Ecology

1.
Wilkinson, C. Status of Coral Reefs of the World: 2008 (Global Coral Reef Monitoring Network and Reef and Rainforest Research Centre, 2008).
2.
Jackson, J. B. C., Donovan, M. K., Cramer, K. L. & Lam, V. V. Status and Trends of Caribbean Coral Reefs: 1970–2012 (Global Coral Reef Monitoring Network, 2014).

3.
Eakin, C. M. et al. Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5, e13969 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast. Shelf Sci. 80, 435–471 (2008).
Article  Google Scholar 

5.
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
CAS  Article  PubMed  Google Scholar 

6.
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
CAS  PubMed  Article  Google Scholar 

7.
De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl Acad. Sci. USA 109, 17995–17999 (2012).
PubMed  Article  Google Scholar 

8.
Gardner, T. A. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).
CAS  PubMed  Article  Google Scholar 

9.
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).
CAS  PubMed  Article  Google Scholar 

10.
ter Steege, H. et al. Estimating the global conservation status of more than 15,000 Amazonian tree species. Sci. Adv. 1, e1500936 (2015).
PubMed  PubMed Central  Article  Google Scholar 

11.
Fauset, S. et al. Hyperdominance in Amazonian forest carbon cycling. Nat. Commun. 6, 6857 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).
CAS  PubMed  Article  Google Scholar 

13.
Connell, J., Hughes, T. & Wallace, C. A 30-year study of coral abundance, recruitment, and disturbance at several scales in space and time. Ecol. Monogr. 67, 461–488 (1997).
Article  Google Scholar 

14.
Hughes, T. P. & Jackson, J. B. C. Population dynamics and life histories of foliaceous corals. Ecol. Monogr. 55, 141–166 (1985).
Article  Google Scholar 

15.
ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).
PubMed  Article  CAS  Google Scholar 

16.
Gaston, K. J. & Blackburn, T. M. How many birds are there? Biodivers. Conserv. 6, 615–625 (1997).
Article  Google Scholar 

17.
Kerry, J. T. & Bellwood, D. R. Do tabular corals constitute keystone structures for fishes on coral reefs? Coral Reefs 34, 41–50 (2015).
Article  Google Scholar 

18.
Connolly, S. R., Hughes, T. P., Bellwood, D. R. & Karlson, R. H. Community structure of corals and reef fishes at multiple scales. Science 309, 1363–1365 (2005).
CAS  PubMed  Article  Google Scholar 

19.
Connolly, S. R., Hughes, T. P. & Bellwood, D. R. A unified model explains commonness and rarity on coral reefs. Ecol. Lett. 20, 477–486 (2017).
PubMed  Article  Google Scholar 

20.
Hubbell, S. P. Estimating the global number of tropical tree species, and Fisher’s paradox. Proc. Natl Acad. Sci. USA 112, 7343–7344 (2015).
CAS  PubMed  Article  Google Scholar 

21.
Hughes, T. P., Bellwood, D. R. & Connolly, S. R. Biodiversity hotspots, centres of endemicity, and the conservation of coral reefs. Ecol. Lett. 5, 775–784 (2002).
Article  Google Scholar 

22.
Hughes, T. P., Bellwood, D. R., Connolly, S. R. & Cornell, H. V. Double jeopardy and global extinction risk in corals and reef fishes. Curr. Biol. 24, 2946–2951 (2014).
CAS  PubMed  Article  Google Scholar 

23.
Kinlan, B. P. & Gaines, S. D. Propagule dispersal in marine and terrestrial environments: a community perspective. Ecology 84, 2007–2020 (2003).
Article  Google Scholar 

24.
Hull, P. M., Darroch, S. A. F. & Erwin, D. H. Rarity in mass extinctions and the future of ecosystems. Nature 528, 345–351 (2015).
CAS  PubMed  Article  Google Scholar 

25.
Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. Adapting the IUCN Red List criteria for invertebrates. Biol. Conserv. 144, 2432–2440 (2011).
Article  Google Scholar 

26.
Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. The underrepresentation and misrepresentation of invertebrates in the IUCN Red List. Biol. Conserv. 149, 147–148 (2012).
Article  Google Scholar 

27.
Estes, J. A., Duggins, D. O. & Rathbun, G. B. The ecology of extinctions in kelp forest communities. Conserv. Biol. 3, 252–264 (1989).
Article  Google Scholar 

28.
Oliver, J. & Babcock, R. Aspects of the fertilization ecology of broadcast spawning corals: sperm dilution effects and in situ measurements of fertilization. Biol. Bull. 183, 409–417 (1992).
CAS  PubMed  Article  Google Scholar 

29.
Knowlton, N., Lang, J. C. & Keller, B. D. Case study of natural population collapse: post-hurricane predation on Jamaican staghorn corals. Smithson. Contrib. Mar. Sci. 31, 1–25 (1990).
Google Scholar 

30.
Gaston, K. J. & Fuller, R. A. Commonness, population depletion and conservation biology. Trends Ecol. Evol. 23, 14–19 (2008).
PubMed  Article  Google Scholar 

31.
Säterberg, T., Sellman, S. & Ebenman, B. High frequency of functional extinctions in ecological networks. Nature 499, 468–470 (2013).
PubMed  Article  CAS  Google Scholar 

32.
Pratchett, M. S. Dietary overlap among coral-feeding butterflyfishes (Chaetodontidae) at Lizard Island, northern Great Barrier Reef. Mar. Biol. 148, 373–382 (2005).
Article  Google Scholar 

33.
Huang, D., Licuanan, W. Y., Baird, A. H. & Fukami, H. Cleaning up the ‘Bigmessidae’: molecular phylogeny of scleractinian corals from Faviidae, Merulinidae, Pectiniidae and Trachyphylliidae. BMC Evol. Biol. 11, 37 (2011).
PubMed  PubMed Central  Article  Google Scholar 

34.
Knowlton, N. & Jackson, J. B. C. New taxonomy and niche partitioning on coral reefs: jack of all trades or master of some? Trends Ecol. Evol. 9, 7–9 (1994).
CAS  PubMed  Article  Google Scholar 

35.
Gilpin, M. E. & Soulé, M. E. in Conservation Biology: The Science of Scarcity and Diversity (ed, Soulé, M. E.) 19–34 (Sinauer Associates, 1986).

36.
Bak, R. P. M. & Meesters, E. H. Population structure as a response of coral communities to global change. Am. Zool. 39, 56–65 (1999).
Article  Google Scholar 

37.
McClanahan, T. R., Ateweberhan, M. & Omukoto, J. Long-term changes in coral colony size distributions on Kenyan reefs under different management regimes and across the 1998 bleaching event. Mar. Biol. 153, 755–768 (2008).
Article  Google Scholar 

38.
Riegl, B. M., Bruckner, A. W., Rowlands, G. P., Purkis, S. J. & Renaud, P. Red Sea coral reef trajectories over 2 decades suggest increasing community homogenization and decline in coral size. PLoS ONE 7, e38396 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).
CAS  Article  Google Scholar 

40.
Global Distribution of Coral Reefs (UNEP-WCMC, WorldFish Centre, WRI & TNC, 2018); https://data.unep-wcmc.org/datasets/

41.
Bruno, J. F. & Valdivia, A. Coral reef degradation is not correlated with local human population density. Sci. Rep. 6, 29778 (2016).

42.
Bruno, J. Data from: Coral reef degradation is not correlated with local human population density. Dryad Digital Repository https://doi.org/10.5061/dryad.48r68 (2016).

43.
Karlson, R. H., Cornell, H. V. & Hughes, T. P. Coral communities are regionally enriched along an oceanic biodiversity gradient. Nature 429, 867–870 (2004).
CAS  PubMed  Article  Google Scholar 

44.
Cornell, H. V., Karlson, R. H. & Hughes, T. P. Scale-dependent variation in coral community similarity across sites, islands, and island groups. Ecology 88, 1707–1715 (2007).
PubMed  Article  Google Scholar 

45.
Cornell, H. V., Karlson, R. H. & Hughes, T. P. Local-regional species richness relationships are linear at very small to large scales in west-central Pacific corals. Coral Reefs 27, 145–151 (2008).
Article  Google Scholar 

46.
Connolly, S. R., Dornelas, M., Bellwood, D. R. & Hughes, T. P. Testing species abundance models: a new bootstrap approach applied to Indo-Pacific coral reefs. Ecology 90, 3138–3149 (2009).
PubMed  Article  Google Scholar 

47.
Reef Habitat Maps (NOAA-NCCOS, accessed 10 November 2017); https://products.coastalscience.noaa.gov/collections/benthic/default.aspx

48.
Purkis, S. J. et al. High-resolution habitat and bathymetry maps for 65,000 sq. km of Earth’s remotest coral reefs. Coral Reefs 38, 467–488 (2019).
Article  Google Scholar 

49.
Roelfsema, C., Phinn, S., Jupiter, S., Comley, J. & Albert, S. Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis. Int. J. Remote Sens. 34, 6367–6388 (2013).
Article  Google Scholar 

50.
Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Article  Google Scholar 

51.
Warton, D. I. & Hui, F. K. C. The arcsine is asinine: the analysis of proportions in ecology. Ecology 92, 3–10 (2011).
PubMed  Article  Google Scholar 

52.
Marsh, L. M., Bradbury, R. H. & Reichelt, R. E. Determination of the physical parameters of coral distributions using line transect data. Coral Reefs 2, 175–180 (1984).
Google Scholar 

53.
Hughes, T. P. Population dynamics based on individual size rather than age: a general model with a reef coral example. Am. Nat. 123, 778–795 (1984).
Article  Google Scholar 

54.
Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: comparative studies of reef-building corals. Ecology 77, 950–963 (1996).
Article  Google Scholar 

55.
Hughes, T. P., Connolly, S. R. & Keith, S. A. Geographic ranges of reef corals (Cnidaria: Anthozoa: Scleractinia) in the Indo-Pacific. Ecology 94, 1659 (2013).
Article  Google Scholar 

56.
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
CAS  PubMed  Article  Google Scholar 

57.
van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).
PubMed  Article  CAS  Google Scholar 

58.
Hubbell, S. P. et al. How many tree species are there in the Amazon and how many of them will go extinct? Proc. Natl Acad. Sci. USA 105, 11498–11504 (2008).
CAS  PubMed  Article  Google Scholar 

59.
Atkinson, A., Siegel, V., Pakhomov, E. A., Jessopp, M. J. & Loeb, V. A re-appraisal of the total biomass and annual production of Antarctic krill. Deep-Sea Res. I 56, 727–740 (2009).
Article  Google Scholar 

60.
Current World Population (Worldometer, accessed 13 May 2020); https://www.worldometers.info/world-population/

61.
California Condor Recovery Program: 2017 Annual Population Status (US Fish and Wildlife Service, 2017).

62.
Goodrich, J. M. et al. Panthera tigris. The IUCN Red List of Threatened Species 2015 Report number e.T15955A50659951 (IUCN, 2015). More

Study site
Small passerines were caught using a 13 m mist net placed between trees and shrubs on the edge of the village of Tarbet, Argyll & Bute (56.21 N 4.71 W), adjacent to a large forestry plantation. Siskins can forage up to at least 5 km from their nest during the breeding season5,18, and the area within 5 km was therefore considered likely to include breeding siskins that would move through the catching site. Plantation forestry species, age, and area were determined from maps from the Forestry Commission compartment data base. There were 1152 ha of plantation forestry within 5 km of the catching site, comprising 79% Sitka spruce, 7% Norway spruce Picea abies, 13% larch and  More

1.
Suess E. Marine cold seeps and their manifestations: geological control, biogeochemical criteria and environmental conditions. Int J Earth Sci. 2014;103:1889–916.
CAS  Article  Google Scholar 
2.
Joye SB. The geology and biogeochemistry of hydrocarbon seeps. Annu Rev Earth Planet Sci. 2020;48:205–31.
CAS  Article  Google Scholar 

3.
Etiope G, Panieri G, Fattorini D, Regoli F, Vannoli P, Italiano F, et al. A thermogenic hydrocarbon seep in shallow Adriatic Sea (Italy): Gas origin, sediment contamination and benthic foraminifera. Mar Pet Geol. 2014;57:283–93.
CAS  Article  Google Scholar 

4.
Kennicutt, MC Habitats and biota of the Gulf of Mexico: before the deepwater horizon oil spill. Ward CH, editor. New York, NY: Springer New York; 2017. p. 275–358.

5.
Ruppel CD, Kessler JD. The interaction of climate change and methane hydrates. Rev Geophys. 2017;55:126–68.
Article  Google Scholar 

6.
Kniemeyer O, Musat F, Sievert SM, Knittel K, Wilkes H, Blumenberg M, et al. Anaerobic oxidation of short-chain hydrocarbons by marine sulphate-reducing bacteria. Nature. 2007;449:898–901.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Jaekel U, Musat N, Adam B, Kuypers M, Grundmann O, Musat F. Anaerobic degradation of propane and butane by sulfate-reducing bacteria enriched from marine hydrocarbon cold seeps. ISME J. 2013;7:885–95.
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Teske A, Carvalho V. Marine hydrocarbon seeps: microbiology and biogeochemistry of a global marine habitat. Cham, Switzerland: Springer Nature; 2020.

9.
Kellogg CA. Enumeration of viruses and prokaryotes in deep-sea sediments and cold seeps of the Gulf of Mexico. Deep Sea Res Part II Top Stud Oceanogr. 2010;57:2002–7.
Article  Google Scholar 

10.
Bryson SJ, Thurber AR, Correa AM, Orphan VJ, Vega Thurber R. A novel sister clade to the enterobacteria microviruses (family Microviridae) identified in methane seep sediments. Environ Microbiol. 2015;17:3708–21.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Paul BG, Bagby SC, Czornyj E, Arambula D, Handa S, Sczyrba A, et al. Targeted diversity generation by intraterrestrial archaea and archaeal viruses. Nat Commun. 2015;6:6585.
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Pan D, Morono Y, Inagaki F, Takai K. An improved method for extracting viruses from sediment: detection of far more viruses in the subseafloor than previously reported. Front Microbiol. 2019;10:878.
PubMed  PubMed Central  Article  Google Scholar 

13.
Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HB, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Jin M, Guo X, Zhang R, Qu W, Gao B, Zeng R. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome. 2019;7:58.
PubMed  PubMed Central  Article  Google Scholar 

15.
Labbe M, Girard C, Vincent WF, Culley AI. Extreme viral partitioning in a marine-derived high arctic lake. mSphere. 2020;5:e00334–00320.
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Okazaki Y, Nishimura Y, Yoshida T, Ogata H, Nakano SI. Genome-resolved viral and cellular metagenomes revealed potential key virus-host interactions in a deep freshwater lake. Environ Microbiol. 2019;21:4740–54.
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Backstrom D, Yutin N, Jorgensen SL, Dharamshi J, Homa F, Zaremba-Niedwiedzka K, et al. Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism. mBio. 2019;10:e02497–02418.
PubMed  PubMed Central  Article  Google Scholar 

18.
Daly RA, Roux S, Borton MA, Morgan DM, Johnston MD, Booker AE, et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat Microbiol. 2019;4:352–61.
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Daly RA, Borton MA, Wilkins MJ, Hoyt DW, Kountz DJ, Wolfe RA, et al. Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales. Nat Microbiol. 2016;1:16146.
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Gregory AC, Zayed AA, Conceicao-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Coutinho FH, Silveira CB, Gregoracci GB, Thompson CC, Edwards RA, Brussaard CPD, et al. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat Commun. 2017;8:15955.
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Chen LX, Meheust R, Crits-Christoph A, McMahon KD, Nelson TC, Slater GF, et al. Large freshwater phages with the potential to augment aerobic methane oxidation. Nat Microbiol. 2020;5:1504–15.
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Cai L, Jorgensen BB, Suttle CA, He M, Cragg BA, Jiao N, et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 2019;13:1857–64.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Danovaro R, Dell’Anno A, Corinaldesi C, Magagnini M, Noble R, Tamburini C, et al. Major viral impact on the functioning of benthic deep-sea ecosystems. Nature. 2008;454:1084–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Middelboe M, Glud RN, Wenzhöfer F, Oguri K, Kitazato H. Spatial distribution and activity of viruses in the deep-sea sediments of Sagami Bay. Jpn Deep Sea Res Part 1 Oceanogr Res Pap. 2006;53:1–13.
Article  Google Scholar 

28.
Danovaro R, Serresi M. Viral density and virus-to-bacterium ratio in deep-sea sediments of the Eastern Mediterranean. Appl Environ Microbiol. 2000;66:1857–61.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Hewson I, Fuhrman JA. Viriobenthos production and virioplankton sorptive scavenging by suspended sediment particles in coastal and pelagic waters. Micro Ecol. 2003;46:337–47.
CAS  Article  Google Scholar 

30.
Corinaldesi C, Dell’Anno A, Danovaro R. Viral infection plays a key role in extracellular DNA dynamics in marine anoxic systems. Limnol Oceanogr. 2007;52:508–16.
CAS  Article  Google Scholar 

31.
Dong X, Greening C, Rattray JE, Chakraborty A, Chuvochina M, Mayumi D, et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nat Commun. 2019;10:1816.
PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Dong X, Rattray JE, Campbell DC, Webb J, Chakraborty A, Adebayo O, et al. Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep. Nat Commun. 2020;11:5825.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Gruber-Vodicka HR, Seah BKB, Pruesse E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems. 2020;5:e00920–00920.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158.
PubMed  PubMed Central  Article  Google Scholar 

36.
Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 2019;36:1925–7.
PubMed  PubMed Central  Google Scholar 

39.
Parks DH, Chuvochina M, Chaumeil PA, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Federhen S. The NCBI taxonomy database. Nucleic Acids Res. 2012;40:D136–43.
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.
PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5:69.
PubMed  PubMed Central  Article  Google Scholar 

44.
Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Marquet M, Hölzer M, Pletz MW, Viehweger A, Makarewicz O, Ehricht R, et al. What the phage: a scalable workflow for the identification and analysis of phage sequences. 2020. https://www.biorxiv.org/content/10.1101/2020.07.24.219899v1.

46.
Kieft K, Zhou Z, Anantharaman K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome. 2020;8:90.
PubMed  PubMed Central  Article  Google Scholar 

47.
Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S, Kyrpides NC. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol. 2020. https://doi.org/10.1101/2020.1105.1106.081778.

48.
Dalcin Martins P, Danczak RE, Roux S, Frank J, Borton MA, Wolfe RA, et al. Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems. Microbiome. 2018;6:138.
PubMed  PubMed Central  Article  Google Scholar 

49.
Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Article  CAS  Google Scholar 

50.
Bin Jang H, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol. 2019;37:632–9.
Article  CAS  Google Scholar 

51.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Roux S, Paez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2020;49:D764–75.

53.
Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUViG). Nat Biotechnol. 2019;37:29–37.
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Castelan-Sanchez HG, Lopez-Rosas I, Garcia-Suastegui WA, Peralta R, Dobson ADW, Batista-Garcia RA, et al. Extremophile deep-sea viral communities from hydrothermal vents: structural and functional analysis. Mar Genom. 2019;46:16–28.
Article  Google Scholar 

55.
Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17:377–86.
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Tominaga K, Morimoto D, Nishimura Y, Ogata H, Yoshida T. In silico prediction of virus-host interactions for marine bacteroidetes with the use of metagenome-assembled genomes. Front Microbiol. 2020;11:738.
PubMed  PubMed Central  Article  Google Scholar 

57.
Ahlgren NA, Ren J, Lu YY, Fuhrman JA, Sun F. Alignment-free d2*oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Res. 2017;45:39–53.
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 2004;32:11–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Skennerton CT, Imelfort M, Tyson GW. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 2013;41:e105.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Dong X, Strous M. An integrated pipeline for annotation and visualization of metagenomic contigs. Front Genet. 2019;10:999.
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Zhou Z, Tran PQ, Breister AM, Liu Y, Kieft K, Cowley ES, et al. METABOLIC: a scalable high-throughput metabolic and biogeochemical functional trait profiler based on microbial genomes. 2020. https://www.biorxiv.org/content/10.1101/761643v1.

62.
Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 2004;5:113.
Article  CAS  Google Scholar 

63.
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 2021;9:37.
PubMed  PubMed Central  Article  Google Scholar 

66.
Vik D, Gazitua MC, Sun CL, Zayed AA, Aldunate M, Mulholland MR et al. Genome-resolved viral ecology in a marine oxygen minimum zone. Environ Microbiol. 2020. https://doi.org/10.1111/1462-2920.15313.

67.
ter Horst AM, Santos-Medellin C, Sorensen JW, Zinke LA, Wilson RM, Johnston ER, et al. Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations. 2020. https://www.biorxiv.org/content/10.1101/2020.12.15.422944v1.full.

68.
Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 2020;48:D265–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
Article  Google Scholar 

71.
Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Jain C, Rodriguez RL, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
PubMed  PubMed Central  Article  CAS  Google Scholar 

73.
Al-Shayeb B, Sachdeva R, Chen LX, Ward F, Munk P, Devoto A, et al. Clades of huge phages from across Earth’s ecosystems. Nature. 2020;578:425–31.
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Ruff SE, Biddle JF, Teske AP, Knittel K, Boetius A, Ramette A. Global dispersion and local diversification of the methane seep microbiome. Proc Natl Acad Sci USA. 2015;112:4015–20.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Trubl G, Jang HB, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:e00076–00018.
CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Roux S, Hallam SJ, Woyke T, Sullivan MB. Viral dark matter and virus-host interactions resolved from publicly available microbial genomes. elife. 2015;4:e08490.
PubMed Central  Article  Google Scholar 

78.
Castelle CJ, Brown CT, Anantharaman K, Probst AJ, Huang RH, Banfield JF. Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nat Rev Microbiol. 2018;16:629–45.
CAS  PubMed  Article  PubMed Central  Google Scholar 

79.
Jarett JK, Dzunkova M, Schulz F, Roux S, Paez-Espino D, Eloe-Fadrosh E, et al. Insights into the dynamics between viruses and their hosts in a hot spring microbial mat. ISME J. 2020;14:2527–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Orsi WD. Ecology and evolution of seafloor and subseafloor microbial communities. Nat Rev Microbiol. 2018;16:671–83.
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Hurwitz BL, Brum JR, Sullivan MB. Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome. ISME J. 2015;9:472–84.
CAS  PubMed  Article  PubMed Central  Google Scholar 

82.
Brum JR, Sullivan MB. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat Rev Microbiol. 2015;13:147–59.
CAS  PubMed  Article  PubMed Central  Google Scholar 

83.
Mara P, Vik D, Pachiadaki MG, Suter EA, Poulos B, Taylor GT, et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 2020;14:3079–92.
CAS  PubMed  PubMed Central  Article  Google Scholar 

84.
Anderson CL, Sullivan MB, Fernando SC. Dietary energy drives the dynamic response of bovine rumen viral communities. Microbiome. 2017;5:155.
PubMed  PubMed Central  Article  Google Scholar 

85.
Gao SM, Schippers A, Chen N, Yuan Y, Zhang MM, Li Q, et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome. 2020;8:89.
PubMed  PubMed Central  Article  Google Scholar 

86.
Zhao R, Summers ZM, Christman GD, Yoshimura KM, Biddle JF. Metagenomic views of microbial dynamics influenced by hydrocarbon seepage in sediments of the Gulf of Mexico. Sci Rep. 2020;10:5772.
CAS  PubMed  PubMed Central  Article  Google Scholar 

87.
Dekas AE, Poretsky RS, Orphan VJ. Deep-sea archaea fix and share nitrogen in methane-consuming microbial consortia. Science. 2009;326:422–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

88.
Zheng, X, Liu, W, Dai, X, Zhu, Y, Wang, J, Zhu, Y et al. Extraordinary diversity of viruses in deep-sea sediments as revealed by metagenomics without prior virion separation. Environ Microbiol. 2020. https://doi.org/10.1111/1462-2920.15154. More

National yield stability
We used the FAOSTAT database (http://www.fao.org/faostat, visited in September 2019) to obtain data on annual crop production (in tons) and area harvested (in hectares) from 1961 to 2010 for 138 crops in 91 populous nations. Following Renard and Tilman16, we accounted for differences among nations in data quality and excluded five nations, namely North Korea, Guinea, Kenya, Mozambique and Zambia, for which at least 20% of the data on area harvested or production were extrapolated by the FAO (see details in16). We calculated for each nation and each year the total annual caloric yield (millions of kcal ha-1). To do so, we first calculated the kcal production of each crop by multiplying the production of each crop by its commodity-specific kilocalorie conversion factor from the USDA Nutrient Database32. In doing so, we were able to compare the production of different crops. Then, we summed these kcal harvests across all crops and divided this value by the sum of harvested area for all crops. We calculated national yield stability (S) as the ratio of mean total annual caloric yield (µT) over its time-detrended standard deviation (σT) for fifty consecutive years (1961–2010). We accounted for a temporal trend of increasing total annual crop yield by implementing a loess regression between annual crop yield and years. σT corresponds to the standard deviation of the residuals of this regression. Finally, we compared this stability index (largely used in the biodiversity-ecological functioning research, e.g.14,16,17,18) with the resilience index used by Zampieri et al.22. Both indices were strongly correlated (r = 0.992), strengthening our findings.
Individual crop yield stability and yield asynchrony
For each country, we quantified the average stability of yields of individual crops as the mean of the inverse of the coefficient of variation of yield of each crop:

$$ {{left( {mathop sum limits_{i = 1}^{N} frac{{mu_{i} }}{{sigma_{i} }}} right)} mathord{left/ {vphantom {{left( {mathop sum limits_{i = 1}^{N} frac{{mu_{i} }}{{sigma_{i} }}} right)} N}} right. kern-nulldelimiterspace} N} $$
(1)

where (mu_{i}) is the temporal mean of crop’s annual kcal yield and (sigma_{i}) its time-detrended standard deviation. Time-detrended crop yield was computed through a loess regression between individual, annual crop yield and years.
We computed the asynchrony between crop yield fluctuations following the index developed by Loreau and De Mazancourt11:

$$ Phi = 1 – frac{{sigma^{2}_{T} }}{{left( {mathop sum nolimits_{i = 1}^{N} sigma_{i} } right)^{2} }} $$
(2)

where Φ is the asynchrony of crop species based on annual caloric yield (millions of kcal ha−1) with (sigma_{T}^{2}) the temporal variance of the time-detrended national yield and (sigma_{i}) the time-detrended standard deviation of each crop’s annual kcal yield. The value of asynchrony varies between zero (perfect synchrony) and one (perfect asynchronous temporal fluctuations).
To test whether yield fluctuations of the most abundant crops have a greater impact on the stability of national food production, we weighted the annual yield of each crop by the proportion of total harvested area occupied by that crop. Average stability of yields of individual crops and yield asynchrony were computed on both the non-weighted and abundance-weighted yields.
Crop diversity
For each country and year, we used both the total number of crop commodities (i.e. crop richness) and the Shannon information index (H′) to quantify crop diversity. H′ weights each crop in a nation by the proportion of total cropland it occupies (pi):

$$ H^{prime } = – mathop sum limits_{i = 1}^{N} left( {p_{i} lnleft[ {p_{i} } right]} right) $$
(3)

with N being the total number of crops grown in a country each year.
The exponential form of the Shannon diversity index gives the effective crop diversity that is the number of crops representing an equal share of harvested area24. In other words, the exponential of the Shannon diversity index weighs all species by their frequency, without favouring either common or rare species24. We averaged the annual effective diversity of crop across the fifty years studied to test the effect of crop diversity on national yield stability.
Agricultural inputs
We extracted the annual national application of nitrogen and the annual cropland area equipped for irrigation from the FAOSTAT database. Because Ireland, New Zealand and Netherlands use much of their fertilizers on pastures rather than croplands, we excluded these nations from our analysis. Similarly, we excluded Egypt because it has 100% of cropland equipped for irrigation. We calculated the annual rates of nitrogen application and irrigation per hectare by dividing their use by the total annual cropland area.
Climate variability
We used global gridded climatic data from the Climate Research Unit of the University of East Anglia33 to compute the year-to-year variability of growing season precipitation and temperature for each country, both strongly affecting the stability of national food production16. From these data, we derived annual precipitation and temperature for each grid cell in a country by taking the sum of monthly precipitation and the mean of monthly temperature values weighted by the proportion of cropland in each grid cell34. We then computed the year-to-year coefficient of variation of cropland-based temperature and precipitation for each country.
Statistical analysis
We used structural equation models (SEMs) to evaluate how irrigation, intensity of use of nitrogen fertilizers and crop diversity affected national yield stability through changes in the average stability of yields of individual crops and asynchrony of yields. SEMs represent a powerful way to disentangle complex mechanisms controlling crop diversity-stability relationships, as previously done in natural ecosystems (e.g.14,15,35,36). We set up two different structural equation models, one based on non-weighted indices of stability of individual crops and asynchrony, the other based on the same indices weighted by the proportion of total harvested area accounted for by each crop. We firstly considered the effects of agricultural inputs and crop diversity on the stability of national food production via the path of average yield stability. The second path quantified the indirect effects of agricultural inputs and crop diversity on national stability via their impacts on crop yield asynchrony. We also accounted for the direct effects of agricultural inputs and crop diversity on national yield stability. Finally, we controlled for the effects of climate variability on total, national yield stability, individual crop yield stability and yield asynchrony. SEMs were run with the lavaan R library37. We used the standardized estimates to compare the relative importance of the different paths. The model fit was evaluated using the Fisher C’score and its associated p values. Because the structural equation model assumes linear relationships between predictors and the dependent variable, we also plotted the relationships between total national yield stability and both asynchrony and average stability of individual crop yield to control for linearity (Fig. 2). Similarly, we investigated the relationships between crop diversity and asynchrony (Fig. 3), as well as between irrigation rate and the average stability of individual crop yield (Fig. 4). More

1.
Díaz, S. et al. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2019).
2.
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).
PubMed  Google Scholar 

3.
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).
PubMed  Google Scholar 

4.
Hughes, J. B., Daily, G. C. & Ehrlich, P. R. Population diversity: its extent and extinction. Science 278, 689–692 (1997).
CAS  PubMed  Google Scholar 

5.
Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10, 121–139 (2017).
PubMed  Google Scholar 

6.
Leigh, D. M. et al. Estimated six per cent loss of genetic variation in wild populations since the Industrial Revolution. Evol. Appl. 12, 1505–1512 (2019).
PubMed  PubMed Central  Google Scholar 

7.
Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl Acad. Sci. USA 114, E6089–E6096 (2017).
CAS  PubMed  Google Scholar 

8.
Laikre, L. et al. Post-2020 goals overlook genetic diversity. Science 367, 1083–1085 (2020).
PubMed  Google Scholar 

9.
The Red List of Threatened Species, Version 2019-3 (IUCN, 2019); http://www.iucnredlist.org

10.
DiBattista, J. D. Patterns of genetic variation in anthropogenically impacted populations. Conserv. Genet. 9, 141–156 (2008).
Google Scholar 

11.
Aguilar, R., Quesada, M., Ashworth, L., Herrerias-Diego, Y. & Lobo, J. Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches. Mol. Ecol. 17, 5177–5188 (2008).
PubMed  Google Scholar 

12.
Willoughby, J. R. et al. The reduction of genetic diversity in threatened vertebrates and new recommendations regarding IUCN conservation rankings. Biol. Conserv. 191, 495–503 (2015).
Google Scholar 

13.
Living Planet Report (WWF, 2018).

14.
Laikre, L. & Ryman, N. Effects on intraspecific biodiversity from harvesting and enhancing natural populations. Ambio 25, 505–509 (1996).
Google Scholar 

15.
Delaney, K. S., Riley, S. P. & Fisher, R. N. A rapid, strong, and convergent genetic response to urban habitat fragmentation in four divergent and widespread vertebrates. PLoS ONE 5, e12767 (2010).
PubMed  PubMed Central  Google Scholar 

16.
Pfenninger, M., Bálint, M. & Pauls, S. U. Methodological framework for projecting the potential loss of intraspecific genetic diversity due to global climate change. BMC Evol. Biol. 12, 224 (2012).
PubMed  PubMed Central  Google Scholar 

17.
Rocha‐Olivares, A., Fleeger, J. W. & Foltz, D. W. Differential tolerance among cryptic species: a potential cause of pollutant-related reductions in genetic diversity. Environ. Toxicol. Chem. 23, 2132–2137 (2004).
PubMed  Google Scholar 

18.
Laikre, L., Schwartz, M. K., Waples, R. S. & Ryman, N. Compromising genetic diversity in the wild: unmonitored large-scale release of plants and animals. Trends Ecol. Evol. 25, 520–529 (2010).
PubMed  Google Scholar 

19.
Channell, R. & Lomolino, M. V. Trajectories to extinction: spatial dynamics of the contraction of geographical ranges. J. Biogeogr. 27, 169–179 (2000).
Google Scholar 

20.
Bijlsma, R. & Loeschcke, V. Genetic erosion impedes adaptive responses to stressful environments. Evol. Appl. 5, 117–129 (2012).
CAS  PubMed  Google Scholar 

21.
Ouborg, N. J., van Treuren, R. & van Damme, J. M. M. The significance of genetic erosion in the process of extinction. Oecologia 86, 359–367 (1991).
CAS  PubMed  Google Scholar 

22.
Lavergne, S. & Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc. Natl Acad. Sci. USA 104, 3883–3888 (2007).
CAS  PubMed  Google Scholar 

23.
Sætre, G.-P. et al. Single origin of human commensalism in the house sparrow. J. Evol. Biol. 25, 788–796 (2012).
PubMed  Google Scholar 

24.
Millette, K. L., Gonzalez, A. & Cristescu, M. E. Breaking ecological barriers: anthropogenic disturbance leads to habitat transitions, hybridization, and high genetic diversity. Sci. Total Environ. 740, 140046 (2020).
CAS  PubMed  Google Scholar 

25.
Millette, K. L. et al. No consistent effects of humans on animal genetic diversity worldwide. Ecol. Lett. 23, 55–67 (2020).
PubMed  Google Scholar 

26.
Allentoft, M. & O’Brien, J. Global amphibian declines, loss of genetic diversity and fitness: a review. Diversity 2, 47–71 (2010).
Google Scholar 

27.
Blomqvist, D., Pauliny, A., Larsson, M. & Flodin, L.-Å. Trapped in the extinction vortex? Strong genetic effects in a declining vertebrate population. BMC Evol. Biol. 10, 33 (2010).
PubMed  PubMed Central  Google Scholar 

28.
Polfus, J. L. et al. Łeghágots’enetę (learning together): the importance of indigenous perspectives in the identification of biological variation. Ecol. Soc. 21, 18 (2016).
Google Scholar 

29.
Marin, K., Coon, A. & Fraser, D. J. Traditional ecological knowledge reveals the extent of sympatric lake trout diversity and habitat preferences. Ecol. Soc. 22, 20 (2017).
Google Scholar 

30.
Small, N. & Munday, M. & Durance, I. The challenge of valuing ecosystem services that have no material benefits. Glob. Environ. Change 44, 57–67 (2017).
Google Scholar 

31.
Satz, D. et al. The challenges of incorporating cultural ecosystem services into environmental assessment. Ambio 42, 675–684 (2013).
PubMed  PubMed Central  Google Scholar 

32.
Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–613 (2010).
CAS  PubMed  Google Scholar 

33.
Rogers, L. A. et al. Centennial-scale fluctuations and regional complexity characterize Pacific salmon population dynamics over the past five centuries. Proc. Natl Acad. Sci. USA 110, 1750–1755 (2013).
CAS  PubMed  Google Scholar 

34.
Brennan, S. R. et al. Shifting habitat mosaics and fish production across river basins. Science 364, 783–786 (2019).
CAS  PubMed  Google Scholar 

35.
Larson, W. A., Lisi, P. J., Seeb, J. E., Seeb, L. W. & Schindler, D. E. Major histocompatibility complex diversity is positively associated with stream water temperatures in proximate populations of sockeye salmon. J. Evol. Biol. 29, 1846–1859 (2016).
CAS  PubMed  Google Scholar 

36.
Freshwater, C. et al. Individual variation, population-specific behaviours and stochastic processes shape marine migration phenologies. J. Anim. Ecol. 88, 67–78 (2018).
PubMed  Google Scholar 

37.
Moore, J. W., McClure, M., Rogers, L. A. & Schindler, D. E. Synchronization and portfolio performance of threatened salmon. Conserv. Lett. 3, 340–348 (2010).
Google Scholar 

38.
Satterthwaite, W. H. & Carlson, S. M. Weakening portfolio effect strength in a hatchery-supplemented Chinook salmon population complex. Can. J. Fish. Aquat. Sci. 72, 1860–1875 (2015).
Google Scholar 

39.
Araki, H., Berejikian, B. A., Ford, M. J. & Blouin, M. S. Fitness of hatchery-reared salmonids in the wild. Evol. Appl. 1, 342–355 (2008).
PubMed  PubMed Central  Google Scholar 

40.
Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318, 100–103 (2007).
CAS  PubMed  Google Scholar 

41.
Carlson, S. M. & Satterthwaite, W. H. Weakened portfolio effect in a collapsed salmon population complex. Can. J. Fish. Aquat. Sci. 68, 1579–1589 (2011).
Google Scholar 

42.
Maldonado, C. et al. Phylogeny predicts the quantity of antimalarial alkaloids within the iconic yellow cinchona bark (Rubiaceae: Cinchona calisaya). Front. Plant Sci. 8, 391 (2017).
PubMed  PubMed Central  Google Scholar 

43.
Cueva-Agila, A. et al. Genetic characterization of fragmented populations of Cinchona officinalis L. (Rubiaceae), a threatened tree of the northern Andean cloud forests. Tree Genet. Genomes 15, 81 (2019).
Google Scholar 

44.
Simpson, R. D., Sedjo, R. A. & Reid, J. W. Valuing biodiversity for use in pharmaceutical research. J. Polit. Econ. 104, 163–185 (1996).
Google Scholar 

45.
Graves, R. A., Pearson, S. M. & Turner, M. G. Species richness alone does not predict cultural ecosystem service value. Proc. Natl Acad. Sci. USA 114, 3774–3779 (2017).
CAS  PubMed  Google Scholar 

46.
Darwin, C. On the Origins of Species by Means of Natural Selection (John Murray, 1859).

47.
Weldon, W. F. R. Mendel’s laws of alternative inheritance in peas. Biometrika 1, 228–254 (1902).
Google Scholar 

48.
Courchamp, F. et al. Rarity value and species extinction: the anthropogenic allee effect. PLoS Biol. 4, e415 (2006).
PubMed  PubMed Central  Google Scholar 

49.
Davis, J. N. Color abnormalities in birds: a proposed nomenclature for birders. Birding 39, 36–46 (2007).
Google Scholar 

50.
Kolbe, J. J. et al. The desire for variety: Italian wall lizard (Podarcis siculus) populations introduced to the United States via the pet trade are derived from multiple native-range sources. Biol. Invasions 15, 775–783 (2013).
Google Scholar 

51.
Tapley, B., Griffiths, R. A. & Bride, I. Dynamics of the trade in reptiles and amphibians within the United Kingdom over a ten-year period. Herpetol. J. 21, 27–34 (2011).
Google Scholar 

52.
Militz, T. A., Foale, S., Kinch, J. & Southgate, P. C. Natural rarity places clownfish colour morphs at risk of targeted and opportunistic exploitation in a marine aquarium fishery. Aquat. Living Resour. 31, 18 (2018).
Google Scholar 

53.
Rowley, J. J. L., Emmett, D. A. & Voen, S. Harvest, trade and conservation of the Asian arowana Scleropages formosus in Cambodia. Aquat. Conserv. Mar. Freshw. Ecosyst. 18, 1255–1262 (2008).
Google Scholar 

54.
Clapp, R. A. Wilderness ethics and political ecology: remapping the Great Bear Rainforest. Polit. Geogr. 23, 839–862 (2004).
Google Scholar 

55.
Cusack, C. M. Save the White Tiger. J Law Soc. Deviance 12, 1 (2016).
Google Scholar 

56.
Zhao, S. et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat. Genet. 45, 67–71 (2013).
CAS  PubMed  Google Scholar 

57.
Gaos, A. R. et al. Hawksbill turtle terra incognita: conservation genetics of eastern Pacific rookeries. Ecol. Evol. 6, 1251–1264 (2016).
PubMed  PubMed Central  Google Scholar 

58.
Read, T. D. et al. Draft sequencing and assembly of the genome of the world’s largest fish, the whale shark: Rhincodon typus Smith 1828. BMC Genom. 18, 532 (2017).
Google Scholar 

59.
Wilting, A. et al. Planning tiger recovery: understanding intraspecific variation for effective conservation. Sci. Adv. 1, e1400175 (2015).
PubMed  PubMed Central  Google Scholar 

60.
Hedrick, P. W. Gene flow and genetic restoration: the florida panther as a case study. Conserv. Biol. 9, 996–1007 (1995).
Google Scholar 

61.
Johnson, W. E. et al. Genetic restoration of the Florida panther. Science 329, 1641–1645 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

62.
Crutsinger, G. M., Souza, L. & Sanders, N. J. Intraspecific diversity and dominant genotypes resist plant invasions. Ecol. Lett. 11, 16–23 (2007).
PubMed  Google Scholar 

63.
Lahr, E. C., Backe, K. M. & Frank, S. D. Intraspecific variation in morphology, physiology, and ecology of wildtype relative to horticultural varieties of red maple (Acer rubrum). Trees 34, 603–614 (2020).
CAS  Google Scholar 

64.
Yoshihara, Y. & Isogai, T. Does genetic diversity of grass improve yield, digestibility, and resistance to weeds, pests and disease infection? Arch. Agron. Soil Sci. 65, 1623–1629 (2019).
Google Scholar 

65.
Busby, P. E., Newcombe, G., Dirzo, R. & Whitham, T. G. Genetic basis of pathogen community structure for foundation tree species in a common garden and in the wild. J. Ecol. 101, 867–877 (2013).
Google Scholar 

66.
Berrang, P., Karnosky, D. F., Mickler, R. A. & Bennett, J. P. Natural selection for ozone tolerance in Populustremuloides. Can. J. Res. 16, 1214–1216 (1986).
CAS  Google Scholar 

67.
Kremp, A. et al. Intraspecific variability in the response of bloom-forming marine microalgae to changed climate conditions. Ecol. Evol. 2, 1195–1207 (2012).
PubMed  PubMed Central  Google Scholar 

68.
Boyden, S., Binkley, D. & Stape, J. L. Competition among eucalyptus trees depends on genetic variation and resource supply. Ecology 89, 2850–2859 (2008).
PubMed  Google Scholar 

69.
Crutsinger, G. M., Reynolds, W. N., Classen, A. T. & Sanders, N. J. Disparate effects of plant genotypic diversity on foliage and litter arthropod communities. Oecologia 158, 65–75 (2008).
PubMed  Google Scholar 

70.
Dubs, F. et al. Positive effects of wheat variety mixtures on aboveground arthropods are weak and variable. Basic Appl. Ecol. 33, 66–78 (2018).
Google Scholar 

71.
Mansion-Vaquié, A., Wezel, A. & Ferrer, A. Wheat genotypic diversity and intercropping to control cereal aphids. Agric. Ecosyst. Environ. 285, 106604 (2019).
Google Scholar 

72.
Tooker, J. F. & Frank, S. D. Genotypically diverse cultivar mixtures for insect pest management and increased crop yields. J. Appl. Ecol. 49, 974–985 (2012).
Google Scholar 

73.
Zhu, Y. et al. Genetic diversity and disease control in rice. Nature 406, 718–722 (2000).
CAS  PubMed  Google Scholar 

74.
Vytopil, E. & Willis, B. L. Epifaunal community structure in Acropora spp. (Scleractinia) on the Great Barrier Reef: implications of coral morphology and habitat complexity. Coral Reefs 20, 281–288 (2001).
Google Scholar 

75.
Mercado-Molina, A. E., Ruiz-Diaz, C. P. & Sabat, A. M. Branching dynamics of transplanted colonies of the threatened coral Acropora cervicornis: morphogenesis, complexity, and modeling. J. Exp. Mar. Biol. Ecol. 482, 134–141 (2016).
Google Scholar 

76.
Lohr, K. E. & Patterson, J. T. Intraspecific variation in phenotype among nursery-reared staghorn coral Acropora cervicornis (Lamarck, 1816). J. Exp. Mar. Biol. Ecol. 486, 87–92 (2017).
Google Scholar 

77.
Morikawa, M. K. & Palumbi, S. R. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc. Natl Acad. Sci. USA 116, 10586–10591 (2019).
CAS  PubMed  Google Scholar 

78.
Contolini, G. M., Reid, K. & Palkovacs, E. P. Climate shapes population variation in dogwhelk predation on foundational mussels. Oecologia 192, 553–564 (2020).
PubMed  Google Scholar 

79.
Allgeier, J. E. et al. Individual behavior drives ecosystem function and the impacts of harvest. Sci. Adv. 6, eaax8329 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

80.
Isaac, M. E. et al. Farmer perception and utilization of leaf functional traits in managing agroecosystems. J. Appl. Ecol. 55, 69–80 (2018).
Google Scholar 

81.
Thomas, E. et al. NTFP harvesters as citizen scientists: validating traditional and crowdsourced knowledge on seed production of Brazil nut trees in the Peruvian Amazon. PLoS ONE 12, e0183743 (2017).
PubMed  PubMed Central  Google Scholar 

82.
Segura, V. et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat. Genet. 44, 825–830 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

83.
Korte, A. & Farlow, A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9, 29 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

84.
Blanchet, S., Prunier, J. G. & De Kort, H. Time to go bigger: emerging patterns in macrogenetics. Trends Genet. 33, 579–580 (2017).
CAS  PubMed  Google Scholar 

85.
Miraldo, A. et al. An Anthropocene map of genetic diversity. Science 353, 1532–1535 (2016).
CAS  PubMed  Google Scholar 

86.
Paz-Vinas, I. et al. Systematic conservation planning for intraspecific genetic diversity. Proc. R. Soc. B Biol. Sci. 285, 20172746 (2018).
Google Scholar 

87.
Coddington, J., Lewin, H. A., Robinson, G. E. & Kress, W. J. The Earth Biogenome Project. Biodivers. Inf. Sci. Stand. 3, e37344 (2019).
Google Scholar 

88.
Crain, R., Cooper, C. & Dickinson, J. L. Citizen science: a tool for integrating studies of human and natural systems. Annu. Rev. Environ. Resour. 39, 641–665 (2014).
Google Scholar 

89.
Kerstes, N. A. G., Breeschoten, T., Kalkman, V. J. & Schilthuizen, M. Snail shell colour evolution in urban heat islands detected via citizen science. Commun. Biol. 2, 264 (2019).
PubMed  PubMed Central  Google Scholar 

90.
Searfoss, A. M., Liu, W. & Creanza, N. Geographically well-distributed citizen science data reveals range-wide variation in the chipping sparrow’s simple song. Anim. Behav. 161, 63–76 (2020).
Google Scholar 

91.
Sauer, J. R., Link, W. A., Fallon, J. E., Pardieck, K. L. & David, J. Ziolkowski Jr. The North American Breeding Bird Survey 1966–2011: summary analysis and species accounts. North Am. Fauna 79, 1–32 (2013).
Google Scholar 

92.
Nugent, J. iNaturalist: citizen science for 21st-century naturalists. Sci. Scope 41, 12 (2018).
Google Scholar 

93.
McKinley, D. C. et al. Citizen science can improve conservation science, natural resource management, and environmental protection. Biol. Conserv. 208, 15–28 (2017).
Google Scholar 

94.
Hedrick, P. W. & Garcia-Dorado, A. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31, 940–952 (2016).
PubMed  Google Scholar 

95.
Waples, R. S. Pacific salmon, Oncorhynchus spp., and the definition of ‘species’ under the endangered species. Act. Mar. Fish. Rev. 53, 11–22 (1991).
Google Scholar 

96.
Moritz, C. Defining ‘evolutionarily significant units’ for conservation. Trends Ecol. Evol. 9, 373–375 (1994).
CAS  PubMed  Google Scholar 

97.
Funk, W. C., McKay, J. K., Hohenlohe, P. A. & Allendorf, F. W. Harnessing genomics for delineating conservation units. Trends Ecol. Evol. 27, 489–496 (2012).
PubMed  PubMed Central  Google Scholar 

98.
Coates, D. J., Byrne, M. & Moritz, C. Genetic diversity and conservation units: dealing with the speciespopulation continuum in the age of genomics. Front. Ecol. Evol. 6, 165 (2018).
Google Scholar 

99.
Whiteley, A. R., Fitzpatrick, S. W., Funk, W. C. & Tallmon, D. A. Genetic rescue to the rescue. Trends Ecol. Evol. 30, 42–49 (2015).
PubMed  Google Scholar 

100.
Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

101.
Des Roches, S. et al. Socio-eco-evolutionary dynamics in cities. Evol. Appl. 14, 248–267 (2021).
PubMed  Google Scholar 

102.
Drury, C. et al. Genomic patterns in Acropora cervicornis show extensive population structure and variable genetic diversity. Ecol. Evol. 7, 6188–6200 (2017).
PubMed  PubMed Central  Google Scholar 

103.
Vasconcelos, R. et al. Combining molecular and landscape tools for targeting evolutionary processes in reserve design: an approach for islands. PLoS ONE 13, e0200830 (2018).
PubMed  PubMed Central  Google Scholar 

104.
Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241 (2002).
Google Scholar 

105.
Hoffmann, A. A., Sgrò, C. M. & Kristensen, T. N. Revisiting adaptive potential, population size, and conservation. Trends Ecol. Evol. 32, 506–517 (2017).
PubMed  Google Scholar 

106.
Carlson, S. M., Cunningham, C. J. & Westley, P. A. H. Evolutionary rescue in a changing world. Trends Ecol. Evol. 29, 521–530 (2014).
PubMed  Google Scholar 

107.
Waldvogel, A.-M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18 (2020).
PubMed  PubMed Central  Google Scholar 

108.
Oke, K. B. et al. Recent declines in salmon body size impact ecosystems and fisheries. Nat. Commun. 11, 4155 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

109.
Thompson, J., Stow, A. & Raftos, D. Lack of genetic introgression between wild and selectively bred Sydney rock oysters Saccostrea glomerata. Mar. Ecol. Prog. Ser. 570, 127–139 (2017).
Google Scholar 

110.
Schindler, D. E., Leavitt, P. R., Brock, C. S., Johnson, S. P. & Quay, P. D. Marine-derived nutrients, commercial fisheries, and production of salmon and lake algae in Alaska. Ecology 86, 3225–3231 (2005).
Google Scholar 

111.
Ainsworth, E. A. The importance of intraspecific variation in tree responses to elevated [CO2]: breeding and management of future forests. Tree Physiol. 36, 679–681 (2016).
CAS  PubMed  Google Scholar  More

MDA-MB-231 generally out-compete MCF-7 cells under all pH, glucose and glutamine levels, and starting frequencies
Fluorescently labeled MCF-7 (GFP) and MDA-MB-231 (RFP) cells allowed us to non-destructively measure population dynamics within each micro-spheroid. We used this spheroid system to: (1) observe the outcome of competition (mixed culture), (2) compare population growth models (mono-cultures), (3) measure intrinsic growth rates (DI), (4) measure carrying capacities (DD), and (5) determine competition coefficients (FD). In experiments 1 and 2, spheroids started with a plating density of 20,000 and 10,000 cells, respectively, with initial conditions of 0%, 20%, 40%, 60%, 80% and 100% MDA-MB-231 cells relative to MCF-7 cells. In experiment 1, additional treatments included neutral (7.4) versus low (6.5) pH and varying levels of glucose (0, 1, 2, 4.5 g/L) (Fig. 1b). Experiment 2 had treatments of pH (7.4, 6.5), glucose (0, 4.5 g/L), and glutamine positive and starved media (Fig. 1c). The ranges of these values encompass both physiologic and extreme tumor conditions. For example, pH ~ 6.5 and very low levels of glucose have been measured near the necrotic core of the tumor. Conversely, high glucose and physiologic pH are present at the tumor edge and near vasculature, and also reflect standard culture conditions for our cell lines. In experiment 1, microspheres were grown for 670 h with fluorescence measured every 24–72 h. In experiment 2, microspheres were grown for 550 h with fluorescence measured every 6–12 h. Growth medium was replaced every 4 days without disturbing the integrity of the microsphere.
Counter to our hypothesis, MDA-MB-231 outcompete MCF-7 cells in physiologic (2 g/L glucose, 7.4 pH) conditions (Fig. 2). However, in more acidic conditions (2 g/L glucose, 6.4 pH) the MCF-7 cells appear to persist, suggesting coexistence (Fig. 3, Supplementary Fig. 1). Furthermore MCF-7 cells persist in either pH condition with high 4 g/L glucose (Fig. 4). This raises two questions: (1) Is the success of MDA-MB-231 in most conditions a consequence of higher initial growth rates, increased carrying capacity, or consistently favorable competition coefficients? and, (2) what characteristics of MCF-7 cells allow them to competitively persist at high glucose conditions?
Figure 2

Photographs of spheroids illustrative of the progression and outcome of competition in normal pH. This series shows the progression of spheroids grown physiological conditions (2 g/L glucose, 7.4 pH). Note that, in this case, the MDA-MB-231 cells appear to take over the spheroids by day 20.

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Figure 3

Photographs of spheroids illustrative of the progression and outcome of competition in acidic pH. This series shows the progression of spheroids grown in acidic conditions (6.5 pH) with 2 g/L glucose. Note that, in this case, the MCF-7 cells appear to persist to the end of the experiment.

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Figure 4

Three time points spanning from the middle to the end for all culture conditions.

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Analysis of growth rate
The growth rate of MCF-7 and MDA-MB-231 cells was quantified by measuring the instantaneous exponential growth rate (in units of per day) for each monoculture during the first 72 h. In general, MCF-7 cells had equal or higher growth rates under all growth conditions when compared to MDA-MB-231 cells. MCF-7 cell growth rate with lower (10,000 initial cells) plating density was reduced when grown in low pH 6.5 (Fig. 5b,c) environments compared to physiologic pH 7.4 (Fig. 5e,f). The higher (20,000 initial cells) plating density resulted in reduced growth rates in normal pH compared to low 6.5 pH (Fig. 5a,d). Not surprisingly, in the harshest environment of low pH 6.5, no glucose, and no glutamine, MCF-7 cells had the lowest, even negative, growth rate (Fig. 5c). Interestingly, the absence of glutamine (Fig. 5c,f) reduced the difference in growth rates between the two cell types, regardless of pH or glucose concentration, consistent with glutamine’s role as a substrate for respiration, which is more pronounced in MCF-7 cells.
Figure 5

Growth rates (per day) of both MCF-7 and MDA-MB-231 cells as measured by the instantaneous exponential growth rate for each monoculture in the first 72 h under all experimental conditions. ANOVA analysis is provided in Supplementary Tables 1–3.

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While the growth rates of MDA-MB-231 cells during these early time points were generally unaffected by changes in environmental conditions, the initial plating density had a significant effect. The higher (20,000 initial cells) plating density (Fig. 5a,d) resulted in slower, even negative, growth rates when compared to the lower (10,000 initial cells) plating density. This suggests that these plating densities may be approaching MDA-MB-231’s carrying capacities.
Analysis of carrying capacity
Due to small differences in the RFU intensity between the GFP and RFP used for the MCF-7 and MDA-MB-231 cells respectively, a direct comparison of carrying capacity using these RFU measurements cannot be made. In this way we split the results in to two figures (Figs. 6 and 7). We see RFU’s higher than 60 in the MCF-7 data while the maximum RFU for MDA-MB-231 data is never greater than 20 (please note the varying y-axis limits in the corresponding figures). Even with this difference in RFU intensity, it is not unreasonable to assume, due to the large difference, the MCF-7 cells do indeed have a higher carrying capacity across all experimental conditions.
Figure 6

Carrying capacities of MCF-7 cells under all experimental conditions, as measured by the mean fluorescence of the final ten time points. ANOVA analysis is provided in Supplementary Tables 4, 5.

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Figure 7

Carrying capacities of MDA-MB-231 cells under all experimental conditions, as measured by the mean fluorescence of the final ten time points. ANOVA analysis is provided in Supplementary Tables 4, 5.

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For MCF-7 cells, an increase in glucose resulted in an increase in carrying capacity across experimental conditions, as would be expected (Fig. 6a–f). Surprisingly, the MCF-7 cells experience a decrease in carrying capacity under normal pH conditions when compared to acidic pH 6.5 conditions suggesting an advantage in acidic conditions.
For the MDA-MB-231 cells, after a high seeding density of 20,000 cells, an increase in glucose resulted in an increase in carrying capacity, just like the MCF-7 cells (Fig. 7a,d). For the low seeding density of 10,000 cells, the opposite occurred (Fig. 7b,c,e, and f), though not as dramatically. Furthermore, the MDA-MB-231 cells experience an increase in carrying capacity in normal pH when compared to acidic pH 6.5 conditions suggesting an advantage in normal pH conditions.
Analysis of initial seeding frequencies
For the analysis of frequency dependent interactions of the two cells lines we analyzed the instantaneous growth rates (in units of per day) during the first 72 h in the mixed spheroids (Figs. 8 and 9). The figures are split between low glucose conditions of 0 g/L (Fig. 8) and high glucose conditions of 4.5 g/L (Fig. 9). Interestingly, under both glucose environments, MDA-MB-231 cells have their maximum growth rates when there are high initial frequencies of MCF-7 cells in the spheroid. This suggests some benefit to the MDA-MB-231 cells from the presence of MCF-7 cells, or less inhibition of growth rates from MCF-7 cells. Furthermore, we see that the MCF-7 cells generally show a maximum growth rate when the seeding frequencies are 40%/60% or 60%/40% (Figs. 8 and 9b,c,e, and f), again suggesting possible benefits to having both cell types in the spheroid.
Figure 8

Based on the first 72 hours, estimates of growth rates (per day) for mixed population tumor spheroids under glucose starved conditions of 0 g/L. ANOVA analysis is provided in Supplementary Tables 1–3.

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Figure 9

Based on the first 72 hours, estimates of growth rates (per day) for mixed population tumor spheroids under glucose rich conditions of 4.5 g/L. ANOVA analysis is provided in Supplementary Tables 1–3.

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In low glucose and at the high seeding density of 20,000 cells, the MCF-7 cells experience a significant decrease in growth rate, even negative growth rates, as the seeding frequency of MDA-MB-231 cells increase. This points to the MDA-MB-231 cells potentially approaching the carrying capacity provided within these nutrient starved conditions, essentially fully depleting all the resources for other cell types. When the glucose is increased, these MCF-7 growth rates increase, suggesting there are now sufficient nutrients for both cell types (Figs. 8 and 9a,d). Upon seeding it appears that both cell types generally have higher initial growth rates when the starting frequencies of MDA-MB-231 are low.
The logistic model best describes individual cell growth
Frequency-dependent effects (here measured as competition coefficients) can only be quantified by modeling the growth of multiple interacting types or species. In order to obtain an accurate estimate of these, the mode of growth must first be determined. In this way, we use the monoculture spheroids of either MCF-7 or MDA-MB-231 alone as a data set to first understand the growth dynamics without the added complexity of the mixed cultured spheroids. Although several growth models have been proposed for spheroids, tumors, and organoids, no specific consensus has been reached34,35. Because of this, we agnostically compared four population growth models: exponential, logistic, Gompertz, and Monod-like (Table 1).
Table 1 The four models considered for estimating the mode of growth of the cancer cells based on data from monoculture spheroids seeded at a low density of 10,000 cells.
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The exponential growth model is commonly used in bacterial studies and assumes unlimited resources. Logistic population growth is the simplest constrained growth model and assumes that per capita population growth rate declines linearly with population size or density36. Although it gives a similar shape, Gompertz growth is exponentially constrained by increasing population. Several authors and works have suggested that the Gompertz equation provides a better fit to the growth of tumors in vivo37. The Monod-like equation imagines resource matching where per capita growth rates are proportional to the amount of resources supplied per individual. It provides a good fit to the population growth curves of bacteria and other single cell organisms grown in chemostats38. Here, we consider all four models when estimating the DI parameter of intrinsic growth rate (r) and the latter three models for estimating the DD parameter of carrying capacity (K).
Using constrained parameter optimization, all four models were fit to the mono-culture spheroid growth of MCF-7 or MDA-MB-231 cells under the eight environmental conditions (Supplementary Fig. 1). Due to the lower number of time points in the experiments with a high seeding density of 20,000 cells, these experiments were not used. Quality of fit for each model was evaluated using adjusted R2, Root Mean Square Error (RMSE), and Akaike Information Criterion (AIC) (Table 2, Supplementary Table 6).
Table 2 Average adjusted R2, RMSE, and AIC when fitting the four growth models to monoculture spheroids.
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Two-way ANOVAs (one for each cell type with pH and nutrient levels as independent variables) showed that adjusted R2’s were similar for the three density-dependent models and significantly lower for the exponential model (Supplementary Table 6). Conversely, the RMSE indicated the best fit for exponential growth (lowest value), lowest quality fit for the Monod-like equation, and similar, intermediate values for the logistic and Gompertz growth models. AIC analysis suggests logistic fits to be the best fit in the majority of scenarios.
While any of these four models could be used to model this system, this analysis suggests either an exponential or logistic model provides the best fit to the data. The principal difference between these two models is that logistic growth includes density-dependence and the growth curve approaches a carrying capacity, whereas exponential growth ultimately leads to extinction (if negative growth rates) or infinitely large population sizes (if positive growth rates). In this way, we evaluated whether the spheroids showed evidence of approaching or reaching a carrying capacity. To do this, we examined the slope of the final ten time points for both the red and green fluorescent values (RFU’s) for all experimental conditions (Supplementary Table 7). As most mono-culture spheroids showed declining slopes, or slopes near to zero, we favor the logistic model for this study.
Parameter estimation for Lotka–Volterra competition model
With the logistic equation showing the best fit for the mono-culture experimental data, we expanded to the Lotka–Volterra competition equations to model the mixed population tumor spheroids. The Lotka–Volterra equations represent a two species extension of logistic growth with the addition of competition coefficients.

$$begin{aligned} frac{{dN_{1} }}{dt} & = N_{1} r_{1} left( {1 – frac{{N_{1} + alpha N_{2} }}{{K_{1} }}} right) \ frac{{dN_{2} }}{dt} & = N_{2} r_{2} left( {1 – frac{{N_{2} + beta N_{1} }}{{K_{2} }}} right) \ end{aligned}$$

where Ni are population sizes, ri are intrinsic growth rates and Ki are carrying capacities. We let species 1 and 2 be MCF-7 and MDA-MB-231, respectively. The competition coefficients (α and β) scale the effects of inter-cell-type competition where α is the effect of MDA-MB-231 on the growth rate of MCF-7 in units of MCF-7, and vice-versa for β. If α (or β) is close to zero, then there is no inter-cell-type suppression; if α (or β) is close to 1 then intra-cell-type competition and inter-cell-type competition are comparable; if α (or β) is > 1 then inter-cell-type competition is severe, and if α (or β) is  (K_{MDA})/β and (K_{MCF})/α  > (K_{MDA}). Similarly, MDA-MB-231 cells are expected to outcompete MCF-7 cells if (K_{MDA})/β  > (K_{MCF}) and (K_{MDA})  > (K_{MCF})/α. Either MCF-7 or MDA-MB-231 will out-compete the other depending upon initial conditions (alternate stable states) when (K_{MCF})  > (K_{MDA})/β and (K_{MDA})  > (K_{MCF})/α. The stable coexistence of MCF-7 and MDA-MB-231 cells is expected when (K_{MDA})/β  > (K_{MCF}) and (K_{MCF})/α  > (K_{MDA}).
To estimate the values for (K_{MCF}), (K_{MDA} ,{ }) α, and β, we fit the co-culture data with low seeding density to the two-species Lotka–Volterra equation creating different fits for each combination of experimental conditions. It is important to note here that the Lotka–Volterra competition equations are not spatially explicit. From Figs. 2, 3 and 4, it can be seen that the cells are segregated within the tumor spheroid. In our application, the Lotka–Volterra competition equations simply provide a first-order linear approximation of between cell-type competition, not a mechanistic model of the consumer-resource dynamics or the spatial dynamics that likely occur in the spheroids.
Using nonlinear constrained optimization, we varied the values for instantaneous growth rates, carrying capacities, and competition coefficients α and β to minimize the RMSE between the Lotka–Volterra model and the experimental data (Fig. 10). In addition to estimating the competition coefficients, this uses the mixed culture data to re-estimate the growth rates and carrying capacities previously estimated from the monoculture spheroids as growth rate and carrying capacity depend on initial seeding density (Figs. 8 and 9).
Figure 10

(a) Estimates for growth rates, carrying capacities, and competition coefficients using nonlinear constrained optimization fitting to the Lotka–Volterra competition growth curves using the low (10,000 cell) seeding density experiments. The average of the four replicates for each experimental condition are shown along with the optimized fit to the Lotka–Volterra model. (b) Optimized parameters for growth rates (per day), carrying capacities, and competition coefficients are shown for each experimental condition. The predicted outcome of competition is given in the last column.

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As in the monoculture parameter estimation, MCF-7 had higher intrinsic growth rates and carrying capacities than MDA-MB-231 under all culture conditions. For all culture conditions, α  > 1 (as high as 12.6) and β ≈ 0 (the magnitude of six of the eight β’s was less than 0.1). Under two conditions (normal Ph and glutamine), the β’s are less than zero allowing for the possibility that MCF-7 cells are actually facilitating the per capita growth rates of the MDA-MB-231. Thus, across all treatments MDA-MB-231 cells had large competitive effects on MCF-7 growth, while the MCF-7 cells had virtually no inhibitory effects on the growth of MDA-MB-231 cells. It appears that this frequency dependent (FD) effect provides a competitive advantage for the MDA-MB-231 cells that allows them to remain in the tumor spheroids despite MCF-7’s higher intrinsic growth rates (DI) and carrying capacities (DD).
The greatest frequency dependent effect of MDA-MB-231 on MCF-7 (highest α) occurred under conditions of 4.5 g/L glucose, 6.5pH, and no glutamine. The greatest effect of MCF-7 cells on MDA-MB-231 (highest β) occurred in the absence of glucose and glutamine at neutral pH. This fits our original predictions; however, the effect of β is not strong enough to rescue MCF-7 cells from competitive exclusion. In accordance with the hypothesis that glucose permits MDA-MB-231 cells to be highly competitive, α’s (the effect of MDA-MB-231 on MCF-7) were generally lower under glucose-starved conditions. Further, both cell types appear to experience decreased growth efficiency and competition when glucose/glutamine starved, and under acidic conditions. This is consistent with more general ecological studies showing that competition between species is generally less under harsh physical conditions39,40.
Using the optimized parameters values calculated by the constrained optimization analysis of the Lotka–Volterra equations can predict the outcome of competition. This analysis suggests that under physiologic pH, regardless of glucose or glutamine concentration, MDA-MB-231 cells will outcompete the MCF-7 cells. Furthermore, under acidic pH, regardless of glucose or glutamine concentration, MDA-MB-231 cells and MCF-7 cells will actually coexist. This is observed experimentally in Figs. 2 and 3 where acidic pH results in coexistence of the two cell types and normal pH results in no MCF-7 cells remaining at the end of the experiment. While a robust result, this does not accord with our original expectations.
In vivo exploration of cell competition
MDA-MB-231 and MCF-7 cells were grown in the mammary fat pads of 8–10 week old nu/nu female mice. Three tumor models were evaluated: only MCF-7, only MDA-MB-231, and 1:1 mix of both cell types with three replicates for each (total of nine mice). Tumor volumes were measured weekly. At 5 weeks, the tumors were harvested and evaluated for ER expression which marks MCF-7 (ER positive) cells providing estimates of the ratio of MDA-MB-231 to MCF-7 cells.
H&E staining indicates that MCF-7 tumors had the greatest viability and lowest necrosis while MDA-MB-231 tumors displayed decreased viability and increased necrosis (Fig. 11a). The small differences in necrotic and viable tissue between the MDA-MB-231 and mixed tumors are indicative of the expansive effect that MDA-MB-231 phenotype has on tumor progression (Fig. 11c).
Figure 11

Results of in vivo mono- and co-culture tumors. (a) Histology slides of MCF-7, MDA-MB-231, and 1:1 co-cultured tumors. Slides were stained with H&E and ER immune stain. (b) Changes in the standardized tumor volume with time (3 mice per treatment). (c) Tumor viability for all three tumor types. Notice a decrease in viability and increase in necrosis in the MDA-MB-231 and mixed tumors. (d) Measures of CAIX, Glut1, and ER biomarker staining for each tumor type. (e) The presence of small clumps of ER staining in mixed tumors reflect the clumping of MCF-7 cells in the progression of the mixed tumor spheroids (these are enlarged photos of the indicated portions of the co-cultured tumors shown in a), suggesting the coexistence of these two phenotypes in certain tumor microenvironments. All error bars show standard error of the mean.

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Changes in tumor size over 5 weeks indicated that MCF-7 cell tumors grew more slowly than MDA-MB-231 tumors and mixed tumors (Fig. 11b; ANCOVA of natural logarithm of standardized size with day as a covariate, F1,49 = 248.0 p  More

1.
Liste, H.-H. & Felgentreu, D. Crop growth, culturable bacteria, and degradation of petrol hydrocarbons (PHCs) in a long-term contaminated field soil. Appl. Soil. Ecol. 31, 43–52 (2006).
Article  Google Scholar 
2.
Smith, M. J., Flowers, T. H., Duncan, H. J. & Alder, J. Effects of polycyclic aromatic hydrocarbons on germination and subsequent growth of grasses and legumes in freshly contaminated soil and soil with aged PAHs residues. Environ. Pollut. 141, 519–525 (2006).
CAS  PubMed  Article  Google Scholar 

3.
Meudec, A., Poupart, N., Dussauze, J. & Deslandes, E. Relationship between heavy fuel oil phytotoxicity and polycyclic aromatic hydrocarbon contamination in Salicornia fragilis. Sci. Total Environ. 381, 146–156 (2007).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Euliss, K., Ho, C.-H., Schwab, A. P., Rock, S. & Banks, M. K. Greenhouse and field assessment of phytoremediation for petroleum contaminants in a riparian zone. Bioresour. Technol. 99, 1961–1971 (2008).
CAS  PubMed  Article  Google Scholar 

5.
Hutchinson, T. C. & Freedman, W. Effects of experimental crude oil spills on subarctic boreal forest vegetation near Norman Wells, N.W.T., Canada. Can. J. Bot. 56, 2424–2433 (1978).
Article  Google Scholar 

6.
Lin, Q. & Mendelssohn, I. A. The combined effects of phytoremediation and biostimulation in enhancing habitat restoration and oil degradation of petroleum contaminated wetlands. Ecol. Eng. 10, 263–274 (1998).
Article  Google Scholar 

7.
Racine, C. H. Long-term recovery of vegetation on two experimental crude oil spills in interior Alaska black spruce taiga. Can. J. Bot. 72, 1171–1177 (1994).
Article  Google Scholar 

8.
Fatima, K., Afzal, M., Imran, A. & Khan, Q. M. Bacterial rhizosphere and endosphere populations associated with grasses and trees to be used for phytoremediation of crude oil contaminated soil. Bull. Environ. Contam. Toxicol. 94, 314–320 (2015).
CAS  PubMed  Article  Google Scholar 

9.
Hashmat, A. J. et al. Characterization of hydrocarbon-degrading bacteria in constructed wetland microcosms used to treat crude oil polluted water. Bull. Environ. Contam. Toxicol. 102, 358–364 (2019).
CAS  PubMed  Article  Google Scholar 

10.
Khan, F. I., Husain, T. & Hejazi, R. An overview and analysis of site remediation technologies. J. Environ. Manag. 71, 95–122 (2004).
Article  Google Scholar 

11.
Sarkar, D., Ferguson, M., Datta, R. & Birnbaum, S. Bioremediation of petroleum hydrocarbons in contaminated soils: comparison of biosolids addition, carbon supplementation, and monitored natural attenuation. Environ. Pollut. 136, 187–195 (2005).
CAS  PubMed  Article  Google Scholar 

12.
Gan, S., Lau, E. V. & Ng, H. K. Remediation of soils contaminated with polycyclic aromatic hydrocarbons (PAHs). J. Hazard. Mater. 172, 532–549 (2009).
CAS  PubMed  Article  Google Scholar 

13.
Anchugova, E. M., Melekhina, E. N., Markarova, MYu. & Shchemelinina, T. N. Approaches to the assessment of the efficiency of remediation of oil-polluted soils. Eurasian Soil Sci. 49, 234–237 (2016).
ADS  CAS  Article  Google Scholar 

14.
Erkenova, M. I., Tolpeshta, I. I., Trofimov, S. Y., Aptikaev, R. S. & Lazarev, A. S. Changes of the content of oil products in the oil-polluted peat soil of a high-moor bog in a field experiment with application of lime and fertilizers. Eurasian Soil Sci. 49, 1310–1318 (2016).
ADS  CAS  Article  Google Scholar 

15.
Sorkhoh, N. A. et al. Bioremediation of volatile oil hydrocarbons by epiphytic bacteria associated with American grass (Cynodon sp.) and broad bean (Vicia faba) leaves. Int. Biodeterior. Biodegrad. 65, 797–802 (2011).
CAS  Article  Google Scholar 

16.
Roy, A. S. et al. Bioremediation potential of native hydrocarbon degrading bacterial strains in crude oil contaminated soil under microcosm study. Int. Biodeterior. Biodegrad. 94, 79–89 (2014).
CAS  Article  Google Scholar 

17.
Cai, B. et al. Comparison of phytoremediation, bioaugmentation and natural attenuation for remediating saline soil contaminated by heavy crude oil. Biochem. Eng. J. 112, 170–177 (2016).
CAS  Article  Google Scholar 

18.
Murygina, V., Gaydamaka, S., Gladchenko, M. & Zubaydullin, A. Method of aerobic-anaerobic bioremediation of a raised bog in Western Siberia affected by old oil pollution. A pilot test. Int. Biodeterior. Biodegrad. 114, 150–156 (2016).
CAS  Article  Google Scholar 

19.
Tahseen, R. et al. Rhamnolipids and nutrients boost remediation of crude oil-contaminated soil by enhancing bacterial colonization and metabolic activities. Int. Biodeterior. Biodegrad. 115, 192–198 (2016).
CAS  Article  Google Scholar 

20.
Baoune, H. et al. Effectiveness of the Zea mays-Streptomyces association for the phytoremediation of petroleum hydrocarbons impacted soils. Ecotoxicol. Environ. Saf. 184, 109591 (2019).
CAS  PubMed  Article  Google Scholar 

21.
Ra, T., Zhao, Y. & Zheng, M. Comparative study on the petroleum crude oil degradation potential of microbes from petroleum-contaminated soil and non-contaminated soil. Int. J. Environ. Sci. Technol. 16, 7127–7136 (2019).
CAS  Article  Google Scholar 

22.
Rajkumari, J., Bhuyan, B., Das, N. & Pandey, P. Environmental applications of microbial extremophiles in the degradation of petroleum hydrocarbons in extreme environments. Environ. Sustain. 2, 311–328 (2019).
CAS  Article  Google Scholar 

23.
Newman, L. A. & Reynolds, C. M. Phytodegradation of organic compounds. Curr. Opin. Biotechnol. 15, 225–230 (2004).
CAS  PubMed  Article  Google Scholar 

24.
Unterbrunner, R. et al. Plant and fertiliser effects on rhizodegradation of crude oil in two soils with different nutrient status. Plant Soil 300, 117–126 (2007).
CAS  Article  Google Scholar 

25.
Muratova, A. Y., Dmitrieva, T. V., Panchenko, L. V. & Turkovskaya, O. V. Phytoremediation of oil-sludge-contaminated soil. Int. J. Phytorem. 10, 486–502 (2008).
CAS  Article  Google Scholar 

26.
Shirdam, R., Zand, A., Bidhendi, G. & Mehrdadi, N. Phytoremediation of hydrocarbon-contaminated soils with emphasis on the effect of petroleum hydrocarbons on the growth of plant species. Phyto 89, 21–29 (2008).
CAS  Article  Google Scholar 

27.
Farias, V. et al. Phytodegradation Potential of Erythrina crista-galli L., Fabaceae petroleum-contaminated soil. Appl. Biochem. Biotechnol. 157, 10–22 (2009).
PubMed  Article  CAS  Google Scholar 

28.
Peng, S., Zhou, Q., Cai, Z. & Zhang, Z. Phytoremediation of petroleum contaminated soils by Mirabilis jalapa L. in a greenhouse plot experiment. J. Hazard. Mater. 168, 1490–1496 (2009).
CAS  PubMed  Article  Google Scholar 

29.
Basumatary, B., Saikia, R., Bordoloi, S., Das, H. C. & Sarma, H. P. Assessment of potential plant species for phytoremediation of hydrocarbon-contaminated areas of upper Assam, India. J. Chem. Technol. Biotechnol. 87, 1329–1334 (2012).
CAS  Article  Google Scholar 

30.
Moubasher, H. A. et al. Phytoremediation of soils polluted with crude petroleum oil using Bassia scoparia and its associated rhizosphere microorganisms. Int. Biodeterior. Biodegrad. 98, 113–120 (2015).
CAS  Article  Google Scholar 

31.
Fatima, K., Imran, A., Amin, I., Khan, Q. M. & Afzal, M. Plant species affect colonization patterns and metabolic activity of associated endophytes during phytoremediation of crude oil-contaminated soil. Environ. Sci. Pollut. Res. Int. 23, 6188–6196 (2016).
CAS  PubMed  Article  Google Scholar 

32.
Khan, S., Afzal, M., Iqbal, S. & Khan, Q. M. Plant–bacteria partnerships for the remediation of hydrocarbon contaminated soils. Chemosphere 90, 1317–1332 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Yavari, S., Malakahmad, A. & Sapari, N. B. A review on phytoremediation of crude oil spills. Water Air Soil Pollut. 226, 279 (2015).
ADS  Article  CAS  Google Scholar 

34.
Okoh, E., Yelebe, Z. R., Oruabena, B., Nelson, E. S. & Indiamaowei, O. P. Clean-up of crude oil-contaminated soils: bioremediation option. Int. J. Environ. Sci. Technol. 17, 1185–1198 (2020).
CAS  Article  Google Scholar 

35.
Naeem, U. & Qazi, M. A. Leading edges in bioremediation technologies for removal of petroleum hydrocarbons. Environ. Sci. Pollut. Res. 27, 27370–27382 (2019).

36.
Tyagi, M., da Fonseca, M. M. R. & de Carvalho, C. C. C. R. Bioaugmentation and biostimulation strategies to improve the effectiveness of bioremediation processes. Biodegradation 22, 231–241 (2011).
CAS  PubMed  Article  Google Scholar 

37.
Fatima, K., Imran, A., Amin, I., Khan, Q. M. & Afzal, M. Successful phytoremediation of crude-oil contaminated soil at an oil exploration and production company by plants-bacterial synergism. Int. J. Phytoremediat. 20, 675–681 (2018).
CAS  Article  Google Scholar 

38.
Walker, D. A. et al. Cumulative impacts of oil fields on Northern Alaskan Landscapes. Science 238, 757–761 (1987).
ADS  CAS  PubMed  Article  Google Scholar 

39.
Maganov, R. U., Markarova, M. Y., Mulyak, V. V., Zagvozdkin, V. E. & Zaikin, I. A. Nature conservation management at the oil and gas companies. Part 1. Reclamation of oil-polluted lands in the Usinsky district of the Komi Republic (Komi Science Center Ural Branch of RAS, Syktyvkar, 2006) ((in Russian)).
Google Scholar 

40.
Vervaeke, P. et al. Phytoremediation prospects of willow stands on contaminated sediment: a field trial. Environ. Pollut. 126, 275–282 (2003).
CAS  PubMed  Article  Google Scholar 

41.
Huang, X.-D., El-Alawi, Y., Gurska, J., Glick, B. R. & Greenberg, B. M. A multi-process phytoremediation system for decontamination of persistent total petroleum hydrocarbons (TPHs) from soils. Microchem. J. 81, 139–147 (2005).
CAS  Article  Google Scholar 

42.
Robson, D. B., Knight, J. D., Farrell, R. E. & Germida, J. J. Natural revegetation of hydrocarbon-contaminated soil in semi-arid grasslands. Can. J. Bot. 82, 22–30 (2004).
Article  Google Scholar 

43.
Grime, J. P. & Pierce, S. The evolutionary strategies that shape ecosystems (Wiley-Blackwell, Chichester, 2012).
Google Scholar 

44.
Ramenskiy, L. G. On principal rules, basic concepts, and terms of land typology, geobotany, and ecology. Sov. Bot. 4, 25–42 (1935).
Google Scholar 

45.
Grime, J. P., Hodgson, J. G. & Hunt, R. Comparative plant ecology: a functional approach to common British species (Unwin Hyman, London, 1988).
Google Scholar 

46.
Thompson, K. Predicting the fate of temperate species in response to human disturbance and global change. In Biodiversity, temperate ecosystems, and global change (eds Boyle, T. J. B. & Boyle, C. E. B.) 61–76 (springer, Berlin, 1994).
Google Scholar 

47.
Massant, W., Godefroid, S. & Koedam, N. Clustering of plant life strategies on meso-scale. Plant Ecol. 205, 47–56 (2009).
Article  Google Scholar 

48.
Novakovsky, A. B. & Panyukov, A. N. Analysis of successional dynamics of a sown meadow using Ramenskii–Grime’s System of ecological strategies. Russ. J. Ecol. 49, 119–127 (2018).
Article  Google Scholar 

49.
Novakovskiy, A. B. & Elsakov, V. V. Hydrometeorological database (HMDB) for practical research in ecology. Data Sci. J. 13, 57–63 (2014).
Article  Google Scholar 

50.
PND F 16.1: 2.21-98. Quantitative chemical analysis of soils. The method of measuring the mass fraction of oil products in soil and soil samples by the fluorimetric method on the Fluorat-02 fluid analyzer. https://www.russiangost.com/p-275219-fr131201213170.aspx

51.
Archegova, I. B., Markarova, M. Y. & Gromova, O. V. Method for reclaiming posttechnogenic lands and lands in remote districts of extreme North. US Patent RU2093974C1 (1997).

52.
Archegova, I. B., Markarova, M. Y. & Gromova, O. V. Method for producing granular fertilizing-seeding material. US Patent RU2099917C1 (1997).

53.
Murygina, V. P., Vojshvillo, N. E. & Kaljuzhnyj, S. V. Biological preparation ‘Roder’ for cleaning soils, soil grounds, sweet and mineralized waters to remove crude oil and petroleum products. US Patent RU2174496C2 (2001).

54.
Murygina, V. P., Markarova, M. Y. & Kalyuzhnyi, S. V. Application of biopreparation “Rhoder” for remediation of oil polluted polar marshy wetlands in Komi Republic. Environ. Int. 31, 163–166 (2005).
CAS  PubMed  Article  Google Scholar 

55.
Lavorel, S. et al. Assessing functional diversity in the field—methodology matters!. Funct. Ecol. 22, 134–147 (2008).
Google Scholar 

56.
Kindt, R. & Coe, R. Tree diversity analysis: a manual and software for common statistical methods for ecological and biodiversity studies (World Agroforestry Centre, Nairobi, 2006).

57.
Hodgson, J. G., Wilson, P. J., Hunt, R., Grime, J. P. & Thompson, K. Allocating C-S-R plant functional types: a soft approach to a hard problem. Oikos 85, 282–294 (1999).
Article  Google Scholar 

58.
Pierce, S., Brusa, G., Vagge, I. & Cerabolini, B. E. L. Allocating CSR plant functional types: the use of leaf economics and size traits to classify woody and herbaceous vascular plants. Funct. Ecol. 27, 1002–1010 (2013).
Article  Google Scholar 

59.
Magguran, A. E. Measuring biological diversity (Blackwell Publishing, Oxford, 2004).
Google Scholar 

60.
Peet, R. K. The measurement of species diversity. Annu. Rev. Ecol. Syst. 5, 285–307 (1974).
Article  Google Scholar 

61.
Kruskal, J. B. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29, 115–129 (1964).
MathSciNet  Article  MATH  Google Scholar 

62.
McCune, B. & Grace, J. B. Analysis of ecological communities (MjM Software Design, Gleneden Beach, 2002).
Google Scholar 

63.
Melekhina, E. N., Markarova, MYu., Shchemelinina, T. N., Anchugova, E. M. & Kanev, V. A. Secondary successions of biota in oil-polluted peat soil upon different biological remediation methods. Eurasian Soil Sci. 48, 643–653 (2015).
ADS  Article  Google Scholar 

64.
Borowik, A., Wyszkowska, J., Gałązka, A. & Kucharski, J. Role of Festuca rubra and Festuca arundinacea in determinig the functional and genetic diversity of microorganisms and of the enzymatic activity in the soil polluted with diesel oil. Environ. Sci. Pollut. Res. Int. 26, 27738–27751 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Wyszkowska, J., Borowik, A. & Kucharski, J. The resistance of Lolium perenne L. × hybridum, Poa pratensis, Festuca rubra, F. arundinacea, Phleum pratense and Dactylis glomerata to soil pollution by diesel oil and petroleum. Plant Soil Environ. 65, 307–312 (2019).
CAS  Article  Google Scholar 

66.
Freedman, W. & Hutchinson, T. Physical and biological effects of experimental crude-oil spills on Low Arctic Tundra in Vicinity of Tuktoyaktuk, Nwt, Canada. Can. J. Bot.-Rev. Can. Bot. 54, 2219–2230 (1976).
Article  Google Scholar 

67.
Kazantseva, M. N. The effect of oil extraction on ground cover of West Siberian taiga forests. Contemp. Probl. Ecol. 4, 582–587 (2011).
Article  Google Scholar 

68.
Lapshina, E. D. & Bleuten, W. Types of deterioration and self-restoration of vegetation of olygotrophic bogs in oil-production areas of Tomsk province, Krylovia. Siberian Bot. J. 1, 129–140 (1999).
Google Scholar 

69.
Cook, R. L., Landmeyer, J. E., Atkinson, B., Messier, J.-P. & Nichols, E. G. Field note: successful establishment of a phytoremediation system at a petroleum hydrocarbon contaminated shallow aquifer: trends, trials, and tribulations. Int. J. Phytorem. 12, 716–732 (2010).
Article  Google Scholar 

70.
Nichols, E. G. et al. Phytoremediation of a petroleum-hydrocarbon contaminated shallow aquifer in Elizabeth City, North Carolina, USA. Remediat. J. 24, 29–46 (2014).
Article  Google Scholar 

71.
Seburn, D. C., Kershaw, G. P. & Kershaw, L. J. Vegetation response to a subsurface crude oil spill on a subarctic right-of-way, Tulita (Fort Norman), Northwest Territories, Canada. Arctic 49, 321–327 (1996).
Article  Google Scholar 

72.
Melekhina, E. N., Markarova, M. Y. U., Anchugova, E. M., Shchemelinina, T. N. & Kanev, V. A. The efficiency assessment of oil polluted soil recultivation methods. Bull. Komi Sci. Center Ural Branch of RAS 27, 61–70 (2016) ((in Russian)).
Google Scholar 

73.
Ma, X. & Burken, J. G. VOCs fate and partitioning in vegetation: use of tree cores in groundwater analysis. Environ. Sci. Technol. 36, 4663–4668 (2002).
ADS  PubMed  Article  Google Scholar 

74.
Haroni, N. N., Badehian, Z., Zarafshar, M. & Bazot, S. The effect of oil sludge contamination on morphological and physiological characteristics of some tree species. Ecotoxicology 28, 507–519 (2019).
CAS  PubMed  Article  Google Scholar 

75.
Banks, M. K., Kulakow, P., Schwab, A. P., Chen, Z. & Rathbone, K. Degradation of crude oil in the rhizosphere of sorghum bicolor. Int. J. Phytorem. 5, 225–234 (2003).
CAS  Article  Google Scholar  More

The following museum acronyms are used (mostly following55):
BMNH, NHM, BM: Natural History Museum, London, formerly British Museum of Natural History (UK);
CAS: California Academy of Sciences, San Francisco (USA);
MHNC: Musée d’histoire naturelle, La Chaux-de-Fonds (CH);
MHNG: Muséum d’Histoire Naturelle, Genève (CH);
MNHN: Muséum national d’Histoire naturelle, Paris (F);
SMNS: Staatliches Museum für Naturkunde, Stuttgart (D);
UWBM The Washington State Museum of Natural History and Culture/Burke Museum, University of Washington (USA);
ZMB: Museum für Naturkunde Berlin, formerly Zoologisches Museum Berlin (D).
Study organism and taxonomic background
Acanthodactylus was first described as a subgenus of Lacerta Cuvier (sic)56, the type species is A. boskianus (Daudin, 1802). The few meristic characters described to be diagnostic for Acanthodactylus56 include: collar connected in the center but free on the sides, tempora squamosal, i.e., temporal region covered by small scales rather than larger shields, ventral scales rectangular and arranged in longitudinal rows, digits acutely fimbriate-denticulate forming “toe fringes”. Fringed toes have evolved in various shapes multiple times in lizards and in Lacertidae, and when triangular, projectional or conical as in Acanthodactylus they are commonly seen as adaptation to windblown sand substrate57.
The enigmatic Acanthodactylus guineensis is among the lesser-known species of the Lacertidae. Only limited information is available regarding the species’ morphology, habitat and distribution (see Meinig & Böhme44 for a review and references therein; and 34, 38, 45, 58). Based on one young specimen, A. guineensis was described as member of the genus Eremias Fitzinger, 1834 by Boulenger40. Boulenger does not mention the slightly projecting third row of scales around the toes and fingers (“fringed toes”) of the type specimen in his quite comprehensive description40, probably because he did not notice it in such a small (SVL 24 mm) specimen and because the projection is indeed rather minor compared to other members of the genus. This is probably also the reason why he did not place guineensis in the genus Acanthodactylus Wiegmann, 1834 but in Eremias Fitzinger, 1834 (in56).
About 30 years later, Boulenger revised the genus Eremias and divided it into five sections he assumed to be natural associations59: (1) Taenieremias Boulenger, 1918—monotypic, type species Eremias guineensis Boulenger, 1887 (currently Acanthodactylus guineensis); (2) Lampreremias Boulenger, 1918—type species Eremias nitida Günther, 1872 (currently Heliobolus nitidus); (3) Pseuderemias Boettger, 1883—type species Eremias mucronata (Blandford, 1870) (currently Pseuderemias mucronata); (4) Mesalina Gray, 1838—type species Eremias rubropunctata (Lichtenstein, 1823) (currently Mesalina rubropunctata); (5) Eremias s. str. Fitzinger, 1834—type Eremias velox (Pallas, 1771) (currently Eremias velox).
Monard47 described Eremias (Taenieremias) benuensis from Cameroon based on a few minor morphological differences compared to E. guineensis, but in 1969 E. benuensis was synonymized with E. guineensis60.
Salvador17 and one year later also Arnold18 in their respective major revisions of the genus Acanthodactylus both found that Eremias (Taenieremias) guineensis agrees with all the characteristic morphological features of Acanthodactylus with the exception of the arrangement of scales around the nostril. However, it was suggested that the E. guineensis condition with an extra suture across the area occupied by the first upper labial scale to produce a smaller first upper labial is easily derived from that found in Acanthodactylus, evidenced by BMNH 1966.430, a juvenile A. erythrurus lineomaculatus (sic) with a similar scale arrangement18. Within Acanthodactylus, both authors placed guineensis in the Western clade and in the Acanthodactylus erythrurus group which was assumed to consist of A. erythrurus, A. savignyi, A. boueti, A. guineensis, and A. blanci which was considered a subspecies of A. savignyi at the time14, 20.
Molecular data and phylogenetic analyses
We aimed at reconstructing a well-supported phylogeny of the genus Acanthodactylus using whole mitochondrial DNA sequences, assembled by means of a shotgun next generation sequencing strategy. Analyses of whole mitogenomes have been shown to resolve many nodes of the lacertid tree with high statistical support (e.g.31). We retrieved muscle tissue from a museum voucher of A. guineensis (ZFMK 59511 from Daroha, near Bobo Dioulasso, Burkina Faso43) that likely was never in contact with formalin for preservation and therefore offered good chances to obtain sufficient amounts of DNA of decent quality for sequencing. We extracted genomic DNA using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) following the protocol provided by the manufacturer. We additionally sequenced a tissue sample of a freshly caught individual of Acanthodactylus schmidti (SB 642) from Abu Dhabi (UAE) to increase sampling of Acanthodactylus spp. in the mitogenomic tree. The lizard was euthanized by injection of an aqueous solution of benzocaine (20%) into the body cavity. Subsequently, a sample of muscle tissue was taken from the thigh, preserved in 98% Ethanol and stored in − 80 °C. Handling, euthanizing and collection of tissue samples of A. schmidti individuals was approved by the NYUAD Institutional Animal Care and Use Committee (IACUC 19-0002) and UAE No Objection Certificate (NOC 8416), and all applied methods were performed in accordance with the relevant guidelines and regulations.
Genomic DNA of SB 642 was extracted from ethanol-preserved muscle tissue using the Qiagen MagAttract HMW DNA Kit (Qiagen, Hilden, Germany) for high molecular weight DNA. We determined DNA yields on a Qubit fluorometer (Qubit, London, UK) with a dsDNA high sensitivity kit. Totals of 52 ng and 80 ng of DNA (diluted in 26 µl 10 mM Tris·Cl, 0.5 mM EDTA, pH 9.0 (AE buffer)) from A. guineensis and A. schmidti, respectively, were used for library preparation.
ZFMK 59511 libraries were prepared with NEB Ultra II FS DNA (New England Biolabs, Ipswich, MA, USA) kit as per protocol instructions with input below 100 ng. For sample SB642, linked reads were generated on a 10X Genomics Chromium following Genome reagent kits v2 instructions. Resulting libraries’ concentration, size distribution and quality were assessed on a Qubit fluorometer (Qubit, London, UK) with a dsDNA high sensitivity kit and on an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) using a High Sensitivity DNA kit. Subsequently, libraries were normalized and pooled, and pools quantified with a KAPA Library quantification kit for Illumina platforms (Roche Sequencing, Pleasanton, CA, USA) on a ABI StepOnePlus qPCR machine (Thermo Fisher Scientific Inc., Waltham, MA, USA), then loaded on a SP flowcell and paired-end sequenced (2 × 150 bp) on an Illumina NovaSeq 6000 next generation sequencer (Illumina, San Diego, CA, USA), and a S2 flowcell for linked read library. Raw reads were deposited in the Sequence Read Archive (SRA) under BioProject ID PRJNA700414 . All mitogenome assemblies and original alignments were deposited in Figshare under  https://doi.org/10.6084/m9.figshare.13754083.v1 .
Raw FASTQ sequenced reads were first assessed for quality using FastQC v0.11.561. The reads where then passed through Trimmomatic v0.3662 (parameters ILLUMINACLIP: trimmomatic_adapter.fa:2:30:10 TRAILING:3 LEADING:3 SLIDINGWINDOW:4:15 MINLEN:36) for quality trimming and adapter sequence removal. Following the quality trimming, the reads were assessed again using FastQC. The executed workflows were performed using BioSAILs63.
For SB 642 (A. schmidti), we received 436,370,000 single-end reads (average read length 139.5 bases (b)) with a raw coverage of 29.29X and a scaffold N50 of 21.12 kilobases (kb). For the MITObim analysis, read number for SB 642 was randomly reduced to 1,000,000 reads (360,463,035 b) using the awk command. For ZFMK 59511 (A. guineensis), we received 57,936,345 paired-end reads (average read length 151 b, raw coverage ~ 10x). Subsequently, the two sets of reads were converted to interleaved format. We used the quality filtered reads to assemble the mitogenomes of A. guineensis and A. schmidti using an iterative mapping strategy in MITObim v. 1.9.164. We used the Acanthodactylus aureus mitogenome (GB accession number xxxxx; assembly method is provided in the next paragraph) as seed for both samples; this rendered an initial mapping of a conserved region from a more distantly related individual unnecessary64. We therefore applied the –quick option in MITObim and iterations were run until no additional reads could be incorporated into the assembly (14 in A. guineensis, eight in A. schmidti).
We also assembled complete or nearly complete mitogenomes for additional twelve species of Gallotiinae and Eremiadini using anchored hybrid enrichment sequence data from Garcia-Porta et al.6 (see Supplementary methods in6 for extraction protocol and sequencing methods and Supplementary Table S1 online in6) with the Podarcis muralis complete mitogenome (GB accession number NC_011607) as reference sequence. The raw data of each individual was quality filtered using Trimmomatic v0.3662 (parameter MINLEN:45) and assembled using MITObim v. 1.9.1. All multiple alignment files generated in the final MITObim iteration were imported to Geneious R11 (https://www.geneious.com) to check for assembly quality and coverage.
The resulting assemblies were annotated with MITOS65 using defaults settings with the vertebrata database as a reference. The annotated assemblies were imported into PhyloSuite66 together with existing mitogenomic sequences of Lacertidae, and Blanus cinereus (Amphisbaenia; outgroup) available in GenBank (as of July 2020). Details of all mitogenomic sequences included in this study and the corresponding GenBank accession numbers are provided in Supplementary Table S1 online. Using PhyloSuite, we exported all protein coding sequences plus the two rRNAs. The resulting files were aligned with MAFFT67 using “auto” settings. Alignments of coding sequences were refined with MACSE68 to account for open reading frame structure during the alignments. The protein coding and rRNAs alignments were finally concatenated into a single alignment delimiting partitions by marker and, for the protein-coding genes, by codons within markers. Mitogenome phylogenies were inferred using a maximum likelihood approach with IQ-TREE v. 1.5.4 software69 and Bayesian inference (BI) analysis with MrBayes 3.270.
For the maximum likelihood approach, best-fitting partitioning schemes and substitution models were selected based on the Akaike Information Criterion (AIC) using the heuristic algorithms implemented in the MFP + MERGE option (Supplementary Table S3 online). After ML inference, branch support was assessed with 1000 ultrafast bootstrap replicates. As the alignments used for phylogenetic inference contained more than one sequence for some species, these terminals in the resulting tree were collapsed a-posteriori for aesthetic reasons using FigTree v1.4 (as depicted in Fig. 1). An uncollapsed version of this tree is presented in Supplementary Fig. S1 online. For the BI analysis, the best-fitting partition/substitution model scheme, as selected by the ModelFinder algorithm in iQTree (Supplementary Table S4 online), was implemented with MrBayes 3.270. Results of two independent runs of 10 million generations, each comprising four Markov Chains (three heated and one cold), were sampled every 1000 generation. Chain mixing and stationery was assessed by examining the standard deviation of split frequencies and by plotting the –lnL per generation using Tracer 1.5 software71. Samples corresponding to the initial phase of the Markov chain (25%) were discarded as burn-in and the remaining results were combined to obtain a majority rule consensus tree and the respective posterior probabilities of nodes.
An additional, more comprehensive, alignment was produced containing all currently known Acanthodactylus species to obtain a higher resolution perspective on the phylogenetic placement of the target species. Since no nuclear data was available for A. guineensis, we extracted from the newly obtained sequences the four best represented mitochondrial gene fragments across Lacertidae, as compiled by Garcia-Porta et al.6, despite the known shortcomings of partial mitochondrial datasets to resolve lacertid relationships9, 72, 73. The corresponding sequences of the ribosomal RNAs (12S, 16S), COB, and NADH-dehydrogenase subunit (ND4) were later added to the alignment of6 using –add and –keeplength options in MAFFT (Supplementary Table S5 online).
Species distribution modeling
As basis for characterizing the species’ bioclimatic envelopes, we compiled an updated distribution map for A. guineensis and A. boueti including new discoveries in museum collections from the current study (see Supplementary Material online), all known records from the literature16, 34, 37, 38, 40, 42, 44, 45, 47, 58, 60, 74,75,76,77 and from the Global Biodiversity Information Facility (GBIF78). We provide all coordinates as latitude (decimal degrees), longitude (decimal degrees). Due to the general scarcity of records for A. guineensis and A. boueti, we added two localities for A. guineensis (12.5°, − 2.5°; 7.5°, 13.5°) and one locality for A. boueti (− 2.5°, 7.5°) published in Trape et al.45 that were not found on GBIF or in other publications, by extracting the center coordinates of the respective 1 × 1 degree grid. These additional localities have coordinates with very low accuracy and the corresponding environmental variables extracted from a 1 × 1 km resolution grid (30 arc-seconds; see below) therefore have high degrees of uncertainty. When examined, the majority of values fell within the total range for the respective environmental variable (the two exceptions are mentioned in the Results section), we consequently decided to keep them for our analysis. We reduced the final locality dataset to one record per km2 (the resolution of the environmental input data, see below) to avoid pseudoreplication using the R package spThin79. We then tested whether the remaining presence points are randomly dispersed or clustered using the Nearest Neighbor Index NNI in the R packages sp80 and spatialEco81. NNI was 1.4 for A. guineensis and 1.2 for A. boueti, indicating that the filtered point dataset consists of randomly distributed points which are not spatially autocorrelated.
As environmental parameters we downloaded spatial layers of the Terrestrial Ecoregions of the World (TEOW)82, global bioclimate and elevation layers39, data on potential evapotranspiration83 and global aridity index84 with a spatial resolution of 30 arc-seconds (~ 1 km2 near the equator). Although the climate datasets are interpolated from data from weather stations much farther apart from one another and are consequently not measurements of actual environmental conditions, they were rigorously cross-validated with observed data (including satellite data) during the development of the dataset and generally showed high correspondence with observations (especially temperature variables)39. Since for many applications such as our species distribution models data at high spatial resolution (i.e., 1 × 1 km) are preferable over lower resolution to capture variation for example across steep climate gradients in mountains39 we chose the 30 arc-seconds dataset over the 5 arc-minutes dataset (~ 9 × 9 km near the equator) for the ecoregions and climate datasets. In addition, we downloaded spatial layers with forest and grassland/scrub/woodland cover from the harmonized soil database (only available in 5 arc-minutes spatial resolution85). We downscaled the forest and grassland/scrub/woodland dataset to 30 arc-seconds resolution despite the resulting slight inaccuracy, which we kept in mind during interpretation but did not consider relevant for the vegetation datasets.
In order to compare the prevailing climate within the distribution range of A. guineensis and A. boueti to all other species of the genus Acanthodactylus we downloaded all available records of the other currently known Acanthodactylus species from GBIF. We cleaned the downloaded datasheet by deleting all entries with missing data for either latitude, longitude, species epithet, or all of those, and removed duplicates or spelling mistakes. We further removed all GBIF entries with imprecise coordinates, i.e. records with coordinates that when plotted landed just outside of coastal areas in the ocean instead of on land (usually coordinates with only two decimals). In addition, we added several curated locality data from Garcia-Porta et al.6 as available from Figshare (https://doi.org/10.6084/m9.figshare.8866271.v1/). Following Tamar et al.14 we treated A. lineomaculatus as a junior synonym of A. erythrurus and changed the respective records in our database accordingly. For species that did not have records published on GBIF we added further locality data from the literature (Supplementary Table S6 online). The final database contained 4286 records from all 44 currently recognized species of Acanthodactylus. We acknowledge that our database is by no means complete, however, we wanted to follow the most conservative approach and use only records that are supported by a voucher specimen and can be traced back using published databases. We trust that our record list covers a nearly complete representation of the ecological conditions inhabited by all currently accepted Acanthodactylus species.
Using the bioclim dataset39 we plotted annual mean temperature (bio1) and annual precipitation (bio12) for all locality records in our Acanthodactylus species database and compared the respective data for A. guineensis and A. boueti with its congeners. We developed species distribution models using Maxent 3.4.146 for A. guineensis and A. boueti. Maxent applies the maximum entropy principle86 for model fitting under the basic premise that the estimated species distribution deviates from a uniform distribution as minimally as required to explain the observations46. We used the cleaned datasets of A. guineensis and A. boueti with only one record per km2 as input presence locality data. We extracted environmental data for all presence sites from the 19 bioclim variables (bio1-19), elevation (alt), aridity index (AI), percentage cover of forest (forest) and grassland, scrub and woodland (grass) per grid cell, as well as four parameters comprising potential evapotranspiration (PET): PET of the wettest, driest, warmest and coldest quarter of the year (PETwet, PETdry, PETwarm, PETcold). To avoid issues resulting from correlated parameters we reduced the number of environmental predictors by performing multicollinearity analyses using the Variance Inflation Factor (VIF) with a threshold  More