Ecology

1.Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshwater Res. 50(8), 839–866. https://doi.org/10.1071/MF99078 (1999).Article 

Google Scholar 
2.Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29(2), 215–233. https://doi.org/10.1016/S0921-8009(99)00009-9 (1999).Article 

Google Scholar 
3.Shahbudin, S., Fikri Akmal, K. F., Faris, S., Normawaty, M. N. & Mukai, Y. Current status of coral reefs in Tioman Island Peninsular Malaysia. Turk. J. Zool. 41(2), 294–305. https://doi.org/10.3906/zoo-1511-42 (2017).Article 

Google Scholar 
4.Anthony, K. R. N. et al. Operationalizing resilience for adaptive coral reef management under global environmental change. Glob. Change Biol. 21(1), 48–61. https://doi.org/10.1111/gcb.12700,Pubmed:25196132 (2015).ADS 
Article 

Google Scholar 
5.Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: timing, extent, and subregional comparisons. PLoS ONE 2(8), e711. https://doi.org/10.1371/journal.pone.0000711,Pubmed:17684557 (2007).ADS 
Article 
PubMed Central 
PubMed 

Google Scholar 
6.Cowburn, B., Samoilys, M. A. & Obura, D. The current status of coral reefs and their vulnerability to climate change and multiple human stresses in the Comoros Archipelago Western Indian Ocean. Mar. Pollut. Bull. 133, 956–969. https://doi.org/10.1016/j.marpolbul.2018.04.065,Pubmed:29778407 (2018).CAS 
Article 

Google Scholar 
7.Schueth, J. D. & Frank, T. D. Reef foraminifera as bioindicators of coral reef health: Low Isles Reef, northern Great Barrier Reef Australia. J. Foram. Res. 38(1), 11–22. https://doi.org/10.2113/gsjfr.38.1.11 (2008).Article 

Google Scholar 
8.Uthicke, S., Thompson, A. & Schaffelke, B. Effectiveness of benthic foraminiferal and coral assemblages as water quality indicators on inshore reefs of the Great Barrier Reef Australia. Coral Reefs 29(1), 209–225. https://doi.org/10.1007/s00338-009-0574-9 (2010).ADS 
Article 

Google Scholar 
9.Natsir, S. M. & Subkhan, M. The distribution of benthic foraminifera in coral reefs community and seagrass bad of Belitung Islands based on FORAM Index. J. Coast. Dev. 15(1), 51–58 (2012).
Google Scholar 
10.Alve, E. Benthic foraminiferal responses to estuarine pollution: a review. J. Foram. Res. 25(3), 190–203. https://doi.org/10.2113/gsjfr.25.3.190 (1995).Article 

Google Scholar 
11.Hallock, P., Lidz, B. H., Cockey-Burkhard, E. M. & Donnelly, K. B. Foraminifera as bioindicators in coral reef assessment and monitoring: the FORAM index. Foraminifera in reef assessment and monitoring. Environ. Monit. Assess. 81(1–3), 221–238 (2003).Article 

Google Scholar 
12.Sen Gupta, B. K. Systematics of modern Foraminifera. In Sen Gupta, B.K. (ed.) Modern Foraminifera (Springer, 2003) 7–36. https://doi.org/10.1007/0-306-48104-9.13.Carnahan, E. A. Foraminiferal Assemblages as Bioindicators of Potentially Toxic Elements in Biscayne Bay, Florida. M.Sc. thesis (U.S.A.: University of South Florida, 2005)14.Barbosa, C. F., Prazeres, M. D. F., Ferreira, B. P. & Seoane, J. C. S. Foraminiferal assemblage and Reef Check census in coral reef health monitoring of East Brazilian margin. Mar. Micropaleontol. 73(1–2), 62–69. https://doi.org/10.1016/j.marmicro.2009.07.002 (2009).ADS 
Article 

Google Scholar 
15.Dimiza, M. D., Koukousioura, O., Triantaphyllou, M. V. & Dermitzakis, M. D. Live and dead benthic foraminiferal assemblages from coastal environments of the Aegean Sea (Greece): distribution and diversity. Rev. Micropaleontol. 59(1), 19–32. https://doi.org/10.1016/j.revmic.2015.10.002 (2016).Article 

Google Scholar 
16.Uthicke, S. & Nobes, K. Benthic foraminifera as ecological indicators for water quality on the Great Barrier Reef. Estuarine Coast. Shelf Sci. 78(4), 763–773. https://doi.org/10.1016/j.ecss.2008.02.014 (2008).ADS 
Article 

Google Scholar 
17.Renema, W. Terrestrial influence as a key driver of spatial variability in large benthic foraminiferal assemblage composition in the Central Indo-Pacific. Earth Sci. Rev. 177, 514–544. https://doi.org/10.1016/j.earscirev.2017.12.013 (2018).ADS 
Article 

Google Scholar 
18.Förderer, M. & Langer, M. R. Exceptionally species-rich assemblages of modern larger benthic foraminifera from nearshore reefs in northern Palawan (Philippines). Rev. Micropaleontol. 100, 65. https://doi.org/10.1016/j.revmic.2019.100387 (2019).Article 

Google Scholar 
19.Eichler, P. P. B. & de Moura, D. S. Symbiont-bearing foraminifera as health proxy in coral reefs in the equatorial margin of Brazil. Environ. Sci. Pollut. Res. 27(12), 13637–13661. https://doi.org/10.1007/s11356-019-07483-y,Pubmed:32034594 (2020).CAS 
Article 

Google Scholar 
20.Renema, W. Is increased calcarinid (foraminifera) abundance indicating a larger role for macro-algae in Indonesian Plio-Pleistocene coral reefs?. Coral Reefs 29(1), 165–173. https://doi.org/10.1007/s00338-009-0568-7 (2010).ADS 
Article 

Google Scholar 
21.Chen, C. & Lin, H. L. Applying benthic Foraminiferal assemblage to evaluate the coral reef condition in Dongsha Atoll lagoon. Zool. Stud. 56, e20. https://doi.org/10.6620/ZS.2017.56-20,Pubmed:31966219 (2017).Article 
PubMed Central 
PubMed 

Google Scholar 
22.Hallock, P. Interoceanic differences in foraminifera with symbiotic algae: a result of nutrient supplies. Mar. Sci. Faculty Publication 1228 (1988). https://scholarcommons.usf.edu/msc_facpub/122823.Langer, M. R., Weinmann, A. E., Lötters, S., Bernhard, J. M. & Rödder, D. Climate-driven range extension of Amphistegina (protista, foraminiferida): Models of current and predicted future ranges [Protista, Foraminiferida]. PLoS ONE 8(2), e54443. https://doi.org/10.1371/journal.pone.0054443,Pubmed:23405081 (2013).ADS 
CAS 
Article 
PubMed Central 
PubMed 

Google Scholar 
24.Culver, S. J. et al. Distribution of foraminifera of the Poverty continental margin, New Zealand: implications for sediment transport. J. Foram. Res. 42(4), 305–326. https://doi.org/10.2113/gsjfr.42.4.305 (2012).Article 

Google Scholar 
25.Szarek, R. Biodiversity and Biogeography of Recent Benthic Foraminiferal Assemblages in the South Western South China Sea (Sunda Shelf). Doctoral dissertation (Kiel, Kiel, Germany: Christian-Albrechts Universität, 2001).26.Prazeres, M., Martínez-Colón, M. & Hallock, P. Foraminifera as bioindicators of water quality: the FoRAM Index revisited. Environ. Pollut. 257, 113612. https://doi.org/10.1016/j.envpol.2019.113612,Pubmed:31784269 (2020).CAS 
Article 

Google Scholar 
27.Toda, T. et al. Community structures of coral reefs around Peninsular Malaysia. J. Oceanogr. 63(1), 113–123. https://doi.org/10.1007/s10872-007-0009-6 (2007).Article 

Google Scholar 
28.Zakai, D. & Chadwick-Furman, N. E. Impacts of intensive recreational diving on reef corals at Eilat, northern Red Sea. Biol. Conserv. 105(2), 179–187. https://doi.org/10.1016/S0006-3207(01)00181-1 (2002).Article 

Google Scholar 
29.Carnahan, E. A., Hoare, A. M., Hallock, P., Lidz, B. H. & Reich, C. D. Foraminiferal assemblages in Biscayne Bay, Florida, USA: responses to urban and agricultural influence in a subtropical estuary. Mar. Pollut. Bull. 59(8–12), 221–233. https://doi.org/10.1016/j.marpolbul.2009.08.008 (2009).CAS 
Article 

Google Scholar 
30.Oliver, L. M. et al. Contrasting responses of coral reef fauna and foraminiferal assemblages to human influence in la Parguera Puerto Rico. Mar. Environ. Res. 99, 95–105. https://doi.org/10.1016/j.marenvres.2014.04.005 (2014).CAS 
Article 

Google Scholar 
31.Unsworth, R. K., Clifton, J. & Smith, D. J. Marine Research and Conservation in the Coral Triangle: The Wakatobi National Park (Nova Science Publishers, 2010).
Google Scholar 
32.Praveena, S. M., Siraj, S. S. & Aris, A. Z. Coral reefs studies and threats in Malaysia: a mini review. Rev. Environ. Sci. Bio Technol. 11(1), 27–39. https://doi.org/10.1007/s11157-011-9261-8 (2012).Article 

Google Scholar 
33.Akmal, K. F., Shahbudin, S., Faiz, M. H. M. & Hamizan, Y. M. Diversity and abundance of scleractinian corals in the East Coast of peninsular Malaysia: a case study of Redang and Tioman Islands. Ocean Sci. J. 54(3), 435–456. https://doi.org/10.1007/s12601-019-0018-6 (2019).ADS 
CAS 
Article 

Google Scholar 
34.Oron, S., Abramovich, S., Almogi-Labin, A., Woeger, J. & Erez, J. Depth related adaptations in symbiont bearing benthic foraminifera: new insights from a field experiment on Operculina ammonoides. Sci. Rep. 8(1), 9560. https://doi.org/10.1038/s41598-018-27838-8,Pubmed:29934603 (2018).ADS 
Article 
PubMed Central 
PubMed 

Google Scholar 
35.Oliver, J. K., Berkelmans, R. & Eakin, C. M. Coral bleaching in space and time in (eds van Oppen, M. J. H. & Lough, J. M.) Coral Bleaching. Ecological Studies. 205 (Springer, 2009) 21–39.36.Harborne, A., Fenner, D., Barnes, A., Beger, M., Harding, S. & Roxburgh, T. Status Report on the Coral Reefs of the East Coast of Peninsular Malaysia. Report Prepared to Department of Fisheries Malaysia, Kuala Lumpur, Malaysia, 361–369 (2000)37.Akhir, M., Fadzil, M., Zakaria, N. Z. & Tangang, F. Intermonsoon variation of physical characteristics and current circulation along the east coast of Peninsular Malaysia. Int. J. Oceanogr., 1–9 (2014)38.Chu, P. C., Qi, Y., Chen, Y., Shi, P. & Mao, Q. South China sea wind-wave characteristics. Part I: validation of WAVEWATCH-III using TOPEX/Poseidon data. J. Atmos. Ocean. Technol. 21(11), 1718–1733. https://doi.org/10.1175/JTECH1661.1 (2004).ADS 
Article 

Google Scholar 
39.Marghany, M. Velocity bunching model for modelling wave spectra along east coast of Malaysia. J. Indian Soc. Remote Sens. 32(2), 185–198. https://doi.org/10.1007/BF03030875 (2004).Article 

Google Scholar 
40.Department of Marine Park, Malaysia. Laporan Tahunan Jabatan Taman Laut Malaysia. Annual report (2012)41.Chia, K. W., Ramachandran, S., Ho, J. A. & Ng, S. S. I. Conflicts to consensus: Stakeholder perspectives of Tioman Island tourism sustainability. Int. J. Bus. Soc. 19, 159 (2018).
Google Scholar 
42.Game, E. T., Meijaard, E., Sheil, D. & McDonald-Madden, E. Conservation in a wicked complex world; challenges and solutions. Conserv. Lett. 7(3), 271–277. https://doi.org/10.1111/conl.12050 (2014).Article 

Google Scholar 
43.Murray, J. W. Ecology and applications of benthic foraminifera. Cambridge University Press (2006)44.Loeblich, A. R. & Tappan, H. Foraminiferal Genera and Their Classification (Van Nostrand Reinhold, 1987).
Google Scholar 
45.Szarek, R., Kuhnt, W., Kawamura, H. & Kitazato, H. Distribution of recent benthic foraminifera on the Sunda Shelf (South China Sea). Mar. Micropaleontol. 61(4), 171–195. https://doi.org/10.1016/j.marmicro.2006.06.005 (2006).ADS 
Article 

Google Scholar 
46.Martin, S. Q. et al. Announcements. J. Foram. Res. 48(4), 388–389. https://doi.org/10.2113/gsjfr.48.4.388 (2018).Article 

Google Scholar 
47.Folk, R. L. Petrology of Sedimentary Rocks (Hemphill Publishing Company, 1980)48.Dean, W. E. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition; comparison with other methods. J. Sediment. Res. 44(1), 242–248 (1974).CAS 

Google Scholar 
49.Heiri, O., Lotter, A. F. & Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol. 25(1), 101–110. https://doi.org/10.1023/A:1008119611481 (2001).ADS 
Article 

Google Scholar 
50.Romesburg, C. Cluster Analysis for Researchers (Lulu Press, 2004).
Google Scholar 
51.Milker, Y. et al. Distribution of recent benthic foraminifera in shelf carbonate environments of the western Mediterranean Sea. Mar. Micropaleontol. 73(3–4), 207–225. https://doi.org/10.1016/j.marmicro.2009.10.003 (2009).ADS 
Article 

Google Scholar  More

1.Grassly, N. C. & Fraser, C. Mathematical models of infectious disease transmission. Nat. Rev. Microbiol. 6, 477–487 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Cressler, C. E., McLeod, D. V., Rozins, C., Van Den Hoogen, J. & Day, T. The adaptive evolution of virulence: A review of theoretical predictions and empirical tests. Parasitology 143, 915–930 (2016).PubMed 
Article 

Google Scholar 
3.Lanzi, G. et al. Molecular and biological characterization of deformed wing virus of honeybees (Apismellifera L.). J. Virol. 80, 4998–5009 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Dainat, B., Evans, J. D., Chen, Y. P., Gauthier, L. & Neumann, P. Dead or alive: Deformed wing virus and Varroa destructor reduce the life span of winter honeybees. Appl. Environ. Microbiol. 78, 981–987 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Highfield, A. C. et al. Deformed wing virus implicated in overwintering honeybee colony losses. Appl. Environ. Microbiol. 75, 7212–7220 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363 (2010).Article 

Google Scholar 
7.De Miranda, J. R. & Genersch, E. Deformed wing virus. J. Invertebr. Pathol. 103, S48–S61 (2010).PubMed 
Article 
CAS 

Google Scholar 
8.Martin, S. J. & Brettell, L. E. Deformed wing virus in honeybees and other insects. Annu. Rev. Virol. 6, 49–69 (2019).CAS 
PubMed 
Article 

Google Scholar 
9.Sumpter, D. J. & Martin, S. J. The dynamics of virus epidemics in Varroa-infested honey bee colonies. J. Anim. Ecol. 73, 51–63 (2004).Article 

Google Scholar 
10.Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. 116, 1792–1801 (2019).CAS 
PubMed 
Article 

Google Scholar 
11.Yang, X. & Cox-Foster, D. L. Impact of an ectoparasite on the immunity and pathology of an invertebrate: Evidence for host immunosuppression and viral amplification. Proc. Natl. Acad. Sci. 102, 7470–7475 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).PubMed 
Article 

Google Scholar 
13.Wilfert, L. et al. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science 351, 594–597 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Dalmon, A. et al. Evidence for positive selection and recombination hotspots in deformed wing virus (DWV). Sci. Rep. 7, 1–12 (2017).Article 
CAS 

Google Scholar 
15.Martin, S. J. et al. Global honey bee viral landscape altered by a parasitic mite. Science 336, 1304–1306 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
16.Moore, J. et al. Recombinants between deformed wing virus and Varroa destructor virus-1 may prevail in Varroa destructor-infested honeybee colonies. J. Gen. Virol. 92, 156–161 (2011).CAS 
PubMed 
Article 

Google Scholar 
17.Ryabov, E. V. et al. A virulent strain of deformed wing virus (DWV) of honeybees (Apis mellifera) prevails after Varroa destructor-mediated, or in vitro, transmission. PLoS Pathog. 10, e1004230 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Ryabov, E. V. et al. Dynamic evolution in the key honey bee pathogen deformed wing virus: Novel insights into virulence and competition using reverse genetics. PLoS Biol. 17, e3000502 (2019).MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Mondet, F. et al. Specific cues associated with honey bee social defence against Varroa destructor infested brood. Sci. Rep. 6, 25444 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Spivak, M. & Danka, R. G. Perspectives on hygienic behavior in Apismellifera and other social insects. Apidologie https://doi.org/10.1007/s13592-020-00784-z (2020).Article 

Google Scholar 
21.Spivak, M. & Gilliam, M. Facultative expression of hygienic behaviour of honey bees in relation to disease resistance. J. Apic. Res. 32, 147–157 (1993).Article 

Google Scholar 
22.Baracchi, D., Fadda, A. & Turillazzi, S. Evidence for antiseptic behaviour towards sick adult bees in honey bee colonies. J. Insect Physiol. 58, 1589–1596 (2012).CAS 
PubMed 
Article 

Google Scholar 
23.Traynor, K. S. et al. Varroa destructor: A complex parasite, crippling honey bees worldwide. Trends Parasitol. 36, 592–606 (2020).CAS 
PubMed 
Article 

Google Scholar 
24.Sun, Q. & Zhou, X. Corpse management in social insects. Int. J:. Biol. Sci. 9, 313 (2013).
Google Scholar 
25.Van Allen, B. G. et al. Cannibalism and infectious disease: Friends or foes?. Am. Nat. 190, 299–312 (2017).PubMed 
Article 

Google Scholar 
26.Bourke, A. F. Queen behaviour, reproduction and egg cannibalism in multiple-queen colonies of the ant Leptothorax acervorum. Anim. Behav. 42, 295–310 (1991).Article 

Google Scholar 
27.Pulliainen, U., Helanterä, H., Sundström, L. & Schultner, E. The possible role of ant larvae in the defence against social parasites. Proc. R. Soc. B 286, 20182867 (2019).CAS 
PubMed 
Article 

Google Scholar 
28.Evans, H. & West-Eberhard, M. The Wasps (Univ. Michigan, 1970).
Google Scholar 
29.Schmickl, T. & Crailsheim, K. Cannibalism and early capping: Strategy of honeybee colonies in times of experimental pollen shortages. J. Comp. Physiol. A 187, 541–547 (2001).CAS 
PubMed 
Article 

Google Scholar 
30.Webster, T. C., Peng, Y. S. & Duffey, S. S. Conservation of nutrients in larval tissue by cannibalizing honey bees. Physiol. Entomol. 12, 225–231 (1987).CAS 
Article 

Google Scholar 
31.Woyke, J. Cannibalism and brood-rearing efficiency in the honeybee. J. Apic. Res. 16, 84–94 (1977).Article 

Google Scholar 
32.Chouvenc, T. Limited survival strategy in starving subterranean termite colonies. Insectes Soc. 67, 71–82 (2020).Article 

Google Scholar 
33.Raina, A. K., Park, Y. I. & Lax, A. Defaunation leads to cannibalism in primary reproductives of the Formosan subterranean termite, Coptotermes formosanus (Isoptera: Rhinotermitidae). Ann. Entomol. Soc. Am. 97, 753–756 (2004).Article 

Google Scholar 
34.Schmickl, T. & Crailsheim, K. Inner nest homeostasis in a changing environment with special emphasis on honey bee brood nursing and pollen supply. Apidologie 35, 249–263 (2004).Article 

Google Scholar 
35.Meunier, J. Social immunity and the evolution of group living in insects. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140102 (2015).Article 

Google Scholar 
36.Rueppell, O., Hayworth, M. K. & Ross, N. Altruistic self-removal of health-compromised honey bee workers from their hive. J. Evol. Biol. 23, 1538–1546 (2010).CAS 
PubMed 
Article 

Google Scholar 
37.Halling, L. & Oldroyd, B. P. Do policing honeybee (Apis mellifera) workers target eggs in drone comb?. Insectes Soc. 50, 59–61 (2003).Article 

Google Scholar 
38.Santomauro, G., Oldham, N. J., Boland, W. & Engels, W. Cannibalism of diploid drone larvae in the honey bee (Apis mellifera) is released by odd pattern of cuticular substances. J. Apic. Res. 43, 69–74 (2004).Article 

Google Scholar 
39.Imdorf, A., Rickli, M., Kilchenmann, V., Bogdanov, S. & Wille, H. Nitrogen and mineral constituents of honey bee worker brood during pollen shortage. Apidologie 29, 315–325 (1998).Article 

Google Scholar 
40.Rudolf, V. H. & Antonovics, J. Disease transmission by cannibalism: Rare event or common occurrence?. Proc. R. Soc. B Biol. Sci. 274, 1205–1210 (2007).Article 

Google Scholar 
41.Chapman, J. W. et al. Age-related cannibalism and horizontal transmission of a nuclear polyhedrosis virus in larval Spodoptera frugiperda. Ecol. Entomol. 24, 268–275 (1999).Article 

Google Scholar 
42.Hamano, K. et al. Waterborne and cannibalism-mediated transmission of the Yellow head virus in Penaeus monodon. Aquaculture 437, 161–166 (2015).Article 

Google Scholar 
43.Möckel, N., Gisder, S. & Genersch, E. Horizontal transmission of deformed wing virus: Pathological consequences in adult bees (Apis mellifera) depend on the transmission route. J. Gen. Virol. 92, 370–377 (2011).PubMed 
Article 
CAS 

Google Scholar 
44.Ryabov, E. V. et al. Development of a honey bee RNA virus vector based on the genome of a deformed wing virus. Viruses 12, 374 (2020).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
45.Posada-Florez, F. et al. Deformed wing virus type A, a major honey bee pathogen, is vectored by the mite Varroa destructor in a non-propagative manner. Sci. Rep. 9, 1–10 (2019).CAS 
Article 

Google Scholar 
46.Bull, J. C. et al. A strong immune response in young adult honeybees masks their increased susceptibility to infection compared to older bees. PLoS Pathog. 8, e1003083 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Shi, M. et al. Redefining the invertebrate RNA virosphere. Nature 540, 539–543 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
48.Masterman, R., Ross, R., Mesce, K. & Spivak, M. Olfactory and behavioral response thresholds to odors of diseased brood differ between hygienic and non-hygienic honey bees (Apis mellifera L.). J. Comp. Physiol. A 187, 441–452 (2001).CAS 
PubMed 
Article 

Google Scholar 
49.Crailsheim, K. Trophallactic interactions in the adult honeybee (Apis mellifera L.). Apidologie 29, 97–112 (1998).Article 

Google Scholar 
50.Nixon, H. & Ribbands, C. R. Food transmission within the honeybee community. Proc. R. Soc. Lond. Ser. B Biol. Sci. 140, 43–50 (1952).ADS 
CAS 
Article 

Google Scholar 
51.Arathi, H. & Spivak, M. Influence of colony genotypic composition on the performance of hygienic behaviour in the honeybee, Apis mellifera L. Anim. Behav. 62, 57–66 (2001).Article 

Google Scholar 
52.Knecht, D. & Kaatz, H. Patterns of larval food production by hypopharyngeal glands in adult worker honey bees. Apidologie 21, 457–468 (1990).Article 

Google Scholar 
53.Li, Z. et al. Transcriptional and physiological responses of hypopharyngeal glands in honeybees (Apis mellifera L.) infected by Nosema ceranae. Apidologie 50, 51–62 (2019).CAS 
Article 

Google Scholar 
54.Lass, A. & Crailsheim, K. Influence of age and caging upon protein metabolism, hypopharyngeal glands and trophallactic behavior in the honey bee (Apis mellifera L.). Insectes Soc. 43, 347–358 (1996).Article 

Google Scholar 
55.Chiou, S.-S. & Chen, W.-J. Mutations in the NS3 gene and 3′-NCR of Japanese encephalitis virus isolated from an unconventional ecosystem and implications for natural attenuation of the virus. Virology 289, 129–136 (2001).CAS 
PubMed 
Article 

Google Scholar 
56.Steel, A., Gubler, D. J. & Bennett, S. N. Natural attenuation of dengue virus type-2 after a series of island outbreaks: A retrospective phylogenetic study of events in the South Pacific three decades ago. Virology 405, 505–512 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.de Souza, F. S., Allsopp, M. H. & Martin, S. J. Deformed wing virus prevalence and load in honeybees in South Africa. Arch. Virol. 166, 237–241 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
58.Martin, S. J. et al. Varroa destructor reproduction and cell re-capping in mite-resistant Apis mellifera populations. Apidologie 51, 369–381 (2020).CAS 
Article 

Google Scholar 
59.Kulhanek, K. et al. Survey-derived best management practices for backyard beekeepers improve colony health and reduce mortality. PLoS ONE 16, e0245490 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS ONE 14, e0218392 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Ryabov, E. V. et al. Recent spread of Varroa destructor virus-1, a honey bee pathogen, in the United States. Sci. Rep. 7, 1–10 (2017).CAS 
Article 

Google Scholar 
62.Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004).
Google Scholar  More

1.Dung, D. T., Thi, L., Phuong, H. & Long, K. D. Insect parasitoid composition on soybean, some eco-biological characteristics of the parasitoid, xanthopimpla punctata fabricius on soybean leaffolder omiodes indicata ( Fabricius ) in Hanoi Vietnam. ISSAAS J. 17, 58–69 (2011).
Google Scholar 
2.Srinivasan, R., Yule, S., Lin, M. Y. & Khumsuwan, C. Recent developments in the biological control of legume pod borer (Maruca vitrata) on yard-long bean. Acta Hort. 1102, 143–149 (2015).Article 

Google Scholar 
3.Van Lenteren, J. C. et al. Environmental risk assessment of exotic natural enemies used in inundative biological control. Biocontrol 48, 3–38 (2003).Article 

Google Scholar 
4.Aboubakar Souna, D. et al. An insight in the reproductive biology of Therophilus javanus (hymenoptera, braconidae, and agathidinae), a potential biological control agent against the legume pod borer (Lepidoptera, Crambidae). Psyche (London) 2017, 1–8 (2017).Article 

Google Scholar 
5.Aboubakar Souna, D. et al. Volatiles from Maruca vitrata (Lepidoptera, Crambidae) host plants influence olfactory responses of the parasitoid Therophilus javanus (Hymenoptera, Braconidae, Agathidinae). Biol. Control 130, 104–109 (2019).CAS 
Article 

Google Scholar 
6.Bellows, T. S. & Van Driesche, R. G. Life Table Construction and Analysis for Evaluating Biological Control Agents. in Handbook of Biological Control 199–223 (Elsevier, 1999). https://doi.org/10.1016/b978-012257305-7/50055-2.7.Maia, A. D. H., Luiz, A. J. & Campanhola, C. Statistical inference on associated fertility life table parameters using jackknife technique: computational aspects. J. Econ. Entomol. 93(2), 511–518 (2000).Article 

Google Scholar 
8.Roy, M., Brodeur, J. & Cloutier, C. Effect of temperature on intrinsic rates of natural increase (rm) of a coccinellid and its spider mite prey. Biocontrol 48, 57–72 (2003).Article 

Google Scholar 
9.Harvey, J. A. Factors affecting the evolution of development strategies in parasitoid wasps: The importance of functional constraints and incorporating complexity. Entomol. Exp. Appl. 117, 1–13 (2005).Article 

Google Scholar 
10.Henderson, R. E., Kuriachan, I. & Vinson, S. B. Postegression feeding enhances growth, survival, and nutrient acquisition in the endoparasitoid Toxoneuron nigriceps (Hymenoptera: Braconidae). J. Insect Sci. 15, 51 (2015).Article 

Google Scholar 
11.Kuriachan, I., Henderson, R., Laca, R. & Vinson, S. B. Post-egression host tissue feeding is another strategy of host regulation by the koinobiont wasp toxoneuron nigriceps. J. Insect Sci. 11, 1–11 (2011).Article 

Google Scholar 
12.Benelli, G. et al. The impact of adult diet on parasitoid reproductive performance. J. Pest. Sci. 90, 807–823 (2017).Article 

Google Scholar 
13.Harvey, J. A. & Strand, M. R. The developmental strategies of endoparasitoid wasps vary with host feeding ecology. Ecology 83, 2439–2451 (2002).Article 

Google Scholar 
14.Harvey, J. A., Bezemer, T. M., Gols, R., Nakamatsu, Y. & Tanaka, T. Comparing the physiological effects and function of larval feeding in closely-related endoparasitoids (Braconidae: Microgastrinae). Physiol. Entomol. 33, 217–225 (2008).Article 

Google Scholar 
15.Harvey, J. A. et al. Development of a solitary koinobiont hyperparasitoid in different instars of its primary and secondary hosts. J. Insect Physiol. 90, 36–42 (2016).CAS 
Article 

Google Scholar 
16.Harvey, J. A. & Malcicka, M. Nutritional integration between insect hosts and koinobiont parasitoids in an evolutionary framework. Entomol. Exp. Appl. 159, 181–188 (2016).Article 

Google Scholar 
17.Gols, R., Ros, V. I. D., Ode, P. J., Vyas, D. & Harvey, J. A. Varying degree of physiological integration among host instars and their endoparasitoid affects stress-induced mortality. Entomol. Exp. Appl. 167, 424–432 (2019).Article 

Google Scholar 
18.Vieira, L. J. P., Franco, G. M. & Sampaio, M. V. Host preference and fitness of Lysiphlebus testaceipes (Hymenoptera: Braconidae) in different instars of the aphid Schizaphis graminum. Neotrop. Entomol. 48, 391–398 (2019).CAS 
Article 

Google Scholar 
19.Harvey, J. A. Dynamic effects of parasitism by an endoparasitoid wasp on the development of two host species: Implications for host quality and parasitoid fitness. Ecol. Entomol. 25, 267–278 (2000).Article 

Google Scholar 
20.Harvey, J. A., Kadash, K. & Strand, M. R. Differences in larval feeding behavior correlate with altered developmental strategies in two parasitic wasps: Implications for the size-fitness hypothesis. Oikos 88, 621–629 (2000).Article 

Google Scholar 
21.van Achterberg, C. & Long, K. D. Revision of the Agathidinae (Hymenoptera, Braconidae) of Vietnam, with the description of forty-two new species and three new genera. Zookeys https://doi.org/10.3897/zookeys.54.475 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
22.Kuriachan, I., Consoli, F. L. & Vinson, S. B. In vitro rearing of Toxoneuron nigriceps (Hymenoptera: Braconidae), a larval endoparasitoid of Heliothis virescens (Lepidoptera: Noctuidae) from early second instar to third instar larvae. J. Insect Physiol. 52, 881–887 (2006).CAS 
Article 

Google Scholar 
23.Pennacchio, F., Vinson, S. B. & Tremblay, E. Growth and development of Cardiochiles nigriceps viereck (hymenoptera, braconidae) larvae and their synchronization with some changes of the hemolymph composition of their host, Heliothis virescens (F.) (Lepidoptera, Noctuidae). Arch. Insect Biochem. Physiol. 24, 65–77 (1993).CAS 
Article 

Google Scholar 
24.Dannon, E. A., Tamò, M., van Huis, A. & Dicke, M. Functional response and life history parameters of Apanteles taragamae, a larval parasitoid of Maruca vitrata. Biocontrol 55, 363–378 (2010).Article 

Google Scholar 
25.Henry, L. M., Gillespie, D. R. & Roitberg, B. D. Does mother really know best? Oviposition preference reduces reproductive performance in the generalist parasitoid Aphidius ervi. Entomol. Exp. Appl. 116, 167–174 (2005).Article 

Google Scholar 
26.Moreau, S. J. M. & Asgari, S. Venom proteins from parasitoid wasps and their biological functions. Toxins (Basel). 7, 2385–2412 (2015).CAS 
Article 

Google Scholar 
27.Aboubakar Souna, D. Assessing the potential of Therophilus javanus, a biological control candidate against the cowpea pod borer Maruca vitrata in West Africa. (University of Montpellier (France); University of Abomey-Calavi (Benin), 2018).28.Beckage, N. E. & Gelman, D. B. Wasp parasitoid disruption of host development: Implications for new biologically based strategies for insect control. Annu. Rev. Entomol. 49, 299–330 (2004).CAS 
Article 

Google Scholar 
29.Qiu, B., Zhou, Z. & Xu, Z. Age Preference and Fitness of Microplitis manilae (Hymenoptera: Braconidae) Reared on Spodoptera exigua (Lepidoptera: Noctuidae). Florida Entomol. 96, 602–609 (2013).Article 

Google Scholar 
30.Mayhew, P. J. Comparing parasitoid life histories. Entomol. Exp. Appl. 159, 147–162 (2016).Article 

Google Scholar 
31.Sithole, R., Chinwada, P. & Lohr, B. L. Effects of host larval stage preferences and diet on life history traits of Diadegma mollipla, an African parasitoid of the Diamondback Moth. Biocontrol Sci. Technol. 28, 172–184 (2018).Article 

Google Scholar 
32.Boulton, R. A., Collins, L. A. & Shuker, D. M. Beyond sex allocation: The role of mating systems in sexual selection in parasitoid wasps. Biol. Rev. https://doi.org/10.1111/brv.12126 (2015).Article 
PubMed 

Google Scholar 
33.Beukeboom, L. W., Ellers, J. & Van Alphen, J. J. M. Absence of single-locus complementary sex determination in the braconid wasps Asobara tabida and Alysia manducator. Heredity (Edinb). 84, 29–36 (2000).Article 

Google Scholar 
34.Zhou, Y., Gu, H. & Dorn, S. Single-locus sex determination in the parasitoid wasp Cotesia glomerata (Hymenoptera: Braconidae). Heredity (Edinb). https://doi.org/10.1038/sj.hdy.6800829 (2006).Article 
PubMed 

Google Scholar 
35.Van Nieuwenhove, G. A. & Ovruski, S. M. Influence of Anastrepha fraterculus (Diptera: Tephritidae) larval instars on the production of Diachasmimorpha longicaudata (Hymneoptera: Braconidae) progeny and their sex ratio. Florida Entomol. 94, 863–868 (2011).Article 

Google Scholar 
36.Huang, Y. B. & Chi, H. Life tables of Bactrocera cucurbitae (Diptera: Tephritidae): With an invalidation of the jackknife technique. J. Appl. Entomol. 137, 327–339 (2013).Article 

Google Scholar 
37.Shapiro, A. S. S. & Wilk, M. B. Biometrika trust an analysis of variance test for normality (Complete Samples ) Published by : Oxford University Press on behalf of Biometrika Trust Stable. Biometrika 52, 591–611 (1965).38.Bretz, F., Hothorn, T. & Westfall, P. H. Multiple comparisons using R. 187 (2011). https://doi.org/10.1128/AAC.03728-14.39.Mangiafico, S. S. rcompanion: Functions to Support Extension Education Program Evaluation. R package version 2.0.0. Cran R (2016). https://doi.org/10.1016/J.AMJSURG.2007.06.026.40.R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria (2019).41.Chi, H. & Su, H.-Y. Age-stage, two-sex life tables of Aphidius gifuensis (Ashmead) (Hymenoptera: Braconidae) and its host Myzus persicae (Sulzer) (Homoptera: Aphididae) with mathematical proof of the relationship between female fecundity and the net reproductive rate. Environ. Entomol. 35, 10–21 (2006).Article 

Google Scholar 
42.Ning, S., Zhang, W., Sun, Y. & Feng, J. Development of insect life tables: Comparison of two demographic methods of Delia antiqua (Diptera: Anthomyiidae) on different hosts. Sci. Rep. 7, 4821 (2017).ADS 
Article 

Google Scholar 
43.Chi, H. TWOSEX-MSChart: a computer program for the age stage, two-sex life table analysis. National Chung Hsing University, Taichung, Taiwan. 2015b Available: http://140.120.197.173/Ecology/ (access (2015).44.Akca, I., Ayvaz, T., Yazici, E., Smith, C. L. & Chi, H. Demography and population projection of Aphis fabae (Hemiptera: Aphididae): with additional comments on life table research criteria. J. Econ. Entomol. 108, 1466–1478 (2015).Article 

Google Scholar  More

In the case of the first studied facility—Facility (I), thermo-visual examinations did not demonstrate any signs of thermal activity in the majority of the area. Above all, the new layered sections of the facility are free from thermal phenomena. However, the central section of the dump is characterized by rather intense thermal phenomena. The cone is also thermally active, yet in this section the thermal activity is not very intensive (see Fig. 1). In the section with the strongest thermal activity, i.e. the southern part of the top, the recorded surface temperature exceeded 600 °C (see Fig. 2). Outside the thermally active zone, the surface temperature did not exceed 25–35 °C.Figure 1Thermo-visual examination of the extractive waste dump (Facility I); October, 2017.Full size imageFigure 2Subsidence (the so-called crater) at the top of Facility I; October, 2017.Full size imageThe tests conducted on the premises of Facility II demonstrated the lack of thermal activity in the majority of the area. Only in the central section, on the top of the older part of the dump, the measurements showed the occurrence of thermal anomalies (see Fig. 3). These were minor areas located along the edge of the scarp on the northern and southern sides where the measured surface temperature was above 80 °C. It is worth mentioning that in 2010, the recorded temperatures for this section were at the level of several hundred degrees Celsius. Outside the sections, the temperature did not exceed 25–40 °C. The relatively high temperature in the area without thermal activity may be explained by the fact that in the morning hours it was exposed to the sun.Figure 3Thermo-visual examination of the extractive waste dump (Facility II); June, 2017.Full size imageDuring the course of the thermo-visual examination of the extractive waste dump in Facility III, no thermal activity was observed in the majority of the area. Only in the central section of the Facility, in the area which is still in operation, the measurements demonstrated the occurrence of thermal anomalies (see Fig. 4). These were minor areas where the local measured surface temperature was above 200 °C. Outside these areas, the temperature did not exceed 20–30 °C.Figure 4Thermo-visual examination of the extractive waste dump (Facility III); October, 2017.Full size imageThe simplest method of analyzing the differences and similarities between the studied objects is their visualization in the space of measured parameters. When two or three parameters are measured, such visualization is not problematic. However, in our study there are nine measured parameters. The graphic presentation of a 9-dimensional space is not possible. This is the rationale of applying the HCA which enables to analyze the similarities between the studied objects (three different dumping facilities in different periods of time) as well as the similarities between the parameters in object space. Nevertheless, the HCA does not allow to simultaneously analyze the relationships between the objects and the measured parameters. This problem was solved by the use of a color map of the experimental data, which enabled an in-depth interpretation of the data structure. In addition, the application of the color map facilitates highlighting the differences and similarities among the clusters showed in the dendrograms, and, in consequence, it helps to distinguish the facilities which are characterized by the highest or the lowest values of the measured parameters. Figure 5 demonstrates the dendrogram for 21 objects representing the studied dumping facilities in different periods of time in the space of 9 measured parameters, the dendrogram for the measured parameters in the object space as well as a color map presenting the values of the measured parameters for particular objects.Figure 5Dendrograms for (a) 21 objects representing the studied dumping facilities (see Table 1) in the space of 9 measured parameters; (b) parameters in the object space; (c) a color map presenting the values of measured parameters for particular dumping facilities.Full size imageBased on the dendrogram presented in Fig. 5a, a clear distinction of the examined objects representing the discussed dumping facilities into two clusters—A and B can be observed. Cluster A includes all samples representing Facility II in the whole period of the monitoring as well as samples representing Facility I taken in Quarter 3, 2017 and in Quarters 1–4, 2018 (objects nos. 1 and 3–6). All samples representing Facility III as well as two samples representing Facility I taken in Quarter 4, 2017 and in Quarter 4, 2018, respectively (objects nos. 2 and 6) are collected in Cluster B. In addition, within each of the clusters certain sub-clusters may be distinguished. Within Cluster A, the following sub-groups can be observed:

sub-group A1 collecting all samples taken from Facility II during the whole period of the monitoring (objects nos. 8–14);

sub-group A2 including samples taken from Facility I in Quarter III, 2017 and in Quarters 1–4, 2018 (objects nos. 1 and 3–6).

In turn, Cluster B contains the following three sub-groups:

sub-group B1 collecting two samples taken from Facility I in Quarter 4, 2017 and 2018, respectively (objects nos. 2 and 6) as well as samples representing Facility III in Quarter 3, 2017, Quarters 1–3, 2018 and Quarter 1, 2019 (objects nos. 15, 17–19 and 21);

sub-group B2 encompassing the remaining two samples taken from Facility III in Quarter 4, 2017 and in Quarter 4, 2018 (objects nos. 16 and 20).

The dendrogram obtained by means of Ward’s linkage method for the measured parameters in the space of 21 objects (see Fig. 5b) enables to distinguish the three principal clusters of parameters listed below:

class A collecting parameters nos. 2, 3 and 4 (describing the concentrations of acenaphtene, fluorene and phenanthrene);

class B containing parameters nos. 1 and 9 (describing the concentrations of naphthalene and chrysene);

class C including the remaining parameters nos. 5, 6, 7 and 8 (describing the concentrations of anthracene, pyrene, fluoranthene and B(a)anthracene).

The PAHs emissions from a burning mine waste dump must be carefully monitored due to their potential toxicity and genotoxicity38,39. PAHs have two different roots; one is incomplete combustion of organic matter, whereas the other one is their production in the geological formation when organic sediments were chemically transformed into fossil fuels. In our study, the first path, namely spontaneous coal waste combustion is observed40,41. It is also worth mentioning that PAHs pose significant human health hazards. The exposure to PAHs may result in skin, lung or stomach cancers in the human organism28.As mentioned before, a considerable drawback of the Hierarchical Clustering Analysis lies in that it does not allow for simultaneous interpretation of the dendrograms describing objects which represent particular samples taken from the extractive waste facility in different periods in the space of measured parameters and parameters in the object space. The lack of the possibility of interpreting the above relationships significantly limits the knowledge of the studied phenomena because the purpose of the analysis is to determine not only the differences among particular samples but also the underlying cause of such differences. Therefore, the dendrogram presenting the examined samples taken from the three selected facilities in different periods of time (see Fig. 5a) was juxtaposed with a color map of the experimental data (see Fig. 5c) demonstrating the values of the measured parameters arranged according to the order of the objects and parameters organization. The juxtaposition enables to determine the reason why the examined samples were distributed in such a way. In addition, the interpretation of the dendrogram for the objects in parameters space complemented with the color map of experimental data allows distinguishing samples which are characterized by the highest values of the measured parameters.Analyzing the dendrogram presented in Fig. 5a together with the color map of the experimental data, it can be observed that all samples within Cluster A were characterized by relatively lower values of the measured parameters. Moreover, sub-group A1 identified within Cluster A was characterized by relatively lowest concentrations of acenaphtene, fluorene, phenanthrene and pyrene (parameters nos. 2, 3, 4, and 7) among all of the examined samples taken from the three selected facilities in the whole period of the monitoring. It confirms the observations which were made previously with the use of a thermo-visual camera which demonstrated that there were no thermal phenomena in the majority of the waste dump areas. The observed sparse emissions of gases result from certain thermal anomalies occurring in the central section of Facility II as well as some small areas located along the northern and southern side of the scarp; however, in any of the places the temperature did not exceed 80 °C (see Fig. 3). The thermo-visual examinations of the remaining sections of the facility showed that the surface temperature did not exceed the level of 25–40 °C and it is a result of sun exposure rather than the occurrence of any thermal phenomena.Sub-group A2 which includes all samples taken from Facility I in Quarter 3, 2017 and Quarters 1–4, 2018 (objects nos. 1 and 3–6) differs from A1 objects in terms of relatively higher concentrations of acenaphtene, fluorene, phenanthrene and pyrene (parameters nos. 2, 3, 4, and 7). Additionally, within sub-group A2, the uniqueness of sample 1 taken in Quarter 3, 2018 (object no. 5) can be observed; it was characterized by the lowest concentration of chryzene (parameter no. 9) of all the examined samples.Despite the fact that the results of the thermo-visual analysis of Facility III in general did not show any major signs of thermal activity, the Hierarchical Clustering Analysis by means of which the samples were examined in terms of the emission of the 9 parameters indicated that some thermal phenomena take place at this dump.Furthermore, the analysis of the color map of the experimental data for the objects grouped in Cluster B makes it abundantly clear that thermal activity takes place in the majority of the monitored areas located at Facility III. What is more, for two samples taken from Facility I in Quarter 4, 2017 and Quarter 4, 2018 (objects nos. 2 and 6), thermal activity was observed. For the two objects, the concentrations of acenaphtene and fluorene are the highest among all of the examined samples (parameters nos. 2 and 3); similarly, the concentrations of phenanthrene, pyrene and B(a)anthracene (parameters nos. 4, 7, and 8) are also high. It confirms the observations made by means of a thermo-visual camera which demonstrated that there were no thermal phenomena if one considers the facility in its entirety. However, there are certain areas in the central section of the facility where thermal phenomena occur.In the case of the samples taken from Facility III in Quarter 3, 2017 as well as in Quarter 2 and Quarter 3, 2018 (objects nos. 15, 18, and 19) within sub-group B1, the values of all measured parameters are only slightly increased, which can indicate the occurrence of thermal phenomena. Yet, it must be kept in mind that the concentrations are decidedly lower than for the remaining samples taken from Facility III. It may be also an indication that self-heating of the coal extractive waste takes place. The other two samples taken from Facility III in Quarter 1, 2018 and Quarter 1, 2019 (objects nos. 17 and 21) are characterized by relatively highest concentrations of pyrene and B(a)anthracene (parameters nos. 7 and 8) as well as high concentrations of anthracene and fluorene (parameters nos. 5 and 6), which can result from an intense thermal activity.A similar observation can be made for the two samples taken from Facility III in Quarter 4, 2017 and Quarter 4, 2018 (objects nos. 16 and 20) which are characterized by the highest concentrations of naphthalene, anthracene, fluoranthene and chrysene of all the examined samples (parameters nos. 1, 5, 6 and 9) as well as high concentrations of phenanthrene (parameter no. 4). Although the results of the thermo-visual analysis of the whole area of Facility III did not demonstrate signs of thermal activity in most of its sections, there exist certain spots with intense thermal processes, which is confirmed by relatively high values of all the measured parameters. More

1.Baker, B. J. et al. Diversity, ecology and evolution of Archaea. Nat. Microbiol. 5, 887–900 (2020).PubMed 
Article 
CAS 

Google Scholar 
2.Baker, B. J., Appler, K. E. & Gong, X. New microbial biodiversity in marine sediments. Ann. Rev. Mar. Sci. 13, 161–175 (2020).PubMed 
Article 

Google Scholar 
3.Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).CAS 
PubMed 
Article 

Google Scholar 
4.Kozubal, M. A. et al. Geoarchaeota: a new candidate phylum in the Archaea from high-temperature acidic iron mats in Yellowstone National Park. ISME J. 7, 622–634 (2013).CAS 
PubMed 
Article 

Google Scholar 
5.Jay, Z. J. et al. Marsarchaeota are an aerobic archaeal lineage abundant in geothermal iron oxide microbial mats. Nat. Microbiol. 3, 732–740 (2018).CAS 
PubMed 
Article 

Google Scholar 
6.Hua, Z. S. et al. Genomic inference of the metabolism and evolution of the archaeal phylum Aigarchaeota. Nat. Commun. 9, 2832 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Seitz, K. W. et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat. Commun. 10, 1822 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Spang, A. et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat. Microbiol. 4, 1138–1148 (2019).CAS 
PubMed 
Article 

Google Scholar 
9.Orsi, W. D. et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat. Microbiol. 5, 248–255 (2020).CAS 
PubMed 
Article 

Google Scholar 
10.Eme, L., Spang, A., Lombard, J., Stairs, C. W. & Ettema, T. J. G. Archaea and the origin of eukaryotes. Nat. Rev. Microbiol. 15, 711–723 (2017).CAS 
PubMed 
Article 

Google Scholar 
11.Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Williams, T. A. et al. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl Acad. Sci. U.S.A. 114, E4602 –E4611 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Spang, A., Caceres, E. F. & Ettema, T. J. G. Genomic exploration of the diversity, ecology, and evolution of the archaeal domain of life. Science 357, eaaf3883 (2017).14.Trembath-Reichert, E. et al. Methyl-compound use and slow growth characterize microbial life in 2-km-deep subseafloor coal and shale beds. Proc. Natl Acad. Sci. USA 114, E9206–E9215 (2017).CAS 
PubMed 
Article 

Google Scholar 
15.Zhuang, G. C., Peña-Montenegro, T. D., Montgomery, A., Hunter, K. S. & Joye, S. B. Microbial metabolism of methanol and methylamine in the Gulf of Mexico: insight into marine carbon and nitrogen cycling. Environ. Microbiol. 20, 4543–4554 (2018).CAS 
PubMed 
Article 

Google Scholar 
16.Chistoserdova, L. Modularity of methylotrophy, revisited. Environ. Microbiol. 13, 2603–2622 (2011).CAS 
PubMed 
Article 

Google Scholar 
17.Chistoserdova, L. & Kalyuzhnaya, M. G. Current trends in methylotrophy. Trends Microbiol. 26, 703–714 (2018).CAS 
PubMed 
Article 

Google Scholar 
18.Sun, J., Mausz, M. A., Chen, Y. & Giovannoni, S. J. Microbial trimethylamine metabolism in marine environments. Environ. Microbiol. 21, 513–520 (2018).PubMed 
Article 

Google Scholar 
19.Zhuang, G.-C., Montgomery, A. & Joye, S. B. Heterotrophic metabolism of C1 and C2 low molecular weight compounds in northern Gulf of Mexico sediments: controlling factors and implications for organic carbon degradation. Geochim. Cosmochim. Acta 247, 243–260 (2019).ADS 
CAS 
Article 

Google Scholar 
20.Vanwonterghem, I. et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat. Microbiol. 1, 16170 (2016).CAS 
PubMed 
Article 

Google Scholar 
21.Lazar, C. S. et al. Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments. Environ. Microbiol. 18, 1200–1211 (2016).CAS 
PubMed 
Article 

Google Scholar 
22.Zhuang, G. Methylotrophic methanogenesis and potential methylated substrates in marine sediment. (University of Bremen, 2014).23.Richards, M. A. et al. Exploring hydrogenotrophic methanogenesis: a genome scale metabolic reconstruction of Methanococcus maripaludis. J. Bacteriol. 198, 3379–3390 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Sousa, D. Z. et al. The deep-subsurface sulfate reducer Desulfotomaculum kuznetsovii employs two methanol-degrading pathways. Nat. Commun. 9, 239 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Dombrowski, N., Teske, A. P. & Baker, B. J. Extensive metabolic versatility and redundancy in microbially diverse, dynamic Guaymas Basin hydrothermal sediments. Nat. Commun. 9, 4999 (2018).26.Fricke, W. F. et al. The genome sequence of Methanosphaera stadtmanae reveals why this human intestinal archaeon is restricted to methanol and H2 for methane formation and ATP synthesis †. J. Bacteriol. 188, 642–658 (2006).27.McKay L., et al. Co-occurring genomic capacity for anaerobic methane and dissimilatory sulfur metabolisms discovered in the Korarchaeota. Nat. Microbiol.4, 614–622 (2019).28.Muñoz-Velasco, I. et al. Methanogenesis on early stages of life: ancient but not primordial. Orig. Life Evol. Biosph. 48, 407–420 (2019).29.Adam, P. S., Borrel, G. & Gribaldo, S. An archaeal origin of the Wood–Ljungdahl H4MPT branch and the emergence of bacterial methylotrophy. Nat. Microbiol. 4, 2155–2163 (2019).30.Swan, B., Reifel, K. & Valentine, D. Periodic sulfide irruptions impact microbial community structure and diversity in the water column of a hypersaline lake. Aquat. Microb. Ecol. 60, 97–108 (2010).Article 

Google Scholar 
31.Adam, P. S., Borrel, G. & Gribaldo, S. Evolutionary history of carbon monoxide dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes. PNAS 115, E5837 (2018).Article 
CAS 

Google Scholar 
32.Orita, I. et al. The ribulose monophosphate pathway substitutes for the missing pentose phosphate pathway in the archaeon Thermococcus kodakaraensis. J. Bacteriol. 188, 4698–4704 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Urschel, M. R., Kubo, M. D., Hoehler, T. M., Peters, J. W. & Boyd, E. S. Carbon source preference in chemosynthetic hot spring communities. Appl. Environ. Microbiol. 81, 3834–3847 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Yokohama, H., Wagner, I. D. & Wiegel, J. Caldicoprobacter oshimai gen. nov., sp. nov., an anaerobic, xylanolytic, extremely thermophilic bacterium isolated from sheep faeces, and proposal of Caldicoprobacteraceae fam. nov. Int. J. Syst. Evol. Microbiol. 60, 67–71 (2010).Article 
CAS 

Google Scholar 
35.Zhang, X. et al. Petroclostridium xylanilyticum gen. Nov., sp. nov., a xylan-degrading bacterium isolated from an oilfield, and reclassification of clostridial cluster iii members into four novel genera in a new hungateiclostridiaceae fam. nov.Int. J. Syst. Evol. Microbiol. 68, 3197–3211 (2018).CAS 
PubMed 
Article 

Google Scholar 
36.Girbal, L., Croux, C., Vasconcelos, I. & Soucaille, P. Regulation of metabolic shifts in Clostridium acetobutylicum ATCC 824. FEMS Microbiol. Rev. 17, 287–297 (1995).CAS 
Article 

Google Scholar 
37.Qi, F. et al. Improvement of butanol production in Clostridium acetobutylicum through enhancement of NAD(P)H availability. J. Ind. Microbiol. Biotechnol. 45, 993–1002 (2018).CAS 
PubMed 
Article 

Google Scholar 
38.Branduardi, P., Longo, V., Berterame, N. M., Rossi, G. & Porro, D. A novel pathway to produce butanol and isobutanol in Saccharomyces cerevisiae. Biotechnol. Biofuels 6, 68 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Johnsen, U. & Schönheit, P. Novel xylose dehydrogenase in the halophilic archaeon Haloarcula marismortui. J. Bacteriol. 186, 6198–6207 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Ravachol, J. et al. Mechanisms involved in xyloglucan catabolism by the cellulosome-producing bacterium Ruminiclostridium cellulolyticum. Sci. Rep. 6, 22770 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Macdonald, S. S., Blaukopf, M. & Withers, S. G. N-acetylglucosaminidases from CAZy family GH3 are really glycoside phosphorylases, thereby explaining their use of histidine as an acid/Base catalyst in place of glutamic acid. J. Biol. Chem. 290, 4887–4895 (2015).CAS 
PubMed 
Article 

Google Scholar 
42.Wang, Y. et al. Environmental conditions constrain the distribution and diversity of Archaeal merA in Yellowstone National Park, Wyoming, U.S.A. Microb. Ecol. 62, 739–752 (2011).CAS 
PubMed 
Article 

Google Scholar 
43.Nunes, C. I. P. et al. ArsC3 from Desulfovibrio alaskensis G20, a cation and sulfate-independent highly efficient arsenate reductase. J. Biol. Inorg. Chem. 19, 1277–1285 (2014).CAS 
PubMed 
Article 

Google Scholar 
44.Silver, S. & Phung, L. T. Genes and enzymes involved in bacterial oxidation and reduction of inorganic arsenic. Appl. Environ. Microbiol. 71, 599–608 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Colman, D. R., Lindsay, M. R. & Boyd, E. S. Mixing of meteoric and geothermal fluids supports hyperdiverse chemosynthetic hydrothermal communities. Nat. Commun. 10, 681 (2019).46.Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of Hydrothermarchaeota in hydrothermal sediment. mSystems 5, e00795-19 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Rabus, R., Venceslau, S. S., Lars, W., Wall, J. D. & Pereira, I. A. C. A post-genomic view of the ecophysiology, catabolism and biotechnological relevance of sulphate-reducing prokaryotes. Adv. Micro. Physiol. 66, 55–321 (2015).CAS 
Article 

Google Scholar 
48.Tóth, A., Takács, M., Groma, G., Rákhely, G. & Kovács, K. L. A novel NADPH-dependent oxidoreductase with a unique domain structure in the hyperthermophilic Archaeon, Thermococcus litoralis. FEMS Microbiol. Lett. 282, 8–14 (2008).PubMed 
Article 
CAS 

Google Scholar 
49.Ma, K., Weiss, R. & Adams, M. W. W. Characterization of hydrogenase II from the hyperthermophilic archaeon Pyrococcus furiosus and assessment of its role in sulfur reduction. J. Bacteriol. 182, 1864–1871 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Jenney, F. E. & Adams, M. W. W. Hydrogenases of the model hyperthermophiles. Ann. N. Y. Acad. Sci. 1125, 252–266 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
51.Van Haaster, D. J., Silva, P. J., Hagedoorn, P. L., Jongejan, J. A. & Hagen, W. R. Reinvestigation of the steady-state kinetics and physiological function of the soluble NiFe-hydrogenase I of Pyrococcus furiosus. J. Bacteriol. 190, 1584–1587 (2008).PubMed 
Article 
CAS 

Google Scholar 
52.Greening, C. et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 10, 761–777 (2016).CAS 
PubMed 
Article 

Google Scholar 
53.Stetter, K. O. Hyperthermophiles in the history of life. Philos. Trans. R. Soc. B Biol. Sci. 361, 1837–1842 (2006).CAS 
Article 

Google Scholar 
54.Collins, T., Gerday, C. & Feller, G. Xylanases, xylanase families and extremophilic xylanases. FEMS Microbiol. Rev. 29, 3–23 (2005).CAS 
PubMed 
Article 

Google Scholar 
55.Schädel, C., Richter, A., Blöchl, A. & Hoch, G. Hemicellulose concentration and composition in plant cell walls under extreme carbon source-sink imbalances. Physiol. Plant. 139, 241–255 (2010).PubMed 

Google Scholar 
56.Chen, S. et al. The Great Oxidation Event expanded the genetic repertoire of arsenic metabolism and cycling. 117, 10414–10421 (2020).57.Rogers, K. L. & Schulte, M. D. Organic sulfur metabolisms in hydrothermal environments. Geobiology 10, 320–332 (2012).CAS 
PubMed 
Article 

Google Scholar 
58.Baker, B. J. et al. Genomic inference of the metabolism of cosmopolitan subsurface Archaea, Hadesarchaea. Nat. Microbiol. 1, 16002 (2016).CAS 
PubMed 
Article 

Google Scholar 
59.Hatzenpichler, R., Krukenberg, V., Spietz, R. L. & Jay, Z. J. Next-generation physiology approaches to study microbiome function at single cell level. Nat. Rev. Microbiol. 18, 241–256 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
61.Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Dick, G. J. et al. Community-wide analysis of microbial genome sequence signatures. Genome Biol. 10, R85 (2009).63.Darling, A. E. et al. PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ 2, e243 (2014).PubMed 
PubMed Central 
Article 

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

Google Scholar 
65.Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 
Article 

Google Scholar 
66.Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).Article 
CAS 

Google Scholar 
67.Aramaki, T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).CAS 
PubMed 
Article 

Google Scholar 
68.Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Søndergaard, D., Pedersen, C. N. S. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci. Rep. 6, 34212 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Zhang, H. et al. DbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46, W95–W101 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.De Anda, V. et al. MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle. Gigascience 6, 1–17 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Zhichao, Z. et al METABOLIC: High-throughput. profiling of microbial genomes for functional traits, biogeochemistry, and community-scale metabolic networks. Preprint at bioRxiv 761643 (2019).73.Rawlings, N. D., Morton, F. R., Kok, C. Y., Kong, J. & Barrett, A. J. MEROPS: the peptidase database. Nucleic Acids Res. 38, D227–D233 (2010).CAS 
PubMed 
Article 

Google Scholar 
74.Taboada, B., Estrada, K., Ciria, R. & Merino, E. Operon-mapper: a web server for precise operon identification in bacterial and archaeal genomes. Bioinformatics 34, 4118–4120 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).CAS 
PubMed 
Article 

Google Scholar 
76.Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Boyd, J. A., Woodcroft, B. J. & Tyson, G. W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 46, e59 (2018).78.Hua, Z. S. et al. Insights into the ecological roles and evolution of methyl-coenzyme M reductase-containing hot spring Archaea. Nat. Commun. 10, 4574 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
79.Chaumeil, P., Mussig, A. J., Parks, D. H. & Hugenholtz, P. Genome analysis GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).80.Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R. J. 5, 144–161 (2013).Article 

Google Scholar  More

In this study we successfully applied a DNA metabarcoding approach to identify consumed food items of plant and animal origin in human stomach content samples, even when digestion was advanced and macroscopic inspection no longer possible. A wide panel of common and less common edible food items were found, including meat, fish, legumes, cereals, nuts, fruits and spices. So far, gastric content analyses in a forensic context are typically based on microscopic and macroscopic identification of food items (reviewed e.g. in1). However, this approach is characterised by low taxonomic resolution, low sensitivity, and proves ineffective when meal leftovers are rendered unidentifiable due to chewing and digestive processes. In the field of molecular ecology, studies on animals have shown that morphological identification of prey items in the stomach underestimates prey diversity, which is particularly true when digestion is advanced (e.g.25). The only study to date applying DNA metabarcoding to infer human diet was based on faecal samples and did not assess any animal components of diet, although including a controlled feeding trial of an animal-based diet12. The comparison of the obtained plant DNA sequences to self-reporting indicated that, while some items were not reported but detected by DNA metabarcoding, all but one self-reported items were detected (the only exception being coffee), thus highlighting the sensitivity of the method. The present study, based on a random sampling of 48 human stomach contents collected during routine autopsies, includes a higher number of vegetal items and shows for the first time the successful detection of dietary items of animal origin. We found no correlation between the diversity of species detected and the time since death or digestion degree, which advocates for the utility of this methodology. The Vert01 primer set, highly specific to vertebrates, enables to distinguish between commonly eaten animal taxa and is clearly advantageous over morphological identification. In line with regional eating habits and previously published diet surveys26, we found within the 48 samples mainly pig, cattle/dairy and OTUs assigned to the plant families Poaceae, Rosaceae and Asteraceae (likely cereals, fruits, lettuces; Fig. 1). We did not detect coffee (Coffea spp.) in any of the stomach content samples, in line with12, which might be due to a degrading effect of roasting procedures on DNA, the absence of this popular beverage in all of the stomach samples being unlikely. Similarly, although common in Swiss eating habits, we also did not detect potato, which is usually eaten boiled or baked. Note that additional edible plant species, not listed in Fig. 2 since not constituting at least 10% of RRA but with 100% match with the database, were also detected (e.g. buckwheat, citrus fruits, flax, mangoes, sesame; Supplementary Table S2). Because we could obviously not compare our results to self-reported diets, we applied very stringent filtering parameters to avoid the occurrence of false positives (see Bioinformatic data treatment). It is beyond the approach of this study to distinguish between the animal source and a final processed food item (e.g. dairy or egg products) based on the obtained DNA sequences. However, this could be achieved by complementing the primer set with a bacterial marker (to e.g. identify the presence of a particular cheese27) or using proteomics (see below).Overall, the Vert01 metabarcode is able to discriminate well among commonly eaten genera. However, owing to its limited taxonomic resolution (72.4% at the species level, based on in silico testing11), species-level distinction is not always possible (e.g. between perch and pikeperch) or between potentially-eaten wild species and their conspecific domestic counterparts (e.g. wild boar and pig). In Fig. 2, we present the taxonomical assignation done using ObiTools together with a common name, selected after manually inspecting each sequence using BLAST and only considering 100% matches with edible species. In some cases, the common name refers to a group of species because the barcode was not specific enough to distinguish between genera or species. This is more relevant concerning plants, as the Sper01 metabarcode length ranges from 10 to 220 bp, implying that some items with shorter metabarcode and/or closely related phylogenetically could not be distinguished to genus or species level due to limited resolutive power. This is related to the nature of this universal plant marker, which has been designed to target a region of the trnL intron of chloroplast DNA which lacks taxonomic resolution within several plant families (only 21.5% resolution at the species level9,11) but has wide taxonomic coverage. This trade-off meant for our study that we could genetically not distinguish between some close species which are clearly different morphologically (e.g. stone fruits, cucurbits). To overcome this issue and increase the taxonomic resolution of the results, it is possible to envisage multiplexing within the same PCR of additional primers specifically targeting groups of species that cannot be identified at the species level by the P6 loop of the trnL intron. Such a strategy has already been implemented to distinguish between Carpinus betulus and Corylus avellana in bison diet28. Furthermore, it must be outlined that by using these primer sets only, diet assessment is not comprehensive as it does not target all possibly present food products. Even so-called universal primers may result in preferential amplification of some taxa over others and non-amplification of target taxa29,30. For this pilot study, we chose to use two universal PCR primer pairs with wide taxonomic coverage but limited specific resolution, in order to detect a broad range of items. To gain resolution for specific vertebrate or plant taxonomic groups (e.g. fish, birds, cereals) or target taxa not covered by these primers and which could be of forensic interest (e.g. marine crustaceans and molluscs, algae, fungi), it is possible to complement Vert01 and Sper01 with additional, taxonomically-restricted PCR metabarcoding primers described in the literature (e.g.31; examples reviewed in11). Taxonomic assignation of an unknown DNA sequence strongly depends on the exhaustiveness and quality of a reference database, either public as e.g. GenBank or custom-made/local (reviewed in32). In case of a priori knowledge of the overall consumed diet in samples, local databases may be restrained to the expected DNA sequences, which subsequently improves taxonomic assignment. For this study we in silico compiled databases containing all possible sequences amplified by our markers, but restricted these to vertebrates and spermatophytes (i.e. seed plants), respectively.The duration of stomach emptying has been estimated by the percentage of a meal present in a stomach3, but this process is influenced by several variables including the type and volume of consumed food, lifestyle and health, and can therefore last from few hours to days2. While one could argue that plant items usually remain longer in the stomach, our findings do not allow to draw robust conclusions about correlations of certain food items and digestion times. In order to establish hypotheses useful for time-frame estimations, additional experiments are necessary. In a controversial case of death, MS-based proteomics provided additional information through the analysis of food-derived proteins and peptides in the gastric content sampled at autopsy, indicating a last breakfast of milk and bread. While this method is certainly promising, it might reveal difficult if digestion is in an advanced stage, and has a less comprehensive scope than a DNA metabarcoding assay33. Furthermore, the effect of food processing techniques on DNA quality must be taken into account since cooking denatures e.g. proteins which in turn renders DNA amplification preferential to immunological approaches1. Different cooking treatments (variable duration of boiling, frying, baking) of tomato seeds showed that DNA extraction yielded in good quality DNA only for fresh seeds34, while digestion did not destroy DNA21. Hence, there might be an implicit bias of DNA metabarcoding to preferentially detect non-processed food (i.e. raw versus cooked). Another issue of environmental DNA-based methods is that it is not possible to distinguish between different states of food products based on DNA sequences. As mentioned before, we could not discriminate between e.g. grapes/wine, fruits/juices, beef meat/dairy products or chicken meat/eggs, since the DNA sequence of a derived product is identical to the DNA sequence of its source. While it is less common to encounter such biases for plants, mainly in cereal-derived products, it has to be taken into account when extrapolating diet patterns from DNA metabarcoding results.Stomach content sampling is invasive, but advantageous or even required with certain animal species and in particular circumstances, including definitely the human forensic context. An advantage of stomach content over faecal samples is that food is in an early stage of digestion before passing through the pyloric sphincter into the intestines, thus the effects of inhibition by bacteria or enzymes and degradation of DNA are less significant11,18. While some food particles such as seeds sometimes remain identifiable, even morphologically, after passing through the digestive system21, others do not and the same applies to DNA which is degraded by the digestive processes taking place in the intestinal tract. In a controlled feeding experiment on insects, the detectability of food DNA in different types of dietary samples showed that regurgitates and entire animals (including stomach content) outperformed faeces regarding detectability of prey DNA13. While food journals in dietary surveys may contain errors or deliberate omissions12, they are a comprehensive and easily accessible method of human diet assessment. However, in case of deceased persons that option is no longer available.Stomach content analyses provided crucial information for criminal investigations about cases of sudden and unexplained death on numerous occasions in recent years, enabling investigators to interpret perimortem events in detail (case examples reviewed in2). The results of this pilot study show that human stomach content analyses by DNA metabarcoding can be used as a complementary tool to traditional forensic macro- and microscopic approaches, with clear advantages such as an almost unlimited flexibility in terms of nature and range of taxa targeted, as well as high sensitivity and taxonomic resolution. Consequently, information that might otherwise remain undetected can be revealed, highlighting timings and circumstances surrounding the last hours of a person and his/her food intake. In a broader perspective, taking into account the potential improvements and refinements described above, and the growing amount of research literature available for wildlife species (i.e. environmental DNA-based studies), our results open up promising and novel prospects in the broader framework of human biomedical investigations of dietary patterns, based on partially or fully digested food found in the gastrointestinal tract or in faecal samples. More

The δ-MAPS analysis is performed onto monthly mean SST anomalies from the Mediterranean Sea Physical Reanalysis (CMEMS MED-Physics25) over the period 1987–2017. The advantage of using a reanalysis resides in the availability of a velocity field consistent with the SSTs that allows us to confirm the coupling between network domains and ocean currents within the euphotic layer.Validation of the δ-MAPS frameworkThe proposed ecoregionalization is first applied to the 2007–2010 period, when the domains, representing ecoregions, can be compared to those identified by Ref.2 using Lagrangian methods. The details of this validation are reported below and relevant figures can be found in the Supplementary Information.The 2007–2010 ecoregions in Figure S1 are consistent with the ones derived in Ref.2 through computationally intensive simulations. The name of each domain corresponds with those used in Ref.2 to ease comparison. It is worthwhile remarking that this work and the one of Ref.2 not only use very different methods to define connectivity, but also different data sources. Our study uses velocity and SST output fields from CMEMS MED Physics reanalysis, while Ref.2 uses the configuration PSY2V3 of the operational system MERCATOR OCEAN with a resolution of 8 km in the horizontal downscaled to a connectivity grid of 50 × 50 km. The data assimilation and clustering algorithms are different and Ref.2 employs a cut-off in addition to the clustering grid downscaling. These differences unavoidably translate into slightly different shapes and patterns of the domains inferred. For example, the D + V area in panel (a) of Figure S1 is effectively two separate ecoregions in Ref.2, in which the Messina Strait is not resolved at the connectivity grid level. However, this separation appears inconsistent with the surface kinetic energy (K.E.) of panel (b) in Figure S1, computed from the horizontal currents, e.g. zonal (u) and meridional (v) velocity components, as K.E. = 1/2 |V|2 where |V|= (u2 + v2)0.5. Indeed, there is no clear separation between the regions north and south at Messina Strait in our dataset. Having detailed this example and acknowledged that some differences should be expected, the overall basin eco-regionalization using δ-MAPS is consistent with that in Ref.2. The spatial accuracy is enough to well separate the main ecological areas, despite small-scale differences (i.e. some km, due to resolution choices).By and large, the SST anomaly domains in Figure S1 are bounded by ocean currents, in agreement with Ref.2. This is due to the dominance of advective forcing by ocean currents on the SSTs at equatorial and mid latitudes, on monthly timescales and spatial scales of few hundreds kilometers24. This link, which is foundational to the proposed methodology, is further quantified as follows: First, we calculate the surface K.E. per unit mass averaged over the time slot of interest; second, we select the points in the validation period (2007–2010) that exceed the 50th percentile of surface K.E. computed for the entire basin over 1987–2017 (e.g. 0.004 m2/s2); third, we compute the domain-boundary matrix augmented by 1 grid point in each direction; finally, we count which fraction of the domain boundaries computed in the boundary matrix overlaps with the K.E. fronts (above the 50th percentile threshold). The fraction obtained is high and equal to 0.73, and remains elevated when increasing the threshold to the 60th percentile (0.66). This procedure was repeated for all the time slots with Δ = 7 years used next in this study, obtaining high and very stable values in each case (mean ± variance = 0.73 ± 0.01 for the 50th percentile threshold, and 0.65 ± 0.01 for the 60th percentile threshold).Additionally, the correlation between the surface K.E. and K.E. at 50 m, 100 m, and 150 m over the whole 1987–2017 period (Figure S2) remains positive and significant, with coefficients for the whole domain (field mean c.c.  ± variance) of 0.83 ± 0.04 at 50 m, 0.68 ± 0.05 at 100 m, and 0.54 ± 0.06 at 150 m, indicating that the link extends to the whole euphotic layer.Mediterranean Sea ecoregions: long-term changesThe space-averaged (e.g. averaged on the whole basin) SSTs over the 1987–2017 period are characterized by a linear warming trend of about 0.04 °C per year, stronger in the eastern portion of the basin (Figure S3 in Supplementary Information). Over the same period, the K.E. per unit mass is characterized by different trends over decadal or quasi-decadal periods (Fig. 2, shown for surface only but the trend extends similarly to 50 m and 100 m depths) and no clear east–west contrast. A positive trend is found in the first part of the curve (1987–2001, 2.3 × 10–4 m2/s2 per year, green line in figure), followed by a central decade without statistically significant changes (2001–2010, blue line), and a steep negative trend afterward (2010–2017, – 4.1 10–4 m2/s2 per year, red line). We refer to 1987–2001, 2001–2010 and 2010–2017, as the UP, MAX and DOWN periods. The dynamical changes associated with the strengthening and weakening of ocean currents are hypothesized to coincide with a reshaping of the sub-basin ecoregions and reciprocal connectivity. The ecoregionalization inference is therefore performed considering time slots of varying length, so that yrend = yrini + Δ with yrini = y0 + n, n = 0,1,…,N, where y0 is the initial year of the dataset (1987) and N is the total number of time slots, each of duration Δ years, between 6 and 8. Time slots overlapping by more than one year among different trends periods are excluded. The choice of Δ = 7 years represents the best trade-off for having enough time slots to quantify the evolution of ecoregions and a sufficiently large number of data points in each time slot for statistical inference. We will focus on this case, but results are verified also for the other Δ values (see Supplementary Information).Figure 2Mean surface kinetic energy timeseries. Monthly time series of deseasonalized surface kinetic energy per unit mass (m2/s2), averaged over the whole Mediterranean Sea between 1987 and 2017. The shaded areas indicate the 1987–1993 (during the UP period), 2004–2010 (during MAX) and 2011–2017 (during DOWN) time slots used in Fig. 3.Full size imageStrength maps for three representative time slots are presented in Fig. 3a,c,e while maps of domain strengths for all Δ = 7 time slots can be found in Figure S4. The mean surface kinetic energy averaged within each timeslot is next compared to the number of ecoregions in corresponding timeslots. The fragmentation level, or the total number of ecoregions, and the mean surface kinetic energy content are highly correlated (Figure S5b in Supplementary Information), with a Pearson’s coefficient of 0.79 for the whole Mediterranean Sea, and 0.8 (0.65) for the eastern (western) basin. The fact that time slots are not independent does not invalidate the analysis, and a large correlation (c.c = 0.73) is retained even when using four non-overlapping time slots. A higher fragmentation occurs whenever the upper ocean layer is more energetic, and this relationship is robust to changes of Δ (see Supplementary Information). The domain strength is next compared to the mean K.E. content. For each timeslot, the domain strength is spatially averaged over the eastern and western basin separately. The correlations between the averaged strengths and the corresponding time slot mean surface K.E. values, both varying as the time slots change, are then calculated for eastern and western basins separately. No linkage is found in the western basin, but a strong anticorrelation describes the relationship in the eastern Mediterranean (c.c. − 0.74). This anticorrelation remains high (− 0.73) also when the eastern basin strengths are related to the whole basin surface K.E. averaged over each timeslot.Figure 3Domains and connectivity networks for the domain containing the Suez Canal. The three 7-year timeslots selected as representative of the UP (a,b), MAX (c,d) and DOWN (e,f) periods. The color of the domains represents their strength (left column), and the red dot shows the location of the Suez Canal. Links in the connectivity nets (right column) are colored according to the correlation between (the domain containing) the Suez Canal and other domains as labeled. Only correlations stronger than 0.35 are plotted. (Domains maps visualization produced with Matlab R2018a, https://www.mathworks.com/).Full size imageWe hypothesize that the amount of K.E. associated with semi-permanent jets, currents or large mesoscale eddies, grouped here together and named KE fronts, can be used as an indicator of their role as connectivity modulators. We identify KE fronts applying a pattern recognition algorithm on the K.E. fields for each time slot. The resulting pictures are processed by an image segmentation technique, based on K-means clustering, to separate the K.E. in four clusters of increasing energy content. The maximum-intensity group is selected as indicators for KE fronts and the number of pixels contained in each cluster is counted and used to estimate the size or abundance of each one. The maximum-intensity cluster well represents the energy-containing structures as measured by the correlation between the mean surface K.E. content in each time slot and the pixels within the corresponding cluster (c.c.  > 0.99). The more pixels reside within each cluster, the larger the KE fronts-populated areas that this cluster approximates. This estimation is carried out for the whole basin, and separately in the eastern and western parts. The number of pixels is then correlated to the number of inferred ecoregions for the whole Mediterranean (c.c. = 0.81), and for eastern (c.c. = 0.81) and western (c.c. = 0.69) basins. Figure S6 in the Supplementary Information compares the clustering maps of a low energy time slot (1987–1993, in panel (a)) and a higher one (2004–2010 in panel (b)), for the whole Mediterranean Sea for the maximum cluster. The number of ecoregions is highly correlated with the KE fronts everywhere and especially in the eastern Mediterranean Sea. The higher level of fragmentation found in the MAX period is thus associated with more abundant and/or larger surface KE fronts, acting as eco-dynamical barriers.To further strengthen this assessment, we consider that energy fronts can act as modulators for SSTa-derived domains. The ecoregionalization over a certain time slot characterizes that time range in one single ecoregion-map but stems from data known at several time points (i.e. monthly SSTa in our case). The resulting domains account therefore for the inherent physical variability of the system over time. A higher (lower) ecoregions fragmentation may therefore by associated with dynamical fronts occurring at different times and not necessarily in the same place, over a certain time range. If this is plausible, we expect to count more (less) occurrences of higher energy in broad areas where the domains are more (less) fragmented. For each time slot, the number of occurrences of a front in each pixel is therefore counted. Specifically, having defined a front as a K.E. realization above the 50th percentile of the overall (1987–2017) time varying surface K.E., we count how many times a front appears in the considered time slot at each pixel. In Figure S7 pixels are colored according to the number of occurrences in each time slot. The result is consistent with the domain fragmentation evolution. The higher fragmentation occurring in timeslots from 2001 to 2010 in the eastern basin is associated with more frequent fronts. Similar considerations hold for the other sub-basins, clearly distinguishing low energy periods from higher ones.Mediterranean ecoregions connectivity networksChanges in functional networks or connectivity among ecoregions can be assessed by comparing a network from each energy period (UP: 1987–1993, MAX: 2004–2010 and DOWN: 2011–2017) (Fig. 3b,d,f for the eastern basin and Figure S8 in the Supplementary Information for the western basin).In 1987–1993 the western basin was characterized by a high mean positive correlation of 0.73, with a strong, non-directional connectivity among the Tyrrhenian and Ligurian-Algero Provençal domains. In 2004–2010 the connectivity was overall weaker, and in particular reduced among Tyrrhenian waters. The connectivity between the Balearic domain (Bal) and the Tyrrhenian ones was also reduced. In 2011–2017 the connectivity was mostly recovered, especially in Tyrrhenian waters. In this period, the Algero-Provençal domain separated from the Ligurian Sea (Lig), enforcing its connectivity with the Balearic and the Alboran ecoregions.In the eastern basin we focus our attention on the ecoregion immediately offshore the Suez Canal (Fig. 3), the major anthropogenic corridor for the introduction of non-indigenous marine species in the Mediterranean Sea, the so-called Lessepsian immigrants32. According to δ-MAPS, connectivity from the domain surrounding Suez was high in the first decade, decreased approaching MAX, remained small until about 2010–2011 with fewer statistically significant links, and increased again in the more recent time slot considered. During the UP and DOWN periods, the strongest connections were with the eastern Levantine (domain N), followed by that with the Aegean, Ionian and Tunisian Seas. During UP the connectivity extended to the Provençal and Algerian Seas, in the western basin, while in DOWN these links were absent and replaced by a connection with the Adriatic Sea.The 1987–1993 and 2011–2017 periods, while not too dissimilar in energy levels, differed indeed for the phase of the Ionian-Adriatic Bimodal Oscillating System or BiOS33,34. The BiOS is a mode of variability characterized by a decadal reversal of the Northern Ionian Gyre (NIG) from cyclonic to anticyclonic, and vice versa. In its anticyclonic spinning the NIG deviates the inflowing Modified Atlantic Water (MAW) from the Sicily Channel towards the northern Ionian, entering the Adriatic Sea and decreasing its salinity and temperature. This prevents a portion of the MAW from reaching the Levantine basin, and enhances the outflow of Levantine waters into the western basin, along a pathway that follows the African coastline. The anticyclonic NIG co-occurs with higher concentrations of Atlantic and Western Mediterranean organisms in the Adriatic Sea. When the NIG is cyclonic, on the other hand, Levantine waters enter the Adriatic Sea, whereas the MAW preferably flows toward the Levantine35 and Lessepsian migrations influence the Adriatic Sea at various latitudes, affecting also phytoplankton phenology33,36,37. The corresponding regions and connectivity networks in the two opposite NIG periods are detailed in Figure S9 in the Supplementary Information. More

1.Becker, G. S. & Lewis, H. G. On the interaction between the quantity and quality of children. Journal of Political Economy 81(2, pt2), s279–s288 (1973).Article 

Google Scholar 
2.Bulatao, R. A. & Lee, R. D. Determinants of Fertility in Developing Countries (Academic Press, 1983).
Google Scholar 
3.Caldwell, J. C. The mechanisms of demographic change in historical perspective. Popul. Stud. 35(1), 1–27 (1981).Article 

Google Scholar 
4.Carlsson, G. The decline of fertility: innovation or adjustment process. Popul. Stud. 20(2), 149–174 (1966).CAS 
Article 

Google Scholar 
5.Easterlin, R. A. & Crimmins, E. M. The Fertility Revolution (University of Chicago Press, 1985).
Google Scholar 
6.Winterhalder, B. & Leslie, P. Risk-sensitive fertility: The variance compensation hypothesis. Evol. Hum. Behav. 23(1), 59–82. https://doi.org/10.1016/S1090-5138(01)00089-7 (2002).Article 

Google Scholar 
7.Sear, R., Lawson, D. W., Kaplan, H. & Shenk, M. K. Understanding Variation in Human Fertility: What Can We Learn from Evolutionary Demography? (The Royal Society, 2016).
Google Scholar 
8.Wood, J. W. Dynamics of Human Reproduction (Aldine de Gruyter, 1994).
Google Scholar 
9.Hruschka, D. J. & Burger, O. How does variance in fertility change over the demographic transition?. Philos. Trans. R. Soc. B 371, 20150155. https://doi.org/10.1098/rstb.2015.0155 (2016).Article 

Google Scholar 
10.Henry, L. Some data on natural fertility. Eugen. Q. 8(2), 81–91 (1961).CAS 
PubMed 
Article 

Google Scholar 
11.Campbell, K. L. & Wood, J. W. Fertility in traditional societies. In Natural Human Fertility Social and Biological Determinants (eds Diggory, P. et al.) 39–61 (MacMillan Press, 1988).
Google Scholar 
12.Ellison, P. T. On Fertile Ground (Harvard University Press, 2001).
Google Scholar 
13.Colleran, H., Jasienska, G., Nenko, I., Galbarczyk, A. & Mace, R. 2015 Fertility decline and the changing dynamics of wealth, status and inequality. Proc. R. Soc. B: Biol. Sci. 282, 20150287 (1806).Article 

Google Scholar 
14.Colleran, H. The cultural evolution of fertility decline. Philos. Trans. R. Soc. B: Biol. Sci. 371(1692), 20150152 (2016).Article 

Google Scholar 
15.Knight, F. H. Risk, Uncertainty, and Profit (Houghton Mifflin, 1921).
Google Scholar 
16.Ellison, P. T. Energetics and reproductive effort. Am. J. Hum. Biol. 15, 342–351 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Jasienska, G. & Ellison, P. Energetic factors and seasonal changes in ovarian function in women from rural Poland. Am. J. Hum. Biol. 16, 563–580 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Jasienska, G. & Ellison, P. T. Physical work causes suppression of ovarian function in women. Proc. R. Soc. Lond. B 265, 1847–1851 (1998).CAS 
Article 

Google Scholar 
19.Kramer, K. L. & McMillan, G. P. The effect of labor saving technology on longitudinal fertility changes. Curr. Anthropol. 47(1), 165–172 (2006).Article 

Google Scholar 
20.Panter-Brick, C. Lactation, birth spacing and maternal workloads among two cases in rural Nepal. J. Biosoc. Sci. 23, 137–154 (1991).CAS 
PubMed 
Article 

Google Scholar 
21.Sear, R., Steele, F., McGregor, I. A. & Mace, R. The effects of kin on child mortality in Gambia. Demography 39(1), 43–63 (2002).PubMed 
Article 

Google Scholar 
22.Valeggia, C. R. & Ellison, P. T. Lactation, energetics, and postpartum fecundity. In Reproductive Ecology and Human Evolution (ed. Ellison, P. T.) 85–105 (Aldine de Gruyter, 2001).
Google Scholar 
23.Gibson, M. & Mace, R. An energy-saving development initiative increases birth rate and childhood malnutrition in rural Ethiopia. PloS Med. 3, 476–484 (2006).Article 

Google Scholar 
24.Kramer, K. L. & McMillan, G. P. Women’s labor, fertility, and the introduction of modern technology in a rural Maya village. J. Anthropol. Res. 55(4), 499–520 (1999).CAS 
PubMed 
Article 

Google Scholar 
25.Low, B., Simon, C. & Anderson, K. An evolutionary ecological perspective on demographic transitions: modeling multiple currencies. Am. J. Hum. Biol. 14(2), 149–167 (2002).PubMed 
Article 

Google Scholar 
26.Low, B. S., Simon, C. S. & Anderson, K. G. The biodemography of modern women: tradeoffs when resources become limiting. In The Biodemography of Human Reproduction and Fertility (ed. Rodgers, J. L.) 105–134 (Kuwer Academic Publishers, 2003).
Google Scholar 
27.Dyson, T. & Murphy, M. The onset of fertility transition. Popul. Dev. Rev. 11(3), 399–440 (1985).Article 

Google Scholar 
28.Early, J. & Headland, T. N. Population Dynamics of a Philippine Rain Forest People (University of Florida Press, 1998).
Google Scholar 
29.Hill, K. & Hurtado, A. M. Ache Life History (Aldine de Gruyter, 1996).
Google Scholar 
30.Kramer, K. L. & Greaves, R. D. Changing patterns of infant mortality and fertility among Pumé foragers and horticulturalists. Am. Anthropol. 109(4), 713–726 (2007).Article 

Google Scholar 
31.Goldstein, J. R. & Klüsener, S. Spatial analysis of the causes of fertility decline in Prussia. Popul. Dev. Rev. 40(3), 497–525 (2014).Article 

Google Scholar 
32.Montgomery, M. R. & Casterline, J. B. The diffusion of fertility control in Taiwan: Evidence from pooled cross-section time-series models. Popul. Stud. 47(3), 457–479 (1993).CAS 
Article 

Google Scholar 
33.Schmertmann, C. P., Assunção, R. M. & Potter, J. E. Knox meets Cox: Adapting epidemiological space-time statistics to demographic studies. Demography 47(3), 629–650 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Galloway, P. R., Hammel, E. A. & Lee, R. D. Fertility decline in Prussia, 1875–1910: A pooled cross-section time series analysis. Popul. Stud. 48(1), 135–158 (1994).CAS 
Article 

Google Scholar 
35.Schmertmann C. P., Potter J. E. & Assunção R. M. 2011 An innovative methodology for space-time analysis with an application to the 1960–2000 Brazilian mortality transition. In Navigating Time and Space in Population Studies 19–36 (Dordrecht, 2011).36.Bongaarts, J., Cleland, J., Townsend, J. W., Bertrand, J. T. & Gupta, M. D. Family Planning Programs for the 21st Century (Population Council, 2012).
Google Scholar 
37.Casterline J. B. Diffusion processes and fertility transition: Introduction. In Diffusion processes and fertility transition: Selected perspectives (ed. Population N.R.C.C.o.) (National Academies Press, US, 2011).38.Cleland, J. The effects of improved survival on fertility: A reassessment. In Global Fertility Transitions Population and Development Review Supplement to Vol 27 (eds. Bulatao R.A., Casterline J.B.) (Population Council, 2001).39.Cleland, J. & Wilson, C. Demand theories of the fertility transition: an iconoclastic view. Popul. Stud. 41(1), 5–30 (1987).Article 

Google Scholar 
40.Montgomery, M. R. & Casterline, J. B. Social learning, social influence, and new models of fertility. Popul. Dev. Rev. 22, 151–175 (1996).Article 

Google Scholar 
41.Sear, R. Evolutionary contributions to the study of human fertility. Popul. Stud. 69(sup1), S39–S55 (2015).Article 

Google Scholar 
42.Knodel, J. & Van de Walle, E. Lessons from the past: Policy implications of historical fertility studies. Popul. Dev. Rev. 5(2), 217–245 (1979).Article 

Google Scholar 
43.Watkins, S. C. From local to national communities: The transformation of demographic regimes in Western Europe, 1870–1960. Popul. Dev. Rev. 16(2), 241–272 (1990).Article 

Google Scholar 
44.Bongaarts, J. & Watkins, S. C. Social interactions and contemporary fertility transitions. Popul. Dev. Rev. 22(4), 639–682 (1996).Article 

Google Scholar 
45.Boyd, R. & Richerson, P. Culture and the Evolutionary Process (Univ. Press, 1985).
Google Scholar 
46.Cavalli-Sforza, L. L. & Feldman, M. W. Cultural Transmission and Evolution: A Quantitative Approach (Princeton University Press, 1981).MATH 

Google Scholar 
47.Colleran, H., Jasienska, G., Nenko, I., Galbarczyk, A. & Mace, R. Community-level education accelerates the cultural evolution of fertility decline. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2013.2732 (2014).Article 

Google Scholar 
48.Conrad, C., Lechner, M. & Werner, W. East German fertility after unification: crisis or adaptation?. Popul. Dev. Rev. 22(2), 331–358. https://doi.org/10.2307/2137438 (1996).Article 

Google Scholar 
49.Easterlin, R. A. Towards a socio-economic theory of fertility: a survey of recent research on economic factors in American fertility. In Fertility and Family Planning: A World View (ed. Behrman, S. J.) 127–156 (University of Michigan Press, 1969).
Google Scholar 
50.Easterlin, R. A. An economic framework for fertility analysis. Stud. Fam. Plann. 6, 54–63 (1975).CAS 
PubMed 
Article 

Google Scholar 
51.Galloway, P. R., Lee, R. D. & Hammel, E. A. Infant mortality and the fertility transition: Macro evidence from Europe and new findings from Prussia. In From Death to Birth Mortality Decline and Reproductive Change (eds Montgomery, M. R. & Cohen, B.) 182–226 (National Academy Press, 1998).
Google Scholar 
52.Kaplan, H. A theory of fertility and parental investment in traditional and modern human societies. Yearb. Phys. Anthropol. 39, 91–135 (1996).Article 

Google Scholar 
53.Lee, R. D. & Bulatao, R. A. The demand for children: a critical essay. In Determinants of Fertility in Developing Countries (eds Bulatao, R. A. & Lee, R. D.) 233–287 (Academic Press, 1983).
Google Scholar 
54.Lesthaeghe R. & Wilson C. Modes of production secularization and the pace of the fertility decline in Western Europe 1870–1930 (1986).55.Turke, P. Evolution and demand for children. Popul. Dev. Rev. 15(1), 61–90 (1989).MathSciNet 
Article 

Google Scholar 
56.Colleran, H. Farming in transition: land and property inheritance in a rural Polish population. Soc. Biol. Hum. Aff. 78, 7–19 (2014).
Google Scholar 
57.González-Bailón, S. & Murphy, T. E. The effects of social interactions on fertility decline in nineteenth-century France: an agent-based simulation experiment. Popul. Stud. 67(2), 135–155 (2013).Article 

Google Scholar 
58.Shenk, M. K., Towner, M. C., Kress, H. C. & Alam, N. A model comparison approach shows stronger support for economic models of fertility decline. Proc. Natl. Acad. Sci. 110(20), 8045–8050. https://doi.org/10.1073/pnas.1217029110 (2013).ADS 
Article 
PubMed 

Google Scholar 
59.Alvergne, A., Gurmu, E., Gibson, M. A. & Mace, R. Social transmission and the spread of modern contraception in rural Ethiopia. PLoS ONE 6, e22515. https://doi.org/10.1371/journal.pone.0022515 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Mace, R. & Colleran, H. Kin influence on the decision to start using modern contraception: a longitudinal study from rural Gambia. Am. J. Hum. Biol. 21, 472–477. https://doi.org/10.1002/ajhb.20940 (2009).Article 
PubMed 

Google Scholar 
61.Montgomery, M., Casterline, J. B. & Heiland, F. Social Networks and the Diffusion of Fertility Control (Population Council, 1998).Book 

Google Scholar 
62.Veile, A. & Kramer, K. L. Pregnancy, birth and babies: motherhood and modernization in a Yucatec village. In Maternal Health, Pregnancy-Related Morbidity and Death among Indigenous Women of Mexico & Central America (ed. Schwartz, D.) 205–224 (Springer, Berlin, 2018).
Google Scholar 
63.Snopkowski, K., Towner, M. C., Shenk, M. K. & Colleran, H. Pathways from education to fertility decline: a multi-site comparative study. Philos. Trans. R. Soc. B: Biol. Sci. 371(1692), 20150156 (2016).Article 

Google Scholar 
64.Schultz T. P. The Fertility Transition: Economic Explanations. Economic Growth Center Discussion Paper No. 833. Available at SSRN: https://ssrn.com/abstract=286291 (2001).65.Becker, S. O., Cinnirella, F. & Woessmann, L. Does women’s education affect fertility? Evidence from pre-demographic transition Prussia. Eur. Rev. Econ. Hist. 17(1), 24–44 (2013).Article 

Google Scholar 
66.Gandrud, C. simPH: an R package for illustrating estimates from cox proportional hazard models including for interactive and nonlinear effects. J. Stat. Softw. 65(3), 1–20 (2015).Article 

Google Scholar 
67.Seiber, E. E., Bertrand, J. T. & Sullivan, T. M. Changes in contraceptive method mix in developing countries. Int. Fam. Plan. Perspect. 33(3), 117–123 (2007).PubMed 
Article 

Google Scholar 
68.Leite, I. D. C., Gupta, N. & Rodrigues, R. D. Female sterilization in Latin America: cross-national perspectives. J. Biosoc. Sci. 36(6), 683 (2004).Article 

Google Scholar 
69.Bertrand, J. T., Sullivan, T. M., Knowles, E. A., Zeeshan, M. F. & Shelton, J. D. Contraceptive method skew and shifts in method mix in low-and middle-income countries. Int. Perspect. Sexual Reprod. Health 40(3), 144–153 (2014).Article 

Google Scholar 
70.Leslie, P. & Winterhalder, B. Demographic consequences of unpredictability in fertility outcomes. Am. J. Hum. Biol. 14(2), 168–183 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
71.Gibson, M. & Mace, R. Labor-saving technology and fertility increase in rural Africa. Curr. Anthropol. 43(4), 631–637 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Alvergne, A., Lawson, D. W., Clarke, P. M. R., Gurmu, E. & Mace, R. Fertility, parental investment, and the early adoption of modern contraception in rural Ethiopia. Am. J. Hum. Biol. 25(1), 107–115 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
73.Mace, R., Allal, N., Sear, R. & Prentice, A. M. The uptake of modern contraception in a Gambian community: the diffusion of an innovation over 25 years. In Social Information Transmission and Human Biology (eds Wells, J. C. K. et al.) 191–206 (Taylor & Francis Group, 2006).
Google Scholar 
74.Lerner, I.M. Heredity, evolution and society. San Francisco: W.H. Freeman (1968)75.Lewontin, R. C. & Levins, R. Biology Under the Influence (Monthly Review Press, 2007).
Google Scholar 
76.Donaldson-Matasci, M. C., Lachmann, M. & Bergstrom, C. T. Phenotypic diversity as an adaptation to environmental uncertainty. Evol. Ecol. Res. 10(4), 493–515 (2008).
Google Scholar 
77.Meyers, L. A. & Bull, J. J. Fighting change with change: adaptive variation in an uncertain world. Trends Ecol. Evol. 17(12), 551–557 (2002).Article 

Google Scholar 
78.Sermonti, G. The butterfly and the lion. In Organisms, Genes and Evolution: Evolutionary Theory at the Crossroads; Proceedings of the 7th International Senckenberg Conference (eds Peters, S. T. & Weingarten, M.) 103 (Franz Steiner Verlag, 2000).
Google Scholar 
79.Boone, J. L. & Kessler, K. L. More status or more children? Social status, fertility reduction and long-term fitness. Evol. Hum. Behav. 20, 257–277 (1999).Article 

Google Scholar 
80.Nolin, D. A. & Ziker, J. P. Reproductive responses to economic uncertainty. Hum. Nat. 27(4), 351–371 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
81.Jensen, R. The (perceived) returns to education and the demand for schooling. Q. J. Econ. 125(2), 515–548 (2010).Article 

Google Scholar 
82.Borgerhoff, M. M. The demographic transition: are we any closer to an evolutionary explanation?. Trends Ecol. Evol. 13, 266–270 (1998).Article 

Google Scholar 
83.Skirbekk, V. Fertility trends by social status. Demogr. Res. 18, 145–180 (2008).Article 

Google Scholar 
84.Vining, D. R. J. Social verses reproductive success: the central theoretical problem of human sociobiology. Behav. Brain Sci. 9(167), 216 (1986).
Google Scholar 
85.Kramer, K. L. Maya Children: Helpers at the Farm (Harvard University Press, 2005).
Google Scholar 
86.Kramer, K. L. & Boone, J. L. Why intensive agriculturalists have higher fertility: a household labor budget approach to subsistence intensification and fertility rates. Curr. Anthropol. 43(3), 511–517 (2002).Article 

Google Scholar 
87.Lee, R. D. & Kramer, K. L. Children’s economic roles in the Maya family life cycle: Cain, Caldwell and Chayanov revisited. Popul. Dev. Rev. 28(3), 475–499 (2002).Article 

Google Scholar 
88.Kramer, K. L. Reconsidering the cost of childbearing: the timing of children’s helping behavior across the life cycle of Maya families. In SocioEconomic Aspects of Human Behavioral Ecology (ed. Alvard, M.) 335–353 (Elsevier, 2004).
Google Scholar 
89.Kramer, K. L., Veile, A. & Otárola-Castillo, E. Sibling competition, growth tradeoffs. Biological vs. statistical significance. PLoS ONE 11(3), e0150126. https://doi.org/10.1371/journal.pone.0150126 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
90.Veile, A. & Kramer, K. L. Shifting weanling’s optimum: breastfeeding ecology and infant health in Yucatan. In Anthropology and Breastfeeding (eds Tomori, C. et al.) Chapter 12 (Routledge Press, 2018).
Google Scholar 
91.Feltz, C. J. & Miller, G. E. An asymptotic test for the equality of coefficients of variation from k populations. Stat. Med. 15(6), 647–658 (1996).Article 

Google Scholar 
92.Marwick, B. & Krishnamoorthy. K. Cvequality: Tests for the Equality of Coefficients of Variation from Multiple Groups. R software package version 0.1.3 (2019). Retrieved from https://github.com/benmarwick/cvequality, on 05/01/2019.93.Cahoy, D. O. A bootstrap test for equality of variances. Comput. Stat. Data Anal. 54(10), 2306–2316 (2010).MathSciNet 
MATH 
Article 

Google Scholar 
94.Therneau, T. A Package for Survival Analysis in S. version 2.38 (2015), https://CRAN.R-project.org/package=survival. More