Ecology

Mohn WW, Mertens B, Neufeld JD, Verstraete W, de Lorenzo V. Distribution and phylogeny of hexachlorocyclohexane-degrading bacteria in soils from Spain. Environ Microbiol. 2006;8:60–8.CAS 

Google Scholar 
Sharma P, Raina V, Kumari R, Malhotra S, Dogra C, Kumari H, et al. Haloalkane Dehalogenase LinB is responsible for β- and δ-hexachlorocyclohexane transformation in Sphingobium indicum B90A. Appl Environ Microbiol. 2006;72:5720–7.CAS 

Google Scholar 
Lal R, Dogra C, Malhotra S, Sharma P, Pal R. Diversity, distribution and divergence of lin genes in hexachlorocyclohexane-degrading sphingomonads. Trends Biotechnol. 2006;24:121–30.CAS 

Google Scholar 
Singh A, Lal R. Sphingobium ummariense sp. nov., a hexachlorocyclohexane (HCH)-degrading bacterium, isolated from HCH-contaminated soil. Int J Syst Evol Microbiol. 2009;59:162–6.CAS 

Google Scholar 
Singh BK, Kuhad RC. Biodegradation of lindane (γ-hexachlorocyclohexane) by the white-rot fungus Trametes hirsutus. Lett Appl Microbiol. 1999;28:238–41.CAS 

Google Scholar 
Kumar D, Pannu R. Perspectives of lindane (γ-hexachlorocyclohexane) biodegradation from the environment: a review. Bioresour Bioprocess. 2018;5:29.CAS 

Google Scholar 
Lal R, Pandey G, Sharma P, Kumari K, Malhotra S, Pandey R, et al. Biochemistry of microbial degradation of hexachlorocyclohexane and prospects for bioremediation. Microbiol Mol Biol Rev. 2010;74:58–80.CAS 

Google Scholar 
Nayyar N, Lal R. Hexachlorocyclohexane contamination and solutions: Brief history and beyond. J Bioremed Biodeg. 2016;07:338.Bumpus JA, Tien M, Wright D, Aust SD. Oxidation of persistent environmental pollutants by a white rot fungus. Science. 1985;228:1434–6.CAS 

Google Scholar 
Phillips TM, Seech AG, Lee H, Trevors JT. Biodegradation of hexachlorocyclohexane (HCH) by microorganisms. Biodegr. 2005;16:363–92.CAS 

Google Scholar 
Dogra C, Raina V, Pal R, Suar M, Lal S, Gartemann KH, et al. Organization of lin genes and IS6100 among different strains of hexachlorocyclohexane-degrading Sphingomonas paucimobilis: Evidence for horizontal gene transfer. J Bacteriol. 2004;186:2225–35.CAS 

Google Scholar 
Pal R, Bala S, Dadhwal M, Kumar M, Dhingra G, Prakash O, et al. Hexachlorocyclohexane-degrading bacterial strains Sphingomonas paucimobilis B90A, UT26 and Sp+, having similar lin genes, represent three distinct species, Sphingobium indicum sp. nov., Sphingobium japonicum sp. nov. and Sphingobium francense sp. nov., and reclassification of [Sphingomonas] chungbukensis as Sphingobium chungbukense comb. nov. Int J Syst Evol Microbiol. 2005;55:1965–72.CAS 

Google Scholar 
Rijnaarts HHM, Bachmann A, Jumelet JC, Zehnder AJB. Effect of desorption and intraparticle mass transfer on the aerobic biomineralization of. alpha-hexachlorocyclohexane in a contaminated calcareous soil. Environ Sci Technol. 1990;24:1349–54.CAS 

Google Scholar 
Harms H, Schlosser D, Wick LY. Untapped potential: exploiting fungi in bioremediation of hazardous chemicals. Nat Rev Microbiol. 2011;9:177–92.CAS 

Google Scholar 
Wick LY Bioavailability as a microbial system property: Lessons learned from biodegradation in the mycosphere. In: Ortega-Calvo JJ, Parsons JR, (eds). Bioavailability of organic chemicals in soil and sediment. Cham: Springer International Publishing; 2020 p. 267–89.Jennings DH. Translocation of Solutes in fungi. Biol Rev. 1987;62:215–43.CAS 

Google Scholar 
Nazir R, Warmink JA, Boersma H, van Elsas JD. Mechanisms that promote bacterial fitness in fungal-affected soil microhabitats. FEMS Microbiol Ecol. 2010;71:169–85.CAS 

Google Scholar 
Worrich A, Stryhanyuk H, Musat N, König S, Banitz T, Centler F, et al. Mycelium-mediated transfer of water and nutrients stimulates bacterial activity in dry and oligotrophic environments. Nat Commun. 2017;8:15472.CAS 

Google Scholar 
Deveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, et al. Bacterial–fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol Rev. 2018;42:335–52.CAS 

Google Scholar 
Kohlmeier S, Smits THM, Ford RM, Keel C, Harms H, Wick LY. Taking the fungal highway: Mobilization of pollutant-degrading bacteria by fungi. Environ Sci Technol. 2005;39:4640–6.CAS 

Google Scholar 
Wick LY, Furuno S, Harms H Fungi as transport vectors for contaminants and contaminant-degrading bacteria. In: Timmis KN, (eds). Handbook of hydrocarbon and lipid microbiology. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 1555–61.Espinosa-Ortiz EJ, Rene ER, Gerlach R. Potential use of fungal-bacterial co-cultures for the removal of organic pollutants. Crit Rev Biotechnol. 2021;0:1–23.
Google Scholar 
Haq IU, Zhang M, Yang P, van Elsas JD The interactions of bacteria with fungi in soil. In: Adv Appl Microbiol. Elsevier; 2014. p. 185–215.Furuno S, Foss S, Wild E, Jones KC, Semple KT, Harms H, et al. Mycelia promote active transport and spatial dispersion of polycyclic aromatic hydrocarbons. Environ Sci Technol. 2012;46:5463–70.CAS 

Google Scholar 
Schamfuß S, Neu TR, van der Meer JR, Tecon R, Harms H, Wick LY. Impact of mycelia on the accessibility of fluorene to PAH-degrading bacteria. Environ Sci Technol. 2013;47:6908–15.
Google Scholar 
Schamfuß S, Neu TR, Harms H, Wick LY. A Whole Cell Bioreporter approach to assess transport and bioavailability of organic contaminants in water unsaturated systems. J Vis Exp. 2014;24:52334.
Google Scholar 
You X, Kallies R, Kühn I, Schmidt M, Harms H, Chatzinotas A, et al. Phage co-transport with hyphal-riding bacteria fuels bacterial invasion in a water-unsaturated microbial model system. ISME J. 2022;16:1275–83.CAS 

Google Scholar 
Wick LY, Remer R, Würz B, Reichenbach J, Braun S, Schäfer F, et al. Effect of fungal hyphae on the access of bacteria to phenanthrene in soil. Environ Sci Technol. 2007;41:500–5.CAS 

Google Scholar 
Boer W, de, Folman LB, Summerbell RC, Boddy L. Living in a fungal world: impact of fungi on soil bacterial niche development. FEMS Microbiol Rev. 2005;29:795–811.
Google Scholar 
Frey‐Klett P, Garbaye J, Tarkka M. The mycorrhiza helper bacteria revisited. N. Phytol. 2007;176:22–36.
Google Scholar 
Hazen TC Cometabolic Bioremediation. In: Timmis KN, editor. Handbook of hydrocarbon and lipid microbiology. Berlin, Heidelberg: Springer; 2010. p. 2505–14.Jehmlich N, Vogt C, Lünsmann V, Richnow HH, von Bergen M. Protein-SIP in environmental studies. Curr Opin Biotechnol. 2016;41:26–33.CAS 

Google Scholar 
Sachsenberg T, Herbst FA, Taubert M, Kermer R, Jehmlich N, von Bergen M, et al. MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics. J Proteome Res. 2015;14:619–27.CAS 

Google Scholar 
Taubert M, Jehmlich N, Vogt C, Richnow HH, Schmidt F, von Bergen M, et al. Time resolved protein-based stable isotope probing (Protein-SIP) analysis allows quantification of induced proteins in substrate shift experiments. Proteomics 2011;11:2265–74.CAS 

Google Scholar 
Lünsmann V, Kappelmeyer U, Benndorf R, Martinez-Lavanchy PM, Taubert A, Adrian L, et al. In situ protein-SIP highlights Burkholderiaceae as key players degrading toluene by para ring hydroxylation in a constructed wetland model: Protein-SIP in a toluene-degrading wetland. Environ Microbiol. 2016;18:1176–86.
Google Scholar 
Khan N, Toscan RB, Lunayo A, Wamalwa B, Muge E, Mulaa FJ, et al. Draft genome sequence of Fusarium equiseti K3, a fungal species isolated from hexachlorocyclohexane-contaminated soil. Microbiol Resour Announc. 2021;10:e00885–21.CAS 

Google Scholar 
Khan N, Brizola Toscan R, Lunayo A, Wamalwa B, Muge E, Mulaa FJ, et al. Draft genome sequences of two Sphingobium species associated with hexachlorocyclohexane (HCH) degradation isolated from an HCH-Contaminated Soil. Microbiol Resour Announc. 2022;11:e00886–21.
Google Scholar 
Yang CH, Menge JA, Cooksey DA. Mutations Affecting Hyphal Colonization and Pyoverdine Production in Pseudomonads Antagonistic toward Phytophthora parasitica. Appl Environ Microbiol. 1994;9:473–81.Senoo K, Wada H. Isolation and identification of an aerobic γ-HCH-decomposing bacterium from soil. Soil Sci Plant Nutr. 1989;35:79–87.CAS 

Google Scholar 
Jehmlich N, Schmidt F, Taubert M, Seifert J, Bastida F, von Bergen M, et al. Protein-based stable isotope probing. Nat Protoc. 2010;5:1957–66.CAS 

Google Scholar 
Graves S, Dorai-Raj HPP and LS with help from S. multcompView: Visualizations of paired comparisons. 2019. Available from: https://CRAN.R-project.org/package=multcompViewWickham H, François R, Henry L, Müller K, RStudio. dplyr: A Grammar of data manipulation. 2022. Available from: https://CRAN.R-project.org/package=dplyrggplot2: Create elegant data visualisations using the grammar of graphics—ggplot2-package. Available from: https://ggplot2.tidyverse.org/reference/ggplot2-package.htmlSchramm FD, Heinrich K, Thüring M, Bernhardt J, Jonas K. An essential regulatory function of the DnaK chaperone dictates the decision between proliferation and maintenance in Caulobacter crescentus. PLoS Genet. 2017;13:e1007148.
Google Scholar 
Li SX, Wu HT, Liu YT, Jiang YY, Zhang YS, Liu WD, et al. The F1Fo-ATP synthase β subunit is required for Candida albicans pathogenicity due to its role in carbon flexibility. Front Microbiol. 2018;9:1025.
Google Scholar 
Godlewska R, Wiśniewska K, Pietras Z, Jagusztyn-Krynicka EK. Peptidoglycan-associated lipoprotein (Pal) of Gram-negative bacteria: function, structure, role in pathogenesis and potential application in immunoprophylaxis. FEMS Microbiol Lett. 2009;298:1–11.CAS 

Google Scholar 
Taubert M, Vogt C, Wubet T, Kleinsteuber S, Tarkka MT, Harms H, et al. Protein-SIP enables time-resolved analysis of the carbon flux in a sulfate-reducing, benzene-degrading microbial consortium. ISME J. 2012;6:2291–301.CAS 

Google Scholar 
Little AEF, Robinson CJ, Peterson SB, Raffa KF, Handelsman J. Rules of engagement: Interspecies interactions that regulate microbial communities. Annu Rev Microbiol. 2008;62:375–401.CAS 

Google Scholar 
Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C. Microbial syntrophy: interaction for the common good. FEMS Microbiol Rev. 2013;37:384–406.CAS 

Google Scholar 
Van Hees PAW, Rosling A, Essén S, Godbold DL, Jones DL, Finlay RD. Oxalate and ferricrocin exudation by the extramatrical mycelium of an ectomycorrhizal fungus in symbiosis with Pinus sylvestris. N Phytol. 2006;169:367–78.
Google Scholar 
Sun YP, Unestam T, Lucas SD, Johanson KJ, Kenne L, Finlay R. Exudation-reabsorption in a mycorrhizal fungus, the dynamic interface for interaction with soil and soil microorganisms. Mycorrhiza 1999;9:137–44.CAS 

Google Scholar 
Leveau JHJ, Preston GM. Bacterial mycophagy: definition and diagnosis of a unique bacterial-fungal interaction. N Phytol. 2008;177:859–76.
Google Scholar 
Bassler BL, Losick R. Bacterially speaking. Cell 2006;125:237–46.CAS 

Google Scholar 
Kim HJ, Boedicker JQ, Choi JW, Ismagilov RF. Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc Natl Acad Sci. 2008;105:18188–93.CAS 

Google Scholar 
Nayyar N, Sangwan N, Kohli P, Verma H, Kumar R, Negi V, et al. Hexachlorocyclohexane: persistence, toxicity and decontamination. Rev Environ Health. 2014;29:49–52.CAS 

Google Scholar 
Willett KL, Ulrich EM, Hites RA. Differential toxicity and environmental fates of hexachlorocyclohexane isomers. Environ Sci Technol. 1998;32:2197–207.CAS 

Google Scholar 
Ellegaard-Jensen L, Knudsen BE, Johansen A, Albers CN, Aamand J, Rosendahl S. Fungal-bacterial consortia increase diuron degradation in water-unsaturated systems. Sci Total Environ. 2014;466–467:699–705.
Google Scholar 
Knudsen BE, Ellegaard-Jensen L, Albers CN, Rosendahl S, Aamand J. Fungal hyphae stimulate bacterial degradation of 2,6-dichlorobenzamide (BAM). Environ Pollut. 2013;181:122–7.CAS 

Google Scholar 
Saez JM, Alvarez A, Fuentes MS, Amoroso MJ, Benimeli CS An Overview on Microbial Degradation of Lindane. In: Singh SN, (eds). Microbe-induced degradation of pesticides. Cham: Springer International Publishing; 2017. p. 191–212.Nzila A. Update on the cometabolism of organic pollutants by bacteria. Environ Pollut. 2013;178:474–82.CAS 

Google Scholar 
Benimeli CS, González AJ, Chaile AP, Amoroso MJ. Temperature and pH effect on lindane removal by Streptomyces sp. M7 in soil extract. J Basic Microbiol. 2007;47:468–73.CAS 

Google Scholar 
Álvarez A, Yañez ML, Benimeli CS, Amoroso MJ. Maize plants (Zea mays) root exudates enhance lindane removal by native Streptomyces strains. Int Biodeterior Biodegrad. 2012;66:14–8.
Google Scholar 
Boltner D, Moreno-Morillas S, Ramos JL. 16S rDNA phylogeny and distribution of lin genes in novel hexachlorocyclohexane-degrading Sphingomonas strains. Environ Microbiol. 2005;7:1329–38.CAS 

Google Scholar  More

Elrick, M. et al. Major Early-Middle Devonian oceanic oxygenation linked to early land plant evolution detected using high-resolution U isotopes of marine limestones. Earth Planet. Sci. Lett. 581, 117410 (2022).CAS 

Google Scholar 
Algeo, T. J. & Scheckler, S. E. Terrestrial-marine teleconnections in the Devonian: Links between the evolution of land plants, weathering processes, and marine anoxic events. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 353(1365), 113–130 (1998).
Google Scholar 
Capel, E. et al. The Silurian-Devonian terrestrial revolution: Diversity patterns and sampling bias of the vascular plant macrofossil record. Earth Sci. Rev. 231, 104085 (2022).
Google Scholar 
Racki, G., Joachimski, M. M. & Morrow, J. R. A major perturbation of the global carbon budget in the Early-Middle Frasnian transition (Late Devonian). Palaeogeogr. Palaeoclimatol. Palaeoecol. 269(3–4), 127–129 (2008).
Google Scholar 
Stein, W. E., Berry, C. M., Hernick, L. V. & Mannolini, F. Surprisingly complex community discovered in the mid-Devonian fossil forest at Gilboa. Nature 483(7387), 78–81 (2012).ADS 
CAS 

Google Scholar 
Retallack, G. J. & Huang, C. Ecology and evolution of Devonian trees in New York, USA. Palaeogeogr. Palaeoclimatol. Palaeoecol. 299(1–2), 110–128 (2011).
Google Scholar 
Qie, W., Algeo, T. J., Luo, G. & Herrmann, A. Global events of the late Paleozoic (early Devonian to Middle Permian): A review. Palaeogeogr. Palaeoclimatol. Palaeoecol. 531, 109259 (2019).
Google Scholar 
Smart, M. S., Filippelli, G., Gilhooly III, W. P., Marshall, J. E. & Whiteside, J. H. Enhanced terrestrial nutrient release during the Devonian emergence and expansion of forests: Evidence from lacustrine phosphorus and geochemical records. GSA Bulletin. Nov. 9 (2022).Śliwiński, M. G., Whalen, M. T. & Day, J. Trace element variations in the Middle Frasnian punctata zone (Late Devonian) in the western Canada sedimentary basin— changes in oceanic bioproductivity and paleoredox spurred by a pulse of terrestrial afforestation?. Geol. Belg. 4, 459–482 (2010).
Google Scholar 
Filippelli, G. M. & Souch, C. Effects of climate and landscape development on the terrestrial phosphorus cycle. Geology 27(2), 171–174 (1999).ADS 
CAS 

Google Scholar 
Filippelli, G. M., Souch, C., Horn, S. P. & Newkirk, D. The pre-Colombian footprint on terrestrial nutrient cycling in Costa Rica: Insights from phosphorus in a lake sediment record. J. Paleolimnol. 43(4), 843–856 (2010).ADS 

Google Scholar 
Pisarzowska, A. & Racki, G. Comparative carbon isotope chemostratigraphy of major Late Devonian biotic crises. In Stratigraphy & Timescales. 387–466, vol. 5. (Academic Press, 2020).Mortlock, R. A. & Froelich, P. N. A simple method for the rapid determination of biogenic opal in pelagic marine sediments. Deep Sea Res. Part A Oceanogr. Res. Pap. 36(9), 1415–1426 (1989).ADS 
CAS 

Google Scholar 
Schieber, J., Krinsley, D. & Riciputi, L. Diagenetic origin of quartz silt in mudstones and implications for silica cycling. Nature 406(6799), 981–985 (2000).ADS 
CAS 

Google Scholar 
Buckman, J., Mahoney, C., März, C., Wagner, T. & Blanco, V. Identifying biogenic silica: Mudrock micro-fabric explored through charge contrast imaging. Am. Miner. 102(4), 833–844 (2017).ADS 

Google Scholar 
Gao, P., He, Z., Lash, G. G., Zhou, Q. & Xiao, X. Controls on silica enrichment of Lower Cambrian organic-rich shale deposits. Mar. Pet. Geol. 130, 105126 (2021).CAS 

Google Scholar 
Schieber, J. Early diagenetic silica deposition in algal cysts and spores; a source of sand in black shales?. J. Sediment. Res. 66(1), 175–183 (1996).
Google Scholar 
Śliwiński, M. G., Whalen, M. T., Newberry, R. J., Payne, J. H. & Day, J. E. Stable isotope (δ13Ccarb and org, δ15Norg) and trace element anomalies during the Late Devonian ‘punctata Event’in the Western Canada Sedimentary Basin. Palaeogeogr. Palaeoclimatol. Palaeoecol. 307(1–4), 245–271 (2011).
Google Scholar 
Papazis, P. K. & Milliken, K. Cathodoluminescent textures and the origin of quartz in the Mississippian Barnett Shale, Fort Worth Basin, Texas. In AAPG Annual Meeting, Volume Abstracts: Calgary, Alberta, American Association of Petroleum Geologists A, 105 (2005).Ross, D. J. & Bustin, R. M. Investigating the use of sedimentary geochemical proxies for paleoenvironment interpretation of thermally mature organic-rich strata: Examples from the Devonian-Mississippian shales, Western Canadian Sedimentary Basin. Chem. Geol. 260(1–2), 1–19 (2009).ADS 
CAS 

Google Scholar 
Götze, J., Plötze, M. & Habermann, D. Origin, spectral characteristics and practical applications of the cathodoluminescence (CL) of quartz–a review. Mineral. Petrol. 71(3), 225–250 (2001).ADS 

Google Scholar 
Milliken, K. L., Ergene, S. M. & Ozkan, A. Quartz types, authigenic and detrital, in the Upper Cretaceous Eagle Ford Formation, south Texas, USA. Sed. Geol. 339, 273–288 (2016).CAS 

Google Scholar 
Blatt, H. Perspectives; Oxygen isotopes and the origin of quartz. J. Sediment. Res. 57(2), 373–377 (1987).ADS 
CAS 

Google Scholar 
Rowe, H. D., Loucks, R. G., Ruppel, S. C. & Rimmer, S. M. Mississippian Barnett Formation, Fort Worth Basin, Texas: Bulk geochemical inferences and Mo–TOC constraints on the severity of hydrographic restriction. Chem. Geol. 257(1–2), 16–25 (2008).ADS 
CAS 

Google Scholar 
Wright, A. M., Ratcliffe, K. T., Zaitlin, B. A. & Wray, D. S. The application of chemostratigraphic techniques to distinguish compound incised valleys in low-accommodation incised-valley systems in a foreland-basin setting: An example from the Lower Cretaceous Mannville Group and Basal Colorado Sandstone (Colorado Group), Western Canadian Sedimentary Basin, in K.T. Ratcliffe, and B.A. Zaitlin (eds.), Application of Modern Stratigraphic Techniques: Theory and Case Histories: SEPM SP PUB no. 94 (2010).Murata, K. J. & Norman, M. B. An index of crystallinity for quartz. Am. J. Sci. 276(9), 1120–1130 (1976).ADS 
CAS 

Google Scholar 
Tréguer, P. J. et al. Reviews and syntheses: The biogeochemical cycle of silicon in the modern ocean. Biogeosciences 18(4), 1269–1289 (2021).ADS 

Google Scholar 
Rivard, B., Harris, N. B., Feng, J. & Dong, T. Inferring total organic carbon and major element geochemical and mineralogical characteristics of shale core from hyperspectral imagery. AAPG Bull. 102(10), 2101–2121 (2018).
Google Scholar 
Lippincott, E. R., Van Valkenburg, A., Weir, C. E. & Bunting, E. N. Infrared studies on polymorphs of silicon dioxide and germanium dioxide. J. Res. Natl. Bur. Stand 61(1), 61–70 (1958).CAS 

Google Scholar 
Salisbury, J. W., D’Aria, D. M. & Jarosewich, E. Midinfrared (2.5–13.5 μm) reflectance spectra of powdered stony meteorites. Icarus 92(2), 280–297 (1991).ADS 
CAS 

Google Scholar 
Wong, P. K., Weissenberger, J. A. W., Gilhooly, M. G., Playton, T. E. & Kerans, C. Revised regional Frasnian sequence stratigraphic framework, Alberta outcrop and subsurface. New Adv. Devonian Carbonates: Outcrop Analogs, Reservoirs, and Chronostratigr. 49(1), 37–85 (2016).
Google Scholar 
Wendte, J. C. Cooking Lake platform evolution and its control on Late Devonian Leduc reef inception and localization, Redwater, Alberta. Bull. Can. Pet. Geol. 42(4), 499–528 (1994).
Google Scholar 
Wendte, J., Stoakes, F. A. & Campbell, C. V. Cyclicity of Devonian strata in the Western Canada Sedimentary Basin. In: Devonian-Early Mississippian Carbonates of the Western Canada Sedimentary Basin: A sequence stratigraphic framework. J. Wendte (ed.). Society of Economic Paleontologists and Mineralogists, Short Course no. 28, p. 25–40 (1995).Stoakes, F. A. Nature and control of shale basin fill and its effect on reef growth and termination: Upper Devonian Duvernay and Ireton Formations of Alberta, Canada. Bull. Can. Pet. Geol. 28(3), 345–410 (1980).
Google Scholar 
Alberta Energy Regulator Duvernay Reserves and Resources Report: A Comprehensive Analysis of Alberta’s Foremost Liquids-Rich Shale Resource, December 2016.Knapp, L. J., McMillan, J. M. & Harris, N. B. A depositional model for organic-rich Duvernay Formation mudstones. Sed. Geol. 347, 160–182 (2017).CAS 

Google Scholar 
Andrichuk, J. M. Stratigraphic evidence for tectonic and current control of Upper Devonian reef sedimentation, Duhamel area, Alberta, Canada. AAPG Bull. 45(5), 612–632 (1961).
Google Scholar 
Harris, N. B., McMillan, J. M., Knapp, L. J. & Mastalerz, M. Organic matter accumulation in the Upper Devonian Duvernay Formation, Western Canada Sedimentary Basin, from sequence stratigraphic analysis and geochemical proxies. Sed. Geol. 376, 185–203 (2018).CAS 

Google Scholar 
Hildred, G. V., Ratcliffe, K. T., Wright, A. M., Zaitlin, B. A. & Wray, D. S. Chemostratigraphic applications to low-accommodation fluvial incised-valley settings: An example from the Lower Mannville Formation of Alberta, Canada. J. Sedim. Res. 80(11), 1032–1045 (2010).
Google Scholar 
Wedepohl, K. H. Environmental influences on the chemical composition of shales and clays. Phys. Chem. Earth 8, 307–333 (1971).ADS 

Google Scholar 
Pearce, T. J., Martin, J. H., Cooper, D. & Wray, D. S. Chemostratigraphy of upper carboniferous (Pennsylvanian) sequences from the Southern North Sea (United Kingdom). Application of Modern Stratigraphic Techniques: Theory and Case Histories. SEPM Spec. Publ. 94, 109–127 (2010).
Google Scholar 
Adachi, M., Yamamoto, K. & Sugisaki, R. Hydrothermal chert and associated siliceous rocks from the northern Pacific their geological significance as indication of ocean ridge activity. Sed. Geol. 47(1–2), 125–148 (1986).CAS 

Google Scholar 
Abercrombie, H. J., Hutcheon, I. E., Bloch, J. D. & Caritat, P. D. Silica activity and the smectite-illite reaction. Geology 22(6), 539–542 (1994).ADS 
CAS 

Google Scholar 
McLennan, S. M. Weathering and global denudation. J. Geol. 101(2), 295–303 (1993).ADS 

Google Scholar 
Nesbitt, H. W. & Young, G. M. Formation and diagenesis of weathering profiles. J. Geol. 97(2), 129–147 (1989).ADS 
CAS 

Google Scholar 
Fedo, C. M., Wayne Nesbitt, H. & Young, G. M. Unraveling the effects of potassium metasomatism in sedimentary rocks and paleosols, with implications for paleoweathering conditions and provenance. Geology 23(10), 921–924 (1995).ADS 
CAS 

Google Scholar 
Nesbitt, H. W. & Young, G. M. Early Proterozoic climates and plate motions inferred from major element chemistry of lutites. Nature 299, 715–717 (1982).ADS 
CAS 

Google Scholar 
von Eynatten, H., Barceló-Vidal, C. & Pawlowsky-Glahn, V. Modelling compositional change: The example of chemical weathering of granitoid rocks. Math. Geol. 35(3), 231–251 (2003).
Google Scholar 
Clark, R. N. & Rencz, A. N. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual Remote Sens. 3(11), 3–58 (1999).
Google Scholar 
Kump, L. R. & Arthur, M. A. Interpreting carbon-isotope excursions: Carbonates and organic matter. Chem. Geol. 161(1–3), 181–198 (1999).ADS 
CAS 

Google Scholar 
Holmden, C. et al. Carbon isotope chemostratigraphy of Frasnian sequences in Western Canada. Saskatchewan Geol. Surv. Summary Investig. 1, 1–6 (2006).
Google Scholar 
Pisarzowska, A. & Racki, G. Isotopic chemostratigraphy across the Early-Middle Frasnian transition (Late Devonian) on the South Polish carbonate shelf: A reference for the global punctata Event. Chem. Geol. 334, 199–220 (2012).ADS 
CAS 

Google Scholar 
Racki, G. & Bultynck, P. Conodont biostratigraphy of the Middle to Upper Devonian boundary Beds in the Kielce area of the Holy Cross Mts. Acta Geol. Pol. 44, 1–25 (1993).
Google Scholar 
Ziegler and Sandberg. The Late Devonian standard conodont zonation CFS, Cour. Forschungsinst. Senckenberg, 121 (1990).Klapper, G., The Montagne Noire Frasnian (Upper Devonian) conodont succession. In McMillan, N.J., et al., eds., Devonian of the world, Volume III: Canadian Society of Petroleum Geologists Memoir 14, p. 449–468 (1988).Jiao, X. et al. Mixed biogenic and hydrothermal quartz in Permian lacustrine shale of Santanghu Basin, NW China: Implications for penecontemporaneous transformation of silica minerals. Int. J. Earth Sci. 107(6), 1989–2009 (2018).CAS 

Google Scholar 
Peltonen, C., Marcussen, Ø., Bjørlykke, K. & Jahren, J. Clay mineral diagenesis and quartz cementation in mudstones: The effects of smectite to illite reaction on rock properties. Mar. Pet. Geol. 26(6), 887–898 (2009).CAS 

Google Scholar 
Pearce, T. J., Besly, B. M., Wray, D. S. & Wright, D. K. Chemostratigraphy: A method to improve interwell correlation in barren sequences—a case study using onshore Duckmantian/Stephanian sequences (West Midlands, UK). Sed. Geol. 124(1–4), 197–220 (1999).CAS 

Google Scholar 
Calvert, S. E. & Pedersen, T. F. Chapter fourteen elemental proxies for palaeoclimatic and palaeoceanographic variability in marine sediments: Interpretation and application. Dev. Mar. Geol. 1, 567–644 (2007).
Google Scholar 
Perri, F., Cirrincione, R., Critelli, S., Mazzoleni, P. & Pappalardo, A. Clay mineral assemblages and sandstone compositions of the Mesozoic Longobucco Group, northeastern Calabria: Implications for burial history and diagenetic evolution. Int. Geol. Rev. 50(12), 1116–1131 (2008).
Google Scholar 
Johnson, J. G., Klapper, G. & Sandberg, C. A. Devonian eustatic fluctuations in Euramerica. Geol. Soc. Am. Bull. 96(5), 567–587 (1985).ADS 

Google Scholar 
Warme, J. E. & Sandberg, C. A. Alamo megabreccia: Record of a Late Devonian impact in southern Nevada. GSA Today 6(1), 1–7 (1996).
Google Scholar 
Ernst, R. E., Rodygin, S. A. & Grinev, O. M. Age correlation of Large Igneous Provinces with Devonian biotic crises. Glob. Planet. Change 185, 103097 (2020).
Google Scholar 
Schiffbauer, J. D. et al. Decoupling biogeochemical records, extinction, and environmental change during the Cambrian SPICE event. Sci. Adv. 3(3), e1602158 (2017).ADS 

Google Scholar 
Duller, R. A., Armitage, J. J., Manners, H. R., Grimes, S. & Jones, T. D. Delayed sedimentary response to abrupt climate change at the Paleocene-Eocene boundary, northern Spain. Geology 47(2), 159–162 (2019).ADS 
CAS 

Google Scholar  More

We used a custom designed biologging tag package with onboard video to describe a 3D high-resolution pursuit between a solitary sailfish and an individual small tuna in open water, representing the first time such an interaction has been documented. The sailfish was tagged at 09:53 on 18 October 2019, and the tag package remained attached to the sailfish for 67 h. However, analyses here are limited to the 24 h period in which the predation event took place (19 October–20 October; ~ 14 h after tagging and ~ 9 h after post-release recovery18) because this coincides with the time period the video camera was recording during daylight hours (on at 0600, off at 1800, sunrise and sunset, respectively) enabling us to ground-truth acceleration signals. Biologging data and accompanying video show the sailfish performing oscillatory dives between the surface and depths of 40–50 m during daylight hours. At night, fewer dives were performed and the sailfish generally remained within the top 10–20 m of the water column (Fig. 1a), leading to a greater range of temperatures experienced during the day (day 20.9–27.9 °C; night 26.5–28.2 °C). Due to the temperature dependence of the estimated active metabolic rate (AMRE), the cooler temperatures at depth led to a reduced AMRE during daylight hours (212.9 ± 89.1 mgO2 kg−1 h−1) compared to night (224.7 ± 44.4 mgO2 kg−1 h−1). Additionally, AMRE initially increases with depth due to increased swim speeds during diving (Fig. 1b), until the thermocline is reached in the 30–40 m depth bin, at which point AMRE decreases with further increased depth (Fig. 1b, c). However, due to thermal inertia of large-bodied fishes19,20,21, it is possible that the sailfish’s body retained heat during the short (14.7 ± 1.7 min) excursions below the thermocline and did not drop to ambient temperature. As such, the metabolic rate calculated at depth may be underestimated with the temperature correction performed here. For example, during the dive in which the predation event occurred (Fig. 1; Table 1), if body temperature was assumed equivalent to surface temperature throughout the dive, estimated metabolic rates would increase by 18% compared to if the metabolic rates were temperature corrected according to the tag’s external temperature reading (Table S2). Yet, because the majority ( > 90%) of time over the 24 h was spent above the thermocline, the temperature correction has little impact on the daily calculated AMRE and subsequent energy expenditure ( More

Eckert, K. L. & Eckert, A. E. An Atlas of Sea Turtle Nesting Habitat for the Wider Caribbean Region Revised edn. WIDECAST Technical Report no. 19 (2019).Guzmán-Hernández, V. Informe técnico 2017 del Programa de conservación de tortugas marinas en Laguna de Términos, Campeche, México. Contiene información de: 1. CPCTM Isla Aguada y 2. Reseña estatal (APFFLT/RPCyGM/CONANP/SEMARNAT, 2018).Guzmán-Hernández, V. et al. Recovery of green turtle populations and their interactions with coastal dune as a baseline for an integral ecological restoration. Acta Bot. Mex. 129, 1. https://doi.org/10.21829/abm129.2022.1954 (2022).Article 

Google Scholar 
Garduño-Andrade, M., Guzmán, V., Miranda, E., Briseño-Dueñas, R. & Abreu-Grobois, F. A. Increases in hawksbill turtle (Eretmochelys imbricata) nestings in the Yucatán Peninsula, Mexico, 1977–1996: Data in support of successful conservation?. Chelonian Conserv. Biol. 3, 286–295 (1999).
Google Scholar 
Guzmán-Hernández, V., Escanero-Figueroa, G. & Márquez, R. Programa tortuguero en el Centro Regional de Investigación Pesquera de Ciudad del Carmen, Campeche: Retrospectiva, avances y perspectivas. In Tortugas Marinas (eds Márquez, R. & Garduño-Dionate, M.) (Instituto Nacional de Pesca, 2014).
Google Scholar 
Guzmán-Hernández, V. Informe técnico 2021 del Programa de conservación de tortugas marinas en Laguna de Términos, Campeche, México. Contiene información de: 1. CPCTM Isla Aguada y 2. Reseña estatal (APFFLT/RPCyGM/CONANP/SEMARNAT, 2022).Troëng, S. & Rankin, E. Long-term conservation efforts contribute to positive green turtle Chelonia mydas nesting trend at Tortuguero, Costa Rica. Biol. Conserv. 121, 111–116 (2005).Article 

Google Scholar 
Lira-Reyes, D. et al. Informe final de la temporada de anidación 2021 del programa para la conservación de la tortuga marina en El Cuyo, Yucatán e Isla Holbox, Quintana Roo (Dirección General de Vida Silvestre de SEMARNAT/Pronatura Península de Yucatán, 2021).Piacenza, S. E., Balazs, G. H., Hargrove, S. K., Richards, P. M. & Heppell, S. S. Trends and variability in demographic indicators of a recovering population of green sea turtles Chelonia mydas. Endanger. Species Res. 31, 103–117 (2016).Article 

Google Scholar 
Iles, T. C. & Beverton, R. J. H. Stock, recruitment and moderating processes in flatfish. J. Sea Res. 39, 41–55 (1998).Article 
ADS 

Google Scholar 
Heppell, S. S., Crowder, L. B., Crouse, D. T., Epperly, S. P. & Frazer, N. B. Population models for Atlantic Loggerheads: Past, present, and future. In Loggerhead Sea Turtles (eds Bolten, A. & Witherington, B.) 255–273 (Smithsonian Institution Press, 2003).
Google Scholar 
Beverton, R. J. H. & Holt, S. J. On the dynamics of exploited fish populations (Fishery Investigation Series II, 1957).Ricker, W. E. Stock and recruitment. J. Fish. Res. Board Can. 11, 559–623 (1954).Article 

Google Scholar 
Cushing, D. H. The dependence of recruitment on parent stock in different groups of fishes. ICES J. Mar. Sci. 33, 340–362. https://doi.org/10.1093/icesjms/33.3.340 (1971).Article 

Google Scholar 
Iles, T. C. A review of stock-recruitment relationships with reference to flatfish populations. Neth. J. Sea Res. 32, 399–420 (1994).Article 

Google Scholar 
Hilborn, R. & Walters, C. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty (Chapman & Hall, 1992).Book 

Google Scholar 
Subbey, S., Devine, J. A., Schaarscmidt, U. & Nash, R. D. M. Modeling and forecasting stock recruitment: Current and future perspectives. ICES J. Mar. Sci. 71, 2307–2322 (2014).Article 

Google Scholar 
del Monte-Luna, P., Villalobos-Ortíz, H. & Arreguín-Sánchez, F. Variability of sea surface temperature in the southwestern Gulf of Mexico. Cont. Shelf Res. 102, 73–79 (2015).Article 
ADS 

Google Scholar 
Van Houtan, K. S. & Halley, J. M. Long-term climate forcing in loggerhead sea turtle nesting. PLoS ONE 6, e19043. https://doi.org/10.1371/journal.pone.0019043 (2011).Article 
ADS 
CAS 

Google Scholar 
SWOT Scientific Advisory Board. The State of the World’s Sea Turtles (SWOT) Minimum Data Standards for Nesting Beach Monitoring, version 1.0 (2011).Cuevas, D., Garrido-Chávez, E. & Raymundo-Sánchez, A. Monitoreo del género Chelonia en playas con alta densidad de anidación: reporte final (PROCER/DGOR/15/2013) para la Comisión Nacional de Áreas Naturales Protegidas (Pronatura Península de Yucatán-Comisión Nacional de Áreas Naturales Protegidas, 2013).Chim-Vera, Y. A. Evaluación del esfuerzo de monitoreo del éxito de emergencia en nidos de tortuga carey y blanca en la Península de Yucatán (Instituto Tecnológico de Conkal, 2009).Guzmán-Hernández, V., Cuevas-Flores, E., García-Alvarado, P. & González-Ruíz, T. Biological monitoring of sea turtles on nesting beaches: Datasets and basic evaluations. In Successful Conservation Strategies for Sea Turtles. Achievements and Challenges (eds Lara-Uc, M. M. et al.) 41–78 (Nova Science Publishers, 2015).
Google Scholar 
Méndez-Matos, V. C., Guzmán-Hernández, V. & Rivas-Hernández, G. Dinámica poblacional de hembras de tortuga blanca (Chelonia mydas) en el estado de Campeche, México. In El uso del conocimiento de las tortugas marinas como herramienta para la restauración de sus poblaciones y hábitats asociados (eds Cuevas, E. A. et al.) 171–187 (Universidad Autónoma del Carmen, 2019).
Google Scholar 
Xavier, R., Cortez, L. P., Cuevas, E., Barata, A. & Queiroz, N. Hawksbill turtle (Eretmochelys imbricata Linnaeus 1766) and green turtle (Chelonia mydas Linnaeus 1754) nesting activity (2002–2004) at El Cuyo beach, Mexico. Amphibia-Reptilia 27, 539–547 (2006).Article 

Google Scholar 
Whiting, A. U., Chaloupka, M. & Limpus, C. J. Sampling nesting sea turtles: Impact of survey error on trend detection. Mar. Ecol. Prog. Ser. 634, 213–223 (2020).Article 
ADS 

Google Scholar 
Harrell, F. E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer Series in Statistics (Springer, 2015).Book 
MATH 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021).Florida Fish and Wildlife Conservation Commission. Index Nesting Beach Survey Totals (1989–2020). https://myfwc.com/research/wildlife/sea-turtles/nesting/beach-survey-totals. Accessed 2022-05-04 (2022).Marjomäki, T. J. Analysis of the spawning stock-recruitment relationship of vendace (Coregonus albula (L.)) with evaluation of alternative models, additional variables, biases and errors. Ecol. Freshw. Fish 13, 46–60 (2004).Article 

Google Scholar 
Arendt, M. D., Schwenter, J. A., Witherington, B. E., Meylan, A. B. & Saba, V. S. Historical versus contemporary climate forcing on the annual nesting variability of loggerhead sea turtles in the Northwest Atlantic Ocean. PLoS ONE 8, e81097. https://doi.org/10.1371/journal.pone.0081097 (2013).Article 
ADS 
CAS 

Google Scholar 
del Monte-Luna, P., Guzmán-Hernández, V., Cuevas, E. A., Arreguín-Sánchez, F. & Lluch-Belda, D. Effect of North Atlantic climate variability on hawksbill turtles in the Southern Gulf of Mexico. J. Exp. Mar. Biol. Ecol. 412, 103–109 (2012).Article 

Google Scholar 
Howard, R., Bell, I. & Pike, D. A. Thermal tolerances of sea turtle embryos: Current understanding and future directions. Endanger. Species Res. 26, 75–86. https://doi.org/10.3354/esr00636 (2014).Article 

Google Scholar 
Broderick, A. C., Godley, B. J. & Hays, G. C. Trophic status drives interannual variability in nesting numbers of marine turtles. Proc. R. Soc. Lond. B. Biol. 268, 1481–1487 (2001).Article 
CAS 

Google Scholar 
Manzano-Sarabia, M. M. & Salinas-Zavala, C. A. Variabilidad estacional e interanual de la concentración de clorofila y temperatura superficial del mar en la región occidental del Golfo de México: 1996–2007. Interciencia 33, 628–634 (2008).
Google Scholar 
Hays, G. C. The implications of variable remigration intervals for the assessment of population size in marine turtles. J. Theor. Biol. 206, 221–227. https://doi.org/10.1006/jtbi.2000.2116 (2000).Article 
ADS 
CAS 

Google Scholar 
Saragoça-Bruno, R., Restrepo, J. A. & Valverde, R. A. Effects of El Niño Southern Oscillation and local ocean temperature on the reproductive output of green turtles (Chelonia mydas) nesting at Tortuguero, Costa Rica. Mar. Biol. 167, 1 (2020).Article 

Google Scholar 
Patrício, A. R., Hawkes, L. A., Monsinjon, J. R., Godley, B. J. & Fuentes, M. M. P. B. Climate change and marine turtles: Recent advances and future directions. Endanger. Species Res. 44, 363–395. https://doi.org/10.3354/esr01110 (2021).Article 

Google Scholar 
Valverde-Cantillo, V., Robinson, N. J. & Santidrián Tomillo, P. Influence of oceanographic conditions on nesting abundance, phenology and internesting periods of east Pacific green turtles. Mar. Biol. 166, 93. https://doi.org/10.1007/s00227-019-3541-1 (2019).Article 

Google Scholar 
Chaloupka, M. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304 (2008).Article 

Google Scholar 
Ariza-Gallego, M., Herrera-Carmona, J., Payán, L. & Giraldo, A. Relationship between sea surface temperature and the nesting of the Olive Ridley sea turtle Lepidochelys olivacea (Testudines: Cheloniidae) in Gorgona Island, Colombian Pacific. Rev. Biol. Trop. 68, 528–540. https://doi.org/10.15517/RBT.V68I2.38642 (2020).Article 

Google Scholar 
Ceriani, S. A., Casale, P., Brost, M., Leone, E. H. & Witherington, B. E. Conservation implications of sea turtle nesting trends: Elusive recovery of a globally important loggerhead population. Ecosphere 10, e02936. https://doi.org/10.1002/ecs2.2936 (2019).Article 

Google Scholar 
Arendt, M. D., Schwenter, J. A., Owens, D. W. & Valverde, R. A. Theoretical modeling and neritic monitoring of loggerhead Caretta caretta [Linnaeus, 1758] sea turtle sex ratio in the southeast United States do not substantiate fears of a male-limited population. Glob. Change Biol. 27, 4849–4859. https://doi.org/10.1111/gcb.15808 (2021).Article 
CAS 

Google Scholar 
Doi, T., Márquez, R., Kimoto, H. & Azeno, N. Diagnosis and Conservation of Hawksbill Turtle Population in the Cuban Archipelago. Technical Report 40 (Japan Bekko Association, 1992).Broderick, A. C. Are green turtles globally endangered?. Glob. Ecol. Biogeogr. 14, 21–26 (2006).Article 

Google Scholar 
Mazaris, A. D., Schofield, G., Gkazinou, C., Almpanidou, V. & Hays, G. C. Global sea turtle conservation successes. Sci. Adv. 3, e1600730. https://doi.org/10.1126/sciadv.1600730 (2017).Article 
ADS 

Google Scholar 
Early-Capistrán, M. M. et al. Integrating local ecological knowledge, ecological monitoring, and computer simulation to evaluate conservation outcomes. Conserv. Lett.https://doi.org/10.1111/conl.12921 (2022).Article 

Google Scholar 
Taylor, M. A., Stephenson, T. S., Chen, A. A. & Stephenson, K. A. Climate change and the Caribbean: Review and response. Caribb. Stud. 40, 169–200 (2012).Article 

Google Scholar  More

Charlesworth, B. & Charlesworth, D. Population genetics from 1966 to 2016. Heredity 118, 2–9 (2017).CAS 

Google Scholar 
Orsini, L., Vanoverbeke, J., Swillen, I., Mergeay, J. & Meester, L. Drivers of population genetic differentiation in the wild: Isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol. Ecol. 22, 5983–5999 (2013).
Google Scholar 
Vendrami, D. L. J. et al. RAD sequencing resolves fine-scale population structure in a benthic invertebrate: Implications for understanding phenotypic plasticity. R. Soc. Open Sci. 4, 160548 (2017).ADS 

Google Scholar 
Dufresnes, C., Rodrigues, N. & Savary, R. Slow and steady wins the race: Contrasted phylogeographic signatures in two Alpine amphibians. Integr. Zool. 17, 181–190 (2021).
Google Scholar 
Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
Google Scholar 
Schwartz, M. K., Luikart, G. & Waples, R. S. Genetic monitoring as a promising tool for conservation and management. Trends in Ecol. Evol. 22, 25–33 (2007).
Google Scholar 
Ottewell, K. M., Bickerton, D. C., Byrne, M. & Lowe, A. J. Bridging the gap: A genetic assessment framework for population-level threatened plant conservation prioritization and decision-making. Divers. Distrib. 22, 174–188 (2016).
Google Scholar 
Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: Revised recommendations for the 50/500 rules, red list criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).
Google Scholar 
Hohenlohe, P. A., Funk, C. W. & Rajora, O. P. Population genomics for wildlife conservation and management. Mol. Ecol. 30, 62–82 (2020).
Google Scholar 
Angelone, S. & Holderegger, R. Population genetics suggests effectiveness of habitat connectivity measures for the European tree frog in Switzerland. J. Appl. Ecol. 46, 879–887 (2009).
Google Scholar 
Griffiths, S. M., Taylor-Cox, E. D., Behringer, D. C., Butler, M. J. IV. & Preziosi, R. F. Using genetics to inform restoration and predict resilience in declining populations of a keystone marine sponge. Biodivers. Conserv. 29, 1383–1410 (2020).
Google Scholar 
Moritz, C. Conservation units and translocations: Strategies for conserving evolutionary processes. Hereditas 130, 217–228 (1999).
Google Scholar 
Bohonak, A. J. Dispersal, gene flow, and population structure. Q. Rev. Biol. 74, 21–45 (1999).CAS 

Google Scholar 
Arguedas, N. & Parker, P. G. Seasonal migration and genetic population structure in house wrens. Condor 102, 517–528 (2000).
Google Scholar 
Quillfeldt, P. et al. Does genetic structure reflect differences in non-breeding movements? A case study in small, highly mobile seabirds. BMC Evol. Biol. 17, 160 (2017).
Google Scholar 
Charlesworth, B., Sniegowski, P. & Stephan, W. The evolutionary dynamics of repetitive DNA in eukaryotes. Nature 371, 215–220 (1994).ADS 
CAS 

Google Scholar 
Schlötterer, C. Evolutionary dynamics of microsatellite DNA. Chromosoma 109, 365–371 (2000).
Google Scholar 
Ellegren, H. Microsatellites: Simple sequences with complex evolution. Nat. Rev. Genet. 5, 435–445 (2004).CAS 

Google Scholar 
Hodel, R. G. J. et al. The report of my death was an exaggeration: A review for researchers using microsatellites in the 21st century. Appl. Plant Sci. 4, 1600025 (2016).
Google Scholar 
Dufresnes, C. & Litvinchuk, S. N. Diversity, distribution and molecular species delimitation in frogs and toads from the Eastern Palearctic. Zool. J. Linn. Soc. 195, 695–760 (2022).
Google Scholar 
Galtier, N., Nabholz, B., Glémin, S. & Hurst, G. D. D. Mitochondrial DNA as a marker of molecular diversity: A reappraisal. Mol. Evol. 18, 4541–4550 (2009).CAS 

Google Scholar 
Zink, R. M. & Barrowclough, G. Mitochondrial DNA under siege in avian phylogeography. Mol. Ecol. 17, 2107–2121 (2008).CAS 

Google Scholar 
Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 

Google Scholar 
Bonnet, T., Leblois, R., Rousset, F. & Crochet, P.-A. A reassessment of explanations for discordant introgressions of mitochondrial and nuclear genomes. Evolution 71, 2140–2218 (2017).
Google Scholar 
Davey, J. W. & Blaxter, M. L. RADSeq: Next-generation population genetics. Brief Funct. Genomics 9, 416–423 (2010).CAS 

Google Scholar 
Lexer, C. et al. ‘Next generation’ biogeography: Towards understanding the drivers of species diversification and persistence. J. Biogeogr. 40, 1013–1022 (2013).
Google Scholar 
Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, e3376 (2008).ADS 

Google Scholar 
Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double Digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).ADS 
CAS 

Google Scholar 
Dufresnes, C. et al. Phylogeography of a cryptic speciation continuum in Eurasian spadefoot toads (Pelobates). Mol. Ecol. 28, 3257–3270 (2019).
Google Scholar 
Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: Analysis of data for pike (Esox Lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).
Google Scholar 
Moussy, C. et al. Migration and dispersal patterns of bats and their influence on genetic structure. Mammal Rev. 43, 183–195 (2013).
Google Scholar 
Berthier, P., Excoffier, L. & Ruedi, M. Recurrent replacement of mtDNA and cryptic hybridization between two sibling bat species Myotis myotis and Myotis blythii. Proc. R. Soc. B: Biol. Sci. 273, 3101–3109 (2007).
Google Scholar 
Wright, P. G. R. et al. Hydrogen isotopes reveal evidence of migration of Miniopterus schreibersii in Europe. BMC Ecol. 20, 52 (2020).CAS 

Google Scholar 
Schnetter, W. Beringungsergebnisse an der Langflügelfledermaus (Miniopterus schreibersi Kühl) im Kaiserstuhl. Bonn. Zool. Beitr. 11, 150–165 (1960).
Google Scholar 
Rodrigues, L. Miniopterus schreibersii. In The Atlas of European Mammals (eds Mitchell-Jones, A. J. et al.) 154–155 (Academic Press, 1999).
Google Scholar 
Rodrigues, L., Ramos Pereira, M. J., Rainho, A. & Palmeirim, J. M. Behavioral determinants of gene flow in the bat Miniopterus schreibersii. Behav. Ecol. Sociobiol. 64, 835–843 (2010).
Google Scholar 
Rodrigues, L. & Palmeirim, J. M. Migratory behaviour of Miniopterus schreibersii (Chiroptera): When, where, and why do cave bats migrate in a Mediterranean region?. J. Zool. 274, 116–125 (2008).
Google Scholar 
Ramos Pereira, M. J., Salgueiro, P., Rodrigues, L., Coelho, M. M. & Palmeirim, J. M. Population structure of a cave-dwelling bat, Miniopterus schreibersii: Does it reflect history and social organization?. J. Hered. 100, 533–544 (2009).
Google Scholar 
Bilgin, R. et al. Circum-Mediterranean phylogeography of a bat coupled with past environmental niche modeling: A new paradigm for the recolonization of Europe?. Mol. Phylogenet. Evol. 99, 323–336 (2016).
Google Scholar 
Gürün, K. et al. A continent-scale study of the social structure and phylogeography of the bent-wing bat, Miniopterus schreibersii (Mammalia: Chiroptera), using new microsatellite data. J. Mammal. 100, 1865–1878 (2019).
Google Scholar 
Gazaryan, S., Bücs, S., Çoraman, E. Miniopterus schreibersii (errata version published in 2021). The IUCN Red List of Threatened Species 2020: e.T81633057A195856522 (2020).Miller-Butterworth, C. M., Jacobs, D. S. & Harley, E. H. Isolation and characterization of highly polymorphic microsatellite loci in Schreibers’ long-fingered bat, Miniopterus schreibersii (Chiroptera: Vespertilionidae). Mol. Ecol. Notes 2, 139–141 (2002).CAS 

Google Scholar 
Wood, R., Weyeneth, N. & Appleton, B. Development and characterisation of 20 microsatellite loci isolated from the large bent-wing bat, Miniopterus schreibersii (Chiroptera: Miniopteridae) and their cross-taxa utility in the family Miniopteridae. Mol. Ecol. Resour. 11, 675–685 (2011).
Google Scholar 
Witsenburg, F. et al. How a haemosporidian parasite of bats gets around: The genetic structure of a parasite, vector and host compared. Mol. Ecol. 24, 926–940 (2015).CAS 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. micro-checker: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Google Scholar 
Parchman, T. L. et al. Genome wide association mapping of an adaptive trait in lodgepole pine. Mol. Ecol. 21, 2991–3005 (2012).CAS 

Google Scholar 
Catchen, J. M., Amores, A., Hohenlohe, P., Cresko, W. & Postlethwait, J. H. Stacks: Building and genotyping loci de novo from short-read sequences. G3 1, 171–182 (2011).CAS 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analyses of population structure. Evolution 38, 1358–1370 (1984).CAS 

Google Scholar 
Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).
Google Scholar 
Frankham, R., Ballou, J. D. & Briscoe, D. A. A Primer of Conservation Genetics (Cambridge University Press, 2004).
Google Scholar 
Weir, B. S. & Goudet, J. A unified characterization of population structure and relatedness. Genetics 206, 2085–2103 (2017).
Google Scholar 
Mantel, N. A. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).CAS 

Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114–138 (1943).CAS 

Google Scholar 
Cavalli-Sforza, L. L. & Edwards, A. W. F. Phylogenetic analysis: Model and estimation procedures. Am. J. Hum. Genet. 19, 233–257 (1967).CAS 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 

Google Scholar 
Goudet, J., Perrin, N. & Waser, P. Tests for sex-biased dispersal using bi-parentally inherited genetic markers. Mol. Ecol. 11, 1103–1114 (2002).CAS 

Google Scholar 
Frichot, E. & François, O. lea: An r package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Google Scholar 
Yannic, G. et al. High connectivity in a long-lived High-Arctic seabird, the ivory gull Pagophila eburnea. Polar Biol. 39, 221–236 (2016).
Google Scholar 
Cumer, T. et al. Landscape and climatic variations of the Quaternary shaped multiple secondary contacts among barn owls (Tyto alba) of the Western Palearctic. Mol. Biol. Evol. 39, msab343 (2022).CAS 

Google Scholar 
Boston, E. S. M., Montgomery, W. I., Hynes, R. & Prodöhl, P. A. New insights on postglacial colonization in western Europe: The phylogeography of the Leisler’s bat (Nyctalus leisleri). Proc. R. Soc. B: Biol. Sci. 282, 20142605 (2015).
Google Scholar 
Razgour, O. et al. The shaping of genetic variation in edge-of-range populations under past and future climate change. Ecol. Lett. 16, 1258–1266 (2013).
Google Scholar 
Petit, E., Balloux, F. & Goudet, J. Sex-biased dispersal in a migratory bat: A characterization using sex-specific demographic parameters. Evolution 55, 635–640 (2001).CAS 

Google Scholar 
Moussy, C. et al. Population genetic structure of serotine bats (Eptesicus serotinus) across Europe and implications for the potential spread of bat rabies (European bat lyssavirus EBLV-1). Heredity 115, 83–92 (2015).CAS 

Google Scholar 
Rossiter, S. J., Benda, P., Dietz, C., Zhang, S. & Jones, G. Rangewide phylogeography in the greater horseshoe bat inferred from microsatellites: Implications for population history, taxonomy and conservation. Mol. Ecol. 16, 4699–4714 (2007).CAS 

Google Scholar 
Dool, S. E. et al. Phylogeography and postglacial recolonization of Europe by Rhinolophus hipposideros: Evidence from multiple genetic markers. Mol. Ecol. 22, 4055–4070 (2013).CAS 

Google Scholar 
Kerth, G. et al. Communally breeding Bechstein’s bats have a stable social system that is independent from the postglacial history and location of the populations. Mol. Ecol. 17, 2368–2381 (2008).CAS 

Google Scholar 
Garrick, R. C., Banusiewicz, J. D., Burgess, S., Hyseni, C. & Symula, R. E. Extending phylogeography to account for lineage fusion. J. Biogeogr. 46, 268–278 (2019).
Google Scholar 
Burri, R. et al. The genetic basis of color-related local adaptation in a ring-like colonization around the Mediterranean. Evolution 70, 140–153 (2016).
Google Scholar 
Taberlet, P., Fumagalli, L., Wust-Saucy, A.-G. & Cosson, J.-F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).CAS 

Google Scholar 
Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999).
Google Scholar 
Gómez, A. & Lunt, D. H. Refugia within refugia: Patterns of phylogeographic concordance in the Iberian Peninsula. In Phylogeography of Southern European Refugia (eds Weiss, S. & Ferrand, N.) 155–188 (Springer, 2007).
Google Scholar 
Vonhof, M. J., Russell, A. L. & Miller-Butterworth, M. Range-wide genetic analysis of little brown bat (Myotis lucifugus) populations: Estimating the risk of spread of white-nose syndrome. PLoS ONE 10, e0128713 (2015).
Google Scholar 
Auteri, G. G. & Knowles, L. L. Decimated little brown bats show potential for adaptive change. Sci. Rep. 10, 3023 (2020).ADS 
CAS 

Google Scholar 
Gignoux-Wolfsohn, S. A. et al. Genomic signatures of selection in bats surviving white-nose syndrome. Mol. Ecol. 30, 5643–5657 (2021).
Google Scholar 
Rivers, N. M., Butlin, R. K. & Altringham, J. D. Autumn swarming behaviour of Natterer’s bats in the UK: Population size, catchment area and dispersal. Biol. Conserv. 127, 215–226 (2006).
Google Scholar 
Reis, N. R., Fregonezi, M. N., Peracchi, A. L. & Rossaneis, B. K. Metapopulation in bats of Southern Brazil. Braz. J. Biol. 72, 605–609 (2012).CAS 

Google Scholar 
Humphrey, S. R. & Oli, M. K. Population dynamics and site fidelity of the cave bat, Myotis velifer, Oklahoma. J. Mammal. 96, 946–956 (2015).
Google Scholar 
Jeffries, D. L. et al. Comparing RADseq and microsatellites to infer complex phylogeographic patterns, an empirical perspective in the Crucian carp, Carassius carassius. L. Mol. Ecol. 25, 2997–3018 (2016).
Google Scholar 
Hodel, R. G. J. et al. Adding loci improves phylogeographic resolution in red mangroves despite increased missing data: Comparing microsatellites and RAD-Seq and investigating loci filtering. Sci. Rep. 7, 17598 (2017).ADS 

Google Scholar 
Lemopoulos, A. et al. Comparing RADseq and microsatellites for estimating genetic diversity and relatedness—Implications for brown trout conservation. Ecol. Evol. 9, 2106–2120 (2019).
Google Scholar 
Zimmerman, S. J., Aldridge, C. L. & Oyler-McCance, S. J. An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genom. 21, 382 (2020).CAS 

Google Scholar 
Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE 7, e45170 (2012).ADS 
CAS 

Google Scholar 
Quetglas, J., Gonzalez, F. & Paz, O. Estudian la extraña mortandad de miles de murcielago de cuevas. Quercus 203, 50 (2003).
Google Scholar 
Negredo, A. et al. Discovery of an ebolavirus-like filovirus in Europe. PLoS Pathog. 7, e1002304 (2011).CAS 

Google Scholar 
Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).
Google Scholar 
Alcalde, J. T., Artácoz, A. & Meijide, F. Recuperación de la colonia de Miniopterus schreibersii de la cueva de Cueva de Ágreda (Soria). Barbastella 5, 32–35 (2012).
Google Scholar 
Kemenesi, G. et al. Re-emergence of Lloviu virus in Miniopterus schreibersii bats, Hungary, 2016. Emerg. Microbes Infect. 7, 66 (2018).
Google Scholar 
Kemenesi, et al. Isolation of infectious Lloviu virus from Schreiber’s bats in Hungary. Nat. Commun. 13, 1706 (2022).ADS 
CAS 

Google Scholar 
Stoffel, C. et al. Genetic consequences of population expansions and contractions in the common hippopotamus (Hippopotamus amphibius) since the late Pleistocene. Mol. Ecol. 24, 2507–2520 (2015).
Google Scholar  More

Quality controls for the validation of MICODE protocolTo validate the MICODE experimental approach in the version of fecal batch of the human proximal colon, we chose to monitor and check some parameters as quality controls (QC) related to metabolites and microbes at the end of fermentations, and in comparison, to the baseline. QCs adopted were; (i) the Firmicutes/Bacteroidetes ratio (F/B), which is related to health and disease11, was maintained at a low level, confirming the capacity to simulate a healthy in vivo condition for 24 h. (ii) The presence of Archea (e.g., Methanobrevibacter smithii and Methanosphaera stadtmanae), which are pretty sensible to oxygen content12, was retained from the baseline to the end point in each vessel and repetition, indicating that the environmental conditions were strictly maintained. (iii) Good’s rarity index of alpha biodiversity remained similar during time of fermentation (p  > 0.05), indicating enough support to the growth of rare species. (iv) Observed OTUs richness index scored approximately 400 OTUs at the end point. (v) The paradigm of prebiotics was confirmed when the positive control (FOS) was applied on MICODE; high probiotic and SCFAs increases and limitation of enteropathogens. (vi) Each GC/MS analysis had quantified some stool-related compounds (urea, 1-propanol, and butylated hydroxy toluene), that ranged across the complete chromatogram and were adsorbed at the same retention times.Changes in bacterial alpha and beta diversitiesThe microbiota diversity indices were analyzed to study the impact of HPBA on microbial population, to assess population’s stability during fermentation, and to compare its microbiota to that of other bioreactors (Figure S1). The baseline of value was compared to the endpoints of fermentation of different treatments. It is undisputable that a part of the effect of reduction in richness (Observed OTUs) was derived by the passage from in vivo to in vitro condition, but the focus must be set on the different trend that other alpha diversity indices had. For example, abundance (Chao 1) for HBPA was significantly higher at the end of fermentation (p  0.05) and HPBA (p  0.05), while oppositely, FOS decreased in evenness (p  > 0.05) and raised in dominance (p  0.05). Among these, 31 variables were significant and their Log2 fold changes in respect to the baseline were compared by post-hoc test (Table 1). The 41 OTUs selected were those that recorded shifts after fermentation and that from literature are susceptible to the effect of prebiotic or fiber substrates. We have included even three OTUs of Archea relative to QC of the experiments (previously discussed).Table 1 Abundances (% ± S.D.) and changes in phylum taxa (Log2 F/C) after 24 h in vitro fecal batch culture fermentations from healthy donors and administrated with HBPA, HB, and FOS as the substrates, and also including a blank control.Full size tableThe first group of OTUs included beneficial or commensal bacteria that usually respond to prebiotics. In this group, three Bifidobacterium were picked showing increases on the substrates and reduction on the blank control. HB and HBPA fostered Bif. bifidum, but just the latter did it significantly, making this taxon grew up to the 3.30% of relative abundance (p  More

Data collectionPeer-reviewed articles published up to Dec 31, 2021 were searched through the Web of Science (http://webofknowledge.com), Google scholar (http://scholar.google.com), and the China National Knowledge Infrastructure (CNKI, http://www.cnki.net). Here we employed a combination of the following search terms: “(tree biomass OR aboveground biomass OR plant biomass OR plant productivity) and (allometric biomass equation OR allometric model OR productivity model OR biomass equation OR biomass model)”. To avoid potential selection bias and duplicates, we conducted a cross-check between the references of relevant articles, which resulted in the selection of 729 relevant articles from the thousands of the appearing articles initially. Subsequently, eligible articles were selected using the following criteria: (1) Allometric models built for specific species with confirmed locations without disturbances were selected, generalized species, large-scales (e.g., province or nation), as well as recently disturbed tree models were excluded. (2) The method employed to develop the model was destructive harvesting and weighing, with at least twenty sample trees, were selected; articles were excluded that did not include measurements and used less than twenty sample trees. (3) The model forms were W = a*Db and LnW = a + b*Ln(D) or W = a*(D2H)b and LnW = a + b*Ln(D2H), where W is the aboveground biomass, and D is the diameter at breast height, H is the tree height, were selected. Consequently, we excluded articles with other variables and other forms of models. Finally, 426 articles remained from the original 729 (Supplementary Fig. S1).We then distilled data from the articles for the following variables: (1) Allometric models, in the form of W = a*Db and LnW = a + b*Ln(D), W = a*(D2H)b and LnW = a + b*Ln(D2H) including the parameters a, b in the D range and H range. (2) Tree species corresponding to the models, including families, genera, and species. (3) Location data, including longitude, latitude, and study sites. (4) Climate data, including mean annual temperature (MAT, °C) and mean annual precipitation (MAP, mm) of the tree species location. (5) Terrain data, including slope and aspect. (6) Soil data, including soil organic carbon (SOC), clay, and soil type.Since not all articles provided the location, climate, soil, and terrain data of the studies, we estimated the missing data as follows, (1) we supplemented the longitude and latitude with the study location using Google Earth. (2) We extracted the missing climate data by using geographic coordinates from WorldClim version 2.0 (http://worldclim.org/current)16. (3) We obtained the shuttle radar topographic mission DEM data with 30 m resolution from NASA, and used SAGA-GIS software to derive various terrain data from the DEM such as altitude, slope, and aspect17, 18. (4) The missing soil data was derived from the Regridded Harmonized World Soil Database v1.219. In particular, we established the soil type according to Soil Taxonomy to increase the accuracy of the analysis and prediction. Furthermore, if the experiments were performed at multiple sites in one study, they were treated as independent observations. In light of above criteria, 817 allometric models in the form of W = a*Db or LnW = a + b*Ln(D) and 612 allometric models in the form of W = a*(D2H)b or LnW = a + b*Ln(D2H) were collected from the 426 articles.Allometric modelThe relationship between the diameter and aboveground biomass was in the form of the power function20:$$begin{array}{c}Wi=atimes D{i}^{b},end{array}$$
(1)
where Wi is the dry mass of the ith tree (kg), Di is diameter at breast height (cm), and a and b are the parameters of the model.$$Wi=atimes (D{i}^{2}Hi{)}^{b},$$
(2)
where Wi is the dry mass of the ith tree (kg), Di is diameter at breast height (cm), Hi is the tree height (cm), and a and b are the parameters of the model.However, a heteroscedasticity exists when directly fitting the tree biomass. The logarithmic transformation of Eq. (1) or Eq. (2), is convenient to facilitate model fitting and deal with heterocedasticity21. The logarithmic transformation allometric model:$$begin{array}{c}Lnleft(Wiright)=a+btimes Lnleft(Diright),end{array}$$
(3)

was used in this function, where a (Eq. 3) represents Ln(a) (Eq. 1), and b (Eq. 3) is the same as b (Eq. 1), respectively.$$begin{array}{c}Lnleft(Wiright)=a+btimes Lnleft(D{i}^{2}Hright),end{array}$$
(4)
was used in this function, where a (Eq. 4) represents Ln(a) (Eq. 2), and b (Eq. 4) is the same as b (Eq. 2), respectively. To unify the models, we transformed the collected Eqs. (1) to (3) and Eqs. (2) to (4).Data analysisTo establish the relationship between variables with parameters a and b for making a parameter prediction on a global scale, Random Forest (RF) (an example of a machine learning model) was employed, which consists of an ensemble of randomized classification and regression trees (CART)21. In short, the RF will generate a number of trees and aggregate these to provide a single prediction. In regression problems the prediction is the average of the individual tree outputs, whereas in classification the trees vote by majority on the correct classification22, 23. Generated trees called ntree are based on a bootstrapped 2/3 sample of the original data to decrease correlations by choosing different training sets in the RF modeling process15. In addition to this normal bagging function, the best split at each node of the tree was searched only among a randomly selected subset (mtry) of predictors24. The tree growing procedure is performed recursively until the size of the node reaches a minimum, k, which is parameterized by the user. For the rest of the original data, RF provides a believable error estimation using the data called Out-Of-Bag (OOB), which is employed to obtain a running unbiased estimate of the classification error as trees are added to the forest15.Predictive variable selectionThe variables included stand factors such as density, family, and diameters, as well as non-stand factors such as MAT, MAP, and SOC. Considering that the prediction was on a global scale, the first step was to exclude the factors that it was not possible to completely extract. Next, we selected variables through the following22: (1) the RF classifier was initially applied using all of the predictor variables, and variable importance was used to rank them based on the mean decrease in accuracy. (2) Removing the least important variables by the variable importance ranking, (3) the training data were then partitioned five-fold for cross-validation and the error rates for each of the five cross-validation partitions were aggregated into a mean error rate, and 20 replicates of the five-fold CV were performed25.By means of the above, eleven variables, including family, genus, species, MAT, MAP, altitude, aspect, SOC, slope, clay, and soil type, were remained to predict parameters. Since the combinations of variables were different, five combinations were performed to make predictions from the eleven variables above. Among the five combinations, each were used by RF to predict and select via the model evaluation index VaR explained and the mean of squared residual (Supplementary Table S1).Optimization of Random Forest parametersRF depends primarily on three parameters that are set by users. (1) ntree, the number of trees in the forest. (2) nodesize, the minimum number of data points in each terminal node. (3) mtry, the number of features tried at each node. To obtain the optimization of RF parameters, we set ntree = 1000, 2000, 3000 and the selection criterion was that ntree was small enough to maximize computational efficiency as well as produced stable OOB error25. As for nodesize, we used 3, 5, 7, and 5 as the default for regression RF, given that the mtry value always is always one third of the number of variables. Here we also set the mtry values (ranging from 2 to 4), which were tested, and we accessed the OOB error rates from 50 replicates for each mtry value25. The primary tuning parameter above were optimized, as well as each combination of the three RF parameters through a grided search, which were used to predict and set RF parameters according to the predictive effect of each combination (Supplementary Table S2).All above data analysis were conducted in R 4.0.326. And the output is the spatial pattern of allometric model parameters at 0.5° resolution.Predicted parameter validationFurther to assess the accuracy of the predicted parameters, we applied them to estimate the AGB at six sites. And the actual AGB of the sites had been obtained via destructive sampling from 209 plots, which were located in Hubei, Liaoning, Gansu, Hebei and Heilongjiang provinces, and Inner Mongolia autonomous region from 2009 to 201327 (Table 1). First, we selected the sample trees according the dominant, average and inferior tree outside the plot. Then the sample trees were felled as carefully as possible and tree height (H), tree diameter in the breast (DBH) and live crown length were recorded. To divide trees into several sub-samples, including branches, leaves, stem wood and stem bark, all of the branches were removed and leaves were picked. Besides, stem was divided into 1 m sections and bark of the stem was removed. Finally, all sub-samples of aboveground part of trees were oven-dried at 80 °C until a constant weight was reached and the sum of all the sun-samples weight was the actual AGB. Through the above process, 249 actual AGB data were obtained. Meanwhile, the predicted parameters of the models together with the DBH and H estimated the predicted AGB. The actual AGB data of 249 sample trees were compared with the predicted AGB by making fitting curves between them in R to show the availability of predicted parameters according root mean square error (RMSE) and R2.Table 1 The basic features of the sampling sites.Full size tableThe experimental research and field studies on plants in this study, including the collection of plant material, complied with the relevant institutional, national, and international guidelines and legislation. And we ensured that we have permission for the plant sampling, all of the steps were allowed in our study for the plant research. In addition, plant identification in this study was conducted by X.Z according to World Plants (https://www.worldplants.de) in the herbarium of School of Forestry & Landscape of Architecture, Anhui Agricultural University, and the voucher specimen of all plant material has been deposited in a publicly available herbarium. More

Rahman, M. A. et al. Comparing the infiltration potentials of soils beneath the canopies of two contrasting urban tree species. Urban For. Urban Green. 38, 22–32. https://doi.org/10.1016/j.ufug.2018.11.002 (2019).Article 

Google Scholar 
Zölch, T., Henze, L., Keilholz, P. & Pauleit, S. Regulating urban surface runoff through nature-based solutions – An assessment at the micro-scale. Environ. Res. 157, 135–144. https://doi.org/10.1016/j.envres.2017.05.023 (2017).Article 
CAS 

Google Scholar 
Barron, O. V., Barr, A. D. & Donn, M. J. Effect of urbanisation on the water balance of a catchment with shallow groundwater. J. Hydrol. 485, 162–176. https://doi.org/10.1016/j.jhydrol.2012.04.027 (2013).Article 
ADS 

Google Scholar 
Rosenzweig, B. R. et al. The value of urban flood modeling. Earth’s Future 9, e2020EF001739. https://doi.org/10.1029/2020EF001739 (2021).Article 
ADS 

Google Scholar 
Pauleit, S., Fryd, O., Backhaus, A. & Jensen, M. B. In Encyclopedia of Sustainability Science and Technology (ed. Meyers, R. A.) 1–29 (Springer, 2020).
Google Scholar 
Rahman, M. A. et al. Traits of trees for cooling urban heat islands: A meta-analysis. Build. Environ. 170, 106606. https://doi.org/10.1016/j.buildenv.2019.106606 (2020).Article 

Google Scholar 
Ziter, C. D., Pedersen, E. J., Kucharik, C. J. & Turner, M. G. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc. Natl. Acad. Sci. USA 116, 7575–7580. https://doi.org/10.1073/pnas.1817561116 (2019).Article 
ADS 
CAS 

Google Scholar 
Waldrop, M. M. News feature: The quest for the sustainable city. Proc. Natl. Acad. Sci. 116, 17134–17138. https://doi.org/10.1073/pnas.1912802116 (2019).Article 
ADS 
CAS 

Google Scholar 
Cleugh, H. A., Bui, E., Simon, D., Xu, J. & Mitchell, V. G. The Impact of Suburban Design on Water Use and Microclimate (2005).Chan, F. K. S. et al. “Sponge City” in China—A breakthrough of planning and flood risk management in the urban context. Land Use Policy 76, 772–778. https://doi.org/10.1016/j.landusepol.2018.03.005 (2018).Article 

Google Scholar 
Morgan, R. P. C. Soil Erosion and Conservation (Wiley, 2005).
Google Scholar 
Xu, C. et al. Surface runoff in urban areas: The role of residential cover and urban growth form. J. Clean. Prod. 262, 121421. https://doi.org/10.1016/j.jclepro.2020.121421 (2020).Article 

Google Scholar 
Ostoić, S. K. & van den Bosch, C. C. K. Exploring global scientific discourses on urban forestry. Urban For. Urban Green. 14, 129–138. https://doi.org/10.1016/j.ufug.2015.01.001 (2015).Article 

Google Scholar 
Rahman, M. A. et al. Tree cooling effects and human thermal comfort under contrasting species and sites. Agric. For. Meteorol. 287, 107947. https://doi.org/10.1016/j.agrformet.2020.107947 (2020).Article 
ADS 

Google Scholar 
Rötzer, T., Rahman, M. A., Moser-Reischl, A., Pauleit, S. & Pretzsch, H. Process based simulation of tree growth and ecosystem services of urban trees under present and future climate conditions. Sci. Total Environ. 676, 651–664. https://doi.org/10.1016/j.scitotenv.2019.04.235 (2019).Article 
ADS 
CAS 

Google Scholar 
Grote, R. et al. Functional traits of urban trees: Air pollution mitigation potential. Front. Ecol. Environ. 14, 543–550. https://doi.org/10.1002/fee.1426 (2016).Article 

Google Scholar 
Pace, R. et al. A single tree model to consistently simulate cooling, shading, and pollution uptake of urban trees. Int. J. Biometeorol. 65, 277–289. https://doi.org/10.1007/s00484-020-02030-8 (2021).Article 
ADS 

Google Scholar 
Kuehler, E., Hathaway, J. & Tirpak, A. Quantifying the benefits of urban forest systems as a component of the green infrastructure stormwater treatment network. Ecohydrology https://doi.org/10.1002/eco.1813 (2017).Article 

Google Scholar 
Rahman, M. A., Moser, A., Gold, A., Rötzer, T. & Pauleit, S. Vertical air temperature gradients under the shade of two contrasting urban tree species during different types of summer days. Sci. Total Environ. 633, 100–111. https://doi.org/10.1016/j.scitotenv.2018.03.168 (2018).Article 
ADS 
CAS 

Google Scholar 
Rahman, M. A., Smith, J. G., Stringer, P. & Ennos, A. R. Effect of rooting conditions on the growth and cooling ability of Pyrus calleryana. Urban For. Urban Green. 10, 185–192. https://doi.org/10.1016/j.ufug.2011.05.003 (2011).Article 

Google Scholar 
Schellekens, J., Scatena, F. N., Bruijnzeel, L. A. & Wickel, A. J. Modelling rainfall interception by a lowland tropical rain forest in northeastern Puerto Rico. J. Hydrol. 225, 168–184. https://doi.org/10.1016/S0022-1694(99)00157-2 (1999).Article 
ADS 

Google Scholar 
Guevara-Escobar, A., González-Sosa, E., Véliz-Chávez, C., Ventura-Ramos, E. & Ramos-Salinas, M. Rainfall interception and distribution patterns of gross precipitation around an isolated Ficus benjamina tree in an urban area. J. Hydrol. 333, 532–541. https://doi.org/10.1016/j.jhydrol.2006.09.017 (2007).Article 
ADS 

Google Scholar 
Xiao, Q. F. & McPherson, E. G. Surface water storage capacity of twenty tree species in Davis, California. J. Environ. Qual. 45, 188–198. https://doi.org/10.2134/jeq2015.02.0092 (2016).Article 
CAS 

Google Scholar 
Xiao, Q. F., McPherson, E. G., Ustin, S. L. & Grismer, M. E. A new approach to modeling tree rainfall interception. J. Geophys. Res. Atmos. 105, 29173–29188. https://doi.org/10.1029/2000jd900343 (2000).Article 
ADS 

Google Scholar 
Carlyle-Moses, D. E. & Gash, J. H. C. In Forest Hydrology and Biogeochemistry: Synthesis of Past Research and Future Directions (eds Levia, D. F. et al.) 407–423 (Springer, 2011).Chapter 

Google Scholar 
Hirano, T. et al. The difference in the short-term runoff characteristic between the coniferous catchment and the deciduous catchment: The effects of storm size on storm generation processes of small forested catchment. J. Jpn. Soc. Hydrol. Water Resour. 22, 24–39. https://doi.org/10.3178/jjshwr.22.24 (2009).Article 

Google Scholar 
Chandler, K. R. & Chappell, N. A. Influence of individual oak (Quercus robur) trees on saturated hydraulic conductivity. For. Ecol. Manage. 256, 1222–1229. https://doi.org/10.1016/j.foreco.2008.06.033 (2008).Article 

Google Scholar 
Stewart, I. D. A systematic review and scientific critique of methodology in modern urban heat island literature. Int. J. Climatol. 31, 200–217. https://doi.org/10.1002/joc.2141 (2011).Article 

Google Scholar 
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214. https://doi.org/10.1038/sdata.2018.214 (2018).Article 

Google Scholar 
Moreno-de las Heras, M., Nicolau, J. M., Merino-Martín, L. & Wilcox, B. P. Plot-scale effects on runoff and erosion along a slope degradation gradient. Water Resour. Res. 46, W04503. https://doi.org/10.1029/2009WR007875 (2010).Article 
ADS 

Google Scholar 
Wu, L., Peng, M., Qiao, S. & Ma, X.-Y. Effects of rainfall intensity and slope gradient on runoff and sediment yield characteristics of bare loess soil. Environ. Sci. Pollut. Res. 25, 3480–3487. https://doi.org/10.1007/s11356-017-0713-8 (2018).Article 

Google Scholar 
Rutter, A. J., Kershaw, K. A., Robins, P. C. & Morton, A. J. A predictive model of rainfall interception in forests, 1. Derivation of the model from observations in a plantation of Corsican pine. Agric. Meteorol. 9, 367–384. https://doi.org/10.1016/0002-1571(71)90034-3 (1971).Article 

Google Scholar 
Gash, J. H. C. An analytical model of rainfall interception by forests. Q. J. R. Meteorol. Soc. 105, 43–55. https://doi.org/10.1002/qj.49710544304 (1979).Article 
ADS 

Google Scholar 
Véliz-Chávez, C., Mastachi-Loza, C. A., Gonzalez-Sosa, E., Becerril-Pia, R. & Ramos-Salinas, N. M. Canopy storage implications on interception loss modeling. Am. J. Plant Sci. 05, 3032–3048. https://doi.org/10.4236/ajps.2014.520320 (2014).Article 

Google Scholar 
Fan, J., Oestergaard, K. T., Guyot, A. & Lockington, D. A. Measuring and modeling rainfall interception losses by a native Banksia woodland and an exotic pine plantation in subtropical coastal Australia. J. Hydrol. 515, 156–165. https://doi.org/10.1016/j.jhydrol.2014.04.066 (2014).Article 
ADS 

Google Scholar 
Ghimire, C. P., Bruijnzeel, L. A., Lubczynski, M. W. & Bonell, M. Rainfall interception by natural and planted forests in the Middle Mountains of Central Nepal. J. Hydrol. 475, 270–280. https://doi.org/10.1016/j.jhydrol.2012.09.051 (2012).Article 
ADS 

Google Scholar 
Pereira, F. L. et al. Modelling interception loss from evergreen oak Mediterranean savannas: Application of a tree-based modelling approach. Agric. For. Meteorol. 149, 680–688. https://doi.org/10.1016/j.agrformet.2008.10.014 (2009).Article 
ADS 

Google Scholar 
Pypker, T. G., Bond, B. J., Link, T. E., Marks, D. & Unsworth, M. H. The importance of canopy structure in controlling the interception loss of rainfall: Examples from a young and an old-growth Douglas-fir forest. Agric. For. Meteorol. 130, 113–129. https://doi.org/10.1016/j.agrformet.2005.03.003 (2005).Article 
ADS 

Google Scholar 
Ringgaard, R., Herbst, M. & Friborg, T. Partitioning forest evapotranspiration: Interception evaporation and the impact of canopy structure, local and regional advection. J. Hydrol. 517, 677–690. https://doi.org/10.1016/j.jhydrol.2014.06.007 (2014).Article 
ADS 

Google Scholar 
Price, A. G. & Carlyle-Moses, D. E. Measurement and modelling of growing-season canopy water fluxes in a mature mixed deciduous forest stand, southern Ontario, Canada. Agric. For. Meteorol. 119, 69–85. https://doi.org/10.1016/S0168-1923(03)00117-5 (2003).Article 
ADS 

Google Scholar 
Fathizadeh, O., Hosseini, S. M., Zimmermann, A., Keim, R. F. & Darvishi Boloorani, A. Estimating linkages between forest structural variables and rainfall interception parameters in semi-arid deciduous oak forest stands. Sci. Total Environ. 601–602, 1824–1837. https://doi.org/10.1016/j.scitotenv.2017.05.233 (2017).Article 
ADS 
CAS 

Google Scholar 
Livesley, S. J., Baudinette, B. & Glover, D. Rainfall interception and stem flow by eucalypt street trees—the impacts of canopy density and bark type. Urban For. Urban Green. 13, 192–197. https://doi.org/10.1016/j.ufug.2013.09.001 (2014).Article 

Google Scholar 
Xiao, Q. & McPherson, E. G. Rainfall interception by Santa Monica’s municipal urban forest. Urban Ecosyst. 6, 291–302. https://doi.org/10.1023/B:UECO.0000004828.05143.67 (2002).Article 

Google Scholar 
Rohatgi, A. WebPlotDigitizer (4.4), 2020).Team, R. C. (R Foundation for Statistical Computing, 2020).García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity–ecosystem stability relationship globally. PNAS 115, 8400–8405. https://doi.org/10.1073/pnas.1800425115 (2018).Article 
ADS 
CAS 

Google Scholar 
Le Provost, G. et al. Land-use history impacts functional diversity across multiple trophic groups. PNAS 117, 1573–1579. https://doi.org/10.1073/pnas.1910023117 (2020).Article 
ADS 
CAS 

Google Scholar 
El Kateb, H., Zhang, H., Zhang, P. & Mosandl, R. Soil erosion and surface runoff on different vegetation covers and slope gradients: A field experiment in Southern Shaanxi Province, China. CATENA 105, 1–10. https://doi.org/10.1016/j.catena.2012.12.012 (2013).Article 

Google Scholar 
Oliveira, P. T. S. et al. The water balance components of undisturbed tropical woodlands in the Brazilian cerrado. Hydrol. Earth Syst. Sci. 19, 2899–2910. https://doi.org/10.5194/hess-19-2899-2015 (2014).Article 
ADS 

Google Scholar 
Hümann, M. et al. Identification of runoff processes – The impact of different forest types and soil properties on runoff formation and floods. J. Hydrol. 409, 637–649. https://doi.org/10.1016/j.jhydrol.2011.08.067 (2011).Article 
ADS 

Google Scholar 
Sun, D. et al. Soil erosion and water retention varies with plantation type and age. For. Ecol. Manage. 422, 1–10. https://doi.org/10.1016/j.foreco.2018.03.048 (2018).Article 

Google Scholar 
Jost, G., Schume, H., Hager, H., Markart, G. & Kohl, B. A hillslope scale comparison of tree species influence on soil moisture dynamics and runoff processes during intense rainfall. J. Hydrol. 420–421, 112–124. https://doi.org/10.1016/j.jhydrol.2011.11.057 (2012).Article 

Google Scholar 
Sadeghi, S. M. M., Attarod, P., Van Stan, J. T. & Pypker, T. G. The importance of considering rainfall partitioning in afforestation initiatives in semiarid climates: A comparison of common planted tree species in Tehran, Iran. Sci. Total Environ. 568, 845–855. https://doi.org/10.1016/j.scitotenv.2016.06.048 (2016).Article 
ADS 
CAS 

Google Scholar 
Pretzsch, H. et al. Climate change accelerates growth of urban trees in metropolises worldwide. Sci. Rep. https://doi.org/10.1038/s41598-017-14831-w (2017).Article 

Google Scholar 
Rahman, M. A., Moser, A., Rötzer, T. & Pauleit, S. Microclimatic differences and their influence on transpirational cooling of Tilia cordata in two contrasting street canyons in Munich, Germany. Agric. For. Meteorol. 232, 443–456. https://doi.org/10.1016/j.agrformet.2016.10.006 (2017).Article 
ADS 

Google Scholar 
Nytch, C. J., Meléndez-Ackerman, E. J., Pérez, M. E. & Ortiz-Zayas, J. R. Rainfall interception by six urban trees in San Juan, Puerto Rico. Urban Ecosyst. 22, 103–115. https://doi.org/10.1007/s11252-018-0768-4 (2018).Article 

Google Scholar 
Rahman, M. A. et al. Comparative analysis of shade and underlying surfaces on cooling effect. Urban For. Urban Green. 63, 127223. https://doi.org/10.1016/j.ufug.2021.127223 (2021).Article 

Google Scholar 
Chen, L., Zhang, Z. & Ewers, B. E. Urban tree species show the same hydraulic response to vapor pressure deficit across varying tree size and environmental conditions. PLoS One https://doi.org/10.1371/journal.pone.0047882 (2012).Article 

Google Scholar 
Moser-Reischl, A., Rahman, M. A., Pauleit, S., Pretzsch, H. & Rötzer, T. Growth patterns and effects of urban micro-climate on two physiologically contrasting urban tree species. Landsc. Urban Plan. 183, 88–99. https://doi.org/10.1016/j.landurbplan.2018.11.004 (2019).Article 

Google Scholar 
Hao, M. et al. Impacts of changes in vegetation on saturated hydraulic conductivity of soil in subtropical forests. Sci. Rep. 9, 8372. https://doi.org/10.1038/s41598-019-44921-w (2019).Article 
ADS 
CAS 

Google Scholar 
Peters, E. B., McFadden, J. P. & Montgomery, R. A. Biological and environmental controls on tree transpiration in a suburban landscape. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2009jg001266 (2010).Article 

Google Scholar 
Komatsu, H., Kume, T. & Otsuki, K. Increasing annual runoff—broadleaf or coniferous forests?. Hydrol. Process. 25, 302–318. https://doi.org/10.1002/hyp.7898 (2011).Article 
ADS 

Google Scholar 
Li, X. et al. Process-based rainfall interception by small trees in Northern China: The effect of rainfall traits and crown structure characteristics. Agric. For. Meteorol. 218–219, 65–73. https://doi.org/10.1016/j.agrformet.2015.11.017 (2016).Article 
ADS 

Google Scholar 
Lukaszkiewicz, J. & Kosmala, M. Determining the age of streetside trees with diameter at breast height-based multifactorial model. Arboricult. Urban For. 34, 137–143. https://doi.org/10.48044/jauf.2008.018 (2008).Article 

Google Scholar 
Buttle, J. M. & Farnsworth, A. G. Measurement and modeling of canopy water partitioning in a reforested landscape: The Ganaraska Forest, southern Ontario, Canada. J. Hydrol. 466–467, 103–114. https://doi.org/10.1016/j.jhydrol.2012.08.021 (2012).Article 

Google Scholar 
Yang, B., Lee, D. K., Heo, H. K. & Biging, G. The effects of tree characteristics on rainfall interception in urban areas. Landsc. Ecol. Eng. 15, 289–296. https://doi.org/10.1007/s11355-019-00383-w (2019).Article 
CAS 

Google Scholar 
Klamerus-Iwan, A. & Witek, W. Variability in the Wettability and Water Storage Capacity of Common Oak Leaves (Quercus robur L). Water 10, 695. https://doi.org/10.3390/w10060695 (2018).Article 
CAS 

Google Scholar 
Van Stan, J. T., Siegert, C. M., Levia, D. F. & Scheick, C. E. Effects of wind-driven rainfall on stemflow generation between codominant tree species with differing crown characteristics. Agric. For. Meteorol. 151, 1277–1286. https://doi.org/10.1016/j.agrformet.2011.05.008 (2011).Article 
ADS 

Google Scholar 
Selbig, W. R. et al. Quantifying the stormwater runoff volume reduction benefits of urban street tree canopy. Sci. Total Environ. 806, 151296. https://doi.org/10.1016/j.scitotenv.2021.151296 (2022).Article 
ADS 
CAS 

Google Scholar 
Centre for Watershed Protection. Review of the Available Literature and Data on the Runoff and Pollutant Removal Capabilities of Urban Trees (Center for Watershed Protection, 2017).
Google Scholar 
Berland, A. et al. The role of trees in urban stormwater management. Landsc. Urban Plan. 162, 167–177. https://doi.org/10.1016/j.landurbplan.2017.02.017 (2017).Article 

Google Scholar 
Pauleit, S. Urban street tree plantings: Indentifying the key requirements. Proc. Inst. Civ. Eng. Municipal Eng. 156, 43–50. https://doi.org/10.1680/muen.2003.156.1.43 (2003).Article 

Google Scholar 
Weller, M. Tree Inventory Data of Central European Cities—Studies on the Composition and Structure of Urban Tree Populations and Derivation of Ecosystem Services. MSC thesis, Technical University of Munich, Germany (2021). More