Philippa Kaur

Yang, B., Pang, X. Y., Hu, B., Bao, W. K. & Tian, G. L. Does thinning-induced gap size result in altered soil microbial community in pine plantation in eastern Tibetan Plateau? Ecol. Evol. 7(9), 2986–2993 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Andrés, B. O., Ricardo, R. P., Raquel, O. & Mirendel, R. Thinning alters the early-decomposition rate and nutrient immobilization-release pattern of foliar litter in Mediterranean oak-pine mixed stands. For. Ecol. Manag. 391, 309–320 (2017).Article 

Google Scholar 
Hart, B. T. N., Smith, J. E., Luoma, D. L. & Hatten, J. A. Recovery of ectomycorrhizal fungus communities fifteen years after fuels reduction treatments in ponderosa pine forests of the Blue Mountains, Oregon. For. Ecol. Manag. 422, 11–22 (2018).Article 

Google Scholar 
Ge, Z. M. et al. Effects of varying thinning regimes on carbon uptake, total stem wood growth, and timber production in Norway spruce (Picea abies) stands in southern Finland under the changing climate. Ann. For. Sci. 68(2), 371–383 (2011).Article 

Google Scholar 
Panayotov, M. et al. Climate extremes during high competition contribute to mortality in unmanaged self-thinning Norway spruce stands in Bulgaria. For. Ecol. Manag. 369, 74–88 (2016).Article 

Google Scholar 
Depauw, L. et al. Interactive effects of past land use and recent forest management on the understorey community in temperate oak forests in South Sweden. J. Veg. Sci. 30(5), 917–928 (2019).Article 

Google Scholar 
Soalleiro, R. R., Murias, M. B. & Gonzalez, J. G. A. Evaluation through a simulation model of nutrient exports in fast-growing southern European pine stands in relation to thinning intensity and harvesting operations. Ann. For. Sci. 64(4), 375–384 (2007).Article 

Google Scholar 
Trentini, C. P. et al. Thinning of loblolly pine plantations in subtropical Argentina: Impact on microclimate and understory vegetation. For. Ecol. Manag. 384, 236–247 (2017).Article 

Google Scholar 
Baena, C. W. et al. Thinning and recovery effects on soil properties in two sites of a Mediterranean forest, in Cuenca Mountain (South-eastern of Spain). For. Ecol. Manag. 308, 223–230 (2013).Article 

Google Scholar 
He, Z. B. et al. Responses of soil organic carbon, soil respiration, and associated soil properties to long-term thinning in a semi-arid spruce plantation in northwestern China. Land Degrad. Dev. 29(12), 4387–4396 (2018).Article 

Google Scholar 
Rambo, T. R. & North, M. P. Canopy microclimate response to pattern and density of thinning in a Sierra Nevada forest. For. Ecol. Manag. 257(2), 435–442 (2009).Article 

Google Scholar 
Zhou, L. L. et al. Thinning increases understory diversity and biomass, and improves soil properties without decreasing growth of Chinese fir in southern China. Environ. Sci. Pollut. Res. 23(23), 24135–24150 (2016).Article 
CAS 

Google Scholar 
Collins, C. G., Carey, C. J., Aronson, E. L., Kopp, C. W. & Diez, J. M. Direct and indirect effects of native range expansion on soil microbial community structure and function. J. Ecol. 104(5), 1271–1283 (2016).Article 

Google Scholar 
Çömez, A., Tolunay, D. & Güner, ŞT. Litterfall and the effects of thinning and seed cutting on carbon input into the soil in Scots pine stands in Turkey. Eur. J. Forest Res. 138(1), 1–14 (2019).Article 

Google Scholar 
Ulvcrona, K. A., Karlsson, K. & Ulvcrona, T. Identifying the biological effects of pre-commercial thinning on diameter growth in young Scots pine stands. Scand. J. For. Res. 29(5), 427–435 (2014).Article 

Google Scholar 
Chen, X. L. et al. Soil microbial functional diversity and biomass as affected by different thinning intensities in a Chinese fir plantation. Appl. Soil. Ecol. 92, 35–44 (2015).Article 

Google Scholar 
Veselá, P., Vašutová, M., Edwards- Jonášová, M. & Cudlin, P. Soil fungal community in norway spruce forests under bark beetle attack. Forests 10(2), 109 (2019).Article 

Google Scholar 
Ardestani, M. M., Jílková, V., Bonkowski, M. & Frouz, J. The effect of arbuscular mycorrhizal fungi Rhizophagus intraradices and soil microbial community on a model plant community in a post-mining soil. Plant Ecol. 220(9), 789–800 (2019).Article 

Google Scholar 
Sapsford, S. J., Paap, T., Hardy, G. E. S. J. & Burgess, T. I. The “chicken or the egg”: Which comes first, forest tree decline or loss of mycorrhizae? Plant Ecol. 218(9), 1093–1106 (2017).Article 

Google Scholar 
Jirout, J., Šimek, M. & Elhottová, D. Inputs of nitrogen and organic matter govern the composition of fungal communities in soil disturbed by overwintering cattle. Soil Biol. Biochem. 43(3), 647–656 (2011).Article 
CAS 

Google Scholar 
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505(7484), 543–545 (2014).Article 
CAS 
PubMed 

Google Scholar 
Iwaoka, C. et al. The impacts of soil fertility and salinity on soil nitrogen dynamics mediated by the soil microbial community beneath the halophytic Shrub Tamarisk. Microb. Ecol. 75(4), 985–996 (2017).Article 
PubMed 

Google Scholar 
Bahnmann, B. et al. Effects of oak, beech and spruce on the distribution and community structure of fungi in litter and soils across a temperate forest. Soil Biol. Biochem. 119, 162–173 (2018).Article 
CAS 

Google Scholar 
Ling, J. J. et al. Genotype by environment interaction analysis of growth of Picea koraiensis families at different sites using BLUP-GGE. New For. 52(1), 113–127 (2021).Article 

Google Scholar 
Zhang, J. B., Wang, L. F., Na, X., Zhang, T. T. & San-Ping, A. N. Primary report on introduction of Picea balfouriana and Picea koraiensis in Gansu. J. Gansu For. Sci. Technol. 44(02), 16–19+29 (2019).
Google Scholar 
Yin, L. M. et al. Arbuscular mycorrhizal trees cause a higher carbon to nitrogen ratio of soil organic matter decomposition via rhizosphere priming than ectomycorrhizal trees. Soil Biol. Biochem. 157, 108246 (2021).Article 
CAS 

Google Scholar 
Zhou, L. & Wang, S. L. Effects of mixed tree species on soil nutrients in Picea koraiensis plantations. J. Northeast For. Univ. 47(2), 37–41 (2019).MathSciNet 
CAS 

Google Scholar 
Cabon, A. et al. Thinning increases tree growth by delaying drought-induced growth cessation in a Mediterranean evergreen oak coppice. For. Ecol. Manag. 409, 333–342 (2018).Article 

Google Scholar 
Splawinski, T. B. et al. Precommercial thinning of Picea mariana and Pinus banksiana: Impact of treatment timing and competitors on growth response. For. Sci. 63(1), 62–70 (2017).Article 

Google Scholar 
Bai, S. H. et al. Effects of forest thinning on soil-plant carbon and nitrogen dynamics. Plant Soil 411(1–2), 437–449 (2016).
Google Scholar 
D’Amato, A. W., Troumbly, S. J., Saunders, M. R., Puettmann, K. J. & Albers, M. A. Growth and survival of Picea glauca following thinning of plantations affected by eastern spruce budworm. North. J. Appl. For. 28(2), 72–78 (2011).Article 

Google Scholar 
Olivar, J., Bogino, S., Rathgeber, C., Bonnesoeur, V. & Bravo, F. Thinning has a positive effect on growth dynamics and growth–climate relationships in Aleppo pine (Pinus halepensis) trees of different crown classes. Ann. For. Sci. 71(3), 395–404 (2014).Article 

Google Scholar 
Weiskittel, A. R., Kenefic, L. S., Seymour, R. S. & Phillips, L. M. Long-term effects of precommercial thinning on the stem dimensions, form and branch characteristics of red spruce and balsam fir crop trees in Maine, USA. Silva Fennica 43(3), 397–409 (2009).Article 

Google Scholar 
Repola, J., Hökkä, H. & Penttilä, T. Thinning intensity and growth of mixed spruce-birch stands on drained peatlands in Finland. Silva Fennica 40(1), 83–99 (2006).Article 

Google Scholar 
Misson, L., Vincke, C. & Devillez, F. Frequency responses of radial growth series after different thinning intensities in Norway spruce (Picea abies (L.) Karst.) stands. For. Ecol. Manag. 177(1–3), 51–63 (2003).Article 

Google Scholar 
Kim, S., Kim, C., Han, S. H., Lee, S. T. & Son, Y. A multi-site approach toward assessing the effect of thinning on soil carbon contents across temperate pine, oak, and larch forests. For. Ecol. Manag. 424, 62–70 (2018).Article 

Google Scholar 
Gliksman, D. et al. Litter decomposition in Mediterranean pine forests is enhanced by reduced canopy cover. Plant Soil 422(1–2), 317–329 (2018).Article 
CAS 

Google Scholar 
Achat, D. L., Fortin, M., Landmann, G., Ringeval, B. & Augusto, L. Forest soil carbon is threatened by intensive biomass harvesting. Sci. Rep. 5, 15991 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Jurgensen, M. F. et al. Impacts of timber harvesting on soil organic matter, nitrogen, productivity, and health of inland northwest forests. For. Sci. 43(2), 234–251 (1997).
Google Scholar 
Blanco, J. A., Imbert, J. B. & Castillo, F. J. Thinning affects nutrient resorption and nutrient-use efficiency in two Pinus sylvestris stands in the pyrenees. Ecol. Appl. 19(3), 682–698 (2009).Article 
PubMed 

Google Scholar 
Steer, J. & Harris, J. A. Shifts in the microbial community in rhizosphere and non-rhizosphere soils during the growth of Agrostis stolonifera. Soil Biol. Biochem. 32(6), 869–878 (2000).Article 
CAS 

Google Scholar 
Coulombe, D., Sirois, L. & Paré, D. Effect of harvest gap formation and thinning on soil nitrogen cycling at the boreal–temperate interface. Can. J. For. Res. 47(3), 308–318 (2017).Article 
CAS 

Google Scholar 
Hagerman, S. M., Jones, M. D., Bradfield, G. E. & SMSakakibara, S. M. Ectomycorrhizal colonization of Picea engelmannii × Picea glauca seedlings planted across cut blocks of different sizes. Can. J. For. Res. 29(12), 1856–1870 (1999).Article 

Google Scholar 
Ogo, S., Yamanaka, T., Akama, K., Nagakura, J. & Yamaji, K. Influence of ectomycorrhizal colonization on cesium uptake by Pinus densiflora seedlings. Mycobiology 46(4), 388–395 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Sebastiana, M. et al. Ectomycorrhizal inoculation with Pisolithus tinctorius reduces stress induced by drought in cork oak. Mycorrhiza 28(3), 247–258 (2018).Article 
CAS 
PubMed 

Google Scholar 
Jurgensen, M., Tarpey, R., Pickens, J., Kolka, R. & Palik, B. Long-term effect of silvicultural thinnings on soil carbon and nitrogen pools. Soil Sci. Soc. Am. J. 76(4), 1418–1425 (2012).Article 
CAS 

Google Scholar 
Mosca, E., Montecchio, L., Barion, G., Dal Cortivo, C. & Vamerali, T. Combined effects of thinning and decline on fine root dynamics in a Quercus robur L. forest adjoining the Italian Pre-Alps. Ann. Bot. 119(7), 1235–1246 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, X. & Christie, P. Changes in soil solution Zn and pH and uptake of Zn by arbuscular mycorrhizal red clover in Zn-contaminated soil. Chemosphere 42(2), 201–207 (2001).Article 
CAS 
PubMed 

Google Scholar 
Hawkes, C. V. et al. Fungal community responses to precipitation. Glob. Change Biol. 17(4), 1637–1645 (2011).Article 

Google Scholar 
McGuire, K. L., Fierer, N., Bateman, C., Treseder, K. K. & Turner, B. L. Fungal community composition in Neotropical rain forests: The influence of tree diversity and precipitation. Microb. Ecol. 63(4), 804–812 (2012).Article 
PubMed 

Google Scholar 
Allison, S. D., Hanson, C. A. & Treseder, K. K. Nitrogen fertilization reduces diversity and alters community structure of active fungi in boreal ecosystems. Soil Biol. Biochem. 39(8), 1878–1887 (2007).Article 
CAS 

Google Scholar 
Van Wyk, D. A. B., Adeleke, R., Rhode, O. H. J., Bezuidenhout, C. C. & Mienie, C. Ecological guild and enzyme activities of rhizosphere soil microbial communities associated with Bt-maize cultivation under field conditions in North West Province of South Africa. J. Basic Microbiol. 57(9), 781–792 (2017).Article 
PubMed 

Google Scholar 
Zhao, C. C. et al. Soil microbial community composition and respiration along an experimental precipitation gradient in a semiarid steppe. Sci. Rep. https://doi.org/10.1038/srep24317 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kowalchuk, G. A., Buma, D. S. & Boer, W. D. Peter GLK & van Veen JA (2002) Effects of above-ground plant species composition and diversity on the diversity of soil-borne microorganisms. Antonie Van Leeuwenhoek 81(1–4), 509 (2002).Article 
PubMed 

Google Scholar  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

Brown, A. R. et al. Assessing risks and mitigating impacts of harmful algal blooms on mariculture and marine fisheries. Rev. Aquac. 12, 1663–1688 (2020).
Google Scholar 
Bechard, A. Red tide at morning, tourists take warning? County-level economic effects of HABS on tourism dependent sectors. Harmful Algae 85, 101689–101689 (2019).Article 

Google Scholar 
Landsberg, J. H. The effects of harmful algal blooms on aquatic organisms. Rev. Fish. Sci. 10, 113–390 (2002).Article 

Google Scholar 
Flewelling, L. J. et al. Brevetoxicosis: Red tides and marine mammal mortalities. Nature 435, 755–756 (2005).Article 
CAS 

Google Scholar 
Gannon, D. P. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).Article 
CAS 

Google Scholar 
Driggers, W. B. et al. Environmental conditions and catch rates of predatory fishes associated with a mass mortality on the West Florida Shelf. Estuar. Coast. Shelf Sci. 168, 40–49 (2016).Article 
CAS 

Google Scholar 
Hallett, C. S., Valesini, F. J., Clarke, K. R. & Hoeksema, S. D. Effects of a harmful algal bloom on the community ecology, movements and spatial distributions of fishes in a microtidal estuary. Hydrobiologia 763, 267–284 (2016).Article 

Google Scholar 
Anderson, D. M. et al. Marine harmful algal blooms (HABs) in the United States: History, current status and future trends. Harmful Algae 102, 101975–101975 (2021).Article 
CAS 

Google Scholar 
Steidinger, K. A. & Haddad, K. Biologic and hydrographic aspects of red tides. Bioscience 31, 814–819 (1981).Article 

Google Scholar 
Soto, I. M. et al. Advection of Karenia brevis blooms from the Florida Panhandle towards Mississippi coastal waters. Harmful Algae 72, 46–64 (2018).Article 

Google Scholar 
Steidinger, K. A. & Ingle, R. M. Observations on the 1971 summer red tide in tampa bay, Florida1. Environ. Lett. 3, 271–278 (1972).Article 
CAS 

Google Scholar 
Liu, Y. et al. Offshore forcing on the “pressure point” of the West Florida Shelf: Anomalous upwelling and its influence on harmful algal blooms. J. Geophys. Res. 121, 5501–5515 (2016).Article 

Google Scholar 
Liu, Y., Weisberg, R. H., Zheng, L., Heil, C. A. & Hubbard, K. A. Termination of the 2018 Florida red tide event: A tracer model perspective. Estuar. Coast. Shelf Sci. 272, 107901 (2022).Article 

Google Scholar 
Weisberg, R. H. & Liu, Y. Local and deep-ocean forcing effects on the West Florida continental shelf circulation and ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.863227 (2022).Article 

Google Scholar 
Walsh, J. J. et al. Red tides in the Gulf of Mexico: Where, when, and why? Journal of Geophysical Research: Oceans 111, (2006).Lapointe, B. E., Herren, L. W., Debortoli, D. D. & Vogel, M. A. Evidence of sewage-driven eutrophication and harmful algal blooms in Florida’s Indian River Lagoon. Harmful Algae 43, 82–102 (2015).Article 
CAS 

Google Scholar 
Medina, M. et al. Nitrogen-enriched discharges from a highly managed watershed intensify red tide (Karenia brevis) blooms in southwest Florida. Sci. Total Environ. 827, 154149–154149 (2022).Article 
CAS 

Google Scholar 
Perkins, S. Ramping up the fight against Florida’s red tides. Proc. Natl. Acad. Sci. U.S.A. 116, 6510–6512 (2019).Article 
CAS 

Google Scholar 
Skripnikov, A. et al. Using localized Twitter activity to assess harmful algal bloom impacts of Karenia brevis in Florida, USA. Harmful Algae 110, 102118–102118 (2021).Article 
CAS 

Google Scholar 
SEDAR. SEDAR 33 Update – Gulf of Mexico gag grouper stock assessment report, 123. https://sedarweb.org/docs/suar/GagUpdateAssessReport_Final_0.pdf (2016).SEDAR. SEDAR 61 – Gulf of Mexico red grouper stock assessment report, 285. https://sedarweb.org/docs/sar/S61_Final_SAR.pdf (2019).SEDAR. SEDAR 10 Stock Assessment Report 2: Gulf of Mexico Gag Grouper, 250. www.sedarweb.org (2004).SEDAR. SEDAR 10 Update – Gulf of Mexico gag grouper stock assessment report. http://www.sedarweb.org (2009).SEDAR. SEDAR 72—Gulf of Mexico gag grouper stock assessment report, 318–318. https://sedarweb.org/docs/sar/S72_SAR_FINAL.pdf%0A (2021).Geary, W. L. et al. A guide to ecosystem models and their environmental applications. Nat. Ecol. Evol. 4, 1459–1471 (2020).Article 

Google Scholar 
Steenbeek, J. et al. Making spatial-temporal marine ecosystem modelling better—A perspective. Environ. Model. Softw. 145, 105209–105209 (2021).Article 

Google Scholar 
Gray DiLeone, A. M. & Ainsworth, C. H. Effects of Karenia brevis harmful algal blooms on fish community structure on the West Florida Shelf. Ecol. Model. 392, 250–267 (2019).Article 

Google Scholar 
Perryman, H. A. et al. A revised diet matrix to improve the parameterization of a West Florida Shelf Ecopath model for understanding harmful algal bloom impacts. Ecol. Model. 416, 108890–108890 (2020).Article 

Google Scholar 
Mayer-Pinto, M., Ledet, J., Crowe, T. P. & Johnston, E. L. Sublethal effects of contaminants on marine habitat-forming species: A review and meta-analysis. Biol. Rev. 95, 1554–1573 (2020).Article 

Google Scholar 
Reis Costa, P. Impact and effects of paralytic shellfish poisoning toxins derived from harmful algal blooms to marine fish. Fish Fish. 17, 226–248 (2016).Article 

Google Scholar 
Dahood, A., de Mutsert, K. & Watters, G. M. Evaluating Antarctic marine protected area scenarios using a dynamic food web model. Biol. Cons. 251, 108766–108766 (2020).Article 

Google Scholar 
de Mutsert, K. et al. Exploring effects of hypoxia on fish and fisheries in the northern Gulf of Mexico using a dynamic spatially explicit ecosystem model. Ecol. Model. 331, 142–150 (2016).Article 

Google Scholar 
de Mutsert, K., Lewis, K. A., White, E. D. & Buszowski, J. End-to-end modeling reveals species-specific effects of large-scale coastal restoration on living resources facing climate change. Front. Mar. Sci. 8, 104–104 (2021).Article 

Google Scholar 
Bauer, B. et al. Erratum: Reducing eutrophication increases spatial extent of communities supporting commercial fisheries: A model case study (ICES Journal of Marine Science (2018) DOI: https://doi.org/10.1093/icesjms/fsy003). ICES Journal of Marine Science, 75, 1155–1155 (2018).Sadchatheeswaran, S., Branch, G. M., Shannon, L. J., Coll, M. & Steenbeek, J. A novel approach to explicitly model the spatiotemporal impacts of structural complexity created by alien ecosystem engineers in a marine benthic environment. Ecol. Model. 459, 109731–109731 (2021).Article 

Google Scholar 
Coll, M. et al. Advancing global ecological modeling capabilities to simulate future trajectories of change in marine ecosystems. Front. Mar. Sci. 7, 741–741 (2020).Article 

Google Scholar 
Hernvann, P. Y. et al. The celtic sea through time and space: Ecosystem modeling to unravel fishing and climate change impacts on food-web structure and dynamics. Front. Mar. Sci. 7, 1018–1018 (2020).Article 

Google Scholar 
Walters, C. Ecospace: Prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems 2, 539–554 (1999).Article 

Google Scholar 
Christensen, V., Walters, C. J., Pauly, D. & Forrest, R. Ecopath with Ecosim version 6 user guide. Fish. Cent. Univ. Br. Columbia Vanc. Can. 281, 1–235 (2008).
Google Scholar 
Okey, T. A., Mahmoudi, B., Mackinson, S., Vasconcellos, M. & Vidal-Hernandez, L. An ecosystem model of the West Florida Shelf for use in fisheries management and ecological research: Volume II. Model construction. Fish. Manag. II, 163–163 (2002).
Google Scholar 
Liu, Y. & Weisberg, R. H. Seasonal variability on the West Florida Shelf. Prog. Oceanogr. 104, 80–98 (2012).Article 

Google Scholar 
Moretzsohn, F., Chávez-Sánchez, J. A. & J.W. Tunnell, Jr. GulfBase: Resource Database for Gulf of Mexico Research. World Wide Web electronic publication (2016).Murawski, S. A., Peebles, E. B., Gracia, A., Tunnell, J. W. & Armenteros, M. Comparative abundance, species composition, and demographics of continental shelf fish assemblages throughout the Gulf of Mexico. Mar. Coast. Fish. 10, 325–346 (2018).Article 

Google Scholar 
Darnell, R. M. The American sea: A natural history of the gulf of Mexico. The American Sea: A Natural History of the Gulf of Mexico, 557, https://doi.org/10.5860/choice.193769 (2015).Brochure, I. Marine recreational information program: Implementation plan (2008).Florida Fish and Wildlife Conservation Commission. Commercial fisheries landings summaries (2021).Murawski, S. A. et al. How did the deepwater horizon oil spill affect coastal and continental shelf ecosystems of the Gulf of Mexico?. Oceanography 29, 160–173 (2016).Article 

Google Scholar 
Chagaris, D. D., Patterson, W. F. & Allen, M. S. Relative effects of multiple stressors on reef food webs in the Northern Gulf of Mexico revealed via ecosystem modeling. Front. Mar. Sci. 7, 513–513 (2020).Article 

Google Scholar 
South, A. rnaturalearth: world map data from Natural Earth. R package version 0.1.0. The R Foundation. https://CRAN.R-project.org/package=rnaturalearth (2017).Colleter, M. et al. Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository. Ecol. Model. 302, 42–53 (2015).Article 

Google Scholar 
Ahrens, R. N., Walters, C. J. & Christensen, V. Foraging arena theory. Fish fish. 13, 41–59 (2012).Article 

Google Scholar 
Christensen, V. et al. Representing variable habitat quality in a spatial food web model. Ecosystems 17, 1397–1412 (2014).Article 
CAS 

Google Scholar 
Steenbeek, J. et al. Bridging the gap between ecosystem modeling tools and geographic information systems: Driving a food web model with external spatial–temporal data. Ecol. Model. 263, 139–151 (2013).Article 

Google Scholar 
Walters, C., Christensen, V., Walters, W. & Rose, K. Representation of multistanza life histories in Ecospace models for spatial organization of ecosystem trophic interaction patterns. Bull. Mar. Sci. 86, 439–459 (2010).
Google Scholar 
Heymans, J. J. et al. Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Model. 331, 173–184 (2016).Article 

Google Scholar 
Okey, T. Simulating community effects of sea floor shading by plankton blooms over the West Florida Shelf. Ecol. Model. 172, 339–359 (2004).Article 

Google Scholar 
Chagaris, D. D. Ecosystem-based Evaluation of Fishery Policies and Tradeoffs on the West Florida Shelf Vol. 53, 1699 (University of Florida, 2013).
Google Scholar 
Chagaris, D. D., Mahmoudi, B., Walters, C. J. & Allen, M. S. Simulating the trophic impacts of fishery policy options on the west florida shelf using ecopath with ecosim. Mar. Coast. Fish. 7, 44–58 (2015).Article 

Google Scholar 
Chagaris, D. et al. An ecosystem-based approach to evaluating impacts and management of invasive lionfish. Fisheries 42, 421–431 (2017).Article 

Google Scholar 
Chagaris, D. West Florida Shelf Ecosystem Model. University of Florida. https://ufdc.ufl.edu/IR00011604/00001%0A West Florida Shelf Ecosystem Model (2021).Vilas, D. Spatiotemporal Ecosystem Dynamics on the West Florida Shelf: Prediction, Validation, and Application to Red Tides and Stock Assessment (University of Florida, 2022).
Google Scholar 
Chassignet, E. P. et al. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system. J. Mar. Syst. 65, 60–83 (2007).Article 

Google Scholar 
NOAA National Geophysical Data Center. U.S. Coastal Relief Model Vol. 3—Florida and East Gulf of Mexico. https://doi.org/10.7289/V5W66HP (2001).NASA. Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Earth Data (2018).Casey, L. Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1950–2020: U.S. Geological Survey, https://doi.org/10.5066/P9P2PF1N (2021).Chagaris, D. & Vilas, D. NOAA RESTORE Science Program: Ecosystem modeling to improve fisheries management in the Gulf of Mexico: model inputs and outputs for the West Florida Shelf, 1985–01–01 to 2018–12–31 (NCEI Accession 0242339), https://doi.org/10.25921/t26e-wj91. (2022).Püts, M. et al. Insights on integrating habitat preferences in process-oriented ecological models—A case study of the southern North Sea. Ecol. Model. 431, 109189–109189 (2020).Article 

Google Scholar 
Vilas, D., Fletcher, R. J. Jr., Siders, Z. A. & Chagaris, D. Understanding the temporal dynamics of estimated environmental niche hypervolumes for marine fishes. Ecol. Evol. 12, e9604 (2022).Article 

Google Scholar 
Grubbs, R. D., Musick, J. A., Conrath, C. L. & Romine, J. G. Long-term movements, migration, and temporal delineation of a summer nursery for Juvenile Sandbar Sharks in the Chesapeake Bay region. In Shark Nursery Grounds of the Gulf of Mexico and the East Coast Waters of the United States. American Fisheries Society Symposium 50 Vol. 50 (eds Grubbs, R. D. et al.) 87–107 (American Fisheries Society, 2007).
Google Scholar 
Addis, D. T., Patterson, W. F., Dance, M. A. & Ingram, G. W. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

Google Scholar 
Akins, J. L., Morris, J. A. & Green, S. J. In situ tagging technique for fishes provides insight into growth and movement of invasive lionfish. Ecol. Evol. 4, 3768–3777 (2014).Article 

Google Scholar 
Chen, Z., Xu, S., Qiu, Y., Lin, Z. & Jia, X. Modeling the effects of fishery management and marine protected areas on the Beibu Gulf using spatial ecosystem simulation. Fish. Res. 100, 222–229 (2009).Article 

Google Scholar 
Steenbeek, J. et al. Ecopath with ecosim as a model-building toolbox: Source code capabilities, extensions, and variations. Ecol. Model. 319, 178–189 (2016).Article 

Google Scholar 
Moriasi, D. N. et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885–900 (2007).Article 

Google Scholar 
Hu, C. et al. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sens. Environ. 97, 311–321 (2005).Article 

Google Scholar 
Chagaris, D., Vilas, D., Siders, Z. A. & Sinnickson, D. Monthly maps of red tide on the West Florida Shelf 2002–2021: A simple approach combining remote sensing and in situ measurements (in prep).Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute. Statewide harmful algal bloom karenia brevis current status map (2022).Wickham, H. et al. ggplot2: Create elegant data visualisations using the grammar of graphics (2016).Landsberg, J. H., Flewelling, L. J. & Naar, J. Karenia brevis red tides, brevetoxins in the food web, and impacts on natural resources: Decadal advancements. Harmful Algae 8, 598–607 (2009).Article 
CAS 

Google Scholar 
Gianelli, I., Ortega, L. & Defeo, O. Modeling short-term fishing dynamics in a small-scale intertidal shellfishery. Fish. Res. 209, 242–250 (2019).Article 

Google Scholar 
Moore, S. K. et al. An index of fisheries closures due to harmful algal blooms and a framework for identifying vulnerable fishing communities on the U.S. West Coast. Mar. Policy 110, 103543–103543 (2019).Article 

Google Scholar 
GSMFC. SEAMAP: Environmental and Biological Atlas of the Gulf of Mexico. www.seamap.org (2020).Bechard, A. Harmful algal blooms and tourism: The economic impact to counties in Southwest Florida. Rev. Reg. Stud. 50, 170–188 (2020).
Google Scholar 
Foley, A. M. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).Article 

Google Scholar 
Karnauskas, M. et al. Timeline of severe red tide events on the West Florida Shelf: insights from oral histories. http://sedarweb.org/docs/wpapers/S61_WP_20_Karnauskasetal_red_tide.pdf (2019).Sagarese, S. R., Gruss, A., Karnauskas, M. & Walter, J. F. Ontogenetic spatial distributions of red grouper (Epinephelus morio) within the northeastern Gulf of Mexico and spatio‐ temporal overlap with red tide events, 35–35. http://sedarweb.org/docs/wpapers/S42_DW_04_Red_tide_distribution.pdf (2014).Sagarese, S. R., Vaughan, N. R., Walter, J. F. & Karnauskas, M. Enhancing single-species stock assessments with diverse ecosystem perspectives: A case study for gulf of mexico red grouper (epinephelus morio) and red tides. Can. J. Fish. Aquat. Sci. 78, 1168–1180 (2021).Article 

Google Scholar 
Sagarese, S. R. & Harford, W. J. Evaluating the risks of red tide mortality misspecification when modeling stock dynamics. Fish. Res. 250, 106271–106271 (2022).Article 

Google Scholar 
Whitehouse, G. A. & Aydin, K. Y. Assessing the sensitivity of three Alaska marine food webs to perturbations: An example of Ecosim simulations using Rpath. Ecol. Model. 429, 109074–109074 (2020).Article 

Google Scholar 
Walter, J. F. et al. Satellite derived indices of red tide severity for input for Gulf of Mexico Gag grouper stock assessment. SEDAR33-DW08 SEDAR. North Charlest. S. C. 43, 40–40 (2013).
Google Scholar 
Jackson, M. C., Pawar, S. & Woodward, G. The temporal dynamics of multiple stressor effects: From individuals to ecosystems. Trends Ecol. Evol. 36, 402–410 (2021).Article 

Google Scholar 
Walters, S., Lowerre-Barbieri, S., Bickford, J., Tustison, J. & Landsberg, J. H. Effects of Karenia brevis red tide on the spatial distribution of spawning aggregations of sand seatrout Cynoscion arenarius in Tampa Bay Florida. Mar. Ecol. Prog. Ser. 479, 191–202 (2013).Article 

Google Scholar 
Reynolds, D. A., Yoo, M. J., Dixson, D. L. & Ross, C. Exposure to the Florida red tide dinoflagellate, Karenia brevis, and its associated brevetoxins induces ecophysiological and proteomic alterations in Porites astreoides. PLoS One 15, e0228414–e0228414 (2020).Article 
CAS 

Google Scholar 
Bornman, E., Cowley, P. D., Adams, J. B. & Strydom, N. A. Daytime intra-estuary movements and harmful algal bloom avoidance by Mugil cephalus (family Mugilidae). Estuar. Coast. Shelf Sci. 260, 107492–107492 (2021).Article 
CAS 

Google Scholar 
Moreira-Santos, M., Ribeiro, R. & Araújo, C. V. M. What if aquatic animals move away from pesticide-contaminated habitats before suffering adverse physiological effects? A critical review. Crit. Rev. Environ. Sci. Technol. 49, 989–1025 (2019).Article 
CAS 

Google Scholar 
Schreck, C. B. & Tort, L. The concept of stress in fish. In Fish Physiology Vol. 35 (eds Schreck, C. B. & Tort, L.) 1–34 (Elsevier, 2016).
Google Scholar 
Madin, E. M. P., Dill, L. M., Ridlon, A. D., Heithaus, M. R. & Warner, R. R. Human activities change marine ecosystems by altering predation risk. Glob. Change Biol. 22, 44–60 (2016).Article 

Google Scholar 
Walsh, J. R., Carpenter, S. R. & Van Der Zanden, M. J. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl. Acad. Sci. U.S.A. 113, 4081–4085 (2016).Article 
CAS 

Google Scholar 
Short, J. W. et al. Evidence for ecosystem-level trophic cascade effects involving gulf menhaden (Brevoortia patronus) triggered by the Deepwater horizon blowout. J. Mar. Sci. Eng. 9, 1–20 (2021).Article 

Google Scholar 
Zohdi, E. & Abbaspour, M. Harmful algal blooms (red tide): A review of causes, impacts and approaches to monitoring and prediction. Int. J. Environ. Sci. Technol. 16, 1789–1806 (2019).Article 

Google Scholar 
Weisberg, R. H., Barth, A., Alvera-Azcarate, A. & Zheng, L. A coordinated coastal ocean observing and modeling system for the West Florida Continental Shelf. Harmful Algae 8, 585–597 (2009).Article 

Google Scholar 
Turley, B. D., Karnauskas, M., Campbell, M. D., Hanisko, D. S. & Kelble, C. R. Relationships between blooms of Karenia brevis and hypoxia across the West Florida Shelf. Harmful Algae 114, 102223 (2022).Article 

Google Scholar 
Fulton, E. A., Smith, A. D., Smith, D. C. & Johnson, P. An integrated approach is needed for ecosystem based fisheries management: Insights from ecosystem-level management strategy evaluation. PLoS One 9, e84242 (2014).Article 

Google Scholar 
Flynn, K. J. & McGillicuddy, D. J. Modeling marine harmful algal blooms: Current status and future prospects. Harmful Algal Blooms https://doi.org/10.1002/9781118994672.ch3 (2018).Article 

Google Scholar 
Thorson, J. T. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161 (2019).Article 

Google Scholar 
Fossum, T. O., Travelletti, C., Eidsvik, J., Ginsbourger, D. & Rajan, K. Learning excursion sets of vector-valued gaussian random fields for autonomous ocean sampling. Ann. Appl. Stat. 15, 597–618 (2021).Article 
MathSciNet 
MATH 

Google Scholar 
Fu, F. X., Place, A. R., Garcia, N. S. & Hutchins, D. A. CO2 and phosphate availability control the toxicity of the harmful bloom dinoflagellate Karlodinium veneficum. Aquat. Microb. Ecol. 59, 55–65 (2010).Article 

Google Scholar 
Hardison, D. R., Sunda, W. G., Shea, D. & Litaker, R. W. Increased toxicity of Karenia brevis during phosphate limited growth: Ecological and evolutionary implications. PLoS One 8, e58545–e58545 (2013).Article 
CAS 

Google Scholar 
Errera, R. M., Yvon-Lewis, S., Kessler, J. D. & Campbell, L. Reponses of the dinoflagellate Karenia brevis to climate change: PCO2 and sea surface temperatures. Harmful Algae 37, 110–116 (2014).Article 
CAS 

Google Scholar 
Wells, M. L. et al. Future HAB science: Directions and challenges in a changing climate. Harmful Algae 91, 101632–101632 (2020).Article 

Google Scholar 
Wolny, J. L. et al. Current and future remote sensing of harmful algal blooms in the chesapeake bay to support the shellfish industry. Front. Mar. Sci. 7, 337–337 (2020).Article 

Google Scholar 
Reum, J. C. P. et al. It’s not the destination, It’s the journey: Multispecies model ensembles for ecosystem approaches to fisheries management. Front. Mar. Sci. 8, 75–75 (2021).Article 

Google Scholar 
Howell, D. et al. Combining ecosystem and single-species modeling to provide ecosystem-based fisheries management advice within current management systems. Front. Mar. Sci. 7, 607831 (2021).Article 

Google Scholar 
McPherson, W. C. and J. C. and J. A. and Y. X. and J. shiny: Web application framework for R. R package version 1.4.0, 115–115 (2019). 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

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