Ecology

De Grave, S., & Fransen, C. H. J. M. Carideorum Catalogus: The Recent Species of the Dendrobranchiate, Stenopodidean, Procarididean and Caridean Shrimps (Crustacea: Decapoda). Zool. Meded. 85, (2011).Garassino, A. The macruran decapod crustaceans of the Lower Cretaceous (Lower Barremian) of Las Hoyas (Cuenca, Spain). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 137, 101–126 (1997).Bravi, S., Coppa, M. G., Garassino, A., & Patricelli, R. Palaemon vesolensis n. sp. (Crustacea, Decapoda) from the Plattenkalk of Vesole Mount (Salerno, Southern Italy). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 140, 141–169 (1999).Colleary, C. et al. Chemical, experimental, and morphological evidence for diagenetically altered melanin in exceptionally preserved fossils. Proc. Natl. Acad. Sci. U.S.A. 11241, 12592–12597 (2015).Article 
ADS 

Google Scholar 
Vinther, J., Briggs, D. E., Clarke, J., Mayr, G. & Prum, R. O. Structural coloration in a fossil feather. Biol. Lett. 6, 128–131 (2010).Article 
PubMed 

Google Scholar 
McNamara, M. E. et al. Fossilised biophotonic nanostructures reveal the original colors of 47 million-year-old moths. PLoS Biol. 9, e1001200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rietschel, S. Taphonomic biasing in the Messel Fauna and Flora. Cour. Forsch. Inst. Senckenberg 107, 169–182 (1988).
Google Scholar 
Wolf, H. W. Schätze im Schiefer (Westermann, 1991).Rabenstein, R. Messel 2000 – Das Weltnaturerbe Deutschlands (eds Forschungsinstitut Senckenberg) (2000).Gruber, G., & Micklich, N. Messel – Treasures of the Eocene (Hessisches Landesmuseum Darmstadt, 2007).Wedmann, S. Annotated taxon-list of the invertebrate animals from the Eocene fossil site Grube Messel near Darmstadt Germany. Cour. Forsch. Inst. Senckenberg 255, 103–110 (2005).
Google Scholar 
Schaal, S. F. K. & Rabenstein, R. D. Tagebau Messel in Linien und Zahlen. Senckenberg Nat. Forsch. Mus. 142, 376–377 (2012).
Google Scholar 
Moshayedi, M., Lenz, O. K., Wilde, V. & Hinderer, M. The recolonisation of volcanically disturbed Eocene habitats of Central Europe: the maar lakes of Messel and Offenthal (SW Germany) compared. Paleobiodivers. Paleoenviron. 100, 951–973 (2020).Article 

Google Scholar 
Schulz, R., Harms, F.-J. & Felder, M. Die Forschungsbohrung Messel 2001: Ein Beitrag zur Entschlüsselung der Genese einer Ölschieferlagerstätte. Z. angew. Geol. 2002, 9–17 (2002).
Google Scholar 
Felder, M. & Harms, F. J. Lithologie und genetische Interpretation der vulkano-sedimentären Ablagerungen aus der Grube Messel anhand der Forschungsbohrung Messel 2001 und weiterer Bohrungen (Eozän, Messel-Formation, Sprendlinger Horst, Südhessen). Cour. Forsch. Inst. Senckenberg 252, 151–203 (2004).
Google Scholar 
Büchel, G. N., & Schaal, S. F. K. The formation of the Messel maar in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Der, G. K. Messeler Ölschiefer – ein Algenlaminit. Cour. Forsch. Inst. Senckenberg 131, 1–143 (1990).
Google Scholar 
Lenz, O. K., Wilde, V. & Riegel, W. Recolonization of a Middle Eocene volcanic site: quantitative palynology of the initial phase of the maar lake of Messel (Germany). Rev. Palaeobot. Palynol. 145, 217–242 (2007).Article 

Google Scholar 
Bauersachs, T., Schouten, S. & Schwark, L. Characterization of the sedimentary organic matter preserved in Messel oil shale by bulk geochemistry and stable isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 410, 390–400 (2014).Article 

Google Scholar 
Mertz, D. F. & Renne, P. R. A numerical age for the Messel fossil deposit (UNESCO world natural heritage site) from 40Ar/39Ar dating. Cour. Forsch. Inst. Senckenberg 255, 67–75 (2005).
Google Scholar 
Lenz, O. K., Wilde, V., Mertz, D. F. & Riegel, W. New palynology-based astronomical and revised 40Ar/39Ar ages for the Eocene maar lake of Messel (Germany). Int. J. Earth Sci. 104, 873–889 (2015).Article 
CAS 

Google Scholar 
Lenz, O. K. & Wilde, V. Changes in Eocene plant diversity and composition of vegetation: The lacustrine archive of Messel (Germany). Paleobiology 44, 709–735 (2018).Article 

Google Scholar 
Lenz, O. K., Wilde, V, Riegel, W., & Harms, F-J. A 600 k.y. record of El Niño–Southern Oscillation (ENSO): evidence for persisting teleconnections during the Middle Eocene greenhouse climate of Central Europe. Geology 38, 627–630 (2010).Lenz, O. K., Wilde, V, & Riegel, W. Paleoclimate – Learning from the past for the future in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 16–23 (Schweizerbart, 2018).Grein, M., Utescher, T., Wilde, V. & Roth-Nebelsick, A. Reconstruction of the middle Eocene climate of Messel using palaeobotanical data. Neues Jb. Geol. Paläontol. Abh. 260, 305–318 (2011).Article 

Google Scholar 
Tütken, T. Isotope compositions (C, O, Sr, Nd) of vertebrate fossils from the Middle Eocene oil shale of Messel, Germany: Implications for their taphonomy and palaeoenvironment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 92–109 (2014).Article 

Google Scholar 
Wilde, V. The fossil flora of Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 42–61 (Schweizerbart, 2018).Smith, K. T., Schaal, S. F. K. & Habersetzer, J. (eds.) Messel: An Ancient Greenhouse Ecosystem. (Schweizerbart, 2018).Wedmann, S., Hörnschemeyer, T., Engel, M. S., Zetter, R. & Grímsson, F. The last meal of an Eocene pollen-feeding fly. Curr. Biol. 31, 2020–2026 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wedmann, S. Jewels in the oil shale – insects and other invertebrates in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Franzen J. L. Odd-toed ungulates – Early horses and tapiromorphs in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 292–301 (Schweizerbart, 2018).Franzen, J. L., Aurich, C. & Habersetzer, J. Description of a well preserved fetus of the European Eocene Equoid Eurohippus messelensis. PLoS ONE 10, e0137985 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Franzen J. L., & Gingerich, P. D. Primates – Rareties in Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 240–247 (Schweizerbart, 2018).Franzen, J. L. et al. Complete primate skeleton from the middle Eocene of Messel in Germany: Morphology and paleobiology. PLoS ONE 4(5), e5723 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Houša, V. Bechleja inopinata n. g., n. sp., nový ráček z českých třetihor (Decapoda, Palaemonidae). Ústřed. Ústavu Geol. Sborník 23, 365–377 (1957).Glaessner, M. F. Decapoda. In Part R Arthropoda 4(2) Treatise on Invertebrate Paleontology (ed Moore, R. C.) (The University of Kansas Press and The Geological Society of America, 1969).De Grave, S., Cai, Y. & Anker, A. Global diversity of shrimps (Crustacea: Decapoda: Caridea) in freshwater. Hydrobiologia 595, 287–293 (2008).Article 

Google Scholar 
Garassino, A. & Bravi, S. Palaemon antonellae new species (Crustacea, Decapoda, Caridea) from the Lower Cretaceous “Platydolomite” of profeti (Caserta, Italy). J. Paleontol. 77, 589–592 (2003).Article 

Google Scholar 
Schweitzer, C., Karasawa, H., Schweigert, G., Feldmann, R. & Garassino, A. Systematic list of fossil decapod crustacean species. Crustac. Monogr. 10, 1–222 (2010).
Google Scholar 
Plotnick, R. E. Taphonomy of a modern shrimp: implications for the arthropod fossil record. Palaios 1, 286–293 (1986).Article 
ADS 

Google Scholar 
Klompmaker, A. A., Portell, R. W. & Frick, M. G. Comparative experimental taphonomy of eight marine arthropods indicates distinct differences in preservation potential. Palaeontology 60, 773–794 (2017).Article 

Google Scholar 
Vannier, J., Schoenemann, B., Gillot, T., Charbonnier, S. & Clarkson, E. Exceptional preservation of eye structure in arthropod visual predators from the Middle Jurassic. Nat. Commun. 7, 1–9 (2016).Article 

Google Scholar 
Jauvion, C., Audo, D., Charbonnier, S. & Vannier, J. Virtual dissection and lifestyle of a 165-million-year-old female polychelidan lobster. Arthropod Struct. Dev. 45, 122–132 (2016).Article 
PubMed 

Google Scholar 
Pazinato, P. G., Jauvion, C., Schweigert, G., Haug, J. T. & Haug, C. After 100 years: a detailed view of an eumalacostracan crustacean from the Upper Jurassic Solnhofen Lagerstätte with raptorial appendages unique to Euarthropoda. Lethaia 54, 55–72 (2021).Article 

Google Scholar 
Briggs, D. E. G. & Kear, A. J. Decay and mineralization of shrimps. Palaios 9, 431–456 (1994).Article 
ADS 

Google Scholar 
Wuttke, M. Conservation-dissolution-transformation. On the behaviour of biogenic materials during fossilization In Messel: an insight into the history of life and of the earth (eds. Schaal, S. & Ziegler, W.) 263–275 (Claredon, 1992).Thompson, J. R. Comments on phylogeny of section Caridea (Decapoda Natantia) and the phylogenetic importance of the Oplophoridea. Proc. Symp. Crustacea Part 1, 314–326 (1967).
Google Scholar 
Ashelby, C. W., De Grave, S. & Johnson, M. L. Preliminary observations on the mandibles of palaemonoid shrimp (Crustacea: Decapoda: Caridea: Palaemonoidea). PeerJ 3, e846 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Felgenhauer, B. E., & Abele, L. G. Phylogenetic relationships among shrimp-like decapods. In Crustacean Phylogeny (ed Schram, F. R.) 291–311 (A. A. Balkema, 1983).Wowor, D., Cai, Y., & Ng, P. K. L. Crustacea: Decapoda, Caridea. In Freshwater Invertebrates of the Malaysian Region (eds Yule, C. M. & Y. H. Sen, Y. H.) 337–357 (Academy of Sciences Malaysia, 2004).Rodd, F. H., & Reznick, D. N. Life History Evolution in Guppies: III. The Impact of Prawn Predation on Guppy Life Histories. Oikos 62, 13–19 (1991).Felgenhauer, B. E. & Abele, L. G. Feeding structures of two atyid shrimps, with comments on Caridean phylogeny. J. Crustac. Biol. 5, 397–419 (1985).Article 

Google Scholar 
de Mazancourt, V., Marquet, G., & Keith, P. The “Pinocchio-shrimp effect”: First evidence of variation in rostrum length with the environment in Caridina H. Milne-Edwards, 1837 (Decapoda: Caridea: Atyidae). J. Crustac. Biol. 37, 249–257 (2017).Zimmermann, G. et al. Geometric morphometrics of carapace of Macrobrachium australe (Crustacea: Palaemonidae) from Reunion Island. Acta Zool. 93, 492–500 (2012).Article 

Google Scholar 
Bauer, R. T. Amphidromy in shrimps: a life cycle between rivers and the sea. Lat. Am. J. Aquat. Res. 41, 633–650 (2013).Article 

Google Scholar 
Jalihal, D. R., Sankolli, K. N. & Shenoy, S. Evolution of larval developmental patterns and the process of freshwaterization in the prawn genus Macrobrachium Bate, 1868 (Decapoda, Palaemonidae). Crustaceana 65, 365–376 (1993).Article 

Google Scholar 
Grande, L. Paleontology of the Green River Formation, with a review of the fish fauna. Bull. Geol. Surv. Wyoming 63, 1–333 (1984).
Google Scholar 
Grande, L. The Lost World of Fossil Lake: snapshots from deep time (University of Chicago Press, 2013).Micklich, N. Peculiarities of the Messel fish fauna and their palaeoecological implications: A case study. Palaeobiodivers. Palaeoenviron. 92, 585–629 (2012).Article 

Google Scholar 
Micklich, N. Actinopterygians—the fishes of the Messel lake. in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 104–111 (Schweizerbart, 2018).Christodoulou, M., Anastasiadou, C., Jugovic, J., & Tzomos, T. Freshwater Shrimps (Atyidae, Palaemonidae, Typhlocarididae) in the Broader Mediterranean Region: Distribution, Life Strategies, Threats, Conservation Challenges and Taxonomic Issues. In A Global Overview of the Conservation of Freshwater Decapod Crustaceans (eds Kawai, T. & Cumberlidge, N.) 199–236 (Springer, 2016).Anger, K. Neotropical Macrobrachium (Caridea: Palaemonidae): On the biology, origin, and radiation of freshwater-invading shrimp. J. Crustac. Biol. 33, 151–183 (2013).Article 

Google Scholar  More

LaBarbera, M. The evolution and ecology of body size. In Patterns and Processes in the History of Life (eds Raup, D. M. & Jablonski, D.) 69–98 (Springer, 1986).
Google Scholar 
Peters, R. H. & Peters, R. H. The Ecological Implications of Body Size Vol. 2 (Cambridge University Press, 1986).
Google Scholar 
Klingenberg, C. P. & Spence, J. On the role of body size for life-history evolution. Ecol. Entomol. 22(1), 55–68 (1997).
Google Scholar 
Blanckenhorn, W. U. The evolution of body size: What keeps organisms small?. Q. Rev. Biol. 75(4), 385–407 (2000).CAS 
PubMed 

Google Scholar 
Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl. Acad. Sci. USA 104(45), 17707–17712 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmidt-Nielsen, K. Scaling in biology: The consequences of size. J. Exp. Zool. 194(1), 287–307 (1975).CAS 
PubMed 

Google Scholar 
Calder, W. A. Size, Function, and Life History (Courier Corporation, 1996).
Google Scholar 
Gould, S. J. Allometry and size in ontogeny and phylogeny. Biol. Rev. 41(4), 587–638 (1966).CAS 
PubMed 

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293(5538), 2248–2251 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Gearty, W. & Payne, J. L. Physiological constraints on body size distributions in Crocodyliformes. Evolution 74(2), 245–255 (2020).PubMed 

Google Scholar 
Maurer, B. A., Brown, J. H. & Rusler, R. D. The micro and macro in body size evolution. Evolution 46(4), 939–953 (1992).PubMed 

Google Scholar 
Hone, D. W. & Benton, M. J. The evolution of large size: How does Cope’s Rule work?. Trends Ecol. Evol. 20(1), 4–6 (2005).PubMed 

Google Scholar 
Reeve, J. P. & Fairbairn, D. J. Predicting the evolution of sexual size dimorphism. J. Evol. Biol. 14(2), 244–254 (2001).
Google Scholar 
Blanckenhorn, W. U. Behavioral causes and consequences of sexual size dimorphism. Ethology 111(11), 977–1016 (2005).
Google Scholar 
Wu, H., Jiang, T., Huang, X. & Feng, J. Patterns of sexual size dimorphism in horseshoe bats: Testing Rensch’s rule and potential causes. Sci. Rep. 8(1), 1–13 (2018).ADS 

Google Scholar 
Cox, R. M., Butler, M. A. & John-Alder, H. B. The evolution of sexual size dimorphism in reptiles. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) 38–49 (Oxford University Press, 2007).
Google Scholar 
Stillwell, R. C., Blanckenhorn, W. U., Teder, T., Davidowitz, G. & Fox, C. W. Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: From physiology to evolution. Annu. Rev. Entomol. 55, 227–245 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rensch, B. Die Abhängigkeit der relativen Sexualdifferenz von der Körpergrösse. Bonn. Zool. Beitr. 1, 58–69 (1950).
Google Scholar 
Rensch, B. Evolution Above the Species Level (Columbia University Press, 1960).
Google Scholar 
Shine, R. Ecological causes for the evolution of sexual dimorphism: A review of the evidence. Q. Rev. Biol. 64(4), 419–461 (1989).CAS 
PubMed 

Google Scholar 
Portik, D. M., Blackburn, D. C. & McGuire, J. A. Macroevolutionary patterns of sexual size dimorphism among African tree frogs (Family: Hyperoliidae). J. Hered. 111(4), 379–391 (2020).PubMed 

Google Scholar 
Ceballos, C. P., Adams, D. C., Iverson, J. B. & Valenzuela, N. Phylogenetic patterns of sexual size dimorphism in turtles and their implications for Rensch’s rule. Evol. Biol. 40(2), 194–208 (2013).
Google Scholar 
Amado, T. F., Martinez, P. A., Pincheira-Donoso, D. & Olalla-Tárraga, M. Á. Body size distributions of anurans are explained by diversification rates and the environment. Glob. Ecol. Biogeogr. 30(1), 154–164 (2021).
Google Scholar 
Starostová, Z., Kubička, L. & Kratochvíl, L. Macroevolutionary pattern of sexual size dimorphism in geckos corresponds to intraspecific temperature-induced variation. J. Evol. Biol. 23(4), 670–677 (2010).PubMed 

Google Scholar 
Herczeg, G., Gonda, A. & Merilä, J. Rensch’s rule inverted–female-driven gigantism in nine-spined stickleback Pungitius pungitius. J. Anim. Ecol. 79(3), 581–588 (2010).PubMed 

Google Scholar 
Liao, W. B. & Chen, W. Inverse Rensch’s rule in a frog with female-biased sexual size dimorphism. Naturwissenschaften 99(5), 427–431 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Cooper, M. I. Sexual size dimorphism and the rejection of Rensch’s rule in Diplopoda (Arthropoda). J. Entomol. Zool. Stud. 6(1), 1582–1587 (2018).
Google Scholar 
Cheng, R. C. & Kuntner, M. Phylogeny suggests nondirectional and isometric evolution of sexual size dimorphism in argiopine spiders. Evolution 68(10), 2861–2872 (2014).PubMed 

Google Scholar 
Webb, T. J. & Freckleton, R. P. Only half right: Species with female-biased sexual size dimorphism consistently break Rensch’s rule. PLoS ONE 2(9), e897 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: Elements of a synthesis. J. Biogeogr. 35(3), 483–500 (2008).
Google Scholar 
Olalla-Tárraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: Anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16(5), 606–617 (2007).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodríguez, M. Á. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Gouveia, S. F. & Correia, I. Geographical clines of body size in terrestrial amphibians: Water conservation hypothesis revisited. J. Biogeogr. 43(10), 2075–2084 (2016).
Google Scholar 
Pincheira-Donoso, D., Meiri, S., Jara, M., Olalla-Tárraga, M. Á. & Hodgson, D. J. Global patterns of body size evolution are driven by precipitation in legless amphibians. Ecography 42(10), 1682–1690 (2019).
Google Scholar 
Nevo, E. Adaptive color polymorphism in cricket frogs. Evolution 27(3), 353–367 (1973).PubMed 

Google Scholar 
Ashton, K. G. Do amphibians follow Bergmann’s rule?. Can. J. Zool. 80(4), 708–716 (2002).MathSciNet 

Google Scholar 
Bergmann, C. Ueber die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Göttinger Studien. 1, 595–708 (1847).
Google Scholar 
Olalla-Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad-scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33(5), 781–793 (2006).
Google Scholar 
Trullas, S. C., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32(5), 235–245 (2007).
Google Scholar 
Rodríguez, M. Á., López-Sañudo, I. L. & Hawkins, B. A. The geographic distribution of mammal body size in Europe. Glob. Ecol. Biogeogr. 15(2), 173–181 (2006).
Google Scholar 
Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodriguez, M. A. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
Google Scholar 
Womack, M. C. & Bell, R. C. Two-hundred million years of anuran body-size evolution in relation to geography, ecology and life history. J. Evol. Biol. 33(10), 1417–1432 (2020).PubMed 

Google Scholar 
Frost, D. R. Amphibian Species of the World: An online reference, version 6. http://research.amnh.org/herpetology/amphibia/index.php. Accessed 12 July 2021 (2021).
Acevedo, A. A., Armesto, O. & Palma, R. E. Two new species of Pristimantis (Anura: Craugastoridae) with notes on the distribution of the genus in northeastern Colombia. Zootaxa 4750(4), 499–523 (2020).
Google Scholar 
Heinicke, M. P., Duellman, W. E. & Hedges, S. B. Major Caribbean and Central American frog faunas originated by ancient oceanic dispersal. Proc. Natl. Acad. Sci. USA 104(24), 10092–10097 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pinto-Sánchez, N. R. et al. The great American biotic interchange in frogs: Multiple and early colonization of Central America by the South American genus Pristimantis (Anura: Craugastoridae). Mol. Phylogenet. Evol. 62(3), 954–972 (2012).PubMed 

Google Scholar 
Zumel, D., Buckley, D. & Ron, S. R. The Pristimantis trachyblepharis species group, a clade of miniaturized frogs: Description of four new species and insights into the evolution of body size in the genus. Zool. J. Linn. Soc. zlab044 (2021).Pincheira-Donoso, D. et al. The multiple origins of sexual size dimorphism in global amphibians. Glob. Ecol. Biogeogr. 30(2), 443–458 (2021).
Google Scholar 
Woolbright, L. L. Sexual selection and size dimorphism in anuran amphibia. Am. Nat. 121(1), 110–119 (1983).
Google Scholar 
Nali, R. C., Zamudio, K. R., Haddad, C. F. & Prado, C. P. Size-dependent selective mechanisms on males and females and the evolution of sexual size dimorphism in frogs. Am. Nat. 184(6), 727–740 (2014).PubMed 

Google Scholar 
Hill, R. et al. Herpetological husbandry observations on the captive reproduction of gaige’s rain frog Pristimantis gaigeae (Dunn 1931). Herpetol. Rev. 41(4), 465 (2010).
Google Scholar 
Rojas-Rivera, A., Cortés-Bedoya, S., Gutiérrez-Cárdenas, P. D. A. & Castellanos, J. M. Pristimantis achatinus (Cachabi robber frog). Parental care and clutch size. Herpetol. Rev. 42, 588–589 (2011).
Google Scholar 
Granados-Pérez, Y. & Ramirez-Pinilla, M. P. Reproductive phenology of three species of Pristimantis in an Andean cloud forest. Revista Acad. Colomb. Ci. Exact. 44(173), 1083–1098 (2020).
Google Scholar 
Levy, D. L. & Heald, R. Biological scaling problems and solutions in amphibians. Cold Spring Harb. Perspect. Biol. 8(1), a019166 (2016).PubMed Central 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Survey Data Series. 691(10), 4–9 (2012).
Google Scholar 
Valenzuela-Sánchez, A., Cunningham, A. A. & Soto-Azat, C. Geographic body size variation in ectotherms: Effects of seasonality on an anuran from the southern temperate forest. Front. Zool. 12(1), 1–10 (2015).
Google Scholar 
Parsons, J. J. The northern Andean environment. Mt. Res. Dev. 2(3), 253–264 (1982).
Google Scholar 
Navas, C. A., Carvajalino-Fernández, J. M., Saboyá-Acosta, L. P., Rueda-Solano, L. A. & Carvajalino-Fernández, M. A. The body temperature of active amphibians along a tropical elevation gradient: Patterns of mean and variance and inference from environmental data. Funct. Ecol. 27(5), 1145–1154 (2013).
Google Scholar 
Swemmer, A. M., Knapp, A. K. & Snyman, H. A. Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands. J. Ecol. 95(4), 780–788 (2007).
Google Scholar 
Losos, J. B. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (Univ. of California Press, 2011).
Google Scholar 
Pincheira-Donoso, D. & Hunt, J. Fecundity selection theory: Concepts and evidence. Biol. Rev. 92(1), 341–356 (2017).PubMed 

Google Scholar 
Morrison, C. & Hero, J. M. Geographic variation in life-history characteristics of amphibians: A review. J. Anim. Ecol. 72(2), 270–279 (2003).
Google Scholar 
Morrow, C. B., Ernest, S. M. & Kerkhoff, A. J. Macroevolution of dimensionless life-history metrics in tetrapods. Proc. Royal Soc. B. 288, 20210200 (2021).
Google Scholar 
Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57(4), 591–601 (2008).PubMed 

Google Scholar 
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368(1618), 20120341 (2013).PubMed 
PubMed Central 

Google Scholar 
Meyer, A. L. & Wiens, J. J. Estimating diversification rates for higher taxa: BAMM can give problematic estimates of rates and rate shifts. Evolution 72(1), 39–53 (2018).PubMed 

Google Scholar 
Rabosky, D. L. et al. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4(1), 1–8 (2013).
Google Scholar 
Mendoza, A. M., Ospina, O. E., Cárdenas-Henao, H. & García-R, J. C. A likelihood inference of historical biogeography in the world’s most diverse terrestrial vertebrate genus: Diversification of direct-developing frogs (Craugastoridae: Pristimantis) across the Neotropics. Mol. Phylogenet. Evol. 85, 50–58 (2015).PubMed 

Google Scholar 
Baker, J., Meade, A., Pagel, M. & Venditti, C. Adaptive evolution toward larger size in mammals. Proc. Natl. Acad. Sci. USA 112(16), 5093–5098 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hariharan, I. K., Wake, D. B. & Wake, M. H. Indeterminate growth: Could it represent the ancestral condition?. Cold Spring Harb. Perspect. Biol. 8(2), a019174 (2016).PubMed Central 

Google Scholar 
Amado, T. F., Bidau, C. J. & Olalla-Tárraga, M. Á. Geographic variation of body size in New World anurans: Energy and water in a balance. Ecography 42(3), 456–466 (2019).
Google Scholar 
Watters, J. L., Cummings, S. T., Flanagan, R. L. & Siler, C. D. Review of morphometric measurements used in anuran species descriptions and recommendations for a standardized approach. Zootaxa 4072, 477–495 (2016).PubMed 

Google Scholar 
Lovich, J. E. & Gibbons, J. W. A review of techniques for quantifying sexual size dimorphism. Growth Dev. Aging. 56, 269–269 (1992).CAS 
PubMed 

Google Scholar 
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29(6), 1695–1701 (2012).CAS 
PubMed 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7(1), 1–8 (2007).
Google Scholar 
Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4(5), e88 (2006).PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree, A Graphical Viewer of Phylogenetic Trees. (2007)Olalla-Tárraga, M. A., Bini, L. M., Diniz-Filho, J. A. & Rodríguez, M. Á. Cross-species and assemblage-based approaches to Bergmann’s rule and the biogeography of body size in Plethodon salamanders of eastern North America. Ecography 33(2), 362–368 (2010).
Google Scholar 
QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. Accessed 10 July 2021 (2022).Wei, T. et al. Package ‘corrplot’. Statistician. 56(316), e24 (2017).
Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51(3), 365–390 (1970).
Google Scholar 
Hawkins, B. A. & Felizola Diniz-Filho, J. A. Beyond Rapoport’s rule: Evaluating range size patterns of New World birds in a two-dimensional framework. Glob. Ecol. Biogeogr. 15(5), 461–469 (2006).
Google Scholar 
Eager, C. standardize: Tools for standardizing variables for regression in R. R package version 0.21 (2017).Meireles, J. E., O’Meara, B. & Cavender-Bares, J. Linking leaf spectra to the plant tree of life. In Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 155–172 (Springer, 2010).
Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401(6756), 877–884 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Pagel, M. The maximum likelihood approach to reconstructing ancestral character states of discrete characters on phylogenies. Syst. Biol. 48(3), 612–622 (1999).
Google Scholar 
Revell, L. J. Phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).
Google Scholar 
Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4(8), 754–759 (2013).
Google Scholar 
Rabosky, D. L. et al. BAMM tools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5(7), 701–707 (2014).
Google Scholar 
Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9(2), e89543 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66(4), 477–498 (2017).PubMed 
PubMed Central 

Google Scholar 
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News. 6(1), 7–11 (2006).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021). https://www.R-project.org. Accessed 1 June 2021 (2021).Fairbairn, D. J. Allometry for sexual size dimorphism: Pattern and process in the coevolution of body size in males and females. Annu. Rev. Ecol. Evol. Syst. 28(1), 659–687 (1997).
Google Scholar 
Fairbairn, D. J. Allometry for sexual size dimorphism: Testing two hypotheses for Rensch’s rule in the water strider Aquarius remigis. Am. Nat. 166(S4), S69–S84 (2005).PubMed 

Google Scholar 
Visser, A. G., Beevers, L. & Patidar, S. Complexity in hydroecological modelling: A comparison of stepwise selection and information theory. River Res. Appl. 34(8), 1045–1056 (2018).
Google Scholar 
Calcagno, V. & de Mazancourt, C. glmulti: An R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34(12), 1–29 (2010).
Google Scholar 
Callaghan, S., Guilyardi, E., Steenman-Clark, L. & Morgan, M. The METAFOR project. in Earth System Modelling-Volume 1 (Springer, 2013).Garamszegi, L. Z. & Mundry, R. Multimodel-inference in comparative analyses. In Modern Phylogenetic Comparative Methods and THEIR Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 305–331 (Springer Berlin, 2014).
Google Scholar  More

OverviewWe compiled systematically sampled empirical taxa occurrence across the landscape, and inferentially assembled respective blue and green local food webs by combining these data with a metaweb approach. We quantified key properties of the inferred food webs, then analysed with GIS-derived environmental information how focal food-web metrics change along elevation and among different land-use types in blue versus green systems. Details are given below.Assemble food webs using a metaweb approachWe applied a metaweb method to obtain the composition and structure of multiple local food webs across a landscape spatial scale10. A metaweb is an accumulation of all interactions (here, trophic relationships) among the focal taxa. In this study, we built our metaweb based on known trophic interactions derived from literature and published datasets, which themselves were all based on primary empirical natural history observations. We further complemented or refined the trophic interactions in the metaweb based on expert knowledge of primary observations that are not yet published or only accessible in grey literature. The expert knowledge covers authors and collaborators who have specific natural history knowledge on Central European plants, herbivorous insects, birds, fish, and aquatic invertebrates. Importantly, these observations were all based on empirical observations and/or unpublished data accumulated over considerable field research experience. The respective literature we referred, as well as the metaweb itself with information source of each trophic link (online repository), are provided in Supplementary Methods. By assuming that any interaction in the metaweb will realise if the interacting taxa co-occur, the metaweb approach allows an inference of local food webs if taxa occurrence is known. Such an assumption of fixed diets may lead to an over-estimation of the locally realised trophic links32, as it essentially ignores the possible intraspecific diet variation caused by resource availability61,62, predation risk63, temperature64, ontogenetic shift65, or other genetic and environmental sources66. Therefore, the food webs we inferred systematically using this method capture trophic relationships driven by community composition (species presence versus absence) but not the above-mentioned processes. Nonetheless, since the trophic interactions were based on empirical observations, the fixed diets can be seen as collapsing all intraspecific variations of diet-determining traits (or trait-matching) at species level, within which we know realisable interactions surely exist. This, together with co-occurrence as a pre-requisite, gives realistic boundaries for the potential interaction realisation, which is plausible and non-biased when applying to localised sites. With this approach, we were addressing a systematic comparison among potential local food webs between the blue and green systems and across the selected gradients. For sensitivity analyses considering the potential inaccuracy of the metaweb approach mentioned here, please see further below Food-web metrics and analyses and Supplementary Discussion.We compiled taxa occurrence of four terrestrial and two aquatic broad taxonomic groups (“focal groups”) to assemble local green and blue communities, respectively and independently, based on the well-resolved data available. Each focal group referred to a distinct taxonomic group, and the within- and among-group trophic relationships captured most of the realised interactions. These focal groups were vascular plants, butterflies, grasshoppers, and birds in the green biome, and stream invertebrates and fishes in the blue biome. Notably, with “butterflies” we refer to their larval stage and accordingly their mostly-herbivorous trophic interactions throughout this study. Larval interactions were also the predominant interaction assessed for stream invertebrates (i.e., all interactions of stream invertebrates focussed on their aquatic stage, which is predominant larval). The occurrence data of these focal groups were compiled from highly standardised multiple-year empirical surveys of various authorities, all conducted by trained biologists with fixed protocols (Supplementary Methods). The information across sites should thus be representative and can be up-scaled to the landscape. The occurrences of plants, butterflies, birds, and stream invertebrates were from the Biodiversity Monitoring Switzerland programme (BDM Coordination Office67) managed by the Swiss Federal Office for the Environment (BAFU/FOEN). The occurrences of grasshoppers and fishes were from the Swiss database on faunistic records, info fauna (CSCF), where we further complemented fish occurrence from the data of Progetto Fiumi Project (Eawag). In terms of biological resolution, taxa were resolved to species level in most cases, while the plant and butterfly groups included some multi-species complexes. Insects of the order Ephemeroptera, Plecoptera, and Trichoptera were resolved to species, while all other stream invertebrates were resolved to family level. These were each treated as a node later in our food-web assembly, and referred to as “species”, as the species within such complexes and families mostly share the same trophic role. Spatially, the occurrence datasets adopted coordinates resolved to 1 × 1 km2. The species that were recorded in the same 1 × 1 km2 grid were considered to co-occurred. We took the co-occurring four/two focal groups to form local green/blue local communities, respectively. To obtain better co-occurrence across group-specific data from different sources (e.g., BDM and info fauna), we intentionally coarsened the grasshopper and fish occurrence to 5 × 5 km2 coordinates. This is arguably a biologically acceptable approximation considering the high mobility of these two groups. Also, we only included known stream-borne fishes and dropped pure lake-borne ones to match our stream-only invertebrate occurrence data. Across all 462 green and 465 blue communities we assembled, we covered 2016 plant, 191 butterfly, 109 grasshopper, 155 bird, 248 stream invertebrate, and 78 stream fish species. Unlike the knowledge of plant occurrence in green communities, we did not have detailed occurrence information of the basal components (e.g., primary producers) in blue ones. Therefore, we assumed three mega nodes—namely plant (including all alive or dead plant materials), plankton (including zooplankton, phytoplankton, and other algae), and detritus—as the basal nodes occurring in all blue communities, without further discrimination of identities or biology within. These adding to our focal groups thus cover major taxonomic groups as well as trophic roles from producers to top consumers in both blue and green systems.Taking the above-assembled local communities then drawing trophic links among species (nodes) according to the metaweb yielded the local food webs (illustrated in Fig. 1), representatively covering the whole Swiss area. Notably, although our understanding of trophic interactions indeed encompassed some links across the blue and green taxa (e.g., between piscivorous birds and fishes), our occurrence datasets did not present sufficient spatial grids where these taxa co-occur. We, therefore, did not include such links, nor assembled blue-green interconnected food webs, but the blue and green food webs separately instead (but see Supplementary Discussion). Also, we dropped isolated nodes, i.e., basal nodes without any co-occurring consumer and consumer nodes without any co-occurring resource, from the inferred food webs. These could possibly be passing-by species that were recorded but had no trophic interaction locally, or those that interact with non-focal taxa whose occurrence information was unknown to us. We thus had to exclude them to focus on evidence-supported occurrences and trophic interactions. Nonetheless, across all cases, isolated nodes were rather rare (averaged less than 3% of species occurred in either blue or green communities).Environmental dataWe acquired environmental data across all of Switzerland (42,000 km2) on a 1 × 1 km2 grid basis (i.e., values are averaged over the grid) from GIS databases, with which we mapped environmental conditions to the grids where we assembled food webs. These included: topographical information from DHM25 (Swisstopo, FOT), land-cover information from CLC (EEA), and climate information (averaged over the decade of 2005–2015) from CHELSA. Among environmental variables, elevation and temperature are essentially highly correlated. In this study, we took elevation as the focal environmental gradient throughout, as after accounting for the main effects of elevation on temperature, the residual temperature was not a good predictor of the food-web metrics we looked at (see next section, and Supplementary Table 4). In other words, by analysing along the elevation gradient, we already captured most of the temperature influences on food webs. Based on the labels provided by the GIS databases, we categorised the originally detailed land cover into the five major land-use types that we used in this study, namely forest, scrubland, open space, farmland, and urban area. Forest includes broad-leaved, coniferous, and mixed forests. Scrub includes bushy and herbaceous vegetation, heathlands, and natural grasslands. Open space encompasses sparsely vegetated areas, such as dunes, bare rocks, glaciers and perpetual snow. Farmland include any form of arable, pastures, and agro-forestry areas. Finally, urban area is where artificial constructions and infrastructure prevail. As each grid could contain multiple land-use types, we then defined the dominant land-use type of the grid as any of the five above that occupied more than 50% of the grid’s area. Analyses separated by land-use types with subsetted food webs (land-use-specific analyses) were based on the grids’ dominant land-use type. There were a few grids where the dominant land-use type did not belong to the focal major five, e.g., wetlands or water bodies, and a few where no single land-use type covered more than 50% of the area. Food webs of these grids were still included in the overall analyses but excluded from any land-use-specific analyses (as revealed in the difference in sample sizes between all versus land-use type subsetted food webs in Fig. 2; analyses details below).Food-web metrics and analysesWe quantified five metrics as the measures of the food webs’ structural and ecological properties. For the fundamental structure of the food webs, the number of nodes (“No. Nodes”) reflects the size of the web, meanwhile represents local species richness (though the few isolated nodes were excluded as above-mentioned). Connectance is the proportion of realised links among all potential ones (thus bounded 0–1), reflecting how connected the web is. We also derived holistic topological measures, namely nestedness and modularity. Nestedness of a food web, on the one hand, describes the tendency that some nodes’ narrower diets being subsets of other’s broader diets. We adopted a recently developed UNODF index68 (bounded 0–1) that is especially suitable for quantifying such a feature in our unipartite food webs. On the other hand, modularity (bounded 0–1 with our index) reflects the tendency of a food web to form modules, where nodes are highly connected within but only loosely connected between. Nestedness and modularity are two commonly investigated structures in ecological networks and have been considered relevant to species feeding ecology24 and the stability of the system69. Finally, we measured the level of consumers’ diet niche overlap of the food webs (Horn’s index70, bounded 0–1), which essentially depends on the arrangement of trophic relationships (thus the structure of the webs), and could have strong ecological implications as niche partitioning has been recognised to be a key mechanism that drives species coexistence71,72. We selected these fundamental and holistic properties as they are potentially more relevant to the processes that may have shaped food webs across a landscape scale (e.g., community assembly), in comparison to some node- or link-centric properties. Also, addressing similar metrics as in the literature13,69 would facilitate potential cross-study comparison or validation.To first gain a glimpse of the structure of the blue and green food webs, we performed a principal component analysis (PCA; Fig. 3a) on the inferred food webs (n = 462 and 465 in green and blue, respectively) taking the four structural metrics (number of nodes, connectance, nestedness, and modularity) as the explaining variables of blue versus green system types. We then confirmed that system type, elevation, and land-use type were all important predictors of food-web metrics (whereas the residual temperature after accounting elevation effects was not) by conducting general linear model analyses, taking the former as interactive predictors while the latter response variables (Supplementary Tables 3, 4). To check how elevation influences food-web properties in blue and green systems separately, and how food-web properties depend on each other, we ran a series of piecewise structural equation modelling (SEM)73 analyses on inferred food webs (Fig. 3b, c) whose dominant land use can be defined (n = 421 and 430 in green and blue, respectively). This was also conducted on subsetted webs of each of the five major land-use types (Supplementary Figs. 1 and 2). The SEM relationships were derived from linear mixed model analyses with dominant land-use type as a random effect (assumption tests see Supplementary Figs. 12–17). The SEM structure of direct effects was set according to the literature13,69 and is illustrated in Fig. 3b. In short, this structure tests the dependencies from elevation (an environmental predictor) to food-web metrics (ecological responses). The further dependencies among food-web metrics themselves were assigned with the principle of pointing from relative lower-level properties to higher-level ones. That is, from number of nodes (purely determined by nodes) to connectance (determined by numbers of nodes and links), further to nestedness and modularity (holistic topologies, determined further by the arrangement of links), then to diet niche overlap (ecological functional outcome).Finally, to check and visualise the exact changing patterns of food webs, we applied generalised additive models (GAMs) to reveal the relationships between food-web metrics and the whole-ranged elevation (Figs. 4 and 5), as well as a particular comparison between food webs in forests and farmlands below 1500 m a.s.l. (Supplementary Fig. 5), as this elevation segment covered most of the sites belonged to these two land-use types. We also performed a series of linear models (LMs) and least-squared slope comparisons based on land-use-specific subsets of food webs (Figs. 4 and 5; Supplementary Figs. 3 and 4), to investigate whether food-web elevational patterns are different among land-use types (assumption tests see Supplementary Tables 5 and 6). In the GAMs analyses, specifically, we simulated two sets of randomised webs, i.e., “keep-group” and “fully”, as the null models to compare with the inferred ones74. Both randomisations generated ten independently simulated webs from each input inferred local food web, keeping the same number of nodes and connectance as of the latter. On the one hand, the keep-group randomisation shuffled trophic links from an input local web but only allowed them to realised fulfilling some pre-set within- and among-group relationships. That is, in green communities, birds can feed on all groups, grasshoppers on any groups but birds, while butterflies only on plants; in blue communities, fishes can feed on all groups, while invertebrates on themselves and the basal resources. These pre-set group-wide relationships captured the majority of realistic trophic interactions compiled in our metaweb. On the other hand, the fully randomised webs shuffled trophic links disregarding the biological identity of nodes. The GAMs of nestedness, modularity, and niche overlap illustrated the patterns of these randomised webs (Fig. 5). Comparing among the three types of webs, the patterns exhibited already by fully randomised webs should be those contributed by variations in web size and connectance, while the difference between keep-group and fully randomised webs by the focal-group composition of local communities, and the difference between inferred and keep-group randomised webs further by the realistic species-specific diets. In addition, we also applied the same GAMs and LMs approach to analyse node richness, as well as both realised and potential diet generality (vulnerability for plants) of each focal group (Supplementary Figs. 6–11). These analyses provided hints about the changes in community composition and species diet breadths along elevation and among land-use types, which helped explain the detected food-web responses in mechanistic ways.In addition, to check if our findings were shaped or strongly influenced by the potential inaccuracy of using the metaweb, we repeated the above PCA, SEM, and GAM analyses as a series of sensitivity analyses. We generated food webs based on our locally inferred ones (i.e., the observations) but with random 10% link removal. This procedure mimics the effect of potential intraspecific diet variation (mentioned earlier) so that some trophic interactions in the metaweb do not realise locally. Overall, these analyses with link removal showed that our conclusions are qualitatively and quantitatively highly robust, and only very minorly affected by the such potential inaccuracy of metawebs, which is also in accordance to other food-web studies (see e.g., Pearse & Altermatt 201575). All details and outcomes of these additional analyses are given in Supplementary discussion.All metric quantification and analyses were performed under R version 4.0.3 (R Core Team76). All applied packages and functions were described in Supplementary Methods, while the R scripts performing these tasks can be accessed at the online repository provided.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Intergovernmental Panel on Climate Change (IPCC). The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2022). https://doi.org/10.1017/9781009157964.Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37 (2012).ADS 

Google Scholar 
Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).PubMed 

Google Scholar 
Hall-Spencer, J. M. & Harvey, B. P. Ocean acidification impacts on coastal ecosystem services due to habitat degradation. Emerg. Top. Life Sci. 3, 197–206 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Straub, S. C. et al. Resistance, extinction, and everything in between—The diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).
Google Scholar 
Connell, S. D., Kroeker, K. J., Fabricius, K. E., Kline, D. I. & Russell, B. D. The other ocean acidification problem: CO2 as a resource among competitors for ecosystem dominance. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120442 (2013).
Google Scholar 
Kroeker, K. J., Micheli, F., Gambi, M. C. & Martz, T. R. Divergent ecosystem responses within a benthic marine community to ocean acidification. Proc. Natl. Acad. Sci. 108, 14515–14520 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Harvey, B. P., Kon, K., Agostini, S., Wada, S. & Hall-Spencer, J. M. Ocean acidification locks algal communities in a species-poor early successional stage. Glob. Change Biol. 27, 2174–2187 (2021).ADS 
CAS 

Google Scholar 
Sunday, J. M. et al. Ocean acidification can mediate biodiversity shifts by changing biogenic habitat. Nat. Clim. Change 7, 81–85 (2017).ADS 
CAS 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).
Google Scholar 
Schiel, D. R. & Foster, M. S. The population biology of large brown seaweeds: Ecological consequences of multiphase life histories in dynamic coastal environments. Annu. Rev. Ecol. Evol. Syst. 37, 343–372 (2006).
Google Scholar 
Wernberg, T. & Filbee-Dexter, K. Missing the marine forest for the trees. Mar. Ecol. Prog. Ser. 612, 209–215 (2019).ADS 

Google Scholar 
Cheminée, A. et al. Nursery value of Cystoseira forests for Mediterranean rocky reef fishes. J. Exp. Mar. Biol. Ecol. 442, 70–79 (2013).
Google Scholar 
Smale, D. A., Burrows, M. T., Moore, P., O’Connor, N. & Hawkins, S. J. Threats and knowledge gaps for ecosystem services provided by kelp forests: A northeast Atlantic perspective. Ecol. Evol. 3, 4016–4038 (2013).PubMed 
PubMed Central 

Google Scholar 
Carbajal, P., Gamarra Salazar, A., Moore, P. J. & Pérez-Matus, A. Different kelp species support unique macroinvertebrate assemblages, suggesting the potential community-wide impacts of kelp harvesting along the Humboldt Current System. Aquat. Conserv. Mar. Freshw. Ecosyst. 32, 14–27 (2022).
Google Scholar 
Filbee-Dexter, K. & Wernberg, T. Rise of turfs: A new battlefront for globally declining kelp forests. Bioscience 68, 64–76 (2018).
Google Scholar 
Pessarrodona, A. et al. Homogenization and miniaturization of habitat structure in temperate marine forests. Glob. Change Biol. 27, 5262–5275 (2021).CAS 

Google Scholar 
Orfanidis, S. et al. Effects of natural and anthropogenic stressors on Fucalean brown seaweeds across different spatial scales in the Mediterranean Sea. Front. Mar. Sci. 8, 1330 (2021).
Google Scholar 
Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl. Acad. Sci. 113, 13785–13790 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Capdevila, P. et al. Warming impacts on early life stages increase the vulnerability and delay the population recovery of a long-lived habitat-forming macroalga. J. Ecol. 107, 1129–1140 (2019).
Google Scholar 
Irving, A. D., Balata, D., Colosio, F., Ferrando, G. A. & Airoldi, L. Light, sediment, temperature, and the early life-history of the habitat-forming alga Cystoseira barbata. Mar. Biol. 156, 1223–1231 (2009).
Google Scholar 
Smith, K. E., Moore, P. J., King, N. G. & Smale, D. A. Examining the influence of regional-scale variability in temperature and light availability on the depth distribution of subtidal kelp forests. Limnol. Oceanogr. 67, 314–328 (2022).ADS 

Google Scholar 
Smale, D. A. et al. Climate-driven substitution of foundation species causes breakdown of a facilitation cascade with potential implications for higher trophic levels. J. Ecol. 00, 1–13 (2022).
Google Scholar 
Hollarsmith, J. A., Buschmann, A. H., Camus, C. & Grosholz, E. D. Varying reproductive success under ocean warming and acidification across giant kelp (Macrocystis pyrifera) populations. J. Exp. Mar. Biol. Ecol. 522, 151247 (2020).
Google Scholar 
Verdura, J. et al. Local-scale climatic refugia offer sanctuary for a habitat-forming species during a marine heatwaves. J. Ecol. 109, 1758–1773 (2021).
Google Scholar 
Mariani, S. et al. Past and present of Fucales from shallow and sheltered shores in Catalonia. Reg. Stud. Mar. Sci. 32, 100824 (2019).
Google Scholar 
Smale, D. A. Impacts of ocean warming on kelp forest ecosystems. New Phytol. 225, 1447–1454 (2020).PubMed 

Google Scholar 
Coelho, S. M., Rijstenbil, J. W. & Brown, M. T. Impacts of anthropogenic stresses on the early development stages of seaweeds. J. Aquat. Ecosyst. Stress Recov. 7, 317–333 (2000).CAS 

Google Scholar 
de Caralt, S., Verdura, J., Vergés, A., Ballesteros, E. & Cebrian, E. Differential effects of pollution on adult and recruits of a canopy-forming alga: Implications for population viability under low pollutant levels. Sci. Rep. 10, 17825 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Capdevila, P. et al. Recruitment patterns in the Mediterranean deep-water alga Cystoseira zosteroides. Mar. Biol. 162, 1165–1174 (2015).CAS 

Google Scholar 
Vadas, R. L., Johnson, S. & Norton, T. A. Recruitment and mortality of early post-settlement stages of benthic algae. Br. Phycol. J. 27, 331–351 (1992).
Google Scholar 
Koch, M., Bowes, G., Ross, C. & Zhang, X.-H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132 (2013).ADS 

Google Scholar 
Shih, P. M. et al. Biochemical characterization of predicted Precambrian RuBisCO. Nat. Commun. 7, 10382 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cornwall, C. E. et al. Inorganic carbon physiology underpins macroalgal responses to elevated CO2. Sci. Rep. 7, 46297 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hepburn, C. D. et al. Diversity of carbon use strategies in a kelp forest community: Implications for a high CO2 ocean. Glob. Change Biol. 17, 2488–2497 (2011).ADS 

Google Scholar 
Porzio, L., Buia, M. C. & Hall-Spencer, J. M. Effects of ocean acidification on macroalgal communities. J. Exp. Mar. Biol. Ecol. 400, 278–287 (2011).CAS 

Google Scholar 
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).ADS 

Google Scholar 
Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms: Biological responses to ocean acidification. Ecol. Lett. 13, 1419–1434 (2010).PubMed 

Google Scholar 
Rindi, F. et al. Coralline algae in a changing Mediterranean Sea: How can we predict their future, if we do not know their present?. Front. Mar. Sci. 6, 723 (2019).
Google Scholar 
James, R. K., Hepburn, C. D., Cornwall, C. E., McGraw, C. M. & Hurd, C. L. Growth response of an early successional assemblage of coralline algae and benthic diatoms to ocean acidification. Mar. Biol. 161, 1687–1696 (2014).CAS 

Google Scholar 
Comeau, S. & Cornwall, C. E. Contrasting effects of ocean acidification on coral reef “animal forests” versus seaweed “kelp forests.” In Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Rossi, S. et al.) 1–25 (Springer International Publishing, 2016) https://doi.org/10.1007/978-3-319-17001-5_29-1.Chapter 

Google Scholar 
Airoldi, L. Effects of disturbance, life histories, and overgrowth on coexistence of algal crusts and turfs. Ecology 81, 798–814 (2000).
Google Scholar 
Asnaghi, V. et al. Colonisation processes and the role of coralline algae in rocky shore community dynamics. J. Sea Res. 95, 132–138 (2015).ADS 

Google Scholar 
Bulleri, F., Bertocci, I. & Micheli, F. Interplay of encrusting coralline algae and sea urchins in maintaining alternative habitats. Mar. Ecol. Prog. Ser. 243, 101–109 (2002).ADS 

Google Scholar 
Villas Bôas, A. B. & Figueiredo, M. A. D. O. Are anti-fouling effects in coralline algae species specific?. Braz. J. Oceanogr. 52, 11–18 (2004).
Google Scholar 
Bulleri, F., Benedetti-Cecchi, L., Acunto, S., Cinelli, F. & Hawkins, S. J. The influence of canopy algae on vertical patterns of distribution of low-shore assemblages on rocky coasts in the northwest Mediterranean. J. Exp. Mar. Biol. Ecol. 267, 89–106 (2002).
Google Scholar 
Maggi, E., Bertocci, I., Vaselli, S. & Benedetti-Cecchi, L. Connell and Slatyer’s models of succession in the biodiversity era. Ecology 92, 1399–1406 (2011).CAS 
PubMed 

Google Scholar 
Irving, A. D., Connell, S. D., Johnston, E. L., Pile, A. J. & Gillanders, B. M. The response of encrusting coralline algae to canopy loss: An independent test of predictions on an Antarctic coast. Mar. Biol. 147, 1075–1083 (2005).
Google Scholar 
Irving, A. D., Connell, S. D. & Elsdon, T. S. Effects of kelp canopies on bleaching and photosynthetic activity of encrusting coralline algae. J. Exp. Mar. Biol. Ecol. 310, 1–12 (2004).
Google Scholar 
Melville, A. J. & Connell, S. D. Experimental effects of kelp canopies on subtidal coralline algae. Austral. Ecol. 26, 102–108 (2001).
Google Scholar 
Breitburg, D. L. Residual effects of grazing: Inhibition of competitor recruitment by encrusting coralline algae. Ecology 65, 1136–1143 (1984).
Google Scholar 
Bulleri, F., Bruno, J. F., Silliman, B. R. & Stachowicz, J. J. Facilitation and the niche: Implications for coexistence, range shifts and ecosystem functioning. Funct. Ecol. 30, 70–78 (2016).
Google Scholar 
van der Heide, T., Angelini, C., de Fouw, J. & Eklöf, J. S. Facultative mutualisms: A double-edged sword for foundation species in the face of anthropogenic global change. Ecol. Evol. 11, 29–44 (2021).PubMed 

Google Scholar 
Molinari-Novoa, E. A. & Guiry, E. Reinstatement of the genera Gongolaria Boehmer and Ericaria Stackhouse (Sargassaceae, Phaeophyceae). Notulae Algarum 1–10 (2020).Celis-Plá, P. S. M., Martinez, B., Korbee, N., Hall-Spencer, J. M. & Figueroa, F. L. Ecophysiological responses to elevated CO2 and temperature in Cystoseira tamariscifolia (Phaeophyceae). Clim. Change 142, 67–81 (2017).ADS 

Google Scholar 
Falace, A. et al. Is the South-Mediterranean canopy-forming Ericaria giacconei (= Cystoseira hyblaea) a loser from ocean warming?. Front. Mar. Sci. 8, 1758 (2021).
Google Scholar 
Hernández, C. A., Sangil, C., Fanai, A. & Hernández, J. C. Macroalgal response to a warmer ocean with higher CO2 concentration. Mar. Environ. Res. 136, 99–105 (2018).PubMed 

Google Scholar 
Falace, A., Kaleb, S., Fuente, G. D. L., Asnaghi, V. & Chiantore, M. Ex situ cultivation protocol for Cystoseira amentacea var. stricta (Fucales, Phaeophyceae) from a restoration perspective. PLoS ONE 13, e0193011 (2018).PubMed 
PubMed Central 

Google Scholar 
Bevilacqua, S. et al. Climatic anomalies may create a long-lasting ecological phase shift by altering the reproduction of a foundation species. Ecology 100, 1–4 (2019).
Google Scholar 
Savonitto, G. et al. Addressing reproductive stochasticity and grazing impacts in the restoration of a canopy-forming brown alga by implementing mitigation solutions. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 1611–1623 (2021).
Google Scholar 
Mangialajo, L. et al. Zonation patterns and interspecific relationships of fucoids in microtidal environments. J. Exp. Mar. Biol. Ecol. 412, 72–80 (2012).
Google Scholar 
Verlaque, M., Boudouresque, C.-F. & Perret-Boudouresque, M. Mediterranean seaweeds listed as threatened under the Barcelona Convention: A critical analysis. Sci. Rep. Port-Cros Natl. Park. 33, 179–214 (2019).
Google Scholar 
Benedetti-Cecchi, L. & Cinelli, F. Effects of canopy cover, herbivores and substratum type on patterns of Cystoseira spp. settlement and recruitment in littoral rockpools. Mar. Ecol. Prog. Ser. 90, 183–191 (1992).ADS 

Google Scholar 
Fuente, G. D. L., Chiantore, M., Asnaghi, V., Kaleb, S. & Falace, A. First ex situ outplanting of the habitat-forming seaweed Cystoseira amentacea var. stricta from a restoration perspective. PeerJ 7, e7290 (2019).
Google Scholar 
Orlando-Bonaca, M. et al. First restoration experiment for Gongolaria barbata in Slovenian coastal waters. What can go wrong?. Plants 10, 239 (2021).PubMed 
PubMed Central 

Google Scholar 
Christie, H. et al. Shifts between sugar kelp and turf algae in Norway: Regime shifts or fluctuations between different opportunistic seaweed species?. Front. Mar. Sci. 6, 72 (2019).
Google Scholar 
Orlando-Bonaca, M., Pitacco, V. & Lipej, L. Loss of canopy-forming algal richness and coverage in the northern Adriatic Sea. Ecol. Indic. 125, 107501 (2021).
Google Scholar 
Thibaut, T., Blanfune, A., Boudouresque, C.-F. & Verlaque, M. Decline and local extinction of Fucales in French Riviera: The harbinger of future extinctions?. Mediterr. Mar. Sci. 16, 206–224 (2015).
Google Scholar 
Thibaut, T., Pinedo, S., Torras, X. & Ballesteros, E. Long-term decline of the populations of Fucales (Cystoseira spp. and Sargassum spp.) in the Albères coast (France, North-western Mediterranean). Mar. Pollut. Bull. 50, 1472–1489 (2005).CAS 
PubMed 

Google Scholar 
Leal, P. P. et al. Copper pollution exacerbates the effects of ocean acidification and warming on kelp microscopic early life stages. Sci. Rep. 8, 14763 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Fernández, P. A., Navarro, J. M., Camus, C., Torres, R. & Buschmann, A. H. Effect of environmental history on the habitat-forming kelp Macrocystis pyrifera responses to ocean acidification and warming: A physiological and molecular approach. Sci. Rep. 11, 2510 (2021).PubMed 
PubMed Central 

Google Scholar 
Lind, A. C. & Konar, B. Effects of abiotic stressors on kelp early life-history stages. Algae 32, 223–233 (2017).CAS 

Google Scholar 
Fernández, P. A. et al. Nitrogen sufficiency enhances thermal tolerance in habitat-forming kelp: Implications for acclimation under thermal stress. Sci. Rep. 10, 3186 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Celis-Plá, P. S. M. et al. Macroalgal responses to ocean acidification depend on nutrient and light levels. Front. Mar. Sci. 2, 26 (2015).
Google Scholar 
Mancuso, F. P. et al. Influence of ambient temperature on the photosynthetic activity and phenolic content of the intertidal Cystoseira compressa along the Italian coastline. J. Appl. Phycol. 31, 3069–3076 (2019).CAS 

Google Scholar 
Vergés, A. et al. The tropicalization of temperate marine ecosystems: Climate-mediated changes in herbivory and community phase shifts. Proc. R. Soc. B Biol. Sci. 281, 20140846 (2014).
Google Scholar 
Vergés, A. et al. Tropical rabbitfish and the deforestation of a warming temperate sea. J. Ecol. 102, 1518–1527 (2014).
Google Scholar 
Gaitán-Espitia, J. D. et al. Interactive effects of elevated temperature and pCO2 on early-life-history stages of the giant kelp Macrocystis pyrifera. J. Exp. Mar. Biol. Ecol. 457, 51–58 (2014).
Google Scholar 
Leal, P. P., Hurd, C. L., Fernández, P. A. & Roleda, M. Y. Ocean acidification and kelp development: Reduced pH has no negative effects on meiospore germination and gametophyte development of Macrocystis pyrifera and Undaria pinnatifida. J. Phycol. 53, 557–566 (2017).CAS 
PubMed 

Google Scholar 
Roleda, M. Y., Morris, J. N., McGraw, C. M. & Hurd, C. L. Ocean acidification and seaweed reproduction: Increased CO2 ameliorates the negative effect of lowered pH on meiospore germination in the giant kelp Macrocystis pyrifera (Laminariales, Phaeophyceae). Glob. Change Biol. 18, 854–864 (2011).ADS 

Google Scholar 
Zhang, X. et al. Elevated CO2 concentrations promote growth and photosynthesis of the brown alga Saccharina japonica. J. Appl. Phycol. https://doi.org/10.1007/s10811-020-02108-1 (2020).Article 

Google Scholar 
Falkenberg, L. J., Russell, B. D. & Connell, S. D. Contrasting resource limitations of marine primary producers: Implications for competitive interactions under enriched CO2 and nutrient regimes. Oecologia 172, 575–583 (2013).ADS 
PubMed 

Google Scholar 
Nagelkerken, I., Russell, B. D., Gillanders, B. M. & Connell, S. D. Ocean acidification alters fish populations indirectly through habitat modification. Nat. Clim. Change 6, 89–93 (2016).ADS 
CAS 

Google Scholar 
Connell, S. D. & Russell, B. D. The direct effects of increasing CO2 and temperature on non-calcifying organisms: increasing the potential for phase shifts in kelp forests. Proc. R. Soc. B Biol. Sci. 277, 1409–1415 (2010).
Google Scholar 
Cornwall, C. E., Comeau, S. & McCulloch, M. T. Coralline algae elevate pH at the site of calcification under ocean acidification. Glob. Change Biol. 23, 4245–4256 (2017).ADS 

Google Scholar 
Martin, S. & Gattuso, J.-P. Response of Mediterranean coralline algae to ocean acidification and elevated temperature. Glob. Change Biol. 15, 2089–2100 (2009).ADS 

Google Scholar 
Cornwall, C. E. et al. Diffusion boundary layers ameliorate the negative effects of ocean acidification on the temperate coralline macroalga Arthrocardia corymbosa. PLoS ONE 9, e97235 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gefen-Treves, S. et al. The microbiome associated with the reef builder Neogoniolithon sp. in the eastern Mediterranean. Microorganisms 9, 1374 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Johnson, C. R. & Mann, K. H. The crustose coralline alga, Phymatolithon Foslie, inhibits the overgrowth of seaweeds without relying on herbivores. J. Exp. Mar. Biol. Ecol. 96, 127–146 (1986).
Google Scholar 
Keats, D. W., Knight, M. A. & Pueschel, C. M. Antifouling effects of epithallial shedding in three crustose coralline algae (Rhodophyta, Coralinales) on a coral reef. J. Exp. Mar. Biol. Ecol. 213, 281–293 (1997).
Google Scholar 
Mancuso, F., D’Hondt, S., Willems, A., Airoldi, L. & Clerck, O. Diversity and temporal dynamics of the epiphytic bacterial communities associated with the canopy-forming seaweed Cystoseira compressa (Esper) Gerloff and Nizamuddin. Front. Microbiol. 7, 476 (2016).PubMed 
PubMed Central 

Google Scholar 
Blanfuné, A., Boudouresque, C. F., Verlaque, M. & Thibaut, T. The ups and downs of a canopy-forming seaweed over a span of more than one century. Sci. Rep. 9, 1–10 (2019).
Google Scholar 
Cebrian, E. et al. A roadmap for the restoration of Mediterranean macroalgal forests. Front. Mar. Sci. 8, 1456 (2021).
Google Scholar 
Gianni, F. et al. Conservation and restoration of marine forests in the Mediterranean Sea and the potential role of Marine Protected Areas. Adv. Oceanogr. Limnol. 4, 83–101 (2013).
Google Scholar 
Gorman, D. & Connell, S. D. Recovering subtidal forests in human-dominated landscapes. J. Appl. Ecol. 46, 1258–1265 (2009).
Google Scholar 
Riquet, F. et al. Highly restricted dispersal in habitat-forming seaweed may impede natural recovery of disturbed populations. Sci. Rep. 11, 16792 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Halpern, B. S., McLeod, K. L., Rosenberg, A. A. & Crowder, L. B. Managing for cumulative impacts in ecosystem-based management through ocean zoning. Ocean Coast. Manag. 51, 203–211 (2008).
Google Scholar 
Verdura, J., Sales, M., Ballesteros, E., Cefalì, M. E. & Cebrian, E. Restoration of a canopy-forming alga based on recruitment enhancement: Methods and long-term success assessment. Front. Plant Sci. 9, 1832 (2018).PubMed 
PubMed Central 

Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
CAS 

Google Scholar 
Dickson, A. G., Sabine, C. L. & Christian, J. R. Guide to Best Practices for Ocean CO2 Measurements. https://repository.oceanbestpractices.org/handle/11329/249 (2007).Spencer Davies, P. Short-term growth measurements of corals using an accurate buoyant weighing technique. Mar. Biol. 101, 389–395. https://doi.org/10.1007/BF00428135 (1989).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. ArXiv14065823 Stat (2015).R: The R Project for Statistical Computing. https://www.r-project.org/.Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, 2018).
Google Scholar 
Lenth, R. V. et al. emmeans: Estimated Marginal Means, aka Least-Squares Means (2022). More

Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. (eds). Global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services. https://doi.org/10.5281/zenodo.3831673 (IPBES Secretariat, 2019).UNEP/FAO. The UN Decade on Ecosystem Restoration 2021-2030 “Prevent, halt and reverse the degradation of ecosystems worldwide.” https://www.decadeonrestoration.org/ (2020).Fischer, J., Riechers, M., Loos, J., Martin-Lopez, B. & Temperton, V. M. Making the UN decade on ecosystem restoration a social-ecological endeavour. Trends Ecol. Evol. 36, 1 (2021).
Google Scholar 
Tolvanen, A. & Aronson, J. Ecological reastoration, ecosystem services, and land use: a European perspective. Ecol. Soc. 21, 47 (2016).
Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
PubMed 

Google Scholar 
Temperton, V. M. et al. Step back from the forest and step up to the Bonn Challenge: how a broad ecological perspective can promote successful landscape restoration. Restor. Ecol. 27, 705–719 (2019).
Google Scholar 
Prach, K. & Hobbs, R. J. Spontaneous succession versus technical reclamation in the restoration of disturbed sites. Restor. Ecol. 16, 363–366 (2008).
Google Scholar 
Prach, K., Šebelíková, L., Řehounková, K. & del Moral, R. Possibilities and limitations of passive restoration of heavily disturbed sites. Landsc. Res. 45, 247–253 (2019).
Google Scholar 
Gilby, B. L. et al. Applying systematic conservation planning to improve the allocation of restoration actions at multiple spatial scales. Restor. Ecol. 29, e13403 (2021).
Google Scholar 
Erdős, L. et al. The edge of two worlds: a new review and synthesis on Eurasian forest-steppes. Appl. Veg. Sci. 21, 345–362 (2018).
Google Scholar 
Poschlod, P. & WallisDeVries, M. F. The historical and socioeconomic perspective of calcareous grasslands. Lessons learnt from the distant and recent past. Biol. Conserv. 104, 361–376 (2022).
Google Scholar 
Wesche, K. et al. The Palaearctic steppe biome: a new synthesis. Biodivers. Conserv. 25, 2197–2231 (2016).
Google Scholar 
Butaye, J., Dries, A. & Honnay, O. Conservation and restoration of calcareous grasslands: a concise review of the effects of fragmentation and management on plant species. Biotechnol. Agron. Soc. Environ. 9, 111–118 (2005).
Google Scholar 
Strassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3, 62–70 (2019).PubMed 

Google Scholar 
Knight, M. L. & Overbeck, G. E. How much does is cost to restore a grassland? Restor. Ecol. 29, e13463 (2021).
Google Scholar 
Albert, Á.-J. et al. Trait-based analysis of spontaneous grassland recovery in sandy old-fields. Appl. Veg. Sci. 17, 214–224 (2014).
Google Scholar 
Crouzeilles, R. et al. Achieving cost-effective landscape-scale forest restoration through targeted natural regeneration. Conserv. Lett. 13, e12709 (2020).
Google Scholar 
Seregélyes, T., Molnár, Z. S., Csomós, Á. & Bölöni, J. Regeneration potential of the Hungarian (semi)-natural habitats I. Concepts and basic data of the MÉTA database. Acta Bot. Hung. 50, 229–248 (2008).
Google Scholar 
Käyhkö, N. & Skånes, H. Change trajectories and key biotopes – Assessing landscape dynamics and sustainability. Landsc. Urban Plan 75, 300–321 (2006).
Google Scholar 
Käyhkö, N. & Skånes, H. Retrospective land cover/land use change trajectories as drivers behind the local distribution and abundance patterns of oaks in south-western Finland. Landsc. Urban Plan 88, 12–22 (2008).
Google Scholar 
Swetnam, R. D. Rural land use in England and Wales between 1930 and 1998: Mapping trajectories of change with a high resolution spatio-temporal dataset. Landsc. Urban Plan 81, 91–103 (2007).
Google Scholar 
Ruiz, J. & Domon, G. 2009. Analysis of landscape pattern change trajectories within areas of intensive agricultural use: case study in a watershed of southern Québec, Canada. Landsc. Ecol. 24, 419–432 (2009).
Google Scholar 
Eremiášová, R. & Skokanová, H. Land use changes (recorded in old maps) and delimitation of the most stable areas from the perspective of land use in the Kašperské Hory region. Landsc. Ecol. 88, 20–34 (2009).
Google Scholar 
Frondoni, R. B. M. & Capotorti, G. A landscape analysis of land cover change in the Municipality of Rome (Italy): spatio-temporal characteristics and ecological implications of land cover transitions from 1954 to 2001. Landsc. Urban Plan 100, 117–128 (2011).
Google Scholar 
Biró, M., Szitár, K., Horváth, F., Bagi, I. & Molnár, Z. S. Detection of long-term landscape changes and trajectories in a Pannonian sand region: comparing land-cover and habitat-based approaches at two spatial scales. Community Ecol. 14, 219–230 (2013).
Google Scholar 
Molnár, Z. S, Biró, M., Bartha, S. & Fekete, G. in Eurasian Steppes. Ecological Problems and Livelihoods in a Changing World (eds Werger, M. J. A. & van Staalduinen, M. A.) Ch. 7 (Springer, 2012).Mezősi, G. in The Physical Geography of Hungary. Geography of the Physical Environment (ed. Mezősi, G) Ch. 11 (Springer, 2017).Biró, M., Bölöni, J. & Molnár, Z. Use of long-term data to evaluate loss and endangerment status of Natura 2000 habitats and effects of protected areas. Conserv. Biol. 32, 660–671 (2018).PubMed 

Google Scholar 
Pe’er, G. et al. Action needed for the EU Common Agricultural Policy to address sustainability challenges. People Nat. 2, 305–316 (2020).
Google Scholar 
Benton, T. G., Bieg, C., Harwatt, H., Pudasaini, R. & Wellesley, L. Food system impacts on biodiversity loss. Three levers for food system transformation in support of nature. Chatham House, the Royal Institute of International Affairs. ISBN: 978 1 78413 433 4 (2021).Kuemmerle, T. et al. Cross-border comparison of post-socialist farmland abandonment in the Carpathians. Ecosystems 11, 614 (2008).
Google Scholar 
Feranec, J. et al. Inventory of major landscape changes in the Czech Republic, Hungary, Romania and Slovak Republic 1970s – 1990s. Int. J. Appl. Earth Observ. Geoinf. 2, 129–139 (2000).
Google Scholar 
Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95, 1511–1534 (2020).PubMed 

Google Scholar 
Csákvári, E. et al. Conservation biology research priorities for 2050: a Central-Eastern European perspective. Biol. Conserv. 264, 109396 (2021).
Google Scholar 
Molnár, Z. S., Bölöni, J. & Horváth, F. Threatening factors encountered: actual endangerment of the Hungarian (semi-)natural habitats. Acta Bot. Hung. 50, 199–217 (2008).
Google Scholar 
Király, G., Molnár, ZS., Bölöni, J., Csiky, J. & Vojtkó, A. Magyarország földrajzi kistájainak növényzete (in Hungarian). MTA ÖBKI, Vácrátót, 248 (2008).Botta-Dukát, Z. Invasion of alien species to Hungarian (semi-)natural habitats. Acta Bot. Hung. 50, 219–227 (2008).
Google Scholar 
Csákvári, E., Bede-Fazekas, Á., Horváth, F., Molnár, Z. & Halassy, M. Do environmental predictors affect the regeneration capacity of sandy habitats? A country-wide survey from Hungary. Glob. Ecol. Conserv. 27, e01547 (2021).
Google Scholar 
Somodi, I. et al. Implementation and application of multiple potential natural vegetation models–a case study of Hungary. J. Veg. Sci. 28, 1260–1269 (2017).
Google Scholar 
Bölöni, J., Molnár, Zs. & Kun, A. (Eds.), Magyarország élőhelyei. A hazai vegetációtípusok leírása és határozója (in Hungarian) (Habitats – Description and Identification of Vegetation Types of Hungary, ÁNÉR 2011). MTA Ökológiai és Botanikai Kutatóintézet, Vácrátót, pp. 439. ISBN 978-963-8391-51 (2011).Choi, Y. D. et al. Ecological restoration for future sustainability in a changing environment. Ecoscience 15, 53–64 (2008).CAS 

Google Scholar 
Valkó, O. et al. Abandonment of croplands: problem or chance for grassland restoration? Case studies from Hungary. Ecosyst. Health Sustain. 2, e01208 (2016).
Google Scholar 
Csecserits, A. et al. Tree plantations are hot-spots of plant invasion in a landscape with heterogeneous land-use. Agric. Ecosyst. Environ. 226, 88–98 (2016).
Google Scholar 
Pyšek P. & Richardson D. M. in Biological Invasions. Ecological Studies (Analysis and Synthesis) (ed. Nentwig, W) Ch. 7 (Springer, 2008).Reis, B. P. et al. The long-term effect of initial restoration intervention, landscape composition, and time on the progress of Pannonic sand grassland restoration. Landsc. Ecol. Eng. https://doi.org/10.1007/s11355-022-00512-y (2022).Article 

Google Scholar 
Ruprecht, E. Successfully recovered grassland: a promising example from Romanian old‐fields. Restor. Ecol. 14, 473–480 (2006).
Google Scholar 
Török, P. et al. Restoring grassland biodiversity: sowing low-diversity seed mixtures can lead to rapid favourable changes. Biol. Conserv. 143, 3 (2010).
Google Scholar 
Török, P., Vida, E., Deák, B., Lengyel, S. & Tóthmérész, B. Grassland restoration on former croplands in Europe: an assessment of applicability of techniques and costs. Biodivers. Conserv. 20, 2311–2332 (2011).
Google Scholar 
Prach, K., Jongepierová, I., Řehounková, K. & Fajmon, K. Restoration of grasslands on ex-arable land using regional and commercial seed mixtures and spontaneous succession: successional trajectories and changes in species richness. Agric. Ecosyst. Environ. 182, 131–136 (2014).
Google Scholar 
Prach, K., Chenoweth, J. & del Moral, R. Spontaneous and assisted restoration of vegetation on the bottom of a former water reservoir, the Elwha River, Olympic National Park, WA, USA. Restor. Ecol. 27, 592–599 (2019).
Google Scholar 
Török, P., Helm, A., Kiehl, K., Buisson, E. & Valkó, O. Beyond the species pool: modification of species dispersal, establishment, and assembly by habitat restoration. Restor. Ecol. 26, S65–S72 (2018).
Google Scholar 
Török, P., Bullock James M, J. M., Jiménez‐Alfaro, B. & Sonkoly, J. The importance of dispersal and species establishment in vegetation dynamics and resilience. J. Veg. Sci. 31, 935–942 (2020).
Google Scholar 
Saura, S., Bodin, Ö. & Fortin, M. J. Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. J. Appl. Ecol. 51, 171–182 (2014).
Google Scholar 
Kirmer, A., Baasch, A. & Tischew, S. Sowing of low and high diversity seed mixtures in ecological restoration of surface mined-land. Appl. Veg. Sci. 15, 198–207 (2012).
Google Scholar 
Llumiquinga, Y. B. et al. Long-term results of initial seeding, mowing and carbon amendment on the restoration of Pannonian sand grassland on old fields. Tuxenia 41, 361–379 (2021).
Google Scholar 
Edwards, A. R. et al. Hay strewing, brush harvesting of seed and soil disturbance as tools for the enhancement of botanical diversity in grasslands. Biol. Conserv. 134, 372–382 (2007).
Google Scholar 
Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. BioScience 65, 1011–1018 (2015).
Google Scholar 
Bussion, E., Archibald, S., Fidelis, A. & Sudling, K. N. Ancient grasslands guide ambitious goals in grassland restoration. Science 377, 594–598 (2022).
Google Scholar 
Csecserits, A. et al. Regeneration of sandy old-field in the forest steppe region of Hungary. Plant Biosyst. 145, 715–726 (2011).
Google Scholar 
Szitár, K. et al. Az országos zöldinfrastruktúrahálózat kijelölésének módszertana többszempontú állapotértékelés alapján. (in Hungarian) (Methodology for designating the national green infrastructure network based on multi-criteria assessment). Term.észetvédelmi K.özlemények 27, 145–157 (2021).
Google Scholar 
Szalai, S., Szinell, C. S. & Zoboki, J. Early warning systems for drought preparedness and drought management. In Proc. Expert Group Meeting (eds Wilhite, D. A., Sivakumar, M. V. K. & Wood, D. A.) (World Meteorological Organization, 2000).Szilassi, P. et al. The link between landscape pattern and vegetation naturalness on a regional scale. Ecol. Indic. 81, 252–259 (2017).
Google Scholar 
Demeter, I., Makádi, M., Végső, B., Aranyos, T. J. & Posta, K. The effect of recycled plant residues on the microbial activity of typical sandy soil of the Nyírség region. In Abstract Book, 18th Alps-Adria Scientific Workshop https://doi.org/10.34116/NTI.2019.AA.13 (2019).Borhidi, A. Social behaviour types, the naturalness and relative ecological indicator values of the higher plants in the Hungarian Flora. Acta Bot. Hung. 39, 97–181 (1995).
Google Scholar 
Horváth, F. et al. Flóra adatbázis 1.2. Taxonlista és attribútum-állomány (Flora database 1.2. Taxon list and attribute file). MTA Ökológiai és Botanikai Kutatóintézet, Vácrátót, ISBN 9638391197 (1995).Király, G. Új Magyar Füvészkönyv. Magyarország hajtásos növényei (New Herbal Guide to the Hungarian Flora). Aggteleki Nemzeti Park Igazgatóság, Jósvafő, Hungary, 628p. (2009).Máté, A. 6260 pannon homoki gyepek. In: Haraszthy, L. (Eds.), Natura 2000 fajok és élőhelyek Magyarországon. (in Hungarian) Pro Vértes Közalapítvány, Csákvár, Hungary, pp. 817-823. ISBN: 9789630888530 (2014).Molnár, Z. S. et al. Magyarországi Élőhelytérképezési Adatbázisának (MÉTA) térképezési módszertani és Adatlapkitöltési Útmutatója (AL-KÚ) 3.3 Kézirat, (Guide on the methods of MÉTA and on the completion of the MÉTA datasheets). MTA ÖBKI, Vácrátót, Hungary, 54 pp. (2003).Molnár, Z. S. et al. A grid-based, satellite-image supported multi-attributed vegetation mapping method (MÉTA). Folia Geobotanica 42, 225–247 (2007).
Google Scholar 
Horváth, F. et al. Fact sheet of the MÉTA database 1.2. Acta Bot. Hung. 50, 11–34 (2008).
Google Scholar 
Bölöni, J., Kun, A. & Molnár, Z. S. Élőhely-ismereti Útmutató (Habitat guide). MTA ÖBKI, Vácrátót, Hungary (2003).European Environment Agency. Corine Land Cover 2006 seamless vector data (Version 17). https://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-3 (2013).European Environment Agency. CLC2006 Technical Guidelines. Report No. 17/2007, ISNN 1725-2237 (2017).ESRI ArcGIS Vers. 10.2. (Environmental System Research Institute Inc., 2013).Pásztor, L. et al. Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools. Hungarian Geogr. Bull. 64, 49–64 (2015).
Google Scholar 
Hijmans, R. J. raster: geographic data analysis and modeling. R package version 2.4-20, https://cran.r-project.org/web/packages/raster/index.html (2015).R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/ (2019).USGS. Shuttle Radar Topography Mission, 3 Arc Second scene SRTM_u03_n045e016-SRTM_ff03_n048e022, Unfilled Unfinished 2.0, Global Land Cover Facility, February 2000. College Park, MD, USA, University of Maryland (2004).SRTM. SRTM Mission Summary. URL: lta.cr.usgs.gov/srtm/mission_summary (2015). [Last accesed: 2016.04.22.].Szalai, S. et al. Climate of the Greater Carpathian Region. Final Technical Report. http://www.carpatclim-eu.org/ (2013).Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22, https://CRAN.R-project.org/doc/Rnews/ (2002).
Google Scholar 
Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees (CRC Press, 1984).Sarica, A., Cerasa, A. & Quattrone, A. Random Forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front. Aging Neurosci. 6, 329 (2017).
Google Scholar 
Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph Stat. 15, 651–674 (2006).
Google Scholar 
Pebesma, E. Simple features for R: standardized support for spatial vector. Data. R. J. 10, 439–446 (2018).
Google Scholar 
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd ed. (Springer, 2013).Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).
Google Scholar 
Bölöni, J., Molnár, Z. S., Horváth, F. & Illyés, E. Naturalness-based habitat quality of the Hungarian (semi-)natural habitats. Acta Bot. Hung. 50, 149–159 (2008).
Google Scholar 
Czúcz, B., Molnár, Z. S., Horváth, F. & Botta-Dukát, Z. The natural capital index of Hungary. Acta Bot. Hung. 50, 161–177 (2008).
Google Scholar  More

Raschke, K., Monteith, J. L. & Weatherley, P. E. How stomata resolve the dilemma of opposing priorities. Phil. Trans. R. Soc. Lond. B 273, 551–560 (1976).Article 
CAS 

Google Scholar 
Brodribb, T. J. & Cochard, H. Hydraulic failure defines the recovery and point of death in water-stressed conifers. Plant Physiol. 149, 575–584 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brodribb, T. J., Bowman, D. J. M. S., Nichols, S., Delzon, S. & Burlett, R. Xylem function and growth rate interact to determine recovery rates after exposure to extreme water deficit. New Phytol. 188, 533–542 (2010).Article 
PubMed 

Google Scholar 
Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).Article 
CAS 
PubMed 

Google Scholar 
Keeling, R. F. et al. Atmospheric evidence for a global secular increase in carbon isotopic discrimination of land photosynthesis. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1619240114 (2017).Guerrieri, R. et al. Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency. Proc. Natl Acad. Sci. USA 116, 16909–16914 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).Article 
PubMed 

Google Scholar 
McDowell, N. G. & Allen, C. D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Change 5, 669–672 (2015).Article 

Google Scholar 
Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).Article 
CAS 
PubMed 

Google Scholar 
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).Article 
CAS 
PubMed 

Google Scholar 
Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).Article 
CAS 

Google Scholar 
Damour, G., Simonneau, T., Cochard, H. & Urban, L. An overview of models of stomatal conductance at the leaf level. Plant Cell Environ. 33, 1419–1438 (2010).PubMed 

Google Scholar 
Wang, Y., Sperry, J. S., Anderegg, W. R. L., Venturas, M. D. & Trugman, A. T. A theoretical and empirical assessment of stomatal optimization modeling. New Phytol. 227, 311–325 (2020).Article 
CAS 
PubMed 

Google Scholar 
Anderegg, W. R. L. et al. Woody plants optimise stomatal behaviour relative to hydraulic risk. Ecol. Lett. 21, 968–977 (2018).Article 
PubMed 

Google Scholar 
Venturas, M. D. et al. A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought. New Phytol. 220, 836–850 (2018).Article 
CAS 
PubMed 

Google Scholar 
Sabot, M. E. B. et al. Plant profit maximization improves predictions of European forest responses to drought. New Phytol. 226, 1638–1655 (2020).Article 
PubMed 

Google Scholar 
Eller, C. B. et al. Stomatal optimization based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate. New Phytol. 226, 1622–1637 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hickler, T., Prentice, I. C., Smith, B., Sykes, M. T. & Zaehle, S. Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model. Glob. Ecol. Biogeogr. 15, 567–577 (2006).Article 

Google Scholar 
Bonan, G. B., Williams, M., Fisher, R. A. & Oleson, K. W. Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum. Geosci. Model Dev. 7, 2193–2222 (2014).Article 

Google Scholar 
Christoffersen, B. O. et al. Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro). Geosci. Model Dev. 9, 4227–4255 (2016).Article 

Google Scholar 
Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, Version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).Article 

Google Scholar 
Cowan, I. R. & Farquhar, G. D. Stomatal function in relation to leaf metabolism and environment. Symp. Soc. Exp. Biol. 31, 471–505 (1977).CAS 
PubMed 

Google Scholar 
Wolf, A., Anderegg, W. R. L. & Pacala, S. W. Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proc. Natl Acad. Sci. USA 113, E7222–E7230 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sperry, J. S. et al. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. Plant Cell Environ. 40, 816–830 (2017).Article 
CAS 
PubMed 

Google Scholar 
Bartlett, M. K., Detto, M. & Pacala, S. W. Predicting shifts in the functional composition of tropical forests under increased drought and CO2 from trade-offs among plant hydraulic traits. Ecol. Lett. 22, 67–77 (2019).Article 
PubMed 

Google Scholar 
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).Article 
PubMed 

Google Scholar 
Wright, I. J., Reich, P. B. & Westoby, M. Least‐cost input mixtures of water and nitrogen for photosynthesis. Am. Nat.161, 98–111 (2003).Article 
PubMed 

Google Scholar 
Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).Article 
CAS 
PubMed 

Google Scholar 
Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e38345 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. New Phytol. 218, 1430–1449 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).Article 

Google Scholar 
Lavergne, A. et al. Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle. New Phytol. 225, 2484–2497 (2020).Article 
CAS 
PubMed 

Google Scholar 
Sperry, J. S. & Love, D. M. What plant hydraulics can tell us about responses to climate-change droughts. New Phytol. 207, 14–27 (2015).Article 
CAS 
PubMed 

Google Scholar 
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).Article 
CAS 
PubMed 

Google Scholar 
Chen, J.-L., Reynolds, J. F., Harley, P. C. & Tenhunen, J. D. Coordination theory of leaf nitrogen distribution in a canopy. Oecologia 93, 63–69 (1993).Article 
PubMed 

Google Scholar 
Buckley, T. N., John, G. P., Scoffoni, C. & Sack, L. How does leaf anatomy influence water transport outside the xylem? Plant Physiol. 168, 1616–1635 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scoffoni, C. et al. Outside-xylem vulnerability, not xylem embolism, controls leaf hydraulic decline during dehydration. Plant Physiol. 173, 1197–1210 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Carminati, A. & Javaux, M. Soil rather than xylem vulnerability controls stomatal response to drought. Trends Plant Sci. 25, 868–880 (2020).Article 
CAS 
PubMed 

Google Scholar 
Klein, T. et al. Xylem embolism refilling and resilience against drought-induced mortality in woody plants: processes and trade-offs. Ecol. Res. 33, 839–855 (2018).CAS 

Google Scholar 
Rodriguez-Dominguez, C. M. & Brodribb, T. J. Declining root water transport drives stomatal closure in olive under moderate water stress. New Phytol. 225, 126–134 (2020).Article 
CAS 
PubMed 

Google Scholar 
Sack, L. & Holbrook, N. M. Leaf hydraulics. Annu. Rev. Plant Biol. 57, 361–381 (2006).Article 
CAS 
PubMed 

Google Scholar 
Bourbia, I., Pritzkow, C. & Brodribb, T. J. Herb and conifer roots show similar high sensitivity to water deficit. Plant Physiol. 186, 1908–1918 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhou, S., Duursma, R. A., Medlyn, B. E., Kelly, J. W. G. & Prentice, I. C. How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress. Agric. Meteorol. 182–183, 204–214 (2013).Article 

Google Scholar 
Kanechi, M., Uchida, N., Yasuda, T. & Yamaguchi, T. Non-stomatal inhibition associated with inactivation of rubisco in dehydrated coffee leaves under unshaded and shaded conditions. Plant Cell Physiol. 37, 455–460 (1996).Article 
CAS 

Google Scholar 
Salmon, Y. et al. Leaf carbon and water status control stomatal and nonstomatal limitations of photosynthesis in trees. New Phytol. 226, 690–703 (2020).Article 
CAS 
PubMed 

Google Scholar 
Dong, N. et al. Components of leaf-trait variation along environmental gradients. New Phytol. 228, 82–94 (2020).Article 
CAS 
PubMed 

Google Scholar 
Martínez‐Vilalta, J., Poyatos, R., Aguadé, D., Retana, J. & Mencuccini, M. A new look at water transport regulation in plants. New Phytol. 204, 105–115 (2014).Article 
PubMed 

Google Scholar 
Bartlett, M. K., Klein, T., Jansen, S., Choat, B. & Sack, L. The correlations and sequence of plant stomatal, hydraulic, and wilting responses to drought. Proc. Natl Acad. Sci. USA 113, 13098–13103 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brodribb, T. J., Holbrook, N. M., Edwards, E. J. & Gutiérrez, M. V. Relations between stomatal closure, leaf turgor and xylem vulnerability in eight tropical dry forest trees. Plant Cell Environ. 26, 443–450 (2003).Article 

Google Scholar 
Martin‐StPaul, N., Delzon, S. & Cochard, H. Plant resistance to drought depends on timely stomatal closure. Ecol. Lett. 20, 1437–1447 (2017).Article 
PubMed 

Google Scholar 
Skelton, R. P. et al. Low vulnerability to xylem embolism in leaves and stems of North American oaks. Plant Physiol. 177, 1066–1077 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).Article 
CAS 
PubMed 

Google Scholar 
Dewar, R. et al. New insights into the covariation of stomatal, mesophyll and hydraulic conductances from optimization models incorporating nonstomatal limitations to photosynthesis. New Phytol. 217, 571–585 (2018).Article 
CAS 
PubMed 

Google Scholar 
Hölttä, T., Lintunen, A., Chan, T., Mäkelä, A. & Nikinmaa, E. A steady-state stomatal model of balanced leaf gas exchange, hydraulics and maximal source–sink flux. Tree Physiol. 37, 851–868 (2017).Article 
PubMed 

Google Scholar 
Pivovaroff, A. L., Sack, L. & Santiago, L. S. Coordination of stem and leaf hydraulic conductance in southern California shrubs: a test of the hydraulic segmentation hypothesis. New Phytol. 203, 842–850 (2014).Article 
PubMed 

Google Scholar 
Boyer, J. S., Wong, S. C. & Farquhar, G. D. CO2 and water vapor exchange across leaf cuticle (epidermis) at various water potentials. Plant Physiol. 114, 185–191 (1997).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Deans, R. M., Brodribb, T. J., Busch, F. A. & Farquhar, G. D. Optimization can provide the fundamental link between leaf photosynthesis, gas exchange and water relations. Nat. Plants 6, 1116–1125 (2020).Article 
CAS 
PubMed 

Google Scholar 
Zhou, S.-X., Medlyn, B. E. & Prentice, I. C. Long-term water stress leads to acclimation of drought sensitivity of photosynthetic capacity in xeric but not riparian Eucalyptus species. Ann. Bot. 117, 133–144 (2016).Article 
CAS 
PubMed 

Google Scholar 
Rungwattana, K. et al. Trait evolution in tropical rubber (Hevea brasiliensis) trees is related to dry season intensity. Funct. Ecol. 32, 2638–2651 (2018).Article 

Google Scholar 
Dybzinski, R., Farrior, C., Wolf, A., Reich, P. B. & Pacala, S. W. Evolutionarily stable strategy carbon allocation to foliage, wood, and fine roots in trees competing for light and nitrogen: an analytically tractable, individual-based model and quantitative comparisons to data. Am. Nat. 177, 153–166 (2011).Article 
PubMed 

Google Scholar 
Hikosaka, K. & Anten, N. P. R. An evolutionary game of leaf dynamics and its consequences for canopy structure. Funct. Ecol. 26, 1024–1032 (2012).Article 

Google Scholar 
Franklin, O. et al. Organizing principles for vegetation dynamics. Nat. Plants 6, 444–453 (2020).Article 
PubMed 

Google Scholar 
Le Quéré, C. et al. Global carbon budget 2017. Earth Syst. Sci. Data 10, 405–448 (2018).Article 

Google Scholar 
Jasechko, S. et al. Terrestrial water fluxes dominated by transpiration. Nature 496, 347–350 (2013).Article 
CAS 
PubMed 

Google Scholar 
Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M. & Guan, K. Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests. New Phytol. 212, 80–95 (2016).Article 
PubMed 

Google Scholar 
Wang, H. et al. Acclimation of leaf respiration consistent with optimal photosynthetic capacity. Glob. Change Biol. 26, 2573–2583 (2020).Article 

Google Scholar 
Papastefanou, P. et al. A dynamic model for strategies and dynamics of plant water-potential regulation under drought conditions. Front. Plant Sci. 11, 373 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Grieu, P., Guehl, J. M. & Aussenac, G. The effects of soil and atmospheric drought on photosynthesis and stomatal control of gas exchange in three coniferous species. Physiol. Plant. 73, 97–104 (1988).Article 

Google Scholar 
Liu, F., Andersen, M. N., Jacobsen, S.-E. & Jensen, C. R. Stomatal control and water use efficiency of soybean (Glycine max L. Merr.) during progressive soil drying. Environ. Exp. Bot. 54, 33–40 (2005).Article 
CAS 

Google Scholar 
Tezara, W., Driscoll, S. & Lawlor, D. W. Partitioning of photosynthetic electron flow between CO2 assimilation and O2 reduction in sunflower plants under water deficit. Photosynthetica 46, 127–134 (2008).Article 
CAS 

Google Scholar 
Liu, C.-C. et al. Influence of drought intensity on the response of six woody karst species subjected to successive cycles of drought and rewatering. Physiol. Plant. 139, 39–54 (2010).Article 
CAS 
PubMed 

Google Scholar 
Posch, S. & Bennett, L. T. Photosynthesis, photochemistry and antioxidative defence in response to two drought severities and with re-watering in Allocasuarina luehmannii. Plant Biol. 11, 83–93 (2009).Article 
CAS 
PubMed 

Google Scholar 
Jiang, M., Kelly, J. W. G., Atwell, B. J., Tissue, D. T. & Medlyn, B. E. Drought by CO2 interactions in trees: a test of the water savings mechanism. New Phytol. 230, 1421–1434 (2021).Article 
CAS 
PubMed 

Google Scholar 
Ennajeh, M., Tounekti, T., Vadel, A. M., Khemira, H. & Cochard, H. Water relations and drought-induced embolism in olive (Olea europaea) varieties ‘Meski’ and ‘Chemlali’ during severe drought. Tree Physiol. 28, 971–976 (2008).Article 
PubMed 

Google Scholar 
Peguero-Pina, J. J., Sancho-Knapik, D., Morales, F., Flexas, J. & Gil-Pelegrín, E. Differential photosynthetic performance and photoprotection mechanisms of three Mediterranean evergreen oaks under severe drought stress. Funct. Plant Biol. 36, 453–462 (2009).Article 
PubMed 

Google Scholar 
Liu, C.-C. et al. Exploitation of patchy soil water resources by the clonal vine Ficus tikoua in karst habitats of southwestern China. Acta Physiol. Plant. 33, 93–102 (2011).Article 

Google Scholar 
Leuning, R. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant Cell Environ. 18, 339–355 (1995).Article 
CAS 

Google Scholar 
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).Article 

Google Scholar 
Brodribb, T. et al. Linking xylem network failure with leaf tissue death. New Phytol. 232, 68–79 (2021).Article 
PubMed 

Google Scholar 
Klein, T. The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Funct. Ecol. 28, 1313–1320 (2014).Article 

Google Scholar  More

Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 1513–1766 (Cambridge Univ. Press, 2021).
Google Scholar 
Hesketh, A. V. & Harley, C. D. G. Glob. Change Biol. https://doi.org/10.1111/gcb.16390 (2022).Article 

Google Scholar 
Jørgensen, L. B., Ørsted, M., Malte, H., Wang, T. & Overgaard, J. Nature https://doi.org/10.1038/s41586-022-05334-4 (2022).Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Nature Clim. Change 2, 686–690 (2012).Article 

Google Scholar 
Cossins, A. R. & Bowler, K. Temperature Biology of Animals (Chapman & Hall, 1987).
Google Scholar 
Dell, A. I., Pawar, S. & Savage, V. M. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).Article 
PubMed 

Google Scholar 
Dillon, M. E. et al. Integr. Comp. Biol. 56, 14–30 (2016).Article 
PubMed 

Google Scholar 
Stillman, J. H. Physiology 34, 86–100 (2019).Article 
PubMed 

Google Scholar 
MacMillan, H. A. J. Exp. Biol. 222, jeb191593 (2019).Article 
PubMed 

Google Scholar 
Kingsolver, J. G. & Umbanhowar, J. J. Exp. Biol. 221, jeb167858 (2018).Article 
PubMed 

Google Scholar  More

Shomurodov, K. F. & Adilov, B. A. Current state of the flora of Vozrozhdeniya Island (Uzbekistan). Arid Ecosyst. 9, 97–103 (2019).
Google Scholar 
Adilov, B. et al. Transformation of vegetative cover on the Ustyurt Plateau of Central Asia as a consequence of the Aral Sea shrinkage. J. Arid Land 13, 71–87 (2020).
Google Scholar 
Kuz’mina, Z. V. & Treshkin, S. E. Soil salinization and dynamics of Tugai vegetation in the southeastern Caspian Sea region and in the Aral Sea coastal region. Eurasian Soil Sci. 30, 642–649 (1997).
Google Scholar 
Kuz’mina, Z. V., Shinkarenko, S. S. & Solodovnikov, D. A. Main tendencies in the dynamics of floodplain ecosystems and landscapes of the lower reaches of the Syr Darya river under modern changing conditions. Arid Ecosyst. 9, 226–236 (2019).
Google Scholar 
Dimeyeva, L. A. Phytogeography of the northeastern coast of the Caspian Sea: Native flora and recent colonizations. J. Arid Land 5, 439–451 (2013).
Google Scholar 
Goryaev, I. A. & Korablev, A. P. Halophytic vegetation in the west caspian lowland. Contemp. Probl. Ecol. 13, 514–521 (2020).
Google Scholar 
Novikova, N. M., Volkova, N. A., Ulanova, S. S. & Chemidov, M. M. Change in vegetation on meliorated solonetcic soils of the Peri-Yergenian plain over 10 years (Republic of Kalmykia). Arid Ecosyst. 10, 194–202 (2020).
Google Scholar 
Ravanbakhsh, M., Amini, T. & Hosseini, S. M. N. Plant species diversity among ecological species groups in the Caspian Sea coastal sand dune; Case study: Guilan Province, North of Iran. Biodiversitas 16, 16–21 (2015).
Google Scholar 
Yan, S., Mu, G., Xu, Y. & Zhao, Z. Quarternary environmental evolution of the Lop Nur region, China. Dili Xuebao/Acta Geogr. Sin. 53, 332–340 (1998).
Google Scholar 
Hao, H., Ferguson, D. K., Chang, H. & Li, C. S. Vegetation and climate of the Lop Nur area, China, during the past 7 million years. Clim. Change 113, 323–338 (2012).ADS 

Google Scholar 
Li, C. et al. Growth and sustainability of Suaeda salsa in the Lop Nur, China. J. Arid Land 10, 429–440 (2018).
Google Scholar 
Barrett, G. Vegetation communities on the shores of a salt lake in semi-arid Western Australia. J. Arid Environ. 67, 77–89 (2006).ADS 

Google Scholar 
Neffar, S., Chenchouni, H. & Si Bachir, A. Floristic composition and analysis of spontaneous vegetation of Sabkha Djendli in north-east Algeria. Plant Biosyst. 150, 396–403 (2016).
Google Scholar 
Yanina, T. A. The Ponto-Caspian region: Environmental consequences of climate change during the Late Pleistocene. Quat. Int. 345, 88–99 (2014).
Google Scholar 
Rychagov, G. I. Pleistocene History of the Caspian Sea (Moscow State University, 1977).
Google Scholar 
Rychagov, G. I. The level mode of the Caspian Sea during the last 10000. Vestn. Mosk. Univ. Seriya 5 Geogr. 2, 38–49 (1993).
Google Scholar 
Kroonenberg, S. B. et al. Solar-forced 2600 BP and Little Ice Age highstands of the Caspian Sea. Quat. Int. 173–174, 137–143 (2007).
Google Scholar 
Kasimov, N. S., Lychagin, M. Y. & Kroonenberg, S. B. Geochemical indication of cyclic fluctuations of the caspian sea level. Vestn. Mosk. Univ. Seriya Geogr. 2, 72–77 (2011).
Google Scholar 
Kroonenberg, S. B., Badyukova, E. N., Storms, J. E. A., Ignatov, E. I. & Kasimov, N. S. A full sea-level cycle in 65 years: Barrier dynamics along Caspian shores. Sediment. Geol. 134, 257–274 (2000).ADS 

Google Scholar 
Bolikhovskaya, N. & Kasimov, N. The evolution of climate and landscapes of the Lower Volga region during the Holocene. Geogr. Environ. Sustain. 3, 78–97 (2010).
Google Scholar 
Magomedov, M.M.-R. & Gasanov, S. M. Features of soil changes under crowns of the shrubberies tamarisk (Tamarix meyeri boiss, T. ramosissima zedeb). South Russ. Ecol. Dev. 6, 12–21 (2014).
Google Scholar 
Du, N. et al. Facilitation or competition? The effects of the shrub species tamarix chinensis on herbaceous communities are dependent on the successional stage in an impacted coastal wetland of North China. Wetlands 37, 899–911 (2017).
Google Scholar 
Jiang, L., Jiapaer, G., Bao, A., Guo, H. & Ndayisaba, F. Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci. Total Environ. 599–600, 967–980 (2017).ADS 
PubMed 

Google Scholar 
Burke, I. C. et al. Plant–soil interactions in temperate grasslands. In Plant-Induced Soil Changes: Processes and Feedbacks (ed. van Breemen, N.) 121–143 (Springer, 1998). https://doi.org/10.1007/978-94-017-2691-7_7.Chapter 

Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Abaturov, B. D. Microdepression microrelief of Caspian Lowland and mechanisms of its formation. Arid. Ecosistemy 16, 31–45 (2010).
Google Scholar 
Sapanov, M. K. The results of soil water investigations in Djanybek stationary. Dokuchaev Soil Bull. 83, 22–40 (2016).
Google Scholar 
Bolshakov, A. F. & Bazykina, G. S. Natural biogeocenoses and the conditions of their existence. In Biogeocenotic Basis of the Reclamation of Semidesert in the Northern Caspain Lowland (ed. Rode, A. A.) 6–34 (Nauka, 1974).
Google Scholar 
Konyushkova, M. V., Nukhimovskaya, Y. D., Gasanova, Z. U. & Stepanova, N. Y. The temporal change in variability of soil salinity and phytodiversity at the coastal plain of the Caspian Sea. Arid Ecosyst. 10, 312–321 (2020).
Google Scholar 
Semenkov, I., Konyushkova, M., Heidari, A., Nukhimovskaya, Y. & Klink, G. Data on the soilscape and vegetation properties at the key site in the NW Caspian Sea coast, Russia. Data Br. 31, 105972 (2020).
Google Scholar 
Konyushkova, M. V. et al. Spatial and seasonal salt translocation in the young soils at the coastal plains of the Caspian Sea. Quat. Int. 590, 15–25 (2021).
Google Scholar 
Semenkov, I., Konyushkova, M., Heidari, A. & Nikolaev, E. Chemical differentiation of recent fine-textured soils on the Caspian Sea coast: A case study in Golestan (Iran) and Dagestan (Russia). Quat. Int. 590, 48–55 (2021).
Google Scholar 
Haghani, S. et al. An early ‘Little Ice Age’ brackish water invasion along the south coast of the Caspian Sea (sediment of Langarud wetland) and its wider impacts on environment and people. Holocene 26, 3–16 (2016).ADS 

Google Scholar 
Panin, G. N., Mamedov, R. M. & Mitrofanov, I. V. Present State of the Caspian Sea (Nauka, 2005).
Google Scholar 
Konyushkova, M. V. et al. The spatial differentiation of soil salinity at the young saline coastal plain of the Caspian region. Dokuchaev Soil Bull. 95, 41–57 (2018).
Google Scholar 
Cherepanov, S. K. Vascular Plants of Russia and Adjacent States (Within the Former USSR) (Cambridge University Press, 1995).
Google Scholar 
Takhtajan, A. Flowering Plants (Springer Science+Business Media B.V, 2009). https://doi.org/10.1007/978-1-4020-9609-9.Book 

Google Scholar 
Govaerts, R., Nic Lughadha, E., Black, N., Turner, R. & Paton, A. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci. Data 8, 215 (2021).PubMed 
PubMed Central 

Google Scholar 
POWO. Plants of the World Online. Facilitated by the Royal Botanic Gardens, Kew (Board of Trustees of the Royal Botanic Gardens, 2022).Chase, M. W. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20 (2016).
Google Scholar 
Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).
Google Scholar 
Clarke, K. R. & Warwick, R. M. A taxonomic distinctness index and its statistical properties. J. Appl. Ecol. 35, 523–531 (1998).
Google Scholar 
Semenkov, I. N. et al. The variability of soils and vegetation of hydrothermal fields in the Valley of Geysers at Kamchatka Peninsula. Sci. Rep. 11, 11077 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Wickham, H. & Henry, L. tidyr: Tidy Messy Data. R Packag. version 1.0.0 (2019).Goryaev, I. A. Regularities of distribution of halophytic vegetation on the Caspian Lowland. Bot. Zhurnal 104, 1072–1089 (2019).
Google Scholar 
Soltanmuradova, Z. I. & Teimurov, A. A. Taxonomic structure of the flora of the Primorskaya Lowland of the Republic of Dagestan. South Russ. Ecol. Dev. 3, 38 (2010).
Google Scholar 
Zörb, C., Sümer, A., Sungur, A., Flowers, T. J. & Özcan, H. Ranking of 11 coastal halophytes from salt marshes in northwest Turkey according their salt tolerance. Turk. J. Botany 37, 1125–1133 (2013).
Google Scholar 
Zhao, Y., Yu, H., Zhang, T. & Guo, J. Mycorrhizal colonization of chenopods and its influencing factors in different saline habitats, China. J. Arid Land 9, 143–152 (2017).
Google Scholar 
Podar, D. et al. Morphological, physiological and biochemical aspects of salt tolerance of halophyte Petrosimonia triandra grown in natural habitat. Physiol. Mol. Biol. Plants 25, 1335–1347 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nayyar, H. & Gupta, D. Differential sensitivity of C3 and C4 plants to water deficit stress: Association with oxidative stress and antioxidants. Environ. Exp. Bot. 58, 106–113 (2006).CAS 

Google Scholar 
Way, D. A., Katul, G. G., Manzoni, S. & Vico, G. Increasing water use efficiency along the C3 to C4 evolutionary pathway: A stomatal optimization perspective. J. Exp. Bot. 65, 3683–3693 (2014).PubMed 
PubMed Central 

Google Scholar 
Atia, A. et al. Ecophysiological aspects in 105 plants species of saline and arid environments in Tunisia. J. Arid Land 6, 762–770 (2014).
Google Scholar 
Pickett, S. T. A. Space-for-time substitution as an alternative to long-term studies. In Long-Term Studies in Ecology 110–135 (1989) https://doi.org/10.1007/978-1-4615-7358-6_5.Walker, L. R., Wardle, D. A., Bardgett, R. D. & Clarkson, B. D. The use of chronosequences in studies of ecological succession and soil development. J. Ecol. 98, 725–736 (2010).
Google Scholar 
Dimeeva, L. A. Dynamics of vegetation in deserts of Aral and Caspian regions. (2011).Yu, K. et al. Late quaternary environments in the Gobi Desert of Mongolia: Vegetation, hydrological, and palaeoclimate evolution. Palaeogeogr. Palaeoclimatol. Palaeoecol. 514, 77–91 (2019).
Google Scholar 
Cao, X., Tian, F., Dallmeyer, A. & Herzschuh, U. Northern Hemisphere biome changes ( >30°N) since 40 cal ka BP and their driving factors inferred from model-data comparisons. Quat. Sci. Rev. 220, 291–309 (2019).ADS 

Google Scholar 
Zhang, D. et al. Response of vegetation to Holocene evolution of westerlies in the Asian Central Arid Zone. Quat. Sci. Rev. 229, 106138 (2020).
Google Scholar 
Lu, K. Q. et al. A new approach to interpret vegetation and ecosystem changes through time by establishing a correlation between surface pollen and vegetation types in the eastern central Asian desert. Palaeogeogr. Palaeoclimatol. Palaeoecol. 551, 109762 (2020).
Google Scholar 
He, Q., Bertness, M. D. & Altieri, A. H. Global shifts towards positive species interactions with increasing environmental stress. Ecol. Lett. 16, 695–706 (2013).PubMed 

Google Scholar 
Ziffer-Berger, J., Weisberg, P. J., Cablk, M. E. & Osem, Y. Spatial patterns provide support for the stress-gradient hypothesis over a range-wide aridity gradient. J. Arid Environ. 102, 27–33 (2014).ADS 

Google Scholar 
Vinogradov, B. V. Plant Indicators and Their Use in the Study of Natural Resources (Visshaya shkola, 1964).
Google Scholar 
Luo, C. et al. Characteristics of the modern pollen distribution and their relationship to vegetation in the Xinjiang region, northwestern China. Rev. Palaeobot. Palynol. 153, 282–295 (2009).
Google Scholar 
Zhao, Y. & Herzschuh, U. Modern pollen representation of source vegetation in the Qaidam Basin and surrounding mountains, north-eastern Tibetan Plateau. Veg. Hist. Archaeobot. 18, 245–260 (2009).
Google Scholar  More