Philippa Kaur

Darwin, C. On the various Contrivances by which British and Foreign Orchids are Fertilised by Insects, and on the good effects of Intercrossing. (John Murray, 1862).Darwin, C. The various Contrivances by which Orchids are Fertilised by Insects. Second edition, revised., (D. Appleton and Company, 1877).Sprengel, C. K. Das entdeckte Geheimnis der Natur im Bau und in der Befruchtung der Blumen. (Vieweg, 1793).Müller, H. Befruchtung der Blumen durch Insekten (Verlag Von Wilhelm Englemann, 1873).Book 

Google Scholar 
Riley, C. V. The yucca moth and yucca pollination. Rep. Missouri Botan. Garden 3, 99–159 (1892).Article 

Google Scholar 
Faegri, K. & Van Der Pijl, L. Principles of Pollination Ecology 3rd edn. (Pergamon, Berlin, 1979).
Google Scholar 
Fenster, C. B., Armbruster, W. S., Wilson, P., Dudash, M. R. & Thomson, J. D. Pollination syndromes and floral specialization. Annu. Rev. Ecol. Evol. Syst. 35, 375–403. https://doi.org/10.1146/annurev.ecolsys.34.011802.132347 (2004).Article 

Google Scholar 
Inouye, D. W. In The Biology of Nectaries (eds Elias, T. S. & Bentley, B. L.) 153–173 (Columbia University Press, 1983).
Google Scholar 
Irwin, R. E., Bronstein, J. L., Manson, J. S. & Richardson, L. Nectar robbing: ecological and evolutionary perspectives. Annu. Rev. Ecol. Evol. Syst. 41, 271–292. https://doi.org/10.1146/annurev.ecolsys.110308.120330 (2010).Article 

Google Scholar 
Irwin, R. E. & Maloof, J. E. Variation in nectar robbing over time, space, and species. Oecologia 133, 525–533. https://doi.org/10.1007/s00442-002-1060-z (2002).ADS 
Article 
PubMed 

Google Scholar 
Maloof, J. E. & Inouye, D. W. Are nectar robbers cheaters or mutualists?. Ecology 81, 2651–2661. https://doi.org/10.1890/0012-9658(2000)081[2651:ANRCOM]2.0.CO;2 (2000).Article 

Google Scholar 
Inouye, D. W. The terminology of floral larceny. Ecology 61, 1251–1253. https://doi.org/10.2307/1936841 (1980).Article 

Google Scholar 
Lyon, D. L. & Chadek, C. Exploitation of nectar resources by hummingbirds, bees (Bombus), and Diglossa baritula and Its role in the evolution of Penstemon kunthii. Condor 73, 246–248. https://doi.org/10.2307/1365847 (1971).Article 

Google Scholar 
Colwell, R. K., Betts, B. J., Bunnell, P., Carpenter, F. L. & Feinsinger, P. Competition for the nectar of Centropogon valerii by the hummingbird Colibri thalassinus and the flower-piercer Diglossa plumbea, and Its evolutionary implications. Condor 76, 447–452. https://doi.org/10.2307/1365817 (1974).Article 

Google Scholar 
Arizmendi, M. C., Dominguez, C. A. & Dirzo, R. The role of an avian nectar robber and of hummingbird pollinators in the reproduction of two plant species. Funct. Ecol. 10, 119–127. https://doi.org/10.2307/2390270 (1996).Article 

Google Scholar 
Arizmendi, M. C. Multiple ecological interactions: Nectar robbers and hummingbirds in a highland forest in Mexico. Can. J. Zool. 79, 997–1006. https://doi.org/10.1139/z01-066 (2001).Article 

Google Scholar 
Navarro, L. Pollination ecology and effect of nectar removal in Macleania bullata (Ericaceae)1. Biotropica 31, 618–625. https://doi.org/10.1111/j.1744-7429.1999.tb00410.x (1999).Article 

Google Scholar 
Traveset, A., Willson, M. F. & Sabag, C. Effect of nectar-robbing birds on fruit set of Fuchsia magellanica in Tierra Del Fuego: A disrupted mutualism. Funct. Ecol. 12, 459–464. https://doi.org/10.1046/j.1365-2435.1998.00212.x (1998).Article 

Google Scholar 
Skutch, A. F. Life histories of Central American birds. Families Fringillidae, Thraupidae Parulidae and Coerebidae. Pacific Coast Avifauna 31, 1–448 (1954).
Google Scholar 
Vuilleumier, F. Systematics and evolution in Diglossa (Aves, Coerebidae). Am. Mus. Novit. 2381, 1–44 (1969).
Google Scholar 
Graves, G. R. Pollination of a Tristerix mistletoe (Loranthaceae) by Diglossa (Aves: Thraupidae). Biotropica 14, 315–317. https://doi.org/10.2307/2388094 (1982).Article 

Google Scholar 
Hernández, H. M. & Toledo, V. M. The role of nectar robbers and pollinators in the reproduction of Erythrina leptorhiza. Ann. Mo. Bot. Gard. 66, 512–520. https://doi.org/10.2307/2398843 (1979).Article 

Google Scholar 
Neill, D. A. Trapliners in the trees: Hummingbird pollination of Erythrina Sect Erythrina (Leguminosae: Papilionoideae). Ann. Missouri Botan. Garden 74, 27–41. https://doi.org/10.2307/2399259 (1987).Article 

Google Scholar 
Hazlehurst, J. A. & Karubian, J. O. Nectar robbing impacts pollinator behavior but not plant reproduction. Oikos 125, 1668–1676. https://doi.org/10.1111/oik.03195 (2016).CAS 
Article 

Google Scholar 
Cuta-Pineda, J. A., Arias-Sosa, L. A. & Pelayo, R. C. The flowerpiercers interactions with a community of high Andean plants. Avian Res. 12, 22. https://doi.org/10.1186/s40657-021-00256-7 (2021).Article 

Google Scholar 
Askins, R. A., Karen, M. E. & Jeffrey, D. W. Flower destruction and nectar depletion by avian nectar robbers on a tropical tree, Cordia sebestena. J. Field Ornithol. 58, 345–349 (1987).
Google Scholar 
McDade, L. A. & Kinsman, S. The impact of floral parasitism in two Neotropical hummingbird-pollinated plant species. Evolution 34, 944–958. https://doi.org/10.2307/2408000 (1980).Article 
PubMed 

Google Scholar 
Ingels, J. Observations of the hummingbirds Orthorhynchus cristatus and Eulampis jugularis of Martinique (West Indies). Gerfaut 66, 129–132 (1976).
Google Scholar 
Feinsinger, P., Beach, J. H., Linhart, Y. B., Busby, W. H. & Murray, K. G. Disturbance, pollinator predictability, and pollination success among Costa Rican cloud forest plants. Ecology 68, 1294–1305. https://doi.org/10.2307/1939214 (1987).Article 

Google Scholar 
Kodric-Brown, A., Brown, J. H., Byers, G. S. & Gori, D. F. Organization of a tropical island community of hummingbirds and flowers. Ecology 65, 1358–1368. https://doi.org/10.2307/1939116 (1984).Article 

Google Scholar 
Lara, C. & Ornelas, J. F. Preferential nectar robbing of flowers with long corollas: Experimental studies of two hummingbird species visiting three plant species. Oecologia 128, 263–273. https://doi.org/10.1007/s004420100640 (2001).ADS 
Article 
PubMed 

Google Scholar 
Hazlehurst, J. A. & Karubian, J. O. Impacts of nectar robbing on the foraging ecology of a territorial hummingbird. Behav. Proc. 149, 27–34. https://doi.org/10.1016/j.beproc.2018.01.001 (2018).Article 

Google Scholar 
Boehm, M. A. Biting the hand that feeds you: Wedge-billed hummingbird is a nectar robber of a sicklebill-adapted Andean bellflower. Acta Amazon. 48, 146–150. https://doi.org/10.1590/1809-4392201703932 (2018).Article 

Google Scholar 
Igić, B., Nguyen, I. & Fenberg, P. B. Nectar robbing in the trainbearers (Lesbia, Trochilidae). PeerJ 8, e9561. https://doi.org/10.7717/peerj.9561 (2020).Article 

Google Scholar 
Lunardi, V. D. O., Silva, É. E., Silva, S. T. A. & Lunardi, D. G. Handroanthus impetiginosus (Bignoniaceae) as an important floral resource for synanthropic birds in the Brazilian semiarid. Oecol. Austr. https://doi.org/10.4257/oeco.2019.2301.12 (2019).Article 

Google Scholar 
Almeida, J. M., Missagia, C. C. C. & Alves, M. A. S. Effects of the availability of floral resources and neighboring plants on nectar robbery in a specialized pollination system. Curr. Zool. https://doi.org/10.1093/cz/zoab083 (2021).Article 

Google Scholar 
Rodríguez-Rodríguez, M. C. & Valido, A. Opportunistic nectar-feeding birds are effective pollinators of bird-flowers from Canary Islands: experimental evidence from Isoplexis canariensis (Scrophulariaceae). Am. J. Bot. 95, 1408–1415. https://doi.org/10.3732/ajb.0800055 (2008).Article 
PubMed 

Google Scholar 
Lohmann, L. G. Untangling the phylogeny of neotropical lianas (Bignonieae, Bignoniaceae). Am. J. Bot. 93, 304–318. https://doi.org/10.3732/ajb.93.2.304 (2006).CAS 
Article 
PubMed 

Google Scholar 
Olmstead, R. G., Zjhra, M. L., Lohmann, L. G., Grose, S. O. & Eckert, A. J. A molecular phylogeny and classification of Bignoniaceae. Am. J. Bot. 96, 1731–1743. https://doi.org/10.3732/ajb.0900004 (2009).CAS 
Article 
PubMed 

Google Scholar 
Lohmann, L. G. & Taylor, C. M. A new generic classification of tribe Bignonieae (Bignoniaceae). Ann. Mo. Bot. Gard. 99, 348–489. https://doi.org/10.3417/2003187 (2014).Article 

Google Scholar 
Gentry, A. H. Coevolutionary patterns in Central American bignoniaceae. Ann. Mo. Bot. Gard. 61, 728–759. https://doi.org/10.2307/2395026 (1974).Article 

Google Scholar 
Bertin, R. I. Floral biology, hummingbird pollination and fruit production of trumpet creeper (Campsis radicans, Bignoniaceae). Am. J. Bot. 69, 122–134. https://doi.org/10.2307/2442837 (1982).Article 

Google Scholar 
Bertin, R. I. Paternity and fruit production in trumpet creeper (Campsis radicans). Am. Nat. 119, 694–709. https://doi.org/10.1086/283943 (1982).Article 

Google Scholar 
Bertin, R. I. & Sullivan, M. Pollen interference and cryptic self-fertility in Campsis radicans. Am. J. Bot. 75, 1140–1147. https://doi.org/10.1002/j.1537-2197.1988.tb08827.x (1988).Article 

Google Scholar 
Bertin, R. I. Paternal success following mixed pollinations of Campsis radicans. Am. Midl. Nat. 124, 153–163. https://doi.org/10.2307/2426088 (1990).Article 

Google Scholar 
Bertin, R. I. Effects of pollination intensity in Campsis radicans. Am. J. Bot. 77, 178–187. https://doi.org/10.1002/j.1537-2197.1990.tb13544.x (1990).Article 
PubMed 

Google Scholar 
Bertin, R. I. & Peters, P. J. Paternal effects on offspring quality in Campsis radicans. Am. Nat. 140, 166–178. https://doi.org/10.1086/285408 (1992).Article 

Google Scholar 
Kartesz, J. T. Campsis radicans. Floristic Synthesis of North America, Version 1.0. Biota of North America Program (BONAP) http://bonap.net/MapGallery/County/Campsis%20radicans.png. (2015).Kolodziejska-Degorska, I. & Zych, M. Bees substitute birds in pollination of ornitogamous climber Campsis radicans [L.] Seem in Poland. Acta Soc. Botanicorum Poloniae 75, 79–85 (2006).Article 

Google Scholar 
Catesby, M. The Natural History of Carolina, Florida and the Bahama islands. Volume 1. (Published by the author, 1731).Audubon, J. J. Ornithological Biography Vol. 3, 638 (Adam and Charles Black, 1835).
Google Scholar 
Audubon, J. J. Ruby-throated Hummingbird, plate CCLIII, The Birds of America Vol. 3 (Havell, 1835).
Google Scholar 
Nuttall, T. Manual of the Ornithology of the United States and of Canada. The Land Birds (Hilliard and Brown, 1832).
Google Scholar 
Stiles, F. G. & Freeman, C. E. Patterns in floral nectar characteristics of some bird-visited plant species from Costa Rica. Biotropica 25, 191–205. https://doi.org/10.2307/2389183 (1993).Article 

Google Scholar 
Stiles, F. G. Ecology, flowering phenology, and hummingbird pollination of some Costa Rican Heliconia species. Ecology 56, 285–301. https://doi.org/10.2307/1934961 (1975).Article 

Google Scholar 
McDade, L. A. & Weeks, J. A. Nectar in hummingbird-pollinated Neotropical plants I: Patterns of production and variability in 12 species. Biotropica 36, 196–215. https://doi.org/10.1111/j.1744-7429.2004.tb00312.x (2004).Article 

Google Scholar 
Wunderle, J. M. Jr. Nectar robbing by Orchard Orioles. Chat 44, 107–108 (1980).
Google Scholar 
Tyler, W. M. in Life histories of North American blackbirds, orioles, tanagers, and allies. Order Passeriformes: Families Ploceidae, Icteridae, and Thraupidae. United States National Museum Bulletin 211 (ed Arthur Cleveland Bent) 247–270 (United States Government Printing Office, 1958).George, F. W. Baltimore Orioles destroying trumpet vine blossoms. Wilson Bull. 46, 64 (1934).
Google Scholar 
Ridgway, R. The birds of North and Middle America, Part 2. Bull. U.S. Natl. Mus. 50, 1–834 (1902).
Google Scholar 
Scharf, W. C. & Kren, J. In Birds of the World (ed. Poole, A. F.) (Cornell Lab of Ornithology, 2020).
Google Scholar 
Morton, E. S. Effective pollination of Erythrina fusca by the Orchard Oriole (Icterus spurius): Coevolved behavioral manipulation?. Ann. Mo. Bot. Gard. 66, 482–489. https://doi.org/10.2307/2398840 (1979).Article 

Google Scholar 
Dickey, D. R. & van Rossem, A. J. The birds of El Salvador. Field Mus. Publ. Zool. 23, 1–609 (1938).
Google Scholar 
Baumel, J. J., King, A. S., Breazile, J. E., Evans, H. E. & Vanden Berge, J. C. (eds). Handbook of Avian Anatomy: Nomina Anatomica Avium, Second Edition. Publications of the Nuttall Ornithological Club no. 23 (Nuttall Ornithological Club, 1993).Beecher, W. J. Adaptations for food-getting in the American blackbirds. Auk 68, 411–440. https://doi.org/10.2307/4080840 (1951).Article 

Google Scholar 
Zusi, R. The role of the depressor mandibulae muscle in kinesis of the avian skull. Proc. U.S. Natl. Mus. 123, 1–28 (1967).Article 

Google Scholar 
Remsen, J. V. Jr. & Robinson, S. K. A classification scheme for foraging behavior of birds in terrestrial habitats. Stud. Avian Biol. 13, 144–160 (1990).
Google Scholar 
Skutch, A. F. Orioles, Blackbirds, and Their Kin (University of Arizona Press, 1996).
Google Scholar 
Hansell, M. P. Bird nests and Construction Behaviour 294 (Cambridge University Press, 2000).Book 

Google Scholar 
Bent, A. C. Life histories of North American blackbirds, orioles, tanagers, and allies. Bull. U.S. Natl. Museum 211, 1–531 (1958).
Google Scholar 
Dennis, J. V. Observations on the orchard oriole in lower Mississippi Delta. Bird-Banding 19, 12–21. https://doi.org/10.2307/4509997 (1948).Article 

Google Scholar 
Wunderle, J. M. & Lodge, D. J. The effect of age and visual cues on floral patch use by bananaquits (Aves: Emberizidae). Anim. Behav. 36, 44–54. https://doi.org/10.1016/S0003-3472(88)80248-3 (1988).Article 

Google Scholar 
Edge, A. A. Characteristics of nectar production and standing crop in Campsis radicans (Bignoniaceae). MSc thesis. (East Tennessee State University, 2010).Galetto, L. Nectary structure and nectar characteristics in some Bignoniaceae. Plant Syst. Evol. 196, 99–121. https://doi.org/10.1007/BF00985338 (1995).Article 

Google Scholar 
Elias, T. S. & Gelband, H. Nectar: Its production and functions in trumpet creeper. Science 189, 289–291. https://doi.org/10.1126/science.189.4199.289 (1975).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Elias, T. S. & Gelband, H. Morphology and anatomy of floral and extrafloral nectaries in Campsis (Bignoniaceae). Am. J. Bot. 63, 1349–1353. https://doi.org/10.1002/j.1537-2197.1976.tb13220.x (1976).Article 

Google Scholar 
Hermans, M. & Rasson, J. P. A new Sobolev test for uniformity on the circle. Biometrika 72, 698–702. https://doi.org/10.2307/2336748 (1985).MathSciNet 
Article 
MATH 

Google Scholar 
Landler, L., Ruxton, G. D. & Malkemper, E. P. The Hermans-Rasson test as a powerful alternative to the Rayleigh test for circular statistics in biology. BMC Ecol. 19, 30. https://doi.org/10.1186/s12898-019-0246-8 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
RStudio Team. RStudio: Integrated Development for R. PBC, Boston, MA http://www.rstudio.com/. (RStudio 2020).Beecher, W. J. Convergent evolution in the American orioles. Wilson Bulletin 62, 50–86 (1950).
Google Scholar 
Wolf, L. L., Hainsworth, F. R. & Stiles, F. G. Energetics of foraging: Rate and efficiency of nectar extraction by hummingbirds. Science 176, 1351–1352. https://doi.org/10.1126/science.176.4041.1351 (1972).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wolf, L. L., Hainsworth, F. R. & Gill, F. B. Foraging efficiencies and time budgets in nectar-feeding birds. Ecology 56, 117–128. https://doi.org/10.2307/1935304 (1975).Article 

Google Scholar 
Alcantara, S. & Lohmann, L. G. Evolution of floral morphology and pollination system in Bignonieae (Bignoniaceae). Am. J. Bot. 97, 782–796. https://doi.org/10.3732/ajb.0900182 (2010).Article 
PubMed 

Google Scholar 
Gentry, A. H. Bignoniaceae: Part II (Tribe Tecomeae). Flora Neotrop. 25, 1–370 (1992).
Google Scholar 
Grant, V. Historical development of ornithophily in the western North American flora. Proc. Natl. Acad. Sci. 91, 10407–10411. https://doi.org/10.1073/pnas.91.22.10407 (1994).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
James, R. L. Some hummingbird flowers east of the Mississippi. Castanea 13, 97–109 (1948).
Google Scholar 
Van Nest, B. N., Edge, A. A., Feathers, M. V., Worley, A. C. & Moore, D. Bees provide pollination service to Campsis radicans (Bignoniaceae), a primarily ornithophilous trumpet flowering vine. Ecol. Entomol. 46, 117–127. https://doi.org/10.1111/een.12947 (2021).Article 

Google Scholar 
Patuxent Wildlife Research Center. Orchard oriole Icterus spurius. BBS summer distribution map, 2011–2015 (relative abundance map). https://www.mbr-pwrc.usgs.gov/bbs/ra2015/ra2015_red_v3.shtml (accessed 7 March 2021) (2021). More

Webb, T. J., vanden Berghe, E. & O’Dor, R. Biodiversity’s big wet secret: The global distribution of marine biological records reveals chronic under-exploration of the deep pelagic ocean. PLoS ONE 5, https://doi.org/10.1371/journal.pone.0010223 (2010).Drazen, J. C. & Sutton, T. T. Dining in the Deep: The Feeding Ecology of Deep-Sea Fishes. Annual Review of Marine Science 9, 337–366, https://doi.org/10.1146/annurev-marine-010816-060543 (2017).ADS 
Article 
PubMed 

Google Scholar 
Brierley, A. S. Diel vertical migration. Current Biology 24, R1074–R1076, https://doi.org/10.1016/j.cub.2014.08.054 (2014).CAS 
Article 
PubMed 

Google Scholar 
Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nature Communications 5, 10, https://doi.org/10.1038/ncomms4271 (2014).CAS 
Article 

Google Scholar 
Anderson, T. R. et al. Quantifying carbon fluxes from primary production to mesopelagic fish using a simple food web model. ICES Journal of Marine Science 76, 690–701, https://doi.org/10.1093/icesjms/fsx234 (2018).Article 

Google Scholar 
Saba, G. K. et al. Toward a better understanding of fish-based contribution to ocean carbon flux. Limnology and Oceanography 66, 1639–1664, https://doi.org/10.1002/lno.11709 (2021).ADS 
CAS 
Article 

Google Scholar 
Koslow, J. A., Kloser, R. J. & Williams, A. Pelagic biomass and community structure over the mid-continental slope off southeastern Australia based upon acoustic and midwater trawl sampling. Marine Ecology Progress Series 146, 21–35, https://doi.org/10.3354/meps146021 (1997).ADS 
Article 

Google Scholar 
Kaartvedt, S., Staby, A. & Aksnes, D. L. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Marine Ecology Progress Series 456, 1–6, https://doi.org/10.3354/meps09785 (2012).ADS 
Article 

Google Scholar 
Lehodey, P., Murtugudde, R. & Senina, I. Bridging the gap from ocean models to population dynamics of large marine predators: A model of mid-trophic functional groups. Progress in Oceanography 84, 69–84, https://doi.org/10.1016/j.pocean.2009.09.008 (2010).ADS 
Article 

Google Scholar 
Van de Putte, A., Flores, H., Volckaert, F. & van Franeker, J. A. Energy content of Antarctic mesopelagic fishes: Implications for the marine food web. Polar Biology 29, 1045–1051, https://doi.org/10.1007/s00300-006-0148-z (2006).Article 

Google Scholar 
Stowasser, G. et al. Food web dynamics in the Scotia Sea in summer: A stable isotope study. Deep-Sea Research Part II-Topical Studies in Oceanography 59, 208–221, https://doi.org/10.1016/j.dsr2.2011.08.004 (2012).ADS 
CAS 
Article 

Google Scholar 
McCormack, S. A. et al. Decades of dietary data demonstrate regional food web structures in the Southern Ocean. Ecology and Evolution 11, 227–241, https://doi.org/10.1002/ece3.7017 (2021).Article 
PubMed 

Google Scholar 
Griffiths, S. P., Olson, R. J. & Watters, G. M. Complex wasp-waist regulation of pelagic ecosystems in the Pacific Ocean. Reviews in Fish Biology and Fisheries 23, 459–475, https://doi.org/10.1007/s11160-012-9301-7 (2013).Article 

Google Scholar 
Saunders, R. A., Hill, S. L., Tarling, G. A. & Murphy, E. J. Myctophid Fish (Family Myctophidae) Are Central Consumers in the Food Web of the Scotia Sea (Southern Ocean). Frontiers in Marine Science 6, https://doi.org/10.3389/fmars.2019.00530 (2019).Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Swimbladder morphology masks Southern Ocean mesopelagic fish biomass. Proceedings of the Royal Society B-Biological Sciences 286, 8, https://doi.org/10.1098/rspb.2019.0353 (2019).Article 

Google Scholar 
Freer, J. J., Tarling, G. A., Collins, M. A., Partridge, J. C. & Genner, M. J. Predicting future distributions of lanternfish, a significant ecological resource within the Southern Ocean. Diversity and Distributions 25, 1259–1272, https://doi.org/10.1111/ddi.12934 (2019).Article 

Google Scholar 
Hidalgo, M. & Browman, H. I. Developing the knowledge base needed to sustainably manage mesopelagic resources Introduction. ICES Journal of Marine Science 76, 609–615, https://doi.org/10.1093/icesjms/fsz067 (2019).Article 

Google Scholar 
Proud, R. et al. From siphonophores to deep scattering layers: Uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES Journal of Marine Science 76, 718–733, https://doi.org/10.1093/icesjms/fsy037 (2019).Article 

Google Scholar 
Caccavo, J. A. et al. Productivity and Change in Fish and Squid in the Southern Ocean. Frontiers in Ecology and Evolution 9, https://doi.org/10.3389/fevo.2021.624918 (2021).Davison, P., Lara-Lopez, A. & Anthony Koslow, J. Mesopelagic fish biomass in the southern California current ecosystem. Deep-Sea Research Part II: Topical Studies in Oceanography 112, 129–142, https://doi.org/10.1016/j.dsr2.2014.10.007 (2015).ADS 
Article 

Google Scholar 
Pakhomov, E. & Yamamura, O. Report of the Advisory Panel on Micronekton Sampling Inter-calibration Experiment. Tech. Rep., PICES (2010).Cheung, W. W. L. et al. Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries 10, 235–251, https://doi.org/10.1111/j.1467-2979.2008.00315.x (2009).Article 

Google Scholar 
Saunders, R. A. & Tarling, G. A. Southern Ocean Mesopelagic Fish Comply with Bergmann’s Rule. American Naturalist 191, 343–351, https://doi.org/10.1086/695767 (2018).Article 

Google Scholar 
Proud, R., Cox, M. J. & Brierley, A. S. Biogeography of the Global Ocean’s Mesopelagic Zone. Current Biology 27, 113–119, https://doi.org/10.1016/j.cub.2016.11.003 (2017).CAS 
Article 
PubMed 

Google Scholar 
Robison, B. H. Conservation of Deep Pelagic Biodiversity. Conservation Biology 23, 847–858, https://doi.org/10.1111/j.1523-1739.2009.01219.x (2009).Article 
PubMed 

Google Scholar 
Constable, A. J. et al. Developing priority variables (“ecosystem Essential Ocean Variables” – eEOVs) for observing dynamics and change in Southern Ocean ecosystems. Journal of Marine Systems 161, 26–41, https://doi.org/10.1016/j.jmarsys.2016.05.003 (2016).ADS 
Article 

Google Scholar 
St John, M. A. et al. A Dark Hole in Our Understanding of Marine Ecosystems and Their Services: Perspectives from the Mesopelagic Community. Frontiers in Marine Science 3, 6, https://doi.org/10.3389/fmars.2016.00031 (2016).Article 

Google Scholar 
Newman, L. et al. Delivering Sustained, Coordinated, and Integrated Observations of the Southern Ocean for Global Impact. Frontiers in Marine Science 6, https://doi.org/10.3389/fmars.2019.00433 (2019).Costello, M. J. & Vanden Berghe, E. ‘Ocean biodiversity informatics’: a new era in marine biology research and management. Marine Ecology Progress Series 316, 203–214, https://doi.org/10.3354/meps316203 (2006).ADS 
Article 

Google Scholar 
Van de Putte, A. et al. From data to marine ecosystem assessments of the Southern Ocean, achievements, challenges, and lessons for the future. Frontiers in Marine Science 8, https://doi.org/10.3389/fmars.2021.637063 (2021).Duhamel, G. et al. Biogeographic Patterns of Fish. In Biogeographic Atlas of the Southern Ocean, 328–362 (Scientific Committee of Antarctic Research, Cambridge, UK, 2014).Piatkowski, U., Rodhouse, P. G., White, M. G., Bone, D. G. & Symon, C. Nekton community of the Scotia Sea as sampled by the RMT-25 during the austral summer. Marine Ecology Progress Series 112, 13–28, https://doi.org/10.3354/meps112013 (1994).ADS 
Article 

Google Scholar 
Collins, M. A. et al. Patterns in the distribution of myctophid fish in the northern Scotia Sea ecosystem. Polar Biology 31, 837–851, https://doi.org/10.1007/s00300-008-0423-2 (2008).Article 

Google Scholar 
Collins, M. A. et al. Latitudinal and bathymetric patterns in the distribution and abundance of mesopelagic fish in the Scotia Sea. Deep-Sea Research Part II-Topical Studies in Oceanography 59, 189–198, https://doi.org/10.1016/j.dsr2.2011.07.003 (2012).ADS 
Article 

Google Scholar 
Loeb, V. J., Hofmann, E. E., Klinck, J. M., Holm-Hansen, O. & White, W. B. ENSO and variability of the Antarctic Peninsula pelagic marine ecosystem. Antarctic Science 21, 135–148, https://doi.org/10.1017/s0954102008001636 (2009).ADS 
Article 

Google Scholar 
Reiss, C. S. et al. Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: implications for top predators and fishery management. Marine Ecology Progress Series 568, 1–16, https://doi.org/10.3354/meps12099 (2017).ADS 
CAS 
Article 

Google Scholar 
Flores, H. et al. Distribution, abundance and ecological relevance of pelagic fishes in the Lazarev Sea, Southern Ocean. Marine Ecology Progress Series 367, 271–282, https://doi.org/10.3354/meps07530 (2008).ADS 
Article 

Google Scholar 
Flores, H. et al. Seasonal changes in the vertical distribution and community structure of Antarctic macrozooplankton and micronekton. Deep-Sea Research Part I-Oceanographic Research Papers 84, 127–141, https://doi.org/10.1016/j.dsr.2013.11.001 (2014).ADS 
Article 

Google Scholar 
Duhamel, G. The Pelagic Fish Community of the Polar Frontal Zone off the Kerguelen Islands. In Fishes of Antarctica, 63–74, https://doi.org/10.1007/978-88-470-2157-0_5 (Springer, Milano, 1998).Duhamel, G., Koubbi, P. & Ravier, C. Day and night mesopelagic fish assemblages off the Kerguelen Islands (Southern Ocean). Polar Biology 23, 106–112, https://doi.org/10.1007/s003000050015 (2000).Article 

Google Scholar 
Duhamel, G., Gasco, N. & Davaine, P. Poissons des îles Kerguelen et Crozet: Guide régional de l’océan Austral (Muséum national d’Histoire naturelle, Paris, 2005).Trebilco, R. et al. Mesopelagic community struture on the southern Kerguelen Axis. In The Kerguelen Plateau: Marine Ecosystem and Fisheries, 49–54 (Australian Antarctic Division, Kingston, Tasmania, 2019).Constable, A. J. & Swadling, K. M. Ecosystem drivers of food webs on the Kerguelen Axis of the Southern Ocean. Deep-Sea Research Part II-Topical Studies in Oceanography 174, 6, https://doi.org/10.1016/j.dsr2.2020.104790 (2020).Article 

Google Scholar 
Van de Putte, A. P., Jackson, G. D., Pakhomov, E., Flores, H. & Volckaert, F. A. M. Distribution of squid and fish in the pelagic zone of the Cosmonaut Sea and Prydz Bay region during the BROKE-West campaign. Deep-Sea Research Part II-Topical Studies in Oceanography 57, 956–967, https://doi.org/10.1016/j.dsr2.2008.02.015 (2010).ADS 
CAS 
Article 

Google Scholar 
Flynn, A. J. & Williams, A. Lanternfish (Pisces: Myctophidae) biomass distribution and oceanographic-topographic associations at Macquarie Island, Southern Ocean. Marine and Freshwater Research 63, 251–263, https://doi.org/10.1071/mf11163 (2012).Article 

Google Scholar 
Sutton, C. A., Kloser, R. J. & Gershwin, L. A. Micronekton in southeastern Australian and the Southern Ocean; A collation of the biomass, abundance, biodiversity and distribution data from CSIRO’s historical mesopelagic depth stratified new samples. CSIRO, Aust. http://hdl.handle.net/102.100.100/365479?index=1 (2018).Gon, O. & Heemstra, P. C. Fishes of the Southern Ocean (J.L.B. Smith Institute of Ichthyology, Grahamstown, South Africa, 1990).Darwin Core Maintenance Group. List of Darwin Core terms (2021).R Core Team. R: A language and environment for statistical computing (2021).Holstein, J. worms: Retrieving Aphia Information from World Register of Marine Species (2018).Bivand, R. et al. maptools: Tools for handling spatial objects. R package version 1.1-1 (2021).Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Research Part I-Oceanographic Research Papers 42, 641–673, https://doi.org/10.1016/0967-0637(95)00021-w (1995).ADS 
Article 

Google Scholar 
Constable, A. J. et al. Climate change and Southern Ocean ecosystems I: how changes in physical habitats directly affect marine biota. Global Change Biology 20, 3004–3025, https://doi.org/10.1111/gcb.12623 (2014).ADS 
Article 
PubMed 

Google Scholar 
Woods, B. et al. Myctobase. Zenodo https://doi.org/10.5281/zenodo.5590999 (2021).Saunders, R. A., Collins, M. A., Stowasser, G. & Tarling, G. A. Southern Ocean mesopelagic fish communities in the Scotia Sea are sustained by mass immigration. Marine Ecology Progress Series 569, 173–185, https://doi.org/10.3354/meps12093 (2017).ADS 
Article 

Google Scholar 
Provoost, P. & Bosch, S. obistools: Tools for data enhancement and quality control (2021).Murphy, E. J. et al. Understanding the structure and functioning of polar pelagic ecosystems to predict the impacts of change, https://doi.org/10.1098/rspb.2016.1646 (2016).McCormack, S. A., Melbourne-Thomas, J., Trebilco, R., Blanchard, J. L. & Constable, A. Alternative energy pathways in Southern Ocean food webs: Insights from a balanced model of Prydz Bay, Antarctica. Deep-Sea Research Part II-Topical Studies in Oceanography 174, https://doi.org/10.1016/j.dsr2.2019.07.001 (2020).Rodhouse, P. G. K. Role of squid in the Southern Ocean pelagic ecosystem and the possible consequences of climate change. Deep-Sea Research Part II-Topical Studies in Oceanography 95, 129–138, https://doi.org/10.1016/j.dsr2.2012.07.001 (2013).ADS 
CAS 
Article 

Google Scholar 
The MathWorks Inc., V.. MATLAB (2019).Potter, D. C., Lough, R. G., Perry, R. I. & Neilson, J. D. Comparison of the mocness and iygpt pelagic samplers for the capture of 0-group cod (gadus morhua) on georges bank. ICES Journal of Marine Science 46, https://doi.org/10.1093/icesjms/46.2.121 (1990).Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. Journal of Animal Ecology 77, 802–813, https://doi.org/10.1111/j.1365-2656.2008.01390.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Oppel, S. et al. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biological Conservation 156, https://doi.org/10.1016/j.biocon.2011.11.013 (2012).McClatchie, S., Thorne, R. E., Grimes, P. & Hanchet, S. Ground truth and target identification for fisheries acoustics. Fisheries Research 47, 173–191, https://doi.org/10.1016/s0165-7836(00)00168-5 (2000).Article 

Google Scholar 
Collins, M., Piatkowski, U. & Saunders, R. A. Distribution of mesopelagic fish in the Scotia Sea from RMT25 and pelagic trawls deployed from RRS James Clark Ross and RRS John Biscoe, UK Polar Data Centre https://doi.org/10.5285/f4dfc0ee-4f61-47c5-a5a8-238e02ff2fdd (2021).Hoddell, R. J., Crossley, C., Hosie, G. & Williams, D. Fish and zooplankton from RMT-8 net hauls on the BROKE voyage. Australian Antarctic Data Centre https://doi.org/10.4225/15/57BA97EA8A22D (2016).Constable, A., Williams, D. & Lamb, T. Heard Island and McDonald Islands (HIMI) Marine Ecosystem. Australian Antarctic Data Centre https://doi.org/10.4225/15/5b31be45e8977 (2018).Van de Putte, A. Fish catches from Rectangular Midwater Trawl – data collected from the BROKE-West voyage of the Aurora Australis, 2006. Australian Antarctic Data Centre https://doi.org/10.4225/15/598d453109182 (2010).Flynn, A. J., Kloser, R. J. & Sutton, C. Micronekton assemblages and bioregional setting of the Great Australian Bight: A temperate northern boundary current system. Deep-Sea Research Part II: Topical Studies in Oceanography 157–158, https://doi.org/10.1016/j.dsr2.2018.08.006 (2018).Oozeki, Y., Hu, F., Tomatsu, C. & Kubota, H. Development of a new multiple sampling trawl with autonomous opening/closing net control system for sampling juvenile pelagic fish. Deep-Sea Research Part I-Oceanographic Research Papers 61, https://doi.org/10.1016/j.dsr.2011.12.001 (2012). More

Global vegetation physiological response to increasing atmospheric CO2 and its reduction of mitigation potentialWe calculate different effects of increasing CO2 on mean annual near-surface air temperature change over global vegetated land. We compare the direct atmospheric radiative effect (RAD)-induced climate warming to the temperature reductions caused by the BGC effect. These two global temperature changes are, in turn, then compared against our main focus of aggregated local PHY-induced temperature contributions (PHYall; Fig.1). The main finding is that the spatial aggregation of PHY feedbacks on temperature over global vegetated land (green bars) offsets a substantial amount of the cooling effect through enhanced terrestrial carbon storage because of the BGC effects (blue bars). Terrestrial carbon storage continuously increases with rising atmospheric CO2, and reaches a global total of 621 ± 260 Pg C under 4 × CO2 (Supplementary Fig. 1). This increased land carbon storage is equivalent to 293 ± 122 ppm of CO2 removal from the atmosphere and results in a temperature cooling of −1.24 ± 0.57 °C. The PHYall-induced land temperature increase is 0.83 ± 0.47 °C, from the ensemble of five ESMs we use (here we excluded bcc-csm1-1; see “Methods”), corresponding to a large offset of the cooling effect by terrestrial ecosystems through BGC. However, our estimated temperature cooling induced by the BGC effect is a transient response. This cooling effect will be larger for subsequently stabilised CO2 concentrations, since terrestrial ecosystems continue to fix carbon until reaching equilibrium. We note that BGC-induced cooling may be overestimated in the absence of land-cover change effect in the simulations, as the latter may reduce terrestrial carbon stores (Supplementary Fig. 2). However, inter-model differences, such as different parameterisation or different biogeochemistry module20, may prevent a definitive answer as to how land-cover change influence global temperature change through BGC (Supplementary Fig. 2–5). We also note that we focus our analysis on CMIP5 data mainly due to the fact that the parameters in Eqs. (1) and (2)21,22 (see “Methods”) for calculating BGC-induced cooling are only currently available for CMIP5 ESMs.Fig. 1: Climate warming mitigation potential of terrestrial ecosystems.Global mean land temperature change due to total CO2 physiological forcing (PHYall), increased terrestrial carbon storage (BGC) and CO2 radiative forcing (RAD). Note axes as coloured, and that the vertical blue axis is temperature cooling through BGC. It is straightforward to compare PHYall-, RAD- and BGC-induced temperature change when the same range and directions of bars are used. Three atmospheric CO2 horizons are selected here, for CO2 concentrations of 500 ppm, 800 ppm and 1032 ppm (4 × CO2). Each bar represents the global area-weighted average from the ensemble of five ESMs. The error bars indicate the standard error of these five models. Note, the bcc-csm1-1 model did not provide diagnostics of carbon storage data and so is not included in calculating the temperature change by carbon storage change.Full size imageThe RAD response to rising CO2 projects a global warming of 5.25 ± 0.65 °C, again under 4 × CO2, corresponding to roughly four times the magnitude of BGC-induced cooling. Hence, PHYall-induced temperature increase adds about 16 ± 8% to the RAD-induced warming globally. The relative magnitude of warming/cooling effects is similar for the lower CO2 levels of 500 ppm and 800 ppm (Fig. 1), illustrating the importance of accounting for PHYall-induced warming and how it affects the ability of terrestrial vegetation to mitigate global warming, irrespective of CO2 concentration. Our identified PHYall feedbacks are a combination of different altered land properties. The balance of changed land components will depend on location, and so spatial variations in the overall warming effect may suggest a reappraisal of some climate change adaptation measures. Hence to aid such assessments, we now consider in detail the global contributions of individual drivers of the PHY-based local warming, and then any geographical variations.In Fig. 2a, we show changes in global vegetated land air temperature associated with PHYall with increasing atmospheric CO2 from our ensemble of six ESMs (see “Methods”). PHYdir (see “Methods”; Supplementary Table 1) is based on the direct CO2 effect on vegetation physiology. PHYall represents all the vegetation physiology-related feedbacks, and captures any additional interactions between RAD and PHYdir, due to all effects not being a simple linear addition of RAD and PHY responses. The difference, PHYall minus PHYdir, is termed PHYint. Although inter-model difference exists, warming levels are projected to increase, by all the ESMs, as CO2 concentration rises, and for both PHYdir and PHYall (Fig. 2a). The interactive effects result in PHYall-induced temperature change being higher than PHYdir throughout the period. For the smaller increases in atmospheric CO2 of up to ~450 ppm, interactive effects dominate with PHYdir being almost zero up to that concentration. However, above the CO2 concentration of 450 ppm, PHYdir increases global warming reaching 0.17 ± 0.06 °C for CO2 of 500 ppm, and climbing to 0.62 ± 0.48 °C under quadrupled atmospheric CO2 (Fig. 2a). At that 4 × CO2 level, interaction term PHYint increases warming by approximately one-fifth of that induced by PHYdir. Hence, the overall physiological feedbacks described by PHYall produce a global temperature increase of 0.74 ± 0.47 °C under 4 × CO2 (again from multi-model mean of six ESMs; see “Methods”).Fig. 2: Change in global mean annual land air temperature and individual climate forcing induced by vegetation physiological response to increasing atmospheric CO2.a Global annual area-weighted temperature change of vegetated land induced by total CO2 physiological forcing (PHYall) and the direct CO2 physiological forcing (PHYdir) in response to increasing atmospheric CO2 concentration. Shaded areas are the standard errors of the six Earth System Models (ESMs) used, and the thick curves are their multi-model means. For each atmospheric CO2 concentration in panel (a), values are based on smoothing using a twenty-year running window (to match with the decomposition results in panel (b). The final temperature change induced by PHYall and PHYdir effects under 4 × CO2 are further marked on the righthand side, with the “+” markers indicating multi-model means. b PHYall-induced climate forcing associated with changes in albedo, aerodynamic resistance (ra), evapotranspiration (ET), downwelling shortwave radiation (SW) and near-surface air emissivity (ɛa). Again, the shaded areas are the standard errors of the models, and the mean is the thick continuous lines. The changes in these variables are calculated using a moving average with a 20-year window. The resulting values under 4 × CO2 are plotted on the righthand side.Full size imageTo better understand the factors influencing CO2 physiological drivers of temperature change, we decompose the global PHYall into individual biophysical components6 (see “Methods”). These five aspects are albedo, aerodynamic resistance (ra), evapotranspiration (ET), downwelling shortwave radiation (SW) and near-surface air emissivity (ɛa). These five-component changes result in climate forcings with different signs and magnitudes (Fig. 2b), and thus perturb the surface energy balance, where positive values correspond to an increase in temperature. Specifically, ET, SW, and albedo generates positive climate forcings that increase local temperature, while ra and ɛa produce negative effects and thus offset local temperature increase. The relative role of each biophysical component in influencing PHYall-induced temperature change remains largely invariant in the transition from low to high CO2 concentrations. In addition, the changes in these five quantities affecting PHYdir show similar variations with that of PHYall as atmospheric CO2 rises (Supplementary Fig. 6). Those results are generally valid for CMIP6 results but with a lower magnitude of change (Supplementary Fig. 7).The vegetation physiological response to rising CO2 causes changes in LAI and stomata closure, which adjust in parallel with more detailed attributes of the land surface. The ESM simulations of changes in LAI and transpiration compare moderately well with available field measurements23,24,25,26,27,28,29,30,31,32 (Supplementary Fig. 8). Here we focus on their effects on the near-surface thermal changes, expressed as climate forcings on near-surface energy fluxes (Fig. 2b). The LAI increase due to elevated CO2 (Supplementary Fig. 9a) leads to decreases in albedo11,33 (Supplementary Fig. 10a; Supplementary Table 2) and ra7 (Supplementary Fig. 10c). These changes are continuous with rising atmospheric CO2 and result in positive and negative effects on global land air temperature change, respectively (Fig. 2b). Specifically, the albedo reduction increases solar radiation absorption by the land surface11,33, and imposes a positive forcing of 0.30 ± 0.14 W m−2 for 500 ppm, and of 0.51 ± 0.45 W m−2 for a quadrupling of CO2 (Fig. 2b). The decreased ra favours the turbulent transport of heat from land to atmosphere7, and leads to a persistent surface cooling with increasing atmospheric CO234,35, which is −1.66 ± 0.44 W m−2 for 500 ppm and −3.15 ± 2.05 W m−2 for 4 × CO2. In contrast, ET decreases considerably (Supplementary Fig. 10e; Supplementary Table 2) due to decreased stomatal conductance responding to increasing atmospheric CO2. These reductions in ET reduce evaporative cooling15,17, and therefore result in a strong positive climate forcing on global warming (Fig. 2b), which increases with rising atmospheric CO2 and reaches 0.88 ± 0.25 W m−2 for atmospheric CO2 of 500 ppm and 2.88 ± 1.49 W m−2 with quadrupled atmospheric CO2 concentration (Fig. 2b).Moreover, ET reduction decreases the inflow of evaporative water to the atmosphere, reducing cloud fraction and water vapour content, thereby feeding back to impose indirect effects that influence temperature change. We quantify these indirect effects through their effects on the components of SW and ɛa, (Fig. 2b). The lower ET values reduce cloud fraction (Supplementary Table 2; Supplementary Fig. 9c) thereby increasing the amount of SW to the land surface6,35,36 (Supplementary Fig. 10g), and producing a moderate positive forcing of 0.67 ± 0.16 W m−2 for 500 ppm and 1.76 ± 1.26 W m−2 under 4 × CO2 (Fig. 2b). However, the reduced cloud fraction and water vapour content decreases ɛa (Supplementary Fig. 10i), which weakens the absorption of longwave radiation37 (Supplementary Fig. 9e). The ɛa changes result in a small negative forcing (−0.28 ± 0.11 W m−2 for 500 ppm and −0.57 ± 0.43 W m−2 for 4 × CO2), and thereby lowering global temperatures. Of particular note is that the direct changes in ET produce the strongest positive forcing for warming (Fig. 2b). Hence, ET changes have a dominant role in influencing the magnitude and sign of PHYall feedbacks on global temperature change. The changes in these five factors for PHYdir are very close in relative terms to those of PHYall (Supplementary Fig. 10), driving climate forcings in a similar magnitude and sign to influence PHYdir-induced global temperature change (Supplementary Fig. 6).Overall, as CO2 rises, these five biophysical factors cause vegetation to amplify warming locally, and when aggregated spatially act to raise planetary global warming. This additional warming effect is primarily driven by changes in ET, with smaller warming contributions from changes in SW and albedo, but also compensated with cooling effects from ra and ɛa changes. The substantial role of ET on biophysical climate feedbacks is consistent with a previous study6, which investigates the biophysical feedbacks because of vegetation greening. That study proposed that CO2-driven greening will enhance ET and thereby producing a net cooling effect. We build on that analysis by here additionally including the CO2-induced partial stomatal closure17,18. We find this inclusion overtakes the LAI influence on ET changes, resulting in large reductions in ET that will instead contribute to net warming as CO2 rises. Large inter-model differences in simulating ra dynamics (Supplementary Fig. 10c, d) mostly explains the substantial spread of ra-induced climate forcing (Fig. 2b). The different extents of LAI increase (Supplementary Fig. 9a, b) also contribute to such differences in simulated ra changes and effects on temperature change. Our noted large cooling through ra changes is also indicated in a recent study7, suggesting the importance of ra in affecting vegetation biophysical climate feedbacks, and the importance of constraining this factor in climate models.Spatial patterns and attributions of vegetation physiologically induced warmingWe present in Fig. 3 the area-weighted regional contributions to global temperature change of the physiological responses and BGC under 4 × CO2. PHYall-based warming and BGC-induced cooling both show larger values in East and Central North America (ENA and CNA), North and Central Europe (NEU and CEU), Amazon (AMZ) and North Asia (NAS). Whereas, the smallest changes are in the Sahel region (SAH) (Fig. 3a). PHYall-induced warming reduces large proportions of the temperature cooling through BGC in the northern mid-to-high latitudes (Fig. 3b). Furthermore, this cooling effect is fully offset by warming through vegetation physiological response in Alaska (ALA), Canada/Greenland/Iceland (CGI), NAS, NEU, SAH, Tibetan Plateau (TIB), Central (CAS) and West Asia (WAS), and West North America (WNA), resulting in slight warming in these regions. Presented as a map of the net effects of BGC and PHYall (Fig. 3b), we further illustrate this overall cooling effect from PHY and BGC for the tropical regions and South Hemisphere. Additionally, the balance of PHY and BGC to influence regional temperature change under relatively low atmospheric CO2 level of 500 ppm (Supplementary Fig. 11) is very close to that for 4 × CO2, suggesting consistency of vegetation biophysical and biogeochemical effects on climate irrespective of CO2 levels. In summary, the PHYall feedbacks offset the cooling benefits from ecosystem “draw-down” of CO2 to a large extent, in line with the global average values presented in Fig. 1. Northern mid-to-high latitudes may contribute less than expected to slow global warming (Fig. 3). However, as also noted for the global change values, the estimated temperature cooling by BGC is a transient effect that would keep increasing as the ecosystems approach their equilibrium. However, BGC-induced cooling may be overestimated without consideration of the effect of land-cover change (Supplementary Fig. 2), which is often associated with the deliberate removal of terrestrial carbon.Fig. 3: Regional contributions to temperature change by PHY and BGC under 4 × CO2.a Contribution of regional vegetation physiological responses (PHYall; green bars) and increased carbon storage (BGC; blue bars) to the overall global temperature change for each of the IPCC AR5 SREX regions. Bars represent area-weighted multi-model means, and the error bars indicate the standard errors of the models for each region. b Spatial distribution of the net effects of warming induced by PHYall and cooling through BGC.Full size imageThe regional pattern in Fig. 3b provides a motivation to investigate further the five climate forcings-driven PHYall contributions (Fig. 2a) to near-surface temperature change triggered by rising CO2. Here, we first examine the much finer spatial patterns and component contributions of PHYall, PHYdir and PHYint on warming for 4 × CO2. Focussing on PHYdir and typically for 4 × CO2, there is a larger local warming in the tropical forests (Fig. 4a), where vegetation shows higher potential to stabilise carbon than other ecosystems10 (Fig. 3). Within these tropical regions, the PHYdir-forced temperature increase is the highest in the Amazon forests (Fig. 4a), reaching approximately 30% of that induced by RAD (Supplementary Fig. 12b). Strong warming by PHYdir is also found in the northern mid-to-high latitudes ( >40°N). In contrast, smaller temperature increases due to PHYdir are seen in arid and semi-arid regions, such as Australia and Sahel (Fig. 4a). The spatial variations of PHYall-forced temperature change (Supplementary Fig. 13a) have strong similarities to those of PHYdir (Fig. 4a). These similarities again suggest a relatively small role of interactions on temperature change (Supplementary Fig. 13b) under quadrupled CO2. Additionally, the agreements across the six ESMs are reasonably high, with all the models agreeing that PHYall, PHYdir and PHYint result in local warming for 4 × CO2 across most of the global vegetated land (Supplementary Fig. 14).Fig. 4: Global patterns of local temperature change and climate forcings through vegetation physiological response to 4 × CO2.Spatial distribution of annual mean temperature change from multi-model ensemble induced by (a). direct CO2 physiological forcing (PHYdir) in response to a 4 × CO2 rise since pre-industrial level. The spatial patterns of the individual climate forcing contributing to PHYdir of panel (a) are as follows. In (b). albedo (α), c Aerodynamic resistance (ra), d Evapotranspiration (ET), e Downwelling shortwave radiation (SW) and (f). near-surface air emissivity (ɛa). In all panels, estimates use the mean of the final 20 years of the simulations (atmospheric CO2 at ~1032 ppm).Full size imageWe next analyse spatially the decomposition of PHYall- and PHYdir-induced temperature change into the five biophysical factors shown in Fig. 2b, and with findings shown in Fig. 4b–f. In response to quadrupled CO2, LAI shows increases over most global vegetated land (Supplementary Fig. 15b), leading to a positive climate forcing (and thus warming) through reducing albedo (Fig. 4b), and especially in the south Sahel and Tibetan Plateau. Moreover, a large albedo decrease occurs in the northern mid-to-high latitudes, such as for most of Siberia. This albedo decrease may be due to LAI increase combined with reduced snow cover promoted by PHY-driven warming17. The LAI increase also contributes to a strong negative forcing through ra decrease7. This ra decrease enhances the energy exchange between land and atmosphere, lowering the local warming effect in response to 4 × CO2, particularly in Australia, Sahel, South Africa and South Asia (Fig. 4c). In comparison, the projected forcing from ET reductions (Supplementary Fig. 15d) causes strongly positive warming, and especially in the tropical and boreal forests (Fig. 4d). The larger ET reductions in the tropical and boreal forests also lead to stronger cloud fraction decreases (Supplementary Fig. 15f). These feedbacks induce a positive climate forcing for additional warming by increasing SW reaching the land surface (Fig. 4e). Simultaneously, the decreased cloud cover and water vapour by the ET reductions generate a cooling effect (Fig. 4f) through decreasing net longwave radiation absorption in most locations. The PHYdir-induced warming and the associated climate forcings at an atmospheric CO2 concentration of 500 ppm (Supplementary Fig. 16) show similar spatial distributions, but smaller magnitude, compared with that adjustment for 4 × CO2 increase (Fig. 4). This result manifests that PHY continues to amplify global warming through the combined climate forcings from our identified five biophysical factors, irrespective of CO2 concentration.Substantial differences exist between PHYall- and PHYdir-induced temperature changes in northwestern Eurasia, implying pronounced PHYint-based feedbacks there under 4 × CO2 (Supplementary Fig. 13b). A relatively large change in SW, inducing a warming effect (Supplementary Fig. 13j), mainly contributes to the large PHYint-coupled changes in this region. Elsewhere, for the Amazon forests, the cooling effect in PHYint, through changes in ET (Supplementary Fig. 13h) and SW (Supplementary Fig. 13j), cancels out the warming due to changes in ra (Supplementary Fig. 13f) and ɛa (Supplementary Fig. 13l). This cancellation causes negligible PHYint feedback on temperature there (Supplementary Fig. 13b). In summary, local warming due to biophysical feedback of increasing CO2 is regulated primarily by the positive forcing from ET reductions (with smaller positive forcings from albedo and SW changes), which is partly compensated by reductions in ra (and a small contribution from ɛa).Strong physiologically induced warming in dense ecosystemsWe further investigate the finding presented in Fig. 4 that PHYdir-forced temperature increase is higher in the tropical forests, while lower in the arid and semi-arid ecosystems. This finding suggests that there may be a relationship between the forced temperature change and background baseline LAI, which we confirm in Fig. 5a (for both PHYall and PHYdir). We find widespread warming amplification with increasing LAI for lower background LAI levels. Such rates of increase in temperature per unit of LAI show evidence of flattening at higher LAI values38. That is, the CO2-fertilised LAI increase saturates in ecosystems with dense canopy cover such as tropical forests (Supplementary Fig. 17a). However, the stomatal closure-induced ET decrease varies almost linearly with baseline LAI, although the reduction breaks down when the baseline LAI approaches six (Supplementary Fig. 17b). Hence, we conclude that ET reductions caused by stomatal closure are the primary cause of PHY-induced temperature rises, although changes in ra because of LAI increases remain an important factor (Fig. 4; Supplementary Fig. 13). Taking all the components together, PHYdir-triggered temperature change increases almost linearly with baseline LAI gradients under 4 × CO2 (Fig. 5a). Of particular interest is that when expressing PHYdir-induced temperature increase as a fraction of warming induced by RAD, it also increases with the baseline LAI value (Fig. 5b). Hence, Fig. 5 provides strong evidence that background vegetation functional structure (LAI) influences the feedback of CO2 physiological forcing on warming in response to 4 × CO2. In terms of change, as the trend of global LAI increase slows down as atmospheric CO2 concentration increases to very high levels (Supplementary Fig. 9a, b), this suggests that the magnitude of the cooling effect (via ra) will decrease relative to the ET-based warming.Fig. 5: Effects of vegetation structure on CO2 physiological forcing in response to 4 × CO2.a Variations of local temperature change induced by total (PHYall) and direct (PHYdir) CO2 physiological forcing, for quadrupled CO2 and presented as a function of baseline pre-industrial leaf area index (LAI). b Variations of the ratio of PHYall- and PHYdir-induced temperature change relative to that of RAD warming, again for 4 × CO2 and as a function of baseline LAI. In both panels, the temperature changes along LAI gradients are smoothed using a five-bin running window for the 0.1 increments in LAI bin size. The shaded areas indicate the standard error among different models.Full size imageImplications and conclusionsTerrestrial ecosystems respond to increasing atmospheric CO2 not only through accumulating carbon (the “BGC” effect) but also through their physiological response (the “PHY” feedback), which can have opposing effects on local surface temperature. As such, to fully understand the net climate benefits of terrestrial ecosystems, comprehensive assessments need to account for both PHY and BGC effects39,40. We find that the warming through PHY feedback largely reduces the capacity of vegetation to slow global warming through BGC. In particular, vegetation transiently operates to amplify local warming in the northern mid-to-high latitudes, as PHY-induced warming is larger than BGC-induced cooling (Fig. 3). Tropical forests have net cooling effects, and forests and ecosystem restoration efforts there would have much higher net cooling benefit for mitigation than if they are placed in the northern mid-to-high latitudes. At the global scale, this PHY-induced warming can offset by up to 67% of the cooling gains from the transient BGC effect (Fig. 1). This is likely an upper-bound value, because the steady-state BGC effect is larger than the transient one. Thus, afforestation can still result in a net cooling effect, especially in the tropics, although the cooling achieved may be less than first expected. Of particular interest is that the PHY-based warming is generally stronger for high baseline LAI values (Fig. 5). This result further suggests the PHY produces extra warming through afforestation due to higher LAI for forests than non-forests. However, this does not contradict the finding for the tropics (where LAI is high) as possibly the most beneficial regions for afforestation, due to the compensating much larger BGC potential to lower temperature for such locations.Our analysis highlights the importance of including PHY feedbacks in any assessments of future levels of global warming. Such understanding may be especially important for the northern mid-to-high latitudes. We point out that only one-third of ESMs we use incorporate dynamic global vegetation schemes (DGVMs). Since land-cover change acts to influence PHY and BGC effects (Supplementary Fig. 2–5), future simulations with dynamic vegetation may allow refining these results. However, we note that inter-model structural differences (e.g. different ratio of transpiration to ET15,41) contribute to large uncertainties in these results. In particular, we note the potential implications of our results for climate mitigation policies with substantial reforestation as a mechanism to slow global warming through emissions offsetting. Reforestation may cause a near-surface cooling through the BGC effect. However, the PHY-based warming effect is stronger for higher LAI values as atmospheric CO2 rises (Fig. 5a). This must be accounted for in any major global reforestation plans with consideration of particular location of reforestation measures42. In general, the effects of anthropogenic land use and land cover change on vegetation biophysical and biogeochemical processes are important and complex with substantial spatial heterogeneity42,43,44,45,46,47. Given the lack of exploitable factorial simulations designed to separate land use and land-cover change effects, these are ignored in the idealised simulations, and may introduce a bias in our results. We note, however, that these idealized “4 × CO2” simulations broadly capture the sign and spatial distributions of land-atmosphere coupling effects on temperature change through comparisons to the ESM results under the RCP8.5 scenario (Supplementary Fig. 18) which include anthropogenic land use and land-cover change. We suggest that future climate projections should much more routinely account for the effect of anthropogenic land use and land-cover change together with PHY and BGC effects42,47, and so that this can be considered in reforestation climate mitigation strategies.An additional caveat of our research is that the PHY and RAD factorial ESM simulations that inform our analysis, are at present only available for the illustrative but potentially unrealistic exponential increase in atmospheric CO2 (1% per year). For this scenario, the systems can exhibit a unrealistic linear behaviour48. PHY and RAD simulations under other scenarios, such as abrupt 4 × CO2 or even historical forcing followed by a potential future scenario, would greatly help to improve projections of PHY feedbacks. We suggest this should be made a high priority of climate modelling exercises, especially if additionally including land use and land-cover change representation. We recognise that the newer CMIP6 ESMs contain improved representation of many processes (e.g. atmospheric aerosols, clouds, and land processes)49. We anticipate our findings to remain broadly valid with the CMIP6 models, although the magnitude of PHY-induced warming is lower for CMIP6 than CMIP5 ESMs18, especially in the northern high latitudes. We hope our analysis provides an incentive to others to undertake such analysis as all the calculations (BGC, PHY, and RAD) become available for that newer CMIP6 ensemble. In summary, our results illustrate that vegetation physiological response to increasing atmospheric CO2 has a substantial local warming effect, which requires consideration alongside the cooling effect vegetation offers by “drawing down” atmospheric carbon dioxide. More

Lindenmayer, D. B., Cunningham, R. B., Donnelly, C. F. & Lesslie, R. On the use of landscape surrogates as ecological indicators in fragmented forests. For. Ecol. Manag. 159(3), 203–216. https://doi.org/10.1016/S0378-1127(01)00433-9 (2002).Article 

Google Scholar 
Hannah, L., Carr, J. L. & Lankerani, A. Human disturbance and natural habitat: a biome level analysis of a global data set. Biodivers. Conserv. 4(2), 128–155. https://doi.org/10.1007/BF00137781 (1995).Article 

Google Scholar 
Sabatini, F. M. et al. Where are europe’s last primary forests?. Divers. Distrib. 24(10), 1426–1439. https://doi.org/10.1111/ddi.12778 (2018).Article 

Google Scholar 
Mikoláš, M. et al. Primary forest distribution and representation in a central european landscape: results of a large-scale field-based census. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2019.117466 (2019).Article 

Google Scholar 
Hilmers, T. et al. Biodiversity along temperate forest succession. J. Appl. Ecol. 55(6), 2756–2766. https://doi.org/10.1111/1365-2664.13238 (2018).Article 

Google Scholar 
Nagel, T. A., Svoboda, M. & Diaci, J. Regeneration patterns after intermediate wind disturbance in an old-growth fagus-abies forest in southeastern Slovenia. For. Ecol. Manag. 226(1–3), 268–278. https://doi.org/10.1016/j.foreco.2006.01.039 (2006).Article 

Google Scholar 
Thorn, S. et al. Estimating retention benchmarks for salvage logging to protect biodiversity. Nat. Commun. 11, 4762. https://doi.org/10.1038/s41467-020-18612-4 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE https://doi.org/10.1371/journal.pone.0185809 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: a review of its drivers. Biol. Conserv. 232, 8–27. https://doi.org/10.1016/j.biocon.2019.01.020 (2019).Article 

Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674. https://doi.org/10.1038/s41586-019-1684-3 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Seibold, S. et al. Experimental studies of dead-wood biodiversity — a review identifying global gaps in knowledge. Biol. Conserv. 191, 139–149. https://doi.org/10.1016/j.biocon.2015.06.006 (2015).Article 

Google Scholar 
Paillet, Y. et al. Biodiversity differences between managed and unmanaged forests: meta-analysis of species richness in Europe. Conserv. Biol. 24(1), 101–112. https://doi.org/10.1111/j.1523-1739.2009.01399.x (2010).Article 
PubMed 

Google Scholar 
Cálix, M., Alexander, K. N. A., Nieto, A., Dodelin, B. et al. European Red List of Saproxylic Beetles (IUCN. 19 s, Brussels, Belgium, 2018). Available at: http://www.iucnredlist.org/initiatives/europe/publicationsSchiegg, K. Effects of dead wood volume and connectivity on saproxylic insect species diversity. Écoscience 7(3), 290–298. https://doi.org/10.1080/11956860.2000.11682598 (2016).Article 

Google Scholar 
Müller, J. et al. Implications from large-scale spatial diversity patterns of saproxylic beetles for the conservation of european beech forests. Insect Conserv. Divers. 6(2), 162–169. https://doi.org/10.1111/j.1752-4598.2012.00200.x (2013).Article 

Google Scholar 
Schneider, A. et al. Animal diversity in beech forests – an analysis of 30 years of intense faunistic research in hessian strict forest reserves. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2021.119564 (2021).Article 

Google Scholar 
Brunet, J., Fritz, Ö. & Richnau, G. Biodiversity in European beech forests—a review with recommendations for sustainable forest management. Ecol. Bull. 53, 77–94 (2010).
Google Scholar 
Bilek, L., Remes, J. & Zahradnik, D. Managed vs. unmanaged. Structure of beech forest stands (Fagus sylvatica L.) after 50 years of development central Bohemia. For. Syst. 20(1), 122–138. https://doi.org/10.5424/fs/2011201-10243 (2011).Article 

Google Scholar 
Müller, J., Bußler, H. & Kneib, T. Saproxylic beetle assemblages related to silvicultural management intensity and stand structures in a beech forest in southern Germany. J. Insect Conserv. 12(2), 107–124. https://doi.org/10.1007/s10841-006-9065-2 (2008).Article 

Google Scholar 
Doerfler, I., Müller, J., Gossner, M. M., Hofner, B. & Weisser, W. W. Success of a deadwood enrichment strategy in production forests depends on stand type and management intensity. For. Ecol. Manag. 400, 607–620. https://doi.org/10.1016/j.foreco.2017.06.013 (2017).Article 

Google Scholar 
Doerfler, I., Gossner, M. M., Müller, J., Seibold, S. & Weisser, W. W. Deadwood enrichment combining integrative and segregative conservation elements enhances biodiversity of multiple taxa in managed forests. Biol. Conserv. 228, 70–78. https://doi.org/10.1016/j.biocon.2018.10.013 (2018).Article 

Google Scholar 
Doerfler, I. et al. Restoration-oriented forest management affects community assembly patterns of deadwood-dependent organisms. J. Appl. Ecol. 57(12), 2429–2440. https://doi.org/10.1111/1365-2664.13741 (2020).Article 

Google Scholar 
Zumr, V., Remeš, J. & Pulkrab, K. How to increase biodiversity of saproxylic beetles in commercial stands through integrated forest management in central Europe. Forests https://doi.org/10.3390/f12060814 (2021).Article 

Google Scholar 
Svoboda, M., Fraver, S., Janda, P., Bače, R. & Zenáhlíková, J. Natural development and regeneration of a central european montane spruce forest. For. Ecol. Manag. 260(5), 707–714. https://doi.org/10.1016/j.foreco.2010.05.027 (2010).Article 

Google Scholar 
Šebková, B. et al. Spatial and volume patterns of an unmanaged submontane mixed forest in central Europe: 160 years of spontaneous dynamics. For. Ecol. Manag. 262(5), 873–885. https://doi.org/10.1016/j.foreco.2011.05.028 (2011).Article 

Google Scholar 
Bílek, L. et al. Gap regeneration in near-natural european beech forest stands in central bohemia – the role of heterogeneity and micro-habitat factors. Dendrobiology https://doi.org/10.12657/denbio.071.006 (2013).Article 

Google Scholar 
Čada, V. et al. Frequent severe natural disturbances and non-equilibrium landscape dynamics shaped the mountain spruce forest in central Europe. For. Ecol. Manag. 363, 169–178. https://doi.org/10.1016/j.foreco.2015.12.023 (2016).Article 

Google Scholar 
Thorn, S. et al. Impacts of salvage logging on biodiversity: a meta-analysis. J. Appl. Ecol. 55(1), 279–289. https://doi.org/10.1111/1365-2664.12945 (2018).Article 
PubMed 

Google Scholar 
Schelhaas, M.-J., Nabuurs, G.-J. & Schuck, A. Natural disturbances in the European forests in the 19th and 20th centuries. Glob. Change Biol. 9(11), 1620–1633. https://doi.org/10.1046/j.1365-2486.2003.00684.x (2003).ADS 
Article 

Google Scholar 
Vera, F. W. M. (ed.) Grazing Ecology and Forest History (CABI, 2000). https://doi.org/10.1079/9780851994420.0000.Book 

Google Scholar 
Vera, F. W. M. The dynamic European forest. Arboric. J. 26(3), 179–211. https://doi.org/10.1080/03071375.2002.9747335 (2012).Article 

Google Scholar 
Swanson, M. E. et al. The forgotten stage of forest succession: early-successional ecosystems on forest sites. Front. Ecol. Environ. 9(2), 117–125. https://doi.org/10.1890/090157 (2011).Article 

Google Scholar 
Lachat, T. et al. Influence of canopy gaps on saproxylic beetles in primeval beech forests: a case study from the Uholka-Shyrokyi Luh forest, Ukraine. Insect Conserv. Divers. 9(6), 559–573. https://doi.org/10.1111/icad.12188 (2016).Article 

Google Scholar 
Gossner, M. M. et al. Current near-to-nature forest management effects on functional trait composition of saproxylic beetles in beech forests. Conserv. Biol. 27(3), 605–614. https://doi.org/10.1111/cobi.12023 (2013).Article 
PubMed 

Google Scholar 
Procházka, J. & Schlaghamerský, J. Does dead wood volume affect saproxylic beetles in montane beech-fir forests of central Europe?. J. Insect Conserv. 23(1), 157–173. https://doi.org/10.1007/s10841-019-00130-4 (2019).Article 

Google Scholar 
Winter, S. & Möller, G. C. Microhabitats in lowland beech forests as monitoring tool for nature conservation. For. Ecol. Manag. 255(3–4), 1251–1261. https://doi.org/10.1016/j.foreco.2007.10.029 (2008).Article 

Google Scholar 
Bouget, C., Larrieu, L. & Brin, A. Key features for saproxylic beetle diversity derived from rapid habitat assessment in temperate forests. Ecol. Ind. 36, 656–664. https://doi.org/10.1016/j.ecolind.2013.09.031 (2014).Article 

Google Scholar 
Sebek, P. et al. Open-grown trees as key habitats for arthropods in temperate woodlands: the diversity, composition, and conservation value of associated communities. For. Ecol. Manag. 380, 172–181. https://doi.org/10.1016/j.foreco.2016.08.052 (2016).Article 

Google Scholar 
Kozel, P. et al. Connectivity and succession of open structures as a key to sustaining light-demanding biodiversity in deciduous forests. J. Appl. Ecol. 58(12), 2951–2961. https://doi.org/10.1111/1365-2664.14019 (2021).Article 

Google Scholar 
Nagel, T. A., Svoboda, M. & Kobal, M. Disturbance, life history traits, and dynamics in an old-growth forest landscape of southeastern Europe. Ecol. Appl. 24(4), 663–679. https://doi.org/10.1890/13-0632.1 (2014).Article 
PubMed 

Google Scholar 
Christensen, M. et al. The forest cycle of Suserup Skov – revisited and revised. Ecol. Bull. 52, 33–42 (2007).
Google Scholar 
Trotsiuk, V., Hobi, M. L. & Commarmot, B. Age structure and disturbance dynamics of the relic virgin beech forest Uholka (Ukrainian Carpathians). For. Ecol. Manag. 265, 181–190. https://doi.org/10.1016/j.foreco.2011.10.042 (2012).Article 

Google Scholar 
Wermelinger, B., Duelli, P. & Obrist, M. K. Dynamics of saproxylic beetles (Coleoptera) in windthrow areas in alpine spruce forests. For. Snow Landsc. Res. 77, 133–148 (2002).
Google Scholar 
Wermelinger, B. et al. Impact of windthrow and salvage-logging on taxonomic and functional diversity of forest arthropods. For. Ecol. Manag. 391, 9–18. https://doi.org/10.1016/j.foreco.2017.01.033 (2017).Article 

Google Scholar 
Meyer, P., Schmidt, M., Feldmann, E., Willig, J. & Larkin, R. Long-term development of species richness in a central European beech (Fagus Sylvatica) forest affected by windthrow—support for the intermediate disturbance hypothesis?. Ecol. Evol. 11(18), 12801–12815. https://doi.org/10.1002/ece3.8028 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Korpeľ, S. Die Urwälder der Westkarpaten (Gustav Fischer, Stuttgart, 1995) (in German).
Google Scholar 
Emborg, J., Christensen, M. & Heilmann-Clausen, J. The structural dynamics of Suserup Skov, a near natural temperate deciduous forest in Denmark. For. Ecol. Manag. 126, 173–189 (2000).Article 

Google Scholar 
Peňa, J., Remeš, J. & Bílek, L. Dynamics of natural regeneration of even-aged beech (Fagus sylvatica L.) stands at different shelterwood densities. J. For. Sci. 56(12), 580–588 (2010).Article 

Google Scholar 
Bílek, L., Peňa, J. F. B., Remeš, J. (2013b). National Nature Reserve Voděradské Bučiny 30 Years of Forestry Research Folia Forestalia Bohemica edn, Vol. 86 (Lesnická práce, 2013).Ruchin, A. B. & Egorov, L. V. Vertical stratification of beetles in deciduous forest communities in the centre of European Russia. Diversity 13, 508. https://doi.org/10.3390/d13110508 (2021).Article 

Google Scholar 
Parmain, G. et al. Can rove beetles (Staphylinidae) be excluded in studies focusing on saproxylic beetles in central European beech forests?. Bull. Entomol. Res. 105(1), 101–109. https://doi.org/10.1017/S0007485314000741 (2015).CAS 
Article 
PubMed 

Google Scholar 
Schmidl, J. & Bußler, H. Ökologische gilden xylobionter Käfer Deutschlands. Nat. Landsch. 36, 202–218 (2004).
Google Scholar 
Seibold, S. et al. Association of extinction risk of saproxylic beetles with ecological degradation of forests in Europe. Conserv. Biol. 29(2), 382–390. https://doi.org/10.1111/cobi.12427 (2015).Article 
PubMed 

Google Scholar 
Hejda, R., Farkač, J. & Chobot, K. Red List of Threatened Species of the Czech Republic Vol. 36, 1–612 (Agentura ochrany přírody a krajiny České republiky, Praha, 2017).
Google Scholar 
Lepš, J., Šmilauer, P. Biostatistika (Nakladatelství Jihočeské univerzity v Českých Budějovicích, 2016)Chao, A. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).
Google Scholar 
Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43, 783–791 (1987).MathSciNet 
CAS 
Article 

Google Scholar 
Colwell, R. K. EstimateS: Statistical Estimation of Species Richness and Shared Species from Samples. Version 9. User’s Guide and application published at: http://purl.oclc.org/estimates (2013).Seibold, S. et al. Experiments with dead wood reveal the importance of dead branches in the canopy for saproxylic beetle conservation. For. Ecol. Manag. 409, 564–570. https://doi.org/10.1016/j.foreco.2017.11.052 (2018).Article 

Google Scholar 
Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67. https://doi.org/10.1890/13-0133.1 (2014).Article 

Google Scholar 
Chao, A., Ma, K. H., Hsieh, T. C. iNEXT (iNterpolation and EXTrapolation)Online: Software for Interpolation and Extrapolation of Species Diversity. ProgramandUser’s Guide published at http://chao.stat.nthu.edu.tw/wordpress/software_download/ (2016).Schenker, N. & Gentleman, J. F. On judging the significance of differences by examining the overlap between confidence intervals. Am. Stat. 55, 182–186 (2001).MathSciNet 
Article 

Google Scholar 
Horak, J. et al. Biodiversity of most dead wood-dependent organisms in thermophilic temperate oak woodlands thrives on diversity of open landscape structures. For. Ecol. Manag. 315, 80–85. https://doi.org/10.1016/j.foreco.2013.12.018 (2014).Article 

Google Scholar 
Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using Canoco (Cambridge University Press, Cambridge, 2010). https://doi.org/10.1017/CBO9780511615146.Book 
MATH 

Google Scholar 
Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using Canoco 5 2nd edn. (New York, 2014).Book 

Google Scholar 
Parisi, F. et al. Spatial patterns of saproxylic beetles in a relic silver fir forest (Central Italy), relationships with forest structure and biodiversity indicators. For. Ecol. Manag. 381, 217–234. https://doi.org/10.1016/j.foreco.2016.09.041 (2016).Article 

Google Scholar 
Siitonen, J. Decaying wood and saproxylic coleoptera in two old spruce forests: a comparison based on two sampling methods. Ann. Zool. Fenn. 31, 89–95 (1994).
Google Scholar 
Alinvi, O., Ball, J. P., Danell, K., Hjältén, J. & Pettersson, R. B. Sampling saproxylic beetle assemblages in dead wood logs: comparing window and eclector traps to traditional bark sieving and a refinement. J. Insect Conserv. 11(2), 99–112. https://doi.org/10.1007/s10841-006-9012-2 (2007).Article 

Google Scholar 
Økland, B. A comparison of three methods of trapping saproxylic beetles. Eur. J. Entomol. 93, 195–209 (1996).
Google Scholar 
Quinto, J., Marcos-García, M. D. L. Á., Brustel, H., Galante, E. & Micó, E. Effectiveness of three sampling methods to survey saproxylic beetle assemblages in mediterranean Woodland. J. Insect Conserv. 17(4), 765–776. https://doi.org/10.1007/s10841-013-9559-7 (2013).Article 

Google Scholar 
Müller, J. et al. Increasing temperature may compensate for lower amounts of dead wood in driving richness of saproxylic beetles. Ecography 38(5), 499–509. https://doi.org/10.1111/ecog.00908 (2015).Article 

Google Scholar 
Schiegg, K. Are there saproxylic beetle species characteristic of high dead wood connectivity?. Ecography 23, 579–587 (2000).Article 

Google Scholar 
Bouget, C., Larrieu, L., Nusillard, B. & Parmain, G. In search of the best local habitat drivers for saproxylic beetle diversity in temperate deciduous forests. Biodivers. Conserv. 22(9), 2111–2130. https://doi.org/10.1007/s10531-013-0531-3 (2013).Article 

Google Scholar 
Brunet, J. & Isacsson, G. Restoration of beech forest for saproxylic beetles—effects of habitat fragmentation and substrate density on species diversity and distribution. Biodivers. Conserv. 18(9), 2387–2404. https://doi.org/10.1007/s10531-009-9595-5 (2009).Article 

Google Scholar 
Eckelt, A. et al. “Primeval forest relict beetles” of central Europe: a set of 168 umbrella species for the protection of primeval forest remnants. J. Insect Conserv. 22(1), 15–28. https://doi.org/10.1007/s10841-017-0028-6 (2018).Article 

Google Scholar 
Speight, M. C. D. (1989). Saproxylic Invertebrates and Their Conservation. Saproxylic Invertebrates and Their Conservation, Vol. 42, Nature and Environmental Series, Strasbourg, 81.Gustafsson, L. et al. Research on retention forestry in northern Europe. Ecol. Process. https://doi.org/10.1186/s13717-019-0208-2 (2020).Article 

Google Scholar 
Zumr, V. & Remeš, J. Saproxylic beetles as an indicator of forest biodiversity and the influence of forest management on their crucial life attributes: review. Rep. For. Res. 65, 242–257 (2020).
Google Scholar 
Bouget, C. & Duelli, P. The effects of windthrow on forest insect communities: a literature review. Biol. Cons. 118(3), 281–299. https://doi.org/10.1016/j.biocon.2003.09.009 (2004).Article 

Google Scholar 
Gran, O. & Götmark, F. Long-term experimental management in Swedish mixed oak-rich forests has a positive effect on saproxylic beetles after 10 years. Biodivers. Conserv. 28, 1451–1472. https://doi.org/10.1007/s10531-019-01736-5 (2019).Article 

Google Scholar 
Fahrig, L. & Storch, D. Why do several small patches hold more species than few large patches?. Glob. Ecol. Biogeogr. 29(4), 615–628. https://doi.org/10.1111/geb.13059 (2020).Article 

Google Scholar 
Müller, J., Engel, H. & Blaschke, M. Assemblages of wood-inhabiting fungi related to silvicultural management intensity in beech forests in southern Germany. Eur. J. For. Res. 126(4), 513–527. https://doi.org/10.1007/s10342-007-0173-7 (2007).Article 

Google Scholar 
Friess, N. et al. Arthropod communities in fungal fruitbodies are weakly structured by climate and biogeography across European beech forests. Divers. Distrib. 25(5), 783–796. https://doi.org/10.1111/ddi.12882 (2019).Article 

Google Scholar 
Brin, A., Brustel, H. & Jactel, H. Species variables or environmental variables as indicators of forest biodiversity: a case study using saproxylic beetles in maritime pine plantations. Ann. For. Sci. https://doi.org/10.1051/forest/2009009 (2009).Article 

Google Scholar 
Müller, J. & Bütler, R. A review of habitat thresholds for dead wood: a baseline for management recommendations in european forests. Eur. J. For. Res. 129(6), 981–992. https://doi.org/10.1007/s10342-010-0400-5 (2010).Article 

Google Scholar 
Alencar, J. B. R., Fonseca, C. R. V., Marra, D. M. & Baccaro, F. B. Windthrows promote higher diversity of saproxylic beetles (Coleoptera: Passalidae) in a central Amazon forest. Insect Conserv. Divers. https://doi.org/10.1111/icad.12523 (2021).Article 

Google Scholar 
Audisio, P. et al. Preliminary re-examination of genus-level taxonomy of the pollen beetle subfamily Meligethinae (Coleoptera: Nitidulidae). Acta Entomol. Musei Natl. Pragae 49(2), 341–504 (2009).
Google Scholar 
Burakowski, B., Mroczkowski, M., Stefańska, J. Chrząszcze – Coleoptera. Ryjkowce – Curculionidae, Część 1. Katalog Fauny Polski Vol. XXIII, no, 19 Warszawa.Laibner, S. Elateridae of the Czech and Slovak Republics (Kabourek, Zlín, 2000).
Google Scholar 
Frank, T. & Reichhart, B. Staphylinidae and Carabidae overwintering in wheat and sown wildflower areas of different age. Bull. Entomol. Res. 94(3), 209–217. https://doi.org/10.1079/BER2004301 (2004).CAS 
Article 
PubMed 

Google Scholar 
Herrmann, S., Kahl, T. & Bauhus, J. Decomposition dynamics of coarse woody debris of three important central European tree species. For. Ecosyst. https://doi.org/10.1186/s40663-015-0052-5 (2015).Article 

Google Scholar 
Hararuk, O., Kurz, W. A. & Didion, M. Dynamics of dead wood decay in swiss forests. For. Ecosyst. https://doi.org/10.1186/s40663-020-00248-x (2020).Article 

Google Scholar 
Jonsell, M., Weslien, J. & Ehnström, B. Substrate requirements of red-listed saproxylic invertebrates in Sweden. Biodivers. Conserv. 7(6), 749–764. https://doi.org/10.1023/A:1008888319031 (1998).Article 

Google Scholar 
Bobiec, A. (ed.) The After Life of a Tree 252 (Warsawa, WWF Poland, 2005).
Google Scholar 
Gossner, M. M. et al. Deadwood enrichment in European forests – which tree species should be used to promote saproxylic beetle diversity?. Biol. Cons. 201, 92–102. https://doi.org/10.1016/j.biocon.2016.06.032 (2016).Article 

Google Scholar 
Vogel, S. et al. Optimizing enrichment of deadwood for biodiversity by varying sun exposure and tree species: an experimental approach. J. Appl. Ecol. 57(10), 2075–2085. https://doi.org/10.1111/1365-2664.13648 (2020).Article 

Google Scholar 
Gough, L. A. et al. Specialists in ancient trees are more affected by climate than generalists. Ecol. Evol. 5(23), 5632–5641. https://doi.org/10.1002/ece3.1799 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Koch Widerberg, M., Ranius, T., Drobyshev, I., Nilsson, U. & Lindbladh, M. Increased openness around retained oaks increases species richness of saproxylic beetles. Biodivers. Conserv. 21(12), 3035–3059. https://doi.org/10.1007/s10531-012-0353-8 (2012).Article 

Google Scholar 
Horák, J., Pavlíček, J., Kout, J. & Halda, J. P. Winners and losers in the wilderness: response of biodiversity to the abandonment of ancient forest pastures. Biodivers. Conserv. 27(11), 3019–3029. https://doi.org/10.1007/s10531-018-1585-z (2018).Article 

Google Scholar 
Vandekerkhove, K. et al. Saproxylic beetles in non-intervention and coppice-with-standards restoration management in meerdaal forest (Belgium): an exploratory analysis. IFor. Biogeosci. For. 9(4), 536–545. https://doi.org/10.3832/ifor1841-009 (2016).Article 

Google Scholar 
Lachat, T. et al. Saproxylic beetles as indicator species for dead-wood amount and temperature in European beech forests. Ecol. Ind. 23, 323–331. https://doi.org/10.1016/j.ecolind.2012.04.013 (2012).Article 

Google Scholar 
Müller, J. et al. Primary determinants of communities in deadwood vary among taxa but are regionally consistent. Oikos 129(10), 1579–1588. https://doi.org/10.1111/oik.07335 (2020).Article 

Google Scholar 
Černecká, Ľ, Mihál, I., Gajdoš, P. & Jarčuška, B. The effect of canopy openness of European beech (Fagus Sylvatica) forests on ground-dwelling spider communities. Insect Conserv. Divers. 13(3), 250–261. https://doi.org/10.1111/icad.12380 (2020).Article 

Google Scholar 
Spitzer, L. et al. Does closure of traditionally managed open woodlands threaten epigeic invertebrates? Effects of coppicing and high deer densities. Biol. Cons. 141(3), 827–837. https://doi.org/10.1016/j.biocon.2008.01.005 (2008).Article 

Google Scholar 
Podrázský, V., Remeš, J. & Farkač, J. Složení společenstev střevlíkovitých brouků (Coleoptera: Carabidae) v lesních porostech s různou druhovou strukturou a systémem hospodaření. Zpr. Lesn. Výzk. 55, 10–15 (2010).
Google Scholar 
Welti, E. A. R. et al. Temperature drives variation in flying insect biomass across a german malaise trap network. Insect Conserv. Divers. https://doi.org/10.1111/icad.12555 (2021).Article 

Google Scholar 
Brang, P. et al. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change. Forestry 87(4), 492–503. https://doi.org/10.1093/forestry/cpu018 (2014).Article 

Google Scholar 
Schall, P. et al. The impact of even-aged and uneven-aged forest management on regional biodiversity of multiple taxa in European beech forests. J. Appl. Ecol. 55(1), 267–278. https://doi.org/10.1111/1365-2664.12950 (2018).Article 

Google Scholar 
Leidinger, J. et al. Shifting tree species composition affects biodiversity of multiple taxa in central European forests. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2021.119552 (2021).Article 

Google Scholar 
Christensen, M. et al. Dead wood in European beech (Fagus Sylvatica) forest reserves. For. Ecol. Manag. 210(1–3), 267–282. https://doi.org/10.1016/j.foreco.2005.02.032 (2005).Article 

Google Scholar 
Plieninger, T. et al. Wood-pastures of Europe: geographic coverage, social-ecological values, conservation management, and policy implications. Biol. Cons. 190, 70–79. https://doi.org/10.1016/j.biocon.2015.05.014 (2015).Article 

Google Scholar 
Weiss, M. et al. The effect of coppicing on insect biodiversity. Small-scale mosaics of successional stages drive community turnover. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2020.118774 (2021).Article 

Google Scholar  More

Giorgi, A., Pentimalli, D., Giupponi, L. & Panseri, S. Quality traits of saffron (Crocus sativus L.) produced in the Italian Alps. Open Agric. 2(1), 52–57 (2017).Article 

Google Scholar 
Winterhalter, P. & Straubinger, M. Saffron—Renewed interest in an ancient spice. Food Rev. Intl. 16(1), 39–59 (2000).CAS 
Article 

Google Scholar 
Schmidt, M., Betti, G. & Hensel, A. Saffron in phytotherapy: Pharmacology and clinical uses. Wien Med. Wochenschr. 157, 315–319 (2007).PubMed 
Article 

Google Scholar 
Siddique, H. R., Fatma, H. & Khan, M. A. Medicinal properties of saffron with special reference to cancer—A review of preclinical studies. in Saffron: The Age-Old Panacea in a New Light (ed. Sarwat,
M. & Sumaiya, S.) 233–244 (Academic Press, 2020).Chapter 

Google Scholar 
Abdullaev, F. I. Cancer chemopreventive and tumoricidal properties of saffron (Crocus sativus L.). Exp. Biol. Med. 227(1), 20–25 (2002).CAS 
Article 

Google Scholar 
Kafi, M., Koocheki, A. & Rashed, M. H. Saffron (Crocus sativus): Production and Processing (Science Publishers, 2006).Book 

Google Scholar 
Mir, G.M. Saffron Agronomy in Kashmir (1992).Melnyk, J. P., Wang, S. & Marcone, M. F. Chemical and biological properties of the world’s most expensive spice: Saffron. Food Res. Int. 43(8), 1981–1989 (2010).CAS 
Article 

Google Scholar 
Menia, M. et al. Production technology of saffron for enhancing productivity. J. Pharmacognosy Phytochem. 7(1), 1033–1039 (2018).
Google Scholar 
Tanra, M. A., Dar, B. A. & Sing, S. Economic analysis of Production and Marketing of saffron in Jammu and Kashmir. J. Social Relevance Concern 5(10), 12–19 (2017).
Google Scholar 
Husaini, A. M., Hassan, B., Ghani, M. Y., Teixeira da Silva, J. A. & Kirmani, N. A. saffron (Crocus sativus Kashmirianus) cultivation in Kashmir: Practices and problems. Functional Plant Sci. Biotechnol. 4(2), 108–115 (2010).
Google Scholar 
Amirnia, R., Bayat, M. & Tajbakhsh, M. Effects of nano fertilizer application and maternal corm weight on flowering at some saffron (Crocus sativus L.) ecotypes. Turkish J. Field Crops. 19(2), 158–168 (2014).Article 

Google Scholar 
Kumar, R. et al. State of art of saffron (Crocus sativus L.) agronomy: A comprehensive review. Food Rev. Int. 25(1), 44–85 (2009).Article 

Google Scholar 
Dhar, A. K. Saffron breeding and agrotechnology. Status Rep. PAFAI J. 12, 18–22 (1990).
Google Scholar 
Ehsanzadeh, P., Yadollahi, A. A. & Maibodi, A. M. Productivity, growth and quality attributes of 10 Iranian saffron accessions under climatic conditions of Chahar-Mahal Bakhtiari, Central Iran. Int. Symp. Saffron Biol. Biotechnol. 650, 183–188 (2003).
Google Scholar 
Duke, J. A. Ecosystematic data on economic plants. Quart. J. Crude Drug Res. 17(3–4), 91–109 (1979).Article 

Google Scholar 
Kanth, R.H., Khanday, B.A. & Tabassum, S. Crop weather relationship for saffron production. Saffron Production in Jammu and Kashmir, Directorate of Extension Education. SKUAST-K, India 170–188 (2008).Shinde, D. A., Talib, A. R. & Gorantiwar, S. M. Composition and classification of some typical soils of saffron growing areas of Jammu and Kashmir. J. Indian Soc. Soil Sci. 32(3), 473–477 (1984).CAS 

Google Scholar 
Nazir, N. A., Khitrov, N. B. & Chizhikova, N. P. Statistical evaluation of soil properties which influence saffron growth in Kashmir. Eurasian Soil Sci. 28(4), 120–138 (1996).
Google Scholar 
Ganai, M. R., Wani, M. A. & Zargar, G. H. Characterization of saffron growing soils of Kashmir. Appl. Biol. Res. 2(1/2), 27–30 (2000).
Google Scholar 
Ganai, M.R.D. Nutrient status of saffron soils and their management. in Proceedings of Seminar-cum-Workshop on saffron (Crocus sativus) 51–54 (2001).Molina, R. V., Valero, M., Navarro, Y., Guardiola, J. L. & Garcia-Luis, A. Temperature effects on flower formation in saffron (Crocus sativus L.). ScientiaHorticulturae 103(3), 361–379 (2005).
Google Scholar 
Galavi, M., Soloki, M., Mousavi, S. R. & Ziyaie, M. Effect of planting depth and soil summer temperature control on growth and yield of saffron (Crocus sativus L.). Asian J. Plant Sci. 7(8), 747 (2008).Article 

Google Scholar 
Kamyabi, S., Habibi Nokhandan, M. & Rouhi, A. Effect of climatic factors affecting saffron using analytic hierarchy process (AHP); Case Study Roshtkhar Region, Iran. (2014).Gupta, R. K. Saffron status and cultivation in northwestern Himalayas. Vegetos 20(1), 1–7 (2007).
Google Scholar 
Qin, A. et al. Maxent modelling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Glob. Ecol. Conserv. 10, 139–146 (2017).Article 

Google Scholar 
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24(1), 38–49 (1997).Article 

Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988).ADS 
MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evaluat. 5(11), 1198–1205 (2014).Article 

Google Scholar 
Hao, T., Elith, J., Arroita, G. G. & Monfort, J. J. L. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25(5), 839–852 (2019).Article 

Google Scholar 
Thuiller, W. BIOMOD—Optimizing predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9, 1353–1362 (2003).ADS 
Article 

Google Scholar 
Mykhailenko, O., Desenko, V., Ivanauskas, L. & Georgiyants, V. Standard operating procedure of Ukrainian saffron cultivation according to with good agricultural and collection practices to assure quality and traceability. Ind. Crops Prod. 151, 112376. https://doi.org/10.1016/j.indcrop.2020.112376 (2020).CAS 
Article 

Google Scholar 
Kothari, D., Thakur, M., Joshi, R., Kumar, A. & Kumar, R. Agro-climatic suitability evaluation for saffron production in areas of western Himalaya. Front. Plant Sci. 12, 657819. https://doi.org/10.3389/fpls.2021.657819 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Mir, J. I., Ahmed, N., Wafai, A. H. & Qadri, R. A. Variability in stigma length and apocarotenoid content in Crocus sativus L. selections of Kashmir. J. Spices Aromatic Crops. 21(2), 169–171 (2012).
Google Scholar 
Nehvi, F. A. et al. New emerging trends on production technology of saffron. II Int. Symp. Saffron Biol. Technol. 739, 375–381 (2006).
Google Scholar 
Golmohammadi, F. Sustainable agriculture and rural development in Iran, Some modern issues in sustainable agriculture and rural development in Iran Germany, LAP LAMBERT Academic Publishing GmbH & Co. LAP Lambert Academic Publishing. Germany. ISBN-13, 978-3 (2012).Golmohammadi, F. Saffron and its importance, medical uses and economical export situation in Iran. in Oral Article Presented in: International Conference on Advances in Plant Sciences 14–18 (2012).Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modelling of species geographic distributions. Ecol. Model. 190(3–4), 231–259 (2006).Article 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species distributions from occurrence data. Ecography 29(2), 129–151 (2006).Article 

Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Article 

Google Scholar 
Wisz, M. S. et al. NCEAS Predicting species distributions working group. Effects of sample size on the performance of species distribution models. Diversity Distributions. 14(5), 763–773 (2008).Article 

Google Scholar 
Rebelo, H. & Jones, G. Ground validation of presence only modelling with rare species: A case study on Barbastella barbastellus (Chiroptera: Vespertilionidae). J. Appl. Ecol. 47(2), 410–420 (2010).Article 

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).Article 

Google Scholar 
Palomera, S. et al. Mapping from heterogeneous biodiversity monitoring data sources. Biodiversity Conservation 21(11), 2927–2948 (2012).Article 

Google Scholar 
Garcia, K., Lasco, R., Ines, A., Lyon, B. & Pulhin, F. Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Appl. Geogr. 44, 12–22 (2013).Article 

Google Scholar 
Marcer, A., Sáez, L., Molowny-Horas, R., Pons, X. & Pino, J. Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biol. Cons. 166, 221–230 (2013).Article 

Google Scholar 
Phillips, S.J., Dudík, M. & Schapire, R.E. A maximum entropy approach to species distribution modelling. in Proceedings of the Twenty-First International Conference on Machine Learning 83 (2004).Baldwin, R. A. Use of maximum entropy modelling in wildlife research. Entropy 11(4), 854–866 (2009).ADS 
Article 

Google Scholar 
Izadpanah, F., Kalantari, S., Hassani, M. E., Naghavi, M. R. & Shokrpour, M. Variation in Saffron (Crocus sativus L.) accessions and Crocus wild species by RAPD analysis. Plant Syst. Evolut. 300, 1941–1944 (2014).Article 

Google Scholar 
Nemati, Z., Harpke, D., Gemicioglu, A., Kerndorff, H. & Blattner, F. R. Saffron (Crocus sativus) is an autotriploid that evolved in Attica (Greece) from wild Crocus cartwrightianus. Mol. Phylogenet. Evol. 136, 14–20 (2019).PubMed 
Article 

Google Scholar 
Proosdij, A. S. J. V., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).Article 

Google Scholar  More

Sakamoto, M. Primary production by phytoplankton community in some Japanese lakes and its dependence on lake depth. Archiv für Hydrobilogie. 62, 1–28 (1966).
Google Scholar 
Vollenweider, R. A. Scientific fundamentals of the eutrophication of lakes and flowing waters, with particular reference to nitrogen and phosphorus as factors in eutrophication (Organisation for Economic Co-operation and Development, 1968).
Google Scholar 
Edmondson, W. T. Phosphorus, nitrogen, and algae in Lake Washington after diversion of sewage. Science 169, 690–691 (1970).ADS 
CAS 
Article 

Google Scholar 
Dillon, P. J. & Rigler, F. H. The phosphorus-chlorophyll relationship in lakes. Limnol. Oceanogr. 19, 767–773 (1974).ADS 
CAS 
Article 

Google Scholar 
Jones, J. R. & Bachmann, R. W. Prediction of phosphorus and chlorophyll levels in lakes. J. Water Pollut. Control Feder. 48, 2176–2182 (1976).CAS 

Google Scholar 
Schindler, D. W. Evolution of phosphorus limitation in lakes. Science 195, 260–262 (1977).ADS 
CAS 
Article 

Google Scholar 
Filstrup, C. T. & Downing, J. A. Relationship of chlorophyll to phosphorus and nitrogen in nutrient-rich lakes. Inland Waters. 7, 385–400 (2017).CAS 
Article 

Google Scholar 
Schindler, D. W. Recent advances in the understanding and management of eutrophication. Limnol. Oceanogr. 51, 356–363 (2006).ADS 
Article 

Google Scholar 
Quinlan, R. et al. Relationships of total phosphorus and chlorophyll in lakes worldwide. Limnol. Oceanogr. 66, 392–404 (2020).ADS 
Article 

Google Scholar 
Yuan, L. L. & Jones, J. R. Rethinking phosphorus–chlorophyll relationships in lakes. Limnol. Oceanogr. 65, 1847–1857 (2020).ADS 
CAS 
Article 

Google Scholar 
Carlson, R. E. A trophic state index for lakes. Limnol. Oceanogr. 11, 361–369 (1977).ADS 
Article 

Google Scholar 
Neveux, J. et al. Comparison of chlorophyll and phaeopigment determinations by spectrophotometric, fluorometric, spectrofluorometric and HPLC methods. Mar. Microb. Food Webs 4, 217–238 (1990).
Google Scholar 
Lampert, W. & Sommer, U. Limnoecology (Oxford University, 2007).
Google Scholar 
Kovalevskaya, R. Z., Zhukava, H. A. & Adamovich, B. V. Modification of the method of spectrophotometric determination of chlorophyll a in the suspended matter of water bodies. J. Appl. Spectrosc. 87, 72–78 (2020).ADS 
CAS 
Article 

Google Scholar 
Søndergaard, M., Lauridsen, T. L., Johansson, L. S. & Jeppesen, E. Nitrogen or phosphorus limitation in lakes and its impact on phytoplankton biomass and submerged macrophyte cover. Hydrobiologia 795, 35–48 (2017).Article 

Google Scholar 
Søndergaard, M., Jensen, J. P., Jeppesen, E. & Møller. P. H. Seasonal dynamics in the concentrations and retention of phosphorus in shallow Danish lakes after reduced loading. Aquat. Ecosyst. Health Manag. 5(1), 19–29 (2002).Magumba, D., Atsushi, M., Michiko, T., Akira, K. & Masao, K. Relationships between Chlorophyll-a, phosphorus and nitrogen as fundamentals for controlling phytoplankton biomass in lakes. Environ. Control. Biol. 51(4), 179–185 (2013).CAS 
Article 

Google Scholar 
Smith, V. H. & Shapiro, J. Chlorophyll-phosphorus relations in individual lakes. Their importance to lake restoration strategies. Environ. Sci. Technol. 15(4), 444–451 (1981).Pothoven, S. A. & Vanderploeg, H. A. Seasonal patterns for Secchi depth, chlorophyll a, total phosphorus, and nutrient limitation differ between nearshore and offshore in Lake Michigan. J. Great Lakes Res. 46, 519–527 (2020).CAS 
Article 

Google Scholar 
Søndergaard, M. & Jeppesen, E. Lake Søbygaard, Denmark: phosphorus dynamics during the first 35 years after an external loading reduction. In: Internal Phosphorus Loading: Causes, Case Studies, and Management (ed. Steinman, A.D. & Spears, B. M.) 285–299 (J. Ross, Plantation, 2020).Guildford, S. J. & Hecky, R. E. Total nitrogen, total phosphorus, and nutrient limitation in lakes and oceans: Is there a common relationship?. Limnol. Oceanogr. 45, 1213–1223 (2000).ADS 
CAS 
Article 

Google Scholar 
Jones, J.R. et al. Nutrients, seston, and transparency of Missouri reservoirs and oxbow lakes. An analysis of regional limnology. Lake Reser. Manag. 24, 155–180 (2008).Pikovsky, A., Rosenblum, M. & Kurths, J. Synchronization. A universal concept in nonlinear sciences (Cambridge University, 2001).Book 

Google Scholar 
Kuramoto, Y. Chemical Oscillations, Waves, and Turbulence (Springer, 1984).Book 

Google Scholar 
Sazonov, A. V. et al. An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG. Biol. Cybern. 100, 129–146 (2009).Article 

Google Scholar 
Medvinsky, A. B. et al. Temperature as a factor affecting fluctuations and predictability of the abundance of lake bacterioplankton. Ecol. Complex. 32, 90–98 (2017).Article 

Google Scholar 
Zhukova, T. V. & Ostapenya, A. P. Estimation of efficiency of nature protection measures in water catchment area of the Naroch lakes. Natural Resources. 3, 68–73 (2000) ((in Russian)).
Google Scholar 
Burlakova, L. E., Karatayev, A. Y. & Padilla, D. K. Changes in the distribution and abundance of Dreissena polymorpha within lakes through time. Hydrobiologia 571, 133–146 (2006).Article 

Google Scholar 
Ostapenia, A. P. et al. Bentification of lake ecosystem: causes, mechanisms, possible consequences, prospect for future research. Trudy BGU. 7, 135–148 (2012) ((in Russian)).
Google Scholar 
Karatayev, A.Y., Burlakova, L.E. & Padilla, D.K. Impacts of Zebra Mussels on aquatic communities and their role as ecosystem engineers. In: Leppäkoski, E., Gollasch, S., Olenin, S. (eds) Invasive Aquatic Species of Europe. Distribution, Impacts and Management (Springer, Dordrecht, 2002).Adamovich, B. V. et al. The divergence of chlorophyll dynamics in the Naroch Lakes. Biophysics 60, 632–638 (2015).CAS 
Article 

Google Scholar 
Zhukova, T. V. et al. Long-term dynamics of suspended matter in Naroch Lakes: Trend or intervation. Inland Water Biol. 10, 250–257 (2017).Article 

Google Scholar 
Adamovich, B. V. et al. Eutrophication, oligotrophication, and benthiphication in Naroch Lakes: 40 years of monitoring. J. Siber. Federal Univ. Biol. 10, 379–394 (2017).Article 

Google Scholar 
Ostapenya A.P. et al. Ecological passport of Lake Myastro (EcoMir, Minsk, 1994) (in Russian).Kantz, H. & Schreiber, T. Nonlinear time series analysis (Cambridge University, 1997).MATH 

Google Scholar 
Kot, M. Elements of mathematical ecology (Cambridge University, 2001).Book 

Google Scholar 
Turchin, P. Complex population dynamics. A Theoretical/Empirical Synthesis (Princeton University, Princeton, 2003).MATH 

Google Scholar 
Cazelles, B. & Stone, L. Detection of imperfect population synchrony in an uncertain world. J. Anim. Ecol. 72, 953–968 (2003).Article 

Google Scholar 
Karatayev, A. Y., Burlakova, L. & Padilla, D. K. The effects of Dreissena polymorpha (Pallas) invasion on aquatic communities in Eastern Europe. J. Shellfish Res. 16, 187–203 (1997).
Google Scholar 
Lia, J. et al. Benthic invaders control the phosphorus cycle in the world’s largest freshwater ecosystem. PNAS 118(6), e2008223118. https://doi.org/10.1073/pnas.2008223118 (2021).CAS 
Article 

Google Scholar 
Mikheyeva, T. M. et al. The dynamics of freshwater phytoplankton stability in the Naroch Lakes (Belarus). Ecol. Ind. 81, 481–490 (2017).Article 

Google Scholar 
Harris, P. H. Phytoplankton ecology. Structure, functioning and flucttuation (Chapman & Hall, London, New York, 1986).Jeppesen, E., Jensen, J. P., Søndergaard, M. & Lauridsen, T. L. Response of fish and plankton to nutrient loading reduction in eight shallow Danish lakes with special emphasis on seasonal dynamics. Freshw. Biol. 50, 1616–1627 (2005).CAS 
Article 

Google Scholar 
Nezlin, N.P. & Li, B-L. Time-series analysis of remote-sensed chlorophyll and environmental factors in the Santa Monica–San Pedro Basin off Southern California. J. Mar. Syst. 39, 185–202 (2003).French, T. D. & Petticrew, E. L. Chlorophyll a seasonality in four shallow eutrophic lakes (northern British Columbia, Canada) and the critical roles of internal phosphorus loading and temperature. Hydrobiologia 575, 285–299 (2007).CAS 
Article 

Google Scholar 
SCOR-UNESCO Working Group no. 17. Determination of photosynthetic pigments in sea-water. Monographs on Oceanologic Methodology 9–18 (UNESSCO, Paris, 1966).Semenov, A. D. Guide on the chemical analysis of continental surface waters (Gidrometeoizdat, 1977) ((in Russian)).
Google Scholar 
Wetzel, R. G. & Likens, G. E. Limnological analysis (Springer, 2000).Book 

Google Scholar 
Steffen, M. & Bartz-Beielstein, T. imputeTS: time series missing value imputation in R. R J. 9(1), 207–218 (2017).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020). More

Climate driversTo explore the impact of climate on forest resilience (see the following sections), we used monthly averaged total precipitation, 2-m air temperature, evapotranspiration deficit and surface solar radiation downwards acquired from the ERA5-Land reanalysis product at 0.1° spatial resolution for the 2000–2020 period (https://cds.climate.copernicus.eu/cdsapp#!/home). Evapotranspiration deficit was quantified as the total precipitation minus evapotranspiration. In this study, we referred to climate regions as defined by the Köppen–Geiger world map of climate classification51 (http://koeppen-geiger.vu-wien.ac.at/present.htm). The original 31 climatic zones were merged into major zones and only those characterized by vegetation cover were included in our study (tropical, arid, temperate and boreal; Extended Data Fig. 8).Vegetation dynamicsNDVI data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra satellite was used to derive changes in global vegetation for the period 2000–2020. We used cloud-free spatial composites provided at 16-day temporal resolution and 0.05° spatial resolution (MOD13C1 Version 6; https://lpdaac.usgs.gov/products/mod13c1v006/) and retained only pixels with good and marginal overall quality. The MODIS-derived NDVI dataset represents a state-of-the-art product of vegetation state whose retrieval algorithm is constantly improved52, and being derived from a unique platform and sensor, it is temporally and spatially consistent. Vegetation dynamics were analysed in terms of kNDVI, a nonlinear generalization of the NDVI based on ref. 22 and derived as follows:$$text{kNDVI=}tanh left({text{NDVI}}^{2}right)$$
(1)
kNDVI has recently been proposed as a strong proxy for ecosystem productivity that shows high correlations with both plot level measurements of primary productivity and satellite retrievals of sun-induced fluorescence22. In addition, kNDVI has been documented to be more closely related to primary productivity, to be resistant to saturation, bias and complex phenological cycles, and to show enhanced robustness to noise and stability across spatial and temporal scales compared to alternative products (for example, NDVI and near-infrared reflectance of vegetation). For these reasons, it has been retained in this study as the preferred metric to describe the state of the forest ecosystem.To obtain an accurate estimate of resilience indicators, vegetation time series need to be stationary without seasonal periodic patterns or long-term trends53. To this aim, vegetation anomalies were obtained from kNDVI data by first subtracting the multi-year 16-day sample mean and then removing linear trends from the resulting time series. Missing data, due for instance to snow cover affecting the retrieval of reflectance properties, have been gap-filled by climatological kNDVI values. The time series of kNDVI-based vegetation anomalies was used to derive resilience indicators and assess their spatial and temporal variations (see next sections).Interannual changes in vegetation were assessed in terms of growing-season-averaged kNDVI. To this end, a climatological growing season that spanned months with at least 75% of days in the greenness phase was derived from the Vegetation Index and Phenology satellite-based product54 (https://vip.arizona.edu/) and acquired for the 2000–2016 period at 0.05° spatial resolution. In addition, forest cover (FC) fraction was derived from the annual land-cover maps of the European Space Agency’s Climate Change Initiative (https://www.esa-landcover-cci.org/)55 over the 2000–2018 period at 300-m spatial resolution. FC was retrieved by summing the fraction of broadleaved deciduous, broadleaved evergreen, needle leaf deciduous and needle leaf evergreen forest. FC was resampled to 0.05° to match the kNDVI spatial resolution.Spatial patterns of slowness and its dependence on environmental factorsIn this study, we quantified the resilience of forest ecosystems—their ability to recover from external perturbations—by the use of the 1-lag TAC (refs. 3,4,5). Such an indicator was initially computed on the whole time series of vegetation anomalies (2000–2020) for forest pixels with less than 50% missing data in the original NDVI and FC greater than 0.05 and referred to in the text as long-term TAC. This analysis was used to assess the spatial patterns of the forest slowness mediated by environmental factors that affect plant growth rates and capacity to recover from perturbations. The long-term TAC was explored both in the geographic and climate space (Extended Data Fig. 1). In the climate space, long-term TAC was binned in a 50 × 50 grid as a function of average annual precipitation and temperature, both computed over the 2000–2020 period, using the average as an aggregation metric weighted by the areal extents of each record. We retained only bins with at least 50 records.To explore the potential drivers of long-term TAC, we developed an RF regression model23 and predicted the observed long-term TAC (response variable) based on a set of environmental features (predictors). The use of machine learning in general and of RF in particular, being nonparametric and nonlinear data-driven methods, does not require a priori assumptions about the functional form relating the key drivers and the response functions. The environmental variables include vegetation properties (FC and growing-season-averaged kNDVI) and climate variables (total precipitation, 2-m air temperature, evapotranspiration deficit and surface solar radiation downwards). Each of the climate variables was expressed in terms of average, coefficient of variation and 1-lag autocorrelation and resampled to 0.05° spatial resolution to match the spatial resolution of kNDVI. All environmental variables were computed annually and then averaged over time, except the autocorrelation that was computed directly for the whole period, analogously to the long-term TAC. This resulted in a set of 14 predictors representing the forest density, the background climate, the climate variability and its TAC in the observational period (Extended Data Table 1). The RF model was developed by splitting the observed long-term TAC into two separate samples: 60% of records were used for model calibration, and the remaining 40% were used to validate model performances in terms of coefficient of determination (R2), mean squared error and percentage bias (PBIAS). Each record refers to a 0.05° pixel. The RF implemented here uses 100 regression trees, whose depth and number of predictors to sample at each node were identified using Bayesian optimization. The general model formulation is as follows:$$text{TAC},=,fleft(Xright)+{varepsilon }_{{rm{f}}}$$
(2)
in which f is the RF regression model, X are the environmental predictors and εf are the residuals. We found that the model explains 87% of the spatial variance (R2) of the observed long-term TAC with a mean squared error of 0.007 and an average overestimation of 0.058 (PBIAS; Extended Data Fig. 2a). By definition, machine learning methods are not based on the mechanistic representation of the phenomena and therefore cannot provide direct information on the underlying processes influencing the system response to drivers. However, some model-agnostic methods can be applied to gain insights into the outputs of RF models. Here we used variable importance metrics to quantify and rank how individual environmental factors influence TAC (Extended Data Fig. 2b). Furthermore, using partial dependence plots derived from the machine learning algorithm RF, we explored the ecosystem response function (TAC) across gradients of vegetation and climate features (Supplementary Discussion 1 and Extended Data Fig. 2c–f).CSD indicatorsTo explore the temporal variation in forest resilience, we used CSD indicators, here quantified in terms of temporal changes in TAC retrieved for two consecutive and independent periods ranging from 2000 to 2010 and from 2011 to 2020, and assessed the significance of the change in the sampled mean aggregated for different climate regions through a two-sided t-test (Fig. 1c). This analysis was complemented by the computation of TAC on the annual scale over a 2-year lagged temporal window (3-year window size) to track the temporal changes in CSD. This resulted in a time series of TAC with an annual time step.We point out that temporal dynamics of annual TAC are driven by two processes: the changes in the resilience of the system that affect the velocity of the recovery from external perturbations and the confounding effects of the changes in autocorrelation of the climate drivers (Xac) that directly affect the autocorrelation of NDVI. Given the specific goals of this study, we factored out the second process from the total TAC signal to avoid that an increasing autocorrelation in the drivers would affect our analysis and conclusions about the resilience and the potential increase in instability56. For this purpose, we disentangled the temporal changes in TAC due to variations in autocorrelation in the climate drivers (({rm{TAC}}| {X}_{{rm{ac}}})) by adopting the space-for-time analogy and applied the RF model (f) at an annual time step (t) in a set of factorial simulations as follows:$${text{TAC}}^{t},{rm{| }},{X}_{{rm{ac}}}=fleft({X}^{t}right)-fleft({X}_{-{rm{ac}}}^{t},{X}_{{rm{ac}}}^{2000}right)$$
(3)
The first term on the right side of equation (3) is the RF model simulation obtained by accounting for the dynamics of all predictors, and the second term is the RF model simulation generated by considering all predictors dynamic except the factors of autocorrelation in climate that are kept constant to their first-year value (year 2000). For such runs, we used predictors computed on an annual scale over a 2-year lagged temporal window, consistently to the TAC time series. We found that the direct effects of autocorrelation in climate have led to a positive trend of TAC in dry zones (due to the increasing autocorrelation of the drivers in these regions) and to an opposite effect in temperate humid forests (Supplementary Fig. 3). To remove these confounding effects, the estimated term ({{rm{TAC}}}^{t}| {X}_{{rm{ac}}}) is factored out from the TACt by subtraction to derive an enhanced estimate of annual resilience that is independent of autocorrelation in climate (Extended Data Fig. 3).Long-term linear trends computed on the resulting enhanced TAC time series (δTAC) represent our reference CSD indicator used in this study to explore the changes in forest resilience. δTAC was quantified for each grid cell (Fig. 1a) and represented in the climate space following the methodology previously described (Fig. 1b). We then assessed the significance of the trends at bin level by applying a two-sided t-test for the sampled trend distributions within each bin. This significance test is independent from the structural temporal dependencies originating from the use of a 2-year lagged temporal window to compute the TAC time series.Following an analogous approach described in equation (3), we disentangled the effect of the variation in forest density, background climate and climate variability on temporal changes in TAC (Fig. 1d,e). We recognize that other environmental factors not explicitly accounted for in our RF model could play a role in modulating the temporal variations in TAC. However, given the comprehensiveness of the suite of predictors used in equation (2) (Extended Data Table 1), it seems plausible that residuals mostly reflect the intrinsic forest resilience, the component intimately connected to the short-term responses of forests to perturbations, which is not directly related to climate variability. Forest ecosystem evolutionary processes could also play a role, but longer time series would be required to reliably capture these dynamics. Furthermore, abrupt declines (ADs) in the vegetation state and following recoveries, similarly to those potentially originating from forest disturbances (for example, wildfires and insect outbreaks), could influence the TAC changes. However, such occurrences, being distributed across the globe throughout the whole period, are expected to only marginally affect the resulting trend in TAC time series.Sensitivity analysisTo assess the robustness of our results with respect to the modelling choices described above, we performed a series of sensitivity analyses for the difference in TAC retrieved for the two independent periods (2000–2010 and 2011–2020). To this aim, we tested their dependence on: the quality flag of the NDVI data used for the analyses (good, good and marginal); the gap-filling procedure tested on different periods (year and growing season); the inclusion or exclusion of forest areas affected by ADs; the threshold on the maximum percentage of missing NDVI data allowed at the pixel level (20%, 50% and 80%); the threshold on the minimum percentage of FC allowed at the pixel level (5%, 50% and 90%); and the pixel spatial resolution used for the analyses (0.05°, 0.25° and 1°). In addition, we tested the sensitivity of the trend in total TAC signal on the moving temporal window length used to calculate autocorrelation at lag 1. Results obtained for the different configurations were compared in terms of frequency distributions, separately for climate regions (Extended Data Fig. 4), and further explored in the climate space (Extended Data Figs. 5 and 6). Outcomes of the sensitivity analysis are discussed in Supplementary Discussion 2.Interplay between GPP and CSDResilience and GPP interact with each other through mutual causal links. On one hand, a reduction in forest resilience makes the system more sensitive to perturbations with potential consequent losses in GPP (ref. 26). On the other hand, a reduction in GPP may lead to a decline in resilience according to the carbon starvation hypothesis, and may be associated with increasing hydraulic failure46. To explore the link between forest resilience and primary productivity, we quantified the correlation between TAC and GPP. Estimates of GPP were derived from the FluxCom Model Tree Ensemble for the 2001–2019 period at 8-daily temporal resolution and 0.0833° spatial resolution and generated using ecosystem GPP fluxes from the FLUXNET network and MODIS remote sensing data as predictor variables36 (http://www.fluxcom.org/). Annual maps of GPP were quantified and resampled to 0.05° to match the temporal and spatial resolution of TAC time series. The Spearman rank correlation (ρ) was then computed between annual GPP and TAC over a 1° spatial moving window to better sample the empirical distribution of the two variables (Fig. 2d). The significance of ρ(GPP,TAC) was assessed over the climate space separately for each bin (Fig. 2e), similarly to the approach used to test the significance of δTAC. Furthermore, we explored the relationships between the trend in GPP (δGPP) and the trend in TAC (δTAC) by clustering the globe according to the directions of the long-term trajectories of the above-mentioned variables (Fig. 2f).Disentangling the impact of forest managementTo characterize TAC on different forest types and disentangle the potential effects originating from forest management, results were separately analysed for intact forests and managed forests. Intact forests were considered those forest pixels constituting the Intact Forest Landscapes57 dataset (https://intactforests.org/). Intact Forest Landscapes identifies the forest extents with no sign of significant human activity over the period 2000–2016 based on Landsat time series. The remaining forests pixels—not labelled as intact—were considered as managed forests (Extended Data Fig. 8). The resulting forest type map is consistent with those used for United Nations Framework Convention on Climate Change reporting58, although with more conservative estimates of intact forests in the boreal zone due to the masking based on FC and percentage of missing data applied in this study.We analysed the differences in long-term TAC (computed for the whole 2000–2020 period) between managed and intact forests by masking out the potential effect of climate background. To this aim, we compared the climate spaces generated separately for managed and intact forests by extracting only those bins that are covered by both forest classes. The resulting distributions—one for each forest class—have the same sample size, and each pair of elements shares the same climate background. Potential confounding environmental effects on average recovery rates are, therefore, minimized. We then applied a two-sided t-test for analysing the significance of the difference in the sampled means (Fig. 2a). An analogous approach was used to test the differences in δTAC and ρ(GPP,TAC) between managed and intact forests (Fig. 2b,c).Early-warning signals of abrupt forest declinesWhen forest ecosystems are subject to an extended and progressive degradation, the loss of resilience can lead to an AD (refs. 3,4,5). Such abrupt changes can trigger a regime shift (tipping point) depending on the capacity of the system to recover from the perturbations (Supplementary Methods 1 and 2). We investigated the potential of changes in TAC as early-warning signals of ADs in intact forests over the 2010–2020 period. To this aim, we quantified at the pixel level ADs as the events occurring on a certain year when the corresponding growing-season average kNDVI was more than n-times local standard deviation below the local mean. Local mean and standard deviation (σ) were computed over the 10-year antecedent temporal window (undisturbed) period and n varies between 1 and 6 with higher values reflecting more severe changes in the state of the system. For each pixel and for each fixed n value, we recorded only the first AD occurrence, thus imposing a univocal record for each abrupt change in the state of the system.We then explored whether the retrieved ADs were statistically associated with antecedent high values of δTAC. To avoid confusion with the attribution of causality, for each AD that occurred at time t (over the 2010–2020 period), we derived the δTAC over the temporal window 2000 − (t − 1). The resulting trend in δTAC is therefore antecedent and independent of the changes in vegetation associated with the AD. Then, for each pixel with an AD at time t, we also extracted randomly one of the undisturbed (with no AD) adjacent pixels and retrieved δTAC over the same temporal window. This analysis produced two distributions of δTAC associated with pixels with and without ADs (AD and no AD, respectively). The two distributions have the same size and each pair of elements shares similar background climate. We calculated the probability of occurrence of AD conditional on the trend in δTAC (({rm{AD}}| delta {rm{TAC}})) as the frequency of ADs for which (delta {rm{TAC}}left(mathrm{AD}right)| > delta {rm{TAC}}left(mathrm{no; AD}right)), and the significance of the difference in the two sampled means (AD and no AD) was evaluated through a two-sided t-test. Probability and significance were assessed for different climate regions and severity of ADs (Fig. 3a). High statistically significant probabilities suggest that the AD is following the drifting towards a critical resilience threshold plausibly associated with changes in environmental drivers.We complemented the aforementioned analyses by retrieving the tolerance and proximity to AD, hereafter determined for a 3σ severity. We first quantified the TAC that proceeded the occurrence of an AD and followed a progressive loss of resilience as captured by positive δTAC. This value, hereafter referred to as abrupt decline temporal autocorrelation (TACAD), reflects the TAC threshold over which we observed an abrupt change in the forest state (Fig. 3b). The tolerance to AD was quantified as the difference between the local TACAD and the TAC value averaged over the 2000–2009 period to characterize the pre-disturbance conditions. The tolerance metric was explored across a gradient of aridity index59 (Fig. 3c).TACAD can be directly retrieved only on those forest pixels that have already experienced an AD. As a considerable fraction of undisturbed forests could potentially be close to their critical TAC threshold, or even have already passed it, it is important to determine their TACAD. To this aim, we developed an RF regression model that expresses the TACAD as a function of the set X of environmental variables used in model f (equation (2)) but excluding the autocorrelation in climate drivers (Xreduced) already disentangled in the TAC signal. The general formulation is as follows:$${{rm{TAC}}}_{{rm{AD}}}=gleft({X}_{text{reduced}}right)+{varepsilon }_{{rm{g}}}$$
(4)
in which g is the RF regression model, Xreduced are the environmental predictors and εg are the residuals. Implementation, calibration and validation of g follow the same rationale described before for the f model. We found that the RF model explains 50% of the variance (R2) of the observed TACAD, with a mean squared error of 0.019 and an average underestimation of 0.86 (PBIAS).The RF model was then used to predict the TACAD over the whole domain of intact forests and served as input to quantify the proximity to AD of undisturbed forest pixels at the end of the observational period (year 2020). Here we defined the proximity metric as the difference between the value of TAC in 2020 and TACAD. Proximity takes negative or zero values when TACAD has already been reached (({{{rm{TAC}}}^{2020}ge {rm{TAC}}}_{{rm{AD}}})) and positive values when there are still margins before reaching the critical threshold (({{{rm{TAC}}}^{2020} < {rm{TAC}}}_{{rm{AD}}})). Together (delta {rm{TAC}} > 0) and ({{{rm{TAC}}}^{2020}ge {rm{TAC}}}_{{rm{AD}}}) therefore represent the most critical conditions, as they indicate that the critical resilience threshold for AD has already been reached and the ecosystem is continuing to lose its capacity to respond to external perturbations. We finally quantified the amount of GPP potentially exposed to such critical conditions by linearly extrapolating the GPP for the year 2020 (available GPP data stop in 2019) and overlaying it on the map of critical conditions (proximity to ({rm{AD}} < 0) and (delta {rm{TAC}} > 0)).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More

Brandes, N. & Linial, M. Giant viruses—big surprises. Viruses 11, 404 (2019).CAS 
Article 

Google Scholar 
Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
Article 

Google Scholar 
Madsen, E. L. Microorganisms and their roles in fundamental biogeochemical cycles. Curr. opinion biotechnology 22, 456–464 (2011).CAS 
Article 

Google Scholar 
Gummow, B. Challenges posed by new and re-emerging infectious diseases in livestock production, wildlife and humans. Livest. Sci. 130, 41–46 (2010).CAS 
Article 

Google Scholar 
Becker, D. J., Streicker, D. G. & Altizer, S. Linking anthropogenic resources to wildlife–pathogen dynamics: a review and meta-analysis. Ecol. letters 18, 483–495 (2015).Article 

Google Scholar 
Woolhouse, M. E. & Gowtage-Sequeria, S. Host range and emerging and reemerging pathogens. Emerg. infectious diseases 11, 1842 (2005).Article 

Google Scholar 
Foster, R. et al. Pathogens co-transported with invasive non-native aquatic species: implications for risk analysis and legislation. NeoBiota 69, 79–102 (2021).Article 

Google Scholar 
Brasier, C. The biosecurity threat to the uk and global environment from international trade in plants. Plant Pathol. 57, 792–808 (2008).Article 

Google Scholar 
Ruiz, G. M. et al. Global spread of microorganisms by ships. Nature 408, 49–50 (2000).ADS 
CAS 
Article 

Google Scholar 
Essl, F. et al. Which taxa are alien? criteria, applications, and uncertainties. BioScience 68, 496–509 (2018).Article 

Google Scholar 
Blackburn, T. M., Bellard, C. & Ricciardi, A. Alien versus native species as drivers of recent extinctions. Front. Ecol. Environ. 17, 203–207 (2019).Article 

Google Scholar 
Hawkins, C. L. et al. Framework and guidelines for implementing the proposed iucn environmental impact classification for alien taxa (eicat). Divers. Distributions 21, 1360–1363 (2015).Article 

Google Scholar 
Corrales, X. et al. Advances and challenges in modelling the impacts of invasive alien species on aquatic ecosystems. Biol. Invasions 22, 907–934 (2020).Article 

Google Scholar 
Regulation, E. Regulation (eu) no 1143/2014 of the European Parliament and of the Council of 22 October 2014 on the prevention and management of the introduction and spread of invasive alien species. Off. J. Eur. Union 57, 35–55 (2014).
Google Scholar 
EU. Regulation (eu) 2016/2031 of the European Parliament of the Council of 26 October 2016 on protective measures against pests of plants, amending regulations (eu) 228/2013,(eu) 652/2014 and (eu) 1143/2014 and repealing council directives 69/464/eec, 74/647/eec, 93/85/eec, 98/57/ec, 2000/29/ec, 2006/91/ec and 2007/33/ec. Off. J. 317, 4–104 (2016).
Google Scholar 
Murtaugh, M. P. et al. The science behind one health: at the interface of humans, animals, and the environment. Tech. Rep. (2017).Ogden, N. H. et al. Emerging infectious diseases and biological invasions: a call for a one health collaboration in science and management. Royal Soc. open science 6, 181577 (2019).ADS 
Article 

Google Scholar 
Roy, H. E. et al. Alien pathogens on the horizon: Opportunities for predicting their threat to wildlife. Conserv. Lett. 10, 477–484 (2017).Article 

Google Scholar 
Ikner, L. A., Gerba, C. P. & Bright, K. R. Concentration and recovery of viruses from water: a comprehensive review. Food Environ. Virol. 4, 41–67 (2012).
Google Scholar 
Taylor, M. W. Introduction: A short history of virology. In Viruses and Man: A History of Interactions, 1–22 (Springer, 2014).Thakur, M. P., Van der Putten, W. H., Cobben, M. M., van Kleunen, M. & Geisen, S. Microbial invasions in terrestrial ecosystems. Nat. Rev. Microbiol. 17, 621–631 (2019).CAS 
Article 

Google Scholar 
Desprez-Loustau, M.-L. et al. The fungal dimension of biological invasions. Trends ecology & evolution 22, 472–480 (2007).Article 

Google Scholar 
Rivett, D. W. et al. Elevated success of multispecies bacterial invasions impacts community composition during ecological succession. Ecol. Lett. 21, 516–524 (2018).Article 

Google Scholar 
Dunn, A. M. & Hatcher, M. J. Parasites and biological invasions: parallels, interactions, and control. TRENDS Parasitol. 31, 189–199 (2015).Article 

Google Scholar 
Pyšek, P. et al. Macroecological framework for invasive aliens (mafia): disentangling large-scale context dependence in biological invasions. (2020).Hulme, P. E. et al. Blurring alien introduction pathways risks losing the focus on invasive species policy. Conserv. Lett. 10, 265–266 (2017).Article 

Google Scholar 
Gilroy, J. J., Avery, J. D. & Lockwood, J. L. Seeking international agreement on what it means to be “native”. Conserv. Lett. 10, 238–247 (2017).Article 

Google Scholar 
Webber, B. L. & Scott, J. K. Rapid global change: implications for defining natives and aliens. Glob. Ecol. Biogeogr. 21, 305–311 (2012).Article 

Google Scholar 
CBD Secretariat. Decision VI/23: Alien species that threaten ecosystems, habitats and species. Document UNEP/CBD/COP/6/23 (2002).World Health Organization. A brief guide to emerging infectious diseases and zoonoses. Tech. Rep. https://apps.who.int/iris/handle/10665/204722 (2014).Firrao, G. et al. Candidatus phytoplasma’, a taxon for the wall-less, non-helical prokaryotes that colonize plant phloem and insects. Int. J. Syst. Evol. Microbiol. 54, 1243–1255 (2004).CAS 
Article 

Google Scholar 
CBD. Pathways of introduction of invasive species, their prioritization and management (Secretariat of the Convention on Biological Diversity Montreal, 2014).OIE. Terrestrial Animal Health Code 2021 (OIE, 2021).Magliozzi, C. et al. bacteria and viruses traits and species-related factors. figshare https://doi.org/10.6084/m9.figshare.18550907.v2 (2022).Katsanevakis, S. et al. Implementing the European policies for alien species: networking, science, and partnership in a complex environment. Manag. Biol. Invasions 4, 3–6 (2013).Article 

Google Scholar 
Tsiamis, K. et al. The EASIN Editorial Board: quality assurance, exchange and sharing of alien species information in europe. Manag. Biol. invasions 7, 321–328 (2016).Article 

Google Scholar 
Wieczorek, J. et al. Darwin core: an evolving community-developed biodiversity data standard. PloS one 7, e29715 (2012).ADS 
CAS 
Article 

Google Scholar 
Darwin Core. Darwin Core quick reference guide. https://dwc.tdwg.org/terms/ (2018).R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis, https://ggplot2.tidyverse.org (Springer-Verlag New York, 2016).Schwarzl, T. ggBubbles: Mini Bubble Plots for Comparison of Discrete Data with ‘ggplot2’ R package version 0.1.4 (2019).Moon, K. R statistics and graphs for medical papers (Hannarae Seoul, 2015).Current, C. Invasive species compendium. Wallingford, UK: CAB Int. Available online: www.cabi.org/isc (accessed on 19 August 2020) (2011).Adams, M. J. & Antoniw, J. F. Dpvweb: An open access internet resource on plant viruses and virus diseases. Outlooks on Pest Manag. 16, 268 (2005).Article 

Google Scholar 
Adams, M. J. & Antoniw, J. F. Dpvweb: a comprehensive database of plant and fungal virus genes and genomes. Nucleic acids research 34, D382–D385 (2006).CAS 
Article 

Google Scholar 
Benson, D. A. et al. Genbank. Nucleic acids research 41, D36–D42 (2012).Article 

Google Scholar  More