitz JS. Revisiting the rules of life for viruses of microorganisms. Nat Rev Microbiol. 2021;19:501–13.CAS 
PubMed 

Google Scholar 
20.Blazanin M, Turner PE. Community context matters for bacteria-phage ecology and evolution. ISME J. 2021;1–10.21.Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23. e14CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Gregory AC, Zablocki O, Zayed AA, Howell A, Bolduc B, Sullivan MB. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host and Microbe. 2020;28:724–40. e8CAS 
PubMed 

Google Scholar 
23.Roux S, Páez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2021;49:1–12.24.Nayfach S, Páez-Espino D, Call L, Low SJ, Sberro H, Ivanova NN, et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome. Nat Microbiol. 2021;6:960–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Roux S, Matthijnssens J, Dutilh BE. Metagenomics in virology. Encycloped Virol. 2021;133–40. Published online 2021 Mar 1. https://doi.org/10.1016/B978-0-12-809633-8.20957-6.26.Warwick-Dugdale J, Solonenko N, Moore K, Chittick L, Gregory AC, Allen MJ, et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ. 2019;7:e6800.PubMed 
PubMed Central 

Google Scholar 
27.Roux S, Solonenko NE, Dang VT, Poulos BT, Schwenck SM, Goldsmith DB, et al. Towards quantitative viromics for both double-stranded and single-stranded DNA viruses. PeerJ. 2016;4:e2777.PubMed 
PubMed Central 

Google Scholar 
28.Simmonds P, Adams MJ, Benkő M, Breitbart M, Brister JR, Carstens EB, et al. Consensus statement: Virus taxonomy in the age of metagenomics. Nat Rev Microbiol. 2017;15:161–8.CAS 
PubMed 

Google Scholar 
29.Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum Information about an Uncultivated Virus Genome (MIUViG): a community consensus on standards and best practices for describing genome sequences from uncultivated viruses. Nat Biotechnol. 2018;37:29–37.PubMed 
PubMed Central 

Google Scholar 
30.Jang HB, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol 2019;37:632–9.
Google Scholar 
31.Nishimura Y, Yoshida T, Kuronishi M, Uehara H, Ogata H, Goto S. ViPTree: the viral proteomic tree server. Bioinformatics. 2017;33:2379–80.32.Moraru C, Varsani A, Kropinski AM. VIRIDIC-a novel tool to calculate the intergenomic similarities of prokaryote-infecting. Viruses. 2020;12:1268.CAS 
PubMed Central 

Google Scholar 
33.Pons JC, Paez-Espino D, Riera G, Ivanova N, Kyrpides NC, Llabrés M. VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families. Bioinformatics. 2021;37:1805–13.34.Bolduc B, Youens-Clark K, Roux S, Hurwitz BL, Sullivan MB. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. ISME J. 2017;11:7–14.PubMed 

Google Scholar 
35.Merchant N, Lyons E, Goff S, Vaughn M, Ware D, Micklos D, et al. The iPlant Collaborative: cyberinfrastructure for enabling data to discovery for the life sciences. PLOS Biol. 2016;14:e1002342.PubMed 
PubMed Central 

Google Scholar 
36.Teytelman L, Stoliartchouk A, Kindler L, Hurwitz BL. Protocols.io: virtual communities for protocol development and discussion. PLOS Biol. 2016;14:e1002538.PubMed 
PubMed Central 

Google Scholar 
37.Kindler L, Stoliartchouk A, Gomez C, Thornton J, Teytelman L, Hurwitz BL. VERVENet: the viral ecology research and virtual exchange network. PeerJ. 2021; in press.38.Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Sousa AL de, Maués D, Lobato A, Franco EF, Pinheiro K, Araújo F, et al. PhageWeb—web interface for rapid identification and characterization of prophages in bacterial genomes. Front Genet. 2018; 9.40.Tynecki P, Guziński A, Kazimierczak J, Jadczuk M, Dastych J, Onisko A. PhageAI—bacteriophage life cycle recognition with machine learning and natural language processing. bioRxiv 2020; 2020.07.11.198606.41.Wommack KE, Bhavsar J, Polson SW, Chen J, Dumas M, Srinivasiah S, et al. VIROME: a standard operating procedure for analysis of viral metagenome sequences. Standards Genom Sci. 2012;6:427–39.
Google Scholar 
42.Roux S, Faubladier M, Mahul A, Paulhe N, Bernard A, Debroas D, et al. Metavir: a web server dedicated to virome analysis. Bioinformatics. 2011;27:3074–5.CAS 
PubMed 

Google Scholar 
43.Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States department of energy systems biology knowledgebase. Nat Biotechnol. 2018;36:566–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.PubMed 
PubMed Central 

Google Scholar 
45.Bolduc B, Jang HB, Doulcier G, You Z-QZ, Roux S, Sullivan MB. vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ. 2017;5:e3243.PubMed 
PubMed Central 

Google Scholar 
46.Hurwitz BL, Westveld AH, Brum JR, Sullivan MB. Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses. Proc Natl Acad Sci. 2014;111:10714–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 2021;9:37.PubMed 
PubMed Central 

Google Scholar 
48.Ren J, Kai S, Chao D, Nathan A, Ahlgren, JA, Fuhrman, YL, et al. Identifying viruses from metagenomic data using deep learning. Quant Biol. 2020;8:64–77. https://doi.org/10.1007/s40484-019-0187-4.49.Amgarten D, Braga LPP, da Silva AM, Setubal JC. MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins. Front Genet. 2018;9:1–8.
Google Scholar 
50.Pratama A, Bolduc B, Zayed AA, Zhong Z-P, Guo J, Vik DR, et al. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation. PeerJ. 2021; In Press.51.Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome. 2020;8:90. https://doi.org/10.1186/s40168-020-00867-0.52.Karner MB, DeLong EF, Karl DM. Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature. 2001;409:507–10.CAS 
PubMed 

Google Scholar 
53.Vik DR, Roux S, Brum JR, Bolduc B, Emerson JB, Padilla CCC, et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ. 2017;5:e3428.PubMed 
PubMed Central 

Google Scholar 
54.Vik D, Bolduc B, Roux S, Krupovic M, Sullivan MB. MArVDv2: a machine learning approach to metagenomic archaeal virus detection. bioRxiv 2021; In Press..55.Tisza MJ, Pastrana DV, Welch NL, Stewart B, Peretti A, Starrett GJ, et al. Discovery of several thousand highly diverse circular DNA viruses. eLife. 2020;9:1–26.
Google Scholar 
56.Tisza MJ, Belford AK, Domínguez-Huerta G, Bolduc B, Buck CB. Cenote-Taker 2 democratizes virus discovery and sequence annotation. Virus Evolut. 2021;7:1–12.
Google Scholar 
57.Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–8900.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage–host relationships. FEMS Microbiol Rev. 2016;40:258–72.CAS 
PubMed 

Google Scholar 
59.Galiez C, Siebert M, Enault F, Vincent J, Söding J. WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics. 2017;33:3113–14.60.Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S & Kyrpides NC. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol 2021;39:578–85. https://doi.org/10.1038/s41587-020-00774-7.61.Gregory AC, Solonenko SA, Ignacio-Espinoza JC, LaButti K, Copeland A, Sudek S, et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC Genom. 2016;17:930.
Google Scholar 
62.Brum JR, Sullivan MB. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat Rev Microbiol. 2015;13:147–59.CAS 
PubMed 

Google Scholar 
63.Gregory AC, Gerhardt K, Zhong Z-P, Bolduc B, Temperton B, Konstantinidis KT, et al. MetaPop: a pipeline for macro- and micro-diversity analyses and visualization of microbial and viral metagenome-derived populations. bioRxiv 2020; 2020.11.01.363960.64.Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.PubMed 
PubMed Central 

Google Scholar 
65.Mitchell AL, Almeida A, Beracochea M, Boland M, Burgin J, Cochrane G, et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 2019;48:D570–D578.PubMed Central 

Google Scholar 
66.Solonenko SA, Ignacio-Espinoza JC, Alberti A, Cruaud C, Hallam S, Konstantinidis K, et al. Sequencing platform and library preparation choices impact viral metagenomes. BMC Genom. 2013;14:320.CAS 

Google Scholar 
67.Wood-Charlson EM, Anubhav, Auberry D, Blanco H, Borkum MI, Corilo YE, et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat Rev Microbiol. 2020;18:313–4.CAS 
PubMed 

Google Scholar  More

1.Baer, S. G. & Birgé, H. E. Soil ecosystem services: An overview. Manag. Soil Health Sustain. Agric. 1, 1–22 (2018).
Google Scholar 
2.Geisen, S., Wall, D. H. & van der Putten, W. H. Challenges and opportunities for soil biodiversity in the anthropocene. Curr. Biol. 29, R1036–R1044 (2019).CAS 
PubMed 

Google Scholar 
3.Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).ADS 
CAS 
PubMed 

Google Scholar 
4.Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Global Change Biol. 21, 973–985 (2015).ADS 

Google Scholar 
5.Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: Biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).PubMed 

Google Scholar 
6.Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. PNAS 111, 5266–5270 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528, 69–76 (2015).ADS 
CAS 
PubMed 

Google Scholar 
8.Smith, P. et al. Global change pressures on soils from land use and management. Global Change Biol. 22, 1008–1028 (2016).ADS 

Google Scholar 
9.Birkhofer, K., Smith, H. G. & Rundlöf, M. Environmental Impacts of Organic Farming. in eLS. 1–7 (John Wiley & Sons Ltd, 2016).10.Bengtsson, J., Ahnström, J. & Weibull, A.-C. The effects of organic agriculture on biodiversity and abundance: A meta-analysis: Organic agriculture, biodiversity and abundance. J. Appl. Ecol. 42, 261–269 (2005).
Google Scholar 
11.Abbott, L. K. & Manning, D. A. C. Soil health and related ecosystem services in organic agriculture. Sustain. Agric. Res. 4, 116 (2015).
Google Scholar 
12.de Graaff, M.-A., Hornslein, N., Throop, H. L., Kardol, P. & van Diepen, L. T. A. Effects of agricultural intensification on soil biodiversity and implications for ecosystem functioning: A meta-analysis. in Advances in Agronomy vol. 155 1–44 (Elsevier, 2019).13.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Pokhrel, Y. et al. Global terrestrial water storage and drought severity under climate change. Nat. Clim. Change 11, 226–233 (2021).ADS 

Google Scholar 
15.Iglesias, A. & Garrote, L. Adaptation strategies for agricultural water management under climate change in Europe. Agric. Water Manage. 155, 113–124 (2015).
Google Scholar 
16.Pörtner, H. O. et al. IPBES-IPCC Co-sponsored Workshop Report Synopsis on Biodiversity and Climate Change. https://zenodo.org/record/4920414 (2021).17.Blankinship, J. C., Niklaus, P. A. & Hungate, B. A. A meta-analysis of responses of soil biota to global change. Oecologia 165, 553–565 (2011).ADS 
PubMed 

Google Scholar 
18.Holmstrup, M. et al. Long-term and realistic global change manipulations had low impact on diversity of soil biota in temperate heathland. Sci. Rep. 7, 41388 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Fry, E. L. et al. Soil multifunctionality and drought resistance are determined by plant structural traits in restoring grassland. Ecology 99, 2260–2271 (2018).PubMed 

Google Scholar 
20.Zhou, Z., Wang, C. & Luo, Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat. Commun. 11, 3072 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Schimel, J. P. Life in dry soils: Effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).
Google Scholar 
22.Kundel, D. et al. Drought effects on nitrogen provisioning in different agricultural systems: Insights gained and lessons learned from a field experiment. Nitrogen 2, 1–17 (2021).
Google Scholar 
23.Abbasi, A. O. et al. Reviews and syntheses: Soil responses to manipulated precipitation changes: An assessment of meta-analyses. Biogeosciences 17, 3859–3873 (2020).ADS 
CAS 

Google Scholar 
24.Webber, H. et al. Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Commun. 9, 4249 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
25.Gomez-Zavaglia, A., Mejuto, J. C. & Simal-Gandara, J. Mitigation of emerging implications of climate change on food production systems. Food Res. Int. 134, 109256 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Yin, R. et al. Soil functional biodiversity and biological quality under threat: Intensive land use outweighs climate change. Soil Biol. Biochem. 147, 107847 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Rawls, W. J., Pachepsky, Y. A., Ritchie, J. C., Sobecki, T. M. & Bloodworth, H. Effect of soil organic carbon on soil water retention. Geoderma 116, 61–76 (2003).ADS 
CAS 

Google Scholar 
28.Lal, R. Soil health and carbon management. Food Energy Secur. 5, 212–222 (2016).
Google Scholar 
29.Iizumi, T. & Wagai, R. Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration. Sci. Rep. 9, 19744 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Fließbach, A., Oberholzer, H.-R., Gunst, L. & Mäder, P. Soil organic matter and biological soil quality indicators after 21 years of organic and conventional farming. Agric. Ecosyst. Environ. 118, 273–284 (2007).
Google Scholar 
31.Gattinger, A. et al. Enhanced top soil carbon stocks under organic farming. PNAS 109, 18226–18231 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Schädler, M. et al. Investigating the consequences of climate change under different land-use regimes: A novel experimental infrastructure. Ecosphere 10, e02635 (2019).
Google Scholar 
33.Birkhofer, K. et al. Ecosystem services: Current challenges and opportunities for ecological research. Front. Ecol. Evol. 2, 87 (2015).
Google Scholar 
34.Birkhofer, K. et al. Relationships between multiple biodiversity components and ecosystem services along a landscape complexity gradient. Biol. Cons. 218, 247–253 (2018).
Google Scholar 
35.Chabert, A. & Sarthou, J.-P. Conservation agriculture as a promising trade-off between conventional and organic agriculture in bundling ecosystem services. Agric. Ecosyst. Environ. 292, 106815 (2020).CAS 

Google Scholar 
36.Felipe-Lucia, M. R. et al. Land-use intensity alters networks between biodiversity, ecosystem functions, and services. PNAS 117, 28140–28149 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Lori, M., Symnaczik, S., Mäder, P., De Deyn, G. & Gattinger, A. Organic farming enhances soil microbial abundance and activity: A meta-analysis and meta-regression. PLoS ONE 12, e0180442 (2017).PubMed 
PubMed Central 

Google Scholar 
38.Kundel, D. et al. Effects of simulated drought on biological soil quality, microbial diversity and yields under long-term conventional and organic agriculture. FEMS Microbiol. Ecol. 96, fiaa205 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Chen, Q.-L. et al. Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils. Soil Biol. Biochem. 141, 107686 (2020).CAS 

Google Scholar 
40.Garland, G. et al. Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems. Nat. Food 2, 28–37 (2021).
Google Scholar 
41.Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
42.Vazquez, C., de Goede, R. G. M., Rutgers, M., de Koeijer, T. J. & Creamer, R. E. Assessing multifunctionality of agricultural soils: Reducing the biodiversity trade-off. Eur. J. Soil. Sci. 72, 1624–1639 (2020).
Google Scholar 
43.Zwetsloot, M. J. et al. Soil multifunctionality: Synergies and trade-offs across European climatic zones and land uses. Eur. J. Soil. Sci. 72, 1640–1654 (2020).
Google Scholar 
44.Delgado-Baquerizo, M. et al. Soil microbial communities drive the resistance of ecosystem multifunctionality to global change in drylands across the globe. Ecol. Lett. 20, 1295–1305 (2017).PubMed 

Google Scholar 
45.Bardgett, R. D. & Caruso, T. Soil microbial community responses to climate extremes: Resistance, resilience and transitions to alternative states. Phil. Trans. R. Soc. B 375, 20190112 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Meyer, S., Kundel, D., Birkhofer, K., Fliessbach, A. & Scheu, S. Soil microarthropods respond differently to simulated drought in organic and conventional farming systems. Ecol. Evol. 11, 10369–10380 (2021).PubMed 
PubMed Central 

Google Scholar 
47.De Smedt, P. et al. Linking macrodetritivore distribution to desiccation resistance in small forest fragments embedded in agricultural landscapes in Europe. Landscape Ecol. 33, 407–421 (2018).
Google Scholar 
48.Liu, W. P. A., Phillips, L. M., Terblanche, J. S., Janion-Scheepers, C. & Chown, S. L. An unusually diverse genus of Collembola in the Cape Floristic Region characterised by substantial desiccation tolerance. Oecologia 195, 873–885 (2021).ADS 
PubMed 

Google Scholar 
49.Birkhofer, K. et al. Long-term organic farming fosters below and aboveground biota: Implications for soil quality, biological control and productivity. Soil Biol. Biochem. 40, 2297–2308 (2008).CAS 

Google Scholar 
50.Mäder, P. Soil fertility and biodiversity in organic farming. Science 296, 1694–1697 (2002).ADS 
PubMed 

Google Scholar 
51.Birkhofer, K., Bezemer, T. M., Hedlund, K. & Setälä, H. Community composition of soil organisms under different wheat farming systems. in Microbial Ecology in Sustainable Agroecosystems 89–111 (CRC press Boca Raton, 2012).52.Birkhofer, K. et al. Soil fauna feeding activity in temperate grassland soils increases with legume and grass species richness. Soil Biol. Biochem. 43, 2200–2207 (2011).CAS 

Google Scholar 
53.Siebert, J. et al. Extensive grassland-use sustains high levels of soil biological activity, but does not alleviate detrimental climate change effects. Adv. Ecol. Res. 60, 25–58 (2019).
Google Scholar 
54.de Vries, F. T. et al. Land use alters the resistance and resilience of soil food webs to drought. Nat. Clim. Change 2, 276–280 (2012).ADS 

Google Scholar 
55.Torode, M. D. et al. Altered precipitation impacts on above-and below-ground grassland invertebrates: Summer drought leads to outbreaks in spring. Front. Plant Sci. 7, 1468 (2016).PubMed 
PubMed Central 

Google Scholar 
56.Jonas, J. L., Wilson, G. W. T., White, P. M. & Joern, A. Consumption of mycorrhizal and saprophytic fungi by Collembola in grassland soils. Soil Biol. Biochem. 39, 2594–2602 (2007).CAS 

Google Scholar 
57.Susanti, W. I., Pollierer, M. M., Widyastuti, R., Scheu, S. & Potapov, A. Conversion of rainforest to oil palm and rubber plantations alters energy channels in soil food webs. Ecol. Evol. 9, 9027–9039 (2019).PubMed 
PubMed Central 

Google Scholar 
58.Seres, A. et al. Collembola decrease the nitrogen uptake of maize through arbuscular mycorrhiza. ekol 28, 242–247 (2009).
Google Scholar 
59.Bender, S. F. & van der Heijden, M. G. A. Soil biota enhance agricultural sustainability by improving crop yield, nutrient uptake and reducing nitrogen leaching losses. J. Appl. Ecol. 52, 228–239 (2015).CAS 

Google Scholar 
60.Carson, J. K. et al. Low pore connectivity increases bacterial diversity in soil. Appl. Environ. Microbiol. 76, 3936–3942 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Krause, H.-M., Fliessbach, A., Mayer, J. & Mäder, P. Implementation and management of the DOK long-term system comparison trial. in Long-Term Farming Systems Research 37–51, (Elsevier, 2020).62.Richner, W. et al. Grundlagen für die Düngung landwirtschaftlicher Kulturen in der Schweiz (GRUD 2017). Agrarforschung Schweiz 8, 47–66 (2017).
Google Scholar 
63.Kundel, D. et al. Design and manual to construct rainout-shelters for climate change experiments in agroecosystems. Front. Environ. Sci. 6, 14 (2018).
Google Scholar 
64.Garland, G. et al. A closer look at the functions behind ecosystem multifunctionality: A review. J. Ecol. 109, 600–613 (2021).
Google Scholar 
65.Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). in Wiley StatsRef: Statistics Reference Online 1–15.66.Fletcher, D. J. & Underwood, A. J. How to cope with negative estimates of components of variance in ecological field studies. J. Exp. Mar. Biol. Ecol. 273, 89–95 (2002).
Google Scholar 
67.Ho, J., Tumkaya, T., Aryal, S., Choi, H. & Claridge-Chang, A. Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16, 565–566 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org.69.Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar  More

Our results show that Chl a alone is not an adequate proxy for prey growth rates in dilution grazing experiments when mixoplankton are present5,10. Chlorophyll is, in any case, a poor proxy for phototrophic plankton biomass31 because of inter-species variations, and also for the photoacclimation abilities of some species (for which very significant changes can occur within a few hours). The problem extends to the involvement of mixoplanktonic prey and grazers. Nevertheless, even very recent studies continue to rely on this parameter for quantifications of grazing despite acknowledging the dominance, both in biomass and abundance, of mixoplanktonic predators in their system30. Moreover, the detailed analysis of the species-specific dynamics revealed that different prey species are consumed at very different rates. In our experiments, and contrary to expectations (see32,33, and Fig. S1 in the Supplementary Information), C. weissflogii was only actively ingested in the ciliate experiment and, according to the results from the control bottles (Table 2), not by M. rubrum (see Fig. 4 and Fig. S1a).Certainly, it is not the first time that a negative selection against diatoms has been seen; for example, Burkill et al.34 noticed that diatoms were less grazed by protist grazers than other phytoplankton species, as assessed by a dilution technique paired with High-Performance Liquid Chromatography for pigment analysis. Using the same method, Suzuki et al.35 reported that diatoms became the dominant phytoplankton group, which suggests that other groups were preferentially fed upon. Calbet et al.36, in the Arctic, also found only occasional grazing over the local diatoms. In our study, diatoms were not only not consumed, but the presence of dinoflagellates appeared to contribute to their growth (Fig. 4), this relationship being partly dependent on the concentration of the predator (see Fig. 2c, d). This result could be a direct consequence of assimilation and use of compounds (e.g.,37,38) released by microplankton such as ammonium (e.g.,39,40) and urea (e.g.,41), which were not supplied in the growth medium, but which would have supported prey growth. Alternatively, this unexpected outcome may have been a consequence of the selective ingestion of R. salina by the two predators, relieving the competition for nutrients and light and resulting in a higher growth rate of the diatom in the presence of the predators. We cannot rule out the fact that diatoms sink faster than flagellates which, as the bottles were not mixed during most of the incubation period (although gently mixed at every sampling point), may have also involuntarily decreased ingestion rates on C. weissflogii. Still, one C. weissflogii cell contains, on average, ca. 2.5 times more Chl a than one R. salina cell (initial value excluded, see Table 3). Taken together with the preference for R. salina it is not surprising that the proportion of total Chl a represented by the diatoms increased over time, in particular in the L/D treatment (Figs. 6a, c and 7a, c).Table 3 Chl a content (pg Chl a cell−1) of the target species at each sampling point as calculated from the control bottles.Full size tableAnother factor clearly highlighted by our experiments, is that protozooplankton themselves contribute a significant portion of the total chlorophyll of the system (due to ingested Chl a), in particular at the beginning of the incubation (see Figs. 6 and 7); this being invariably ignored in a traditional dilution experiment. The high Chl a detected inside the protozooplanktonic grazers at the beginning of the incubations could suggest that the system was initially not in equilibrium, and that this was the result of superfluous feeding (e.g.,42). This would, nevertheless, be surprising since we required ca. 1 h to collect the initial samples (t = 0 h) after joining all the organisms together (see the section “Dilution grazing experiments” in the “Methods” section); previous studies, like the one on G. dominans and Oxyrrhis marina by Calbet et al.42, showed that the hunger response and consequent vacuole replenishment occurred in ca. 100 min for very high prey concentrations and it is expected to decrease at lower prey concentrations as the ones used in our study. Therefore, even if one assumes that the first 4 h of incubation are a result of superfluous feeding, after 24 h, the “estimated”, “observed”, and “from dilution slope” grazing estimates are not significantly different to those displayed in Fig. 5 (P  > 0.05 in all instances) and, therefore, we can assume that the hunger response was likely irrelevant (e.g.,43) and did not mask our results. In any case, as stated before, an actual field grazing dilution experiment also suffers from similar problems, because grazers and prey are suddenly diluted and not pre-adapted to distinct food concentrations. Nevertheless, this is not novel information, since Chl a and its degradation products have been found inside several protozooplankton species from different phylogenetic groups immediately after feeding44 and even after some days without food45. An increase in intracellular Chl a concentrations immediately after feeding has also been found in mixoplankton46,47, on which this increase is derived both from ingested prey as well as from new synthesis of their own Chl a. Additionally, several experiments with Live Fluorescently Labelled Algae (LFLA) show that predators (irrespective of their trophic mode) seem to maximise the concentration of intracellular prey shortly after the initiation of the incubation (e.g.,48; Ferreira et al., submitted). Indeed, some authors have even been able to measure photosynthesis in protozooplankton, like the ciliates Mesodinium pulex49 and Strombidinopsis sp.50.The fact that Chl a is a poor indicator of phytoplankton biomass and the inherent consequences discussed so far can be solved by the quantification of the prey community abundance (e.g.,51) by microscopy or by the use of signature pigments for each major phytoplankton group. The latter method, however, is not as thorough as the former, since rare are the cases where one pigment is exclusively associated with a single group of organisms (see52 and references therein). In any case, any pigment-based proxy is subject to the same problems, as identified by Kruskopf & Flynn31. Irrespective of the quantification method, it has been made evident that the different algae are consumed at different rates (e.g., pigments10,34,35; microscopy5,36).Prey selection in protistan grazers is a common feature (e.g.,23,26,27,28). Given the diversity of grazers in natural communities and the array of preferred prey that each particular species possesses, it is logical to think that dilution experiments will capture the net community response properly. Likewise, grazers interact with each other through toxins, competition, and intraguild predation among other factors. An example of intraguild predation could be the observed on K. armiger by G. dominans (see Figs. 2f and 4 and Table 1), which caused an average loss of ca. 18.72 pg of K. armiger carbon per G. dominans per hour in the D treatment. Interestingly, in the same treatment, a slight negative effect of K. armiger on its predator G. dominans can also be deduced (i.e., positive g, Table 1), resulting in an average loss of ca. 0.33 pg G. dominans carbon per K. armiger per hour. This could be a consequence of algal toxins, since K. armiger is a known producer of karmitoxin22, whose presence may have negative effects even on metazoan grazers21. Regarding ciliates, none of the species used is a known producer of toxic compounds, which suggests that the average loss of ca. 1.25 pg M. rubrum carbon per hour in the D treatment was due to S. arenicola predation. Altogether, it seems clear from our data that intraguild predation cannot be ignored when analysing dilution experiments (Fig. 4). Furthermore, our results clearly show that single functional responses cannot be used to extrapolate community grazing impacts, as evidenced by the differences in estimated and measured ingestion rates based on the disappearance of prey in combined grazers experiments (Fig. 5). Nevertheless, this is a relatively common procedure (e.g.,53 and references therein). Often in modelling approaches, individual predator’s functional responses have been used to extrapolate prey selectivity and community grazing responses27; in reality complex prey selectivity functions are required to satisfactorily describe prey selectivity and inter-prey allelopathic interactions54.It is, however, also evident that the measured ingestion rates in combined grazers experiments were not the same as those calculated from the slope of the dilution grazing experiment. This raises the question of why was that the case. It is well known that phytoplankton cultures, when extremely diluted, show a lag phase of different duration55 which has been attributed to the net leakage of metabolites56. Assuming that the duration of the lag phase will be dependent on the level of dilution, it seems reasonable to deduce that after ca. 24 h the instantaneous growth rates (µ) in the most diluted treatments will be lower than that of the undiluted treatments. This has consequences, not only for the estimated prey growth rates but also for the whole assessment of the grazing rate, due to the flattening of the regression line (i.e., the decrease in the computed growth rate). This artefact may be more evident in cultures acclimated to very particular conditions (as the laboratory cultures used in this study) than in nature.Another important finding of our research is the importance of light on the correct expression of the feeding activity by both mixoplankton and protozooplankton. We noticed that irrespective of the light conditions, all species exhibited a diurnal feeding rhythm (R. salina panels in Figs. 2 and 3), which is in accordance with earlier observations on protists (e.g.,29,57,58). The presence of light typically increased the ingestion rates. Additionally, the ingestion rates differed during the night period between L/D and D treatments, which implies that receiving light during the day is also vital in modulating the night behaviour of protoozoo- and mixoplankton. In particular, mixoplankton grazing is usually affected by light conditions, typically increasing (e.g.,32,59), but also sometimes decreasing(e.g.,60) in the presence of light. Different irradiance levels can also affect the magnitude of ingestion rates both in protozoo- and mixoplankton (see61 and references therein).For those reasons, we hoped for a rather consistent pattern among our protists that would help us discriminate mixoplankton in dilution grazing experiments. As a matter of fact, based on the results from Arias et al.29, we expected that in the dinoflagellate experiment, the D treatment would have inhibited only the grazing of K. armiger, enabling a simple discrimination between trophic modes. The reality did not meet the expectations since the day and night-time carbon-specific ingestion rates (as assessed using the control bottles, Table 2) of K. armiger were respectively higher and equal than those of G. dominans. Conversely, in the ciliate experiment, protozooplankton were the major grazers in our incubations regardless of the day period and light conditions. This response was not as straightforward as one would expect it to be because M. rubrum has been recently suggested to be a species complex containing at least 7 different species (62 and references therein), which hinders any possible conjecture on their grazing impact. Indeed, the uneven responses found between and within trophic modes precluded such optimistic hypothetical procedure.The D treatment in the present paper illustrated the importance of mimicking natural light conditions, a factor also addressed in the original description of the technique by Landry and Hassett1. It is crucial for the whole interpretation of the dilution technique that incubations should be conducted in similar light (and temperature) conditions as the natural ones to allow for the continued growth of the phototrophic prey. However, here we want to stress another aspect of the incubations: should they start during the day or the night? Considering our (and previous) results on diel feeding rhythms, and on the contribution of each species to the total Chl a pool, it is clear that different results will be obtained if the incubations are started during the day or the night. Besides, whether day or night, organisms are also likely to be in a very different physiological state (either growing or decreasing). Therefore, we recommend that dilution experiments conducted in the field should always be started at the same period of the day to enable comparisons (see also Anderson et al.14 for similar conclusions on bacterivory exerted by small flagellates). Ideally, incubations would be started at different times of the day to capture the intricacies of the community dynamics on a diel cycle. Nevertheless, should the segmented analysis be impossible, we argue that the right time to begin the incubations would be during the night, as this is the time where ingestion rates by protozooplankton are typically lower (e.g.,29,57,58, this study) and would, consequently, reduce their quota of Chl a in the system.Lastly, we want to stress that we are aware that our study does not represent natural biodiversity because our experiments were conducted in the laboratory with a few species. Nevertheless, we attempted to use common species of wide distribution for each major group of protists to provide a better institutionalisation of our conclusions. Further to the choice of predator and prey is their concentrations and proportions. Being a laboratory experiment designed to understand fundamental mechanisms within a dilution grazing experiment, we departed from near saturating food conditions from where we started the dilution series. In nature, the concentrations that we used may be high but are not unrealistic, and actually lower than in many bloom scenarios. We included diatoms at high concentrations, even knowing that they are not the preferred prey of most grazers34, because diatoms are very abundant in many natural ecosystems and to stress the point of food selection within the experiment. For sure, using different proportions of prey would have rendered different results. However, as previously mentioned, our aim was not to seek flaws in the dilution technique, but to understand the role of mixoplankton in these experiments and the complex trophic interactions that may occur within. Ultimately, with our choice of prey and their concentrations, we have proven that when there is no selection for a massively abundant prey, the use of Chl a as a proxy for community abundances may underestimate actual grazing rates.Some other aspects of our experiments may also be criticised because they do not fully match a standard dilution experiment. For instance, we manipulated light, adding complexity to the study. However, this manipulation enabled the deepening into the drivers of the mixoplanktonic and protozooplanktonic grazing responses. Another characteristic, perhaps awkward, of our study is that we allowed the grazers to deplete their prey before starting the experiment. One may argue this procedure does not mimic the natural previous trophic history a grazer may have in nature. Yet, in nature, when facing a dilution experiment, it is impossible to ascertain whether the organisms are encountering novel prey or not. Indeed, they (prey and predator) could have just migrated into such conditions, or be subject to famine, or just moved from a food patch. In any case, it is true that a consistent “hunger response” would have affected our initial grazing values, biasing grazing rate estimates. To overcome this artefact, we let the grazers feed for about one hour before starting the actual dilution assay (see the “Methods” section). From that point on, any dilution is, in fact, an abrupt alteration of the food scenario, which is likely more important than the previous trophic history of the grazer.In summary, with these laboratory experiments, we have presented evidence calling for a revision of the use of chlorophyll in dilution grazing experiments5,10, and we have highlighted the need to observe the organismal composition of both initial and final communities to better understand the dynamics during the dilution grazing experiments51. This approach will not incorporate mixoplanktonic activity into the dilution technique per se however if combined with LFLA (see5,17), a semi-quantitative approach to disentangle the contribution of mixoplankton to community grazing could be achieved (although not perfect). An alternative (and perhaps more elegant) solution could be the integration of the experimental technique with in silico modelling. The modelling approaches of the dilution technique have already been used, for example, to disentangle niche competition63 and to explore nonlinear grazer responses20. We believe that our experimental design and knowledge of the previously indicated data could be of use for the configuration of a dilution grazing model, which could then be validated in the field (and, optimistically, coupled to the ubiquitous application of the dilution technique across the globe). We cannot guarantee that having a properly constructed model that mimics the dilution technique will be the solution to the mixoplankton paradigm. However, it may provide a step towards that goal as it could finally shed much-needed light on the mixo- and heterotrophic contributions to the grazing pressure of a given system. To quote from the commentary of Flynn et al.6, it could provide the answer to the question of whether mixoplankton are de facto “another of the Emperor’s New Suit of Clothes” or, “on the other hand (…) collectively worthy of more detailed inclusion in models”. More

Our results showed that covariates at different geographical scales (national and local) may have important effects on the risk of snakebite, both for humans and animals. The results indicate that the risk of snakebite in the Terai varies at national scale between clusters and at local scale between households. The evaluation of the final models without spatial random components and the worsening of the models’ goodness of fit as a result highlighted how snakebite risk and its determining factors are indeed spatially structured.A strong association between high snakebite incidence and mortality, and poverty was established from the analysis of 138 countries affected by the disease32. In this study, we identified the PPI, an indicator for poverty, as a highly influential risk-increasing factor for humans. This not only confirms the critical role of poverty as a driver for this Neglected Tropical Disease, but also offers the possibility to use a standardized index at individual household scale for similar studies. Chaves et al.33 used the Poverty Gap, which is a simpler index expressing how far a person is from the average national poverty line, but to our knowledge, no study has used PPI for snakebite in any way. Applying PPI as a snakebite risk predictor also addresses previous expert calls for an Ecohealth approach to consider the relationship between the structural characteristics of houses, poverty, and snakebite34.Three of the survey covariates had significant effects on the odds of snakebite. Food storage and straw storage increased them, while sleeping on the floor reduce them. The effect of the first two covariates is likely to be related to prey availability, represented by rodents, which are attracted by food and shelter sources. Both food and straw are very often stored near dwellings, which in the end multiply the number of possible encounters between humans, domestic animals, and the hunting snakes20. The expected snakebite risk reduction effect by sleeping on the floor is more complex though. Previously, a higher snakebite incidence was reported among rural Hindus in Maharashtra, India, due to their custom of sleeping on the floor35,36, while in Nepal, Chappuis et al. did not find any protective effect or significant difference in snakebite cases between sleeping on a cot or on the floor37. This result, nevertheless, might be influenced again by regional customs that make sleeping on the floor more common in eastern Terai (71.1% of all affirmative answers to this question), and second, by the commonly acknowledged prevalence of kraits (Bungarus spp.) in western Terai, which are the species most commonly linked to bites to people sleeping on the floor while hunting at night inside houses22,38. This geographic separation, between the human behaviour and the distribution of the species considered to cause most bites linked to it, could explain the observed shift in the odds towards a reduction effect. This effect should be further explored in localized studies designed to capture behavioural differences in humans and snakes.For both the general human risk model and its equivalent prediction model, the covariate Distance to water had a significant risk-increasing effect. For each additional km in distance from permanent water sources, the odds of snakebite increased by 1.38 and 1.51 times, respectively. From a human perspective and in this socio-economic framework, it would be important to consider not only the distance to water, but also the path taken to get the water (or any other resource). If this path would lead a person through grasslands and open fields, this could imply an increased risk of snakebite. From an ecological perspective, there are two important aspects to consider in relation to water sources. One is, as in this study, the distance from large, constant water sources, which usually represent stable environments subject to less hydric stress. The second (not considered here) are the human-made water sources, such as ponds, reservoirs, and paddy fields that change often, are usually closer to human dwellings, and are known to attract some medically important venomous snakes (MIVS)5. Studies on snake migration and home range use have concluded that depending on species and ecological conditions, snakes can move between a few tens of meters per day and more than 10 km between seasons, while searching for water and prey resources38,39,40,41. In sub-tropical regions like the Terai, snakes living closer to continuous sources of water and vegetation should have easier access to a wider variety of prey. On the contrary, those living in agricultural areas might need to scout farther in the search for resources, encountering human-made waterbodies and prey, such as rodents42 and amphibians, abundant in this region10. Further studies considering all sources of water, and species ecology, biology and richness would be necessary to completely understand the effect of this and similar eco-physiological covariates.Another important factor was the NDVI, which is a commonly used value to express photosynthetic activity, leaf production and in summary the ‘greenness’ of the environment43. As is the case for other covariates, its interpretation depends on the study circumstances. In Iran, it was considered an indicator of prey availability for snakes and linked to snake habitat suitability14. Elevated NDVI values have been associated with higher number of hospitalizations in Nigeria and northern Ghana, in particular during the periods of high agricultural activity, which is also related to higher snake-human contact and higher snakebite incidence43. In our study, its ‘protective’ effect can indeed be the consequence of better access to prey associated with healthier ecosystems, explained in the Terai by the higher NDVI values of the multiple dense forests distributed along the region. In addition, the averaged NDVI values for agricultural areas should be lower than those for perennial forests, because they include the highs and lows of production and harvest.Environmental drivers like temperature and precipitation are common factors in geospatial analyses of snakebite13,14,17,44. They are found in many cases to be the main factors modulating the incidence or risk of snakebite, while varying in importance according to study conditions. For example, in Iran, precipitation seasonality was the most prevalent climatic covariate determining the habitat suitability leading to snakebite risk14, while in Mozambique, temperature seasonality was the predominant covariate13. Despite the Terai’s sub-tropical climate, the range of the average minimum temperature of the coldest month (BIO6) was 1.8–10.9 °C. For our snakebite risk analysis in animals, an increase of 10 °C of BIO6 between any two points represented an increase in the odds of snakebite of 23.41 times. For snakes, this range could be the difference between total lethargy and partial activity45, which could lead to increased numbers of snakebites. In addition, and according to the production and holding practices of domestic animals in the Terai, this temperature range can also represent the difference between animals (mainly ruminants) being kept in sheds when at the lower range limits, or being let out of them at the upper limits, which would again increase the chances of encounters with snakes.Similarly, for the animal model, pig density and sheep density, significantly influenced the variation in the risk of snakebite for animals in the Terai. This could be due to the conditions in which the animals and their feed are kept, favouring environments that are beneficial for either snakes or their prey. At more local scales, rather than the distribution, the presence of other animal species could instead be the factor associated with higher snakebite rates12. However, since the available data on domestic animal density was produced more than 10 years ago, and the animal population has grown substantially in the last years in Nepal, this outcome should be interpreted with caution.For the animal risk, the possession of an animal shed also significantly increased the odds of snakebite. Similar to straw storage, animal sheds and similar constructions offer some shelter and at the same time attract small (prey) animals, both of which are likely to attract snakes, increasing snakebite risk for the animals using the shed. If in addition, the sheds function as poultry coops, the snake hunting behaviour might be instead targeted towards chicks and chickens12. Mitigation measures such as raising the coop’s floor or securing openings with fine metal mesh have been suggested to reduce this risk12.The human modification of terrestrial systems was the only non-significant covariate in the animal risk model. However, as its strong, risk-reducing effect still seems to explain a lot of the response variation, it was retained. Its change in one unit, i.e., going from a pristine to fully modified environment, decreased the odds of snakebite by 0.13 (equivalent to 7.69 times), which agrees with previous national survey results from Sri Lanka21.For our human risk prediction model, four covariates were either significant or helped to explain the changes in the response. Distance to water and NDVI were clearly significant, and precipitation of the driest quarter (BIO17) and the mean annual temperature (BIO1) helped to explain some of the response variation with convincing, unambiguous effects. For BIO17, an increase of 100 mm of rain during the driest months of the year represented an odds-reduction effect equivalent to 8.33 times. This agrees with the results of distance to water, suggesting that the additional availability of resources during water shortage periods, i.e., almost four times more rain (BIO17 range: 18–71 mm), could locally improve ecological conditions for snakes also leading to less scouting and fewer human encounters. Previous studies have analysed the multilevel ecological effects of droughts, e.g., reducing snake prey and leading snakes to engage in riskier behaviours46,47. For BIO1, the protective effect was weaker. An increase of 10 °C represented a reduction of the odds of snakebite equivalent to 3.57 times. Average temperatures for specific locations are difficult to interpret, since they might depend on mild highs and lows, strong highs and lows, or relative combinations of both. Thus, despite having a relatively important effect on the response, this effect still might be the consequence of confounding and unknown interactions.Several other evaluated covariates, for both humans and animals, showed a negligible effect on describing the response, were not significant while having very large uncertainties, or both. Consequently, they were discarded as predicting factors. For the list of baseline covariates evaluated, see supplementary Table S1. For a complete list of available survey covariates, see Alcoba et al.27.Some of our discarded covariates have been important in other studies, for example, to quantify snakebite risk based on reclassification methods of covariates such as habitat suitability, species presence, or envenoming severity13,14,17,44,48. These methods are especially relevant when one species (or very few) is the cause of most snakebite cases, and has differentiated optimal and sub-optimal habitats. In Nepal, and particularly in the Terai, there are at least two, and sometimes more than 10 MIVS with overlapping distributions49. Thus, it could be said that practically the whole region offers suitable habitat for multiple MIVS. In addition, the impossibility of reliably identifying the species having bitten the surveyed victims hindered the use of single species in the analysis. In our analysis, species richness was removed, as it showed almost no effect on the response. A recent meta-analysis reported an equivalent result at global scale, finding no significant difference between the number of venomous snake species in tropical and temperate locations, while the number of snakebites is clearly higher in tropical areas50. These results suggested that high incidence of snakebite is unrelated to species richness, but instead related to other factors like the number of people working in agricultural environments21,32,50. Another important driver of snakebite incidence has been population density50. In our study, however, any possible effect from population density on the risk was diminished by the random selection of households at specific numbers during study design. This was later confirmed by the minimal effect of population density as covariate in the human risk analysis.This study presents a few limitations. For instance, despite the capacity of the INLA method to borrow strength from neighbouring observations and areas, the selection of adequate covariates with enough explanatory power still depends greatly on the number of snakebite cases, which even for a national scale study like this remains small. Also, some of the covariates with the strongest explanatory power came from our household survey, which prevented their use for generalized spatial prediction models. Concerning the animal risk analysis, due to the small number of snakebite cases we opted to aggregate all animal species and consider a grouped response. Thus, for a spatial analysis of animal risk, it was not worth it to consider each species, since that would dilute further an already sparse dataset in individual models and selection processes. Moreover, the data gathered for animals was dependent on the random selection of (human) households and unrelated to the current distribution of animal populations. This, in addition to the possible number of dry bites that go unnoticed, might be responsible for the low number of animal victims recorded (even combined across all species), making a more detailed analysis unfeasible.Despite the large number of covariates examined during our analysis, very few were useful to predict snakebite risk along the Terai. It is possible that confounders or other difficult-to-measure covariates could better explain the complex relationship between the ecology and biology of MIVS, socio-economic factors, human behavioural traits, and the circumstances around domestic animal keeping. This needs to be further explored, following a recent call for an overarching One Health and Ecohealth approach to better understand the drivers for snakebite risk, incidence, and mortality under specific situations34.In conclusion, snakebite is a multi-factorial disease and there is no possible universal approach to model its risk. Each model should be individually designed for each set of socio-economical, geographic, ecological, cultural, and environmental circumstances19. To better understand and address the snakebite problem, it is necessary to approach it, whenever possible, with local data collected at a national scale, so that the conclusions drawn can fuel appropriate national public health policies and actions. As long as people work, live, and keep their domestic animals in close contact with natural environments with MIVS, the risk of snakebite will be present. However, better understanding of the factors influencing that risk at the most granular scale possible, and the estimation of the populations at risk, can help to better target prevention and mitigation measures. For humans, this evidence can channel efforts towards improved access to treatment through the optimized stockpiling of antivenom, and the improvement, relocation or new construction of treating facilities, but more importantly, towards community education and sensitization in preventive campaigns51. Part of that preventive and educative efforts can be done at household level, by promoting and facilitating the use of protective equipment such as rubber boots, or the guidance on how to improve and adapt their immediate surroundings to make them ecologically less attractive for snakes and their prey. For domestic animals, this information could help better target awareness-raising activities for animal owners and implement mitigation strategies. For animals at higher risk, tailored interventions such as the improvement of housing conditions, regular cleaning of sheds and surrounding areas (e.g., from food waste and surrounding vegetation), and using light when animals are walked out of the enclosure at night could be deployed specifically as snakebite prevention measures52. It is also important to highlight that many of the factors analysed in this study affect most directly the snakes themselves, not only as snakebite agents, but also as a diverse group of species, differently affected by ecological, climatic and environmental factors in a multiplicity of settings shared with humans and domestic animals. It is therefore necessary to further investigate how those factors influence the behavioural and ecological traits of MIVS in order to truly understand this disease from a One Health viewpoint. At stake is the reduction of snakebite envenoming incidence rates in humans and animals, and of its possible long-term sequelae on human populations. More