Ecology

de Menocal, P. B. Plio-Pleistocene African climate. Science 270, 53–59 (1995).ADS 
Article 

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

Google Scholar 
Donges, J. F. et al. Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proc. Natl Acad. Sci. U.S.A. 108, 20422–20427 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maslin, M. A. et al. East african climate pulses and early human evolution. Quat. Sci. Rev. 101, 1–17 (2014).ADS 
Article 

Google Scholar 
Larrasoaña, J. C., Roberts, A. P. & Rohling, E. J. Dynamics of Green Sahara periods and their role in hominin evolution. PLoS One 8, 76514 (2013).ADS 
Article 
CAS 

Google Scholar 
Castañeda, I. S. et al. Wet phases in the Sahara/Sahel region and human migration patterns in North Africa. Proc. Natl Acad. Sci. USA. 106, 20159–20163 (2009).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
United Nations World Food Programme. Scaling up for resilient individuals, communities and systems in the Sahel Operational Reference Note. (2018).Barbier, B., Yacouba, H., Karambiri, H., Zoromé, M. & Somé, B. Human vulnerability to climate variability in the sahel: Farmers’ adaptation strategies in northern burkina faso. Environ. Manag. 43, 790–803 (2009).ADS 
Article 

Google Scholar 
Mohamed, A. Ben Climate change risks in Sahelian Africa. Reg. Environ. Chang. 11, 109–117 (2011).Article 

Google Scholar 
Biasutti, M. Forced Sahel rainfall trends in the CMIP5 archive. J. Geophys. Res. Atmos. 118, 1613–1623 (2013).ADS 
Article 

Google Scholar 
Roudier, P., Sultan, B., Quirion, P. & Berg, A. The impact of future climate change on West African crop yields: what does the recent literature say? Glob. Environ. Chang 21, 1073–1083 (2011).Article 

Google Scholar 
Keeling, R. F. & Keeling, C. D. Atmospheric monthly in situ CO2 data—Mauna Loa Observatory, Hawaii. In Scripps CO2 Program Data. UC San Diego Library Digital Collections. https://doi.org/10.6075/J08W3BHW (2017).Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Tiedemann, R., Sarnthein, M. & Shackleton, N. J. Astronomic timescale for the Pliocene Atlantic δ18O and dust flux records of Ocean Drilling Program Site 659. Paleoceanography 9, 619–638 (1994).ADS 
Article 

Google Scholar 
de Menocal, P. B., Ruddiman, W. F. & Pokras, E. M. Influences of high‐ and low‐latitude processes on African terrestrial climate: Pleistocene eolian records from equatorial atlantic Ocean Drilling Program Site 663. Paleoceanography 8, 209–242 (1993).ADS 
Article 

Google Scholar 
Kuechler, R. R., Dupont, L. M. & Schefuß, E. Hybrid insolation forcing of Pliocene monsoon dynamics in West Africa. Clim. Past 14, 73–84 (2018).Article 

Google Scholar 
Rose, C. et al. Changes in northeast African hydrology and vegetation associated with pliocene-pleistocene sapropel cycles. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150243 (2016).Article 
CAS 

Google Scholar 
Tierney, J. E., Pausata, F. S. R. & De Menocal, P. B. Rainfall regimes of the Green Sahara. Sci. Adv. 3, e1601503 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tierney, J. E. & Russell, J. M. Abrupt climate change in southeast tropical Africa influenced by Indian monsoon variability and ITCZ migration. Geophys. Res. Lett. 34, 1–6 (2007).Article 
CAS 

Google Scholar 
Skonieczny, C. et al. Monsoon-driven Saharan dust variability over the past 240,000 years. Sci. Adv. 5, eaav1887 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McGee, D. et al. The magnitude, timing and abruptness of changes in North African dust deposition over the last 20,000 yr. Earth Planet. Sci. Lett. 371–372, 163–176 (2013).ADS 
Article 
CAS 

Google Scholar 
Bosmans, J. H. C., Hilgen, F. J., Tuenter, E. & Lourens, L. J. Obliquity forcing of low-latitude climate. Clim. Past 11, 1335–1346 (2015).Article 

Google Scholar 
Bosmans, J. H. C., Drijfhout, S. S., Tuenter, E., Hilgen, F. J. & Lourens, L. J. Response of the North African summer monsoon to precession and obliquity forcings in the EC-Earth GCM. Clim. Dyn. 44, 279–297 (2014).Article 

Google Scholar 
Mantsis, D. F. et al. The response of large-scale circulation to obliquity-induced changes in meridional heating gradients. J. Clim. 27, 5504–5516 (2014).ADS 
Article 

Google Scholar 
Rachmayani, R., Prange, M. & Schulz, M. Intra-interglacial climate variability: model simulations of Marine Isotope Stages 1, 5, 11, 13, and 15. Clim. Past 12, 677–695 (2016).Article 

Google Scholar 
Chou, C. & Neelin, J. D. Mechanisms limiting the northward extent of the northern summer monsoons over North America, Asia, and Africa. J. Clim. 16, 406–425 (2003).ADS 
Article 

Google Scholar 
Bischoff, T., Schneider, T. & Meckler, A. N. A conceptual model for the response of tropical rainfall to orbital variations. J. Clim. 30, 8375–8391 (2017).ADS 
Article 

Google Scholar 
Ehleringer, J. R., Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and climate.Bond, W. J. & Midgley, G. F. A proposed CO2-controlled mechanism of woody plant invasion in grasslands and savannas. Glob. Chang. Biol. 6, 865–869 (2000).ADS 
Article 

Google Scholar 
Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A. & Bond, W. J. Deciphering the distribution of the savanna biome. N. Phytol. 191, 197–209 (2011).Article 

Google Scholar 
Vallé, F., Dupont, L. M., Leroy, S. A. G. G., Schefuß, E. & Wefer, G. Pliocene environmental change in West Africa and the onset of strong NE trade winds (ODP Sites 659 and 658). Palaeogeogr. Palaeoclimatol. Palaeoecol. 414, 403–414 (2014).Article 

Google Scholar 
Leroy, S. & Dupont, L. Development of vegetation and continental aridity in northwestern Africa during the Late Pliocene: the pollen record of ODP site 658. Palaeogeogr. Palaeoclimatol. Palaeoecol. 109, 295–316 (1994).Article 

Google Scholar 
Huang, Y., Dupont, L., Sarnthein, M., Hayes, J. M. & Eglinton, G. Mapping of C4 plant input from North West Africa into North East Atlantic sediments. Geochim. Cosmochim. Acta 64, 3505–3513 (2000).ADS 
CAS 
Article 

Google Scholar 
Buitenwerf, R., Bond, W. J., Stevens, N. & Trollope, W. S. W. Increased tree densities in South African savannas: >50 years of data suggests CO2 as a driver. Glob. Chang. Biol. 18, 675–684 (2012).ADS 
Article 

Google Scholar 
Stevens, N., Lehmann, C. E. R., Murphy, B. P. & Durigan, G. Savanna woody encroachment is widespread across three continents. Glob. Chang. Biol. 23, 235–244 (2017).ADS 
PubMed 
Article 

Google Scholar 
Stevens, N., Erasmus, B. F. N., Archibald, S. & Bond, W. J. Woody encroachment over 70 years in South African savannahs: overgrazing, global change or extinction aftershock? Philos. Trans. R. Soc. B Biol. Sci. 371, (2016).Kgope, B. S., Bond, W. J. & Midgley, G. F. Growth responses of African savanna trees implicate atmospheric [CO2] as a driver of past and current changes in savanna tree cover. Austral. Ecol. 35, 451–463 (2010).Article 

Google Scholar 
Scheff, J., Seager, R., Liu, H., Coats, S. & Observatory, L. E. Are glacials dry? Consequences for paleoclimatology and for greenhouse warming. J. Clim. 30, 6593–6609 (2017).ADS 
Article 

Google Scholar 
Bragg, F. J. et al. Stable isotope and modelling evidence for CO2 as a driver of glacial-interglacial vegetation shifts in southern Africa. Biogeosciences 10, 2001–2010 (2013).ADS 
CAS 
Article 

Google Scholar 
Bhattacharya, T., Tierney, J. E., Addison, J. A. & Murray, J. W. Ice-sheet modulation of deglacial North American monsoon intensification. Nat. Geosci. 11, 848–852 (2018).ADS 
CAS 
Article 

Google Scholar 
DiNezio, P. N. et al. Glacial changes in tropical climate amplified by the Indian Ocean. Sci. Adv. 4, 1–12 (2018).Article 

Google Scholar 
Kuechler, R. R., Schefuß, E., Beckmann, B., Dupont, L. & Wefer, G. NW African hydrology and vegetation during the Last Glacial cycle reflected in plant-wax-specific hydrogen and carbon isotopes. Quat. Sci. Rev. 82, 56–67 (2013).ADS 
Article 

Google Scholar 
Raymo, M. E. & Nisancioglu, K. H. The 41 kyr world: Milankovitch’s other unsolved mystery. Paleoceanography 18, 1011 (2003).Davis, B. A. S. & Brewer, S. Orbital forcing and role of the latitudinal insolation/temperature gradient. Clim. Dyn. 32, 143–165 (2009).Article 

Google Scholar 
Bosmans, J. H. C. et al. Precession and obliquity forcing of the freshwater budget over the Mediterranean. Quat. Sci. Rev. 123, 16–30 (2015).ADS 
Article 

Google Scholar 
McGee, D., Broecker, W. S. & Winckler, G. Gustiness: the driver of glacial dustiness? Quat. Sci. Rev. 29, 2340–2350 (2010).ADS 
Article 

Google Scholar 
Bradtmiller, L. I. et al. Changes in biological productivity along the northwest African margin over the past 20,000 years. Paleoceanography 31, 185–202 (2016).ADS 
Article 

Google Scholar 
Guan, K., Wood, E. F. & Caylor, K. K. Multi-sensor derivation of regional vegetation fractional cover in Africa. Remote Sens. Environ. 124, 653–665 (2012).ADS 
Article 

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

Google Scholar 
Sage, R. F. The evolution of C4 photosynthesis. N. Phytol. 161, 341–370 (2004).CAS 
Article 

Google Scholar 
Lloyd, J. et al. Contributions of woody and herbaceous vegetation to tropical savanna ecosystem productivity: a quasi-global estimate. Tree Physiol. 28, 451–468 (2008).PubMed 
Article 

Google Scholar 
Archibald, S. & Hempson, G. P. Competing consumers: contrasting the patterns and impacts of fire and mammalian herbivory in Africa. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150309 (2016).Elderfield, H. et al. Evolution of ocean temperature. Science 337, 704–709 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hooghiemstra, H., Lézine, A. M., Leroy, S. A. G., Dupont, L. & Marret, F. Late Quaternary palynology in marine sediments: a synthesis of the understanding of pollen distribution patterns in the NW African setting. Quat. Int. 148, 29–44 (2006).Article 

Google Scholar 
Dupont, L. M. Vegetation zones in NW Africa during the brunhes chron reconstructed from marine palynological data. Quat. Sci. Rev. 12, 189–202 (1993).ADS 
Article 

Google Scholar 
Dallmeyer, A., Claussen, M., Lorenz, S. J. & Shanahan, T. The end of the African humid period as seen by a transient comprehensive Earth system model simulation of the last 8000 years. Clim 16, 117–140 (2020).ADS 

Google Scholar 
Collins, J. A. et al. Interhemispheric symmetry of the tropical African rainbelt over the past 23,000 years. Nat. Geosci. 4, 42–45 (2011).ADS 
CAS 
Article 

Google Scholar 
Pastouret, L., Chamley, H., Delibrias, G., Duplessy, J. & Thiede, J. Late quaternary climatic changes in western tropical africa deduced from deep-sea sedimentation off the Niger delta. Oceanol. Acta 1, 217–232 (1978).CAS 

Google Scholar 
Tierney, J. E., Lewis, S. C., Cook, B. I., LeGrande, A. N. & Schmidt, G. A. Model, proxy and isotopic perspectives on the East African Humid Period. Earth Planet. Sci. Lett. 307, 103–112 (2011).ADS 
CAS 
Article 

Google Scholar 
COHMAP members. Climatic changes of the last 18,000 years: observations and model simulations. Science 241, 1043–1052 (1988).Article 

Google Scholar 
Street-Perrott, F. A., Marchand, D. S., Roberts, N. & Harrison, S. P. Global lake-level variations from 18,000 to 0 years ago: a palaeoclimate analysis. U.S. Department of Energy Technical Report 46, 20545 (1989).de Menocal, P. B. & Tierney, J. E. Green Sahara: African humid periods paced by Earth’ s orbital changes. Nat. Educ. Knowl. 3(10):12 (2012).Sage, R. F. & Kubien, D. S. Quo vadis C4? An ecophysiological perspective on global change and the future of C4 plants. Photosynth. Res. 77, 209–225 (2003).CAS 
PubMed 
Article 

Google Scholar 
Sarnthein, M., Tetzlaff, G., Koopmann, B., Wolter, K. & Pflaumann, U. Glacial and interglacial wind regimes over the eastern subtropical Atlantic and North-West Africa. Nature 293, 193–196 (1981).ADS 
Article 

Google Scholar 
Rowland, G. H. et al. The spatial distribution of aeolian dust and terrigenous fluxes in the tropical Atlantic ocean since the last glacial maximum. Paleoceanogr. Paleoclimatol. 36, 1–17 (2021).Article 

Google Scholar 
Polissar, P. J., Rose, C., Uno, K. T., Phelps, S. R. & DeMenocal, P. Synchronous rise of African C4 ecosystems 10 million years ago in the absence of aridification. Nat. Geosci. 12, 657–660 (2019).ADS 
CAS 
Article 

Google Scholar 
Jullien, E. et al. Low-latitude “dusty events” vs. high-latitude “icy Heinrich events”. Quat. Res. 68, 379–386 (2007).Article 

Google Scholar 
Pye, K. Aeolian Dust and Dust Deposits. (Academic Press, 1987).Skonieczny, C. et al. A three-year time series of mineral dust deposits on the West African margin: sedimentological and geochemical signatures and implications for interpretation of marine paleo-dust records. Earth Planet. Sci. Lett. 364, 145–156 (2013).ADS 
CAS 
Article 

Google Scholar 
Malaizé, B. et al. The impact of African aridity on the isotopic signature of Atlantic deep waters across the Middle Pleistocene Transition. Quat. Res. 77, 182–191 (2012).Article 
CAS 

Google Scholar 
Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ 18O records. Paleoceanography 20, 1–17 (2005).
Google Scholar 
Polissar, P. J. & D’Andrea, W. J. Uncertainty in paleohydrologic reconstructions from molecular D values. Geochim. Cosmochim. Acta 129, 146–156 (2014).ADS 
CAS 
Article 

Google Scholar 
Eggleston, S., Schmitt, J., Bereiter, B., Schneider, R. & Fischer, H. Evolution of the stable carbon isotope composition of atmospheric CO2 over the last glacial cycle. Paleoceanography 31, 434–452 (2016).ADS 
Article 

Google Scholar 
Tierney, J. E. & deMenocal, P. B. Abrupt shifts in Horn of Africa hydroclimate since the last glacial maximum. Science 342, 843–846 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Schrag, D. P. et al. The oxygen isotopic composition of seawater during the Last Glacial Maximum. Quat. Sci. Rev. 21, 331–342 (2002).ADS 
Article 

Google Scholar 
Vogts, A., Moossen, H., Rommerskirchen, F. & Rullkötter, J. Distribution patterns and stable carbon isotopic composition of alkanes and alkan-1-ols from plant waxes of African rain forest and savanna C3 species. Org. Geochem. 40, 1037–1054 (2009).CAS 
Article 

Google Scholar 
Garcin, Y. et al. Reconstructing C3 and C4 vegetation cover using n-alkane carbon isotope ratios in recent lake sediments from Cameroon, Western Central Africa. Geochim. Cosmochim. Acta 142, 482–500 (2014).ADS 
CAS 
Article 

Google Scholar 
White, F. The Vegetation of Africa. (UNESCO 1983).Ritchie, J. C., Eyles, C. H. & Haynes, C. V. Sediment and pollen evidence for an early to mid-Holocene humid period in the eastern Sahara. Nature 314, 352–355 (1985).ADS 
Article 

Google Scholar 
Watrin, J. et al. Plant migration and plant communities at the time of the ‘green Sahara’. Comptes Rendus—Geosci. 341, 656–670 (2009).ADS 
Article 

Google Scholar 
Hély, C. et al. Holocene changes in African vegetation: tradeoff between climate and water availability. Clim 10, 681–686 (2014).ADS 

Google Scholar 
Lézine, A. M. Timing of vegetation changes at the end of the Holocene Humid Period in desert areas at the northern edge of the Atlantic and Indian monsoon systems. Comptes Rendus—Geosci. 341, 750–759 (2009).ADS 
Article 

Google Scholar 
Dupont, L. M. & Hooghiemstra, H. The Saharan-Sahelian boundary during the Brunhes chron. Acta Bot. Neerl. 38, 405–415 (1989).Article 

Google Scholar 
Sachse, D. et al. Molecular paleohydrology: interpreting the hydrogen-isotopic composition of lipid biomarkers from photosynthesizing organisms. Annu. Rev. Earth Planet. Sci. 40, 221–249 (2012).ADS 
CAS 
Article 

Google Scholar 
Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).ADS 
Article 

Google Scholar 
Worden, J. et al. Importance of rain evaporation and continental convection in the tropical water cycle. Nature 445, 528–532 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Risi, C., Bony, S. & Vimeux, F. Influence of convective processes on the isotopic composition (δ18O and δD) of precipitation and water vapor in the tropics: 2 Physical interpretation of the amount effect. J. Geophys. Res. Atmos. 113, D19306 (2008).ADS 
Article 
CAS 

Google Scholar 
Risi, C. et al. What controls the isotopic composition of the African monsoon precipitation? Insights from event-based precipitation collected during the 2006 AMMA field campaign. Geophys. Res. Lett. 35, L24808 (2008).ADS 
Article 
CAS 

Google Scholar 
Badewien, T., Vogts, A. & Rullkötter, J. n-Alkane distribution and carbon stable isotope composition in leaf waxes of C3 and C4 plants from Angola. Org. Geochem. 89–90, 71–79 (2015).Bezabih, M., Pellikaan, W. F., Tolera, A. & Hendriks, W. H. Evaluation of n-alkanes and their carbon isotope enrichments (d 13 C) as diet composition markers. Anim. Int. J. Anim. Biosci. 5, 57–66 (2011).CAS 
Article 

Google Scholar 
Kristen, I. et al. Biomarker and stable carbon isotope analyses of sedimentary organic matter from Lake Tswaing: evidence for deglacial wetness and early Holocene drought from South Africa. 143–160 https://doi.org/10.1007/s10933-009-9393-9 (2010).Magill, C. R., Ashley, G. M. & Freeman, K. H. Water, plants, and early human habitats in eastern Africa. Proc. Natl Acad. Sci. 110, 1175–1180 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cheddadi, R., Carré, M., Nourelbait, M., François, L. & Rhoujjati, A. Early Holocene greening of the Sahara requires Mediterranean winter rainfall. 1–7 https://doi.org/10.1073/pnas.2024898118 (2021).Niedermeyer, E. M. et al. Orbital- and millennial-scale changes in the hydrologic cycle and vegetation in the western African Sahel: insights from individual plant wax δD and δ13C. Quat. Sci. Rev. 29, 2996–3005 (2010).ADS 
Article 

Google Scholar 
Adkins, J., deMenocal, P. & Eshel, G. The ‘African humid period’ and the record of marine upwelling from excess 230Th in Ocean Drilling Program Hole 658C. Paleoceanography 21, 1–14 (2006).Article 

Google Scholar 
Mcgee, D. Glacial—interglacial precipitation changes. Annu. Rev. Mar. Sci. 12, 525–557 (2020).Weldeab, S., Lea, D. W., Schneider, R. R. & Andersen, N. 155,000 Years of West African monsoon and ocean thermal evolution. Science 316, 1303–1307 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Schefuß, E., Schouten, S. & Schneider, R. R. Climatic controls on central African hydrology during the past 20,000 years. Nature 437, 1003–1006 (2005).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Weijers, J. W. H., Schefuß, E., Schouten, S. & Damsté, J. S. S. Coupled thermal and hydrological evolution of tropical Africa over the last deglaciation. Science 315, 1701–1704 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lezine, A. M. & Cazet, J. P. High-resolution pollen record from core KW31, Gulf of Guinea, documents the history of the lowland forests of West Equatorial Africa since 40,000 yr ago. Quat. Res. 64, 432–443 (2005).Article 

Google Scholar 
Marret, F., Scourse, J. D., Versteegh, G., Fred Jansen, J. H. & Schneider, R. Integrated marine and terrestrial evidence for abrupt Congo River palaeodischarge fluctuations during the last deglaciation. J. Quat. Sci. 16, 761–766 (2001).Article 

Google Scholar 
Dupont, L. & Behling, H. Land-sea linkages during deglaciation: High-resolution records from the eastern Atlantic off the coast of Namibia and Angola (ODP site 1078). Quat. Int. 148, 19–28 (2006).Article 

Google Scholar 
Maley, J. & Brenac, P. Vegetation dynamics, palaeoenvironments and climatic changes in the forests of western Cameroon during the last 28,000 years B.P. Rev. Palaeobot. Palynol. 99, 157–187 (1998).Article 

Google Scholar 
Giresse, P., Maley, J. & Brenac, P. Late Quaternary palaeoenvironments in the Lake Barombi Mbo (West Cameroon) deduced from pollen and carbon isotopes of organic matter. Palaeogeogr. Palaeoclimatol. Palaeoecol. 107, 65–78 (1994).Article 

Google Scholar 
Maley, J. The African rain forest vegetation and palaeoenvironments during late quaternary. Clim. Change 19, 79–98 (1991).ADS 
Article 

Google Scholar 
Talbot, M. R. & Johannessen, T. A high resolution paleoclimatic record for the last 27,500 years in tropical West Africa from the carbon and nitrogen isotopic composition of lacustrine organic matter. Earth Planet. Sci. Lett. 110, 23–37 (1992).Anhuf, D. et al. Paleo-environmental change in Amazonian and African rainforest during the LGM. Palaeogeogr. Palaeoclimatol. Palaeoecol. 239, 510–527 (2006).Article 

Google Scholar 
Elenga, H. et al. Pollen-based biome reconstruction for southern Europe and Africa 18,000 yr BP. J. Biogeogr. 27, 621–634 (2000).Article 

Google Scholar 
Gasse, F., Chalié, F., Vincens, A., Williams, M. A. J. & Williamson, D. Climatic patterns in equatorial and southern Africa from 30,000 to 10,000 years ago reconstructed from terrestrial and near-shore proxy data. Quat. Sci. Rev. 27, 2316–2340 (2008).ADS 
Article 

Google Scholar 
Wu, H., Guiot, J., Brewer, S. & Guo, Z. Climatic changes in Eurasia and Africa at the last glacial maximum and mid-Holocene: reconstruction from pollen data using inverse vegetation modelling. Clim. Dyn. 29, 211–229 (2007).Article 

Google Scholar 
Harrison, S. P. & Prentice, C. I. Climate and CO2 controls on global vegetation distribution at the last glacial maximum: analysis based on palaeovegetation data, biome modelling and palaeoclimate simulations. Glob. Chang. Biol. 9, 983–1004 (2003).ADS 
Article 

Google Scholar 
Prentice, I. C., Cleator, S. F., Huang, Y. H., Harrison, S. P. & Roulstone, I. Reconstructing ice-age palaeoclimates: quantifying low-CO2 effects on plants. Glob. Planet. Change 149, 166–176 (2017).ADS 
Article 

Google Scholar 
Prentice, I. C., Villegas-Diaz, R. & Harrison, S. P. Accounting for atmospheric carbon dioxide variations in pollen-based reconstruction of past hydroclimates. Glob. Planet. Change 103790 https://doi.org/10.1016/j.gloplacha.2022.103790 (2022).Abell, J. T., Winckler, G., Anderson, R. F. & Herbert, T. D. Poleward and weakened westerlies during Pliocene warmth. Nature 589, 70–75 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Winckler, G., Anderson, R. F. & Schlosser, P. Equatorial Pacific productivity and dust flux during the mid-Pleistocene climate transition. Paleoceanography 20, 1–10 (2005).Article 

Google Scholar 
McGee, D. & Mukhopadhyay, S. Extraterrestrial He in sediments: from recorder of asteroid collisions to timekeeper of global environmental changes. in Advances in Isotope Geochemistry 155–176 (Springer, 2013). https://doi.org/10.1007/978-3-642-28836-4_7Costa, K. & McManus, J. Efficacy of 230Th normalization in sediments from the Juan de Fuca Ridge, northeast Pacific Ocean. Geochim. Cosmochim. Acta 197, 215–225 (2017).ADS 
CAS 
Article 

Google Scholar 
Nier, A. O. & Schlutter, D. J. Extraction of helium from individual interplanetary dust particles by step-heating. Meteoritics 27, 166–173 (1992).ADS 
CAS 
Article 

Google Scholar 
McGee, D. et al. Tracking eolian dust with helium and thorium: impacts of grain size and provenance. Geochim. Cosmochim. Acta 175, 47–67 (2016).ADS 
CAS 
Article 

Google Scholar 
Bhattacharya, A. Application of the Helium Isotopic System to Accretion of Terrestrial and Extraterrestrial Dust Through the Cenozoic. (Harvard University, 2012).Ebisuzaki, W. A method to estimate the statistical significance of a correlation when the data are serially correlated. J. Clim. 10, 2147–2153 (1997).ADS 
Article 

Google Scholar 
Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).ADS 
Article 

Google Scholar 
Grinsted, A., Moore, J. C. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 515–533 (2004).Article 

Google Scholar 
Berger, A. L. Long-term variations of daily insolation and Quaternary climatic changes. J. Atmos. Sci. 35, 2361–2367 (1978).ADS 
Article 

Google Scholar 
Berger, A. & Loutre, M. F. Insolation values for the climate of the last 10 million years. Quat. Sci. Rev. 10, 297–317 (1991).ADS 
Article 

Google Scholar 
Eisenman, I. & Huybers, P. J. daily_insolation. (2006).Thyng, K. M., Greene, C. A., Hetland, R. D., Zimmerle, H. M. & DiMarco, S. F. True colors of oceanography. Oceanography 29, 9–13 (2016).Article 

Google Scholar 
Lüthi, D. et al. High-resolution carbon dioxide concentration record 650,000–800,000 years before present. Nature 453, 379–382 (2008).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Rommerskirchen, F. et al. A north to south transect of Holocene southeast Atlantic continental margin sediments: relationship between aerosol transport and compound-specific δ13C land plant biomarker and pollen records. Geochem. Geophys. Geosyst. 4, (2003).Zhao, M., Dupont, L., Eglinton, G. & Teece, M. n-Alkane and pollen reconstruction of terrestrial climate and vegetation for N.W. Africa over the last 160 kyr. Org. Geochem. 34, 131–143 (2003).CAS 
Article 

Google Scholar 
Küechler, R. R. A Revised Orbital Forcing Concept of West African Climate and Vegetation Variability During the Pliocene and the Last Glacial Cycle-Molecular Isotopic Approach and Proxy Calibration. (University of Bremen, 2015). More

Haque, M. N. Dietary manipulation: A sustainable way to mitigate methane emissions from ruminants. J. Anim. Sci. Technol. 60, 1–10. https://doi.org/10.1186/s40781-018-0175-7(2018) (2018).Article 

Google Scholar 
IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press (in press).Lauder, A. R. et al. Offsetting methane emissions—An alternative to emission equivalence metrics. Int. J. Greenh. 12, 419–429. https://doi.org/10.1016/j.ijggc.2012.11.028 (2013).CAS 
Article 

Google Scholar 
Hill, J., McSweeney, C., Wright, A. G., Bishop-Hurley, G. & Kalantar-Zadeh, K. Measuring methane production from ruminants. Trends Biotechnol. 34, 26–35. https://doi.org/10.1016/j.tibtech.2015.10.004 (2016).CAS 
Article 
PubMed 

Google Scholar 
Van Zanten, H. H. E. et al. Defining a land boundary for sustainable livestock consumption. Glob Change Biol. 24, 4185–4194. https://doi.org/10.1111/gcb.14321 (2018).ADS 
Article 

Google Scholar 
Naumann, H. D., Tedeschi, L. O., Zeller, W. E. & Huntley, N. F. The role of condensed tannins in ruminant animal production: Advances, limitations and future directions. Rev. Bras. de Zootec. 46, 929–949. https://doi.org/10.1590/S1806-92902017001200009 (2017).Article 

Google Scholar 
Mueller-Harvey, I. Unravelling the conundrum of tannins in animal nutrition and health. J. Sci. Food Agric. 86, 2010–2037. https://doi.org/10.1002/jsfa.2577 (2006).CAS 
Article 

Google Scholar 
Burggraaf, V. T. et al. Morphology and agronomic performance of white clover with increased flowering and condensed tannin concentration. N. Z. J. Agric. Res. 49, 147–155. https://doi.org/10.1080/00288233.2006.9513704 (2006).CAS 
Article 

Google Scholar 
Einarsson, R. et al. Crop production and nitrogen use in European cropland and grassland 1961–2019. Sci. Data 8, 288. https://doi.org/10.1038/s41597-021-01061-z (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Salminen, J.-P. & Karonen, M. Chemical ecology of tannins and other phenolics: we need a change in approach. Funct. Ecol. 25, 325–338. https://doi.org/10.1111/j.1365-2435.2010.01826.x (2011).Article 

Google Scholar 
Zeller, W. E. Activity, purification, and analysis of condensed tannins: current state of affairs and future endeavors. Crop Sci. 59, 886–904. https://doi.org/10.2135/cropsci2018.05.0323 (2019).CAS 
Article 

Google Scholar 
Barbehenn, R. V. & Peter Constabel, C. Tannins in plant–herbivore interactions. Phytochemistry 72, 1551–1565. https://doi.org/10.1016/j.phytochem.2011.01.040 (2011).CAS 
Article 
PubMed 

Google Scholar 
Chung, Y. H. et al. Enteric methane emission, diet digestibility, and nitrogen excretion from beef heifers fed sainfoin or alfalfa1. J. Anim. Sci. 91, 4861–4874. https://doi.org/10.2527/jas.2013-6498 (2013).CAS 
Article 
PubMed 

Google Scholar 
Christensen, R. G. et al. Effects of feeding birdsfoot trefoil hay on neutral detergent fiber digestion, nitrogen utilization efficiency, and lactational performance by dairy cows1. J. Dairy Sci. 98, 7982–7992. https://doi.org/10.3168/jds.2015-9348 (2015).CAS 
Article 
PubMed 

Google Scholar 
Jonker, A. & Yu, P. The occurrence, biosynthesis, and molecular structure of proanthocyanidins and their effects on legume forage protein precipitation, digestion and absorption in the ruminant digestive tract. Int. J. Mol. Sci. 18, 1105. https://doi.org/10.3390/ijms18051105 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Barry, T. N. & McNabb, W. C. The implications of condensed tannins on the nutritive value of temperate forages fed to ruminants. Br. J. Nutr. 81, 263–272. https://doi.org/10.1017/S0007114599000501 (1999).CAS 
Article 
PubMed 

Google Scholar 
Verma, S., Taube, F. & Malisch, C. S. Examining the variables leading to apparent incongruity between antimethanogenic potential of tannins and their observed effects in ruminants—A review. Sustainability 13, 2743. https://doi.org/10.3390/su13052743 (2021).CAS 
Article 

Google Scholar 
Malisch, C. S. et al. Large variability of proanthocyanidin content and composition in Sainfoin (Onobrychis viciifolia). J. Agric. Food Chem. 63, 10234–10242. https://doi.org/10.1021/acs.jafc.5b04946 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Verma, S., Salminen, J.-P., Taube, F. & Malisch, C. S. Large inter- and intraspecies variability of polyphenols and proanthocyanidins in eight temperate forage species indicates potential for their exploitation as nutraceuticals. J. Agric. Food Chem. 69, 12445–12455. https://doi.org/10.1021/acs.jafc.1c03898 (2021).CAS 
Article 
PubMed 

Google Scholar 
Lorenz, H., Reinsch, T., Kluß, C., Taube, F. & Loges, R. Does the admixture of forage herbs affect the yield performance, yield stability and forage quality of a grass clover ley?. Sustainability 12, 5842. https://doi.org/10.3390/su12145842 (2020).Article 

Google Scholar 
Hofer, D. et al. Yield of temperate forage grassland species is either largely resistant or resilient to experimental summer drought. J. Appl. Ecol. 53, 1023–1034. https://doi.org/10.1111/1365-2664.12694 (2016).Article 

Google Scholar 
Mueller-Harvey, I. et al. Benefits of condensed tannins in forage legumes fed to ruminants : Importance of structure, concentration and diet compsition. Crop Sci. 59, 861–885. https://doi.org/10.2135/cropsci2017.06.0369 (2017).CAS 
Article 

Google Scholar 
Loza, C. et al. Assessing the potential of diverse forage mixtures to reduce enteric methane emissions in vitro. Animals 11, 1126. https://doi.org/10.3390/ani11041126 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Min, B. R. et al. Dietary mitigation of enteric methane emissions from ruminants: A review of plant tannin mitigation options. Anim. Nutr. 6, 231–236. https://doi.org/10.1016/j.aninu.2020.05.002 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
van Gastelen, S., Dijkstra, J. & Bannink, A. Are dietary strategies to mitigate enteric methane emission equally effective across dairy cattle, beef cattle, and sheep?. J. Dairy Sci. 102, 6109–6130. https://doi.org/10.3168/jds.2018-15785 (2019).CAS 
Article 
PubMed 

Google Scholar 
Hatew, B. et al. Relationship between in vitro and in vivo methane production measured simultaneously with different dietary starch sources and starch levels in dairy cattle. Anim. Feed Sci. Technol. 202, 20–31. https://doi.org/10.1016/j.anifeedsci.2015.01.012 (2015).CAS 
Article 

Google Scholar 
Storm, I. M. L. D., Hellwing, A. L. F., Nielsen, N. I. & Madsen, J. Methods for measuring and estimating methane emission from ruminants. Animals 2, 160–183. https://doi.org/10.3390/ani2020160 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Dewhurst, R. J., Delaby, L., Moloney, A., Boland, T. & Lewis, E. Nutritive value of forage legumes used for grazing and silage. Irish J. Agric. Food Res. 48, 167–187 (2009).CAS 

Google Scholar 
Hakl, J., Fuksa, P., Konečná, J. & Šantrůček, J. Differences in the crude protein fractions of lucerne leaves and stems under different stand structures. Grass Forage Sci. 71, 413–423. https://doi.org/10.1111/gfs.12192 (2016).CAS 
Article 

Google Scholar 
Jayanegara, A., Makkar, H. & Becker, K. The use of principal component analysis in identifying and integrating variables related to forage quality and methane production. J. Indones. Trop. Anim. 34, 241–247. https://doi.org/10.14710/jitaa.34.4.241-247 (2009).Article 

Google Scholar 
Maccarana, L. et al. Methodological factors affecting gas and methane production during in vitro rumen fermentation evaluated by meta-analysis approach. J. Anim. Sci. Biotechnol. 7, 35–35. https://doi.org/10.1186/s40104-016-0094-8 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Baruah, L., Malik, P. K., Kolte, A. P., Dhali, A. & Bhatta, R. Methane mitigation potential of phyto-sources from Northeast India and their effect on rumen fermentation characteristics and protozoa in vitro. Vet. World 11, 809–818. https://doi.org/10.14202/vetworld.2018.809-818 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hassanat, F. & Benchaar, C. Assessment of the effect of condensed (acacia and quebracho) and hydrolysable (chestnut and valonea) tannins on rumen fermentation and methane production in vitro. J. Sci. Food Agric. 93, 332–339. https://doi.org/10.1002/jsfa.5763 (2013).CAS 
Article 
PubMed 

Google Scholar 
Naumann, H. et al. Relationships between structures of condensed tannins from texas legumes and methane production during in vitro rumen digestion. Molecules 23, 2123. https://doi.org/10.3390/molecules23092123 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Jayanegara, A., Makkar, H. P. S. & Becker, K. Addition of purified tannin sources and polyethylene glycol treatment on methane emission and rumen fermentation in vitro. Media Peternakan 38, 57–63. https://doi.org/10.5398/medpet.2015.38.1.57 (2015).Article 

Google Scholar 
Jayanegara, A., Goel, G., Makkar, H. P. S. & Becker, K. Divergence between purified hydrolysable and condensed tannin effects on methane emission, rumen fermentation and microbial population in vitro. Anim. Feed Sci. Technol. 209, 60–68. https://doi.org/10.1016/j.anifeedsci.2015.08.002 (2015).CAS 
Article 

Google Scholar 
Hatew, B. et al. Diversity of condensed tannin structures affects rumen in vitro methane production in sainfoin (Onobrychis viciifolia) accessions. Grass Forage Sci. 70, 474–490. https://doi.org/10.1111/gfs.12125 (2015).CAS 
Article 

Google Scholar 
Huyen, N. T. et al. Structural features of condensed tannins affect in vitro ruminal methane production and fermentation characteristics. J. Agric. Sci. 154, 1474–1487. https://doi.org/10.1017/S0021859616000393 (2016).CAS 
Article 

Google Scholar 
Salami, S. A. et al. Characterisation of the ruminal fermentation and microbiome in lambs supplemented with hydrolysable and condensed tannins. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiy061 (2018).Article 
PubMed 

Google Scholar 
Salminen, J. P., Karonen, M. & Sinkkonen, J. Chemical ecology of tannins: Recent developments in tannin chemistry reveal new structures and structure-activity patterns. Chem.-Eur. J. 17, 2806–2816. https://doi.org/10.1002/chem.201002662 (2011).CAS 
Article 
PubMed 

Google Scholar 
Bezabih, M., Pellikaan, W. F., Tolera, A., Khan, N. A. & Hendriks, W. Chemical composition and in vitro total gas and methane production of forage species from the Mid Rift Valley grasslands of Ethiopia. Grass Forage Sci. 69, 635–643. https://doi.org/10.1111/gfs.12091 (2013).CAS 
Article 

Google Scholar 
Navarrete, S., Kemp, P. D., Pain, S. J. & Back, P. J. Bioactive compounds, aucubin and acteoside, in plantain (Plantago lanceolata L.) and their effect on in vitro rumen fermentation. Anim. Feed Sci. Technol. 222, 158–167. https://doi.org/10.1016/j.anifeedsci.2016.10.008 (2016).CAS 
Article 

Google Scholar 
Basha, N. A., Scogings, P. F. & Nsahlai, I. V. Effects of season, browse species and polyethylene glycol addition on gas production kinetics of forages in the subhumid subtropical savannah, South Africa. J. Sci. Food Agric. 93, 1338–1348. https://doi.org/10.1002/jsfa.5895 (2013).CAS 
Article 
PubMed 

Google Scholar 
O’Donovan, L. & Brooker, J. D. Effect of hydrolysable and condensed tannins on growth, morphology and metabolism of Streptococcus gallolyticus (S. caprinus) and Streptococcus bovis. Microbiology 147, 1025–1033. https://doi.org/10.1099/00221287-147-4-1025 (2001).CAS 
Article 
PubMed 

Google Scholar 
Bhatta, R. et al. Difference in the nature of tannins on in vitro ruminal methane and volatile fatty acid production and on methanogenic archaea and protozoal populations. J. Dairy Sci. 92, 5512–5522. https://doi.org/10.3168/jds.2008-1441 (2009).CAS 
Article 
PubMed 

Google Scholar 
Naumann, H. D. et al. Effect of molecular weight and concentration of legume condensed tannins on in vitro larval migration inhibition of Haemonchus contortus. Vet. Parasitol. 199, 93–98. https://doi.org/10.1016/j.vetpar.2013.09.025 (2014).CAS 
Article 
PubMed 

Google Scholar 
Jayanegara, A., Goel, G., Makkar, H.P.S., & Becker, K. Reduction in
methane emissions from ruminants by plant secondary metabolites: Effects of polyphenols and saponins. Food and Agriculture Organization of the United Nations (FAO) Rome, Italy, 151–157. ISBN 978-92-5-106697-3 (2010).Hatew, B. et al. Impact of variation in structure of condensed tannins from sainfoin (Onobrychis viciifolia) on in vitro ruminal methane production and fermentation characteristics. J. Anim. Physiol. Anim. Nutr. 100, 348–360. https://doi.org/10.1111/jpn.12336 (2016).CAS 
Article 

Google Scholar 
Waghorn, G. C., Douglas, G. B., Niezen, J. H., McNabb, W. C. & Foote, A. G. Forages with condensed tannins-their management and nutritive value for ruminants. Proc. N. Z. Grassl. Assoc., 60, 89−98 (1998).Woodward, S. L., Waghorn, G. C. & Lassey, K. Early indications that feeding Lotus will reduce methane emissions from ruminants. Proc. N. Z. Soc. Anim. Prod. 61, 23–26 (2001).
Google Scholar 
Molle, G. et al. Responses to condensed tannins of flowering sulla (Hedysarum coronarium L.) grazed by dairy sheep: Part 1: Effects on feeding behaviour, intake, diet digestibility and performance. Livest. Sci. 123, 138–146. https://doi.org/10.1016/j.livsci.2008.11.018 (2009).Article 

Google Scholar 
Orlandi, T., Kozloski, G. V., Alves, T. P., Mesquita, F. R. & Ávila, S. C. Digestibility, ruminal fermentation and duodenal flux of amino acids in steers fed grass forage plus concentrate containing increasing levels of Acacia mearnsii tannin extract. Anim. Feed Sci. Technol. 210, 37–45. https://doi.org/10.1016/j.anifeedsci.2015.09.012 (2015).CAS 
Article 

Google Scholar 
Patra, A. K. & Yu, Z. Effects of adaptation of in vitro rumen culture to garlic oil, nitrate, and saponin and their combinations on methanogenesis, fermentation, and abundances and diversity of microbial populations. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01434 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Niderkorn, V. et al. Effect of increasing the proportion of chicory in forage-based diets on intake and digestion by sheep. Animal 13, 718–726. https://doi.org/10.1017/S1751731118002185 (2019).CAS 
Article 
PubMed 

Google Scholar 
Lee, J., Hemmingson, N., Minneé, E. & Clark, C. Management strategies for chicory (Cichorium intybus) and plantain (Plantago lanceolata): Impact on dry matter yield, nutritive characteristics, and plant density. Crop Pasture Sci. 66, 168. https://doi.org/10.1071/CP14181 (2015).CAS 
Article 

Google Scholar 
Cong, W.-F., Jing, J., Rasmussen, J., Søegaard, K. & Eriksen, J. Forbs enhance productivity of unfertilised grass-clover leys and support low-carbon bioenergy. Sci. Rep. 7, 1422. https://doi.org/10.1038/s41598-017-01632-4 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sanderson, M. A., Labreveux, M., Hall, M. H. & Elwinger, G. F. Nutritive value of chicory and English plantain forage. Crop Sci. 43, 1797. https://doi.org/10.2135/cropsci2003.1797 (2003).CAS 
Article 

Google Scholar 
Van Soest, P. J., Robertson, J. B. & Lewis, B. A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2 (1991).Article 
PubMed 

Google Scholar 
Engström, M. T. et al. Rapid qualitative and quantitative analyses of proanthocyanidin oligomers and polymers by UPLC-MS/MS. J. Agric. Food Chem. 62, 3390–3399. https://doi.org/10.1021/jf500745y (2014).CAS 
Article 
PubMed 

Google Scholar 
Menke, K. & Steingass, H. Estimation of the energetic feed value obtained from chemical analysis and in vitro gas production using rumen fluid. Anim. Res. Dev. 28, 7–55 (1988).
Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Venables, B. & Ripley, B. Generalised linear models. In Modern Applied Statistics With S.(4th edition) 183–208 (Springer, 2013). More

Genome’s collection and phylogenetic analysisThe study examined the genomes of 139 isolates, 61 of environmental samples (ENV) and 78 clinical (CLI) (Supplementary Table 1, Supplementary Fig. 1), with origin in 21 countries: USA (23/139, 17%), UK, Portugal and Spain (each 15/139, 33%), China (14/139, 10%), Germany (13/139, 9%), Thailand (11/139, 8%) and other countries (each  More

Dolezal AG, Toth AL. Feedbacks between nutrition and disease in honey bee health. Curr Opin Insect Sci. 2018;26:114–9.PubMed 
Article 

Google Scholar 
Scofield HN, Mattila HR. Honey bee workers that are pollen stressed as larvae become poor foragers and waggle dancers as adults. PLoS ONE. 2015;10:e0121731.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, et al. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA. 2013;110:3229–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Akman Gündüz E, Douglas AE. Symbiotic bacteria enable insect to use a nutritionally inadequate diet. Proc R Soc B Biol Sci. 2009;276:987–91.Article 
CAS 

Google Scholar 
Wu D, Daugherty SC, Van Aken SE, Pai GH, Watkins KL, Khouri H, et al. Metabolic Complementarity and Genomics of the Dual Bacterial Symbiosis of Sharpshooters. PLoS Biol 2006;4:e188.Bing X, Attardo GM, Vigneron A, Aksoy E, Scolari F, Malacrida A, et al. Unravelling the relationship between the tsetse fly and its obligate symbiont Wigglesworthia: transcriptomic and metabolomic landscapes reveal highly integrated physiological networks. Proc R Soc B Biol Sci. 2017; 284:20170360.Itoh H, Jang S, Takeshita K, Ohbayashi T, Ohnishi N, Meng X-Y, et al. Host–symbiont specificity determined by microbe–microbe competition in an insect gut. Proc Natl Acad Sci USA. 2019;116:22673–82.CAS 
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 
Oliver KM, Degnan PH, Hunter MS, Moran NA. Bacteriophages encode factors required for protection in a symbiotic mutualism. Science 2009;325:992–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shin SC, Kim SH, You H, Kim B, Kim AC, Lee KA, et al. Drosophila microbiome modulates host developmental and metabolic homeostasis via insulin signaling. Science 2011;334:670–4.CAS 
PubMed 
Article 

Google Scholar 
Dedeine F, Vavre F, Fleury F, Loppin B, Hochberg ME, Boulétreau M. Removing symbiotic Wolbachia bacteria specifically inhibits oogenesis in a parasitic wasp. Proc Natl Acad Sci USA. 2001;98:6247–52.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Koropatnick TA, Engle JT, Apicella MA, Stabb EV, Goldman WE, McFall-Ngai MJ. Microbial factor-mediated development in a host-bacterial mutualism. Science 2004;306:1186–8.CAS 
PubMed 
Article 

Google Scholar 
Chun CK, Troll JV, Koroleva I, Brown B, Manzella L, Snir E, et al. Effects of colonization, luminescence, and autoinducer on host transcription during development of the squid-vibrio association. Proc Natl Acad Sci USA. 2008;105:11323–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shikuma NJ, Pilhofer M, Weiss GL, Hadfield MG, Jensen GJ, Newman DK. Marine tubeworm metamorphosis induced by arrays of bacterial phage tail-like structures. Science 2014;343:529–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Freiberg C, Fellay R, Bairoch A, Broughton WJ, Rosenthal A, Perret X. Molecular basis of symbiosis between Rhizobium and legumes. Nature 1997;387:394–401.CAS 
PubMed 
Article 

Google Scholar 
Médigue C, Masson-Boivin C, Gilbert LB, Cruveiller S, Gris C, Batut J, et al. Experimental evolution of a plant pathogen into a legume symbiont. PLoS Biol. 2010;8:e1000280.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Brucker RM, Bordenstein SR. Speciation by symbiosis. Trends Ecol Evol. 2012;27:443–51.PubMed 
Article 

Google Scholar 
Moran NA, McCutcheon JP, Nakabachi A. Genomics and evolution of heritable bacterial symbionts. Annu Rev Genet. 2008;42:165–90.CAS 
PubMed 
Article 

Google Scholar 
Moran NA, Tran P, Gerardo NM. Symbiosis and insect diversification: an ancient symbiont of sap-feeding insects from the bacterial phylum Bacteroidetes. Appl Environ Microbiol. 2005;71:8802–10.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lee FJ, Miller KI, McKinlay JB, Newton ILG. Differential carbohydrate utilization and organic acid production by honey bee symbionts. FEMS Microbiol Ecol. 2018;94:fiy113.CAS 
Article 

Google Scholar 
Lee FJ, Rusch DB, Stewart FJ, Mattila HR, Newton ILG. Saccharide breakdown and fermentation by the honey bee gut microbiome. Environ Microbiol. 2015;17:796–815.CAS 
PubMed 
Article 

Google Scholar 
Zheng H, Nishida A, Kwong WK, Koch H, Engel P, Steele MI, et al. Metabolism of toxic sugars by strains of the bee gut symbiont Gilliamella apicola. MBio 2016;7:e01326–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kešnerová L, Mars RAT, Ellegaard KM, Troilo M, Sauer U, Engel P. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 2017;15:e2003467.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gallai N, Salles JM, Settele J, Vaissière BE. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol Econ. 2009;68:810–21.Article 

Google Scholar 
Brodschneider R, Gray A, Adjlane N, Ballis A, Brusbardis V, Charrière JD, et al. Multi-country loss rates of honey bee colonies during winter 2016/2017 from the COLOSS survey. J Apic Res. 2018;57:452–7.Article 

Google Scholar 
Kulhanek K, Steinhauer N, Rennich K, Caron DM, Sagili RR, Pettis JS, et al. A national survey of managed honey bee 2015-6 annual colony losses in the USA. J Apic Res. 2017;56:328–40.Article 

Google Scholar 
Goulson D, Nicholls E, Botías C, Rotheray EL. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 2015;347:1255957.PubMed 
Article 
CAS 

Google Scholar 
Dolezal AG, Carrillo-Tripp J, Judd TM, Allen Miller W, Bonning BC, Toth AL. Interacting stressors matter: Diet quality and virus infection in honeybee health. R Soc Open Sci. 2019;6:81803.Article 
CAS 

Google Scholar 
St Clair AL, Zhang G, Dolezal AG, O’Neal ME, Toth AL, et al. Diversified farming in a monoculture landscape: effects on honey bee health and wild bee communities. Environ Entomol. 2020;49:753–64.Article 

Google Scholar 
Naug D. Nutritional stress due to habitat loss may explain recent honeybee colony collapses. Biol Conserv. 2009;142:2369–72.Article 

Google Scholar 
Taha EKA, Al-Kahtani S, Taha R. Protein content and amino acids composition of bee-pollens from major floral sources in Al-Ahsa, eastern Saudi Arabia. Saudi J Biol Sci. 2019;26:232–7.CAS 
PubMed 
Article 

Google Scholar 
de Groot AP. Amino acid requirements for growth of the honeybee (Apis mellifica L.). Experientia 1952;8:192–4.Article 

Google Scholar 
Brodschneider R, Crailsheim K. Nutrition and health in honey bees. Apidologie 2010;41:278–94.Article 

Google Scholar 
Keller I, Fluri P, Imdorf A. Pollen nutrition and colony development in honey bees – Part II. Bee World. 2005;86:27–34.Article 

Google Scholar 
Huang Z. Pollen nutrition affects honey bee stress resistance. Terr Arthropod Rev. 2012;5:175–89.Article 

Google Scholar 
van Dooremalen C, Stam E, Gerritsen L, Cornelissen B, van der Steen J, van Langevelde F, et al. Interactive effect of reduced pollen availability and Varroa destructor infestation limits growth and protein content of young honey bees. J Insect Physiol. 2013;59:487–93.PubMed 
Article 
CAS 

Google Scholar 
Feldhaar H, Straka J, Krischke M, Berthold K, Stoll S, Mueller MJ, et al. Nutritional upgrading for omnivorous carpenter ants by the endosymbiont Blochmannia. BMC Biol. 2007;5:48.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sannino DR, Dobson AJ, Edwards K, Angert ER, Buchon N. The Drosophila melanogaster gut microbiota provisions thiamine to its host. MBio 2018;9:e00155–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hammer TJ, Moran NA. Links between metamorphosis and symbiosis in holometabolous insects. Philos Trans R Soc B Biol Sci. 2019;374:20190068.CAS 
Article 

Google Scholar 
Kowallik V, Mikheyev AS. Honey bee larval and adult microbime life stages are effectively decoupled with vertical transmisson overcoming early life perturbations. mBio 2021;12:e02966–21.CAS 
PubMed Central 
Article 

Google Scholar 
Storelli G, Defaye A, Erkosar B, Hols P, Royet J, Leulier F. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-dependent nutrient sensing. Cell Metab. 2011;14:403–14.CAS 
PubMed 
Article 

Google Scholar 
Wright GA, Nicolson SW, Shafir S. Nutritional physiology and ecology of honey bees. Annu Rev Entomol. 2017;63:327–44.PubMed 
Article 
CAS 

Google Scholar 
Tarpy DR, Mattila HR, Newton ILG. Development of the honey bee gut microbiome throughout the queen-rearing process. Appl Environ Microbiol. 2015;81:3182–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Corby-Harris V, Snyder LA, Schwan MR, Maes P, McFrederick QS, Anderson KE. Origin and effect of Alpha 2.2 Acetobacteraceae in honey bee larvae and description of Parasaccharibacter apium gen. nov., sp. nov. Appl Environ Microbiol. 2014;80:7460–72.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vojvodic S, Rehan SM, Anderson KE. Microbial gut diversity of Africanized and European honey bee larval instars. PLoS ONE. 2013;8:72106.Article 
CAS 

Google Scholar 
Kwong WK, Medina LA, Koch H, Sing KW, Soh EJY, Ascher JS, et al. Dynamic microbiome evolution in social bees. Sci Adv. 2017;3:e1600513.PubMed 
PubMed Central 
Article 

Google Scholar 
Cohen O, Ashkenazy H, Belinky F, Huchon D, Pupko T. GLOOME: Gain loss mapping engine. Bioinformatics 2010;26:2914–5.CAS 
PubMed 
Article 

Google Scholar 
Price MN, Deutschbauer AM, Arkin AP. GapMind: Automated annotation of amino acid biosynthesis. mSystems 2020;5:e00291–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schmehl DR, Tomé HVV, Mortensen AN, Martins GF, Ellis JD. Protocol for the in vitro rearing of honey bee (Apis mellifera L.) workers. J Apic Res. 2016;55:113–29.Article 

Google Scholar 
Li H, Tennessen JM. Preparation of Drosophila larval samples for gas chromatography-mass spectrometry (GC-MS)-based metabolomics. J Vis Exp. 2018;136:e57847.
Google Scholar 
Rortais A, Arnold G, Halm MP, Touffet-Briens F. Modes of honeybees exposure to systemic insecticides: Estimated amounts of contaminated pollen and nectar consumed by different categories of bees. Apidologie 2005;36:71–83.CAS 
Article 

Google Scholar 
Buttstedt A, Mureşan CI, Lilie H, Hause G, Ihling CH, Schulze SH, et al. How honeybees defy gravity with royal jelly to raise queens. Curr Biol. 2018;28:1095–1100.CAS 
PubMed 
Article 

Google Scholar 
Fratini F, Cilia G, Mancini S, Felicioli A. Royal jelly: An ancient remedy with remarkable antibacterial properties. Microbiol Res. 2016;192:130–41.CAS 
PubMed 
Article 

Google Scholar 
Fontana R, Mendes MA, De Souza BM, Konno K, César LMM, Malaspina O, et al. Jelleines: A family of antimicrobial peptides from the royal jelly of honeybees (Apis mellifera). Peptides 2004;25:919–28.CAS 
PubMed 
Article 

Google Scholar 
Rokop ZP, Horton MA, Newton ILG. Interactions between cooccurring lactic acid bacteria in honey bee hives. Appl Environ Microbiol. 2015;81:7261–70.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crailsheim K, Brodschneider R, Aupinel P, Behrens D, Genersch E, Vollmann J, et al. Standard methods for artificial rearing of Apis mellifera larvae. J Apic Res. 2013;52:1–16.Article 

Google Scholar 
Smith EA, Newton ILG. Genomic signatures of honey bee association in an acetic acid symbiont. Genome Biol Evol. 2020;12:1882–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kaftanoglu O, Linksvayer TA, Page RE. Rearing honey bees, Apis mellifera, in vitro 1: Effects of sugar concentrations on survival and development. J Insect Sci. 2011;11:96.PubMed 
PubMed Central 
Article 

Google Scholar 
Aupinel P, Fortini D, Dufour H, Tasei J-N, Michaud B, Odoux J-F, et al. Improvement of artificial feeding in a standard in vitro method for rearing Apis mellifera larvae. Bull Insectol. 2005;58:107–11.
Google Scholar 
Hansen AK, Moran NA. Aphid genome expression reveals host-symbiont cooperation in the production of amino acids. Proc Natl Acad Sci USA. 2011;108:2849–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCutcheon JP, McDonald BR, Moran NA. Convergent evolution of metabolic roles in bacterial co-symbionts of insects. Proc Natl Acad Sci USA. 2009;106:15394–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shigenobu S, Watanabe H, Hattori M, Sakaki Y, Ishikawa H. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. Aps Nat. 2000;407:81–86.CAS 

Google Scholar 
Gil R, Silva FJ, Zientz E, Delmotte F, González-Candelas F, Latorre A, et al. The genome sequence of Blochmannia floridanus: Comparative analysis of reduced genomes. Proc Natl Acad Sci USA. 2003;100:9388–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCutcheon JP, Moran NA. Extreme genome reduction in symbiotic bacteria. Nat Rev Microbiol. 2012;10:13–26.CAS 
Article 

Google Scholar 
Wernegreen JJ, Lazarus AB, Degnan PH. Small genome of Candidatus Blochmannia, the bacterial endosymbiont of Camponotus, implies irreversible specialization to an intracellular lifestyle. Microbiology 2002;148:2551–6.CAS 
PubMed 
Article 

Google Scholar 
McCutcheon JP, Moran NA. Parallel genomic evolution and metabolic interdependence in an ancient symbiosis. Proc Natl Acad Sci USA. 2007;104:19392–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bennett GM, Mccutcheon JP, Macdonald BR, Romanovicz D, Moran NA. Differential genome evolution between companion symbionts in an insect-bacterial symbiosis. mBio 2014;5:e01697–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Husnik F, Nikoh N, Koga R, Ross L, Duncan RP, Fujie M, et al. Horizontal gene transfer from diverse bacteria to an insect genome enables a tripartite nested mealybug symbiosis. Cell 2013;153:1567.CAS 
PubMed 
Article 

Google Scholar 
Bao XY, Yan JY, Yao YL, Wang Y, Bin, Visendi P, Seal S, et al. Lysine provisioning by horizontally acquired genes promotes mutual dependence between whitefly and two intracellular symbionts. PLOS Pathog. 2021;17:e1010120.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cotte JF, Casabianca H, Giroud B, Albert M, Lheritier J, Grenier-Loustalot MF. Characterization of honey amino acid profiles using high-pressure liquid chromatography to control authenticity. Anal Bioanal Chem. 2004;378:1342–50.CAS 
PubMed 
Article 

Google Scholar 
Baker HG. Non-sugar chemical constituents of nectar. Apidologie 1977;8:349–56.Article 

Google Scholar 
Nyholm SV, McFall-Ngai MJ. The winnowing: Establishing the squid – Vibrios symbiosis. Nat Rev Microbiol. 2004;2:632–42.CAS 
PubMed 
Article 

Google Scholar 
Kikuchi Y, Hosokawa T, Fukatsu T. Insect-microbe mutualism without vertical transmission: a stinkbug acquires a beneficial gut symbiont from the environment every generation. Appl Environ Microbiol. 2007;73:4308–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Itoh H, Jang S, Takeshita K, Ohbayashi T, Ohnishi N, Meng XY, et al. Host–symbiont specificity determined by microbe–microbe competition in an insect gut. Proc Natl Acad Sci USA. 2019;116:22673–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oono R, Anderson CG, Denison RF. Failure to fix nitrogen by non-reproductive symbiotic rhizobia triggers host sanctions that reduce fitness of their reproductive clonemates. Proc R Soc B Biol Sci. 2011;278:2698–703.Article 

Google Scholar 
Brown BP, Wernegreen JJ. Genomic erosion and extensive horizontal gene transfer in gut-associated Acetobacteraceae. BMC Genom. 2019;20:1–15.CAS 
Article 

Google Scholar 
Vitreschak AG, Rodionov DA, Mironov AA, Gelfand MS. Riboswitches: the oldest mechanism for the regulation of gene expression? Trends Genet. 2004;20:44–50.CAS 
PubMed 
Article 

Google Scholar 
Meijuan X, Rao Z, Yang J, Dou W, Xu Z. The effect of a LYSE exporter overexpression on L-arginine production in Corynebacterium crenatum. Curr Microbiol. 2013;67:271–8.Article 
CAS 

Google Scholar 
Indurthi SM, Chou H-T, Lu C-D. Molecular characterization of lysR-lysXE, gcdR-gcdHG and amaR-amaAB operons for lysine export and catabolism: a comprehensive lysine catabolic network in Pseudomonas aeruginosa PAO1. Microbiology 2016;162:876–88.CAS 
Article 

Google Scholar 
Pathania A, Sardesai AA. Distinct paths for basic amino acid export in Escherichia coli: YbjE (LysO) mediates export of L-lysine. J Bacteriol. 2015;197:2036–47.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miller DL, Smith EA, Newton ILG. A bacterial symbiont protects honey bees from fungal disease. mBio 2021;12:e00503–21.CAS 
PubMed Central 
Article 

Google Scholar  More

More than three billion people rely on the ocean to make a living, most of whom are in developing countries. For some 17% of the world’s population, fisheries and aquaculture provide the main source of animal protein. For the least-developed countries, fish contributes about 29% of animal protein intake; in other developing countries, it accounts for 19%1.As the global population increases, the demand for seafood is expected to rise, too. Already, Africa and Asia have seen fish production double over the past few decades. Globally, fish consumption is set to rise by around 15% by 20302.Although ocean ecosystems are strained by climate change, overfishing and more, studies nevertheless suggest that seafood can be expanded sustainably to meet future food demands3. Last year, international efforts promoting this approach included the Blue Food Assessment (a joint initiative of 25 research institutions) and the United Nations Food Systems Summit.Success will depend on small-scale fisheries. Small operations tend to deliver both food and income directly to the people who need them most, and locals have a strong incentive to make their practices sustainable. What’s more, these fisheries can be remarkably efficient. Almost everything that hand-to-mouth fisheries catch is consumed. By contrast, around 20% of the fish caught by industrial fleets is estimated to be wasted, mainly because of unwanted by-catch4. So, whereas large-scale operators land more fish, small-scale fisheries provide a larger share of the fish that is actually consumed.Small fishers rarely have the right resources to expand their operations, or even to survive. If they do scale up, they might lose some of their current advantages or engage in the same harmful practices as do large commercial fisheries. Managed with care, however, small fisheries could provide win–wins for livelihoods and the environment. Making this happen should be high on the agenda at the UN Ocean Conference in Lisbon this month.As someone who has studied food security and policymaking for decades, here I suggest ways to support and strengthen artisanal fishing operations.Small reformsThe potential and importance of small-scale fisheries has been increasingly recognized over the past decade. In 2014, the UN Food and Agriculture Organization (FAO) provided voluntary guidelines to support sustainable small-scale fisheries, aimed at improving food security and eradicating poverty. A forthcoming report by the FAO, Duke University in Durham, North Carolina, and the non-profit organization WorldFish, headquartered in Penang, Malaysia, will conclude a remarkable initiative to collate case studies, questionnaire results and data sets to help get fishers a seat at policymakers’ tables. The UN General Assembly has declared 2022 the International Year of Artisanal Fisheries and Aquaculture.Most nations already have management policies for marine ecosystems that provide for small-scale fisheries. In India, Indonesia, Malaysia and Sri Lanka, for example, there is a ban on trawling within about 8 kilometres of the coastline to prevent industrial fishers from scooping up large catches, which protects those regions for local fishers. Countries such as Costa Rica ease access by exempting small-scale fisheries from licences, and Angola exempts subsistence and artisanal fishers from paying licensing fees5.But this is not enough. Small-scale fishers’ rights to access are often poorly defined, ineffectively enforced or unfairly distributed4. The boundaries of exclusive economic zones (EEZs) — the parts of the coast belonging to a given nation — are often poorly policed, and large-scale vessels regularly swoop in and take sea life through bottom trawling, something that small fishers seldom practice. Large-scale bottom-trawlers account for 26% of the global fisheries catch, with more than 99% of that occurring in the EEZs of coastal countries6. Even when there are well-meaning policies to protect local fishers, foreign vessels can take advantage. For instance, a 2018 investigation by the Environmental Justice Foundation in London found that around 90% of Ghana’s industrial fishing fleet was linked to Chinese ownership, despite Ghanaian laws expressly forbidding foreign ownership or control of its boats. Clearer definitions of the terms fisher, fishing and fishing vessel to make provisions for small-scale operators could help, in part, to avoid such abuse.Government subsidies also require reform. One estimate found that large-scale fishers receive about three-and-a-half times more subsidies than small-scale fishers do7. This widens the existing advantages of large operations in terms of vessels and gear, infrastructure (including cold storage), processing capacity and access to cheap fuel. By giving large-scale fishers the capacity to catch even more, it can have the perverse effect of encouraging overfishing8. Instead, subsidies and other funds should be directed towards small-scale fishers to let them expand their access to markets, while keeping them from adopting the negative practices of large-scale operations.More for consumptionThe total global loss and waste from fisheries is estimated at between 30% and 35% annually1. This could increase as smaller operations broaden their markets. A 2015 estimate of the Volta Basin coast in West Africa attributed 65% of fish-production losses to a lack of technology and good manufacturing practices, and to a lack of infrastructure such as decent roads and cold storage9. The study found that fish were rarely lost to physical damage during the process; most waste resulted from spoilage. Such losses limit the sale of fish locally and to distant markets.Public and private investment in cold-storage facilities and processing equipment (such as for drying, fermentation, pickling or smoking) could help. Current funding for fishery conservation projects comes from development partners, regional banks, the World Bank, private foundations and other agencies — with some entities also providing microloans to small-scale fisheries — but these efforts are uncoordinated and inadequate.One promising strategy is to pair international or national funding with direct contracts for feeding programmes linked to schools, hospitals and similar facilities. Such arrangements would provide small fisheries with large, consistent markets and storage infrastructure that boosts local consumption and does not incentivize overfishing.

Artisanal fishers at a fish-processing cooperative in Santa Rosa de Salinas, Ecuador.Credit: Camilo Pareja/AFP/Getty

Other strategies pair local fishers with conservation efforts. As fishing operations scale up, fish entrails and other waste cannot simply be thrown into the sea: care must be taken not to contaminate the environment. One option is to fund ecosystem-restoration projects that also benefit local fisheries. For example, the Mikoko Pamoja (Mangroves Together) project in Gazi Bay, Kenya, restores and conserves degraded mangrove forests, which act as nurseries for young fish. The restoration thus earns saleable carbon credits while enhancing nearby fishery grounds for the local community.Consumers could support small fisheries by buying local, because shorter supply chains mean more income for the fishers. The use of ecolabels — which seek to promote sustainably managed fisheries by certifying that a product has a reduced environmental impact — could also encourage consumer adoption, and help consumers to make informed choices.However, such certification is costly to obtain and maintain, and requires compliance, monitoring and reporting. Certification can distort market opportunities, effectively excluding small enterprises from entering international markets. These programmes can also have unintended consequences: most certification programmes focus on environmental sustainability and pay less attention to social responsibility elements, such as fairness in access to resources, markets and wages.Instead, simple incentive programmes could be implemented by funders, managers and local governments trying to promote sustainable fisheries. For example, local markets could display a rating system for individual fishers or small entrepreneurs. This could include various elements of sustainability other than environmental ones — such as providing information on the type of fishing gear, location of the catch and freshness. Promoting the rating as a social responsibility concept would inform consumers of the need to support sustainable fisheries. The rating system could be conducted by community members trained in inspection and enforcement.Local controlDiverse efforts are needed to protect small fisheries’ access and to boost local consumption and reduce waste, and must be tailored to local community conditions. The 2021 UN Food Systems Summit was a ‘people’s summit’ that elevated roles for Indigenous peoples and civil-society groups, yet the voice of fishing communities was notably absent.Few governments take an integrated approach to the development, implementation and enforcement of policies. For example, policies governing urban development tend not to consider the implications on the ocean, fish and fishers. In the late 2000s, for instance, fishers were initially denied access to traditional public fishing zones along the beach front in Durban, South Africa, following upgrades to the port and the development of a private marina and hotel. (Fishers later reclaimed some of the zones after protests and engagement with the authorities10.)Cooperatives can help on several fronts: by coordinating fishing activities, sharing information (about weather, sea conditions or fish movement) and advocating effectively for human and social rights. For instance, CoopeSoliDar, a small-scale fisheries management cooperative in San José, Costa Rica, has helped to strengthen collective action to sustainably use molluscs, alleviate poverty and strengthen the representation of women and young people in community decision-making. Governments can help by creating a legal framework to establish cooperatives and include them in decisions to manage marine resources.Local communities can also stand up for themselves. For example, a class action by a group of 5,000 artisanal fishers in South Africa in 2004 argued against a policy they said did not give them recognition or access to food and fishing rights that were established in the country’s constitution. The court ruled in the group’s favour in 2007, and the resulting legal framework granted small-scale fishers collective community fishing rights, recognizing community members as bona fide fishers11.Integrated inputsSmall fisheries do not operate in isolation. Unlike terrestrial resources, the ocean is an extensive, global commons without clear territorial boundaries. Issues as diverse as climate change, ocean acidification, overfishing and pollution by nutrients and plastics and other chemicals all affect local fishers. But such system interactions get scant attention when fisheries policies focus on a single seafood stock or individual fishing area.Whereas the concept of integrated land management has been part of the development agenda for a few decades, integrated marine management is only now emerging. To work, it must involve all relevant stakeholders, including small-scale fishers.A context-specific strategy in the Seychelles is a leading example of such integration. Communities, financing partners and the government worked together to create the Seychelles Marine Spatial Plan Initiative, which protects 30% of the archipelago’s waters and boosts climate resilience. The Seychelles faces significant threats from rising sea levels and warmer air and water temperatures that put fisheries, infrastructure, tourism and its rich biodiversity at risk.In an example in the Coral Triangle region (encompassing Indonesia, Malaysia, Papua New Guinea, the Philippines, the Solomon Islands and East Timor), local communities gave their input to a marine protection plan. This led to a greater understanding of how practices such as overfishing and taking undersized stock sustains marine and coastal resources, and how managing these helps to address food security, climate change and threats to marine biodiversity. Such cooperation between fishing communities and governments in managing marine protected areas is essential to the preservation of future fish stocks (see go.nature.com/3xvkqxj).Fishers should be actively engaged in relevant meetings held by the UN and national and local councils, so that they can weigh in on matters that affect fishing access, their livelihoods and environmental concerns. Both fishers and organizers must help to build empowerment mechanisms to make sure their voices are heard, such as providing translation services and scheduling meetings at accessible locations. This is important not just for the fishers’ human rights, but also because much can be learnt from artisanal fishers’ local knowledge.Moves that would, for instance, restrict the fishing season or areas so that stocks or biodiversity can recover should include compensation mechanisms that will secure fishers’ cooperation and livelihoods. Social-protection measures such as food and income assistance can also help to tide fishers over.When fish swim in schools, they move more efficiently, forage better and are protected from predators. The same might be said for small-scale fishers, but those networks should extend to local and international communities, too. Collaborative problem-solving and an integrated food system can deliver seafood protein, sustainably, to a world that increasingly needs it. More

Aissi A, Beghami Y, Heuertz M (2019) Le chêne faginé (Quercus faginea, Fagaceae) en Algérie: potentiel germinatif et variabilité morphologique des glands et des semis. Plant Ecol Evol 152:437–449Article 

Google Scholar 
Aissi A, Beghami Y, Lepais O, Véla E (2021) Morphological and taxonomic analysis of Quercus faginea (Fagaceae) complex in Algeria. Botany 99:99–113Article 

Google Scholar 
Alberto F, Niort J, Derory J, Lepais O, Vitalis R, Galop D et al. (2010) Population differentiation of sessile oak at the altitudinal front of migration in the French Pyrenees. Mol Ecol 19:2626–2639CAS 
PubMed 
Article 

Google Scholar 
Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell PubDe Barba M, Miquel C, Lobréaux S, Quenette PY, Swenson JE, Taberlet P (2016) High-throughput microsatellite genotyping in ecology: Improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Mol Ecol Resour 17:492–507PubMed 
Article 
CAS 

Google Scholar 
Barthe S, Gugerli F, Barkley NA, Maggia L, Cardi C, Scotti I (2012) Always look on both sides: phylogenetic information conveyed by simple sequence repeat allele sequences. PLoS One 7:e40699CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–35PubMed 
PubMed Central 
Article 

Google Scholar 
Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004) GENETIX 4.05, logiciel sous Windows TM pour la génétique des populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5171, Université de Montpellier II, Montpellier (France)
Google Scholar 
Bradbury IR, Wringe BF, Watson B, Paterson I, Horne J, Beiko R et al. (2018) Genotyping-by-sequencing of genome-wide microsatellite loci reveals fine-scale harvest composition in a coastal Atlantic salmon fishery. Evol Appl 11:918–930CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buschbom J, Yanbaev Y, Degen B (2011) Efficient long-distance gene flow into an isolated relict oak stand. J Hered 102:464–472PubMed 
Article 

Google Scholar 
Chapuis M, Raynal L, Plantamp C, Meynard CN, Blondin L, Marin J et al. (2020) A young age of subspecific divergence in the desert locust inferred by ABC Random Forest. Mol Ecol 29:4542–4558PubMed 
Article 

Google Scholar 
Cornuet J-M, Ravigné V, Estoup A (2010) Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0). BMC Bioinforma 11:401Article 
CAS 

Google Scholar 
Crow JF, Aoki K (1984) Group selection for a polygenic behavioral trait: Estimating the degree of population subdivision. Proc Natl Acad Sci USA 81:6073–6077CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Curto M, Winter S, Seiter A, Schmid L, Scheicher K, Barthel LMF et al. (2019) Application of a SSR-GBS marker system on investigation of European Hedgehog species and their hybrid zone dynamics. Ecol Evol 9:2814–2832PubMed 
PubMed Central 
Article 

Google Scholar 
Darby BJ, Erickson SF, Hervey SD, Ellis-Felege SN (2016) Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing. Ecol Evol 6:4502–4512PubMed 
PubMed Central 
Article 

Google Scholar 
Dickey AM, Hall PM, Shatters RG, Mckenzie CL (2013) Evolution and homoplasy at the Bem6 microsatellite locus in three sweetpotato whitefly (Bemisia tabaci) cryptic species. BMC Res Notes 6:249PubMed 
PubMed Central 
Article 

Google Scholar 
Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2013) NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214PubMed 
Article 

Google Scholar 
Durand J, Bodenes C, Chancerel E, Frigerio JM, Vendramin G, Sebastiani F et al. (2010) A fast and cost-effective approach to develop and map EST-SSR markers: oak as a case study. BMC Genomics 11:570PubMed 
PubMed Central 
Article 

Google Scholar 
Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361Article 

Google Scholar 
Estoup A, Jarne P, Cornuet J-M (2002) Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Mol Ecol 11:1591–1604CAS 
PubMed 
Article 

Google Scholar 
Estoup A, Raynal L, Verdu P, Marin J-M (2018) Model choice using Approximate Bayesian Computation and Random Forests: analyses based on model grouping to make inferences about the genetic history of Pygmy human populations. J la Société Fr Stat 159:167–190
Google Scholar 
Excoffier L, Dupanloup I, Huerta-Sánchez E, Sousa VC, Foll M (2013) Robust demographic inference from genomic and SNP data. PLoS Genet 9:e1003905PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567PubMed 
Article 

Google Scholar 
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–87CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feliner GN (2014) Patterns and processes in plant phylogeography in the Mediterranean Basin. A review. Perspect Plant Ecol, Evol Syst 16:265–278Article 

Google Scholar 
Flagel L, Brandvain Y, Schrider DR (2019) The unreasonable effectiveness of convolutional neural networks in population genetic inference. Mol Biol Evol 36:220–238CAS 
PubMed 
Article 

Google Scholar 
Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180:977–993PubMed 
PubMed Central 
Article 

Google Scholar 
Gaggiotti OE, Chao A, Peres-Neto P, Chiu CH, Edwards C, Fortin MJ et al. (2018) Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales. Evol Appl 11:1176–1193PubMed 
PubMed Central 
Article 

Google Scholar 
García Murillo P., Harvey-Brown Y (2017) Quercus canariensis. In: The IUCN Red List of Threatened Species,, p e.T78809256A80570536GBIF Secratariat (2021a) Quercus faginea Lam. In: GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omeiaccessed via GBIF.org on 2022-05-10GBIF Secratariat (2021b). Quercus canariensis Willd. In: GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omeiaccessed via GBIF.org on 2022-05-10Gómez A, Lunt DH (2007) Refugia within refugia: Patterns of phylogeographic concordance in the Iberian peninsula. In: Weiss S, Ferrand N (eds) Phylogeography of Southern European Refugia. Springer Netherlands, Dordrecht, p 155–188Chapter 

Google Scholar 
Gorener V, Harvey-Brown Y, Barstow M (2017) Quercus canariensis. IUCN red List Threat species e.T7880925Goudet J (1995) FSTAT (Version 1.2): A computer program to calculate F-statistics. J Hered 86:485–486Article 

Google Scholar 
Hampe A, Petit RJ (2005) Conserving biodiversity under climate change: the rear edge matters. Ecol Lett 8:461–7PubMed 
Article 

Google Scholar 
Hardy OJ, Charbonnel N, Fréville H, Heuertz M (2003) Microsatellite allele sizes: a simple test to assess their significance on genetic differentiation. Genetics 163:1467–82CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620Article 
CAS 

Google Scholar 
Harvey-Brown Y, García Murillo PG, Buira A (2017) Quercus faginea. IUCN Red List Threat Species: e.T78916251A80570540.Henriques R, von der Heyden S, Matthee CA (2016) When homoplasy mimics hybridization: a case study of Cape hakes (Merluccius capensis and M. paradoxus). PeerJ 4:e1827PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

Google Scholar 
Hey J, Won YJ, Sivasundar A, Nielsen R, Markert JA (2004) Using nuclear haplotypes with microsatellites to study gene flow between recently separated Cichlid species. Mol Ecol 13:909–919CAS 
PubMed 
Article 

Google Scholar 
Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA et al. (2020) Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol Conserv 248:108654Article 

Google Scholar 
Hoogenboom J, de Knijff P, Laros JFJ, de Leeuw RH, van der Gaag KJ, Sijen T (2016) FDSTools: A software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise. Forensic Sci Int Genet 27:27–40PubMed 
Article 
CAS 

Google Scholar 
Hothorn T, Hornik K, van de Wiel MA, Zeileis A (2008) Implementing a class of permutation tests: the coin package. J Stat Softw 28:1–23Article 

Google Scholar 
Jamieson IG, Allendorf FW (2012) How does the 50/500 rule apply to MVPs? Trends Ecol Evol 27:578–584PubMed 
Article 

Google Scholar 
Jerome D, Vasquez F (2018) Quercus faginea. IUCN Red List Threat Species e.T7891625Kalinowski ST (2005) HP-RARE 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189CAS 
Article 

Google Scholar 
Kampfer S, Lexer C, Glössl J, Steinkellner H (1998) Characterization of (GA)n microsatellite loci from Quercus robur. Hereditas 129:183–186CAS 
Article 

Google Scholar 
Kivelä M, Arnaud-Haond S, Saramäki J (2015) EDENetworks: A user-friendly software to build and analyse networks in biogeography, ecology and population genetics. Mol Ecol Resour 15:117–122PubMed 
Article 

Google Scholar 
Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour 15:1179–1191CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Layton KKS, Dempson B, Snelgrove PVR, Duffy SJ, Messmer AM, Paterson IG et al. (2020) Resolving fine‐scale population structure and fishery exploitation using sequenced microsatellites in a northern fish. Evol Appl: eva.12922.Lepais O, Chancerel E, Boury C, Salin F, Manicki A, Taillebois L et al. (2020) Fast sequence-based microsatellite genotyping development workflow. PeerJ 8:e9085PubMed 
PubMed Central 
Article 

Google Scholar 
Lepais O, Leger V, Gerber S (2006) Short note: high throughput microsatellite genotyping in oak species. Silvae Genet 55:238Article 

Google Scholar 
Lepais O, Muller SD, Ben Saad-Limam S, Benslama M, Rhazi L, Belouahem-Abed D et al. (2013) High genetic diversity and distinctiveness of rear-edge climate relicts maintained by ancient tetraploidisation for Alnus glutinosa. PLoS One 8:e75029CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leroy T, Roux C, Villate L, Bodénès C, Romiguier J, Paiva JAP et al. (2017) Extensive recent secondary contacts between four European white oak species. N. Phytol 214:865–878CAS 
Article 

Google Scholar 
Lye GC, Lepais O, Goulson D (2011) Reconstructing demographic events from population genetic data: the introduction of bumblebees to New Zealand. Mol Ecol 20:2888–900CAS 
PubMed 
Article 

Google Scholar 
Magri D, Fineschi S, Bellarosa R, Buonamici A, Sebastiani F, Schirone B et al. (2007) The distribution of Quercus suber chloroplast haplotypes matches the palaeogeographical history of the western Mediterranean. Mol Ecol 16:5259–66CAS 
PubMed 
Article 

Google Scholar 
Marin J, Pudlo P, Estoup A, Robert C (2018) Likelihood-free model choice. In: Sisson S A, Fan Y, Beaumont M (eds) Handbook of Approximate Bayesian Computation, CRC Press, pp. 153.Médail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. J Biogeogr 36:1333–1345Article 

Google Scholar 
Moracho E, Moreno G, Jordano P, Hampe A (2016) Unusually limited pollen dispersal and connectivity of Pedunculate oak (Quercus robur) refugial populations at the species’ southern range margin. Mol Ecol 25:3319–3331CAS 
PubMed 
Article 

Google Scholar 
Mountain JL, Knight A, Jobin M, Gignoux C, Miller A, Lin AA et al. (2002) SNPSTRs: Empirically derived, rapidly typed, autosomal haplotypes for inference of population history and mutational processes. Genome Res 12:1766–1772CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Muir G, Lowe AJ, Fleming CC, Vogl C (2004) High nuclear genetic diversity, high levels of outcrossing and low differentiation among remnant populations of Quercus petraea at the margin of its range in Ireland. Ann Bot 93:691–697CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Neophytou C, Gärtner SM, Vargas-Gaete R, Michiels H-G (2015) Genetic variation of Central European oaks: shaped by evolutionary factors and human intervention? Tree Genet Genomes 11:1–15Article 

Google Scholar 
Payseur BA, Cutter AD (2006) Integrating patterns of polymorphism at SNPs and STRs. Trends Genet 22:424–429CAS 
PubMed 
Article 

Google Scholar 
Petit RJ, Brewer S, Bordács S, Burg K, Cheddadi R, Coart E et al. (2002) Identification of refugia and post-glacial colonisation routes of European white oaks based on chloroplast DNA and fossil pollen evidence. Ecol Manag 156:49–74Article 

Google Scholar 
Petit RJ, Hampe A, Cheddadi R (2005) Climate changes and tree phylogeography in the Mediterranean. Taxon 54:877–885Article 

Google Scholar 
Press MO, Hall AN, Morton EA, Queitsch C (2019) Substitutions are boring: Some arguments about parallel mutations and high mutation rates. Trends Genet 35:253–264CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Press MO, Mccoy RC, Hall AN, Akey JM, Queitsch C (2018) Massive variation of short tandem repeats with functional consequences across strains of Arabidopsis thaliana. Genome Res 28:1169–1178CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–59CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pudlo P, Marin J-M, Estoup A, Cornuet J-M, Gautier M, Robert CP (2016) Reliable ABC model choice via random forests. Bioinformatics 32:859–866CAS 
PubMed 
Article 

Google Scholar 
Ramakrishnan U, Mountain JL (2004) Precision and accuracy of divergence time estimates from STR and SNPSTR variation. Mol Biol Evol 21:1960–1971CAS 
PubMed 
Article 

Google Scholar 
Raynal L, Marin J-M, Pudlo P, Ribatet M, Robert CP, Estoup A (2019) ABC random forests for Bayesian parameter inference. Bioinformatics 35:1720–1728CAS 
PubMed 
Article 

Google Scholar 
Rodríguez-Sánchez F, Hampe A, Jordano P, Arroyo J (2010) Past tree range dynamics in the Iberian Peninsula inferred through phylogeography and palaeodistribution modelling: A review. Rev Palaeobot Palynol 162:507–521Article 

Google Scholar 
Šarhanová P, Pfanzelt S, Brandt R, Himmelbach A, Blattner FR (2018) SSR-seq: Genotyping of microsatellites using next-generation sequencing reveals higher level of polymorphism as compared to traditional fragment size scoring. Ecol Evol 8:10817–10833PubMed 
PubMed Central 
Article 

Google Scholar 
Scotti-Saintagne C, Mariette S, Porth I, Goicoechea PG, Barreneche T, Bodénès C et al. (2004) Genome scanning for interspecific differentiation between two closely related oak species [Quercus robur L. and Q. petraea (Matt.) Liebl.]. Genetics 168:1615–26CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vartia S, Villanueva-Cañas JL, Finarelli J, Farrell ED, Collins PC, Hughes GM et al. (2016) A novel method of microsatellite genotyping-by-sequencing using individual combinatorial barcoding. R Soc Open Sci 3:150565PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Viruel J, Haguenauer A, Juin M, Mirleau F, Bouteiller D, Boudagher-Kharrat M et al. (2018) Advances in genotyping microsatellite markers through sequencing and consequences of scoring methods for Ceratonia siliqua (Leguminosae). Appl Plant Sci 6:e01201PubMed 
PubMed Central 
Article 

Google Scholar 
Wang J (2016) Individual identification from genetic marker data: developments and accuracy comparisons of methods. Mol Ecol Resour 16:163–175CAS 
PubMed 
Article 

Google Scholar 
Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol Appl 3:244–262PubMed 
Article 

Google Scholar 
Xie KT, Wang G, Thompson AC, Wucherpfennig JI, Reimchen TE, MacColl ADC et al. (2019) DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science (80-) 363:81–84CAS 
Article 

Google Scholar  More

In our simulations for the autotrophs, we varied two of the three trade-off properties (level of defence, plasticity costs and defence costs; see Fig. 1b) at a time and kept the third one constant. This results in three constellations reflecting three different trade-offs between these properties (Table 1):

parallel: trade-off between defence and plasticity costs;

crossing: trade-off between defence costs and plasticity costs;

angle: trade-off between defence and defence costs.

Table 1 Description of the three constellations parallel, crossing, and angle defining the position of the four phenotypes in the trait space of defence and growth rate.Full size tableIn all three constellations, the autotrophic species B spanned the entire defence range, i.e. it had a completely undefended phenotype Bu and a maximally defended phenotype Bd. A either had a more limited defence range (in constellations parallel and angle) or spanned the entire range as well (in constellation crossing), representing three distinct ways that the trade-off between defence, growth rate, and plasticity range may play out. For each constellation, we varied the maximum switching rate χmax over 5 orders of magnitude to investigate the effect of plasticity (Table 1, middle row). This parameter determines how rapidly a species can switch between phenotypes (see “Methods”, “Exchange rates”); higher values indicate faster adaptation. These results were also compared with a non-plastic baseline scenario where both phenotypes of each species are presented but χmax = 0 (Table 1, upper row), as well as a rigid scenario where the species have only a single phenotype (Table 1, bottom row). All parameters and their values can be found in Supplementary Table S1.In the following, we give a detailed description of the results for constellation parallel, where the autotroph species A and B have the same defence costs resulting in parallel trade-off lines between defence and growth rate, while varying the level of defence for A and varying the plasticity costs for B (Table 1, left column). We start with examining patterns for the phenotype biomasses, coexistence and community stability in the non-plastic baseline scenario “parallel 0”, and then compare the corresponding scenarios with a low exchange rate (“parallel 0.01”) and a high exchange rate (“parallel 1”). We next discuss the other two constellations (crossing and angle, Table 1) more briefly. Finally, we generalize across all scenarios and focus on the coexistence, the degree of maladaptive switching, and the consumer and total autotroph biomasses.Non-plastic baseline dynamics: scenario parallel 0
In this scenario, four single phenotypes unconnected by exchange compete with each other. Thus, species coexistence here depends entirely on phenotype coexistence: the trade-offs have to be such that for each species, at least one phenotype is a good enough competitor to survive. Which phenotypes survive depend on the two trade-off parameters, defence of the defended phenotype of species A (dAu) and plasticity costs for species B (pcB), which thus determine whether coexistence is possible.The defence costs were kept constant at an intermediate value of 0.3 for both species, resulting in parallel trade-off lines (Table 1, scenario “parallel 0”). The undefended phenotype of A, Au, is a growth-specialist with the highest growth rate of all phenotypes. The defended phenotype of the same species, Ad, has a defence between 0 and 0.9 and a relatively high growth rate, and can be viewed as a generalist. Species B has variable plasticity costs that lower the growth rate of both phenotypes. The defended phenotype of species B, Bd, has the lowest growth rate of all phenotypes but is very well-defended, and thus a defence-specialist. Its undefended phenotype, Bu, is as undefended as Au but has a lower growth rate; it is thus always an inferior competitor and inevitably goes extinct (Fig. 2c).Figure 2Biomasses, coexistence and trait space for scenario parallel 0. Biomasses of the four autotrophic phenotypes (a–d), their coexistence patterns (e), the consumer biomass (f) and the autotrophs’ trait values (g–j) (higher biomasses are shown by darker colours). Lines in (a–f) separate the regions I–III of different coexistence patterns. Note that in (a–f), the y-axis is reversed to show increasing fitness along all axes. An exemplary trait combination for every region is shown in (g–j); larger symbols indicate the surviving phenotypes. Shaded areas in (e) depict oscillating systems (quarter-lag predator–prey cycles in dense shading, antiphase cycles in loose shading).Full size imageAs Bu never survives, coexistence of the autotroph species requires the survival of defence-specialist Bd. Bd can only survive if Ad is not too defended, because Ad has a higher growth rate than Bd and will outcompete Bd in the “defended” niche otherwise (region Ib; Fig. 2d,h). A second criterion is that the plasticity costs for B must not be too high, because then the benefits of the defence of Bd no longer outweigh the costs, and it will go extinct even if there are no other highly defended phenotypes around (region Ia; Fig. 2d,g). In the regions where Bd goes extinct, species coexistence is not possible (Fig. 2e). The generalist Ad either survives by itself (region Ia in Fig. 2b,g) if its defence is low to intermediate, or together with the growth-specialist Au if its defence is high (region Ib in Fig. 2a,b,h). In the regions II and III where Bd survives, it never survives on its own, but always together with one of the phenotypes of A. It coexists with the growth-specialist Au if the plasticity costs are very low (region II in Fig. 2,i), and together with Ad if they are low to intermediate (region III in Fig. 2,j). These two regions do support species coexistence (Fig. 2e).In three of the four regions (Ib, II and III in Fig. 2f), consumer biomass is low, because the final community always contains a well-defended phenotype (Ad in region Ib, and Bd in regions II and III); the overall level of defence of the community is relatively high in these regions (Supplementary Figure S1). Conversely, consumer biomass is relatively high in region Ia, because the only surviving autotroph phenotype is relatively fast-growing and fairly undefended (Fig. 2f,g). The regions where a well-defended phenotype survives often show antiphase cycles (Ib, II and III in Fig. 2e). These cycles do not occur in the region where only Ad survives (Ia in Fig. 2e); but regular quarter-lag predator–prey cycles can be found here if Ad is almost entirely undefended.While the community defence (i.e. mean defence of the autotroph community) depends strongly on the coexisting phenotypes, the community growth rate is roughly constant because over the entire trait space, at least one phenotype with a high growth rate always survives (Supplementary Figure S1). The standing variance of the community defence was high when two phenotypes coexist as they occupy different niches along the defence axis (Fig. 2h–j). In contrast, the variance of the community growth rate was very low and almost constant across all regions.Effect of phenotypic plasticityEven a little bit of plasticity in the scenario parallel 0.01 (χmax = 0.01) can change the above patterns for coexistence, stability, and average consumer biomass (Fig. 3a–d). While the autotrophs are intuitively expected to benefit from being plastic, the effect of plasticity on consumer biomass always turned out to be positive (Fig. 3a). This may be explained by the fact that switching was always, on average, maladaptive (Fig. 3c,d), measured by the adaptation index Φ (see Eqs. (11–13) in “Methods”). This index combines information on the net “flow” of individuals due to switching (i.e. whether more undefended individuals switch to defended or vice versa) with the fitness difference between the two phenotypes, and thus measures whether overall, more individuals switch from a low-fitness to a high-fitness phenotype (adaptive) or the reverse (maladaptive). This index can approach zero, but is always negative at equilibrium (see Appendix B), indicating maladaptive switching.Figure 3Consumer biomass, autotroph coexistence and maladaptive switching for the scenarios parallel 0.01 (a–d) and parallel 1 (e–h). Consumer biomass (a,e), the autotroph coexistence patterns (b,f), and the autotrophs’ maladaptive switching Φ (c,d,g,h) (higher biomasses or more intensive maladaptive switching are shown by darker colours). Lines separate the regions I–III of different autotroph coexistence. The y-axis is reversed to follow the pattern of increasing fitness. Grey areas in (c,d,g,h) depict areas where the species was extinct. Shaded areas in b and f depict oscillating systems (quarter-lag predator–prey cycles in dense shading, antiphase cycles in loose shading).Full size imageThe most striking effect of plasticity was on coexistence, which was affected both positively and negatively by plasticity in different regions of the parameter space (Fig. 3b, Supplementary Figure S4a–d). A negative effect on coexistence is seen in region II, where the autotroph species previously coexisted (Fig. 2e), while with plasticity, B outcompeted A (Fig. 3b). Without plasticity, coexistence was possible in this region because Au and Bd survived; importantly, Au outcompeted Bu due to its higher growth rate, even though the difference between their growth rates is very small in this region (Fig. 2i). Plasticity reverses the competitive exclusion pattern between the two undefended phenotypes: Bu receives a constant flow of biomass from the well-defended Bd, which compensates for its slightly lower growth rate and allows it to outcompete Au. Thus, coexistence is reduced as a direct consequence of maladaptive switching.Plasticity can also promote coexistence, as the coexistence region now extends into former region Ib where the generalist Ad is highly defended (Fig. 2b, Supplementary Figure S1a). This is also an effect of maladaptive switching, though in this case the effect is indirect, mediated through the effect of plasticity on consumer biomass. Without plasticity, coexistence was impossible in region Ib because Bd was always outcompeted by Ad: even though the latter had a slightly lower level of defence, this was outweighed by its higher growth rate, making Ad the superior competitor over Bd. However, plasticity changes this because maladaptive switching increases the consumer biomass, which in turn alters the cost/ benefit balance of defence: Bd derives a stronger benefit from its high level of defence, which now outweighs the cost and allows it to survive. Coexistence through this mechanism is not possible when the plasticity costs for B are too high or when Ad is too well-defended, explaining the narrowing of the coexistence “tail” for high defence of Ad (Fig. 3b).While the patterns of coexistence changed when allowing for plasticity, the patterns in the trait values were nearly indistinguishable from the previous scenario (Supplementary Figure S1, S2). Finally, plasticity had a strong impact on the community dynamics, as most of the antiphase cycles were stabilized (Ib, II, III in Fig. 3b). Their area decreased sharply as these cycles were characterized by asynchronous dynamics between the two prey phenotypes, which were reduced by plasticity. In contrast, the area of the quarter-lag predator–prey cycles remained unaffected by plasticity.All the above patterns were found to a far stronger degree with a higher amount of plasticity (χmax = 1; Fig. 3e–h, Supplementary Figure S4e–h). Consumer biomass increased strongly everywhere (cf. Fig. 3a,e), reflecting the strong increase in the degree of maladaptive switching (cf. Fig. 3c,d,g,h). The higher exchange rates led to more synchronization between the phenotypes, extinguishing the antiphase cycles completely (Fig. 3f). It also decreased the biomass of both defended phenotypes (cf. Supplementary Figure S4b,d,f,h). This in turn led to a lower community defence and a higher community growth rate (Supplementary Figure S3) both contributing to a higher consumer biomass. Finally, there was a sharp decrease in the coexistence region for high plasticity (Fig. 3e). Region II, where B outcompetes A through maladaptive switching, doubled in size due to the much higher degree of maladaptive switching (Fig. 3g,h). Region I, where A outcompetes B, now also increased, when the level of defence of Ad is relatively low (Fig. 3e). This is again an indirect effect of maladaptive switching causing a strong increase in consumer biomass, affecting the cost/ benefit balance of defence: while Bd derives a strong benefit from its high level of defence, Bu is completely undefended, and is at an extra disadvantage because of its low growth rate. Thus, while Bd would have been able to survive by itself, the high exchange rate causes a strong source-sink dynamic that drives B extinct.Effect of plasticity in constellations crossing and angle
In constellation crossing the trade-off lines of both species cross in the trait space, as the level of defence is the same for both defended phenotypes; species B has a lower growth rate for its undefended phenotype than species A due to plasticity costs, while its defence costs are low and thus the growth rate of its defended phenotype is higher than for species A (Table 1, Supplementary Figure S5). Without plasticity the crossing trade-off lines lead to coexistence of both species in all simulations as Au and Bd were always the only survivors, mostly showing antiphase oscillations (Supplementary Figure S5).Allowing for phenotypic plasticity has the same results as were observed for constellation parallel: consumer biomass sharply increases (Fig. 4a,e); antiphase cycles are dampened or absent; and the area of coexistence decreases (Fig. 4b,f). All these changes are more pronounced for higher exchange rates (cf. Fig. 4a,b,e,f). Again, the biomass of the defended phenotypes decreased for high exchange rates (Supplementary Figure S6). Switching was always maladaptive for high exchange rates (Fig. 4g,h), and mostly maladaptive for low exchange rates (Fig. 4c,d). As was seen for constellation parallel, maladaptive switching was the reason for the decrease in coexistence. B can outcompete A when B has low plasticity costs. Bd has a much higher growth rate than Ad, while the undefended phenotypes have similar growth rates. The direction of competitive exclusion between Au and Bu is thus easily reversed by Bd donating biomass to the sink Bu, allowing B to occupy both niches and outcompete A (region II in Fig. 4b,f). The same mechanism happens in reverse for high plasticity and defence costs of B: the differences in growth rate for the undefended phenotypes are high, while the defended phenotypes have very similar growth rates. Au can support Ad, and A outcompetes B (region III in Fig. 4b,f).Figure 4Coexistence and maladaptive switching for scenario crossing 0.01 (a-d) and crossing 1 (e–h). Consumer biomass (a,e), the autotroph coexistence patterns (b,f), and the autotrophs’ maladaptive switching Φ (c,d,g,h) (higher biomasses or more intensive maladaptive switching are shown by darker colours). Lines separate the regions I–III of different autotroph coexistence. The x- and y-axis are reversed to follow the pattern of increasing fitness. Shaded areas in (b) depict antiphase cycles. Grey areas in (c,d,g,h) depict areas where the species was extinct. Shaded grey areas depict areas without simulations (cf. “Methods”). Note that (c,d,g,h) have each a different colour scale.Full size imageIn constellation angle there are no plasticity costs, and thus the undefended phenotypes Au and Bu have identical growth rates. The defended phenotypes take the same places in trait space as in the parallel constellation: Ad is a generalist, with a lower level of defence and a relatively high growth rate due to low defence costs, whereas Bd is a defence-specialist with a high level of defence but a low growth rate. This leads to the trade-off lines forming an angle (see Table 1). Without phenotypic plasticity, the coexistence patterns are the same as in constellation parallel, except that no competitive exclusion occurs between the undefended phenotypes; instead, they (neutrally) coexist in regions Ib, II and III (Supplementary Figure S7; cf. Fig. 2).With plasticity, neutral coexistence vanished: the defended phenotype that survived (Ad in region Ib, Bd in region III) could support the undefended phenotype of its own species, driving the other species extinct (Fig. 5b,f). As in the other constellations, the area of coexistence and the biomasses of the defended phenotypes decreased and antiphase cycles vanished with increasing χmax (Fig. 5b,f, Supplementary Figure S8), while maladaptive switching and the consumer biomass increased (Fig. 5).Figure 5Coexistence and maladaptive switching for scenario angle 0.01 (a–d) and angle 1 (e–h). Consumer biomass (a,e), the autotroph coexistence patterns (b,f), and the autotrophs’ maladaptive switching Φ (c,d,g,h) (higher biomasses or more intensive maladaptive switching are shown by darker colours). Lines separate the regions I–III of different autotroph coexistence. The y-axis is reversed to follow the pattern of increasing fitness. Shaded areas in (b) depict antiphase cycles. Grey areas in (c,d,g,h) depict areas where the species was extinct. Note that (c,d,g,h) have each a different colour scale.Full size imageGeneral resultsAs plasticity had very similar effects across all three constellations, we here generalize our results: we compare the three constellations for exchange rates over 5 orders of magnitude, as well as the non-plastic scenario and the rigid scenario (Table 1). That is, all simulations from one scenario (e.g. parallel 0) were summarized into one bar respective point in Fig. 6.Figure 6General patterns for coexistence, maladapative switching and biomasses. Share of surviving species in percent (A, B, coexistence or neutral coexistence) (a–c), median absolute value of maladaptive switching Φ (d–f) and median of total autotroph biomass (A + B), median consumer biomass C and share of defended phenotypes ((Ad + Bd)/(A + B)) (g–i) for the three constellations and increasing maximum exchange rates χmax. χmax = 0 denotes the non-plastic scenarios; *denotes the rigid scenarios. Maladaptive switching and the share of defended phenotypes do not apply for the rigid scenarios.Full size imageFor all constellations, the fraction of simulation runs leading to coexistence was highest in the non-plastic scenario and decreased with increasing χmax (Fig. 6a–c). In constellation parallel the share of coexistence for increasing χmax continuously decreased from 51 to 3% (Fig. 6a). In crossing, the share decreased from full to no coexistence (Fig. 6b). In angle, the share of coexistence was 88% in the non-plastic scenario when taking also neutral coexistence into account (Fig. 6c). Its share decreased to 9% for a χmax of 10 and increased again to 25% for the rigid scenario. Maladaptive switching increased for both species and all constellations for increasing χmax (Fig. 6d–f). The increased plasticity led to a lower total autotroph biomass and a lower share of defended phenotypes (Fig. 6g–i), which resulted in higher consumer biomass (Fig. 6g–i).Interestingly, and counterintuitively, the above patterns show that increasing the speed of plasticity (by increasing χmax) makes the system behave more like the rigid system. The coexistence patterns in scenarios with high χmax approach those of the rigid scenarios in two of the constellations (Fig. 6a,b). Similarly, the total autotroph and consumer biomasses approach the ones in the rigid scenarios (Fig. 6g–i). Thus, we found the higher χmax make the autotrophs not more adaptive, but behave more like non-adaptive species. More

Insects and plantsLarvae of fall armyworm (FAW, Spodoptera frugiperda) were cultured at the Department of Entomology at the Max Planck Institute for Chemical Ecology, and reared on a semi-artificial diet based on pinto bean59, and maintained under controlled light and temperature conditions (12:12 h light/dark, 21 °C).Feeding experiments3rd–4th instar FAW larvae were utilized for all experiments. Insects were starved overnight prior to feeding experiments. The following day insects were fed with a semi-artificial, pinto bean-based diet or put on maize leaves in small plastic cups and allowed to feed on the respective diets for a day. Insects were dissected in cold phosphate buffered saline (PBS, pH = 7.4) to harvest larval tissues (guts, Malphigian tubules, fat bodies, cuticle), which were stored at − 80 °C until further use. For droplet feeding, 12.5 mM DIMBOA was prepared by dissolving the compound in DMSO. This DIMBOA solution was further diluted in 10% aqueous sucrose solution. The larvae were stimulated with forceps to encourage regurgitation, and 2 μL DIMBOA-sucrose solution was administered directly to the larval mouthparts. Insects were then fed on semi-artificial diet for up to 6 h; following which gut tissue was dissected using cold phosphate buffer and the tissue samples were stored at − 80 °C until further use.Insect cell culturesSpodoptera frugiperda Sf9 cells and Trichoplusia ni Hi5 cells were cultured in Sf-900 II serum-free medium (Gibco) and ExpressFive serum-free medium (Gibco), respectively. Adherent cultures were maintained at 27 °C, and sub-cultured every 3–4 days.Cell treatmentsInsect cells were seeded in 6 well culture plates (Corning) and left at 27 °C overnight. For transcript stability tests, a fresh cycloheximide (CHX) stock (50 mg/mL) was prepared in ethanol and added to the cultured cells at a concentration of 50 µg/mL. Incubations with CHX were performed up to 6 h. For testing substrate specificity, cells were then treated with the following compounds for 1 h—DIMBOA (25–100 μM), indole (50–100 μM), quercetin (50–100 μM), and esculetin (50–100 μM). All the stocks were prepared in DMSO and cells treated with the corresponding volume of pure DMSO served as a control. The range of concentrations used for the substrates was based on previous work38.RNA extraction, reverse transcription and real time-PCR analysisTissue samples from the larvae were homogenized and total RNA extracted using the innuPREP RNA Mini Kit (Analytik Jena). Cell cultures used for RNA extraction were obtained during sub-culturing at full confluency, and centrifuged at 500×g for 5 min. The culture medium was discarded, and the fresh pellets were directly used for RNA extraction. RNA concentrations were measured with the NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific). First strand cDNA was synthesized from 1 μg total RNA using SuperScript III Reverse Transcriptase and OligodT primers from Invitrogen. Sequences were successfully amplified using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 35 cycles of 10 s at 98 °C, 20 s at 55 °C, 45 s at 72 °C; and 5 min at 72 °C). The PCR products were purified with a PCR cleanup kit (Qiagen) and cloned into pCR-Blunt II-TOPO vector (Life Technologies) and transformed into NEB cells (Life Technologies), which were plated on selective LB agar medium containing 100 μg/mL ampicillin and incubated overnight at 37 °C. Positive colonies were identified by PCR using vector-specific M13 primers. Positive clones were confirmed by sequencing. Real time PCR analyses were carried out using Brilliant III SYBR Master Mix, employing SYBR Green chemistry. Relative quantification of the transcript levels was done using the 2−∆∆Ct method60. SfRPL10 was used as reference gene for all analyses. The primer pairs used for distinguishing between the variants are listed in Supplementary Table 1. As the expression of full-length and variants of SfUGT33F28 differed according to the strains, tissues, and treatments being analyzed, variant expression is reported as ratios relative to the canonical transcript to facilitate comparisons.Preparation of minigenes for alternative splicing studiesGenomic DNA was isolated from S. frugiperda larvae using the cetyl trimethyl ammonium bromide (CTAB) protocol61. DNA concentration was measured with the NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific). The minigene was amplified using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 35 cycles of 10 s at 98 °C, 30 s at 55–60 °C, 1 min 30 s at 72 °C; and 10 min at 72 °C), cloned into a pCR-Blunt II-TOPO vector (Life Technologies) and sequenced using M13 primers. The confirmed sequence was eventually cloned into a pIB/V5-His-TOPOvector (Life Technologies) and transformed into NEB cells (Life Technologies). Positive colonies were identified by colony PCR using vector-specific OpIE2 primers, sub-cultured overnight at 37 °C in liquid LB medium containing 100 μg/mL ampicillin and used for plasmid DNA purification with the NucleoSpin Plasmid kit (Macherey-Nagel). Concentration and purity of the obtained construct was assessed by the NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific) and the correct orientation of the PCR products was confirmed by DNA sequencing.Nuclear protein isolationNuclear proteins were isolated from insect cells62 using the protocol originally described with few modifications. Cells grown to concentrations of up to 1 × 106 cells/well were harvested and washed with PBS (pH 7.4). The extracts were centrifuged at 12,000×g for 10 min and pellets were re-suspended in 400 μL cell lysis buffer (10 mM HEPES, pH 7.5, 10 mM KCl, 0.1 mM EDTA pH 8.0, 1 mM DTT, 0.5% Nonidet-40 and 10 μL protease inhibitor cocktail). Cells were allowed to swell on ice for 20 min with intermittent mixing. Suspensions were vortexed to disrupt the cell membranes and then centrifuged at 12,000×g for 10 min at 4 °C. Pelleted nuclei were washed thrice with cell lysis buffer, re-suspended in 50 μL nuclear extraction buffer (20 mM HEPES pH 7.5, 400 mM KCl, 1 mM EDTA pH 8.0, 1 mM DTT, 10% glycerol and protease inhibitor) and incubated on ice for 30 min. Nuclear fractions were collected by centrifugation at 12,000g for 15 min at 4 °C. Protein concentrations were measured by Bradford and extracts were stored at − 80 °C until further use.Electrophoretic mobility shift assay (EMSA)EMSA was performed using the LightShift Chemiluminescent EMSA kit (Thermo Scientific) following the manufacturer’s instructions. Genomic DNA fragments of 20–25 bp corresponding to the 5′ flanking region of UGT33F28 exon 1 (with and without AhR-ARNT motif deletion) were synthesized with covalently linked biotin (Sigma). The DNA probes used in the experiment are listed in Supplementary Table 6. EMSA was performed in 20 µL reactions containing 20 fmol biotinylated DNA probe with 3.5–4 µg nuclear protein extracted from insect cells, according to manufacturer’s instructions. A reaction comprising the above along with the excess of unlabeled canonical DNA probe (200 molar excess) was further employed as a control. The reaction was assembled at room temperature and incubated for 30 min. The reactions were separated on a 5% TBE gel in 0.5X TBE at 100 V for 60 min. The samples were then transferred to a positively charged nylon membrane (Hybond N+, Amersham Bioscience) using semi-dry transfer at 15 V for 30 min. The membrane was cross-linked for 1 min using the auto cross-link function on the UV cross-linker (Stratagene). The biotinylated DNA–protein complex was detected by the streptavidin–horseradish peroxidase conjugated antibody provided in the kit. The membrane was washed and incubated with the chemiluminescence substrate for 5 min and the signals were developed by exposing the membrane to an X-ray film for 1 min.Streptavidin affinity purificationStreptavidin agarose (Sigma-Aldrich) was employed for protein purification. Briefly, 50–100 μL of agarose was packed into a 1.5 mL Eppendorf tube for each sample. The agarose was allowed to settle with a short centrifugation (500×g, 5 min) and the supernatant was discarded. The agarose was washed 4–5 times with binding buffer (PBS containing 1 mM EDTA, 1 mM DTT, 4 µg poly dI. dC as non-specific competitor DNA and protease inhibitor). Simultaneously, the binding reaction with the nuclear protein fraction and the DNA probe was assembled as described above. A 100 μg amount of total nuclear protein was incubated with 4 μg of biotinylated DNA probe at room temperature for 20 min. The reaction was loaded onto the streptavidin column equilibrated with the binding buffer and incubated for another 1 h at room temperature with gentle shaking. Subsequently, the agarose was washed 4–5 times with the binding buffer. After the final wash, the supernatant was aspirated and 10 μL was left above the beads. For protein separation, 20–30 μL pf the SDS loading buffer was added onto the agarose, boiled at 95 °C for 5 min and the sample thus obtained was utilized for electrophoresis.Deletion mutagenesisFor deletion mutagenesis, a pair of primers flanking the sequence to be deleted (non-overlapping) was designed. The pCR-Blunt II-TOPO vector (Life Technologies) clone for the SfUGT33F28 exon 1–2 minigene was utilized as a template. Sequence was successfully amplified using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 20 cycles of 10 s at 98 °C, 30 s at 55–60 °C, 4 min at 72 °C; and 10 min at 72 °C). A DpnI digest was performed to remove the background DNA, followed by ligation and transformation into fresh cells. The sequence of the mutant TOPO clone was then confirmed and utilized as a template for cloning into pIB/V5-His-TOPO vector (Life Technologies) for transfection into insect cells.Cloning and heterologous expression of SfUGTsSequences were amplified from S. frugiperda gut cDNA samples using Phusion High Fidelity DNA Polymerase (New England Biolabs) (PCR protocol: 30 s at 98 °C; 35 cycles of 10 s at 98 °C, 20 s at 55–60 °C, 45 s at 72 °C; and 5 min at 72 °C). The resulting amplified products were purified with a PCR cleanup kit (Qiagen) and incubated with GoTaq DNA polymerase (Promega) for 15 min at 72 °C in order to add A overhangs. The products were cloned into the pIB/V5-His-TOPO vector (Life Technologies) and transformed into NEB cells (Life Technologies), which were plated on selective LB agar medium containing 100 μg/mL ampicillin and incubated overnight at 37 °C. Positive colonies were identified by PCR using vector-specific OpIE2 primers, sub-cultured overnight at 37 °C in liquid LB medium containing 100 μg/mL ampicillin and used for plasmid DNA purification with the NucleoSpin Plasmid kit (Macherey-Nagel). Concentration and purity of the obtained constructs were assessed by NanoDrop 2000 UV–Vis Spectrophotometer (Thermo Scientific) and the correct orientation of the PCR products was confirmed by DNA sequencing.Insect cell transfectionFor transfection, Sf9 cells and Hi5 cells were sub-cultured at full confluency in a 6-well plate in a 1:3 dilution and left overnight to adhere to the flask surface. The medium was replaced, and transfections were carried out using FuGENE HD Transfection Reagent (Promega) in a 1:3 plasmid/lipid ratio (1.7 μg plasmid and 5.0 μL lipid for 3 mL medium). Cells were incubated for 48–72 h at 27 °C and re-suspended in fresh medium containing 50 μg/mL blasticidin for 2 weeks. Stable cell cultures were subsequently maintained at 10 μg/mL blasticidin.Cell lysate preparationCells were obtained from cultures 2 weeks post transfection growing stably on 50 μg/mL blasticidin. A 1 mL quantity of cells was harvested for each construct and re-suspended into 100 µL buffer. Protein concentrations were measured using the Bradford reagent, and 1–2 μg of the cell lysate was used for enzyme assays.Microsome preparationFor microsome extraction, confluent, stably transfected cells from five T-75 flasks (10 mL culture) per recombinant plasmid were harvested by scraping the cells off the bottom using a sterile cell scraper (Sarstedt AG, Nuembrecht, Germany). The obtained cell suspensions were combined into a 50 mL falcon tube and centrifuged at 1000×g for 15 min at 4 °C (AvantiTM J-20 XP Centrifuge, Beckman Coulter, Krefeld, Germany). The supernatant was discarded, the cells were washed twice with ice-cold PBS buffer (pH 7.4) and centrifuged at 1000×g for 15 min. The resulting cell pellet was re-suspended in 10 mL hypotonic buffer (20 mM Tris, 5 mM EDTA, 1 mM DTT, 20% glycerol, pH 7.5), containing 0.1% BenzonaseR nuclease and 100 μL Protease Inhibitor Cocktail (Serva) followed by incubation on ice for 30 min. After cell lysis, the cells were homogenized by 20–30 strokes in a Potter–Elvehjem tissue grinder (Kontes Glass Co., Vineland, USA) and were subsequently mixed with an equal volume of sucrose buffer (20 mM Tris, 5 mM EDTA, 1 mM DTT, 500 mM sucrose, 20% glycerol, pH 7.5). The homogenate was centrifuged at 1200×g and 4 °C for 10 min (AvantiTM J-20 XP Centrifuge, Beckman Coulter), and the supernatant was transferred into Beckman polycarbonate ultracentrifugation bottles (25 × 89 mm) (Beckman Coulter) and centrifuged at 100,000×g and 4 °C for 1.5 h in a fixed angle Type 70 Ti rotor (OptimaTM L-90K Ultracentrifuge, Beckman Coulter). After ultracentrifugation, the clear supernatant, containing the cytosolic fraction, was aliquoted into 1.5 mL Eppendorf tubes. The pellet, containing the microsomal fractions, was re-suspended in 1 mL of phosphate buffer (100 mM K2HPO4, pH 7.0), containing 10 μL Protease Inhibitor Cocktail (Serva) and stored at − 80 °C until further use. Typically, 5–10 μg of the microsome fraction so obtained was utilized for the enzyme assays.Cross-linking assaysCross-linking assays were performed using dimethyl suberimidate (DMS) as the cross-linking agent. A fresh stock of DMS (5 mg/mL) was prepared in 0.2 M triethanolamine (pH 8.0) at the start of each assay. DMS was added to a final concentration of 2.5 mg/mL to insect cell microsomes with gentle shaking up to 3 h, and samples were subsequently stored at − 20 °C until further use. All protein samples were electrophoresed using a 12% Mini-PROTEAN tris glycine gel, blotted onto PVDF membrane using wet transfer at 70 V for 30–45 min, followed by detection using the V5-HRP conjugate.V5-based affinity purificationAnti-V5 agarose affinity gel (Sigma-Aldrich) was employed for protein purification. Briefly, 50–75 μL of the agarose was packed into a 1.5 mL Eppendorf tube for each sample. The agarose was allowed to settle with a short centrifugation and the supernatant was discarded. The agarose was washed 4–5 times with PBS (pH 7.4). Samples to be purified were incubated with 5% digitonin on ice for 20 min and subject to centrifugation at 16,000×g for 30 min. Clarified cell lysate or microsomal extract was added onto the resin (up to 200 μL, volume adjusted by addition of PBS) and incubated for 1.5 h on a shaker. Subsequently, the agarose was washed 4–5 times with PBS. After the final wash, the supernatant was aspirated and 10 μL was left above the beads. This fraction was used for both protein electrophoresis and enzyme assays (separate purifications). For SDS-PAGE, 20–30 μL pf the SDS loading buffer was added onto the agarose, boiled at 95 °C for 5 min and sample thus obtained was utilized for electrophoresis.LC–MS/MS peptide analysisProtein bands of Coomassie Brilliant blue R250 stained gels were cut from the gel matrix and tryptic digestion was carried out63. For LC–MS/MS analysis of the resulting peptides, samples were reconstituted in 20 μL aqueous 1% formic acid, and 1 μL was injected onto an UPLC M-class system (Waters, Manchester, UK) coupled to a Synapt G2-si mass spectrometer (Waters, Manchester, UK). Samples were first pre-concentrated and desalted using a Symmetry C18 trap column (100 Å, 180 µm × 20 mm, 5 µm particle size) at a flow rate of 15 µL/min (0.1% aqueous formic acid). Peptides were eluted onto a ACQUITY UPLC HSS T3 analytical column (100 Å, 75 µm × 200 mm, 1.8 µm particle size) at a flow rate of 350 nL/min with the following gradient: 3–15% over 3 min, 15–20% B over 7 min, 20–40% B over 30 min, 40–50% B over 5 min, 50–70% B over 5 min, 70–95% B over 3 min, isocratic at 95% B for 1 min, and a return to 1% B over 1 min. Phases A and B were composed of 0.1% formic acid and 100% acetonitrile in 0.1% formic acid, respectively). The analytical column was re-equilibrated for 10 min prior to the next injection. The eluted peptides were transferred into the mass spectrometer operated in V-mode with a resolving power of at least 20,000 full width at half height FWHM. All analyses were performed in a positive ESI mode. A 100 fmol/μL sample of human Glu-Fibrinopeptide B in 0.1% formic acid/acetonitrile (1:1 v/v) was infused at a flow rate of 1 μL/min through the reference sprayer every 45 s to compensate for mass shifts in MS and MS/MS fragmentation mode. Data were acquired using data-dependent acquisition (DDA). The acquisition cycle for DDA analysis consisted of a survey scan covering the range of m/z 400–1800 Da followed by MS/MS fragmentation of the ten most intense precursor ions collected at 0.5 s intervals in the range of 50–2000 m/z. Dynamic exclusion was applied to minimize multiple fragmentations for the same precursor ions. MS data were collected using MassLynx v4.1 software (Waters, Manchester, UK).Data processing and protein identificationDDA raw data were processed and searched against a sub-database containing common contaminants (human keratins and trypsin) using ProteinLynx Global Server (PLGS) version 2.5.2 (Waters, Manchester, UK). Spectra remaining unmatched by database searching were interpreted de novo to yield peptide sequences and subjected to homology-based searching using the MS BLAST program64 installed on a local server. MS BLAST searches were performed against a Spodoptera frugiperda database obtained by in silico translation of the S. frugiperda transcriptome37 and against arthropoda database (NCBI). PKL-files of MS/MS spectra were generated and searched against Spodoptera frugiperda database combined with NCBI nr (downloaded on May 24, 2020) using MASCOT software version 2.6.2. The following searching parameters were applied: fixed precursor ion mass tolerance of 15 ppm for the survey peptide, fragment ion mass tolerance of 0.1 Da, 1 missed cleavage, fixed carbamidomethylation of cysteines and possible oxidation of methionine.Enzymatic assaysFor UGT assays, samples from insect cell cultures (transient or stable) were prepared in phosphate buffer (pH 7.0, 100 mM). Typical enzyme reactions included 5–10 µg cell microsomal extracts, 2 μL of 12.5 mM DIMBOA in DMSO (25 nmol), 4 μL of 12.5 mM UDP-glucose in water (50 nmol), and phosphate buffer (pH 7.0, 100 mM) to give an assay volume of 50 μL. Controls containing either boiled enzymatic preparation, or only the protein suspension and buffer were included. After incubation at 30 °C for 60 min, the enzyme reactions were interrupted by adding 50 μL of 1:1 (v:v) methanol/formic acid solution. For enzyme assays involving resin purified microsomal extracts, equal amounts of extracts were employed for resin purification and the enzyme assay (buffer + substrate) was pipetted directly onto the resin. Post incubation, samples were centrifuged, supernatant was collected, and reaction was stopped by addition of methanol/formic acid solution. Assays were centrifuged at 5000g for 5 min and the obtained supernatant was collected and analyzed by LC–MS/MS.Chromatographic methodsFor all analytical chromatography procedures, formic acid (0.05%) in water and acetonitrile were used as mobile phases A and B, respectively, and the column temperature was maintained at 25 °C. Analyses of enzymatic assays and plant samples used a Zorbax Eclipse XDB-C18 column (50 × 4.6 mm, 1.8 μm, Agilent Technologies) with a flow rate of 1.1 mL/min and with the following elution profile: 0–0.5 min, 95% A; 0.5–6 min, 95–67.5% A; 6.02–7 min, 100% B; 7.1–9.5 min, 95% A. LC–MS/MS analyses were performed on an Agilent 1200 HPLC system (Agilent Technologies) coupled to an API 6500 tandem spectrometer (AB Sciex) equipped with a turbospray ion source operating in negative ionization mode. Multiple reaction monitoring (MRM) was used to monitor analyte parent ion to product ion conversion with parameters from the literature for DIMBOA65 and DIMBOA-Glc16. Analyst (version 1.6.3, Applied Biosystems) software was used for data acquisition and processing.Statistical analysisAll statistical analyses were carried out using SigmaPlot 12.0 and R studio (version 3.6.3). Data were tested for homogeneity of variance and normality and were appropriately transformed to meet these criteria where required. The specific statistical method used for each data set is described in the figure legends. More