More stories

  • in

    Weakened resilience of benthic microbial communities in the face of climate change

    Yao C-L, Somero GN. The impact of ocean warming on marine organisms. Chin Sci Bull. 2014;59:468–79.
    Google Scholar 
    Frölicher TL, Fischer EM, Gruber N. Marine heatwaves under global warming. Nature. 2018;560:360–4.PubMed 

    Google Scholar 
    Bindoff NL, Cheung WWL, Kairo JG, Arístegui J, Guinder VA, Hallberg R, et al. Changing ocean, marine ecosystems, and dependent communities. Switzerland: Intergovernmental Panel on Climate Change (IPCC); 2019.Breitburg D, Levin LA, Oschlies A, Grégoire M, Chavez FP, Conley DJ, et al. Declining oxygen in the global ocean and coastal waters. Science. 2018;359:eaam7240.PubMed 

    Google Scholar 
    Collins M, Knutti R, Arblaster J, Dufresne J-L, Fichefet T, Friedlingstein P, et al. Long-term climate change: projections, commitments and irreversibility. In: Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. United Kingdom and New York, NY, USA: Cambridge; 2013.Mackenzie BR, Schiedek D. Daily ocean monitoring since the 1860s shows record warming of northern European seas. Glob Change Biol. 2007;13:1335–47.
    Google Scholar 
    Gruner DS, Bracken MES, Berger SA, Eriksson BK, Gamfeldt L, Matthiessen B, et al. Effects of experimental warming on biodiversity depend on ecosystem type and local species composition. Oikos. 2017;126:8–17.
    Google Scholar 
    Forsman A, Berggren H, Åström M, Larsson P. To what extent can existing research help project climate change impacts on biodiversity in aquatic environments? A review of methodological approaches. Multidiscipl Digital Publishing Inst. 2016;4:75.
    Google Scholar 
    HELCOM. Eutrophication in the Baltic Sea—An integrated thematic assessment of the effects of nutrient enrichment and eutrophication in the Baltic Sea region. Baltic Sea Environ Proc. 2009. Report No.: 115B.Carstensen J, Andersen JH, Gustafsson BG, Conley DJ. Deoxygenation of the Baltic Sea during the last century. Proc Natl Acad Sci USA. 2014;111:5628–33.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Broman E, Sjostedt J, Pinhassi J, Dopson M. Shifts in coastal sediment oxygenation cause pronounced changes in microbial community composition and associated metabolism. Microbiome. 2017;5:96.PubMed 
    PubMed Central 

    Google Scholar 
    Schmidtko S, Stramma L, Visbeck M. Decline in global oceanic oxygen content during the past five decades. Nature. 2017;542:335–9.CAS 
    PubMed 

    Google Scholar 
    Brewer PG, Peltzer ET. Depth perception: the need to report ocean biogeochemical rates as functions of temperature, not depth. Philos Trans R Soc Mathemat Phys Eng. 2017;375:20160319.
    Google Scholar 
    Laruelle GG, Cai W-J, Hu X, Gruber N, Mackenzie FT, Regnier P. Continental shelves as a variable but increasing global sink for atmospheric carbon dioxide. Nat Commun. 2018;9:454.PubMed 
    PubMed Central 

    Google Scholar 
    Gilbert D, Rabalais NN, Díaz RJ, Zhang J. Evidence for greater oxygen decline rates in the coastal ocean than in the open ocean. Biogeosciences. 2010;7:2283–96.CAS 

    Google Scholar 
    Kauppi L, Norkko J, Ikonen J, Norkko A. Seasonal variability in ecosystem functions: quantifying the contribution of invasive species to nutrient cycling in coastal ecosystems. Marine Ecol Progr Series. 2017;572:193–207.CAS 

    Google Scholar 
    Lu X, Zhou F, Chen F, Lao Q, Zhu Q, Meng Y, et al. Spatial and seasonal variations of sedimentary organic matter in a subtropical bay: implication for human interventions. Int J Environ Res Public Health. 2020;17:1362.CAS 
    PubMed Central 

    Google Scholar 
    Turner JT. Zooplankton fecal pellets, marine snow, phytodetritus and the ocean’s biological pump. Progr Oceanograph. 2015;130:205–48.
    Google Scholar 
    Gupta A, Gupta R, Singh RL. Microbes and environment. In: Singh R (eds) Principles and Applications of Environmental Biotechnology for a Sustainable Future. Applied Environmental Science and Engineering for a Sustainable Future. Springer, Singapore; 2017:43–84.American Society for Microbiology. Microbes and Climate Change: Report on an American Academy of Microbiology and American Geophysical Union Colloquium held in Washington, DC, in March 2016. Washington (DC): American Society for Microbiology; 2017.Sarmento H, Montoya JM, Vazquez-Dominguez E, Vaque D, Gasol JM. Warming effects on marine microbial food web processes: how far can we go when it comes to predictions? Philos Trans R Soc B Biol Sci. 2010;365:2137–49.
    Google Scholar 
    IPCC. Climate Change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press (In Press); 2021.Moberg A, Humborg C. Second assessment of climate change for the Baltic Sea basin. Second assessment of climate change for the Baltic Sea basin. Berlin Heidelberg: Springer; 2008.
    Google Scholar 
    Humborg C, Geibel MC, Sun X, McCrackin M, Mörth C-M, Stranne C, et al. High emissions of carbon dioxide and methane from the coastal Baltic Sea at the end of a summer heat wave. Front Marine Sci. 2019;6:493.
    Google Scholar 
    Smith TP, Thomas TJH, García-Carreras B, Sal S, Yvon-Durocher G, Bell T, et al. Community-level respiration of prokaryotic microbes may rise with global warming. Nat Commun. 2019;10:5124.PubMed 
    PubMed Central 

    Google Scholar 
    Broman E, Li L, Fridlund J, Svensson F, Legrand C, Dopson M. Spring and late summer phytoplankton biomass impact on the coastal sediment microbial community structure. Microbial Ecol. 2018;77:288–303.
    Google Scholar 
    Gao Y, Cornwell JC, Stoecker DK, Owens MS. Influence of cyanobacteria blooms on sediment biogeochemistry and nutrient fluxes. Limnol Oceanograph. 2014;59:959–71.CAS 

    Google Scholar 
    Sawicka JE, Brüchert V. Annual variability and regulation of methane and sulfate fluxes in Baltic Sea estuarine sediments. Biogeosciences. 2017;14:325–39.CAS 

    Google Scholar 
    Berner RA. A new geochemical classification of sedimentary environments. J Sediment Res. 1981;51:359–65.CAS 

    Google Scholar 
    Nealson KH. Sediment bacteria: who’s there, what are they doing, and what’s new? Ann Rev Earth Planet Sci. 1997;25:403–34.CAS 

    Google Scholar 
    EPA. Quality criteria for water. Washington D.C., USA: Office of Water Regulations and Standards; 1986.
    Google Scholar 
    Tamme R, Hiiesalu I, Laanisto L, Szava-Kovats R, Pärtel M. Environmental heterogeneity, species diversity and co-existence at different spatial scales. J Veget Sci. 2010;21:796–801.
    Google Scholar 
    Klier J, Dellwig O, Leipe T, Jürgens K, Herlemann DPR. Benthic bacterial community composition in the oligohaline-marine transition of surface sediments in the Baltic Sea based on rRNA analysis. Front Microbiol. 2018;9:236.PubMed 
    PubMed Central 

    Google Scholar 
    Broman E, Sachpazidou V, Pinhassi J, Dopson M. Oxygenation of hypoxic coastal Baltic Sea sediments impacts on chemistry, microbial community composition, and metabolism. Front Microbiol. 2017;8:2453.PubMed 
    PubMed Central 

    Google Scholar 
    Orlygsson J, Kristjansson JK. The family Hydrogenophilaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The Prokaryotes: Alphaproteobacteria and Betaproteobacteria. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 859–68.Liu Z, Frigaard NU, Vogl K, Iino T, Ohkuma M, Overmann J, et al. Complete genome of Ignavibacterium album, a metabolically versatile, flagellated, facultative anaerobe from the phylum Chlorobi. Front Microbiol. 2012;3:185.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Watanabe M, Kojima H, Fukui M. Desulfoplanes formicivorans gen. nov., sp. nov., a novel sulfate-reducing bacterium isolated from a blackish meromictic lake, and emended description of the family Desulfomicrobiaceae. Int J Syst Evol Microbiol. 2015;65:1902–7.CAS 
    PubMed 

    Google Scholar 
    Galushko A, Desulfocapsaceae JK. Bergey’s Manual of Systematics of Archaea and Bacteria. Hoboken, New Jersey: Wiley; 2015. p. 1–6.
    Google Scholar 
    Dyksma S, Bischof K, Fuchs BM, Hoffmann K, Meier D, Meyerdierks A, et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 2016;10:1939–53.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ye Q, Wu Y, Zhu Z, Wang X, Li Z, Zhang J. Bacterial diversity in the surface sediments of the hypoxic zone near the Changjiang Estuary and in the east China Sea. Microbiologyopen. 2016;5:323–39.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fahrbach M, Kuever J, Remesch M, Huber BE, Kampfer P, Dott W, et al. Steroidobacter denitrificans gen. nov., sp. nov., a steroidal hormone-degrading gammaproteobacterium. Int J Syst Evol Microbiol. 2008;58:2215–23.CAS 
    PubMed 

    Google Scholar 
    Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front Microbiol. 2017;8:682.PubMed 
    PubMed Central 

    Google Scholar 
    Reyes C, Schneider D, Thürmer A, Kulkarni A, Lipka M, Sztejrenszus SY, et al. Potentially active iron, sulfur, and sulfate reducing bacteria in Skagerrak and Bothnian bay sediments. Geomicrobiol J. 2017;34:840–50.CAS 

    Google Scholar 
    Lovley DR, Roden EE, Phillips EJP, Woodward JC. Enzymatic iron and uranium reduction by sulfate-reducing bacteria. Marine Geol. 1993;113:41–53.CAS 

    Google Scholar 
    Funkey CP, Conley DJ, Reuss NS, Humborg C, Jilbert T, Slomp CP. Hypoxia sustains cyanobacteria blooms in the Baltic sea. Environ Sci Technol. 2014;48:2598–602.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boden R, Hutt LP, Rae AW. Reclassification of Thiobacillus aquaesulis (Wood & Kelly, 1995) as Annwoodia aquaesulis gen. nov., comb. nov., transfer of Thiobacillus (Beijerinck, 1904) from the Hydrogenophilales to the Nitrosomonadales, proposal of Hydrogenophilalia class. nov. within the ‘Proteobacteria’, and four new families within the orders Nitrosomonadales and Rhodocyclales. Int J Syst Evol Microbiol. 2017;67:1191–205.CAS 
    PubMed 

    Google Scholar 
    Howarth R, Unz RF, Seviour EM, Seviour RJ, Blackall LL, Pickup RW, et al. Phylogenetic relationships of filamentous sulfur bacteria (Thiothrix spp. and Eikelboom type 021N bacteria) isolated from waste water treatment plants and description of Thiothrix eikelboomii sp. nov., Thiothrix unzii sp. nov., Thiothrix fructosivorans sp. nov. and Thiothrix defluvii sp. nov. Int J Syst Evol Microbiol. 1999;49:1817–27.CAS 

    Google Scholar 
    Imhoff JF. The family Chromatiaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The Prokaryotes: Gammaproteobacteria. Berlin, Heidelberg: Springer Berlin Heidelberg; 2014. p. 151–78.Bižić M, Klintzsch T, Ionescu D, Hindiyeh MY, Günthel M, Muro-Pastor AM, et al. Aquatic and terrestrial cyanobacteria produce methane. Sci Adv. 2020;6:eaax5343.PubMed 
    PubMed Central 

    Google Scholar 
    Rana K, Rana N, Singh B. Chapter 10 – Applications of sulfur oxidizing bacteria. In: Salwan R, Sharma V, editors. Physiological and Biotechnological Aspects of Extremophiles. London, UK: Academic Press; 2020. p. 131–6.
    Google Scholar 
    Zhuang W-Q, Yi S, Bill M, Brisson VL, Feng X, Men Y, et al. Incomplete Wood-Ljungdahl pathway facilitates one-carbon metabolism in organohalide-respiring Dehalococcoides mccartyi. Proc Natl Acad Sci USA. 2014;111:6419–24.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Roncarati D, Scarlato V. Regulation of heat-shock genes in bacteria: from signal sensing to gene expression output. FEMS Microbiol Rev. 2017;41:549–74.CAS 
    PubMed 

    Google Scholar 
    Nagar SD, Aggarwal B, Joon S, Bhatnagar R, Bhatnagar S. A network biology approach to decipher stress response in bacteria using Escherichia coli as a model. OMICS. 2016;20:310–24.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jonas K, Liu J, Chien P, Laub MT. Proteotoxic stress induces a cell-cycle arrest by stimulating lon to degrade the replication initiator DnaA. Cell. 2013;154:623–36.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Miss P. Oskarshamn power plant and Clab—Annual report over the radioecological environmental control under 2020. Reg.Nr.2021-02902. Made public 2021-03-21 (In Swedish). Oskarshamn, Sweden; 2021.Lindh MV, Figueroa D, Sjostedt J, Baltar F, Lundin D, Andersson A, et al. Transplant experiments uncover Baltic Sea basin-specific responses in bacterioplankton community composition and metabolic activities. Front Microbiol. 2015;6:223.PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. Vienna, Austria: Foundation for Statistical Computing; 2018.
    Google Scholar  More

  • in

    Field trials reveal the complexities of deploying and evaluating the impacts of yeast-baited ovitraps on Aedes mosquito densities in Trinidad, West Indies

    Bhatt, S. et al. The global distribution and burden of dengue. Nature 496, 504–509 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gratz, N. G. Critical review of the vector status of Aedes albopictus. Med. Vet. Entomol. 18, 215–227 (2004).CAS 
    PubMed 

    Google Scholar 
    Weaver, S. C., Charlier, C., Nasilakis, N. & Lecuit, M. Zika, chikungunya, and other emerging vector-borne viral diseases. Annu. Rev. Med. 69, 1–14 (2018).
    Google Scholar 
    Wilder-Smith, A. et al. Epidemic arboviral diseases: priorities for research and public health. Lancet Infect. Dis. 17, e101–e106 (2017).PubMed 

    Google Scholar 
    Felicetti, T., Manfroni, G., Cecchetti, V. & Cannalire, R. Broad-spectrum flavivirus inhibitors: a medicinal chemistry point of view. Chem. Med. Chem. 15, 2391–2419 (2020).CAS 
    PubMed 

    Google Scholar 
    da Silveira, L. T. C., Bernardo, T. & Santos, M. Systemic review of dengue vaccine efficacy. BMC Inf. Dis. 19, 750 (2019).
    Google Scholar 
    Katzelnich, L. C. et al. Zika virus infection enhances future risk of severe dengue disease. Science 369, 1123–1128 (2020).ADS 

    Google Scholar 
    Rezza, G. & Weaver, S. C. Chikungunya as a paradigm for emerging viral diseases: evaluating disease impact and hurdles to vaccine development. PLoS Negl. Trop. Dis. 13, e0006919 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Reiter, P. & Gubler, D. J. Surveillance and control of urban dengue vectors. In Dengue and dengue hemorrhagic fever (eds Gubler, D. J. & Kuno, G.) 425–462 (CAB International, 1997).
    Google Scholar 
    Moyes, C. L. et al. Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS Negl. Trop. Dis. 11, e0005625 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Bowman, L. R., Donegan, S. & McCall, P. J. Is dengue vector control deficient in effectiveness or evidence?: systematic review and meta-analysis. PLoS Negl. Trop. Dis. 10, e0004551 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Erlanger, T. E., Keiser, J. & Utzinger, J. Effect of dengue vector control interventions on entomological parameters in developing countries: a systematic review and meta-analysis. Med. Vet. Entomol. 22, 203–221 (2008).CAS 
    PubMed 

    Google Scholar 
    Banerjee, S., Aditya, G. & Saha, G. K. Household disposables as breeding habitats of dengue vectors: linking wastes and public health. Waste Manag. 33, 233–239 (2013).PubMed 

    Google Scholar 
    Chadee, D. D., Doon, R. & Severson, D. W. Surveillance of dengue fever cases using a novel Aedes aegypti population sampling method in Trinidad, West Indies: the cardinal points approach. Acta Trop. 104, 1–7 (2007).PubMed 

    Google Scholar 
    Barrera, R., Acevedo, V. & Amador, M. Role of abandoned and vacant houses on Aedes aegypti productivity. J. Med. Entomol. 104, 145–150 (2020).
    Google Scholar 
    Chadee, D. D. & Rahaman, A. Use of water drums by humans and Aedes aegypti in Trinidad. J. Vector Ecol. 25, 28–35 (2000).CAS 
    PubMed 

    Google Scholar 
    Padmanabha, H., Soto, E., Mosquera, M., Lord, C. C. & Lounibos, L. P. Ecological links between water storage behaviors and Aedes aegypti production: implications for dengue vector control in variable climates. EcoHealth 7, 78–90 (2010).CAS 
    PubMed 

    Google Scholar 
    Colton, Y. M., Chadee, D. D. & Severson, D. W. Natural skip oviposition of the mosquito Aedes aegypti indicated by codominant genetic markers. Med. Vet. Entomol. 17, 195–204 (2003).CAS 
    PubMed 

    Google Scholar 
    Davis, T. J., Kaufman, P. E., Hogsette, J. A. & Kline, D. I. The effects of larval habitat quality on Aedes albopictus skip oviposition. J. Am. Mosq. Control Assoc. 31, 321–328 (2015).PubMed 

    Google Scholar 
    David, M. R., Lourenco-de-Oliveira, R. & de Freitas, R. M. Container productivity, daily survival rates and dispersal of Aedes aegypti mosquitoes in a high income dengue epidemic neighbourhood of Rio de Janeiro: presumed influence of differential urban structure on mosquito biology. Mem. Inst. Oswaldo Cruz 104, 927–932 (2009).PubMed 

    Google Scholar 
    Focks, D. A. & Chadee, D. D. Pupal survey: an epidemiologically significant surveillance method for Aedes aegypti: an example using data from Trinidad. Am. J. Trop. Med. Hyg. 56, 159–167 (1997).CAS 
    PubMed 

    Google Scholar 
    Morrison, A. C. et al. Temporal and geographic patterns of Aedes aegypti (Diptera: Culicidae) production in Iquitos, Peru. J. Med. Entomol. 41, 1123–1142 (2004).PubMed 

    Google Scholar 
    Chadee, D. D. Oviposition strategies adopted by gravid Aedes aegypti (L.) (Diptera: Culicidae) as detected by ovitraps in Trinidad, West Indies (2002–2006). Acta Trop. 111, 279–283 (2009).CAS 
    PubMed 

    Google Scholar 
    Chadee, D. D. Seasonal incidence and horizontal distribution patterns of oviposition by Aedes aegypti in an urban environment in Trinidad, West Indies. J. Am. Mosq. Control Asso. 8, 281–284 (1992).CAS 

    Google Scholar 
    Fay, R. W. & Eliason, D. A. A preferred oviposition site as a surveillance method for Aedes aegypti. Mosq. News 26, 531–535 (1966).
    Google Scholar 
    Johnson, B. J., Ritchie, S. A. & Fonseca, D. M. The state of the art of lethal oviposition trap-based mass interventions for arboviral control. Insects 8, 5 (2017).PubMed Central 

    Google Scholar 
    Eiras, A. E., Buhagiar, T. S. & Ritchie, S. A. Development of the gravid Aedes trap for the capture of adult female container-exploiting mosquitoes (Diptera: Culicidae). J. Med. Entomol. 51, 200–209 (2014).PubMed 

    Google Scholar 
    Mackay, A. J., Amador, M. & Barrera, R. An improvied autocidal gravid ovitrap for the control and surveillance of Aedes aegypti. Parasit. Vectors 6, 225 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Olson, K. E. & Blair, C. D. Arbovirus-mosquito interactions: RNAi pathway. Curr. Opin. Virol. 15, 119–126 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hapairai, L. K. et al. Lure-and-kill yeast interfering RNA larvicides targeting neural genes in the human disease vector mosquito Aedes aegypti. Sci. Rep. 7, 13223 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mysore, K. et al. Yeast interfering RNA larvicides targeting neural genes induce high rates of Anopheles larval mortality. Malaria J. 16, 461 (2017).
    Google Scholar 
    Mysore, K. et al. Characterization of a broad-based mosquito yeast interfering RNA larvicide with a conserved target site in mosquito semaphorin-1a genes. Parasit. Vectors 12, 256 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Mysore, K. et al. Characterization of a yeast interfering RNA larvicide with a target site conserved in the synaptotagmin gene of multiple disease vector mosquitoes. PLoS Negl. Trop. Dis 13, e0007422 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hapairai, L. K. et al. Evaluation of large volume yeast interfering RNA lure-and-kill ovitraps for attraction and control of Aedes mosquitoes. Med. Vet. Entomol. 35, 361–370 (2021).CAS 
    PubMed 

    Google Scholar 
    Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
    Google Scholar 
    Braks, M. A. H., Honorio, N. A., Lourenco-de-Oliveira, R., Juliano, S. A. & Lounibos, L. P. Convergent habitat segregation of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in southeastern Brazil and Florida. J. Med. Entomol. 40, 785–794 (2003).PubMed 

    Google Scholar 
    Kumari, R., Kumar, K. & Chauhan, L. S. First dengue virus detection in Aedes albopictus from Delhi, India: Its breeding ecology and role in dengue transmission. Trop. Med. Int. Health 16, 949–954 (2012).
    Google Scholar 
    Apostol, B. L., Black, W. C. IV., Reiter, P. & Miller, B. R. Use of randomly amplified polymorphic DNA amplified by polymerase chain reaction markers to estimate the number of Aedes aegypti families at oviposition sites in San Juan, Puerto Rico. Am. J. Trop. Med. Hyg. 51, 89–97 (1994).CAS 
    PubMed 

    Google Scholar 
    Corbet, P. S. & Chadee, D. D. An improved method for detecting substrate preferences shown by mosquitoes that exhibit ‘skip oviposition’. Physiol. Entomol. 18, 114–118 (1993).
    Google Scholar 
    Reinbold-Wasson, D. D. & Reiskind, M. H. Comparative skip-oviposition behavior among container breeding Aedes spp. mosquitoes (Diptera: Culicidae). J. Med. Entomol. https://doi.org/10.1093/jme/tjab084 (2021).Article 
    PubMed 

    Google Scholar 
    Barrera, R. Spatial stability of adult Aedes aegypti populations. Am. J. Trop. Med. Hyg. 85, 1087–1092 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Barrera, R., Amador, M., Ruiz-Valcarcel, J. & Acevedo, V. Factors modulating captures of gravid Aedes aegypti females. J. Am. Mosq. Control Assoc. 36, 66–73 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Moura, M. B. C. M. et al. Spatio-temporal dynamics of Aedes aegypti and Aedes albopictus oviposition in an urban area of northeastern Brazil. Trop. Med. Int. Health 25, 1510–1521 (2020).PubMed 

    Google Scholar 
    Crawford, J. E. et al. Efficient production of male Wolbachia-infected Aedes aegypti mosquitoes enables large-scale suppression of wild populations. Nat. Biotechnol. 38, 482–492 (2020).CAS 

    Google Scholar 
    Lau, K. W. et al. Vertical distribution of Aedes mosquitoes in multiple story buildings in Selangor and Kuala Lumpur, Malaysia. Trop. Biomed. 30, 36–45 (2013).CAS 
    PubMed 

    Google Scholar 
    Perich, M. J. et al. Field evaluation of a lethal ovitrap against dengue vectors in Brazil. Med. Vet. Entomol. 17, 205–210 (2003).CAS 
    PubMed 

    Google Scholar 
    Serpa, L. L. N. et al. Study of the the distribution and abundance of the eggs of Aedes aegypti and Aedes albopictus according to the habitat and meteorlogical variables, municipality of Sao Sebastiao, Sao Paulo state, Brazil. Parasit. Vectors 6, 321 (2014).
    Google Scholar 
    Sithiprasasna, R. et al. Field evaluation of a lethal ovitrap for the control of Aedes aegypti (Diptera: Culicidae) in Thailand. J. Med. Entomol. 40, 455–462 (2003).PubMed 

    Google Scholar 
    Barrera, R., Amador, M., Munoz, J. & Acevedo, V. Integrated vector control of Aedes aegypti mosquitoes around target houses. Parasit. Vectors 11, 88 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Naranjo, D. P. et al. Vector control programs in Saint Johns County, Florida and Guayas, Ecuador: Successes and barriers to integrated vector management. BMC Public Health 14, 674 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Regis, L. N. et al. Sustained reduction of the dengue vector population resulting from an integrated control strategy applied in two Brazilian cities. PLoS ONE 8, e67682 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stewart, A. T. M. et al. Community acceptance of yeast interfering RNA larvicide technology for control of Aedes mosquitoes in Trinidad. PLoS ONE 15, e0237675 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Winter, N. et al. Assessment of Trinidad community stakeholder perspectives on the use of yeast interfering RNA-baited ovitraps for biorational control of Aedes mosquitoes. PLoS ONE 16, e0252997 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chadee, D. D. & Corbet, P. S. Seasonal incidence and diel patterns of oviposition in the field of the mosquito, Aedes aegypti (L.) (Diptera: Culicidae) in Trinidad, West Indies: a preliminary study. Ann. Trop. Med. Parasitol. 81, 151–161 (1987).CAS 
    PubMed 

    Google Scholar 
    Edman, J. D. et al. Aedes aegypti (Diptera: Culicdae) movement influenced by availability of oviposition sites. J. Med. Entomol. 35, 578–583 (1998).CAS 
    PubMed 

    Google Scholar 
    Reiter, P., Amador, M. A., Anderson, R. A. & Clark, G. G. Short report: dispersal of Aedes aegypti in an urban area after blood feeding as demonstrated by rubidium-marked eggs. Am. J. Trop. Med. Hyg. 52, 177–179 (1995).CAS 
    PubMed 

    Google Scholar 
    Mysore, K. et al. Preparation and use of a yeast shRNA delivery system for gene silencing in mosquito larvae. Methods Mol. Biol. 1858, 213–231 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chadee, D. D., Fat, F. H. & Persad, R. C. First record of Aedes albopictus from Trinidad, West Indies. J. Am. Mosq. Control Assoc. 19, 438–439 (2003).PubMed 

    Google Scholar 
    Clemons, A., Mori, A., Haugan, M., Severson, D. W. & Duman-Scheel, M. Culturing and egg collection of Aedes aegypti. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.prot5507 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stan Developmental Team. Stan Modeling Language Users Guide and Reference Manual, v.2.22.1 https://mc-stan.org (2020). More

  • in

    Seasonal and temporal patterns of rainfall shape arthropod community composition and multi-trophic interactions in an arid environment

    Holmgren, M. et al. Extreme climatic events shape arid and semiarid ecosystems. Front. Ecol. Environ. 4, 87–95 (2006).
    Google Scholar 
    Ummenhofer, C. C. & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review. Philos. Trans. R. Soc. B-Biol. Sci. 372, 20160135. https://doi.org/10.1098/rstb.2016.0135 (2017).Chesson, P. et al. Resource pulses, species interactions, and diversity maintenance in arid and semi-arid environments. Oecologia 141, 236–253 (2004).ADS 
    PubMed 

    Google Scholar 
    McCluney, K. E. et al. Shifting species interactions in terrestrial dryland ecosystems under altered water availability and climate change. Biol. Rev. 87, 563–582 (2012).PubMed 

    Google Scholar 
    Reyer, C. P. O. et al. A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability. Glob. Change Biol. 19, 75–89 (2013).ADS 

    Google Scholar 
    Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in and and semi-arid ecosystems. Oecologia 141, 211–220 (2004).ADS 
    PubMed 

    Google Scholar 
    Borer, E. T., Seabloom, E. W. & Tilman, D. Plant diversity controls arthropod biomass and temporal stability. Ecol. Lett. 15, 1457–1464 (2012).PubMed 

    Google Scholar 
    Kwok, A. B. C., Wardle, G. M., Greenville, A. C. & Dickman, C. R. Long-term patterns of invertebrate abundance and relationships to environmental factors in arid Australia. Austral Ecol. 41, 480–491 (2016).
    Google Scholar 
    Prugh, L. R. et al. Ecological winners and losers of extreme drought in California. Nat. Climate Change 8, 819–824 (2018).ADS 

    Google Scholar 
    Deguines, N., Brashares, J. S. & Prugh, L. R. Precipitation alters interactions in a grassland ecological community. J. Anim. Ecol. 86, 262–272 (2017).PubMed 

    Google Scholar 
    Ripple, W. J. et al. What is a trophic cascade?. Trends Ecol. Evol. 31, 842–849 (2016).PubMed 

    Google Scholar 
    Greenville, A. C., Wardle, G. M. & Dickman, C. R. Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature. Ecol. Evol. 2, 2645–2658 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Molyneux, J., Pavey, C. R., James, A. I. & Carthew, S. M. Persistence of ground-dwelling invertebrates in desert grasslands during a period of low rainfall—Part 2. J. Arid. Environ. 157, 39–47 (2018).ADS 

    Google Scholar 
    Seymour, C. L., Simmons, R. E., Joseph, G. S. & Slingsby, J. A. On bird functional diversity: Species richness and functional differentiation show contrasting responses to rainfall and vegetation structure in an arid landscape. Ecosystems 18, 971–984 (2015).
    Google Scholar 
    Prather, C. M. et al. Invertebrates, ecosystem services and climate change. Biol. Rev. 88, 327–348 (2013).PubMed 

    Google Scholar 
    Del Toro, I., Ribbons, R. R. & Pelini, S. L. The little things that run the world revisited: a review of ant-mediated ecosystem services and disservices (Hymenoptera: Formicidae). Myrmecol. News 17, 133–146 (2012).
    Google Scholar 
    Gerlach, J., Samways, M. & Pryke, J. Terrestrial invertebrates as bioindicators: an overview of available taxonomic groups. J. Insect Conserv. 17, 831–850 (2013).
    Google Scholar 
    Doblas-Miranda, E., Sanchez-Pinero, F. & Gonzalez-Megias, A. Different microhabitats affect soil macroinvertebrate assemblages in a Mediterranean arid ecosystem. Appl. Soil Ecol. 41, 329–335 (2009).
    Google Scholar 
    Hadley, N. F. & Szarek, S. R. Productivity of desert ecosystems. Bioscience 31, 747–753 (1981).
    Google Scholar 
    Barnett, K. L. & Facey, S. L. Grasslands, invertebrates, and precipitation: A review of the effects of climate change. Front. Plant Sci. 7, 1196 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Zhu, H. et al. Effects of altered precipitation on insect community composition and structure in a meadow steppe. Ecol. Entomol. 39, 453–461 (2014).
    Google Scholar 
    Palmer, C. M. Chronological changes in terrestrial insect assemblages in the arid zone of Australia. Environ. Entomol. 39, 1775–1787 (2010).PubMed 

    Google Scholar 
    Liu, R. T., Zhu, F. & Steinberger, Y. Ground-active arthropod responses to rainfall-induced dune microhabitats in a desertified steppe ecosystem, China. J. Arid Land 8, 632–646 (2016).
    Google Scholar 
    Mendelsohn, J., Jarvis, A., Roberts, C. & Robertson, T. Atlas of Namibia: A portrait of the land and its people. 3rd edn, (Sunbird Publishers, 2009).Theron, L. Temporal and spatial composition of arboreal insects along the Omaruru river, Namibia Magister scientiae thesis, University of the Free State Bloemfontein, (2010).Wagner, T. C., Richter, J., Joubert, D. F. & Fischer, C. A dominance shift in arid savanna: An herbaceous legume outcompetes local C4 grasses. Ecol. Evol. 8, 6779–6787 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Wagner, T. C., Hane, S., Joubert, D. F. & Fischer, C. Herbaceous legume encroachment reduces grass productivity and density in arid rangelands. PLoS ONE 11, e0166743; https://doi.org/10.1371/journal.pone.0166743 (2016).Picker, M., Griffiths, C. & Weaving, A. Field Guide to Insects of Southern Africa. (Struik Nature, 2004).Scholtz, C. H. & Holm, E. Insects of Southern Africa. 2nd edn, (Protea Book House, 2008).Blaum, N., Seymour, C., Rossmanith, E., Schwager, M. & Jeltsch, F. Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators. Biodivers. Conserv. 18, 1187–1199 (2009).
    Google Scholar 
    Franca, L. F., Figueiredo-Paixao, V. H., Duarte-Silva, T. A. & dos Santos, K. B. The effects of rainfall and arthropod abundance on breeding season of insectivorous birds, in a semi-arid neotropical environment. Zoologia-Curitiba. https://doi.org/10.3897/zoologia.37.e37716 (2020).Wagner, T. C., Uiseb, K. & Fischer, C. Rolling pits of Hartmann’s mountain zebra (Zebra equus hartmannae) increase vegetation diversity and landscape heterogeneity in the Pre-Namib. Ecol. Evol. 11, 13036–13051 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).Oksanen, J., et al. vegan: Community Ecology Package. R package version 2.5-7. (2020).Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).
    Google Scholar 
    Anderson, M. J. in Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan et al.) (2017).Stopher, K. V., Bento, A. I., Clutton-Brock, T. H., Pemberton, J. M. & Kruuk, L. E. B. Multiple pathways mediate the effects of climate change on maternal reproductive traits in a red deer population. Ecology 95, 3124–3138 (2014).
    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 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-PLUS. (Springer Verlag, 2000).Zhang, D. rsq: R-Squared and related measures. R package version 2.2. (2021).Barnes, A. D. et al. Direct and cascading impacts of tropical land-use change on multi-trophic biodiversity. Nat. Ecol. Evol. 1, 1511–1519 (2017).PubMed 

    Google Scholar 
    Henschel, J. R. Long-term population dynamics of Namib desert Tenebrionid beetles reveal complex relationships to pulse-reserve conditions. Insects 12, 804. https://doi.org/10.3390/insects12090804 (2021).Cloudsley-Thompson, J. L. The adaptational diversity of desert biota. Environ. Conserv. 20, 227–231 (1993).
    Google Scholar 
    Sømme, L. in Invertebrates in Hot and Cold Arid Environments 135–157 (Springer, 1995).Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Henschel, J., Klintenberg, P., Roberts, C. & Seely, M. Long-term ecological research from an arid, variable, drought-prone environment. Sécheresse 18, 342–347 (2007).
    Google Scholar 
    Cloudsley-Thompson, J. L. Adaptations of arthropoda to arid environments. Annu. Rev. Entomol. 20, 261–283 (1975).CAS 
    PubMed 

    Google Scholar 
    Schuldt, A. et al. Belowground top-down and aboveground bottom-up effects structure multitrophic community relationships in a biodiverse forest. Sci. Rep. 7 (2017).Vidal, M. C. & Murphy, S. M. Bottom-up vs. top-down effects on terrestrial insect herbivores: a meta-analysis. Ecol. Lett. 21, 138–150 (2018).Báez, S., Collins, S. L., Lightfoot, D. & Koontz, T. L. Bottom-up regulation of plant community structure in an aridland ecosystem. Ecology 87, 2746–2754 (2006).PubMed 

    Google Scholar 
    Gibb, H. et al. Testing top-down and bottom-up effects on arid zone beetle assemblages following mammal reintroduction. Austral Ecol. 43, 288–300 (2018).
    Google Scholar 
    Coll, M. & Guershon, M. Omnivory in terrestrial arthropods: Mixing plant and prey diets. Annu. Rev. Entomol. 47, 267–297 (2002).CAS 
    PubMed 

    Google Scholar 
    Karolyi, F., Hansal, T., Krenn, H. W. & Colville, J. F. Comparative morphology of the mouthparts of the megadiverse South African monkey beetles (Scarabaeidae: Hopliini): feeding adaptations and guild structure. PeerJ 4, e1597; https://doi.org/10.7717/peerj.1597 (2016).Greenslade, P. Survival of Collembola in arid environments: Observations in South Australia and the Sudan. J. Arid. Environ. 4, 219–228 (1981).ADS 

    Google Scholar 
    Fattorini, S. Effects of fire on tenebrionid communities of a Pinus pinea plantation: A case study in a Mediterranean site. Biodivers. Conserv. 19, 1237–1250 (2009).
    Google Scholar 
    Sanders, N. J., Moss, J. & Wagner, D. Patterns of ant species richness along elevational gradients in an arid ecosystem. Glob. Ecol. Biogeogr. 12, 93–102 (2003).
    Google Scholar  More

  • in

    Anti-pulling force and displacement deformation analysis of the anchor pulling system of the new debris flow grille dam

    Design parametersA new type of Debris-flow grille dam is proposed to be built with a height of 8 m. Column section 500 mm × 700 mm, spacing 5000 mm. The cross section of the beam is 400 mm × 300 mm, and the spacing is 4000 mm. The section steel adopts I-steel 45a, the spacing is 250 mm. The counterfort wall is 300 mm thick and 6500 mm high. Pile foundation adopts manual digging pile, pile by 1000 mm, 5000 mm deep. The concrete is C30; Stressed bar is HRB335; Stirrups is HRB300; Stay Cable is 3 (emptyset) s15.2. The design size of the anchor piers is shown in Fig. 12. In the Figure where (T = 2 times 10^{5} N); (L_{l} = 8500;{text{mm}}); (E_{l} = 1.95 times 10^{5} ;{text{N/mm}}^{2}); (A_{l} = 420;{text{mm}}); (D_{e} = 1000;{text{mm}}); (L_{m} = 1200;{text{mm}}); (E_{e} = 3.0 times 10^{4} ;{text{N/mm}}^{2}); (H = 1000;{text{mm}}); (mu = 0.2); (E = 20;{text{N/mm}}^{2}). The parameter of gully bed soil is shown in Table 1.Figure 12The parameters of anchor piers.Full size imageTable 1 The parameters of gully bed soil.Full size tableAnalysis of results(1) The effect of the elastic modulus and Poisson’s ratio of the surrounding soil on the displacement deformation of the anchor-pulling system.The elastic modulus (E) and Poisson’s ratio (mu) are important parameters for calculating the displacement deformation of soil. They have something to do with both the properties of materials and the stress level. To analyze the effect of the physical parameter variation of the surrounding soil on the displacement deformation of the anchor-pulling system, we can study changing the elastic modulus and Poisson’s ratio. The variation range of the elastic modulus is 15–45 N/mm2, and the variation range of Poisson’s ratio is 0.15–0.25.Figure 13 shows the variation curve in which the displacement deformation increases with the elastic modulus of the soil around the anchor pier. We can see that as the elastic modulus of the soil around the anchor pier increases, the displacement deformation decreases gradually. When the elastic modulus is in the range of 15–35 N/mm2, the curve is steep, and the decrease in deformation is apparent. After 35 N/mm2, the curve becomes smooth, and the decrease in deformation tends to be stable.Figure 13The effect of the elastic modulus E(15–45 N/mm2) of the surrounding soil on the displacement of the anchor-pulling system.Full size imageIn Fig. 14, the displacement deformation increases linearly with Poisson’s ratio of the soil around the anchor pier. However, the total impact is not large. From calculation, the variation of elastic modulus of the soil around the anchor pier has nothing to do with elastic deformation of the stayed cable ((S_{1} )), but mainly influences relative shear displacement between anchor piers and the surrounding soil ((S_{2} )) and the compression performance of the soil on the front of anchor piers ( (S_{3} )). where ((S_{2} )) accounted for 89% and (left( {S_{3} } right)) accounted for 11%. When the Poisson ratio increases, the displacement deformation also increases. Poisson’s ratio has the greatest influence on the relative shear displacement ((S_{2} )) of the anchor pier and soil, accounting for approximately 96.4%. The design parameters should be selected correctly during design. The influence of parameters on the deformation of anchor system is analyzed by using control variable method. The influence of a single variable on the results can be intuitively obtained. However, the elastic modulus E and Poisson ‘ s ratio (mu) of rock and soil are not independent. Therefore, Matlab is used to analyze the influence of the two aspects on the deformation of the tensile anchor system, and the results are shown in Fig. 15. It can be seen from Fig. 15 that the influence of elastic modulus E on the deformation of tensile anchor system is much greater than that of Poisson’s ratio (mu). And the variation of the curve is basically the same, so the interaction between the two is weak.Figure 14The effect of Poisson’s ratio (mu)(0.15–0.26) of the surrounding soil on the displacement of the anchor-pulling system.Full size imageFigure 15Influence of elastic modulus E (15–45 N/mm2) and Poisson’s ratio (mu left( {0.15 – 0.26} right)) on deformation of anchor system.Full size image(2) The effect of the design parameters of anchor piers on the displacement deformation of the anchor-pulling system.The design parameters of anchor piers include the equivalent width (D_{e}), length (L_{m}) and height (H). Different design parameters have varying effects on the displacement deformation of the anchor-pulling system. Keep other parameters unchanged and let ( D_{e} ) vary in 0.5–1.5 m, (L_{m}) vary in 0.6–2.0 m, and (H) vary in 0.5–1.5 m. Analyzing their effect on the displacement deformation of the anchor-pulling system, the results are shown in Figs. 16 and 17.Figure 16The effect of equivalent width (D_{e})(500–1500 mm) on the displacement of the anchor-pulling system.Full size imageFigure 17The effect of equivalent length (L_{m})(600–2000 mm) on the displacement of the anchor-pulling system.Full size imageAs illustrated in Figs. 16 and 17, the effects of the design parameters of the anchor piers on the displacement deformation of the anchor-pulling system are almost the same. As the size increases, the displacement deformation gradually decreases, and the front section decreases quickly, while the rear section becomes gradually smooth. Here, the equivalent width (D_{e}) and length (L_{m}) mainly affect the compression performance of the soil on the front of anchor piers (left( {S_{3} } right)). The anchor piers can be seen as rigid bodies where horizontal displacement takes place. Increasing the size means increasing the contact area between the anchor pier and soil body. With this increase, the compression performance of the soil on the front of the anchor piers decreases. However, the effect of the height (H) on the displacement deformation of the anchor-pulling system is the contribution to the relative shear displacement between the anchor piers and the surrounding soil ((S_{2} )). When (H) grows, ((S_{2} )) grows accordingly. However, theoretically, the larger the effect of the size, the better it is. Because of the constraint of topographic conditions, construction conditions and economic benefits in practical engineering, it is necessary to choose the best size. the anchor pier provides enough anchor force and saves all kinds of resources. The best design dimensions suggested are (D_{e}) = 1.2 m–1.8 m, (L_{m}) = 1.5 m–2.5 m, and (H) = 1.0 m–1.6 m.It can be seen from Fig. 18 that the width (D_{e}) and the height (L_{m}) of anchor pier influence each other greatly. When (D_{e}) is 600 mm, with the increase of (L_{m}), the deformation of tension anchor system will first decrease and then increase. When (D_{e}) is greater than 800 mm, with the increase of (L_{m}), the deformation of tension anchor system will continue to decrease. And with the increase of (L_{m}), the decreasing trend is more obvious. When (L_{m}) is 500 mm, with the increase of the height of the anchor pier (D_{e}), the deformation of the anchor system will increase first. When (L_{m}) is greater than 800 mm, with the increase of (D_{e}), the deformation of the anchor system will continue to decrease. But the decreasing trend is not much different.Figure 18Influence of Anchor Pier Width (D_{e} left( {500 – 1500;{text{mm}}} right)) and Anchor Pier Height (L_{m} left( {600 – 2000;{text{mm}}} right)) on Deformation of Anchorage System.Full size imageThe numerical validationThe establishment of the finite element modelWhen the finite element model of the anchor-pulling system and surrounding soil is created, the constitutive model of the surrounding soil uses the Mohr–Coulomb elastoplastic model. The anchor pier and surrounding soil use eight nodes as oparametric elements, such as solid45, of which the basic grid unit is cubic units. When the grid is divided, the grid between the anchor pier and the surrounding soil contact is dense. The LINK10 unit is used to simulate cables, which have a bilinear stiffness matrix. It can simulate not only tensile bar units but also compressed bar units. For example, when the pull-up option is used alone, if the unit is under pressure, its stiffness disappears, so it can be used to simulate the relaxation of cables or chains. This feature is very significant for the static problem of wire rope, which uses a unit to simulate the entire cable. It can also be used for dynamic analysis with inertial or damping effects when the needed relaxation unit should pay attention to its performance rather than its movement. The soil is homogeneous. The soil physical parameters and structure design parameters are consistent with the theoretical calculation parameters mentioned above. The tensile force of the cable is exerted on the nodes as a force. The top surface of the model is free, and the normal displacements of the remaining faces are constrained such that the displacements are zero. The contact of the anchor pier and surrounding soils is a rigid-flexible surface-to-surface contact element to reflect the interaction. The surface of the anchor pier is regarded as the “target” surface, and the surface of the soil body is regarded as the “contact” surface. The coefficient of friction and normal penalty stiffness are 0.35 and 0.15, respectively. The scope of interaction between the anchor pier and the surrounding soil in the model is taken as 15 m × 11 m × 12 m, referring to past experience in engineering and the research data of the effect scope that the related anchors have had on the soil. The values of the model geometric parameters and physical and mechanical parameters are the same as in “Design parameters” section. The finite element model is shown in Fig. 19.Figure 19Finite element model of the anchor-pulling system and surrounding soil.Full size imageResearch on finite element model gridIn order to verify the convergence of numerical simulation, the soil was divided into three different mesh sizes. Condition 1 is fine finite element meshing. The stress nephogram of condition 1 is shown in Fig. 20. Condition 2 is medium finite element mesh. The stress nephogram of condition 1 is shown in Fig. 21. Condition 3 is coarse finite element mesh. The stress nephogram of condition 1 is shown in Fig. 22. See Table 2 for specific grid division.Figure 20Condition 1 stress cloud diagram.Full size imageFigure 21Condition 1 stress cloud diagram.Full size imageFigure 22Condition 1 stress cloud diagram.Full size imageTable 2 Mesh size of three working conditions.Full size tableIt can be seen from the stress nephogram of the three working conditions that the thicker the grid is, the greater the displacement of the anchor system is. The maximum displacement difference between condition 2 and condition 3 is 2.6%; the maximum displacement of condition 1 is 17% different from that of condition 2. The finer the mesh, the more accurate the numerical simulation results. But with the increase in computing time. It can be seen from Table 2 that the maximum iteration of condition 1 is 10 times, and the result will converge. The maximum iterations of condition 2 and 3 only need 7 times, and the results can converge.The calculation resultsFigure 23 and Fig. 24 are the displacement nephograms of the soil around the anchor piers for 100 kN and 400 kN, respectively. The soil displacement increases with increasing load, the affected area will increase and become uniform, and the area under load will also increase. The soil within the range of 1–3 m around the anchor pier is greatly affected, accounting for 80% of the total force. The soil around the anchor pier should be reinforced, and the anchoring force should be enhanced in the design.Figure 23Displacement fringe of soil around the anchor piers for 100 kN.Full size imageFigure 24Displacement fringe of soil around the anchor piers for 400 kN.Full size imageIn order to further study the influence of anchorage pier size on the displacement and deformation of anchorage system, finite element models with different sizes are established by finite element method. The stress nephogram is shown in Figs. 25, 26 and 27.Figure 25Top 800 mm, bottom 800 mm anchor pier stress nephogram.Full size imageFigure 26Top 1000 mm, bottom 1000 mm anchor pier stress nephogram.Full size imageFigure 27Top 800 mm, bottom 1000 mm anchor pier stress nephogram.Full size imageFrom Figs. 25, 26 and 27, it can be seen that when the anchor pier is rectangular, the deformation of the tensile anchor system decreases with the increase of the size of the anchor pier, but the degree is small. When the anchor pier is trapezoidal, the material is small, but the deformation is more ideal than the rectangular. It can be seen that reasonable selection of anchor pier size is crucial, not blindly increase the size of anchor pier.Figure 28 shows that the displacement of the soil around the anchor pier increases with increasing load, and the added value is obvious at approximately 2–3 mm. Figure 29 shows that the increase in load has a great effect on the soil in front of the anchor pier. As the load increases, the compressive deformation of the soil gradually increases. As the distance from the anchor pier increases, the displacement of the soil decreases, and the scope of influence gradually decreases. The displacement of the soil tends to be stable beyond 4–5 m from the anchor pier.Figure 28The displacement of soil around anchor pier.Full size imageFigure 29The horizontal displacement of soil along cable axis.Full size imageComparison of theoretical calculation and numerical simulation results at the time of load variationTo verify the correctness of the theoretical calculation, we compare the theoretical calculation with numerical simulation results of displacement deformation of anchor-pulling system under different pulling force of stayed cable. The results are shown in Fig. 30, see Table 3 for data.Figure 30Comparison of theoretical calculation and numerical simulation results.Full size imageTable 3 Comparison between theoretical calculation and numerical simulation.Full size tableAs seen from Fig. 30, the theoretical and numerical simulation results are consistent, showing a linear growth trend. The slope difference of the two straight lines is approximately 5%, which meets the accuracy requirements of geotechnical engineering. As the restraint effect of the surrounding soil on the anchor pier is not fully considered, the theoretical calculation result is too large. The deformation of anchor (left( {S_{1} } right)) in displacement deformation is the same, and the relative shear displacement (left( {S_{2} } right)) of the anchor pier and the soil and the compressive deformation ((S_{3} )) of the soil at the front end of the anchor pier are 1.25 times and 1.08 times the numerical simulation results, respectively. The change in (left( {S_{2} } right)) in the calculation results is large and should be taken into account in the design. More

  • in

    Variation in diet composition and its relation to gut microbiota in a passerine bird

    Büyükdeveci, M. E., Balcázar, J. L., Demirkale, İ & Dikel, S. Effects of garlic-supplemented diet on growth performance and intestinal microbiota of rainbow trout (Oncorhynchus mykiss). Aquaculture 486, 170–174 (2018).
    Google Scholar 
    Maklakov, A. A. et al. Sex-specific fitness effects of nutrient intake on reproduction and lifespan. Curr. Biol. 18, 1062–1066 (2008).CAS 
    PubMed 

    Google Scholar 
    Totsch, S. K. et al. Effects of a Standard American Diet and an anti-inflammatory diet in male and female mice. Eur. J. Pain 22, 1203–1213 (2018).CAS 
    PubMed 

    Google Scholar 
    Green, D. A. & Millar, J. S. Changes in gut dimensions and capacity of Peromyscus maniculatus relative to diet quality and energy needs. Can. J. Zool. 65, 2159–2162 (1987).
    Google Scholar 
    Jones, V. A. et al. Crohn’s disease: Maintenance of remission by diet. Lancet 2, 177–180 (1985).CAS 
    PubMed 

    Google Scholar 
    Hirai, T. Ontogenetic change in the diet of the pond frog, Rana nigromaculata. Ecol. Res. 17, 639–644 (2002).
    Google Scholar 
    Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sender, R., Fuchs, S. & Milo, R. Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell 164, 337–340 (2016).CAS 
    PubMed 

    Google Scholar 
    Reikvam, D. H. et al. Depletion of murine intestinal microbiota: effects on gut mucosa and epithelial gene expression. PLoS ONE 6, e17996 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sommer, F. & Bäckhed, F. The gut microbiota-masters of host development and physiology. Nat. Rev. Microbiol. 11, 227–238 (2013).CAS 
    PubMed 

    Google Scholar 
    Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Youngblut, N. D. et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 10, 2200 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Zhu, Y. et al. Beef, chicken, and soy proteins in diets induce different gut microbiota and metabolites in rats. Front. Microbiol. 8, 1395 (2017).Zimmer, J. et al. A vegan or vegetarian diet substantially alters the human colonic faecal microbiota. Eur. J. Clin. Nutr. 66, 53–60 (2012).CAS 
    PubMed 

    Google Scholar 
    McKenney, E. A., Rodrigo, A. & Yoder, A. D. Patterns of gut bacterial colonization in three primate species. PLoS ONE 10, e0124618 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Bergmann, G. T. Microbial community composition along the digestive tract in forage- and grain-fed bison. BMC Vet. Res. 13, 253 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Phillips, C. D. et al. Microbiome structural and functional interactions across host dietary niche space. Integr. Comp. Biol. 57, 743–755 (2017).CAS 
    PubMed 

    Google Scholar 
    Song, S. J. et al. Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. mBio 11, e02901–19 (2020).Bodawatta, K. H., Sam, K., Jønsson, K. A. & Poulsen, M. Comparative analyses of the digestive tract microbiota of New Guinean passerine birds. Front. Microbiol. 9, 1830 (2018).Capunitan, D. C., Johnson, O., Terrill, R. S. & Hird, S. M. Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea. Mol. Ecol. 29, 829–847 (2020).CAS 
    PubMed 

    Google Scholar 
    Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. T. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Waite, D. W. & Taylor, M. W. Characterizing the avian gut microbiota: membership, driving influences, and potential function. Front. Microbiol 5, 223 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Loo, W. T., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. An inter-island comparison of Darwin’s finches reveals the impact of habitat, host phylogeny, and island on the gut microbiome. PLoS ONE 14, e0226432 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Loo, W. T., García-Loor, J., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. Host phylogeny, diet, and habitat differentiate the gut microbiomes of Darwin’s finches on Santa Cruz Island. Sci. Rep. 9, 1–12 (2019).
    Google Scholar 
    Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE 15, e0220926 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Orłowski, G. & Karg, J. Diet of nestling Barn Swallows Hirundo rustica in rural areas of Poland. Cent. Eur. J. Biol. 6, 1023–1035 (2011).
    Google Scholar 
    Wiesenborn, W. D. & Heydon, S. L. Diets of breeding southwestern willow flycatchers in different habitats. Wilson J. Ornithol. 119, 547–557 (2007).
    Google Scholar 
    Moreby, S. J. An aid to the identification of arthropod fragments in the faeces of gamebird chicks (Galliformes). Ibis 130, 519–526 (1988).
    Google Scholar 
    Zeale, M. R. K., Butlin, R. K., Barker, G. L. A., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Mol. Ecol. Resour. 11, 236–244 (2011).CAS 
    PubMed 

    Google Scholar 
    Bolnick, D. I. et al. Individuals’ diet diversity influences gut microbial diversity in two freshwater fish (threespine stickleback and Eurasian perch). Ecol. Lett. 17, 979–987 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Bolnick, D. I. et al. Individual diet has sex-dependent effects on vertebrate gut microbiota. Nat. Commun. 5, 4500 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14, 1160–1170 (2014).CAS 
    PubMed 

    Google Scholar 
    Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. & Taberlet, P. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biol. Lett. 10, 20140562 (2014).Elbrecht, V. et al. Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects. PeerJ 4, e1966 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—Sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Piñol, J., San Andrés, V., Clare, E. L., Mir, G. & Symondson, W. O. C. A pragmatic approach to the analysis of diets of generalist predators: The use of next-generation sequencing with no blocking probes. Mol. Ecol. Resour. 14, 18–26 (2014).PubMed 

    Google Scholar 
    Góngora, E., Elliott, K. H. & Whyte, L. Gut microbiome is affected by inter-sexual and inter-seasonal variation in diet for thick-billed murres (Uria lomvia). Sci. Rep. 11, 1200 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Teyssier, A. et al. Diet contributes to urban-induced alterations in gut microbiota: Experimental evidence from a wild passerine. Proc. R. Soc. B 287, 20192182 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Kreisinger, J. et al. Temporal stability and the effect of transgenerational transfer on fecal microbiota structure in a long distance migratory bird. Front. Microbiol. 8, 50 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Petrželková, A. et al. Brood parasitism and quasi-parasitism in the European barn swallow (Hirundo rustica rustica). Behav. Ecol. Sociobiol. 69, 1405–1414 (2015).
    Google Scholar 
    Kreisinger, J. et al. Fecal microbiota associated with phytohaemagglutinin-induced immune response in nestlings of a passerine bird. Ecol. Evol. 8, 9793–9802 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Elbrecht, V. & Leese, F. Validation and development of COI metabarcoding primers for freshwater macroinvertebrate bioassessment. Front. Environ. Sci. 5, 11 (2017).Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinform. 15, 182 (2014).
    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2018).Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Meth 13, 581–583 (2016).CAS 

    Google Scholar 
    Pafčo, B. et al. Metabarcoding analysis of strongylid nematode diversity in two sympatric primate species. Sci. Rep. 8, 5933 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wright, E. S. RNAconTest: Comparing tools for noncoding RNA multiple sequence alignment based on structural consistency. RNA 26, 531–540 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes—A 2019 update. Nucleic Acids Res. 48, D445–D453 (2020).CAS 
    PubMed 

    Google Scholar 
    Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 12, 385 (2011).
    Google Scholar 
    Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: Repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
    Google Scholar 
    Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).
    Google Scholar 
    Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24 (1999).
    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-2. 2018. (2018).Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    Hui, F. K. C. boral–Bayesian ordination and regression analysis of multivariate abundance data in R. Methods Ecol. Evol. 7, 744–750 (2016).
    Google Scholar 
    Aivelo, T. & Norberg, A. Parasite-microbiota interactions potentially affect intestinal communities in wild mammals. J. Anim. Ecol. 87, 438–447 (2018).PubMed 

    Google Scholar 
    Caviedes-Vidal, E. et al. The digestive adaptation of flying vertebrates: High intestinal paracellular absorption compensates for smaller guts. Proc. Natl. Acad. Sci. U.S.A. 104, 19132–19137 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McWhorter, T. J., Caviedes-Vidal, E. & Karasov, W. H. The integration of digestion and osmoregulation in the avian gut. Biol. Rev. Camb. Philos. Soc. 84, 533–565 (2009).PubMed 

    Google Scholar 
    Grigolo, C. P. et al. Diet heterogeneity and antioxidant defence in Barn Swallow Hirundo rustica nestlings. Avocetta 43, 1 (2019).
    Google Scholar 
    Law, A. A. et al. Diet and prey selection of barn swallows (Hirundo rustica) at Vancouver International Airport. Canadian Field-Naturalist 131, 26 (2017).
    Google Scholar 
    McClenaghan, B., Nol, E. & Kerr, K. C. R. DNA metabarcoding reveals the broad and flexible diet of a declining aerial insectivore. Auk 136, uky003 (2019).Turner, A. K. The use of time and energy by aerial feeding birds (University of Stirling, 1981).
    Google Scholar 
    Bryant, D. M. & Turner, A. K. Central place foraging by swallows (Hirundinidae): The question of load size. Anim. Behav. 30, 845–856 (1982).
    Google Scholar 
    Møller, A. P. Advantages and disadvantages of coloniality in the swallow, Hirundo rustica. Anim. Behav. 35, 819–832 (1987).
    Google Scholar 
    Brodmann, P. A. & Reyer, H.-U. Nestling provisioning in water pipits (Anthus spinoletta): Do parents go for specific nutrients or profitable prey?. Oecologia 120, 506–514 (1999).ADS 
    PubMed 

    Google Scholar 
    Herlugson, C. J. Food of adult and nestling Western and Mountain bluebirds. Murrelet 63, 59–65 (1982).
    Google Scholar 
    Batt, B. D. J., Anderson, M. G. & Afton, A. D. Ecology and management of breeding waterfowl (Univ of Minnesota Press, 1992).
    Google Scholar 
    Douglas, D. J. T., Evans, D. M. & Redpath, S. M. Selection of foraging habitat and nestling diet by Meadow Pipits Anthus pratensis breeding on intensively grazed moorland. Bird Study 55, 290–296 (2008).
    Google Scholar 
    Waugh, D. R. Predation strategies in aerial feeding birds (University of Stirling, 1978).
    Google Scholar 
    Kropáčková, L. et al. Co-diversification of gastrointestinal microbiota and phylogeny in passerines is not explained by ecological divergence. Mol. Ecol. 26, 5292–5304 (2017).PubMed 

    Google Scholar 
    Kohl, K. D. et al. Physiological and microbial adjustments to diet quality permit facultative herbivory in an omnivorous lizard. J. Exp. Biol. 219, 1903–1912 (2016).PubMed 

    Google Scholar 
    Baxter, N. T. et al. Intra- and interindividual variations mask interspecies variation in the microbiota of sympatric Peromyscus populations. Appl. Environ. Microbiol. 81, 396–404 (2015).ADS 
    PubMed 

    Google Scholar 
    Holmes, I. A., Monagan, I. V. Jr., Rabosky, D. L. & Davis Rabosky, A. R. Metabolically similar cohorts of bacteria exhibit strong cooccurrence patterns with diet items and eukaryotic microbes in lizard guts. Ecol. Evol. 9, 12471–12481 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. et al. Diet diversity is associated with beta but not alpha diversity of pika gut microbiota. Front. Microbiol. 7, 1169 (2016).Li, H. et al. Diet simplification selects for high gut microbial diversity and strong fermenting ability in high-altitude pikas. Appl. Microbiol. Biotechnol. 102, 6739–6751 (2018).CAS 
    PubMed 

    Google Scholar 
    Ambrosini, R. et al. Cloacal microbiomes and ecology of individual barn swallows. FEMS Microbiol. Ecol. 95, fiz061 (2019).Kreisinger, J., Čížková, D., Kropáčková, L. & Albrecht, T. Cloacal microbiome structure in a long-distance migratory bird assessed using deep 16sRNA pyrosequencing. PLoS ONE 10, e0137401 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Noguera, J. C., Aira, M., Pérez-Losada, M., Domínguez, J. & Velando, A. Glucocorticoids modulate gastrointestinal microbiome in a wild bird. R. Soc. Open Sci. 5, 171743 (2018).Shehzad, W. et al. Carnivore diet analysis based on next-generation sequencing: Application to the leopard cat (Prionailurus bengalensis) in Pakistan. Mol. Ecol. 21, 1951–1965 (2012).CAS 
    PubMed 

    Google Scholar 
    Vestheim, H. & Jarman, S. N. Blocking primers to enhance PCR amplification of rare sequences in mixed samples—A case study on prey DNA in Antarctic krill stomachs. Front. Zool. 5, 12 (2008).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Novel wheat varieties facilitate deep sowing to beat the heat of changing climates

    World Food and Agriculture—Statistical Yearbook 2020 (FAO, 2020).Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).CAS 

    Google Scholar 
    Hedden, P. The genes of the Green Revolution. Trends Genet. 19, 5–9 (2003).CAS 

    Google Scholar 
    Hochman, Z., Gobbett, D. L. & Horan, H. Climate trends account for stalled wheat yields in Australia since 1990. Glob. Change Biol. 23, 2071–2081 (2017).
    Google Scholar 
    Wang, B. et al. Australian wheat production expected to decrease by the late 21st century. Glob. Change Biol. 24, 2403–2415 (2018).
    Google Scholar 
    Rebetzke, G. J. et al. Genotypic increases in coleoptile length improves stand establishment, vigour and grain yield of deep-sown wheat. Field Crops Res. 100, 10–23 (2007).
    Google Scholar 
    Gan, Y., Stobbe, E. H. & Moes, J. Relative date of wheat seedling emergence and its impact on grain yield. Crop Sci. 32, 1275–1281 (1992).
    Google Scholar 
    Rebetzke, G., Ingvordsen, C., Bovill, W., Trethowan, R. & Fletcher, A. in Australian Agriculture in 2020: From Conservation to Automation (eds Pratley, J. & Kirkegaard, J.) 273–288 (Agronomy Australia and Charles Sturt Univ., 2019).Schillinger, W. F., Donaldson, E., Allan, R. E. & Jones, S. S. Winter wheat seedling emergence from deep sowing depths. Agron. J. 90, 582–586 (1998).
    Google Scholar 
    Hunt, J. R. et al. Early sowing systems can boost Australian wheat yields despite recent climate change. Nat. Clim. Change 9, 244–247 (2019).
    Google Scholar 
    Richards, R. The effect of dwarfing genes in spring wheat in dry environments. I. Agronomic characteristics. Aust. J. Agric. Res. 43, 517–527 (1992).
    Google Scholar 
    Rebetzke, G. et al. Quantitative trait loci on chromosome 4B for coleoptile length and early vigour in wheat (Triticum aestivum L.). Aust. J. Agric. Res. 52, 1221–1234 (2001).CAS 

    Google Scholar 
    Rebetzke, G., Richards, R., Sirault, X. & Morrison, A. Genetic analysis of coleoptile length and diameter in wheat. Aust. J. Agric. Res. 55, 733–743 (2004).
    Google Scholar 
    Rebetzke, G. J., Zheng, B. & Chapman, S. C. Do wheat breeders have suitable genetic variation to overcome short coleoptiles and poor establishment in the warmer soils of future climates? Funct. Plant Biol. 43, 961–972 (2016).
    Google Scholar 
    Rebetzke, G. J. et al. Height reduction and agronomic performance for selected gibberellin-responsive dwarfing genes in bread wheat (Triticum aestivum L.). Field Crops Res. 126, 87–96 (2012).
    Google Scholar 
    Zhao, Z., Rebetzke, G. J., Zheng, B., Chapman, S. C. & Wang, E. Modelling impact of early vigour on wheat yield in dryland regions. J. Exp. Bot. 70, 2535–2548 (2019).CAS 

    Google Scholar 
    Brown, H. E. et al. Plant Modelling Framework: software for building and running crop models on the APSIM platform. Environ. Model. Softw. 62, 385–398 (2014).
    Google Scholar 
    Holzworth, D. P. et al. APSIM—evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350 (2014).
    Google Scholar 
    Smith, C. J. et al. Using fertiliser to maintain soil inorganic nitrogen can increase dryland wheat yield with little environmental cost. Agric. Ecosyst. Environ. 286, 106644 (2019).CAS 

    Google Scholar 
    Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).
    Google Scholar 
    Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3, 17102 (2017).
    Google Scholar 
    Anderson, W. K., Stephens, D. & Siddique, K. H. M. in Innovations in Dryland Agriculture (eds Farooq, M. & Siddique, K. H. M.) 299–319 (Springer International, 2016).Flohr, B. M., Hunt, J. R., Kirkegaard, J. A., Evans, J. R. & Lilley, J. M. Genotype × management strategies to stabilise the flowering time of wheat in the south-eastern Australian wheatbelt. Crop Pasture Sci. 69, 547–560 (2018).
    Google Scholar 
    Rebetzke, G., Botwright, T., Moore, C., Richards, R. & Condon, A. Genotypic variation in specific leaf area for genetic improvement of early vigour in wheat. Field Crops Res. 88, 179–189 (2004).
    Google Scholar 
    Richards, R. A. & Lukacs, Z. Seedling vigour in wheat—sources of variation for genetic and agronomic improvement. Aust. J. Agric. Res. 53, 41–50 (2002).CAS 

    Google Scholar 
    López-Castañeda, C. & Richards, R. A. Variation in temperate cereals in rainfed environments III. Water use and water-use efficiency. Field Crops Res. 39, 85–98 (1994).
    Google Scholar 
    Zerner, M. C., Rebetzke, G. J. & Gill, G. S. Genotypic stability of weed competitive ability for bread wheat (Triticum aestivum) genotypes in multiple environments. Crop Pasture Sci. 67, 695–702 (2016).
    Google Scholar 
    Allan, R. E., Vogel, O. A. & Peterson, C. J. Jr Seedling emergence rate of fall-sown wheat and its association with plant height and coleoptile length. Agron. J. 54, 347–350 (1962).
    Google Scholar 
    Towards a Global Programme on Sustainable Dryland Agriculture (FAO, 2020); https://www.fao.org/3/nd366en/nd366en.pdfAntle, J. M., Cho, S., Tabatabaie, S. H. & Valdivia, R. O. Economic and environmental performance of dryland wheat-based farming systems in a 1.5 C world. Mitig. Adapt. Strateg. Glob. Change 24, 165–180 (2019).
    Google Scholar 
    Kirkegaard, J. & Hunt, J. Increasing productivity by matching farming system management and genotype in water-limited environments. J. Exp. Bot. 61, 4129–4143 (2010).CAS 

    Google Scholar 
    Rebetzke, G. J. et al. Agronomic assessment of the durum Rht18 dwarfing gene in bread wheat. Crop Pasture Sci. https://doi.org/10.1071/CP21645 (2022).Bathgate, J. The Influence of Wheat (Triticum aestivum L.) Semi-dwarfing Genes and the Lcol-A1 QTL on the Coleoptile, Seedling Vigour, and Establishment from Deep Sowing. Honours thesis, Charles Sturt Univ. (2021).Brown, H., Huth, N. & Holzworth, D. Crop model improvement in APSIM: using wheat as a case study. Eur. J. Agron. 100, 141–150 (2018).
    Google Scholar 
    Botwright, T., Rebetzke, G., Condon, T. & Richards, R. The effect of rht genotype and temperature on coleoptile growth and dry matter partitioning in young wheat seedlings. Funct. Plant Biol. 28, 417–423 (2001).
    Google Scholar 
    Ellis, M. H. et al. The effect of different height reducing genes on the early growth of wheat. Funct. Plant Biol. 31, 583–589 (2004).CAS 

    Google Scholar 
    Whan, B. The association between coleoptile length and culm length in semidwarf and standard wheats. J. Aust. Inst. Agric. Sci. 42, 194–196 (1976).
    Google Scholar 
    Whan, B. The emergence of semidwarf and standard wheats, and its association with coleoptile length. Aust. J. Exp. Agric. 16, 411–416 (1976).
    Google Scholar 
    Bush, M. & Evans, L. Growth and development in tall and dwarf isogenic lines of spring wheat. Field Crops Res. 18, 243–270 (1988).
    Google Scholar 
    Rebetzke, G. J., Bonnett, D. G. & Ellis, M. H. Combining gibberellic acid-sensitive and insensitive dwarfing genes in breeding of higher-yielding, sesqui-dwarf wheats. Field Crops Res. 127, 17–25 (2012).
    Google Scholar 
    Miralles, D., Calderini, D., Pomar, K. & D’Ambrogio, A. Dwarfing genes and cell dimensions in different organs of wheat. J. Exp. Bot. 49, 1119–1127 (1998).CAS 

    Google Scholar 
    Radford, B. Effect of constant and fluctuating temperature regimes and seed source on the coleoptile length of tall and semidwarf wheats. Aust. J. Exp. Agric. 27, 113–117 (1987).
    Google Scholar 
    Botwright, T., Rebetzke, G., Condon, A. & Richards, R. Influence of variety, seed position and seed source on screening for coleoptile length in bread wheat (Triticum aestivum L.). Euphytica 119, 349–356 (2001).
    Google Scholar 
    Cornish, P. & Hindmarsh, S. Seed size influences the coleoptile length of wheat. Aust. J. Exp. Agric. 28, 521–523 (1988).
    Google Scholar 
    Zheng, B., Chenu, K. & Doherty, A. The APSIM-Wheat Module (7.5 R3008) (APSIM Initiative, 2015); https://www.apsim.info/wp-content/uploads/2019/09/WheatDocumentation.pdfZadoks, J. C., Chang, T. T. & Konzak, C. F. A decimal code for the growth stages of cereals. Weed Res. 14, 415–421 (1974).
    Google Scholar 
    Bell, L. W., Lilley, J. M., Hunt, J. R. & Kirkegaard, J. A. Optimising grain yield and grazing potential of crops across Australia’s high-rainfall zone: a simulation analysis. 1. Wheat. Crop Pasture Sci. 66, 332–348 (2015).
    Google Scholar 
    Flohr, B. M., Hunt, J. R., Kirkegaard, J. A. & Evans, J. R. Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia. Field Crops Res. 209, 108–119 (2017).
    Google Scholar 
    Chen, C. et al. Spatial patterns of estimated optimal flowering period of wheat across the southwest of Western Australia. Field Crops Res. 247, 107710 (2020).
    Google Scholar 
    Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16, 309–330 (2001).
    Google Scholar 
    Liu, B. et al. Global wheat production with 1.5 and 2.0 °C above pre-industrial warming. Glob. Change Biol. 25, 1428–1444 (2019).
    Google Scholar 
    Zhao, Z., Wang, E., Rebetzke, G. J. & Kirkegaard, J. A. Supporting data for ‘Sowing deep to beat the heat using novel genetics adapts wheat to a changing climate’. CSIRO Data Access Portal https://data.csiro.au/collection/csiro:53658 (2022).Holzworth, D. et al. APSIM Next Generation: overcoming challenges in modernising a farming systems model. Environ. Model. Softw. 103, 43–51 (2018).
    Google Scholar 
    APSIM Initiative. Source code of APSIM Next Generation. GitHub https://github.com/APSIMInitiative/ApsimX (2021). More

  • in

    Pronounced loss of Amazon rainforest resilience since the early 2000s

    DatasetsWe use the Amazon basin (http://worldmap.harvard.edu/data/geonode:amapoly_ivb, accessed 28 January 2021) as our region of study. To determine the grid cells that are contained within Brazil for a subset of analysis, we use the ‘maps’ package in R (v.3.3.0; https://CRAN.R-project.org/package=maps). This is also used in the plotting of country outlines. The main dataset used to determine forest health is from VODCA33, of which we use the Ku-band product. These data are available at 0.25° × 0.25° at a monthly resolution from January 1988 to December 2016. We also use NOAA AVHRR NDVI34. For precipitation data, we use the CHIRPS dataset40 downloaded from Google Earth Engine at a monthly resolution. Finally, to determine land cover types, we used the IGBP MODIS land cover dataset MCD12C1 (ref. 37). All these datasets are at a higher spatial resolution than the VODCA dataset and thus we downscale them to match the lower resolution. Our SST data comes from HadISST49, where we define a North Atlantic region (15–70° W, 5–25° N), for which we take the spatial mean. The mean monthly cycle is then removed to produce anomalies.For the vegetation datasets that we measure the resilience indicators on (below), we use STL decomposition (seasonal and trend decomposition using Loess)51 using the stl() function in R. This splits time series in each grid cell into an overall trend, a repeating annual cycle (by using the ‘periodic’ option for the seasonal window) and a residual component. We use the residual component in our resilience analysis. The first 3 yr of data had large jumps in VOD which were seen when testing other regions of the world as well as in the Amazon region. Hence, we restrict our analysis to the period January 1991 to December 2016.To test the robustness of the detrending, we also vary the size of the trend window in the stl() function. The results from these alternatively detrended time series are shown in Supplementary Fig. 4. The results are also robust to varying the window used to calculate the seasonal component rather than using ‘periodic’; at the strictest plausible value of 13, we still see the same increases in AR(1) (Supplementary Fig. 5).For the AMO index shown in Supplementary Fig. 13, data come from the Kaplan SST dataset and can be downloaded from https://psl.noaa.gov/data/timeseries/AMO/.Grid cell selectionWe use the IGBP MODIS land cover dataset at the resolution described above to determine which grid cells to use in our analysis. The dataset is available at an annual resolution from 2001 to 2018 (but we only use the time series up to 2016 to match the time span of our VOD and NDVI datasets). To focus on changes in forest resilience, we use grid cells where the evergreen BL fraction is ≥80% in 2001. Grid cells are treated as human land-use area if the built-up, croplands or vegetation mosaics fraction is >0%. We remove grid cells that have human land use in them from our forest analysis, regardless of if there is ≥80% BL fraction in the grid cell.We measure the minimum distance between forested Amazon basin grid cells and human land-use grid cells in 2016 (believing this to be the most cautious and least biased way to measure distance) using the latitude and longitude of each grid point and computing the great-circle distance. We use human land-use grid cells over a larger area than the basin, so that we can determine the closest distance to human land use, regardless of whether this human land use lies within the basin. We also measure the minimum distance from human land use or roads in Brazil, where we have reliable data on state and federal roads (https://datacatalog.worldbank.org/dataset/brazil-road-network-federal-and-state-highways). As in the main text, we reiterate that these minimum distances can be viewed as the maximum distance from human land use as our data will not include roads for the full Amazon basin, or non-federal or non-state roads in Brazil that will have human activity associated with them.To ensure that the pattern of changes in resilience is not a consequence of more settlements being in the southeast of the region, combined with the gradient of rainfall from northwest to southeast typical of the rainforest, we measure the correlation between MAP and the distances from the urban grid cells, which is very weak (Spearman’s ρ = 0.109, P  More

  • in

    Hibernation slows epigenetic ageing in yellow-bellied marmots

    Flatt, T. A new definition of aging? Front. Genet. 3, 148 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Berdasco, M. & Esteller, M. Hot topics in epigenetic mechanisms of aging: 2011. Aging Cell 11, 181–186 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jylhävä, J., Pedersen, N. L. & Hägg, S. Biological age predictors. EBioMedicine 21, 29–36 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Wagner, K. H., Cameron-Smith, D., Wessner, B. & Franzke, B. Biomarkers of aging: from function to molecular biology. Nutrients 8, 338 (2016).
    Google Scholar 
    Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging 7, 1159–1170 (2015).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Johnson, T. E. Recent results: biomarkers of aging. Exp. Gerontol. 41, 1243–1246 (2006).CAS 
    PubMed 
    PubMed Central 

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

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

    Google Scholar 
    Unnikrishnan, A. et al. The role of DNA methylation in epigenetics of aging. Pharmacol. Ther. 195, 172–185 (2019).CAS 
    PubMed 
    PubMed Central 

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

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

    Google Scholar 
    Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987 (2014).CAS 
    PubMed 
    PubMed Central 

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

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

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

    Google Scholar 
    Ito, G., Yoshimura, K. & Momoi, Y. Analysis of DNA methylation of potential age-related methylation sites in canine peripheral blood leukocytes. J. Vet. Med. Sci. 79, 745–750 (2017).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Lowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. Genome Biol. 19, 22 (2018).PubMed 
    PubMed Central 

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

    Google Scholar 
    Zaghlool, S. B. et al. Association of DNA methylation with age, gender, and smoking in an Arab population. Clin. Epigenetics 7, 6 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Gao, X., Zhang, Y., Breitling, L. P. & Brenner, H. Relationship of tobacco smoking and smoking-related DNA methylation with epigenetic age acceleration. Oncotarget 7, 46878–46889 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Marioni, R. E. et al. The epigenetic clock and telomere length are independently associated with chronological age and mortality. Int. J. Epidemiol. 45, 424–432 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Marioni, R. E. et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 16, 25 (2015).PubMed 
    PubMed Central 

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

    Google Scholar 
    Chen, B. H. et al. DNA methylation‐based measures of biological age: meta‐analysis predicting time to death. Aging 8, 1844–1859 (2016).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Horvath, S. & Levine, A. J. HIV-1 infection accelerates age according to the epigenetic clock. J. Infect. Dis. 212, 1563–1573 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Horvath, S. et al. Accelerated epigenetic aging in Down syndrome. Aging Cell 14, 491–495 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parrott, B. B. & Bertucci, E. M. Epigenetic aging clocks in ecology and evolution. Trends Ecol. Evol. 34, 767–770 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Wagner, W. Epigenetic aging clocks in mice and men. Genome Biol. 18, 107 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Wang, T. et al. Quantitative translation of dog-to-human aging by conserved remodeling of the DNA methylome. Cell Syst. 11, 176–185 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Wilkinson, G. S. & Adams, D. M. Recurrent evolution of extreme longevity in bats. Biol. Lett. 15, 20180860 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Austad, S. N. Comparative biology of aging. J. Gerontol. A 64, 199–201 (2009).
    Google Scholar 
    Wu, C. W. & Storey, K. B. Life in the cold: links between mammalian hibernation and longevity. Biomol. Concepts 7, 41–52 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Turbill, C., Bieber, C. & Ruf, T. Hibernation is associated with increased survival and the evolution of slow life histories among mammals. Proc. R. Soc. Lond. B 278, 3355–3363 (2011).
    Google Scholar 
    Chen, Y. et al. Mechanisms for increased levels of phosphorylation of elongation factor-2 during hibernation in ground squirrels. Biochemistry 40, 11565–11570 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Knight, J. E. et al. mRNA stability and polysome loss in hibernating Arctic ground squirrels (Spermophilus parryii). Mol. Cell. Biol. 20, 6374–6379 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yan, J., Barnes, B. M., Kohl, F. & Marr, T. G. Modulation of gene expression in hibernating arctic ground squirrels. Physiol. Genomics 32, 170–181 (2008).CAS 

    Google Scholar 
    Van Breukelen, F. & Martin, S. L. Molecular adaptations in mammalian hibernators: unique adaptations or generalized responses? J. Appl. Physiol. 92, 2640–2647 (2002).
    Google Scholar 
    Morin, P. & Storey, K. B. Evidence for a reduced transcriptional state during hibernation in ground squirrels. Cryobiology 53, 310–318 (2006).CAS 

    Google Scholar 
    van Breukelen, F. & Martin, S. L. Reversible depression of transcription during hibernation. J. Comp. Physiol. B 172, 355–361 (2002).
    Google Scholar 
    Azzu, V. & Valencak, T. G. Energy metabolism and ageing in the mouse: a mini-review. Gerontology 63, 327–336 (2017).
    Google Scholar 
    Schrack, J. A., Knuth, N. D., Simonsick, E. M. & Ferrucci, L. ‘IDEAL’ aging is associated with lower resting metabolic rate: the Baltimore Longitudinal Study of Aging. J. Am. Geriatr. Soc. 62, 667–672 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Al-attar, R. & Storey, K. B. Suspended in time: molecular responses to hibernation also promote longevity. Exp. Gerontol. 134, 110889 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Carey, H. V., Andrews, M. T. & Martin, S. L. Mammalian hibernation: cellular and molecular responses to depressed metabolism and low temperature. Physiol. Rev. 83, 1153–1181 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Turbill, C., Ruf, T., Smith, S. & Bieber, C. Seasonal variation in telomere length of a hibernating rodent. Biol. Lett. 9, 20121095 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Turbill, C., Smith, S., Deimel, C. & Ruf, T. Daily torpor is associated with telomere length change over winter in Djungarian hamsters. Biol. Lett. 8, 304–307 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Armitage, K. B., Blumstein, D. T. & Woods, B. C. Energetics of hibernating yellow-bellied marmots (Marmota flaviventris). Comp. Biochem. Physiol. A 134, 101–114 (2003).
    Google Scholar 
    Armitage, K. B. in Molecules to Migration: the Pressures of Life (eds Morris, S. & Vosloo, A.) 591–602 (Medimond Publishing, 2008).Haghani, A. et al. DNA methylation networks underlying mammalian traits. Preprint at bioRxiv https://doi.org/10.1101/2021.03.16.435708 (2021).Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.01.18.426733 (2021).Yang, S. et al. Rare mutations in AHDC1 in patients with obstructive sleep apnea. Biomed. Res. Int. https://doi.org/10.1155/2019/5907361 (2019).De Paoli-Iseppi, R. et al. Measuring animal age with DNA methylation: from humans to wild animals. Front. Genet. 8, 106 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Arneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences. Nat. Commun. 13, 783 (2022).CAS 

    Google Scholar 
    Armitage, K. B. Reproductive strategies of yellow-bellied marmots: energy conservation and differences between the sexes. J. Mammal. 79, 385–393 (1998).
    Google Scholar 
    Armitage, K. B. in Adaptive Strategies and Diversity in Marmots (eds Ramousse, R. et al.) 133–142 (International Marmot Network, 2003).Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics 14, 912–926 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Snir, S., VonHoldt, B. M. & Pellegrini, M. A statistical framework to identify deviation from time linearity in epigenetic aging. PLoS Comput. Biol. 12, e1005183 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Farrell, C., Snir, S. & Pellegrini, M. The epigenetic pacemaker: modeling epigenetic states under an evolutionary framework. Bioinformatics 36, 4662–4663 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marioni, R. E. et al. Tracking the epigenetic clock across the human life course: a meta-analysis of longitudinal cohort data. J. Gerontol. A 74, 57–61 (2019).
    Google Scholar 
    El Khoury, L. Y. et al. Systematic underestimation of the epigenetic clock and age acceleration in older subjects. Genome Biol. 20, 283 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Kilgore, D. L. & Armitage, K. B. Energetics of yellow-bellied marmot populations. Ecology 59, 78–88 (1978).
    Google Scholar 
    Armitage, K. B. Social and population dynamics of yellow-bellied marmots: results from long-term research. Annu. Rev. Ecol. Syst. 22, 379–407 (1991).
    Google Scholar 
    Webb, D. R. Environmental harshness, heat stress, and Marmota flaviventris. Oecologia 44, 390–395 (1980).
    Google Scholar 
    Armitage, K. B. Evolution of sociality in marmots. J. Mammal. 80, 1–10 (1999).
    Google Scholar 
    Allainé, D. Sociality, mating system and reproductive skew in marmots: evidence and hypotheses. Behav. Processes 51, 21–34 (2000).
    Google Scholar 
    Arnold, W. The evolution of marmot sociality. II. Costs and benefits of joint hibernation. Behav. Ecol. Sociobiol. 27, 239–246 (1990).
    Google Scholar 
    Villanueva-Cañas, J. L., Faherty, S. L., Yoder, A. D. & Albà, M. M. Comparative genomics of mammalian hibernators using gene networks. Integr. Comp. Biol. 54, 452–462 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Lyman, C. P., O’Brien, R. C., Greene, G. C. & Papafrangos, E. D. Hibernation and longevity in the Turkish hamster Mesocricetus brandti. Science 212, 668–670 (1981).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kirby, R., Johnson, H. E., Alldredge, M. W. & Pauli, J. N. The cascading effects of human food on hibernation and cellular aging in free-ranging black bears. Sci. Rep. 9, 2197 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Giroud, S. et al. Late-born intermittently fasted juvenile garden dormice use torpor to grow and fatten prior to hibernation: consequences for ageing processes. Proc. R. Soc. Lond. B 281, 20141131 (2014).
    Google Scholar 
    Hoelzl, F. et al. Telomeres are elongated in older individuals in a hibernating rodent, the edible dormouse (Glis glis). Sci. Rep. 6, 36856 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Haussmann, M. F. & Mauck, R. A. Telomeres and longevity: testing an evolutionary hypothesis. Mol. Biol. Evol. 25, 220–228 (2008).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    van Lieshout, S. H. J. et al. Individual variation in early-life telomere length and survival in a wild mammal. Mol. Ecol. 28, 4152–4165 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Lowe, D., Horvath, S. & Raj, K. Epigenetic clock analyses of cellular senescence and ageing. Oncotarget 7, 8524–8531 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Kabacik, S., Horvath, S., Cohen, H. & Raj, K. Epigenetic ageing is distinct from senescence-mediated ageing and is not prevented by telomerase expression. Aging 10, 2800–2815 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Keil, G., Cummings, E. & Magalhães, J. P. Being cool: how body temperature influences ageing and longevity. Biogerontology 16, 383–397 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Means, L. W., Higgins, J. L. & Fernandez, T. J. Mid-life onset of dietary restriction extends life and prolongs cognitive functioning. Physiol. Behav. 54, 503–508 (1993).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Speakman, J. R. & Mitchell, S. E. Caloric restriction. Mol. Aspects Med. 32, 159–221 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Walford, R. L. & Spindler, S. R. The response to calorie restriction in mammals shows features also common to hibernation: a cross-adaptation hypothesis. J. Gerontol. A 52, B179–B183 (1997).CAS 

    Google Scholar 
    Conti, B. et al. Transgenic mice with a reduced core body temperature have an increased life span. Science 314, 825–828 (2006).CAS 

    Google Scholar 
    Conti, B. Considerations on temperature, longevity and aging. Cell. Mol. Life Sci. 65, 1626–1630 (2008).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gribble, K. E., Moran, B. M., Jones, S., Corey, E. L. & Mark Welch, D. B. Congeneric variability in lifespan extension and onset of senescence suggest active regulation of aging in response to low temperature. Exp. Gerontol. 114, 99–106 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Johns, D. W. & Armitage, K. B. Behavioral ecology of alpine yellow-bellied marmots. Behav. Ecol. Sociobiol. 5, 133–157 (1979).
    Google Scholar 
    Armitage, K. B. Social behaviour of a colony of the yellow-bellied marmot (Marmota flaviventris). Anim. Behav. 10, 319–331 (1962).
    Google Scholar 
    Armitage, K. B. Vernal behaviour of the yellow-bellied marmot (Marmota flaviventris). Anim. Behav. 13, 59–68 (1965).
    Google Scholar 
    Armitage, K. B., Melcher, J. C. & Ward, J. M. Oxygen consumption and body temperature in yellow-bellied marmot populations from montane-mesic and lowland-xeric environments. J. Comp. Physiol. B 160, 491–502 (1990).
    Google Scholar 
    Sheriff, M. J., Williams, C. T., Kenagy, G. J., Buck, C. L. & Barnes, B. M. Thermoregulatory changes anticipate hibernation onset by 45 days: data from free-living arctic ground squirrels. J. Comp. Physiol. B 182, 841–847 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Schwartz, C., Hampton, M. & Andrews, M. T. Hypothalamic gene expression underlying pre-hibernation satiety. Genes Brain Behav. 14, 310–318 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maegawa, S. et al. Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res. 20, 332–340 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hampton, M., Melvin, R. G. & Andrews, M. T. Transcriptomic analysis of brown adipose tissue across the physiological extremes of natural hibernation. PLoS ONE 8, e85157 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Lindner, M. et al. Temporal changes in DNA methylation and RNA expression in a small song bird: within- and between-tissue comparisons. BMC Genomics 22, 36 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schwartz, C., Hampton, M. & Andrews, M. T. Seasonal and regional differences in gene expression in the brain of a hibernating mammal. PLoS ONE 8, e58427 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dopico, X. C. et al. Widespread seasonal gene expression reveals annual differences in human immunity and physiology. Nat. Commun. 6, 7000 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jansen, H. T. et al. Hibernation induces widespread transcriptional remodeling in metabolic tissues of the grizzly bear. Commun. Biol. 2, 336 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Viitaniemi, H. M. et al. Seasonal variation in genome-wide DNA methylation patterns and the onset of seasonal timing of reproduction in great tits. Genome Biol. Evol. 11, 970–983 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Johnston, R. A., Paxton, K. L., Moore, F. R., Wayne, R. K. & Smith, T. B. Seasonal gene expression in a migratory songbird. Mol. Ecol. 25, 5680–5691 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boyer, B. B. & Barnes, B. M. Molecular and metabolic aspects of mammalian hibernation. Bioscience 49, 713–724 (1999).
    Google Scholar 
    Siutz, C., Ammann, V. & Millesi, E. Shallow torpor expression in free-ranging common hamsters with and without food supplements. Front. Ecol. Evol. 6, 190 (2018).
    Google Scholar 
    Langer, F., Havenstein, N. & Fietz, J. Flexibility is the key: metabolic and thermoregulatory behaviour in a small endotherm. J. Comp. Physiol. B 188, 553–563 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Bieber, C., Turbill, C. & Ruf, T. Effects of aging on timing of hibernation and reproduction. Sci. Rep. 8, 13881 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Storey, K. B. & Storey, J. M. Aestivation: signaling and hypometabolism. J. Exp. Biol. 215, 1425–1433 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Krivoruchko, A. & Storey, K. B. Forever young: mechanisms of natural anoxia tolerance and potential links to longevity. Oxid. Med. Cell. Longev. 3, 186–198 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Storey, K. B. & Storey, J. M. Metabolic rate depression in animals: transcriptional and translational controls. Biol. Rev. 79, 207–233 (2004).PubMed 
    PubMed Central 

    Google Scholar 
    Puspitasari, A. et al. Hibernation as a tool for radiation protection in space exploration. Life 11, 54 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Blumstein, D. T. Yellow-bellied marmots: insights from an emergent view of sociality. Philos. Trans. R. Soc. Lond. B 368, 20120349 (2013).
    Google Scholar 
    Armitage, K. B. & Downhower, J. F. Demography of yellow-bellied marmot populations. Ecology 55, 1233–1245 (1974).
    Google Scholar 
    Zhou, W., Triche, T. J., Laird, P. W. & Shen, H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucleic Acids Res. 46, e123 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Labarre, B. A. et al. MethylToSNP: identifying SNPs in Illumina DNA methylation array data. Epigenetics Chromatin 12, 79 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Snir, S., Wolf, Y. I. & Koonin, E. V. Universal pacemaker of genome evolution. PLoS Comput. Biol. 8, e1002785 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).
    Google Scholar 
    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. Epigenomics 10, 695–706 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wood, S. & Scheipl, F. gamm4: Generalized additive mixed models using mgcv and lme4, R package version 0.2-3 (2014); http://cran.r-project.org/package=gamm4R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).RStudio Team. RStudio: Integrated Development Environment for R (RStudio Inc., 2019).Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).Kluyver, T. et al. in Positioning and Power in Academic Publishing: Players, Agents and Agendas (eds Loizides, F. & Scmidt, B.) 87–90 (IOS Press, 2016); https://doi.org/10.3233/978-1-61499-649-1-87Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots https://cran.r-project.org/package=ggpubr (2020).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).
    Google Scholar 
    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
    Google Scholar 
    Mclean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pinho, G. M. et al. Hibernation slows epigenetic ageing in yellow-bellied marmots data sets. OSF https://doi.org/10.17605/OSF.IO/E42ZV (2021). More