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1.
Conrad R. Soil microorganisms as controllers of atmospheric trace gases (H2, CO, CH4, OCS, N2O, and NO). Microbiol Rev. 1996;60:609–40.
CAS PubMed PubMed Central Article Google Scholar
2.
Rhee TS, Brenninkmeijer CAM, Röckmann T. The overwhelming role of soils in the global atmospheric hydrogen cycle. Atmos Chem Phys. 2006;6:1611–25.
CAS Article Google Scholar
3.
Downey NVS, Randerson JT, Eiler JM. Molecular hydrogen uptake by soils in forest, desert, and marsh ecosystems in California. J Geophys Res. 2008;113:G03037.
Article Google Scholar
4.
Schmitt S, Hanselmann A, Wollschläger U, Hammer S, Levin I. Investigation of parameters controlling the soil sink of atmospheric molecular hydrogen. Tellus B Chem Phys Meter. 2009;61:416–23.
Article CAS Google Scholar
5.
Novelli PC, Lang PM, Masarie KA, Hurst DF, Myers R, Elkins JW. Molecular hydrogen in the troposphere: Global distribution and budget. J Geophys Res. 1999;104:30427–44.
CAS Article Google Scholar
6.
Downey NVS, Randerson JT, Eiler JM. Temperature and moisture dependence of soil H2 uptake measured in the laboratory. Geophys Res Lett. 2006;33:1–5.
Google Scholar
7.
Häring V, Conrad R. Demonstration of two different H2-oxidizing activities in soil using an H2 consumption and a tritium exchange assay. Biol Fertil Soils. 1994;17:125–8.
Article Google Scholar
8.
Schuler S, Conrad R. Soils contain two different activities for oxidation of hydrogen. FEMS Microbiol Ecol. 1990;73:77–84.
CAS Article Google Scholar
9.
Constant P, Chowdhury SP, Hesse L, Pratscher J, Conrad R. Genome data mining and soil survey for the novel group 5 [NiFe]-hydrogenase to explore the diversity and ecological importance of presumptive high-affinity H2-oxidizing bacteria. Appl Environ Microbiol. 2011;77:6027–35.
CAS PubMed PubMed Central Article Google Scholar
10.
Greening C, Constant P, Hards K, Morales SE, Oakeshott JG, Russell RJ, et al. Atmospheric hydrogen scavenging: from enzymes to ecosystems. Appl Environ Microbiol. 2015;81:1190–9.
PubMed PubMed Central Article CAS Google Scholar
11.
Maimaiti J, Zhang Y, Yang J, Cen YP, Layzell DB, Peoples M, et al. Isolation and characterization of hydrogen-oxidizing bacteria induced following exposure of soil to hydrogen gas and their impact on plant growth. Environ Microbiol. 2007;9:435–44.
CAS PubMed Article Google Scholar
12.
Constant P, Poissant L, Villemur R. Isolation of Streptomyces sp. PCB7, the first microorganism demonstrating high-affinity uptake of tropospheric H2. ISME J. 2008;2:1066–76.
CAS PubMed Article Google Scholar
13.
Constant P, Hallenbeck PC. Chapter 5 – Hydrogenase. In: Pandey A, Chang JS, Hallenbeck PC, Larroche C, editors. Biohydrogen, 1st edition. Amsterdam: Elsevier; 2013.
14.
Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilized energy source for microbial growth and survival. ISME J. 2016;10:761–77.
CAS PubMed Article Google Scholar
15.
Constant P, Hallenbeck PC. Chapter 3 – Hydrogenase. In . Editors: Pandey A, Mohan SV, Chang JS, Hallenbeck PC, Larroche C, editors. Biohydrogen, 2nd edition. Amsterdam: Elsevier; 2019;49–78.
16.
Piché-Choquette S, Constant P. Molecular hydrogen, a neglected key driver of soil biogeochemical processes. Appl Environ Microbiol. 2019;85:e02418–18.
PubMed PubMed Central Article Google Scholar
17.
Greening C, Berney M, Hards K, Cook GM, Conrad R. A soil actinobacterium scavenges atmospheric H2 using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. Proc Natl Acad Sci USA. 2014;111:4257–61.
CAS PubMed Article Google Scholar
18.
Constant P, Chowdhury SP, Pratscher J, Conrad R. Streptomycetes contributing to atmospheric molecular hydrogen soil uptake are widespread and encode a putative high‐affinity [NiFe]‐hydrogenase. Environ Microbiol. 2010;12:821–9.
CAS PubMed Article Google Scholar
19.
Meredith LK, Rao D, Bosak T, Klepec-Ceraj V, Tada KR, Hansel CM, et al. Consumption of atmospheric hydrogen during the life cycle of soil‐dwelling actinobacteria. Environ Microbiol Rep. 2014;6:226–38.
CAS PubMed Article Google Scholar
20.
Piché-Choquette S, Khdhiri M, Constant P. Survey of high-affinity H2-oxidizing bacteria in soil reveals their vast diversity yet underrepresentation in genomic databases. Micro Ecol. 2017;74:771–5.
Article CAS Google Scholar
21.
Greening C, Carere CR, Rushton-Green R, Harold LK, Hards K, Taylor MC, et al. Persistence of the dominant soil phylum Acidobacteria by trace gas scavenging. Proc Natl Acad Sci USA. 2015;112:10497–502.
CAS PubMed Article Google Scholar
22.
Islam ZF, Cordero PRF, Feng J, Chen YJ, Bay SK, Jirapanjawat T, et al. Two Chloroflexi classes independently evolved the ability to persist on atmospheric hydrogen and carbon monoxide. ISME J. 2019;13:1801–13.
CAS PubMed PubMed Central Article Google Scholar
23.
Mohammadi S, Pol A, van Alen TA, Jetten MSM, Op den Camp HJM. Methylacidiphilum fumariolicum SolV, a thermoacidophilic ‘Knallgas’ methanotroph with both an oxygen-sensitive and -insensitive hydrogenase. ISME J. 2017;11:945–58.
CAS PubMed Article Google Scholar
24.
Janssen PH. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl Environ Microbiol. 2006;72:1719–28.
CAS PubMed PubMed Central Article Google Scholar
25.
Barns SM, Cain EC, Sommerville L, Kuske CR. Acidobacteria phylum sequences in uranium-contaminated subsurface sediments greatly expand the known diversity within the phylum. Appl Environ Microbiol. 2007;73:3113–6.
CAS PubMed PubMed Central Article Google Scholar
26.
Dedysh SN, Yilmaz P. Refining the taxonomic structure of the phylum Acidobacteria. Int J Syst Evol Microbiol. 2018;68:3796–806.
CAS PubMed Article PubMed Central Google Scholar
27.
Kielak AM, Barreto CC, Kowalchuk GA, van Veen JA, Kuramae EE. The ecology of acidobacteria: moving beyond genes and genomes. Front Microbiol. 2016;7:744.
PubMed PubMed Central Google Scholar
28.
Myers MR, King GM. Isolation and characterization of Acidobacterium ailaaui sp. nov., a novel member of Acidobacteria sub-division I, from a geothermally-heated Hawaiian microbial mat. Int J Syst Evol Microbiol. 2016;66:5328–35.
CAS PubMed Article PubMed Central Google Scholar
29.
Eichorst SA, Trojan D, Roux S, Herbold C, Rattei T, Woebken D. Genomic insights into the Acidobacteria reveal strategies for their success in terrestrial environments. Environ Microbiol. 2018;20:1041–63.
CAS PubMed PubMed Central Article Google Scholar
30.
Gödde R, Meuser K, Conrad R. Hydrogen consumption and carbon monoxide production in soils with different properties. Biol Fertil Soils. 2000;32:129–34.
Article Google Scholar
31.
Meredith LK, Commane R, Keenan TF, Klosterman ST, Munger JW, Templer PH, et al. Ecosystem fluxes of hydrogen in a mid‐latitude forest driven by soil microorganisms and plants. Glob Change Biol. 2017;23:906–19.
Article Google Scholar
32.
Turlapati SA, Minocha R, Bhiravarasa PS, Tisa LS, Thomas WK, Minocha SC. Chronic N-amended soils exhibit an altered bacterial community structure in Harvard Forest, MA, USA. FEMS Microbiol Ecol. 2012;83:478–93.
PubMed Article CAS PubMed Central Google Scholar
33.
Søndergaard D, Pedersen CNS, Greening C. HydDB: A web tool for hydrogenase classification and analysis. Sci Rep. 2016;6:1–8.
Article CAS Google Scholar
34.
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.
CAS PubMed Article PubMed Central Google Scholar
35.
Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2019;20:1160–6.
CAS PubMed Article PubMed Central Google Scholar
36.
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.
PubMed PubMed Central Article CAS Google Scholar
37.
Eddy SR. A new generation of homology search tools based on probabilistic inference. Genome Inf. 2009;23:205–11.
Google Scholar
38.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS PubMed PubMed Central Article Google Scholar
39.
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.
CAS PubMed PubMed Central Article Google Scholar
40.
Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–50.
CAS PubMed PubMed Central Article Google Scholar
41.
Kaiser C, Koranda M, Kitzler B, Fuchslueger L, Schnecker J, Schweiger P, et al. Belowground carbon allocation by trees drives seasonal patterns of extracellular enzyme activities by altering microbial community composition in a beech forest soil. N. Phytol. 2010;187:843–58.
CAS Article Google Scholar
42.
Spohn M, Pötsch EM, Eichorst SA, Woebken D, Wanek W, Richter A. Soil microbial carbon use efficiency and biomass turnover in a long-term fertilization experiment in a temperate grassland. Soil Biol Biochem. 2016;97:168–75.
CAS Article Google Scholar
43.
Šťovíček A, Kim M, Or D, Gillor O. Microbial community response to hydration-desiccation cycles in desert soil. Sci Rep. 2017;7:1–19.
Article CAS Google Scholar
44.
Angel R. Total nucleic acid extraction from soil. Protocol Exchange. 2012; https://doi.org/10.1038/protex.2012.046.
45.
Rose TM, Schultz ER, Henikoff JG, Pietrokovski S, McCallum CM, Henikoff S. Consensus-degenerate hybrid oligonucleotide primers for amplification of distantly related sequences. Nucl Acids Res. 1998;26:1628–35.
CAS PubMed Article Google Scholar
46.
Herbold CW, Pelikan C, Kuzyk O, Hausmann B, Angel R, Berry D, et al. A flexible and economical barcoding approach for highly multiplexed amplicon sequencing of diverse target genes. Front Microbiol. 2015;6:731.
PubMed PubMed Central Article Google Scholar
47.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-Source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.
CAS PubMed PubMed Central Article Google Scholar
48.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.
PubMed PubMed Central Article Google Scholar
49.
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90 K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
PubMed PubMed Central Article CAS Google Scholar
50.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.
CAS PubMed Article PubMed Central Google Scholar
51.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
CAS PubMed PubMed Central Article Google Scholar
52.
Constant P, Chowdhury SP, Hesse L, Conrad R. Co-localization of atmospheric H2 oxidation activity and high affinity H2-oxidizing bacteria in non-axenic soil and sterile soil amended with Streptomyces sp. PCB7. Soil Biol Biochem. 2011;43:1888–93.
CAS Article Google Scholar
53.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS PubMed PubMed Central Article Google Scholar
54.
Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–37.
CAS PubMed PubMed Central Article Google Scholar
55.
Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.
CAS PubMed Article Google Scholar
56.
Eichorst SA, Kuske CR, Schmidt TM. Influence of plant polymers on the distribution and cultivation of bacteria in the phylum Acidobacteria. Appl Environ Microbiol. 2011;77:586–96.
CAS PubMed Article Google Scholar
57.
Koch IH, Gich F, Dunfield PF, Overmann J. Edaphobacter modestus gen. nov., sp. nov. and Edaphobacter aggregans sp. nov., two novel acidobacteria isolated from alpine and forest soils. Int J Syst Evol Microbiol. 2008;58:1114–22.
CAS PubMed Article Google Scholar
58.
Eichorst SA, Breznak JA, Schmidt TM. Isolation and characterization of soil bacteria that define Terriglobus gen. nov., in the phylum Acidobacteria. Appl Environ Microbiol. 2007;73:2708–17.
CAS PubMed PubMed Central Article Google Scholar
59.
Ritz C, Baty F, Streibig JC, Gerhard D. Dose-response analysis using R. PLoS ONE. 2015;10:e0146021.
PubMed PubMed Central Article CAS Google Scholar
60.
Joseph SJ, Hugenholtz P, Sangwan P, Osborne CA, Janssen PH. Laboratory cultivation of widespread and previously uncultured soil bacteria. Appl Environ Microbiol. 2003;69:7210–5.
CAS PubMed PubMed Central Article Google Scholar
61.
Männistö MK, Rawat S, Starovoytov V, Haggblom MM. Granulicella arctica sp. nov., Granulicella mallensis sp. nov., Granulicella tundricola sp. nov. and Granulicella sapmiensis sp. nov., novel acidobacteria from tundra soil. Int J Syst Evol Microbiol. 2012;62:2097–106.
PubMed Article CAS Google Scholar
62.
Crowe MA, Power JF, Morgan XC, Dunfield PF, Lagutin K, Rijpstra WIC, et al. Pyrinomonas methylaliphatogenes gen. nov., sp. nov., a novel group 4 thermophilic member of the phylum Acidobacteria from geothermal soils. Int J Syst Evol Microbiol. 2014;64:220–7.
CAS PubMed Article Google Scholar
63.
Belova SE, Ravin NV, Pankratov TA, Rakitin AL, Ivanova AA, Beletsky AV, et al. Hydrolytic capabilities as a key to environmental success: chitinolytic and cellulolytic acidobacteria from acidic sub-arctic soils and boreal peatlands. Front Microbiol. 2018;9:1–14.
Article Google Scholar
64.
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.
CAS PubMed Article Google Scholar
65.
Ward NL, Challacombe JF, Janssen PH, Henrissat B, Coutinho PM, Wu M, et al. Three genomes in the phylum Acidobacteria provide insight into their lifestyles in soils. Appl Environ Microbiol. 2009;75:2046–56.
CAS PubMed PubMed Central Article Google Scholar
66.
Schäfer C, Bommer M, Hennig SE, Jeoung JH, Dobbek H, Lenz O. Structure of an actinobacterial-type [NiFe]-hydrogenase reveals insight into O2-tolerant H2 oxidation. Structure. 2016;24:285–92.
PubMed Article CAS Google Scholar
67.
Liot Q, Constant P. Breathing air to save energy–new insights into the ecophysiological role of high-affinity [NiFe]-hydrogenase in Streptomyces avermitilis. Microbiologyopen. 2016;5:47–59.
CAS PubMed Article Google Scholar
68.
Jones RT, Robeson MS, Lauber CL, Hamady M, Knight R, Fierer N. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. ISME J. 2009;3:442–53.
CAS PubMed PubMed Central Article Google Scholar
69.
Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA. 1998;95:6578–83.
CAS PubMed Article PubMed Central Google Scholar
70.
Kolter R, Siegele DA, Tormo A. The stationary phase of the bacterial life cycle. Ann Rev Microbiol. 1993;47:855–74.
CAS Article Google Scholar
71.
Lennon JTJ, Jones SES. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev. 2011;9:119–30.
CAS Google Scholar
72.
Jones SE, Lennon JT. Dormancy contributes to the maintenance of microbial diversity. Proc Natl Acad Sci USA. 2010;107:5881–6.
CAS PubMed Article PubMed Central Google Scholar
73.
Morita RY. Is H2 the universal energy source for long-term survival? Micro Ecol. 1999;38:307–20.
CAS Article Google Scholar
74.
Cordero PRF, Bayly K, Man Leung P, Huang C, Islam ZF, Schittenhelm RB, et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 2019;13:2868–81.
CAS PubMed PubMed Central Article Google Scholar More
1.
Milton, K. in Machiavellian Intelligence: Social Expertise and the Evolution of Intellect in Monkeys, Apes, and Humans 285–305 (Clarendon, Oxford, 1988).
2.
DeCasien, A. R., Williams, S. A. & Higham, J. P. Primate brain size is predicted by diet but not sociality. Nat. Ecol. Evol. 1, 0112 (2017).
Article Google Scholar
3.
Sayol, F. et al. Environmental variation and the evolution of large brains in birds. Nat. Commun. 7, 13971 (2016).
ADS CAS PubMed PubMed Central Article Google Scholar
4.
Powell, L. E., Isler, K. & Barton, R. A. Re-evaluating the link between brain size and behavioural ecology in primates. Proc. R. Soc. B Biol. Sci. 284, 1–8 (2017).
Google Scholar
5.
Byrne, R. W. & Whiten, A. in Machiavellian Intelligence: Social Expertise and the Evolution of Intellect in Monkeys, Apes, and Humans. https://doi.org/10.2307/2804121 (Clarendon, Oxford, 1988).
6.
Dunbar, R. I. M. The social brain hypothesis. Evol. Anthropol. Rev. 6, 178–190 (1998).
Article Google Scholar
7.
Ashton, B. J., Thornton, A. & Ridley, A. R. An intraspecific appraisal of the social intelligence hypothesis. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170288 (2018).
Article Google Scholar
8.
Holekamp, K. E. Questioning the social intelligence hypothesis. Trends Cogn. Sci. 11, 65–69 (2007).
PubMed Article Google Scholar
9.
Kern, J. M. & Radford, A. N. Experimental evidence for delayed contingent cooperation among wild dwarf mongooses. Proc. Natl Acad. Sci. USA 115, 6255–6260 (2018).
CAS PubMed Article Google Scholar
10.
Borgeaud, C. & Bshary, R. Wild vervet monkeys trade tolerance and specific coalitionary support for grooming in experimentally induced conflicts. Curr. Biol. 25, 3011–3016 (2015).
CAS PubMed Article PubMed Central Google Scholar
11.
Moll, H. & Tomasello, M. Cooperation and human cognition: the Vygotskian intelligence hypothesis. Philos. Trans. R. Soc. B Biol. Sci. 362, 639–648 (2007).
Article Google Scholar
12.
van Schaik, C. P., Isler, K. & Burkart, J. M. Explaining brain size variation: from social to cultural brain. Trends Cogn. Sci. 16, 277–284 (2012).
PubMed Article PubMed Central Google Scholar
13.
Lucas, J. R., Gentry, K. E., Sieving, K. E. & Freeberg, T. M. Communication as a fundamental part of Machiavellian intelligence. J. Comp. Psychol. 132, 442–454 (2018).
PubMed Article PubMed Central Google Scholar
14.
Dunbar, R. I. M. & Shultz, S. Why are there so many explanations for primate brain evolution? Philos. Trans. R. Soc. B Biol. Sci. 372, 20160244 (2017).
Article Google Scholar
15.
Dunbar, R. I. M. & Shultz, S. Understanding primate brain evolution. Philos. Trans. R. Soc. B Biol. Sci. 362, 649–658 (2007).
CAS Article Google Scholar
16.
Shultz, S. & Dunbar, R. I. M. Social bonds in birds are associated with brain size and contingent on the correlated evolution of life-history and increased parental investment. Biol. J. Linn. Soc. 100, 111–123 (2010).
Article Google Scholar
17.
Shultz, S. & Dunbar, R. I. M. The evolution of the social brain: anthropoid primates contrast with other vertebrates. Proc. R. Soc. B Biol. Sci. 274, 2429–2436 (2007).
Article Google Scholar
18.
Lemoine, S. et al. Between-group competition impacts reproductive success in wild chimpanzees. Curr. Biol. 30, 312–318.e3 (2020).
CAS PubMed PubMed Central Article Google Scholar
19.
Adams, E. S. Approaches to the study of territory size and shape. Annu. Rev. Ecol. Syst. 32, 277–303 (2001).
Article Google Scholar
20.
Hardy, I. C. W. & Briffa, M. Animal Contests (Cambridge Univ. Press, Cambridge, 2013).
Google Scholar
21.
Radford, A. N., Majolo, B. & Aureli, F. Within-group behavioural consequences of between-group conflict: a prospective review. Proc. R. Soc. B Biol. Sci. 283, 20161567 (2016).
Article Google Scholar
22.
van Schaik, C. P. in Comparative Socioecology (eds. Standen, V. & Foley, R. A.) 195–218 (Blackwell, 1989).
23.
Wrangham, R. W. An ecological model of female-bonded primate groups. Behaviour 75, 262–300 (1980).
Article Google Scholar
24.
Sterck, E. H. M. The evolution of female social relationships in nonhuman primates. Behav. Ecol. Sociol. 291–309 (1997).
25.
Wilson, M. L. & Wrangham, R. W. Intergroup relations in chimpanzees. Annu. Rev. Anthropol. 32, 363–392 (2003).
Article Google Scholar
26.
Moser-Purdy, C., MacDougall-Shackleton, E. A. & Mennill, D. J. Enemies are not always dear: male song sparrows adjust dear enemy effect expression in response to female fertility. Anim. Behav. 126, 17–22 (2017).
Article Google Scholar
27.
Gherardi, F. Fighting behavior in hermit crabs: the combined effect of resource-holding potential and resource value in Pagurus longicarpus. Behav. Ecol. Sociobiol. 59, 500–510 (2006).
Article Google Scholar
28.
Alexander, R. D. in The Human Revolution (eds. Mellars, P. & Stringer, C.) 455–513 (Edinburgh Univ. Press, Edinburgh, 1984).
29.
Hamilton, W. D. in ASA Studies 4: Biosocial Anthropology (ed. Fox, R.) 133–153 (Malaby, 1975).
30.
Grueter, C. C. Home range overlap as a driver of intelligence in primates. Am. J. Primatol. 77, 418–424 (2015).
PubMed Article Google Scholar
31.
Reichert, M. S. & Quinn, J. L. Cognition in contests: mechanisms, ecology, and evolution. Trends Ecol. Evol. 32, 773–785 (2017).
PubMed Article Google Scholar
32.
Shettleworth, S. J. Cognition, Evolution, and Behavior (Oxford Univ. Press, 2010).
33.
Young, A. J., Spong, G. & Clutton-Brock, T. Subordinate male meerkats prospect for extra-group paternity: alternative reproductive tactics in a cooperative mammal. Proc. R. Soc. B Biol. Sci. 274, 1603–1609 (2007).
Article Google Scholar
34.
Radford, A. N. & du Plessis, M. A. Territorial vocal rallying in the green woodhoopoe: factors affecting contest length and outcome. Anim. Behav. 68, 803–810 (2004).
Article Google Scholar
35.
Geissmann, T. & Orgeldinger, M. The relationship between duet songs and pair bonds in siamangs, Hylobates syndactylus. Anim. Behav. 60, 805–809 (2000).
CAS PubMed Article Google Scholar
36.
Ridley, A. R. in Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution and Behavior. 115–132. https://doi.org/10.1017/CBO9781107338357.008 (Cambridge Univ. Press, 2016).
37.
Bee, M. A. Habituation and sensitization of aggression in bullfrogs (Rana catesbeiana): testing the dual-process theory of habituation. J. Comp. Psychol. 115, 307–316 (2001).
CAS PubMed Article Google Scholar
38.
Sheehan, M. J. & Tibbetts, E. A. Selection for individual recognition and the evolution of polymorphic identity signals in Polistes paper wasps. J. Evol. Biol. 23, 570–577 (2010).
CAS PubMed Article Google Scholar
39.
Cant, M. A., Otali, E. & Mwanguhya, F. Fighting and mating between groups in a cooperatively breeding mammal, the banded mongoose. Ethology 108, 541–555 (2002).
Article Google Scholar
40.
Braga Goncalves, I. & Radford, A. N. Experimental evidence that intruder and group member attributes affect outgroup defence and associated within-group interactions in a social fish. Proc. R. Soc. B Biol. Sci. 286, 20191261 (2019).
Article Google Scholar
41.
Szipl, G., Ringler, E. & Bugnyar, T. Attacked ravens flexibly adjust signalling behaviour according to audience composition. Proc. R. Soc. B Biol. Sci. 285, 20180375 (2018).
Article Google Scholar
42.
Noser, R. & Byrne, R. W. Mental maps in chacma baboons (Papio ursinus): Using inter-group encounters as a natural experiment. Anim. Cogn. 10, 331–340 (2007).
PubMed Article Google Scholar
43.
Radford, A. N. Preparing for battle? Potential intergroup conflict promotes current intragroup affiliation. Biol. Lett. 7, 26–29 (2011).
PubMed Article Google Scholar
44.
Christensen, C. & Radford, A. N. Dear enemies or nasty neighbors? Causes and consequences of variation in the responses of group-living species to territorial intrusions. Behav. Ecol. 29, 1004–1013 (2018).
Article Google Scholar
45.
Temeles, E. J. The role of neighbours in territorial systems: when are they ‘dear enemies’? Anim. Behav. 47, 339–350 (1994).
Article Google Scholar
46.
Radford, A. N. Group-specific vocal signatures and neighbour-stranger discrimination in the cooperatively breeding green woodhoopoe. Anim. Behav. 70, 1227–1234 (2005).
Article Google Scholar
47.
Hyman, J. & Hughes, M. Territory owners discriminate between aggressive and nonaggressive neighbours. Anim. Behav. 72, 209–215 (2006).
Article Google Scholar
48.
Monclús, R., Saavedra, I. & de Miguel, J. Context-dependent responses to neighbours and strangers in wild European rabbits (Oryctolagus cuniculus). Behav. Process. 106, 17–21 (2014).
Article Google Scholar
49.
Thompson, F. J., Marshall, H. H., Vitikainen, E. I. K. & Cant, M. A. Causes and consequences of intergroup conflict in cooperative banded mongooses. Anim. Behav. 126, 31–40 (2017).
Article Google Scholar
50.
McComb, K., Packer, C. & Pusey, A. Roaring and numerical assessment in contests between groups of female lions, Panthera leo. Anim. Behav. 47, 379–387 (1994).
Article Google Scholar
51.
Descovich, K. A., Lisle, A. T., Johnston, S., Nicolson, V. & Phillips, C. J. C. Differential responses of captive southern hairy-nosed wombats (Lasiorhinus latifrons) to the presence of faeces from different species and male and female conspecifics. Appl. Anim. Behav. Sci. 138, 110–117 (2012).
Article Google Scholar
52.
Christensen, C., Kern, J. M., Bennitt, E. & Radford, A. N. Rival group scent induces changes in dwarf mongoose immediate behavior and subsequent movement. Behav. Ecol. 27, 1627–1634 (2016).
Google Scholar
53.
Trimmer, P. C. & Houston, A. I. An evolutionary perspective on information processing. Top. Cogn. Sci. 6, 312–330 (2014).
PubMed Article PubMed Central Google Scholar
54.
Mares, R., Young, A. J., Levesque, D. L., Harrison, N. & Clutton-Brock, T. H. Responses to intruder scents in the cooperatively breeding meerkat: sex and social status differences and temporal variation. Behav. Ecol. 22, 594–600 (2011).
Article Google Scholar
55.
Humphries, D. J., Finch, F. M., Bell, M. B. V. & Ridley, A. R. Vocal cues to identity: pied babblers produce individually distinct but not stable loud calls. Ethology 122, 609–619 (2016).
Article Google Scholar
56.
Burgener, N., Dehnhard, M., Hofer, H. & East, M. L. Does anal gland scent signal identity in the spotted hyaena? Anim. Behav. 77, 707–715 (2009).
Article Google Scholar
57.
Lanchester, F. W. W. Aircraft in Warfare: the Dawn of the Fourth Arm. (Constable and Company Limited, 1916).
58.
Wilson, M. L., Britton, N. F. & Franks, N. R. Chimpanzees and the mathematics of battle. Proc. R. Soc. B Biol. Sci. 269, 1107–1112 (2002).
Article Google Scholar
59.
Plowes, N. J. R. & Adams, E. S. An empirical test of Lanchester’s square law: mortality during battles of the fire ant Solenopsis invicta. Proc. R. Soc. B Biol. Sci. 272, 1809–1814 (2005).
Article Google Scholar
60.
Radford, A. N. Territorial vocal rallying in the green woodhoopoe: influence of rival group size and composition. Anim. Behav. 66, 1035–1044 (2003).
Article Google Scholar
61.
van Schaik, C. P. et al. Male monkeys use punishment and coercion to de-escalate costly intergroup fights. Proc. R. Soc. B Biol. Sci. 285, 20172323 (2018).
Article Google Scholar
62.
Boydston, E. E., Morelli, T. L. & Holekamp, K. E. Sex differences in territorial behavior exhibited by the spotted hyena (Hyaenidae, Crocuta crocuta). Ethology 107, 369–385 (2001).
Article Google Scholar
63.
McComb, K., Pusey, A., Packer, C. & Grinnell, J. Female lions can identify potentially infanticidal males from their roars. Proc. R. Soc. B Biol. Sci. 252, 59–64 (1993).
ADS CAS Article Google Scholar
64.
Koch, F., Signer, J., Kappeler, P. M. & Fichtel, C. Intergroup encounters in Verreaux’s sifakas (Propithecus verreauxi): who fights and why? Behav. Ecol. Sociobiol. 70, 797–808 (2016).
PubMed PubMed Central Article Google Scholar
65.
Schindler, S., Radford, A. N. & Schindler, S. Factors influencing within-group conflict over defence against conspecific outsiders seeking breeding positions. Proc. R. Soc. B Biol. Sci. 285, 20181669 (2018).
Article Google Scholar
66.
Arseneau-Robar, T. J. M. et al. Female monkeys use both the carrot and the stick to promote male participation in intergroup fights. Proc. R. Soc. B Biol. Sci. 283, 20161817 (2016).
Article Google Scholar
67.
Radford, A. N. Duration and outcome of intergroup conflict influences intragroup affiliative behaviour. Proc. R. Soc. B Biol. Sci. 275, 2787–2791 (2008).
Article Google Scholar
68.
Tibbetts, E. A., Agudelo, J., Pandit, S. & Riojas, J. Transitive inference in Polistes paper wasps. Biol. Lett. 15, 20190015 (2019).
PubMed PubMed Central Article Google Scholar
69.
Grosenick, L., Clement, T. S. & Fernald, R. D. Fish can infer social rank by observation alone. Nature 445, 429–432 (2007).
ADS CAS PubMed Article PubMed Central Google Scholar
70.
Arseneau-Robar, T. J. M., Taucher, A. L., Schnider, A. B., van Schaik, C. P. & Willems, E. P. Intra- and interindividual differences in the costs and benefits of intergroup aggression in female vervet monkeys. Anim. Behav. 123, 129–137 (2017).
Article Google Scholar
71.
Kotrschal, A., Räsänen, K., Kristjánsson, B. K., Senn, M. & Kolm, N. Extreme sexual brain size dimorphism in sticklebacks: a consequence of the cognitive challenges of sex and parenting? PLoS ONE 7, e30055 (2012).
ADS CAS PubMed PubMed Central Article Google Scholar
72.
Garamszegi, L. Z., Eens, M., Erritzøe, J. & Møller, A. P. Sperm competition and sexually size dimorphic brains in birds. Proc. R. Soc. B Biol. Sci. 272, 159–166 (2005).
Article Google Scholar
73.
Willems, E. P. & Van Schaik, C. P. Collective action and the intensity of between-group competition in nonhuman primates. Behav. Ecol. 26, 625–631 (2015).
Article Google Scholar
74.
Kotrschal, A. et al. Artificial selection on relative brain size in the guppy reveals costs and benefits of evolving a larger brain. Curr. Biol. 23, 168–171 (2013).
CAS PubMed PubMed Central Article Google Scholar
75.
Kotrschal, A., Corral-Lopez, A. & Kolm, N. Large brains, short life: selection on brain size impacts intrinsic lifespan. Biol. Lett. 15 (2019).
76.
Tsuboi, M. et al. Comparative support for the expensive tissue hypothesis: big brains are correlated with smaller gut and greater parental investment in Lake Tanganyika cichlids. Evolution 69, 190–200 (2015).
PubMed Article Google Scholar
77.
Kotrschal, A., Kolm, N. & Penn, D. J. Selection for brain size impairs innate, but not adaptive immune responses. Proc. R. Soc. B Biol. Sci. 283 (2016).
78.
Gervais, M. M., Kline, M., Ludmer, M., George, R. & Manson, J. H. The strategy of psychopathy: primary psychopathic traits predict defection on low-value relationships. Proc. R. Soc. B Biol. Sci. 280, 20122773 (2013).
Article Google Scholar
79.
Creel, S. & Christianson, D. Relationships between direct predation and risk effects. Trends Ecol. Evol. 23, 194–201 (2008).
PubMed Article Google Scholar
80.
Bruintjes, R., Lynton-Jenkins, J., Jones, J. W. & Radford, A. N. Out-group threat promotes within-group affiliation in a cooperative fish. Am. Nat. 187, 274–282 (2015).
PubMed Article Google Scholar
81.
Mosser, A. & Packer, C. Group territoriality and the benefits of sociality in the African lion, Panthera leo. Anim. Behav. 78, 359–370 (2009).
Article Google Scholar
82.
Crofoot, M. C., Gilby, I. C., Wikelski, M. C. & Kays, R. W. Interaction location outweighs the competitive advantage of numerical superiority in Cebus capucinus intergroup contests. Proc. Natl Acad. Sci. USA 105, 577–581 (2008).
ADS CAS PubMed Article Google Scholar
83.
Shaw, R. C., Boogert, N. J., Clayton, N. S. & Burns, K. C. Wild psychometrics: evidence for ‘general’ cognitive performance in wild New Zealand robins, Petroica longipes. Anim. Behav. 109, 101–111 (2015).
Article Google Scholar
84.
Buechel, S. D., Boussard, A., Kotrschal, A., van Der Bijl, W. & Kolm, N. Brain size affects performance in a reversal-learning test. Proc. R. Soc. B Biol. Sci. 285, 20172031 (2018).
Article Google Scholar
85.
Kotrschal, A., Deacon, A. E., Magurran, A. E. & Kolm, N. Predation pressure shapes brain anatomy in the wild. Evol. Ecol. 31, 619–633 (2017).
PubMed PubMed Central Article Google Scholar
86.
Herculano-Houzel, S. & Lent, R. Isotropic fractionator: a simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 25, 2518–2521 (2005).
CAS PubMed PubMed Central Article Google Scholar
87.
Ashton, B. J., Ridley, A. R., Edwards, E. K. & Thornton, A. Cognitive performance is linked to group size and affects fitness in Australian magpies. Nature 61, 5985–5991 (2018).
Google Scholar
88.
Taborsky, B. & Oliveira, R. F. Social competence: an evolutionary approach. Trends Ecol. Evol. 27, 679–688 (2012).
PubMed Article Google Scholar
89.
Gonda, A., Herczeg, G. & Merilä, J. Evolutionary ecology of intraspecific brain size variation: a review. Ecol. Evol. 3, 2751–2764 (2013).
PubMed PubMed Central Article Google Scholar
90.
Morris-Drake, A. et al. Experimental field evidence that out-group threats influence within-group behavior. Behav. Ecol. 30, 1425–1435 (2019).
PubMed PubMed Central Article Google Scholar
91.
Hellmann, J. K. & Hamilton, I. M. Intragroup social dynamics vary with the presence of neighbors in a cooperatively breeding fish. Curr. Zool. 65, 21–31 (2018).
PubMed PubMed Central Article Google Scholar
92.
Healy, S. D. & Rowe, C. A critique of comparative studies of brain size. Proc. R. Soc. B Biol. Sci. 274, 453–464 (2007).
Article Google Scholar
93.
Kotrschal, A. et al. The benefit of evolving a larger brain: big-brained guppies perform better in a cognitive task. Anim. Behav. 86, e4–e6 (2013).
PubMed PubMed Central Article Google Scholar
94.
Whiten, A. Social, Machiavellian and cultural cognition: a golden age of discovery in comparative and evolutionary psychology. J. Comp. Psychol. 132, 437–441 (2018).
PubMed Article PubMed Central Google Scholar
95.
Radford, A. N. & Bruintjes, R. Expanding the link between out-group threats and in-group behavior (a reply to Kavaliers and Choleris). Am. Nat. 189, 459–462 (2017).
PubMed Article PubMed Central Google Scholar
96.
Brown, M. Food and range defence in group-living primates. Anim. Behav. 85, 807–816 (2013).
Article Google Scholar
97.
Mirville, M. O. et al. Factors influencing individual participation during intergroup interactions in mountain gorillas. Anim. Behav. 144, 75–86 (2018).
Article Google Scholar
98.
Sheldahl, L. A. & Martins, E. P. The territorial behavior of the western fence lizard, Sceloporus occidentalis. Herpetologica 56, 469–479 (2000).
Google Scholar
99.
Ward, M. P., Alessi, M., Benson, T. J. & Chiavacci, S. J. The active nightlife of diurnal birds: extraterritorial forays and nocturnal activity patterns. Anim. Behav. 88, 175–184 (2014).
Article Google Scholar
100.
Feldblum, J. T., Manfredi, S., Gilby, I. C. & Pusey, A. E. The timing and causes of a unique chimpanzee community fission preceding Gombe’s “Four-Year War”. Am. J. Phys. Anthropol. 166, 730–744 (2018).
PubMed Article PubMed Central Google Scholar More
Our work was conducted under a permit from the University of North Florida Institutional Animal Care and Use Committee (permit #18-005).
Field methods
We performed nighttime spotlight surveys with an outboard motorboat throughout 2019 to determine relative alligator abundance, distribution, and habitat selection. This technique is an established method for estimating relative population sizes in crocodilians across heterogeneous habitat32. However, a limitation of spotlight surveys is the variation in detection probability caused by different environmental conditions or observers29. To control for these effects, we implemented a standardized survey protocol33,34. All surveys covered the first 8 km of nine tributaries within the lower St. Johns River system, starting at the point where each tributary meets the main channel of the river (Fig. 1). We limited our surveys to the first 8 km because some tributaries contained low bridges that blocked boat access after this point. We chose tributaries that were surrounded by different amounts of urban land cover such that our surveys spanned an urbanization gradient from approximately 5 to 80% urban land cover within 1 km of the river’s edge (Fig. 2). GIS analyses also revealed that land use patterns around the St. Johns River are dynamic, with different urban land cover proportions at 0.1, 1, 3, and 5 km from the water’s edge for each tributary (Fig. 2). To reduce temporal bias, we conducted surveys over the span of 1 year and segregated sampling periods into four distinct seasons (winter [Dec–Feb], spring [Mar–May], summer [Jun–Aug], and fall [Sep–Nov]. We surveyed each tributary one time during the middle month of each season, resulting in a total of four surveys per tributary. We surveyed the tributaries in a quasi-random fashion because the tributaries closest to the mouth of the St. Johns River are under significant tidal influence, so we timed surveys of those tributaries during periods of high tide in order to access the full survey area. We only performed surveys when rainfall was absent and wind speeds were below 16 km/h since these factors have been shown to affect alligator detection probability24. Quasi-random sampling over the span of a year was best suited to randomize environmental conditions that affect nighttime spotlight survey counts, such as water level, temperature, moon phase, and moon illumination24,31,35,36.
Figure 1
Map of the tributaries surrounding the lower St. Johns River that were surveyed as part of our study (white areas). From northeast to south: Clapboard Creek, Dunn Creek, Broward River, Trout River, Arlington River, Ortega River, Doctors Lake, Julington Creek, and Black Creek. The map was created with ArcGIS Pro 2.6 (https://arcgis.pro/).
Full size image
Figure 2
Levels of urban development (FLUCCS code 1000) surrounding the tributaries of the St. Johns River that were surveyed in this study. Land use was quantified using 0.1, 1, 3, and 5 km buffers around each tributary transect.
Full size image
We began all surveys no earlier than 30 min after sunset and we maintained a constant boat speed of 10–12 km/h. At the start and end of each survey we recorded moon phase, weather conditions, visibility, ambient light, air temperature, water temperature, and salinity. We detected alligator eyeshine primarily using two 1200 lm handheld spotlights, but we also used additional handheld lights (6000 lumens) often throughout the surveys. As soon as we detected eyeshine we approached the alligator at reduced speed. We placed each individual into a size class (30–90 cm [juvenile], 90–180 cm [sub-adult], 180–270 cm [adult], 270–360 cm [large adult], + 360 cm [largest adult]) by estimating the distance between the eyes and the tip of the snout37,38. If an alligator submerged before size estimation could take place, we recorded its length as unknown or simply larger or smaller than 180 cm. At each sighting we recorded global positioning system location using the on-deck boat navigation unit. We measured environmental characteristics at each sighting using a YSI meter (Pro2030; YSI; Yellow Springs, Ohio, USA), a thermometer, and a sky quality meter (SQM; Unihedron; Grimsby, Ontario, Canada).
We recorded information about habitat characteristics for each sighting following previous studies13,26. We first visually characterized habitat in a 10 m radius circle centered on the alligator sighting location (“used habitat”). We recorded the proportion of open water, emergent vegetation, floating vegetation, anthropogenic structure, and dry ground within the circle, as well as the alligator’s distance from shore, vegetation, and anthropogenic structure. We then visually classified the same habitat characteristics in a 20 × 100 m plot centered on the alligator sighting location and stretching along the shoreline (“available habitat”). If an alligator sighting occurred entirely in open water, then we shifted the available habitat plot to the closest shoreline. For each used habitat circle and available habitat plot, we classified the respective shorelines as natural, hardened, or mixed, depending on if the shore was totally vegetated, subject to anthropogenic armoring, or a mixture of the two types, respectively. We also estimated the proportion of shoreline found within these areas that were covered in naturally growing vegetation rather than anthropogenically altered lawns.
Land use classification
We used ArcGIS Pro (ESRI; Redlands, CA, USA) for all spatial data manipulation and visualization. We acquired land use and cover data from the St. Johns River Water Management District (SJRWMD) via the Florida Geographic Data Library. For all analyses we used data from the most recent SJRWMD dataset, which was from 2014.
We split a 100 k definition polygon of the St. Johns River to create smaller units representing each tributary transect. The resulting features consisted of the main portion of each tributary surveyed where lower order streams that were not surveyed were deleted. Because the extent to which alligators respond to land use changes was not known a priori, we buffered the transect polygon feature for each tributary to 0.1, 1, 3, and 5 km to further clip the SJRWMD land cover and use data layer. By creating four buffers for each of the nine tributaries, we generated a total of 36 land cover and use layers.
We classified land use types through the Florida Land Use and Cover Classification System (FLUCCS), as cited in SJRWMD metadata documentation. This hierarchical coding scheme contains four levels, of which we used the highest level (level 1) designation. This particular level classifies land use into nine distinct categories. These categories included urban and built-up; agriculture; upland nonforested; upland forests; water; wetlands; barren land; transportation, communication, and utilities; and special classification. For the purposes of this study, we only included defined terrestrial land use types in statistical analyses. These land use types were urban and built-up (e.g., residential, industrial, and recreational areas), agriculture (e.g., cropland, pastures, aquaculture), upland nonforested (e.g., shrub and brushland), upland forests (e.g., coniferous forests, hardwood forests, tree plantations), wetlands (e.g., freshwater/saltwater marshes, mangrove swamps, wet prairies), barren land (e.g., beaches other than swimming beaches, borrow areas, spoil areas), and transportation, communication and utilities (e.g., highways, electrical power facilities, wastewater treatment facilities). We calculated the proportions of each land use type using each respective land use shape area divided by total shape area.
Statistical analyses
To determine if environmental conditions and/or land use characteristics affect broad scale alligator distribution, we performed multiple analyses using SPSS (IBM; Armonk, NY, USA). We included all alligator sightings in our analyses, but we did not apply population estimate correction equations to the alligator counts because they tend to underestimate population numbers in crocodilians39. Sighting data used in statistical analyses therefore represent relative alligator abundance, not estimates of true alligator population size. We first checked normality for each variable using Kolmogorov–Smirnov and Shapiro–Wilk tests to determine if parametric or nonparametric tests were appropriate. Normality varied greatly across the suite of variables; therefore, Spearman’s rho and Pearson’s correlation coefficient were used when appropriate. We then performed simple linear regression to determine if there were any direct relationships between relative alligator abundance and individual variables. We performed these tests for alligator counts in each tributary by season and for the average number of sightings per tributary across seasons. We also averaged environmental variables for each tributary by season and for the average value per tributary across seasons. We tested for the effect of land use at all four buffer sizes for each tributary, including all relevant terrestrial land use types.
We then performed multiple linear regression analyses in a stepwise manner. This modeling system excluded variables found to be highly correlated with other variables (multicollinear) and retained variables that significantly contributed to the model (P ≤ 0.05). We then performed these tests on modified datasets that did not contain the two most saline tributaries to further validate preliminary findings. When more than one significant model was produced for a given data set, we calculated AICc values to rank models while penalizing model complexity and accounting for our small sample sizes.
To evaluate habitat selection, we compared percent shoreline vegetation and the proportions of habitat characteristics found in the 10 m radius circle to those found in the remaining areas of each respective 20 × 100 m plot using the Wilcoxon signed rank test. When comparisons could be made between two normally distributed groups of data, we used a paired sample t test instead. While comparing used to available habitat data was the basis of the tests, the amount of data per analysis differed between analysis groups. The first group was composed of all habitat selection data across time and space. This “global” dataset was the most robust in terms of sample size but may have been biased by double counting individuals across time. The second group was divided by season, so analyses were performed on all data collected within a season across space. This group removed the bias of double counting individuals but may be affected by variation in the number of sightings per season and tributary. More
1.
Flint, H. J., Scott, K. P., Louis, P. & Duncan, S. H. The role of the gut microbiota in nutrition and health. Nat. Rev. Gastroenterol. Hepatol. 9, 577. https://doi.org/10.1038/nrgastro.2012.156 (2012).
CAS Article PubMed Google Scholar
2.
Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267. https://doi.org/10.1126/science.1223813 (2012).
ADS CAS Article Google Scholar
3.
Zhang, Z., Jiao, S., Li, X. & Li, M. Bacterial and fungal gut communities of Agrilus mali at different developmental stages and fed different diets. Sci. Rep. 8, 15634 (2018).
ADS PubMed PubMed Central Article CAS Google Scholar
4.
Engel, P. & Moran, N. A. The gut microbiota of insects–diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013).
CAS PubMed Article Google Scholar
5.
Paine, T., Raffa, K. & Harrington, T. Interactions among scolytid bark beetles, their associated fungi, and live host conifers. Annu. Rev. Entomol. 42, 179–206 (1997).
CAS PubMed Article Google Scholar
6.
Yun, J. H. et al. Insect gut bacterial diversity determined by environmental habitat, diet, developmental stage, and phylogeny of host. Appl. Environ. Microbiol. 80, 5254–5264 (2014).
PubMed PubMed Central Article CAS Google Scholar
7.
Fukatsu, T. & Ishikawa, H. A novel eukaryotic extracellular symbiont in an aphid, Astegopteryx styraci (Homoptera, Aphididae, Hormaphidinae). J. Insect Physiol. 38, 765–773 (1992).
Article Google Scholar
8.
Malacrinò, A., Schena, L., Campolo, O., Laudani, F. & Palmeri, V. Molecular analysis of the fungal microbiome associated with the olive fruit fly Bactrocera oleae. Fungal Ecol. 18, 67–74 (2015).
Article Google Scholar
9.
Vega, F. E. & Blackwell, M. Insect-Fungal Associations: Ecology and Evolution (Oxford University Press, Oxford, 2005).
Google Scholar
10.
Stefanini, I. Yeast-insect associations: it takes guts. Yeast 35, 315–330 (2018).
CAS PubMed PubMed Central Article Google Scholar
11.
Boyce, A. Bionomics of the walnut husk fly, Rhagoletis completa. Hilgardia 8, 363–579 (1934).
Article Google Scholar
12.
Fanson, B. G. & Taylor, P. W. Protein: carbohydrate ratios explain life span patterns found in Queensland fruit fly on diets varying in yeast: sugar ratios. Age 34, 1361–1368 (2012).
CAS PubMed Article Google Scholar
13.
Moadeli, T., Mainali, B., Ponton, F. & Taylor, P. Evaluation of yeasts in gel larval diet for Queensland fruit fly, Bactrocera tryoni. J. Appl. Entomol. 142, 679–688 (2018).
CAS Article Google Scholar
14.
Nash, W. J. & Chapman, T. Effect of dietary components on larval life history characteristics in the Medfly (Ceratitis capitata: Diptera, Tephritidae). PLoS ONE 9, e86029 (2014).
ADS PubMed PubMed Central Article CAS Google Scholar
15.
Nestel, D. & Nemny-Lavy, E. Nutrient balance in medfly, Ceratitis capitata, larval diets affects the ability of the developing insect to incorporate lipid and protein reserves. Entomol. Exp. Appl. 126, 53–60. https://doi.org/10.1111/j.1570-7458.2007.00639.x (2008).
CAS Article Google Scholar
16.
Nestel, D., Nemny-Lavy, E. & Chang, C. L. Lipid and protein loads in pupating larvae and emerging adults as affected by the composition of Mediterranean fruit fly (Ceratitis capitata) meridic larval diets. Arch. Insect Biochem. Physiol. 56, 97–109. https://doi.org/10.1002/arch.20000 (2004).
CAS Article PubMed Google Scholar
17.
Mori, B. A. et al. Enhanced yeast feeding following mating facilitates control of the invasive fruit pest Drosophila suzukii. J. Appl. Ecol. 54, 170–177. https://doi.org/10.1111/1365-2664.12688 (2017).
Article Google Scholar
18.
Stamps, J. A., Yang, L. H., Morales, V. M. & Boundy-Mills, K. L. Drosophila regulate yeast density and increase yeast community similarity in a natural substrate. PLoS ONE 7, e42238. https://doi.org/10.1371/journal.pone.0042238 (2012).
ADS CAS Article PubMed PubMed Central Google Scholar
19.
Anagnostou, C., Dorsch, M. & Rohlfs, M. Influence of dietary yeasts on Drosophila melanogaster life-history traits. Entomol. Exp. Appl. 136, 1–11. https://doi.org/10.1111/j.1570-7458.2010.00997.x (2010).
Article Google Scholar
20.
Rohlfs, M. & Kürschner, L. Saprophagous insect larvae, Drosophila melanogaster, profit from increased species richness in beneficial microbes. J. Appl. Entomol. 134, 667–671. https://doi.org/10.1111/j.1439-0418.2009.01458.x (2010).
Article Google Scholar
21.
Menezes, C. et al. A Brazilian social bee must cultivate fungus to survive. Curr. Biol. 25, 2851–2855. https://doi.org/10.1016/j.cub.2015.09.028 (2015).
CAS Article PubMed Google Scholar
22.
Yun, J. H., Jung, M. J., Kim, P. S. & Bae, J. W. Social status shapes the bacterial and fungal gut communities of the honey bee. Sci. Rep. 8, 2019. https://doi.org/10.1038/s41598-018-19860-7 (2018).
ADS CAS Article PubMed PubMed Central Google Scholar
23.
DeLeon-Rodriguez, C. M. & Casadevall, A. Cryptococcus neoformans: tripping on acid in the phagolysosome. Front. Microbiol. 7, 164. https://doi.org/10.3389/fmicb.2016.00164 (2016).
Article PubMed PubMed Central Google Scholar
24.
Hajek, A. & St. Leger, R. Interactions between fungal pathogens and insect hosts. Annu. Rev. Entomol. 39, 293–322. https://doi.org/10.1146/annurev.en.39.010194.001453 (1994).
Article Google Scholar
25.
Lu, H. L., Wang, J. B., Brown, M. A., Euerle, C. & Leger, R. J. S. Identification of Drosophila mutants affecting defense to an entomopathogenic fungus. Sci. Rep. 5, 12350 (2015).
ADS PubMed PubMed Central Article Google Scholar
26.
Almeida, J. E., Batista Filho, A., Oliveira, F. C. & Raga, A. Pathogenicity of the entomopathogenic fungi and nematode on medfly Ceratitis capitata (Wied.)(Diptera: Tephritidae). BioAssay https://doi.org/10.14295/BA.v2 (2007).
Article Google Scholar
27.
Lacey, L. A., Frutos, R., Kaya, H. & Vail, P. Insect pathogens as biological control agents: do they have a future?. Biol. Control 21, 230–248 (2001).
Article Google Scholar
28.
Ortu, S., Cocco, A. & Dau, R. Evaluation of the entomopathogenic fungus Beauveria bassiana strain ATCC 74040 for the management of Ceratitis capitata. B. Insectol. 62, 245–252 (2009).
Google Scholar
29.
Quesada-Moraga, E., Ruiz-García, A. & Santiago-Alvarez, C. Laboratory evaluation of entomopathogenic fungi Beauveria bassiana and Metarhizium anisopliae against puparia and adults of Ceratitis capitata (Diptera: Tephritidae). J. Econ. Entomol. 99, 1955–1966 (2006).
CAS PubMed Article Google Scholar
30.
Clarke, A. R., Powell, K. S., Weldon, C. W. & Taylor, P. W. The ecology of Bactrocera tryoni (Diptera: Tephritidae): what do we know to assist pest management?. Ann. Appl. Biol. 158, 26–54 (2011).
Article Google Scholar
31.
Dominiak, B. C. & Daniels, D. Review of the past and present distribution of Mediterranean fruit fly (Ceratitis capitata Wiedemann) and Queensland fruit fly (Bactrocera tryoni Froggatt) in Australia. Aust. J. Entomol. 51, 104–115 (2012).
Article Google Scholar
32.
Sutherst, R. W., Collyer, B. S. & Yonow, T. The vulnerability of Australian horticulture to the Queensland fruit fly, Bactrocera (Dacus) tryoni, under climate change. Aust. J. Agric. Res. 51, 467–480 (2000).
Article Google Scholar
33.
Dominiak, B., Westcott, A. & Barchia, I. Release of sterile Queensland fruit fly, Bactrocera tryoni (Froggatt) (Diptera: Tephritidae), at Sydney, Australia. Aust. J. Exp. Agric. 43, 519–528 (2003).
Article Google Scholar
34.
Deutscher, A. T. et al. Near full-length 16S rRNA gene next-generation sequencing revealed Asaia as a common midgut bacterium of wild and domesticated Queensland fruit fly larvae. Microbiome 6, 85 (2018).
PubMed PubMed Central Article Google Scholar
35.
Drew, R., Courtice, A. & Teakle, D. Bacteria as a natural source of food for adult fruit flies (Diptera: Tephritidae). Oecologia 60, 279–284. https://doi.org/10.1007/BF00376839 (1983).
ADS CAS Article PubMed Google Scholar
36.
Lloyd, A., Drew, R., Teakle, D. & Hayward, A. Bacteria associated with some Dacus species (Diptera: Tephritidae) and their host fruit in Queensland. Aust. J. Biol. Sci. 39, 361–368 (1986).
Article Google Scholar
37.
Morrow, J. L., Frommer, M., Shearman, D. C. & Riegler, M. The microbiome of field-caught and laboratory-adapted Australian tephritid fruit fly species with different host plant use and specialisation. Microb. Ecol. 70, 498–508 (2015).
CAS PubMed Article Google Scholar
38.
Murphy, K. M., Teakle, D. S. & MacRae, I. C. Kinetics of colonization of adult Queensland fruit flies (Bactrocera tryoni) by dinitrogen-fixing alimentary tract bacteria. Appl. Environ. Microbiol. 60, 2508–2517 (1994).
CAS PubMed PubMed Central Article Google Scholar
39.
Thaochan, N., Drew, R., Hughes, J., Vijaysegaran, S. & Chinajariyawong, A. Alimentary tract bacteria isolated and identified with API-20E and molecular cloning techniques from Australian tropical fruit flies Bactrocera cacuminata and B. tryoni. J. Insect Sci. 10, 131 (2010).
CAS PubMed PubMed Central Article Google Scholar
40.
Majumder, R., Sutcliffe, B., Taylor, P. W. & Chapman, T. A. Next-Generation Sequencing reveals relationship between the larval microbiome and food substrate in the polyphagous Queensland fruit fly. Sci. Rep. 9, 1–12 (2019).
Article CAS Google Scholar
41.
Shuttleworth, L. A., Khan, M. A. M., Collins, D., Osborne, T. & Reynolds, O. L. Wild bacterial probiotics fed to larvae of mass-reared Queensland fruit fly [Bactrocera tryoni (Froggatt)] do not impact long-term survival, mate selection, or locomotor activity. Insect Sci. 27, 745–755 (2020).
CAS PubMed Article Google Scholar
42.
Shuttleworth, L. A. et al. A walk on the wild side: gut bacteria fed to mass-reared larvae of Queensland fruit fly [Bactrocera tryoni (Froggatt)] influence development. BMC Biotechnol. 19, 1–11 (2019).
Article CAS Google Scholar
43.
Woruba, D. N. et al. Diet and irradiation effects on the bacterial community composition and structure in the gut of domesticated teneral and mature Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae). BMC Microbiol. 19, 281 (2019).
CAS PubMed PubMed Central Article Google Scholar
44.
Majumder, R., Sutcliffe, B., Chapman, T. A. & Taylor, P. W. Microbiome of the Queensland fruit fly through metamorphosis. Microorganisms 8, 795 (2020).
PubMed Central Article PubMed Google Scholar
45.
Deutscher, A. T., Reynolds, O. L. & Chapman, T. A. Yeast: an overlooked component of Bactrocera tryoni (Diptera: Tephritidae) larval gut microbiota. J. Econ. Entomol. 110, 298–300 (2016).
Google Scholar
46.
Piper, A. M., Farnier, K., Linder, T., Speight, R. & Cunningham, J. P. Two gut-associated yeasts in a tephritid fruit fly have contrasting effects on adult attraction and larval survival. J. Chem. Ecol. 43, 891–901 (2017).
CAS PubMed Article Google Scholar
47.
Toju, H., Tanabe, A. S., Yamamoto, S. & Sato, H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS ONE 7, e40863 (2012).
ADS CAS PubMed PubMed Central Article Google Scholar
48.
Schmidt, P. A. et al. Illumina metabarcoding of a soil fungal community. Soil Biol. Biochem. 65, 128–132 (2013).
CAS Article Google Scholar
49.
Yun, J. H., Jung, M. J., Kim, P. S. & Bae, J. W. Social status shapes the bacterial and fungal gut communities of the honey bee. Sci. Rep. 8, 1–11 (2018).
Article CAS Google Scholar
50.
Ravenscraft, A., Berry, M., Hammer, T., Peay, K. & Boggs, C. Structure and function of the bacterial and fungal gut microbiota of Neotropical butterflies. Ecol. Monogr. 89, e01346 (2019).
Article Google Scholar
51.
Mohammed, W. S., Ziganshina, E. E., Shagimardanova, E. I., Gogoleva, N. E. & Ziganshin, A. M. Comparison of intestinal bacterial and fungal communities across various xylophagous beetle larvae (Coleoptera: Cerambycidae). Sci. Rep. 8, 10073 (2018).
ADS PubMed PubMed Central Article CAS Google Scholar
52.
Sutcliffe, B. et al. Diverse fungal lineages in subtropical ponds are altered by sediment-bound copper. Fungal Ecol. 34, 28–42 (2018).
Article Google Scholar
53.
Hamby, K. A., Hernández, A., Boundy-Mills, K. & Zalom, F. G. Associations of yeasts with spotted-wing Drosophila (Drosophila suzukii; Diptera: Drosophilidae) in cherries and raspberries. Appl. Environ. Microbiol. 78, 4869–4873 (2012).
CAS PubMed PubMed Central Article Google Scholar
54.
Kurtzman, C., Fell, J. W. & Boekhout, T. The Yeasts: A Taxonomic Study (Elsevier, Amsterdam, 2011).
Google Scholar
55.
Marchesi, J. R. Prokaryotic and eukaryotic diversity of the human gut. Adv. Appl. Microbiol. 72, 43–62 (2010).
PubMed Article Google Scholar
56.
Xiang, H. et al. Microbial communities in the larval midgut of laboratory and field populations of cotton bollworm (Helicoverpa armigera). Can. J. Microbiol. 52, 1085–1092 (2006).
CAS PubMed Article Google Scholar
57.
Kudo, R., Masuya, H., Endoh, R., Kikuchi, T. & Ikeda, H. Gut bacterial and fungal communities in ground-dwelling beetles are associated with host food habit and habitat. ISME J. 13, 676 (2019).
CAS PubMed Article Google Scholar
58.
Broderick, N. A., Raffa, K. F., Goodman, R. M. & Handelsman, J. Census of the bacterial community of the gypsy moth larval midgut by using culturing and culture-independent methods. Appl. Environ. Microbiol. 70, 293–300 (2004).
CAS PubMed PubMed Central Article Google Scholar
59.
Colman, D. R., Toolson, E. C. & Takacs-Vesbach, C. Do diet and taxonomy influence insect gut bacterial communities?. Mol. Ecol. 21, 5124–5137 (2012).
CAS PubMed Article Google Scholar
60.
Quan, A. S. & Eisen, M. B. The ecology of the Drosophila-yeast mutualism in wineries. PLoS ONE 13, e0196440 (2018).
PubMed PubMed Central Article CAS Google Scholar
61.
Starmer, W. T. & Lachance, M. A. Yeast ecology. Yeasts 7, 65–83 (2011).
Article Google Scholar
62.
Molnárová, J., Vadkertiová, R. & Stratilová, E. Extracellular enzymatic activities and physiological profiles of yeasts colonizing fruit trees. J. Basic Microbiol. 54, S74–S84 (2014).
PubMed Article CAS Google Scholar
63.
White, I. M. & Elson-Harris, M. M. Fruit Flies of Economic Significance: Their Identification and Bionomics (CAB International, Wallingford, 1992).
Google Scholar
64.
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).
CAS PubMed Google Scholar
65.
Benson, D. A. et al. GenBank. Nucleic Acids Res. 46, D41–D47 (2018).
CAS PubMed Article Google Scholar
66.
Australia, P. H. The Australian Handbook for the Identification of Fruit Flies. Vol. Version 1.0 (ed. Woods N) 234 (2011).
67.
Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts. Mol. Ecol. 2, 113–118 (1993).
CAS PubMed Article Google Scholar
68.
Hoggard, M. et al. Characterizing the human mycobiota: a comparison of small subunit rRNA, ITS1, ITS2, and large subunit rRNA genomic targets. Front. Microbiol. 9, 2208 (2018).
PubMed PubMed Central Article Google Scholar
69.
Fouts, D. E. et al. Next generation sequencing to define prokaryotic and fungal diversity in the bovine rumen. PLoS ONE 7, e48289 (2012).
ADS CAS PubMed PubMed Central Article Google Scholar
70.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335 (2010).
CAS PubMed PubMed Central Article Google Scholar
71.
Greenfield, P. Greenfield Hybrid Analysis Pipeline (GHAP) v1 (CSIRO, Canberra, 2017).
Google Scholar
72.
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
CAS PubMed Article Google Scholar
73.
Maidak, B. L. et al. The ribosomal database project (RDP). Nucleic Acids Res. 24, 82–85 (1996).
CAS PubMed PubMed Central Article Google Scholar
74.
Deshpande, V. et al. Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences. Mycologia 108, 1–5 (2016).
PubMed Article Google Scholar
75.
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).
CAS PubMed Article Google Scholar
76.
Clarke, K. & Ainsworth, M. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Prog. Ser. 92, 205–205 (1993).
ADS Article Google Scholar More
1.
Wen, A. et al. Detection, distribution, and genetic variability of ‘Candidatus Liberibacter’ species associated with zebra complex disease of potato in North America. Plant Dis. 93, 1102–1115 (2009).
CAS PubMed Article Google Scholar
2.
Munyaneza, J. E. et al. First report of “Candidatus Liberibacter solanacearum” associated with psyllid-affected carrots in Europe. Plant Dis. 94, 639 (2010).
CAS PubMed Article Google Scholar
3.
Nelson, W. R. et al. A new haplotype of ‘Candidatus Liberibacter solanacearum’ identified in the Mediterranean region. Eur. J. Plant Pathol. 135, 633–639 (2013).
Article Google Scholar
4.
Teresani, G. R. et al. Association of ‘Candidatus Liberibacter solanacearum’ with a vegetative disorder of celery in Spain and development of a real-time PCR method for its detection. Phytopathology 104, 804–811 (2014).
CAS PubMed Article Google Scholar
5.
Swisher Grimm, K. D. & Garczynski, S. F. Identification of a new haplotype of ‘Candidatus Liberibacter solanacearum’ in Solanum tuberosum. Plant Dis. 103, 468–474 (2019).
CAS PubMed Article PubMed Central Google Scholar
6.
Mauck, K. E., Sun, P., Meduri, V. & Hansen, A. K. N. C. Liberibacter psyllaurous haplotype resurrected from a 49-year-old specimen of Solanum umbelliferum: a native host of the psyllid vector. Sci. Rep. 9, 9530 (2019).
ADS PubMed PubMed Central Article CAS Google Scholar
7.
Haapalainen, M. et al. A novel haplotype of ‘Candidatus Liberibacter solanacearum ’ found in Apiaceae and Polygonaceae family plants. Eur. J. Plant Pathol. https://doi.org/10.1007/s10658-019-01890-0 (2019).
Article Google Scholar
8.
Contreras-Rendón, A., Sánchez-Pale, J. R., Fuentes-Aragón, D., Alanís-Martínez, I. & Silva-Rojas, H. V. Conventional and qPCR reveals the presence of ‘Candidatus Liberibacter solanacearum’ haplotypes A, and B in Physalis philadelphica plant, seed, and Βactericera cockerelli psyllids, with the assignment of a new haplotype H in Convolvul. Antonie Van Leeuwenhoek 113, 533–551 (2020).
PubMed Article CAS PubMed Central Google Scholar
9.
Haapalainen, M. et al. Genetic variation of ‘Candidatus Liberibacter solanacearum’ haplotype C and identification of a novel haplotype from Trioza urticae and stinging nettle. Phytopathology 1–49 (2018).
10.
Munyaneza, J. E., Sengoda, V. G., Aguilar, E., Bextine, B. & McCue, K. F. First report of ‘Candidatus Liberibacter solanacearum’ associated with psyllid-infested tobacco in Nicaragua. Plant Dis. 97, 1244 (2013).
CAS PubMed Article Google Scholar
11.
Hajri, A., Loiseau, M., Cousseau-Suhard, P., Renaudin, I. & Gentit, P. Genetic characterization of ‘Candidatus Liberibacter solanacearum’ haplotypes associated with Apiaceous crops in France. Plant Dis. 101, 1383–1390 (2017).
CAS PubMed Article Google Scholar
12.
Haapalainen, M. et al. Frequency and occurrence of the carrot pathogen ‘Candidatus Liberibacter solanacearum’ haplotype C in Finland. Plant Pathol. 66, 559–570 (2017).
CAS Article Google Scholar
13.
Nissinen, A. I., Haapalainen, M., Jauhiainen, L., Lindman, M. & Pirhonen, M. Different symptoms in carrots caused by male and female carrot psyllid feeding and infection by ‘Candidatus Liberibacter solanacearum’. Plant Pathol. 63, 812–820 (2014).
Article Google Scholar
14.
Munyaneza, J. E. et al. First report of “Candidatus Liberibacter solanacearum” associated with psyllid-affected carrots in Sweden. Plant Dis. 96, 453–453 (2012).
CAS PubMed Article Google Scholar
15.
Ouvrard, D. 2018 Psyl’list – The World Psylloidea Database. https://www.hemiptera-databases.com/psyllist (2019).
16.
Bell, J. et al. Detection and monitoring of psyllid vectors of ‘Candidatus Liberibacter solanacearum’ in Scotland – Final report of project RRL/001/14. (2017).
17.
Teresani, G. R. et al. Transmission of ‘Candidatus Liberibacter solanacearum’ by Bactericera trigonica Hodkinson to vegetable hosts. Spanish J. Agric. Res. 15, e1011 (2017).
Article Google Scholar
18.
Antolínez, C. A., Fereres, A. & Moreno, A. Risk assessment of ‘Candidatus Liberibacter solanacearum’ transmission by the psyllids Bactericera trigonica and B. tremblayi from Apiaceae crops to potato. Sci. Rep. 7, 45534 (2017).
ADS PubMed PubMed Central Article CAS Google Scholar
19.
Mawassi, M. et al. ‘Candidatus Liberibacter solanacearum’ is tightly associated with Carrot Yellows symptoms in Israel and transmitted by the prevalent psyllid vector Bactericera trigonica. Phytopathology 108, 1056–1066 (2018).
CAS PubMed Article Google Scholar
20.
Antolínez, C. et al. Seasonal abundance of psyllid species on carrots and potato crops in Spain. Insects 10, 287 (2019).
PubMed Central Article PubMed Google Scholar
21.
Loiseau, M. et al. First report of ‘Candidatus Liberibacter solanacearum’ in carrot in France. Plant Dis. 98, 839 (2014).
CAS PubMed Article Google Scholar
22.
Tahzima, R. et al. First report of ‘Candidatus, Liberibacter solanacearum’ on carrot in Africa. Plant Dis. 98, 1426 (2014).
CAS PubMed Article Google Scholar
23.
Alfaro-Fernández, A., Siverio, F., Cebrián, M. C., Villaescusa, F. J. & Font, M. I. ‘Candidatus Liberibacter solanacearum’ associated with Bactericera trigonica-affected carrots in the Canary Islands. Plant Dis. 96, 581 (2012).
PubMed Article Google Scholar
24.
Othmen, S. B. et al. ‘Candidatus Liberibacter solanacearum’ haplotypes D and E in carrot plants and seeds in Tunisia. J. Plant Pathol. 100, 197–207 (2018).
Article Google Scholar
25
Othmen, S. B. et al. Bactericera trigonica and B. nigricornis (Hemiptera: Psylloidea) in Tunisia as potential vectors of ‘Candidatus Liberibacter solanacearum’ on Apiaceae. Orient. Insects https://doi.org/10.1080/00305316.2018.1536003 (2018).
Article Google Scholar
26.
Monger, W. A. & Jeffries, C. J. First report of ’ Candidatus Liberibacter solanacearum’ in parsley (Petroselinum crispum ) seed. New Dis. Rep. 34, 31 (2016).
Article Google Scholar
27.
Torres, G. L. et al. Horizontal transmission of ‘Candidatus Liberibacter solanacearum’ by Bactericera cockerelli (Hemiptera: Triozidae) on Convolvulus and Ipomoea (Solanales: Convolvulaceae). PLoS ONE 10, 1–11 (2015).
CAS Google Scholar
28.
Munyaneza, J. E., Sengoda, V. G., Aguilar, E., Bextine, B. R. & McCue, K. F. First report of ‘Candidatus Liberibacter solanacearum’ infecting eggplant in Honduras. Plant Dis. 97, 1654 (2013).
CAS PubMed Article Google Scholar
29.
Aguilar, E., Sengoda, V. G., Bextine, B., McCue, K. F. & Munyaneza, J. E. First report of ‘Candidatus Liberibacter solanacearum’ on tobacco in Honduras. Plant Dis. 97, 1376 (2013).
CAS PubMed Article Google Scholar
30.
Munyaneza, J. E., Sengoda, V. G., Crosslin, J. M., De la Rosa-Lozano, G. & Sanchez, A. First report of ‘Candidatus Liberibacter psyllaurous’ in potato tubers with Zebra Chip Disease in Mexico. Dis. Notes 93, 552–552 (2009).
CAS Google Scholar
31.
Teulon, D. A., Workman, P. J., Thomas, K. L. & Nielsen, M. C. Bactericera cockerelli: Incursion, dispersal and current distribution on vegetable crops in New Zealand. New Zeal. Plant Prot. 62, 136–144 (2009).
Article Google Scholar
32.
Castillo Carrillo, C., Fu, Z. & Burckhardt, D. First record of the Tomato Potato Psyllid Bactericera cockerelli from South America. Bull. Insectol. 72, 85–91 (2019).
Google Scholar
33.
EPPO. PM 9/25 (1) Bactericera cockerelli and ‘Candidatus Liberibacter solanacearum’. EPPO Bull.47, 513–523 (2017).
34.
Hodkinson, I. D. Life cycle variation and adaptation in jumping plant lice (Insecta: Hemiptera: Psylloidea): a global synthesis. J. Nat. Hist. 43, 65–179 (2009).
Article Google Scholar
35.
Thinakaran, J. et al. Association of Potato Psyllid (Bactericera cockerelli; Hemiptera: Triozidae) with Lycium spp. (Solanaceae) in potato growing regions of Washington, Idaho, and Oregon. Am. J. Potato Res. 94, 490–499 (2017).
Article Google Scholar
36.
Kaur, N. et al. Survival and development of Potato Psyllid (Hemiptera: Triozidae) on Convolvulaceae: effects of a plant-fungus symbiosis (Periglandula). PLoS ONE 13, 1–19 (2018).
Google Scholar
37
Cooper, W. R., Horton, D. R., Miliczky, E., Wohleb, C. H. & Waters, T. D. The weed link in Zebra Chip epidemiology: suitability of non-crop Solanaceae and Convolvulaceae to Potato Psyllid and “Candidatus Liberibacter solanacearum”. Am. J. Potato Res. https://doi.org/10.1007/s12230-019-09712-z (2019).
Article Google Scholar
38.
Munyaneza, J. E., Mustafa, T., Fisher, T. W., Sengoda, V. G. & Horton, D. R. Assessing the likelihood of transmission of ‘Candidatus Liberibacter solanacearum’ to carrot by Potato Psyllid, Bactericera cockerelli (Hemiptera: Triozidae). PLoS ONE 11, 1–16 (2016).
Article CAS Google Scholar
39.
Nissinen, A. I. et al. Can Carrot Psyllid (Trioza apicalis) transmit ‘Candidatus Liberibacter solanacearum’ to potato? Proceedings of the 12th Annual Zebra Chip Report. Sess. 194–198 (2012).
40.
Haapalainen, M. et al. Carrot pathogen ‘Candidatus Liberibacter solanacearum’ Haplotype C detected in symptomless potato plants in Finland. Potato Res. 61, 31–50 (2018).
CAS Article Google Scholar
41.
Teresani, G. et al. Search for potential vectors of ‘Candidatus Liberibacter solanacearum’: population dynamics in host crops. Span, J. Agric. Res. 13, 1–11 (2015).
Article Google Scholar
42.
Antolinez, C. A., Fereres, A. & Moreno, A. Risk assessment of ‘Candidatus Liberibacter solanacearum’ transmission by the psyllids Bactericera trigonica and B. tremblayi from Apiaceae crops to potato. Sci. Rep. 7, 1–10 (2017).
Article CAS Google Scholar
43.
Sjölund, M. J. et al. First report of ’Candidatus Liberibacter solanacearum ’ in the United Kingdom in the psyllid Trioza anthrisci. New Dis. Rep. 36, 4 (2017).
Article Google Scholar
44.
Munyaneza, J. E. et al. Association of “Candidatus Liberibacter solanacearum” with the psyllid, Trioza apicalis (Hemiptera: Triozidae) in Europe. J. Econ. Entomol. 103, 1060–1070 (2010).
CAS PubMed Article Google Scholar
45.
Munyaneza, J. E., Sengoda, V. G., Sundheim, L. & Meadow, R. Survey of ‘Candidatus Liberibacter solanacearum’ in carrot crops affected by the psyllid Trioza apicalis (Hemiptera: Triozidae) in Norway. J. Plant Pathol. 96, 397–402 (2014).
Google Scholar
46.
Liefting, L. W., Weir, B. S., Pennycook, S. R. & Clover, G. R. G. ‘Candidatus Liberibacter solanacearum’, associated with plants in the family Solanaceae. Int. J. Syst. Evol. Microbiol. 59, 2274–2276 (2009).
CAS PubMed Article Google Scholar
47.
Hansen, A. K., Trumble, J. T., Stouthamer, R. & Paine, T. D. A new huanglongbing species, ‘Candidatus Liberibacter psyllaurous’, found to infect tomato and potato, is vectored by the psyllid Bactericera cockerelli (Sulc). Appl. Environ. Microbiol. 74, 5862–5865 (2008).
CAS PubMed PubMed Central Article Google Scholar
48.
Munyaneza, J. E., Sengoda, V. G., Crosslin, J. M., Garzon-Tiznado, J. A. & Cardenas-Valenzuela, O. G. First report of ‘Candidatus Liberibacter solanacearum’ in tomato plants in Mexico. Plant Dis. 93, 1076 (2009).
PubMed Google Scholar
49.
Crosslin, J. M., Lin, H. & Munyaneza, J. E. Detection of ‘Candidatus Liberibacter solanacearum’ in the potato psyllid, Bactericera cockerelli (Sulc), by conventional and real-time PCR. Southwest. Entomol. 36, 125–135 (2011).
Article Google Scholar
50.
Loiseau, M. et al. Lack of evidence of vertical transmission of ‘Candidatus Liberibacter solanacearum’ by carrot seeds suggests that seed is not a major transmission pathway. Plant Dis. 101, 2104–2109 (2017).
CAS PubMed Article Google Scholar
51.
Bertolini, E. et al. Transmission of ‘Candidatus Liberibacter solanacearum’ in carrot seeds. Plant Pathol. 64, 276–285 (2015).
CAS Article Google Scholar
52.
Carminati, G., Satta, E., Paltrinieri, S. & Bertaccini, A. Simultaneous evaluation of ‘ Candidatus Phytoplasma’ and ‘Candidatus Liberibacter solanacearum’ seed transmission in carrot. Phytopathogenic Mol. 9, 141 (2019).
Article Google Scholar
53.
Bantock, T. & Botting, J. British Bugs: an online identification guide to UK Hemiptera. https://www.britishbugs.org.uk/index.htmlhttps://www.britishbugs.org.uk/index.html (2018).
54.
Hodkinson, I. D. & White, I. M. Homoptera Psylloidea. Handbooks for the Identification of British Insects (Royal Entomology Society of London, London, 1979).
55.
Munyaneza, J. E. et al. First report of ‘Candidatus Liberibacter solanacearum’ associated with psyllid-infested carrots in Germany. Plant Dis. 99, 1269 (2015).
Article Google Scholar
56.
Sjölund, M. J., Arnsdorf, Y. M., Carnegie, M., Fornefeld, E. & Will, T. ‘Candidatus Liberibacter solanacearum’ detected in Trioza urticae using suction trap-based monitoring of psyllids in Germany. J. Plant Dis. Prot. 126, 89–92 (2018).
Article Google Scholar
57.
Hajri, A., Cousseau-suhard, P., Gentit, P. & Loiseau, M. New insights into the genetic diversity of the bacterial plant pathogen ‘Candidatus Liberibacter solanacearum ’ as revealed by a new multilocus sequence analysis scheme. bioRxiv (preprint server) https://doi.org/https://doi.org/10.1101/623405.
58.
Ossiannilsson, F. The Psylloidea (Homoptera) of Fennoscandia and Denmark. Fauna Entomologica Scandinavia (E. J. Brill, 1992).
59.
Tishetshkin, D. Y. The possibility to use bioacoustic characters in the taxonomy of the jumping plant lice with an example of the genus Craspedolepta (Homoptera, Psyllinea, Aphalaridae) and description of a new species from Transbaikalia. Entomol. Rev. 87, 561–570 (2007).
Article Google Scholar
60.
Bird, J. M. & Hodkinson, I. D. Species at the edge of their range: The significance of the thermal environment for the distribution of congeneric Craspedolepta species (Sternorrhyncha: Psylloidea) living on Chamerion angustifolium (Onagraceae). Eur. J. Entomol. 96, 103–109 (1999).
Google Scholar
61.
Brunt, A. et al. Plant Viruses Online: Descriptions and Lists from the VIDE Database. Version: 20th August 1996. https://biology.anu.edu.au/Groups/MES/vide/https://biology.anu.edu.au/Groups/MES/vide/.
62.
Sjölund, M. J., Ouvrard, D., Kenyon, D. & Highet, F. Developing an RT-PCR assay for the identification of psyllid species. Proc. Crop Prot. North. Britain 279–282 (2016).
63.
Percy, D. M. Radiation, diversity, and host-plant interactions among island and continental legume-feeding psyllids. Evolution (N.Y.) 57, 2540–2556 (2003).
Google Scholar
64.
Li, W. et al. Multiplex real-time PCR for detection, identification and quantification of ‘Candidatus Liberibacter solanacearum’ in potato plants with zebra chip. J. Microbiol. Methods 78, 59–65 (2009).
CAS PubMed Article Google Scholar
65.
Peccoud, J., Labonne, G. & Sauvion, N. Molecular test to assign individuals within the Cacopsylla pruni complex. PLoS ONE 8, 1–8 (2013).
Article CAS Google Scholar
66.
EPPO. PM 7/129 (1) DNA barcoding as an identification tool for a number of regulated pests. EPPO Bull.46, 501–537 (2016). More
1.
Adams, M., Raadik, T. A., Burridge, C. P. & Georges, A. Global biodiversity assessment and hyper-cryptic species complexes: more than one species of elephant in the room?. Syst. Biol. 63, 518–533 (2014).
PubMed Article Google Scholar
2.
Pérez-Ponce de León, G. & Poulin, R. Taxonomic distribution of cryptic diversity among metazoans: not so homogeneous after all. Biol. Lett. 12, 20160371. https://doi.org/10.1098/rsbl.2016.0371 (2016).
Article PubMed PubMed Central Google Scholar
3.
Pfenninger, M. & Schwenk, K. Cryptic animal species are homogeneously distributed among taxa and biogeographical regions. BMC Evol. Biol. 7, 121. https://doi.org/10.1186/1471-2148-7-121 (2007).
Article PubMed PubMed Central Google Scholar
4.
Keshavmurthy, S. et al. DNA barcoding reveals the coral “laboratory-rat”, Stylophora pistillata encompasses multiple identities. Sci. Rep. 3, 1520. https://doi.org/10.1038/srep01520 (2013).
CAS Article PubMed PubMed Central Google Scholar
5.
Mamos, T., Wattier, R., Burzyński, A. & Grabowski, M. The legacy of a vanished sea: a high level of diversification within a European freshwater amphipod species complex driven by 15 My of Paratethys regression. Mol. Ecol. 25, 785–810 (2016).
Article Google Scholar
6.
Bickford, D. et al. Cryptic species as a window on diversity and conservation. Trends Ecol. Evol. 22, 148–155 (2007).
PubMed Article Google Scholar
7.
Brodersen, J. & Seehausen, O. Why evolutionary biologists should get seriously involved in ecological monitoring and applied biodiversity assessment programs. Evol. Appl. 7, 968–983 (2014).
PubMed PubMed Central Article Google Scholar
8.
Eme, D. et al. Do cryptic species matter in macroecology? Sequencing European groundwater crustaceans yields smaller ranges but does not challenge biodiversity determinants. Ecography 41, 424–436 (2018).
Article Google Scholar
9.
Galipaud, M., Gauthey, Z., Turlin, J., Bollache, L. & Lagrue, C. Mate choice and male–male competition among morphologically cryptic but genetically divergent amphipod lineages. Behav. Ecol. Sociobiol. 69, 1907–1916 (2015).
Article Google Scholar
10.
Galipaud, M., Bollache, L. & Lagrue, C. Variations in infection levels and parasite-induced mortality among sympatric cryptic lineages of native amphipods and a congeneric invasive species: Are native hosts always losing?. Int. J. Parasitol. Parasites Wildl. 6, 439–447 (2017).
PubMed PubMed Central Article Google Scholar
11.
Westram, A. M., Baumgartner, C., Keller, I. & Jokela, J. Are cryptic host species also cryptic to parasites? Host specificity and geographical distribution of acanthocephalan parasites infecting freshwater Gammarus. Infect. Genet. Evol. 11, 1083–1090 (2011).
CAS PubMed Article Google Scholar
12.
Fišer, Ž, Altermatt, F., Zakšek, V., Knapič, T. & Fišer, C. Morphologically cryptic amphipod species are “ecological clones” at regional but not at local scale: a case study of four Niphargus species. PLoS ONE 10, e0134384. https://doi.org/10.1371/journal.pone.0134384 (2015).
CAS Article PubMed PubMed Central Google Scholar
13.
Eisenring, M., Altermatt, F., Westram, A. M. & Jokela, J. Habitat requirements and ecological niche of two cryptic amphipod species at landscape and local scales. Ecosphere 7, e01319. https://doi.org/10.1002/ecs2.1319 (2016).
Article Google Scholar
14.
Westram, A. M., Jokela, J. & Keller, I. Hidden biodiversity in an ecologically important freshwater amphipod: Differences in genetic structure between two cryptic species. PLoS ONE 8, e69576. https://doi.org/10.1371/journal.pone.0069576 (2013).
ADS CAS Article PubMed PubMed Central Google Scholar
15.
Mutanen, M., Kaila, L. & Tabell, J. Wide-ranging barcoding aids discovery of one-third increase of species richness in presumably well-investigated moths. Sci. Rep. 3, 2901. https://doi.org/10.1038/srep02901 (2013).
ADS Article PubMed PubMed Central Google Scholar
16.
Cook, B. D., Page, T. J. & Hughes, J. M. Importance of cryptic species for identifying ‘representative’ units of biodiversity for freshwater conservation. Biol. Conserv. 141, 2821–2831 (2008).
Article Google Scholar
17.
Fišer, C., Robinson, C. T. & Malard, F. Cryptic species as a window into the paradigm shift of the species concept. Mol. Ecol. 27, 613–635 (2018).
PubMed Article Google Scholar
18.
Bálint, M., Barnard, P. C., Schmitt, T., Ujvárosi, L. & Popescu, O. Differentiation and speciation in mountain streams: a case study in the caddisfly Rhyacophila aquitanica (Trichoptera). J. Zool. Syst. Evol. Res. 46, 340–345 (2008).
Article Google Scholar
19.
Major, K., Soucek, D. J., Giordano, R., Wetzel, M. J. & Soto-Adames, F. The common ecotoxicology laboratory strain of Hyalella azteca is genetically distinct from most wild strains sampled in eastern North America: common lab strain of H. azteca is distinct from wild strains. Environ. Toxicol. Chem. https://doi.org/10.1002/etc.2355 (2013).
Article PubMed Google Scholar
20.
Feckler, A., Thielsch, A., Schwenk, K., Schulz, R. & Bundschuh, M. Differences in the sensitivity among cryptic lineages of the Gammarus fossarum complex. Sci. Total Environ. 439, 158–164 (2012).
ADS CAS PubMed Article PubMed Central Google Scholar
21.
Feckler, A. et al. Cryptic species diversity: an overlooked factor in environmental management?. J. Appl. Ecol. 51, 958–967 (2014).
Article Google Scholar
22.
Caputo, D. R., Robson, S. C., Werner, I. & Ford, A. T. Complete transcriptome assembly and annotation of a critically important amphipod species in freshwater ecotoxicological risk assessment: Gammarus fossarum. Environ. Int. 137, 105319 (2020).
CAS PubMed Article PubMed Central Google Scholar
23.
Cogne, Y. et al. De novo transcriptomes of 14 gammarid individuals for proteogenomic analysis of seven taxonomic groups. Sci. Data 6, 184. https://doi.org/10.1038/s41597-019-0192-5 (2019).
CAS Article PubMed PubMed Central Google Scholar
24.
Costa-Silva, G. J., Rodriguez, M. S., Roxo, F. F., Foresti, F. & Oliveira, C. Using different methods to access the difficult task of delimiting species in a complex Neotropical hyperdiverse group. PLoS ONE 10, e0135075 (2015).
PubMed PubMed Central Article CAS Google Scholar
25.
Dincă, V. et al. DNA barcode reference library for Iberian butterflies enables a continental-scale preview of potential cryptic diversity. Sci. Rep. 5, 12395. https://doi.org/10.1371/journal.pone.0135075 (2015).
ADS CAS Article PubMed PubMed Central Google Scholar
26.
Trontelj, P. et al. A molecular test for cryptic diversity in ground water: how large are the ranges of macro-stygobionts?. Freshw. Biol. 54, 727–744 (2009).
CAS Article Google Scholar
27.
Jackson, J. K. et al. Cryptic biodiversity in streams: a comparison of macroinvertebrate communities based on morphological and DNA barcode identifications. Freshw. Sci. 33, 312–324 (2014).
Article Google Scholar
28.
Grabowski, M., Mamos, T., Bacela-Spychalska, K., Rewicz, T. & Wattier, R. A. Neogene paleogeography provides context for understanding the origin and spatial distribution of cryptic diversity in a widespread balkan freshwater amphipod. PeerJ https://doi.org/10.7717/peerj.3016 (2017).
Article PubMed PubMed Central Google Scholar
29.
Grabowski, M., Wysocka, A. & Mamos, T. Molecular species delimitation methods provide new insight into taxonomy of the endemic gammarid species flock from the ancient Lake Ohrid. Zool. J. Linn. Soc. https://doi.org/10.1093/zoolinnean/zlw025 (2017).
Article Google Scholar
30.
Hogg, I. D., Larose, C., de Lafontaine, Y. & Doe, K. G. Genetic evidence for a Hyalella species complex within the Great Lakes—St. Lawrence River drainage basin: implications for ecotoxicology and conservation biology. Can. J. Zool. 76, 1134–1140 (1998).
Article Google Scholar
31.
Hogg, I. D., Stevens, M. I., Schnabel, K. E. & Ann Chapman, M. Deeply divergent lineages of the widespread New Zealand amphipod Paracalliope fluviatilis revealed using allozyme and mitochondrial DNA analyses. Freshw. Biol. 51, 236–248 (2006).
CAS Article Google Scholar
32.
Katouzian, A.-R. et al. Drastic underestimation of amphipod biodiversity in the endangered Irano-Anatolian and Caucasus biodiversity hotspots. Sci. Rep. 6, 22507. https://doi.org/10.1038/srep22507 (2016).
ADS CAS Article PubMed PubMed Central Google Scholar
33.
Mamos, T., Wattier, R., Majda, A., Sket, B. & Grabowski, M. Morphological vs. molecular delineation of taxa across montane regions in Europe: the case study of Gammarusbalcanicus Schäferna, (Crustacea: Amphipoda). J. Zool. Syst. Evol. Res. 52, 237–248 (2014).
Article Google Scholar
34.
Murphy, N. P., Adams, M., Guzik, M. T. & Austin, A. D. Extraordinary micro-endemism in Australian desert spring amphipods. Mol. Phylogenet. Evol. 66, 645–653 (2013).
CAS PubMed Article Google Scholar
35.
Murphy, N. P., King, R. A. & Delean, S. Species, ESUs or populations? Delimiting and describing morphologically cryptic diversity in Australian desert spring amphipods. Invertebr. Syst. 29, 457 (2015).
Article Google Scholar
36.
Seidel, R. A., Lang, B. K. & Berg, D. J. Phylogeographic analysis reveals multiple cryptic species of amphipods (Crustacea: Amphipoda) in Chihuahuan Desert springs. Biol. Conserv. 142, 2303–2313 (2009).
Article Google Scholar
37.
Sutherland, D. L., Hogg, I. D. & Waas, J. R. Phylogeography and species discrimination in the Paracalliopefluviatilis species complex (Crustacea: Amphipoda): can morphologically similar heterospecifics identify compatible mates?: Mate discrimination in P. fluviatilis. Biol. J. Linn. Soc. 99, 196–205 (2009).
Article Google Scholar
38.
Witt, J. D. S., Threloff, D. L. & Hebert, P. D. N. DNA barcoding reveals extraordinary cryptic diversity in an amphipod genus: implications for desert spring conservation. Mol. Ecol. 15, 3073–3082 (2006).
CAS PubMed Article Google Scholar
39.
Witt, J. D. S. & Hebert, P. D. N. Cryptic species diversity and evolution in the amphipod genus Hyalella within central glaciated North America: a molecular phylogenetic approach. Can. J. Fish Aquat. Sci. 57, 12 (2000).
Article Google Scholar
40.
Blackman, R. et al. Detection of a new non-native freshwater species by DNA metabarcoding of environmental samples—first record of Gammarus fossarum in the UK. Aquat. Invasions 12, 177–189 (2017).
Article Google Scholar
41.
Dangles, O., Gessner, M. O., Guerold, F. & Chauvet, E. Impacts of stream acidification on litter breakdown: implications for assessing ecosystem functioning. J. Appl. Ecol. 41, 365–378 (2004).
CAS Article Google Scholar
42.
Felten, V., Tixier, G., Guérold, F., De Crespin De Billy, V. & Dangles, O. Quantification of diet variability in a stream amphipod: implications for ecosystem functioning. Fundam. Appl. Limnol. Arch. Für Hydrobiol. 170, 303–313 (2008).
Article Google Scholar
43.
Besse, J.-P., Geffard, O. & Coquery, M. Relevance and applicability of active biomonitoring in continental waters under the Water Framework Directive. TrAC Trends Anal. Chem. 36, 113–127 (2012).
CAS Article Google Scholar
44.
Schmidlin, L., von Fumetti, S. & Nagel, P. Temperature effects on the feeding and electron transport system (ETS) activity of Gammarus fossarum. Aquat. Ecol. 49, 71–80 (2015).
Article Google Scholar
45.
Labaude, S., Rigaud, T. & Cézilly, F. Additive effects of temperature and infection with an acanthocephalan parasite on the shredding activity of Gammarus fossarum (Crustacea: Amphipoda): the importance of aggregative behavior. Glob. Change Biol. 23, 1415–1424 (2017).
ADS Article Google Scholar
46.
Kunz, P. Y., Kienle, C. & Gerhardt, A. Gammarus spp. in aquatic ecotoxicology and water quality assessment: Toward integrated multilevel tests. In Reviews of Environmental Contamination and Toxicology Vol. 205 (ed. Whitacre, D. M.) 1–76 (Springer, New York, 2010).
Google Scholar
47.
Mehennaoui, K. et al. Gammarus fossarum (Crustacea, Amphipoda) as a model organism to study the effects of silver nanoparticles. Sci. Total Environ. 566–567, 1649–1659 (2016).
ADS PubMed Article CAS Google Scholar
48.
Besse, J.-P. et al. Caged Gammarus fossarum (Crustacea) as a robust tool for the characterization of bioavailable contamination levels in continental waters: towards the determination of threshold values. Water Res. 47, 650–660 (2013).
CAS PubMed Article Google Scholar
49.
Gouveia, D. et al. Ecotoxico-Proteomics for aquatic environmental monitoring: First in situ application of a new proteomics-based multibiomarker assay using caged amphipods. Environ. Sci. Technol. 51, 13417–13426 (2017).
ADS CAS PubMed Article Google Scholar
50.
Trapp, J. et al. Proteogenomics of Gammarus fossarum to document the reproductive system of amphipods. Mol. Cell. Proteomics 13, 3612–3625 (2014).
CAS PubMed PubMed Central Article Google Scholar
51.
Sanchez-Thirion, K. et al. High food quality increases infection of Gammarus pulex (Crustacea: Amphipoda) by the acanthocephalan parasite Pomphorhynchus laevis. Int. J. Parasitol. 49, 805–817 (2019).
CAS PubMed Article Google Scholar
52.
Bigot-Clivot, A. et al. Bioaccumulation of toxoplasma and cryptosporidium by the freshwater crustacean Gammarus fossarum: involvement in biomonitoring surveys and trophic transfer. Ecotoxicol. Environ. Saf. 133, 188–194 (2016).
CAS PubMed Article Google Scholar
53.
Chen, H.-Y., Grabner, D. S., Nachev, M., Shih, H.-H. & Sures, B. Effects of the acanthocephalan Polymorphus minutus and the microsporidian Dictyocoela duebenum on energy reserves and stress response of cadmium exposed Gammarus fossarum. PeerJ 3, e1353. https://doi.org/10.7717/peerj.1353 (2015).
CAS Article PubMed PubMed Central Google Scholar
54.
Müller, J. Mitochondrial DNA variation and the evolutionary history of cryptic Gammarus fossarum types. Mol. Phylogenet. Evol. 15, 260–268 (2000).
PubMed Article CAS Google Scholar
55.
Weiss, M., Macher, J. N., Seefeldt, M. A. & Leese, F. Molecular evidence for further overlooked species within the Gammarus fossarum complex (Crustacea: Amphipoda). Hydrobiologia 721, 165–184 (2014).
CAS Article Google Scholar
56.
Weiss, M. & Leese, F. Widely distributed and regionally isolated! Drivers of genetic structure in Gammarus fossarum in a human-impacted landscape. BMC Evol. Biol. 16, 153 (2016).
PubMed PubMed Central Article CAS Google Scholar
57.
Lagrue, C. et al. Confrontation of cryptic diversity and mate discrimination within Gammarus pulex and Gammarus fossarum species complexes. Freshw. Biol. 59, 2555–2570 (2014).
Article Google Scholar
58.
Copilaş-Ciocianu, D. & Petrusek, A. The southwestern Carpathians as an ancient centre of diversity of freshwater gammarid amphipods: insights from the Gammarus fossarum species complex. Mol. Ecol. 24, 3980–3992 (2015).
PubMed Article Google Scholar
59.
Copilaş-Ciocianu, D., Rutová, T., Pařil, P. & Petrusek, A. Epigean gammarids survived millions of years of severe climatic fluctuations in high latitude refugia throughout the Western Carpathians. Mol. Phylogenet. Evol. 112, 218–229 (2017).
PubMed Article Google Scholar
60.
Hebert, P. D. N., Ratnasingham, S. & de Waard, J. R. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. Lond. B Biol. Sci. 270, S96–S99 (2003).
CAS Google Scholar
61.
Kekkonen, M. & Hebert, P. D. N. DNA barcode-based delineation of putative species: efficient start for taxonomic workflows. Mol. Ecol. Resour. 14, 706–715 (2014).
PubMed PubMed Central Article Google Scholar
62.
Copilaş-Ciocianu, D. & Petrusek, A. Phylogeography of a freshwater crustacean species complex reflects a long-gone archipelago. J. Biogeogr. 44, 421–432 (2017).
Article Google Scholar
63.
Grabner, D. S. et al. Invaders, natives and their enemies: distribution patterns of amphipods and their microsporidian parasites in the Ruhr Metropolis, Germany. Parasit. Vectors 8, 419 (2015).
PubMed PubMed Central Article Google Scholar
64.
Copilaș-Ciocianu, D., Zimța, A. & Petrusek, A. Integrative taxonomy reveals a new Gammarus species (Crustacea, Amphipoda) surviving in a previously unknown southeast European glacial refugium. J. Zool. Syst. Evol. Res. 57, 272–297 (2019).
Article Google Scholar
65.
Copilaş-Ciocianu, D., Zimţa, A.-A., Grabowski, M. & Petrusek, A. Survival in northern microrefugia in an endemic Carpathian gammarid (Crustacea: Amphipoda). Zool. Scr. 47, 357–372 (2018).
Article Google Scholar
66.
Hou, Z., Li, J. & Li, S. Diversification of low dispersal crustaceans through mountain uplift: a case study of Gammarus (Amphipoda: Gammaridae) with descriptions of four novel species: Diversification of Gammarus species. Zool. J. Linn. Soc. 170, 591–633 (2014).
Article Google Scholar
67.
Wellborn, G. A. & Cothran, R. D. Niche diversity in crustacean cryptic species: complementarity in spatial distribution and predation risk. Oecologia 154, 175–183 (2007).
ADS PubMed Article Google Scholar
68.
Delić, T., Švara, V., Coleman, C. O., Trontelj, P. & Fišer, C. The giant cryptic amphipod species of the subterranean genus Niphargus (Crustacea, Amphipoda). Zool. Scr. 46, 740–752 (2017).
Article Google Scholar
69.
McInerney, C. E. et al. The ancient Britons: groundwater fauna survived extreme climate change over tens of millions of years across NW Europe. Mol. Ecol. 23, 1153–1166 (2014).
PubMed Article Google Scholar
70.
Cooper, S. J. B. et al. Subterranean archipelago in the Australian arid zone: mitochondrial DNA phylogeography of amphipods from central Western Australia: Phylogeography of subterranean amphipods. Mol. Ecol. 16, 1533–1544 (2007).
CAS PubMed Article Google Scholar
71.
Ethridge, J. Z., Gibson, J. R. & Nice, C. C. Cryptic diversity within and amongst spring-associated Stygobromus amphipods (Amphipoda: Crangonyctidae): Stygobromus amphipod cryptic diversity. Zool. J. Linn. Soc. 167, 227–242 (2013).
Article Google Scholar
72.
Beermann, J. et al. Cryptic species in a well-known habitat: applying taxonomics to the amphipod genus Epimeria (Crustacea, Peracarida). Sci. Rep. 8, 6893. https://doi.org/10.1038/s41598-018-25225-x (2018).
ADS CAS Article PubMed PubMed Central Google Scholar
73.
Baird, H. P., Miller, K. J. & Stark, J. S. Evidence of hidden biodiversity, ongoing speciation and diverse patterns of genetic structure in giant Antarctic amphipods: Genetic diversity in antarticamphipods. Mol. Ecol. 20, 3439–3454 (2011).
PubMed Article Google Scholar
74.
Havermans, C. et al. Genetic and morphological divergences in the cosmopolitan deep-sea amphipod Eurythenes gryllus reveal a diverse abyss and a bipolar species. PLoS ONE 8, e74218. https://doi.org/10.1371/journal.pone.0074218 (2013).
ADS CAS Article PubMed PubMed Central Google Scholar
75.
Havermans, C. Have we so far only seen the tip of the iceberg? Exploring species diversity and distribution of the giant amphipod Eurythenes. Biodiversity 17, 12–25 (2016).
Article Google Scholar
76.
Coleman, C. O. Taxonomy in times of the taxonomic impediment—examples from the community of experts on amphipod crustaceans. J. Crustac. Biol. 35, 729–740 (2015).
Article Google Scholar
77.
Arfianti, T., Wilson, S. & Costello, M. J. Progress in the discovery of amphipod crustaceans. PeerJ 6, e5187 (2018).
PubMed PubMed Central Article Google Scholar
78.
Popov, S. V. et al. Lithological-Paleogeographic maps of Partethys—10 maps late Eocene to Plioccene. Courier Foschung-Intitut Senckberg 1–46 (2004).
79.
Frenzel, B., Pécsi, M. & Velichko, A. A. Atlas of Paleoclimates and Paleoenvironments of the Northern Hemisphere: Late Pleistocene, Holocene (Geographical Research Institute Hungarian Academy of Science; G. Fischer, Budapest, 1992).
Google Scholar
80.
Macneil, C., Dick, J. T. A. & Elwood, R. W. The trophic ecology of freshwater Gammarus spp (Crustacea; Amphipoda): Problems and perspective concerning the functional feeding group concept. Biol. Rev. 72, 349–364 (2007).
Article Google Scholar
81.
Rudolph, K., Coleman, C. O., Mamos, T. & Grabowski, M. Description and post-glacial demography of Gammarusjazdzewskii sp. nov. (Crustacea: Amphipoda) from Central Europe. Syst. Biodivers. 16, 587–603 (2018).
Article Google Scholar
82.
Westram, A. M., Jokela, J., Baumgartner, C. & Keller, I. Spatial distribution of cryptic species diversity in European freshwater amphipods (Gammarus fossarum) as revealed by pyrosequencing. PLoS ONE 6, e23879. https://doi.org/10.1371/journal.pone.0023879 (2011).
ADS CAS Article PubMed PubMed Central Google Scholar
83.
Lefébure, T. et al. Phylogeography of a subterranean amphipod reveals cryptic diversity and dynamic evolution in extreme environments: cryptic and dynamic evolution in subsurface. Mol. Ecol. 15, 1797–1806 (2006).
PubMed Article CAS Google Scholar
84.
Lefébure, T., Douady, C. J., Malard, F. & Gibert, J. Testing dispersal and cryptic diversity in a widely distributed groundwater amphipod (Niphargus rhenorhodanensis). Mol. Phylogenet. Evol. 42, 676–686 (2007).
PubMed Article CAS Google Scholar
85.
Raffard, A., Santoul, F., Cucherousset, J. & Blanchet, S. The community and ecosystem consequences of intraspecific diversity: a meta-analysis: the ecological effects of intraspecific diversity. Biol. Rev. 94, 648–661 (2019).
PubMed Article Google Scholar
86.
Jabłońska, A., Wrzesińska, W., Zawal, A., Pešić, V. & Grabowski, M. Long-term within-basin isolation patterns, different conservation units, and interspecific mitochondrial DNA introgression in an amphipod endemic to the ancient Lake Skadar system, Balkan Peninsula. Freshw. Biol. 65, 209–225 (2020).
Article CAS Google Scholar
87.
Stein, E. D., Martinez, M. C., Stiles, S., Miller, P. E. & Zakharov, E. V. Is DNA barcoding actually cheaper and faster than traditional morphological methods: results from a survey of freshwater bioassessment efforts in the United States?. PLoS ONE 9, e95525. https://doi.org/10.1371/journal.pone.0095525 (2014).
ADS CAS Article PubMed PubMed Central Google Scholar
88.
Valentini, A., Pompanon, F. & Taberlet, P. DNA barcoding for ecologists. Trends Ecol. Evol. 24, 110–117 (2009).
PubMed Article Google Scholar
89.
Weigand, H. et al. DNA barcode reference libraries for the monitoring of aquatic biota in Europe: Gap-analysis and recommendations for future work. Sci. Total Environ. 678, 499–524 (2019).
ADS CAS PubMed Article Google Scholar
90.
Delić, T., Trontelj, P., Rendoš, M. & Fišer, C. The importance of naming cryptic species and the conservation of endemic subterranean amphipods. Sci. Rep. 7, 3391. https://doi.org/10.1038/s41598-017-02938-z (2017).
ADS CAS Article PubMed PubMed Central Google Scholar
91.
Wysocka, A. et al. Origin of the Lake Ohrid gammarid species flock: ancient local phylogenetic lineage diversification. J. Biogeogr. 41, 1758–1768 (2014).
Article Google Scholar
92.
Esmaeili-Rineh, S., Sari, A., Delić, T., Moškrič, A. & Fišer, C. Molecular phylogeny of the subterranean genus Niphargus (Crustacea: Amphipoda) in the Middle East: a comparison with European Niphargids. Zool. J. Linn. Soc. 175, 812–826 (2015).
Article Google Scholar
93.
Hou, Z., Sket, B., Fiser, C. & Li, S. Eocene habitat shift from saline to freshwater promoted Tethyan amphipod diversification. Proc. Natl. Acad. Sci. 108, 14533–14538 (2011).
ADS CAS PubMed Article Google Scholar
94.
AltschuP, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic Local Alignment Search Tool. 8.
95.
Karaman, G. & Pinkster, S. Freshwater Gammarus species from Europe, North Africa and adjacent regions of Asia (Crustacea-Amphipoda). Part I. Gammarus pulex-group and related species. Bijdragen tot de Dierkunde 1–97 (1977).
96.
Piscart, C. & Bollache, L. Crustacés amphipodes de surface : gammares d’eau douce (Association Française de Limnologie, Thonon-les-Bains, 2012).
Google Scholar
97.
Eggers, T. & Martens, A. A key of the freshwater Amphipods of Germany. Lauterbornia 1–68 (2001).
98.
Jazdzewski, K. Morfologia, taksonomia i wystepowanie w Polsce kielzy z rodzajów Gammarus Fabr. i Chaetogammarus Mart. (Crustacea, Amphipoda). Acta Universitatis Lodziensis 1–187 (1975).
99.
Karaman, G. S. Crustacea (Amphipoda di acqua dolce. Calderini, Bologna, 1993).
Google Scholar
100.
Hillis, D. M., Moritz, C. & Mable, B. K. Molecular Systematics (Sinauer Associates Inc, Sunderland, 1996).
Google Scholar
101.
Casquet, J., Thebaud, C. & Gillespie, R. G. Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders: Quick and easy dsDNA extraction for barcoding. Mol. Ecol. Resour. 12, 136–141 (2012).
CAS PubMed Article PubMed Central Google Scholar
102.
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol 6, 294–299 (1994).
Google Scholar
103.
Costa, F. O., Henzler, C. M., Lunt, D. H., Whiteley, N. M. & Rock, J. Probing marine Gammarus (Amphipoda) taxonomy with DNA barcodes. Syst. Biodivers. 7, 365–379 (2009).
Article Google Scholar
104.
Astrin, J. J. & Stüben, P. E. Phylogeny in cryptic weevils: molecules, morphology and new genera of western Palaearctic Cryptorhynchinae (Coleoptera:Curculionidae). Invertebr. Syst. 22, 503–522 (2008).
Article Google Scholar
105.
Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).
CAS PubMed PubMed Central Article Google Scholar
106.
Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).
CAS Article Google Scholar
107.
Librado, P. & Rozas, J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
CAS PubMed Article PubMed Central Google Scholar
108.
Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).
CAS PubMed Article PubMed Central Google Scholar
109.
Ratnasingham, S. & Hebert, P. D. N. A DNA-based registry for all animal species: the barcode index number (BIN) System. PLoS ONE 8, e66213. https://doi.org/10.1371/journal.pone.0066213 (2013).
ADS CAS Article PubMed PubMed Central Google Scholar
110.
Zhang, J., Kapli, P., Pavlidis, P. & Stamatakis, A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29, 2869–2876 (2013).
CAS PubMed PubMed Central Article Google Scholar
111.
Puillandre, N. et al. Large-scale species delimitation method for hyperdiverse groups: large-scale species delimitation. Mol. Ecol. 21, 2671–2691 (2012).
CAS PubMed Article Google Scholar
112.
Ratnasingham, S. & Hebert, P. D. N. BOLD: The Barcode of life data system (https://www.barcodinglife.org). Mol. Ecol. Notes7, 355–364 (2007).
113.
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS PubMed PubMed Central Article Google Scholar
114.
Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537. https://doi.org/10.1371/journal.pcbi.1003537 (2014).
CAS Article PubMed PubMed Central Google Scholar
115.
Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17, 42 (2017).
PubMed PubMed Central Article Google Scholar
116.
Baele, G., Lemey, P. & Vansteelandt, S. Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution. BMC Bioinform. 14, 85 (2013).
Article Google Scholar
117.
Brower, A. V. Rapid morphological radiation and convergence among races of the butterfly Heliconius erato inferred from patterns of mitochondrial DNA evolution. Proc. Natl. Acad. Sci. 91, 6491–6495 (1994).
ADS CAS PubMed Article Google Scholar
118.
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).
CAS PubMed PubMed Central Article Google Scholar
119.
Felsenstein, J. Confidence limits on phylogenies: an approach using the boostrap. Evolution 39, 783–791 (1985).
PubMed Article Google Scholar
120.
Miller, M. P. Alleles In Space (AIS): computer software for the joint analysis of interindividual spatial and genetic information. J. Hered. 96, 722–724 (2005).
CAS PubMed Article Google Scholar More
1.
Darwin, C. On the Origin of Species (John Murray, London, 1859).
Google Scholar
2.
Gause, G. F. The Struggle for Existence (Williams & Wilkins, Philadelphia, 1934).
Google Scholar
3.
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Article Google Scholar
4.
Vamosi, S. M., Heard, S. B., Vamosi, J. C. & Webb, C. O. Emerging patterns in the comparative analysis of phylogenetic community structure. Mol. Ecol. 18, 572–592 (2009).
CAS Article Google Scholar
5.
Biere, A. & Bennett, A. E. Three-way interactions between plants, microbes and insects. Funct. Ecol. 27, 567–573 (2013).
Article Google Scholar
6.
Biesmeijer, J. C. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).
ADS CAS Article Google Scholar
7.
Harvey, J. A. et al. International scientists formulate a roadmap for insect conservation and recovery. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-019-1079-8 (2020).
Article Google Scholar
8.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
PubMed Central Article CAS PubMed Google Scholar
9.
Leather, S. R. “Ecological Armageddon”—more evidence for the drastic decline in insect numbers: Insect declines. Ann. Appl. Biol. 172, 1–3 (2018).
Article Google Scholar
10.
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).
Article Google Scholar
11.
Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426 (2020).
Article Google Scholar
12.
Ford, H. A., Barrett, G. W., Saunders, D. A. & Recher, H. F. Why have birds in the woodlands of Southern Australia declined?. Biol. Conserv. 97, 71–88 (2001).
Article Google Scholar
13.
Córdoba-Aguilar, A. & Rocha-Ortega, M. Damselfly (Odonata: Calopterygidae) population decline in an urbanizing watershed. J. Insect Sci. 19, 30 (2019).
PubMed Central Article PubMed Google Scholar
14.
Kalkman, V. J. et al. Diversity and conservation of European dragonflies and damselflies (Odonata). Hydrobiologia 811, 269–282 (2018).
Article Google Scholar
15.
Rosenberg, K. V. et al. Decline of the North American avifauna. Science https://doi.org/10.1126/science.aaw1313 (2019).
Article PubMed PubMed Central Google Scholar
16.
Rodhouse, T. J. et al. Evidence of region-wide bat population decline from long-term monitoring and Bayesian occupancy models with empirically informed priors. Ecol. Evol. https://doi.org/10.1002/ece3.5612 (2019).
Article PubMed Central PubMed Google Scholar
17.
Kaunisto, K. M. et al. Threats from the air: Damselfly predation on diverse prey taxa. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13184 (2020).
Article PubMed PubMed Central Google Scholar
18.
Simmons, B. I. et al. Worldwide insect declines: An important message, but interpret with caution. Ecol. Evol. 9, 3678–3680 (2019).
PubMed Central Article PubMed Google Scholar
19.
Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on earth and in the ocean?. PLoS Biol. 9, e1001127 (2011).
CAS PubMed Central Article PubMed Google Scholar
20.
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on earth. Proc. Natl. Acad. Sci. 115, 6506–6511 (2018).
CAS Article Google Scholar
21.
Nyffeler, M., Şekercioğlu, ÇH. & Whelan, C. J. Insectivorous birds consume an estimated 400–500 million tons of prey annually. Sci. Nat. 105, 47 (2018).
Article CAS Google Scholar
22.
Vesterinen, E. J. et al. What you need is what you eat? Prey selection by the bat Myotis daubentonii. Mol. Ecol. 25, 1581–1594 (2016).
CAS Article Google Scholar
23.
Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. & Lilley, T. M. Table for five, please: Dietary partitioning in boreal bats. Ecol. Evol. 8, 10914–10937 (2018).
PubMed Central Article PubMed Google Scholar
24.
Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. S. & Lilley, T. M. Data from: Table for five, please: Dietary partitioning in boreal bats. Dryad Dataset https://doi.org/10.5061/dryad.6880rf1 (2019).
Article Google Scholar
25.
Kaunisto, K. M., Roslin, T., Sääksjärvi, I. E. & Vesterinen, E. J. Pellets of proof: First glimpse of the dietary composition of adult odonates as revealed by metabarcoding of feces. Ecol. Evol. 7, 8588–8598 (2017).
PubMed Central Article PubMed Google Scholar
26.
Kaunisto, K. M., Roslin, T. L., Sääksjärvi, I. E. & Vesterinen, E. J. Data from: Pellets of proof: first glimpse of the dietary composition of adult odonates as revealed by metabarcoding of feces. Dryad Dataset https://doi.org/10.5061/dryad.5n92p (2018).
Article Google Scholar
27.
Vesterinen, E. J. et al.Threats from the air: damselfly predation on diverse prey taxa. 1438406240 bytes (2019) https://doi.org/10.5061/DRYAD.ZS7H44J4Z.
28.
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406 (2019).
Article Google Scholar
29.
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).
Article Google Scholar
30.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018).
31.
Oksanen, J. et al. vegan: Community Ecology Package. (2013).
32.
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).
ADS CAS Article Google Scholar
33.
Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).
ADS CAS Article Google Scholar
34.
Fuszara, E. et al. Population changes in Natterer’s bat (Myotis nattereri) and Daubenton’s bat (M. daubentonii) in winter roosts of central Poland. Pol. J. Ecol. 58, 769–781 (2010).
Google Scholar
35.
Kim, K. C. & Byrne, L. B. Biodiversity loss and the taxonomic bottleneck: Emerging biodiversity science. Ecol. Res. 21, 794 (2006).
Article Google Scholar
36.
Sekercioglu, C. H. et al. Disappearance of insectivorous birds from tropical forest fragments. Proc. Natl. Acad. Sci. USA. 99, 263–267 (2002).
ADS CAS Article Google Scholar
37.
Spiller, K. J. & Dettmers, R. Evidence for multiple drivers of aerial insectivore declines in North America. Condor 121, 10 (2019).
Article Google Scholar
38.
Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl. Acad. Sci. 115, E10397–E10406 (2018).
CAS Article Google Scholar
39.
Warren, M. S. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).
ADS CAS Article PubMed PubMed Central Google Scholar
40.
Ochieng, H., de Ruyter van Steveninck, E. D. & Wanda, F. M. Mouthpart deformities in Chironomidae (Diptera) as indicators of heavy metal pollution in northern Lake Victoria, Uganda. Afr. J. Aquat. Sci. 33, 135–142 (2008).
CAS Article Google Scholar
41.
Luoto, T. P. Hydrological change in lakes inferred from midge assemblages through use of an intralake calibration set. Ecol. Monogr. 80, 303–329 (2010).
Article Google Scholar
42.
Aquatic insects of North Europe—A Taxonomic Handbook. vol. 2 (Apollo Books, 1997).
43.
Wirta, H. K. et al. Exposing the structure of an Arctic food web. Ecol. Evol. 5, 3842–3856 (2015).
PubMed Central Article PubMed Google Scholar
44.
Vesterinen, E. J., Lilley, T., Laine, V. N. & Wahlberg, N. Next generation sequencing of fecal DNA reveals the dietary diversity of the widespread insectivorous predator Daubenton’s bat (Myotis daubentonii) in southwestern Finland. PLoS ONE 8, e82168 (2013).
ADS PubMed Central Article CAS PubMed Google Scholar
45.
Clare, E. L., Fraser, E. E., Braid, H. E., Fenton, M. B. & Hebert, P. D. N. Species on the menu of a generalist predator, the eastern red bat (Lasiurus borealis): Using a molecular approach to detect arthropod prey. Mol. Ecol. 18, 2532–2542 (2009).
Article Google Scholar
46.
Clare, E. L. et al. The diet of Myotis lucifugus across Canada: Assessing foraging quality and diet variability. Mol. Ecol. 23, 3618–3632 (2014).
Article Google Scholar
47.
Rytkönen, S. et al. From feces to data: A metabarcoding method for analyzing consumed and available prey in a bird-insect food web. Ecol. Evol. 9, 631–639 (2019).
Article Google Scholar
48.
Eitzinger, B. et al. Assessing changes in arthropod predator–prey interactions through DNA-based gut content analysis—variable environment, stable diet. Mol. Ecol. 28, 266–280 (2019).
CAS Article Google Scholar
49.
Schmidt, N. M., Mosbacher, J. B., Eitzinger, B., Vesterinen, E. J. & Roslin, T. High resistance towards herbivore-induced habitat change in a high Arctic arthropod community. Biol. Lett. 14, 20180054 (2018).
PubMed Central Article CAS PubMed Google Scholar
50.
Schmidt, N. M., Mosbacher, J. B., Vesterinen, E. J., Roslin, T. & Michelsen, A. Limited dietary overlap amongst resident Arctic herbivores in winter: Complementary insights from complementary methods. Oecologia 187, 689–699 (2018).
ADS Article Google Scholar
51.
Gripenberg, S. et al. A highly resolved food web for insect seed predators in a species-rich tropical forest: Host use by insect seed predators. Ecol. Lett. https://doi.org/10.1111/ele.13359 (2019).
Article PubMed Central PubMed Google Scholar
52.
Basset, Y. et al. A cross-continental comparison of assemblages of seed- and fruit-feeding insects in tropical rain forests: Faunal composition and rates of attack. J. Biogeogr. 45, 1395–1407 (2018).
Article Google Scholar
53.
Raitif, J., Plantegenest, M., Agator, O., Piscart, C. & Roussel, J.-M. Seasonal and spatial variations of stream insect emergence in an intensive agricultural landscape. Sci. Total Environ. 644, 594–601 (2018).
ADS CAS Article Google Scholar
54.
Rogers, L. E., Buschbom, R. L. & Watson, C. R. Length-weight relationships of shrub-steppe invertebrates1. Ann. Entomol. Soc. Am. 70, 51–53 (1977).
Article Google Scholar
55.
De Felici, L., Piersma, T. & Howison, R. A. Abundance of arthropods as food for meadow bird chicks in response to short- and long-term soil wetting in Dutch dairy grasslands. PeerJ 7, e7401 (2019).
PubMed Central Article PubMed Google Scholar
56.
Aziz, M. A. et al. Using non-invasively collected genetic data to estimate density and population size of tigers in the Bangladesh Sundarbans. Glob. Ecol. Conserv. 12, 272–282 (2017).
Article Google Scholar
57.
Greenop, A., Woodcock, B. A., Wilby, A., Cook, S. M. & Pywell, R. F. Functional diversity positively affects prey suppression by invertebrate predators: A meta-analysis. Ecology 99, 1771–1782 (2018).
PubMed Central Article PubMed Google Scholar
58.
Kissick, A. L., Dunning, J. B., Fernandez-Juricic, E. & Holland, J. D. Different responses of predator and prey functional diversity to fragmentation. Ecol. Appl. 28, 1853–1866 (2018).
Article PubMed PubMed Central Google Scholar
59.
Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).
Article PubMed PubMed Central Google Scholar More
