Philippa Kaur

Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 
Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis. (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in press).Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).Wood, T. E. et al. in Ecosystem Consequences of Soil Warming: Microbes, Vegetation, Fauna and Soil Biogeochemistry (ed. Mohan, J.) Ch. 14 (Academic Press, 2019).Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).CAS 
PubMed 
Article 

Google Scholar 
van Gestel, N. et al. Predicting soil carbon loss with warming. Nature 554, E4–E5 (2018).PubMed 
Article 
CAS 

Google Scholar 
Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–104 (2017).CAS 
PubMed 
Article 

Google Scholar 
Romero-Olivares, A. L., Allison, S. D. & Treseder, K. K. Soil microbes and their response to experimental warming over time: a meta-analysis of field studies. Soil Biol. Biochem. 107, 32–40 (2017).CAS 
Article 

Google Scholar 
Anderson-Teixeira, K. J., Wang, M. M. H., McGarvey, J. C. & LeBauer, D. S. Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db). Glob. Change Biol. 22, 1690–1709 (2016).Article 

Google Scholar 
Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584, 234–237 (2020).CAS 
PubMed 
Article 

Google Scholar 
Kimball, B. A. et al. Infrared heater system for warming tropical forest understory plants and soils. Ecol. Evol. 8, 1932–1944 (2018).DeAngelis, K. M. et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00104 (2015)Bååth, E. Temperature sensitivity of soil microbial activity modeled by the square root equation as a unifying model to differentiate between direct temperature effects and microbial community adaptation. Glob. Change Biol. 24, 2850–2861 (2018).Article 

Google Scholar 
Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).CAS 
Article 

Google Scholar 
Ratkowsky, D. A., Olley, J., Mcmeekin, T. A. & Ball, A. Relationship between temperature and growth-rate of bacterial cultures. J. Bacteriol. 149, 1–5 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rinnan, R., Rousk, J., Yergeau, E., Kowalchuk, G. A. & Bååth, E. Temperature adaptation of soil bacterial communities along an Antarctic climate gradient: predicting responses to climate warming. Glob. Change Biol. 15, 2615–2625 (2009).Article 

Google Scholar 
Nottingham, A. T., Bååth, E., Reischke, S., Salinas, N. & Meir, P. Adaptation of soil microbial growth to temperature: using a tropical elevation gradient to predict future changes. Glob. Change Biol. https://doi.org/10.1111/gcb.14502 (2019).Li, J. Q., Bååth, E., Pei, J. M., Fang, C. M. & Nie, M. Temperature adaptation of soil microbial respiration in alpine, boreal and tropical soils: an application of the square root (Ratkowsky) model. Glob. Change Biol. 27, 1281–1292 (2021).CAS 
Article 

Google Scholar 
Rousk, J., Frey, S. D. & Bååth, E. Temperature adaptation of bacterial communities in experimentally warmed forest soils. Glob. Change Biol. 18, 3252–3258 (2012).Article 

Google Scholar 
Nottingham, A. T. et al. Annual to decadal temperature adaptation of the soil bacterial community after translocation across an elevation gradient in the Andes. Soil Biol. Biochem. 158, 108217 (2021).CAS 
Article 

Google Scholar 
Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).PubMed 
Article 

Google Scholar 
Donhauser, J., Niklaus, P. A., Rousk, J., Larose, C. & Frey, B. Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils. Soil Biol. Biochem. 148, 107873 (2020).CAS 
Article 

Google Scholar 
Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).Pold, G., Melillo, J. M. & DeAngelis, K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00480 (2015).Zhou, J. Z. et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat. Commun. 7, 12083 (2016).Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).Wu, L. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat. Microbiol. 7, 1054–1062 (2022).Oliverio, A. M., Bradford, M. A. & Fierer, N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob. Change Biol. 23, 2117–2129 (2017).Article 

Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).CAS 
PubMed 
Article 

Google Scholar 
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Env. Resour. 43, 193–218 (2018).Article 

Google Scholar 
Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. https://doi.org/10.3389/Fmicb.2013.00333 (2013).Pietikäinen, J., Pettersson, M. & Bååth, E. Comparison of temperature effects on soil respiration and bacterial and fungal growth rates. FEMS Microbiol. Ecol. 52, 49–58 (2005).PubMed 
Article 
CAS 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
Article 

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

Google Scholar 
Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).PubMed 
Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).PubMed 
Article 

Google Scholar 
LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bagchi, R. et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature 506, 85–88 (2014).CAS 
PubMed 
Article 

Google Scholar 
Lapebie, P., Lombard, V., Drula, E., Terrapon, N. & Henrissat, B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat. Commun. https://doi.org/10.1038/s41467-019-10068-5 (2019).Makhalanyane, T. P. et al. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39, 203–221 (2015).CAS 
PubMed 
Article 

Google Scholar 
Aydogan, E. L., Moser, G., Muller, C., Kampfer, P. & Glaeser, S. P. Long-term warming shifts the composition of bacterial communities in the phyllosphere of Galium album in a permanent grassland field-experiment. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.00144 (2018).Hu, D. Y., Zang, Y., Mao, Y. J. & Gao, B. L. Identification of molecular markers that are specific to the class thermoleophilia. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01185 (2019).Mohan, J. E. et al. Mycorrhizal fungi mediation of terrestrial ecosystem responses to global change: mini-review. Fungal Ecol. 10, 3–19 (2014).Article 

Google Scholar 
Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Agren, G. I. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79–91 (2012).CAS 
PubMed 
Article 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
Reed, S. C. et al. Soil biogeochemical responses of a tropical forest to warming and hurricane disturbance. Adv. Ecol. Res. 62, 225–252 (2020).Article 

Google Scholar 
Nottingham, A. T., Turner, B. L., Stott, A. W. & Tanner, E. V. J. Nitrogen and phosphorus constrain labile and stable carbon turnover in lowland tropical forest soils. Soil Biol. Biochem. 80, 26–33 (2015).CAS 
Article 

Google Scholar 
Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).Kemmitt, S. J. et al. Mineralization of native soil organic matter is not regulated by the size, activity or composition of the soil microbial biomass—a new perspective. Soil Biol. Biochem. 40, 61–73 (2008).CAS 
Article 

Google Scholar 
Nannipieri, P., Trasar-Cepeda, C. & Dick, R. P. Soil enzyme activity: a brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fert. Soils 54, 11–19 (2018).CAS 
Article 

Google Scholar 
Wallenstein, M., Allison, S., Ernakovich, J., Steinweg, J. M. & Sinsabaugh, R. in Soil Enzymology. Soil Biology Vol. 22 (eds Shukla, G. & Varma, A.) Ch. 13 (Springer, 2011).Zhou, X. Y., Chen, L., Xu, J. M. & Brookes, P. C. Soil biochemical properties and bacteria community in a repeatedly fumigated-incubated soil. Biol. Fert. Soils 56, 619–631 (2020).CAS 
Article 

Google Scholar 
Sanchez-Julia, M. & Turner, B. L. Abiotic contribution to phenol oxidase activity across a manganese gradient in tropical forest soils. Biogeochemistry https://doi.org/10.1007/s10533-021-00764-0 (2021).Razavi, B. S., Liu, S. B. & Kuzyakov, Y. Hot experience for cold-adapted microorganisms: temperature sensitivity of soil enzymes. Soil Biol. Biochem. 105, 236–243 (2017).CAS 
Article 

Google Scholar 
Pinney, M. M. et al. Parallel molecular mechanisms for enzyme temperature adaptation. Science 371, eaay2784 (2021).CAS 
PubMed 
Article 

Google Scholar 
Fanin, N. et al. Soil enzymes in response to climate warming: mechanisms and feedbacks. Funct. Ecol. https://doi.org/10.1111/1365-2435.14027 (2022).Hall, S. J. & Silver, W. L. Iron oxidation stimulates organic matter decomposition in humid tropical forest soils. Glob. Change Biol. 19, 2804–2813 (2013).Article 

Google Scholar 
Freeman, C., Ostle, N. & Kang, H. An enzymic ‘latch’ on a global carbon store. Nature 409, 149 (2001).CAS 
PubMed 
Article 

Google Scholar 
Sarmiento, C. et al. Soilborne fungi have host affinity and host-specific effects on seed germination and survival in a lowland tropical forest. Proc. Natl Acad. Sci. USA 114, 11458–11463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Condit, R., Perez, R., Lao, S., Aguilar, S. & Hubbell, S. P. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. For. Ecosyst. https://doi.org/10.1186/s40663-017-0103-1 (2017).Woodring, W. P. Geology of Barro Colorado Island. Smithson. Misc. Collect. 135, 1–39 (1958).
Google Scholar 
Sanchez, P. A. & Logan, T. J. Myths and science about the chemistry and fertility of soils in the tropics. SSSA Spec. Publ. 29, 35–46 (1992).CAS 

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

Google Scholar 
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).CAS 
Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
Jenkinson, D. S., Brookes, P. C. & Powlson, D. S. Measuring soil microbial biomass. Soil Biol. Biochem. 36, 5–7 (2004).CAS 
Article 

Google Scholar 
Kouno, K., Tuchiya, Y. & Ando, T. Measurement of soil microbial biomass phosphorus by an anion-exchange membrane method. Soil Biol. Biochem. 27, 1353–1357 (1995).CAS 
Article 

Google Scholar 
Tabatabai, M. A. in Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties (ed. Page, A.L.) 778–833 (SSSA, 1994).Marx, M. C., Wood, M. & Jarvis, S. C. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol. Biochem. 33, 1633–1640 (2001).CAS 
Article 

Google Scholar 
Price, N. & Stevens, L. Fundamentals of Enzymology: Cell and Molecular Biology of Catalytic Proteins (Oxford Univ. Press, 1999).Hagerty, S. B., Allison, S. D. & Schimel, J. P. Evaluating soil microbial carbon use efficiency explicitly as a function of cellular processes: implications for measurements and models. Biogeochemistry 140, 269–283 (2018).CAS 
Article 

Google Scholar 
Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3, 395–398 (2013).CAS 
Article 

Google Scholar 
Spohn, M. et al. Soil microbial carbon use efficiency and biomass turnover in a long-term fertilization experiment in a temperate grassland. Soil Biol. Biochem. 97, 168–175 (2016).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. et al. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).Article 

Google Scholar 
Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2019).CAS 
Article 

Google Scholar 
Bååth, E., Pettersson, M. & Söderberg, K. H. Adaptation of a rapid and economical microcentrifugation method to measure thymidine and leucine incorporation by soil bacteria. Soil Biol. Biochem. 33, 1571–1574 (2001).Article 

Google Scholar 
Bárcenas-Moreno, G., Gomez-Brandon, M., Rousk, J. & Bååth, E. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob. Change Biol. 15, 2950–2957 (2009).Article 

Google Scholar 
Smirnova, E., Huzurbazar, S. & Jafari, F. PERFect: PERmutation Filtering test for microbiome data. Biostatistics 20, 615–631 (2019).PubMed 
Article 

Google Scholar 
Alberdi, A. & Gilbert, M. T. P. hilldiv: an R package for the integral analysis of diversity based on Hill numbers. Preprint at bioRxiv https://doi.org/10.1101/545665 (2019).Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 
Article 

Google Scholar 
Oksanen, J. et al. vegan: Community ecology package, R Package version 2 https://cran.r-project.org/web/packages/vegan/ (2018).Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. https://doi.org/10.1186/gb-2011-12-6-r60 (2011).Roesch, L. F. W. et al. PIME: a package for discovery of novel differences among microbial communities. Mol. Ecol. Resour. 20, 415–428 (2020).CAS 
PubMed 
Article 

Google Scholar 
Roberts, D.W. labdsv: Ordination and multivariate analysis for ecology. R package version 2.0-1 https://cran.r-project.org/web/packages/labdsv/ (2019).Cao, Y. et al. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics 38, 4027–4029 (2022).Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. Peerj 3, e1319 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Peterson, R. A. & Cavanaugh, J. E. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J. Appl. Stat. 47, 2312–2327 (2020).PubMed 
Article 

Google Scholar  More

Ray DK, Mueller ND, West PC, Foley JA. Yield trends are insufficient to double global crop production by 2050. PLoS ONE. 2013;8:1–8.
Google Scholar 
United Nations Department of Economic and Social Affairs. World population prospects: the 2017 revision. 2017. https://www.un.org/development/desa/publications/world-population-prospects-the-2017-revision.html.Pe’er G, Dicks LV, Visconti P, Arlettaz R, Báldi A, Benton TG, et al. EU agricultural reform fails on biodiversity. Science. 2014;344:1090–2.PubMed 

Google Scholar 
Jack CN, Petipas RH, Cheeke TE, Rowland JL, Friesen ML. Microbial inoculants: silver bullet or microbial Jurassic Park? Trends Microbiol. 2020;29:299–308.PubMed 

Google Scholar 
Saad M, Eida A, Hirt H. Tailoring plant-associated microbial inoculants in agriculture: a roadmap for successful application. J Exp Bot. 2020;71:3878–901.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, le Roux X, Salles JF. The legacy of microbial inoculants in agroecosystems and potential for tackling climate change challenges. iScience. 2022;25:103821.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bounaffaa M, Florio A, le Roux X, Jayet PA. Economic and environmental analysis of maize inoculation by plant growth promoting rhizobacteria in the French Rhône-Alpes region. Ecol Econ. 2018;146:334–46.
Google Scholar 
Bashan Y, de-Bashan LE, Prabhu SR, Hernandez JP. Advances in plant growth-promoting bacterial inoculant technology: formulations and practical perspectives (1998-2013). Plant Soil. 2014;378:1–33.CAS 

Google Scholar 
Mallon C, van Elsas J, Salles J. Microbial invasions: the process, patterns, and mechanisms. Trends Microbiol. 2015;23:719–29.CAS 
PubMed 

Google Scholar 
Mawarda PC, le Roux X, van Elsas JD, Salles JF. Deliberate introduction of invisible invaders: a critical appraisal of the impact of microbial inoculants on soil microbial communities. Soil Biol Biochem.2020;148:1–13.
Google Scholar 
Mallon C, Poly F, le Roux X, Marring I, van Elsas J, Salles J. Resource pulses can alleviate the biodiversity-invasion relationship in soil microbial communities. Ecology. 2015;96:915–26.PubMed 

Google Scholar 
Xing J, Jia X, Wang H, Ma B, Salles JF, Xu J. The legacy of bacterial invasions on soil native communities. Environ Microbiol. 2020;23:1–13.
Google Scholar 
Eisenhauer N, Schulz W, Scheu S, Jousset A. Niche dimensionality links biodiversity and invasibility of microbial communities. Funct Ecol. 2013;27:282–8.
Google Scholar 
Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, et al. Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev. 2018;43:293–323.
Google Scholar 
Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.CAS 
PubMed 

Google Scholar 
Sherr BF, Sherr EB, Berman T. Grazing, growth, and ammonium excretion rates of a heterotrophic microflagellate fed with four species of bacteria. Appl Environ Microbiol. 1983;45:1196–201.CAS 
PubMed 
PubMed Central 

Google Scholar 
Koller R, Rodriguez A, Robin C, Scheu S, Bonkowski M. Protozoa enhance foraging efficiency of arbuscular mycorrhizal fungi for mineral nitrogen from organic matter in soil to the benefit of host plants. New Phytol. 2013;199:203–11.CAS 
PubMed 

Google Scholar 
Geisen S, Koller R, Hünninghaus M, Dumack K, Urich T, Bonkowski M. The soil food web revisited: diverse and widespread mycophagous soil protists. Soil Biol Biochem. 2016;94:10–18.CAS 

Google Scholar 
Long JJ, Jahn CE, Sánchez-Hidalgo A, Wheat W, Jackson M, Gonzalez-Juarrero M, et al. Interactions of free-living amoebae with rice bacterial pathogens Xanthomonas oryzae pathovars oryzae and oryzicola. PLoS ONE. 2018;13:e0202941.PubMed 
PubMed Central 

Google Scholar 
Iavicoli A, Boutet E, Buchala A, Métraux JP. Induced systemic resistance in Arabidopsis thaliana in response to root inoculation with Pseudomonas fluorescens CHA0. Mol Plant Microbe Interact. 2003;16:851–8.CAS 
PubMed 

Google Scholar 
Jousset A, Rochat L, Scheu S, Bonkowski M, Keel C. Predator-prey chemical warfare determines the expression of biocontrol genes by rhizosphere-associated pseudomonas fluorescens. Appl Environ Microbiol. 2010;76:5263–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Berney C, Romac S, Mahé F, Santini S, Siano R, Bass D. Vampires in the oceans: predatory cercozoan amoebae in marine habitats. ISME J. 2013;7:2387–99.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jousset A, Scheu S, Bonkowski M. Secondary metabolite production facilitates establishment of rhizobacteria by reducing both protozoan predation and the competitive effects of indigenous bacteria. Funct Ecol. 2008;22:714–9.
Google Scholar 
Jousset A, Lara E, Wall LG, Valverde C. Secondary metabolites help biocontrol strain Pseudomonas fluorescens CHA0 to escape protozoan grazing. Appl Environ Microbiol. 2006;72:7083–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mallon CA, le Roux X, van Doorn GS, Dini-Andreote F, Poly F, Salles JF. The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader’s niche. ISME J. 2018;12:728–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mawarda PC, Lakke SL, Dirk van Elsas J, Salles JF. Temporal dynamics of the soil bacterial community following Bacillus invasion. iScience. 2022;25:1–17.
Google Scholar 
Yi Y, de Jong A, Spoelder J, Theo J, van Elsas JD, Kuipers OP. Draft genome sequence of Bacillus mycoides M2E15, a strain isolated from the endosphere of potato. Genome Announc. 2016;4:e00031.PubMed 
PubMed Central 

Google Scholar 
Loznik B, Oosterkamp PJ. Fertilizer comprising protozoa and bacteria. World Intelectual Property Organization; 2017. https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2017105238.Guo S, Xiong W, Hang X, Gao Z, Jiao Z, Liu H, et al. Protists as main indicators and determinants of plant performance. Microbiome. 2021;9:1–11.
Google Scholar 
Bargabus RL, Zidack NK, Sherwood JE, Jacobsen BJ. Characterisation of systemic resistance in sugar beet elicited by a non-pathogenic, phyllosphere-colonizing Bacillus mycoides, biological control agent. Physiol Mol Plant Pathol. 2002;61:289–98.CAS 

Google Scholar 
Neher OT, Johnston MR, Zidack NK, Jacobsen BJ. Evaluation of Bacillus mycoides isolate BmJ and B. mojavensis isolate 203-7 for the control of anthracnose of cucurbits caused by Glomerella cingulata var. orbiculare. Biol Control. 2009;48:140–6.
Google Scholar 
Gao Z. Soil protists: from traits to ecological functions. University of Utrecht; 2020. https://dspace.library.uu.nl/handle/1874/400054.Amacker N, Gao Z, Hu J, Jousset ALC, Kowalchuk GA, Geisen S. Protist feeding patterns and growth rate are related to their predatory impacts on soil bacterial communities. FEMS Microbiol Ecol. 2022;98:1–11.
Google Scholar 
Wright DA, Killham K, Glover LA, Prosser JI. Role of pore size location in determining bacterial activity during predation by protozoa in soil. Appl Environ Microbiol. 1995;61:3537–43.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright D, Killham K, Glover L, Biota JP-SS. The effect of location in soil on protozoal grazing of a genetically modified bacterial inoculum. In: Brussaard L, Kooistra MJ, editors. Soil structure/soil biota interrelationships. Amsterdam: Elsevier; 1993.p.633–40.
Google Scholar 
Thewes S, Soldati T, Eichinger L. Editorial: amoebae as host models to study the interaction with pathogens. Front Cell Infect Microbiol. 2019;9:47.PubMed 
PubMed Central 

Google Scholar 
Kuppardt A, Fester T, Härtig C, Chatzinotas A. Rhizosphere protists change metabolite profiles in Zea mays. Front Microbiol. 2018;9:857.PubMed 
PubMed Central 

Google Scholar 
Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A, Becker A, et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotechnol. 2016;34:942–9.CAS 
PubMed 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 

Google Scholar 
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ritz K. The plate debate: cultivable communities have no utility in contemporary environmental microbial ecology. FEMS Microbiol Ecol. 2007;60:358–62.CAS 
PubMed 

Google Scholar 
Amacker N, Gao Z, Agaras BC, Latz E, Kowalchuk GA, Valverde CF, et al. Biocontrol traits correlate with resistance to predation by protists in soil pseudomonads. Front Microbiol. 2020;11:3164.
Google Scholar 
Glücksman E, Bell T, Griffiths RI, Bass D. Closely related protist strains have different grazing impacts on natural bacterial communities. Environ Microbiol. 2010;12:3105–13.PubMed 

Google Scholar 
Saleem M, Fetzer I, Dormann CF, Harms H, Chatzinotas A. Predator richness increases the effect of prey diversity on prey yield. Nat Commun. 2012;3:1–7.
Google Scholar 
Hünninghaus M, Koller R, Kramer S, Marhan S, Kandeler E, Bonkowski M. Changes in bacterial community composition and soil respiration indicate rapid successions of protist grazers during mineralization of maize crop residues. Pedobiologia. 2017;62:1–8.
Google Scholar 
van Elsas J, Chiurazzi M, Mallon C, Elhottova D, Krištůfek V, Salles J. Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci USA 2012;109:1159–64.PubMed 
PubMed Central 

Google Scholar 
Horňák K, Corno G. Every coin has a back side: invasion by limnohabitans planktonicus promotes the maintenance of species diversity in bacterial communities. PLoS ONE. 2012;7:e51576.PubMed 
PubMed Central 

Google Scholar 
Gómez P, Paterson S, de Meester L, Liu X, Lenzi L, Sharma MD, et al. Local adaptation of a bacterium is as important as its presence in structuring a natural microbial community. Nat Commun. 2016;7:1–8.
Google Scholar 
Heilbronner S, Krismer B, Brötz-Oesterhelt H, Peschel A. The microbiome-shaping roles of bacteriocins. Nat Rev Microbiol. 2021;19:726–39.CAS 
PubMed 

Google Scholar 
Xiong W, Li R, Guo S, Karlsson I, Jiao Z, Xun W, et al. Microbial amendments alter protist communities within the soil microbiome. Soil Biol Biochem. 2019;135:379–82.CAS 

Google Scholar 
Schneider FD, Scheu S, Brose U. Body mass constraints on feeding rates determine the consequences of predator loss. Ecol Lett. 2012;15:436–43.PubMed 

Google Scholar 
Brose U, Archambault P, Barnes AD, Bersier L-F, Boy T, Canning-Clode J, et al. Predator traits determine food-web architecture across ecosystems. Nat Ecol Evol. 2019;3:919–27.PubMed 

Google Scholar 
van Elsas JD, Trevors JT, Jansson JK, Nannipieri P, editors. Modern soil microbiology. 3rd ed. Boca Raton: CRC Press; 2019.Berga M, Székely AJ, Langenheder S. Effects of disturbance intensity and frequency on bacterial community composition and function. PLoS ONE. 2012;7:e365969.
Google Scholar 
Wang Z, Chen Z, Kowalchuk GA, Xu Z, Fu X, Kuramae EE. Succession of the resident soil microbial community in response to periodic inoculations. Appl Environ Microbiol. 2021;87:e00046.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Land use changes has led to the disappearance of trees from many dryland landscapes in recent centuries, like in western North American and northern China, often accompanied by desertification. Reforestation has the potential to restore these ecosystems and help keep more carbon in soils, especially when natural regeneration is being outpaced by human pressures.
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Smol, J. P. & Stoermer, E. F. The Diatoms: Application for the Environmental and Earth Sciences (Cambridge University Press, 2010).Charles, D. F. Relationships between surface sediment diatom assemblages and lake water characteristics in Adirondack lakes. Ecology 66, 994–111 (1985).Article 

Google Scholar 
Whitehead, D. R., Charles, D. F., Jackson, S. T., Reed S. E. & Sheehan, M. C. In Diatoms and Lake Acidity (eds J. P. Smol et al.) 251–274 (W. Junk, 1986).Whitehead, D. R. et al. The developmental history of Adirondack (N.Y.) lakes. J. Paleolimnol. 2, 185–206 (1989).ADS 
Article 

Google Scholar 
Whitehead, D. R., Charles, D. F. & Goldstein, R. A. The PIRLA project (Paleoecological Investigation of Recent Lake Acidification): an introduction to the synthesis of the project. J. Paleolimnol. 3, 187–194 (1990).ADS 
Article 

Google Scholar 
Dixit, S. S. et al. Diatom assemblages from Adirondack lakes (New York, USA) and the development of inference models for retrospective environmental assessment. J. Paleolimnol. 8, 27–47 (1993).ADS 
Article 

Google Scholar 
Dixit, S. S. & Smol, J. P. Diatom evidence of past water quality changes in Adirondack seepage lakes (New York, USA). Diatom Res. 1, 113–129 (1995).Article 

Google Scholar 
Allen, A. P. et al. Concordance of taxonomic composition patterns across multiple lake assemblages: effects of scale, body size, and land use. Can. J. Fish. Aquat. 56, 2029–2040 (1999).Article 

Google Scholar 
Pither, J. & Aarssen, L. W. The evolutionary species pool hypothesis and patterns of freshwater diatom diversity along a pH gradient. J. Biogeogr. 32, 503–513 (2005).Article 

Google Scholar 
Winegardner, A. K., Legendre, P., Beisner, B. E. & Gregory-Eaves, I. Diatom diversity patterns over the past c. 150 years across the conterminous United States of America: Identifying mechanisms behind beta diversity. Global Ecol. Biogeogr. 26, 1303–1315 (2017).Article 

Google Scholar 
Dixit, S. S. & Smol, J. P. Diatoms as indicators in the Environmental Monitoring and Assessment Program-Surface Waters (EMAP-SW). Environ. Monit. Assess. 31, 275–37 (1994).PubMed 

Google Scholar 
Dixit, S. S. et al. Assessing water quality changes in the lakes of the northeastern United States using sediment diatoms. Can. J. Fish. Aquatic Sci. 56, 131–152 (1999).Article 

Google Scholar 
Stevenson, R. J., Zalack, J. & Wolin, J. A multimetric index of lake diatom condition using surface sediment assemblages. Freshw. Sci. 32, 1005–1025 (2013).Article 

Google Scholar 
Liu, B. & Stevenson, R. J. Improving assessment accuracy for lake biological condition by classifying lakes with diatom typology, varying metrics and modeling multimetric indices. Sci. Total Environ. 609, 263–271 (2017).ADS 
Article 

Google Scholar 
Herlihy, A. T. et al. Using multiple approaches to develop nutrient criteria for lakes in the conterminous USA. Freshw. Sci. 32, 367–384 (2013).Article 

Google Scholar 
Bachmann, R. W., Hoyer, M. V. & Canfield, D. E. The extent that natural lakes in the United States of America have been changed by cultural eutrophication. Limnol. Oceanogr. 58, 945–950 (2013).ADS 
Article 

Google Scholar 
McDonald, C. P. et al. Comment on Bachmann et al. (2013): A nonrepresentative sample cannot describe the extent of cultural eutrophication of natural lakes in the United States. Limnol. Oceanogr. 59, 2226–2230 (2014).ADS 
Article 

Google Scholar 
Smith, V. H. et al. Comment: Cultural eutrophication of natural lakes in the United States is real and widespread. Limnol. Oceanogr. 59, 2217–2225 (2014).ADS 
Article 

Google Scholar 
Bachmann, R. W., Hoyer, M. V. & Canfield, D. E. Response to comments: Quantification of the extent of cultural eutrophication of natural lakes in the United States. Limnol. Oceanogr. 59, 2231–2239 (2014).ADS 
Article 

Google Scholar 
Bachmann, R. W., Hoyer, M. V., Croteau, A. C. & Canfield, D. E. Factors related to Secchi depths and their stability over time as determined from a probability sample of US lakes. Environ. Monit. Assess. 189, 206 (2017).Article 

Google Scholar 
Stager, J. C., Leavitt, P. R. & Dixit, S. S. Assessing impacts of past human activity on the water quality of Upper Saranac lake, New York. Lake Reserv. Manag. 13, 175–184 (1997).Article 

Google Scholar 
Dixit, S. S., Dixit, A. S., Smol, J. P., Hughes, R. M. & Paulsen, S. G. Water Quality Changes from Human Activities in Three Northeastern USA Lakes. Lake Reserv. Manag. 16, 35–321 (2000).Article 

Google Scholar 
Köster, D. et al. Paleolimnological assessment of human-induced impacts on Walden Pond (Massachusetts, USA) using diatoms and stable isotopes. Aquat. Ecosyst. Health 8, 117–131 (2005).Article 

Google Scholar 
Enache, M. D., Charles, D. F., Belton, T. J. & Callinan, C. W. Total phosphorus changes in New York and New Jersey lakes (USA) inferred from sediment cores. Lake Reserv. Manag. 28, 293–310 (2012).Article 

Google Scholar 
Rowell, H. C. et al. Quantitative paleolimnological inference models applied to a high-resolution biostratigraphic study of lake degradation and recovery, Onondaga Lake, New York (USA). J Paleolimnol. 55, 241–258 (2016).Article 

Google Scholar 
Tyree, M. A., Bishop, I. W., Hawkins, C. P., Mitchell, R. & Spaulding, S. A. Reduction of taxonomic bias in diatom species data. Limnol. Oceanogr. Methods 18, 271–279 (2020).Article 

Google Scholar 
Stribling, J. B., Pavlik, K. L., Holdsworth, S. M. & Leppo, E. W. Data quality, performance, and uncertainty in taxonomic identification for biological assessments. J. North Am. Benthol. Soc. 27, 906–919 (2008).Article 

Google Scholar 
Thomson, S. A. et al. Towards a global list of accepted species II. Consequences of inadequate taxonomic list governance. Org. Divers. Evol. 21, 623–630 (2021).Article 

Google Scholar 
Spaulding, S. A. et al. Diatoms of North America https://diatoms.org/ (2020).Lee, S. S., Bishop, I. W., Spaulding, S. A., Mitchell, R. M. & Yuan, L. L. Taxonomic harmonization may reveal a stronger association between diatom assemblages and total phosphorus in large datasets. Ecol. Indic. 102, 166–174 (2019).Article 

Google Scholar 
Cumming, B. F. et al. How Much Acidification Has Occurred in Adirondack Region Lakes (New York, USA) since Preindustrial Times? Can. J. Fish. Aquat. 49, 128–141 (1992).Article 

Google Scholar 
Larsen, D. P., Stevens, D. L., Selle, A. R. & Paulsen, S. G. Environmental Monitoring and Assessment Program, EMAP-Surface Waters: A northeast lakes pilot. Lake Reserv. Manag. 7, 1–11 (1991).Article 

Google Scholar 
Hughes, R. M., Paulsen, S. G. & Stoddard, J. L. EMAP-surface waters: A multiassemblage, probability survey of ecological integrity in the USA. Hydrobiologia 422, 429–443 (2000).Article 

Google Scholar 
Larsen, D. P., Thornton, K. W., Urquhart, N. S. & Paulsen, S. G. The role of sample surveys for monitoring the condition of the nation’s lakes. Environ. Monit. Assess. 32, 101–34 (1994).Article 

Google Scholar 
U.S. Environmental Protection Agency. Environmental Monitoring & Assessment Program. Northeast Lakes 1991-94 Data Sets. https://archive.epa.gov/emap/archive-emap/web/html/nelakes.html (2016).U.S. Environmental Protection Agency. National Lakes Assessment: A Collaborative Survey of the Nation’s Lakes. Report No. EPA-841-R-09-001. (U.S. Environmental Protection Agency, 2009).U.S. Environmental Protection Agency. 2012 National Lakes Assessment. Field Operations Manual. Report No. EPA 841-B-11-003. (U.S. Environmental Protection Agency, 2011)Charles, D. F., Knowles, C. & Davis, R. S. Protocols for the Analysis of Algal Samples Collected as Part of the U.S. Geological Survey National Water-Quality Assessment Program. https://water.usgs.gov/nawqa/protocols/algprotocol/algprotocol.pdf Report (2002).Krammer, K. Diatoms of Europe V. 1. (Gantner Verlag, 2000)Lange-Bertalot, H. Diatoms of Europe V. 2. (Gantner Verlag, 2001)Krammer, K. Diatoms of Europe V. 3. (Gantner Verlag, 2002)Krammer, K. Diatoms of Europe V. 4. (Gantner Verlag, 2003)Siver, P. A. & Hamilton, P. B. Iconographia Diatomologica V. 22. (Gantner Verlag, 2011).Levkov, Z., Metzeltin, D. & Pavlov, A. Diatoms of Europe V. 7. (Gantner Verlag, 2013)Levkov, Z., Mitić-Kopanja, D. & Reichardt, E. Diatoms of Europe V. 8. (Koeltz Botanical Books, 2016).Lange-Bertalot, H., Hofmann, G., Werum, M. & Cantonati, M. Freshwater Benthic Diatoms of Central Europe (Koeltz Botanical Books, 2017).Guiry, M. D. & Guiry, G. M. AlgaeBase https://www.algaebase.org (2021).Kociolek, J. P. et al. DiatomBase http://www.diatombase.org (2021).De Cáceres, M. Package ‘indicspecies’ https://cran.r-project.org/web/packages/indicspecies/indicspecies.pdf (2020).Legendre, P. & Birks, H. J. B. In Tracking Environmental Change Using Lake Sediments. V. 5: Data Handling and Numerical Techniques (eds Birks H. J. B. et al.) 201–248 (Springer Dordrecht, 2012).Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).ADS 
Article 

Google Scholar 
Oksanen, J. et al. Package ‘vegan’ https://cran.r-project.org/web/packages/vegan/vegan.pdf (2020).Spaulding, S. A. Diatom Laboratory: Research Labs & Groups: INSTAAR: CU-Boulder https://instaar.colorado.edu/research/labs-groups/diatom-laboratory//research-detail (2021).Conservation Gateway. Northeast Lake and Pond Classification System. http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/freshwater/Pages/Northeast-Lakes.aspx (2021).Soranno, P. & Cheruvelil, K. LAGOS-NE-LIMNO v1.087.3: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925–2013. Environmental Data Initiative https://doi.org/10.6073/pasta/08c6f9311929f4874b01bcc64eb3b2d7 (2019).U.S. Geological Survey. National Hydrography Dataset (NHD). USGS Unnumbered Series. (U.S. Geological Survey, 2001).Potapova, M. G., Lee, S. S., Spaulding, S. A. & Schulte, N. O. A harmonized dataset of sediment diatoms from hundreds of lakes in the northeastern United States. U.S. EPA Office of Research and Development (ORD) https://doi.org/10.23719/1524246 (2022).U.S. Environmental Protection Agency. National Aquatic Resource Surveys. National Lakes Assessment 2007 (data and metadata files) https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys (2010).U.S. Environmental Protection Agency. National Aquatic Resource Surveys. National Lakes Assessment 2017 (data and metadata files). http://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys (2021). More

Snir, A. et al. The origin of cultivation and proto-weeds, long before Neolithic farming. PLoS ONE 10(7), e0131422. https://doi.org/10.1371/journal.pone.0131422 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Groman-Yaroslavski, I., Weiss, E. & Nadel, D. Composite sickles and cereal harvesting methods at 23,000-years-old Ohalo II Israel. PLoS ONE 11(11), e0167151. https://doi.org/10.1371/journal.pone.0167151 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Edwards, P. C. A 14000 year-old hunter-gatherer’s toolkit. Antiquity 81(314), 865–876. https://doi.org/10.1017/S0003598X0009596X (2007).Article 

Google Scholar 
Le Dosseur, G. Bone Objects in the Southern Levant from the Thirteenth to the Fourth Millennia. Bulletin du Centre de recherche français à Jérusalem 12, 111–125 (2003).
Google Scholar 
Garrard, A., & Yazbeck, C. The Natufian of Moghr el-Ahwal in the Qadisha valley, northern Lebanon. in Natufian Foragers in the Levant. International Monographs in Prehistory (eds. Bar-Yosef, O. & Valla, F. R.). 17–47. (Michigan, Ann Arbor, 2013).Belfer-Cohen, A. The Natufian in the Levant. Annu. Rev. Anthropol. 20, 167–186. https://doi.org/10.1146/annurev.an.20.100191.001123 (1991).Article 

Google Scholar 
Stordeur, D. Le Natoufien et son évolution à travers les artefacts en os in Natufian Foragers in the Levant. International Monographs in Prehistory (eds. Bar-Yosef, O. & Valla, F. R.). 457–482. (Michigan, Ann Arbor, 2013).Rosen, S. A. Lithics after the Stone Age: a handbook of stone tools from the Levant. (Rowman Altamira, 1997).Anderson, P. C. Prehistory of agriculture: new experimental and ethnographic approaches. (Cotsen Institute of Archaeology Press, 1999).Ibáñez, J. J., González Urquijo, J. E., & Rodríguez, A. The evolution of technology during the PPN in the Middle euphrates. A view from use wear analysis of lithic tools. in Systèmes techniques et communautés du Néolithique Préceramique au Proche Orient. Technical Systems and Near Eastern PPN Communities (eds. Astruc, L., Binder, D. & Briois, F.) 153–165 (Editions APDCA, 2007).Maeda, O., Lucas, L., Silva, F., Tanno, K. I. & Fuller, D. Q. Narrowing the harvest: Increasing sickle investment and the rise of domesticated cereal agriculture in the Fertile Crescent. Quatern. Sci. Rev. 145, 226–237. https://doi.org/10.1016/j.quascirev.2016.05.032 (2016).ADS 
Article 

Google Scholar 
Pichon, F. Exploitation of the cereals during the Pre-pottery Neolithic of Dja’de-el-Mughara: Preliminary results of the functional study of the glossy blades. Quatern. Int. 427, 138–151. https://doi.org/10.1016/j.quaint.2016.01.064 (2017).Article 

Google Scholar 
Borrell, F., & Molist, M. Projectile Points, Sickle Blades and Glossed Points. Tools and Hafting Systems at Tell Halula (Syria) during the 8th millennium cal. BC Paléorient, 33(2), 59–77 (2007). https://doi.org/10.2307/41496812.Douka, K., Efstratiou, N., Hald, M., Henriksen, P. & Karetsou, A. Dating Knossos and the arrival of the earliest Neolithic in the southern Aegean. Antiquity 91(356), 304–321. https://doi.org/10.15184/aqy.2017.29 (2017).Article 

Google Scholar 
Perlès, C. From the Near East to Greece: Let’s reverse the focus. Cultural elements that didn’t transfer. in How did farming reach Europe? (ed. Lichter, C.) 275–290 (Istanbul, Ege Yayınları, 2005).Gijn A.L. van & Wentink K. The role of flint in mediating identities: The microscopic evidence. in Mobilty, meaning & transformations of things, shifting contexts of material culture through time and space. (eds. Hahn, H.P. & Weiss, H.) 120–132 (Oxford, Oxbow Books, 2013).Guilaine, J. The neolithic transition: From the Eastern to the Western Mediterranean. in Times of Neolithic Transition along the Western Mediterranenn. (eds. O., García-Puchol & D. C., Salazar-García) 15–31 (New York, Springer, 2017). https://doi.org/10.1007/978-3-319-52939-4_2.Forenbaher, S. & Miracle, P. T. The spread of farming in the Eastern Adriatic. Antiquity 79(305), 514–528 (2005).Article 

Google Scholar 
Gabriele, M. et al. Long-distance mobility in the North-Western Mediterranean during the Neolithic transition using high resolution pottery sourcing. J. Archaeol. Sci. Rep. 28, 102050. https://doi.org/10.1016/j.jasrep.2019.102050 (2019).Article 

Google Scholar 
Manen, C., Perrin, T., Guilaine, J., Bouby, L., Bréhard, S., Briois, F., Durand, F., Marinval, P. & Vigne, J. D. The Neolithic transition in the western Mediterranean: A complex and non-linear diffusion process—the radiocarbon record revisited. Radiocarbon 61(2), 531–571 (2019). https://doi.org/10.1017/RDC.2018.98Ibáñez, J. J., Clemente Conte, I., Gassin, B., Gibaja, J. F., Gonzáles Urquijo, J. E., Márquez, B., Philibert, S., Rodríguez Rodríguez, A. Harvesting technology during the Neolithic in south-west Europe. in Prehistoric technology 40 years later: functional studies and the Russian legacy (eds. Longo L. & Skakun, N.) 183–95 (Oxford, Archaeopress, 2008).Gibaja, J. F., Ibáñez, J. J., González Urquijo, J. E. Neolithic Sickles in the Iberian Peninsula. in Exploring and Explaining Diversity in Agricultural Technology, EARTH 2 (eds. van Gijn, A., Whittaker, P. & Anderson, P.) 112–118 (Oxford, Oxbow Books, 2014).Mazzucco, N., Capuzzo, G., Petrinelli-Pannocchia, C., Ibáñez, J. J., Gibaja, J. F. Harvesting tools and the spread of the Neolithic into the Central-Western Mediterranean area. Quat. Int. 470(Part B), 511–528 (2018). https://doi.org/10.1016/j.quaint.2017.04.018.Mazzucco, N., Guilbeau, D., Kačar, S., Podrug, E., Forenbaher, S., Radić, D., Moore, A. T. M. The time is ripe for a change. The evolution of harvesting technologies in Central Dalmatia during the Neolithic period (6th millennium cal BC). J. Anthropol. Archaeol. 51, 88–103 (2018). https://doi.org/10.1016/j.jaa.2018.06.003Mazzucco, N. et al. Migration, adaptation, innovation: The spread of Neolithic harvesting technologies in the Mediterranean. PLoS ONE 15(4), e0232455. https://doi.org/10.1371/journal.pone.0232455 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fugazzola Delpino, M. A., D’Eugenio, G. & Pessina, A. “La Marmotta” (Anguillara Sabazia, RM): Scavi 1989—un abitato perilacustre di età Neolitica. Bull. Paletnol. Ital. 84, 181–315 (1993).
Google Scholar 
Fugazzola Delpino, M. A., Pessina, A. Le village néolithique submergé de La Marmotta (lac de Bracciano, Rome). in Le Néolithique du Nord-Ouest méditerranéen (ed. Vaquer, J.) 35–38 (Société préhistorique française, Paris, 1999)Fugazzola Delpino, M. A. La Marmotta. in Le ceramiche impresse nel Neolitico antico. Italia e Mediterraneo (eds. Fugazzola, M.A., Pessina, A. & Tiné, V) 373–395 (Istituto Poligrafico e Zecca dello Stato, Roma, 2002).Grantham, G. L. faucille et la faux. Études rurales 151–152, 103–131 (1999).Article 

Google Scholar 
Sigaut, F. Identification des techniques de récolte des graines alimentaires. J. Agric. Trad. Bot. Appl. 25(3), 145–161 (1978).
Google Scholar 
Anderson, P. C., Sigaut, F. Introduction: reasons for variability in harvesting techniques and tools. in Exploring and Explaining Diversity in Agricultural Technology, EARTH 2 (eds. van Gijn, A., Whittaker, P. & Anderson, P.) 85–93 (Oxford, Oxbow Books, 2014).Halstead, P. Two oxen ahead: Pre-mechanized farming in the Mediterranean (John Wiley & Sons, 2014).Book 

Google Scholar 
Fugazzola Delpino, M. A. & Mineo, M. La piroga neolitica di Bracciano (La Marmotta 1). Bull. Paletnol. Ital. 86, 197–266 (1995).
Google Scholar 
Fugazzola Delpino, M. A., Tinazzi, O. Dati di cronologia da un villaggio del Neolitico Antico. Le indagini dendrocronologiche condotte sui legni de La Marmotta (lago di Bracciano-Roma). in Miscellanea in ricordo di Francesco Nicosia, 1–10 (Studia Erudita, Fabrizio Serra Editore, 2010).Salavert, A. et al. Direct dating reveals the early history of opium poppy in western Europe. Sci. Rep. 10, 20263. https://doi.org/10.1038/s41598-020-76924-3 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ghiselli, L. et al. Nutritional characteristics of ancient Tuscan varieties of Triticum aestivum L. Ital. J. Agron. 11(4), 237–245 (2016).Article 

Google Scholar 
Pichon, F. Une moisson expérimentale de céréales, Séranon (août 2016), ArchéOrient – Le Blog, 14 octobre2016, (2016). https://archeorient.hypotheses.org/6667.Banks, W. E. & Kay, M. High-resolution casts for lithic use-wear analysis. Lithic Technol. 28(1), 27–34. https://doi.org/10.1080/01977261.2003.11721000 (2003).Article 

Google Scholar 
Ibáñez, J. J., Anderson, P. C., Gonzalez-Urquijo, J. & Gibaja, J. Cereal cultivation and domestication as shown by microtexture analysis of sickle gloss through confocal microscopy. J. Archaeol. Sci. 73, 62–81. https://doi.org/10.1016/j.jas.2016.07.011 (2016).Article 

Google Scholar 
Caruso Fermé, L. Modalidades de adquisición y uso del material leñoso entre grupos cazadores-recolectores patagónicos (Argentina). Métodos y técnicas de estudios del material leñoso arqueológico. PhD Dissertation (Universidad Autónoma de Barcelona, Barcelona, 2012).Caruso Fermé, L., Clemente, I., Civalero, M.T. A use-wear analysis of wood technology of patagonian hunter-gatherers. The case of Cerro Casa de Piedra 7, Argentina. J. Archaeol. Sci. 15, 315–321 (2015). https://doi.org/10.1016/j.jas.2015.03.015.Caruso Fermé, L., Aschero, C. Manufacturing and use of the wooden artifacts. A use-wear analysis of wood technology in hunter-gatherer groups (Cerro Casa de Piedra 7 site, Argentina). J. Archaeol. Sci. 31, 102291 (2020). https://doi.org/10.1016/j.quaint.2020.10.067.Schweingruber, F. H. Anatomy of European wood: An atlas for the identification of European trees, shrubs and dwarf shrubs (Paul Haupt, 1990).
Google Scholar 
Rageot, M. et al. Birch bark tar production: Experimental and biomolecular approaches to the study of a common and widely used prehistoric adhesive. J. Archaeol. Method Theory 26, 276–312. https://doi.org/10.1007/s10816-018-9372-4 (2019).Article 

Google Scholar 
Rageot, M. et al. New insights into Early Celtic consumption practices: Organic residue analyses of local and imported pottery from Vix-Mont Lassois. PLoS ONE 14(6), e0218001. https://doi.org/10.1371/journal.pone.0218001 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Arobba, D., Caramiello, R., Martino, G. P. Analisi paleobotaniche di resine dal relitto navale romano del Golfo Dianese. Rivista di Studi Liguri, LXIII-LXIV: 339–355 (1999).Marshall, D. M. Archaeological pollen: extraction from ancient resins. The American Association of Stratigraphic Palynologists. Prog. and Abstr., 38th Ann. Mtg., 34 (2005).Berglund, B. E., Ralska-Jasiewiczowa, M. Pollen analysis and pollen diagrams. in Handbook of Holocene Palaeoecology and Palaeohydrology. (eds. Berglund, B. E.) 455–484 (Chichester, Wiley, 1986).Traverse, A. Paleopalynology. Second Edition, 813 p. (Dordrecht, Springer, 2007).Punt W. (ed.) The Northwest European pollen flora (NEPF), vol. 2 (1980), vol. 3 (1981), vol. 4 (1984) vol. 5 (1988), vol. 6 (1991), vol. 7 (1996), vol. 8 (2003) (Elsevier, Wim Punt, Amsterdam, 1980–2003)Fægri, K. & Iversen, J. Textbook of pollen analysis (John Wiley and Sons, 1989).
Google Scholar 
Moore, P. D., Webb, J. A. & Collinson, M. E. Pollen analysis 2nd edn. (Blackwell, 1991).
Google Scholar 
Beug, H.-J. Leitfaden der Pollenbestimmung für Mitteleuropa und angrenzende Gebiete (Pfeil, 2004).
Google Scholar 
Reille, M. Pollen et spores d’Europe et d’Afrique du Nord. (Marseille, Laboratoire de Botanique Historique et Palynologie, 1992).Katz, O. et al. Rapid phytolith extraction for analysis of phytolith concentrations and assemblages during an excavation: An application at Tell es-Safi/Gath Israel. J. Archaeol. Sci. 37(7), 1557–1563. https://doi.org/10.1016/j.jas.2010.01.016 (2010).Article 

Google Scholar 
Brown, D. A. Prospects and limits of a phytolith key for grasses in the central United States. J. Archaeol. Sci. 11, 345–368. https://doi.org/10.1016/0305-4403(84)90016-5 (1984).Article 

Google Scholar 
Rosen, A. M. Preliminary identification of silica skeletons from Near Eastern archaeological sites: an anatomical approach. in Phytolith Systematics: Emerging Issues, Advances in Archaeological and Museum Science (eds. Rapp, G. Jr. & Mulholland, S. C.) 129–148 (New York, Plenum Press, 1992)Mulholland, S. C., Rapp Jr. G. A morphological classification of grass silica-bodies. in Phytolith Systematics: Emerging Issues, Advances in Archaeological and Museum Science (eds. Rapp, G. Jr. & Mulholland, S. C.) 65–89 (New York, Plenum Press, 1992)Piperno, D. R. Phytoliths: A comprehensive Guide for Archaeologists and Paleoecologists (Altamira Press, 2006).
Google Scholar 
Albert, R. M., & Weiner, S. Study of phytoliths in prehistoric ash layers from Kebara and Tabun caves using a quantitative approach. in Phytoliths: applications in earth sciences and human history, (eds. Meunier, J.D. & Colin, F.) 251–266 (Tokyo, Balkema Publisher, 2001)Albert, R. M. et al. Phytolith-rich layers from the Late Bronze and Iron Ages at Tel Dor (Israel): Mode of formation and archaeological significance. J. Archaeol. Sci. 35(1), 57–75. https://doi.org/10.1016/j.jas.2007.02.015 (2008).Article 

Google Scholar 
Albert, R. M., Ruíz, J. A. & Sans, A. PhytCore ODB: A new tool to improve efficiency in the management and exchange of information on phytoliths. J. Archaeol. Sci. 68, 98–105 (2016).Article 

Google Scholar 
Portillo, M., Kadowaki, S., Nishiaki, Y. & Albert, R. M. Early Neolithic household behavior at Tell Seker al-Aheimar (Upper Khabur, Syria): A comparison to ethnoarchaeological study of phytoliths and dung spherulites. J. Archaeol. Sci. 42, 107–118 (2014).Article 

Google Scholar 
Tsartsidou, G. et al. The phytolith archaeological record: strengths and weaknesses evaluated based on a quantitative modern reference collection from Greece. J. Archaeol. Sci. 34, 1262–1275. https://doi.org/10.1016/j.jas.2006.10.017 (2007).Article 

Google Scholar 
Neumann, K., Strömberg , A. E. C., Ball, T., Albert, R. M., Vrydaghs, L. Scott-Cummings, L. (International Committee for Phytolith Taxonomy ICPT). International Code for Phytolith Nomenclature (ICPN) 2.0. Annals of Botany, 124(2): 189–199 (2019).Anderson, P. C. Insight into plant harvesting and other activities at Hatoula, as revealed by microscopic functional analysis of selected chipped stone tools. Le site de Hatoula en Judée occidental. (eds. Lechevallier, M. & Ronen, A.) 277–293 (Paris, Association Paléorient, 1994)Fugazzola Delpino, M.A. La vita quotidiana del Neolitico. Il sito della Marmotta sul Lago di Bracciano. in Settemila anni fa il primo pane. Ambienti e culture delle società neolitiche (eds. Pessina, A. & Muscio G.) 185–192 (Udine, Museo Friulano di Storia Naturale, 1998–1999)Mineo, M. Monossili d’Europa: costruite anche per le rotte marine? in Ubi minor: le isole minori del Mediterraneo centrale: dal Neolitico ai primi contatti coloniali (eds. Guidi, A., Cazzella, A. & Nomi, F.). Scienze dell’Antichità 22, 453–475 (2016)Helwig, K., Monahan, V. & Poulin, J. The identification of hafting adhesive on a slotted antler point from a southwest Yukon ice patch. Am. Antiq. 73, 279–288. https://doi.org/10.1017/S000273160004227X (2008).Article 

Google Scholar 
Steigenberger, G. & Herm, C. Natural resins and balsams from an eighteenth-century pharmaceutical collection analysed by gas chromatography/mass spectrometry. Anal. Bioanal. Chem. 401, 1771–1784. https://doi.org/10.1007/s00216-011-5169-y (2011).CAS 
Article 
PubMed 

Google Scholar 
van den Berg, K. J., Boon, J. J., Pastorova, I. & Spetter, L. F. M. Mass spectrometric methodology for the analysis of highly oxidized diterpenoid acids in Old Master paintings. J. Mass Spectrom. 35, 512–533. https://doi.org/10.1002/(SICI)1096-9888(200004)35:4%3c512::AID-JMS963%3e3.0.CO;2-3 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Behre K. E. Anthropogenic Indicators in Pollen Diagrams, A.A. (Rotterdam, Balkema, 1986).Mercuri, A. M. et al. Anthropogenic Pollen Indicators (API) from archaeological sites as local evidence of human-induced environments in the Italian peninsula. Ann. Bot. 3, 143–153 (2013).
Google Scholar 
Andersen, S.-T., Identification of wild grass and cereal pollen. in Danmarks Geologiske Undersøgelse (ed. Aaby, B.) 69–92 (Geological Survey of Denmark, 1978).Bottema, S. Cereal-type pollen in the Near East as indicators of wild or domestic crops. in Préhistoire de l’agriculture: nouvelles approches expérimentales et ethnographiques (ed. Anderson P. C.) 95–106 (Paris, CRA, 1992). https://doi.org/10.1007/BF00217499.Lagerås, P. Long-term history of land-use and vegetation at Femtingagölen—a small lake in the Småland Uplands, southern Sweden. Veg. Hist. Archaeobot. 5, 215–228 (1996).Article 

Google Scholar 
Joly, C., Barillé, L., Barreau, M., Mancheron, A. & Visset, L. Grain and annulus diameter as criteria for distinguishing pollen grains of cereals from wild grasses. Rev. Palaeobot. Palynol. 146, 221–233. https://doi.org/10.1016/j.revpalbo.2007.04.003 (2007).Article 

Google Scholar 
Punt, W. Umbelliferae. Rev. Palaeobot. Palynol. 42, 155–364 (1984).Article 

Google Scholar 
Ellis, M. B. & Ellis, J. P. Microfungi of Land Plants. An Identification Handbook (London, Croom Helm, 1985) (Figure 1270).Ellis, M. B. & Ellis, J. P. Microfungi of Land Plants. An Identification Handbook (London, Croom Helm, 1985) (Figures 174; 176).Rottoli, M., Pessina, A. Neolithic agriculture in Italy: an update of archaeobotanical data with particular emphasis on northern settlements. in The Origins and Spread of Domestic Plants in Southwest Asia and Europe. (eds. Colledge, S. & Conolly, J.) 141–154 (Routledge, New York, 2016)Gurova, M. Prehistoric sickles in the collection of the National Museum of Archaeology in Sofia. in Southeast Europe and Anatolia in Prehistory: Essays in Honor of Vassil Nikolov on his 65th Anniversary (eds. Bacvarov, K. & Gleser, E.) 159–165 (Bonn, Verlag Dr. Rudolf Habelt GmbH, 2016)Sidéra, I. Nouveaux éléments d’origine proche-orientale dans le Néolithique ancien balkanique. in Analyse de l’industrie osseuse. in Préhistoire d’Anatolie. Genèse de deux mondes (ed. Otte, M.), 215–239 (Liège, ERAUL, 1997)Mellaart, J. Excavations at Hacılar: Fourth preliminary report, 1960. Anat. Stud. Anat. Stud. 11, 39–75 (1961).Article 

Google Scholar 
Nag, P. K., Goswami, A., Ashtekar, S. P. & Pradhan, C. K. Ergonomics in sickle operation. Appl. Ergon. 19(3), 233–239 (1988).CAS 
Article 

Google Scholar 
Astruc, L., Tkaya, M. B. & Torchy, L. D. l’efficacité des faucilles néolithiques au Proche-Orient: approche expérimentale. Bulletin de la Société préhistorique française 109(4), 671–687 (2012).Article 

Google Scholar 
Sigaut, F. Les techniques de récolte des grains : identification, localisation, problèmes d’interprétation. in Rites et rythmes agraires (ed. Cauvin, M.-C.) 31–43 (Lyon, Maison de l’Orient et de la Méditerranée Jean Pouilloux, 1991)Magri, D. Late Quaternary vegetation history at Lagaccione near Lago di Bolsena (central Italy). Rev. Palaeobot. Palynol. 106(3–4), 171–208 (1999).Article 

Google Scholar 
Gale, R., & Cutler, D. F. Plants in archaeology: identification manual of vegetative plant materials used in Europe and the Southern Mediterranean to c. 1500 (Westbury and Royal Botanic Gardens, Kew, 2000).Chabal, L. & Feugère, M. L. Le mobilier organique des puits antiques et autres contextes humides de Lattara. Lattara 18, 137–188 (2005).
Google Scholar 
Chabal, L. (ed.) Quatre puits de l’agglomération routière gallo-romaine d’Ambrussum (Villetelle, Hérault). Supplément. Revue Archéologique de Narbonnaise, 42: 65–71 (2013).Caruso Fermé, L. & Piqué Huerta, R. Landscape and forest exploitation at the ancient Neolithic site of La Draga (Banyoles, Spain). The Holocene, 24(3): 266 (2014).Boschian, G. Il Riparo “Ermanno de Pompeis” presso l’Eremo di San Bartolomeo di Legio. Scavi 1990–1999. in Atti della XXXVI Riunione Scientifica IIPP, Preistoria e Protostoria dell’Abruzzo, Chieti-Celano, 27–30 settembre 2001, 105–116 (IIPP; Firenze, 2003).Radi, G. & Danese, E. L’abitato di Colle Santo Stefano di Ortucchio (L’Aquila). in Atti della XXXVI Riunione Scientifica IIPP, Preistoria e Protostoria dell’Abruzzo, Chieti-Celano, 27–30 settembre 2001, 145–161 (IIPP; Firenze, 2003).De Francesco, A. M., Bocci, M., Crisci, G. M., & Francaviglia, V. Obsidian provenance at several Italian and Corsican archaeological sites using the non-destructive X-ray fluorescence method. in Obsidian and ancient manufactured glass (eds. Liritzis, I., & Stevenson, C. M.) 115–129 (Albuquerque, UNM Press, 2012).Degano, I. et al. Hafting of Middle Paleolithic tools in Latium (central Italy): New data from Fossellone and Sant’Agostino caves. PLoS ONE 14, e0213473 (2019).CAS 
Article 

Google Scholar 
Nardella, F. et al. Chemical investigations of bitumen from Neolithic archaeological excavations in Italy by GC/MS combined with principal component analysis. Anal. Methods 11, 1449–1459. https://doi.org/10.1039/c8ay02429d (2019).CAS 
Article 

Google Scholar 
Rageot, M. et al. Management systems of adhesive materials throughout the Neolithic in the North-West Mediterranean. J. Archaeol. Sci. 126, 105309 (2021).Article 

Google Scholar 
Binder, D., Bourgeois, G., Benoist, F. & Vitry, C. Identification de brai de bouleau (betula) dans le néolithique de Giribaldi (Nice, France) par la spectrométrie de masse. Revue d’Archéométrie 14, 37–42 (1990).Article 

Google Scholar 
Vuorela, I. Relative pollen rain around cultivated fields. Acta Bot. Fenn. 102, 1–27 (1973).
Google Scholar 
Robinson, M. & Hubbard, R. N. L. B. The transport of pollen in the bracts of hulled cereals. J. Archaeol. Sci. 4(2), 197–199. https://doi.org/10.1016/0305-4403(77)90067-X (1977).Article 

Google Scholar 
Hall, V.A., The role of harvesting techniques in the dispersal of pollen grains of Cerealia. Pollen et Spores, XXX, 2, pp. 265–270.Portillo, M., Llergo, Y., Ferrer, A. & Albert, R. M. Tracing microfossil residues of cereal processing in the archaeobotanical record: an experimental approach. Veg. Hist. Archaeobot. 26(1), 59–74. https://doi.org/10.1007/s00334-016-0571-1 (2017).Article 

Google Scholar 
Negri, G. Nuovo erbario figurato (Hoepli ed., Milano, 1981).Paris R. R. & Moyse H. Matière Médicale. Vol 2°, (Masson, Paris. 1976).Bulgarelli, G. & Flamigni, S. Le piante tossiche e velenose (Hoepli ed., Milano, 2010).Les, D. H. Aquatic Dicotyledons of North America: Ecology, Life History, and Systematics (CRC Press, 2017).Book 

Google Scholar 
Curti, L. Herbarium, un’inedita collezione di piante del XVIII secolo conservata presso l’orto Botanico dell’Università di Padova (Offset Invicta S.p.A., Padova, 1992).Rottoli, M. Zafferanone selvatico (Carthamus lanatus) e cardo della Madonna (Silybum marianum), piante raccolte o coltivate nel Neolitico antico a “La Marmotta”? Bollettino di Paletnologia Italiana, 91–92, 47–61 (2000–2001).Rottoli, M. “La Marmotta”, Anguillara Sabazia (RM), scavi 1989. Analisi paletnobotaniche: prime risultanze. Bullettino di Paletnologia Italiana 84, 305–315 (1993).Van Geel, B. Non-pollen palynomorphs. in Tracking Environmental Change Using Lake Sediments: Terrestrial, vol. 3. (ed. Smol, J. P., Birks, H. J. B., Last W. M.) 99–119 (Algal and Siliceous Indicators, New York, 2001)Hawksworth, David L., van Geel, Bas, Wiltshire, Patricia E. J. The enigma of the Diporotheca palynomorph. Rev. Palaeobot. Palynol. 235, 94–98 (2016). https://doi.org/10.1016/j.revpalbo.2016.09.010.Krug, J. C., Benny, G. L., Keller, H. W. Coprophilous fungi. In Biodiversity of Fungi. Inventory and Monitoring Methods (ed. Foster M., Bill, G.) 467–499 (Elsevier Science, Amsterdam, 2004). More

Model equationsOur host-parasite mathematical model involves the following host population components: ‘susceptible’ hosts denoted by (S), and hosts infected by k distinct types of parasites ((k=1,2,…,n)), the corresponding population numbers of infected hosts are denoted by (I_{i_1,i_2,…,i_k}), where each index (i_j) can take a value from 1, …, n (to avoid repeated counting of the same infection configuration, we require throughout the paper that (i_1 More

Study subjectsSubjects in our study are individually recognized wild capuchins found in and around the Lomas Barbudal Biological Reserve in Guanacaste, Costa Rica. This population has been under observation since 1990 (Perry 2012; Perry et al. 2012), including near continuous observation from January 2002 through March 2020.Data collectionWe use proximity data on subjects collected during group scan sampling between January 2001 and March 2020 (Altmann 1974). Included in scans are the identity of the subject, and the identity of other individuals within approximately 4 meters of them. Scans have been collected on all individuals in study groups since 2002, and on all adults and subadults since 2001. Scans are taken opportunistically, without regard to time of day. At least 10 min separate consecutive scans of the same individual to reduce the non-independence of scans taken close in time.Data in this manuscript were collected by 124 observers, with an average of 7.1 data collectors per month. Observers typically work in teams of two to three and rotate across different groups to reduce potential observer bias. Observers also rotate across observer teams to avoid observer drift in coding, since observer teams could potentially start to code behaviors differently from each other in the absence of overlap in observer composition.Initial pedigree constructionOf the 376 individuals in our behavioral dataset, 280 (74.5%) were first seen within three months of their births, and we could confidently assign maternity to them based on demographic (pregnancies) and behavioral data (primary nursing) even prior to genotyping. Of the remaining individuals, 41 (10.9%) were males of unknown origin that immigrated into our study population, while the rest were natal to our study groups but were first seen as older infants ( >3 months), juveniles, or (sub)adults (14.6%) and required genotyping to assign/confirm maternity. Paternity was assigned based on genetic information when possible (but see Non-genotyped individuals).In total, 287 subjects (76.3%) had two assigned parents, 37 had one assigned parent (9.8%), and 52 (13.8%) had no assigned parent based on demographic, behavioral, and/or genetic parentage information. Most individuals with no assigned parents were immigrant males (78.9%).GenotypingInformation on genetic parentage assignment (at up to 18 microsatellite loci) in our study population is available from previously published work (1996–2005 (Muniz et al. 2006), 2005–2012 (Godoy et al. 2016b)). Partial genotypes (up to 14 loci) have been generated for individuals in this study which more recently entered the study population through birth or immigration (n = 91, 2012–2020) (See SI File 1). Briefly, DNA was extracted primarily from non-invasively collected fecal samples, and occasionally from tissue samples obtained from deceased individuals, then amplified at up to 18 autosomal tetranucleotide microsatellite loci (Muniz and Vigilant 2008) using either a 1-step or 2-step PCR protocol (Arandjelovic et al. 2009). There were no significant deviations from Hardy-Weinberg equilibrium, and no evidence of linkage disequilibrium between loci was found (Muniz 2008).DNA samples were run at a minimum in triplicate, but additional PCRs were performed on low quality samples (e.g., with low quantities of DNA). Genotypes at each of the loci were assigned to be heterozygous when each allele was seen at least twice in independent PCRs, and assigned as homozygous when the allele was seen in at least three independent PCRs in absence of a second allele.Amplicons were analyzed using an ABI PRISM3100 automated sequencer and GeneMapper Software (Applied Biosystems, Foster City, CA, USA). Likelihood-based parentage assignments were performed using CERVUS 2.0 or 3.0 (Marshall et al. 1998; Kalinowski et al. 2007). The average exclusionary power of the 18 microsatellites was 0.9888 for the first parent and 0.9998 for second parent (Muniz et al. 2006).Individuals with unknown parents (e.g., immigrant males, founders) were genotyped twice (i.e., using two independent DNA samples) following the procedures described above to guard against sample mix up. Known mother-offspring pairs were confirmed by ascertaining the absence of Mendelian mismatches across all loci for the pair, though one mismatch was allowed to account for null alleles, mutations, and genotyping errors. We detected one null allele in the population in 19 individuals and traced it back to a male who was either the father or grandfather of those individuals (Muniz et al. 2006; Godoy et al. 2016b).Candidate males for paternity assignment were chosen based on group membership around the time of an infant’s conception (typically 1–10 males). In cases when conceptions occurred prior to the habituation of a study group, we used the identities of all adult males present when the group was first observed. Candidate mothers were similarly chosen for individuals that were first seen as older infants, juveniles, or (sub)adults. For individuals born post-group habituation, CERVUS has always assigned paternity from the pool of potential candidate fathers. Parent-offspring pairs and trios were allowed one mismatch (excluding those at the locus with the known null allele).Pedigree updatingNon-genotyped individualsDuring stable tenures, alpha males in our population sire approximately 73% of infants born in their groups, including 90% of offspring born to unrelated females (Godoy et al. 2016a). There is strong evidence of inbreeding avoidance between alpha males and their female descendants, with relatedness to females as the primary factor constraining alpha male monopolization of paternity within groups (Muniz et al. 2006, 2010; Godoy et al. 2016a, 2016b; Wikberg et al. 2017, 2018). We used this information to update our pedigree, filling missing father information with the identity of the alpha male around the time of a non-genotyped individual’s conception, but only if their mother was not the daughter or granddaughter of the alpha male (i.e., with inbreeding avoidance). This approach allowed us to assign presumed paternity to 21 non-genotyped individuals (5.6% of subjects) who were natal to our study groups.Individuals with missing or incomplete parentageOut of the original four study groups (from which fissions led to eight additional study groups), we lacked parentage information (i.e., neither parent was sampled) for 12 individuals first seen at the time of habituation. We had incomplete parentage on an additional 11 adults (i.e., only one parent was sampled). We used the software program COLONY version 2.0.6.7 to look for evidence of whether these individuals were related to each other at the level of full sibling (Jones and Wang 2010). We also looked for potential full sibling pairs among the non-natal immigrant males in the population, since co-migrant males are typically kin (Perry 2012; Wikberg et al. 2014, 2018). We assigned five full sibling pairs among co-migrant males, and four full sibling pairs among natal founders. For any remaining co-migrant males and natal founder pairs that were not assigned as full siblings, we assumed these to be either paternal (migrants) or maternal (natal) half siblings, as is typical in this study population (Perry 2012). These assignments are likely to have some error. However, based on what we know about kinship in capuchins, it would introduce more error to assume that these pairs are unrelated.We pruned our modified population pedigree using the R package pedantics version 1.01 (Morrissey and Wilson 2010), to include only individuals that were linked to the subjects in our behavioral dataset. The reduction in missing data can improve convergence and mixing of models (Hadfield 2010). The pruned pedigree contained 419 individuals, with 353 maternities, 354 paternities, 209 full sibships, 413 maternal half sibships, and 1496 paternal half sibships. Maximum pedigree depth was six generations (mean = 3.03).Sociality measures (response variables)We generated two related proximity-based measures of sociality—(1) whether an individual was seen in proximity of another monkey (within ~4 meters) during a scan (i.e., they were not alone), and (2) the number of partners an individual has nearby (within ~4 meters) during a scan. The former is measure of the propensity of an individual to be social versus alone, while the latter is more indicative of the gregariousness of an individual. These two phenotypes are not independent, as they are generated from the same data (Fig. 1a).Fig. 1: Distribution of sociality, sampling, group size, and alpha tenure length.The scatterplot in a shows the proportion of scans per individual per month where the subjects were recorded in proximity of others on the x-axis, and the average number of social partners per scan per month for subjects on the y-axis. The sizes of the circles in a are proportional to sample size (range: 5–317 scans per data point). The figure in b shows the number of calendar years of data sampling per subject (range: 1–20), c variation in group size, and d the number of calendar years represented by different alpha tenures in the dataset. Note that d does not represent the full diversity of alpha tenure lengths in the population, only within the dataset: some tenure lengths are left-truncated as data from 1990–2000 are not included in this dataset. Figure produced in R using ggplot2 version 3.3.5 (Wickham 2016) and cowplot version 1.1.1 (Wilke 2020). The capuchin image was generated in R using sketcher version 0.1.3 (Tsuda 2020) based on an image taken by Nicholas Schleissmann.Full size imageWe compiled the scans of individuals by month (mean: 31.9, range: 5–317 scans per month) so that we had counts of (1) the total number of scans where an individual was social and (2) the total number of partners an individual had. With these counts we could look at the (1) proportion of time spent social (versus alone) and (2) the average number of partners an individual had, while still preserving information about sampling density (number of scans).To be included in any month, subjects needed to have at least five scan samples in that period. As we are interested in the repeatability of our measures of social behavior, subjects had to have at least six months of data to be included.We excluded dependent infants (less than one year of age) as potential social partners of their mothers. We also excluded these dependent infants as subjects, since an infant is expected to be in close proximity of its mother, particularly during the first half of their first year of life (Godoy 2010; Perry 2012). Including data from infants would likely introduce upward bias to heritability estimates, because mothers and their dependent offspring (whom share high relatedness) would often be in close proximity of each other, and their measures of proximity to others would thus also be highly correlated.On average, subjects spent just over half of their sampled time within approximately four meters of another monkey (mean: 0.539, standard deviation: 0.193) and had approximately one social partner per scan (mean: 1.057, standard deviation: 0.619) (Fig. 1a). Our dataset consisted of 22,138 monthly sociality scores on 376 subjects generated from 641 140 scans (mean: 56.5 months per subject, range: 6–184 months per subject). Almost all subjects (99.7%, i.e., all but one) were represented by data across more than one calendar year (25, 50, 75% quantiles: 4, 7, 10 different years of data collection) (Fig. 1b).Fixed effectsWe included age (as a cubic function) and sex in our models, as well as their interaction to account for differences in how male and female capuchins sexually mature and age. Age in our dataset was right-skewed with higher representation at younger ages (mean: 9.3 years, standard deviation: 6.9) (Fig. 2). To put the ages in developmental context, mean age at first live birth is around 6.3 years for females in this population, though females can begin reproducing in their 5th year (Perry et al. 2012). Males as young as six years old have been known to sire offspring (Godoy et al. 2016b), but males tend to not reach full adult size until their 10th year (Jack et al. 2014).Fig. 2: Sociality as a function of age and sex.Circles represent individual monthly data. The sizes of the circles are proportional to sample size (range: 5–317 shows per data point). Circles in a represent the proportion of time individuals were seen in proximity of others (not alone) per month, while in b represent the average number of partners for individuals per month. Solid lines represent estimated sociality scores based on age and sex, with all other fixed effects set to the mean. The two x-axes represent age as z-scores and in years. Figure produced in R using ggplot2 version 3.3.5 (Wickham 2016).Full size imageSeasonal environmental changes, such as in food abundance, or temperature and rain, can lead to changes in how individuals cluster near others, for example, because of how food resources become distributed in the environment. For example, in black-crested gibbons (Nomascus concolor), group averages of dyadic proximity have been documented to decrease from the dry season to wet season, with increased average group proximity during cold months and lowered proximity during warm months (Guan et al. 2013). We account for seasonal variation by modelling monthly changes as a sine wave, through inclusion of the sine and cosine functions of a transformed month variable (See SI File 1 for further details).Central America is a region of ENSO-related precipitation, where the El Niño-Southern Oscillation (ENSO) has an impact on large scale patterns of temperature and precipitation (Ropelewski and Halpert 1987). Bimonthly rainfall anomalies are linked with both the warm El Niño and cool La Niña phases in a neighboring tropical dry forest in Costa Rica, where long-term monitoring of wild white-faced capuchins has shown declines in reproductive output associated with El Niño-like conditions (Campos et al. 2015). To account for the large-scale influence of ENSO on group dynamics, we included a factor variable for three different ENSO phases (Average/Neutral, Cool/La Niña, and Warm/El Niño). We used the bi-monthly Multivariate El Niño/Southern Oscillation (ENSO) index (MEI.v2) obtained from the Physical Sciences Laboratory of the National Oceanic and Atmospheric Administration (https://psl.noaa.gov/enso/mei/, retrieved: 2021-11-06) to determine the different phases. MEI.v2 is a composite index of five different variables (sea level pressure, sea surface temperature, surface zonal winds, surface meridional winds, and Outgoing Longwave Radiation) used to create a time series of ENSO conditions from 1979 to present (Zhang et al. 2019). Warm phases correspond to MEI.v2 values of 0.5 or higher, while cool phases correspond to values of −0.5 or lower.Demographic differences between groups and within groups across time can also lead to variation in behavior. For example, group size has been found to correlate with the amount of time that individuals spend grooming in various primate species (Dunbar 1991; Lehmann et al. 2007). Group size is also associated with higher sociality measures such as both the number of strong and weak ties that individuals form in diverse clades of primates (Schülke et al. 2022). We attempt to account for variation that arises from such demographic differences by including group size (mean: 24.7, standard deviation: 7.9) (Fig. 1c) as a fixed effect.In our models, group size and cubic age were centered and scaled to a mean of zero and a standard deviation of one.Random effectsAll models include the identity of the subject (VID, n = 376) as a random factor, as well as subject identity nested within year (VID:Year, n = 3150), the identity of each subject’s mother (VM, n = 142), maternal identity nested within group of residence within year of data collection (VM:GroupAlpha:Year, n = 2085), and a special variable known as the animal term to account for additive genetic variance (VA). These components contribute to long- and/or short-term repeatability of individuals. All models also include year of data collection (VYear, n = 20), month nested within year (VMonth:Year, n = 224), and the identity of each subject’s group of residence both across years (VGroupAlpha, n = 56) and within years (VGroupAlpha:Year, n = 200).VID in the models (since the models also additionally estimate VM and VA) can be thought of as estimating the “permanent environment variance” (i.e., VPE) of an individual, which is the “individual-specific variation in environmental conditions that permanently affect the phenotype (e.g. early-life conditions)” (Dingemanse et al. 2010). VID:Year captures the variance explained by the repeated sampling of the same individuals within a particular year. We use it to estimate the proportion of the phenotypic variance due to similarity in the trait within individuals from data taken closer in time (within the same year). During such a relatively short period, individuals are more likely to be stable in important social traits such as kin availability, dominance rank for adults, and maternal dominance rank for infants and young juveniles.VM estimates the variance explained by maternal effects (m2), specifically similarity between maternal siblings. Maternal identities were not available for all subjects, namely 11 immigrant males of unknown origin who were not assigned by COLONY as having a full sibling. We created unique dummy codes for their maternal identities, so that no two of these individuals shared the same mother. We additionally nested maternal identities (VM:GroupAlpha:Year) to account for similarity between maternal siblings residing in the same group in the same year. Such a nested structure might capture potential upward biases on heritability due to maternal kin biases in spatial association among siblings residing in the same group.We estimate h2 in our models by fitting a random effects term (VA), referred to as the animal term, which in the R package MCMCglmm links to the identities of individuals in our population pedigree (Hadfield 2010; see below for details on the implementation of the models in MCMCglmm). Inclusion of the animal term provides our models with an additive genetic variance component based on the estimated coefficients of relatedness between individuals in our pedigree. In short, if animals that share more alleles are also more like each other in their behavior, then variation in the behavior may well be due to genetic variation in the population (under the assumption that phenotypic similarity is not due to a shared environment, or is adequately controlled for by fixed and random effects in the model).VYear and VMonth:Year were included in order to account for temporal variation in sociality scores not captured by the fixed effects of seasonality or ENSO phase. These could arise from, for example, observer drift in coding (i.e., measurement error) or prevailing environmental conditions (e.g., drought) that could lead to changes to how individuals cluster near others. There were 218 unique observer combinations across the 224 months represented in the dataset, so VMonth:Year should also capture variance due to any differences between observer teams, though we cannot separate out the unique influence of observers.VGroupAlpha represents variance arising from the different alpha tenures within groups in our study population. VGroupAlpha captures both variance due to group of residence effects and the additional influence of alpha tenures within those groups. In capuchins, alpha males are ‘keystone’ individuals, whose influence is disproportionate relative to that of others in the population, and thus play important roles in establishing group dynamics (Jack and Fedigan 2018). Including group of residence, as defined by alpha tenure, is also important because it helps to account for the higher relatedness within groups within alpha tenures which results from high male reproductive skew toward alpha males. At Lomas Barbudal, males can remain in their alpha position for upwards of 18 years. Alpha tenures in this dataset spanned one to 14 years (Fig. 1d), so we additionally nested the identity of alpha males per group within years (VGroupAlpha:Year) so as to separate the within-year and across-year influences of group of residence.Statistical methodsWe ran analyses in R 4.1.2 (R Core Team 2021), using a Bayesian method with the R package MCMCglmm version 2.32 (Hadfield 2010). Data and code used to run all models is provided in the Supplementary Information.For our binary response variable (social versus alone), which was pooled into monthly units, we fit models with a binomial distribution and logit link function (family = “multinomial2”), with the number of scans each individual was documented social (‘successes’) versus the number of times alone (‘failures’).For our other response variable (number of partners), which was also pooled into monthly units, we fit models with a Poisson distribution (family = “poisson”), with the total number (sum) of partners per month. We included the natural log of the number of scans per month as a fixed effect to account for sampling effort. We set a strong prior for the log of sampling effort so that the rate at which events occurred was 1 (i.e., we could look at average number of partners per scan).We used a parameter-expanded prior (V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000) and two inverse Wishart priors (V = 1, nu = 0.002; V = 1, nu = 0.02) for the G structures in our models (i.e., random effects variance components). The prior on the residual variance component was set to one for both the binomial and Poisson models. Estimates for variance components were robust against the choice of prior (SI Fig. 3). We therefore only report findings from models run with parameter-expanded priors in the main text.Pilot runs (thin = 10, burnin = 3000, nitt = 13,000) indicated that autocorrelation values would remain high for some variance components in models run with parameter-expanded priors, even with large thinning intervals. We therefore increased the number of iterations to guarantee effective sample sizes of at least 1000, but ideally closer to 4000. All models were run with a long burn-in period of at least 10,000 iterations.We ran multiple chains (n = 4) of each model and assessed convergence of the chains visually (SI Files 2a-b), as well as through the Gelman-Rubin criterion implemented via the ‘gelman.diag’ function from the coda package in R (version 0.19-4) (Plummer et al. 2006). Scale reduction factors were below 1.02, signifying good convergence. We used Heidelberger and Welch’s convergence diagnostic test for stationarity to check convergence of each chain using the ‘heidel.diag’ function from the coda package. Results are presented from the first chain of each model.Reduced modelsInclusion of fixed effects can potentially have an impact on the estimates of variance components in models because total phenotypic variance (VP) is estimated (and partitioned among the different random effects) after conditioning on the fixed effects. Heritability estimates, for example, can be higher because the variance explained by the fixed effects structure (VFE) is not included in VP, thus making the relative contribution of VA to VP larger compared to the same model without fixed effects (Wilson 2008). Conversely, not adequately controlling for relevant fixed effects that contribute to phenotypic variance among and within individuals may potentially lead to an underestimation of VA and associated heritability (h2).We ran multiple reduced versions of our models to look at the impact of fixed effects on our variance components. We began with an intercept-only version (i.e., no fixed effects), then built-up complexity by adding in versions with the properties of the individuals first (age, sex), then properties of the group (group size), and subsequently environmental properties (seasonality, ENSO phases). Outputs for these reduced models are provided in the Supplementary Information (SI Table 2, SI Table 3).We provide the deviance information criterion (DIC) values for models (automatically generated by the MCMCglmm package). DIC is a generalization for multi-level models of the Akaike Information Criterion (AIC); and as in AIC, lower DIC values indicate better fit.Transformations from unobserved latent scale to observed data scaleOutputs from our MCMCglmm models were on the unobserved latent scale. We used the R package QGglmm (version 0.7.4) to additionally compute parameters of interest on the observed data scale (de Villemereuil et al. 2016; de Villemereuil 2018). We used the functions ‘QCicc’ to compute Intra-Class Correlation (ICC) coefficients and ‘QGparams’ to compute additive genetic variance and thus narrow-sense heritability (h2) on the observed data scale. We implemented the ‘QGparams’ and ‘QGicc’ functions with parameters model = ‘binomN.logit’ and n.obs = 32 (the average number of scans per subject per month in our dataset) for the binomial model and model = ‘Poisson.log’ for the Poisson model. The choice of value for n.obs is somewhat arbitrary, and we show the consequences for changes in values of this parameter (i.e., higher estimates with increasing values of n.obs) in SI Fig. 4.Closed form solutions in QGglmm are not available for integrating over posterior distributions generated from binomial models with logit link functions (de Villemereuil 2016). Consequently, using the ‘QGicc’ function is particularly slow. We therefore estimate ICCs from our binomial models using a random subset of the posterior (n = 1000 iterations).The code used for transforming the MCMCglmm outputs from the latent scale to the original data scale are available online (see DATA AVAILABILITY).Repeatability and the proportion of variance explained by variance componentsTotal phenotypic variance (VP) was the sum of estimates from all variance components and residual variance in a model (VP = VID + VID:Year + VM + VM:GroupAlpha:Year + VA + VGroupAlpha + VGroupAlpha:Year + VMonth:Year + VYear + Vresidual). The proportion of variance explained by each variance component was calculated by including its estimate in the numerator while including total phenotypic variance in the denominator. So, for example the proportion of variance explained by year of data collection was calculated as (left( {frac{{V_{Year}}}{{V_P}}} right)).Long-term repeatability was calculated with the sum of VID, VM, and VA in the numerator. Short-term repeatability was calculated similarly but with inclusion of within-series variances (VID + VM + VA + VID:Year + VM:GroupAlpha:Year) in the numerator to capture additional consistency in among-individual differences resulting from greater environmental similarity within a time series (i.e., year).We report posterior modes and 95% Highest Posterior Density intervals (i.e., 95HPDI in square brackets). Unless mentioned otherwise, we present results on the unobserved latent scale, and without the variance from the fixed effects (VFE) incorporated into VP. For completeness, estimates with VFE included in VP and transformations to the observed data scale are also provided in SI Table 3. More

Wang C, Wang S. Insect pathogenic fungi: genomics, molecular interactions, and genetic improvements. Annu Rev Entomol. 2017;62:73–90.CAS 
PubMed 
Article 

Google Scholar 
Lacey LA, Grzywacz D, Shapiro-Ilan DI, Frutos R, Brownbridge M, Goettel MS. Insect pathogens as biological control agents: Back to the future. J Invertebr Pathol. 2015;132:1–41.CAS 
PubMed 
Article 

Google Scholar 
Butt TM, Coates CJ, Dubovskiy IM, Ratcliffe NA Entomopathogenic fungi: new insights into host-pathogen interactions. Advances in Genetics. 2016. Elsevier Ltd.Lu HL, St. Leger RJ. Insect immunity to entomopathogenic fungi. Adv Genet. 2016;94:251–85.CAS 
PubMed 
Article 

Google Scholar 
Yuan S, Tao X, Huang S, Chen S, Xu A. Comparative immune systems in animals. Annu Rev Anim Biosci. 2014;2:235–58.CAS 
PubMed 
Article 

Google Scholar 
Flórez LV, Biedermann PHW, Engl T, Kaltenpoth M. Defensive symbioses of animals with prokaryotic and eukaryotic microorganisms. Nat Prod Rep. 2015;32:904–36.PubMed 
Article 

Google Scholar 
Oliver KM, Smith AH, Russell JA. Defensive symbiosis in the real world – advancing ecological studies of heritable, protective bacteria in aphids and beyond. Funct Ecol. 2014;28:341–55.Article 

Google Scholar 
Scarborough CL, Ferrari J, Godfray HC. Aphid protected from pathogen. Science 2005;310:1781.CAS 
PubMed 
Article 

Google Scholar 
Łukasik P, van Asch M, Guo H, Ferrari J, Charles H. Unrelated facultative endosymbionts protect aphids against a fungal pathogen. Ecol Lett. 2013;16:214–8.PubMed 
Article 

Google Scholar 
Flórez LV, Scherlach K, Gaube P, Ross C, Sitte E, Hermes C, et al. Antibiotic-producing symbionts dynamically transition between plant pathogenicity and insect-defensive mutualism. Nat Commun. 2017;8:15172.PubMed 
PubMed Central 
Article 

Google Scholar 
Flórez LV, Scherlach K, Miller IJ, Rodrigues A, Kwan JC, Hertweck C, et al. An antifungal polyketide associated with horizontally acquired genes supports symbiont-mediated defense in Lagria villosa beetles. Nat Commun. 2018;9:2478.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kaltenpoth M, Göttler W, Herzner G, Strohm E. Symbiotic bacteria protect wasp larvae from fungal infestation. Curr Biol. 2005;15:475–9.CAS 
PubMed 
Article 

Google Scholar 
Kroiss J, Kaltenpoth M, Schneider B, Schwinger MG, Hertweck C, Maddula RK, et al. Symbiotic streptomycetes provide antibiotic combination prophylaxis for wasp offspring. Nat Chem Biol. 2010;6:261–3.CAS 
PubMed 
Article 

Google Scholar 
Kaltenpoth M, Goettler W, Koehler S, Strohm E. Life cycle and population dynamics of a protective insect symbiont reveal severe bottlenecks during vertical transmission. Evol Ecol. 2010;24:463–77.Article 

Google Scholar 
Wang X, Yang X, Zhou F, Tian ZQ, Cheng J, Michaud JP, et al. Symbiotic bacteria on the cuticle protect the oriental fruit moth Grapholita molesta from fungal infection. Biol Control. 2022;169:104895.CAS 
Article 

Google Scholar 
Wang L, Feng Y, Tian J, Xiang M, Sun J, Ding J, et al. Farming of a defensive fungal mutualist by an attelabid weevil. ISME J. 2015;9:1793–801.PubMed 
PubMed Central 
Article 

Google Scholar 
Currie CR, Stuart AE. Weeding and grooming of pathogens in agriculture by ants. Proc R Soc B Biol Sci. 2001;268:1033–9.CAS 
Article 

Google Scholar 
Currie CR, Scottt JA, Summerbell RC, Malloch D. Fungus-growing ants use antibiotic-producing bacteria to control garden parasites. Nature 1999;398:701–4.CAS 
Article 

Google Scholar 
Currie CR, Bot ANM, Boomsma JJ. Experimental evidence of a tripartite mutualism: Bacteria protect ant fungus gardens from specialized parasites. Oikos 2003;101:91–102.Article 

Google Scholar 
Um S, Fraimout A, Sapountzis P, Oh D-CC, Poulsen M. The fungus-growing termite Macrotermes natalensis harbors bacillaene-producing Bacillus sp. that inhibit potentially antagonistic fungi. Sci Rep. 2013;3:3250.PubMed 
PubMed Central 
Article 

Google Scholar 
Grubbs KJ, Surup F, Biedermann PHW, McDonald BR, Klassen JL, Carlson CM, et al. Cycloheximide-producing streptomyces associated with xyleborinus saxesenii and xyleborus affinis fungus-farming ambrosia beetles. Front Microbiol. 2020;11:1–12.Article 

Google Scholar 
Piel J. Metabolites from symbiotic bacteria. Nat Prod Rep. 2009;26:338–62.CAS 
PubMed 
Article 

Google Scholar 
Van Arnam EB, Currie CR, Clardy J. Defense contracts: Molecular protection in insect-microbe symbioses. Chem Soc Rev. 2018;47:1638–51.PubMed 
Article 

Google Scholar 
Beemelmanns C, Guo H, Rischer M, Poulsen M. Natural products from microbes associated with insects. Beilstein J Org Chem. 2016;12:314–27.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lackner G, Peters EE, Helfrich EJN, Piel J. Insights into the lifestyle of uncultured bacterial natural product factories associated with marine sponges. Proc Natl Acad Sci USA. 2017;114:E347–E356.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schoenian I, Spiteller M, Ghaste M, Wirth R, Herz H, Spiteller D. Chemical basis of the synergism and antagonism in microbial communities in the nests of leaf-cutting ants. Proc Natl Acad Sci USA. 2011;108:1955–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kaltenpoth M, Strupat K, Svatoš A. Linking metabolite production to taxonomic identity in environmental samples by (MA)LDI-FISH. ISME J. 2016;10:527–31.PubMed 
Article 

Google Scholar 
Geier B, Sogin EM, Michellod D, Janda M, Kompauer M, Spengler B, et al. Spatial metabolomics of in situ host–microbe interactions at the micrometre scale. Nat Microbiol. 2020;5:498–510.CAS 
PubMed 
Article 

Google Scholar 
De Roode JC, Lefèvre T. Behavioral immunity in insects. Insects 2012;3:789–820.PubMed 
PubMed Central 
Article 

Google Scholar 
Kerwin AH, Gromek SM, Suria AM, Samples RM, Deoss DJ, O’Donnell K, et al. Shielding the next generation: Symbiotic bacteria from a reproductive organ protect bobtail squid eggs from fungal fouling. mBio. 2019;10:e02376-19.PubMed 
PubMed Central 
Article 

Google Scholar 
Soler JJ, Martín-Vivaldi M, Ruiz-Rodríguez M, Valdivia E, Martín-Platero AM, Martínez-Bueno M, et al. Symbiotic association between hoopoes and antibiotic-producing bacteria that live in their uropygial gland. Funct Ecol. 2008;22:864–71.Article 

Google Scholar 
Bunker ME, Elliott G, Martin MO, Arnold AE, Weiss SL. Vertically transmitted microbiome protects eggs from fungal infection and egg failure. Anim Microbiome. 2021;3:43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nyholm SV. In the beginning: Egg-microbe interactions and consequences for animal hosts: Egg microbiomes in animals. Philos Trans R Soc B Biol Sci. 2020;375:20190593.CAS 
Article 

Google Scholar 
Smith DFQ, Dragotakes Q, Kulkarni M, Hardwick M, Casadevall A, Microbiology M, et al. Melanization is an important antifungal defense mechanism in Galleria mellonella hosts. bioRxiv 2022.04.02.486843.Yokoi K, Hayakawa Y, Kato D, Minakuchi C, Tanaka T, Ochiai M, et al. Prophenoloxidase genes and antimicrobial host defense of the model beetle, Tribolium castaneum. J Invertebr Pathol. 2015;132:190–200.CAS 
PubMed 
Article 

Google Scholar 
Zhang J, Huang W, Yuan C, Lu Y, Yang B, Wang CY, et al. Prophenoloxidase-mediated ex vivo immunity to delay fungal infection after insect ecdysis. Front Immunol. 2017;8:1–14.
Google Scholar 
Zhang J, Lu A, Kong L, Zhang Q, Ling E. Functional analysis of insect molting fluid proteins on the protection and regulation of ecdysis. J Biol Chem. 2014;289:35891–906.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soluk DA. Postmolt susceptibility of ephemerella larvae to predatory stoneflies: constraints on defensive armour. Oikos 1990;58:336.Article 

Google Scholar 
Kanyile SN, Engl T, Kaltenpoth M. Nutritional symbionts enhance structural defence against predation and fungal infection in a grain pest beetle. J Exp Biol. 2022;225:1–9.Article 

Google Scholar 
Flórez LV, Kaltenpoth M. Symbiont dynamics and strain diversity in the defensive mutualism between Lagria beetles and Burkholderia. Environ Microbiol. 2017;19:3674–88.PubMed 
Article 
CAS 

Google Scholar 
Uberti A, Smaniotto MA, Giacobbo CL, Lovatto M, Lugaresi A, Girardi GC. Novo inseto praga na cultura do pessegueiro: biologia de Lagria villosa Fabricius, 1783 (Coleoptera: Tenebrionidae) alimentados com pêssego. Sci Electron Arch. 2017;10:72–76.
Google Scholar 
Stammer HJ. Die Symbiose der Lagriiden (Coleoptera). Z für Morphol und Ökologie der Tiere. 1929;15:1–34.Article 

Google Scholar 
Boucias DG, Pendland JC Principles of Insect Pathology. 1998. Springer Science + Business Media, LLC, New York.Garcia MA, Pierozzi IJ. Aspectos da biologia e ecologia de Lagria villosa Fabricius, 1781 (Coleoptera, Lagriidae). Rev Bras Biol. 1982;42:415–20.
Google Scholar 
Vega FE, Posada F, Catherine Aime M, Pava-Ripoll M, Infante F, Rehner SA. Entomopathogenic fungal endophytes. Biol Control. 2008;46:72–82.Article 

Google Scholar 
Kabaluk JT, Ericsson JD. Metarhizium anisopliae seed treatment increases yield of field corn when applied for wireworm control. Agron J. 2007;99:1377–81.Article 

Google Scholar 
Hallouti A, Ait Hamza M, Zahidi A, Ait Hammou R, Bouharroud R, Ait Ben Aoumar A, et al. Diversity of entomopathogenic fungi associated with Mediterranean fruit fly (Ceratitis capitata (Diptera: Tephritidae)) in Moroccan Argan forests and nearby area: impact of soil factors on their distribution. BMC Ecol. 2020;20:1–13.Article 
CAS 

Google Scholar 
Iwanicki NS, Pereira AA, Botelho ABRZ, Rezende JM, Moral RDA, Zucchi MI, et al. Monitoring of the field application of Metarhizium anisopliae in Brazil revealed high molecular diversity of Metarhizium spp in insects, soil and sugarcane roots. Sci Rep. 2019;9:1–12.CAS 
Article 

Google Scholar 
Roberts DW, St. Leger RJ. Metarhizium spp., cosmopolitan insect-pathogenic fungi: Mycological aspects. Adv Appl Microbiol. 2004;54:1–70.CAS 
PubMed 
Article 

Google Scholar 
Wierz JC, Gaube P, Klebsch D, Kaltenpoth M, Flórez LV. Transmission of bacterial symbionts with and without genome erosion between a beetle host and the plant environment. Front Microbiol. 2021;12:715601.PubMed 
PubMed Central 
Article 

Google Scholar 
Gillespie JP, Bailey AM, Cobb B, Vilcinskas A. Fungi as elicitors of insect immune responses. Arch Insect Biochem Physiol. 2000;44:49–68.CAS 
PubMed 
Article 

Google Scholar 
Ortiz-Urquiza A, Keyhani NO. Action on the surface: Entomopathogenic fungi versus the insect cuticle. Insects 2013;4:357–74.PubMed 
PubMed Central 
Article 

Google Scholar 
Grizanova EV, Coates CJ, Dubovskiy IM, Butt TM. Metarhizium brunneum infection dynamics differ at the cuticle interface of susceptible and tolerant morphs of Galleria mellonella. Virulence 2019;10:999–1012.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eaton WD, Love DC, Botelho C, Meyers TR, Imamura K, Koeneman T. Preliminary results on the seasonality and life cycle of the parasitic dinoflagellate causing bitter crab disease in Alaskan Tanner crabs (Chionoecetes bairdi). J Invertebr Pathol. 1991;57:426–34.CAS 
PubMed 
Article 

Google Scholar 
Field RH, Chapman CJ, Taylor AC, Neil DM, Vickerman K. Infection of the Norway lobster Nephrops norvegicus by a Hematodinium-like species of dinoflagellate on the west coast of Scotland. Dis Aquat Organ. 1992;13:1–15.Article 

Google Scholar 
Threlkeld ST, Chiavelli DA, Willey RL. The organization of zooplankton epibiont communities. Trends Ecol Evol. 1993;8:317–21.CAS 
PubMed 
Article 

Google Scholar 
Duneau D, Ebert D. The role of moulting in parasite defence. Proc R Soc B Biol Sci. 2012;279:3049–54.Article 

Google Scholar 
Vandenberg JD, Ramos M, Altre JA. Dose-Response and Age- and Temperature-Related Susceptibility of the Diamondback Moth (Lepidoptera: Plutellidae) to Two Isolates of Beauveria bassiana (Hyphomycetes: Moniliaceae). Environ Entomol. 1998;27:1017–21.Article 

Google Scholar 
Vey A, Fargues J. Histological and ultrastructural studies of Beauveria bassiana infection in Leptinotarsa decemlineta larvae during ecdysis. J Invertebr Pathol. 1977;30:207–15.Article 

Google Scholar 
Reynolds SE, Samuels RI. Physiology and biochemistry of insect moulting fluid. Adv Insect Phys. 1996;26:157–232.CAS 
Article 

Google Scholar 
Lopanik NB. Chemical defensive symbioses in the marine environment. Funct Ecol. 2014;28:328–40.Article 

Google Scholar 
Sen R, Ishak HD, Estrada D, Dowd SE, Hong E, Mueller UG. Generalized antifungal activity and 454-screening of Pseudonocardia and Amycolatopsis bacteria in nests of fungus-growing ants. Proc Natl Acad Sci USA. 2009;106:17805–10.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Currie CR, Poulsen M, Mendenhall J, Boomsma JJ, Billen J. Coevolved crypts and exocrine glands support mutualistic bacteria in fungus-growing ants. Science 2006;311:81–3.CAS 
PubMed 
Article 

Google Scholar 
Li H, Sosa-Calvo J, Horn HA, Pupo MT, Clardy J, Rabeling C, et al. Convergent evolution of complex structures for ant-bacterial defensive symbiosis in fungus-farming ants. Proc Natl Acad Sci USA. 2018;115:10720–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kaltenpoth M, Roeser-Mueller K, Koehler S, Peterson A, Nechitaylo TY, Stubblefield JW, et al. Partner choice and fidelity stabilize coevolution in a Cretaceous-age defensive symbiosis. Proc Natl Acad Sci. 2014;111:6359–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Engl T, Kroiss J, Kai M, Nechitaylo TY, Svatoš A, Kaltenpoth M. Evolutionary stability of antibiotic protection in a defensive symbiosis. Proc Natl Acad Sci USA. 2018;115:E2020–E2029.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gil-Turnes MS, Hay ME, Fenical W. Symbiotic marine bacteria chemically defend crustacean embryos from a pathogenic fungus. Science 1989;246:116–8.CAS 
PubMed 
Article 

Google Scholar 
Gil-Turnes MS, Fenical W. Embryos of Homarus americanus are protected by epibiotic bacteria. Biol Bull. 1992;182:105–8.CAS 
PubMed 
Article 

Google Scholar 
Hoffmann KH Insect Molecular Biology and Ecology. 2015. CRC Press.Eisner T, Morgan RC, Attygalle AB, Smedley SR, Herath KB, Meinwald J. Defensive production of quinoline by a phasmid insect (Oreophoetes peruana). J Exp Biol. 1997;200:2493–2500.CAS 
PubMed 
Article 

Google Scholar 
Waterworth SC, Flórez LV, Rees ER, Hertweck C, Kaltenpoth M, Kwan JC. Horizontal gene transfer to a defensive symbiont with a reduced genome in a multipartite beetle microbiome. mBio. 2020;11:e02430-19.PubMed 
PubMed Central 
Article 

Google Scholar 
Niehs SP, Kumpfmüller J, Dose B, Little RF, Ishida K, Flórez LV, et al. Insect‐associated bacteria assemble the antifungal butenolide gladiofungin by non‐canonical polyketide chain termination. Angew Chem. 2020;132:23322–6.Article 

Google Scholar 
Dose B, Niehs SP, Scherlach K, Flórez LV, Kaltenpoth M, Hertweck C. Unexpected bacterial origin of the antibiotic icosalide: two-tailed depsipeptide assembly in multifarious Burkholderia symbionts. ACS Chem Biol. 2018;13:2414–20.CAS 
PubMed 
Article 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22.CAS 
PubMed 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.Article 
CAS 

Google Scholar 
Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and ‘all-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 2014;42:643–8.Article 
CAS 

Google Scholar 
Weiss B, Kaltenpoth M. Bacteriome-localized intracellular symbionts in pollen-feeding beetles of the genus Dasytes (Coleoptera, Dasytidae). Front Microbiol. 2016;7:1–10.Article 

Google Scholar 
Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paschke C, Leisner A, Hester A, Maass K, Guenther S, Bouschen W, et al. Mirion – A software package for automatic processing of mass spectrometric images. J Am Soc Mass Spectrom. 2013;24:1296–306.CAS 
PubMed 
Article 

Google Scholar  More