Ecology

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

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

Koskella, B. The phyllosphere. Curr. Biol. 30, R1143–R1146 (2020).Article 
CAS 
PubMed 

Google Scholar 
Lu, N. et al. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods 15, 17 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Arye, G. C. & Harel, A. in Microbial Genomics in Sustainable Agroecosystems (eds Tripathi, V. et al.) 39–65 (Springer, 2020).Universal plant healthcare. Nat. Plants 6, 47 (2020).Fones, H. N. et al. Threats to global food security from emerging fungal and oomycete crop pathogens. Nat. Food 1, 332–342 (2020).Article 

Google Scholar 
Li, W., Deng, Y., Ning, Y., He, Z. & Wang, G. L. Exploiting broad-spectrum disease resistance in crops: from molecular dissection to breeding. Annu. Rev. Plant Biol. 71, 575–603 (2020).Article 
CAS 
PubMed 

Google Scholar 
Matsumoto, H. et al. Bacterial seed endophyte shapes disease resistance in rice. Nat. Plants 7, 60–72 (2021).Article 
CAS 
PubMed 

Google Scholar 
Thomazella, D. P. T. et al. Loss of function of a DMR6 ortholog in tomato confers broad-spectrum disease resistance. Proc. Natl Acad. Sci. USA 118, e2026152118 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Guo, Y. Molecular design for rice breeding. Nat. Food 2, 849–849 (2021).Article 

Google Scholar 
Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).Article 
PubMed 

Google Scholar 
Berg, G. et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome 8, 103 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Carrion, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).Article 
CAS 
PubMed 

Google Scholar 
Chialva, M., Lanfranco, L. & Bonfante, P. The plant microbiota: composition, functions, and engineering. Curr. Opin. Biotechnol. 73, 135–142 (2021).Article 
PubMed 

Google Scholar 
Liu, H., Brettell, L. E. & Singh, B. Linking the phyllosphere microbiome to plant health. Trends Plant Sci. 25, 841–844 (2020).Article 
CAS 
PubMed 

Google Scholar 
Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).Article 
CAS 
PubMed 

Google Scholar 
Liu, H., Brettell, L. E., Qiu, Z. & Singh, B. K. Microbiome-mediated stress resistance in plants. Trends Plant Sci. 25, 733–743 (2020).Article 
CAS 
PubMed 

Google Scholar 
Xu, P. et al. Temporal metabolite responsiveness of microbiota in the tea plant phyllosphere promotes continuous suppression of fungal pathogens. J. Adv. Res. 39, 49–60 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, M. & Cernava, T. Overhauling the assessment of agrochemical-driven interferences with microbial communities for improved global ecosystem integrity. Environ. Sci. Ecotechnol. 4, 100061 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hegazi, N., Hartmann, A. & Ruppel, S. The plant microbiome: exploration of plant–microbe interactions for improving agricultural productivity. J. Adv. Res. 19, 1–2 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mittelviefhaus, M., Muller, D. B., Zambelli, T. & Vorholt, J. A. A modular atomic force microscopy approach reveals a large range of hydrophobic adhesion forces among bacterial members of the leaf microbiota. ISME J. 13, 1878–1882 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sapkota, R., Knorr, K., Jorgensen, L. N., O’Hanlon, K. A. & Nicolaisen, M. Host genotype is an important determinant of the cereal phyllosphere mycobiome. New Phytol. 207, 1134–1144 (2015).Article 
CAS 
PubMed 

Google Scholar 
Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10, e1004283 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Horton, M. W. et al. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5, 5320 (2014).Article 
PubMed 

Google Scholar 
Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7, 12151 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shakir, S., Zaidi, S. S., de Vries, F. T. & Mansoor, S. Plant genetic networks shaping phyllosphere microbial community. Trends Genet. 37, 306–316 (2021).Article 
CAS 
PubMed 

Google Scholar 
Xiong, C. et al. Plant developmental stage drives the differentiation in ecological role of the maize microbiome. Microbiome 9, 171 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Laforest-Lapointe, I., Paquette, A., Messier, C. & Kembel, S. W. Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature 546, 145–147 (2017).Article 
CAS 
PubMed 

Google Scholar 
Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580, 653–657 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pang, Z. et al. Linking plant secondary metabolites and plant microbiomes: a review. Front. Plant Sci. 12, 621276 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pfeilmeier, S. et al. The plant NADPH oxidase RBOHD is required for microbiota homeostasis in leaves. Nat. Microbiol. 6, 852–864 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gupta, R. et al. Cytokinin drives assembly of the phyllosphere microbiome and promotes disease resistance through structural and chemical cues. ISME J. 16, 122–137 (2022).Article 
CAS 
PubMed 

Google Scholar 
Massoni, J. et al. Consistent host and organ occupancy of phyllosphere bacteria in a community of wild herbaceous plant species. ISME J. 14, 245–258 (2020).Article 
CAS 
PubMed 

Google Scholar 
Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ren, G. et al. Response of soil, leaf endosphere and phyllosphere bacterial communities to elevated CO2 and soil temperature in a rice paddy. Plant Soil 392, 27–44 (2015).Article 
CAS 

Google Scholar 
Meyer, K.M. et al. Plant neighborhood shapes diversity and reduces interspecific variation of the phyllosphere microbiome. ISME J. 16, 1376–1387 (2022).Article 
PubMed 

Google Scholar 
Qiu, Y. et al. Warming and elevated ozone induce tradeoffs between fine roots and mycorrhizal fungi and stimulate organic carbon decomposition. Sci. Adv. 7, eabe9256 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yu, H., Zhang, Y. & Tan, W. The “neighbor avoidance effect” of microplastics on bacterial and fungal diversity and communities in different soil horizons. Environ. Sci. Ecotechnol. 8, 100121 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Q. et al. Interactive effects of ozone exposure and nitrogen addition on the rhizosphere bacterial community of poplar saplings. Sci. Total Environ. 754, 142134 (2021).Article 
CAS 
PubMed 

Google Scholar 
Zhang, H., Jiang, Q., Wang, J., Li, K. & Wang, F. Analysis on the impact of two winter precipitation episodes on PM2.5 in Beijing. Environ. Sci. Ecotechnol. 5, 100080 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng, Z. et al. Ozone pollution threatens the production of major staple crops in East Asia. Nat. Food 3, 47–56 (2022).Article 
CAS 

Google Scholar 
Zhu, Y. G. et al. Impacts of global change on the phyllosphere microbiome. New Phytol. 234, 1977–1986 (2021).Article 

Google Scholar 
Sawada, H. et al. Elevated ozone deteriorates grain quality of japonica rice cv. Koshihikari, even if it does not cause yield reduction. Rice 9, 7 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Agathokleous, E. et al. Ozone affects plant, insect, and soil microbial communities: a threat to terrestrial ecosystems and biodiversity. Sci. Adv. 6, eabc1176 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mieczan, T. & Bartkowska, A. The effect of experimentally simulated climate warming on the microbiome of carnivorous plants—a microcosm experiment. Glob. Ecol. Conserv. 34, e02040 (2022).Article 

Google Scholar 
Liu, H. et al. Evidence for the plant recruitment of beneficial microbes to suppress soil-borne pathogens. New Phytol. 229, 2873–2885 (2021).Article 
CAS 
PubMed 

Google Scholar 
Gao, M. et al. Disease-induced changes in plant microbiome assembly and functional adaptation. Microbiome 9, 187 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Snelders, N. C. et al. Microbiome manipulation by a soil-borne fungal plant pathogen using effector proteins. Nat. Plants 6, 1365–1374 (2020).Article 
CAS 
PubMed 

Google Scholar 
Humphrey, P. T. & Whiteman, N. K. Insect herbivory reshapes a native leaf microbiome. Nat. Ecol. Evol. 4, 221–229 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Laforest-Lapointe, I., Messier, C. & Kembel, S. W. Tree leaf bacterial community structure and diversity differ along a gradient of urban intensity. mSystems 2, e00087–17 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Imperato, V. et al. Characterisation of the Carpinus betulus L. phyllomicrobiome in urban and forest areas. Front. Microbiol. 10, 1110 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Perreault, R. & Laforest-Lapointe, I. Plant–microbe interactions in the phyllosphere: facing challenges of the anthropocene. ISME J. 16, 339–345 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Jain, A., Ranjan, S., Dasgupta, N. & Ramalingam, C. Nanomaterials in food and agriculture: an overview on their safety concerns and regulatory issues. Crit. Rev. Food Sci. Nutr. 58, 297–317 (2018).Article 
CAS 
PubMed 

Google Scholar 
Sillen, W. M. A. et al. Nanoparticle treatment of maize analyzed through the metatranscriptome: compromised nitrogen cycling, possible phytopathogen selection, and plant hormesis. Microbiome 8, 127 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Berg, G. & Cernava, T. The plant microbiota signature of the Anthropocene as a challenge for microbiome research. Microbiome 10, 54 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Fan, X. et al. Microenvironmental interplay predominated by beneficial Aspergillus abates fungal pathogen incidence in paddy environment. Environ. Sci. Technol. 53, 13042–13052 (2019).Article 
CAS 
PubMed 

Google Scholar 
Chen, Y. et al. Wheat microbiome bacteria can reduce virulence of a plant pathogenic fungus by altering histone acetylation. Nat. Commun. 9, 3429 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, M., Hashimoto, M. & Hashidoko, Y. Repression of tropolone production and induction of a Burkholderia plantarii pseudo-biofilm by carot-4-en-9,10-diol, a cell-to-cell signaling disrupter produced by Trichoderma virens. PLoS ONE 8, e78024 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bauermeister, A., Mannochio-Russo, H., Costa-Lotufo, L. V., Jarmusch, A. K. & Dorrestein, P. C. Mass spectrometry-based metabolomics in microbiome investigations. Nat. Rev. Microbiol. 20, 143–160 (2022).Article 
CAS 
PubMed 

Google Scholar 
Matsumoto, H. et al. Reprogramming of phytopathogen transcriptome by a non-bactericidal pesticide residue alleviates its virulence in rice. Fundam. Res. 2, 198–207 (2022).Article 
CAS 

Google Scholar 
Hou, S. et al. A microbiota–root–shoot circuit favours Arabidopsis growth over defence under suboptimal light. Nat. Plants 7, 1078–1092 (2021).Mathur, M., Nair, A. & Kadoo, N. Plant–pathogen interactions: microRNA-mediated trans-kingdom gene regulation in fungi and their host plants. Genomics 112, 3021–3035 (2020).Article 
CAS 
PubMed 

Google Scholar 
Kaur, C. et al. Microbial methylglyoxal metabolism contributes towards growth promotion and stress tolerance in plants. Environ. Microbiol. 24, 2817–2836 (2021).Article 
PubMed 

Google Scholar 
Castrillo, G. et al. Root microbiota drive direct integration of phosphate stress and immunity. Nature 543, 513–518 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Korenblum, E. et al. Rhizosphere microbiome mediates systemic root metabolite exudation by root-to-root signaling. Proc. Natl Acad. Sci. USA 117, 3874–3883 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chisholm, S. T., Coaker, G., Day, B. & Staskawicz, B. J. Host–microbe interactions: shaping the evolution of the plant immune response. Cell 124, 803–814 (2006).Article 
CAS 
PubMed 

Google Scholar 
Jones, J. D. & Dangl, J. L. The plant immune system. Nature 444, 323–329 (2006).Article 
CAS 
PubMed 

Google Scholar 
Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).Article 
CAS 
PubMed 

Google Scholar 
Vogel, C., Bodenhausen, N., Gruissem, W. & Vorholt, J. A. The Arabidopsis leaf transcriptome reveals distinct but also overlapping responses to colonization by phyllosphere commensals and pathogen infection with impact on plant health. New Phytol. 212, 192–207 (2016).Article 
CAS 
PubMed 

Google Scholar 
Dodds, P. N. & Rathjen, J. P. Plant immunity: towards an integrated view of plant–pathogen interactions. Nat. Rev. Genet. 11, 539–548 (2010).Article 
CAS 
PubMed 

Google Scholar 
Stringlis, I. A. et al. Root transcriptional dynamics induced by beneficial rhizobacteria and microbial immune elicitors reveal signatures of adaptation to mutualists. Plant J. 93, 166–180 (2018).Article 
CAS 
PubMed 

Google Scholar 
He, J. et al. A LysM receptor heteromer mediates perception of arbuscular mycorrhizal symbiotic signal in rice. Mol. Plant 12, 1561–1576 (2019).Article 
CAS 
PubMed 

Google Scholar 
Bozsoki, Z. et al. Ligand-recognizing motifs in plant LysM receptors are major determinants of specificity. Plant Sci. 369, 663–670 (2020).CAS 

Google Scholar 
Stringlis, I. A., Zhang, H., Pieterse, C. M. J., Bolton, M. D. & de Jonge, R. Microbial small molecules—weapons of plant subversion. Nat. Prod. Rep. 35, 410–433 (2018).Article 
CAS 
PubMed 

Google Scholar 
Kong, H. G., Song, G. C., Sim, H. J. & Ryu, C. M. Achieving similar root microbiota composition in neighbouring plants through airborne signalling. ISME J. 15, 397–408 (2021).Article 
CAS 
PubMed 

Google Scholar 
Vacher, C. et al. The phyllosphere: microbial jungle at the plant–climate interface. Annu. Rev. Ecol. Evol. Syst. 47, 1–24 (2016).Article 

Google Scholar 
Thapa, S. & Prasanna, R. Prospecting the characteristics and significance of the phyllosphere microbiome. Ann. Microbiol. 68, 229–245 (2018).Article 
CAS 

Google Scholar 
Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Muller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).Article 
CAS 
PubMed 

Google Scholar 
Chen, X., Wicaksono, W. A., Berg, G. & Cernava, T. Bacterial communities in the plant phyllosphere harbour distinct responders to a broad-spectrum pesticide. Sci. Total Environ. 751, 141799 (2021).Article 
CAS 
PubMed 

Google Scholar 
Liu, Y. X. et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell 12, 315–330 (2021).Article 
PubMed 

Google Scholar 
Hosokawa, M. et al. Droplet-based microfluidics for high-throughput screening of a metagenomic library for isolation of microbial enzymes. Biosens. Bioelectron. 67, 379–385 (2015).Article 
CAS 
PubMed 

Google Scholar 
Kehe, J. et al. Massively parallel screening of synthetic microbial communities. Proc. Natl Acad. Sci. USA 116, 12804–12809 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, J. et al. High-throughput cultivation and identification of bacteria from the plant root microbiota. Nat. Protoc. 16, 988–1012 (2021).Article 
CAS 
PubMed 

Google Scholar 
Grosskopf, T. & Soyer, O. S. Synthetic microbial communities. Curr. Opin. Microbiol. 18, 72–77 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vogel, C. M., Potthoff, D. B., Schafer, M., Barandun, N. & Vorholt, J. A. Protective role of the Arabidopsis leaf microbiota against a bacterial pathogen. Nat. Microbiol. 6, 1537–1548 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Finkel, O. M. et al. A single bacterial genus maintains root growth in a complex microbiome. Nature 587, 103–108 (2020).Wagner, M. R. et al. Microbe-dependent heterosis in maize. Proc. Natl Acad. Sci. USA 118, e2021965118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schafer, M., Vogel, C. M., Bortfeld-Miller, M., Mittelviefhaus, M. & Vorholt, J. A. Mapping phyllosphere microbiota interactions in planta to establish genotype–phenotype relationships. Nat. Microbiol. 7, 856–867 (2022).Article 
CAS 
PubMed 

Google Scholar 
Han, B. & Huang, X. Sequencing-based genome-wide association study in rice. Curr. Opin. Plant Biol. 16, 133–138 (2013).Article 
CAS 
PubMed 

Google Scholar 
Roman-Reyna, V. et al. The rice leaf microbiome has a conserved community structure controlled by complex host-microbe interactions. Cell Host Microbe https://doi.org/10.2139/ssrn.3382544 (2019).Deng, S. et al. Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome. ISME J. 15, 3181–3194 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wagner, M. R., Busby, P. E. & Balint-Kurti, P. Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance. New Phytol. 225, 2152–2165 (2020).Article 
CAS 
PubMed 

Google Scholar 
Wagner, M. R., Roberts, J. H., Balint-Kurti, P. & Holland, J. B. Heterosis of leaf and rhizosphere microbiomes in field-grown maize. New Phytol. 228, 1055–1069 (2020).Article 
CAS 
PubMed 

Google Scholar 
Nobori, T. et al. Transcriptome landscape of a bacterial pathogen under plant immunity. Proc. Natl Acad. Sci. USA 115, E3055–E3064 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Xu, L. et al. Holo-omics for deciphering plant–microbiome interactions. Microbiome 9, 69 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
French, E., Kaplan, I., Iyer-Pascuzzi, A., Nakatsu, C. H. & Enders, L. Emerging strategies for precision microbiome management in diverse agroecosystems. Nat. Plants 7, 256–267 (2021).Article 
PubMed 

Google Scholar 
Lemmon, Z. H. et al. Rapid improvement of domestication traits in an orphan crop by genome editing. Nat. Plants 4, 766–770 (2018).Article 
CAS 
PubMed 

Google Scholar 
Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018).Article 

Google Scholar 
Zhou, L., Zhang, C., Liu, F., Qiu, Z. & He, Y. Application of deep learning in food: a review. Compr. Rev. Food Sci. Food Saf. 18, 1793–1811 (2019).Article 
PubMed 

Google Scholar 
Moreno-Indias, I. et al. Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions. Front. Microbiol. 12, 635781 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Song, P., Wang, J., Guo, X., Yang, W. & Zhao, C. High-throughput phenotyping: breaking through the bottleneck in future crop breeding. Crop J. 9, 633–645 (2021).Article 

Google Scholar  More

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

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

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

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

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