Philippa Kaur

Pitcher, T. J. et al. Seamounts: Ecology, Fisheries & Conservation (Blackwell Publishing, 2007).Book 

Google Scholar 
Harris, P. T., Macmillan-Lawler, M., Rupp, J. & Baker, E. K. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).Article 

Google Scholar 
Wessel, P., Sandwell, D. T. & Kim, S.-S. The global seamount census. Oceanography 23, 24–33 (2010).Article 

Google Scholar 
Etnoyer, P. J. et al. BOX 12|How large is the seamount biome?. Oceanography 23, 206–209 (2010).Article 

Google Scholar 
De Forges, B. R., Koslow, J. A. & Pooro, G. C. B. Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature 405, 944–947 (2000).Article 
PubMed 

Google Scholar 
Rowden, A. A., Dower, J. F., Schlacher, T. A., Consalvey, M. & Clark, M. R. Paradigms in seamount ecology: Fact, fiction and future. Mar. Ecol. 31, 226–241 (2010).Article 

Google Scholar 
Pinheiro, H. T. et al. Fish biodiversity of the Vitória-Trindade seamount chain, southwestern Atlantic: An updated database. PLoS ONE 10, 1–17 (2015).Article 

Google Scholar 
Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. PNAS 107, 9711 (2010).Article 

Google Scholar 
Rowden, A. A. et al. A test of the seamount oasis hypothesis: Seamounts support higher epibenthic megafaunal biomass than adjacent slopes. Mar. Ecol. 31, 95–106 (2010).Article 

Google Scholar 
Busch, K. et al. On giant shoulders: How a seamount affects the microbial community composition of seawater and sponges. Biogeosciences 17, 3471–3486 (2020).Article 
CAS 

Google Scholar 
Zhao, Y. et al. Virioplankton distribution in the tropical western Pacific Ocean in the vicinity of a seamount. Microbiol Open 9, e1031 (2020).Article 

Google Scholar 
Arístegui, J. et al. Plankton metabolic balance at two North Atlantic seamounts. Deep-Sea Res. II 56, 2646–2655 (2009).Article 

Google Scholar 
Dower, J. F. & Mackast, D. L. “Seamount effects” in the zooplankton community near Cobb Seamount. Deep-Sea Res. I 43, 837–858 (1996).Article 

Google Scholar 
O’Hara, T. D., Rowden, A. A. & Bax, N. J. A Southern Hemisphere bathyal fauna is distributed in latitudinal bands. Curr. Biol. 21, 226–230 (2011).Article 
PubMed 

Google Scholar 
Williams, A., Althaus, F., Clark, M. R. & Gowlett-Holmes, K. Composition and distribution of deep-sea benthic invertebrate megafauna on the Lord Howe Rise and Norfolk Ridge, southwest Pacific Ocean. Deep-Sea Res. II 58, 948–958 (2011).Article 
CAS 

Google Scholar 
Bridges, A. E. H., Barnes, D. K. A., Bell, J. B., Ross, R. E. & Howell, K. L. Benthic assemblage composition of South Atlantic seamounts. Front. Mar. Sci. 8, 660648 (2021).Article 

Google Scholar 
Lapointe, A. E., Watling, L., France, S. C. & Auster, P. J. Megabenthic assemblages in the lower bathyal (700–3000 m) on the New England and corner rise seamounts Northwest Atlantic. Deep-Sea Res. I 165, 103366 (2020).Article 

Google Scholar 
Clark, M. R. & Bowden, D. A. Seamount biodiversity: High variability both within and between seamounts in the Ross Sea region of Antarctica. Hydrobiologia 761, 161–180 (2015).Article 
CAS 

Google Scholar 
McClain, C. R., Lundsten, L., Barry, J. & DeVogelaere, A. Assemblage structure, but not diversity or density, change with depth on a northeast Pacific seamount. Mar. Ecol. 31, 14–25 (2010).Article 

Google Scholar 
Long, D. J. & Baco, A. R. Rapid change with depth in megabenthic structure-forming communities of the Makapu’u deep-sea coral bed. Deep-Sea Res. II 99, 158–168 (2014).Article 

Google Scholar 
Thresher, R. et al. Strong septh-related zonation of megabenthos on a rocky continental margin (∼ 700–4000 m) off southern Tasmania Australia. PLoS ONE 9, e85872 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
O’Hara, T. D., Consalvey, M., Lavrado, H. P. & Stocks, K. I. Environmental predictors and turnover of biota along a seamount chain. Mar. Ecol. 31, 84–94 (2010).Article 

Google Scholar 
Boschen, R. E. et al. Megabenthic assemblage structure on three New Zealand seamounts: Implications for seafloor massive sulfide mining. Mar. Ecol. Prog. Ser. 523, 1–14 (2015).Article 

Google Scholar 
Caratori Tontini, F. et al. Crustal magnetization of brothers volcano, New Zealand, measured by autonomous underwater vehicles: Geophysical expression of a submarine hydrothermal system. Econ. Geol. 107, 1571–1581 (2012).Article 

Google Scholar 
Rex, M. A., Etter, R. J., Clain, A. J. & Hill, M. S. Bathymetric patterns of body size in deep-sea gastropods. Evolution (N Y) 53, 1298–1301 (1999).
Google Scholar 
O’Hara, T. D. Seamounts: Centres of endemism or species richness for ophiuroids?. Glob. Ecol. Biogeogr. 16, 720–732 (2007).Article 

Google Scholar 
Clark, M. R. et al. The ecology of seamounts: Structure, function, and human impacts. Ann. Rev. Mar. Sci. 2, 253–278 (2010).Article 
PubMed 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).Article 
PubMed 

Google Scholar 
Levin, L. A. & Thomas, C. L. The influence of hydrodynamic regime on infaunal assemblages inhabiting carbonate sediments on central Pacific seamounts. Deep Sea Res. A 36, 1897–1915 (1989).Article 

Google Scholar 
Puerta, P. et al. Variability of deep-sea megabenthic assemblages along the western pathway of the Mediterranean outflow water. Deep-Sea Res. I 185, 103791 (2022).Article 

Google Scholar 
Tapia-Guerra, J. M. et al. First description of deep benthic habitats and communities of oceanic islands and seamounts of the Nazca Desventuradas Marine Park Chile. Sci. Rep. 11, 6209 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morgan, N. B., Goode, S., Roark, E. B. & Baco, A. R. Fine scale assemblage structure of benthic invertebrate megafauna on the North Pacific seamount Mokumanamana. Front. Mar. Sci. 6, 715 (2019).Article 

Google Scholar 
Perez, J. A. A., Kitazato, H., Sumida, P. Y. G., Sant’Ana, R. & Mastella, A. M. Benthopelagic megafauna assemblages of the Rio Grande Rise (SW Atlantic). Deep-Sea Res. I 134, 1–11 (2018).Article 

Google Scholar 
Poore, G. C. B. et al. Invertebrate diversity of the unexplored marine western margin of Australia: Taxonomy and implications for global biodiversity. Mar. Biodivers. 45, 271–286 (2015).Article 

Google Scholar 
Henry, L. A., Moreno Navas, J. & Roberts, J. M. Multi-scale interactions between local hydrography, seabed topography, and community assembly on cold-water coral reefs. Biogeosciences 10, 2737–2746 (2013).Article 

Google Scholar 
Meyer, K. S. et al. Rocky islands in a sea of mud: Biotic and abiotic factors structuring deep-sea dropstone communities. Mar. Ecol. Prog. Ser. 556, 45–57 (2016).Article 

Google Scholar 
Stratmann, T., Soetaert, K., Kersken, D. & van Oevelen, D. Polymetallic nodules are essential for food-web integrity of a prospective deep-seabed mining area in Pacific abyssal plains. Sci. Rep. 11, 12238 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Genin, A., Dayton, P. K., Lonsdale, P. F. & Spiess, F. N. Corals on seamount peaks provide evidence of current acceleration over deep-sea topography. Nature 322, 59–61 (1986).Article 

Google Scholar 
Roberts, J. M., Wheeler, A. J. & Freiwald, A. Reefs of the deep: The biology and geology of cold-water coral ecosystems. Science 1979(312), 543–547 (2006).Article 

Google Scholar 
Kutti, T., Bannister, R. J. & Fosså, J. H. Community structure and ecological function of deep-water sponge grounds in the Traenadypet MPA-Northern Norwegian continental shelf. Cont. Shelf Res. 69, 21–30 (2013).Article 

Google Scholar 
Beazley, L., Kenchington, E. L., Murillo, F. J. & Sacau, M. D. M. Deep-sea sponge grounds enhance diversity and abundance of epibenthic megafauna in the Northwest Atlantic. ICES J. Mar. Sci. 70, 1471–1490 (2013).Article 

Google Scholar 
Buhl-Mortensen, L. et al. Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Mar. Ecol. 31, 21–50 (2010).Article 

Google Scholar 
Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 1–11 (2018).Article 
CAS 

Google Scholar 
Yesson, C., Clark, M. R., Taylor, M. L. & Rogers, A. D. The global distribution of seamounts based on 30 arc seconds bathymetry data. Deep-Sea Res. I 58, 442–453 (2011).Article 

Google Scholar 
ICES. Report of the ICES-NAFO Working Group on Deep-Water Ecology (WGDEC), 9–13 March 2009, ICES CM2009ACOM:23. 2009.Cárdenas, P. & Rapp, H. T. Demosponges from the Northern mid-Atlantic ridge shed more light on the diversity and biogeography of North Atlantic deep-sea sponges. J. Mar. Biol. Assoc. U.K. 95, 1475–1516 (2015).Article 

Google Scholar 
Cárdenas, P. et al. Taxonomy, biogeography and DNA barcodes of Geodia species (Porifera, Demospongiae, Tetractinellida) in the Atlantic boreo-arctic region. Zool. J. Linn. Soc. 169, 251–311 (2013).Article 

Google Scholar 
Roberts, E. M. et al. Oceanographic setting and short-timescale environmental variability at an Arctic seamount sponge ground. Deep-Sea Res. I 138, 98–113 (2018).Article 

Google Scholar 
Roberts, E. et al. Water masses constrain the distribution of deep-sea sponges in the North Atlantic Ocean and Nordic seas. Mar. Ecol. Prog. Ser. 659, 75–96 (2021).Article 

Google Scholar 
Morganti, T. M. et al. Giant sponge grounds of central Arctic seamounts are associated with extinct seep life. Nat. Commun. 13, 638 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morganti, T. M. et al. In situ observation of sponge trails suggests common sponge locomotion in the deep central Arctic. Curr. Biol. 31, R368–R370 (2021).Article 
CAS 
PubMed 

Google Scholar 
Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep-Sea Res. I 153, 103137 (2019).Article 

Google Scholar 
McIntyre, F. D., Drewery, J., Eerkes-Medrano, D. & Neat, F. C. Distribution and diversity of deep-sea sponge grounds on the Rosemary bank seamount NE Atlantic. Mar. Biol. 163, 143 (2016).Article 

Google Scholar 
Buhl-Mortensen, P. & Buhl-Mortensen, L. Diverse and vulnerable deep-water biotopes in the Hardangerfjord. Mar. Biol. Res. 10, 253–267 (2014).Article 

Google Scholar 
de Clippele, L. H. et al. The effect of local hydrodynamics on the spatial extent and morphology of cold-water coral habitats at Tisler Reef Norway. Coral Reefs 37, 253–266 (2018).Article 
PubMed 

Google Scholar 
Dunlop, K., Harendza, A., Plassen, L. & Keeley, N. Epifaunal habitat Associations on mixed and hard bottom substrates in coastal waters of Northern Norway. Front. Mar. Sci. 7, 568802 (2020).Article 

Google Scholar 
Fiore, C. L. & Cox Jutte, P. Characterization of macrofaunal assemblages associated with sponges and tunicates collected off the southeastern United States. Biology 129, 105–120 (2010).
Google Scholar 
Murillo, F. J. et al. Deep-sea sponge grounds of the Flemish Cap, Flemish Pass and the Grand Banks of Newfoundland (Northwest Atlantic Ocean): Distribution and species composition. Mar. Biol. Res. 8, 842–854 (2012).Article 

Google Scholar 
Purser, A. et al. Local variation in the distribution of benthic megafauna species associated with cold-water coral reefs on the Norwegian margin. Cont. Shelf Res. 54, 37–51 (2013).Article 

Google Scholar 
Klitgaard, A. B. & Tendal, O. S. Distribution and species composition of mass occurrences of large-sized sponges in the northeast Atlantic. Prog. Oceanogr. 61, 57–98 (2004).Article 

Google Scholar 
Klitgaard, A. B. The fauna associated with outer shelf and upper slope sponges (porifera, demospongiae) at the faroe islands, northeastern Atlantic. Sarsia 80, 1–22 (1995).Article 

Google Scholar 
Cárdenas, P. & Moore, J. A. First records of Geodia demosponges from the New England seamounts, an opportunity to test the use of DNA mini-barcodes on museum specimens. Mar. Biodivers. 49, 163–174 (2019).Article 

Google Scholar 
Schejter, L., Chiesa, I. L., Doti, B. L. & Bremec, C. Mycale (Aegogropila) magellanica (Porifera: Demospongiae) in the southwestern Atlantic Ocean: Endobiotic fauna and new distributional information. Sci. Mar. 76, 753–761 (2012).
Google Scholar 
Beaulieu, S. E. Life on glass houses: Sponge stalk communities in the deep sea. Mar. Biol. 138, 803–817 (2001).Article 

Google Scholar 
Goren, L., Idan, T., Shefer, S. & Ilan, M. Macrofauna inhabiting massive demosponges from shallow and mesophotic habitats along the Israeli Mediterranean coast. Front. Mar. Sci. 7, 612779 (2021).Article 

Google Scholar 
Kersken, D. et al. The infauna of three widely distributed sponge species (Hexactinellida and Demospongiae) from the deep Ekström Shelf in the Weddell Sea Antarctica. Deep-Sea Res. II 108, 101–112 (2014).Article 

Google Scholar 
Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep Sea Res. 1 Oceanogr. Res. Pap. 153, 103137 (2019).Article 

Google Scholar 
Bart, M. C., Hudspith, M., Rapp, H. T., Verdonschot, P. F. M. & de Goeij, J. M. A Deep-Sea Sponge Loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

Google Scholar 
de Goeij, J. M. et al. Surviving in a marine desert: The sponge loop retains resources within coral reefs. Science 1979(342), 108–110 (2013).Article 

Google Scholar 
Pawlik, J. R. & Mcmurray, S. E. The emerging ecological and biogeochemical importance of sponges on coral reefs. (2019) https://doi.org/10.1146/annurev-marine-010419Wassmann, P., Slagstad, D. & Ellingsen, I. Primary production and climatic variability in the European sector of the Arctic Ocean prior to 2007: Preliminary results. Polar Biol. 33, 1641–1650 (2010).Article 

Google Scholar 
Arrigo, K. R., van Dijken, G. & Pabi, S. Impact of a shrinking Arctic ice cover on marine primary production. Geophys. Res. Lett. 35, L19603 (2008).Article 

Google Scholar 
Dunne, J. P., Sarmiento, J. L. & Gnanadesikan, A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Glob. Biogeochem. Cycles 21, GB4006 (2007).Article 

Google Scholar 
Wei, C.-L. et al. Global patterns and predictions of seafloor biomass using random forests. PLoS ONE 5, e15323 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stratmann, T. et al. The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities. Sci. Data 7, 206 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McClain, C. R., Lundsten, L., Ream, M., Barry, J. & DeVogelaere, A. Endemicity, biogeography, composition, and community structure on a Northeast Pacific seamount. PLoS ONE 4, e4141 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Walter, M., Köhler, J., Myriel, H., Steinmacher, B. & Wisotzki, A. Physical oceanography measured on water bottle samples during POLARSTERN cruise PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.871927 (2017).van Appen, W.-J., Latarius, K. & Kanzow, T. Physical oceanography and current meter data from mooring F6–17. PANGAEA https://doi.org/10.1594/PANGAEA.870845 (2017).Ruhl, H. A. & Smith, K. L. Shifts in deep-sea community structure linked to climate and food supply. Science 1979(305), 513–515 (2004).Article 

Google Scholar 
Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 1979(339), 1430–1432 (2013).Article 

Google Scholar 
Rybakova, E., Kremenetskaia, A., Vedenin, A., Boetius, A. & Gebruk, A. Deep-sea megabenthos communities of the Eurasian Central Arctic are influenced by ice-cover and sea-ice algal falls. PLoS ONE 14, e0211009 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhulay, I., Bluhm, B. A., Renaud, P. E., Degen, R. & Iken, K. Functional pattern of benthic epifauna in the Chukchi borderland Arctic deep sea. Front. Mar. Sci. 8, 609956 (2021).Article 

Google Scholar 
Boetius, A. & Purser, A. The expedition PS101 of the research vessel Polarstern to the Arctic Ocean in 2016. Berichte zur Polar-und Meeresforschung = Rep Polar Mar Res https://doi.org/10.2312/BzPM_0706_2017 (2017).Article 

Google Scholar 
Simon-Lledó, E. et al. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Prog. Oceanogr. 187, 102405 (2020).Article 

Google Scholar 
Simon-Lledó, E. et al. Preliminary observations of the abyssal megafauna of Kiribati. Front. Mar. Sci. 6, 1–13 (2019).Article 

Google Scholar 
Zhulay, I., Iken, K., Renaud, P. E. & Bluhm, B. A. Epifaunal communities across marine landscapes of the deep Chukchi Borderland (Pacific Arctic). Deep Sea Res. 1 Oceanogr. Res. Pap. 151, 103065 (2019).Article 

Google Scholar 
Åström, E. K. L., Sen, A., Carroll, M. L. & Carroll, J. L. Cold seeps in a warming Arctic: Insights for benthic ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00244 (2020).Article 

Google Scholar 
Pedersen, R. B. et al. Discovery of a black smoker vent field and vent fauna at the Arctic Mid-Ocean Ridge. Nat. Commun. 1, 1–6 (2010).Article 
CAS 

Google Scholar 
Åström, E. K. L. et al. Methane cold seeps as biological oases in the high-Arctic deep sea. Limnol. Oceanogr. 63, S209–S231 (2018).Article 

Google Scholar 
Rybakova Goroslavskaya, E., Galkin, S., Bergmann, M., Soltwedel, T. & Gebruk, A. Density and distribution of megafauna at the Håkon Mosby mud volcano (the Barents Sea) based on image analysis. Biogeosciences 10, 3359–3374 (2013).Article 

Google Scholar 
Sweetman, A. K., Levin, L. A., Rapp, H. T. & Schander, C. Faunal trophic structure at hydrothermal vents on the southern mohn’s ridge, arctic ocean. Mar. Ecol. Prog. Ser. 473, 115–131 (2013).Article 

Google Scholar 
Decker, C. & Olu, K. Does macrofaunal nutrition vary among habitats at the Hakon Mosby mud volcano?. Cah. Biol. Mar. 51, 361–367 (2010).
Google Scholar 
Macdonald, I. R., Bluhm, B. A., Iken, K., Gagaev, S. & Strong, S. Benthic macrofauna and megafauna assemblages in the Arctic deep-sea Canada Basin. Deep-Sea Res. II 57, 136–152 (2010).Article 

Google Scholar 
Taylor, J., Krumpen, T., Soltwedel, T., Gutt, J. & Bergmann, M. Dynamic benthic megafaunal communities: Assessing temporal variations in structure, composition and diversity at the Arctic deep-sea observatory HAUSGARTEN between 2004 and 2015. Deep Sea Res. 1 Oceanogr. Res. Pap. 122, 81–94 (2017).Article 

Google Scholar 
Vedenin, A. A. et al. Uniform bathymetric zonation of marine benthos on a Pan-Arctic scale. Prog. Oceanogr. 202, 102764 (2022).Article 

Google Scholar 
Bart, M. C. et al. A deep-sea sponge loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

Google Scholar 
Guihen, D., White, M. & Lundälv, T. Temperature shocks and ecological implications at a cold-water coral reef. ANZIAM J. https://doi.org/10.1017/S1755267212000413 (2014).Article 

Google Scholar 
Strand, R. et al. The response of a boreal deep-sea sponge holobiont to acute thermal stress. Sci. Rep. 7, 1660 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hanz, U. et al. The important role of sponges in carbon and nitrogen cycling in a deep-sea biological hotspot. Funct. Ecol. 36, 2188–2199 (2022).Article 
CAS 

Google Scholar 
Maier, S. R. et al. Reef communities associated with ‘dead’ cold-water coral framework drive resource retention and recycling in the deep sea. Deep-Sea Res. I 175, 103574 (2021).Article 
CAS 

Google Scholar 
Bart, M. C. et al. Dissolved organic carbon (DOC) is essential to balance the metabolic demands of four dominant North-Atlantic deep-sea sponges. Limnol. Oceanogr. https://doi.org/10.1002/lno.11652 (2020).Article 

Google Scholar 
Bart, M. C. et al. Differential processing of dissolved and particulate organic matter by deep-sea sponges and their microbial symbionts. Sci. Rep. 10, 1–13 (2020).Article 

Google Scholar 
Maier, S. R. et al. Recycling pathways in cold-water coral reefs: Use of dissolved organic matter and bacteria by key suspension feeding taxa. Sci. Rep. 10, 9942 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
International Hydrographic Bureau. 16th meeting of the GEBCO sub-committee on undersea feature names (SCUFN). Preprint at (2003).Torres-Valdés, S., Morische, A. & Wischnewski, L. Revision of nutrient data from Polarstern expedition PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.908179 (2019).Purser, A. et al. Ocean floor observation and bathymetry system (OFOBS): A new towed camera/sonar system for deep-sea habitat surveys. IEEE J. Ocean. Eng. 44, 87–99 (2019).Article 

Google Scholar 
Marcon, Y. & Purser, A. PAPARA(ZZ)I : An open-source software interface for annotating photographs of the deep-sea. SoftwareX 6, 69–80 (2017).Article 

Google Scholar 
Greene, H. G., Bizzarro, J. J., O’Connell, V. M. & Brylinsky, C. K. Construction of digital potential marine benthic habitat maps using a coded classification scheme and its application. Spec. Pap.: Geol. Assoc. Canada 47, 141–155 (2007).
Google Scholar 
Horton, T. et al. Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications. Front. Mar. Sci. 8, 620702 (2021).Article 

Google Scholar 
Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application (Cambridge University Press, 1997).Book 
MATH 

Google Scholar 
Rodgers, J. L. The bootstrap, the jackknife, and the randomization test: A sampling taxonomy. Multivar. Behav. Res. 34, 441–456 (1999).Article 
CAS 

Google Scholar 
Crowley, P. H. Resampling methods for computation-intensive data analysis in ecology and evolution. Annu. Rev. Ecol. Syst. 23, 405–447 (1992).Article 

Google Scholar 
Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

Google Scholar 
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
R-Core Team. R: A language and environment for statistical computing. Preprint at https://www.r-project.org/ (2017).Oksanen, J. et al. vegan: Community ecology package. Preprint at (2017).Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260 (2013).Article 

Google Scholar 
Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. 69, 1–17 (2016).Article 

Google Scholar 
Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).Article 
CAS 
PubMed 

Google Scholar 
de Kluijver, A. Fatty acid analysis sponges. protocols.io 1, 1–14. https://doi.org/10.17504/protocols.io.bhnpj5dn (2021).Article 

Google Scholar 
de Kluijver, A. et al. Bacterial precursors and unsaturated long-chain fatty acids are biomarkers of North-Atlantic deep-sea demosponges. PLoS ONE 16, e0241095 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More

Alpha diversity differences among communitiesNematode gut microbiomes were assigned into their respective species categories of E. antarcticus and P. murrayi based on 18S host data that was consistent with morphology (see Methods “Microinvertebrate haplotypes”). In contrast, due to recovery of three undiscernible 18S tardigrade haplotypes, the gut microbiomes were assigned to Tardigrada. Mat bacterial communities were significantly (Tukey’s HSD, P  0.65, χ2(1)  0.38, χ2(3)  More

Differences in environmental factorsEnvironmental factors showed value differences between forest types, while the significance of differences differed among variables, which were both found with corrected values and original measurements (Fig. 1).Figure 1The differences in environmental factors between naturally regenerating forests (NF) and planted forests (PF) in China. The environmental factors include three annual climatic factors (a–c), three seasonal temperature factors (d–f), three seasonal precipitation factors (g–i), three biotic factors (j–l), and two soil factors (m,n). Three annual climatic factors include mean annual air temperature (MAT, a), mean annual precipitation (MAP, b), and aridity index (AI, c) defined as the ratio of MAP to annual potential evapotranspiration. Three seasonal temperature factors include the temperature of the warmest month (Tw, d), the temperature of the coldest month (Tc, e), temperature annual range (TR, f). Three seasonal precipitation factors include precipitation of the wettest month (Pw, g), precipitation of the driest month (Pd, h), and precipitation seasonality (Ps, i) defined as the standard deviation of monthly precipitation during the measuring year. Three biological factors include the mean annual leaf area index (LAI, j), the maximum leaf area index (MLAI, k), and stand age (SA, l). Two soil factors include soil organic carbon content (SOC, m) and soil total nitrogen content (STN, n). The differences are tested for each variable with one-way analysis of variance (ANOVA), where * and ** indicate significant differences between forest types at significance levels of α = 0.05 and α = 0.01, respectively. The corrected values are mean values during 2003–2019 after correcting the original measurements with the interannual trend (See methods), which are listed in each panel, while original measurements are mean values during the measuring period of each ecosystem, which are not shown in each panel.Full size imageFor annual climatic factors, the significant difference between NF and PF only appeared in MAT (Fig. 1a). The mean MAT of NF was 10.50 ± 7.81 °C, which was significantly lower than that of PF (15.65 ± 6.23 °C) (p  0.05) (Fig. 2c). Even considering the significant effects of MAT on ER, ANCOVA results obtained by fixing MAT as a covariant also suggested that ER values did not significantly differ between forest types (F = 0.01, p  > 0.05). Fixing other variables as a covariant also drew a similar result.Therefore, NF showed a lower NEP resulting from the lower GPP than PF, while their differences were not statistically significant (Fig. 2).Differences in NEP latitudinal patternsCarbon fluxes showed divergent latitudinal patterns between NF and PF, while their latitudinal patterns varied among carbon fluxes, which were both found with corrected values and original measurements (Fig. 3).Figure 3The latitudinal patterns of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  0.05).The ER of NF showed a significant decreasing latitudinal pattern (Fig. 3e), while that of PF exhibited no significant latitudinal pattern (Fig. 3f). The increasing latitude caused the ER of NF to significantly decrease. Each unit increase in latitude led to a 28.71 gC m−2 year−1 decrease in ER, with an R2 of 0.31. However, the increasing latitude contributed little to the ER spatial variation of PF (p  > 0.05).In addition, the latitudinal patterns of carbon fluxes and their differences between forest types were also obtained with the original measurements (Fig. 3, grey points). The latitudinal patterns of random error adding carbon fluxes were comparable to those of our corrected carbon fluxes (Fig. 3), which confirmed that the latitudinal patterns of carbon fluxes and their differences between forest types would not be affected by the uncertainties in generating the corrected carbon fluxes.Therefore, among NFs, the similar decreasing latitudinal patterns of GPP and ER meant that NEP showed no significant latitudinal pattern, while the significant decreasing latitudinal pattern of GPP and no significant latitudinal pattern of ER caused NEP to show a decreasing latitudinal pattern among PFs.Differences in the environmental effects on NEP spatial variationsEnvironmental factors, including the annual climatic factors, seasonal temperature factors, seasonal precipitation factors, biological factors, and soil factors, exerted divergent effects on the spatial variations of NEP and its components, which also differed between forest types (Table 1). No factor was found to affect that the spatial variation of NEP among NFs, while most annual and seasonal climatic factors were found to affect that among PFs. The spatial variations of GPP and ER among NFs were both affected by most annual and seasonal climatic factors and LAI, while those among PFs were primarily shaped by most annual and seasonal climatic factors. Though LAI showed no significant effect on GPP and ER spatial variations among PFs, SA exerted a significant negative effect. In addition, the spatial variations of soil variables contributed little to the spatial variations of carbon fluxes. Therefore, among NFs, most annual and seasonal climatic factors and LAI were found to affect GPP and ER spatial variations, while no factor was found to significantly influent the NEP spatial variation. However, among PFs, most annual and seasonal climatic factors were found to affect the spatial variations of NEP and its components, while LAI showed no significant effect. Using the original measurements also generated the similar correlation coefficients (Supplementary Table S1).Table 1 Correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF).Full size tableGiven the high correlations among annual climatic factors and seasonal climatic factors (Supplementary Table S2), the partial correlation analysis was applied to determine which factors should be employed to reveal the mechanisms underlying the spatial variations of NEP. Partial correlation analysis showed that MAT and MAP exerted the most important roles in spatial variations of NEP and its components (Table 2). After controlling MAT (or MAP), other factors seldom showed significant correlation with carbon fluxes, especially fixing MAT (Table 2). In addition, MAT and MAP exerted similar effects on the spatial variations of NEP and its components (Table 1). Using the original measurements also generated the similar partial correlation coefficients (Supplementary Table S3). Therefore, we only presented the effects of MAT on carbon flux spatial variations and their differences between forest types in detail.Table 2 Partial correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF) with fixing mean annual air temperature (MAT) or mean annual precipitation (MAP).Full size tableThe increasing MAT increased carbon fluxes, while the increasing rates differed between forest types (Fig. 4). The increasing MAT contributed little to the NEP spatial variation of NF but raised the NEP of PF (Fig. 4a,b). Each unit increase in MAT caused the NEP of PF to increase at a rate of 27.77 gC m−2 year−1, with an R2 of 0.31 (Fig. 4b). The increasing MAT significantly raised GPP in NF and PF (Fig. 4c,d). For NF, each unit increase in MAT increased GPP at a rate of 43.76 gC m−2 year−1, with an R2 of 0.49 (Fig. 4c), while each unit increase in MAT increased the GPP of PF at a rate of 69.18 gC m−2 year−1, with an R2 of 0.57 (Fig. 4d). The GPP increasing rates did not significantly differ between NF and PF (F = 1.52, p  > 0.05). The increasing MAT also raised ER in both NF and PF (Fig. 4e,f), whose increasing rates were 38.97 gC m−2 year−1 (Fig. 4e) and 36.79 gC m−2 year−1 (Fig. 4f), respectively, while their differences were not statistically significant (F = 0.01, p  > 0.05). In addition, using the original measurements also generated the similar spatial variations and their differences between forest types (Fig. 4). Furthermore, the random error adding carbon fluxes responded similarly to those of our correcting carbon fluxes (Fig. 4), indicating that the effects of MAT on carbon fluxes would not be affected by the uncertainties in our correcting carbon fluxes. Therefore, the similar responses of GPP and ER to MAT made MAT contribute little to NEP spatial variations among NFs, while GPP and ER showed divergent response rates to MAT, which made NEP increase with MAT among PFs.Figure 4The effects of mean annual air temperature (MAT) on the spatial variations of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  More

McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. 110, 3229–3236 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Archibald, J. M. Endosymbiosis and eukaryotic cell evolution. Curr. Biol. 25, R911–R921 (2015).Article 
CAS 
PubMed 

Google Scholar 
Moran, N. A. Symbiosis as an adaptive process and source of phenotypic complexity. Proc. Natl. Acad. Sci. U.S.A. 104(Suppl 1), 8627–8633 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hurst, G. D. D. Extended genomes: Symbiosis and evolution. Interface Focus. https://doi.org/10.1098/rsfs.2017.0001 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Moran, N. A., McCutcheon, J. P. & Nakabachi, A. Genomics and evolution of heritable bacterial symbionts. Annu. Rev. Genet. 42, 165–190 (2008).Article 
CAS 
PubMed 

Google Scholar 
Kikuchi, Y., Hosokawa, T. & Fukatsu, T. Insect-microbe mutualism without vertical transmission: A stinkbug acquires a beneficial gut symbiont from the environment every generation. Appl. Environ. Microbiol. 73, 4308–4316 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kikuchi, Y., Hosokawa, T. & Fukatsu, T. An ancient but promiscuous host-symbiont association between Burkholderia gut symbionts and their heteropteran hosts. ISME J. 5, 446–460 (2011).Article 
PubMed 

Google Scholar 
Hu, Y. et al. Herbivorous turtle ants obtain essential nutrients from a conserved nitrogen-recycling gut microbiome. Nat. Commun. 9, 2440. https://doi.org/10.1038/s41467-018-03357-y (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Salem, H. et al. Drastic genome reduction in an herbivore’s pectinolytic symbiont. Cell 171, 1520–1531 (2017).Article 
CAS 
PubMed 

Google Scholar 
Bennett, G. M. & Moran, N. A. Heritable symbiosis: The advantages and perils of an evolutionary rabbit hole. Proc. Natl. Acad. Sci. 112, 10169–10176 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Campbell, M. A. et al. Changes in endosymbiont complexity drive host-level compensatory adaptations in cicadas. MBio 9, e02104-18 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Buchner, P. Symbiosis in animals which suck plant juices. In Endosymbiosis of Animals with Plant Microorganisms 210–432 (Interscience, 1965).
Google Scholar 
McCutcheon, J. P., McDonald, B. R. & Moran, N. A. Convergent evolution of metabolic roles in bacterial co-symbionts of insects. Proc. Natl. Acad. Sci. 106, 15394–15399 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Christensen, H. & Fogel, M. L. Feeding ecology and evidence for amino acid synthesis in the periodical cicada (Magicicada). J. Insect Physiol. 57, 211–219 (2011).Article 
CAS 
PubMed 

Google Scholar 
McCutcheon, J. P., McDonald, B. R. & Moran, N. A. Origin of an alternative genetic code in the extremely small and GC-rich genome of a bacterial symbiont. PLoS Genet. 5, e1000565 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Campbell, M. A. et al. Genome expansion via lineage splitting and genome reduction in the cicada endosymbiont Hodgkinia. Proc. Natl. Acad. Sci. 112, 10192–10199 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Müller, H. J. Neuere vorstellungen über verbreitung und phylogenie der endosymbiosen der zikaden. Z. Morphol. Oekol. Tiere 61, 190–210 (1962).Article 

Google Scholar 
Müller, H. J. Zur systematik und phylogenie der zikaden-endosymbiosen. Biol. Zent. 68, 343–368 (1949).
Google Scholar 
Matsuura, Y. et al. Recurrent symbiont recruitment from fungal parasites in cicadas. Proc. Natl. Acad. Sci. 115, E5970–E5979 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhou, W. et al. Analysis of inter-individual bacterial variation in gut of cicada Meimuna mongolica (Hemiptera: Cicadidae). J. Insect Sci. 15, 1–6 (2015).Article 

Google Scholar 
Zheng, Z., Wang, D., He, H. & Wei, C. Bacterial diversity of bacteriomes and organs of reproductive, digestive and excretory systems in two cicada species (Hemiptera: Cicadidae). PLoS One 12, 1–21 (2017).
Google Scholar 
Wang, D., Huang, Z., He, H. & Wei, C. Comparative analysis of microbial communities associated with bacteriomes, reproductive organs and eggs of the cicada Subpsaltria yangi. Arch. Microbiol. 200, 227–235 (2018).Article 
CAS 
PubMed 

Google Scholar 
Dillon, R. J. & Dillon, V. M. The gut bacteria of insects: Nonpathogenic interactions. Annu. Rev. Entomol. 49, 71–92 (2004).Article 
CAS 
PubMed 

Google Scholar 
Ng, S. H., Stat, M., Bunce, M. & Simmons, L. W. The influence of diet and environment on the gut microbial community of field crickets. Ecol. Evol. 8, 4704–4720 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Nishida, A. H. & Ochman, H. Rates of gut microbiome divergence in mammals. Mol. Ecol. 27, 1884–1897 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Douglas, A. E. & Werren, J. H. Holes in the hologenome: Why host–microbe symbioses are not holobionts. MBio 7, e02099 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grueneberg, J., Engelen, A. H., Costa, R. & Wichard, T. Macroalgal morphogenesis induced by waterborne compounds and bacteria in coastal seawater. PLoS One 11, e0146307 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Lin, J. D., Lemay, M. A. & Parfrey, L. W. Diverse bacteria utilize alginate within the microbiome of the giant kelp Macrocystis pyrifera. Front. Microbiol. 9, 1914 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Coon, K. L., Vogel, K. J., Brown, M. R. & Strand, M. R. Mosquitoes rely on their gut microbiota for development. Mol. Ecol. 23, 2727–2739 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Coon, K. L., Brown, M. R. & Strand, M. R. Mosquitoes host communities of bacteria that are essential for development but vary greatly between local habitats. Mol. Ecol. 25, 5806–5826 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kwong, W. K. et al. Dynamic microbiome evolution in social bees. Sci. Adv. 3, 1–17 (2017).Article 

Google Scholar 
Brooks, A. W., Kohl, K. D., Brucker, R. M., van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis: Relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 14, 1–29 (2016).Article 

Google Scholar 
Kropáčková, L. et al. Codiversification of gastrointestinal microbiota and phylogeny in passerines is not explained by ecological divergence. Mol. Ecol. 26, 5292–5304 (2017).Article 
PubMed 

Google Scholar 
Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403. https://doi.org/10.3389/fmicb.2015.01403 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Hu, Y., Lukasik, P., Moreau, C. S. & Russell, J. A. Correlates of gut community composition across an ant species (Cephalotes varians) elucidate causes and consequences of symbiotic variability. Mol. Ecol. 23, 1284–1300 (2014).Article 
PubMed 

Google Scholar 
Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnz117 (2019).Article 
PubMed 

Google Scholar 
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl. Acad. Sci. 114, 9641–9646 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shapira, M. Gut microbiotas and host evolution: Scaling up symbiosis. Trends Ecol. Evol. 31, 539–549 (2016).Article 
PubMed 

Google Scholar 
Marshall, D. C. et al. Inflation of molecular clock rates and dates: Molecular phylogenetics, biogeography, and diversification of a global cicada radiation from Australasia (Hemiptera: Cicadidae: Cicadettini). Syst. Biol. 65, 16–34 (2016).Article 
PubMed 

Google Scholar 
Lane, D. H. The recognition concept of speciation applied in an analysis of putative hybridization in New Zealand cicadas of the genus Kikihia (Insects: Hemiptera: Tibicinidae). Speciation and the Recognition Concept: Theory and Application (The Johns Hopkins Univ Press, 1995).
Google Scholar 
Cooley, J. R. & Marshall, D. C. Sexual signaling in periodical cicadas, Magicicada spp. (Hemiptera: Cicadidae). Behaviour 138, 827–855 (2001).Article 

Google Scholar 
Fleming, C. A. Adaptive Radiation in New Zealand Cicadas (American Philosophical Society, 1975).
Google Scholar 
Dugdale, J. S. & Fleming, C. A. New Zealand cicadas of the genus Maoricicada (Homoptera: Tibicinidae). N. Z. J. Zool. 5, 295–340 (1978).Article 

Google Scholar 
Marshall, D. C., Hill, K. B. R., Cooley, J. R. & Simon, C. Hybridization, mitochondrial DNA phylogeography, and prediction of the early stages of reproductive isolation: Lessons from New Zealand cicadas (genus Kikihia). Syst. Biol. 60, 482–502 (2011).Article 
PubMed 

Google Scholar 
Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Report No.: e27295v2. PeerJ https://doi.org/10.7287/peerj.preprints.27295v2 (2018).Article 

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

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Davis, N. M., Proctor, D., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226. https://doi.org/10.1101/221499 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemmon, A. R., Emme, S. A. & Lemmon, E. M. Anchored hybrid enrichment for massively high-throughput phylogenomics. Syst. Biol. 61, 727–744 (2012).Article 
CAS 
PubMed 

Google Scholar 
Simon, C. et al. Off-target capture data, endosymbiont genes and morphology reveal a relict lineage that is sister to all other singing cicadas. Biol. J. Linn. Soc. Lond. https://doi.org/10.1093/biolinnean/blz120 (2019).Article 

Google Scholar 
Owen, C. L. et al. Detecting and removing sample contamination in phylogenomic data: An example and its implications for Cicadidae phylogeny (Insecta: Hemiptera). Syst. Biol. 71, 1504–1523 (2022).Article 
PubMed 

Google Scholar 
Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner. Report No.: LBNL-7065E. https://www.osti.gov/biblio/1241166-bbmap-fast-accurate-splice-aware-aligner (Lawrence Berkeley National Lab. (LBNL), 2014).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).Article 
MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nurk, S. et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In Research in Computational Molecular Biology 158–170 (Springer, 2013).Chapter 

Google Scholar 
Łukasik, P. et al. One hundred mitochondrial genomes of cicadas. J. Hered. 110, 247–256 (2019).Article 
PubMed 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. https://doi.org/10.1093/bib/bbx108 (2017).Article 
PubMed Central 

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

Google Scholar 
Hahn, C., Bachmann, L. & Chevreux, B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—A baiting and iterative mapping approach. Nucleic Acids Res. 41, e129 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Miller, M. A., Pfeiffer, W., Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. 2010 Gateway Computing Environments Workshop (GCE) 1–8 (2010).Buckley, T. R., Cordeiro, M., Marshall, D. C. & Simon, C. Differentiating between hypotheses of lineage sorting and introgression in New Zealand alpine cicadas (Maoricicada Dugdale). Syst. Biol. 55, 411–425 (2006).Article 
PubMed 

Google Scholar 
Marshall, D. C., Slon, K., Cooley, J. R., Hill, K. B. R. & Simon, C. Steady Plio-Pleistocene diversification and a 2-million-year sympatry threshold in a New Zealand cicada radiation. Mol. Phylogenet. Evol. 48, 1054–1066 (2008).Article 
PubMed 

Google Scholar 
Bator, J., Marshall, D. C., Leston, A., Cooley, J. & Simon, C. Phylogeography of the endemic red-tailed cicadas of New Zealand (Hemiptera: Cicadidae: Rhodopsalta): Molecular, morphological and bioacoustical confirmation of the existence of Hudson’s Rhodopsalta microdora. Zool. J. Linn. Soc. 195, 1219–1244 (2022).Article 

Google Scholar 
Brumfield, K. D. et al. Gut microbiome insights from 16S rRNA analysis of 17-year periodical cicadas (Hemiptera: Magicicada spp.) Broods II, VI, and X. Sci. Rep. 12, 16967. https://doi.org/10.1038/s41598-022-20527-7 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rakitov, R. A. Structure and function of the Malpighian tubules, and related behaviors in juvenile cicadas: Evidence of homology with spittlebugs (Hemiptera: Cicadoidea & Cercopoidea). Zool. Anz. 241, 117–130 (2002).Article 

Google Scholar 
Andersen, P. C., Brodbeck, B. V. & Mizell, R. F. Feeding by the leafhopper, Homalodisca coagulata, in relation to xylem fluid chemistry and tension. J. Insect Physiol. 38, 611–622 (1992).Article 
CAS 

Google Scholar 
Cheung, W. W. K. & Marshall, A. T. Water and ion regulation in cicadas in relation to xylem feeding. J. Insect Physiol. 19, 1801–1816 (1973).Article 
CAS 

Google Scholar 
Williams, K. S. & Simon, C. The ecology, behavior, and evolution of periodical cicadas. Annu. Rev. Entomol. 40, 269–295 (1995).Article 
CAS 

Google Scholar 
Logan, D. P., Rowe, C. A. & Maher, B. J. Life history of chorus cicada, an endemic pest of kiwifruit (Cicadidae: Homoptera). N. Z. Entomol. 37, 96–106 (2014).Article 

Google Scholar 
Buckley, T. R. & Simon, C. Evolutionary radiation of the cicada genus Maoricicada Dugdale (Hemiptera: Cicadoidea) and the origins of the New Zealand alpine biota. Biol. J. Linn. Soc. Lond. 91, 419–435 (2007).Article 

Google Scholar 
Banker, S. E., Wade, E. J. & Simon, C. The confounding effects of hybridization on phylogenetic estimation in the New Zealand cicada genus Kikihia. Mol. Phylogenet. Evol. 116, 172–181 (2017).Article 
PubMed 

Google Scholar 
Groussin, M. et al. Unraveling the processes shaping mammalian gut microbiomes over evolutionary time. Nat. Commun. 8, 14319 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sanders, J. G. et al. Stability and phylogenetic correlation in gut microbiota: Lessons from ants and apes. Mol. Ecol. 23, 1268–1283 (2014).Article 
PubMed 

Google Scholar 
Wang, J. et al. Analysis of intestinal microbiota in hybrid house mice reveals evolutionary divergence in a vertebrate hologenome. Nat. Commun. 6, 6440 (2015).Article 
CAS 
PubMed 

Google Scholar 
Brucker, R. M. & Bordenstein, S. R. The hologenomic basis of speciation. Science 466, 667–669 (2013).Article 

Google Scholar 
Chandler, J. A. & Turelli, M. Comment on “The hologenomic basis of speciation: Gut bacteria cause hybrid lethality in the genus Nasonia”. Science 345, 1011 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, Z. et al. Changes in the rumen microbiome and metabolites reveal the effect of host genetics on hybrid crosses. Environ. Microbiol. Rep. 8, 1016–1023 (2016).Article 
CAS 
PubMed 

Google Scholar 
Weintraub, P. G. & Beanland, L. Insect vectors of phytoplasmas. Annu. Rev. Entomol. 51, 91–111 (2006).Article 
CAS 
PubMed 

Google Scholar 
Hopkins, D. L. Xylella fastidiosa: Xylem-limited bacterial pathogen of plants. Annu. Rev. Phytopathol. 27, 271–290 (1989).Article 

Google Scholar 
Karban, R. Why cicadas (Hemiptera: Cicadidae) develop so slowly. Biol. J. Linn. Soc. Lond. 135, 291–298 (2021).Article 

Google Scholar 
Krell, R. K., Boyd, E. A., Nay, J. E., Park, Y.-L. & Perring, T. M. Mechanical and insect transmission of Xylella fastidiosa to Vitis vinifera. Am. J. Enol. Vitic. 58, 211–216 (2007).Article 
CAS 

Google Scholar 
Paião, F., Meneguim, A. M., Casagrande, E. C., Lovato, L. & Leite, R. P. Levantamento de espécies de cigarras e transmissão de Xylella fastidiosa em cafeeiro. http://www.sbicafe.ufv.br/handle/123456789/1457 (2003).Elbeaino, T. et al. Identification of three potential insect vectors of Xylella fastidiosa in southern Italy. Phytopathol. Mediterr. 53, 328–332 (2014).
Google Scholar  More

I was filled with hope when I read the first draft of the Global Biodiversity Framework (GBF) in mid-2021. It seemed that the parties to the United Nations Convention on Biodiversity had learnt from bitter experience — the failure of the Aichi Biodiversity Targets, set for the previous decade. Instead of vague aims, the draft framework incorporated most of the advice that the scientific community, myself included, had marshalled. It contained ambitious quantitative thresholds, such as those for the area of ecosystem to be protected, the percentage of genetic diversity to be maintained, and percentage reductions for overall extinction rates, pesticide use and subsidies harmful to biodiversity.Then came the square brackets. In the world of policy, these mark proposed amendments that the parties do not yet agree on. The square brackets proliferated at an alarming rate throughout the GBF text, enclosing, neutralizing and paralysing goals and targets. By July 2021, in a version about 10,200 words long, there were more than 900 pairs of square brackets.Brackets germinated with particular vigour in sections that could make the greatest difference for a better future because of their precision, ambition or conceptual novelty. Almost all quantitative thresholds had been bracketed or had disappeared.
The United Nations must get its new biodiversity targets right
I applaud the new prominence given to gender justice (with a new dedicated Target 22) and to financial resources and capacity building (Target 19). I wonder why other key aspects have not received the same treatment, and have instead been compressed almost beyond recognition. For example, the first draft highlighted that species, ecosystems, genetic diversity and nature’s contribution to people each needed their own specific, verifiable outcomes. Now they have coagulated into one vague yet verbose paragraph.This thicket of square brackets smothers the GBF and the hopes of those of us who see transformative change as the only way forward for life on Earth as we know it.In a titanic effort, a streamlined proposal from the Informal Group on the GBF has halved the brackets to be considered by the parties when they meet in Montreal, Canada, for the 15th Conference of the Parties (COP15) on 7–19 December.We need a text with teeth — and far fewer brackets. This much we have learnt in the 30 years since the foundational 1992 Rio Summit drew attention to the impact of human activities on the environment: a strong, precise, ambitious text does not in itself ensure successful implementation, but a weak, vague, toothless text almost guarantees failure.It was no surprise when the Convention on Biological Diversity officially declared the failure of its ten-year Aichi Targets. People involved at the international interface of biodiversity science and policy were already discussing how to do better in the next decade with the GBF.
Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
The scientific community rose to the occasion. In just three years, we produced the first-ever intergovernmental appraisal of life on Earth and what it means to people: The Global Assessment Report on Biodiversity and Ecosystem Services from IPBES (the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services), which I co-chaired. It was ready in time for the original 2020 date for COP15, before the global disruption caused by COVID-19. It was the most comprehensive ever synthesis of published information on the topic, an inclusive conceptual framework involving various disciplines and knowledge systems, and unprecedented participation of Indigenous peoples.Then, in 2020, we assembled an interdisciplinary team of more than 60 biodiversity scientists across the world, and within a few months produced detailed suggestions for the goals of the GBF. Since then, we have made the best of the many pandemic postponements by issuing a stream of specific, evidence-based recommendations on targets, scenarios and implementation.The scientific advice is convergent. First, the GBF needs to explicitly address each facet of biodiversity; none is a good substitute or umbrella for the others. Second, the biodiversity goals must be more ambitious than ever, accompanied by equally ambitious targets for concrete action and sufficient resources to make them happen. Third, the targets need to be precise, traceable and coordinated.Fourth, formally protecting a proportion of the planet’s most pristine ecosystems will by itself fall far short. Nature must be mainstreamed, incorporated in decisions made for the landscapes in which we live and work every day, well beyond protected areas. Finally, and most crucially, targets must focus on the root causes of biodiversity loss: the ways in which we consume, trade and allocate subsidies, incentives and safeguards.From previous experience, I expected objections to certain sections— pesticides and subsidies, say — but they are everywhere. Only 2 of the 22 targets have no brackets. Ironing out objections takes precious time. Because the framework can be enshrined only by consensus, too many objections can lead to too much compromise.Now, to avert failure, we exhort the governments gathering in Montreal to be brave, long-sighted and open-hearted, and to produce a visionary, ambitious biodiversity framework, grounded in knowledge. The awareness and mobilization of their constituencies has never been greater, the evidence in their hands never clearer. If not now, when?

Competing Interests
The author declares no competing interests. More

Dataset descriptionSample collectionOur research complies with all relevant ethical regulations following policies at the University of California, San Diego (UCSD). Animal samples that were sequenced were not collected at UCSD and are not for vertebrate animals research at UCSD following the UCSD Institutional Animal Care and Use Committee (IACUC). Samples were contributed by 34 principal investigators of the Earth Microbiome Project 500 (EMP500) Consortium and are samples from studies at their respective institutions (Supplementary Table 1). Relevant permits and ethics information for each parent study are described in the ‘Permits for sample collection’ section below. Samples were contributed as distinct sets referred to here as studies, where each study represented a single environment (for example, terrestrial plant detritus). To achieve more even coverage across microbial environments, we devised an ontology of sample types (microbial environments), the EMP Ontology (EMPO) (http://earthmicrobiome.org/protocols-and-standards/empo/)1, and selected samples to fill out EMPO categories as broadly as possible. EMPO recognizes strong gradients structuring microbial communities globally, and thus classifies microbial environments (level 4) on the basis of host association (level 1), salinity (level 2), host kingdom (if host-associated) or phase (if free-living) (level 3) (Fig. 1a). As we anticipated previously1, we have updated the number of levels as well as states therein for EMPO (Fig. 1b) on the basis of an important additional salinity gradient observed among host-associated samples when considering the previously unreported shotgun metagenomic and metabolomic data generated here (Fig. 3c,d). We note that although we were able to acquire samples for all EMPO categories, some categories are represented by a single study.Samples were collected following the Earth Microbiome Project sample submission guide50. Briefly, samples were collected fresh, split into 10 aliquots and then frozen, or alternatively collected and frozen, and subsequently split into 10 aliquots with minimal perturbation. Aliquot size was sufficient to yield 10–100 ng genomic DNA (approximately 107–108 cells). To leave samples amenable to chemical characterization (metabolomics), buffers or solutions for sample preservation (for example, RNAlater) were avoided. Ethanol (50–95%) was allowed as it is compatible with LC–MS/MS although it should also be avoided if possible.Sampling guidance was tailored for four general sample types: bulk unaltered (for example, soil, sediment, faeces), bulk fractionated (for example, sponges, corals, turbid water), swabs (for example, biofilms) and filters. Bulk unaltered samples were split fresh (or frozen), sampled into 10 pre-labelled 2 ml screw-cap bead beater tubes (Sarstedt, 72.694.005 or similar), ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Bulk fractionated samples were fractionated as appropriate for the sample type, split into 10 pre-labelled 2 ml screw-cap bead beater tubes, ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Swabs were collected as 10 replicate swabs using 5 BD SWUBE dual cotton swabs with wooden stick and screw cap (281130). Filters were collected as 10 replicate filters (47 mm diameter, 0.2 um pore size, polyethersulfone (preferred) or hydrophilic PTFE filters), placed in pre-labelled 2 ml screw-cap bead beater tubes, and flash frozen in liquid nitrogen (if possible). All sample types were stored at –80 °C if possible, otherwise –20 °C.To track the provenance of sample aliquots, we employed a QR coding scheme. Labels were affixed to aliquot tubes before shipping when possible. QR codes had the format ‘name.99.s003.a05’, where ‘name’ is the PI name, ‘99’ is the study ID, ‘s003’ is the sample number and ‘a05’ is the aliquot number. QR codes (version 2, 25 pixels × 25 pixels) were printed on 1.125’ × 0.75’ rectangular and 0.437’ circular cap Cryogenic Direct Thermal labels (GA International, DFP-70) using a Zebra model GK420d printer and ZebraDesigner Pro 3 software for Windows. After receipt but before aliquots were stored in freezers, QR codes were scanned into a sample inventory spreadsheet using a QR scanner.Sample metadataEnvironmental metadata were collected for all samples on the basis of the EMP Metadata Guide, which combines guidance from the Genomics Standards Consortium MIxS (Minimum Information about any Sequence) standard74 and the Qiita Database (https://qiita.ucsd.edu)51. The metadata guide provides templates and instructions for each MIxS environmental package (that is, sample type). Relevant information describing each PI submission, or study, was organized into a separate study metadata file (Supplementary Table 1).MetabolomicsLC–MS/MS sample extraction and preparationTo profile metabolites among all samples, we used LC–MS/MS, a versatile method that detects tens of thousands of metabolites in biological samples. All solvents and reactants used were LC–MS grade. To maximize the biomass extracted from each sample, the samples were prepared depending on their sampling method (for example, bulk, swabs, filter and controls). The bulk samples were transferred into a microcentrifuge tube (polypropylene, PP) and dissolved in 7:3 MeOH:H2O using a volume varying from 600 µl to 1.5 ml, depending on the amounts of sample available, and homogenized in a tissue lyser (QIAGEN) at 25 Hz for 5 min. Then, the tubes were centrifuged at 2,000 × g for 15 min, and the supernatant was collected in a 96-well plate (PP). For swabs, the swabs were transferred into a 96-well plate (PP) and dissolved in 1.0 ml of 9:1 ethanol:H2O. The prepared plates were sonicated for 30 min, and after 12 h at 4 °C, the swabs were removed from the wells. The filter samples were dissolved in 1.5 ml of 7:3 MeOH:H2O in microcentrifuge tubes (PP) and sonicated for 30 min. After 12 h at 4 °C, the filters were removed from the tubes. The tubes were centrifuged at 2,000 × g for 15 min, and the supernatants were transferred to 96-well plates (PP). The process control samples (bags, filters and tubes) were prepared by adding 3.0 ml of 2:8 MeOH:H2O and recovering 1.5 ml after 2 min. After the extraction process, all sample plates were dried with a vacuum concentrator and subjected to solid phase extraction (SPE). SPE was used to remove salts that could reduce ionization efficiency during mass spectrometry analysis, as well as the most polar and non-polar compounds (for example, waxes) that cannot be analysed efficiently by reversed-phase chromatography. The protocol was as follows: the samples (in plates) were dissolved in 300 µl of 7:3 MeOH:H2O and put in an ultrasound bath for 20 min. SPE was performed with SPE plates (Oasis HLB, hydrophilic-lipophilic-balance, 30 mg with particle sizes of 30 µm). The SPE beds were activated by priming them with 100% MeOH, and equilibrated with 100% H2O. The samples were loaded on the SPE beds, and 100% H2O was used as wash solvent (600 µl). The eluted washing solution was discarded, as it contains salts and very polar metabolites that subsequent metabolomics analysis is not designed for. The sample elution was carried out sequentially with 7:3 MeOH:H2O (600 µl) and 100% MeOH (600 µl). The obtained plates were dried with a vacuum concentrator. For mass spectrometry analysis, the samples were resuspended in 130 µl of 7:3 MeOH:H2O containing 0.2 µM of amitriptyline as an internal standard. The plates were centrifuged at 30 × g for 15 min at 4 °C. Samples (100 µl) were transferred into new 96-well plates (PP) for mass spectrometry analysis.LC–MS/MS sample analysisThe extracted samples were analysed by ultra-high performance liquid chromatography (UHPLC, Vanquish, Thermo Fisher) coupled to a quadrupole-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher) operated in data-dependent acquisition mode (LC–MS/MS in DDA mode). Chromatographic separation was performed using a Kinetex C18 1.7 µm (Phenomenex), 100 Å pore size, 2.1 mm (internal diameter) × 50 mm (length) column with a C18 guard cartridge (Phenomenex). The column was maintained at 40 °C. The mobile phase was composed of a mixture of (A) water with 0.1% formic acid (v/v) and (B) acetonitrile with 0.1% formic acid. Chromatographic elution method was set as follows: 0.00–1.00 min, isocratic 5% B; 1.00–9.00 min, gradient from 5% to 100% B; 9.00–11.00 min, isocratic 100% B; followed by equilibration 11.00–11.50 min, gradient from 100% to 5% B; 11.50–12.50 min, isocratic 5% B. The flow rate was set to 0.5 ml min−1.The UHPLC was interfaced to the orbitrap using a heated electrospray ionization source with the following parameters: ionization mode, positive; spray voltage, +3,496.2 V; heater temperature, 363.90 °C; capillary temperature, 377.50 °C; S-lens RF, 60 arbitrary units (a.u.); sheath gas flow rate, 60.19 a.u.; and auxiliary gas flow rate, 20.00 a.u. The MS1 scans were acquired at a resolution (at m/z 200) of 35,000 in the m/z 100–1500 range, and the fragmentation spectra (MS2) scans at a resolution of 17,500 from 0 to 12.5 min. The automatic gain control target and maximum injection time were set at 1.0 × 106 and 160 ms for MS1 scans, and set at 5.0 × 105 and 220 ms for MS2 scans, respectively. Up to three MS2 scans in data-dependent mode (Top 3) were acquired for the most abundant ions per MS1 scans using the apex trigger mode (4–15 s), dynamic exclusion (11 s) and automatic isotope exclusion. The starting value for MS2 was m/z 50. Higher-energy collision induced dissociation (HCD) was performed with a normalized collision energy of 20, 30 and 40 eV in stepped mode. The major background ions originating from the SPE were excluded manually from the MS2 acquisition. Analyses were randomized within plate and blank samples analysed every 20 injections. A quality control mix sample assembled from 20 random samples across the sample types was injected at the beginning, the middle and the end of each plate sequence. The chromatographic shift observed throughout the batch was estimated as less than 2 s, and the relative standard deviation of ion intensity was 15% per replicate.LC–MS/MS data processingThe mass spectrometry data were centroided and converted from the proprietary format (.raw) to the m/z extensible markup language format (.mzML) using ProteoWizard (ver. 3.0.19, MSConvert tool)75. The mzML files were then processed with MZmine 2 toolbox76 using the ion-identity networking modules77 that allow advanced detection for adduct/isotopologue annotations. The MZmine processing was performed on Ubuntu 18.04 LTS 64-bits workstation (Intel Xeon E5-2637, 3.5 GHz, 8 cores, 64 Gb of RAM) and took ~3 d. The MZmine project, the MZmine batch file (.XML format) and results files (.MGF and .CSV) are available in the MassIVE dataset MSV000083475. The MZmine batch file contains all the parameters used during the processing. In brief, feature detection and deconvolution was performed with the ADAP chromatogram builder78 and local minimum search algorithm. The isotopologues were regrouped and the features (peaks) were aligned across samples. The aligned peak list was gap filled and only peaks with an associated fragmentation spectrum and occurring in a minimum of three files were conserved. Peak shape correlation analysis grouped peaks originating from the same molecule and annotated adduct/isotopologue with ion-identity networking77. Finally, the feature quantification table results (.CSV) and spectral information (.MGF) were exported with the GNPS module for feature-based molecular networking analysis on GNPS79 and with SIRIUS export modules.LC–MS/MS data annotationThe results files of MZmine (.MGF and .CSV files) were uploaded to GNPS (http://gnps.ucsd.edu)52 and analysed with the feature-based molecular networking workflow79. Spectral library matching was performed against public fragmentation spectra (MS2) spectral libraries on GNPS and the NIST17 library.For the additional annotation of small peptides, we used the DEREPLICATOR tools available on GNPS80,81. We then used SIRIUS82 (v. 4.4.25, headless, Linux) to systematically annotate the MS2 spectra. Molecular formulae were computed with the SIRIUS module by matching the experimental and predicted isotopic patterns83, and from fragmentation trees analysis84 of MS2. Molecular formula prediction was refined with the ZODIAC module using Gibbs sampling85 on the fragmentation spectra (chimeric spectra or those with poor fragmentation were excluded). In silico structure annotation using structures from biodatabase was done with CSI:FingerID86. Systematic class annotations were obtained with CANOPUS41 and used the NPClassifier ontology87.The parameters for SIRIUS tools were set as follows, for SIRIUS: molecular formula candidates retained, 80; molecular formula database, ALL; maximum precursor ion m/z computed, 750; profile, orbitrap; m/z maximum deviation, 10 ppm; ions annotated with MZmine were prioritized and other ions were considered (that is, [M+H3N+H]+, [M+H]+, [M+K]+, [M+Na]+, [M+H-H2O]+, [M+H-H4O2]+, [M+NH4]+); for ZODIAC: the features were split into 10 random subsets for lower computational burden and computed separately with the following parameters: threshold filter, 0.9; minimum local connections, 0; for CSI:FingerID: m/z maximum deviation, 10 ppm; and biological database, BIO.To establish putative microbially related secondary metabolites, we collected annotations from spectral library matching and the DEREPLICATOR+ tools and queried them against the largest microbial metabolite reference databases (Natural Products Atlas88 and MIBiG89). Molecular networking79 was then used to propagate the annotation of microbially related secondary metabolites throughout all molecular families (that is, the network component).LC–MS/MS data analysisWe combined the annotation results from the different tools described above to create a comprehensive metadata file describing each metabolite feature observed. Using that information, we generated a feature-table including only secondary metabolite features determined to be microbially related. We then excluded very low-intensity features introduced to certain samples during the gap-filling step described above. These features were identified on the basis of presence in negative controls that were universal to all sample types (that is, bulk, filter and swab) and by their relatively low per-sample intensity values. Finally, we excluded features present in positive controls for sampling devices specific to each sample type (that is, bulk, filter or swab). The final feature-table included 618 samples and 6,588 putative microbially related secondary metabolite features that were used for subsequent analysis.We used QIIME 2’s90 (v2020.6) ‘diversity’ plugin to quantify alpha-diversity (that is, feature richness) for each sample and ‘deicode’91 to quantify beta-diversity (that is, robust Aitchison distances, which are robust to both sparsity and compositionality in the data) between each pair of samples. We parameterized our robust Aitchison principal components analysis (RPCA)91 to exclude samples with fewer than 500 features and features present in fewer than 10% of samples. We used the ‘taxa’ plugin to quantify the relative abundance of microbially related secondary metabolite pathways and superclasses (that is, on the basis of NPClassifier) within each environment (that is, for each level of EMPO 4), and ‘songbird’ v1.0.492 to identify sets of microbially related secondary metabolites whose abundances were associated with certain environments. We parameterized our ‘songbird’ model as follows: epochs, 1,000,000; differential prior, 0.5; learning rate, 1.0 × 10−5; summary interval, 2; batch size, 400; minimum sample count, 0; and training on 80% of samples at each level of EMPO 4 using ‘Animal distal gut (non-saline)’ as the reference environment. Environments with fewer than 10 samples were excluded to optimize model training (that is, ‘Animal corpus (non-saline)’, ‘Animal proximal gut (non-saline)’, ‘Surface (saline)’). The output from ‘songbird’ includes a rank value for each metabolite in every environment, which represents the log fold change for a given metabolite in a given environment92. We compared log fold changes for each metabolite from this run to those from (1) a replicate run using the same reference environment and (2) a run using a distinct reference environment: ‘Water (saline)’. We found strong Spearman correlations in both cases (Supplementary Table 8), and therefore focused on results from the original run using ‘Animal distal gut (non-saline)’ as the reference environment, as it has previously been shown to be relatively unique among other habitats. In addition to summarizing the top 10 metabolites for each environment (Supplementary Table 3), we used the log fold change values in our multi-omics analyses described below.We used the RPCA biplot and QIIME 2’s90 EMPeror93 to visualize differences in composition among samples, as well as the association with samples of the 25 most influential microbially related secondary metabolite features (that is, those with the largest magnitude across the first three principal component loadings). We tested for significant differences in metabolite composition across all levels of EMPO using PERMANOVA implemented with QIIME 2’s ‘diversity’ plugin90 and using our robust Aitchison distance matrix as input. In parallel, we used the differential abundance results from ‘songbird’ described above to identify specific microbially related secondary metabolite pathways and superclasses that varied strongly across environments. We then went back to our metabolite feature-table to visualize differences in the relative abundances of those pathways and superclasses within each environment by first selecting features and calculating log-ratios using ‘qurro’94, and then plotting using the ‘ggplot2’ package95 in R96 v4.0.0. We tested for significant differences in relative abundances across environments using Kruskal–Wallis tests implemented with the base ‘stats’ package in R96.GC–MS sample extraction and preparationTo profile volatile small molecules among all samples in addition to what was captured with LC–MS/MS, we used gas chromatography coupled with mass spectrometry (GC–MS). All solvents and reactants were GC–MS grade. Two protocols were used for sample extraction, one for the 105 soil samples and a second for the 356 faecal and sediment samples that were treated as biosafety level 2. The 105 soil samples were received at the Pacific Northwest National Laboratory and processed as follows. Each soil sample (1 g) was weighed into microcentrifuge tubes (Biopur Safe-Lock, 2.0 ml, Eppendorf). H2O (1 ml) and one scoop (~0.5 g) of a 1:1 (v/v) mixture of garnet (0.15 mm, Omni International) and stainless steel (0.9–2.0 mm blend, Next Advance) beads and one 3 mm stainless steel bead (Qiagen) were added to each tube. Samples were homogenized in a tissue lyser (Qiagen) for 3 min at 30 Hz and transferred into 15 ml polypropylene tubes (Olympus, Genesee Scientific). Ice-cold water (1 ml) was used to rinse the smaller tube and combined into the 15 ml tube. Chloroform:methanol (10 ml, 2:1 v/v) was added and samples were rotated at 4 °C for 10 min, followed by cooling at −70 °C for 10 min and centrifuging at 150 × g for 10 min to separate phases. The top and bottom layers were combined into 40 ml glass vials and dried using a vacuum concentrator. Chloroform:methanol (1 ml, 2:1) was added to each large glass vial and the sample was transferred into 1.5 ml tubes and centrifuged at 1,300 × g. The supernatant was transferred into glass vials and dried for derivatization.The remaining 356 samples received from UCSD that included faecal and sediment samples were processed as follows: 100 µl of each sample was transferred to a 2 ml microcentrifuge tube using a scoop (MSP01, Next Advance). The final volume of the sample was brought to 1.5 ml, ensuring that the solvent ratio is 3:8:4 H2O:CHCl3:MeOH by adding the appropriate volumes of H2O, MeOH and CHCl3. After transfer, one 3 mm stainless steel bead (QIAGEN), 400 µl methanol and 300 µl H2O were added to each tube and the samples were vortexed for 30 s. Then, 800 µl chloroform was added and samples were vortexed for 30 s. After centrifuging at 150 × g for 10 min to separate phases, the top and bottom layers were combined in a vial and dried for derivatization.The samples were derivatized for GC–MS analysis as follows: 20 µl of a methoxyamine solution in pyridine (30 mg ml−1) was added to the sample vial and vortexed for 30 s. A bath sonicator was used to ensure that the sample was completely dissolved. Samples were incubated at 37 °C for 1.5 h while shaking at 1,000 r.p.m. N-methyl-N-trimethylsilyltrifluoroacetamide (80 µl) and 1% trimethylchlorosilane solution was added and samples were vortexed for 10 s, followed by incubation at 37 °C for 30 min, with 1,000 r.p.m. shaking. The samples were then transferred into a vial with an insert.An Agilent 7890A gas chromatograph coupled with a single quadrupole 5975C mass spectrometer (Agilent) and an HP-5MS column (30 m × 0.25 mm × 0.25 μm; Agilent) was used for untargeted analysis. Samples (1 μl) were injected in splitless mode, and the helium gas flow rate was determined by the Agilent Retention Time Locking function on the basis of analysis of deuterated myristic acid (Agilent). The injection port temperature was held at 250 °C throughout the analysis. The GC oven was held at 60 °C for 1 min after injection, and the temperature was then increased to 325 °C at a rate of 10 °C min−1, followed by a 10 min hold at 325 °C. Data were collected over the mass range of m/z 50–600. A mixture of FAMEs (C8–C28) was analysed each day with the samples for retention index alignment purposes during subsequent data analysis.GC–MS data processing and annotationThe data were converted from vendor’s format to the .mzML format and processed using GNPS GC–MS data analysis workflow (https://gnps.ucsd.edu)97. The compounds were identified by matching experimental spectra to the public libraries available at GNPS, as well as NIST 17 and Wiley libraries. The data are publicly available at the MassIVE depository (https://massive.ucsd.edu); dataset ID: MSV000083743. The GNPS deconvolution is available in GNPS (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=d5c5135a59eb48779216615e8d5cb3ac), as is the library search (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=59b20fc8381f4ee6b79d35034de81d86).GC–MS data analysisFor multi-omics analyses including GC–MS data, we first removed noisy (that is, suspected background contaminants and artifacts) features by excluding those with balance scores 1.5–2 kb DNA fragments’ (Oxford Nanopore Technologies). The resulting product consists of uniquely tagged rRNA operon amplicons. The uniquely tagged rRNA operons were amplified in a second PCR, where the reaction (100 µl) contained 2 U Platinum SuperFi DNA Polymerase High Fidelity (Thermo Fisher) and a final concentration of 1X SuperFi buffer, 0.2 mM of each dNTP, and 500 nM of each forward and reverse synthetic primer targeting the tailed primers from above. The PCR cycling parameters consisted of an initial denaturation (3 min at 95 °C) and then 25–35 cycles of denaturation (15 s at 95 °C), annealing (30 s at 60 °C) and extension (6 min at 72 °C), followed by final extension (5 min at 72 °C). The PCR product was purified using the custom bead purification protocol above. Batches of 25 amplicon libraries were barcoded and sent for PacBio Sequel II library preparation and sequencing (Sequel II SMRT Cell 8M and 30 h collection time) at the DNA Sequencing Center at Brigham Young University. Circular consensus sequencing (CCS) reads were generated using CCS v.3.4.1 (https://github.com/PacificBiosciences/ccs) using default settings. UMI consensus sequences were generated using the longread_umi pipeline (https://github.com/SorenKarst/longread_umi) with the following command: longread_umi pacbio_pipeline -d ccs_reads.fq -o out_dir -m 3500 -M 6000 -s 60 -e 60 -f CAAGCAGAAGACGGCATACGAGAT -F AGRGTTYGATYMTGGCTCAG -r AATGATACGGCGACCACCGAGATC -R CGACATCGAGGTGCCAAAC -U ‘0.75;1.5;2;0’ -c 2.Amplicon data analysisFor multi-omics analyses including amplicon sequence data, we processed each dataset for comparison of beta-diversity. For all amplicon data except that for bacterial full-length rRNA amplicons, raw sequence data were converted from bcl to fastq, and then multiplexed files for each sequencing run uploaded as separate preparations to Qiita (study: 13114).For each 16S sequencing run, in Qiita, data were demultiplexed, trimmed to 150 bp and denoised using Deblur122 to generate a feature-table of sub-operational taxonomic units (sOTUs) per sample, using default parameters. We then exported feature-tables and denoised sequences from each sequencing run, used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised reads across sequencing runs, and placed all denoised reads into the GreenGenes 13_8 phylogeny123 via fragment insertion using QIIME 2’s90 SATé-Enabled Phylogenetic Placement (SEPP)124 plugin to produce a phylogeny for diversity analyses. To allow for phylogenetically informed diversity analyses, reads not placed during SEPP (that is, 513 sOTUs, 0.1% of all sOTUs) were removed from the merged feature-table. We then used QIIME 2’s ‘feature-table’ plugin to exclude singleton sOTUs and rarefy the data to 5,000 reads per sample. Rarefaction depths for all amplicon analyses were chosen to best normalize sampling effort per sample while maintaining ≥75% of samples representative of Earth’s environments, and also to maintain consistency with the analyses from EMP release 1. We then used QIIME 2’s90 ‘diversity’ plugin to estimate alpha-diversity (that is, sOTU richness) and beta-diversity (that is, unweighted UniFrac distances). The final feature-table for 16S beta-diversity analysis included 681 samples and 93,260 features. We performed a comparative analysis of the data including and excluding the reads not placed during SEPP, and note that both alpha-diversity (that is, sOTU richness) and beta-diversity (that is, sample–sample RPCA distances) were highly correlated between datasets (Spearman r = 1.0) (Supplementary Fig. 5). We thus proceeded with the SEPP-filtered dataset and used phylogenetically informed diversity metrics where applicable.For 18S data, we used QIIME 2’s90 ‘demux’ plugin’s ‘emp-paired’ method125,126 to first demultiplex each sequencing run, and then the ‘cutadapt’ plugin’s127 ‘trim-paired’ method to trim sequencing primers from reads. We then exported trimmed reads, concatenated R1 and R2 read files per sample, and denoised reads using Deblur’s122,128 ‘workflow’ with default settings, trimming reads to 90 bp, and taking the ‘all.biom’ and ‘all.seqs’ output, for each sequencing run. We then used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised sequences across sequencing runs, and then the ‘feature-classifier’ plugin’s ‘classify-sklearn’ method to classify taxonomy for each sOTU via pre-fitted machine-learning classifiers129 and the SILVA 138 reference database130. We then used QIIME 2’s90 ‘feature-table’ plugin to exclude reads assigned to bacteria and archaea, singleton sOTUs and samples with a total frequency of More

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
PubMed 

Google Scholar 
Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116 (2014).PubMed 

Google Scholar 
Pendrill, F. et al. Agricultural and forestry trade drives large share of tropical deforestation emissions. Glob. Environ. Change 56, 1–10 (2019).
Google Scholar 
Haupt, F., Bakhtary, H., Schulte, I., Galt, H. & Streck, C. Progress on Corporate Commitments and their Implementation (Tropical Forest Alliance, 2018); https://www.tropicalforestalliance.org/assets/Uploads/Progress-on-Corporate-Commitments-and-their-Implementation.pdfAustin, K. G. et al. Mapping and monitoring zero-deforestation commitments. Bioscience 71, 1079–1090 (2021).PubMed 
PubMed Central 

Google Scholar 
Leijten, F. C., Sim, S., King, H. & Verburg, P. H. Which forests could be protected by corporate zero deforestation commitments? A spatial assessment. Environ. Res. Lett. 15, 064021 (2020).
Google Scholar 
Garrett, R. D. et al. Criteria for effective zero-deforestation commitments. Glob. Environ. Change https://doi.org/10.1016/j.gloenvcha.2018.11.003 (2019).Lehmann, C. E. R. & Parr, C. L. Tropical grassy biomes: linking ecology, human use and conservation. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2016.0329 (2016).Miles, L. et al. A global overview of the conservation status of tropical dry forests. J. Biogeogr. 33, 491–505 (2006).
Google Scholar 
Gibbs, H. K. et al. Brazil’s soy moratorium. Science https://doi.org/10.1126/science.aaa0181 (2015).Jopke, P. & Schoneveld, G. C. Corporate Commitments to Zero Deforestation: An Evaluation of Externality Problems and Implementation Gaps (CIFOR, 2018); https://doi.org/10.17528/cifor/006827Parr, C. L., Lehmann, C. E. R., Bond, W. J., Hoffmann, W. A. & Andersen, A. N. Tropical grassy biomes: misunderstood, neglected, and under threat. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2014.02.004 (2014).Ratnam, J. et al. When is a ‘forest’ a savanna, and why does it matter? Glob. Ecol. Biogeogr. https://doi.org/10.1111/j.1466-8238.2010.00634.x (2011).Sanchez-Azofeifa, G. A. et al. Research priorities for neotropical dry forests. Biotropica 37, 477–485 (2005).
Google Scholar 
Vijay, V., Pimm, S. L., Jenkins, C. N. & Smith, S. J. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS ONE 11, e0159668 (2016).PubMed 
PubMed Central 

Google Scholar 
Principles & Criteria for the Production of Sustainable Palm Oil (RSPO, 2018).Rosoman, G. et al. (eds) The HCS Approach Toolkit (HCS Approach Steering Group, 2017).Brown, E. & Senior, M. J. M. (eds) Common Guidance for the Identification of High Conservation Values (HCV Resource Network, 2017).Furumo, P. R. & Aide, T. M. Characterizing commercial oil palm expansion in Latin America: land use change and trade. Environ. Res. Lett. 12, 024008 (2017).
Google Scholar 
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience https://doi.org/10.1093/biosci/bix014 (2017).Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth Syst. Sci. Data 13, 1211–1231 (2021).
Google Scholar 
Woittiez, L. S., van Wijk, M. T., Slingerland, M., van Noordwijk, M. & Giller, K. E. Yield gaps in oil palm: a quantitative review of contributing factors. Eur. J. Agron. 83, 57–77 (2017).
Google Scholar 
Kuepper, B., Drost, S. & Piotrowski, M. Latin American Palm Oil Linked to Social Risks, Local Deforestation (Chain Reaction Research, 2021); https://chainreactionresearch.com/wp-content/uploads/2021/12/Latin-American-Palm-Oil-Linked-to-Social-Issues-Local-Deforestation-1.pdfHoyle, D. et al. RSPO New Planting Procedures: Summary Report of ESIA, HCV Assessments and Management Plan (Terea, Proforest and Olam Palm Gabon, 2017).Universal Mill List (World Resources Institute, Rainforest Alliance, Proforest & Daemeter, 2018); https://data.globalforestwatch.org/documents/gfw::universal-mill-list/aboutPirker, J., Mosnier, A., Kraxner, F., Havlík, P. & Obersteiner, M. What are the limits to oil palm expansion? Glob. Environ. Change 40, 73–81 (2016).
Google Scholar 
Fischer, G. et al. Global Agro-Ecological Zones 4 (GAEZ v4) – Model Documentation (FAO, 2021); https://doi.org/10.4060/cb4744enGlobal Agro-Ecological Zoning Version 4 (GAEZ v4) (FAO & IIASA, 2021); http://www.fao.org/gaez/Tao, H. H. et al. Long-term crop residue application maintains oil palm yield and temporal stability of production. Agron. Sustain. Dev. https://doi.org/10.1007/s13593-017-0439-5 (2017).Wei, L., John Martin, J. J., Zhang, H., Zhang, R. & Cao, H. Problems and prospects of improving abiotic stress tolerance and pathogen resistance of oil palm. Plants 10, 2622 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Corley, R. H. & Tinker, P. B. The Oil Palm (Wiley-Blackwell, 2016).Barona, E., Ramankutty, N., Hyman, G. & Coomes, O. T. The role of pasture and soybean in deforestation of the Brazilian Amazon. Environ. Res. Lett. 5, 024002 (2010).
Google Scholar 
ten Kate, A., Kuepper, B. & Piotrowski, M. NDPE Policies Cover 83% of Palm Oil Refineries; Implementation at 78% (Chain Reaction Research, 2020); https://chainreactionresearch.com/wp-content/uploads/2020/04/NDPE-Policies-Cover-83-of-Palm-Oil-Refining-Market.pdfThe Trase Yearbook: The State Of Forest Risk Supply Chains (Trase, 2020); https://insights.trase.earth/yearbook/summaryAustin, K. G. et al. Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017).
Google Scholar 
Furumo, P. R., Rueda, X., Rodríguez, J. S. & Parés Ramos, I. K. Field evidence for positive certification outcomes on oil palm smallholder management practices in Colombia. J. Clean. Prod. 245, 118891 (2020).
Google Scholar 
Carlson, K. M. et al. Effect of oil palm sustainability certification on deforestation and fire in Indonesia. Proc. Natl Acad. Sci. USA 115, 121–126 (2018).CAS 
PubMed 

Google Scholar 
Heilmayr, R., Carlson, K. M. & Benedict, J. J. Deforestation spillovers from oil palm sustainability certification. Environ. Res. Lett. 15, 075002 (2020).CAS 

Google Scholar 
Impact (RSPO, 2022); https://www.rspo.org/impactBastos Lima, M. G., Persson, U. M. & Meyfroidt, P. Leakage and boosting effects in environmental governance: a framework for analysis. Environ. Res. Lett. 14, 105006 (2019).
Google Scholar 
Corley, R. H. V. How much palm oil do we need? Environ. Sci. Policy https://doi.org/10.1016/j.envsci.2008.10.011 (2009).FAOSTAT: Food and Agriculture Data (FAO, 2020); https://www.fao.org/faostat/en/Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).
Google Scholar 
Murphy, B. P., Andersen, A. N. & Parr, C. L. The underestimated biodiversity of tropical grassy biomes. Phil. Trans. R. Soc. B 371, 20150319 (2016).PubMed 
PubMed Central 

Google Scholar 
Smith, J. R., Hendershot, J. N., Nova, N. & Daily, G. C. The biogeography of ecoregions: descriptive power across regions and taxa. J. Biogeogr. https://doi.org/10.1111/jbi.13871 (2020).Klink, C. A. & Machado, R. B. Conservation of the Brazilian Cerrado. Conserv. Biol. 19, 707–713 (2005).
Google Scholar 
Strassburg, B. B. N. et al. Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-017-0099 (2017).le Polain de Waroux, Y. et al. The restructuring of South American soy and beef production and trade under changing environmental regulations. World Dev. 121, 188–202 (2019).
Google Scholar 
Nepstad, L. S. et al. Pathways for recent Cerrado soybean expansion: extending the soy moratorium and implementing integrated crop livestock systems with soybeans. Environ. Res. Lett. 14, 044029 (2019).
Google Scholar 
Searchinger, T. D. et al. High carbon and biodiversity costs from converting Africa’s wet savannahs to cropland. Nat. Clim. Change https://doi.org/10.1038/nclimate2584 (2015).Cardoso Da Silva, J. M. & Bates, J. M. Biogeographic patterns and conservation in the South American Cerrado: a tropical savanna hotspot. BioScience 52, 225–233 (2002).
Google Scholar 
Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL 7, 217–240 (2021).Hill, T. C., Williams, M., Bloom, A. A., Mitchard, E. T. A. & Ryan, C. M. Are inventory based and remotely sensed above-ground biomass estimates consistent? PLoS ONE https://doi.org/10.1371/journal.pone.0074170 (2013).Ryan, C. M. et al. Ecosystem services from southern African woodlands and their future under global change. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2015.0312 (2016).Grace, J., Jose, J. S., Meir, P., Miranda, H. S. & Montes, R. A. Productivity and carbon fluxes of tropical savannas. J. Biogeogr. 33, 387–400 (2006).
Google Scholar 
Scharlemann, J. P., Tanner, E. V., Hiederer, R. & Kapos, V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag. 5, 81–91 (2014).CAS 

Google Scholar 
Quezada, J. C., Etter, A., Ghazoul, J., Buttler, A. & Guillaume, T. Carbon neutral expansion of oil palm plantations in the Neotropics. Sci. Adv. 5, eaaw4418 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Aleman, J. C., Blarquez, O. & Staver, C. A. Land-use change outweighs projected effects of changing rainfall on tree cover in sub-Saharan Africa. Glob. Change Biol. https://doi.org/10.1111/gcb.13299 (2016).Espírito-Santo, M. M. et al. Understanding patterns of land-cover change in the Brazilian Cerrado from 2000 to 2015. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2015.0435 (2016).Overbeck, G. E. et al. Conservation in Brazil needs to include non-forest ecosystems. Divers. Distrib. https://doi.org/10.1111/ddi.12380 (2015).Hoekstra, J. M., Boucher, T. M., Ricketts, T. H. & Roberts, C. Confronting a biome crisis: global disparities of habitat loss and protection. Ecol. Lett. https://doi.org/10.1111/j.1461-0248.2004.00686.x (2005).RTRS Standard for Responsible Soy Production Version 3.1 (RTRS, 2017); https://responsiblesoy.org/wp-content/uploads/2019/08/RTRS%20Standard%20Responsible%20Soy%20production%20V3.1%20ING-LOW.pdfBatlle-Bayer, L., Batjes, N. H. & Bindraban, P. S. Changes in organic carbon stocks upon land use conversion in the Brazilian Cerrado: a review. Agric. Ecosyst. Environ. 137, 47–58 (2010).CAS 

Google Scholar 
Rockström, J., Falkenmark, M., Lannerstad, M. & Karlberg, L. The planetary water drama: dual task of feeding humanity and curbing climate change. Geophys. Res. Lett. 39, LXXXXX (2012).
Google Scholar 
Ocampo-Peñuela, N., Garcia-Ulloa, J., Ghazoul, J. & Etter, A. Quantifying impacts of oil palm expansion on Colombia’s threatened biodiversity. Biol. Conserv. https://doi.org/10.1016/j.biocon.2018.05.024 (2018).Gilroy, J. J. et al. Minimizing the biodiversity impact of Neotropical oil palm development. Glob. Change Biol. 21, 1531–1540 (2015).
Google Scholar 
Bonn Challenge 2020 Report (IUCN, 2020); https://www.bonnchallenge.org/resources/bonn-challenge-2020-reportGilroy, J. J. et al. Cheap carbon and biodiversity co-benefits from forest regeneration in a hotspot of endemism. Nat. Clim. Change 4, 503–507 (2014).
Google Scholar 
Evans, M. C. et al. Carbon farming via assisted natural regeneration as a cost-effective mechanism for restoring biodiversity in agricultural landscapes. Environ. Sci. Policy 50, 114–129 (2015).CAS 

Google Scholar 
Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W. & Mortensen, D. A. Agriculture in 2050: recalibrating targets for sustainable intensification. BioScience https://doi.org/10.1093/biosci/bix010 (2017).Beyer, R. & Rademacher, T. Species richness and carbon footprints of vegetable oils: can high yields outweigh palm oil’s environmental impact? Sustainability 13, 1813 (2021).
Google Scholar 
Lee, J. S. H., Ghazoul, J., Obidzinski, K. & Koh, L. P. Oil palm smallholder yields and incomes constrained by harvesting practices and type of smallholder management in Indonesia. Agron. Sustain. Dev. 34, 501–513 (2014).
Google Scholar 
Murphy, D. J. The future of oil palm as a major global crop: opportunities and challenges. J. Oil Palm Res. 26, 1–24 (2014).
Google Scholar 
Giam, X., Koh, L. P. & Wilcove, D. S. Tropical crops: cautious optimism. Science https://doi.org/10.1126/science.346.6212.928-a (2014).Villoria, N. B., Golub, A., Byerlee, D. & Stevenson, J. Will yield improvements on the forest frontier reduce greenhouse gas emissions? A global analysis of oil palm. Am. J. Agric. Econ. 95, 1301–1308 (2013).
Google Scholar 
Koh, L. P. & Lee, T. M. Sensible consumerism for environmental sustainability. Biol. Conserv. https://doi.org/10.1016/j.biocon.2011.10.029 (2012).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Harris, N., Goldman, E. & Gibbes, S. Spatial Database of Planted Trees (SDPT) Version 1.0 (World Resources Institute, 2019); https://data.globalforestwatch.org/datasets/tree-plantationsSutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018).
Google Scholar 
Global Land Cover (Copernicus, 2019); https://lcviewer.vito.be/Tsendbazar, N.-E. et al. Copernicus Global Land Operations ‘Vegetation and Energy’ ‘CGLOPS−1’ Validation Report. Moderate Dynamic Land Cover 100m Version 2 (WUR, 2019); https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_VR_LC100m-V2.0_I1.00.pdfSantoro, M. et al. GlobBiomass – global datasets of forest biomass. PANGAEA https://doi.org/10.1594/PANGAEA.894711 (2018).Santoro, M. et al. A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations. Geophys. Res. Abstr. https://doi.org/10.1002/joc.5086 (2018).Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science https://doi.org/10.1126/science.1244693 (2013).Gumbricht, T. et al. Tropical and Subtropical Wetlands Distribution Version 7 (CIFOR, 2017); https://doi.org/10.17528/CIFOR/DATA.00058The IUCN Red List of Threatened Species Version 2018−1 (IUCN, 2018); https://www.iucnredlist.orgBird Species Distribution Maps of the World Version 6.0 (BirdLife International & Handbook of the Birds of the World, 2016); http://datazone.birdlife.org/species/requestdisR Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Silalertruksa, T. et al. Environmental sustainability of oil palm cultivation in different regions of Thailand: greenhouse gases and water use impact. J. Clean. Prod. 167, 1009–1019 (2017).CAS 

Google Scholar 
Fourcade, Y., Engler, J. O., Rödder, D. & Secondi, J. Mapping species distributions with Maxent using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9, e97122 (2014).PubMed 
PubMed Central 

Google Scholar 
Liu, Z. et al. Shifts in the extent and location of rice cropping areas match the climate change pattern in China during 1980–2010. Reg. Environ. Change https://doi.org/10.1007/s10113-014-0677-x (2015).Singh, K., McClean, C. J., Büker, P., Hartley, S. E. & Hill, J. K. Mapping regional risks from climate change for rainfed rice cultivation in India. Agric. Syst. 156, 76–84 (2017).PubMed 
PubMed Central 

Google Scholar 
Estes, L. D. et al. Comparing mechanistic and empirical model projections of crop suitability and productivity: implications for ecological forecasting. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.12034 (2013).Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modelling (2016).Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
Google Scholar 
Merow, C., Smith, M. J., Silander, J. A., Merow, C. & Silander, J. A. A practical guide to Maxent for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).VanDerWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol. Modell. 220, 589–594 (2009).
Google Scholar 
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. F. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Modell. 199, 142–152 (2006).
Google Scholar 
Engler, R., Guisan, A. & Rechsteiner, L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. https://doi.org/10.1111/j.0021-8901.2004.00881.x (2004).Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1. (International Food Policy Research Institute, 2019); https://doi.org/10.7910/DVN/PRFF8V/M2EMBNHofste, R. W. et al. Aqueduct 3.0: Updated Decision-Relevant Global Water Risk Indicators (World Resources Institute, 2019); https://www.wri.org/research/aqueduct-30-updated-decision-relevant-global-water-risk-indicatorsCarr, M. K. V. The water relations and irrigation requirements of oil palm (Elaeis guineensis): a review. Exp. Agric. 47, 629–652 (2011).
Google Scholar 
Yusop, Z., Hui, C. M., Garusu, G. J. & Katimon, A. Estimation of evapotranspiration in oil palm catchments by short-time period water-budget method. Malays. J. Civ. Eng. 20, 160–174 (2008).
Google Scholar 
Hargreaves, G. H. & Allen, R. G. History and evaluation of Hargreaves evapotranspiration equation. J. Irrig. Drain. Eng. 129, 53–63 (2003).
Google Scholar 
Trabucco, A. & Zomer, R. J. Global aridity index and potential evapotranspiration (ET0) climate database v2. Figshare https://doi.org/10.6084/m9.figshare.7504448.v3 (2019).Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC & IUCN, 2020); www.protectedplanet.net/en/thematic-areas/wdpaDudley, N. (ed.) Guidelines for Applying Protected Area Management Categories (IUCN, 2008).Juffe-Bignoli, D. et al. World Database on Protected Areas User Manual 1.5 (UNEP-WCMC, 2017); https://www.protectedplanet.net/en/resources/wdpa-manualChave, J. J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).CAS 
PubMed 

Google Scholar 
Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. https://doi.org/10.1371/journal.pbio.0050157 (2007).Beyer, R. M. & Manica, A. Historical and projected future range sizes of the world’s mammals, birds, and amphibians. Nat. Commun. 11, 5633 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Beyer, R. M. & Manica, A. Global and country-level data of the biodiversity footprints of 175 crops and pasture. Data Brief 36, 106982 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cobertura de la Tierra 100K Periodo 2018 (IDEAM, Instituto de Hidrología, Meteorología y Estudios Ambientales, 2021); http://www.siac.gov.co/catalogo-de-mapasSouza, C. M. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and Earth Engine. Remote Sens. 12, 2735 (2020).
Google Scholar 
MapBiomas Project – Collection 6 of the Annual Series of Land Use and Land Cover Maps of Brazil (MapBiomas, 2021); https://plataforma.brasil.mapbiomas.org/Veldman, J. W. & Putz, F. E. Grass-dominated vegetation, not species-diverse natural savanna, replaces degraded tropical forests on the southern edge of the Amazon Basin. Biol. Conserv. https://doi.org/10.1016/j.biocon.2011.01.011 (2011).Portillo-Quintero, C. A. & Sánchez-Azofeifa, G. A. Extent and conservation of tropical dry forests in the Americas. Biol. Conserv. https://doi.org/10.1016/j.biocon.2009.09.020 (2010).Veldman, J. W. et al. Toward an old-growth concept for grasslands, savannas, and woodlands. Front. Ecol. Environ. https://doi.org/10.1890/140270 (2015).Zaloumis, N. P. & Bond, W. J. Reforestation or conservation? The attributes of old growth grasslands in South Africa. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2015.0310 (2016).Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. https://doi.org/10.1038/s41893-018-0100-6 (2018).Djoudi, H., Vergles, E., Blackie, R. R., Koame, C. K. & Gautier, D. Dry forests, livelihoods and poverty alleviation: understanding current trends. Int. For. Rev. https://doi.org/10.1505/146554815815834868 (2015).Ground-Truthing to Improve Due Diligence on Human Rights in Deforestation-Risk Supply Chains (Forest Peoples Programme, 2020); https://www.forestpeoples.org/en/ground-truthing-to-improve-due-diligenceDrost, S., Rijk, G. & Piotrowski, M. Oil Palm Growers Exposed to USD 0.4-5.9B in Social Compensation Risk (Chain Reaction Research, 2019); https://chainreactionresearch.com/wp-content/uploads/2019/12/Social-compensation-risks-for-palm-growers-4.pdf More

Study sites and coloniesAll the experiments were carried out during the apple and pear blooming seasons of 2007, 2008, 2011, 2013 and 2014 in different locations of the province of Rio Negro, Argentina, while some laboratory experiments performed in the city of Buenos Aires. We used individual foragers of Apis mellifera L. and their colonies containing a mated queen, brood, and food reserves in ten-frame Langstroth hives. All beehives used had similar sizes and the same management history from the beekeeper. The honey bees studied belonged to commercial Langstroth-type hives rented to pollinate these plots. Each hive had a fertilized queen, 3 or 4 capped brood frames, reserves and approximately 15,000 individuals56.Testing generalization of memories from pear mimic odours to pear and apple natural floral scentsThe absolute conditioning assays were performed in the laboratories of the School of Exacts and Natural Sciences of the University of Buenos Aires (34° 32′ S, 58° 26′ W), Buenos Aires, Argentina. We used honey bee foragers collected at the entrance of the hives settle in the experimental field of the School of Exacts and Natural Sciences. The apple (‘Granny Smith’ and ‘Red Delicious’ varieties) and pear (‘Packham’ and ‘D’anjou’ varieties) bud samples that we used as conditioned stimuli (CS) during the conditioning were collected at the end of the blossom of 2011 in Ingeniero Huergo (39° 03′ 27.5″ S; 67° 13′ 53.5″ W), province of Río Negro, Argentina, and taken to the laboratory in the city of Buenos Aires, Argentina, to be used within the following 2 days.We first developed the three different synthetic mixtures (PM, PMI and PMII) that could be generalized to the fragrance of the pear flower by foraging bees. The pear synthetic mixtures were formulated considering the previously reported volatile profile of pear blossoms57. Then, we chose the synthetic mixture most perceptually similar to the pear flower fragrance and measured its generalisation response to the apple flower fragrance to test the compounds’ specificity. The chemical compounds used to prepare the different synthetic mixtures for the behavioural assays were obtained from Sigma-Aldrich, Steinheim, Germany. The compounds used for the three pear mixtures (PM, PMI and PMII) were composed by alpha-pinene, 2-ethyl-hexanol, (R)-(+)-limonene, and (±)-linalool. For details of the PM and mixture proportions see Patent PCT/IB2018/05555058.To test generalization, we took advantages of the fact that honey bees reflexively extend their proboscises when sugar solution is applied to their antennae59. The proboscis extension reflex (PER) can be used to condition bees to an odour if a neutral olfactory stimulus (CS) is paired with a sucrose reward as unconditioned stimulus, US60. Conditioned honey bees extend their proboscises towards the odour alone, a response that indicates that this stimulus has been learned and predicts the oncoming food reward. Conditioned bees can generalize such a learned response to a novel odour if it is perceived like the conditioned one (CS). Then we performed three absolute PER conditionings where we paired each of the three PMs with a sucrose-water solution (30%) reward along three learning trials (exp. 4.2a). Afterwards, pear floral scent was presented as novel odour to test generalization. Based on the generalization level to the pear odour, we chose the synthetic mixture that showed the highest generalisation towards pear flower fragrance, and we used it in all the experiments that follow. In an additional 3-trial PER conditioning with the chosen mixture, we quantified generalisation towards both the pear and apple fragrances as novel stimuli (exp. 4.2b).The experimental bees were all foragers, captured from colonies that had no access to any pear and/or apple tree, hence completely naïve for the CSs. Immediately after capture, bees were anaesthetized at 4 °C and harnessed in metal tubes so that they could only move their mouthparts and antennae60. They were fed 30% weight/weight unscented sucrose solution for about three seconds and kept in a dark incubator (30 °C, 55% relative humidity) for about two hours. Only those bees that showed the unconditioned response (the reflexive extension of the proboscis after applying a 30% w/w sucrose solution to the antennae) and did not respond to the mechanical air flow stimulus were used. Trials lasted 46 s and presented three steps: 20 s of clean air, 6 s of odour presentation (CS) and the last 20 s of clean air. During rewarded trials (CS), the reward (US, a drop of 30% w/w sucrose solution) was delivered upon the last 3 s of CS presentation. The synthetic mixtures (PM) were delivered in a constant air flow (15 ml/s) that passed through a 1 ml syringe containing 4 µl of the synthetic mixture on a small strip of filter paper. On the other hand, pear and apple floral volatiles were swept from a 100 g of fresh pear buds (var. ‘D’Anjou’ and ‘Packham’) or apple buds (var. ‘Granny Smith’, ‘Gala’ and ‘Red Delicious’) inside a kitasato by means of an air flow (54 ml/s).Testing discrimination between mimics and natural floral scentsThe differential conditioning assays were performed in a field laboratory in Ingeniero Huergo, province of Río Negro, Argentina. Conditioning trials with AM as CS were carried out in September 2007 and 2008, prior to the beginning of flowering of the fruit trees. Conditioning trials with PM as CS were carried out in September 2011 in the same area (Ingeniero Huergo, province of Río Negro, Argentina). Apple and pear bud samples used as CS were collected in plots that start blooming located around Ingeniero Huergo, but distant (more than 1 km) from the plot where we collected the bees. The bud samples presented the following varieties: M. domesticus sp., ‘Granny Smith’, ‘Gala’, and ‘Red Delicious’; P. communis sp., ‘Packham’ and ‘D’Anjou’.With the aim to develop a synthetic mixture that presents difficult to discriminate with the fragrance of the apple flower by foraging bees, an apple synthetic mixture (AM) was formulated considering the previously reported volatile profile of apple blossoms61. The chemical compounds used to prepare the apple synthetic mixtures for the behavioural assays were obtained from Sigma-Aldrich, Steinheim, Germany. Apple mimic (AM) was composed by benzaldehyde, limonene and citral. For details of the AM proportions see Patent AR2011010244162. Jasmine mimic (JM) was a commercial extract obtained from Firmenich S.A.I.C. y F, Argentina.If the synthetic mixture chosen were perceptually similar to the apple flower fragrance, experimental bees should have difficult to discriminate to the apple flower fragrance to test the compounds’ specificity. Thus, we performed differential PER conditioning between synthetic mixtures (AM and Jasmine mimic, JM) or between synthetic mixtures (AM or JM) and the apple natural fragrance. We followed a differential PER conditioning34 to assess to what extent the bees were able to discriminate the synthetic mimics from their natural flower scents. PER differential conditioning consisted of four pairs of trials, four rewarded trials (CS+) and four non-rewarded trials (CS−) that were presented in a pseudo-randomized manner. Conditionings were performed using the synthetic mixtures PM and AM and the natural floral scents, pear and apple, either as CS+ and CS−. We followed the same procedure that in 3.3 to capture the bees and to present the stimuli during trials.Feeding protocolWe used the offering of scented sucrose solution in the hive as a standardized procedure to establish long-term olfactory memory in honey bees23,24,24,26,63. Scented sucrose solution was obtained by diluting 50 µl of PM or AM per litre of sucrose solution (50% weight/weight, henceforth: w/w). For the ‘apple’ series, colonies were fed 1500 ml of sugar solution offered in an internal plastic feeder for 2 days, about 3 days before the apple trees began to bloom. For the ‘pear’ series, hives were fed 500 ml of sugar solution that we spread over the top of the central frames. Both feeding procedures have been found to be functional for establishing olfactory in-hive memories26. Depending on the pear varieties, the scented sucrose solution was offered when the pear trees were 10–40% in bloom.Colony activityThe effects of the AM-treatment on colony nest entrance activity were studied in 18 colonies located in an agricultural setting of apple and pear trees in Ingeniero Huergo, on an 8-ha plot, half of which was planted with apple trees (varieties: ‘Granny Smith’, ‘Gala’ and ‘Red Delicious’) and the other 4 ha with pear trees (varieties: ‘Packham’ and ‘D’anjou’). The effect of the PM-treatment on colony activity was studied in 14 colonies located in three adjoining pear plots (total surface: 8 ha) in Otto Krause (39° 06′ 22″ S 66° 59′ 46″ O, Supplementary Fig. S5), province of Río Negro, Argentina. The varieties of these plots corresponded to ‘Packham’ and ‘Williams’. Pollen collection (exp. 4.5.2) was also studied in colonies located in these plots.We focused on the nest entrance activity since once the first successful foragers return to the hive and display dances and/or unload the food collected, it promotes the activation or reactivation of inactive foragers and, in a minor proportion, those hive mates ready to initiate foraging tasks39,65,66,67,67. Then, we choose number of incoming bees as an indicator of colony foraging activity, since most of these bees are expected to return from foraging sites33. Thus, we compared the activity level at the nest entrance between 7 SS + PM-treated colonies and 7 SS-treated colonies. We also compared the nest entrance activity level between 5 colonies treated with SS + AM and 5 colonies fed with SS. This activity value was estimated by the number of incoming foragers at the entrance of the hive for one minute, every morning at the same time (10:30 a.m.) during the entire experiment (9 consecutive days). A first measurement was done one day before feeding the colonies (used as covariate) and 7 measurements afterwards.We measured the amount of pollen loads collected by two colonies: one fed with SS + PM and one fed with SS. Pollen loads were collected using conventional pollen traps (frontal-entrance trap), consisting of a wooden structure with a removable metal mesh inside. Pollen samples were collected for 3 days, two hours per day during the late morning, 3, 7 and 8 days after the offering of SS + PM or SS. Pollen pellets identified based on pollen colour as coming from the pear flower or from other species were separated and counted. In addition, we estimated the weight of pear pollen loads during a 5 days period, from 6 to 10 days after the offering of scented or unscented sucrose solution. To reduce measurement error, pollen loads were weighed in groups of 10.Crop yieldPear crop yield was studied in pear plots in General Roca (39° 02′ 00″ S; 67° 35′ 00″ O, Supplementary Fig. S4, Supplementary Table S3), province of Río Negro, Argentina. In an area of 15.2 ha (4 plots of 3.8 ha each), 45 beehives were equidistantly located in groups. We measured the number of fruits per tree set of 30 trees in the surrounding areas of the PM-treated colonies (2 groups of 8 hives) and control colonies (2 groups of 8 hives). A third group category contained 13 untreated colonies. The varieties of the pear trees were ‘D’Anjou’ and ‘Packham’.Apple crop yield estimated by means of number of fruits per plant was studied in General Roca (Supplementary Fig. S2, Supplementary Table S1), province of Río Negro, Argentina. We measured fruit set in the two plots that covered a surface of 3.8 ha and contained a total of 74 colonies distributed in groups (the control plot, 39 SS-treated-colonies treated with SS; and the treated plot, 35 SS + AM-treated-colonies treated with SS + AM). The varieties of the apple trees were ‘Red Delicious’ (clone 1), ‘Royal Gala’ and ‘Granny Smith’.A second studied on apple fruit yield by means of kg of fruits per hectare was performed in Coronel Belisle (39° 11′ 00″ S 65° 59′ 00″ O, Supplementary Fig. S3, Supplementary Table S2), province of Río Negro, Argentina. Four apple plots with ‘Granny Smith’, ‘Hi Early’ and ‘Red Delicious’, clone 1 varieties of 15.4 ha each were randomly assigned to different treatments (treated plot 1, 40 SS + AM-treated-hives treated with SS + AM; treated plot 2, 40 SS + AM-treated-hives treated with SS + AM; control plot 1, 40 SS-treated-hives treated with SS; control plot 2, 40 SS-treated-hives treated with SS).During the fruit harvest, the fruit yield was estimated in the surroundings (150 m around) of two groups of 8 colonies each. We fed one group SS + PM and the other unscented sucrose solution (SS). Yield was estimated as the number of fruits per trees in 30 randomly selected trees within each area, alternating the counts between the North and South faces of the plots. Following the same procedure, we also estimated the number of fruits per trees in the surroundings of two groups of 14 colonies each that pollinated apple crops. Again, we fed one group SS + AM and the other SS. Additionally, a total of 218 colonies in General Roca and 180 colonies in Coronel Belisle have been separated in the two experimental groups, in which yield had been provided by the producer and expressed in kg of fruits per ha. It is worth remarking that in some plots the distance between treated and control beehive groups was around 300 m, suggesting that might have been overlapping flying areas between treated and control hives. Additionally, the apple fields studied in the surrounding of Coronel Belisle, presented many trees without flowers. It was considered that the absence of flowers in numerous trees would bias the counts performed in those fields. Then, to quantify this situation, which might be associated with the masting phenomenon68, samples with the proportions of trees without flowers for every 20 trees in each plot was done. Trees that had between 80 and 100% of their surface devoid of flowers were considered “without flowers” trees, and “trees with available flowers” those that had more than 20% of their surface covered with flowers. An average of 30% of the trees within these plots were devoid of flowers. Thus, a correction factor was considered to evaluate the yield data provided by the grower per plot analysed (Supplementary Table S4).StatisticsAll statistical analyses were performed with R Core Team 201969. For Experiment 4.2 and 4.3, we analysed PER proportion by means of a binomial multiplicative generalized linear mixed model using the “glmer” function of the ‘lme4’ package70.For experiment 4.2a we considered the pear mimics (three-level factor corresponding to PM, PMI and PMII) and the event (two-level factor corresponding to 3rd trial and test) as fixed factors and each “bee” as a random factor.For experiment 4.2b we considered the tested odours (three-level factor corresponding to Apple, Pear and PM) as fixed factors.For experiment 4.3 we considered the tested odours (two-level factor corresponding to CS+ and CS−) as fixed factors. Post hoc contrasts were conducted on models to assess effects and significance between fixed factors using the “emmeans” function of the ‘emmeans’ package version 1.7.071 with a significance level of 0.05.For experiment 4.5.1 we analysed “rate of incoming bees” using a generalized linear mixed model. As Poisson model for incoming bees was overdispersed72, we used a negative binomial distribution using the ‘glmmTMB’ package (function ‘glmmTMB’73. We considered “treatment” [two-level factor corresponding to SS + AM (or SS + PM) and SS], “days” (7-level factor corresponding to the date after treatment), the rate of incoming bees before the offering of food (to control for pre-existing colony differences) as covariate (a quantitative fixed effects variable), and “colony” as a random factor.For experiment 4.6, we analysed fruits per trees by means of a negative binomial multiplicative generalized linear mixed model using the “log” function of the ‘ml’ package70. Post hoc contrasts were conducted on models to assess effects and significance between fixed factors using the “emmeans” function of the ‘emmeans’ package version 1.8.071 with a significance level of 0.05. For experiment 4.6b we analysed “yield” (as weight of fruits per unit area) using a general linear mixed model. We checked homoscedasticity and normality assumptions (Levene and Shapiro–Wilk tests, respectively). We considered “treatment” (two-level factor corresponding to SS + AM and SS) and “apple varieties” (3-level factor corresponding to Hi Early, Granny Smith and Chañar 28) as fixed factors and “location” (2-level factor corresponding to General Roca and Coronel Belisle) as random factors. More