Philippa Kaur

1.
Hop H, Pearson T, Hegseth EN, Kovacs KM, Wiencke C, Kwasniewski S, et al. The marine ecosystem of Kongsfjorden, Svalbard. Polar Res. 2002;21:167–208.
Article  Google Scholar 
2.
Arndt S, Jørgensen BB, LaRowe DE, Middelburg JJ, Pancost RD, Regnier P. Quantifying the degradation of organic matter in marine sediments: a review and synthesis. Earth-Sci Rev. 2013;123:53–86.
CAS  Article  Google Scholar 

3.
Dunne JP, Sarmiento JL, Gnanadesikan A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Glob Biogeochem Cycles. 2007;21:1–16.
Article  CAS  Google Scholar 

4.
Christian JR, Karl DM. Bacterial ectoenzymes in m`arine waters: activity ratios and temperature responses in three oceanographic provinces. Limnol Oceanogr. 1995;40:1042–9.
CAS  Article  Google Scholar 

5.
Fabiano M, Pusceddu A. Total and hydrolizable particulate organic matter (carbohydrates, proteins and lipids) at a coastal station in Terra Nova Bay (Ross Sea, Antarctica). Polar Biol. 1998;19:125–32.
Article  Google Scholar 

6.
Bradley JA, Amend JP, LaRowe DE. Necromass as a limited source of energy for microorganisms in marine sediments. J Geophys Res Biogeosci. 2018;123:577–90.
Article  Google Scholar 

7.
Wehrmann LM, Formolo MJ, Owens JD, Raiswell R, Ferdelman TG, Riedinger N, et al. Iron and manganese speciation and cycling in glacially influenced high-latitude fjord sediments (West Spitsbergen, Svalbard): evidence for a benthic recycling-transport mechanism. Geochim Cosmochim Acta. 2014;141:628–55.
CAS  Article  Google Scholar 

8.
Burdige DJ. Preservation of organic matter in marine sediments: controls, mechanisms, and an imbalance in sediment organic carbon budgets? Chem Rev. 2007;107:467–85.
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Hedges JI, Oades JM. Comparative organic geochemistries of soils and marine sediments. Org Geochem. 1997;27:319–61.
CAS  Article  Google Scholar 

10.
McCarthy M, Pratum T, Hedges J, Benner R. Chemical composition of dissolved organic nitrogen in the ocean. Nature. 1997;390:150–4.
CAS  Article  Google Scholar 

11.
Vetter YA, Deming JW. Extracellular enzyme activity in the Arctic Northeast Water polynya. Mar Ecol Prog Ser. 1994;114:23–34.
CAS  Article  Google Scholar 

12.
Parsons TR, Stephens K, Strickland JDH. On the chemical composition of eleven species of marine phytoplankters. J Fish Res Board Can. 1961;18:1001–16.
CAS  Article  Google Scholar 

13.
Hudson BJ, Karis IG. The lipids of the alga Spirulina. J Sci Food Agric. 1974;25:759–63.
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Wakeham SG, Lee C, Farrington JW, Gagosian RB. Biogeochemistry of particulate organic matter in the oceans: results from sediment trap experiments. Deep Sea Res A. 1984;31:509–28.
CAS  Article  Google Scholar 

15.
Harvey HR, Rodger Harvey H, Fallon RD, Patton JS. The effect of organic matter and oxygen on the degradation of bacterial membrane lipids in marine sediments. Geochim Cosmochim Acta. 1986;50:795–804.
CAS  Article  Google Scholar 

16.
Sousa DZ, Smidt H, Alves MM, Stams AJM. Ecophysiology of syntrophic communities that degrade saturated and unsaturated long-chain fatty acids. FEMS Microbiol Ecol. 2009;68:257–72.
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Meyer-Reil L-A. Ecological aspects of enzymatic activity in marine sediments. Brock/Springer Series in Contemporary Bioscience; Springer New York New York, NY 1991. p. 84–95.

18.
Beulig F, Røy H, Glombitza C, Jørgensen BB. Control on rate and pathway of anaerobic organic carbon degradation in the seabed. Proc Natl Acad Sci USA. 2018;115:367–72.
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Arnosti C. Microbial extracellular enzymes and the marine carbon cycle. Ann Rev Mar Sci. 2011;3:401–25.
PubMed  Article  PubMed Central  Google Scholar 

20.
Arnosti C. Contrasting patterns of peptidase activities in seawater and sediments: an example from Arctic fjords of Svalbard. Mar Chem. 2015;168:151–6.
CAS  Article  Google Scholar 

21.
Muyzer G, Stams AJM. The ecology and biotechnology of sulphate-reducing bacteria. Nat Rev Microbiol. 2008;6:441–54.
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Webster G, Watt LC, Rinna J, Fry JC, Evershed RP, Parkes RJ, et al. A comparison of stable-isotope probing of DNA and phospholipid fatty acids to study prokaryotic functional diversity in sulfate-reducing marine sediment enrichment slurries. Environ Microbiol. 2006;8:1575–89.
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Müller AL, Pelikan C, de Rezende JR, Wasmund K, Putz M, Glombitza C, et al. Bacterial interactions during sequential degradation of cyanobacterial necromass in a sulfidic arctic marine sediment. Environ Microbiol. 2018;20:2927–40.
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Knoblauch C, Sahm K, Jørgensen BB. Psychrophilic sulfate-reducing bacteria isolated from permanently cold arctic marine sediments: description of Desulfofrigus oceanense gen. nov., sp. nov., Desulfofrigus fragile sp. nov., Desulfofaba gelida gen. nov., sp. nov., Desulfotalea psychrophila gen. nov., sp. nov. and Desulfotalea arctica sp. nov. Int J Syst Bacteriol. 1999;49:1631–43.
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Sahm K, Knoblauch C, Amann R. Phylogenetic affiliation and quantification of psychrophilic sulfate-reducing isolates in marine Arctic sediments. Appl Environ Microbiol. 1999;65:3976–81.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Na H, Lever MA, Kjeldsen KU, Schulz F, Jørgensen BB. Uncultured desulfobacteraceae and crenarchaeotal group C3 incorporate 13C-acetate in coastal marine sediment. Environ Microbiol Rep. 2015;7:614–22.
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Wasmund K, Mußmann M, Loy A. The life sulfuric: microbial ecology of sulfur cycling in marine sediments. Environ Microbiol Rep. 2017;9:323–44.
CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Lloyd KG, Schreiber L, Petersen DG, Kjeldsen KU, Lever MA, Steen AD, et al. Predominant archaea in marine sediments degrade detrital proteins. Nature. 2013;496:215–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Zinke LA, Glombitza C, Bird JT, Røy H, Jørgensen BB, Lloyd KG, et al. Microbial organic matter degradation potential in Baltic Sea sediments influenced by depositional conditions and in situ geochemistry. Appl Environ Microbiol. 2018;85:e02164–18.
Article  Google Scholar 

30.
Orsi WD, Richards TA, Francis WR. Predicted microbial secretomes and their target substrates in marine sediment. Nat Microbiol. 2018;3:32–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Baker BJ, Lazar CS, Teske AP, Dick GJ. Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria. Microbiome. 2015;3:14.
PubMed  PubMed Central  Article  Google Scholar 

32.
Boyer T, Levitus S, Garcia H, Locarnini RA, Stephens C, Antonov J. Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25 grid. Int J Climatol. 2005;25:931–45.
Article  Google Scholar 

33.
Glombitza C, Jaussi M, Røy H, Seidenkrantz M-S, Lomstein BA, Jørgensen BB. Formate, acetate, and propionate as substrates for sulfate reduction in sub-arctic sediments of Southwest Greenland. Front Microbiol. 2015;6:846.
PubMed  PubMed Central  Article  Google Scholar 

34.
Graue J, Engelen B, Cypionka H. Degradation of cyanobacterial biomass in anoxic tidal-flat sediments: a microcosm study of metabolic processes and community changes. ISME J. 2012;6:660–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Newport PJ, Nedwell DB. The mechanisms of inhibition of Desulfovibrio and Desulfotomaculum species by selenate and molybdate. J Appl Bacteriol. 1988;65:419–23.
CAS  Article  Google Scholar 

36.
Danovaro R, Dell’Anno A, Fabiano M. Bioavailability of organic matter in the sediments of the Porcupine Abyssal Plain, northeastern Atlantic. Mar Ecol Prog Ser. 2001;220:25–32.
CAS  Article  Google Scholar 

37.
Pusceddu A, Dell’Anno A, Fabiano M, Danovaro R. Quantity and bioavailability of sediment organic matter as signatures of benthic trophic status. Mar Ecol Prog Ser. 2009;375:41–52.
CAS  Article  Google Scholar 

38.
Glombitza C, Pedersen J, Røy H, Jørgensen BB. Direct analysis of volatile fatty acids in marine sediment porewater by two-dimensional ion chromatography-mass spectrometry. Limnol Oceanogr Methods. 2014;12:455–68.
CAS  Article  Google Scholar 

39.
Dumont MG, Radajewski SM, Miguez CB, McDonald IR, Murrell JC. Identification of a complete methane monooxygenase operon from soil by combining stable isotope probing and metagenomic analysis. Environ Microbiol. 2006;8:1240–50.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Neufeld JD, Vohra J, Dumont MG, Lueders T, Manefield M, Friedrich MW, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Pelikan C, Herbold CW, Hausmann B, Müller AL, Pester M, Loy A. Diversity analysis of sulfite- and sulfate-reducing microorganisms by multiplex dsrA and dsrB amplicon sequencing using new primers and mock community-optimized bioinformatics. Environ Microbiol. 2016;18:2994–3009.
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Herbold CW, Pelikan C, Kuzyk O, Hausmann B, Angel R, Berry D, et al. A flexible and economical barcoding approach for highly multiplexed amplicon sequencing of diverse target genes. Front Microbiol. 2016;6:731.
Google Scholar 

43.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Tikhonov M, Leach RW, Wingreen NS. Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution. ISME J. 2015;9:68–80.
PubMed  Article  CAS  PubMed Central  Google Scholar 

45.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Lagkouvardos I, Joseph D, Kapfhammer M, Giritli S, Horn M, Haller D, et al. IMNGS: a comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci Rep. 2016;6:33721.
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.
PubMed  PubMed Central  Article  CAS  Google Scholar 

52.
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.
PubMed  PubMed Central  Article  CAS  Google Scholar 

53.
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019 Nov 15:btz848. https://doi.org/10.1093/bioinformatics/btz848. Epub ahead of print. PMID: 31730192.

60.
Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
CAS  PubMed  Article  PubMed Central  Google Scholar 

61.
Minh BQ, Nguyen MAT, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30:1188–95.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2007;23:127–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
PubMed  PubMed Central  Article  CAS  Google Scholar 

64.
Vallenet D, Calteau A, Cruveiller S, Gachet M, Lajus A, Josso A, et al. MicroScope in 2017: an expanding and evolving integrated resource for community expertise of microbial genomes. Nucleic Acids Res. 2017;45:D517–28.
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. The RAST server: rapid annotations using subsystems technology. BMC Genomics. 2008;9:75.
PubMed  PubMed Central  Article  CAS  Google Scholar 

66.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2013;42:D222–30.
PubMed  PubMed Central  Article  CAS  Google Scholar 

69.
Haft DH, Selengut JD, White O. The TIGRFAMs database of protein families. Nucleic Acids Res. 2003;31:371–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2018;46:2699.
PubMed  PubMed Central  Article  CAS  Google Scholar 

71.
Kall L, Krogh A, Sonnhammer ELL. Advantages of combined transmembrane topology and signal peptide prediction-the Phobius web server. Nucleic Acids Res. 2007;35:W429–32.
PubMed  PubMed Central  Article  Google Scholar 

72.
Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010;26:1608–15.
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Rawlings ND. MEROPS: the peptidase database. Nucleic Acids Res. 2000;28:323–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Lenfant N, Hotelier T, Velluet E, Bourne Y, Marchot P, Chatonnet A. ESTHER, the database of the α/β-hydrolase fold superfamily of proteins: tools to explore diversity of functions. Nucleic Acids Res. 2013;41:D423–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–95. https://doi.org/10.1093/nar/gkt1178.

76.
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402.
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Steen AD, Kevorkian RT, Bird JT, Dombrowski N, Baker BJ, Hagen SM, et al. Kinetics and identities of extracellular peptidases in subsurface sediments of the White Oak River Estuary, North Carolina. Appl Environ Microbiol. 2019;85:e00102–19.

78.
Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.
PubMed  PubMed Central  Article  CAS  Google Scholar 

80.
Berger SA, Krompass D, Stamatakis A. Performance, accuracy, and Web server for evolutionary placement of short sequence reads under maximum likelihood. Syst Biol. 2011;60:291–302.
PubMed  PubMed Central  Article  Google Scholar 

81.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Zhao J-S, Manno D, Hawari J. Psychrilyobacter atlanticus gen. nov., sp. nov., a marine member of the phylum Fusobacteria that produces H2 and degrades nitramine explosives under low temperature conditions. Int J Syst Evol Microbiol. 2009;59:491–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

83.
Hedges JI, Oades JM. Comparative organic geochemistries of soils and marine sediments. Org Geochem. 1997;27:319–61.
CAS  Article  Google Scholar 

84.
Wakeham SG, Canuel EA. Degradation and preservation of organic matter in marine sediments. In: The handbook of environmental chemistry; Springer Berlin Heidelberg Berlin, Heidelberg 2006. p. 295–321.

85.
Bienhold C, Boetius A, Ramette A. The energy–diversity relationship of complex bacterial communities in Arctic deep-sea sediments. ISME J. 2011;6:724–32.
PubMed  PubMed Central  Article  CAS  Google Scholar 

86.
Finke N, Vandieken V, Jørgensen BB. Acetate, lactate, propionate, and isobutyrate as electron donors for iron and sulfate reduction in Arctic marine sediments, Svalbard. FEMS Microbiol Ecol. 2007;59:10–22.
CAS  PubMed  Article  PubMed Central  Google Scholar 

87.
Glombitza C, Egger M, Røy H, Jørgensen BB. Controls on volatile fatty acid concentrations in marine sediments (Baltic Sea). Geochim Cosmochim Acta. 2019;258:226–41.
CAS  Article  Google Scholar 

88.
Kubo K, Lloyd KG, F Biddle J, Amann R, Teske A, Knittel K. Archaea of the Miscellaneous Crenarchaeotal Group are abundant, diverse and widespread in marine sediments. ISME J. 2012;6:1949–65.
CAS  PubMed  PubMed Central  Article  Google Scholar 

89.
Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.
CAS  PubMed  PubMed Central  Article  Google Scholar  More

Greenhouse experiments and sampling
Wheat (Triticum turgidum cv. Negev) was cultivated in sandy loam soil (19% clay, 6% silt, 75% sand) classified as Calcic Haploxerept. The soil was obtained from intensive agriculture field located in Eshkol region, Israel (31.248,949, 34.379,872). Potatoes, wheat and peanuts were previously grown in this field. Initial soil parameters were: pH 8.78 ± 0.04, electrical conductivity 99 ± 1 (µS/m), NO3-N 0.22 ± 0.02 (mg/kg), NH4 0.30 ± 0.01 (mg/kg), P-PO4 0.09 ± 0.01(mg/kg), total soluble organic carbon 4.0 ± 0.04 (mg/kg) and total soluble nitrogen 0.70 ± 0.02 (mg/kg).
The plants were grown for 6 weeks (from December 2016 to February 2017) as described previously [25]. Briefly, 750 g of soil was distributed in a 700-mL plastic pot, with four seeds per pot. Those pots were able to sustain up to four wheat plants for six weeks under the experimental conditions. The wheat was grown in a greenhouse with two closed-system chambers at day/night temperatures of 25 °C/18 °C ± 1 °C, and with an automatically adjusted CO2-supply system (Emproco Ltd., Ashkelon, Israel). The photoperiod was 9 h and the daily light integral was 12.5 MJ/day. Wheat plants were grown in a sequence of three independent experimental cycles of 6 weeks each (five pots per treatment per cycle), with a 1-week shift between cycles. Plants were grown under either ambient (400 ppm) or elevated (850 ppm) atmospheric CO2 levels. Nutrient solution was prepared with 90% nitrogen supplied as nitrate and 10% supplied as ammonium using KNO3 and NH4NO3 to provide final concentrations of 30, 70 and 100 ppm nitrate [26]. Other macronutrients were supplied in each treatment at the following rate: P-15 ppm, K-150 ppm, Mg-24 ppm, Ca-120 ppm and S-40 ppm provide by NH4NO3, KNO3, CaCl2, KCl, MgCl2 and KH2PO4 salts. 40 ppm S and Ca were present in the tap water. Micronutrients were supplied at a rate of 1.3 ppm Fe, 0.7 ppm Mn, 0.3 ppm Zn, 0.05 ppm Cu, and 0.0375 ppm Mo using Korotin (Haifa Chemicals, Israel), a commercial micronutrient mix. Each pot was irrigated with 50 mL of the nutrient solution four times a week. The total amount of nitrogen in the 30 ppm nitrate treatment was 36 mg/pot (equivalent of ca. 73 kg N/ha), 70 ppm nitrate treatment was 84 mg/pot (equivalent of ca. 170 kg N/ha) and in the 100 ppm treatment, 120 mg/pot (equivalent of ca. 250 kg N/ha).
Soil and plant analyses
At the end of the 6th week of growth, 15 pots (5 pots per cycle) from each treatment were sampled for soil, shoots and roots, and the following parameters were measured: soil nitrate and ammonia content, soil EC and soil pH, shoot and root dry biomass, nitrogen concentration and content in shoot and roots. Soil properties and relevant methods were as described previously [25]. Briefly, soil EC and pH were determined in a solution of 1:5 air dry sieved soil:distilled water (w/v). Nitrate and ammonium concentrations were determined using an autoanalyzer (Lachat Instruments, Milwaukee, WI or Gallery Plus, Thermo Fisher Scientific, Waltham, MA, USA). Sampled shoots and roots were dried at 60 °C for 48 h, ground and weighed to obtain dry biomass. Total nitrogen concentration was determined using an autoanalyzer (Lachat Instruments or Gallery Plus) following digestion with sulfuric acid and peroxide [27].
Root DNA extraction for sequencing and qPCR
At the end of the 6th week of wheat growth, pots were randomly selected for DNA extraction. To obtain the root-surface-associated microbiome, wheat roots were collected in triplicate from each of the three cycles and were vortexed three time with 85% saline solution, until no visible soil particles were attached to the roots. Total DNA was extracted from 0.4 g of complete root system, using the Exgene Soil DNA mini isolation kit (GeneAll, Seoul, Korea) according to the manufacturer’s instructions.
Generation of qPCR plasmid standards
Plasmids containing the 16S rRNA gene were generated as described previously [28, 29]. Each PCR amplification product was ligated into pGEM-T Easy Vector (Promega, Madison, WI, USA) and plasmids were transformed into BioSuper Escherichia coli DH5α competent cells (Bio-Lab, Jerusalem, Israel). Circular plasmid DNAs were used as the standards to create calibration curves at 10-fold dilutions for gene quantification by real-time qPCR.
Assessment of gene copy numbers by qPCR
Copy numbers of the total bacterial community (16S rRNA gene) and translation elongation factor 1 (TEF, a plant housekeeping gene) were assessed using selected primers (Table S1) in roots of 6-week-old wheat plants with the StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Triplicates from whole genomic DNA were diluted to 6 ng/µL and 1 µL was used in a 20-µL final reaction volume together with 50 µM forward and reverse primers and 10 µL 1X FAST MasterMix (Thermo Fisher Scientific). Three biological and three technical replicates were analyzed for each root DNA sample. Reaction efficiency was monitored in each run by means of an internal standard curve (constructed plasmids) using duplicates of 10-fold dilutions of standards ranging from 108–102 copies per reaction. Efficiency was 89–98% for all target genes and runs, and R2 values were greater than 0.99. Copy numbers of the target genes were calculated based on the relative calibration curve of the plasmid copy numbers. All data analyses were conducted using StepOne software v2.3 (Applied Biosystems).
Shotgun sequencing
Root DNA was extracted from each of the biological triplicates, in each of the three cycles. For sequencing, the DNA of the triplicates was combined, resulting in three biological replicates per treatment (one from each batch) and 18 samples altogether. Shotgun metagenome libraries were prepared using the Celero DNA-Seq library preparation kit (NuGen, Takara Bio, USA) with enzymatic shearing, according to the manufacturer’s instructions. All libraries were then pooled in equal volumes and size selection (350–400 bp fragments) was performed using a Blue PippinPrep instrument (Sage Scientific). The libraries were then sequenced using an Illumina MiniSeq instrument employing a mid-output kit. Based on the number of reads per sample, the samples were repooled with varying volumes, and size selection was performed again using the same size range. The final size-selected pool was sequenced on an Illumina NovaSeq instrument with an S4 flow cell, employing 2 × 150 base reads. Library preparation and pooling were performed at the University of Illinois at Chicago Sequencing Core (UICSQC), and sequencing was performed by Novogene Corporation (Chula Vista, CA, USA).
In total, we obtained 310 Gb of information, with 30–44 million sequences per root sample. These sequence data were submitted to the Sequence Read Archive (SRA) of the NCBI databases under accession numbers SUB6631533 and SUB8385777, BioProject: PRJNA592741.
All reads were subjected to quality control using FastQC v0.11.3 [30] and barcode trimming using Trimmomatics v0.32 [31]. Reads were mapped to the whole wheat metagenome using Bowtie2 v2.3.5.1 [32], and mapped reads were filtered out from each sample. Then, short Illumina reads from triplicates of each nitrate treatment (30, 70 and 100 ppm) were assembled using SPADES v3.13.0 [33] into longer contigs, to create three wheat root microbiome catalogs for each treatment separately. The 30 ppm nitrate catalog had 677,271 contigs with N50 of 964 bp, 70 ppm nitrate catalog had 644,394 contigs with N50 of 971 bp, and the 100 ppm catalog had 677,271 contigs with N50 of 964 bp. Those three catalogs were combined and Prodigal v2.6.2 [34] was used for protein-coding gene prediction. To create a non-redundant set of genes, we used CD-HIT-EST software v4.8.1 [35] with a similarity threshold of 95%. Those genes were used as the root gene catalog, which included 35 million partial genes. This gene catalog was searched against the non-redundant NCBI protein database using DIAMOND sensitive algorithm v0.9.24.125 [36] to assign taxonomic and functional annotations. Results were then uploaded to MEGAN Ultimate edition software v6.15.2 [37]. The LCA (lowest common ancestor) algorithm was applied (parameters used with minimum bit-score of 70, minimum support of 5% and 30% top threshold) to compute the assignment of genes to specific taxa. For functional annotation, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [38] was used. Following annotation, to generate taxonomic and functional count tables, each library was mapped to the gene catalog with Trinity mapping software v2.8.4 [39], with Bowtie2-modified parameters (–no-unal –gbar 99999999 -k 250 –dpad 0 –mp 1,1 –np 1 –score-min L,0,−0.9 -L 20 -i S,1,0.50).
Data analyses
Significance of interactions between CO2 and nitrate levels on soil and plant parameters was calculated using two-way ANOVA the least-squares method, in JMP 14 Pro software (SAS Institute Inc., Cary, NC, USA). Differences between soil and plant parameters as influenced by interactions between CO2 and nitrate levels was calculated using Student’s t test in JMP 14 Pro software and statistical significance was set at P  More

1.
Feschotte, C. & Gilbert, C. Endogenous viruses: insights into viral evolution and impact on host biology. Nat. Rev. Genet. 13, 283–296 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Holmes, E. C. The evolution of endogenous viral elements. Cell Host Microbe 10, 368–377 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Fischer, M. G. Giant viruses come of age. Curr. Opin. Microbiol. 31, 50–57 (2016).
PubMed  Article  PubMed Central  Google Scholar 

4.
Wilhelm, S. W. et al. A student’s guide to giant viruses infecting small eukaryotes: from Acanthamoeba to zooxanthellae. Viruses 9, 46 (2017).
PubMed Central  Article  CAS  Google Scholar 

5.
Abergel, C., Legendre, M. & Claverie, J.-M. The rapidly expanding universe of giant viruses: Mimivirus, Pandoravirus, Pithovirus and Mollivirus. FEMS Microbiol. Rev. 39, 779–796 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Weynberg, K. D., Allen, M. J. & Wilson, W. H. Marine prasinoviruses and their tiny plankton hosts: a review. Viruses 9, 43 (2017).
PubMed Central  Article  CAS  Google Scholar 

7.
Bhattacharya, D. & Medlin, A. L. Algal phylogeny and the origin of land plants. Plant Physiol. 116, 9–15 (1998).
CAS  PubMed Central  Article  Google Scholar 

8.
Jeanniard, A. et al. Towards defining the chloroviruses: a genomic journey through a genus of large DNA viruses. BMC Genomics 14, 158 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Moniruzzaman, M., Martinez-Gutierrez, C. A., Weinheimer, A. R. & Aylward, F. O. Dynamic genome evolution and complex virocell metabolism of globally-distributed giant viruses. Nat. Commun. 11, 1710 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Filée, J. Genomic comparison of closely related giant viruses supports an accordion-like model of evolution. Front. Microbiol. 6, 593 (2015).
PubMed  PubMed Central  Google Scholar 

11.
Van Etten, J. L. et al. Chloroviruses have a sweet tooth. Viruses 9, 88 (2017).
PubMed Central  Article  CAS  Google Scholar 

12.
Schvarcz, C. R. & Steward, G. F. A giant virus infecting green algae encodes key fermentation genes. Virology 518, 423–433 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Sun, C., Feschotte, C., Wu, Z. & Mueller, R. L. DNA transposons have colonized the genome of the giant virus Pandoravirus salinus. BMC Biol. 13, 38 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Marcet-Houben, M. & Gabaldón, T. Acquisition of prokaryotic genes by fungal genomes. Trends Genet. 26, 5–8 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Rossoni, A. W. et al. The genomes of polyextremophilic cyanidiales contain 1% horizontally transferred genes with diverse adaptive functions. eLife 8, e45017 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Filée, J. Multiple occurrences of giant virus core genes acquired by eukaryotic genomes: the visible part of the iceberg? Virology 466–467, 53–59 (2014).
PubMed  Article  CAS  PubMed Central  Google Scholar 

17.
Maumus, F. & Blanc, G. Study of gene trafficking between Acanthamoeba and giant viruses suggests an undiscovered family of amoeba-infecting viruses. Genome Biol. Evol. 8, 3351–3363 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Gallot-Lavallée, L. & Blanc, G. A glimpse of nucleo-cytoplasmic large DNA virus biodiversity through the eukaryotic genomics window. Viruses 9, 17 (2017).
PubMed Central  Article  Google Scholar 

19.
Maumus, F., Epert, A., Nogué, F. & Blanc, G. Plant genomes enclose footprints of past infections by giant virus relatives. Nat. Commun. 5, 4268 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Guglielmini, J., Woo, A. C., Krupovic, M., Forterre, P. & Gaia, M. Diversification of giant and large eukaryotic dsDNA viruses predated the origin of modern eukaryotes. Proc. Natl Acad. Sci. USA 116, 19585–19592 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Forterre, P. & Gaïa, M. Giant viruses and the origin of modern eukaryotes. Curr. Opin. Microbiol. 31, 44–49 (2016).
PubMed  Article  PubMed Central  Google Scholar 

22.
Piacente, F., Gaglianone, M., Laugieri, M. E. & Tonetti, M. G. The autonomous glycosylation of large DNA viruses. Int. J. Mol. Sci. 16, 29315–29328 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Schulz, F. et al. Giant virus diversity and host interactions through global metagenomics. Nature 578, 432–436 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Abrahão, J. et al. Tailed giant Tupanvirus possesses the most complete translational apparatus of the known virosphere. Nat. Commun. 9, 749 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Wilson, W. H. et al. Complete genome sequence and lytic phase transcription profile of a Coccolithovirus. Science 309, 1090–1092 (2005).
ADS  Article  CAS  Google Scholar 

26.
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016). 
Article  CAS  Google Scholar 

27.
Koonin, E. V. & Krupovic, M. The depths of virus exaptation. Curr. Opin. Virol. 31, 1–8 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Ochman, H., Lawrence, J. G. & Groisman, E. A. Lateral gene transfer and the nature of bacterial innovation. Nature 405, 299–304 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Groisman, E. A. & Ochman, H. Pathogenicity islands: bacterial evolution in quantum leaps. Cell 87, 791–794 (1996).
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Martin, W. F. Too much eukaryote LGT. BioEssays 39, 1700115 (2017).
Article  Google Scholar 

31.
Keeling, P. J. & Palmer, J. D. Horizontal gene transfer in eukaryotic evolution. Nat. Rev. Genet. 9, 605–618 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Cock, J. M. et al. The Ectocarpus genome and the independent evolution of multicellularity in brown algae. Nature 465, 617–621 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Delaroque, N., Maier, I., Knippers, R. & Müller, D. G. Persistent virus integration into the genome of its algal host, Ectocarpus siliculosus (Phaeophyceae). J. Gen. Virol. 80, 1367–1370 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Delaroque, N. & Boland, W. The genome of the brown alga Ectocarpus siliculosus contains a series of viral DNA pieces, suggesting an ancient association with large dsDNA viruses. BMC Evol. Biol. 8, 110 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
ADS  MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Yutin, N., Wolf, Y. I., Raoult, D. & Koonin, E. V. Eukaryotic large nucleo-cytoplasmic DNA viruses: clusters of orthologous genes and reconstruction of viral genome evolution. Virol. J. 6, 223 (2009).
PubMed  PubMed Central  Article  CAS  Google Scholar 

39.
Filée, J., Siguier, P. & Chandler, M. I am what I eat and I eat what I am: acquisition of bacterial genes by giant viruses. Trends Genet. 23, 10–15 (2007).
PubMed  Article  CAS  PubMed Central  Google Scholar 

40.
Filée, J., Pouget, N. & Chandler, M. Phylogenetic evidence for extensive lateral acquisition of cellular genes by nucleocytoplasmic large DNA viruses. BMC Evol. Biol. 8, 320 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

41.
Hoff, K. J. & Stanke, M. Predicting genes in single genomes with AUGUSTUS. Curr. Protoc. Bioinformatics 65, e57 (2019).
PubMed  PubMed Central  Google Scholar 

42.
Stanke, M. & Morgenstern, B. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. 33, W465–W467 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

45.
Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).
PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Federhen, S. The NCBI Taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: efficient manipulation of biological strings. R package version 2.56.0  https://bioconductor.org/packages/Biostrings (2020).

49.
Bao, Z. & Eddy, S. R. Automated de novo identification of repeat sequence families in sequenced genomes. Genome Res. 12, 1269–1276 (2002).
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Delcher, A. L., Phillippy, A., Carlton, J. & Salzberg, S. L. Fast algorithms for large-scale genome alignment and comparison. Nucleic Acids Res. 30, 2478–2483 (2002).
PubMed  PubMed Central  Article  Google Scholar 

51.
Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Haft, D. H. et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Moniruzzaman, M. et al. Virus–host relationships of marine single-celled eukaryotes resolved from metatranscriptomics. Nat. Commun. 8, 16054 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
PubMed  PubMed Central  Google Scholar 

57.
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Lechner, M. et al. Proteinortho: detection of (co-)orthologs in large-scale analysis. BMC Bioinformatics 12, 124 (2011).
PubMed  PubMed Central  Article  Google Scholar 

60.
Csardi G, N. T. The igraph software package for complex network research. InterJournal Complex Systems 1695, 1–9 (2006).

61.
Burns, J. A., Paasch, A., Narechania, A. & Kim, E. Comparative genomics of a bacterivorous green algae reveals evolutionary causalities and consequences of phago-mixotrophic mode of nutrition. Genome Biol. Ecol. 7, 3047–3061 (2015).
CAS  Article  Google Scholar 

62.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Martinez-Gutierrez, C. A. & Aylward, F. O. Strong purifying selection is associated with genome streamlining in epipelagic Marinimicrobia. Genome Biol. Evol. 11, 2887–2894 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar  More

1.
Pinedo, M.C. Ocorrência de pinípedes na costa brasileira. Garcia de Orta Serie de Zoologia 15, 37–48 (1990).
2.
Rosas, F. C. W., Pinedo, M. C., Marmotel, M. & Haimovici, M. Seasonal movements of the South American sea lion (Otaria flavescens Shaw, 1800) of the Rio Grande do Sul coast Brazil. Mammalia 58, 51–59 (1994).
Article  Google Scholar 

3.
Simões-Lopes, P. C., Drehmer, C. J. & Ott, P. H. Nota sobre os Otariidae e Phocidae (Mammalia: Carnivora) da costa norte do Rio Grande do Sul e Santa Catarina Brasil. Biociências 3, 173–181 (1995).
Google Scholar 

4.
Oliveira, L.R. Carnívoros marinhos in Mamíferos do Rio Grande do Sul (eds. Weber, M.M., Roman, C. & Cáceres, N.C.) 405-227 (Editora UFSM, 2013).

5.
Oliveira, L.R., Danilewicz, D., Martins, M.B., Ott, P.H., Moreno, I.B., Caon, G. New records of the Antarctic fur seal, Arctocephalus gazella (Peters, 1875) to the Brazilian coast. Com. Museu de Ciência e Tecnologia da PUCRS 14, 201–207 (2001).

6.
Oliveira, L. R., Machado, R., Alievi, M. M. & Würdig, N. L. Crabeater seal (Lobodon carcinophaga) on the coast of Rio Grande do Sul State, Brazil. LAJAM 5, 145–148 (2006).
Article  Google Scholar 

7.
Frainer, G., Heissler, V. L. & Moreno, I. B. A wandering Weddell seal (Leptonychotes weddellii) at Trindade Island, Brazil: the extreme sighting of a circumpolar species. Polar Biol. 41, 579–582 (2017).
Article  Google Scholar 

8.
Milmann, L., Machado, R., Oliveira, L.R., Ott, P.H. Far away from home: presence of fur seal (Arcocephalus sp.) in the equatorial Atlantic Ocean. Polar Biol. 42, 817–822 (2019).

9.
Rocha-Campos, C.C., Câmara, I.G. Plano de ação nacional para conservação dos mamíferos aquáticos: grandes cetáceos e pinípedes. Instituto Chico Mendes de Conservação da Biodiversidade. 156 (ICMBio, 2011). https://www.icmbio.gov.br/cma/images/stories/pans_grandes_cetaceos_e_pinipedes/Pequenos_cet%C3%A1ceos_PAN.pdf.

10.
Pavanato, H., Silva, K. G., Estima, S. C., Monteiro, D. S. & Kinas, P. G. Occupancy dynamics of South American sea lions in Brazilian Haul-outs. Braz. J. Biol. 73, 855–862 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Campagna, C. The breeding cycle of the southern sea lion, Otaria byronia. Mar. Mammal Sci. 1, 210–218 (1985).
Article  Google Scholar 

12.
Vaz-Ferreira, R. Arctocephalus australis (Zimmermann): South American fur seal. Mammals Seas FAO Fish. Ser. 4, 497–508 (1982).
Google Scholar 

13.
Francu-Treco, V., Costa, P., Scharam, Y., Tassino, B. & Inchausti, P. Sex on the rocks: reproductive tactics and breeding success of South American fur seal males. Behav. Ecol. 25, 1513–1523 (2014).
Article  Google Scholar 

14.
Campagna, C. et al. Movements and location at sea of South American sea lions (Otaria flavescens). J. Zool. 257, 205–220 (2001).
Article  Google Scholar 

15.
Bastida, R. & Rodríguez, D. Hallazgo de un apostadero estacional de lobos marinos de dos pelos, Arctocephalus australis (Zimmermann, 1783), en bajos fondos frente a la costa de Mar del Plata (Provincia de Buenos Aires, Argentina). Anales 4ª Reunión de Trabajo de Especialistas en Mamíferos Acuáticos de América del Sur 1–22 (1994).

16.
Sanfelice, D., Vasques, V. C. & Crespo, E. A. Ocupação sazonal por duas espécies de Otariidae (Mammalia, Carnivora) da Reserva Ecológica Ilha dos Lobos, Rio Grande do Sul Brasil. Iheringia, Sér. Zool. 87, 101–110 (1999).
Google Scholar 

17.
Giardino, G. V. et al. Travel for sex: Long-range breeding dispersal and winter haulout fidelity in Southern sea lion males. Mammal. Biol. 81, 89–95 (2014).
Article  Google Scholar 

18.
Oliveira, L. R. et al. Morphological and genetic evidence for two evolutionarily significant units (ESUS) in the South American fur seal Arctocephalus australis. Conserv. Genet. 9, 1451–1466 (2008).
Article  Google Scholar 

19.
Oliveira, L. R. et al. Ancient female philopatry, asymmetric male gene flow, and synchronous population expansion support the influence of climatic oscillations on the evolution of South American sea lion (Otaria flavescens). PLoS ONE 12, e0179442 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

20.
Cárdenas-Alayza, S., Crespo, E.A., Oliveira, L., R. Otaria byronia. The IUCN Red List of Threatened Species 2016: e.T41665A61948292. https://doi.org/https://doi.org/10.2305/IUCN.UK.2016-1.RLTS.T41665A61948292.en (2016).

21.
Páez, E. Situación de la administración del recurso lobos y leones marinos en Uruguay in Bases para la conservación y el manejo de la costa Uruguaya (eds. Menafra, R., Rodríguez-Gallego, L., Scarabino, F., Conde, D. 577–583 (Sociedad Uruguaya para la Conservación de la Naturaleza, Montevideo 2006).

22.
Franco-Trecu, V. Tácticas comportamentales de forrajeo y apareamiento y dinámica poblacional de dos especies de otáridos simpátricas con tendencias poblacionales contrastantes. PhD Thesis. Universidad de la República (UdelaR) Montevideo, Uruguay (2015). https://hdl.handle.net/20.500.12008/6895.

23.
Crespo, E. A., Oliva, D., Dans, S. & Sepúlveda, M. Estado de situación del lobo marino común en su área de distribución (Editorial Universidad de Valparaíso, Valparaíso, Chile, 2012).
Google Scholar 

24.
Baylis, A. M. M. et al. Disentangling the cause of a catastrophic population decline in a large marine mammal. Ecology 96, 2834–2847 (2015).
PubMed  Article  PubMed Central  Google Scholar 

25.
Cárdenas-Alayza, S., Oliveira, L.R., Crespo, E.A. Arctocephalus australis. The IUCN Red List of Threatened Species 2016: e.T2055A45223529. https:// doi.org/https://doi.org/10.2305/IUCN.UK.2016-1.RLTS.T2055A45223529.en. (2016).

26.
Baylis, A. M. M. et al. Re-evaluating the population size of South American fur seals and conservation implications. Aquat. Conserv. Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3194 (2019).
Article  Google Scholar 

27.
Harwood, J. & Prime, J. H. Some Factors affecting the size of British grey seal populations. J. Appl. Ecol. 15, 401–411 (1978).
Article  Google Scholar 

28.
Páez, E. Utilización de Boostrap y analisis de poder en estimaciones de abundancia de cachorros de Arctocephalus australis [Using Bootstrap and power analysis in abundance estimates of Arctocephalus australis pups] in Sinopsis de la Biologıa y Ecologıa de las Poblaciones de Lobos Finos y Leones Marinos de Uruguay [Synopsis of the biology and ecology of populations of fur seals and sea lions of Uruguay] (eds. Rey, M., Amestoy, F.) 55–70 (Proyecto URU/92/003, INAPE, Montevideo, Uruguay, 2000).

29.
Franco-Trecu, V. et al. Abundance and population trends of the South American Fur Seal (Arctocephalus australis) in Uruguay. Aquat. Mammals 45, 48–55 (2019).
Article  Google Scholar 

30.
Crespo, E.A. & Oliveira, L.R. South American fur seal (Arctocephalus australis, Zimmerman 1783) in Ecology and Conservation of Pinnipeds in Latin America (eds. Heckel, G., Schramm, Y.) (Springer Nature, in press).

31.
Sanfelice, D., Vasques, V. C., Romanowski, H. P. & Cappozzo, H. L. Activity budget in South American Sea Lions (Otaria flavescens) in the most northern South-Atlantic haul-out site. Bol. Soc. Bras. Mastozool. 73, 87–91 (2015).
Google Scholar 

32.
McIntosh, R. R. et al. Understanding meta-population trends of the Australian fur seal, with insights for adaptive monitoring. PLoS ONE 13, e0200253 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Eberhardt, L. L., Chapman, D. G. & Gilbert, J. R. A review of marine mammal census methods. Wildl. Monogr. 63, 5–46 (1979).
Google Scholar 

34.
Forney, K.A. Surveys in Encyclopedia of Marine Mammals (eds. Perrin, W.F., Wursig, B., Thewissen, J.G.M.) 129–1131 (Academic Press, 2009).

35.
Grandi, M. F., Dans, S. L. & Crespo, E. A. Social composition and spatial distribution of colonies in an expanding population of south American sea lions. J. Mammal. 89, 1218–1228 (2008).
Article  Google Scholar 

36.
Lowry, M.S., W.L. Perryman, M.S. Lynn, R.L. Westlake, F.J. Counts of northern elephant seals, Mirounga angustirostris, from large-format aerial photographs taken at rookeries in southern California during the breeding season. Fish. Bull. Natl Ocean. Atmos. Admin. 94, 176–185 (1996).

37.
Adame, K., Pardo, M. A., Salvadeo, C., Beier, E. & Elorriaga-Verplancken, F. R. Detectability and categorization of California sea lions using an unmanned aerial vehicle. Mar. Mammal Sci. 33, 913–925 (2017).
Article  Google Scholar 

38.
Hiby, A. R., Thompson, D. & Ward, A. J. Census of grey seals by aerial photography. Photogram. Rec. 12, 589–594 (1988).
Article  Google Scholar 

39.
Heide-Jorgensen, M. P. Aerial digital photographic surveys of narwhals, Monodon monoceros, in northwest Greenland. Mar. Mammal Sci. 20, 246–261 (2004).
Article  Google Scholar 

40.
Silva, K.G. Os pinípedes no Brasil: ocorrências, estimativas populacionais e conservação. PhD thesis. Fundação Universidade Federal de Rio Grande, Rio Grande (2004).

41.
Silva, K.G., Araújo, T.G., Crivellaro, C.V.L., Menezes, R.B. Os Mamíferos Marinhos do Litoral do Rio Grande do Sul (NEMA, 2014).

42.
Small, R. J., Pendleton, G. W. & Pitcher, K. W. Trends in abundance of Alaska harbor seals, 1983–2001. Mar. Mammal Sci. 19, 344–362 (2003).
Article  Google Scholar 

43.
Sepúlveda, M. et al. Distribution and abundance of the South American sea lion Otaria flavescens (Carnivora: Otariidae) along the central coast off Chile. Rev. Chil. Hist. Nat.  84, 97–106 (2011).
Article  Google Scholar 

44.
Li, J. & Heap, A. D. A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecol. Inf. 6, 228–241 (2011).
Article  Google Scholar 

45.
Vaz-Ferreira, R. Otaria flavescens (Shaw): South American sea lion. Mammals in the Seas. FAO Fisheries series 4, 477–495 (1982).

46.
Castilho, P. V. & Simões-Lopes, P. C. Sea mammals in archaeological sites on the southern coast of Brazil. Rev. Mus. Arqueol. Etnol. 18, 101–113 (2008).
Article  Google Scholar 

47.
Engel, M. T., Marchini, S., Pont, A. C., Machado, R. & Oliveira, L. R. Perceptions and attitudes of stakeholders towards the Wildlife Refuge of Ilha dos Lobos, a marine protected area in Brazil. Mar. Policy 45, 45–51 (2014).
Article  Google Scholar 

48.
Warneke, R. M. Dispersal and mortality of juvenile fur seals, Arctocephalus pusillus doriferus, in Bass Strait, Southeastern Australia. Rapports et Proces Verbaux des Reunions du Conseil International pour l’Exploration de la Mer 169, 296–302 (1975).
Google Scholar 

49.
Riedman, M. The Pinnipeds. 439 (University of California Press, 1990).

50.
Brasil. Decreto no. 88.463, de 4 de julho de 1983. Cria a Reserva Ecológica Ilha dos Lobos, e dá outras providencias. Diário Oficial da República Federativa do Brasil 129, 12009 (1983).

51.
Brasil. Decreto de 4 de julho de 2005. Presidência da República-Casa Civil- Subchefia para Assuntos Jurídicos. 04 de julho de 2005. https://www.planalto.gov.br/ccivil_03/_Ato2004-2006/2005/Dnn/Dnn10578.htm (2005).

52.
Groch, K. R., Palazzo, J. T., Flores, P. A. C., Adler, F. R. & Fabian, M. E. Recent rapid increases in the right whale (Eubalaena australis) population off southern Brazil. Latin Am. J. Aquat. Mammals 4, 41–47 (2005).
Google Scholar 

53.
Danilewicz, D., Moreno, I. B., Tavares, M. & Sucunza, F. Southern right whales (Eubalaena australis) off Torres, Brazil: group characteristics, movements, and insights into the role of the Brazilian-Uruguayan wintering ground. Mammalia 81, 225–234 (2016).
Google Scholar 

54.
Bartheld, J.L., Pavés, H., Contreras, F. Cuantificación poblacional de lobos marinos en el litoral de la I a IV Regiones. Final report proyecto FIP 2006-50 (2008).

55.
King, J.E. Seals of the World (British Museum of Natural History, 1983).

56.
Crespo. E.A. Dinámica poblacional del lobo marino de un pelo Otaria flavescens (Shaw, 1800), en el norte del Litoral Patagónico. PhD Thesis Ciencias Biológicas Facultad de Ciencias Exactas y Naturales. Universidad Nacional de Buenos Aires, Argentina (1988). https://digital.bl.fcen.uba.ar/Download/Tesis/Tesis_2107_Crespo.pdf.

57.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

58.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, https://www.R-project.org/ (2019).

59.
ESRI. ArcGIS Desktop: Release 10.5 Redlands (Environmental Systems Research Institute, 2018).

60.
Bastida, R. & Rodríguez, D. Mamíferos marinos de Patagonia y Antártida (Editorial Vazquez Mazzini, 2003).

61.
Silverman, B.W. Density Estimation for Statistics and Data Analysis (Chapman and Hall, 1986).

62.
Diggle, P. J. A kernel method for smoothing point process data. Appl. Stat. Amsterdam 34, 138–147 (1985).
MATH  Article  Google Scholar 

63.
Druck, S., Carvalho, M.S., Câmara, G., Monteiro, A.V.M. Análise Espacial de Dados Geográficos. (EMBRAPA, 2004).

64.
Lewis, M. Elefante marino del sur: biología de la especie, descripción general de la agrupación de la Península Valdés y protocolos de trabajo. Informes Técnicos del Plan de Manejo Integrado de la Zona Costera Patagónica. Puerto Madryn. Argentina 16, 1–29 (1996).
Google Scholar 

65.
Szteren, D. Otaria flavescens and Arctocephaus australis abundance in poorly known sites: a spatial expansion of colonies?. Braz. J. Oceanogr. 63, 337–346 (2015).
Article  Google Scholar 

66.
Seeliger, U., Odebrecht, C., Castelo, J.P. Os ecossistemas costeiro e marinho do extremo sul do Brasil. (Ecoscientia, 1998).

67.
Oliveira, L.R., Ott, P.H., Malabarba, L.R. Ecologia alimentar dos pinípedes do sul do Brasil e uma avaliação de suas interações com atividades pesqueiras in Ecologia de mamíferos (eds. Reis, N.R., Peracchi, A.L., Santos, G.A.S.D.) 93–109 (Technical Books Editora, Londrina, 2008).

68.
Machado, R. et al. Trophic overlap between marine mammals and fisheries in subtropical waters in the western South Atlantic. Mar. Ecol. Prog. Ser. 639, 215–232 (2020).
ADS  Article  Google Scholar 

69.
Machado, R. et al. Changes in the feeding ecology of South American sea lions on the southern Brazilian coast over the last two decades of excessive fishing exploration. Hydrobiologia 819, 17–37 (2008).
Article  Google Scholar 

70.
Machado, R., Oliveira, L. R. & Montealegre-Quijano, S. Incidental catch of South American sea lion in a pair trawl off southern Brazil. Neotropic. Biol. Conserv. 10, 43–47 (2015).
Google Scholar 

71.
Pont, A. C. et al. The human dimension of the conflict between fishermen and South American sea lions in southern Brazil. Hydrobiologia 767, 1–16 (2016).
Article  CAS  Google Scholar 

72.
Moreno, I. B., Danilewicz, D., Tavares, M., Ott, P. H. & Machado, R. Descrição da pesca costeira de média escala no litoral norte do Rio Grande do Sul: comunidades pesqueiras de Imbé/Tramandaí e Passo de Torres/Torres. Boletim do Instituto de Pesca (Online) 35, 129–140 (2009).
Google Scholar 

73.
Oliveira, L. R. Caracterização dos padrões de ocorrência dos pinípedes (Carnivora: Pinnipedia) ocorrentes no litoral do Rio Grande do Sul, Brasil, entre 1993 e 1999. Master Dissertation. Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brasil (1999).

74.
Oliveira, A. et al. Occurrence of pinnipeds in Santa Catarina between 2000 and 2010. Latin Am. J. Aquat. Mammals 9, 145–149 (2011).
Article  Google Scholar 

75.
Prado, J.H.F., Mattos, P.H., Silva, K.G., Secchi, E.R. Long-Term Seasonal and Interannual Patterns of Marine Mammal Strandings in Subtropical Western South Atlantic. PLoS ONE 11, e.0146339 (2016).

76.
Baldassin, P., Armorim, D.B., Werneck, M.R. Pathologies of Pinnipeds in Brazil in Pinnipeds Bio-Ecology, Threats and Conservation (eds. Avalva, J.) 269–285 (Ed. Taylor & Francis Group) (2017).

77.
Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).
Article  Google Scholar 

78.
Dantas, G. et al. Evidence for northward extension of the winter range of Magellanic penguins along the Brazilian coast. Mar. Ornithol. 41, 195–197 (2013).
Google Scholar 

79.
Marques, F. P., Cardoso, L. G., Haimovici, M. & Bugoni, L. Trophic ecology of Magellanic Penguins (Spheniscus magellanicus) during the non-breeding period. Estuar. Coast Shelf. Sci. 210, 109–122 (2018).
ADS  CAS  Article  Google Scholar 

80.
Garcia-Borboroglu, P. et al. Magellanic penguin mortality in 2008 along the SW Atlantic Coast. Mar. Pollut. Bull. 60, 1652–1657 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
de Paula, A.A., Ott, P.H., Tavares, M. et al. Host–parasite relationship in Magellanic Penguins (Spheniscus magellanicus) during their long northward journey to the Brazilian coast. Polar Biol. 43, 1261–1272 (2020).
Article  Google Scholar 

82.
Rosas, F. C. W., Haimovici, M. & Pinedo, M. C. Age and growth of the South American sea lion, Otaria flavescens (Shaw, 1800), in southern Brazil. J. Mammal. 74, 141–147 (1993).
Article  Google Scholar 

83.
Machado, R. et al. Mortalidade de Otaria flavescens devido a interações com a atividade pesqueira no sul do Brasil in 15a Reunión de Trabajo de Expertos en Mamíferos Acuáticos de América del Sur y 9º Congreso de la Sociedad Latino Americana de Especialistas en Mamíferos Acuáticos (SOLAMAC), Puerto Madryn (2012).

84.
Drehmer, C.J. Variação geográfica em Otaria byronia (de Blainville, 1820) (Pinnipedia, Otariidae) com base na morfometria sincraniana. PhD thesis, Universidade Federal do Rio Grande do Sul, Porto Alegre (2005). https://hdl.handle.net/10183/8135.

85.
Muelbert, M. M. C. & Oliveira, L. R. First records of stranded pregnant female South American fur seals, Arctocephalus australis, in the southern Brazilian cost. LAJAM 5, 67–68 (2006).
Article  Google Scholar 

86.
Castello, H. P. & Pinedo, M. C. Os visitantes ocasionais de nosso litoral. Natureza em Revista 3, 40–46 (1977).
Google Scholar 

87.
Lodi, L. & Siciliano, S. A southern elephant seal in Brazil. Mar. Mammal Sci. 5, 513 (1989).
Google Scholar 

88.
Moura, J., Di Dario, B., Lima, L. & Siciliano, S. Southern elephant seals (Mirounga leonina) along the Brazilian coast: review and additional records. Mar. Biodivers. Rec. 3, 1–5 (2010).
Article  Google Scholar 

89.
Lewis, M., Campagna, C., Marin, M. R. & Fernandez, T. Southern elephant seals north of the Antarctic Polar Front. Antarct. Sci. 18, 213–221 (2006).
ADS  Article  Google Scholar 

90.
Kirkwood, R. & Goldsworthy, S. Fur seals and sea lions. (CSIRO Publishing, 2013). More

1.
Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Chang. 2, 111–115 (2012).
ADS  Article  Google Scholar 
2.
Dainese, M. et al. Human disturbance and upward expansion of plants in a warming climate. Nat. Clim. Chang. 7, 577–580 (2017).
ADS  Article  Google Scholar 

3.
Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl Acad. Sci. USA 105, 11823–11826 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Lamprecht, A., Semenchuk, P. R., Steinbauer, K., Winkler, M. & Pauli, H. Climate change leads to accelerated transformation of high-elevation vegetation in the central Alps. N. Phytol. 220, 447–459 (2018).
Article  Google Scholar 

5.
Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Dullinger, S. et al. Post-glacial migration lag restricts range filling of plants in the European Alps. Glob. Ecol. Biogeogr. 21, 829–840 (2012).
Article  Google Scholar 

7.
Rumpf, S. B. et al. Extinction debts and colonization credits of non-forest plants in the European Alps. Nat. Commun. 10, 4293 (2019).

8.
Cannone, N. & Pignatti, S. Ecological responses of plant species and communities to climate warming: upward shift or range filling processes? Clim. Change 123, 201–214 (2014).
ADS  Article  Google Scholar 

9.
Pauli, H., Gottfried, M., Reiter, K., Klettner, C. & Grabherr, G. Signals of range expansions and contractions of vascular plants in the high Alps: observations (1994–2004) at the GLORIA* master site Schrankogel, Tyrol, Austria. Glob. Chang. Biol. 13, 147–156 (2007).
ADS  Article  Google Scholar 

10.
Pounds, J. A., Fogden, M. P. L., Savage, J. M. & Gorman, G. C. Tests of null models for amphibian declines on a tropical mountain. Conserv. Biol. 11, 1307–1322 (1997).
Article  Google Scholar 

11.
Beaugrand, G., Brander, K. M., Alistair Lindley, J., Souissi, S. & Reid, P. C. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Lehikoinen, A. et al. Declining population trends of European mountain birds. Glob. Chang. Biol. 25, 577–588 (2019).
ADS  PubMed  Article  PubMed Central  Google Scholar 

13.
Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Lenoir, J. & Svenning, J. C. In Encyclopedia of Biodiversity 599–611 (Academic, 2013).

15.
Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

16.
Carboni, M. et al. Simulating plant invasion dynamics in mountain ecosystems under global change scenarios. Glob. Chang. Biol. 24, e289–e302 (2018).
PubMed  Article  PubMed Central  Google Scholar 

17.
Tattoni, C., Ianni, E., Geneletti, D., Zatelli, P. & Ciolli, M. Landscape changes, traditional ecological knowledge and future scenarios in the Alps: a holistic ecological approach. Sci. Total Environ. 579, 27–36 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Mair, L. et al. Abundance changes and habitat availability drive species’ responses to climate change. Nat. Clim. Chang. 4, 127–131 (2014).
ADS  Article  Google Scholar 

19.
Opdam, P. & Wascher, D. Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biol. Conserv. 117, 285–297 (2004).
Article  Google Scholar 

20.
Troia, M. J., Kaz, A. L., Niemeyer, J. C. & Giam, X. Species traits and reduced habitat suitability limit efficacy of climate change refugia in streams. Nat. Ecol. Evol. 3, 1321–1330 (2019).
PubMed  Article  PubMed Central  Google Scholar 

21.
Elsen, P. R., Monahan, W. B. & Merenlender, A. M. Topography and human pressure in mountain ranges alter expected species responses to climate change. Nat. Commun. 11, 1974 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Freeman, B. G., Lee-Yaw, J. A., Sunday, J. M. & Hargreaves, A. L. Expanding, shifting and shrinking: The impact of global warming on species’ elevational distributions. Glob. Ecol. Biogeogr. 27, 1268–1276 (2018).
Article  Google Scholar 

23.
Lenoir, J. & Svenning, J. C. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).
Article  Google Scholar 

24.
Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).

25.
Platts, P. J. et al. Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups. Sci. Rep. 9, 15039 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Dullinger, I. et al. A socio-ecological model for predicting impacts of land-use and climate change on regional plant diversity in the Austrian Alps. Glob. Chang. Biol. 26, 2336–2352 (2020).
ADS  PubMed Central  Article  Google Scholar 

27.
Kull, T. & Hutchings, M. J. A comparative analysis of decline in the distribution ranges of orchid species in Estonia and the United Kingdom. Biol. Conserv. 129, 31–39 (2006).
Article  Google Scholar 

28.
Wraith, J. & Pickering, C. A continental scale analysis of threats to orchids. Biol. Conserv. 234, 7–17 (2019).
Article  Google Scholar 

29.
Wraith, J., Norman, P. & Pickering, C. Orchid conservation and research: an analysis of gaps and priorities for globally red listed species. Ambio 49, 1601–1611 (2020).
PubMed  Article  PubMed Central  Google Scholar 

30.
Phillips, R. D., Reiter, N. & Peakall, R. Orchid conservation: from theory to practice. Ann. Bot. 126, 345–362 (2020).
PubMed  Article  PubMed Central  Google Scholar 

31.
van der Meer, S., Jacquemyn, H., Carey, P. D. & Jongejans, E. Recent range expansion of a terrestrial orchid corresponds with climate-driven variation in its population dynamics. Oecologia 181, 435–448 (2016).
ADS  PubMed  Article  PubMed Central  Google Scholar 

32.
Vogt-Schilb, H. et al. Responses of orchids to habitat change in Corsica over 27 years. Ann. Bot. 118, 115–123 (2016).
PubMed  PubMed Central  Article  Google Scholar 

33.
Vogt-Schilb, H., Munoz, F., Richard, F. & Schatz, B. Recent declines and range changes of orchids in Western Europe (France, Belgium and Luxembourg). Biol. Conserv. 190, 133–141 (2015).
Article  Google Scholar 

34.
Perazza, G., & & Lorenz, R. Le Orchidee dell’Italia Nordorientale. Atlante Corologico e Guida al Riconoscimento (Osiride, 2013).

35.
Sletvold, N., Dahlgren, J. P., Øien, D.-I., Moen, A. & Ehrlén, J. Climate warming alters effects of management on population viability of threatened species: results from a 30-year experimental study on a rare orchid. Glob. Chang. Biol. 19, 2729–2738 (2013).
ADS  PubMed  Article  PubMed Central  Google Scholar 

36.
Auffret, A. G., Kimberley, A., Plue, J. & Waldén, E. Super-regional land-use change and effects on the grassland specialist flora. Nat. Commun. 9, 3464 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

37.
Vilà‐Cabrera, A., Premoli, A. C. & Jump, A. S. Refining predictions of population decline at species’ rear edges. Glob. Chang. Biol. 25, 1549–1560 (2019).
ADS  PubMed  Article  PubMed Central  Google Scholar 

38.
Matthies, D., Bräuer, I., Maibom, W. & Tscharntke, T. Population size and the risk of local extinction: empirical evidence from rare plants. Oikos 105, 481–488 (2004).
Article  Google Scholar 

39.
Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Wilcox, R. R. Introduction to Robust Estimation and Hypothesis Testing 4th edn (Academic, 2016).

41.
Lenoir, J., Gegout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. USA 110, 18561–18565 (2013).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

43.
De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).
PubMed  Article  PubMed Central  Google Scholar 

44.
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Bertrand, R. et al. Ecological constraints increase the climatic debt in forests. Nat. Commun. 7, 12643 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Lenoir, J. et al. Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography 33, 295–303 (2010).
Google Scholar 

47.
Colwell, R. K. & Lees, D. C. The mid-domain effect: geometric constraints on the geography of species richness. Trends Ecol. Evol. 15, 70–76 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Rumpf, S. B., Hülber, K., Zimmermann, N. E. & Dullinger, S. Elevational rear edges shifted at least as much as leading edges over the last century. Glob. Ecol. Biogeogr. 28, 533–543 (2019).
Article  Google Scholar 

49.
Gibson-Reinemer, D. K. & Rahel, F. J. Inconsistent range shifts within species highlight idiosyncratic responses to climate warming. PLoS ONE 10, e0132103 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Vittoz, P., Randin, C., Dutoit, A., Bonnet, F. & Hegg, O. Low impact of climate change on subalpine grasslands in the Swiss Northern Alps. Glob. Chang. Biol. 15, 209–220 (2009).
ADS  Article  Google Scholar 

51.
Vogt-Schilb, H., Geniez, P., Pradel, R., Richard, F. & Schatz, B. Inter-annual variability in flowering of orchids: lessons learned from 8 years of monitoring in a Mediterranean region of France. Eur. J. Environ. Sci. 3, 129–137 (2013).
Google Scholar 

52.
Cotto, O. et al. A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nat. Commun. 8, 15399 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Tye, M., Dahlgren, J. P., Øien, D.-I., Moen, A. & Sletvold, N. Demographic responses to climate variation depend on spatial- and life history-differentiation at multiple scales. Biol. Conserv. 228, 62–69 (2018).
Article  Google Scholar 

54.
Aeschimann, D., Lauber, K., Moser, D. M. & Theurillat, J. P. Flora Alpina: Atlas des 4500 Plantes Vasculaires des Alpes (Aeschimann/Lauber, Belin, 2004).

55.
Di Piazza, A., & Eccel, E. Analisi di Serie di Temperatura e Precipitazione in Trentino nel Periodo 1958–2010 (Provincia Autonoma di Trento, 2012).

56.
Provincia Autonoma di Trento. Urbanistica – Banche Dati – Repertorio Cartografico (Provincia Autonoma di Trento, 2009).

57.
Monteiro, A. T., Fava, F., Hiltbrunner, E., Della Marianna, G. & Bocchi, S. Assessment of land cover changes and spatial drivers behind loss of permanent meadows in the lowlands of Italian Alps. Landsc. Urban Plan. 100, 287–294 (2011).
Article  Google Scholar 

58.
Eccel, E., Zollo, A. L., Mercogliano, P. & Zorer, R. Simulations of quantitative shift in bio-climatic indices in the viticultural areas of Trentino (Italian Alps) by an open source R package. Comput. Electron. Agric. 127, 92–100 (2016).
Article  Google Scholar 

59.
Verheyen, K. et al. Combining biodiversity resurveys across regions to advance global change research. Bioscience 67, 73–83 (2017).
Article  Google Scholar 

60.
Landolt, E. et al. Flora Indicativa: Okologische Zeigerwerte und Biologische Kennzeichen zur Flora der Schweiz und der Alpen (Haupt, 2010).

61.
Akinwande, M. O., Dikko, H. G. & Samson, A. Variance inflation factor: as a condition for the inclusion of suppressor variable(s) in regression analysis. Open J. Stat. 05, 754–767 (2015).
Article  Google Scholar 

62.
Kéry, M., Gardner, B. & Monnerat, C. Predicting species distributions from checklist data using site-occupancy models. J. Biogeogr. 37, 1851–1862 (2010).

63.
Harrison, X. A. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2, e616 (2014).
PubMed  PubMed Central  Article  Google Scholar 

64.
Hothorn, T., Bretz, F., Westfall, P. & Heiberger, R. M. multcomp: simultaneous inference for general linear hypotheses. R package version 0.992-4. http://132.180.15.2/math/statlib/R/CRAN/doc/packages/multcomp.pdf (2007).

65.
Mair, P. & Wilcox, R. Robust statistical methods in R using the WRS2 package. Behav. Res. Methods 52, 464–488 (2020).
PubMed  Article  PubMed Central  Google Scholar 

66.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

67.
Aikio, S., Duncan, R. P. & Hulme, P. E. Herbarium records identify the role of long-distance spread in the spatial distribution of alien plants in New Zealand. J. Biogeogr. 37, 1740–1751 (2010).
Article  Google Scholar 

68.
Ripley, B., Venables, B., Bates, D., Hornik, K. & Firth, D. Package ‘MASS’. http://www.stats.ox.ac.uk/pub/MASS4/ (2010).

69.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

70.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017). More

1.
World Health Organization. World malaria report 2019 (WHO, Geneva, 2019).
Google Scholar 
2.
Wirtz, R. A. & Burkot, T. R. Detection of malarial parasites in mosquitoes. In Advances in Disease Vector Research (eds Maudlin, I. & Sinha, R. C.) (Sprinter, New York, 1991).
Google Scholar 

3.
Trape, J. F. & Rogier, C. Combating malaria morbidity and mortality by reducing transmission. Parasitol. Today 12, 236–240 (1996).
CAS  PubMed  Article  Google Scholar 

4.
Mala, A. O. et al. Plasmodium falciparum transmission and aridity: a Kenyan experience from the dry lands of Baringo and its implications for Anopheles arabiensis control. Malar. J. 10, 121 (2011).
PubMed  PubMed Central  Article  Google Scholar 

5.
Macdonald, G. The Epidemiology and Control of Malaria (Oxford Univ. Press, London, 1957).
Google Scholar 

6.
Gillies, M. & de Meillon, B. The Anophelini of Africa South of the Sahara (Ethiopian Zoogeographical Region) (South African Institute of Medical Research, Johannesburg, 1968).
Google Scholar 

7.
Service, M. W. Mosquito (Diptera: Culicidae) dispersal—the long and short of it. J. Med. Entomol. 34, 579–588 (1997).
Article  Google Scholar 

8.
Hemming-Schroeder, E., Lo, E., Salazar, C., Puente, S. & Yan, G. Landscape genetics: a toolbox for studying vector-borne diseases. Front. Ecol. Evol. 6, 21 (2018).
ADS  Article  Google Scholar 

9.
Ramsdale, C. D. & Fontaine, R. E. Ecological Investigations of Anopheles gambiae and Anopheles funestus (World Health Organization, Geneva, 1970).
Google Scholar 

10.
Charlwood, J. D., Vij, R. & Billingsley, P. F. Dry season refugia of malaria-transmitting mosquitoes in a dry savannah zone of east Africa. Am. J. Trop. Med. Hyg. 62, 726–732 (2000).
CAS  PubMed  Article  Google Scholar 

11.
Aniedu, I. Dynamics of malaria transmission near two permanent breeding sites in Baringo district, Kenya. Indian J. Med. Res. 105, 206–211 (1997).
CAS  PubMed  Google Scholar 

12.
Kamau, L. et al. Analysis of genetic variability in Anopheles arabiensis and Anopheles gambiae using microsatellite loci. Insect Mol. Biol. 8, 287–297 (1999).
CAS  PubMed  Article  Google Scholar 

13.
Lehmann, T. et al. Genetic differentiation of Anopheles gambiae populations from East and West Africa: comparison of microsatellite and allozyme loci. Heredity 77, 192–200 (1996).
CAS  PubMed  Article  Google Scholar 

14.
Kamau, L., Lehmann, T., Hawley, W. A., Orago, A. S. & Collins, F. H. Microgeographic genetic differentiation of Anopheles gambiae mosquitoes from Asembo Bay, western Kenya: a comparison with Kilifi in coastal Kenya. Am. J. Trop. Med. Hyg. 58, 64–66 (1998).
CAS  PubMed  Article  Google Scholar 

15.
Storfer, A. et al. Putting the ‘landscape’ in landscape genetics. Heredity 98, 128–142 (2007).
CAS  PubMed  Article  Google Scholar 

16.
Biek, R. & Real, L. A. The landscape genetics of infectious disease emergence and spread. Mol. Ecol. 19, 3515–3531 (2010).
PubMed  PubMed Central  Article  Google Scholar 

17.
Storfer, A., Murphy, M. A., Spear, S. F., Holderegger, R. & Waits, L. P. Landscape genetics: Where are we now?. Mol. Ecol. 19, 3496–3514 (2010).
PubMed  Article  Google Scholar 

18.
Medley, K. A., Jenkins, D. G. & Hoffman, E. A. Human-aided and natural dispersal drive gene flow across the range of an invasive mosquito. Mol. Ecol. 24, 284–295 (2015).
PubMed  Article  Google Scholar 

19.
Blanchong, J. A. et al. Landscape genetics and the spatial distribution of chronic wasting disease. Biol. Lett. 4, 130–133 (2008).
PubMed  Article  Google Scholar 

20.
Cullingham, C. I., Kyle, C. J., Pond, B. A., Rees, E. E. & White, B. N. Differential permeability of rivers to raccoon gene flow corresponds to rabies incidence in Ontario, Canada. Mol. Ecol. 18, 43–53 (2009).
PubMed  Google Scholar 

21.
Côté, H., Garant, D., Robert, K., Mainguy, J. & Pelletier, F. Genetic structure and rabies spread potential in raccoons: the role of landscape barriers and sex-biased dispersal. Evol. Appl. 5, 393–404 (2012).
PubMed  PubMed Central  Article  Google Scholar 

22.
Guivier, E. et al. Landscape genetics highlights the role of bank vole metapopulation dynamics in the epidemiology of Puumala hantavirus. Mol. Ecol. 20, 3569–3583 (2011).
CAS  PubMed  Google Scholar 

23.
Carrel, M., Wan, X. F., Nguyen, T. & Emch, M. Genetic variation of highly pathogenic H5N1 avian influenza viruses in Vietnam shows both species-specific and spatiotemporal associations. Avian Dis. 55, 659–666 (2011).
PubMed  PubMed Central  Article  Google Scholar 

24.
Lo, E. et al. Transmission dynamics of co-endemic Plasmodium vivax and P. falciparum in Ethiopia and prevalence of antimalarial resistant genotypes. PLoS Negl. Trop. Dis. 11, e0005806 (2017).
PubMed  PubMed Central  Article  Google Scholar 

25.
Lo, E. et al. Frequent spread of Plasmodium vivax malaria maintains high genetic diversity at the Myanmar–China Border, without distance and landscape barriers. J. Infect. Dis. 216, 1254–1263 (2017).
PubMed  PubMed Central  Article  Google Scholar 

26.
Lehmann, T. et al. Microgeographic structure of Anopheles gambiae in western Kenya based on mtDNA and microsatellite loci. Mol. Ecol. 6, 243–253 (1997).
CAS  PubMed  Article  Google Scholar 

27.
Bayoh, M. N. et al. Anopheles gambiae: historical population decline associated with regional distribution of insecticide-treated bed nets in western Nyanza Province, Kenya. Malar. J. 9, 62 (2010).
PubMed  PubMed Central  Article  Google Scholar 

28.
Kitau, J. et al. Species shifts in the Anopheles gambiae complex: do LLINs successfully control Anopheles arabiensis?. PLoS ONE 7, e31481 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Mwangangi, J. M. et al. The role of Anopheles arabiensis and Anopheles coustani in indoor and outdoor malaria transmission in Taveta District, Kenya. Parasit. Vectors 6, 114 (2013).
PubMed  PubMed Central  Article  Google Scholar 

30.
Ototo, E. N. et al. Surveillance of malaria vector population density and biting behaviour in western Kenya. Malar. J. 14, 244 (2015).
PubMed  PubMed Central  Article  Google Scholar 

31.
Sougoufara, S., Harry, M., Doucouré, S., Sembène, P. M. & Sokhna, C. Shift in species composition in the Anopheles gambiae complex after implementation of long-lasting insecticidal nets in Dielmo, Senegal. Med. Vet. Entomol. 30, 365–368 (2016).
CAS  PubMed  Article  Google Scholar 

32.
Hemming-Schroeder, E. et al. Emerging pyrethroid resistance among Anopheles arabiensis in Kenya. Am. J. Trop. Med. Hyg. 98, 704–709 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Githeko, A. K. et al. Some observations on the biting behavior of Anopheles gambiae ss, Anopheles arabiensis, and Anopheles funestus and their implications for malaria control. Exp. Parasitol. 82, 306–315 (1996).
CAS  PubMed  Article  Google Scholar 

34.
Massebo, F., Balkew, M., Gebre-Michael, T. & Lindtjørn, B. Blood meal origins and insecticide susceptibility of Anopheles arabiensis from Chano in South-West Ethiopia. Parasit. Vectors 6, 44 (2013).
PubMed  PubMed Central  Article  Google Scholar 

35.
Tirados, I., Costantini, C., Gibson, G. & Torr, S. J. Blood-feeding behaviour of the malarial mosquito Anopheles arabiensis: implications for vector control. Med. Vet. Entomol. 20, 425–437 (2006).
CAS  PubMed  Article  Google Scholar 

36.
Sinka, M. E. et al. The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis. Parasit. Vectors 3, 117 (2010).
PubMed  PubMed Central  Article  Google Scholar 

37.
Charlwood, J. D. et al. The rise and fall of Anopheles arabiensis (Diptera: Culicidae) in a Tanzanian village. Bull. Entomol. Res. 85, 37–44 (1995).
Article  Google Scholar 

38.
Drake, J. M. & Beier, J. C. Ecological niche and potential distribution of Anopheles arabiensis in Africa in 2050. Malar. J. 13, 213 (2014).
PubMed  PubMed Central  Article  Google Scholar 

39.
Donnelly, M. J., Cuamba, N., Charlwood, J. D., Collins, F. H. & Townson, H. Population structure in the malaria vector, Anopheles arabiensis Patton, in East Africa. Heredity 83, 408–417 (1999).
PubMed  Article  Google Scholar 

40.
Donnelly, M. J. & Townson, H. Evidence for extensive genetic differentiation among populations of the malaria vector Anopheles arabiensis in Eastern Africa. Insect Mol. Biol. 9, 357–367 (2000).
CAS  PubMed  Article  Google Scholar 

41.
Donnelly, M. J., Licht, M. C. & Lehmann, T. Evidence for recent population expansion in the evolutionary history of the malaria vectors Anopheles arabiensis and Anopheles gambiae. Mol. Biol. Evol. 18, 1353–1364 (2001).
CAS  PubMed  Article  Google Scholar 

42.
Minakawa, N. et al. Spatial distribution of anopheline larval habitats in Western Kenyan highlands: effects of land cover types and topography. Am. J. Trop Med. Hyg. 73, 157–165 (2005).
PubMed  Article  Google Scholar 

43.
Muturi, E. J. et al. Population genetic structure of Anopheles arabiensis (Diptera: Culicidae) in a rice growing area of central Kenya. J. Med. Entomol. 47, 144–151 (2014).
Article  Google Scholar 

44.
Gray, E. M. & Bradley, T. J. Physiology of desiccation resistance in Anopheles gambiae and Anopheles arabiensis. Am. J. Trop Med. Hyg. 73, 553–559 (2005).
PubMed  Article  Google Scholar 

45.
Yamana, T. K. & Eltahir, E. A. Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa. Parasit. Vectors 6, 235 (2013).
PubMed  PubMed Central  Article  Google Scholar 

46.
Nkumama, I. N., O’Meara, W. P. & Osier, F. H. Changes in malaria epidemiology in Africa and new challenges for elimination. Trends Parasitol. 33, 128–140 (2017).
PubMed  Article  Google Scholar 

47.
Chen, H. et al. Monooxygenase levels and knockdown resistance (kdr) allele frequencies in Anopheles gambiae and Anopheles arabiensis in Kenya. J. Med. Entomol. 45, 242–250 (2014).
Article  Google Scholar 

48.
Severson, D. W. RFLP analysis of insect genomes. In The Molecular Biology of Insect Disease Vectors (eds Crampton, J. M. et al.) (Springer, Dordrecht, 1997).
Google Scholar 

49.
Scott, J. A., Brogdon, W. G. & Collins, F. H. Identification of single specimens of the Anopheles gambiae complex by the polymerase chain reaction. Am. J. Trop. Med. Hyg. 49, 520–529 (1993).
CAS  PubMed  Article  Google Scholar 

50.
Zheng, L., Benedict, M. Q., Cornel, A. J., Collins, F. H. & Kafatos, F. C. An integrated genetic map of the African human malaria vector mosquito, Anopheles gambiae. Genetics 143, 941–952 (1996).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Oetting, W. S. et al. Linkage analysis with multiplexed short tandem repeat polymorphisms using infrared fluorescence and M13 tailed primers. Genomics 30, 450–458 (1995).
CAS  PubMed  Article  Google Scholar 

52.
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).
PubMed  Article  Google Scholar 

53.
Raymond, M. & Rousset, F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).
Article  Google Scholar 

54.
Rousset, F. Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).
PubMed  Article  Google Scholar 

55.
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Article  Google Scholar 

56.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

57.
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour 15, 1179–1191 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Bates, D. et al. Package ‘lme4’. Convergence 12, 2 (2015).
Google Scholar 

59.
Beerli, P. & Felsenstein, J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Natl. Acad. Sci. 98, 4563–4568 (2001).
ADS  CAS  PubMed  MATH  Article  Google Scholar 

60.
Cushman, S., Storfer, A. & Waits, L. Landscape Genetics: Concepts, Methods, Applications (Wiley, West Sussex, 2015).
Google Scholar 

61.
Roy, J. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. J. R. Meteor. Soc. 25, 1965–1978 (2005).
Google Scholar 

62.
Channan, S., Collins, K. & Emanuel, W. R. Global Mosaics of the Standard MODIS Land Cover Type Data (University of Maryland and the Pacific Northwest National Laboratory, College Park, 2014).
Google Scholar 

63.
Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
ADS  Article  Google Scholar 

64.
Tatem, A. J. WorldPop, open data for spatial demography. Sci. Data 4, 1–4 (2017).
Article  Google Scholar 

65.
McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89, 2712–2724 (2008).
PubMed  Article  Google Scholar 

66.
Adamack, A. T. & Gruber, B. PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).
Article  Google Scholar 

67.
Peterman, W. E. ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol. Evol. 9, 1638–1647 (2018).
Article  Google Scholar 

68.
Peterman, W. E. et al. A comparison of popular approaches to optimize landscape resistance surfaces. Landsc. Ecol. 34, 2197–2208 (2019).
Article  Google Scholar 

69.
Oyler-McCance, S. J., Fedy, B. C. & Landguth, E. L. Sample design effects in landscape genetics. Conserv. Genet. 14, 275–285 (2013).
Article  Google Scholar  More

1.
Branca, G., Lipper, L., McCarthy, N. & Jolejole, M. C. Food security, climate change, and sustainable land management. A review. Agrono. Sustain. Dev. 33, 635–650 (2013).
Article  Google Scholar 
2.
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Tilman, D. et al. Forecasting agriculturally driven global environmental change. Science 292, 281–284 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Huang, Y. & Tang, Y. An estimate of greenhouse gas (N2O and CO2) mitigation potential under various scenarios of nitrogen use efficiency in Chinese croplands. GCB Bioenergy 16, 2958–2970 (2010).
Google Scholar 

5.
Gan, Y. T. et al. Improving farming practices reduces the carbon footprint of spring wheat production. Nat. Commun. 5, 5012. https://doi.org/10.1038/ncomms6012 (2014).
ADS  Article  PubMed  PubMed Central  Google Scholar 

6.
Cameron, K. C., Di, H. J. & Moir, J. Nitrogen losses from the soil/plant system: A review. Ann. Appl. Biol. 162, 145–173 (2013).
CAS  Article  Google Scholar 

7.
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Lithourgidis, A. S., Dordas, C. A., Damalas, C. A. & Vlachostergios, D. N. Annual intercrops: An alternative pathway for sustainable agriculture. Aust. J. Crop Sci. 5, 396–410 (2011).
Google Scholar 

9.
Tsubo, M., Walker, S. & Mukhala, E. Comparisons of radiation use efficiency of mono-/inter-cropping systems with different row orientations. Field Crop Res. 71, 17–29 (2001).
Article  Google Scholar 

10.
Li, L. et al. Root distribution and interactions between intercropped species. Oecologia 147, 280–290 (2006).
ADS  PubMed  Article  Google Scholar 

11.
Li, L. et al. Wheat/maize or wheat/soybean strip intercropping: I. Yield advantage and interspecific interactions on nutrients. Field Crop Res. 71, 123–137 (2001).
Article  Google Scholar 

12.
Brooker, R. W., Karley, A. J., Newton, A. C., Pakeman, R. J. & Schöb, C. Facilitation and sustainable agriculture: A mechanistic approach to reconciling crop production and conservation. Funct. Ecol. 30, 98–107 (2016).
Article  Google Scholar 

13.
Zhang, F. & Li, L. Using competitive and facilitative interactions in intercropping systems enhances crop productivity and nutrient-use efficiency. Plant Soil 248, 305–312 (2003).
CAS  Article  Google Scholar 

14.
Li, Q. Z. et al. Overyielding and interspecific interactions mediated by nitrogen fertilization in strip intercropping of maize with faba bean, wheat and barley. Plant Soil 339, 147–161 (2010).
Article  CAS  Google Scholar 

15.
Klimek-Kopyra, A., Zaja¸c, T. & Re¸bilas, K. A mathematical model for the evaluation of cooperation and competition effects in intercrops. Eur. J. Agron. 51, 9–17 (2013).
Article  Google Scholar 

16.
Li, L., Yang, S. C., Li, X. L., Zhang, F. S. & Christie, P. Interspecific complementary and competitive interactions between intercropped maize and faba bean. Plant Soil 212, 105–114 (1999).
CAS  Article  Google Scholar 

17.
Bedoussac, L. & Justes, E. A comparison of commonly used indices for evaluating species interactions and intercrop efficiency: Application to durum wheat–winter pea intercrops. Field Crop Res. 124, 25–36 (2011).
Article  Google Scholar 

18.
Hu, F. et al. Boosting system productivity through the improved coordination of interspecific competition in maize/pea strip intercropping. Field Crop Res. 198, 50–60 (2016).
Article  Google Scholar 

19.
Andersen, M., Hauggaard-Nielsen, H., Weiner, J. & Jensen, E. Competitive dynamics in two- and three-component intercrops. J. Appl. Ecol. 44, 545–551 (2007).
Article  Google Scholar 

20.
Li, L. et al. Wheat/maize or wheat/soybean strip intercropping: II. Recovery or compensation of maize and soybean after wheat harvesting. Field Crop Res. 71, 173–181 (2001).
Article  Google Scholar 

21.
Chai, Q., Qin, A., Gan, Y. & Yu, A. Higher yield and lower carbon emission by intercropping maize with rape, pea, and wheat in arid irrigation areas. Agrono. Sustain. Dev. 34, 535–543 (2013).
Article  CAS  Google Scholar 

22.
Hu, F. et al. Improving N management through intercropping alleviates the inhibitory effect of mineral N on nodulation in pea. Plant Soil 412, 235–251 (2017).
CAS  Article  Google Scholar 

23.
FAO/UNESCO. Soil Map of the World: Revised Legend/prepared by the Foodand Agriculture Organization of the United Nations. UNESCO (1988).

24.
Gan, Y. T. et al. Ridge-furrow mulching systems-an innovative technique for boosting crop productivity in semiarid rain-fed environments. Adv. Agron. 118, 429–476 (2013).
Article  Google Scholar 

25.
Yin, W. et al. Straw retention combined with plastic mulching improves compensation of intercropped maize in arid environment. Field Crop Res. 204, 42–51 (2017).
Article  Google Scholar 

26.
Willey, R. W. & Rao, M. R. A. Competitive ratio for quantifying competition between intercrops. Exp. Agric. 16, 117–125 (1980).
Article  Google Scholar 

27.
Fageria, N. K. & Baligar, V. C. Enhancing nitrogen use efficiency in crop plants. Adv. Agron. 88, 97–185 (2005).
CAS  Article  Google Scholar 

28.
Malézieux, E. et al. Mixing plant species in cropping systems: Concepts, tools and models. A review. Agrono. Sustain. Dev. 29, 43–62 (2009).
Article  Google Scholar 

29.
Gómez-Rodrı́guez, O., Zavaleta-Mejı́a, E., González-Hernández, V. A., Livera-Muñoz, M. & Cárdenas-Soriano, E. Allelopathy and microclimatic modification of intercropping with marigold on tomato early blight disease development. Field Crop Res. 83, 27–34 (2003).
Article  Google Scholar 

30.
Corre-Hellou, G., Fustec, J. & Crozat, Y. Interspecific competition for soil N and its interaction with N2 fixation, leaf expansion and crop growth in pea–barley intercrops. Plant Soil 282, 195–208 (2006).
CAS  Article  Google Scholar 

31.
Hauggaard-Nielsen, H., Ambus, P. & Jensen, E. S. The comparison of nitrogen use and leaching in sole cropped versus intercropped pea and barley. Nutr. Cycl. Agroecosys. 65, 289–300 (2003).
CAS  Article  Google Scholar 

32.
Andersen, M., Hauggaard-Nielsen, H., Ambus, P. & Jensen, E. Biomass production, symbiotic nitrogen fixation and inorganic N use in dual and tri-component annual intercrops. Plant Soil 266, 273–287 (2004).
CAS  Article  Google Scholar 

33.
Hou, Z., Li, P., Li, B., Gong, J. & Wang, Y. Effects of fertigation scheme on N uptake and N use efficiency in cotton. Plant Soil 290, 115–126 (2007).
CAS  Article  Google Scholar 

34.
Ghosh, P. K., Mohanty, M., Bandyopadhyay, K. K., Painuli, D. K. & Misra, A. K. Growth, competition, yields advantage and economics in soybean/pigeonpea intercropping system in semi-arid tropics of India. II. Effect of nutrient management. Field Crop Res 96, 90–97 (2006).
Article  Google Scholar 

35.
Li, S. X., Wang, Z. H., Hu, T. T., Gao, Y. J. & Stewart, B. A. Nitrogen in dryland soils of China and its management. Adv. Agron. 101, 123–181 (2009).
Article  Google Scholar 

36.
Hardarson, G., Zapata, F. & Danso, S. K. A. Effect of plant genotype and nitrogen fertilizer on symbiotic nitrogen fixation by soybean cultivars. Plant Soil 82, 397–405 (1984).
CAS  Article  Google Scholar 

37.
Li, C. et al. The dynamic process of interspecific interactions of competitive nitrogen capture between intercropped wheat (Triticum aestivum L.) and Faba Bean (Vicia faba L.). PLoS ONE 9, e115804. https://doi.org/10.1371/journal.pone.0119659 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Hauggaard-Nielsen, H. & Jensen, E. S. Evaluating pea and barley cultivars for complementarity in intercropping at different levels of soil N availability. Field Crop Res. 72, 185–196 (2001).
Article  Google Scholar 

39.
Herridge, D. F., Peoples, M. B. & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant Soil 311, 1–18 (2008).
CAS  Article  Google Scholar 

40.
Boucher, D. H. & Espinosa, M. J. Cropping system and growth and nodulation responses of beans to nitrogen in Tabasco, Mexico. Trop. Agric. 59, 279–282 (1982).
Google Scholar 

41.
Jensen, E. S. Grain yield, symbiotic N2 fixation and interspecific competition for inorganic N in pea-barley intercrops. Plant Soil 182, 25–38 (1996).
CAS  Article  Google Scholar 

42.
Gooding, M. J. et al. Intercropping with pulses to concentrate nitrogen and sulphur in wheat. J. Agric. Sci. 145, 469–479 (2007).
CAS  Article  Google Scholar 

43.
Rusinamhodzi, L., Murwira, H. K. & Nyamangara, J. Cotton–cowpea intercropping and its N2 fixation capacity improves yield of a subsequent maize crop under Zimbabwean rain-fed conditions. Plant Soil 287, 327–336 (2006).
CAS  Article  Google Scholar 

44.
Xiao, Y., Li, L. & Zhang, F. Effect of root contact on interspecific competition and N transfer between wheat and fababean using direct and indirect 15N techniques. Plant Soil 262, 45–54 (2004).
CAS  Article  Google Scholar 

45.
Jamont, M., Piva, G. & Fustec, J. Sharing N resources in the early growth of rapeseed intercropped with faba bean: Does N transfer matter?. Plant Soil 371, 641–653 (2013).
CAS  Article  Google Scholar  More

1.
Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).
ADS  Article  Google Scholar 
2.
Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11, 2341–2356 (2014).
ADS  CAS  Article  Google Scholar 

3.
Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).
ADS  CAS  Article  Google Scholar 

4.
Wieder, W. R. et al. Explicitly representing soil microbial processes in Earth system models. Glob. Biogeochem. Cycles 29, 1782–1800 (2015).
ADS  CAS  Article  Google Scholar 

5.
Li, J., Wang, G., Allison, S., Mayes, M. & Luo, Y. Soil carbon sensitivity to temperature and carbon use efficiency compared across microbial-ecosystem models of varying complexity. Biogeochemistry 119, 67–84 (2014).
Article  Google Scholar 

6.
Luo, Y. Q. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Glob. Biogeochem. Cycles 30, 40–56 (2016).
ADS  CAS  Article  Google Scholar 

7.
German, D. P., Marcelo, K. R. B., Stone, M. M. & Allison, S. D. The Michaelis-Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study. Glob. Change Biol. 18, 1468–1479 (2012).
ADS  Article  Google Scholar 

8.
Wang, G. S., Post, W. M. & Mayes, M. A. Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecol. Appl. 23, 255–272 (2013).
PubMed  Article  Google Scholar 

9.
Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E. & Pacala, S. W. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nat. Clim. Change 4, 1099–1102 (2014).
ADS  CAS  Article  Google Scholar 

10.
Wang, G. S. et al. Microbial dormancy improves development and experimental validation of ecosystem model. ISME J. 9, 226–237 (2015).
CAS  PubMed  Article  Google Scholar 

11.
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).
ADS  CAS  Article  Google Scholar 

12.
Georgiou K., Abramoff R. Z., Harte J., Riley W. J. & Torn M. S. Microbial community-level regulation explains soil carbon responses to long-term litter manipulations. Nat. Commun. 8, 1223 (2017).

13.
Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2019).
CAS  Article  Google Scholar 

14.
Chenu C., Rumpel C. & Lehmann J. in Soil Microbiology, Ecology and Biochemistry 4th edn (ed Paul E. A.) Ch. 13 (Academic Press, 2015).

15.
Jagadamma, S., Mayes, M. A., Steinweg, J. M. & Schaeffer, S. M. Substrate quality alters the microbial mineralization of added substrate and soil organic carbon. Biogeosciences 11, 4665–4678 (2014).
ADS  Article  CAS  Google Scholar 

16.
Stewart, C. E., Paustian, K., Conant, R. T., Plante, A. F. & Six, J. Soil carbon saturation: Implications for measurable carbon pool dynamics in long-term incubations. Soil Biol. Biochem. 41, 357–366 (2009).
CAS  Article  Google Scholar 

17.
Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513, 81–8 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

18.
Hagerty, S. B. et al. Accelerated microbial turnover but constant growth efficiency with warming in soil. Nat. Clim. Change 4, 903–906 (2014).
ADS  CAS  Article  Google Scholar 

19.
Li, J. et al. Reduced carbon use efficiency and increased microbial turnover with soil warming. Glob. Change Biol. 25, 900–910 (2019).
ADS  Article  Google Scholar 

20.
Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry 127, 173–188 (2016).
CAS  Article  Google Scholar 

21.
Sinsabaugh, R. L., Moorhead, D. L., Xu, X. & Litvak, M. E. Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production. New Phytol. 214, 1518–1526 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Ye, J. S., Bradford, M. A., Dacal, M., Maestre, F. T. & Garca-Palacios, P. Increasing microbial carbon use efficiency with warming predicts soil heterotrophic respiration globally. Glob. Change Biol. 25, 3354–3364 (2019).
ADS  Article  Google Scholar 

23.
Xu, X. et al. Global pattern and controls of soil microbial metabolic quotient. Ecol. Monogr. 87, 429–441 (2017).
Article  Google Scholar 

24.
Ye, J.-S., Bradford, M. A., Maestre, F. T., Li, F.-M. & García-Palacios, P. Compensatory thermal adaptation of soil microbial respiration rates in global croplands. Glob. Biogeochem. Cycles 34, e2019GB006507 (2020).
ADS  CAS  Google Scholar 

25.
Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil 241, 155–176 (2002).
CAS  Article  Google Scholar 

26.
Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Manzoni, S. et al. Optimal metabolic regulation along resource stoichiometry gradients. Ecol. Lett. 20, 1182–1191 (2017).
PubMed  Article  PubMed Central  Google Scholar 

28.
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
ADS  Article  Google Scholar 

29.
Abramoff, R. Z., Torn, M. S., Georgiou, K., Tang, J. & Riley, W. J. Soil organic matter temperature sensitivity cannot be directly inferred from spatial gradients. Glob. Biogeochem. Cycles 33, 761–776 (2019).
ADS  CAS  Article  Google Scholar 

30.
Colores, G. M., Schmidt, S. K. & Fisk, M. C. Estimating the biomass of microbial functional groups using rates of growth-related soil respiration. Soil Biol. Biochem. 28, 1569–1577 (1996).
CAS  Article  Google Scholar 

31.
Van de Werf, H. & Verstraete, W. Estimation of active soil microbial biomass by mathematical analysis of respiration curves: calibration of the test procedure. Soil Biol. Biochem. 19, 261–265 (1987).
Article  Google Scholar 

32.
Sinsabaugh, R. L., Manzoni, S., Moorhead, D. L. & Richter, A. Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecol. Lett. 16, 930–939 (2013).
PubMed  Article  PubMed Central  Google Scholar 

33.
Schnecker, J., Bowles, T., Hobbie, E. A., Smith, R. G. & Grandy, A. S. Substrate quality and concentration control decomposition and microbial strategies in a model soil system. Biogeochemistry 144, 47–59 (2019).
CAS  Article  Google Scholar 

34.
Kluber, A. et al. Soil Respiration and Microbial Biomass from Soil Incubations with 13C Labeled Additions. (Oak Ridge National Laboratory, TES SFA, US Department of Energy, Oak Ridge, Tennessee, USA, 2020).

35.
Wang, G. S. et al. Soil moisture drives microbial controls on carbon decomposition in two subtropical forests. Soil Biol. Biochem. 130, 185–194 (2019).
CAS  Article  Google Scholar 

36.
Wang, K. F. et al. Modeling global soil carbon and soil microbial carbon by integrating microbial processes into the ecosystem process model TRIPLEX-GHG. J. Adv. Model Earth Syst. 9, 2368–2384 (2017).
ADS  Article  Google Scholar 

37.
He, Y. J. et al. Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests. J. Geophys. Res. Biogeosci. 120, 2596–2611 (2015).
CAS  Article  Google Scholar 

38.
Beare, M. H., Neely, C. L., Coleman, D. C. & Hargrove, W. L. Characterization of a substrate-induced respiration method for measuring fungal, bacterial and total microbial biomass on plant residues. Agric. Ecosyst. Environ. 34, 65–73 (1991).
Article  Google Scholar 

39.
Stenström, J., Svensson, K. & Johansson, M. Reversible transition between active and dormant microbial states in soil. FEMS Microbiol. Ecol. 36, 93–104 (2001).
PubMed  Article  PubMed Central  Google Scholar 

40.
Kaprelyants, A. S., Gottschal, J. C. & Kell, D. B. Dormancy in non-sporulating bacteria. FEMS Microbiol. Rev. 10, 271–285 (1993).
CAS  PubMed  Article  Google Scholar 

41.
Frey, S. D., Drijber, R., Smith, H. & Melillo, J. Microbial biomass, functional capacity, and community structure after 12 years of soil warming. Soil Biol. Biochem. 40, 2904–2907 (2008).
CAS  Article  Google Scholar 

42.
Canham, C. D. W., Cole, J. & Lauenroth, W. K. Models In Ecosystem Science (Princeton University Press, 2003).

43.
Vereecken, H. et al. Modeling Soil Processes: Review, Key Challenges, and New Perspectives. Vadose Zone J. 15, 1–57 (2016).

44.
Fuhrer, T., Fischer, E. & Sauer, U. Experimental identification and quantification of glucose metabolism in seven bacterial species. J. Bacteriol. 187, 1581–1590 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Fontaine, S. et al. Mechanisms of the priming effect in a savannah soil amended with cellulose. Soil Sci. Soc. Am. J. 68, 125–131 (2004).
ADS  CAS  Article  Google Scholar 

46.
Sinsabaugh, R. L. et al. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).
Article  Google Scholar 

47.
Wang, G. S., Mayes, M. A., Gu, L. H. & Schadt, C. W. Representation of dormant and active microbial dynamics for ecosystem modeling. PLoS ONE 9, e89252 (2014).

48.
Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–105 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

49.
Hartley, I. P., Heinemeyer, A. & Ineson, P. Effects of three years of soil warming and shading on the rate of soil respiration: substrate availability and not thermal acclimation mediates observed response. Glob. Change Biol. 13, 1761–1770 (2007).
ADS  Article  Google Scholar 

50.
Knorr, W., Prentice, I. C., House, J. I. & Holland, E. A. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Melillo, J. M. et al. Soil warming and carbon-cycle feedbacks to the climate system. Science 298, 2173–2176 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Luo, Y. Q. et al. Ecological forecasting and data assimilation in a data-rich era. Ecol. Appl. 21, 1429–1442 (2011).
PubMed  Article  PubMed Central  Google Scholar 

53.
Melillo, J. M., Steudler, P. A., Mohan, J. E. Prospect Hill soil warming experiment at Harvard Forest since 1991. Harvard Forest Data Archive HF005-05 Harvard Forest, Petersham, MA http://harvardforestfasharvardedu 8080 (1999).

54.
Zhou, J. Z. et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat. Clim. Change 2, 106–110 (2012).
ADS  CAS  Article  Google Scholar 

55.
Ye, J.-S., Bradford, M. A., Maestre, F. T., Li, F.-M. & García-Palacios, P. Compensatory thermal adaptation of soil microbial respiration rates in global croplands. Glob. Biogeochem. Cycles 34, e2019GB006507 (2020).

56.
Wang, G. S. & Chen, S. L. A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil. Geoderma 170, 206–216 (2012).
ADS  CAS  Article  Google Scholar 

57.
R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statitical Computing, Vienna, Austria, 2019).

58.
Batstone, D. J., Pind, P. F. & Angelidaki, I. Kinetics of thermophilic, anaerobic oxidation of straight and branched chain butyrate and valerate. Biotechnol. Bioeng. 84, 195–204 (2003).
CAS  PubMed  Article  Google Scholar 

59.
Wang, G. S., Barber, M. E., Chen, S. L. & Wu, J. Q. SWAT modeling with uncertainty and cluster analyses of tillage impacts on hydrological processes. Stoch. Environ. Res. Risk Assess. 28, 225–238 (2014).
Article  Google Scholar 

60.
Sulman, B. N. et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141, 109–123 (2018).
CAS  Article  Google Scholar 

61.
Abramoff, R. et al. The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century. Biogeochemistry 137, 51–71 (2018).
Article  Google Scholar 

62.
Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
van Gestel, N. et al. Predicting soil carbon loss with warming reply. Nature 554, E7–E8 (2018).
Article  CAS  Google Scholar 

64.
Jian, S. Y. et al. Soil extracellular enzyme activities, soil carbon and nitrogen storage under nitrogen fertilization: a meta-analysis. Soil Biol. Biochem. 101, 32–43 (2016).
CAS  Article  Google Scholar  More