Ecology

1.
Banks, S. C. et al. How does ecological disturbance influence genetic diversity?. Trends Ecol. Evol. 28, 670–679 (2013).
PubMed  Article  Google Scholar 
2.
Coltman, D. W., Pilkington, J. G., Smith, J. A. & Pemberton, J. M. Parasite-mediated selection against inbred soay sheep in a free living island population. Evolution 53, 1259–1267 (1999).
PubMed  Google Scholar 

3.
Hedrick, P. W. & Fredrickson, R. Genetic rescue guidelines with examples from Mexican wolves and Florida panthers. Cons. Genet. 11, 615–626 (2010).
Article  Google Scholar 

4.
Frankham, R. Genetics and extinction. Biol. Cons. 126, 131–140 (2005).
Article  Google Scholar 

5.
Markert, J. A. et al. Population genetic diversity and fitness in multiple environments. BMC. Evol Biol. 10, 205 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

6.
Willoughby, J. R. et al. The reduction of genetic diversity in threatened vertebrates and new recommendations regarding IUCN conservation rankings. Biol. Cons. 191, 495–503 (2015).
Article  Google Scholar 

7.
Gray, T. N. E. et al. Rucervus eldii. The IUCN red list of threatened species. e.T4265A22166803 (2015). https://dx.doi.org/10.2305/IUCN.UK.2015-2.RLTS.T4265A22166803.en. Downloaded on 19 January 2020.

8.
Grubb, P. Artiodactyla. In Mammal Species of the World (eds Wilson, D. E. & Reeder, D. M.) 637–722 (Johns Hopkins University Press, Baltimore, 2005).
Google Scholar 

9.
Salter, R. E. & Sayer, J. A. The brow-antlered deer in Myanmar—Its distribution and status. Oryx. 20, 241–245 (1986).
Article  Google Scholar 

10.
McShea, W. J., Leimgruber, P., Aung, M., Monfort, S. L. & Wemmer, C. Range collapse of a tropical cervid (Cervus eldi) and the extent of remaining habitat in central Myanmar. Anim. Conserv. 2, 173–183 (1999).
Article  Google Scholar 

11.
Zhang, Q., Zeng, Z., Ji, Y., Zhang, D. & Song, Y. Microsatellite variation in China’s Hainan Eld’s deer (Cervus eldi hainanus) and implications for their conservation. Cons. Genet. 9, 507–514 (2008).
CAS  Article  Google Scholar 

12.
Zhang, Q., Zeng, Z., Sun, L. & Song, Y. The origin and phylogenetics of Hainan Eld’s deer and implications for Eld’ s deer conservation. Acta. Ther. Sin. 29, 365–371 (2009).
CAS  Google Scholar 

13.
Ranjitsinh, M. K. Keibul Lamjao Sanctuary and the Brow-antlered deer—1972 with notes on a visit in 1975. J. Bom. Nat. His. Soc. 72, 243–255 (1975).
Google Scholar 

14.
Hussain, S. A. & Badola, R. Conservation Ecology of Sangai and Its Wetland Habitat. Study Report Vol. I (Wildlife Institute of India, Dehra Dun, 2013).
Google Scholar 

15.
McShea, W. J., Aung, M., Songer, M. & Connette, G. M. The challenges of protecting an endangered species in the developing world: A case history of Eld’s Deer conservation in Myanmar. Case Stud. Environ. 2, 1–9 (2018).
Article  Google Scholar 

16.
Ginsburg, L., Ingavat, R. & Sen, S. A Middle Pleistocene (Loagian) cave fauna in Northern Thailand. Comptes Rendus de l’Académie des Sciences Paris. 294, 295–297 (1982).
Google Scholar 

17.
Tougard, C. Y., Chaimanee, V., Sutheethron, S. & Triamwichanon, Jaeger, J. J. Extension of the geographic distribution of the giant panda (Ailuropoda) and reasons for its progressive disappearance in Southeast Asia during the Latest Middle Pleistocene. C. R. Acad. Sci. Paris. 323, 973–979 (1996).
CAS  Google Scholar 

18.
Corbett, G. B. & Hill, J. E. The Mammals of the Indomalay Region: A Systematic Review. Natural History Museum Publications (Oxford University Press, Oxford, 1992).
Google Scholar 

19.
Woodruff, D. S. & Turner, L. M. The Indochinese-Sundaic zoogeographic transition: A description and analysis of terrestrial mammal species distributions. J. Biogeo. 36, 803–821 (2009).
Article  Google Scholar 

20.
Hassanin, A. & Ropiquet, A. Molecular phylogeny of the tribe Bovini (Bovidae, Bovinae) and the taxonomic status of the kouprey, Bos sauveli Urbain, 1937. Mol. Phylo. Evol. 33, 896–907 (2004).
CAS  Article  Google Scholar 

21.
Meijaard, E. Solving mammalian riddles. A reconstruction of the Tertiary and Quaternary distribution of mammals and their palaeoenvironments in island South-East Asia. PhD Thesis, The Australian National University, Canberra (2004).

22.
Ropiquet, A. & Hassanin, A. Molecular evidence for the polyphyly of the genus Hemitragus (Mammalia, Bovidae). Mol. Phylo. Evol. 36, 154–168 (2005).
CAS  Article  Google Scholar 

23.
Bird, M. I., Taylor, D. & Hunt, C. Palaeoenvironments of insular Southeast Asia during the Last Glacial Period: A Savanna corridor in Sundaland?. Quat. Sci. Rev. 24, 2228–2242 (2005).
ADS  Article  Google Scholar 

24.
Geist, V. Deer of the World: Their Evolution, Behaviour, and Ecology (Stackpole Books, Mechanicsburg, 1998).
Google Scholar 

25.
Ellerman, J. R. & Morrison-Scott, T. C. S. Checklist of Palaearctic and Indian Mammals, 1758 to 1947 (British Museum Natural History, London, 1951).
Google Scholar 

26.
Gilbert, C., Ropiquet, A. & Hassanin, A. Mitochondrial and nuclear phylogenies of Cervidae (Mammalia, Ruminantia): Systematics, morphology, and biogeography. Mol. Phylo. Evol. 40, 101–117 (2006).
CAS  Article  Google Scholar 

27.
Hassanin, A. et al. Pattern and timing of diversification of cetartiodactyla (mammalia, laurasiatheria), as revealed by a comprehensive analysis of mitochondrial genomes. C. R. Biol. 335, 32–50 (2012).
PubMed  Article  Google Scholar 

28.
Pitra, C., Fickel, J., Meijaard, E. & Groves, C. P. Evolution and phylogeny of old world deer. Mol. Phyl. Evol. 33, 880–895 (2004).
CAS  Article  Google Scholar 

29.
Balakrishnan, C. N., Monfort, S. L., Gaur, A., Singh, L. & Sorenson, M. D. Phylogeography and conservation genetics of Eld’s deer (Cervus eldi). Mol. Ecol. 12, 1–10 (2003).
CAS  PubMed  Article  Google Scholar 

30.
Thomas, O. The nomenclature and the geographical forms of the panolia deer (Rucervus eldi) and its relatives. J. Bom. Nat. His. Soci. 23, 363–367 (1918).
Google Scholar 

31.
Angom, S., Kumar, A., Gupta, S. K. & Hussain, S. A. Analysis of mtDNA control region of an isolated population of Eld’s deer (Rucervus eldii) reveals its vulnerability to inbreeding. Mito. DNA. Part B. 2, 277–280 (2017).
Article  Google Scholar 

32.
Xia, X., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylo. Evol. 26, 1–7 (2002).
Article  Google Scholar 

33.
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).
PubMed  Article  Google Scholar 

34.
Hughes, A. R., Inouye, B. D., Johnson, M. T. J., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).
PubMed  Article  Google Scholar 

35.
Haq, B. U., Hardenbol, J. & Vail, P. R. The chronology of fluctuating sea level since the Triassic. Sci. 235, 1156–1165 (1987).
ADS  CAS  Article  Google Scholar 

36.
Suraprasit, K., Jongautchariyakul, S., Yamee, C., Pothichaiya, C. & Bocherens, H. New fossil and isotope evidence for the Pleistocene zoogeographic transition and hypothesized savanna corridor in peninsular Thailand. Quat. Sci. Rev. 221, 105861 (2019).
Article  Google Scholar 

37.
Suraprasit, K. et al. The middle Pleistocene vertebrate fauna from Khok Sung (Nakhon Ratchasima, Thailand): Biochronological and paleobiogeographical implications. Zoo Keys. 613, 1–157 (2016).
Google Scholar 

38.
Nautiyal, C. M. & Chauhan, M. S. Late Holocene vegetation and climate change in Loktak Lake region, Manipur, based on pollen and chemical evidence. Palaeob. 58, 21–28 (2009).
Google Scholar 

39.
Tripathi, S., Singh, Y. R., Nautiyal, C. M. & Thakur, B. Vegetation history, monsoonal fluctuations and anthropogenic impact during the last 2330 years from Loktak Lake (Ramsar site), Manipur, Northeast India: A pollen-based study. Palynology 42, 406–419 (2017).
Article  Google Scholar 

40.
Leonard, J. A. et al. Phylogeography of vertebrates on the Sunda Shelf: A multi-species comparison. J. Biogeogr. 42, 871–879 (2015).
Article  Google Scholar 

41.
Naish, D. Eld’s deer: Endangered, persisting in fragmented populations, and morphologically weird… but it wasn’t always so. Scientific American Blog Network. https://blogs.scientificamerican.com/tetrapod-zoology/elds-deer-endangered-fragmented-weird/. Accessed on 20 April, 2020 (2015).

42.
National Studbook of Sangai (Rucervus eldii eldii), Wildlife Institute of India, Dehradun and Central Zoo Authority (2018) New Delhi. TR. No. 2018/07. https://wii.gov.in/research_report2018.

43.
Angom, S., Tuboi, C., Ghazi, M. G. U., Badola, R. & Hussain, S. A. Demographic and genetic structure of a severely fragmented population of the endangered hog deer (Axis porcinus) in the Indo Burma biodiversity hotspot. PLoS ONE 15, e0210382 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Hartl, D. L. & Clark, A. G. Organisation of genetic variation. In Principles of Population Genetics (eds Hartl, D. L. & Clark, A. G.) 74–110 (Sinauer Associates, Sunderland, 1997).
Google Scholar 

45.
Sharma, C. & Chauhan, M. S. Vegetation and climate since Last Glacial Maxima in Darjeeling (Mirik Lake), Eastern Himalaya. in Proc. 29th Int. Geol. Congr. Part B, 279.e288 (1994).

46.
Tripathi, S., Thakur, B., Nautiyal, C. M. & Bera, S. K. Floristic and climatic reconstruction in the Indo-Burma region for the last 13,000 cal. yr: A palynological interpretation from the endangered wetlands of Assam, northeast India. The Holocene. 30, 1–17 (2019).
Google Scholar 

47.
Mehrotra, N., Shah, S. K. & Bhattacharyya, A. Review of palaeoclimate records from Northeast India based on pollen proxy data of Late Pleistocene-Holocene. Quat. Inter. 325, 41–54 (2014).
Article  Google Scholar 

48.
Singh, N. R. Fluvial regime of the Manipur river basin and Loktak Lake with study of backflow. M. Tech thesis. Indian Institute of Technology (2006).

49.
Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Article  Google Scholar 

50.
Slatkin, M. & Excoffier, L. Serial founder effects during range expansion: A spatial analog of genetic drift. Genetics 191, 171–181 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol. Cons. 170, 56–63 (2014).
Article  Google Scholar 

52.
Hassanin, A., Ropiquet, A., Couloux, A. & Cruaud, C. Evolution of the mitochondrial genome in mammals living at high altitude: New insights from a study of the tribe Caprini (Bovidae, Antilopinae). J. Mol. Evol. 68, 293–310 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

53.
Moore, S. S., Barendse, W., Berger, K. T., Armitage, S. M. & Hetzel, D. J. S. Bovine and ovine DNA microsatellites from the EMBL and GenBank databases. Anim. Genet. 23, 463–467 (1992).
CAS  PubMed  Article  Google Scholar 

54.
Gaur, A. et al. Development and characterisation of 10 novel microsatellite markers from chital deer (Cervus axis) and their cross-amplification in other related species. Mol. Ecol. Not. 3, 607–609 (2003).
CAS  Article  Google Scholar 

55.
Bishop, M. D. et al. A genetic linkage map for cattle. Genet. 136, 619–639 (1994).
CAS  Article  Google Scholar 

56.
Marshall, T. C., Slate, J., Kruuk, L. E. & Pemberton, J. M. Statistical confidence for likelihood-based paternity inference in natural populations. Mol. Ecol. 7, 639–655 (1998).
CAS  PubMed  Article  Google Scholar 

57.
DeWoody, J. A., Honeycutt, R. L. & Skow, L. C. Microsatellite markers in white-tailed deer. J. Hered. 86, 317–319 (1995).
CAS  PubMed  Article  Google Scholar 

58.
Jones, K. C., Levine, K. F. & Banks, J. D. DNA-based genetic markers in black-tailed and mule deer for forensic applications. California Dept Fish Game. 86, 115–126 (2000).
Google Scholar 

59.
Vaiman, D., Osta, R., Mercier, D., Grohs, C. & Leveziel, H. Characterization of five new bovine dinucleotide repeats. Anim. Genet. 23, 537–541 (1992).
CAS  PubMed  Article  Google Scholar 

60.
Brezinsky, L., Kemp, S. J. & Teale, A. J. ILSTS005: A polymorphic bovine microsatellite. Anim. Genet. 24, 75–76 (1993).
CAS  PubMed  Article  Google Scholar 

61.
Zhang, Q., Ji, Y. J., Zeng, Z. G., Song, Y. L. & Zhang, D. X. Polymorphic microsatellite DNA markers for the vulnerable Hainan Eld’s deer (Cervus eldi hainanus) in China. Act. Zoo. Sin. 51, 530–534 (2005).
CAS  Google Scholar 

62.
Buchanan, F. C. & Crawford, A. M. Ovine dinucleotide repeat polymorphism at the MAF70 locus. Anim. Genet. 23, 185 (1992).
CAS  PubMed  Article  Google Scholar 

63.
Poetsch, M., Seefeldt, S., Maschke, M. & Lignitz, E. Analysis of microsatellite polymorphism in red deer, roe deer, and fallow deer possible employment in forensic applications. Foren. Sci. Int. 6, 1–8 (2001).
Article  Google Scholar 

64.
Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucl. Aci. Res. 22, 4673–4680 (1994).
CAS  Article  Google Scholar 

65.
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Librado, P. & Rozas, J. DnaSPv5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
CAS  Article  Google Scholar 

67.
Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Article  Google Scholar 

68.
Cheng, L., Connor, T. R., Sirén, J., Aanensen, D. M. & Corander, J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol. Biol. Evol. 30, 1224–1228 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Corander, J., Marttinen, P., Siren, J. & Tang, J. Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. BMC. Bioinf. 9, 539 (2008).
Article  CAS  Google Scholar 

70.
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
PubMed  PubMed Central  Article  Google Scholar 

71.
Akaike, H.  A new look at the statistical model identification. IEEE Trans. Autom. Control. 19, 716-723 (1974).
ADS  MathSciNet  MATH  Article  Google Scholar 

72.
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Meth. 9, 772 (2012).
CAS  Article  Google Scholar 

73.
Posada, D. jModelTest: Phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256 (2008).
CAS  PubMed  Article  Google Scholar 

74.
Grant, J. R. & Stothard, P. The CG View Server: A comparative genomics tool for circular genomes. Nucl. Aci. Res. 36, 181–184 (2008).
Article  CAS  Google Scholar 

75.
Xia, X. & Xie, Z. DAMBE: Software package for data analysis in molecular biology and evolution. J. Hered. 92, 371–373 (2001).
CAS  PubMed  Article  Google Scholar 

76.
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29, 1695–1701 (2012).
CAS  PubMed  Article  Google Scholar 

78.
Bibi, F. A multi-calibrated mitochondrial phylogeny of extant Bovidae (artiodactyla, ruminantia) and the importance of the fossil record to systematics. BMC. Evol. Biol. 13, 166 (2013).
PubMed  PubMed Central  Article  Google Scholar 

79.
Dong, W., Pan, Y. & Liu, J. The earliest Muntiacus (Artiodactyla, Mammalia) from the Late Miocene of Yuanmou, southwestern, China. C. R. Palevol. 3, 379–386 (2004).
Article  Google Scholar 

80.
Hulce, D., Li, X., Snyder-Leiby, T. & Liu, C. S. J. GeneMarker® genotyping software: Tools to increase the statistical power of DNA fragment analysis. J. Biomol. Tech. 22, S35–S36 (2011).
PubMed Central  PubMed  Google Scholar 

81.
Valiere, N. GIMLET: A computer program for analysing genetic individual identification data. Mol. Ecol. Not. 2, 377–379 (2002).
CAS  Google Scholar 

82.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 099–1106 (2007).
Article  Google Scholar 

84.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genet. 155, 945–959 (2000).
CAS  Google Scholar 

85.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  Article  Google Scholar 

86.
Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: A website and program for visualising STRUCTURE output and implementing the Evanno method. Cons. Genet. Res. 4, 359–361 (2012).
Article  Google Scholar 

87.
Rosenberg, N. A. DISTRUCT: A program for the graphical display of population structure. Mol. Ecol. Not. 4, 137–138 (2004).
Article  Google Scholar 

88.
Archer, F. I., Adams, P. E. & Schneiders, B. B. strataG: An r package for manipulating, summarising and analysing population genetic data. Mol. Ecol. Res. 17, 5–11 (2017).
CAS  Article  Google Scholar 

89.
Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: A program for detecting recent effective population size reductions from allele data frequencies. J. Hered. 90, 502–503 (1999).
Article  Google Scholar 

90.
Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genet. 144, 2001–2014 (1996).
CAS  Article  Google Scholar 

91.
Luikart, G., Allendorf, F. W., Cornuet, J. M. & Sherwin, W. B. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. 89, 238–247 (1998).
CAS  PubMed  Article  Google Scholar 

92.
Peel, D., Waples, R. S., Macbeth, G. M., Do, C. & Ovenden, J. R. Accounting for missing data in the estimation of contemporary genetic effective population size (Ne). Mol. Ecol. Res. 13, 243–253 (2013).
CAS  Article  Google Scholar 

93.
Waples, R. S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Cons. Genet. 7, 167–184 (2006).
Article  Google Scholar 

94.
Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).
PubMed  Article  Google Scholar 

95.
Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size from genetic data. Mol. Ecol. Res. 14, 209–214 (2014).
CAS  Article  Google Scholar 

96.
Nikolic, N. & Chevalet, C. Detecting past changes in effective population size. Evol. Appl. 7, 663–681 (2014).
PubMed  PubMed Central  Article  Google Scholar 

97.
Chevalet, C. & Nikolic, N. The distribution of coalescence times and distances between microsatellite alleles with changing effective population size. Theor. Popul. Biol. 77, 152–163 (2010).
PubMed  MATH  Article  Google Scholar 

98.
Dallas, J. F. Estimation of microsatellite mutation rates in recombinant inbred strains of mouse. Mam. Gen. 3, 452–456 (1992).
CAS  Article  Google Scholar 

99.
Weber, J. L. & Wong, C. C. Mutation of human short tandem repeats. Hum. Mol. Genet. 2, 1123–1128 (1993).
CAS  PubMed  Article  Google Scholar 

100.
Brinkmann, B., Klintschar, M., Neuhuber, F., Huhne, J. & Rolf, B. Mutation rate in human microsatellites: Influence of the structure and length of the tandem repeat. Am. J. Hum. Genet. 62, 1408–1415 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

101.
Sajantila, A., Lukka, M. & Syvänen, A. Experimentally observed germline mutations at human micro- and minisatellite loci. Eur. J. Hum. Genet. 7, 263–266 (1999).
CAS  PubMed  Article  Google Scholar 

102.
Ellegren, H. Microsatellite mutations in the germline: Implications for evolutionary inference. Trends. Genet. 16, 551–558 (2000).
CAS  PubMed  Article  Google Scholar 

103.
Hrbek, T., de Brito, R. A., Wang, B., Pletscher, L. S. & Cheverud, J. M. Genetic characterisation of a new set of recombinant inbred lines (LGXSM) formed from the intercross of SM/J and LG/J inbred mouse strains. Mam. Gen. 17, 417–429 (2006).
CAS  Article  Google Scholar  More

Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
Tomas Roslin

University of Helsinki, Helsinki, Finland
Tomas Roslin, Laura Antão, Maria Hällfors, Coong Lo, Juri Kurhinen & Otso Ovaskainen

EarthCape OY, Helsinki, Finland
Evgeniy Meyke

Department of Computer Science, Aalto University, Espoo, Finland
Gleb Tikhonov

Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo University, Mieres, Spain
Maria del Mar Delgado

3237 Biology-Psychology Building, University of Maryland, College Park, MD, USA
Eliezer Gurarie

National Park Orlovskoe Polesie, Oryol, Russian Federation
Marina Abadonova

Institute of Botany, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
Ozodbek Abduraimov, Azizbek Mahmudov & Mirabdulla Turgunov

Kostomuksha Nature Reserve, Kostomuksha, Russian Federation
Olga Adrianova, Irina Gaydysh & Natalia Sikkila

Altai State Nature Biosphere Reserve, Gorno-Altaysk, Russian Federation
Tatiana Akimova, Svetlana Chuhontseva, Elena Gorbunova, Yury Kalinkin, Helen Korolyova, Oleg Mitrofanov, Miroslava Sahnevich, Vladimir Yakovlev & Tatyana Zubina

Kabardino-Balkarski Nature Reserve, Kashkhatau, Russian Federation
Muzhigit Akkiev

FSE Zapovednoe Podlemorye, Ust-Bargizin, Russian Federation
Aleksandr Ananin, Evgeniya Bukharova & Natalia Luzhkova

Institute of General and Experimental Biology, Siberian Branch, Russian Academy of Sciences, Ulan-Ude, Russian Federation
Aleksandr Ananin

State Nature Reserve Stolby, Krasnoyarsk, Russian Federation
Elena Andreeva, Nadezhda Goncharova, Alexander Hritankov, Anastasia Knorre, Vladimir Kozsheechkin & Vladislav Timoshkin

Carpathian Biosphere Reserve, Rakhiv, Ukraine
Natalia Andriychuk, Alla Kozurak & Anatoliy Vekliuk

Nizhne-Svirsky State Nature Reserve, Lodeinoe Pole, Russian Federation
Maxim Antipin

State Nature Reserve Prisursky, Cheboksary, Russian Federation
Konstantin Arzamascev

Zapovednoe Pribajkalje (Bajkalo-Lensky State Nature Reserve, Pribajkalsky National Park), Irkutsk, Russian Federation
Svetlana Babina

Darwin Nature Biosphere Reserve, Borok, Russian Federation
Miroslav Babushkin, Andrey Kuznetsov, Natalia Nemtseva, Irina Rybnikova & Nicolay Zelenetskiy

Volzhsko-Kamsky National Nature Biosphere Rezerve, Sadovy, Russian Federation
Oleg Bakin, Elena Chakhireva & Alexey Pavlov

FGBU National Park Shushenskiy Bor, Shushenskoe, Russian Federation
Anna Barabancova & Andrej Tolmachev

Voronezhsky Nature Biosphere Reserve, Voronezh, Russian Federation
Inna Basilskaja & Inna Sapelnikova

Baikalsky State Nature Biosphere Reserve, Tankhoy, Russian Federation
Nina Belova, Olga Ermakova, Irina Kozyr, Aleksandra Krasnopevtseva & Nikolay Volodchenkov

Visimsky Nature Biosphere Reserve, Kirovgrad, Russian Federation
Natalia Belyaeva & Rustam Sibgatullin

Kondinskie Lakes National Park named after L. F. Stashkevich, Sovietsky, Russian Federation
Tatjana Bespalova, Alena Butunina, Aleksandra Esengeldenova, Natalia Korotkikh & Evgeniy Larin

FSBI United Administration of the Kedrovaya Pad’ State Biosphere Nature Reserve and Leopard’s Land National Park, Vladivostok, Russian Federation
Evgeniya Bisikalova

Pechoro-Ilych State Nature Reserve, Yaksha, Russian Federation
Anatoly Bobretsov, Murad Kurbanbagamaev, Irina Megalinskaja, Viktor Teplov, Valentina Teplova & Tatiana Tertitsa

A. N. Severtsov Institute of Ecology and Evolution, Moscow, Russian Federation
Vladimir Bobrov & Igor Pospelov

Komsomolskiy Department, FGBU Zapovednoye Priamurye, Komsomolsk-on-Amur, Russian Federation
Vadim Bobrovskyi, Olga Kuberskaya, Polina Van & Vladimir Van

Tigirek State Nature Reserve, Barnaul, Russian Federation
Elena Bochkareva & Evgeniy A. Davydov

Institute of Systematics and Ecology of Animals, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russian Federation
Elena Bochkareva

State Nature Reserve Bolshaya Kokshaga, Yoshkar-Ola, Russian Federation
Gennady Bogdanov

Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences, Ekaterinburg, Russian Federation
Vladimir Bolshakov

Sikhote-Alin State Nature Biosphere Reserve named after K. G. Abramov, Terney, Russian Federation
Svetlana Bondarchuk, Sergey Elsukov, Ludmila Gromyko, Irina Nesterova & Elena Smirnova

FSBI Prioksko-Terrasniy State Reserve, Danky, Russian Federation
Yuri Buyvolov & Galina Sokolova

Lomonosov Moscow State University, Moscow, Russian Federation
Anna Buyvolova & Ilya Prokhorov

National Park Meshchera, Gus-Hrustalnyi, Russian Federation
Yuri Bykov, Zoya Drozdova & Svetlana Mayorova

South Urals Federal Research Center of Mineralogy and Geoecology, Ilmeny State Reserve, Ural Branch, Russian Academy of Sciences, Miass, Russian Federation
Olga Chashchina, Nadezhda Kuyantseva & Valery Zakharov

FGBU National Park Kenozersky, Arkhangelsk, Russian Federation
Nadezhda Cherenkova, Svetlana Drovnina & Alexander Samoylov

FGBU GPZ Kologrivskij les im. M.G. Sinicina, Kologriv, Russian Federation
Sergej Chistjakov

Altai State University, Barnaul, Russian Federation
Evgeniy A. Davydov

Pryazovskyi National Nature Park, Melitopol’, Ukraine
Viktor Demchenko, Elena Diadicheva & Valeri Sanko

State Nature Reserve Privolzhskaya Lesostep, Penza, Russian Federation
Aleksandr Dobrolyubov & Aleksey Kudryavtsev

Komarov Botanical Institute, Russian Academy of Sciences, Saint Petersburg, Russian Federation
Ludmila Dostoyevskaya, Violetta Fedotova & Pavel Lebedev

Sary-Chelek State Nature Reserve, Aksu, Kyrgyzstan
Akynaly Dubanaev

Institute for Evolutionary Ecology NAS Ukraine, Kiev, Ukraine
Yuriy Dubrovsky

FGBU State Nature Reserve Kuznetsk Alatau, Mezhdurechensk, Russian Federation
Lidia Epova

Kerzhenskiy State Nature Biosphere Reserve, Nizhny Novgorod, Russian Federation
Olga S. Ermakova

FSBI United Administration of the Mordovia State Nature Reserve and National Park Smolny, Republic of Mordovia, Saransk, Russian Federation
Elena Ershkova

Ogarev Mordovia State University, Saransk, Russian Federation
Elena Ershkova

Bryansk Forest Nature Reserve, Nerussa, Russian Federation
Oleg Evstigneev, Evgeniya Kaygorodova, Sergey Kossenko, Sergey Kruglikov & Elena Sitnikova

Pinezhsky State Nature Reserve, Pinega, Russian Federation
Irina Fedchenko, Lyudmila Puchnina, Svetlana Rykova & Andrei Sivkov

The Central Chernozem State Biosphere Nature Reserve named after Professor V.V. Alyokhin, Kurskiy, Russian Federation
Tatiana Filatova

Tyumen State University, Tyumen, Russian Federation
Sergey Gashev

Reserves of Taimyr, Norilsk, Russian Federation
Anatoliy Gavrilov, Leonid Kolpashikov, Elena Pospelova & Violetta Strekalovskaya

Chatkalski National Park, Toshkent, Uzbekistan
Dmitrij Golovcov

National Park Ugra, Kaluga, Russian Federation
Tatyana Gordeeva & Viktorija Teleganova

Kaniv Nature Reserve, Kaniv, Ukraine
Vitaly Grishchenko, Yuliia Kulsha, Vasyl Shevchyk & Eugenia Yablonovska-Grishchenko

Smolenskoe Poozerje National Park, Przhevalskoe, Russian Federation
Vladimir Hohryakov, Gennadiy Kosenkov & Ksenia Shalaeva

FSBI Zeya State Nature Reserve, Zeya, Russian Federation
Elena Ignatenko, Klara Pavlova & Sergei Podolski

Polistovsky State Nature Reserve, Pskov, Russian Federation
Svetlana Igosheva & Tatiana Novikova

Ural State Pedagogical University, Yekaterinburg, Russian Federation
Uliya Ivanova, Margarita Kupriyanova, Tamara Nezdoliy, Nataliya Skok & Oksana Yantser

Institute of Mathematical Problems of Biology RAS—the Branch of the Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, Russian Federation
Natalya Ivanova & Maksim Shashkov

Kronotsky Federal Nature Biosphere Reserve, Yelizovo, Russian Federation
Fedor Kazansky & Darya Panicheva

Zhiguli Nature Reserve, P. Bakhilova Polyana, Russian Federation
Darya Kiseleva

Institute for Ecology and Geography, Siberian Federal University, Krasnoyarsk, Russian Federation
Anastasia Knorre

Central Forest State Nature Biosphere Reserve, Tver, Russian Federation
Evgenii Korobov, Elena Shujskaja, Sergei Stepanov & Anatolii Zheltukhin

National Park Bashkirija, Nurgush, Russian Federation
Elvira Kotlugalyamova & Lilija Sultangareeva

State Nature Reserve Kurilsky, Juzhno-Kurilsk, Russian Federation
Evgeny Kozlovsky

Vodlozersky National Park, Karelia, Petrozavodsk, Russian Federation
Elena Kulebyakina & Viktor Mamontov

State Nature Reserve Kivach, Kondopoga, Russian Federation
Anatoliy Kutenkov, Nadezhda Kutenkova, Anatoliy Shcherbakov, Svetlana Skorokhodova, Alexander Sukhov & Marina Yakovleva

South-Ural Federal University, Miass, Russian Federation
Nadezhda Kuyantseva

Saint-Petersburg State Forest Technical University, St. Petersburg, Russian Federation
Pavel Lebedev

Astrakhan Biosphere Reserve, Astrakhan, Russian Federation
Kirill Litvinov

FSBI United Administration of the Lazovsky State Reserve and National Park Zov Tigra, Lazo, Russian Federation
Lidiya Makovkina, Aleksandr Myslenkov & Inna Voloshina

State Nature Reserve Tungusskiy, Krasnoyarsk, Russian Federation
Artur Meydus, Julia Raiskaya & Vladimir Sopin

Krasnoyarsk State Pedagogical University named after V.P. Astafyev, Krasnoyarsk, Russian Federation
Artur Meydus

Institute of Geography, Russian Academy of Sciences, Moscow, Russian Federation
Aleksandr Minin

Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, Russian Federation
Aleksandr Minin

Carpathian National Nature Park, Yaremche, Ukraine
Mykhailo Motruk

State Environmental Institution National Park Braslav lakes, Braslav, Belarus
Nina Nasonova

National Park Synevyr, Synevyr-Ostriki, Ukraine
Tatyana Niroda, Ivan Putrashyk, Yurij Tyukh & Yurij Yarema

Pasvik State Nature Reserve, Nikel, Russian Federation
Natalja Polikarpova

Mari Chodra National Park, Krasnogorsky, Russian Federation
Tatiana Polyanskaya

State Nature Reserve Vishersky, Krasnovishersk, Russian Federation
Irina Prokosheva

State Nature Reserve Olekminsky, Olekminsk, Russian Federation
Yuri Rozhkov, Olga Rozhkova & Dmitry Tirski

Crimea Nature Reserve, Alushta, Republic of Crimea
Marina Rudenko

Forest Research Institute Karelian Research Centre, Russian Academy of Sciences, Petrozavodsk, Russian Federation
Sergei Sazonov, Lidia Vetchinnikova & Juri Kurhinen

Black Sea Biosphere Reserve, Hola Prystan’, Ukraine
Zoya Selyunina

Institute of Physicochemical and Biological Problems in Soil Sciences, Russian Academy of Sciences, Pushchino, Russian Federation
Maksim Shashkov

State Nature Reserve Nurgush, Kirov, Russian Federation
Sergej Shubin & Ludmila Tselishcheva

Caucasian State Biosphere Reserve of the Ministry of Natural Resources, Maykop, Russian Federation
Yurii Spasovski

National Nature Park Vyzhnytskiy, Berehomet, Ukraine
Vitalіy Stratiy

National Park Khvalynsky, Khvalynsk, Russian Federation
Guzalya Suleymanova

State Research Center Arctic and Antarctic Research Institute, Saint Petersburg, Russian Federation
Aleksey Tomilin

Information-Analytical Centre for Protected Areas, Moscow, Russian Federation
Aleksey Tomilin

State Nature Reserve Malaya Sosva, Sovetskiy, Russian Federation
Aleksander Vasin & Aleksandra Vasina

Krasnoyarsk State Medical University named after Prof. V.F.Voino-Yasenetsky, Krasnoyarsk, Russian Federation
Vladislav Vinogradov

Surhanskiy State Nature Reserve, Sherabad, Uzbekistan
Tura Xoliqov

Mordovia State Nature Reserve, Pushta, Russian Federation
Andrey Zahvatov

Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
Otso Ovaskainen

The data were collected by the 195 authors starting from M.A. and ending with T.Z. in the author list. J.K., E.M., C.L., G.T. and E.G. contributed to the establishment and coordination of the collaborative network and to the compilation and curation of the resulting dataset. T.R., O.O., L.A., M.H. and M.d.M.D. conceived the idea behind the current study and wrote the first draft of the paper, with O.O. conducting the analyses. All authors provided useful comments on earlier drafts. More

Samples
We sampled 73 adult fox skulls (Vulpes vulpes) from three separate sample groups: wild (8 F, 12 M), unselected (15 F, 8 M), and domesticated (15 F, 15 M). Domesticated and unselected skulls from the RFF experiment were generously provided by Dr. Trut and transported to Harvard in 2004. Unfortunately, we do not know how these foxes were chosen, but have no reason not to assume that they were selected randomly from both populations. Wild fox skulls in the study were sampled from the collections of the Museum of Comparative Zoology, Harvard University. All but two wild foxes were trapped in Canada east of Quebec between 1894 and 1952, with the majority (70%) between 1894 and 1900 (Table S1). We excluded from the study sample all juvenile skulls, as determined through lower third molar eruption and fusion of the cranial suture between the basioccipital and basisphenoid16, and those skulls that had evidence of damage or disease. After applying these exclusion criteria, we arrived at our final sample of 73 skulls.
3D landmarks
To prevent movement during measurement, each skull was embedded in styrofoam and secured to the workspace desk before 3D coordinates of 29 landmarks, listed, defined and displayed in Table S2 and Fig S1, were collected on the left half of each skull by a single analyst (TMK). Of these 29 landmarks, 17% are on the cranial base, 27% are on the neurocranium, and 55% are on the face. 3D landmark coordinates were measured with a Microscribe G2 (Positional Accuracy ± 0.38 mm, Revware, Inc.). This machine consists of a mobile robotic arm tipped with a probe. After calibration, the probe tip is placed on each landmark to record its XYZ coordinates. To avoid having to move the skulls during measuring and to limit the number of variables in our final GM analysis (too high a number may be a problem given our small sample size), we restricted landmark measurements to one half of each skull. We assume that the fluctuating asymmetry between each fox population is negligible and stable as has previously been shown in comparisons across a domestic-wild hybridization zone in mice17. In most cases, landmark positions were lightly marked with pencil to ensure proper probe placement.
Linear and endocranial volume measurements
Six linear measurements were taken on each skull using digital calipers (Fowler High Precision, Positional Accuracy ± 0.03 mm): total skull length, snout length, cranial vault height, cranial vault width, bi-zygomatic width and upper jaw width (Fig. 1). Endocranial volume was measured using plastic beads. Each cranium was filled up to the level of the foramen magnum and repeatedly shaken and tamped down until no more beads could be added. The beads were then funneled into a graduated cylinder to obtain a volumetric measurement.
Figure 1

Schematic diagram of the six linear measurements taken on the fox skulls. 1: Bi-zygomatic width. 2: Cranial vault width. 3: Upper jaw width. 4: Total skull length. 5: Snout length. 6: Cranial vault height.

Full size image

Geometric morphometrics
Generalized Procrustes analysis was conducted in R v. 4.0.218 using the geomorph v. 3.3.1 package19. Landmark configurations from each specimen were translated to the origin, rescaled to centroid size, and optimally rotated (using a least-squares criterion) until the coordinates of homologous landmarks aligned as closely as possible. These steps place all specimens in the same shape space, centered on the mean shape. An orthogonal projection into a linear tangent space was applied so statistical analyses could be performed on the resulting tangent space coordinates. For Procrustes superimposition, we used the default parameters of the gpagen function in geomorph.
Repeatability analyses
Landmark measurement repeatability was evaluated through repeated measurements of three fox skulls (domesticated male ID# TM23, domesticated female ID# TF476, unselected female ID# UF1058) on ten separate occasions. In this case, repeatability encompasses both Microscribe and operator error. Generalized Procrustes Analysis (GPA) was used on these landmark coordinates to ensure that they were in the same 3D location relative to one another. The average Procrustes distance (PD) between all ten iterations of the same specimen was then compared to the average Procrustes distance within the population-sex grouping to which the skull belonged. To do this, we calculated a sensitivity ratio based on the formula: (Mean Inter-specimen PD – Mean Intra-specimen PD)/Mean Intra-specimen PD. This created a sensitivity ratio that reflects how sensitive the Microscribe measurements are with respect to the average difference among foxes of the same population-sex category. Averaging the sensitivity ratios for our three skulls, we find that the difference between replicates is roughly 3.7 times smaller than the differences within population-sex groupings. This indicates that the Microscribe G2 is robust enough to detect subtle individual differences in measured landmark coordinates. Linear and endocranial measurement repeatability was quantified through a similar method where repeat measurements were taken on 3 domesticated female fox skulls on 15 separate occasions. Sensitivity ratios were deteremined for each measurement (i.e. total skull length, snout length etc.) by calculating the standard deviation of each repeated measurement on a single specimen, averaging the three specimens’ standard deviations for that measurement, and then comparing that value to the population (domesticated female) standard deviation for that measurement. With the exception of cranial vault height (see limitations), the replicate standard deviations of each measurement were roughly a third (or less) of the population standard deviations (Table S3).
Statistical analyses
All statistical analyses were performed in R18. For all parametric inferences, we report point and interval (95% confidence) estimates of effect sizes, while for permutation-based inferences we report point estimates and p-values. All p-values involving multiple comparisons were adjusted for family-wise error using the sequential Bonferroni method.
3D shape comparisons
To test hypotheses about shape differences among the three populations of foxes, we used a permutation-based Procrustes MANOVA to regress tangent space coordinates on population identity and sex in the geomorph v. 3.3.1 package. Because we are unable to detect significant differences in allometry among populations with a permutation-based Procrustes MANOVA of tangent space coordinates on the interaction term of population identity and centroid size, the tangent space coordinates were not corrected for any scaling effects (see Supplemental information and Fig S2). Given this result, we control only for isometric size in geometric morphometric analyses (i.e. no correction for scaling in tangent space coordinates) as well as in our linear measurements. To determine how skull shape differed between fox populations, we performed pairwise comparisons of shape using Procrustes distances. We additionally performed pairwise comparisons between groups of the shape variance within a group (as assessed by the dispersion of residuals around the mean shape for a given population)20. All pairwise comparisons were made using the RRPP v. 0.6.1 package21,22. Permutation-based p-values for the pairwise comparisons were corrected for family-wise error using the sequential Bonferroni method. To visualize changes in skull shape between populations, a principal components analysis (PCA) was performed on the tangent space coordinates. Skull warp changes along the first principal component were graphed to visualize shape changes along this axis. Size differences between populations were assessed via a linear model using a weighted least squares (WLS) estimator, where centroid size was regressed on population identity and sex. The WLS estimator allowed for separate residual variances for each combination of population and sex, so that heteroskedasticity across these groups could be accounted for in the model. Variance in centroid size was assessed with a Levene’s test based on absolute deviations from the median and was performed using the car package v. 3.0-1023.
Linear and endocranial volume comparisons
Prior to modeling linear and volumetric data, we created size-adjusted versions of our variables to account for a difference in isometric size between wild and RFF populations. Normalizing to size allows us to parse out the effects of size selection from those of selection for docility as they likely have overlapping effects on craniofacial shape. We adjusted for size by normalizing each linear measurement and the cube root of endocranial volume by centroid size. We used centroid size rather than the geometric mean of the six linear measurements because centroid size was calculated using a larger sample of craniometric landmarks and is therefore the better proxy of overall cranial size. We performed size corrections on the raw measurements instead of including a size variable in the models because it allows the size-correction to be intrinsic to each fox rather than depending on the size of every fox in the model.
To determine if there were population-level differences in size-corrected linear and volumetric variables, we used a linear model with a generalized least squares (GLS) estimator from the nlme v. 3.1-150 package24 to regress all 7 skull variables simultaneously as correlated responses on population identity and sex (see Supplementary Methods for details of estimation strategy and model specification and Figs. S3, S4). We report estimates of pairwise percent differences between population means for each skull variable. We use this method because the 7 linear and volumetric skull variables were correlated in two ways (see Fig. S3). First, they were measured on the same specimens, and second, they represent non-independent aspects of shape variation. Modeling these response variables in 7 separate general linear models (e.g., ANOVA) would result in biologically unrealistic inferences because these correlations would be artificially fixed at zero. In addition, since skull variables exhibited varying amounts of dispersion, the GLS model allowed for different residual variances for each response variable.
Sexual dimorphism comparisons
Sexual dimorphism within a species is often represented as dimorphism in size as well as shape25. Therefore, in contrast to the previous analyses, we assess the degree of sexual dimorphism in both size and shape. We used a similar GLS model to determine the degree of sexual dimorphism of the raw (non-size corrected) variables within each population. To estimate sex-specific effects, we added an additional interaction term between sex and population identity in this model. We report the degree of dimorphism using estimates of mean differences between males and females for a given skull variable, within a population. For both models using linear and volumetric data, we performed model selection for variance components and correlation structures using the Bayesian information criterion, since this has been shown to provide a good balance between parsimony and over-fitting for explanatory models26. Linear model (GLS) assumptions were checked using diagnostic plots of standardized residuals and fitted values (see Fig. S5). More

1.
Kenrick, P., Wellman, C. H., Schneider, H. & Edgecombe, G. D. A timeline for terrestrialization: consequences for the carbon cycle in the Palaeozoic. Philos. Trans. R. Soc. B 367, 519–536 (2012).
Article  Google Scholar 
2.
Kennedy, M., Droser, M., Mayer, L. M., Pevear, D. & Mrofka, D. Late Precambrian oxygenation; inception of the clay mineral factory. Science 311, 1446–1449 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Naranjo-Ortiz, M. A. & Gabaldón, T. Fungal evolution: major ecological adaptations and evolutionary transitions. Biol. Rev. 94, 1443–1476 (2019).
PubMed  Article  Google Scholar 

4.
Heckman, D. S. et al. Molecular evidence for the early colonization of land by fungi and plants. Science 293, 1129–1133 (2001).
CAS  PubMed  Article  Google Scholar 

5.
Lutzoni, F. et al. Contemporaneous radiations of fungi and plants linked to symbiosis. Nat. Commun. 9, 5451 (2018).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Chang, Y. et al. Phylogenomic analyses indicate that early fungi evolved digesting cell walls of algal ancestors of land plants. Genome Biol. Evol. 7, 1590–1601 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Taylor, T. N., Krings, M. & Taylor, E. L. Fossil Fungi. 1st edn (Academic Press, 2015).

8.
Berbee, M. L. et al. Genomic and fossil windows into the secret lives of the most ancient fungi. Nat. Rev. Microbiol. 18, 717–730 (2020).
Google Scholar 

9.
Bengtson, S. et al. Fungus-like mycelial fossils in 2.4-billion-year-old vesicular basalt. Nat. Ecol. Evol. 1, 0141 (2017).
Article  Google Scholar 

10.
Loron, C. C. et al. Early fungi from the Proterozoic era in Arctic Canada. Nature 570, 232–235 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Bonneville, S. et al. Molecular identification of fungi microfossils in a Neoproterozoic shale rock. Sci. Adv. 6, eaax7599 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Butterfield, N. J. Probable Proterozoic fungi. Paleobiology 31, 165–182 (2005).
Article  Google Scholar 

13.
Yuan, X., Xiao, S. & Taylor, T. N. Lichen-like symbiosis 600 million years ago. Science 308, 1017–1020 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

14.
Smith, M. R. Cord-forming Palaeozoic fungi in terrestrial assemblages. Bot. J. Linn. Soc. 180, 452–460 (2016).
Article  Google Scholar 

15.
Krings, M., Harper, C. J. & Taylor, E. L. Fungi and fungal interactions in the Rhynie chert: a review of the evidence, with the description of Perexiflasca tayloriana gen. et sp. nov. Philos. Trans. R. Soc. B 373, 20160500 (2018).
Article  Google Scholar 

16.
Condon, D. et al. U-Pb ages from the Neoproterozoic Doushantuo Formation, China. Science 308, 95–98 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Zhou, C., Huyskens, M. H., Lang, X., Xiao, S. & Yin, Q.-Z. Calibrating the terminations of Cryogenian global glaciations. Geology 47, 251–254 (2019).
ADS  CAS  Article  Google Scholar 

18.
Jiang, G., Kennedy, M. J., Christie-Blick, N., Wu, H. & Zhang, S. Stratigraphy, sedimentary structures, and textures of the late Neoproterozoic Doushantuo cap carbonate in South China. J. Sediment. Res. 76, 978–995 (2006).
ADS  CAS  Article  Google Scholar 

19.
Hoffman, P. F. & Macdonald, F. A. Sheet-crack cements and early regression in Marinoan (635 Ma) cap dolostones: regional benchmarks of vanishing ice-sheets? Earth Planet. Sci. Lett. 300, 374–384 (2010).
ADS  CAS  Article  Google Scholar 

20.
Gan, T. et al. Miniature paleo-speleothems from the earliest Ediacaran (635 Ma) Doushantuo cap dolostone in South China and their implications for terrestrial ecosystems. EarthArXiv, https://doi.org/10.31223/osf.io/srkcp (2019).

21.
Zhou, C., Bao, H., Peng, Y. & Yuan, X. Timing the deposition of 17O-depleted barite at the aftermath of Nantuo glacial meltdown in South China. Geology 38, 903–906 (2010).
ADS  CAS  Article  Google Scholar 

22.
Zhou, G., Luo, T., Zhou, M., Xing, L. & Gan, T. A ubiquitous hydrothermal episode recorded in the sheet-crack cements of a Marinoan cap dolostone of South China: implication for the origin of the extremely 13C-depleted calcite cement. J. Asian Earth Sci. 134, 63–71 (2017).
ADS  Article  Google Scholar 

23.
Muscente, A. D., Czaja, A. D., Tuggle, J., Winkler, C. & Xiao, S. Manganese oxides resembling microbial fabrics and their implications for recognizing inorganically preserved microfossils. Astrobiology 18, 249–258 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

24.
García-Ruiz, J. M. et al. Self-assembled silica-carbonate structures and detection of ancient microfossils. Science 302, 1194 (2003).
ADS  PubMed  Article  CAS  Google Scholar 

25.
Rouillard, J., García-Ruiz, J. M., Gong, J. & van Zuilen, M. A. A morphogram for silica-witherite biomorphs and its application to microfossil identification in the early earth rock record. Geobiology 16, 279–296 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Hofmann, B. A., Farmer, J. D., Blanckenburg, F. V. & Fallick, A. E. Subsurface filamentous fabrics: an evaluation of origins based on morphological and geochemical criteria, with implications for exopaleontology. Astrobiology 8, 87–117 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

27.
Rasmussen, B. Filamentous microfossils in a 3,235-million-year-old volcanogenic massive sulphide deposit. Nature 405, 676–679 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

28.
Schopf, J. W. et al. Sulfur-cycling fossil bacteria from the 1.8-Ga Duck Creek Formation provide promising evidence of evolution’s null hypothesis. Proc. Natl Acad. Sci. USA 112, 2087–2092 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Teske, A. & Nelson, D. C. in The Prokaryotes: Volume 6: Proteobacteria: Gamma Subclass (eds Dworkin, M. et al.) 784–810 (Springer, 2006).

30.
Zhou, X. et al. Biogenic iron-rich filaments in the quartz veins in the uppermost Ediacaran Qigebulake Formation, Aksu area, northwestern Tarim Basin, China: implications for iron oxidizers in subseafloor hydrothermal systems. Astrobiology 15, 523–537 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

31.
Thurston, E. L. & Ingram, L. O. Morphology and fine structure of Fischerella ambigua. J. Phycol. 7, 203–210 (1971).
Google Scholar 

32.
Iyengar, M. O. P. & Desikachary, T. V. Mastigocladopsis jogensis gen. et sp. nov., a new member of the stigonemataceæ. Proc. Ind. Acad. Sci. B 24, 55–59 (1946).
Google Scholar 

33.
Komárek, J. Cyanoprokaryota: 3. Teil/Part 3: Heterocytous Genera. (Springer Spektrum, 2013).

34.
Castenholz, R. W. in Bergey’s Manual of Systematic Bacteriology (eds Boone, et al.) 473–599 (Springer, 2001).

35.
Bartley, J. K. Actualistic taphonomy of cyanobacteria: implications for the Precambrian fossil record. Palaios 11, 571–586 (1996).
ADS  Article  Google Scholar 

36.
Bold, H. C. & Wynne, M. J. Introduction to the Algae: Structure and Reproduction. (Prentice-Hall, 1978).

37.
Butterfield, N. J. A vaucheriacean alga from the middle Neoproterozoic of Spitsbergen: implications for the evolution of Proterozoic eukaryotes and the Cambrian explosion. Paleobiology 30, 231–252 (2004).
Article  Google Scholar 

38.
Tang, Q., Pang, K., Yuan, X. & Xiao, S. A one-billion-year-old multicellular chlorophyte. Nat. Ecol. Evol. 4, 543–549 (2020).
PubMed  Article  Google Scholar 

39.
Leliaert, F. & Coppejans, E. A revision of Cladophoropsis Børgesen (Siphonocladales, Chlorophyta). Phycologia 45, 657–679 (2006).
Article  Google Scholar 

40.
Zhao, Z.-J., Zhu, H., Hu, Z.-Y. & Liu, G.-X. Occurrence of true branches in Rhizoclonium (Cladophorales, Ulvophyceae) and the reinstatement of Rhizoclonium pachydermum Kjellman. Phytotaxa 166, 273–284 (2014).
Article  Google Scholar 

41.
Entwisle, T. J. A monograph of Vaucheria (Vaucheriaceae, Chrysophyta) in south-eastern mainland Australia. Aust. Syst. Bot. 1, 1–77 (1988).
Article  Google Scholar 

42.
Boo, S. M. & Cho, T. O. The Morphology of Griffithsia tomo-yamadae Okamura (Ceramiaceae, Rhodophyta): a little-known species from the northeast Pacific. Bot. Mar. 44, 109–118 (2001).
Article  Google Scholar 

43.
Ferrer, N. C. & Caceres, E. J. Spirogyra salmonispora sp. nov. (Zygnematophyceae, Chiorophyta), a new freshwater species of the section Conjugata. Arch. Protistenk. 146, 101–105 (1995).
Article  Google Scholar 

44.
Li, Q., Chen, X., Jiang, Y. & Jiang, C. in Actinobacteria: Basics and Biotechnological Applications (eds Dhanasekaran, D. & Jiang, Y.) 59–86 (IntechOpen, 2016).

45.
Goodfellow, M. et al. Bergey’s Manual of Systematic Bacteriology: Volume Five The Actinobacteria, Part A and B. (Springer-Verlag, 2012).

46.
Erikson, D. The morphology, cytology, and taxonomy of the Actinomycetes. Annu. Rev. Microbiol. 3, 23–54 (1949).
Article  Google Scholar 

47.
Gregory, K. F. Hyphal anastomosis and cytological aspects of Streptomyces scabies. Can. J. Microbiol. 2, 649–655 (1956).
Article  Google Scholar 

48.
Higgins, M. L. & Silvey, J. K. G. Slide culture observations of two freshwater Actinomycetes. Trans. Am. Micros. Soc. 85, 390–398 (1966).
CAS  Article  Google Scholar 

49.
Spatafora, J. W. et al. A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data. Mycologia 108, 1028–1046 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
O’Donnell, K. L. Zygomycetes in Culture. (Department of Botany, University of Georgia, 1979).

51.
Fischer, M. S. & Glass, N. L. Communicate and fuse: how filamentous fungi establish and maintain an interconnected mycelial network. Front. Microbiol. 10, 619 (2019).
PubMed  PubMed Central  Article  Google Scholar 

52.
Webster, J. in Introduction to Fungi (3rd Edn.) (eds Webster, J. & Weber, R.) 165–225 (Cambridge University Press, 2007).

53.
Drake, H. et al. Anaerobic consortia of fungi and sulfate reducing bacteria in deep granite fractures. Nat. Commun. 8, 55 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

54.
Bengtson, S. et al. Deep-biosphere consortium of fungi and prokaryotes in Eocene subseafloor basalts. Geobiology 12, 489–496 (2014).
CAS  PubMed  Article  Google Scholar 

55.
Ivarsson, M. et al. Fossilized fungi in subseafloor Eocene basalts. Geology 40, 163–166 (2012).
ADS  CAS  Article  Google Scholar 

56.
Northup, D. et al. Biological investigations in Lechuguilla Cave. NSS Bull. 56, 54–63 (1994).
Google Scholar 

57.
Duane, M. J. Unusual preservation of crustaceans and microbial colonies in a vadose zone, northwest Morocco. Lethaia 36, 21–32 (2003).
Article  Google Scholar 

58.
Kretzschmar, M. Fossile pilze in eisen-stromatolithen von warstein (rheinisches schiefergebirge). Facies 7, 237–259 (1982).
Article  Google Scholar 

59.
Nieves-Rivera, Á. M., Santos-Flores, C. J., Dugan, F. M. & Miller, T. E. Guanophilic fungi in three caves of southwestern Puerto Rico. Int. J. Speleol. 38, 61–70 (2009).
Article  Google Scholar 

60.
Nováková, A. Microscopic fungi isolated from the Domica Cave system (Slovak Karst National Park, Slovakia). A review. Int. J. Speleol. 38, 71–82 (2009).
Article  Google Scholar 

61.
Popović, S. et al. Cyanobacteria, algae and microfungi present in biofilm from Božana Cave (Serbia). Int. J. Speleol. 44, 141–149 (2015).
Article  Google Scholar 

62.
Schopf, J. W. Microflora of the Bitter Springs Formation, late Precambrian, central Australia. J. Paleontol. 42, 651–688 (1968).
Google Scholar 

63.
Strother, P. K. Systematics and evolutionary significance of some new cryptospores from the Cambrian of eastern Tennessee, USA. Rev. Palaeobot. Palynol. 227, 28–41 (2016).
Article  Google Scholar 

64.
Prave, A. R. Life on land in the Proterozoic: evidence from the Torridonian rocks of northwest Scotland. Geology 30, 811–814 (2002).
ADS  Article  Google Scholar 

65.
Blank, C. E. Origin and early evolution of photosynthetic eukaryotes in freshwater environments: reinterpreting Proterozoic paleobiology and biogeochemical processes in light of trait evolution. J. Phycol. 49, 1040–1055 (2013).
CAS  PubMed  Article  Google Scholar 

66.
Sánchez-Baracaldo, P., Raven, J. A., Pisani, D. & Knoll, A. H. Early photosynthetic eukaryotes inhabited low-salinity habitats. Proc. Natl. Acad. Sci. USA 114, E7737–E7745 (2017).
PubMed  Article  CAS  Google Scholar 

67.
Föllmi, K. B. The phosphorus cycle, phosphogenesis and marine phosphate-rich deposits. Earth Sci. Rev. 40, 55–124 (1996).
ADS  Article  Google Scholar 

68.
Sahoo, S. K. et al. Ocean oxygenation in the wake of the Marinoan glaciation. Nature 489, 546–549 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

69.
Guo, Z., Peng, X., Czaja, A. D., Chen, S. & Ta, K. Cellular taphonomy of well-preserved Gaoyuzhuang microfossils: a window into the preservation of ancient cyanobacteria. Precambrian Res. 304, 88–98 (2018).
ADS  CAS  Article  Google Scholar 

70.
Czaja, A. D., Beukes, N. J. & Osterhout, J. T. Sulfur-oxidizing bacteria prior to the Great Oxidation Event from the 2.52 Ga Gamohaan Formation of South Africa. Geology 44, 983–986 (2016).
ADS  CAS  Article  Google Scholar 

71.
Pang, K. et al. The nature and origin of nucleus-like intracellular inclusions in Paleoproterozoic eukaryote microfossils. Geobiology 11, 499–510 (2013).
CAS  PubMed  Google Scholar 

72.
Zhang, J. et al. Improved precision and spatial resolution of sulfur isotope analysis using NanoSIMS. J. Anal. Spectrom. 20, 1934–1943 (2014).
CAS  Article  Google Scholar 

73.
Chen, L. et al. Extreme variation of sulfur isotopic compositions in pyrite from the Qiuling sediment-hosted gold deposit, West Qinling orogen, central China: an in situ SIMS study with implications for the source of sulfur. Mineral. Depos. 50, 643–656 (2015).
ADS  CAS  Article  Google Scholar 

74.
Roberts, N. M. W. & Walker, R. J. U-Pb geochronology of calcite-mineralized faults: absolute timing of rift-related fault events on the northeast Atlantic margin. Geology 44, 531–534 (2016).
ADS  CAS  Article  Google Scholar 

75.
Roberts, N. M. W. et al. A calcite reference material for LA-ICP-MS U-Pb geochronology. Geochem. Geophys. Geosyst. 18, 2807–2814 (2017).
ADS  CAS  Article  Google Scholar 

76.
Hu, Z. et al. Signal enhancement in laser ablation ICP-MS by addition of nitrogen in the central channel gas. J. Anal. Spectrom. 23, 1093–1101 (2008).
CAS  Article  Google Scholar 

77.
Liu, Y. et al. Reappraisement and refinement of zircon U-Pb isotope and trace element analyses by LA-ICP-MS. Chin. Sci. Bull. 55, 1535–1546 (2010).
CAS  Article  Google Scholar 

78.
Zhang, Y. & Yuan, X. New data on multicellular thallophytes and fragments of cellular tissues from late Proterozoic phosphate rocks, South China. Lethaia 25, 1–18 (1992).
Article  Google Scholar 

79.
Nie, W., Ma, D., Pan, J., Zhou, J. & Wu, K. δ13C excursions of phosphorite-bearing rocks in Neoproterozoic-Early Cambrian interval in Guizhou, South China: implications for palaeoceanic evolutions. J. Nanjing Univ. Nat. Sci. 42, 257–268 (2006).
CAS  Google Scholar 

80.
Barfod, G. H. et al. New Lu-Hf and Pb-Pb age constraints on the earliest animal fossils. Earth Planet. Sci. Lett. 201, 203–212 (2002).
ADS  CAS  Article  Google Scholar 

81.
Igisu, M. et al. Micro-FTIR spectroscopic signatures of bacterial lipids in Proterozoic microfossils. Precambrian Res. 173, 19–26 (2009).
ADS  CAS  Article  Google Scholar 

82.
Wang, X.-H. Interfacial electrochemistry of pyrite oxidation and flotation: II. FTIR studies of xanthate adsorption on pyrite surfaces in neutral pH solutions. J. Colloid Interface Sci. 171, 413–428 (1995).
ADS  CAS  Article  Google Scholar 

83.
Igisu, M. et al. FTIR microspectroscopy of Ediacaran phosphatized microfossils from the Doushantuo Formation, Weng’an, South China. Gondwana Res. 25, 1120–1138 (2014).
ADS  CAS  Article  Google Scholar 

84.
Beyssac, O., Goffé, B., Chopin, C. & Rouzaud, J. N. Raman spectra of carbonaceous material in metasediments: a new geothermometer. J. Metamorphic. Geol. 20, 859–871 (2002).
ADS  CAS  Article  Google Scholar 

85.
Turcotte, S. B. et al. Application of Raman spectroscopy to metal-sulfide surface analysis. Appl. Opt. 32, 935–938 (1993).
ADS  CAS  PubMed  Article  Google Scholar  More

1.
Mehta, R. S. Ecomorphology of the moray bite: Relationship between dietary extremes and morphological diversity. Physiol. Biochem. Zool. 82, 90–103 (2009).
PubMed  Article  ADS  PubMed Central  Google Scholar 
2.
Mehta, R. S. & Wainwright, P. C. Raptorial jaws in the throat help moray eels swallow large prey. Nature 449, 79–82 (2007).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

3.
Robins, C. R. The phylogenetic relationships of the anguilliform fishes. Fishes Western N. Atl. 1, 9–23 (1989).
Google Scholar 

4.
Greenwood, P. H. Notes on the anatomy and classification of elopomorph fishes. Bull. Mus. Comp. Zool. 32, 65–102 (1977).

5.
Nelson, G. J. Relationships of clupeomorphs, with remarks on the structure of the lower jaw in fishes. Interrelat. Fishes 333–349 (1973).

6.
Inoue, J. G. et al. Deep-ocean origin of the freshwater eels. Biol. Let. 6, 363–366. https://doi.org/10.1098/rsbl.2009.0989 (2010).
Article  Google Scholar 

7.
Santini, F. et al. A multi-locus molecular timescale for the origin and diversification of eels (Order: Anguilliformes). Mol. Phylogenet. Evol. 69, 884–894. https://doi.org/10.1016/j.ympev.2013.06.016 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

8.
Inoue, J. G., Miya, M., Tsukamoto, K. & Nishida, M. Complete Mitochondrial DNA Sequence of Conger myriaster (Teleostei: Anguilliformes): Novel gene order for vertebrate mitochondrial genomes and the phylogenetic implications for Anguilliform Families. J. Mol. Evol. 52, 311–320 (2001).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

9.
Tang, K. L. & Fielitz, C. Phylogeny of moray eels (Anguilliformes: Muraenidae), with a revised classification of true eels (Teleostei: Elopomorpha: Anguilliformes). Mitochondrial DNA. 24, 55–66 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Russell, B. & Houston, W. Offshore fishes of the Arafura Sea. Beagle Rec. Mus. Art Galleries Northern Territory 6, 69–84 (1989).
Google Scholar 

11.
Chen, D., Ye, Y., Chen, J., Zhan, P. & Lou, Y. Molecular nutritional characteristics of vinasse pike eel (Muraenesox cinereus) during pickling. Food Chem. 224, 359–364. https://doi.org/10.1016/j.foodchem.2016.12.089 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
Boore, J. L. Animal mitochondrial genomes. Nucleic Acids Res. 27, 1767–1780 (1999).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Lu, Z. Z. et al. Complete mitochondrial genome of Ophichthus brevicaudatus reveals novel gene order and phylogenetic relationships of Anguilliformes. Int. J. Biol. Macromol. 135, 609–618. https://doi.org/10.1016/j.ijbiomac.2019.05.139 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Bibb, M., Etten, R., Wright, C., Walberg, M. & Clayton, D. Sequence and gene organization of mouse mitochondrial DNA. Cell 26, 167–180. https://doi.org/10.1016/0092-8674(81)90300-7 (1981).
CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Anderson, S. B. et al. Sequence and organization of the human mitochondrial genome. Nature 290, 457–465. https://doi.org/10.1038/290457a0 (1981).
CAS  Article  PubMed  ADS  PubMed Central  Google Scholar 

16.
Roe, B. A., Ma, D. P., Wilson, R. K. & Wong, F. H. The complete nucleotide sequence of the Xenopus laevis mitochondrial genome. J. Biol. Chem. 260, 9759–9774 (1985).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Brown, W., George, M. J. & Wilson, A. C. Rapid evolution of animal mitochondrial DNA. Proc. Natl. Acad. Sci. USA 76, 1967–1971. https://doi.org/10.1073/pnas.76.4.1967 (1979).
CAS  Article  PubMed  ADS  PubMed Central  Google Scholar 

18.
Macey, J. R., Larson, A., Ananjeva, N. B., Fang, Z. & Papenfuss, T. J. Two novel gene orders and the role of light-strand replication in rearrangement of the vertebrate mitochondrial genome. Mol. Biol. Evol. 14, 91–104. https://doi.org/10.1093/oxfordjournals.molbev.a025706 (1997).
CAS  Article  PubMed  PubMed Central  Google Scholar 

19.
Zhang, J. Y., Zhang, L. P., Yu, D. N., Storey, K. B. & Zheng, R. Q. Complete mitochondrial genomes of Nanorana taihangnica and N. yunnanensis (Anura: Dicroglossidae) with novel gene arrangements and phylogenetic relationship of Dicroglossidae. BMC Evol. Biol. 18, 1–13 (2018).
Article  CAS  Google Scholar 

20.
Yan, J., Li, H. & Zhou, K. Evolution of the mitochondrial genome in snakes: Gene rearrangements and phylogenetic relationships. BMC Genom. 9, 569. https://doi.org/10.1186/1471-2164-9-569 (2008).
CAS  Article  Google Scholar 

21.
Liu, J., Yu, J., Zhou, M. & Yang, J. Complete mitochondrial genome of Japalura flaviceps: Deep insights into the phylogeny and gene rearrangements of Agamidae species. Int. J. Biol. Macromol. 125, 423–431. https://doi.org/10.1016/j.ijbiomac.2018.12.068 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Verkuil, Y. I., Piersma, T. & Baker, A. J. A novel mitochondrial gene order in shorebirds (Scolopacidae, Charadriiformes). Mol. Phylogenet. Evol. 57, 411–416. https://doi.org/10.1016/j.ympev.2010.06.010 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Eberhard, J. R. & Wright, T. F. Rearrangement and evolution of mitochondrial genomes in parrots. Mol. Phylogenet. Evol. 94, 34–46 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Pääbo, S., Thomas, W. K., Whitfield, K. M., Kumazawa, Y. & Wilson, A. C. Rearrangements of mitochondrial transfer RNA genes in marsupials. J. Mol. Evol. 33, 426–430. https://doi.org/10.1007/bf02103134 (1991).
Article  PubMed  ADS  PubMed Central  Google Scholar 

25.
Gong, L., Shi, W., Yang, M., Li, D. & Kong, X. Novel gene arrangement in the mitochondrial genome of Bothus myriaster(Pleuronectiformes: Bothidae): Evidence for the dimer-mitogenome and non-random loss model. Mitochondrial DNA Part A. 27, 3089–3092 (2015).
Article  CAS  Google Scholar 

26.
Miya, M. N. Organization of the Mitochondrial Genome of a Deep-Sea Fish, Gonostoma gracile (Teleostei: Stomiiformes): First example of transfer RNA gene rearrangements in Bony Fishes. Mar. Biotechnol. 1, 416–0426 (1999).
CAS  Article  Google Scholar 

27.
Shi, W., Miao, X. G. & Kong, X. Y. A novel model of double replications and random loss accounts for rearrangements in the Mitogenome of Sssamariscus latus (Teleostei: Pleuronectiformes). BMC Genom. 15, 352 (2014).
Article  CAS  Google Scholar 

28.
Kong, X. et al. A novel rearrangement in the mitochondrial genome of tongue sole, Cynoglossus semilaevis: Control region translocation and a tRNA gene inversion. Genome. 52, 975–984. https://doi.org/10.1139/g09-069 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Gong, L., Shi, W., Si, L. Z. & Kong, X. Y. Rearrangement of mitochondrial genome in fishes. Zool. Res. 34, 666–673 (2013).
CAS  Google Scholar 

30.
Inoue, J. G., Masaki, M., Katsumi, T. & Mutsumi, N. evolution of the deep-sea gulper eel mitochondrial genomes: Large-scale gene rearrangements originated within the eels. Mol. Biol. Evol. 20, 1917–1924 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Ishikawa, S., Kimura, Y., Tokai, T., Tsukamoto, K. & Nishida, M. Gene rearrangement around the control region in the mitochondrial genome of conger myriaster. Fish. Sci. 66, 1186–1188 (2002).
Article  Google Scholar 

32.
Miya, M., Kawaguchi, A. & Nishida, M. Mitogenomic exploration of higher teleostean phylogenies: A case study for moderate-scale evolutionary genomics with 38 newly determined complete mitochondrial DNA sequences. Mol. Biol. Evol. 18, 1993–2009 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Miya, M. T. et al. Major patterns of higher teleostean phylogenies: A new perspective based on 100 complete mitochondrial DNA sequences. Mol. Phylogenet. Evol. 26, 121–138 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Poulton, J. et al. Families of mtDNA re-arrangements can be detected in patients with mtDNA deletions: Duplications may be a transient intermediate form. Hum. Mol. Genet. 2, 23–30 (1993).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Lunt, D. H. & Hyman, B. C. Animal mitochondrial DNA recombination. Nature 387, 247 (1997).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

36.
Ladoukakis, E. D. & Zouros, E. Recombination in animal mitochondrial DNA: Evidence from published sequences. Mol. Biol. Evol. 18, 2127–2131 (2001).
CAS  PubMed  Article  Google Scholar 

37.
Sammler, S., Bleidorn, C. & Tiedemann, R. Full mitochondrial genome sequences of two endemic Philippine hornbill species (Aves: Bucerotidae) provide evidence for pervasive mitochondrial DNA recombination. BMC Genom. 12, 35. https://doi.org/10.1186/1471-2164-12-35 (2011).
CAS  Article  Google Scholar 

38.
Atsushi, K. et al. Phylogeny, recombination, and mechanisms of stepwise mitochondrial genome reorganization in mantellid frogs from madagascar. Mol. Biol. Evol. 5, 874–891 (2008).
Google Scholar 

39.
Arndt, A. & Smith, M. J. Mitochondrial gene rearrangement in the sea cucumber genus Cucumaria. Mol. Biol. Evol. 8, 1009–1016 (1998).
Article  Google Scholar 

40.
Moritz, C., Dowling, T. E. & Brown, W. M. Evolution of animal mitochondrial DNA: Relevance for population biology and systematics. Annu. Rev. Ecol. Syst. 18, 269–292 (1987).
Article  Google Scholar 

41.
Erin, E. S. et al. Multiple independent origins of mitochondrial control region duplications in the order Psittaciformes. Mol. Phylogenet. Evol. 64, 342–356. https://doi.org/10.1016/j.ympev.2012.04.009 (2012).
Article  Google Scholar 

42.
Mauro, D. S., Gower, D. J., Rafael, Z. & Mark, W. A hotspot of gene order rearrangement by tandem duplication and random loss in the vertebrate mitochondrial genome. Mol. Biol. Evol. 23, 227–234 (2006).
Article  CAS  Google Scholar 

43.
Lavrov, D. V., Boore, J. L. & Brown, W. M. Complete mtDNA sequences of two millipedes suggest a new model for mitochondrial gene rearrangements: Duplication and nonrandom loss. Mol. Biol. Evol. 19, 163–169 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Smith, M. J., Arndt, A., Gorski, S. & Fajber, E. The phylogeny of echinoderm classes based on mitochondrial gene arrangements. J. Mol. Evol. 36, 545–554 (1993).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

45.
Schierup, M. H. & Hein, J. Consequences of recombination on traditional phylogenetic analysis. Genetics 156, 879–891 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Zhi, J. J. et al. Comparative mitochondrial genomics of snakes: Extraordinary substitution rate dynamics and functionality of the duplicate control region. BMC Evol. Biol. 7, 123. https://doi.org/10.1186/1471-2148-7-123 (2007).
CAS  Article  Google Scholar 

47.
Shi, W., Dong, X. L., Wang, Z. M., Miao, X. G. & Kong, X. Y. Complete mitogenome sequences of four flatfishes (Pleuronectiformes) reveal a novel gene arrangement of L-strand coding genes. BMC Evol. Biol. 13, 173 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Kumazawa, Y., Ota, H., Nishida, M. & Ozawa, T. The complete nucleotide sequence of a snake (Dinodon semicarinatus) mitochondrial genome with two identical control regions. Genetics 150, 313–329 (1998).
CAS  PubMed  PubMed Central  Google Scholar 

49.
Liu, Y. et al. Mitochondrial genome of the yellow catfish Pelteobagrus fulvidraco and insights into Bagridae phylogenetics. Genomics 111, 1258–1265. https://doi.org/10.1016/j.ygeno.2018.08.005 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Gong, L., Lü, Z. M., Guo, B. Y., Ye, Y. Y. & Liu, L. Q. Characterization of the complete mitochondrial genome of the tidewater goby, Eucyclogobius newberryi (Gobiiformes; Gobiidae; Gobionellinae) and its phylogenetic implications. Conserv. Genet. Resour. 10, 93–97 (2017).
Article  Google Scholar 

51.
Lin, J. P. et al. The first complete mitochondrial genome of the sand dollar Sinaechinocyamus mai (Echinoidea: Clypeasteroida). Genomics 112, 1686–1693. https://doi.org/10.1016/j.ygeno.2019.10.007 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Prabhu, V. R. et al. Characterization of the complete mitochondrial genome of Barilius malabaricus and its phylogenetic implications. Genomics 112, 2154–2163. https://doi.org/10.1016/j.ygeno.2019.12.009 (2020).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Xu, T. J., Cheng, Y. Z., Sun, Y. N., Shi, G. & Wang, R. X. The complete mitochondrial genome of bighead croaker, Collichthys niveatus (Perciformes, Sciaenidae): Structure of control region and phylogenetic considerations. Mol. Biol. Rep. 38, 4673–4685. https://doi.org/10.1007/s11033-010-0602-4 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

54.
Ojala, D., Montoya, J. & Attardi, G. tRNA punctuation model of RNA processing in human mitochondria. Nature 290, 470–474. https://doi.org/10.1038/290470a0 (1981).
CAS  Article  PubMed  ADS  PubMed Central  Google Scholar 

55.
Vandana, R. P. et al. Characterization of the complete mitochondrial genome of Barilius malabaricus and its phylogenetic implications. Genomics 112, 2154–2163 (2019).
Google Scholar 

56.
Wang, X., Wang, J., He, S. & Mayden, R. L. The complete mitochondrial genome of the Chinese hook snout carp Opsariichthys bidens (Actinopterygii: Cypriniformes) and an alternative pattern of mitogenomic evolution in vertebrate. Gene 399, 0–19 (2007).
CAS  Article  Google Scholar 

57.
Gong, L., Liu, B., Lü, Z. M. & Liu, L. Q. Characterization of the complete mitochondrial genome of Wuhaniligobius polylepis (Gobiiformes: Gobiidae) and phylogenetic studies of Gobiiformes. Mitochondrial DNA Part B 3, 1117–1119. https://doi.org/10.1080/23802359.2018.1519380 (2018).
Article  Google Scholar 

58.
Dowton, M. & Campbell, N. J. H. Intramitochondrial recombination—is it why some mitochondrial genes sleep around?. Trends Ecol. Evol. 16, 269–271 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Kong, X. D. et al. A novel rearrangement in the mitochondrial genome of tongue sole, Cynoglossus semilaevis: Control region translocation and a tRNA gene inversion. Genome 52, 975–984. https://doi.org/10.1139/g09-069 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Shi, W., Gong, L., Wang, S. Y., Miao, X. G. & Kong, X. Y. Tandem duplication and random loss for mitogenome rearrangement in symphurus (Teleost: Pleuronectiformes). BMC Genom. 16, 355 (2015).
Article  CAS  Google Scholar 

61.
Gong, L. et al. Large-scale mitochondrial gene rearrangements in the hermit crab Pagurus nigrofascia and phylogenetic analysis of the Anomura. Gene 695, 75–83 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Wang, Z. W. et al. Complete mitochondrial genome of Parasesarma affine (Brachyura: Sesarmidae): Gene rearrangements in Sesarmidae and phylogenetic analysis of the Brachyura. Int. J. Biol. Macromol. 118, 31–40 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Inoue, J. G., Miya, M., Tsukamoto, K. & Nishida, M. Evolution of the deep-sea gulper eel mitochondrial genomes: Large-scale gene rearrangements originated within the eels. Mol. Biol. Evol. 20, 1917–1924. https://doi.org/10.1093/molbev/msg206 (2003).
CAS  Article  PubMed  PubMed Central  Google Scholar 

64.
Inoue, J. G. et al. Deep-ocean origin of the freshwater eels. Biol. Lett. 6, 363–366. https://doi.org/10.1098/rsbl.2009.0989 (2010).
Article  PubMed  PubMed Central  Google Scholar 

65.
Chen, J. N., López, J. A., Lavoué, S., Miya, M. & Chen, W. J. Phylogeny of the Elopomorpha (Teleostei): Evidence from six nuclear and mitochondrial markers. Mol. Phylogenet. Evol. 70, 152–161 (2014).
PubMed  Article  PubMed Central  Google Scholar 

66.
Reece, J. S., Bowen, B. W., Smith, D. G. & Larson, A. Molecular phylogenetics of moray eels (Muraenidae) demonstrates multiple origins of a shell-crushing jaw (Gymnomuraena, Echidna) and multiple colonizations of the Atlantic Ocean. Mol. Phylogenet. Evol. 57, 829–835 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Johnson, G. D., Ida, H., Sakaue, J., Sado, T. & Asahida, T. A “living fossil” eel (Anguilliformes: Protanguillidae, fam. nov.) from an undersea cave in Palau. Proc. R. Soc. B Biol. Sci. 279, 934–943. https://doi.org/10.1098/rspb.2011.1289 (2012).
Article  Google Scholar 

68.
Kumazawa, Y. & Nishida, M. Variations in mitochondrial tRNA gene organization of reptiles as phylogenetic markers. Mol. Biol. Evol. 12, 759–772. https://doi.org/10.1093/oxfordjournals.molbev.a040254 (1995).
CAS  Article  PubMed  PubMed Central  Google Scholar 

69.
Loh, K. H. et al. Next-generation sequencing yields the complete mitochondrial genome of the longfang moray, Enchelynassa canina (Anguilliformes: Muraenidae). Mitochondrial DNA Part A 27, 2431–2432 (2015).
Article  CAS  Google Scholar 

70.
Loh, K. H. et al. Next generation sequencing yields the complete mitochondrial genome of the Zebra moray, Gymnomuraena zebra (Anguilliformes: Muraenidae). Mitochondrial DNA Part A 27, 1–2 (2015).
Google Scholar 

71.
Perna, N. T. & Kocher, T. D. Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J. Mol. Evol. 41, 353–358 (1995).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

72.
Sudhir, K. et al. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
Article  CAS  Google Scholar 

73.
Paul, S. & Wishart, D. S. Circular genome visualization and exploration using CGView. Bioinformatics 21, 537–539 (2004).
Google Scholar 

74.
Nelson, J. S. Fishes of the Word 4th edn. (Wiley, New York, 2006).
Google Scholar 

75.
Xia, X. DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol. Biol. Evol. 35, 1550–1552. https://doi.org/10.1093/molbev/msy073 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

76.
Shi, W., Kong, X., Wang, Z. M. & Jiang, J. X. Utility of tRNA Genes from the Complete Mitochondrial Genome of Psetta maxima for Implying a Possible Sister-group Relationship to the Pleuronectiformes. Zool. Stud. 50, 665–681 (2011).
CAS  Google Scholar 

77.
Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948. https://doi.org/10.1093/bioinformatics/btm404 (2007).
CAS  Article  Google Scholar 

78.
Hall, T. BioEdit: A user-friendly biological sequence alignment program for Windows 95/98/NT. Nucleic Acids Sympos. Ser. 41, 95–98 (1999).
CAS  Google Scholar 

79.
Gerard, T. & Jose, C. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 56, 564–577 (2007).
Article  CAS  Google Scholar 

80.
Gascuel, O. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
PubMed  Article  CAS  PubMed Central  Google Scholar 

81.
Huelsenbeck, J. P. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
PubMed  PubMed Central  Article  Google Scholar 

82.
Posada, D. & Crandall, K. A. MODELTEST: Testing the model of DNA substitution. Bioinformatics 14, 817–818 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Nylander, J. A. A., Fredrik, R., Huelsenbeck, J. P. & Nieves-Aldrey, J. Bayesian phylogenetic analysis of combined data. Syst. Biol. 53, 47–67 (2004).
PubMed  Article  PubMed Central  Google Scholar 

84.
Sitnikova, T., Rzhetsky, A. & Nei, M. Interior-branch and bootstrap tests of phylogenetic trees. Mol. Biol. Evol. 12, 319–333 (1995).
CAS  PubMed  PubMed Central  Google Scholar 

85.
Antezana, M. When being “most likely” is not enough: Examining the performance of three uses of the parametric bootstrap in phylogenetics. J. Mol. Evol. 56, 198–222 (2003).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar  More

1.
Gause, G. F. Experimental populations of microscopic organisms. Ecology 18, 173–179 (1937).
Google Scholar 
2.
DeBach, P. & Sundby, R. A. Competitive displacement between ecological homologues. Hilgardia 34, 105–166 (1963).
Article  Google Scholar 

3.
Johnson, C. A. & Bronstein, J. L. Coexistence and competitive exclusion in mutualism. Ecology 100, e02708 (2019).
PubMed  Article  PubMed Central  Google Scholar 

4.
Gause, G. F. Experimental analysis of Vito Volterra’s mathematical theory of the struggle for existence. Science 79, 16–17 (1934).
ADS  CAS  Article  Google Scholar 

5.
Hutchinson, G. E. Concluding remarks Cold Spring Harbor Symp. Quant. Biol. 22, 415–427 (1958).
Article  Google Scholar 

6.
MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).
Article  Google Scholar 

7.
Kooyers, N. J., James, B. & Blackman, B. K. Competition drives trait evolution and character displacement between Mimulus species along an environmental gradient. Evolution 71, 1205–1221 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Whittaker, R. H., Levin, S. A. & Root, R. B. Niche, Habitat, and Ecotope. Am. Nat. 107, 321–338 (1973).
Article  Google Scholar 

9.
Wilson, R. P. Resource partitioning and niche hyper-volume overlap in free-living Pygoscelid penguins. Funct. Ecol. 24, 646–657 (2010).
Article  Google Scholar 

10.
Berendse, F. Interspecific competition and niche differentiation between Plantago Ianceolata and Anthoxanthum odoratum in a natural hayfield. J. Ecol. 71, 379–390 (1983).
Article  Google Scholar 

11.
Peterson, A. T. & Holt, R. D. Niche differentiation in Mexican birds: using point occurrences to detect ecological innovation. Ecol. Lett. 6, 774–782 (2003).
Article  Google Scholar 

12.
Preez, B., Purdon, J., Trethowan, P., Macdonald, D. W. & Loveridge, A. J. Dietary niche differentiation facilitates coexistence of two large carnivores. J. Zool. 302, 149–156 (2017).
Article  Google Scholar 

13.
Pratte, I., Robertson, G. J. & Mallory, M. L. Four sympatrically nesting auks show clear resource segregation in their foraging environment. Mar. Ecol. Prog. Ser. 572, 243–254 (2017).
ADS  Article  Google Scholar 

14.
Cody, M. L. Coexistence, coevolution and convergent evolution in seabird communities. Ecology 54, 31–44 (1973).
Article  Google Scholar 

15.
Hjernquist, M. B., Hjernquist, M., Hjernquist, B. & Thuman Hjernquist, K. A. Common Guillemot Uria aalge differentiate their niche to coexist with colonizing Great Cormorants Phalacrocorax carbo. Atlantic Seabird 7, 83–89 (2005).
Google Scholar 

16.
Gaston, A. J. Seabirds: A Natural History (Yale University Press, New Haven, 2004).
Google Scholar 

17.
Frere, E., Quintana, F., Gandini, P. & Wilson, R. P. Foraging behaviour and habitat partitioning of two sympatric cormorants in Patagonia, Argentina. Ibis 150, 558–564 (2008).
Article  Google Scholar 

18.
Linnebjerg, J. F., Reuleaux, A., Mouritsen, K. N. & Frederiksen, M. Foraging ecology of three sympatric breeding alcids in a declining colony in southwest Greenland. Waterbirds 38, 143–152 (2015).
Article  Google Scholar 

19.
Symons, S. C. Ecological Segregation Between Two Closely Related Species: Exploring Atlantic Puffin and Razorbill Foraging Hotspots (M. Sc. thesis, University of New Brunswick) (2018).

20.
Jenkins, E. J. & Davoren, G. K. Seabird species-and assemblage-level isotopic niche shifts associated with changing prey availability during breeding in coastal Newfoundland. Ibis https://doi.org/10.1111/ibi.12873 (2020).
Article  Google Scholar 

21.
Gaston, A. J., Ydenberg, R. C. & Smith, G. E. J. Ashmole’s halo and population regulation in seabirds. Mar. Ornithol. 35, 119–126 (2007).
Google Scholar 

22.
Sánchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).
ADS  Article  Google Scholar 

23.
Elliott, K. H. et al. Central-place foraging in an Arctic seabird provides evidence for Storer-Ashmole’s halo. Auk 126, 613–625 (2009).
Article  Google Scholar 

24.
Wakefield, E. D. et al. Space partitioning without territoriality in gannets. Science 341, 68–70 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Linnebjerg, J. F. et al. Sympatric breeding auks shift between dietary and spatial resource partitioning across the annual cycle. PLoS ONE 8, e72987 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Masello, J. F. et al. Diving seabirds share foraging space and time within and among species. Ecosphere 1, 1–28 (2010).
Article  Google Scholar 

27.
Hinke, J. T. et al. Spatial and isotopic niche partitioning during winter in chindstrap and Adélie penguins from the South Shetland Islands. Ecosphere 6, 1–32 (2015).
Article  Google Scholar 

28.
Gulka, J., Ronconi, R. A. & Davoren, G. K. Spatial segregation contrasting dietary overlap: niche partitioning of two sympatric alcids during shifting resource availability. Mar. Biol. 166, 155 (2019).
Article  Google Scholar 

29.
Ricklefs, R. E. & White, S. C. Growth and energetics of chicks of the Sooty Tern (Sterna fuscata) and Common Tern (S. hirundo). Auk 98, 361–378 (1981).
Google Scholar 

30.
Lance, M. M. & Thompson, C. W. Overlap in diets and foraging of Common Murres and Rhinoceros Auklets after the breeding season. Auk 122, 887–901 (2005).
Article  Google Scholar 

31.
Maynard, L. D. & Davoren, G. K. Inter-colony and interspecific differences in the isotopic niche of two sympatric gull species in Newfoundland. Mar. Ornithol. 48, 103–109 (2020).
Google Scholar 

32.
Thaxter, C. B. et al. Influence of wing loading on the trade-off between pursuit-diving and flight in common guillemots and razorbills. J. Exp. Biol. 213, 1018–1025 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Thaxter, C. B. et al. Modelling the effects of prey size and distribution on prey capture rates of two sympatric marine predators. PLoS ONE 8, e79915 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Shoji, A., Aris-Brosou, S. & Elliott, K. H. Physiological constraints and dive behavior scale in tandem with body mass in auks: A comparative analysis. Comp. Biochem. Physiol. A 196, 54–60 (2016).
CAS  Article  Google Scholar 

35.
Guigueno, M. F., Shoji, A., Elliott, K. H. & Aris-Brosou, S. Flight costs in volant vertebrates: A phylogenetically-controlled meta-analysis of birds and bats. Comp. Biochem. Physiol. A 235, 193–201 (2019).
CAS  Article  Google Scholar 

36.
Robertson, G. S., Bolton, M. & Monaghan, P. Influence of diet and foraging strategy on reproductive success in two morphologically similar sympatric seabirds. Bird Study 63, 316–329 (2016).
Article  Google Scholar 

37.
Gaston, A. J. & Jones, I. L. The Auks: Alcidae (Oxford University Press, USA, 1998).
Google Scholar 

38.
Orians, G. H. & Pearson, N. E. On the theory of central place foraging. Analysis of ecological systems. Ohio State Univ. Press Columbus 2, 155–177 (1979).
Google Scholar 

39.
Rail, J. F. & Cotter, R. C. Seventeenth census of seabird populations in the sanctuaries of the North Shore of the Gulf of St. Lawrence, 2010. Can. Field-Nat. 121, 287–294 (2015).
Article  Google Scholar 

40.
Lavoie, R. A., Rail, J. F. & Lean, D. R. Diet composition of seabirds from Corossol Island, Canada, using direct dietary and stable isotope analyses. Waterbirds 35, 402–419 (2012).
Article  Google Scholar 

41.
Elliott, K. H. et al. High flight costs, but low dive costs, in auks support the biomechanical hypothesis for flightlessness in penguins. Proc. Natl. Acad. Sci. USA 110, 9380–9384 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Croll, D. A., Gaston, A. J., Burger, A. E. & Konnoff, D. Foraging behavior and physiological adaptation for diving in Thick-billed Murres. Ecology 73, 344–356 (1992).
Article  Google Scholar 

43.
Thaxter, C. B. et al. Sex-specific food provisioning in a monomorphic seabird, the common guillemot Uria aalge: nest defence, foraging efficiency or parental effort?. J. Avian Biol. 40, 75–84 (2009).
Article  Google Scholar 

44.
Gremillet, D. & Boulinier, T. Spatial ecology and conservation of seabirds facing global climate change a review. Mar. Ecol. Prog. Ser. 391, 121–137 (2009).
ADS  Article  Google Scholar 

45.
Domalik, A. D., Hipfner, J. M., Studholme, K. R., Crossin, G. T. & Green, D. J. At-sea distribution and fine-scale habitat use patterns of zooplanktivorous Cassin’s auklets during the chick-rearing period. Mar. Biol. 165, 177 (2018).
Article  Google Scholar 

46.
Lack, D. Ecological Isolation in Birds (Blackwell, Oxford, 1971).
Google Scholar 

47.
Wanless, S., Harris, M. P. & Morris, J. A. A comparison of feeding areas used by individual Common Murres (Uria aalge), Razorbills (Alca torda) and an Atlantic Puffin (Fratercula arctica) during the breeding season. Colonial Waterbirds 13, 16–24 (1990).
Article  Google Scholar 

48.
Paredes, R., Jones, I. L., Boness, D. J., Tremblay, Y. & Renner, M. Sex-specific differences in diving behaviour of two sympatric Alcini species: thick-billed murres and razorbills. Can. J. Zool. 86, 610–622 (2008).
Article  Google Scholar 

49.
Kotzerka, J., Garthe, S. & Hatch, S. A. GPS tracking devices reveal foraging strategies of Black-legged Kittiwakes. J. Ornithol. 151, 459–467 (2009).
Article  Google Scholar 

50.
John, M. A. S., MacDonald, J. S., Harrison, P. J., Beamish, R. J. & Choromanski, E. The Fraser River plume: some preliminary observations on the distribution of juvenile salmon, herring, and their prey. Fish. Oceanogr. 1, 153–162 (1992).
Article  Google Scholar 

51.
Phillips, E. M., Horne, J. K., Adams, J. & Zamon, J. E. Selective occupancy of a persistent yet variable coastal river plume by two seabird species. Mar. Ecol. Prog. Ser. 594, 245–261 (2018).
ADS  Article  Google Scholar 

52.
Shoji, A. et al. Foraging behaviour of sympatric razorbills and puffins. Mar. Ecol. Prog. Ser. 520, 257–267 (2015).
ADS  Article  Google Scholar 

53.
Chimienti, M. et al. Taking movement data to new depths: inferring prey availability and patch profitability from seabird foraging behavior. Ecol. Evol. 7, 10252–10256 (2017).
PubMed  PubMed Central  Article  Google Scholar 

54.
Gaglio, D., Cook, T. R., Connan, M., Ryan, P. G. & Sherley, R. B. Dietary studies in birds: testing a non-invasive method using digital photography in seabirds. Methods Ecol. Evol. 8, 214–222 (2017).
Article  Google Scholar 

55.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. (2019)

56.
Calenge, C. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).
Article  Google Scholar 

57.
Hijmans, R. J. Geosphere: Spherical Trigonometry. R Package Version 1.5–10 (2019).

58.
Burger, A. E. Arrival and departure behavior of common murres at colonies: evidence for an information halo?. Colonial Waterbirds 20, 55–65 (1997).
Article  Google Scholar 

59.
Bradstreet, M. S. W. & Brown, R. G. B. Feeding studies. In Population Estimation, Productivity, and Food Habits of Nesting Seabirds at Cape Pierce and the Pribilof Islands, Bering Sea (ed. Johnson, S. R.) 257–306 (Anchorage, Alaska, 1985).
Google Scholar 

60.
Vaughn, H. R. H. Flight speed of guillemots, razorbills and puffins. Br. Birds 31, 123 (1937).
Google Scholar 

61.
Fifield, D. A., Lewis, K. P., Gjerdrum, C., Robertsoan, G. J. & Wells, R. Offshore seabird monitoring program. Environ. Stud. Res. Funds Rep. 183, 68 (2009).
Google Scholar 

62.
Pennycuick, C. J. Actual and “optimum” flight speeds: field data reassessed. J. Exp. Biol. 200, 2355–2361 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

63.
Elliott, K. H. et al. Windscapes shape seabird instantaneous energy costs but adult behavior buffers impact on offspring. Mov. Ecol. 2, 17 (2014).
PubMed  PubMed Central  Article  Google Scholar 

64.
Gaston, A. J. et al. Modeling foraging range for breeding colonies of thick-billed murres Uria lomvia in the Eastern Canadian Arctic and potential overlap with industrial development. Biol. Conserv. 168, 134–143 (2013).
Article  Google Scholar 

65.
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).
MATH  Article  Google Scholar 

66.
Neuhäuser, M. Wilcoxon–Mann–Whitney Test. In International Encyclopedia of Statistical Science (ed. Lovric, M.) (Springer, Berlin, Heidelberg, 2011).
Google Scholar 

67.
Haynes, W. Bonferroni Correction in Encyclopedia of Systems Biology (Dubitzky, W., Wolkenhauer, O., Cho, K.H., Yokota, H.) (Springer, New York, 2013).

68.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

69.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, New York, 2016).
Google Scholar 

70.
Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.4. https://CRAN.R-project.org/package=ggspatial (2020).

71.
South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0. https://CRAN.R-project.org/package=rnaturalearth (2017).

72.
South, A. rnaturalearthdata: World Vector Map Data from Natural Earth used in ‘rnaturalearth’. https://CRAN.R-project.org/package=rnaturalearthdata (2017).

73.
Pabesma, E. Simple features for R: standardized support for spatial vector data. R J 10, 439–446 (2018).
Article  Google Scholar 

74.
Fieberg, J. & Kochanny, C. O. Quantifying home-range overlap: The importance of the utilization distribution. J. Wildl. Manag. 69, 1346–1359 (2005).
Article  Google Scholar 

75.
Delord, K. et al. Movements of three alcid species breeding sympatrically in Saint Pierre and Miquelon, northwestern Atlantic Ocean. J. Ornithol. 161, 359–371 (2019).
Article  Google Scholar 

76.
Wei, T. & Simko, V. R package “corrplot”: Visualization of a Correlation Matrix (Version 0.84) (2017). https://github.com/taiyun/corrplot.

77.
Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).
Article  Google Scholar 

78.
Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. https://CRAN.R-project.org/package=factoextra, (Version 1.0.3) (2016).

79.
Rasband, W. S. ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, https://imagej.nih.gov/ij/ (1997–2018).

80.
Lambert, J. D. & Bernier, B. Observations on 4RST capelin in the Gulf of St. Lawrence (A retrospective, 1984–1987). CAFSAC Res. Document 89 (1989).

81.
Elliott, K. H. & Gaston, A. J. Mass-length relationships and energy content of fishes and invertebrates delivered to nesting Thick-billed Murres Uria lomvia in the Canadian Arctic, 1981–2007. Mar. Ornithol. 36, 25–34 (2008).
Google Scholar 

82.
Noble, V. R. & Clark, D. S. Seasonal length: weight relationships of Grenadiers, Chimaeras, and Atlantic Herring caught by Fisheries and Oceans Canada’s Maritimes Region Ecosystem Surveys, using different measurement techniques at sea. Can. Data Rep. Fish. Aquat. Sci. 1291, 4–14 (2019).
Google Scholar 

83.
Silva, J. F., Ellis, J. R. & Ayers, R. A. Length-weight relationships of marine fish collected from around the British Isles. Science 150, 109 (2013).
Google Scholar 

84.
Morin, R., Ricard, D., Benoît, H. & Surette, T. A review of the biology of Atlantic hagfish (Myxine glutinosa), its ecology, and its exploratory fishery in the southern Gulf of St. Lawrence (NAFO Div. 4T). DFO Canadian Science Advisory Secretariat, v + 39 (2017).

85.
Erguden, D., Turan, F. & Turan, C. Length–weight and length–length relationships for four shad species along the western Black Sea coast of Turkey. J. Appl. Ichthyol. 27, 942–944 (2011).
Article  Google Scholar 

86.
Sievert, C. plotly for R. https://plotly-book.cpsievert.me. [p506] (2018).

87.
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr (2019).

88.
Zeileisk, A., Kleiber, C. & Jackman, S. Regression models for count data in R. J. Stat. Softw. 27, 1–25 (2008).
Google Scholar 

89.
Chambers, J. M. Linear models. In Statistical Models (eds Chambers, J. M. & Hastie, T. J.) (Wadsworth and Brooks/Cole, Belmont, 1992).
Google Scholar 

90.
Ahlmann-Eltze, C. ggsignif: Significance Brackets for ggplot2. https://CRAN.R-project.org/package=ggsignif (2019). More

Through a three-year investigation, we found that a moderate decrease of nitrogen from 280 to 140–210 kg N ha−1 did not markedly influence the populations of cereal aphids or the parasitism rate. However, a moderate decrease of nitrogen input from 280 to 140–210 kg N ha−1 maximized the fitness of two predominant Aphidiinae parasitoid species, suggesting parasitoid control of cereal aphid would get benefit from the moderate decrease of nitrogen fertilizer. Those results showed that moderately decreasing nitrogen fertilizer could boost the parasitoid control of cereal aphids. Our research suggests that moderately decreasing nitrogen input is qualitatively beneficial to parasitoids but would not control cereal aphids quantitatively.
Effect of decreasing nitrogen fertilizer on the cereal aphid population
This study demonstrated that nitrogen fertilizer has the potential to positively influence densities of S. avenae and R. padi among all manipulated nitrogen fertilizer levels (70–280 kg N ha−1) (Fig. 1). Similar conclusions have been documented in research linked with aphids, including cereal aphids5,17,24. First, the plant usually responds monotonously and positively to nitrogen fertilizer. The percentage of nitrogen in the dry weight of tobacco leaves was positively associated with fertilizer levels25. Nitrogen fertilizer in the range of 0–225 kg N ha−1 improved nitrogen concentration of canola throughout the growing season26. It has been reported that fertilization has a positive influence on plants, indicating a cascading effect on herbivorous pests24,26,27. Nitrogen input could enhance the nutritional quality of the host, as nitrogen input increases sugars and amino acids availability for aphids, thereby accelerating the population growth of the herbivores28,29. Second, fertilization negatively affects plant defensive responses to herbivores and lessens the amounts of toxins in host plants27. For example, nitrogen fertilizer employed for walnut seedlings decreased the allocation to defensive toxins such as juglone, thereby lowering resistance to walnut aphids30. Third, fertilization alters the microclimate of crops and thereby contributes to the population growth of aphids17,31.
However, only the lowest nitrogen level manipulated in our experiment (70 kg N ha−1) significantly reduced the population of cereal aphids compared with the conventional nitrogen level (280 kg N ha−1) in 2016 and 2017 (Fig. 1). Those results showed that the magnitude of decreasing fertilizer input from the conventional level (280 kg N ha−1) to a moderate level (140–210 kg N ha−1) was insufficient to contain the population of cereal aphids. The performance of cereal aphids could remain unaffected when fertilizer input was decreased to a low level, as aphids could adapt to the pressure of deficient nutrition by sucking more strongly10. Therefore, to reduce the population of cereal aphids, the nitrogen level should be decreased to 70 kg·N·ha−1 or lower. Similarly, as fertilizer was applied to tobacco in the range of 0–200 ppm N, the nymph weights of whiteflies on tobacco plants did not diminish markedly until the nitrogen concentration level was reduced from 200 to 0 ppm N25.
Nevertheless, cereal yield responds to nitrogen levels as a negatively accelerating curve based on previous studies7,9. Far lower nitrogen input sharply reduces grain yield, and moderate nitrogen fertilizer is always imperative in agricultural production2,7. Therefore, the tradeoff between maintaining the essential grain yield and reduction of the pest population would not have been optimized solely by decreasing nitrogen input.
The wheat variety adopted in our experiment was susceptible to cereal aphids. The landscape around our field employed in this experiment was predominated by winter wheat, and thus the landscape was extremely simplified. By comparison, use of a resistant variety and intercropping wheat with another crop mediated the impact of nitrogen input on densities of cereal aphids10,12. If these factors are taken into consideration, it then seems more unlikely that the pest population can be controlled solely by decreasing nitrogen input in complex realistic agricultural environments.
Effect of decreasing nitrogen fertilizer on the densities of parasitoids and parasitism rate
The results showed that the parasitism rate remained unchanged with nitrogen input (Fig. 2), similar to the results of Garratt, who pointed out that fertilizer levels did not affect the parasitism rate in a cereal-aphid-parasitoid system, as the densities of aphids and their parasitoids increased synchronously with the amount of fertilizer18. Similar findings were observed in a walnut aphid-Aphidiinae parasitoid system24. Mixed results were reported in previous studies5,11. The densities of cereal aphids and parasitoids increased when input of nitrogen fertilizer increased from 115 to 170 kg N ha−1, while the parasitism rate increased steadily5.
Parasitoids are subject to pressures derived from higher trophic level. Coincidental intraguild predation is ubiquitous in the form of parasitized aphids suffering from predation. The effect of coincidental intraguild predation on biocontrol and the abundance of parasitoids remains controversial32,33. Importantly, the Aphidiinae parasitoids have the potential to identify the odors of ladybird beetles and reduce searching efficiency by themselves and their offspring, a trait-mediated indirect effect unrelated with the densities of ladybird beetles34. It is possible that the behavior of Aphidiinae parasitoids and the parasitism rate could have been mediated indirectly by ladybird beetles and other predators. Furthermore, the hyperparasitoids also could have relieved biocontrol by Aphidiinae parasitoids35. Hence, the higher trophic level could relieve the effects of nitrogen levels on densities of parasitoids and the parasitism rate.
Effect of decreasing nitrogen fertilizer on the body size of Aphidiinae parasitoids
This research has shown that nitrogen fertilizer application impacted the body sizes of the two Aphidiinae parasitoids (Figs. 3, 4). It has been reported that the body sizes of parasitoids increased monotonically with nitrogen fertilizer under low densities of aphids in the laboratory18,22, meanwhile the dispersion capacity of parasitoid adults, the fecundity of adult females, the emergence rate, the adult longevity of parasitoids, and the parasitism rate increased with the body sizes of parasitoids19,20,22. In contrast to previous reports, this field study found that a moderate decrease in nitrogen application from 280 to 140–210 kg N ha−1 maximized the body sizes of parasitoids. The body sizes of parasitoids depend negatively on the abundance of parasitoids and positively on the hosts diversity19,36,37. Hence, combining the positive effect of the abundance of aphids and of the nitrogen input with the negative effect of parasitoid abundance, it is assumed that an equilibrium should emerge balancing the positive effect of abundance of aphids and the negative effect of abundance of parasitoids. Analogously, It has been reported that an optimized nitrogen level maximized the ratio of predators to prey in a canola-mustard aphid-predatory gall midge system26.
Manipulating nitrogen fertilizer to maximize the fitness of parasitoids plays a crucial role in natural pest control. Increasing the body sizes of parasitoids means greater fertility and dispersal ability of adults20,21, higher fitness of offspring38, and the resulting greater capacity to control the aphid. Thus, decreasing nitrogen fertilizer from the conventional level to more environmentally-friendly magnitudes (140–210 kg N ha−1) could increase the fitness of Aphidiinae parasitoids and boost the biocontrol by parasitoids. Regrettably, this research study did not validate such a viewpoint since the parasitism rate was not maximized under the moderate nitrogen levels. First, there may be hysteresis effects. The parasitoids that were measured for body sizes came from mummies that were sampled in the flowering phases. These parasitoids came into play and mummified cereal aphids after more than ten days. The mummies remained scarce before the flowering phase. Thus, a notable lag occurred and the effect of parasitoid fitness on the parasitism rate could have been unobservable in this study. Second, apart from affecting parasitoid fitness, nitrogen application affected pest fitness. A moderate amount nitrogen maximized the performance of the green peach aphid and the Bertha armyworm23,39. A positive relationship between aphid weight and hind tibia length of parasitoids has been reported18. Combined with the finding in this study that the body sizes of parasitoids were maximized by moderate nitrogen levels, these results imply that the fitness of cereal aphids also benefited from moderate nitrogen levels. However, the densities of cereal aphids in moderate nitrogen levels were similar to those under higher nitrogen levels, suggesting that there could be a compensation between the effect of nitrogen input on fitness of cereal aphids and the effect of nitrogen input on fitness of parasitoids. Currently, long-term agricultural intensification limited biocontrol of parasitoids5. Previous study has reported that the parasitoids were more strongly influenced by agricultural intensification compared to cereal aphids5,13,14. If serious agricultural intensification had mediated, for example decreasing nitrogen fertilizer to an optimized extent, the equilibrium between the impact of moderate decreasing nitrogen fertilizer on parasitoids and the counterpart on cereal aphids would be reshaped. Thus, the positive influence of decreasing nitrogen fertilizer on parasitoids would prevail. Coincidentally, such a magnitude of decreasing nitrogen application would maintain the current wheat yield and lessen the potential environmental risks9.
Relationship between the parasitism rate and the population growth of cereal aphids
From flowering to milking phase, the population of the cereal aphid R. padi that escaped from Aphidiinae parasitoids increased substantially in both 2017 and 2018, while the population of the cereal aphid S. avenae decreased markedly in both 2016 and 2017 (Table 1). Combining the differences between dynamics of the two cereal aphid species with the fact that the Aphidiinae parasitoids rarely parasitize R. padi in China40, it is apparent that the Aphidiinae parasitoids play a pivotal role in suppressing the cereal aphid S. avenae. Furthermore, a higher parasitism rate had a greater suppression effect on the population of the cereal aphid S. avenae, in line with previous research6,14,41.
Year-to-year fluctuation of the cereal aphids-Aphidiinae parasitoids interaction
Obvious fluctuations in the cereal aphids-Aphidiinae parasitoids interaction across years have been documented in this study. Such population fluctuations of aphids and their natural enemies are ubiquitous14,17,42. It has been assumed that a disadvantageous climate accounted for the fluctuations17. The climate changes could not have been manipulated in our study, but they play essential roles in population fluctuations43. Climate warming induced an outbreak of the cereal aphids, but the parasitism rate remained unchanged43,44. Lack of Aphidiinae parasitoids caused higher populations of the cereal aphid S. avenae in a simulated warmed wheat field. However, abundant Aphidiinae parasitoids retained effective suppression of the cereal aphids even when the wheat field was warmed45. The synchronization of parasitoids with pests is vitally important for maintaining biocontrol46, while climate change has the potential to mismatch the pests with parasitoids and cause strong population fluctuations of pests and natural enemies47.
In this study, the parasitism rate was evaluated according to the densities of discernible mummies, a conventional method widely adopted5,6,24. We keep in mind that this method neglects the fact that the symptomless aphids that have been parasitized. Consequently, the parasitism rare was underestimated and the annual fluctuations of abundance of the parasitoids and the parasitism rate were magnified, especially early in the season. Molecular detection, which has the capacity to evaluate whether symptomless aphids have been parasitized and if so by which parasitoid species, presents an exceedingly promising alternative for exploring the aphid-parasitoid interaction11,33. This burgeoning method should be employed to more accurately evaluate the aphids-parasitoids interaction. More

According to7, identifying fragile ecological areas is imperative for ecological protection and environmental organization and management. Therefore, assessing ecological vulnerability is crucial for the study of ecosystem vulnerability45. Based on the current conditions and previous predictions, the EVI was classified from the lowest vulnerability (potential) to the highest vulnerability (high), as shown in Table 4. Overall, this study obtained three main results, which are highlighted below.
The first result concerned the spatial variation in EVI. In the composite system, the EVI (EVIPCA) varied from north to south, with Littoral being a vulnerable province and Alibori being a stable province. In the additive system, EVI (EVIad), both southern and northern Benin were identified as vulnerable, especially northern Benin, and Littoral (which was identified as vulnerable by the composite system) and central Atacora (which was identified as potentially vulnerable by the composite system), respectively, were identified as vulnerable.
The second result was the calculation of the spatial autocorrelation coefficients (Moran’s I) of each EVI, which were IPCA = 0.955256 and IAD = 0.989222 for the composite and additive systems, respectively. Both of these values are very high and are better than those reported in46. Although the spatial variations in these systems were obviously different, their Moran’s I values remained very high. However, according to Moran’s I, the spatial autocorrelation of the additive system was higher than that of the composite system. The principal component analysis approach assumes no prior relationship between the different factors and allows their relationships to develop from the statistical analysis, thus indicating the regional spatial variability of the components8. The observed discrepancies in spatial variation outcomes did not mean that there was a lack of spatial organization between the components. Therefore, graphic dissimilarities (differences in spatial distributions) do not challenge the spatial layout of the components or notably, their correlations.
The third result was from the cluster analysis, showing high-high clusters in the south for the composite system and in the north for the additive system. We deduce that regardless of the system used to calculate vulnerability, ecosystems in central Benin are still relatively stable. Central Benin has a moderate population density and moderate soil organic carbon levels. Littoral has a high population density rate, while Borgou has a high soil organic carbon level. These outcomes reveal that southern Benin is seriously threatened according to the composite system and that northern Benin is seriously threatened according to the additive system. These findings were explained and discussed with reference to available studies.
We used IDW interpolation, as opposed to41, who used kriging interpolation. We note that the indicators used in that study were slightly different from those in this study and were not classified similarly; in addition, different analysis assumptions were applied. His results show a strong positive correlation between sensitivity and the additive EVI (EVIAD), which is slightly different from the results of our study. In this study, we found a moderate correlation between these two factors. This difference in the outcomes can be attributed to the difference in the indicators and their distribution in the system. Nonetheless, that study showed that additive vulnerability is primarily influenced by adaptation, exposure and sensitivity; our study led us to put these elements in the order of adaptation, sensitivity and exposure. Both studies placed adaptation in the same position. Although the considered variables were different, we reached the same conclusion regarding adaptation, which can be considered a strength of our additive system.
Densely populated areas were determined to be very vulnerable47. High sensitivity rates were detected in southern Benin, including in Littoral, Atlantique, and Oueme. Housing and density indicators were classified as sensitivity variables, which means that density is still a threat to ecosystem stability. Littoral Province, the economic capital of Benin, which has the highest population density (more than 8000 inhabitants per square kilometer, according to the averaged raw data), and Atlantique and Oueme provinces, newly developed residential areas, were classified as extremely vulnerable. Alibori Province, the largest and least populated province, was classified as the most stable area in the composite system. We can deduce from this analysis that the population density also has a great impact on the composite system. In the additive system, Littoral remained an extremely vulnerable area, and central Atacora and Collines were the most stable areas. This outcome confirms that density in Littoral is a serious challenge to stability according to both systems.
However, the composite system than the additive system is more credible since it is based on SPSS, a statistical software, and is therefore empirical. In contrast, the additive system can be unreliable, since the indicators, as a whole, are classified according to the user. This classification method is subjective, and therefore theoretical (here, we based our indicators on expert advice and IPCC recommendations); hence, it leaves room for doubt. This study found that coastal zones, i.e., Littoral, are the most vulnerable33,34,48. This finding indicates the reality for our study. The extremely vulnerable areas identified by the composite system were high per capita density areas, which emphasized that density was a decisive indicator in our composite system. This analysis uncovered significant spatial variation in population vulnerability in southern Benin. According to the raw data we collected, the average density per capita in Borgou is 35.909%, while in Littoral, it is 8003.636%, i.e., 223 times higher than that in Borgou. Borgou is made up of several communes, while Littoral consists only of Cotonou, the economic and administrative capital of Benin, which is a highly desirable area. The demand for buildings has forced people to occupy some natural drainage channels, making this commune vulnerable to flooding. Southern Benin is less spacious but has more inhabitants than northern Benin because almost the entire administrative system of the country is located there, as well as one of the largest markets in West Africa. There is a need for an efficient decentralization process according to the determined standards. Our study revealed that regions with lower density per capita were the least vulnerable.
The additive system found that the areas with high bush fires and soil organic carbon rates were the most vulnerable. Thus, vulnerability is specific to the context34, since the factors that make a region or a community vulnerable can vary among different regions and community. The vulnerability of the northern area that was highlighted by the additive system can be explained by the practice of intensive agriculture (soil organic carbon) and the bush fires involved in these practices. Northern Benin is an agricultural area, and cotton cultivation is common; hence, there are high levels of pesticide use. Agriculture is very important for the Beninese economy and hence pesticides are used. Vulnerability in southern Benin is related to climate, flooding, and the high population density, while vulnerability in northern Benin is related to bush fires and soil organic matter levels. Although the systems and indicator groupings were different, they reached the same conclusion about Littoral Province. In the additive system, the vulnerable areas corresponded to areas with high soil organic carbon.
It is important to point out that this study suffers from certain limitations38. For example, data for all the indicators from the same time period were not always available, some required data were inaccessible and some data were gathered from the public domain. This can be interpreted as a weakness of our system. Since public-domain data are not accurate, they can result in biased outputs, which should not be ignored. The determined spatial and temporal variation, as well as the type of degradation under consideration, depends on the input data sets for the analysis and modeling39. Using automatic linear modeling model building (ALMMB), our results were improved.
The main objective of automatic linear modeling model building (ALMMB) was to improve the present study outcomes by enhancing the accuracy of the established system based on the adjusted chi-square Pearson correlation. Using automatic linear modeling regression combined with the best subsets method in SPSS 23, we tried to enhance each observed vulnerability level. Table 7 displays both the observed and enhanced rates for each EVI, and Fig. 6 displays the map of the enhanced values. We note that the potentially vulnerable areas32 increase or decrease in size less than the highly vulnerable areas.
Table 7 Observed and enhanced rate for EVI.
Full size table

Figure 6

Improving composite and additive EVI map.

Full size image

Based on Table 8, in the composite system, increases in both the potentially and highly vulnerable areas were highlighted. The observed potentially vulnerable area was 48,600 km2, and the enhanced potentially vulnerable area was 60,269 km2. The observed highly vulnerable area was 3729 km2, and the enhanced highly vulnerable area was 4812 km2; the differences in these values were 11,669 km2 and 1083 km2, respectively. A decrease in the potentially vulnerable area and an increase in the highly vulnerable area were noted in the additive system. In the additive system, the observed potentially vulnerable area was 36,450 km2, and the enhanced potentially vulnerable area was 32,119 km2, for a difference of 4331 km2. The observed highly vulnerable area was 3007 km2, and the enhanced highly vulnerable area was 6977 km2, for a difference of 3970 km2, i.e., more than the double the observed value. However, according to the enhanced composite model, much attention should be paid to all southern provinces, especially Zou, Oueme and Plateau. Figure 6 displays the enhanced vulnerability mapping for a) the composite system and b) the additive system. Figure 7 summarizes the different classified areas and their differences.
Table 8 Observed and enhanced vulnerability areas. Note Classif. = classification, Dif. = difference, Qualif. = qualification, Inc. = increase and Reg. = regression.
Full size table

Figure 7

Synthesis of different classified areas.

Full size image

In summary, the composite system was vulnerable to climate and flooding (and to some extent to population density as well, as in Littoral), while the additive system was vulnerable to bush fires and soil organic matter. Littoral was identified as a vulnerable area in both systems. Finally, to improve the accuracy of our results, we used ALMMB. The results showed both increases and decreases in the size of vulnerable areas. The present study used a combination of GIS, PCA and ALMMB to accurately assess the vulnerability of terrestrial ecosystems in Benin. More