Ecology

Inferring leading-following networks
Defining leading-following events
Unlike studies on collective motion where group movement is tracked continuously5,15, our datasets contain only discrete records of bat appearances at experimental boxes. Quantifying individual influence is, thus, contingent on a rigorous method for inferring leading-following events from discrete recordings of animal occurrences. To denote the information that individuals possess about the location of experimental boxes, we refine the nomenclature used by Kerth and Reckardt3. An individual bat is said to be naïve at time ({{{mathbf {t}}}}_{{{mathbf {1}}}}) regarding a given box, if it has not been recorded by the reading device in that box for all times ({{mathbf {t}}} 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