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    Ruminant inner ear shape records 35 million years of neutral evolution

    Zachos, J. C., Pagani, M., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Erwin, D. H. Climate as a driver of evolutionary change. Curr. Biol. 19, R575–R583 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mayhew, P. J., Jenkins, G. B. & Benton, T. G. A long-term association between global temperature and biodiversity, origination and extinction in the fossil record. Proc. R. Soc. Lond. B 275, 47–53 (2008).
    Google Scholar 
    Raia, P. et al. Past extinctions of Homo species coincided with increased vulnerability to climatic change. One Earth 3, 480–490 (2020).Article 
    ADS 

    Google Scholar 
    deMencoal, P. Climate and human evolution. Science 331, 540–542 (2011).Article 
    ADS 

    Google Scholar 
    Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016).Article 

    Google Scholar 
    Potts, R. & Faith, J. T. Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution. J. Hum. Evol. 87, 5–20 (2015).Article 
    PubMed 

    Google Scholar 
    Clavel, J. & Morlon, H. Accelerated body size evolution during cold climatic periods in the Cenozoic. Proc. Natl Acad. Sci. USA 114, 4183–4188 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Mihlbachler, M. C., Rivals, F., Solounias, N. & Semprebon, G. M. Dietary change and evolution of horses in North America. Science 331, 1178–1181 (2011).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Mennecart, B. et al. Bony labyrinth morphology clarifies the origin and evolution of deer. Sci. Rep. 7, 13176 (2017).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Ponce, deLeón et al. Human bony labyrinth is an indicator of population history and dispersal from Africa. Proc. Natl Acad. Sci. USA 115, 4128–4133 (2018).Article 
    ADS 

    Google Scholar 
    Luo, Z.-X. The inner ear and its bony housing in tritylodontids and implications for the evolution of the mammalian ear. Bull. Mus. Comp. Zool. 156, 81–97 (2001).
    Google Scholar 
    Ekdale, E. G. Comparative anatomy of the bony labyrinth (inner ear) of placental mammals. PLoS ONE 8, e66624 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    O’Leary, M. A. An anatomical and phylogenetic study of the osteology of the petrosal of extant and extinct artiodactylans (Mammalia) and relatives. Bull. Am. Mus. Nat. Hist. 335, 1–206 (2010).Article 

    Google Scholar 
    Costeur, L. et al. The bony labyrinth of toothed whales reflects both phylogeny and habitat preferences. Sci. Rep. 8, 7841 (2018).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Spoor, F., Bajpai, S., Hussain, S. T., Kumar, K. & Thewissen, J. G. M. Vestibular evidence for the evolution of aquatic behavior in early cetaceans. Nature 417, 163–166 (2002).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Davies, K. T. J., Bates, P. J. J., Maryanto, I., Cotton, J. A. & Rossiter, S. J. The evolution of bat vestibular systems in the face of potential antagonistic selection pressures for flight and echolocation. PLoS ONE 8, e61998 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Park, T., Mennecart, B., Costeur, L., Grohé, C. & Cooper, N. Convergent evolution in toothed whale cochleae. BMC Evol. Biol. 1, 195 (2019).Article 

    Google Scholar 
    Benoit, J. et al. A test of the lateral semicircular canal correlation to head posture, diet and other biological traits in “ungulate” mammals. Sci. Rep. 10, 19602 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Morimoto, N. et al. Variation of bony labyrinthine morphology in Mio-Plio-Pleistocene and modern anthropoids. Am. J. Phys. Anthropol. 2020, 1–17 (2020).
    Google Scholar 
    DeMiguel, D., Azanza, B. & Morales, J. Key innovations in ruminant evolution: A paleontological perspective. Int. Zool. 9, 412–433 (2014).Article 

    Google Scholar 
    Gunz, P., Ramsier, M., Kuhrig, M., Hublin, J. & Spoor, F. The mammalian bony labyrinth reconsidered, introducing a comprehensive geometric morphometric approach. J. Anat. 220, 529–543 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grohe, C., Tseng, Z. J., Lebrun, R., Boistel, R. & Flynn, J. J. Bony labyrinth shape variation in extant Carnivora: a case study of Musteloidea. J. Anat. 228, 366–383 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Urciuoli, A. et al. A comparative analysis of the vestibular apparatus in Epipliopithecus vindobonensis: Phylogenetic implications. J. Hum. Evol. 151, 102930 (2021).Article 
    PubMed 

    Google Scholar 
    IUCN. The IUCN red list of threatened species. Version 2021-1. https://www.iucnredlist.org. Accessed 17 June 2021.Kingdon, J. & Hoffmann. M. Mammals of Africa. Volume VI pigs, hippopotamuses, chevrotains, Giraffes, deer and bovids 704 (Bloomsbury Publishing, 2013).Chen, L. et al. Large-scale ruminant genome sequencing provides insights into their evolution and distinct traits. Science 364 eaav6202 (2019).Hassanin, A. et al. Pattern and timing of diversification of Cetartiodactyla (Mammalia, Laurasiatheria), as revealed by a comprehensive analysis of mitochondrial genomes. C. R. Biol. 335, 32–50 (2012).Article 
    PubMed 

    Google Scholar 
    Wang, Y. et al. Genetic basis of ruminant headgear and rapid antler regeneration. Science 364, 1153 (2019).Article 

    Google Scholar 
    Myers, E. A. & Bubrink, F. T. Ecological opportunity: Trigger of adaptative radiation. Nat. Educ. Knowl. 3, 23 (2012).
    Google Scholar 
    Gentry, A. W. Bovidae. In Cenozoic mammals of Africa (eds Werdelin, L. & Sanders, W. J.) 741–796 (University of California Press, 2010).Harris, J. M., Solounias, N. & Geraads, D. Giraffoidea. In Werdelin, L. & Sanders, W. J. Cenozoic mammals of Africa. 797–812 (University of California Press, 2010).Clauss, M. & Rössner, G. E. Old world ruminant morphophysiology, life history, and fossil record: exploring key innovations of a diversification sequence. Ann. Zool. Fenn. 51, 80–94 (2014).Article 

    Google Scholar 
    Johnston, A. R. & Anthony, N. M. A multi-locus species phylogeny of African forest duikers in the subfamily Cephalophinae: evidence for a recent radiation in the Pleistocene. BMC Evol. Biol. 12, 120 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cooney, C. R. & Thomas, G. H. Heterogeneous relationships between rates of speciation and body size evolution across vertebrate clades. Nat. Ecol. Evol. 5, 101–110 (2020).Article 
    PubMed 

    Google Scholar 
    Köhler, M. & Moyà-Solà, S. Physiological and life history strategies of a fossil large mammal in a resource-limited environment. Proc. Natl Acad. Sci. USA 106, 20354–22035 (2009).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Bibi, F. A multi-calibrated mitochondrial phylogeny of extant Bovidae (Artiodactyla, Ruminantia) and the importance of the fossil record to systematics. BMC Evol. Biol. 13, 1–15 (2013).Article 

    Google Scholar 
    Geraads, D. A reassessment of the Bovidae (Mammalia) from the Nawata Formation of Lothagam, Kenya, and the late Miocene diversification of the family in Africa. J. Syst. Palaeontol. 17, 1–14 (2017).
    Google Scholar 
    Mennecart, B., Aiglstorfer, M., Li, Y., Li, C. & Wang, S. Ruminants reveal Eocene Asiatic palaeobiogeographical provinces as the origin of diachronous mammalian Oligocene dispersals into Europe. Sci. Rep. 11, 17710 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Rössner, G. E. Family tragulidae. In: The evolution of artiodactyls (eds Prothero, D. R. & Foss S. C.) (The Johns Hopkins University Press, Baltimore, 2007).Sánchez, I. M., Quiralte, V., Morales, J. & Pickford, M. A new genus of tragulid ruminant from the early Miocene of Kenya. Acta Palaeontol. Pol. 55, 177–187 (2010).Article 

    Google Scholar 
    Sánchez, I. M., Quiralte, V., Ríos, M., Morales, J. & Pickford, M. First African record of the Miocene Asian mouse-deer Siamotragulus (Mammalia, Ruminantia, Tragulidae): implications for the phylogeny and evolutionary history of the advanced selenodont tragulids. J. Syst. Palaeontol. 13, 543–556 (2015).Article 

    Google Scholar 
    Mennecart, B. et al. The first French tragulid skull (Mammalia, Ruminantia, Tragulidae) and associated tragulid remains from the Middle Miocene of Contres (Loir-et-Cher, France). C. R. Palevol 17, 189–200 (2018).Article 

    Google Scholar 
    Bobe, R. & Eck, G. C. Responses of African bovids to Pliocene climatic change. Paleobiology 27, 1–47 (2001).Article 

    Google Scholar 
    Strömberg, C. A. E. Decoupled taxonomic radiation and ecological expansion of open-habitat grasses in the Cenozoic of North America. Proc. Natl Acad. Sci. USA 102, 11980–11984 (2005).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Kaya, F. et al. The rise and fall of the Old World savannah fauna and the origins of the African savannah biome. Nat. Ecol. Evol. 2, 241–246 (2017).Article 

    Google Scholar 
    Gravilets, S. & Losos, J. B. Adaptive radiation: contrasting theory with data. Science 323, 732–737 (2009).Article 
    ADS 

    Google Scholar 
    Moen, D. & Morlon, H. Why does diversification slow down? Trends Ecol. Evol. 29, 190–197 (2014).Article 
    PubMed 

    Google Scholar 
    Couvreur, T. L. P. et al. Tectonics, climate and the diversification of the tropical African terrestrial flora and fauna. Biol. Rev. 96, 16–51 (2020).Article 
    PubMed 

    Google Scholar 
    Fontoura, E., Darival Ferreira, J., Bubadué, J., Ribeiro, A. M. & Kerber, L. Virtual brain endocast of Antifer (Mammalia: Cervidae), an extinct large cervid from South America. J. Morphol. 281, 1–18 (2020).Article 

    Google Scholar 
    Trauth M. A. et al. Recurring types of variability and transitions in the ∼620 kyr record of climate change from the Chew Bahir basin, southern Ethiopia Quaternary. Sci. Rev. https://doi.org/10.1016/j.quascirev.2020.106777 (2021).Janis, C. M. & Manning, E. Antilocapridae. In Evolution of tertiary mammals of North America (eds Janis, C. M., Scott, K. M. & Jacobs, L. L.) 491–507 (Cambridge University Press, 1998).Klimova, A., Munguia-Vega, A., Hoffman, J. I. & Culver, M. Genetic diversity and demography of two endangered captive pronghorn subspecies from the Sonoran Desert. J. Mammal. 95, 1263–1277 (2014).Article 

    Google Scholar 
    Evin, A., et al. Size and shape of the semicircular canal of the inner ear: A new marker of pig domestication? J. Exp. Zool. B Mol. Dev. Evol. https://doi.org/10.1002/jez.b.23127 (2022).Sánchez, I. M., Cantalapiedra, J. L., Ríos, M., Quiralte, V. & Morales, J. Systematics and evolution of the Miocene three-horned Palaeomerycid ruminants (Mammalia, Cetartiodactyla). PLoS ONE 10, e0143034 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wiley, D. Landmark Editor 3.6 (Institute for Data Analysis and Visualization, Davis, CA, University of California, 2006).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2022). https://www.R-project.org/.Gunz, P. & Mitteroecker, P. Semilandmarks: a method for quantifying curves and surfaces. Hystrix 24, 103–109 (2013).
    Google Scholar 
    Adams, D. C. & Otárola-Castillo, E. geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).Article 

    Google Scholar 
    Adams, D. C., Collyer, M. L., Kaliontzopoulou, A. geomorph: software for geometric morphometric analyses. R package version 3.2.1 software (2020).Gunz, P., Mitteroecker, P., Bookstein, F. L. Semilandmarks in three dimensions. In Modern morphometrics in physical anthropology. Springer, pp. 73–98 (2005).Maddison, W. P., Maddison, D. R. Mesquite: a modular system for evolutionary analysis. Version 3.04. (2010).Gromolard, C. & Guérin, C. Mise au point sur Parabos cordieri (de Christol), un Bovidé (Mammalia, Artiodactyla) du Pliocène d’Europe occidentale. Géobios 13, 741–755 (1980).Article 

    Google Scholar 
    Duvernois, M.-P. Mise au point sur le genre Leptobos (Mammalia, Artiodactyla, Bovidae); implications biostratigraphiques et phylogénétiques. Géobios 25, 155–166 (1992).Article 

    Google Scholar 
    Janis, C. M., Manning, E. Dromomerycidae. In Evolution of Tertiary mammals of North America Volume1: Terrestrial carnivores, ungulates, and ungulatelike mammals (eds. Janis, C. M., Scott, K. M., Jacobs L. L.) 477–490 (Cambridge University Press, 1998).Birungi, J. & Arctander, P. Molecular systematics and phylogeny of the reduncini (artiodactyla: bovidae) inferred from the analysis of mitochondrial cytochrome b gene sequences. J. Mamm. Evol. 8, 125–147 (2001).Article 

    Google Scholar 
    Lalueza-Fox, C. et al. Molecular dating of caprines using ancient DNA sequences of Myotragus balearicus, an extinct endemic Balear mammal. BMC Evol. Biol. 5, 1–11 (2005).Article 

    Google Scholar 
    Marot, J. D. Molecular phylogeny of terrestrial artiodactyls, conflict and resolution. In The evolution of artiodactyls (eds Prothero, D. R., Foss, S. C.) 4–18 (The Johns Hopkins University Press, 2007).Webb, D. S. Hornless ruminants. In Evolution of Tertiary mammals of North America Volume1: Terrestrial carnivores, ungulates, and ungulatelike mammals (eds Janis, C. M., Scott, K. M., Jacobs, L. L.) 463–476 (Cambridge University Press, 1998).Mennecart, B. & Métais, G. Mosaicomeryx gen. nov., a ruminant mammal from the Oligocene of Europe and the significance of ‘gelocids’. J. Syst. Palaeontol. 13, 581–600 (2015).Article 

    Google Scholar 
    Sánchez, I. M., DeMiguel, D., Quiralte, V. & Morales, J. The first known Asian Hispanomeryx (Mammalia, Ruminantia, Moschidae.). J. Vert. Paleontolo. 31, 1397–1403 (2011).Heckeberg, N. S., Erpenbeck, D., Wörheide, G. & Rössner, G. Systematic relationships of five newly sequenced cervid species. PeerJ 4, e2307 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ríos, M., Sánchez, I. M. & Morales, J. A new giraffid (Mammalia, Ruminantia, Pecora) from the late Miocene of Spain, and the evolution of the sivathere-samothere lineage. PLoS ONE 12, e0185378 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vislobokova, I. New data on late Miocene mammals of Kohfidisch, Austria. Paleontol. J. 41, 451–460 (2007).Article 

    Google Scholar 
    Aiglstorfer, M., Rössner, G. E. & Böhme, M. Dorcatherium naui and pecoran ruminants from the late Middle Miocene Gratkorn locality (Austria). Palaeobiodivers. Palaeoenviron. 94, 83–123 (2014).Article 

    Google Scholar 
    Janis, C. M. & Scott, K. M. The interrelationships of higher ruminant families with special emphasis on the members of the Cervoidea. Am. Mus. Novit. 2893, 1–85 (1987).
    Google Scholar 
    Leinders, J. Hoplitomerycidae fam. nov. (Ruminantia, Mammalia) from Neogene fissure fillings in Gargano (Italy). Scr. Geol. 70, 1–68 (1984).
    Google Scholar 
    Hassanin, A. & Douzery, E. Molecular and morphological phylogenies of Ruminantia, and the alternative position of the Moschidae. Syst. Biol. 52, 206–228 (2003).Article 
    PubMed 

    Google Scholar 
    Métais, G. & Vislobokova, I. Basal ruminants. In The evolution of artiodactyls (eds Prothero, D. R. & Foss, S. C.) 189–212 (The Johns Hopkins University Press, 2007).Mennecart, B., Zoboli, D., Costeur, L. & Pillola, G. L. On the systematic position of the oldest insular ruminant Sardomeryx oschiriensis (Mammalia, Ruminantia) and the early evolution of the Giraffomorpha. J. Syst. Palaeontol. 17, 691–704 (2019).Article 

    Google Scholar 
    Aiglstorfer, M. et al. Musk Deer on the Run – Dispersal of Miocene Moschidae in the Context of Environmental Changes. In Evolution of Cenozoic land mammal faunas and ecosystems: 25 years of the NOW database of fossil mammals. (eds Casanovas-Vilar, I., van den Hoek Ostende, L. W., Janis, C. M. & Saarinen J.) (Cham: Springer, in press).Klingenberg, C. P. MorphoJ: an integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).Article 
    PubMed 

    Google Scholar 
    Schlager, S. Morpho and Rvcg – Shape analysis in R. In Zheng, G., Li, S., Szekely, G. Statistical shape and deformation analysis, 217–256 (MA: Academic Press, 2017).Klingenberg, C. P. & Gidaszewski, N. A. Testing and quantifying phylogenetic signals and homoplasy in morphometric data. Syst. Biol. 59, 245–261 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Marriott, F. H. C. Barnard’s monte carlo tests: how many simulations? Appl. Stat. 28, 75–77 (1979).Article 

    Google Scholar 
    Edgington, E. S. Randomization tests (Marcel Dekker, 1987).Tzeng, T. D. & Yeh, S. Y. Permutation tests for difference between two multivariate allometric patterns. Zool. Stud. 38, 10–18 (1999).
    Google Scholar 
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

    Google Scholar 
    Renaud, S., Dufour, A.-B., Hardouin, E. A., Ledevin, R. & Auffray, C. Once upon multivariate analyses: when they tell several stories about biological evolution. PLoS ONE 10, e0132801 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mitteroecker, P. & Bookstein, F. Linear discrimination, ordination, and the visualization of selection gradients in modern morphometrics. Evol. Biol. 38, 100–114 (2011).Article 

    Google Scholar 
    Raia, P., Castiglione, S., Serio, C., Mondanaro, A. & Raia, M. P. Package ‘RRphylo’. CRAN Repos. 4, 1–31 (2018).
    Google Scholar 
    Castiglione, S. et al. A new method for testing evolutionary rate variation and shifts in phenotypic evolution. Methods Ecol. Evol. 9, 974–983 (2018).Article 

    Google Scholar 
    Morlon, H. et al. “RPANDA: an R package for macroevolutionary analyses on phylogenetic trees.”. Methods Ecol. Evol. 7, 589–597 (2016).Article 

    Google Scholar 
    Costeur, L., Mennecart, B., Müller, B., Schulz, G. Observations on the scaling relationship between bony labyrinth, skull size and body mass in ruminants. Proc. SPIE 11113, https://doi.org/10.1117/12.2530702 (2019).Costeur, L., Mennecart, B., Müller, B. & Schulz, G. Prenatal growth stages show the development of the ruminant bony labyrinth and petrosal bone. J. Anat. 230, 347–353 (2017).Article 
    PubMed 

    Google Scholar 
    Mennecart, B. & Costeur, L. Shape variation and ontogeny of the ruminant bony labyrinth, an example in Tragulidae. J. Anat. 229, 422–435 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clauss, M., Steuer, P., Müller, D. W. H., Codron, D. & Hummel, J. Herbivory and body size: allometries of diet quality and gastrointestinal physiology, and implications for herbivore ecology and dinosaur gigantism. PLoS One 8, e68714 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    du Toit, J. T. & Owen-Smith, N. Body size, population metabolism, and habitat specialization among large African herbivores. Am. Nat. 133, 736–740 (1989).Article 

    Google Scholar 
    Mennecart B., Becker D., & Berger J. -P. Mandible shape of ruminants: between phylogeny and feeding habits. In: Ruminants: Anatomy, behavior, and diseases, (ed. Mendes R. E.) 205–226 (Nova Science Publishers, 2012).Bokma, F. et al. Testing for Depéret’s rule (body size increase) in mammals using combined extinct and extant data. Syst. Biol. 65, 98–108 (2016).Article 
    PubMed 

    Google Scholar 
    Besiou, E., Choupa, M. N., Lyras, G. & van der Geer, A. Body mass divergence in sympatric deer species of Pleistocene Crete (Greece). Palaeontol. Electron. 25, a23 (2022).
    Google Scholar 
    Mennecart B., Métais G., Tissier J., Rössner G. E., & Costeur L. 3D models related to the publication: Reassessment of the enigmatic ruminant Miocene genus Amphimoschus Bourgeois, 1873 (Mammalia, Artiodactyla, Ruminantia, Pecora). MorphoMuseuM 7, e131 (2021).Mennecart, B., Perthuis de, A. D. & Costeur, L. 3D models related to the publication: The first French tragulid skull (Mammalia, Ruminantia, Tragulidae) and associated tragulid remains from the Middle Miocene of Contres (Loir-et-Cher, France). MorphoMuseuM 3, e4 (2018).Article 

    Google Scholar 
    Aiglstorfer, M., Costeur, L., Mennecart, B. & Heizmann, E. P. J. Micromeryx? eiselei – a new moschid species from Steinheim am Albuch, Germany, and the first comprehensive description of moschid cranial material from the Miocene of Central Europe. MorphoMuseuM 3, e4 (2107).Article 

    Google Scholar 
    Costeur, L. & Mennecart, B. 3D models related to the publication: Prenatal growth stages show the development of the ruminant bony labyrinth and petrosal bone. MorphoMuseuM 2, e3 (2016).Article 

    Google Scholar 
    Mennecart, B. & Costeur, L. 3D models related to the publication: a Dorcatherium (Mammalia, Ruminantia, Middle Miocene) petrosal bone and the tragulid ear region. MorphoMuseuM 2, e2 (2016).Article 

    Google Scholar 
    Mennecart, B. et al. Allometric and phylogenetic aspects of stapes morphology in ruminantia (Mammalia, Artiodactyla). Front. Earth Sci. 8, 176 (2020). More

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    Increases in reef size, habitat and metacommunity complexity associated with Cambrian radiation oxygenation pulses

    The rise of animals (metazoans) is a seminal event in the history of life. The Cambrian Radiation ~540 Ma marks the appearance of abundant and diverse metazoans and increasing ecosystem complexity in the fossil record1. A causal relationship between the redox and fossil records is proposed, where oxygen provision reached a threshold, or series of thresholds, which allowed for the diversification of metazoans with increasing metabolic demands2. Global geochemical data, however, suggest that oxygenation was not a simple, linear process, but rather occurred episodically via a series of short-lived pulses (1–3 Myr), or ‘oceanic oxygenation events’ (OOEs)3,4. Early and even later Cambrian seas likely had shallower, and more dynamic, oxygen minimum zones (OMZs) than modern oceans5,6. Such pulses of increased oxygenation (and related changes in productivity) are hypothesised to have increased the extent of shallow-ocean oxygenation and hence to have promoted diversification7. But what remains unquantified is the community-wide response of metazoans to such redox cycles, an insight into the evolutionary processes involved, and hence whether these pulses were indeed a driving force for the Cambrian Radiation.In order to test the hypothesis that oxic pulses led to diversification and potentially ecological development, a correlation between increased oxygenation, rates of origination, and metrics of metazoan ecosystem complexity needs to be demonstrated. Early Cambrian marine environments were heterogeneous with respect to oxygen provision and nutrient load at a regional scale, so in order to investigate potential correlations, we require the integration of global and local redox proxies, and biotic records in the same stratigraphically well-constrained geological successions.During the early Cambrian, the Siberian Platform was a vast isolated, tropical continent almost entirely covered by an epicontinental sea (Fig. 1a)8,9. The platform supported a single metacommunity, i.e. a species pool with many local, interacting communities e.g.10, representing a third of total early Cambrian metazoan benthic diversity with widespread metazoan (archaeocyath sponge) reefs that formed bioherms (Fig. 1b)7,11. Dynamic and synchronous changes of body size in archaeocyath sponges, hyoliths, and helcionelloid molluscs through the early Cambrian on the Siberian Platform have been quantified, which coincide with elevated biodiversity and rates of origination: these have been proposed to follow OOEs12. Here we consider temporal changes in both the position of archaeocyath sponge reefs as a function of relative water depth, and in individual reef size (diameter), as well as the ecological complexity of the reef-building and dwelling communities by quantification of changing reef community membership of sessile archaeocyath sponge, coralomorph, and cribricyath species, on the Siberian Platform.Fig. 1: Palaeogeographic and stratigraphic position of the early Cambrian archaeocyath reefs of the Lena-Aldan area on the Siberian Platform.a Early Cambrian palaeofacies zonation map of the Siberian Platform. b Cross section to show relative positions of sampled transects along the Lena River11,40,66,67,68. c Lithostratigraphy, biostratigraphy, carbon isotope (δ13C)29,31,32 and carbonate-associated sulfate sulfur isotope (δ34SCAS)7 data for sections from the middle Lena River (Isit’, Zhurinsky Mys, Achchagy-Kyyry-Taas, and Achchagy-Tuoydakh). S.E.—Sinsk Event; Tolb.—Tolba Formation; ATD., BOT., N.-D., TOM.—Atdabanian, Botoman, Nemakit-Daldynian, and Tommotian local stages, respectively.Full size imageTo quantify ecological complexity, we used metacommunity analyses, which compare the structure between communities in terms of taxa (generally species) compositions spatially and temporally10 (see Methods). The ‘Elements of Metacommunity Structure’ framework used here is a hierarchical analysis that identifies properties in site-by-species presence/absence matrices that are related to the underlying processes, such as species interactions, dispersal, and environmental filtering that shape species distributions10. Application to various marine and terrestrial palaeocommunities has demonstrated the robustness of these methods to fossil data and sample size variations13,14. There are fourteen different types of metacommunity structure which are determined by the calculation of three metacommunity metrics: Coherence, Turnover, and Boundary Clumping, which reveal different controlling processes of underlying metacommunity structure10,15,16,17,18.The most ecologically complex metacommunities are classified as Clementsian, and have positive coherence, turnover and boundary clumping16. Clementsian metacommunities contain groups of taxa with similar range boundaries that respond to the environment synchronously as taxa have physiological or evolutionary trade-offs within the communities associated with environmental thresholds19. By contrast, when taxa respond individualistically to the underlying environment, without accounting for other taxa within the community, the structure is Gleasonian, and is defined by positive coherence and turnover but no significant boundary clumping16. When coherence is positive, but turnover is not significantly different from random, then the resultant metacommunity structures are known as quasi-structures (e.g. quasi-Clementsian), which reflect weaker underlying structuring processes.We determined the metacommunity structure for archaeocyath sponge species on the Siberian Platform throughout their early Cambrian record using an entire previously published data set11 then on a sub-set of metacommunities which had a sufficient number of reef sites to be suitable for analyses, i.e. with a sufficient number of sites to be statistically significant. Further, to investigate the effects of water depth on metacommunity structure, we used Spearman rank correlations to test whether the metacommunity ranking (as determined by reciprocal averaging, a type of correspondence analysis which ordinates the sites based on their species composition17), is significantly correlated to water depth. Finally, to quantify how pairwise associations between taxa change between the three temporally different metacommunities, we determined which pairwise taxa co-occurrences are significantly non-random using a combinatorics approach, and whether any non-random co-occurrences are positive or negative20.Species richness estimates are highly sensitive to differences in sampling. When comparing species richness of assemblages from several time intervals, it is advisable to standardise sampling across those assemblages to ensure that changes in species richness are not attributable to sampling differences. One approach is to subsample each time interval down to a standardised number of individuals (size-based rarefaction), but this approach can underestimate changes in richness because it tends to sample low-richness assemblages more completely than high-richness ones21. Coverage-based rarefaction, where each sample is down-sampled to a standardised level of taxonomic completeness, avoids this potential issue. The coverage of a sample is the proportion of species in the assemblage which are represented in that sample, and it can be estimated by subtracting the proportion of singletons in a sample from 1 (e.g.22; see also21 for details). We used the estimateD function from R package iNEXT23 to produce coverage-standardised species richness estimates with 95% confidence intervals, by repeatedly down-sampling the sampled assemblage from each time interval to match the coverage of the lowest-coverage interval. We did this by setting datatype = “abundance”, base = “coverage” and leaving all other arguments as default.In sum, we test the biotic response to OOEs by compiling metrics of archaeocyath reef size, location, and metacommunity complexity, integrated with existing data on archaeocyath individual size, species richness and origination and extinction rates12 and high-resolution geochemistry4,7 recalculated to the same stratigraphic scale, on the Siberian Platform over 11 Myr through Cambrian stages 2–3 (mid-Tommotian to early Botoman on the Siberian stratigraphic scale; 525–514 Ma). These results are used to quantify the community-wide response of metazoans to extrinsic redox cycles, and hence gain insight into the evolutionary processes involved.Geological setting and evolution of redoxDuring the early Cambrian shallow marine carbonates associated with evaporites and siliciclastics dominated the inner Siberian Platform, passing to shallow marginal carbonates of transitional facies known as the transitional zone (or the Anabar-Sinsk), thence to deep ramp and slope settings that accumulated organic-rich limestone and shale (Fig. 1a)24,25,26. Archaeocyathan reefs or bioherms were almost entirely restricted to the transitional facies. Such reefs appeared and proliferated during Cambrian stages 2 and 3 (Tommotian, Atdabanian and earliest Botoman), disappeared at the beginning of Stage 4 (middle Botoman) and re-appeared briefly at the end of this stage (Toyonian).We integrate palaeontological (archaeocyath species number and individual size), palaeoecological (reef size and palaeodepth location) and chemostratigraphic information (carbon isotope cycles 5p, 6p, and II–VII) for sections of the Aldan, Selinde and Lena rivers with sub-metre-scale lithostratigraphic subdivisions27,28,29,30,31,32,33 (Figs. 1c, 2a–c, 3a). This results in negligible uncertainty associated with sample heights, which are fixed relative to a consistent datum within each section.Fig. 2: Lithostratigraphy, biostratigraphy and carbon isotope (δ13C) data for sections of the Aldan and Selinde rivers bearing the earlierst archaeocyath reef communities of the Siberian Platform.a Dvortsy27,28,30 b Ulakhan-Sulugur33,34, and c Selinde69,70.Full size imageFig. 3: Summary of geochemical and biotic changes through the early Cambrian, Siberian Platform, and uranium isotope data representing a global record.a International and Siberian timescale, within age model C of 57. ND—Nemakit-Daldynian regional stage; U’-Y—Ust’-Yudoma Formation. b Summary of carbon and sulphur isotopes (from the Lena River, Siberia7). c Uranium isotopes from Siberia (grey; Sukharikha and Bol’shaya Kuonamka rivers), South China (blue), and Morocco (orange) (all data points are larger than 2SE)4. d Archaeocyath sponge species diversity and maximum diameter12. Plotted richness values are the species richness estimator21 with accompanying 95% confidence interval, calculated using the estimated function from R package iNEXT62. e Rates of archaeocyath sponge species origination and extinction12. f Reef location as a function of relative water depth (Supplementary Table 1). FWWB—Fair weather wave base. SWB—Storm weather wave base. g Reef/bioherm diameter, coloured by relative water depth (see column f, and Supplementary Table 2). h Number of reef community types (Supplementary Table 3). i Archaeocyath reef ecosystem complexity, with percentage of species co-occurrence as changing proportions of total non-random and positive and negative. G = Gleasonian, QG = Quasi-Gleasonian, C = Clementsian.Full size imageThroughout Cambrian stages 2 and 3, high-amplitude positive δ13C carbon isotope excursions show a strong positive covariation with the sulphur isotope composition of carbonate-associated sulphate (δ34SCAS) in sections from the Lena River (Fig. 3b)7. The rising limbs of these excursions are interpreted as intervals of progressive burial of reductants under anoxic bottom water conditions, and a progressive increase in atmospheric oxygen7. Coincident δ13C and δ34SCAS peaks (numbered II–VII) correspond with a pulse of atmospheric oxygen into the shallow marine environment (creating an OOE), followed by a corresponding decrease in reductant burial under more widespread marine oxia (falling limbs of δ13C and δ34SCAS), and leading to gradual de-oxygenation over Myr7. In addition, phosphorous retention might have occurred under oxic shallow marine conditions, acting to reduce primary productivity and further oxygenate the shallow marine environment in the short-term ( More

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    Multiple drivers and lineage-specific insect extinctions during the Permo–Triassic

    Fossil record of insectsWe compiled all species-level fossil occurrences of insects using https://paleobiodb.org/ (PBDB) as a starting point (downloaded October 12, 2021). The dataset obtained from PBDB contained initially 5808 occurrences for a period ranging from the Asselian to the Rhaetian. The dataset was cleaned of synonyms, outdated combinations, nomina dubia, and other erroneous and doubtful records, based on revisions provided in the literature and/or on the expertise of the authors. After correction, including data addition from the literature, our dataset was composed of 3636 species (1784 genera, and 418 families) for 17,250 occurrences resulting from an in-depth study and curation of the entire bibliography of fossil insects, spanning from the Asselian (lowermost Permian) to the Rhaetian (uppermost Triassic). Although most of the taxa included in the datasets are nominal taxa (published and named), a few unnamed taxa (genera or species) that are considered separate from others were also included, although not formally named in the literature or not published yet. These unpublished taxa are identifiable by the notation ‘fam. nov.’ or ‘gen. nov.’ following their names.Occurrences used here are specimens originating from a given stratigraphic horizon assigned to a given taxon. The age of each occurrence is based on data from PBDB, corrected with a more precise age (generally stage, sometimes substage), and the age of each time bin boundaries relies on the stratigraphic framework proposed in the International Chronostratigraphic Chart (updated to correspond with the ICS 2022/0295). Similarly, the ages of some species assigned to the wrong stage were corrected. In fact, some species from the French Permian deposit of Lodève were initially considered to be of Artinskian age in PBDB but most species from this deposit originate from the Merifons member, which is of Kungurian age96.Our data compilation allows a robust integration of data before and after our period of interest (i.e. the lower Permian and all geologic stages after the Carnian) to encompass occurrences of genera that may survive until the Late Triassic and to generate a sufficient background for the model to correctly estimate the extinction events around the P/T boundary. Since we used different datasets, the differences between genus-level or family-level occurrence numbers are explained by the systematic placement of some specimens that can only be placed confidently in a family but not in a genus (Supplementary Table 1). Tentative species identifications originally placed with uncertainty (reported as ‘aff.’ or ‘?’) were always included at a higher taxonomic level. Uncertain generic attributions were integrated as occurrences at the family level (e.g. a fossil initially considered Tupus? is recorded as an occurrence of Meganeuridae). Our total dataset was subdivided into smaller datasets, which represent orders or other subclades of insects (e.g. Mecoptera, Holometabola and Polyneoptera). Note that all the ichnospecies—a species name assigned to trace fossils (e.g. resting trace, nest and leaf damage)— and insect eggs (e.g. Clavapartus latus, Furcapartus exilis and Monilipartus tenuis) were not included in the analyses97. To prevent potential issues regarding the diversification estimates for clades with poor delineation, we refrained from analysing several orders that serve as taxonomic ‘wastebaskets’ (e.g. Grylloblattodea). These groups are poorly defined, likely polyphyletic or paraphyletic, and not supported by apomorphic characters—e.g. the monophyly of the ‘Grylloblattodea’ (Grylloblattida Walker, 1914 plus numerous fossil families and genera of uncertain affinities) is not supported by any synapomorphy, nor the relationships within this group. The occurrences assigned to these orders were rather included in analyses conducted at a higher taxonomic level (at the Polyneoptera level in the case of the ‘Grylloblattodea’). The detail of the composition of all the datasets is given in Supplementary Table 14, and each dataset is available in Supplementary Data 1.Studying extinction should, when possible, rely on species-level diversity to better circumscribe extinction events at this taxonomic rank, which is primarily affected by extinction98,99,100. However, in palaeoentomology, species-level occurrence data may contain less information than genus-level data, mainly because species are most of the time only known from one deposit, resulting in reduced life span, and are also sometimes poorly defined. Insects are also less prone to long-lasting genera or species than other lineages, maybe because of the relatively short time between generations (allowing for rapid evolution) or because morphological characters are better preserved or more diagnostic than in other lineages (i.e. wing venation), allowing easier differentiation. Another argument for the use of genus-level datasets is the possibility to add occurrences represented by fossils that cannot be assigned at the species level because of poor preservation or an insufficient number of specimens/available characters. By extension, the genus life span provides clues as to survivor taxa and times of origination during periods of post-extinction or recovery. A genus encompassing extinction events indicates that at least one species of this genus crossed the extinction. To get the best signal and infer a robust pattern of insect dynamics around the P/T events, we have chosen to analyse our dataset at different taxonomic ranks (e.g. genus, family and order levels) to extract as much evidence as possible.To further support our choice to work at these different levels, most recent works aiming to decipher the diversification and extinction in insect lineages have worked using a combination of analyses21,22,26; this also applies to non-insect clades51,101,102. This multi-level approach should maximise our understanding of the Permo–Triassic events.Assessing optimal parameters and preliminary testsPrior to choosing the settings for the final analyses (see detail in Dynamics of origination and extinction), a series of tests were carried out to better evaluate the convergence of our analyses. First, we analysed our genus-level dataset with PyRate36 running for 10 million generations and sampling every 10,000 generations, on ten randomly replicated datasets using the reversible-jump Markov Chain Monte Carlo (RJMCMC) model37 and the parameters of PyRate set by default. As the convergence was too low, new settings were used, notably increasing the number of generations to 50 million generations and monitoring the MCMC mixing and effective sample size (ESS) each 10 million generations. We modified the minimal interval between two shifts (-min_dt option, testing 0.5, 1.5 and 2), and found no major difference in diversification patterns between our tests. We have opted for 50 million generations with a predefined time frame set for bins corresponding to the Permian and Triassic stages, and a minimum interval between two shifts of two Ma. These parameters allow for maintaining a short bin frame and high convergence values while correctly identifying periods of diversification and extinction. For each analysis, ten datasets were generated using the extract.ages function to randomly resample the age of fossil occurrences within their respective temporal ranges (i.e. resampled ages are randomly drawn between the minimum and the maximum ages of the geological stratum). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 after excluding the first 10% of the samples as a burn-in period. The parameters are considered convergent when their ESS are greater than 200.Dynamics of origination and extinctionWe carried out the analyses of the fossil datasets based on the Bayesian framework implemented in the programme PyRate36. We analysed the fossil datasets under two models: the birth–death model with constrained shifts (BDCS38) and the RJMCMC (-A 4 option37). These models allow for a simultaneous estimate for each taxon: (1) the parameters of the preservation process (Supplementary Fig. 17), (2) the times of origination (Ts) and extinction (Te) of each taxon, (3) the origination and extinction rates and their variation through time for each stage and (4) the number and magnitude of shifts in origination and extinction rates.All analyses were set with the best-fit preservation process after comparing (-PPmodeltest option) the homogeneous Poisson process (-mHPP option), the non-homogeneous Poisson process (default option), and the time-variable Poisson process (-qShift option). The preservation process infers the individual origination and extinction times of each taxon based on all fossil occurrences and on an estimated preservation rate, denoted q, expressed as expected occurrences per taxon per Ma. The time-variable Poisson process assumes that preservation rates are constant within a predefined time frame but may vary over time (here, set for bins corresponding to stages). This model is thus appropriate when rates over time are heterogeneous.We ran PyRate for 50 million MCMC generations and a sampling every 50,000 generations for the BDCS and RJMCMC models with time bins corresponding to Permian and Triassic stages (-fixShift option). All analyses were set with a time-variable Poisson process (-qShift option) of preservation and accounted for varying preservation rates across taxa using the Gamma model (-mG option), that is, with gamma-distributed rate heterogeneity with four rate categories36. As explained above, the minimal interval between two shifts (-min_dt option) was modified and a value of 2 was used. The default prior to the vector of preservation rates is a single gamma distribution with shape = 1.5 and rate = 1.5. We reduced the subjectivity of this parameter, and favoured a better adequation to the data, allowing PyRate to estimate the rate parameter of the prior from the data by setting the rate parameter to 0 (-pP option). Therefore, PyRate assigns a vague exponential hyper-prior to the rate and samples the rate along with all other model parameters. Similarly, because our dataset does not encompass the entire fossil record of insects, we assumed that a possible edge effect may interfere with our analyses, with a strong diversification during the lowermost Permian and, conversely a strong extinction during the uppermost Triassic. Because the RJMCMC and BDCS algorithms look for rate shifts, we constrained the algorithm to only search for shifts (-edgeShift option) within the following time range 295.0 to 204.5 Ma. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 after excluding the first 10% of the samples as a burn-in period. The parameters are considered convergent when their ESS are greater than 200.We then combined the posterior estimates of the origination and extinction rates across all replicates to generate rates through-time plots (origination, extinction, and net diversification). Shifts of diversification were considered significant when log Bayes factors were >6 in the RJMCMC model, while we considered shifts to be significant in the BDCS model when mean rates in a time bin did not overlap with the 95% credibility interval (CI) of the rates of adjacent time bins.We replicated all the analyses on ten randomly generated datasets of each clade and calculated estimates of the Ts and the Te as the average of the posterior samples from each replicate. Thus, we obtained ten posterior estimates of the Ts and Te for all taxa and we used these values to estimate the past diversity dynamics by calculating the number of living taxa at each time point. For all the subsequent analyses, we used the estimated Ts and Te of all taxa to test whether or not the origination and the extinction rate dynamics were correlated with particular abiotic factors, as suggested by the drastic changes in environmental conditions known during the Permo–Triassic. We used proxies for abiotic factors, such as global continental fragmentation or the dynamic of major clades of plants, and for biotic factors via species interaction within and between ecological guilds. This approach avoids re-modelling preservation and re-estimating times of origination and extinction, which reduces drastically the computational burden, while still allowing to account for the preservation process and the uncertainties associated with fossil ages. Similarly, the times of origination and extinction used in all the subsequent analyses were obtained while accounting for the heterogeneity of preservation, origination and extinction rates. To discuss the magnitude of the periods of extinction and diversification, we compared the magnitude of these events to the background origination and extinction rates (i.e. not during extinction or diversification peaks).The PyRate approach has proven to be robust following a series of tests and simulations that reflect commonly observed biases when modelling past diversity dynamics31,38. These simulations were based on datasets simulated under a range of potential biases (i.e. violations of the sampling assumptions, variable preservation rates, and incomplete taxon sampling) and reflecting the limitations of the fossil record. Simulation results showed that PyRate is able to correctly estimate the dynamics of origination and extinction rates, including sudden rate changes and mass extinction, even if the preservation levels are low (down to 1–3 fossil occurrences per species on average), the taxon sampling is partial (up to 80% missing) or if the datasets have a high proportion of singletons (exceeding 30% of the taxa in some cases). The strongest bias in birth–death rate estimates is caused by incomplete data (i.e. missing lineages) altering the distribution of taxa; a pervasive effect often mentioned for phylogeny-based models104,105,106. However, in the case of PyRate, the simulations confirm the absence of consistent biases due to an incomplete fossil record36. Finally, the recently implemented RJMCMC model was shown to be very accurate for estimating origination and extinction rates (i.e. more accurate than the BDCS model, the boundary-crossing and three-time methods) and is able to recover sudden extinction events regardless of the biases in the fossil dataset37.The severity of extinctions and survivorsFor each event—the Roadian–Wordian, the LPME, and the Ladinian–Carnian—we quantified the percentage of extinctions and survivors at the genus level. We used the Te and Ts from our RJMCMC analysis and computed the mean for the Te (Tem) and for the Ts (Tsm) of each genus. We then filtered our dataset to keep only the genera with a Tsm older than the upper boundary of the focal event, i.e., we only kept the genera that appeared before the end of the event. Then, we discarded the genera that have disappeared before the lower boundary of the focal event, i.e. Tem older that the lower boundary of the event. The remaining genera, which corresponds to all the genera (total) present during the crisis (Ttgen), can be classified into two categories, ‘survivor genera’ (Sgen), i.e. those that survived the crisis, and those that died: ‘extinct genera’ (Egen). The survivors have a Tem younger than the upper boundary of the focal event, while the ‘extinct genera’ died out during the event and have a Tem between the lower and upper boundaries of the event of interest. To obtain the percentage of survivors, we used the following formula: (Sgen/Ttgen) × 100. Similarly, the percentage of extinction is calculated as: (Egen/Ttgen) × 100.Age-dependent extinction modelWe assessed the effect of taxon age on the extinction probability by fitting the age-dependent extinction (ADE; -ADE 1 option) model50. This model estimates the probability for a lineage to become extinct as a function of its age, also named longevity, which is the elapsed time since its origination. It is recommended to run the ADE model over time windows with roughly constant origination and extinction rates, as convergence is difficult—but not impossible—to reach in extinction or diversification contexts50. We ran PyRate for 50 million MCMC generations with a sampling every 50,000 generations, with a time-variable Poisson process of preservation (-qShift option), while accounting for varying preservation rates across taxa using the Gamma model (-mG option). We replicated the analyses on ten randomised datasets and combined the posterior estimates across all replicates. We estimated the shape (Φ) and scale (Ψ) parameters of the Weibull distribution, and the taxon longevity in a million years. According to ref. 50, there is no evidence of age-dependent extinction rates if Φ = 1. However, the extinction rate is higher for young species and decreases with species age if Φ  1. Although ADE models are prone to high error rates when origination and extinction rates increase or decrease through time, simulations with PyRate have shown that fossil-based inferences are robust50. We investigated the effect of ADE during three different periods (-filter option) as follows: (1) between 264.28 Ma and 255 Ma (pre-decline), (2) between 254.5 Ma and 251.5 Ma (decline) and (3) between 234 Ma and 212 Ma (post-crisis). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Selection of abiotic and biotic variablesTo test correlations of insect diversification with environmental changes, we examined the link between a series of environmental variables and origination/extinction rates over a period encompassing the GEE, the LPME and the CPE but also for each extinction event. We focused on the role of nine variables, also called proxies, which have been demonstrated or assumed to be linked to extinctions and changes in insect diversity26,67.The variations in the atmospheric CO2 and O2 concentrations are thought to be correlated with the diversification of several insect lineages, including the charismatic giant Meganeuridae65,66,67. Because the increase of O2 concentration has likely driven the diversification of some insects, its diminution may have resulted in the extinction or decline of some lineages. Therefore, we investigated the potential correlation of the variations of this variable with insect dynamics using data from ref. 55. We extracted the data, with 1-million-year time intervals, spanning the Permo–Triassic.Similarly, the modification of CO2 concentration, notably its increase, is known to promote speciation in some modern insect groups107. Therefore, a similar effect may have occurred during the Permian and Triassic but remains to be tested. We based our analyses on the dataset of ref. 108. We used their cleaned dataset and extracted all verified values for the Permo–Triassic interval. Because the initial data (i.e. independent estimates) were made in various locations for the same age, different values of the CO2 concentration are provided. We incorporated all these values in our analysis, allowing PyRate to search for a correlation for each value of the CO2 concentration. We obtain a final correlation independent of the sampling location, in line with our large-scale analysis.The continental fragmentation, as approximated by plate tectonic change over time, has recently been proposed as a driver of Plecoptera dynamics26. Because the period studied encompasses a major geological event, the fragmentation of the supercontinent Pangea, we investigated the effect of continental fragmentation on insect diversification dynamics. We retrieved the index of continental fragmentation developed by ref. 69 using paleogeographic reconstructions for 1-million-year time intervals. This index approaches 1 when all plates are disjoined (complete plate fragmentation) and approaches 0 when the continental aggregation is maximal.Climate change (variations in warming and cooling periods) is a probable driver of diversification changes over the history of insects21,109. Temperature is likely directly linked with insect dynamics109 but also with their food sources, notably plants110. Because it was demonstrated that modification of temperature impacted floral assemblages110, we tested the correlation between temperature variations and the diversification dynamic of insects. Major trends in global climate change through time are typically estimated from relative proportions of different oxygen isotopes (δ18O) in samples of benthic foraminiferan shells111. We used the data from ref. 112, converted to absolute temperatures following the methodology described in Condamine et al.113 (see their section Global temperature variations through time). The resulting temperature data reflects planetary-scale climatic trends, with time intervals inferior to 1-million-year, which can be expected to have led to temporally coordinated diversification changes in several clades rather than local or seasonal fluctuations.The fluctuation in relative diversity of gymnosperms, non-Polypodiales ferns, Polypodiales ferns, spore-plants, and later the rise of angiosperms has likely driven the diversification of numerous insects57,60,61,114. Close interactions between insects and plants are well-recorded during the Permian and Triassic57,60,61. In fact, herbivorous insects are known to experience high selection pressure from bottom-up forces, resulting from interactions with their hosts or feeding plants30,72. Therefore, it appears crucial to investigate the effect of these modifications on the insects’ past dynamics. We used the data from ref. 38 for the different plant lineages (all with 1-million-year time intervals). All the datasets for these variables are available in the publications cited aside from each variable or in Supplementary Data 1.Multivariate birth–death modelWe used the multivariate birth–death (MBD) model to assess to what extent biotic and abiotic factors can explain temporal variation in origination and extinction rates55. The model is described in ref. 55, where origination and extinction rates can change through time in relation to environmental variables so that origination and extinction rates depend on the temporal variations of each factor. The strength and sign (positive or negative) of the correlations are jointly estimated for each variable. The sign of the correlation parameters indicates the sign of the resulting correlation. When their value is estimated around zero, no correlation is estimated. An MCMC algorithm combined with a horseshoe prior, controlling for over-parameterisation and for the potential effects of multiple testing, jointly estimates the baseline origination (λ0) and extinction (µ0) rates and all correlation parameters (Gλ and Gµ)55. The horseshoe prior is used to discriminate which correlation parameters should be treated as noise (shrunk around 0) and which represent a true signal (i.e. significantly different from 0). In the MBD model, a correlation parameter is estimated to quantify independently the role of each variable on the origination and the extinction.We ran the MBD model using 20 (for short intervals) or 50 million MCMC generations and sampling every 20,000 or 50,000 to approximate the posterior distribution of all parameters (λ0, µ0, nine Gλ, nine Gµ and the shrinkage weights of each correlation parameter, ωG). The MBD analyses used the Ts and the Te derived from our previous analyses under the RJMCMC model. The results of the MBD analyses were summarised by calculating the posterior mean and 95% CI of all correlation parameters and the mean of the respective shrinkage weights (across ten replicates), as well as the mean and 95% CI of the baseline origination and extinction rates. We carried out six analyses, over: (1) the Permo–Triassic (between 298.9 and 201.3 Ma); (2) the Roadian–Wordian (R/W) boundary (between 270 and 265 Ma), (3) the LPME (between 254.5 and 250 Ma), (4) the Ladinian–Carnian (L/C) boundary (between 240 to 234 Ma), (5) the Permian period (between 298.9 and 251.902 Ma) and (6) the Triassic period (between 251.902 and 201.3 Ma). We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Multiple clade diversity-dependence modelTo assess the potential effect of diversity-dependence on the diversity dynamics of three or four insect guilds, we used the multiple clade diversity-dependence (MCDD) model in which origination and extinction rates are correlated with the diversity trajectory of other clades31. This model postulates that competitive interactions linked with an increase in diversity results in decreasing origination rates and/or increasing extinction rates. The MCDD model allows for testing diversity-dependence between genera of a given clade or between genera of distinct clades sharing a similar ecology.We estimated the past diversity dynamics for three (i.e. herbivores, predators, and a guild composed of generalists + detritivores/fungivores dubbed ‘others’) or four insect groups or guilds (i.e. herbivores, predators, generalists and detritivores/fungivores) by calculating the number of living species at every point in time based on the times of origination (Ts) and extinction (Te) estimated under the RJMCMC model (see above) (Supplementary Figs. 19–24). We defined our four insect groups with a cautious approach i.e. insect genera, families or orders for which nothing is known about the ecology or about the ecology of their close relatives were not considered for the analysis. For example, no diet was assigned to Diptera, Mecoptera or Glosselytrodea. The ecology of the Triassic Diptera and Permo–Triassic Mecoptera is difficult to establish because extant Diptera and Mecoptera have a wide diversity of ecology. Fossil Mecoptera are also putatively involved in numerous interactions with plants (species with elongated mouthparts), suggesting a placement in the herbivore group, while other species were likely predators. Therefore, we cannot decide to which group each species belongs. Similarly, nothing is known about the body and mouthparts of the Glosselytrodea, most of the time described based on isolated wings; we did not assign the order to any group. The definition and delineation of insect clades have also challenged the placement of several orders (e.g. ‘Grylloblattodea’) in one of our four groups. The order ‘Grylloblattodea’ is poorly delineated and mostly serves as a taxonomic ‘wastebasket’ to which it is impossible to assign a particular ecology. Finally, genera, species, or families not placed in a higher clade (e.g. Meshemipteron, Perielytridae) were not included in the analysis. Oppositely, the guilds ‘herbivores’ and ‘predators’ are well defined, and their ecology is evidenced by the morphology of their representatives and the principle of actualism. For example, the ecology of Meganeurites gracilipes (Meganeuridae) has been deeply studied, and its enlarged compound eyes, its sturdy mandibles with acute teeth, its tarsi and tibiae bearing strong spines, and the presence of a pronounced thoracic skewness are specialisations today found in dragonflies that capture their prey while in flight115. All Odonatoptera are well-known predator insects. The raptorial forelegs of the representatives of the order Titanoptera and their mouthparts with strong mandibles are linked with predatory habits81. The Palaeodictyopteroidea were herbivorous insects with long, beak-like, piercing mouthparts, and probably a sucking organ81,82. Most Hemiptera are confidently considered herbivorous insects by comparison with their extant representatives. For example, the Cicadomorpha or Sternorrhyncha are known to feed on plants and their fossil representatives likely possessed the same ecology because of similar morphologies116. Some hemipteran families (e.g. Nabidae) are predators and we cautiously distinguished herbivorous and carnivorous taxa among Hemiptera. The detail of the ecological assignations for the 1009 genera included in our analyses can be found in Supplementary Data 1 (Table MCCD).We calculated ten diversity trajectories from the ten replicated analyses under the RJMCMC model. The estimation of past species diversity might be biased by low preservation rates or taxonomic uncertainties. However, such trajectory curves are likely to provide a reasonably accurate representation of the past diversity changes in the studied clades, notably because the preservation during the Permian and Triassic period is relatively good for insects (i.e. no gaps).Our MCDD analyses comprise all the insect genera spanning from the lowermost Permian to the uppermost Triassic and were run and repeated on ten replicates (using the Te and Ts estimated under the RJMCMC model) with 50 million MCMC generations and a sampling frequency of 50,000. For each of the four insect groups, we computed the median and the 95% CI of the baseline origination and extinction rates (λi and µi), the within-group diversity-dependence parameters gλi and gµi, and the between-groups diversity dependence parameters gλij and gµij. The mean of the sampled diversity dependence parameters (e.g. gλij) was used as a measure of the intensity of the negative (if positive) or positive interactions (if negative) between each pair of groups. The interactions were considered significant when their median was different from 0 and the 95% CI did not overlap with 0. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.We cross-validated the result of the MCDD model using the MBD model. The MBD model can be used to run a multiple clade diversity-dependence analysis by providing the diversity trajectories of insect guilds as a continuous variable. These data are directly generated by PyRate using the lineages-through-time generated by the RJMCMC analyses (-ltt option). We ran the MBD model using 50 million MCMC generations and sampling every 50,000 to approximate the posterior distribution of all parameters (λ0, µ0, four Gλ, four Gµ and the shrinkage weights of each correlation parameter, ωG). We carried out three analyses, over the period encompassing the three extinction events (between 275 and 230 Ma): (1) for herbivores; (2) for predators; and (3) for ‘others’. For each analysis, the lineages-through-time data of the two other guilds are used as continuous variables to investigate a diversity dependence effect. We monitored chain mixing and ESS by examining the log files in Tracer 1.7.1103 and considered the convergence of parameters sufficient when their ESS were greater than 200.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

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    Algal sensitivity to nickel toxicity in response to phosphorus starvation

    Effect of phosphorus starved cultures of Dunaliella tertiolecta on growth represented as optical density under stress of nickel ionsIn the case of normal culture, phosphorus starved control culture (without nickel stress), and phosphorus-starved treated cultures, data presented in Table 1 and graphed in figure (S1, Supplementary Data) clearly showed a progressive increase in optical density with increasing culturing period in case of normal culture, phosphorus-starved control culture, and phosphorus-starved treated cultures. Our findings are consistent with those of18 who found that in phosphorus starved cultures of three algae species, Microcystic aeruginosa, Chlorella pyrenoidesa, and Cyclotella sp., the biomass, specific growth rate, and Chl-a all declined significantly.The optical density achieved during the four periods of culturing was lower in phosphorus-depleted control cultures than in normal cultures (i.e., cultures contained phosphorus). When compared to a normal control (without nickel addition), the optical density was reduced by 9.1% after 4 days of culturing under phosphorus deprivation and by 10.0 percent after 8 days of culturing. In the case of 5 mg/L dissolved nickel, however, the obtained optical density values in phosphorus starved treatment cultures rose with the increase in culturing period during all culturing periods as compared to phosphorus-starved control (without nickel addition) cultures.At 10 mg/L dissolved nickel and after 4 days of culturing, the optical density although less than those in case of concentration 5 mg/L, yet it was higher than control (− P) but by increasing the culturing period more than 4 days, the optical density was less than control (− P). Our results are similar to those of19 who observed that the decrease in cell division rate signaled the onset of P-deficiency. The cultures that showed no significant increase in cell number for at least three consecutive days under the experimental conditions were considered P-depleted. In addition20, observed that the growth rate of Dunaliella prava was found to be dramatically lowered when phosphorus was limited. The content of chlorophyll fractions, total soluble carbohydrates, and proteins all fell considerably as a result of phosphorus restriction.The results concerning the effect of dissolved nickel on the growth of Dunaliella tertiolecta under conditions of phosphorus limitation show that phosphorus starved Dunaliella had lower growth as compared to the control (phosphorus-containing culture medium). These results are in agreement with those obtained by7 who reported that the optical density of Chlorella kessleri cell suspension decreased with phosphorus deficiency compared to control. Also21, found that Chlorella vulgaris cells grew 30–40% slower in phosphorus-starved cultures than in control cultures. Furthermore22, showed that diatoms were unable to thrive when phosphorus levels were insufficient. Diatom dominances were reduced to 45 and 55% in enclosures where phosphate was not provided23 observed that, under salt stress, Chlorella’s metabolic rate was substantially lower than Dunaliella’s.It can be concluded that when microorganisms are deprived of phosphorus, dissolved nickel uptake decreases, resulting in an increase in algal metabolism24. Also25, examined the effects of phosphorus and nitrogen starvation on the life cycle of Emiliania huxleyi (Haptophyta) and proved that various biochemical pathways’ metabolic load increased under P-starvation while it decreased under N-starvation.Effect of phosphorus starved cultures of Dunaliella tertiolecta on chlorophylls content under stress of nickel ionsTable 2 and figure (S2, Supplementary Data) show the sequences of change in the amount of chlorophylls a and b in phosphorus-depleted cultures of Dunaliella tertiolecta in response to various dissolved nickel concentrations. The results show that total chlorophyll content rose steadily until the end of the experiment under normal conditions (a control containing phosphorus). These results are in harmony with those obtained by24. The ratio between chlorophylls “a” and “b” remained nearly constant till the end of the 12th day. At the 16th day of culturing, the ratio decreased from 2.9:1 to 2.4:1. On the contrary, the total chlorophylls under control (in the absence of nickel element) in case of phosphorus-starved cultures showed a progressive increase up to the 12th day. At the 12th day the total chlorophylls in case of phosphorus-starved cultures decreased by 10.7% compared to the normal control. At the 16th day, the total chlorophylls in case of untreated phosphorus starved culture decreased by 20.8% compared to those obtained at normal control26. Reported that the chlorophyll content of Chlorella sorokiniana was significantly reduced due to a lack of nitrogen and phosphorus in the medium.Table 2 Effect of different concentrations of dissolved nickel (mg/L) on chlorophylls content (µg/ml) of Dunaliella tertiolecta under the stress of phosphorus starvation.Full size tableThe total chlorophyll content of Dunaliella tertiolecta in the phosphorus-starved cultures treated with 5 mg/L of dissolved nickel increased gradually until the 12th day, when the content of the total chlorophylls reached 2.11 µg/ml, i.e., higher than the phosphorus-starved control (− P) by 15.3%. At the 16th day, the total chlorophylls, although lower than those obtained at the 12th day, were still higher than the control (− P). At a concentration of 10 mg/L of dissolved nickel, slight increase in the content of total chlorophylls was recorded from the beginning to the end of the culturing period, i.e., from the 4th to the 16th day. At the other concentrations of dissolved nickel (15, 20, and 25 mg/L), a pronounced decrease in the total chlorophylls could be observed from the 4th to the 16th day of culturing compared to control (− P). Our results are going with an agreement with those obtained by27 who found that chlorophylls were inhibited maximum at higher dissolved nickel concentrations but activated at lower values. The normal ratio between chlorophylls “a” and “b” (3:1) was upset after the 8th day of culturing under concentrations 5, 10, and 15 mg/L of dissolved nickel. At 20 and 25 mg/L of dissolved nickel, this ratio was unstable from the beginning to the end of the experiment. The fact that dissolved nickel is extremely mobile and hence only absorbed to a minimal level may explain the sensitivity of the tested alga to nickel in response to phosphorus deficiency, and an increase in phosphorus concentration favors its absorption by microorganisms28. It can be concluded that when microorganisms are deprived of phosphorus, dissolved nickel uptake decreases, resulting in an increase in algal metabolism.Effect of different concentrations of dissolved nickel on photosynthesis (O2-evolution) of phosphorus starved cells of Dunaliella tertiolecta
    Data represented in Table 3 and graphed in figure (S3, Supplementary Data S3) showed that the effect of phosphorus limitation on the photosynthetic activity of Dunaliella tertiolecta in response to five different concentrations of dissolved nickel revealed that, under phosphorus limiting conditions, the amount of O2-evolution was lower than in untreated cultures (the control). The evolution of O2 after 4 days of culturing in case of phosphorus starved control decreased by 8.7% compared to normal control, while after 12 days it decreased by 30.4%. The rate of O2-evolution at different concentrations of dissolved nickel over 5 mg/L caused successive reductions in the O2-evolution of phosphorus starved cells. Application of 5 mg/L of dissolved nickel, the results cleared that the rate of O2-evolution increased under the effect of all tested concentrations till the end of the experiment. It is clear from our data that the rate of O2-evolution depended mainly on the concentration of the nickel element and the length of culturing period. The lower the rate of O2-evolution, the higher the element’s concentration, and the longer the culturing period. This coincided with the findings of7 who found that low phosphorus treatment causes Chlorella kessleri to lose its photosynthetic activity. In this regard, it was discovered that phosphorus deficiency resulted in a decrease in photosynthetic electron transport activity29 found that the O2-evolution of Chlamydomon reinhardtii declined by 75%. This decrease reflects damage of PSII and the generation of PSII QB-non reducing centers.Table 3 Effect of different concentrations of dissolved nickel (mg/L) on photosynthetic activity (O2-evolution calculated as µ mol O2 mg chl-1 h-1) on phosphorus supplemented and starved cells of Dunaliella tertiolecta.Full size tableAlso30 found that P- deficiency has been correlated with lower photosynthetic rates. In the case of the treated phosphorus-starved cultures with lower concentrations (5 mg/L) of dissolved nickel, the rate of photosynthesis increased when compared to the phosphorus-starved control, but was less than that of the normal control (without nickel treatment). On the contrary, it was found that, in the treated phosphorus-starved cultures at concentrations of 10, 15, 20 and 25 mg/L of the tested element, the rate of photosynthesis decreased from the beginning to the end of the experiment. With increasing concentration, duration of the culturing period, and kind of element, the condition of decrease in O2-evolution became more pronounced; the same results were also recorded by24. The stimulation of growth and photosynthesis in the presence of some concentrations of dissolved nickel under phosphorus-limiting conditions is observed by31 they report that in Cu2+ sensitive Scenedesmus acutus, intracellular polyphosphate plays a key role in shielding photosynthesis from Cu2+ toxicity but not in copper resistant species.Effect of different concentrations of dissolved nickel on respiration (O2-uptake) of phosphorus starved cells of Dunaliella tertiolectaData obtained in Table 4 and graphed in figure (S4, Supplementary Data S4) concerning the rate of respiration of Dunaliella tertiolecta under phosphorus-limiting conditions was higher than that of untreated phosphorus-starved (control) for a short period of time only, i.e., after 4 days, at concentrations 5, 10 and 15 mg/L of dissolved nickel, After 8 days of culturing, the rate of O2- uptake increased only at 5 mg/L of dissolved nickel, while at the other concentrations it decreased gradually with increasing the concentration of the element. This finding is consistent with the findings of23, who discovered that Dunaliella cells increased their O2 absorption and evolution rates in the presence of 2 M salt NaCl in the media. In terms of oxygen uptake rate, Dunaliella cells demonstrated an increase in salt concentrations. In 1.5 M NaCl, it increased significantly by 60–80%.Table 4 Effect of different concentrations of dissolved nickel (mg/L) on respiration activity (O2-uptake calculated as µ mol O2 h-1) on phosphorus supplemented and starved cells of Dunaliella tertiolecta.Full size tableConcerning the increase in respiration in P-depleted green alga species cultures5 suggested that Scenedesmus, for example, can utilize the energy stored in starch and lipids for active phosphorus uptake from lake sediments. This process is aided by an increase in phosphatase production32 and these cells’ ability to operate anaerobically33. When unicellular green algae or higher plants are exposed to P deficiency, the majority of newly fixed carbon appears to be allocated to the synthesis of non-phosphorylated storage polyglucans (i.e., starch) or sucrose, with less photosynthetic activity directed to respiratory metabolism and other biosynthesis pathways34. It can be concluded from the obtained results that, when the alga was cultivated under phosphorus deficiency and treated with varied amounts of dissolved nickel, the growth was the most sensitive characteristic, followed by photosynthesis, and then dark respiration. In the few comparative studies with several species of green algae, growth was more sensitive than the other physiological processes examined. Out of them35, reported that growth was more susceptible to phosphorus deficiency in Chlorella pyrenoidosa and Asterionella gracilis than photosynthesis and respiration (the least sensitive processes). Growth was also more sensitive than photosynthesis in Nitzschia closterium 36 . Another important fact reported by37 is that under low phosphorus conditions, Dunaliella parva accumulates lipids rather than carbohydrates. These findings imply that phosphorus stress may prevent starch and/or protein production, leading to an increase in carbon flux to lipids. More

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    Sap flow of sweet cherry reveals distinct effects of humidity and wind under rain covered and netted protected cropping systems

    Jensen, M. H. & Malter, A. J. Protected Agriculture—A Global Review. World Bank Technical Paper Number 253 (World Bank, 1995).
    Google Scholar 
    Meli, T., Riesen, W. & Widmer, A. Protection of sweet cherry hedgerows with polyethylene films. Acta Hortic. 155, 463–467 (1984).Article 

    Google Scholar 
    Janick, J. (ed.) Horticultural Reviews Vol. 30, 115–162 (Wiley, 2004).
    Google Scholar 
    Janke, R. R., Altamimi, M. E. & Khan, M. The use of high tunnels to produce fruit and vegetable crops in North America. Agric. Sci. 08, 692–715. https://doi.org/10.4236/as.2017.87052 (2017).Article 

    Google Scholar 
    Alarcon, J. J. et al. Sap flow as an indicator of transpiration and the water status of young apricot trees. Plant Soil 227, 77–85. https://doi.org/10.1023/A:1026520111166 (2000).Article 
    CAS 

    Google Scholar 
    Ferrara, G. & Flore, J. Comparison between different methods for measuring tranpiration in potted apple trees. Biol. Plant. 46, 41–47 (2003).Article 

    Google Scholar 
    Nicolás, E., Torrecillas, A., Amico, J. D. & Alarcón, J. J. Sap flow, gas exchange, and hydraulic conductance of young apricot trees growing under a shading net and different water supplies. J. Plant Physiol. 162, 439–447. https://doi.org/10.1016/j.jplph.2004.05.014 (2005).Article 
    CAS 

    Google Scholar 
    Green, S. & Romero, R. Can we improve heat-pulse to measure low and reverse flows. Acta Hortic. 951, 19–30 (2012).Article 

    Google Scholar 
    Noitsakis, B. & Nastis, A. S. Seasonal changes of water potential, stomatal conductance and transpiration in the leaf of cherry trees grown in shelter. CIHEAM 12, 267–270 (1995).
    Google Scholar 
    Lang, G. A. High tunnel tree fruit production: The final frontier. HortTechnology 19, 50–55 (2009).Article 

    Google Scholar 
    Lang, G. A. Tree fruit production in high tunnels: Current status and case study of sweet cherries. Acta Hortic. 987, 73–82 (2013).Article 

    Google Scholar 
    Meland, M., Frøynes, O. & Kaiser, C. High tunnel production systems improve yields and fruit size of sweet cherry. Acta Hortic. 1161, 117–124. https://doi.org/10.17660/ActaHortic.2017.1161.20 (2017).Article 

    Google Scholar 
    Cohen, S., Moreshet, S., Guillou, L. L., Simon, J.-C. & Cohen, M. Response of citrus trees to modified radiation regime in semi-arid conditions. J. Exp. Bot. 48, 35–44. https://doi.org/10.1093/jxb/48.1.35 (1997).Article 
    CAS 

    Google Scholar 
    Zeppel, M., Murray, B. R., Barton, C. & Eamus, D. Seasonal responses of xylem sap velocity to VPD and solar radiation during drought in a stand of native trees in temperate Australia. Funct. Plant Biol. 31, 461–470 (2004).Article 

    Google Scholar 
    Bonada, M., Buesa, I., Moran, M. A. & Sadras, V. O. Interactive effects of warming and water deficit on Shiraz vine transpiration in the Barossa Valley, Australia. OENO One 52, 189–202. https://doi.org/10.20870/oeno-one.2018.52.2.2141 (2018).Article 
    CAS 

    Google Scholar 
    Wang, K. Y., Kellomaki, S., Zha, T. & Peltola, H. Annual and seasonal variation of sap flow and conductance of pine trees grown in elevated carbon dioxide and temperature. J. Exp. Bot. 56, 155–165. https://doi.org/10.1093/jxb/eri013 (2005).Article 
    CAS 

    Google Scholar 
    Laplace, S., Chu, C. & Kume, S. Wind speed response of sap flow in five subtropical trees based on wind tunnel experiments. Br. J. Environ. Clim. Change 3, 160–171. https://doi.org/10.9734/BJECC/2013/3842 (2013).Article 

    Google Scholar 
    Kellomäki, S. & Wang, K. Y. Sap flow in Scots pine growing under conditions of year-round carbon dioxide enrichment and temperature elevation. Plant, Cell Environ. 21, 969–981. https://doi.org/10.1046/j.1365-3040.1998.00352.x (2002).Article 

    Google Scholar 
    Urban, J., Ingwers, M., McGuire, M. A. & Teskey, R. O. Stomatal conductance increases with rising temperature. Plant Signal. Behav. 12, 3–6. https://doi.org/10.1080/15592324.2017.1356534 (2017).Article 
    CAS 

    Google Scholar 
    Wu, J. et al. Nocturnal sap flow is mainly caused by stem refilling rather than nocturnal transpiration for Acer truncatum in urban environment. Urban For. Urban Green. 56, 126800. https://doi.org/10.1016/j.ufug.2020.126800 (2020).Article 

    Google Scholar 
    Chen, Y.-J. et al. Time lags between crown and basal sap flows in tropical lianas and co-occurring trees. Tree Physiol. 36, 736–747. https://doi.org/10.1093/treephys/tpv103 (2015).Article 

    Google Scholar 
    Marshall, D. C. Measurment of sap flow in conifers by heat transport. Plant Physiol. 33, 385–396 (1958).Article 
    CAS 

    Google Scholar 
    Swanson, R. H. & Whitfield, W. A. A numerical analysis of heat pulse velocity theory and practice. J. Exp. Bot. 32, 221–239 (1981).Article 

    Google Scholar 
    Green, S., Clothier, B. & Jardine, B. Theory and practical application of heat pulse to measure sap flow. Am. Soc. Agron. 95, 1371–1379 (2003).Article 

    Google Scholar 
    Goodwin, I., Cornwall, D. & Green, S. R. Pear transpiration and basal crop coefficients estimated by sap flow. Acta Hortic. 951, 183–190. https://doi.org/10.17660/ActaHortic.2012.951.22 (2012).Article 

    Google Scholar 
    Fernandez, J. E. et al. Heat-pulse measurements of sap flow in olives for automating irrigation, tests, root flow and diagnostics of water stress. Agric. Water Manag. 51, 99–123 (2001).Article 

    Google Scholar 
    Green, S. R. & Clothier, B. Water use of kiwifruit vines and apple trees by the heat-pulse technique. J. Exp. Bot. 39, 115–123 (1988).Article 

    Google Scholar 
    Green, S. R. et al. Measurement of sap flow in young apple trees using the average gradient heat-pulse method. Acta Hortic. 1222, 173–178. https://doi.org/10.17660/ActaHortic.2018.1222.35 (2018).Article 

    Google Scholar 
    Green, S., Clothier, B. & Perie, E. A re-analysis of heat pulse theory across a wide range of sap flows. Acta Hortic. 846, 95–104 (2009).Article 

    Google Scholar 
    Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop Evapotranspiration Guidelines for Computing Crop Water Requirements, FAO Irrigation and Drainage Paper 56 300 (FAO, 1998).
    Google Scholar 
    R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2010).Hastie, T. & Tibshirani, R. Generalized Additive Models (Chapman and Hall/CRC, 1990).MATH 

    Google Scholar 
    Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723. https://doi.org/10.1109/TAC.1974.1100705 (1974).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Sams, C. E. & Flore, J. A. The influence of leaf age, leaf position on the shoot, and environmental variables on net photosynthetic rate of sour cherry (Prunus cerasus L. ’Montmorency’). J. Am. Soc. Hortic. Sci. 107, 339–344 (1982).Article 

    Google Scholar 
    Wallberg, B. N. & Sagredo, K. X. Vegetative and reproductive development of “Lapins” sweet cherry trees under rain protective cropping. Int. Soc. Hortic. Sci. 1058, 411–417 (2014).
    Google Scholar 
    Lang, G. A. Growing sweet cherries under plastic covers and tunnels: Physiological aspects and practical considerations. Acta Hortic. 1020, 303–312. https://doi.org/10.17660/ActaHortic.2014.1020.43 (2014).Article 

    Google Scholar 
    Goodwin, I., McClymont, L., Turpin, S. & Darbyshire, R. Effectiveness of netting in decreasing fruit surface temperature and sunburn damage of red-blushed pear. N. Z. J. Crop. Hortic. Sci. 46, 334–345. https://doi.org/10.1080/01140671.2018.1432492 (2018).Article 
    CAS 

    Google Scholar 
    Mika, A., Buler, Z., Wójcik, K. & Konopacka, D. Influence of the plastic cover on the protection of sweet cherry fruit against cracking, on the microclimate under cover and fruit quality. J. Hortic. Res. 27, 31–38. https://doi.org/10.2478/johr-2019-0018 (2019).Article 
    CAS 

    Google Scholar 
    Blanco, V., Zoffoli, J. P. & Ayala, M. High tunnel cultivation of sweet cherry (Prunus avium L.): Physiological and production variables. Sci. Hortic. 251, 108–117. https://doi.org/10.1016/j.scienta.2019.02.023 (2019).Article 

    Google Scholar 
    Sams, C. E. & Flore, J. A. Net photosynthetic rate of sour cherry (Prunus cerasus L. ‘Montmorency’) during the growing season with particular reference to fruiting. Photosynth. Res. 4, 307–316. https://doi.org/10.1007/BF00054139 (1983).Article 

    Google Scholar 
    Lange, O. L., Schulze, E. D., Evenari, M., Kappen, L. & Buschbom, U. The temperature-related photosynthesis capacity of plants under desert conditions. Oecologia 17, 97–110. https://doi.org/10.1007/BF00346273 (1974).Article 
    CAS 

    Google Scholar 
    Beckman, T. G., Perry, R. L. & Flore, J. A. Short-term flooding affects gas exchange characteristics of containerized sour cherry trees. HortScience 27, 1297. https://doi.org/10.21273/hortsci.27.12.1297 (1992).Article 

    Google Scholar 
    Lei, H., Zhi-Shan, Z. & Xin-Rong, L. Sap flow of Artemisia ordosica and the influence of environmental factors in a revegetated desert area: Tengger Desert, China. Hydrol. Processes 24, 1248–1253. https://doi.org/10.1002/hyp.7584 (2010).Article 

    Google Scholar 
    Juhász, A., Hrotko, K. & Tokei, L. Air and Water Components of the Environment, 76–82.Ravi, S. & D’Odorico, P. A field-scale analysis of the dependence of wind erosion threshold velocity on air humidity. Geophys. Res. Lett. 32, 023675. https://doi.org/10.1029/2005gl023675 (2005).Article 

    Google Scholar 
    Holmes, M. & Farrell, D. South African Avocado Growers Association Yearbook Vol. 16, 59–64 (1993).Jones, H. G. Plants and Microclimate: A quantitative Approach to Environmental Plant Physiology 3rd edn. (Cambridge University Press, 2014).
    Google Scholar 
    Juhász, Á., Sepsi, P., Nagy, Z., Tőkei, L. & Hrotkó, K. Water consumption of sweet cherry trees estimated by sap flow measurement. Sci. Hortic. 164, 41–49. https://doi.org/10.1016/j.scienta.2013.08.022 (2013).Article 

    Google Scholar 
    Gussakovsky, E. E., Salomon, E., Ratner, K., Shahak, Y. & Driesenaar, A. R. J. Photoinhibition (light stress) in citrus leaves. Acta Hortic. 349, 139–143 (1993).Article 

    Google Scholar 
    Grappadelli, L. C. & Lakso, A. N. Is maximizing orchard light interception always the best choice? Acta Hortic. 732, 507–518. https://doi.org/10.17660/ActaHortic.2007.732.77 (2007).Article 

    Google Scholar  More

  • in

    Vegetation assessments under the influence of environmental variables from the Yakhtangay Hill of the Hindu-Himalayan range, North Western Pakistan

    Khan, M. et al. Plant species and communities assessment in interaction with edaphic and topographic factors; an ecological study of the mount Eelum District Swat Pakistan. Saudi J. Biol. Sci. 24(4), 778–786 (2017).Article 

    Google Scholar 
    Ur Rahman, A. et al. Impact of multiple environmental factors on species abundance in various forest layers using an integrative modeling approach. Global Ecol. Conserv. 29, e01712 (2021).Article 

    Google Scholar 
    Arneth, A., Uncertain future for vegetation cover. Nature 524(7563), 44–45.Goldsmith, F., Description and analysis of vegetation. Methods Plant Ecol. (1976).Rahman, I. U. et al. First insights into the floristic diversity, biological spectra and phenology of Manoor Valley Pakistan. Pak. J. Bot 50(3), 1113–1124 (2018).
    Google Scholar 
    Khan, S.M., Plant communities and vegetation ecosystem services in the Naran Valley, Western Himalaya, 2012, University of Leicester.Haq, F., Ahmad, H. & Iqbal, Z. Vegetation description and phytoclimatic gradients of subtropical forests of Nandiar Khuwar catchment District Battagram. Pak. J. Bot 47(4), 1399–1405 (2015).
    Google Scholar 
    Iqbal, M. et al. A novel approach to phytosociological classification of weeds flora of an agro-ecological system through Cluster, two way cluster and indicator species analyses. Ecol. Ind. 84, 590–606 (2018).Article 

    Google Scholar 
    Shaw, M. R. et al. Grassland responses to global environmental changes suppressed by elevated CO2. Science 298(5600), 1987–1990 (2002).Article 

    Google Scholar 
    Drenovsky, R.E., Effects of mineral nutrient deficiencies on plant performance in the desert shrubs Chrysothamnus nauseosus ssp. consimilis and Sarcobatus vermiculatus2002: University of California, Davis.Iqbal, M. et al. Vegetation classification of the Margalla Foothills, Islamabad under the influence of edaphic factors and anthropogenic activities using modern ecological tools. Pak. J. Bot 53(5), 1831–1843 (2021).Article 

    Google Scholar 
    Bai, Y. et al. Landscape-level dynamics of grassland-forest transitions in British Columbia. J. Range Manag. 57(1), 66–75 (2004).Article 

    Google Scholar 
    Zhao, T. et al. Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm. Remote Sens. Environ. 257, 112321 (2021).Article 

    Google Scholar 
    Austin, M., Chapter 2: Vegetation and environment: discontinuities and continuities. IN VAN DER MAAREL, E.(Ed.) Végétation ecology. Etats‐Unis, 2005, Blackwell Publishing.Peña-Claros, M. et al. Soil effects on forest structure and diversity in a moist and a dry tropical forest. Biotropica 44(3), 276–283 (2012).Article 

    Google Scholar 
    Miao, R. et al. Effects of long-term grazing exclusion on plant and soil properties vary with position in dune systems in the Horqin Sandy Land. CATENA 209, 105860 (2022).Article 

    Google Scholar 
    Abbas, Z. et al. Plant communities and anthropo-natural threats in the Shigar valley,(Central Karakorum) Baltistan-Pakistan. Pak. J. Bot. 52, 987–994 (2020).Article 

    Google Scholar 
    Anwar, S., et al., Plant diversity and communities pattern with special emphasis on the indicator species of a dry temperate forest: A case study from Liakot area of the Hindu Kush mountains, Pakistan. Trop. Ecol. 1–16 (2022).Mumshad, M. et al. Phyto-ecological studies and distribution pattern of plant species and communities of Dhirkot, Azad Jammu and Kashmir, Pakistan. PLoS ONE 16(10), e0257493 (2021).Article 

    Google Scholar 
    Baldeck, C. A. et al. Soil resources and topography shape local tree community structure in tropical forests. Proc. R. Soc. B Biol. Sci. 280(1753), 20122532 (2013).Article 

    Google Scholar 
    Guerra, T. N. F. et al. Influence of edge and topography on the vegetation in an Atlantic Forest remnant in northeastern Brazil. J. For. Res. 18(2), 200–208 (2013).Article 

    Google Scholar 
    Townsend, A. R., Asner, G. P. & Cleveland, C. C. The biogeochemical heterogeneity of tropical forests. Trends Ecol. Evol. 23(8), 424–431 (2008).Article 

    Google Scholar 
    Becknell, J. M. & Powers, J. S. Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest. Can. J. For. Res. 44(6), 604–613 (2014).Article 

    Google Scholar 
    Geri, F., Rocchini, D. & Chiarucci, A. Landscape metrics and topographical determinants of large-scale forest dynamics in a Mediterranean landscape. Landsc. Urban Plan. 95(1–2), 46–53 (2010).Article 

    Google Scholar 
    Lomolino, M. V. Elevation gradients of species-density: historical and prospective views. Glob. Ecol. Biogeogr. 10(1), 3–13 (2001).Article 

    Google Scholar 
    Zhang, K. et al. An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis. Environ. Model. Softw. 148, 105279 (2022).Article 

    Google Scholar 
    Liu, Y. et al. A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds. J. Hydrol. 590, 125440 (2020).Article 

    Google Scholar 
    Mir, A. Y. et al. Ethnopharmacology and phenology of high-altitude medicinal plants in Kashmir Northern Himalaya. Ethnobot. Res. Appl. 22, 1–15 (2021).
    Google Scholar 
    Vetaas, O. R. & Grytnes, J. A. Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal. Glob. Ecol. Biogeogr. 11(4), 291–301 (2002).Article 

    Google Scholar 
    Li, W. et al. Fine root biomass and morphology in a temperate forest are influenced more by the nitrogen treatment approach than the rate. Ecol. Ind. 130, 108031 (2021).Article 

    Google Scholar 
    Su, N. et al. Landscape context determines soil fungal diversity in a fragmented habitat. CATENA 213, 106163 (2022).Article 

    Google Scholar 
    Yang, Y., et al., Nitrogen fertilization weakens the linkage between soil carbon and microbial diversity: a global meta‐analysis. Global Change Biol. (2022).Ahmad, Z. et al. Weed species composition and distribution pattern in the maize crop under the influence of edaphic factors and farming practices: A case study from Mardan Pakistan. Saudi J. Biol. Sci. 23(6), 741–748 (2016).Article 

    Google Scholar 
    Rahman, A. U. et al. Ecological assessment of plant communities and associated edaphic and topographic variables in the Peochar Valley of the Hindu Kush mountains. Mt. Res. Dev. 36(3), 332–341 (2016).Article 

    Google Scholar 
    Ashton, P. S. A contribution of rain forest research to evolutionary theory. Ann. Mo. Bot. Gard. 64(4), 694–705 (1977).Article 

    Google Scholar 
    Yang, Y. et al. Negative effects of multiple global change factors on soil microbial diversity. Soil Biol. Biochem. 156, 108229 (2021).Article 

    Google Scholar 
    Pärtel, M. Local plant diversity patterns and evolutionary history at the regional scale. Ecology 83(9), 2361–2366 (2002).Article 

    Google Scholar 
    Taylor, D.R., Aarssen, L.W., & Loehle, C. On the relationship between r/K selection and environmental carrying capacity: A new habitat templet for plant life history strategies. Oikos 239–250 (1990).Knapp, A. K. et al. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 298(5601), 2202–2205 (2002).Article 

    Google Scholar 
    Zscheischler, J. et al. Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes. J. Geophys. Res. Biogeosci. 121(8), 2186–2198 (2016).Article 

    Google Scholar 
    Gao, C. et al. Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region China. Urban Clim. 40, 101005 (2021).Article 

    Google Scholar 
    Wang, S. et al. Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards. J. Hydrol. 603, 126964 (2021).Article 

    Google Scholar 
    Zhang, K. et al. The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: An analysis using radar-derived estimates of root-zone soil moisture. J. Geophys. Res. Biogeosci. 124(11), 3208–3231 (2019).Article 

    Google Scholar 
    Yang, Y. et al. Increasing contribution of microbial residues to soil organic carbon in grassland restoration chronosequence. Soil Biol. Biochem. 170, 108688 (2022).Article 

    Google Scholar 
    Li, J. et al. Differential mechanisms drive species loss under artificial shade and fertilization in the Alpine Meadow of the Tibetan Plateau. Front. Plant Sci. 13, 832473–832473 (2022).Article 

    Google Scholar 
    Fischer, C. et al. How do earthworms, soil texture and plant composition affect infiltration along an experimental plant diversity gradient in grassland?. PLoS ONE 9(6), e98987 (2014).Article 

    Google Scholar 
    Zhao, T. et al. Soil moisture experiment in the Luan River supporting new satellite mission opportunities. Remote Sens. Environ. 240, 111680 (2020).Article 

    Google Scholar 
    Marandi, A., Polikarpus, M. & Jõeleht, A. A new approach for describing the relationship between electrical conductivity and major anion concentration in natural waters. Appl. Geochem. 38, 103–109 (2013).Article 

    Google Scholar 
    Xu, J. et al. Modeling of coupled transfer of water, heat and solute in saline loess considering sodium sulfate crystallization. Cold Reg. Sci. Technol. 189, 103335 (2021).Article 

    Google Scholar 
    Chen, X. et al. Spatiotemporal characteristics and attribution of dry/wet conditions in the Weihe River Basin within a typical monsoon transition zone of East Asia over the recent 547 years. Environ. Model. Softw. 143, 105116 (2021).Article 

    Google Scholar 
    Ali, G., Siddique, S. & Suliman, M. Effect of canopy cover on natural regeneration of pinus wallichiana in moist temperate forest of Yakh Tangay, District Shangla Swat Pakistan. FUUAST J. Biol. 8(2), 193–201 (2018).
    Google Scholar 
    Zhang, K. et al. Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China. Nat. Hazard. 19(1), 93–105 (2019).Article 

    Google Scholar 
    Khan, W. et al. Vegetation mapping and multivariate approach to indicator species of a forest ecosystem: A case study from the Thandiani sub Forests Division (TsFD) in the Western Himalayas. Ecol. Ind. 71, 336–351 (2016).Article 

    Google Scholar 
    Iqbal, J. & Ahmed, M. Vegetation description of some pine forests of Shangla district of Khyber Pakhtunkhwa Pakistan: A preliminary study. FUUAST J. Biol. 4(1), 83–88 (2014).
    Google Scholar 
    Sparrow, B. D. et al. A vegetation and soil survey method for surveillance monitoring of rangeland environments. Front. Ecol. Evol. 8, 157 (2020).Article 

    Google Scholar 
    Esri, R., ArcGIS desktop: release 10. Environmental Systems Research Institute, CA (2011).Salzer, D., & Willoughby, J. Standardize this! The futility of attempting to apply a standard quadrat size and shape to rare plant monitoring. in Proceedings of the symposium of the North Coast Chapter of the California Native Plant Society: the ecology and management of rare plants of northwestern California. Arcata, CA. Sacramento, CA: The California Native Plant Society (2004).Bano, S. et al. Eco-Floristic studies of native plants of the Beer Hills along the Indus River in the districts Haripur and Abbottabad Pakistan. Saudi J. Biol. Sci. 25(4), 801–810 (2018).Article 

    Google Scholar 
    Perveen, A. & Qaiser, M. Pollen flora of Pakistan–XXXI Betulaceae. Pak. J. Bot. 31, 243–246 (1999).
    Google Scholar 
    Raunkiaer, C., The life forms of plants and statistical plant geography; being the collected papers of C. Raunkiaer. The life forms of plants and statistical plant geography; being the collected papers of C. Raunkiaer. (1934).Hussain, S.S., Pakistan manual of plant ecology1984: National Book Foundation.Kamran, S. et al. The role of graveyards in species conservation and beta diversity: A vegetation appraisal of sacred habitats from Bannu Pakistan. J. For. Res. 31(4), 1147–1158 (2020).Article 

    Google Scholar 
    Manan, F. et al. Environmental determinants of plant associations and evaluation of the conservation status of Parrotiopsis jacquemontiana in Dir, the Hindu Kush Range of Mountains. Trop. Ecol. 61(4), 509–526 (2020).Article 

    Google Scholar 
    Tfaily, M. M. et al. Sequential extraction protocol for organic matter from soils and sediments using high resolution mass spectrometry. Anal. Chim. Acta 972, 54–61 (2017).Article 

    Google Scholar 
    Chaney, R., Slonim, S., & Slonim, S. Determination of calcium carbonate content in soils, in Geotechnical properties, behavior, and performance of calcareous soils1982, ASTM International.McCune, B., & Mefford, M. PC-ORD, Multivariate analysis of ecological data, Version 5 for Windows edition. MjM Software Design, Gleneden Beach, Oregon USA (2005).Lepš, J., & Šmilauer, P. Multivariate analysis of ecological data using CANOCO2003: Cambridge university press.Xie, W. et al. A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin county, China. ISPRS Int. J. Geo Inf. 10(2), 93 (2021).Article 

    Google Scholar 
    Li, L., Lei, Y. & Pan, D. Economic and environmental evaluation of coal production in China and policy implications. Nat. Hazards 77(2), 1125–1141 (2015).Article 

    Google Scholar 
    Team, R.C., R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2013).Anwar, S. et al. Floristic composition and ecological gradient analyses of the Liakot Forests in the Kalam region of District Swat Pakistan. J. For. Res. 30(4), 1407–1416 (2019).Article 

    Google Scholar 
    Haq, S. M. et al. Exploring and understanding the floristic richness, life-form, leaf-size spectra and phenology of plants in protected forests: A case study of Dachigam National Park in Himalaya Asia. Acta Ecol. Sin. 41(5), 479–490 (2021).Article 

    Google Scholar 
    Ilyas, M. et al. A Preliminary checklist of the vascular flora of Kabal Valley, Swat Pakistan. Pak. J. Bot 45(2), 605–615 (2013).
    Google Scholar 
    Amjad, M. S. et al. Floristic composition, biological spectrum and phenological pattern of vegetation in the subtropical forest of Kotli District, AJK Pakistan. Pure Appl. Biol. (PAB) 6(2), 426–447 (2017).
    Google Scholar 
    Shaheen, H. et al. Species diversity, community structure, and distribution patterns in western Himalayan alpine pastures of Kashmir Pakistan. Mount. Res. Dev. 31(2), 153–159 (2011).Article 

    Google Scholar 
    Abbas, Z. et al. Ethnobotany of the balti community, tormik valley, karakorum range, baltistan, pakistan. J. Ethnobiol. Ethnomed. 12(1), 1–16 (2016).Article 

    Google Scholar 
    Ahmed, M. et al. Phytosociology and structure of Himalayan forests from different climatic zones of Pakistan. Pak. J. Bot. 38(2), 361 (2006).MathSciNet 

    Google Scholar 
    Shehzadi, S. et al. Floristic compositions along an 18-Km long transect in Ayubia National Park District Abbottabad Pakistan. Pak. J. Bot. 41(5), 2115–2127 (2009).
    Google Scholar 
    Khan, W., et al., Life forms, leaf size spectra and diversity indices of plant species grown in the Thandiani forests, district Abbottabad, Khyber Pakhtunkhwa, Pakistan. Saudi J. Biol. Sci.Kharkwal, G. et al. Phytodiversity and growth form in relation to altitudinal gradient in the Central Himalayan (Kumaun) region of India. Curr. Sci. 1, 873–878 (2005).
    Google Scholar 
    Bennie, J. et al. Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol. Model. 216(1), 47–59 (2008).Article 

    Google Scholar 
    Choudhary, K. & Nama, K. S. Phyto-diversity of Mukundara hills national park of Kota district, Rajasthan India. Adv. Appl. Sci. Res. 5(1), 18–23 (2014).
    Google Scholar 
    Shimwell, D.W., Description and classification of vegetation (1971).Malik, Z.H., Comparative study of vegetation of GungaChotti and Bedori Hills, Distric Bagh, Azad Jammu and Kashmir with special reference to range conditions, 2005, University of Peshawar, Pakistan.Khan, W. et al. Life forms, leaf size spectra, regeneration capacity and diversity of plant species grown in the Thandiani forests, district Abbottabad, Khyber Pakhtunkhwa Pakistan. Saudi J. Biol. Sci. 25(1), 94–100 (2018).Article 

    Google Scholar 
    Grytnes, J. A. & Vetaas, O. R. Species richness and altitude: A comparison between null models and interpolated plant species richness along the Himalayan altitudinal gradient Nepal. Am. Nat. 159(3), 294–304 (2002).Article 

    Google Scholar 
    Majid, A., Khan, M. & Calixto, E. Ecological assessment of plant communities along the edaphic and topographic gradients of biha valley, District Swat Pakistan. Appl. Ecol. Environ. Res. 16(5), 5611–5631 (2018).Article 

    Google Scholar 
    Khan, S.M., et al., Vegetation dynamics in the Western Himalayas, diversity indices and climate change. Sci. Tech. Dev. 31(3), 232–243 (2012).Khan, S. M. et al. Identifying plant species and communities across environmental gradients in the Western Himalayas: Method development and conservation use. Eco. Inform. 14, 99–103 (2013).Article 

    Google Scholar 
    Shaheen, H. & Shinwari, Z. K. Phyto diversity and endemic richness of Karambar lake vegetation from Chitral Hindukush-Himalayas. Pak. J. Bot 44(1), 17–21 (2012).
    Google Scholar 
    Wana, D., Plant communities and diversity along altitudinal gradients from Lake Abaya to Chencha Highlands, 2002, MA Thesis, School of Graduate Studies, Addis Ababa University. Addis Ababa.Canfora, L. et al. Is soil microbial diversity affected by soil and groundwater salinity? Evidences from a coastal system in central Italy. Environ. Monit. Assess. 189(7), 1–15 (2017).Article 

    Google Scholar 
    Liu, S., et al., The distribution characteristics and human health risks of high-fluorine groundwater in coastal plain: A case study in Southern Laizhou Bay, China. Front. Environ. Sci. 568 (2022).Niu, Y. et al. Vegetation distribution along mountain environmental gradient predicts shifts in plant community response to climate change in alpine meadow on the Tibetan Plateau. Sci. Total Environ. 650, 505–514 (2019).Article 

    Google Scholar 
    Nadal-Romero, E. et al. Effects of slope angle and aspect on plant cover and species richness in a humid Mediterranean badland. Earth Surf. Proc. Land. 39(13), 1705–1716 (2014).Article 

    Google Scholar  More

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    Phytoplankton in the middle

    Marine phytoplankton both follow and actively influence the environment they inhabit. Unpacking the complex ecological and biogeochemical roles of these tiny organisms can help reveal the workings of the Earth system.
    Phytoplankton are the workers of an ocean-spanning factory converting sunlight and raw nutrients into organic matter. These little organisms — the foundation of the marine ecosystem — feed into a myriad of biogeochemical cycles, the balance of which help control the distribution of carbon on the Earth surface and ultimately the overall climate state. As papers in this issue of Nature Geoscience show, phytoplankton are far from passive actors in the global web of biogeochemical cycles. The functioning of phytoplankton is not just a matter for biologists, but is also important for geoscientists seeking to understand the Earth system more broadly.Phytoplankton are concentrated where local nutrient and sea surface temperatures are optimal, factors which aren’t always static in time. Prominent temperature fluctuations, from seasonal to daily cycles, are reflected in phytoplankton biomass, with cascading effects on other parts of marine ecosystems, such as economically-important fisheries. In an Article in this issue, Keerthi et al., show that phytoplankton biomass, tracked by satellite measurements of chlorophyll for relatively small ( More

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    Sewage surveillance of antibiotic resistance holds both opportunities and challenges

    Huijbers, P. M. C., Flach, C.-F. & Larsson, D. G. J. A conceptual framework for the environmental surveillance of antibiotics and antibiotic resistance. Environ. Int. 130, 104880 (2019).Article 

    Google Scholar 
    Aarestrup, F. M. & Woolhouse, M. E. J. Using sewage for surveillance of antimicrobial resistance. Science 367, 630–632 (2020).Article 

    Google Scholar 
    European Commission. Proposal for a revised Urban Wastewater Treatment Directive. European Commission https://environment.ec.europa.eu/publications/proposal-revised-urban-wastewater-treatment-directive_en (2022).US Centres for Disease Control and Prevention. COVID-19 impacts on environment (e.g., water, soil) and sanitation: addressing antimicrobials and antimicrobial resistant threats in the environment. US Centres for Disease Control and Prevention https://www.cdc.gov/drugresistance/pdf/covid19/COVID19-Impacts-AR-Environment-Sanitation-508.pdf (2021).Flach, C.-F., Hutinel, M., Razavi, M., Åhrén, C. & Larsson, D. G. J. Monitoring of hospital sewage shows both promise and limitations as an early-warning system for carbapenemase-producing Enterobacterales in a low-prevalence setting. Water Res. 200, 117261 (2021).Article 

    Google Scholar 
    Larsson, D. G. J. & Flach, C.-F. Antibiotic resistance in the environment. Nat. Rev. Microbiol. 20, 257–269 (2022).Article 

    Google Scholar 
    Newton, R. J. et al. Sewage reflects the microbiomes of human populations. mBio 6, e02574 (2015).Article 

    Google Scholar 
    Huijbers, P. M. C., Larsson, D. G. J. & Flach, C. F. Surveillance of antibiotic resistant Escherichia coli in human populations through urban wastewater in ten European countries. Environ. Pollut. 261, 114200 (2020).Article 

    Google Scholar 
    Laxminarayan, R. & Macauley, M. K. The Value of Infromation: Methodological Frontiers and New Applications in Environment and Health 1st edn (Springer Dordrecht, 2012).Munk, P. et al. Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance. Nat. Commun. 13, 7251 (2022).Article 

    Google Scholar  More