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    Routes and rates of bacterial dispersal impact surface soil microbiome composition and functioning

    Ronce O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu Rev Ecol Evol Syst. 2007;38:231–53.Article 

    Google Scholar 
    Shmida A, Wilson MV. Biological determinants of species diversity. J Biogeogr. 1985;12:1–20.Article 

    Google Scholar 
    Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.PubMed 
    Article 

    Google Scholar 
    Slatkin M. Gene flow and the geographic structure of natural populations. Science. 1987;236:787–92.CAS 
    PubMed 
    Article 

    Google Scholar 
    Baas-Becking, LGM. Geobiology or introduction to environmental science (Translated from Dutch). The Hague: W.P. Van Stockum & Zoon; 1934.Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
    PubMed 
    Article 

    Google Scholar 
    Peay KG, Schubert MG, Nguyen NH, Bruns TD. Measuring ectomycorrhizal fungal dispersal: macroecological patterns driven by microscopic propagules. Mol Ecol. 2012;21:4122–36.PubMed 
    Article 

    Google Scholar 
    Andam CP, Doroghazi JR, Campbell AN, Kelly PJ, Choudoir MJ, Buckley DH. A latitudinal diversity gradient in terrestrial bacteria of the genus Streptomyces. mBio. 2016;7:e02200–15.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Choudoir MJ, Barberán A, Menninger HL, Dunn RR, Fierer N. Variation in range size and dispersal capabilities of microbial taxa. Ecology. 2018;99:322–34.PubMed 
    Article 

    Google Scholar 
    Hanson CA, Müller AL, Loy A, Dona C, Appel R, Jørgensen BB, et al. Historical factors associated with past environments influence the biogeography of thermophilic endospores in Arctic marine sediments. Front Microbiol. 2019;10:245.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Albright MBN, Martiny JBH. Dispersal alters bacterial diversity and composition in a natural community. ISME J. 2018;12:296–9.PubMed 
    Article 

    Google Scholar 
    Evans SE, Bell-Dereske LP, Dougherty KM, Kittredge HA. Dispersal alters soil microbial community response to drought. Environ Microbiol. 2020;22:905–16.CAS 
    PubMed 
    Article 

    Google Scholar 
    Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.PubMed 
    Article 

    Google Scholar 
    Cevallos-Cevallos JM, Danyluk MD, Gu G, Vallad GE, van Bruggen AHC. Dispersal of Salmonella typhimurium by rain splash onto tomato plants. J Food Prot. 2012;75:472–9.PubMed 
    Article 

    Google Scholar 
    Lindström ES, Langenheder S. Local and regional factors influencing bacterial community assembly. Environ Microbiol Rep. 2012;4:1–9.PubMed 
    Article 

    Google Scholar 
    Rime T, Hartmann M, Frey B. Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier. ISME J. 2016;10:1625–41.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lindström ES, Östman Ö. The importance of dispersal for bacterial community composition and functioning. PLoS ONE. 2011;6:e25883.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Declerck SAJ, Winter C, Shurin JB, Suttle CA, Matthews B. Effects of patch connectivity and heterogeneity on metacommunity structure of planktonic bacteria and viruses. ISME J. 2013;7:533–42.PubMed 
    Article 

    Google Scholar 
    Souffreau C, Pecceu B, Denis C, Rummens K, De Meester L. An experimental analysis of species sorting and mass effects in freshwater bacterioplankton. Freshw Biol. 2014;59:2081–95.Article 

    Google Scholar 
    Comte J, Langenheder S, Berga M, Lindström ES. Contribution of different dispersal sources to the metabolic response of lake bacterioplankton following a salinity change. Environ Microbiol. 2017;19:251–60.CAS 
    PubMed 
    Article 

    Google Scholar 
    Albright MBN, Sevanto S, Gallegos-Graves LV, Dunbar J. Biotic interactions are more important than propagule pressure in microbial community invasions. mBio. 2020;11:e02089–20.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Galès A, Latrille E, Wéry N, Steyer JP, Godon JJ. Needles of Pinus halepensis as biomonitors of bioaerosol emissions. PLoS ONE. 2014;9:e112182.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bell E, Blake LI, Sherry A, Head IM, Hubert CRJ. Distribution of thermophilic endospores in a temperate estuary indicate that dispersal history structures sediment microbial communities. Environ Microbiol. 2018;20:1134–47.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Leung MHY, Wilkins D, Li EKT, Kong FKF, Lee PKH. Indoor-air microbiome in an urban subway network: diversity and dynamics. Appl Environ Microbiol. 2014;80:6760–70.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Maignien L, DeForce EA, Chafee ME, Murat Eren A, Simmons SL. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio. 2014;5:e00682–13.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bell T. Experimental tests of the bacterial distance-decay relationship. ISME J. 2010;4:1357–65.PubMed 
    Article 

    Google Scholar 
    Kaneko R, Kaneko S. The effect of bagging branches on levels of endophytic fungal infection in Japanese beech leaves. For Pathol. 2004;34:65–78.Article 

    Google Scholar 
    Vannette RL, Fukami T. Dispersal enhances beta diversity in nectar microbes. Ecol Lett. 2017;20:901–10.PubMed 
    Article 

    Google Scholar 
    Satou M, Kubota M, Nishi K. Measurement of horizontal and vertical movement of Ralstonia solanacearum in soil. J Phytopathol. 2006;154:592–7.CAS 
    Article 

    Google Scholar 
    Veen GF, Snoek BL, Bakx-Schotman T, Wardle DA, van der Putten WH. Relationships between fungal community composition in decomposing leaf litter and home-field advantage effects. Funct Ecol. 2019;33:1524–35.Article 

    Google Scholar 
    Liu G, Cornwell WK, Pan X, Ye D, Liu F, Huang Z, et al. Decomposition of 51 semidesert species from wide-ranging phylogeny is faster in standing and sand-buried than in surface leaf litters: implications for carbon and nutrient dynamics. Plant Soil. 2015;396:175–87.CAS 
    Article 

    Google Scholar 
    Kimball S, Goulden ML, Suding KN, Parker S. Altered water and nitrogen input shifts succession in a southern California coastal sage community. Ecol Appl. 2014;24:1390–404.PubMed 
    Article 

    Google Scholar 
    Finks SS, Weihe C, Kimball S, Allison SD, Martiny AC, Treseder KK, et al. Microbial community response to a decade of simulated global changes depends on the plant community. Elementa. 2021;9:124.
    Google Scholar 
    Khalili B, Weihe C, Kimball S, Schmidt KT, Martiny JBH. Optimization of a method to quantify soil bacterial abundance by flow cytometry. mSphere. 2019;4:e00435–19.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci USA. 1985;82:6955–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    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 
    Article 

    Google Scholar 
    Looby CI, Maltz MR, Treseder KK. Belowground responses to elevation in a changing cloud forest. Ecol Evol. 2016;6:1996–2009.PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–D264.CAS 
    PubMed 
    Article 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
    PubMed 
    Article 

    Google Scholar 
    Smith DJ, Ravichandar JD, Jain S, Griffin DW, Yu H, Tan Q, et al. Airborne bacteria in Earth’s lower stratosphere resemble taxa detected in the troposphere: results from a new NASA Aircraft Bioaerosol Collector (ABC). Front Microbiol. 2018;9:1752.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bryan NC, Christner BC, Guzik TG, Granger DJ, Stewart MF. Abundance and survival of microbial aerosols in the troposphere and stratosphere. ISME J. 2019;13:2789–99.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Matulich KL, Weihe C, Allison SD, Amend AS, Berlemont R, Goulden ML, et al. Temporal variation overshadows the response of leaf litter microbial communities to simulated global change. ISME J. 2015;9:2477–89.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kim N, Zabaloy MC, Villamil MB, Riggins CW, Rodríguez-Zas S. Microbial shifts following five years of cover cropping and tillage practices in fertile agroecosystems. Microorganisms. 2020;8:1773.CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Gurfield N, Grewal S, Cua LS, Torres PJ, Kelley ST. Endosymbiont interference and microbial diversity of the Pacific coast tick, Dermacentor occidentalis, in San Diego County, California. PeerJ. 2017;5:e3202.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Knights D, Kuczynski J, Charlson ES, Zaneveld J, Mozer MC, Collman RG, et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods. 2011;8:761–3.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bertolini V, Gandolfi I, Ambrosini R, Bestetti G, Innocente E, Rampazzo G, et al. Temporal variability and effect of environmental variables on airborne bacterial communities in an urban area of Northern Italy. Appl Microbiol Biotechnol. 2013;97:6561–70.CAS 
    PubMed 
    Article 

    Google Scholar 
    Voříšková J, Baldrian P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 2013;7:477–86.PubMed 
    Article 
    CAS 

    Google Scholar 
    Rastogi G, Coaker GL, Leveau JHJ. New insights into the structure and function of phyllosphere microbiota through high-throughput molecular approaches. FEMS Microbiol Lett. 2013;348:1–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    Lindow SE, Leveau JHJ. Phyllosphere microbiology. Curr Opin Biotechnol. 2002;13:238–43.CAS 
    PubMed 
    Article 

    Google Scholar 
    Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Kapturska D, et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol Ecol. 2016;25:4059–74.CAS 
    PubMed 
    Article 

    Google Scholar 
    Austin AT, Vivanco L. Plant litter decomposition in a semi-arid ecosystem controlled by photodegradation. Nature. 2006;442:555–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Glassman SI, Weihe C, Li J, Albright MBN, Looby CI, Martiny AC, et al. Decomposition responses to climate depend on microbial community composition. Proc Natl Acad Sci USA. 2018;115:11994–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Punnapayak H, Sudhadham M, Prasongsuk S, Pichayangkura S. Characterization of Aureobasidium pullulans isolated from airborne spores in Thailand. J Ind Microbiol Biotechnol. 2003;30:89–94.CAS 
    PubMed 
    Article 

    Google Scholar 
    Elmassry MM, Ray N, Sorge S, Webster J, Merry K, Caserio A, et al. Investigating the culturable atmospheric fungal and bacterial microbiome in West Texas: implication of dust storms and origins of the air parcels. FEMS Microbes. 2020;1:xtaa009.Article 

    Google Scholar 
    Van Diepen LTA, Frey SD, Landis EA, Morrison EW, Pringle A. Fungi exposed to chronic nitrogen enrichment are less able to decay leaf litter. Ecology. 2017;98:5–11.PubMed 
    Article 

    Google Scholar 
    Du X, Guo Q, Gao X, Ma K. Seed rain, soil seed bank, seed loss and regeneration of Castanopsis fargesii (Fagaceae) in a subtropical evergreen broad-leaved forest. Ecol Manag. 2007;238:212–9.Article 

    Google Scholar 
    Work TT, Buddle CM, Korinus LM, Spence JR. Pitfall trap size and capture of three taxa of litter-dwelling arthropods: implications for biodiversity studies. Environ Entomol. 2002;31:438–48.Article 

    Google Scholar 
    Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol Lett. 2004;7:601–13.Article 

    Google Scholar 
    Evans S, Martiny JBH, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.PubMed 
    Article 

    Google Scholar 
    Cadotte MW. Dispersal and species diversity: a meta-analysis. Am Nat. 2006;167:913–24.PubMed 
    Article 

    Google Scholar 
    Schmidt SK, Nemergut DR, Darcy JL, Lynch R. Do bacterial and fungal communities assemble differently during primary succession? Mol Ecol. 2014;23:254–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Baker NR, Khalili B, Martiny JBH, Allison SD. Microbial decomposers not constrained by climate history along a Mediterranean climate gradient in southern California. Ecology. 2018;99:1441–52.PubMed 
    Article 

    Google Scholar 
    Martiny JBH, Martiny AC, Weihe C, Lu Y, Berlemont R, Brodie EL, et al. Microbial legacies alter decomposition in response to simulated global change. ISME J. 2017;11:490–9.PubMed 
    Article 

    Google Scholar 
    Santander MV, Mitts BA, Pendergraft MA, Dinasquet J, Lee C, Moore AN, et al. Tandem fluorescence measurements of organic matter and bacteria released in sea spray aerosols. Environ Sci Technol. 2021;55:5171–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hobbie SE. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol Evol. 2015;30:357–63.PubMed 
    Article 

    Google Scholar  More

  • in

    Permissive aggregative group formation favors coexistence between cooperators and defectors in yeast

    Szathmáry E. Toward major evolutionary transitions theory 2.0. Proc Natl Acad Sci USA. 2015;112:10104–11. https://doi.org/10.1073/pnas.1421398112CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Niklas KJ, Newman SA. The origins of multicellular organisms. Evol Dev. 2013;15:41–52. https://doi.org/10.1111/ede.12013Article 
    PubMed 

    Google Scholar 
    Pfeiffer T, Bonhoeffer S. An evolutionary scenario for the transition to undifferentiated multicellularity. Proc Natl Acad Sci USA. 2003;100:1095–8. https://doi.org/10.1073/pnas.0335420100CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fisher RM, Regenberg B, Multicellular group formation in Saccharomyces cerevisiae. Proc Royal Soc B: Biol Sci. 2019;286. https://doi.org/10.1098/rspb.2019.1098Umen JG. Green algae and the origins of multicellularity in the plant kingdom. Cold Spring Harb Perspect Biol. 2014;6:a016170 https://doi.org/10.1101/cshperspect.a016170Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Knoll AH. The multiple origins of complex multicellularity. Annu Rev Earth Planet Sci. 2011;39:217–39. https://doi.org/10.1146/annurev.earth.031208.100209CAS 
    Article 

    Google Scholar 
    Bonner JT. The origins of multicellularity. Integr Biol Issues N. Rev. 1998;1:27–36.Article 

    Google Scholar 
    Tarnita CE, Taubes CH, Nowak MA. Evolutionary construction by staying together and coming together. J Theor Biol. 2013;320:10–22. https://doi.org/10.1016/j.jtbi.2012.11.022Article 
    PubMed 

    Google Scholar 
    Ratcliff WC, Denison RF, Borrello M, Travisano M. Experimental evolution of multicellularity. Proc Natl Acad Sci USA. 2012;109:1595–1600. https://doi.org/10.1073/pnas.1115323109Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Koschwanez JH, Foster KR, Murray AW. Sucrose utilization in budding yeast as a model for the origin of undifferentiated multicellularity. PLoS Biol. 2011;9:e1001122 https://doi.org/10.1371/journal.pbio.1001122CAS 
    Article 
    PubMed 

    Google Scholar 
    Kuzdzal-Fick JJ, Chen L, Balázsi G. Disadvantages and benefits of evolved unicellularity versus multicellularity in budding yeast. Ecol Evol. 2019;9:8509–23. https://doi.org/10.1002/ece3.5322Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brückner S, Schubert R, Kraushaar T, Hartmann R, Hoffmann D, Jelli E, et al. Kin discrimination in social yeast is mediated by cell surface receptors of the flo11 adhesin family. eLife 2020;9. https://doi.org/10.7554/eLife.55587Smukalla S, Caldara M, Pochet N, Beauvais A, Guadagnini S, Yan C, et al. FLO1 is a variable green beard gene that drives biofilm-like cooperation in budding yeast. Cell. 2008;135:726–37. https://doi.org/10.1016/j.cell.2008.09.037CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Driscoll WW, Travisano M, Synergistic cooperation promotes multicellular performance and unicellular free-rider persistence. Nat Commun. 2017;8. https://doi.org/10.1038/ncomms15707Pentz JT, Márquez-Zacarías P, Bozdag GO, Burnetti A, Yunker PJ, Libby E, et al. Ecological advantages and evolutionary limitations of aggregative multicellular development. Curr Biol. 2020;30:4155–.e6. https://doi.org/10.1016/j.cub.2020.08.006.CAS 
    Article 
    PubMed 

    Google Scholar 
    Goossens K, Willaert R. Flocculation protein structure and cell-cell adhesion mechanism in Saccharomyces cerevisiae. Biotechnol Lett. 2010;32:1571–85. https://doi.org/10.1007/s10529-010-0352-3CAS 
    Article 
    PubMed 

    Google Scholar 
    Di Gianvito P, Tesnière C, Suzzi G, Blondin B, Tofalo R. FLO5 gene controls flocculation phenotype and adhesive properties in a Saccharomyces cerevisiae sparkling wine strain. Sci Rep. 2017;7:1–12. https://doi.org/10.1038/s41598-017-09990-9CAS 
    Article 

    Google Scholar 
    Veelders M, Brückner S, Ott D, Unverzagt C, Mösch HU, Essen LO. Structural basis of flocculin-mediated social behavior in yeast. Proc Natl Acad Sci USA. 2010;107:22511–6. https://doi.org/10.1073/pnas.1013210108Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Verstrepen KJ, Jansen A, Lewitter F, Fink GR. Intragenic tandem repeats generate functional variability. Nat Genet. 2005;37:986–90. https://doi.org/10.1038/ng1618CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Verstrepen KJ, Klis FM. Flocculation, adhesion and biofilm formation in yeasts. Mol Microbiol. 2006;60:5–15. https://doi.org/10.1111/j.1365-2958.2006.05072.xCAS 
    Article 
    PubMed 

    Google Scholar 
    Verstrepen KJ, Reynolds TB, Fink GR. Origins of variation in the fungal cell surface. Nat Rev Microbiol. 2004;2:533–40. https://doi.org/10.1038/nrmicro927CAS 
    Article 
    PubMed 

    Google Scholar 
    Kraushaar T, Brückner S, Veelders M, Rhinow D, Schreiner F, Birke R, et al. Interactions by the fungal Flo11 adhesin depend on a fibronectin type III-like adhesin domain girdled by aromatic bands. Structure. 2015;23:1005–17. https://doi.org/10.1016/j.str.2015.03.021CAS 
    Article 
    PubMed 

    Google Scholar 
    Chen L, Noorbakhsh J, Adams RM, Samaniego-Evans J, Agollah G, Nevozhay D, et al. Two-dimensionality of yeast colony expansion accompanied by pattern formation. PLoS Comput Biol. 2014;10. https://doi.org/10.1371/journal.pcbi.1003979Oppler ZJ, Parrish ME, Murphy HA, Variation at an adhesin locus suggests sociality in natural populations of the yeast saccharomyces cerevisiae. Proc Royal Soc B: Biol Sci. 2019;286. https://doi.org/10.1098/rspb.2019.1948Lo WS, Dranginis AM. The cell surface flocculin Flo11 is required for pseudohyphae formation and invasion by Saccharomyces cerevisiae. Mol Biol Cell. 1998;9:161–71. https://doi.org/10.1091/mbc.9.1.161CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    El-Kirat-Chatel S, Beaussart A, Vincent SP, Abellán Flos M, Hols P, Lipke PN, et al. Forces in yeast flocculation. Nanoscale. 2015;7:1760–7. https://doi.org/10.1039/c4nr06315eCAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kobayashi O, Hayashi N, Kuroki R, Sone H. Region of Flo1 proteins responsible for sugar recognition. J Bacteriol. 1998;180:6503–10. https://doi.org/10.1128/jb.180.24.6503-6510.1998CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kapsetaki SE, West SA. The costs and benefits of multicellular group formation in algae. Evolution. 2019;73:1296–308. https://doi.org/10.1111/evo.13712Article 
    PubMed 

    Google Scholar 
    Quintero-Galvis JF, Paleo-López R, Solano-Iguaran JJ, Poupin MJ, Ledger T, Gaitan-Espitia JD, et al. Exploring the evolution of multicellularity in Saccharomyces cerevisiae under bacteria environment: An experimental phylogenetics approach. Ecol Evol. 2018;8:4619–30. https://doi.org/10.1002/ece3.3979Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goossens KV, Ielasi FS, Nookaew I, Stals I, Alonso-Sarduy L, Daenen L, et al. Molecular mechanism of flocculation self-recognition in yeast and its role in mating and survival. mBio. 2015;6:1–16. https://doi.org/10.1128/mBio.00427-15CAS 
    Article 

    Google Scholar 
    Hamilton WD. The genetical evolution of social behaviour. I. J Theor Biol. 1964;7:1–16. https://doi.org/10.1016/0022-5193(64)90038-4CAS 
    Article 
    PubMed 

    Google Scholar 
    Queller DC, Ponte E, Bozzaro S, Strassmann JE. Single-gene greenbeard effects in the social amoeba Dictyostelium discoideum. Science. 2003;299:105–6. https://doi.org/10.1126/science.1077742CAS 
    Article 
    PubMed 

    Google Scholar 
    Foty RA, Steinberg MS. The differential adhesion hypothesis: A direct evaluation. Dev Biol. 2005;278:255–63. https://doi.org/10.1016/j.ydbio.2004.11.012CAS 
    Article 
    PubMed 

    Google Scholar 
    Nowak MA. Five rules for the evolution of cooperation. Science. 2006;314:1560–3. https://doi.org/10.1126/science.1133755Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nadell CD, Foster KR, Xavier JB. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput Biol. 2010;6:e1000716 https://doi.org/10.1371/journal.pcbi.1000716CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 2014;24:50–55. https://doi.org/10.1016/j.cub.2013.10.030CAS 
    Article 
    PubMed 

    Google Scholar 
    Liu CG, Li ZY, Hao Y, Xia J, Bai FW, Mehmood MA, Computer simulation elucidates yeast flocculation and sedimentation for efficient industrial fermentation. Biotechnol J. 2018;13. https://doi.org/10.1002/biot.201700697Boraas ME, Seale DB, Boxhorn JE. Phagotrophy by flagellate selects for colonial prey: A possible origin of multicellularity. Evol Ecol. 1998;12:153–64. https://doi.org/10.1023/A:1006527528063Article 

    Google Scholar 
    Staps M, van Gestel J, Tarnita CE. Emergence of diverse life cycles and life histories at the origin of multicellularity. Nat Ecol Evol. 2019;3:1197–205. https://doi.org/10.1038/s41559-019-0940-0Article 
    PubMed 

    Google Scholar 
    De Vargas Roditi L, Boyle KE, Xavier JB. Multilevel selection analysis of a microbial social trait. Mol Syst Biol. 2013;9:684 https://doi.org/10.1038/msb.2013.42Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Damore JA, Gore J. Understanding microbial cooperation. J Theor Biol. 2012;299:31–41. https://doi.org/10.1016/j.jtbi.2011.03.008Article 
    PubMed 

    Google Scholar 
    Denoth Lippuner A, Julou T, Barral Y. Budding yeast as a model organism to study the effects of age. FEMS Microbiol Rev. 2014;38:300–25. https://doi.org/10.1111/1574-6976.12060CAS 
    Article 
    PubMed 

    Google Scholar 
    Janssens GE, Veenhoff LM. The natural variation in lifespans of single yeast cells is related to variation in cell size, ribosomal protein, and division time. PLoS ONE. 2016;11:e0167394 https://doi.org/10.1371/journal.pone.0167394CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ross-Gillespie A, Gardner A, West SA, Griffin AS. Frequency dependence and cooperation: Theory and a test with bacteria. Am Nat. 2007;170:331–42. https://doi.org/10.1086/519860Article 
    PubMed 

    Google Scholar 
    Healey D, Axelrod K, Gore J. Negative frequency-dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population. Mol Syst Biol. 2016;12:877 https://doi.org/10.15252/msb.20167033CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Harrow GL, Lees JA, Hanage WP, Lipsitch M, Corander J, Colijn C, et al. Negative frequency-dependent selection and asymmetrical transformation stabilise multi-strain bacterial population structures. ISME J. 2021;15:1523–38. https://doi.org/10.1038/s41396-020-00867-wCAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Avilés L. Solving the freeloaders paradox: Genetic associations and frequency-dependent selection in the evolution of cooperation among nonrelatives. Proc Natl Acad Sci USA. 2002;99:14268–73. https://doi.org/10.1073/pnas.212408299CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fisher RM, Cornwallis CK, West SA. Group formation, relatedness, and the evolution of multicellularity. Curr Biol. 2013;23:1120–5. https://doi.org/10.1016/j.cub.2013.05.004CAS 
    Article 
    PubMed 

    Google Scholar 
    Pentz JT, Travisano M, Ratcliff WC, Clonal development is evolutionarily superior to aggregation in wild-collected Saccharomyces cerevisiae. In Artificial Life 14 – Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, 2014;550–4. 10.7551/978-0-262-32621-6-ch088.Melbinger A, Cremer J, Frey E, The emergence of cooperation from a single mutant during microbial life cycles. J Royal Soc Interface. 2015;12. https://doi.org/10.1098/rsif.2015.0171 More

  • in

    Individual variability in foraging success of a marine predator informs predator management

    Krause, M. & Robins, K. Charismatic species and beyond: How cultural schemas and organisational routines shape conservation. Conserv. Soc. 15, 313–321 (2017).
    Google Scholar 
    Marshall, K. N., Stier, A. C., Samhouri, J. F., Kelly, R. P. & Ward, E. J. Conservation challenges of predator recovery. Conserv. Lett. 9, 70–78 (2016).
    Google Scholar 
    Bearzi, G., Holcer, D. & Di Sciara, G. N. The role of historical dolphin takes and habitat degradation in shaping the present status of northern Adriatic cetaceans. Aquat. Conserv. Mar. Freshw. Ecosyst. 14, 363–379 (2004).
    Google Scholar 
    Lavigne, D. M. Marine mammals and fisheries: The role of science in the culling debate. In Marine Mammals: Fisheries Tourism and Management Issues (eds Gales, N. et al.) 31–47 (CSIRO Publishing, 2003).
    Google Scholar 
    Bowen, W. D. & Lidgard, D. Marine mammal culling programs: Review of effects on predator and prey populations. Mamm. Rev. 43, 207–220 (2013).
    Google Scholar 
    Svanbäck, R. & Persson, L. Individual diet specialization, niche width and population dynamics: Implications for trophic polymorphisms. J. Anim. Ecol. 73, 973–982 (2004).
    Google Scholar 
    Butler, J. R. A. et al. The Moray Firth Seal Management Plan: An adaptive framework for balancing the conservation of seals, salmon, fisheries and wildlife tourism in the UK. Aquat. Conserv. Mar. Freshw. Ecosyst. 18, 1025–1038 (2008).
    Google Scholar 
    Graham, I. M., Harris, R. N., Matejusová, I. & Middlemas, S. J. Do ‘rogue’ seals exist? Implications for seal conservation in the UK. Anim. Conserv. 14, 587–598 (2011).
    Google Scholar 
    Linnell, J. D. C., Aanes, R., Swenson, J. E., Odden, J. & Smith, M. E. Large carnivores that kill livestock: Do ‘problem individuals’ really exist?. Wildl. Soc. Bull. 27, 698–705 (1999).
    Google Scholar 
    Tidwell, K. S., van der Leeuw, B. K., Magill, L. N., Carrothers, B. A. & Wertheimer, R. H. Evaluation of pinniped predation on adult salmonids and other fish in the Bonneville Dam tailrace (2017).Guillemette, M. & Brousseau, P. Does culling predatory gulls enhance the productivity of breeding common terns?. J. Appl. Ecol. 38, 1–8 (2001).
    Google Scholar 
    Rudolf, V. H. W. & Rasmussen, N. L. Population structure determines functional differences among species and ecosystem processes. Nat. Commun. 4, 2318 (2013).ADS 
    PubMed 

    Google Scholar 
    Harmon, L. J. et al. Evolutionary diversification in stickleback affects ecosystem functioning. Nature 458, 1167–1170 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Adams, J. et al. A century of Chinook salmon consumption by marine mammal predators in the Northeast Pacific Ocean. Ecol. Inform. 34, 44–51 (2016).
    Google Scholar 
    Chasco, B. et al. Competing tradeoffs between increasing marine mammal predation and fisheries harvest of Chinook salmon. Sci. Rep. 7, 1–14 (2017).CAS 

    Google Scholar 
    Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialisation in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164 (2006).ADS 

    Google Scholar 
    Thiemann, G. W., Iverson, S. J., Stirling, I. & Obbard, M. E. Individual patterns of prey selection and dietary specialization in an Arctic marine carnivore. Oikos 120, 1469–1478 (2011).
    Google Scholar 
    Königson, S., Fjälling, A., Berglind, M. & Lunneryd, S. G. Male gray seals specialize in raiding salmon traps. Fish. Res. 148, 117–123 (2013).
    Google Scholar 
    Sih, A., Sinn, D. L. & Patricelli, G. L. On the importance of individual differences in behavioural skill. Anim. Behav. 155, 307–317 (2019).
    Google Scholar 
    Bjorkland, R. H. et al. Stable isotope mixing models elucidate sex and size effects on the diet of a generalist marine predator. Mar. Ecol. Prog. Ser. 526, 213–225 (2015).ADS 

    Google Scholar 
    Schwarz, D. et al. Large-scale molecular diet analysis in a generalist marine mammal reveals male preference for prey of conservation concern. Ecol. Evol. 8, 9889–9905 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Tinker, M. T., Costa, D. P., Estes, J. A. & Wieringa, N. Individual dietary specialization and dive behaviour in the California sea otter: Using archival time-depth data to detect alternative foraging strategies. Deep. Res. Part II Top. Stud. Oceanogr. 54, 330–342 (2007).ADS 

    Google Scholar 
    Voelker, M. R., Schwarz, D., Thomas, A., Nelson, B. W. & Acevedo-Gutiérrez, A. Large-scale molecular barcoding of prey DNA reveals predictors of intrapopulation feeding diversity in a marine predator. Ecol. Evol. 10, 9867–9885 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).MathSciNet 
    PubMed 

    Google Scholar 
    Harcourt, R. Individual variation in predation on fur seals by southern sea lions (Otaria byronia) in Peru. Can. J. Zool. 71, 1908–1911 (1993).
    Google Scholar 
    Marine Mammal Commission. Marine Mammal Protection Act. Marine Mammal Protection Act Amendment 1–56 (U.S. Fish and Wildlife Service, 2004). https://doi.org/10.1002/tcr.201190008.Book 

    Google Scholar 
    National Marine Fisheries Service. Willamette Falls Pinniped-Fishery Interaction Task Force Marine Mammal Protection Act, Section 120 (National Marine Fisheries Service, 2018).
    Google Scholar 
    Jefferson, T. A., Smultea, M. A., Ward, E. J. & Berejikian, B. Estimating the stock size of harbor seals (Phoca vitulina richardii) in the inland waters of Washington State using line-transect methods. PLoS ONE 16, e0241254 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jeffries, S., Huber, H., Calambokidis, J. & Laake, J. Trends and status of harbor seals in Washington State: 1978–1999. J. Wildl. Manag. 67, 208–219 (2003).
    Google Scholar 
    Thomas, A. C., Lance, M. M., Jeffries, S. J., Miner, B. G. & Acevedo-Gutiérrez, A. Harbor seal foraging response to a seasonal resource pulse, spawning Pacific herring. Mar. Ecol. Prog. Ser. 441, 225–239 (2011).ADS 

    Google Scholar 
    Chasco, B. et al. Estimates of chinook salmon consumption in Washington State inland waters by four marine mammal predators from 1970 to 2015. Can. J. Fish. Aquat. Sci. 74, 1173–1194 (2017).
    Google Scholar 
    Farrer, J. & Acevedo-Gutiérrez, A. Use of haul-out sites by harbor seals (Phoca vitulina) in Bellingham: Implications for future development. Northwest. Nat. 91, 74–79 (2010).
    Google Scholar 
    Steingass, S., Jeffries, S., Hatch, D. & Dupont, J. Field report: 2020 pinniped research and management activities at Bonneville Dam (2020).Tidwell, K. S., Carrothers, B. A., Blumstein, D. T. & Schakner, Z. A. Steller sea lion (Eumetopias jubatus) response to non-lethal hazing at Bonneville Dam. Front. Conserv. Sci. 2, 1–9 (2021).
    Google Scholar 
    Hiruki, L. M., Schwartz, M. K. & Boveng, P. L. Hunting and social behaviour of leopard seals (Hydrurga leptonyx) at Seal Island, South Shetland Islands, Antarctica. J. Zool. 249, 97–109 (1999).
    Google Scholar 
    Ainley, D. G., Ballard, G., Karl, B. J. & Dugger, K. M. Leopard seal predation rates at penguin colonies of different size. Antarct. Sci. 17, 335–340 (2005).ADS 

    Google Scholar 
    Páez-Rosas, D. et al. Hunting and cooperative foraging behavior of Galapagos sea lion: An attack to large pelagics. Mar. Mammal Sci. 36, 386–391 (2020).
    Google Scholar 
    Macneale, K. H., Kiffney, P. M. & Scholz, N. L. Pesticides, aquatic food webs, and the conservation of Pacific salmon. Front. Ecol. Environ. 8, 475–482 (2010).
    Google Scholar 
    Roni, P., Anders, P. J., Beechie, T. J. & Kaplowe, D. J. Review of tools for identifying, planning, and implementing habitat restoration for Pacific salmon and steelhead. North Am. J. Fish. Manag. 38, 355–376 (2018).
    Google Scholar 
    Morissette, L., Christensen, V. & Pauly, D. Marine mammal impacts in exploited ecosystems: Would large scale culling benefit fisheries?. PLoS ONE 7, 1–18 (2012).
    Google Scholar 
    Thompson, D., Coram, A. J., Harris, R. N. & Sparling, C. E. Review of non-lethal seal control options to limit seal predation on salmonids in rivers and at finfish farms. Scott. Mar. Freshw. Sci. 12, 137 (2021).
    Google Scholar 
    Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
    Google Scholar 
    Fairbanks, C. & Penttila, D. Bellingham Bay Forage Fish Spawning Assessment (2016).Madsen, S. W. & Nightengale, T. Whatcom Creek Ten-Years After: Summary Report (Department of Public Works, 2009). https://doi.org/10.2307/j.ctt20krzd7.7.Book 

    Google Scholar 
    Martin, P. & Bateson, P. Measuring Behaviour: An Introductory Guide (Cambridge University Press, 2007).
    Google Scholar 
    Bolger, D. T., Morrison, T. A., Vance, B., Lee, D. & Farid, H. A computer-assisted system for photographic mark-recapture analysis. Methods Ecol. Evol. 3, 813–822 (2012).
    Google Scholar 
    Harrison, P. J. et al. Incorporating movement into models of grey seal population dynamics. J. Anim. Ecol. 75, 634–645 (2006).PubMed 

    Google Scholar 
    Thompson, P. M. & Wheeler, H. Photo-ID-based estimates of reproductive patterns in female harbor seals. Mar. Mammal Sci. 24, 138–146 (2008).
    Google Scholar 
    Washington Department of Fish and Wildlife. Whatcom Creek Hatchery (WDFW, 2019).
    Google Scholar 
    R Core Team. R: A language and environment for statistical computing (R Core Team, 2020).
    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

    Google Scholar 
    Lloyd-Smith, J. O. Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases. PLoS ONE 2, 1–8 (2007).
    Google Scholar 
    Zhang, D. rsq: R-Squared and Related Measures. R package version 2.1 (2020).Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).ADS 

    Google Scholar 
    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 
    Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
    MATH 

    Google Scholar  More

  • in

    Albedo changes caused by future urbanization contribute to global warming

    Land coverUrban landscapes are characterized by small clusters of patches, forming land mosaics that are distinct from natural landscapes. An accurate estimation of climate forcing requires a land cover dataset at high resolutions that does not omit small urban patches. In this study, the RF estimates are based on 500-m and 1-km land cover datasets. This fine resolution is necessary to preserve spatial details of small urban patches while avoiding the large underestimation of urban land areas at coarse resolution (e.g., ~19% underestimation at 10 km compared to that at 1 km)3. We used 500-m resolution MODIS Land Cover product (MCD12Q1v006) for historical land cover changes. For future urban land cover distributions, we used the global urban land expansion products simulated under the SSPs for 2030–2100 (i.e., Chen-2020)4. The simulation performance was tested using historical urban expansion from 2000 to 2015 based on Global Human Settlement Layer51, where the agreement between simulated and observed urban land was evaluated using the Figure of Merit (FoM) indicator52 that has showed similar or better values than those reported in other existing land simulation applications4. The high-resolution Chen-2020 also shows very high spatial consistency with the prominent coarse resolution global urban land projection LUH2 that is recommended in CMIP64. Considering different scenarios is also necessary to account for the uncertainties of future socioeconomic and environmental conditions, so we included simulated urban lands under three scenarios (Supplementary Table 1): Sustainability -SSP1, Middle of the Road – SSP2, and Fossil-fueled Development – SSP553. Within each SSP scenario, the product provides a likelihood map of each grid becoming urban, based on 100 urbanization simulations. We used the likelihood map to account for spatial uncertainties of urban expansion by deriving 90% confidence intervals of projected urban land demand within a SSP scenario. We used the MODIS IGBP Land Cover classes (Supplementary Table 2) and resampled the original 500-m resolution MODIS products in 2018 to 1-km resolution to match the future simulations when it was used as a baseline year. To isolate the independent effect of urbanization (vs other types of land uses) in future estimates, land covers that are not converted to urban are assumed to have the same cover types as in 2018 (i.e., the baseline year). Though there are other global land cover products for current periods, we choose the MODIS IGBP land cover products because the albedo look-up maps (LUMs) were based on IGBP land cover types (see Albedo Look-Up Maps).To further evaluate the uncertainties caused by different projections of future urbanization, we also included the other two SSPs from Chen-2020, and another two 1-km resolution urban land cover products projected for the future for the purpose of comparison. The other two products include four projections of SRES scenarios (i.e., A1, B1, A1B, and B2) (i.e., Li-2017 mentioned above)3 and one without scenario description but assumed historical development would continue (i.e., Zhou-2019 mentioned above)2. These projections of future urban land expansion were calibrated with different historical urban land products and can be regarded as independent.Albedo look-up maps (LUMs)Albedo Look-Up Maps (LUMs)31 were derived from the intersection of MODIS land cover54 and surface albedo55 products, which are used to determine the albedo values for each IGBP land cover type by month and by location. Monthly means of white-sky (i.e., diffuse surface illumination condition) and black sky (i.e., direct surface illumination condition) during 2001–2011 were processed for snow-free and snow-covered periods for each of the 17 IGBP land cover classes at spatial resolutions of 0.05°−1°31. The LUMs have been verified by comparing the reconstructed albedo using the LUMs with the original MODIS albedo, which shows very similar values31. We used the LUMs at a resolution of 1° due to the significantly fewer missing values, to assure the spatial continuity of albedo changes at a global scale while keeping the matches with the 1° resolution of radiation data and RF kernels. The underlying assumption is that albedo of the same land cover type varies insignificantly within a 1° grid.Snow and radiation productSnow cover can significantly change the albedo of land regardless of cover types (Supplementary Fig. 4). In this study, we tally monthly albedo using snow-free and snow-covered categories in estimating RF. Past and present snow-free and snow-covered conditions were derived from level 3 MODIS/Terra Snow Cover (MOD10CM.006)56 at 0.05° spatial resolution and resampled to a 1° spatial resolution. Monthly means of 2001–2005 vs 2015–2019 were used for 2001 and 2018 respectively. For future periods, ensemble mean snow cover for each year and month, projected under the CMIP5 framework for three Representative Concentration Pathway (RCP) scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) were used (for more details see Supplementary Note 2B). By comparing the model outputs with MODIS observations for a recent decade (2006–2015), we found that the multi-model mean snow cover was systematically biased compared to MODIS observations. Consequently, we calibrated the ensemble mean projections by subtracting the biases for the grids. In each 10th year of the future (e.g., 2030, 2040, etc.), the decadal monthly mean snow cover (e.g., 2026–2035 for 2030, and 2036–2045 for 2040, etc.) was used for the year.We used the long-term monthly averages (1981–2010) of diffuse and direct incoming surface solar radiation reanalysis Gaussian grid product from National Centers for Environmental Prediction (NCEP)57. Visible and near infrared beam downward radiation and diffuse downward radiation from NCEP were used to compute the white-sky and black-sky fractions. As for snow cover, ensemble mean shortwave radiation at surface (SWSF) and at top-of-atmosphere (SWTOA) projected from CMIP5 models (Supplementary Note 3C) for RCP2.6, RCP4.5, and RCP8.5 were collected for empirically computing future albedo kernels (see section 3.4 below).Radiative kernelsRadiative kernels were used to compute top-of-atmosphere RF due to small perturbations of temperature, water vapor, and albedo. We used the latest state-of-the-art albedo kernels calculated with CESM v1.1.258 to compute RF in 2018 relative to 2001. In brief, the albedo kernel is the change in top-of-atmosphere radiative flux for a 0.01 change in surface albedo. The CESM1.1.2 kernels are separated into clear- and all-sky illumination conditions. We used the all-sky kernels because we include both black-sky and white-sky albedos. For future periods, because there are no available radiative kernels produced from general circulation models, we approximated the future kernels using an empirical parameterization following Bright et al.59:$${K}_{m}left(iright)={{SW}}^{{SF}}(i)times {sqrt}left(frac{{{SW}}^{{SF}}(i)}{{{SW}}^{{TOA}}(i)}right)/(-100)$$
    (1)
    where m is the month, i is the location, and SWSF and SWTOA are the surface and top-of-atmosphere shortwave radiation; dividing by −100 is for matching the CESM1.1.2 kernel definition of a 0.01 change in surface albedo.Estimation of albedo change and RFWe analyzed the RF in 2018 due to albedo changes caused by urbanization since 2001 (2018–2001), and in the future from 2030 to 2100 at decadal intervals (i.e., 2030, 2040, 2050, …, and 2100) since 2018 under three illustrative scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, which combine SSP-based urbanization projections and RCP-based climate projections. The three illustrative scenarios were selected following the scenario designation of the latest IPCC report50 and represent low greenhouse gas (GHG) emissions with CO2 emissions declining to net zero around or after 2050, intermediate GHG emissions with CO2 emissions remaining around current levels until the mid-century, and very high CO2 emissions that roughly double from current levels by 2050, respectively. We selected 2018 as the baseline year to divide the past from the future because 2018 was the latest year with available MODIS land cover products at the time of this study. We used ArcGIS 10.6 to produce spatial maps of all variables, including area of each land cover type within a 1° × 1°-grid, snow cover, albedo, radiation, and kernels, and R 3.6.1 to compute the RF.We focused only on albedo changes induced by urbanization, including the conversions from all other 16 IGBP land cover types to urban land. The changes of albedo for each grid (x, y) of a month (m) were obtained by computing the difference between albedo of that grid in the baseline year (t = t0) and in a later year (t = t1) with urban expansion:$${triangle alpha }_{m,t1-t0}(x,y)={alpha }_{m,t=t1}(x,y)-{alpha }_{m,t=t0}(x,y)$$
    (2)
    where αm, t = t1 (x, y) and αm, t = t0) (x, y) is the albedo for each grid (x,y) of a month (m) at the base year and later year respectively; the grid-scale albedo is computed as the weighted sum of albedo by land cover types with the weighing factor corresponding to areal percentage of a land cover within the grid. The albedo for each land cover type of a grid was then obtained by applying the albedo LUMs that provide spatially continuous black-sky, white-sky, snow-covered, and snow-free albedo maps for a given month for each land cover. Firstly, monthly mean albedo is computed as:$${alpha }_{m,t}(x,y)=mathop{sum }limits_{l=1}^{17}mathop{sum }limits_{s=0}^{1}mathop{sum }limits_{r=0}^{1}{{f}_{l,t}(x,y){f}_{s,m,t}(x,y)f}_{r,m,t}(x,y)left({alpha }_{l,s,r,m}(x,y)right)$$
    (3)
    where m is the month, t is the year, l is the land cover type, fl is the proportion of a cover type within the grid, fs,m,t is the fraction for snow-covered (s = 0) and snow-free (s = 1) conditions of the time (m, t), fr,m,t (x, y) is the fraction for white-sky (r = 0) or black-sky (r = 1) conditions of the time, and αl,s,r,m (x, y) is the albedo for land cover type l in month m that is extracted from the albedo LUMs corresponding to snow condition (s) and radiation condition (r). The annual mean albedo change is reported as the mean of monthly albedo change:$${triangle alpha }_{t1-t0}(x,y)=frac{1}{12}mathop{sum }limits_{m=1}^{m=12}({alpha }_{m,t=t1}(x,y)-{alpha }_{m,t=t0}(x,y))$$
    (4)
    The conversion of other land covers to urban land can contribute differently to the global RF, as the total area that is converted into urban land is different among non-urban land covers and the albedo differences between urban land and non-urban land cover types vary. To estimate the proportional contributions of different land conversions, we first decomposed the total albedo of each grid into the proportion of each land cover type:$${alpha }_{l,m,t}(x,y)={f}_{l,m,t}(x,y)mathop{sum }limits_{s=0}^{1}mathop{sum }limits_{r=0}^{1}{f}_{s,m,t}(x,y){f}_{r,m,t}(x,y)left({alpha }_{l,s,r,m}(x,y)right)$$
    (5)
    The global RF due to albedo change caused by conversion from each non-urban land cover type (l ≠ 13) to urban land (l = 13) (see Supplementary Table 2 land cover labels) was calculated as:$${{RF}}_{triangle alpha ,l(lne 13),{global}}=frac{1}{{A}_{{Earth}}}mathop{sum }limits_{i=1}^{n}mathop{sum }limits_{m=1}^{12}{({alpha }_{13,m,t=t1}left(iright)-{alpha }_{l,m,t=t0}left(iright))Delta p}_{lto 13}left(iright){Area}left(iright){K}_{m}(i)$$
    (6)
    where i refers to a grid, n is the total number of pixels on global lands, AEarth is the global surface area (5.1  ×  108 km2), α13,m,t = t1) (i) is the albedo of urban land in month m in the later year with urban expansion, αl,m,t = t0 (i) is the albedo of a targeted non-urban land cover type in the base year t0, Δpl→13 is the percentage of the non-urban land cover type that is converted to urban land in the year t1 compared to year t0, Area(i) is the area of the pixel, and Km (i) is the radiative kernel at the grid.The global RF due to urbanization-induced albedo changes was then calculated as:$${{RF}}_{triangle alpha ,{global}}=mathop{sum }limits_{l=1}^{17}{{RF}}_{triangle alpha ,l,{global}}(l,ne, 13)$$
    (7)
    GWP: CO2-equivalentWe followed GWP calculations by explicitly accounting for the lifetime and dynamic behavior of CO2 to convert RF to CO2 equivalent60,61:$${GWP}[{kg},{of},{{CO}}_{2}-{eq}]=frac{{int }_{t=0}^{{TH}}{{RF}}_{triangle alpha ,{global}}(t)}{{k}_{{CO}_2}{int }_{t=0}^{{TH}}{y}_{{{CO}}_{2}}(t)}$$
    (8)
    where kCO2 is radiative efficiency of CO2 in the atmosphere (W/m2/kg) at a constant background concentration of 389 ppmv, which is taken as 1.76  ×  1015 W/m2/kg62, and RF∆α,global is the global RF caused by albedo changes (W/m2). ({y}_{{{CO}}_{2}}) is the impulse-response function (IRF) for CO2 that ranges from 1 at the time of the emission pulse (t = 0) to 0.41 after 100 years, and here it is set to a mean value of 0.52 over 100 years60. The time horizon (TH) of our GWP calculations was fixed at 100 years following IPCC standards and previous studies60,63,64.Global mean surface air temperature changeWe estimated the 100-year global mean surface temperature change for the estimated RF by adopting an equilibrium climate sensitivity (ECS), defined as the global mean surface air temperature increase that follows a doubling of pre-industrial atmospheric carbon dioxide (RF = 3.7 W/m2). Given a value of RF induced by a forcing agent, the temperature change is estimated as RF/3.7 × ECS. To consider the uncertainties of ECS, we adopted a mean value of 3 °C and a very likely (90% confidence interval) range of 2–5 °C following IPCC AR650. Without knowing the exact distribution shape of ECS and future albedo-change-induced RF, we created a log-normal distribution (Supplementary Note 4) to approximate their asymmetric distribution through numerical simulation. We then conducted Monte Carlo simulations that draw 5000 random samples from each distribution to jointly estimate the uncertainties of global mean surface air temperature changes. We report the mean and 90% interval ranges of the change in temperature. More

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    Sex differences in the winter activity of desert hedgehogs (Paraechinus aethiopicus) in a resource-rich habitat in Qatar

    Nagy, K. A. Field metabolic rate and food requirement scaling in mammals and birds. Ecol. Monogr. 57, 111–128 (1987).Article 

    Google Scholar 
    Anderson, K. J. & Jetz, W. The broad-scale ecology of energy expenditure of endotherms. Ecol. Lett. 8, 310–318 (2005).Article 

    Google Scholar 
    Terrien, J., Perret, M. & Aujard, F. Behavioral thermoregulation in mammals: A review. Front. Biosci. 16, 1428–1444 (2011).Article 

    Google Scholar 
    Mery, F. & Burns, J. G. Behavioural plasticity: An interaction between evolution and experience. Evol. Ecol. 24, 571–583 (2010).Article 

    Google Scholar 
    Brockmann, H. J. The evolution of alternative strategies and tactics. Adv. Study Behav. 30, 1–51 (2001).Article 

    Google Scholar 
    Milling, C. R., Rachlow, J. L., Johnson, T. R., Forbey, J. S. & Shipley, L. A. Seasonal variation in behavioral thermoregulation and predator avoidance in a small mammal. Behav. Ecol. 28, 1236–1247 (2017).Article 

    Google Scholar 
    Guiden, P. W. & Orrock, J. L. Seasonal shifts in activity timing reduce heat loss of small mammals during winter. Anim. Behav. 164, 181–192 (2020).Article 

    Google Scholar 
    Cotton, C. L. & Parker, K. L. Winter activity patterns of northern flying squirrels in sub-boreal forests. Can. J. Zool. 78, 1896–1901 (2000).Article 

    Google Scholar 
    Long, R. A., Martin, T. J. & Barnes, B. M. Body temperature and activity patterns in free-living arctic ground squirrels. J. Mammal. 86, 314–322 (2005).Article 

    Google Scholar 
    Zschille, J., Stier, N. & Roth, M. Gender differences in activity patterns of American mink Neovison vison in Germany. Eur. J. Wildl. Res. 56, 187–194 (2010).Article 

    Google Scholar 
    Geiser, F. Hibernation. Curr. Biol. 23, R188–R193 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gür, M. K. & Gür, H. Age and sex differences in hibernation patterns in free-living Anatolian ground squirrels. Mamm. Biol. 80, 265–272 (2015).Article 

    Google Scholar 
    Kisser, B. & Goodwin, H. T. Hibernation and overwinter body temperatures in free-ranging thirteen-lined ground squirrels, Ictidomys tridecemlineatus. Am. Midl. Nat. 167, 396–409 (2012).Article 

    Google Scholar 
    Dmi’el, R. & Schwarz, M. Hibernation patterns and energy expenditure in hedgehogs from semi-arid and temperate habitats. J. Comp. Physiol. B 155, 117–123 (1984).Article 

    Google Scholar 
    Abu Baker, M. A. et al. Caught basking in the winter sun: Preliminary data on winter thermoregulation in the Ethiopian hedgehog, Paraechinus aethiopicus in Qatar. J. Arid Environ. 125, 52–55 (2016).ADS 
    Article 

    Google Scholar 
    McKechnie, A. E. & Mzilikazi, N. Heterothermy in Afrotropical mammals and birds: A review. Integr. Comp. Biol. 51, 349–363 (2011).PubMed 
    Article 

    Google Scholar 
    Wacker, C. B., McAllan, B. M., Körtner, G. & Geiser, F. The role of basking in the development of endothermy and torpor in a marsupial. J. Comp. Physiol. B 187, 1029–1038 (2017).PubMed 
    Article 

    Google Scholar 
    Brown, K. J. & Downs, C. T. Basking behaviour in the rock hyrax (Procavia capensis) during winter. Afr. Zool. 42, 70–79 (2007).Article 

    Google Scholar 
    Humphries, M. M., Thomas, D. W. & Kramer, D. L. The role of energy availability in mammalian hibernation: A cost-benefit approach. Physiol. Biochem. Zool. 76, 165–179 (2003).PubMed 
    Article 

    Google Scholar 
    Field, K. A. et al. Effect of torpor on host transcriptomic responses to a fungal pathogen in hibernating bats. Mol. Ecol. 27, 3727–3743 (2018).CAS 
    Article 

    Google Scholar 
    Bieber, C., Cornils, J. S., Hoelzl, F., Giroud, S. & Ruf, T. The costs of locomotor activity? Maximum body temperatures and the use of torpor during the active season in edible dormice. J. Comp. Physiol. B 187, 803–814 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eto, T. et al. Individual variation of daily torpor and body mass change during winter in the large Japanese field mouse (Apodemus speciosus). J. Comp. Physiol. B 188, 1005–1014 (2018).PubMed 
    Article 

    Google Scholar 
    Zervanos, S. M., Maher, C. R. & Florant, G. L. Effect of body mass on hibernation strategies of woodchucks (Marmota monax). (2014).Ford, R. G. Home range in a patchy environment: Optimal foraging predictions. Am. Zool. 23, 315–326 (1983).Article 

    Google Scholar 
    Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B 185, 575–586 (2015).PubMed 
    Article 

    Google Scholar 
    Batavia, M., Nguyen, G., Harman, K. & Zucker, I. Hibernation patterns of Turkish hamsters: Influence of sex and ambient temperature. J. Comp. Physiol. B 183, 269–277 (2013).PubMed 
    Article 

    Google Scholar 
    Kato, G. A. et al. Individual differences in torpor expression in adult mice are related to relative birth mass. J. Exp. Biol. 221, jeb171983 (2018).PubMed 
    Article 

    Google Scholar 
    Williams, C. T. et al. Sex-dependent phenological plasticity in an arctic hibernator. Am. Nat. 190, 854–859 (2017).PubMed 
    Article 

    Google Scholar 
    Healy, J. E., Burdett, K. A., Buck, C. L. & Florant, G. L. Sex differences in torpor patterns during natural hibernation in golden-mantled ground squirrels (Callospermophilus lateralis). J. Mammal. 93, 751–758 (2012).Article 

    Google Scholar 
    Wang, Y., Yuan, L.-L., Peng, X., Wang, Y. & Yang, M. Experimental study on hibernation patterns in different ages and sexes of daurian ground squirrel (Spermophilus Dauricus). Shenyang Shifan Daxue Xuebao (Ziran Kexue Ban) 27, 351–355 (2009).
    Google Scholar 
    Siutz, C., Franceschini, C. & Millesi, E. Sex and age differences in hibernation patterns of common hamsters: Adult females hibernate for shorter periods than males. J. Comp. Physiol. B 186, 801–811 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Michener, G. R. Sexual differences in over-winter torpor patterns of Richardson’s ground squirrels in natural hibernacula. Oecologia 89, 397–406 (1992).ADS 
    PubMed 
    Article 

    Google Scholar 
    Boyles, J. G., Bennett, N. C., Mohammed, O. B. & Alagaili, A. N. Torpor patterns in Desert Hedgehogs (Paraechinus aethiopicus) represent another new point along a thermoregulatory continuum. Physiol. Biochem. Zool. 90, 445–452 (2017).PubMed 
    Article 

    Google Scholar 
    Reeve, N. Hedgehogs (Poyser, 1994).
    Google Scholar 
    He, K. et al. An estimation of erinaceidae phylogeny: A combined analysis approach. PLoS One 7, e39304 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schoenfeld, M. & Yom-Tov, Y. The biology of two species of hedgehogs, Erinaceus europaeus concolor and Hemiechinus auritus aegyptius, Israel. Mammalia 49, 339–356 (1985).Article 

    Google Scholar 
    Haigh, A., O’Riordan, R. M. & Butler, F. Nesting behaviour and seasonal body mass changes in a rural Irish population of the Western hedgehog (Erinaceus europaeus). Acta Theriol. (Warsz) 57, 321–331 (2012).Article 

    Google Scholar 
    Rasmussen, S. L., Berg, T. B., Dabelsteen, T. & Jones, O. R. The ecology of suburban juvenile European hedgehogs (Erinaceus europaeus) in Denmark. Ecol. Evol. 9, 13174–13187 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jensen, A. B. Overwintering of European hedgehogs (Erinaceus europaeus) in a Danish rural area. Acta Theriol. (Warsz) 49, 145–155 (2004).Article 

    Google Scholar 
    Jackson, D. B. The breeding biology of introduced hedgehogs (Erinaceus europaeus) on a Scottish Island: Lessons for population control and bird conservation. J. Zool. 268, 303–314 (2006).Article 

    Google Scholar 
    Rautio, A., Valtonen, A., Auttila, M. & Kunnasranta, M. Nesting patterns of European hedgehogs (Erinaceus europaeus) under northern conditions. Acta Theriol. (Warsz) 59, 173–181 (2014).Article 

    Google Scholar 
    Hallam, S. L. & Mzilikazi, N. Heterothermy in the southern African hedgehog, Atelerix frontalis. J. Comp. Physiol. B 181, 437–445 (2011).PubMed 
    Article 

    Google Scholar 
    South, K. E., Haynes, K. & Jackson, A. C. Hibernation Patterns of the European Hedgehog, Erinaceus europaeus, at a Cornish Rescue Centre. Animals 10, 1418 (2020).PubMed Central 
    Article 

    Google Scholar 
    Gillies, A. C., Ellison, G. T. H. & Skinner, J. D. The effect of seasonal food restriction on activity, metabolism and torpor in the South African hedgehog (Atelerix frontalis). J. Zool. 223, 117–130 (1991).Article 

    Google Scholar 
    Gazzard, A. & Baker, P. J. Patterns of feeding by householders affect activity of hedgehogs (Erinaceus europaeus) during the hibernation period. Animals 10, 1344 (2020).PubMed Central 
    Article 

    Google Scholar 
    Dmiel, R. & Schwarz, M. Hibernation patterns and energy expenditure in hedgehogs from semi-arid and temperate habitats. J. Comp. Physiol. B 155, 117–123 (1984).Article 

    Google Scholar 
    Fowler, P. A. & Racey, P. A. Daily and seasonal cycles of body temperature and aspects of heterothermy in the hedgehog Erinaceus europaeus. J. Comp. Physiol. B 160, 299–307 (1990).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rutovskaya, M. V. et al. The dynamics of body temperature of the Eastern European hedgehog (Erinaceus roumanicus) during winter hibernation. Biol. Bull. 46, 1136–1145 (2019).Article 

    Google Scholar 
    Harrison, D. L. & Bates, P. J. J. The Mammals of Arabia Vol 354 (Harrison Zoological Museum Sevenoaks, 1991).
    Google Scholar 
    Al-Musfir, H. M. & Yamaguchi, N. Timings of hibernation and breeding of Ethiopian Hedgehogs, Paraechinus aethiopicus in Qatar. Zool. Middle East 45, 3–10 (2008).Article 

    Google Scholar 
    Pettett, C. E., Al-Hajri, A., Al-Jabiry, H., Macdonald, D. W. & Yamaguchi, N. A comparison of the Ranging behaviour and habitat use of the Ethiopian hedgehog (Paraechinus aethiopicus) in Qatar with hedgehog taxa from temperate environments. Sci. Rep. 8, 1–10 (2018).Article 
    CAS 

    Google Scholar 
    Abu Baker, M. A., Reeve, N., Conkey, A. A. T., Macdonald, D. W. & Yamaguchi, N. Hedgehogs on the move: Testing the effects of land use change on home range size and movement patterns of free-ranging Ethiopian hedgehogs. PLoS One 12, e0180826 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Yamaguchi, N., Al-Hajri, A. & Al-Jabiri, H. Timing of breeding in free-ranging Ethiopian hedgehogs, Paraechinus aethiopicus, from Qatar. J. Arid Environ. 99, 1–4 (2013).ADS 
    Article 

    Google Scholar 
    Alagaili, A. N., Bennett, N. C., Mohammed, O. B. & Hart, D. W. The reproductive biology of the Ethiopian hedgehog, Paraechinus aethiopicus, from central Saudi Arabia: The role of rainfall and temperature. J. Arid Environ. 145, 1–9 (2017).ADS 
    Article 

    Google Scholar 
    Pettett, C. E. et al. Daily energy expenditure in the face of predation: Hedgehog energetics in rural landscapes. J. Exp. Biol. 220, 460–468 (2017).PubMed 
    Article 

    Google Scholar 
    Kraus, C., Eberle, M. & Kappeler, P. M. The costs of risky male behaviour: Sex differences in seasonal survival in a small sexually monomorphic primate. Proc. R. Soc. B Biol. Sci. 275, 1635–1644 (2008).Article 

    Google Scholar 
    Mzilikazi, N. & Lovegrove, B. G. Reproductive activity influences thermoregulation and torpor in pouched mice, Saccostomus campestris. J. Comp. Physiol. B 172, 7–16 (2002).PubMed 
    Article 

    Google Scholar 
    Richter, M. M., Barnes, B. M., O’reilly, K. M., Fenn, A. M. & Buck, C. L. The influence of androgens on hibernation phenology of free-living male arctic ground squirrels. Horm. Behav. 89, 92–97 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Haigh, A., Butler, F. & O’Riordan, R. M. Courtship behaviour of western hedgehogs (Erinaceus europaeus) in a rural landscape in Ireland and the first appearance of offspring. Lutra 55, 41–54 (2012).
    Google Scholar 
    Nicol, S. C., Morrow, G. E. & Harris, R. L. Energetics meets sexual conflict: The phenology of hibernation in Tasmanian echidnas. Funct. Ecol. 33, 2150–2160 (2019).Article 

    Google Scholar 
    Pettett, C. W., Macdonald, D., Al-Hajiri, A., Al-Jabiry, H. & Yamaguchi, N. Characteristics and demography of a free-ranging Ethiopian Hedgehog, Paraechinus aethiopicus, population in Qatar. Animals 10, 951 (2020).PubMed Central 
    Article 

    Google Scholar 
    Kenward, R. E. A Manual for Wildlife Radio Tagging (Academic Press, 2000).
    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2021).
    Google Scholar 
    Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).
    Google Scholar  More

  • in

    Hydrologic regime alteration and influence factors in the Jialing River of the Yangtze River, China

    Ge, J., Peng, W., Wei, H. W., Qu, X. & Singh, S. Quantitative assessment of flow regime alteration using a revised range of variability methods. Water 10(5), 597 (2018).Article 

    Google Scholar 
    Latrubesse, E. M. et al. Damming the rivers of the Amazon basin. Nature 546(7658), 363–369 (2017).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Meade, R. H. & Moody, J. A. Causes for the decline of suspended-sediment discharge in the Mississippi River system, 1940–2007. Hydrol. Process 24(1), 35–49 (2010).
    Google Scholar 
    Fathi, M. M., Awadallah, A. G., Abdelbaki, A. M. & Haggag, M. A new Budyko framework extension using time series SARIMAX model. J. Hydrol. 570(2019), 827–838 (2019).ADS 
    Article 

    Google Scholar 
    Wang, H., Liu, J. & Guo, W. The variation and attribution analysis of the runoff and sediment in the lower reach of the Yellow River during the past 60 years. Water Supply 21(6), 3193–3209 (2021).Article 

    Google Scholar 
    Guo, S. L., Guo, J., Hou, Y., Xiong, L. & Hong, X. Prediction of future runoff change based on Budyko hypothesis in Yangtze River basin. Adv. Water Sci. 26(02), 151–160 (2015).
    Google Scholar 
    Zhang, X., Dong, Z., Gupta, H., Wu, G. & Li, D. Impact of the three gorges dam on the hydrology and ecology of the Yangtze River. Water 590(8), 1–18 (2016).ADS 
    CAS 

    Google Scholar 
    Zhang, J., Zhang, M., Song, Y. & Lai, Y. Hydrological simulation of the Jialing River Basin using the MIKE SHE model in changing climate. J. Water Clim. Change 12(6), 1–20 (2021).
    Google Scholar 
    Richter, B. D., Baumgartner, J. V., Powell, J. & Braun, P. D. A method for assessing hydrologic alteration within ecosystems. Conserv. Biol. 10(4), 1163–1174 (1996).Article 

    Google Scholar 
    Richter, B. D., Baumgartner, J. V., Wigington, B. & Braun, D. How much water does a river need?. Freshw. Biol. 37(1), 231–249 (1997).Article 

    Google Scholar 
    Richter, B. D., Baumgartner, J. V., Braun, D. P. & Powell, J. A spatial assessment of hydrologic alteration within a river network. Regul. River Res. Manag. 14(4), 329–340 (1998).Article 

    Google Scholar 
    Guo, W., Xu, G., Shao, J., Bing, J. & Chen, X. Research on the middle and lower reaches of the Yangtze River and lake’s hydrological alterations based on RVA. In IOP Conference Series: Earth and Environmental Science Vol 153, No 6, 062047.1–062047.8 (2018).Guo, W., Li, Y., Wang, H. & Zha, H. Assessment of eco-hydrological regime of lower reaches of Three Gorges Reservoir based on IHA-RVA. Resour. Environ. Yangtze Basin 27(09), 2014–2021 (2018).
    Google Scholar 
    Zuo, Q. & Liang, S. Effects of dams on river flow regime based on IHA/RVA. Proc. Int. Assoc. Hydrol. Sci. 368(368), 275–276 (2015).
    Google Scholar 
    Mwedzi, T., Katiyo, L., Mugabe, F. T., Bere, T. & Kuoika, O. L. A spatial assessment of stream-flow characteristics and hydrologic alterations, post dam construction in the Manyame catchment, Zimbabwe. Water Sa 42(2), 194–202 (2016).CAS 
    Article 

    Google Scholar 
    Liu, J., Chen, J., Xu, J., Lin, Y. & Zhou, M. Attribution of runoff variation in the headwaters of the Yangtze River based on the Budyko hypothesis. Int. J. Environ. Res. Public Health 16(14), 2506.1-2506.15 (2019).
    Google Scholar 
    Yan, D. Using budyko-type equations for separating the impacts of climate and vegetation change on runoff in the source area of the yellow river. Water 12(12), 3418.1-3418.15 (2020).ADS 

    Google Scholar 
    Gunkel, A. & Lange, J. Water scarcity, data scarcity and the Budyko curve—An application in the Lower Jordan River Basin. J. Hydrol. Reg. Stud. 12(C), 136–149 (2017).Article 

    Google Scholar 
    Fathi, M. M., Awadallah, A. G., Abdelbaki, A. M. & Haggag, M. A new Budyko framework extension using time series SARIMAX model. J. Hydrol. 570, 827–838 (2019).ADS 
    Article 

    Google Scholar 
    Li, Y., Fan, J. & Liao, Y. Variation characteristics of streamflow and sediment in the Jialing river basin in the past 60 years. Mt. Res. 38(03), 339–348 (2020).
    Google Scholar 
    Liu, Y., Li, F. & Xu, X. Impacts of hydropower development on hydrological regime in mainstream of mid-lower Jialing River. Yangtze River 45(05), 10–15 (2014).
    Google Scholar 
    Zhou, Y. et al. Distinguishing the multiple controls on the decreased sediment flux in the Jialing River basin of the Yangtze River, Southwestern China. CATENA 193(C), 104593.1-104593.11 (2020).
    Google Scholar 
    Zeng, X. et al. Changes and relationships of climatic and hydrological droughts in the Jialing River Basin, China. PLoS ONE 10(11), e0141648 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Yan, M., Fang, G. H., Dai, L. H., Tan, Q. F. & Huang, X. F. Optimizing reservoir operation considering downstream ecological demands of water quantity and fluctuation based on IHA parameters. J. Hydrol. 4(2021), 126647 (2021).Article 

    Google Scholar 
    Wei, R., Liu, J., Zhang, T., Zeng, Q. & Dong, X. Attribution analysis of runoff variation in the upper-middle reaches of Yalong river. Resour. Environ. Yangtze Basin 29(07), 1643–1652 (2020).
    Google Scholar 
    Xie, J. H., Yu, J. H., Chem, H. S. & Hsu, P. C. Sources of subseasonal prediction skill for heatwaves over the Yangtze river basin revealed from three S2S models. Adv. Atmos. Sci. 37(12), 1435–1450 (2020).Article 

    Google Scholar 
    Guo, W., Li, Y., Wang, H. & Cha, H. Temporal variations and influencing factors of river runoff and sediment regimes in the Yangtze River, China. Desalin. Water Treat. 174(2020), 258–270 (2020).Article 

    Google Scholar 
    Tian, X. et al. Hydrologic alteration and possible underlying causes in the Wuding River, China. Sci. Total Environ. 693, 133556.1-133556.9 (2019).Article 
    CAS 

    Google Scholar 
    Tang, B., Wang, W. C. & Fan, X. Study on the influence of reservoir dispatch of the upper Yangtze river on the runoff control. E3S Web Conf. 283(18), 01030 (2021).
    Google Scholar 
    Liu, Y. et al. Characteristics and resource status of main commercial fish in the middle reaches of Jialing River, China. J. Appl. Environ. Biol. 27(04), 837–847 (2021).
    Google Scholar 
    Sun, Z., Zhang, M. & Chen, Y. Protection of the rare and endemic fish in the conservation area located in the upstream of the Yangtze River. Freshw. Fish. 44(06), 3–8 (2014).
    Google Scholar 
    Chen, Q. H. et al. Impacts of climate change and LULC change on runoff in the Jinsha River Basin. J. Geogr. Sci. 30(01), 85–102 (2020).Article 

    Google Scholar 
    Cui, L., Wang, Z. & Deng, L. Vegetation dynamics based on NDVI in Yangtze River Basin (China) during 1982–2015. IOP Conf. Ser. Materials Sci. Eng. 780(2020), 062049 (2020).Article 

    Google Scholar 
    Wang, Y., Wang, S., Wu, M. & Wang, S. Impacts of the land use and climate changes on the hydrological characteristics of Jialing River Basin. Res. Soil Water Conserv. 26(01), 135–142 (2019).
    Google Scholar 
    Wu, Y. L. & Pu, H. W. Y. The influence of hydropower station on sand content detection in Jialing River. Technol. Dev. Enterp. 38(9), 55–58 (2019).
    Google Scholar 
    Zhuo, Z., Qian, Z., Jiang, H., Wang, H. & Guo, W. Evaluation of hydrological regime in Xiangjiang basin on IHA-RVA method. China Rural Water Hydropower 8(2020), 188–192 (2020).
    Google Scholar 
    Chen, L. et al. Temporal characteristics detection and attribution analysis of hydrological time-series variation in the seagoing river of southern China under environmental change. Acta Geophys. 66(5), 1151–1170 (2018).ADS 
    Article 

    Google Scholar 
    Zhang, R., Liu, J., Mao, G. & Wang, L. Flow regime alterations of upper Heihe River based on improved RVA. Arid Zone Res. 38(01), 29–38 (2021).
    Google Scholar 
    Sun, Y. & Wang, X. Changes in runoff and driving force analysis in the key section of the Yellow River diversion project. J. Hydroecol. 41(06), 19–26 (2020).
    Google Scholar 
    Zhang, L., Dawes, W. R. & Walker, G. R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37(3), 701–708 (2001).ADS 
    Article 

    Google Scholar 
    Fu, B. Calculation of soil evaporation. Acta Meteor. Sin. 02(1981), 226–236 (1981).
    Google Scholar 
    Liu, J., Zhang, Q., Singh, V. P. & Shi, P. Contribution of multiple climatic variables and human activities to streamflow changes across China. J. Hydrol. 545(2016), 145–162 (2016).
    Google Scholar 
    Yang, D., Zhang, S. & Xu, X. Attribution analysis for runoff decline in Yellow River Basin during past fifty years based on Budyko hypothesis. Sci. Sinica 45(10), 1024–1034 (2015).
    Google Scholar 
    Schreiber, P. Ber die Beziehungen zwischen dem Niederschlag und der Wasserführung der Flüsse in Mitteleuropa. Meteorol. Z. 21, 441–452 (1904).Budyko, M. Evaporation under Natural Conditions (Gidrometeorizdat, Leningrad, Russia, 1948).Pike, J. The estimation of annual run-off from meteorological data in a tropical climate. J. Hydrol. 2, 116–123 (1964).Ol’dekop, E. On evaporation from the surface of river basins. Trans. Meteorol. Obs. 4, 200 (1911). More

  • in

    Rapid Eocene diversification of spiny plants in subtropical woodlands of central Tibet

    Reich, P. B. et al. The evolution of plant functional variation: traits, spectra, and strategies. Int. J. Plant Sci. 164, S143–S164 (2003).
    Google Scholar 
    Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).
    Google Scholar 
    Liu, X. J. & Ma, K. P. Plant functional traits concepts, applications and future directions. Sci. Sin. Vitae 45, 325–339 (2015).
    Google Scholar 
    Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).
    Google Scholar 
    Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl Acad. Sci. USA 112, 797–802 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barton, K. E. Tougher and thornier: general patterns in the induction of physical defence traits. Func. Ecol. 30, 181–187 (2016).
    Google Scholar 
    Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).PubMed 

    Google Scholar 
    Wright, S. J. et al. Functional traits and the growth–mortality trade-off in tropical trees. Ecology 91, 3664–3674 (2010).PubMed 

    Google Scholar 
    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ruiz-Jaen, M. C. & Potvin, C. Can we predict carbon stocks in tropical ecosystems from tree diversity? Comparing species and functional diversity in a plantation and a natural forest. New Phytol. 189, 978–987 (2011).PubMed 

    Google Scholar 
    Grubb, P. J. A positive distrust in simplicity-lessons from plant defences and from competition among plants and among animals. J. Ecol. 80, 585–610 (1992).
    Google Scholar 
    Hanley, M. E., Lamont, B. B., Fairbanks, M. M. & Rafferty, C. M. Plant structural traits and their role in anti-herbivore defence. Perspect. Plant Ecol. 8, 157–178 (2007).
    Google Scholar 
    Burns, K. C. Spinescence in the New Zealand flora: parallels with Australia. N. Z. J. Bot. 54, 273–289 (2016).
    Google Scholar 
    Goheen, J. R., Young, T. P., Keesing, F. & Palmer, T. M. Consequences of herbivory by native ungulates for the reproduction of a savanna tree. J. Ecol. 95, 129–138 (2007).
    Google Scholar 
    Goldel, B., Kissling, W. D. & Svenning, J.-C. Geographical variation and environmental correlates of functional trait distributions in palms (Arecaceae) across the New World. Bot. J. Linn. Soc. 179, 602–617 (2015).
    Google Scholar 
    Alves-Silva, E. & Del-Claro, K. Herbivory causes increases in leaf spinescence and fluctuating asymmetry as a mechanism of delayed induced resistance in a tropical savanna tree. Plant Ecol. Evol. 149, 73–80 (2016).
    Google Scholar 
    Cooper, S. M. & Ginnett, T. F. Spines protect plants against browsing by small climbing mammals. Oecologia 113, 219–221 (1998).ADS 
    PubMed 

    Google Scholar 
    Charles-Dominique, T. et al. Spiny plants, mammal browsers, and the origin of African savannas. Proc. Natl Acad. Sci. USA 113, E5572–E5579 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ratnam, J., Tomlinson, K. W., Rasquinha, D. N. & Sankaran, M. Savannahs of Asia: antiquity, biogeography, and an uncertain future. Philos. Trans. R. Soc. B. 371, 20150305 (2016).
    Google Scholar 
    Scholes, R. & Archer, S. Tree-grass interactions in savannas. Annu. Rev. Ecol. Syst. 28, 517–544 (1997).
    Google Scholar 
    Cerling, T. E. Development of grasslands and savannas in East Africa during the Neogene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 97, 241–247 (1992).
    Google Scholar 
    Brown, R. W. Additions to the flora of the Green River formation. U. S. Geol. Surv. Prof. Paper, U. S. Gov. Print. Off. 154-J, 279–292 (1929).Manchester, S. Oligocene fossil plants of the John Day Formation, Oregon. Or. Geol. 49, 115d–127d (1987).
    Google Scholar 
    Meyer, H. W. & Manchester, S. R. Oligocene Bridge Creek flora of the John Day Formation, Oregon (Univ. California Press, 1997).Lancucka-Srodoniowa, M. Tortonian flora from the “Gdow Bay” in the south of Poland. Acta Palaeobot. 7, 1–134 (1966).
    Google Scholar 
    Yuan, J. et al. Rapid drift of the Tethyan Himalaya terrane before two-stage India-Asia collision. Natl Sci. Rev. 8, nwaa173 (2021).PubMed 

    Google Scholar 
    Spicer, R. A. et al. Why the ‘Uplift of the Tibetan Plateau’is a myth. Natl Sci. Rev. 8, nwaa091 (2021).PubMed 

    Google Scholar 
    Spicer, R. A. Tibet, the Himalaya, Asian monsoons and biodiversity–In what ways are they related? Plant Divers. 39, 233–244 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    DeCelles, P. G., Kapp, P., Gehrels, G. E. & Ding, L. Paleocene-Eocene foreland basin evolution in the Himalaya of southern Tibet and Nepal: implications for the age of initial India-Asia collision. Tectonics 33, 824–849 (2014).ADS 

    Google Scholar 
    Royden, L. H., Burchfiel, B. C. & van der Hilst, R. D. The geological evolution of the Tibetan Plateau. Science 321, 1054–1058 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Deng, T., Wu, F. X., Zhou, Z. K. & Su, T. Tibetan Plateau: an evolutionary junction for the history of modern biodiversity. Sci. China Earth Sci. 63, 172–187 (2020).ADS 

    Google Scholar 
    Favre, A. et al. The role of the uplift of the Qinghai‐Tibetan Plateau for the evolution of Tibetan biotas. Biol. Rev. 90, 236–253 (2015).PubMed 

    Google Scholar 
    Su, T. et al. A Middle Eocene lowland humid subtropical “Shangri-La” ecosystem in central Tibet. Proc. Natl Acad. Sci. USA 117, 32989–32995 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Scientific Expedition Team to the Qinghai-Xizang Plateau. Vegetation of Xizang (Tibet) (Sci. Press, 1988).Liu. X. H. Paleoelevation History and Evolution of the Cenozoic Lunpola basin, Central Tibet. Doctoral thesis (Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 2018).Xiong, Z. Y. et al. The rise and demise of the Paleogene Central Tibetan Valley. Sci. Adv. 8, eabj0944 (2022).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Reichgelt, T., West, C. K. & Greenwood, D. R. The relation between global palm distribution and climate. Sci. Rep. 8, 4721 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Farnsworth, A. et al. Paleoclimate model-derived thermal lapse rates: towards increasing precision in paleoaltimetry studies. Earth Planet. Sci. Lett. 564, 116903 (2021).CAS 

    Google Scholar 
    Spicer, R. A. et al. Why do foliar physiognomic climate estimates sometimes differ from those observed? Insights from taphonomic information loss and a CLAMP case study from the Ganges Delta. Palaeogeogr. Palaeoclimatol. Palaeoecol. 302, 381–395 (2011).
    Google Scholar 
    Walter, H. Vegetation of the Earth and Ecological Systems of the Geo-biosphere (Springer Berlin Heidelb., 1973).Burley, J. Encyclopedia of Forest Sciences (Acad. Press, 2004).Deng, T. et al. A mammalian fossil from the Dingqing Formation in the Lunpola Basin, northern Tibet, and its relevance to age and paleo-altimetry. Sci. Bull. 57, 261–269 (2012).CAS 

    Google Scholar 
    Ma, P. F. et al. Late Oligocene-early Miocene evolution of the Lunpola Basin, central Tibetan Plateau, evidences from successive lacustrine records. Gondwana Res. 48, 224–236 (2017).ADS 

    Google Scholar 
    Hempson, G. P., Archibald, S. & Bond, W. J. A continent-wide assessment of the form and intensity of large mammal herbivory in Africa. Science 350, 1056–1061 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Spicer, R. A. The formation and interpretation of plant fossil assemblages. Adv. Bot. Res. 16, 95–191 (1989).
    Google Scholar 
    Gibson, D. J. Grasses and Grassland Ecology (Oxford Univ. Press, 2009).Eltringham, S. K. The Hippos: Natural History and Conservation (Princeton Univ. Press, 1999).Jiang, H. et al. Oligocene Koelreuteria (Sapindaceae) from the Lunpola Basin in central Tibet and its implication for early diversification of the genus. J. Asian Earth Sci. 175, 99–108 (2019).ADS 

    Google Scholar 
    Liu, J. et al. Biotic interchange through lowlands of Tibetan Plateau suture zones during Paleogene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 524, 33–40 (2019).
    Google Scholar 
    Jia, L. B. et al. First fossil record of Cedrelospermum (Ulmaceae) from the Qinghai-Tibetan Plateau: implications for morphological evolution and biogeography. J. Syst. Evol. 57, 94–104 (2019).
    Google Scholar 
    Su, T. et al. No high Tibetan Plateau until the Neogene. Sci. Adv. 5, eaav2189 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, Y. L., Li, B. Y. & Zheng, D. A discussion on the boundary and area of the Tibetan Plateau in China. Geol. Res. 21, 1–8 (2002).
    Google Scholar 
    Yao, T. D. et al. From Tibetan Plateau to Third Pole and Pan-Third Pole. Bull. Chin. Acad. Sci. 32, 924–931 (2017).
    Google Scholar 
    Spicer, R. A., Farnsworth, A. & Su, T. Cenozoic topography, monsoons and biodiversity conservation within the Tibetan Region: an evolving story. Plant Divers. 42, 229–254 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Liu, X. H., Xu, Q. & Ding, L. Differential surface uplift: Cenozoic paleoelevation history of the Tibetan Plateau. Sci. China Earth Sci. 59, 2105–2120 (2016).ADS 
    CAS 

    Google Scholar 
    Ding, L., Li, Z. Y. & Song, P. P. Core fragments of Tibetan Plateau from Gondwanaland united in Northern Hemisphere. Bull. Chin. Acad. Sci. 32, 945–950 (2017).
    Google Scholar 
    Deng, T. & Ding, L. Paleoaltimetry reconstructions of the Tibetan Plateau: progress and contradictions. Natl Sci. Rev. 2, 417–437 (2015).CAS 

    Google Scholar 
    Li, S. F. et al. Orographic evolution of northern Tibet shaped vegetation and plant diversity in eastern Asia. Sci. Adv. 7, eabc7741 (2021).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ding, L. et al. The Andean-type Gangdese Mountains: Paleoelevation record from the Paleocene–Eocene Linzhou Basin. Earth Planet. Sci. Lett. 392, 250–264 (2014).ADS 
    CAS 

    Google Scholar 
    Deng, T. et al. Review: implications of vertebrate fossils for paleo-elevations of the Tibetan Plateau. Glob. Planet. Change 174, 58–69 (2019).ADS 

    Google Scholar 
    Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hopkins, W. G. Introduction to Plant Physiology (John Wiley & Sons, 1999).Sun, J. M., Liu, W. G., Liu, Z. H. & Fu, B. H. Effects of the uplift of the Tibetan Plateau and retreat of Neotethys ocean on the stepwise aridification of mid-latitude Asian interior. Bull. Chin. Acad. Sci. 32, 951–958 (2017).
    Google Scholar 
    Zong, G. F. Cenezoic Mammals and Environment of Hengduan Mountains Region (China Ocean Press, 1996).Deng, T. et al. An Oligocene giant rhino provides insights into Paraceratherium evolution. Commun. Biol. 4, 639 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Young, T. P., Stanton, M. L. & Christian, C. E. Effects of natural and simulated herbivory on spine lengths of Acacia drepanolobium in Kenya. Oikos 101, 171–179 (2003).
    Google Scholar 
    Karban, R. & Myers, J. H. Induced plant responses to herbivory. Annu. Rev. Ecol. Syst. 20, 331–348 (1989).
    Google Scholar 
    Huntly, N. Herbivores and the dynamics of communities and ecosystems. Annu. Rev. Ecol. Syst. 22, 477–503 (1991).
    Google Scholar 
    Asner, G. P. et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proc. Natl Acad. Sci. USA 106, 4947–4952 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sankaran, M., Augustine, D. J. & Ratnam, J. Native ungulates of diverse body sizes collectively regulate long‐term woody plant demography and structure of a semi‐arid savanna. J. Ecol. 101, 1389–1399 (2013).
    Google Scholar 
    Staver, A. C. & Bond, W. J. Is there a ‘browse trap’? Dynamics of herbivore impacts on trees and grasses in an African savanna. J. Ecol. 102, 595–602 (2014).
    Google Scholar 
    Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Spicer, R. A. et al. The topographic evolution of the Tibetan Region as revealed by palaeontology. Palaeobio. Palaeoenv. 101, 213–243 (2021).
    Google Scholar 
    Rowley, D. B. & Currie, B. S. Palaeo-altimetry of the late Eocene to Miocene Lunpola basin, central Tibet. Nature 439, 677–681 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Sun, J. M. et al. Palynological evidence for the latest Oligocene-early Miocene paleoelevation estimate in the Lunpola Basin, central Tibet. Palaeogeogr. Palaeoclimatol. Palaeoecol. 399, 21–30 (2014).
    Google Scholar 
    DeCelles, P. G., Kapp, P., Ding, L. & Gehrels, G. E. Late Cretaceous to middle Tertiary basin evolution in the central Tibetan Plateau: Changing environments in response to tectonic partitioning, aridification, and regional elevation gain. Geol. Soc. Am. Bull. 119, 654–680 (2007).ADS 

    Google Scholar 
    Tang, H. et al. Extinct genus Lagokarpos reveals a biogeographic connection between Tibet and other regions in the Northern Hemisphere during the Paleogene. J. Syst. Evol. 57, 670–677 (2019).
    Google Scholar 
    Wang, T. X. et al. Fossil fruits of Illigera (Hernandiaceae) from the Eocene of central Tibetan Plateau. J. Syst. Evol. 59, 1276–1286 (2021).
    Google Scholar 
    Del Rio, C. et al. Asclepiadospermum gen. nov., the earliest fossil record of Asclepiadoideae (Apocynaceae) from the early Eocene of central Qinghai-Tibetan Plateau, and its biogeographic implications. Am. J. Bot. 107, 126–138 (2020).PubMed 

    Google Scholar 
    Xu, Z. Y. The Tertiary and its petroleum potential in the Lunpola Basin, Tibet. Oil Gas. Geol. 1, 153–158 (1980).
    Google Scholar 
    Zhang, K. X. et al. Paleogene-Neogene stratigraphic realm and sedimentary sequence of the Qinghai-Tibet Plateau and their response to uplift of the plateau. Sci. China Earth Sci. 53, 1271–1294 (2010).ADS 

    Google Scholar 
    Wu, Y. F. & Chen, Y. Y. Fossil cyprinid fishes from the late Tertiary of north Xizang, China. Vertebrata Palasiat. 18, 15–20 (1980).
    Google Scholar 
    Wu, F. X., Miao, D. S., Chang, M. M., Shi, G. L. & Wang, N. Fossil climbing perch and associated plant megafossils indicate a warm and wet central Tibet during the late Oligocene. Sci. Rep. 7, 878 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cai, C. Y., Huang, D. Y., Wu, F. X., Zhao, M. & Wang, N. Tertiary water striders (Hemiptera, Gerromorpha, Gerridae) from the central Tibetan Plateau and their palaeobiogeographic implications. J. Asian Earth Sci. 175, 121–127 (2017).ADS 

    Google Scholar 
    Low, S. L. et al. Oligocene Limnobiophyllum (Araceae) from the central Tibetan Plateau and its evolutionary and palaeoenvironmental implications. J. Syst. Palaeontol. 18, 415–431 (2020).
    Google Scholar 
    Bell, A. D. & Bryan, A. Plant Form: An Illustrated Guide to Flowering Plant Morphology (Timber Press, 2008).Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Google Scholar 
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 35, 526–528 (2019).CAS 
    PubMed 

    Google Scholar 
    Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics. 24, 129–131 (2008).CAS 
    PubMed 

    Google Scholar 
    Maddison, W. P. Confounding asymmetries in evolutionary diversification and character change. Evolution 60, 1743–1746 (2006).PubMed 

    Google Scholar 
    Forest, C. E., Molnar, P. & Emanuel, K. A. Palaeoaltimetry from energy conservation principles. Nature 374, 347–350 (1995).ADS 
    CAS 

    Google Scholar 
    Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@ Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).ADS 
    CAS 

    Google Scholar 
    Gough, D. O. Solar interior structure and luminosity variations. Sol. Phys. 74, 21–34 (1981).ADS 
    CAS 

    Google Scholar 
    Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cox, P. M. Description of the “TRIFFID” Dynamic Global Vegetation Model. 1–16 (Met Office Hadley Centre, 2001).Cox, P., Huntingford, C. & Harding, R. A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J. Hydrol. 212, 79–94 (1998).ADS 

    Google Scholar 
    McInerney, F. A., Strömberg, C. A. E. & White, J. W. C. The Neogene transition from C3 to C4 grasslands in North America stable carbon isotope ratios of fossil phytoliths. Paleobiology 37, 23–49 (2011).
    Google Scholar 
    Lu, H. Y. et al. Phytoliths as quantitative indicators for the reconstruction of past environmental conditions in China II: palaeoenvironmental reconstruction in the Loess Plateau. Quat. Sci. Rev. 25, 945–959 (2006).ADS 

    Google Scholar  More

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    Houseflies harbor less diverse microbiota under laboratory conditions but maintain a consistent set of host-associated bacteria

    The copy numbers for 16S and ITS1 rRNA, and the sequencing depth for all samples are presented in Supplementary File 3 (qPCR data, Sequencing Rarefaction Curves). An average of 14,265.25 reads per housefly sample for the V4 16SrRNA and 16,149.4 reads per housefly sample for the ITS1 were retained after quality filtering. After quality filtering of the egg-laying substrate samples, an average of 10,371.75 reads were retained per sample for the V4 16SrRNA, and an average of 25,479.75 reads were retained per sample for the ITS1 region. The extracted DNA from newly emerged adult houseflies of the Spanish laboratory strain (12 samples in total, newly emerged adults, three replicates from four generations, strain SP100) returned a low copy number for the fungal ITS1 (qPCR data, Supplementary File 3) and a low number of acquired sequencing reads; they were therefore omitted from any further analysis of the fungal microbiota. In addition, the mitochondrial COI phylogeny showed that the Dutch wild-caught strain and the Dutch laboratory strain, which were sampled from the same locality at different times, are in close proximity and form a separate clade from the Spanish lab strain phylotypes (Supplementary File 2).The housefly microbiota alpha-diversity is determined by sampling environmentAbsolute richness (number of ASVs), Shannon index, and Phylogenetic diversity for all housefly strains and developmental stages are shown in Fig. 1. The highest bacterial alpha diversity was observed for the wild-caught housefly population GK0. Strain was an important factor for separating Shannon biodiversity levels both for newly emerged (F = 4.37, P  More