Ecology

1.Csiki-Sava, Z., Buffetaut, E., Ősi, A., Pereda-Suberbiola, X. & Brusatte, S. L. Island life in the Cretaceous – faunal composition, biogeography, evolution, and extinction of land-living vertebrates on the Late Cretaceous European archipelago. ZooKeys. https://doi.org/10.3897/zookeys.469.8439 (2015).2.Benton, M. J. et al. Dinosaurs and the island rule: The dwarfed dinosaurs from Haţeg Island. Palaeogeogr. Palaeoclimatol. Palaeoecol. 293, 438–454 (2010).
Google Scholar 
3.Scotese, C. R. An Atlas of phanerozoic Paleogeographic maps: The seas come in and the seas go out. Annu. Rev. Earth Planet. Sci. 49, 679–728 (2021).CAS 

Google Scholar 
4.Randazzo, V. et al. The migration path of Gondwanian dinosaurs toward Adria: New insights from the Cretaceous of NW Sicily (Italy). Cretac. Res. 126, 104919 (2021).5.Dal Sasso, C., Pierangelini, G., Famiani, F., Cau, A. & Nicosia, U. First sauropod bones from Italy offer new insights on the radiation of Titanosauria between Africa and Europe. Cretac. Res. 64, 88–109 (2016).
Google Scholar 
6.Vlahović, I., Tišljar, J., Velić, I. & Matičec, D. Evolution of the Adriatic Carbonate Platform: Palaeogeography, main events and depositional dynamics. Palaeogeogr. Palaeoclimatol. Palaeoecol. 220, 333–360 (2005).
Google Scholar 
7.Dalla Vecchia, F. M. Tethyshadros insularis, a new hadrosauroid dinosaur (Ornithischia) from the Upper Cretaceous of Italy. J. Vertebr. Paleontol. 29, 1100–1116 (2009).
Google Scholar 
8.Tarlao, A., Tentor, M., Tunis, G. & Venturini, S. Evidence of a tectonic phase in the Lower Senonian of the Villaggio del Pescatore area. Gortania Atti Museo Friulano Storia Naturale 135–142 (1993).9.Tarlao, A., Tentor, M., Tunis, G., Venturini, S. Stop 4: Villaggio del Pescatore. Atti Museo Geologico Paleontologico Monfalcone 135–142 (1995).10.Alessandro Palci. Ricostruzione Paleoambientale del Sito Fossilifero Senoniano del Villaggio del Pescatore (Trieste). (Università degli Studi di Trieste, 2003).11.Arbulla, D., Caffa, M., Cotza, F., Cucchi, F., Flora, O., Masetti, D., Pittau, P., Pugliese, N., Stenni, B., Tarlao, A., Tunisè, G. & Zini, L. The Santonina-Campanian succession of the Villaggio del Pescatore (Trieste karst) yielding the hadrosaur: palaeoecology, stratigraphy, sedimentology and geochemistry. in Sixth European workshop on vertebrate palaeontology abstracts (2001).12.Arbulla, D., Cotza, F., Cucchi, F., Dalla Vecchia, F. M., De Giusto, A., Flora, O., Masetti, D., Palci, A., Pittau, P., Pugliese, N., Stenni, B., Tarlao, A., Tunis, G. & Zini, L. La successione Santoniano–Campaniana del Villaggio del Pescatore (Carso Triestino) nel quale sono stati rinvenuti i resti di dinosauro. in Guida alle escursioni/excursions guide, Società Paleontologica Italiana 20–27 (EUT Edizioni Università di Trieste, 2006).13.Dal Sasso, C. Dinosaurs of Italy. Comptes Rendus Palevol. 2, 45–66 (2003).
Google Scholar 
14.Dal Sasso, C. & Brillante, G. Dinosaurs of Italy. (Indiana University Press, 2005).15.Dalla Vecchia, F., Tunis, G., Venturini, S. & Tarlao, A. Dinosaur track sites in the upper Cenomanian (Late Cretaceous) of Istrian Peninsula (Croatia). Boll. Della Soc. Paleontol. Ital. 40, 25–54 (2001).16.Dalla Vecchia, F. M. Observations on the presence of plant-eating dinosaurs in an oceanic carbonate platform. Nat. Nascosta 27 (2003).17.Nicosia, U., Avanzini, M., Barbera, C., Conti, M. A., Dalla Vecchia, F. M. e altri 15 coautori in ordine alfabetico. I vertebrati continentali del Paleozoico e Mesozoico. in Paleontologia dei Vertebrati in Italia vol. Memorie Museo Civico di Storia Naturale Verona 41–66 (Bonfiglio L., 2005).18.Dalla Vecchia, F. M. The impact of dinosaur palaeoichnology in palaeoenvironmental and palaeogeographic reconstructions: The case of the Periadriatic carbonate platforms. Oryctos 8, 19 (2008).
Google Scholar 
19.Delfino, M., Martin, J. E. & Buffetaut, E. A new species of Acynodon (Crocodylia) from the upper cretaceous (Santonian–Campanian) of Villaggio del Pescatore, Italy. Palaeontology 51, 1091–1106 (2008).
Google Scholar 
20.Dalla Vecchia, F. M. The unusual tail of Tethyshadros insularis (Dinosauria, Hadrosauroidea) from the Adriatic Island of the European Archipelago. Rivista Italiana di Paleontologia e Stratigrafia 126, 46 (2020).
Google Scholar 
21.Benson, R. B. J., Hunt, G., Carrano, M. T. & Campione, N. Cope’s rule and the adaptive landscape of dinosaur body size evolution. Palaeontology 61, 13–48 (2018).
Google Scholar 
22.Dalla Vecchia, F. Telmatosaurus and the other hadrosaurids of the Cretaceous European Archipelago. An update. Nat. Nascosta 39, 1–18 (2009).
Google Scholar 
23.Soul, L. & Wright, D. Phylogenetic Comparative Methods: A User’s Guide for Paleontologists. (2020) https://doi.org/10.32942/osf.io/ytm5x.24.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
Google Scholar 
25.Chiocchini, M., Farinacci, A., Mancinelli, A., Molinari, V. & Potetti, M. Biostratigrafia a foraminiferi, dasicladali e calpionelle delle successioni carbonatiche mesozoiche dell’Appennino centrale (Italia). in Biostratigrafia dell’Italia centrale. Studi Geologici Camerti vol. Biostratigrafia dell’Italia centrale 129 (Mancinelli, A, 1994).26.Chiocchini, M., Pampaloni, M. L., Pichezzi, R. M. Microfacies and microfossils of the Mesozoic carbonate successions of Latium and Abruzzi (Central Italy), Vol. 269 (Memorie per Servire alla Descrizione della Carta Geologica D’Italia, ISPRA, Dipartimento Difesa del Suolo, Roma, 2012).27.Frijia, G., Parente, M., Di Lucia, M. & Mutti, M. Carbon and strontium isotope stratigraphy of the Upper Cretaceous (Cenomanian-Campanian) shallow-water carbonates of southern Italy: Chronostratigraphic calibration of larger foraminifera biostratigraphy. Cretac. Res. 53, 110–139 (2015).
Google Scholar 
28.Steuber, T., Korbar, T., Jelaska, V. & Gušić, I. Strontium-isotope stratigraphy of Upper Cretaceous platform carbonates of the island of Brač (Adriatic Sea, Croatia): Implications for global correlation of platform evolution and biostratigraphy. Cretac. Res. 26, 741–756 (2005).
Google Scholar 
29.Schlüter, M., Steuber, T. & Parente, M. Chronostratigraphy of Campanian-Maastrichtian platform carbonates and rudist associations of Salento (Apulia, Italy). Cretac. Res. 29, 100–114 (2008).
Google Scholar 
30.Consorti, L., Frijia, G. & Caus, E. Rotaloidean foraminifera from the Upper Cretaceous carbonates of Central and Southern Italy and their chronostratigraphic age. Cretac. Res. 70, 226–243 (2017).
Google Scholar 
31.Fourcade, E. Murciella cuvillieri n. gen. n. sp. nouveau foraminifère du Sénonien supérieur du sud-est de l’Espagne. Rev. Micropaléontol. 147–155 (1966).32.Fleury, J.-J. Rhapydioninidés du Campanien-Maastrichtien en région méditerranéenne : Les genres Murciella, Sigalveolina n. gen. et Cyclopseudedomia. Carnets Géologie Noteb. Geol. 18, 233–280 (2018).33.Dercourt et al. Atlas of Peri-Tethys Palaeogeographical Maps-digital-CCGM-CGMW. https://ccgm.org/en/atlases/189-atlas-of-peri-tethys-palaeogeographical-maps-.html (2000).34.Bosellini, A. Dinosaurs, “re-write” the geodynamics of the eastern Mediterranean and the paleogeography of the Apulia Platform. Earth-Sci. Rev. 59, 211–234 (2002).ADS 

Google Scholar 
35.Zarcone, G. et al. A possible bridge between Adria and Africa: New palaeobiogeographic and stratigraphic constraints on the Mesozoic palaeogeography of the Central Mediterranean area. Earth-Sci. Rev. 103, 154–162 (2010).ADS 

Google Scholar 
36.Ustaszewski, K. et al. Late Cretaceous intra-oceanic magmatism in the internal Dinarides (northern Bosnia and Herzegovina): Implications for the collision of the Adriatic and European plates. Lithos 108, 106–125 (2009).ADS 
CAS 

Google Scholar 
37.Dragičević, I. & Velić, I. The Northeastern Margin of the Adriatic Carbonate Platform. Geol. Croat. 55, 185–232 (2002).
Google Scholar 
38.Petti, F. et al. Cretaceous tetrapod tracks from Italy: A treasure trove of exceptional biodiversity. J. Mediterr. Earth Sci. 12, 167–191 (2020).
Google Scholar 
39.Blatnik, M. et al. Late Cretaceous and Paleogene Paleokarsts of the Northern Sector of the Adriatic Carbonate Platform. in Karstology in the classical Karst (eds. Knez, M., Otoničar, B., Petrič, M., Pipan, T. & Slabe, T.) 11–31 (Springer, 2020). https://doi.org/10.1007/978-3-030-26827-5_2.40.Boscarolli, D. & Dalla Vecchia, F. The Upper Hauterivian-Lower Barremian dinosaur site of Bale/valle (SW Istria, Croatia). (1999).41.Dalla Vecchia, F. M. Theropod footprints in the Cretaceous Adriatic-Dinaric Carbonate Platform (Italy and Croatia). Gaia 15, 355–367 (1998).
Google Scholar 
42.Dalla Vecchia, F. Cretaceous dinosaurs in the Adriatic-Dinaric Carbonate Platform (Italy and Croatia): Paleoenvironmental implications and paleogeographical hypotheses. Mem. Della Soc. Geol. Ital. 57, 89–100 (2002).
Google Scholar 
43.Dalla Vecchia, F., Vlahović, I., Posocco, L., Tarlao, A. & Tentor, M. Late Barremian and late Albian (Early Cretaceous) dinosaur tracksites in the main Brioni/Brijun island (SW Istria, Croatia). Nat. Nascosta 25, 1–36 (2002).44.Dalla Vecchia, F. M. et al. New dinosaur track sites in the Albian (Early Cretaceous) of the Istrian peninsula (Croatia) part I—Stratigraphy and sedimentology part II—paleontology. 69 (2000).45.Debeljak, I., Košir, A. & Otoničar, B. A preliminary note on dinosaurs and non-dinosaurian reptiles from the Upper Cretaceous carbonate platform succession at Kozina (SW Slovenia). Razpr. Slov. Akad. Znan. Umet. Razred Za Naravosl. Vede Diss. Acad. Sci. Artium Slov. Cl. IV Hist. Nat. 3–25 (1999).46.Debeljak, I., Košir, A., Buffetaut, E. & Otoničar, B. The Late Cretaceous dinosaurs and crocodiles of Kozina (SW Slovenia): A preliminary study. Mem. Della Soc. Geol. Ital. 57, 193–201 (2002).
Google Scholar 
47.Mezga, A. et al. A new dinosaur tracksite in the Cenomanian of Istria, Croatia. Riv. Ital. Paleontol. E Stratigr. 112, 435–445 (2006).
Google Scholar 
48.Mezga, A., Meyer, C. A., Tešović, B. C., Bajraktarević, Z. & Gušić, I. The first record of dinosaurs in the Dalmatian part (Croatia) of the Adriatic-Dinaric carbonate platform (ADCP). Cretac. Res. 27, 735–742 (2006).
Google Scholar 
49.Weishampel, D., Norman, D. & Grigorescu, D. Telmatosaurus transsylvanicus from the Late Cretaceous of Romania: the most basal hadrosaurid dinosaur. Palaeontology 36, 361–385 (1993).
Google Scholar 
50.Richard Owen. Report on British fossil reptiles, Part 2. in vol. 11 60–204 (1842).51.Seeley, H. G. On the classification of the fossil animals commonly named Dinosauria. Proc. R. Soc. Lond. 43, 165–171 (1887).
Google Scholar 
52.Marsh, O. C. Principal characters of American Jurassic dinosaurs, IV. Am. J. Sci. s3–21, 167–170 (1881).53.Sereno, P. C. The Origin and Evolution of Dinosaurs. Annu. Rev. Earth Planet. Sci. 25, 435–489 (1997).ADS 
CAS 

Google Scholar 
54.Cope, E. D. Synopsis of the extinct Batrachia, Reptilia, and Aves of North America. Trans. Am. Philos. Soc. 1–252 (1870).55.Madzia, D., Jagt, J. W. M. & Mulder, E. W. A. Osteology, phylogenetic affinities and taxonomic status of the enigmatic late Maastrichtian ornithopod taxon Orthomerus dolloi (Dinosauria, Ornithischia). Cretac. Res. 108, 104334 (2020).56.Norman, D. On the history, osteology, and systematic position of the Wealden (Hastings Group) dinosaur Hypselospinus fittoni (Iguanodontia: Styracosterna). Zool. J. Linn. Soc. 173, 92 (2014).57.Xing, H., Mallon, J. C. & Currie, M. L. Supplementary cranial description of the types of Edmontosaurus regalis (Ornithischia: Hadrosauridae), with comments on the phylogenetics and biogeography of Hadrosaurinae. PLOS ONE 12, e0175253 (2017).58.Sues, H.-D. & Averianov, A. A new basal hadrosauroid dinosaur from the Late Cretaceous of Uzbekistan and the early radiation of duck-billed dinosaurs. Proc. R. Soc. B Biol. Sci. 276, 2549–2555 (2009).
Google Scholar 
59.Shibata, M., Jintasakul, P., Azuma, Y. & You, H.-L. A New Basal Hadrosauroid Dinosaur from the Lower Cretaceous Khok Kruat Formation in Nakhon Ratchasima Province, Northeastern Thailand. PLOS ONE 10, e0145904 (2015).60.McDonald, A. T., Bird, J., Kirkland, J. I. & Dodson, P. Osteology of the Basal Hadrosauroid Eolambia caroljonesa (Dinosauria: Ornithopoda) from the Cedar Mountain Formation of Utah. PLoS ONE 7, e45712 (2012).61.Gates, T., Horner, J., Hanna, R. & Nelson, R. New Unadorned Hadrosaurine Hadrosaurid (Dinosauria, Ornithopoda) from the Campanian of North America. J. Vertebr. Paleontol. 31, 798–811 (2011).
Google Scholar 
62.Prieto-Marquez, A. New information on the cranium of Brachylophosaurus canadensis (Dinosauria, Hadrosauridae), with a revision of its phylogenetic position. J. Vertebr. Paleontol. 25, 144–156 (2005).
Google Scholar 
63.Gates, T. A. & Lamb, J. Redescription of Lophorhothon atopus (Ornithopoda: Dinosauria) from the Late Cretaceous of Alabama based on new material. Can. J. Earth Sci. https://doi.org/10.1139/cjes-2020-0173 (2021).Article 

Google Scholar 
64.Prieto-Márquez, A., Erickson, G. M. & Ebersole, J. A. Anatomy and osteohistology of the basal hadrosaurid dinosaur Eotrachodon from the uppermost Santonian (Cretaceous) of southern Appalachia. PeerJ 4, e1872 (2016).65.You, H.-L. & Li, D.-Q. A new basal hadrosauriform dinosaur (Ornithischia: Iguanodontia) from the Early Cretaceous of northwestern China. Can. J. Earth Sci. 46, 949–957 (2009).ADS 

Google Scholar 
66.Fowler, E. A. F. & Horner, J. R. A New Brachylophosaurin Hadrosaur (Dinosauria: Ornithischia) with an Intermediate Nasal Crest from the Campanian Judith River Formation of Northcentral Montana. PLOS ONE 10, e0141304 (2015).67.Gates, T. A., Hall, B. & Lamb, J. P. A redescription of Lophorhothon atopus (Ornithopoda: Dinosauria) from the Cretaceous of Alabama based on new material. Can. J. Earth Sci. 58, 918–935 (2021).
Google Scholar 
68.Gates, T. A., Evans, D. C. & Sertich, J. J. W. Description and rediagnosis of the crested hadrosaurid (Ornithopoda) dinosaur Parasaurolophus cyrtocristatus on the basis of new cranial remains. PeerJ 9, e10669 (2021).69.Griffin, C. T. et al. Assessing ontogenetic maturity in extinct saurian reptiles. Biol. Rev. 96, 470–525. https://doi.org/10.1111/brv.12666?af=R (2021).
Google Scholar 
70.Bailleul, A. M., Scannella, J. B., Horner, J. R. & Evans, D. C. Fusion patterns in the skulls of modern archosaurs reveal that sutures are ambiguous maturity indicators for the Dinosauria. PLOS ONE 11, e0147687 (2016).71.Francillon-Vieillot, H. et al. Microstructure and mineralization of vertebrate skeletal tissues. in Skeletal biomineralization: Patterns, processes and evolutionary trends 175–234 (American Geophysical Union (AGU), 1989). https://doi.org/10.1029/SC005p0175.72.Woodward Ballard, H., Horner, J. & Farlow, J. Osteohistological evidence for determinate growth in the American Alligator. J. Herpetol. 45, 339–342 (2011).73.Horner, J. R., Ricqlès, A. D. & Padian, K. Long bone histology of the hadrosaurid dinosaur Maiasaura peeblesorum: Growth dynamics and physiology based on an ontogenetic series of skeletal elements. J. Vertebr. Paleontol. 20, 115–129 (2000).
Google Scholar 
74.Erickson, G. M. Assessing dinosaur growth patterns: A microscopic revolution. Trends Ecol. Evol. 20, 677–684 (2005).PubMed 

Google Scholar 
75.Hone, D. W. E., Farke, A. A. & Wedel, M. J. Ontogeny and the fossil record: What, if anything, is an adult dinosaur?. Biol. Lett. 12, 20150947 (2016).PubMed 
PubMed Central 

Google Scholar 
76.Sharma, P. P., Clouse, R. M. & Wheeler, W. C. Hennig’s semaphoront concept and the use of ontogenetic stages in phylogenetic reconstruction. Cladistics 33, 93–108 (2017).PubMed 

Google Scholar 
77.Campione, N. E., Brink, K. S., Freedman, E. A., McGarrity, C. T. & Evans, D. C. ‘Glishades ericksoni’, an indeterminate juvenile hadrosaurid from the Two Medicine Formation of Montana: implications for hadrosauroid diversity in the latest Cretaceous (Campanian-Maastrichtian) of western North America. Palaeobiodivers. Palaeoenviron. 93, 65–75 (2013).
Google Scholar 
78.Kobayashi, Y., Takasaki, R., Kubota, K. & Fiorillo, A. R. A new basal hadrosaurid (Dinosauria: Ornithischia) from the latest Cretaceous Kita-ama Formation in Japan implies the origin of hadrosaurids. Sci. Rep. 11(1), 1–15. https://www.nature.com/articles/s41598-021-87719-5 (2021).79.A new brachylophosaurin (Dinosauria: Hadrosauridae) from the Upper Cretaceous Menefee Formation of New Mexico [PeerJ]. https://peerj.com/articles/11084/.80.Prieto-Marquez, A. & Carrera Farias, M. The late-surviving early diverging Ibero-Armorican ‘duck-billed’ dinosaur Fylax and the role of the Late Cretaceous European Archipelago in hadrosauroid biogeography. Acta Palaeontol. Pol. 66, 425–435 (2021).81.Prieto-Márquez, A., Dalla Vecchia, F. M., Gaete, R. & Galobart, À. Diversity, Relationships, and biogeography of the Lambeosaurine Dinosaurs from the European Archipelago, with Description of the New Aralosaurin Canardia garonnensis. PLoS ONE 8, e69835 (2013).82.Longrich, N. R., Suberbiola, X. P., Pyron, R. A. & Jalil, N.-E. The first duckbill dinosaur (Hadrosauridae: Lambeosaurinae) from Africa and the role of oceanic dispersal in dinosaur biogeography. Cretac. Res. 120, 104678 (2021).83.Brown, C. M., Evans, D. C., Campione, N. E., O’Brien, L. J. & Eberth, D. A. Evidence for taphonomic size bias in the Dinosaur Park Formation (Campanian, Alberta), a model Mesozoic terrestrial alluvial-paralic system. Palaeogeogr. Palaeoclimatol. Palaeoecol. 372, 108–122 (2013).
Google Scholar 
84.Mallon, J. C., Evans, D. C., Ryan, M. J. & Anderson, J. S. Feeding height stratification among the herbivorous dinosaurs from the Dinosaur Park Formation (upper Campanian) of Alberta, Canada. BMC Ecol. 13, 14 (2013).PubMed 
PubMed Central 

Google Scholar 
85.Mallon, J. C. Competition structured a Late Cretaceous megaherbivorous dinosaur assemblage. Sci. Rep. 9, 15447 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
86.Campione, N. E. & Evans, D. C. The accuracy and precision of body mass estimation in non-avian dinosaurs. Biol. Rev. 95, 1759–1797 (2020).PubMed 

Google Scholar 
87.Benítez-López, A. et al. The island rule explains consistent patterns of body size evolution in terrestrial vertebrates. Nat. Ecol. Evol. 5, 768–786 (2021).PubMed 

Google Scholar 
88.Fabbri M. et al. A shift in ontogenetic timing produced the unique sauropod skull. Evolution 75, 819–831 (2021).PubMed 

Google Scholar 
89.Chinsamy, A. & Raath, M. A. Preparation of fossil bone for histological examination. (1992).90.Fabbri, M., Wiemann, J., Manucci, F. & Briggs, D. E. G. Three-dimensional soft tissue preservation revealed in the skin of a non-avian dinosaur. Palaeontology 63, 185–193 (2020).
Google Scholar 
91.Takasaki R., et al. Re-examination of the cranial osteology of the Arctic Alaskan hadrosaurine with implications for its taxonomic status. https://doi.org/10.1371/journal.pone.0232410 (2020).92.Raven, T. J. & Maidment, S. C. R. The systematic position of the enigmatic thyreophoran dinosaur Paranthodon africanus, and the use of basal exemplifiers in phylogenetic analysis. PeerJ 6, e4529 (2018).93.Goloboff, P. A. Extended implied weighting. Cladistics 30, 260–272 (2014).PubMed 

Google Scholar 
94.Goloboff, P. A., Farris, J. S. & Nixon, K. C. TNT, a free program for phylogenetic analysis. Cladistics 24, 774–786 (2008).
Google Scholar 
95.Herne, M. C., Nair, J. P., Evans, A. R. & Tait, A. M. New small-bodied ornithopods (Dinosauria, Neornithischia) from the Early Cretaceous Wonthaggi Formation (Strzelecki Group) of the Australian-Antarctic rift system, with revision of Qantassaurus intrepidus Rich and Vickers-Rich, 1999. J. Paleontol. 93, 543–584 (2019).
Google Scholar 
96.Madzia, D. & Cau, A. Inferring ‘weak spots’ in phylogenetic trees: application to mosasauroid nomenclature. PeerJ 5, e3782 (2017).97.Madzia, D. & Cau, A. Estimating the evolutionary rates in mosasauroids and plesiosaurs: discussion of niche occupation in Late Cretaceous seas. PeerJ 8, e8941 (2020).98.Nicholl, C. S. C., Rio, J. P., Mannion, P. D. & Delfino, M. A re-examination of the anatomy and systematics of the tomistomine crocodylians from the Miocene of Italy and Malta. J. Syst. Palaeontol. 18, 1853–1889 (2020).
Google Scholar 
99.Rio, J. P., Mannion, P. D., Tschopp, E., Martin, J. E. & Delfino, M. Reappraisal of the morphology and phylogenetic relationships of the alligatoroid crocodylian Diplocynodon hantoniensis from the late Eocene of the United Kingdom. Zool. J. Linn. Soc. 188, 579–629 (2020).
Google Scholar 
100.Groh, S. S., Upchurch, P., Barrett, P. M. & Day, J. J. The phylogenetic relationships of neosuchian crocodiles and their implications for the convergent evolution of the longirostrine condition. Zool. J. Linn. Soc. 188, 473–506 (2020).
Google Scholar 
101.Bell, M. A. & Lloyd, G. T. Strap: An R package for plotting phylogenies against stratigraphy and assessing their stratigraphic congruence. Palaeontology 58, 379–389 (2015).
Google Scholar 
102.Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341–1351 (1997).PubMed 

Google Scholar 
103.Butler, M. A. & King, A. A. Phylogenetic comparative analysis: A modeling approach for adaptive evolution. Am. Nat. 164, 683–695 (2004).PubMed 

Google Scholar 
104.Beaulieu, J. M., Jhwueng, D.-C., Boettiger, C. & O’Meara, B. C. Modeling stabilizing selection: Expanding the Ornstein–Uhlenbeck model of adaptive evolution. Evolution 66, 2369–2383 (2012).PubMed 

Google Scholar 
105.Hunt, G. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology 38, 351–373 (2012).
Google Scholar 
106.Grosheny, D. et al. The Cenomanian-Turonian Boundary Event (CTBE) in northern Lebanon as compared to regional data—Another set of evidences supporting a short-lived tectonic pulse coincidental with the event?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 447–461 (2017).
Google Scholar 
107.Prieto-Márquez, A. Global historical biogeography of hadrosaurid dinosaurs. Zool. J. Linn. Soc. 159, 503–525 (2010).
Google Scholar 
108.Nariaki Sugiura. Further analysts of the data by akaike’ s information criterion and the finite corrections: Further analysts of the data by Akaike’s: Communications in Statistics—Theory and Methods: Vol 7, No 1. https://doi.org/10.1080/03610927808827599 (1978).109.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach. (Springer, Berlin, 2002). https://doi.org/10.1007/b97636.110.Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
111.Gates, T. A., Organ, C. & Zanno, L. E. Bony cranial ornamentation linked to rapid evolution of gigantic theropod dinosaurs. Nat. Commun. 7, 12931 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4, 754–759 (2013).
Google Scholar 
113.Pennell, M. W. et al. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 30, 2216–2218 (2014). More

Alcala N, Goldberg A, Ramakrishnan U, Rosenberg NA (2019) Coalescent theory of migration network motifs. Mol Biol Evol 36:2358–2374CAS 
PubMed 
PubMed Central 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CAS 
PubMed 
PubMed Central 

Google Scholar 
Anjana S, Sammeta SP, Das R (2020) Developing ancestry informative marker panel for Nigeria-Cameroonian chimpanzees. J Genet 99:1–7
Google Scholar 
Armstrong E, Khan A, Taylor RW, Gouy A, Greenbaum G, Thiéry A et al. (2021) Recent evolutionary history of tigers highlights contrasting roles of genetic drift and selection. Mol Biol Evol 38:2366–2379CAS 
PubMed 
PubMed Central 

Google Scholar 
Ballou JD (1992) Genetic and demographic considerations in endangered species captive breeding and reintroduction programs. In: Wildlife 2001: populations, Springer: Dordrecht, pp. 262–275Balloux F, Lugon‐Moulin N (2002) The estimation of population differentiation with microsatellite markers. Mol Ecol 11:155–165PubMed 

Google Scholar 
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120CAS 
PubMed 
PubMed Central 

Google Scholar 
Brandies P, Peel E, Hogg CJ, Belov K (2019) The value of reference genomes in the conservation of threatened species. Genes (Basel) 10:846CAS 

Google Scholar 
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
PubMed Central 

Google Scholar 
Chapman JR, Nakagawa S, Coltman DW, Slate J, Sheldon BC (2009) A quantitative review of heterozygosity–fitness correlations in animal populations. Mol Ecol 18:2746–2765CAS 
PubMed 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Roy R, Venkatesh N (2019) Using ancestry informative markers (AIMs) to detect fine structure within gorilla populations. Front Genet 10:43CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2018) An ancestry informative marker set which recapitulates the known fine structure of populations in South Asia. Genome Biol Evol 10:2408–2416PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2019) Application of the geographic population genetic structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas. BMC Bioinforma 20:35
Google Scholar 
Dudash MR, Fenster CB (2000) Inbreeding and outbreeding depression in fragmented populations. Conserv Biol Ser 35–54Esposito U, Das R, Syed S, Pirooznia M, Elhaik E (2018) Ancient ancestry informative markers for identifying fine-scale ancient population genetic structure in Eurasians. Genes (Basel) 9:625
Google Scholar 
Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH et al. (2020) Dense sampling of bird diversity increases power of comparative genomics. Nature 587:252–257CAS 
PubMed 
PubMed Central 

Google Scholar 
Frantz AC, Pourtois JT, Heuertz M, Schley L, Flamand MC, Krier A et al. (2006) Genetic structure and assignment tests demonstrate illegal translocation of red deer (Cervus elaphus) into a continuous population. Mol Ecol 15:3191–3203CAS 
PubMed 

Google Scholar 
Friar EA, Boose DL, LaDoux T, Roalson EH, Robichaux RH (2001) Population genetic structure in the endangered Mauna Loa silversword, Argyroxiphium kauense (Asteraceae), and its bearing on reintroduction. Mol Ecol 10:1657–1663CAS 
PubMed 

Google Scholar 
Fuentes‐Pardo AP, Ruzzante DE (2017) Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations. Mol Ecol 26:5369–5406PubMed 

Google Scholar 
Goodrich J, Lynam A, Miquelle D, Wibisono H, Kawanishi K, Pattanavibool A et al. (2015) Panthera tigris. In: The IUCN Red List of Threatened Species 2015, p. 15955 50659951Ivy JA, Lacy RC (2010) Using molecular methods to improve the genetic management of captive breeding programs for threatened species. In: Molecular approaches in natural resource conservation and management, pp. 267–295Jangtarwan K, Koomgun T, Prasongmaneerut T, Thongchum R, Singchat W, Tawichasri P et al. (2019) Take one step backward to move forward: Assessment of genetic diversity and population structure of captive Asian woolly-necked storks (Ciconia episcopus). PLoS One 14:e0223726CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiménez‐Mena B, Schad K, Hanna N, Lacy RC (2016) Pedigree analysis for the genetic management of group‐living species. Ecol Evol 6:3067–3078PubMed 
PubMed Central 

Google Scholar 
Kanthaswamy S, Johnson Z, Trask JS, Smith DG, Ramakrishnan R, Bahk J et al. (2014) Development and validation of a SNP‐based assay for inferring the genetic ancestry of rhesus macaques (Macaca mulatta). Am J Primatol 76:1105–1113CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Bhattacharjee S, Sharma S, Chugani AN, Sivaraman K et al. (2020) Are shed hair genomes the most effective noninvasive resource for estimating relationships in the wild? Ecol Evol 10:4583–4594PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Shukla H, Viswanathan A, van der Valk T, Borthakur U, et al. (2021). Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. bioRxivKhan A, Tyagi A (2021) Considerations for initiating a wildlife genomics research project in South and South-East Asia. J Indian Inst Sci 101:243–256. https://doi.org/10.1007/s41745-021-00243-3Article 

Google Scholar 
Kirk H, Freeland JR (2011) Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci 12:3966–3988PubMed 
PubMed Central 

Google Scholar 
Kolipakam V, Singh S, Pant B, Qureshi Q, Jhala YV (2019) Genetic structure of tigers (Panthera tigris tigris) in India and its implications for conservation. Glob. Ecol Conserv 20:710
Google Scholar 
Kunde MN, Martins RF, Premier J, Fickel J, Förster DW (2020) Population and landscape genetic analysis of the Malayan sun bear Helarctos malayanus. Conserv Genet 21:123–135CAS 

Google Scholar 
Laikre L, Palm S, Ryman N (2005) Genetic population genetic structure of fishes: implications for coastal zone management. AMBIO A J Hum Environ 34:111–119
Google Scholar 
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357CAS 
PubMed 
PubMed Central 

Google Scholar 
Liang Z, Bu L, Qin Y, Peng Y, Yang R, Zhao Y (2019) Selection of optimal ancestry informative markersfor classification and ancestry proportion estimation in pigs. Front genet 10:183PubMed 
PubMed Central 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079PubMed 
PubMed Central 

Google Scholar 
Lott MJ, Wright BR, Kemp LF, Johnson RN, Hogg CJ (2020) Genetic Management of Captive and Reintroduced Bilby Populations. J Wildl Manag 84:20–32
Google Scholar 
Luo SJ, Johnson WE, Martenson J, Antunes A, Martelli P, Uphyrkina O et al. (2008) Subspecies genetic assignments of worldwide captive tigers increase conservation value of captive populations. Curr Biol 18:592–596CAS 
PubMed 

Google Scholar 
Miller W, Hayes VM, Ratan A, Petersen DC, Wittekindt NE, Miller J et al. (2011) Genetic diversity and population structure of the endangered marsupial Sarcophilus harrisii (Tasmanian devil) Proc Natl Acad Sci 108:12348–12353CAS 
PubMed 
PubMed Central 

Google Scholar 
Mondol S, Bruford MW, Ramakrishnan U (2013) Demographic loss, genetic structure and the conservation implications for Indian tigers. Proc R Soc B Biol Sci 280:20130496
Google Scholar 
Muñoz I, Henriques D, Johnston JS, Chávez-Galarza J, Kryger P, Pinto MA (2015) Reduced SNP Panels for Genetic Identification and Introgression Analysis in the Dark Honey Bee (Apis mellifera mellifera) PLOS One 10:e0124365PubMed 
PubMed Central 

Google Scholar 
Nassir R, Kosoy R, Tian C, White PA, Butler LM, Silva G et al. (2009) An ancestry informative marker set for determining continental origin: validation and extension using human genome diversity panels. BMC Genet 10:39PubMed 
PubMed Central 

Google Scholar 
Natesh M, Atla G, Nigam P, Jhala YV, Zachariah A, Borthakur U et al. (2017) Conservation priorities for endangered Indian tigers through a genomic lens. Sci Rep. 7:1–11
Google Scholar 
Natesh M, Taylor RW, Truelove NK, Hadly EA, Palumbi SR, Petrov DA et al. (2019) Empowering conservation practice with efficient and economical genotyping from poor quality samples. Methods Ecol evolution 10:853–859
Google Scholar 
Patterson N, Price AL, Reich D (2006) Population genetic structure and eigenanalysis. PLoS Genet 2:190
Google Scholar 
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CAS 
PubMed 

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

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CAS 
PubMed 
PubMed Central 

Google Scholar 
Putnam AS, Ivy JA (2014) Kinship-based management strategies for captive breeding programs when pedigrees are unknown or uncertain. J Hered 105:303–311PubMed 

Google Scholar 
Rosenberg NA, Li LM, Ward R, Pritchard JK (2003) Informativeness of genetic markers for inference of ancestry. Am J Hum Genet 73:1402–1422CAS 
PubMed 
PubMed Central 

Google Scholar 
Sagar V, Kaelin CB, Natesh M, Reddy PA, Mohapatra RK, Chhattani H et al. (2021) High frequency of an otherwise rare phenotype in a small and isolatedtiger population. Proc Natl Acad Sci p. 118Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham RK (2012) Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28:1811–1817CAS 
PubMed 

Google Scholar 
Schlaepfer DR, Braschler B, Rusterholz HP, Baur B (2018) Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta‐analysis. Ecosphere 9:2488
Google Scholar 
Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C et al. (2003) Skin pigmentation, biogeographical ancestry and admixture mapping. Hum Genet 112:387–399PubMed 

Google Scholar 
Somenzi E, Ajmone-Marsan P, Barbato M (2020) Identification of ancestry informative marker (AIM) panels to assess hybridisation between feral and domestic sheep. Animals 10:582PubMed Central 

Google Scholar 
Supple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genome Biol 19:1–12
Google Scholar 
Svardal H, Jasinska AJ, Apetrei C, Coppola G, Huang Y, Schmitt CA et al. (2017) Ancient hybridization and strong adaptation to viruses across African vervet monkey populations. Nat genet 49:1705–1713CAS 
PubMed 
PubMed Central 

Google Scholar 
Vongpaisarnsin K, Listman JB, Malison RT, Gelernter J (2015) Ancestry informative markers for distinguishing between Thai populations based on genome-wide association datasets. Leg Med 17:245–250CAS 

Google Scholar 
Wasser SK, Brown L, Mailand C, Mondol S, Clark W, Laurie C et al. (2015) Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science (80-) 349:84–87CAS 

Google Scholar 
Wilkinson S, Wiener P, Archibald AL, Law A, Schnabel RD, McKay SD et al. (2011) Evaluation of approaches for identifying population informative markers from high density SNP chips. BMC genetics 12:1–14Wright S (1969) Evolution and the genetics of populationsWultsch C, Caragiulo A, Dias-Freedman I, Quigley H, Rabinowitz S, Amato G (2016) Genetic diversity and population genetic structure of Mesoamerican jaguars (Panthera onca): implications for conservation and management. PLoS One 11:162377
Google Scholar  More

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

Probing directional interactions of OMM12 strains using spent culture mediaTo characterize directional interactions of the OMM12 consortium members, we chose an in vitro approach to explore how the bacterial strains alter their chemical environment by growth to late stationary phase.Growth of the individual monocultures in a rich culture medium that supports growth of all members (AF medium, Methods, Table S1) was monitored over time (Fig. S1; SI data table 1) and growth rates (Table S2) were determined. Strains were grouped by growth rate (GR) into fast growing strains (GR  > 1.5 h–1, E. faecalis KB1, B. animalis YL2, C. innocuum I46 and B. coccoides YL58), strains with intermediate growth rate (GR  > 1 h–1, M. intestinale YL27, F. plautii YL31, E. clostridioformis YL32, B. caecimuris I48 and L.reuteri I49) and slow growing strains (GR  More

1.Food and Agriculture Organization & World Health Organization. Health and nutrition properties of probiotics in food including powder milk with live lactic acid bacteria. (FAO food and nutrition paper, 85, 2001).2.Barba-Vidal, E., Martín-Orúe, S. M. & Castillejos, L. Review: Are we using probiotics correctly in post-weaning piglets?. Animal 12, 2489–2498 (2018).CAS 
PubMed 

Google Scholar 
3.Bernardeau, M., Lehtinen, M. J., Forssten, S. D. & Nurminen, P. Importance of the gastrointestinal life cycle of Bacillus for probiotic functionality. J. Food Sci. Technol. 54, 2570–2584 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Hong, H. A., Duc, L. H. & Cutting, S. M. The use of bacterial spore formers as probiotics. FEMS Microbiol. Rev. 29, 813–835 (2005).CAS 
PubMed 

Google Scholar 
5.Duc, L. H., Hong, H. A. & Cutting, S. M. Germination of the spore in the gastrointestinal tract provides a novel route for heterologous antigen delivery. Vaccine 21, 4215–4224 (2003).CAS 

Google Scholar 
6.Leser, T. D., Knarreborg, A. & Worm, J. Germination and outgrowth of Bacillus subtilis and Bacillus licheniformis spores in the gastrointestinal tract of pigs. J. Appl. Microbiol. 104, 1025–1033 (2008).CAS 
PubMed 

Google Scholar 
7.Cutting, S. M. Bacillus probiotics. Food Microbiol. 28, 214–220 (2011).PubMed 

Google Scholar 
8.Prieto, M. L. et al. Assessment of the bacteriocinogenic potential of marine bacteria reveals lichenicidin production by seaweed-derived Bacillus spp. Mar. Drugs 10, 2280–2299 (2012).CAS 
PubMed 

Google Scholar 
9.Prieto, M. L. et al. In vitro assessment of marine Bacillus for use as livestock probiotics. Mar. Drugs 12, 2422–2445 (2014).PubMed 
PubMed Central 

Google Scholar 
10.Prieto, M. L. et al. Evaluation of the efficacy and safety of a marine-derived Bacillus strain for use as an in-feed probiotic for newly weaned pigs. PLoS ONE 9, e88599 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
11.National Research Council. Nutrient Requirements of Swine (The National Academies Press, 2012).
12.Berends, B. R., Urlings, H. A. P., Snijders, J. M. A. & Van Knapen, F. Identification and quantification of risk factors in animal management and transport regarding Salmonella spp. in pigs. Int. J. Food Microbiol. 30, 37–53. https://doi.org/10.1016/0168-1605(96)00990-7 (1996).CAS 
Article 
PubMed 

Google Scholar 
13.Miller, M. F., Carr, M. A., Bawcom, D. B., Ramsey, C. B. & Thompson, L. D. Microbiology of pork carcasses from pigs with differing origins and feed withdrawal times†. J. Food Prot. 60, 242–245. https://doi.org/10.4315/0362-028x-60.3.242 (1997).Article 
PubMed 

Google Scholar 
14.Adewole, D. I., Kim, I. H. & Nyachoti, C. M. Gut health of pigs: Challenge models and response criteria with a critical analysis of the effectiveness of selected feed additives—A review. Asian-Austr. J. Anim. Sci. 29, 909–924 (2016).CAS 

Google Scholar 
15.Department of Agriculture and Food and Rural Development. European communities (pig carcase (grading)) (amendment) regulations. (S.I. No. 413/2001, 2001).16.Gardiner, G. E. et al. Relative ability of orally administered Lactobacillus murinus to predominate and persist in the porcine gastrointestinal tract. Appl. Environ. Microbiol. 70, 1895–1906 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.McCormack, U. M. et al. Exploring a possible link between the intestinal microbiota and feed efficiency in pigs. Appl. Environ. Microbiol. 83, e00380-e417 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Buzoianu, S. G. et al. High-throughput sequence-based analysis of the intestinal microbiota of weanling pigs fed genetically modified MON810 maize expressing Bacillus thuringiensis Cry1Ab (Bt maize) for 31 days. Appl. Environ. Microbiol. 78, 4217–4224 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).20.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

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

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

Google Scholar 
23.R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).24.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 

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

Google Scholar 
26.Foster, Z. S. L., Sharpton, T. J. & Grünwald, N. J. Metacoder: An R package for visualization and manipulation of community taxonomic diversity data. PLOS Comput. Biol. 13, e1005404 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
27.Jadamus, A., Vahjen, W. & Simon, O. Growth behaviour of a spore forming probiotic strain in the gastrointestinal tract of broiler chicken and piglets. Arch. Tierernahr. 54, 1–17 (2001).CAS 
PubMed 

Google Scholar 
28.Duc, L. H., Hong, H. A., Barbosa, T. M., Henriques, A. O. & Cutting, S. M. Characterization of Bacillus probiotics available for human use. Appl. Environ. Microbiol. 70, 2161–2171 (2004).ADS 
CAS 
PubMed Central 

Google Scholar 
29.Tam, N. K. M. et al. The intestinal life cycle of Bacillus subtilis and close relatives. J. Bacteriol. 188, 2692–2700 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Casula, G. & Cutting, S. M. Bacillus Probiotics: Spore germination in the gastrointestinal tract. Appl. Environ. Microbiol. 68, 2344–2352 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Kidder, D. E. & Manners, M. J. Digestion in the pig. (Scientechnica, 1978).32.Crespo-Piazuelo, D. et al. Maternal supplementation with Bacillus altitudinis spores improves porcine offspring growth performance and carcass weight. Br. J. Nutr. https://doi.org/10.1017/S0007114521001203 (2021).Article 
PubMed 

Google Scholar 
33.Zhou, H., Wang, C., Ye, J., Chen, H. & Tao, R. Effects of dietary supplementation of fermented Ginkgo biloba L. residues on growth performance, nutrient digestibility, serum biochemical parameters and immune function in weaned piglets. Anim. Sci. J. 86, 790–799 (2015).CAS 
PubMed 

Google Scholar 
34.Kim, S. J., Kwon, C. H., Park, B. C., Lee, C. Y. & Han, J. H. Effects of a lipid-encapsulated zinc oxide dietary supplement, on growth parameters and intestinal morphology in weanling pigs artificially infected with enterotoxigenic Escherichia coli. J. Anim. Sci. Technol. 57, 4 (2015).PubMed 
PubMed Central 

Google Scholar 
35.Pérez, V. G. et al. Additivity of effects from dietary copper and zinc on growth performance and fecal microbiota of pigs after weaning. J. Anim. Sci. 89, 414–425 (2011).PubMed 

Google Scholar 
36.Ventrella, D. et al. The biomedical piglet: establishing reference intervals for haematology and clinical chemistry parameters of two age groups with and without iron supplementation. BMC Vet. Res. 13, 23 (2017).PubMed 
PubMed Central 

Google Scholar 
37.Thorn, C. E. Hematology of the pig. in Schalm’s Veterinary Hematology, 6th edition (eds. Weiss, D. J. & Wardrop, K. J.) 843–851 (2010). https://doi.org/10.1111/j.1939-165X.2011.00324.x38.Morrow-Tesch, J. L., McGlone, J. J. & Salak-Johnson, J. L. Heat and social stress effects on pig immune measures. J. Anim. Sci. 72, 2599–2609 (1994).CAS 
PubMed 

Google Scholar 
39.Schmid, L., Heit, W. & Flury, R. Agranulocytosis associated with semisynthetic penicillins and cephalosporins. Report of 7 cases. Blut 48, 11–18 (1984).CAS 
PubMed 

Google Scholar 
40.Kloubert, V. et al. Influence of zinc supplementation on immune parameters in weaned pigs. J. Trace Elem. Med. Biol. 49, 231–240 (2018).CAS 
PubMed 

Google Scholar 
41.The European Agency for the Evaluation of Medicinal Products (EMEA). Commitee for veterinary medicinal products: Apramycin. (1999).42.Frese, S. A., Parker, K., Calvert, C. C. & Mills, D. A. Diet shapes the gut microbiome of pigs during nursing and weaning. Microbiome 3, 28 (2015).PubMed 
PubMed Central 

Google Scholar 
43.Slifierz, M. J., Friendship, R. M. & Weese, J. S. Longitudinal study of the early-life fecal and nasal microbiotas of the domestic pig. BMC Microbiol. 15, 184 (2015).PubMed 
PubMed Central 

Google Scholar 
44.Ivarsson, E., Roos, S., Liu, H. Y. & Lindberg, J. E. Fermentable non-starch polysaccharides increases the abundance of Bacteroides-Prevotella-Porphyromonas in ileal microbial community of growing pigs. Animal 8, 1777–1787 (2014).CAS 
PubMed 

Google Scholar 
45.Pajarillo, E. A. B., Chae, J.-P., Balolong, M. P., Bum Kim, H. & Kang, D.-K. Assessment of fecal bacterial diversity among healthy piglets during the weaning transition. J. Gen. Appl. Microbiol. 60, 140–146 (2014).CAS 

Google Scholar 
46.Yu, T. et al. Low-molecular-weight chitosan supplementation increases the population of Prevotella in the cecal contents of weanling pigs. Front. Microbiol. 8, 1–9 (2017).
Google Scholar 
47.Shen, J. et al. Coated zinc oxide improves intestinal immunity function and regulates microbiota composition in weaned piglets. Br. J. Nutr. 111, 2123–2134 (2014).CAS 
PubMed 

Google Scholar 
48.Rattigan, R., Sweeney, T., Vigors, S., Rajauria, G. & O’Doherty, J. V. Effects of reducing dietary crude protein concentration and supplementation with laminarin or zinc oxide on the faecal scores and colonic microbiota in newly weaned pigs. J. Anim. Physiol. Anim. Nutr. (Berl) 104, 1471–1483 (2020).CAS 

Google Scholar 
49.López-Colom, P., Estellé, J., Bonet, J., Coma, J. & Martín-Orúe, S. M. Applicability of an unmedicated feeding program aimed to reduce the use of antimicrobials in nursery piglets: Impact on performance and fecal microbiota. Animals 10, 242 (2020).PubMed Central 

Google Scholar 
50.Wei, X. et al. ZnO modulates swine gut microbiota and improves growth performance of nursery pigs when combined with peptide cocktail. Microorganisms 8, 146 (2020).CAS 
PubMed Central 

Google Scholar 
51.Vahjen, W., Pieper, R. & Zentek, J. Increased dietary zinc oxide changes the bacterial core and enterobacterial composition in the ileum of piglets. J. Anim. Sci. 89, 2430–2439 (2011).CAS 
PubMed 

Google Scholar 
52.Pieper, R., Vahjen, W., Neumann, K., Van Kessel, A. G. & Zentek, J. Dose-dependent effects of dietary zinc oxide on bacterial communities and metabolic profiles in the ileum of weaned pigs. J. Anim. Physiol. Anim. Nutr. (Berl) 96, 825–833 (2012).CAS 

Google Scholar 
53.Yu, T. et al. Dietary high zinc oxide modulates the microbiome of ileum and colon in weaned piglets. Front. Microbiol. 8, 1–12 (2017).
Google Scholar 
54.Xia, T. et al. Dietary ZnO nanoparticles alters intestinal microbiota and inflammation response in weaned piglets. Oncotarget 8, 64878–64891 (2017).PubMed 
PubMed Central 

Google Scholar 
55.Poulsen, A.-S.R. et al. Impact of Bacillus spp. spores and gentamicin on the gastrointestinal microbiota of suckling and newly weaned piglets. PLoS ONE 13, e0207382 (2018).PubMed 
PubMed Central 

Google Scholar 
56.Hagerty, S. L., Hutchison, K. E., Lowry, C. A. & Bryan, A. D. An empirically derived method for measuring human gut microbiome alpha diversity: Demonstrated utility in predicting health-related outcomes among a human clinical sample. PLoS ONE 15, e0229204 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).ADS 

Google Scholar 
58.Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Ryden, R. & Moore, B. J. The in vitro activity of apramycin, a new aminocyditol antibiotic. J. Antimicrob. Chemother. 3, 609–613 (1977).CAS 
PubMed 

Google Scholar 
60.Jones, N., Ray, B., Ranjit, K. T. & Manna, A. C. Antibacterial activity of ZnO nanoparticle suspensions on a broad spectrum of microorganisms. FEMS Microbiol. Lett. 279, 71–76 (2008).CAS 
PubMed 

Google Scholar 
61.Gardiner, G. E., Metzler-Zebeli, B. U. & Lawlor, P. G. Impact of intestinal microbiota on growth and feed efficiency in pigs: A review. Microorganisms 8, 1886 (2020).CAS 
PubMed Central 

Google Scholar 
62.Ghanbari, M., Klose, V., Crispie, F. & Cotter, P. D. The dynamics of the antibiotic resistome in the feces of freshly weaned pigs following therapeutic administration of oxytetracycline. Sci. Rep. 9, 4062 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
63.Zeineldin, M., Aldridge, B. & Lowe, J. Antimicrobial effects on swine gastrointestinal microbiota and their accompanying antibiotic resistome. Front. Microbiol. 10, 1035 (2019).PubMed 
PubMed Central 

Google Scholar 
64.On, S. L. W. Identification methods for campylobacters, helicobacters, and related organisms. Clin. Microbiol. Rev. 9, 405–422 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Bergström, S., Garon, C. F., Barbour, A. G. & MacDougall, J. Extrachromosomal elements of spirochetes. Res. Microbiol. 143, 623–628 (1992).PubMed 

Google Scholar 
66.Oh, J. K. et al. Association between the body weight of growing pigs and the functional capacity of their gut microbiota. Anim. Sci. J. 91, e13418 (2020).CAS 
PubMed 

Google Scholar 
67.Ruiz, V. L. A. et al. Case–control study of pathogens involved in piglet diarrhea. BMC Res. Notes 9, 22 (2016).PubMed 
PubMed Central 

Google Scholar 
68.Yang, Q. et al. Longitudinal development of the gut microbiota in healthy and diarrheic piglets induced by age-related dietary changes. Microbiologyopen 8, e923 (2019).PubMed 
PubMed Central 

Google Scholar 
69.Wang, S. et al. Combined supplementation of Lactobacillus fermentum and Pediococcus acidilactici promoted growth performance, alleviated inflammation, and modulated intestinal microbiota in weaned pigs. BMC Vet. Res. 15, 239 (2019).PubMed 
PubMed Central 

Google Scholar 
70.Looft, T. et al. Bacteria, phages and pigs: The effects of in-feed antibiotics on the microbiome at different gut locations. ISME J. 8, 1566–1576 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Quan, J. et al. A global comparison of the microbiome compositions of three gut locations in commercial pigs with extreme feed conversion ratios. Sci. Rep. 8, 4536 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
72.Ramayo-Caldas, Y. et al. Phylogenetic network analysis applied to pig gut microbiota identifies an ecosystem structure linked with growth traits. ISME J. 10, 2973–2977 (2016).PubMed 
PubMed Central 

Google Scholar 
73.Che, L. et al. Inter-correlated gut microbiota and SCFAs changes upon antibiotics exposure links with rapid body-mass gain in weaned piglet model. J. Nutr. Biochem. 74, 108246 (2019).CAS 
PubMed 

Google Scholar 
74.Machiels, K. et al. A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut 63, 1275–1283 (2014).CAS 
PubMed 

Google Scholar 
75.Segain, J. P. et al. Butyrate inhibits inflammatory responses through NFkappaB inhibition: Implications for Crohn’s disease. Gut 47, 397–403 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Roediger, W. E. The colonic epithelium in ulcerative colitis: An energy-deficiency disease?. Lancet (London, England) 2, 712–715 (1980).CAS 

Google Scholar 
77.Jewell, K. A., Scott, J. J., Adams, S. M. & Suen, G. A phylogenetic analysis of the phylum Fibrobacteres. Syst. Appl. Microbiol. 36, 376–382. https://doi.org/10.1016/j.syapm.2013.04.002 (2013).CAS 
Article 
PubMed 

Google Scholar 
78.Abdul Rahman, N. et al. A phylogenomic analysis of the bacterial phylum Fibrobacteres. Front. Microbiol. 6, 1469–1469. https://doi.org/10.3389/fmicb.2015.01469 (2016).Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).
Google Scholar 
2.Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).CAS 

Google Scholar 
3.Smith, W. K. et al. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sens. Environ. 233, 111401 (2019).
Google Scholar 
4.Verma, M. et al. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set. Biogeosciences 11, 2185–2200 (2014).
Google Scholar 
5.MacBean, N. et al. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems. Environ. Res. Lett. 16, 094023 (2021).CAS 

Google Scholar 
6.Wang, L., Manzoni, S., Ravi, S., Riveros-Iregui, D. & Caylor, K. Dynamic interactions of ecohydrological and biogeochemical processes in water-limited systems. Ecosphere 6, 1–27 (2015).
Google Scholar 
7.Oleson, K. W. et al. Technical Description of the Community Land Model (CLM). Technical Note NCAR/TN-461+ STR (NCAR, 2004).8.Bonan, G. B. & Levis, S. Evaluating aspects of the community land and atmosphere models (CLM3 and CAM3) using a dynamic global vegetation model. J. Clim. 19, 2290–2301 (2006).
Google Scholar 
9.Brovkin, V., Ganopolski, A. & Svirezhev, Y. A continuous climate-vegetation classification for use in climate-biosphere studies. Ecol. Modell. 101, 251–261 (1997).
Google Scholar 
10.Foley, J. A. et al. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochem. Cycles 10, 603–628 (1996).CAS 

Google Scholar 
11.Haxeltine, A. & Prentice, I. C. BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Global Biogeochem. Cycles 10, 693–709 (1996).CAS 

Google Scholar 
12.Sitch, S. The Role of Vegetation Dynamics in the Control of Atmospheric CO2 Content. Dissertation, Lund Univ. (2000).13.Levis, S., Bonan, G. B., Vertenstein, M. & Oleson, K. W. The Community Land Model’s Dynamic Global Vegetation Model (CLM-DGVM): Technical Description and User’s Guide. NCAR Technical Note TN-459+ IA 50 (NCAR, 2004).14.Woodward, F. I., Lomas, M. R. & Betts, R. A. Vegetation-climate feedbacks in a greenhouse world. Philos. Trans. R. Soc. Lond. B Biol. Sci. 353, 29–39 (1998).
Google Scholar 
15.Hickler, T., Prentice, I. C., Smith, B., Sykes, M. T. & Zaehle, S. Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model. Glob. Ecol. Biogeogr. 15, 567–577 (2006).
Google Scholar 
16.Turner, D. P. et al. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sens. Environ. 102, 282–292 (2006).
Google Scholar 
17.Loik, M. E., Breshears, D. D., Lauenroth, W. K. & Belnap, J. A multi-scale perspective of water pulses in dryland ecosystems: climatology and ecohydrology of the western USA. Oecologia 141, 269–281 (2004).
Google Scholar 
18.Austin, A. T. et al. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–235 (2004).
Google Scholar 
19.Biederman, J. A. et al. Terrestrial carbon balance in a drier world: the effects of water availability in southwestern North America. Glob. Change Biol. 22, 1867–1879 (2016).
Google Scholar 
20.Wilcox, B. P., Sorice, M. G. & Young, M. H. Dryland ecohydrology in the Anthropocene: taking stock of human–ecological interactions. Geogr. Compass 5, 112–127 (2011).21.Biederman, J. A. et al. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America. Glob. Change Biol. 23, 4204–4221 (2017).
Google Scholar 
22.Lauenroth, W. K. & Bradford, J. B. Ecohydrology of dry regions of the United States: precipitation pulses and intraseasonal drought. Ecohydrology 2, 173–181 (2009).
Google Scholar 
23.Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220 (2004).
Google Scholar 
24.Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).CAS 

Google Scholar 
25.Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Change 9, 880–885 (2019).
Google Scholar 
26.Bradford, J. B., Schlaepfer, D. R., Lauenroth, W. K. & Palmquist, K. A. Robust ecological drought projections for drylands in the 21st century. Glob. Change Biol. 26, 3906–3919 (2020).
Google Scholar 
27.Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).
Google Scholar 
28.Jung, M. et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116, G00J07 (2011).
Google Scholar 
29.Jung, M. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516–520 (2017).CAS 

Google Scholar 
30.Xiao, J. et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data. Remote Sens. Environ. 114, 576–591 (2010).
Google Scholar 
31.Tramontana, G. et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13, 4291–4313 (2016).CAS 

Google Scholar 
32.Joiner, J. & Yoshida, Y. Satellite-based reflectances capture large fraction of variability in global gross primary production (GPP) at weekly time scales. Agric. For. Meteorol. 291, 108092 (2020).
Google Scholar 
33.Aguiar, M. R. & Sala, O. E. Patch structure, dynamics and implications for the functioning of arid ecosystems. Trends Ecol. Evol. 14, 273–277 (1999).CAS 

Google Scholar 
34.Bacour, C. et al. Improving estimates of gross primary productivity by assimilating solar-induced fluorescence satellite retrievals in a terrestrial biosphere model using a process-based SIF model. J. Geophys. Res. Biogeosci. 124, 3281–3306 (2019).
Google Scholar 
35.MacBean, N. et al. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data. Sci. Rep. 8, 1973 (2018).
Google Scholar 
36.Xiao, J. et al. Assessing net ecosystem carbon exchange of U.S. terrestrial ecosystems by integrating eddy covariance flux measurements and satellite observations. Agric. For. Meteorol. 151, 60–69 (2011).
Google Scholar 
37.Ropelewski, C. F. & Halpert, M. S. Global and REGIONAL SCALE PRECIPITATION PATTERNS ASSociated with the El Niño/Southern Oscillation. Mon. Wea. Rev. 115, 1606–1626 (1987).
Google Scholar 
38.Trenberth, K. E. The definition of El Niño. Bull. Amer. Meteor. Soc. 78, 2771–2778 (1997).
Google Scholar 
39.Boening, C., Willis, J. K., Landerer, F. W., Nerem, R. S. & Fasullo, J. The 2011 La Niña: so strong, the oceans fell. Geophys. Res. Lett. 39, L19602 (2012).40.Kogan, F. & Guo, W. Strong 2015–2016 El Niño and implication to global ecosystems from space data. Int. J. Remote Sens. 38, 161–178 (2017).
Google Scholar 
41.Berntson, G. G., Lozano, D. L. & Chen, Y. J. Filter properties of root mean square successive difference (RMSSD) for heart rate. Psychophysiology 42, 246–252 (2005).
Google Scholar 
42.von Neumann, J., Kent, R. H., Bellinson, H. R. & Hart, B. I. The mean square successive difference. Ann. Math. Stat. 12, 153–162 (1941).
Google Scholar 
43.Jenerette, G. D., Barron-Gafford, G. A., Guswa, A. J., McDonnell, J. J. & Villegas, J. C. Organization of complexity in water limited ecohydrology. Ecohydrology 5, 184–199 (2012).
Google Scholar 
44.IPCC 2013. Climate Change 2013 – The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2014).45.Breshears, D. D. et al. The critical amplifying role of increasing atmospheric moisture demand on tree mortality and associated regional die-off. Front. Plant Sci. 4, 266 (2013).46.Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, nclimate3114 (2016).
Google Scholar 
47.Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, art129 (2015).
Google Scholar 
48.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 

Google Scholar 
49.Cook, B. I., Ault, T. R. & Smerdon, J. E. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082 (2015).
Google Scholar 
50.Huang, J., Yu, H., Dai, A., Wei, Y. & Kang, L. Drylands face potential threat under 2 °C global warming target. Nat. Clim. Change 7, 417–422 (2017).
Google Scholar 
51.Easterling, D. R. et al. Climate extremes: observations, modeling, and impacts. Science 289, 2068–2074 (2000).CAS 

Google Scholar 
52.MacDonald, G. M. Water, climate change, and sustainability in the Southwest. Proc. Natl Acad. Sci. USA 107, 21256–21262 (2010).CAS 

Google Scholar 
53.van Dijk, A. I. J. M. et al. The Millennium Drought in southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour. Res. 49, 1040–1057 (2013).
Google Scholar 
54.Collier, N. et al. The International Land Model Benchmarking (ILAMB) System: design, theory, and implementation. J. Adv. Model. Earth Syst. 10, 2731–2754 (2018).
Google Scholar 
55.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
56.R Core Team. R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).57.Kuhn, M. caret: Classification and regression training. R package version 6.0-88. https://CRAN.R-project.org/package=caret (2021).58.Didan, K. MOD13C1 MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13C1.006 (NASA EOSDIS Land Processes DAAC, 2015).59.Hijmans, R. J. raster: Geographic data analysis and modeling. R package version 3.5-2. https://CRAN.R-project.org/package=raster (2021).60.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Google Scholar 
61.Climatic Research Unit (University of East Anglia) & Met Office. CRU TS Version 4.04. http://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.04/ (CRU, 2020).62.Hijmans, R. J. geosphere: Spherical trigonometry. Package version 1.5-10. https://CRAN.R-project.org/package=geosphere (2019).63.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
64.Barnes, M. L. et al. Vegetation productivity responds to sub-annual climate conditions across semiarid biomes. Ecosphere 7, n/a–n/a (2016).
Google Scholar 
65.Vicente-Serrano, S. M. et al. Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interact. 16, 1–27 (2012).
Google Scholar 
66.Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Climate 23, 1696–1718 (2010).
Google Scholar 
67.Beguería, S. & Vicente-Serrano, S. M. SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index. R package version 1.7. https://CRAN.R-project.org/package=SPEI (2017).68.Beguería, S., Vicente-Serrano, S. M. & Angulo-Martínez, M. A Multiscalar Global Drought Dataset: the SPEI base: a new gridded product for the analysis of drought variability and impacts. Bull. Am. Meteorol. Soc. 91, 1351–1356 (2010).
Google Scholar 
69.Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M. & El Kenawy, A. A New Global 0.5° Gridded Dataset (1901–2006) of a Multiscalar Drought Index: comparison with current drought index datasets based on the Palmer Drought Severity Index. J. Hydrometeorol. 11, 1033–1043 (2010).
Google Scholar 
70.Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).
Google Scholar 
71.Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).
Google Scholar 
72.Sörensen, L. A spatial analysis approach to the global delineation of dryland areas of relevance to the CBD Programme of Work on Dry and Subhumid Lands. Dataset based on spatial analysis between WWF terrestrial ecoregions (WWF-US, 2004) and aridity zones https://www.unep-wcmc.org/resources-and-data/a-spatial-analysis-approach-to-the-global-delineation-of-dryland-areas-of-relevance-to-the-cbd-programme-of-work-on-dry-and-subhumid-lands (CRU/UEA; UNE, 2007). Data accessed: 6/27/2021.73.Miles, L. et al. A global overview of the conservation status of tropical dry forests. J. Biogeogr. 33, 491–505 (2006).
Google Scholar 
74.Freitag, D. Information Extraction from HTML: Application of a General Machine Learning Approach, 517–523 (AAAI/IAAI, 1998).75.Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A. & Van Der Laan, M. J. Survival ensembles. Biostatistics 7, 355–373 (2006).
Google Scholar 
76.Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for random forests. BMC Bioinformatics 9, 307 (2008).
Google Scholar 
77.Strobl, C., Boulesteix, A.-L., Zeileis, A. & Hothorn, T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 8, 25 (2007).
Google Scholar 
78.Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006. https://doi.org/10.5067/MODIS/MOD17A2H.006 (NASA EOSDIS Land Processes DAAC, 2015).79.Jung, M. et al. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences 17, 1343–1365 (2020).CAS 

Google Scholar 
80.Jung, M. et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data 6, 74 (2019).
Google Scholar 
81.Von Neumann, J., Kent, R., Bellinson, H. & Hart, B. The mean square successive difference. Ann. Math. Stat. 12, 153–162 (1941).82.Revelle, W. R. psych: Procedures for personality and psychological research. R package version 2.1.6. https://CRAN.R-project.org/package=psych (2021).83.Farella, M. Code and data for ‘Improved dryland carbon flux predictions with explicit consideration of water–carbon coupling’. zenodo https://doi.org/10.5281/ZENODO.5540015 (2021). 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