Ecology

Butchart, S. H. M. et al. Global biodiversity: Indicators of recent declines. Science 328(5982), 1164–1168. https://doi.org/10.1126/science.1187512 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Dirzo, R. et al. Defaunation in the anthropocene. Science 345(6195), 401–406. https://doi.org/10.1126/science.1251817 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bradshaw, C. J. A. et al. Underestimating the challenges of avoiding a ghastly future. Front. Conserv. Sci. https://doi.org/10.3389/fcosc.2020.615419 (2021).Article 

Google Scholar 
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived?. Nature 471(7336), 51–57. https://doi.org/10.1038/nature09678 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ceballos, G. et al. Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. Proc. Natl. Acad. Sci. U.S.A. 117(24), 13596–13602. https://doi.org/10.1073/pnas.1922686117 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McGowan, P. J., Traylor-Holzer, K. & Leus, K. IUCN guidelines for determining when and how ex situ management should be used in species conservation. Conserv. Lett. 10(3), 361–366. https://doi.org/10.1111/conl.12285 (2016).Article 

Google Scholar 
Clout, M. N. & Merton, D. V. Saving the Kakapo: The conservation of the world’s most peculiar parrot. Bird Conserv. Int. 8(3), 281–296. https://doi.org/10.1017/s0959270900001933 (1998).Article 

Google Scholar 
Milinkovitch, M. C. et al. Genetic analysis of a successful repatriation programme: Giant Galápagos tortoises. Proc. R. Soc. B Biol. Sci. 271(1537), 341–345. https://doi.org/10.1098/rspb.2003.2607 (2004).CAS 
Article 

Google Scholar 
Ryder, O. A. & Wedemeyer, E. A. A cooperative breeding programme for the Mongolian wild horse Equus przewalskii in the United States. Biol. Conserv. 22(4), 259–271. https://doi.org/10.1016/0006-3207(82)90021-0 (1982).Article 

Google Scholar 
Mallinson, J. J. C. Conservation breeding programmes: An important ingredient for species survival. Biodivers. Conserv. 4(6), 617–635. https://doi.org/10.1007/bf00222518 (1995).Article 

Google Scholar 
Seddon, P. J., Armstrong, D. P. & Maloney, R. F. Developing the science of reintroduction biology. Conserv. Biol. 21(2), 303–312. https://doi.org/10.1111/j.1523-1739.2006.00627.x (2007).Article 
PubMed 

Google Scholar 
Bowkett, A. E. Recent captive-breeding proposals and the return of the ark concept to global species conservation. Conserv. Biol. 23(3), 773–776. https://doi.org/10.1111/j.1523-1739.2008.01157.x (2009).Article 
PubMed 

Google Scholar 
Shan, L. et al. Large-scale genetic survey provides insights into the captive management and reintroduction of giant pandas. Mol. Biol. Evol. 31(10), 2663–2671. https://doi.org/10.1093/molbev/msu210 (2014).CAS 
Article 
PubMed 

Google Scholar 
Fischer, J. & Lindenmayer, D. An assessment of the published results of animal relocations. Biol. Conserv. 96(1), 1–11. https://doi.org/10.1016/s0006-3207(00)00048-3 (2014).Article 

Google Scholar 
Christie, M. R., Marine, M. L., French, R. A. & Blouin, M. S. Genetic adaptation to captivity can occur in a single generation. Proc. Natl. Acad. Sci. U.S.A. 109(1), 238–242. https://doi.org/10.1073/pnas.1111073109 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fraser, D. J. et al. Population correlates of rapid captive-induced maladaptation in a wild fish. Evol. Appl. 12(7), 1305–1317. https://doi.org/10.1111/eva.12649 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Ralls, K., Brugger, K. & Ballou, J. Inbreeding and juvenile mortality in small populations of ungulates. Science 206(4422), 1101–1103. https://doi.org/10.1126/science.493997 (1979).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Charlesworth, D. & Charlesworth, B. Inbreeding depression and its evolutionary consequences. Annu. Rev. Ecol. Evol. Syst. 18(1), 237–268. https://doi.org/10.1146/annurev.es.18.110187.001321 (1987).Article 

Google Scholar 
Ralls, K., Ballou, J. D. & Templeton, A. Estimates of lethal equivalents and the cost of inbreeding in mammals. Conserv. Biol. 2(2), 185–193. https://doi.org/10.1111/j.1523-1739.1988.tb00169.x (1988).Article 

Google Scholar 
Hedrick, P. W. & Kalinowski, S. T. Inbreeding depression in conservation biology. Annu. Rev. Ecol. Evol. Syst. 31(1), 139–162. https://doi.org/10.1146/annurev.ecolsys.31.1.139 (2000).Article 

Google Scholar 
Frankham, R. Introduction to Conservation Genetics 2nd edn. (Cambridge University Press, 2010).Book 

Google Scholar 
Laikre, L. Conservation genetics of Nordic carnivores: Lessons from zoos. Hereditas 130(3), 203–216. https://doi.org/10.1111/j.1601-5223.1999.00203.x (2004).Article 

Google Scholar 
Gomendio, M., Cassinello, J. & Roldan, E. R. S. A comparative study of ejaculate traits in three endangered ungulates with different levels of inbreeding: Fluctuating asymmetry as an indicator of reproductive and genetic stress. Proc. R. Soc. B Biol. Sci. 267(1446), 875–882. https://doi.org/10.1098/rspb.2000.1084 (2000).CAS 
Article 

Google Scholar 
Swinnerton, K. J., Groombridge, J. J., Jones, C. G., Burn, R. W. & Mungroo, Y. Inbreeding depression and founder diversity among captive and free-living populations of the endangered pink pigeon Columba mayeri. Anim. Conserv. 7(4), 353–364. https://doi.org/10.1017/s1367943004001556 (2004).Article 

Google Scholar 
Farquharson, K. A., Hogg, C. J. & Grueber, C. E. Offspring survival changes over generations of captive breeding. Nat. Commun. https://doi.org/10.1038/s41467-021-22631-0 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Kleiman, D. G., Thompson, K. V. & Baer, C. K. Wild Mammals in Captivity: Principles and Techniques for Zoo Management 2nd edn. (University of Chicago Press, 2021).
Google Scholar 
Ralls, K. & Ballou, J. D. Captive breeding and reintroduction. In Encyclopedia of Biodiversity (ed. Levin, S. A.) 662–667 (Academic Press, 2013). https://doi.org/10.1016/b978-0-12-384719-5.00268-9.Chapter 

Google Scholar 
Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17(1), 230–237. https://doi.org/10.1046/j.1523-1739.2003.01236.x (2003).Article 

Google Scholar 
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. U.S.A. 101(42), 15261–15264. https://doi.org/10.1073/pnas.0403809101 (2003).ADS 
CAS 
Article 

Google Scholar 
Willi, Y., van Buskirk, J. & Hoffmann, A. A. Limits to the adaptive potential of small populations. Annu. Rev. Ecol. Evol. Syst. 37(1), 433–458. https://doi.org/10.1146/annurev.ecolsys.37.091305.110145 (2006).Article 

Google Scholar 
Habel, J. C., Husemann, M., Finger, A., Danley, P. D. & Zachos, F. E. The relevance of time series in molecular ecology and conservation biology. Biol. Rev. 89(2), 484–492. https://doi.org/10.1111/brv.12068 (2013).Article 
PubMed 

Google Scholar 
Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science 318(5847), 100–103. https://doi.org/10.1126/science.1145621 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Purohit, D. et al. Genetic effects of long-term captive breeding on the endangered pygmy hog. PeerJ 9, e12212. https://doi.org/10.7717/peerj.12212 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Hahn, E. E. & Culver, M. Genetic diversity and structure in Arizona pronghorn following conservation efforts. Conserv. Sci. Pract. https://doi.org/10.1111/csp2.498 (2021).Article 

Google Scholar 
Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Gen. 10(3), 195–205. https://doi.org/10.1038/nrg2526 (2009).CAS 
Article 

Google Scholar 
Wang, J., Santiago, E. & Caballero, A. Prediction and estimation of effective population size. Heredity 117(4), 193–206. https://doi.org/10.1038/hdy.2016.43 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
O’Gara, W., Yoakum, J. D. & McCabe, R. E. Pronghorn: Ecology and Managment (University Press of Colorado, 2004).
Google Scholar 
Janis, C. M., Scott, K. M. & Jacobs, L. L. Evolution of Tertiary Mammals of North America: Terrestrial Carnivores, Ungulates, and Ungulate like Mammals Vol. 1 (Cambridge University Press, 2005).
Google Scholar 
Nelson, E. W. Status of the Pronghorn Antelope, 1922–1924 (U.S Department Agriculture Bulletin, 1925).Book 

Google Scholar 
O’Gara, B. W. & McCabe, R. E. From exploitation to conservation. In Pronghorn: Ecology and Management (eds O’Gara, B. W. & Yoakum, J. D.) 41–73 (University Press Colorado, 2004).
Google Scholar 
Cancino, J., Ortega-Rubio, A. & Sanchez-Pacheco, J. A. Status of an endangered subspecies: The peninsular pronghorn at Baja California. J. Arid Environ. 32(4), 463–467. https://doi.org/10.1006/jare.1996.0039 (1996).ADS 
Article 

Google Scholar 
Laliberte, A. S. & Ripple, W. J. Range contractions of North American carnivores and ungulates. Bioscience 54(2), 123–138. https://doi.org/10.1641/0006-3568 (2004).Article 

Google Scholar 
Medellín, R. A. et al. History, ecology, and conservation of the pronghorn antelope, bighorn sheep, and black bear in Mexico. In Biodiversity, Ecosystems, and Conservation in Northern Mexico (eds Cartron, J.-L. et al.) 387–405 (Oxford University Press, 2005).
Google Scholar 
Lee, T. E., Bickham, J. W. & Scott, M. D. Mitochondrial DNA and allozyme analysis of North American pronghorn populations. J. Wildl. Manag. 58(2), 307–318. https://doi.org/10.2307/3809396 (1994).Article 

Google Scholar 
IUCN SSC Antelope Specialist Group. Antilocapra americana ssp. peninsularis. The IUCN Red List of Threatened Species 2021: e.T1679A200726719. https://doi.org/10.2305/IUCN.UK.2021-2.RLTS.T1679A200726719.en (2021).SEMARNAT. Norma Oficial Mexicana NOM-059-SEMARNAT-2010, Protección ambiental– Especies nativas de México de flora y fauna silvestres– Categorías de riesgo y especificaciones para su inclusión, exclusión o cambio– Lista de especies en riesgo. Diario Oficial de la Federación 30 diciembre (2010).U. S. Fish and Wildlife Service. Recovery Plan for the Sonoran pronghorn (Antilocapra americana sonoriensis), Second Revision. (U.S. Fish and Wildlife Service, Southwest Region, Albuquerque, 2016).Cancino, J., Sanchez-Sotomayor, V. & Castellanos, R. From the field: Capture, hand-raising, and captive management of peninsular pronghorn. Wildl. Soc. Bull. 33(1), 61–65. https://doi.org/10.2193/0091-7648 (2005).Article 

Google Scholar 
Horne, J. S., Hervert, J. J., Woodruff, S. P. & Mills, L. S. Evaluating the benefit of captive breeding and reintroductions to endangered Sonoran pronghorn. Biol. Conserv. 196, 133–146. https://doi.org/10.1016/j.biocon.2016.02.005 (2016).Article 

Google Scholar 
CONANP. Programa de Acción para la Conservación de la Especie: Berrendo (Antilocapra americana), 2009 año del berrendo. Secretaria del Medio Ambiente y Recursos Naturales (SEMARNAT). www.conanp.gob.mx (2009).Cancino, J., Rodríguez-Estrella, R. & Miller, P. Using population viability analysis for management recommendations of the endangered endemic peninsular pronghorn. Acta Zool. Mex. 26(1), 173–189 (2010).
Google Scholar 
Danoff-Burg, J. A. & Mulroe, K. Peninsular Pronghorn Species Action Plan (2021) (in press).Stephen, C. L. et al. Population genetic analysis of sonoran pronghorn (Antilocapra americana sonoriensis). J. Mammal. 86(4), 782–792. https://doi.org/10.1644/1545-1542 (2005).Article 

Google Scholar 
Stephen, C. L., Whittaker, D. G., Gillis, D., Cox, L. L. & Rhodes, O. E. Genetic consequences of reintroductions: An example from oregon pronghorn antelope (Antilocapra americana). J. Wildl. Manag. 69(4), 1463–1474. https://doi.org/10.2193/0022-541x (2005).Article 

Google Scholar 
Barnow-Meyer, K. & Byers, J. Genetic diversity and gene flow in Yellowstone Basin pronghorn (Antilocapra americana). UW Natl. Parks Serv. Res. Station Annu. Rep. 31, 65–72. https://doi.org/10.13001/uwnpsrc.2008.3705 (2008).Article 

Google Scholar 
LaCava, M. E. F. et al. Pronghorn population genomics show connectivity in the core of their range. J. Mammal. 101(4), 1061–1071. https://doi.org/10.1093/jmammal/gyaa054 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Klimova, A., Munguia-Vega, A., Hoffman, J. I. & Culver, M. Genetic diversity and demography of two endangered captive pronghorn subspecies from the Sonoran Desert. J. Mammal. 95(6), 1263–1277. https://doi.org/10.1644/13-mamm-a-321 (2014).Article 

Google Scholar 
Hahn, E. E., Klimova, A., Munguía-Vega, A., Clark, K. B. & Culver, M. Use of museum specimens to refine historical pronghorn subspecies boundaries. J. Wildl. Manag. 84(3), 524–533. https://doi.org/10.1002/jwmg.21810 (2020).Article 

Google Scholar 
Axelrod, D. I. The evolution of desert vegetation in western North America. Carnegie Instit. Wash. Publ. 590, 215–306 (1950).
Google Scholar 
Dolby, G. A., Bennett, S. E. K., Lira-Noriega, A., Wilder, B. T. & Munguía-Vega, A. Assessing the geological and climatic forcing of biodiversity and evolution Surrounding the Gulf of California. J. Southwest. 57, 391–455. https://doi.org/10.1353/jsw.2015.0005 (2015).Article 

Google Scholar 
Gedir, J. V., Cain, J. W., Harris, G. & Turnbull, T. T. Effects of climate change on long-term population growth of pronghorn in an arid environment. Ecosphere 6(10), art189. https://doi.org/10.1890/es15-00266.1 (2015).Article 

Google Scholar 
Cornuet, J. M. et al. DIYABC v2.0: A software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30(8), 1187–1189. https://doi.org/10.1093/bioinformatics/btt763 (2014).CAS 
Article 
PubMed 

Google Scholar 
Islas-Espinoza, M. & de las Heras, A. Peninsular pronghorn conservation: Too many paradigms, too few indicators. In Sustainability Indicators in Practice (eds Latawiec, A. & Agol, D.) 126–145 (De Gruyter Open Poland, 2015). https://doi.org/10.1515/9783110450507-012.Chapter 

Google Scholar 
Willoughby, J. R. et al. The impacts of inbreeding, drift and selection on genetic diversity in captive breeding populations. Mol. Ecol. 24(1), 98–110. https://doi.org/10.1111/mec.13020 (2014).CAS 
Article 
PubMed 

Google Scholar 
Crow, J. F. & Kimura, M. An Introduction in Population Genetics Theory (Harper and Row, 1970).MATH 

Google Scholar 
Falconer, D. S. Introduction to Quantitative Genetics 3rd edn. (Longman Scientific and Technical, 1989).
Google Scholar 
Ballou, J. D. Strategies for maintaining genetic diversity in captive populations through reproductive technology. Zoo Biol. 3(4), 311–323. https://doi.org/10.1002/zoo.1430030404 (1984).Article 

Google Scholar 
Ballou, J. D. & Lacy, R. C. Identifying genetically important individuals for management of genetic diversity in pedigreed populations. In Population Management for Survival and Recovery (eds Ballou, J. D. et al.) 76–111 (Columbia Press, 1995).
Google Scholar 
Montgomery, M. E. et al. Minimizing kinship in captive breeding programs. Zoo Biol. 16(5), 377–389. https://doi.org/10.1002/(sici)1098-2361 (1997).Article 

Google Scholar 
Dunn, S. J., Clancey, E., Waits, L. P. & Byers, J. A. Inbreeding depression in pronghorn (Antilocapra americana) fawns. Mol. Ecol. 20(23), 4889–4898. https://doi.org/10.1111/j.1365-294x.2011.05327.x (2011).Article 
PubMed 

Google Scholar 
Hoffman, J. I. et al. High-throughput sequencing reveals inbreeding depression in a natural population. Proc. Natl. Acad. Sci. U.S.A. 111(10), 3775–3780. https://doi.org/10.1073/pnas.1318945111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kardos, M. et al. The crucial role of genome-wide genetic variation in conservation. Proc. Natl. Acad. Sci. U.S.A. 118(48), e2104642118. https://doi.org/10.1073/pnas.2104642118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zoonomia Consortium. A comparative genomics multitool for scientific discovery and conservation. Nature 587(7833), 240–245. https://doi.org/10.1038/s41586-020-2876-6 (2020).ADS 
CAS 
Article 

Google Scholar 
Ceballos, F. C., Joshi, P. K., Clark, D. W., Ramsay, M. & Wilson, J. F. Runs of homozygosity: Windows into population history and trait architecture. Nat. Rev. Genet. 19(4), 220–234. https://doi.org/10.1038/nrg.2017.109 (2018).CAS 
Article 
PubMed 

Google Scholar 
Supple, M. A. & Shapiro, B. Conservation of biodiversity in the genomics era. Genome Biol. https://doi.org/10.1186/s13059-018-1520-3 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hohenlohe, P. A. & Rajora, O. P. Population Genomics: Wildlife (Springer, 2020).
Google Scholar 
Chalmers, G. A. & Barrett, M. W. Capture myopathy in pronghorns in Alberta, Canada. J. Am. Vet. Med. Assoc. 171(9), 918–923 (1977).CAS 
PubMed 

Google Scholar 
Sotelo-Gallardo, H., Contreras Balderas, A. J. & Espinosa Treviño, A. Comparación de dos métodos de liberación del berrendo, Antilocapra americana (Artiodactyla: Antilocapridae) en Coahuila, México. Rev. Biol. Trop. 65(3), 1208. https://doi.org/10.15517/rbt.v65i3.29447 (2017).Article 

Google Scholar 
Breed, D. et al. Conserving wildlife in a changing world: Understanding capture myopathy—A malignant outcome of stress during capture and translocation. Conserv. Physiol. https://doi.org/10.1093/conphys/coz027 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Snyder, N. F. et al. Limitations of captive breeding in endangered species recovery. Conserv. Biol. 10(2), 338–348. https://doi.org/10.1046/j.1523-1739.1996.10020338.x (1996).Article 

Google Scholar 
Bonebrake, T. C., Christensen, J., Boggs, C. L. & Ehrlich, P. R. Population decline assessment, historical baselines, and conservation. Conserv. Lett. 3(6), 371–378. https://doi.org/10.1111/j.1755-263x.2010.00139.x (2010).Article 

Google Scholar 
Grismer, L. L. & McGuire, J. A. The oases of central Baja California, Mexico. Part I. A preliminary account of the relict mesophilic herpetofauna and the status of the oases. Bull. South. Calif. Acad. Sci. 92, 2–24 (1993).
Google Scholar 
Welsh, H. H., Clark, W. H., Franco-Vizcaíno, E. & Valdéz-Villavicencio, J. H. Herpetofauna associated with palm oases across the Californian-Sonoran transition in Northern Baja California, Mexico. Southwest. Nat. 55(4), 581–585. https://doi.org/10.1894/pas-15.1 (2010).Article 

Google Scholar 
Mann, D. H., Groves, P., Gaglioti, B. V. & Shapiro, B. A. Climate-driven ecological stability as a globally shared cause of Late Quaternary megafaunal extinctions: The Plaids and Stripes Hypothesis. Biol. Rev. 94(1), 328–352. https://doi.org/10.1111/brv.12456 (2018).Article 

Google Scholar 
Brown, D. E., Warnecke, D. & McKinney, T. Effects of midsummer drought on mortality of doe pronghorn (Antilocapra americana). Southwest. Nat. 51(2), 220–225. https://doi.org/10.1894/0038-4909 (2006).Article 

Google Scholar 
Simpson, D. C., Harveson, L. A., Brewer, C. E., Walser, R. E. & Sides, A. R. Influence of precipitation on pronghorn demography in Texas. J. Wildl. Manag. 71(3), 906–910. https://doi.org/10.2193/2005-753 (2007).Article 

Google Scholar 
McKinney, T., Brown, D. E. & Allison, L. Winter precipitation and recruitment of pronghorns in Arizona. Southwest. Nat. 53(3), 319–325. https://doi.org/10.1894/cj-147.1 (2008).Article 

Google Scholar 
Otte, A. Partners save the Sonoran pronghorn. Endang. Species Bull. 31, 22–23 (2006).
Google Scholar 
McCullough, D. R. & Barrett, R. H. Wildlife 2001: Populations (Springer, 1992).Book 

Google Scholar 
Percie Du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 20. PLOS Biol. 18(7), e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Carling, M. D., Passavant, C. W. & Byers, J. A. DNA microsatellites of pronghorn (Antilocapra americana). Mol. Ecol. Not. 3(1), 10–11. https://doi.org/10.1046/j.1471-8286.2003.00334.x (2002).Article 

Google Scholar 
Dunn, S. J. et al. Ten polymorphic microsatellite markers for pronghorn (Antilocapra americana). Conserv. Genet. Resour. 2(1), 81–84. https://doi.org/10.1007/s12686-009-9166-9 (2010).Article 

Google Scholar 
Munguia-Vega, A., Klimova, A. & Culver, M. New microsatellite loci isolated via next-generation sequencing for two endangered pronghorn from the Sonoran Desert. Conserv. Genet. Resour. 5(1), 125–127. https://doi.org/10.1007/s12686-012-9749-8 (2012).Article 

Google Scholar 
Boutin-Ganache, I., Raposo, M., Raymond, M. & Deschepper, C. F. M13-Tailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods. Biotechniques 31(1), 25–28. https://doi.org/10.2144/01311bm02 (2001).Article 

Google Scholar 
Amos, W. et al. Automated binning of microsatellite alleles: Problems and solutions. Mol. Ecol. Not. 7(1), 10–14. https://doi.org/10.1111/j.1471-8286.2006.01560.x (2006).CAS 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.Jombart, T. & Ahmed, I. Adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27(21), 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281. https://doi.org/10.7717/peerj.281 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Adamack, A. T. & Gruber, B. PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5(4), 384–387. https://doi.org/10.1111/2041-210x.12158 (2014).Article 

Google Scholar 
Agapow, P. M. & Burt, A. Indices of multilocus linkage disequilibrium. Mol. Ecol. Not. 1(1–2), 101–102. https://doi.org/10.1046/j.1471-8278.2000.00014.x (2001).CAS 
Article 

Google Scholar 
Paradis, E. pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 26(3), 419–420. https://doi.org/10.1093/bioinformatics/btp696 (2010).CAS 
Article 
PubMed 

Google Scholar 
Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Not. 5(1), 184–186. https://doi.org/10.1111/j.1471-8286.2004.00828.x (2005).Article 

Google Scholar 
Aparicio, J. M., Ortego, J. & Cordero, P. J. What should we weigh to estimate heterozygosity, alleles or loci?. Mol. Ecol. 15(14), 4659–4665. https://doi.org/10.1111/j.1365-294x.2006.03111.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Alho, J. S., Välimäki, K. & Merilä, J. Rhh: An R extension for estimating multilocus heterozygosity and heterozygosity–heterozygosity correlation. Mol. Ecol. Res. 10(4), 720–722. https://doi.org/10.1111/j.1755-0998.2010.02830.x (2010).Article 

Google Scholar 
Stoffel, M. A. et al. inbreedR: An R package for the analysis of inbreeding based on genetic markers. Methods Ecol. Evol. 7(11), 1331–1339. https://doi.org/10.1111/2041-210x.12588 (2016).Article 

Google Scholar 
Wang, J. Coancestry: A program for simulating, estimating and analysing relatedness and inbreeding coefficients. Mol. Ecol. Res. 11(1), 141–145. https://doi.org/10.1111/j.1755-0998.2010.02885.x (2010).ADS 
Article 

Google Scholar 
Wang, J. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genet. Res. 89(3), 135–153. https://doi.org/10.1017/s0016672307008798 (2007).CAS 
Article 
PubMed 

Google Scholar 
Marshall, T. C. et al. Estimating the prevalence of inbreeding from incomplete pedigrees. Proc. R. Soc. B Biol. Sci. 269(1500), 1533–1539. https://doi.org/10.1098/rspb.2002.2035 (2002).CAS 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162(4), 2025–2035. https://doi.org/10.1093/genetics/162.4.2025 (2002).Article 
PubMed 
PubMed Central 

Google Scholar 
Bertorelle, G., Benazzo, A. & Mona, S. ABC as a flexible framework to estimate demography over space and time: Some cons, many pros. Mol. Ecol. 19(13), 2609–2625. https://doi.org/10.1111/j.1365-294x.2010.04690.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Fagundes, N. J. R. et al. Statistical evaluation of alternative models of human evolution. Proc. Natl. Acad. Sci. U.S.A. 104(45), 17614–17619. https://doi.org/10.1073/pnas.0708280104 (2007).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Losos, E. & Leigh, E. G. Tropical Forest Diversity and Dynamism: Findings from a Large-Scale Plot Network (Univ. Chicago Press, 2004).Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 

Google Scholar 
Hansen, M. C. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–854 (2013).CAS 
PubMed 

Google Scholar 
Chazdon, R. L. Beyond deforestation: restoring degraded lands. Science 1458, 1458–1460 (2008).
Google Scholar 
Global Forest Resources Assessment 2010 (FAO, 2010).Rozendaal, D. M. A. & Chazdon, R. L. Demographic drivers of tree biomass change during secondary succession in northeastern Costa Rica. Ecol. Appl. 25, 506–516 (2015).PubMed 

Google Scholar 
Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).CAS 
PubMed 

Google Scholar 
Chazdon, R. L., Broadbent, E. N., Rozendaal, D. M. A., Bongers, F. & Al, E. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).PubMed 
PubMed Central 

Google Scholar 
Lohbeck, M. et al. Functional diversity changes during tropical forest succession. Perspect. Plant Ecol. Evol. Syst. 14, 89–96 (2012).
Google Scholar 
Poorter, L. et al. Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat. Ecol. Evol. 3, 928–934 (2019).PubMed 

Google Scholar 
Townsend, A. R., Cleveland, C. C., Houlton, B. Z., Alden, C. B. & White, J. W. Multi-element regulation of the tropical forest carbon cycle. Front. Ecol. Environ. 9, 9–17 (2011).
Google Scholar 
Medvigy, D. et al. Observed variation in soil properties can drive large variation in modelled forest functioning and composition during tropical forest secondary succession. New Phytol. 223, 1820–1833 (2019).Powers, J. S., Mar, E. & Marín-Spiotta, E. Ecosystem processes and biogeochemical cycles during secondary tropical forest succession. Annu. Rev. Ecol. Evol. Syst. 48, 497–519 (2017).
Google Scholar 
Davidson, E. A. et al. Recuperation of nitrogen cycling in Amazonian forests following agricultural abandonment. Nature 447, 995–998 (2007).CAS 
PubMed 

Google Scholar 
Davidson, E. A. & Martinelli, L. A. in Amazonia and Global Change (eds Keller, M. et al.) 299–309 (American Geophysical Union, 2013).Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur? Biogeochemistry 13, 87–115 (1991).
Google Scholar 
Batterman, S. A. et al. Key role of symbiotic dinitrogen fixation in tropical forest secondary succession. Nature 502, 224–227 (2013).CAS 
PubMed 

Google Scholar 
Bauters, M., Mapenzi, N., Kearsley, E., Vanlauwe, B. & Boeckx, P. Facultative nitrogen fixation by legumes in the central Congo basin is downregulated during late successional stages. Biotropica 48, 281–284 (2016).
Google Scholar 
Van Langenhove, L. et al. Regulation of nitrogen fixation from free-living organisms in soil and leaf litter of two tropical forests of the Guiana shield. Plant Soil 450, 93–110 (2020).PubMed 

Google Scholar 
Vitousek, P. M. Litterfall, nutrient cycling, and nutrient limitation in tropical forests. Ecology 65, 285–298 (1984).CAS 

Google Scholar 
Kaspari, M. et al. Multiple nutrients limit litterfall and decomposition in a tropical forest. Ecol. Lett. 11, 35–43 (2008).PubMed 

Google Scholar 
Cleveland, C. C. et al. Relationships among net primary productivity, nutrients and climate in tropical rain forest: a pan-tropical analysis. Ecol. Lett. 14, 939–947 (2011).PubMed 

Google Scholar 
Chadwick, O. A., Derry, L. A., Vitousek, P. M., Huebert, B. J. & Hedin, L. O. Changing sources of nutrients during four million years of ecosystem development. Nature 397, 491–497 (1999).CAS 

Google Scholar 
Hedin, L. O. et al. Nutrient losses over four million years of tropical forest development. Ecology 84, 2231–2255 (2003).
Google Scholar 
Sanchez, P. A., Villachica, J. H. & Bandy, D. E. Soil fertility dynamics after clearing a tropical rainforest in Peru. Soil Sci. Soc. Am. J. 47, 1171 (1983).CAS 

Google Scholar 
Davidson, E. A. et al. Nitrogen and phosphorus limitation of biomass growth in a tropical secondary forest. Ecol. Appl. 14, 150–163 (2004).
Google Scholar 
Wardle, D. A., Walker, L. R. & Bardgett, R. D. Ecosystem properties and forest decline in contrasting long-term chronosequences. Science 305, 509–513 (2004).CAS 
PubMed 

Google Scholar 
Wassen, M. J., Venterink, H. O., Lapshina, E. D. & Tanneberger, F. Endangered plants persist under phosphorus limitation. Nature 437, 547–550 (2005).CAS 
PubMed 

Google Scholar 
Waring, B. G., Becknell, J. M. & Powers, J. S. Nitrogen, phosphorus, and cation use efficiency in stands of regenerating tropical dry forest. Oecologia 178, 887–897 (2015).PubMed 

Google Scholar 
De longe, M., D’odorico, P. & Lawrence, D. Feedbacks between phosphorus deposition and canopy cover: the emergence of multiple stable states in tropical dry forests. Glob. Change Biol. 14, 154–160 (2008).
Google Scholar 
Bauters, M. et al. Fire-derived phosphorus fertilization of African Tropical Forests. Nat. Commun. 12, 5129 (2021).Vitousek, P. M. & Reiners, W. A. Ecosystem succession and nutrient retention: a hypothesis. Bioscience 25, 376–381 (1975).CAS 

Google Scholar 
Gallarotti, N. et al. In-depth analysis of N 2O fluxes in tropical forest soils of the Congo Basin combining isotope and functional gene analysis. ISME J. 15, 3357–3374 (2021).Gorham, E., Vitousek, P. M. & Reiners, W. A. The regulation of chemical budgets over the course of terrestrial ecosystem succession. Annu. Rev. Ecol. Syst. 10, 53–84 (1979).CAS 

Google Scholar 
Markewitz, D., Davidson, E., Moutinho, P. & Nepstad, D. Nutrient loss and redistribution after forest clearing on a highly weathered soil in Amazonia. Ecol. Appl. 14, 177–199 (2004).
Google Scholar 
Lawrence, D. et al. Ecological feedbacks following deforestation create the potential for a catastrophic ecosystem shift in tropical dry forest. Proc. Natl Acad. Sci. USA 104, 20696–20701 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Veldkamp, E., Schmidt, M., Powers, J. S. & Corre, M. D. Deforestation and reforestation impacts on soils in the tropics. Nat. Rev. Earth Environ. 1, 590–605 (2020).
Google Scholar 
Sanchez, P. A. Properties and Management of Soils in the Tropics (John Wiley and Sons, 1976).Turner, B. L. & Engelbrecht, B. M. J. Soil organic phosphorus in lowland tropical rain forests. Biogeochemistry 103, 297–315 (2011).Sullivan, B. W. et al. Biogeochemical recuperation of lowland tropical forest during succession. Ecology 100, e02641 (2019).Sardans, J. et al. Empirical support for the biogeochemical niche hypothesis in forest trees. Nat. Ecol. Evol. 13, 184–194 (2021).White, P. J. & Broadley, M. R. Calcium in plants. Ann. Bot. 92, 487–511 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).PubMed 

Google Scholar 
Huggett, B. A., Schaberg, P. G., Hawley, G. J. & Eagar, C. Long-term calcium addition increases growth release, wound closure, and health of sugar maple (Acer saccharum) trees at the Hubbard Brook Experimental Forest. Can. J. For. Res. 37, 1692–1700 (2007).CAS 

Google Scholar 
Marschner, P. Marschner’s Mineral Nutrition of Higher Plants 3rd edn (Elsevier/Academic Press 2002).Walker, L. R., Wardle, D. A., Bardgett, R. D. & Clarkson, B. D. The use of chronosequences in studies of ecological succession and soil development. J. Ecol. 98, 725–736 (2010).
Google Scholar 
Bauters, M. et al. Soil nutrient depletion and tree functional composition shift following repeated clearing in secondary forests of the Congo Basin. Ecosystems 24, 1422–1435 (2021).Turner, B. L., Brenes-arguedas, T. & Condit, R. Pervasive phosphorus limitation of tree species but not communities in tropical forests. Nature 555, 367–370 (2018).CAS 
PubMed 

Google Scholar 
Wright, S. J. Plant responses to nutrient addition experiments conducted in tropical forests. Ecol. Monogr. 89, e01382 (2019).Lugli, L. F. et al. Rapid responses of root traits and productivity to phosphorus and cation additions in a tropical lowland forest in Amazonia. New Phytol. 230, 116–128 (2021).Vitousek, P. M. M. & Sanford, R. L. Nutrient cycling in moist tropical forest. Annu. Rev. Ecol. Syst. 17, 137–167 (1986).
Google Scholar 
Kaspari, M. & Powers, J. S. Biogeochemistry and geographical ecology: embracing all twenty-five elements required to build organisms. Am. Nat. 188, S62–S73 (2016).PubMed 

Google Scholar 
Nykvist, N. in Soils of Tropical Forest Ecosystems (eds Schulte, A. & Ruhiyat, D.) 87–91 (Springer, 1998).Bunyavejchewin, S., Sinbumroong, A., Turner, B. L. & Davies, S. J. Natural disturbance and soils drive diversity and dynamics of seasonal dipterocarp forest in Southern Thailand. J. Trop. Ecol. 35, 95–107 (2019).
Google Scholar 
Quesada, C. A. et al. Variations in chemical and physical properties of Amazon forest soils in relation to their genesis. Biogeosciences 7, 1515–1541 (2010).CAS 

Google Scholar 
Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).Makelele, I. A. et al. Afrotropical secondary forests exhibit fast diversity and functional recovery, but slow compositional and carbon recovery after shifting cultivation. J. Veg. Sci. 32, e13071 (2021).Van Langenhove, L. et al. Atmospheric deposition of elements and its relevance for nutrient budgets of tropical forests. Biogeochemistry 149, 175–193 (2020).
Google Scholar 
Staelens, J. et al. Calculating dry deposition and canopy exchange with the canopy budget model: review of assumptions and application to two deciduous forests. Water Air Soil Pollut. 191, 149–169 (2008).CAS 

Google Scholar 
Hofhansl, F. et al. Topography strongly affects atmospheric deposition and canopy exchange processes in different types of wet lowland rainforest, southwest Costa Rica. Biogeochemistry 106, 371–396 (2011).
Google Scholar 
Schrijver, A. De, Nachtergale, L. & Staelens, J. Comparison of throughfall and soil solution chemistry between a high-density Corsican pine stand and a naturally regenerated silver birch stand. Environ Pollut. 131, 93–105 (2004).Eriksson, E. & Khunakasem, V. Chloride concentration in groundwater, recharge rate and rate of deposition of chloride in the Israel coastal plain. J. Hydrol. 7, 178–197 (1969).
Google Scholar 
Malhi, Y. et al. An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR). J. Veg. Sci. 13, 439 (2002).
Google Scholar 
Réjou-Méchain, M., Tanguy, A., Piponiot, C., Chave, J. & Hérault, B. biomass: an R package for estimating above-ground biomass and its uncertainty in tropical forests. Methods Ecol. Evol. 8, 1163–1167 (2017).
Google Scholar 
Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).
Google Scholar 
Malhi, Y. et al. The Global Ecosystems Monitoring network: monitoring ecosystem productivity and carbon cycling across the tropics. Biol. Conserv. 253, 108889 (2021).D’Angelo, E., Crutchfield, J. & Vandiviere, M. Rapid, sensitive, microscale determination of phosphate in water and soil. J. Environ. Qual. 30, 2206–2209 (2001).Rowland, A. P. & Haygarth, P. M. Determination of total dissolved phosphorus in soil solutions. J. Environ. Qual. 26, 410–415 (1997).CAS 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 

Google Scholar 
Brookes, P. C., Powlson, D. S. & Jenkinson, D. S. Measurement of microbial biomass phosphorus in soil. Soil Biol. Biochem. 14, 319–329 (1982).CAS 

Google Scholar 
Kaiser, C. et al. Belowground carbon allocation by trees drives seasonal patterns of extracellular enzyme activities by altering microbial community composition in a beech forest soil. New Phytol. 187, 843–858 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Poorter, L. et al. Multidimensional tropical forest recovery. Science 374, 1370–1376 (2021). More

Sample collection and hydrographySampling was conducted aboard the University of Victoria’s MSV John Strickland either weekly, biweekly or monthly between 11 March 2010 and 15 November 2017 in Saanich Inlet at 48.59°N, 123.50°W (Fig. 1). To standardize measurements and due to biological significance, seawater was collected from the euphotic zone. Sampling depths corresponded to approximately 100, 50, 15, and 1% of the photosynthetically active radiation (PAR) at the surface (Io). These “light” depths were either determined using a CTD-mounted PAR sensor or a Secchi disk. CTD profiles were performed prior to each seawater cast to measure depth, temperature and conductivity of the water column, and PAR, fluorescence, and dissolved oxygen (when available).Seawater from each light depth was collected using Niskin or GO-FLO bottles on either a rosette sampler or an oceanographic wire. When possible, individual samples were collected directly from the Niskin or GO-FLO bottles. When time was not sufficient to allow direct sampling, bulk samples of seawater from each depth were collected into acid-washed polyethylene carboys, kept cold in the dark, and homogenized before sub-sampling for the individual measurements.Dissolved nutrientsFor the measurements of nitrite (NO2−), nitrate and nitrite (NO3− + NO2−), phosphate (PO43−) and Si(OH)4, seawater samples from each light depth were syringe-filtered through a combusted 0.7 µm (nominal porosity) glass fibre filter into acid-washed 30-mL polypropylene bottles and immediately frozen. All nutrient samples were stored at −20 °C until analysis. Concentrations of NO2−, NO3− + NO2−, PO43−, and Si(OH)4 were determined using an Astoria Nutrient Autoanalyzer (Astoria-Pacific, OR, USA) following the methodology of Barwell-Clarke and Whitney22. During 2014 and 2015, samples for the measurement of Si(OH)4 were collected separately from those for the other nutrients, filtered with a 0.6 µm polycarbonate membrane filter and stored at 4 °C. During this period, Si(OH)4 concentrations were determined manually using the molybdate blue colorimetric methodology23. Replicate (2 or 3) nutrient samples were taken at each depth; average data are presented in the published dataset and the figures (Fig. 2).Fig. 2Dissolved macronutrient concentrations in the euphotic zone of Saanich Inlet from March 11, 2010 to November 15, 2017. Left panels show depth-integrated concentrations (black bars on top) and time-series profiles (filled contour/scatter plots on bottom) for (A) nitrate plus nitrite (NO3− + NO2−), (B) phosphate (PO43−) and (C) silicic acid (Si(OH)4). In the time-series profiles, 2012–2013 data are not interpolated due to single-depth sampling. Grey shaded regions in top panels indicate the phytoplankton growing seasons considered for this study (March 1st – October 30th). Right panels show monthly-averaged depth profiles for the entire 8-year period, illustrating euphotic zone seasonality for each nutrient. The color scale bars on the far right apply to both the time-series vertical profiles and the 8-year seasonal plots. Sampling depths are indicated by round symbols. The year labels are positioned under the tick marks corresponding to January.Full size imageSuspended particulate matterTotal chlorophyll-aChlorophyll-a (Chl-a) was used as a proxy for phytoplankton biomass (Fig. 3A). For total Chl-a analysis, seawater samples (0.25–1 L) were gently vacuum filtered onto 0.7 µm (nominal porosity) glass fiber filters, which were then stored at −20 °C until analysis. Chl-a concentrations were determined using the acetone extraction and acidification method24,25. Acidification of samples decreased the likelihood of overestimation of Chl-a concentrations due to the presence of chlorophyll degradation products26. Filters were submerged in 10 mL of 90% acetone, sonicated for 10 minutes in an ice bath, and left to extract at −20 °C for 22 h. Following the extraction period, samples were allowed to equilibrate to room temperature (~2 h). Fluorescence of the acetone solution containing the extracted Chl-a was measured before and after acidification with 1.2 N hydrochloric acid using a Turner 10-AU fluorometer. The final concentrations of total Chl-a were calculated from measurements made before (Fo) and after (Fa) acidification using Eq. (1)25. The coefficient (τ) of Eq. (1), adapted from Strickland and Parsons25, was derived from a calibration of the Turner 10-AU fluorometer with known pure chlorophyll standards (Table 2).$${rm{C}}{rm{h}}{rm{l}} mbox{-} {rm{a}}(mu g,{L}^{-1})=frac{tau }{tau -1}ast ({rm{F}}{rm{o}}-{rm{F}}{rm{a}})ast 0.814ast left(frac{{rm{V}}{rm{o}}{rm{l}}.{rm{A}}{rm{c}}{rm{e}}{rm{t}}{rm{o}}{rm{n}}{rm{e}},{rm{e}}{rm{x}}{rm{t}}{rm{r}}{rm{a}}{rm{c}}{rm{t}}{rm{e}}{rm{d}}}{{rm{V}}{rm{o}}{rm{l}}.{rm{S}}{rm{e}}{rm{a}}{rm{w}}{rm{a}}{rm{t}}{rm{e}}{rm{r}},{rm{f}}{rm{i}}{rm{l}}{rm{t}}{rm{e}}{rm{r}}{rm{e}}{rm{d}}}right)$$
(1)
Fig. 3Biological particulate concentrations in the euphotic zone of Saanich Inlet from March 11, 2010 – November 15, 2017. Left panels show time-series profiles (filled contour/scatter plots) of (A) total chlorophyll-a (Total Chl-a), (B) particulate carbon (PC), (C) particulate nitrogen (PN), and (D) particulate biogenic silica (bSiO2). The 2012–2013 data are not interpolated due to single-depth sampling. In A, the bar plot in the top panel shows percent contribution of different size fractions to total Chl-a. In (B–D), black bars in top panels show depth-integrated concentrations. Grey shaded regions in bar plots indicate phytoplankton growing seasons considered for this study (March 1st – October 30th). Right panels show monthly-averaged depth profiles for the entire 8-year period, illustrating euphotic zone seasonality for each particulate. The color scale bars on the far right apply to both the time-series vertical profiles and the 8-year seasonal plots. Sampling depths are indicated by round symbols. The year labels are positioned under the tick marks corresponding to January.Full size imageSize fractionated chlorophyll-aTo determine the percent contributions of “pico” (0.7–2 µm), “small nano” (2–5 µm), “large nano” (5–20 µm) and “micro” ( >20 µm) phytoplankton to total Chl-a, seawater samples (0.25–1 L) separate from those used for total Chl-a) were consecutively filtered through 20, 5 and 2 µm polycarbonate membrane filters and 0.7 µm (nominal porosity) glass fiber filters. Between 2013 and 2017, the “pico” and “small nano” size classes were collected as one fraction (0.7–5 µm). Analysis of Chl-a concentrations for each size fraction followed the same procedure outlined for total Chl-a.Particulate carbon and nitrogenParticulate C and N measurements were obtained from seawater samples incubated for carbon (ρC) and nitrate uptake (ρNO3) rates (see section on “Uptake rates of carbon and nitrate” for methodology) (Fig. 3B,C). PC and PN measurements presented in this dataset were taken at the end of ρC and ρNO3 incubations; however, original (‘ambient’) values can be back calculated by subtracting the amount of C and N taken up during the incubation period from the final PC and PN values. The differences between after-incubation PC and PN data and back-calculated ambient values were not significantly different than the measurement error.Particulate biogenic silicaParticulate biogenic silica was used as a proxy for siliceous phytoplankton biomass (Fig. 3D). Seawater samples (0.5–1 L) from each depth were gently vacuum filtered through 0.6 µm polycarbonate membrane filters. Filters were folded and placed in polypropylene centrifuge tubes, dried for 48 h at 60 °C, and then stored in a desiccator at room temperature until analysis. Filters were digested with 4 mL of 0.2 M NaOH for 30–45 min in a water bath at 95 °C27. After digestion, samples were neutralized with 0.1 N HCl and cooled rapidly in an ice bath. Samples were centrifuged to separate out the undissolved lithogenic silica, and colorimetric analysis was performed on the supernatant. The transmittance of the samples, standards, and reverse-order reagent blanks were read at 820 nm using a Beckman DU 530 ultraviolet-visible (UV/Vis) spectrophotometer27,28.Uptake rates of carbon and nitrateSeawater samples (~0.5–1 L) were gently collected into clear polycarbonate bottles. One additional sample was collected from the 100% light depth, into a dark polycarbonate bottle, which did not allow light penetration. After the addition of the isotopic tracers (see below), bottles were placed into an acrylic incubator with constant seawater flow to maintain surface seawater temperature. Three acrylic tubes wrapped in colored and neutral density photo-film (to obtain 50, 15, and 1% of surface PAR) were used to incubate sample bottles under the same in-situ light conditions from which samples were collected. Samples from the 100% light level were placed inside the same acrylic incubator, but outside of the film-covered tubes. A LI-COR® LI-190 Quantum sensor was installed next to the incubator and continuously recorded incoming PAR for the entire incubation period. During sampling in 2010 and 2011, all experiments were performed using a shipboard incubator. For sampling from 2012 onwards, all experiments were done using an incubator on land (University of Victoria Aquatic Facility), which was connected to a seawater system maintained at local surface seawater temperature (approximately 9–12 °C depending on the time of year).Rates of C (ρC) and NO3 (ρNO3) uptake were determined using a stable isotope tracer-technique29,30 (Fig. 4). A single seawater sample from each light depth received a dual spike, with NaH13CO3 (99% 13C purity, Cambridge Isotope Laboratories) for the determination of ρC and Na15NO3 (98 + % 15N purity, Cambridge Isotopes Laboratories) for the determination of ρNO3. Isotope additions were made at approximately 10% of ambient dissolved inorganic carbon (DIC) and NO3− concentrations.Fig. 4Carbon and nitrate uptake rates in the euphotic zone of Saanich Inlet from March 11, 2010 – November 15, 2017. Left panels show depth-integrated rates (black-bars on top) and time-series profiles (filled contour/scatter plots below) of (A) carbon (ρC) and (B) nitrate (ρNO3) uptake rates. In the time-series profiles, 2012–2013 data are not interpolated due to single-depth sampling. Grey shaded regions in depth-integrated plots indicate phytoplankton growing seasons for this study (March 1st – October 30th). Right panels show monthly-averaged depth profiles for the entire 8-year period, illustrating euphotic zone seasonality for carbon and nitrate uptake. The color scale bars on the far right apply to both the total time-series vertical profiles and the 8-year seasonal plots. Sampling depths are indicated by round symbols. The year labels are positioned under the tick marks corresponding to January.Full size imageSpiked seawater samples were incubated for 24 h, except from 2010 to 2013 when the incubation period was 4 to 6 hr. After incubation, the entire sample was gently vacuum filtered onto a combusted 0.7 µm (nominal porosity) glass fibre filter. Filters were dried for 48 h at 60 °C and kept in a desiccator at room temperature until analysis. Filters were packed into pellets and sent to the Stable Isotope Facility at the University of California (UC) Davis for analysis of 13C and 15N enrichment, and total C and N content by continuous flow isotope ratio mass spectrometry and elemental analysis, respectively. For these measurements, UC Davis uses either an Elementar Vario EL Cube or Micro Cube elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany) interfaced to either a PDZ Europa 20–20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK) or an Isoprime VisION IRMS (Elementar UK Ltd, Cheadle, UK).Carbon and NO3− uptake rates were calculated using Eq. 3 of Hama et al.29, and Eq. 3 and 6 of Dugdale and Wilkerson30, respectively.For samples incubated for less than 24 h, the daily C or NO3− uptake rates (ρX) were calculated using a PAR extrapolation method shown in Eq. (2):$$rho X(mu mol,{L}^{-1}da{y}^{-1})=left(rho X(mu mol,{L}^{-1}h{r}^{-1})divleft(frac{PAR,during,incubation}{Total,Daily,PAR}right)right)ast 24$$
(2)
Additionally, to account for NO3− uptake occurring under no light, ρNO3− was measured in dark bottles and this rate was added to the ρNO3− of each sample incubated for less than 24 h. The ρNO3− DARK was calculated following Eq. (3):$$rho N{O}_{3}DARK(mu mol,{L}^{-1}da{y}^{-1})=left(rho N{O}_{3}DARK(mu mol,{L}^{-1}h{r}^{-1})divleft(frac{Total,Daily,PAR-PAR,during,incubation}{Total,Daily,PAR}right)right)ast 24$$
(3)
PAR data used in Eqs. (2) and (3) came from the LI-COR® LI-190 Quantum sensor that was mounted beside the incubator. The seawater DIC value for each sample was calculated using a regression equation relating water density to DIC for Saanich Inlet31. Ambient NO3− concentrations were measured as described above. More

Monitoring sites and speciesThe monitoring survey was conducted at five sites in Fukushima Prefecture (sites 1–4 and A1) and at one site in Ibaraki Prefecture (site 5), Japan (Fig. 1). Sites 1, 2, and A1 are located in Kawauchi Village, site 3 in Otama Village, site 4 in Tadami Town, and site 5 in Ishioka City. Monitoring at sites 1–5 was started in 2011 or 2012, and site A1 was additionally monitored since 2017. The tree species, age, mean diameter at breast height, initial deposition density of 137Cs, and sampling year of each sample at each site are listed in Table 1. The dominant tree species in the contaminated area, namely, Japanese cedar (Cryptomeria japonica [L.f.] D.Don), Japanese cypress (Chamaecyparis obtusa [Siebold et Zucc.] Endl.), konara oak (Quercus serrata Murray), and Japanese red pine (Pinus densiflora Siebold et Zucc.) were selected for monitoring. Japanese chestnut (Castanea crenata Siebold et Zucc.) was supplementally added in 2017. The cedar, cypress, and pine are evergreen coniferous species, and the oak and chestnut are deciduous broad-leaved species. Sites 1 and 3 each have three plots, and each plot contains a different monitoring species. Site A1 has one plot containing two different monitoring species, and the remaining sites each have one plot with one monitoring species, giving ten plots in total.Figure 1Locations of the monitoring sites and initial deposition densities of 137Cs (decay-corrected to July 2, 2011) following the Fukushima nuclear accident in Fukushima and Ibaraki Prefectures. Open circles indicate the monitoring sites and the cross mark indicates the Fukushima Dai-ichi Nuclear Power Plant. Data on the deposition density were provided by MEXT19,20 and refined by Kato et al.21. The map was created using R (version 4.1.0)22 with ggplot2 (version 3.3.5)23 and sf (version 1.0–0)24 packages.Full size imageTable 1 Description of the sampled trees and monitoring sites.Full size tableSample collection and preparationBulk sampling of bark and wood disks was conducted by felling three trees per year at all sites during 2011–20168,25 and at sites 3–5 and A1 during 2017–2020. Partial sampling from six trees per year was conducted at sites 1 and 2 during 2017–2020 (from seven trees at site 2 in 2017) to sustain the monitoring trees. All the samples were obtained from the stems around breast height. During the partial sampling, bark pieces sized approximately 3 cm × 3 cm (axial length × tangential length) were collected from four directions of the tree stem using a chisel, and 12-mm-diameter wood cores were collected from two directions of the tree stem using an automatic increment borer (Smartborer, Seiwa Works, Tsukuba, Japan) equipped with a borer bit (10–101-1046, Haglöf Sweden, Långsele, Sweden). Such partial sampling increases the observational errors in the bark and wood 137Cs concentrations in individual trees26. To mitigate this error and maintain an accurate mean value of the 137Cs concentration, we increased the number of sampled trees from three to six. The sampling was conducted mainly in July–September of each year; the exceptions were site-5 samples in 2011 and 2012, which were collected irregularly during January–February of the following year. The collected bark pieces were separated into outer and inner barks, and the wood disks and cores were split into sapwood and heartwood. The outer and inner bark samples during 2012–2016 were obtained by partial sampling of barks sized approximately 10 cm × 10 cm from 2–3 directions on 2–3 trees per year.The bulk samples of bark, sapwood, and heartwood were air-dried and then chipped into flakes using a cutting mill with a 6-mm mesh sieve (UPC-140, HORAI, Higashiosaka, Japan). The pieces of the outer and inner bark were chipped into approximately 5 mm × 5 mm pieces using pruning shears, and the cores of the sapwood and heartwood were chipped into semicircles of thickness 1–2 mm. Each sample was packed into a container for radioactivity measurements and its mass was measured after oven-drying at 75 °C for at least 48 h. Multiplying this mass by the conversion factor (0.98 for bark and 0.99 for wood)8 yielded the dry mass at 105 °C.Radioactivity measurementsThe radioactivity of 137Cs in the samples was determined by γ-ray spectrometry with a high-purity Ge semiconductor detector (GEM20, GEM40, or GWL-120, ORTEC, Oak Ridge, TN). For measurements, the bulk and partial samples were placed into Marinelli containers (2.0 L or 0.7 L) and cylindrical containers (100 mL or 5 mL), respectively. The peak efficiencies of the Marinelli containers, the 100-mL container, and the 5-mL container were calibrated using standard sources of MX033MR, MX033U8PP (Japan Radioisotope Association, Tokyo, Japan), and EG-ML (Eckert & Ziegler Isotope Products, Valencia, CA), respectively. For the measurement of the 5-mL container, a well-type Ge detector (GWL-120) was used under the empirical assumption that the difference in γ-ray self-absorption between the standard source and the samples is negligible27. The measurement was continued until the counting error became less than 5% (higher counting errors were allowed for small or weakly radioactive samples). The activity concentration of 137Cs in the bark (whole) collected by partial sampling was calculated as the mass-weighted mean of the concentrations in the outer and inner barks; meanwhile, the concentration in the wood (whole) was calculated as the cross-sectional-area-weighted mean of sapwood and heartwood concentrations. The activity concentrations were decay-corrected to September 1, 2020, to exclude the decrease due to the radioactive decay.Trend analysesThe yearly representative values (true states) of 137Cs activity concentration in each stem part in each plot were estimated using a DLM, a state-space model in which the noise follows a normal distribution and the relationship between variables is linear. One basic DLM is the local linear trend model defined by the following equations:$$Y_{t} = mu _{t} + varepsilon _{t} ,quad quad quad varepsilon _{t} sim Normal left( {0,sigma _{varepsilon }^{2} } right)$$
(1)
$$mu_{t} = mu_{t – 1} + beta_{t – 1} + eta_{t} ,quad quad quad eta_{t} sim Normal left( {0,sigma_{eta }^{2} } right)$$
(2)
$$beta_{t} = beta_{t – 1} + zeta_{t} ,quad quad quad zeta_{t} sim Normal left( {0,sigma_{zeta }^{2} } right)$$
(3)
where Yt, μt, and βt are the observation values, level (true state), and slope, respectively, and εt, ηt, and ζt denote their corresponding noises. The subscript t is the time index. The noises εt, ηt, and ζt follow normal distributions with a mean of 0 and variances of ({sigma }_{varepsilon }^{2}), ({sigma }_{eta }^{2}), and ({sigma }_{zeta }^{2}), respectively. To detect relatively long-term trends, we employed the smooth local linear trend model28 (also called the smooth trend model, integrated random walk model, or second-order trend model), which is obtained by considering that μt and βt are driven by the same noise. The trend changes are assumed to be smoother in this model than in the local linear trend model28,29. Combining Eqs. (2) and (3), μt in the smooth local linear trend model is finally obtained as$$mu_{t} = 2mu_{t – 1} – mu_{t – 2} + eta_{t} ,quad quad quad eta_{t} sim Normal left( {0,sigma_{eta }^{2} } right)$$
(4)
The parameters μt, ({sigma }_{eta }^{2}), and ({sigma }_{varepsilon }^{2}) of each stem part in each plot were determined by Bayesian estimation with a Markov chain Monte Carlo (MCMC) method. The Bayesian estimation was performed in R (version 4.1.0)22 with the rstan package (version 2.21.2)30. Uninformative prior distributions were used for μ1, μ2, ({sigma }_{eta }^{2}), and ({sigma }_{varepsilon }^{2}). The log-transformed values of the 137Cs activity concentration (decay-corrected to September 1, 2020) were given as Yt (the observed values of multiple individuals in each year were passed via the segment function of Stan). MCMC sampling was conducted for four chains of 50,000 iterations (the first 25,000 were discarded as warmup), obtaining 100,000 MCMC samples for each parameter. The MCMC was judged to have converged when the maximum value of Rhat was less than 1.05 and the divergent transitions after warmup were fewer than 1,000 (i.e., less than 1% of the MCMC sample size). On the datasets of the outer and inner barks from site-3 oaks and all stem parts from site-A1 pines and chestnuts, the MCMC converged poorly owing to the small number of monitoring years. Thus, the temporal trends in these datasets were not analyzed (the observational data at site A1 are shown in Supplementary Fig. S1 and Table S1).To detect decadal trends rather than yearly variations, we determined the temporal trends in the true state (μ) by setting 2–4 delimiting years and examining whether μ varied significantly from one delimiting year to the next. As the delimiting years, we selected the initial and final years of monitoring and the years in which the median µ was highest (µ-max year) and lowest (µ-min year). When the µ-max year and/or the µ-min year coincided with the initial year and/or final year of monitoring, the number of delimiting years reduced from four to two or three. The trend in µ between two delimiting years was determined to be increasing and decreasing when the 95% credible interval of µ2nd delimiting year − µ1st delimiting year (obtained from the MCMC samples) was higher and lower than zero, respectively. A flat trend (no significant variation) was detected when the 95% credible interval included zero. If the 3rd and 4th delimiting years existed, the trends between the 2nd and 3rd delimiting years and between the 3rd and 4th delimiting years were determined in the same manner.The 137Cs CRs of outer bark/inner bark, heartwood/sapwood, and inner bark/sapwood were also subjected to the above trend analyses. On datasets with less than five years of monitoring, the MCMC did not converge so the trend analysis was not attempted. More

Thamdrup, B. New Pathways and processes in the global nitrogen cycle. Annu. Rev. Ecol. Evol. Syst. 43, 407–428 (2012).
Google Scholar 
Lam, P. et al. Revising the nitrogen cycle in the Peruvian oxygen minimum zone. Proc. Natl. Acad. Sci. USA 106, 4752–4757 (2009).CAS 

Google Scholar 
Behrendt, A., de Beer, D. & Stief, P. Vertical activity distribution of dissimilatory nitrate reduction in coastal marine sediments. Biogeosciences 10, 7509–7523 (2013).
Google Scholar 
Fossing, H. et al. Concentration and transport of nitrate by the mat-forming sulphur bacterium. Thioploca. Nature 374, 713–715 (1995).CAS 

Google Scholar 
McHatton, S. C., Barry, J. P., Jannasch, H. W. & Nelson, D. C. High nitrate concentrations in vacuolate, autotrophic marine Beggiatoa spp. Appl. Environ. Microbiol. 62, 954–958 (1996).CAS 

Google Scholar 
Kamp, A., Høgslund, S., Risgaard-Petersen, N. & Stief, P. Nitrate storage and dissimilatory nitrate reduction by eukaryotic microbes. Front. Microbiol. 6, 1492 (2015).
Google Scholar 
Eppley, R. W. & Rogers, J. N. Inorganic nitrogen assimilation of Ditylum brightwellii, a marine plankton diatom. J. Phycol. 6, 344–351 (1970).CAS 

Google Scholar 
Lomas, M. & Glibert, P. Comparisons of nitrate uptake, storage, and reduction in marine diatoms and flagellates. J. Phycol. 36, 903–913 (2000).CAS 

Google Scholar 
Jørgensen, B. B. & Gallardo, A. Thioploca spp.: filamentous sulfur bacteria with nitrate vacuoles. FEMS Microbiol. Ecol. 28, 301–313 (1999).
Google Scholar 
Schulz, H. N. et al. Dense populations of a giant sulfur bacterium in Namibian shelf sediments. Science 284, 493–495 (1999).CAS 

Google Scholar 
Risgaard-Petersen, N. et al. Evidence for complete denitrification in a benthic foraminifer. Nature 443, 93–96 (2006).CAS 

Google Scholar 
Kamp, A., de Beer, D., Nitsch, J. L., Lavik, G. & Stief, P. Diatoms respire nitrate to survive dark and anoxic conditions. Proc. Natl. Acad. Sci. USA 108, 5649–5654 (2011).CAS 

Google Scholar 
Stief, P. et al. Dissimilatory nitrate reduction by Aspergillus terreus isolated from the seasonal oxygen minimum zone in the Arabian Sea. BMC Microbiol. 14, 35 (2014).
Google Scholar 
Høgslund, S., Cedhagen, T., Bowser, S. S. & Risgaard-Petersen, N. Sinks and sources of intracellular nitrate in gromiids. Front. Microbiol. 8, 617 (2017).
Google Scholar 
Harold, F. M. The Vital Force: A Study of Bioenergetics (WH Freeman & Co., 1986).Katz, M. E., Finkel, Z. V., Grzebyk, D., Knoll, A. H. & Falkowski, P. G. Evolutionary trajectories and biogeochemical impacts of marine eukaryotic phytoplankton. Annu. Rev. Ecol. Evol. Syst. 35, 523–556 (2004).
Google Scholar 
Villareal, T. A., Altabet, M. A. & Culverrymsza, K. Nitrogen transport by vertically migrating diatom mats in the North Pacific Ocean. Nature 363, 709–712 (1993).CAS 

Google Scholar 
Kamp, A., Stief, P. & Schulz, H. N. Anaerobic sulfide oxidation with nitrate by a freshwater Beggiatoa enrichment culture. Appl. Environ. Microbiol. 72, 4755–4760 (2006).CAS 

Google Scholar 
Merz, E. et al. Nitrate respiration and diel migration patterns of diatoms are linked in sediments underneath a microbial mat. Environ. Microbiol. 23, 1422–1435 (2021).CAS 

Google Scholar 
Leblanc, K. et al. A global diatom database–abundance, biovolume and biomass in the world ocean. Earth Syst. Sci. Data 4, 149–165 (2012).
Google Scholar 
Benoiston, A. S. et al. The evolution of diatoms and their biogeochemical functions. Phil. Trans. R. Soc. B 372, 20160397 (2017).
Google Scholar 
Nelson, D. M., Tréguer, P., Brzezinski, M. A., Leynaert, A. & Queguiner, B. Production and dissolution of biogenic silica in the ocean-revised global estimates, comparison with regional data and relationship to biogenic sedimentation. Global Biogeochem. Cycl. 9, 359–372 (1995).CAS 

Google Scholar 
Sarthou, G., Timmermans, K. R., Blain, S. & Tréguer, P. Growth physiology and fate of diatoms in the ocean: a review. J. Sea Res. 53, 25–42 (2005).CAS 

Google Scholar 
Dortch, Q., Clayton, J. R., Thoresen, S. S. & Ahmed, S. I. Species differences in accumulation of nitrogen pools in phytoplankton. Mar. Biol. 81, 237–250 (1984).CAS 

Google Scholar 
Kamp, A., Stief, P., Knappe, J. & de Beer, D. Response of the ubiquitous pelagic diatom Thalassiosira weissflogii to darkness and anoxia. PLoS ONE 8, e82605 (2013).
Google Scholar 
Kamp, A., Stief, P., Bristow, L. A., Thamdrup, B. & Glud, R. N. Intracellular nitrate of marine diatoms as a driver of anaerobic nitrogen cycling in sinking aggregates. Front. Microbiol. 7, 1669 (2016).
Google Scholar 
Needoba, J. A. & Harrison, P. J. Influence of low light and a light:dark cycle on NO3− uptake, intracellular NO3−, and nitrogen isotope fractionation by marine phytoplankton. J. Phycol. 40, 505–516 (2004).CAS 

Google Scholar 
Lomas, M. W. & Glibert, P. M. Temperature regulation of nitrate uptake: A novel hypothesis about nitrate uptake and reduction in cool-water diatoms. Limnol. Oceanogr. 44, 556–572 (1999).CAS 

Google Scholar 
Lomas, M. W., Rumbley, C. J. & Glibert, P. M. Ammonium release by nitrogen sufficient diatoms in response to rapid increases in irradiance. J. Plankton Res. 22, 2351–2366 (2000).CAS 

Google Scholar 
Van Tol, H. M. & Armbrust, E. V. Genome-scale metabolic model of the diatom Thalassiosira pseudonana highlights the importance of nitrogen and sulfur metabolism in redox balance. PLoS ONE 16, e0241960 (2021).
Google Scholar 
Piña-Ochoa, E. et al. Widespread occurrence of nitrate storage and denitrification among Foraminifera and Gromiida. Proc. Natl. Acad. Sci. USA 107, 1148–1153 (2010).
Google Scholar 
García-Robledo, E., Corzo, A., Papaspyrou, S., Jimenez-Arias, J. L. & Villahermosa, D. Freeze-lysable inorganic nutrients in intertidal sediments: dependence on microphytobenthos abundance. Mar. Ecol. Prog. Ser. 403, 155–163 (2010).
Google Scholar 
Marchant, H. K., Lavik, G., Holtappels, M. & Kuypers, M. M. M. The fate of nitrate in intertidal permeable sediments. PLoS ONE 9, e104517 (2014).
Google Scholar 
Villareal, T. A. & Lipschultz, F. Internal nitrate concentrations in single cells of large phytoplankton from the Sargasso Sea. J. Phycol. 31, 689–696 (1995).CAS 

Google Scholar 
Smith, G. J., Zimmerman, R. C. & Alberte, R. S. Molecular and physiological responses of diatoms to variable levels of irradiance and nitrogen availability: Growth of Skeletonema costatum in simulated upwelling conditions. Limnol. Oceanogr. 37, 989–1007 (1992).CAS 

Google Scholar 
Montagnes, D. J. S. & Franklin, D. J. Effect of temperature on diatom volume, growth rate, and carbon and nitrogen content: Reconsidering some paradigms. Limnol. Oceanogr. 46, 2008–2018 (2001).CAS 

Google Scholar 
Smith, S. R. et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat. Commun. 10, 4552 (2019).
Google Scholar 
Behrenfeld, M. J. et al. Thoughts on the evolution and ecological niche of diatoms. Ecol. Monogr. 91, e01457 (2021).
Google Scholar 
Bourke, M. F. et al. Metabolism in anoxic permeable sediments is dominated by eukaryotic dark fermentation. Nat. Geosci. 10, 30–35 (2017).CAS 

Google Scholar 
Härnström, K., Ellegaard, M., Andersen, T. J. & Godhe, A. Hundred years of genetic structure in a sediment revived diatom population. Proc. Natl. Acad. Sci. USA 108, 4252–4257 (2011).
Google Scholar 
Pelusi, A., Santelia, M. E., Benvenuto, G., Godhe, A. & Montresor, M. The diatom Chaetoceros socialis: spore formation and preservation. Europ. J. Phycol. 55, 1–10 (2020).CAS 

Google Scholar 
Petterson, K. & Sahlsten, E. Diel patterns of combined nitrogen uptake and intracellular storage of nitrate by phytoplankton in the open Skagerrak. J. Exp. Mar. Biol. Ecol. 138, 167–182 (1990).
Google Scholar 
Petterson, K. Seasonal uptake of carbon and nitrogen and intracellular storage of nitrate in planktonic organisms in the Skagerrak. J. Exp. Mar. Biol. Ecol. 151, 121–1137 (1991).
Google Scholar 
Bode, A., Botas, J. A. & Fernandez, E. Nitrate storage by phytoplankton in a coastal upwelling environment. Mar. Biol. 129, 399–406 (1997).CAS 

Google Scholar 
Stief, P., Kamp, A., Thamdrup, B. & Glud, R. N. Anaerobic nitrogen turnover by sinking diatom aggregates at varying ambient oxygen levels. Front. Microbiol. 7, 98 (2016).
Google Scholar 
Jensen, M. M. et al. Intensive nitrogen loss over the Omani Shelf due to anammox coupled with dissimilatory nitrite reduction to ammonium. ISME J. 5, 1660–1670 (2011).CAS 

Google Scholar 
Magalhaes, C. M., Wiebe, W. J., Joye, S. B. & Bordalo, A. A. Inorganic nitrogen dynamics in intertidal rocky biofilms and sediments of the Douro River estuary (Portugal). Estuaries 28, 592–607 (2005).CAS 

Google Scholar 
Burgin, A. J. & Hamilton, S. K. Have we overemphasized the role of denitrification in aquatic ecosystems? A review of nitrate removal pathways. Front. Ecol. Environ. 5, 89–96 (2007).
Google Scholar 
Kühl, M., Glud, R. N., Ploug, H. & Ramsing, N. B. Microenvironmental control of photosynthesis and photosynthesis-coupled respiration in an epilithic cyanobacterial biofilm. J. Phycol. 32, 799–812 (1996).
Google Scholar 
Heisterkamp, I. M. et al. Shell biofilm-associated nitrous oxide production in marine molluscs: processes, precursors and relative importance. Environ. Microbiol. 15, 1943–1955 (2013).CAS 

Google Scholar 
Fernandez-Mendez, M. et al. Composition, buoyancy regulation and fate of ice algal aggregates in the Central Arctic Ocean. PLoS ONE 9, e107452 (2014).
Google Scholar 
Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 339, 1430–1432 (2013).CAS 

Google Scholar 
Abed, R. M. M. & Garcia-Pichel, F. Long-term compositional changes after transplant in a microbial mat cyanobacterial community revealed using a polyphasic approach. Environ. Microbiol. 3, 53–62 (2001).CAS 

Google Scholar 
Al-Najjar, M. A. A., de Beer, D., Kühl, M. & Polerecky, L. Light utilization efficiency in photosynthetic microbial mats. Environ. Microbiol. 14, 982–992 (2012).CAS 

Google Scholar 
Heisterkamp, I. M., Kamp, A., Schramm, A. T., de Beer, D. & Stief, P. Indirect control of the intracellular nitrate pool of intertidal sediment by the polychaete Hediste diversicolor. Mar. Ecol. Prog. Ser. 445, 181–192 (2012).
Google Scholar 
García-Robledo, E., Corzo, A. & Papaspyrou, S. A fast and direct spectrophotometric method for the sequential determination of nitrate and nitrite at low concentrations in small volumes. Mar. Chem. 162, 30–36 (2014).
Google Scholar 
Grasshoff, K. In Methods of Seawater Analysis (eds Grasshoff, K., Ehrhardt, M., Kremling, K.) 143–150 (Verlag Chemie Weinheim, 1983).Braman, R. S. & Hendrix, S. A. Nanogram nitrite and nitrate determination in environmental and biological materials by vanadium(III) reduction with chemiluminescence detection. Anal. Chem. 61, 2715–2718 (1989).CAS 

Google Scholar 
Meier, D. V. et al. Limitation of microbial processes at saturation-level salinities in a microbial mat covering a coastal salt flat. Appl. Environ. Microbiol. 87, e00698–21 (2021).CAS 

Google Scholar 
Sode, K., Horikoshi, K., Takeyama, H., Nakamura, N. & Matsunaga, T. Online monitoring or marine cyanobacterial cultivation based on phycocyanin fluorescence. J. Biotechnol. 21, 209–217 (1991).CAS 

Google Scholar 
Berns, D. S., Scott, E. & Oreilly, K. T. C-phycocyanin-minimum molecular weight. Science 145, 1054–1055 (1964).CAS 

Google Scholar 
Hillebrand, H., Durselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Google Scholar 
Zimmermann, J., Jahn, R. & Gemeinholzer, B. Barcoding diatoms: evaluation of the V4 subregion on the 18S rRNA gene, including new primers and protocols. Org. Divers. Evol. 11, 173–192 (2011).
Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 

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

Google Scholar 
Round, F. E., Crawford, R. M. & Mann, D. G. The Diatoms: Biology and Morphology of the Genera. 747p (Cambridge University Press, 1990).Medlin, L. K. Evolution of the diatoms: major steps in their evolution and a review of the supporting molecular and morphological evidence. Phycologia 55, 79–103 (2016).CAS 

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

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer Verlag, 2016).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).Stief, P. Intracellular Nitrate Storage by Diatoms-Source data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.19790176.v1 (2022). More

An experiment in secondary forests in the Democratic Republic of the Congo finds that calcium, an overlooked soil nutrient, is scarcer than phosphorus, and represents a potentially greater limitation on tropical forest growth.Ecology can reveal distributional patterns and dynamics in nature. One approach used is studying the elemental composition of plants, which has been linked to ecological processes such as growth, diversity or water use efficiency. More recently, elemental composition has been detected as a cofactor in governing the carbon sink capacity of plants, and therefore climate change mitigation1,2,3. This discovery has added an extra layer of urgency to the field, which now aims to better understand and predict global change. The study of nitrogen and/or phosphorus has until now received most of the attention of plant ecologists: nitrogen is the most abundant element in dry leaves after hydrogen and carbon, forms the main structure of proteins and is strongly linked to photosynthesis4. Phosphorus represents around one-tenth of nitrogen abundance in leaves and is key in energy storage and nucleic acids. However, although these represent only two of the many chemical elements that are in flux throughout ecosystems, whether others may have a dominant role in ecosystem dynamics is an open question. Writing in Nature Ecology & Evolution, Bauters et al.5 share some evidence to motivate broadening out from the dominant focus on nitrogen and phosphorus in terrestrial ecology, by revealing a crucial limiting role of calcium in the dynamics of tropical forest succession. More

Sabkha soil analysisSoil biochemical analysis shows higher concentration of salt in the middle (average of 200 ppt) which decreases to 20 ppt outside of the sabkha (Fig. 1B). Soil conductivity and total dissolved solids (TDS) were positively correlated with salinity (r = 0.8, p  More

Reich, P. B. et al. The evolution of plant functional variation: traits, spectra, and strategies. Int. J. Plant Sci. 164, S143–S164 (2003).
Google Scholar 
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).
Google Scholar 
Liu, X. J. & Ma, K. P. Plant functional traits concepts, applications and future directions. Sci. Sin. Vitae 45, 325–339 (2015).
Google Scholar 
Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).
Google Scholar 
Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl Acad. Sci. USA 112, 797–802 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Barton, K. E. Tougher and thornier: general patterns in the induction of physical defence traits. Func. Ecol. 30, 181–187 (2016).
Google Scholar 
Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).PubMed 

Google Scholar 
Wright, S. J. et al. Functional traits and the growth–mortality trade-off in tropical trees. Ecology 91, 3664–3674 (2010).PubMed 

Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Ruiz-Jaen, M. C. & Potvin, C. Can we predict carbon stocks in tropical ecosystems from tree diversity? Comparing species and functional diversity in a plantation and a natural forest. New Phytol. 189, 978–987 (2011).PubMed 

Google Scholar 
Grubb, P. J. A positive distrust in simplicity-lessons from plant defences and from competition among plants and among animals. J. Ecol. 80, 585–610 (1992).
Google Scholar 
Hanley, M. E., Lamont, B. B., Fairbanks, M. M. & Rafferty, C. M. Plant structural traits and their role in anti-herbivore defence. Perspect. Plant Ecol. 8, 157–178 (2007).
Google Scholar 
Burns, K. C. Spinescence in the New Zealand flora: parallels with Australia. N. Z. J. Bot. 54, 273–289 (2016).
Google Scholar 
Goheen, J. R., Young, T. P., Keesing, F. & Palmer, T. M. Consequences of herbivory by native ungulates for the reproduction of a savanna tree. J. Ecol. 95, 129–138 (2007).
Google Scholar 
Goldel, B., Kissling, W. D. & Svenning, J.-C. Geographical variation and environmental correlates of functional trait distributions in palms (Arecaceae) across the New World. Bot. J. Linn. Soc. 179, 602–617 (2015).
Google Scholar 
Alves-Silva, E. & Del-Claro, K. Herbivory causes increases in leaf spinescence and fluctuating asymmetry as a mechanism of delayed induced resistance in a tropical savanna tree. Plant Ecol. Evol. 149, 73–80 (2016).
Google Scholar 
Cooper, S. M. & Ginnett, T. F. Spines protect plants against browsing by small climbing mammals. Oecologia 113, 219–221 (1998).ADS 
PubMed 

Google Scholar 
Charles-Dominique, T. et al. Spiny plants, mammal browsers, and the origin of African savannas. Proc. Natl Acad. Sci. USA 113, E5572–E5579 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ratnam, J., Tomlinson, K. W., Rasquinha, D. N. & Sankaran, M. Savannahs of Asia: antiquity, biogeography, and an uncertain future. Philos. Trans. R. Soc. B. 371, 20150305 (2016).
Google Scholar 
Scholes, R. & Archer, S. Tree-grass interactions in savannas. Annu. Rev. Ecol. Syst. 28, 517–544 (1997).
Google Scholar 
Cerling, T. E. Development of grasslands and savannas in East Africa during the Neogene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 97, 241–247 (1992).
Google Scholar 
Brown, R. W. Additions to the flora of the Green River formation. U. S. Geol. Surv. Prof. Paper, U. S. Gov. Print. Off. 154-J, 279–292 (1929).Manchester, S. Oligocene fossil plants of the John Day Formation, Oregon. Or. Geol. 49, 115d–127d (1987).
Google Scholar 
Meyer, H. W. & Manchester, S. R. Oligocene Bridge Creek flora of the John Day Formation, Oregon (Univ. California Press, 1997).Lancucka-Srodoniowa, M. Tortonian flora from the “Gdow Bay” in the south of Poland. Acta Palaeobot. 7, 1–134 (1966).
Google Scholar 
Yuan, J. et al. Rapid drift of the Tethyan Himalaya terrane before two-stage India-Asia collision. Natl Sci. Rev. 8, nwaa173 (2021).PubMed 

Google Scholar 
Spicer, R. A. et al. Why the ‘Uplift of the Tibetan Plateau’is a myth. Natl Sci. Rev. 8, nwaa091 (2021).PubMed 

Google Scholar 
Spicer, R. A. Tibet, the Himalaya, Asian monsoons and biodiversity–In what ways are they related? Plant Divers. 39, 233–244 (2017).PubMed 
PubMed Central 

Google Scholar 
DeCelles, P. G., Kapp, P., Gehrels, G. E. & Ding, L. Paleocene-Eocene foreland basin evolution in the Himalaya of southern Tibet and Nepal: implications for the age of initial India-Asia collision. Tectonics 33, 824–849 (2014).ADS 

Google Scholar 
Royden, L. H., Burchfiel, B. C. & van der Hilst, R. D. The geological evolution of the Tibetan Plateau. Science 321, 1054–1058 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Deng, T., Wu, F. X., Zhou, Z. K. & Su, T. Tibetan Plateau: an evolutionary junction for the history of modern biodiversity. Sci. China Earth Sci. 63, 172–187 (2020).ADS 

Google Scholar 
Favre, A. et al. The role of the uplift of the Qinghai‐Tibetan Plateau for the evolution of Tibetan biotas. Biol. Rev. 90, 236–253 (2015).PubMed 

Google Scholar 
Su, T. et al. A Middle Eocene lowland humid subtropical “Shangri-La” ecosystem in central Tibet. Proc. Natl Acad. Sci. USA 117, 32989–32995 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scientific Expedition Team to the Qinghai-Xizang Plateau. Vegetation of Xizang (Tibet) (Sci. Press, 1988).Liu. X. H. Paleoelevation History and Evolution of the Cenozoic Lunpola basin, Central Tibet. Doctoral thesis (Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 2018).Xiong, Z. Y. et al. The rise and demise of the Paleogene Central Tibetan Valley. Sci. Adv. 8, eabj0944 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Reichgelt, T., West, C. K. & Greenwood, D. R. The relation between global palm distribution and climate. Sci. Rep. 8, 4721 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Farnsworth, A. et al. Paleoclimate model-derived thermal lapse rates: towards increasing precision in paleoaltimetry studies. Earth Planet. Sci. Lett. 564, 116903 (2021).CAS 

Google Scholar 
Spicer, R. A. et al. Why do foliar physiognomic climate estimates sometimes differ from those observed? Insights from taphonomic information loss and a CLAMP case study from the Ganges Delta. Palaeogeogr. Palaeoclimatol. Palaeoecol. 302, 381–395 (2011).
Google Scholar 
Walter, H. Vegetation of the Earth and Ecological Systems of the Geo-biosphere (Springer Berlin Heidelb., 1973).Burley, J. Encyclopedia of Forest Sciences (Acad. Press, 2004).Deng, T. et al. A mammalian fossil from the Dingqing Formation in the Lunpola Basin, northern Tibet, and its relevance to age and paleo-altimetry. Sci. Bull. 57, 261–269 (2012).CAS 

Google Scholar 
Ma, P. F. et al. Late Oligocene-early Miocene evolution of the Lunpola Basin, central Tibetan Plateau, evidences from successive lacustrine records. Gondwana Res. 48, 224–236 (2017).ADS 

Google Scholar 
Hempson, G. P., Archibald, S. & Bond, W. J. A continent-wide assessment of the form and intensity of large mammal herbivory in Africa. Science 350, 1056–1061 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Spicer, R. A. The formation and interpretation of plant fossil assemblages. Adv. Bot. Res. 16, 95–191 (1989).
Google Scholar 
Gibson, D. J. Grasses and Grassland Ecology (Oxford Univ. Press, 2009).Eltringham, S. K. The Hippos: Natural History and Conservation (Princeton Univ. Press, 1999).Jiang, H. et al. Oligocene Koelreuteria (Sapindaceae) from the Lunpola Basin in central Tibet and its implication for early diversification of the genus. J. Asian Earth Sci. 175, 99–108 (2019).ADS 

Google Scholar 
Liu, J. et al. Biotic interchange through lowlands of Tibetan Plateau suture zones during Paleogene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 524, 33–40 (2019).
Google Scholar 
Jia, L. B. et al. First fossil record of Cedrelospermum (Ulmaceae) from the Qinghai-Tibetan Plateau: implications for morphological evolution and biogeography. J. Syst. Evol. 57, 94–104 (2019).
Google Scholar 
Su, T. et al. No high Tibetan Plateau until the Neogene. Sci. Adv. 5, eaav2189 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, Y. L., Li, B. Y. & Zheng, D. A discussion on the boundary and area of the Tibetan Plateau in China. Geol. Res. 21, 1–8 (2002).
Google Scholar 
Yao, T. D. et al. From Tibetan Plateau to Third Pole and Pan-Third Pole. Bull. Chin. Acad. Sci. 32, 924–931 (2017).
Google Scholar 
Spicer, R. A., Farnsworth, A. & Su, T. Cenozoic topography, monsoons and biodiversity conservation within the Tibetan Region: an evolving story. Plant Divers. 42, 229–254 (2020).PubMed 
PubMed Central 

Google Scholar 
Liu, X. H., Xu, Q. & Ding, L. Differential surface uplift: Cenozoic paleoelevation history of the Tibetan Plateau. Sci. China Earth Sci. 59, 2105–2120 (2016).ADS 
CAS 

Google Scholar 
Ding, L., Li, Z. Y. & Song, P. P. Core fragments of Tibetan Plateau from Gondwanaland united in Northern Hemisphere. Bull. Chin. Acad. Sci. 32, 945–950 (2017).
Google Scholar 
Deng, T. & Ding, L. Paleoaltimetry reconstructions of the Tibetan Plateau: progress and contradictions. Natl Sci. Rev. 2, 417–437 (2015).CAS 

Google Scholar 
Li, S. F. et al. Orographic evolution of northern Tibet shaped vegetation and plant diversity in eastern Asia. Sci. Adv. 7, eabc7741 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ding, L. et al. The Andean-type Gangdese Mountains: Paleoelevation record from the Paleocene–Eocene Linzhou Basin. Earth Planet. Sci. Lett. 392, 250–264 (2014).ADS 
CAS 

Google Scholar 
Deng, T. et al. Review: implications of vertebrate fossils for paleo-elevations of the Tibetan Plateau. Glob. Planet. Change 174, 58–69 (2019).ADS 

Google Scholar 
Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Hopkins, W. G. Introduction to Plant Physiology (John Wiley & Sons, 1999).Sun, J. M., Liu, W. G., Liu, Z. H. & Fu, B. H. Effects of the uplift of the Tibetan Plateau and retreat of Neotethys ocean on the stepwise aridification of mid-latitude Asian interior. Bull. Chin. Acad. Sci. 32, 951–958 (2017).
Google Scholar 
Zong, G. F. Cenezoic Mammals and Environment of Hengduan Mountains Region (China Ocean Press, 1996).Deng, T. et al. An Oligocene giant rhino provides insights into Paraceratherium evolution. Commun. Biol. 4, 639 (2021).PubMed 
PubMed Central 

Google Scholar 
Young, T. P., Stanton, M. L. & Christian, C. E. Effects of natural and simulated herbivory on spine lengths of Acacia drepanolobium in Kenya. Oikos 101, 171–179 (2003).
Google Scholar 
Karban, R. & Myers, J. H. Induced plant responses to herbivory. Annu. Rev. Ecol. Syst. 20, 331–348 (1989).
Google Scholar 
Huntly, N. Herbivores and the dynamics of communities and ecosystems. Annu. Rev. Ecol. Syst. 22, 477–503 (1991).
Google Scholar 
Asner, G. P. et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proc. Natl Acad. Sci. USA 106, 4947–4952 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sankaran, M., Augustine, D. J. & Ratnam, J. Native ungulates of diverse body sizes collectively regulate long‐term woody plant demography and structure of a semi‐arid savanna. J. Ecol. 101, 1389–1399 (2013).
Google Scholar 
Staver, A. C. & Bond, W. J. Is there a ‘browse trap’? Dynamics of herbivore impacts on trees and grasses in an African savanna. J. Ecol. 102, 595–602 (2014).
Google Scholar 
Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Spicer, R. A. et al. The topographic evolution of the Tibetan Region as revealed by palaeontology. Palaeobio. Palaeoenv. 101, 213–243 (2021).
Google Scholar 
Rowley, D. B. & Currie, B. S. Palaeo-altimetry of the late Eocene to Miocene Lunpola basin, central Tibet. Nature 439, 677–681 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Sun, J. M. et al. Palynological evidence for the latest Oligocene-early Miocene paleoelevation estimate in the Lunpola Basin, central Tibet. Palaeogeogr. Palaeoclimatol. Palaeoecol. 399, 21–30 (2014).
Google Scholar 
DeCelles, P. G., Kapp, P., Ding, L. & Gehrels, G. E. Late Cretaceous to middle Tertiary basin evolution in the central Tibetan Plateau: Changing environments in response to tectonic partitioning, aridification, and regional elevation gain. Geol. Soc. Am. Bull. 119, 654–680 (2007).ADS 

Google Scholar 
Tang, H. et al. Extinct genus Lagokarpos reveals a biogeographic connection between Tibet and other regions in the Northern Hemisphere during the Paleogene. J. Syst. Evol. 57, 670–677 (2019).
Google Scholar 
Wang, T. X. et al. Fossil fruits of Illigera (Hernandiaceae) from the Eocene of central Tibetan Plateau. J. Syst. Evol. 59, 1276–1286 (2021).
Google Scholar 
Del Rio, C. et al. Asclepiadospermum gen. nov., the earliest fossil record of Asclepiadoideae (Apocynaceae) from the early Eocene of central Qinghai-Tibetan Plateau, and its biogeographic implications. Am. J. Bot. 107, 126–138 (2020).PubMed 

Google Scholar 
Xu, Z. Y. The Tertiary and its petroleum potential in the Lunpola Basin, Tibet. Oil Gas. Geol. 1, 153–158 (1980).
Google Scholar 
Zhang, K. X. et al. Paleogene-Neogene stratigraphic realm and sedimentary sequence of the Qinghai-Tibet Plateau and their response to uplift of the plateau. Sci. China Earth Sci. 53, 1271–1294 (2010).ADS 

Google Scholar 
Wu, Y. F. & Chen, Y. Y. Fossil cyprinid fishes from the late Tertiary of north Xizang, China. Vertebrata Palasiat. 18, 15–20 (1980).
Google Scholar 
Wu, F. X., Miao, D. S., Chang, M. M., Shi, G. L. & Wang, N. Fossil climbing perch and associated plant megafossils indicate a warm and wet central Tibet during the late Oligocene. Sci. Rep. 7, 878 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cai, C. Y., Huang, D. Y., Wu, F. X., Zhao, M. & Wang, N. Tertiary water striders (Hemiptera, Gerromorpha, Gerridae) from the central Tibetan Plateau and their palaeobiogeographic implications. J. Asian Earth Sci. 175, 121–127 (2017).ADS 

Google Scholar 
Low, S. L. et al. Oligocene Limnobiophyllum (Araceae) from the central Tibetan Plateau and its evolutionary and palaeoenvironmental implications. J. Syst. Palaeontol. 18, 415–431 (2020).
Google Scholar 
Bell, A. D. & Bryan, A. Plant Form: An Illustrated Guide to Flowering Plant Morphology (Timber Press, 2008).Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics. 24, 129–131 (2008).CAS 
PubMed 

Google Scholar 
Maddison, W. P. Confounding asymmetries in evolutionary diversification and character change. Evolution 60, 1743–1746 (2006).PubMed 

Google Scholar 
Forest, C. E., Molnar, P. & Emanuel, K. A. Palaeoaltimetry from energy conservation principles. Nature 374, 347–350 (1995).ADS 
CAS 

Google Scholar 
Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@ Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).ADS 
CAS 

Google Scholar 
Gough, D. O. Solar interior structure and luminosity variations. Sol. Phys. 74, 21–34 (1981).ADS 
CAS 

Google Scholar 
Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cox, P. M. Description of the “TRIFFID” Dynamic Global Vegetation Model. 1–16 (Met Office Hadley Centre, 2001).Cox, P., Huntingford, C. & Harding, R. A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J. Hydrol. 212, 79–94 (1998).ADS 

Google Scholar 
McInerney, F. A., Strömberg, C. A. E. & White, J. W. C. The Neogene transition from C3 to C4 grasslands in North America stable carbon isotope ratios of fossil phytoliths. Paleobiology 37, 23–49 (2011).
Google Scholar 
Lu, H. Y. et al. Phytoliths as quantitative indicators for the reconstruction of past environmental conditions in China II: palaeoenvironmental reconstruction in the Loess Plateau. Quat. Sci. Rev. 25, 945–959 (2006).ADS 

Google Scholar  More