Philippa Kaur

1.UNDP-GEF. The ecological atlas of the Baikal basin. United Nations Office for Project Sercives (UNOPS). 2015. p 145. http://baikal.iwlearn.org/en.2.Moore MV, Hampton SE, Izmest’eva LR, Silow EA, Peshkova EV, Pavlov BK. Climate change and the world’s “Sacred Sea”—Lake Baikal, Siberia. Bioscience. 2009;59:405–17.Article 

Google Scholar 
3.Granin NG, Aslamov IA, Kozlov VV, Makarov MM, Kirillin G, McGinnis DF, et al. Methane hydrate emergence from Lake Baikal: direct observations, modelling, and hydrate footprints in seasonal ice cover. Sci Rep. 2019;9:19361.CAS 
Article 

Google Scholar 
4.Glöckner FO, Zaichikov E, Belkova N, Denissova L, Pernthaler J, Pernthaler A, et al. Comparative 16S rRNA analysis of lake bacterioplankton reveals globally distributed phylogenetic clusters including an abundant group of actinobacteria. Appl Environ Microbiol. 2000;66:5053–65.Article 

Google Scholar 
5.Kurilkina MI, Zakharova YR, Galachyants YP, Petrova DP, Bukin YS, Domysheva VM et al. Bacterial community composition in the water column of the deepest freshwater Lake Baikal as determined by next-generation sequencing. FEMS Microbiol Ecol. 2016;92:fiw094.6.Zakharenko AS, Galachyants YP, Morozov IV, Shubenkova OV, Morozov AA, Ivanov VG, et al. Bacterial communities in areas of oil and methane seeps in pelagic of Lake Baikal. Micro Ecol. 2019;78:269–85.CAS 
Article 

Google Scholar 
7.Yi Z, Berney C, Hartikainen H, Mahamdallie S, Gardner M, Boenigk J, et al. High-throughput sequencing of microbial eukaryotes in Lake Baikal reveals ecologically differentiated communities and novel evolutionary radiations. FEMS Microbiol Ecol. 2017;93:10.Article 

Google Scholar 
8.David GM, Moreira D, Reboul G, Annenkova NV, Galindo LJ, Bertolino P, et al. Environmental drivers of plankton protist communities along latitudinal and vertical gradients in the oldest and deepest freshwater lake. Environ Microbiol. 2021;23:1436–51.CAS 
Article 

Google Scholar 
9.Annenkova NV, Giner CR, Logares R. Tracing the origin of planktonic protists in an ancient lake. Microorganisms. 2020;8:543.10.Lomakina AV, Mamaeva EV, Galachyants YP, Petrova DP, Pogodaeva TV, Shubenkova OV, et al. Diversity of archaea in bottom sediments of the discharge areas with oil- and gas-bearing fluids in Lake Baikal. Geomicrobiol J. 2018;35:50–63.CAS 
Article 

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

Google Scholar 
12.Biddle JF, Fitz-Gibbon S, Schuster SC, Brenchley JE, House CH. Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. Proc Natl Acad Sci USA. 2008;105:10583–1058.CAS 
Article 

Google Scholar 
13.Spring S, Bunk B, Sproer C, Rohde M, Klenk HP. Genome biology of a novel lineage of planctomycetes widespread in anoxic aquatic environments. Environ Microbiol. 2018;20:2438–55.CAS 
Article 

Google Scholar 
14.Podosokorskaya OA, Kadnikov VV, Gavrilov SN, Mardanov AV, Merkel AY, Karnachuk OV, et al. Characterization of Melioribacter roseus gen. nov., sp. nov., a novel facultatively anaerobic thermophilic cellulolytic bacterium from the class Ignavibacteria, and a proposal of a novel bacterial phylum Ignavibacteriae. Environ Microbiol. 2013;15:1759–71.CAS 
Article 

Google Scholar 
15.Dombrowski N, Seitz KW, Teske AP, Baker BJ. Genomic insights into potential interdependencies in microbial hydrocarbon and nutrient cycling in hydrothermal sediments. Microbiome 2017;5:106.Article 

Google Scholar 
16.Roberts SL, Swann GEA, McGowan S, Panizzo VN, Vologina EG, Sturm M, et al. Diatom evidence of 20th century ecosystem change in Lake Baikal, Siberia. PLoS One. 2018;13:e0208765.Article 

Google Scholar 
17.Mukherjee I, Hodoki Y, Okazaki Y, Fujinaga S, Ohbayashi K, Nakano SI. Widespread dominance of kinetoplastids and unexpected presence of diplonemids in deep freshwater lakes. Front Microbiol. 2019;10:2375.Article 

Google Scholar 
18.Zemskaya TI, Cabello-Yeves PJ, Pavlova ON, Rodriguez-Valera F. Microorganisms of Lake Baikal—the deepest and most ancient lake on Earth. Appl Microbiol Biotechnol. 2020;104:6079–90.19.Sheik CS, Jain S, Dick GJ. Metabolic flexibility of enigmatic SAR324 revealed through metagenomics and metatranscriptomics. Environ Microbiol. 2014;16:304–17.CAS 
Article 

Google Scholar 
20.Paver SF, Muratore D, Newton RJ, Coleman ML. Reevaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems. mSystems. 2018; 3:e00232–18. 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

1.Watling, J. I. et al. Support for the habitat amount hypothesis from a global synthesis of species density studies. Ecol. Lett. 23, 674–681 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Hanski, I. Metapopulation dynamics. Nature 396, 41–49 (1998).ADS 
CAS 
Article 

Google Scholar 
4.Ćosović, M., Bugalho, M. N., Thom, D. & Borges, J. G. Stand structural characteristics are the most practical biodiversity indicators for forest management planning in Europe. Forests 11, 343 (2020).Article 

Google Scholar 
5.Bouvet, A. et al. Effects of forest structure, management and landscape on bird and bat communities. Environ. Conserv. 43, 148–160 (2016).Article 

Google Scholar 
6.Froidevaux, J. S., Zellweger, F., Bollmann, K., Jones, G. & Obrist, M. K. From field surveys to LiDAR: shining a light on how bats respond to forest structure. Remote Sens. Environ. 175, 242–250 (2016).ADS 
Article 

Google Scholar 
7.Fuentes-Montemayor, E. et al. Species mobility and landscape context determine the importance of local and landscape-level attributes. Ecol. Appl. 27, 1541–1554 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Jung, K., Kaiser, S., Böhm, S., Nieschulze, J. & Kalko, E. K. Moving in three dimensions: effects of structural complexity on occurrence and activity of insectivorous bats in managed forest stands. J. Appl. Ecol. 49, 523–531 (2012).Article 

Google Scholar 
9.Langridge, J., Pisanu, B., Laguet, S., Archaux, F. & Tillon, L. The role of complex vegetation structures in determining hawking bat activity in temperate forests. For. Ecol. Manag. 448, 559–571 (2019).Article 

Google Scholar 
10.Müller, J. et al. From ground to above canopy—Bat activity in mature forests is driven by vegetation density and height. For. Ecol. Manag. 306, 179–184 (2013).Article 

Google Scholar 
11.Renner, S. C. et al. Divergent response to forest structure of two mobile vertebrate groups. For. Ecol. Manag. 415, 129–138 (2018).Article 

Google Scholar 
12.Fuentes-Montemayor, E., Goulson, D., Cavin, L., Wallace, J. M. & Park, K. J. Fragmented woodlands in agricultural landscapes: the influence of woodland character and landscape context on bats and their insect prey. Agr. Ecosyst. Environ. 172, 6–15 (2013).Article 

Google Scholar 
13.Rachwald, A., Boratyński, J. S., Krawczyk, J., Szurlej, M. & Nowakowski, W. K. Natural and anthropogenic factors influencing the bat community in commercial tree stands in a temperate lowland forest of natural origin (Białowieża Forest). For. Ecol. Manag. 479, 118544 (2021).Article 

Google Scholar 
14.Alder, D., Poore, A., Norrey, J., Newson, S. & Marsden, S. Irregular silviculture positively influences multiple bat species in a lowland temperate broadleaf woodland. For. Ecol. Manag. 118786, 1613 (2020).
Google Scholar 
15.Carr, A., Zeale, M. R., Weatherall, A., Froidevaux, J. S. & Jones, G. Ground-based and LiDAR-derived measurements reveal scale-dependent selection of roost characteristics by the rare tree-dwelling bat Barbastella barbastellus. For. Ecol. Manag. 417, 237–246 (2018).Article 

Google Scholar 
16.Kortmann, M. et al. Beauty and the beast: how a bat utilizes forests shaped by outbreaks of an insect pest. Anim. Conserv. 21, 21–30 (2018).Article 

Google Scholar 
17.Ruczyński, I., Nicholls, B., MacLeod, C. & Racey, P. Selection of roosting habitats by Nyctalus noctula and Nyctalus leisleri in Białowieża Forest—adaptive response to forest management?. For. Ecol. Manag. 259, 1633–1641 (2010).Article 

Google Scholar 
18.Ober, H. K. & Hayes, J. P. Influence of forest riparian vegetation on abundance and biomass of nocturnal flying insects. For. Ecol. Manag. 256, 1124–1132 (2008).Article 

Google Scholar 
19.Russo, D. et al. Identifying key research objectives to make European forests greener for bats. Front. Ecol. Evol. 4, 87 (2016).Article 

Google Scholar 
20.Kaňuch, P. et al. Relating bat species presence to habitat features in natural forests of Slovakia (Central Europe). Mamm. Biol. 73, 147–155 (2008).Article 

Google Scholar 
21.Kirkpatrick, L. et al. Bat use of commercial coniferous plantations at multiple spatial scales: management and conservation implications. Biol. Cons. 206, 1–10 (2017).Article 

Google Scholar 
22.Vasko, V. et al. Within-season changes in habitat use of forest-dwelling boreal bats. Ecol. Evol. 10, 4164–4174 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Węgiel, A. et al. The foraging activity of bats in managed pine forests of different ages. Eur. J. Forest Res. 138, 383–396 (2019).Article 

Google Scholar 
24.Bender, M. J., Castleberry, S. B., Miller, D. A. & Wigley, T. B. Site occupancy of foraging bats on landscapes of managed pine forest. For. Ecol. Manag. 336, 1–10 (2015).Article 

Google Scholar 
25.Apoznański, G. et al. Use of coniferous plantations by bats in western Poland during summer. Balt. For. 26, 232 (2020).Article 

Google Scholar 
26.Buchholz, S., Kelm, V. & Ghanem, S. J. Mono-specific forest plantations are valuable bat habitats: implications for wind energy development. Eur. J. Wildl. Res. 67, 1–12 (2021).Article 

Google Scholar 
27.Charbonnier, Y. et al. Deciduous trees increase bat diversity at stand and landscape scales in mosaic pine plantations. Landscape Ecol. 31, 291–300 (2016).Article 

Google Scholar 
28.Arroyo‐Rodríguez, V. et al. Designing optimal human‐modified landscapes for forest biodiversity conservation. Ecol. Lett. In Press. (2020).29.Dunning, J. B., Danielson, B. J. & Pulliam, H. R. Ecological processes that affect populations in complex landscapes. Oikos 15, 169–175 (1992).Article 

Google Scholar 
30.Hatfield, J. H. et al. Mediation of area and edge effects in forest fragments by adjacent land use. Conserv. Biol. 34, 395–404 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Barbaro, L. et al. Biotic predictors complement models of bat and bird responses to climate and tree diversity in European forests. Proc. R. Soc. B 286, 20182193 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Ethier, K. & Fahrig, L. Positive effects of forest fragmentation, independent of forest amount, on bat abundance in eastern Ontario, Canada. Landsc. Ecol. 26, 865–876 (2011).Article 

Google Scholar 
33.Rodríguez-San Pedro, A. & Simonetti, J. A. The relative influence of forest loss and fragmentation on insectivorous bats: does the type of matrix matter?. Landsc. Ecol. 30, 1561–1572 (2015).Article 

Google Scholar 
34.Charbonnier, Y. M. et al. Bat and bird diversity along independent gradients of latitude and tree composition in European forests. Oecologia 182, 529–537 (2016).ADS 
PubMed 
Article 

Google Scholar 
35.Dietz, C., Nill, D. & von Helversen, O. Bats of Britain, Europe and Northwest Africa. (A & C Black, 2009).36.Law, B., Park, K. J. & Lacki, M. J. in Bats in the Anthropocene: conservation of bats in a changing world (eds Christian C Voigt & T Kingston) 105–150 (Springer, 2016).37.Carr, A., Weatherall, A. & Jones, G. The effects of thinning management on bats and their insect prey in temperate broadleaved woodland. For. Ecol. Manag. 457, 117682 (2020).Article 

Google Scholar 
38.Müller, J. et al. Aggregative response in bats: prey abundance versus habitat. Oecologia 169, 673–684 (2012).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Ware, R. L., Garrod, B., Macdonald, H. & Allaby, R. G. Guano morphology has the potential to inform conservation strategies in British bats. PLoS ONE 15, e0230865 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Kirkpatrick, L., Bailey, S. & Park, K. J. Negative impacts of felling in exotic spruce plantations on moth diversity mitigated by remnants of deciduous tree cover. For. Ecol. Manag. 404, 306–315 (2017).Article 

Google Scholar 
41.Fuentes-Montemayor, E., Goulson, D., Cavin, L., Wallace, J. M. & Park, K. J. Factors influencing moth assemblages in woodland fragments on farmland: implications for woodland management and creation schemes. Biol. Cons. 153, 265–275 (2012).Article 

Google Scholar 
42.Rainho, A., Augusto, A. M. & Palmeirim, J. M. Influence of vegetation clutter on the capacity of ground foraging bats to capture prey. J. Appl. Ecol. 47, 850–858 (2010).Article 

Google Scholar 
43.Blakey, R. V., Law, B. S., Kingsford, R. T. & Stoklosa, J. Terrestrial laser scanning reveals below-canopy bat trait relationships with forest structure. Remote Sens. Environ. 198, 40–51 (2017).ADS 
Article 

Google Scholar 
44.Laforge, A. et al. Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data. (Submitted).45.Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J. Biogeogr. 31, 79–92 (2004).Article 

Google Scholar 
46.Summerville, K. S. & Crist, T. O. Contrasting effects of habitat quantity and quality on moth communities in fragmented landscapes. Ecography 27, 3–12 (2004).Article 

Google Scholar 
47.Vinet, O., Sane, F. & Chaigne, A. Radiopistage de la barbastelle (Barbastella barbastellus) en forêt domaniale de l’Aigoual. (Nimes, France, 2013).
48.Obrist, M. K. et al. Response of bat species to sylvo-pastoral abandonment. For. Ecol. Manag. 261, 789–798 (2011).Article 

Google Scholar 
49.Norberg, U. M. & Rayner, J. M. Ecological morphology and flight in bats (Mammalia; Chiroptera): wing adaptations, flight performance, foraging strategy and echolocation. Philos. Trans. R. Soc. Lond. B Biol. Sci. 316, 335–427 (1987).ADS 
Article 

Google Scholar 
50.Swift, S. & Racey, P. Gleaning as a foraging strategy in Natterer’s bat Myotis nattereri. Behav. Ecol. Sociobiol. 52, 408–416 (2002).Article 

Google Scholar 
51.Brigham, R., Grindal, S., Firman, M. & Morissette, J. The influence of structural clutter on activity patterns of insectivorous bats. Can. J. Zool. 75, 131–136 (1997).Article 

Google Scholar 
52.Bender, M. J., Perea, S., Castleberry, S. B., Miller, D. A. & Wigley, T. B. Influence of insect abundance and vegetation structure on site-occupancy of bats in managed pine forests. For. Ecol. Manag. 482, 118839 (2021).Article 

Google Scholar 
53.Ancillotto, L. et al. The importance of non-forest landscapes for the conservation of forest bats: lessons from barbastelles (Barbastella barbastellus). Biodivers. Conserv. 24, 171–185 (2015).Article 

Google Scholar 
54.Plank, M., Fiedler, K. & Reiter, G. Use of forest strata by bats in temperate forests. J. Zool. 286, 154–162 (2012).Article 

Google Scholar 
55.Kusch, J., Weber, C., Idelberger, S. & Koob, T. Foraging habitat preferences of bats in relation to food supply and spatial vegetation structures in a western European low mountain range forest. Folia Zool. 53, 113–128 (2004).
Google Scholar 
56.Siemers, B. M. & Schnitzler, H.-U. Natterer’s bat (Myotis nattereri Kuhl, 1818) hawks for prey close to vegetation using echolocation signals of very broad bandwidth. Behav. Ecol. Sociobiol. 47, 400–412 (2000).Article 

Google Scholar 
57.Arrizabalaga-Escudero, A. et al. Trophic requirements beyond foraging habitats: the importance of prey source habitats in bat conservation. Biol. Conserv. 191, 512–519 (2015).Article 

Google Scholar 
58.Carr, A. et al. Moths consumed by the Barbastelle Barbastella barbastellus require larval host plants that occur within the bat’s foraging habitats. Acta Chiropterologica 22, 257–269 (2021).
Google Scholar 
59.van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol. Lett. 21, 31–42 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
60.Lindenmayer, D., Franklin, J. & Fischer, J. General management principles and a checklist of strategies to guide forest biodiversity conservation. Biol. Conserv. 131, 433–445 (2006).Article 

Google Scholar 
61.Wolters, V., Bengtsson, J. & Zaitsev, A. S. Relationship among the species richness of different taxa. Ecology 87, 1886–1895 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Larrieu, L. et al. Cost-efficiency of cross-taxon surrogates in temperate forests. Ecol. Ind. 87, 56–65 (2018).Article 

Google Scholar 
63.Westgate, M. J., Tulloch, A. I., Barton, P. S., Pierson, J. C. & Lindenmayer, D. B. Optimal taxonomic groups for biodiversity assessment: a meta-analytic approach. Ecography 40, 539–548 (2017).Article 

Google Scholar 
64.Larrieu, L. et al. Assessing the potential of routine stand variables from multi-taxon data as habitat surrogates in European temperate forests. Ecol. Ind. 104, 116–126 (2019).Article 

Google Scholar 
65.Bitterlich, W. The relascope idea. Relative measurements in forestry. Farnham Royal: Commonwealth Agricultural Bureaux, Slough. (1984).
66.Bachelot, B. Sky: canopy openness analyzer package. R package version 1.0. https://cran.r-project.org/web/packages/Sky/index.html. (2016).67.R Development Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. (2019).68.Blondel, J. & Cuvillier, R. Une méthode simple et rapide pour décrire les habitats d’oiseaux: le stratiscope. Oikos 29, 326–331 (1977).Article 

Google Scholar 
69.Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).Article 

Google Scholar 
70.Froidevaux, J. S., Zellweger, F., Bollmann, K. & Obrist, M. K. Optimizing passive acoustic sampling of bats in forests. Ecol. Evol. 4, 4690–4700 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
71.Bas, Y., Bas, D. & Julien, J.-F. Tadarida: a toolbox for animal detection on acoustic recordings. J. Open Res. Softw. 5, 6 (2017).Article 

Google Scholar 
72.Barré, K. et al. Accounting for automated identification errors in acoustic surveys. Methods Ecol. Evol. 10, 1171–1188 (2019).Article 

Google Scholar 
73.Russo, D., Ancillotto, L. & Jones, G. Bats are still not birds in the digital era: echolocation call variation and why it matters for bat species identification. Can. J. Zool. 96, 63–78 (2018).Article 

Google Scholar 
74.Obrist, M. K., Boesch, R. & Flückiger, P. F. Variability in echolocation call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach. Mammalia 68, 307–322 (2004).Article 

Google Scholar 
75.Barataud, M. Acoustic ecology of european bats: species identification, study of their habitats and foraging behaviour. Paris: Muséum national d’Histoire naturelle & Mèze: Biotope (Inventaires & biodiversité) 352, 115 (2015).
Google Scholar 
76.Truxa, C. & Fiedler, K. Attraction to light-from how far do moths (Lepidoptera) return to weak artificial sources of light?. Eur. J. Entomol. 109, 1053 (2012).Article 

Google Scholar 
77.Froidevaux, J. S., Fialas, P. C. & Jones, G. Catching insects while recording bats: impacts of light trapping on acoustic sampling. Remote Sens. Ecol. Conserv. 4, 240–247 (2018).Article 

Google Scholar 
78.Andreas, M., Reiter, A., Cepáková, E. & Uhrin, M. Body size as an important factor determining trophic niche partitioning in three syntopic rhinolophid bat species. Biologia 68, 170–175 (2013).Article 

Google Scholar 
79.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R J. 9, 378–400 (2017).Article 

Google Scholar 
80.Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. Model Sel. Multimodel Inference 2, 15 (2002).MATH 

Google Scholar 
81.Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
82.Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.2.0. https://cran.r-project.org/web/packages/DHARMa/index.html. (2017).83.Mazerolle, M. J. AICcmodavg. R package version 2.3-1. https://cran.r-project.org/web/packages/AICcmodavg/index.html. (2020).84.Grueber, C., Nakagawa, S., Laws, R. & Jamieson, I. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
86.Arnold, T. W. Uninformative parameters and model selection using Akaike’s Information Criterion. J. Wildl. Manag. 74, 1175–1178 (2010).Article 

Google Scholar 
87.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.0. https://cran.r-project.org/web/packages/emmeans/index.html. (2020). More

1.Hamdan, A. et al. A preliminary study of mirror-induced self-directed behaviour on wildlife at the Royal Belum Rainforest Malaysia. Sci. Rep. 10, 14105. https://doi.org/10.1038/s41598-020-71047-1 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
2.Lazarus, B. A. et al. Topographical differences impacting wildlife dynamics at natural salt licks in the Royal Belum Rainforest. Asian J. Conserv. Biol. 8(2), 97–101 (2019).
Google Scholar 
3.Brightsmith, D. J., Taylor, J. & Phillips, T. D. The roles of soil characteristics and toxin adsorption in avian geophagy. Biotropica 40, 766–774 (2008).Article 

Google Scholar 
4.Ayotte, J. B., Parker, K. L., Arocena, J. & Gillingham, M. P. Chemical composition of lick soils: functions of soil ingestion by four ungulate species. J. Mammal. 87(5), 878–888 (2006).Article 

Google Scholar 
5.Matsubayashi, H. et al. Importance of natural licks for mammals in Bornean Inland Tropical Rainforest. Ecol. Res. 22, 742 (2006).Article 

Google Scholar 
6.Tracy, B. F. & McNaughton, S. J. Elemental analysis of mineral licks from the Serengeti National Park, the Konza Prairie and Yellowstone National Park. Ecography 18, 91–94 (1995).Article 

Google Scholar 
7.Razali, N. B. et al. Physical factors at salt licks influenced the frequency of wildlife visitation in the Malaysian tropical rainforest. Trop. Zool. 33(3), 83–96. https://doi.org/10.4081/tz.2020.69 (2020).Article 

Google Scholar 
8.Owen-Smith, N. & Mills, M. Predator-prey size relationships in an African large-mammal food web. J. Anim. Ecol. 77, 173–183 (2008).Article 

Google Scholar 
9.Mathers, K. L., Rice, S. P. & Wood, P. J. Predator, prey, and substrate interactions: the role of faunal activity and substrate characteristics. Ecosphere 10(1), e02545 (2019).Article 

Google Scholar 
10.Sobral, M. et al. Mammal diversity influences the carbon cycle through trophic interactions in the Amazon. Nat. Ecol. Evol. 1, 1670–1676 (2017).Article 

Google Scholar 
11.Stevens, A. Dynamics of predation. Nat. Educ. Knowl. 3(10), 46 (2010).
Google Scholar 
12.Lima, S. T. Putting predators back into behavioral predator–prey interactions. Trends Ecol. Evol. 17(2), 70–75 (2002).Article 

Google Scholar 
13.Cuyper, A. D. et al. Predator size and prey size–gut capacity ratios determine kill frequency and carcass production in terrestrial carnivorous mammals. Oikos https://doi.org/10.1111/oik.05488 (2018).Article 

Google Scholar 
14.Terborgh, J. et al. Ecological meltdown in predator-free forest fragments. Science 294(5548), 1923–1926 (2001).ADS 
CAS 
Article 

Google Scholar 
15.Couturier, S. & Barrete, C. The behaviour of moose at natural mineral springs in Quebec. Can. J. Zool. 66, 522–528 (1987).Article 

Google Scholar 
16.Ruggiero, R. D. & Fay, J. M. Utilization of termitarium soils by elephants and its ecological implications. Afr. J. Ecol. 32, 222–232 (1994).Article 

Google Scholar 
17.Shahfiz, M. A. et al. Checklist of vertebrates at Primary Linkages 2 (PL2) of the central forest spine ecological corridor in Belum Temengor Forest Reserves, Perak, Peninsular Malaysia. Malays. For. 82(2), 463–485 (2019).
Google Scholar 
18.Liyana, N. M., Othman, Z., Wahid, A. R. & Hakimie, A. A. Habitat suitability prediction model of wildlife at Royal Belum State Park using geographical information system. Int. J. Geoinform. 12(2), 1–8 (2016).
Google Scholar 
19.Kawanishi, K. et al. The Malayan tiger. In In Noyes Series in Animal Behavior, Ecology, Conservation and Management, Tigers of the World 2nd edn (eds Tilson, R. & Nyhus, P. J.) 367–376 (William Andrew Publishing, Norwich, 2010).
Google Scholar 
20.Lynam, A. J., Laidlaw, R., Wan Noordin, W. S., Elagupillay, S. & Bennett, E. L. Assessing the conservation status of the tiger Panthera tigris at priority sites in Peninsular Malaysia. Oryx 41(4), 454–462. https://doi.org/10.1017/S0030605307001019 (2007).Article 

Google Scholar 
21.Kawanishi, K., Rayan, M. D., Gumal, M. T. & Shepherd, C. R. Extinction process of the sambar in Peninsular Malaysia. Deer Spec. Group Newsl. N. 26, 48–59 (2014).
Google Scholar 
22.Simcharoen, A. et al. Female tiger Panthera tigris home range size and prey abundance: important metrics for management. Oryx 48(3), 370–377. https://doi.org/10.1017/S0030605312001408 (2014).Article 

Google Scholar 
23.Kedri, K. et al. Distribution and ecology of Rafflesia in Royal Belum state park, Perak, Malaysia. Int. J. Eng. Technol. 7(229), 292–296 (2018).Article 

Google Scholar 
24.Misni, A., Rauf, A., Rasam, A. & Buyadi, A. S. N. Spatial analysis of habitat conservation for hornbills: a case study of Royal Belum-Temengor forest complex in Perak Sate Park Malaysia. Pertanika J. Soc. Sci. Hum. 25(S), 11–20 (2017).
Google Scholar 
25.Rovero, F., Zimmermann, F., Berzi, D. & Meek, P. Which camera trap type and how many do I need? A review of camera features and study designs for a range of wildlife research applications. Hystrix 2, 6318 (2013).
Google Scholar 
26.Liu, N., Zhao, Q., Zhang, N., Cheng, X., & Zhu, J. Pose-guided complementary features learning for Amur tiger re-identification, in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 286–293. https://doi.org/10.1109/ICCVW.2019.00038 (2019).27.Sharma, S., Jhala, Y. & Sawarkar, V. B. Identification of individual tigers (Panthera tigris) from their pugmarks. J. Zool. 267, 9–18 (2005).Article 

Google Scholar 
28.Cho, Y. et al. The tiger genome and comparative analysis with lion and snow leopard genomes. Nat. Commun. 4, 2433 (2013).ADS 
Article 

Google Scholar 
29.Kerley, L. L. Using dogs for tiger conservation and research. Integr. Zool. 5, 390–396 (2010).Article 

Google Scholar 
30.Li, S., Li, J., Tang, H., Qian, R., & Lin, W. ATRW: a benchmark for Amur tiger re-identification in the wild, in Proceedings of the 28th ACM International Conference on Multimedia (MM ’20), October 12–16, 2020, Seattle, WA, USA. https://doi.org/10.1145/3394171.3413569 (ACM, New York, NY, USA, 2020).31.Shi, C. et al. Amur tiger stripes: Individual identification based on deep convolutional neural network. Integr. Zool. 15(6), 461–470 (2020).Article 

Google Scholar 
32.McCullough, D. R., Pei, K. C. J. & Wang, Y. Home range, activity patterns, and habitat relations of Reeves’ muntjacs in Taiwan. J. Wildl. Manag. 64(2), 430. https://doi.org/10.2307/3803241 (2000).Article 

Google Scholar 
33.Chatterjee, D., Sankar, K., Qureshi, Q., Malik, P. K. & Nigam, P. Ranging pattern and habitat use of sambar (Rusa unicolor) in Sariska Tiger Reserve, Rajasthan, western India. DSG Newsl. 26, 60–71 (2014).
Google Scholar 
34.Garza, S. J., Tabak, M. A., Miller, R. S., Farnsworth, M. L. & Burdett, C. L. Abiotic and biotic influences on home-range size of wild pigs (Sus scrofa). J. Mammal. 99(1), 97–107. https://doi.org/10.1093/jmammal/gyx154 (2018).Article 

Google Scholar 
35.Sankar, K. et al. Home range, habitat use and food habits of re-introduced gaur (Bos gaurus gaurus) in Bandhavgarh Tiger Reserve, Central India. Trop. Conserv. Sci. 6(1), 50–69 (2013).Article 

Google Scholar 
36.Simcharoen, A. et al. Ecological Factors that influence sambar (Rusa unicolor) distribution and abundance in western Thailand: Implications for tiger conservation. Raffles Bull. Zool. 62, 100–106 (2014).
Google Scholar 
37.Mark Rayan, D. & Linkie, M. Managing threatened ungulates in logged-primary forest mosaics in Malaysia. PLoS ONE 15(12), e0243932. https://doi.org/10.1371/journal.pone.0243932 (2020).CAS 
Article 

Google Scholar 
38.McClure, M. L. et al. Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States. PLoS ONE 10(8), e0133771. https://doi.org/10.1371/journal.pone.0133771 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Ickes, K. Hyper-abundance of native wild pigs (Sus scrofa) in a lowland dipterocarp rain forest of Peninsular Malaysia. Biotropica 33(4), 682–690 (2001).Article 

Google Scholar 
40.Saunders, G. & McLeod, S. Predicting home range size from the body mass or population densities of feral pigs, sus scrofa (Artiodactyla: Suidae). Aust. J. Ecol. 24, 538–543 (1999).Article 

Google Scholar 
41.Abrams, P. A. & Matsuda, H. Prey adaptation as a cause of predator-prey cycles. Evolution 51, 1742–1750 (1997).Article 

Google Scholar 
42.Zhang, C., Minghai, Z. & Philip, S. Does prey density limit Amur tiger (Panthera tigris altaica) recovery in north-eastern China. Wildl. Biol. 19(4), 452–461 (2013).Article 

Google Scholar 
43.Majumder, A. et al. Home ranges of Bengal tiger (Panthera tigris tigris L.) in Pench Tiger Reserve, Madhya Pradesh, Central India. Wildl. Biol. Pract. 8, 36–49 (2012).
Google Scholar  More

Host and parasitoid linesBlack bean aphids (A. fabae) were reared on their host plant Vicia faba (Fabaceae) in a climate chamber at 22 °C with a 16-h photoperiod to ensure clonal reproduction. Two sublines of A. fabae clone A06-407 were used: the original A06-407 clone, which was free of any known defensive endosymbionts, and the modified A06-407 clone harboring the H. defensa strain H76 (Vorburger et al. 2009; Dennis et al. 2017). The original (H. defensa negative) and the modified (H. defensa positive, harboring the H. defensa strain H76) aphid lines are in the following called H− and H+, respectively.We used two experimentally evolved populations of the parasitoid wasp L. fabarum. One was adapted to the presence of Hamiltonella in host aphids, the other was not. Wasp populations were established by Dennis et al. (2017) from a mixture of nine collections of sexually reproducing, haplo-diploid L. fabarum from six locations across Switzerland. Experimental evolution was conducted by rearing wasps exclusively on H− or H+ aphids, leading to counteradaptation in the H+ treatment; wasps reared on H+ aphids evolved an improved ability to parasitize H+ aphids compared to wasps reared on H− aphids (see Dennis et al. 2017 for more details). After maintaining treatments for 24 generations in 4 replicate populations each, replicates were combined and treatments were continued unreplicated at a population size of 200 individuals (see Rossbacher and Vorburger 2020 for details). Until the onset of the experiments presented here, parasitoid populations had been reared for approximately 140 generations on either H− or H+ aphids (since September 2013). At this point, the population reared on H+ aphids was able to parasitize H+ aphids nearly as well as H− aphids, whereas the population reared on H− aphids was only able to parasitize H− aphids but not H+ aphids. In the following, we refer to the wasp population adapted to H+ aphids as R ( = Resistant to Hamiltonella) and to the population adapted to H− aphids as S ( = Susceptible to Hamiltonella).Experiment 1: characterization of general inheritance patternsTo determine whether the evolved ability to parasitize H+ aphids is mainly determined by the larval or the maternal genotype, and whether it shows a dominant or recessive inheritance pattern, crossing experiments were combined with no-choice bioassays over two generations of wasps. In the first generation, all possible combinations of males and females from the R and S populations were crossed in order to quantify their ability to reproduce on H+ aphids (Table 1). Assuming that this ability is governed by a single Mendelian locus with two alleles (R and S), which are fixed in the respective populations (likely an oversimplification), allowed us to postulate three mutually exclusive hypotheses (H1–H3) that make different predictions for the outcome of these crosses (Table 1). To indicate genotypes and ploidy, crosses are depicted in the following as, e.g., RR × S, meaning that a (diploid) female from the R population was crossed with a (haploid) male from the S population.Table 1 Prediction of female offspring survival and reproduction in experimental crosses of evolved Lysiphlebus fabarum populations under three different hypotheses.Full size table(H1) The counteradaptation is larval and dominant. Under H1, RR × R, RR × S, and SS × R crosses are expected to produce female offspring on H+ aphids, as homozygous RR and heterozygous RS female larvae would be of the R phenotype and thus counteradapted. Homozygous SS daughters from SS × S crosses would fail to develop. If the counteradaptation was larval but inherited in an intermediate rather than dominant fashion, the expectation remains the same as under H1, albeit with the possibility that RR × S and SS × R crosses produce fewer female offspring than RR × R crosses.(H2) The counteradaptation is larval and recessive. Under H2, only RR × R crosses would produce female offspring on H+ aphids. RR × S, SS × R, and SS × S crosses are expected to not produce any female offspring as their heterozygous (RS) or homozygous (SS) daughters would be of the S phenotype and thus not counteradapted.(H3) The counteradaptation is maternal. Under H3, the RR × R and RR × S crosses are expected to produce female offspring and the SS × R and SS × S crosses are not, as the genotype of the mother is decisive for offspring survival. If both maternal and larval effects were at play, the sex ratio in offspring from the RR×S crosses is expected to be male biased compared to RR × R crosses, due to a disadvantage of RS larvae compared to RR larvae, while haploid male larvae have an R genotype in either case.To isolate wasps prior to use in experiments, mummies (parasitized aphids approaching parasitoid emergence) were collected and stored individually in 1.5 ml Eppendorf tubes. Thus, adult wasps had never encountered another wasp or aphid before (naive virgins). Zero-to-3 days after hatching, the wasps were paired and given 20–120 min for mating in 1.5 ml Eppendorf tubes. Although there was no control whether mating occurred in the given amount of time, mating was usually observed within the first 30 s of having wasp pairs in the same tube. Then the wasps were released on a caged plant with an aphid colony consisting of a known number of 0–48-h-old H+ aphid nymphs. The mean ± standard deviation (SD) number of aphid nymphs provided per cross was 43.5 ± 14.9. Adult wasps were removed from colonies 24 h after release. Nine days after adding wasps, plants were enclosed in cellophane bags and left to dry out at 22 °C for hatching and subsequent sexing and counting of wasp offspring. Differences in numbers of female offspring between the different crosses of the first generation were analyzed with Mann–Whitney U tests. A generalized linear model (GLM) was used to analyze differences in sex ratios. Statistical analyses were performed using R version 3.5.2 (R Core Team 2018).Because findings from the first generation of crosses supported H3 (see “Results”), two extensions of H3 (H3.1 and H3.2) were tested in a second generation of crosses to determine whether the maternal counteradaptation was dominant or recessive (Table 1). To this end, we tested the ability of 20 virgin female offspring from 10 RR × S crosses (i.e., heterozygous RS females) to reproduce on H+ aphids. The mean ± SD number of aphid nymphs provided per RS female was 21.9 ± 9.5.(H3.1) The counteradaptation is maternal and dominant. Under H3.1, RS females are expected to reproduce successfully on H+ aphids, because they are of the R phenotype. They are expected to produce only male offspring as they are virgins (arrhenotokous parthenogenesis). This scenario is indistinguishable from cytoplasmic inheritance, which would require further examination.(H3.2) The counteradaptation is maternal and recessive. Under H3.2, RS females are not expected to reproduce on H+ aphids, because they are of the S phenotype.Experiment 2: crosses and phenotyping for QTL studyTo obtain a mapping population and phenotype data, a crossing scheme similar to the one by Pannebakker et al. (2011) was realized (Fig. 1). The crossing design relied on two main assumptions: First, we assumed that the alleles responsible for the counteradaptation are fixed in alternative states in the R and S populations. Second, due to the findings from the first experiment, we assumed the counteradaptation to be recessive and determined by the maternal genotype (see “Results”). In the first generation (P generation), a single S female was crossed with an R male to produce heterozygous female RS offspring (F1 generation). F1 females were allowed to reproduce as naive virgins to produce a recombinant male-only mapping population (F2 generation, Fig. 1A). F2 males were then backcrossed into the R background (each male with one RR female) to produce F3 female offspring for phenotyping (Fig. 1B). All reproduction up to the emergence of F3 females took place on H− aphids (Fig. 1) to avoid any selection. P individuals, F1 females and F2 males were stored in 1.5 ml Eppendorf tubes at −80 °C for subsequent genotyping.Fig. 1: Experimental crossing procedure for QTL analysis.Crossing design used to obtain a F2 mapping population for genotyping (A) and a F3 population for phenotyping (B). In a first step, two P generation individuals (parents), a diploid female from the symbiont-susceptible population, and a haploid male from the symbiont-resistant population were crossed to obtain 17 heterozygous F1 hybrid females. F1 hybrid females were allowed to reproduce as virgins—i.e., arrhenotokous parthenogenesis—to obtain 354 recombinant F2 males (mapping population), which were either carrying the S (susceptible) or the R (resistant) genotype. Recombinant F2 males were backcrossed with females of the resistant population to produce semi-recombinant F3 females. Sister F3 females have identical chromosomes of paternal origin and are thus considered clonal sibships. Two hundred and forty-four clonal sibships consisting of one to two sister F3 females were allowed to reproduce as virgins on a colony of symbiont-protected (H+) aphid hosts for phenotyping. Bar colors represent genomic regions originating from different parental populations and letters under sex symbols indicate the ploidy levels and genotypes.Full size imagePhenotyping was conducted by letting naive virgin F3 females oviposit for 24 h on colonies with a known number of approximately 24–72-h-old H+ aphid nymphs and subsequently counting their offspring as previously described. The average ± SD number of aphid nymphs provided was 40.9 ± 13.6. Wasps were added to the aphid colonies in an open Eppendorf tube. If possible, two sister F3 females from the same recombinant F2 father were added to each aphid colony in order to reduce the occurrence of false negatives, i.e., random failures to reproduce that are unrelated to the females’ genotype, e.g., due to harmful handling or death before oviposition. F3 sister females are identical concerning their paternal chromosome set and share the same R population background concerning their maternal chromosome set. They are considered clonal sibships (Pannebakker et al. 2011).The phenotype we measured was the number of wasp offspring produced per H+ aphid colony. This measure exhibited strong variation and zero inflation. To improve its value as a proxy for counteradaptation, the measure was corrected for certain variables in the phenotyping set-up that could have influenced offspring production independent of the F3 genotype. We used the zeroinfl function of the R-package pscl (Zeileis et al. 2008) to fit the following full model by zero-inflated Poisson regression:n_offspring ~ n_nymphs + n_wasps_added + all_removed + any_found_dead + any_in_tube | n_nymphs + n_wasps_added + all_removed + any_found_dead + any_in_tubewhere n_offspring is the number of offspring wasps produced, n_nymphs is the number of aphid nymphs, i.e., potential hosts, provided, n_wasps_added is a factor describing whether one or two wasps were added to the aphid colony, all_removed is a factor describing whether all wasps could be recovered 24 h after adding them to the aphid colony, any_found_dead is a factor describing whether any of the wasps were dead after 24 h, and any_in_tube is a factor describing whether any of the wasps were found in the tube rather than on the plant after 24 h. Parameters before and after the | symbol are components of the Poisson and the zero-inflation part of the model, respectively. The full model was reduced to a minimal model by performing backwards elimination with the function be.zerofinl from the R-package mpath (Wang 2020). The final minimal model was:n_offspring ~ n_nymphs + all_removed + any_in_tube | n_wasps_added + any_in_tube.Residuals of the minimal model were used as the corrected count phenotype for QTL mapping. We also assessed offspring presence presence/absence as an additional binary phenotype. Due to its simplicity, a binary phenotype may be less prone to environmental variation and more appropriate if counteradaptation is a Mendelian trait.DNA extraction and sequencingDNA extraction from 354 F2 males, 17 F1 females, and the two P individuals was performed adapting the LGC-sbeadex Livestock D protocol (LGC Genomics, Berlin, Germany). In addition to these experimental individuals, 30 wasps from an asexual, isofemale line of L. fabarum (line CV17-84) were processed to quantify genotyping error. Due to their mode of reproduction and maintenance at small population size, CV17-84 individuals are expected to be genetically nearly identical. F2, F1, and P individuals and three pools of 10 CV17-84 wasps each were crushed in liquid nitrogen prior to lysis. Extraction from individual samples was downscaled and included the following adaptations: lysis was done with PN buffer during 2 h at 60 °C with 1:10 protease solution, the lysate was incubated with binding mix during 20 min and elution was done at 60 °C. Extraction from pooled samples was, besides doubling the amount of protease, done following the manual. DNA concentration of each sample was measured using a Spark 10 M Multimode Microplate Reader (Tecan, Switzerland). Quality of DNA obtained with the used protocols was tested on a Nanodrop spectrophotometer (Thermo Fisher Scientific, USA) and on agarose gels. ddRAD library preparation was adapted from the protocol by Peterson et al. (2012). Restriction enzymes MfeI and TaqI were used for double digestion of up to 50 ng DNA per sample. After ligation of barcoded adapters to each individual sample, samples were combined in 12 pools with 24–36 samples each. Eleven pools contained one sample of 50 ng DNA from the CV17-84 wasps and 23–35 other samples (F2, F1, or P). Fragment size selection was performed on each pool with AMPure XP beads (Beckman Coulter, USA) (0.6× and 0.09×) and followed by selection of biotinylated P2 adapters. This was followed by PCR with KAPA HiFi HotStart ReadyMix (Roche, Switzerland) to amplify DNA and add 12 different Illumina primers to identify pools. Pools were then purified and combined into a final library. Mean fragment size of the library was 606 bp, as measured with the 2200 TapeStation (Agilent, USA), which corresponds to a mean insert size of 470 bp. The library was sequenced in a single lane of an SP flow cell on an Illumina NovaSeq 6000 System with 2 × 150 bp paired-end sequencing (at Functional Genomic Center, Zürich). A total of 307.2 million paired-end reads were obtained from P, F1, and F2 individuals and 11 CV17-84 control samples.GenotypingWe used the dDocent pipeline (Puritz et al. 2014; Puritz et al. 2014) for genotyping. Reads were demultiplexed with the process_radtags function of the STACKS package (v 2.14, Catchen et al. 2013) with disabled filtering of degraded cut sites, which led to 304.2 million demultiplexed paired-end reads. BWA-MEM (v 0.7.17, Li and Durbin 2010) was used with default settings to map reads to the reference genome of L. fabarum (Lf_genome_V1.0.fa, Dennis et al. 2020). On average (±SD), 1.585 (±1.058) million reads were assigned per sample during demultiplexing. out of which an average of 81.66% were mapped and retained after filtering for mapping quality (Supplementary Table S1). We called 547,092 variants using freebayes (v 1.3.1, Garrison and Marth 2012) with the default settings from dDocent pipeline specifying population (corresponding to the generation P, F1, F2, or CV17-84) and ploidy of individuals. The VCF-file was then split into a dataset containing 355 haploid individuals, i.e., males (one P, 354 F2) and a dataset with 29 diploid individuals i.e., females (1 P, 11 CV17-84, 17 F1). The dataset with diploids was filtered following the dDocent filtering pipeline up until removing indels, retaining 2456 single-nucleotide polymorphisms (SNPs). The following changes were made to the tutorial: the minimum quality score (–minQ) was set to 20, the minimum mean depth (–min-meanDP) was set to 10, and the maximum mean depth (–max-meanDP) was set to 400. The haploid dataset was then transformed to allelic primitives and filtered to contain only the 2456 SNPs that were retained in the diploid dataset. The VCF files containing haploid and diploid samples were then transformed to SNP tables using samtools (v 1.9, Li et al. 2009) and custom bash scripts. A custom R-script was then used to filter the SNP tables and create an input file for linkage mapping with MSTmap (Wu et al. 2008). The retained SNPs are homozygous in the mother, biallelic among the two parent individuals, and known in both parent individuals. Additionally, we tested for segregation distortion, removing SNPs that deviate significantly from an allele frequency of 50% based on a chi-square test with Bonferroni-corrected false-discovery rate of 5%. For each allele in each offspring (F2) male, alleles were recoded as “A” for maternal, “B” for paternal, and “U” for unknown. SNPs missing in >50% of individuals and individuals with >50% unknown genotypes were removed. The dataset used for linkage mapping contained 351 F2 individuals and 1838 SNPs of which 3 were removed by MSTmap internal filters leading to a final dataset of 1835 SNPs contained in the linkage map.Quantification of genotyping errorGenotyping error rate was quantified by counting mismatches between the supposedly identical genotypes of 11 CV17-84 DNA samples that were sequenced as part of 11 different pools. The 1835 SNPs used for QTL mapping were used as a template to filter SNPs in the dataset with CV17-84 individuals with vcftools (–positions flag). A SNP table containing CV17-84 genotypes was then analyzed in R to quantify genotyping error. For each pair of CV17-84 samples, the proportion of genotype mismatches was counted and averaged over all comparisons to obtain an estimate of mean genotyping error. Unknown genotypes were not counted as mismatch. The mean percentage of pairwise mismatches among the 11 CV17-84 samples ranged from 0.8392 to 1.706% with an average of 1.207%. The average mismatch measure was employed as an estimate for the genotyping error during analyses with R/qtl (Broman et al. 2003).Linkage map and QTL mappingLinkage mapping was performed with MSTmap (Wu et al. 2008) using the following settings: population_type = DH, distance_function = kosambi, cut_off_p_value = 0.000001, no_map_dist = 15.0, no_map_size = 2, missing_threshold = 0.25, estimation_before_clustering = no, detect_bad_data = yes, objective_function = COUNT. The resulting distance matrix was processed with R to contain only marker locations, Linkage group (LG) ID, and map distance. The new linkage map was edited in order to use the same LG IDs and orientations as in the linkage map by Dennis et al. (2020).Phenotype data, genotype data, and the new linkage map were merged with a custom R script to produce an input file for R/qtl (Broman et al. 2003). After reading the dataset with R/qtl, its cross type was transformed to recombinant-inbred by selfing (convert2riself function) because this expects no heterozygotes and genotype frequencies at 0.5, which fits our crossing scheme. We tested for duplicated genotypes ( >90% similarity between individuals), checked for switched markers using the checkAlleles function, and plotted recombination fractions (Supplementary Fig. S1), none of which indicated any problems. Intermarker distance was estimated with the est.map function, setting map function to “kosambi” and tolerance to 10−4. The resulting map was used as new linkage map with cM as map unit. Conditional genotype probabilities were calculated at a step size of 0.1 cM. The scanone function was used to calculate logarithmic of the odds (LOD) scores over the genome using the default (EM) algorithm with nonparametric and binary model for the corrected count phenotype and the additional binary phenotype, respectively. Significance thresholds were calculated by conducting 1000 permutations and choosing a 5% cut-off corresponding to the significance threshold at an alpha of 5%. The 95% approximate Bayes confidence interval was then calculated for the chromosome with significant LOD score. After simulating genotypes 1000 times with a step size of 0.1 cM and pulling genotype probabilities at the peak LOD, the explained phenotypic variance was estimated with the fitqtl function.Candidate gene identificationAs RADseq loci are usually short and represent a small proportion of the genome, they are unlikely located in candidate genes themselves. The 95% approximate Bayes confidence interval of the single significant QTL we identified includes all markers on scaffold tig00000002, upwards of 311,170 (bp). Thus, we considered tig00000002 from position 311,170 on as region for searching candidate genes. Gene annotations were retrieved from the recently published L. fabarum genome (Dennis et al. 2020). In addition, we identified putative venom and toxin genes in the L. fabarum genome in order to explore this function among candidate genes. To do so, we collected venom protein sequences from several parasitoid wasp species: Nasonia vitripennis (Danneels et al. 2010), Chelonus inanitus (Vincent et al. 2010), Microplitis demolitor (Burke and Strand 2014), Fopius arisanus (Geib et al. 2017), Diachasma alloeum (Tvedte et al. 2019), Cotesia congregata (Gauthier et al. 2021), Leptopilina boulardi, Leptopilina heterotoma (Goecks et al. 2013), and Aphidius ervi (Colinet et al. 2014); and retrieved candidate animal toxin proteins (7151 sequences) from the UniProt Animal Toxin Annotation Program database (UATdb, Jungo et al. 2012). These proteins were then matched to L. fabarum proteins by blastp (-e-value  More

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