Philippa Kaur

1.Lloyd, G. T. et al. Dinosaurs and the Cretaceous terrestrial revolution. Proc. R. Soc. B 275, 2483–2490 (2008).PubMed 
PubMed Central 

Google Scholar 
2.Bininda-Emonds, O. R. P. et al. The delayed rise of present-day mammals. Nature 446, 507–512 (2007).CAS 
PubMed 

Google Scholar 
3.Herrera-Flores, J. A., Stubbs, T. L. & Benton, M. J. Ecomorphological diversification of squamates in the Cretaceous. R. Soc. Open Sci. 8, 201961 (2021).PubMed 
PubMed Central 

Google Scholar 
4.Benton, M. J. The origins of modern biodiversity on land. Phil. Trans. R. Soc. B 365, 3667–3679 (2010).PubMed 
PubMed Central 

Google Scholar 
5.Roelants, K. et al. Global patterns of diversifcation in the history of modern amphibians. Proc. Natl Acad. Sci. USA 104, 887–892 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Grosberg, R. K., Vermeij, G. J. & Wainwright, P. C. Biodiversity in water and on land. Curr. Biol. 22, 900–903 (2012).
Google Scholar 
7.Condamine, F. L., Silvestro, D., Koppelhus, E. B. & Antonelli, A. The rise of angiosperms pushed conifers to decline during global cooling. Proc. Natl Acad. Sci. USA 117, 28867–28875 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Buggs, R. J. The deepening of Darwin’s abominable mystery. Nat. Ecol. Evol. 1, 0169 (2017).
Google Scholar 
9.Friis, E. M., Crane, P. R., Pedersen, K. R., Stampanoni, M. & Marone, F. Exceptional preservation of tiny embryos documents seed dormancy in early angiosperms. Nature 528, 551–554 (2015).PubMed 

Google Scholar 
10.Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution (Cambridge Univ. Press, 2011).11.Friis, E. M., Pedersen, K. R. & Crane, P. R. Cretaceous angiosperm flowers: Innovation and evolution in plant reproduction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 232, 251–293 (2006).
Google Scholar 
12.Soltis, P. S., Folk, R. A. & Soltis, D. E. Darwin review: angiosperm phylogeny and evolutionary radiations. Proc. R. Soc. B 286, 20190099 (2019).PubMed Central 

Google Scholar 
13.Bond, W. J. & Scott, A. C. Fire and the spread of flowering plants in the Cretaceous. New Phytol. 188, 1137–1150 (2010).PubMed 

Google Scholar 
14.Bond, W. J. & Midgley, J. J. Fire and the angiosperm revolutions. Int. J. Plant Sci. 173, 569–583 (2012).
Google Scholar 
15.Belcher, C. M. & Hudspith, V. A. Changes to Cretaceous surface fire behaviour influenced the spread of the early angiosperms. New Phytol. 213, 1521–1532 (2017).CAS 
PubMed 

Google Scholar 
16.He, T., Lamont, B. B. & Pausas, J. G. Fire as a key driver of Earth’s biodiversity. Biol. Rev. 94, 1983–2010 (2019).PubMed 

Google Scholar 
17.Cruickshank, R. D. & Ko, K. Geology of an amber locality in the Hukawng Valley, Northern Myanmar. J. Asian Earth Sci. 21, 441–455 (2003).
Google Scholar 
18.Shi, G. H. et al. Age constraint on Burmese amber based on U–Pb dating of zircons. Cretac. Res. 37, 155–163 (2012).
Google Scholar 
19.Yu, T. et al. An ammonite trapped in Burmese amber. Proc. Natl Acad. Sci. USA 166, 11345–11350 (2019).
Google Scholar 
20.Xing, L. D. & Qiu, L. Zircon U–Pb age constraints on the Hkamti amber biota in northern Myanmar. Palaeogeogr. Palaeoclimatol. Palaeoecol. 558, 109960 (2020).
Google Scholar 
21.Xia, F. Y., Yang, G., Zhang, Q. & Shi, G. L. Amber Lives Through Time and Space (Beijing Science Press, 2015).22.Poinar, G. O. & Brown, A. E. A green algae (Chaetophorales: Chaetophoraceae) in Burmese amber. Hist. Biol. 33, 323–327 (2019).
Google Scholar 
23.Liu, Z. J., Huang, D., Cai, C. Y. & Wang, X. The core eudicot boom registered in Myanmar amber. Sci. Rep. 8, 16765 (2018).PubMed 
PubMed Central 

Google Scholar 
24.Poinar, G. O. & Chambers, K. L. Tropidogyne pentaptera sp. nov., a new mid-Cretaceous fossil angiosperm flower in Burmese amber. Palaeodiversity 10, 135–140 (2017).
Google Scholar 
25.Poinar, G. O. & Chambers, K. L. Palaeoanthella huangii gen. and sp. nov., an Early Cretaceous flower (Angiospermae) in Burmese amber. SIDA 21, 2087–2092 (2005).
Google Scholar 
26.Goldblatt, P. An analysis of the flora of Southern Africa: its characteristics, relationships, and orgins. Ann. Mo. Bot. Gard. 65, 369–436 (1978).
Google Scholar 
27.Verboom, G. A. et al. in Fynbos: Ecology, Evolution and Conservation of a Megadiverse Region (eds Allsopp, N. et al.) 93–118 (Oxford Univ. Press, 2014).28.Hauenschild, F., Favre, A., Michalak, I. & Muellner-Riehl, A. N. The influence of the Gondwanan breakup on the biogeographic history of the ziziphoids (Rhamnaceae). J. Biogeogr. 45, 2669–2677 (2018).
Google Scholar 
29.Onstein, R. E. & Linder, H. P. Beyond climate: convergence in fast evolving sclerophylls in Cape and Australian Rhamnaceae predates the mediterranean climate. J. Ecol. 104, 665–677 (2016).
Google Scholar 
30.Brown, S., Scott, A. C., Glasspool, I. J. & Collinson, M. E. Cretaceous wildfires and their impact on the Earth system. Cretac. Res. 36, 162–190 (2012).
Google Scholar 
31.Richardson, J. E. et al. Rapid and recent origin of species richness in the Cape flora of South Africa. Nature 412, 181–183 (2001).CAS 
PubMed 

Google Scholar 
32.Pillans, N. S. The genus Phylica. J. S. Afr. Bot. 8, 1–164 (1942).
Google Scholar 
33.Rebelo, T. et al. in The vegetation of South Africa, Lesotho and Swaziland (eds Mucina, L. & Rutherford, M. C.) 52–219 (South African National Biodiversity Institute, 2006).34.Gimingham, C. H. & Cowling, R. The ecology of fynbos: nutrients, fire and diversity. J. Ecol. 81, 195–196 (1993).
Google Scholar 
35.Richardson, J. E., Fay, M. F., Cronk, Q. C. B. & Cronk, M. W. Species delimitation and the origin of populations in island representatives of Phylica (Rhamnaceae). Evolution 57, 816–827 (2003).PubMed 

Google Scholar 
36.Richardson, J. E. Molecular Systematics of the Genus Phylica L. With an Emphasis on the Island Species (Edinburgh Univ. Press, 1999).37.Schirarend, C. & Köhler, E. World Pollen and Spore Flora: Rhamnaceae Juss (Scandinavian Univ. Press, 1993).38.Medan, D. & Schirarend, C. in Flowering plants · Dicotyledons (ed. Kubitzki, K.) 320–338 (Springer, 2004).39.Gotelli, M. M., Galati, B. G. & Medan, D. Morphological and ultrastructural studies of floral nectaries in Rhamnaceae. J. Torrey Bot. Soc. 144, 63–73 (2017).
Google Scholar 
40.Friedrich, O., Norris, R. D. & Erbacher, J. Evolution of middle to Late Cretaceous oceans–a 55 m.y. record of Earth’s temperature and carbon cycle. Geology 40, 107–110 (2012).CAS 

Google Scholar 
41.Lenton, T. M., Daines, S. J. & Mills, B. J. W. COPSE reloaded: an improved model of biogeochemical cycling over Phanerozoic time. Earth Sci. Rev. 178, 1–28 (2018).CAS 

Google Scholar 
42.Huber, B. T., Hodell, D. A. & Hamilton, C. P. Middle-Late Cretaceous climate of the southern high latitudes: stable isotopic evidence for minimal equator-to-pole thermal gradients. Geol. Soc. Am. Bull. 107, 1164–1191 (1995).
Google Scholar 
43.Belcher, C. M., Yearsley, J. M., Hadden, R. M., Mcelwain, J. C. & Rein, G. Baseline intrinsic flammability of Earth’s ecosystems estimated from paleoatmospheric oxygen over the past 350 million years. Proc. Natl Acad. Sci. USA 107, 22448–22453 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Berner, R. A., Beerling, D. J., Dudley, R., Robinson, J. M. & Wildman, R. A. Phanerozoic atmospheric oxygen. Annu. Rev. Earth Planet. Sci. 31, 105–134 (2003).CAS 

Google Scholar 
45.Glasspool, I. J. & Scott, A. C. Phanerozoic concentrations of atmospheric oxygen reconstructed from sedimentary charcoal. Nat. Geosci. 3, 627–630 (2010).CAS 

Google Scholar 
46.Poulsen, C. J., Tabor, C. & White, J. D. Long-term climate forcing by atmospheric oxygen concentrations. Science 348, 1238–1241 (2015).CAS 
PubMed 

Google Scholar 
47.Hudspith, V. A. & Belcher, C. M. Fire biases the production of charred flowers: implications for the Cretaceous fossil record. Geology 45, 727–730 (2017).
Google Scholar 
48.Scott, A. C. Charcoal recognition, taphonomy and uses in palaeoenvironmental analysis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 291, 11–39 (2010).
Google Scholar 
49.Scott, A. C. The use of charcoal to interpret Cretaceous wildfires and volcanic activity. Glob. Geol. 22, 217–241 (2019).
Google Scholar 
50.Scott, A. C., Cripps, J. A., Nichols, G. J. & Collinson, M. E. The taphonomy of charcoal following a recent heathland fire and some implications for the interpretation of fossil charcoal deposits. Palaeogeogr. Palaeoclimatol. Palaeoecol. 164, 1–31 (2000).
Google Scholar 
51.Whtilock, C., Higuera, P. E., McWethy, D. B. & Briles, C. E. Paleoecological perspectives on fire ecology: revisiting the fire-regime concept. Open Ecol. J. 3, 6–23 (2010).
Google Scholar 
52.Bond, W. J. & Keeley, J. E. Fire as global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends Ecol. Evol. 20, 387–394 (2005).PubMed 

Google Scholar 
53.Bowman, D. M. J. S. et al. Fire in the Earth system. Science 324, 481–484 (2009).CAS 
PubMed 

Google Scholar 
54.Crisp, M. D., Burrows, G. E., Cook, L. G., Thornhill, A. H. & Bowman, D. M. J. S. Flammable biomes dominated by eucalypts originated at the Cretaceous–Paleogene boundary. Nat. Commun. 2, 193 (2011).PubMed 

Google Scholar 
55.Pausas, J. G. & Keeley, J. E. A burning story: the role of fire in the history of life. Bioscience 59, 593–601 (2009).
Google Scholar 
56.Scott, A. C. Burning Planet. The Story of Fire Through Time (Oxford Univ. Press, 2018).57.Scott, A. C. Fire: A Very Short Introduction (Oxford Univ. Press, 2020).58.Scott, A. C., Bowman, D. J. M. S., Bond, W. J., Pyne, S. J. & Alexander M. Fire on Earth: An Introduction (J. Wiley & Sons Press, 2014).59.Keeley, J. E., Pausas, J. G., Rundel, P. W., Bond, W. J. & Bradstock, R. A. Fire as an evolutionary pressure shaping plant traits. Trends Plant Sci. 16, 406–411 (2011).CAS 
PubMed 

Google Scholar 
60.Lenton,T. M. in Fire Phenomena and the Earth System: An Interdisciplinary Guide to Fire Science (ed. Belcher, C. M.) 289–308 (J. Wiley & Sons Press, 2013).61.Herendeen, P. S., Magallon-Puebla, S., Lupia, R., Crane, P. R. & Kobylinska, J. A preliminary conspectus of the Allon flora from the Late Cretaceous (Late Santonian) of the central Georgia, USA. Ann. Mo. Bot. Gard. 86, 407–471 (1999).
Google Scholar 
62.He, T., Pausas, J. G., Belcher, C. M., Schwilk, D. W. & Lamont, B. B. Fire-adapted traits of Pinus arose in the fiery Cretaceous. New Phytol. 194, 751–759 (2012).PubMed 

Google Scholar 
63.Cornwell, W. K. et al. Flammability across the gymnosperm phylogeny: the importance of litter particle size. New Phytol. 206, 672–681 (2015).PubMed 

Google Scholar 
64.Lamont, B. B. & He, T. Fire-adapted Gondwanan angiosperm floras evolved in the Cretaceous. BMC Evol. Biol. 12, 223 (2012).PubMed 
PubMed Central 

Google Scholar 
65.He, T., Lamont, B. B. & Manning, J. A. Cretaceous origin for fire adaptations in the Cape flora. Sci. Rep. 6, 34880 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.He, T., Lamont, B. B. & Downes, K. S. Banksia born to burn. New Phytol. 191, 184–196 (2011).PubMed 

Google Scholar 
67.Midgley, J. & Bond, W. Pushing back in time, the role of fire in plant evolution. New Phytol. 191, 5–7 (2011).PubMed 

Google Scholar 
68.Scott, A. C. The Pre-Quaternary history of fire. Palaeogeogr. Palaeoclimatol. Palaeoecol. 164, 281–329 (2000).
Google Scholar 
69.Midgley, J. J., Kruger, L. M. & Skelton, R. How do fires kill plants? The hydraulic death hypothesis and Cape Proteaceae “fire-resisters”. S. Afr. J. Bot. 77, 381–386 (2011).
Google Scholar 
70.Lamont, B. B., Groom, P. K., Williams, M. & He, T. LMA, density and thickness: recognizing different leaf shapes and correcting for their non-laminarity. New Phytol. 207, 942–947 (2015).PubMed 

Google Scholar 
71.Lamont, B. B., He, T. & Yan, Z. Evolutionary history of fire-stimulated resprouting, flowering, seed release and germination. Biol. Rev. 94, 903–928 (2019).PubMed 

Google Scholar 
72.Schwilk, D. W. & Kerr, B. Genetic niche-hiking: an alternative explanation for the evolution of flammability. Oikos 99, 431–442 (2002).
Google Scholar 
73.Kilian, D. & Cowling, R. M. Comparative seed biology and co-existence of two fynbos shrub species. J. Veg. Sci. 3, 637–646 (1992).
Google Scholar 
74.Hall, S. A., Newton, R. J., Holmes, P. M., Gaertner, M. & Esler, K. J. Heat and smoke pre‐treatment of seeds to improve restoration of an endangered Mediterranean climate vegetation type. Austral Ecol. 42, 354–366 (2017).
Google Scholar 
75.Ruprecht, E., Fenesi, A., Fodor, E. I., Kuhn, T. & Tklyi, J. Shape determines fire tolerance of seeds in temperate grasslands that are not prone to fire. Perspect. Plant Ecol. 17, 397–404 (2015).
Google Scholar 
76.Mohr, B. A. R. & Friis, E. M. Early angiosperms from the Lower Cretaceous Crato Formation (Brazil), a preliminary report. Int. J. Plant Sci. 161, 155–167 (2000).
Google Scholar 
77.Forest, F. et al. Preserving the evolutionary potential of floras in biodiversity hotspots. Nature 445, 757–760 (2007).CAS 
PubMed 

Google Scholar 
78.Linder, H. P. Evolution of diversity: the Cape flora. Trends Plant Sci. 10, 536–541 (2005).CAS 
PubMed 

Google Scholar 
79.Linder, H. P. The radiation of the Cape flora, southern Africa. Biol. Rev. 78, 597–638 (2003).CAS 
PubMed 

Google Scholar 
80.Poinar, G. O. Burmese amber: evidence of Gondwanan origin and Cretaceous dispersion. Hist. Biol. 31, 1304–1309 (2019).
Google Scholar 
81.Oliveira, I. D. S. et al. Earliest onychophoran in amber reveals Gondwanan migration patterns. Curr. Biol. 26, 2594–2601 (2016).CAS 
PubMed 

Google Scholar 
82.Poinar, G. O., Lambert, J. B. & Wu, Y. Araucarian source of fossiliferous Burmese amber: spectroscopic and anatomical evidence. J. Bot. Res. Inst. Tex. 1, 449–455 (2007).
Google Scholar 
83.Cai, C. Y. et al. Basal polyphagan beetles in mid-Cretaceous amber from Myanmar: biogeographic implications and long-term morphological stasis. Proc. R. Soc. B 286, 2175 (2019).
Google Scholar 
84.Zhang, W., Li, H., Shih, C., Zhang, A. & Ren, D. Phylogenetic analyses with four new Cretaceous bristletails reveal inter-relationships of Archaeognatha and Gondwana origin of Meinertellidae. Cladistics 34, 384–406 (2018).PubMed 

Google Scholar 
85.Westerweel, J. et al. Burma Terrane part of the Trans-Tethyan Arc during collision with India according to palaeomagnetic data. Nat. Geosci. 12, 5–6 (2019).
Google Scholar 
86.Metcalfe, I. in Biogeography and Geological Evolution of SE Asia (eds Hall, R. & Holloway, J. D.) 25–41 (Backhuys Publishers Press,1998).87.Li, J., Wu, Y., Peng, J. & Batten, D. J. Palynofloral evolution on the northern margin of the Indian Plate, southern Xizang, China during the Cretaceous period and its phytogeographic significance. Palaeogeogr. Palaeoclimatol. Palaeoecol. 515, 107–122 (2019).
Google Scholar 
88.Smith, A. G., Smith, D. G. & Funnell B. M. Atlas of Mesozoic and Cenozoic Coastlines (Cambridge Univ. Press, 2004).89.Klages, J. P. et al. Temperate rainforests near the South Pole during peak Cretaceous warmth. Nature 580, 81–86 (2020).CAS 
PubMed 

Google Scholar 
90.Coetzee, J. A. & Muller, J. The phytogeographic significance of some extinct Gondwana pollen types from the Tertiary of the southwestern Cape (South Africa). Ann. Mo. Bot. Gard. 71, 1088–1099 (1984).
Google Scholar 
91.De Villiers, S. E. & Cadman, A. The palynology of Tertiary sediments from a palaeochannel in Namaqualand, South Africa. Palaeontol. Afr. 34, 69–99 (1997).
Google Scholar 
92.De Villiers, S. E. & Cadman, A. An analysis of the palynomorphs obtained from Tertiary sediments at Koingnaas, Namaqualand, South Africa. J. Afr. Earth Sci. 33, 17–47 (2001).
Google Scholar 
93.Sandersen, A., Scott, L., McLachlan, I. R. & Hancox, P. J. Cretaceous biozonation based on terrestrial palynomorphs from two wells in the offshore Orange Basin of South Africa. Palaeontol. Afr. 46, 21–41 (2011).
Google Scholar 
94.Hooghiemstra, H., Lézine, A. M., Leroy, S. A. G., Dupont, L. & Marret, F. Late Quaternary palynology in marine sediments: a synthesis of the understanding of pollen distribution patterns in the NW African setting. Quat. Int. 148, 29–44 (1988).
Google Scholar 
95.Scholtz, A. The palynology of the upper lacustrine sediments of the Arnot Pipe, Banke, Namaqualand. Ann. S. Afr. Mus. 95, 1–109 (1985).
Google Scholar 
96.Sciscio, L. et al. Fluctuations in Miocene climate and sea levels along the south-western South African coast: inferences from biogeochemistry, palynology and sedimentology. Palaeontol. Afr. 48, 2–18 (2013).
Google Scholar 
97.Roberts, D. L. et al. Miocene fluvial systems and palynofloras at the southwestern tip of Africa: implications for regional and global fluctuations in climate and ecosystems. Earth Sci. Rev. 124, 184–201 (2013).
Google Scholar 
98.Roberts, D. L. et al. Palaeoenvironments during a terminal Oligocene or early Miocene transgression in a fluvial system at the southwestern tip of Africa. Glob. Planet. Change 150, 1–23 (2017).
Google Scholar 
99.Grimaldi, D., Engel, M. S. & Nascimbene, P. Fossiliferous Cretaceous amber from Myanmar (Burma): its rediscovery, biotic diversity, and paleontological significance. Am. Mus. Novit. 3361, 1–72 (2002).
Google Scholar 
100.Mao, Y. et al. Various amberground marine animals on Burmese amber with discussions on its age. Palaeoentomology 1, 91–103 (2018).
Google Scholar 
101.Smith, R. D. & Ross, A. J. Amberground pholadid bivalve borings and inclusions in Burmese amber: implications for proximity of resin-producing forests to brackish waters, and the age of the amber. Earth Env. Sci. Trans. R. Soc. Edinb. 107, 239–247 (2018).
Google Scholar 
102.Schmidt, A. R. & Dilcher, D. L. Aquatic organisms as amber inclusions and examples from a modern swamp forest. Proc. Natl Acad. Sci. USA 104, 16581–16585 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
103.Cole, L. E., Bhagwat, S. A. & Willis, K. J. Fire in the swamp forest: palaeoecological insights into natural and human-induced burning in intact tropical peatlands. Front. For. Glob. Change 2, 48 (2019).
Google Scholar 
104.Labandeira, C. C. in Reading and Writing of the Fossil Record: Preservational Pathways to Exceptional Fossilization. The Paleontological Society Papers (eds Laflamme, M. et al.) 163–216 (Cambridge Univ. Press, 2014).105.Seyfullah, L. J. et al. Production and preservation of resins–past and present. Biol. Rev. 93, 1684–1714 (2018).PubMed 

Google Scholar 
106.Putz, M. K. & Taylor, E. L. Wound response in fossil trees assemblages from Antarctica and its potential as a palaeoenvironmental indicator. IAWA J. 17, 77–88 (1996).
Google Scholar 
107.McKellar, R. C. et al. Insect outbreaks produce distinctive carbon isotope signatures in defensive resins and fossiliferous ambers. Proc. R. Soc. B 278, 3219–3224 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
108.Pausas, J. G. Generalized fire response strategies in plants and animals. Oikos 128, 147–153 (2019).
Google Scholar 
109.Schmidt, A. R. et al. Arthropods in amber from the Triassic Period. Proc. Natl Acad. Sci. USA 109, 14796–14801 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
110.Silvestro, D. et al. Fossil data support a pre-Cretaceous origin of flowering plants. Nat. Ecol. Evol. 5, 449–457 (2021).PubMed 

Google Scholar 
111.Donoghue, P. Evolution: the flowering of land plant evolution. Curr. Biol. 29, 753–756 (2019).
Google Scholar 
112.Thulin, M. et al. Family relationships of the enigmatic rosid genera Barbeya and Dirachma from the Horn of Africa region. Plant Syst. Evol. 213, 103–119 (1998).
Google Scholar 
113.Wilf, P., Carvalho, M. R., Gandolfo, M. A. & Cúneo, N. R. Eocene lantern fruits from Gondwanan Patagonia and the early origins of Solanaceae. Science 355, 71–75 (2017).CAS 
PubMed 

Google Scholar  More

1.Blicharska, M. & Mikusiński, G. Incorporating social and cultural significance of large old trees in conservation policy. Conserv. Biol. 28, 1558–1567 (2014).PubMed 

Google Scholar 
2.Lindenmayer, D. B. & Laurance, W. F. The ecology, distribution, conservation and management of large old trees. Biol. Rev. Camb. Phil. Soc. 92, 1434–1458 (2017).
Google Scholar 
3.Munné-Bosch, S. Limits to tree growth and longevity. Trends Plant Sci. 23, 985–993 (2018).PubMed 

Google Scholar 
4.Lindenmayer, D. B. Conserving large old trees as small natural features. Biol. Conserv. 211, 51–59 (2017).
Google Scholar 
5.Lutz, J. A. et al. Global importance of large-diameter trees. Glob. Ecol. Biogeogr. 27, 849–864 (2018).
Google Scholar 
6.Slik, J. W. F. et al. Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics: large trees and tropical forest biomass. Glob. Ecol. Biogeogr. 22, 1261–1271 (2013).
Google Scholar 
7.McMahon, S. M., Arellano, G. & Davies, S. J. The importance and challenges of detecting changes in forest mortality rates. Ecosphere 10, e02615 (2019).
Google Scholar 
8.Vieira, S. et al. Slow growth rates of Amazonian trees: consequences for carbon cycling. Proc. Natl Acad. Sci. USA 102, 18502–18507 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Martınez-Ramos, M. & Alvarez-Buylla, E. R. How old are tropical rain forest trees? Trends Plant Sci. 3, 400–405 (1998).
Google Scholar 
10.Schöngart, J., Bräuning, A., Barbosa, A. C. M. C., Lisi, C. S. & de Oliveira, J. M. in Dendroecology: Tree-Ring Analyses Applied to Ecological Studies (eds Amoroso, M. M. et al.) 35–73 (Springer, 2017).11.Brienen, R. J. W. & Zuidema, P. A. Lifetime growth patterns and ages of Bolivian rain forest trees obtained by tree ring analysis. J. Ecol. 94, 481–493 (2006).
Google Scholar 
12.Piovesan, G. & Biondi, F. On tree longevity. New Phytol. 231, 1318–1337 (2021).PubMed 

Google Scholar 
13.Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Condit, R., Hubbell, S. P. & Foster, R. B. Mortality rates of 205 neotropical tree and shrub species and the impact of a severe drought. Ecol. Monogr. 65, 419–439 (1995).
Google Scholar 
15.Acker, S. A. et al. Recent tree mortality and recruitment in mature and old-growth forests in western Washington. Ecol. Manage. 336, 109–118 (2015).
Google Scholar 
16.Thomas, R. Q., Kellner, J. R., Clark, D. B. & Peart, D. R. Low mortality in tall tropical trees. Ecology 94, 920–929 (2013).
Google Scholar 
17.Stephenson, N. L. & Mantgem, P. J. Forest turnover rates follow global and regional patterns of productivity. Ecol. Lett. 8, 524–531 (2005).PubMed 

Google Scholar 
18.Drobyshev, I. et al. Lifespan and mortality of old oaks—combining empirical and modelling approaches to support their management in Southern Sweden. Ann. Sci. 65, 401–401 (2008).
Google Scholar 
19.Richardson, S. J. et al. Large-tree growth and mortality rates in forests of the central North Island, New Zealand. N. Z. J. Ecol. 33, 208–215 (2009).
Google Scholar 
20.Chambers, J. Q., Higuchi, N. & Schimel, J. P. Ancient trees in Amazonia. Nature 391, 135–136 (1998).CAS 

Google Scholar 
21.Laurance, W. F., Nascimento, H. E. M., Laurance, S. G., Condit, R., D’Angelo, S. & Andrade, A. Inferred longevity of Amazonian rainforest trees based on a long-term demographic study. Ecol. Manage. 190, 131–143 (2004).
Google Scholar 
22.Fichtler, E., Clark, D. A. & Worbes, M. Age and long-term growth of trees in an old-growth tropical rain forest, based on analyses of tree rings and C-14. Biotropica 35, 306–317 (2003).
Google Scholar 
23.Foster, D. R. Land-use history (1730–1990) and vegetation dynamics in central New England, USA. J. Ecol. 80, 753–771 (1992).
Google Scholar 
24.Senf, C., Buras, A., Zang, C. S., Rammig, A. & Seidl, R. Excess forest mortality is consistently linked to drought across Europe. Nat. Commun. 11, 6200 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.van Mantgem, P. J. et al. Widespread increase of tree mortality rates in the western United States. Science 323, 521–524 (2009).PubMed 

Google Scholar 
26.Qiu, T. et al. Is there tree senescence? The fecundity evidence. Proc. Natl Acad. Sci. USA 118, https://doi.org/10.1073/pnas.2106130118 (2021).27.Barrett, S. C. H. Influences of clonality on plant sexual reproduction. Proc. Natl Acad. Sci. USA 112, 8859–8866 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Thomas, H. Senescence, ageing and death of the whole plant. New Phytol. 197, 696–711 (2013).PubMed 

Google Scholar 
29.Munné-Bosch, S. Long-lived trees are not immortal. Trends Plant Sci. 25, 846–849 (2020).PubMed 

Google Scholar 
30.Sillett, S. C. et al. Comparative development of the four tallest conifer species. Ecol. Manage. 480, 118688 (2021).
Google Scholar 
31.Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. Nature 428, 851–854 (2004).CAS 
PubMed 

Google Scholar 
32.Thomas, H. Ageing in plants. Mech. Ageing Dev. 123, 747–753 (2002).PubMed 

Google Scholar 
33.Dahlgren, J. P., García, M. B. & Ehrlén, J. Nonlinear relationships between vital rates and state variables in demographic models. Ecology 92, 1181–1187 (2011).PubMed 

Google Scholar 
34.Klimešová, J., Malíková, L., Rosenthal, J. & Šmilauer, P. Potential bud bank responses to apical meristem damage and environmental variables: matching or complementing axillary meristems? PLoS ONE 9, e88093 (2014).PubMed 
PubMed Central 

Google Scholar 
35.Plomion, C. et al. Oak genome reveals facets of long lifespan. Nat. Plants 4, 440–452 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Hanlon, V. C. T., Otto, S. P. & Aitken, S. N. Somatic mutations substantially increase the per-generation mutation rate in the conifer Picea sitchensis. Evol. Lett. 1, 95 (2019).
Google Scholar 
37.Amaral, J. et al. Advances and promises of epigenetics for forest trees. Trees Livelihoods 11, 976 (2020).
Google Scholar 
38.Carbó, M. et al. in Epigenetics in Plants of Agronomic Importance: Fundamentals and Applications: Transcriptional Regulation and Chromatin Remodelling in Plants (eds Alvarez-Venegas, R. et al.) 381–403 (Springer, 2019).39.Sow, M. D. et al. in Advances in Botanical Research (eds Mirouze, M. et al.) Vol. 88, 387–453 (Academic Press, 2018).40.Das, A., Battles, J., Stephenson, N. L. & van Mantgem, P. J. The contribution of competition to tree mortality in old-growth coniferous forests. Ecol. Manage. 261, 1203–1213 (2011).
Google Scholar 
41.Etzold, S. et al. One century of forest monitoring data in Switzerland reveals species-and site-specific trends of climate-induced tree mortality. Front. Plant Sci. 10, https://doi.org/10.3389/fpls.2019.00307 (2019).42.McNellis, B. E., Smith, A. M. S., Hudak, A. T. & Strand, E. K. Tree mortality in western U.S. forests forecasted using forest inventory and Random Forest classification. Ecosphere 12, https://doi.org/10.1002/ecs2.3419 (2021).43.Piovesan, G. et al. Lessons from the wild: slow but increasing long-term growth allows for maximum longevity in European beech. Ecology 100, e02737 (2019).PubMed 

Google Scholar 
44.Piovesan, G. et al. Radiocarbon dating of Aspromonte sessile oaks reveals the oldest dated temperate flowering tree in the world. Ecology 101, e03179 (2020).PubMed 

Google Scholar 
45.Körner, C. A matter of tree longevity. Science 355, 130–131 (2017).PubMed 

Google Scholar 
46.Poulter, B. et al. The global forest age dataset and its uncertainties (GFADv1.1). PANGAEA https://doi.org/10.1594/PANGAEA.889943 (2019).47.Di Filippo, A., Biondi, F., Piovesan, G. & Ziaco, E. Tree ring-based metrics for assessing old-growth forest naturalness. J. Appl. Ecol. 54, 737–749 (2017).
Google Scholar 
48.Caetano-Andrade, V. L. et al. Tropical trees as time capsules of anthropogenic activity. Trends Plant Sci. 25, 369–380 (2020).CAS 
PubMed 

Google Scholar 
49.Roskilly, B., Keeling, E., Hood, S., Giuggiola, A. & Sala, A. Conflicting functional effects of xylem pit structure relate to the growth–longevity trade-off in a conifer species. Proc. Natl Acad. Sci. USA 116, 15282–15287 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Kingman, J. F. C. The coalescent. Stoch. Process. Appl. 13, 235–248 (1982).
Google Scholar 
51.Joly, S., McLenachan, P. A. & Lockhart, P. J. A statistical approach for distinguishing hybridization and incomplete lineage sorting. Am. Nat. 174, E54–E70 (2009).PubMed 

Google Scholar 
52.Leaché, A. D., Harris, R. B., Rannala, B. & Yang, Z. The influence of gene flow on species tree estimation: a simulation study. Syst. Biol. 63, 17–30 (2014).PubMed 

Google Scholar 
53.Yu, Y., Dong, J., Liu, K. J. & Nakhleh, L. Maximum likelihood inference of reticulate evolutionary histories. Proc. Natl Acad. Sci. USA 111, 16448–16453 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Zhou, Y. et al. Importance of incomplete lineage sorting and introgression in the origin of shared genetic variation between two closely related pines with overlapping distributions. Heredity 118, 211–220 (2017).CAS 
PubMed 

Google Scholar 
55.Petit, R. J. & Hampe, A. Some evolutionary consequences of being a tree. Annu. Rev. Ecol. Evol. Syst. 37, 187–214 (2006).
Google Scholar 
56.Tejo, C. F. & Fontúrbel, F. E. A vertical forest within the forest: millenary trees from the Valdivian rainforest as biodiversity hubs. Ecology 100, e02584 (2019).PubMed 

Google Scholar 
57.Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507, 90–93 (2014).CAS 
PubMed 

Google Scholar  More

Study areaThe study was conducted in central Hungary, ca. 25–60 km south of Budapest, at around the settlements Alsónémedi (47°18′; 19°09′), Apaj (47°06′; 19°05′), Kunszentmiklós (47°01′; 19°07′) and Tass (47°01′; 19°01′) during the 2020 and 2021 breeding seasons. We also used heterospecific controls with Eurasian collared doves for comparisons conducted in the year 2016. In this study area common cuckoos can be found in high densities in their breeding season (May and June). They almost exclusively parasitize great reed warblers (Acrocephalus arundinaceus) locally, a large host which breeds in narrow reed-beds along small irrigation and flood-relief channels47.All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. Local animal ethics regulations and agreements were followed for fieldwork. All work complied with the Hungarian laws, and the Middle-Danube-Valley Inspectorate for Environmental Protection, Nature Conservation and Water Management, Budapest, provided permission for research (permit no. PE/KTF/17190-3/2015).Playback filesWe used cuckoo calls recorded in May between 2016 and 2019. Recording were made with a Telinga Universal parabola dish, equipped with a Sennheiser ME-62 microphone, a K6 powering module, a FEL MX mono preamp, and a Marantz PMD-620 MKII recorder (sampling rate: 48 kHz, 24-bit quality)30.We constructed ten different sound files for playback from the basic “cu-coo” calls:Heterospecific (negative) control(1) The calls of a neutral species from the local avifauna, the Eurasian collared dove, were used for interspecific vocalization control.Natural (positive) control(2) Normal (natural) “cu-coo” calls.Experimental treatments; one-note calls(3) Deleting the second note, i.e. contained “cu”, only.(4) Deleting the first note, i.e. contained “coo”, only.Two-note calls(5) Reversal of the basic “cu-coo” call, i.e. “coo-cu”.(6) Repeating the first note, and deleting the second note, i.e. “cu-cu”.(7) Repeating the second note, and deleting the first note, i.e. “coo-coo”.Three-note calls(8) Repeating the first note, i.e. “cu-cu-coo”.(9) Repeating the second note. i.e. “cu-coo-coo”.Three-note natural(10) Normal (but rare and context specific) “nat. cu-cu-coo”.The experimental 3-note variant of the calls (“cu-cu-coo”; call type No. (8)) differs from our natural 3-note calls (“nat. cu-cu-coo”; call type No. (10)) in two out of the three acoustic parameters (length: F1,18 = 79.258, P  More

1.Rinke C, Chuvochina M, Mussig AJ, Chaumeil P-A, Davín AA, Waite DW, et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat Microbiol. 2021;6:946–59.CAS 
PubMed 

Google Scholar 
2.Karner MB, DeLong EF, Karl DM. Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature. 2001;409:507–10.CAS 
PubMed 

Google Scholar 
3.Buckley DH, Graber JR, Schmidt TM. Phylogenetic analysis of nonthermophilic members of the kingdom Crenarchaeota and their diversity and abundance in soils. Appl Environ Microbiol. 1998;64:4333–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Casamayor EO, Schäfer H, Bañeras L, Pedrós-Alió C, Muyzer G. Identification of and spatio-temporal differences between microbial assemblages from two neighboring sulfurous lakes: Comparison by microscopy and denaturing gradient gel electrophoresis. Appl Environ Microbiol. 2000;66:499–508.CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Francis CA, Roberts KJ, Beman JM, Santoro AE, Oakley BB. Ubiquity and diversity of ammonia-oxidizing Archaea in water columns and sediments of the ocean. Proc Natl Acad Sci USA. 2005;102:14683–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
6.Stahl DA, de la Torre JR. Physiology and diversity of ammonia-oxidizing archaea. Annu Rev Microbiol. 2012;66:83–101.CAS 
PubMed 

Google Scholar 
7.DeLong EF. Archaea in coastal marine environments. Proc Natl Acad Sci USA. 1992;89:5685–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Fuhrman JA, McCallum K, Davis AA. Novel major archaebacterial group from marine plankton. Nature. 1992;356:148–9.CAS 
PubMed 

Google Scholar 
9.Qin W, Zheng Y, Zhao F, Wang Y, Urakawa H, Martens-Habbena W, et al. Alternative strategies of nutrient acquisition and energy conservation map to the biogeography of marine ammonia-oxidizing archaea. ISME J. 2020;14:2596–609.
Google Scholar 
10.Aylward FO, Santoro AE. Heterotrophic Thaumarchaea with small genomes are widespread in the dark ocean. mSystems. 2020;5:e00415–00420.CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Reji L, Francis CA. Metagenome-assembled genomes reveal unique metabolic adaptations of a basal marine Thaumarchaeota lineage. ISME J. 2020;14:2105–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Wang Y, Huang J-M, Cui G-J, Nunoura T, Takaki Y, Li W-L, et al. Genomics insights into ecotype formation of ammonia-oxidizing archaea in the deep ocean. Environ Microbiol. 2019b;21:716–29.CAS 
PubMed 

Google Scholar 
13.Zhong H, Lehtovirta-Morley L, Liu J, Zheng Y, Lin H, Song D, et al. Novel insights into the Thaumarchaeota in the deepest oceans: their metabolism and potential adaptation mechanisms. Microbiome. 2020;8:78.PubMed 
PubMed Central 

Google Scholar 
14.Wang B, Qin W, Ren Y, Zhou X, Jung M-Y, Han P, et al. Expansion of Thaumarchaeota habitat range is correlated with horizontal transfer of ATPase operons. ISME J. 2019a;13:3067–79.CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Sheridan PO, Raguideau S, Quince C, Holden J, Zhang L, Gaze WH, et al. Gene duplication drives genome expansion in a major lineage of Thaumarchaeota. Nat Commun. 2020;11:5494.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Ren M, Feng X, Huang Y, Wang H, Hu Z, Clingenpeel S, et al. Phylogenomics suggests oxygen availability as a driving force in Thaumarchaeota evolution. ISME J. 2019;13:2150–61.PubMed 
PubMed Central 

Google Scholar 
17.Yang Y, Zhang C, Lenton TM, Yan X, Zhu M, Zhou M, et al. The evolution pathway of ammonia-oxidizing archaea shaped by major geological events. Mol Biol Evol. 2021;38:3637–48.PubMed 
PubMed Central 

Google Scholar 
18.Alves RJE, Minh BQ, Urich T, von Haeseler A, Schleper C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat Commun. 2018;9:1517.PubMed 
PubMed Central 

Google Scholar 
19.Llirós M, Casamayor EO, Borrego C. High archaeal richness in the water column of a freshwater sulfurous karstic lake along an interannual study. FEMS Microbiol Ecol. 2008;66:331–42.PubMed 

Google Scholar 
20.Wang Z, Wang Z, Huang C, Pei Y. Vertical distribution of ammonia-oxidizing archaea (AOA) in the hyporheic zone of a eutrophic river in North China. World J Microbiol Biotechnol. 2014;30:1335–46.CAS 
PubMed 

Google Scholar 
21.Mußmann M, Brito I, Pitcher A, Sinninghe Damsté JS, Hatzenpichler R, Richter A, et al. Thaumarchaeotes abundant in refinery nitrifying sludges express amoA but are not obligate autotrophic ammonia oxidizers. Proc Natl Acad Sci USA. 2011;108:16771–6.PubMed 
PubMed Central 

Google Scholar 
22.Biller S, Mosier A, Wells G, Francis C. Global biodiversity of aquatic ammonia-oxidizing archaea is partitioned by habitat. Front Microbiol. 2012;3:252.23.Beman JM, Francis CA. Diversity of ammonia-oxidizing archaea and bacteria in the sediments of a hypernutrified subtropical estuary: Bahia del Tobari, Mexico. Appl Environ Microbiol. 2006;72:7767–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Auguet J-C, Nomokonova N, Camarero L, Casamayor EO. Seasonal changes of freshwater ammonia-oxidizing archaeal assemblages and nitrogen species in oligotrophic alpine lakes. Appl Environ Microbiol. 2011;77:1937–45.CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Small GE, Bullerjahn GS, Sterner RW, Beall BFN, Brovold S, Finlay JC, et al. Rates and controls of nitrification in a large oligotrophic lake. Limnol Oceanogr. 2013;58:276–86.CAS 

Google Scholar 
26.Herber J, Klotz F, Frommeyer B, Weis S, Straile D, Kolar A, et al. A single Thaumarchaeon drives nitrification in deep oligotrophic Lake Constance. Environ Microbiol. 2020;22:212–28.CAS 
PubMed 

Google Scholar 
27.Auguet J-C, Triadó-Margarit X, Nomokonova N, Camarero L, Casamayor EO. Vertical segregation and phylogenetic characterization of ammonia-oxidizing Archaea in a deep oligotrophic lake. ISME J. 2012;6:1786–97.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Podowski JC, Paver SF, Newton RJ, Coleman ML. Genome streamlining, proteorhodopsin, and organic nitrogen metabolism in freshwater nitrifiers. bioRxiv. 2021;2021.2001.2019.427344.29.Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A, Becker A, et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotech. 2016;34:942–9.CAS 

Google Scholar 
30.Restrepo-Ortiz CX, Auguet J-C, Casamayor EO. Targeting spatiotemporal dynamics of planktonic SAGMGC-1 and segregation of ammonia-oxidizing thaumarchaeota ecotypes by newly designed primers and quantitative polymerase chain reaction. Environ Microbiol. 2014;16:689–700.CAS 
PubMed 

Google Scholar 
31.Liu S, Wang H, Chen L, Wang J, Zheng M, Liu S, et al. Comammox Nitrospira within the Yangtze River continuum: community, biogeography, and ecological drivers. ISME J. 2020;14:2488–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Santos-Júnior CD, Sarmento H, de Miranda FP, Henrique-Silva F, Logares R. Uncovering the genomic potential of the Amazon River microbiome to degrade rainforest organic matter. Microbiome. 2020;8:151.PubMed 
PubMed Central 

Google Scholar 
33.Jung M-Y, Sedlacek CJ, Kits KD, Mueller AJ, Rhee S-K, Hink L, et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 2022;16:272–83.CAS 
PubMed 

Google Scholar 
34.Kim BK, Jung MY, Yu DS, Park SJ, Oh TK, Rhee SK, et al. Genome sequence of an ammonia-oxidizing soil archaeon, “Candidatus Nitrosoarchaeum koreensis” MY1. J Bacteriol. 2011;193:5539–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Jung MY, Park SJ, Kim SJ, Kim JG, Sinninghe Damste JS, Jeon CO, et al. A mesophilic, autotrophic, ammonia-oxidizing archaeon of thaumarchaeal group I.1a cultivated from a deep oligotrophic soil horizon. Appl Environ Microbiol. 2014;80:3645–55.PubMed 
PubMed Central 

Google Scholar 
36.Lebedeva EV, Hatzenpichler R, Pelletier E, Schuster N, Hauzmayer S, Bulaev A, et al. Enrichment and genome sequence of the group i.1a ammonia-oxidizing archaeon “Ca. Nitrosotenuis uzonensis” representing a clade globally distributed in thermal habitats. PLoS One. 2013;8:e80835.PubMed 
PubMed Central 

Google Scholar 
37.Li Y, Ding K, Wen X, Zhang B, Shen B, Yang Y. A novel ammonia-oxidizing archaeon from wastewater treatment plant: Its enrichment, physiological and genomic characteristics. Sci Rep. 2016;6:23747.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Sauder LA, Albertsen M, Engel K, Schwarz J, Nielsen PH, Wagner M, et al. Cultivation and characterization of Candidatus Nitrosocosmicus exaquare, an ammonia-oxidizing archaeon from a municipal wastewater treatment system. ISME J. 2017;11:1142–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Wang Y, Qin W, Jiang X, Ju F, Mao Y, Zhang A, et al. Seasonal prevalence of ammonia-oxidizing archaea in a full-scale municipal wastewater treatment plant treating saline wastewater revealed by a 6-year time-series analysis. Environ Sci Technol. 2021;55:2662–73.CAS 
PubMed 

Google Scholar 
40.Xing P, Tao Y, Luo J, Wang L, Li B, Li H, et al. Stratification of microbiomes during the holomictic period of Lake Fuxian, an alpine monomictic lake. Limnol Oceanogr. 2020;65:S134–S148.
Google Scholar 
41.Cabello-Yeves PJ, Zemskaya TI, Rosselli R, Coutinho FH, Zakharenko AS, Blinov VV, et al. Genomes of novel microbial lineages assembled from the sub-ice waters of Lake Baikal. Appl Environ Microbiol. 2017;84:e02132–02117.PubMed 
PubMed Central 

Google Scholar 
42.Cabello-Yeves PJ, Zemskaya TI, Zakharenko AS, Sakirko MV, Ivanov VG, Ghai R et al. Microbiome of the deep Lake Baikal, a unique oxic bathypelagic habitat. Limnol Oceanogr. 2020;65:1471–88.43.Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci USA. 2014;111:12504–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Bayer B, Vojvoda J, Offre P, Alves RJ, Elisabeth NH, Garcia JA, et al. Physiological and genomic characterization of two novel marine thaumarchaeal strains indicates niche differentiation. ISME J. 2016;10:1051–63.CAS 
PubMed 

Google Scholar 
45.Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Hink L, Gubry-Rangin C, Nicol GW, Prosser JI. The consequences of niche and physiological differentiation of archaeal and bacterial ammonia oxidisers for nitrous oxide emissions. ISME J. 2018;12:1084–93.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Martens-Habbena W, Berube PM, Urakawa H, de la Torre JR, Stahl DA. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature. 2009;461:976–9.CAS 
PubMed 

Google Scholar 
48.Mayr MJ, Zimmermann M, Guggenheim C, Brand A, Bürgmann H. Niche partitioning of methane-oxidizing bacteria along the oxygen–methane counter gradient of stratified lakes. ISME J. 2020;14:274–87.CAS 
PubMed 

Google Scholar 
49.Reis PCJ, Thottathil SD, Ruiz-González C, Prairie YT. Niche separation within aerobic methanotrophic bacteria across lakes and its link to methane oxidation rates. Environ Microbiol. 2020;22:738–51.CAS 
PubMed 

Google Scholar 
50.Tran PQ, Bachand SC, McIntyre PB, Kraemer BM, Vadeboncoeur Y, Kimirei IA, et al. Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika. ISME J 2021;15:1971–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Sauder LA, Engel K, Lo C-C, Chain P, Neufeld JD. Candidatus Nitrosotenuis aquarius, an ammonia-oxidizing archaeon from a freshwater aquarium biofilter. Appl Environ Microbiol. 2018;84:e01430-18.52.Hug LA, Thomas BC, Brown CT, Frischkorn KR, Williams KH, Tringe SG, et al. Aquifer environment selects for microbial species cohorts in sediment and groundwater. ISME J. 2015;9:1846–56.PubMed 
PubMed Central 

Google Scholar 
53.Barco RA, Garrity GM, Scott JJ, Amend JP, Nealson KH, Emerson D. A genus definition for Bacteria and Archaea based on a standard genome relatedness index. MBio. 2020;11:e02475–02419.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Haas S, Desai DK, LaRoche J, Pawlowicz R, Wallace DWR. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environ Microbiol. 2019;21:3927–52.CAS 
PubMed 

Google Scholar 
55.Herbold CW, Lehtovirta-Morley LE, Jung M-Y, Jehmlich N, Hausmann B, Han P, et al. Ammonia-oxidising archaea living at low pH: Insights from comparative genomics. Environ Microbiol. 2017;19:4939–52.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Shen M, Li Q, Ren M, Lin Y, Wang J, Chen L, et al. Trophic status is associated with community structure and metabolic potential of planktonic microbiota in plateau lakes. Front Microbiol. 2019;10:2560–2560.PubMed 
PubMed Central 

Google Scholar 
57.Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.PubMed 
PubMed Central 

Google Scholar 
58.Swan BK, Tupper B, Sczyrba A, Lauro FM, Martinez-Garcia M, González JM, et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc Natl Acad Sci USA. 2013;110:11463–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Grzymski JJ, Dussaq AM. The significance of nitrogen cost minimization in proteomes of marine microorganisms. ISME J. 2012;6:71–80.CAS 
PubMed 

Google Scholar 
60.Bragg JG, Hyder CL. Nitrogen versus carbon use in prokaryotic genomes and proteomes. Proc R Soc Lond B Biol Sci. 2004;271:S374–7.CAS 

Google Scholar 
61.Mende DR, Bryant JA, Aylward FO, Eppley JM, Nielsen T, Karl DM, et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat Microbiol. 2017;2:1367–73.CAS 
PubMed 

Google Scholar 
62.Baudouin-Cornu P, Schuerer K, Marlière P, Thomas D. Intimate evolution of proteins: Proteome atomic content correlates with genome base composition. J Biol Chem. 2004;279:5421–8.CAS 
PubMed 

Google Scholar 
63.Santoro AE, Dupont CL, Richter RA, Craig MT, Carini P, McIlvin MR, et al. Genomic and proteomic characterization of Candidatus Nitrosopelagicus brevis: An ammonia-oxidizing archaeon from the open ocean. Proc Natl Acad Sci USA. 2015;112:1173–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Luo H, Tolar BB, Swan BK, Zhang CL, Stepanauskas R, Ann Moran M, et al. Single-cell genomics shedding light on marine Thaumarchaeota diversification. ISME J. 2014;8:732–6.CAS 
PubMed 

Google Scholar 
65.Reji L, Tolar BB, Smith JM, Chavez FP, Francis CA. Depth distributions of nitrite reductase (nirK) gene variants reveal spatial dynamics of thaumarchaeal ecotype populations in coastal Monterey Bay. Environ Microbiol. 2019;21:4032–45.CAS 
PubMed 

Google Scholar 
66.Hallam SJ, Konstantinidis KT, Putnam N, Schleper C, Watanabe Y, Sugahara J, et al. Genomic analysis of the uncultivated marine crenarchaeote Cenarchaeum symbiosum. Proc Natl Acad Sci USA. 2006;103:18296–301.CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Martiny JBH, Jones SE, Lennon JT, Martiny AC. Microbiomes in light of traits: A phylogenetic perspective. Science. 2015;350:aac9323.PubMed 

Google Scholar 
68.Logares R, Bråte J, Bertilsson S, Clasen JL, Shalchian-Tabrizi K, Rengefors K. Infrequent marine–freshwater transitions in the microbial world. Trends Microbiol. 2009;17:414–22.CAS 
PubMed 

Google Scholar 
69.Paver SF, Muratore D, Newton RJ, Coleman ML, Flynn TM. Reevaluating the salty divide: Phylogenetic specificity of transitions between marine and freshwater systems. mSystems. 2018;3:e00232–00218.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Henson MW, Lanclos VC, Faircloth BC, Thrash JC. Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J. 2018;12:1846–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Luo H, Csűros M, Hughes AL, Moran MA, Azam F, Zehr J. Evolution of divergent life history strategies in marine Alphaproteobacteria. MBio. 2013;4:e00373–00313.PubMed 
PubMed Central 

Google Scholar 
72.Zaremba-Niedzwiedzka K, Viklund J, Zhao W, Ast J, Sczyrba A, Woyke T, et al. Single-cell genomics reveal low recombination frequencies in freshwater bacteria of the SAR11 clade. Genome Biol. 2013;14:R130.PubMed 
PubMed Central 

Google Scholar 
73.Fillol M, Auguet J-C, Casamayor EO, Borrego CM. Insights in the ecology and evolutionary history of the Miscellaneous Crenarchaeotic Group lineage. ISME J. 2016;10:665–77.PubMed 

Google Scholar 
74.Siuda W, Kiersztyn B. Urea in lake ecosystem: The origin, concentration and distribution in relation to trophic state of the Great Mazurian Lakes (Poland). Pol J Ecol. 2015;63:110–23. 114
Google Scholar 
75.Spang A, Poehlein A, Offre P, Zumbragel S, Haider S, Rychlik N, et al. The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ Microbiol. 2012;14:3122–45.CAS 
PubMed 

Google Scholar 
76.Kitzinger K, Padilla CC, Marchant HK, Hach PF, Herbold CW, Kidane AT, et al. Cyanate and urea are substrates for nitrification by Thaumarchaeota in the marine environment. Nat Microbiol. 2019;4:234–43.CAS 
PubMed 

Google Scholar 
77.Kerou M, Offre P, Valledor L, Abby SS, Melcher M, Nagler M, et al. Proteomics and comparative genomics of Nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers. Proc Natl Acad Sci USA. 2016;113:E7937–E7946.CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Carini P, Dupont Christopher L, Santoro, Alyson E. Patterns of thaumarchaeal gene expression in culture and diverse marine environments. Environ Microbiol. 2018;20:2112–24.CAS 
PubMed 

Google Scholar 
79.Bogard MJ, Donald DB, Finlay K, Leavitt PR. Distribution and regulation of urea in lakes of central North America. Freshw Biol. 2012;57:1277–92.CAS 

Google Scholar 
80.Glibert PM, Harrison J, Heil C, Seitzinger S. Escalating worldwide use of urea – a global change contributing to coastal eutrophication. Biogeochemistry. 2006;77:441–63.CAS 

Google Scholar 
81.Alonso-Sáez L, Waller AS, Mende DR, Bakker K, Farnelid H, Yager PL, et al. Role for urea in nitrification by polar marine Archaea. Proc Natl Acad Sci USA. 2012;109:17989–94.PubMed 
PubMed Central 

Google Scholar 
82.Tolar BB, Wallsgrove NJ, Popp BN, Hollibaugh JT. Oxidation of urea-derived nitrogen by thaumarchaeota-dominated marine nitrifying communities. Environ Microbiol. 2017;19:4838–50.CAS 
PubMed 

Google Scholar 
83.Gunde-Cimerman N, Plemenitaš A, Oren A. Strategies of adaptation of microorganisms of the three domains of life to high salt concentrations. FEMS Microbiol Rev. 2018;42:353–75.CAS 
PubMed 

Google Scholar 
84.Hagemann M. Molecular biology of cyanobacterial salt acclimation. FEMS Microbiol Rev. 2011;35:87–123.CAS 
PubMed 

Google Scholar 
85.Blount P, Iscla I. Life with bacterial mechanosensitive channels, from discovery to physiology to pharmacological target. Microbiol Mol Biol Rev. 2020;84:e00055–00019.CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Martinac B, Bavi N, Ridone P, Nikolaev YA, Martinac AD, Nakayama Y, et al. Tuning ion channel mechanosensitivity by asymmetry of the transbilayer pressure profile. Biophys Rev. 2018;10:1377–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Widderich N, Czech L, Elling FJ, Konneke M, Stoveken N, Pittelkow M, et al. Strangers in the archaeal world: osmostress-responsive biosynthesis of ectoine and hydroxyectoine by the marine thaumarchaeon Nitrosopumilus maritimus. Environ Microbiol. 2016;18:1227–48.CAS 
PubMed 

Google Scholar 
88.Jung H, Hilger D, Raba M. The Na+/L-proline transporter PutP. Front Biosci-Landmark. 2012;17:745–59.CAS 

Google Scholar 
89.Tyedmers J, Mogk A, Bukau B. Cellular strategies for controlling protein aggregation. Nat Rev Mol Cell Biol. 2010;11:777–88.CAS 
PubMed 

Google Scholar 
90.Li D-C, Yang F, Lu B, Chen D-F, Yang W-J. Thermotolerance and molecular chaperone function of the small heat shock protein HSP20 from hyperthermophilic archaeon, Sulfolobus solfataricus P2. Cell Stress Chaperones. 2012;17:103–8.PubMed 

Google Scholar 
91.Qin W, Amin SA, Lundeen RA, Heal KR, Martens-Habbena W, Turkarslan S, et al. Stress response of a marine ammonia-oxidizing archaeon informs physiological status of environmental populations. ISME J. 2017a;12:508–19.PubMed 
PubMed Central 

Google Scholar 
92.Phadtare S, Inouye M. Role of CspC and CspE in regulation of expression of RpoS and UspA, the stress response proteins in Escherichia coli. J Bacteriol. 2001;183:1205–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Albers S-V, Jarrell KF. The archaellum: An update on the unique archaeal motility structure. Trends Microbiol. 2018;26:351–62.CAS 
PubMed 

Google Scholar 
94.Mosier AC, Lund MB, Francis CA. Ecophysiology of an ammonia-oxidizing archaeon adapted to low-salinity habitats. Micro Ecol. 2012;64:955–63.CAS 

Google Scholar 
95.Qin W, Heal KR, Ramdasi R, Kobelt JN, Martens-Habbena W, Bertagnolli AD, et al. Nitrosopumilus maritimus gen. nov., sp. nov., Nitrosopumilus cobalaminigenes sp. nov., Nitrosopumilus oxyclinae sp. nov., and Nitrosopumilus ureiphilus sp. nov., four marine ammonia-oxidizing archaea of the phylum Thaumarchaeota. Int J Syst Evol Microbiol. 2017b;67:5067–79.PubMed 

Google Scholar 
96.Dupuis M-È, Villion M, Magadán AH, Moineau S. CRISPR-Cas and restriction–modification systems are compatible and increase phage resistance. Nat Commun. 2013;4:2087.PubMed 

Google Scholar 
97.Krupovic M, Makarova KS, Wolf YI, Medvedeva S, Prangishvili D, Forterre P, et al. Integrated mobile genetic elements in Thaumarchaeota. Environ Microbiol. 2019;21:2056–78.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Critical transition theory tells us that, as exogenous parameters drive the system towards a bifurcation and the emergence of a new equilibrium, we can see evidence of the upcoming transition through decreasing resistance to each oscillation peak15, like a ball rolling around in a cup whose walls are becoming less and less steep1,6. We find evidence of this critical slowing down within the year-to-year deceleration of rust growth rates just prior to their annual peak, or λ, our formal slowing down measure. This is coupled with a delay in the initiation month (takeoff) of the oscillation itself. The oscillations collapse in 2019 as the system apparently switches to a more benign non-epidemic state.In the mid-elevation zone of southwestern Mexico where we conducted our study, the general expectation from climate change, in addition to increased temperature, is reduced rainfall19,20, a pattern broadly in evidence for the past five years (Fig. S2). With regard to the coffee rust disease, this precipitation trend is most closely associated with the change in λ, and reflects the obligate range of associated moisture and temperature conditions required for the rust to flourish9,17. On the other hand, rust-resistant replanting has been occurring throughout coffee farms in much of Mesoamerica since the rust outbreak in 2012–201321,22. This could limit plant-to-plant spread by reducing opportunities for direct contact with an infected plant, as well as decrease the environmental spore load by reducing the contributing pool of infected plants in the broader region. On our site, the slowing down patterns of both λ and the rust takeoff point were associated with more rust-resistant replanting on a local level, while the significant linear delay in the year-to-year rust takeoff over the study period may reflect larger-scale effects emerging from increased resistance across the farm.Our preliminary treatment of April as the annual rust season initiation point corresponded generally with a seasonal pattern of increasing rainfall. This approach might be assumed by a manager experiencing the system in real time without knowledge of how the rust season will play out that year. Such an assumption works well in the first two seasons of our data, where the rust increase and rainy season both appear to begin in April. However, this association decouples in subsequent years. Evidently, the critical slowing down we initially estimated in Fig. 1B is partitioned into two components: one that imposes a lag on the initiation of the disease, and another that imposes a decelerating approach to the peak rust intensity, necessitating a joint model of the initiation month and the critical slowing down warning signal (λ). That the takeoff had a significant delaying trend over the study period suggests that the initiation of the rust season can also be seen as a measure of critical slowing down, in that a slower approach to the equilibrium point (the seasonal rust peak) will likely be reflected in a failure to even recognize the increase when it is still very small. Thus, the conspicuous change in takeoff point over time (Fig. 2A, B) itself suggests a critical slowing down and the negative values of λ in Fig. 1B may be mainly a reflection of a lag in takeoff time. The overall increasing lag in rust takeoff each year could mean that the transmission factor itself may be exhibiting a critical slowing down as the critical transition is approached, due to the progressive influence of parameters outside of the present analysis. Given the basic biology of the disease10, this trend may stem from a year-to-year secular decline in the environmental spore load causing initiation of the disease season to be deterred by a small amount each year.The evident relationship between critical slowing down and reduced rainfall is suggestive of a connection to climate change, while a slow secular increase in the proportion of rust-resistant varieties could have inhibited local or regional spread, leading to an effective “herd immunity” to the disease. The joint operation of reduced rainfall and fewer susceptible plants raises the possibility of dual bifurcations and hysteretic zones in the dynamic landscape, which we illustrate in a qualitative fashion (generalized to climate and management) in Fig. 4a. The initial outbreak, which was unexpected in light of the historically low, but persistent, levels of rust in the region7,8, was likely a critical transition precipitated by interactions between local and regional processes14. As foregrounded in Fig. 4b, we propose that a possible parameter driving the system to this initial bifurcation was recent increases in precipitation, as evidenced in local rainfall records (Fig. S2) and regional trends19. This could have propelled the system past a hysteretic phase space to where seasonal conditions dictated that the system jump to a high rust intensity equilibrium, represented conceptually in Fig. 4a by the dotted trajectory leading to the upper surface of the landscape that corresponds to an epidemic state of the rust. Likewise, in the years following the outbreak, our findings suggest that, while the system still tracked precipitation, progressive replanting of resistant varieties emerged as another parameter axis (management) that drove the system through a second critical transition back to a low rust equilibrium (Fig. 4c). Although the dynamical landscape in Fig. 4 is a qualitative representation, the trajectory along the upper surface helps to visualize how two exogenous forces, operating separately, both contributed to the critical transition we observed in our data. The trajectory we propose brings attention to the interesting possibility that the main driver of slowing down shifted from climate (precipitation) to management (replanting), leading to the second critical transition. Indeed, though the average yearly replanting rate remained roughly the same year-to-year (Fig. S1), we note that much of the cutting prior to the collapse in 2019 seemed to be concentrated in April that year (Fig. 1D), accompanied presumably by a similarly timed replanting campaign.Figure 4Envisioning the combination of climate and management effects in a joint hysteretic framing, stemming from gradual change in both forcing parameters. (A) Relative positions of rust intensity for each year are illustrated in their approximate positions with red arrows (other trajectories could be imagined based on the data presented herein). Inset plots provide a conceptualized view of the dynamics between the two forcing variables on rust intensity: (B) management (in our case, resistant variety replanting) and (C) climate (precipitation). In the inset plots, arrows indicate directions of change; solid and dashed lines indicate stable and unstable equilibria, respectively.Full size imageRecent work on critical transitions suggests that perturbances driving a system to transition are more realistically not distinct or isolated, and that the stochastic and deterministic elements of the system can therefore be entangled or even interdependent4. Likewise, we find that the variability in the environmental covariates of monthly rainfall and resistant variety replanting better explained patterns in λ than a linear trend leading up to the transition, as represented by the year variable. The correlation between the quadrat grouping offset estimates from the λ and takeoff components of the multivariate model also suggest that slowing down and delayed takeoff were associated at the individual quadrat level (Fig. 3C). Accounting for this spatial effect, these two components do not appear to be correlated by year (Fig. 3D). This suggests that the shared variability between these two indicators reflects variability in spatial environment within the plot rather than idiosyncratic effects of unique years. Besides the direct effect of resistant varieties, local stochasticity and spread dynamics may also play a role. Local growing conditions, such as variability in shade from overstory trees, can affect dispersal through rainfall splash and wind23,24. Additional management factors may also play a role, such as the vegetation structure and the presence of paths25, as well as the physical relationship between coffee plants26.Our observations of the rust dynamics themselves allow us to detect the general signals anticipating a critical transition, though the drivers may emerge from a complex system of dialectical interactions that must be considered in their whole7,27. The concept of critical slowing down thus may lend itself to application across coffee-growing regions, where predicted effects of climate change and other geographic conditions may differ9,19. Since the emergence of the rust outbreak, recommendations and protocols have been published for monitoring rust levels, potentially providing managers with regular data in changes in rust intensity for many areas9,28. As the resilience of a system can be interpreted through measuring critical slowing down prior to catastrophe2, as well as, in our case, the “exit time” from an undesirable regime4, we demonstrate that such concepts may be applied to this monitoring data to gain some insight into the system’s status. Future studies could explore signs of critical slowing down across coffee-growing regions and management systems to see how these signals predict significant changes and respond to local drivers, potentially adding to the vocabulary of agroecological management.In sum, it is clear that both a lag in takeoff point for the seasonal oscillation and the rate of approach to the peak each year seem to conspire to produce a critical slowing down, strong evidence that the decline in the disease in 2019–2020 is indeed a critical transition, regardless of the underlying mechanism. While our model suggests that two exogenous forces, rainfall and resistant variety replanting, may be driving the slowing down in our case, the underlying dynamical landscape is likely not unique to our site. More generally, the phenomenon of multivariate bifurcations leading to subsequent critical transitions (e.g., Fig. 4) is perhaps more common than thought29. Examinations of critical transitions should therefore consider the larger dynamical landscape for the possibility of subsequent transitions. More

1.Pennington, R. T., Lehmann, C. E. R. & Rowland, L. M. Tropical savannas and dry forests. Curr. Biol. 28, R541–R545 (2018).CAS 
PubMed 

Google Scholar 
2.Piao, S. et al. Interannual variation of terrestrial carbon cycle: Issues and perspectives. Glob. Change Biol. 26, 300–318 (2019).ADS 

Google Scholar 
3.Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants. 5, 944–951 (2019).CAS 
PubMed 

Google Scholar 
4.Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
CAS 
PubMed 

Google Scholar 
5.Moro, M. F., Nic Lughadha, E., de Araújo, F. S. & Martins, F. R. A phytogeographical metaanalysis of the semiarid caatinga domain in Brazil. Bot. Rev. 82, 91–148 (2016).6.Terra, M., et al. Water availability drives gradients of tree diversity, structure and functional traits in the Atlantic–Cerrado–Caatinga transition, Brazil. J. Plant Ecol. 11, 803–814 (2018).7.Leite, M. B., Xavier, R. O., Oliveira, P. T. S., Silva, F. K. G. & Silva Matos, D. M. Groundwater depth as a constraint on the woody cover in a Neotropical Savanna. Plant Soil. 426, 1–15 (2018).8.Silvertown, J., Araya, Y. & Gowing, D. Hydrological niches in terrestrial plant communities: a review. J. Ecol. 103, 93–108 (2014).
Google Scholar 
9.Poulter, B. et al. Plant functional type classification for earth system models: results from the European Space Agency’s Land Cover Climate Change Initiative. Geosci. Model Dev. 8, 2315–2328 (2015).ADS 

Google Scholar 
10.Congalton, R. G., Gu, J., Yadav, K., Thenkabail, P. & Ozdogan, M. Global land cover mapping: A review and uncertainty analysis. Remote Sens. 6(12), 12070–12093 (2014).ADS 

Google Scholar 
11.Phiri, D. & Morgenroth, J. Developments in Landsat land cover classification methods: A review. Remote Sens. 9(9), 967 (2017).
Google Scholar 
12.Joshi, N. et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8(1), 70 (2016).ADS 

Google Scholar 
13.Xiao, J. et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sens. Environ. 233, 111383 (2019).ADS 

Google Scholar 
14.Françoso, R. D. et al. Delimiting floristic biogeographic districts in the Cerrado and assessing their conservation status. Biodivers. Conserv. 29, 1477–1500 (2019).
Google Scholar 
15.Eiten, G. Delimitation of the cerrado concept. Plant Ecol. 36, 169–178 (1978).
Google Scholar 
16.Ribeiro, J.F. & Walter, B.M.T. As principais fitofisionomias do bioma Cerrado in Cerrado: ecologia e flora (ed. Sano, S.M., Almeida, S.P. & Ribeiro, J.F.) 151–212 (EMBRAPA, 2008).17.Oliveria, P.S. & Marquis, R.J. The Cerrados of Brazil: ecology and natural history of a neotropical savanna. (Columbia University Press, 2002).18.Buchhorn, M. et al. Copernicus Global Land Cover Layers—Collection 2. Remote Sens. 12, 1044 (2020).ADS 

Google Scholar 
19.European Space Agency. Data. https://climate.esa.int/en/projects/land-cover/data/ (2021).20.Pellegrini, A. F. A. Nutrient limitation in tropical savannas across multiple scales and mechanisms. Ecology 97, 313–324 (2016).PubMed 

Google Scholar 
21.Vourlitis, G. L. et al. Variations in stand structure and diversity along a soil fertility gradient in a Brazilian savanna (Cerrado) in Southern Mato Grosso. Soil Sci. Soc. Am. J. 77, 1370–1379 (2013).ADS 
CAS 

Google Scholar 
22.Abrahão, A. et al. Soil types select for plants with matching nutrient-acquisition and use traits in hyperdiverse and severely nutrient-impoverished campos rupestres and cerrado in Central Brazil. J. Ecol. 107, 1302–1316 (2018).
Google Scholar 
23.de Assis, A. C. C., Coelho, R. M., da Silva Pinheiro, E. & Durigan, G. Water availability determines physiognomic gradient in an area of low-fertility soils under Cerrado vegetation. Plant Ecol. 212, 1135–1147 (2011).24.Oliveira, P. T. S. et al. Groundwater recharge decrease with increased vegetation density in the Brazilian cerrado. Ecohydrology. 10, e1759 (2016).25.de Oliveira Xavier, R., Leite, M. B., Dexter, K. & da Silva Matos, D. M. Differential effects of soil waterlogging on herbaceous and woody plant communities in a Neotropical savanna. Oecologia. 190, 471–483 (2019).26.Zappi, D. C., Moro, M. F., Meagher, T. R. & Nic Lughadha, E. Plant biodiversity drivers in Brazilian campos rupestres: insights from phylogenetic structure. Front. Plant Sci. 8, (2017).27.Neri, A. V., Schaefer, C. E. G. R., Souza, A. L., Ferreira-Junior, W. G. & Meira-Neto, J. A. A. Pedology and plant physiognomies in the cerrado, Brazil. An. Acad. Bras. Ciênc. 85, 87–102 (2013).CAS 
PubMed 

Google Scholar 
28.Simon, M. F. & Pennington, T. Evidence for Adaptation to Fire Regimes in the Tropical Savannas of the Brazilian Cerrado. Int. J. Plant Sci. 173, 711–723 (2012).
Google Scholar 
29.de Castro, E. A. & Kauffman, J. B. Ecosystem structure in the Brazilian Cerrado: a vegetation gradient of aboveground biomass, root mass and consumption by fire. J. Trop. Ecol. 14, 263–283 (1998).
Google Scholar 
30.da Silva, D. M. & Batalha, M. A. Soil–vegetation relationships in cerrados under different fire frequencies. Plant Soil 311, 87–96 (2008).CAS 

Google Scholar 
31.Durigan, G. Zero-fire: Not possible nor desirable in the Cerrado of Brazil. Flora. 268, 151612 (2020).32.Lloyd, J. & Veenendaal, E. M. Are fire mediated feedbacks burning out of control? (2016)33.Bueno, M. L. et al. The environmental triangle of the Cerrado Domain: Ecological factors driving shifts in tree species composition between forests and savannas. J. Ecol. 106, 2109–2120 (2018).
Google Scholar 
34.Alencar, A. et al. Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. Remote Sens. 12, 924 (2020).ADS 

Google Scholar 
35.INPE. Projeto TerraClass Cerrado Mapeamento do Uso e Cobertura Vegetal do Cerrado. http://www.inpe.br/cra/projetos_pesquisas/dados_terraclass.php (2019)36.Sano, E. E., Rosa, R., Brito, J. L. S. & Ferreira, L. G. Land cover mapping of the tropical savanna region in Brazil. Environ. Monit. Assess. 166, 113–124 (2009).PubMed 

Google Scholar 
37.Sano, E. E. et al. Cerrado ecoregions: A spatial framework to assess and prioritize Brazilian savanna environmental diversity for conservation. J. Environ. Manag. 232, 818–828 (2019).
Google Scholar 
38.Monteiro, L. M. et al. Evaluating the impact of future actions in minimizing vegetation loss from land conversion in the Brazilian Cerrado under climate change. Biodivers. Conserv. 29, 1701–1722 (2018).
Google Scholar 
39.Silva, J. F., Farinas, M. R., Felfili, J. M. & Klink, C. A. Spatial heterogeneity, land use and conservation in the cerrado region of Brazil. J. Biogeogr. 33, 536–548 (2006).
Google Scholar 
40.Strassburg, B. B. N. et al. Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol. 1, 0099 (2017).
Google Scholar 
41.Soares-Filho, B. et al. Cracking Brazil’s Forest Code. Science 344, 363–364 (2014).ADS 
CAS 
PubMed 

Google Scholar 
42.Gomes, L., Miranda, H. S. & Bustamante, M. M. C. How can we advance the knowledge on the behavior and effects of fire in the Cerrado biome? For. Ecol. Manag. 417, 281–290 (2018).43.Hartley, A. J., MacBean, N., Georgievski, G. & Bontemps, S. Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sens. Environ. 203, 71–89 (2017).ADS 

Google Scholar 
44.Cava, M. G. B., Pilon, N. A. L., Ribeiro, M. C. & Durigan, G. Abandoned pastures cannot spontaneously recover the attributes of old-growth savannas. J. Appl. Ecol. 55, 1164–1172 (2017).
Google Scholar 
45.Brancalion, P. H. S. et al. Governance innovations from a multi-stakeholder coalition to implement large-scale Forest Restoration in Brazil. World Dev. Perspect. 3, 15–17 (2016).
Google Scholar 
46.Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Phil. Trans. R. Soc. B. 375, 20190120 (2020).PubMed 
PubMed Central 

Google Scholar 
47.MMA. Plano de Manejo Parque Nacional Chapada dos Veadeiros. Ministro de Estado do Meio Ambiente. Brasília. (2009).48.Hunke, P., Roller, R., Zeilhofer, P., Schröder, B. & Mueller, E. N. Soil changes under different land-uses in the Cerrado of Mato Grosso, Brazil. Geoderma Reg. 4, 31–43 (2015).
Google Scholar 
49.Sampaio, A.B. et. al. Guia de restauração do Cerrado: volume 1: semeadura direta. Embrapa Cerrados-Livro técnico (INFOTECA-E, 2015).50.Schmidt, I. B. et al. Tailoring restoration interventions to the grassland-savanna-forest complex in central Brazil. Restor. Ecol. 27, 942–948 (2019).
Google Scholar 
51.Schmidt, I. B. et al. Community-based native seed production for restoration in Brazil: the role of science and policy. Plant Biol. J. 21, 389–397 (2018).
Google Scholar 
52.Strassburg, B. B. N. et al. Author Correction: Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 4, 765–765 (2020).PubMed 

Google Scholar 
53.Assis, G. B., Pilon, N. A. L., Siqueira, M. F. & Durigan, G. Effectiveness and costs of invasive species control using different techniques to restore cerrado grasslands. Restor. Ecol. 29, (2020).54.Torello-Raventos, M. et al. On the delineation of tropical vegetation types with an emphasis on forest/savanna transitions. Plant Ecol. Divers. 6, 101–137 (2013).
Google Scholar 
55.da Silva, D. P., Amaral, A. G., Bijos, N. R. & Munhoz, C. B. R. Is the herb-shrub composition of veredas (Brazilian palm swamps) distinguishable?. Acta Bot. Bras. 32, 47–54 (2017).
Google Scholar 
56.Munhoz, C. B. R. & Felfili, J. M. Florística do estrato herbáceo-subarbustivo de um campo limpo úmido em Brasília, Brasil. Biota. Neotrop. 7, 205–215 (2007).
Google Scholar 
57.Franco, A. C. et al. Leaf functional traits of Neotropical savanna trees in relation to seasonal water deficit. Trees 19, 326–335 (2004).
Google Scholar 
58.Oliveras, I. & Malhi, Y. Many shades of green: the dynamic tropical forest–savannah transition zones. Phil. Trans. R. Soc. B. 371, 20150308 (2016).PubMed 
PubMed Central 

Google Scholar 
59.Cianciaruso, MV. & Batalha, MA. A year in a Cerrado wet grassland: a non-seasonal island in a seasonal savanna environment. Braz. J. Biol. 68, 495–501 (2008).60.MapBiomas. MapBiomas v5.0. https://mapbiomas.org (2021).61.Souza, C. M. Jr. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with landsat archive and earth engine. Remote Sens. 12, 2735 (2020).ADS 

Google Scholar 
62.Gorelick, N. et al. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).ADS 

Google Scholar 
63.Crouzeilles, R. et al. There is hope for achieving ambitious Atlantic Forest restoration commitments. Perspect. Ecol. Conserv. 17, 80–83 (2019).
Google Scholar 
64.Smith, C. C. et al. Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon. Glob. Change Biol. 26, 7006–7020 (2020).ADS 

Google Scholar 
65.Rosan, T. M. et al. Extensive 21st-Century Woody Encroachment in South America’s Savanna. Geophys. Res. Lett. 46, 6594–6603 (2019).ADS 

Google Scholar 
66.Schwieder, M. et al. Mapping Brazilian savanna vegetation gradients with Landsat time series. Int. J. Appl. Earth Obs. Geoinf. 52, 361–370 (2016).ADS 

Google Scholar 
67.Ribeiro, F. F. et al. Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas. Remote Sens. 12, 1721 (2020).ADS 

Google Scholar 
68.Jacon, A. D., Galvão, L. S., dos Santos, J. R. & Sano, E. E. Seasonal characterization and discrimination of savannah physiognomies in Brazil using hyperspectral metrics from Hyperion/EO-1. Int. J. Remote Sens. 38, 4494–4516 (2017).
Google Scholar 
69.Neves, A. K. et al. Hierarchical mapping of Brazilian Savanna (Cerrado) physiognomies based on deep learning. J. App. Remote Sens. 15, 044504–1–044504–23 (2021).70.de Souza Mendes, F., Baron, D., Gerold, G., Liesenberg, V. & Erasmi, S. Optical and SAR remote sensing synergism for mapping vegetation types in the endangered cerrado/amazon ecotone of nova mutum—mato grosso. Remote Sens. 11, 1161 (2019).ADS 

Google Scholar 
71.Sano, E. E., Ferreira, L. G., Asner, G. P. & Steinke, E. T. Spatial and temporal probabilities of obtaining cloud-free Landsat images over the Brazilian tropical savanna. Int. J. Remote Sens. 28, 2739–2752 (2007).
Google Scholar 
72.Flores-Anderson, A.I., Herndon, K.E., Thapa, R.B. & Cherrington, E. The SAR handbook: comprehensive methodologies for forest monitoring and biomass estimation. (SERVIR, 2019).73.Bendini, H. N. et al. Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series. Int. J. Appl. Earth. Obs. Geoinf. 82, 101872 (2019).74.Bendini, H. N. et al. Combining environmental and Landsat analysis ready data for vegetation mapping: a case study in the Brazilian savanna biome. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLIII-B3–2020, 953–960 (2020).75.ECMWF. Climate reanalysis. https://climate.copernicus.eu/climate-reanalysis (2021).76.UNESCO. MINOR MODIFICATIONS PROPOSAL TO THE BOUNDARIES of Cerrado Protected Areas World Heritage: Chapada dos Veadeiros and Emas National Parks. (UNESCO Brasília, 2019)77.ICUN. Advisory mission to Cerrado Protected Areas World Heritage Property (Chapada Dos Veadeiros component) (Brazil). (International Union for Conservation of Nature, 2016)78.EMBRAPA. Sistema brasileiro de classificação dos solos. (EMBRAPA, 2006)79.IBGE. Mapa de Solos do Brasil do IBGE escala 1:250.000 https://www.ibge.gov.br/geociencias/downloads-geociencias.html (IBGE, 2020).80.Rodrigues, J. A. et al. How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections. Int. J. Appl. Earth. Obs. Geoinf. 78, 318–331 (2019).ADS 

Google Scholar 
81.NASA, MCD64A1 v6. https://lpdaac.usgs.gov/products/mcd64a1v006/ (2021)82.GEE. Earth Engine Data Catalog. https://developers.google.com/earth-engine/datasets (2021)83.Vreugdenhil, M. et al. Sensitivity of sentinel-1 backscatter to vegetation dynamics: an Austrian case study. Remote Sens. 10, 1396 (2018).ADS 

Google Scholar 
84.Harfenmeister, K., Spengler, D. & Weltzien, C. Analyzing temporal and spatial characteristics of crop parameters using sentinel-1 backscatter data. Remote Sens. 11, 1569 (2019).ADS 

Google Scholar 
85.European Space Agency. Level-2A Algorithm Overview https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm (2021)86.GEE. Sentinel-2 MSI: MultiSpectral Instrument, Level-2A. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR (2021).87.GEE. USGS Landsat 8 Level 2, Collection 2, Tier 1. https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 (2021)88.Xue, J. & Su, B. Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. 2017, 1353691 (2017).
Google Scholar 
89.Parente, L. & Ferreira, L. Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016. Remote Sens. 10, 606 (2018).ADS 

Google Scholar 
90.Hill, M. J., Zhou, Q., Sun, Q., Schaaf, C. B. & Palace, M. Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation. Int. J. Remote Sens. 38, 874–905 (2017).
Google Scholar 
91.Nomura, K. & Mitchard, E. More than meets the eye: using sentinel-2 to map small plantations in complex forest landscapes. Remote Sens. 10, 1693 (2018).ADS 

Google Scholar 
92.Hagen-Zanker, A. A computational framework for generalized moving windows and its application to landscape pattern analysis. Int. J. Appl. Earth. Obs. Geoinf. 44, 205–216 (2016).ADS 

Google Scholar 
93.Wantzen, K. M. et al. Soil carbon stocks in stream-valley-ecosystems in the Brazilian Cerrado agroscape. Agric. Ecosyst. Environ. 151, 70–79 (2012).CAS 

Google Scholar 
94.ESA. Copernicus DEM: Global and European Digital Elevation Model (COP-DEM). https://spacedata.copernicus.eu/web/cscda/dataset-details?articleId=394198 (2021)95.Breiman, L. Mach. Learn. 45, 5–32 (2001).96.Chen, D. & Wei, H. The effect of spatial autocorrelation and class proportion on the accuracy measures from different sampling designs. ISPRS J. Photogramm. Remote Sens. 64, 140–150 (2009).ADS 

Google Scholar 
97.Olofsson, P., Foody, G. M., Stehman, S. V. & Woodcock, C. E. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens. Environ. 129, 122–131 (2013).ADS 

Google Scholar 
98.Foody, G. M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 80, 185–201 (2002).ADS 

Google Scholar 
99.Jank, L., Barrios, S. C., do Valle, C. B., Simeão, R. M. & Alves, G. F. The value of improved pastures to Brazilian beef production. Crop Pasture Sci. 65, 1132 (2014).100.Oliveira, J. et al. Choosing pasture maps: An assessment of pasture land classification definitions and a case study of Brazil. Int. J. Appl. Earth. Obs. Geoinf. 93, 102205 (2020).101.Pereira, O., Ferreira, L., Pinto, F. & Baumgarten, L. Assessing pasture degradation in the Brazilian cerrado based on the analysis of MODIS NDVI time-series. Remote Sens. 10, 1761 (2018).ADS 

Google Scholar 
102.Meirelles, M.L., Ferreira, E.A.B. and Franco, A.C. Dinâmica sazonal do carbono em campo úmido do cerrado. Embrapa Cerrados-Documentos (INFOTECA-E, 2006).103.França, A. M. S., Paiva, R. J. O., Sano, E. E. & Carvalho, A. M. Estimates for carbon stocks in soil under humid grassland areas in the federal district of Brazil. OJE 04, 777–787 (2014).
Google Scholar 
104.Silveira, F. A. O. et al. Ecology and evolution of plant diversity in the endangered campo rupestre: a neglected conservation priority. Plant Soil. 403, 129–152 (2015).
Google Scholar 
105.Pereira, E. G., Siqueira-Silva, A. I., de Souza, A. E., Melo, N. M. J. & Souza, J. P. Distinct ecophysiological strategies of widespread and endemic species from the megadiverse campo rupestre. Flora 238, 79–86 (2018).
Google Scholar 
106.Moreira, S. N., Pott, V. J., Pott, A., da Silva, R. H. & Júnior, G. A. D. Flora and vegetation structure of Vereda in southwestern Cerrado. Oecol. Aust. 23, 776–798 (2019).
Google Scholar 
107.Pinto, J. R. R., Lenza, E. & Pinto, A. de S. Composição florística e estrutura da vegetação arbustivo-arbórea em um cerrado rupestre, Cocalzinho de Goiás, Goiás. Rev. Bras. Bot. 32, (2009).108.Gomes, L., Lenza, E., Maracahipes, L., Marimon, B. S. & Oliveira, E. A. de. Comparações florísticas e estruturais entre duas comunidades lenhosas de cerrado típico e cerrado rupestre, Mato Grosso, Brasil. Acta Bot. Bras. 25, 865–875 (2011).109.Gomes, D.L. Classificação fitofisionômica do cerrado no Parque Nacional da Chapada dos Veadeiros, GO, com a aplicação de uma análise combinatória com filtros adaptativos em imagens TM Landsat. (Dissertação de Mestrado, Brasília, 2008).110.Neyret, M. et al. Examining variation in the leaf mass per area of dominant species across two contrasting tropical gradients in light of community assembly. Ecol. Evol. 6, 5674–5689 (2016).PubMed 
PubMed Central 

Google Scholar 
111.Abreu, R. C. R. et al. The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. 3, e1701284 (2017).112.Morais, V. A. et al. Carbon and biomass stocks in a fragment of cerradão in Minas Gerais state, Brazil. Cerne 19, 237–245 (2013).
Google Scholar 
113.Bispo, P. da C. et al. Woody aboveground biomass mapping of the Brazilian savanna with a multi-sensor and machine learning approach. Remote Sens. 12, 2685 (2020).114.Taberelli, M. & Gascon, C. Lessons from fragmentation research: improving management and policy guidelines for biodiversity conservation. Conserv. Biol. 19, 734–739 (2005).
Google Scholar 
115.Holder, D. N. H., Dockary, M. & Barber, J. The red edge of plant leaf reflectance. Int. J. Remote Sens. 4, 273–288 (1983).
Google Scholar 
116.Li, J. & Roy, D. A global analysis of sentinel-2A, sentinel-2B and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens. 9, 902 (2017).ADS 

Google Scholar 
117.Hunter, F. D. L., Mitchard, E. T. A., Tyrrell, P. & Russell, S. Inter-seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sens. 12, 198 (2020).ADS 

Google Scholar 
118.Ramos, D. M., Diniz, P., Ooi, M. K. J., Borghetti, F. & Valls, J. F. M. Avoiding the dry season: dispersal time and syndrome mediate seed dormancy in grasses in Neotropical savanna and wet grasslands. J. Veg. Sci. 28, 798–807 (2017).
Google Scholar 
119.de Camargo, M. G. G., de Carvalho, G. H., Alberton, B. de C., Reys, P. & Morellato, L. P. C. Leafing patterns and leaf exchange strategies of a cerrado woody community. Biotropica. 50, 442–454 (2018).120.Rüetschi, M., Schaepman, M. & Small, D. Using multitemporal sentinel-1 C-band backscatter to monitor phenology and classify deciduous and coniferous forests in Northern Switzerland. Remote Sens. 10, 55 (2017).ADS 

Google Scholar 
121.Sano, E. E., Ferreira, L. G. & Huete, A. R. Synthetic aperture radar (L band) and optical vegetation indices for discriminating the Brazilian savanna physiognomies: a comparative analysis. Earth Interact. 9, 1–15 (2005).
Google Scholar 
122.Joshi, N. et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 8, 70 (2016).ADS 

Google Scholar 
123.Nicolau, A. P., Flores-Anderson, A., Griffin, R., Herndon, K. & Meyer, F. J. Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest. Int. J. Appl. Earth. Obs. Geoinf. 94, 102214 (2021).124.Notarnicola, C. & Posa, F. Inferring vegetation water content from C- and L-band SAR images. IEEE Trans. Geosci. Remote Sens. 45, 3165–3171 (2007).ADS 

Google Scholar 
125.El Hajj, M., Baghdadi, N., Bazzi, H. & Zribi, M. Penetration analysis of SAR signals in the C and L bands for wheat, maize, and grasslands. Remote Sens. 11, 31 (2018).ADS 

Google Scholar 
126.JAXA. Global PALSAR-2/PALSAR/JERS-1 Mosaic and Forest/Non-Forest map. https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm (JAXA, 2021).127.Zimbres, B. et al. Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome. For. Ecol. Manag. 499, 119615 (2021).128.Ryan, C. M. et al. Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery. Glob. Change Biol. 18, 243–257 (2011).ADS 

Google Scholar 
129.Yu, Y. & Saatchi, S. Sensitivity of L-Band SAR backscatter to aboveground biomass of global forests. Remote Sens. 8, 522 (2016).ADS 

Google Scholar 
130.Pilon, N. A. L. et al. The diversity of post-fire regeneration strategies in the cerrado ground layer. J. Ecol. 109, 154–166 (2020).
Google Scholar 
131.Schmidt, I. B. & Eloy, L. Fire regime in the Brazilian Savanna: Recent changes, policy and management. Flora. 268, 151613 (2020).132.Boschetti, L. et al. Global validation of the collection 6 MODIS burned area product. Remote Sens. Environ. 235, 111490 (2019).133.Humber, M. L., Boschetti, L., Giglio, L. & Justice, C. O. Spatial and temporal intercomparison of four global burned area products. Int. J. Digit. Earth. 12, 460–484 (2018).PubMed 
PubMed Central 

Google Scholar 
134.Arruda, V. L. S., Piontekowski, V. J., Alencar, A., Pereira, R. S. & Matricardi, E. A. T. An alternative approach for mapping burn scars using Landsat imagery, Google Earth Engine, and Deep Learning in the Brazilian Savanna. Remote Sens. Appl. Soc. Environ. 22, 100472 (2021).135.Santos, F. L. M. et al. Assessing VIIRS capabilities to improve burned area mapping over the Brazilian Cerrado. Int. J. Remote Sens. 41, 8300–8327 (2020).
Google Scholar 
136.Marques, E. Q. et al. Redefining the Cerrado-Amazonia transition: implications for conservation. Biodivers. Conserv. 29, 1501–1517 (2019).
Google Scholar 
137.Marimon, B. S. et al. Disequilibrium and hyperdynamic tree turnover at the forest–cerrado transition zone in southern Amazonia. Plant Ecol. Divers. 7, 281–292 (2013).
Google Scholar 
138.Mellor, A., Boukir, S., Haywood, A. & Jones, S. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS J. Photogramm. Remote Sens. 105, 155–168 (2015).ADS 

Google Scholar 
139.DRYFLOR. Plant diversity patterns in neotropical dry forests and their conservation implications. Science. 353, 1383–1387 (2016). More

Species composition of stone wall treesFamilies and genera of stone wall treesThere were 96 stone wall trees belonging to 6 genera and 5 families in Macao. Among them, Moraceae and Ficus appeared the most frequently, both reaching 85 times, accounting for 88.5% (Table 1). It showed that Moraceae, a kind of tropical distribution family, was dominant in the stone wall trees communities, which meant that stone wall trees species in Macao appeared distinctly tropical nature18.Table 1 Frequency of occurrence of stone wall Trees in different families and genera.Full size tableSpecies of stone wall treesThere were 16 species of the stone wall trees in Macao including Bridelia tomentosa, Celtis sinensis, Eriobotrya japonica, Ficus altissima, F. benjamina, F. elastica, F. hispida, F. microcarpa, F. pandurata, F. subpisocarpa, F. tinctoria subsp. gibbosa, F. rumphii, F. variegata, F. virens, Leucaena leucocephala, and Trema cannabina (Fig. 2).Figure 216 species of stone wall trees in Macao (photo was taken by Professor Qin Xingsheng).Full size imageBased on the frequency of occurrence of various tree species, the frequency was concentrated in the range of 1–5%. Among them, Ficus microcarpa had the highest frequency, reaching 58 times, with a frequency of 60.4% (Fig. 3). This tree species is robust, adaptable and fast growing, which is the main population of Ficus19.Figure 3Frequency distribution of stone wall tree species in Macao.Full size imageStone wall trees in the historic center of MacaoThe historic center of Macao, covering an area of about 2.8 km2, is the heartland of Macao’s historical and cultural heritage, which plays a significant role in the cultural heritage around the world18. The historic center of Macao provides valuable historical and cultural resources that enable Macao to transform into a world tourism center20.A total of 14 plots were located in the historic Center of Macao (Fig. 4), with 45 stone wall trees, accounting for 47.9% of the total number of trees in the survey. Among them, Jardim Luís de Camões has the largest number of 9 stone wall trees. The park, built in the mid-eighteenth century, is one of the oldest gardens in Macao and has the largest number of old trees in Macao. The park had provided good time and environmental conditions for the growth of stone wall trees.Figure 4(a) Schematic diagram of distribution and number of stone wall trees in the historic Center of Macao. (b) Schematic diagram of historic center of Macao. (URL of the Macao map: https://www.d-maps.com/m/asia/china/macau/macau02.gif).Full size imageAccording to Decree No. 56/84/M of the Macao Special Administrative Region Government Printing Department, immovable property that represents the creation of man, or the development of nature or technology and has cultural significance is considered tangible cultural property. The occurrence of the stone wall tree was inextricably linked to ancient wall-building techniques of that time, which was of great significance for the study of the technological development and ecological landscape of the historic center of Macao. The concept of “historic urban landscape” was proposed by Zhang Song20, who argued that cities were organisms in continuous evolution, emphasizing respect for the interrelationship between natural and man-made environments. The stone wall trees in the historic center of Macao have been associated with the local culture and ecology tightly and should be preserved as important urban landscape.Symbiotic relationship between tree and stone wallsAs shown in the table below (Table 2), it was found that most of the stone wall trees had root systems that were not only superficially attached to the wall but also extended to the top or bottom of the wall. In particular, Ficus spp. whose strong root system could closely mosaic with the wall, thus forming a strong symbiosis.Table 2 The relationship between the root system of the stone wall tree and the wall.Full size tableStone walls can imitate the traditional nature-accommodating features to permit spontaneous establishment of a diverse plant assemblage. Besides vegetative diversities in terms of species composition, growth form and biomass structure, stone walls can support a mass collection of urban wildlife and provide various ecosystem service. It is highly recommended that modern urban design be created to embrace stone wall landscape as an integral part of naturalistic or ecological design.Vision for the establishment of the stone wall tree trail system in the historic of MacaoThe traditional street environment in the Macao Peninsula is a kind of distinctive urban landscape, which can highlight the specificity and value of the urban context. The combination of the stone wall trees and walls, together with the traditional streets, form a spatial urban landscape. Starting from the location of the stone wall tree landscape, the dots and lines are prospective to promote the establishment of a comprehensive stone wall tree landscape trail system (Fig. 5), so that the public can make use of the existing biological resources to have a better understanding of the land on which they live.Figure 5Schematic diagram of the stone wall trees trail system on the Macao Peninsula (URL of the Macao map: https://www.d-maps.com/m/asia/china/macau/macau02.gif and the finished map is created by Meisi Chen through the Photoshop CS6 and Arc GIS 10.2).Full size imageSince 2012, the Macao Government has been implementing the “Strolling along Macao Street” project, which aims at studying and exploring the history and culture of the streets of Macao through an in-depth cultural tourism route and promoting it to different levels of society. The establishment of the stone wall tree trail system can rely on this project to raise the public’s awareness of the protection and cultural identity of the stone wall tree landscape through a variety of ways. For example, route design competition, photography competition and exhibition, recruitment of “Stonewall Tree Protection Ambassadors” and other forms of participation, so that the public could complete the “role change” in the high degree of such participation—from “onlookers” to “bystanders”.Survey results of associated plant speciesSpecies composition and occurrence of frequencyThe survey showed that there were 101 species of stone wall tree associated plants in Macao, under 88 genera and 51 families. Most associated plants belonged to Euphorbiaceae, Compositae, and Araceae.There were 85 species with a frequency of 1–5 times, accounting for 84.2% of total species. A total of 11 species appeared 11–15 times, accounting for 4.0% (Fig. 6). There were a total of 4 species that appeared more than 15 times. They were Cocculus orbiculatus, Pteris cretica, Paederia scandens, and Pyrrosia adnascens. Most of the associated species appeared only 1–5 times, indicating that most plants were selective and accidental for the growth conditions of stone wall sites.Figure 6Occurrence frequency in various species of associated plants.Full size imageLife form compositionHerbaceous plants with 37 species, accounting the percentage of 52.3% (Fig. 7), were dominant in the associated plant species because the seeds of herbaceous plants are lighter and can be propagated to the wall surface by wind force.Figure 7Life form of associated plants with stone wall trees in Macao.Full size imageSimilarity analysis of the associated plants in MacaoIn order to compare the similarity of associated plant species in different environment, the surveyed sample sites for this study were divided into three categories: motorized lanes, non-motorized lanes, and park habitats (Table 3). According to Jaccard’s similarity principle, Sj is extremely dissimilar when it is 0.00–0.25, and the analysis showed that the similarity of companion plant species in all three habitats was extremely dissimilar. Therefore, it indicated that the companion plants in different habitats had obvious diversity and uniqueness.Table 3 Jaccard similarity index for companion plant species composition among three habitats.Full size table More

ALAN advances timing of activity and BMAL1 expressionDaily cycles of activity were strongly affected by the ALAN treatment (GAMM, p = 0.001, Fig. 2A and Fig. S2; Table S4). In the 5 lux group birds were generally active 6–7 h before lights-on, whereas birds in the other two light treatments (0.5 and 1.5 lux) advanced morning activity to a much lesser extent. Because of the advanced onset of activity, 40% of the overall diel activity in the 5 lux group occurred during the night, compared to 11 and 14% in the 0.5 and 1.5 lux groups, and less than 1% in the control dark group. Thus, with increasing ALAN, nocturnal activity also increased (LMM, treatment p  0.1 for pairwise comparison), and thereafter their timing remained stable. The group exposed to 5 lux showed a much larger instantaneous phase advance of almost five hours (mean ± SEM = 289 ± 21 min), and thereafter continued to gradually phase-advance until reaching a stable phase after 10 days (interaction treatment × day, p  More