Ecology

1.Bickford D, Lohman DJ, Sodhi NS, Ng PKL, Meier R, Winker K, et al. Cryptic species as a window on diversity and conservation. Trends Ecol Evol. 2007;22:148–55.PubMed 
PubMed Central 
Article 

Google Scholar 
2.Tewksbury JJ, Anderson JGT, Bakker JD, Billo TJ, Dunwiddie PW, Groom MJ, et al. Natural history’s place in science and society. Bioscience. 2014;64:300–10.Article 

Google Scholar 
3.Leliaert F, Verbruggen H, Vanormelingen P, Steen F, López-Bautista JM, Zuccarello GC, et al. DNA-based species delimitation in algae. Eur J Phycol. 2014;49:179–96.Article 

Google Scholar 
4.Potter D, LaJeunesse TC, Saunders GW, Anderson RA. Convergent evolution masks extensive biodiversity among marine coccoid picoplankton. Biodivers Conserv. 1997;6:99–107.Article 

Google Scholar 
5.de Vargas C, Norris R, Zaninetti L, Gibb SW, Pawlowski J. Molecular evidence of cryptic speciation in planktonic foraminifers and their relation to oceanic provinces. Proc Natl Acad Sci USA. 1999;96:2864–8.PubMed 
Article 
PubMed Central 

Google Scholar 
6.John U, Litaker RW, Montresor M, Murray S, Brosnahan ML, Anderson DM. Formal revision of the alexandrium tamarense species complex (dinophyceae) taxonomy: the introduction of five species with emphasis on molecular-based (rDNA) classification. Protist. 2014;165:779–804.PubMed 
PubMed Central 
Article 

Google Scholar 
7.Hoppenrath M, Reñé A, Satta CT, Yamaguchi A, Leander BS. Morphology and molecular phylogeny of a new marine, sand-dwelling dinoflagellate genus, Pachena (Dinophyceae), with descriptions of three new species. J Phycol. 2020;56:798–817.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Sproles AE, Oakley CA, Krueger T, Grossman AR, Weis VM, Meibom A, et al. Sub-cellular imaging shows reduced photosynthetic carbon and increased nitrogen assimilation by the non-native endosymbiont Durusdinium trenchii in the model cnidarian Aiptasia. Environ Microbiol. 2020;22:3741–53.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Hume BCC, Mejia-Restrepo A, Voolstra CR, Berumen ML. Fine-scale delineation of Symbiodiniaceae genotypes on a previously bleached central Red Sea reef system demonstrates a prevalence of coral host-specific associations. Coral Reefs. 2020;39:583–601.Article 

Google Scholar 
10.Gabay Y, Parkinson JE, Wilkinson SP, Weis VM, Davy SK. Inter-partner specificity limits the acquisition of thermotolerant symbionts in a model cnidarian-dinoflagellate symbiosis. ISME J. 2019;13:2489–99.PubMed 
PubMed Central 
Article 

Google Scholar 
11.Tivey TR, Parkinson JE, Weis VM. Host and symbiont cell cycle coordination is mediated by symbiotic state, nutrition, and partner identity in a model cnidarian-dinoflagellate symbiosis. MBio. 2020;11:1–17.Article 

Google Scholar 
12.Lawson CA, Possell M, Seymour JR, Raina JB, Suggett DJ. Coral endosymbionts (Symbiodiniaceae) emit species-specific volatilomes that shift when exposed to thermal stress. Sci Rep. 2019;9:1–11.
Google Scholar 
13.Reich HG, Rodriguez IB, LaJeunesse TC, Ho TY. Endosymbiotic dinoflagellates pump iron: differences in iron and other trace metal needs among the Symbiodiniaceae. Coral Reefs. 2020;39:915–27.Article 

Google Scholar 
14.de Queiroz A, Gatesy J. The supermatrix approach to systematics. Trends Ecol Evol. 2007;22:34–41.PubMed 
Article 
PubMed Central 

Google Scholar 
15.de Queiroz K. Species concepts and species delimitation. Syst Biol. 2007;56:879–86.PubMed 
PubMed Central 
Article 

Google Scholar 
16.Schönrogge K, Barr B, Wardlaw JC, Napper E, Gardner MG, Breen J, et al. When rare species become endangered: Cryptic speciation in myrmecophilous hoverflies. Biol J Linn Soc. 2002;75:291–300.Article 

Google Scholar 
17.Pettay DT, Wham DC, Pinzón JH, LaJeunesse TC. Genotypic diversity and spatial-temporal distribution of Symbiodinium clones in an abundant reef coral. Mol Ecol. 2011;20:5197–212.PubMed 
PubMed Central 
Article 

Google Scholar 
18.Baums IB, Devlin-Durante MK, LaJeunesse TC. New insights into the dynamics between reef corals and their associated dinoflagellate endosymbionts from population genetic studies. Mol Ecol. 2014;23:4203–15.PubMed 
Article 
PubMed Central 

Google Scholar 
19.Pinzón JH, LaJeunesse TC. Species delimitation of common reef corals in the genus Pocillopora using nucleotide sequence phylogenies, population genetics and symbiosis ecology. Mol Ecol. 2011;20:311–25.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Sampayo EM, Dove S, LaJeunesse TC. Cohesive molecular genetic data delineate species diversity in the dinoflagellate genus Symbiodinium. Mol Ecol. 2009;18:500–19.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Lien AY, Fukami H, Yamashita Y, Lien Y, Fukami H, Yamashita Y. Symbiodinium Clade C dominates zooxanthellate corals (Scleractinia) in the temperate region of Japan. Zool Sci. 2012;29:173–80.Article 

Google Scholar 
22.LaJeunesse TC. ‘Species’ radiations of symbiotic dinoflagellates in the Atlantic and Indo-Pacific since the Miocene-Pliocene transition. Mol Biol Evol. 2005;22:570–81.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Wham DC, Carmichael M, LaJeunesse TC. Microsatellite loci for Symbiodinium goreaui and other Clade C Symbiodinium. Conserv Genet Resour. 2014;6:127–9.Article 

Google Scholar 
24.LaJeunesse TC, Pettay DT, Sampayo EM, Phongsuwan N, Borwn B, Obura DO, et al. Long standing environmental conditions, geographic isolation and host-symbiont specificity influence the relative ecological dominance and genetic diversification of coral endosymbionts in the genus Symbiodinium. J Biogeogr. 2010;11:674–5.
Google Scholar 
25.Thornhill DJ, Lewis AM, Wham DC, LaJeunesse TC. Host-specialist lineages dominate the adaptive radiation of reef coral endosymbionts. Evolution. 2014;68:352–67.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.LaJeunesse TC, Bhagooli R, Hidaka M, DeVantier L, Done T, Schmidt GW, et al. Closely related Symbiodinium spp. differ in relative dominance in coral reef host communities across environmental, latitudinal and biogeographic gradients. Mar Ecol Prog Ser. 2004;284:147–61.Article 

Google Scholar 
27.Fitt WK, Gates RD, Hoegh-Guldberg O, Bythell JC, Jatkar A, Grottoli AG, et al. Response of two species of Indo-Pacific corals, Porites cylindrica and Stylophora pistillata, to short-term thermal stress: The host does matter in determining the tolerance of corals to bleaching. J Exp Mar Bio Ecol. 2009;373:102–10.Article 

Google Scholar 
28.Hume B, D’Angelo C, Burt J, Baker AC, Riegl B, Wiedenmann J. Corals from the Persian/Arabian Gulf as models for thermotolerant reef-builders: Prevalence of clade C3 Symbiodinium, host fluorescence and ex situ temperature tolerance. Mar Pollut Bull. 2013;72:313–22.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Hoadley KD, Lewis AM, Wham DC, Pettay DT, Grasso C, Smith R, et al. Host – symbiont combinations dictate the photo-physiological response of reef-building corals to thermal stress. Sci Rep. 2019:9:1–15.30.Sampayo EM, Ridgway T, Bongaerts P, Hoegh-Guldberg O. Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc Natl Acad Sci USA. 2008;105:10444–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Lee SY, Jeong HJ, LaJeunesse TC. Cladocopium infistulum sp. nov. (Dinophyceae), a thermally tolerant dinoflagellate symbiotic with giant clams from the western Pacific Ocean. Phycologia. 2020;59:515–26.Article 

Google Scholar 
32.LaJeunesse TC, Wham DC, Pettay DT, Parkinson JE, Keshavmurthy S, Chen CA. Ecologically differentiated stress-tolerant endosymbionts in the dinoflagellate genus Symbiodinium (Dinophyceae) Clade D are different species. Phycologia. 2014;53:305–19.Article 

Google Scholar 
33.Lewis AM, Chan AN, LaJeunesse TC. New species of closely related endosymbiotic dinoflagellates in the greater caribbean have niches corresponding to host coral phylogeny. J Eukaryot Microbiol. 2019;66:469–82.PubMed 
Article 
PubMed Central 

Google Scholar 
34.Veron JEN. Corals of the world. In: Stafford-Smith M, editor. Australian Institute of Marine Science; Townsville, Australia, 2000.35.Richmond RH. Energetics, competency, and long-distance dispersal of planula larvae of the coral Pocillopora damicornis. Mar Biol. 1987;93:527–33.Article 

Google Scholar 
36.Harrison PL, Wallace CC. A review of reproduction, larval dispersal and settlement of scleractinian corals. In: Dubinsky Z, editor. Ecosystems of the World 25 Coral Reefs; New York, NY, USA, 1990. p. 133–9637.Glynn PW. Coral reef bleaching: ecological perspectives. Coral Reefs. 1993;12:1–17.Article 

Google Scholar 
38.LaJeunesse TC, Smith R, Walther M, Pinzon J, Pettay DT, McGinley M, et al. Host-symbiont recombination versus natural selection in the response of coral-dinoflagellate symbioses to environmental disturbance. Proc R Soc B Biol Sci. 2010;277:2925–34.Article 

Google Scholar 
39.Stella JS, Pratchett MS, Hutchings PA, Jones GP. Coral-associated invertebrates: diversity, ecological importance and vulnerability to disturbance. Oceanogr Mar Biol Annu Rev. 2011;49:43–104.
Google Scholar 
40.Austin AD, Austin SA, Sale PF. Community structure of the fauna associated with the coral Pocillopora damicornis (L.) on the Great Barrier Reef. Mar Freshw Res. 1980;31:163–74.Article 

Google Scholar 
41.Glynn PW, Maté JL, Baker AC. Coral bleaching and mortality in Panama and Ecuador during the 1997 – 1998 El Niño – southern oscillation event: spatial/temporal patterns and comparisons with the 1982 – 1983 event. Bull Mar Sci. 2001;69:79–109.
Google Scholar 
42.Johnston EC, Forsman ZH, Flot J, Schmidt-Roach S, Pinzón H, Knapp ISS, et al. A genomic glance through the fog of plasticity and diversification in Pocillopora. Sci Rep. 2017;7:5991.43.Iglesias-Prieto R, Beltrán VH, LaJeunesse TC, Reyes-Bonilla H, Thomé PE. Different algal symbionts explain the vertical distribution of dominant reef corals in the eastern Pacific. Proc R Soc B Biol Sci. 2004;271:1757–63.CAS 
Article 

Google Scholar 
44.Bahr KD, Tran T, Jury CP, Toonen RJ. Abundance, size, and survival of recruits of the reef coral Pocillopora acuta under ocean warming and acidification. PLoS ONE. 2020;15:1–13.Article 
CAS 

Google Scholar 
45.Flot JF, Tillier S. The mitochondrial genome of Pocillopora (Cnidaria: Scleractinia) contains two variable regions: The putative D-loop and a novel ORF of unknown function. Gene. 2007;401:80–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.LaJeunesse TC, Loh WKW, Van Woesik R, Schmidt GW, Fitt WK. Low symbiont diversity in southern Great Barrier Reef corals, relative to those of the Caribbean. Limnol Oceanogr. 2003;48:2046–54.Article 

Google Scholar 
47.Tonk L, Sampayo EM, LaJeunesse TC, Schrameyer V, Hoegh-Guldberg O. Symbiodinium (Dinophyceae) diversity in reef-invertebrates along an offshore to inshore reef gradient near Lizard Island, Great Barrier Reef. J Phycol. 2014;50:552–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Magalon H, Baudry E, Husté A, Adjeroud M, Veuille M. High genetic diversity of the symbiotic dinoflagellates in the coral Pocillopora meandrina from the South Pacific. Mar Biol. 2006;148:913–22.Article 

Google Scholar 
49.Pinzón JH, Sampayo E, Cox E, Chauka LJ, Chen CA, Voolstra CR, et al. Blind to morphology: genetics identifies several widespread ecologically common species and few endemics among Indo-Pacific cauliflower corals (Pocillopora, Scleractinia). J Biogeogr. 2013;40:1595–608.Article 

Google Scholar 
50.Silverstein RN, Correa AMS, LaJeunesse TC, Baker AC. Novel algal symbiont (Symbiodinium spp.) diversity in reef corals of Western Australia. Mar Ecol Prog Ser. 2011;422:63–75.Article 

Google Scholar 
51.Wham DC, LaJeunesse TC. Symbiodinium population genetics: testing for species boundaries and analysing samples with mixed genotypes. Mol Ecol. 2016;25:2699–712.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Baums IB, Devlin-durante M, Laing BAA, Feingold J, Smith T, Bruckner A, et al. Marginal coral populations: the densest known aggregation of Pocillopora in the Galápagos Archipelago is of asexual origin. Front Mar Sci. 2014;1:1–11.Article 

Google Scholar 
53.McGinley MP, Aschaffenburg MD, Pettay DT, Smith RT, LaJeunesse TC, Warner ME. Symbiodinium spp. in colonies of eastern Pacific Pocillopora spp. are highly stable despite the prevalence of low-abundance background populations. Mar Ecol Prog Ser. 2012;462:1–7.Article 

Google Scholar 
54.Camp EF, Nitschke MR, Rodolfo-metalpa R, Gardner SG, Smith DJ, Zampighi M, et al. Reef-building corals thrive within hot-acidic and deoxygenated waters. Sci Rep. 2017;7:2434.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.LaJeunesse TC, Trench RK. Biogeography of two species of Symbiodinium (Freudenthal) inhabiting the intertidal sea anemone Anthopleura elegantissima (Brandt). Biol Bull. 2000;199:126–34.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.LaJeunesse TC. Investigating the biodiversity, ecology, and phylogeny of endosymbiotic dinoflagellates in the genus Symbiodinium using the its region: In search of a “species” level marker. J Phycol. 2001;880:866–80.Article 

Google Scholar 
57.Moore RB, Ferguson KM, Loh WKW, Hoegh-Guldberg O, Carter DA. Highly organized structure in the non-coding region of the psbA minicircle from clade C Symbiodinium. Int J Syst Evol Microbiol. 2003;53:1725–34.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.LaJeunesse TC, Thornhill DJ. Improved resolution of reef-coral endosymbiont (Symbiodinium) species diversity, ecology, and evolution through psbA non-coding region genotyping. PLoS ONE. 2011;6:e29013.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Swofford D. PAUP 4.0: Phylogenetic analysis using parsimony. Washington DC, USA: Smithson Inst.; 2014.60.Ronquist F, Teslenko M, Van Der Mark P, Ayres DL, Darling A, Höhna S, et al. Mrbayes 3.2: efficient bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61:539–42.PubMed 
PubMed Central 
Article 

Google Scholar 
61.Nylander JAA. MrModeltest v2. Uppsala, Sweden: Progr Distrib by author Evol Biol Centre, Uppsala Univ.; 2004.62.Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D, et al. BEAST 2: a software platform for bayesian evolutionary analysis. PLoS Comput Biol. 2014;10:1–6.Article 
CAS 

Google Scholar 
63.Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA. Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst Biol. 2018;67:901–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Jackson JBC, O’Dea A. Timing of the oceanographic and biological isolation of the Caribbean sea from the tropical eastern pacific ocean. Bull Mar Sci. 2013;89:779–800.
Google Scholar 
65.Haug G, Tiedemann R. Effect of the formation of the Isthmus of Panama on Atlantic Ocean thermohaline circulation. Nature. 1998;394:1699–701.
Google Scholar 
66.O’Dea A, Lessios HA, Coates AG, Eytan RI, Restrepo-Moreno SA, Cione AL, et al. Formation of the Isthmus of Panama. Sci Adv. 2016;2:1–12.Article 

Google Scholar 
67.Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007;7:214.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Bouckaert R, Heled J DensiTree 2: seeing trees through the forest. 2014. https://www.biorxiv.org/content/10.1101/012401v1.69.Bay LK, Howells EJ, van Oppen MJH. Isolation, characterisation and cross amplification of thirteen microsatellite loci for coral endo-symbiotic dinoflagellates (Symbiodinium clade C). Conserv Genet Resour. 2009;1:199–203.Article 

Google Scholar 
70.Peakall R, Smouse PE. GenALEx 6.5: genetic analysis in excel. population genetic software for teaching and research-an update. Bioinformatics. 2012;28:2537–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Davies SW, Moreland KN, Wham DC, Kanke MR, Matz MV. Cladocopium community divergence in two Acropora coral hosts across multiple spatial scales. Mol Ecol. 2020;29:4559–72.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Earl DA, vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4:359–61.Article 

Google Scholar 
73.Liu H, Stephens TG, González-Pech RA, Beltran VH, Lapeyre B, Bongaerts P, et al. Symbiodinium genomes reveal adaptive evolution of functions related to coral-dinoflagellate symbiosis. Commun Biol. 2018;1:95.PubMed 
PubMed Central 
Article 

Google Scholar 
74.Raymond M, Rousset F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Heredity. 1995:248–9.75.Rousset F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour. 2008;86:103–6.PubMed 
PubMed Central 
Article 

Google Scholar 
76.Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9:2579–605.
Google Scholar 
78.LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol. 2018;28:2570–2580.e6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.LaJeunesse TC, Bonilla HR, Warner ME, Wills M, Schmidt GW, Fitt WK. Specificity and stability in high latitude eastern Pacific coral-algal symbioses. Limnol Oceanogr. 2008;53:719–27.Article 

Google Scholar 
80.Ramsby BD, Hill MS, Thornhill DJ, Steenhuizen SF, Achlatis M, Lewis AM, et al. Sibling species of mutualistic Symbiodinium Clade G from bioeroding sponges in the western Pacific and western Atlantic oceans. J Phycol. 2017;53:951–60.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Prada C, McIlroy SE, Beltrán DM, Valint DJ, Ford SA, Hellberg ME, et al. Cryptic diversity hides host and habitat specialization in a gorgonian-algal symbiosis. Mol Ecol. 2014;23:3330–40.PubMed 
Article 
PubMed Central 

Google Scholar 
82.Wham DC, Ning G, LaJeunesse TC. Symbiodinium glynnii sp. nov., a species of stress-tolerant symbiotic dinoflagellates from pocilloporid and montiporid corals in the Pacific Ocean. Phycologia. 2017;56:396–409.CAS 
Article 

Google Scholar 
83.Mayr E. The growth of biological thought: Diversity, evolution, and inheritance. Cambridge, MA, USA: Belknap Press of Harvard University Press; 1982.84.Arnaud-Haond S, Duarte CM, Alberto F, Serrão EA. Standardizing methods to address clonality in population studies. Mol Ecol. 2007;16:5115–39.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Jeong HJ, Lee SY, Kang NS, Yoo YD, Lim AS, Lee MJ, et al. Genetics and morphology characterize the dinoflagellate Symbiodinium voratum, n. sp., (dinophyceae) as the sole representative of Symbiodinium Clade E. J Eukaryot Microbiol. 2014;61:75–94.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Blank RJ, Trench RK. Speciation and symbiotic dinoflagellates. Science. 1985;229:656–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Suggett DJ, Moore CM, Hickman AE, Geider RJ. Interpretation of fast repetition rate (FRR) fluorescence: Signatures of phytoplankton community structure versus physiological state. Mar Ecol Prog Ser. 2009;376:1–19.Article 

Google Scholar 
88.Suggett DJ, Goyen S, Evenhuis C, Szabó M, Pettay DT, Warner ME, et al. Functional diversity of photobiological traits within the genus Symbiodinium appears to be governed by the interaction of cell size with cladal designation. New Phytol. 2015;208:370–81.PubMed 
Article 
PubMed Central 

Google Scholar 
89.Geider R, Piatt T, Raven J. Size dependence of growth and photosynthesis in diatoms: a synthesis. Mar Ecol Prog Ser. 1986;30:93–104.CAS 
Article 

Google Scholar 
90.Finkel ZV. Light absorption and size scaling of light-limited metabolism in marine diatoms. Limnol Oceanogr. 2001;46:86–94.CAS 
Article 

Google Scholar 
91.Irwin AJ, Finkel ZV, Schofield OME, Falkowski PG. Scaling-up from nutrient physiology to the size-structure of phytoplankton communities. J Plankton Res. 2006;28:459–71.Article 

Google Scholar 
92.Wu Y, Campbell DA, Irwin AJ, Suggett DJ, Finkel ZV. Ocean acidification enhances the growth rate of larger diatoms. Limnol Oceanogr. 2014;59:1027–34.CAS 
Article 

Google Scholar 
93.Rowan R. Coral bleaching: thermal adaptation in reef coral symbionts. Nature. 2004;430:742.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Berkelmans R, Van, Oppen MJH. The role of zooxanthellae in the thermal tolerance of corals: a “nugget of hope” for coral reefs in an era of climate change. Proc R Soc B Biol Sci. 2006;273:2305–12.Article 

Google Scholar 
95.Abrego D, Ulstrup KE, Willis BL, Van Oppen MJH. Species-specific interactions between algal endosymbionts and coral hosts define their bleaching response to heat and light stress. Proc R Soc B Biol Sci. 2008;275:2273–82.CAS 
Article 

Google Scholar 
96.Schluter D. Evidence for ecological speciation and its alternative. Science. 2009;323:737–41.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.Hendry AP, Nosil P, Rieseberg LH. The speed of ecological speciation. Funct Ecol. 2007;21:455–64.PubMed 
PubMed Central 
Article 

Google Scholar 
98.Glynn PW, Gassman NJ, Eakin CM, Cortes J, Smith DB, Guzman HM. Reef coral reproduction in the eastern Pacific: Costa Rica, Panama, and Galapagos Islands (Ecuador). Mar Biol. 1991;109:355–68.Article 

Google Scholar 
99.Hirose M, Kinzie RA, Hidaka M. Early development of zooxanthella-containing eggs of the corals Pocillopora verrucosa and P. eydouxi with special reference to the distribution of zooxanthellae. Biol Bull. 2000;199:68–75.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Chavez-Romo H. Sexual reproduction of the coral Pocillopora damicornis in the southern Gulf of California. Mex Cienc Mar. 2007;33:495–501.Article 

Google Scholar 
101.Russell SL, Chappell L, Sullivan W. A symbiont’s guide to the germline. 1st ed. In: Current topics in developmental biology. Vol 135. Amsterdam, The Netherlands: Elsevier Inc.; 2019. p. 351.102.LaJeunesse TC, Thornhill DJ, Cox EF, Stanton FG, Fitt WK, Schmidt GW. High diversity and host specificity observed among symbiotic dinoflagellates in reef coral communities from Hawaii. Coral Reefs. 2004;23:596–603.
Google Scholar 
103.Rowan ROB, Powers DA. A molecular genetic classification of zooxanthellae and the evolution of animal-algal symbioses. Science. 1991;251:1348–51.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Zachos JC, Dickens GR, Zeebe RE. An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics. Nature. 2008;451:279–83.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.LaJeunesse TC, Parkinson JE, Reimer JD. A genetics-based description of Symbiodinium minutum sp. nov. and S. psygmophilum sp. nov. (dinophyceae), two dinoflagellates symbiotic with cnidaria. J Phycol. 2012;48:1380–91.PubMed 
Article 
PubMed Central 

Google Scholar 
106.Pettay DT, LaJeunesse TC. Long-range dispersal and high-latitude environments influence the population structure of a “stress-tolerant” dinoflagellate endosymbiont. PLoS ONE. 2013;8:1–12.Article 
CAS 

Google Scholar 
107.Wicks LC, Sampayo E, Gardner JPA, Davy SK. Local endemicity and high diversity characterise high-latitude coral- Symbiodinium partnerships. Coral Reefs. 2010;29:989–1003.Article 

Google Scholar 
108.Sampayo EM, Franceschinis L, Hoegh-Guldberg O, Dove S. Niche partitioning of closely related symbiotic dinoflagellates. Mol Ecol. 2007;16:3721–33.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
109.Thompson JN. The geographic mosaic of coevolution. Chicago, IL, USA: University of Chicago Press; 2005.110.Sampayo EM, Ridgway T, Franceschinis L, Roff G, Hoegh-Guldberg O, Dove S. Coral symbioses under prolonged environmental change: living near tolerance range limits. Sci Rep. 2016;6:36271.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
111.Janis CM. Tertiary mammal evolution in the context of changing climates, vegetation, and tectonic events. Annu Rev Ecol Syst. 1993;24:467–500.Article 

Google Scholar 
112.Willeit M, Ganopolski A, Calov R, Brovkin V. Mid-Pleistocene transition in glacial cycles explained by declining CO2 and regolith removal. Sci Adv. 2019;5:eaav7337.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
113.Palumbi SR, Barshis DJ, Traylor-Knowles N, Bay RA. Mechanisms of reef coral resistance to future climate change. Science. 2014;344:895–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
114.Pandolfi JM, Jackson JBC, Geister J. Geologically sudden extinction of two widespread late Pleistocene Caribbean reef corals. In: Evolutionary patterns: growth, form and tempo in the fossil record. Chicago, IL, USA: University of Chicago Press; 2001. p. 120–58.115.Toth LT, Aronson RB, Cobb KM, Cheng H, Edwards RL, Grothe PR, et al. Climatic and biotic thresholds of coral-reef shutdown. Nat Clim Chang. 2015;5:369–74.Article 

Google Scholar 
116.Baums IB, Baker AC, Davies SW, Grottoli AG, Kenkel CD, Kitchen SA, et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol Appl. 2019;29:1–23.Article 

Google Scholar  More

1.Brilli M, Mengoni A, Fondi M, Bazzicalupo M, Liò P, Fani R. Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network. BMC Bioinformatics. 2008;9:551.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Dziewit L, Pyzik A, Szuplewska M, Matlakowska R, Mielnicki S, Wibberg D, et al. Diversity and role of plasmids in adaptation of bacteria inhabiting the Lubin copper mine in Poland, an environment rich in heavy metals. Front Microbiol. 2015;6:152.PubMed 
PubMed Central 

Google Scholar 
3.Matyar F, Kaya A, Dinçer S. Antibacterial agents and heavy metal resistance in Gram-negative bacteria isolated from seawater, shrimp and sediment in Iskenderun Bay, Turkey. Sci Total Environ. 2008;407:279–85.CAS 
PubMed 
Article 

Google Scholar 
4.Dagan T, Artzy-Randrup Y, Martin W. Modular networks and cumulative impact of lateral transfer in prokaryote genome evolution. Proc Natl Acad Sci USA. 2008;105:10039–44.CAS 
PubMed 
Article 

Google Scholar 
5.Heuer H, Smalla K. Plasmids foster diversification and adaptation of bacterial populations in soil. FEMS Microbiol Rev. 2012;36:1083–104.CAS 
PubMed 
Article 

Google Scholar 
6.Morozova D, Möhlmann D, Wagner D. Survival of methanogenic archaea from Siberian permafrost under simulated Martian thermal conditions. Orig Life Evol Biosph. 2007;37:189–200.CAS 
PubMed 
Article 

Google Scholar 
7.Schimel J, Balser TC, Wallenstein M. Microbial stress-response physiology and its implications for ecosystem functioning. Ecology. 2007;88:1386–94.Article 

Google Scholar 
8.Margesin R, Miteva V. Diversity and ecology of psychrophilic microorganisms. Res Microbiol. 2011;162:346–61.PubMed 
Article 

Google Scholar 
9.Dutta H, Dutta A. The microbial aspect of climate change. Energy, Ecol Environ. 2016;1:209–32.Article 

Google Scholar 
10.Leplae R, Lima-Mendez G, Toussaint A. A first global analysis of plasmid encoded proteins in the ACLAME database. FEMS Microbiol Rev. 2006;30:980–94.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Couturier M, Bex F, Bergquist PL, Maas WK. Identification and classification of bacterial plasmids. Microbiol Rev. 1988;52:375–95.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Carattoli A, Bertini A, Villa L, Falbo V, Hopkins KL, Threlfall EJ. Identification of plasmids by PCR-based replicon typing. J Microbiol Methods. 2005;63:219–28.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Johnson TJ, Wannemuehler YM, Johnson SJ, Logue CM, White DG, Doetkott C, et al. Plasmid replicon typing of commensal and pathogenic Escherichia coli isolates. Appl Environ Microbiol. 2007;73:1976–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science. 1997;278:631–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Smalla K, Jechalke S, Top EM. Plasmid detection, characterization, and ecology. Microbiol Spectr. 2015;3:PLAS-0038-2014.16.Top EM, Holben WE, Forney LJ. Characterization of diverse 2,4-dichlorophenoxyacetic acid-degradative plasmids isolated from soil by complementation. Appl Environ Microbiol. 1995;61:1691–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Sayler GS, Hooper SW, Layton AC, King JMH. Catabolic plasmids of environmental and ecological significance. Microb Ecol. 1990;19:1–20.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Elsas JD, Bailey MJ. The ecology of transfer of mobile genetic elements. FEMS Microbiol Ecol. 2006;42:187–97.Article 

Google Scholar 
19.Barrón MD, La C, Merlin C, Guilloteau H, Montargès-Pelletier E, Bellanger X. Suspended materials in river waters differentially enrich class 1 integron- and IncP-1 plasmid-carrying bacteria in sediments. Front Microbiol. 2018;9:1443.Article 

Google Scholar 
20.Dziewit L, Bartosik D. Plasmids of psychrophilic and psychrotolerant bacteria and their role in adaptation to cold environments. Front Microbiol. 2014;5:596.PubMed 
PubMed Central 
Article 

Google Scholar 
21.McCann CM, Christgen B, Roberts JA, Su JQ, Arnold KE, Gray ND, et al. Understanding drivers of antibiotic resistance genes in High Arctic soil ecosystems. Environ Int. 2019;125:497–504.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Nesme J, Simonet P. The soil resistome: a critical review on antibiotic resistance origins, ecology and dissemination potential in telluric bacteria. Environ Microbiol. 2015;17:913–30.PubMed 
Article 
PubMed Central 

Google Scholar 
23.Sjölund M, Bonnedahl J, Hernandez J, Bengtsson S, Cederbrant G, Pinhassi J, et al. Dissemination of multidrug-resistant bacteria into the Arctic. Emerg Infect Dis. 2008;14:70–72.PubMed 
PubMed Central 
Article 

Google Scholar 
24.Perron GG, Whyte L, Turnbaugh PJ, Goordial J, Hanage WP, Dantas G, et al. Functional characterization of bacteria isolated from ancient Arctic soil exposes diverse resistance mechanisms to modern antibiotics. PLoS One. 2015;10:e0069533.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Hernández J, González-Acuña D. Anthropogenic antibiotic resistance genes mobilization to the polar regions. Infect Ecol Epidemiol. 2016;6:32112.PubMed 
PubMed Central 

Google Scholar 
26.Tan L, Li L, Ashbolt N, Wang X, Cui Y, Zhu X, et al. Arctic antibiotic resistance gene contamination, a result of anthropogenic activities and natural origin. Sci Total Environ. 2018;621:1176–84.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Wang F, Stedtfeld RD, Kim OS, Chai B, Yang L, Stedtfeld TM, et al. Influence of soil characteristics and proximity to antarctic research stations on abundance of antibiotic resistance genes in soils. Environ Sci Technol. 2016;50:12621–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Winkel M, Mitzscherling J, Overduin PP, Horn F, Winterfeld M, Rijkers R, et al. Anaerobic methanotrophic communities thrive in deep submarine permafrost. Sci Rep. 2018;8:1291.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Liebner S, Harder J, Wagner D. Bacterial diversity and community structure in polygonal tundra soils from Samoylov Island, Lena Delta, Siberia. Int Microbiol. 2008;11:195–202.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Taş N, Prestat E, Wang S, Wu Y, Ulrich C, Kneafsey T, et al. Landscape topography structures the soil microbiome in Arctic polygonal tundra. Nat Commun. 2018;9:777.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Reasoner DJ, Geldreich EE. A new medium for the enumeration and subculture of bacteria from potable water. Appl Environ Microbiol. 1985;49:1–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Muyzer G, De Waal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Mizrahi-Man O, Davenport ER, Gilad Y. Taxonomic classification of bacterial 16S rRNA genes using short sequencing reads: evaluation of effective study designs. PLoS One. 2013;8:e53608.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10.Article 

Google Scholar 
35.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
37.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:2584.Article 

Google Scholar 
38.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Hammer DAT, Ryan PD, Hammer Ø, Harper DAT. Past: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:1–9.
Google Scholar 
40.Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, et al. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Krawczyk PS, Lipinski L, Dziembowski A. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 2018;46:e35.PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
43.Kahlke T, Ralph PJ. BASTA—taxonomic classification of sequences and sequence bins using last common ancestor estimations. Methods Ecol Evol. 2019;10:100–3.Article 

Google Scholar 
44.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003;4:41.PubMed 
PubMed Central 
Article 

Google Scholar 
46.Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Third Int AAAI Conf Weblogs Soc Media. San Jose, California, USA. 2009.47.Jacomy M, Venturini T, Heymann S, Bastian M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS One. 2014;9:e98679.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA, Baylay AJ, et al. The comprehensive antibiotic resistance database. Antimicrob Agents Chemother. 2013;57:3348–57.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DG. BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res. 2014;42:D737–43.CAS 
PubMed 
Article 

Google Scholar 
50.Caswell TA, Droettboom M, Hunter J, Lee A, Firing E, Stansby D, et al. matplotlib/matplotlib: REL: v3.1.1. 2019.51.Pham VHT, Kim J. Improvement for isolation of soil bacteria by using common culture media. J Pure Appl Microbiol. 2016;10:49–60.
Google Scholar 
52.Romaniuk K, Ciok A, Decewicz P, Uhrynowski W, Budzik K, Nieckarz M, et al. Insight into heavy metal resistome of soil psychrotolerant bacteria originating from King George Island (Antarctica). Polar Biol. 2018;41:1319–33.Article 

Google Scholar 
53.Amann RI, Ludwig W, Schleifer KH. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol Rev. 1995;59:143–69.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Tamaki H, Sekiguchi Y, Hanada S, Nakamura K, Nomura N, Matsumura M, et al. Comparative analysis of bacterial diversity in freshwater sediment of a shallow eutrophic lake by molecular and improved cultivation-based techniques. Appl Environ Microbiol. 2005;71:2162–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Stewart EJ. Growing unculturable bacteria. J Bacteriol. 2012;194:4151–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Ganzert L, Jurgens G, Munster U, Wagner D. Methanogenic communities in permafrost-affected soils of the Laptev Sea coast, Siberian Arctic, characterized by 16S rRNA gene fingerprints. FEMS Microbiol Ecol. 2007;59:476–88.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Bajerski F, Ganzert L, Mangelsdorf K, Padur L, Lipski A, Wagner D. Chryseobacterium frigidisoli sp. nov., a psychrotolerant species of the family Flavobacteriaceae isolated from sandy permafrost from a glacier forefield. Int J Syst Evol Microbiol. 2013;63:2666–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Filippidou S, Wunderlin T, Junier T, Jeanneret N, Dorador C, Molina V, et al. A combination of extreme environmental conditions favor the prevalence of endospore-forming Firmicutes. Front Microbiol. 2016;7:1707.PubMed 
PubMed Central 
Article 

Google Scholar 
59.Kuramae EE, Yergeau E, Wong LC, Pijl AS, Veen JA, Kowalchuk GA. Soil characteristics more strongly influence soil bacterial communities than land-use type. FEMS Microbiol Ecol. 2012;79:12–24.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Filippidou S, Junier T, Wunderlin T, Lo CC, Li PE, Chain PS, et al. Under-detection of endospore-forming Firmicutes in metagenomic data. Comput Struct Biotechnol J. 2015;13:299–306.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Dziewit L, Cegielski A, Romaniuk K, Uhrynowski W, Szych A, Niesiobedzki P, et al. Plasmid diversity in arctic strains of Psychrobacter spp. Extremophiles. 2013;17:433–44.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Mindlin S, Petrenko A, Kurakov A, Beletsky A, Mardanov A, Petrova M. Resistance of permafrost and modern Acinetobacter lwoffiistrains to heavy metals and arsenic revealed by genome analysis. Biomed Res Int. 2016;2016:3970831.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Moghadam MS, Albersmeier A, Winkler A, Cimmino L, Rise K, Hohmann-Marriott MF, et al. Isolation and genome sequencing of four Arctic marine Psychrobacter strains exhibiting multicopper oxidase activity. BMC Genomics. 2016;17:117.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ. Co-occurrence of resistance genes to antibiotics, biocides and metals reveals novel insights into their co-selection potential. BMC Genomics. 2015;16:964.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Carroll LM, Gaballa A, Guldimann C, Sullivan G, Henderson LO, Wiedmann M. Identification of novel mobilized colistin resistance gene mcr-9 in a multidrug-resistant, colistin-susceptible Salmonella enterica serotype Typhimurium isolate. MBio. 2019;10:e00853–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Bleich A, Fox JG. The mammalian microbiome and its importance in laboratory animal research. ILAR J. 2015;56:153–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Grond K, Sandercock BK, Jumpponen A, Zeglin LH. The avian gut microbiota: community, physiology and function in wild birds. J Avian Biol. 2018;49:e01788.Article 

Google Scholar 
68.Anganova EV, Savchenkov MF, Stepanenko LA, Savilov ED. Microbiological monitoring of opportunistic Enterobacteriaceae of the Lena river. Gig Sanit. 2016;95:1124–8.69.Tignat-Perrier R, Dommergue A, Thollot A, Keuschnig C, Magand O, Vogel TM, et al. Global airborne microbial communities controlled by surrounding landscapes and wind conditions. Sci Rep. 2019;9:14441.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Mu C, Zhang F, Chen X, Ge S, Mu M, Jia L, et al. Carbon and mercury export from the Arctic rivers and response to permafrost degradation. Water Res. 2019;161:54–60.CAS 
PubMed 
Article 

Google Scholar 
71.Petrova M, Kurakov A, Shcherbatova N, Mindlin S. Genetic structure and biological properties of the first ancient multiresistance plasmid pKLH80 isolated from a permafrost bacterium. Microbiology. 2014;160:2253–63.CAS 
PubMed 
Article 

Google Scholar 
72.Afouda P, Dubourg G, Levasseur A, Fournier P-E, Delerce J, Mediannikov O, et al. Culturing ancient bacteria carrying resistance genes from permafrost and comparative genomics with modern isolates. Microorganisms. 2020;8:1522.CAS 
PubMed Central 
Article 
PubMed 

Google Scholar  More

1.Sedano, F. & Randerson, J. T. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences 11, 3739–3755 (2014).ADS 

Google Scholar 
2.Veraverbeke, S. et al. Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Chang. 7, 529–534 (2017).ADS 

Google Scholar 
3.Calef, M. P., McGuire, A. D. & Chapin, F. S. Human influences on wildfire in Alaska from 1988 through 2005: an analysis of the spatial patterns of human impacts. Earth Interact. 12, 1–17 (2008).ADS 

Google Scholar 
4.McCarty, J. L., Smith, T. E. L. & Turetsky, M. R. Arctic fires re-emerging. Nat. Geosci. 13, 658–660 (2020).ADS 
CAS 

Google Scholar 
5.Irannezhad, M., Liu, J., Ahmadi, B. & Chen, D. The dangers of Arctic zombie wildfires. Science 369, 1171 (2020).ADS 

Google Scholar 
6.Rein, G. in Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science (ed. Belcher, C. M.) 15–34 (Wiley-Blackwell, 2013).7.Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Overland, J. E., Wang, M., Walsh, J. E. & Stroeve, J. C. Future Arctic climate changes: adaptation and mitigation time scales. Earth’s Future 2, 68–74 (2014).ADS 

Google Scholar 
9.Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, GB2023 (2009).ADS 

Google Scholar 
10.Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).ADS 
CAS 

Google Scholar 
11.Turetsky, M. R. et al. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat. Geosci. 4, 27–31 (2011).ADS 
CAS 

Google Scholar 
12.Walker, X. J. et al. Soil organic layer combustion in boreal black spruce and jack pine stands of the Northwest Territories, Canada. Int. J. Wildl. Fire 27, 125–134 (2018).
Google Scholar 
13.Turetsky, M. R. et al. Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci. 8, 11–14 (2015).ADS 
CAS 

Google Scholar 
14.Flannigan, M. D. et al. Fuel moisture sensitivity to temperature and precipitation: climate change implications. Clim. Change 134, 59–71 (2016).ADS 
CAS 

Google Scholar 
15.Coops, N. C., Hermosilla, T., Wulder, M. A., White, J. C. & Bolton, D. K. A thirty year, fine-scale, characterization of area burned in Canadian forests shows evidence of regionally increasing trends in the last decade. PLoS One 13, e0197218 (2018).PubMed 
PubMed Central 

Google Scholar 
16.USDA Forest Service, USFS-USDI and NASF. Large Fire Cost Reduction Action Plan https://www.fs.usda.gov/sites/default/files/media_wysiwyg/5100_largefirecostreductionaction_mar_03.pdf (2003).17.Podur, J. & Wotton, M. Will climate change overwhelm fire management capacity? Ecol. Modell. 221, 1301–1309 (2010).
Google Scholar 
18.Tymstra, C., Stocks, B. J., Cai, X. & Flannigan, M. D. Wildfire management in Canada: review, challenges and opportunities. Prog. Disaster Sci. 5, 100045 (2020); erratum 8, 100045 (2020).
Google Scholar 
19.Stocks, B. J. et al. Large forest fires in Canada, 1959–1997. J. Geophys. Res. 107, https://doi.org/10.1029/2001JD000484 (2002).20.Wiggins, E. B. et al. Evidence for a larger contribution of smoldering combustion to boreal forest fire emissions from tower observations in Alaska. Atmos. Chem. Phys. https://doi.org/10.5194/acp-2019-1067 (in the press).21.Rein, G., Garcia, J., Simeoni, A., Tihay, V. & Ferrat, L. Smouldering natural fires: comparison of burning dynamics in boreal peat and Mediterranean humus. WIT Trans. Ecol. Environ. 119, 183–192 (2008).
Google Scholar 
22.Baber, C. & McMaster, R. 2019 Alaska Statewide Annual Operating Plan. https://fire.ak.blm.gov/administration/asma.php (Alaska Statewide Master Agreement, 2019).23.Alaska Interagency Coordination Center. 2010 Alaska fire statistics. https://www.frames.gov/catalog/12055 (Wildland Fire Summary and Statistics Annual Report, 2010).24.Alaska Division of Forestry. State Forestry monitoring hot spots that overwintered from Deshka Landing Fire. https://akfireinfo.com/2020/04/10/state-forestry-monitoring-hot-spots-that-overwintered-from-deshka-landing-fire/ (2020).25.Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
26.Kasischke, E. S., Rupp, T. S. & Verbyla, D. L. in Alaska’s Changing Boreal Forest (eds Chapin, F. S. III, Oswood, M. et al.) 285–301 (Oxford Univ. Press, 2006).27.Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase western U.S. forest wildfire activity. Science 313, 940–943 (2006).ADS 
CAS 

Google Scholar 
28.Painter, T. H. et al. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens. Environ. 113, 868–879 (2009).ADS 

Google Scholar 
29.Scholten, R. C., Jandt, R. R., Miller, E. A., Rogers, B. M. & Veraverbeke, S. ABoVE: Ignitions, burned area and emissions of fires in AK, YT, and NWT, 2001–2018. https://doi.org/10.3334/ORNLDAAC/1812 (2020).30.Xiao, J. & Zhuang, Q. Drought effects on large fire activity in Canadian and Alaskan forests. Environ. Res. Lett. 2, 044003 (2007).ADS 

Google Scholar 
31.Flannigan, M. D. et al. Global wildland fire season severity in the 21st century. For. Ecol. Manage. 294, 54–61 (2013).
Google Scholar 
32.Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Adams, W. H. The Role of Fire in the Alaska Taiga. An Unsolved Problem (Bureau of Land Management, State Office, Anchorage, AK, 1974); preprint at https://scholarworks.alaska.edu/handle/11122/6675 (2016).34.Certini, G. Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10 (2005).ADS 
PubMed 

Google Scholar 
35.Kane, E. S., Kasischke, E. S., Valentine, D. W., Turetsky, M. R. & McGuire, A. D. Topographic influences on wildfire consumption of soil organic carbon in interior Alaska: implications for black carbon accumulation. J. Geophys. Res. Biogeosci. 112, 1–11 (2007).
Google Scholar 
36.Hoy, E. E., Turetsky, M. R. & Kasischke, E. S. More frequent burning increases vulnerability of Alaskan boreal black spruce forests. Environ. Res. Lett. 11, 095001 (2016).ADS 

Google Scholar 
37.Miyanishi, K. & Johnson, E. A. Process and patterns of duff consumption in the mixedwood boreal forest. Can. J. For. Res. 32, 1285–1295 (2002).
Google Scholar 
38.Kasischke, E. S. & Turetsky, M. R. Recent changes in the fire regime across the North American boreal region — spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33, https://doi.org/10.1029/2006GL025677 (2006).39.Johnstone, J. F. et al. Factors shaping alternate successional trajectories in burned black spruce forests of Alaska. Ecosphere 11, https://doi.org/10.1002/ecs2.3129 (2020).40.Mekonnen, Z. A., Riley, W. J., Randerson, J. T., Grant, R. F. & Rogers, B. M. Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire. Nat. Plants 5, 952–958 (2019).
Google Scholar 
41.Andreae, M. O. & Merlet, P. Emission of trace gases and aerosols from biomass burning. Glob. Biogeochem. Cycles 15, 955–966 (2001).ADS 
CAS 

Google Scholar 
42.Dean, J. F. et al. Methane feedbacks to the global climate system in a warmer world. Rev. Geophys. 56, 207–250 (2018).ADS 

Google Scholar 
43.Beaudoin, A., Bernier, P. Y., Villemaire, P., Guindon, L. & Guo, X. J. Tracking forest attributes across Canada between 2001 and 2011 using a k nearest neighbors mapping approach applied to MODIS imagery. Can. J. For. Res. 48, 85–93 (2018).
Google Scholar 
44.Veraverbeke, S., Rogers, B. M. & Randerson, J. T. Daily burned area and carbon emissions from boreal fires in Alaska. Biogeosci. Discuss. 12, 3579–3601 (2015).ADS 
CAS 

Google Scholar 
45.Kasischke, E. S. et al. Quantifying burned area for North American forests: implications for direct reduction of carbon stocks. J. Geophys. Res. Biogeosci. 116, 1–17 (2011).
Google Scholar 
46.Farukh, M. A. & Hayasaka, H. Active forest fire occurrences in severe lightning years in Alaska. J. Nat. Disaster Sci. 33, 71–84 (2012).
Google Scholar 
47.Burrows, W. R. & Kochtubajda, B. A decade of cloud-to-ground lightning in Canada: 1999-2008. Part 1: flash density and occurrence. Atmos.-Ocean 48, 177–194 (2010).
Google Scholar 
48.Bieniek, P. A. et al. Lightning variability in dynamically downscaled simulations of Alaska’s present and future summer climate. J. Appl. Meteorol. Climatol. 59, 1139–1152 (2020).ADS 

Google Scholar 
49.Kochtubajda, B. et al. Exceptional cloud-to-ground lightning during an unusually warm summer in Yukon, Canada. J. Geophys. Res. Atmos. 116, https://doi.org/10.1029/2011JD016080 (2011).50.Kochtubajda, B., Stewart, R. & Tropea, B. Lightning and weather associated with the extreme 2014 wildfire season in Canada’s Northwest Territories. In Proceedings of the 24th International Lightning Detection Conference 1–4 (VAISALA, 2016).51.Dowdy, A. J. & Mills, G. A. Atmospheric and fuel moisture characteristics associated with lightning-attributed fires. J. Appl. Meteorol. Climatol. 51, 2025–2037 (2012).ADS 

Google Scholar 
52.Larjavaara, M., Pennanen, J. & Tuomi, T. J. Lightning that ignites forest fires in Finland. Agric. For. Meteorol. 132, 171–180 (2005).ADS 

Google Scholar 
53.Duncan, B. W., Adrian, F. W. & Stolen, E. D. Isolating the lightning ignition regime from a contemporary background fire regime in east-central Florida, USA. Can. J. For. Res. 40, 286–297 (2010).
Google Scholar 
54.Veraverbeke, S. et al. Mapping the daily progression of large wildland fires using MODIS active fire data. Int. J. Wildl. Fire 23, 655–667 (2014).
Google Scholar 
55.Statistics Canada. Road Network File 2010. https://www150.statcan.gc.ca/n1/en/catalogue/92-500-X (2016).56.Government of Yukon. Corporate Spatial Warehouse. ftp://ftp.geomaticsyukon.ca/GeoYukon/Transportation/Roads_1M/ (2018).57.Rittger, K., Painter, T. H. & Dozier, J. Assessment of methods for mapping snow cover from MODIS. Adv. Water Resour. 51, 367–380 (2013).ADS 

Google Scholar 
58.Gallant, A. L., Binnian, E. F., Omernik, J. M. & Shasby, M. B. Ecoregions of Alaska (Professional Paper 1567, USGS, 1995).59.Canadian Council on Ecological Areas (CCEA). Canada ecozones. https://ccea-ccae.org/ecozones-downloads/ (2016).60.Mesinger, F. et al. North American regional reanalysis. Bull. Am. Meteorol. Soc. 87, 343–360 (2006).ADS 

Google Scholar 
61.Van Wagner, C. E. Development and Structure of the Canadian Fire Weather Index System. Forestry Technical Report Vol. 35 (Canadian Forestry Service Headquarters, Ottawa, 1987).62.York, A. D. & Jandt, R. R. Opportunities to Apply Remote Sensing in Boreal/Arctic Wildfire Management & Science: A Workshop Report www.frames.gov/catalog/57849 (University of Alaska, Fairbanks, 2019).63.Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).ADS 

Google Scholar 
64.Welch, B. L. The significance of the difference between two means when the population variances are unequal. Biometrika 29, 350–362 (1938).MATH 

Google Scholar 
65.Welch, B. L. The generalization of ‘Student’s’ problem when several different population variances are involved. Biometrika 34, 28–35 (1947).MathSciNet 
CAS 
MATH 

Google Scholar 
66.Morin, P. et al. ArcticDEM; a publically available, high resolution elevation model of the Arctic. Geophys. Res. Abstr. 18, EGU2016-8396 (2016).
Google Scholar 
67.Porter, C. et al. ArcticDEM. https://doi.org/10.7910/DVN/OHHUKH (Harvard Dataverse, 2018).68.Dai, C., Durand, M., Howat, I. M., Altenau, E. H. & Pavelsky, T. M. Estimating river surface elevation from arcticDEM. Geophys. Res. Lett. 45, 3107–3114 (2018).ADS 

Google Scholar 
69.Hansen, M. C. et al. Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact. 7, 1–15 (2003).
Google Scholar 
70.Pettinari, M. L. & Chuvieco, E. Generation of a global fuel data set using the fuel characteristic classification system. Biogeosciences 13, 2061–2076 (2016).ADS 

Google Scholar 
71.Ottmar, R. D., Sandberg, D. V., Riccardi, C. L. & Prichard, S. J. An overview of the fuel characteristic classification system — quantifying, classifying, and creating fuelbeds for resource planning. Can. J. For. Res. 37, 2383–2393 (2007).
Google Scholar 
72.Riccardi, C. L. et al. The fuelbed: a key element of the fuel characteristic classification system. Can. J. For. Res. 37, 2394–2412 (2007).
Google Scholar 
73.Beaudoin, A., Bernier, P. Y., Villemaire, P., Guindon, L. & Guo, X. Species Composition, Forest Properties and Land Cover Types Across Canada’s Forests at 250m Resolution for 2001 and 2011. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 (Natural Resources Canada, Canadian Forest Service, Laurentian Forest Centre, 2017).74.Hugelius, G. et al. The northern circumpolar soil carbon database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3–13 (2013).ADS 

Google Scholar  More

1.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
2.Alroy, J. The shifting balance of diversity among major marine animal groups. Science 321, 1191–1194 (2010).ADS 
Article 
CAS 

Google Scholar 
3.Sakamoto, M., Benton, M. J. & Venditti, C. Dinosaurs in decline tens of millions of years before their final extinction. Proc. Nat. Acad. Sci. USA 113, 5036–5040 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Schluter, D. & Pennell, M. W. Speciation gradients and the distribution of biodiversity. Nature 546, 48–55 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Silvestro, D., Schitzler, J., Liow, L. H., Antonelli, A. & Salamin, N. Bayesian estimation of speciation and extinction from incomplete fossil occurrence data. Syst. Biol. 63, 349–367 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Maliet, O., Hartig, F. & Morlon, H. A model with many small shifts for estimating species-specific diversification rates. Nat. Ecol. Evolution 3, 1086–1092 (2019).Article 

Google Scholar 
8.Etienne, R. S. et al. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. R. Soc. B. Biol. Sci. 279, 1300–1309 (2011).Article 

Google Scholar 
9.Alfaro, M. E. et al. Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates. Proc. Nat. Acad. Sci. USA 106, 13410–13414 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Raup, D. M. Mathematical models of cladogenesis. Paleobiology 11, 42–52 (1985).Article 

Google Scholar 
11.Magallon, S. & Sanderson, M. J. Absolute diversification rates in angiosperm clades. Evolution 55, 1762–1780 (2001).CAS 
PubMed 
Article 

Google Scholar 
12.Yan, H.-F. et al. What explains high plant richness in East Asia? Time and diversification in the tribe Lysimachieae (Primulaceae). N. Phytol. 219, 436–448 (2018).Article 

Google Scholar 
13.Lu, H. P., Yeh, Y. C., Shiah, F. K., Gong, G. C. & Hsieh, C. H. Evolutionary constraints on species diversity in marine bacterioplankton communities. ISME J. 13, 1032–1041 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
14.Miller, E. C., Hayashi, K. T., Song, D. Y. & Wiens, J. J. Explaining the ocean’s richest biodiversity hotspot and global patterns of fish diversity. Proc. R. Soc. B. Biol. Sci. 285, 20181314 (2018).15.Tedesco, P. A., Paradis, E., Leveque, C. & Hugueny, B. Explaining global-scale diversification patterns in actinopterygian fishes. J. Biogeogr. 44, 773–783 (2017).Article 

Google Scholar 
16.Lenzner, B. et al. Role of diversification rates and evolutionary history as a driver of plant naturalization success. New Phytol. 229, 2998–3008 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Gohli, J. et al. Biological factors contributing to bark and ambrosia beetle species diversifcation. Evolution 71, 1258–1272 (2017).PubMed 
Article 

Google Scholar 
18.Wiens, J. J., Lapoint, R. T. & Whiteman, N. K. Herbivory increases diversification across insect clades. Nat. Comm. 6, 8370 (2015).ADS 
CAS 
Article 

Google Scholar 
19.Castro-Insua, A., Gomez-Rodriguez, C., Wiens, J. J. & Baselga, A. Climatic niche divergence drivers patterns of diversification and richness among mammal families. Sci. Rep. 8, 8781 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Dorchin, N., Harris, K. M. & Stireman, J. O. Phylogeny of the gall midges (Diptera, Cecidomyiidae, Cecidomyiinae): systematics, evolution of feeding modes and diversification rates. Mol. Phyl. Evol. 140, 106602 (2019).Article 

Google Scholar 
21.Lu, L. et al. Why is fruit colour so variable? Phylogenetic analyses reveal relationships between fruit-colour evolution, biogeography and diversification. Glob. Ecol. Biogeogr. 28, 891–903 (2019).Article 

Google Scholar 
22.Hernandez-Hernandez, T. & Wiens, J. J. Why are there so many flowering plants? A multi-scale analysis of plant diversification. Am. Nat. 195, 948–963 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Paradis, E. Analysis of diversification: combining phylogenetic and taxonomic data. Proc. R. Soc. B. Biol. Sci. 270, 2499–2505 (2003).Article 

Google Scholar 
24.Ricklefs, R. E. Global variation in the diversification rate of passerine birds. Ecology 87, 2468–2478 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Ricklefs, R. E. Estimating diversification rates from phylogenetic information. Trends Ecol. Evol. 22, 601–610 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Alroy, J. Geographical, environmental, and intrinsic biotic controls on Phanerozoic marine diversification. Paleontology 53, 1211–1235 (2010).Article 

Google Scholar 
27.Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Sepkoski, J. J. A kinetic-model of phanerozoic taxonomic diversity III. post-paleozoic families and mass extinctions. Paleobiology 10, 246–267 (1984).Article 

Google Scholar 
29.Budd, G. E. & Mann, R. P. History is written by the victors: the effect of the push of the past on the fossil record. Evolution 72, 2276–2291 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Nee, S., May, R. M. & Harvey, P. H. The reconstructed evolutionary process. Philos. Trans. R. Soc. Lond. B. 344, 305–311 (1994).ADS 
CAS 
Article 

Google Scholar 
31.Diaz, L. F. H., Harmon, L. J., Sugawara, M. T. C., Miller, E. T. & Pennell, M. W. Macroevolutionary diversification rates show time dependency. Proc. Nat. Acad. Sci. USA 116, 7403–7408 (2019).Article 
CAS 

Google Scholar 
32.Gingerich, P. D. Rates of evolution on the time scale of the evolutionary process. Genetica 112-113, 127–144 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Uyeda, J. C., Hansen, T. F., Arnold, S. J. & Pienaar, J. The million-year wait for macroevolutionary bursts. Proc. Nat. Acad. Sci. USA 108, 15908–15913 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Sepkoski, J. J. A kinetic model of Phanerozoic taxonomic diversity I. Analysis of marine orders. Paleobiology 4, 223–251 (1978).Article 

Google Scholar 
35.Raup, D. M., Gould, S. J., Schopf, T. J. M. & Simberloff, D. Stochastic models of phylogeny and evolution of diversity. J. Geol. 81, 525–542 (1973).ADS 
Article 

Google Scholar 
36.Foote, M. Pulsed origination and extinction in the marine realm. Paleobiology 31, 6–20 (2005).Article 

Google Scholar 
37.Stanley, S. M. Macroevolution: Pattern and Process. (Freeman, 1979).38.Strathmann, R. R. & Slatkin, M. The improbability of animal phyla with few species. Paleobiology 9, 97–106 (1983).Article 

Google Scholar 
39.Marshall, C. R. Five paleobiological laws needed to understand the evolution of the living biota. Nat. Ecol. Evolut. 1, 0165 (2017).Article 

Google Scholar 
40.Louca, S. & Pennell, M. W. Phylogenies of extant species are consistent with an infinite array of diversification histories. Nature 580, 502–505 (2019).ADS 
Article 
CAS 

Google Scholar 
41.Kozak, K. H. & Wiens, J. J. Testing the relationships between diversification, species richness, and trait evolution. Syst. Biol. 65, 975–988 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Wiens, J. J. & Scholl, J. P. Diversification rates, clade ages, and macroevolutionary methods. Proc. Nat. Acad. Sci. USA 116, 24400 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Wiens, J. J. Faster diversification on land than sea helps explain global biodiversity patterns among habitats and animal phyla. Ecol. Lett. 18, 1234–1241 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Nee, S. Birth-death models in macroevolution. Ann. Rev. Ecol. Evol. Syst. 37, 1–17 (2006).Article 

Google Scholar 
45.Scholl, J. P. & Wiens, J. J. Diversification rates and species richness across the Tree of Life. Proc. R. Soc. Lond. B 283, 20161334 (2016).
Google Scholar 
46.Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Foote, M., Cooper, R. A., Crampton, J. S. & Sadler, P. M. Diversity-dependent evolutionary rates in early Palaeozoic zooplankton. Proc. R. Soc. Lond. B 285, 20180122 (2018).
Google Scholar 
48.Sadler, P. M., Cooper, R. A. & Melchin, M. J. Sequencing the graptoloid clade: building a global diversity curve from local range charts, regional composites and global time-lines. Proc. Yorks. Geol. Soc. 58, 329–343 (2011).Article 

Google Scholar 
49.Grassle, J. F. The Ocean Biogeographic Information System (OBIS): an on-line, worldwide atlas for accessing, modeling and mapping marine biological data in a multidimensional geographic context. Oceanography 13, 5–7 (2000).Article 

Google Scholar 
50.Le Loeuff, J. Paleobiogeography and biodiversity of Late Maastrichtian dinosaurs: how many dinosaur species went extinction at the Cretaceous–Tertiary boundary? Bull. Soc. Geìol. Fr. 183, 547–559 (2012).Article 

Google Scholar 
51.Benson, R. B. J. Dinosaur macroevolution and macroecology. Ann. Rev. Ecol. Evol. Syst. 49, 379–408 (2018).Article 

Google Scholar 
52.Adrain, J. M. A synopsis of Ordovician trilobite distribution and diversity. Geol. Soc. Lond. Memoirs. 38, 297–336 (2013).Article 

Google Scholar 
53.Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M. The Geological Timescale 2012. (Elsevier, 2012).54.Benson, R. B. J. et al. Rates of dinosaur body mass evolution indicate 170 million years of sustained ecological innovation on the avian stem lineage. PLoS Biol. 12, e1001853 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.Alroy, J. et al. Phanerozoic trends in the global diversity of marine invertebrates. Science 321, 97–100 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Rabosky, D. L. Ecological limits on clade diversification in higher taxa. Am. Nat. 173, 662–674 (2009).PubMed 
Article 

Google Scholar 
57.Foote, M. Symmetric waxing and waning of invertebrate genera. Paleobiology 33, 517–529 (2007).Article 

Google Scholar 
58.Quental, T. B. & Marshall, C. R. How the Red Queen drives terrestrial mammals to extinction. Science 341, 290–292 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Host and siteB. ermanii Chamiss, a deciduous broad-leaved tree species, occurs naturally in open spaces within subalpine and boreal forests in Northeast China, Japan and the Russian Far East.40,41,42 B. ermanii is also a typical tree species of the timberline, because it can resist to severe frost, strong wind and low temperature by ecophysiological and ecomorphological flexiblity.43,44,45 In addition, a rich mycobiome of B. ermanii has been reported,16,46,47 which may facilitate the survival and spread of the host plant.On the northern slope of Changbai Mountain, Northeast China, B. ermanii grows over a broad elevation range from ca. 1700 to 2100 m a.s.l., and forms pure stands along >300 m vertical belt below the timberline.42,48 The establishment of Changbai Nature Reserve (CNR) in 1960 gave strict protection to the Erman’s birch (B. ermanii) forests.49 All our sampling was located within the core region of the CNR. The region has a typical continental temperate monsoon climate, with higher elevations experiencing lower temperature and greater precipitation.50 Soils of the Erman’s birch forests are Permi-Gelic Cambosols, whereas the soils beyond the upper and lower limits of B. ermanii zone are Permafrost cold Cambosols and Umbri-Gelic Cambosols, respectively. Climate change, particularly rising temperature, threatens the populations of B. ermanii and the whole Erman’s birch forest ecosystem in Changbai Mountain.50,51Field sample collectionWe sampled fine roots and neighboring soils for B. ermanii individuals at six elevation-related habitats of B. ermanii on 2–8 September, 2018 (Fig. 1a). These six elevation habitats include the upper limit (2069–2116 m), tree islands (1997–2042 m), treeline (1949–1992 m), pure stands (including two sub-sites isolated by over 2 km; 1900–1926 m), ecotone of dark coniferous forests and Erman’s birch forests (1742–1765 m) and lower limit (sparse individuals in coniferous forests; 1688–1706 m), respectively. In each habitat, 14 trees were randomly selected, and all trees were located more than 20 m apart from other sampled trees to ensure independence of each sample. Neighboring soils and fine roots were collected according to the protocols of Yang et al.28 and Lankau and Keymer,52 respectively. Briefly, with the trunk as center and the DBH as distance from the stem, we collected four soil cores (diameter = 3.5 cm, depth = 10 cm) after removal of litter and mixed them as a single composite soil sample (Fig. 1b). We excavated surface soils near the base of trees and traced roots from the base to terminal fine roots in three directions. The fine roots of three directions were combined as a composite fine root sample, and each raw sub-fine-root section was nearly 6 cm wide and 8 cm long (Fig. 1c, d). All the samples were brought back to the laboratory with ice bags within 8 h. Soil was sieved through a 2-mm mesh and divided into two subsamples: one was stored at 4 °C to determine the soil properties, whereas the other was stored at −40 °C for subsequent DNA extraction. Fine roots were rinsed with sterile water and cut into 1.5-cm segments: one subsample (ca. 80%) was stored at 4 °C to determine root biochemical traits, and the other subsample (ca. 20%) was stored at −40 °C for subsequent DNA extraction. In the field, tree height, canopy diameter, DBH, elevation, slope, latitude, and longitude of each sampled tree were recorded. In total, 84 fine roots and 84 neighboring soils were collected.Fig. 1: Sampling map and procedures in this study.a Sampling map in the core region of CNR: the icons with different colors represent the tree individuals of B. ermanii. Contours were fitted in a map of Google Earth. b The sampling procedure of neighboring soils: each red point represent one soil core with depth 0–10 cm and diameter 3.5 cm. c The sampling procedure of fine roots: each red square (nearly 6 × 8 cm) represent a sub-fine-root system (namely, d).Full size imageMeasurement of soil properties and root traitsWe measured 28 soil properties, including soil pH, moisture, conductivity, dissolved organic carbon, dissolved organic nitrogen (DON), ammonium nitrogen, nitrate nitrogen, total carbon, total nitrogen, total phosphate, total potassium, total calcium, total magnesium, total manganese, total iron, total aluminum, available phosphate, available potassium, available calcium, available magnesium, available manganese, available iron, available aluminum, C/N ratio, C/P ratio and the proportions of clay, silt and sand. The measurement methods of soil pH, moisture, ammonium nitrogen, nitrate nitrogen, total carbon, total nitrogen and total content of other elements followed our recent study.28 In addition, soil conductivity was determined with a soil to water ratio of 1:5 by conductivity meter (Mettler Toledo FE30, Shanghai, China). Mehlich 353 and three-acid-system (nitric acid, perchloric acid, and hydrofluoric acid) were used to extract the available and total content of elements, respectively. Total and available content of phosphate, potassium, calcium, magnesium, manganese, iron, and aluminum were measured using an ICP Optima 8000 (Perkin-Elmer, Waltham, MA, USA). The proportions of clay, silt, and sand were measured by Laser Particle Sizer LS13320 (Beckman, Brea, CA, USA).Eighteen root traits, including root total carbon (RTC), root total nitrogen (RTN), root phosphate, root potassium, root calcium, root magnesium, root manganese, root iron, root aluminum, root C/N ratio, root N/P ratio, lignin, cellulose, hemicellulose, soluble sugar, soluble protein, free amino acid (FAA) and free fatty acids (FFA), were also measured. Specifically, RTC and RTN were determined with a carbon–hydrogen–nitrogen (CHN) elemental analyzer (2400 II CHN elemental analyzer; PerkinElmer, Boston, MA, USA). Root phosphate, root potassium, root calcium, root magnesium, root manganese, root iron, and root aluminum were measured in ICP Optima 8000 (Perkin-Elmer, Waltham, MA, USA). Soluble protein was measured by a dye-binding assay.54 FAA was analyzed by the amino acid analyzer L-8800 (Hitachi, Tokyo, Japan) with leucine as the standard sample. FFA was determined by NEFA FS kits (Diasys, Holzheim, Garman) and the automatic biochemical analyzer AU680 (Olympus, Tokyo, Japan). The measurement methods of lignin, cellulose, hemicellulose, and soluble sugar followed that of our previous study.16Calculation of distance to forest edgeThe location of each tree individual was determined by latitude and longitude. A high-resolution map (treecover2000) on global forest cover at a spatial resolution of 30 m was used as a base map.55 In the map, the areas where forest cover was more than 30% were defined as the “mainland” in the IBT framework and shown as the green grids in ArcGIS (Fig. S1). This standard referred to the proposal of Convention on Climate Change Kyoto.56 Then, we calculated the minimum distance of each tree to the neighboring forest edge (i.e., green grids) by using the function Near of the Proximity tool box in ArcGIS 10.0 (ESRI, Redlands, CA, USA).Sequencing and bioinformaticsSoil total DNA was extracted from 0.5 g of soil by using FastDNA® Spin kit for Soil (MP Biomedicals, Solon, Ohio, USA). Total DNA of fine roots was extracted from 0.3 g of plant tissue by using Qiagen Plant DNeasy kits (Qiagen, Hilden, Germany). PCR procedures, including primers (ITS1-F: CTTGGTCATTTAGAGGAAGTAA, ITS2: GCTGCGTTCTTCATCGATGC) and conditions were described in our previous studies.16,28 The PCR products of all samples were normalized to equimolar amounts and sequenced on the Illumina MiSeq PE300 platform of the Majorbio Company, Shanghai, China.We first merged the paired-end reads using FLASH.57 QIIME 1.9.058 and Cutadapt 1.9.159 were applied for quality filtering, trimming, and chimera removal. Altogether 8,238,146 sequences passed quality filtering (parameters: minlength = 240; maxambigs = 0; phred quality threshold = 30). ITSx 1.0.11 was used to remove the flanking small ribosomal subunit (SSU) and 5.8 S genes,60 leaving the ITS1 region for further analyses. The putative chimeric sequences were removed using a combination of de novo and reference-based chimera checking, with the parameter –non_chimeras_rentention = union in QIMME.61 The remaining sequences were then clustered into operational taxonomic units (OTUs) at 97% similarity threshold by using USEARCH.62 Singletons were also removed during the USERCH clustering process. Fungal taxonomy was assigned to each OTU by using the Ribosomal Database Project Classifier with minimum confidence of 0.8.63 The UNITE v.8.0 (http://unite.ut.ee) release for QIIME served as a reference database for fungal taxonomy.64 The OTU table was then curated with LULU, a post-clustering OTU table curation method, to improve diversity estimates.65After removing non-fungal sequences, the final data set included 7,849,126 fungal sequences covering 6663 OTUs in 168 samples (minimum 4662; maximum 70,779; mean 46,721 sequences per sample). The rarefaction curves of the average observed OTU number are shown in Fig. S2. FUNGuild was used to assign each OTU to a putative functional guild, and the assignments with confidence ranking “possible” were assigned as “unknown” as recommended by the authors.66 We further modified the assignment of EcM fungi (the subject in the present study) and their lineages according to.31 For some OTUs that were simultaneously assigned to endophytic, saprotrophic, or pathogenic fungi, we considered these as endophytes in roots and saprotrophs in soil samples.StatisticsAll statistical analyses were conducted in R 3.5.267 and AMOS 21.0 (AMOS IBM, New York, USA). In order to analyze the alpha diversities of soil fungi and the three most dominant guilds (viz., EcM, endophytic and saprotrophic fungi) at the same sequencing depth, the data set was subsampled to 4662 reads with 30 iterations. The mean number of observed OTUs was used to represent the diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi, as previously implemented in.15,68 Numbers of EcM fungal lineages and saprotrophic genera, families, orders, and classes of each sample were also calculated based on the same subsampling.First, linear and quadratic regression models were used to determine the effect of elevation on diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi. The model with lowest Akaike’s information criterion (AIC) value was selected. In order to account for spatial effects, linear mixed-effects models (LMMs) were fitted using the lme4 package69 to analyze the variation in diversities of total fungi, EcM fungi, endophytic fungi, and saprotrophic fungi along the elevation gradient with latitude and longitude as random factors. Corrected Akaike Information Criterion (AICc) for small data sets was used to identify the best mixed-effects model from linear and quadratic polynomial models. The significance of each LMM was tested by the function Anova in the car package.70 Marginal (m) and conditional (c) R2 were calculated by the function r.squaredGLMM in the MuMIn package.71 Marginal R2 (R2m) represents the variance explained by fixed effects, whereas conditional R2 (R2c) represents the variance explained by both fixed and random effects.Second, to test the application of IBT on EcM fungal diversity, DBH and RTC of B. ermanii were chosen as proxies of island area and energy, respectively, whereas DFE was chosen as the proxy of island isolation (i.e., island distance to mainland). Linear regression models were used to assess the species-area, species-energy, and species-isolation relationships. In order to account for spatial effects, LMMs were also used for these independent relationships with latitude and longitude as random factors as described above. Classical power-law function models were used to identify the species-area relationship for EcM fungal diversities in roots and soils using z-values in the formula S = CAz 72 to compare EcM fungi with macroorganisms in previous studies. Furthermore, ordinary least squares (OLS) multiple regression models were performed to identify the relative contributions of DBH, RTC, and DFE on pattern of EcM fungal diversity when considering other predictive variables. Here, five spatial vectors (PCNM1-5) with significant positive spatial autocorrelation (Fig. S3) were obtained by the principal coordinates of neighbor matrices (PCNM) method,73 and added into OLS multiple regression models to consider the possible geographic effect. EcM fungal diversities in roots and soils, 28 soil properties, 18 root traits, elevation, slopes, DBH, tree height, canopy diameter, and DFE were standardized (average = 0 and SD = 1) before the OLS multiple regression analysis. AIC was used to identify the best OLS multiple regression model, as implemented in the MASS package.74 Variance inflation factor (VIF) was calculated for each model by the function vif in the car package. We used the criterion VIF  More

1.Oke, T. R. The heat island of the urban boundary layer: characteristics, causes and effects. In Wind climate in cities (eds Cermak, J. E. et al.) 81–107 (Springer, 1995). https://doi.org/10.1007/978-94-017-3686-2_5.
Google Scholar 
2.Wang, C., Wang, Z. H. & Yang, J. Cooling effect of urban trees on the built environment of contiguous United States. Earth’s Future 6, 1066–1081. https://doi.org/10.1029/2018EF000891 (2018).ADS 
Article 

Google Scholar 
3.Pauleit, S. Urban street tree plantings: identifying the key requirements. Proc. Inst. Civ. Eng. Munic. Eng. 156, 43–50. https://doi.org/10.1680/muen.2003.156.1.43 (2003).Article 

Google Scholar 
4.Frantzeskaki, N. et al. Nature-based solutions for urban climate change adaptation: linking science, policy, and practice communities for evidence-based decision-making. BioScience 69, 455–466. https://doi.org/10.1093/biosci/biz042 (2019).Article 

Google Scholar 
5.Armson, D., Stringer, P. & Ennos, A. R. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban For. Urban Green. 11, 245–255. https://doi.org/10.1016/j.ufug.2012.05.002 (2012).Article 

Google Scholar 
6.Oke, T. R. The micrometeorology of the urban forest. Philos. Trans. R. Soc. Lond. B 324, 335–349. https://doi.org/10.1098/rstb.1989.0051 (1989).ADS 
Article 

Google Scholar 
7.Chow, W. T. & Brazel, A. J. Assessing xeriscaping as a sustainable heat island mitigation approach for a desert city. Build. Environ. 47, 170–181. https://doi.org/10.1016/j.buildenv.2011.07.027 (2012).Article 

Google Scholar 
8.Wang, K., Ma, Q., Wang, X. & Wild, M. Urban impacts on mean and trend of surface incident solar radiation. Geophys. Res. Lett. 41, 4664–4668. https://doi.org/10.1002/2014GL060201 (2014).ADS 
Article 

Google Scholar 
9.Bassuk, N. & Whitlow, T. Environmental stress in street trees. Arboricult. J. 12, 195–201. https://doi.org/10.1080/03071375.1988.9746788 (1988).Article 

Google Scholar 
10.Nowak, D. J., Kuroda, M. & Crane, D. E. Tree mortality rates and tree population projections in Baltimore, Maryland, USA. Urban For. Urban Green. 2, 139–147. https://doi.org/10.1078/1618-8667-00030 (2004).Article 

Google Scholar 
11.Monteith, J. . & Unsworth, M. . Principles of Environmental Physics: Plants, Animals, and the Atmosphere 4th edn. (Elsevier Ltd., 2013).
Google Scholar 
12.Simon, H. et al. Modeling transpiration and leaf temperature of urban trees—a case study evaluating the microclimate model ENVI-met against measurement data. Landsc. Urban Plan. 174, 33–40. https://doi.org/10.1016/j.landurbplan.2018.03.003 (2018).Article 

Google Scholar 
13.González-Dugo, M. P., Moran, M. S., Mateos, L. & Bryant, R. Canopy temperature variability as an indicator of crop water stress severity. Irrig. Sci. 24, 1–8. https://doi.org/10.1007/s00271-005-0023-7 (2006).Article 

Google Scholar 
14.Han, M., Zhang, H., DeJonge, K. C., Comas, L. H. & Trout, T. J. Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricult. Water Manag. 177, 400–409. https://doi.org/10.1016/j.agwat.2016.08.031 (2016).Article 

Google Scholar 
15.Hou, M., Tian, F., Zhang, T. & Huang, M. Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery. Agricult. Water Manag. 222, 182–192. https://doi.org/10.1016/j.agwat.2019.06.005 (2019).Article 

Google Scholar 
16.Heilman, J. L., Brittin, C. L. & Zajicek, J. M. Water use by shrubs as affected by energy exchange with building walls. Agricult. For. Meteorol. 48, 345–357. https://doi.org/10.1016/0168-1923(89)90078-6 (1989).ADS 
Article 

Google Scholar 
17.Perini, K. & Magliocco, A. Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban For. Urban Green. 13, 495–506. https://doi.org/10.1016/j.ufug.2014.03.003 (2014).Article 

Google Scholar 
18.Soer, G. J. Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface temperatures. Remote Sens. Environ. 9, 27–45. https://doi.org/10.1016/0034-4257(80)90045-0 (1980).ADS 
Article 

Google Scholar 
19.Spronken-Smith, R. A. & Oke, T. R. The thermal regime of urban parks in two cities with different summer climates. Int. J. Remote Sens. 19, 2085–2104. https://doi.org/10.1080/014311698214884 (1998).ADS 
Article 

Google Scholar 
20.Rahman, M. A. et al. Traits of trees for cooling urban heat islands: a meta-analysis. Build. Environ. 170, 106606. https://doi.org/10.1016/j.buildenv.2019.106606 (2020).Article 

Google Scholar 
21.Leuzinger, S., Vogt, R. & Körner, C. Tree surface temperature in an urban environment. Agricult. For. Meteorol. 150, 56–62. https://doi.org/10.1016/j.agrformet.2009.08.006 (2010).ADS 
Article 

Google Scholar 
22.Meier, F. & Scherer, D. Spatial and temporal variability of urban tree canopy temperature during summer 2010 in Berlin, Germany. Theor. Appl. Climatol. 110, 373–384. https://doi.org/10.1007/s00704-012-0631-0 (2012).ADS 
Article 

Google Scholar 
23.Ballester, C., Jiménez-Bello, M. A., Castel, J. R. & Intrigliolo, D. S. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agric. For. Meteorol. 168, 120–129. https://doi.org/10.1016/j.agrformet.2012.08.005 (2013).ADS 
Article 

Google Scholar 
24.Jones, H. G. Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Adv. Botan. Res. 41, 107–163. https://doi.org/10.1016/s0065-2296(04)41003-9 (2004).ADS 
Article 

Google Scholar 
25.Givoni, B. Impact of planted areas on urban environmental quality: a review. Atmos. Environ. Part B Urban Atmos. 25, 289–299. https://doi.org/10.1016/0957-1272(91)90001-U (1991).ADS 
Article 

Google Scholar 
26.Kalnay, E. & Cai, M. Impact of urbanization and land-use change on climate. Nature 423, 528–531. https://doi.org/10.1038/nature01675 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
27.A. Coutts, A. et al. Impacts of water sensitive urban design solutions on human thermal comfort. p. 20 (online link: https://watersensitivecities.org.au/wp-content/uploads/2016/07/TMR_B3-1_WSUD_thermal_comfort_no2.pdf) (2014).28.Coutts1, A. et al. The Impacts of WSUD Solutions on Human Thermal Comfort Green Cities and Micro-climate-B3.1-2-2014 Contributing Authors. Tech. Rep. (1968).29.Gunawardena, K. R., Wells, M. J. & Kershaw, T. Utilising green and bluespace to mitigate urban heat island intensity. Sci. Total Environ. 584–585, 1040–1055. https://doi.org/10.1016/j.scitotenv.2017.01.158 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
30.Völker, S., Baumeister, H., Classen, T., Hornberg, C. & Kistemann, T. Evidence for the temperature-mitigating capacity of urban blue space—a health geographic perspective. Erdkunde 67, 355–371. https://doi.org/10.3112/erdkunde.2013.04.05 (2013).Article 

Google Scholar 
31.Hu, L. & Li, Q. Greenspace, bluespace, and their interactive influence on urban thermal environments. Environ. Res. Lett. 15, 034041. https://doi.org/10.1088/1748-9326/ab6c30 (2020).ADS 
Article 

Google Scholar 
32.Theeuwes, N. E., Solcerová, A. & Steeneveld, G. J. Modeling the influence of open water surfaces on the summertime temperature and thermal comfort in the city. J. Geophys. Res. Atmos. 118, 8881–8896. https://doi.org/10.1002/jgrd.50704 (2013).ADS 
Article 

Google Scholar 
33.Ziter, C. D., Pedersen, E. J., Kucharik, C. J. & Turner, M. G. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc. Natl. Acad. Sci. U. S. A. 116, 7575–7580. https://doi.org/10.1073/pnas.1817561116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Johnson, S., Ross, Z., Kheirbek, I. & Ito, K. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Clim. 31, 100583. https://doi.org/10.1016/j.uclim.2020.100583 (2020).Article 

Google Scholar 
35.Wetherley, E. B., McFadden, J. P. & Roberts, D. A. Megacity-scale analysis of urban vegetation temperatures. Remote Sens. Environ. 213, 18–33. https://doi.org/10.1016/j.rse.2018.04.051 (2018).ADS 
Article 

Google Scholar 
36.Zhou, W., Wang, J. & Cadenasso, M. L. Effects of the spatial configuration of trees on urban heat mitigation: a comparative study. Remote Sens. Environ. 195, 1–12. https://doi.org/10.1016/j.rse.2017.03.043 (2017).ADS 
Article 

Google Scholar 
37.Weng, Q., Lu, D. & Schubring, J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 89, 467–483. https://doi.org/10.1016/j.rse.2003.11.005 (2004).ADS 
Article 

Google Scholar 
38.Hulley, G., Hook, S., Fisher, J. & Lee, C. ECOSTRESS, A NASA Earth-Ventures Instrument for studying links between the water cycle and plant health over the diurnal cycle. In International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, 5494–5496 (Institute of Electrical and Electronics Engineers Inc., 2017). https://doi.org/10.1109/IGARSS.2017.8128248.39.Wood, S. N. Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics 62, 1025–1036. https://doi.org/10.1111/j.1541-0420.2006.00574.x (2006).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
40.Duan, S.-B. et al. Estimation of diurnal cycle of land surface temperature at high temporal and spatial resolution from clear-sky MODIS data. Remote Sens. 6, 3247–3262. https://doi.org/10.3390/rs6043247 (2014).ADS 
Article 

Google Scholar 
41.Hu, L., Sun, Y., Collins, G. & Fu, P. Improved estimates of monthly land surface temperature from MODIS using a diurnal temperature cycle (DTC) model. ISPRS J. Photogramm. Remote Sens. 168, 131–140. https://doi.org/10.1016/j.isprsjprs.2020.08.007 (2020).ADS 
Article 

Google Scholar 
42.New York City Department of Information Technology and Telecommunications (NYC DoITT). Land Cover Raster Data 6-inch Resolution (2017).43.New York City Department of Information Technology and Telecommunications (NYC DoITT). New York City Building Footprint (2017).44.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).Book 

Google Scholar 
45.Cârlan, I., Mihai, B. A., Nistor, C. & Große-Stoltenberg, A. Identifying urban vegetation stress factors based on open access remote sensing imagery and field observations. Ecol. Inform. 55, 101032. https://doi.org/10.1016/j.ecoinf.2019.101032 (2020).Article 

Google Scholar 
46.Cregg, B. & Dix, M. E. Tree moisture stress and insect damage in urban areas in relation to heat island effects. J. Arboricult. 27, 8–17 (2001).
Google Scholar 
47.Novem, D. & Falxa, N. United States Department of Agriculture The Urban Forest of New York City. Tech. Rep. https://doi.org/10.2737/NRS-RB-117 (2018).48.Shaker, R. R., Altman, Y., Deng, C., Vaz, E. & Forsythe, K. W. Investigating urban heat island through spatial analysis of New York City streetscapes. J. Clean. Prod. 233, 972–992. https://doi.org/10.1016/j.jclepro.2019.05.389 (2019).Article 

Google Scholar 
49.Meir, T., Orton, P. M., Pullen, J., Holt, T. & Thompson, W. T. Forecasting the New York City urban heat island and sea breeze during extreme heat events. Weather Forecast. 28, 1460–1477. https://doi.org/10.1175/WAF-D-13-00012.1 (2013).ADS 
Article 

Google Scholar 
50.Ramamurthy, P., González, J., Ortiz, L., Arend, M. & Moshary, F. Impact of heatwave on a megacity: an observational analysis of New York City during July 2016. Environ. Res. Lett. 12, 054011. https://doi.org/10.1088/1748-9326/aa6e59 (2017).ADS 
Article 

Google Scholar 
51.Gedzelman, S. D. et al. Mesoscale aspects of the Urban Heat Island around New York City. Theor. Appl. Climatol. 75, 29–42. https://doi.org/10.1007/s00704-002-0724-2 (2003).ADS 
Article 

Google Scholar 
52.Cavender, N. & Donnelly, G. Intersecting urban forestry and botanical gardens to address big challenges for healthier trees, people, and cities. Plants People Planet 1, 315–322. https://doi.org/10.1002/ppp3.38 (2019).Article 

Google Scholar 
53.Marias, D. E., Meinzer, F. C. & Still, C. Impacts of leaf age and heat stress duration on photosynthetic gas exchange and foliar nonstructural carbohydrates in Coffea arabica. Ecol. Evol. 7, 1297–1310. https://doi.org/10.1002/ece3.2681 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Brune, M. Urban trees under climate change. Potential impacts of dry spells and heat waves in three Germany regions in the 1950s. Report 20, Climate Service Center Germany, Hamburg 123 (2016).55.Jim, C. Y. Green-space preservation and allocation for sustainable greening of compact cities. Cities 21, 311–320. https://doi.org/10.1016/j.cities.2004.04.004 (2004).Article 

Google Scholar 
56.Santamouris, M. Cooling the cities—a review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments. Sol. Energy 103, 682–703. https://doi.org/10.1016/j.solener.2012.07.003 (2014).ADS 
Article 

Google Scholar 
57.Drescher, M. Urban heating and canopy cover need to be considered as matters of environmental justice. Proc. Natl. Acad. Sci. U. S. A. 116, 26153–26154. https://doi.org/10.1073/pnas.1917213116 (2019).ADS 
CAS 
Article 
PubMed Central 

Google Scholar 
58.Roloff, A. . Bäume in der Stadt (Ulmer, E, 2013).
Google Scholar 
59.Bruse, M. ENVI-met 3.0: Updated Model Overview. Tech. Rep. (2004).60.US Census Bureau. State and County Quick Facts https://www.census.gov/quickfacts/fact/table/US/PST045219 (2019).61.Shorris, A. Cool Neighborhoods NYC: A Comprehensive Approach to Keep Communities Safe in Extreme Heat. Tech. Rep.62.Hulley, G. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Mission Level 2 Product Specification Document. Tech. Rep. (2018).63.Stark, P. & Parker, R. Bounded-Variable Least-squares: An Algorithm and Applications. Tech. Rep. 394, (1995). More

1.Collingsworth, P. D. et al. Climate change as a long-term stressor for the fisheries of the Laurentian Great Lakes of North America. Rev. Fish Biol. Fish. 27, 363–391 (2017).Article 

Google Scholar 
2.Hilborn, R. & Walters, C. J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and the Uncertainty (Routledge, Chapman and Hall Inc., 1992).Book 

Google Scholar 
3.Bean, C. W., Winfield, I. J. & Fletcher, J. M. Stock assessment of the Arctic charr (Salvelinus alpinus) population in Loch Ness, UK in stock assessment. In Inland Fisheries (ed. Cowx, I. G.) 206–223 (Blackwell Scientific Publications, 1996).
Google Scholar 
4.Emmrich, M. et al. Strong correspondence between gillnet catch per effort and hydroacoustically derived fish biomass in stratified lakes. Freshw. Biol. 57, 2436–2448 (2012).Article 

Google Scholar 
5.CEN (European Committee for Standardization). Water quality – sampling of fish with multi-mesh gillnets. European Committee for Standardization, European Standard EN 14757:2015 (Brussels, 2015).6.Murphy, B. & Willis, D. W. Fisheries Techniques 2nd edn. (American Fisheries Society, 1996).
Google Scholar 
7.Kinzelbach, R. Neozoans in European waters—Exemplifying the worldwide process of invasion and species mixing. Cell. Mol. Life Sci. 51, 526–538 (1995).CAS 
Article 

Google Scholar 
8.Cerwenka, A. F. Phenotypic and genetic differentiation of invasive gobies in the upper Danube River. Dissertation (Technische
Universität München, 2014).9.Byström, P. et al. Declining coastal piscivore populations in the Baltic Sea: Where and when do sticklebacks matter?. Ambio 44, 462–471 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Ustups, D. et al. Diet overlap between juvenile flatifish and the invasive round goby in the central Baltic Sea. J. Sea Res. 107, 121–129 (2016).ADS 
Article 

Google Scholar 
11.Jackson, D. A. & Harvey, H. H. Qualitative and quantitative sampling of lake fish communities. Can. J. Fish. Aquat. Sci. 54, 2807–2813 (1997).Article 

Google Scholar 
12.Argyle, R. L. Acoustics as a tool for the assessment of Great Lakes forage fishes. Fish. Res. 14, 179–196 (1992).Article 

Google Scholar 
13.Jurvelius, J., Leinikki, J., Mamylov, V. & Pushkin, S. Stock assessment of pelagic three-spined stickleback (Gasterosteus aculeatus): A simultaneous up- and down-looking echo-sounding study. Fish. Res. 27, 227–241 (1996).Article 

Google Scholar 
14.Horne, J. K. Acoustic approaches to remote species identification: A review. Fish. Oceanogr. 9, 356–371 (2000).Article 

Google Scholar 
15.Godlewska, M., Świerzowski, A. & Winfield, I. J. Hydroacoustics as a tool for studies of fish and their habitat. Ecohydrol. Hydrobiol. 4, 417–427 (2004).
Google Scholar 
16.Muška, M. et al. Real-time distribution of pelagic fish: combining hydroacoustics, GIS and spatial modelling at a fine spatial scale. Sci. Rep. 8, 5381. https://doi.org/10.1038/s41598-018-23762-z (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Berger, L. et al. Acoustic target classification. ICES Coop. Res. Rep. https://doi.org/10.17895/ices.pub.4567 (2018).ADS 
Article 

Google Scholar 
18.Emmrich, M., Helland, I. P., Busch, S., Schiller, S. & Mehner, T. Hydroacoustic estimates of fish densities in comparison with stratified pelagic trawl sampling in two deep, coregonid-dominated lakes. Fish. Res. 105, 178–186 (2010).Article 

Google Scholar 
19.DuFour, M. R., Qian, S. S., Mayer, C. M. & Vandergoot, C. S. Embracing uncertainty to reduce bias in hydroacoustic species apportionment. Fish. Res. https://doi.org/10.1016/j.fishres.2020.105750 (2021).Article 

Google Scholar 
20.Cabreira, A. G., Tripode, M. & Madirolas, A. Artificial neural networks for fish-species identification. ICES J. Mar. Sci. 66, 1119–1129 (2009).Article 

Google Scholar 
21.Robotham, H., Bosch, P., Gutierrez-Estrada, J., Castillo, J. & Pulido-Calvo, I. Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks. Fish. Res. 102, 115–122 (2010).Article 

Google Scholar 
22.Taylor, J. C. & Maxwell, D. L. Hydroacoustics: lakes and reservoirs. in Salmonid Field Protocols Handbook: Techniques for Assessing Status and Trends in Salmon and Trout Populations (ed. Johnson, D. H. et al.) 153–172 (American Fisheries Society in association with State of the Salmon, 2007).23.Parker-Stetter, S. L., Rudstam, L. G., Sullivan, L. G. & Warner, D. M. Standard operating procedures for fisheries acoustic surveys in the Great Lakes. Great Lakes Fisheries Commission Special Publication 09-01 (2009).24.Guillard, J., Perga, M. E., Colon, M. & Angeli, N. Hydroacoustic assessment of young-of-the-year perch, Perca fluviatilis, population dynamics in an oligotrophic lake (Lake Annecy, France). Fish. Manag. Ecol. 13, 319–327 (2006).Article 

Google Scholar 
25.Winfield, I. J., Fletcher, J. M., James, J. B. & Bean, J. B. Assessment of fish populations in still waters using hydroacoustics and survey gill netting: Experiences with Arctic charr (Salvelinus alpinus) in the UK. Fish. Res. 96, 30–38 (2009).Article 

Google Scholar 
26.Yule, D. L., Lori, M. E., Cachera, S., Colon, M. & Guillard, J. Comparing two fish sampling standards over time: Largely congruent results but with caveats. Freshw. Biol. 58, 2074–2088 (2013).Article 

Google Scholar 
27.DuFour, M. R., Song, S. Q., Mayer, C. M. & Vandergoot, C. S. Evaluating catchability in a large-scale gillnet survey using hydroacoustics: Making the case for coupled surveys. Fish. Res. 211, 309–318 (2019).Article 

Google Scholar 
28.Haralabous, J. & Georgakarakos, S. Artificial neural networks as a tool for species identification of fish schools. ICES J. Mar. Sci. 53, 173–180 (1996).Article 

Google Scholar 
29.Zakharia, M. E., Magand, F., Hetroit, F. & Diner, N. Wideband sounder for fish species identification at sea. ICES J. Mar. Sci. 53, 203–208 (1996).Article 

Google Scholar 
30.Fernandes, P. G. Classification trees for species identification of fish-school echo traces. ICES J. Mar. Sci. 66, 1073–1080 (2009).Article 

Google Scholar 
31.Eckmann, R. A hydroacoustic study of the pelagic spawning behavior of whitefish (Coregonus lavaretus) in lake constance. Can. J. Fish. Aquat. Sci. 48, 995–1002 (1991).Article 

Google Scholar 
32.Eckmann, R. & Engesser, B. Reconstructing the build-up of a pelagic stickleback (Gasterosteus aculeatus) population using hydroacoustics. Fish. Res. 210, 189–192 (2018).Article 

Google Scholar 
33.Peltonen, H., Ruuhijärvi, J., Malinen, T. & Horppila, J. Estimation of roach (Rutilus rutilus (L.)) and smelt (Osmerus eperlanus (L.)) stocks with virtual population analysis. Hydroacoustics and Gillnet CPUE. Fish. Res. 44, 25–36 (1999).Article 

Google Scholar 
34.MacLennan, D. N., Fernandes, P. G. & Dalen, J. A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 59, 365–369 (2002).Article 

Google Scholar 
35.Korneliussen, R. J. The acoustic identification of Atlantic mackerel. ICES J. Mar. Sci. 67, 1749–1758 (2010).Article 

Google Scholar 
36.Langkau, M. C., Balk, H., Schmidt, M. B. & Borcherding, J. Can acoustic shadows identify fish species? A novel application of imaging sonar data. Fish. Manag. Ecol. 19, 313–322 (2012).Article 

Google Scholar 
37.Boswell, K. M., Wilson, M. P. & Cowan, J. H. Jr. A semiautomated approach to estimating fish size, abundance, and behavior from dual-frequency identification sonar (DIDSON) data. N. Am. J. Fish. Manag. 28, 799–807 (2008).Article 

Google Scholar 
38.Crossman, J. A., Martel, G., Johnson, P. N. & Bray, K. The use of Dual-Frequency Identification SONar (DIDSON) to document white sturgeon activity in the Columbia River, Canada. J. Appl. Ichthyol. 27, 53–57 (2011).Article 

Google Scholar 
39.Rakowitz, G. et al. Use of high-frequency imaging sonar (DIDSON) to observe fish behavior towards a surface trawl. Fish. Res. 123–124, 37–48 (2012).Article 

Google Scholar 
40.Skowronski, M. D. & Harris, J. G. Automatic detection of microchiroptera echolocation calls from field recordings using machine learning algorithms. J. Acoust. Soc. Am. 119, 1817–1833 (2005).Article 

Google Scholar 
41.Witten, I. H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques 4th edn. (Morgan Kaufmann USA, 2017).MATH 

Google Scholar 
42.Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science 349(6245), 255–260 (2015).ADS 
MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
43.Jech, J. M., Lawson, G. L. & Lavery, A. C. Wideband (15–260 kHz) acoustic volume backscattering spectra of Northern krill (Meganyctiphanes norvegica) and butterfish (Peprilus triacanthus). ICES J. Mar. Sc. 74, 2249–2261 (2017).Article 

Google Scholar 
44.Lavery, A. C., Bassett, C., Lawson, G. L. & Jech, J. M. Exploiting signal processing approaches for broadband echosounders. ICES J. of Mar. Sci. 74, 2262–2275 (2017).Article 

Google Scholar 
45.Bassett, C., De Robertis, A. & Wilson, C. D. Broadband echosounder measurements of the frequency response of fishes and euphausiids in the Gulf of Alaska. ICES J. Mar. Sci. 75, 1131–1142 (2018).Article 

Google Scholar 
46.Demer, D. A. et al. 2016 USA–Norway EK80 Workshop Report: Evaluation of a wideband echosounder for fisheries and marine ecosystem science. ICES Coop. Res. Rep. https://doi.org/10.17895/ices.pub.2318 (2017).Article 

Google Scholar 
47.Tuzlukov, V. Signal Processing in Radar Systems 1st edn. (CRC Press Taylor & Francis Group USA, 2013).MATH 

Google Scholar 
48.Baer, J., Eckmann, R., Rösch, R., Arlinghaus, R. & Brinker, A. Managing upper lake constance fishery in a multi-sector policy landscape: Beneficiary and victim of a century of anthropogenic trophic change. In Inter-Sectoral Governance of Inland Fisheries (eds Song, A. M. et al.) 32–47 (TBTI Publication Series, 2017).
Google Scholar 
49.Roch, S., von Ammon, L., Geist, J. & Brinker, A. Foraging habits of invasive three-spined sticklebacks (Gasterosteus aculeatus)—Impacts on fisheries yield in Upper Lake Constance. Fish. Res. 204, 172–180 (2018).Article 

Google Scholar 
50.Rösch, R., Baer, J. & Brinker, A. Impact of the invasive three-spined stickleback (Gasterosteus aculeatus) on relative abundance and growth of native pelagic whitefish (Coregonus wartmanni) in Upper Lake Constance. Hydrobiol. 824, 255–270 (2018).Article 
CAS 

Google Scholar 
51.Balk, H., & Lindem, T. Sonar4 and Sonar5-Pro Post processing systems Operator manual, version 6.0.3. (2018).52.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
53.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News. 2, 18–22 (2002).
Google Scholar 
54.Degan, D. J. & Wilson, W. Comparison of four hydroacoustic frequencies for sampling pelagic fish populations in Lake Texoma. N. Am. J. Fish. Manag. 15, 924–932 (1995).Article 

Google Scholar 
55.Godlewska, M. et al. Hydroacoustic measurements at two frequencies: 70 and 120 kHz—Consequences for fish stock estimation. Fish. Res. 96, 11–16 (2009).Article 

Google Scholar 
56.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
MATH 

Google Scholar 
57.Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 
Article 

Google Scholar 
58.Oppel, S. et al. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biol. Cons. 156, 94–104 (2012).Article 

Google Scholar 
59.Kuhn, M. Caret: Classification and Regression Training. R package version 6.0-81. https://CRAN.R-project.org/package=caret (2018).60.Archer, K. J. & Kimes, R. V. Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52, 2249–2260 (2008).MathSciNet 
MATH 
Article 

Google Scholar 
61.Lawson, G. J., Barange, M. & Fréon, P. Species identification of pelagic fish schools on the South African continental shelf using acoustic descriptors and ancillary information. ICES J. Mar. Sci. 58, 275–287 (2001).Article 

Google Scholar 
62.Simmonds, E. J., Armstrong, F. & Copland, P. J. Species identification using wideband backscatter with neural network and discriminant analysis. ICES J. Mar. Sci. 53, 189–195 (1996).Article 

Google Scholar 
63.Bergström, U. et al. Stickleback increase in the Baltic Sea—A thorny issue for coastal predatory fish. Estuar. Coast. Shelf Sci. 163, 134–142 (2015).ADS 
Article 

Google Scholar 
64.Pepin, T. & Shears, T. H. Influence of body size and alternate prey abundance on the risk of predation to fish larvae. Mar. Ecol. Prog. Ser. 128, 279–285 (1995).ADS 
Article 

Google Scholar 
65.Frouzová, J., Kubečka, J., Balk, H. & Frouz, J. Target strength of some European fish species and its dependence onfish body parameters. Fish. Res. 75, 86–96 (2005).Article 

Google Scholar 
66.Marques, D. A., Lucek, K., Sousa, V. C., Excoffier, L. & Seehausen, O. Admixture between old lineages facilitated contemporary ecological speciation in Lake Constance stickleback. Nat. Commun. 10, 4240. https://doi.org/10.1038/s41467-019-12182-w (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Study site and subject detailsThe study was conducted at the Inkawu Vervet Project (IVP) in a 12,000-hectare private game reserve: Mawana (28° 00.327 S, 031° 12.348 E) in KwaZulu Natal province, South Africa. The vegetation of the study site consisted in a savannah characterized by a mosaic of grasslands and clusters of trees of the typical savannah thornveld, bushveld and thicket patches. We studied two groups of wild vervet monkeys (Chlorocebus pygerythrus): ‘Noha’ (NH) and ‘Kubu’ (KB). NH was composed of 34 individuals (6 adult males; 9 adult females; 6 juvenile males; 7 juvenile females; 5 infant males; 1 infant female) and KB was composed of 19 individuals (1 adult male; 6 adult females; 3 juvenile males; 4 juvenile females; 3 infant males; 2 infant females; Table S1). Males were considered as adults once they dispersed, and females were considered as adults after they gave their first birth. Individuals that did not fulfil these criteria were considered as juveniles28 and infants were aged less than 1 year old. In EWA models, infants and juveniles were lumped in a single category “juveniles”. Each group had been habituated to the presence of human observers: since 2010 for NH and since 2013 for KB. All individuals were identifiable thanks to portrait photographs and specific individual body and face features (scars, colours, shape etc.).This research adhered to the “Guidelines for the use of animals in research” of Association for Study of Animal Behaviour, was approved by the relevant local authority, Ezemvelo KZN Wildlife, South Africa and complied with the ARRIVE guidelines.Hierarchy establishmentAgonistic interactions (aggressor behaviour: stare, chase, attack, hit, bite, take place; victim behaviour: retreat, flee, leave, avoid, jump aside) were collected from May 2018 to October 2018, aside from experiment days, on all the adults and juveniles of both groups via ad libitum sampling50 and food competition tests (i.e. corn provided to the whole group from a plastic box). Data were collected by CC, MBC and different observers from the IVP team. Before beginning data collection, observers had to pass an inter-observer reliability test with 80% reliability for each data category between two observers. Data were collected on tablets (Vodacom Smart Tab 2) equipped with Pendragon version 8.Individual hierarchical ranks were determined by the outcome of dyadic agonistic interactions recorded ad libitum and through food competition tests using Socprog software version 2.751. Hierarchies in both groups were significantly linear (NH: h′ = 0.27; P  More