Philippa Kaur

Morphological distribution of alpine meadow rootsAs shown in Fig. 2, in XM, the alpine meadow roots are mainly distributed at the level of 0–20 cm. The distribution of alpine meadow roots is wider than that of CM, which is mainly distributed from 0 to 40 cm. The alpine meadow roots in XM have a larger surface area, a larger projected area, and a larger volume than that in CM. In XM, the distribution law of alpine meadow roots is horizontal divergence, while the distribution of alpine meadow roots in CM shows vertical extension, but the alpine meadow roots all decrease gradually from the top to the bottom in the XM and CM. At the same soil depth, the total length, total surface area, total projected area, and total volume of alpine meadow roots in XM are larger than those in CM.Figure 2Distribution of alpine meadow roots at CM and XM. The figure is generated with use of Excel 2019 (https://www.microsoft.com/zh-cn/microsoft-365/excel).Full size imageAccording to the diameter of the roots, the alpine meadow roots in the Nagqu River Basin can be divided into three types: 0–0.5, 0.5–1, and 1–1.5 mm (Figs. 3 and 4). In CM and XM the length of the alpine meadow roots decreases with the increase in the soil depth. Additionally, at a soil depth of 0–60 cm, roots with a length of 0–0.5 mm account for the largest proportion, while roots with a length of 1–1.5 mm account for the smallest proportion. With the increase in soil depth, the proportion of the roots with lengths of 0.5–1 and 1–1.5 mm roots gradually decreases or even disappears, while the proportion with a length of 0–0.5 mm gradually increases.Figure 3Distribution of root length, surface area, projected area and volume. The figure is generated with use of Excel 2019 (https://www.microsoft.com/zh-cn/microsoft-365/excel).Full size imageFigure 4Distribution and ratio of root length, surface area, projected area and volume. The figure is generated with use of Excel 2019 (https://www.microsoft.com/zh-cn/microsoft-365/excel).Full size imageAs shown in Figs. 3 and 4, the surface area and the projected area of the alpine meadow roots in the Nagqu River Basin experience similar changes with depth. In CM, with the increase in soil depth, the proportion of roots with a surface area of 0–0.5 mm gradually increases and the proportion of roots with a surface area of 0.5–1 and 1–1.5 mm gradually decreases. In the 10–15 cm soil, roots with a surface area of 0.5–1 mm account for the largest proportion in CM; meanwhile, in XM the roots with a surface area of 0–0.5 mm account for the largest proportion in the 10–20 cm soil.In CM, the roots with a volume of 0.5–1 mm roots account for the largest proportion in the 10–20 cm soil. However, the roots with a volume of 0.5–1 mm account for the largest proportion in the 10–15 cm soil, while the roots with a volume of 0–0.5 mm account for the largest proportion in the 15–20 cm soil.In short, with the increase in soil depth, the length, surface area, projected area, and volume of the alpine meadow roots gradually decrease in the Nagqu River Basin, the proportion of 0–0.5 mm roots increases while the proportion of 0.5–1 mm roots decreases. The distribution of alpine meadow roots in CM shows a vertical extension, while in XM it shows horizontal divergence. In addition, compared with CM, the total length, total surface area, and total volume of 0–0.5 mm roots in XM increase by 20.95 cm, 1.90 cm2, and 0.014 cm3, and the corresponding specific gravity increases by 9.09%, 13.50%, and 12.14%. The total length, total surface area, and total volume of 0.5–1 and 1–1.5 mm roots in XM show smaller changes, and the corresponding specific gravity decreases.Distribution of soil moisture and nutrientsAs shown in Fig. 5, at the same soil depth, compared with LFM, the moisture in the 0–20, 20–40, and 40–60 cm soil in HFM was reduced by 30.74%, 52.89%, and 47.52%, and even the maximum soil moisture in the HFM was lower than that in the minimum soil moisture in LFM.Figure 5Soil moisture distribution in LFM and HFM. The figure is generated with use of Excel 2019 (https://www.microsoft.com/zh-cn/microsoft-365/excel).Full size imageIn LFM, as the soil depth increases, the soil moisture gradually increases. The 10–20 cm soil had the lowest moisture content in LFM of around 9.11%, the 20–40 cm soil moisture increased by 27.4%, and the 40–60 cm soil moisture increased by 45.9%. In HFM, the moisture content was 6.31% in the 0–20 cm soil. The moisture was the highest in the 40–60 cm soil. Compared with the 0–20 cm, the moisture was increased by 10.6%. The moisture was the lowest in the 20–40 cm soil, being reduced by 13.3% compared to the 0–20 cm soil. Therefore, HFM and LFM have different soil moisture distributions. In the 0–60 cm soil layer of HFM, the middle soil (20–40 cm) had a lower moisture content, while the surface (0–20 cm) and deep soil layers (40–60 cm) had higher moisture contents.In contrast to the distribution of soil moisture, the distribution of soil nutrients in HFM and LFM was the same: the soil nutrients gradually decreased from the surface to the bottom (Fig. 6). In LFM, the 0–20 cm-depth soil had the highest nutrient content, and the available phosphorous (AP), hydrolysable nitrogen (HN), available K (AK), and microbial biomass carbon (MBC) contents were 2.7, 109.83, 140.11, and 149.38 mg/kg, respectively. With the increase in soil depth, compared with the 0–20 cm soil, the contents of AP, HN, AK, and MBC in the 20–40 cm-depth soil were reduced by 33.33%, 33.44%, 5.45%, and 55.64%, while the content of AP, HN, AK, and MBC in the 40–60 cm-depth soil were reduced by 31.48%, 31.83%, 11.13%, and 66.28%, respectively. In HFM, the nutrient content in the 0–20 cm soil layer was also the largest, and the AP, HN, AK, and MBC has contents of 3.7, 86.17, 107.42, and 120.11 mg/kg, respectively. Compared with the 0–20 cm soil, the contents of AP, HN, AK, and MBC in the 20–40 cm-depth soil decreased by 43.24%, 29.11%, 27.07%, and 60.26%, respectively, while the contents of AP, HN, AK, and MBC in the 40–60 cm-depth soil decreased by 64.86%, 82.79%, 53.04%, and 83.88%, respectively.Figure 6Soil nutrients distribution in LFM and HFM, HN is hydrolysable nitrogen, AP is available phosphorus, AK is available K, MBC is microbial biomass carbon. The figure is generated with use of Excel 2019 (https://www.microsoft.com/zh-cn/microsoft-365/excel).Full size imageMeanwhile, in the same depth of soil, in LFM the content of HN, AK, and MBC is greater than that in HFM. Compared with the LFM, the contents of HN, AK, and MBC in the 0–20 cm soil layer in HFM are reduced by 21.54%, 23.33%, and 19.59%; the HN, AK, and MBC in the 20–40 cm are decreased by 16.43%, 40.86%, and 27.98%; and the HN, AK, and MBC in the 40–60 cm decreased by 80.19%, 59.49%, 61.56%, respectively. However, the AP in the 0–20 and 20–40 cm depths in the HFM is greater than in the LFM. It may be that the higher FTCF causes more damage to Bradyrhizobium, Mesorhizobium, and Pseudomonas in the soil of the Nagqu River Basin, the competitiveness of Bacillus decreases and the abundance increases, while the phosphate-dissolving ability of Bacillus may lead to an increase in the phosphorus content in the soil26.Correlation analysisAs shown in Fig. 7, the NFTC, FTCD, FTCF, and daily average temperature difference (DATD) all have a significant negative correlation with the top soil moisture in the Nagqu River Basin. This shows that the top soil moisture is not just affected by repeated FTC. Additionally, the effect of FTC on the top soil moisture is not transient; the longer FTC exists, the greater the impact on the top soil moisture will be.Figure 7Correlation between FTCF and top soil moisture and nutrients content, HN is hydrolysable nitrogen, AP is available phosphorus, AK is available K, MBC is microbial biomass carbon, SM is soil moisture, FTCF is the freeze–thaw cycle frequency, FTCD is the number of freezing–thawing cycle days, NFTC is the number of freezing–thawing cycles, DATD is the daily average temperature difference, and *indicate the correlation coefficient is statistically significant at the P = 0.05 level. The figure is generated with use of R language 3.6.3 (https://www.r-project.org/) and Visio 2019 (https://www.microsoft.com/zh-cn/microsoft-365/visio/flowchart-software).Full size imageMeanwhile, the contents of HN, AK, and MBC in the top soil have no obvious correlation with NFTC and FTCD but have a significant negative correlation with FTCF. This shows that, compared with NFTC and FTCD, FTCF is more suitable for measuring the influence of FTC characteristics on soil properties. With the increase in FTCF, the damage to the soil structure increases and the contents of HN, AK, and MBC in the top soil significantly decrease, but the AP shows different changes under the influence of microorganisms28. More

Sun, J. et al. Synchronous turnover of flora, fauna, and climate at the Eocene-Oligocene Boundary in Asia. Sci. Rep. 4, 7463 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tiffney, B. H. Perspectives on the origin of the floristic similarity between eastern Asia and eastern North America. J. Arnold Arbor. Harv. Univ. 66, 73–94 (1985).
Google Scholar 
Tiffney, B. H. The Eocene North Atlantic land bridge: its importance in Tertiary and modern phytogeography of the Northern Hemisphere. J. Arnold Arbor. Harv. Univ. 66, 243–273 (1985).
Google Scholar 
Donoghue, M. J. A phylogenetic perspective on the distribution of plant diversity. Proc. Natl Acad. Sci. USA 105, 11549–11555 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edwards, E. J. et al. Convergence, consilience, and the evolution of temperate deciduous forests. Am. Nat. 190, S87–S104 (2017).PubMed 

Google Scholar 
Segovia, R. A. et al. Freezing and water availability structure the evolutionary diversity of trees across the Americas. Sci. Adv. 6, eaaz5373 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tiffney, B. H. & Manchester, S. R. The use of geological and paleontological evidence in evaluating plant phylogeographic hypotheses in the Northern Hemisphere Tertiary. Int. J. Plant Sci. 162, S3–S17 (2001).
Google Scholar 
Tierney, J. E. et al. Past climates inform our future. Science 370, eaay3701 (2020).CAS 
PubMed 

Google Scholar 
Axelrod, D. I. Biogeography of oaks in the Arcto-Tertiary province. Ann. Mo. Bot. Gard. 70, 629–657 (1983).
Google Scholar 
Axelrod, D. I., Ai-Shehbaz, I. & Raven, P. H. History of the Modern Flora of China. (Springer, 1996).Cavender-Bares, J. Diversification, adaptation, and community assembly of the American oaks (Quercus), a model clade for integrating ecology and evolution. N. Phytol. 221, 669–692 (2019).
Google Scholar 
Delcourt, H. R. & A., D. P. North American Terrestrial Vegetation. (Cambridge University Press, 2000).Olson, J. S., Watts, J. A. & Allison, L. J. Carbon in Live Vegetation of Major World Ecosystems (1983).Soepadmo, E. Flora Malesiana Series I. Vol. 7 (Noordhoff International Publishing, 1972).Vogt, K. A. et al. Review of root dynamics in forest ecosystems grouped by climate, climatic forest type and species. Plant Soil 187, 159–219 (1996).CAS 

Google Scholar 
Whitmore, T. C. Tropical Rain Forest of the Far East. (Oxford University Press, 1984).Zhu, H. Ecological and biogeographical studies on the tropical rain forest of south Yunnan, SW China with a special reference to its relation with rain forests of tropical Asia. J. Biogeogr. 24, 647–662 (1997).
Google Scholar 
Averill, C., Bhatnagar, J. M., Dietze, M. C., Pearse, W. D. & Kivlin, S. N. Global imprint of mycorrhizal fungi on whole-plant nutrient economics. Proc. Natl Acad. Sci. USA 116, 23163–23168 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, F., Kohler, A., Murat, C., Veneault-Fourrey, C. & Hibbett, D. S. Unearthing the roots of ectomycorrhizal symbioses. Nat. Rev. Microbiol. 14, 760–773 (2016).CAS 
PubMed 

Google Scholar 
Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis, 2nd edn. (Academic Press, 1997).Abrahamson, W. G. & Melika, G. Gall-inducing insects (Cynipinae) provide insights into plant systematic relationships. Am. J. Bot. 85, 111–111 (1998).
Google Scholar 
Raman, A. Nutritional diversity in gall-inducing insects and their evolutionary relationships with flowering plants. Int. J. Ecol. Environ. Sci. 22, 133–143 (1996).
Google Scholar 
Stone, G. N. et al. Extreme host plant conservatism during at least 20 million years of host plant pursuit by oak gallwasps. Evolution 63, 854–869 (2009).CAS 
PubMed 

Google Scholar 
Johnson, W. C. & Webb, T. The role of bluejays (Cyanocitta cristata L.) in the postglacial dispersal of fagaceous trees in eastern North America. J. Biogeogr. 16, 561–571 (1989).
Google Scholar 
Koenig, W. D. & Haydock, J. Oaks, acorns, and the geographical ecology of acorn woodpeckers. J. Biogeogr. 26, 159–165 (1999).
Google Scholar 
Payne, J. & Francis, C. M. A Field Guide to the Mammals of Borneo. (Sabah Society with World Wildlife Fund Malaysia, 1985).Steele, M. A. Oak Seed Dispersal. (The Johns Hopkins University Press, 2021).Vander Wall, S. B. The evolutionary ecology of nut dispersal. Bot. Rev. 67, 74–117 (2001).
Google Scholar 
Vander Wall, S. B. How plants manipulate the scatter-hoarding behaviour of seed-dispersing animals. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 365, 989–997 (2010).
Google Scholar 
Barrón, E. et al. in Oaks physiological ecology. Exploring the functional diversity of genus Quercus L. (eds. Eustaquio Gil-Pelegrín, José Javier Peguero-Pina, & Domingo Sancho-Knapik) 39–105 (Springer International Publishing, 2017).Crepet, W. L. & Nixon, K. C. Earliest megafossil evidence of Fagaceae: Phylogenetic and biogeographic implications. Am. J. Bot. 76, 842–855 (1989).
Google Scholar 
Denk, T. & Grimm, G. W. Significance of pollen characteristics for infrageneric classification and phylogeny in Quercus (Fagaceae). Int. J. Plant Sci. 170, 926–940 (2009).
Google Scholar 
Denk, T., Grímsson, F. & Zetter, R. Fagaceae from the early Oligocene of Central Europe: Persisting new world and emerging old world biogeographic links. Rev. Palaeobot. Palynol. 169, 7–20 (2012).
Google Scholar 
Grímsson, F., Grimm, G. W., Zetter, R. & Denk, T. Cretaceous and Paleogene Fagaceae from North America and Greenland: Evidence for a Late Cretaceous split between Fagus and the remaining Fagaceae. Acta Palaeobotanica 56, 247–305 (2016).
Google Scholar 
Jones, J. H. Evolution of the Fagaceae: the implications of foliar features. Ann. Mo. Bot. Gard. 73, 228–275 (1986).
Google Scholar 
Manchester, S. R. Biogeographical relationships of North American Tertiary floras. Ann. Mo. Bot. Gard. 86, 472–522 (1999).
Google Scholar 
Sadowski, E. M., Schmidt, A. R. & Denk, T. Staminate inflorescences with in situ pollen from Eocene Baltic amber reveal high diversity in Fagaceae (oak family). Willdenowia 50, 405–517 (2020).
Google Scholar 
Bouchal, J., Zetter, R., Grimsson, F. & Denk, T. Evolutionary trends and ecological differentiation in early Cenozoic Fagaceae of western North America. Am. J. Bot. 101, 1332–1349 (2014).PubMed 
PubMed Central 

Google Scholar 
Naryshkina, N. N. & Evstigneeva, T. A. Fagaceae in the Eocene palynoflora of the south of primorskii region: New data on taxonomy and morphology. Paleontol. J. 54, 429–439 (2020).
Google Scholar 
Sadowski, E.-M., Hammel, J. U. & Denk, T. Synchrotron X-ray imaging of a dichasium cupule of Castanopsis from Eocene Baltic amber. Am. J. Bot. 105, 2025–2036 (2018).PubMed 

Google Scholar 
Gandolfo, M. A., Nixon, K. C., Crepet, W. L. & Grimaldi, D. A. A late Cretaceous fagalean inflorescence preserved in amber from New Jersey. Am. J. Bot. 105, 1424–1435 (2018).PubMed 

Google Scholar 
Hipp, A. L. et al. Genomic landscape of the global oak phylogeny. N. Phytol. 226, 1198–1212 (2020).CAS 

Google Scholar 
Wilf, P., Nixon, K. C., Gandolfo, M. A. & Cuneo, N. R. Eocene Fagaceae from Patagonia and Gondwanan legacy in Asian rainforests. Science 364, eaaw5139 (2019).CAS 
PubMed 

Google Scholar 
Petit, R. J. & Hampe, A. Some evolutionary consequences of being a tree. Annu. Rev. Ecol., Evol. Syst. 37, 187–214 (2006).
Google Scholar 
Smith, S. A. & Donoghue, M. J. Rates of molecular evolution are linked to life history in flowering plants. Science 322, 86–89 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Boucher, F. C., Verboom, G. A., Musker, S. & Ellis, A. G. Plant size: A key determinant of diversification? N. Phytol. 216, 24–31 (2017).
Google Scholar 
Parins-Fukuchi, C., Stull, G. W. & Smith, S. A. Phylogenomic conflict coincides with rapid morphological innovation. Proc. Natl Acad. Sci. USA 118, e2023058118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Denk, T., Grimm, G. W., Manos, P. S., Deng, M. & Hipp, A. L. in Oaks Physiological Ecology. Exploring the Functional Diversity of Genus Quercus L. Vol. 7 Tree Physiology (eds. GilPelegrin, E., PegueroPina, J. J., & SanchoKnapik, D.) 13–38 (Springer International Publishing Ag, Gewerbestrasse 11, Cham, Ch-6330, Switzerland, 2017).Deng, M., Jiang, X. L., Hipp, A. L., Manos, P. S. & Hahn, M. Phylogeny and biogeography of East Asian evergreen oaks (Quercus section Cyclobalanopsis; Fagaceae): Insights into the Cenozoic history of evergreen broad-leaved forests in subtropical Asia. Mol. Phylogen. Evol. 119, 170–181 (2018).
Google Scholar 
Hipp, A. L. et al. Sympatric parallel diversification of major oak clades in the Americas and the origins of Mexican species diversity. N. Phytol. 217, 439–452 (2018).CAS 

Google Scholar 
Crowl, A. A. et al. Uncovering the genomic signature of ancient introgression between white oak lineages (Quercus). N. Phytol. 226, 1158–1170 (2020).CAS 

Google Scholar 
Hauser, D. A., Keuter, A., McVay, J. D., Hipp, A. L. & Manos, P. S. The evolution and diversification of the red oaks of the California Floristic Province (Quercus section Lobatae, series Agrifoliae). Am. J. Bot. 104, 1581–1595 (2017).PubMed 

Google Scholar 
McVay, J. D., Hauser, D., Hipp, A. L. & Manos, P. S. Phylogenomics reveals a complex evolutionary history of lobed-leaf white oaks in western North America. Genome 60, 733–742 (2017).PubMed 

Google Scholar 
McVay, J. D., Hipp, A. L. & Manos, P. S. A genetic legacy of introgression confounds phylogeny and biogeography in oaks. Proc. R. Soc. B. 284, 20170300 (2017).PubMed 
PubMed Central 

Google Scholar 
Manos, P. S., Doyle, J. J. & Nixon, K. C. Phylogeny, biogeography, and processes of molecular differentiation in Quercus subgenus Quercus (Fagaceae). Mol. Phylogen. Evol. 12, 333–349 (1999).CAS 

Google Scholar 
Pham, K. K., Hipp, A. L., Manos, P. S. & Cronn, R. C. A time and a place for everything: phylogenetic history and geography as joint predictors of oak plastome phylogeny. Genome 60, 720–732 (2017).PubMed 

Google Scholar 
Simeone, M. C. et al. Plastome data reveal multiple geographic origins of Quercus Group Ilex. PeerJ 4, e1897 (2016).PubMed 
PubMed Central 

Google Scholar 
Oh, S.-H. & Manos, P. S. Molecular phylogenetics and cupule evolution in Fagaceae as inferred from nuclear CRABS CLAW sequences. Taxon 57, 434–451 (2008).
Google Scholar 
Renne, P. R. et al. Time scales of critical events around the Cretaceous-Paleogene boundary. Science 339, 684–687 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Koenen, E. J. M. et al. The origin of the Legumes is a complex paleopolyploid phylogenomic tangle closely associated with the Cretaceous–Paleogene (K–Pg) mass extinction event. Syst. Biol. 70, 508–526 (2021).PubMed 
PubMed Central 

Google Scholar 
Wang, W. et al. Menispermaceae and the diversification of tropical rainforests near the Cretaceous-Paleogene boundary. N. Phytol. 195, 470–478 (2012).
Google Scholar 
Suh, A., Smeds, L. & Ellegren, H. The dynamics of incomplete lineage sorting across the ancient adaptive radiation of Neoavian birds. PLoS Biol. 13, e1002224 (2015).PubMed 
PubMed Central 

Google Scholar 
Feng, Y. J. et al. Phylogenomics reveals rapid, simultaneous diversification of three major clades of Gondwanan frogs at the Cretaceous-Paleogene boundary. Proc. Natl Acad. Sci. USA 114, E5864–E5870 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Alfaro, M. E. et al. Explosive diversification of marine fishes at the Cretaceous-Palaeogene boundary. Nat. Ecol. Evol. 2, 688–696 (2018).PubMed 

Google Scholar 
Meredith, R. W. et al. Impacts of the Cretaceous terrestrial revolution and KPg extinction on mammal diversification. Science 334, 521–524 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Martinez, I. & Gonzalez-Taboada, F. Seed dispersal patterns in a temperate forest during a mast event: performance of alternative dispersal kernels. Oecologia 159, 389–400 (2009).ADS 
PubMed 

Google Scholar 
Larson-Johnson, K. Phylogenetic investigation of the complex evolutionary history of dispersal mode and diversification rates across living and fossil Fagales. N. Phytol. 209, 418–435 (2016).CAS 

Google Scholar 
Xiang, X. G. et al. Large-scale phylogenetic analyses reveal fagalean diversification promoted by the interplay of diaspores and environments in the Paleogene. Perspect. Plant Ecol. Evol. Syst. 16, 101–110 (2014).
Google Scholar 
Casanovas-Vilar, I. et al. Oldest skeleton of a fossil flying squirrel casts new light on the phylogeny of the group. Elife 7, e39270 (2018).PubMed 
PubMed Central 

Google Scholar 
Huchon, D. et al. Rodent phylogeny and a timescale for the evolution of glires: Evidence from an extensive taxon sampling using three nuclear genes. Mol. Biol. Evol. 19, 1053–1065 (2002).CAS 
PubMed 

Google Scholar 
Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Roth, V. L. & Mercer, J. M. The Effects of Cenozoic Global Change on Squirrel Phylogeny. Science 299, 1568–1572 (2003).ADS 
PubMed 

Google Scholar 
Jonsson, K. A. et al. A supermatrix phylogeny of corvoid passerine birds (Ayes: Corvides). Mol. Phylogen. Evol. 94, 87–94 (2016).
Google Scholar 
Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Benz, B. W., Robbins, M. B. & Peterson, A. T. Evolutionary history of woodpeckers and allies (Aves: Picidae): Placing key taxa on the phylogenetic tree. Mol. Phylogen. Evol. 40, 389–399 (2006).CAS 

Google Scholar 
Lutzoni, F. et al. Contemporaneous radiations of fungi and plants linked to symbiosis. Nat. Commun. 9, 5451 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bonfante, P. & Genre, A. Mechanisms underlying beneficial plant-fungus interactions in mycorrhizal symbiosis. Nat. Commun. 1, 48 (2010).ADS 
PubMed 

Google Scholar 
Miyauchi, S. et al. Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nat. Commun. 11, 5125 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Varga, T. et al. Megaphylogeny resolves global patterns of mushroom evolution. Nat. Ecol. Evol. 3, 668–678 (2019).PubMed 
PubMed Central 

Google Scholar 
Yang, Y. Y., Qu, X. J., Zhang, R., Stull, G. W. & Yi, T. S. Plastid phylogenomic analyses of Fagales reveal signatures of conflict and ancient chloroplast capture. Mol. Phylogen. Evol. 163, 107232 (2021).
Google Scholar 
Whittemore, A. T. & Schaal, B. A. Interspecific gene flow in sympatric oaks. Proc. Natl Acad. Sci. USA 88, 2540–2544 (1991).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kremer, A. & Hipp, A. L. Oaks: an evolutionary success story. N. Phytol. 226, 987–1011 (2020).
Google Scholar 
Petit, R. et al. Chloroplast DNA variation in European white oaks phylogeography and patterns of diversity based on data from over 2600 populations. Ecol. Manag. 176, 595–599 (2003).
Google Scholar 
Petit, R. et al. Chloroplast DNA footprints of postglacial recolonization by oaks. Proc. Natl Acad. Sci. USA 94, 9996–10001 (1997).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Petit, R. J. & Excoffier, L. Gene flow and species delimitation. Trends Ecol. Evol. 24, 386–393 (2009).PubMed 

Google Scholar 
Premoli, A. C., Mathiasen, P., Cristina Acosta, M. & Ramos, V. A. Phylogeographically concordant chloroplast DNA divergence in sympatric Nothofagus s.s. How deep can it be? N. Phytol. 193, 261–275 (2012).CAS 

Google Scholar 
Tsuda, Y., Semerikov, V., Sebastiani, F., Vendramin, G. G. & Lascoux, M. Multispecies genetic structure and hybridization in the Betula genus across Eurasia. Mol. Ecol. 26, 589–605 (2017).PubMed 

Google Scholar 
Zhang, B. W. et al. Phylogenomics reveals an ancient hybrid origin of the persian walnut. Mol. Biol. Evol. 36, 2451–2461 (2019).CAS 

Google Scholar 
Bock, R. Witnessing genome evolution: experimental reconstruction of endosymbiotic and horizontal gene transfer. Annu. Rev. Genet. 51, 1–22 (2017).CAS 
PubMed 

Google Scholar 
Hill, W. G. Disequilibrium among several linked neutral genes in finite population: II. Variances and covariances of disequilibria. Theor. Popul. Biol. 6, 184–198 (1974).CAS 
PubMed 
MATH 

Google Scholar 
Huerta-Sanchez, E. et al. Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA. Nature 512, 194–197 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, Y. et al. Megabase-scale presence-absence variation with Tripsacum origin was under selection during maize domestication and adaptation. Genome Biol. 22, 237 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, X. et al. The history and evolution of the Denisovan-EPAS1 haplotype in Tibetans. Proc. Natl Acad. Sci. USA 118, e2020803118 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cavender-Bares, J., Gonzalez-Rodriguez, A., Pahlich, A., Koehler, K. & Deacon, N. Phylogeography and climatic niche evolution in live oaks (Quercus series Virentes) from the tropics to the temperate zone. J. Biogeogr. 38, 962–981 (2011).
Google Scholar 
Chen, D. et al. Phylogeography of Quercus variabilis based on chloroplast DNA sequence in East Asia: multiple glacial refugia and mainland-migrated island populations. PLoS ONE 7, e47268 (2012).ADS 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Leroy, T. et al. Adaptive introgression as a driver of local adaptation to climate in European white oaks. N. Phytol. 226, 1171–1182 (2020).
Google Scholar 
O’Donnell, S. T., Fitz-Gibbon, S. T. & Sork, V. L. Ancient introgression between distantly related White Oaks (Quercus sect. Quercus) shows evidence of climate-associated asymmetric gene exchange. J. Hered. 112, 663–670 (2021).PubMed 

Google Scholar 
Nagamitsu, T., Uchiyama, K., Izuno, A., Shimizu, H. & Nakanishi, A. Environment-dependent introgression from Quercus dentata to a coastal ecotype of Quercus mongolica var. crispula in northern Japan. N. Phytol. 226, 1018–1028 (2020).CAS 

Google Scholar 
Maxwell, L. M., Walsh, J., Olsen, B. J. & Kovach, A. I. Patterns of introgression vary within an avian hybrid zone. BMC Ecol. Evol. 21, 14 (2021).PubMed 
PubMed Central 

Google Scholar 
Hewitt, G. M. Hybrid zones-natural laboratories for evolutionary studies. Trends Ecol. Evol. 3, 158–167 (1988).CAS 
PubMed 

Google Scholar 
Gernandt, D. S., Resendiz Arias, C., Terrazas, T., Aguirre Dugua, X. & Willyard, A. Incorporating fossils into the Pinaceae tree of life. Am. J. Bot. 105, 1329–1344 (2018).CAS 
PubMed 

Google Scholar 
Rose, J. P., Toledo, C. A. P., Lemmon, E. M., Lemmon, A. R. & Sytsma, K. J. Out of sight, out of mind: Widespread nuclear and plastid-nuclear discordance in the flowering plant genus Polemonium (Polemoniaceae) suggests widespread historical gene flow despite limited nuclear signal. Syst. Biol. 70, 162–180 (2021).CAS 
PubMed 

Google Scholar 
Truffaut, L. et al. Fine-scale species distribution changes in a mixed oak stand over two successive generations. N. Phytol. 215, 126–139 (2017).CAS 

Google Scholar 
Petit, R. J., Bodénès, C., Ducousso, A., Roussel, G. & Kremer, A. Hybridization as a mechanism of invasion in oaks. N. Phytol. 161, 151–164 (2003).
Google Scholar 
Sork, V. L. et al. Phylogeny and introgression of California scrub White Oaks (Quercus section Quercus). Int. Oaks J. 27, 61–74 (2016).
Google Scholar 
Quang, N. D., Ikeda, S. & Harada, K. Nucleotide variation in Quercus crispula Blume. Heredity (Edinb.) 101, 166–174 (2008).CAS 

Google Scholar 
Graham, A. The role of land bridges, ancient environments, and migrations in the assembly of the North American flora. J. Syst. Evol. 56, 405–429 (2018).
Google Scholar 
Suarez-Gonzalez, A., Lexer, C. & Cronk, Q. C. B. Adaptive introgression: a plant perspective. Biol. Lett. 14, e47268 (2018).
Google Scholar 
Abbott, R. J., Barton, N. H. & Good, J. M. Genomics of hybridization and its evolutionary consequences. Mol. Ecol. 25, 2325–2332 (2016).PubMed 

Google Scholar 
Goulet, B. E., Roda, F. & Hopkins, R. Hybridization in plants: Old ideas, new techniques. Plant Physiol. 173, 65–78 (2017).CAS 
PubMed 

Google Scholar 
Mitchell, N. et al. Correlates of hybridization in plants. Evol. Lett. 3, 570–585 (2019).PubMed 
PubMed Central 

Google Scholar 
Payseur, B. A. & Rieseberg, L. H. A genomic perspective on hybridization and speciation. Mol. Ecol. 25, 2337–2360 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bodenes, C. et al. Comparative mapping in the Fagaceae and beyond with EST-SSRs. BMC Plant Biol. 12, 153 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cannon, C. H. & Petit, R. J. The oak syngameon: More than the sum of its parts. N. Phytol. 226, 978–983 (2020).
Google Scholar 
Chen, S. C., Cannon, C. H., Kua, C. S., Liu, J. J. & Galbraith, D. W. Genome size variation in the Fagaceae and its implications for trees. Tree Genet. Genom. 10, 977–988 (2014).
Google Scholar 
Kremer, A. et al. Genomics of Fagaceae. Tree Genet. Genom. 8, 583–610 (2012).
Google Scholar 
Neale, D. B., Martinez-Garcia, P. J., De La Torre, A. R., Montanari, S. & Wei, X. X. Novel insights into tree biology and genome evolution as revealed through genomics. Annu. Rev. Plant Biol. 68, 457–483 (2017).CAS 
PubMed 

Google Scholar 
Staton, M. et al. Substantial genome synteny preservation among woody angiosperm species: Comparative genomics of Chinese chestnut (Castanea mollissima) and plant reference genomes. BMC Genomics 16, 744 (2015).PubMed 
PubMed Central 

Google Scholar 
Manos, P. S. & Stanford, A. M. The historical biogeography of Fagaceae: tracking the tertiary history of temperate and subtropical forests of the Northern Hemisphere. Int. J. Plant Sci. 162, S77–S93 (2001).
Google Scholar 
Salojarvi, J. et al. Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch. Nat. Genet. 49, 904–912 (2017).CAS 
PubMed 

Google Scholar 
Mishra, B. et al. A reference genome of the European beech (Fagus sylvatica L.). Gigascience 7, giy063 (2018).ADS 
PubMed Central 

Google Scholar 
Xing, Y. et al. Hybrid de novo genome assembly of Chinese chestnut (Castanea mollissima). Gigascience 8, giz112 (2019).PubMed 
PubMed Central 

Google Scholar 
Sork, V. L. et al. High-quality genome and methylomes illustrate features underlying evolutionary success of oaks. Preprint at bioRxiv https://doi.org/10.1101/2021.04.09.439191 (2021).Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).PubMed 
PubMed Central 

Google Scholar 
Camacho, C. et al. BLAST plus: architecture and applications. BMC Bioinforma. 10, 421 (2009).
Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at http://arxiv.org/abs/1303.3997v2 (2013).McKenna, A. et al. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bertels, F., Silander, O. K., Pachkov, M., Rainey, P. B. & van Nimwegen, E. Automated reconstruction of whole-genome phylogenies from short-sequence reads. Mol. Biol. Evol. 31, 1077–1088 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Haudry, A. et al. An atlas of over 90,000 conserved noncoding sequences provides insight into crucifer regulatory regions. Nat. Genet. 45, 891–898 (2013).CAS 
PubMed 

Google Scholar 
Hupalo, D. & Kern, A. D. Conservation and functional element discovery in 20 angiosperm plant genomes. Mol. Biol. Evol. 30, 1729–1744 (2013).CAS 
PubMed 

Google Scholar 
Dierckxsens, N., Mardulyn, P. & Smits, G. NOVOPlasty: De novo assembly of organelle genomes from whole genome data. Nucleic Acids Res. 45, e18 (2017).PubMed 

Google Scholar 
Qu, X. J., Moore, M. J., Li, D. Z. & Yi, T. S. PGA: A software package for rapid, accurate, and flexible batch annotation of plastomes. Plant Methods 15, 50 (2019).PubMed 
PubMed Central 

Google Scholar 
Kearse, M. et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 

Google Scholar 
Katoh, K. & Standley, D. M. Mafft multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ronquist, F. et al. Mrbayes 3.2: Efficient bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
PubMed Central 

Google Scholar 
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. Partitionfinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).CAS 
PubMed 

Google Scholar 
Akaike, H. New look at statistical-model identification. Ieee Trans. Autom. Control AC19, 716–723 (1974).ADS 
MathSciNet 
MATH 

Google Scholar 
Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: Polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinforma. 19, 153 (2018).
Google Scholar 
Chifman, J. & Kubatko, L. Quartet inference from SNP data under the coalescent model. Bioinformatics 30, 3317–3324 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
PAUP*. Phylogenetic analysis using parsimony (* and other methods). v. Version 4. (Sinauer Associates, Sunderland, 2003).Yang, Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
PubMed 

Google Scholar 
dos Reis, M. & Yang, Z. Approximate likelihood calculation on a phylogeny for bayesian estimation of divergence times. Mol. Biol. Evol. 28, 2161–2172 (2011).PubMed 

Google Scholar 
Chen, D. et al. Divergence time estimation of Galliformes based on the best gene shopping scheme of ultraconserved elements. BMC Ecol. Evol. 21, 209 (2021).PubMed 
PubMed Central 

Google Scholar 
Smith, S. A., Brown, J. W. & Walker, J. F. So many genes, so little time: A practical approach to divergence-time estimation in the genomic era. PLoS ONE 13, e0197433 (2018).PubMed 
PubMed Central 

Google Scholar 
Johns, C. A., Toussaint, E. F. A., Breinholt, J. W. & Kawahara, A. Y. Origin and macroevolution of micro-moths on sunken Hawaiian Islands. Proc. R. Soc. B. 285, 20181047 (2018).PubMed 
PubMed Central 

Google Scholar 
Rabosky, D. L. et al. BAMMtools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).
Google Scholar 
Salichos, L., Stamatakis, A. & Rokas, A. Novel information theory-based measures for quantifying incongruence among phylogenetic trees. Mol. Biol. Evol. 31, 1261–1271 (2014).CAS 
PubMed 

Google Scholar 
Smith, S. A., Moore, M. J., Brown, J. W. & Yang, Y. Analysis of phylogenomic datasets reveals conflict, concordance, and gene duplications with examples from animals and plants. BMC Evol. Biol. 15, 150 (2015).PubMed 
PubMed Central 

Google Scholar 
Xia, X. H., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylogen. Evol. 26, 1–7 (2003).CAS 

Google Scholar 
Xia, X. DAMBE7: New and improved tools for data analysis in molecular biology and evolution. Mol. Biol. Evol. 35, 1550–1552 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
McInerney, J. O. GCUA: general codon usage analysis. Bioinformatics 14, 372–373 (1998).CAS 
PubMed 

Google Scholar 
Folk, R. A., Mandel, J. R. & Freudenstein, J. V. Ancestral gene flow and parallel organellar genome capture result in extreme phylogenomic discord in a lineage of angiosperms. Syst. Biol. 66, 320–337 (2017).CAS 
PubMed 

Google Scholar 
Sukumaran, J. & Holder, M. T. DendroPy: A Python library for phylogenetic computing. Bioinformatics 26, 1569–1571 (2010).CAS 
PubMed 

Google Scholar 
Olave, M., Avila, L. J., Sites, J. W., Morando, M. & Freckleton, R. Detecting hybridization by likelihood calculation of gene tree extra lineages given explicit models. Methods Ecol. Evol. 9, 121–133 (2017).
Google Scholar 
Than, C. & Nakhleh, L. Species tree inference by minimizing deep coalescences. PLoS Comp. Biol. 5, e1000501 (2009).ADS 
MathSciNet 

Google Scholar 
Malinsky, M., Matschiner, M. & Svardal, H. Dsuite – Fast D-statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021).PubMed 

Google Scholar 
Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Solis-Lemus, C. & Ane, C. Inferring phylogenetic networks with maximum pseudolikelihood under incomplete lineage sorting. PLoS Genet. 12, e1005896 (2016).PubMed 
PubMed Central 

Google Scholar 
Solis-Lemus, C., Bastide, P. & Ane, C. Phylonetworks: A package for phylogenetic networks. Mol. Biol. Evol. 34, 3292–3298 (2017).CAS 
PubMed 

Google Scholar 
Hejase, H. A. & Liu, K. J. A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation. BMC Bioinforma. 17, 422 (2016).
Google Scholar 
Shen, X. X., Hittinger, C. T. & Rokas, A. Contentious relationships in phylogenomic studies can be driven by a handful of genes. Nat. Ecol. Evol. 1, 126 (2017).PubMed 
PubMed Central 

Google Scholar 
Browning, B. L. & Browning, S. R. Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194, 459–471 (2013).PubMed 
PubMed Central 

Google Scholar  More

.readcube-buybox { display: none !important;}

Some species of coral might be able to adapt to a world altered by climate change, at least if countries curb their greenhouse-gas emissions1.

Access options

Access through your institution

Change institution

Buy or subscribe

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

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-022-00719-x

ReferencesMcLachlan, R. H. et al. Sci. Rep. 12, 3712 (2022).PubMed 
Article 

Google Scholar 
Download references

Subjects

Ecology

Latest on:

Ecology

Discovery of a Ni2+-dependent guanidine hydrolase in bacteria
Article 09 MAR 22

Rewilding Argentina: lessons for the 2030 biodiversity targets
Comment 07 MAR 22

How itchy vicuñas remade a vast wilderness
Research Highlight 04 MAR 22

Jobs

Postdoctoral Research Fellow

Center for Genomic Medicine (CGM), MGH
Boston, MA, United States

wiss. Mitarbeiter/in (m/w/d)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Doctoral Candidate (PhD student) in Hybrid Satellite-Terrestrial Connectivity Solutions for Emerging IoT/MTC Systems

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
Luxembourg, Luxembourg

Research Associate (Postdoc) in Artificial Intelligence for Space Applications

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
Luxembourg, Luxembourg More

Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).CAS 

Google Scholar 
Davy, R. & Outten, S. The Arctic surface climate in CMIP6: status and developments since CMIP5. J. Clim. 33, 8047–8068 (2020).
Google Scholar 
Voigt, C. et al. Ecosystem carbon response of an Arctic peatland to simulated permafrost thaw. Glob. Change Biol. 25, 1746–1764 (2019).
Google Scholar 
Swindles, G. T. et al. The long-term fate of permafrost peatlands under rapid climate warming. Sci. Rep. 5, 17951 (2015).CAS 

Google Scholar 
Du, R. et al. The role of peat on permafrost thaw based on field observations. Catena 208, 105772 (2022).CAS 

Google Scholar 
Chaudhary, N. et al. Modelling past and future peatland carbon dynamics across the pan-Arctic. Glob. Change Biol. 26, 4119–4133 (2020).
Google Scholar 
Müller, J. & Joos, F. Committed and projected future changes in global peatlands—continued transient model simulations since the Last Glacial Maximum. Biogeosciences 18, 3657–3687 (2021).
Google Scholar 
Seppälä, M. Synthesis of studies of palsa formation underlining the importance of local environmental and physical characteristics. Quatern. Res. 75, 366–370 (2011).
Google Scholar 
Karjalainen, O. et al. High potential for loss of permafrost landforms in a changing climate. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/abafd5 (2020).Fan, X., Duan, Q., Shen, C., Wu, Y. & Xing, C. Global surface air temperatures in CMIP6: historical performance and future changes. Environ. Res. Lett. 15, 104056 (2020).
Google Scholar 
Tebaldi, C. et al. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn. 12, 253–293 (2021).
Google Scholar 
Zoltai, S. & Tarnocai, C. Properties of a wooded palsa in northern Manitoba. Arct. Alp. Res. 3, 115–129 (1971).
Google Scholar 
Minke, M., Donner, N., Karpov, N. S., de Klerk, P. & Joosten, H. Distribution, diversity, development and dynamics of polygon mires: examples from Northeast Yakutia (Siberia). Peatl. Int. 1, 36–40 (2007).
Google Scholar 
O’Neill, H. B., Wolfe, S. A. & Duchesne, C. New ground ice maps for Canada using a paleogeographic modelling approach. Cryosphere 13, 753–773 (2019).
Google Scholar 
Fronzek, S., Luoto, M. & Carter, T. R. Potential effect of climate change on the distribution of palsa mires in subarctic Fennoscandia. Clim. Res. 32, 1–12 (2006).
Google Scholar 
Luoto, M., Fronzek, S. & Zuidhoff, F. S. Spatial modelling of palsa mires in relation to climate in northern Europe. Earth Surf. Process. Landf. 29, 1373–1387 (2004).
Google Scholar 
Peregon, A., Maksyutov, S., Kosykh, N. P. & Mironycheva-Tokareva, N. P. Map-based inventory of wetland biomass and net primary production in Western Siberia. J. Geophys. Res. Biogeosci. 113, G01007 (2008).
Google Scholar 
Terentieva, I., Lapshina, E. D., Sabrekov, A. F., Maksyutov, S. S. & Glagolev, M. V. Mapping of West Siberian wetland complexes using landsat imagery: implications for methane emissions. Biogeosciences 13, 4615–4626 (2016).CAS 

Google Scholar 
Zoltai, S., Siltanen, R. M. & Johnson, J. D. A Wetland Data Base for the Western Boreal, Subarctic, and Arctic Regions of Canada (Natural Resources Canada, Canadian Forest Service, 2000).Fewster, R. E. et al. Drivers of Holocene palsa distribution in North America. Quat. Sci. Rev. https://doi.org/10.1016/j.quascirev.2020.106337 (2020).Parviainen, M. & Luoto, M. Climate envelopes of mire complex types in Fennoscandia. Geogr. Ann. A 89, 137–151 (2007).
Google Scholar 
Aalto, J., Harrison, S. & Luoto, M. Statistical modelling predicts almost complete loss of major periglacial processes in northern Europe by 2100. Nat. Commun. 8, 515 (2017).
Google Scholar 
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Google Scholar 
Brunner, L. et al. Reduced global warming from CMIP6 projections when weighting models by performance and independence. Earth Syst. Dyn. 11, 995–1012 (2020).
Google Scholar 
O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
Google Scholar 
Aalto, J., Venäläinen, A., Heikkinen, R. K. & Luoto, M. Potential for extreme loss in high‐latitude Earth surface processes due to climate change. Geophys. Res. Lett. 41, 3914–3924 (2014).
Google Scholar 
Halsey, L. A., Vitt, D. H. & Zoltai, S. C. Disequilibrium response of permafrost in boreal continental western Canada to climate change. Climatic Change 30, 57–73 (1995).
Google Scholar 
Camill, P. & Clark, J. S. Climate change disequilibrium of boreal permafrost peatlands caused by local processes. Am. Nat. 151, 207–222 (1998).CAS 

Google Scholar 
Borge, A. F., Westermann, S., Solheim, I. & Etzelmüller, B. Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years. Cryosphere 11, 1–16 (2017).
Google Scholar 
Åkerman, H. J. & Johansson, M. Thawing permafrost and thicker active layers in sub‐arctic Sweden. Permafr. Periglac. Process. 19, 279–292 (2008).
Google Scholar 
Olvmo, M., Holmer, B., Thorsson, S., Reese, H. & Lindberg, F. Sub-arctic palsa degradation and the role of climatic drivers in the largest coherent palsa mire complex in Sweden (Vissátvuopmi), 1955–2016. Sci. Rep. 10, 8937 (2020).CAS 

Google Scholar 
Payette, S., Delwaide, A., Caccianiga, M. & Beauchemin, M. Accelerated thawing of subarctic peatland permafrost over the last 50 years. Geophys. Res. Lett. 31, L18208 (2004).
Google Scholar 
Treat, C. C. et al. Effects of permafrost aggradation on peat properties as determined from a pan‐Arctic synthesis of plant macrofossils. J. Geophys. Res. Biogeosci. 121, 78–94 (2016).CAS 

Google Scholar 
Dearborn, K. D., Wallace, C. A., Patankar, R. & Baltzer, J. L. Permafrost thaw in boreal peatlands is rapidly altering forest community composition. J. Ecol. 109, 1452–1467 (2021).CAS 

Google Scholar 
Olefeldt, D. & Roulet, N. T. Effects of permafrost and hydrology on the composition and transport of dissolved organic carbon in a subarctic peatland complex. J. Geophys. Res. Biogeosci. 117, G01005 (2012).
Google Scholar 
Burd, K., Estop-Aragonés, C., Tank, S. E. & Olefeldt, D. Lability of dissolved organic carbon from boreal peatlands: interactions between permafrost thaw, wildfire, and season. Can. J. Soil Sci. 100, 503–515 (2020).
Google Scholar 
Klaminder, J., Yoo, K., Rydberg, J. & Giesler, R. An explorative study of mercury export from a thawing palsa mire. J. Geophys. Res. Biogeosci. 113, G04034 (2008).
Google Scholar 
Luoto, M. & Seppälä, M. Thermokarst ponds as indicators of the former distribution of palsas in Finnish Lapland. Permafr. Periglac. Process. 14, 19–27 (2003).
Google Scholar 
Vitt, D. H., Halsey, L. A. & Zoltai, S. C. The changing landscape of Canada’s western boreal forest: the current dynamics of permafrost. Can. J. For. Res. 30, 283–287 (2000).
Google Scholar 
Turetsky, M., Wieder, R., Vitt, D., Evans, R. & Scott, K. The disappearance of relict permafrost in boreal North America: effects on peatland carbon storage and fluxes. Glob. Change Biol. 13, 1922–1934 (2007).
Google Scholar 
Jorgenson, M. T. et al. Resilience and vulnerability of permafrost to climate change. Can. J. For. Res. 40, 1219–1236 (2010).
Google Scholar 
Magnússon, R. Í. et al. Rapid vegetation succession and coupled permafrost dynamics in Arctic thaw ponds in the Siberian lowland tundra. J. Geophys. Res. Biogeosci. 125, e2019JG005618 (2020).
Google Scholar 
Zoltai, S. Permafrost distribution in peatlands of west-central Canada during the Holocene warm period 6000 years bp. Géogr. Phys. Quat. 49, 45–54 (1995).
Google Scholar 
Seppälä, M. An expermental study of the formation of palsas. In Proc. 4th Canadian Permafrost Conference (ed. French, H. M.) 36–42 (National Research Council of Canada, Ottawa, 1982).Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, L13402 (2010).
Google Scholar 
Serreze, M. C. & Barry, R. G. Processes and impacts of Arctic amplification: a research synthesis. Glob. Planet. Change 77, 85–96 (2011).
Google Scholar 
Thurner, M. et al. Carbon stock and density of northern boreal and temperate forests. Glob. Ecol. Biogeogr. 23, 297–310 (2014).
Google Scholar 
Seppälä, M. The origin of palsas. Geogr. Ann. A 68, 141–147 (1986).
Google Scholar 
Mamet, S. D., Chun, K. P., Kershaw, G. G., Loranty, M. M. & Peter Kershaw, G. Recent increases in permafrost thaw rates and areal loss of palsas in the western Northwest Territories, Canada. Permafr. Periglac. Process. 28, 619–633 (2017).
Google Scholar 
Camill, P. Permafrost thaw accelerates in boreal peatlands during late-20th century climate warming. Climatic Change 68, 135–152 (2005).CAS 

Google Scholar 
Quinton, W. L. & Baltzer, J. The active-layer hydrology of a peat plateau with thawing permafrost (Scotty Creek, Canada). Hydrol. J. 21, 201–220 (2013).
Google Scholar 
Turetsky, M. R., Wieder, R. K. & Vitt, D. H. Boreal peatland C fluxes under varying permafrost regimes. Soil Biol. Biochem. 34, 907–912 (2002).CAS 

Google Scholar 
Schädel, C. et al. Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils. Nat. Clim. Change 6, 950–953 (2016).
Google Scholar 
Myers‐Smith, I. H. & Hik, D. S. Climate warming as a driver of tundra shrubline advance. J. Ecol. 106, 547–560 (2018).
Google Scholar 
Gibson, C. M. et al. Wildfire as a major driver of recent permafrost thaw in boreal peatlands. Nat. Commun. 9, 3041 (2018).
Google Scholar 
Gibson, C. M., Estop-Aragonés, C., Flannigan, M., Thompson, D. K. & Olefeldt, D. Increased deep soil respiration detected despite reduced overall respiration in permafrost peat plateaus following wildfire. Environ. Res. Lett. 14, 125001 (2019).CAS 

Google Scholar 
Estop-Aragonés, C. et al. Respiration of aged soil carbon during fall in permafrost peatlands enhanced by active layer deepening following wildfire but limited following thermokarst. Environ. Res. Lett. 13, 085002 (2018).
Google Scholar 
Treat, C. C. et al. Predicted vulnerability of carbon in permafrost peatlands with future climate change and permafrost thaw in western Canada. J. Geophys. Res. Biogeosci. 126, e2020JG005872 (2021).CAS 

Google Scholar 
Jones, M. C. et al. Rapid carbon loss and slow recovery following permafrost thaw in boreal peatlands. Glob. Change Biol. 23, 1109–1127 (2017).
Google Scholar 
Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).CAS 

Google Scholar 
Heffernan, L., Estop‐Aragonés, C., Knorr, K. H., Talbot, J. & Olefeldt, D. Long‐term impacts of permafrost thaw on carbon storage in peatlands: deep losses offset by surficial accumulation. J. Geophys. Res. Biogeosci. 125, e2019JG005501 (2020).CAS 

Google Scholar 
Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Change 8, 907–913 (2018).CAS 

Google Scholar 
Qiu, C. et al. The role of northern peatlands in the global carbon cycle for the 21st century. Glob. Ecol. Biogeogr. 29, 956–973 (2020).
Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Google Scholar 
Hugelius, G. et al. Maps of Northern Peatland Extent, Depth, Carbon Storage and Nitrogen Storage Version 1.0 (Bolin Centre for Climate Research, 2020); https://doi.org/10.17043/hugelius-2020Brownrigg, R. mapdata: Extra Map Databases. R version 2.3.0 https://CRAN.R-project.org/package=mapdata (2018).Olefeldt, D., Turetsky, M. R., Crill, P. M. & McGuire, A. D. Environmental and physical controls on northern terrestrial methane emissions across permafrost zones. Glob. Change Biol. 19, 589–603 (2013).
Google Scholar 
Pissart, A. Palsas, lithalsas and remnants of these periglacial mounds: a progress report. Prog. Phys. Geog. 26, 605–621 (2002).
Google Scholar 
Wolfe, S. A., Stevens, C. W., Gaanderse, A. J. & Oldenborger, G. A. Lithalsa distribution, morphology and landscape associations in the Great Slave Lowland, Northwest Territories, Canada. Geomorphology 204, 302–313 (2014).
Google Scholar 
Lara, M. J., Nitze, I., Grosse, G. & McGuire, A. D. Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska. Sci. Data 5, 180058 (2018).
Google Scholar 
Jorgenson, M. T. & Grunblatt, J. Landscape-Level Ecological Mapping of Northern Alaska and Field Site Photography (Arctic Landscape Conservation Cooperative, U.S. Fish & Wildlife Service, 2013).Xu, J., Morris, P. J., Liu, J. & Holden, J. PEATMAP: refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134–140 (2018).
Google Scholar 
ESRI. ArcMap v.10.6.1 (Environmental Systems Research Institute, 2018).Brown, J., Ferrians, O. Jr, Heginbottom, J. A. & Melnikov, E. Circum-Arctic Map of Permafrost and Ground-Ice Conditions (US Geological Survey, 1997).QGIS v.3.12 (QGIS Association, 2020); https://qgis.orgHugelius, G. et al. The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3 (2013).
Google Scholar 
Everett, K. R. in Developments in Soil Science Vol. 11 (eds Wilding, L. P. et al.) 1–53 (Elsevier, 1983).Tokarska, K. B. et al. Past warming trend constrains future warming in CMIP6 models. Sci. Adv. 6, eaaz9549 (2020).CAS 

Google Scholar 
Forster, P. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) Ch. 7 (IPCC, Cambridge Univ. Press, 2021).Flynn, C. M. & Mauritsen, T. On the climate sensitivity and historical warming evolution in recent coupled model ensembles. Atmos. Chem. Phys. 20, 7829–7842 (2020).CAS 

Google Scholar 
Morris, P. J. et al. Global peatland initiation driven by regionally asynchronous warming. Proc. Natl Acad. Sci. USA 115, 4851–4856 (2018).CAS 

Google Scholar 
Latombe, G. et al. Comparison of spatial downscaling methods of general circulation model results to study climate variability during the Last Glacial Maximum. Geosci. Model Dev. 11, 2563–2579 (2018).
Google Scholar 
Galar, M., Fernández, A., Barrenechea, E., Bustince, H. & Herrera, F. An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit. 44, 1761–1776 (2011).
Google Scholar 
Petrucci, C. J. A primer for social worker researchers on how to conduct a multinomial logistic regression. J. Soc. Serv. Res. 35, 193–205 (2009).
Google Scholar 
Fronzek, S. et al. Evaluating sources of uncertainty in modelling the impact of probabilistic climate change on sub-arctic palsa mires. Nat. Hazards Earth Syst. Sci. 11, 2981–2995 (2011).
Google Scholar 
Aalto, J. & Luoto, M. Integrating climate and local factors for geomorphological distribution models. Earth Surf. Process. Landf. 39, 1729–1740 (2014).
Google Scholar 
Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).
Google Scholar 
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Google Scholar 
Anisimov, O. A. & Nelson, F. E. Permafrost zonation and climate change in the northern hemisphere: results from transient general circulation models. Climatic Change 35, 241–258 (1997).
Google Scholar 
Armstrong, R. A. When to use the Bonferroni correction. Ophthalm. Physiol. Opt. 34, 502–508 (2014).
Google Scholar 
Menard, S. Standards for standardized logistic regression coefficients. Soc. Forces 89, 1409–1428 (2011).
Google Scholar 
Powers, D. M. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Mach. Learn. Technol. 2, 37–63 (2011).
Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133, 225–245 (2000).
Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Fewster, R. E. et al. Imminent loss of climate space for permafrost peatlands in Europe and Western Siberia. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01296-7 (2022). More

Lindquist, B. The main varieties of Picea abies (L.) Karst. In Europe, with a contribution to the theory of a forest vegetation in Scandinavia during the last Pleistocene glaciation. Acta Horti Bergiani 14, 249–342 (1948).
Google Scholar 
Parducci, L. et al. Glacial survival of boreal trees in northern Scandinavia. Science 335, 1083–1086 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Parducci, L. et al. Response to comment on “Glacial survival of boreal trees in Northern Scandinavia.”. Science 338, 9–10 (2012).
Google Scholar 
Birks, H. H. et al. Comment on “Glacial survival of boreal trees in Northern Scandinavia.”. Science 338, 9–11 (2012). 2012.
Google Scholar 
Chen, J. et al. Genomic data provide new insights on the demographic history and the extent of recent material transfers in Norway spruce. Evolut. Appl. 12, 1539–1551 (2019).
Google Scholar 
Giesecke, T. & Bennett, K. D. The Holocene spread of Picea abies (L.) Karst. in Fennoscandia and adjacent areas. J. Biogeogr. 31, 1523–1548 (2004).
Google Scholar 
Latałowa, M. & van der Knaap, W. O. Late Quaternary expansion of Norway spruce Picea abies (L.) Karst. in Europe according to pollen data. Quat. Sci. Rev. 25, 2780–2805 (2006).ADS 

Google Scholar 
Brewer, S. et al. Late-glacial and Holocene European pollen data. J. Maps 13, 921–928 (2017).
Google Scholar 
Heikkilä, M., Fontana, S. L. & Seppä, H. Rapid Lateglacial tree population dynamics and ecosystem changes in the eastern Baltic region. J. Quat. Sci. 24, 802–815 (2009).
Google Scholar 
Giesecke, T., Brewer, S., Finsinger, W., Leydet, M. & Bradshaw, R. H. W. Patterns and dynamics of European vegetation change over the last 15,000 years. J. Biogeogr. 44, 1441–1456 (2017).
Google Scholar 
Segerström, U. & Von Stedingk, H. Early-Holocene spruce, Picea abies (L.) Karst., in west central Sweden as revealed by pollen analysis. Holocene 13, 897–906 (2003).ADS 

Google Scholar 
Kullman, L. New and Firm Evidence for Mid-Holocene Appearance of Piceaa abies in the Scandes Mountains, Sweden. J. Ecol. 83, 439 (1995).
Google Scholar 
Kullman, L. Boreal tree taxa in the central Scandes during the Late-Glacial: Implications for Late-Quaternary forest history. J. Biogeogr. 29, 1117–1124 (2002).
Google Scholar 
Öberg, L. & Kullman, L. Ancient Subalpine Clonal Spruces (Picea abies): Sources of Postglacial Vegetation History in the Swedish Scandes. Arctic 64, 183–196 (2011).
Google Scholar 
Paus, A., Velle, G. & Berge, J. The Lateglacial and early Holocene vegetation and environment in the Dovre mountains, central Norway, as signalled in two Lateglacial nunatak lakes. Quat. Sci. Rev. 30, 1780–1796 (2011).ADS 

Google Scholar 
Birks, H. H., Larsen, E. & Birks, H. J. B. Did tree-Betula, Pinus and Picea survive the last glaciation along the west coast of Norway? A review of the evidence, in light of Kullman (2002). J. Biogeogr. 32, 461–1471 (2005).
Google Scholar 
Tollefsrud, M. M. et al. Genetic consequences of glacial survival and postglacial colonization in Norway spruce: Combined analysis of mitochondrial DNA and fossil pollen. Mol. Ecol. 17, 4134–4150 (2008).CAS 
PubMed 

Google Scholar 
Tsuda, Y. et al. The extent and meaning of hybridization and introgression between Siberian spruce (Picea obovata) and Norway spruce (Picea abies): cryptic refugia as stepping stones to the west? Mol. Ecol. 25, 2773–2789 (2016).CAS 
PubMed 

Google Scholar 
Hughes, A. L. C., Gyllencreutz, R., Lohne, Ø. S., Mangerud, J. & Svendsen, J. I. The last Eurasian ice sheets – a chronological database and time-slice reconstruction, DATED-1. Boreas 45, 1–45 (2016).
Google Scholar 
Gowan, E. J. et al. A new global ice sheet reconstruction for the past 80 000 years. Nat. Commun. 12, 1199 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemdahl, G. & Berglund, B. E. Senglaciala granskogar i Sydsverige? [Late glacial spruce forests in southern Sweden?]. Sven. Botanisk Tidskr. 99, 183–186 (2005).
Google Scholar 
Hicks, S. When no pollen does not mean no trees. Vegetation Hist. Archaeobotany 15, 253–261 (2006).
Google Scholar 
Birks, H. H., Larsen, E. & Birks, H. J. B. On the presence of late-glacial trees in western Norway and the Scandes: A further comment. J. Biogeogr. 33, 376–377 (2006).
Google Scholar 
Kullman, L. Ecological tree line history and palaeoclimate – review of megafossil evidence from the Swedish Scandes. Boreas 42, 555–567 (2013).
Google Scholar 
Giesecke, T. Changing Plant Distributions and Abundances. In: S. A. Elias (ed.) The encyclopedia of quaternary science, vol 3, pp. 854– 860. Elsevier, Amsterdam, NL (2013).Tollefsrud, M. M. et al. Combined analysis of nuclear and mitochondrial markers provide new insight into the genetic structure of North European Picea abies. Heredity 102, 549–562 (2009).CAS 
PubMed 

Google Scholar 
Edwards, M. E., Armbruster, W. S. & Elias, S. E. Constraints on post-glacial boreal tree expansion out of far-northern refugia. Glob. Ecol. Biogeogr. 23, 1198–1208 (2014).
Google Scholar 
Li, L., et al. Teasing apart the joint effect of demography and natural selection in the birth of a contact zone. bioRxiv https://doi.org/10.1101/2022.01.11.475794 (2022).Parducci, L. et al. Ancient plant DNA in lake sediments. N. Phytologist 214, 924–942 (2017).CAS 

Google Scholar 
Alsos, I. G. et al. Plant DNA metabarcoding of lake sediments: How does it represent the contemporary vegetation. PLoS ONE 13, e0195403 (2018).PubMed 
PubMed Central 

Google Scholar 
Naydenov, K., Senneville, S., Beaulieu, J., Tremblay, F. & Bousquet, J. Glacial vicariance in Eurasia: Mitochondrial DNA evidence from Scots pine for a complex heritage involving genetically distinct refugia at mid-northern latitudes and in Asia Minor. BMC Evolut. Biol. 7, 233 (2007).
Google Scholar 
Kullman, L. Palaeoecological, Biogeographical and Palaeoclimatological Implications of Early Holocene Immigration of Larix sibirica Ledeb. into the Scandes Mountains, Sweden. Glob. Ecol. Biogeogr. Lett. 7, 181 (1998).
Google Scholar 
Kullman, L. Early postglacial appearance of tree species in northern Scandinavia: review and perspective. Quat. Sci. Rev. 27, 2467–2472 (2008).ADS 

Google Scholar 
Suyama, Y. & Matsuki, Y. MIG-seq: An effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Sci. Rep. 5, 16963 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zale, R. et al. Growth of plants on the Late Weichselian ice-sheet during Greenland interstadial-1? Quat. Sci. Rev. 185, 222–229 (2018).ADS 

Google Scholar 
Kylander, M. E., Klaminder, J., Wohlfarth, B. & Löwemark, L. Geochemical responses to paleoclimatic changes in southern Sweden since the late glacial: the Hässeldala Port lake sediment record. J. Paleolimnol. 50, 57–70 (2013).ADS 

Google Scholar 
Ampel, L., Kylander, M. E., Steinthorsdottir, M. & Wohlfarth, B. Abrupt climate change and early lake development – the Lateglacial diatom flora at Hässeldala Port, southeastern Sweden. Boreas 44, 94–102 (2015).
Google Scholar 
Wohlfarth, B. et al. Hässeldala – a key site for Last Termination climate events in northern Europe. Boreas 46, 143–161 (2017).
Google Scholar 
Wohlfarth, B. et al. Climate and environment in southwest Sweden 15.5–11.3 cal. ka BP. Boreas 47, 687–710 (2018).
Google Scholar 
Parducci, L., et al. Shotgun environmental DNA, pollen, and macrofossil analysis of lateglacial lake sediments from southern Sweden. Front. Ecol. Evol., 7, 189 (2019).Nilsson, T. Die pollenanalytische zonengliederung der spät- und postglazialen bildungen schonens. GFF 57, 385–562 (1935).
Google Scholar 
Lindbladh, M. När granen kom till byn några tankar kring granens invandring i södra Sverige. Sven. Botanisk Tidskr. 98, 249–262 (2004).
Google Scholar 
Schenk, F. et al. Floral evidence for high summer temperatures in southern Scandinavia during 15–11 cal ka BP. Quat. Sci. Rev. 233, 106243 (2020).
Google Scholar 
Nikolov, N., & Helmisaari, H. Silvics of the circumpolar boreal forest tree species. In A Systems Analysis of the Global Boreal Forest (pp. 13–84). Cambridge University Press (1992).Wohlfarth, B. Ice-free conditions in Sweden during Marine Oxygen Isotope Stage 3? Boreas 39, 377–398 (2010).
Google Scholar 
Sarala, P., Väliranta, M., Eskola, T. & Vaikutiené, G. First physical evidence for forested environment in the Arctic during MIS 3. Sci. Rep. 6, 29054 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Svenning, J.-C., Normand, S. & Kageyama, M. Glacial refugia of temperate trees in Europe: insights from species distribution modelling. J. Ecol. 96, 1117–1127 (2008).
Google Scholar 
Wang, X., Bernhardsson, C. & Ingvarsson, P. K. Demography and Natural Selection Have Shaped Genetic Variation in the Widely Distributed Conifer Norway Spruce (Picea abies). Genome Biol. Evolution 12, 3803–3817 (2020).
Google Scholar 
Binney, H. A. et al. The distribution of late-Quaternary woody taxa in northern Eurasia: evidence from a new macrofossil database. Quat. Sci. Rev. 28, 2445–2464 (2009).ADS 

Google Scholar 
Krüger, S., Dörfler, W., Bennike, O. & Wolters, S. Life in Doggerland – palynological investigations of the environment of prehistoric hunter-gatherer societies in the North Sea Basin. EG – Quat. Sci. J. 66, 3–13 (2017).
Google Scholar 
Alsos, I. G. et al. Last Glacial Maximum environmental conditions at Andøya, northern Norway; evidence for a northern ice-edge ecological “hotspot.”. Quat. Sci. Rev. 239, 106364 (2020).
Google Scholar 
Alsos, I. G. et al. Frequent Long-Distance Plant Colonization in the Changing Arctic. Science 316, 1606–1609 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Epp, L. S. et al. New environmental metabarcodes for analysing soil DNA: Potential for studying past and present ecosystems. Mol. Ecol. 21, 1821–1833 (2012).CAS 
PubMed 

Google Scholar 
Capo, E. et al. Lake Sedimentary DNA Research on Past Terrestrial and Aquatic Biodiversity: Overview and Recommendations. Quaternary 4, 6 (2021).
Google Scholar 
Kullman, L. Immigration of Picea abies into North-Central Sweden. New evidence of regional expansion and tree-limit evolution. Nord. J. Bot. 21, 39–54 (2001).
Google Scholar 
Dabney, J., Meyer, M. & Pääbo, S. Ancient DNA damage. Cold Spring Harb. Perspect. Biol. 5, a012567 (2013).PubMed 
PubMed Central 

Google Scholar 
Ficetola, G. F. et al. Replication levels, false presences, and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecol. Resour. 15, 543–556 (2015).CAS 
PubMed 

Google Scholar 
Nystedt, B. et al. The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Genomics (2013) arXiv:1303.3997Chang, C. C. et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).PubMed 
PubMed Central 

Google Scholar 
Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, 2074–2093 (2006).CAS 

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

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-Statistics for the Analysis of Population Structure. Evolution 38, 1358 (1984).CAS 
PubMed 

Google Scholar 
Pembleton, L. W., Cogan, N. O. I. & Forster, J. W. StAMPP: An R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol. Ecol. Resour. 13, 946–952 (2013).CAS 
PubMed 

Google Scholar 
Karney, C. F. F. Algorithms for geodesics. J. Geod. 87, 43–55 (2013).ADS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing https://www.r-project.org/ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016). More

Danovaro, R., Snelgrove, P. V. & Tyler, P. Challenging the paradigms of deep-sea ecology. Trends Ecol. Evol. 29, 465–475 (2014).PubMed 

Google Scholar 
Smith, C. R., Hoover, D. J. & Doan, S. E. Phytodetritus at the abyssal seafloor across 10° of latitude in the central equatorial Pacific. Oceanogr. Lit. Rev. 4, 318 (1997).
Google Scholar 
Buesseler, K. O. et al. Revisiting carbon flux through the ocean’s twilight zone. Science 316, 567–570 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Rex, M. A. et al. Global bathymetric patterns of standing stock and body size in the deep-sea benthos. Mar. Ecol. Prog. Ser. 317, 1–8 (2006).ADS 

Google Scholar 
Clough, L. M., Renaud, P. E. & Ambrose, W. G. Jr. Impacts of water depth, sediment pigment concentration, and benthic macrofaunal biomass on sediment oxygen demand in the western Arctic Ocean. Can. J. Fish. Aquat. Sci. 62, 1756–1765 (2005).CAS 

Google Scholar 
Gorska, B., Soltwedel, T., Schewe, I. & Wlodarska-Kowalczuk, M. Bathymetric trends in biomass size spectra, carbon demand, and production of Arctic benthos (76–5561 m, Fram Strait). Prog. Oceanogr. 186, 102370 (2020).
Google Scholar 
Stratmann, T. et al. The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities. Sci. Data 7, 206 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Glud, R. N. Oxygen dynamics of marine sediments. Mar. Biol. Res. 4, 243–289 (2008).
Google Scholar 
Zeppilli, D. et al. Characteristics of meiofauna in extreme marine ecosystems: a review. Mar. Biodivers. 48, 35–71 (2018).
Google Scholar 
Rosli, N., Leduc, D., Rowden, A. A. & Probert, P. K. Review of recent trends in ecological studies of deep-sea meiofauna, with focus on patterns and processes at small to regional spatial scales. Mar. Biodivers. 48, 13–34 (2018).
Google Scholar 
Schratzberger, M. & Ingels, J. Meiofauna matters: the roles of meiofauna in benthic ecosystems. J. Exp. Mar. Biol. Ecol. 502, 12–25 (2018).
Google Scholar 
Berg, P., Rysgaard, S., Funch, P. & Sejr, M. K. Effects of bioturbation on solutes and solids in marine sediments. Aquat. Microb. Ecol. 26, 81–94 (2001).
Google Scholar 
Aller, R. C. & Aller, J. Y. Meiofauna and solute transport in marine muds. Limnol. Oceanogr. 37, 1018–1033 (1992).ADS 
CAS 

Google Scholar 
Leduc, D. et al. Comparison between infaunal communities of the deep floor and edge of the Tonga Trench: possible effects of differences in organic matter supply. Deep Sea Res. Part Oceanogr. Res. Pap. 116, 264–275 (2016).ADS 

Google Scholar 
Schmidt, C. & Martínez Arbizu, P. Unexpectedly higher metazoan meiofauna abundances in the Kuril-Kamchatka Trench compared to the adjacent abyssal plains. Deep Sea Res. Part II Top. Stud. Oceanogr. 111, 60–75 (2015).ADS 
CAS 

Google Scholar 
Danovaro, R., Gambi, C. & DellaCroce, N. Meiofauna hotspot in the Atacama Trench, eastern south Pacific Ocean. Deep Sea Res. Part Oceanogr. Res. Pap. 49, 843–857 (2002).ADS 
CAS 

Google Scholar 
Ichino, M. C. et al. The distribution of benthic biomass in hadal trenches: a modelling approach to investigate the effect of vertical and lateral organic matter transport to the seafloor. Deep Sea Res. Part Oceanogr. Res. Pap. 100, 21–33 (2015).ADS 
CAS 

Google Scholar 
Shirayama, Y. The abundance of deep-sea meiobenthos in the western pacific in relation to environmental-factors. Oceanol. Acta 7, 113–121 (1984).
Google Scholar 
Leduc, D. & Rowden, A. A. Nematode communities in sediments of the Kermadec Trench, Southwest Pacific Ocean. Deep Sea Res. Part Oceanogr. Res. Pap. 134, 23–31 (2018).ADS 

Google Scholar 
Brandt, A., Brix, S., Riehl, T. & Malyutina, M. Biodiversity and biogeography of the abyssal and hadal Kuril-Kamchatka trench and adjacent NW Pacific deep-sea regions. Prog. Oceanogr. 181, 102232 (2020).
Google Scholar 
Schmidt, C., Escobar Wolf, K., Lins, L., Martínez Arbizu, P. & Brandt, A. Meiofauna abundance and community patterns along a transatlantic transect in the Vema Fracture Zone and in the hadal zone of the Puerto Rico trench. Deep Sea Res. Part II Top. Stud. Oceanogr. 148, 223–235 (2018).ADS 

Google Scholar 
Jamieson, A. J., Fujii, T., Mayor, D. J., Solan, M. & Priede, I. G. Hadal trenches: the ecology of the deepest places on Earth. Trends Ecol. Evol. 25, 190–197 (2010).PubMed 

Google Scholar 
Jamieson, A. J. Ecology of deep oceans: hadal trenches. eLS https://doi.org/10.1002/9780470015902.a0023606 (2011).Article 

Google Scholar 
Stewart, H. A. & Jamieson, A. J. Habitat heterogeneity of hadal trenches: Considerations and implications for future studies. Prog. Oceanogr. 161, 47–65 (2018).ADS 

Google Scholar 
Wenzhöfer, F. et al. Benthic carbon mineralization in hadal trenches: Assessment by in situ O2 microprofile measurements. Deep Sea Res. Part Oceanogr Res Pap. 116, 276–286 (2016).ADS 

Google Scholar 
Glud, R. N. et al. Hadal trenches are dynamic hotspots for early diagenesis in the deep sea. Commun. Earth Environ. 2, 1–8 (2021).ADS 

Google Scholar 
Glud, R. N. et al. High rates of microbial carbon turnover in sediments in the deepest oceanic trench on Earth. Nat. Geosci. 6, 284–288 (2013).ADS 
CAS 

Google Scholar 
Xu, Y. et al. Distribution, source, and burial of sedimentary organic carbon in Kermadec and Atacama Trenches. J. Geophys. Res. Biogeosciences 126, e2020JG006189 (2021).ADS 
CAS 

Google Scholar 
Itou, M., Matsumura, I. & Noriki, S. A large flux of particulate matter in the deep Japan Trench observed just after the 1994 Sanriku-Oki earthquake. Deep Sea Res. Part Oceanogr. Res. Pap. 47, 1987–1998 (2000).ADS 
CAS 

Google Scholar 
Oguri, K. et al. Hadal disturbance in the Japan Trench induced by the 2011 Tohoku-Oki Earthquake. Sci. Rep. 3, 1–6 (2013).
Google Scholar 
Luo, M. et al. Benthic carbon mineralization in hadal trenches: insights from in situ determination of benthic oxygen consumption. Geophys. Res. Lett. 45, 2752–2760 (2018).ADS 
CAS 

Google Scholar 
Itoh, M. et al. Bathymetric patterns of meiofaunal abundance and biomass associated with the Kuril and Ryukyu trenches, western North Pacific Ocean. Deep Sea Res. Part Oceanogr. Res. Pap. 58, 86–97 (2011).ADS 

Google Scholar 
Tietjen, J. H., Deming, J. W., Rowe, G. T., Macko, S. & Wilke, R. J. Meiobenthos of the hatteras abyssal plain and Puerto Rico trench: abundance, biomass and associations with bacteria and particulate fluxes. Deep Sea Res. Part Oceanogr. Res. Pap. 36, 1567–1577 (1989).ADS 

Google Scholar 
Richardson, M. D., Briggs, K. B., Bowles, F. A. & Tietjen, J. H. A depauperate benthic assemblage from the nutrient-poor sediments of the Puerto Rico Trench. Deep Sea Res. Part Oceanogr. Res. Pap. 42, 351–364 (1995).ADS 

Google Scholar 
Tietjen, J. H. Ecology of deep-sea nematodes from the Puerto Rico trench area and Hatteras Abyssal plain. Deep Sea Res. Part Oceanogr. Res. Pap. 36, 1579–1594 (1989).ADS 

Google Scholar 
Shirayama, Y. & Kojima, S. Abundance of deep-sea meiobenthos off Sanriku, Northeastern Japan. J. Oceanogr. 50, 109–117 (1994).
Google Scholar 
Ingels, J. et al. Preferred use of bacteria over phytoplankton by deep-sea nematodes in polar regions. Mar. Ecol. Prog. Ser. 406, 121–133 (2010).ADS 
CAS 

Google Scholar 
Guilini, K., Oevelen, D. V., Soetaert, K., Middelburg, J. J. & Vanreusela, A. Nutritional importance of benthic bacteria for deep-sea nematodes from the Arctic ice margin: Results of an isotope tracer experiment. Limnol. Oceanogr. 55, 1977–1989 (2010).ADS 
CAS 

Google Scholar 
Moens, T., Verbeeck, L., de Maeyer, A., Swings, J. & Vincx, M. Selective attraction of marine bacterivorous nematodes to their bacterial food. Mar. Ecol. Prog. Ser. 176, 165–178 (1999).ADS 

Google Scholar 
Schmidt, C., Sattarova, V. V., Katrynski, L. & Arbizu, P. M. New insights from the deep: Meiofauna in the Kuril-Kamchatka Trench and adjacent abyssal plain. Prog. Oceanogr. 173, 192–207 (2019).ADS 

Google Scholar 
Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).ADS 
CAS 

Google Scholar 
Neira, C., Sellanes, J., Levin, L. A. & Arntz, W. E. Meiofaunal distributions on the Peru margin: relationship to oxygen and organic matter availability. Deep Sea Res. Part Oceanogr. Res. Pap. 48, 2453–2472 (2001).ADS 
CAS 

Google Scholar 
Soltwedel, T. Metazoan meiobenthos along continental margins: a review. Prog. Oceanogr. 46, 59–84 (2000).ADS 

Google Scholar 
Rowe, G. T., Sibuet, M., Deming, J., Tietjen, J. & Khripounoff, A. Organic carbon turnover time in deep-sea benthos. Prog. Oceanogr. 24, 141–160 (1990).ADS 

Google Scholar 
Tselepides, A. et al. Organic matter composition of the continental shelf and bathyal sediments of the Cretan Sea (NE Mediterranean). Prog. Oceanogr. 46, 311–344 (2000).ADS 

Google Scholar 
Hansen, J. & Josefson, A. Pools of chlorophyll and live planktonic diatoms in aphotic marine sediments. Mar. Biol. 139, 289–299 (2001).CAS 

Google Scholar 
Hargraves, P. E. & French, S. Survival characteristics of marine diatom resting spores. in JOURNAL OF PHYCOLOGY vol. 11 6–6 (PHYCOLOGICAL SOC AMER INC 810 EAST 10TH ST, LAWRENCE, KS 66044, 1975).Schauberger, C. et al. Spatial variability of prokaryotic and viral abundances in the Kermadec and Atacama Trench regions. Limnol. Oceanogr. 66(6), 2095–2109 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
van Oevelen, D. et al. Carbon flows in the benthic food web at the deep-sea observatory HAUSGARTEN (Fram Strait). Deep Sea Res. Part Oceanogr. Res. Pap. 58, 1069–1083 (2011).ADS 

Google Scholar 
Heip, C. H. R. et al. The role of the benthic biota in sedimentary metabolism and sediment-water exchange processes in the Goban Spur area (NE Atlantic). Deep Sea Res. Part II Top. Stud. Oceanogr. 48, 3223–3243 (2001).ADS 
CAS 

Google Scholar 
Rowe, G. T. et al. Comparative biomass structure and estimated carbon flow in food webs in the deep Gulf of Mexico. Deep Sea Res. Part II Top. Stud. Oceanogr. 55, 2699–2711 (2008).ADS 
CAS 

Google Scholar 
Baguley, J. G., Montagna, P. A., Hyde, L. J. & Rowe, G. T. Metazoan meiofauna biomass, grazing, and weight-dependent respiration in the Northern Gulf of Mexico deep sea. Deep Sea Res. Part II Top. Stud. Oceanogr. 55, 2607–2616 (2008).ADS 
CAS 

Google Scholar 
Maciute, A. et al. A microsensor-based method for measuring respiration of individual nematodes. Methods Ecol. Evol. 12(10), 1841–1847. https://doi.org/10.1111/2041-210X.13674 (2021).
Article 

Google Scholar 
Montagna, P. A. In situ measurement of meiobenthic grazing rates on sediment bacteria and edaphic diatoms. (1984).Danovaro, R. Detritus-Bacteria-Meiofauna interactions in a seagrass bed (Posidonia oceanica) of the NW Mediterranean. Mar. Biol. 127, 1–13 (1996).CAS 

Google Scholar 
Pape, E., van Oevelen, D., Moodley, L., Soetaert, K. & Vanreusel, A. Nematode feeding strategies and the fate of dissolved organic matter carbon in different deep-sea sedimentary environments. Deep Sea Res. Part Oceanogr. Res. Pap. 80, 94–110 (2013).ADS 
CAS 

Google Scholar 
Wieser, W. Beziehungen zwischen Mundhöhlengestalt, Ernährungsweise und Vorkommen bei freilebenden mari- nen Nematoden. Ark. För Zool. 2, 439–484 (1953).
Google Scholar 
Moens, T. & Vincx, M. Observations on the feeding ecology of estuarine nematodes. J. Mar. Biol. Assoc. U. K. 77, 211–227 (1997).
Google Scholar 
Moens, T. et al. Carbon sources of Antarctic nematodes as revealed by natural carbon isotope ratios and a pulse-chase experiment. Polar Biol. 31, 1–13 (2007).
Google Scholar 
Ingels, J., Kiriakoulakis, K., Wolff, G. A. & Vanreusel, A. Nematode diversity and its relation to the quantity and quality of sedimentary organic matter in the deep Nazaré Canyon, Western Iberian Margin. Deep Sea Res. Part Oceanogr. Res. Pap. 56, 1521–1539 (2009).ADS 
CAS 

Google Scholar 
Gambi, C., Vanreusel, A. & Danovaro, R. Biodiversity of nematode assemblages from deep-sea sediments of the Atacama Slope and Trench (South Pacific Ocean). Deep Sea Res. Part Oceanogr. Res. Pap. 50, 103–117 (2003).ADS 

Google Scholar 
Vanhove, S., Vermeeren, H. & Vanreusel, A. Meiofauna towards the South Sandwich Trench (750–6300 m), focus on nematodes. Deep Sea Res. Part II Top Stud. Oceanogr. 51, 1665–1687 (2004).ADS 

Google Scholar 
Jumars, P. A. & Hessler, R. R. Hadal community structure: implications from the Aleutian Trench. J. Mar. Res. 34, 547–560 (1976).
Google Scholar 
Kim, D.-S. & Min, W.-G. Meiobenthic communities in extreme deep-sea environment. Korean J. Fish. Aquat. Sci. 39, 203–213 (2006).
Google Scholar 
Wenzhöfer, F. The Expedition SO261 of the Research Vessel SONNE to the Atacama Trench in the Pacific Ocean in 2018. Berichte Zur Polar- Meeresforsch. Rep. Polar Mar. Res. 729, 111. https://doi.org/10.2312/BzPM_0729_2019 (2019).Article 

Google Scholar 
Scholl, D. W., Christensen, M. N., von Huene, R. & Marlow, M. S. Peru-Chile trench sediments and sea-floor spreading. Geol. Soc. Am. Bull. 81, 1339–1360 (1970).ADS 

Google Scholar 
Fisher, R. L. & Raitt, R. W. Topography and structure of the Peru-Chile trench. In Deep Sea Research and Oceanographic Abstracts vol. 9 423–443 (Elsevier, 1962).Bandy, O. L. & Rodolfo, K. S. Distribution of foraminifera and sediments, Peru-Chile Trench area. In Deep Sea Research and Oceanographic Abstracts vol. 11 817–837 (Elsevier, 1964).Lutz, M. J., Caldeira, K., Dunbar, R. B. & Behrenfeld, M. J. Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. J. Geophys. Res. Oceans https://doi.org/10.1029/2006JC003706 (2007).Article 

Google Scholar 
Carrie, J., Sanei, H. & Stern, G. Standardisation of Rock-Eval pyrolysis for the analysis of recent sediments and soils. Org. Geochem. 46, 38–53 (2012).CAS 

Google Scholar 
Shuman, F. R. & Lorenzen, C. J. Quantitative degradation of chlorophyll by a marine herbivore 1. Limnol. Oceanogr. 20, 580–586 (1975).ADS 
CAS 

Google Scholar 
Glud, R. N. et al. In situ microscale variation in distribution and consumption of 2: a case study from a deep ocean margin sediment (Sagami Bay, Japan). Limnol. Oceanogr. 54, 1–12 (2009).ADS 
CAS 

Google Scholar 
Revsbech, N. P. An oxygen microelectrode with a guard cathode. Linnol. Oceanogr. 34, 474–487 (1989).ADS 
CAS 

Google Scholar 
Berg, P., Risgaard-Petersen, N. & Rysgaard, S. Interpretation of measured concentration profiles in sediment pore water. Limnol. Oceanogr. 43, 1500–1510 (1998).ADS 
CAS 

Google Scholar 
Feller, R. J. & Warwick, R. M. Energetics. in Feller, R.J. and Warwick, R.M. (1988) Energetics. In: Higgins, R.P. and Thiel, H., (eds.) Introduction to the study of meiofauna. Smithsonian Institution Press, Washington, D.C, pp. 181–196. (eds. Higgins, R. P. & Thiel, H.) 181–196 (Smithsonian Institution Press, 1988).Mahaut, M.-L., Sibuet, M. & Shirayama, Y. Weight-dependent respiration rates in deep-sea organisms. Deep Sea Res. Part Oceanogr. Res. Pap. 42, 1575–1582 (1995).ADS 

Google Scholar  More

Soil pH, biomass and Cd content of peanutSoil pHFigure 1 shows that, in this study, application of slaked lime significantly increased soil pH in nearly all growth stages (p  C1200  > C900  > C600  > C300  > C0. Among the soil characteristics, soil pH is considered as an important index that impact Cd uptake by crops, since pH can obviously affect the speciation and solubility of Cd in soil liquids15. The use of slaked lime can neutralize excessive H+ concentrations in soil solutions and decrease Cd solubility33, but there were no observable differences among the different growth stages.Figure 1Effects of slaked lime application on soil pH values. The values are means (± SD) of three replicates. Bar groups with different capital letters indicate significant differences (p  More