Philippa Kaur

Lindenmayer, D. B. & Laurance, W. F. The ecology, distribution, conservation and management of large old trees. Biol. Rev. Camb. Phil. Soc. 92, 1434–1458 (2017).Article 

Google Scholar 
Voigt, C. C., Borissov, I. & Kelm, D. H. Bats fertilize roost trees. Biotropica 47, 403–406 (2015).Article 

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

Google Scholar 
Sponsel, L. E. Spiritual Ecology: A Quiet Revolution (ABC-CLIO, 2012).Omura, H. Trees, forests and religion in Japan. Mt. Res. Dev. 24, 179–182 (2004).Article 

Google Scholar 
Heintzman, P. Nature-based recreation and spirituality: a complex relationship. Leis. Sci. 32, 72–89 (2009).Article 

Google Scholar 
Irvine, K. N., Hoesly, D., Bell-Williams, R. & Warber, S. L. in Biodiversity and Health in the Face of Climate Change (eds Marselle, M. R. et al.) 213–247 (Springer, 2019).Millennium Ecosystem Assessment Ecosystems and Human Well-Being: Synthesis (Island Press, 2005).Vihervaara, P., Rönkä, M. & Walls, M. Trends in ecosystem service research: early steps and current drivers. Ambio 39, 314–324 (2010).Article 

Google Scholar 
Brown, J. H. Macroecology (Univ. Chicago Press, 1995).Piovesan, G. & Biondi, F. On tree longevity. New Phytol. 231, 1318–1337 (2021).Article 

Google Scholar 
Matsui, K. Geography of Religion in Japan: Religious Space, Landscape, and Behavior (Springer, 2013).Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Giant Trees Follow-Up Survey Report, the Sixth Census of the National Survey of the Natural Environment (in Japanese) (Biodiversity Center of Japan & Ministry of the Environment, 2001); https://www.biodic.go.jp/reports2/6th/kyojuflup/6_kyojuflup.pdfMakino, K. Folkloristics of Giant Trees (in Japanese) (Kobunsha, 1986).Daniel, T. C. et al. Contributions of cultural services to the ecosystem services agenda. Proc. Natl Acad. Sci. USA 109, 8812–8819 (2012).Article 
CAS 

Google Scholar 
Muthukrishna, M. et al. Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) psychology: measuring and mapping scales of cultural and psychological distance. Psychol. Sci. 31, 678–701 (2020).Article 

Google Scholar 
Twigger-Ross, C. L. & Uzzell, D. L. Place and identity processes. J. Environ. Psychol. 16, 205–220 (1996).Article 

Google Scholar 
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, 2011).Watanabe, T., Matsunaga, K., Kanazawa, Y., Suzuki, K. & Rotherham, I. D. Landforms and distribution patterns of giant Castanopsis sieboldii trees in urban areas and western suburbs of Tokyo, Japan. Urban For. Urban Green. 60, 126997 (2021).Article 

Google Scholar 
Uryu, S. jpmesh: Utilities for Japanese Mesh Code. R package version 2.1.0 https://CRAN.R-project.org/package=jpmesh (2022).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Yamanouchi, T. et al. A Checklist of Japanese Plant Names (Japan Node of Global Biodiversity Information Facility, 2019); https://www.gbif.jp/v2/activities/wamei_checklist.html More

Wohlleben, P. The Hidden Life of Trees: What They Feel, How They Communicate—Discoveries From a Secret World Vol. 1 (Greystone Books, 2016).Simard, S. W. Finding the Mother Tree: Discovering the Wisdom of the Forest (Knopf Doubleday Publishing Group, 2022).Powers, R. The Overstory (W. W. Norton & Company, 2018).Jabr, F. The social life of forests. New York Times Magazine https://www.nytimes.com/interactive/2020/12/02/magazine/tree-communication-mycorrhiza.html (2020).Kaplan, S. With forests in peril, she’s on a mission to save ‘mother trees’. Washington Post (27 December 2022).Chung, D. & Williams, R. T. Talking trees. Natl Geogr. 233, 6 (2018).
Google Scholar 
Grant, R. Do trees talk to each other? Smithsonian Magazine https://www.smithsonianmag.com/science-nature/the-whispering-trees-180968084/ (2018).Schwartzberg, L. Fantastic Fungi. Moving Art (2019).Druyan, A. Cosmos: Possible Worlds: the Search for Intelligent Life on Earth (2020).Mills, M. C’mon C’mon (2020).Simard, S. W. How trees talk to each other. YouTube https://www.youtube.com/watch?v=Un2yBgIAxYs (2016).Abumrad J & Krulwich, R. From tree to shining tree. Radiolab https://radiolab.org/episodes/from-tree-to-shining-tree (2016).Geddes, L. Unearthing the secret social lives of trees. The Guardian Science Weekly https://www.theguardian.com/science/audio/2021/apr/29/unearthing-the-secret-social-lives-of-trees-podcast (2021).Davies, D. Trees talk to each other. ‘Mother Tree’ ecologist hears lessons for people, too. National Public Radio https://www.npr.org/sections/health-shots/2021/05/04/993430007/trees-talk-to-each-other-mother-tree-ecologist-hears-lessons-for-people-too (2021).Braff, Z. Midnight train to Royston. Ted Lasso (2021).Murphy, R. Welcome, friends. The Watcher (2022).Milović, M., Kebert, M. & Orlović, S. How mycorrhizas can help forests to cope with ongoing climate change? Pregledni Članci Rev. 5, 279–286 (2021).
Google Scholar 
Simard, S. W. & Austin, M. E. in Climate Change and Variabilty (eds Simard, S. W. & Austin, M. E.) 275–302 (IntechOpen Europe, 2010).Domínguez-Núñez, J. A. in Structure and Functions of the Pedosphere (eds Giri, B. et al.) 365–391 (Springer, 2022).Authier, L., Violle, C. & Richard, F. Ectomycorrhizal networks in the anthropocene: from natural ecosystems to urban planning. Front. Plant Sci. 13, 900231 (2022).Article 

Google Scholar 
Selosse, M.-A., Richard, F., He, X. & Simard, S. W. Mycorrhizal networks: des liaisons dangereuses? Trends Ecol. Evol. 21, 621–628 (2006).Article 

Google Scholar 
Newman, E. Mycorrhizal links between plants—their functioning and ecological significance. Adv. Ecol. Res. 18, 243–270 (1988).Article 

Google Scholar 
Bonello, P., Bruns, T. D. & Gardes, M. Genetic structure of a natural population of the ectomycorrhizal fungus Suillus pungens. New Phytol. 138, 533–542 (1998).Article 
CAS 

Google Scholar 
Dahlberg, A. & Stenlid, J. Size, distribution and biomass of genets in populations of Suillus bovinus (L.: Fr.) Roussel revealed by somatic incompatibility. New Phytol. 128, 225–234 (1994).Article 

Google Scholar 
Kretzer, A. M., Dunham, S., Molina, R. & Spatafora, J. W. Microsatellite markers reveal the below ground distribution of genets in two species of Rhizopogon forming tuberculate ectomycorrhizas on Douglas fir. New Phytol. 161, 313–320 (2004).Article 
CAS 

Google Scholar 
Figueiredo, A. F., Boy, J. & Guggenberger, G. Common mycorrhizae network: a review of the theories and mechanisms behind underground interactions. Front. Fungal Biol. 2, https://doi.org/10.3389/ffunb.2021.735299 (2021).Leake, J. et al. Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Can. J. Bot. 82, 1016–1045 (2004).Article 

Google Scholar 
Trappe, J. M. & Fogel, R. in The Belowground Ecosystem: a Synthesis of Plant-Associated Processes (ed. Marshall J. K.) 205–214 (Colorado State Univ., 1977).Beiler, K. J., Durall, D. M., Simard, S. W., Maxwell, S. A. & Kretzer, A. M. Architecture of the wood-wide web: Rhizopogon spp. genets link multiple Douglas-fir cohorts. New Phytol. 185, 543–553 (2010).Article 
CAS 

Google Scholar 
Beiler, K. J., Simard, S. W. & Durall, D. M. Topology of tree–mycorrhizal fungus interaction networks in xeric and mesic Douglas-fir forests. J. Ecol. 103, 616–628 (2015).Article 

Google Scholar 
Beiler, K. J., Simard, S. W., LeMay, V. & Durall, D. M. Vertical partitioning between sister species of Rhizopogon fungi on mesic and xeric sites in an interior Douglas-fir forest. Mol. Ecol. 21, 6163–6174 (2012).Article 

Google Scholar 
Lian, C., Narimatsu, M., Nara, K. & Hogetsu, T. Tricholoma matsutake in a natural Pinus densiflora forest: correspondence between above- and below-ground genets, association with multiple host trees and alteration of existing ectomycorrhizal communities. New Phytol. 171, 825–836 (2006).Article 

Google Scholar 
Van Dorp, C. H., Simard, S. W. & Durall, D. M. Resilience of Rhizopogon–Douglas-fir mycorrhizal networks 25 years after selective logging. Mycorrhiza 30, 467–474 (2020).Article 

Google Scholar 
Cazzolla Gatti, R. et al. The number of tree species on Earth. Proc. Natl Acad. Sci. USA 119, e2115329119 (2022).Article 

Google Scholar 
Tedersoo, L. & Bahram, M. Mycorrhizal types differ in ecophysiology and alter plant nutrition and soil processes. Biol. Rev. 94, 1857–1880 (2019).Article 

Google Scholar 
Setälä, H. Growth of birch and pine seedlings in relation to grazing by soil fauna on ectomycorrhizal fungi. Ecology 76, 1844–1851 (1995).Article 

Google Scholar 
Kanters, C., Anderson, I. C. & Johnson, D. Chewing up the wood-wide web: selective grazing on ectomycorrhizal fungi by collembola. Forests 6, 2560–2570 (2015).Article 

Google Scholar 
Horton, T. R., Bruns, T. D. & Parker, V. T. Ectomycorrhizal fungi associated with Arctostaphylos contribute to Pseudotsuga menziesii establishment. Can. J. Bot. 77, 93–102 (1999).
Google Scholar 
Kennedy, P. G., Izzo, A. D. & Bruns, T. D. There is high potential for the formation of common mycorrhizal networks between understorey and canopy trees in a mixed evergreen forest. J. Ecol. 91, 1071–1080 (2003).Article 

Google Scholar 
Kennedy, P. G., Smith, D. P., Horton, T. R. & Molina, R. J. Arbutus menziesii (Ericaceae) facilitates regeneration dynamics in mixed evergreen forests by promoting mycorrhizal fungal diversity and host connectivity. Am. J. Bot. 99, 1691–1701 (2012).Article 

Google Scholar 
Horton, T. R., Molina, R. & Hood, K. Douglas-fir ectomycorrhizae in 40- and 400-year-old stands: mycobiont availability to late successional western hemlock. Mycorrhiza 15, 393–403 (2005).Article 
CAS 

Google Scholar 
Buscardo, E. et al. Is the potential for the formation of common mycorrhizal networks influenced by fire frequency? Soil Biol. Biochem. 46, 136–144 (2012).Article 
CAS 

Google Scholar 
Hewitt, R. E., Chapin, F. S. III, Hollingsworth, T. N. & Taylor, D. L. The potential for mycobiont sharing between shrubs and seedlings to facilitate tree establishment after wildfire at Alaska arctic treeline. Mol. Ecol. 26, 3826–3838 (2017).Article 

Google Scholar 
Jia, S., Nakano, T., Hattori, M. & Nara, K. Root-associated fungal communities in three Pyroleae species and their mycobiont sharing with surrounding trees in subalpine coniferous forests on Mount Fuji, Japan. Mycorrhiza 27, 733–745 (2017).Article 
CAS 

Google Scholar 
Hortal, S. et al. Beech roots are simultaneously colonized by multiple genets of the ectomycorrhizal fungus Laccaria amethystina clustered in two genetic groups. Mol. Ecol. 21, 2116–2129 (2012).Article 
CAS 

Google Scholar 
Wadud, M. A., Nara, K., Lian, C., Ishida, T. A. & Hogetsu, T. Genet dynamics and ecological functions of the pioneer ectomycorrhizal fungi Laccaria amethystina and Laccaria laccata in a volcanic desert on Mount Fuji. Mycorrhiza 24, 551–563 (2014).Article 

Google Scholar 
Germain, S. J. & Lutz, J. A. Shared friends counterbalance shared enemies in old forests. Ecology 102, e03495 (2021).Article 

Google Scholar 
Simard, S. W. et al. Partial retention of legacy trees protect mycorrhizal inoculum potential, biodiversity, and soil resources while promoting natural regeneration of interior Douglas-fir. Front. For. Glob. Change 3, https://doi.org/10.3389/ffgc.2020.620436 (2021).Björkman, E. Monotropa hypopitys L.—an epiparasite on tree roots. Physiol. Plant. 13, 308–327 (1960).Article 

Google Scholar 
Simard, S. W. et al. Net transfer of carbon between ectomycorrhizal tree species in the field. Nature 388, 579–582 (1997).Article 
CAS 

Google Scholar 
Read, D. The ties that bind. Nature 388, 517–518 (1997).Article 
CAS 

Google Scholar 
Aleklett, K. & Boddy, L. Fungal behaviour: a new frontier in behavioural ecology. Trends Ecol. Evol. 36, 787–796 (2021).Article 

Google Scholar 
Franklin, O., Näsholm, T., Högberg, P. & Högberg, M. N. Forests trapped in nitrogen limitation—an ecological market perspective on ectomycorrhizal symbiosis. New Phytol. 203, 657–666 (2014).Article 
CAS 

Google Scholar 
Hasselquist, N. J. et al. Greater carbon allocation to mycorrhizal fungi reduces tree nitrogen uptake in a boreal forest. Ecology 97, 1012–1022 (2016).
Google Scholar 
Näsholm, T. et al. Are ectomycorrhizal fungi alleviating or aggravating nitrogen limitation of tree growth in boreal forests? New Phytol. 198, 214–221 (2013).Article 

Google Scholar 
Hoeksema, J. D. in Mycorrhizal Networks (ed. Horton, T. R.) 255–277 (Springer Netherlands, 2015).Teste, F. P. & Simard, S. W. Mycorrhizal networks and distance from mature trees alter patterns of competition and facilitation in dry Douglas-fir forests. Oecologia 158, 193–203 (2008).Article 

Google Scholar 
Teste, F. P., Simard, S. W., Durall, D. M., Guy, R. D. & Berch, S. M. Net carbon transfer between Pseudotsuga menziesii var. glauca seedlings in the field is influenced by soil disturbance. J. Ecol. 98, 429–439 (2010).Article 
CAS 

Google Scholar 
Teste, F. P. et al. Access to mycorrhizal networks and roots of trees: importance for seedling survival and resource transfer. Ecology 90, 2808–2822 (2009).Article 

Google Scholar 
Lerat, S. et al. 14C transfer between the spring ephemeral Erythronium americanum and sugar maple saplings via arbuscular mycorrhizal fungi in natural stands. Oecologia 132, 181–187 (2002).Article 

Google Scholar 
Klein, T., Siegwolf, R. T. W. & Korner, C. Belowground carbon trade among tall trees in a temperate forest. Science 352, 342–344 (2016).Article 
CAS 

Google Scholar 
He, X., Bledsoe, C. S., Zasoski, R. J., Southworth, D. & Horwath, W. R. Rapid nitrogen transfer from ectomycorrhizal pines to adjacent ectomycorrhizal and arbuscular mycorrhizal plants in a California oak woodland. New Phytol. 170, 143–151 (2006).Article 
CAS 

Google Scholar 
Schoonmaker, A. L., Teste, F. P., Simard, S. W. & Guy, R. D. Tree proximity, soil pathways and common mycorrhizal networks: their influence on the utilization of redistributed water by understory seedlings. Oecologia 154, 455–466 (2007).Article 

Google Scholar 
Warren, J. M., Brooks, J. R., Meinzer, F. C. & Eberhart, J. L. Hydraulic redistribution of water from Pinus ponderosa trees to seedlings: evidence for an ectomycorrhizal pathway. New Phytol. 178, 382–394 (2008).Article 
CAS 

Google Scholar 
Bingham, M. A. & Simard, S. W. Seedling genetics and life history outweigh mycorrhizal network potential to improve conifer regeneration under drought. For. Ecol. Manag. 287, 132–139 (2013).Article 

Google Scholar 
Kranabetter, J. M. Understory conifer seedling response to a gradient of root and ectomycorrhizal fungal contact. Can. J. Bot. 83, 638–646 (2005).Article 

Google Scholar 
Liang, M. et al. Soil fungal networks maintain local dominance of ectomycorrhizal trees. Nat. Commun. 11, 2636 (2020).Article 
CAS 

Google Scholar 
Liang, M. et al. Soil fungal networks moderate density-dependent survival and growth of seedlings. New Phytol. 230, 2061–2071 (2021).Article 

Google Scholar 
McGuire, K. L. Common ectomycorrhizal networks may maintain monodominance in a tropical rain forest. Ecology 88, 567–574 (2007).Article 

Google Scholar 
Pec, G. J., Simard, S. W., Cahill, J. F. & Karst, J. The effects of ectomycorrhizal fungal networks on seedling establishment are contingent on species and severity of overstorey mortality. Mycorrhiza 30, 173–183 (2020).Article 

Google Scholar 
Corrales, A., Mangan, S. A., Turner, B. L. & Dalling, J. W. An ectomycorrhizal nitrogen economy facilitates monodominance in a neotropical forest. Ecol. Lett. 19, 383–392 (2016).Article 

Google Scholar 
Booth, M. G. Mycorrhizal networks mediate overstorey–understorey competition in a temperate forest. Ecol. Lett. 7, 538–546 (2004).Article 

Google Scholar 
Booth, M. G. & Hoeksema, J. D. Mycorrhizal networks counteract competitive effects of canopy trees on seedling survival. Ecology 91, 2294–2302 (2010).Article 

Google Scholar 
Brearley, F. Q. et al. Testing the importance of a common ectomycorrhizal network for dipterocarp seedling growth and survival in tropical forests of Borneo. Plant Ecol. Divers. 9, 563–576 (2016).Article 

Google Scholar 
Dehlin, H. et al. Tree seedling performance and below-ground properties in stands of invasive and native tree species. N. Z. J. Ecol. 32, 67–79 (2008).
Google Scholar 
Newbery, D. M. & Neba, G. A. Micronutrients may influence the efficacy of ectomycorrhizas to support tree seedlings in a lowland African rain forest. Ecosphere 10, e02686 (2019).Article 

Google Scholar 
Oliveira, I. R. et al. Nutrient deficiency enhances the rate of short-term belowground transfer of nitrogen from Acacia mangium to Eucalyptus trees in mixed-species plantations. For. Ecol. Manag. 491, 119192 (2021).Article 

Google Scholar 
Paula, R. R. et al. Evidence of short-term belowground transfer of nitrogen from Acacia mangium to Eucalyptus grandis trees in a tropical planted forest. Soil Biol. Biochem. 91, 99–108 (2015).Article 
CAS 

Google Scholar 
Nygren, P. & Leblanc, H. A. Dinitrogen fixation by legume shade trees and direct transfer of fixed N to associated cacao in a tropical agroforestry system. Tree Physiol. 35, 134–147 (2015).Article 
CAS 

Google Scholar 
Liu, Y., Chen, H. & Mou, P. Spatial patterns nitrogen transfer models of ectomycorrhizal networks in a Mongolian scotch pine plantation. J. For. Res. 29, 339–346 (2018).Article 
CAS 

Google Scholar 
Bingham, M. A. & Simard, S. Ectomycorrhizal networks of Pseudotsuga menziesii var. glauca trees facilitate establishment of conspecific seedlings under drought. Ecosystems 15, 188–199 (2012).Article 
CAS 

Google Scholar 
Robinson, D. & Fitter, A. The magnitude and control of carbon transfer between plants linked by a common mycorrhizal network. J. Exp. Bot. 50, 9–13 (1999).Article 
CAS 

Google Scholar 
Chen, W., Koide, R. T. & Eissenstat, D. M. Root morphology and mycorrhizal type strongly influence root production in nutrient hot spots of mixed forests. J. Ecol. 106, 148–156 (2018).Article 
CAS 

Google Scholar 
Jones, M. D., Durall, D. M. & Tinker, P. B. A comparison of arbuscular and ectomycorrhizal Eucalyptus coccifera: growth response, phosphorus uptake efficiency and external hyphal production. New Phytol. 140, 125–134 (1998).Article 

Google Scholar 
Pickles, B. J. et al. Transfer of 13C between paired Douglas-fir seedlings reveals plant kinship effects and uptake of exudates by ectomycorrhizas. New Phytol. 214, 400–411 (2017).Article 
CAS 

Google Scholar 
Teste, F. P., Simard, S. W. & Durall, D. M. Role of mycorrhizal networks and tree proximity in ectomycorrhizal colonization of planted seedlings. Fungal Ecol. 2, 21–30 (2009).Article 

Google Scholar 
Bingham, M. A. & Simard, S. W. Mycorrhizal networks affect ectomycorrhizal fungal community similarity between conspecific trees and seedlings. Mycorrhiza 22, 317–326 (2012).Article 

Google Scholar 
Pec, G. J. et al. Change in soil fungal community structure driven by a decline in ectomycorrhizal fungi following a mountain pine beetle (Dendroctonus ponderosae) outbreak. New Phytol. 213, 864–873 (2017).Article 
CAS 

Google Scholar 
Coomes, D. A. & Grubb, P. J. Impacts of root competition in forests and woodlands: a theoretical framework and review of experiments. Ecol. Monogr. 70, 171–207 (2000).Article 

Google Scholar 
Finlay, R. D. & Read, D. J. The structure and function of the vegetative mycelium of ectomycorrhizal plants. New Phytol. 103, 143–156 (1986).Article 

Google Scholar 
Brownlee, C., Duddridge, J. A., Malibari, A. & Read, D. J. The structure and function of mycelial systems of ectomycorrhizal roots with special reference to their role in forming inter-plant connections and providing pathways for assimilate and water transport. Plant Soil 71, 433–443 (1983).Article 

Google Scholar 
Wu, B., Nara, K. & Hogetsu, T. Can 14C-labeled photosynthetic products move between Pinus densiflora seedlings linked by ectomycorrhizal mycelia? New Phytol. 149, 137–146 (2001).Article 
CAS 

Google Scholar 
Anten, N. P. R. & Chen, B. J. W. Detect thy family: mechanisms, ecology and agricultural aspects of kin recognition in plants. Plant Cell Environ. 44, 1059–1071 (2021).Article 
CAS 

Google Scholar 
Dominguez, P. G. & Niittylä, T. Mobile forms of carbon in trees: metabolism and transport. Tree Physiol. 42, 458–487 (2021).Article 

Google Scholar 
Yu, R.-P., Lambers, H., Callaway, R. M., Wright, A. J. & Li, L. Belowground facilitation and trait matching: two or three to tango. Trends Plant Sci. 26, 1227–1235 (2021).Article 
CAS 

Google Scholar 
Simard, S. W. in The Word for World is Still Forest (eds Springer, A. & Turpin, E.) 66–72 (K Verlag and Haus der Kulturen der Welt, 2017).Simard, S. W. in Memory and Learning in Plants (eds Baluska, F. et al.) 191–213 (Springer, 2018).Boyno, G. & Demir, S. Plant–mycorrhiza communication and mycorrhizae in inter-plant communication. Symbiosis 86, 155–168 (2022).Article 

Google Scholar 
Rasheed, M. U., Brosset, A. & Blande, J. D. Tree communication: the effects of “wired” and “wireless” channels on interactions with herbivores. Curr. For. Rep. 9, 33–47 (2023).
Google Scholar 
Song, Y. Y., Simard, S. W., Carroll, A., Mohn, W. W. & Zeng, R. S. Defoliation of interior Douglas-fir elicits carbon transfer and stress signalling to ponderosa pine neighbors through ectomycorrhizal networks. Sci. Rep. 5, 8495 (2015).Article 
CAS 

Google Scholar 
Gorzelak, M. A. Kin-Selected Signal Transfer Through Mycorrhizal Networks in Douglas-Fir. PhD thesis, Univ. British Columbia (2017).Asay, A. K. Mycorrhizal Facilitation of Kin Recognition in Interior Douglas-Fir (Pseudotsuga menziesii var. glauca). MSc thesis, Univ. British Columbia (2013).Orrego, G. Western Hemlock Regeneration on Coarse Woody Debris is Facilitated by Linkage into a Mycorrhizal Network in an Old-Growth Forest. MSc thesis, Univ. British Columbia (2018).Diédhiou, A. G. et al. Multi-host ectomycorrhizal fungi are predominant in a Guinean tropical rainforest and shared between canopy trees and seedlings. Environ. Microbiol. 12, 2219–2232 (2010).
Google Scholar 
Grelet, G.-A. et al. New insights into the mycorrhizal Rhizoscyphus ericae aggregate: spatial structure and co-colonization of ectomycorrhizal and ericoid roots. New Phytol. 188, 210–222 (2010).Article 
CAS 

Google Scholar 
Van der Heijden, M. G. A. & Horton, T. R. Socialism in soil? The importance of mycorrhizal fungal networks for facilitation in natural ecosystems. J. Ecol. 97, 1139–1150 (2009).Article 

Google Scholar 
Babikova, Z., Johnson, D., Bruce, T., Pickett, J. & Gilbert, L. Underground allies: how and why do mycelial networks help plants defend themselves? BioEssays 36, 21–26 (2014).Article 

Google Scholar 
Alaux, P.-L., Zhang, Y., Gilbert, L. & Johnson, D. Can common mycorrhizal fungal networks be managed to enhance ecosystem functionality? Plants People Planet 3, 433–444 (2021).Article 

Google Scholar 
Simard, S. W. et al. Mycorrhizal networks: mechanisms, ecology and modelling. Fungal Biol. Rev. 26, 39–60 (2012).Article 

Google Scholar 
Flinn, K. The idea that trees talk to cooperate is misleading. Scientific American https://www.scientificamerican.com/article/the-idea-that-trees-talk-to-cooperate-is-misleading/ (2021).Högberg, P. & Högberg, M. N. Does successful forest regeneration require the nursing of seedlings by nurse trees through mycorrhizal interconnections. For. Ecol. Manag. 516, 120252 (2022).Article 

Google Scholar 
Teste, F. P., Jones, M. D. & Dickie, I. A. Dual-mycorrhizal plants: their ecology and relevance. New Phytol. 225, 1835–1851 (2020).Article 

Google Scholar 
Toju, H., Guimarães, P. R., Olesen, J. M. & Thompson, J. N. Assembly of complex plant–fungus networks. Nat. Commun. 5, 5273 (2014).Article 
CAS 

Google Scholar 
Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis 3rd edn (Elsevier, 2008).Nara, K. Ectomycorrhizal networks and seedling establishment during early primary succession. New Phytol. 169, 169–178 (2006).Article 
CAS 

Google Scholar 
Arnebrant, K., Ek, H., Finlay, R. D. & Söderström, B. Nitrogen translocation between Alnus glutinosa (L.) Gaertn. seedlings inoculated with Frankia sp. and Pinus contorta Doug, ex Loud seedlings connected by a common ectomycorrhizal mycelium. New Phytol. 124, 231–242 (1993).Article 

Google Scholar 
Finlay, R. D. Functional aspects of phosphorus uptake and carbon translocation in incompatible ectomycorrhizal associations between Pinus sylvestris and Suillus grevillei and Boletinus cauipes. New Phytol. 112, 185–192 (1989).Article 
CAS 

Google Scholar 
Cahanovitc, R., Livne-Luzon, S., Angel, R. & Klein, T. Ectomycorrhizal fungi mediate belowground carbon transfer between pines and oaks. ISME J. 16, 1420–1429 (2022).Article 
CAS 

Google Scholar 
Teste, F. P., Veneklass, E. J., Dixon, K. W. & Lambers, H. Is nitrogen transfer among plants enhanced by contrasting nutrient-acquisition strategies? Plant Cell Environ. 38, 50–60 (2015).Article 
CAS 

Google Scholar 
Simard, S. W. et al. Reciprocal transfer of carbon isotopes between ectomycorrhizal Betula papyrifera and Pseudotsuga menziesii. New Phytol. 137, 529–542 (1997).Article 
CAS 

Google Scholar 
Egerton-Warburton, L. M., Querejeta, J. I. & Allen, M. F. Common mycorrhizal networks provide a potential pathway for the transfer of hydraulically lifted water between plants. J. Exp. Bot. 58, 1473–1483 (2007).Article 
CAS 

Google Scholar 
He, X., Critchley, C., Ng, H. & Bledsoe, C. Nodulated N2-fixing Casuarina cunninghamiana is the sink for net N transfer from non-N2-fixing Eucalyptus maculata via an ectomycorrhizal fungus Pisolithus sp. using 15NH4+ or 15NO3− supplied as ammonium nitrate. New Phytol. 167, 897–912 (2005).Article 
CAS 

Google Scholar 
He, X., Critchley, C., Ng, H. & Bledsoe, C. Reciprocal N (15NH4+ or 15NO3−) transfer between nonN2-fixing Eucalyptus maculata and N2-fixing Casuarina cunninghamiana linked by the ectomycorrhizal fungus Pisolithus sp. New Phytol. 163, 629–640 (2004).Article 

Google Scholar 
Bingham, M. A. & Simard, S. W. Do mycorrhizal network benefits to survival and growth of interior Douglas-fir seedlings increase with soil moisture stress? Ecol. Evol. 1, 306–316 (2011).Article 

Google Scholar 
Babikova, Z. et al. Underground signals carried through common mycelial networks warn neighbouring plants of aphid attack. Ecol. Lett. 16, 835–843 (2013).Article 

Google Scholar 
Birch, J. D., Simard, S. W., Beiler, K. J. & Karst, J. Beyond seedlings: ectomycorrhizal fungal networks and growth of mature Pseudotsuga menziesii. J. Ecol. 109, 806–818 (2021).Article 
CAS 

Google Scholar 
Färkkilä, S. M. A. et al. Fluorescent nanoparticles as tools in ecology and physiology. Biol. Rev. 96, 2392–2424 (2021).Article 

Google Scholar  More

Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).
Google Scholar 
Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).
Google Scholar 
Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).
Google Scholar 
Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).ADS 
CAS 

Google Scholar 
Alexander Pyron, R. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280, 1–10 (2013).
Google Scholar 
Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).
Google Scholar 
Wright, S., Keeling, J. & Gillman, L. The road from Santa Rosalia: a faster tempo of evolution in tropical climates. Proc. Natl Acad. Sci. USA 103, 7718–7722 (2006).ADS 
CAS 

Google Scholar 
Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).
Google Scholar 
Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
CAS 

Google Scholar 
Igea, J. & Tanentzap, A. J. Angiosperm speciation speeds up near the poles. Ecol. Lett. 23, 1–40 (2020).
Google Scholar 
Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007).ADS 
CAS 

Google Scholar 
Rabosky, D. L. & Huang, H. A robust semi-parametric test for detecting trait-dependent diversification. Syst. Biol. 65, 181–193 (2016).
Google Scholar 
Hansen, J. et al. Global temperature change. Proc. Natl Acad. Sci. USA 103, 14288–14293 (2006).ADS 
CAS 

Google Scholar 
Huey, R. B. & Kingsolver, J. G. Climate warming, resource availability, and the metabolic meltdown of ectotherms. Am. Nat. 194, E140–E150 (2019).
Google Scholar 
Gerringer, M. E., Linley, T. D., Jamieson, A. J., Goetze, E. & Drazen, J. C. Pseudoliparis swirei sp. Nov.: A newly-discovered hadal snailfish (Scorpaeniformes: Liparidae) from the Mariana Trench. Zootaxa 4358, 161–177 (2017).
Google Scholar 
Childress, J. J. Are there physiological and biochemical adaptations of metabolism in deep-sea animals? Trends Ecol. Evol. 10, 30–36 (1995).CAS 

Google Scholar 
Seibel, B. A. & Drazen, J. C. The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities. Philos. Trans. R. Soc. B Biol. Sci. 362, 2061–2078 (2007).CAS 

Google Scholar 
Eme, D., Anderson, M. J., Myers, E. M. V., Roberts, C. D. & Liggins, L. Phylogenetic measures reveal eco-evolutionary drivers of biodiversity along a depth gradient. Ecography 43, 689–702 (2020).
Google Scholar 
Costello, M. J. & Chaudhary, C. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Curr. Biol. 27, R511–R527 (2017).CAS 

Google Scholar 
Brown, A. & Thatje, S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: Physiological contributions to adaptation of life at depth. Biol. Rev. 89, 406–426 (2014).
Google Scholar 
Zintzen, V., Anderson, M. J., Roberts, C. D., Harvey, E. S. & Stewart, A. L. Effects of latitude and depth on the beta diversity of New Zealand fish communities. Sci. Rep. 7, 1–10 (2017).CAS 

Google Scholar 
Coleman, R. R., Copus, J. M., Coffey, D. M., Whitton, R. K. & Bowen, B. W. Shifting reef fish assemblages along a depth gradient in Pohnpei, Micronesia. PeerJ 2018, 1–30 (2018).
Google Scholar 
Neat, F. C. & Campbell, N. Proliferation of elongate fishes in the deep sea. J. Fish. Biol. 83, 1576–1591 (2013).CAS 

Google Scholar 
Martinez, C. M. et al. The deep sea is a hot spot of fish body shape evolution. Ecol. Lett. 24, 1788–1799 (2021).
Google Scholar 
Webb, P. Introduction to Oceanography (Online OER textbook, 2017).Hanly, P. J., Mittelbach, G. G. & Schemske, D. W. Speciation and the latitudinal diversity gradient: Insights from the global distribution of endemic fish. Am. Nat. 189, 604–615 (2017).
Google Scholar 
Tedesco, P. A., Paradis, E., Lévêque, C. & Hugueny, B. Explaining global-scale diversification patterns in actinopterygian fishes. J. Biogeogr. 44, 773–783 (2017).
Google Scholar 
Cooney, C. R., Seddon, N. & Tobias, J. A. Widespread correlations between climatic niche evolution and species diversification in birds. J. Anim. Ecol. 85, 869–878 (2016).
Google Scholar 
Title, P. O. & Burns, K. J. Rates of climatic niche evolution are correlated with species richness in a large and ecologically diverse radiation of songbirds. Ecol. Lett. 18, 433–440 (2015).
Google Scholar 
Seeholzer, G. F., Claramunt, S. & Brumfield, R. T. Niche evolution and diversification in a Neotropical radiation of birds (Aves: Furnariidae). Evolution 71, 702–715 (2017).
Google Scholar 
Kozak, K. H. & Wiens, J. J. Accelerated rates of climatic-niche evolution underlie rapid species diversification. Ecol. Lett. 13, 1378–1389 (2010).
Google Scholar 
Schnitzler, J., Graham, C. H., Dormann, C. F., Schiffers, K. & Peter Linder, H. Climatic niche evolution and species diversification in the cape flora, South Africa. J. Biogeogr. 39, 2201–2211 (2012).
Google Scholar 
Ghezelayagh, A. et al. Prolonged morphological expansion of spiny-rayed fishes following the end-Cretaceous. Nat. Ecol. Evol. 1–10. https://doi.org/10.1038/s41559-022-01801-3 (2022).Polato, N. R. et al. Narrow thermal tolerance and low dispersal drive higher speciation in tropical mountains. Proc. Natl Acad. Sci. USA 115, 12471–12476 (2018).ADS 
CAS 

Google Scholar 
Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).
Google Scholar 
O’Hara, T. D., Hugall, A. F., Woolley, S. N. C., Bribiesca-Contreras, G. & Bax, N. J. Contrasting processes drive ophiuroid phylodiversity across shallow and deep seafloors. Nature 565, 636–639 (2019).ADS 

Google Scholar 
Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639 (2010).
Google Scholar 
Hulsey, C. D., Roberts, R. J., Loh, Y. H. E., Rupp, M. F. & Streelman, J. T. Lake Malawi cichlid evolution along a benthic/limnetic axis. Ecol. Evol. 3, 2262–2272 (2013).CAS 

Google Scholar 
Woolley, S. N. C. et al. Deep-sea diversity patterns are shaped by energy availability. Nature 533, 393–396 (2016).ADS 
CAS 

Google Scholar 
Pigot, A. L., Owens, I. P. F. & Orme, C. D. L. The environmental limits to geographic range expansion in birds. Ecol. Lett. 13, 705–715 (2010).
Google Scholar 
Gerringer, M. E., Linley, T. D. & Nielsen, J. G. Revision of the depth record of bony fishes with notes on hadal snailfishes (Liparidae, Scorpaeniformes) and cusk eels (Ophidiidae, Ophidiiformes). Mar. Biol. 168, 1–9 (2021).
Google Scholar 
Kolora, S. R. R. et al. Origins and evolution of extreme life span in Pacific Ocean rockfishes. Science 374, 842–847 (2021).ADS 
CAS 

Google Scholar 
Rutschmann, S. et al. Parallel ecological diversification in Antarctic notothenioid fishes as evidence for adaptive radiation. Mol. Ecol. 20, 4707–4721 (2011).
Google Scholar 
Wilson, L. A. B., Colombo, M., Hanel, R., Salzburger, W. & Sánchez-Villagra, M. R. Ecomorphological disparity in an adaptive radiation: opercular bone shape and stable isotopes in Antarctic icefishes. Ecol. Evol. 3, 3166–3182 (2013).
Google Scholar 
Ingram, T. Speciation along a depth gradient in a marine adaptive radiation. Proc. R. Soc. B. 278, 613–618 (2011).
Google Scholar 
Hyde, J. R., Kimbrell, C. A., Budrick, J. E., Lynn, E. A. & Vetter, R. D. Cryptic speciation in the vermilion rockfish (Sebastes miniatus) and the role of bathymetry in the speciation process. Mol. Ecol. 17, 1122–1136 (2008).CAS 

Google Scholar 
Kai, Y., Orr, J. W., Sakai, K. & Nakabo, T. Genetic and morphological evidence for cryptic diversity in the Careproctus rastrinus species complex (Liparidae) of the North Pacific. Ichthyol. Res. 58, 143–154 (2011).
Google Scholar 
Gerringer, M. E. et al. Habitat influences skeletal morphology and density in the snailfishes (family Liparidae). Front. Zool. 18, 1–22 (2021).
Google Scholar 
Saveliev, P. A. & Metelyov, E. A. Species composition and distribution of eelpouts (Zoarcidae, Perciformes, Actinopterygii) in the northwestern Sea of Okhotsk in summer. Prog. Oceanogr. 196, 102605 (2021).
Google Scholar 
Quattrini, A. M. et al. Niche divergence by deep-sea octocorals in the genus Callogorgia across the continental slope of the Gulf of Mexico. Mol. Ecol. 22, 4123–4140 (2013).
Google Scholar 
Zardus, J. D., Etter, R. J., Chase, M. R., Rex, M. A. & Boyle, E. E. Bathymetric and geographic population structure in the pan-Atlantic deep-sea bivalve Deminucula atacellana (Schenck, 1939). Mol. Ecol. 15, 639–651 (2006).CAS 

Google Scholar 
Schüller, M. Evidence for a role of bathymetry and emergence in speciation in the genus Glycera (Glyceridae, Polychaeta) from the deep Eastern Weddell Sea. Polar Biol. 34, 549–564 (2011).
Google Scholar 
Smith, W. L., Everman, E. & Richardson, C. Phylogeny and taxonomy of flatheads, scorpionfishes, sea robins, and stonefishes (Percomorpha: Scorpaeniformes) and the evolution of the lachrymal saber. Copeia 106, 94–119 (2018).
Google Scholar 
Jamon, M., Renous, S., Gasc, J. P., Bels, V. & Davenport, J. Evidence of force exchanges during the six-legged walking of the bottom-dwelling fish,Chelidonichthys lucerna. J. Exp. Zool. 307A, 542–547 (2007).
Google Scholar 
McCune, A. R. & Carlson, R. L. Twenty ways to lose your bladder: common natural mutants in zebrafish and widespread convergence of swim bladder loss among teleost fishes. Evol. Dev. 6, 246–259 (2004).
Google Scholar 
Rabosky, D. L. Speciation rate and the diversity of fishes in freshwaters and the oceans. J. Biogeogr. 47, 1207–1217 (2020).
Google Scholar 
Daane, J. M. et al. Historical contingency shapes adaptive radiation in Antarctic fishes. Nat. Ecol. Evol. 3, 1102–1109 (2019).
Google Scholar 
Mu, Y. et al. Whole genome sequencing of a snailfish from the Yap Trench (~7,000 m) clarifies the molecular mechanisms underlying adaptation to the deep sea. PLoS Genet. 17, e1009530 (2021).CAS 

Google Scholar 
Yancey, P. H., Gerringer, M. E., Drazen, J. C., Rowden, A. A. & Jamieson, A. Marine fish may be biochemically constrained from inhabiting the deepest ocean depths. Proc. Natl Acad. Sci. USA 111, 4461–4465 (2014).ADS 
CAS 

Google Scholar 
Janzen, D. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Google Scholar 
Kozak, K. H. & Wiens, J. J. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. R. Soc. B: Biol. Sci. 274, 2995–3003 (2007).
Google Scholar 
Sheldon, K. S., Huey, R. B., Kaspari, M. & Sanders, N. J. Fifty years of mountain passes: a perspective on Dan Janzen’s classic article. Am. Nat. 191, 553–565 (2018).
Google Scholar 
Muñoz, M. M. & Bodensteiner, B. L. Janzen’s hypothesis meets the bogert effect: connecting climate variation, thermoregulatory behavior, and rates of physiological evolution. Integr. Organ. Biol. 1, oby002 (2019).
Google Scholar 
Santidrián Tomillo, P., Fonseca, L., Paladino, F. V., Spotila, J. R. & Oro, D. Are thermal barriers ‘higher’ in deep sea turtle nests? PLoS ONE 12, 1–14 (2017).
Google Scholar 
Brown, J. H. Why marine islands are farther apart in the tropics. Am. Nat. 183, 842–846 (2014).
Google Scholar 
Jablonski, D. et al. Out of the tropics, but how? Fossils, bridge species, and thermal ranges in the dynamics of the marine latitudinal diversity gradient. Proc. Natl Acad. Sci. USA 110, 10487–10494 (2013).ADS 
CAS 

Google Scholar 
Hattermann, T. Antarctic thermocline dynamics along a narrow shelf with easterly winds. J. Phys. Oceanogr. 48, 2419–2443 (2018).ADS 

Google Scholar 
Robison, B. H. What drives the diel vertical migrations of Antarctic midwater fish? J. Mar. Biol. Ass. 83, 639–642 (2003).
Google Scholar 
Bourgeaud, L. et al. Climatic niche change of fish is faster at high latitude and in marine environments. Preprint at bioRxiv https://doi.org/10.1101/853374 (2019).Pie, M. R. et al. The evolution of latitudinal range limits in tropical reef fishes: heritability, limits, and inverse Rapoport’s rule. J. Biogeogr. 00, 1–12 (2021).
Google Scholar 
Powell, M. G. & Glazier, D. S. Asymmetric geographic range expansion explains the latitudinal diversity gradients of four major taxa of marine plankton. Paleobiology 43, 196–208 (2017).
Google Scholar 
Lawson, A. M. & Weir, J. T. Latitudinal gradients in climatic-niche evolution accelerate trait evolution at high latitudes. Ecol. Lett. 17, 1427–1436 (2014).
Google Scholar 
Boag, T. H., Gearty, W. & Stockey, R. G. Metabolic tradeoffs control biodiversity gradients through geological time. Curr. Biol. 31, 2906–2913.e3 (2021).CAS 

Google Scholar 
Near, T. J. et al. Ancient climate change, antifreeze, and the evolutionary diversification of Antarctic fishes. Proc. Natl Acad. Sci. USA 109, 3434–3439 (2012).ADS 
CAS 

Google Scholar 
Hotaling, S., Borowiec, M. L., Lins, L. S. F., Desvignes, T. & Kelley, J. L. The biogeographic history of eelpouts and related fishes: Linking phylogeny, environmental change, and patterns of dispersal in a globally distributed fish group. Mol. Phylogenet. Evol. 162, 107211 (2021).
Google Scholar 
Thatje, S., Hillenbrand, C.-D., Mackensen, A. & Larter, R. Life hung by a thread: endurance of Antarctic fauna in glacial periods. Ecology 89, 682–692 (2008).
Google Scholar 
Keller, I. & Seehausen, O. Thermal adaptation and ecological speciation. Mol. Ecol. 21, 782–799 (2012).CAS 

Google Scholar 
Deutsch, C., Penn, J. L. & Seibel, B. Metabolic trait diversity shapes marine biogeography. Nature 585, 557–562 (2020).ADS 
CAS 

Google Scholar 
Labeyrie, L. D., Duplessy, J. C. & Blanc, P. L. Variations in mode of formation and temperature of oceanic deep waters over the past 125,000 years. Nature 327, 477–482 (1987).ADS 
CAS 

Google Scholar 
Boag, T. H., Stockey, R. G., Elder, L. E., Hull, P. M. & Sperling, E. A. Oxygen, temperature and the deep-marine stenothermal cradle of Ediacaran evolution. Proc. R. Soc. B: Biol. Sci. 285, 2011724 (2018).
Google Scholar 
Koslow, J. A. Community structure in North Atlantic deep-sea fishes. Prog. Oceanogr. 31, 321–338 (1993).ADS 

Google Scholar 
Brunn, A. The abyssal fauna: its ecology, distribution, and origin. Nature 177, 1105–1108 (1956). Fr.ADS 

Google Scholar 
Gaither, M. R. et al. Depth as a driver of evolution in the deep sea: Insights from grenadiers (Gadiformes: Macrouridae) of the genus Coryphaenoides. Mol. Phylogenet. Evol. 104, 73–82 (2016).
Google Scholar 
Eastman, J. T. Evolution and diversification of Antarctic notothenioid fishes. Am. Zool. 31, 93–110 (1991).
Google Scholar 
Quattrini, A. M., Gómez, C. E. & Cordes, E. E. Environmental filtering and neutral processes shape octocoral community assembly in the deep sea. Oecologia 183, 221–236 (2017).ADS 

Google Scholar 
Stefanoudis, P. V. et al. Depth-dependent structuring of reef fish assemblages from the shallows to the rariphotic zone. Front. Mar. Sci. 6, 1–16 (2019).
Google Scholar 
Zintzen, V., Anderson, M. J., Roberts, C. D. & Diebel, C. E. Increasing variation in taxonomic distinctness reveals clusters of specialists in the deep sea. Ecography 34, 306–317 (2011).
Google Scholar 
Price, S. A., Claverie, T., Near, T. J. & Wainwright, P. C. Phylogenetic insights into the history and diversification of fishes on reefs. Coral Reefs 34, 997–1009 (2015).ADS 

Google Scholar 
Weber, M. G., Wagner, C. E., Best, R. J., Harmon, L. J. & Matthews, B. Evolution in a Community Context: On Integrating Ecological Interactions and Macroevolution. Trends Ecol. Evol. 32, 291–304 (2017).
Google Scholar 
Linley, T. D. et al. Fishes of the hadal zone including new species, in situ observations and depth records of Liparidae. Deep Sea Res. Part I Oceanogr. Res. Pap. 114, 99–110 (2016).ADS 

Google Scholar 
Jamieson, A. J., Linley, T. D., Eigler, S. & Macdonald, T. A global assessment of fishes at lower abyssal and upper hadal depths (5000 to 8000 m). Deep Sea Res. Part I Oceanogr. Res. Pap. 103642. https://doi.org/10.1016/j.dsr.2021.103642 (2021).Boers, N. Observation-based early-warning signals for a collapse of the Atlantic meridional overturning circulation. Nat. Clim. Chang. 11, 680–688 (2021).ADS 

Google Scholar 
Paulus, E. Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front. Mar. Sci. 8, 667048 (2021).Froese, R. & Pauly, D. FishBase. FishBase www.fishbase.org (2019).Boettiger, C., Lang, D. T. & Wainwright, P. C. Rfishbase: exploring, manipulating and visualizing FishBase data from R. J. Fish. Biol. 81, 2030–2039 (2012).CAS 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 

Google Scholar 
Karstensen, J., Stramma, L. & Visbeck, M. Oxygen minimum zones in the eastern tropical Atlantic and Pacific oceans. Prog. Oceanogr. 77, 331–350 (2008).ADS 

Google Scholar 
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. Part I: Oceanogr. Res. Pap. 126, 85–102 (2017).ADS 

Google Scholar 
Alfaro, M. E. et al. Explosive diversification of marine fishes at the Cretaceous–Palaeogene boundary. Nat. Ecol. Evol. 2, 688–696 (2018).
Google Scholar 
Magnuson-Ford, K. & Otto, S. P. Linking the investigations of character evolution and species diversification. Am. Nat. 180, 225–245 (2012).
Google Scholar 
Goldberg, E. E. & Igić, B. Tempo and mode in plant breeding system evolution. Evolution 66, 3701–3709 (2012).
Google Scholar 
Rabosky, D. L. & Goldberg, E. E. Model inadequacy and mistaken inferences of trait-dependent speciation. Syst. Biol. 64, 340–355 (2015).CAS 

Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
Google Scholar 
Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. (2019).Collyer, M. L. & Adams, D. C. RRPP: An r package for fitting linear models to high-dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).
Google Scholar 
Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).
Google Scholar 
Freckleton, R. P., Phillimore, A. B. & Pagel, M. Relating traits to diversification: a simple test. Am. Nat. 172, 102–115 (2008).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 

Google Scholar 
Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).ADS 
CAS 

Google Scholar 
May, M. R. & Moore, B. R. A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation. Syst. Biol. 69, 530–544 (2020).
Google Scholar 
Höhna. et al. RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Syst. Biol. 65, 726–736 (2016).
Google Scholar 
Burress, E. D. & Muñoz, M. M. Ecological opportunity from innovation, not islands, drove the anole lizard adaptive radiation. Syst. Biol. 0, 1–12 (2021).
Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 

Google Scholar 
Ives, A. R. & Helmus, M. R. Phylogenetic metrics of community similarity. Am. Nat. 176, E128–E142 (2010).
Google Scholar 
Costello, M. J. & Breyer, S. Ocean depths: the mesopelagic and implications for global warming. Curr. Biol. 27, R36–R38 (2017).CAS 

Google Scholar  More

As part of the streamflow data release, NEON released four relevant data products: Gauge Height26, Elevation of Surface Water29, Stage-discharge Rating Curves30, and Continuous Discharge15. Data users are able to download this full suite of information and protocols to inform decisions on data usage and applicability. We evaluated the quality of the Continuous Discharge product using all four relevant NEON data products, considering the validity of model inputs as well as the goodness-of-fit of final streamflow estimates. We analyzed 1) the fit of the regression between manual stage height readings and continuous pressure transducer data used to estimate continuous stream surface elevation, 2) the fit of rating curves transforming stream surface elevation to streamflow, and 3) the proportion of streamflow estimates over the maximum manually-measured streamflow.Stage classificationThe rating curve models predicting streamflow required continuous stream stage estimates as model inputs. NEON predicted continuous gauge height with a two step approach. First, continuous in-stream transducer readings were converted to water height by applying an offset between the transducer elevation and the staff gauge (Eq. 1). This offset is derived from the NEON geolocation database as the difference between the location of the pressure transducer and the staff gauge27. The offset changes only when the location of either the staff gauge or transducer moves.$${h}_{wc}=frac{{P}_{sw}}{p,ast ,g},ast ,1000+{h}_{stage}$$
(1)
Conversion of pressure data to water height used by NEON27 where hwc is the estimated water column height (m), Psw is calibrated surface water pressure (kPa), p is the density of water (999 kg/m3), g is the acceleration due to gravity (9.81 m/s2), and hstage is the offset between the pressure transducer and the staff gauge (m).Then, NEON uses a linear regression between manually-measured reference stage height and the calculated gauge height from Eq. 1, yielding final predictions of continuous stream gauge height27. In an ideal setting, stage and gauge height should correlate perfectly28. In the field, sensor uncertainty, manual reference measurement error, and shifting conditions in the stream can convolute the relationship. We tested the goodness of fit between continuously estimated stream gauge height values and manual stage measurements using the Nash-Sutcliffe model efficiency coefficient (Eq. 2). Nash-Sutcliffe coefficient is a commonly used metric in hydrology used to evaluate how well a model performed relative to observed values (manually measured stage and calculated gauge height). For the purposes of this discussion, manual reference measurements will be referred to as ‘stage’ and automated, sensed readings as ‘gauge height’.$$NSE=1-frac{Sigma {left({Q}_{o}-{Q}_{m}right)}^{2}}{Sigma {left({Q}_{o}-{bar{Q}}_{o}right)}^{2}}$$
(2)
Equation 2 presents Nash-Sutcliffe model efficiency coefficient, where Qo is an observed value (streamflow or stage height), Qm is a modeled value, and ({bar{Q}}_{o}) is the mean of observed values.Stage, gauge height, and regression data were sourced from the NEON Continuous Discharge product, representing what was directly applied to streamflow estimation. Up to 26 stage measurements were available per year. We examined every regression between stage and gauge height (one per site year in which data was available) and classified each as either ‘good’, ‘fair’, or ‘poor’ quality based on their goodness of fit. Regressions with a NSE (Eq. 2) of 0.90 or greater were considered good, those with a NSE of less than 0.90 but greater than or equal to 0.75 were considered fair, and those with an NSE of less than 0.75 were considered poor (Fig. 2).Drift detectionBecause electronic instruments, such as pressure transducers, can have systematic directional drift, referred to as ‘drift’, during deployment, we developed an approach to detect periods of time when NEON’s Elevation of Surface Water product drifted. We used two methods to assess and flag the potential for instrument drift at monthly time steps. First, we flagged any period the manually measured stage fell outside NEON’s uncertainty bound for gauge height made at the same time. From this, we calculated the proportion of stage measurements outside of the gauge height uncertainty bounds per month. This proved to be a relatively lenient filter that missed periods of manually identified drift. We found adding a second filter that flagged any month where the difference between the manually measured stage and gauge height exceeded 6 cm, was effective in catching the majority of periods where drift was identified. Second, we calculated the average differences between stage and gauge height for each month (Fig. 3). To determine appropriate cut-off values to classify areas of potential drift, we manually audited and flagged periods of observable directional drift. Our goal was to set a maximum cut-off difference which retained as much usable data as possible while still capturing 70% of the manually flagged directional drift periods. Applying this method, we determined a cut-off value of 6 cm average monthly deviation between observed and predicted stage values.Using these two filters in combination, we again classified data into three groups: ‘likely no drift’, ‘potential drift’, and ‘not assessed’. Site-months with no more than 50% of stage measurements outside of the gauge height time series uncertainty and an average difference between stage and gauge height less than 6 cm were considered to have ‘likely no drift’. Site-months with either more than 50% of stage readings outside of the gauge height time series uncertainty or an average difference between stage and gauge height more than 6 cm were deemed to have ‘potential drift’. Site-months with no stage measurements could not be evaluated and were considered ‘not assessed’. Although this approach to identify drift is imperfect, in that slight drift could be missed and times without manual measurements are not possible to assess, we believe this is a helpful method given the data available from NEON and the fact drift has been observed when visually inspecting data (Fig. 3).Rating curve classificationTo evaluate how well rating curves predicted streamflow, we assessed each rating curve used to convert stage to discharge. NEON prepares a new rating curve for each site’s water year (beginning on October 1st)27. In cases where NEON reported multiple rating curves for a site’s water year each curve was assessed separately across the time series which it was used. We classified rating curves into three tiers based on two metrics: the Nash-Sutcliffe coefficient (Eq. 2) between observed and predicted streamflow, and the percentage of continuous discharge values above the maximum manually measured gauging used to construct the rating curve.First, we calculated the Nash-Sutcliffe coefficient for each rating curve to estimate how well rating curves captured the variation in the stage-streamflow relationship. We used the reported values for modeled and manually measured streamflow from the ‘Y1simulated’ and ‘Y1observed’ columns in the ‘sdrc_resultsResiduals’ table of the Stage-discharge rating curves product. NEON generally conducts between 12 and 24 manual gaugings per year to build and maintain the stage-discharge relationship.Second, we calculated the percentage of continuous streamflow values outside the range of manually measured estimates of streamflow. This was useful to assess if the stage-discharge relationship is representative of observed flow conditions. The relationship between discharge and stage is often nonlinear, with inflection points around changes in channel morphology making gauging the stream at high and low flow conditions critical to building a reliable rating curve16. A rating curve based on a large number of direct field measurements all taken during a narrow range of baseflows, for example, could generate a rating curve with a high Nash-Sutcliffe coefficient that is unreliable when extrapolated to high or low flow events. Using these two metrics, we were able to classify rating curves into categories of relative quality. To calculate the percentage of values in the continuous streamflow product that fall outside the range of manually gauged streamflow values, we extracted the maximum and minimum gauging values from the ‘sdrc_resultsResiduals’ table in the Stage-discharge Rating Curve product. We then compared the predicted values derived from each rating curve (as reported in the ‘csd_continuousDischarge’ table) to the extracted range and calculated the proportion of values which fell outside of it.We used the Nash-Sutcliffe coefficient and percentage of streamflow values over the maximum observed field measurements to classify rating curves into three categories outlined in Table 1.To integrate stage-gauge regressions, drift detections, and rating curve classification, we produced a summary table with classifications for all three tests and the corresponding metrics used in each classification (Fig. 5). The table is grouped by month and site so users can query sites and determine which months have the appropriate data for their needs. More

Orchard site selectionThe survey was conducted from 2018 to 2019 in the Circum-Bohai Bay region, which included Shandong, Hebei, and Liaoning provinces and Beijing, and the Loess Plateau region, which included Shanxi and Shaanxi provinces. Five typical production counties were selected in each province or city. Representative orchards were selected according to the production of the main varieties in each county (orchard area was greater than 1.0 ha; the pear trees were 15 to 25 years old; and the yield of orchards ranged from 40 to 60 t ha−1). A total of 225 orchards were investigated (Fig. 1), including 150 in the Circum-Bohai Bay region and 75 in the Loess Plateau region (Table 1).Fig. 1The locations of the 225 pear orchards.Full size imageTable 1 Numbers of pear orchard and main cultivated varieties investigated in Circum-Bohai Bay and Loess Plateau.Full size tableSample collection and pretreatmentSoil and leaf samples were collected at the stage in which the growth of new shoots ceased, from July 1 to July 1510. Eleven sampling sites were determined in each orchard according to an “S” shape sampling method (Fig. 2), and soil samples from the 0–20 cm, 20–40 cm and 40–60 cm layers were collected. The soil samples of the same soil layer at each sampling site were mixed into one sample. Then, the soil samples were air-dried, ground and sifted with a nylon sieve for determination of nutrient concentrations.Fig. 2The “S” shape sampling method. The red dots are the sampling locations.Full size imageTen to fifteen pear trees in each orchard of the same size and vigour and 5 to 10 mature leaves from the middle of a long shoot from the periphery of each tree were selected for leaf sampling11. Then, all the leaves from the same orchard were mixed into one leaf sample. The leaves were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the leaf samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Fruit samples were collected at the ripening stage. Pear trees from which leaf samples were collected from each orchard were selected for fruit sample collection. Three to five peripheral fruits of the same size were collected from each tree, and fruit samples from the same orchard were mixed into one sample. The fruits were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again, cut into slices and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the fruit samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Sample determinationVarious indicators of soil and plant samples were determined according to the method of Cui et al.12 and Bao13.Soil pH determinationA potentiometric method was used to measure soil pH. Carbon dioxide-free water was added to soil that had been passed through a 2 mm sieve at a water-soil ratio of 2.5:1. The soil solution was stirred for 1 min and left undisturbed for 30 min. Each soil sample was measured more than three times with a pH meter (FE20K PLUS PH, Mettler-Toledo, Switzerland), and the difference in the parallel determination results was less than 0.2 pH units. The electrode was washed with deionized water and dried with filter paper after each sample measurement. A calibration solution was used to calibrate the electrode between measurements after every 10 soil samples.Soil organic matter determinationSoil organic matter was measured according to the Schollenberger method using chromic acid redox titration. Five millilitres of a 0.8 M 1/6 K2Cr2O7 solution was added to a test tube with approximately 0.5000 g of soil that had been passed through a 0.25 mm sieve. The mixture was then added to 5 mL concentrated sulfuric acid and shaken gently to disperse the soil. The tube was placed in a phosphoric acid bath, heated to 170 °C and boiled for 5 min. To condense the water vapour that escaped during the heating process, a small funnel was placed on the top of the test tube. The substances in the test tube and funnel were transferred to a conical flask after cooling. Then, the solution was added to 1,10-phenanthroline hydrate and titrated with 0.2 M FeSO4 until it turned maroon. A blank experiment was performed when each batch of samples was measured. The soil organic matter content was calculated according to the following formula:$${rm{omega }}left({rm{OM}}right)=frac{left({rm{V}}-{rm{V}}0right)times {rm{c}}times 3times 1.724times {rm{f}}}{{rm{m}}}$$
(1)
ω(OM): soil organic matter content; c: standard FeSO4 solution concentration; V: volume of the standard FeSO4 used in titration; V0: volume of standard FeSO4 used in titrating control sample; 3: molar mass of a quarter of carbon; 1.724: the conversion factor from organic carbon to organic matter; f: oxidation correction coefficient (the value was 1.1); m: mass of oven-dried soil sample.Soil total N determinationTotal N was determined by the semitrace Kjeldahl method. Approximately 1.0000 g of air-dried soil that had been passed through a 0.25 mm sieve was added to a digestion tube. Meanwhile, the soil moisture content was measured to calculate the mass of the oven-dried soil. Two grams of accelerator and 5 mL of concentrated sulfuric acid were added to the tube. The tube was then covered with a small funnel, and the sample was digested at 360 °C for 15–20 min. The mixture was digested for 1 h until the colour changed from brown to greyish green or greyish white. Two digested soilless samples were used as controls. After the digestion tube cooled, it was placed in a distiller, and a small amount of deionized water was added. Five millilitres of a 2% boric acid indicator was added to a 150 mL conical flask, and the flask was placed at the end of the condenser tube. Then, the digestion solution was distilled until the distillate volume was approximately 75 mL. The distillate was titrated with 0.01 M standard hydrochloric acid to a purplish red colour endpoint. The soil total N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
(2)
ω(N): soil total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried soil sample.Soil alkaline hydrolysable N determinationApproximately 2.00 g of air-dried soil that have been passed through a 2 mm sieve was placed in the outer chamber of a diffuser. The diffuser was gently rotated to evenly distribute the soil in the outer chamber. Two millilitres of H3BO3 indicator was placed in the inner chamber of the diffusion dish. The edge of the frosted glass surface of the diffuser was coated with alkaline glycerin and covered with frosted glass. The diffuser was covered tightly and secured with rubber bands after 10.00 mL of 1 M NaOH was injected into the diffuser through a hole in the frosted glass. The diffuser was placed in a 40 °C incubator for alkaline hydrolysis diffusion for 24 h. Then, the mixture was titrated with 0.01 M standard hydrochloric acid until it turned purplish red. A blank test was performed at the same time as the samples. The soil alkaline hydrolysable N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
(3)
ω(N): soil alkaline hydrolysable N concentration; c: standard acid solution concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of air-dried soil sample.Soil available P determinationApproximately 2.50 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle and 50 mL of 0.5 M NaHCO3 was added. After the bottle was shaken for 30 min, the mixture was immediately filtered with phosphorus-free filter paper. Ten millilitres of the filtrate was accurately measured into a conical flask, and 5.00 mL of Mo-Sb-Vc colour developer and 10 mL of deionized water were added. The absorbance of the mixture was measured at approximately 700 nm after 30 min using a UV-Vis spectrophotometer (UV1900PC, AuCy Instrument, Shanghai, China). Finally, the P concentration was calculated according to a standard curve prepared with solutions of different P concentrations. A blank test was performed at the same time that the samples were determined.Soil available K determinationApproximately 5.00 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle, and 50 mL of 1.0 M NH4OAc was added. After the sample was shaken for 30 min, the mixture was immediately filtered with dry filter paper. The concentration of K in the filtrate was determined directly by a flame photometer (LM12-FP6430, Haifuda, China) according to a standard curve prepared with solutions of different K concentrations. A blank test was performed at the same time that the samples were determined.Leaf and fruit N determinationApproximately 0.3000 g of plant powder that had been passed through a 0.5 mm sieve was placed into a digestion tube and 5 mL concentrated sulfuric acid was added. Then, the digestion tube was placed onto a digestion stove at 360 °C after two doses of 2 mL H2O2, and the sample was digested until the mixture turned brown. After the tube cooled, 2 mL H2O2 was added, and the digestion was continued for 5 min. This process was repeated until the mixture turned clear. The mixture was diluted to 100 mL in a volumetric flask for testing after it cooled. Then, 5 to 10 mL of the liquid to be tested was accurately measured into a distiller for distillation. The distillation and titration processes were the same as those used for ammonium in the Soil total N determination section. A blank test was performed at the same time as sample measurement. The leaf or fruit N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14times {rm{V}}1}{{rm{m}}times {rm{V}}2}$$
(4)
ω(N): total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried sample; V1: volume of the digestion solution after constant volume; V2: measured volume of digestion solution after constant volume.Leaf and fruit P, K, Ca, Fe, Mn, Cu, Zn, B determinationApproximately 0.5000 g of plant powder that had been passed through a 0.5 mm sieve was placed in a digestion tube and a 10 mL mixture of concentrated nitric acid and hypochlorous acid (4:1) was added. After the sample was left undisturbed for more than 4 h, it was placed onto a digestion stove and heated to 150 °C so that NO2 could volatilize slowly. Then, the temperature was appropriately increased to a temperature not higher than 250 °C until the digestive solution was transparent and approximately 2 mL remained. The solution was transferred into a volumetric flask after cooling and adjusted to a constant volume of 50 mL. The solution was then filtered, and the concentration of each element in the solution was determined by a plasma emission spectrometer (ICP-OES, OPTIMA 3300 DV, 75 Perkin-Elmer, USA). A blank test was performed at the same time as sample measurement. The leaf or fruit P, K, Ca, Fe, Mn, Cu, Zn, and B concentrations were calculated according to the following formula:$${rm{omega }}({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})=frac{rho ({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})times {rm{V}}times {rm{f}}}{{rm{m}}}$$
(5)
ω(P, K, Ca, Fe, Mn, Cu, Zn, B): P, K, Ca, Fe, Mn, Cu, Zn, B concentration in leaf or fruit; ρ(P, K, Ca, Fe, Mn, Cu, Zn, B): the concentration of P, K, Ca, Fe, Mn, Cu, Zn or B in the liquid to be measured; V: volume of the liquid to be measured after constant volume; f: dilution ratio of the liquid to be measured; m: mass of oven-dried sample. More

Myxococcota and Bdellovibrionota were active constituents of activated sludge microbiotaTo explore the predating activity and diversity of predatory bacteria in activated sludge, 13C-labeled Escherichia coli and Pseudomonas putida cells (determined as 97.09 and 97.30 atom% 13C, respectively) were added to the sludge microcosms for maximumly eight days of incubation, and 13C incorporation was examined using rRNA-SIP to identify prokaryotic and eukaryotic microorganisms involved in actively consuming the 13C-labeled prey cells. Bacterial 16S rRNA gene amplicon sequencing-based analysis indicated the relative contribution of 47.9% and 42.7% of the obtained sequences by the added biomass upon amendment in the 13C-E. coli (Fig. 1A) and 13C-P. putida (Fig. 1B) microcosms, which dropped below 1.0% after 16 h and eight days of incubation, respectively. The overall bacterial community structure at the steady state was highly comparable to that of the control microcosms (Fig. 1C), indicating that the prey cell amendments did not induce too strong fluctuation in the microbiota structure during the SIP experiment that prevented disentangling the indigenous community dynamics.Fig. 1: The dynamics of the prokaryotic communities and mineralization of the added 13C-biomass during the microcosm experiment.The overall prokaryotic communities were obtained by 16S rRNA gene amplicon sequencing of the total DNA from the activated sludge microcosms amended with 13C-E. coli (A) and 13C-P. putida (B) cells, and the control group (C) without amendment. The structure of the active prokaryotic communities was inferred based on amplicon sequencing of the light rRNA fractions from the microcosms amended with 13C-E. coli (D) and 13C-P. putida (E) cells. The temporal change in the proportion of produced 13CO2 in total CO2 indicated the mineralization of the 13C-labeled cells of E. coli and P. putida in duplicate microcosms (F). Relative sequence abundance of the ten most abundant prokaryotic phyla, together with the genera Escherichia-Shigella and Pseudomonas, was shown.Full size imageThe metabolically active bacterial communities, as inferred by 16S rRNA gene transcripts of the light rRNA fractions from the microcosms, were rather consistent throughout the experiment (Fig. 1D, E), but they showed clear compositional differences compared to the overall prokaryotic communities inferred by 16S rRNA gene amplicon sequences (Fig. 1A, B). Myxococcota and Bdellovibrionota species showed an average relative abundance of 17.5 (±0.7) % and 2.7 (±0.2) % in the 16S rRNA gene transcripts, respectively, which were significantly higher than 5.4 (±0.6) % and 1.3 (±0.1) % in the 16S rRNA genes of bacterial communities (p 1% in the 13C-heavy fractions, strong 13C-labeling was found for the as-yet-uncultivated myxobacterial mle1-27 clade (average EF 2.66 across time and treatments), which contributed to 10.3% to 38.9% of the 16S rRNA gene transcripts in the 13C-heavy fractions, indicating its high metabolic activity in consuming the 13C-labeled biomass of both E. coli and P. putida. Comparatively, Haliangium spp. and uncultured Polyangiaceae belonging to Myxococcota, as well as the as-yet-uncultivated OM27 clade belonging to Bdellovibrionota, also exhibited strong 13C-labeling (maximum EF across time: 2.4–39.5), but almost exclusively in the microcosms amended with 13C-E. coli cells (Fig. 2A). The as-yet-uncultivated myxobacterial VHS-B3-70 clade exhibited the strongest enrichment (average EF 16.67 across time and treatments) but made up only 0.2% to 2.3% of 16S rRNA gene transcripts of the 13C-heavy fraction (Fig. 2A). Overall, our microcosm experiment tracking added 13C-labeled prey bacterial cells with rRNA-SIP suggested prominent predatory activity of Myxococcota and Bdellovibrionota lineages including largely as-yet-uncultivated ones (e.g., the mle1-27, VHS-B3-70, and OM27 clades) in activated sludge.Fig. 2: The enrichment of incorporators of added 13C-biomass in heavy rRNA fractions and the temporal labeling patterns.13C-labeled prokaryotic (A) and micro-eukaryotic (B) genus-level taxa were identified by SIP in the microcosms added with E. coli and P. putida after one, two, and four days of incubation. Enrichment factor (EF) was calculated for microorganisms using heavy and light rRNA gradient fractions of the 13C- and 12C-microcosms to infer 13C-labeling. Taxa with an EF  > 0.1 in at least one of the treatment groups at one sampling time point was considered labeled. The area of circles indicates the relative sequence abundance of the labeled taxa in heavy 13C-rRNA. The negative EFs and positive EFs 1% in the heavy rRNA fractions of at least one of the 13C-E. coli and 13C-P. putida microcosms at a sampling point.Full size imageMyxococcota and Bdellovibrionota predated more selectively than protistsFor the micro-eukaryotes, several taxa belonging to Ciliophora, especially Cyrtophoria spp. and Telotrochidium spp., and also Peritrichia spp., Vaginicola spp., Aspidisca spp., and Epistylis spp., were highly enriched (maximum EF across time and treatments: 0.9–6.7) in the 13C-heavy rRNA fractions (Fig. 3B), in agreement with the dominance of Ciliophora in the micro-eukaryotic rRNA gene transcripts (Fig. 2B). The Candida-Lodderomyces clade and Cyberlindnera-Candida clade within Ascomycota, Magnoliophyta spp. within Phragmoplastophyta, and Poteriospumella spp. and unclassified Chromulinales within Ochrophyta were also strongly labeled (maximum EF: 13.5–242.5, Fig. 2B). Moreover, the 13C-biomass incorporation by micro-eukaryotes was independent of whichever prey bacteria (Fig. 2B, D), revealing no detectable prey preference in the metabolically active micro-eukaryotic predators. On the contrary, differential labeling by 13C-E. coli and 13C-P. putida cells was frequently observed for the predatory bacteria (Fig. 2A, C). The most obvious example was the OM27 clade ASVs belonging to Bdellovibrionota, which were found to incorporate 13C-labeled biomass exclusively of E. coli (Fig. 2C). Comparatively, Haliangium-affiliated ASV27 and ASV63 were labeled only by 13C-E. coli, ASV57 labeled by both 13C-E. coli and 13C-P. putida, while ASV72 and ASV76 were also labeled by 13C-P. putida, but only at a later sampling point (Fig. 2C). These results on the divergent labeling patterns with the tested prey bacteria together strongly implied population-specific predating behaviors of predatory bacteria in activated sludge.Fig. 3: In situ relative abundance of Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge at a local WWTP (WWTP01) based on sampling over two years.The abundance of the abundant genera belonging to Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge were compared according to amplicon sequencing-based analysis of bacterial 16S rRNA gene V3-V4 region. The top 10 abundant genus-level taxa across samples collected from eight samplings are shown, with the putative predators identified by SIP in the microcosm experiment highlighted. The asterisk denotes significant difference in relative abundance between aerobic and anaerobic sludges (p 0.1% in the activated sludge of WWTP01, including the putative predators identified in the microcosm experiment, i.e., Haliangium spp. (2.8 ± 0.7%) which represented the most abundant myxobacterial lineage in the activated sludge, uncultured Polyangiaceae (0.4 ± 0.1%), and the mle1-27 clade (0.2 ± 0.0%; Fig. 3). Moreover, Pajaroellobacter (1.2 ± 0.2%), Nannocystis (0.4 ± 0.1%), Phaselicystis (0.3 ± 0.1%), and several other myxobacterial clades, although not identified as putative predators in the microcosm experiment, were among the abundant myxobacteria in situ in the activated sludge. Although the myxobacterial genera showed comparable relative abundance in the anaerobic tanks, fed by returned activated sludge, to their counterparts in the aerobic tanks, the obligately aerobic myxobacteria were presumably metabolically inactive in the anerobic sludge. Unlike Myxococcota, members of Bdellovibrionota altogether showed significantly higher relative abundance in the aerobic sludge (1.0 ± 0.2%) than in the anaerobic sludge (0.6 ± 0.1%, paired samples Wilcoxon test p  More

Davidson, S. C. et al. Ecological insights from three decades of animal movement tracking across a changing arctic. Science 370, 712–715 (2020).ADS 
CAS 

Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Chang. 8, 224–228 (2018).ADS 

Google Scholar 
Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).ADS 
CAS 

Google Scholar 
Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Sci. B. 278, 3437–3443 (2011).
Google Scholar 
González, A. M., Bayly, N. J. & Hobson, K. A. Earlier and slower or later and faster: spring migration pace linked to departure time in a Neotropical migrant songbird. J. Anim. Ecol. 89, 2840–2851 (2020).
Google Scholar 
Liedvogel, M., Åkesson, S. & Bensch, S. The genetics of migration on the move. Trends Ecol. Evol. 26, 561–569 (2011).
Google Scholar 
Caprioli, M. et al. Clock gene variation is associated with breeding phenology and maybe under directional selection in the migratory barn swallow. PLoS ONE 7, e35140 (2012).ADS 
CAS 

Google Scholar 
Mettler, R., Segelbacher, G. & Schaefer, M. H. Interactions between a candidate gene for migration (ADCYAP1), morphology and sex predict spring arrival in blackcap populations. PLoS ONE 10, e0144587 (2015).
Google Scholar 
Bazzi, G. et al. Clock gene polymorphism and scheduling of migration: a geolocator study of the barn swallow Hirundo rustica. Sci. Rep. 5, 12443 (2015).ADS 

Google Scholar 
Saino, N. et al. Polymorphism at the Clock gene predicts phenology of long-distance migratoin in birds. Mol. Ecol. 24, 1758–1773 (2015).CAS 

Google Scholar 
Bossu, C. M. et al. Clock-linked genes underlie seasonal migratory timing in a diurnal raptor. Proc. R. Soc. B. 289, 20212507 (2022).CAS 

Google Scholar 
O’Malley, K. G., Ford, M. J. & Hard, J. J. Clock polymorphism in Pacific salmon: evidence for variable selection along a latitudinal gradient. Proc. R. Soc. B. 277, 3703–3714 (2010).
Google Scholar 
Peterson, M. P. et al. Variation in candidate genes CLOCK and ADCYAP1 does not consistently predict differences in migratory behavior in the songbird genus Junco. F1000Research 2 (2013).McKinnon, E. A. & Ten Love, O. P. years tracking the migrations of small landbirds: Lessons learned in the golden age of bio-logging. Auk 135, 834–856 (2018).
Google Scholar 
Fraser, K. C. et al. Continent-wide tracking to determine migratory connectivity and tropical habitat associations of a declining aerial insectivore. Proc. R. Soc. B. 279, 4901–4906 (2012).
Google Scholar 
Neufeld, L. R. et al. Breeding latitude is associated with the timing of nesting and migration around the annual calendar among purple martin Progne subis populations. J. Ornithol. 162, 1009–1024 (2021).
Google Scholar 
Peona, V. et al. Identifying the causes and consequences of assembly gaps using a multiplatform genome assembly of a bird-of-paradise. Mol. Ecol. 21(1), 263–286 (2020).
Google Scholar 
Coelho, L. A., Musher, L. J. & Cracraft, J. A multireference-based whole genome assembly for the obligate ant-following antbird, Rhegmatorhina melanosticta (Thamnophilidae). Diversity 11(19), 144 (2019).CAS 

Google Scholar 
Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).CAS 

Google Scholar 
Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: Towards a genomic predictor of bleaching. Science 369(6501) (2019).Jones, S., Pfister-Genskow, M., Benca, R. M. & Cirelli, C. Molecular correlates of sleep and wakefulness in the brain of the white-crowned sparrow. J. Neurochem. 105, 46–62 (2008).CAS 

Google Scholar 
Ma, C. et al. Sleep regulation by neurotensinergic neurons in a thalamo-amygdala circuit. Neuron 103 (2019).Wong, J. M. & Eirin-Lopez, J. M. Evolution of methyltransferase-like (METTL) proteins in metazoan: a complex gene family involved in epitranscriptomic regulation and other epigenetic processes. Mol. Biol. Evol. 38, 5309–5327 (2021).CAS 

Google Scholar 
Jia, Z. et al. ACSS3 in brown fast drives propionate catabolism and its deficiency leads to autophagy and systemic metabolic dysfunction. Clin. Transl. Med. 12, e665 (2022).CAS 

Google Scholar 
Muller, F. et al. Towards a conceptual framework for explaining variation in nocturnal departure time of songbird migrants. Mov. Ecol. 4, 24 (2016).
Google Scholar 
Fraser, K. C. et al. Individual variability in migration timing can explain long-term population-level advances in a songbird. Front. Ecol. Evol. 7, 324 (2019).ADS 

Google Scholar 
Barret, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23(1), 38–44 (2008).
Google Scholar 
Colodro-Conde, L. et al. A direct test of the diathesis-stress model for depression. Mol. Psychiatry 23, 1590–1596 (2017).
Google Scholar 
Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLOS Genetics 9(4) (2013).Lavallée, C. D. et al. The use of nocturnal flights for barrier crossing in a diurnally migrating songbird. Mov. Ecol. 9, 21 (2021).
Google Scholar 
Saino, N. et al. Migration phenology and breeding success are predicted by methylation of a photoperiodic gene in the barn swallow. Sci. Rep. 7, 45412 (2017).ADS 
CAS 

Google Scholar 
Henry, R. A. et al. Changing the selectivity of p300 by acetyl-CoA modulation of histone acetylation. ACS Chem. Biol 10, 146–156 (2015).CAS 

Google Scholar 
Sun, H., Skorgerbø, G., Wang, Z., Liu, W. & Li, Y. Structural relationships between highly conserved elements and genes in vertebrate genomes. PLoS ONE 3, e3727 (2008).ADS 

Google Scholar 
Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 

Google Scholar 
Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2013).CAS 

Google Scholar 
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 

Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).ADS 

Google Scholar 
Coombe, L. et al. ARKS: Chromosome-scale scaffolding of human genome drafts with linked read kmers. BMC Bioinform. 19, 1–10 (2018).
Google Scholar 
Campbell, M. S., Holt, C., Moore, B. & Yandell, M. Genome annotation and curation using MAKER and MAKER‐P. Curr. Protocols Bioinform. 48, 4.11.1–4.11.39 (2014).Malmberg, M. M. et al. Evaluation and recommendations for routine genotyping using skim whole genome re-sequencing in canola. Front. Plant. Sci. 9 (2018).Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).CAS 

Google Scholar 
Golicz, A. A., Bayer, P. E. & Edwards, D. Skim-based genotyping by sequencing. Methods Mol. Biol. 1245, 257–270 (2015).CAS 

Google Scholar 
Hill, R. D. Theory of geolocation by light levels. In B. J. L. Boeuf, & R. M. Laws (Ed.), Elephant seals: Population ecology, behaviour and physiology, pp. 227–236. Berkeley, CA: University of California Press (1994).Wotherspoon, S., Summer, M. & Lisovski, S. BAStag: basic data processing for light based geolocation archival tags. Version 0.1.3. (2016).Lisovski, S. & Hahn, S. GeoLight-processing and anslysing light-based geolocator data in R. Methods Ecol. Evol. 3, 1055–1059 (2012).
Google Scholar 
Gompert, Z., Lucas, L. K., Nice, C. C. & Buerkle, C. A. Genome divergence and the genetic architecture of barriers to gene flow between Lycaeides idas and L. melissa. Evolution 67, 2498–2514 (2013).
Google Scholar 
Pfeifer, S. P. et al. The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow. Mol. Biol. Evol. 35, 792–806 (2018).CAS 

Google Scholar 
Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 

Google Scholar 
Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analysis. Nat Protoc 15, 2759–2772 (2020).CAS 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 

Google Scholar 
Cruickshank, T. E. & Hahn, M. W. Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol. Ecol. 23, 3133–3157 (2014).
Google Scholar 
Vijay, N. et al. Evolution of heterogeneous genome differentiation across multiple contact zones in a crow species complex. Nat. Commun. 7, 13195 (2016).ADS 
CAS 

Google Scholar 
Delmore, K. et al. The evolutionary history and genomics of European blackcap migration. eLife 9, e54462 (2020). More

Literature search and screeningOur analysis included a systematic literature search and was conducted by following the PRISMA protocol55 (Supplementary Fig. 7). We searched through Web of Science and China National Knowledge Infrastructure (CNKI) platforms by using keywords listed in Supplementary Table 3. A total of 3299 potentially relevant articles were found (Mandarin and English). The availability of peer-reviewed datasets associated with these published articles11,15,56,57,58,59 and online databases (The Sustainable Wetlands Adaptation and Mitigation Program (SWAMP) database, https://www2.cifor.org/swamp) were also considered. We then removed a significant number of articles through title screening, leaving 551 articles for further inspection.For these remaining articles, we used a four-step critique process to screen their title, abstract, and full text. We determined that firstly, they must provide carbon density data for at least one of the four mangrove carbon pools (i.e., aboveground biomass, belowground biomass, sediment organic carbon, or total ecosystem carbon). Secondly, articles needed to state the forest age or the starting date of the restoration action. For those studies providing only age intervals (e.g., 10–25 years, >66 years), we excluded them from the analysis. Thirdly, a description of prior land use was required. From these, mangrove restoration could be divided into two categories—reforestation and afforestation—on whether mangroves previously existed in that location. For reforestation, the initial conditions for inclusion were: (1) abandoned agricultural/aquacultural sites built previously by excavating mangrove forests, (2) clear-felled mangrove lands after wars, timber harvest, and silvicultural management, and (3) mangrove forests with mortality due to spraying of defoliants and hydrological alteration caused by the construction of embankments. We compared the carbon densities of reforested mangroves among sites with different causes of degradation/deforestation, and no significant difference is found (Supplementary Fig. 9). For those reforested mangroves, we assumed they would be protected and conserved by local governments and non-government organizations, so that there will not be human-driven degradation or deforestation in the near future. However, we acknowledge that a fraction of mangrove reforestation is managed for wood production, which means logging would happen at a certain interval after reforestation at these sites. For these logging sites, we used their reported measurements after clear-cut, such as 0-, 5-, 10-, 15-, and 25-year post-harvest sites in Sundarbans, Bangladesh60. On the other hand, the future occurrence of natural-driven deforestation (e.g., cyclones) is difficult to predict, and thus not considered in our study. For afforestation, the initial condition for inclusion was the presence of non-mangrove habitat immediately before afforestation began, such as mudflats, seagrass, saltmarsh, coral reef, or denuded areas. In most cases, reforestation and afforestation were undertaken through active planting without much re-engineering4, but for reforestation, natural regeneration could have, and in many places likely did, augment recruitment61. Moreover, we only considered mangrove succession that started from near-barren land with an insignificant amount of biomass, and introductions of exotic species to degraded areas with sparse trees were not incorporated. Lastly, if the forest age or prior land use type was not given, the articles needed to specify the location of sampling plots (latitude, longitude). With the coordinates matching, prior land use type and establishment dates were sometimes identifiable through remote sensing (Supplementary Fig. 10). For those articles sharing the same restoration sites but showing different aspects of the data collection, we combined the results and considered the collective work as one source. Based on the space-for-time method, data in the control sites before mangrove restoration actions were also collected as a paired site of restoration (e.g., abandoned ponds before mangrove reforestation; mudflats before mangrove afforestation). In total, we obtained data from 379 mangrove restoration sites described by 106 articles.Data extractionWe extracted aboveground living biomass carbon (AGC), belowground living biomass carbon (BGC), sediment carbon (SCS), and total ecosystem carbon (TECS) density from the 106 original data sources. In most cases, numeric values were provided. For those data not provided numerically but graphed, we determined values from figures with the application of GetData Graph Digitizer (http://getdata-graph-digitizer.com/).Among the articles, aboveground and belowground biomass (Mg ha−1) data were obtained using either a harvesting method (empirical) or an allometric method (calculation). Aboveground biomass represented the sum of stem, leaf, and branch dry weight, and we included prop root biomass when Rhizophora spp. were present. For soil coring methods that determined belowground biomass or sediment carbon density, belowground biomass was considered the dry weight of living coarse and fine roots multiplied by the ratio of core area to land surface area62. For allometric methods, trunk diameter at breast height (DBH, ~1.3 m) and tree height were used to calculate aboveground and belowground biomass by species-specific or common allometric equations63. These equations were also used to calculate the belowground biomass when articles provided plot information (DBH, height) but not belowground biomass (Supplementary Table 4). Total biomass was calculated as the sum of aboveground and belowground biomass. Deadwood and pneumatophore biomass were not included in our analysis; these data are rarely provided and/or methods of determination are inconsistent among global studies64. Some articles provided total biomass and shoot/root biomass ratio (S/R), and in such cases, above- and belowground biomass data were obtained through calculation as follows:$${{{{{rm{Aboveground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{frac{S}{R}}{frac{S}{R}+1}$$
(1)
$${{{{{rm{Belowground}}}}}},{{{{{rm{biomass}}}}}}={{{{{rm{Total}}}}}},{{{{{rm{biomass}}}}}}times frac{1}{frac{S}{R}+1}$$
(2)
For those articles measuring carbon content, study-specific carbon conversion factors were used to transform biomass to biomass carbon density (Mg C ha−1). If carbon content data were not provided, we converted aboveground and belowground biomass to carbon density by applying a conversion of 0.47 and 0.39, respectively65. The aboveground biomass carbon density was divided by its corresponding age to get the average aboveground biomass carbon accumulation rate (Mg C ha−1 yr−1).For sediment carbon density (SCS, Mg C ha−1), we selected the top 1 m because this depth equated to the most commonly reported depth and could reflect the impact of root mass input in the deeper depth66, which is also consistent with recent blue carbon standing stock assessment guidance64,67. Sediment carbon stock was calculated by multiplying sediment organic carbon content (SOC, %) by bulk density (BD, g cm−3), integrated over depth (cm). For studies that reported sediment carbon stock to More