Philippa Kaur

To authentically be a welcoming space for Black scholars, we need to accept the full expression of Black excellence in all its forms. Concurrently, that means interrogating how societal norms and stereotypes coerce Black scientists to conform or assimilate to a strict definition of professionalism6. We do not accept species uniformity in promoting healthy ecosystems, so why would we expect assimilation of personalities, values and cultures? Recent social media movements, including but not limited to #BlackBirdersWeek, #BlackintheIvory and #BlackinNature, illustrate the myriad forms of Black scholarship, education and outreach5. Undervaluing these stories, narratives and identities negates the positive contributions our non-Black colleagues make in fighting structural racism.
Support and fight alongside your Black colleagues against racial oppression, especially when it is inconvenient and outside our academic walls. This is especially pertinent for field biologists, as our right to belong in nature without fear of persecution or violence is under constant threat25,26. The compounding and pervasive impacts of environmental racism in conservation and environmental movements all contribute to marginalizing Black scholars’ contributions to field ecology and biology16,25,26. Authentically recognizing Black excellence will likely mean confronting authority figures (that is, police, deans, chancellors, society presidents, department chairs and so on) and using your privilege to protect the rights of your colleagues. More

1.
Teem, J. L. et al. Genetic biocontrol for invasive species. Front. Bioeng. Biotechnol. 8, 452. https://doi.org/10.3389/fbioe.2020.00452 (2020).
Article  PubMed  PubMed Central  Google Scholar 
2.
McFarlane, G. R., Whitelaw, C. B. A. & Lillico, S. G. CRISPR-based gene drives for pest control. Trends Biotechnol. 36, 130–133. https://doi.org/10.1016/j.tibtech.2017.10.001 (2018).
CAS  Article  PubMed  Google Scholar 

3.
Dearden, P. K. et al. The potential for the use of gene drives for pest control in New Zealand: a perspective. J. R. Soc. N. Z. 48, 225–244. https://doi.org/10.1080/03036758.2017.1385030 (2017).
Article  Google Scholar 

4.
Esvelt, K. M., Smidler, A. L., Catteruccia, F. & Church, G. M. Concerning RNA-guided gene drives for the alteration of wild populations. eLife 3, e03401. https://doi.org/10.7554/eLife.03401 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Barrangou, R. & Doudna, J. A. Applications of CRISPR technologies in research and beyond. Nat. Biotechnol. 34, 933–941. https://doi.org/10.1038/nbt.3659 (2016).
CAS  Article  PubMed  Google Scholar 

6.
Kandul, N. P. et al. Transforming insect population control with precision guided sterile males with demonstration in flies. Nat. Commun. 10, 84. https://doi.org/10.1038/s41467-018-07964-7 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

7.
Kyrou, K. et al. A CRISPR-Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nat. Biotechnol. 36, 1062–1066. https://doi.org/10.1038/nbt.4245 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

8.
Drury, D. W., Dapper, A. L., Siniard, D. J., Zentner, G. E. & Wade, M. J. CRISPR/Cas9 gene drives in genetically variable and nonrandomly mating wild populations. Sci. Adv. 3, e1601910. https://doi.org/10.1126/sciadv.1601910 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Hammond, A. M. et al. The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito. PLoS Genet. 13, e1007039. https://doi.org/10.1371/journal.pgen.1007039 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Webber, B. L., Raghu, S. & Edwards, O. R. Opinion: is CRISPR-based gene drive a biocontrol silver bullet or global conservation threat?. Proc. Natl. Acad. Sci. U.S.A. 112, 10565–10567. https://doi.org/10.1073/pnas.1514258112 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Wilkins, K. E., Prowse, T. A. A., Cassey, P., Thomas, P. Q. & Ross, J. V. Pest demography critically determines the viability of synthetic gene drives for population control. Math. Biosci. 305, 160–169. https://doi.org/10.1016/j.mbs.2018.09.005 (2018).
MathSciNet  Article  PubMed  MATH  Google Scholar 

12.
de la Filia, A. G., Bain, S. A. & Ross, L. Haplodiploidy and the reproductive ecology of Arthropods. Curr. Opin. Insect Sci. 9, 36–43. https://doi.org/10.1016/j.cois.2015.04.018 (2015).
Article  Google Scholar 

13.
Deredec, A., Burt, A. & Godfray, H. C. The population genetics of using homing endonuclease genes in vector and pest management. Genetics 179, 2013–2026. https://doi.org/10.1534/genetics.108.089037 (2008).
Article  PubMed  PubMed Central  Google Scholar 

14.
Rode, N. O., Estoup, A., Bourguet, D., Courtier-Orgogozo, V. & Débarre, F. Population management using gene drive: molecular design, models of spread dynamics and assessment of ecological risks. Conserv. Genet. 20, 671–690. https://doi.org/10.1007/s10592-019-01165-5 (2019).
CAS  Article  Google Scholar 

15.
Alphey, N. & Bonsall, M. B. Interplay of population genetics and dynamics in the genetic control of mosquitoes. J. R. Soc. Interface 11, 20131071. https://doi.org/10.1098/rsif.2013.1071 (2014).
Article  PubMed  PubMed Central  Google Scholar 

16.
Prowse, T. A. A. et al. Dodging silver bullets: good CRISPR gene-drive design is critical for eradicating exotic vertebrates. Proc. R. Soc. B https://doi.org/10.1098/rspb.2017.0799 (2017).
Article  PubMed  Google Scholar 

17.
Lowe, S., Browne, M., Boudjelas, S. & De Poorter, M. 100 of the World’s Worst Invasive Alien Species. A Selection from the Global Invasive Species Database Vol. 12 (The Invasive Species Specialist Group (ISSG) a specialist group of the Species Survival Commission (SSC) of the World Conservation Union (IUCN), Auckland, 2000).
Google Scholar 

18.
Lester, P. J. & Beggs, J. R. Invasion success and management strategies for social Vespula wasps. Annu. Rev. Entomol. 64, 51–71. https://doi.org/10.1146/annurev-ento-011118-111812 (2019).
CAS  Article  PubMed  Google Scholar 

19.
Lester, P. J. et al. Determining the origin of invasions and demonstrating a lack of enemy release from microsporidian pathogens in common wasps (Vespula vulgaris). Divers. Distrib. 20, 964–974. https://doi.org/10.1111/ddi.12223 (2014).
Article  Google Scholar 

20.
Harris, R. J. Diet of the wasps Vespula vulgaris and V. germanica in honeydew beech forest of the South Island, New Zealand. N. Z. J. Zool. 18, 159–169 (1991).
Article  Google Scholar 

21.
Grangier, J. & Lester, P. J. A novel interference behaviour: invasive wasps remove ants from resources and drop them from a height. Biol. Lett. 7, 664–667. https://doi.org/10.1098/rsbl.2011.0165 (2011).
Article  PubMed  PubMed Central  Google Scholar 

22.
Wilson, P. R., Karl, B. J., Toft, R. J., Beggs, J. R. & Taylor, R. H. The role of introduced predators and competitors in the decline of kaka (Nestor meridionalis) populations in New Zealand. Biol. Conserv. 83, 175–185. https://doi.org/10.1016/S0006-3207(97)00055-4 (1998).
Article  Google Scholar 

23.
Dobelmann, J. et al. Fitness in invasive social wasps: the role of variation in viral load, immune response and paternity in predicting nest size and reproductive output. Oikos 126, 1208–1218. https://doi.org/10.1111/oik.04117 (2017).
CAS  Article  Google Scholar 

24.
Sekine, K., Furusawa, T. & Hatakeyama, M. The boule gene is essential for spermatogenesis of haploid insect male. Dev. Biol. 399, 154–163. https://doi.org/10.1016/j.ydbio.2014.12.027 (2015).
CAS  Article  PubMed  Google Scholar 

25.
Ferree, P. M. et al. Identification of genes uniquely expressed in the germ-line tissues of the jewel wasp Nasonia vitripennis. G3-Genes Genom. Genet. 5, 2647–2653. https://doi.org/10.1534/g3.115.021386 (2015).
CAS  Article  Google Scholar 

26.
Mikhaylova, L. M., Boutanaev, A. M. & Nurminsky, D. I. Transcriptional regulation by Modulo integrates meiosis and spermatid differentiation in male germ line. Proc. Natl. Acad. Sci. U.S.A. 103, 11975–11980. https://doi.org/10.1073/pnas.0605087103 (2006).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Parsch, J., Meiklejohn, C. D., Hauschteck-Jungen, E., Hunziker, P. & Hartl, D. L. Molecular evolution of the ocnus and janus genes in the Drosophila melanogaster species subgroup. Mol. Biol. Evol. 18, 801–811. https://doi.org/10.1093/oxfordjournals.molbev.a003862 (2001).
CAS  Article  PubMed  Google Scholar 

28.
Dang, Y. et al. Optimizing sgRNA structure to improve CRISPR-Cas9 knockout efficiency. Genome Biol. 16, 280. https://doi.org/10.1186/s13059-015-0846-3 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Yarrington, R. M., Verma, S., Schwartz, S., Trautman, J. K. & Carroll, D. Nucleosomes inhibit target cleavage by CRISPR-Cas9 in vivo. Proc. Natl. Acad. Sci. U.S.A. 115, 9351–9358. https://doi.org/10.1073/pnas.1810062115 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

30.
Chaverra-Rodriguez, D. et al. Targeted delivery of CRISPR-Cas9 ribonucleoprotein into arthropod ovaries for heritable germline gene editing. Nat. Commun. 9, 3008. https://doi.org/10.1038/s41467-018-05425-9 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

31.
Noble, C. et al. Daisy-chain gene drives for the alteration of local populations. Proc. Natl. Acad. Sci. U.S.A. 116, 8275–8282. https://doi.org/10.1073/pnas.1716358116 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
KaramiNejadRanjbar, M. et al. Consequences of resistance evolution in a Cas9-based sex conversion-suppression gene drive for insect pest management. Proc. Natl. Acad. Sci. U.S.A. 115, 6189–6194. https://doi.org/10.1073/pnas.1713825115 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Brenton-Rule, E. C. et al. The origins of global invasions of the German wasp (Vespula germanica) and its infection with four honey bee viruses. Biol. Invasions 20, 3445–3460. https://doi.org/10.1007/s10530-018-1786-0 (2018).
Article  Google Scholar 

34.
Schmack, J. M. et al. Lack of genetic structuring, low effective population sizes and major bottlenecks characterise common and German wasps in New Zealand. Biol. Invasions 21, 3185–3201. https://doi.org/10.1007/s10530-019-02039-0 (2019).
Article  Google Scholar 

35.
Tanaka, H., Stone, H. A. & Nelson, D. R. Spatial gene drives and pushed genetic waves. Proc. Natl. Acad. Sci. U.S.A. 114, 8452–8457. https://doi.org/10.1073/pnas.1705868114 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Hammond, A. et al. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat. Biotechnol. 34, 78–83. https://doi.org/10.1038/nbt.3439 (2016).
CAS  Article  PubMed  Google Scholar 

37.
Marshall, J. M., Buchman, A., Sanchez, C. H. & Akbari, O. S. Overcoming evolved resistance to population-suppressing homing-based gene drives. Sci. Rep. 7, 3776. https://doi.org/10.1038/s41598-017-02744-7 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Eckhoff, P. A., Wenger, E. A., Godfray, H. C. & Burt, A. Impact of mosquito gene drive on malaria elimination in a computational model with explicit spatial and temporal dynamics. Proc. Natl. Acad. Sci. U.S.A. 114, E255–E264. https://doi.org/10.1073/pnas.1611064114 (2017).
CAS  Article  PubMed  Google Scholar 

39.
North, A., Burt, A. & Godfray, H. C. Modelling the spatial spread of a homing endonuclease gene in a mosquito population. J. Appl. Ecol. 50, 1216–1225. https://doi.org/10.1111/1365-2664.12133 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Kirk, N., Kannemeyer, R., Greenaway, A., MacDonald, E. & Stronge, D. Understanding attitudes on new technologies to manage invasive species. Pac. Conserv. Biol. https://doi.org/10.1071/pc18080 (2019).
Article  Google Scholar 

41.
Mercier, O. R., KingHunt, A. & Lester, P. J. Novel biotechnologies for eradicating wasps: seeking Māori studies students’ perspectives with Q method. Kōtuitui N. Z. J. Soc. Sci. 14, 136–156. https://doi.org/10.1080/1177083x.2019.1578245 (2019).
Article  Google Scholar 

42.
Peters, R. S. et al. Evolutionary history of the Hymenoptera. Curr. Biol. 27, 1013–1018. https://doi.org/10.1016/j.cub.2017.01.027 (2017).
CAS  Article  PubMed  Google Scholar 

43.
Stein, K. J. & Fell, R. D. Correlation of queen sperm content with colony size in yellowjackets (Hymenoptera: Vespidae). Environ. Entomol. 23, 1497–1500. https://doi.org/10.1093/ee/23.6.1497 (1994).
Article  Google Scholar 

44.
Lester, P. J., Haywood, J., Archer, M. E. & Shortall, C. R. The long-term population dynamics of common wasps in their native and invaded range. J. Anim. Ecol. 86, 337–347. https://doi.org/10.1111/1365-2656.12622 (2017).
Article  PubMed  Google Scholar 

45.
Burt, A. & Deredec, A. Self-limiting population genetic control with sex-linked genome editors. Proc. R. Soc. B https://doi.org/10.1098/rspb.2018.0776 (2018).
Article  PubMed  Google Scholar 

46.
Prowse, T. A., Adikusuma, F., Cassey, P., Thomas, P. & Ross, J. V. A Y-chromosome shredding gene drive for controlling pest vertebrate populations. eLife 8, e41873. https://doi.org/10.7554/eLife.41873 (2019).
Article  PubMed  PubMed Central  Google Scholar 

47.
Li, J. et al. Can CRISPR gene drive work in pest and beneficial haplodiploid species?. Evol. Appl. https://doi.org/10.1111/eva.13032 (2020).
Article  PubMed  PubMed Central  Google Scholar 

48.
Esvelt, K. M. & Gemmell, N. J. Conservation demands safe gene drive. PLoS Biol. 15, e2003850. https://doi.org/10.1371/journal.pbio.2003850 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

49.
Piaggio, A. J. et al. Is it time for synthetic biodiversity conservation?. Trends Ecol. Evol. 32, 97–107. https://doi.org/10.1016/j.tree.2016.10.016 (2017).
Article  PubMed  Google Scholar 

50.
Edgington, M. P., Harvey-Samuel, T. & Alphey, L. Population-level multiplexing, a promising strategy to manage the evolution of resistance against gene drives targeting a neutral locus. Evol. Appl. https://doi.org/10.1111/eva.12945 (2020).
Article  Google Scholar 

51.
Sumner, S., Law, G. & Cini, A. Why we love bees and hate wasps. Ecol. Entomol. 43, 836–845. https://doi.org/10.1111/een.12676 (2018).
Article  Google Scholar 

52.
Southon, R. J., Fernandes, O. A., Nascimento, F. S. & Sumner, S. Social wasps are effective biocontrol agents of key lepidopteran crop pests. Proc. R. Soc. B https://doi.org/10.1098/rspb.2019.1676 (2019).
Article  PubMed  Google Scholar 

53.
Harris, R. J., Thomas, C. D. & Moller, H. The influence of habitat use and foraging on the replacement of one introduced wasp species by another in New Zealand. Ecol. Entomol. 16, 441–448. https://doi.org/10.1111/j.1365-2311.1991.tb00237.x (1991).
Article  Google Scholar 

54.
Lester, P. J. et al. Critical issues facing New Zealand entomology. N. Z. Entomol. 37, 1–13. https://doi.org/10.1080/00779962.2014.861789 (2014).
Article  Google Scholar 

55.
Hare, K. M. et al. Intractable: species in New Zealand that continue to decline despite conservation efforts. J. R. Soc. N. Z. 49, 301–319. https://doi.org/10.1080/03036758.2019.1599967 (2019).
Article  Google Scholar 

56.
Hu, X. F., Zhang, B., Liao, C. H. & Zeng, Z. J. High-Efficiency CRISPR/Cas9-mediated gene editing in honeybee (Apis mellifera) embryos. G3-Genes Genom. Genet. 9, 1759–1766. https://doi.org/10.1534/g3.119.400130 (2019).
CAS  Article  Google Scholar 

57.
Yan, H. et al. An engineered orco mutation produces aberrant social behavior and defective neural development in ants. Cell 170, 736-747 e739. https://doi.org/10.1016/j.cell.2017.06.051 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Oksanen, J. et al. vegan: community ecology package. (R package version 2.4-0. https://CRAN.R-project.org/package=vegan, 2016). More

1.
Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature464, 1334–1337 (2010).
Google Scholar 
2.
Serreze, M. C. & Barry, R. G. Processes and impacts of Arctic amplification: a research synthesis. Glob. Plan. Change77.1, 85–96 (2011).
Google Scholar 

3.
Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (IPCC, Cambridge Univ. Press, 2013).

4.
Zhang, X. et al. Enhanced poleward moisture transport and amplified northern high-latitude wetting trend. Nat. Clim. Change3, 47–51 (2013).
Google Scholar 

5.
Kattsov, V. M. et al. Simulation and projection of Arctic freshwater budget components by the IPCC AR4 global climate models. J. Hydrometeor.8, 571–589 (2007).
Google Scholar 

6.
Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change7, 263–267 (2017).
Google Scholar 

7.
Vihma, T. Effects of Arctic sea ice decline on weather and climate: a review. Surv. Geophys.35, 1175–1214 (2014).
Google Scholar 

8.
van der Kolk, H. J. et al. Potential Arctic tundra vegetation shifts in response to changing temperature, precipitation and permafrost thaw. Biogeosci13, 6229 (2016).
Google Scholar 

9.
Fujinami, H., Yasunari, T. & Watanabe, T. Trend and interannual variation in summer precipitation in eastern Siberia in recent decades. Int. J. Climatol.36, 355–368 (2016).
Google Scholar 

10.
Kopec, B. G., Feng, X., Michel, F. A. & Posmentier, E. S. Influence of sea ice on Arctic precipitation. Proc. Nat. Acad. Sci. USA113, 46–51 (2016).
Google Scholar 

11.
Bintanja, R. & Selten, F. M. Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat. Nature509, 479–482 (2014).
Google Scholar 

12.
Park, T. et al. Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Env. Res. Lett.11, 084001 (2016).
Google Scholar 

13.
Koenigk, T. et al. Arctic climate change in 21st century CMIP5 simulations with EC-Earth. Clim. Dyn. 111–12, 2719–2743 (2013).
Google Scholar 

14.
Peterson, B. J. et al. Increasing river discharge to the Arctic Ocean. Science 13298, 2171–2173 (2002).
Google Scholar 

15.
Euskirchen, E. S. et al. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high‐latitude ecosystems. Glob. Change Biol.12, 731–750 (2006).
Google Scholar 

16.
Neumann, R. B. Warming effects of spring rainfall increase methane emissions from thawing permafrost. Geophys. Res. Lett.46, 1393–1401 (2019).
Google Scholar 

17.
Vincent, W. F., Lemay, M. & Allard, M. Arctic permafrost landscapes in transition: towards an integrated Earth system approach. Arct. Sci.3, 39–64 (2017).
Google Scholar 

18.
Payette, S., Delwaide, A., Caccianiga, M. & Beauchemin, M. Accelerated thawing of subarctic peatland permafrost over the last 50 years. Geophys. Res. Lett.31, 18 (2014).
Google Scholar 

19.
Åkerman, H. J. & Johansson, M. Thawing permafrost and thicker active layers in sub‐arctic Sweden. Permafr. Periglac. Proc.19, 279–292 (2008).
Google Scholar 

20.
Iijima, Y. et al. Abrupt increases in soil temperatures following increased precipitation in a permafrost region, central Lena River basin, Russia. Permafr. Periglac. Proc.21, 30–41 (2010).
Google Scholar 

21.
Jorgenson, M. T., Shur, Y. L. & Pullman, E. R. Abrupt increase in permafrost degradation in Arctic Alaska. Geophys. Res. Lett.33, 2 (2006).
Google Scholar 

22.
Kokfelt, U. et al. Ecosystem responses to increased precipitation and permafrost decay in subarctic Sweden inferred from peat and lake sediments. Glob. Change Biol.15, 1652–1663 (2009).
Google Scholar 

23.
Loranty, M. M. et al. Changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosci.15, 5287–5313 (2018).
Google Scholar 

24.
Shiklomanov, N. I., Streletskiy, D. A., Little, J. D. & Nelson, F. E. Isotropic thaw subsidence in undisturbed permafrost landscapes. Geophys. Res. Lett.40, 6356–6361 (2013).
Google Scholar 

25.
Shur, Y. L. & Jorgenson, M. T. Patterns of permafrost formation and degradation in relation to climate and ecosystems. Permafr. Periglac. Proc.18, 7–19 (2007).
Google Scholar 

26.
Jorgenson, M. T. et al. Resilience and vulnerability of permafrost to climate change. Can. J. Res.40, 1219–1236 (2010).
Google Scholar 

27.
Chapin, F. S. III, Van Cleve, K. & Chapin, M. C. Soil temperature and nutrient cycling in the tussock growth form of Eriophorum vaginatum. J. Ecol. Mar.1, 169–189 (1979).
Google Scholar 

28.
Fisher, J. P. et al. The influence of vegetation and soil characteristics on active‐layer thickness of permafrost soils in boreal forest. Glob. Change Biol.22, 3127–3140 (2016).
Google Scholar 

29.
Brown, D. et al. Interactive effects of wildfire and climate on permafrost degradation in Alaskan lowland forests. J. Geophys. Res. Biogeosci.120, 1619–1637 (2015).
Google Scholar 

30.
Jafarov, E. E., Romanovsky, V. E., Genet, H., McGuire, A. D. & Marchenko, S. S. The effects of fire on the thermal stability of permafrost in lowland and upland black spruce forests of interior Alaska in a changing climate. Environ. Res. Lett.8, 035030 (2013).
Google Scholar 

31.
Vogelmann, J. E. et al. Completion of the 1990s National Land Cover Data Set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogram. Engin. Rem. Sens.67, 6 (2001).
Google Scholar 

32.
Van Tatenhove, F. G. & Olesen, O. B. Ground temperature and related permafrost characteristics in West Greenland. Permafr. Periglac. Proc.5, 199–215 (1994).
Google Scholar 

33.
Hinkel, K. M., Paetzold, F., Nelson, F. E. & Bockheim, J. G. Patterns of soil temperature and moisture in the active layer and upper permafrost at Barrow, Alaska: 1993–1999. Glob. Planet. Change29, 293–309 (2001).
Google Scholar 

34.
Harris, S. A. Causes and consequences of rapid thermokarst development in permafrost or glacial terrain. Permafr. Periglac. Proc.13, 237–242 (2002).
Google Scholar 

35.
Ling, F. & Zhang, T. A numerical model for surface energy balance and thermal regime of the active layer and permafrost containing unfrozen water. Cold Reg. Sci. Technol.38, 1–5 (2004).
Google Scholar 

36.
Price, A. G., Dunham, K., Carleton, T. & Band, L. Variability of water fluxes through the black spruce (Picea mariana) canopy and feather moss (Pleurozium schreberi) carpet in the boreal forest of Northern Manitoba. J. Hydrol.196, 310–323 (1997).
Google Scholar 

37.
Hamada, S. et al. Hydrometeorological behaviour of pine and larch forests in eastern Siberia. Hydrol. Proc.18, 23–39 (2004).
Google Scholar 

38.
Goetz, J. D. & Price, J. S. Role of morphological structure and layering of Sphagnum and Tomenthypnum mosses on moss productivity and evaporation rates. Can. J. Soil Sci.95, 109–124 (2015).
Google Scholar 

39.
Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci.9, 312 (2016).
Google Scholar 

40.
Gibson, C. M. et al. Wildfire as a major driver of recent permafrost thaw in boreal peatlands. Nat. Comm.9, 3041 (2018).
Google Scholar 

41.
Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat. Comm.10, 1329 (2019).
Google Scholar 

42.
Johnstone, J. F. et al. Fire, climate change, and forest resilience in interior Alaska. Can. J. Res.40, 1302–1312 (2010).
Google Scholar 

43.
Juszak, I., Eugster, W., Heijmans, M. M. & Schaepman-Strub, G. Contrasting radiation and soil heat fluxes in Arctic shrub and wet sedge tundra. Biogeosci.13, 404940–404964 (2016).
Google Scholar 

44.
Shur, Y., Hinkel, K. M. & Nelson, F. E. The transient layer: implications for geocryology and climate‐change science. Permafr. Periglac. Proc.16, 5–17 (2005).
Google Scholar 

45.
Barker, A. J. et al. Late season mobilization of trace metals in two small Alaskan arctic watersheds as a proxy for landscape scale permafrost active layer dynamics. Chem. Geo.381, 180–193 (2014).
Google Scholar 

46.
Khosh, M. S. et al. Seasonality of dissolved nitrogen from spring melt to fall freezeup in Alaskan Arctic tundra and mountain streams. J. Geophys. Res. Biogeosci.122, 1718–1737 (2017).
Google Scholar 

47.
Loiko, S. V. et al. Abrupt permafrost collapse enhances organic carbon, CO2, nutrient and metal release into surface waters. Chem. Geo.471, 153–165 (2017).
Google Scholar 

48.
Jorgenson, M. T., Racine, C. H., Walters, J. C. & Osterkamp, T. E. Permafrost degradation and ecological changes associated with a warming climate in central Alaska. Clim. Change48, 551–579 (2001).
Google Scholar 

49.
Douglas, T., Jones, M. C., Hiemstra, C. A. & Arnold, J. R. Sources and sinks of carbon in boreal ecosystems of interior Alaska: A review. Elem. Sci. Anth.12, 2 (2014).
Google Scholar 

50.
Douglas, T. A. et al. Degrading permafrost mapped with electrical resistivity tomography, airborne imagery and LiDAR, and seasonal thaw measurements. Geophys81, WA71-WA85 (2015).
Google Scholar  More

The relevant bands that were used for sample evaluation are compiled in Table 1. Band positions are indicated according to Smith47 and Guo and Bustin48. The list is limited to visible band maxima. Aging/oxidation lead to interactions (e.g. H-bonds) and subsequently to broadening of bands49. Strong bands of inorganic components overlap smaller organic bands. Nevertheless, underlying features are included by multivariate data analysis of the spectral pattern. Data analyses were performed with selected wavenumber regions.
Table 1 Wavenumber position and assignment of functional groups.
Full size table

Stepwise procedure in the interpretation of sample sets from additional sites
Principal Component Analysis (PCA) of infrared spectra (wavenumber regions of 4,000–2,400 cm−1 and 1,800–400 cm−1) is performed for all samples (reference samples and dated sample sets). If their scores correspond to the age determined by the reference method, it is indicative that alteration proceeds according to the spectral pattern of the reference samples. Otherwise, further investigations are necessary as described below.
Trend of the spectral pattern of natural charcoal aging
Due to the determined age, it can be assumed that all samples were pyrolyzed at temperatures  > 400 °C. Charcoals produced at lower temperatures are more affected by microbial degradation as shown by incubation50, field experiments42 and chemical oxidation17. During the aging process a common succession of the spectral changes is noticeable. Figure 1a and b display the PCA (4,000–2,400 cm−1 and 1,800–400 cm−1) of reference samples from Austria (recent, A, B, C) and sample sets of charcoals from Brazil (Rio) and Germany (Wittnau WI and Iznang IZ). Despite the heterogeneity within the group, their scores in PC1 are proportional to age. The loadings plot (Fig. 1b) clearly reveals the relevance of the organic bands with regard to the aging process.
Figure 1

(a) Scores plot and (b) corresponding loadings plot of the PCA based on the wavenumber regions 4,000–2,400 cm−1 and 1,800–400 cm−1, (c) average spectra (wavenumber range 1,800–1,100 cm−1) of Austrian reference samples (rec, A1800 CE, B13th–early 15th cent. CE, C15th–13th cent. BCE) and samples from Brazil (Rio18th–19th cent. CE) and Germany (WI15th–17th cent. CE and IZ3280–3250 BCE).

Full size image

The average infrared spectra of each sample set are shown in Fig. 1c to support the loadings interpretation. They feature the characteristic development of relevant bands in the region from 1,800 cm−1 to 1,100 cm−1: the increase in intensity of the carboxylate bands (1,585–1,565 cm−1 and 1,385–1,375 cm−1) and the concomitant increase, followed by a relative decrease in the oldest samples, of the carboxylic acid bands at about 1705 cm−1 and 1,260–1,250 cm−1. This decline is observed starting from the fifteenth to the thirteenth century BCE (reference samples “C”). The samples from Brazil (Rio), Wittnau (WI) and Iznang (IZ) fit in the series according to their determined age. Emerging functional groups and changes of band intensities in the carboxylic/carboxylate region are paralleled by a corresponding increase of the O–H stretch band in the spectral region 3,500–2,500 cm−1, which is in accordance with the increasing hydrophilicity due to oxidation (as shown in Fig. 3). Despite different sampling sites (Germany, Brazil) and therefore different climatic conditions, the oxidation process follows a useful regularity. The degree of carbonization seems more important than some differences in the environment, as confirmed by litterbag experiments, where degradation was generally highest in 500 °C chars and lowest in 300 °C chars, independent of storage conditions such as soil surface, litter, or layer of limestone chips42. Changes regarding drought, humidity and temperatures might be counterbalanced over the long-lasting residence time in the environment. Nevertheless, some environmental conditions have a strong impact on alteration or preservation15. Heterogeneity within groups is pronounced from the nineteenth to the thirteenth century CE, whereas older groups become more uniform. Over a period of millennia, the relevance of individual degrees of carbonization or environmental exposure abates and only samples with high degrees of carbonization and appropriate environmental conditions remain.
Archeological sites Bodnegg and Olzreute
Two sample sets (Bodnegg and Olzreute) do not fit in the series of reference samples in terms of their spectral features. Despite the age of several thousand years (3950–3650 BCE and 2900–2820 BCE, respectively), sample positions in the scores plot of the PCA (not shown), based on all reference samples and the wavenumber regions 4,000–2,400 cm−1 and 1,800–400 cm−1, are close to recent samples (rec) or overlap samples from reference set “A” (about 1800 CE). This first PCA was the basis for the second PCA (Fig. 2), which emphasizes these two relevant periods. This second PCA used the wavenumber regions 4,000–2,400 cm−1 and 1,800–1,100 cm−1. The wavenumber region from 1,100 to 400 cm−1 was excluded, as mineral compounds were not visible in the spectra.
Figure 2

Scores plot of the PCA based on infrared spectra (wavenumber regions 4,000–2,400 cm−1 and 1,800–1,100 cm−1) of reference samples (rec, A) and (a) samples from Bodnegg (BO) and (b) samples from Olzreute (OL); squares indicate samples (BO1, BO2, OL1, OL2) for single spectra (Fig. 3) and 14C analyses (see below).

Full size image

Figure 3

Average infrared spectra (wavenumber regions 4,000–2,400 cm−1 and 1,800–1,100 cm−1) of reference samples (rec, A, B) and single spectra of marked samples (Fig. 2) from Bodnegg (BO1 and BO2) and Olzreute (OL1 and OL2).

Full size image

According to their position in the scores plot, close to either recent samples (rec) or reference samples “A”, single spectra of the marked samples in Fig. 2 from both groups (BO1 and BO2, OL1 and OL2) are displayed in Fig. 3. The wide variability of intensities in the carboxyl/carboxylate region indicates different partial oxidation degrees among different BO and OL samples (within-group variation).
As the spectra of BO2 and OL2 feature properties of reference samples C (increase of the bands at 1,585–1,565 cm−1 and 1,385–1,375 cm−1 and the concomitant decrease of the bands at 1,705 cm−1 and 1,260 cm−1), a SIMCA (Soft Independent Modeling of Class Analogy) was calculated to find out the membership of the samples.
Most of the samples are located in the area “neither–nor”, but closer to the class “rec” than “C”. Only 7 out of 125 samples from Bodnegg, and none from Olzreute, are assigned to the class “C”, as would be expected from their determined age.
The similarity to recent samples or reference samples “A” (i.e., low degree of partial oxidation) indicates some protective mechanism from aging or high charcoal recalcitrance, which could be provided by a high degree of carbonization. The minor evidence of aging and the discrepancy between the indicated age and the spectral signature required additional investigation.
The scores plot of the PCA (Fig. 2) and the Coomans plot (Fig. 4) reveal the heterogeneity within these groups (BO and OL), which raises the question of whether charcoals with a wider range of age coexist in the same sites. In such cases FT-IR spectroscopy provides advantageous information as it allows analyses of huge sample sets due to low costs and a rapid procedure. The spectral feature can be used for sample screening to confirm previous dating results or to initiate additional investigations. Two samples from each site, Olzreute and Bodnegg, with the highest distance in the scores plot (see squares in Fig. 2) were subjected to 14C-dating, which confirmed the same age for both contrasting BO- and OL-samples. Therefore, we can conclude that a high degree of carbonization and/or special environmental conditions are responsible for the preservation.
Figure 4

Coomans plot representing the membership of samples from Bodnegg (BO) and Olzreute (OL); classification by a SIMCA model based on infrared spectra (wavenumber regions 4,000–2,400 cm−1 and 1,800–1,100 cm−1) with defined classes “rec” and “C”15th–13th cent. BCE; significance level 5% (black lines).

Full size image

In the next step, the carbonization temperature that the wood was exposed to, was determined based on spectral characteristics using an established prediction model51. It has to be emphasized that the prediction model has been calibrated with fresh charcoals from laboratory experiments51 and applied on recent traditional kiln processes52. The aging effect is not considered in the model. Prediction results for the current sample sets are presented in Fig. 5a and indicate that many charcoal samples from Olzreute (OL) and Bodnegg (BO) were subjected to similar temperatures as kiln samples52. Comparison of standard deviations of all sample sets (Fig. 5b) confirms that the application of the temperature prediction model is limited to charcoal samples that are similar to recent charcoals. High standard deviations indicate that the material departed from material characteristics of the calibrated recent charcoals.
Figure 5

Boxplots representing (a) the temperature prediction of recent samples and samples from Bodnegg (BO) and Olzreute (OL) and (b) the standard deviations of temperature prediction for all charcoal sample sets.

Full size image

It has to be emphasized that the sample set “rec” comprises samples with a high carbonization degree. It is not evident that such carbonization conditions can be presupposed for all pyrolysis processes to which charcoals were subjected at archeological sites. Reflectance measurements might provide more information about the production temperature, at least for  > 400 °C35,48. The boxplots show that most samples BO and OL had been exposed to temperatures  > 400 °C, half of them even  > 580 °C corresponding to high thermal alteration.
Apart from the high degree of carbonization in the BO and OL samples, environmental conditions have to be considered. According to the archeological information about the sampling sites, charcoals from Bodnegg and Olzreute were embedded in a permanently wet peat. As oxygen availability and accessibility are essential factors for degradation, anoxic environments such as waterlogged ecosystems, peats and river sediments foster preservation15. Anaerobic environmental conditions over longer periods of time, together with the high degree of carbonization, seem to be responsible for the good state of preservation. Lab experiments with a strong chemical oxidative reagent revealed a considerable resistance of charcoals produced at 600 °C against oxidation17.
Sample set with partially high content of mineral compounds (site Speckhau)
The spectral pattern of the sample set from Speckhau (SP) conspicuously indicates the environment where the charcoals were buried. These charcoals originated from a tumulus and were embedded in a mineral matrix. Mineral components (silicates, clay) had permeated the pores and could not be removed without additional chemical methods. According to Huisman et al.53, who investigated remains of a Neolithic settlement, alkalinity is a main factor for charcoal alteration and pedological processes that lead to clay coatings. Figure 6a displays the average spectra of both samples containing high mineral contents (SP-H) and samples with a low content (SP-L).
Figure 6

(a) Average FTIR-ATR spectra of samples with low (SP-L) and high (SP-H) mineral content; (b) scores plot of the PCA based on infrared spectra (wavenumber range 4,000–2,400 cm−1 and 1,800–400 cm−1) for all samples (SP) and reference samples (rec, A, B, C); (c) corresponding loadings plot of the 1st and the 2nd PC; (d) scores plot of the Varimax-Rotation based on infrared spectra (wavenumber range 4,000–2,400 cm−1 and 1,800–400 cm−1) for all samples (SP) and reference samples (rec, A, B, C); (e) corresponding loadings plot of the rotated component 1 and rotated component 2.

Full size image

The high mineral content with intense infrared bands (e.g. Si–O at about 1,030 cm−1)54 obliterates other bands, with organic indicator bands disappearing almost completely. In 21 (SP-L) out of 40 samples the characteristic indicator bands of aging are at least visible. The PCA (Fig. 6b) based on infrared spectra of the whole sample set in the wavenumber range 4,000–2,400 cm−1 and 1,800–400 cm−1 illustrates the conspicuous heterogeneity due to different portions of mineral components. The corresponding loadings plot (Fig. 6c) of the 1st and the 2nd Principal Component (PC) reveals the spectral regions that are responsible for the main variance (explained variance by PC1 and PC2: 74% and 18%, respectively). Besides the characteristic spectral regions that represent the aging process ( > 1,200 cm−1), the dominant contribution of mineral components with atom pairs with large reduced mass, such as Si–O, Al–O, Fe–O etc., resulting in stretching bands at low wavenumbers (region  More

1.
Cragg, S. M. et al. Lignocellulose degradation mechanisms across the tree of Life. Curr. Opin. Chem. Bio. 29, 108–119. https://doi.org/10.1016/j.cbpa.2015.10.018 (2015).
Article  CAS  Google Scholar 
2.
Sticklen, M. B. Plant genetic engineering for biofuel production: towards affordable cellulosic ethanol. Nat. Rev. Genet. 9, 433–443. https://doi.org/10.1038/nrg2336 (2008).
Article  PubMed  CAS  Google Scholar 

3.
Guerriero, G., Hausman, J., Strauss, J., Ertan, H. & Siddiqui, K. S. Lignocellulosic biomass: biosynthesis, degradation, and industrial utilization. Eng. Life Sci. 16, 1–16. https://doi.org/10.1002/elsc.201400196 (2016).
Article  CAS  Google Scholar 

4.
Morrison, M., Pope, P. B., Denman, S. E. & McSweeney, C. S. Plant biomass degradation by gut microbiomes: more of the same or something new?. Curr. Opin. Biotechnol. 20, 358–363. https://doi.org/10.1016/j.copbio.2009.05.004 (2009).
Article  PubMed  CAS  Google Scholar 

5.
Karasov, W. H., del Rio, C. M. & Caviedes-Vidal, E. Ecological physiology of diet and digestive systems. Annu. Rev. Physiol. 73, 69–93. https://doi.org/10.1146/annurev-physiol-012110-142152 (2011).
Article  PubMed  CAS  Google Scholar 

6.
Engel, P. & Moran, N. A. The gut microbiota of insects — diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735. https://doi.org/10.1111/1574-6976.12025 (2013).
Article  PubMed  CAS  Google Scholar 

7.
Hansen, A. K. & Moran, N. A. The impact of microbial symbionts on host plant utilization by herbivorous insects. Mol. Ecol. 23, 1473–1496. https://doi.org/10.1111/mec.12421 (2013).
Article  PubMed  Google Scholar 

8.
Kohl, K. D., Connelly, J. W., Dearing, M. D. & Forbey, J. S. Microbial detoxification in the gut of a specialist avian herbivore, the Greater Sage-Grouse. FEMS Microbiol.Lett. 363, fnw144. https://doi.org/10.1093/femsle/fnw144 (2016).
Article  PubMed  CAS  Google Scholar 

9.
Mueller, U. G., Gerardo, N. M., Aanen, D. K., Six, D. L. & Schultz, T. R. The evolution of agriculture in insects. Annu. Rev. Ecol. Evol. Syst. 36, 563–595. https://doi.org/10.1146/annurev.ecolsys.36.102003.152626 (2005).
Article  Google Scholar 

10.
Mayhé-Nunes, A. J. & Jaffé, K. On the biogeography of Attini (Hymenoptera: Formicidae). Ecotropicos 11, 45–54 (1998).
Google Scholar 

11.
Ward, P. S., Brady, S. G., Fisher, B. L. & Schultz, T. R. The evolution of myrmicine ants: phylogeny and biogeography of a hyperdiverse ant clade (Hymenoptera: Formicidae). Syst. Entomol. 40, 61–81. https://doi.org/10.1111/syen.12090 (2015).
Article  Google Scholar 

12.
Jordal, B. H. & Cognato, C. Molecular phylogeny of bark and ambrosia beetles reveals multiple origins of fungus farming during periods of global warming. BMC Evol. Biol. 12, 133. https://doi.org/10.1186/1471-2148-12-133 (2012).
Article  PubMed  PubMed Central  CAS  Google Scholar 

13.
Nobre, T., Rouland-Lefevre, C. & Aanen, D. K. Comparative biology of fungus cultivation in termites and ants. In Biology of termites: a modern synthesis, Chapter 8, 193–210 (eds Bignell, D. E. et al.) (Springer, Berlin, 2011).
Google Scholar 

14.
Aylward, F. O. et al. Leucoagaricus gongylophorus produces diverse enzymes for the degradation of recalcitrant plant polymers in leaf-cutter ant fungus gardens. Appl. Environ. Microbiol. 79, 3770–3778. https://doi.org/10.1128/AEM.03833-12 (2013).
Article  PubMed  PubMed Central  CAS  Google Scholar 

15.
Khadempour, L. et al. The fungal cultivar of leaf-cutter ants produces specific enzymes in response to different plant substrates. Mol. Ecol. 25, 5795–5805. https://doi.org/10.1111/mec.13872 (2016).
Article  PubMed  PubMed Central  CAS  Google Scholar 

16.
Vigueras, G. et al. Growth and enzymatic activity of Leucoagaricus gongylophorus, a mutualistic fungus isolated from the leaf-cutting ant Atta mexicana, on cellulose and lignocellulosic biomass. Lett. Appl. Microbiol. 65, 173–181. https://doi.org/10.1111/lam.12759 (2017).
Article  PubMed  CAS  Google Scholar 

17.
Poulsen, M. et al. Complementary symbiont contributions to plant decomposition in a fungus-farming termite. Proc. Natl. Acad. Sci. USA 111, 14500–14505. https://doi.org/10.1073/pnas.1319718111 (2014).
ADS  Article  PubMed  CAS  Google Scholar 

18.
Hyodo, F., Inoue, T., Azuma, J. I., Tayasu, I. & Abe, T. Role of the mutualistic fungus in lignin degradation in the fungus-growing termite Macrotermes gilvus (Isoptera; Macrotermitinae). Soil Biol. Biochem. 32, 653–658. https://doi.org/10.1016/S0038-0717(99)00192-3 (2000).
Article  CAS  Google Scholar 

19.
Hyodo, F. et al. Differential role of symbiotic fungi in lignin degradation and food provision for fungus-growing termites (Macrotermitinae: Isoptera). Funct. Ecol. 17, 186–193. https://doi.org/10.1046/j.1365-2435.2003.00718.x (2003).
Article  Google Scholar 

20.
De Fine Lich, H. H. & Biedermann, P. H. W. Patterns of functional enzyme activity in fungus farming ambrosia beetles. Front. Zool. 9, 13. https://doi.org/10.1186/1742-9994-9-13 (2012).
Article  CAS  Google Scholar 

21.
Lange, L. & Grell, M. N. The prominent role of fungi and fungal enzymes in the ant–fungus biomass conversion symbiosis. Appl. Microbiol. Biotechnol. 98, 4839–4851. https://doi.org/10.1007/s00253-014-5708-5 (2014).
Article  PubMed  CAS  Google Scholar 

22.
Collins, N. M. The role of termites in the decomposition of wood and leaf litter in the Southern Guinea savanna of Nigeria. Oecologia 51, 389–399. https://doi.org/10.1007/BF00540911 (1981).
ADS  Article  PubMed  CAS  Google Scholar 

23.
Beaver, R. A. Insect-fungus relationships in the bark and ambrosia beetles. In Insect-fungus interactions (eds Wilding, N. et al.) 121–143 (Academic Press, Cambridge, 1989).
Google Scholar 

24.
Kok, L. T., Norrisd, M. & Chu, H. M. Sterol metabolism as a basis for mutualistic symbiosis. Nature 225, 661–662. https://doi.org/10.1038/225661b0 (1970).
ADS  Article  PubMed  CAS  Google Scholar 

25.
Six, D. L. Ecological and evolutionary determinants of bark beetle-fungus symbioses. Insects 3, 339–366. https://doi.org/10.3390/insects3010339 (2012).
Article  PubMed  PubMed Central  Google Scholar 

26.
Pinto-Tomás, A. A. et al. Symbiotic nitrogen fixation in the fungus gardens of leaf-cutter ants. Science 326, 1120–1123. https://doi.org/10.1126/science.1173036 (2009).
ADS  Article  PubMed  CAS  Google Scholar 

27.
Suen, G. et al. An insect herbivore microbiome with high plant biomass degrading capacity. PLoS Genet. 6, e1001129. https://doi.org/10.1371/journal.pgen.1001129 (2010).
Article  PubMed  PubMed Central  CAS  Google Scholar 

28.
Aylward, F. O. et al. Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens. ISME J. 6, 1688–1701. https://doi.org/10.1038/ismej.2012.10 (2012).
Article  PubMed  PubMed Central  CAS  Google Scholar 

29.
Haanstad, J. O. & Norris, D. M. Microbial symbiotes of the ambrosia beetle Xyletorinus politus. Microb. Ecol. 11, 267–276. https://doi.org/10.1007/BF02010605 (1985).
Article  PubMed  CAS  Google Scholar 

30.
Grubbs, K. J. et al. Genome sequence of Streptomyces griseus  strain XyelbKG-1, an ambrosia beetle associated actinomycete. J. Bacteriol. 193, 2890–2891. https://doi.org/10.1128/JB.00330-11 (2011).
Article  PubMed  PubMed Central  CAS  Google Scholar 

31.
Scott, J. J. et al. Bacterial protection of beetle-fungus mutualism. Science 322, 63. https://doi.org/10.1126/science.1160423 (2008).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

32.
Boone, C. K. Bacteria associated with a tree-killing insect reduce concentrations of plant defense compounds. J. Chem. Ecol. 39, 1003–1006. https://doi.org/10.1007/s10886-013-0313-0 (2013).
Article  PubMed  CAS  Google Scholar 

33.
Xu, L.-T., Lu, M. & Sun, J.-H. Invasive bark beetle-associated microbes degrade a host defensive monoterpene. Insect Sci. 23, 183–190. https://doi.org/10.1111/1744-7917.12255 (2016).
Article  PubMed  CAS  Google Scholar 

34.
Um, S., Fraimout, A., Sapountzis, P., Oh, D.-C. & Poulsen, M. The fungus-growing termite Macrotermes natalensis harbors bacillaene-producing Bacillus sp. that inhibit potentially antagonistic fungi. Sci. Rep. 3, 3250. https://doi.org/10.1038/srep03250 (2013).
Article  PubMed  PubMed Central  Google Scholar 

35.
Li, H. et al. Lignocellulose pretreatment in a fungus-cultivating termite. Proc. Natl. Acad. Sci. USA 114, 4709–4714. https://doi.org/10.1073/pnas.1618360114 (2017).
ADS  Article  PubMed  CAS  Google Scholar 

36.
Aylward, F. O. et al. Convergent bacterial microbiotas in the fungal agricultural systems of insects. mBio 5, e02077-14. https://doi.org/10.1128/mBio.02077-14 (2014).
Article  PubMed  PubMed Central  Google Scholar 

37.
Stayton, C. T. The definition, recognition, and interpretation of convergent evolution, and two new measures for quantifying and assessing the significance of convergence. Evolution 69, 2140–2153. https://doi.org/10.1111/evo.12729 (2015).
Article  PubMed  Google Scholar 

38.
Arbuckle, K. & Speed, M. P. Analysing convergent evolution: a practical guide to methods. In Evolutionary biology: convergent evolution, evolution of complex traits, concepts and methods, Chapter 2, (ed. Pontarotti, P.) 23–36 (Springer, Berlin , 2016).
Google Scholar 

39.
Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, 9323. https://doi.org/10.1126/science.aac9323 (2015).
ADS  Article  CAS  Google Scholar 

40.
Rabeling, C., Verhaagh, M. & Engels, W. Comparative study of nest architecture and colony structure of the fungus-growing ants, Mycocepurus goeldii and M. smithii. J. Insect. Sci. 7, 40. https://doi.org/10.1673/031.007.4001 (2007).
Article  PubMed  PubMed Central  CAS  Google Scholar 

41.
Zanetti, R. et al. An overview of integrated management of leaf-cutting ants (Hymenoptera: Formicidae) in Brazilian forest plantations. Forests 5, 439–454. https://doi.org/10.3390/f5030439 (2014).
Article  Google Scholar 

42.
Markowitz, V. M. et al. IMG/M-HMP: a metagenome comparative analysis system for the human microbiome project. PLoS ONE 7, e40151. https://doi.org/10.1371/journal.pone.0040151 (2012).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

43.
Adams, A. S. et al. Mountain pine beetles colonizing historical and naïve host trees are associated with a bacterial community highly enriched in genes contributing to terpene metabolism. Appl. Environ. Microbiol. 79, 3468–3475. https://doi.org/10.1128/AEM.00068-13 (2013).
Article  PubMed  PubMed Central  CAS  Google Scholar 

44.
Solheim, H. Oxygen deficiency and spruce resin inhibition of growth of blue stain fungi associated with Ips typographus. Mycol. Res. 95, 1387–1392. https://doi.org/10.1016/S0953-7562(09)80390-0 (1991).
Article  Google Scholar 

45.
Schuurman, G. H. Ecosystem influences of fungus-growing termites in the dry Paleotropics. In Soil ecology and ecosystem services, Chapter 34 (eds Wall, D. H. et al.) 173–188 (Oxford University Press, Oxford, 2012).
Google Scholar 

46.
Somera, A. F., Lima, A. M., Santos-Neto, A. J., Lanças, F. M. & Bacci, M. Jr. Leaf-cutter ant fungus gardens are biphasic mixed microbial bioreactors that convert plant biomass to polyols with biotechnological applications. Appl. Environ. Microbiol. 81, 4525–4535. https://doi.org/10.1128/AEM.00046-15 (2015).
Article  PubMed  PubMed Central  CAS  Google Scholar 

47.
Ballard, R. W., Palleroni, N. J., Doudoroff, M., Stanier, R. Y. & Mandel, M. Taxonomy of the aerobic pseudomonads: Pseudomonas cepacia, P. marginata, P. alliicola and P. caryophylli. J. Gen. Microbiol. 60, 199–214. https://doi.org/10.1099/00221287-60-2-199 (1970).
Article  PubMed  CAS  Google Scholar 

48.
O’Hara, C. M. Manual and automated instrumentation for identification of Enterobacteriaceae and other aerobic gram-negative Bacilli. Clin. Microbiol. Rev. 18, 147–162. https://doi.org/10.1128/CMR.18.1.147-162.2005 (2005).
Article  PubMed  PubMed Central  CAS  Google Scholar 

49.
Brune, A., Miambi, E. & Breznak, J. A. Roles of oxygen and the intestinal microflora in the metabolism of lignin-derived phenylpropanoids and other monoaromatic compounds by termites. Appl. Environ. Microbiol. 61, 2688–2695 (1995).
Article  CAS  Google Scholar 

50.
White, B. A., Lamed, R., Bayer, E. A. & Flint, H. J. Biomass utilization by gut microbiomes. Annu. Rev. Microbiol. 68, 279–296. https://doi.org/10.1146/annurev-micro-092412-155618 (2014).
Article  PubMed  CAS  Google Scholar 

51.
de Vos, W. Microbial biofilms and the human intestinal microbiome. npj Biofilms Microbio. 1, 15005. https://doi.org/10.1038/npjbiofilms.2015.5 (2015).
Article  CAS  Google Scholar 

52.
Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345. https://doi.org/10.1016/j.cell.2016.05.041 (2016).
Article  PubMed  CAS  Google Scholar 

53.
Leng, R. A. Biofilm compartmentalisation of the rumen microbiome: modification of fermentation and degradation of dietary toxins. Anim. Prod. Sci. 57, 2188–2203. https://doi.org/10.1071/AN17382 (2017).
Article  CAS  Google Scholar 

54.
Kohl, K. D. et al. Metagenomic sequencing provides insights into microbial detoxification in the guts of small mammalian herbivores (Neotoma spp.). FEMS Microbiol. Ecol. 94, fiy184. https://doi.org/10.1093/femsec/fiy184 (2018).
Article  CAS  Google Scholar 

55.
Burke, C., Steinberg, P., Rusch, D., Kjelleberg, S. & Thomas, T. Bacterial community assembly based on functional genes rather than species. Proc. Natl. Acad. Sci USA 108, 14288–14293. https://doi.org/10.1073/pnas.1101591108 (2011).
ADS  Article  Google Scholar 

56.
Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277. https://doi.org/10.1126/science.aaf4507 (2016).
ADS  Article  PubMed  CAS  Google Scholar 

57.
Louca, S. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943. https://doi.org/10.1038/s41559-018-0519-1 (2018).
Article  PubMed  Google Scholar 

58.
Jurburg, S. D. & Salles, J. F. Functional redundancy and ecosystem function—the soil microbiota as a case study. In Biodiversity in ecosystems—linking structure and function (eds Lo, Y.-H. et al.) 29–49 (INTECH, New York, 2015).
Google Scholar 

59.
Grell, M. N. et al. The fungal symbiont of Acromyrmex leaf-cutting ants expresses the full spectrum of genes to degrade cellulose and other plant cell wall polysaccharides. BMC Genomics 14, 928. https://doi.org/10.1186/1471-2164-14-928 (2013).
Article  PubMed  PubMed Central  CAS  Google Scholar 

60.
Žifčáková, L. et al. Feed in summer, rest in winter: microbial carbon utilization in forest topsoil. Microbiome 5, 122. https://doi.org/10.1186/s40168-017-0340-0 (2017).
Article  PubMed  PubMed Central  Google Scholar 

61.
Jing, T., Qi, F. & Wang, Z. Most dominant roles of insect gut bacteria: digestion, detoxification, or essential nutrient provision? Microbiome 8, 38. https://doi.org/10.1186/s40168-020-00823-y (2020).
Article  PubMed  PubMed Central  Google Scholar 

62.
Howard, J. J., Cazin, J. & Wiemer, D. F. Toxicity of terpenoid deterrents to the leafcutting ant Atta cephalotes and its mutualistic fungus. J. Chem. Ecol. 14, 59–69. https://doi.org/10.1007/BF01022531 (1988).
Article  PubMed  CAS  Google Scholar 

63.
Keeling, C. I. & Bohlmann, J. Diterpene resin acids in conifers. Phytochemistry 67, 2415–2423. https://doi.org/10.1016/j.phytochem.2006.08.019 (2006).
Article  PubMed  CAS  Google Scholar 

64.
Zhu, L. et al. Potential mechanism of detoxification of cyanide compounds by gut microbiomes of bamboo-eating pandas. MSphere 3, e00229-18. https://doi.org/10.1128/mSphere.00229-18 (2018).
Article  PubMed  PubMed Central  Google Scholar 

65.
Cheng, X. et al. Metagenomic analysis of the pinewood nematode microbiome reveals a symbiotic relationship critical for xenobiotics degradation. Sci. Rep. 3, 1869. https://doi.org/10.1038/srep01869 (2013).
Article  PubMed  PubMed Central  CAS  Google Scholar 

66.
Flemming, H. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575. https://doi.org/10.1038/nrmicro.2016.94 (2016).
Article  PubMed  CAS  Google Scholar 

67.
Sivadon, P., Barnier, C., Urios, L. & Grimaud, R. Biofilm formation as a microbial strategy to assimilate particulate substrates. Environ. Microbiol. Rep. 11, 749–764. https://doi.org/10.1111/1758-2229.12785 (2019).
Article  PubMed  CAS  Google Scholar 

68.
Brethauer, S., Shahab, R. L. & Studer, M. H. Impacts of biofilms on the conversion of cellulose. Appl. Microbiol. Biotechnol. 104, 5201–5212. https://doi.org/10.1007/s00253-020-10595-y (2020).
Article  PubMed  PubMed Central  CAS  Google Scholar 

69.
Macfarlane, S. & Macfarlane, G. T. Composition and metabolic activities of bacterial biofilms colonizing food residues in the human gut. Appl. Environ. Microbiol. 72, 6204–6211. https://doi.org/10.1128/AEM.00754-06 (2006).
Article  PubMed  PubMed Central  CAS  Google Scholar 

70.
Deveau, A. et al. Bacterial–fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol. Rev. 42, 335–352. https://doi.org/10.1093/femsre/fuy008 (2018).
Article  PubMed  CAS  Google Scholar 

71.
Purahong, W. et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol. Ecol. 25, 4059–4074. https://doi.org/10.1111/mec.13739 (2016).
Article  PubMed  CAS  Google Scholar 

72.
Frey-Klett, P. et al. Bacterial-fungal interactions: hyphens between agricultural, clinical, environmental, and food microbiologists. Microbiol. Mol. Biol. Rev. 75, 583–609. https://doi.org/10.1128/MMBR.00020-11 (2011).
Article  PubMed  PubMed Central  CAS  Google Scholar 

73.
Martin, M. M. Biochemical implications of insect mycophagy. Biol. Rev. 54, 1–21. https://doi.org/10.1111/j.1469-185X.1979.tb00865.x (1979).
Article  CAS  Google Scholar 

74.
Brabcová, V., Nováková, M., Davidová, A. & Baldrian, P. Dead fungal mycelium in forest soil represents a decomposition hotspot and a habitat for a specific microbial community. New Phytol. 210, 1369–1381. https://doi.org/10.1111/nph.13849 (2016).
Article  PubMed  CAS  Google Scholar 

75.
Brabcová, V., Štursová, M. & Baldrian, P. Nutrient content affects the turnover of fungal biomass in forest topsoil and the composition of associated microbial communities. Soil Biol. Biochem. 118, 187–198. https://doi.org/10.1016/j.soilbio.2017.12.012 (2018).
Article  CAS  Google Scholar 

76.
de Boer, W. D., Folman, L. B., Summerbell, R. C. & Boddy, L. Living in a fungal world: impact of fungi on soil bacterial niche development. FEMS Microbiol. Rev. 29, 795–811. https://doi.org/10.1016/j.femsre.2004.11.005 (2005).
Article  PubMed  CAS  Google Scholar 

77.
Leveau, J. H. & Preston, G. M. Bacterial mycophagy: definition and diagnosis of a unique bacterial–fungal interaction. New Phytol. 177, 859–876. https://doi.org/10.1111/j.1469-8137.2007.02325.x (2008).
Article  PubMed  Google Scholar 

78.
Carrasco, J. & Preston, G. M. Growing edible mushrooms: a conversation between bacteria and fungi. Environ. Microbiol. 22, 858–872. https://doi.org/10.1111/1462-2920.14765 (2020).
Article  PubMed  Google Scholar 

79.
Warmink, J. A., Nazir, R. & Van Elsas, J. D. Universal and species-specific bacterial ‘fungiphiles’ in the mycospheres of different basidiomycetous fungi. Environ. Microbiol. 11, 300–312. https://doi.org/10.1111/j.1462-2920.2008.01767.x (2009).
Article  PubMed  CAS  Google Scholar 

80.
Guennoc, C., Rose, C., Labbé, J. & Deveau, A. Bacterial biofilm formation on the hyphae of ectomycorrhizal fungi: a widespread ability under controls?. FEMS Microbiol. Ecol. 94, 093. https://doi.org/10.1093/femsec/fiy093 (2018).
Article  CAS  Google Scholar 

81.
Figueiredo, A. R. T. D. & Kramer, J. Cooperation and conflict within the microbiota and their effects on animal hosts. Front. Ecol. Evol. 8, 132. https://doi.org/10.3389/fevo.2020.00132 (2020).
Article  Google Scholar 

82.
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666. https://doi.org/10.1126/science.aad2602 (2015).
ADS  Article  PubMed  CAS  Google Scholar 

83.
Donaldson, G., Lee, S. & Mazmanian, S. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32. https://doi.org/10.1038/nrmicro3552 (2016).
Article  PubMed  CAS  Google Scholar 

84.
Tropini, C., Earle, K. A., Huang, K. C. & Sonnenburg, J. L. The gut microbiome: connecting spatial organization to function. Cell Host Microbe 21, 433–442. https://doi.org/10.1016/j.chom.2017.03.010 (2017).
Article  PubMed  PubMed Central  CAS  Google Scholar 

85.
Adair, K. L. & Douglas, A. E. Making a microbiome: the many determinants of host-associated microbial community composition. Curr. Opin. Microbiol. 35, 23–29. https://doi.org/10.1016/j.mib.2016.11.002 (2017).
Article  PubMed  Google Scholar 

86.
Shafquat, A., Joice, R., Simmons, S. & Huttenhower, C. Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol. 22, 261–266. https://doi.org/10.1016/j.tim.2014.01.011 (2014).
Article  PubMed  PubMed Central  CAS  Google Scholar 

87.
Hernandez-Agreda, A., Gates, R. D. & Ainsworth, T. D. Defining the core microbiome in corals’ microbial soup. Trends Microbiol. 25, 125–140. https://doi.org/10.1016/j.tim.2016.11.003 (2017).
Article  PubMed  CAS  Google Scholar 

88.
Ramadhar, T. et al. Bacterial symbionts in agricultural systems provide a strategic source for antibiotic discovery. J. Antibiot. 67, 53–58. https://doi.org/10.1038/ja.2013.77 (2014).
Article  PubMed  CAS  Google Scholar 

89.
Van Arnam, E. B., Currie, C. R. & Clardy, J. Defense contracts: molecular protection in insect-microbe symbioses. Chem. Soc. Rev. 47, 1638–1651. https://doi.org/10.1039/C7CS00340D (2018).
Article  PubMed  Google Scholar 

90.
Berasategui, A. et al. Potential applications of insect symbionts in biotechnology. Appl. Microbiol. Biotechnol. 100, 1567–1577. https://doi.org/10.1007/s00253-015-7186-9 (2016).
Article  PubMed  CAS  Google Scholar 

91.
Cox, M. P., Peterson, D. A. & Biggs, P. J. SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics 11, 485. https://doi.org/10.1186/1471-2105-11-485 (2010).
Article  PubMed  PubMed Central  Google Scholar 

92.
Li, D., Liu, C., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676. https://doi.org/10.1093/bioinformatics/btv033 (2015).
Article  PubMed  CAS  Google Scholar 

93.
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864. https://doi.org/10.1093/bioinformatics/btr026 (2011).
Article  PubMed  PubMed Central  CAS  Google Scholar 

94.
Markowitz, V. M. et al. IMG/M 4 version of the integrated metagenome comparative analysis system. Nucleic Acids Res. 42, D568–D573. https://doi.org/10.1093/nar/gkt919 (2014).
Article  PubMed  CAS  Google Scholar 

95.
Patil, K. R., Roune, L. & MChardy, A. C. The PhyloPythiaS web server for taxonomic assignment of metagenome sequences. PLoS One 7, e38581. https://doi.org/10.1371/journal.pone.0038581 (2012).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

96.
Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. Proc. Natl Acad. Sci. USA 109, 11002–11007. https://doi.org/10.1073/pnas.1202970109 (2012).
ADS  Article  PubMed  Google Scholar 

97.
Kešnerová, L., Moritz, R. & Engel, P. Bartonella apis sp. Nov., a honey bee gut symbiont of the class Alphaproteobacteria. Int. J. Syst. Evol. Microbiol. 66, 414–421. https://doi.org/10.1099/ijsem.0.000736 (2016).
Article  CAS  Google Scholar 

98.
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).
Article  PubMed  PubMed Central  CAS  Google Scholar 

99.
Guindon, S. & Gascuel, O. A Simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704. https://doi.org/10.1080/10635150390235520https://doi.org/10.1080/10635150390235520 (2003).
Article  PubMed  Google Scholar 

100.
Hammer, Ř., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electronica 4, 1. https://palaeo-electronica.org/2001_1/past/issue1_01.htm (2001).

101.
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494https://doi.org/10.1093/bioinformatics/btu494 (2014).
Article  PubMed  PubMed Central  CAS  Google Scholar 

102.
Fan, H., Ives, A. R., Surget-Groba, Y. & Cannon, C. H. An assembly and alignment-free method of phylogeny reconstruction from next-generation sequencing data. BMC Genomics 16, 522. https://doi.org/10.1186/s12864-015-1647-5 (2015).
Article  PubMed  PubMed Central  CAS  Google Scholar 

103.
Letunic, I. & Bork, P. Interactive tree of life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128. https://doi.org/10.1093/bioinformatics/btl529 (2007).
Article  PubMed  CAS  Google Scholar 

104.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).
Article  PubMed  CAS  Google Scholar 

105.
White, J. R. et al. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, 1000352. https://doi.org/10.1371/journal.pcbi.1000352 (2009).
Article  CAS  Google Scholar 

106.
Cantarel, B. L. et al. The carbohydrate-active enZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 37, D233–D238. https://doi.org/10.1093/nar/gkn663 (2009).
Article  PubMed  CAS  Google Scholar 

107.
Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46, W95–W101. https://doi.org/10.1093/nar/gky418 (2018).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

108.
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA & GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731. https://doi.org/10.1016/j.jmb.2015.11.006 (2016).
Article  PubMed  CAS  Google Scholar 

109.
Barcoto, M. O. Fungus-growing insects host a convergent microbiome with functional similarities to other lignocellulose-feeding insects. Masters dissertation, São Paulo State University (2017). More

1.
Zhao, Y. et al. Nature 494, 357–360 (2013).
CAS  Article  Google Scholar 
2.
Giovannoni, S. J. Annu. Rev. Mar. Sci. 9, 231–255 (2017).
Article  Google Scholar 

3.
Morris, R. M., Cain, K. R., Hvorecny, K. L. & Kollman, J. M. Nat. Microbiol. https://doi.org/10.1038/s41564-020-0725-x (2020).
Article  PubMed  Google Scholar 

4.
Zhao, Y. et al. Environ. Microbiol. 21, 1989–2001 (2019).
CAS  Article  Google Scholar 

5.
Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. ISME J. 11, 1511–1520 (2017).
Article  Google Scholar 

6.
Nanda, A. M., Thormann, K. & Frunzke, J. J. Bacteriol. 197, 410–419 (2015).
Article  Google Scholar 

7.
Liu, X. et al. Environ. Microbiol. 21, 4212–4232 (2019).
CAS  Article  Google Scholar 

8.
Feiner, R. et al. Nat. Rev. Microbiol. 13, 641–650 (2015).
CAS  Article  Google Scholar 

9.
Våge, S., Storesund, J. E. & Thingstad, T. F. Nature 499, E3–E4 (2013).
Article  Google Scholar  More

1.
Zanuncio, J. C. et al. Population dynamics of Lepidoptera pests in Eucalyptus urophylla plantations in the Brazilian Amazonia. Forests 5, 72–87 (2014).
Google Scholar 
2.
Macedo-Reis, L., Soares, L., Faria, M., Espírito-Santo, M. & Zanuncio, J. Survival of a lepidopteran defoliator of Eucalyptus is influenced by local hillside and forest remnants in Brazil. Fla. Entomol. 96, 941–947 (2013).
Google Scholar 

3.
Loetti, V. & Bellocq, I. Effects of the insecticides methoxyfenozide and cypermethrin on non-target arthropods: a field experiment. Austral Entomol. 56, 255–260 (2017).
Google Scholar 

4.
Lundström, N. L. P., Zhang, H. & Brännström, Å. Pareto-efficient biological pest control enable high efficacy at small costs. Ecol. Model. 364, 89–97 (2017).
Google Scholar 

5.
Costa, L. G., Giordano, G., Guizzetti, M. & Vitalone, A. Neurotoxicity of pesticides: a brief review. Front. Biosci. 13, 1240–1249 (2008).
CAS  PubMed  Google Scholar 

6.
de S Pereira, K., Guedes, N. M. P., Serrão, J. E., Zanuncio, J. C. & Guedes, R. N. C. Superparasitism, immune response and optimum progeny yield in the gregarious parasitoid Palmistichus elaeisis. Pest Manag. Sci. 73, 1101–1109 (2017).
PubMed  Google Scholar 

7.
Pereira, F. F., Zanuncio, T. V., Zanuncio, J. C., Pratissoli, D. & Tavares, M. T. Species of lepidoptera defoliators of eucalyptus as new host for the parasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). Braz. Arch. Biol. Technol. 51, 259–262 (2008).
Google Scholar 

8.
Barbosa, R. H., Zanuncio, J. C., Pereira, F. F., Kassab, S. O. & Rossoni, C. Foraging activity of Palmistichus elaeisis (Hymenoptera: Eulophidae) at various densities on pupae of the Eucalyptus defoliator Thyrinteina arnobia (Lepidoptera: Geometridae). Fla. Entomol. 99, 686–690 (2016).
Google Scholar 

9.
Zanuncio, J., Pereira, F., Jacques, G., Tavares, M. & Serrão, J. Tenebrio molitor Linnaeus (Coleoptera: Tenebrionidae), a new alternative host to rear the pupae parasitoid Palmistichus elaeisis Delvare & Lasalle (Hymenoptera: Eulophidae). Coleopt. Bull. 62, 64–66 (2008).
Google Scholar 

10.
Roubos, C. R., Rodriguez-Saona, C. & Isaacs, R. Mitigating the effects of insecticides on arthropod biological control at field and landscape scales. Biol. Control 75, 28–38 (2014).
CAS  Google Scholar 

11.
Addison, P. J. & Barker, G. M. Effect of various pesticides on the non-target species Microctonus hyperodae, a biological control agent of Listronotus bonariensis. Entomol. Exp. Appl. 119, 71–79 (2006).
CAS  Google Scholar 

12.
Banks, J. E., Stark, J. D., Vargas, R. I. & Ackleh, A. S. Parasitoids and ecological risk assessment: can toxicity data developed for one species be used to protect an entire guild?. Biol. Control 59, 336–339 (2011).
Google Scholar 

13.
Bayram, A., Salerno, G., Onofri, A. & Conti, E. Lethal and sublethal effects of preimaginal treatments with two pyrethroids on the life history of the egg parasitoid Telenomus busseolae. Biocontrol 55, 697–710 (2010).
CAS  Google Scholar 

14.
Desneux, N., Decourtye, A. & Delpuech, J.-M. The sublethal effects of pesticides on beneficial arthropods. Annu. Rev. Entomol. 52, 81–106 (2007).
CAS  PubMed  Google Scholar 

15.
Delpuech, J. M. & Delahaye, M. The sublethal effects of deltamethrin on Trichogramma behaviors during the exploitation of host patches. Sci. Total Environ. 447, 274–279 (2013).
ADS  CAS  PubMed  Google Scholar 

16.
De La Cruz, R. A. et al. Side-effects of pesticides on the generalist endoparasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). Sci. Rep. 7, 1–8 (2017).
CAS  Google Scholar 

17.
Wang, Y. et al. Susceptibility to selected insecticides and risk assessment in the insect egg parasitoid Trichogramma confusum (Hymenoptera: Trichogrammatidae). J. Econ. Entomol. 106, 142–149 (2013).
CAS  PubMed  Google Scholar 

18.
Zanuncio, T. V. et al. Fertility and life expectancy of the predator Supputius cincticeps (Heteroptera: Pentatomidae) exposed to sublethal doses of permethrin. Biol. Res. 38, 31–39 (2005).
CAS  PubMed  Google Scholar 

19.
Wang, Y. et al. Toxicity risk of insecticides to the insect egg parasitoid Trichogramma evanescens Westwood (Hymenoptera: Trichogrammatidae). Pest Manag. Sci. 70, 398–404 (2014).
CAS  PubMed  Google Scholar 

20.
Biondi, A., Zappalà, L., Stark, J. D. & Desneux, N. Do biopesticides affect the demographic traits of a parasitoid wasp and its biocontrol services through sublethal effects?. PLoS ONE 8, 1–11 (2013).
Google Scholar 

21.
Cônsoli, F. L., Botelho, P. S. M. & Parra, J. R. P. Selectivity of insecticides to the egg parasitoid Trichogramma galloi Zucchi, 1988, (Hym., Trichogrammatidae). J. Appl. Entomol. 125, 37–43 (2001).
Google Scholar 

22.
Soderlund, D. M. Toxicology and mode of action of pyrethroid insecticides. In Hayes’ Handbook of Pesticide Toxicology 1665–1686 (Elsevier Inc., 2010). doi:10.1016/B978-0-12-374367-1.00077-X.

23.
Youssef, A. I. et al. The side-effects of plant protection products used in olive cultivation on the hymenopterous egg parasitoid Trichogramma cacoeciae Marchal. J. Appl. Entomol. 128, 593–599 (2004).
CAS  Google Scholar 

24.
Alix, A., Cortesero, A. M., Nénon, J. P. & Anger, J. P. Selectivity assessment of chlorfenvinphos reevaluated by including physiological and behavioral effects on an important beneficial insect. Environ. Toxicol. Chem. 20, 2530–2536 (2001).
CAS  PubMed  Google Scholar 

25.
Fontes, J., Roja, I. S., Tavares, J. & Oliveira, L. Lethal and sublethal effects of various pesticides on Trichogramma achaeae (Hymenoptera: Trichogrammatidae). J. Econ. Entomol. 111, 1219–1226 (2018).
CAS  PubMed  Google Scholar 

26.
Bayram, A., Salerno, G., Onofri, A. & Conti, E. Sub-lethal effects of two pyrethroids on biological parameters and behavioral responses to host cues in the egg parasitoid Telenomus busseolae. Biol. Control 53, 153–160 (2010).
CAS  Google Scholar 

27.
Zantedeschi, R. et al. Toxicity of soybean-registered agrochemicals to Telenomus podisi and Trissolcus basalis immature stages. Phytoparasitica 46, 203–212 (2018).
CAS  Google Scholar 

28.
Thubru, D. P., Firake, D. M. & Behere, G. T. Assessing risks of pesticides targeting lepidopteran pests in cruciferous ecosystems to eggs parasitoid, Trichogramma brassicae (Bezdenko). Saudi J. Biol. Sci. 25, 680–688 (2018).
CAS  PubMed  Google Scholar 

29.
Fernández, M. D. M., Medina, P., Fereres, A., Smagghe, G. & Viñuela, E. Are mummies and adults of Eretmocerus mundus (Hymenoptera: Aphelinidae) compatible with modern insecticides?. J. Econ. Entomol. 108, 2268–2277 (2015).
Google Scholar 

30.
Liu, F., Zhang, X., Gui, Q. Q. & Xu, Q. J. Sublethal effects of four insecticides on Anagrus nilaparvatae (Hymenoptera: Mymaridae), an important egg parasitoid of the rice planthopper Nilaparvata lugens (Homoptera: Delphacidae). Crop Prot. 37, 13–19 (2012).
Google Scholar 

31.
Pastori, P. L. et al. Reproduction of Trichospilus diatraeae (Hymenoptera: Eulophidae) in pupae of two lepidopterans defoliators of eucalypt. Rev. Colomb. Entomol. 38, 91–93 (2012).
Google Scholar 

32.
Harvey, J. A. & Gols, R. Effects of plant-mediated differences in host quality on the development of two related endoparasitoids with different host-utilization strategies. J. Insect Physiol. 107, 110–115 (2018).
CAS  PubMed  Google Scholar 

33.
Pereira, F. F. et al. The density of females of Palmistichus elaeisis Delvare and Lasalle (Hymenoptera: Eulophidae) affects their reproductive performance on pupae of Bombyx mori L. (Lepidoptera: Bombycidae). An. Acad. Bras. Cienc. 82, 323–331 (2010).
PubMed  Google Scholar 

34.
Desneux, N., Denoyelle, R. & Kaiser, L. A multi-step bioassay to assess the effect of the deltamethrin on the parasitic wasp Aphidius ervi. Chemosphere 65, 1697–1706 (2006).
ADS  CAS  PubMed  Google Scholar 

35.
Desneux, N., Ramirez-Romero, R. & Kaiser, L. Multistep bioassay to predict recolonization potential of emerging parasitoids after a pesticide treatment. Environ. Toxicol. Chem. 25, 2675–2682 (2006).
CAS  PubMed  Google Scholar 

36.
Agrolink. Bula Decis 25 EC. www.agrolink.com.br https://www.agrolink.com.br/agrolinkfito/produto/decis-25-ec_8.html (2018).

37.
Khan, M. A., Khan, H. & Ruberson, J. R. Lethal and behavioral effects of selected novel pesticides on adults of Trichogramma pretiosum (Hymenoptera: Trichogrammatidae). Pest Manag. Sci. 71, 1640–1648 (2015).
CAS  PubMed  Google Scholar 

38.
Stark, J. D., Banks, J. E. & Vargas, R. How risky is risk assessment: the role that life history strategies play in susceptibility of species to stress. Proc. Natl. Acad. Sci. 101, 732–736 (2004).
ADS  CAS  PubMed  Google Scholar 

39.
Rieth, J. P. & Levin, M. D. The repellent effect of two pyrethroid insecticides on the honey bee. Physiol. Entomol. 13, 213–218 (1988).
CAS  Google Scholar 

40.
Valente, E. C. N., Broglio, S. M. F., dos Passos, E. M. & de Lima, A. S. T. Desempenho de Trichogramma galloi (Hymenoptera: Trichogrammatidae) sobre ovos de Diatraea spp. (Lepidoptera: Crambidae). Pesqui. Agropecu. Bras. 51, 293–300 (2016).
Google Scholar 

41.
Rossoni, C. et al. Development of Eulophidae (Hymenoptera) parasitoids in Diatraea saccharalis (Lepidoptera: Crambidae) pupae exposed to entomopathogenic fungi. Can. Entomol. 148, 716–723 (2016).
Google Scholar 

42.
Harvey, J. A., Poelman, E. H. & Tanaka, T. Intrinsic inter- and intraspecific competition in parasitoid wasps. Annu. Rev. Entomol. 58, 333–351 (2013).
CAS  PubMed  Google Scholar 

43.
Jervis, M. A., Ellers, J. & Harvey, J. A. Resource acquisition, allocation, and utilization in parasitoid reproductive strategies. Annu. Rev. Entomol. 53, 361–385 (2008).
CAS  PubMed  Google Scholar 

44.
Souza, D., Monteiro, A. B. & Faria, L. D. B. Morphometry, allometry, and fluctuating asymmetry of egg parasitoid Trichogramma pretiosum under insecticide influence. Entomol. Exp. Appl. 166, 298–303 (2018).
CAS  Google Scholar 

45.
Rasool, S. et al. Effect of host size on larval competition of the gregarious parasitoid Bracon hebetor (Say.) (Hymenoptera: Braconidae). Pak. J. Zool. 49, 1085–1085 (2017).
Google Scholar 

46.
Menezes, C. W. G. et al. Reproductive and toxicological impacts of herbicides used in Eucalyptus culture in Brazil on the parasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). Weed Res. 52, 520–525 (2012).
CAS  Google Scholar 

47.
Andrade, G. S. et al. Oogenesis pattern and type of ovariole of the parasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae). An. Acad. Bras. Cienc. 84, 767–774 (2012).
PubMed  Google Scholar 

48.
Menezes, C. W. G. et al. Palmistichus elaeisis (Hymenoptera: Eulophidae) as an indicator of toxicity of herbicides registered for corn in Brazil. Chil. J. Agric. Res. 74, 361–365 (2014).
Google Scholar 

49.
I. R. A. C. Method No: 007: Leaf eating Lepidoptera and Coleoptera. Available online: uploads/ 2009, (2010).

50.
Finney, D. J. P. A. Probit Analysis 318 (Cambridge University Press, London , 1971).
Google Scholar  More

1.
Domínguez-Rodrigo, M. Is the “Savanna Hypothesis” a dead concept for explaining the emergence of the earliest hominins?. Curr. Anthropol. 55, 59–81 (2014).
Google Scholar 
2.
Cerling, T. E. et al. Woody cover and hominin environments in the past 6 million years. Nature 476, 52–56 (2011).
ADS  Google Scholar 

3.
Potts, R. Hominin evolution in settings of strong environmental variability. Q. Sci. Rev. 73, 1–13 (2013).
ADS  Google Scholar 

4.
Magill, C. R., Ashley, G. M. & Freeman, K. H. Ecosystem variability and early human habitats in eastern Africa. Proc. Natl Acad. Sci. USA 110, 1167–1174 (2013).
ADS  CAS  PubMed  Google Scholar 

5.
Levin, N. E. Environment and climate of early human evolution. Annu. Rev. Earth Planet. Sci. 43, 405–429 (2015).
ADS  CAS  Google Scholar 

6.
Uno, K. T., Polissar, P. J., Jackson, K. E. & deMenocal, P. B. Neogene biomarker record of vegetation change in eastern Africa. Proc. Natl Acad. Sci. USA 113, 6355–6363 (2016).
ADS  CAS  PubMed  Google Scholar 

7.
Jacobs, B. F. Paleobotanical studies from tropical Africa: relevance to the evolution of forest, woodland, and savannah biomes. Phil. Trans. R. Soc. B 359, 1573–1583 (2004).
PubMed  Google Scholar 

8.
Beerling, D. J. & Osborne, C. P. The origin of the savanna biome. Glob. Chang. Biol. 12, 2023–2031 (2006).
ADS  Google Scholar 

9.
Bond, W. J. What limits trees in C4 grasslands and savannas?. Annu. Rev. Ecol. Evol. Syst. 39, 641–659 (2008).
Google Scholar 

10.
Cerling, T. E. et al. Global vegetation change through the Miocene/Pliocene boundary. Nature 389, 153–158 (1997).
ADS  CAS  Google Scholar 

11.
Ehleringer, J. R., Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and climate. Oecologia 112, 285–299 (1997).
ADS  PubMed  Google Scholar 

12.
Beerling, D. J. & Royer, D. L. Convergent cenozoic CO2 history. Nat. Geosci. 4, 418–420 (2011).
ADS  CAS  Google Scholar 

13.
Pagani, M., Zachos, J. C., Freeman, K. H., Tipple, B. & Bohaty, S. Marked decline in atmospheric carbon dioxide concentrations during the Paleogene. Science 309, 600–603 (2005).
ADS  CAS  PubMed  Google Scholar 

14.
Pagani, M., Freeman, K. H. & Arthur, M. A. Late Miocene atmospheric CO2 concentrations and the expansion of C4 grasses. Science 285, 876–879 (1999).
CAS  PubMed  Google Scholar 

15.
Bolton, C. T. et al. Decrease in coccolithophore calcification and CO2 since the middle Miocene. Nat. Commun. 7, 10284 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

16.
Herbert, T. D. et al. Late Miocene global cooling and the rise of modern ecosystems. Nat. Geosci. 9, 843–847 (2016).
ADS  CAS  Google Scholar 

17.
Sponheimer, M. et al. Isotopic evidence of early hominin diets. Proc. Natl Acad. Sci. USA 110, 10513–10518 (2013).
ADS  CAS  Google Scholar 

18.
Feakins, S. J. et al. Northeast African vegetation change over 12 my. Geology 41, 295–298 (2013).
ADS  Google Scholar 

19.
Ségalen, L., Lee-Thorp, J. A. & Cerling, T. E. Timing of C4 grass expansion across sub-Saharan Africa. J. Hum. Evol. 53, 549–559 (2007).
PubMed  Google Scholar 

20.
Pennington, R. T., Cronk, Q. C. B. & Richardson, J. A. Introduction and synthesis: plant phylogeny and the origin of major biomes. Phil. Trans. R. Soc. B. 359, 1455–1464 (2004).
PubMed  Google Scholar 

21.
Pennington, R. T., Richardson, J. E. & Lavin, M. Insights into the historical construction of species-rich biomes from dated plant phylogenies, neutral ecological theory and phylogenetic community structure. New Phytol. 172, 605–616 (2006).
PubMed  Google Scholar 

22.
Bytebier, B., Antonelli, A., Bellstedt, D. U. & Linder, H. P. Estimating the age of fire in the Cape flora of South Africa from an orchid phylogeny. Proc. R. Soc. B 278, 188–195 (2010).
PubMed  Google Scholar 

23.
Crisp, M. D., Burrows, G. E., Cook, L. G., Thornhill, A. H. & Bowman, D. M. Flammable biomes dominated by eucalypts originated at the Cretaceous-Palaeogene boundary. Nat. Comm. 2, 193 (2011).
ADS  Google Scholar 

24.
Vicentini, A., Barber, J. C., Aliscioni, S. S., Giussani, L. M. & Kellogg, E. A. The age of the grasses and clusters of origins of C4 photosynthesis. Glob. Chang. Biol. 14, 2963–2977 (2008).
ADS  Google Scholar 

25.
Scheiter, S. et al. Fire and fire-adapted vegetation promoted C4 expansion in the Late Miocene. New Phytol. 195, 653–666 (2012).
PubMed  Google Scholar 

26.
Ramírez, S. R., Gravendeel, B., Singer, R. B., Marshall, C. R. & Pierce, N. E. Dating the origin of the Orchidaceae from a fossil orchid with its pollinator. Nature 448, 1042–1045 (2007).
ADS  Google Scholar 

27.
Losos, J. B. & Schluter, D. Analysis of an evolutionary species–area relationship. Nature 408, 847–850 (2000).
ADS  CAS  PubMed  Google Scholar 

28.
Linder, H. P. & Verboom, G. A. The evolution of regional species richness: the history of the southern African flora. Annu. Rev. Ecol. Evol. Syst. 46, 393–412 (2015).
Google Scholar 

29.
Simon, M. F. et al. Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. Proc. Natl Acad. Sci. USA 106, 20359–20364 (2009).
ADS  CAS  PubMed  Google Scholar 

30.
Cardoso, D. et al. A molecular-dated phylogeny and biogeography of the monotypic legume genus Haplormosia, a missing African branch of the otherwise American-Australian Brongniartieae clade. Mol. Phylogenet. Evol. 107, 431–442 (2017).
PubMed  Google Scholar 

31.
Fritz, S. A. & Purvis, A. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conserv. Biol. 24, 1042–1051 (2010).
PubMed  Google Scholar 

32.
Linder, H. P. East African Cenozoic vegetation history. Evol. Anthropol. 26, 300–312 (2017).
PubMed  Google Scholar 

33.
Retallack, G. J. Middle Miocene fossil plants from Fort Ternan (Kenya) and evolution of African grasslands. Paleobiology 18, 383–400 (1992).
Google Scholar 

34.
Uno, K. T. et al. Late Miocene to Pliocene carbon isotope record of differential diet change among East African herbivores. Proc. Natl. Acad. Sci. USA 108, 6509–6514 (2011).
ADS  CAS  PubMed  Google Scholar 

35.
Dart, R. A. Australopithecus africanus: the man-ape of South Africa. Nature 115, 195–199 (1925).
ADS  Google Scholar 

36.
Dembo, M. et al. The evolutionary relationships and age of Homo naledi: An assessment using dated Bayesian phylogenetic methods. J. Hum. Evol. 97, 17–26 (2016).
PubMed  Google Scholar 

37.
Davies, T. J. & Buckley, L. B. Phylogenetic diversity as a window into the evolutionary and biogeographic histories of present-day richness gradients for mammals. Phil. Trans. R. Soc. B 366, 2414–2425 (2011).
PubMed  Google Scholar 

38.
Maurin, O. et al. Savanna fire and the origins of the ‘underground forests’ of Africa. New Phytol. 204, 201–214 (2014).
PubMed  Google Scholar 

39.
Charles-Dominique, T. et al. Spiny plants, mammal browsers, and the origin of African savannas. Proc. Natl Acad. Sci. USA 113, E5572–E5579 (2016).
CAS  PubMed  Google Scholar 

40.
Bibi, F. A multi-calibrated mitochondrial phylogeny of extant Bovidae (Artiodactyla, Ruminantia) and the importance of the fossil record to systematics. BMC Evol. Biol. 13, 166 (2013).
PubMed  PubMed Central  Google Scholar 

41.
Nyakatura, K. & Bininda-Emonds, O. Updating the evolutionary history of Carnivora (Mammalia): a new species-level supertree complete with divergence time estimates. BMC Biol. 10, 12 (2012).
PubMed  PubMed Central  Google Scholar 

42.
Kyalangalilwa, B., Boatwright, J. S., Daru, B. H., Maurin, O. & van der Bank, M. Phylogenetic position and revised classification of Acacia s.l. (Fabaceae: Mimosoideae) in Africa, including new combinations Vachellia and Senegalia. Bot. J. Linn. Soc. 172, 500–523 (2013).
Google Scholar 

43.
Silvestro, D. & Michalak, I. raxmlGUI: a graphical front-end for RAxML. Org. Divers. Evol. 12, 335–337 (2012).
Google Scholar 

44.
Webb, C. O. & Donoghue, M. J. Phylomatic: tree assembly for applied phylogenetics. Mol. Ecol. Notes 5, 181–183 (2005).
Google Scholar 

45.
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian Phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Bell, C. D., Soltis, D. E. & Soltis, P. S. The age and diversification of the angiosperms re-revisited. Am. J. Bot. 97, 1296–1303 (2010).
PubMed  Google Scholar 

47.
Phillips, S. J. et al. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 

48.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 

49.
Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393 (2005).
Google Scholar 

50.
Blach-Overgaard, A., Svenning, J. C., Dransfield, J., Greve, M. & Balslev, H. Determinants of palm species distributions across Africa: the relative roles of climate, non-climatic environmental factors, and spatial constraints. Ecography 33, 380–391 (2010).
Google Scholar 

51.
Ratnam, J. et al. When is a ‘forest’ a savanna, and why does it matter?. Glob. Ecol. Biogeogr. 20, 653–660 (2011).
Google Scholar 

52.
Koenker, R. quantreg: Quantile Regression. R package version 5.05 (https://CRAN.R-project.org/package=quantreg, 2013).

53.
Richardson, J. E. et al. Rapid and recent origin of species richness in the Cape flora of South Africa. Nature 412, 181–183 (2001).
ADS  CAS  PubMed  Google Scholar 

54.
Verboom, G. A. et al. Origin and diversification of the Greater Cape flora: ancient species repository, hot-bed of recent radiation, or both?. Mol. Phylog. Evol. 51, 44–53 (2009).
Google Scholar 

55.
Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115–9120 (2000).
ADS  CAS  PubMed  Google Scholar 

56.
deMenocal, P. B. African climate change and faunal evolution during the Pliocene-Pleistocene. Earth Planet. Sci. Lett. 220, 3–24 (2004).
ADS  CAS  Google Scholar  More