Ecology

1.
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring along the Great Barrier Reef following mass coral bleaching. Nature 560, 92–96 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Bruno, J. F., Côté, I. M. & Toth, L. T. Climate change, coral loss, and the curious case of the parrotfish paradigm: Why don’t marine protected areas improve reef resilience?. Ann. Rev. Mar. Sci. 11, 307–334 (2019).
PubMed  Article  Google Scholar 

4.
Jouffray, J.-B. et al. Identifying multiple coral reef regimes and their drivers across the Hawaiian archipelago. Philos. Trans. R. Soc. B 370, 20130268 (2015).
Article  Google Scholar 

5.
Holbrook, S. J., Schmitt, R. J., Adam, T. C. & Brooks, A. J. Coral reef resilience, tipping points and the strength of herbivory. Sci. Rep. 6, 35817 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Smith, J. E. et al. Re-evaluating the health of coral reef communities: baselines and evidence for human impacts across the central Pacific. Proc. R. Soc. B 283, 20151985 (2016).
PubMed  Article  CAS  Google Scholar 

7.
Bellwood, D. R. et al. Coral reef conservation in the Anthropocene: confronting spatial mismatches and prioritizing functions. Biol. Conserv. 236, 604–615 (2019).
Article  Google Scholar 

8.
Liao, Z., Yu, K., Wang, Y., Huang, X. & Xu, L. Coral–algal interactions at Weizhou Island in the northern South China Sea: variations by taxa and the exacerbating impact of sediments trapped in turf algae. PeerJ 7, e6590 (2019).
PubMed  PubMed Central  Article  Google Scholar 

9.
McCook, L. Competition between corals and algal turfs along a gradient of terrestrial influence in the nearshore central Great Barrier Reef. Coral Reefs 19, 419–425 (2001).
ADS  Article  Google Scholar 

10.
McCook, L. J., Jompa, J. & Diaz-Pulido, G. Competition between corals and algae on coral reefs: a review of evidence and mechanisms. Coral Reefs 19, 400–417 (2001).
ADS  Article  Google Scholar 

11.
Vermeij, M. J. A. et al. The effects of nutrient enrichment and herbivore abundance on the ability of turf algae to overgrow coral in the Caribbean. PLoS ONE 5, e14312 (2010).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Cheal, A. J. et al. Coral–macroalgal phase shifts or reef resilience: links with diversity and functional roles of herbivorous fishes on the Great Barrier Reef. Coral Reefs 29, 1005–1015 (2010).
ADS  Article  Google Scholar 

13.
Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

14.
Ceccarelli, D. M., Jones, G. P. & McCook, L. J. Territorial damselfishes as determinants of the structure of benthic communities on coral reefs. Oceanogr. Mar. Biol. Annu. Rev. 39, 355–389 (2001).
Google Scholar 

15.
Hata, H. & Ceccarelli, D. Farming behaviour of territorial damselfishes. In Biology of damselfishes (eds Frédérich, B. & Parmentier, E.) 122–152 (CRC Press, Boca Raton, 2016).
Google Scholar 

16.
Emslie, M. J. et al. Regional-scale variation in the distribution and abundance of farming damselfishes on Australia’s Great Barrier Reef. Mar. Biol. 159, 1293–1304 (2012).
Article  Google Scholar 

17.
Glaser, M. et al. Breaking resilience for a sustainable future: thoughts for the Anthropocene. Front. Mar. Sci. 5, 34 (2018).
Article  Google Scholar 

18.
Randazzo Eisemann, Á, Montero Muñoz, J. L., McField, M., Myton, J. & Arias-González, J. E. The effect of algal-gardening damselfish on the resilience of the Mesoamerican Reef. Front. Mar. Sci. 6, 414 (2019).
Article  Google Scholar 

19.
Ceccarelli, D. M. et al. Long-term dynamics and drivers of coral and macroalgal cover on inshore reefs of the Great Barrier Reef Marine Park. Ecol. Appl. 30, e02008 (2020).
PubMed  Article  Google Scholar 

20.
Kaufman, L. The three spot damselfish: effects on benthic biota of Caribbean coral reefs. InProceedings of the Third International Coral Reef Symposium1, 559–564 (1977).

21.
Kaufman, L. There was biological disturbance on Pleistocene coral reefs. Paleobiology 7, 527–532 (1981).
Article  Google Scholar 

22.
Jones, G. P., Santana, L., McCook, L. J. & McCormick, M. I. Resource use and impact of three herbivorous damselfishes on coral reef communities. Mar. Ecol. Prog. Ser. 328, 215–224 (2006).
ADS  Article  Google Scholar 

23.
Raymundo, L. J. et al. Coral Disease Handbook: Guidelines for Assessment, Monitoring & Management. (Coral Reef Targeted Research and Capacity Building for Management Program, 2008).

24.
Bruckner, A. & Bruckner, R. Mechanical lesions and corallivory. In Diseases of Coral (eds Woodley, C. et al.) 242–265 (Wiley, New York, 2016).
Google Scholar 

25.
Hata, H. & Kato, M. Monoculture and mixed-species algal farms on a coral reef are maintained through intensive and extensive management by damselfishes. J. Exp. Mar. Biol. Ecol. 313, 285–296 (2004).
Article  Google Scholar 

26.
Hata, H. & Kato, M. Weeding by the herbivorous damselfish Stegastes nigricans in nearly monocultural algae farms. Mar. Ecol. Prog. Ser. 237, 227–231 (2002).
ADS  Article  Google Scholar 

27.
Hata, H. & Kato, M. Demise of monocultural algal farms by exclusion of territorial damselfish. Mar. Ecol. Prog. Ser. 263, 159–167 (2003).
ADS  Article  Google Scholar 

28.
Pruitt, J. N. et al. Collective aggressiveness of an ecosystem engineer is associated with coral recovery. Behav. Ecol. 29, 1216–1224 (2018).
Google Scholar 

29.
Kamath, A. et al. Potential feedback between coral presence and farmerfish collective behavior promotes coral recovery. Oikos 128, 482–492 (2019).
Article  Google Scholar 

30.
Gochfeld, D. J. Territorial damselfishes facilitate survival of corals by providing an associational defense against predators. Mar. Ecol. Prog. Ser. 398, 137–148 (2010).
ADS  Article  Google Scholar 

31.
Biodiversity Center of Japan. Monitoring Sites 1000 Annual Reports: the Coral Reefs. (Biodiversity Center of Japan, 2019).

32.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018).
Google Scholar 

33.
Titlyanov, E. A., Titlyanova, T. V., Yakovleva, I. M. & Sergeeva, O. S. Three stages of injuries regeneration on scleractinian corals. Galaxea 8, 39–50 (2006).
Article  Google Scholar 

34.
Titlyanov, E. A. & Titlyanova, T. V. Coral–algal competition on damaged reefs. Russ. J. Mar. Biol. 34, 199–219 (2008).
Article  Google Scholar 

35.
Titlyanov, E. A., Titlyanova, T. V. & Chapman, D. J. Dynamics and patterns of algal colonization on mechanically damaged and dead colonies of the coral Porites lutea. Bot. Mar. 51, 285–296 (2008).
Article  Google Scholar 

36.
White, J. S. & O’Donnell, J. L. Indirect effects of a key ecosystem engineer alter survival and growth of foundation coral species. Ecology 91, 3538–3548 (2010).
PubMed  Article  Google Scholar 

37.
Gleason, M. G. Coral recruitment in Moorea, French Polynesia: the importance of patch type and temporal variation. J. Exp. Mar. Biol. Ecol. 207, 79–101 (1996).
Article  Google Scholar 

38.
Ceccarelli, D. M. Modification of benthic communities by territorial damselfish: a multi-species comparison. Coral Reefs 26, 853–866 (2007).
ADS  Article  Google Scholar 

39.
Letourneur, Y., Harmelin-Vivien, M. & Galzin, R. Impact of Hurricane Firinga on fish community structure on fringing reefs of Reunion Island SW Indian Ocean. Environ. Biol. Fishes 37, 109–120 (1993).
Article  Google Scholar 

40.
Sammarco, P. W. & Carleton, J. H. Damselfish territoriality and coral community structure: reduced grazing, coral recruitment, and effects on coral spat. In Proceedings of the 4h International Coral Reef Symposium2, 525–535 (1981).

41.
Letourneur, Y. Spatial and temporal variability in territoriality of a tropical benthic damselfish on a coral reef (Réunion island). Environ. Biol. Fishes 57, 377–391 (2000).
Article  Google Scholar 

42.
Klumpp, D. W., McKinnon, D. & Daniel, P. Damselfish territories: zones of high productivity on coral reefs. Mar. Ecol. Prog. Ser. 40, 41–51 (1987).
ADS  Article  Google Scholar 

43.
Letourneur, Y., Galzin, R. & Harmelin-Vivien, M. Temporal variations in the diet of the damselfish Stegastes nigricans (Lacépède) on a Réunion fringing reef. J. Exp. Mar. Biol. Ecol. 217, 1–18 (1997).
Article  Google Scholar 

44.
Schopmeyer, S. A. & Lirman, D. Occupation dynamics and impacts of damselfish territoriality on recovering populations of the threatened staghorn coral, Acropora cervicornis. PLoS ONE 10, e0141302 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Sammarco, P. W., Risk, M. J. & Rose, C. Effects of grazing and damselfish territoriality on intertidal bioerosion of dead corals: indirect effects. J. Exp. Mar. Biol. Ecol. 112, 185–199 (1987).
Article  Google Scholar 

46.
Strömberg, H. & Kvarnemo, C. Effects of territorial damselfish on cryptic bioeroding organisms on dead Acropora formosa. J. Exp. Mar. Biol. Ecol. 327, 91–102 (2005).
Article  Google Scholar  More

1.
Greenfield, H. J. The secondary products revolution: The past, the present and the future. World Archaeol. 42, 29–54 (2010).
Google Scholar 
2.
Marciniak, A. The secondary products revolution: Empirical evidence and its current zooarchaeological critique. J. World Prehist. 24, 117–130 (2011).
Google Scholar 

3.
Sherratt, A. The secondary exploitation of animals in the Old World. World Archaeol. 15, 90–104 (1983).
Google Scholar 

4.
Patel, A. K. & Meadow, R. H. South Asian contribution to animal domestication and pastoralism: Bones, genes and archaeology. In The Oxford Handbook of Zooarchaeology (eds Albarella, U. et al.) 1–27 (Oxford University Press, Oxford, 2017). https://doi.org/10.1093/oxfordhb/9780199686476.001.0001.
Google Scholar 

5.
Meadow, R. H. & Patel, A. K. Prehistoric pastoralism in northwestern South Asia from the Neolithic through the Harappan period. In Indus Ethnobiology: New perspective from the field (eds Weber, S. A. & Belcher, W. R.) 65–94 (Lexingtion Books, Lanham, 2003).
Google Scholar 

6.
Wright, R. P. The Ancient Indus: Urbanism, Economy, and Society (Cambridge University Press, Cambridge, 2010).
Google Scholar 

7.
Fuller, D. Q. Indus and non-Indus agricultural traditions: Local developments and crop adoptions on the Indian peninsula. In Indus Ethnobiology (eds Weber, S. & Belcher, W. R.) 243–396 (Lexington Books, Lanham, 2003).
Google Scholar 

8.
Meadow, R. H. Pre- and Proto-Historic agriculture and pastoral transformations in northwestern and South Asia. Rev. Archaeol. 19, 12–21 (1998).
Google Scholar 

9.
Pokharia, A. K. et al. Archaeobotany and archaeology at Kanmer, a Harappan site in Kachchh, Gujarat: Evidence for adaptation in response to climatic variability. Curr. Sci. 100, 1833–1846 (2011).
Google Scholar 

10.
Rissman, P. C. Migratory Pastoralism in Western India in the Second Millennium B.C.: The Evidence from Oriyo Timbo (Chiroda). (University of Microfilms International, 1985).

11.
Weber, S., Kashyap, A. & Harriman, D. Does size matter: The role and significance of cereal grains in the Indus Civilization. Archaeol. Anthropol. Sci. 2, 35–43 (2010).
Google Scholar 

12.
Weber, S. A. Plants and Harappan subsistence: An example of stability and change from Rojdi (Oxford & IBH and American Institute of Indian Studies, Oxford, 1991).
Google Scholar 

13.
Goyal, P. et al. Subsistence system, paleoecology, and 14C chronology at Kanmer, a Harappan site in Gujarat India. Radiocarbon 55, 141–150 (2013).
CAS  Google Scholar 

14.
Pokharia, A. K., Kharakwal, J. S. & Srivastava, A. Archaeobotanical evidence of millets in the Indian subcontinent with some observations on their role in the Indus Civilization. J. Archaeol. Sci. 42, 442–455 (2014).
Google Scholar 

15.
Pokharia, A. K. et al. Altered cropping pattern and cultural continuation with declined prosperity following abrupt and extreme arid event at ~4,200 yrs BP: Evidence from an Indus archaeological site Khirsara, Gujarat, western India. PLoS ONE 12, 1–17 (2017).
Google Scholar 

16.
Chase, B. Social change at the Harappan settlement of Gola Dhoro: A reading from animal bones. Antiquity 84, 528–543 (2010).
Google Scholar 

17.
Chase, B. On the pastoral economies of Harappan Gujarat: Faunal analyses at Shikarpur in context. Herit. J. Multidiscip. Stud. Archaeol. 2, 1–22 (2014).
Google Scholar 

18.
Chase, B., Ajithprasad, P., Rajesh, S. V., Patel, A. & Sharma, B. Materializing Harappan identities: Unity and diversity in the borderlands of the Indus Civilization. J. Anthropol. Archaeol. 35, 63–78 (2014).
Google Scholar 

19.
Belcher, W. R. Marine exploitation in the Third Millennium BC- The eastern coast of Pakistan. Paleorient 31, 79–85 (2005).
Google Scholar 

20.
Belcher, W. R. Fish exploitation of the Indus Valley Tradition. In Indus Ethnobiology 95–174 (Lexington Books, Lanham, 2003).
Google Scholar 

21.
Chase, B., Meiggs, D., Ajithprasad, P. & Slater, P. A. What is left behind: Advancing interpretation of pastoral land-use in Harappan Gujarat using herbivore dung to examine bioshphere strontium isotope (87Sr/86Sr) variation. J. Archaeol. Sci. 92, 1–12 (2018).
Google Scholar 

22.
Chase, B., Meiggs, D., Ajithprasad, P. & Slater, P. A. Pastoral land-use of the Indus Civilization in Gujarat: Faunal analyses and biogenic isotopes at Bagasra. J. Archaeol. Sci. 50, 1–15 (2014).
Google Scholar 

23.
Miller, L. J. Secondary products and urbanism in South Asia: The evidence for traction at Harappa. In Indus Ethnobiology: New perspective from the field (eds Weber, S. A. & Belcher, W. R.) 251–326 (Lexington Books, Lanham, 2003).
Google Scholar 

24.
Bourgeois, G. & Gouin, P. Résultats D ’ une analyse de traces organiques fossiles dans une ‘Faisselle’ Harappéenne. Paléorient 21, 125–128 (1995).
Google Scholar 

25.
Evershed, R. P., Dudd, S. N., Copley, M. S. & Mukherjee, A. Identification of animal fats via compound specific δ13C values of individual fatty acids: Assessments of results for reference fats and lipid extracts of archaeological pottery vessels. Doc. Praehist. 29, 73–96 (2002).
Google Scholar 

26.
Copley, M. S., Berstan, R., Straker, V., Payne, S. & Evershed, R. P. Dairying in antiquity. II. Evidence from absorbed lipid residues dating to the British Bronze Age. J. Archaeol. Sci. 32, 505–521 (2005).
Google Scholar 

27.
Spangenberg, J. E., Jacomet, S. & Schibler, J. Chemical analyses of organic residues in archaeological pottery from Arbon Bleiche 3, Switzerland—evidence for dairying in the late Neolithic. J. Archaeol. Sci. 33, 1–13 (2006).
Google Scholar 

28.
Craig, O. E. et al. Stable isotope analysis of Late Upper Palaeolithic human and faunal remains from Grotta del Romito (Cosenza) Italy. J. Archaeol. Sci. 37, 2504–2512 (2010).
Google Scholar 

29.
Craig, O. E. et al. Earliest evidence for the use of pottery. Nature 496, 351–354 (2013).
CAS  PubMed  ADS  Google Scholar 

30.
Craig, O. E., Taylor, G., Mulville, J., Collins, M. J. & Parker, P. M. The identification of prehistoric dairying activities in the Western Isles of Scotland: an integrated biomolecular approach. J. Archaeol. Sci. 32, 91–103 (2005).
Google Scholar 

31.
Evershed, R. P. Organic residue analysis in archaeology: The archaeological biomarker revolution. Archaeometry 50, 895–924 (2008).
CAS  Google Scholar 

32.
Craig, O. E., Love, G. D., Isaksson, S., Taylor, G. & Snape, C. E. Stable carbon isotopic characterisation of free and bound lipid constituents of archaeological ceramic vessels released by solvent extraction, alkaline hydrolysis and catalytic hydropyrolysis. J. Anal. Appl. Pyrolysis 71, 613–634 (2004).
CAS  Google Scholar 

33.
Dudd, S. N. & Evershed, R. P. Direct demonstration of milk as an element of archaeological economies. Science 282, 1478–1481 (1998).
CAS  PubMed  Google Scholar 

34.
Buonasera, T. Y., Tremayne, A. H., Darwent, C. M., Eerkens, J. W. & Mason, O. K. Lipid biomarkers and compound specific δ13C analysis indicate early development of a dual-economic system for the Arctic Small Tool tradition in northern Alaska. J. Archaeol. Sci. 61, 129–138 (2015).
CAS  Google Scholar 

35.
Meier-Augenstein, W. Stable isotope analysis of fatty acids by gas chromatography–isotope ratio mass spectrometry. Anal. Chim. Acta 465, 63–79 (2002).
CAS  Google Scholar 

36.
Mottram, H. R., Dudd, S. N., Lawrence, G. J., Stott, A. W. & Evershed, R. P. New chromatographic, mass spectrometric and stable isotope approaches to the classification of degraded animal fats preserved in archaeological pottery. J. Chromatogr. A 833, 209–221 (1999).
CAS  Google Scholar 

37.
Gregg, M. W., Banning, E. B., Gibbs, K. & Slater, G. F. Subsistence practices and pottery use in Neolithic Jordan: Molecular and isotopic evidence. J. Archaeol. Sci. 36, 937–946 (2009).
Google Scholar 

38.
Copley, M. S. et al. Direct chemical evidence for widespread dairying in prehistoric Britain. Proc. Natl. Acad. Sci. 100, 1524–1529 (2003).
CAS  PubMed  ADS  Google Scholar 

39.
Dunne, J., di Lernia, S., Chłodnicki, M., Kherbouche, F. & Evershed, R. P. Timing and pace of dairying inception and animal husbandry practices across Holocene North Africa. Quat. Int. https://doi.org/10.1016/j.quaint.2017.06.062 (2017).
Article  Google Scholar 

40.
Dunne, J. et al. First dairying in green Saharan Africa in the fifth millennium BC. Nature 486, 390–394 (2012).
CAS  PubMed  ADS  Google Scholar 

41.
Copley, M. S., Clark, K. & Evershed, R. P. Organic-residue analysis of pottery vessels and clay balls. In Changing Materialities at Çatalhoyuk: Reports from the 1995–99 Seasons 169–174 (McDonald Institute for Archaeological Research, Cambridge, 2005).
Google Scholar 

42.
Roffet-Salque, M., Lee, M. R. F., Timpson, A. & Evershed, R. P. Impact of modern cattle feeding practices on milk fatty acid stable carbon isotope compositions emphasise the need for caution in selecting reference animal tissues and products for archaeological investigations. Archaeol. Anthropol. Sci. 9, 1343–1348 (2017).
Google Scholar 

43.
Craig, O. E. et al. Ancient lipids reveal continuity in culinary practices across the transition to agriculture in Northern Europe. Proc. Natl. Acad. Sci. 108, 17910–17915 (2011).
CAS  PubMed  ADS  Google Scholar 

44.
Salque, M. et al. Earliest evidence for cheese making in the sixth millennium BC in northern Europe. Nature 493, 522–525 (2013).
CAS  PubMed  ADS  Google Scholar 

45.
Correa-Ascencio, M., Robertson, I. G., Cabrera-Cortés, O., Cabrera-Castro, R. & Evershed, R. P. Pulque production from fermented agave sap as a dietary supplement in Prehispanic Mesoamerica. Proc. Natl. Acad. Sci. 111, 14223–14228 (2014).
CAS  PubMed  ADS  Google Scholar 

46.
Kimpe, K., Jacobs, P. A. & Waelkens, M. Analysis of oil used in late Roman oil lamps with different mass spectrometric techniques revealed the presence of predominantly olive oil together with traces of animal fat. J. Chromatogr. A 937, 87–95 (2001).
CAS  PubMed  Google Scholar 

47.
Eerkens, J. The preservation and identification of Piñon resins by GC-MS in pottery from the western Great Basin. Archaeometry 44, 95–105 (2002).
CAS  Google Scholar 

48.
Gregg, M. W., Brettell, R. & Stern, B. Bitumen in Neolithic Iran: Biomolecular and isotopic evidence. In Archaeological Chemistry 137–151 (American Chemical Society, Washington, 2007).
Google Scholar 

49.
Lucquin, A., March, R. J. & Cassen, S. Analysis of adhering organic residues of two “coupes-à-socles” from the Neolithic funerary site “La Hougue Bie” in Jersey: evidences of birch bark tar utilisation. J. Archaeol. Sci. 34, 704–710 (2007).
Google Scholar 

50.
Stacey, R., Cartwright, C., Tanimoto, S. & Villing, A. Coatings and contents: Investigations of residues on four fragmentary sixth-century B.C. vessels from Naukratis (Egypt). Br. Museum Tech. Res. Bull. 4, 19–26 (2010).
Google Scholar 

51.
Brecoulaki, H., Andreotti, A., Bonaduce, I., Colombini, M. P. & Lluveras, A. Characterization of organic media in the wall-paintings of the “Palace of Nestor” at Pylos, Greece: Evidence for a secco painting techniques in the Bronze Age. J. Archaeol. Sci. 39, 2866–2876 (2012).
CAS  Google Scholar 

52.
Spades, S. & Russ, J. GC–MS analysis of lipids in prehistoric rock paints and associated oxalate coatings from the Lower Pecos Region, Texas. Archaeometry 47, 115–126 (2005).
CAS  Google Scholar 

53.
Eckmeier, E. & Wiesenberg, G. L. B. Short-chain n-alkanes (C16–20) in ancient soil are useful molecular markers for prehistoric biomass burning. J. Archaeol. Sci. 36, 1590–1596 (2009).
Google Scholar 

54.
Chakraborty, K. S. et al. Enamel isotopic data from the domesticated animals at Kotada Bhadli, Gujarat, reveals specialized animal husbandry during the Indus Civilization. J. Archaeol. Sci. Rep. 21, 2 (2018).
Google Scholar 

55.
Chakraborty, K. S. Subsistence-Based Economy and the Regional Interaction Processes of the Indus Civilization Borderland in Kachchh, Gujarat: A Bio-Molecular Perspective (University of Toronto, Toronto, 2019).
Google Scholar 

56.
Shirvalkar, P. & Rawat, Y. S. Excavation at Kotada Bhadli, District Kachchh, Gujarat: A perliminary report. Puratattva 42, 182–201 (2012).
Google Scholar 

57.
Goyal, P. Observations on faunal remains recovered from Kotada Bhadli. In Excavation at Kotada Bhadli (eds Shirvalkar, P. & Prasad, E.) 136–149 (Archaeological Survey of India, New Delhi, 2020).
Google Scholar 

58.
Joglekar, P. P. & Goyal, P. Faunal remains from Shikarpur, a Harappan site in Gujarat India. Iran. J. Archaeol. Stud. 1, 15–25 (2011).
Google Scholar 

59.
Goyal, P. & Joglekar, P. P. Archaeozoological remains from the site of Kanmer. In Excavation at Kanmer (2005–2006 to 2008–2009): Kanmer archaeological research project an Indo-Japanese collaboration (eds Kharakwal, J. S. et al.) 767–794 (Indus Project Research Institute for Humanity and Nature, Kyoto, 2012).
Google Scholar 

60.
Meadow, R. H. & Patel, A. K. Prehistoric pastoralism in northwestern South Asia from the Neolithic through the Harappan Period. In Indus Ethnobiology: New perspective from the field (eds Weber, S. & Belcher, W. R.) 65–94 (Lexington Books, Lanham, 2003).
Google Scholar 

61.
Patel, A. The Primary Pastoral economy of Dholavira: A first look at animals and urban life in third millennium Kutch. In South Asian Archaeology 1995 (ed. Allchin, B.) 101–113 (The Ancient India and Iran Trust, Cambridge, 1997).
Google Scholar 

62.
Miller, L. J. Urban Economies in Early States: The Secondary Products Revolution in the Indus Civilization (New York University, New York, 2004).
Google Scholar 

63.
Halstead, P. Mortality models and milking: Problems of uniformitarianism, optimality and equifinality reconsidered. Anthropozoologica https://doi.org/10.4319/lo.2013.58.2.0489 (1998).
Article  Google Scholar 

64.
Gillis, R. E. et al. The evolution of dual meat and milk cattle husbandry in Linearbandkeramik societies. Proc. R. Soc. B Biol. Sci. 284, 2 (2017).
Google Scholar 

65.
Sternberg, L. O., Deniro, M. J. & Johnson, H. B. Isotope ratios of cellulose from plants having different photosynthetic pathways. Plant Physiol. 74, 557–561 (1984).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Zhang, C. et al. Diets and environments of late Cenozoic mammals in the Qaidam Basin, Tibetan Plateau: Evidence from stable isotopes. Earth Planet. Sci. Lett. 333–334, 70–82 (2012).
ADS  Google Scholar 

67.
Cerling, T. E. et al. Global vegetation change through the Miocene/Pliocene boundary. Nature 389, 153–158 (1997).
CAS  ADS  Google Scholar 

68.
Sternberg, L. O., Deniro, M. J. & Ting, I. P. Carbon, hydrogen, and oxygen isotope ratios of cellulose from plants having intermediary photosynthetic modes. Plant Physiol. 74, 104–107 (1984).
CAS  PubMed  PubMed Central  Google Scholar 

69.
Smith, B. N. & Epstein, S. Two categories of 13C/12C ratios for higher plants. Plant Physiol. 47, 380–384 (1971).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Correa-Ascencio, M. & Evershed, P. R. High throughput screening of organic residues in archaeological potsherds using direct acidified methanol extraction. Anal. Methods 6, 1330–1340 (2014).
CAS  Google Scholar 

71.
Evershed, R. P., Heron, C. & Goad, L. J. Analysis of organic residues of archaeological origin by high-temperature gas chromatography and gas chromatography-mass spectrometry. Analyst 115, 1339–1342 (1990).
CAS  ADS  Google Scholar 

72.
Papakosta, V., Smittenberg, R. H., Gibbs, K., Jordan, P. & Isaksson, S. Extraction and derivatization of absorbed lipid residues from very small and very old samples of ceramic potsherds for molecular analysis by gas chromatography-mass spectrometry (GC-MS) and single compound stable carbon isotope analysis by gas chromatogra. Microchem. J. 123, 196–200 (2015).
CAS  Google Scholar 

73.
Demirci, Ö, Lucquin, A., Craig, O. E. & Raemaekers, D. C. M. First lipid residue analysis of Early Neolithic pottery from Swifterbant ( the Netherlands, ca 4300–4000 BC ). Archaeol. Anthropol. Sci. https://doi.org/10.1007/s12520-020-01062-w (2020).
Article  Google Scholar 

74.
Carrer, F. et al. Chemical analysis of pottery demonstrates prehistoric origin for high-altitude alpine dairying. PLoS ONE 11, e0151442 (2016).
PubMed  PubMed Central  Google Scholar 

75.
Heron, C. et al. First molecular and isotopic evidence of millet processing in prehistoric pottery vessels. Sci. Rep. 6, 1–9 (2016).
Google Scholar 

76.
Pecci, A. & Cau Ontiveros, M. A. Report on the Analyses of the Organic Residues in Archaeological Samples from the Project ‘Excavating the Roman Peasant’. University of Barcelona (2010).

77.
Gregg, M. W. & Slater, G. F. A new method for extraction, isolation and transesterification of free fatty acids from archaeological pottery. Archaeometry 52, 833–854 (2010).
CAS  Google Scholar 

78.
Copley, M. S. et al. Detection of palm fruit lipids in archaeological pottery from Qasr Ibrim, Egyptian Nubia. Proc. R. Soc. B Biol. Sci. 268, 593–597 (2001).
CAS  Google Scholar 

79.
Evershed, R. P., Copley, M. S., Dickson, L. & Hansel, F. A. Experimental evidence for the processing of marine animal products and other commodities containing polyunsaturated fatty acids in pottery vessels. Archaeometry 50, 101–113 (2008).
CAS  Google Scholar 

80.
Hansel, F. A., Copley, M. S., Madureira, L. A. S. & Evershed, R. P. Thermally produced ω-(o-alkylphenyl)alkanoic acids provide evidence for the processing of marine products in archaeological pottery vessels. Tetrahedron Lett. 45, 2999–3002 (2004).
CAS  Google Scholar 

81.
Meadow, R. H. Prehistoric wild sheep and sheep domestication on the eastern margin of the Middle East. in Animal Domestication and Its Cultural Context (eds. Crabtree, P. J., Campana, D. V. & Ryan, K.) 24–36 (University Museum, Univerity of Pennsylvania, 1989).

82.
Craig, O. E. et al. Distinguishing wild ruminant lipids by gas chromatography/combustion/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 26, 2359–2364 (2012).
CAS  PubMed  ADS  Google Scholar 

83.
Pokharia, A. K. Floral Remains. in Excavation at Kanmer (2005–06 to 2008–09): Kanmer archaeological research project an Indo-Japanese collaboration (eds. Kharakwal, J. S., Rawat, Y. S. & Osada, T.) 795–812 (Indus project, research institute for humanity and Nature, 2012).

84.
Steele, V. J., Stern, B. & Stott, A. W. Olive oil or lard?: distinguishing plant oils from animal fats in the archeological record of the eastern Mediterranean using gas chromatography/combustion/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 3478–3484 (2010).
CAS  PubMed  ADS  Google Scholar 

85.
Spangenberg, J. E. & Ogrinc, N. Authentication of vegetable oils by bulk and molecular carbon isotope analyses with emphasis on olive oil and pumpkin seed oil. J. Agric. Food Chem. 49, 1534–1540 (2001).
CAS  PubMed  Google Scholar 

86.
Hammann, S. & Cramp, L. J. E. Towards the detection of dietary cereal processing through absorbed lipid biomarkers in archaeological pottery. J. Archaeol. Sci. 93, 74–81 (2018).
CAS  Google Scholar 

87.
Colonese, A. C. et al. New criteria for the molecular identification of cereal grains associated with archaeological artefacts. Sci. Rep. 7, 1–7 (2017).
CAS  Google Scholar 

88.
Courel, B. et al. Organic residue analysis shows sub-regional patterns in the use of pottery by Northern European hunter-gatherers. R. Soc. Open Sci. 7, 2 (2020).
Google Scholar 

89.
Hendy, J. et al. Ancient proteins from ceramic vessels at Çatalhöyük West reveal the hidden cuisine of early farmers. Nat. Commun. 9, 2 (2018).
Google Scholar 

90.
Chase, B., Meiggs, D. & Ajithprasad, P. Pastoralism, climate change, and the transformation of the Indus Civilization in Gujarat: Faunal analyses and biogenic isotopes. J. Anthropol. Archaeol. 59, 101173 (2020).
Google Scholar 

91.
Margabandhu, C. Technology of trasport vehicles in early India. In Radiocarbon and Indian Archaeology (eds Agarwal, D. P. & Ghosh, A.) 182–189 (Munshiram Manoharlal Publishers, New Delhi, 1973).
Google Scholar 

92.
Fairservis, W. J. Cattle. Exped. Mag. 28, 43–50 (1986).
Google Scholar 

93.
Chase, B. Family matters in Gujarat. In Walking with the Unicorn: Social Organization and Material Culture in Ancient South Asia (eds Frenez, D. et al.) 90–103 (Archaeopress Publishing LTD, Oxford, 2018).
Google Scholar  More

1.
Makino, S. Biology of Latibulus argiolus (Hymenoptera, Ichneumonidae), a Parasitoid of the Paper Wasp Polistes biglumis (Hymenoptera, Vespidae). Kontyû 51, 426–434 (1983).
Google Scholar 
2.
Oh, S.-H., An, S.-L. & Lee, J.-W. Review of Korean Latibulus (Hymenoptera: Ichneumonidae: Cryptinae) and a key to the world species. Can. Entomol. 144, 509–525 (2012).
Article  Google Scholar 

3.
Quicke, D. L. J. The Braconid and Ichneumonid Parasitoid Wasps: Biology Systematics, Evolution and Ecology (Wiley Blackwell, Amsterdam, 2015).
Google Scholar 

4.
Paukku, S. & Kotiaho, J. S. Female oviposition decisions and their impact on progeny life-history traits. J. Insect Behav. 21, 505–520 (2008).
Article  Google Scholar 

5.
Rusina, L. Y. The role of parasitoids in regulation of Polistes Wasp population (Hymenoptera, Vespidae: Polistinae). Entomol. Rev. 93, 271–280 (2012).
Article  Google Scholar 

6.
Coelho, N. H. P. et al. Understanding genetic diversity, spatial genetic structure, and mating system through microsatellite markers for the conservation and sustainable use of Acrocomia aculeata (Jacq.) Lodd. Ex Mart. Conserv. Genet. 19, 879–891 (2018).
CAS  Article  Google Scholar 

7.
Manlik, O. et al. Demography and genetics suggest reversal of dolphin source-sink dynamics, with implications for conservation. Mar. Mammal Sci. 35, 732–759 (2019).
Article  Google Scholar 

8.
Nowicki, P. et al. What keeps “living dead” alive: demography of a small and isolated population of Maculinea (=Phengaris) alcon. J. Insect Conserv. 23, 201–210 (2019).
Article  Google Scholar 

9.
Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).
Article  Google Scholar 

10.
Olafsson, K., Hjorleifsdottir, S., Pampoulie, C., Hreggvidsson, G. O. & Gudjonsson, S. Novel set of multiplex assays (SalPrint15) for efficient analysis of 15 microsatellite loci of contemporary samples of the Atlantic salmon (Salmo salar). Mol. Ecol. Resour. 10, 533–537 (2010).
CAS  Article  Google Scholar 

11.
Randi, E. et al. Multilocus detection of wolf x dog hybridization in Italy, and guidelines for marker selection. PLoS ONE 9, e86409 (2014).
ADS  Article  Google Scholar 

12.
Hung, C.-M., Yu, A.-Y., Lai, Y.-T. & Shaner, P.-J.L. Developing informative microsatellite markers for nonmodel species using reference mapping against a model species’ genome. Sci. Rep. 6, 23087 (2016).
ADS  CAS  Article  Google Scholar 

13.
Marcus, T., Assmann, T., Durka, W. & Drees, C. A suite of multiplexed microsatellite loci for the ground beetle Abax parallelepipedus (Piller and Mitterpacher, 1783) (Coleoptera, Carabidae). Conserv. Genet. Resour. 5, 1151–1156 (2013).
Article  Google Scholar 

14.
Panagiotopoulou, H., Baca, M., Baca, K., Stanković, A. & Żmihorski, M. Optimization and validation of a multiplex assay for microsatellite loci analysis in the field cricket, Gryllus campestris (Orthoptera: Gryllidae). J. Asia-Pac. Entomol. 18, 421–424 (2015).
CAS  Article  Google Scholar 

15.
PacBio Pacific Biosciences, Procedure & Checklist: 2 kb Template Preparation and Sequencing. https://www.pacb.com/wp-content/uploads/2015/09/Procedure-Checklist-2-kb-Template-Preparation-and-Sequencing.pdf.

16.
Faircloth, B. C. MSATCOMMANDER: detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).
CAS  Article  Google Scholar 

17.
Zhang, Z., Schwartz, S., Wagner, L. & Webb, M. A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 7, 203–214 (2000).
CAS  Article  Google Scholar 

18.
Morgulis, A. et al. Database indexing for production MegaBLAST searches. Bioinformatics 24, 1757–1764 (2008).
CAS  Article  Google Scholar 

19.
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
CAS  Article  Google Scholar 

20.
Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234 (2000).
CAS  Article  Google Scholar 

21.
Austin, J. D. et al. Permanent genetic resources added to Molecular Ecology Resources Database 1 February 2011–31 March 2011. Mol. Ecol. Resour. 11, 757–758 (2011).
CAS  Article  Google Scholar 

22.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).
CAS  Article  Google Scholar 

23.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Article  Google Scholar 

24.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
Article  Google Scholar 

25.
Rousset, F. GENEPOP’007: a complete reimplementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).
Article  Google Scholar 

26.
Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225 (1989).
Article  Google Scholar 

27.
Pompanon, F., Bonin, A., Bellemain, E. & Taberlet, P. Genotyping errors: causes, consequences and solutions. Nat. Rev. Genet. 6, 846–847 (2005).
Article  Google Scholar 

28.
Grohme, M. A., Soler, R. F., Wink, M. & Frohme, M. Microsatellite marker discovery using single molecule real-time circular consensus sequencing on the Pacific Biosciences RS. Biotechniques 55, 253–256 (2013).
CAS  Article  Google Scholar 

29.
Liljegren, M. M., de Muinck, E. J. & Trosvik, P. Microsatellite length scoring by single molecule real time sequencing-effects of sequence structure and PCR regime. PLoS ONE 11, e0159232 (2016).
Article  Google Scholar 

30.
Dutta, N. et al. Microsatellite marker set for genetic diversity assessment of primitive Chitala chitala (Hamilton, 1822) derived through SMRT sequencing technology. Mol. Biol. Rep. 46, 41–49 (2018).
Article  Google Scholar 

31.
Wei, N., Bemmels, J. B. & Dick, C. W. The effects of read length, quality and quantity on microsatellite discovery and primer development: from Illumina to Pac Bio. Mol. Ecol. Resour. 14, 953–965 (2014).
CAS  PubMed  Google Scholar 

32.
Corner, S., Yuzbasiyan-Gurkan, V., Agnew, D. & Venta, P. J. Development of a 12-plex of new microsatellite markers using a novel universal primer method to evaluate the genetic diversity of jaguars (Panthera onca) from North American zoological institutions. Conservation Genet. Resour. 11, 487–497 (2019).
Article  Google Scholar 

33.
Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).
CAS  Article  Google Scholar 

34.
Blondin, L. et al. Characterization and comparison of microsatellite markers derived from genomic and expressed libraries for the desert locust. J. Appl. Entomol. 137, 673–683 (2013).
Article  Google Scholar 

35.
Chistiakov, D. A., Hellemans, B. & Volckaert, F. A. Microsatellites and their genomic distribution, evolution, function and applications: a review with special reference to fish genetics. Aquaculture 255, 1–29 (2006).
CAS  Article  Google Scholar 

36.
Cheng, L., Zhang, Y., Lu, C.-Y., Li, C. & Sun, X.-W. Development and characterization of four moderate multiplex microsatellite panels in crucian carp (Carassius auratus). Conserv. Genet. Resour. 5, 821–823 (2013).
Article  Google Scholar  More

1.
Maddrell, S. & Gardiner, B. Excretion of alkaloids by Malpighian tubules of insects. J. Exp. Biol. 64, 267–281 (1976).
CAS  PubMed  Google Scholar 
2.
Després, L., David, J.-P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).
PubMed  Article  Google Scholar 

3.
Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).
PubMed  Article  CAS  Google Scholar 

4.
Richardson, L. L. et al. Secondary metabolites in floral nectar reduce parasite infections in bumblebees. Proc. R. Soc. B Biol. Sci. 282, 20142471 (2015).
Article  Google Scholar 

5.
Manson, J. S., Otterstatter, M. C. & Thomson, J. D. Consumption of a nectar alkaloid reduces pathogen load in bumble bees. Oecologia 162, 81–89 (2010).
ADS  PubMed  Article  Google Scholar 

6.
Alaux, C. et al. Interactions between Nosema microspores and a neonicotinoid weaken honeybees (Apis mellifera). Environ. Microbiol. 12, 774–782 (2010).
PubMed  PubMed Central  Article  Google Scholar 

7.
Vidau, C. et al. Exposure to sublethal doses of fipronil and thiacloprid highly increases mortality of honeybees previously infected by Nosema ceranae. PLoS ONE 6, e21550 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
McMillan, L. E., Miller, D. W. & Adamo, S. A. Eating when ill is risky: immune defense impairs food detoxification in the caterpillar Manduca sexta. J. Exp. Biol. 221, jeb173336 (2018).
PubMed  Article  Google Scholar 

9.
King, R. L. & Taylor, A. B. Malpighamœba locustae, n. sp. (Amoebidae), a protozoan parasitic in the Malpighian tubes of grasshoppers. Trans. Am. Microsc. Soc. 55, 6–10 (1936).
Article  Google Scholar 

10.
Taylor, A. B. & King, R. L. Further studies on the parasitic amebae found in grasshoppers. Trans. Am. Microsc. Soc. 56, 172–176 (1937).
Article  Google Scholar 

11.
Bailey, L. Honey bee pathology. Annu. Rev. Entomol. 13, 191–212 (1968).
Article  Google Scholar 

12.
Harry, O. G. & Finlayson, L. H. Histopathology of secondary infections of Malpighamoeba locustae (Protozoa, Amoebidae) in the desert locust, Schistocerca gregaria (Orthoptera, Acrididae). J. Invertebr. Pathol. 25, 25–33 (1975).
Article  Google Scholar 

13.
Harry, O. G. & Finlayson, L. H. The life-cycle, ultrastructure and mode of feeding of the locust amoeba Malpighamoeba locustae. Parasitology 72, 127 (1976).
Article  Google Scholar 

14.
Liu, T. P. Scanning electron microscope observations on the pathological changes of Malpighian tubules in the worker honeybee, Apis mellifera, infected by Malpighamoeba mellificae. J. Invertebr. Pathol. 46, 125–132 (1985).
Article  Google Scholar 

15.
Wright, S. H. & Dantzler, W. H. Molecular and cellular physiology of renal organic cation and anion transport. Physiol. Rev. 84, 987–1049 (2004).
CAS  PubMed  Article  Google Scholar 

16.
Gaertner, L. S., Murray, C. L. & Morris, C. E. Transepithelial transport of nicotine and vinblastine in isolated Malpighian tubules of the tobacco hornworm (Manduca sexta) suggests a P-glycoprotein-like mechanism. J. Exp. Biol. 201, 2637–2645 (1998).
CAS  PubMed  Google Scholar 

17.
Rheault, M. R., Plaumann, J. S. & O’Donnell, M. J. Tetraethylammonium and nicotine transport by the Malpighian tubules of insects. J. Insect Physiol. 52, 487–498 (2006).
CAS  PubMed  Article  Google Scholar 

18.
Leader, J. P. & O’Donnell, M. J. Transepithelial transport of fluorescent p-glycoprotein and MRP2 substrates by insect Malpighian tubules: confocal microscopic analysis of secreted fluid droplets. J. Exp. Biol. 208, 4363–4376 (2005).
CAS  PubMed  Article  Google Scholar 

19.
Rossi, M., De Battisti, D. & Niven, J. E. Transepithelial transport of P-glycoprotein substrate by the Malpighian tubules of the desert locust. PLoS ONE 14, e0223569 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Dermauw, W. & Van Leeuwen, T. The ABC gene family in arthropods: comparative genomics and role in insecticide transport and resistance. Insect Biochem. Mol. Biol. 45, 89–110 (2014).
CAS  PubMed  Article  Google Scholar 

21.
Eytan, G. D., Regev, R., Oren, G., Hurwitz, C. D. & Assaraf, Y. G. Efficiency of P-glycoprotein–mediated exclusion of rhodamine dyes from multidrug-resistant cells is determined by their passive transmembrane movement rate. Eur. J. Biochem. 248, 104–112 (1997).
CAS  PubMed  Article  Google Scholar 

22.
Murray, C. L. A P-glycoprotein-like mechanism in the nicotine-resistant insect, Manduca sexta (University of Ottawa, Ottawa, 1996).
Google Scholar 

23.
O’Donnell, M. Insect excretory mechanisms. Adv. Insect Physiol. 35, 1–122 (2008).
Article  Google Scholar 

24.
Berridge, M. J. The physiology of excretion in the cotton stainer, Dysdercus fasciatus, Signoret. IV. Hormonal control of excretion. J. Exp. Biol. 44, 553–566 (1966).
CAS  PubMed  Google Scholar 

25.
Ramsay, J. A. Active transport of water by the Malpighian tubules of the stick insect, Dixippus Morosus (Orthoptera, Phasmidae). J. Exp. Biol. 31, 104–113 (1954).
CAS  Google Scholar 

26.
Maddrell, S. Active transport of water by insect Malpighian tubules. J. Exp. Biol. 207, 894–896 (2004).
PubMed  Article  Google Scholar 

27.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2019). https://www.R-project.org/.

29.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

30.
Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. in Model Selection Multimodel Inference 2nd edn (Springer, New York, 2002).

31.
Maddrell, S. H. P. & O’Donnell, M. J. Insect Malpighian tubules: V-ATPase action in ion and fluid transport. J. Exp. Biol. 172, 417–429 (1992).
CAS  PubMed  Google Scholar 

32.
Wieczorek, H., Beyenbach, K. W., Huss, M. & Vitavska, O. Vacuolar-type proton pumps in insect epithelia. J. Exp. Biol. 212, 1611–1619 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Garrett, M. A., Bradley, T. J., Meredith, J. E. & Phillips, J. E. Ultrastructure of the Malpighian tubules of Schistocerca gregaria. J. Morphol. 195, 313–325 (1988).
PubMed  Article  Google Scholar 

34.
Ugwu, M. C., Oli, A., Esimone, C. O. & Agu, R. U. Organic cation rhodamines for screening organic cation transporters in early stages of drug development. J. Pharmacol. Toxicol. Methods 82, 9–19 (2016).
CAS  PubMed  Article  Google Scholar 

35.
Maddrell, S. H. P., Gardiner, B. O. C., Pilcher, D. E. M. & Reynolds, S. E. Active transport by insect Malpighian tubules of acidic dyes and of acylamides. J. Exp. Biol. 61, 357–377 (1974).
CAS  PubMed  Google Scholar 

36.
Hinks, C. F. & Ewen, A. B. Pathological effects of the parasite Malameba locustae in males of the migratory grasshopper Melanoplus sanguinipes and its interaction with the insecticide, cypermethrin. Entomol. Exp. Appl. 42, 39–44 (1986).
CAS  Article  Google Scholar 

37.
Sreeramulu, K., Liu, R. & Sharom, F. J. Interaction of insecticides with mammalian P-glycoprotein and their effect on its transport function. Biochim. Biophys. Acta BBA Biomembr. 1768, 1750–1757 (2007).
CAS  Article  Google Scholar 

38.
Bernays, E. A. & Chapman, R. F. Plant chemistry and acridoid feeding behaviour. Biochem. Asp. Plant Anim. Coevol. 99, 41 (1978).
Google Scholar 

39.
Habig, W. H., Pabst, M. J. & Jakoby, W. B. Glutathione S-transferases the first enzymatic step in mercapturic acid formation. J. Biol. Chem. 249, 7130–7139 (1974).
CAS  PubMed  PubMed Central  Google Scholar 

40.
Stahlschmidt, Z. R., Acker, M., Kovalko, I. & Adamo, S. A. The double-edged sword of immune defence and damage control: do food availability and immune challenge alter the balance?. Funct. Ecol. 29, 1445–1452 (2015).
Article  Google Scholar 

41.
Jeschke, V., Gershenzon, J. & Vassão, D. G. A mode of action of glucosinolate-derived isothiocyanates: detoxification depletes glutathione and cysteine levels with ramifications on protein metabolism in Spodoptera littoralis. Insect Biochem. Mol. Biol. 71, 37–48 (2016).
CAS  PubMed  Article  Google Scholar 

42.
Phillips, J. E. Rectal absorption in the desert locust, Schistocerca gregaria Forskal. I. Water. J. Exp. Biol. 41, 15–38 (1964).
CAS  PubMed  Google Scholar 

43.
Phillips, J. Comparative physiology of insect renal function. Am. J. Physiol. Regul. Integr. Comp. Physiol. 241, R241–R257 (1981).
CAS  Article  Google Scholar 

44.
Proux, J. Lack of responsiveness of Malpighian tubules to the AVP-like insect diuretic hormone on migratory locusts infected with the protozoan Malameba locustae. J. Invertebr. Pathol. 58, 353–361 (1991).
CAS  Article  Google Scholar 

45.
Phillips, J. E. Rectal absorption in the desert locust, Schistocerca gregaria Forskal. II. Sodium, potassium and chloride. J. Exp. Biol. 41, 39–67 (1964).
CAS  PubMed  Google Scholar 

46.
Misof, B. et al. Phylogenomics resolves the timing and pattern of insect evolution. Science 346, 763–767 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

47.
Venter, I. G. Egg development in the brown locust, Locustana pardalina (Walker), with special reference to the effect of infestation by Malameba locustae. South Afr. J. Agric. Sci. 9, 429–434 (1966).
Google Scholar  More

Study areas
Time series of population survey data were used from nonmigratory elk populations in three different locations along the West Coast of the USA (Fig. 4). Five of the populations were in the Prairie Creek drainage (Davison), the Lower Redwood Creek drainage (Levee Soc), the Stone Lagoon area, the Gold Bluffs region, and the Bald Hills region of Redwood National and State Parks (RNSP), Humboldt County, California (41.2132° N, 124.0046° W). These populations occupy an area of about 380 km2. The climate in this region was mild, with cool summers and rainy winters. Annual precipitation was usually between 120 and 180 cm and most of the precipitation fell between October and April. Snow was rare since average winter temperatures rarely dropped below freezing and ranged from 3 to 5 °C. Average summer temperatures ranged from 10 to 27 °C, depending on the distance inland. Elk in RNSP were not legally hunted, and displayed strong social bonding between females, juveniles, and sub-adult males7.
Figure 4

Map of study areas in Arid Lands Ecology (ALE) Reserve, southern part of Redwood National and State Parks, and Tomales Point Elk Reserve in Point Reyes National Seashore. This map was created in ArcMap (Version 10.6; https://desktop.arcgis.com/en/arcmap/).

Full size image

An elk population in the Point Reyes National Seashore inhabited part of the Point Reyes Peninsula in Marin County, California (38.0723° N, 122.8817° W). The elk were restricted to an area of 10.52 km2 on the northern tip of the peninsula by a 3-m-tall fence. The climate of this study area was Mediterranean, with an average annual precipitation of 87 cm27. Most of the precipitation fell from autumn to early spring. Temperatures averaged about 7 °C in winter and 13 °C in summer27,35.
Another elk population was in the Arid Lands Ecology (ALE) Reserve and occupied a 300 km2 area within the U.S. Department of Energy’s Hanford Site, Washington (46.68778° N, 119.6292° W). The climate in this area was semi-arid with dry, hot summers and wet, moderately-cold winters. Average summer temperatures were around 20 °C and average winter temperatures were around 5 °C with an average annual precipitation of 16 cm, half of which fell in the winter as rain36.
Population surveys
In RNSP, females, juveniles, and subadult males were often in the same group and tended to use open meadow habitat more frequently than adult males37,38. These behavioral patterns likely explain why females, juveniles, and subadult males were sighted more frequently than adult males7. Moreover, in size-dimorphic ungulates such as elk, recruitment was strongly correlated with female abundance and weakly correlated with male abundance7,13,39. In RNSP, the abundance of groups of females, juveniles, and subadult males drove the dynamics of the group and of adult males7. Therefore, for the RNSP populations, we used herd counts where a herd was comprised of females, juveniles, and subadult males. We also used herd counts for the Point Reyes and ALE Reserve populations to remain consistent.
Systematic herd surveys of elk were conducted during January from 1997 to 2019 in RNSP. Surveys in the Davison meadows, the Levee Soc area, the Stone Lagoon area, the Gold Bluffs region, and the Bald Hills region were conducted by driving specified routes 4 to 10 times on different days throughout the month of January. The time series for these five herds ranged from 19 to 23 years of data. The elk were counted and classified by age and sex as adult males, subadult males, females, and juveniles. Females could not be visually differentiated into adult and subadult age categories37. The highest count of females, juveniles, and subadult males from the surveys conducted each year was used as an index of abundance of each herd since the detection probabilities were high both on an absolute basis ( > 0.8) and relative to variation in detection probabilities (CVsighting = 0.05)7,40. For the Bald Hills herd, which is the only herd in RNSP where harvests occurred, we added hunter harvests to the highest count of each year to account for this source of mortality. These harvests occurred only when elk from the Bald Hills herd left RNSP.
Elk population surveys were conducted at the Point Reyes National Seashore from 1982 to 2008. Weekly surveys were conducted after the mating season. Surveys were conducted on foot or horseback of female elk that were ear-tagged or had a collar containing radio telemetry32,35. Individuals counted were classified as females, juveniles, subadult males, and adult males. Data were not available for the years 1984 to 1989 and 1993, so the time series included 20 years of data. We used the highest count of females, juveniles, and subadult males in each year in our analyses. This herd was also not hunted.
Elk population surveys in the ALE Reserve were conducted in winters after hunting and before parturition. From 1982 to 2000, biologists used aerial telemetry studies, in which they located all collared elk during each survey and classified them by sex and age. We used the total counts of females, juveniles and subadult males. For years in which multiple surveys were conducted, we used the highest count in each year as an index of abundance for that year25,41. We omitted population survey data collected in 1982 from our analysis because individuals were not classified by sex and age in this year. Consequently, the time series included 18 years of data. For all years of data used, we added hunter harvests to the highest count of each year to account for this source of mortality. The count in 2000 was much lower than in the previous year, likely due in part to a large wildfire which occurred in the summer of 2000, which probably had an immediate effect of reducing available elk forage in the reserve and caused elk to spend more time outside of the ALE Reserve42,43. In addition, the highest recorded number of elk (about 291) were harvested that year43.
Ricker growth models
We fit linearized Ricker growth models simultaneously to the seven time series to estimate population growth parameters as well as temporal variation in r and β. We estimated K as the x-intercept of the Ricker growth model (i.e., when r = 0). Notably, preliminary analyses showed that not accounting for observer error did not bias our results (see Supplementary Information).
We used a Bayesian Markov Chain Monte Carlo (MCMC) algorithm with 3 chains, 150,000 iterations, a burn-in period of 75,000, an adaptation period of 75,000, and no thinning. We used Bayesian inference and MCMC because these methods offer advantages when fitting hierarchical models to model variation in ecological data44,45. We conducted these analyses in the RJAGS program (JAGS Version 4.0.0; https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/Windows/) in RStudio (R Version 3.5.0; https://cran.r-project.org/bin/windows/base/old/3.5.0/). We used uninformative priors for the y-intercept (i.e., rmax) and the slope (i.e., β) in order to allow solely the data to influence posterior estimates of these parameters. Informative priors were not necessary as long as parameter estimates from each chain converged. Convergence among chains was determined when the Gelman-Rubin diagnostic ((hat{R})) was less than 1.01, and through visual checks of trace and density plots46.
The estimate of rmax borrowed information among herds because this parameter should be similar among populations within a species22. Therefore, we modeled rmax for each herd (j) as a random effect following a normal distribution with (mu_{{r_{max} }} sim Normalleft( {0, 0.001} right)) and (sigma_{{r_{max} }} sim Uniformleft( {0, 100} right)). To model temporal variation in r for each herd, we included a zero-centered random effect which was also modeled following the normal distribution (gamma_{t,j} sim Normalleft( {0,sigma_{{gamma_{j} }} } right)), where (sigma_{{gamma_{j} }} sim Uniformleft( {0, 100} right)). The estimate of β did not borrow information among herds because this parameter can vary widely among herds18. The prior for β for each herd (j) followed the normal distribution (beta_{j} sim Normalleft( {0, 0.001} right)). To model temporal variation in β for each herd, we modified how we modeled β by using a normal distribution ({beta_{{delta }_{t,j}}} sim Normalleft( {mu_{{beta_{{delta }_{j} }}}} , {sigma_{{beta_{{delta }_{j}} }} } right)), where ({mu_{{beta_{{delta }_{j}} }}} sim Normalleft( {0, 0.001} right)) and ({sigma_{{beta_{{delta }_{j}} }}} sim Uniformleft( {0, 100} right)). Thus, there were four possible Ricker growth models for each herd; (1) no temporal variation in r and β,

$$ r_{t} = r_{max} + beta N_{t} + varepsilon , $$
(3)

(2) temporal variation in r,

$$ r_{t} = r_{max} + beta N_{t,} + gamma_{t} + varepsilon , $$
(4)

(3) temporal variation in β,

$$ r_{t} = r_{max} + {beta_{{delta }_{t}}} N_{t} + varepsilon , $$
(5)

and
(4) temporal variation in both rmax and β

$$ r_{t} = r_{max} + {beta_{{delta }_{t}}} N_{t} + gamma_{t} + varepsilon . $$
(6)

The residual variance was modeled as (varepsilon sim Uniformleft( {0,100} right)). We fit the model with no temporal variation (Eq. (3)) in either parameter to all seven time series simultaneously. All parameters except for rmax were estimated independently for each herd. For each time series of population survey data, we determined whether models with more parameters provided a better fit. We did so by fitting each possible growth model (Eqs. (4)–(6)) to each time series one at a time, while modeling all other time series with no temporal variation in rmax or β (Eq. (3)). The model with the lowest mean deviance from RJAGS by more than 2 was selected for that herd47.
Environmental and demographic stochasticity
We estimated fluctuation in abundance which can be attributed to demographic and environmental stochasticity for herds with different K for each herd. The stochasticity model was outlined by Ferguson and Ponciano9;

$$ Varleft( {N_{t – 1} } right) = Var_{dem} left( {N_{t – 1} } right) + Var_{r} left( {N_{t – 1} } right) + Var_{{upbeta }} left( {N_{t – 1} } right), $$
(7)

where (Varleft( {N_{t – 1} } right)) was total population stochasticity, (Var_{dem} left( {N_{t – 1,} } right)) was population abundance fluctuation due to demographic stochasticity, (Var_{r} left( {N_{t – 1} } right)) was population abundance fluctuation due to changes in r (i.e., density-independent environmental stochasticity), and (Var_{beta } left( {N_{t – 1} } right)) was population abundance fluctuation due to changes in β. The model assumes density-dependent survival following the Ricker model. Demographic stochasticity was calculated as follows;

$$ Var_{dem} left( {N_{t – 1} } right) = alpha N_{t – 1} e^{{ – beta_{Delta } left( {N_{t – 1} } right)}} left( {1 – e^{{ – beta_{Delta } left( {N_{t – 1} } right)}} } right) + sigma_{dem}^{2} N_{t – 1} e^{{ – 2beta_{Delta } left( {N_{t – 1} } right)}} $$
(8)

where (sigma_{dem}^{2}) was assumed to be equal to α9. Environmental stochasticity that is expressed as changes in r, otherwise known as density-independent or additive stochasticity, was calculated as follows;

$$ Var_{r} left( {N_{t – 1} } right) = sigma_{{beta_{Delta } }}^{2} alpha^{2} N_{t – 1}^{2} e^{{ – 2beta_{Delta } left( {N_{t – 1} } right)}} , $$
(9)

and environmental stochasticity that is expressed as changes in β, otherwise known as density-dependent or multiplicative stochasticity, was calculated as follows;

$$ Var_{{upbeta }} left( {N_{t – 1} } right) = sigma_{{beta_{Delta } }}^{2} alpha^{2} N_{t – 1}^{2} left( {N_{t – 1} } right)^{2} e^{{ – 2beta_{Delta } left( {N_{t – 1} } right)}} . $$
(10)

Population growth parameters from the selected Ricker growth model for each herd were used in these equations to estimate each of these sources of stochasticity for each herd across abundances ranging from five to above K. The relative total population stochasticity was expressed as the total population stochasticity at K for each herd divided by that herd’s K. More

1.
Sulser, T. B., Mason-D’Croz, D., Robinson, S., Wiebe, K. & Rosegrant, M. W. Africa in the global agricultural economy in 2030 and 2050. In ReSAKSS Annual Trends and Outlook report 2014 (eds Badiane, O. & Makombe, T.). International Food Policy Research Institute (IFPRI). (2015).
2.
Van Ittersum, M. K. et al. Can sub-Saharan Africa feed itself?. Proc. Natl. Acad. Sci. U.S.A. 113, 14964–14969 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Thierfelder, C., Matemba-Mutasa, R. & Rusinamhodzi, L. Yield response of maize (Zea mays L.) to conservation agriculture cropping system in Southern Africa. Soil Till. Res. 146, 230–242 (2015).
Article  Google Scholar 

5.
Good, A. G. & Beatty, P. H. Fertilizing nature: a tragedy of excess in the commons. PLoS Biol. 9, e1001124 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Reicosky, D. C., Sauer, T. J. & Hatfi, J. L. Challenging balance between productivity and environmental quality: tillage impacts. In Soil Management: Building a Stable Base for Agriculture Vol. 1373 (eds Hatfield, J. L. & Sauer, T. J.) 13–37 (ASA and SSSA, Madison, 2011).
Google Scholar 

7.
Thierfelder, C., Rusinamhodzi, L., Setimela, P., Walker, F. & Eash, N. S. Conservation agriculture and drought-tolerant germplasm: reaping the benefits of climate-smart agriculture technologies in central Mozambique. Renew. Agric. Food Syst. 31, 414–428 (2016).
Article  Google Scholar 

8.
Vanlauwe, B. et al. Agronomic use efficiency of N fertilizer in maize-based systems in sub-Saharan Africa within the context of integrated soil fertility management. Plant Soil 339, 35–50 (2011).
CAS  Article  Google Scholar 

9.
Giller, K. E. et al. A research agenda to explore the role of conservation agriculture in African smallholder farming systems. Field Crops Res. 124, 468–472 (2011).
Article  Google Scholar 

10.
Jaleta, M., Kassie, M. & Shiferaw, B. Tradeoffs in crop residue utilization in mixed crop—livestock systems and implications for conservation agriculture. Agric. Syst. 121, 96–105 (2013).
Article  Google Scholar 

11.
Baudron, F., Delmotte, S., Corbeels, M., Herrera, J. M. & Tittonell, P. Multi-scale trade-off analysis of cereal residue use for livestock feeding vs. soil mulching in the Mid-Zambezi Valley, Zimbabwe. Agric. Syst. 134, 97–106 (2015).
Article  Google Scholar 

12.
Valbuena, D. et al. Conservation agriculture in mixed crop-livestock systems: scoping crop residue trade-offs in Sub-Saharan Africa and South Asia. Field Crop Res. 132, 175–184 (2012).
Article  Google Scholar 

13.
Guto, S. N. Chakula bila kulima? Trade-offs concerning soil and water conservation in heterogeneous smallholder farms of Central Kenya. Ph.D. Thesis (2011).

14.
Chivenge, P., Vanlauwe, B. & Six, J. Does the combined application of organic and mineral nutrient sources influence maize productivity? A meta-analysis. Plant Soil 342, 1–30 (2011).
CAS  Article  Google Scholar 

15.
Bullock, D. G. & Anderson, D. S. Evaluation of the Minolta SPAD-502 chlorophyll meter for nitrogen management in corn. J. Plant Nutr. 21, 741–755 (1998).
CAS  Article  Google Scholar 

16.
Markwell, J., Osterman, J. C. & Mitchell, J. L. Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynth. Res. 46, 467–472 (1995).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Cerovic, Z. G., Masdoumier, G., Ghozlen, N. B. & Latouche, G. A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiol. Plantarum. 146, 251–260 (2012).
CAS  Article  Google Scholar 

18.
Atzberger, C. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sens. 5, 949–981 (2013).
ADS  Article  Google Scholar 

19.
Sakamoto, T. et al. An alternative method using digital cameras for continuous monitoring of crop status. Agric. For. Meteorol. 113, 154–155 (2012).
Google Scholar 

20.
Fernandez-Gallego, J. A. et al. Low-cost assessment of grain yield in durum wheat using RGB images. Eur. J. Agron. 105, 146–156 (2019).
Article  Google Scholar 

21.
Araus, J. L. & Kefauver, S. C. Breeding to adapt agriculture to climate change: affordable phenotyping solutions. Curr. Opin. Plant Biol. 45, 237–247 (2018).
PubMed  Article  PubMed Central  Google Scholar 

22.
Gracia-Romero, A. et al. Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe. Remote Sens. 10(2), 349 (2018).
ADS  Article  Google Scholar 

23.
Araus, J. L. & Cairns, J. E. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19, 52–61 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Yousfi, S., Serret, M. D., MArquez A. J., Voltas, J. & Araus, J. L. Combined use of δ13C, δ18O and δ15N tracks nitrogen metabolism and genotypic adaptation of durum wheat to salinity and water deficit. New Phytol. 194, 230–244 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Vergara-Díaz, O. et al. A novel remote sensing approach for prediction of Maize yield under different conditions of nitrogen fertilization. Front. Plant Sci. 7, 1–13 (2016).
Article  Google Scholar 

26.
Araus, L., Sánchez, C. & Cabrera-Bosquet, L. Is heterosis in maize mediated through better water use?. New Phytol. 187(2), 392–406 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Thierfelder, C. & Wall, P. C. Investigating conservation agriculture (CA) systems in Zambia and Zimbabwe to mitigate future effects of climate change. J. Crop Improv. 24, 113–121 (2010).
Article  Google Scholar 

28.
Rusinamhodzi, L. et al. A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions. Agron. Sustain. Dev. 31, 657–673 (2011).
Article  Google Scholar 

29.
Kafesu, N. et al. Comparative fertilization effects on maize productivity under conservation and conventional tillage on sandy soils in a smallholder cropping system in Zimbabwe. Field Crops Res. 218, 106–114 (2018).
Article  Google Scholar 

30.
Arvidsson, J., Etana, A. & Rydberg, T. Crop yield in Swedish experiments with shallow tillage and no-tillage 1983–2012. Eur. J. Agron. 52, 307–315 (2014).
Article  Google Scholar 

31.
Habig, J. & Swanepoel, C. Effects of conservation agriculture and fertilization on soil microbial diversity and activity. Environmental 2, 358–384 (2015).
Google Scholar 

32.
Fonte, S. J., Quansah, G. W. & Six, J. Fertilizer and residue quality effects on organic matter stabilization in soil aggregates. Soil Biol. Biochem. 73(3), 961–966 (2009).
CAS  Google Scholar 

33.
Gabriel, J. L. et al. Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosyst. Eng. 160, 124–133 (2017).
Article  Google Scholar 

34.
Choi, W.-J., Lee, S.-M., Ro, H.-M., Kim, K.-C. & Yoo, S.-H. Natural 15N abundances of maize and soil amended with urea and composted pig manure. Plant Soil 245, 223–232 (2002).
CAS  Article  Google Scholar 

35.
Bateman, A. S., Kelly, S. D. & Jickells, T. D. Nitrogen isotope relationships between crops and fertilizer: implications for using nitrogen isotope analysis as an indicator of agricultural regime. J. Agric. Food Chem. 53, 5760–5765 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Serret, M. D., Ortiz-Monasterio, I., Pardo, A. & Araus, J. L. The effects of urea fertilization and genotype on yield, nitrogen use efficiency, δ15N and δ13C in wheat. Ann. Appl. Biol. 153, 243–257 (2008).
CAS  Google Scholar 

37.
Amundson, R. et al. Global patterns of the isotopic composition of soil and plant nitrogen. Glob. Biogeochem. Cycles https://doi.org/10.1029/2002GB001903 (2003).
Article  Google Scholar 

38.
Farquhar, G. D., Ehleringer, R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537 (1989).
CAS  Article  Google Scholar 

39.
Monneveux, P., Sheshshayee, M. S., Akhter, J. & Ribaut, J. M. Using carbon isotope discrimination to select maize (Zea mays L.) inbred lines and hybrids for drought tolerance. Plant Sci. 173, 390–396 (2007).
CAS  Article  Google Scholar 

40.
Cabrera-Bosquet, L., Molero, G., Nogués, S. & Araus, J. L. Water and nitrogen conditions affect the relationships of Δ13C and Δ18O to gas exchange and growth in durum wheat. J. Exp. Bot. 60, 1633–1644 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Farquhar, G. D. On the nature of carbon isotope discrimination in C4 species. Aust. J. Plant Physiol. 10, 205 (1983).
CAS  Google Scholar 

42.
Evans, J. R. Nitrogen and photosynthesis in the flag leaf of wheat (Triticum aestivum L.). Plant Phys. 5, 297–302 (1983).
CAS  Article  Google Scholar 

43.
Szabó, É. Effect of some physiological properties on the quality parameters of different winter wheat varieties in a long-term experiment. Cereal Res. Commun. 42, 126–138 (2013).
Article  Google Scholar 

44.
Giunta, F., Motzo, R. & Deidda, M. SPAD readings and associated leaf traits in durum wheat, barley and triticale cultivars. Euphytica 125, 197–205 (2002).
CAS  Article  Google Scholar 

45.
Zhang, Y., Tremblay, N. & Zhu, J. Evaluation of the Multiplex® fluorescence sensor for the assessment of corn nitrogen status. J. Food Agric. Environ. 10(1), 1008–1016 (2012).
CAS  Google Scholar 

46.
Cairns, J. E., Sanchez, C., Vargas, M., Ordoñez, R. & Araus, J. L. Dissecting maize productivity: ideotypes associated with grain yield under drought stress and well-watered conditions. J. Integr. Plant Biol. 54, 1007–1020 (2012).
PubMed  Article  Google Scholar 

47.
Buchaillot, M. L. et al. Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques. Sensors 19(8), 1815 (2019).
Article  Google Scholar 

48.
Monneveux, P., Sanchez, C. & Tiessen, A. Future progress in drought tolerance in maize needs new secondary traits and cross combinations. J. Agric. Sci. 146, 287–300 (2008).
Article  Google Scholar 

49.
Hu, H. et al. Assessment of chlorophyll content based on image color analysis, comparison with SPAD-502. In 2nd International Conference on Information Engineering and Computer Science—Proceedings, ICIECS 2010 (2010).

50.
Sevik, H., Belkaylai, N. & Aktar, G. Change of chlorophyll amount in some landscape plants. J. Biotech. Sci. 2(1), 10–16 (2014).
Google Scholar 

51.
Sevik, H., Karakas, H. & Karaca, U. Color—chlorophyll relationship of some indoor ornamental plants. Int. J. Eng. Sci. Res. Technol. 2, 1706–1712 (2013).
Google Scholar 

52.
Pointer, M. R. A comparison of the CIE 1976 colour spaces. Color Res. App. 6, 108–118 (2009).
Article  Google Scholar 

53.
Cooper, F. G. Munsell Manual of Color (Munsell Color Company Inc, Boston, 1929).
Google Scholar 

54.
Carper, W. J., Lillesand, T. M. & Kiefer, R. W. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogram. Eng. Remote Sens. 56(4), 459–467 (1990).
Google Scholar 

55.
Hunt, E. R. Jr., Cavigelli, M., Daughtry, C. S. T., Mcmurtrey Iii, J. & Walthall, C. L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric. 6, 359–378 (2005).
Article  Google Scholar 

56.
Hunt, R. & Perry, E. M. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Obs. Geoinf. 21, 103–112 (2013).
ADS  Article  Google Scholar 

57.
Gracia-Romero, A. et al. Comparative performance of ground vs. Aerially assessed rgb and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Front. Plant Sci. 8, 2004 (2017).
PubMed  PubMed Central  Article  Google Scholar 

58.
Süß, A. et al. Measuring leaf chlorophyll content with the Konica Minolta SPAD-502Plus—theory, measurement, problems, interpretation. EnMAP Field Guides Technical Report, GFZ Data Services (2015).

59.
Casadesús, J. et al. Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann. App. Biol. 150, 227–236 (2007).
Article  Google Scholar 

60.
Erdle, K., Mistele, B. & Schmidhalter, U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 124, 74–84 (2011).
Article  Google Scholar 

61.
Zaman-Allah, M. et al. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 11, 35 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Gracia-Romero, A., Kefauver, S. C., Fernandez-Gallego, J. A., Vergara-Díaz, O. & Araus, J. L. UAV and ground image-based phenotyping: a proof of concept with Durum wheat. Remote Sens. 11(10), 1244 (2019).
ADS  Article  Google Scholar 

63.
Cerovic, Z. G. et al. Nondestructive diagnostic test for nitrogen nutrition of grapevine (Vitis vinifera L.) based on dualex leaf-clip measurements in the field. J. Agric. Food Chem. 63, 3669–3680 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Bendig, J. et al. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens. 6, 10395–10412 (2014).
ADS  Article  Google Scholar 

65.
Coplen, T. B. & Zhu, X. K. Explanatory glossary of terms used in expression of relative isotope ratios and gas ratios. IUPAC Recommendations 1–27 (2008). More

1.
Mousseau, T. A. & Fox, C. W. The adaptive significance of maternal effects. Trends Ecol. Evol. 13, 403–407 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Hagan, H. R. A brief analysis of viviparity in insects. J. N. Y. Entomol. Soc. 56, 63–68 (1948).
Google Scholar 

3.
Resetarits, W. J. Jr. Oviposition site choice and life history evolution. Am. Zool. 36, 205–215 (1996).
Article  Google Scholar 

4.
Schwarzkopf, L. & Andrews, R. M. Are moms manipulative or just selfish? Evaluating the “maternal manipulation hypothesis” and implications for life-history studies of reptiles. Herpetologica 68, 147–159 (2012).
Article  Google Scholar 

5.
Bernardo, J. Maternal effects in animal ecology. Am. Zool. 36, 83–105 (1996).
Article  Google Scholar 

6.
Réale, D. & Roff, D. A. Quantitative genetics of oviposition behaviour and interactions among oviposition traits in the sand cricket. Anim. Behav. 64, 397–406 (2002).
Article  Google Scholar 

7.
McGaugh, S. E., Schwanz, L. E., Bowden, R. M., Gonzalez, J. E. & Janzen, F. J. Inheritance of nesting behaviour across natural environmental variation in a turtle with temperature-dependent sex determination. Proc. R. Soc. B Biol. Sci. 277, 1219–1226 (2010).
Article  Google Scholar 

8.
Seymour, R. S. & Ackerman, R. A. Adaptations to underground nesting in birds and reptiles. Am. Zool. 20, 437–447 (1980).
Article  Google Scholar 

9.
Booth, D. T. Influence of incubation temperature on hatchling phenotype in reptiles. Physiol. Biochem. Zool. 79, 274–281 (2006).
PubMed  Article  PubMed Central  Google Scholar 

10.
Deeming, D. C. in Temperature-Dependent Sex Determination in Vertebrates (eds Valenzuela, N. & Lance, V. A.) 33–41 (Smithsonian Books, 2004).

11.
Deeming, D. C. & Ferguson, M. in Egg Incubation: Its Effects on Embryonic Development in Birds and Reptiles (eds Deeming, D. C. & Ferguson, M. W. J.) 147–171 (Cambridge University Press, Cambridge, 1991).

12.
Schwarzkopf, L. & Brooks, R. J. Nest-site selection and offspring sex ratio in painted turtles, Chrysemys picta. Copeia 1987, 53–61 (1987).
Article  Google Scholar 

13.
Refsnider, J. M. & Janzen, F. J. Putting eggs in one basket: ecological and evolutionary hypotheses for variation in oviposition-site choice. Annu. Rev. Ecol. Evol. Syst. 41, 39–57 (2010).
Article  Google Scholar 

14.
Doody, J. S. et al. Nest site choice compensates for climate effects on sex ratios in a lizard with environmental sex determination. Evol. Ecol. 20, 307–330 (2006).
Article  Google Scholar 

15.
Ewert, M. A., Lang, J. W. & Nelson, C. E. Geographic variation in the pattern of temperature-dependent sex determination in the American snapping turtle (Chelydra serpentina). J. Zool. 265, 81–95 (2005).
Article  Google Scholar 

16.
Doody, J. S. Superficial lizards in cold climates: nest site choice along an elevational gradient. Austral. Ecol. 34, 773–779 (2009).
Article  Google Scholar 

17.
Doody, J. S. & Moore, J. A. Conceptual model for thermal limits on the distribution of reptiles. Herpetol. Conserv. Biol. 5, 283–289 (2010).
Google Scholar 

18.
Delmas, V., Bonnet, X., Girondot, M. & Prévot-Julliard, A.-C. Varying hydric conditions during incubation influence egg water exchange and hatchling phenotype in the red-eared slider turtle. Physiol. Biochem. Zool. 81, 345–355 (2008).
PubMed  Article  PubMed Central  Google Scholar 

19.
Fitch, H. S. Reproductive cycles in lizards and snakes. Univ. Kans. Mus. Nat. Hist. Misc. Publ. 52, 1–247 (1970).
Google Scholar 

20.
Gutzke, W. H., Packard, G. C., Packard, M. & Boardman, T. J. Influence of the hydric and thermal environments on eggs and hatchlings of painted turtles (Chrysemys picta). Herpetologica 43, 393–404 (1987).
Google Scholar 

21.
Muth, A. Physiological ecology of desert iguana (Dipsosaurus dorsalis) eggs: temperature and water relations. Ecology 61, 1335–1343 (1980).
Article  Google Scholar 

22.
Plumer, M. & Snell, H. Nest site selection and water relations of eggs in the snake, Opheodrys aestirus. Copeia 1988, 58–61 (1988).
Article  Google Scholar 

23.
Reedy, A. M., Zaragoza, D. & Warner, D. A. Maternally chosen nest sites positively affect multiple components of offspring fitness in a lizard. Behav. Ecol. 24, 39–46 (2013).
Article  Google Scholar 

24.
Socci, A. M., Schlaepfer, M. A. & Gavin, T. A. The importance of soil moisture and leaf cover in a female lizard’s (Norops polylepis) evaluation of potential oviposition sites. Herpetologica 61, 233–240 (2005).
Article  Google Scholar 

25.
Warner, D. A. & Andrews, R. M. Laboratory and field experiments identify sources of variation in phenotypes and survival of hatchling lizards. Biol. J. Lin. Soc. 76, 105–124 (2002).
Article  Google Scholar 

26.
Li, S. R. et al. Female lizards choose warm, moist nests that improve embryonic survivorship and offspring fitness. Funct. Ecol. 32, 416–423 (2018).
Article  Google Scholar 

27.
Warner, D. A., Jorgensen, C. F. & Janzen, F. J. Maternal and abiotic effects on egg mortality and hatchling size of turtles: temporal variation in selection over seven years. Funct. Ecol. 24, 857–866 (2010).
Article  Google Scholar 

28.
Black, C. P., Birchard, G. F., Schuett, G. W. & Black, V. D. in Respiration and Metabolism of Embryonic Vertebrates (ed Seymour, R. S.) 137–145 (Springer, Berlin, 1984).

29.
Hayes, W. K., Carter, R. L., Cyril, S. & Thornton, B. in Iguanas: Biology and Conservation (eds Alberts, A. C., Carter, R. L., Hayes, W. K., & Martins, E. P.) 232–257 (University of California Press, 2004).

30.
Iverson, J. B., Hines, K. N. & Valiulis, J. M. The nesting ecology of the Allen Cays rock iguana, Cyclura cychlura inornata in the Bahamas. Herpetol. Monogr. 18, 1–36 (2004).
Article  Google Scholar 

31.
Kam, Y.-C. Effects of simulated flooding on metabolism and water balance of turtle eggs and embryos. J. Herpetol. 28, 173–178 (1994).
Article  Google Scholar 

32.
Moll, E. O. & Legler, J. M. The life history of a neotropical slider turtle, Pseudemys scripta (Schoepff), in Panama. Bull. Los Angel.Cty. Mus.Nat. Hist. 11, 1–102 (1971).
Google Scholar 

33.
Tracy, C. R. Water relations of parchment-shelled lizard (Sceloporus undulatus) eggs. Copeia 3, 478–482 (1980).
Article  Google Scholar 

34.
Mortimer, J. A. The influence of beach sand characteristics on the nesting behavior and clutch survival of green turtles (Chelonia mydas). Copeia 1990, 802–817 (1990).
Article  Google Scholar 

35.
Platt, S. G. & Thorbjarnarson, J. B. Nesting ecology of the American crocodile in the coastal zone of Belize. Copeia 2000, 869–873 (2000).
Article  Google Scholar 

36.
Snell, H. L. & Tracy, C. R. Behavioral and morphological adaptations by Galapagos land iguanas (Conolophus subcristatus) to water and energy requirements of eggs and neonates. Am. Zool. 25, 1009–1018 (1985).
Article  Google Scholar 

37.
Thompson, M., Packard, G., Packard, M. & Rose, B. Analysis of the nest environment of tuatara Sphenodon punctatus. J. Zool. 238, 239–251 (1996).
Article  Google Scholar 

38.
Bodensteiner, B. L., Mitchell, T. S., Strickland, J. T. & Janzen, F. J. Hydric conditions during incubation influence phenotypes of neonatal reptiles in the field. Funct. Ecol. 29, 710–717 (2015).
Article  Google Scholar 

39.
Doody, J. S., James, H., Colyvas, K., Mchenry, C. R. & Clulow, S. Deep nesting in a lizard, déjà vu devil’s corkscrews: first helical reptile burrow and deepest vertebrate nest. Biol. J. Lin. Soc. 116, 13–26 (2015).
Article  Google Scholar 

40.
Doody, J. S. et al. Cryptic and complex nesting in the yellow-spotted monitor, Varanus panoptes. J. Herpetol. 48, 363–370 (2014).
Article  Google Scholar 

41.
Doody, J. S. et al. Deep, helical, communal nesting and emergence in the sand monitor: ecology informing paleoecology?. J. Zool. 305, 88–95 (2018).
Article  Google Scholar 

42.
Doody, J. S. et al. Deep communal nesting by yellow-spotted monitors in a desert ecosystem: indirect evidence for a response to extreme dry conditions. Herpetologica 74, 306–310 (2018).
Article  Google Scholar 

43.
Bureau of Meteorology. Average Annual, Seasonal and Monthly Rainfall, https://www.bom.gov.au/jsp/ncc/climate_averages/rainfall/index.jsp (2019).

44.
Cogger, H. Reptiles and Amphibians of Australia (CSIRO Publishing, 2014).

45.
Doody, J. S. et al. Chronic effects of an invasive species on an animal community. Ecology 98, 2093–2101 (2017).
PubMed  Article  PubMed Central  Google Scholar 

46.
Doody, J. S. et al. Invasive toads shift predator–prey densities in animal communities by removing top predators. Ecology 96, 2544–2554 (2015).
PubMed  Article  PubMed Central  Google Scholar 

47.
Shea, G. & Sadlier, R. An ovigerous argus monitor, Varanus panoptes panoptes. Herpetofauna 31, 132–133 (2001).
Google Scholar 

48.
Doody, J. S. et al. Impacts of the invasive cane toad on aquatic reptiles in a highly modified ecosystem: the importance of replicating impact studies. Biol. Invasions 16, 2303–2309 (2014).
Article  Google Scholar 

49.
Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).
MathSciNet  Article  Google Scholar 

50.
Telemeco, R. S., Elphick, M. J. & Shine, R. Nesting lizards (Bassiana duperreyi) compensate partly, but not completely, for climate change. Ecology 90, 17–22 (2009).
PubMed  Article  PubMed Central  Google Scholar 

51.
Wilson, D. S. Nest-site selection: microhabitat variation and its effects on the survival of turtle embryos. Ecology 79, 1884–1892 (1998).
Article  Google Scholar 

52.
Refsnider, J., Bodensteiner, B., Reneker, J. & Janzen, F. Nest depth may not compensate for sex ratio skews caused by climate change in turtles. Anim. Conserv. 16, 481–490 (2013).
Article  Google Scholar 

53.
Morjan, C. L. Variation in nesting patterns affecting nest temperatures in two populations of painted turtles (Chrysemys picta) with temperature-dependent sex determination. Behav. Ecol. Sociobiol. 53, 254–261 (2003).
Article  Google Scholar 

54.
Refsnider, J. M. & Janzen, F. J. Behavioural plasticity may compensate for climate change in a long-lived reptile with temperature-dependent sex determination. Biol. Conserv. 152, 90–95 (2012).
Article  Google Scholar 

55.
Georges, A., Limpus, C. & Stoutjesdijk, R. Hatchling sex in the marine turtle Caretta caretta is determined by proportion of development at a temperature, not daily duration of exposure. J. Exp. Zool. 270, 432–444 (1994).
Article  Google Scholar 

56.
Barbault, R. Population dynamics and reproductive patterns of three African skinks. Copeia 1976, 483–490 (1976).
Article  Google Scholar 

57.
Brown, G. & Shine, R. Why do most tropical animals reproduce seasonally? Testing hypotheses on an Australian snake. Ecology 87, 133–143 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Van Dyke, J. U. in Reproductive Biology and Phylogeny of Lizards and Tuatara (ed Rheubert, J. L.) 121–155 (CRC Press, New York, 2014).

59.
James, C. & Shine, R. The seasonal timing of reproduction. Oecologia 67, 464–474 (1985).
ADS  PubMed  Article  PubMed Central  Google Scholar 

60.
Packard, G. C., Miller, K. & Packard, M. J. A protocol for measuring water potential in subterranean nests of reptiles. Herpetologica 48, 202–209 (1992).
Google Scholar 

61.
Taylor, J. A. & Tulloch, D. Rainfall in the wet-dry tropics: extreme events at Darwin and similarities between years during the period 1870–1983 inclusive. Aust. J. Ecol. 10, 281–295 (1985).
Article  Google Scholar 

62.
de Almeida Prado, C. P., Uetanabaro, M. & Lopes, F. S. Reproductive strategies of Leptodactylus chaquensis and L. podicipinus in the Pantanal Brazil. J. Herpetol. 34, 135–139 (2000).
Article  Google Scholar 

63.
Newton, I. Population limitation in birds: the last 100 years. Brit. Birds 100, 518–539 (2007).
Google Scholar 

64.
James, C. D. & Whitford, W. G. An experimental study of phenotypic plasticity in the clutch size of a lizard. Oikos 70, 49–56 (1994).
Article  Google Scholar 

65.
Jolly, C. J., Shine, R. & Greenlees, M. J. The impacts of a toxic invasive prey species (the cane toad, Rhinella marina) on a vulnerable predator (the lace monitor, Varanus varius). Biol. Invasions 18, 1499–1509 (2016).
Article  Google Scholar 

66.
Christian, K. in Varanoid Lizards of the World (eds Pianka, E. R. & King, D. R.) 423–429 (Indiana University Press, 2004).

67.
Christian, K. A., Corbett, L., Green, B. & Weavers, B. W. Seasonal activity and energetics of two species of varanid lizards in tropical Australia. Oecologia 103, 349–357 (1995).
ADS  PubMed  Article  PubMed Central  Google Scholar 

68.
Warner, D. A., Du, W.-G. & Georges, A. Introduction to the special issue—Developmental plasticity in reptiles: physiological mechanisms and ecological consequences. J. Exp. Zool. A Ecol. Int. Physiol. 329, 153–161 (2018).
Google Scholar 

69.
While, G. M. et al. Patterns of developmental plasticity in response to incubation temperature in reptiles. J. Exp. Zool. Part A Ecol. Integr. Physiol. 329, 162–176 (2018).
Google Scholar 

70.
Siepielski, A. M. et al. Precipitation drives global variation in natural selection. Science 355, 959–962 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
ADS  CAS  Article  Google Scholar 
2.
Dewhirts, F. E. et al. The human oral microbiome. J. Bacteriol. 192, 5002–5017 (2012).
Google Scholar 

3.
Krishnan, K., Chen, T. & Paster, B. J. A practical guide to the oral microbiome and its relation to health and disease. Oral Dis. 23, 276–286 (2017).
CAS  Article  Google Scholar 

4.
Edlund, A. et al. Uncovering complex microbiome activities via transcriptomics during 24 hours of biofilm assembly and maturation. Microbiome 6, 217 (2018).
Article  Google Scholar 

5.
Marsh, P.D. et al. in Marsh and Martin´s Oral Microbiology (ed Marsh P.D. & Martin M.) 81-109-000 (Elsevier, 2016).

6.
Peterson, S. N. et al. The dental plaque microbiome in health and disease. PLoS ONE 8(3), e58487. https://doi.org/10.1371/journal.pone.0058487 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

7.
Segata, N. et al. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 13, R42 (2012).
CAS  Article  Google Scholar 

8.
Solbiati, J. & Frias-Lopez, J. Metatranscriptome of the oral microbiome in health and disease. J. Dent. Res. 97(5), 492–500 (2018).
CAS  Article  Google Scholar 

9.
Hajishengallis, G., Darveau, R. P. & Curtis, M. A. The keystone-pathogen hypothesis. Nat. Rev. Microbiol. 10(10), 717–725 (2012).
CAS  Article  Google Scholar 

10.
Marsh, P. D. In Sickness and in health—What does the oral microbiome mean to us? an ecological perspective. Adv. Dent. Res. 29(1), 60–65 (2018).
CAS  Article  Google Scholar 

11.
Struzycka, I. The oral microbiome in dental caries. Pol. J. Microbiol. 63(2), 127–135 (2014).
Article  Google Scholar 

12.
Fakhruddin, K. S., Ngo, H. C. & Samaranayake, L. P. Cariogenic microbiome and microbiota of the early primary dentition: A contemporary overview. Oral Dis. 25, 982–995 (2019).
Article  Google Scholar 

13.
Theilade, D. E., Slots, J. & Fejerskov, O. The ultrastructure of black stain on human primary teeth. Scan. J. Dent. Res. 81, 528–352 (1973).
CAS  Google Scholar 

14.
Żyła, T., Kawala, B., Antoszewska-Smith, J. & Kawala, M. Black stain and dental caries: a review of the literature. Biomed. Res. Int. 2015, 469392 (2015).
Article  Google Scholar 

15.
Albelda-Bernardo, M. A., Jovani-Sancho, M. D. M., Veses, V. & Sheth, C. C. Remediation of adult black dental stains by phototherapy. BDJ Open 4, 17035 (2018).
CAS  Article  Google Scholar 

16.
Ortiz-López, C. S., Veses, V., Garcia-Bautista, J. A. & Jovani-Sancho, M. D. M. Risk factors for the presence of dental black plaque. Sci. Rep. 8(1), 16752 (2018).
ADS  Article  Google Scholar 

17.
Reid, J. S., Beeley, J. A. & MacDonald, D. G. Investigations into black extrinsic tooth stain. J. Dent. Res. 56, 895–899 (1977).
CAS  Article  Google Scholar 

18.
Zhang, F. et al. A preliminary study on the relationship between iron and black extrinsic tooth stain in children. Lett. Appl. Microbiol. 64(6), 424–429 (2017).
CAS  Article  Google Scholar 

19.
Slots, J. The microflora of black stain of human primary teeth. Scan. J. Dent. Res. 82, 484–490 (1974).
CAS  Google Scholar 

20.
Saba, C. et al. Black stains in the mixed dentition: a PCR microbiological study of the etiopathogenic bacteria. J. Clin. Pediatr. Dent. 30, 219–224 (2006).
CAS  Article  Google Scholar 

21.
Li, Y. et al. Analysis of the microbiota of black stain in the primary dentition. PLoS ONE 10(9), e0137030 (2015).
Article  Google Scholar 

22.
Chen, L., Zhang, Q., Wang, Y., Zhang, K. & Zou, J. Comparing dental plaque microbiome diversity of extrinsic black stain in the primary dentition using Illumina MiSeq sequencing technique. BMC Oral Health 19(1), 269 (2019).
CAS  Article  Google Scholar 

23.
Li, Y. et al. Oral Microbial community typing of caries and pigment in primary dentition. BMC Genomics 17, 558 (2016).
Article  Google Scholar 

24.
Petersen, P. E. Global policy for improvement of oral health in the 21st century–implications to oral health research of World Health Assembly 2007, World Health Organization. Comm. Dent. Oral Epidemiol. 37(1), 1–8 (2009).
Article  Google Scholar 

25.
Schoiler, K. et al. Bacterial biofilm composition in healthy subjects with and without caries experience. J. Oral Microbiol. 11(1), 1633194 (2019).
Article  Google Scholar 

26.
Zaura, E., Keijser, B. J. F., Huse, S. M. & Crielaard, W. Defining the healthy “core microbiome” of oral microbial communities. BMC Microbiol. 9, 259 (2009).
Article  Google Scholar 

27.
Belstrøm, D. et al. Metagenomic and metatranscriptomic analysis of saliva reveals disease-associated microbiota in patients with periodontitis and dental caries. NPJ Biofilms Microbiomes 3, 23 (2017).
Article  Google Scholar 

28.
Pozhitkov, A. E. et al. Towards microbiome transplant as a therapy for periodontitis: an exploratory study of periodontitis microbial signature contrasted by oral health, caries and edentulism. BMC Oral Health 15, 125 (2014).
Article  Google Scholar 

29.
Jakubovics, N. S. Intermicrobial interactions as a driver for community composition and stratification of oral biofilms. J. Mol. Biol. 427(23), 3662–3675 (2015).
CAS  Article  Google Scholar 

30.
Takeshita, T. et al. Dental plaque development on a hydroxyapatite disk in young adults observed by using a barcoded pyrosequencing approach. Sci. Rep. 5, 8136 (2015).
CAS  Article  Google Scholar 

31.
Aruni, W. A., Dou, Y., Mishra, A. & Fletcher, H. M. The biofilm community: rebels with a cause. Curr. Oral Health Rep. 2, 48–56 (2015).
Article  Google Scholar 

32.
Gasparetto, A. et al. Prevalence of black tooth stains and dental caries in Brazilian schoolchildren. Braz. Dent. J. 14, 157–161 (2003).
Article  Google Scholar 

33.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 1091. https://doi.org/10.1038/s41587-019-0252-6 (2019).
CAS  Article  PubMed  Google Scholar 

34.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650. https://doi.org/10.1093/molbev/msp077 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73(16), 5261–5267 (2007).
CAS  Article  Google Scholar 

39.
Douglas, G.M. et al. PICRUSt2: An improved and customizable approach for metagenome inference. bioRxiv 672295; 10.1101/672295 (2020)

40.
Ye, Y. & Doak, T.G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 5(8), e1000465 (2009).
Article  Google Scholar  More