Philippa Kaur

1.Thompson JR, Rivera HE, Closek CJ, Medina M. Microbes in the coral holobiont: partners through evolution, development, and ecological interactions. Front Cell Infect Microbiol. 2014;4:176.PubMed 

Google Scholar 
2.Pogoreutz C, Voolstra CR, Rädecker N, Weis V, Cardenas A, Raina J-B. The coral holobiont highlights the dependence of cnidarian animal hosts on their associated microbes. In: Bosch TCG, Hadfield MG, editors. Cellular dialogues in the holobiont. CRC Press; 2020. p. 91–118.3.Stanley GD, van de Schootbrugge B. The evolution of the coral–algal symbiosis. In: van Oppen MJH, Lough JM, editors. Coral bleaching: patterns, processes, causes and consequences. Berlin, Heidelberg: Springer; 2009. p. 7–19.4.LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol. 2018;28:2570–80.e6.CAS 
PubMed 

Google Scholar 
5.Muscatine L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs. 1990;25:75–87.
Google Scholar 
6.Stolarski J, Kitahara MV, Miller DJ, Cairns SD, Mazur M, Meibom A. The ancient evolutionary origins of Scleractinia revealed by azooxanthellate corals. BMC Evol Biol. 2011;11:316.PubMed 
PubMed Central 

Google Scholar 
7.Frankowiak K, Wang XT, Sigman DM, Gothmann AM, Kitahara MV, Mazur M, et al. Photosymbiosis and the expansion of shallow-water corals. Sci Adv. 2016;2:e1601122.PubMed 
PubMed Central 

Google Scholar 
8.Wild C, Hoegh-Guldberg O, Naumann MS, Colombo-Pallotta MF, Ateweberhan M, Fitt WK, et al. Climate change impedes scleractinian corals as primary reef ecosystem engineers. Mar Freshw Res. 2011;62:205–15.CAS 

Google Scholar 
9.Hughes TP, Barnes ML, Bellwood DR, Cinner JE, Cumming GS, Jackson JBC. et al. Coral reefs in the Anthropocene. Nature. 2017;546:82–90.CAS 
PubMed 

Google Scholar 
10.Kleypas J, Allemand D, Anthony K, Baker AC, Beck MW, Hale LZ, et al. Designing a blueprint for coral reef survival. Biol Conserv. 2021;257:109107.
Google Scholar 
11.Anthony KRN, Hoogenboom MO, Maynard JA, Grottoli AG, Middlebrook R. Energetics approach to predicting mortality risk from environmental stress: a case study of coral bleaching. Funct Ecol. 2009;23:539–50.
Google Scholar 
12.Hughes TP, Anderson KD, Connolly SR, Heron SF, Kerry JT, Lough JM. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science. 2018;359:80–83.CAS 
PubMed 

Google Scholar 
13.Voolstra CR, Suggett DJ, Peixoto RS, John E, Parkinson KM, Quigley CB, Silveira M, et al. Extending the natural adaptive capacity of coral holobionts. Nat Rev Earth Environ. 2021;2:747–62; https://doi.org/10.1038/s43017-021-00214-3.Article 

Google Scholar 
14.Suggett DJ, Smith DJ. Coral bleaching patterns are the outcome of complex biological and environmental networking. Glob Chang Biol. 2020;26:68–79.PubMed 

Google Scholar 
15.Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci USA. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
16.Morris LA, Voolstra CR, Quigley KM, Bourne DG, Bay LK. Nutrient availability and metabolism affect the stability of coral–Symbiodiniaceae symbioses. Trends Microbiol. 2019;27:678–89.CAS 
PubMed 

Google Scholar 
17.Muscatine L, Porter JW. Reef Corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience. 1977;27:454–60.
Google Scholar 
18.Falkowski PG, Dubinsky Z, Muscatine L, Porter JW. Light and the bioenergetics of a symbiotic coral. Bioscience. 1984;34:705–9.CAS 

Google Scholar 
19.Falkowski PG, Dubinsky Z, Muscatine L, McCloskey L. Population control in symbiotic corals. Bioscience. 1993;43:606–11.
Google Scholar 
20.Peng S-E, Chen C-S, Song Y-F, Huang H-T, Jiang P-L, Chen W-NU, et al. Assessment of metabolic modulation in free-living versus endosymbiotic Symbiodinium using synchrotron radiation-based infrared microspectroscopy. Biol Lett. 2012;8:434–7.PubMed 

Google Scholar 
21.Krueger T, Horwitz N, Bodin J, Giovani M-E, Escrig S, Fine M, et al. Intracellular competition for nitrogen controls dinoflagellate population density in corals. Proc R Soc B. 2020;287:20200049.CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Roberty S, Béraud E, Grover R, Ferrier-Pagès C. Coral croductivity is co-limited by bicarbonate and ammonium availability. Microorganisms. 2020;8:640CAS 
PubMed Central 

Google Scholar 
23.Baker DM, Freeman CJ, Wong JCY, Fogel ML, Knowlton N. Climate change promotes parasitism in a coral symbiosis. ISME J. 2018;12:921–30.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Cunning R, Muller EB, Gates RD, Nisbet RM. A dynamic bioenergetic model for coral- Symbiodinium symbioses and coral bleaching as an alternate stable state. J Theor Biol. 2017;431:49–62.CAS 
PubMed 

Google Scholar 
25.Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.PubMed 

Google Scholar 
26.Wiedenmann J, D’Angelo C, Smith EG, Hunt AN, Legiret F-E, Postle AD, et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat Clim Chang. 2012;3:160–4.
Google Scholar 
27.Houlbrèque F, Ferrier-Pagès C. Heterotrophy in tropical scleractinian corals. Biol Rev Camb Philos Soc. 2009;84:1–17.PubMed 

Google Scholar 
28.Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.CAS 
PubMed 

Google Scholar 
29.Grover R, Maguer J-F, Allemand D, Ferrier-Pagès C. Uptake of dissolved free amino acids by the scleractinian coral Stylophora pistillata. J Exp Biol. 2008;211:860–5.CAS 
PubMed 

Google Scholar 
30.Lema KA, Willis BL, Bourne DG. Corals form characteristic associations with symbiotic nitrogen-fixing bacteria. Appl Environ Microbiol. 2012;78:3136–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1187.PubMed 
PubMed Central 

Google Scholar 
32.Olson ND, Lesser MP. Diazotrophic diversity in the Caribbean coral, Montastraea cavernosa. Arch Microbiol. 2013;195:853–9.CAS 
PubMed 

Google Scholar 
33.Lesser MP, Morrow KM, Pankey SM, Noonan SHC. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 2018;12:813–24.CAS 
PubMed 

Google Scholar 
34.Moynihan MA, Goodkin NF, Morgan KM, Kho PYY, dos Santos AL, Lauro FM, et al. Coral-associated nitrogen fixation rates and diazotrophic diversity on a nutrient-replete equatorial reef. ISME J. 2021; https://doi.org/10.1038/s41396-021-01054-1.35.Tilstra A, Pogoreutz C, Rädecker N, Ziegler M, Wild C, Voolstra CR. Relative diazotroph abundance in symbiotic Red Sea corals decreases with water depth. Front Mar Sci. 2019;6:372.
Google Scholar 
36.Bednarz VN, Cardini U, van Hoytema N, Al-Rshaidat MMD, Wild C. Seasonal variation in dinitrogen fixation and oxygen fluxes associated with two dominant zooxanthellate soft corals from the northern Red Sea. Mar Ecol Prog Ser. 2015;519:141–52.
Google Scholar 
37.Rädecker N, Meyer FW, Bednarz VN, Cardini U, Wild C. Ocean acidification rapidly reduces dinitrogen fixation associated with the hermatypic coral Seriatopora hystrix. Mar Ecol Prog Ser. 2014;511:297–302.
Google Scholar 
38.Cardini U, Bednarz VN, Naumann MS, van Hoytema N, Rix L, Foster RA, et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc R Soc B. 2015;282:20152257.PubMed 
PubMed Central 

Google Scholar 
39.Bednarz VN, van de Water JAJM, Grover R, Maguer J-F, Fine M, Ferrier-Pagès C. Unravelling the importance of diazotrophy in corals – combined assessment of nitrogen assimilation, diazotrophic community and natural stable isotope signatures. Front Microbiol. 2021;12:1638.
Google Scholar 
40.Santos HF, Carmo FL, Duarte G, Dini-Andreote F, Castro CB, Rosado AS, et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 2014;8:2272–9.PubMed 
PubMed Central 

Google Scholar 
41.Cardini U, van Hoytema N, Bednarz VN, Rix L, Foster RA, Al-Rshaidat MMD, et al. Microbial dinitrogen fixation in coral holobionts exposed to thermal stress and bleaching. Environ Microbiol. 2016;18:2620–33.CAS 
PubMed 

Google Scholar 
42.Bednarz VN, van de Water JAJM, Rabouille S, Maguer J-F, Grover R, Ferrier-Pagès C. Diazotrophic community and associated dinitrogen fixation within the temperate coral Oculina patagonica. Environ Microbiol. 2019;21:480–95.CAS 
PubMed 

Google Scholar 
43.Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Chang Biol. 2017;23:3838–48.PubMed 

Google Scholar 
44.Bednarz VN, Grover R, Maguer J-F, Fine M, Ferrier-Pagès C. The assimilation of diazotroph-derived nitrogen by scleractinian corals depends on their metabolic status. mBio. 2017;8:e02058–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Petrou K, Nunn BL, Padula MP, Miller DJ, Nielsen DA. Broad scale proteomic analysis of heat-destabilised symbiosis in the hard coral Acropora millepora. Sci Rep. 2021;11:19061.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Roth F, Rädecker N, Carvalho S, Duarte CM, Saderne V, Anton A, et al. High summer temperatures amplify functional differences between coral‐ and algae‐dominated reef communities. Ecology. 2021;102:e03226.PubMed 

Google Scholar 
47.Andersson AF, Lindberg M, Jakobsson H, Bäckhed F, Nyrén P, Engstrand L. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS ONE. 2008;3:e2836.PubMed 
PubMed Central 

Google Scholar 
48.Bayer T, Neave MJ, Alsheikh-Hussain A, Aranda M, Yum LK, Mincer T, et al. The microbiome of the Red Sea coral Stylophora pistillata is dominated by tissue-associated Endozoicomonas bacteria. Appl Environ Microbiol. 2013;79:4759–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Gaby JC, Buckley DH. A comprehensive evaluation of PCR primers to amplify the nifH gene of nitrogenase. PLoS ONE. 2012;7:e42149.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
51.R Core Team. R: a language and environment for statistical computing computer program. Vienna, Austria: R Foundation for Statistical Computing; 2021.52.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 

Google Scholar 
53.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community Ecol Package. 2007;10:631–7.
Google Scholar 
55.Roesch LFW, Dobbler PT, Pylro VS, Kolaczkowski B, Drew JC, Triplett EW. pime: A package for discovery of novel differences among microbial communities. Mol Ecol Resour. 2020;20:415–28.CAS 
PubMed 

Google Scholar 
56.Guo K. microbial: do 16s data analysis and generate figures. R package version 1.14.4. 2021.57.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
Google Scholar 
58.Wang Q, Quensen JF, 3rd, Fish JA, Lee TK, Sun Y, Tiedje JM. et al. Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool. mBio. 2013;4:e00592–13.PubMed 
PubMed Central 

Google Scholar 
59.Meunier V, Geissler L, Bonnet S, Rädecker N, Perna G, Grosso O, et al. Microbes support enhanced nitrogen requirements of coral holobionts in a high CO2 environment. Mol Ecol. 2021;30:5888–99 .60.Angel R, Nepel M, Panhölzl C, Schmidt H, Herbold CW, Eichorst SA, et al. Evaluation of primers targeting the diazotroph functional gene and development of NifMAP – a bioinformatics pipeline for analyzing nifH amplicon data. Front Microbiol. 2018;9:703.PubMed 
PubMed Central 

Google Scholar 
61.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Frank IE, Turk-Kubo KA, Zehr JP. Rapid annotation of nifH gene sequences using classification and regression trees facilitates environmental functional gene analysis. Environ Microbiol Rep. 2016;8:905–16.PubMed 

Google Scholar 
63.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 

Google Scholar 
64.Hardy RW, Holsten RD, Jackson EK, Burns RC. The acetylene-ethylene assay for N2 fixation: laboratory and field evaluation. Plant Physiol. 1968;43:1185–207.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Breitbarth E, Mills MM, Friedrichs G, LaRoche J. The Bunsen gas solubility coefficient of ethylene as a function of temperature and salinity and its importance for nitrogen fixation assays: Bunsen coefficient and N2fixation. Limnol Oceanogr Methods 2004;2:282–8.
Google Scholar 
66.Zehr JP, Montoya JP. Measuring N2 fixation in the field. In: Bothe H, Ferguson SJ, Newton WE, editors. Biology of the nitrogen cycle. Elsevier; 2007. p. 193–205.67.Soper FM, Simon C, Jauss V. Measuring nitrogen fixation by the acetylene reduction assay (ARA): is 3 the magic ratio?. Biogeochemistry. 2021;152:345–51.CAS 

Google Scholar 
68.Lavy A, Eyal G, Neal B, Keren R, Loya Y, Ilan M. A quick, easy and non‐intrusive method for underwater volume and surface area evaluation of benthic organisms by 3D computer modelling. Methods Ecol Evol. 2015;6:521–31.
Google Scholar 
69.Wilson ST, Böttjer D, Church MJ, Karl DM. Comparative assessment of nitrogen fixation methodologies, conducted in the oligotrophic North Pacific Ocean. Appl Environ Microbiol. 2012;78:6516–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Hoppe P, Cohen S, Meibom A. NanoSIMS: Technical aspects and applications in cosmochemistry and biological geochemistry. Geostand Geoanal Res. 2013;37:111–54.CAS 

Google Scholar 
71.Neave MJ, Rachmawati R, Xun L, Michell CT, Bourne DG, Apprill A, et al. Differential specificity between closely related corals and abundant Endozoicomonas endosymbionts across global scales. ISME J. 2017;11:186–200.PubMed 

Google Scholar 
72.Savary R, Barshis DJ, Voolstra CR, Cárdenas A, Evensen NR, Banc-Prandi G, et al. Fast and pervasive transcriptomic resilience and acclimation of extremely heat tolerant coral holobionts from the northern Red Sea. Proc Natl Acad Sci USA. 2021;118:e2023298118.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Lesser MP, Mazel CH, Gorbunov MY, Falkowski PG. Discovery of symbiotic nitrogen-fixing cyanobacteria in corals. Science. 2004;305:997–1000.CAS 
PubMed 

Google Scholar 
74.Lesser MP, Falcón LI, Rodríguez-Román A, Enríquez S, Hoegh-Guldberg O, Iglesias-Prieto R. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar Ecol Prog Ser. 2007;346:143–52.CAS 

Google Scholar 
75.Tilstra A, El-Khaled YC, Roth F, Rädecker N, Pogoreutz C, Voolstra CR, et al. Denitrification aligns with N2 fixation in Red Sea corals. Sci Rep. 2019;9:19460.PubMed 
PubMed Central 

Google Scholar 
76.Rädecker N, Pogoreutz C, Ziegler M, Ashok A, Barreto MM, Chaidez V, et al. Assessing the effects of iron enrichment across holobiont compartments reveals reduced microbial nitrogen fixation in the Red Sea coral Pocillopora verrucosa. Ecol Evol. 2017;7:6614–21.PubMed 
PubMed Central 

Google Scholar 
77.Ainsworth TD, Fine M, Blackall LL, Hoegh-Guldberg O. Fluorescence in situ hybridization and spectral imaging of coral-associated bacterial communities. Appl Environ Microbiol. 2006;72:3016–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
78.van de Water JAJM, Ainsworth TD, Leggat W, Bourne DG, Willis BL, van Oppen MJH. The coral immune response facilitates protection against microbes during tissue regeneration. Mol Ecol. 2015;24:3390–404.PubMed 

Google Scholar 
79.Wada N, Ishimochi M, Matsui T, Pollock FJ, Tang S-L, Ainsworth TD, et al. Characterization of coral-associated microbial aggregates (CAMAs) within tissues of the coral Acropora hyacinthus. Sci Rep. 2019;9:14662.PubMed 
PubMed Central 

Google Scholar 
80.Udvardi M, Poole PS. Transport and metabolism in legume-rhizobia symbioses. Annu Rev Plant Biol. 2013;64:781–805.CAS 
PubMed 

Google Scholar 
81.Pernice M, Meibom A, Van Den Heuvel A, Kopp C, Domart-Coulon I, Hoegh-Guldberg O, et al. A single-cell view of ammonium assimilation in coral–dinoflagellate symbiosis. ISME J. 2012;6:1314–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
82.Shashar N, Cohen Y, Loya Y, Sar N. Nitrogen fixation (acetylene reduction) in stony corals: evidence for coral-bacteria interactions. Mar Ecol Prog Ser. 1994;111:259–64.CAS 

Google Scholar 
83.Inomura K, Bragg J, Riemann L, Follows MJ. A quantitative model of nitrogen fixation in the presence of ammonium. PLoS ONE. 2018;13:e0208282.PubMed 
PubMed Central 

Google Scholar 
84.Agawin NSR, Rabouille S, Veldhuis MJW, Servatius L, Hol S, van Overzee HMJ, et al. Competition and facilitation between unicellular nitrogen-fixing cyanobacteria and non-nitrogen-fixing phytoplankton species. Limnol Oceanogr. 2007;52:2233–48.CAS 

Google Scholar 
85.El-Khaled YC, Roth F, Tilstra A, Rädecker N, Karcher DB, Kürten B, et al. In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser. 2020;645:55–66.CAS 

Google Scholar 
86.Fine M, Loya Y. Endolithic algae: an alternative source of photoassimilates during coral bleaching. Proc R Soc B. 2002;269:1205–10.PubMed 
PubMed Central 

Google Scholar 
87.Sangsawang L, Casareto BE, Ohba H, Vu HM, Meekaew A, Suzuki T, et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R Soc Open Sci. 2017;4:171201.PubMed 
PubMed Central 

Google Scholar 
88.Fine M, Roff G, Ainsworth TD, Hoegh-Guldberg O. Phototrophic microendoliths bloom during coral “white syndrome. Coral Reefs. 2006;25:577–81.
Google Scholar 
89.Fine M, Steindler L, Loya Y. Endolithic algae photoacclimate to increased irradiance during coral bleaching. Mar Freshw Res. 2004;55:115–21.CAS 

Google Scholar 
90.Meunier V, Bonnet S, Benavides M, Ravache A, Grosso O, Lambert C, et al. Diazotroph-derived nitrogen assimilation strategies differ by scleractinian coral species. Front Mar Sci. 2021;8:1018.
Google Scholar  More

1.Huang, J. et al. Recently amplified arctic warming has contributed to a continual global warming trend. Nat. Clim. Chang. 7(12), 875–879. https://doi.org/10.1038/s41558-017-0009-5 (2017).ADS 
Article 

Google Scholar 
2.Zhang, X. et al. Changes in temperature and precipitation across Canada. In Canada’s Changing Climate Report (eds Bush, E. & Lemmen, D. S.) 112–193 (Ottawa, 2019).
Google Scholar 
3.Koenigk, T. et al. Arctic climate change in 21st century CMIP5 simulations with EC-Earth. Clim. Dyn. 40(11–12), 2719–2743. https://doi.org/10.1007/s00382-012-1505-y (2013).Article 

Google Scholar 
4.Arndt, K. A., Lipson, D. A., Hashemi, J., Oechel, W. C. & Zona, D. Snow melt stimulates ecosystem respiration in Arctic ecosystems. Glob. Change Biol. 26(9), 5042–5051. https://doi.org/10.1111/gcb.15193 (2020).ADS 
Article 

Google Scholar 
5.Commane, R. et al. Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra. Proc. Natl. Acad. Sci. U.S.A. 114(21), 5361–5366. https://doi.org/10.1073/pnas.1618567114 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Euskirchen, E. S., Bret-Harte, M. S., Shaver, G. R., Edgar, C. W. & Romanovsky, V. E. Long-term release of carbon dioxide from Arctic Tundra Ecosystems in Alaska. Ecosystems 20(5), 960–974. https://doi.org/10.1007/s10021-016-0085-9 (2017).CAS 
Article 

Google Scholar 
7.Webb, E. E. et al. Increased wintertime CO2 loss as a result of sustained tundra warming. J. Geophys. Res. Biogeosci. 121, 249–265. https://doi.org/10.1002/2014JG002795 (2016).CAS 
Article 

Google Scholar 
8.Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Chang. 9(11), 852–857. https://doi.org/10.1038/s41558-019-0592-8 (2019).ADS 
CAS 
Article 

Google Scholar 
9.Rafat, A. et al. Non-growing season carbon emissions in a northern peatland are projected to increase under global warming. Nature Communications Earth & Enviornment 2(1), 111. https://doi.org/10.1038/s43247-021-00184-w (2021).ADS 
Article 

Google Scholar 
10.Yarwood, S. A. The role of wetland microorganisms in plant-litter decomposition and soil organic matter formation: A critical review. FEMS Microbiol. Ecol. 94(11), 1–17. https://doi.org/10.1093/femsec/fiy175 (2018).CAS 
Article 

Google Scholar 
11.Yu, Z. C. Northern peatland carbon stocks and dynamics: A review. Biogeosciences 9(10), 4071–4085. https://doi.org/10.5194/bg-9-4071-2012 (2012).ADS 
CAS 
Article 

Google Scholar 
12.Keenan, T. F. & Williams, C. A. The terrestrial carbon sink. Annu. Rev. Environ. Resour. 43, 219–243. https://doi.org/10.1146/annurev-environ-102017-030204 (2018).Article 

Google Scholar 
13.Stocker, B. D., Yu, Z., Massa, C. & Joos, F. Holocene peatland and ice-core data constraints on the timing and magnitude of CO2 emissions from past land use. Proc. Natl. Acad. Sci. 114(7), 1492–1497. https://doi.org/10.1073/pnas.1613889114 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Webster, K. L. et al. Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands. Carbon Balance Manage. 13(1), 5. https://doi.org/10.1186/s13021-018-0105-5 (2018).CAS 
Article 

Google Scholar 
15.Byun, E., Finkelstein, S. A., Cowling, S. A. & Badiou, P. Potential carbon loss associated with post-settlement wetland conversion in southern Ontario, Canada. Carbon Balance Manag 13(1), 6. https://doi.org/10.1186/s13021-018-0094-4 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Lei, J. et al. Temporal changes in global soil respiration since 1987. Nat. Commun. 12(1), 1–9. https://doi.org/10.1038/s41467-020-20616-z (2021).ADS 
CAS 
Article 

Google Scholar 
17.Bona, K. A. et al. The Canadian model for peatlands (CaMP): A peatland carbon model for national greenhouse gas reporting. Ecol. Model. 431, 109164. https://doi.org/10.1016/j.ecolmodel.2020.109164 (2020).Article 

Google Scholar 
18.Brooks, P. D., McKnight, D. & Elder, K. Carbon limitation of soil respiration under winter snowpacks: Potential feedbacks between growing season and winter carbon fluxes. Glob. Change Biol. 11(2), 231–238. https://doi.org/10.1111/j.1365-2486.2004.00877.x (2005).ADS 
Article 

Google Scholar 
19.Helbig, M. et al. Direct and indirect climate change effects on carbon dioxide fluxes in a thawing boreal forest–wetland landscape. Glob. Change Biol. 23(8), 3231–3248. https://doi.org/10.1111/gcb.13638 (2017).ADS 
Article 

Google Scholar 
20.Zhang, T., Wang, G., Yang, Y., Mao, T. & Chen, X. Non-growing season soil CO2 flux and its contribution to annual soil CO2 emissions in two typical grasslands in the permafrost region of the Qinghai-Tibet Plateau. Eur. J. Soil Biol. 71, 45–52. https://doi.org/10.1016/j.ejsobi.2015.10.004 (2015).ADS 
CAS 
Article 

Google Scholar 
21.Grosse, G. et al. Vulnerability of high-latitude soil organic carbon in North America to disturbance. J. Geophys. Res. 116, G00K06. https://doi.org/10.1029/2010JG001507 (2011).CAS 
Article 

Google Scholar 
22.Hamdi, S., Moyano, F., Sall, S., Bernoux, M. & Chevallier, T. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol. Biochem. 58, 115–126. https://doi.org/10.1016/j.soilbio.2012.11.012 (2013).CAS 
Article 

Google Scholar 
23.Conant, R. T. et al. Temperature and soil organic matter decomposition rates—synthesis of current knowledge and a way forward. Glob. Change Biol. 17(11), 3392–3404. https://doi.org/10.1111/j.1365-2486.2011.02496.x (2011).ADS 
Article 

Google Scholar 
24.Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440(7081), 165–173. https://doi.org/10.1038/nature04514 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
25.Fang, C., Smith, P., Moncrieff, J. B. & Smith, J. U. Similar response of labile and resistant soil organic matter pools to changes in temperature. Nature 433(7021), 57–59. https://doi.org/10.1038/nature03138 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
26.Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Chang. 7(11), 817–822. https://doi.org/10.1038/nclimate3421 (2017).ADS 
CAS 
Article 

Google Scholar 
27.Li, J. et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob. Change Biol. 26(3), 1873–1885. https://doi.org/10.1111/gcb.14838 (2020).ADS 
Article 

Google Scholar 
28.Li, J., Pei, J., Pendall, E., Fang, C. & Nie, M. Spatial heterogeneity of temperature sensitivity of soil respiration: A global analysis of field observations. Soil Biol. Biochem. 141, 107675. https://doi.org/10.1016/j.soilbio.2019.107675 (2020).CAS 
Article 

Google Scholar 
29.Niu, B. et al. Warming homogenizes apparent temperature sensitivity of ecosystem respiration. Sci. Adv. 7(15), eabc7358. https://doi.org/10.1126/sciadv.abc7358 (2021).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Wang, J., Wu, Q., Yuan, Z. & Kang, H. Soil respiration of alpine meadow is controlled by freeze-Thaw processes of active layer in the permafrost region of the Qinghai-Tibet Plateau. Cryosphere 14(9), 2835–2848. https://doi.org/10.5194/tc-14-2835-2020 (2020).ADS 
Article 

Google Scholar 
31.Wang, Q. et al. Global synthesis of temperature sensitivity of soil organic carbon decomposition: Latitudinal patterns and mechanisms. Funct. Ecol. 33(3), 514–523. https://doi.org/10.1111/1365-2435.13256 (2019).Article 

Google Scholar 
32.Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Chang. 6(8), 751–758. https://doi.org/10.1038/nclimate3071 (2016).ADS 
CAS 
Article 

Google Scholar 
33.Pi, K. et al. The cold region critical zone in transition: Responses to climate warming and land use change. Annu. Rev. Environ. Resour. 46(1), 1–24. https://doi.org/10.1146/annurev-environ-012220-125703 (2021).Article 

Google Scholar 
34.Fuss, C. B. et al. Nitrate and dissolved organic carbon mobilization in response to soil freezing variability. Biogeochemistry 131(1–2), 35–47. https://doi.org/10.1007/s10533-016-0262-0 (2016).CAS 
Article 

Google Scholar 
35.Meyer, N., Welp, G. & Amelung, W. The Temperature sensitivity (Q10) of soil respiration: Controlling factors and spatial prediction at regional scale based on environmental soil classes. Glob. Biogeochem. Cycles 32(2), 306–323. https://doi.org/10.1002/2017GB005644 (2018).ADS 
CAS 
Article 

Google Scholar 
36.Moyano, F. E. et al. The moisture response of soil heterotrophic respiration: Interaction with soil properties. Biogeosciences 9(3), 1173–1182. https://doi.org/10.5194/bg-9-1173-2012 (2012).ADS 
CAS 
Article 

Google Scholar 
37.Schipper, L. A. et al. Shifts in temperature response of soil respiration between adjacent irrigated and non-irrigated grazed pastures. Agr. Ecosyst. Environ. 285, 106620. https://doi.org/10.1016/j.agee.2019.106620 (2019).CAS 
Article 

Google Scholar 
38.Alster, C. J., von Fischer, J. C., Allison, S. D. & Treseder, K. K. Embracing a new paradigm for temperature sensitivity of soil microbes. Glob. Change Biol. 26(6), 3221–3229. https://doi.org/10.1111/gcb.15053 (2020).ADS 
Article 

Google Scholar 
39.Baldwin, K. et al. Vegetation Zones of Canada: a Biogeoclimatic Perspective. Sault Ste. Marie, ON, Canada: Natural Resources Canada, Canadian Forest Service. Great Lake Forestry Center. https://open.canada.ca/data/en/dataset/22b0166b-9db3-46b7-9baf-6584a3acc7b1 (2019).40.Beck, H. E. et al. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data 5, 1–12. https://doi.org/10.1038/sdata.2018.214 (2018).Article 

Google Scholar 
41.Gardner, W. H. Water content. In Methods of soil analysis: Physical and mineralogical methods, agronomy series 9 (Part 1) (ed. Klute, A.) 493–544 (Soil Science Society of America, 1986). https://doi.org/10.2136/sssabookser5.1.2ed.c21.Chapter 

Google Scholar 
42.Webster, K. L., Creed, I. F., Bourbonnière, R. A. & Beall, F. D. Controls on the heterogeneity of soil respiration in a tolerant hardwood forest. J. Geophys. Res. 113(G3), G03018. https://doi.org/10.1029/2008JG000706 (2008).ADS 
Article 

Google Scholar 
43.Quinton, W. L. & Baltzer, J. L. The active-layer hydrology of a peat plateau with thawing permafrost (Scotty Creek, Canada). Hydrogeol. J. 21(1), 201–220. https://doi.org/10.1007/s10040-012-0935-2 (2013).ADS 
Article 

Google Scholar 
44.Davidson, E. A., Savage, K., Verchot, L. V. & Navarro, R. Minimizing artifacts and biases in chamber-based measurements of soil respiration. Agric. For. Meteorol. 113(1–4), 21–37. https://doi.org/10.1016/S0168-1923(02)00100-4 (2002).ADS 
Article 

Google Scholar 
45.Rezanezhad, F., Couture, R. M., Kovac, R., O’Connell, D. & Van Cappellen, P. Water table fluctuations and soil biogeochemistry: An experimental approach using an automated soil column system. J. Hydrol. 509, 245–256. https://doi.org/10.1016/j.jhydrol.2013.11.036 (2014).ADS 
CAS 
Article 

Google Scholar 
46.Fang, C. & Moncrieff, J. B. The dependence of soil CO2 efflux on temperature. Soil Biol. Biochem. 33(2), 155–165. https://doi.org/10.1016/S0038-0717(00)00125-5 (2001).CAS 
Article 

Google Scholar 
47.Alster, C. J., Koyama, A., Johnson, N. G., Wallenstein, M. D. & von Fischer, J. C. Temperature sensitivity of soil microbial communities: An application of macromolecular rate theory to microbial respiration. J. Geophys. Res. Biogeosci. 121(6), 1420–1433. https://doi.org/10.1002/2016JG003343 (2016).Article 

Google Scholar 
48.Hobbs, J. K. et al. Change in heat capacity for enzyme catalysis determines temperature dependence of enzyme catalyzed rates. ACS Chem. Biol. 8(11), 2388–2393. https://doi.org/10.1021/cb4005029 (2013).CAS 
Article 
PubMed 

Google Scholar 
49.Robinson, J. M. et al. Rapid laboratory measurement of the temperature dependence of soil respiration and application to changes in three diverse soils through the year. Biogeochemistry 133(1), 101–112. https://doi.org/10.1007/s10533-017-0314-0 (2017).CAS 
Article 

Google Scholar 
50.Schipper, L. A., Hobbs, J. K., Rutledge, S. & Arcus, V. L. Thermodynamic theory explains the temperature optima of soil microbial processes and high Q10 values at low temperatures. Glob. Change Biol. 20(11), 3578–3586. https://doi.org/10.1111/gcb.12596 (2014).ADS 
Article 

Google Scholar 
51.Webster, K. L., Creed, I. F., Malakoff, T. & Delaney, K. Potential Vulnerability of Deep Carbon Deposits of Forested Swamps to Drought. Soil Sci. Soc. Am. J. 78(3), 1097–1107. https://doi.org/10.2136/sssaj2013.10.0436 (2014).ADS 
CAS 
Article 

Google Scholar 
52.Loranty, M. M. et al. Reviews and syntheses: Changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosciences 15(17), 5287–5313. https://doi.org/10.5194/bg-15-5287-2018 (2018).ADS 
CAS 
Article 

Google Scholar 
53.Roy-Léveillée, P., Burn, C. R. & Mcdonald, I. D. Vegetation-Permafrost Relations within the Forest-Tundra Ecotone near Old Crow, Northern Yukon, Canada. Permafr. and Periglac. Process. 25(2), 127–135. https://doi.org/10.1002/ppp.1805 (2014).Article 

Google Scholar 
54.Zhang, Y., Sherstiukov, A. B., Qian, B., Kokelj, S. V. & Lantz, T. C. Impacts of snow on soil temperature observed across the circumpolar north. Environ. Res. Lett. 13(4), 1e7. https://doi.org/10.1088/1748-9326/aab1e7 (2018).CAS 
Article 

Google Scholar 
55.Sjögersten, S. et al. Temperature response of ex-situ greenhouse gas emissions from tropical peatlands: Interactions between forest type and peat moisture conditions. Geoderma 324, 47–55. https://doi.org/10.1016/j.geoderma.2018.02.029 (2018).ADS 
CAS 
Article 

Google Scholar 
56.Bradford, M. A. et al. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat. Ecol. Evol. 3(2), 223–231. https://doi.org/10.1038/s41559-018-0771-4 (2019).Article 
PubMed 

Google Scholar 
57.Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Chang. 3(4), 395–398. https://doi.org/10.1038/nclimate1796 (2013).ADS 
CAS 
Article 

Google Scholar 
58.Moinet, G. Y. K. et al. Temperature sensitivity of decomposition decreases with increasing soil organic matter stability. Sci. Total Environ. 704, 135460. https://doi.org/10.1016/j.scitotenv.2019.135460 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
59.Naylor, D. et al. Soil microbiomes under climate change and implications for carbon cycling. Annu. Rev. Environ. Resour. 45, 29–59. https://doi.org/10.1146/annurev-environ-012320-082720 (2020).Article 

Google Scholar 
60.Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. 4, 1–16. https://doi.org/10.3389/fmicb.2013.00333 (2013).Article 

Google Scholar 
61.Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445. https://doi.org/10.1146/annurev-ecolsys-112414-054234 (2017).Article 

Google Scholar 
62.Hararuk, O., Shaw, C. & Kurz, W. A. Constraining the organic matter decay parameters in the CBM-CFS3 using Canadian National Forest Inventory data and a Bayesian inversion technique. Ecol. Model. 364, 1–12. https://doi.org/10.1016/j.ecolmodel.2017.09.008 (2017).CAS 
Article 

Google Scholar 
63.Franzluebbers, A. J. Microbial activity in response to water-filled pore space of variably eroded southern Piedmont soils. Appl. Soil. Ecol. 11(1), 91–101. https://doi.org/10.1016/S0929-1393(98)00128-0 (1999).Article 

Google Scholar 
64.Rezanezhad, F. et al. Structure of peat soils and implications for water storage, flow and solute transport: A review update for geochemists. Chem. Geol. 429, 75–84. https://doi.org/10.1016/j.chemgeo.2016.03.010 (2016).ADS 
CAS 
Article 

Google Scholar 
65.Stirling, E., Fitzpatrick, R. W. & Mosley, L. M. Drought effects on wet soils in inland wetlands and peatlands. Earth Sci. Rev. 210, 103387. https://doi.org/10.1016/j.earscirev.2020.103387 (2020).CAS 
Article 

Google Scholar 
66.Wickland, K. P. & Neff, J. C. Decomposition of soil organic matter from boreal black spruce forest: Environmental and chemical controls. Biogeochemistry 87(1), 29–47. https://doi.org/10.1007/s10533-007-9166-3 (2008).Article 

Google Scholar 
67.Arnold, C., Ghezzehei, T. A. & Berhe, A. A. Decomposition of distinct organic matter pools is regulated by moisture status in structured wetland soils. Soil Biol. Biochem. 81, 28–37. https://doi.org/10.1016/j.soilbio.2014.10.029 (2015).CAS 
Article 

Google Scholar 
68.Moyano, F. E., Manzoni, S. & Chenu, C. Responses of soil heterotrophic respiration to moisture availability: An exploration of processes and models. Soil Biol. Biochem. 59, 72–85. https://doi.org/10.1016/j.soilbio.2013.01.002 (2013).CAS 
Article 

Google Scholar 
69.Sierra, C. A., Malghani, S. & Loescher, H. W. Interactions among temperature, moisture, and oxygen concentrations in controlling decomposition rates in a boreal forest soil. Biogeosciences 14(3), 703–710. https://doi.org/10.5194/bg-14-703-2017 (2017).ADS 
CAS 
Article 

Google Scholar 
70.McCarter, C. P. R. et al. Pore-scale controls on hydrological and geochemical processes in peat: Implications on interacting processes. Earth Sci. Rev. 207, 103227. https://doi.org/10.1016/j.earscirev.2020.103227 (2020).CAS 
Article 

Google Scholar 
71.Strack, M. et al. Effect of water table drawdown on peatland dissolved organic carbon export and dynamics. Hydrol. Process. 22(17), 3373–3385. https://doi.org/10.1002/hyp.6931 (2008).ADS 
CAS 
Article 

Google Scholar 
72.Leclair, M., Whittington, P. & Price, J. Hydrological functions of a mine-impacted and natural peatland-dominated watershed, James Bay Lowland. J. Hydrol. Reg. Stud. 4, 732–747. https://doi.org/10.1016/j.ejrh.2015.10.006 (2015).Article 

Google Scholar 
73.Treat, C. C. et al. Effects of permafrost aggradation on peat properties as determined from a pan-Arctic synthesis of plant macrofossils. J. Geophys. Res. Biogeosci. 121(1), 78–94. https://doi.org/10.1002/2015JG003061 (2016).CAS 
Article 

Google Scholar 
74.Günther, A. et al. Prompt rewetting of drained peatlands reduces climate warming despite methane emissions. Nat. Commun. 11(1), 1–5. https://doi.org/10.1038/s41467-020-15499-z (2020).CAS 
Article 

Google Scholar 
75.Schädel, C. et al. Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils. Nat. Clim. Chang. 6(10), 950–953. https://doi.org/10.1038/nclimate3054 (2016).ADS 
CAS 
Article 

Google Scholar 
76.Hemes, K. S., Chamberlain, S. D., Eichelmann, E., Knox, S. H. & Baldocchi, D. D. A biogeochemical compromise: The high methane cost of sequestering carbon in restored wetlands. Geophys. Res. Lett. 45, 6081–6091. https://doi.org/10.1029/2018GL077747 (2018).ADS 
CAS 
Article 

Google Scholar 
77.Davidson, E. A., Samanta, S., Caramori, S. S. & Savage, K. The Dual Arrhenius and Michaelis-Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Glob. Change Biol. 18, 371–384. https://doi.org/10.1111/j.1365-2486.2011.02546.x (2012).ADS 
Article 

Google Scholar 
78.Matzner, E. & Borken, W. Do freeze-thaw events enhance C and N losses from soils of different ecosystems? A review. Eur. J. Soil Sci. 59(2), 274–284. https://doi.org/10.1111/j.1365-2389.2007.00992.x (2008).Article 

Google Scholar 
79.Song, Y., Zou, Y., Wang, G. & Yu, X. Altered soil carbon and nitrogen cycles due to the freeze-thaw effect: A meta-analysis. Soil Biol. Biochem. 109, 35–49. https://doi.org/10.1016/j.soilbio.2017.01.020 (2017).CAS 
Article 

Google Scholar 
80.Wang, J. et al. Effects of freezing-thawing cycle on peatland active organic carbon fractions and enzyme activities in the Da Xing’anling Mountains. Northeast China. Environmental Earth Sciences 72(6), 1853–1860. https://doi.org/10.1007/s12665-014-3094-z (2014).CAS 
Article 

Google Scholar 
81.Wu, H., Xu, X., Cheng, W., Fu, P. & Li, F. Antecedent soil moisture prior to freezing can affect quantity, composition and stability of soil dissolved organic matter during thaw. Sci. Rep. 7(1), 1–12. https://doi.org/10.1038/s41598-017-06563-8 (2017).ADS 
CAS 
Article 

Google Scholar 
82.Bao, T., Xu, X., Jia, G., Billesbach, D. P. & Sullivan, R. C. Much stronger tundra methane emissions during autumn freeze than spring thaw. Glob. Change Biol. 27(2), 376–387. https://doi.org/10.1111/gcb.15421 (2021).ADS 
Article 

Google Scholar 
83.Chang, K. Y., Riley, W. J., Crill, P. M., Grant, R. F. & Saleska, S. R. Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity. Biogeosciences 17(22), 5849–5860. https://doi.org/10.5194/bg-17-5849-2020 (2020).ADS 
CAS 
Article 

Google Scholar 
84.Neumann, R. B. et al. Warming Effects of Spring Rainfall Increase Methane Emissions From Thawing Permafrost. Geophys. Res. Lett. 46(3), 1393–1401. https://doi.org/10.1029/2018GL081274 (2019).ADS 
Article 

Google Scholar 
85.Rezanezhad, F., Price, J. S. & Craig, J. R. The effects of dual porosity on transport and retardation in peat: A laboratory experiment. Can. J. Soil Sci. 92(5), 723–732. https://doi.org/10.4141/CJSS2011-050 (2012).Article 

Google Scholar 
86.Raz-Yaseef, N. et al. Large CO2 and CH4 emissions from polygonal tundra during spring thaw in northern Alaska. Geophys. Res. Lett. 44(1), 504–513. https://doi.org/10.1002/2016GL071220 (2017).ADS 
CAS 
Article 

Google Scholar 
87.Waldrop, M. P. et al. Carbon fluxes and microbial activities from boreal peatlands experiencing permafrost thaw. J. Geophys. Res. Biogeosci. 126(3), e2020JG005869. https://doi.org/10.1029/2020JG005869 (2021).ADS 
CAS 
Article 

Google Scholar 
88.Pulliainen, J. et al. Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature 581(7808), 294–298. https://doi.org/10.1038/s41586-020-2258-0 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar  More

1.United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019: Highlights (ST/ESA/SER.A/423).2.Leij-Halfwerk, S. et al. Prevalence of protein-energy malnutrition risk in European older adults in community, residential and hospital settings, according to 22 malnutrition screening tools validated for use in adults >/=65 years: A systematic review and meta-analysis. Maturitas 126, 80–89 (2019).PubMed 

Google Scholar 
3.Keller, H. H., Ostbye, T. & Goy, R. Nutritional risk predicts quality of life in elderly community-living Canadians. J. Gerontol. A Biol. Sci. Med. Sci. 59(1), 68–74 (2004).PubMed 

Google Scholar 
4.Correia, M. I. & Waitzberg, D. L. The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clin. Nutr. 22(3), 235–239 (2003).PubMed 

Google Scholar 
5.van der Pols-Vijlbrief, R., Wijnhoven, H. A., Schaap, L. A., Terwee, C. B. & Visser, M. Determinants of protein-energy malnutrition in community-dwelling older adults: A systematic review of observational studies. Ageing Res Rev. 18, 112–131 (2014).PubMed 

Google Scholar 
6.Wysokinski, A., Sobow, T., Kloszewska, I. & Kostka, T. Mechanisms of the anorexia of aging—A review. Age (Dordr). 37(4), 9821 (2015).PubMed 

Google Scholar 
7.Poisson, P., Laffond, T., Campos, S., Dupuis, V. & Bourdel-Marchasson, I. Relationships between oral health, dysphagia and undernutrition in hospitalised elderly patients. Gerodontology 33(2), 161–168 (2016).PubMed 

Google Scholar 
8.Besnard, P. et al. Obese subjects with specific gustatory papillae microbiota and salivary cues display an impairment to sense lipids. Sci. Rep. 8(1), 6742 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
9.Feng, Y. et al. The associations between biochemical and microbiological variables and taste differ in whole saliva and in the film lining the tongue. Biomed. Res. Int. 2018, 2838052. https://doi.org/10.1155/2018/2838052 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Cattaneo, C. et al. New insights into the relationship between taste perception and oral microbiota composition. Sci. Rep. 9(1), 3549 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
11.Cattaneo, C., Riso, P., Laureati, M., Gargari, G. & Pagliarini, E. Exploring associations between interindividual differences in taste perception, oral microbiota composition, and reported food intake. Nutrients 11(5), 1167 (2019).CAS 
PubMed Central 

Google Scholar 
12.Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 486(7402), 207–214 (2012).13.Neyraud, E. & Morzel, M. Biological films adhering to the oral soft tissues: Structure, composition, and potential impact on taste perception. J. Texture Stud. 50(1), 19–26 (2019).PubMed 

Google Scholar 
14.Francois, A. et al. Olfactory epithelium changes in germfree mice. Sci. Rep. 6, 24687 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Paillaud, E. et al. Oral candidiasis and nutritional deficiencies in elderly hospitalised patients. Br. J. Nutr. 92(5), 861–867 (2004).CAS 
PubMed 

Google Scholar 
16.Sakashita, S., Takayama, K., Nishioka, K. & Katoh, T. Taste disorders in healthy “carriers” and “non-carriers” of Candida albicans and in patients with candidosis of the tongue. J. Dermatol. 31(11), 890–897 (2004).PubMed 

Google Scholar 
17.Hoogendijk, E. O. et al. The Longitudinal Aging Study Amsterdam: Cohort update 2016 and major findings. Eur. J. Epidemiol. 31(9), 927–945 (2016).PubMed 
PubMed Central 

Google Scholar 
18.Hoogendijk, E. O. et al. The Longitudinal Aging Study Amsterdam: Cohort update 2019 and additional data collections. Eur. J. Epidemiol. 35(1), 61–74 (2020).PubMed 

Google Scholar 
19.Huisman, M. et al. Cohort profile: The Longitudinal Aging Study Amsterdam. Int. J. Epidemiol. 40(4), 868–876 (2011).PubMed 

Google Scholar 
20.Cederholm, T. et al. Diagnostic criteria for malnutrition—An ESPEN consensus statement. Clin. Nutr. 34(3), 335–340 (2015).CAS 
PubMed 

Google Scholar 
21.Hanisah, R., Suzana, S. & Lee, F. S. Validation of screening tools to assess appetite among geriatric patients. J. Nutr. Health Aging. 16(7), 660–665 (2012).CAS 
PubMed 

Google Scholar 
22.Dewhirst, F. E. et al. The human oral microbiome. J. Bacteriol. 192(19), 5002–5017 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Kaci, G. et al. Anti-inflammatory properties of Streptococcus salivarius, a commensal bacterium of the oral cavity and digestive tract. Appl. Environ. Microbiol. 80(3), 928–934 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
24.Asakawa, M. et al. Tongue microbiota and oral health status in community-dwelling elderly adults. mSphere 3(4), e00332–18 (2018).PubMed 
PubMed Central 

Google Scholar 
25.Zupancic, K., Kriksic, V., Kovacevic, I. & Kovacevic, D. Influence of oral probiotic Streptococcus salivarius K12 on ear and oral cavity health in humans: Systematic review. Probiotics Antimicrob. Proteins 9(2), 102–110 (2017).CAS 
PubMed 

Google Scholar 
26.Tagg, J. R. & Dierksen, K. P. Bacterial replacement therapy: Adapting ‘germ warfare’ to infection prevention. Trends Biotechnol. 21(5), 217–223 (2003).CAS 
PubMed 

Google Scholar 
27.Guglielmetti, S. et al. Oral bacteria as potential probiotics for the pharyngeal mucosa. Appl. Environ. Microbiol. 76(12), 3948–3958 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Huttenbrink, K.B., Hummel, T., Berg, D., Gasser, T. & Hahner, A. Olfactory dysfunction: common in later life and early warning of neurodegenerative disease. Dtsch Arztebl Int. 110(1–2), 1–7, e1 (2013).29.Pavlovic, J., Racic, M., Ivkovic, N. & Jatic, Z. Comparison of nutritional status between nursing home residents and community dwelling older adults: A cross-sectional study from bosnia and herzegovina. Mater. Sociomed. 31(1), 19–24 (2019).PubMed 
PubMed Central 

Google Scholar 
30.Fluitman, K. S. et al. Poor taste and smell are associated with poor appetite, macronutrient intake, and dietary quality but not with undernutrition in older adults. J. Nutr. 151(3), 605–614 (2021).PubMed 
PubMed Central 

Google Scholar 
31.Whigham, L. D., Schoeller, D. A., Johnson, L. K. & Atkinson, R. L. Effect of clothing weight on body weight. Int. J. Obes. (Lond.) 37(1), 160–161 (2013).CAS 

Google Scholar 
32.Kyle, U. G., Genton, L., Karsegard, L., Slosman, D. O. & Pichard, C. Single prediction equation for bioelectrical impedance analysis in adults aged 20–94 years. Nutrition 17(3), 248–253 (2001).CAS 
PubMed 

Google Scholar 
33.Sergi, G. et al. Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free-living Caucasian older adults. Clin. Nutr. 34(4), 667–673 (2015).PubMed 

Google Scholar 
34.Cruz-Jentoft, A. J. et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 48(1), 16–31 (2019).PubMed 

Google Scholar 
35.Kiesswetter, E., Hengeveld, L. M., Keijser, B. J., Volkert, D. & Visser, M. Oral health determinants of incident malnutrition in community-dwelling older adults. J. Dent. 85, 73–80 (2019).PubMed 

Google Scholar 
36.Beukers, M. H. et al. Development of the HELIUS food frequency questionnaires: ethnic-specific questionnaires to assess the diet of a multiethnic population in The Netherlands. Eur. J. Clin. Nutr. 69(5), 579–584 (2015).CAS 
PubMed 

Google Scholar 
37.Visser, M., Elstgeest, L. E. M., Winkens, L. H. H., Brouwer, I. A. & Nicolaou, M. Relative validity of the HELIUS Food Frequency Questionnaire for measuring dietary intake in older adult participants of the Longitudinal Aging Study Amsterdam. Nutrients 12(7), 1998 (2020).PubMed Central 

Google Scholar 
38.Garretsen, H. R. F. & Knibbe, R. A. Alcohol prevalentie onderzoek Rotterdam/Limburg, Landelijk Eindrapport, Ministerie van Welzijn, Volksgezondheid en Cultuur, Leidschendam (1983).39.Radloff, L. The CES-D scale: A self-reported depression scale for research in the general population. Appl. Psychol. Meas. 1, 385–401 (1977).
Google Scholar 
40.Folstein, M. F., Folstein, S. E. & McHugh, P. R. “Mini-mental state” A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12(3), 189–198 (1975).CAS 
PubMed 

Google Scholar 
41.Landis, B. N. et al. “Taste Strips”—a rapid, lateralized, gustatory bedside identification test based on impregnated filter papers. J. Neurol. 256(2), 242–248 (2009).PubMed 

Google Scholar 
42.Mueller, C. A., Pintscher, K. & Renner, B. Clinical test of gustatory function including umami taste. Ann. Otol. Rhinol. Laryngol. 120(6), 358–362 (2011).PubMed 

Google Scholar 
43.Hummel, T., Kobal, G., Gudziol, H. & Mackay-Sim, A. Normative data for the “Sniffin’ Sticks” including tests of odor identification, odor discrimination, and olfactory thresholds: An upgrade based on a group of more than 3,000 subjects. Eur. Arch. Otorhinolaryngol. 264(3), 237–243 (2007).CAS 
PubMed 

Google Scholar 
44.Arganini, C. & Sinesio, F. Chemosensory impairment does not diminish eating pleasure and appetite in independently living older adults. Maturitas 82(2), 241–244 (2015).PubMed 

Google Scholar 
45.de Jong, N., Mulder, I., de Graaf, C. & van Staveren, W. A. Impaired sensory functioning in elders: the relation with its potential determinants and nutritional intake. J. Gerontol. A Biol. Sci. Med. Sci. 54(8), B324–B331 (1999).PubMed 

Google Scholar 
46.Fluitman, K. S. et al. The association of olfactory function with BMI, appetite, and prospective weight change in Dutch community-dwelling older adults. J. Nutr. Health Aging 23(8), 746–752 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Gopinath, B. et al. Olfactory impairment in older adults is associated with poorer diet quality over 5 years. Eur. J. Nutr. 55(3), 1081–1087 (2016).CAS 
PubMed 

Google Scholar 
48.Karpa, M. J. et al. Prevalence and neurodegenerative or other associations with olfactory impairment in an older community. J. Aging Health. 22(2), 154–168 (2010).PubMed 

Google Scholar 
49.Smoliner, C., Fischedick, A., Sieber, C. C. & Wirth, R. Olfactory function and malnutrition in geriatric patients. J. Gerontol. A Biol. Sci. Med. Sci. 68(12), 1582–1588 (2013).PubMed 

Google Scholar 
50.Keijser, B. J. F. et al. Dose-dependent impact of oxytetracycline on the veal calf microbiome and resistome. BMC Genom. 20(1), 65 (2019).
Google Scholar 
51.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U. S. A. 108(Suppl 1), 4516–4522 (2011).ADS 
CAS 
PubMed 

Google Scholar 
52.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Guiver, M., Levi, K. & Oppenheim, B. A. Rapid identification of candida species by TaqMan PCR. J. Clin. Pathol. 54(5), 362–366 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria (2018).55.Wickham, H. Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

Google Scholar 
56.Oksanen, J., et al. Vegan: Community Ecology Package. R package 2018.57.Cailliez, F. The analytical solution of the additive constant problem. Psychometrika 48(2), 305–308 (1983).MathSciNet 
MATH 

Google Scholar 
58.Love, M. I., Wolfgang, H. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12), 550 (2014).PubMed 
PubMed Central 

Google Scholar  More

1.Sih, A., Crowley, P., McPeek, M., Petranka, J. & Strohmeier, K. Predation, competition, and prey communities: A review of field experiments. Annu. Rev. Ecol. S. 16, 269–311 (1985).
Google Scholar 
2.Schmitz, O. J. et al. From individuals to ecosystem function: Toward an integration of evolutionary and ecosystem ecology. Ecology 89, 2436–2445 (2008).PubMed 
PubMed Central 

Google Scholar 
3.Sih, A., Englund, G. & Wooster, D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 13, 350–355 (1998).CAS 
PubMed 

Google Scholar 
4.Holt, R. D. Predation, apparent competition, and structure of prey communities. Theor. Popul. Biol. 12, 197–229 (1977).MathSciNet 
CAS 
PubMed 

Google Scholar 
5.Bonsall, M. B. & Hassell, M. P. Apparent competition structures ecological assemblages. Nature 388, 371–373 (1997).CAS 
ADS 

Google Scholar 
6.Tuda, M. & Shimada, M. Complexity, evolution, and persistence in host–parasitoid experimental systems with Callosobruchus beetles as the host. Adv. Ecol. Res. 37, 37–75 (2005).
Google Scholar 
7.Briggs, C. J., Nisbet, R. M. & Murdoch, W. W. Coexistence of competing parasitoid species on a host with a variable life cycle. Theor. Popul. Biol. 44, 341–373 (1993).MATH 

Google Scholar 
8.Peri, E., Cusumano, A., Amodeo, V., Wajnberg, E. & Colazza, S. Intraguild interactions between two egg parasitoids of a true bug in semi-field and field conditions. PLoS ONE 9, e99876 (2014).PubMed 
PubMed Central 
ADS 

Google Scholar 
9.Pekas, A., Tena, A., Harvey, J. A., Garcia-Marí, F. & Frago, E. Host size and spatiotemporal patterns mediate the coexistence of specialist parasitoids. Ecology 97, 1345–1356 (2016).PubMed 

Google Scholar 
10.DeLong, J. P. & Vasseur, D. A. Mutual interference is common and mostly intermediate in magnitude. BMC Ecol. 11, 1 (2011).PubMed 
PubMed Central 

Google Scholar 
11.Hassell, M. P. & Varley, G. C. New inductive population model for insect parasites and its bearing on biological control. Nature 223, 1133–1137 (1969).CAS 
PubMed 
ADS 

Google Scholar 
12.Hassell, M. P. Mutual interference between searching insect parasites. J. Anim. Ecol. 40, 473–486 (1971).
Google Scholar 
13.Charnov, E. L., Orians, G. H. & Hyatt, K. Ecological implications of resource depression. Am. Nat. 110, 247–259 (1976).
Google Scholar 
14.Free, C. A., Beddington, J. R. & Lawton, J. H. On the inadequacy of simple models of mutual interference for parasitism and predation. J. Anim. Ecol. 46, 543–554 (1977).
Google Scholar 
15.Visser, M. E., Jones, T. H. & Driessen, G. Interference among insect parasitoids: A multi-patch experiment. J. Anim. Ecol. 68, 108–120 (1999).
Google Scholar 
16.Beddington, J. R. Mutual interference between parasites or predators and its effect on searching efficiency. J. Anim. Ecol. 44, 331–340 (1975).
Google Scholar 
17.DeAngelis, D. L., Goldstein, R. A. & O’Neill, R. V. A model for trophic interaction. Ecology 56, 881–892 (1975).
Google Scholar 
18.Arditi, R., Callois, J. M., Tyutyunov, Y. & Jost, C. Does mutual interference always stability predator–prey dynamics? A comparison of models. C. R. Biol. 327, 1037–1057 (2004).PubMed 

Google Scholar 
19.Abrams, P. A. Why ratio dependence is (still) a bad model of predation. Biol. Rev. 90, 794–814 (2015).PubMed 

Google Scholar 
20.Pedersen, B. S. & Mills, N. J. Single vs. multiple introduction in biological control: The roles of parasitoid efficiency, antagonism, and niche overlap. J. Appl. Ecol. 41, 973–984 (2004).
Google Scholar 
21.Amarasekare, P. Interference competition and species coexistence. Proc. R. Soc. B 269, 2550–2641 (2002).
Google Scholar 
22.Mohamad, R., Wajnberg, E., Monge, J. P. & Goubault, M. The effect of direct interspecific competition on patch exploitation strategies in parasitoid wasps. Oecologia 177, 305–315 (2015).PubMed 
ADS 

Google Scholar 
23.Elliott, J. M. Interspecific interference and the functional response of four species of carnivorous stoneflies. Freshw. Biol. 48, 1527–1539 (2004).
Google Scholar 
24.Nakamichi, Y., Tuda, M. & Wajnberg, E. Intraspecific interference between native parasitoids modified by a non-native parasitoid and its consequence on population dynamics. Ecol. Entomol. 45, 1263–1271 (2020).
Google Scholar 
25.Trivers, R. L. & Willard, D. E. Natural selection of parental ability to vary the sex ratio of offspring. Science 179, 90–92 (1973).CAS 
PubMed 
ADS 

Google Scholar 
26.Appleby, B. M., Petty, S. J., Blakey, J. K., Rainey, P. & Macdonald, D. W. Does variation of sex ratio enhance reproductive success of offspring in tawny owls (Strix aluco)?. Proc. R. Soc. B 264, 1111–1116 (1997).PubMed Central 
ADS 

Google Scholar 
27.Nishimura, K. & Jahn, G. C. Sex allocation of three solitary ectoparasitic wasp species on bean weevil larvae: Sex ratio change with host quality and local mate competition. J. Ethol. 14, 27–33 (1996).
Google Scholar 
28.Shimada, M. & Fujii, K. Niche modification and stability of competitive systems. I. Niche modification process. Res. Popul. Ecol. 27, 185–201 (1985).
Google Scholar 
29.Utida, S. Population fluctuation, an experimental and theoretical approach. Cold Spring Harb. Symp. Quant. Biol. 22, 139–151 (1957).
Google Scholar 
30.Utida, S. Cyclic fluctuations of population density intrinsic to the host–parasitoid system. Ecology 38, 442–449 (1957).
Google Scholar 
31.Fujii, K. Studies on the interspecies competition between the azuki bean weevil and the southern cowpea weevil. III. Some characteristics of strains of two species. Res. Popul. Ecol. 10, 87–98 (1968).
Google Scholar 
32.Bellows, T. S. Analytical models for laboratory populations of Callosobruchus chinensis and C. maculatus (Coleoptera, Bruchidae). J. Anim. Ecol. 51, 263–287 (1982).
Google Scholar 
33.Tuda, M. Density dependence depends on scale; at larval resource patch and at whole population. Res. Popul. Ecol. 35, 261–271 (1993).
Google Scholar 
34.Tuda, M. & Shimada, M. Developmental schedules and persistence of experimental host–parasitoid systems at two different temperatures. Oecologia 103, 283–291 (1995).PubMed 
ADS 

Google Scholar 
35.Tuda, M., Chou, L.-Y., Niyomdham, C., Buranapanichpan, S. & Tateishi, Y. Ecological factors associated with pest status in Callosobruchus (Coleoptera: Bruchidae): High host specificity of non-pests to Cajaninae (Fabaceae). J. Stored Prod. Res. 41, 31–45 (2005).
Google Scholar 
36.Tuda, M., Rönn, J., Buranapanichpan, S., Wasano, N. & Arnqvist, G. Evolutionary diversification of the bean beetle genus Callosobruchus (Coleoptera: Bruchidae): Traits associated with stored-product pest status. Mol. Ecol. 15, 3541–3551 (2006).CAS 
PubMed 

Google Scholar 
37.Tuda, M. Applied evolutionary ecology of insects in the subfamily Bruchinae (Coleoptera: Chrysomelidae). Appl. Entomol. Zool. 42, 337–346 (2007).
Google Scholar 
38.Clausen, C. P. Introduced Parasites and Predators of Arthropod Pests and Weeds: A World Review (United States Department of Agriculture Handbook, 1978).
Google Scholar 
39.Schmale, I., Wäckers, F. L., Cardona, C. & Dorn, S. Control potential of three hymenopteran parasitoid species against the bean weevil in stored beans: The effect of adult parasitoid nutrition on longevity and progeny production. Biol. Control 21, 134–139 (2001).
Google Scholar 
40.Vamosi, S. M., den Hollander, M. D. & Tuda, M. Egg dispersion is more important than competition type for herbivores attacked by a parasitoid. Popul. Ecol. 53, 319–326 (2011).
Google Scholar 
41.Shimada, M. Population fluctuation and persistence of one-host–two parasitoid systems depending on resource distribution: From parasitizing behavior to population dynamics. Res. Popul. Ecol. 41, 69–79 (1999).
Google Scholar 
42.Baker, J. E., Perez-Mendoza, J. & Beeman, R. W. Multiple mating potential in a pteromalid wasp determined by using an insecticide resistance marker. J. Entomol. Sci. 33, 165–170 (1998).
Google Scholar 
43.Yamamura, K. Transformation using (x + 0.5) to stabilize the variance of populations. Res. Popul. Ecol. 41, 229–234 (1999).
Google Scholar 
44.Hamilton, W. D. Extraordinary sex ratios. Science 156, 477–488 (1967).CAS 
PubMed 
ADS 

Google Scholar 
45.Waage, J. K. & Lane, J. B. The reproductive strategy of a parasitic wasp: II. Sex allocation and local mate competition in Trichogramma evanescens. J. Anim. Behav. 53, 417–426 (1984).
Google Scholar 
46.Strand, M. R. Variable sex ratio strategy of Telonomus heliothidis (Hymenoptera: Scelionidae): Adaptation to host and conspecific density. Oecologia 77, 219–224 (1988).CAS 
PubMed 
ADS 

Google Scholar 
47.Hassell, M. P. The Dynamics of Arthropod Predator-Prey Systems (Princeton University Press, 1978).MATH 

Google Scholar 
48.Godfray, H. C. J. Parasitoids: Behavioral and Evolutionary Ecology (Princeton University Press, 1994).
Google Scholar 
49.Wen, B., Smith, L. & Brower, J. H. Competition between Anisopteromalus calandrae and Choetospila elegans (Hymenoptera: Pteromalidae) at different parasitoid densities on immature maize weevils (Coleoptera: Curculionidae) in corn. Environ. Entomol. 23, 367–373 (1994).
Google Scholar 
50.Wen, B. & Brower, J. H. Competition between Anisopteromalus calandrae and Choetospila elegans (Hymenoptera: Pteromalidae) at different parasitoid densities on immature rice weevils (Coleoptera: Curculionidae) in wheat. Biol. Control 5, 151–157 (1995).
Google Scholar 
51.Campan, E. & Benrey, B. Behavior and performance of a specialist and a generalist parasitoid of bruchids on wild and cultivated beans. Biol. Control 30, 220–228 (2004).
Google Scholar 
52.Choi, W. I., Yoon, T. J. & Ryoo, M. I. Host-size-dependent feeding behaviour and progeny sex ratio of Anisopteromalus calandrae (Hym., Pteromalidae). J. Appl. Entomol. 125, 71–77 (2001).
Google Scholar 
53.Wai, K. M. Intra- and interspecific larval competition among wasps parasitic to bean weevil larvae. Thesis—University of Tsukuba, D.Sc. (A), no. 714 (1990).54.Heimpel, G. E. & Cock, M. J. W. Shifting paradigms in the history of classical biological control. Biocontrol 63, 27–37 (2018).
Google Scholar 
55.Miksanek, J. R. & Heimpel, G. E. Density-dependent lifespan and estimation of life expectancy for a parasitoid with implications for population dynamics. Oecologia 194, 311–320 (2020).PubMed 
ADS 

Google Scholar 
56.Kidd, N. A. C. & Jervis, M. A. The effects of host-feeding behaviour on the dynamics of parasitoid–host interactions, and the implications for biological control. Res. Popul. Ecol. 31, 235–274 (1989).
Google Scholar 
57.Comins, H. N. & Wellings, P. W. Density-related parasitoid sex-ratio: Influence on host–parasitoid population dynamics. J. Anim. Ecol. 54, 583–594 (1985).
Google Scholar 
58.Hassell, M. P., Waage, J. K. & May, R. M. Variable parasitoid sex ratios and their effect on host–parasitoid dynamics. J. Anim. Ecol. 52, 889–904 (1983).
Google Scholar 
59.Skalski, G. T. & Gilliam, J. F. Functional responses with predator interference: Viable alternatives to the Holling Type II model. Ecology 82, 3083–3092 (2001).
Google Scholar 
60.Kratina, P., Vos, M., Bateman, A. & Anholt, B. R. Functional responses modified by predator density. Oecologia 159, 425–433 (2008).PubMed 
ADS 

Google Scholar 
61.Freedman, H. I. Stability analysis of a predator–prey system with mutual interference and density-dependent death rates. Bull. Math. Biol. 41, 67–78 (1979).MathSciNet 
MATH 

Google Scholar 
62.Erbe, L. H. & Freedman, H. I. Modeling persistence and mutual interference among subpopulations of ecological communities. Bull. Math. Biol. 47, 295–304 (1985).MathSciNet 
MATH 

Google Scholar 
63.Alonso, D., Bartumeus, F. & Catalan, J. Mutual interference between predators can give rise to Turing spatial patterns. Ecology 83, 28–34 (2002).
Google Scholar 
64.May, R. M. & Hassell, M. P. The dynamics of multiparasitoid–host interactions. Am. Nat. 117, 234–261 (1981).MathSciNet 

Google Scholar 
65.Wajnberg, E., Curty, C. & Colazza, S. Genetic variation in the mechanisms of direct mutual interference in a parasitic wasp: Consequences in terms of patch-time allocation. J. Anim. Ecol. 73, 1179–1189 (2004).
Google Scholar 
66.Okuyama, T. Parasitoid aggregation and interference in host–parasitoid dynamics. Ecol. Entomol. 41, 473–479 (2016).
Google Scholar 
67.Jeffs, C. T. & Lewis, O. T. Effects of climate warming on host–parasitoid interactions. Ecol. Entomol. 38, 209–218 (2013).
Google Scholar 
68.Laws, A. N. Climate change effects on predator–prey interactions. Curr. Opin. Insect Sci. 23, 28–34 (2017).PubMed 

Google Scholar 
69.Tougeron, K., Brodeur, J., Le Lann, C. & van Baaren, J. How climate change affects the seasonal ecology of insect parasitoids. Ecol. Entomol. 45, 167–181 (2020).
Google Scholar 
70.Tuda, M. & Bonsall, M. B. Evolutionary and population dynamics of host–parasitoid interactions. Res. Popul. Ecol. 41, 81–91 (1999).
Google Scholar 
71.Outreman, Y. et al. Multi-scale and antagonist selection on life-history traits in parasitoids: A community ecology perspective. Funct. Ecol. 32, 736–751 (2018).
Google Scholar  More

DatasetsIn this study, datasets from two fundamentally different real-world ecological use cases were employed. The objects of interest in these images were manually counted in previous studies2,8,36,37, without the aim of DL applications.Microscopic images of otolith ringsThe first dataset consists of 3585 microscopic images of otoliths (i.e., hearing stones) of plaice (Pleuronectes platessa). Newly settled juvenile plaice of various length classes were collected at stations along the North Sea and Wadden Sea coast during 23 sampling campaigns conducted over 6 years. Each individual fish was measured, the sagittal otoliths were removed and microscopic images of two zoom levels ((10times 20) and (10times 10), depending on fish length) were made. Post-settlement daily growth rings outside the accessory growth centre were then counted by eye6,7. In this dataset, images of otoliths with less than 16 and more than 45 rings were scarce (Fig. 6). Therefore, a stratified random design was used to select 120 images to evaluate the model performance over the full range of ring counts: all 3585 images were grouped in eight bins according to their label (Fig. 6) and from each bin 15 images were randomly selected for the test set. Out of the remaining 3465 images, 80% of the images were randomly selected for training and 20% were used as a validation set, which is used to estimate the model performance and optimise hyperparameters during training.Figure 6Distribution of the labels (i.e., number of post-settlement rings) of all images in the otolith dataset ((n=3585)).Full size imageAerial images of sealsThe second dataset consists of 11,087 aerial images (named ‘main dataset’ from now onwards) of hauled out grey seals (Halichoerus grypus) and harbour seals (Phoca vitulina), collected between 2005 and 2019 in the Dutch part of the Wadden Sea2,36. Surveys for both species were performed multiple times each year: approximately three times during pupping season and twice during the moult8. During these periods, seals haul out on land in larger numbers. Images were taken manually through the airplane window whenever seals were sighted, while flying at a fixed height of approximately 150m, using different focal lengths (80-400mm). Due to variations in survey conditions (e.g., weather, lighting) and image composition (e.g., angle of view, distance towards seals), this main dataset is highly variable. Noisy labels further complicated the use of this dataset: seals present in multiple (partially) overlapping images were counted only once, and were therefore not included in the count label of each image. Recounting the seals on all images in this dataset to deal with these noisy labels would be a tedious task, compromising one of the main aims of this study of reducing annotation efforts. Instead, only a selection of the main dataset was recounted and used for training and testing. First, 100 images were randomly selected (and recounted) for the test set. In the main dataset, images with a high number of seals were scarce, while images with a low number of seals were abundant (Fig. 7, panel A). Therefore, as with the otoliths, all 11,087 images were grouped into 20 bins according to their label (Fig. 7, panel A), after which five images were randomly selected from each bin for the test set. Second, images of sufficient quality and containing easily identifiable were selected from the main dataset (and recounted) for training and validation, until 787 images were retained (named ‘seal subset 1’). In order to create images with zero seals (i.e., just containing the background) and to remove seals that are only partly photographed along the image borders, some of these images were cropped. The dimensions of those cropped images were preserved and, if required, the image-level annotation was modified accordingly. The resulting ‘seal subset 1’ only contains images with zero to 99 seals (Fig. 7, panel B). These 787 images were then randomly split in a training (80%) and validation set (20%). In order to still take advantage of the remaining 10,200 images from the main dataset, a two-step label refinement was performed (see the section “Dealing with noisy labels: two-step label refinement” below).Figure 7Distribution of the labels (i.e., number of seals) in (A) the seal main dataset ((n=11{,}087)), (B) ‘seal subset 1’ ((n=787)) and (C) ‘seal subset 2’ ((n=100)).Full size imageConvolutional neural networksCNNs are a particular type of artificial neural network. Similar to a biological neural network, where many neurons are connected by synapses, these models consist of a series of connected artificial neurons (i.e., nodes), grouped into layers that are applied one by one. In a CNN, each layer receives an input and produces an output by performing a convolution between the neurons (now organised into a rectangular filter) and each spatial input location and its surroundings. This convolution operator computes a dot product at each location in the input (image or previous layer’s output), encoding the correlation between the local input values and the learnable filter weights (i.e., neurons). After this convolution, an activation function is applied so that the final output of the network can represent more than just a linear combination of the inputs. Each layer performs calculations on the inputs it receives from the previous layer, before sending it to the next layer. Regular layers that ingest all previous outputs rather than a local neighbourhood are sometimes also employed at the end; these are called “fully-connected” layers. The number of layers determines the depth of the network. More layers introduce a larger number of free (learnable) parameters, as does a higher number of convolutional filters per layer or larger filter sizes. A final layer usually projects the intermediate, high-dimensional outputs into a vector of size C (the number of categories) in the case of classification, into a single number in the case of regression (ours), or into a custom number of outputs representing arbitrarily complex parameters, such as the class label and coordinates of a bounding box in the case of object detection. During training, the model is fed with many labelled examples to learn the task at hand: the parameters of the neurons are updated to minimise a loss (provided by an error function measuring the discrepancy between predictions and labels; in our case this is the Huber loss as described below). To do so, the gradient and its derivative with respect to each neuron in the last layer is computed; modifying neurons by following their gradients downwards allows reducing the loss (and thereby improving model prediction) for the current image accordingly. Since the series of layers in a CNN can be seen as a set of nested, differentiable functions, the chain rule can be applied to also compute gradients for the intermediate, hidden layers and modify neurons therein backwards until the first layer. This process is known as backpropagation38. With the recent increase of computational power and labelled dataset sizes, these models are now of increasing complexity (i.e., they have higher numbers of learnable parameters in the convolutional filters and layers).CNNs come in many layer configurations, or architectures. One of the most widely used CNN architecture is the ResNet20, which introduced the concept of residual blocks: in ResNets, the input to a residual block (i.e., a group of convolutional layers with nonlinear activations) is added to its output in an element-wise manner. This allows the block to focus on learning residual patterns on top of its inputs. Also, it enables learning signals to by-pass entire blocks, which stabilises training by avoiding the problem of vanishing gradients39. As a consequence, ResNets were the first models that could be trained even with many layers in series and provided a significant increase in accuracy.Model selection and trainingFor the otolith dataset, we employed ResNet20 architectures of various depths (i.e., ResNet18, ResNet34, ResNet50, ResNet101 and ResNet152, where the number corresponds to the number of hidden layers in the model, see Supplementary S1). These ResNet models were pretrained on ImageNet40, which is a large benchmark dataset containing millions of natural images annotated with thousands of categories. Pre-training on ImageNet is a commonly employed methodology to train a CNN efficiently, as it will already have learned how to recognise common recurring features, such as edges and basic geometrical patterns, which would have to be learned from zero otherwise. Therefore, pre-training reduces the required amount of training data significantly.Figure 8Schematic representation of the CNN used in this study. The classification output layer of the pretrained ResNet18 is replaced by two fully-connected layers. The model is trained with a Huber loss.Full size imageWe modified the ResNet architecture to perform a regression task. To do so, we replaced the classification output layer with two fully-connected layers that map to 512 neurons after the first layer and to a single continuous variable after the second layer23 (Fig. 8). Since the final task to be performed is regression, the loss function is a loss function that is tailored for regression. In our experiments we tested both a Mean Squared Error and a Smooth L1 (i.e., Huber) loss21 (see Supplementary S1). The Huber loss is more robust against outliers and is defined as follows:$$begin{aligned} {mathscr {L}}(y,{hat{y}})=frac{1}{n}sum _i^{n} z_i end{aligned}$$
(1)
where (z_i) is given by$$begin{aligned} z_i= {left{ begin{array}{ll} 0.5times (y_i-{hat{y}}_i)^2, &{}quad text {if } |y_i-{hat{y}}_i| More

1.Vickery, J. A. et al. The decline of Afro-Palaearctic migrants and an assessment of potential causes. Ibis (Lond. 1759) 156, 1–22 (2014).
Google Scholar 
2.Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).ADS 
CAS 
PubMed 

Google Scholar 
3.Knight, S. M. et al. Constructing and evaluating a continent-wide migratory songbird network across the annual cycle. Ecol. Monogr. 88, 445–460 (2018).
Google Scholar 
4.Alves, J. A. et al. Costs, benefits, and fitness consequences of different migratory strategies. Ecology 94, 11–17 (2013).ADS 
PubMed 

Google Scholar 
5.van Wijk, R. E., Schaub, M. & Bauer, S. Dependencies in the timing of activities weaken over the annual cycle in a long-distance migratory bird. Behav. Ecol. Sociobiol. 71, 71–73 (2017).
Google Scholar 
6.Donald, P. F., Sanderson, F. J., Burfield, I. J. & van Bommel, F. P. J. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agric. Ecosyst. Environ. 116, 189–196 (2006).
Google Scholar 
7.Bowler, D. E., Heldbjerg, H., Fox, A. D., Jong, M. & Böhning-Gaese, K. Long-term declines of European insectivorous bird populations and potential causes. Conserv. Biol. 0, 1–11 (2019).
Google Scholar 
8.Harrison, X. A., Blount, J. D., Inger, R., Norris, D. R. & Bearhop, S. Carry-over effects as drivers of fitness differences in animals. J. Anim. Ecol. 80, 4–18 (2010).PubMed 

Google Scholar 
9.Emmenegger, T., Hahn, S. & Bauer, S. Individual migration timing of common nightingales is tuned with vegetation and prey phenology at breeding sites. BMC Ecol. 14, 1–8 (2014).
Google Scholar 
10.Morrison, C. A., Alves, J. A., Gunnarsson, T. G., Þórisson, B. & Gill, J. A. Why do earlier-arriving migratory birds have better breeding success?. Ecol. Evol. 9, 8856–8864 (2019).PubMed 
PubMed Central 

Google Scholar 
11.Cooper, N. W., Murphy, M. T., Redmond, L. J. & Dolan, A. C. Reproductive correlates of spring arrival date in the Eastern Kingbird Tyrannus tyrannus. J. Ornithol. 152, 143–152 (2011).
Google Scholar 
12.Nilsson, C., Klaassen, R. H. G. & Alerstam, T. Differences in speed and duration of bird migration between spring and autumn. Am. Nat. 181, 837–845 (2013).PubMed 

Google Scholar 
13.Gow, E. A. et al. Effects of spring migration distance on tree swallow reproductive success within and among flyways. Front. Ecol. Evol. 7, 380 (2019).ADS 

Google Scholar 
14.Saino, N. et al. Sex-dependent carry-over effects on timing of reproduction and fecundity of a migratory bird. J. Anim. Ecol. 86, 239–249 (2017).PubMed 

Google Scholar 
15.Briedis, M., Hahn, S. & Adamík, P. Cold spell en route delays spring arrival and decreases apparent survival in a long-distance migratory songbird. BMC Ecol. 17, 1–8 (2017).
Google Scholar 
16.McKinnon, E. A., Macdonald, C. M., Gilchrist, H. G. & Love, O. P. Spring and fall migration phenology of an arctic-breeding passerine. J. Ornithol. 157, 681–693 (2016).
Google Scholar 
17.Woodworth, B. K. et al. Differential migration and the link between winter latitude, timing of migration, and breeding in a songbird. Oecologia 181, 413–422 (2016).ADS 
PubMed 

Google Scholar 
18.Saino, N. et al. Ecological conditions during winter predict arrival date at the breeding quarters in a trans-Saharan migratory bird. Ecol. Lett. 7, 21–25 (2004).
Google Scholar 
19.Norris, D. R., Marra, P. P., Kyser, T. K., Sherry, T. W. & Ratcliffe, L. M. Tropical winter habitat limits reproductive success on the temperate breeding grounds in a migratory bird. Proc. R. Soc. B Biol. Sci. 271, 59–64 (2004).
Google Scholar 
20.Bearhop, S., Hilton, G. M., Votier, S. C. & Waldron, S. Stable isotope ratios indicate that body condition in migrating passerines is influenced by winter habitat. Proc. R. Soc. London B Biol. Sci. 271, S215–S218 (2004).
Google Scholar 
21.Ockendon, N., Leech, D. & Pearce-Higgins, J. W. Climatic effects on breeding grounds are more important drivers of breeding phenology in migrant birds than carry- over effects from wintering grounds. Biol. Lett. 9 (2013).22.Arbeiter, S., Schulze, M., Tamm, P. & Hahn, S. Strong cascading effect of weather conditions on prey availability and annual breeding performance in European bee-eaters Merops apiaster. J. Ornithol. 157, 155–163 (2016).
Google Scholar 
23.Harrison, X. A. et al. Environmental conditions during breeding modify the strength of mass-dependent carry-over effects in a migratory bird. PLoS ONE 8, e77783 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Swift, R. J., Rodewald, A. D., Johnson, J. A., Andres, B. A. & Senner, N. R. Seasonal survival and reversible state effects in a long-distance migratory shorebird. J. Anim. Ecol. 89, 2043–2055 (2020).PubMed 

Google Scholar 
25.Brust, V., Bastian, H. V., Bastian, A. & Schmoll, T. Determinants of between-year burrow re-occupation in a colony of the European bee-eater Merops apiaster. Ecol. Evol. 5, 3223–3230 (2015).PubMed 
PubMed Central 

Google Scholar 
26.Lessells, C. M. & Krebs, J. R. Age and breeding performance of European bee-eaters. Auk 106, 375–382 (1989).
Google Scholar 
27.Pârâu, L. G. et al. Dynamics in numbers of group-roosting individuals in relation to pair-sleeping occurrence and onset of egg-laying in European Bee-eaters Merops apiaster. J. Ornithol. 158, 1119–1122 (2017).
Google Scholar 
28.Hoi, H., Darolová, A., Krištofík, J. & Hoi, C. The effect of the ectoparasite Carnus hemapterus on immune defence, condition, and health of nestling European Bee-eaters. J. Ornithol. 159, 291–302 (2018).
Google Scholar 
29.Kapun, M., Darolová, A., Krištofik, J., Mahr, K. & Hoi, H. Distinct colour morphs in nestling European Bee-eaters Merops apiaster: Is there an adaptive value?. J. Ornithol. 152, 1001–1005 (2011).
Google Scholar 
30.Lessells, C. M. & Avery, M. I. Hatching asynchrony in european bee-eaters merops apiaster. J. Anim. Ecol. 58, 815–835 (1989).
Google Scholar 
31.Arbeiter, S., Schulze, M., Todte, I. & Hahn, S. Das Zugverhalten und die Ausbreitung von in Sachsen-Anhalt brütenden Bienenfressern (Merops apiaster). Berichte der Vogelwarte Hiddensee 21, 33–40 (2012).
Google Scholar 
32.Dhanjal-Adams, K. L. et al. Spatiotemporal group dynamics in a long-distance migratory bird. Curr. Biol. 28, 2824-2830.e3 (2018).CAS 
PubMed 

Google Scholar 
33.Hahn, S. et al. Range-wide migration corridors and non-breeding areas of a northward expanding Afro-Palaearctic migrant, the European Bee-eater Merops apiaster. Ibis (Lond. 1859) 162, 345–355 (2019).
Google Scholar 
34.Fry, C. H. The bee-eaters. (T & A D Polyser Ltd, 1984).35.Ramos, R. et al. Population genetic structure and long-distance dispersal of a recently expanding migratory bird. Mol. Phylogenet. Evol. 99, 194–203 (2016).PubMed 

Google Scholar 
36.Jacobsen, L. B. et al. Annual spatiotemporal migration schedules in three larger insectivorous birds: European nightjar, common swift and common cuckoo. Anim. Biotelem1 5, 1–11 (2017).
Google Scholar 
37.Åkesson, S., Klaassen, R., Holmgren, J., Fox, J. W. & Hedenström, A. Migration routes and strategies in a highly aerial migrant, the common Swift Apus apus, revealed by light-level. Geolocators. PLoS One 7, e41195 (2012).ADS 
PubMed 

Google Scholar 
38.Carneiro, C., Gunnarsson, T. G. & Alves, J. A. Faster migration in autumn than in spring: seasonal migration patterns and non-breeding distribution of Icelandic Whimbrels Numenius phaeopus islandicus. J. Avian Biol. 50 (2019).39.Sapir, N. et al. Migration by soaring or flapping: numerical atmospheric simulations reveal that turbulence kinetic energy dictates bee-eater flight mode. Proc. R. Soc. B Biol. Sci. 278, 3380–3386 (2011).
Google Scholar 
40.Lemke, H. W. et al. Annual cycle and migration strategies of a Trans-Saharan migratory songbird: a geolocator study in the great reed warbler. PLoS ONE 8, e79209 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Briedis, M. et al. A full annual perspective on sex-biased migration timing in long-distance migratory birds. Proc. R. Soc. B Biol. Sci. 286, 20182821 (2019).
Google Scholar 
42.Fransson, T. Timing and speed of migration in North and West European populations of Sylvia warblers. J. Avian Biol. 26, 39–48 (1995).
Google Scholar 
43.Briedis, M., Hahn, S., Krist, M. & Adamík, P. Finish with a sprint: evidence for time-selected last leg of migration in a long-distance migratory songbird. Ecol. Evol. 8, 6899–6908 (2018).PubMed 
PubMed Central 

Google Scholar 
44.Alerstam, T. Strategies for the transition to breeding in time-selected bird migration. Ardea 94, 347–357 (2006).
Google Scholar 
45.Arizaga, J., Willemoes, M., Unamuno, E., Unamuno, J. M. & Thorup, K. Following year-round movements in Barn Swallows using geolocators: could breeding pairs remain together during the winter?. Bird Study 62, 141–145 (2015).
Google Scholar 
46.Tøttrup, A. P. et al. Drought in Africa caused delayed arrival of European songbirds. Science 338, 1307 (2012).ADS 
PubMed 

Google Scholar 
47.Smith, R. J. & Moore, F. R. Arrival timing and seasonal reproductive performance in a long-distance migratory landbird. Behav. Ecol. Sociobiol. 57, 231–239 (2005).
Google Scholar 
48.IPMA. Climate bulletin, June 2017, Portugal. http://www.ipma.pt/resources.www/docs/im.publicacoes/edicoes.online/20170719/bXUzZOgrqXmTjnUVRtro/cli_20170601_20170630_pcl_mm_co_pt.pdf (2017).49.Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘cold-blooded’ animals against climate warming. Proc. Natl. Acad. Sci. U.S.A. 106, 3835–3840 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Cunningham, S. J., Martin, R. O., Hojem, C. L. & Hockey, P. A. R. Temperatures in excess of critical thresholds threaten nestling growth and survival in a rapidly-warming Arid Savanna: a study of common fiscals. PLoS ONE 8 (2013).51.Cruz-Mcdonnell, K. K. & Wolf, B. O. Rapid warming and drought negatively impact population size and reproductive dynamics of an avian predator in the arid southwest. Glob. Chang. Biol. 22, 237–253 (2016).ADS 
PubMed 

Google Scholar 
52.Shukla, P. R. et al. Technical summary. IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (2019).53.Persson, C. Age structure, sex ratios and survival rates in a south Swedish Sand martin (Riparia riparia) population, 1964 to 1984. J. Zool. 1, 639–670 (1987).
Google Scholar 
54.Costa, J. S., Rocha, A. D., Correia, R. A. & Alves, J. A. Developing and validating a nestling photographic aging guide for cavity-nesting birds: an example with the European Bee-eater (Merops apiaster). Avian Res. 11, 1–8 (2020).
Google Scholar 
55.Lisovski, S., Wotherspoon, S. & Sumner, M. TwGeos: Basic data processing for light-level geolocation archival tags. R package version 0.1.2. (2016). 56.Lisovski, S. et al. Geolocation by light: accuracy and precision affected by environmental factors. Methods Ecol. Evol. 3, 603–612 (2012).
Google Scholar 
57.Wotherspoon, S., Sumner, M. & Lisovski, S. R package SGAT: solar/satellite geolocation for animal tracking (2016).58.Lisovski, S. et al. Light-level geolocator analyses: a user’s guide. J. Anim. Ecol. 89, 221–236 (2019).PubMed 

Google Scholar 
59.Lisovski, S. & Hahn, S. GeoLight—processing and analysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
Google Scholar 
60.Mazerolle, M. J. AICcmodavg: model selection and multimodel inference based on (Q)AIC(c). R package version 2.2–2. (2019).61.Team, R. C. R: a language and environment for statistical computing. (2017). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More