Ecology

1.Roselló-Mora R, Amann R. The species concept for prokaryotes. FEMS Microbiol Rev. 2001;25:39–67.
Google Scholar 
2.Certini G, Campbell CD, Edwards AC. Rock fragments in soil support a different microbial community from the fine earth. Soil Biol Biochem. 2004;36:1119–28.CAS 

Google Scholar 
3.Carson JK, Rooney D, Gleeson DB, Clipson N. Altering the mineral composition of soil causes a shift in microbial community structure. FEMS Microbiol Ecol. 2007;61:414–23.CAS 
PubMed 

Google Scholar 
4.Uroz S, Kelly LC, Turpault M, Lepleux C, Frey-Klett P. The mineralosphere concept: mineralogical control of the distribution and function of mineral-associated bacterial communities. Trends Microbiol. 2015;23:751–62.CAS 
PubMed 

Google Scholar 
5.Ahmed E, Hugerth LW, Logue JB, Brüchert V, Andersson AF, Holmström SJ. Mineral type structures soil microbial communities. Geomicrobiol J 2017;34:538–45.CAS 

Google Scholar 
6.Whitman T, Neurath R, Perera A, Chu-Jacoby I, Ning D, Zhou J, et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ Microbiol. 2018;20:4444–60.CAS 
PubMed 

Google Scholar 
7.Kandeler E, Gebala A, Boeddinghaus RS, Müller K, Rennert T, Soares M, et al. The mineralosphere—succession and physiology of bacteria and fungi colonising pristine minerals in grassland soils under different land-use intensities. Soil Biol Biochem. 2019;136:107534.CAS 

Google Scholar 
8.Hassink J, Bouwman LA, Zwart KB, Bloem J, Brussaard L. Relationships between soil texture, physical protection of organic-matter, soil biota, and C-mineralization and N-mineralization in grassland soils. Geoderma 1993;57:105–28.CAS 

Google Scholar 
9.Mayer LM, Schick LL, Hardy KR, Wagai R, McCarthy J. Organic matter in small mesopores in sediments and soils. Geochim Cosmochim Acta. 2004;68:3868–72.
Google Scholar 
10.Chenu C, Stotzky G. Interaction between microorganisms and soil particles: an overview. In: Huang PM, Bollag JM, Senesi N, editors. Interactions between soil particles and microorganism: impact on the terrestrial ecosystem. New York: Wiley; 2002. p. 3–40.11.Hemkemeyer M, Pronk GJ, Heister K, Kögel-Knabner I, Martens R, Tebbe CC. Artificial soil studies reveal domain-specific preferences of microorganisms for the colonisation of different soil minerals and particle size fractions. FEMS Microbiol Ecol. 2014;90:770–82.CAS 
PubMed 

Google Scholar 
12.Six J, Elliott ET, Paustian K. Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture. Soil Biol Biochem. 2000;32:2099–103.CAS 

Google Scholar 
13.Totsche KU, Amelung W, Gerzabek MH, Guggenberger G, Klumpp E, Knief C, et al. Microaggregates in soils. J Plant Nutr Soil Sci. 2018;181:104–36.CAS 

Google Scholar 
14.Rasmussen C, Southard RJ, Horwath WR. Litter type and soil minerals control temperate forest soil carbon response to climate change. Glob Change Biol 2008;14:2064–80.
Google Scholar 
15.Hemingway JD, Rothman DH, Grant KE, Rosengard SZ, Eglinton TI, Derry LA, et al. Mineral protection regulates long-term global preservation of natural organic carbon. Nature 2019;570:228–31.CAS 
PubMed 

Google Scholar 
16.Ranjard L, Richaume A. Quantitative and qualitative microscale distribution of bacteria in soil. Res Microbiol. 2001;152:707–16.CAS 
PubMed 

Google Scholar 
17.Poll C, Thiede A, Wermbter N, Sessitsch A, Kandeler E. Micro-scale distribution of microorganisms and microbial enzyme activities in a soil with long-term organic amendment. Eur J Soil Sci. 2003;54:715–24.
Google Scholar 
18.Neumann D, Heuer A, Hemkemeyer M, Martens R, Tebbe CC. Response of microbial communities to long-term fertilization depends on their microhabitat. FEMS Microbiol Ecol. 2013;86:71–84.CAS 
PubMed 

Google Scholar 
19.Nie M, Pendall E, Bell C, Wallenstein MD. Soil aggregate size distribution mediates microbial climate change feedbacks. Soil Biol Biochem. 2014;68:357–365.CAS 

Google Scholar 
20.Chenu C, Hassink J, Bloem J. Short-term changes in the spatial distribution of microorganisms in soil aggregates as affected by glucose addition. Biol Fertil Soils. 2001;34:349–56.CAS 

Google Scholar 
21.Saidy AR, Smernik RJ, Baldock JA, Kaiser K, Sanderman J. The sorption of organic carbon onto differing clay minerals in the presence and absence of hydrous iron oxide. Geoderma. 2013;209:15–21.
Google Scholar 
22.Mikutta R, Kleber M, Torn MS, Jahn R. Stabilization of soil organic matter: association with minerals or chemical recalcitrance? Biogeochemistry 2006;77:25–56.CAS 

Google Scholar 
23.Gadd GM. Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology. 2010;156:609–43.CAS 
PubMed 

Google Scholar 
24.Lehmann J, Kleber M. The contentious nature of soil organic matter. Nature 2015;528:60–68.CAS 
PubMed 

Google Scholar 
25.Sokol NW, Sanderman J, Bradford MA. Pathways of mineral‐associated soil organic matter formation: integrating the role of plant carbon source, chemistry, and point of entry. Glob Change Biol. 2019;25:12–24.
Google Scholar 
26.Skjemstad JO, Janik LJ, Head MJ, McClure SG. High energy ultraviolet photo‐oxidation: a novel technique for studying physically protected organic matter in clay‐and silt‐sized aggregates. J Soil Sci. 1993;44:485–99.CAS 

Google Scholar 
27.Goldfarb KC, Karaoz U, Hanson CA, Santee CA, Bradford MA, Treseder KK, et al. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front Microbiol. 2011;2:1–10.
Google Scholar 
28.Kleber M, Sollins P, Sutton R. A conceptual model of organo-mineral interactions in soils: self-assembly of organic molecular fragments into zonal structures on mineral surfaces. Biogeochemistry 2007;85:9–24.
Google Scholar 
29.Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM. Mineral control of soil organic carbon storage and turnover content. Nature 1997;389:3601–3.
Google Scholar 
30.Dahlgren RA, Saigusa M, Ugolini FC. The nature, properties and management of volcanic soils. Adv Agron. 2004;82:113–82.CAS 

Google Scholar 
31.Mikutta R, Kleber M, Jahn R. Poorly crystalline minerals protect organic carbon in clay subfractions from acid subsoil horizons. Geoderma 2005;128:106–15.CAS 

Google Scholar 
32.Keiluweit M, Bougoure JJ, Nico PS, Pett-Ridge J, Weber PK, Kleber M. Mineral protection of soil carbon counteracted by root exudates. Nat Clim Change. 2015;5:588–95.CAS 

Google Scholar 
33.Rasmussen C, Throckmorton H, Liles G, Heckman K, Meding S, Horwath WR. Controls on soil organic carbon partitioning and stabilization in the California Sierra Nevada. Soil Syst. 2018;2:1–18.
Google Scholar 
34.Zhou Z, Wang C, Luo Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat Comm. 2020;11:1–10.CAS 

Google Scholar 
35.Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microb. 2015;81:7570–81.CAS 

Google Scholar 
36.Hayer M, Schwartz E, Marks JC, Koch BJ, Morrissey EM, Schuettenberg AA, et al. Identification of growing bacteria during litter decomposition in freshwater through H218O quantitative stable isotope probing. Environ Microbiol Rep. 2016;8:975–82.CAS 
PubMed 

Google Scholar 
37.Papp K, Hungate BA, Schwartz E. Microbial rRNA synthesis and growth compared through quantitative stable isotope probing with H218O. Appl Environ Microbiol. 2018;84:1–17.
Google Scholar 
38.Finley BK, Dijkstra P, Rasmussen C, Schwartz E, Liu XA, van Gestel N, et al. Soil mineral assemblage and substrate quality effects on microbial priming. Geoderma2018;322:38–47.CAS 

Google Scholar 
39.Rasmussen C, Southard RJ, Horwath WR. Mineral control of organic carbon mineralization in a range of temperate conifer forest soils. Glob Change Biol. 2006;12:834–47.
Google Scholar 
40.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012;22:939–46.CAS 
PubMed 
PubMed Central 

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

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

Google Scholar 
44.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;41:590–6.
Google Scholar 
45.Morrissey EM, Mau RL, Schwartz E, McHugh TA, Dijkstra P, Koch BJ, et al. Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 2017;11:1890–9.PubMed 
PubMed Central 

Google Scholar 
46.R Core Team. R: a language and environment for statistical computiong. Vienna: R Foundation for Statistical Computing; 2021. https://www.R-project.org/.47.Dowle M, Srinivasan A. data.table: Extensions of ‘data.frame’. R package version 1.13.6. 2020.48.Oksanen J, Blanchet FG, Kindt R, Legendre P, O’hara RB, Simpson GL, et al. Vegan: community ecology package. R package version 1.17-4. 2010. http://cran.r-project.org.49.Morrissey EM, Mau RL, Hayer M, Liu XJ, Schwartz E, Dijkstra P, et al. Evolutionary history constrains microbial traits across environmental variation. Nat Ecol Evol. 2019;3:1064–9.PubMed 

Google Scholar 
50.Pinheiro J, Bates D, DebRoy S, Sarkar D. R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3. 1–137, 2018. https://CRAN.R-project.org/package=nlme .51.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2018;35:526–8.
Google Scholar 
52.Barter RL, Yu B. Superheat: an R package for creating beautiful and extendable heatmaps for visualizing complex data. J Comput Graph Stat. 2018;27:910–22.PubMed 
PubMed Central 

Google Scholar 
53.Demoling F, Figueroa D, Bååth E. Comparison of factors limiting bacterial growth in different soils. Soil Biol Biochem. 2007;39:2485–95.CAS 

Google Scholar 
54.Kaiser K, Zech W. Sorption of dissolved organic nitrogen by acid subsoil horizons and individual mineral phases. Eur J Soil Sci. 2000;51:403–11.CAS 

Google Scholar 
55.Barnhisel RI, Bertsch PM. Chlorites and hydroxy-interlayered vermiculite and smectite. In: Dixon JB, Weed SB editors. Minerals in soils environments, 2nd edn. Madison: Soil Science Society of America, Inc.; 1989. p. 729–88.56.Zunino H, Borie F, Aguilera S, Martin JP, Haider K. Decomposition of C-14- labeled glucose, plant and microbial products and phenols in volcanic ash-derived soils of Chile. Soil Biol Biochem. 1982;14:37–43.CAS 

Google Scholar 
57.Baldock JA, Nelson PN. In: Sumner ME editor. Handbook of soil science. Boca Raton: CRC Press; 2000. B25–B84.58.Matus F, Rumpel C, Neculman R, Panichini M, Mora ML. Soil carbon storage and stabilisation in andic soils: a review. Catena. 2014;120:102–10.CAS 

Google Scholar 
59.Nottingham AT, Griffiths H, Chamberlain PM, Stott AW, Tanner EVJ. Soil priming by sugar and leaf-litter substrates: a link to microbial groups. Appl Soil Ecol. 2009;42:183–90.
Google Scholar 
60.McMahon SK, Williams MA, Bottomley PJ, Myrold DD. Dynamics of microbial communities during decomposition of carbon-13 labeled ryegrass fractions in soil. Soil Sci Soc Am J 2005;69:1238–47.CAS 

Google Scholar 
61.Vieira S, Sikorski J, Gebala A, Boeddinghaus RS, Marhan S, Rennert T, et al. Bacterial colonization of minerals in grassland soils is selective and highly dynamic. Environ Microbiol. 2020;22:917–33.CAS 
PubMed 

Google Scholar 
62.Mille-Lindblom C, Fischer H, Tranvik LJ. Antagonism between bacteria and fungi: substrate competition and a possible tradeoff between fungal growth and tolerance towards bacteria. Oikos 2006;113:233–42.
Google Scholar  More

1.Baines SB, Pace ML. The production of dissolved organic matter by phytoplankton and its importance to bacteria: patterns across marine and freshwater systems. Limnol Oceanogr. 1991;36:1078–90.
Google Scholar 
2.Williams PJLeB. Heterotrophic bacteria and the dynamics of dissolved organic material. In: Kirchman DL (ed). Microbial Ecology of the Oceans, 1st edn. New York: Wiley-Liss; 2000. p. 153–200.3.Thornton DCO. Dissolved organic matter (DOM) release by phytoplankton in the contemporary and future ocean. Eur J Phycol. 2014;49:20–46.CAS 

Google Scholar 
4.Nagata T. Organic matter-bacteria interactions in seawater. In: Kirchman DL, (ed). Microbial Ecology of the Oceans. Hoboken: John Wiley and Sons, Inc; 2008. p. 207–41.
Google Scholar 
5.Kujawinski EB. The impact of microbial metabolism on marine dissolved organic matter. Ann Rev Mar Sci. 2011;3:567–99.PubMed 

Google Scholar 
6.Azam F, Fenchel T, Field JG, Gray JS, Meyerreil LA, Thingstad F. The ecological role of water-column microbes in the sea. Mar Ecol Prog Ser. 1983;10:257–63.
Google Scholar 
7.Cole JJ, Findlay S, Pace ML. Bacterial production in fresh and saltwater ecosystems – a cross-system overview. Mar Ecol Prog Ser. 1988;43:1–10.
Google Scholar 
8.Moran MA, Kujawinski EB, Stubbins A, Fatland R, Aluwihare LI, Buchan A, et al. Deciphering ocean carbon in a changing world. Proc Nat Acad Sci. 2016;113:3143–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Becker KW, Collins JR, Durham BP, Groussman RD, White AE, Fredricks HF, et al. Daily changes in phytoplankton lipidomes reveal mechanisms of energy storage in the open ocean. Nat Comm. 2018;9:5179.
Google Scholar 
10.Boysen AK, Carlson LT, Durham BP, Groussman RD, Aylward FO, Ribalet F, et al. Diel oscillations of particulate metabolites reflect synchronized microbial activity in the North Pacific Subtropical Gyre. bioRxiv. 2020: 2020.05.09.086173.11.Durham BP, Boysen AK, Carlson LT, Groussman RD, Heal KR, Cain KR, et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat Microbiol. 2019;4:1706–15.CAS 
PubMed 

Google Scholar 
12.Burney CM, Davis PG, Johnson KM, Sieburth JM. Diel relationships of microbial trophic groups and in situ dissolved carbohydrate dynamics in the Caribbean Sea. Mar Biol. 1982;67:311–22.CAS 

Google Scholar 
13.Gasol JM, Doval MD, Pinhassi J, Calderon-Paz JI, Guixa-Boixareu N, Vaque D, et al. Diel variations in bacterial heterotrophic activity and growth in the northwestern Mediterranean Sea. Mar Ecol Prog Ser. 1998;164:107–24.
Google Scholar 
14.Kuipers B, van Noort GJ, Vosjan J, Herndl GJ. Diel periodicity of bacterioplankton in the euphotic zone of the subtropical Atlantic Ocean. Mar Ecol Prog Ser. 2000;201:13–25.
Google Scholar 
15.Ottesen EA, Young CR, Gifford SM, Eppley JM, Marin R, Schuster SC, et al. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science 2014;345:207–12.CAS 
PubMed 

Google Scholar 
16.Aylward FO, Eppley JM, Smith JM, Chavez FP, Scholin CA, DeLong EF. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc Nat Acad Sci. 2015;112:5443–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Frischkorn KR, Haley ST, Dyhrman ST. Coordinated gene expression between Trichodesmium and its microbiome over day–night cycles in the North Pacific Subtropical Gyre. ISME J 2018;12:997–1007.PubMed 
PubMed Central 

Google Scholar 
18.Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 

Google Scholar 
19.Bjornsen PK. Phytoplankton exudation of organic-matter – why do healthy cells do it. Limnol Oceanogr. 1988;33:151–4.
Google Scholar 
20.Fogg GE. The ecological significance of extracellular products of phytoplankton photosynthesis. Bot Mar. 1983;26:3–14.CAS 

Google Scholar 
21.Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 2015;522:98–101.CAS 
PubMed 

Google Scholar 
22.Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria‐phytoplankton model system. Environ Microbiol. 2017;19:3500–13.CAS 
PubMed 

Google Scholar 
23.Guerrini F, Mazzotti A, Boni L, Pistocchi R. Bacterial-algal interactions in polysaccharide production. Aquat Micro Ecol. 1998;15:247–53.
Google Scholar 
24.Armbrust EV, Berges JA, Bowler C, Green BR, Martinez D, Putnam NH, et al. The genome of the diatom Thalassiosira pseudonana: ecology, evolution, and metabolism. Science 2004;306:79–86.CAS 
PubMed 

Google Scholar 
25.Moran MA, Buchan A, Gonzalez JM, Heidelberg JF, Whitman WB, Kiene RP, et al. Genome sequence of Silicibacter pomeroyi reveals adaptations to the marine environment. Nature 2004;432:910–3.CAS 
PubMed 

Google Scholar 
26.Uitz J, Claustre H, Gentili B, Stramski D. Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Global Biogeochem Cycles. 2010;24.27.Buchan A, LeCleir GR, Gulvik CA, Gonzalez JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
PubMed 

Google Scholar 
28.Luo HW, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.PubMed 
PubMed Central 

Google Scholar 
29.Nowinski B, Moran MA. Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater. Nat Microbiol. 2021;6:524.CAS 
PubMed 

Google Scholar 
30.Denger K, Lehmann S, Cook AM. Molecular genetics and biochemistry of N-acetyltaurine degradation by Cupriavidus necator H16. Microbiology 2011;157:2983–91.CAS 
PubMed 

Google Scholar 
31.Schulz A, Stoveken N, Binzen IM, Hoffmann T, Heider J, Bremer E. Feeding on compatible solutes: a substrate-induced pathway for uptake and catabolism of ectoines and its genetic control by EnuR. Environ Microbiol. 2017;19:926–46.CAS 
PubMed 

Google Scholar 
32.Crossette E, Gumm J, Langenfeld K, Raskin L, Duhaime M, Wigginton K. Metagenomic quantification of genes with internal standards. mBio. 2021;12:e03173-20.PubMed 
PubMed Central 

Google Scholar 
33.Gifford SM, Becker JW, Sosa OA, Repeta DJ, DeLong EF. Quantitative transcriptomics reveals the growth-and nutrient-dependent response of a streamlined marine methylotroph to methanol and naturally occurring dissolved organic matter. mBio. 2016;7:e01279-16.PubMed 
PubMed Central 

Google Scholar 
34.Moran MA, Satinsky B, Gifford SM, Luo HW, Rivers A, Chan LK, et al. Sizing up metatranscriptomics. ISME J 2013;7:237–43.CAS 
PubMed 

Google Scholar 
35.Guillard RRL, Hargraves PE. Stichochrysis immobilis is a diatom, not a chyrsophyte. Phycologia 1993;32:234–6.
Google Scholar 
36.Uchimiya M, Tsuboi Y, Ito K, Date Y, Kikuchi J. Bacterial substrate transformation tracked by stable-isotope-guided NMR metabolomics: application in a natural aquatic microbial community. Metabolites 2017;7:52.PubMed Central 

Google Scholar 
37.Lewis IA, Schommer SC, Markley JL. rNMR: open source software for identifying and quantifying metabolites in NMR spectra. Mag Res Chem. 2009;47:S123–S6.CAS 

Google Scholar 
38.Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu YF, et al. HMDB 3.0-the human metabolome database in 2013. Nuc Acids Res 2013;41:D801–D7.CAS 

Google Scholar 
39.Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, et al. BioMagResBank Nuc Acids Res. 2008;36:D402–D8.CAS 

Google Scholar 
40.Toukach PV, Egorova KS. Carbohydrate structure database merged from bacterial, archaeal, plant and fungal parts. Nuclic Acids Res. 2016;44:D1229–D36.CAS 

Google Scholar 
41.Landa M, Burns AS, Durham BP, Esson K, Nowinski B, Sharma S, et al. Sulfur metabolites that facilitate oceanic phytoplankton-bacteria carbon flux. ISME J. 2019;13:2536–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Boroujerdi AFB, Lee PA, DiTullio GR, Janech MG, Vied SB, Bearden DW. Identification of isethionic acid and other small molecule metabolites of Fragilariopsis cylindrus with nuclear magnetic resonance. Anal Bioanal Chem. 2012;404:777–84.CAS 
PubMed 

Google Scholar 
43.Walejko JM, Chelliah A, Keller-Wood M, Gregg A, Edison AS. Global metabolomics of the placenta reveals distinct metabolic profiles between maternal and fetal placental tissues following delivery in non-labored women. Metabolites 2018;8:10.PubMed Central 

Google Scholar 
44.Schwämmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering of omics data. Bioinformatics 2018;34:2965–72.PubMed 

Google Scholar 
45.Thaben PF, Westermark PO. Detecting rhythms in time series with RAIN. J Biol Rhythms. 2014;29:391–400.PubMed 
PubMed Central 

Google Scholar 
46.Welsh J (2020). CirHeatmap. Available from: https://github.com/joadwe/cirheatmap.47.Landa M, Burns AS, Roth SJ, Moran MA. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 2017;11:2677–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Satinsky BM, Gifford SM, Crump BC, Moran MA Use of internal standards for quantitative metatranscriptome and metagenome analysis. In: DeLong EF (ed). Methods in Enzymology. 2013. 531: p. 237-50.49.Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 2015;31:166–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 

Google Scholar 
51.Becker S, Tebben J, Coffinet S, Wiltshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Nat Acad Sci. 2020;117:6599–607.CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Neidhardt F, Ingraham J, Schaechter S Physiology of the bacterial cell: a molecular approach. Massachusetts: Sinauer Associates Inc.; 1990.53.Lidbury I, Murrell JC, Chen Y. Trimethylamine N-oxide metabolism by abundant marine heterotrophic bacteria. Proc Nat Acad Sci. 2014;111:2710–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Mayer J, Huhn T, Habeck M, Denger K, Hollemeyer K, Cook AM. 2,3-Dihydroxypropane-1-sulfonate degraded by Cupriavidus pinatubonensis JMP134: purification of dihydroxypropanesulfonate 3-dehydrogenase. Microbiology 2010;156:1556–64.CAS 
PubMed 

Google Scholar 
55.Mou XZ, Sun SL, Rayapati P, Moran MA. Genes for transport and metabolism of spermidine in Ruegeria pomeroyi DSS-3 and other marine bacteria. Aquat Micro Ecol. 2010;58:311–21.
Google Scholar 
56.Biller SJ, Coe A, Roggensack SE, Chisholm SW Heterotroph interactions alter Prochlorococcus transcriptome dynamics during extended periods of darkness. mSystems. 2018; 3 https://doi.org/10.1128/mSystems.00040-18.57.Harding L, Meeson B, Prézelin B, Sweeney B. Diel periodicity of photosynthesis in marine phytoplankton. Mar Biol. 1981;61:95–105.
Google Scholar 
58.Harding L, Prezelin B, Sweeney B, Cox J. Diel oscillations of the photosynthesis-irradiance (PI) relationship in natural assemblages of phytoplankton. Mar Biol. 1982;67:167–78.
Google Scholar 
59.Blough NV, Zepp RG Reactive oxygen species in natural waters. Active oxygen in chemistry. Dordrecht: Springer; 1995. p. 280–333.60.Zafiriou OC, Joussot-Dubien J, Zepp RG, Zika RG. Photochemistry of natural waters. Environ Sci Technol. 1984;18:358A–71A.CAS 

Google Scholar 
61.Ziegelhoffer EC, Donohue TJ. Bacterial responses to photo-oxidative stress. Nat Rev Microbiol. 2009;7:856–63.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Lubin EA, Henry JT, Fiebig A, Crosson S, Laub MT. Identification of the PhoB regulon and role of PhoU in the phosphate starvation response of Caulobacter crescentus. J Bacteriol. 2016;198:187–200.CAS 
PubMed 

Google Scholar 
63.Yang C, Huang TW, Wen SY, Chang CY, Tsai SF, Wu WF, et al. Genome-wide PhoB binding and gene expression profiles reveal the hierarchical gene regulatory network of phosphate starvation in Escherichia coli. Plos One. 2012;7:e47314.CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Hsieh YJ, Wanner BL. Global regulation by the seven-component Pi signaling system. Curr Opin Microbiol. 2010;13:198–203.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Muratore D, Boysen AK, Harke MJ, Becker KW, Casey JR, Coesel SN, et al. Community-scale synchronization and temporal partitioning of gene expression, metabolism, and lipid biosynthesis in oligotrophic ocean surface waters. bioRxiv. 2020: 2020.05.15.098020.66.Giedroc DP. Hydrogen peroxide sensing in Bacillus subtilis: it is all about the (metallo)regulator. Mol Microbiol. 2009;73:1–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131:281–5.CAS 
PubMed 

Google Scholar 
68.Weinitschke S, Sharma PI, Stingl U, Cook AM, Smits TH. Gene clusters involved in isethionate degradation by terrestrial and marine bacteria. Appl Environ Microbiol. 2010;76:618–21.CAS 
PubMed 

Google Scholar 
69.Nikaido H. Molecular basis of bacterial outer membrane permeability revisited. Microbiol Mol Biol Rev. 2003;67:593–656.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Hellebust JA. Excretion of some organic compounds by marine phytoplankton 1. Limnol Oceanogr. 1965;10:192–206.
Google Scholar 
71.Behrenfeld MJ, Halsey KH, Milligan AJ. Evolved physiological responses of phytoplankton to their integrated growth environment. Philos Trans R Soc B: Biol Sci. 2008;363:2687–703.CAS 

Google Scholar 
72.Kiene RP, Linn LJ, Bruton JA. New and important roles for DMSP in marine microbial communities. J Sea Res. 2000;43:209–24.CAS 

Google Scholar 
73.Fredrickson KA, Strom SL. The algal osmolyte DMSP as a microzooplankton grazing deterrent in laboratory and field studies. J Plankton Res. 2009;31:135–52.
Google Scholar 
74.Sunda W, Kieber DJ, Kiene RP, Huntsman S. An antioxidant function for DMSP and DMS in marine algae. Nature 2002;418:317–20.CAS 
PubMed 

Google Scholar 
75.Lidbury I, Kimberley G, Scanlan DJ, Murrell JC, Chen Y. Comparative genomics and mutagenesis analyses of choline metabolism in the marine Roseobacter clade. Environ Microbiol. 2015;17:5048–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Cunliffe M. Purine catabolic pathway revealed by transcriptomics in the model marine bacterium Ruegeria pomeroyi DSS-3. FEMS Microbiol Ecol. 2016;92:fiv150.PubMed 

Google Scholar 
77.Durham BP, Sharma S, Luo HW, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Nat Acad Sci. 2015;112:453–7.CAS 
PubMed 

Google Scholar  More

1.Beissinger, S. R. & Westphal, M. I. On the use of demographic models of population viability in endangered species management. J. Wildl. Manag. 62, 821–841 (1998).
Google Scholar 
2.Campana, S. Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. J. Fish. Biol. 59, 197–242 (2001).
Google Scholar 
3.Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Jarman, S. N. et al. Molecular biomarkers for chronological age in animal ecology. Mol. Ecol. 24, 4826–4847 (2015).CAS 
PubMed 

Google Scholar 
5.Thompson, M. J., vonHoldt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging 9, 1055–1068 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.De Paoli-Iseppi, R. et al. Measuring animal age with DNA methylation: from humans to wild animals. Front. Genet. 8, 106 (2017).PubMed 
PubMed Central 

Google Scholar 
7.Bell, C. G. et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 20, 249 (2019).PubMed 
PubMed Central 

Google Scholar 
8.Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).CAS 
PubMed 

Google Scholar 
10.Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 e956 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, 68 (2017).PubMed 
PubMed Central 

Google Scholar 
12.Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction, and rapamycin treatment. Genome Biol. 18, 57 (2017).PubMed 
PubMed Central 

Google Scholar 
13.Nussey, D. H., Froy, H., Lemaitre, J. F., Gaillard, J. M. & Austad, S. N. Senescence in natural populations of animals: widespread evidence and its implications for bio-gerontology. Ageing Res. Rev. 12, 214–225 (2013).PubMed 

Google Scholar 
14.Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).PubMed 
PubMed Central 

Google Scholar 
15.Voisin, S. et al. An epigenetic clock for human skeletal muscle. J. Cachexia Sarcopenia Muscle https://doi.org/10.1002/jcsm.12556 (2020).16.De Paoli-Iseppi, R. et al. Age estimation in a long-lived seabird (Ardenna tenuirostris) using DNA methylation-based biomarkers. Mol. Ecol. Resour. 19, 411–425 (2019).PubMed 

Google Scholar 
17.Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8, 9998 (2018).PubMed 
PubMed Central 

Google Scholar 
18.Chen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 8, 1844–1865 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Christiansen, L. et al. DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell 15, 149–154 (2016).CAS 
PubMed 

Google Scholar 
20.Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi‐ supercentenarians and their offspring. Aging 7, 1159–1170 (2018).
Google Scholar 
21.Marioni, R. E. et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 16, 25 (2015).PubMed 
PubMed Central 

Google Scholar 
22.Perna, L. et al. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenetics 8, 64 (2016).PubMed 
PubMed Central 

Google Scholar 
23.Mitchell, C., Schneper, L. M. & Notterman, D. A. DNA methylation, early life environment, and health outcomes. Pediatr. Res. 79, 212–219 (2016).CAS 
PubMed 

Google Scholar 
24.Pérez, R. F., Santamarina, P., Fernández, A. F., & Fraga, M. F. Epigenetics and Lifestyle: The Impact of Stress, Diet, and Social Habits on Tissue Homeostasis. In Epigenetics and Regeneration (ed. Palacios, D.) pp. 461–489 (Academic Press, 2019).25.Szyf, M., Tang, Y. Y., Hill, K. G. & Musci, R. The dynamic epigenome and its implications for behavioral interventions: a role for epigenetics to inform disorder prevention and health promotion. Transl. Behav. Med. 6, 55–62 (2016).PubMed 
PubMed Central 

Google Scholar 
26.Lee, R. S. et al. Chronic corticosterone exposure increases expression and decreases deoxyribonucleic acid methylation of Fkbp5 in mice. Endocrinology 151, 4332–4343 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Zannas, A. S. et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 16, 266 (2015).PubMed 
PubMed Central 

Google Scholar 
28.Biemont, C. Inbreeding effects in the epigenetic era. Nat. Rev. Genet. 11, 234 (2010).CAS 
PubMed 

Google Scholar 
29.Venney, C. J., Johansson, M. L. & Heath, D. D. Inbreeding effects on gene-specific DNA methylation among tissues of Chinook salmon. Mol. Ecol. 25, 4521–4533 (2016).CAS 
PubMed 

Google Scholar 
30.Vergeer, P., Wagemaker, N. C. & Ouborg, N. J. Evidence for an epigenetic role in inbreeding depression. Biol. Lett. 8, 798–801 (2012).PubMed 
PubMed Central 

Google Scholar 
31.Han, W. et al. Genome-wide analysis of the role of DNA methylation in inbreeding depression of reproduction in Langshan chicken. Genomics 112, 2677–2687 (2020).CAS 
PubMed 

Google Scholar 
32.Thompson, M. J. et al. A multi-tissue full lifespan epigenetic clock for mice. Aging 10, 2832–2854 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Zhang, Q. et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 11, 54 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics 14, 912–926 (2019).PubMed 
PubMed Central 

Google Scholar 
35.Pinho, G. M. et al. Hibernation slows epigenetic aging in yellow-bellied marmots. Preprint at bioRxiv https://doi.org/10.1101/2021.03.07.434299 (2021).36.Moehlman, P. D. Equids: Zebras, Asses, and Horses Status Survey and Conservation Action Plan Vol. 37, 190 pp (IUCN/SSC Equid Specialist Group, 2002).37.Moehlman, P. D. & King, S. R. B. IUCN SSC Equid Specialist Group 2020 Report. https://www.iucn.org/commissions/ssc-groups/mammals/mammals-a-e/equid (2020).38.Rubinacci, S., Ribeiro, D. M., Hofmeister, R. & Delaneau, O. Efficient phasing and imputation of low-coverage sequencing data using large reference panels. Nat. Genet. 53, 120–126 (2021).CAS 
PubMed 

Google Scholar 
39.Ceballos, F. C., Hazelhurst, S. & Ramsay, M. Runs of homozygosity in sub-Saharan African populations provide insights into complex demographic histories. Hum. Genet. 138, 1123–1142 (2019).CAS 
PubMed 

Google Scholar 
40.Curik, I., Ferenčaković, M. & Sölkner, J. Inbreeding and runs of homozygosity: a possible solution to an old problem. Livest. Sci. 166, 26–34 (2014).
Google Scholar 
41.Anderson, J. A. et al. The costs of competition: high social status males experience accelerated epigenetic aging in wild baboons. eLife 10, e66128 (2020).
Google Scholar 
42.McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. https://doi.org/10.1038/nbt.1630 (2010).43.Gronniger, E. et al. Aging and chronic sun exposure cause distinct epigenetic changes in human skin. PLoS Genet. 6, e1000971 (2010).PubMed 
PubMed Central 

Google Scholar 
44.Robeck, T. R. et al. Multi-species and multi-tissue methylation clocks for age estimation in toothed whales and dolphins. Commun. Biol. https://doi.org/10.1038/s42003-021-02179-x (2021).45.Jonsson, H. et al. Speciation with gene flow in equids despite extensive chromosomal plasticity. Proc. Natl Acad. Sci. USA 111, 18655–18660 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Vilstrup, J. T. et al. Mitochondrial phylogenomics of modern and ancient equids. PLoS One 8, e55950 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Jensen-Seaman, M. I. & Hooper-Boyd, K. A. in Encyclopedia of Life Sciences (ELS) (John Wiley & Sons, Ltd., 2008).48.Farrell, C., Snir, S. & Pellegrini, M. The epigenetic pacemaker—modeling epigenetic states under an evolutionary framework. Bioinformatics https://doi.org/10.1093/bioinformatics/btaa585 (2020).49.Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. Epigenomics 10, 695–706 (2018).CAS 
PubMed 

Google Scholar 
50.Charlesworth, B. & Hughes, K. A. Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence. Proc. Natl Acad. Sci. USA 93, 6140–6145 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Fox, C. W. Inbreeding depression increases with maternal age. Evolut. Ecol. Res. 12, 961–972 (2010).
Google Scholar 
52.Benton, C. H. et al. Inbreeding intensifies sex- and age-dependent disease in a wild mammal. J. Anim. Ecol. 87, 1500–1511 (2018).PubMed 

Google Scholar 
53.Mayne, B., Berry, O., Davies, C., Farley, J. & Jarman, S. A genomic predictor of lifespan in vertebrates. Sci. Rep. 9, 17866 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.McClain, A. T. & Faulk, C. The evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging 10, 561–572 (2018).
Google Scholar 
55.Alpi, A. F., Pace, P. E., Babu, M. M. & Patel, K. J. Mechanistic insight into site-restricted monoubiquitination of FANCD2 by Ube2t, FANCL, and FANCI. Mol. Cell 32, 767–777 (2008).CAS 
PubMed 

Google Scholar 
56.Kannan, M. B., Solovieva, V. & Blank, V. The small MAF transcription factors MAFF, MAFG, and MAFK: current knowledge and perspectives. Biochim. Biophys. Acta 1823, 1841–1846 (2012).CAS 
PubMed 

Google Scholar 
57.Li, Z. et al. PBX3 is an important cofactor of HOXA9 in leukemogenesis. Blood 121, 1422–1431 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Malecki, M. T. et al. Mutations in NEUROD1 are associated with the development of type 2 diabetes mellitus. Nat. Genet. 23, 323–328 (1999).CAS 
PubMed 

Google Scholar 
59.Ding, Q., Joshi, P. S., Xie, Z. H., Xiang, M. & Gan, L. BARHL2 transcription factor regulates the ipsilateral/contralateral subtype divergence in postmitotic dI1 neurons of the developing spinal cord. Proc. Natl Acad. Sci. USA 109, 1566–1571 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Mo, Z., Li, S., Yang, X. & Xiang, M. Role of the Barhl2 homeobox gene in the specification of glycinergic amacrine cells. Development 131, 1607–1618 (2004).CAS 
PubMed 

Google Scholar 
61.Giampietro, C. et al. The alternative splicing factor Nova2 regulates vascular development and lumen formation. Nat. Commun. 6, 8479 (2015).CAS 
PubMed 

Google Scholar 
62.Yano, M., Hayakawa-Yano, Y., Mele, A. & Darnell, R. B. Nova2 regulates neuronal migration through an RNA switch in disabled-1 signaling. Neuron 66, 848–858 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Deneen, B. et al. The transcription factor NFIA controls the onset of gliogenesis in the developing spinal cord. Neuron 52, 953–968 (2006).CAS 
PubMed 

Google Scholar 
64.Hiraike, Y. et al. NFIA co-localizes with PPARgamma and transcriptionally controls the brown fat gene program. Nat. Cell Biol. 19, 1081–1092 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Caricasole, A., Sala, C., Roncarati, R., Formenti, E. & Terstappen, G. C. Cloning and characterization of the human phosphoinositide-specific phospholipase C-beta 1 (PLCβ1). Biochim. Biophys. Acta 1517, 63–72 (2000).CAS 
PubMed 

Google Scholar 
66.McOmish, C. E., Burrows, E. L., Howard, M. & Hannan, A. J. PLC-beta1 knockout mice as a model of disrupted cortical development and plasticity: behavioral endophenotypes and dysregulation of RGS4 gene expression. Hippocampus 18, 824–834 (2008).CAS 
PubMed 

Google Scholar 
67.Mittelstaedt, T., Alvarez-Baron, E. & Schoch, S. RIM proteins and their role in synapse function. Biol. Chem. 391, 599–606 (2010).CAS 
PubMed 

Google Scholar 
68.Schoch, S. et al. RIM1α forms a protein scaffold for regulating neurotransmitter release at the active zone. Nature 415, 321–326 (2002).CAS 
PubMed 

Google Scholar 
69.Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.01.18.426733 (2021).70.Nishikawa, K. et al. Maf promotes osteoblast differentiation in mice by mediating the age-related switch in mesenchymal cell differentiation. J. Clin. Invest. 120, 3455–3465 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Saidak, Z., Hay, E., Marty, C., Barbara, A. & Marie, P. J. Strontium ranelate rebalances bone marrow adipogenesis and osteoblastogenesis in senescent osteopenic mice through NFATc/Maf and Wnt signaling. Aging Cell 11, 467–474 (2012).CAS 
PubMed 

Google Scholar 
72.McClay, J. L. et al. A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects. Hum. Mol. Genet. 23, 1175–1185 (2014).CAS 
PubMed 

Google Scholar 
73.Ambeskovic, M. et al. Ancestral stress programs sex-specific biological aging trajectories and non-communicable disease risk. Aging 12, 3828–3847 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Burger, C., Lopez, M. C., Baker, H. V., Mandel, R. J. & Muzyczka, N. Genome-wide analysis of aging and learning-related genes in the hippocampal dentate gyrus. Neurobiol. Learn Mem. 89, 379–396 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Horvath, S. et al. DNA methylation clocks show slower progression of aging in naked mole-rat queens. Preprint at bioRxiv https://doi.org/10.1101/2021.03.15.435536 (2021).76.Rapoport, S. I., Primiani, C. T., Chen, C. T., Ahn, K. & Ryan, V. H. Coordinated expression of phosphoinositide metabolic genes during development and aging of human dorsolateral prefrontal cortex. PLoS One 10, e0132675 (2015).PubMed 
PubMed Central 

Google Scholar 
77.Dube, J. B. et al. Genetic determinants of “cognitive impairment, no dementia”. J. Alzheimers Dis. 33, 831–840 (2013).CAS 
PubMed 

Google Scholar 
78.Hinney, A. et al. Genetic variation at the CELF1 (CUGBP, elav-like family member 1 gene) locus is genome-wide associated with Alzheimer’s disease and obesity. Am. J. Med. Genet. B Neuropsychiatr. Genet. 165B, 283–293 (2014).PubMed 

Google Scholar 
79.Ntalla, I. et al. Replication of established common genetic variants for adult BMI and childhood obesity in Greek adolescents: the TEENAGE study. Ann. Hum. Genet. 77, 268–274 (2013).PubMed 
PubMed Central 

Google Scholar 
80.Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Gao, Z. et al. Neurod1 is essential for the survival and maturation of adult-born neurons. Nat. Neurosci. 12, 1090–1092 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
82.Badawi, Y. & Nishimune, H. Presynaptic active zones of mammalian neuromuscular junctions: Nanoarchitecture and selective impairments in aging. Neurosci. Res. 127, 78–88 (2018).CAS 
PubMed 

Google Scholar 
83.Tollervey, J. R. et al. Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res. 21, 1572–1582 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Kim, B. H., Nho, K. & Lee, J. M., Alzheimer’s Disease Neuroimaging, I. Genome-wide association study identifies susceptibility loci of brain atrophy to NFIA and ST18 in Alzheimer’s disease. Neurobiol. Aging 102, 200 e201–200 e211 (2021).
Google Scholar 
85.Horvath, S. et al. DNA methylation aging and transcriptomic studies in horses. Preprint at bioRxiv https://doi.org/10.1101/2021.03.11.435032 (2021).86.Benayoun, B. A., Pollina, E. A. & Brunet, A. Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat. Rev. Mol. Cell Biol. 16, 593–610 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Quach, A. et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging 9, 419–446 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Crary-Dooley, F. K. et al. A comparison of existing global DNA methylation assays to low-coverage whole-genome bisulfite sequencing for epidemiological studies. Epigenetics 12, 206–214 (2017).PubMed 
PubMed Central 

Google Scholar 
89.Reed, K., Poulin, M. L., Yan, L. & Parissenti, A. M. Comparison of bisulfite sequencing PCR with pyrosequencing for measuring differences in DNA methylation. Anal. Biochem. 397, 96–106 (2010).CAS 
PubMed 

Google Scholar 
90.Tost, J., Dunker, J. & Gut, I. G. Analysis and quantification of multiple methylation variable positions in CpG islands by Pyrosequencing. Biotechniques 35, 152–156 (2003).CAS 
PubMed 

Google Scholar 
91.Karesh, W. B. in Zoo and Wild Animal Medicine: Current Therapy (eds Fowler Murray, E. & Eric Miller, R.) 298−308 (Saunders Elsevier, 2008).92.Chiou, K. L. & Bergey, C. M. Methylation-based enrichment facilitates low-cost, noninvasive genomic scale sequencing of populations from feces. Sci. Rep. 8, 1975 (2018).PubMed 
PubMed Central 

Google Scholar 
93.Orkin, J. D. et al. The genomics of ecological flexibility, large brains, and long lives in capuchin monkeys revealed with fecalFACS. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2010632118 (2021).94.Snyder-Mackler, N. et al. Efficient genome-wide sequencing and low-coverage pedigree analysis from noninvasively collected samples. Genetics 203, 699–714 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Harley, E. H., Knight, M. H., Lardner, C., Wooding, B. & Gregor, M. The Quagga project: progress over 20 years of selective breeding. South African J. Wildlife Res. https://doi.org/10.3957/056.039.0206 (2009).96.Arneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences Preprint at bioRxiv https://doi.org/10.1101/2021.01.07.425637 (2021).97.Kalbfleisch, T. S. et al. Improved reference genome for the domestic horse increases assembly contiguity and composition. Commun. Biol. 1, 197 (2018).PubMed 
PubMed Central 

Google Scholar 
98.Wade, C. M. et al. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science https://doi.org/10.1126/science.1178158 (2009).99.Zhou, W., Triche, T. J. Jr., Laird, P. W. & Shen, H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).PubMed 
PubMed Central 

Google Scholar 
100.Bocklandt, S. et al. Epigenetic predictor of age. PLoS One 6, e14821 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
102.Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).PubMed 
PubMed Central 

Google Scholar 
103.R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).104.Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).105.Larison, B. et al. Population structure, inbreeding and stripe pattern abnormalities in plains zebras. Mol. Ecol. 30, 379–390 (2021).CAS 
PubMed 

Google Scholar 
106.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows−Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
107.Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).108.Freed, D., Aldana, R., Weber, J. A. & Edwards, J. S. The Sentieon Genomics Tools—A fast and accurate solution to variant calling from next-generation sequence data. Preprint at bioRxiv https://doi.org/10.1101/115717 (2017).109.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
110.Meyermans, R., Gorssen, W., Buys, N. & Janssens, S. How to study runs of homozygosity using PLINK? A guide for analyzing medium density SNP data in livestock and pet species. BMC Genomics 21, 94 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
111.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).CAS 
PubMed 
PubMed Central 

Google Scholar 
112.McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359–372 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
113.Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
114.Zeileis, A. Econometric computing with HC and HAC covariance matrix estimators. J. Stat. Softw. 11, 1–17 (2004).
Google Scholar 
115.Zeileis, A., Köll, S. & Graham, N. Various versatile variances: an object-oriented implementation of clustered covariances in R. J. Stat. Softw. 95, 1–36 (2020).
Google Scholar 
116.Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).
Google Scholar 
117.Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A. & Williams, R. M. J. Adjustment During Army Life (Princeton University Press, 1949). More

1.Schmidt-Nielsen, K. Scaling: Why is Animal Size So Important? (Cambrige University Press, 1984).Book 

Google Scholar 
2.Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406. https://doi.org/10.1038/nclimate1259 (2011).ADS 
Article 

Google Scholar 
3.Yom-Tov, Y., Heggberget, T. M., Wiig, O. & Yom-Tov, S. Body size changes among otters, Lutra lutra, in Norway: The possible effects of food availability and global warming. Oecologia 150, 155–160. https://doi.org/10.1007/s00442-006-0499-8 (2006).ADS 
Article 
PubMed 

Google Scholar 
4.Bergmann, C. Ueber die Verhältnisse der Wärmeökonomie der Tiere zu ihrer Grösse. Gött Stud. 3, 595–708 (1847).
Google Scholar 
5.Dehnel, A. Studies on the genus Sorex L.. Ann. Univ. Mariae Curie Sklodowska 5, 17–102 (1949).
Google Scholar 
6.Foster, J. B. Evolution of mammals on islands. Nature 202, 234–235. https://doi.org/10.1038/202234a0 (1964).ADS 
Article 

Google Scholar 
7.Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108. https://doi.org/10.1111/j.1558-5646.1956.tb02836.x (1956).Article 

Google Scholar 
8.Allen, J. A. The Influence of physical conditions in the genesis of species. Radic. Rev. 1, 108–140 (1877).
Google Scholar 
9.Blackburn, T. M., Gaston, K. J. & Loder, N. Geographic gradients in body size: A clarification of Bergmann’s rule. Divers. Distrib. 5, 165–174. https://doi.org/10.1046/j.1472-4642.1999.00046.x (1999).Article 

Google Scholar 
10.Riemer, K., Guralnick, R. P. & White, E. P. No general relationship between mass and temperature in endothermic species. Elife 7, 16. https://doi.org/10.7554/eLife.27166 (2018).Article 

Google Scholar 
11.Ashton, K. G. Patterns of within-species body size variation of birds: Strong evidence for Bergmann’s rule. Glob. Ecol. Biogeogr. 11, 505–523. https://doi.org/10.1046/j.1466-822X.2002.00313.x (2002).Article 

Google Scholar 
12.Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351. https://doi.org/10.1046/j.1365-2699.2003.00837.x (2003).Article 

Google Scholar 
13.Reig, S. Geographic variation in pine marten (Martes martes) and beech marten (M. foina) in Europe. J. Mammal. 73, 744–769. https://doi.org/10.2307/1382193 (1992).Article 

Google Scholar 
14.Blackburn, T. M. & Hawkins, B. A. Bergmann’s rule and the mammal fauna of northern North America. Ecography 27, 715–724. https://doi.org/10.1111/j.0906-7590.2004.03999.x (2004).Article 

Google Scholar 
15.Diniz, J. A. F., Bini, L. M., Rodriguez, M. A., Rangel, T. & Hawkins, B. A. Seeing the forest for the trees: Partitioning ecological and phylogenetic components of Bergmann’s rule in European Carnivora. Ecography 30, 598–608. https://doi.org/10.1111/j.2007.0906-7590.04988.x (2007).Article 

Google Scholar 
16.Hoy, S. R., Peterson, R. O. & Vucetich, J. A. Climate warming is associated with smaller body size and shorter lifespans in moose near their southern range limit. Glob. Change Biol. 24, 2488–2497. https://doi.org/10.1111/gcb.14015 (2018).ADS 
Article 

Google Scholar 
17.Martin, J. M., Mead, J. I. & Barboza, P. S. Bison body size and climate change. Ecol. Evol. 8, 4564–4574. https://doi.org/10.1002/ece3.4019 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Ozgul, A. et al. The dynamics of phenotypic change and the shrinking sheep of St. Kilda. Science 325, 464–467. https://doi.org/10.1126/science.1173668 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Prokosch, J., Bernitz, Z., Bernitz, H., Erni, B. & Altwegg, R. Are animals shrinking due to climate change? Temperature-mediated selection on body mass in mountain wagtails. Oecologia 189, 841–849. https://doi.org/10.1007/s00442-019-04368-2 (2019).ADS 
Article 
PubMed 

Google Scholar 
20.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055. https://doi.org/10.1038/nature08649 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
21.Schloss, C. A., Nunez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl. Acad. Sci. U.S.A. 109, 8606–8611. https://doi.org/10.1073/pnas.1116791109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Williams, J. E. & Blois, J. L. Range shifts in response to past and future climate change: Can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts? J. Biogeogr. 45, 2175–2189. https://doi.org/10.1111/jbi.13395 (2018).Article 

Google Scholar 
23.Gordon, C. J. Effects of ambient temperature and exposure to 2450-MHz microwave radiation of evaporative heat loss in the mouse. J. Microw. Power Electromagn. Energy 17, 145–150 (1982).CAS 

Google Scholar 
24.Zub, K., Piertney, S., Szafranska, P. A. & Konarzewski, M. Environmental and genetic influences on body mass and resting metabolic rates (RMR) in a natural population of weasel Mustela nivalis. Mol. Ecol. 21, 1283–1293. https://doi.org/10.1111/j.1365-294X.2011.05436.x (2012).Article 
PubMed 

Google Scholar 
25.Leyequien, E., de Boer, W. F. & Cleef, A. Influence of body size on coexistence of bird species. Ecol. Res. 22, 735–741. https://doi.org/10.1007/s11284-006-0311-6 (2007).Article 

Google Scholar 
26.Briscoe, N. J., Krockenberger, A., Handasyde, K. A. & Kearney, M. R. Bergmann meets Scholander: Geographical variation in body size and insulation in the koala is related to climate. J. Biogeogr. 42, 791–802. https://doi.org/10.1111/JBI.12445 (2015).Article 

Google Scholar 
27.Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming? Trends Ecol. Evol. 26, 285–291. https://doi.org/10.1016/J.TREE.2011.03.005 (2011).Article 
PubMed 

Google Scholar 
28.Reyer, C. et al. Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide. Ann. For. Sci. 71, 211–225. https://doi.org/10.1007/s13595-013-0306-8 (2014).Article 

Google Scholar 
29.Laidre, K. L. et al. Transient benefits of climate change for a high-Arctic polar bear (Ursus maritimus) subpopulation. Glob. Change Biol. 26, 6251–6265. https://doi.org/10.1111/gcb.15286 (2020).ADS 
Article 

Google Scholar 
30.Yunger, J. A. Response of two low-density populations of Peromyscus leucopus to increased food availability. J. Mammal. 83, 267–279. https://doi.org/10.1644/1545-1542(2002)083%3c0267:rotldp%3e2.0.co;2 (2002).Article 

Google Scholar 
31.Monterroso, P., Francisco, D. R., Lukacs, P. M., Alves, P. C. & Ferreras, P. Ecological traits and the spatial structure of competitive coexistence among carnivores. Ecology. https://doi.org/10.1002/ecy.3059 (2020).Article 
PubMed 

Google Scholar 
32.Dayan, T. & Simberloff, D. Ecological and community-wide character displacement: The next generation. Ecol. Lett. 8, 875–894. https://doi.org/10.1111/j.1461-0248.2005.00791.x (2005).Article 

Google Scholar 
33.Creel, S. & Creel, N. M. Limitation of African wild dogs by competition with larger carnivores. Conserv. Biol. 10, 526–538. https://doi.org/10.1046/j.1523-1739.1996.10020526.x (1996).Article 

Google Scholar 
34.Wereszczuk, A. & Zalewski, A. Spatial niche segregation of sympatric stone marten and pine marten—Avoidance of competition or selection of optimal habitat? PLoS ONE 10, e0139852. https://doi.org/10.1371/journal.pone.0139852 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Pereboom, V. et al. Movement patterns, habitat selection, and corridor use of a typical woodland-dweller species, the European pine marten (Martes martes), in fragmented landscape. Can. J. Zool. 86, 983–991. https://doi.org/10.1139/Z08-076 (2008).Article 

Google Scholar 
36.Virgos, E., Zalewski, A., Rosalino, L. M. & Mergey, M. Habitat ecology of Martens species in Europe. A review of the evidence. In Biology and Conservation of Martens, Sables and Fishers: A New Synthesis (eds Aubry, K. B. et al.) 255–266 (Cornell University Press, 2012).
Google Scholar 
37.Goszczyński, J., Posłuszny, M., Pilot, M. & Gralak, B. Patterns of winter locomotion and foraging in two sympatric marten species: Martes martes and Martes foina. Can. J. Zool. 85, 239–249. https://doi.org/10.1139/Z06-212 (2007).ADS 
Article 

Google Scholar 
38.Larroque, J., Ruette, S., Vandel, J. M. & Devillard, S. Where to sleep in a rural landscape? A comparative study of resting sites pattern in two syntopic Martes species. Ecography 38, 1129–1140. https://doi.org/10.1111/ecog.01133 (2015).Article 

Google Scholar 
39.Monakhov, V. G. & Hamilton, M. J. Spatial trends in the size structure of pine Marten Martes martes Linnaeus, 1756 (Mammalia: Mustelidae) within the species range. Russ. J. Ecol. 51, 250–259. https://doi.org/10.1134/s1067413620030108 (2020).CAS 
Article 

Google Scholar 
40.Meiri, S., Dayan, T. & Simberloff, D. Carnivores, biases and Bergmann’s rule. Biol. J. Linn. Soc. 81, 579–588. https://doi.org/10.1111/j.1095-8312.2004.00310.x (2004).Article 

Google Scholar 
41.Keinath, D. A. et al. A global analysis of traits predicting species sensitivity to habitat fragmentation. Glob. Ecol. Biogeogr. 26, 115–127. https://doi.org/10.1111/geb.12509 (2017).Article 

Google Scholar 
42.Bailey, L. D. et al. Using different body size measures can lead to different conclusions about the effects of climate change. J. Biogeogr. 47, 1687–1697. https://doi.org/10.1111/jbi.13850 (2020).Article 

Google Scholar 
43.Buskirk, S. W. & Harlow, H. J. Body-fat dynamics of the American marten (Martes americana) in winter. J. Mammal. 70, 191–193. https://doi.org/10.2307/1381687 (1989).Article 

Google Scholar 
44.Wereszczuk, A.et al. Various responses of pine marten
morphology and demography to temporal climate changes and primary productivity. PREPRINT (Version 1) available at
Research Square https://doi.org/10.21203/rs.3.rs-1021314/v1 (2021)45.Desy, E. A. & Batzli, G. O. Effects of food availability and predation on prairie vole demography—A field experiment. Ecology 70, 411–421. https://doi.org/10.2307/1937546 (1989).Article 

Google Scholar 
46.Geist, V. Bergmann rule is invalid. Can. J. Zool. 65, 1035–1038. https://doi.org/10.1139/z87-164 (1987).Article 

Google Scholar 
47.Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563. https://doi.org/10.1126/science.1082750 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
48.Svensson, B. M., Carlsson, B. A. & Melillo, J. M. Changes in species abundance after seven years of elevated atmospheric CO2 and warming in a Subarctic birch forest understorey, as modified by rodent and moth outbreaks. PeerJ 6, e4843. https://doi.org/10.7717/peerj.4843 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Zalewski, A., Jedrzejewski, W. & Jedrzejewska, B. Mobility and home range use by pine martens (Martes martes) in a Polish primeval forest. Ecoscience 11, 113–122. https://doi.org/10.1080/11956860.2004.11682815 (2004).Article 

Google Scholar 
50.Krebs, C. J., Cowcill, K., Boonstra, R. & Kenney, A. J. Do changes in berry crops drive population fluctuations in small rodents in the southwestern Yukon? J. Mammal. 91, 500–509. https://doi.org/10.1644/09-mamm-a-005.1 (2010).Article 

Google Scholar 
51.Selas, V., Kobro, S. & Sonerud, G. A. Population fluctuations of moths and small rodents in relation to plant reproduction indices in southern Norway. Ecosphere 4, 1–11. https://doi.org/10.1890/es13-00228.1 (2013).Article 

Google Scholar 
52.Yom-Tov, Y., Yom-Tov, S. & Jarrell, G. Recent increase in body size of the American marten Martes americana in Alaska. Biol. J. Linn. Soc. 93, 701–707. https://doi.org/10.1111/j.1095-8312.2007.00950.x (2008).Article 

Google Scholar 
53.Caryl, F. M., Quine, C. P. & Park, K. J. Martens in the matrix: the importance of nonforested habitats for forest carnivores in fragmented landscapes. J. Mammal. 93, 464–474. https://doi.org/10.1644/11-mamm-a-149.1 (2012).Article 

Google Scholar 
54.Zalewski, A. Factors affecting the duration of activity by pine martens (Martes martes) in the Bialowieza National Park, Poland. J. Zool. 251, 439–447. https://doi.org/10.1111/j.1469-7998.2000.tb00799.x (2000).Article 

Google Scholar 
55.Zalewski, A. Factors affecting selection of resting site type by pine marten in primeval deciduous forests (Bialowieza National Park, Poland). Acta Theriol. 42, 271–288. https://doi.org/10.4098/AT.arch.97-29 (1997).Article 

Google Scholar 
56.Gilbert, J. H., Zollner, P. A., Green, A. K., Wright, J. L. & Karasov, W. H. Seasonal field metabolic rates of American martens in Wisconsin. Am. Midl. Nat. 162, 327–334. https://doi.org/10.1674/0003-0031-162.2.327 (2009).Article 

Google Scholar 
57.Zub, K., Szafranska, P. A., Konarzewski, M. & Speakman, J. R. Effect of energetic constraints on distribution and winter survival of weasel males. J. Anim. Ecol. 80, 259–269. https://doi.org/10.1111/j.1365-2656.2010.01762.x (2011).Article 
PubMed 

Google Scholar 
58.Hantak, M. M., McLean, B. S., Li, D. & Guralnick, R. P. Mammalian body size is determined by interactions between climate, urbanization, and ecological traits. Commun. Biol. https://doi.org/10.1038/s42003-021-02505-3 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Yom-Tov, Y., Yom-Tov, S. & Baagoe, H. Increase of skull size in the red fox (Vulpes vulpes) and Eurasian badger (Meles meles) in Denmark during the twentieth century: An effect of improved diet? Evol. Ecol. Res. 5, 1037–1048 (2003).
Google Scholar 
60.Wereszczuk, A., Leblois, R. & Zalewski, A. Genetic diversity and structure related to expansion history and habitat isolation: Stone marten populating rural-urban habitats. BMC Ecol. 17, 46. https://doi.org/10.1186/s12898-017-0156-6 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
61.Phillips, B. L., Brown, G. P., Webb, J. K. & Shine, R. Invasion and the evolution of speed in toads. Nature 439, 803. https://doi.org/10.1038/439803a (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
62.Sidorovich, V., Kruuk, H. & Macdonald, D. W. Body size, and interactions between European and American mink (Mustela lutreola and M. vison) in Eastern Europe. J. Zool. 248, 521–527. https://doi.org/10.1017/s0952836999008110 (1999).Article 

Google Scholar 
63.Pagh, S., Hansen, M. S., Jensen, B., Pertoldi, C. & Chriel, M. Variability in body mass and sexual dimorphism in Danish red foxes (Vulpes vulpes) in relation to population density. Zool. Ecol. 28, 1–9. https://doi.org/10.1080/21658005.2017.1409997 (2018).Article 

Google Scholar 
64.Zalewski, A. & Bartoszewicz, M. Phenotypic variation of an alien species in a new environment: The body size and diet of American mink over time and at local and continental scales. Biol. J. Linn. Soc. 105, 681–693. https://doi.org/10.1111/j.1095-8312.2011.01811.x (2012).Article 

Google Scholar 
65.Balestrieri, A. et al. Range expansion of the pine marten (Martes martes) in an agricultural landscape matrix (NW Italy). Mamm. Biol. 75, 412–419. https://doi.org/10.1016/j.mambio.2009.05.003 (2010).Article 

Google Scholar 
66.Rosellini, S., Osorio, E., Ruiz-Gonzalez, A., Isabel, A. P. & Barja, I. Monitoring the small-scale distribution of sympatric European pine martens (Martes martes) and stone martens (Martes foina): A multievidence approach using faecal DNA analysis and camera-traps. Wildl. Res. 35, 434–440. https://doi.org/10.1071/wr07030 (2008).Article 

Google Scholar 
67.Delibes, M. Interspecific competition and the habitat of the stone marten Martes foina (Erxleben 1777) in Europe. Acta Zool. Fennica 174, 229–231 (1983).
Google Scholar 
68.Zabala, J., Zuberogoitia, I. & Antonio Martinez-Climent, J. Testing for niche segregation between two abundant carnivores using presence-only data. Folia Zool. 58, 385–395 (2009).
Google Scholar 
69.Jacob, D. et al. Climate impacts in Europe under +1.5 degrees C global warming. Earths Fut. 6, 264–285. https://doi.org/10.1002/2017ef000710 (2018).ADS 
Article 

Google Scholar 
70.Fewster, R. M., Buckland, S. T., Siriwardena, G. M., Baillie, S. R. & Wilson, J. D. Analysis of population trends for farmland birds using generalized additive models. Ecology 81, 1970–1984. https://doi.org/10.2307/177286 (2000).Article 

Google Scholar 
71.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017).Book 

Google Scholar 
72.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).73.Lenssen, N. J. L. et al. Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos. 124, 6307–6326. https://doi.org/10.1029/2018jd029522 (2019).ADS 
Article 

Google Scholar  More

1.Mckinney, M. L. Effects of urbanization on species richness : a review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).
Google Scholar 
2.Anderson, P. M. L., Okereke, C., Rudd, A. & Parnell, S. Urbanization, biodiversity and ecosystem services: challenges and opportunities a global assessment (Springer, Berlin, 2013). https://doi.org/10.1007/978-94-007-7088-1.Book 

Google Scholar 
3.Newhouse, M. J., Marra, P. P. & Johnson, L. S. Reproductive success of house wrens in suburban and rural landscapes. Wilson J. Ornithol. 120, 99–104 (2008).
Google Scholar 
4.Biard, C. et al. Growing in Cities: An Urban Penalty for Wild Birds? A Study of Phenotypic Differences between Urban and Rural Great Tit Chicks (Parus major). Front. Ecol. Evol. 5, (2017). https://doi.org/10.3389/fevo.2017.000795.Seress, G. et al. Urbanization, nestling growth and reproductive success in a moderately declining house sparrow population. J. Avian Biol. 43, 403–414 (2012).
Google Scholar 
6.Glądalski, M. et al. Differences in the breeding success of Blue Tits Cyanistes caeruleus between a forest and an urban area : a long-term study. Acta Ornithol. 52, 59–68 (2017).
Google Scholar 
7.Teglhøj, P. G. A comparative study of insect abundance and reproductive success of barn swallows Hirundo rustica in two urban habitats. J. Avian Biol. 48, 846–853 (2017).
Google Scholar 
8.Chamberlain, D. E. et al. Avian productivity in urban landscapes: A review and meta-analysis. Ibis (Lond. 1859). 151, 1–18 (2009).
Google Scholar 
9.Chatelain, M. et al. Urban metal pollution explains variation in reproductive outputs in great tits and blue tits. Sci. Total Environ. 776, 145966 (2021).ADS 
CAS 

Google Scholar 
10.Capilla-Lasheras, P. et al. A global meta-analysis reveals more variable life histories in urban birds compared to their non-urban neighbours. Preprint (2021). https://doi.org/10.1101/2021.09.24.461498.11.Caizergues, A. et al. An avian urban morphotype: how the city environment shapes greattit morphology at different life stages. Urban Ecosyst. 24, 929–941 (2021).
Google Scholar 
12.Corsini, M. et al. Growing in the city: Urban evolutionary ecology of avian growth rates. Evol. Appl. 14, 69–84 (2021).PubMed 

Google Scholar 
13.Seress, G. & Liker, A. Habitat urbanization and its effects on birds. Acta Zool. Acad. Sci. Hungaricae 61, 373–408 (2015).
Google Scholar 
14.Bailly, J. et al. From eggs to fledging: negative impact of urban habitat on reproduction in two tit species. J. Ornithol. 157, 377–392 (2016).
Google Scholar 
15.Seress, G. et al. Impact of urbanization on abundance and phenology of caterpillars and consequences for breeding in an insectivorous bird. Ecol. Appl. 28, 1143–1156 (2018).PubMed 

Google Scholar 
16.Seress, G., Sándor, K., Evans, K. L. & Liker, A. Food availability limits avian reproduction in the city: an experimental study on great tits Parus major. J. Anim. Ecol. 89, 1570–1580 (2020).PubMed 

Google Scholar 
17.Krištín, A. & Patočka, J. Birds as predators of Lepidoptera: Selected examples. Biologia (Bratisl). 52, 319–326 (1997).
Google Scholar 
18.Perrins, C. M. Tits and their caterpillar food supply. Ibis (Lond. 1859). 133, 49–54 (1991).
Google Scholar 
19.Ramsay, S. L. & Houston, D. C. Amino acid composition of some woodland arthropods and its implications for breeding tits and other passerines. Ibis (Lond. 1859). 145, 227–232 (2003).
Google Scholar 
20.Partali, V., Liaaen-Jensen, S., Slagsvold, T. & Lifjeld, J. T. Carotenoids in food chain studies—II. The food chain of Parus SPP. Monitored by carotenoid analysis. Comp. Biochem. Physiol. Part B Comp. Biochem. 87, 885–888 (1987).
Google Scholar 
21.Isaksson, C., Johansson, A. & Andersson, S. Egg yolk carotenoids in relation to habitat and reproductive investment in the great Tit Parus major. Physiol. Biochem. Zool. 81, 112–118 (2008).CAS 
PubMed 

Google Scholar 
22.Isaksson, C., Örnborg, J., Stephensen, E. & Andersson, S. Plasma glutathione and carotenoid coloration as potential biomarkers of environmental stress in great tits. EcoHealth 2, 138–146 (2005).
Google Scholar 
23.Arnold, K. E., Ramsay, S. L., Henderson, L. & Larcombe, S. D. Seasonal variation in diet quality: antioxidants, invertebrates and blue tits Cyanistes caeruleus. Biol. J. Linn. Soc. 99, 708–717 (2010).
Google Scholar 
24.Fenoglio, M. S., Rossetti, M. R. & Videla, M. Negative effects of urbanization on terrestrial arthropod communities: a meta-analysis. Glob. Ecol. Biogeogr. 29, 1412–1429 (2020).
Google Scholar 
25.Piano, E. et al. Urbanization drives cross-taxon declines in abundance and diversity at multiple spatial scales. Glob. Chang. Biol. 26, 1196–1211 (2020).ADS 
PubMed 

Google Scholar 
26.Nadolski, J., Marciniak, B., Loga, B., Michalski, M. & Bańbura, J. Long-term variation in the timing and height of annual peak abundance of caterpillars in tree canopies: Some effects on a breeding songbird. Ecol. Indic. 121, 107120 (2021).
Google Scholar 
27.Sepp, T., McGraw, K. J., Kaasik, A. & Giraudeau, M. A review of urban impacts on avian life-history evolution: does city living lead to slower pace of life?. Glob. Chang. Biol. 24, 1452–1469 (2018).ADS 
PubMed 

Google Scholar 
28.Miyashita, T., Shinkai, A. & Chida, T. The effects of forest fragmentation on web spider communities in urban areas. Biol. Conserv. 86, 357–364 (1998).
Google Scholar 
29.Merckx, T. et al. Body-size shifts in aquatic and terrestrial urban communities. Nature 558, 113–116 (2018).ADS 
CAS 
PubMed 

Google Scholar 
30.Ishitani, M., Kotze, D. J. & Niemelä, J. Changes in carabid beetle assemblages across an urban-rural gradient in Japan. Ecography (Cop.) 26, 481–489 (2003).
Google Scholar 
31.Pollock, C. J., Capilla-Lasheras, P., McGill, R. A. R., Helm, B. & Dominoni, D. M. Integrated behavioural and stable isotope data reveal altered diet linked to low breeding success in urban-dwelling blue tits (Cyanistes caeruleus). Sci. Rep. 7, 5014 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
32.Jarrett, C., Powell, L. L., McDevitt, H., Helm, B. & Welch, A. J. Bitter fruits of hard labour: diet metabarcoding and telemetry reveal that urban songbirds travel further for lower-quality food. Oecologia 193, 377–388 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
33.Isaksson, C. & Andersson, S. Carotenoid diet and nestling provisioning in urban and rural great tits Parus major. J. Avian Biol. 38, 564–572 (2007).
Google Scholar 
34.Sinkovics, C. A fiókatáplálék mennyisége , minősége és szezonalitása városi és erdei széncinege (Parus major) populációkban. (Szent István University, 2014).35.Tremblay, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis (Lond. 1859). 147, 17–24 (2005).
Google Scholar 
36.Schwagmeyer, P. L. & Mock, D. W. Parental provisioning and offspring fitness: size matters. Anim. Behav. 75, 291–298 (2008).
Google Scholar 
37.Lease, H. M. & Wolf, B. O. Lipid content of terrestrial arthropods in relation to body size, phylogeny, ontogeny and sex. Physiol. Entomol. 36, 29–38 (2011).CAS 

Google Scholar 
38.Riddington, R. & Gosler, A. G. Differences in reproductive success and parental qualities between habitats in the Great Tit Parus major. Ibis (Lond. 1859) 137, 371–378 (1995).
Google Scholar 
39.Mennechez, G. & Clergeau, P. Effect of urbanisation on habitat generalists: starlings not so flexible?. Acta Oecologica 30, 182–191 (2006).ADS 

Google Scholar 
40.Shawkey, M. D., Bowman, R. & Woolfenden, G. E. Why is brood reduction in Florida Scrub-Jays higher in suburban than in wildland habitats?. Can. J. Zool. 82, 1427–1435 (2004).
Google Scholar 
41.Robb, G. N., McDonald, R. A., Chamberlain, D. E. & Bearhop, S. Food for thought: supplementary feeding as a driver of ecological change in avian populations. Front. Ecol. Environ. 6, 476–484 (2008).
Google Scholar 
42.Sauter, A., Bowman, R., Schoech, S. J. & Pasinelli, G. Does optimal foraging theory explain why suburban Florida scrub-jays (Aphelocoma coerulescens) feed their young human-provided food ?. Behav. Ecol. Sociobiol. 60, 465–474 (2006).
Google Scholar 
43.Heiss, R. S., Clark, A. B. & McGowan, K. J. Growth and nutritional state of American Crow nestlings vary between urban and rural habitats. Ecol. Appl. 19, 829–839 (2009).PubMed 

Google Scholar 
44.Graveland, J. & van Gijzen, T. Arthropods and seeds are not sufficient as calcium sources for shell formation and skeletal growth in passerines. Ardea 82, 299–314 (1994).
Google Scholar 
45.Ricklefs, R. In Avian Biology (eds. Farner, D., King, J. & Parkes, K.) 1–83 (Academic Press, 1983).46.Peach, W. J., Vincent, K. E., Fowler, J. A. & Grice, P. V. Reproductive success of house sparrows along an urban gradient. Anim. Conserv. 11, 493–503 (2008).
Google Scholar 
47.Johnston, R. D. Effects of diet quality on the nestling growth of a wild insectivorous passerine, the house martin Delichon urbica. Funct. Ecol. 7, 255–266 (1993).
Google Scholar 
48.Marciniak, B., Nadolski, J., Nowakowska, M., Loga, B. & Bańbura, J. Habitat and annual variation in arthropod abundance affects Blue Tit Cyanistes caeruleus reproduction. Acta Ornithol. 42, 53–62 (2007).
Google Scholar 
49.Pagani-Núñez, E. & Senar, J. C. One hour of sampling is enough: great tit Parus major parents feed their nestlings consistently across time. Acta Ornithol. 48, 194–200 (2013).
Google Scholar 
50.Betts, M. M. The behaviour of a pair of great tits at the nest. Br. Birds 48, 77–82 (1955).
Google Scholar 
51.Van Balen, J. H. A comparative study of the breeding ecology of the great tit Parus major in different habitats. Ardea 61, 1–93 (1973).
Google Scholar 
52.Seress, G. et al. Effects of capture and video-recording on the behavior and breeding success of Great Tits in urban and forest habitats. J. F. Ornithol. 88, 299–312 (2017).
Google Scholar 
53.Free Software Foundation. vlc. (1991).54.Sinkovics, C., Seress, G., Fábián, V., Sándor, K. & Liker, A. Obtaining accurate measurements of the size and volume of insects fed to nestlings from video recordings. J. F. Ornithol. 89, 165–172 (2018).
Google Scholar 
55.R Core Team. R: A language and environment for statistical computing. (2017). Available at: https://www.r-project.org/.56.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. (2021). R package version 3.1-153, https://CRAN.R-project.org/package=nlme.57.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2018). R package version 1.3.1. https://CRAN.R-project.org/package=emmeans58.Venables, W. N. & Ripley, B. D. Modern applied statistics with S (Springer, Berlin, 2002).MATH 

Google Scholar 
59.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2011).
Google Scholar 
60.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 3, 346–363 (2008).MathSciNet 
MATH 

Google Scholar 
61.Ruxton, G. D. & Beauchamp, G. Time for some a priori thinking about post hoc testing. Behav. Ecol. 19, 690–693 (2008).
Google Scholar 
62.Bolker, B. M. et al. Generalized linear mixed models :a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
63.Vincze, E. et al. Great tits take greater risk toward humans and sparrowhawks in urban habitats than in forests. Ethology 125, 686–701 (2019).
Google Scholar 
64.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer, New York, 2009).MATH 

Google Scholar 
65.Serrano-Davies, E. & Sanz, J. J. Habitat structure modulates nestling diet composition and fitness of Blue Tits Cyanistes caeruleus in the Mediterranean region. Bird Study 64, 295–305 (2017).
Google Scholar 
66.Senar, J. C., Manzanilla, A. & Mazzoni, D. A comparison of the diet of urban and forest great tits in a Mediterranean habitat. Anim. Biodivers. Conserv. 44(2), 321–327 (2021).
Google Scholar 
67.Narango, D. L., Tallamy, D. W. & Marra, P. P. Nonnative plants reduce population growth of an insectivorous bird. Proc. Natl. Acad. Sci. 115, 201809259 (2018).
Google Scholar 
68.de Satgé, J. et al. Urbanisation lowers great tit Parus major breeding success at multiple spatial scales. J. Avian Biol. 50, (2019). https://doi.org/10.1111/jav.0210869.Baldan, D. & Ouyang, J. Q. Urban resources limit pair coordination over offspring provisioning. Sci. Rep. 10, 15888 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Mennechez, G. & Clergeau, P. In Avian Ecology and Conservation in an Urbanizing World (ed. Marzluff, J. M.) 275–287 (Springer, 2001). https://doi.org/10.1007/978-1-4615-1531-9_1371.Meyrier, E. et al. Happy to breed in the city? Urban food resources limit reproductive output in Western Jackdaws. Ecol. Evol. 7, 1363–1374 (2017).PubMed 
PubMed Central 

Google Scholar 
72.Kingsolver, J. G. & Woods, H. A. Thermal sensitivity of growth and feeding in Manduca sexta caterpillars. Physiol. Zool. 70, 631–638 (1997).CAS 
PubMed 

Google Scholar 
73.Warren, M. S. et al. The decline of butterflies in Europe: Problems, significance, and possible solutions. Proc. Natl. Acad. Sci. 118, e2002551117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Burghardt, K. T., Tallamy, D. W., Philips, C. & Shropshire, K. J. Non-native plants reduce abundance, richness, and host specialization in lepidopteran communities. Ecosphere 1, 1–22 (2010).
Google Scholar 
75.Tallamy, D. W. & Shriver, W. G. Are declines in insects and insectivorous birds related?. Condor 123, 1–8 (2021).
Google Scholar 
76.Mackenzie, J. A., Hinsley, S. A. & Harrison, N. M. Parid foraging choices in urban habitat and the consequences for fitness. Ibis (Lond. 1859) 156, 591–605 (2014).
Google Scholar 
77.Narango, D. L., Tallamy, D. W. & Marra, P. P. Native plants improve breeding and foraging habitat for an insectivorous bird. Biol. Conserv. 213, 42–50 (2017).
Google Scholar 
78.Cholewa, M. & Wesołowski, T. Nestling food of European hole-nesting passerines: do we know enough to test the adaptive hypotheses on breeding seasons?. Acta Ornithol. 46, 105–116 (2011).
Google Scholar  More

1.Kinoshita, S., Structural Colors in the Realm of Nature (World Scientific, 2008).2.Sun, J., Bhushan, B. & Tong, J. Structural coloration in nature. RSC Adv. 3, 14862–14889 (2013).CAS 
ADS 

Google Scholar 
3.Whitney, H. M. et al. Floral iridescence, produced by diffractive optics, acts as a cue for animal pollinators. Science 323, 130–133 (2009).CAS 
PubMed 
ADS 

Google Scholar 
4.Whitney, H. M., Kolle, M., Alvarez-Fernandez, R., Steiner, U. & Glover, B. J. Contributions of iridescence to floral patterning. Commun. Integr. Biol. 2, 230–232 (2009).PubMed 
PubMed Central 

Google Scholar 
5.Moyroud, E. et al. Disorder in convergent floral nanostructures enhances signalling to bees. Nature 550, 469–474 (2017).CAS 
PubMed 
ADS 

Google Scholar 
6.Mason, C. W. Structural colors in feathers. II. J. Phys. Chem. 27, 401–448 (2005).
Google Scholar 
7.Mason, C. W. Structural colors in insects. III. J. Phys. Chem. 31, 1856–1872 (2005).
Google Scholar 
8.Roberts, N. W., Marshall, N. J. & Cronin, T. W. High levels of reflectivity and pointillist structural color in fish, cephalopods, and beetles. Proc. Natl. Acad. Sci. 109, E3387–E3387 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
9.Zi, J. et al. Coloration strategies in peacock feathers. Proc. Natl. Acad. Sci. 100, 12576–12578 (2003).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
10.McCoy, D. E., Feo, T., Harvey, T. A. & Prum, R. O. Structural absorption by barbule microstructures of super black bird of paradise feathers. Nat. Commun. 9, 1–8 (2018).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
11.Teyssier, J., Saenko, S. V., Van Der Marel, D. & Milinkovitch, M. C. Photonic crystals cause active colour change in chameleons. Nat. Commun. 6, 1–7 (2015).
Google Scholar 
12.Cooper, K. M., Hanlon, R. T. & Budelmann, B. U. Physiological color change in squid iridophores. Cell Tissue Res. 259, 15–24 (1990).CAS 
PubMed 

Google Scholar 
13.Glover, B. J. & Whitney, H. M. Structural colour and iridescence in plants: The poorly studied relations of pigment colour. Ann. Bot. 105, 505–511 (2010).PubMed 
PubMed Central 

Google Scholar 
14.Shi, N. N. et al. Keeping cool: Enhanced optical reflection and radiative heat dissipation in Saharan silver ants. Science 349, 298–301 (2015).CAS 
PubMed 
ADS 

Google Scholar 
15.Preciado, J. A. et al. Radiative properties of polar bear hair. Am. Soc. Mech. Eng. Bioeng. Div. 54, 57–58 (2002).
Google Scholar 
16.Bosi, S. G., Hayes, J., Large, M. C. J. & Poladian, L. Color, iridescence, and thermoregulation in Lepidoptera. Appl. Opt. 47, 5235–5241 (2008).PubMed 
ADS 

Google Scholar 
17.Kinoshita, S., Yoshioka, S., Fujii, Y. & Okamoto, N. Photophysics of structural color in the Morpho butterflies. Forma-Tokyo 17, 103–121 (2002).
Google Scholar 
18.Tabata, H., Kumazawa, K., Funakawa, M., Takimoto, J. I. & Akimoto, M. Microstructures and optical properties of scales of butterfly wings. Opt. Rev. 3, 139–145 (1996).
Google Scholar 
19.Krishna, A. et al. Infrared optical and thermal properties of microstructures in butterfly wings. Proc. Natl. Acad. Sci. USA 117, 1566–1572 (2020).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
20.Tsai, C. C. et al. Physical and behavioral adaptations to prevent overheating of the living wings of butterflies. Nat. Commun. 11, 1–14 (2020).ADS 

Google Scholar 
21.Wilts, B. D., Vey, A. J. M., Briscoe, A. D. & Stavenga, D. G. Longwing (Heliconius) butterflies combine a restricted set of pigmentary and structural coloration mechanisms. BMC Evol. Biol. 17, 1–12 (2017).
Google Scholar 
22.Berthier, S. Thermoregulation and spectral selectivity of the tropical butterfly Prepona meander: A remarkable example of temperature auto-regulation. Appl. Phys. A Mater. Sci. Process. 80, 1397–1400 (2005).CAS 
ADS 

Google Scholar 
23.Vukusic, P. & Sambles, J. R. Photonic structures in biology. Nature 424, 852–855 (2003).CAS 
PubMed 
ADS 

Google Scholar 
24.Siddique, R. H., Diewald, S., Leuthold, J. & Hölscher, H. Theoretical and experimental analysis of the structural pattern responsible for the iridescence of Morpho butterflies. Opt. Express 21, 14351–14361 (2013).PubMed 
ADS 

Google Scholar 
25.Steindorfer, M. A., Schmidt, V., Belegratis, M., Stadlober, B. & Krenn, J. R. Detailed simulation of structural color generation inspired by the Morpho butterfly. Opt. Express 20, 21485–21494 (2012).PubMed 
ADS 

Google Scholar 
26.Munro, J. T. et al. Climate is a strong predictor of near-infrared reflectance but a poor predictor of colour in butterflies. Proc. R. Soc. B Biol. Sci. 286, 20190234 (2019).
Google Scholar 
27.Incropera, F. P., DeWitt, D. P., Bergman, T. L. & Lavine, A. S. Fundamentals of Heat and Mass Transfer (Wiley, 2006).28.DeWitt, D. P., Incropera, F. P. “Physics of thermal radiation” in Theory and Practice of Radiation Thermometry, (1988), pp. 19–89.29.Howell, J. R., Menguc, M. P., Siegel, R. Thermal Radiation Heat Transfer (CRC Press, 2016).30.Lord, S. D. A new software tool for computing earth’s atmospheric transmission of near- and far-infrared radiation. NASA Tech. Memo. 103957 (1992).31.Raman, A. P., Anoma, M. A., Zhu, L., Rephaeli, E. & Fan, S. Passive radiative cooling below ambient air temperature under direct sunlight. Nature 515, 540–544 (2014).CAS 
PubMed 
ADS 

Google Scholar 
32.Krishna, A. & Lee, J. Morphology-driven emissivity of microscale tree-like structures for radiative thermal management. Nanoscale Microscale Thermophys. Eng. 22, 124–136 (2018).CAS 
ADS 

Google Scholar 
33.Zhai, Y. et al. Scalable-manufactured randomized glass-polymer hybrid metamaterial for daytime radiative cooling. Science 355, 1062–1066 (2017).CAS 
PubMed 
ADS 

Google Scholar 
34.Zhang, X. A. et al. Dynamic gating of infrared radiation in a textile. Science 623, 1–15 (2019).
Google Scholar 
35.Xu, C., Stiubianu, G. T. & Gorodetsky, A. A. Adaptive infrared-reflecting systems inspired by cephalopods. Science 359, 1495–1500 (2018).CAS 
PubMed 
ADS 

Google Scholar 
36.Xie, D. et al. Broadband omnidirectional light reflection and radiative heat dissipation in white beetles: Goliathus goliatus. Soft Matter 15, 4294–4300 (2019).CAS 
PubMed 
ADS 

Google Scholar 
37.Heinrich, B. Thermoregulation in endothermic insects. Science 185, 747–756 (1974).CAS 
PubMed 
ADS 

Google Scholar 
38.Kingsolver, J. G. Thermoregulation and flight in Colias butterflies: elevational patterns and mechanistic limitations. Ecology 64, 534–545 (1983).
Google Scholar 
39.Rawlins, J. E. Thermoregulation by the black swallowtail butterfly, Papilio polyxenes (Lepidoptera: Papilionidae). Ecology 61, 345–357 (1980).
Google Scholar 
40.Clench, H. K. Behavioral thermoregulation in butterflies. Ecology 47, 1021–1034 (1966).
Google Scholar 
41.Bonebrake, T. C., Boggs, C. L., Stamberger, J. A., Deutsch, C. A. & Ehrlich, P. R. From global change to a butterfly flapping: Biophysics and behaviour affect tropical climate change impacts. Proc. R. Soc. B Biol. Sci. 281, 20141264 (2014).
Google Scholar 
42.Nève, G. & Hall, C. Variation of thorax flight temperature among twenty Australian butterflies (Lepidoptera: Papilionidae, Nymphalidae, Pieridae, Hesperiidae, Lycaenidae). Eur. J. Entomol. 113, 571–578 (2016).
Google Scholar 
43.MacLean, H. J., Higgins, J. K., Buckley, L. B. & Kingsolver, J. G. Morphological and physiological determinants of local adaptation to climate in Rocky Mountain butterflies. Conserv. Physiol. 4, 1 (2016).
Google Scholar 
44.Tsai, C. C., et al., Butterflies regulate wing temperatures using radiative cooling in 2017 Conference on Lasers and Electro-Optics (CLEO), (IEEE, 2017), p. 9.45.Watanabe, K., Hoshino, T., Kanda, K., Haruyama, Y. & Matsui, S. Brilliant blue observation from a Morpho-butterfly-scale quasi-structure. Jpn. J. Appl. Phys. 44, L48–L50 (2005).CAS 
ADS 

Google Scholar 
46.Wilts, B. D., Giraldo, M. A. & Stavenga, D. G. Unique wing scale photonics of male Rajah Brooke’s birdwing butterflies. Front. Zool. 13, 1–12 (2016).
Google Scholar 
47.De Keyser, R., Breuker, C. J., Hails, R. S., Dennis, R. L. H. & Shreeve, T. G. Why small is beautiful: Wing colour is free from thermoregulatory constraint in the small lycaenid butterfly, Polyommatus icarus. PLoS One 10, e0122663 (2015).
Google Scholar 
48.Biró, L. P. et al., Role of photonic-crystal-type structures in the thermal regulation of a lycaenid butterfly sister species pair. Phys. Rev. E Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top. 67, 7 (2003).49.Sala-Casanovas, M., Krishna, A., Yu, Z. & Lee, J. Bio-inspired stretchable selective emitters based on corrugated nickel for personal thermal management. Nanoscale Microscale Thermophys. Eng. 23, 173–187 (2019).CAS 
ADS 

Google Scholar 
50.Phan, L. et al. Reconfigurable infrared camouflage coatings from a cephalopod protein. Adv. Mater. 25, 5621–5625 (2013).CAS 
PubMed 

Google Scholar 
51.Pris, A. D. et al. Towards high-speed imaging of infrared photons with bio-inspired nanoarchitectures. Nat. Photonics 6, 564–564 (2012).CAS 
ADS 

Google Scholar 
52.Krishna, A. et al. Ultraviolet to mid-infrared emissivity control by mechanically reconfigurable graphene. Nano Lett. 19, 5086–5092 (2019).CAS 
PubMed 
ADS 

Google Scholar 
53.Moharam, M. G. & Gaylord, T. K. Rigorous coupled-wave analysis of planar-grating diffraction. J. Opt. Soc. Am. 71, 811 (1981).ADS 

Google Scholar 
54.Moharam, M. G. Coupled-wave analysis of two-dimensional dielectric gratings in Holographic Optics: Design and Applications, (1988), p. 8.55.Peng, S. & Morris, G. M. Efficient implementation of rigorous coupled-wave analysis for surface-relief gratings. J. Opt. Soc. Am. A 12, 1087 (1995).ADS 

Google Scholar 
56.Moharam, M. G., Gaylord, T. K., Grann, E. B. & Pommet, D. A. Formulation for stable and efficient implementation of the rigorous coupled-wave analysis of binary gratings. J. Opt. Soc. Am. A 12, 1068 (1995).ADS 

Google Scholar 
57.Taflove, A., Hagness, S. C. Computational Electrodynamics: The Finite-Difference Time-Domain Method (Artech House, 2005).58.Fang, J. et al. Enhanced photocatalytic hydrogen production on three-dimensional gold butterfly wing scales/CdS nanoparticles. Appl. Surf. Sci. 427, 807–812 (2018).CAS 
ADS 

Google Scholar 
59.Wilts, B. D., Leertouwer, H. L. & Stavenga, D. G. Imaging scatterometry and microspectrophotometry of lycaenid butterfly wing scales with perforated multilayers. J. R. Soc. Interface 6, S185–S192 (2009).PubMed 

Google Scholar 
60.Aideo, S. N., Mohanta, D. Investigation of manifestation of optical properties of butterfly wings with nanoscale zinc oxide incorporation. J. Phys: Confer. Ser. 765, 012019 (2016).61.Guan, Y. et al. Ordering of hollow Ag-Au nanospheres with butterfly wings as a biotemplate. Sci. Rep. 8, 1–7 (2018).
Google Scholar 
62.Simonsen, T. J. et al. Phylogenetics and divergence times of Papilioninae (Lepidoptera) with special reference to the enigmatic genera Teinopalpus and Meandrusa. Cladistics 27, 113–137 (2011).PubMed 

Google Scholar 
63.Wilts, B. D., Pirih, P., Arikawa, K. & Stavenga, D. G. Shiny wing scales cause spec(tac)ular camouflage of the angled sunbeam butterfly, Curetis acuta. Biol. J. Linn. Soc. 109, 279–289 (2013).
Google Scholar 
64.Wu, L., Han, Z., Qiu, Z., Guan, H. & Ren, L. The microstructures of butterfly wing scales in northeast of China. J. Bionic Eng. 4, 47–52 (2007).CAS 

Google Scholar 
65.Azofeifa, D. E., Arguedas, H. J. & Vargas, W. E. Optical properties of chitin and chitosan biopolymers with application to structural color analysis. Opt. Mater. (Amst) 35, 175–183 (2012).CAS 
ADS 

Google Scholar 
66.Vargas, W. E., Azofeifa, D. E. & Arguedas, H. J. Índices de refracción de la quitina, el quitosano y el ácido úrico con aplicación en análisis de color estructural. Opt. Pura y Apl. 46, 55–72 (2013).
Google Scholar 
67.Herman, A., Vandenbem, C., Deparis, O., Simonis, P. & Vigneron, J. P. Nanoarchitecture in the black wings of Troides magellanus : A natural case of absorption enhancement in photonic materials. Nanophotonic Mater. VIII 8094, 80940H (2011).
Google Scholar 
68.Yoshioka, S. & Kinoshita, S. Wavelength-selective and anisotropic light-diffusing scale on the wing of the Morpho butterfly. Proc. Biol. Sci. 271, 581–587 (2004).PubMed 
PubMed Central 

Google Scholar 
69.Catalanotti, S. et al. The radiative cooling of selective surfaces. Sol. Energy 17, 83–89 (1975).ADS 

Google Scholar 
70.Long Kou, J., Jurado, Z., Chen, Z., Fan, S. & Minnich, A. J. Daytime radiative cooling using near-black infrared emitters. ACS Photonics 4, 626–630 (2017).
Google Scholar 
71.Wasserthal, L. T. The role of butterfly wings in regulation of body temperature. J. Insect Physiol. 21, 1921–1930 (1975).
Google Scholar 
72.Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).ADS 

Google Scholar 
73.New, M., Lister, D., Hulme, M. & Makin, I. A high-resolution data set of surface climate over global land areas. Clim. Res. 21, 1–25 (2002).
Google Scholar 
74.Weather Spark Weather Data. https://weatherspark.com (July 10, 2019).75.Weather Underground Historical Weather. https://www.wunderground.com/history/ (August 2, 2018).76.Liu, F. et al. Replication of homologous optical and hydrophobic features by templating wings of butterflies Morpho menelaus. Opt. Commun. 284, 2376–2381 (2011).CAS 
ADS 

Google Scholar 
77.Chen, T., Cong, Q., Qi, Y., Jin, J. & Choy, K. L. Hydrophobic durability characteristics of butterfly wing surface after freezing cycles towards the design of nature inspired anti-icing surfaces. PLoS ONE 13, e0188775 (2018).PubMed 
PubMed Central 

Google Scholar 
78.Fang, Y., Sun, G., Wang, T. Q., Cong, Q. & Ren, L. Q. Hydrophobicity mechanism of non-smooth pattern on surface of butterfly wing. Chin. Sci. Bull. 52, 711–716 (2007).
Google Scholar 
79.Garland, T., Harvey, P. H. & Ives, A. R. Procedures for the analysis of comparative data using phylogenetically independent contrasts. Syst. Biol. 41, 18–32 (1992).
Google Scholar 
80.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
Google Scholar 
81.Felsenstein, J. Phylogenies and quantitative characters. Annu. Rev. Ecol. Syst. 19, 445–471 (1988).
Google Scholar 
82.Espeland, M. et al. A comprehensive and dated phylogenomic analysis of butterflies. Curr. Biol. 28, 770–778.e5 (2018).CAS 
PubMed 

Google Scholar 
83.Maddison, W. P. & Maddison, D. R. Mesquite: a modular system for evolutionary analysis. 2010. Version 2, 73 (2008).
Google Scholar 
84.Cai, W., Shalaev, V. M. Optical Metamaterials, 10th Ed. (Springer, 2010).85.Zheludev, N. I. & Kivshar, Y. S. From metamaterials to metadevices. Nat. Mater. 11, 917–924 (2012).CAS 
PubMed 
ADS 

Google Scholar 
86.Chen, Z., Zhu, L., Raman, A. & Fan, S. Radiative cooling to deep sub-freezing temperatures through a 24-h day-night cycle. Nat. Commun. 7, 1–5 (2016).
Google Scholar 
87.Mandal, J. et al. Hierarchically porous polymer coatings for highly efficient passive daytime radiative cooling. Science 362, 315–319 (2018).CAS 
PubMed 
ADS 

Google Scholar 
88.Lenert, A. et al. A nanophotonic solar thermophotovoltaic device. Nat. Nanotechnol. 9, 126–130 (2014).CAS 
PubMed 
ADS 

Google Scholar 
89.Quintiere, J. Radiative characteristics of fire fighters’ coat fabrics. Fire Technol. 10, 153–161 (1974).CAS 

Google Scholar 
90.Energy Sector Management Assistance Program (ESMAP). Global Solar Atlas 2.1: Technical Report. https://globalsolaratlas.info (World Bank, December 2019).91.Yoshioka, S. & Kinoshita, S. Direct determination of the refractive index of natural multilayer systems. Phys. Rev. E 83, 051917 (2011).ADS 

Google Scholar 
92.Leertouwer, H. L., Wilts, B. D. & Stavenga, D. G. Refractive index and dispersion of butterfly chitin and bird keratin measured by polarizing interference microscopy. Opt. Express 19, 24061–24066 (2011).CAS 
PubMed 
ADS 

Google Scholar  More

1.Wang, C., Horby, P. W., Hayden, F. G. & Gao, G. F. A novel coronavirus outbreak of global health concern. Lancet 395, 470–473 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C. & Garry, R. F. The proximal origin of SARS-CoV-2. Nat. Med. 26, 450–452 (2020).CAS 

Google Scholar 
3.Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Woo, P. C. Y. et al. Discovery of seven novel mammalian and avian coronaviruses in the genus deltacoronavirus supports bat coronaviruses as the gene source of alphacoronavirus and betacoronavirus and avian coronaviruses as the gene source of gammacoronavirus and deltacoronavirus. J. Virol. 86, 3995–4008 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Wong, A. C. P., Li, X., Lau, S. K. P. & Woo, P. C. Y. Global epidemiology of bat coronaviruses. Viruses 11, 174 (2019).CAS 
PubMed Central 

Google Scholar 
6.Lacroix, A. et al. Genetic diversity of coronaviruses in bats in Lao PDR and Cambodia. Infect. Genet. Evol. 48, 10–18 (2017).CAS 
PubMed 

Google Scholar 
7.Tsuda, S. et al. Genomic and serological detection of bat coronavirus from bats in the Philippines. Arch. Virol. 157, 2349–2355 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Han, Y. et al. Identification of diverse bat alphacoronaviruses and betacoronaviruses in China provides new insights into the evolution and origin of coronavirus-related diseases. Front. Microbiol. 10, 20 (2019).
Google Scholar 
9.Xu, L. et al. Detection and characterization of diverse alpha- and betacoronaviruses from bats in China. Virol. Sin. 31, 69–77 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Chen, Y.-N. et al. Detection of the severe acute respiratory syndrome-related coronavirus and alphacoronavirus in the bat population of Taiwan. Zoonoses Public Health 63, 608–615 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Wacharapluesadee, S. et al. Group C betacoronavirus in bat guano fertilizer, Thailand. Emerg. Infect. Dis. 19, 1349–1351 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Latinne, A. et al. Origin and cross-species transmission of bat coronaviruses in China. Nat. Commun. 11, 4235 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Drexler, J. F. et al. Amplification of emerging viruses in a bat colony. Emerg. Infect. Dis. 17, 449–456 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Amman, B. R. et al. Seasonal pulses of marburg virus circulation in Juvenile Rousettus aegyptiacus bats coincide with periods of increased risk of human infection. PLoS Pathog. 8, 25 (2012).
Google Scholar 
15.Peel, A. J. et al. The effect of seasonal birth pulses on pathogen persistence in wild mammal populations. Proc. R. Soc. Lond. B Biol. Sci. 281, 20132962 (2014).
Google Scholar 
16.Hayman, D. T. S. Biannual birth pulses allow filoviruses to persist in bat populations. Proc. R. Soc. Lond. B Biol. Sci. 282, 20142591 (2015).
Google Scholar 
17.Gloza-Rausch, F. et al. Detection and prevalence patterns of Group I Coronaviruses in Bats, Northern Germany. Emerg. Infect. Dis. 14, 626–631 (2008).PubMed 
PubMed Central 

Google Scholar 
18.Annan, A. et al. Human betacoronavirus 2c EMC/2012-related viruses in bats, Ghana and Europe. Emerg. Infect. Dis. 19, 456–459 (2013).PubMed 
PubMed Central 

Google Scholar 
19.Anthony, S. J. et al. Global patterns in coronavirus diversity. Virus Evol. 3, 25 (2017).
Google Scholar 
20.Montecino-Latorre, D. et al. Reproduction of East-African bats may guide risk mitigation for coronavirus spillover. One Health Outlook 2, 2 (2020).PubMed 
PubMed Central 

Google Scholar 
21.Maganga, G. D. et al. Genetic diversity and ecology of coronaviruses hosted by cave-dwelling bats in Gabon. Sci. Rep. 10, 7314 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, e1006698 (2017).PubMed 
PubMed Central 

Google Scholar 
23.Wacharapluesadee, S. et al. A longitudinal study of the prevalence of Nipah virus in Pteropus lylei bats in Thailand: Evidence for seasonal preference in disease transmission. Vector-Borne Zoonot. Dis. 10, 183–190 (2010).
Google Scholar 
24.Cappelle, J. et al. Nipah virus circulation at human-bat interfaces, Cambodia. Bull. World Health Organ. 98, 539–547 (2020).PubMed 
PubMed Central 

Google Scholar 
25.Thavry, H., Cappelle, J., Bumrungsri, S., Thona, L. & Furey, N. M. The diet of the cave nectar bat (#Eonycteris spelaea# Dobson) suggests it pollinates economically and ecologically significant plants in Southern Cambodia. Zool. Stud. 56, 25 (2017).
Google Scholar 
26.Lim, T., Cappelle, J., Hoem, T. & Furey, N. Insectivorous bat reproduction and human cave visitation in Cambodia: A perfect conservation storm?. PLoS One 13, e0196554 (2018).PubMed 
PubMed Central 

Google Scholar 
27.Sikes, R. S. 2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mammal. 97, 663–688 (2016).PubMed 
PubMed Central 

Google Scholar 
28.Anthony, E. L. P. Age determination in bats. In Ecological and Behavioral Methods for the Study of Bats 47–58 (Smithsonian Press, 1988).
Google Scholar 
29.Racey, P. A. Reproductive assessment. In Behavioural and Ecological Methods for the Study of Bats 249–264 (Johns Hopkins University Press, 2009).
Google Scholar 
30.Watanabe, S. et al. Bat coronaviruses and experimental infection of bats, the Philippines. Emerg. Infect. Dis. 16, 1217–1223 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Quan, P.-L. et al. Identification of a severe acute respiratory syndrome coronavirus-like virus in a leaf-nosed bat in Nigeria. MBio 1, 25 (2010).
Google Scholar 
32.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
34.Burgin, C. Family Rhinolophidae, horseshoe bats. In Handbook of the Mammals of the World Vol. 9 260–332 (Lynx Edicions, 2019).
Google Scholar 
35.Martín-Martín, A., Orduna-Malea, E., Thelwall, M. & Delgado López-Cózar, E. Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories. J. Informetr. 12, 1160–1177 (2018).
Google Scholar 
36.Racey, P. A. & Entwistle, E. Life history and reproductive strategies of bats. Reprod. Biol. Bats 20, 363–468 (2000).
Google Scholar 
37.Furey, N. M., Mackie, I. J. & Racey, P. A. Reproductive phenology of bat assemblages in Vietnamese karst and its conservation implications. Acta Chiropterol. 13, 341–354 (2011).
Google Scholar 
38.Sterling, E. J., Hurley, M. M. & Le, D. M. Vietnam: A Natural History (Yale University Press, 2006).
Google Scholar 
39.Van, N. K., Hzien, N. T., Loc, P. K. & Hiep, N. T. Bioclimatic Diagrams of Vietnam (Vietnam National University Publishing House, 2000).
Google Scholar 
40.Plowright, R. K. et al. Transmission or within-host dynamics driving pulses of zoonotic viruses in reservoir-host populations. PLoS Negl. Trop. Dis. 10, 0004796 (2016).
Google Scholar 
41.Peel, A. J. et al. Support for viral persistence in bats from age-specific serology and models of maternal immunity. Sci. Rep. 8, 3859 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
42.Wanger, T. C., Darras, K., Bumrungsri, S., Tscharntke, T. & Klein, A.-M. Bat pest control contributes to food security in Thailand. Biol. Conserv. 171, 220–223 (2014).
Google Scholar 
43.Furey, N. M., Racey, P. A., Ith, S., Touch, V. & Cappelle, J. Reproductive ecology of wrinkle-lipped free-tailed bats Chaerephon plicatus (Buchannan, 1800) in relation to Guano production in Cambodia. Diversity 10, 91 (2018).
Google Scholar 
44.Ades, G. W. J. & Dudgeon, D. Insect seasonality in Hong Kong: A monsoonal environment in the northern tropics (1999).45.Kai, K. H. & Corlett, R. T. Seasonality of forest invertebrates in Hong Kong, South China. J. Trop. Ecol. 18, 637–644 (2002).
Google Scholar 
46.Kingston, T., Lim, B. L. & Zubaid, A. Bats of Krau Wildlife Reserve (Universiti Kebangsaan Malaysia, 2006).
Google Scholar 
47.Nurul-Ain, E., Rosli, H. & Kingston, T. Resource availability and roosting ecology shape reproductive phenology of rain forest insectivorous bats. Biotropica 49, 382–394 (2017).
Google Scholar 
48.Fleming, T. H., Hooper, E. T. & Wilson, D. E. Three Central American Bat Communities: Structure, reproductive cycles, and movement patterns. Ecology 53, 555–569 (1972).
Google Scholar 
49.Bernard, R. T. & Cumming, G. S. African bats: Evolution of reproductive patterns and delays. Q. Rev. Biol. 72, 253–274 (1997).CAS 
PubMed 

Google Scholar 
50.Nguyen, S. T. et al. Bats (Chiroptera) of Bidoup Nui Ba National Park, Dalat Plateau, Vietnam. Mammal Stud. 46, 53–68 (2021).
Google Scholar 
51.Plowright, R. K. et al. Urban habituation, ecological connectivity and epidemic dampening: The emergence of Hendra virus from flying foxes (Pteropus spp.). Proc. R. Soc. B Biol. Sci. 278, 3703–3712 (2011).
Google Scholar 
52.Peel, A. J. et al. Synchronous shedding of multiple bat paramyxoviruses coincides with peak periods of Hendra virus spillover. Emerg. Microbes Infect. 8, 1314–1323 (2019).PubMed 
PubMed Central 

Google Scholar  More

Dataset of beneficial plantsI collated a species-level dataset of plant benefits (presence/absence data) starting from the information gathered by Kleunen et al.32. These authors extracted data from the WEP database (National Plant Germplasm System GRIN-GLOBAL; https://npgsweb.ars-grin.gov/gringlobal/taxon/taxonomysearcheco.aspx, Accessed 7 Jan 2016), which is based on the book by Wiersema and León20. Their dataset included 84 categories and subcategories of plant benefits pertaining human and animal nutrition, materials, fuels, medicine, useful poisons, social and environmental benefits. Subcategories of benefits, which often included very few records, were merged here into 25 standard and major categories following the guidelines in the Economic Botany Data Collection Standard33 as in Molina-Venegas et al.13, namely ornamental plants, soil improvers, hedging/shelter, human food, human-food additives, vertebrate food, invertebrate food, fuelwood, charcoal, other biofuels, timber, cane/stems, fibres, tannins/dyestuffs, beads, gums/resins, lipids, waxes, essential oils/scents, latex/rubber, medicines, invertebrate poison, vertebrate poison, smoking materials/drugs and symbolic/inspirational plants (Fig. 1). A few records (n = 93) that could not be assigned to any of the above categories were disregarded, and so was the category ‘gene source’ because unlike other benefits, any species is intrinsically a potential gene donor and hence there is not a clear link between the benefit and species features. Note that this is not to say that preserving genetic diversity, which indeed is the underlying message of this research, is a meaningless goal. Infraspecific taxa were collapsed at the species level, and the very few fern taxa in the original database32 were excluded. In total, I gathered 15,834 plant-benefit records sorted in a matrix of 25 types of benefits and 9521 species of seed plants. Most species (83.74%) provided only one or two benefits representing 62.83% of the records in the dataset, and the maximum number of benefits per species was 10 (only three species). Although the WEP database is the largest species-level database on plant benefits32, it does not claim to be comprehensive20. Yet, the size of the dataset I gathered here represented 76.19% of the total seed-plant genus-level records collated for the same types of benefits in a more comprehensive survey by Molina-Venegas et al.13 that based on Mabberley’s Plant-book34. Moreover, the total number of records per category (at the genus-level) strongly correlated between the datasets (Pearson r = 0.94, p  More