Ecology

May, R. M. Biological populations with nonoverlapping generations: stable points, stable cycles, and chaos. Science 186, 645–647 (1974).CAS 
PubMed 
Article 

Google Scholar 
Beddington, J. R., Free, C. A. & Lawton, J. H. Dynamic complexity in predator–prey models framed in difference equations. Nature 255, 58–60 (1975).Article 

Google Scholar 
Hastings, A., Hom, C. L., Ellner, S., Turchin, P. & Godfray, H. C. J. Chaos in ecology: is Mother Nature a strange attractor? Annu. Rev. Ecol. Syst. 24, 1–33 (1993).Article 

Google Scholar 
Cressie, N. & Wikle, C. K. Statistics for Spatio-Temporal Data (John Wiley & Sons, 2011).The State of World Fisheries and Aquaculture 2020 (FAO, 2020).Hastings, A. & Powell, T. Chaos in a three-species food chain. Ecology 72, 896–903 (1991).Article 

Google Scholar 
Huisman, J. & Weissing, F. J. Biodiversity of plankton by species oscillations and chaos. Nature 402, 407–410 (1999).Article 

Google Scholar 
Doebeli, M. & Ispolatov, I. Chaos and unpredictability in evolution. Evolution 68, 1365–1373 (2014).PubMed 
Article 

Google Scholar 
Pearce, M. T., Agarwala, A. & Fisher, D. S. Stabilization of extensive fine-scale diversity by ecologically driven spatiotemporal chaos. Proc. Natl Acad. Sci. USA 117, 14572–14583 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Costantino, R. F., Desharnais, R. A., Cushing, J. M. & Dennis, B. Chaotic dynamics in an insect population. Science 275, 389–391 (1997).CAS 
PubMed 
Article 

Google Scholar 
Becks, L., Hilker, F. M., Malchow, H., Jürgens, K. & Arndt, H. Experimental demonstration of chaos in a microbial food web. Nature 435, 1226–1229 (2005).CAS 
PubMed 
Article 

Google Scholar 
Benincá, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).PubMed 
Article 
CAS 

Google Scholar 
Tilman, D. & Wedin, D. Oscillations and chaos in the dynamics of a perennial grass. Nature 353, 653–655 (1991).Article 

Google Scholar 
Turchin, P. & Ellner, S. P. Living on the edge of chaos: population dynamics of fennoscandian voles. Ecology 81, 3099–3116 (2000).Article 

Google Scholar 
Ferrari, M. J. et al. The dynamics of measles in sub-Saharan Africa. Nature 451, 679–684 (2008).CAS 
PubMed 
Article 

Google Scholar 
Benincà, E., Ballantine, B., Ellner, S. P. & Huisman, J. Species fluctuations sustained by a cyclic succession at the edge of chaos. Proc. Natl Acad. Sci. USA 112, 6389–6394 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hassell, M. P., Lawton, J. H. & May, R. M. Patterns of dynamical behaviour in single-species populations. J. Anim. Ecol. 45, 471–486 (1976).Article 

Google Scholar 
Sibly, R. M., Barker, D., Hone, J. & Pagel, M. On the stability of populations of mammals, birds, fish and insects. Ecol. Lett. 10, 970–976 (2007).PubMed 
Article 

Google Scholar 
Shelton, A. O. & Mangel, M. Fluctuations of fish populations and the magnifying effects of fishing. Proc. Natl Acad. Sci USA. 108, 7075–7080 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Salvidio, S. Stability and annual return rates in amphibian populations. Amphib. Reptil. 32, 119–124 (2011).Article 

Google Scholar 
Snell, T. W. & Serra, M. Dynamics of natural rotifer populations. Hydrobiologia 368, 29–35 (1998).Article 

Google Scholar 
Gross, T., Ebenhöh, W. & Feudel, U. Long food chains are in general chaotic. Oikos 109, 135–144 (2005).Article 

Google Scholar 
Ispolatov, I., Madhok, V., Allende, S. & Doebeli, M. Chaos in high-dimensional dissipative dynamical systems. Sci. Rep. 5, 12506 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clark, T. J. & Luis, A. D. Nonlinear population dynamics are ubiquitous in animals. Nat. Ecol. Evol. 4, 75–81 (2020).CAS 
PubMed 
Article 

Google Scholar 
Sivakumar, B., Berndtsson, R., Olsson, J. & Jinno, K. Evidence of chaos in the rainfall-runoff process. Hydrol. Sci. J. 46, 131–145 (2001).CAS 
Article 

Google Scholar 
Hanski, I., Turchin, P., Korpimäki, E. & Henttonen, H. Population oscillations of boreal rodents: regulation by mustelid predators leads to chaos. Nature 364, 232–235 (1993).CAS 
PubMed 
Article 

Google Scholar 
Turchin, P. & Taylor, A. D. Complex dynamics in ecological time series. Ecology 73, 289–305 (1992).Article 

Google Scholar 
Munch, S. B., Brias, A., Sugihara, G. & Rogers, T. L. Frequently asked questions about nonlinear dynamics and empirical dynamic modelling. ICES J. Mar. Sci. 77, 1463–1479 (2020).Article 

Google Scholar 
Sugihara, G. & May, R. M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990).CAS 
PubMed 
Article 

Google Scholar 
Ellner, S. P. & Turchin, P. Chaos in a noisy world: new methods and evidence from time-series analysis. Am. Nat. 145, 343–375 (1995).Article 

Google Scholar 
Nychka, D., Ellner, S., Gallant, A. R. & McCaffrey, D. Finding chaos in noisy systems. J. R. Stat. Soc. B 54, 399–426 (1992).
Google Scholar 
Webber, C. L. & Zbilut, J. P. Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol. 76, 965–973 (1994).PubMed 
Article 

Google Scholar 
Bandt, C. & Pompe, B. Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88, 174102 (2002).PubMed 
Article 
CAS 

Google Scholar 
Luque, B., Lacasa, L., Ballesteros, F. & Luque, J. Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80, 46103 (2009).CAS 
Article 

Google Scholar 
Toker, D., Sommer, F. T. & D’Esposito, M. A simple method for detecting chaos in nature. Commun. Biol. 3, 11 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pikovsky, A. & Politi, A. Lyapunov Exponents: A Tool to Explore Complex Dynamics (Cambridge Univ. Press, 2016).Rosenstein, M. T., Collins, J. J. & De Luca, C. J. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65, 117–134 (1993).Article 

Google Scholar 
Dämmig, M. & Mitschke, F. Estimation of Lyapunov exponents from time series: the stochastic case. Phys. Lett. A 178, 385–394 (1993).Article 

Google Scholar 
Prendergast, J., Bazeley-White, E., Smith, O., Lawton, J. & Inchausti, P. The Global Population Dynamics Database (KNB, 2010); https://doi.org/10.5063/F1BZ63Z8Thibaut, L. M. & Connolly, S. R. Hierarchical modeling strengthens evidence for density dependence in observational time series of population dynamics. Ecology 101, e02893 (2020).PubMed 
Article 

Google Scholar 
Knape, J. & de Valpine, P. Are patterns of density dependence in the Global Population Dynamics Database driven by uncertainty about population abundance? Ecol. Lett. 15, 17–23 (2012).PubMed 
Article 

Google Scholar 
Takens, F. in Dynamical Systems and Turbulence (eds Rand, D. A. & Young, L. S.) 366–381 (Springer, 1981).Sugihara, G. Nonlinear forecasting for the classification of natural time series. Philos. Trans. R. Soc. A 348, 477–495 (1994).
Google Scholar 
Loh, J. et al. The Living Planet Index: using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. B 360, 289–295 (2005).Article 

Google Scholar 
Kendall, B. E. Cycles chaos, and noise in predator–prey dynamics. Chaos Solitons Fractals 12, 321–332 (2001).Article 

Google Scholar 
Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).CAS 
PubMed 
Article 

Google Scholar 
Anderson, D. M. & Gillooly, J. F. Allometric scaling of Lyapunov exponents in chaotic populations. Popul. Ecol. 62, 364–369 (2020).Article 

Google Scholar 
Graham, D. W. et al. Experimental demonstration of chaotic instability in biological nitrification. ISME J. 1, 385–393 (2007).CAS 
PubMed 
Article 

Google Scholar 
Turchin, P. Nonlinear time-series modeling of vole population fluctuations. Res. Popul. Ecol. 38, 121–132 (1996).Article 

Google Scholar 
Becks, L. & Arndt, H. Different types of synchrony in chaotic and cyclic communities. Nat. Commun. 4, 1359 (2013).PubMed 
Article 
CAS 

Google Scholar 
Becks, L. & Arndt, H. Transitions from stable equilibria to chaos, and back, in an experimental food web. Ecology 89, 3222–3226 (2008).PubMed 
Article 

Google Scholar 
Rezende, E. L., Albert, E. M., Fortuna, M. A. & Bascompte, J. Compartments in a marine food web associated with phylogeny, body mass, and habitat structure. Ecol. Lett. 12, 779–788 (2009).PubMed 
Article 

Google Scholar 
Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).CAS 
PubMed 
Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2020-2 (IUCN, 2020); https://www.iucnredlist.orgFreckleton, R. P. & Watkinson, A. R. Are weed population dynamics chaotic? J. Appl. Ecol. 39, 699–707 (2002).Article 

Google Scholar 
May, R. M. Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976).CAS 
PubMed 
Article 

Google Scholar 
Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the ocean? PLoS Biol. 9, e1001127 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: a global meta-analysis. Fish Fish. 19, 964–973 (2018).Article 

Google Scholar 
Boettiger, C., Harte, T., Chamberlain, S. & Ram, K. rgpdd: R Interface to the Global Population Dynamics Database. https://docs.ropensci.org/rgpdd, https://github.com/ropensci/rgpdd (2019).Brook, B. W., Traill, L. W. & Bradshaw, C. J. A. Minimum viable population sizes and global extinction risk are unrelated. Ecol. Lett. 9, 375–382 (2006).PubMed 
Article 

Google Scholar 
Baars, J. W. M. Autecological investigations of marine diatoms, 2. Generation times of 50 species. Hydrobiol. Bull. 15, 137–151 (1981).Article 

Google Scholar 
Lavigne, A. S., Sunesen, I. & Sar, E. A. Morphological, taxonomic and nomenclatural analysis of species of Odontella, Trieres and Zygoceros (Triceratiaceae, Bacillariophyta) from Anegada Bay (Province of Buenos Aires, Argentina). Diatom Res. 30, 307–331 (2015).Article 

Google Scholar 
Anderson, D. M. & Gillooly, J. F. Physiological constraints on long-term population cycles: a broad-scale view. Evol. Ecol. Res. 18, 693–707 (2017).
Google Scholar 
Janes, M. J. Oviposition studies on the chinch bug, Blissus leucopterus (Say). Ann. Entomol. Soc. Am. 28, 109–120 (1935).Article 

Google Scholar 
Cook, L. M. Food-plant specialization in the moth Panaxia dominula L. Evolution 15, 478–485 (1961).Article 

Google Scholar 
Casey, T. M. Flight energetics of sphinx moths: power input during hovering flight. J. Exp. Biol. 64, 529–543 (1976).CAS 
PubMed 
Article 

Google Scholar 
Kobayashi, A., Tanaka, Y. & Shimada, M. Genetic variation of sex allocation in the parasitoid wasp Heterospilus prosopidis. Evolution 57, 2659–2664 (2003).PubMed 
Article 

Google Scholar 
Hozumi, N. & Miyatake, T. Body-size dependent difference in death-feigning behavior of adult Callosobruchus chinensis. J. Insect Behav. 18, 557–566 (2005).Article 

Google Scholar 
Huntley, M. E. & Lopez, M. D. G. Temperature-dependent production of marine copepods: a global synthesis. Am. Nat. 140, 201–242 (1992).CAS 
PubMed 
Article 

Google Scholar 
Cohen, R. E. & Lough, R. G. Length–weight relationships for several copepods dominant in the Georges Bank–Gulf of Maine area. J. Northwest Atl. Fish. Sci. 2, 47–52 (1981).Article 

Google Scholar 
World Register of Marine Species (WoRMS, accessed 1 November 2020); https://doi.org/10.14284/170Nakamura, Y. Growth and grazing of a large heterotrophic dinoflagellate, Noctiluca scintillans, in laboratory cultures. J. Plankton Res. 20, 1711–1720 (1998).Article 

Google Scholar 
Boulding, E. G. & Platt, T. Variation in photosynthetic rates among individual cells of a marine dinoflagellate. Mar. Ecol. Prog. Ser. 29, 199–203 (1986).CAS 
Article 

Google Scholar 
Rimet, F. et al. The Observatory on LAkes (OLA) database: sixty years of environmental data accessible to the public. J. Limnol. https://doi.org/10.4081/jlimnol.2020.1944 (2020).Rudstam, L. Zooplankton Survey of Oneida Lake, New York, 1964 to Present (KNB, 2020); https://knb.ecoinformatics.org/view/kgordon.17.99https://knb.ecoinformatics.org/knb/metacat/kgordon.17.67/defaultDumont, H. J., Van de Velde, I. & Dumont, S. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecologia 19, 75–97 (1975).PubMed 
Article 

Google Scholar 
Geller, W. & Müller, H. Seasonal variability in the relationship between body length and individual dry weight as related to food abundance and clutch size in two coexisting Daphnia species. J. Plankton Res. 7, 1–18 (1985).Article 

Google Scholar 
Branstrator, D. K. Contrasting life histories of the predatory cladocerans Leptodora kindtii and Bythotrephes longimanus. J. Plankton Res. 27, 569–585 (2005).Article 

Google Scholar 
Rosen, R. A. Length–dry weight relationships of some freshwater zooplankton. J. Freshw. Ecol. 1, 225–229 (1981).Article 

Google Scholar 
Peters, R. H. & Downing, J. A. Empirical analysis of zooplankton filtering and feeding rates. Limnol. Oceanogr. 29, 763–784 (1984).Article 

Google Scholar 
Eckmann, J. P., Kamphorst, S. O. & Ruelle, D. Recurrence plots of dynamical systems. Europhys. Lett. 4, 973–977 (1987).Article 

Google Scholar 
Luque, B., Lacasa, L., Ballesteros, F. J. & Robledo, A. Analytical properties of horizontal visibility graphs in the Feigenbaum scenario. Chaos 22, 013109 (2012).PubMed 
Article 

Google Scholar 
McCaffrey, D. F., Ellner, S., Gallant, A. R. & Nychka, D. W. Estimating the Lyapunov exponent of a chaotic system with nonparametric regression. J. Am. Stat. Assoc. 87, 682–695 (1992).Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Ricker, W. E. Stock and recruitment. J. Fish. Board Can. 11, 559–623 (1954).Article 

Google Scholar  More

The data set consists of tree-ring width measurements in Decadal/Tuscon RWL format24, COFECHA25 listings for every RWL file, online-only Tables 1 and 2 with the description for every living-tree and historical chronology. In each RWL file the measurements for each tree denoted by a number are usually represented by several cores denoted by the letters a,b,c, etc., e.g. T15S1a and T15S1b are two cores for the first tree at the site T15S, T15S15a and T15S15b are two cores for the 15th tree at the site. The historical chronologies usually contain several codes referring to different sources of materials, but the numbering is the same – numbers denote different beams from each source and letters a-d denote the measurements along different radii from each beam.Missing values in RWL files are denoted either by zeroes in the case of missing rings or by −888 in the case of missing core segments. The description of each site contains the information on the location, geographical coordinates, number of trees and samples, information on series intercorrelation, average mean sensitivity, quality of the cross-dating, and related publications (online-only Tables 1, 2). Some sites also have descriptions of vegetation and soils. The RWL files of the measurements and the related COFECHA quality control listings are publicly available in ITRDB. The ITRDB codes and links are provided in the online-only Tables 1 and 2. The whole data set is also available as a standalone set of files26 in Figshare repository, where RWL files are named as the site code plus ‘.rwl’ extension, the COFECHA listings are named as the site code plus ‘COF.txt’. For example, the site T15S is represented by the files ‘T15S.rwl’ and ‘T15SCOF.txt’. Supplementary Tables 1 and 2 represent printable versions of Online-only Tables 1 and 2, respectively.Below we describe the sources of material for each historical chronology.KirillovMaterials for the Kirillov chronology were collected over many years from archaeological excavations in the town of Kirillov, Vologda region. They include wood samples obtained from architectural buildings and various small archaeological excavations in the vicinity of the Kirillo-Belozersky monastery (59.86°N, 38.37°E). During restoration work in 1969, 1971, 1985, and 1987, samples of wooden ties and piles of foundations from brick defensive walls and monastery buildings were collected. The archaeological part of the collection also contains samples from wooden log cabins, wells, and log heaps (remnants of buildings demolished during renovation) and discovered during rescue excavations in 1994, 1998–2000, 2007, 2008, 2011, 2015, 2016, and 2018. The samples were processed in the Laboratory of Natural Science Methods in Archaeology, Institute of Archaeology RAS. Unfortunately, most of the original material has not been archived after the measurements were made. The Kirillov chronology was calendar dated with living trees from the Vologda region (sites KOV and SHBO) and materials from the Museum of Wooden Architecture of the Vologda Region “Semyonkovo”27.VologdaThe collection consists of materials from wooden buildings in the city of Vologda (59.22°N, 39.89°E). The data was assembled by D. Kats in the 1990s and later archived at the Institute of Plant and Animal Ecology in Ekaterinburg. In 2009 the collection was transferred again, and now resides at the Institute of Geography RAS, where ring-widths were measured a second time. The data set includes the samples from 19th century wooden houses on Gogol Street, numbers 3 and 5 (codes AU and AV), from Gertsen Street number 58 (code BA), from the Spaso-Prilutskiy Monastery in the northern outskirts of Vologda (code BB), and from samples of unknown origin from the 18th century (code M). The Vologda chronology was calendar dated with the Kirillov chronology.NovgorodMaterials in the Novgorod chronology are derived from archaeological excavations in the city of Velikiy Novgorod (58.52°N, 31.27°E), in addition to samples from wooden buildings of the Novgorod Region. The latter include materials from building transferred to the Museum of Wooden Architecture “Vitoslavlitsy” from the Novgorod region. These include the Chapel of Magdalena (code N04A), the Church of St. Nicolay from the village of Visokiy Ostrov (code N09A), and a church from the village of Tukholi (code N11A). Archaeological materials come from the city of Novgorod, from the excavation of Yaroslavovo Dvorische (archaeologist A.V. Andrienko, code N02A28), as well as excavations on Telegina-Redyatina Street (code ‘tere’), Posolskaya Street (code ‘posol’), Znamenskaya Street (code ‘znam’), Troitskaya Street (codes ‘35a-1-b1’ and ‘16a-1-v2’), and B. Konyushennaya Street (code ‘kon’), which were directed by archaeologist O.I. Oleynikov. The Novgorod chronology was calendar dated using the russ1 chronology from the ITRDB (with a correction for the known error of 1 year29), and by crossdating with the Kirillov and Vologda chronologies.ArkhangelskThe Arkhangelsk chronology includes samples from houses and churches from the northwestern part of the Arkhangelsk region (63.4–64.7°N, 37.4–43.4°E). These include wooden houses from the town of Pinega, Kudrina Street 45 and 55 (codes I15A and I14A, 64.70°N, 43.39°E), the house of the Bazheniny family in the village of Vavchuga, Kholmogorskoye district (code I21A, 64.23°N, 41.92°E), the Church of Introduction in the village of Vorzogory (code I02A, 63.89°N, 37.67°E), the Church of Vladimir in the village of Medvedevskaya (code I04A, 63.81°N, 38.32°E), and from the the Ensemble of the Church in the village of Piyala (codes I08A, I09A, P, 63.43°N, 39.08°E), all located in the Onezhskiy District. The chronology was calendar dated using a living pine tree-ring series (code I24S, 64.11°N, 38.03°E) in addition to crossdating with the Solovki chronology30.KareliaThis chronology includes materials from eight churches in the Republic of Karelia, all located along the shores of Onega Lake (60.80–62.72°N, 33.06–35.27°E)31. Most of these measurements are of lower precision than of the other data in this study (0.05 mm versus 0.001 mm) however, they are vital to the dendrochronological dating in the region. The Karelia chronology was calendar dated using the Solovki and Arkhangelsk chronologies.Zapadnaya Dvina (ZD1, ZD2)Tree-ring chronologies ZD1 and ZD2 were constructed with subfossil oak trees sampled in the alluvial deposits of the Zapadnaya Dvina River and its tributary, the Velesa River. The sample sites include reaches of both rivers upstream of their confluence (56.06°N, 31.97°E). Subfossil oak tree trunks were discovered in the riverbed as well as in riverbank alluvial deposits and oxbow lakes. The ZD1 and ZD2 chronologies do not overlap with the living oak tree-ring series from the region, but were crossdated with chronologies from Belarus and from the Baltic region. ZD1 (CE 572–1382) was calendar dated with oak samples from the Church of the Saviour’s Transfiguration in Polotsk (Belarus) which spans CE 869-112232; it also crossdates with subfossil oak series from Smarhon, Belarus33 and the Baltic 1 chronology34. A detailed report was previously published elsewhere14. The calendar age of the ZD2 chronology (CE 1346–1762) was established by comparison with the 2021BLT3 chronology35.KostromaMaterials for the Kostroma chronology come from archaeological excavations in the City of Kostroma and from the wooden buildings from the surrounding Kostroma Region. They include materials from a church in the Andreevskoye village (code K2A, 58.16°N, 41.30°E), two buildings from the Museum of Wooden Architecture in the Kostroma region, which include the house of Skobyolkin (code K13A), and the Church of Ilijah the Prophet (code K14A). The other materials come from the ‘Melochniye Ryady’ excavations in the center of Kostroma, (archaeologist A.Lazarev, code K09A). The chronology was calendar dated using the Kirillov and Vologda chronologies.SmolenskSeven beams of pine come from archaeological excavations at Pobedy Square in the city of Smolensk (54.78°N, 32.05°E)36. They were crossdated using the chronology from the Dannenshtern House in Riga37. The material of the Dannenshtern House likely comes from near the headwaters of the Kasplya tributary of the Daugava River (Zapadnaya Dvina River) located near Smolensk.SolovkiThe Solovki chronology consists of measurements from living trees (pines PDB and spruce PDEL; 65.12°N, 35.57°E), beams in a church on Malaya Muksalma Island (code MMCH; 65.01°N, 36.00°E), a building built for resin extraction (code SMOL), a barn (code SOLAM), and from a monastery outbuilding (or skit) on Sekirnaya Hill (code SLKL; 64.08°N, 35.57°E). Also included in the chronology are series from a satellite monastery building on Bolshaya Muksalma Island (code BMSK; 65.03°N, 35.90°E), series from a bathhouse nearby (code BMBN), samples from the Church of Andrew the First-Called on Zayatskiy Island (code B24A; 64.97°N, 35.65°E), series from a 19th century building (code SOLIZ), along with archaeological materials from the monastery (codes B27A, B28A), and a barn on Anzer Island (codes B39A, B38A; 65.19°N, 35.98°E). The earliest part of the chronology consists of ring-width series from beams from the 16th century Spaso-Preobrazhenskiy Cathedral (code SP; 65.02°N, 35.71°E). More

van den Hoogen J, Geisen S, Routh D. Soil nematode abundance and functional group composition at a global scale. Nature 2019;572:194–98.PubMed 

Google Scholar 
Yeates GW, Bongers T, Degoede RGM, Freckman DW, Georgieva SS. Feeding habits in soil nematode families and genera – an outline for soil ecologists. J Nematol. 1993;25:315–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nicol JM, Turner SJ, Coyne DL, Nijs Ld, Hockland S, Maafi ZT. Current nematode threats to world agriculture. In: Jones J, Gheysen G, Fenoll C, editors. Genomics and Molecular Genetics of Plant-Nematode Interactions. Dordrecht: Springer; 2011. p. 21–43.Decraemer W, Hunt D. Structure and Classification. In: R. N. Perry, M. Moens, Eds. Plant Nematology. CABI, Wallingford, Oxfordshire, UK and Boston, USA, 2005, pp. 26–27.Fleming TR, Maule AG, Fleming CC. Chemosensory responses of plant parasitic nematodes to selected phytochemicals reveal long-term habituation traits. J Nematol. 2017;49:462–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Murungi LK, Kirwa H, Coyne D, Teal PEA, Beck JJ, Torto B. Identification of key root volatiles signaling preference of tomato over spinach by the root knot nematode Meloidogyne incognita. J AgricFood Chem. 2018;66:7328–36.CAS 

Google Scholar 
Wang CL, Masler EP, Rogers ST. Responses of Heterodera glycines and Meloidogyne incognita infective juveniles to root tissues, root exudates, and root extracts from three plant species. Plant Dis. 2018;102:1733–40.CAS 
PubMed 

Google Scholar 
Sikder MM, Vestergård M. Impacts of root metabolites on soil nematodes. Front Plant Sci. 2020;10:1792.PubMed 
PubMed Central 

Google Scholar 
van Dam NM, Tytgat TOG, Kirkegaard JA. Root and shoot glucosinolates: A comparison of their diversity, function and interactions in natural and managed ecosystems. Phytochem Rev. 2009;8:171–86.CAS 

Google Scholar 
Bressan M, Roncato MA, Bellvert F, et al. Exogenous glucosinolate produced by Arabidopsis thaliana has an impact on microbes in the rhizosphere and plant roots. ISME J. 2009;3:1243–57.CAS 
PubMed 

Google Scholar 
Mucha S, Heinzlmeir S, Kriechbaumer V, Strickland B, Kirchhelle C, Choudhary M, et al. The formation of a camalexin biosynthetic metabolon. Plant Cell. 2019;31:2697–710.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kettles GJ, Drurey C, Schoonbeek HJ, Maule AJ, Hogenhout SA. Resistance of Arabidopsis thaliana to the green peach aphid, Myzus persicae, involves camalexin and is regulated by microRNAs. N. Phytol. 2013;198:1178–90.CAS 

Google Scholar 
Tsuji J, Jackson EP, Gage DA, Hammerschmidt R, Somerville SC. Phytoalexin accumulation in Arabidopsis thaliana during the hypersensitive reaction to Pseudomonas syringae pv. syringae. Plant Physiol. 1992;98:1304–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Thomma BPHJ, Nelissen I, Eggermont K, Broekaert WF. Deficiency in phytoalexin production causes enhanced susceptibility of Arabidopsis thaliana to the fungus Alternaria brassicicola. Plant J 1999;19:163–71.CAS 
PubMed 

Google Scholar 
Teixeira MA, Wei LH, Kaloshian I. Root-knot nematodes induce pattern-triggered immunity in Arabidopsis thaliana roots. N Phytol. 2016;211:276–87.CAS 

Google Scholar 
Shah SJ, Anjam MS, Mendy B, Anwer MA, Habash SS, Lozano-Torres JL, et al. Damage-associated responses of the host contribute to defence against cyst nematodes but not root-knot nematodes. J Exp Bot. 2017;68:5949–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ali MA, Wieczorek K, Kreil DP, Bohlmann H. The beet cyst nematode Heterodera schachtii modulates the expression of WRKY transcription factors in syncytia to favour its development in Arabidopsis roots. PLoS One. 2014;9:e102360.PubMed 
PubMed Central 

Google Scholar 
Lazzeri L, Curto G, Leoni O, Dallavalle E. Effects of glucosinolates and their enzymatic hydrolysis products via myrosinase on the root-knot nematode Meloidogyne incognita (Kofoid et White) Chitw. J Agric Food Chem. 2004;52:6703–07.CAS 
PubMed 

Google Scholar 
Avato P, D’Addabbo T, Leonetti P, Argentieri MP. Nematicidal potential of Brassicaceae. Phytochem Rev. 2013;12:791–802.CAS 

Google Scholar 
Mathesius U. Flavonoid functions in plants and their interactions with other organisms. Plants (Basel) 2018;7:30.
Google Scholar 
Weston LA, Mathesius U. Flavonoids: Their structure, biosynthesis and role in the rhizosphere, including allelopathy. J Chem Ecol. 2013;39:283–97.CAS 
PubMed 

Google Scholar 
Badri DV, Loyola-Vargas VM, Broeckling CD, De-la-Pena C, Jasinski M, Santelia D, et al. Altered profile of secondary metabolites in the root exudates of Arabidopsis ATP-binding cassette transporter mutants. Plant Physiol. 2008;146:762–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Cesco S, Neumann G, Tomasi N, Pinton R, Weisskopf L. Release of plant-borne flavonoids into the rhizosphere and their role in plant nutrition. Plant Soil. 2010;329:1–25.CAS 

Google Scholar 
Drewnowski A, Gomez-Carneros C. Bitter taste, phytonutrients, and the consumer: A review. Am J Clin Nutr. 2000;72:1424–35.CAS 
PubMed 

Google Scholar 
Chin S, Behm CA, Mathesius U. Functions of flavonoids in plant-nematode interactions. Plants (Basel) 2018;7:1–17.
Google Scholar 
Kaplan DT, Keen NT, Thomason IJ. Association of glyceollin with the incompatible response of soybean roots to Meloidogyne incognita. Physiol Plant Pathol. 1980;16:309–18.CAS 

Google Scholar 
Aoudia H, Ntalli N, Aissani N, Yahiaoui-Zaidi R, Caboni P. Nematotoxic phenolic compounds from Melia azedarach against Meloidogyne incognita. J AgricFood Chem. 2012;60:11675–80.CAS 

Google Scholar 
Kennedy MJ, Niblack TL, Krishnan HB. Infection by Heterodera glycines elevates isoflavonoid production and influences soybean nodulation. J Nematol. 1999;31:341–47.CAS 
PubMed 
PubMed Central 

Google Scholar 
Collingborn FMB, Gowen SR, Mueller-Harvey I. Investigations into the biochemical basis for nematode resistance in roots of three Musa cultivars in response to Radopholus similis infection. J Agric Food Chem. 2000;48:5297–301.CAS 
PubMed 

Google Scholar 
Cook R, Tiller SA, Mizen KA, Edwards R. Isoflavonoid metabolism in resistant and susceptible cultivars of white clover infected with the stem nematode Ditylenchus dipsaci. J Plant Physiol. 1995;146:348–54.CAS 

Google Scholar 
Kirwa HK, Murungi LK, Beck JJ, Torto B. Elicitation of differential responses in the root-knot nematode Meloidogyne incognita to tomato root exudate cytokinin, flavonoids, and alkaloids. J AgricFood Chem. 2018;66:11291–300.CAS 

Google Scholar 
Wuyts N, Swennen R, De, Waele D. Effects of plant phenylpropanoid pathway products and selected terpenoids and alkaloids on the behaviour of the plant-parasitic nematodes Radopholus similis. Pratylenchus penetrans Meloidogyne Incogn Nematol. 2006;8:89–101.CAS 

Google Scholar 
Hartwig UA, Joseph CM, Phillips DA. Flavonoids released naturally from alfalfa seeds enhance growth rate of Rhizobium meliloti. Plant Physiol. 1991;95:797–803.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hassan S, Mathesius U. The role of flavonoids in root-rhizosphere signalling: Opportunities and challenges for improving plant-microbe interactions. J Exp Bot. 2012;63:3429–44.CAS 
PubMed 

Google Scholar 
Kudjordjie EN, Sapkota R, Nicolaisen M. Arabidopsis assemble distinct root-associated microbiomes through the synthesis of an array of defense metabolites. PLoS One. 2021;10:e0259171.
Google Scholar 
Rønn R, Vestergård M, Ekelund F. Interactions between bacteria, protozoa and nematodes in soil. Acta Protozool. 2012;51:223–35.
Google Scholar 
Thakur MP, Geisen S. Trophic regulations of the soil microbiome. Trends Microbiol. 2019;27:771–80.CAS 
PubMed 

Google Scholar 
Elhady A, Gine A, Topalovic O, Jacquiod S, Sorensen SJ, Sorribas FJ, et al. Microbiomes associated with infective stages of root-knot and lesion nematodes in soil. PLoS One. 2017;12:e0177145.PubMed 
PubMed Central 

Google Scholar 
Toju H, Tanaka Y. Consortia of anti-nematode fungi and bacteria in the rhizosphere of soybean plants attacked by root-knot nematodes. R Soc Open Sci. 2019;6:181693.CAS 
PubMed 
PubMed Central 

Google Scholar 
Topalović O, Bredenbruch S, Schleker ASS, Heuer H. Microbes attaching to endoparasitic phytonematodes in soil trigger plant defense upon root penetration by the nematode. Front Plant Sci 2020;11:138.PubMed 
PubMed Central 

Google Scholar 
Schaad NW, Walker JT. The use of density-gradient centrifugation for the purification of eggs of Meloidogyne spp. J Nematol. 1975;7:203–04.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hooper DJ, Hallmann J, Subbotin SA. Methods for extraction, processing and detection of plant and soil nematodes. In: Luc M, Sikora RA, Bridge J, editors. Plant parasitic nematodes in subtropical and tropical agriculture. Second ed. Wallingford, UK: CABI Publishing; 2005. p. 53.Topalovic O, Elhady A, Hallmann J, Richert-Poggeler KR, Heuer H. Bacteria isolated from the cuticle of plant-parasitic nematodes attached to and antagonized the root-knot nematode Meloidogyne hapla. Sci Rep. 2019;9:11477.PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.Porazinska DL, Giblin-Davis RM, Faller L, Farmerie W, Kanzaki N, Morris K, et al. Evaluating high-throughput sequencing as a method for metagenomic analysis of nematode diversity. Mol Ecol Resour. 2009;9:1439–50.CAS 
PubMed 

Google Scholar 
Sapkota R, Nicolaisen M. High-throughput sequencing of nematode communities from total soil DNA extractions. BMC Ecol. 2015;15:3.PubMed 
PubMed Central 

Google Scholar 
Sikder MM, Vestergård M, Sapkota R, Kyndt T, Nicolaisen M. Evaluation of metabarcoding primers for analysis of soil nematode communities. Diversity (Basel) 2020;12:388.CAS 

Google Scholar 
Ihrmark K, Bodeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, et al. New primers to amplify the fungal ITS2 region – evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol. 2012;82:666–77.CAS 
PubMed 

Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 

Google Scholar 
Sapkota R, Skantar AM, Nicolaisen M. A TaqMan real-time PCR assay for detection of Meloidogyne hapla in root galls and in soil. Nematol. 2016;18:147–54.CAS 

Google Scholar 
Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: A versatile open source tool for metagenomics. Peer J. 2016;4:e2584.PubMed 
PubMed Central 

Google Scholar 
Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol Evol. 2013;4:914–19.
Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D6.CAS 
PubMed 

Google Scholar 
Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–D8.CAS 
PubMed 

Google Scholar 
UNITE. UNITE QIIME release for Fungi [Internet]. UNITE Community. 2020.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–36.CAS 
PubMed 
PubMed Central 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J, Blanchet FG, Kindt R, Friendly M, Legendre P, McGlinn D, et al. Vegan: Community Ecology Package. Ordination methods, diversity analysis and other functions for community and vegetation ecologists. R Package Version 2.5-5 ed: The Comprehensive R Archive Network; 2019.Love MI, Huber W, Anders S. Moderated estimation of fold change anddispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 

Google Scholar 
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics 2014;30:3123–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kudjordjie EN, Sapkota R, Steffensen SK, Fomsgaard IS, Nicolaisen M. Maize synthesized benzoxazinoids affect the host associated microbiome. Microbiome 2019;7:59.PubMed 
PubMed Central 

Google Scholar 
McCarthy DJ, Chen YS, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40:4288–97.CAS 
PubMed 
PubMed Central 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139–40.CAS 
PubMed 

Google Scholar 
Frerigmann H, Gigolashvili T. MYB34, MYB51, and MYB122 distinctly regulate indolic glucosinolate biosynthesis in Arabidopsis thaliana. Mol Plant. 2014;7:814–28.CAS 
PubMed 

Google Scholar 
Schulz E, Tohge T, Zuther E, Fernie AR, Hincha DK. Flavonoids are determinants of freezing tolerance and cold acclimation in Arabidopsis thaliana. Sci Rep. 2016;6:34027.Borevitz JO, Xia Y, Blount J, Dixon RA, Lamb C. Activation tagging identifies a conserved MYB regulator of phenylpropanoid biosynthesis. Plant Cell. 2000;12:2383–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Du SS, Zhang HM, Bai CQ, Wang CF, Liu QZ, Liu ZL, et al. Nematocidal flavone-C-glycosides against the root-knot nematode (Meloidogyne incognita) from Arisaema erubescens tubers. Molecules 2011;16:5079–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhou DM, Feng H, Schuelke T, De Santiago A, Zhang QM, Zhang JF, et al. Rhizosphere microbiomes from root knot nematode non-infested plants suppress nematode Infection. Micro Ecol. 2019;78:470–81.CAS 

Google Scholar 
Topalović O, Vestergård M. Can microorganisms assist the survival and parasitism of plant-parasitic nematodes? Trends Parasitol. 2021;37:947–58.PubMed 

Google Scholar 
De Mesel I, Derycke S, Moens T, Van der Gucht K, Vincx M, Swings J. Top-down impact of bacterivorous nematodes on the bacterial community structure: a microcosm study. Environ Microbiol. 2004;6:733–44.PubMed 

Google Scholar 
Adam M, Westphal A, Hallmann J, Heuer H. Specific microbial attachment to root knot nematodes in suppressive soil. Appl Environ Microbiol. 2014;80:2679–86.PubMed 
PubMed Central 

Google Scholar 
Ramyabharathi S, Sankari Meena K, Rajendran L, Karthikeyan G, Jonathan EI, Raguchander T. Biocontrol of wilt-nematode complex infecting gerbera by Bacillus subtilis under protected cultivation. Egypt J Biol Pest Co. 2018;28:21.
Google Scholar 
Jamal Q, Cho JY, Moon JH, Munir S, Anees M, Kim KY. Identification for the first time of cyclo (D-Pro-L-Leu) produced by Bacillus amyloliquefaciens y1 as a nematocide for control of Meloidogyne incognita. Molecules 2017;22:1839.PubMed Central 

Google Scholar 
Moosavi MR, Zare R. Fungi as biological control agents of plant-parasitic nematodes. In: Mérillon J-M, Ramawat KG, editors. Plant Defence: Biological Control. Progress in Biological Control 22. 2nd Edition ed. Switzerland: Springer; 2020. p. 333–84.Ashrafi S, Stadler M, Dababat AA, Richert-Poggeler KR, Finckh MR, Maier W. Monocillium gamsii sp nov and Monocillium bulbillosum: two nematode-associated fungi parasitising the eggs of Heterodera filipjevi. Mycokeys. 2017;27:21–38.
Google Scholar 
Nuaima RH, Ashrafi S, Maier W, Heuer H. Fungi isolated from cysts of the beet cyst nematode parasitized its eggs and counterbalanced root damages. J Pest Sci. 2021;94:563–72.
Google Scholar 
Iqbal M, Dubey M, McEwan K, Menzel U, Franko MA, Viketoft M, et al. Evaluation of Clonostachys rosea for control of plant parasitic nematodes in soil and in roots of carrot and wheat. Phytopathology 2018;108:52–59.CAS 
PubMed 

Google Scholar 
DiLegge MJ, Manter DK, Vivanco JM. A novel approach to determine generalist nematophagous microbes reveals Mortierella globalpina as a new biocontrol agent against Meloidogyne spp. nematodes. Sci Rep. 2019;9:7521.PubMed 
PubMed Central 

Google Scholar 
Goswami J, Pandey RK, Tewari JP, Goswami BK. Management of root knot nematode on tomato through application of fungal antagonists, Acremonium strictum and Trichoderma harzianum. J Environ Sci Health. 2008;43:237–40.CAS 

Google Scholar 
Chen Q, Peng D. Nematode chitin and application. In: Yang Q, Fukamizo T, editors. Targeting Chitin-containing Organisms. Advances in Experimental Medicine and Biology. 1142. Singapore: Springer; 2019. pp. 209–219.Zhou WQ, Verma VC, Wheeler TA, Woodward JE, Starr JL, Sword GA. Tapping into the cotton fungal phytobiome for novel nematode biological control tools. Phytobiomes J 2020;4:19–26.
Google Scholar 
Alcazar R, von Reth M, Bautor J, Chae E, Weigel D, Koornneef M, et al. Analysis of a plant complex resistance gene locus underlying immune-related hybrid incompatibility and its occurrence in nature. PLoS Genet. 2014;10:e1004848.PubMed 
PubMed Central 

Google Scholar 
Mikkelsen MD, Hansen CH, Wittstock U, Halkier BA. Cytochrome P450CYP79B2 from Arabidopsis catalyzes the conversion of tryptophan to indole-3-acetaldoxime, a precursor of indole glucosinolates and indole-3-acetic acid. J Biol Chem. 2000;275:33712–17.CAS 
PubMed 

Google Scholar 
Hull AK, Vij R, Celenza JL. Arabidopsis cytochrome P450s that catalyze the first step of tryptophan-dependent indole-3-acetic acid biosynthesis. Proc Natl Acad Sci USA. 2000;97:2379–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhao YD, Hull AK, Gupta NR, Goss KA, Alonso J, Ecker JR, et al. Trp-dependent auxin biosynthesis in Arabidopsis: involvement of cytochrome P450s CYP79B2 and CYP79B3. GenesDev. 2002;16:3100–12.CAS 

Google Scholar 
Schlaeppi K, Bodenhausen N, Buchala A, Mauch F, Reymond P. The glutathione-deficient mutant pad2-1 accumulates lower amounts of glucosinolates and is more susceptible to the insect herbivore Spodoptera littoralis. Plant J. 2008;55:774–86.CAS 
PubMed 

Google Scholar 
Schuhegger R, Nafisi M, Mansourova M, Petersen BL, et al. CYP71B15 (PAD3) catalyzes the final step in camalexin biosynthesis. Plant Physiol. 2006;141:1248–54.CAS 
PubMed 
PubMed Central 

Google Scholar 
Glawischnig E. The role of cytochrome P450 enzymes in the biosynthesis of camalexin. Biochem Soc Trans. 2006;34:1206–8.CAS 
PubMed 

Google Scholar 
Haughn GW, Davin L, Giblin M, Underhill EW. Biochemical genetics of plant secondary metabolites in Arabidopsis thaliana: The glucosinolates. Plant Physiol. 1991;97:217–26.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kroymann J, Textor S, Tokuhisa JG, Falk KL, Bartram S, Gershenzon J, et al. A gene controlling variation in Arabidopsis glucosinolate composition is part of the methionine chain elongation pathway. Plant Physiol. 2001;127:1077–88.CAS 
PubMed 
PubMed Central 

Google Scholar 
Textor S, de Kraker JW, Hause B, Gershenzon J, Tokuhisa JG. MAM3 catalyzes the formation of all aliphatic glucosinolate chain lengths in Arabidopsis. Plant Physiol. 2007;144:60–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Barth C, Jander G. Arabidopsis myrosinases TGG1 and TGG2 have redundant function in glucosinolate breakdown and insect defense. Plant J. 2006;46:549–62.CAS 
PubMed 

Google Scholar 
Dong XY, Braun EL, Grotewold E. Functional conservation of plant secondary metabolic enzymes revealed by complementation of Arabidopsis flavonoid mutants with maize genes. Plant Physiol. 2001;127:46–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
Peer WA, Brown DE, Tague BW, Muday GK, Taiz L, Murphy AS. Flavonoid accumulation patterns of transparent testa mutants of Arabidopsis. Plant Physiol. 2001;126:536–48.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gonzalez A, Brown M, Hatlestad G, Akhavan N, Smith T, Hembd A, et al. TTG2 controls the developmental regulation of seed coat tannins in Arabidopsis by regulating vacuolar transport steps in the proanthocyanidin pathway. Dev Biol. 2016;419:54–63.CAS 
PubMed 

Google Scholar 
Walker AR, Davison PA, Bolognesi-Winfield AC, James CM, Srinivasan N, Blundell TL, et al. The TRANSPARENT TESTA GLABRA1 locus, which regulates trichome differentiation and anthocyanin biosynthesis in Arabidopsis, encodes a WD40 repeat protein. Plant Cell. 1999;11:1337–49.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).PubMed 

Google Scholar 
IPBES. Global Assessment Report on Biodiversity and Ecosystem Service. Debating Nature’s Value (IPBES, 2019).Harrison, S. Plant community diversity will decline more than increase under climatic warming. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190106 (2020).
Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science (80-.). 1327, eaax3100 (2019).Chapin, F. S. et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).CAS 
PubMed 

Google Scholar 
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).PubMed 

Google Scholar 
Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science (80-.). 348, 336–340 (2015).CAS 

Google Scholar 
Díaz, S., Fargione, J., Chapin, F. S. & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol. 4, e277 (2006).PubMed 
PubMed Central 

Google Scholar 
Pennekamp, F. et al. Biodiversity increases and decreases ecosystem stability. Nature 563, 109–112 (2018).CAS 
PubMed 

Google Scholar 
Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Natl Acad. Sci. USA 117, 24345–24351 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Global evidence of positive biodiversity effects on spatial ecosystem stability in natural grasslands. Nat. Commun. 10, 1–9 (2019).
Google Scholar 
Poorter, L. et al. Diversity enhances carbon storage in tropical forests. Glob. Ecol. Biogeogr. 24, 1314–1328 (2015).
Google Scholar 
Schnabel, F. et al. Drivers of productivity and its temporal stability in a tropical tree diversity experiment. Glob. Chang. Biol. 25, 4257–4272 (2019).PubMed 

Google Scholar 
Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl Acad. Sci. USA 111, 13697–13702 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mori, A. S. Advancing nature-based approaches to address the biodiversity and climate emergency. Ecol. Lett. 23, 1729–1732 (2020).PubMed 

Google Scholar 
Mazzochini, G. G. et al. Plant phylogenetic diversity stabilizes large-scale ecosystem productivity. Glob. Ecol. Biogeogr. 28, 1430–1439 (2019).
Google Scholar 
Manhães, A. P., Mazzochini, G. G., Oliveira-Filho, A. T., Ganade, G. & Carvalho, A. R. Spatial associations of ecosystem services and biodiversity as a baseline for systematic conservation planning. Divers. Distrib. 22, 932–943 (2016).
Google Scholar 
García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity–ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).PubMed 
PubMed Central 

Google Scholar 
De Keersmaecker, W. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015).
Google Scholar 
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).CAS 
PubMed 

Google Scholar 
Linscheid, N. et al. Towards a global understanding of vegetation-climate dynamics at multiple timescales. Biogeosciences 17, 945–962 (2020).
Google Scholar 
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science (80-.). 300, 1560–1563 (2003).CAS 

Google Scholar 
Quetin, G. R. & Swann, A. L. S. Empirically derived sensitivity of vegetation to climate across global gradients of temperature and precipitation. J. Clim. 30, 5835–5849 (2017).
Google Scholar 
Cavender-bares, J. et al. The role of diversification in community assembly of the oaks (Quercus L.) across the continental U. S. Am. J. Bot. 105, 565–586 (2018).PubMed 

Google Scholar 
Woodward, F. I., Lomas, M. R. & Kelly, C. K. Global climate and the distribution of plant biomes. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 1465–1476 (2004).CAS 

Google Scholar 
Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).PubMed 

Google Scholar 
Cavender-Bares, J., Ackerly, D. D., Hobbie, S. E. & Townsend, P. A. Evolutionary legacy effects on ecosystems: biogeographic origins, plant traits, and implications for management in the era of global change. Annu. Rev. Ecol. Evol. Syst. 47, 433–462 (2016).
Google Scholar 
Harrison, S., Spasojevic, M. J. & Li, D. Climate and plant community diversity in space and time. Proc. Natl Acad. Sci. USA 117, 4464–4470 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Šímová, I. et al. Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species. J. Biogeogr. 45, 895–916 (2018).
Google Scholar 
Lamanna, C. et al. Functional trait space and the latitudinal diversity gradient. Proc. Natl Acad. Sci. USA 111, 13745–13750 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Craven, D. et al. A cross-scale assessment of productivity–diversity relationships. Glob. Ecol. Biogeogr. 29, 1940–1955 (2020).
Google Scholar 
White, H. J. et al. Ecosystem stability at the landscape scale is primarily associated with climatic history. Funct. Ecol. 1–13 https://doi.org/10.1111/1365-2435.13957 (2021).Enquist, B. J. et al. Scaling from Traits to Ecosystems: Developing a General Trait Driver Theory via Integrating Trait-Based and Metabolic Scaling Theories. Advances in Ecological Research. Vol. 52 (Elsevier Ltd., 2015).Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).PubMed 
PubMed Central 

Google Scholar 
Barry, K. E. et al. A graphical null model for scaling biodiversity–ecosystem functioning relationships. J. Ecol. 109, 1549–1560 (2021).
Google Scholar 
Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).PubMed 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
PubMed 

Google Scholar 
Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).PubMed 
PubMed Central 

Google Scholar 
Bond, E. M. & Chase, J. M. Biodiversity and ecosystem functioning at local and regional spatial scales. Ecol. Lett. 5, 467–470 (2002).
Google Scholar 
Delsol, R., Loreau, M. & Haegeman, B. The relationship between the spatial scaling of biodiversity and ecosystem stability. Glob. Ecol. Biogeogr. 27, 439–449 (2018).PubMed 
PubMed Central 

Google Scholar 
Price, G. R. The nature of selection. J. Theor. Biol. 175, 389-396 (1995).Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J. Ecol. 89, 118–125 (2001).
Google Scholar 
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).PubMed 
PubMed Central 

Google Scholar 
Cadotte, M., Dinnage, R. & Tilman, D. Phylogenetic diversity promotes ecosytem stability. Ecology 93, S223–S233 (2012).
Google Scholar 
Veron, S., Davies, T. J., Cadotte, M. W., Clergeau, P. & Pavoine, S. Predicting loss of evolutionary history: Where are we? Biol. Rev. 92, 271–291 (2017).PubMed 

Google Scholar 
Tucker, C. M., Davies, T. J., Cadotte, M. W. & Pearse, W. D. On the relationship between phylogenetic diversity and trait diversity. Ecology 99, 1473–1479 (2018).PubMed 

Google Scholar 
Faith, D. P. Systematics and conservation: on predicting the feature diversity of subsets of taxa. Cladistics 8, 361–373 (1992).PubMed 

Google Scholar 
Hisano, M., Searle, E. B. & Chen, H. Y. H. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems. Biol. Rev. 93, 439–456 (2018).PubMed 

Google Scholar 
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity–ecosystem-function relationships. Ecology 92, 1573–1581 (2011).PubMed 

Google Scholar 
Cadotte, M. W., Cardinale, B. J. & Oakley, T. H. Evolutionary history and the effect of biodiversity on plant productivity. Proc. Natl Acad. Sci. USA 105, 17012–17017 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Venail, P. et al. Species richness, but not phylogenetic diversity, influences community biomass production and temporal stability in a re-examination of 16 grassland biodiversity studies. Funct. Ecol. 29, 615–626 (2015).
Google Scholar 
Enquist, B., Condit, R., Peet, R., Schildhauer, M. & Thiers, B. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ Prepr. 4, e2615v2 (2016).Maitner, B. S. et al. The bien R package: a tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).
Google Scholar 
Mori, A. S. Resilience in the studies of biodiversity–ecosystem functioning. Trends Ecol. Evol. 31, 87–89 (2016).PubMed 

Google Scholar 
Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).
Google Scholar 
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 

Google Scholar 
Huete, A., Chris, J. & Leeuwen, W. Van. MODIS vegetation index (MOD 13). Algorithm theoretical basis document vol. 3 https://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (1999).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 

Google Scholar 
MacIas-Fauria, M., Forbes, B. C., Zetterberg, P. & Kumpula, T. Eurasian Arctic greening reveals teleconnections and the potential for structurally novel ecosystems. Nat. Clim. Chang. 2, 613–618 (2012).
Google Scholar 
Garcia, R. A., Cabeza, M., Rahbek, C. & Araújo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science (80-.). 344, 1247579 (2014).Zhang, Y. et al. Precipitation and carbon-water coupling jointly control the interannual variability of global land gross primary production. Sci. Rep. 6, 39748 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).CAS 
PubMed 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933 (2001).
Google Scholar 
Srivastava, D. S. et al. Phylogenetic diversity and the functioning of ecosystems. Ecol. Lett. 15, 637–648 (2012).PubMed 

Google Scholar 
Parker, I. M. et al. Phylogenetic structure and host abundance drive disease pressure in communities. Nature 520, 542–544 (2015).CAS 
PubMed 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).PubMed 

Google Scholar 
Brun, P. et al. Plant community impact on productivity: Trait diversity or key(stone) species effects? Ecol. Lett. 25, 913–925 (2022).PubMed 

Google Scholar 
Aubin, I. et al. Traits to stay, traits to move: a review of functional traits to assess sensitivity and adaptive capacity of temperate and boreal trees to climate change. Environ. Rev. 24, 164–186 (2016).
Google Scholar 
Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1216065111 (2014).Díaz, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).
Google Scholar 
Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).CAS 
PubMed 

Google Scholar 
Ye, J. S., Pei, J. Y. & Fang, C. Under which climate and soil conditions the plant productivity–precipitation relationship is linear or nonlinear? Sci. Total Environ. 616–617, 1174–1180 (2018).PubMed 

Google Scholar 
Allan, E. et al. More diverse plant communities have higher functioning over time due to turnover in complementary dominant species. Proc. Natl Acad. Sci. U. S. A. 108, 17034–17039 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. 104, 13384–13389 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Chang. 11, 543–550 (2021).
Google Scholar 
Kattge, J. et al. TRY plant trait database–enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).PubMed 

Google Scholar 
Feeley, K. J., Bravo-Avila, C., Fadrique, B., Perez, T. M. & Zuleta, D. Climate-driven changes in the composition of New World plant communities. Nat. Clim. Chang. 10, 965–970 (2020).CAS 

Google Scholar 
Li, D., Miller, J. E. D. & Harrison, S. Climate drives loss of phylogenetic diversity in a grassland community. Proc. Natl Acad. Sci. USA 116, 19989–19994 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Madani, N. et al. Future global productivity will be affected by plant trait response to climate. Sci. Rep. 8, 2870 (2018).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing Version 3.5.2. (R Core Team, 2018).Ammer, C. Diversity and forest productivity in a changing climate. N. Phytol. 221, 50–66 (2019).
Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS 
PubMed 

Google Scholar 
Larue, E. A., Hardiman, B. S., Elliott, J. M. & Fei, S. Structural diversity as a predictor of ecosystem function. Environ. Res. Lett. 14, 114011 (2019).Phillips, S. J. & Dudìk, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography (Cop.). 31, 161–175 (2008).
Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 
Diniz-Filho, J. A. F. & Bini, L. M. Modelling geographical patterns in species richness using eigenvector-based spatial filters. Glob. Ecol. Biogeogr. 14, 177–185 (2005).
Google Scholar 
Merow, C., Smith, M. J. & Silander, J. a. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (Cop.). 36, 1058–1069 (2013).
Google Scholar 
Merow, C. BIEN range methods description. http://bien.nceas.ucsb.edu/bien/wp-content/uploads/2017/06/BIEN3RangeMethodsSummary.pdf (2017).Schrodt, F. et al. BHPMF-a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Glob. Ecol. Biogeogr. 24, 1510–1521 (2015).
Google Scholar 
Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).PubMed 

Google Scholar 
Guo, W. Y. et al. Half of the world’s tree biodiversity is unprotected and is increasingly threatened by human activities. Preprint at bioRxiv https://doi.org/10.1101/2020.04.21.052464 (2020).Guo, W., Serra-diaz, J. M., Schrodt, F. & Eiserhardt, W. L. Paleoclimate and current climate collectively shape the phylogenetic and functional diversity of trees worldwide. Preprint at bioRxiv https://doi.org/10.1101/2020.06.02.128975 (2020).Diniz-Filho, J. A. F. et al. On the selection of phylogenetic eigenvectors for ecological analyses. Ecography (Cop.). 35, 239–249 (2012).
Google Scholar 
Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).
Google Scholar 
Santos, T. PVR: Phylogenetic eigenvectors regression and phylogentic signal-representation curve. R package version 0.3. Available at: http://CRAN.R-project.org/package=PVR (2018).Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gerhold, P., Cahill, J. F., Winter, M., Bartish, I. V. & Prinzing, A. Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better). Funct. Ecol. 29, 600–614 (2015).
Google Scholar 
Kendall, M. & Stuart, A. The Advanced Theory of Statistics (Macmillan, 1983).Pavoine, S. & Bonsall, M. B. Measuring biodiversity to explain community assembly: a unified approach. Biol. Rev. Camb. Philos. Soc. 86, 792–812 (2011).CAS 
PubMed 

Google Scholar 
Tucker, C. M. et al. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715 (2017).PubMed 

Google Scholar 
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 
PubMed 

Google Scholar 
Cornwell, W. K., Schwilk, L. D. W. & Ackerly, D. D. A trait-based test for habitat filtering: convex hull volume. Ecology 87, 1465–1471 (2006).PubMed 

Google Scholar 
Villéger, S., Maire, E. & Leprieur, F. On the risks of using dendrograms to measure functional diversity and multidimensional spaces to measure phylogenetic diversity: a comment on Sobral et al. (2016). Ecol. Lett. 20, 554–557 (2017).PubMed 

Google Scholar 
Laliberté, E., Legendre, P. & Shipley, B. FD: measuring functional diversity from multiple traits, an other tools for functional ecology. R package version 1.0-12 (Comprehensive R Archive Network, Vienna, Austria, 2015).Podani, J. & Schmera, D. On dendrogram-based measures of functional diversity. Oikos 115, 179–185 (2006).
Google Scholar 
Poos, M. S., Walker, S. C. & Jackson, D. A. Functional-diversity indices can be driven by methodological choices and species richness. Ecology 90, 341–347 (2009).PubMed 

Google Scholar 
Gotelli, N. J. & Graves, G. R. Null Models in Ecology (Smithsonian Institution Press, 1996).Swenson, N. G. Functional and Phylogenetic Ecology in R. (Springer, 2014).Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography (Cop.). 30, 609–628 (2007).
Google Scholar 
Kissling, W. D. & Carl, G. Spatial autocorrelation and the selection of simultaneous autoregressive models. Glob. Ecol. Biogeogr. 17, 59–71 (2008).
Google Scholar 
Bivand, R. spatialreg: Spatial Regression Analysis (R package version 1.1-5, 2019). More

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

Google Scholar 
Hou, E. Q. et al. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nat. Commun. 11, 637 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cordell, D., Drangert, J.-O. & White, S. The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19, 292–305 (2009).Article 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
PubMed 
Article 

Google Scholar 
Chen, X. L., Chen, H. Y. H., Searle, E. B., Chen, C. & Reich, P. B. Negative to positive shifts in diversity effects on soil nitrogen over time. Nat. Sustain. 4, 225–234 (2021).Article 

Google Scholar 
Oelmann, Y. et al. Plant diversity effects on aboveground and belowground N pools in temperate grassland ecosystems: development in the first 5 years after establishment. Glob. Biogeochem. Cy. 25, GB2014 (2011).Article 
CAS 

Google Scholar 
Fornara, D. A. et al. Plant effects on soil N mineralization are mediated by the composition of multiple soil organic fractions. Ecol. Res. 26, 201–208 (2011).CAS 
Article 

Google Scholar 
Wright, A. J., Wardle, D. A., Callaway, R. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390 (2017).PubMed 
Article 

Google Scholar 
Oelmann, Y. et al. Above- and belowground biodiversity jointly tighten the P cycle in agricultural grasslands. Nat. Commun. 12, 4431 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, L. et al. Diversity enhances agricultural productivity via rhizosphere phosphorus facilitation on phosphorus-deficient soils. Proc. Natl Acad. Sci. USA 104, 11192–11196 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, L., Tilman, D., Lambers, H. & Zhang, F. S. Plant diversity and overyielding: insights from belowground facilitation of intercropping in agriculture. New Phytol. 203, 63–69 (2014).PubMed 
Article 
CAS 

Google Scholar 
Hacker, N. et al. Plant diversity shapes microbe–rhizosphere effects on P mobilisation from organic matter in soil. Ecol. Lett. 18, 1356–1365 (2015).PubMed 
Article 

Google Scholar 
Vance, C. P., Uhde-Stone, C. & Allan, D. L. Phosphorus acquisition and use: critical adaptations by plants for securing a nonrenewable resource. New Phytol. 157, 423–447 (2003).CAS 
PubMed 
Article 

Google Scholar 
Chen, J. et al. Long-term nitrogen loading alleviates phosphorus limitation in terrestrial ecosystems. Glob. Change Biol. 26, 5077–5086 (2020).Article 

Google Scholar 
Hinsinger, P. et al. P for two, sharing a scarce resource: soil phosphorus acquisition in the rhizosphere of intercropped species. Plant Physiol. 156, 1078–1086 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, X. J. et al. Plant diversity and species turnover co-regulate soil nitrogen and phosphorus availability in Dinghushan forests, southern China. Plant Soil 464, 257–272 (2021).CAS 
Article 

Google Scholar 
Hooper, D. U. & Vitousek, P. M. Effects of plant composition and diversity on nutrient cycling. Ecol. Monogr. 68, 121–149 (1998).Article 

Google Scholar 
Alberti, G. et al. Tree functional diversity influences belowground ecosystem functioning. Appl. Soil Ecol. 120, 160–168 (2017).Article 

Google Scholar 
Maddhesiya, P. K., Singh, K. & Singh, R. P. Effects of perennial aromatic grass species richness and microbial consortium on soil properties of marginal lands and on biomass production. Land Degrad. Dev. 32, 1008–1021 (2021).Article 

Google Scholar 
Zhang, C. B. et al. Effects of plant diversity on nutrient retention and enzyme activities in a full-scale constructed wetland. Bioresour. Technol. 101, 1686–1692 (2010).CAS 
PubMed 
Article 

Google Scholar 
Štursová, M. & Baldrian, P. Effects of soil properties and management on the activity of soil organic matter transforming enzymes and the quantification of soil-bound and free activity. Plant Soil 338, 99–110 (2011).Article 
CAS 

Google Scholar 
Wu, H. et al. Linkage between tree species richness and soil microbial diversity improves phosphorus bioavailability. Funct. Ecol. 33, 1549–1560 (2019).Article 

Google Scholar 
Steinauer, K. et al. Plant diversity effects on soil microbial functions and enzymes are stronger than warming in a grassland experiment. Ecology 96, 99–112 (2015).PubMed 
Article 

Google Scholar 
Zhang, D. S. et al. Increased soil phosphorus availability induced by faba bean root exudation stimulates root growth and phosphorus uptake in neighbouring maize. New Phytol. 209, 823–831 (2016).CAS 
PubMed 
Article 

Google Scholar 
Berendse, F., van Ruijven, J., Jongejans, E. & Keesstra, S. Loss of plant species diversity reduces soil erosion resistance. Ecosystems 18, 881–888 (2015).CAS 
Article 

Google Scholar 
Forrester, D. I. & Bauhus, J. A review of processes behind diversity–productivity relationships in forests. Curr. Rep. 2, 45–61 (2016).Article 
CAS 

Google Scholar 
Batterman, S. A. et al. Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees. Ecol. Lett. 21, 1486–1495 (2018).PubMed 
Article 

Google Scholar 
Chen, C., Chen, H. Y. H., Chen, X. & Huang, Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat. Commun. 10, 1332 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hisano, M., Chen, H. Y. H., Searle, E. B. & Reich, P. B. Species-rich boreal forests grew more and suffered less mortality than species-poor forests under the environmental change of the past half-century. Ecol. Lett. 22, 999–1008 (2019).PubMed 
Article 

Google Scholar 
Chen, X. & Chen, H. Y. H. Plant diversity loss reduces soil respiration across terrestrial ecosystems. Glob. Change Biol. 25, 1482–1492 (2019).Article 

Google Scholar 
Chen, X. & Chen, H. Y. H. Plant mixture balances terrestrial ecosystem C:N:P stoichiometry. Nat. Commun. 12, 4562 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reich, P. B. et al. Species and functional group diversity independently influence biomass accumulation and its response to CO2 and N. Proc. Natl Acad. Sci. USA 101, 10101–10106 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, X. et al. Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis. Biol. Rev. 95, 167–183 (2020).Article 

Google Scholar 
Zhang, Y., Chen, H. Y. H. & Reich, P. B. Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J. Ecol. 100, 742–749 (2012).Article 

Google Scholar 
Alewell, C. et al. Global phosphorus shortage will be aggravated by soil erosion. Nat. Commun. 11, 4546 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mueller, K. E., Tilman, D., Fornara, D. A. & Hobbie, S. E. Root depth distribution and the diversity–productivity relationship in a long-term grassland experiment. Ecology 94, 787–793 (2013).Article 

Google Scholar 
Tang, X. Y. et al. Intercropping legumes and cereals increases phosphorus use efficiency; a meta-analysis. Plant Soil 460, 89–104 (2021).CAS 
Article 

Google Scholar 
Karanika, E. D., Alifragis, D. A., Mamolos, A. P. & Veresoglou, D. S. Differentiation between responses of primary productivity and phosphorus exploitation to species richness. Plant Soil 297, 69–81 (2007).CAS 
Article 

Google Scholar 
Bünemann, E. K., Prusisz, B. & Ehlers, K. in Phosphorus in Action: Biological Processes in Soil Phosphorus Cycling (eds Bünemann, E. et al.) 37–57 (Springer, 2011).Ma, Z. L. & Chen, H. Y. H. Effects of species diversity on fine root productivity in diverse ecosystems: a global meta-analysis. Glob. Ecol. Biogeogr. 25, 1387–1396 (2016).Article 

Google Scholar 
Mellado-Vazquez, P. G. et al. Plant diversity generates enhanced soil microbial access to recently photosynthesized carbon in the rhizosphere. Soil Biol. Biochem. 94, 122–132 (2016).CAS 
Article 

Google Scholar 
Qin, Y. et al. Arbuscular mycorrhizal fungus differentially regulates P mobilizing bacterial community and abundance in rhizosphere and hyphosphere. Appl. Soil Ecol. 170, 104294 (2022).Article 

Google Scholar 
Rojo, M. J., Carcedo, S. G. & Mateos, M. P. Distribution and characterization of phosphatase and organic phosphorus in soil fractions. Soil Biol. Biochem. 22, 169–174 (1990).CAS 
Article 

Google Scholar 
Barrow, N. The effects of pH on phosphate uptake from the soil. Plant Soil 410, 401–410 (2017).CAS 
Article 

Google Scholar 
Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).CAS 
PubMed 
Article 

Google Scholar 
Yu, R. P., Li, X. X., Xiao, Z. H., Lambers, H. & Li, L. Phosphorus facilitation and covariation of root traits in steppe species. New Phytol. 226, 1285–1298 (2020).CAS 
PubMed 
Article 

Google Scholar 
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine 6, e1000097 (2009).Jenkins, D. G. & Quintana-Ascencio, P. F. A solution to minimum sample size for regressions. PLoS ONE 15, e0229345 (2020)..Rohatgi, A. WebPlotDigitizer v.4.5 (Automeris, 2021); https://automeris.io/WebPlotDigitizerJobbagy, E. G. & Jackson, R. B. The distribution of soil nutrients with depth:global patterns and the imprint of plants. Biogeochemistry 53, 51–77 (2001).CAS 
Article 

Google Scholar 
Trabucco, A. & Zomer, R. Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database (CGIAR, 2009); http://www.cgiar-csi.org/data/global-aridity-and-pet-databaseBridgham, S. D., Pastor, J., Mcclaugherty, C. A. & Richardson, C. J. Nutrient-use efficiency: a litterfall index, a model, and a test along a nutrient-availability gradient in North Carolina peatlands. Am. Nat. 145, 1–21 (1995).Article 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).CAS 
PubMed 
Article 

Google Scholar 
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).CAS 
PubMed 
Article 

Google Scholar 
Bates, D. et al. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-10 https://cran.r-project.org/web/packages/lme4/index.html (2017).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
Article 

Google Scholar 
MuMIn: Multi-model inference. R package version 1.42.1 (2018).Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Koricheva, J., Gurevitch, J. & Mengersen, K. Handbook of Meta-analysis in Ecology and Evolution (Princeton Univ. Press, 2013).Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).Article 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Long, J. A. Interactions: comprehensive, user-friendly toolkit for probing interactions. R package version 1.1.5 https://cran.r-project.org/package=interactions (2021).Adams, D. C., Gurevitch, J. & Rosenberg, M. S. Resampling tests for meta-analysis of ecological data. Ecology 78, 1277–1283 (1997).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). More

Daims H, Lücker S, Wagner M. A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends Microbiol. 2016;24:699–712.CAS 
Article 

Google Scholar 
Ehrich S, Behrens D, Lebedeva E, Ludwig W, Bock E. A new obligately chemolithoautotrophic, nitrite-oxidizing bacterium, Nitrospira moscoviensis sp. nov. and its phylogenetic relationship. Arch Microbiol. 1995;164:16–23.CAS 
Article 

Google Scholar 
Koch H, Galushko A, Albertsen M, Schintlmeister A, Gruber-Dorninger C, Lücker S, et al. Growth of nitrite-oxidizing bacteria by aerobic hydrogen oxidation. Science. 2014;345:1052–4.CAS 
Article 

Google Scholar 
Koch H, Lücker S, Albertsen M, Kitzinger K, Herbold C, Spieck E, et al. Expanded metabolic versatility of ubiquitous nitrite-oxidizing bacteria from the genus Nitrospira. Proc Natl Acad Sci USA. 2015;112:11371–6.CAS 
Article 

Google Scholar 
Daims H, Lebedeva EV, Pjevac P, Han P, Herbold C, Albertsen M, et al. Complete nitrification by Nitrospira bacteria. Nature. 2015;528:504–9.CAS 
Article 

Google Scholar 
van Kessel MAHJ, Speth DR, Albertsen M, Nielsen PH, Op den Camp HJM, Kartal B, et al. Complete nitrification by a single microorganism. Nature. 2015;528:555–9.Article 

Google Scholar 
Lücker S, Wagner M, Maixner F, Pelletier E, Koch H, Vacherie B, et al. A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria. Proc Natl Acad Sci USA. 2010;107:13479–84.Article 

Google Scholar 
Mundinger AB, Lawson CE, Jetten MSM, Koch H, Lücker S. Cultivation and transcriptional analysis of a canonical Nitrospira under stable growth conditions. Front Microbiol. 2019;10:1325.Morita RY. Is H2 the universal energy source for long-term survival? Micro Ecol. 1999;38:307–20.CAS 
Article 

Google Scholar 
Bay SK, Dong X, Bradley JA, Leung PM, Grinter R, Jirapanjawat T, et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat Microbiol. 2021;6:246–56.CAS 
Article 

Google Scholar 
Constant P, Poissant L, Villemur R. Isolation of Streptomyces sp. PCB7, the first microorganism demonstrating high-affinity uptake of tropospheric H2. ISME J. 2008;2:1066–76.CAS 
Article 

Google Scholar 
Greening C, Carere CR, Rushton-Green R, Harold LK, Hards K, Taylor MC, et al. Persistence of the dominant soil phylum Acidobacteria by trace gas scavenging. Proc Natl Acad Sci USA. 2015;112:10497–502.CAS 
Article 

Google Scholar 
Islam ZF, Cordero PRF, Feng J, Chen Y-J, Bay SK, Jirapanjawat T, et al. Two Chloroflexi classes independently evolved the ability to persist on atmospheric hydrogen and carbon monoxide. ISME J. 2019;13:1801.CAS 
Article 

Google Scholar 
Islam ZF, Welsh C, Bayly K, Grinter R, Southam G, Gagen EJ, et al. A widely distributed hydrogenase oxidises atmospheric H2 during bacterial growth. ISME J. 2020;14:2649–58.CAS 
Article 

Google Scholar 
Schmitz RA, Pol A, Mohammadi SS, Hogendoorn C, van Gelder AH, Jetten MSM, et al. The thermoacidophilic methanotroph Methylacidiphilum fumariolicum SolV oxidizes subatmospheric H2 with a high-affinity, membrane-associated [NiFe] hydrogenase. ISME J. 2020;14:1223–32.CAS 
Article 

Google Scholar 
Ortiz M, Leung PM, Shelley G, Jirapanjawat T, Nauer PA, Van Goethem M, et al. Multiple energy sources and metabolic strategies sustain microbial diversity in Antarctic desert soils. Proc Natl Acad Sci. 2021;118:e2025322118.CAS 
Article 

Google Scholar 
Greening C, Berney M, Hards K, Cook GM, Conrad R. A soil actinobacterium scavenges atmospheric H2 using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. Proc Natl Acad Sci USA. 2014;111:4257–61.CAS 
Article 

Google Scholar 
Myers MR, King GMY. Isolation and characterization of Acidobacterium ailaaui sp. nov., a novel member of Acidobacteria subdivision 1, from a geothermally heated Hawaiian microbial mat. Int J Syst Evol Microbiol. 2016;66:5328–35.CAS 
Article 

Google Scholar 
Cordero PRF, Grinter R, Hards K, Cryle MJ, Warr CG, Cook GM, et al. Two uptake hydrogenases differentially interact with the aerobic respiratory chain during mycobacterial growth and persistence. J Biol Chem. 2019;294:18980–91.CAS 
Article 

Google Scholar 
Sander R. Compilation of Henry’s law constants (version 4.0) for water as solvent. Atmos Chem Phys. 2015;15:4399–981.CAS 
Article 

Google Scholar 
Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.CAS 
Article 

Google Scholar 
Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10:1794–805.CAS 
Article 

Google Scholar 
Shah AD, Goode RJA, Huang C, Powell DR, Schittenhelm RB. LFQ-Analyst: an easy-to-use interactive web platform to analyze and visualize label-free proteomics data preprocessed with MaxQuant. J Proteome Res. 2020;19:204–11.CAS 
Article 

Google Scholar 
Nowka B, Daims H, Spieck E. Comparative oxidation kinetics of nitrite-oxidizing bacteria: nitrite availability as key factor for niche differentiation. Appl Environ Microbiol. 2014;81:745–53.Thauer RK, Jungermann K, Decker K. Energy conservation in chemotrophic anaerobic bacteria. Bacteriol Rev. 1977;41:809.Article 

Google Scholar 
Greening C, Villas-Bôas SG, Robson JR, Berney M, Cook GM. The growth and survival of Mycobacterium smegmatis is enhanced by co-metabolism of atmospheric H2. PLoS ONE. 2014;9:e103034.Article 

Google Scholar 
Constant P, Chowdhury SP, Pratscher J, Conrad R. Streptomycetes contributing to atmospheric molecular hydrogen soil uptake are widespread and encode a putative high-affinity [NiFe]-hydrogenase. Environ Microbiol. 2010;12:821–9.CAS 
Article 

Google Scholar 
Häring V, Conrad R. Demonstration of two different H2-oxidizing activities in soil using an H2 consumption and a tritium exchange assay. Biol Fertil Soils. 1994;17:125–8.Article 

Google Scholar 
Yang Y, Daims H, Liu Y, Herbold CW, Pjevac P, Lin J-G, et al. Activity and metabolic versatility of complete ammonia oxidizers in full-scale wastewater treatment systems. mBio. 2020;11:e03175–19.Chadwick GL, Hemp J, Fischer WW, Orphan VJ. Convergent evolution of unusual complex I homologs with increased proton pumping capacity: energetic and ecological implications. ISME J. 2018;12:2668–80.CAS 
Article 

Google Scholar 
Alberty RA. Standard apparent reduction potentials of biochemical half reactions and thermodynamic data on the species involved. Biophys Chem. 2004;111:115–22.CAS 
Article 

Google Scholar 
Burns LC, Stevens RJ, Smith RV, Cooper JE. The occurrence and possible sources of nitrite in a grazed, fertilized, grassland soil. Soil Biol Biochem. 1995;27:47–59.CAS 
Article 

Google Scholar 
Zhang M, Yuan D, Chen G, Li Q, Zhang Z, Liang Y. Simultaneous determination of nitrite and nitrate at nanomolar level in seawater using on-line solid phase extraction hyphenated with liquid waveguide capillary cell for spectrophotometric detection. Microchim Acta. 2009;165:427–35.CAS 
Article 

Google Scholar 
Daims H, Nielsen JL, Nielsen PH, Schleifer K-H, Wagner M. In situ characterization of Nitrospira-like nitrite-oxidizing bacteria active in wastewater treatment plants. Appl Environ Microbiol. 2001;67:5273–84.CAS 
Article 

Google Scholar 
Lebedeva EV, Alawi M, Maixner F, Jozsa P-G, Daims H, Spieck E. Physiological and phylogenetic characterization of a novel lithoautotrophic nitrite-oxidizing bacterium, ‘Candidatus Nitrospira bockiana’. Int J Syst Evol Microbiol. 2008;58:242–50.CAS 
Article 

Google Scholar 
Lebedeva EV, Off S, Zumbrägel S, Kruse M, Shagzhina A, Lücker S, et al. Isolation and characterization of a moderately thermophilic nitrite-oxidizing bacterium from a geothermal spring. FEMS Microbiol Ecol. 2011;75:195–204.CAS 
Article 

Google Scholar 
Watson SW, Bock E, Valois FW, Waterbury JB, Schlosser U. Nitrospira marina gen. nov. sp. nov.: a chemolithotrophic nitrite-oxidizing bacterium. Arch Microbiol. 1986;144:1–7.Article 

Google Scholar 
Maixner F, Noguera DR, Anneser B, Stoecker K, Wegl G, Wagner M, et al. Nitrite concentration influences the population structure of Nitrospira-like bacteria. Environ Microbiol. 2006;8:1487–95.CAS 
Article 

Google Scholar 
Sorokin DY, Lucker S, Vejmelkova D, Kostrikina NA, Kleerebezem R, Rijpstra WIC, et al. Nitrification expanded: discovery, physiology and genomics of a nitrite-oxidizing bacterium from the phylum Chloroflexi. ISME J. 2012;6:2245–56.CAS 
Article 

Google Scholar 
Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 2016;10:761–77.CAS 
Article 

Google Scholar 
Daebeler A, Kitzinger K, Koch H, Herbold CW, Steinfeder M, Schwarz J, et al. Exploring the upper pH limits of nitrite oxidation: diversity, ecophysiology, and adaptive traits of haloalkalitolerant. Nitrospira ISME J. 2020;14:2967–79.CAS 
Article 

Google Scholar 
Suarez C, Sedlacek CJ, Gustavsson DJI, Eiler A, Modin O, Hermansson M, et al. Disturbance-based management of ecosystem services and disservices in partial nitritation anammox biofilms. 2021. https://www.biorxiv.org/content/10.1101/2021.07.05.451122v1. More

Edelist, D., Rilov, G., Golani, D., Carlton, J. T. & Spanier, E. Restructuring the Sea: profound shifts in the world’s most invaded marine ecosystem. Divers. Distrib. 19, 69–77, https://doi.org/10.1111/ddi.12002 (2013).Article 

Google Scholar 
Parravicini, V., Azzurro, E., Kulbicki, M. & Belmaker, J. Niche shift can impair the ability to predict invasion risk in the marine realm: an illustration using Mediterranean fish invaders. Ecol. Lett. 18, 246–253, https://doi.org/10.1111/ele.12401 (2015).Article 
PubMed 

Google Scholar 
Galil, B. S. et al. International arrivals: widespread bioinvasions in European Seas. Ethol. Ecol. Evol. 26, 152–171, https://doi.org/10.1080/03949370.2014.897651 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Golani, D. & Fricke, R. Checklist of the Red Sea Fishes with delineation of the Gulf of Suez, Gulf of Aqaba, endemism and Lessepsian migrants. Zootaxa 4509, 1–215, https://doi.org/10.11646/zootaxa.4509.1.1 (2018).Article 
PubMed 

Google Scholar 
Zenetos, A. et al. Uncertainties and validation of alien species catalogues: The Mediterranean as an example. Estuar. Coast. Shelf Sci. 191, 171–187, https://doi.org/10.1016/j.ecss.2017.03.031 (2017).Article 

Google Scholar 
Katsanevakis, S. et al. Advancing marine conservation in European and contiguous seas with the MarCons Action. Res. Ideas Outcomes 3, e11884, https://doi.org/10.3897/rio.3.e11884 (2017).Article 

Google Scholar 
Schroeder, K., Chiggiato, J., Bryden, H. L., Borghini, M. & Ben Ismail, S. Abrupt climate shift in the Western Mediterranean Sea. Sci. Rep. 6, 23009, https://doi.org/10.1038/srep23009 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vargas-Yáñez, M. et al. Warming trends and decadal variability in the Western Mediterranean shelf. Glob. Planet. Change 63, 177–184, https://doi.org/10.1016/j.gloplacha.2007.09.001 (2008).Article 

Google Scholar 
D’Amen, M. & Azzurro, E. Lessepsian fish invasion in Mediterranean marine protected areas: a risk assessment under climate change scenarios. ICES J. Mar. Sci. 77, 388–397, https://doi.org/10.1093/icesjms/fsz207 (2020).Article 

Google Scholar 
Golani, D., Azzurro, E., Dulčić, J., Massutí, E. & Orsi-Relini, L. Atlas of Exotic Species in the Mediterranean Sea. F. Briand, Ed. 365 pages. CIESM Publishers, Paris, Monaco (2021).Editorial Board. AquaNIS. Information system on Aquatic Non-Indigenous and Cryptogenic Species. World Wide Web electronic publication. Version 2.36+ (2015).Roy, D. et al. DAISIE – Inventory of alien invasive species in Europe. https://doi.org/10.15468/ybwd3x (2020).European Commission – Joint Research Centre – European Alien Species Information Network (EASIN).Uludag, A, Scalera, R., Trichkova, T., Tomov, R. & Rat, M. East and South European Network for Invasive Alien Species (ESENIAS): Development, networking and role in the invasive alien species research and policy-making in Europe. (2016).Zenetos, A. et al. ELNAIS: A collaborative network on Aquatic Alien Species in Hellas (Greece). REABIC 6, 185–196, https://doi.org/10.3391/mbi.2015.6.2.09 (2015).Article 

Google Scholar 
European Network on Invasive Alien Species. NOBANIS (Gateway to information on Invasive Alien species in North and Central Europe) (2013).MAMIAS – Marine Mediterranean Invasive Alien Species. (2014).MedMIS – Mediterranean Marine Invasive SpeciesKatsanevakis, S. et al. Identifying where vulnerable species occur in a data-poor context: combining satellite imaging and underwater occupancy surveys. Mar. Ecol. Prog. Ser. 577, 17–32, https://doi.org/10.3354/meps12232 (2017).Article 

Google Scholar 
Galil, B. S. Alien species in the Mediterranean Sea—which, when, where, why? In Challenges to Marine Ecosystems (eds. Davenport, J. et al.) 105–116, https://doi.org/10.1007/978-1-4020-8808-7_10 (Springer Netherlands (2008).Galil, B. S. Taking stock: inventory of alien species in the Mediterranean sea. Biol. Invasions 11, 359–372, https://doi.org/10.1007/s10530-008-9253-y (2009).Article 

Google Scholar 
Nunes, A. L., Orizaola, G., Laurila, A. & Rebelo, R. Rapid evolution of constitutive and inducible defenses against an invasive predator. Ecology 95, 1520–1530, https://doi.org/10.1890/13-1380.1 (2014).Article 
PubMed 

Google Scholar 
Zenetos, A. et al. Annotated list of marine alien species in the Mediterranean with records of the worst invasive species. Mediterr. Mar. Sci. 6, 63–118, https://doi.org/10.12681/mms.186 (2005).Article 

Google Scholar 
Zenetos, A. et al. Additions to the annotated list of marine alien biota in the Mediterranean with special emphasis on Foraminifera and Parasites. Mediterr. Mar. Sci. 9, 119–166, https://doi.org/10.12681/mms.146 (2008).Article 

Google Scholar 
Zenetos, A. et al. Alien species in the Mediterranean sea by 2010. A contribution to the application of european union’s marine strategy framework directive (MSFD). Part I. Spatial distribution. https://doi.org/10.12681/mms.87 (2010)Zenetos, Α et al. Alien species in the Mediterranean Sea by 2012. A contribution to the application of European Union’s Marine Strategy Framework Directive (MSFD). Part 2. Introduction trends and pathways. Mediterr. Mar. Sci. 13, 328–352, https://doi.org/10.12681/mms.327 (2012).Article 

Google Scholar 
Dimitriadis, C. et al. Updating the occurrences of Pterois miles in the Mediterranean Sea, with considerations on thermal boundaries and future range expansion. Mediterr. Mar. Sci. 21, 62–69, https://doi.org/10.12681/mms.21845 (2020).Article 

Google Scholar 
Carlton, J. T. Pattern, process, and prediction in marine invasion ecology. Biol. Conserv. 78, 97–106, https://doi.org/10.1016/0006-3207(96)00020-1 (1996).Article 

Google Scholar 
Olenin, S., Minchin, D., Daunys, D. & Zaiko, A. Pathways of aquatic invasions in Europe. Atlas of biodiversity risk 138–139 (2010).Essl, F. et al. A Conceptual Framework for Range-Expanding Species that Track Human-Induced Environmental Change. BioScience 69, 908–919 (2019).Article 

Google Scholar 
Golani, D., Orsi-Relini, L., Massuti, E. & Quignard, J. P. CIESM Atlas of Exotic Species in the Mediterranean. vol. 1 (2002).D’Amen, M. & Azzurro, E. Integrating univariate niche dynamics in species distribution models: A step forward for marine research on biological invasions. J. Biogeogr. 47, 686–697, https://doi.org/10.1111/jbi.13761 (2020).Article 

Google Scholar 
Azzurro, E., Smeraldo, S. & D’Amen, M. ORMEF: Occurrence Records of Mediterranean Exotic Fishes database. SEANOE. https://doi.org/10.17882/84182 (2021).Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018, https://doi.org/10.1038/sdata.2016.18 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Fricke, R., Eschmeyer, W. N. & Van Der Laan, R. Eschmeyer’s Catalog of Fishes: genera, species, references. California Academy of Sciences (2022).Azzurro, E., Goren, M., Diamant, A., Galil, B. & Bernardi, G. Establishing the identity and assessing the dynamics of invasion in the Mediterranean Sea by the dusky sweeper, Pempheris homboidei Kossmann & Räuber, 1877 (Pempheridae, Perciformes). Biol. Invasions 17, 815–826, https://doi.org/10.1007/s10530-014-0836-5 (2015).Article 

Google Scholar 
Evans, J. & Schembri, P. On the occurrence of Cephalopholis hemistiktos and C. taeniops (Actinopterygii, Perciformes, Serranidae) in Malta, with corrections of previous misidentifications. Acta Ichthyol. Piscat. 47, 197–200, https://doi.org/10.3750/AIEP/02064 (2017).Article 

Google Scholar 
Dragicevic, B. et al. New Mediterranean Biodiversity Records (December 2019). https://doi.org/10.12681/mms.20913 (2019).UNEP/MAP – United Nation Environment Programme – Mediterranean Action Plan. Integrated Monitoring and Assessment Programme of the Mediterranean Sea and Coast and Related Assessment Criteria (IMAP). (2016). More

Hewitt, G. M. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Drovetski, S. V. et al. A test of the European Pleistocene refugial paradigm, using a Western Palaearctic endemic bird species. Proc. R. Soc. B 285, 20181606 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Hewitt, G. M. Quaternary phylogeography: the roots of hybrid zones. Genetica 139, 617–638 (2011).PubMed 
Article 

Google Scholar 
Nadachowska-Brzyska, K., Li, C., Smeds, L., Zhang, G. & Ellegren, H. Temporal dynamics of avian populations during Pleistocene revealed by whole-genome sequences. Curr. Biol. 25, 1375–1380 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Newton, I. Speciation and Biogeography of Birds (Academic Press, 2003).
Google Scholar 
Pellegrino, I. et al. Phylogeography and Pleistocene refugia of the Little Owl Athene noctua inferred from mtDNA sequence data. Ibis 156, 639–657 (2014).Article 

Google Scholar 
Tietze, D. T. Bird Species: How they Arise, Modify and Vanish (Springer Nature, 2018).Book 

Google Scholar 
Carrera, L., Pavia, M., Peresani, M. & Romandini, M. Late Pleistocene fossil birds from Buso Doppio del Broion Cave (North-Eastern Italy): implications for palaeoecology, palaeoenvironment and palaeoclimate. Boll. Soc. Paleontol. I(57), 145–174 (2018).
Google Scholar 
Carrera, L., Pavia, M., Romandini, M. & Peresani, M. Avian fossil assemblages at the onset of the LGM in the eastern Alps: a palaecological contribution from the Rio Secco Cave (Italy). C. R. Palevol 17, 166–177 (2018).Article 

Google Scholar 
Carrera, L., Scarponi, D., Martini, F., Sarti, L. & Pavia, M. Mid-Late Pleistocene Neanderthal landscapes in southern Italy: paleoecological contributions of the avian assemblage from Grotta del Cavallo, Apulia, southern Italy. Palaeogeogr. Palaeocl. 567, 110256 (2021).Article 

Google Scholar 
Clark, P. U. et al. The last glacial maximum. Science 325, 710–714 (2009).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Hampe, A. & Jump, A. S. Climate relicts: past, present, future. Annu. Rev. Ecol. Evol. S. 42, 313–333 (2011).Article 

Google Scholar 
Holm, S. R. & Svenning, J. C. 180,000 years of climate change in Europe: avifaunal responses and vegetation implications. PLoS ONE 9, e94021 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Sanchez Marco, A. Avian zoogeographical patterns during the Quaternary in the Mediterranean region and paleoclimatic interpretation. Ardeola 51, 91–132 (2004).
Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. S. 40, 677–697 (2009).Article 

Google Scholar 
Gavin, D. G. et al. Climate refugia: joint inference from fossil records, species distribution models and phylogeography. New Phytol. 204, 37–54 (2014).PubMed 
Article 

Google Scholar 
Nogués-Bravo, D. Predicting the past distribution of species climatic niches. Glob. Ecol. Biogeogr. 18, 521–531 (2009).Article 

Google Scholar 
Svenning, J. C., Fløjgaard, C., Marske, K. A., Nogues-Bravo, D. & Normand, S. Applications of species distribution modeling to paleobiology. Quat. Sci. Rev. 30, 2930–2947 (2011).Article 
ADS 

Google Scholar 
Varela, S., Lobo, J. M. & Hortal, J. Using species distribution models in paleobiogeography: a matter of data, predictors and concepts. Palaeogeogr. Palaeocl. 310, 451–463 (2011).Article 

Google Scholar 
Arcones, A., Ponti, R., Ferrer, X. & Vieites, D. R. Pleistocene glacial cycles as drivers of allopatric differentiation in Arctic shorebirds. J. Biogeogr. 48, 747–759 (2021).Article 

Google Scholar 
Kozma, R., Melsted, P., Magnússon, K. P. & Höglund, J. Looking into the past–the reaction of three grouse species to climate change over the last million years using whole genome sequences. Mol. Ecol. 25, 570–580 (2016).PubMed 
Article 

Google Scholar 
Lagerholm, V. K. et al. Range shifts or extinction? Ancient DNA and distribution modelling reveal past and future responses to climate warming in cold-adapted birds. Glob. Change Biol. 23, 1425–1435 (2017).Article 
ADS 

Google Scholar 
Metcalf, J. L. et al. Integrating multiple lines of evidence into historical biogeography hypothesis testing: a Bison bison case study. Proc. R. Soc. B 281, 20132782. https://doi.org/10.1098/rspb.2013.2782 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Perktaş, U., Peterson, A. T. & Dyer, D. Integrating morphology, phylogeography, and ecological niche modeling to explore population differentiation in North African Common Chaffinches. J. Ornithol. 158, 1–13 (2017).Article 

Google Scholar 
Perktaş, U., De Silva, T. N., Quintero, E. & Tavşanoğlu, Ç. Adding ecology into phylogeography: ecological niche models and phylogeography in tandem reveals the demographic history of the subalpine warbler complex. Bird Study 66, 234–242 (2019).Article 

Google Scholar 
Fløjgaard, C., Normand, S., Skov, F. & Svenning, J. C. Ice age distributions of European small mammals: insights from species distribution modelling. J. Biogeogr. 36, 1152–1163 (2009).Article 

Google Scholar 
Lima-Ribeiro, M. S., Varela, S., Nogués-Bravo, D. & Diniz-Filho, J. A. F. Potential suitable areas of giant ground sloths dropped before its extinction in South America: the evidences from bioclimatic envelope modeling. Nat. Conserv. 10, 145–151 (2012).Article 

Google Scholar 
Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359–364 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Martínez-Meyer, E., Townsend Peterson, A. & Hargrove, W. W. Ecological niches as stable distributional constraints on mammal species, with implications for Pleistocene extinctions and climate change projections for biodiversity. Glob. Ecol. Biogeogr. 13, 305–314 (2004).Article 

Google Scholar 
Nogués-Bravo, D., Rodríguez, J., Hortal, J., Batra, P. & Araújo, M. B. Climate change, humans, and the extinction of the woolly mammoth. PLoS Biol. 6, e79 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Waltari, E. et al. Locating Pleistocene refugia: comparing phylogeographic and ecological niche model predictions. PLoS ONE 2, e563 (2007).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Barrientos, R. et al. Refugia, colonization and diversification of an arid-adapted bird: coincident patterns between genetic data and ecological niche modelling. Mol. Ecol. 23, 390–407 (2014).PubMed 
Article 

Google Scholar 
Huntley, B. & Green, R. E. Bioclimatic models of the distributions of Gyrfalcons and ptarmigan. In Gyrfalcons and Ptarmigan in a Changing World Vol. II (eds Watson, R. T. et al.) 329–338 (The Peregrine Fund, 2011).
Google Scholar 
Huntley, B., Allen, J. R. M., Barnard, P., Collingham, Y. C. & Holliday, P. R. Species distribution models indicate contrasting late-Quaternary histories for Southern and Northern Hemisphere bird species. Glob. Ecol. Biogeogr. 22, 277–288 (2013).Article 

Google Scholar 
Kiss, O. et al. Past and future climate-driven shifts in the distribution of a warm-adapted bird species, the European Roller Coracias garrulus. Bird Study 67, 143–159 (2020).Article 

Google Scholar 
Koparde, P., Mehta, P., Mukherjee, S. & Robin, V. V. Quaternary climatic fluctuations and resulting climatically suitable areas for Eurasian owlets. Ecol. Evol. 9, 4864–4874 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Peterson, A. T. & Ammann, C. M. Global patterns of connectivity and isolation of populations of forest bird species in the late Pleistocene. Glob. Ecol. Biogeogr. 22, 596–606 (2013).Article 

Google Scholar 
Peterson, A. T., Martínez-Meyer, E. & González-Salazar, C. Reconstructing the Pleistocene geography of the Aphelocoma jays (Corvidae). Divers. Distrib. 10, 237–246 (2004).Article 

Google Scholar 
Ponti, R., Arcones, A., Ferrer, X. & Vieites, D. R. Lack of evidence of a Pleistocene migratory switch in current bird long-distance migrants between Eurasia and Africa. J. Biogeogr. 47, 1564–1573 (2020).Article 

Google Scholar 
Ruegg, K. C., Hijmans, R. J. & Moritz, C. Climate change and the origin of migratory pathways in the Swainson’s thrush Catharus ustulatus. J. Biogeogr. 33, 1172–1182 (2006).Article 

Google Scholar 
Smith, S. E., Gregory, R. D., Anderson, B. J. & Thomas, C. D. The past, present and potential future distributions of cold-adapted bird species. Divers. Distrib. 19, 352–362 (2013).Article 

Google Scholar 
Sutton, L. J. et al. Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution. Ecol. Evol. 11, 481–497 (2021).PubMed 
Article 

Google Scholar 
Varela, S., Lima-Ribeiro, M. S., Diniz-Filho, J. A. F. & Storch, D. Differential effects of temperature change and human impact on European Late Quaternary mammalian extinctions. Glob. Change Biol. 21, 1475–1481 (2015).Article 
ADS 

Google Scholar 
Scridel, D. et al. Thermal niche predicts recent changes in range size for bird species. Clim. Res. 73, 207–216 (2017).Article 

Google Scholar 
Barnagaud, J. Y. et al. Relating Habitat and Climatic Niches in Birds. PLoS Biol. 7, e32819 (2012).CAS 
ADS 

Google Scholar 
Devictor, V., Julliard, R., Jiguet, F. & Couvet, D. Birds are tracking climate warming, but not fast enough. Proc. R. Soc. Lond. [Biol.] 275, 2743–2748 (2008).
Google Scholar 
Gaüzère, P., Jiguet, F. & Devictor, V. Rapid adjustment of bird community compositions to local climatic variations and its functional consequences. Glob. Change Biol. 21, 3367–3378 (2015).Article 
ADS 

Google Scholar 
Jiguet, F., Gadot, A., Julliard, R., Newson, S. & Couvet, D. Climate envelope, life history traits and the resilience of birds facing global change. Glob. Change Biol. 13, 1673–1685 (2007).Article 
ADS 

Google Scholar 
Jiguet, F. et al. Bird population trends are linearly affected by climate change along species thermal ranges. Proc. R. Soc. Lond. [Biol.] 277, 3601–3608 (2010).
Google Scholar 
Jiguet, F. et al. Population trends of European common birds are predicted by characteristics of their climatic niche. Glob. Change Biol. 16, 497–505 (2010).Article 
ADS 

Google Scholar 
Lindström, Å., Green, M., Paulson, G., Smith, H. G. & Devictor, V. Rapid changes in bird community composition at multiple temporal and spatial scales in response to recent climate change. Ecography 36, 313–322 (2013).Article 

Google Scholar 
Pearce-Higgins, J. W., Eglington, S. M., Martay, B. & Chamberlain, D. E. Drivers of climate change impacts on bird communities. J. Anim. Ecol. 84, 943–954 (2015).PubMed 
Article 

Google Scholar 
Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016).CAS 
PubMed 
Article 
ADS 

Google Scholar 
BirdLife International. Crex crex. The IUCN Red List of Threatened Species 2016: e.T22692543A86147127. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T22692543A86147127.en (2016).BirdLife International. Perdix perdix. The IUCN Red List of Threatened Species 2016: e.T22678911A85929015. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T22678911A85929015.en (2016).BirdLife International. Pyrrhocorax graculus. The IUCN Red List of Threatened Species 2016: e.T22705921A87386602. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T22705921A87386602.en (2016).BirdLife International. Coturnix coturnix. The IUCN Red List of Threatened Species 2018: e.T22678944A131904485. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22678944A131904485.en (2018).BirdLife International. Athene noctua. The IUCN Red List of Threatened Species 2019: e.T22689328A155470112. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2019-3.RLTS.T22689328A155470112.en (2019).BirdLife International. Bubo scandiacus. The IUCN Red List of Threatened Species 2020: e.T22689055A181375387. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2020-3.RLTS.T22689055A181375387.en (2020).Cramp, S. The Complete Birds of the Western Palearctic on CD-ROM (Oxford University Press, 1998).
Google Scholar 
Tyrberg, T. Pleistocene Birds of the Palearctic: A Catalogue. (Publications of the Nuttall Ornithological Club No. 27, 1998).Tyrberg, T. Pleistocene Birds of the Palaearctic. http://web.telia.com/~u11502098/pleistocene.pdf (2008).Pellegrino, I. et al. Evidence for strong genetic structure in European populations of the little owl Athene noctua. J. Avian Biol. 46, 462–475 (2015).Article 

Google Scholar 
van Nieuwenhuyse, D., Génot, J. C. & Johnson, D. H. The Little Owl: Conservation, Ecology and Behavior of Athene noctua (Cambridge University Press, 2008).
Google Scholar 
Dupont, L. M. Vegetation zones in NW Africa during the Brunhes chron reconstructed from marine palynological data. Quat. Sci. Rev. 12, 189–202 (1993).Article 
ADS 

Google Scholar 
Hoag, C. & Svenning, J. C. African environmental change from the Pleistocene to the Anthropocene. Annu. Rev. Env. Resour. 42, 27–54 (2017).Article 

Google Scholar 
Hoelzmann, P. et al. Palaeoenvironmental changes in the arid and sub arid belt (Sahara-Sahel-Arabian Peninsula) from 150 kyr to present. In Past Climate Variability Through Europe and Africa (eds Battarbee, R. W. et al.) 219–256 (Springer, 2004).Chapter 

Google Scholar 
Larrasoaña, J. C., Roberts, A. P. & Rohling, E. J. Dynamics of green Sahara periods and their role in hominin evolution. PLoS ONE 8, e76514 (2013).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Bech, N., Novoa, C., Allienne, J. F., Boissier, J. & Bro, E. Quantifying genetic distance between wild and captive strains of the grey partridge Perdix perdix in France: conservation implications. Biodivers. Conserv. 29, 609–624 (2020).Article 

Google Scholar 
Liukkonen-Anttila, T., Uimaniemi, L., Orell, M. & Lumme, J. Mitochondrial DNA variation and the phylogeography of the grey partridge (Perdix perdix) in Europe: from Pleistocene history to present day populations. J. Evolut. Biol. 15, 971–982 (2002).CAS 
Article 

Google Scholar 
Potapova, O. Snowy owl Nyctea scandiaca (Aves: Strigiformes) in the Pleistocene of the Ural Mountains with notes on its ecology and distribution in the Northern Palearctic. Deinsea 8, 103–126 (2001).
Google Scholar 
Mourer-Chauviré, C. Les oiseaux du Pléistocène moyen et supérieur de France. Doc. Lab. Géol. Fac. Sci. Lyon 64, 1–624 (1975).
Google Scholar 
Mourer-Chauviré, C. Les oiseaux dans les habitats pale´olithiques: gibier des hommes ou proies des rapaces? In Animal and Archaeology: 2. Shell Middens, Fishes and Birds (eds Grigson, C. & Clutton-Brock, J.) 111–124 (British Archaeological Reports International Series 183, 1983).
Google Scholar 
Meijer, H. J., Pavia, M., Madurell-Malapeira, J. & Alba, D. M. A revision of fossil eagle owls (Aves: Strigiformes: Bubo) from Europe and the description of a new species, Bubo ibericus, from Cal Guardiola (NE Iberian Peninsula). Hist. Biol. 29, 822–832 (2017).Article 

Google Scholar 
Sanchez Marco, A. Aves fósiles de la Península Ibérica, Canarias y Baleares: balance de los estudios realizados. Investig. Rev. PH Inst. Andal. Patrim. Hist. 94, 154–181 (2018).
Google Scholar 
Sardella, R. et al. Grotta Romanelli (Southern Italy, Apulia): legacies and issues in excavating a key site for the Pleistocene of the Mediterranean. Riv. Ital. Paleontol. S. 124, 247–264 (2018).
Google Scholar 
Rustioni, M., Ferretti, M. P., Mazza, P., Pavia, M. & Varola, A. The vertebrate fauna from Cardamone (Apulia, southern Italy): an example of Mediterranean mammoth fauna. Deinsea 9, 395–404 (2003).
Google Scholar 
Bedetti, C. & Pavia, M. Reinterpretation of the Late Pleistocene Ingarano Cave deposit based on the fossil bird association (Apulia, South-eastern Italy). Riv. Ital. Paleontol. S. 113, 487–507 (2007).
Google Scholar 
Tyrberg, T. Arctic, montane and steppe birds as glacial relicts in West Palearctic. Ornithol. Verh. 25, 29–49 (1991).
Google Scholar 
Bruderer, B. & Salewski, V. Evolution of bird migration in a biogeographical context. J. Biogeogr. 35, 1951–1959 (2008).Article 

Google Scholar 
Finlayson, C. Avian Survivors. The History and Biogeography of Palearctic Birds (T. & A.D. Poyser, 2011).
Google Scholar 
Louchart, A. Emergence of long distance bird migrations: a new model integrating global climate changes. Naturwissenschaften 95, 1109–1119 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).PubMed 
Article 

Google Scholar 
Somveille, M. et al. Simulation-based reconstruction of global bird migration over the past 50,000 years. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Fiedler, W. Recent changes in migratory behaviour of birds: a compilation of field observations and ringing data. In Avian Migration (eds Berthold, P. et al.) 21–38 (Springer, 2003).Chapter 

Google Scholar 
Milá, B., Smith, T. B. & Wayne, R. K. Postglacial population expansion drives the evolution of long-distance migration in a songbird. Evolution 60, 2403–2409 (2006).PubMed 
Article 

Google Scholar 
Zink, R. M. The evolution of avian migration. Biol. J. Linn. Soc. 104, 237–250 (2011).Article 

Google Scholar 
Zink, R. M. & Gardner, A. S. Glaciation as a migratory switch. Sci. Adv. 3, e1603133 (2017).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Matthiesen, D. G. Avian medullary bone in the fossil record, an example from the Early Pleistocene of Olduvai Gorge, Tanzania. J. Vertebr. Paleontol. 9, 34A (1990).
Google Scholar 
Ponti, R., Arcones, A., Ferrer, X. & Vieites, D. R. Seasonal climatic niches diverge in migratory birds. Ibis 162, 318–330 (2020).Article 

Google Scholar 
Cohen, K. M. & Gibbard, P. L. Global chronostratigraphical correlation table for the last 2.7 million years, version 2019 QI-500. Quat. Int. 500, 20–31 (2019).Article 

Google Scholar 
Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, PA1003. https://doi.org/10.1029/2004PA001071 (2005).Article 
ADS 

Google Scholar 
Vermeersch, P. M. Radiocarbon Palaeolithic Europe Database, Version 26. https://ees.kuleuven.be/geography/projects/14c-palaeolithic/index.html (2019).d’Errico, F., Banks, W. E., Vanhaeren, M., Laroulandie, V. & Langlais, M. PACEA geo-referenced radiocarbon database. Paleoanthropology https://doi.org/10.4207/PA.2011.ART40 (2011).Article 

Google Scholar 
Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360. https://doi.org/10.1017/S0033822200033865 (2009).Article 

Google Scholar 
Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1897. https://doi.org/10.2458/azu_js_rc.55.16947 (2013).CAS 
Article 

Google Scholar 
Serjeantson, D. Birds: a seasonal resource. Environ. Archaeol. 3, 23–33 (1998).Article 

Google Scholar 
Serjeantson, D. Birds. Cambridge Manuals in Archaeology (Cambridge University Press, 2009).
Google Scholar 
Lima-Ribeiro, M. S. et al. EcoClimate: a database of climate data from multiple models for past, present, and future for macroecologists and biogeographers. Biodivers. Inform. 10, 1–21 (2015).Article 

Google Scholar 
Varela, S., Lima-Ribeiro, M. S. & Terribile, L. C. A short guide to the climatic variables of the last glacial maximum for biogeographers. PLoS ONE 10, e0129037 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

Google Scholar 
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
PubMed 
Article 

Google Scholar 
Leathwick, J. R., Elith, J., Francis, M. P., Hastie, T. & Taylor, P. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Mar. Ecol. Prog. Ser. 321, 267–281 (2006).Article 
ADS 

Google Scholar 
Leathwick, J. R., Elith, J., Chadderton, W. L., Rowe, D. & Hastie, T. Dispersal, disturbance and the contrasting biogeographies of New Zealand’s diadromous and non-diadromous fish species. J. Biogeogr. 35, 1481–1497 (2008).Article 

Google Scholar 
Therneau, T. & Atkinson, B. Rpart: Recursive Partitioning and Regression Trees. R package version 4.1-15. https://CRAN.R-project.org/package=rpart (2019).Kuhn, M. Caret: Classification and Regression Training. R package version 6.0-88. https://CRAN.R-project.org/package=caret (2021). More