Philippa Kaur

Špinar, Z. V. Revize nĕkterých moravských diskosauriscidů (Labyrinthodontia). Rozpravy Ústředního Ústavu Geologického. 15, 1–115 (1952).
Google Scholar 
Klembara, J. & Meszároš, Š. New finds of Discosauriscus austriacus (Makowsky 1876) from the Lower Permian of the Boskovice Furrow (Czecho-Slovakia). Geol. Carpath. 43, 305–312 (1992).
Google Scholar 
Klembara, J. The external gills and ornamentation of the skull roof bones of the Lower Permian tetrapod Discosauriscus austriacus (Makowsky 1876) with remarks to its ontogeny. Paläontol. Z. 69, 265–281 (1995).
Google Scholar 
Klembara, J. The cranial anatomy of Discosauriscus Kuhn, a seymouriamorph tetrapod from the Lower Permian of the Boskovice Furrow (Czech Republic). Philos. Trans. R. Soc. B 352, 257–302 (1997).ADS 

Google Scholar 
Calábková, G., Březina, J. & Madzia, D. Evidence of large terrestrial seymouriamorphs in the lowermost Permian of the Czech Republic. Pap. Palaeontol. https://doi.org/10.1002/spp2.1428 (2022).Article 

Google Scholar 
Makowsky, A. Über einen neuen Labyrinthodonten ‘Archegosaurus austriacus nov. spec’. Sitzungsberichte der keiserischen Akademie der Wissenschaft. 73, 155–166 (1876).
Google Scholar 
Fritsch, H. A. Neue Übersicht der in der Gaskohle und den Kalksteinen der Permformation in Böhmen vorgefundenen Tierreste. Sitzungsberichte der königlichen böhmische Gesellschaft der Wissenschaften in Prag 1879, 184–195 (1880).
Google Scholar 
Klembara, J. A new discosauriscid seymouriamorph tetrapod from the Lower Permian of Moravia, Czech Republic. Acta Palaeontol. Pol. 50, 25–48 (2005).
Google Scholar 
Klembara, J. New cranial and dental features of Discosauriscus austriacus (Seymouriamorpha, Discosauriscidae) and the ontogenetic conditions of Discosauriscus. Spec. Pap. Palaeontol. 81, 61–69 (2009).
Google Scholar 
Klembara, J. A new find of discosauriscid seymouriamorph from the Lower Permian of Boskovice Basin in Moravia (the Czech Republic). Fossil Imprint 72, 117–121 (2016).
Google Scholar 
Augusta, J. Spodnopermaská zvířena a květena z nového naleziště za pilou dolu “Antonín” u Zbýšova na Moravě. Věstník Státního geologického Ústavu. 22(4), 187–224 (1947).
Google Scholar 
Milner, A. W., Klembara, J. & Dostál, O. A zatrachydid temnospondyl from the Lower Permian of the Boskovice Furrow in Moravia (Czech Republic). J. Vertebr. Paleontol. 27, 711–715 (2007).
Google Scholar 
Klembara, J. & Steyer, S. A new species of Sclerocephalus (Temnospondyli: Stereospondylomorpha) from the Early Permian of the Boskovice Basin (Czech Republic). J. Paleontol. 86, 302–310 (2012).
Google Scholar 
Zajíc, J. & Štamberg, S. Selected important fossiliferous horizons of the Boskovice Basin in the light of the new zoopaleontological data. Acta Musei Reginaehradecensis A 30, 5–15 (2004).
Google Scholar 
Štamberg, S. & Zajíc, J. Carboniferous and Permian faunas and Their Occurrence in the Limnic Basins of the Czech Republic Museum of Eastern Bohemia (Hradec Králové, 2008).Calábková, G. & Nosek, V. Stopy velkého čtvernožce z permu boskovické brázdy. Sborník Muzea Brněnska. 59–68 (2022).Calábková, G., Březina, J., Nosek, V. & Madzia, D. High diversity of tetrapods in the lower Permian of the Boskovice Basin, Czech Republic. In 21st Slovak-Czech-Polish Paleontological Conference, Bratislava, Slovakia 113–114 (2022).Fritsch, H. A. Über die Fauna der Gaskohle der Pilsner und Rakonitzer Beckens. In Věstník Královské české společnosti nauk. Třída mathematicko-přírodovědecká. 70–79. (Praha, 1875).Fritsch, A. Fauna der Gaskohle und der Kalksteine der Permformation Böhmens. II/2. Prague: F. Řivnáč. 33–64 (1885).Fritsch, H. A. Ueber neue Wirbelthiere aus der Permformation Böhmens nebst einer Uebersicht der aus derselben bekannt gewordenen Arten. Sitzungsberichte der königl. böhmischen Gesellschaft der Wissenschaften, mathematischnaturwissenschaftliche Classe 52, 17 (1895).Švestka, F. Příspěvek k dnešní bilanci nálezů rostlinných fossilií z uhelné pánve rosicko-oslavanské a památné Rybičkové skály pod spodnopermským Konvizem u Padochova. Příroda. 35(5), 116–119 (1943).
Google Scholar 
Švestka, F. Druhý příspěvek k fytopaleontologickému Průzkumu spodního perrnu a permokarbonu Oslavan, Padochova a Zbýšova. Příroda. 36, 159–165 (1944).
Google Scholar 
Fritsch, A. Fauna der Gaskohle und der Kalksteine der Permformation Böhmens II/4. Prague: F. Řivnáč. 93–114 (1889).Reisz, R. R. Pennsylvanian Pelycosaurs from Linton, Ohio and Nýřany, Czechoslovakia. J. Paleontol. 49, 522–527 (1975).
Google Scholar 
Fröbisch, J., Schoch, R. R., Müller, J., Schindler, T. & Schweiss, D. A new basal sphenacodontid synapsid from the Late Carboniferous of the Saar-Nahe Basin, Germany. Acta Palaeontol. Pol. 56, 113–120 (2011).
Google Scholar 
Spindler, F., Voigt, S. & Fischer, J. Edaphosauridae (Synapsida, Eupelycosauria) from Europe and their relationship to North American representatives. PalZ. 94, 125–153 (2019).
Google Scholar 
Jaroš, J. Litostratigrafie permokarbonu Boskovické brázdy. Věstník Ústředního ústavu geologického 38, 115–118 (1963).
Google Scholar 
Jaroš J. & Malý, L. Boskovická brázda. 208–223. In Geologie a ložiska svrchnopaleozoických limnických pánví České republiky (ed. PEšEK, J.) (Český geologický ústav, 2001).Pešek, J. Late Paleozoic limnic basins and coal deposits of the Czech Republic. Folia Musei Rerum Naturalium Bohemiae occidentalis: Geologica et Paleobiologica, 1 (2004).Jaroš, J. Geologický vývoj a stavba boskovické brázdy. PhD thesis, Charles University, Prague, Czech Republic (1962).Houzar, S., Hršelová, P., Gilíková, H., Buriánek, D. & Nehyba, S. Přehled historie vyzkumů permokarbonskych sedimentů jižni časti boskovicke brazdy (Čast 2. Geologie a petrografie). Acta Musei Moraviae Scientiae Geologicae. 102, 3–65 (2017).
Google Scholar 
Opluštil, S., Jirásek, J., Schmitz, M. & Matýsek, D. Biotic changes around the radioisotopically constrained Carboniferous-Permian boundary in the Boskovice Basin (Czech Republic). Bull. Geosci. 92, 95–122 (2017).
Google Scholar 
Dopita, M., Havlena, V. & Pešek, J. Ložiska fosilních paliv. Vyd. 1. Nakladatelství technické literatury, Praha (1985).Pešek, J., Holub, V., Jaroš, J., Malý, L., Martínek, K., Prouza, V., Spudil, J. & Tasler, R. Geologie a ložiska svrchnopaleozoických limnických pánví České republiky. Český geologický ústav, Praha (2001).Šimůnek, Z. & Martínek, K. A study of Late Carboniferous and Early Permian plant assemblages from the Boskovice Basin, Czech Republic. Rev. Palaeobot. Palynol. 155, 275–307 (2009).
Google Scholar 
Kukalová, J. On the Family Blattinopsidae Bolton, 1925 (Insecta, Protorthoptera). Rozpravy Československé akademie věd, Rada matematických a přírodních věd 69, 1–27 (1959).
Google Scholar 
Kukalová, J. Permian protelytroptera, coleoptera and protorthoptera (insecta) of Moravia. Sborník geologických věd, Paleontonologie. 6, 61–98 (1965).
Google Scholar 
Schneider, J. W. Zur Entomofauna des Jungpalaozoikums der Boskovicer Furche (ČSSR), Teil 1: Mylacridae (Insecta, Blattoidea). Freiberger Forschungshefte C 357, 43–55 (1980).
Google Scholar 
Schneider, J. W. Zur Entomofauna des Jungpalaozoikums der Boskovicer Furche (ČSSR), Teil 2: Phyloblattidae (Insecta, Blattoidea). Freiberger Forschungshefte C 395, 19–37 (1984).
Google Scholar 
Zajíc, J. Sladkovodní mikrovertebrátní společenstva svrchního Stefanu a spodního autunu Čech. Závěrečný zpráva za grant GAČR, MS, Česká geologický Ústav, 1–61. Praha (1996).Zajíc, J., Martínek, K., Šimůnek Z. & Drábková, J. Permokarbon Boskovické brázdy ve výkopu pro rozšíření tranzitního plynovodu. Zprávy o geologických výzkumech v roce 1995, 179–182. Praha. (1996).Ivanov, M. Přehled historie paleontologickeho badani v permokarbonu boskovicke brazdy na Moravě. Acta Musei Moraviae Scientiae Geologicae. 88, 3–112 (2003).
Google Scholar 
Zajíc, J. Vertebrate biozonation of the Permo-Carboniferous lakes of the Czech Republic: New data. Acta Musei Reginaehradecensis A 30, 15–16 (2004).
Google Scholar 
Zajíc, J. Permian acanthodians of the Czech Republic Czech Geological Survey Special Paper. 18, 1–42 (2005).Štamberg, S. Fossiliferous Early Permian horizons of the Krkonoše Piedmont Basin and the Boskovice Graben (Bohemian Massif) in view of the occurrence of actinopterygians. Paläontologie, Stratigraphie, Fazies (22). Freiberger Forschungshefte, C, 548, 45–60 (2014).Kukalová, J. Permian insects of Moravia. Part I: Miomoptera. Sborník geologických věd, Paleontonologie 1, 7–52 (1963).
Google Scholar 
Kukalová, J. Permian insects of Moravia. Part II: Liomopteridae. Sborník geologických věd, Paleontonologie. 3, 3–118 (1964).
Google Scholar 
Štamberg, S. Permo-Carboniferous actinopterygians of the Boskovice Graben. Part 1. Neslovicella, Bourbonnella, Letovichthys. Museum of Eastern Bohemia in Hradec Králové (2007).Klembara, J. The skeletal anatomy and relationships of a new discosauriscid seymouriamorph from the Lower Permian of Moravia (Czech Republic). Ann. Carnegie Museum 77, 451–484 (2009).
Google Scholar 
Klembara, J. & Mikudíková, M. New cranial material of Discosauriscus pulcherrimus (Seymouriamorpha, Discosauriscidae) from the Lower Permian of the Boskovice Basin (Czech Republic). Earth Environ. Sci. Trans. R. Soc. Edinb. 109, 225–236 (2018).
Google Scholar 
Leonardi, G. Glossary and Manual of Tetrapod Footprint Palaeoichnology 1–117 (Departamento Nacional de Producao Mineral, 1987).
Google Scholar 
Porter, S., Roussel, M. & Soressi, M. A simple photogrammetry rig for the reliable creation of 3D artifact models in the field: Lithic examples from the early upper paleolithic sequence of Les Cottés (France). Adv. Archaeol. Pract. 4, 1–86 (2016).
Google Scholar 
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J. & Reynolds, J. M. ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 179, 300–314 (2012).ADS 

Google Scholar 
Yilmaz, H., Yakar, M., Gulec, S. & Dulgerler, O. Importance of digital close-range photogrammetry in documentation of cultural heritage. J. Cult. Herit. 8(4), 428–433 (2007).
Google Scholar 
Haeckel, E. Generelle Morphologie der Organismen (Reimer, 1866).
Google Scholar 
Osborn, H. F. The reptilian subclasses Diapsida and Synapsida and the early history of the Diaptosauria. Mem. Am. Mus. Nat. Hist. 1, 265–270 (1903).
Google Scholar 
Romer, A. S. & Price, L. I. Review of the Pelycosauria. Geol. Soc. Am. Spec. Pap. 28, 1–538 (1940).
Google Scholar 
Geinitz, H. B. Beiträge zur Kenntnis der organischen Überreste in der Dyas (oder permischen Formation zum Theil) und über den Namen Dyas: Neues Jahrbuch für Mineralogie, Geologie und Paläontologie. 385–398 (1863).Voigt, S. & Lucas, S. G. Outline of a Permian tetrapod footprint ichnostratigraphy. 387–404. In The Permian Timescale: An Introduction (eds. Lucas, S. G. and Shen, S. Z.) 450 (Geological Society, London, Special Publications, 2016). https://doi.org/10.1144/SP450.10 (2016).Voigt, S. & Ganzelewski, M. Toward the origin of amniotes: Diadectomorph and synapsid footprints from the early Late Carboniferous of Germany. Acta Palaeontol. Pol. 55, 57–72 (2010).
Google Scholar 
Marchetti, L. et al. Defining the morphological quality of fossil footprints. Problems and principles of preservation in tetrapod ichnology with examples from the Palaeozoic to the present. Earth Sci. Rev. 193, 109–145 (2019).ADS 

Google Scholar 
Voigt, S. Die Tetrapodenichnofauna des kontinentalen Oberkarbon und Perm im Thüringer Wald—Ichnotaxonomie, Paläoökologie und Biostratigraphie. Cuvillier, Göttingen (2005).Voigt, S. & Lucas, S. G. On a diverse tetrapod ichnofauna from early Permian red beds in San Miguel County, north-central New Mexico: New Mexico Geological Society. Guidebook. 66, 241–252 (2015).
Google Scholar 
Tilton, J. L. Permian vertebrate tracks in West Virginia. Bull. Geol. Soc. Am. 42, 547–556 (1931).
Google Scholar 
Van Allen, H. E. K., Calder, J. H. & Hunt, A. P. The trackway record of a tetrapod community in a walchian conifer forest from the Permo-Carboniferous of Nova Scotia. N. M. Mus. Nat. Hist. Sci. Bull. 30, 322–332 (2005).
Google Scholar 
Gand, G. Les traces de Vertébrés Tétrapodes du Permien français: Paléontologie, stratigraphie, paléoenvironnements (Bourgogne University, 1987).
Google Scholar 
Sacchi, E., Cifelli, R., Citton, P., Nicosia, U. & Romano, M. Dimetropus osageorum n. isp. from the Early Permian of Oklahoma (USA): A trace and its trackmaker. Ichnos 21, 175–192 (2014).
Google Scholar 
Buchwitz, M. & Voigt, S. On the morphological variability of Ichniotherium tracks and evolution of locomotion in the sistergroup of amniotes. PeerJ 6, e4346. https://doi.org/10.7717/peerj.4346 (2018).Article 
CAS 

Google Scholar 
Mujal, E., Marchetti, L., Schoch, R. R. & Fortuny, J. Upper Paleozoic to lower mesozoic tetrapod ichnology revisited: Photogrammetry and relative depth pattern inferences on functional prevalence of autopodia. Front. Earth Sci. 8(248), 1–23 (2020).
Google Scholar 
Lucas, S. G., Kollar, A. D., Berman, D. S. & Henrici, A. C. Pelycosaurian-grade (Amniota: Synapsida) footprints from the Lower Permian Dunkard Group of Pennsylvania and West Virginia. Ann. Carnegie Mus. 83(4), 287–294 (2016).
Google Scholar 
Haubold, H., Hunt, A. P., Lucas, S. G. & Lockley, M. G. Wolfcampian (Early Permian) vertebrate tracks from Arizona and New Mexico. N. M. Mus. Nat. Hist. Sci. Bull. 6, 135–165 (1995).
Google Scholar 
Meade, L. E., Jones, A. S. & Butler, R. J. A revision of tetrapod footprints from the late Carboniferous of the West Midlands, UK. PeerJ 4, e2718. https://doi.org/10.7717/peerj.2718 (2016).Article 

Google Scholar 
Haubold, H. Die Tetrapodenfährten des Buntsandsteins. Paläontologische Abhandlungen A. IV, 395–548 (1971).Gand, G. & Haubold, H. Traces de Vertébrés du Permien du bassin de Saint-Affrique (Description, datation, comparaison avec celles du bassin de Lodève). Géologie Méditerranéenne 11, 321–348 (1984).
Google Scholar 
Voigt, S., Niedźwiedski, G., Raczyński, P., Mastaler, K. & Ptaszyński, T. Early Permian tetrapod ichnofauna from the Intra-Sudetic Basin, SW Poland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 313–314, 173–180 (2012).
Google Scholar 
Niedźwiedzki, G. & Bojanowski, M. A supposed eupelycosaur body impression from the Early Permian of the Intra-Sudetic Basin, Poland. Ichnos Int. J. Plant Anim. Traces. 19(3), 150–155 (2012).
Google Scholar 
Marchetti, L. New occurrences of tetrapod ichnotaxa from the Permian Orobic Basin (Northern Italy) and critical discussion of the age of the ichnoassociation. Pap. Palaeontol. 2, 363–386. https://doi.org/10.1002/spp2.1045 (2016).Article 

Google Scholar 
Mujal, E. et al. Palaeoenvironmental reconstruction and early Permian ichnoassemblage from the NE Iberian Peninsula (Pyrenean Basin). Geol. Mag. 153, 578–600 (2016).ADS 

Google Scholar 
Matamales-Andreu, R., Mujal, E., Galobart, A. & Fortuny, J. Insights on the evolution of synapsid locomotion based on tetrapod tracks from the lower Permian of Mallorca (Balearic Islands, western Mediterranean). Palaeogeogr. Palaeoclimatol. Palaeoecol. 579, 110589 (2021).
Google Scholar 
Matamales-Andreu, R. et al. Early–middle Permian ecosystems of equatorial Pangaea: Integrated multi-stratigraphic and palaeontological review of the Permian of Mallorca (Balearic Islands, western Mediterranean. Earth Sci. Rev. 228, 103948 (2022).
Google Scholar 
Voigt, S., Lagnaoui, A., Hminna, A., Saber, H. & Schneider, J. W. Revisional notes on the Permian tetrapod ichnofauna from the Tiddas Basin, central Morocco. Palaeogeogr. Palaeoclimatol. Palaeoecol. 302, 474–483 (2011).
Google Scholar 
Voigt, S., Saber, H., Schneider, J. W., Hmich, D. & Hminna, A. Late Carboniferous-early Permian tetrapod ichnofauna from the Khenifra Basin, central Morocco. Geobios 44, 309–407 (2011).
Google Scholar 
Lagnaoui, A. et al. Late Carboniferous tetrapod footprints from the Souss Basin, Western High Atlas Mountains, Morocco. Ichnos https://doi.org/10.1080/10420940.2017.1320284 (2017).Article 

Google Scholar 
Fichter, J. Aktuopaläontologische Studien zur Lokomotion rezenter Urodelen und Lacertilier sowie paläontologische Untersuchungen an Tetrapodenfährten des Rotliegenden (Unter-Perm) SW-Deutschlands. PhD thesis. Johannes-Gutenberg University, Mainz (1979).Haubold, H. The Early Permian tetrapod ichnofauna of Tambach, the changing concepts in ichnotaxonomy. Hallesches Jahrb. Geowiss. B 20, 1–16 (1998).Haubold, H. Tetrapodenfährten aus dem Perm—Kenntnisstand und Progress 2000. Hallesches Jahrb. Geowiss. B 22, 1–16 (2000).Romano, M., Citton, P. & Nicosia, U. Corroborating trackmaker identification through footprint functional analysis: The case study of Ichniotherium and Dimetropus. Lethaia 49(1), 102–116. https://doi.org/10.1111/let.12136 (2016).Article 

Google Scholar 
Ford, D. P. & Benson, J. B. R. The phylogeny of early amniotes and the affinities of Parareptilia and Varanopidae. Nat. Ecol. Evol. 4, 57–65. https://doi.org/10.1038/s41559-019-1047-3 (2020).Article 

Google Scholar 
Modesto, S. P. Rooting about reptile relationships. Nat. Ecol. Evol. 4, 10–11 (2020).
Google Scholar 
Spindler, F. et al. First arboreal ’pelycosaurs’ (Synapsida: Varanopidae) from the early Permian Chemnitz Fossil Lagerstätte, SE Germany, with a review of varanopid phylogeny. PalZ. 92, 315–364 (2018).
Google Scholar 
Haubold, H. & Sarjeant, W. A. S. Tetrapodenfährten aus den Keele und Enville Groups (Permokarbon: Stefan und Autun) von Shropshire und South Staffordshire. Großbritannien. Z. geol. Wiss 1, 895–933 (1973).
Google Scholar 
Kümmell, S., Abdala, F., Sassoon, J. & Abdala, V. Evolution and identity of synapsid carpal bones. Acta Palaeontol. Pol. 65(4), 649–678 (2020).
Google Scholar 
Berman, D. S. et al. New primitive caseid (Synapsida, Caseasauria) from the Early Permian of Germany. Ann. Carnegie Museum 86(1), 47–74 (2020).
Google Scholar 
Spindler, F., Falconnet, J. & Fröbisch, J. Callibrachion and Datheosaurus, Two Historical and Previously Mistaken Basal Caseasaurian Synapsids From Europe. Acta Palaeontol. Pol. 61(3), 597–616 (2016).
Google Scholar 
Reisz, R. R., Madin, H. C., Fröbisch, J. & Falconnet, J. A new large caseid (Synapsida, Caseasauria) from the Permian of Rodez (France), including a reappraisal of “Casea” rutena Sigogneau-Russell & Russell, 1974. Geodiversitas 33(2), 227–246. https://doi.org/10.5252/g2011n2a2 (2011).Article 

Google Scholar 
Voigt, S. & Lucas, S. G. Permian tetrapod ichnodiversity of the Prehistoric Trackways National Monument (south-central New Mexico, USA). N. M. Mus. Nat. Hist. Sci. Bull. 65, 153–167 (2015).
Google Scholar 
Brand, L. R. Variations in salamander trackways resulting from substrate differences. J. Paleontol. 70, 1004–1010 (1996).
Google Scholar 
Krapovickas, V., Marsicano, C. A., Mancuso, A. C., de la Fuente, M. S. & Ottone, E. G. Tetrapod and invertebrate trace fossils from aeolian deposits of the lower Permian of central-western Argentina. Hist. Biol. 27, 827–842 (2015).
Google Scholar 
Benson, R. B. J. Interrelationships of basal synapsids: Cranial and postcranial morphological partitions suggest different topologies. J. Syst. Paleontol. 10, 601–624 (2012).
Google Scholar 
Spindler, F. The basal Sphenacodontia—Systematic revision and evolutionary implications. PhD Thesis, Technische Universität Bergakademie Freiberg, Germany (2015).Spindler, F. Re-evaluation of an early sphenacodontian synapsid from the Lower Permian of England. Earth Environ. Sci. Trans. R. Soc. Edinb. 111, 27–37 (2020).
Google Scholar 
Reisz, R. R. & Fröbisch, J. The oldest caseid synapsid from the Late Pennsylvanian of Kansas, and the evolution of herbivory in terrestrial vertebrates. PLoS ONE 9(4), e94518. https://doi.org/10.1371/journal.pone.00945 (2014) (1–9).Article 
ADS 

Google Scholar 
Werneburg, R., Spindler, F., Falconnet, J., Steyer, J.-S., Vianey-Liaud, M & Schneider, J. W. New caseid synapsid from the Permian (Guadalupian) of the Lodève basin (Occitanie, France). Palaeo Vertebrata 1–36 (2022).Ronchi, A., Sacchi, E., Romano, M. & Nicosia, U. A huge caseid pelycosaur from north-western Sardinia and its bearing on European Permian stratigraphy and palaeobiogeography. Acta Palaeontol. Pol. 56, 723–738 (2011).
Google Scholar 
Romano, M. & Nicosia, U. Alierasaurus ronchii, gen. et. Sp. nov., a caseid from the Permian of Sardinia, Italy. J. Vertebr. Paleontol. 34, 900–913 (2014).
Google Scholar 
Maddin, H. C., Sidor, C. A. & Reisz, R. R. Cranial anatomy of Ennatosaurus tecton (Synapsida: Caseidae) from the Middle Permian of Russia and the evolutionary relationships of Caseidae. J. Vertebr. Paleontol. 28, 160–180 (2008).
Google Scholar 
Langiaux, J., Parriat, H. & Sotty, D. Faune fossile du bassin de Blanzy-Montceau. La Physiophilie. 80, 55–67 (1974).
Google Scholar 
Gaudry, A. Sur un reptile très perfectionné trouvé dans le terrain permien. Comptes rendus hebdomadaires des Séances de l’Académie des Sciences. 91(16), 669–671 (1880).
Google Scholar 
Reisz, R. R. Handbuch der Paläoherpetologie. Teil 17A, Pelycosauria. (Gustav Fischer Verlag, 1986).Ziegler, J. et al. U-Pb ages of magmatic and detrital zircon of the Döhlen Basin: Geological history of a Permian strike-slip basin in the Elbe Zone (Germany). Int. J. Earth Sci. 108, 887–910 (2019).
Google Scholar  More

Lal, R. Soil Carbon sequestration impacts on global climate change and food security. Science 30, 1623–1627 (2004).ADS 

Google Scholar 
Stockmann, U. et al. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ. 164, 80–99 (2013).CAS 

Google Scholar 
Batjes, N. H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 47(2), 151–163 (1996).CAS 

Google Scholar 
Michalzik, B., Kalbitz, K., Park, J. H., Solinger, S. & Matzner, E. Fluxes and concentrations of dissolved organic carbon and nitrogen: A synthesis for temperate forests. Biogeochemistry 52, 173–205 (2001).
Google Scholar 
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 

Google Scholar 
Song, M. H. et al. Shifts in priming partly explain impacts of long-term nitrogen input in different chemical forms on soil organic carbon storage. Glob. Chang. Biol. 24, 4160–4172 (2018).ADS 

Google Scholar 
Okolo, C. C. et al. Priming effect in semi-arid soils of northern Ethiopia under different land use types. Biogeochemistry https://doi.org/10.1007/s10533-022-00905-z (2022).Article 

Google Scholar 
Eze, P. N., Udeigwe, T. K. & Stietiya, M. H. Distribution and potential source evaluation of heavy metals in prominent soils of Accra plains, Ghana. Geoderma 156(3–4), 357–362 (2010).ADS 
CAS 

Google Scholar 
Eze, P. N., Mbakwe, I. & Okolo, C. C. Ecosystem functions of the soil highlighted in Igbo proverbs. In IUSS Global Soil Proverbs: Cultural Language of the Soil (eds Yang, J. E. et al.) (Schweizerbart and Borntraeger Science Publishers, 2019).
Google Scholar 
Nottingham, A. T. et al. Adaptation of soil microbial growth to temperature: Using a tropical elevation gradient to predict future changes. Glob. Chang. Biol. 25, 827–838 (2019).ADS 

Google Scholar 
Paul, K. I., Polglase, P. J., Nyakuengama, J. G. & Khanna, P. K. Change in soil carbon following afforestation. Forest Ecol. Manag. 168, 241–257 (2002).
Google Scholar 
Batjes, N. H. Options for increasing carbon sequestration in West Africa soils: An exploratory study with special focus on Senegal. Land Degrad. Dev. 12, 131–142 (2001).
Google Scholar 
Powlson, D. S., Whitmore, A. P. & Goulding, K. W. T. Soil carbon sequestration to mitigate climate change: A critical re-examination to identify the true and the false. Eur. J. Soil Sci. 62, 42–55 (2011).CAS 

Google Scholar 
Zhang, K., Dang, H., Zhang, Q. & Cheng, X. Soil carbon dynamics following land-use change varied with temperature and precipitation gradients: Evidence from stable isotopes. Glob. Chang. Biol. 21, 2762–2772 (2015).ADS 

Google Scholar 
Gebresamuel, G. et al. Nutrient Balance of farming systems in tigray, Northern Ethiopia. J. Soil Sci. Plant Nutr. 21, 315–328 (2021).CAS 

Google Scholar 
IPCC, Climate Change: The physical science basis. Contribution of working Group I to the Fourth Assessment. In Report of the Intergovernmental Panel on Climate Change (Eds. Solomon, S., Quin, D and Manning, M). (Cambridge University Press, Cambridge, UK) (2007).Yang, Y. S., Xie, J. S. & Sheng, H. The impact of land use/cover change on storage and quality of soil organic carbon in mid-subtropical mountainous area of southern China. J. Geo. Sci. 19, 49–57 (2009).
Google Scholar 
Akinyemi, F. O., Tlhalerwa, L. T. & Eze, P. N. Land degradation assessment in an African dryland context based on the composite Land Degradation Index and mapping method. Geocarto Int. 36(16), 1838–1854 (2021).
Google Scholar 
Button, E. S. et al. Deep-C storage: Biological, chemical and physical strategies to enhance carbon stocks in agricultural subsoils. Soil Biol. Biochem. 170, 108697 (2022).CAS 

Google Scholar 
Rumpel, C. & Kögel-Knabner, I. Deep soil organic matter: A key but poorly understood component of terrestrial C cycle. Plant Soil 338(1), 143–158 (2011).CAS 

Google Scholar 
Lal, R., Lorenz, K., Huttle, R. F., Schneider, B. U. & Von, B. J. Terrestrial biosphere as a source and sink of atmospheric carbon dioxide. In Recarbonization of the Biosphere: Ecosystems and the Global Cycle (eds Lal, R. et al.) (Springer, 2012).
Google Scholar 
Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555–559 (2020).ADS 
CAS 

Google Scholar 
Salome, C., Nunan, N., Pouteau, V., Lerchw, T. Z. & Chenu, C. Carbon dynamics in topsoil and in subsoil may be controlled by different regulatory mechanisms. Glob. Chang. Biol. 16, 416–426 (2010).ADS 

Google Scholar 
Sithole, N. J., Magwaza, L. S. & Thibaud, G. R. Long-term impact of no-till conservation agriculture and N-fertilizer on soil aggregate stability, infiltration and distribution of C in different size fractions. Soil Tillage Res. 190, 147–156 (2019).
Google Scholar 
Tashi, S., Singh, B., Keitel, C. & Adams, M. Soil carbon and nitrogen stocks in forests along an altitudinal gradient in the eastern Himalayas and a meta-analysis of global data. Glob. Chang. Biol. 22, 2255–2268 (2016).ADS 

Google Scholar 
Zhou, Z., Wang, C. & Luo, Y. Effects of forest degradation on microbial communities and soil carbon cycling: A global meta-analysis. Global Ecol. Biogeography 27, 110–124 (2018).
Google Scholar 
Mhete, M., Eze, P. N., Rahube, T. O. & Akinyemi, F. O. Soil properties influence bacterial abundance and diversity under different land-use regimes in semi-arid environments. Sci. African 7, e00246 (2020).
Google Scholar 
Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Chang. 8, 885–889 (2018).ADS 
CAS 

Google Scholar 
Murty, D., Kirschbaum, M. U. F., Mcmurtrie, R. E. & Mcgilvray, H. Does conversion of forest to agricultural land change soil carbon and nitrogen? A review of the literature. Glob. Chang. Biol. 8, 105–123 (2002).ADS 

Google Scholar 
Veldkamp, E., Schmidt, M., Powers, J. S. & Corre, M. D. Deforestation and reforestation impacts on soils in the tropics. Nat. Rev. Earth Environ. 1, 590–605 (2020).ADS 

Google Scholar 
Kebonye, N. M., Eze, P. N., Ahado, S. K. & John, K. Structural equation modeling of the interactions between trace elements and soil organic matter in semiarid soils. Intl. J. Environ. Sci. Technol. 17(4), 2205–2214 (2020).CAS 

Google Scholar 
Del Galdo, L., Six, J., Peressotti, A. & Cotrufo, M. F. Assessing the impact of land-use change on soil C sequestration in agricultural soils by means of organic matter fraction and stable C isotopes. Glob. Chang. Biol. 9, 1204–1213 (2003).ADS 

Google Scholar 
Lal, R. Carbon sequestration in dry land ecosystems of West Asia and North Africa. Land Degrad. Dev. 13, 45–59 (2002).
Google Scholar 
Gebresamuel, G., Singh, B. R., Mitiku, H., Borresen, T. & Lal, R. Carbon Stocks in Ethiopian Soils in relation to land use and soil management. Land Degrad. Dev. 19(4), 351–367 (2008).
Google Scholar 
Fisseha, I., Mats, O. & Karl, S. Effect of land use changes on soil carbon status of some soil types in the Ethiopian Rift Valley. J. Drylands 4(1), 289–299 (2011).
Google Scholar 
Shiferaw, A., Hans, H. & Gete, Z. A review on soil carbon sequestration in Ethiopia to Mitigate land degradation and climate change. J. Environ. Earth Sci. 3(12), 187–201 (2013).
Google Scholar 
Bazezew, M. N., Teshome, S. & Eyale, B. Above- and below-ground reserved carbon in danaba community forest of Oromia Region, Ethiopia: Implications for CO2 emission balance. Am. J. Environ. Prot. 4(2), 75–82 (2015).
Google Scholar 
Berihu, T. et al. Soil carbon and nitrogen losses following deforestation in Ethiopia. Agron. Sust. Dev. 37, 1 (2017).CAS 

Google Scholar 
Gebresamuel, G. et al. Changes in soil organic carbon stock and nutrient status after conversion of pasture land to cultivated land in semi-arid areas of northern Ethiopia. Arch. Agron. Soil Sci. https://doi.org/10.1080/03650340.2020.1823372 (2022).Article 

Google Scholar 
Hoyle, F. C., Baldock, J. A. & Murphy, D. V. Soil organic carbon: Role in rainfed farming systems: With particular reference to Australian Conditions. In Rainfed Farming Systems (eds Tow, P. et al.) (Springer, 2011). https://doi.org/10.1007/978-1-4020-9132-2_14.Chapter 

Google Scholar 
Mekuria, W. et al. Restoration of degraded landscapes for ecosystem services in North-Western Ethiopia. Heliyon 4, e00764. https://doi.org/10.1016/j.heliyon.2018 (2018).Article 

Google Scholar 
Okolo, C. C. et al. Assessing the sustainability of land use management of Northern Ethiopian drylands by various indicators for soil health. Ecol. Indic. 112, 106092. https://doi.org/10.1016/j.ecolind.2020.106092 (2020).Article 
CAS 

Google Scholar 
WRB. International Union of Soil Science Working Group. In World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. FAO, Rome (2014).NMA 2018. National Metrological Agency (NMA), 2018. The National Metrological Agency of Ethiopia Mekelle center, Tigray Regional State, Mekelle, Ethiopia.Anikwe, M. A. N., Obi, M. E. & Agbim, N. N. Effect of crop and soil management practices soil compactibility in maize and groundnut plots in a Paleustult in Southeastern Nigeria. Plant Soils. 253, 457–465 (2003).CAS 

Google Scholar 
Anikwe, M. A. N. Carbon storage in soils of southeastern Nigeria under different management practices. Carbon Bal. Manag. https://doi.org/10.1186/1750-0680-5-5 (2010).Article 

Google Scholar 
IPCC Guidelines for National Greenhouse Gas Inventories. In Vol. 4: Agriculture, Forestry and other Land Use (eds. Eggleston, S., Buendia, K., Miwa, K., Ngara, T. and Tanabe, K.) (Institute for Global Environmental Strategies, 2006).McKenzie, N., Ryan, P., Fogarty, P. & Wood, J. Sampling, measurement and analytical protocols for carbon estimation in soil, litter and coarse woody debris. National Carbon Accounting System Technical Report No. 14. Australian Greenhouse Office, Canberra (2000).Nelson, D. W. & Sommers, L. E. Total carbon, total organic carbon and organic matter. In Methods of Soil Analysis. Part 3: Chemical Methods. Agronomy Monograph No. 9 (Ed. Sparks, D.L) 961–1010. (American Society of Agronomy, 1996).Bremner, J. M. & Mulvaney, C. S. Nitrogen-total. In Chemical and Microbiological Properties (eds Keeney, D. R. et al.) 595–624 (American Society of Agronomy and Soil Science Society of America, 1982).
Google Scholar 
McLean, E. O. Soil pH and lime requirement. In Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties. 2nd edn. Agronomy monograph No. 9 (Eds. Page, A.L., Miller, R.H and Keeney, D.R). 199–224. (American Society of Agronomy, 1982).Rhoades, J. D. Cation exchange capacity. In Methods of Soil Analysis: Part 2 Chemical and Microbial Properties. Agronomy Monograph No. 9. (Eds. Page, A.L., Miller, R.H and Keeney, D.R) pp. 149–157 (American Society of Agronomy, 1982).Blake, G. R. & Hartge, K. H. Bulk density. In Methods of Soil Analysis. Part 1: Physical and Mineralogical Properties. 2nd edn. Agronomy Monograph No. 9 (ed. Klute, A) 363–382. (American Society of Agronomy, 1986).Gee, G. W. & Bauder, J. W. Particle size analysis. In Methods of Soil Analysis. Part 1: Physical and Mineralogical Properties. 2nd edn. Agronomy Monograph No. 9. (Ed. A Klute) 91–100. (American Society of Agronomy, 1986).Gelaw, A. M., Singh, B. R. & Lal, R. Soil organic carbon and total nitrogen stocks under different land uses in a semi-arid watershed in Tigray, Northern Ethiopia. Agric. Ecosyst. Environ. 188, 256–263 (2014).
Google Scholar 
Puget, P. & Lal, R. Soil organic carbon and nitrogen in a Mollisol in Central Ohio as affected by tillage and land use. Soil Tillage Res. 80, 201–213 (2005).
Google Scholar 
Chan, Y. Increasing soil organic carbon of agricultural land. Primefact 735, 1–5 (2008).
Google Scholar 
Worku, G., Bantider, A. & Temesgen, H. Effects of land use/land cover change on some soil physical and chemical properties in Ameleke micro-watershed Gedeo and Borena Zones. South Ethiopia. J. Environ. Earth Sci. 4, 13–24 (2014).
Google Scholar 
Assefa, D. et al. Deforestation and land use strongly effect soil organic carbon and nitrogen stock in Northwest Ethiopia. CATENA 153, 89–99 (2017).CAS 

Google Scholar 
Gessesse, T. A., Khamzina, A., Gebresamuel, G. & Amelung, W. Terrestrial carbon stocks following 15 years of integrated watershed management intervention in semi-arid Ethiopia. CATENA 190, 104543 (2020).CAS 

Google Scholar 
Haileslassie, A., Priess, J., Veldkamp, E., Teketay, D. & Lesschen, J. P. Assessment of soil nutrient depletion and its spatial variability on smallholders’ mixed farming systems in Ethiopia using partial versus full nutrient balances. Agric. Ecosyst. Environ. 108, 1–16 (2005).
Google Scholar 
Lemenih, M., Lemma, B. & Teketay, D. Changes in soil carbon and total nitrogen following reforestation of previously cultivated land in the highlands of Ethiopia. Ethiopian J. Sci. 28(2), 99–108 (2005).
Google Scholar 
Lemenih, M., Karltun, E. & Olsson, M. Soil organic matter dynamics after deforestation along a farm field chronosequences in southern highlands of Ethiopia. Agric. Ecosyst. Environ. 109, 9–19 (2005).
Google Scholar 
Okebalama, C. B., Igwe, C. A. & Okolo, C. C. Soil organic carbon levels in soils of contrasting land uses in Southeastern Nigeria. Trop. Subtrop. Agroecosyst. 20, 493–504 (2017).CAS 

Google Scholar 
Nwite, J. N., Orji, J. E. & Okolo, C. C. Effect of different land use systems on soil carbon storage and structural indices in Abakaliki, Nigeria. Indian J. Ecol. 45(3), 522–527 (2018).
Google Scholar 
Don, A., Schumacher, J. & Freibauer, A. Impact of tropical land-use change on soil organic carbon stocks–a meta-analysis. Glob. Chang. Biol. 17, 1658–1670 (2011).ADS 

Google Scholar 
Zinn, Y. L., Marrenjo, G. J. & Silva, C. A. Soil C: N ratos are unresponsive to land use change in Brazil: A comparative analysis. Agric. Ecosyst. Environ. 255, 62–72 (2018).CAS 

Google Scholar 
Lou, Y. L., Xu, M. G., Chen, X. N., He, X. H. & Zhao, K. Stratification of soil organic C, N and C: N ratio as affected by conservation tillage in two maize fields of China. CATENA 95, 124–130 (2012).CAS 

Google Scholar 
Xiao, X., Kuang, X., Sauer, T. J., Heitman, J. L. & Horton, R. Bare soil carbon dioxide fluxes with time and depth determined by high-resolution gradient-based measurements and surface chambers. Soil Sci. Soc. Am. 79, 1073–1083 (2015).CAS 

Google Scholar 
Wang, X. et al. Forest soil profile inversion and mixing change the vertical stratification of soil CO2 concentration without altering soil surface CO2 Flux. Forests 10, 192 (2019).
Google Scholar 
Bates, C. T. et al. Conversion of marginal land into switchgrass conditionally accrues soil carbon but reduces methane consumption. ISME J. 16, 10 (2021).
Google Scholar 
Slessarev, E. W. et al. Quantifying the effects of switchgrass (Panicum virgatum) on deep organic C stocks using natural abundance 14C in three marginal soils. GCB Bioenergy 12, 834–847 (2020).CAS 

Google Scholar 
Balesdent, J., Besnard, E., Arrouays, D. & Chenu, C. The dynamics of carbon in particle size fractions of soil in a forest-cultivation sequence. Plant Soil 201, 49–57 (1998).CAS 

Google Scholar 
Birch, H. F. & Friend, M. T. The organ matter and nitrogen status of east African soils. J. Soil Sci. 7, 156–167 (1956).CAS 

Google Scholar 
Deng, L., Zhu, G., Tang, Z. & Shangguan, Z. Global patterns of the effects of land-usechanges on soil carbon stocks. Glob. Ecol. Conserv. 5, 127–138 (2016).
Google Scholar 
Post, W. M. & Kwon, K. C. Soil carbon sequestration and land-use change: Processes and potential. Glob. Chang. Biol. 6, 317–327 (2000).ADS 

Google Scholar 
Feng, X. & Simpson, M. J. Temperature responses of individual soil organic matter components. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2008JG000743 (2008).Article 

Google Scholar 
Chen, S., Huang, Y., Zou, J. & Shi, Y. Mean residence time of global topsoil organic carbon depends on temperature, precipitation and soil nitrogen. Glob. Planet. Chang. 100, 99–108 (2013).ADS 

Google Scholar 
Alemayehu, K. & Sheleme, B. Effects of different land use systems on selected soi properties in South Ethiopia. J. Soil Sci. Environ. Manag. 4(5), 100–107 (2013).
Google Scholar 
Bockheim, J. G. Soil endemism and its relation to soil formation theory. Geoderma 129, 109–124 (2005).ADS 

Google Scholar 
Ukaegbu, E. P., Osuaku, S. K. & Okolo, C. C. Suitability assessment of soils supporting oilpalm plantations in the coastal plains sand, Imo State Nigeria. Int. J. Agric. For. 5(2), 113–120 (2015).
Google Scholar 
Okolo, C. C. et al. Impact of open cast mine land use on soil physical properties in Enyigba, Southeastern Nigeria and the implication for sustainable land use management. Niger. J. Soil Sci. 25(1), 95–101 (2015).
Google Scholar 
Nwite, J. N. & Okolo, C. C. Soil water relations of an Ultisol amended with agro-wastes and its effect on grain yield of maize (Zea Mays L.) in Abakaliki, Southeastern Nigeria. Eur. J. Sci. Res. 141, 126–140 (2016).
Google Scholar 
Nwite, J. N. & Okolo, C. C. Organic carbon dynamics and changes in some physical properties of soil and their effect on grain yield of maize under conservative tillage practices in Abakaliki, Nigeria. Afr. J. Agric. Res. 12(26), 2215–2222 (2017).CAS 

Google Scholar 
Mbah, C. N., Njoku, C., Okolo, C. C., Attoe, E. & Osakwe, U. C. Amelioration of a degraded Ultisol with hardwood biochar: Effects on soil physico-chemical properties and yield of cucumber (Cucumis sativus L). Afr. J. Agric. Res. 12(21), 1781–1792 (2017).CAS 

Google Scholar 
Nandan, R. et al. Impact of conservation tillage in rice–based cropping systems on soil aggregation, carbon pools and nutrients. Geoderma 340, 104–114 (2019).ADS 
CAS 

Google Scholar 
Sharma, K.L. Effect of agroforestry systems on soil quality–monitoring and assessment. Central Research Institute for Dryland Agriculture. 2011. http://www.crida.in/DRM1-WinterSchool/KLS.pdf/. Accessed on 30 Dec 2018.Okolo, C. C., Gebresamuel, G., Zenebe, A., Haile, M. & Eze, P. N. Accumulation of organic carbon in various soil aggregate sizes under different land use systems in a semi-arid environment. Agric. Ecosyst. Environ. 297, 106924. https://doi.org/10.1016/j.agee.2020.106924 (2020).Article 
CAS 

Google Scholar 
Okolo, C. C., Gebresamuel, G., Retta, A. N., Zenebe, A. & Haile, M. Advances in quantifying soil organic carbon under different land uses in Ethiopia: A review and synthesis. Bull. Natl. Res. Cent. 43(99), 2019. https://doi.org/10.1186/s42269-019-0120-z (2019).Article 

Google Scholar  More

Heyes, B. Y. C. M. Social learning in animals: Categories and mechanisms. Biol. Rev. 69(2), 207–231. https://doi.org/10.1111/j.1469-185X.1994.tb01506.x (1994).Article 
CAS 

Google Scholar 
Hoppitt, W. & Laland, K. N. Social processes influencing learning in animals: A review of the evidence. Adv. Study Behav. 38, 105–165. https://doi.org/10.1016/S0065-3454(08)00003-X (2008).Article 

Google Scholar 
Kendal, R. L., Coolen, I. & Laland, K. N. Adaptive trade-offs in the use of social and personal information. In Cognitive Ecology II (eds Dukas, R. & Ratcliffe, J. M.) 249–271 (University of Chicago Press, 2009).Chapter 

Google Scholar 
Marshall-Pescini, S. & Whiten, A. Social learning of nut-cracking behavior in East African sanctuary-living chimpanzees (Pan troglodytes schweinfurthii). J. Comp. Psychol. 122(2), 186. https://doi.org/10.1037/0735-7036.122.2.186 (2008).Article 

Google Scholar 
Hobaiter, C., Poisot, T., Zuberbühler, K., Hoppitt, W. & Gruber, T. Social network analysis shows direct evidence for social transmission of tool use in wild chimpanzees. PLoS Biol. 12(9), e1001960. https://doi.org/10.1371/journal.pbio.1001960 (2014).Article 
CAS 

Google Scholar 
Coelho, C. G. et al. Social learning strategies for nut-cracking by tufted capuchin monkeys (Sapajus spp.). Anim. Cogn. 18(4), 911–919. https://doi.org/10.1007/s10071-015-0861-5 (2015).Article 
CAS 

Google Scholar 
Boyd, R. & Richerson, P. J. Culture and the evolutionary process (University of Chicago press, 1985).
Google Scholar 
Laland, K. N. Social learning strategies. Anim. Learn. Behav. 32(1), 4–14. https://doi.org/10.3758/BF03196002 (2004).Article 

Google Scholar 
Kendal, R. L. Animal ‘culture wars’: Evidence from the Wild?. Psychologist 21(4), 312–315 (2008).
Google Scholar 
Kendal, R. L., Kendal, J. R., Hoppitt, W. & Laland, K. N. Identifying social learning in animal populations: A new ‘option-bias’ method. PLoS ONE 4(8), e6541. https://doi.org/10.1371/journal.pone.0006541 (2009).Article 
ADS 
CAS 

Google Scholar 
Giraldeau, L. A., Valone, T. J. & Templeton, J. J. Potential disadvantages of using socially acquired information. Philos. Trans. R. Soc. Lond. Series B. 357(1427), 1559–1566. https://doi.org/10.1098/rstb.2002.1065 (2002).Article 

Google Scholar 
Kendal, R. L., Coolen, I., van Bergen, Y. & Laland, K. N. Trade-offs in the adaptive use of social and asocial learning. Adv. Study Behav. 35, 333–379. https://doi.org/10.1016/S0065-3454(05)35008-X (2005).Article 

Google Scholar 
Galef, B. G. Jr. Why behaviour patterns that animals learn socially are locally adaptive. Anim. Behav. 49(5), 1325–1334. https://doi.org/10.1006/anbe.1995.0164 (1995).Article 

Google Scholar 
Kendal, R. L. et al. Social learning strategies: Bridge-building between fields. Trends Cogn. Sci. 22(7), 651–665. https://doi.org/10.1016/j.tics.2018.04.003 (2018).Article 

Google Scholar 
Rendell, L. et al. Cognitive culture: Theoretical and empirical insights into social learning strategies. Trends Cogn. Sci. 15(2), 68–76. https://doi.org/10.1016/j.tics.2010.12.002 (2011).Article 

Google Scholar 
Dindo, M., Thierry, B. & Whiten, A. Social diffusion of novel foraging methods in brown capuchin monkeys (Cebus apella). Proc. R. Soc. B 275(1631), 187–193. https://doi.org/10.1098/rspb.2007.1318 (2008).Article 

Google Scholar 
Reader, S. M. & Biro, D. Experimental identification of social learning in wild animals. Learn. Behav. 38(3), 265–283. https://doi.org/10.3758/LB.38.3.265 (2010).Article 

Google Scholar 
Hoppitt, W. & Laland, K. N. Social Learning: An Introduction to Mechanisms, Methods, and Models (Princeton University Press, 2013).Book 

Google Scholar 
Byrne, R. W. & Russon, A. E. Learning by imitation: A hierarchical approach. Behav. Brain Sci. 21(5), 667–684. https://doi.org/10.1017/S0140525X9833174X (1998).Article 
CAS 

Google Scholar 
Kendal, R. L. et al. Evidence for social learning in wild lemurs (Lemur catta). Learn. Behav. 38(3), 220–234. https://doi.org/10.3758/LB.38.3.220 (2010).Article 

Google Scholar 
Lonsdorf, E. V. & Bonnie, K. E. Opportunities and constraints when studying social learning: Developmental approaches and social factors. Learn. Behav. 38(3), 195–205. https://doi.org/10.3758/LB.38.3.195 (2010).Article 

Google Scholar 
Coussi-korbel, S. & Fragaszy, M. On the relation between social dynamics and social learning. Anim. Behav. 50(6), 1441–1453. https://doi.org/10.1016/0003-3472(95)80001-8 (1995).Article 

Google Scholar 
Franz, M. & Nunn, C. L. Network-based diffusion analysis: A new method for detecting social learning. Proc. R. Soc. Lond B 276(1663), 1829–1836. https://doi.org/10.1098/rspb.2008.1824 (2009).Article 

Google Scholar 
Hoppitt, W., Boogert, N. J. & Laland, K. N. Detecting social transmission in networks. J. Theor. Biol. 263(4), 544–555. https://doi.org/10.1016/j.jtbi.2010.01.004 (2010).Article 
ADS 
MATH 

Google Scholar 
Hoppitt, W. & Laland, K. N. Detecting social learning using networks: A users guide. Am. J. Primatol. 73(8), 834–844. https://doi.org/10.1002/ajp.20920 (2011).Article 

Google Scholar 
Hasenjager, M. J., Leadbeater, E. & Hoppitt, W. Detecting and quantifying social transmission using network-based diffusion analysis. J. Anim. Ecol. 90(1), 8–26. https://doi.org/10.1111/1365-2656.13307 (2021).Article 

Google Scholar 
Schnoell, A. V. & Fichtel, C. Wild red-fronted lemurs (Eulemur rufifrons) use social information to learn new foraging techniques. Anim. Cogn. 15(4), 505–516. https://doi.org/10.1007/s10071-012-0477-y (2012).Article 

Google Scholar 
Coelho, C. Social Dynamics and Diffusion of Novel Behaviour Patterns in Wild Capuchin Monkeys (Sapajus libidinosus) Inhabiting the Serra da Capivara National Park. (Unpublished Doctoral Dissertation) (Durham University, 2015).
Google Scholar 
Claidière, N., Messer, E. J., Hoppitt, W. & Whiten, A. Diffusion dynamics of socially learned foraging techniques in squirrel monkeys. Curr. Biol. 23(13), 1251–1255. https://doi.org/10.1016/j.cub.2013.05.036 (2013).Article 
CAS 

Google Scholar 
van Leeuwen, E. J., Staes, N., Verspeek, J., Hoppitt, W. J. & Stevens, J. M. Social culture in bonobos. Curr. Biol. 30(6), R261–R262. https://doi.org/10.1016/j.cub.2020.02.038 (2020).Article 
CAS 

Google Scholar 
Canteloup, C., Hoppitt, W. & van de Waal, E. Wild primates copy higher-ranked individuals in a social transmission experiment. Nat. Commun. 11(1), 1–10. https://doi.org/10.1038/s41467-019-14209-8 (2020).Article 
CAS 

Google Scholar 
Kawai, M. Newly-acquired pre-cultural behavior of the natural troop of Japanese monkeys on Koshima Islet. Primates 6(1), 1–30. https://doi.org/10.1007/BF01794457 (1965).Article 

Google Scholar 
Huffman, M. A., Leca, J. B. & Nahallage, C. A. Cultured Japanese macaques: A multidisciplinary approach to stone handling behavior and its implications for the evolution of behavioral tradition in nonhuman primates. In The Japanese Macaques (eds Nakagawa, N. et al.) 191–219 (Springer, 2010). https://doi.org/10.1007/978-4-431-53886-8_9.Chapter 

Google Scholar 
Drapier, M. & Thierry, B. Social transmission of feeding techniques in Tonkean macaques?. Int. J. Primatol. 23(1), 105–122. https://doi.org/10.1023/A:1013201924975 (2002).Article 

Google Scholar 
Ducoing, A. M. & Thierry, B. Tool-use learning in Tonkean macaques (Macaca tonkeana). Anim. Cogn. 8(2), 103–113. https://doi.org/10.1007/s10071-004-0240-0 (2005).Article 

Google Scholar 
Ferrari, P. F. et al. Neonatal imitation in rhesus macaques. PLoS Biol. 4(9), e302. https://doi.org/10.1371/journal.pbio.0040302 (2006).Article 
CAS 

Google Scholar 
Leca, J. B., Gunst, N. & Huffman, M. A. The first case of dental flossing by a Japanese macaque (Macaca fuscata): Implications for the determinants of behavioral innovation and the constraints on social transmission. Primates 51(1), 13. https://doi.org/10.1007/s10329-009-0159-9 (2010).Article 

Google Scholar 
Macellini, S. et al. Individual and social learning processes involved in the acquisition and generalization of tool use in macaques. Philos. Trans. R. Soc. B 367(1585), 24–36. https://doi.org/10.1098/rstb.2011.0125 (2012).Article 
CAS 

Google Scholar 
Redshaw, J. Re-analysis of data reveals no evidence for neonatal imitation in rhesus macaques. Biol. Let. 15(7), 20190342. https://doi.org/10.1098/rsbl.2019.0342 (2019).Article 

Google Scholar 
Hook, M. A. et al. Inter-group variation in abnormal behavior in chimpanzees (Pan troglodytes) and rhesus macaques (Macaca mulatta). Appl. Anim. Behav. Sci. 76(2), 165–176. https://doi.org/10.1016/S0168-1591(02)00005-9 (2002).Article 

Google Scholar 
Watanabe, K., Urasopon, N. & Malaivijitnond, S. Long-tailed macaques use human hair as dental floss. Am. J. Primatol. 69(8), 940–944. https://doi.org/10.1002/ajp.20403 (2007).Article 

Google Scholar 
Gumert, M. D., Kluck, M. & Malaivijitnond, S. The physical characteristics and usage patterns of stone axe and pounding hammers used by long-tailed macaques in the Andaman Sea region of Thailand. Am. J. Primatol. 71(7), 594–608. https://doi.org/10.1002/ajp.20694 (2009).Article 

Google Scholar 
Tan, A. W., Hemelrijk, C. K., Malaivijitnond, S. & Gumert, M. D. Young macaques (Macaca fascicularis) preferentially bias attention towards closer, older, and better tool users. Anim. Cogn. 21(4), 551–563. https://doi.org/10.1007/s10071-018-1188-9 (2018).Article 

Google Scholar 
Bandini, E. & Tennie, C. Exploring the role of individual learning in animal tool-use. PeerJ 8, e9877. https://doi.org/10.7717/peerj.9877 (2020).Article 

Google Scholar 
Leca, J. B., Gunst, N., & Huffman, M. A. Japanese macaque cultures: Inter-and intra-troop behavioural variability of stone handling patterns across 10 troops. Behaviour, 251–281. https://www.jstor.org/stable/4536445 (2007).Tanaka, I. Matrilineal distribution of louse egg-handling techniques during grooming in free-ranging Japanese macaques. Am. J. Phys. Anthropol. 98(2), 197–201. https://doi.org/10.1002/ajpa.1330980208 (1995).Article 
CAS 

Google Scholar 
Tanaka, I. Social diffusion of modified louse egg-handling techniques during grooming in free-ranging Japanese macaques. Anim. Behav. 56(5), 1229–1236. https://doi.org/10.1006/anbe.1998.0891 (1998).Article 
CAS 

Google Scholar 
Whiten, A. & van de Waal, E. The pervasive role of social learning in primate lifetime development. Behav. Ecol. Sociobiol. 72(5), 1–16. https://doi.org/10.1007/s00265-018-2489-3 (2018).Article 

Google Scholar 
Widdig, A., Streich, W. J. & Tembrock, G. Coalition formation among male Barbary macaques (Macaca sylvanus). Am. J. Primatol. 50(1), 37–51. https://doi.org/10.1002/(SICI)1098-2345(200001)50:1%3c37::AID-AJP4%3e3.0.CO;2-3 (2000).Article 
CAS 

Google Scholar 
Thierry, B. Unity in diversity: Lessons from macaque societies. Evol. Anthropol. 16(6), 224–238. https://doi.org/10.1002/evan.20147 (2007).Article 

Google Scholar 
Berghänel, A., Ostner, J., Schröder, U. & Schülke, O. Social bonds predict future cooperation in male Barbary macaques, Macaca sylvanus. Anim. Behav. 81(6), 1109–1116. https://doi.org/10.1016/j.anbehav.2011.02.009 (2011).Article 

Google Scholar 
Carne, C., Wiper, S. & Semple, S. Reciprocation and interchange of grooming, agonistic support, feeding tolerance, and aggression in semi-free-ranging Barbary macaques. Am. J. Primatol. 73(11), 1127–1133. https://doi.org/10.1002/ajp.20979 (2011).Article 

Google Scholar 
Molesti, S. & Majolo, B. Cooperation in wild Barbary macaques: Factors affecting free partner choice. Anim. Cogn. 19(1), 133–146. https://doi.org/10.1007/s10071-015-0919-4 (2016).Article 

Google Scholar 
Rebout, N., Desportes, C. & Thierry, B. Resource partitioning in tolerant and intolerant macaques. Aggress. Behav. 43(5), 513–520. https://doi.org/10.1002/ab.21709 (2017).Article 

Google Scholar 
Amici, F., Caicoya, A. L., Majolo, B. & Widdig, A. Innovation in wild Barbary macaques (Macaca sylvanus). Sci. Rep. 10(1), 1–12. https://doi.org/10.1038/s41598-020-61558-2 (2020).Article 
CAS 

Google Scholar 
Fischer, J. Emergence of individual recognition in young macaques. Anim. Behav. 67(4), 655–661. https://doi.org/10.1016/j.anbehav.2003.08.006 (2004).Article 

Google Scholar 
Seyfarth, R. M. & Cheney, D. L. Production, usage, and comprehension in animal vocalizations. Brain Lang. 115(1), 92–100. https://doi.org/10.1016/j.bandl.2009.10.003 (2010).Article 

Google Scholar 
Garcia-Nisa, I. Communication and cultural transmission in populations of semi free-ranging Barbary macaques (Macaca sylvanus). (Doctoral dissertation). Durham University, United Kingdom. http://etheses.dur.ac.uk/14140/ (2021).Hoppitt, W. The conceptual foundations of network-based diffusion analysis: Choosing networks and interpreting results. Philos. Trans. R. Soc. B 372(1735), 20160418. https://doi.org/10.1098/rstb.2016.0418 (2017).Article 

Google Scholar 
Hikami, K., Hasegawa, Y. & Matsuzawa, T. Social transmission of food preferences in Japanese monkeys (Macaca fuscata) after mere exposure or aversion training. J. Comp. Psychol. 104(3), 233. https://doi.org/10.1037/0735-7036.104.3.233 (1990).Article 
CAS 

Google Scholar 
Deaner, R. O., Khera, A. V. & Platt, M. L. Monkeys pay per view: Adaptive valuation of social images by rhesus macaques. Curr. Biol. 15(6), 543–548. https://doi.org/10.1016/j.cub.2005.01.044 (2005).Article 
CAS 

Google Scholar 
Gariépy, J. F. et al. Social learning in humans and other animals. Front. Neurosci. 8, 58. https://doi.org/10.3389/fnins.2014.00058 (2014).Article 

Google Scholar 
Barrett, B. J., McElreath, R. L. & Perry, S. E. Pay-off-biased social learning underlies the diffusion of novel extractive foraging traditions in a wild primate. Proc. R. Soc. B 284(1856), 20170358. https://doi.org/10.1098/rspb.2017.0358 (2017).Article 

Google Scholar 
Kuester, J. & Paul, A. Group fission in Barbary macaques (Macaca sylvanus) at Affenberg Salem. Int. J. Primatol. 18(6), 941–966. https://doi.org/10.1023/A:1026396113830 (1997).Article 

Google Scholar 
Whitehead, H. Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis (University of Chicago Press, 2008).Book 

Google Scholar 
Hoppitt, W. (2011). NBDA User Guide V1.2. https://lalandlab.st-andrews.ac.uk/freeware/ 28 Sept 2016.Fleiss, J. L., Levin, B. & Paik, M. C. Statistical Methods for Rates and Proportions 3rd edn. (Wiley, 2003).Book 
MATH 

Google Scholar 
McHugh, M. L. Interrater reliability: the kappa statistic. Biochemia medica: Biochemia medica, 22(3), 276–282. https://hrcak.srce.hr/89395 (2012).Hair, J. F., Anderson, R. E., Babin, B. J. & Black, W. C. Multivariate Data Analysis: A Global Perspective Vol. 7 (Pearson Education, 2010).
Google Scholar 
Campbell, L. A., Tkaczynski, P. J., Lehmann, J., Mouna, M. & Majolo, B. Social thermoregulation as a potential mechanism linking sociality and fitness: Barbary macaques with more social partners form larger huddles. Sci. Rep. 8(1), 1–8. https://doi.org/10.1038/s41598-018-24373-4 (2018).Article 
CAS 

Google Scholar 
Barrett, L., Henzi, S. P., Weingrill, T., Lycett, J. E. & Hill, R. A. Market forces predict grooming reciprocity in female baboons. Proc. R. Soc. Lond. Ser. B 266(1420), 665–670. https://doi.org/10.1098/rspb.1999.0687 (1999).Article 

Google Scholar 
Henzi, S. P. et al. Effect of resource competition on the long-term allocation of grooming by female baboons: Evaluating Seyfarth’s model. Anim. Behav. 66(5), 931–938. https://doi.org/10.1006/anbe.2003.2244 (2003).Article 

Google Scholar 
Ueno, M. & Nakamichi, M. Grooming facilitates huddling formation in Japanese macaques. Behav. Ecol. Sociobiol. 72(6), 1–10. https://doi.org/10.1007/s00265-018-2514-6 (2018).Article 

Google Scholar 
Carter, A. J., Tico, M. T. & Cowlishaw, G. Sequential phenotypic constraints on social information use in wild baboons. Elife 5, e13125. https://doi.org/10.7554/eLife.13125.001 (2016).Article 

Google Scholar 
Barelli, C., Reichard, U. H. & Mundry, R. Is grooming used as a commodity in wild white-handed gibbons, Hylobates lar?. Anim. Behav. 82(4), 801–809. https://doi.org/10.1016/j.anbehav.2011.07.012 (2011).Article 

Google Scholar 
Schülke, O., Dumdey, N. & Ostner, J. Selective attention for affiliative and agonistic interactions of dominants and close affiliates in macaques. Sci. Rep. 10(1), 1–8. https://doi.org/10.1038/s41598-020-62772-8 (2020).Article 
CAS 

Google Scholar 
Heesen, M., Macdonald, S., Ostner, J. & Schülke, O. Ecological and social determinants of group cohesiveness and within-group spatial position in wild Assamese macaques. Ethology 121(3), 270–283. https://doi.org/10.1111/eth.12336 (2015).Article 

Google Scholar 
Ortiz, K. M. Female feeding competition in a folivorous primate (Propithecus verreauxi) with formalized dominance hierarchies: contest or scramble? (Doctoral dissertation). University of Texas, USA. https://repositories.lib.utexas.edu/handle/2152/34120 (2015).Jurczyk, V., Fröber, K. & Dreisbach, G. Increasing reward prospect motivates switching to the more difficult task. Mot. Sci. 5(4), 295–313. https://doi.org/10.1037/mot0000119 (2019).Article 

Google Scholar 
Rathke, E. M. & Fischer, J. Differential ageing trajectories in motivation, inhibitory control and cognitive flexibility in Barbary macaques (Macaca sylvanus). Philos. Trans. R. Soc. B 375(1811), 20190617. https://doi.org/10.1098/rstb.2019.0617 (2020).Article 

Google Scholar 
Kendal, R. et al. Chimpanzees copy dominant and knowledgeable individuals: Implications for cultural diversity. Evol. Hum. Behav. 36(1), 65–72. https://doi.org/10.1016/j.evolhumbehav.2014.09.002 (2015).Article 

Google Scholar 
van de Waal, E., Claidière, N. & Whiten, A. Social learning and spread of alternative means of opening an artificial fruit in four groups of vervet monkeys. Anim. Behav. 85(1), 71–76. https://doi.org/10.1016/j.anbehav.2012.10.008 (2013).Article 

Google Scholar 
Luncz, L. V. & Boesch, C. Tradition over trend: Neighboring chimpanzee communities maintain differences in cultural behavior despite frequent immigration of adult females. Am. J. Primatol. 76(7), 649–657. https://doi.org/10.1002/ajp.22259 (2014).Article 

Google Scholar 
van Leeuwen, E. J., Acerbi, A., Kendal, R. L., Tennie, C. & Haun, D. B. A reappreciation of ‘conformity’. Anim. Behav. 122, e5–e10. https://doi.org/10.1016/j.anbehav.2016.09.010 (2016).Article 

Google Scholar 
Horner, V. & Whiten, A. Causal knowledge and imitation/emulation switching in chimpanzees (Pan troglodytes) and children (Homo sapiens). Anim. Cogn. 8(3), 164–181. https://doi.org/10.1007/s10071-004-0239-6 (2005).Article 

Google Scholar 
Wood, L. The influence of model-based biases and observer prior experience on social learning mechanisms and strategies. (Doctoral dissertation). Durham University, United Kingdom. http://etheses.dur.ac.uk/7274/ (2013).van Leeuwen, E. J., Cronin, K. A., Schütte, S., Call, J. & Haun, D. B. Chimpanzees (Pan troglodytes) flexibly adjust their behaviour in order to maximize payoffs, not to conform to majorities. PLoS ONE 8(11), e80945. https://doi.org/10.1371/journal.pone.0080945 (2013).Article 
CAS 

Google Scholar 
Vale, G. L., Flynn, E. G., Lambeth, S. P., Schapiro, S. J. & Kendal, R. L. Public information use in chimpanzees (Pan troglodytes) and children (Homo sapiens). J. Comp. Psychol. 128(2), 215–223. https://doi.org/10.1037/a0034420 (2014).Article 

Google Scholar 
Canteloup, C., Cera, M. B., Barrett, B. J. & van de Waal, E. Processing of novel food reveals payoff and rank-biased social learning in a wild primate. Sci. Rep. 11(1), 1–13. https://doi.org/10.1038/s41598-021-88857-6 (2021).Article 
CAS 

Google Scholar 
Boccaletti, S. et al. The structure and dynamics of multilayer networks. Phys. Rep. 544(1), 1–122. https://doi.org/10.1016/j.physrep.2014.07.001 (2014).Article 
ADS 
CAS 

Google Scholar 
Kivela, M. et al. Multilayer networks. J. Complex Netw. 2(3), 203e271. https://doi.org/10.1093/comnet/cnu016 (2014).Article 

Google Scholar 
Snijders, L. & Naguib, M. Communication in animal social networks: A missing link. Adv. Study Behav. 49, 297–359. https://doi.org/10.1016/bs.asb.2017.02.004 (2017).Article 

Google Scholar 
Finn, K. R., Silk, M. J., Porter, M. A. & Pinter-Wollman, N. The use of multilayer network analysis in animal behaviour. Anim. Behav. 149, 7–22. https://doi.org/10.1016/j.anbehav.2018.12.016 (2019).Article 

Google Scholar  More

Study areaThe study area is located in Lintong District, Xi’an City, Shaanxi Province, China (34° 21′ 59.94″, 109° 12′ 51.012″) (Meteorologists, 2020b). The study area is located in northwestern China (Fig. 1), which is a Warm temperate semi-humid continental climate with distinct cold, warm, dry and wet seasons. Winter is cold, windy, foggy, and with little rain or snow. Spring is warm, dry, windy, and variable. The summer is hot and rainy, with prominent droughts and thunderstorms, and high wind. Autumn is cool, the temperature drops rapidly and autumn showers are obvious. The annual average temperature is 13.0–13.7 °C, the coldest January average temperature is −1.2–0 °C, the hottest July average temperature is 26.3–26.6 °C, the annual extreme minimum temperature is −21.2 °C, Lantian December 28, 1991, the annual extreme maximum temperature is 43.4 °C, Chang’an June 19, 1966. Annual precipitation is 522.4–719.5 mm, increasing from north to south. July and September are the two obvious peak precipitation months. The annual sunshine hours range from 1646.1 to 2114.9 h. The dominant wind direction varies from place to place, with the northeast wind in Xi’an, west wind in Zhouzhi and Huxian, east-northeast wind in Gaoling and Lintong, southeast wind in Chang’an, and northwest wind in Lantian. Meteorological disasters include drought, continuous rain, heavy rain, flooding, urban flooding, hail, gale, dry hot wind, high temperature, lightning, sand and dust, fog, haze, cold wave, and low-temperature freeze.
Figure 1Location of the field of study (The satellite imagery supporting this study was obtained using Baidu Maps (Android version—16.4.0.1195). The URL is (https://map.baidu.com/@14256795.568410998,5210675.606268121,8.67z.).Full size imageWheat (XiNong 805) was planted on September 24, 2019 and matured for harvest on May 28, 2020 (We warrant that we have the right to collect and manage wheat (XiNong 805). In addition, the study is in compliance with relevant institutional, national, and international guidelines.). Among the six strategies in the experiment (Table 1), we focused on strategies 1 and 4, fixed irrigation dates optimization and fixed fertilizer application dates optimization. Based on the custom of the study area, three days of diffuse irrigation were selected for Strategy 1. Three days of fertilization of the urea and three days of irrigation were selected for Strategy 4. The best practice for Strategy 1 was total irrigation of 201 mm for the total season and a total of 7388 kg/ha of wheat was obtained for this simulation, while the best practice for Strategy 4 was total irrigation of 197 mm for the total season and a total fertilizer application of 282 kg/ha for the total season. A total of 7894 kg/ha of wheat was obtained for this simulation.Table 1 Details of the 6 strategies of the experimental setup.Full size tableDSSAT modelDSSAT, one of the most widely used crop growth models, is an integrated computer system developed by the University of Hawaii under the authority of the U.S. Agency for International Development (USAID). It aims to aggregate various crop models and standardize the format of model input and output variables to facilitate the diffusion and application of models7, thereby accelerating the diffusion of agricultural technology and providing decision making and countermeasures for the rational and efficient use of natural resources in developing countries.
The DSSAT 4.5 model integrates all crop models into the simulation pathway-based CSM (Cropping System Model) farming system model, which uses a set of simulated soil moisture, nitrogen, and carbon dynamics codes, while crop growth and development are stimulated through the CERES37,38, CROPGRO39, CROPSIM, and SUBSOR modules. DSSAT is applicable to single sites or same type zones and can be extrapolated to the regional level through Geographic Information System (GIS).DSSAT–CSM simulates the growth process of crops grown on a uniform land area under prescribed or simulated management40, and the changes in soil water, carbon and nitrogen with under tillage systems. The DSSAT model is a decision support system supported by crop simulation models, which, in addition to data support, provides methods for calculating and solving problems, and provides decision-maker with the results of their decisions. It also provides scientific decisions for farmers to provide different cultivation management measures (e.g., proper fertilization and irrigation for crops) in different climatic years.Inputs and outputs of the modelThe DSSAT model has four main user-editable input files and various output files. The input files include crop management7,41, soil, weather, and cultivar parameter files; the output files include three types: (1) output files, (2) seasonal output files, and (3) diagnostic and management files.Crop management data: Crop management data provides basic information about crop growth. Detailed and accurate parameter provision is the basis for improving the accuracy of model simulation. Crop management parameters include crop variety, soil type, meteorological name, previous season crop, sowing period, sowing density, sowing depth, irrigation amount and time, fertilizer application amount and time, the initial condition of the soil, pest management, tillage frequency and method, etc. Some of these parameters are not easily available in field experiments and can be obtained from other test sites or from existing documentation. On the other hand, if there are missing values in the model, it will increase the simulation error of the model (this situation is hard to avoid). Therefore, in this study, the parameters were selected based on the principle of being both detailed and easily available.Soil data Soil data contains various parameters of the soil section plane, including soil color, soil slope, soil capacity, organic carbon, soil nitrogen content, drainage properties, the proportion of clay, particles, and stones in the soil. Similar to the governing documents, the more complete the parameters the smaller the error value of the simulation. The various physical and chemical properties of the soil for this study were obtained from the China Soil Database at the time of the study. The various physical and chemical properties of the soil for this study were obtained from the China Soil Database.Weather data The DSSAT model uses daily weather data as weather input data for the model. The model requires a minimum of four daily weather data in order to accurately simulate the water cycle in soil plants (Fig. 2). These are:(1) daily solar radiation energy (MJM); (2) daily maximum temperature (°C); (3) daily minimum temperature (°C); and (4) daily precipitation (mm). Weather data were obtained from the China Meteorological Administration. Weather data were obtained from the China Meteorological Administration.Figure 2Precipitation and maximum and minimum temperatures during 2019–2020.Full size imageModel calibration Adjusting the cultivar parameter is very important to accurately simulate the local growing environment. In this experiment, we collected field data for 2019 and 2020, and adjusted the parameters in the cultivar parameter files by trial-and-error method to make the simulation process more closely match the actual local crop growth process.Multi-objective optimization algorithmMulti-objective optimization techniques have been successfully applied in many real-world problems. In general42,43,44, MOPs produce a set of optimal solutions that together represent a trade-off between conflicting objectives, and such solutions are called Pareto optimal solutions (PS). These PS cannot make any solution better without compromising the other solutions. Therefore, when solving multi-objective problems, more PS are needed to find. Some MOPs aim to find all PS or at least a representative subset of them.A multi-objective optimization problem can be stated as follows:$$mathrm{min }Fleft(xright)={({f}_{1}left(xright),dots ,{f}_{k}(x))}^{T}$$
(1)
$$mathrm{subject;to};xin Omega$$
(2)
where (Omega) is the decision space,(F:Omega to {R}^{k}) consists of (k) real-value objective functions and ({R}^{k}) is called the objective space. The attainable objective set is defined as the set ({F(x)in Omega }).NSGA-II optimizerWe use non-dominated sorting genetic algorithm (NSGA-II) for Multiobjective optimization in R language. The NSGA-II algorithm is a classical multi-objective evolutionary algorithm with remarkable results in solving 2-objective and 3-objective problems45. It maintains the convergence speed and diversity of solutions by fast non-dominated sorting and crowding distance, selects the next population by elite selection strategy.Objective functionThe multi-objective optimization problem varies one or more variables to maximize or minimize two or more objective problems. In the case of crop production, where decision-makers change irrigation and fertilizer application to maximize benefits, this study focuses on when to apply irrigation or fertilizer on the field and how much irrigation or nitrogen fertilizer to apply.There are many crop models available that can be used as optimization objective functions, and DSSAT is definitely the best choice because it is easy to use and well-proven36. The user runs the model by entering defined soil, weather, variety, and crop management files, which are fed into the core of the model, the Crop Simulation Model (CSM). The model simulates the growth, development, and yield of crops grown on a uniform land area under management, as well as changes in soil water, carbon, and nitrogen over time under cropping systems. The CSM itself is a highly modular model system consisting of a number of sub-modules. Researchers have validated the output of these sub-modules as a whole under various crops, climate, and soil conditions.Using DSSAT, it is easy to design a set of objective functions and optimize them, as in our case.$$mathrm{Max}:Y=mathrm{DSSAT}left.left( {i}_{a0},dots ,{i}_{mathrm{aj}},{f}_{mathrm{a}0},dots ,{f}_{mathrm{ad}},{D}_{i}right.right)$$
(3)
$$mathrm{Min}:I=sum_{n=0}^{j}{i}_{an}$$
(4)
$$mathrm{Min}:F=sum_{m=0}^{d}{f}_{am}$$
(5)
where (Y) is yield,(I) is the total amount of irrigation, (F) is the total amount of nitrogen application, ({i}_{an}) is the amount of irrigation at one time, ({f}_{am}) is the amount of nitrogen applied at one time, (j) is a number of applications of irrigation, and (d) is a number of nitrogen applications. ({D}_{i}) is a random date combination of irrigation time and fertilizer application time.All other variables (e.g., climate, soil, location, crop variety) are kept constant during the optimization process. The irrigation unit is mm and the nitrogen application unit is kg/ha, the irrigation and nitrogen application amounts are positive integers by default (integer arithmetic reduces the program running time).Data-driven evolutionary algorithmsIn general, the key to DDEAs is to reduce the required FEs and assist evolution through data. The data is generally utilized through surrogate model. The use of suitable surrogate model can be used in place of real FEs46. Thus, DDEAs have more advantages over EAs in solving expensive problems.In terms of algorithmic framework, DDEAs contain two parts: surrogate model management (SMM) and evolutionary optimization part (EOP)47,48. The SMM part is used in order to obtain better approximations, while EOPs will use surrogate models in EAs to assist evolution. DDEAs can be divided into two types: online DDEAs and offline DDEAs23. Online DDEAs can be evaluated by real FEs with more new data. This new information can provide SMM with more information and construct a more accurate surrogate model49. Since DSSAT can obtain new data through FEs during the EOP process, the method used in this paper is online DDEAs. In contrast, offline DDEAs can only drive evolution through historical data.Radial Basis Function (RBF) network is a single hidden layer feedforward neural network that uses a radial basis function as the activation function for the hidden layer neurons, while the output layer is a linear combination of the outputs of the hidden layer neurons. RBF was used to approximate each objective function. According to the investigation of multi-objective optimization problems with high computational cost, radial basis functions are often used as the surrogate model, mainly because RBF networks can approximate arbitrary nonlinear functions with arbitrary accuracy and have global approximation capability, which fundamentally solves the local optimum problem of BP networks, and the topology is compact, the structural parameters can be learned separately, and the convergence speed is fast.In this paper, a new data-driven approach is proposed and place it in the lower-level optimization of the framework. RBF is utilized as the surrogate model and NSGA-II as the optimizer. Details are described in Algorithm 1.Data-driven method details
In step 1, the initial parent population is generated by randomly selecting points and the size is (N). In step 2, we run DSSAT (N) times to determine the objective function values of the (N) initial population solutions. Next, the algorithm then loops through the generations. At the beginning of each loop, surrogate models, which one objective train one surrogate and denoted by ({s}_{t}^{left({f}_{1}right)}) , were trained by the already obtained objective function values (step 3.1). The trial offspring ({P}_{i}^{^{prime}}left(tright)={ {x}_{1}^{^{prime}}left(tright),dots ,{x}_{u}^{^{prime}}left(tright)}) are generated by SBX and PM (step 3.2), then the trained surrogate model is used to predict the objective function values of trial offspring (step 3.3). The predicted objective function values are sorting by Pareto non-dominated and crowding distance (step 3.4), then (r) offspring (Q_{i} left( t right) = left{ {x^{primeprime}_{1} left( t right), ldots ,x^{primeprime}_{r} left( t right)} right}) are selected from the trial offspring (step 3.5).The offspring are evaluated by the DSSAT (step 3.6), and after combining the parent population and offspring population (step 3.7), the new parent population are selected by Pareto non-dominated and crowding distance sorting (step 3.8).Maximum extension distanceMED guides a small number of individuals to approximate the entire PF. MED is defined as follow:$$mathrm{MED}left({P}_{t}^{left(qright)}right)=mathrm{ND}left({P}_{t}^{left(qright)}right)times mathrm{TD}left({P}_{t}^{left(qright)}right)$$
(6)
where$$mathrm{ND}left({P}_{t}^{left(qright)}right)=underset{z,qne z}{mathrm{min}}sum_{m=1}^{M}left|{f}_{m}^{left(qright)}-{f}_{m}^{left(zright)}right|$$$$mathrm{TD}left({P}_{t}^{left(qright)}right)=sum_{z=1}^{P}sum_{m=1}^{M}left|{f}_{m}^{left(qright)}-{f}_{m}^{left(zright)}right|$$({P}_{t}^{left(qright)}) is the qth individual in population Pt at the tth generation. (mathrm{ND}left({P}_{t}^{left(qright)}right)) calculates the minimum distance between ({P}_{t}^{left(qright)}) and ({P}_{t}^{left(zright)}). The larger (mathrm{ND}left({P}_{t}^{left(qright)}right)) value means a better individual diversity. (mathrm{TD}left({P}_{t}^{left(qright)}right)) calculates the summation of distance between ({P}_{t}^{left(qright)}) and ({P}_{t}^{left(zright)}). The larger (mathrm{TD}left({P}_{t}^{left(qright)}right)) value means that the solution ({P}_{t}^{left(qright)}) has moved away from other individuals. A larger MED value means that an individual extends the overall boundary and an individual acquires better diversity.Modeling processTo maximize crop yield and optimize the use efficiency of water and fertilizer in a given environment, BSBOP framework is proposed. Crop growth is simulated by DSSAT, the data-driven approach reduces the runtime of the overall framework while finding optimal management strategies. The overall framework includes four main parts: upper-level screening, upper-level optimization, lower-level optimization and lower-level screening (Fig. 3).Figure 3Proposed integrated bi-level screening, bi-level optimization and DSSAT framework.Full size imageUpper-level screening The weather file in DSSAT was loaded by R language. The data are pre-processed with precipitation and solar radiation information to narrow down the date range for irrigation and fertilizer application. In other words, the date ranges for selecting irrigation and fertilization are restricted by the ULS.Upper-level optimization Generating random combinations of dates by the Latin hypercube sampling method (LHS). The upper-level screening starts with referencing the two variables (number of irrigation and nutrient application events). LHS uses these variables to generate a series of uniformly distributed random day combinations. For example, date combinations generated by the LHS could be May 15, July 18 and August 1 for irrigation and May 30, June 30 and July 18 for nutrient application. From the series of uniformly distributed random day combinations, one will be selected and incorporated into the lower-level optimization.Lower-level optimization The agricultural management strategy is optimized by the online data-driven approach proposed in Algorithm 1. Assuming three irrigation and three nitrogen application events are given, these events will be incorporated into the LOP, which consists of the RBF and NSGA-II. The population size of this paper is 105. The number of iterations varies according to the different strategies, and the objective function values are calculated by DSSAT. The main idea of applying Evolutionary multi-objective algorithms(EMO) and RBF to DSSAT is to generate a large number of trial offspring by traditional Simulated Binary Crossover (SBX) and Polynomial Mutation (PM), and then evaluate them using the trained surrogate model50. The objective values of the evaluation were then ranked by Pareto non-dominated and crowding distance, and the top 105 individuals were selected from a large number of trial offspring, after which a small number of individuals out of 105 were selected by Maximum Extension Distance (MED) for real function evaluation, and then combine the parents and offspring to select the next generation of parents by Pareto non-dominated and crowding distance sorting. Furthermore, in the numerical experiments, to ensure the superiority of the algorithm and reduce the experimental complexity, we use a relatively simple radial basis function (RBF) surrogate. The NSGA-II algorithm can be used for both bi-objective and tri-objective problems, so it can optimize the system by starting with the most critical objective and then adding additional objectives. For each solution in the population, the objective functions (1: maximize yield, 2: minimize irrigation application, 3: minimize nitrogen fertilizer application) will be evaluated by invoking the DSSAT model for these dates and the amount of fertilizer irrigation applied. Populations will be tested against the termination criteria (maximum number of iterations allowed). If the termination criteria are not satisfied, the population evolves and is re-evaluated again. The process is repeated until the termination criterion is satisfied and then the local Pareto front of the selected day combination is stored. After each iteration of the UOP, the new local Pareto is combined with the global Pareto frontier. In the next step, if there are any remaining day combinations, the above process is repeated for each new day combination until all generated random day combinations have been processed.Lower-level screening Firstly, the K-means method is used to screen the global Pareto solutions with higher yield. Then, secondary screening takes economic efficiency as the objective and optimizes it by Differential Evolution (DE) algorithm. Finally, the locally appropriate solution is intelligently selected.Optimization strategies and configurationDue to the complexity of the problem, a BSBOP framework was proposed in this study. Due to a large number of variables behind irrigation and fertilization, traversal date for optimization appears to be particularly difficult and time-consuming, assuming that only irrigation is optimized for 120 days of the growth cycle and the decision-maker has 0-150 mm of water per day, then there are ({151}^{120}) different solutions. If both irrigation and fertilization are considered, then there are ({151}^{120}cdot {151}^{120}) different solutions. Therefore, this study tries to reduce the number of variables while minimizing the running time of the algorithm.Here we hypothesize that more precision and effective agricultural management can be implemented through the proposed framework. Not only can crop yields be increased, but also irrigation application and fertilizer application can be reduced, while the solutions obtained have important guidance for decision-makers: such as the selection of irrigation and fertilizer application dates during the growing season of the crop, the selection of irrigation and fertilizer application amounts, and the relationship between economic benefits and application costs. To test this hypothesis, different optimization strategies were developed and evaluated (Table 1). Each optimization strategy was aimed at maximizing yield while minimizing resource wastage.The various strategies are listed below (Table 1). Strategy 1—Fixed irrigation dates: Keeping the number of irrigation days and all parameters constant, only the amount of irrigation on each date is changed, trying to reduce the amount of irrigation as much as possible, make it easy to compare the results with best practices. Strategy 2—Optimal irrigation dates: Traverse through the irrigation dates to optimize irrigation, and try to find a better combination of irrigation dates (optimal dates) and better amount of irrigation over the wheat growth cycle. Strategy 3—Optimal irrigation dates based on surrogate model: RBF is added to Strategy 2, which makes it possible to reduce lots of time. Strategy 4—Fixed fertilizer application date: Using the optimal irrigation date found in Strategy 2 while keeping the number of days of fertilization and all other parameters constant, irrigation and fertilization are optimized in an attempt to minimize the amount of irrigation and fertilizer applied. Strategy 5—Optimal fertilizer application date: while ensuring the optimal irrigation date, traverse the fertilizer application date for optimization, trying to find out the potential yield of the crop. Strategy 6—Optimal fertilizer application date based on surrogate model: RBF is introduced based on Strategy 5. The time consumption was reduced.The stopping criterion in this study is when the optimization results converge visually. The algorithm population size was set to 105, and the generation of offspring used traditional polynomial Mutation. The number of hidden layers of the surrogate model is equal to the dimension of the decision variables, the learning rate is 0.01, the Gaussian kernel function is chosen as the activation function of the hidden layer in the RBF network. The neurons centers are generated by the K-means clustering method. The width parameter of the function is generated by calculating the variance of each cluster. The optimization weight parameters are selected by the recursive least square method. This is because the use of the least square method is likely to encounter situations where matrix inversion is troublesome. Therefore, recursive least squares (RLS) is often used to give a recursive form of the matrix in which the inverse needs to be found, making it computationally easier. More

Tréguer, P. J. et al. Reviews and syntheses: The biogeochemical cycle of silicon in the modern ocean. Biogeosciences 18, 1269–1289 (2021).Article 
ADS 

Google Scholar 
Tréguer, P. et al. Influence of diatom diversity on the ocean biological carbon pump. Nat. Geosci. 11, 27–37 (2018).Article 
ADS 

Google Scholar 
Benoiston, A.-S. et al. The evolution of diatoms and their biogeochemical functions. Phil. Trans. R. Soc. B 372, 20160397 (2017).Article 

Google Scholar 
de Goeij, J. M. et al. Surviving in a marine desert: The sponge loop retains resources within coral reefs. Science 342, 108–110 (2013).Article 
ADS 

Google Scholar 
Folkers, M. & Rombouts, T. Sponges revealed: a synthesis of their overlooked ecological functions within aquatic ecosystems. In YOUMARES 9—The Oceans: Our Research, Our Future (eds. Jungblut, S. et al.) 181–193 (Springer International Publishing, 2020).Kristiansen, S. & Hoell, E. E. The importance of silicon for marine production. Hydrobiologia 484, 21–31 (2002).Article 
CAS 

Google Scholar 
Henderson, M. J., Huff, D. D. & Yoklavich, M. M. Deep-sea coral and sponge taxa increase demersal fish diversity and the probability of fish presence. Front. Mar. Sci. 7, 593844 (2020).Article 

Google Scholar 
McGrath, E. C., Woods, L., Jompa, J., Haris, A. & Bell, J. J. Growth and longevity in giant barrel sponges: Redwoods of the reef or pines in the Indo-Pacific?. Sci. Rep. 8, 15317 (2018).Article 
ADS 

Google Scholar 
Jochum, K. P., Wang, X. H., Vennemann, T. W., Sinha, B. & Muller, W. E. G. Siliceous deep-sea sponge Monorhaphis chuni: A potential paleoclimate archive in ancient animals. Chem. Geol. 300, 143–151 (2012).Article 
ADS 

Google Scholar 
Maldonado, M. et al. Sponge grounds as key marine habitats: A synthetic review of types, structure, functional roles, and conservation concerns. In Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds. Rossi, S. et al.) vol. 1 145–184 (Springer International Publishing, 2017).Maldonado, M. et al. Sponge skeletons as an important sink of silicon in the global oceans. Nat. Geosci. 12, 815–822 (2019).Article 
ADS 
CAS 

Google Scholar 
Maldonado, M. et al. Siliceous sponges as a silicon sink: An overlooked aspect of benthopelagic coupling in the marine silicon cycle. Limnol. Oceanogr. 50, 799–809 (2005).Article 
ADS 
CAS 

Google Scholar 
López-Acosta, M. et al. Sponge contribution to the silicon cycle of a diatom-rich shallow bay. Limnol. Oceanogr. 67, 2431–2447 (2022).Article 
ADS 

Google Scholar 
Maldonado, M. et al. Massive silicon utilization facilitated by a benthic-pelagic coupled feedback sustains deep-sea sponge aggregations. Limnol. Oceanogr. 66, 366–391 (2021).Article 
ADS 
CAS 

Google Scholar 
Wulff, J. L. Ecological interactions of marine sponges. Can. J. Zool. 84, 146–166 (2006).Article 

Google Scholar 
Pawlik, J. R., Loh, T.-L. & McMurray, S. E. A review of bottom-up vs. top-down control of sponges on Caribbean fore-reefs: What’s old, what’s new, and future directions. PeerJ 6, 4343 (2018).Article 

Google Scholar 
Dayton, P. K., Robilliard, G. A., Paine, R. T. & Dayton, L. B. Biological Accommodation in the Benthic Community at McMurdo Sound, Antartica. Ecol. Monogr. 44, 105–128 (1974).Article 

Google Scholar 
Meylan, A. Spongivory in hawksbill turtles: A diet of glass. Science 239, 393–395 (1988).Article 
ADS 
CAS 

Google Scholar 
Wulff, J. Sponge-feeding by Caribbean angelfishes, trunk-fishes, and filefishes. In Sponges in time and space 265–271 (A. A. Balkema, 1994).Santos, C. P., Coutinho, A. B. & Hajdu, E. Spongivory by Eucidaris tribuloides from Salvador, Bahia (Echinodermata: Echinoidea). J. Mar. Biol. Ass. 82, 295–297 (2002).Article 

Google Scholar 
Chu, J. W. F. & Leys, S. P. The dorid nudibranchs Peltodoris lentiginosa and Archidoris odhneri as predators of glass sponges. Invertebr. Biol. 131, 75–81 (2012).Article 

Google Scholar 
Maschette, D. et al. Characteristics and implications of spongivory in the Knifejaw Oplegnathus woodwardi (Waite) in temperate mesophotic waters. J. Sea Res. 157, 101847 (2020).Article 

Google Scholar 
Knowlton, A. L. & Highsmith, R. C. Nudibranch-sponge feeding dynamics: Benefits of symbiont-containing sponge to Archidoris montereyensis (Cooper, 1862) and recovery of nudibranch feeding scars by Halichondria panicea (Pallas, 1766). J. Exp. Mar. Biol. Ecol. 327, 36–46 (2005).Article 

Google Scholar 
Bloom, S. A. Morphological correlations between dorid nudibranch predators and sponge prey. Veliger 18, 289–301 (1976).
Google Scholar 
Faulkner, D. & Ghiselin, M. Chemical defense and evolutionary ecology of dorid nudibranchs and some other opisthobranch gastropods. Mar. Ecol. Prog. Ser. 13, 295–301 (1983).Article 
ADS 

Google Scholar 
Bloom, S. A. Specialization and noncompetitive resource partitioning among sponge-eating dorid nudibranchs. Oecologia 49, 305–315 (1981).Article 
ADS 

Google Scholar 
Clark, K. B. Nudibranch life cycles in the Northwest Atlantic and their relationship to the ecology of fouling communities. Helgolander Wiss. Meeresunters 27, 28–69 (1975).Article 
ADS 

Google Scholar 
Wulff, J. Regeneration of sponges in ecological context: Is regeneration an integral part of life history and morphological strategies?. Integr. Comp. Biol. 50, 494–505 (2010).Article 

Google Scholar 
Wu, Y.-C., Franzenburg, S., Ribes, M. & Pita, L. Wounding response in Porifera (sponges) activates ancestral signaling cascades involved in animal healing, regeneration, and cancer. Sci. Rep. 12, 1307 (2022).Article 
ADS 
CAS 

Google Scholar 
Turner, T. The marine sponge Hymeniacidon perlevis is a globally-distributed exotic species. Aquat. Invasions 15, 542–561 (2020).Article 

Google Scholar 
Ackers, R. G., Moss, D. & Picton, B. E. In Sponges of the British Isles (‘Sponge V’). vol. A Colour Guide and Working Document (Marine Conservation Society, 1992).Lima, P. O. V. & Simone, L. R. L. Anatomical review of Doris verrucosa and redescription of Doris januarii (Gastropoda, Nudibranchia) based on comparative morphology. J. Mar. Biol. Ass. 95, 1203–1220 (2015).Article 

Google Scholar 
Avila, C. et al. Biosynthetic origin and anatomical distribution of the main secondary metabolites in the nudibranch mollusc Doris verrucosa. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 97, 363–368 (1990).Article 

Google Scholar 
Urgorri, V. & Besteiro, C. The feeding habits of the nudibranchs of Galicia. Iberus 4, 51–58 (1984).
Google Scholar 
Aminot, A. & Kerouel, R. In Dosage automatique des nutriments dans les eaux marines: Méthodes en flux continu. Méthodes d’analyse en milieu marin, Ed. Ifremer 188 (2007).Hydes, D. J. & Liss, P. S. Fluorimetric method for the determination of low concentrations of dissolved aluminium in natural waters. Analyst 101, 922 (1976).Article 
ADS 
CAS 

Google Scholar 
López-Acosta, M., Leynaert, A., Coquille, V. & Maldonado, M. Silicon utilization by sponges: An assessment of seasonal changes. Mar. Ecol. Prog. Ser. 605, 111–123 (2018).Article 
ADS 

Google Scholar 
Grall, J., Le-Loch, F., Guyonnet, B. & Riera, P. Community structure and food web based on stable isotopes (δ15N and δ13C) analysis of a North Eastern Atlantic maerl bed. J. Exp. Mar. Biol. Ecol. 338, 1–15 (2006).Article 
CAS 

Google Scholar 
Cebrian, E., Uriz, M. J., Garrabou, J. & Ballesteros, E. Sponge Mass Mortalities in a warming Mediterranean sea: Are cyanobacteria-harboring species worse off?. PLoS ONE 6, e20211 (2011).Article 
ADS 
CAS 

Google Scholar 
McClintock, J. B. Investigation of the relationship between invertebrate predation and biochemical composition, energy content, spicule armament and toxicity of benthic sponges at McMurdo Sound, Antartica. Mar. Biol. 94, 479–487 (1987).Article 
CAS 

Google Scholar 
Cockburn, T. C. & Reid, R. G. B. Digestive tract enzymes in two Aeolid nudibranchs (opisthobranchia: Gastropoda). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 65, 275–281 (1980).Article 

Google Scholar 
De Caralt, S., Uriz, M. & Wijffels, R. Grazing, differential size-class dynamics and survival of the Mediterranean sponge Corticium candelabrum. Mar. Ecol. Prog. Ser. 360, 97–106 (2008).Article 
ADS 

Google Scholar 
Ragueneau, O., De-Blas-Varela, E., Tréguer, P., Quéguiner, B. & Del Amo, Y. Phytoplankton dynamics in relation to the biogeochemical cycle of silicon in a coastal ecosystem of western Europe. Mar. Ecol. Prog. Ser. 106, 157–172 (1994).Article 
ADS 

Google Scholar 
Turon, X., Tarjuelo, I. & Uriz, M. J. Growth dynamics and mortality of the encrusting sponge Crambe crambe (Poecilosclerida) in contrasting habitats: Correlation with population structure and investment in defence: Growth and mortality of encrusting sponges. Funct. Ecol. 12, 631–639 (1998).Article 

Google Scholar 
Hoppe, W. F. Growth, regeneration and predation in three species of large coral reef sponges. Mar. Ecol. Prog. Ser. 50, 117–125 (1988).Article 
ADS 

Google Scholar 
Ayling, A. L. Growth and regeneration rates in thinly encrusting Demospongiae from temperate waters. Biol. Bull. 165, 343–352 (1983).Article 

Google Scholar 
Fillinger, L., Janussen, D., Lundälv, T. & Richter, C. Rapid glass sponge expansion after climate-induced Antarctic ice shelf collapse. Curr. Biol. 23, 1330–1334 (2013).Article 
CAS 

Google Scholar 
Dayton, P. K. et al. Benthic responses to an Antarctic regime shift: Food particle size and recruitment biology. Ecol. Appl. 29, 1 (2019).Article 

Google Scholar 
Guy, G. & Metaxas, A. Recruitment of deep-water corals and sponges in the Northwest Atlantic Ocean: Implications for habitat distribution and population connectivity. Mar. Biol. 169, 107 (2022).Article 

Google Scholar 
Beucher, C., Treguer, P., Corvaisier, R., Hapette, A. M. & Elskens, M. Production and dissolution of biosilica, and changing microphytoplankton dominance in the Bay of Brest (France). Mar. Ecol. Prog. Ser. 267, 57–69 (2004).Article 
ADS 

Google Scholar 
López-Acosta, M., Leynaert, A. & Maldonado, M. Silicon consumption in two shallow-water sponges with contrasting biological features. Limnol. Oceanogr. 61, 2139–2150 (2016).Article 
ADS 

Google Scholar 
Ellwood, M. J., Wille, M. & Maher, W. Glacial silicic acid concentrations in the Southern Ocean. Science 330, 1088–1091 (2010).Article 
ADS 
CAS 

Google Scholar 
Maldonado, M. et al. Cooperation between passive and active silicon transporters clarifies the ecophysiology and evolution of biosilicification in sponges. Sci. Adv. 6, eaba9322 (2020).Article 
ADS 
CAS 

Google Scholar 
Palumbi, S. R. Tactics of acclimation: morphological changes of sponges in an unpredictable environment. Science 225, 1478–1480 (1984).Article 
ADS 
CAS 

Google Scholar 
Broadribb, M., Bell, J. J. & Rovellini, A. Rapid acclimation in sponges: Seasonal variation in the organic content of two intertidal sponge species. J. Mar. Biol. Ass. 101, 983–989 (2021).Article 
CAS 

Google Scholar 
Schönberg, C. H. L. & Barthel, D. Inorganic skeleton of the demosponge Halichondria panacea. Seasonality in spicule production in the Baltic Sea. Mar. Biol. 130, 133–140 (1997).Article 

Google Scholar 
Sheild, C. J. & Witman, J. D. The impact of Henricia sanguinolenta (O. F. Müller) (Echinodermata: Asteroidea) predation on the finger sponges, Isodictya spp.. J. Exp. Mar. Biol. Ecol. 166, 107–133 (1993).Article 

Google Scholar 
Lewis, J. R., Bowman, R. S., Kendall, M. A. & Williamson, P. Some geographical components in population dynamics: Possibilities and realities in some littoral species. Neth. J. Sea Res. 16, 18–28 (1982).Article 

Google Scholar 
Ashton, G. V. et al. Predator control of marine communities increases with temperature across 115 degrees of latitude. Science 376, 1215–1219 (2022).Article 
ADS 
CAS 

Google Scholar 
Knowlton, A. & Highsmith, R. Convergence in the time-space continuum: A predator-prey interaction. Mar. Ecol. Prog. Ser. 197, 285–291 (2000).Article 
ADS 

Google Scholar  More

.readcube-buybox { display: none !important;}
Agricultural expansion can plunder forests, but it is not the only human activity to do so. Researchers have found that more than one-third of the loss of Earth’s large, intact forests is driven by production for export — especially of wood, minerals and energy1.

Access options

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0 0;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50%0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:””;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to Nature and 55 other Nature journal$29.99monthlySubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueAll prices are NET prices.VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00All prices are NET prices.

Additional access options:

doi: https://doi.org/10.1038/d41586-023-00119-9

References

Subjects

Conservation biology More

Box, J. E. et al. Key indicators of Arctic climate change: 1971–2017. Environ. Res. Lett. 14, 045010 (2019).ADS 
CAS 

Google Scholar 
Previdi, M., Smith, K. L. & Polvani, L. M. Arctic amplification of climate change: a review of underlying mechanisms. Environ. Res. Lett. 16, 093003 (2021).ADS 
CAS 

Google Scholar 
Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ. 3, 1–10 (2022).ADS 

Google Scholar 
Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).ADS 

Google Scholar 
Kopec, B. G., Feng, X., Michel, F. A. & Posmentier, E. S. Influence of sea ice on Arctic precipitation. Proc. Natl. Acad. Sci. 113, 46–51 (2016).ADS 
CAS 

Google Scholar 
Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth Environ. 3, 10–23 (2022).ADS 

Google Scholar 
Overland, J. et al. The urgency of Arctic change. Polar Sci. 21, 6–13 (2019).ADS 

Google Scholar 
Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).ADS 
CAS 

Google Scholar 
Ciavarella, A. et al. Prolonged Siberian heat of 2020 almost impossible without human influence. Clim. Change 166, 9 (2021).ADS 

Google Scholar 
Dobricic, S., Russo, S., Pozzoli, L., Wilson, J. & Vignati, E. Increasing occurrence of heat waves in the terrestrial Arctic. Environ. Res. Lett. 15, 024022 (2020).ADS 

Google Scholar 
Graham, R. M. et al. Increasing frequency and duration of Arctic winter warming events. Geophys. Res. Lett. 44, 6974–6983 (2017).ADS 

Google Scholar 
Knight, J. & Harrison, S. The impacts of climate change on terrestrial Earth surface systems. Nat. Clim. Change 3, 24–29 (2013).ADS 

Google Scholar 
Pearson, R. G. et al. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Change 3, 673–677 (2013).ADS 

Google Scholar 
Beck, P. S. A. et al. Changes in forest productivity across Alaska consistent with biome shift. Ecol. Lett. 14, 373–379 (2011).
Google Scholar 
Reichle, L. M., Epstein, H. E., Bhatt, U. S., Raynolds, M. K. & Walker, D. A. Spatial Heterogeneity of the Temporal Dynamics of Arctic Tundra Vegetation. Geophys. Res. Lett. 45, 9206–9215 (2018).ADS 

Google Scholar 
Sturm, M., Racine, C. & Tape, K. Increasing shrub abundance in the Arctic. Nature 411, 546–547 (2001).ADS 
CAS 

Google Scholar 
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).ADS 

Google Scholar 
Phoenix, G. K. & Bjerke, J. W. Arctic browning: extreme events and trends reversing arctic greening. Glob. Change Biol. 22, 2960–2962 (2016).ADS 

Google Scholar 
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).ADS 
CAS 

Google Scholar 
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).
Google Scholar 
Virkkala, A.-M. et al. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob. Change Biol. 27, 4040–4059 (2021).CAS 

Google Scholar 
Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).ADS 

Google Scholar 
Rienecker, M. M. et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Clim. 24, 3624–3648 (2011).ADS 

Google Scholar 
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 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
Google Scholar 
Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Sci. Data 7, 248 (2020).
Google Scholar 
Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).
Google Scholar 
Niittynen, P., Heikkinen, R. K. & Luoto, M. Snow cover is a neglected driver of Arctic biodiversity loss. Nat. Clim. Change 8, 997–1001 (2018).ADS 

Google Scholar 
Slatyer, R. A., Umbers, K. D. L. & Arnold, P. A. Ecological responses to variation in seasonal snow cover. Conserv. Biol. 36, e13727 (2022).
Google Scholar 
Serreze, M. C. et al. Arctic rain on snow events: bridging observations to understand environmental and livelihood impacts. Environ. Res. Lett. 16, 105009 (2021).ADS 

Google Scholar 
López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Change Biol. 27, 1704–1720 (2021).ADS 

Google Scholar 
Ennos, A. R. Wind as an ecological factor. Trends Ecol. Evol. 12, 108–111 (1997).CAS 

Google Scholar 
Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).ADS 

Google Scholar 
Boussetta, S. et al. ECLand: The ECMWF Land Surface Modelling System. Atmosphere 12, 723 (2021).ADS 
CAS 

Google Scholar 
Munõz-Sabater, J. ERA5-Land hourly data from 1981 to present. ECMWF https://doi.org/10.24381/cds.e2161bac (2019). Munõz-Sabater, J. ERA5-Land hourly data from 1950 to 1980. ECMWF https://doi.org/10.24381/cds.e2161bac (2021).Hoyer, S. & Hamman, J. xarray: N-D labeled Arrays and Datasets in Python. J. Open Res. Softw. 5, 10 (2017).
Google Scholar 
Sen, P. K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).MATH 

Google Scholar 
Theil, H. A rank-invariant method of linear and polynomial regression analysis I, II and III. Indag. Math. 173 (1950).Hussain, M. M. & Mahmud, I. pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J. Open Source Softw. 4, 1556 (2019).ADS 

Google Scholar 
Aalto, J. et al. High-resolution analysis of observed thermal growing season variability over northern Europe. Clim. Dyn. 58, 1477–1493 (2022).
Google Scholar 
Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 035004 (2018).ADS 

Google Scholar 
Barichivich, J., Briffa, K. R., Osborn, T. J., Melvin, T. M. & Caesar, J. Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob. Biogeochem. Cycles 26 (2012).Wu, F., Jiang, Y., Wen, Y., Zhao, S. & Xu, H. Spatial synchrony in the start and end of the thermal growing season has different trends in the mid-high latitudes of the Northern Hemisphere. Environ. Res. Lett. 16, 124017 (2021).ADS 

Google Scholar 
Ruosteenoja, K., Räisänen, J., Venäläinen, A. & Kämäräinen, M. Projections for the duration and degree days of the thermal growing season in Europe derived from CMIP5 model output. Int. J. Climatol. 36, 3039–3055 (2016).
Google Scholar 
Niittynen, P. & Luoto, M. The importance of snow in species distribution models of arctic vegetation. Ecography 41, 1024–1037 (2018).
Google Scholar 
McMaster, G. S. & Wilhelm, W. W. Growing degree-days: one equation, two interpretations. Agric. For. Meteorol. 87, 291–300 (1997).ADS 

Google Scholar 
Körner, C. Plant adaptation to cold climates. F1000Research 5, F1000 Faculty Rev-2769 (2016).Niittynen, P. et al. Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nat. Clim. Change 10, 1143–U134 (2020).ADS 

Google Scholar 
Cohen, J., Ye, H. & Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 42, 7115–7122 (2015).ADS 

Google Scholar 
Mooney, P. A. & Li, L. Near future changes to rain-on-snow events in Norway. Environ. Res. Lett. 16, 064039 (2021).ADS 

Google Scholar 
Preece, C., Callaghan, T. V. & Phoenix, G. K. Impacts of winter icing events on the growth, phenology and physiology of sub-arctic dwarf shrubs. Physiol. Plant. 146, 460–472 (2012).CAS 

Google Scholar 
Putkonen, J. & Roe, G. Rain-on-snow events impact soil temperatures and affect ungulate survival. Geophys. Res. Lett. 30, (2003).Treharne, R., Bjerke, J. W. & Tømmervik, H. & Phoenix, G. K. Development of new metrics to assess and quantify climatic drivers of extreme event driven Arctic browning. Remote Sens. Environ. 243, 111749 (2020).ADS 

Google Scholar 
Bokhorst, S. et al. Impacts of extreme winter warming events on plant physiology in a sub-Arctic heath community. Physiol. Plant. 140, 128–140 (2010).CAS 

Google Scholar 
Russo, S., Sillmann, J. & Fischer, E. M. Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environ. Res. Lett. 10, 124003 (2015).ADS 

Google Scholar 
Alduchov, O. A. & Eskridge, R. E. Improved Magnus Form Approximation of Saturation Vapor Pressure. J. Appl. Meteorol. Climatol. 35, 601–609 (1996).ADS 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).
Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
CAS 

Google Scholar 
De Frenne, P. et al. Forest microclimates and climate change: Importance, drivers and future research agenda. Glob. Change Biol. 27, 2279–2297 (2021).ADS 

Google Scholar 
Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).ADS 
CAS 

Google Scholar 
Berner, L. T., Jantz, P., Tape, K. D. & Goetz, S. J. Tundra plant above-ground biomass and shrub dominance mapped across the North Slope of Alaska. Environ. Res. Lett. 13, 035002 (2018).ADS 

Google Scholar 
Walker, D. A. et al. Phytomass, LAI, and NDVI in northern Alaska: Relationships to summer warmth, soil pH, plant functional types, and extrapolation to the circumpolar Arctic. J. Geophys. Res. Atmospheres 108, (2003).Williams, C. M., Henry, H. A. L. & Sinclair, B. J. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biol. Rev. 90, 214–235 (2015).
Google Scholar 
Peng, S. et al. Change in snow phenology and its potential feedback to temperature in the Northern Hemisphere over the last three decades. Environ. Res. Lett. 8, 014008 (2013).ADS 

Google Scholar 
Wheeler, J. A. et al. Increased spring freezing vulnerability for alpine shrubs under early snowmelt. Oecologia 175, 219–229 (2014).ADS 
CAS 

Google Scholar 
Zhu, L., Ives, A. R., Zhang, C., Guo, Y. & Radeloff, V. C. Climate change causes functionally colder winters for snow cover-dependent organisms. Nat. Clim. Change 9, 886–893 (2019).ADS 

Google Scholar 
Vitasse, Y. et al. ‘Hearing’ alpine plants growing after snowmelt: ultrasonic snow sensors provide long-term series of alpine plant phenology. Int. J. Biometeorol. 61, 349–361 (2017).ADS 

Google Scholar 
Kling, M. M. & Ackerly, D. D. Global wind patterns and the vulnerability of wind-dispersed species to climate change. Nat. Clim. Change 10, 868–875 (2020).ADS 

Google Scholar 
Dial, R. J., Maher, C. T., Hewitt, R. E. & Sullivan, P. F. Sufficient conditions for rapid range expansion of a boreal conifer. Nature 608, 546–551 (2022).ADS 
CAS 

Google Scholar 
Nathan, R. et al. Mechanisms of long-distance dispersal of seeds by wind. Nature 418, 409–413 (2002).ADS 
CAS 

Google Scholar 
Sakai, A. Mechanism of Desiccation Damage of Conifers Wintering in Soil-Frozen Areas. Ecology 51, 657–664 (1970).
Google Scholar 
Wilson, J. W. Notes on Wind and its Effects in Arctic-Alpine Vegetation. J. Ecol. 47, 415–427 (1959).
Google Scholar 
Rantanen, M. et al. Bioclimatic atlas of the terrestrial Arctic, figshare, https://doi.org/10.6084/m9.figshare.c.6216368 (2023).Räisänen, J. Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change. The Cryosphere 15, 1677–1696 (2021).ADS 

Google Scholar 
Xu, J., Ma, Z., Yan, S. & Peng, J. Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China. J. Hydrol. 605, 127353 (2022).
Google Scholar 
Behrangi, A., Singh, A., Song, Y. & Panahi, M. Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations. Geophys. Res. Lett. 46, 11358–11366 (2019).ADS 

Google Scholar 
Menne, M. J., Williams, C. N., Gleason, B. E., Rennie, J. J. & Lawrimore, J. H. The Global Historical Climatology Network Monthly Temperature Dataset, Version 4. J. Clim. 31, 9835–9854 (2018).ADS 

Google Scholar 
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An Overview of the Global Historical Climatology Network-Daily Database. J. Atmospheric Ocean. Technol. 29, 897–910 (2012).ADS 

Google Scholar 
Atlaskin, E. & Vihma, T. Evaluation of NWP results for wintertime nocturnal boundary-layer temperatures over Europe and Finland. Q. J. R. Meteorol. Soc. 138, 1440–1451 (2012).ADS 

Google Scholar 
Lindsay, R., Wensnahan, M., Schweiger, A. & Zhang, J. Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic. J. Clim. 27, 2588–2606 (2014).ADS 

Google Scholar 
Wang, C., Graham, R. M., Wang, K., Gerland, S. & Granskog, M. A. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution. The Cryosphere 13, 1661–1679 (2019).ADS 

Google Scholar 
Wesslén, C. et al. The Arctic summer atmosphere: an evaluation of reanalyses using ASCOS data. Atmospheric Chem. Phys. 14, 2605–2624 (2014).ADS 

Google Scholar  More

Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518. https://doi.org/10.1038/nature06970 (2008).Article 
ADS 
CAS 

Google Scholar 
Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).Article 
ADS 
CAS 

Google Scholar 
Lafferty, K. D., Dobson, A. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. 103, 11211–11216 (2006).Article 
ADS 
CAS 

Google Scholar 
Amundsen, P. A. et al. Food web topology and parasites in the pelagic zone of a subarctic lake. J. Anim. Ecol. 78, 563–572. https://doi.org/10.1111/j.1365-2656.2008.01518.x (2009).Article 

Google Scholar 
Thompson, R. M., Mouritsen, K. N. & Poulin, R. Importance of parasites and their life cycle characteristics in determining the structure of a large marine food web. J. Anim. Ecol. 74, 77–85. https://doi.org/10.1111/j.1365-2656.2004.00899.x (2005).Article 

Google Scholar 
Thieltges, D. W. et al. Parasites as prey in aquatic food webs: Implications for predator infection and parasite transmission. Oikos 122, 1473–1482. https://doi.org/10.1111/j.1600-0706.2013.00243.x (2013).Article 

Google Scholar 
Sato, T. et al. Nematomorph parasites drive energy flow through a riparian ecosystem. Ecology 92, 201–207 (2011).Article 

Google Scholar 
Lafferty, K. D. & Kuris, A. M. Trophic strategies, animal diversity and body size. Trends Ecol. Evol. 17, 507–513 (2002).Article 

Google Scholar 
Goedknegt, M. A. et al. Trophic relationship between the invasive parasitic copepod Mytilicola orientalis and its native blue mussel (Mytilus edulis) host. Parasitology 145, 814–821. https://doi.org/10.1017/S0031182017001779 (2018).Article 
CAS 

Google Scholar 
Timi, J. T. & Poulin, R. Why ignoring parasites in fish ecology is a mistake. Int. J. Parasitol. 50, 755–761. https://doi.org/10.1016/j.ijpara.2020.04.007 (2020).Article 

Google Scholar 
Barber, I. & Svensson, P. A. Effects of experimental Schistocephalus solidus infections on growth, morphology and sexual development of female three-spined sticklebacks Gasterosteus aculeatus. Parasitology 126, 359–367. https://doi.org/10.1017/s0031182002002925 (2003).Article 
CAS 

Google Scholar 
Scharsack, J. P., Koch, K. & Hammerschmidt, K. Who is in control of the stickleback immune system: Interactions between Schistocephalus solidus and its specific vertebrate host. Proc. Biol. Sci. 274, 3151–3158. https://doi.org/10.1098/rspb.2007.1148 (2007).Article 

Google Scholar 
Hopkins, C. A. Studies on cestode metabolism. I. glycogen metabolism in Schistocephalus solidus In vivo. J. Parasitol. 36, 384–390 (1950).Article 
CAS 

Google Scholar 
Körting, W. & Barrett, J. Carbohydrate catabolism in the plerocercoids of Schistocephalus solidus (Cestoda: Pseudophyllidea). Int. J. Parasitol. 7, 411–417 (1977).Article 

Google Scholar 
Hebert, F. O., Grambauer, S., Barber, I., Landry, C. R. & Aubin-Horth, N. Major host transitions are modulated through transcriptome-wide reprogramming events in Schistocephalus solidus, a threespine stickleback parasite. Mol. Ecol. 26, 1118–1130. https://doi.org/10.1111/mec.13970 (2017).Article 
CAS 

Google Scholar 
Berger, C. S. et al. The parasite Schistocephalus solidus secretes proteins with putative host manipulation functions. Parasites Vectors 14, 436. https://doi.org/10.1186/s13071-021-04933-w (2021).Article 
CAS 

Google Scholar 
Jolles, J. W., Mazue, G. P. F., Davidson, J., Behrmann-Godel, J. & Couzin, I. D. Schistocephalus parasite infection alters sticklebacks’ movement ability and thereby shapes social interactions. Sci. Rep. 10, 12282. https://doi.org/10.1038/s41598-020-69057-0 (2020).Article 
ADS 
CAS 

Google Scholar 
Scharsack, J. P. et al. Climate change facilitates a parasite’s host exploitation via temperature-mediated immunometabolic processes. Glob. Change Biol. 27, 94–107. https://doi.org/10.1111/gcb.15402 (2021).Article 
ADS 
CAS 

Google Scholar 
Kochneva, A., Borvinskaya, E. & Smirnov, L. Zone of interaction between the parasite and the host: Protein profile of the body cavity fluid of Gasterosteus aculeatus L. infected with the Cestode Schistocephalus solidus (Muller, 1776). Acta Parasitol. 66, 569–583. https://doi.org/10.1007/s11686-020-00318-8 (2021).Article 
CAS 

Google Scholar 
Barber, I. & Scharsack, J. P. The three-spined stickleback-Schistocephalus solidus system: An experimental model for investigating host-parasite interactions in fish. Parasitology 137, 411–424. https://doi.org/10.1017/S0031182009991466 (2010).Article 
CAS 

Google Scholar 
Weber, J. N., Steinel, N. C., Shim, K. C. & Bolnick, D. I. Recent evolution of extreme cestode growth suppression by a vertebrate host. Proc. Natl. Acad. Sci. U. S. A. 114, 6575–6580. https://doi.org/10.1073/pnas.1620095114 (2017).Article 
ADS 
CAS 

Google Scholar 
Sabadel, A. J. M., Stumbo, A. D. & MacLeod, C. D. Stable-isotope analysis: A neglected tool for placing parasites in food webs. J. Helminthol. 93, 1–7. https://doi.org/10.1017/S0022149X17001201 (2019).Article 
CAS 

Google Scholar 
Hayes, J. M. Factors controlling 13C contents of sedimentary organic compounds: Principles and evidence. Mar. Geol. 113, 111–125 (1993).Article 
ADS 
CAS 

Google Scholar 
France, R. L. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnol. Oceanogr. 40, 1310–1313 (1995).Article 
ADS 

Google Scholar 
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326. https://doi.org/10.1007/s00442-017-3881-9 (2017).Article 
ADS 
CAS 

Google Scholar 
McMahon, K. W., Fogel, M. L., Elsdon, T. S. & Thorrold, S. R. Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. J. Anim. Ecol. 79, 1132–1141. https://doi.org/10.1111/j.1365-2656.2010.01722.x (2010).Article 

Google Scholar 
Liu, H.-z, Luo, L. & Cai, D.-l. Stable carbon isotopic analysis of amino acids in a simplified food chain consisting of the green alga Chlorella spp., the calanoid copepod Calanus sinicus, and the Japanese anchovy (Engraulis japonicus). Can. J. Zool. 96, 23–30. https://doi.org/10.1139/cjz-2016-0170 (2018).Article 
CAS 

Google Scholar 
Wang, Y. V. et al. Know your fish: A novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chem. 256, 380–389. https://doi.org/10.1016/j.foodchem.2018.02.095 (2018).Article 
CAS 

Google Scholar 
Whiteman, J. P., Kim, S. L., McMahon, K. W., Koch, P. L. & Newsome, S. D. Amino acid isotope discrimination factors for a carnivore: Physiological insights from leopard sharks and their diet. Oecologia 188, 977–989. https://doi.org/10.1007/s00442-018-4276-2 (2018).Article 
ADS 

Google Scholar 
Rogers, M., Bare, R., Gray, A., Scott-Moelder, T. & Heintz, R. Assessment of two feeds on survival, proximate composition, and amino acid carbon isotope discrimination in hatchery-reared Chinook salmon. Fish. Res. 219, 105303. https://doi.org/10.1016/j.fishres.2019.06.001 (2019).Article 

Google Scholar 
Choy, K., Smith, C. I., Fuller, B. T. & Richards, M. P. Investigation of amino acid δ13C signatures in bone collagen to reconstruct human palaeodiets using liquid chromatography–isotope ratio mass spectrometry. Geochim. Cosmochim. Acta 74, 6093–6111. https://doi.org/10.1016/j.gca.2010.07.025 (2010).Article 
ADS 
CAS 

Google Scholar 
Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).Article 
CAS 

Google Scholar 
Raghavan, M., McCullagh, J. S., Lynnerup, N. & Hedges, R. E. Amino acid delta13C analysis of hair proteins and bone collagen using liquid chromatography/isotope ratio mass spectrometry: Paleodietary implications from intra-individual comparisons. Rapid Commun. Mass Spectrom. 24, 541–548. https://doi.org/10.1002/rcm.4398 (2010).Article 
ADS 
CAS 

Google Scholar 
Honch, N. V., McCullagh, J. S. & Hedges, R. E. Variation of bone collagen amino acid delta13C values in archaeological humans and fauna with different dietary regimes: Developing frameworks of dietary discrimination. Am. J. Phys. Anthropol. 148, 495–511. https://doi.org/10.1002/ajpa.22065 (2012).Article 

Google Scholar 
Mora, A. et al. High-resolution palaeodietary reconstruction: Amino acid δ 13 C analysis of keratin from single hairs of mummified human individuals. Quatern. Int. 436, 96–113. https://doi.org/10.1016/j.quaint.2016.10.018 (2017).Article 

Google Scholar 
Matos, M. P. V., Konstantynova, K. I., Mohr, R. M. & Jackson, G. P. Analysis of the (13)C isotope ratios of amino acids in the larvae, pupae and adult stages of Calliphora vicina blow flies and their carrion food sources. Anal. Bioanal. Chem. 410, 7943–7954. https://doi.org/10.1007/s00216-018-1416-9 (2018).Article 
CAS 

Google Scholar 
Bontempo, L. et al. Bulk and compound-specific stable isotope ratio analysis for authenticity testing of organically grown tomatoes. Food Chem. 318, 126426. https://doi.org/10.1016/j.foodchem.2020.126426 (2020).Article 
CAS 

Google Scholar 
Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
CAS 

Google Scholar 
Newsome, S. D., Fogel, M. L., Kelly, L. & del Rio, C. M. Contributions of direct incorporation from diet and microbial amino acids to protein synthesis in Nile tilapia. Funct. Ecol. 25, 1051–1062. https://doi.org/10.1111/j.1365-2435.2011.01866.x (2011).Article 

Google Scholar 
Larsen, T. et al. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLoS ONE 8, e73441. https://doi.org/10.1371/journal.pone.0073441 (2013).Article 
ADS 
CAS 

Google Scholar 
Thieltges, D. W., Goedknegt, M. A., O’Dwyer, K., Senior, A. M. & Kamiya, T. Parasites and stable isotopes: A comparative analysis of isotopic discrimination in parasitic trophic interactions. Oikos 128, 1329–1339. https://doi.org/10.1111/oik.06086 (2019).Article 

Google Scholar 
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. Camb. Philos. Soc. 87, 545–562. https://doi.org/10.1111/j.1469-185X.2011.00208.x (2011).Article 

Google Scholar 
Wang, Y. V., Wan, A. H. L., Krogdahl, A., Johnson, M. & Larsen, T. (13)C values of glycolytic amino acids as indicators of carbohydrate utilization in carnivorous fish. PeerJ 7, e7701. https://doi.org/10.7717/peerj.7701 (2019).Article 

Google Scholar 
Hesse, T. et al. Insights into amino acid fractionation and incorporation by compound-specific carbon isotope analysis of three-spined sticklebacks. Sci. Rep. 12, 11690. https://doi.org/10.1038/s41598-022-15704-7 (2022).Article 
ADS 
CAS 

Google Scholar 
Riekenberg, P. M. et al. Stable nitrogen isotope analysis of amino acids as a new tool to clarify complex parasite–host interactions within food webs. Oikos 130, 1650–1664. https://doi.org/10.1111/oik.08450 (2021).Article 
CAS 

Google Scholar 
Carleton, S. A. & Del Rio, C. M. Growth and catabolism in isotopic incorporation: A new formulation and experimental data. Funct. Ecol. 24, 805–812. https://doi.org/10.1111/j.1365-2435.2010.01700.x (2010).Article 

Google Scholar 
Perga, M. E. & Gerdeaux, D. ‘Are fish what they eat’ all year round?. Oecologia 144, 598–606. https://doi.org/10.1007/s00442-005-0069-5 (2005).Article 
ADS 
CAS 

Google Scholar 
Grey, J. Trophic fractionation and the effects of diet switch on the carbon stable isotopic ‘signatures’ of pelagic consumers. SIL Proc. 1922–2010(27), 3187–3191. https://doi.org/10.1080/03680770.1998.11898266 (2000).Article 

Google Scholar 
Danfaer, A. Nutrient metabolism and utilization in the liver. Livest. Prod. Sci. 39, 115–127 (1994).Article 

Google Scholar 
Read, C. P. & Simmons, J. E. Biochemistry and physiology of tapeworms. Physiol. Rev. 43, 263–305 (1963).Article 
CAS 

Google Scholar 
Nachev, M. et al. Understanding trophic interactions in host-parasite associations using stable isotopes of carbon and nitrogen. Parasites Vectors 10, 1–9. https://doi.org/10.1186/s13071-017-2030-y (2017).Article 
CAS 

Google Scholar 
Kanaya, G. et al. Application of stable isotopic analyses for fish host–parasite systems: An evaluation tool for parasite-mediated material flow in aquatic ecosystems. Aquat. Ecol. 53, 217–232. https://doi.org/10.1007/s10452-019-09684-6 (2019).Article 
CAS 

Google Scholar 
Gilbert, B. M. et al. You are how you eat: differences in trophic position of two parasite species infecting a single host according to stable isotopes. Parasitol. Res. 119, 1393–1400. https://doi.org/10.1007/s00436-020-06619-1 (2020).Article 

Google Scholar 
Gilbert, B. M. et al. Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host—Parasite system from the Vaal River System, South Africa. Int. J. Parasitol. Parasites Wildl. 12, 134–141. https://doi.org/10.1016/j.ijppaw.2020.05.011 (2020).Article 

Google Scholar 
Felig, P. The glucose-alanine cycle. Metabolism 22, 179–207 (1973).Article 
CAS 

Google Scholar 
Dale, R. A. Catabolism of threonine in mammals by coupling of L-threonine 3-dehydrogenase with 2-amino-3-oxobutyrate-CoA ligase. Biochem. Biophys. Acta. 544, 496–503 (1978).Article 
CAS 

Google Scholar 
Jordan, P. M. & Akhtar, M. The mechanism of action of serine Transhydroxymethylase. Biochem. J. 116, 277–286 (1970).Article 
CAS 

Google Scholar 
Linstead, D. J., Klein, R. A. & Cross, G. A. M. Threonine catabolism in Trypanosoma brucei. J. Gen. Microbiol. 101, 243–251 (1977).Article 
CAS 

Google Scholar 
Hare, P. E., Fogel, M. L., Stafford, T. W. Jr., Mitchell, A. D. & Hoering, T. C. The isotopic composition of carbon and nitrogen in individual amino acids isolated from modern and fossil proteins. J. Archaeol. Sci. 18, 277–292 (1991).Article 

Google Scholar 
Petzke, K. J., Boeing, H., Klaus, S. & Metges, C. C. Carbon and nitrogen stable isotopic composition of hair protein and amino acids can be used as biomarkers for animal-derived dietary protein intake in humans. J. Nutr. 135, 1515–1520 (2005).Article 
CAS 

Google Scholar 
McMahon, K. W., Polito, M. J., Abel, S., McCarthy, M. D. & Thorrold, S. R. Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua). Ecol. Evol. 5, 1278–1290. https://doi.org/10.1002/ece3.1437 (2015).Article 

Google Scholar 
Fuller, B. T. & Petzke, K. J. The dietary protein paradox and threonine (15) N-depletion: Pyridoxal-5’-phosphate enzyme activity as a mechanism for the delta (15) N trophic level effect. Rapid Commun. Mass Spectrom. 31, 705–718. https://doi.org/10.1002/rcm.7835 (2017).Article 
ADS 
CAS 

Google Scholar 
Bowyer, A. et al. Structure and function of the l-threonine dehydrogenase (TkTDH) from the hyperthermophilic archaeon Thermococcus kodakaraensis. J. Struct. Biol. 168, 294–304. https://doi.org/10.1016/j.jsb.2009.07.011 (2009).Article 
CAS 

Google Scholar 
Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: Reaction mechanism, physiological significance and hyperglycinemia. Proc. Jpn. Acad. https://doi.org/10.2183/pjab/84.246 (2008).Article 

Google Scholar 
Locasale, J. W. Serine, glycine and one-carbon units: Cancer metabolism in full circle. Nat. Rev. Cancer 13, 572–583. https://doi.org/10.1038/nrc3557 (2013).Article 
CAS 

Google Scholar 
Kalhan, S. C. & Hanson, R. W. Resurgence of serine: An often neglected but indispensable amino Acid. J. Biol. Chem. 287, 19786–19791. https://doi.org/10.1074/jbc.R112.357194 (2012).Article 
CAS 

Google Scholar 
Larsen, T., Wang, Y. V. & Wan, A. H. L. Tracing the Trophic fate of aquafeed macronutrients with carbon isotope ratios of amino acids. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.813961 (2022).Article 

Google Scholar 
Sweeting, C. J., Polunin, N. V. & Jennings, S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601. https://doi.org/10.1002/rcm.2347 (2006).Article 
ADS 
CAS 

Google Scholar 
Tarallo, A., Bailey, C., Agnisola, C. & D’Onofrio, G. A theoretical evaluation of the respiration rate partition in the Gasterosteus aculeatus-Schistocephalus solidus host-parasite system. Int. Aquat. Res. 13, 185. https://doi.org/10.22034/IAR.2021.1924974.1142 (2021).Article 

Google Scholar 
Takizawa, Y. et al. A new insight into isotopic fractionation associated with decarboxylation in organisms: Implications for amino acid isotope approaches in biogeoscience. Progress Earth Planet. Sci. https://doi.org/10.1186/s40645-020-00364-w (2020).Article 

Google Scholar 
Ron-Harel, N. et al. T cell activation depends on extracellular alanine. Cell Rep. 28, 3011-3021.e4. https://doi.org/10.1016/j.celrep.2019.08.034 (2019).Article 
CAS 

Google Scholar 
Wang, W. et al. Glycine metabolism in animals and humans: Implications for nutrition and health. Amino Acids 45, 463–477. https://doi.org/10.1007/s00726-013-1493-1 (2013).Article 
CAS 

Google Scholar 
Mathis, D. & Shoelson, S. E. Immunometabolism: An emerging frontier. Nat. Rev. Immunol. 11, 81. https://doi.org/10.1038/nri2922 (2011).Article 
CAS 

Google Scholar 
Guo, C. et al. Live Edwardsiella tarda vaccine enhances innate immunity by metabolic modulation in zebrafish. Fish Shellfish Immunol. 47, 664–673. https://doi.org/10.1016/j.fsi.2015.09.034 (2015).Article 
CAS 

Google Scholar 
Peuss, R. et al. Adaptation to low parasite abundance affects immune investment and immunopathological responses of cavefish. Nat. Ecol. Evol. 4, 1416–1430. https://doi.org/10.1038/s41559-020-1234-2 (2020).Article 

Google Scholar 
Smyth, J. D. Fertilization of Schistocephalus solidus in vitro. Exp. Parasitol. 3, 64–71 (1954).Article 
CAS 

Google Scholar 
Schärer, L. & Wedekind, C. Lifetime reproductive output in a hermaphrodite cestode when reproducing alone or in pairs. Evol. Ecol. 13, 381–394 (1999).Article 

Google Scholar 
McCullagh, J. S. Mixed-mode chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 483–494. https://doi.org/10.1002/rcm.4322 (2010).Article 
ADS 
CAS 

Google Scholar 
Dunn, P. J., Honch, N. V. & Evershed, R. P. Comparison of liquid chromatography-isotope ratio mass spectrometry (LC/IRMS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for the determination of collagen amino acid delta13C values for palaeodietary and palaeoecological reconstruction. Rapid Commun. Mass Spectrom. 25, 2995–3011. https://doi.org/10.1002/rcm.5174 (2011).Article 
ADS 
CAS 

Google Scholar 
Fry, B., Carter, J. F., Yamada, K., Yoshida, N. & Juchelka, D. Position-specific (13) C/(12) C analysis of amino acid carboxyl groups—Automated flow-injection-analysis based on reaction with ninhydrin. Rapid Commun. Mass Spectrom. https://doi.org/10.1002/rcm.8126 (2018).Article 

Google Scholar 
Marks, R. G. H., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. How to couple LC-IRMS with HRMS─A proof-of-concept study. Anal Chem 94, 2981–2987 (2022).Article 
CAS 

Google Scholar 
Sun, Y. et al. A method for stable carbon isotope measurement of underivatized individual amino acids by multi-dimensional high-performance liquid chromatography and elemental analyzer/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 34, e8885. https://doi.org/10.1002/rcm.8885 (2020).Article 
CAS 

Google Scholar 
Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).Article 
ADS 
CAS 

Google Scholar 
Köster, D., Villalobos, I. M. S., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. New concepts for the determination of oxidation efficiencies in liquid chromatography-isotope ratio mass spectrometry. Anal. Chem. 91, 5067–5073. https://doi.org/10.1021/acs.analchem.8b05315 (2019).Article 
CAS 

Google Scholar 
Boschker, H. T., Moerdijk-Poortvliet, T. C., van Breugel, P., Houtekamer, M. & Middelburg, J. J. A versatile method for stable carbon isotope analysis of carbohydrates by high-performance liquid chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 22, 3902–3908. https://doi.org/10.1002/rcm.3804 (2008).Article 
ADS 
CAS 

Google Scholar  More