Ecology

Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
CAS 

Google Scholar 
McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the Anthropogene. Trends Ecol. Evol. 30, 104–113 (2015).
Google Scholar 
Bradshaw, C. J. A., Sodhi, N. S. & Brook, B. W. Tropical turmoil: A biodiversity tragedy in progress. Front. Ecol. Environ. 7, 79–87 (2009).
Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
CAS 

Google Scholar 
Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294, 804–808 (2001).ADS 
CAS 

Google Scholar 
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS 
CAS 

Google Scholar 
Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 

Google Scholar 
Pasari, J. R., Levi, T., Zavaleta, E. S. & Tilman, D. Several scales of biodiversity affect ecosystem multifunctionality. Proc. Natl. Acad. Sci. U.S.A. 110, 10219–10222 (2013).ADS 
CAS 

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Google Scholar 
Murphy, G. E. P. & Romanuk, T. N. A meta-analysis of declines in local species richness from human disturbances. Ecol. Evol. 4, 91–103 (2014).
Google Scholar 
Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).ADS 
CAS 

Google Scholar 
de Coster, G., Banks-Leite, C. & Metzger, J. P. Atlantic forest bird communities provide different but not fewer functions after habitat loss. Proc. R. Soc. B 282, 20142844 (2015).
Google Scholar 
Riemann, J. C., Ndriantsoa, S. H., Rödel, M.-O. & Glos, J. Functional diversity in a fragmented landscape—habitat alterations affect functional trait composition of frog assemblages in Madagascar. Global Ecol. Conserv. 10, 173–183 (2017).
Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: A few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).CAS 

Google Scholar 
Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).
Google Scholar 
van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multi-functionality. Proc. Natl. Acad. Sci. U.S.A. 113, 3557–3562 (2016).ADS 

Google Scholar 
Mori, A. S., Isbell, F. & Seidl, R. β-diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).
Google Scholar 
Dehling, J. M. & Dehling, D. M. Conserving ecological functions of frog communities in Borneo requires diverse forest landscapes. Global Ecol. Conserv. 26, e01481 (2021).
Google Scholar 
Hector, A. & Bagchi, R. Biodiversity and ecosystem multifunctionality. Nature 448, 188–190 (2007).ADS 
CAS 

Google Scholar 
Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).ADS 
CAS 

Google Scholar 
Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl. Acad. Sci. U.S.A. 100, 12765–12770 (2003).ADS 
CAS 

Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 

Google Scholar 
Felipe-Lucia, M. R. et al. Land-use intensity alters networks between biodiversity, ecosystem functions, and services. Proc. Natl. Acad. Sci. U.S.A. 117, 28140–28149 (2020).ADS 
CAS 

Google Scholar 
Tilman, D. Functional diversity in Encyclopedia of biodiversity, Vol. 3. (ed. Levin S. A.) 109–120 (Academic Press, 2001)Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).
Google Scholar 
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity-ecosystem function relationships. Ecology 92, 1573–1581 (2011).
Google Scholar 
Lean, C. & Maclaurin, J. The value of phylogenetic diversity in Biodiversity conservation and phylogenetic systematics. Topics in Biodiversity and Conservation 14. (eds. Pellens, R., Grandcolas, P.) 19–38 (Springer, 2016).Owen, N. R., Gumbs, R., Gray, C. L. & Faith, D. P. Global conservation of phylogenetic diversity captures more than just functional diversity. Nat. Commun. 10, 859 (2019).ADS 

Google Scholar 
Gumbs, R., Williams, R. C., Lowney, A. M. & Smith, D. Spatial and species-level metrics reveal global patterns of irreplaceable and imperiled gecko phylogenetic diversity. Israel J. Ecol. Evolut. 66, 239–252 (2020).
Google Scholar 
Brooks, D. R., Mayden, R. L. & McLennan, D. A. Phylogeny and biodiversity: Conserving our evolutionary legacy. Trends Ecol. Evol. 7, 55–59 (1992).CAS 

Google Scholar 
Phillimore, A. B. et al. Biogeographical basis of recent phenotypic divergence among birds: a global study of subspecies richness. Evolution 61, 942–957 (2007).
Google Scholar 
Miraldo, A. et al. An Anthropocene map of genetic diversity. Science 353, 1532–1535 (2016).ADS 
CAS 

Google Scholar 
Smith, B. T., Seeholzer, G. F., Harvey, M. G., Cuervo, A. M. & Brumfield, R. T. A latitudinal phylogeographic diversity gradient in birds. PLoS Biol. 15, e2001073 (2017).
Google Scholar 
Tucker, C. M. et al. Assessing the utility of conserving evolutionary history. Biol. Rev. 94, 1740–1760 (2019).
Google Scholar 
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).
Google Scholar 
Villéger, S., Miranda, J. R., Hernández, D. F. & Mouillot, D. Contrasting changes in taxonomic vs. functional diversity of tropical fish communities after habitat degradation. Ecological Applications 20, 1512–1522 (2010).Gibbons, J. W. et al. Remarkable amphibian biomass and abundance in an isolated wetland: Implications for wetland conservation. Conserv. Biol. 20, 1457–1465 (2006).
Google Scholar 
Hocking, D. J. & Babbitt, K. J. Amphibian contributions to ecosystem services. Herpetol. Conserv. Biol. 9, 1–17 (2014).
Google Scholar 
Beebee, T. J. C. Amphibian breeding and climate change. Nature 374, 219–220 (1995).ADS 
CAS 

Google Scholar 
Kiesecker, J. M., Blaustein, A. R. & Belden, L. K. Complex causes of amphibian population declines. Nature 410, 681–684 (2001).ADS 
CAS 

Google Scholar 
Cheng, T. L., Rovito, S. M., Wake, D. B. & Vredenburg, V. T. Coincident mass extirpation of neotropical amphibians with the emergence of the infection fungal pathogen Batrachochytrium dendrobatidis. Proc. Natl. Acad. Sci. U.S.A. 108, 9502–9507 (2011).ADS 
CAS 

Google Scholar 
Wake, D. B. & Vredenburg, V. T. Are we in the midst of the sixth mass extinction? A view from the world of amphibians. Proc. Natl. Acad. Sci. U.S.A. 105, 11466–11473 (2008).ADS 
CAS 

Google Scholar 
Ernst, R. & Rödel, M.-O. Patterns of community composition in two tropical tree frog assemblages: Separating spatial structure and environmental effects in disturbed and undisturbed forests. J. Trop. Ecol. 24, 111–120 (2008).
Google Scholar 
Gardner, T. A. et al. The value of primary, secondary, and plantation forests for a Neotropical Herpetofauna. Conserv. Biol. 21, 775–787 (2007).
Google Scholar 
Gardner, T. A., Fitzherbert, E. B., Drewes, R. C., Howell, K. M. & Caro, T. Spatial and temporal patterns of abundance and diversity of an East African leaf litter amphibian fauna. Biotropica 39, 105–113 (2007).
Google Scholar 
Gillespie, G. R. et al. Conservation of amphibians in Borneo: relative value of secondary tropical forest and non-forest habitats. Biol. Cons. 152, 136–144 (2012).
Google Scholar 
Angarita-M., O., Montes-Correa, A. C. & Renjifo, J. M. Amphibians and reptiles of an agroforestry system in the Colombian Caribbean. Amphibian & Reptile Conservation 8, 33–52 (2015).Jiménez-Robles, O., Guayasamin, J. M., Ron, S. R. & De la Riva, I. Reproductive traits associated with species turnover of amphibians in Amazonia and its Andean slopes. Ecol. Evol. 7, 2489–2500 (2017).
Google Scholar 
Ernst, R., Linsenmair, K. E. & Rödel, M.-O. Diversity erosion beyond the species level: dramatic loss of functional diversity after selective logging in two tropical amphibian communities. Biol. Cons. 133, 143–155 (2006).
Google Scholar 
Oda, F. H. et al. Anuran species richness, composition, and breeding habitat preferences: a comparison between forest remnants and agricultural landscapes in Southern Brazil. Zool. Stud. 55, 34 (2016).
Google Scholar 
Sinsch, U., Lümkemann, K., Rosar, K., Schwarz, C. & Dehling, J. M. Acoustic niche partitioning in an anuran community inhabiting an Afromontane wetland (Butare, Rwanda). African Zool. 47, 60–73 (2012).
Google Scholar 
Tumushimire, L., Mindje, M., Sinsch, U. & Dehling, J. M. The anuran diversity of cultivated wetlands in Rwanda: Melting pot of generalists?. Salamandra 56, 99–112 (2020).
Google Scholar 
REMA. Rwanda State of Environment and Outlook Report 2017 – Achieving Sustainable Urbanization. (Rwanda Environment Management Authority, Government of Rwanda, 2017).Su, J. C., Debinski, D. M., Jakubauskas, M. E. & Kindscher, K. Beyond species richness: Community similarity as a measure of cross-taxon congruence for coarse-filter conservation. Conserv. Biol. 18, 167–173 (2004).
Google Scholar 
Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).ADS 
CAS 

Google Scholar 
Zimkus, B. M., Rödel, M.-O. & Hillers, A. Complex patterns of continental speciation: Molecular phylogenetics and biogeography of sub-Saharan puddle frogs (Phrynobatrachus). Mol. Phylogenet. Evol. 55, 883–900 (2010).
Google Scholar 
Dehling, J. M. & Sinsch, U. Partitioning of morphospace in larval and adult reed frogs (Anura: Hyperoliidae: Hyperolius) of the Central African Albertine Rift. Zool. Anz. 280, 65–77 (2019).
Google Scholar 
Mazel, F. et al. Prioritizing phylogenetic diversity captures functional diversity unreliably. Nat. Commun. 9, 2888 (2018).ADS 

Google Scholar 
Haddad, C. F. B. & Prado, C. P. A. Reproductive modes and their unexpected diversity in the Atlantic forest of Brazil. Bioscience 55, 207–217 (2005).
Google Scholar 
Capinha, C., Essl, F., Seebens, H., Moser, D. & Pereira, H. M. The dispersal of alien species redefines biogeography in the Anthropocene. Science 348, 1248–1251 (2015).ADS 
CAS 

Google Scholar 
Alroy, J. Effects of habitat disturbance on tropical forest biodiversity. Proc. Natl. Acad. Sci. U.S.A. 114, 6056–6061 (2017).ADS 
CAS 

Google Scholar 
Dehling, J. M. & Sinsch, U. Diversity of Ptychadena in Rwanda and taxonomic status of P. chrysogaster Laurent, 1954 (Amphibia, Anura, Ptychadenidae). ZooKeys 356, 69–102 (2013).IUCN. The IUCN Red List of Threatened Species. Version 2020–1. https://www.iucnredlist.org (2020).Portillo, F., Greenbaum, E., Menegon, M., Kusamba, C. & Dehling, J. M. Phylogeography and species boundaries of Leptopelis (Anura: Arthroleptidae) from the Albertine Rift. Mol. Phylogenet. Evol. 82, 75–86 (2015).
Google Scholar 
Channing, A., Dehling, J. M., Lötters, S. & Ernst, R. Species boundaries and taxonomy of the African River Frogs (Anura: Pyxicephalidae: Amietia). Zootaxa 4155, 1–76 (2016).CAS 

Google Scholar 
Rödel, M.-O. & Ernst, R. Measuring and monitoring amphibian diversity in tropical forests. I. An evaluation of methods with recommendations for standardization. Ecotropica 10, 1–14 (2004).Channing, A. & Howell, K. M. Amphibians of East Africa. (Chimaira, 2006).Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evolut. 2, 850–858 (2018).
Google Scholar 
Villéger, S., Mason, N. W. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).
Google Scholar 
Maire, E., Grenouillet, G., Brosse, S. & Villéger, S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Glob. Ecol. Biogeogr. 24, 728–740 (2015).
Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Cons. 61, 1–10 (1992).
Google Scholar 
Dehling, D. M. et al. Functional and phylogenetic diversity and assemblage structure of frugivorous birds along an elevational gradient in the tropical Andes. Ecography 37, 1047–1055 (2014).
Google Scholar 
Baselga, A. et al. betapart: partitioning beta diversity into turnover and nestedness components. R package version 1.5.6. https://CRAN.R-project.org/package=betapart (2022).Dehling, D. M. et al. Specialists and generalists fulfil important and complementary functional roles in ecological processes. Funct. Ecol. 35, 1810–1821 (2021).CAS 

Google Scholar 
Dehling, D. M., Barreto, E. & Graham, C. H. The contribution of mutualistic interactions to functional and phylogenetic diversity. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.05.006 (2022).Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2021). More

German CR, Von Damm KL. Hydrothermal processes. In: Holland HD, Turekian KK and Elderfield H, editors. Treatise geochem, Vol. 6. The oceans and marine geochemistry. Oxford, UK:Elsevier-Pergamon, 2004;181–222.Bell JB, Woulds C, Oevelen DV. Hydrothermal activity, functional diversity and chemoautotrophy are major drivers of seafloor carbon cycling. Sci Rep. 2017;7:1–3.
Google Scholar 
McCollom TM. Geochemical constraints on primary productivity in submarine hydrothermal vent plumes. Deep Res Part I Oceanogr Res Pap. 2000;47:85–101.CAS 

Google Scholar 
Tunnicliffe V, Baross JA, Gebruk AV, Giere O, Holland ME, Koschinsky A, et al. Group report: what are the interactions between biotic processes at vents and physical, chemical, and geological conditions. In: Halbach PE, Tunnicliffe V, and Hein JR, editors. Energy and Mass Transfer in Marine Hydrothermal Systems. Berlin-Dahlem:University Press; 2003;251–70.Nakamura K, Takai K. Theoretical constraints of physical and chemical properties of hydrothermal fluids on variations in chemolithotrophic microbial communities in seafloor hydrothermal systems. Prog Earth Planet Sci. 2014;1:1–24.
Google Scholar 
Wang W, Li Z, Zeng L, Dong C, Shao Z. The oxidation of hydrocarbons by diverse heterotrophic and mixotrophic bacteria that inhabit deep-sea hydrothermal ecosystems. ISME J. 2020;14:1994–2006.CAS 

Google Scholar 
Sinha RK, Krishnan KP, Kurian PJ. Complete genome sequence and comparative genome analysis of Alcanivorax sp. IO_7, a marine alkane-degrading bacterium isolated from hydrothermally-influenced deep seawater of southwest Indian ridge. Genomics 2021;113:884–91.CAS 

Google Scholar 
Li J, Yang J, Sun M, Su L, Wang H, Gao J, et al. Distribution and succession of microbial communities along the dispersal pathway of hydrothermal plumes on the Southwest Indian Ridge. Front Mar Sci. 2020;7:581381.
Google Scholar 
Meier DV, Bach W, Girguis PR, Gruber-Vodicka HR, Reeves EP, Richter M, et al. Heterotrophic Proteobacteria in the vicinity of diffuse hydrothermal venting. Environ Microbiol. 2016;18:4348–68.
Google Scholar 
Li WL, Huang JM, Zhang PW, Cui GJ, Wei ZF, Wu YZ, et al. Periodic and spatial spreading of alkanes and Alcanivorax bacteria in deep waters of the Mariana Trench. Appl Environ Microbiol. 2019;85:e02089–18.CAS 

Google Scholar 
Brooijmans RJW, Pastink MI, Siezen RJ. Hydrocarbon-degrading bacteria: The oil-spill clean-up crew. Micro Biotechnol. 2009;2:587.CAS 

Google Scholar 
Scoma A, Barbato M, Borin S, Daffonchio D, Boon N. An impaired metabolic response to hydrostatic pressure explains Alcanivorax borkumensis recorded distribution in the deep marine water column. Sci Rep. 2016;6:1–3.
Google Scholar 
Lai Q, Wang L, Liu Y, Fu Y, Zhong H, Wang B, et al. Alcanivorax pacificus sp. nov., isolated from a deep-sea pyrene-degrading consortium. Int J Syst Evol Microbiol. 2011;61:1370–4.CAS 

Google Scholar 
Wu Y, Lai Q, Zhou Z, Qiao N, Liu C, Shao Z. Alcanivorax hongdengensis sp. nov., an alkane-degrading bacterium isolated from surface seawater of the straits of Malacca and Singapore, producing a lipopeptide as its biosurfactant. Int J Syst Evol Microbiol. 2009;59:1474–9.CAS 

Google Scholar 
Fernández-Martínez J, Pujalte MJ, García-Martínez J, Mata M, Garay E, Rodríguez-Valera F. Description of Alcanivorax venustensis sp. nov. and reclassification of Fundibacter jadensis DSM 12178T (Bruns and Berthe-Corti 1999) as Alcanivorax jadensis comb. nov., members of the emended genus Alcanivorax. Int J Syst Evol Microbiol. 2003;53:331–8.
Google Scholar 
Radwan SS, Khanafer MM, Al-Awadhi HA. Ability of the so-called obligate hydrocarbonoclastic bacteria to utilize nonhydrocarbon substrates thus enhancing their activities despite their misleading name. BMC Microbiol. 2019;19:1–2.
Google Scholar 
Kalscheuer R, Stöveken T, Malkus U, Reichelt R, Golyshin PN, Sabirova JS, et al. Analysis of storage lipid accumulation in Alcanivorax borkumensis: Evidence for alternative triacylglycerol biosynthesis routes in bacteria. J Bacteriol. 2007;189:918–28.CAS 

Google Scholar 
Timm C, Davy B, Haase K, Hoernle KA, Graham IJ, De Ronde CEJ, et al. Subduction of the oceanic Hikurangi Plateau and its impact on the Kermadec arc. Nat Commun. 2014;5:1–9.
Google Scholar 
Haase KM, Beier C, Bach W, Kleint C, Anderson MO, Rubin K, et al. SO-263 Cruise Report: Tonga Rift. 2018. https://doi.org/10.13140/RG.2.2.23035.16169.Gartman A, Hannington M, Jamieson JW, Peterkin B, Garbe-Schönberg D, Findlay AJ, et al. Boiling-induced formation of colloidal gold in black smoker hydrothermal fluids. Geology 2018;46:39–42.CAS 

Google Scholar 
Falkenberg JJ, Keith M, Haase KM, Bach W, Klemd R, Strauss H, et al. Effects of fluid boiling on Au and volatile element enrichment in submarine arc-related hydrothermal systems. Geochim Cosmochim Acta. 2021;307:105–32.CAS 

Google Scholar 
Peters C, Strauss H, Haase K, Bach W, de Ronde CEJ, Kleint C, et al. SO2 disproportionation impacting hydrothermal sulfur cycling: Insights from multiple sulfur isotopes for hydrothermal fluids from the Tonga-Kermadec intraoceanic arc and the NE Lau Basin. Chem Geol. 2021;586:120586.CAS 

Google Scholar 
Baker ET, Walker SL, Massoth GJ, Resing JA. The NE Lau Basin: Widespread and abundant hydrothermal venting in the back-arc region behind a superfast subduction zone. Front Mar Sci. 2019;6:382.
Google Scholar 
Kim J, Lee KY, Kim JH. Metal-bearing molten sulfur collected from a submarine volcano: Implications for vapor transport of metals in seafloor hydrothermal systems. Geology 2011;39:351–4.CAS 

Google Scholar 
Klose L, Keith M, Hafermaas D, Kleint C, Bach W, Diehl A, et al. Trace element and isotope systematics in vent fluids and sulphides from Maka volcano, North Eastern Lau Spreading Centre: Insights into three-component fluid mixing. Front Earth Sci. 2021;9:1–26.
Google Scholar 
Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS 

Google Scholar 
Dede B, Hansen CT, Neuholz R, Schnetger B, Kleint C, Walker S, et al. Niche differentiation of sulfur-oxidizing bacteria (SUP05) in submarine hydrothermal plumes. ISME J. 2022;16:1479–90.CAS 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.
Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.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 

Google Scholar 
McMurdie PJ, Holmes S. Phyloseq: An R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 

Google Scholar 
Diehl A, Bach W. MARHYS (MARine HYdrothermal Solutions) Database: A global compilation of marine hydrothermal vent fluid, end member, and seawater compositions. Geochem Geophys Geosystems. 2020;21:e2020GC009385.
Google Scholar 
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 

Google Scholar 
Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 2012;28:1823–9.CAS 

Google Scholar 
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar A, et al. ARB: A software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 

Google Scholar 
Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.CAS 

Google Scholar 
Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014;30:1312–3.CAS 

Google Scholar 
Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS 

Google Scholar 
Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.CAS 

Google Scholar 
Daims H, Brühl A, Amann R, Schleifer KH, Wagner M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: Development and evaluation of a more comprehensive probe set. Syst Appl Microbiol. 1999;22:434–44.CAS 

Google Scholar 
Wallner G, Amann R, Beisker W. Optimizing fluorescent in situ hybridization with rRNA‐targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 1993;14:136–43.CAS 

Google Scholar 
Stahl DA, Amann R. Development and application of nucleic acid probes in bacterial systematics. In: Nucleic acid techniques in bacterial systematics. Stackebrandt, E, Goodfellow M, editors. Chichester, UK: John Wiley & Sons Ltd; 1991. pp. 205–48.Manz W, Amann R, Ludwig W, Wagner M, Schleifer KH. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: Problems and solutions. Syst Appl Microbiol. 1992;15:593–600.
Google Scholar 
Eilers H, Pernthaler J, Glöckner FO, Amann R. Culturability and in situ abundance of pelagic Bacteria from the North Sea. Appl Environ Microbiol. 2000;66:3044–51.CAS 

Google Scholar 
Syutsubo K, Kishira H, Harayama S. Development of specific oligonucleotide probes for the identification and in situ detection of hydrocarbon-degrading Alcanivorax strains. Environ Microbiol. 2001;3:371–9.CAS 

Google Scholar 
Morris RM, Rappé MS, Urbach E, Connon SA, Giovannoni SJ. Prevalence of the Chloroflexi-related SAR202 bacterioplankton cluster throughout the mesopelagic zone and deep ocean. Appl Environ Microbiol. 2004;70:2836–42.CAS 

Google Scholar 
Bushnell B BBMap (version 35.14). 2015. https://sourceforge.net/projects/bbmap/.Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinforma. 2010; http://www.bioinformatics.babraham.ac.uk/projects/.Rodriguez-R LM, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: Fast estimation of metagenomic coverage and sequence diversity. mSystems 2018;3:e00039–18.
Google Scholar 
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.
Google Scholar 
Kopylova E, Noé L, Touzet H. SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012;28:3211–7.CAS 

Google Scholar 
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.CAS 

Google Scholar 
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 2013;29:1072–5.CAS 

Google Scholar 
Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 

Google Scholar 
Eren AM, Kiefl E, Shaiber A, Veseli I, Miller SE, Schechter MS, et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol. 2021;6:3–6.CAS 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 

Google Scholar 
Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotech. 2017;35:725–31.CAS 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
Google Scholar 
Seemann T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 

Google Scholar 
Priest T, Heins A, Harder J, Amann R, Fuchs BM. Niche partitioning of the ubiquitous and ecologically relevant NS5 marine group. ISME J. 2022;16:1570–82.CAS 

Google Scholar 
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.CAS 

Google Scholar 
Karthikeyan S, Rodriguez‐R LM, Heritier‐Robbins P, Hatt JK, Huettel M, Kostka JE, et al. Genome repository of oil systems: An interactive and searchable database that expands the catalogued diversity of crude oil‐associated microbes. Environ Microbiol. 2020;22:2094–106.CAS 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.CAS 

Google Scholar 
Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.CAS 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 

Google Scholar 
Gomes AÉI, Stuchi LP, Siqueira NMG, Henrique JB, Vicentini R, Ribeiro ML, et al. Selection and validation of reference genes for gene expression studies in Klebsiella pneumoniae using Reverse Transcription Quantitative real-time PCR. Sci Rep. 2018;8:1–4.
Google Scholar 
Guidi L, Chaffron S, Bittner L, Eveillard D, Larhlimi A, Roux S, et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 2016;532:465–70.CAS 

Google Scholar 
Duarte CM. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol Oceanogr Bull. 2015;24:11–4.
Google Scholar 
Anantharaman K, Breier JA, Dick GJ. Metagenomic resolution of microbial functions in deep-sea hydrothermal plumes across the Eastern Lau Spreading Center. ISME J. 2016;10:225–39.CAS 

Google Scholar 
Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front Microbiol. 2017;8:682.
Google Scholar 
Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Addendum: Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl.nov.). Front Microbiol. 2017;9:772.
Google Scholar 
Green DH, Llewellyn LE, Negri AP, Blackburn SI, Bolch CJS. Phylogenetic and functional diversity of the cultivable bacterial community associated with the paralytic shellfish poisoning dinoflagellate Gymnodinium catenatum. FEMS Microbiol Ecol. 2004;47:345–57.CAS 

Google Scholar 
Ramasamy KP, Rajasabapathy R, Lips I, Mohandass C, James RA. Genomic features and copper biosorption potential of a new Alcanivorax sp. VBW004 isolated from the shallow hydrothermal vent (Azores, Portugal). Genomics 2020;112:3268–73.CAS 

Google Scholar 
Barbato M, Scoma A, Mapelli F, De Smet R, Banat IM, Daffonchio D, et al. Hydrocarbonoclastic Alcanivorax isolates exhibit different physiological and expression responses to N-dodecane. Front Microbiol. 2016;7:2056.
Google Scholar 
Sevilla E, Yuste L, Rojo F. Marine hydrocarbonoclastic bacteria as whole-cell biosensors for n-alkanes. Micro Biotechnol. 2015;8:693–706.CAS 

Google Scholar 
Tivey MK. Black and white smokers. In: Harff J, Meschede M, Petersen S, Thiede Jö, editors. Encyclopedia of Marine Geosciences. Dordrecht: Springer Netherlands; 2016. p. 58–62.Djurhuus A, Mikalsen SO, Giebel HA, Rogers AD. Cutting through the smoke: The diversity of microorganisms in deep-sea hydrothermal plumes. R Soc Open Sci. 2017;4:160829.
Google Scholar 
Leahy JG, Colwell RR. Microbial degradation of hydrocarbons in the environment. Microbiol Rev. 1990;54:305–15.CAS 

Google Scholar 
Atlas R, Bragg J. Bioremediation of marine oil spills: When and when not – The Exxon Valdez experience. Micro Biotechnol. 2009;2:213–21.CAS 

Google Scholar 
Reva ON, Hallin PF, Willenbrock H, Sicheritz-Ponten T, Tümmler B, Ussery DW. Global features of the Alcanivorax borkumensis SK2 genome. Environ Microbiol. 2008;10:614–25.CAS 

Google Scholar 
Gregory GJ, Morreale DP, Carpenter MR, Kalburge SS, Boyd EF. Quorum sensing regulators AphA and OpaR control expression of the operon responsible for biosynthesis of the compatible solute ectoine. Appl Environ Microbiol. 2019;85:e01543–19.CAS 

Google Scholar 
Richter AA, Mais CN, Czech L, Geyer K, Hoeppner A, Smits SHJ, et al. Biosynthesis of the stress-protectant and chemical chaperon ectoine: biochemistry of the transaminase EctB. Front Microbiol. 2019;10:2811.
Google Scholar 
Schneiker S, Dos Santos VAPM, Bartels D, Bekel T, Brecht M, Buhrmester J, et al. Genome sequence of the ubiquitous hydrocarbon-degrading marine bacterium Alcanivorax borkumensis. Nat Biotechnol. 2006;24:997–1004.CAS 

Google Scholar 
Wang W, Shao Z. Enzymes and genes involved in aerobic alkane degradation. Front Microbiol. 2013;4:116.
Google Scholar 
Barclay W, Rodd JA, Pflueger JC, Havard KR, Helu SP. Oil plays in the kingdom of Tonga, Southwest Pacific. PESA J. 1993;21:79–92.
Google Scholar 
Chadwick WW, Rubin KH, Merle SG, Bobbitt AM, Kwasnitschka T, Embley RW. Recent eruptions between 2012-2018 discovered at West Mata submarine volcano (NE Lau Basin, SW Pacific) and characterized by new ship, AUV, and ROV data. Front Mar Sci. 2019;6:495.
Google Scholar 
Baumberger T, Lilley MD, Lupton JE, Baker ET, Resing JA, Buck NJ, et al. Dissolved gas and metal composition of hydrothermal plumes from a 2008 submarine eruption on the Northeast Lau Spreading Center. Front Mar Sci. 2020;7:171.
Google Scholar 
Lupton J, Rubin KH, Arculus R, Lilley M, Butterfield D, Resing J, et al. Helium isotope, C/3 He, and Ba‐Nb‐Ti signatures in the northern Lau Basin: Distinguishing arc, back‐arc, and hotspot affinities. Geochem Geophys. 2015;16:1133–55.CAS 

Google Scholar 
Graham DW. Noble gas isotope geochemistry of mid-ocean ridge and ocean island basalts: Characterization of mantle source reservoirs. In: Porcelli D, Wieler R, Ballentine C, editors. Noble gases in Geochemistry and cosmochemistry, Rev Mineral Geochem. Vol 47. Washington D.C.: Mineral Soc. Of Am; 2002. p. 247–318.Lupton JE, Arculus RJ, Greene RR, Evans LJ, Goddard CI. Helium isotope variations in seafloor basalts from the Northwest Lau Backarc Basin: Mapping the influence of the Samoan hotspot. Geophys Res Lett. 2009;36:L17313.
Google Scholar 
Gordon GW. Naturally occurring organohalogen compounds – A comprehensive survey. Prog Chem Org Nat Prod. 1996;68:1–423.
Google Scholar 
Spietz RL, Butterfield DA, Buck NJ, Larson BI, Chadwick WW, Walker SL, et al. Deep-sea volcanic eruptions create unique chemical and biological linkages between the subsurface lithosphere and the oceanic hydrosphere. Oceanography. 2018;31:128–35.
Google Scholar 
Huber JA, Butterfield DA, Baross JA. Bacterial diversity in a subseafloor habitat following a deep-sea volcanic eruption. FEMS Microbiol Ecol. 2003;43:393–409.CAS 

Google Scholar  More

Brown, J. S., Laundre, J. W. & Gurung, M. The ecology of fear: optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399 (1999).
Google Scholar 
Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the ‘landscape of fear’ in Yellowstone National Park, US.A. Can. J. Zool. 79, 1401–1409 (2001).
Google Scholar 
Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).CAS 

Google Scholar 
Laundre, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).
Google Scholar 
Loggins, A. A., Shrader, A. M., Monadjem, A. & McCleery, R. A. Shrub cover homogenizes small mammals’ activity and perceived predation risk. Sci. Rep. 9, 16857 (2019).
Google Scholar 
Whittingham, M. J. & Evans, K. L. The effects of habitat structure on predation risk of birds in agricultural landscapes. Ibis 146, 210–220 (2004).
Google Scholar 
Marshall, K. L. A., Philpot, K. E. & Stevens, M. Microhabitat choice in island lizards enhances camouflage against avian predators. Sci. Rep. 6, 19815 (2016).CAS 

Google Scholar 
Stevens, M., Troscianko, J., Wilson-Aggarwal, J. K. & Spottiswoode, C. N. Improvement of individual camouflage through background choice in ground-nesting birds. Nat. Ecol. Evol. 1, 1325–1333 (2017).
Google Scholar 
Wilson-Aggarwal, J. K., Troscianko, J. T., Stevens, M. & Spottiswoode, C. N. Escape distance in ground-nesting birds differs with individual level of camouflage. Am. Nat. 188, 231–239 (2016).
Google Scholar 
Troscianko, J., Wilson-Aggarwal, J., Stevens, M. & Spottiswoode, C. N. Camouflage predicts survival in ground-nesting birds. Sci. Rep. 6, 19966 (2016).CAS 

Google Scholar 
Gaston, K. J., Duffy, J. P., Gaston, S., Bennie, J. & Davies, T. W. Human alteration of natural light cycles: causes and ecological consequences. Oecologia 176, 917–931 (2014).
Google Scholar 
Gaston, K. J., Davies, T. W., Nedelec, S. L. & Holt, L. A. Impacts of artificial light at night on biological timings. Annu. Rev. Ecol. Evol. Syst. 48, 49–68 (2017).
Google Scholar 
Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).
Google Scholar 
Gaston, K. J. et al. Pervasiveness of biological impacts of artificial light at night. Integr. Comp. Biol. 61, 1098–1110 (2021).
Google Scholar 
Sanders, D., Frago, E., Kehoe, R., Patterson, C. & Gaston, K. J. A meta-analysis of biological impacts of artificial light at night. Nat. Ecol. Evol. 5, 74–81 (2021).
Google Scholar 
Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160248 (2017).
Google Scholar 
Underwood, C. N., Davies, T. W. & Queir Os, A. M. Artificial light at night alters trophic interactions of intertidal invertebrates. J. Anim. Ecol. 86, 781–789 (2017).
Google Scholar 
Burger, J., Howe, M. A., Hahn, D. C. & Chase, J. Effects of tide cycles on habitat selection and habitat partitioning by migrating shorebirds. Auk 94, 743–758 (1977).
Google Scholar 
Granadeiro, J. P., Dias, M. P., Martins, R. C. & Palmeirim, J. M. Variation in numbers and behaviour of waders during the tidal cycle: implications for the use of estuarine sediment flats. Acta Oecologica 29, 293–300 (2006).
Google Scholar 
Lourenço, P. M. et al. The energetic importance of night foraging for waders wintering in a temperate estuary. Acta Oecologica 34, 122–129 (2008).
Google Scholar 
McNeil, R., Drapeau, P. & Goss-Custard, J. D. The occurrence and adaptive significance of nocturnal habits in waterfowl. Biol. Rev. 67, 381–419 (1992).
Google Scholar 
Martin, G. R. Visual fields and their functions in birds. J. Ornithol. 148, 547–562 (2007).
Google Scholar 
Martin, G. R. What is binocular vision for? A birds’ eye view. J. Vis. 9, 1–19 (2009).
Google Scholar 
Davies, T. W., Duffy, J. P., Bennie, J. & Gaston, K. J. The nature, extent, and ecological implications of marine light pollution. Front. Ecol. Environ. 12, 347–355 (2014).
Google Scholar 
Leopold, M. F., Philippart, C. J. M. & Yorio, P. Nocturnal feeding under artificial light conditions by Brown-hooded Gull (Larus maculipennis) in Puerto Madryn harbour (Chubut Province, Argentina). Hornero 25, 55–60 (2010).
Google Scholar 
Pugh, A. R. & Pawson, S. M. Artificial light at night potentially alters feeding behaviour of the native southern black-backed gull (Larus dominicanus). Notornis 63, 37–39 (2016).
Google Scholar 
Santos, C. D. et al. Effects of artificial illumination on the nocturnal foraging of waders. Acta Oecologica 36, 166–172 (2010).
Google Scholar 
Montevecchi, W. A. Influences of Artificial Light on Marine Birds. in Ecological Consequences of Artificial Night Lighting (eds. Rich, C. & Longcore, T.) 94–113 (Island Press, 2006).Dwyer, R. G., Bearhop, S., Campbell, H. A. & Bryant, D. M. Shedding light on light: benefits of anthropogenic illumination to a nocturnally foraging shorebird. J. Anim. Ecol. 82, 478–485 (2013).
Google Scholar 
Blumstein, D. T. Developing an evolutionary ecology of fear: how life history and natural history traits affect disturbance tolerance in birds. Anim. Behav. 71, 389–399 (2006).
Google Scholar 
Stankowich, T. & Blumstein, D. T. Fear in animals: a meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 272, 2627–2634 (2005).
Google Scholar 
Caro, T. Antipredator Defenses in Birds and Mammals. (University of Chicago Press, 2005).Tillmann, J. E. Fear of the dark: night-time roosting and anti-predation behaviour in the grey partridge (Perdix perdix L.). Behaviour 146, 999–1023 (2009).
Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2022-1. https://www.iucnredlist.org/species/22693190/117917038 (2022).Brown, D. et al. The Eurasian Curlew—the most pressing bird conservation priority in the UK? Br. Birds 108, 660–668 (2015).
Google Scholar 
Franks, S. E., Douglas, D. J. T., Gillings, S. & Pearce-Higgins, J. W. Environmental correlates of breeding abundance and population change of Eurasian Curlew Numenius arquata in Britain. Bird. Study 64, 393–409 (2017).
Google Scholar 
Desholm, M. & Kahlert, J. Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005).
Google Scholar 
Clarke, J. A. Moonlight’s influence on predator/prey interactions between short-eared owls (Asio flammeus) and Deermice (Peromyscus maniculatus). Behav. Ecol. Sociobiol. 13, 205–209 (1983).
Google Scholar 
Mandelik, Y., Jones, M. & Dayan, T. Structurally complex habitat and sensory adaptations mediate the behavioural responses of a desert rodent to an indirect cue for increased predation risk. Evol. Ecol. Res. 5, 501–515 (2003).
Google Scholar 
Alexander, R. D. The Evolution of Social Behavior | Annual Review of Ecology, Evolution, and Systematics. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).
Google Scholar 
Pulliam, H. R. On the advantages of flocking. J. Theor. Biol. 38, 419–422 (1973).CAS 

Google Scholar 
Barnard, C. J. Flock feeding and time budgets in the house sparrow (Passer domesticus L.). Anim. Behav. 28, 295–309 (1980).
Google Scholar 
Cooper, W. E. Jr. et al. Effects of risk, cost, and their interaction on optimal escape by nonrefuging Bonaire whiptail lizards, Cnemidophorus murinus. Behav. Ecol. 14, 288–293 (2003).
Google Scholar 
Lagos, P. A. et al. Flight initiation distance is differentially sensitive to the costs of staying and leaving food patches in a small-mammal prey. Can. J. Zool. 87, 1016–1023 (2009).
Google Scholar 
Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).
Google Scholar 
Tucker, V. A., Tucker, A. E., Akers, K. & Enderson, J. H. Curved flight paths and sideways vision in peregrine falcons (Falco peregrinus). J. Exp. Biol. 203, 3755–3763 (2000).CAS 

Google Scholar 
Carr, J. M. & Lima, S. L. Wintering birds avoid warm sunshine: predation and the costs of foraging in sunlight. Oecologia 174, 713–721 (2014).
Google Scholar 
van den Hout, P. J. & Martin, G. R. Extreme head-tilting in shorebirds: predator detection and sun avoidance. Wader Study Group Bull. 118, 18–21 (2011).
Google Scholar 
Ferguson, J. W. H., Galpin, J. S. & de Wet, M. J. Factors affecting the activity patterns of black-backed jackals Canis mesomelas. J. Zool. 214, 55–69 (1988).
Google Scholar 
Pyke, G. H. Optimal foraging theory: a critical review. Annu. Rev. Ecol. Syst. 15, 523–575 (1984).
Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton University Press, 1986).Mouritsen, K. N. Predator avoidance in night-feeding dunlins calidris alpina: a matter of concealment. Ornis Scand. 23, 195–198 (1992).
Google Scholar 
Blumstein, D. T. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manag. 67, 852–857 (2003).
Google Scholar 
Troscianko, J. OSpRad; an open-source, low-cost, high-sensitivity spectroradiometer (p. 2022.12.09.519768). bioRxiv https://doi.org/10.1101/2022.12.09.519768 (2022).Article 

Google Scholar 
Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.4. http://florianhartig.github.io/DHARMa/ (2022).Core Team, R. R: a Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2022).
Google Scholar  More

Lovegrove, B. G. A phenology of the evolution of endothermy in birds and mammals. Biol. Rev. 92, 1213–1240 (2017).
Google Scholar 
Cuthill, I. C. et al. The biology of color. Science 357, 1–7 (2017).
Google Scholar 
Stuart-Fox, D., Newton, E. & Clusella-Trullas, S. Thermal consequences of colour and near-infrared reflectance. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160345 (2017).
Google Scholar 
Smith, K. R. et al. Color change for thermoregulation versus camouflage in free-ranging lizards. Am. Nat. 188, 668–678 (2016).
Google Scholar 
Rudh, A. & Qvarnström, A. Adaptive colouration in amphibians. Semin. Cell Dev. Biol. 24, 553–561 (2013).
Google Scholar 
Geen, M. R. S. & Johnston, G. R. Coloration affects heating and cooling in three color morphs of the Australian bluetongue lizard, Tiliqua scincoides. J. Therm. Biol. 43, 54–60 (2014).
Google Scholar 
Tattersall, G. J., Eterovick, P. C. & de Andrade, D. V. Tribute to R. G. Boutilier: skin colour and body temperature changes in basking Bokermannohyla alvarengai (Bokermann 1956). J. Exp. Biol. 209, 1185–1196 (2006).
Google Scholar 
Tattersall, G. J., Hillman, S. S., Drewes, R. C. & Sokol, O. M. The thermogenesis of digestion in rattlesnakes. J. Exp. Biol. 207, 579–585 (2004).
Google Scholar 
Seebacher, F. & Murray, S. A. Transient receptor potential ion channels control thermoregulatory behaviour in reptiles. PLoS One 2, e281, 1–7 (2007).Forget-Klein, É. & Green, D. M. Toads use the subsurface thermal gradient for temperature regulation underground. J. Therm. Biol. 99, 1–9 (2021).
Google Scholar 
Kiefer, M. C., Van Sluys, M. & Rocha, C. F. D. Thermoregulatory behaviour in Tropidurus torquatus (Squamata, Tropiduridae) from Brazilian coastal populations: an estimate of passive and active thermoregulation in lizards. Acta Zool. 88, 81–87 (2007).
Google Scholar 
Spencer, K. et al. Growth at cold temperature increases the number of motor neurons to optimize locomotor function. Curr. Biol. 29, 1787–1799.e5 (2019).CAS 

Google Scholar 
Herrel, A. & Bonneaud, C. Temperature dependence of locomotor performance in the tropical clawed frog, Xenopus tropicalis. J. Exp. Biol. 215, 2465–2470 (2012).
Google Scholar 
Casterlin, M. E. & Reynolds, W. W. Diel activity and thermoregulatory behavior of a fully aquatic frog: Xenopus laevis. Hydrobiologia 75, 189–191 (1980).
Google Scholar 
Guo, K. et al. The thermal dependence and molecular basis of physiological color change in Takydromus septentrionalis (Lacertidae). Biol. Open 10, 1–9 (2021).
Google Scholar 
De Velasco, J. B. & Tattersall, G. J. The influence of hypoxia on the thermal sensitivity of skin colouration in the bearded dragon, Pogona vitticeps. J. Comp. Physiol. B. 178, 867–875 (2008).CAS 

Google Scholar 
Stuart-Fox, D. & Moussalli, A. Camouflage, communication and thermoregulation: lessons from colour changing organisms. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 463–470 (2009).
Google Scholar 
Sanabria, E. A., Vaira, M., Quiroga, L. B., Akmentins, M. S. & Pereyra, L. C. Variation of thermal parameters in two different color morphs of a diurnal poison toad, Melanophryniscus rubriventris (Anura: Bufonidae). J. Therm. Biol. 41, 1–5 (2014).
Google Scholar 
Clusella-Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal benefits of melanism in cordylid lizards: a theoretical and field test. Ecology 90, 2297–2312 (2009).
Google Scholar 
Duarte, R. C., Flores, A. A. V. & Stevens, M. Camouflage through colour change: mechanisms, adaptive value and ecological significance. Philos. Trans. R. Soc. B: Biol. Sci. 372, 1–7 (2017).Bertolesi, G. E. & McFarlane, S. Seeing the light to change colour: an evolutionary perspective on the role of melanopsin in neuroendocrine circuits regulating light-mediated skin pigmentation. Pigment Cell Melanoma Res. 31, 354–373 (2018).CAS 

Google Scholar 
Bertolesi, G. E. et al. The regulation of skin pigmentation in response to environmental light by pineal type II opsins and skin melanophore melatonin receptors. J. Photochem. Photobiol. B Biol. 212, 112024 (2020).CAS 

Google Scholar 
Bagnara, J. T. Pineal regulation of the body lightening reaction in amphibian larvae. Sci. (80-.). 132, 1481–1483 (1960).CAS 

Google Scholar 
Bertolesi, G. E., Song, Y. N., Atkinson-Leadbeater, K., Yang, J.-L. J. & McFarlane, S. Interaction and developmental activation of two neuroendocrine systems that regulate light-mediated skin pigmentation. Pigment Cell Melanoma Res. 30, 413–423 (2017).CAS 

Google Scholar 
Wang, H. & Siemens, J. TRP ion channels in thermosensation, thermoregulation and metabolism. Temp. (Austin, Tex.) 2, 178–187 (2015).
Google Scholar 
Hoffstaetter, L. J., Bagriantsev, S. N. & Gracheva, E. O. TRPs et al.: a molecular toolkit for thermosensory adaptations. Pflug. Arch. Eur. J. Physiol. 470, 745–759 (2018).CAS 

Google Scholar 
Kashio, M. Thermosensation involving thermo-TRPs. Mol. Cell. Endocrinol. 520, 1–8 (2021).
Google Scholar 
Señarís, R., Ordás, P., Reimúndez, A. & Viana, F. Mammalian cold TRP channels: impact on thermoregulation and energy homeostasis. Pflug. Arch. 470, 761–777 (2018).
Google Scholar 
Guo, H., Carlson, J. A. & Slominski, A. Role of TRPM in melanocytes and melanoma. Exp. Dermatol. 21, 650–654 (2012).CAS 

Google Scholar 
Kadowaki, T. Evolutionary dynamics of metazoan TRP channels. Pflug. Arch. 467, 2043–2053 (2015).CAS 

Google Scholar 
Saito, S. & Tominaga, M. Evolutionary tuning of TRPA1 and TRPV1 thermal and chemical sensitivity in vertebrates. Temp. (Austin, Tex.) 4, 141–152 (2017).
Google Scholar 
Saito, S. et al. Analysis of transient receptor potential ankyrin 1 (TRPA1) in frogs and lizards illuminates both nociceptive heat and chemical sensitivities and coexpression with TRP vanilloid 1 (TRPV1) in ancestral vertebrates. J. Biol. Chem. 287, 30743–30754 (2012).CAS 

Google Scholar 
Saito, S. et al. Evolution of heat sensors drove shifts in thermosensation between xenopus species adapted to different thermal niches. J. Biol. Chem. 291, 11446–11459 (2016).CAS 

Google Scholar 
Gracheva, E. O. et al. Molecular basis of infrared detection by snakes. Nature 464, 1006–1011 (2010).CAS 

Google Scholar 
Laursen, W. J., Anderson, E. O., Hoffstaetter, L. J., Bagriantsev, S. N. & Gracheva, E. O. Species-specific temperature sensitivity of TRPA1. Temp. (Austin, Tex.) 2, 214–226 (2015).
Google Scholar 
Bertolesi, G. E., Hehr, C. L. & McFarlane, S. Melanopsin photoreception in the eye regulates light-induced skin colour changes through the production of α-MSH in the pituitary gland. Pigment Cell Melanoma Res. 28, 559–571 (2015).CAS 

Google Scholar 
Bagnara, J. T. The pineal and the body lightening reaction of larval amphibians. Gen. Comp. Endocrinol. 3, 86–100 (1963).CAS 

Google Scholar 
Nisembaum, L. et al. In the heat of the night: thermo-TRPV channels in the salmonid pineal photoreceptors and modulation of melatonin secretion. Endocrinology 156, 4629–4638 (2015).CAS 

Google Scholar 
Schartl, M. et al. What is a vertebrate pigment cell? Pigment Cell Melanoma Res. 29, 8–14 (2016).
Google Scholar 
Slominski, A. Cooling skin cancer: menthol inhibits melanoma growth. Focus on ‘TRPM8 activation suppresses cellular viability in human melanoma’. Am. J. Physiol. – Cell Physiol. 295, C293–C295 (2008).CAS 

Google Scholar 
Yamamura, H., Ugawa, S., Ueda, T., Morita, A. & Shimada, S. TRPM8 activation suppresses cellular viability in human melanoma. Am. J. Physiol. Cell Physiol. 295, C296–C301 (2008).CAS 

Google Scholar 
Knowlton, W. M. et al. A sensory-labeled line for cold: TRPM8-expressing sensory neurons define the cellular basis for cold, cold pain, and cooling-mediated analgesia. J. Neurosci. 33, 2837–2848 (2013).CAS 

Google Scholar 
Weyer-Menkhoff, I., Pinter, A., Schlierbach, H., Schänzer, A. & Lötsch, J. Epidermal expression of human TRPM8, but not of TRPA1 ion channels, is associated with sensory responses to local skin cooling. Pain 160, 2699–2709 (2019).Kumasaka, M., Sato, S., Yajima, I. & Yamamoto, H. Isolation and developmental expression of tyrosinase family genes in Xenopus laevis. Pigment Cell Res. 16, 455–462 (2003).CAS 

Google Scholar 
Rodionov, V. I., Hope, A. J., Svitkina, T. M. & Borisy, G. G. Functional coordination of microtubule-based and actin-based motility in melanophores. Curr. Biol. 8, 165–169 (1998).CAS 

Google Scholar 
Session, A. M. et al. Genome evolution in the allotetraploid frog Xenopus laevis. Nature 538, 336–343 (2016).CAS 

Google Scholar 
Gosset, J. R. et al. A cross-species translational pharmacokinetic-pharmacodynamic evaluation of core body temperature reduction by the TRPM8 blocker PF-05105679. Eur. J. Pharm. Sci. 109S, S161–S167 (2017).
Google Scholar 
Winchester, W. J. et al. Inhibition of TRPM8 channels reduces pain in the cold pressor test in humans. J. Pharmacol. Exp. Ther. 351, 259–269 (2014).
Google Scholar 
Bianchi, B., Smith, P. A. & Abriel, H. The ion channel TRPM4 in murine experimental autoimmune encephalomyelitis and in a model of glutamate-induced neuronal degeneration. Mol. Brain 11, 1–10 (2018).
Google Scholar 
Li, K., Shi, Y., Gonye, E. C. & Bayliss, D. A. TRPM4 contributes to subthreshold membrane potential oscillations in multiple mouse pacemaker neurons. eNeuro 8, 1–13 (2021).
Google Scholar 
Dong, W. et al. Visual avoidance in Xenopus tadpoles is correlated with the maturation of visual responses in the optic tectum. J. Neurophysiol. 101, 803–815 (2009).
Google Scholar 
Bertolesi, G. E., Debnath, N., Atkinson-Leadbeater, K., Niedzwiecka, A. & McFarlane, S. Distinct type II opsins in the eye decode light properties for background adaptation and behavioural background preference. Mol. Ecol. 30, 6659–6676 (2021).CAS 

Google Scholar 
Viczian, A. S. & Zuber, M. E. A simple behavioral assay for testing visual function in xenopus laevis. J. Vis. Exp. 12, 51726 (2014).
Google Scholar 
Myers, B. R., Sigal, Y. M. & Julius, D. Evolution of thermal response properties in a cold-activated TRP channel. PLoS One 4, e5741 (2009).
Google Scholar 
Furman, B. L. S. et al. Pan-African phylogeography of a model organism, the African clawed frog ‘Xenopus laevis’. Mol. Ecol. 24, 909–925 (2015).CAS 

Google Scholar 
Wilson, R. S., James, R. S. & Johnston, I. A. Thermal acclimation of locomotor performance in tadpoles and adults of the aquatic frog Xenopus laevis. J. Comp. Physiol. B. 170, 117–124 (2000).CAS 

Google Scholar 
Kashiwagi, K. et al. Xenopus tropicalis: an ideal experimental animal in amphibia. Exp. Anim. 59, 395–405 (2010).CAS 

Google Scholar 
Martínez-Freiría, F., Toyama, K. S., Freitas, I. & Kaliontzopoulou, A. Thermal melanism explains macroevolutionary variation of dorsal pigmentation in Eurasian vipers. Sci. Rep. 10, 72871–1 (2020).Tanaka, K. Does the thermal advantage of melanism produce size differences in color-dimorphic snakes? Zool. Sci. 26, 698–703 (2009).
Google Scholar 
Moreno Azócar, D. L., Nayan, A. A., Perotti, M. G. & Cruz, F. B. How and when melanic coloration is an advantage for lizards: the case of three closely-related species of Liolaemus. Zool. (Jena.) 141, 125774 (2020).
Google Scholar 
Azócar, D. L. M. et al. Effect of body mass and melanism on heat balance in Liolaemus lizards of the goetschi clade. J. Exp. Biol. 219, 1162–1171 (2016).
Google Scholar 
Smith, K. R. et al. Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards. Proc. R. Soc. B Biol. Sci. 283, 20160626 (2016).
Google Scholar 
Rowe, J. W. et al. Thermal and substrate color-induced melanization in laboratory reared red-eared sliders (Trachemys scripta elegans). J. Therm. Biol. 61, 125–132 (2016).
Google Scholar 
Larsen, E. H. Dual skin functions in amphibian osmoregulation. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 253, 110869 (2021).CAS 

Google Scholar 
Franco-Belussi, L., Sköld, H. N. & De Oliveira, C. Internal pigment cells respond to external UV radiation in frogs. J. Exp. Biol. 219, 1378–1383 (2016).
Google Scholar 
Langhelle, A., Lindell, M. J. & Nyström, P. Effects of ultraviolet radiation on amphibian embryonic and larval development. J. Herpetol. 33, 449–456 (1999).
Google Scholar 
Mueller, K. P. & Neuhauss, S. C. F. Sunscreen for fish: co-option of UV light protection for camouflage. PLoS One 9, e87372 (2014).
Google Scholar 
Perotti, M. G., Diéguez, M. & Del, C. Effect of UV-B exposure on eggs and embryos of patagonian anurans and evidence of photoprotection. Chemosphere 65, 2063–2070 (2006).CAS 

Google Scholar 
Nilsson Sköld, H., Aspengren, S. & Wallin, M. Rapid color change in fish and amphibians – function, regulation, and emerging applications. Pigment Cell Melanoma Res. 26, 29–38 (2013).
Google Scholar 
Vences, M. et al. Field body temperatures and heating rates in a montane frog population: the importance of black dorsal pattern for thermoregulation on JSTOR. Ann. Zool. Fennici 39, 209–220 (2002).
Google Scholar 
Lindgren, J. et al. Skin pigmentation provides evidence of convergent melanism in extinct marine reptiles. Nature 506, 484–488 (2014).CAS 

Google Scholar 
Bonino, M. F., Cruz, F. B. & Perotti, M. G. Does temperature at local scale explain thermal biology patterns of temperate tadpoles? J. Therm. Biol. 94, 102744 (2020).
Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 

Google Scholar 
Liu, T. et al. RNA interference-mediated depletion of TRPM8 enhances the efficacy of epirubicin chemotherapy in prostate cancer LNCaP and PC3 cells. Oncol. Lett. 15, 4129–4136 (2018).
Google Scholar 
Kashina, A. S. et al. Protein Kinase A, which regulates intracellular transport, forms complexes with molecular motors on organelles. Curr. Biol. 14, 1877–1881 (2004).CAS 

Google Scholar  More

Daskalova, G. N. et al. Landscape-scale forest loss as a catalyst of population and biodiversity change. Science 368(6497), 1341–1347 (2020).ADS 
CAS 

Google Scholar 
Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366(6470), 1236–1239 (2019).ADS 
CAS 

Google Scholar 
Siddig, A. A., Ellison, A. M., Ochs, A., Villar-Leeman, C. & Lau, M. K. How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecol. Ind. 60, 223–230 (2016).
Google Scholar 
Thancharoen, A. Well managed firefly tourism: A good tool for firefly conservation in Thailand. Lampyrid. 2, 142–148 (2012).
Google Scholar 
Hwang, Y. T., Moon, J., Lee, W. S., Kim, S. A. & Kim, J. Evaluation of firefly as a tourist attraction and resource using contingent valuation method based on a new environmental paradigm. J. Qual. Assur. Hosp. Tour. 21(3), 320–336 (2019).Carlson, A. D. & Copeland, J. Flash communication in fireflies. Q. Rev. Biol. 60(4), 415–436 (1985).
Google Scholar 
Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).
Google Scholar 
Lewis, S. M. et al. A global perspective on firefly extinction threats. Bioscience 70(2), 157–167 (2020).
Google Scholar 
Cao, C. Q., Zhang, Y., Wang, Y. Z. & He, H. Progress in the research, protection, development and utilization of fireflies. J. Environ. Entomol.1–36 (2022).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403(6772), 853–858 (2000).ADS 
CAS 

Google Scholar 
Thorn, J. S., Nijman, V., Smith, D. & Nekaris, K. A. I. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates:Nycticebus). Divers. Distrib. 15(2), 289–298 (2009).
Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40(1), 677–697 (2009).
Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).
Google Scholar 
Hirzel, A. H., Hausser, J., Chessel, D. & Perrin, N. Ecological-Niche Factor Analysis: How to compute habitat-suitability maps without absence data?. Ecology 83(7), 2027–2036 (2002).
Google Scholar 
Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (General). 135(3), 370–384 (1972).
Google Scholar 
Hastie, T. J. Generalized additive models. Statistical models in S. Routledge. 249–307 (2017).Stockwell, D. R. & Noble, I. R. Induction of sets of rules from animal distribution data: A robust and informative method of data analysis. Math. Comput. Simul. 33(5–6), 385–390 (1992).
Google Scholar 
Beaumont, L. J., Hughes, L. & Poulsen, M. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186(2), 251–270 (2005).
Google Scholar 
Jung, J. M., Lee, W. H. & Jung, S. Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomol. Res. 46(4), 223–235 (2016).
Google Scholar 
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2), 161–175 (2008).
Google Scholar 
Moreno, R., Zamora, R., Molina, J. R., Vasquez, A. & Herrera, M. Á. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Eco. Inform. 6(6), 364–370 (2011).
Google Scholar 
Wang, Z. et al. Prediction of potential distribution of the invasive Chrysanthemum Lace Bug, Corythucha marmorata in China based on Maxent. J. Environ. Entomol. 41(3), 626–633 (2019).
Google Scholar 
Li, A. et al. MaxEnt modeling to predict current and future distributions of Batocera lineolata (Coleoptera: Cerambycidae) under climate change in China. Ecoscience 27(1), 23–31 (2020).
Google Scholar 
Sutherland, L. N., Powell, G. S. & Bybee, S. M. Validating species distribution models to illuminate coastal fireflies in the South Pacific (Coleoptera: Lampyridae). Sci. Rep. 11(1), 1–12 (2021).ADS 

Google Scholar 
Fu, X. H., Ballantyne, L. A. & Lambkin, C. Emeia gen. nov., a new genus of Luciolinae fireflies from China (Coleoptera: Lampyridae) with an unusual trilobite-like larva, and a redescription of the genus Curtos Motschulsky. Zootaxa. 3403(1), 1–53 (2012).Idris, N. S. et al. The dynamics of landscape changes surrounding a firefly ecotourism area. Glob. Ecol. Conserv. 29, e01741 (2021).
Google Scholar 
Santiago-Blay, J. A. Silent Sparks: The Wondrous World of Fireflies. Life: The Excitement of Biology. (2016).Picchi, M. S., Avolio, L., Azzani, L., Brombin, O. & Camerini, G. Fireflies and land use in an urban landscape: the case of Luciola italica L.(Coleoptera: Lampyridae) in the city of Turin. J. Insect Conserv. 17(4), 797–805 (2013).Pearsons, K. A., Lower, S. E. & Tooker, J. F. Toxicity of clothianidin to common Eastern North American fireflies. PeerJ 9, e12495 (2021).
Google Scholar 
Madruga Rios, O. & Hernández Quinta, M. Larval Feeding Habits of the Cuban Endemic FireflyAlecton discoidalisLaporte (Coleoptera: Lampyridae). Psyche J. Entomol. 2010, 1–5 (2010).Roberge, J. M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18(1), 76–85 (2004).
Google Scholar 
Bowen-Jones, E. & Entwistle, A. Identifying appropriate flagship species: The importance of culture and local contexts. Oryx 36(2), 189–195 (2002).
Google Scholar 
Walpole, M. J. & Leader-Williams, N. Tourism and flagship species in conservation. Biodivers. Conserv. 11(3), 543–547 (2002).Zhejiang Provincial Bureau of Statistics. Zhejiang physical geography profile, http://tjj.zj.gov.cn/col/col1525489/index.html (2022).Zhejiang Provincial Forestry Department. Announcement of Forest Resources and Their Ecological Function Value in Zhejiang Province. Zhejiang Daily. https://doi.org/10.38328/n.cnki.nzjrb.2016.002829 (2016).Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).
Google Scholar 
Brown, J. L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5(7), 694–700 (2014).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).
Google Scholar 
Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F.-C. & Taneja, J. Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sens. 13(5), 922 (2021).ADS 

Google Scholar 
WAN, J. et al. Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling. J. Integr. Agric. 19(8), 2072–2082 (2020).Zhou, R. et al. Projecting the potential distribution of glossina morsitans (Diptera: Glossinidae) under climate change using the MaxEnt model. Biology. 10(11), 1150 (2021).
Google Scholar 
Hill, M. P., Hoffmann, A. A., McColl, S. A. & Umina, P. A. Distribution of cryptic blue oat mite species in Australia: current and future climate conditions. Agric. For. Entomol. 14(2), 127–137 (2011).
Google Scholar 
Su, H., Bista, M. & Li, M. Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models. Sci. Rep. 11(1), 1 (2021).ADS 
CAS 

Google Scholar 
Proosdij, A. J., Sosef, M., Wieringa, J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).
Google Scholar 
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2), 145–151 (2008).
Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43(6), 1223–1232 (2006).
Google Scholar 
Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6(1), 337–348 (2016).
Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988).ADS 
CAS 
MATH 

Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133(3), 225–245 (2000).
Google Scholar 
Gama, M., Crespo, D., Dolbeth, M. & Anastácio, P. M. Ensemble forecasting of Corbicula fluminea worldwide distribution: projections of the impact of climate change. Aquat. Conserv. Mar. Freshwat. Ecosyst. 27(3), 675–684 (2017).
Google Scholar 
Zhao, Y., Deng, X., Xiang, W., Chen, L. & Ouyang, S. Predicting potential suitable habitats of Chinese fir under current and future climatic scenarios based on Maxent model. Eco. Inform. 64, 101393 (2021).
Google Scholar 
Evans, T. R., Salvatore, D., van de Pol, M. & Musters, C. J. M. Adult firefly abundance is linked to weather during the larval stage in the previous year. Ecol. Entomol. 44(2), 265–273 (2018).Chettri, B., Bhupathy, S. & Acharya, B. K. Distribution pattern of reptiles along an eastern Himalayan elevation gradient India. Acta Oecol. 36(1), 16–22 (2010).ADS 

Google Scholar 
Brown, J. H. Mammals on mountainsides: elevational patterns of diversity. Global Ecol. Biogeogr. 10(1), 101–109 (2001).Gairola, S., Sharma, C. M., Ghildiyal, S. K. & Suyal, S. Tree species composition and diversity along an altitudinal gradient in moist tropical montane valley slopes of the Garhwal Himalaya India. For. Sci. Technol. 7(3), 91–102 (2011).
Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5), 773–785 (2006).
Google Scholar 
Abe, N. Kansei estimation on luminescence of Firefly-Kansei information measurement and welfare utilization. J. Japan Soc. Kansei Eng. 3(2), 41–50 (2004).
Google Scholar 
Buckley, R. et al. Economic value of protected areas via visitor mental health. Nat. Commun. 10(1), 1 (2019).
Google Scholar 
Lewis, S. M. et al. Firefly tourism: Advancing a global phenomenon toward a brighter future. Conserv. Sci. Pract. 3(5), 1 (2021).
Google Scholar  More

Saxon E, Bertozzi C. Cell surface engineering by a modified Staudinger reaction. Science. 2000;287:2007–10.CAS 

Google Scholar 
Staudinger H, Meyer J. Über neue organische Phosphorverbindungen III. Phosphinmethylenderivate und Phosphinimine. Helv Chim Acta. 1919;2:635–46. https://doi.org/10.1002/hlca.19190020164.Article 
CAS 

Google Scholar 
Laughlin ST, Bertozzi CR. Metabolic labeling of glycans with azido sugars and subsequent glycan-profiling and visualization via Staudinger ligation. Nat Protoc. 2007;2:2930–44.CAS 

Google Scholar 
Oliveira BL, Guo Z, Bernardes GJL. Inverse electron demand Diels–Alder reactions in chemical biology. Chem Soc Rev. 2017;46:4895–950.CAS 

Google Scholar 
Lang K, Chin JW. Bioorthogonal reactions for labeling proteins. ACS Chem Biol. 2014;9:16–20. https://doi.org/10.1021/cb4009292.Article 
CAS 

Google Scholar 
Kolb HC, Finn MG, Sharpless K. Click chemistry: diverse chemical function from a few good reactions. Angew Chemie-Int Ed. 2001;40:2004–21.CAS 

Google Scholar 
Tornøe C, Christensen C, Meldal M. Peptidotriazoles on Solid Phase: [1,2,3]-Triazoles by regiospecific Copper(I)-Catalyzed 1,3-Dipolar Cycloadditions of Terminal Alkynes to Azides. J Org Chem. 2002;67:3057–64. https://doi.org/10.1021/jo011148j.Article 
CAS 

Google Scholar 
Bakkum T, Leeuwen T, van, Sarris AJC, Elsland DM, van, Poulcharidis D, Overkleeft HS, et al. Quantification of bioorthogonal stability in immune phagocytes using flow cytometry reveals rapid degradation of strained alkynes. ACS Chem Biol. 2018;13:1173–9. https://doi.org/10.1021/acschembio.8b0035.Article 
CAS 

Google Scholar 
Wang Q, Chan T, Hilgraf R, Fokin R, Sharpless K, Finn M. Bioconjugation by copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:3192–3.CAS 

Google Scholar 
Link A, Tirrell D. Cell surface labeling of Escherichia coli via copper(I)-catalyzed [3+2] cycloaddition. J Am Chem Soc. 2003;125:11164–5.CAS 

Google Scholar 
Dieterich D, Link A, Tirrell D, Schuman E. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc Natl Acad Sci USA. 2006;103:9482–7.CAS 

Google Scholar 
McKay C, Finn M. Click chemistry in complex mixtures: bioorthogonal bioconjugation. Chem Biol. 2014;21:1075–101.CAS 

Google Scholar 
Agard N, Prescher J, Bertozzi C. A strain-promoted [3 + 2] Azide−Alkyne cycloaddition for covalent modification of biomolecules in living systems. J Am Chem Soc. 2004;126:15046–7. https://doi.org/10.1021/ja044996f.Article 
CAS 

Google Scholar 
Weissleder R, Hilderbrand S. Tetrazine-based cycloadditions: application to pretargeted live cell imaging. Bioconjug Chem. 2008;19:2297–9.
Google Scholar 
Scinto SL, Bilodeau DA, Hincapie R, Lee W, Nguyen SS, Xu M, et al. Bioorthogonal chemistry. Nat Rev Methods. 2021;1:1–23.
Google Scholar 
Sletten E, Bertozzi C. Bioorthogonal chemistry: fishing for selectivity in a sea of functionality. Angew Chem Int Ed Engl. 2009;48:6974–98.CAS 

Google Scholar 
Moses JE, Moorhouse AD. The growing applications of click chemistry. Chem Soc Rev. 2007;36:1249–62.CAS 

Google Scholar 
Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
CAS 

Google Scholar 
Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol. 2020;184:241–56.
Google Scholar 
Siegrist M, Whiteside S, Jewett J, Aditham A, Cava F, Bertozzi C. (D)-Amino acid chemical reporters reveal peptidoglycan dynamics of an intracellular pathogen. ACS Chem Biol. 2013;8:500–5.CAS 

Google Scholar 
Liechti G, Kuru E, Hall E, Kalinda A, Brun YV, VanNieuwenhze M, et al. A new metabolic cell wall labeling method reveals peptidoglycan in Chlamydia trachomatis. Nature. 2014;506:507. https://doi.org/10.1038/nature12892.Article 
CAS 

Google Scholar 
Pilhofer M, Aistleitner K, Biboy J, Gray J, Kuru E, Hall E, et al. Discovery of chlamydial peptidoglycan reveals bacteria with murein sacculi but without FtsZ. Nat Commun. 2013;4:1–7.
Google Scholar 
Taylor JA, Bratton BP, Sichel SR, Blair KM, Jacobs HM, Demeester KE, et al. Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in helicobacter pylori. Elife. 2020;9:e52482.CAS 

Google Scholar 
Kuru E, Hughes HV, Brown PJ, Hall E, Tekkam S, Cava F, et al. In situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent D-amino acids. Angew Chemie Int Ed. 2012;51:12519–23. https://doi.org/10.1002/anie.201206749.Article 
CAS 

Google Scholar 
van Teeseling MCF, Mesman RJ, Kuru E, Espaillat A, Cava F, Brun YV, et al. Anammox Planctomycetes have a peptidoglycan cell wall. Nat Commun. 2015;6:6878. https://doi.org/10.1038/ncomms7878.Article 
CAS 

Google Scholar 
Wang W, Yang Q, Du Y, Zhou X, Du X, Wu Q. et al. Metabolic labeling of Peptidoglycan with NIR-II dye enables in vivo imaging of gut microbiota. Angew Chemie Int Ed. 2020;59:2628–33. https://doi.org/10.1002/anie.201910555.Article 
CAS 

Google Scholar 
Wang W, Zhu Y, Chen X. imaging of gram-negative and gram-positive microbiotas in the mouse gut. Biochemistry. 2017;56:3889–93.CAS 

Google Scholar 
Geva-Zatorsky N, Alvarez D, Hudak JE, Reading NC, Erturk-Hasdemir D, Dasgupta S, et al. In vivo imaging and tracking of host-microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat Med. 2015;21:1091–100.CAS 

Google Scholar 
Besanceney-Webler C, Jiang H, Wang W, Baughn AD, Wu P. Metabolic labeling of fucosylated glycoproteins in Bacteroidales species. Bioorg Med Chem Lett. 2011;21:4989–92.CAS 

Google Scholar 
Han Z, Thuy-Boun PS, Pfeiffer W, Vartabedian VF, Torkamani A, Teijaro JR, et al. Identification of an N-acetylneuraminic acid-presenting bacteria isolated from a human microbiome. Sci Rep. 2021;11:1–12.
Google Scholar 
Becam J, Walter T, Burgert A, Schlegel J, Sauer M, Seibel J, et al. Antibacterial activity of ceramide and ceramide analogs against pathogenic Neisseria. Sci Rep. 2017;7:1–12.CAS 

Google Scholar 
Nilsson I, Lee SY, Sawyer WS, Baxter Rath CM, Lapointe G, Six DA. Metabolic phospholipid labeling of intact bacteria enables a fluorescence assay that detects compromised outer membranes. J Lipid Res. 2020;61:870–83.CAS 

Google Scholar 
Evershed RP, Crossman ZM, Bull ID, Mottram H, Dungait JAJ, Maxfield PJ, et al. 13C-Labelling of lipids to investigate microbial communities in the environment. Curr Opin Biotechnol. 2006;17:72–82.CAS 

Google Scholar 
Salic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc Natl Acad Sci USA. 2008;105:2415–20. https://doi.org/10.1073/pnas.0712168105.Article 

Google Scholar 
Smriga S, Samo TJ, Malfatti F, Villareal J, Azam F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat Microb Ecol. 2014;72:269–80.
Google Scholar 
Beauchemina ET, Hunter C, Maurice CF. Actively replicating gut bacteria identified by 5-ethynyl-2’-deoxyuridine (EdU) click chemistry and cell sorting. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.07.20.500840v2.Sinclair L, Barthelemy C, Cantrell D. Single cell glucose uptake assays: a cautionary tale. Immunometabolism. 2020;2. https://pubmed.ncbi.nlm.nih.gov/32879737/.Hu F, Chen DZ, Zhang DL, Shen Y, Wei L, Min PW. Vibrational imaging of glucose uptake activity in live cells and tissues by stimulated Raman scattering. Angew Chem Int Ed Engl. 2015;54:9821.CAS 

Google Scholar 
Kiick K, Saxon E, Tirrell D, Bertozzi C. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc Natl Acad Sci USA. 2002;99:19–24.CAS 

Google Scholar 
Kiick K, Tirrell D. Protein engineering by in vivo incorporation of non-natural amino acids: control of incorporation of methionine analogues by Methionyl-tRNA Synthetase. Tetrahedron. 2000;56:9487–93.CAS 

Google Scholar 
Ignacio B, Bakkum T, Bonger K, Martin N, van Kasteren S. Metabolic labeling probes for interrogation of the host-pathogen interaction. Org Biomol Chem. 2021;19:2856–70.CAS 

Google Scholar 
Bagert JD, Kessel JC, van, Sweredoski MJ, Feng L, Hess S, Bassler BL, et al. Time-resolved proteomic analysis of quorum sensing in Vibrio harveyi. Chem Sci. 2016;7:1797–806.CAS 

Google Scholar 
Babin BM, Atangcho L, Van Eldijk MB, Sweredoski MJ, Moradian A, Hess S, et al. Selective proteomic analysis of antibiotic-tolerant cellular subpopulations in pseudomonas aeruginosa biofilms. 2017. https://doi.org/10.1128/mBio.01593-17.Hatzenpichler R, Scheller S, Tavormina PL, Babin BM, Tirrell DA, Orphan VJ. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol. 2014;16:2568–90. https://doi.org/10.1111/1462-2920.12436.Article 
CAS 

Google Scholar 
Samo TJ, Smriga S, Malfatti F, Sherwood BP, Azam F. Broad distribution and high proportion of protein synthesis active marine bacteria revealed by click chemistry at the single cell level. Front Mar Sci. 2014;0:48.
Google Scholar 
Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal-bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–78. https://doi.org/10.1073/pnas.1603757113.Article 
CAS 

Google Scholar 
Couradeau E, Sasse J, Goudeau D, Nath N, Hazen TC, Bowen BP, et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun. 2019;10:1–10.CAS 

Google Scholar 
Leizeaga A, Estrany M, Forn I, Sebastián M. Using click-chemistry for visualizing in situ changes of translational activity in planktonic marine bacteria. Front Microbiol. 2017;0:2360.
Google Scholar 
Lindivat M, Larsen A, Hess-Erga OK, Bratbak G, Hoell IA. Bioorthogonal non-canonical amino acid tagging combined with flow cytometry for determination of activity in aquatic microorganisms. Front Microbiol. 2020;0:1929.
Google Scholar 
Chen L, Zhao B, Li X, Cheng Z, Wu R, Xia Y. Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging. Chem Eng J. 2021;418:129411.CAS 

Google Scholar 
McKay LJ, Smith HJ, Barnhart EP, Schweitzer HD, Malmstrom RR, Goudeau D, et al. Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane. ISME J. 2021;16:915–26.
Google Scholar 
Du Z, Behrens SF. Tracking de novo protein synthesis in the activated sludge microbiome using BONCAT-FACS. Water Res. 2021;205:117696.CAS 

Google Scholar 
Valentini TD, Lucas SK, Binder KA, Cameron LC, Motl JA, Dunitz JM, et al. Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota. Nat Commun. 2020;11:1–11.
Google Scholar 
Taguer M, Shapiro BJ, Maurice CF. Translational activity is uncoupled from nucleic acid content in bacterial cells of the human gut microbiota. Gut Microbes. 2021;13:1–15.
Google Scholar 
Banahene N, Kavunja HW, Swarts BM. Chemical reporters for bacterial glycans: development and applications. Chem Rev. 2021;122:3336–413. https://doi.org/10.1021/acs.chemrev.1c00729.Article 
CAS 

Google Scholar 
Kavunja HW, Piligian BF, Fiolek TJ, Foley HN, Nathan TO, Swarts BM. A chemical reporter strategy for detecting and identifying O-mycoloylated proteins in Corynebacterium. Chem Commun. 2016;52:13795–8.CAS 

Google Scholar 
Demeester KE, Liang H, Jensen MR, Jones ZS, D’Ambrosio EA, Scinto SL, et al. Synthesis of functionalized N-Acetyl Muramic acids to probe bacterial cell wall recycling and biosynthesis. J Am Chem Soc. 2018;140:9458–65. https://doi.org/10.1021/jacs.8b03304.Article 
CAS 

Google Scholar 
Moulton KD, Adewale AP, Carol HA, Mikami SA, Dube DH. Metabolic glycan labeling-based screen to identify bacterial glycosylation genes. ACS Infect Dis. 2020;6:3247–59. https://doi.org/10.1021/acsinfecdis.0c00612.Article 
CAS 

Google Scholar 
Keller LJ, Babin BM, Lakemeyer M, Bogyo M. Activity-based protein profiling in bacteria: Applications for identification of therapeutic targets and characterization of microbial communities. Curr Opin Chem Biol. 2020;54:45–53.CAS 

Google Scholar 
Speers AE, Adam GC, Cravatt BF. Activity-based protein profiling in vivo using a copper(I)-catalyzed azide-alkyne [3 + 2] cycloaddition. J Am Chem Soc. 2003;125:4686–7. https://doi.org/10.1021/ja034490.Article 
CAS 

Google Scholar 
Krysiak J, Sieber SA. Activity-based protein profiling in bacteria. Methods Mol Biol. 2017;1491:57–74.CAS 

Google Scholar 
Jariwala PB, Pellock SJ, Cloer EW, Artola M, Simpson JB, Bhatt AP, et al. Discovering the microbial enzymes driving drug toxicity with activity-based protein profiling. ACS Chem Biol. 2020;15:217–25. https://doi.org/10.1021/acschembio.9b00788.Article 
CAS 

Google Scholar 
Kovalyova Y, Hatzios SK. Activity-based protein profiling at the host-pathogen interface. Curr Top Microbiol Immunol. 2019;420:73–91.CAS 

Google Scholar 
Sakoula D, Smith GJ, Frank J, Mesman RJ, Kop LFM, Blom P, et al. Universal activity-based labeling method for ammonia- and alkane-oxidizing bacteria. ISME J. 2021;16:958–71.
Google Scholar 
Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2020;19:55–71.
Google Scholar 
Fitzpatrick CR, Salas-González I, Conway JM, Finkel OM, Gilbert S, Russ D, et al. The plant microbiome: from ecology to reductionism and beyond. 101146/annurev-micro-022620-014327. 2020;74:81–100. https://www.annualreviews.org/doi/abs/10.1146/annurev-micro-022620-014327.Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, Whitlock MC. Experimental evolution. Trends Ecol Evol. 2012;27:547–60.
Google Scholar 
Lenski RE. Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations. ISME J. 2017;11:2181–94.CAS 

Google Scholar 
Rodríguez-Verdugo A. Evolving Interactions and Emergent Functions in Microbial Consortia. mSystems. 2021;6. https://pubmed.ncbi.nlm.nih.gov/34427521/.Pascual-García A, Bonhoeffer S, Bell T. Metabolically cohesive microbial consortia and ecosystem functioning. Philos Trans R Soc B. 2020;375. https://royalsocietypublishing.org/doi/full/10.1098/rstb.2019.0245.Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 

Google Scholar 
Balaban NQ, Helaine S, Lewis K, Ackermann M, Aldridge B, Andersson DI, et al. Definitions and guidelines for research on antibiotic persistence. Nat Rev Microbiol. 2019;17:441–8.CAS 

Google Scholar 
Vermeersch L, Perez-Samper G, Cerulus B, Jariani A, Gallone B, Voordeckers K, et al. On the duration of the microbial lag phase. Curr Genet. 2019;65:721–7.CAS 

Google Scholar 
Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, et al. Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA. 2014;111:7427–32.CAS 

Google Scholar 
Zhang Z, Du C, de Barsy F, Liem M, Liakopoulos A, van Wezel GP, et al. Antibiotic production in Streptomyces is organized by a division of labor through terminal genomic differentiation. Sci Adv. 2020;6:eaay5781.CAS 

Google Scholar 
Mavridou DAI, Gonzalez D, Kim W, West SA, Foster KR. Bacteria use collective behavior to generate diverse combat strategies. Curr Biol. 2018;28:345–355.e4.CAS 

Google Scholar 
Levin AM, de Vries RP, Conesa A, de Bekker C, Talon M, Menke HH, et al. Spatial differentiation in the vegetative mycelium of Aspergillus niger. Eukaryot Cell. 2007;6:2311–22.CAS 

Google Scholar 
Zacchetti B, Wösten HAB, Claessen D. Multiscale heterogeneity in filamentous microbes. Biotechnol Adv. 2018;36:2138–49.CAS 

Google Scholar 
Bleichrodt R-J, Vinck A, Read ND, Wösten HAB. Selective transport between heterogeneous hyphal compartments via the plasma membrane lining septal walls of Aspergillus niger. Fungal Genet Biol. 2015;82:193–200.CAS 

Google Scholar 
Nürnberg DJ, Mariscal V, Bornikoel J, Nieves-Morión M, Krauß N, Herrero A, et al. Intercellular diffusion of a fluorescent sucrose analog via the septal junctions in a Filamentous Cyanobacterium. MBio. 2015;6. https://journals.asm.org/doi/full/10.1128/mBio.02109-14.Pasulka AL, Thamatrakoln K, Kopf SH, Guan Y, Poulos B, Moradian A, et al. Interrogating marine virus-host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Environ Microbiol. 2018;20:671–92. https://doi.org/10.1111/1462-2920.13996.Article 
CAS 

Google Scholar 
Berjón-Otero M, Duponchel S, Hackl T, Fischer M. Visualization of giant virus particles using BONCAT labeling and STED microscopy. bioRxiv. 2020;2020.07.14.202192. https://www.biorxiv.org/content/10.1101/2020.07.14.202192v1.Steward KF, Eilers B, Tripet B, Fuchs A, Dorle M, Rawle R, et al. Metabolic implications of using BioOrthogonal Non-Canonical Amino Acid Tagging (BONCAT) for tracking protein synthesis. Front Microbiol. 2020;0:197.
Google Scholar 
van Elsland DM, Pujals S, Bakkum T, Bos E, Oikonomeas-Koppasis N, Berlin I, et al. Ultrastructural Imaging of Salmonella–Host interactions using super-resolution correlative light-electron microscopy of bioorthogonal pathogens. ChemBioChem. 2018;19:1766–70. https://doi.org/10.1002/cbic.201800230.Article 
CAS 

Google Scholar 
Michels DE, Lomenick B, Chou T-F, Sweredoski MJ, Pasulka A. Amino acid analog induces stress response in marine Synechococcus. Appl Environ Microbiol. 2021;87:1–18. https://doi.org/10.1128/AEM.00200-21.Article 

Google Scholar 
Hong V, Steinmetz NF, Manchester M, Finn MG. Labeling live cells by copper-catalyzed alkyne−azide click chemistry. Bioconjug Chem. 2010;21:1912–6. https://doi.org/10.1021/bc100272z.Article 
CAS 

Google Scholar 
van Geel R, Pruijn G, van Delft F, Boelens W. Preventing thiol-yne addition improves the specificity of strain-promoted azide-alkyne cycloaddition. Bioconjug Chem. 2012;23:392–8.
Google Scholar 
Patterson DM, Nazarova LA, Prescher JA. Finding the Right (Bioorthogonal) Chemistry. ACS Chem Biol. 2014;9:592–605. https://doi.org/10.1021/cb400828a.Article 
CAS 

Google Scholar 
Ignacio BJ, Dijkstra J, Garcia NM, Slot EFJ, van Weijsten MJ, Storkebaum E, et al. THRONCAT: Efficient metabolic labeling of newly synthesized proteins using a bioorthogonal threonine analog. bioRxiv. 2022. https://www.biorxiv.org/content/10.1101/2022.03.29.486210v1.Wright MH. Chemical proteomics of host–microbe interactions. Proteomics. 2018;18:1700333. https://doi.org/10.1002/pmic.201700333.Article 
CAS 

Google Scholar 
Yu H, Schomaker J. Recent developments and strategies for mutually orthogonal bioorthogonal reactions. Chembiochem. 2021;22:3254–62.
Google Scholar 
Willems LI, Li N, Florea BI, Ruben M, van der Marel GA, Overkleeft HS. Triple bioorthogonal ligation strategy for simultaneous labeling of multiple enzymatic activities. Angew Chemie Int Ed. 2012;51:4431–4. https://doi.org/10.1002/anie.201200923.Article 
CAS 

Google Scholar 
Simon C, Lion C, Spriet C, Baldacci-Cresp F, Hawkins S, Biot C. One, two, three: a bioorthogonal triple labelling strategy for studying the dynamics of plant cell wall formation in vivo. Angew Chemie Int Ed. 2018;57:16665–71. https://doi.org/10.1002/anie.201808493.Article 
CAS 

Google Scholar 
Chio TI, Gu H, Mukherjee K, Tumey LN, Bane SL. Site-specific bioconjugation and multi-bioorthogonal labeling via rapid formation of a boron–nitrogen heterocycle. Bioconjug Chem. 2019;30:1554–64. https://doi.org/10.1021/acs.bioconjchem.9b0024.Article 
CAS 

Google Scholar 
Bakkum T, Heemskerk MT, Bos E, Groenewold M, Oikonomeas-Koppasis N, Walburg KV, et al. Bioorthogonal correlative light-electron microscopy of mycobacterium tuberculosis in macrophages reveals the effect of antituberculosis drugs on subcellular bacterial distribution. ACS Cent Sci. 2020;6:1997–2007. https://doi.org/10.1021/acscentsci.0c00539.Article 
CAS 

Google Scholar  More

Ackerly, D. D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 164, S165–S184 (2003).Article 

Google Scholar 
Kellermann, V., Van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).Article 
CAS 

Google Scholar 
Hansen, M. M., Olivieri, I., Waller, D. M. & Nielsen, E. E. Monitoring adaptive genetic responses to environmental change. Mol. Ecol. 21, 1311–1329 (2012).Article 

Google Scholar 
Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).Article 

Google Scholar 
Becker, M. et al. Hybridization may facilitate in situ survival of endemic species through periods of climate change. Nat. Clim. Change 3, 1039–1043 (2013).Article 

Google Scholar 
Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).Article 

Google Scholar 
Todesco, M. et al. Hybridization and extinction. Evol. Appl. 9, 892–908 (2016).Article 
CAS 

Google Scholar 
Rhymer, J. M. & Simberloff, D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).Article 

Google Scholar 
Taylor, S. A. & Larson, E. L. Insights from genomes into the evolutionary importance and prevalence of hybridization in nature. Nat. Ecol. Evol. 3, 170–177 (2019).Article 

Google Scholar 
vonHoldt, B. M., Brzeski, K. E., Wilcove, D. S. & Rutledge, L. Y. Redefining the role of admixture and genomics in species conservation. Conserv. Lett. 11, e12371 (2018).Article 

Google Scholar 
Hamilton, J. A. & Miller, J. M. Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conserv. Biol. 30, 33–41 (2016).Article 

Google Scholar 
Ralls, K., Sunnucks, P., Lacy, R. C. & Frankham, R. Genetic rescue: a critique of the evidence supports maximizing genetic diversity rather than minimizing the introduction of putatively harmful genetic variation. Biol. Conserv. 251, 108784 (2020).Article 

Google Scholar 
Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal) adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

Google Scholar 
Rellstab, C., Dauphin, B. & Exposito‐Alonso, M. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14, 1202–1212 (2021).Article 

Google Scholar 
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).Article 
CAS 

Google Scholar 
Rellstab, C. et al. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol. Ecol. 25, 5907–5924 (2016).Article 

Google Scholar 
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).Article 

Google Scholar 
Exposito-Alonso, M. et al. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Nat. Ecol. Evol. 2, 352–358 (2018).Article 

Google Scholar 
Kindt, R. AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change. PeerJ 9, e11534 (2021).Article 

Google Scholar 
Gain, C. & François, O. LEA 3: factor models in population genetics and ecological genomics with R. Mol. Ecol. Resour. 21, 2738–2748 (2020).Article 

Google Scholar 
Aguirre-Liguori, J. A., Ramírez-Barahona, S. & Gaut, B. S. The evolutionary genomics of species’ responses to climate change. Nat. Ecol. Evol. 5, 1350–1360 (2021).Article 

Google Scholar 
Taylor, S. A., Larson, E. L. & Harrison, R. G. Hybrid zones: windows on climate change. Trends Ecol. Evol. 30, 398–406 (2015).Article 

Google Scholar 
Hoffmann, A. A. & Sgro, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
McGuigan, K., Franklin, C. E., Moritz, C. & Blows, M. W. Adaptation of rainbow fish to lake and stream habitats. Evolution 57, 104–118 (2003).
Google Scholar 
Smith, S., Bernatchez, L. & Beheregaray, L. RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species. BMC Genomics 14, 375 (2013).Article 
CAS 

Google Scholar 
Smith, S. et al. Latitudinal variation in climate‐associated genes imperils range edge populations. Mol. Ecol. 29, 4337–4349 (2020).Article 
CAS 

Google Scholar 
Sandoval-Castillo, J. et al. Adaptation of plasticity to projected maximum temperatures and across climatically defined bioregions. Proc. Natl Acad. Sci. USA 117, 17112–17121 (2020).Article 
CAS 

Google Scholar 
Brauer, C., Unmack, P. J., Smith, S., Bernatchez, L. & Beheregaray, L. B. On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system. Mol. Ecol. 27, 3484–3497 (2018).Article 
CAS 

Google Scholar 
Attard, C. R. et al. Fish out of water: genomic insights into persistence of rainbowfish populations in the desert. Evolution 76, 171–183 (2022).Article 

Google Scholar 
Gates, K. et al. Environmental selection, rather than neutral processes, best explain patterns of diversity in a tropical rainforest fish. Preprint at bioRxiv https://doi.org/10.1101/2022.1105.1113.491913 (2022).Article 

Google Scholar 
McCairns, R. J. S., Smith, S., Sasaki, M., Bernatchez, L. & Beheregaray, L. B. The adaptive potential of subtropical rainbowfish in the face of climate change: heritability and heritable plasticity for the expression of candidate genes. Evol. Appl. 9, 531–545 (2016).Article 
CAS 

Google Scholar 
McGuigan, K., Zhu, D., Allen, G. & Moritz, C. Phylogenetic relationships and historical biogeography of melanotaeniid fishes in Australia and New Guinea. Mar. Freshwat. Res. 51, 713–723 (2000).Article 

Google Scholar 
Unmack, P. J. et al. Malanda Gold: the tale of a unique rainbowfish from the Atherton Tablelands, now on the verge of extinction. Fish. Sahul. 30, 1039–1054 (2016).
Google Scholar 
Moritz, C. Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst. Biol. 51, 238–254 (2002).Article 

Google Scholar 
Pope, L., Estoup, A. & Moritz, C. Phylogeography and population structure of an ecotonal marsupial, Bettongia tropica, determined using mtDNA and microsatellites. Mol. Ecol. 9, 2041–2053 (2000).Article 
CAS 

Google Scholar 
Hugall, A., Moritz, C., Moussalli, A. & Stanisic, J. Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875). Proc. Natl Acad. Sci. USA 99, 6112–6117 (2002).Article 
CAS 

Google Scholar 
Moritz, C. et al. Identification and dynamics of a cryptic suture zone in tropical rainforest. Proc. R. Soc. B. 276, 1235–1244 (2009).Article 
CAS 

Google Scholar 
Phillips, B. L., Baird, S. J. & Moritz, C. When vicars meet: a narrow contact zone between morphologically cryptic phylogeographic lineages of the rainforest skink, Carlia rubrigularis. Evolution 58, 1536–1548 (2004).
Google Scholar 
Krosch, M. N., Baker, A. M., Mckie, B. G., Mather, P. B. & Cranston, P. S. Deeply divergent mitochondrial lineages reveal patterns of local endemism in chironomids of the Australian Wet Tropics. Austral Ecol. 34, 317–328 (2009).Article 

Google Scholar 
Williams, S. E., Bolitho, E. E. & Fox, S. Climate change in Australian tropical rainforests: an impending environmental catastrophe. Proc. R. Soc. B. 270, 1887–1892 (2003).Article 

Google Scholar 
Whitehead, P. et al. Temporal development of the Atherton Basalt Province, north Queensland. Aust. J. Earth Sci. 54, 691–709 (2007).Article 
CAS 

Google Scholar 
Moy, K. G., Unmack, P. J., Lintermans, M., Duncan, R. P. & Brown, C. Barriers to hybridisation and their conservation implications for a highly threatened Australian fish species. Ethology 125, 142–152 (2019).Article 

Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).Article 
CAS 

Google Scholar 
Buerkle, C. A. Maximum‐likelihood estimation of a hybrid index based on molecular markers. Mol. Ecol. Notes 5, 684–687 (2005).Article 
CAS 

Google Scholar 
Anderson, E. & Thompson, E. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160, 1217–1229 (2002).Article 
CAS 

Google Scholar 
Dorion, S. & Landry, J. Activation of the mitogen-activated protein kinase pathways by heat shock. Cell Stress Chaperones 7, 200 (2002).Article 
CAS 

Google Scholar 
Blumstein, M. et al. Protocol for projecting allele frequency change under future climate change at adaptive-associated loci. STAR Protoc. 1, 100061 (2020).Article 

Google Scholar 
Gougherty, A. V., Keller, S. R. & Fitzpatrick, M. C. Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat. Clim. Change 11, 166–171 (2021).Article 

Google Scholar 
Blumstein, M. et al. A new perspective on ecological prediction reveals limits to climate adaptation in a temperate tree species. Curr. Biol. 30, 1447–1453. e1444 (2020).Article 
CAS 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).Article 
CAS 

Google Scholar 
Goicoechea, P. G. et al. Adaptive introgression promotes fast adaptation in oaks marginal populations. Preprint available at bioRxiv https://doi.org/10.1101/731919 (2019).Lavergne, S. & Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc. Natl Acad. Sci. USA 104, 3883–3888 (2007).Article 
CAS 

Google Scholar 
De Carvalho, D. et al. Admixture facilitates adaptation from standing variation in the European aspen (Populus tremula L.), a widespread forest tree. Mol. Ecol. 19, 1638–1650 (2010).Article 

Google Scholar 
De-Kayne, R. et al. Genomic architecture of adaptive radiation and hybridization in Alpine whitefish. Nat. Commun. 13, 4479 (2022).Article 
CAS 

Google Scholar 
Baskett, M. L. & Gomulkiewicz, R. Introgressive hybridization as a mechanism for species rescue. Theor. Ecol. 4, 223–239 (2011).Article 

Google Scholar 
Meier, J. I. et al. The coincidence of ecological opportunity with hybridization explains rapid adaptive radiation in Lake Mweru cichlid fishes. Nat. Commun. 10, 1–11 (2019).Article 
CAS 

Google Scholar 
Svardal, H. et al. Ancestral hybridization facilitated species diversification in the Lake Malawi cichlid fish adaptive radiation. Mol. Biol. Evol. 37, 1100–1113 (2020).Article 
CAS 

Google Scholar 
Racimo, F., Sankararaman, S., Nielsen, R. & Huerta-Sánchez, E. Evidence for archaic adaptive introgression in humans. Nat. Rev. Genet. 16, 359–371 (2015).Article 
CAS 

Google Scholar 
Jeong, C. et al. Admixture facilitates genetic adaptations to high altitude in Tibet. Nat. Commun. 5, 1–7 (2014).Article 

Google Scholar 
Nolte, A. W., Freyhof, J., Stemshorn, K. C. & Tautz, D. An invasive lineage of sculpins, Cottus sp. (Pisces, Teleostei) in the Rhine with new habitat adaptations has originated from hybridization between old phylogeographic groups. Proc. R. Soc. B. 272, 2379–2387 (2005).Article 

Google Scholar 
Fitzpatrick, M. C., Chhatre, V. E., Soolanayakanahally, R. Y. & Keller, S. R. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol. Ecol. Resour. 21, 2749–2765 (2021).Article 
CAS 

Google Scholar 
Schneider, C., Cunningham, M. & Moritz, C. Comparative phylogeography and the history of endemic vertebrates in the Wet Tropics rainforests of Australia. Mol. Ecol. 7, 487–498 (1998).Article 

Google Scholar 
Hewitt, G. M. Quaternary phylogeography: the roots of hybrid zones. Genetica 139, 617–638 (2011).Article 

Google Scholar 
Pfennig, K. S., Kelly, A. L. & Pierce, A. A. Hybridization as a facilitator of species range expansion. Proc. R. Soc. B. 283, 20161329 (2016).Article 

Google Scholar 
Soulé, M. E. What is conservation biology? A new synthetic discipline addresses the dynamics and problems of perturbed species, communities, and ecosystems. Bioscience 35, 727–734 (1985).
Google Scholar 
Biermann, C. & Havlick, D. Genetics and the question of purity in cutthroat trout restoration. Restor. Ecol. 29, e13516 (2021).Article 

Google Scholar 
Fredrickson, R. J. & Hedrick, P. W. Dynamics of hybridization and introgression in red wolves and coyotes. Conserv. Biol. 20, 1272–1283 (2006).Article 

Google Scholar 
Hirashiki, C., Kareiva, P. & Marvier, M. Concern over hybridization risks should not preclude conservation interventions. Conserv. Sci. Pract. 3, e424 (2021).
Google Scholar 
Unmack, P. J., Allen, G. R. & Johnson, J. B. Phylogeny and biogeography of rainbowfishes (Melanotaeniidae) from Australia and New Guinea. Mol. Phylogenet. Evol. 67, 15–27 (2013).Article 

Google Scholar 
Allen, G. Rainbowfishes in Nature and the Aquarium (Tetra Publications, 1995).Seehausen, O. Hybridization and adaptive radiation. Trends Ecol. Evol. 19, 198–207 (2004).Article 

Google Scholar 
Pusey, B., Kennard, M. J. & Arthington, A. H. Freshwater Fishes of North-eastern Australia (CSIRO Publishing, 2004).Zhu, D., Degnan, S. & Moritz, C. Evolutionary distinctiveness and status of the endangered Lake Eacham rainbowfish (Melanotaenia eachamensis). Conserv. Biol. 12, 80–93 (1998).Article 

Google Scholar 
McGuigan, K., Chenoweth, S. F. & Blows, M. W. Phenotypic divergence along lines of genetic variance. Am. Nat. 165, 32–43 (2005).Article 

Google Scholar 
Sunnucks, P. & Hales, D. F. Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol. Biol. Evol. 13, 510–524 (1996).Article 
CAS 

Google Scholar 
Peterson, B., Weber, J., Kay, E., Fisher, H. & Hoekstra, H. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).Article 
CAS 

Google Scholar 
Catchen, J. M., Amores, A., Hohenlohe, P., Cresko, W. & Postlethwait, J. H. Stacks: building and genotyping loci de novo from short-read sequences. G3: Genes Genomes Genet. 1, 171–182 (2011).Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).Article 
CAS 

Google Scholar 
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).Article 
CAS 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Article 

Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F‐statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Bailey, R. ribailey/gghybrid: gghybrid R package for Bayesian hybrid index and genomic cline estimation. v2.0.0 https://doi.org/10.5281/zenodo.3676498 (2020).Wringe, B. hybriddetective: automates the process of detecting hybrids from genetic data. R package version 0.1.0.9000 https://github.com/bwringe/hybriddetective (2016).Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).Article 
CAS 

Google Scholar 
Malinsky, M., Matschiner, M. & Svardal, H. Dsuite‐Fast D‐statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021).Article 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).Article 
CAS 

Google Scholar 
Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).Article 
CAS 

Google Scholar 
Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).Article 
CAS 

Google Scholar 
Malinsky, M. et al. Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake. Science 350, 1493–1498 (2015).Article 
CAS 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Article 
CAS 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

Google Scholar 
Karger, D. N. et al. CHELSA climatologies at high resolution for the Earth’s land surface areas (v.1.0). https://doi.org/10.1594/WDCC/CHELSA_v1 (2016).Ackerley, D. & Dommenget, D. Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures. Geosci. Model Dev. 9, 2077–2098 (2016).Article 

Google Scholar 
Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim: high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 1–9 (2018).Article 

Google Scholar 
Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21,000 years at regional and global scales. Ecography 40, 1348–1358 (2017).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD–a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
Lemus-Canovas, M., Lopez-Bustins, J. A., Martin-Vide, J. & Royé, D. synoptReg: an R package for computing a synoptic climate classification and a spatial regionalization of environmental data. Environ. Model. Softw. 118, 114–119 (2019).Article 

Google Scholar 
Hao, T., Elith, J., Guillera‐Arroita, G. & Lahoz‐Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25, 839–852 (2019).Article 

Google Scholar 
Galpern, P., Peres‐Neto, P. R., Polfus, J. & Manseau, M. MEMGENE: spatial pattern detection in genetic distance data. Methods Ecol. Evol. 5, 1116–1120 (2014).Article 

Google Scholar 
Peres‐Neto, P. R. & Galpern, P. memgene: spatial pattern detection in genetic distance data using Moran’s eigenvector maps. R package version 1.0.1 https://cran.r-project.org/web/packages/memgene/ (2019).Oksanen, J. et al. vegan: community ecology package. R package version 2.3–0 https://cran.r-project.org/web/packages/vegan/ (2015).Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L. & Lasky, J. R. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecol. 25, 104–120 (2015).Article 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).Article 
CAS 

Google Scholar 
Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).Article 
CAS 

Google Scholar 
Brauer, C. J. et al. Data for ‘Natural hybridisation reduces vulnerability to climate change’. figshare https://doi.org/10.6084/m9.figshare.21692918 (2022).Brauer, C. J. et al. Code for ‘Natural hybridisation reduces vulnerability to climate change’. GitHub https://github.com/pygmyperch/NER (2022). More

Vegetation and land-cover dataTo monitor vegetation at the global scale, we use three datasets: (1) vegetation optical depth (VOD, 0.25°, Ku-Band, daily 1987–201723) (Fig. 1A), (2) AVHRR GIMMSv3g normalized difference vegetation index (NDVI, 1/12°, bi-weekly 1981–201524) (Fig. 1B), and (3) MODIS MOD13 NDVI at 0.05° (16-day, 2000–202125). We correct for spurious values in the NDVI data (e.g., cloud contamination) using the method of Chen et al.43. We resample the VOD data using bi-weekly medians to agree with the NDVI data time sampling.For all three vegetation datasets, we remove seasonality and long-term trends using seasonal trend decomposition by Loess4,44 based on the proposed optimal parameters listed in Cleveland et al.44 (code available on Zenodo45). That is, we use a period of 24 (bi-monthly, 1 year), 47 for the trend smoother (just under 2 years) and 25 for low-pass (just over 1 year). We only use the STL residual—the de-seasoned and de-trended NDVI and VOD time series—in our analysis.To contextualize our understanding of vegetation resilience, we use MODIS MCD12Q1 land cover46 (Fig. 1C) as well as a global average aridity index based on WorldCLIM data31 (Fig. 1D). We exclude from our analysis anthropogenic and non-vegetated landscapes (e.g., permanent snow and ice, desert, urban), as well as any land covers which have changed (e.g., forest to grassland) during the period 2001–2020.Precipitation data and variability metricsTo measure precipitation at the global scale, we rely upon ERA5 data (~30 km, monthly, 1981–2021)33. We process global-scale precipitation metrics using the Google Earth Engine47 platform. We further use the sum of soil moisture from the surface down to 28 cm of depth (first two layers of the ECMWF Integrated Forecasting System soil moisture estimates) to quantify soil moisture means and inter-annual variability33.It is well-documented that vegetation resilience is responsive to the MAP of certain regions1. However, the role of precipitation variability in controlling vegetation resilience has not been well-studied. Here we examine precipitation variability in terms of both intra- and inter-annual patterns. Intra-annual precipitation variability is determined in terms of the Walsh-Lawler Seasonality index32 (Fig. 1D), calculated using monthly data from ERA533.Partly due to the fact that precipitation is non-negative, simple inter-annual variability metrics such as the standard deviation of annual precipitation sums are biased by the absolute precipitation sums; higher precipitation regions have a higher possible range of variability. To limit the influence of MAP, we hence investigate the standard deviation of annual precipitation sums normalized by the MAP, over the period 1981–2021, based on ERA5 data33 (Fig. 1F). We motivate our normalization by MAP with the strong linear relationship between MAP and MAP standard deviation (Supplementary Fig. S2). We further confirm our discovered relationships (Fig. 5) using only those regions where MAP was between the 40 and 60th percentile of MAP for a given land cover (Supplementary Figs. S11,S12). This serves as an additional check that our normalization of MAP standard deviation by MAP does not bias the inferred relationship between vegetation resilience and precipitation variability. Similarly, we generate a normalized inter-annual soil moisture variability by normalizing year-on-year soil moisture standard deviation (Supplementary Fig. S8) by long-term mean soil moisture (Supplementary Fig. S5).Empirical resilience estimationResilience is defined as the ability of a system to recover from perturbations, and can be quantified empirically by the speed of recovery to the previous state16,17. To measure resilience on the global scale, we employ a recently introduced methodology4 which we will briefly summarize in the following.We first identify sharp transitions in the vegetation time series using an 18-point (9 month) moving window to define local slopes throughout the time series48. We then identify slopes above the 99th percentile, and define connected regions as individual perturbations. The highest peak (largest instantaneous slope) within each connected region is then labeled as an individual disturbance.The employed approach does not delineate every rapid transition in a time series due to our reliance on percentiles; our dataset will be inherently biased towards the largest transitions. Furthermore, the same transitions are not guaranteed to be captured for both NDVI and VOD data in each location, as the percentiles will naturally vary between the datasets. Finally, our method will in some cases produce false positives, especially in cases where a given time series does not have any significant rapid transitions. To limit the influence of false positives on our results, we discard any perturbations where the time series does not drop significantly, and where the period before and after a given transition does not pass a two-sample Kolmogorov–Smirnov test4.Finally, using our global set of time-series transitions, we can identify each local vegetation (NDVI or VOD) minima, and use the five following years of data to fit an exponential function to the residual time series, assuming that the recovery after a perturbation to a vegetation state x0 follows approximately the equation$$x(t),approx ,{x}_{0}{e}^{rt}$$
(1)
where x(t) denotes the vegetation state at time t after the perturbation. Negative r indicates that the vegetation system will return to the original stable state at rate ∣r∣. For positive r, the initial perturbation would be amplified, suggesting a non-resilient vegetation state. Our empirical recovery rates are defined as the fitted exponent r, obtained for each detected transition in the NDVI and VOD residual time series. We finally use the coefficient of determination R2 to remove instances where the fitted exponential poorly matches the underlying data4.For the empirical estimate of the restoring rate obtained from fitting an exponential to the recovery after an abrupt negative deviation of VOD or NDVI, abrupt changes in the mean state induced by changing sensors rather than an actual vegetation shift may impact the results. However, all datasets used here are tightly cross-calibrated to eliminate mean-shifts when new instruments are introduced23,24. It is therefore unlikely that changes in the instrumentation of the various datasets unduly influence our empirical estimates of λ.Dynamical system metrics of resilienceThe lag-one autocorrelation (AC1) has previously been proposed to measure the stability of real-world dynamical systems in general, and the resilience of vegetation systems in particular1,19,20,21,49. Based on the concept of critical slowing down, the AC1 has, together with the variance, also been suggested as an early-warning indicator for forthcoming critical transitions50,51. Mathematically, the suitability of the variance and AC1 as resilience measures and early-warning indicators can be motivated as follows4,52,53. First, linearize the system around a given stable state x*:$$dbar{x}=lambda bar{x}dt+sigma dW$$
(2)
for (bar{x}: !!=x-{x}^{*}), assuming a Wiener Process W with standard deviation σ. The dynamics are stable for λ  More