Philippa Kaur

Yuuka Fukui
Yuuka Fukui received Ph.D. degree from Keio University in 2012 under the supervision of Prof. Keiji Fujimoto. She was a JSPS research fellow (DC2) from 2010 to 2012. She joined the laboratory of Prof. Keiji Fujimoto at Keio university as a research associate in 2012 and was promoted to an assistant professor in 2017. Her research interests focus on the design and synthesis of polymeric materials (particles, membranes, porous structures) and organic-inorganic hybrid materials inspired from biological systems.About the award article: The authors reported a new technique to prepare nanoparticles from biomass-derived polymers, which will be utilized as an eco-friendly alternative to synthetic particulate plastics. Nanosized agarose gel particles were produced via sol-to-gel transition of agarose inside water nanodroplets prepared by W/O miniemulsion method. Subsequently, the water evaporation was carried out to generate xerogel nanoparticles (AgarX). The morphologies and crystal structure of AgarX were controlled by changing the pressure and temperature during the water evaporation. The resultant AgarX possessed high crystallinity and exhibited a water dispersibility and a water resistance.

Mikihiro Hayashi
Mikihiro Hayashi received his Ph.D. degree from Nagoya University (Prof. Yushu Matsushita group) in 2015. During his doctor course, he had been selected as a JSPS research fellow (DC2) and experienced researches in ESPCI Paris-Tech (Prof. Ludwik Leibler) and in Shanghai Jiao Tong University (Prof. Xinyuan Zhu). He then re-joined Ludwik Leibler’s group as a postdoc, and experienced another postdoc in Prof. Masatoshi Tokita in Tokyo institute of technology. In 2017, he became an assistant professor in Prof. Akinori Takasu group (Nagoya institute of technology), and currently manages his own laboratory as a PI. His research interest is the design of functional cross-linked materials.About the award article: the authors reported a preparation vitrimer-like elastomers with dynamic bond-exchangeable cross-links. A poly(ethyl acrylate)-based copolymer bearing random pyridine groups was synthesized, which was cross-linked by quaternization reaction with dibromo cross-linkers. In this system, the bond exchange was operated via trans-N-alkylation of the quaternized pyridine groups, showing useful sustainable functions, such as reprocessability, recyclability, and dissolution ability in some selective solvents.

Ryohei Ishige
Ryohei Ishige received his Ph.D. from Tokyo Institute of Technology in 2011 under the supervision of Prof. Junji Watanabe. He joined Prof. Atsushi Takahara’s laboratory at Kyushu University (2011–2013) and Prof. Yoshinobu Tsujii’s laboratory at Kyoto University (2013–2014). From 2014, he joined Prof. Shinji Ando’s laboratory at Tokyo Institute of Technology as an assistant professor and was promoted to an associate professor in 2021. His research interests are liquid-crystalline aromatic polymers and those structure-property relationships.About the award article: the authors developed a novel analytical technique integrating spectroscopies (infrared pMAIRS, and spectroscopic ellipsometry) and scattering methods (GI-WAXS), applied to the process where thin film polyimide, PI, is generated from linear poly(amic ester), PAE, precursors whose backbone consists of para-linkage. They revealed that PAE-based thin PI films form heterogeneous structure composed of non-oriented amorphous region and oriented ordered region which includes anisotropic nanopores causing structural birefringence. This method enables comprehensive evaluation of the evolution in complex hierarchical structures following chemical reactions for every noncrystalline thin film polymers.

Ryohei Kakuchi
Ryohei Kakuchi received his Ph.D. degree from the Hokkaido University in 2009 with a JSPS (Japan Society for Promotion of Science) research fellowship. After the Ph.D., he has made postdoctoral works in Germany from 2009 to 2014 and joined Kanazawa University as a research assistant professor in 2014. Based on the Leading Initiative for Excellent Young Researchers program, he was then appointed as an assistant professor (PI) at Gunma University in 2017. His research interests are the novel polymer synthesis based on unique organic transformation reactions including multicomponent reactions.About the award article: The authors proposed a new synthetic strategy to utilize wood-biomass sourced compounds in a green fashion. To achieve sustainable material chemistry, the intrinsic reactivity of lignin-derived poly(methacrylated vanillin) (PMV) was spotlighted because many multicomponent reactions employ aldehydes as a reactant. First, the Passerini three-component reaction (Passerini-3CR) of the PMV was revealed to proceed with >90% aldehyde conversions. Taking advantage of this high reactivity of the PMV, its immobilized cellulose fabric, a wood-biomass sourced organic hybrid, was revealed to accept the surface Passerini-3CR with amino acid derivatives, thereby demonstrating a fully bio-based material fabrication. More

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
Article 

Google Scholar 
Gibbs, H. K. et al. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc. Natl Acad. Sci. USA 107, 16732–16737 (2010).CAS 
Article 

Google Scholar 
Payn, T. et al. Changes in planted forests and future global implications. Ecol. Manag. 352, 57–67 (2015).Article 

Google Scholar 
Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environ. Res. Lett. 14, 055003 (2019).Article 

Google Scholar 
Hurni, K. & Fox, J. The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014. J. Land Use Sci. 13, 198–219 (2018).Article 

Google Scholar 
Vijay, V. et al. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS ONE 11, e0159668 (2016).Heilmayr, R., Echeverría, C. & Lambin, E. F. Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nat. Sustain. 3, 701–709 (2020).Article 

Google Scholar 
le Maire, G., Dupuy, S., Nouvellon, Y., Loos, R. A. & Hakamada, R. Mapping short-rotation plantations at regional scale using MODIS time series: case of eucalypt plantations in Brazil. Remote Sens. Environ. 152, 136–149 (2014).Article 

Google Scholar 
Wang, M. M. H., Carrasco, L. R. & Edwards, D. P. Reconciling rubber expansion with biodiversity conservation. Curr. Biol. 30, 3825–3832 (2020).CAS 
Article 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

Google Scholar 
Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2019).Sloan, S., Meyfroidt, P., Rudel, T. K., Bongers, F. & Chazdon, R. The forest transformation: planted tree cover and regional dynamics of tree gains and losses. Glob. Environ. Change 59, 101988 (2019).Article 

Google Scholar 
Petersen, R. et al. Mapping Tree Plantations with Multispectral Imagery: Preliminary Results for Seven Tropical Countries (WRI, 2016).Erb, K.-H. et al. Land management: data availability and process understanding for global change studies. Glob. Change Biol. 23, 512–533 (2017).Article 

Google Scholar 
Souza, C. M. et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat Archive and Earth Engine. Remote Sens. 12, 2735 (2020).Article 

Google Scholar 
Miettinen, J. et al. Extent of industrial plantations on Southeast Asian peatlands in 2010 with analysis of historical expansion and future projections. GCB Bioenergy 4, 908–918 (2012).Article 

Google Scholar 
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).Puyravaud, J.-P., Davidar, P. & Laurance, W. F. Cryptic destruction of India’s native forests. Conserv. Lett. 3, 390–394 (2010).Article 

Google Scholar 
Fagan, M. E. et al. Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data. Remote Sens. Environ. 216, 415–426 (2018).Article 

Google Scholar 
Tropek, R. et al. Comment on “High-resolution global maps of 21st-century forest cover change”. Science 344, 981 (2014).CAS 
Article 

Google Scholar 
Global Forest Resources Assessment 2020 (FAO, 2020).FAOSTAT Agricultural Statistics Database (FAO, 2019); http://faostat.fao.org/site/291/default.aspxCook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).CAS 
Article 

Google Scholar 
Hurni, K., Schneider, A., Heinimann, A., Nong, D. H. & Fox, J. Mapping the expansion of boom crops in mainland Southeast Asia using dense time stacks of Landsat data. Remote Sens. 9, 320 (2017).Article 

Google Scholar 
Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).Article 

Google Scholar 
Torbick, N., Ledoux, L., Salas, W. & M. Zhao, M. Regional mapping of plantation extent using multisensor imagery. Remote Sens. 8, 236 (2016).Azizan, F. A., Kiloes, A. M., Astuti, I. S. & Abdul Aziz, A. Application of optical remote sensing in rubber plantations: a systematic review. Remote Sens. 13, 429 (2021).Article 

Google Scholar 
Bégué, A. et al. Remote sensing and cropping practices: a review. Remote Sens. 10, 99 (2018).Article 

Google Scholar 
Bey, A. & Meyfroidt, P. Improved land monitoring to assess large-scale tree plantation expansion and trajectories in Northern Mozambique. Environ. Res. Commun. 3, 115009 (2021).Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000 (2018).Article 

Google Scholar 
Féret, J.-B. & Asner, G. P. Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sens. Environ. 115, 2415–2422 (2011).Article 

Google Scholar 
Poortinga, A. et al. Mapping plantations in Myanmar by fusing Landsat-8, Sentinel-2 and Sentinel-1 data along with systematic error quantification. Remote Sens. 11, 831 (2019).Article 

Google Scholar 
Gutiérrez-Vélez, V. H. et al. High-yield oil palm expansion spares land at the expense of forests in the Peruvian Amazon. Environ. Res. Lett. 6, 044029 (2011).Article 

Google Scholar 
Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth Syst. Sci. Data. 13, 1211–1231 (2021).Article 

Google Scholar 
Ordway, E. M., Naylor, R. L., Nkongho, R. N. & Lambin, E. F. Oil palm expansion and deforestation in Southwest Cameroon associated with proliferation of informal mills. Nat. Commun. 10, 114 (2019).CAS 
Article 

Google Scholar 
Heilmayr, R., Echeverría, C., Fuentes, R. & Lambin, E. F. A plantation-dominated forest transition in Chile. Appl. Geogr. 75, 71–82 (2016).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. R. The trouble with trees: afforestation plans for Africa. Trends Ecol. Evol. 34, 963–965 (2019).Article 

Google Scholar 
Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Fagan, M. E. A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps. Glob. Change Biol. 26, 4679–4690 (2020).Bastin, J. F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).CAS 
Article 

Google Scholar 
Fagan, M. E., Reid, J. L., Holland, M. B., Drew, J. G. & Zahawi, R. A. How feasible are global forest restoration commitments? Conserv. Lett. 13, e12700 (2020).Article 

Google Scholar 
Malkamäki, A. et al. A systematic review of the socio-economic impacts of large-scale tree plantations, worldwide. Glob. Environ. Change 53, 90–103 (2018).Article 

Google Scholar 
Schwartz, N. B., Aide, T. M., Graesser, J., Grau, H. R. & Uriarte, M. Reversals of reforestation across Latin America limit climate mitigation potential of tropical forests. Front. For. Glob. Change 3, 85 (2020).Article 

Google Scholar 
Noojipady, P. et al. Managing fire risk during drought: the influence of certification and El Niño on fire-driven forest conversion for oil palm in Southeast Asia. Earth Syst. Dynam. 8, 749–771 (2017).Bullock, E. L., Woodcock, C. E., Souza, C. Jr. & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. Glob. Change Biol. 26, 2956–2969 (2020).Article 

Google Scholar 
Sloan, S. & Sayer, J. A. Forest Ecology and Management Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. Ecol. Manag. 352, 134–145 (2015).Article 

Google Scholar 
Heinrich, V. H. A. et al. Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nat. Commun. 12, 1785 (2021).CAS 
Article 

Google Scholar 
Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).Article 

Google Scholar 
Bernal, B., Murray, L. T. & Pearson, T. R. H. Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag. 13, 22 (2018).CAS 
Article 

Google Scholar 
Li, W., Goodchild, M. F. & Church, R. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. Int. J. Geogr. Inf. Sci. 27, 1227–1250 (2013).Article 

Google Scholar 
Asner, G. P. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote Sens. 22, 3855–3862 (2001).Article 

Google Scholar 
Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).Article 
CAS 

Google Scholar 
Gutiérrez-Vélez, V. H. & DeFries, R. Annual multi-resolution detection of land cover conversion to oil palm in the Peruvian Amazon. Remote Sens. Environ. 129, 154–167 (2013).Article 

Google Scholar 
Reiche, J. et al. Combining satellite data for better tropical forest monitoring. Nat. Clim. Change 6, 120–122 (2016).Article 

Google Scholar 
Erinjery, J. J., Singh, M. & Kent, R. Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sens. Environ. 216, 345–354 (2018).Article 

Google Scholar 
Shimada, M. et al. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens. Environ. 155, 13–31 (2014).Article 

Google Scholar 
Torres, R. et al. GMES Sentinel-1 mission. Remote Sens. Environ. 120, 9–24 (2012).Article 

Google Scholar 
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).Article 

Google Scholar 
World Database on Protected Areas User Manual 1.4 (UNEP-WCMC, 2016).AutoML: Automatic Machine Learning (H2O.ai, 2020); https://h2o-release.s3.amazonaws.com/h2o/rel-yau/5/docs-website/h2o-docs/automl.htmlHealey, S. P. et al. Mapping forest change using stacked generalization: an ensemble approach. Remote Sens. Environ. 204, 717–728 (2018).Article 

Google Scholar 
Lagomasino, D. et al. Measuring mangrove carbon loss and gain in deltas. Environ. Res. Lett. 14, 25002 (2019).Article 

Google Scholar 
Bunting, P. et al. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669 (2018).Article 

Google Scholar 
Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).Article 

Google Scholar 
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).Article 

Google Scholar 
Stehman, S. V. Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. Int. J. Remote Sens. 35, 4923–4939 (2014).Article 

Google Scholar 
Olofsson, P. et al. Mitigating the effects of omission errors on area and area change estimates. Remote Sens. Environ. 236, 111492 (2020).Article 

Google Scholar 
Database of Global Administrative Areas (GADM) v.3.6 (GADM, 2018); https://gadm.org/download_country_v3.htmlHijmans, R. J., Williams, E., Vennes, C. M. & Hijmans, M. R. J. Package ‘geosphere’ version 1.5-10. Spherical trigonometry (2017).Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots: Distribution and Protection of Conservation Priority Areas (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, 2011).Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017). More

Santana, S. E., Alfaro, J. L. & Alfaro, M. E. Adaptive evolution of facial colour patterns in Neotropical primates. Proc. R. Soc. B Biol. Sci. 279, 2204–2211 (2012).
Google Scholar 
Santana, S. E., Alfaro, J. L., Noonan, A. & Alfaro, M. E. Adaptive response to sociality and ecology drives the diversification of facial colour patterns in catarrhines. Nat. Commun. 4, 25 (2013).
Google Scholar 
Kobayashi, H. & Kohshima, S. Unique morphology of the human eye and its adaptive meaning: Comparative studies on external morphology of the primate eye. J. Hum. Evol. 40, 419–435 (2001).CAS 
PubMed 

Google Scholar 
Tomasello, M., Hare, B., Lehmann, H. & Call, J. Reliance on head versus eyes in the gaze following of great apes and human infants: The cooperative eye hypothesis. J. Hum. Evol. 52, 314–320 (2007).PubMed 

Google Scholar 
Farroni, T. et al. Newborns’ preference for face-relevant stimuli: Effects of contrast polarity. Proc. Natl. Acad. Sci. USA 102, 17245–17250 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Farroni, T., Massaccesi, S., Pividori, D. & Johnson, M. H. Gaze following in newborns. Infancy 5, 39–60 (2004).
Google Scholar 
Itakura, S. & Tanaka, M. Use of experimenter-given cues during object-choice tasks by chimpanzees (Pan troglodytes), an orangutan (Pongo pygmaeus), and human infants (Homo sapiens). J. Comp. Psychol. 112, 119–126 (1998).CAS 
PubMed 

Google Scholar 
Yorzinski, J. L., Thorstenson, C. A. & Nguyen, T. P. Sclera and iris color interact to influence gaze perception. Front. Psychol. 12, 1–11 (2021).
Google Scholar 
Yorzinski, J. L., Harbourne, A. & Thompson, W. Sclera color in humans facilitates gaze perception during daytime and nighttime. PLoS One 16, 1–15 (2021).
Google Scholar 
Yorzinski, J. L. & Miller, J. Sclera color enhances gaze perception in humans. PLoS One 15, 1–14 (2020).
Google Scholar 
Tomasello, M., Call, J. & Hare, B. Five primate species follow the visual gaze of conspecifics. Anim. Behav. 55, 1063–1069 (1998).CAS 
PubMed 

Google Scholar 
Kano, F. & Call, J. Cross-species variation in gaze following and conspecific preference among great apes, human infants and adults. Anim. Behav. 91, 137–150 (2014).
Google Scholar 
Kano, F., Kawaguchi, Y. & Yeow, H. Experimental evidence for the gaze-signaling hypothesis: White sclera enhances the visibility of eye gaze direction in humans and chimpanzees. bioRxiv 2021.09.21.461201 (2021).Perea-García, J. O., Kret, M. E., Monteiro, A. & Hobaiter, C. Scleral pigmentation leads to conspicuous, not cryptic, eye morphology in chimpanzees. Proc. Natl. Acad. Sci. USA 116, 19248–19250 (2019).PubMed 
PubMed Central 

Google Scholar 
Mearing, A. S. & Koops, K. Quantifying gaze conspicuousness: Are humans distinct from chimpanzees and bonobos ?. J. Hum. Evol. 157, 103043 (2021).PubMed 

Google Scholar 
Mearing, A. S., Burkart, J. M., Dunn, J., Street, S. E. & Koops, K. The evolutionary origins of primate scleral coloration. bioRxiv 40, 2021.07.25.453695 (2021).Mayhew, J. A. & Gómez, J. C. Gorillas with white sclera: A naturally occurring variation in a morphological trait linked to social cognitive functions. Am. J. Primatol. 77, 869–877 (2015).PubMed 

Google Scholar 
Caspar, K. R., Biggemann, M., Geissmann, T. & Begall, S. Ocular pigmentation in humans, great apes, and gibbons is not suggestive of communicative functions. Sci. Rep. 11, 1–14 (2021).
Google Scholar 
Kano, F. et al. What is unique about the human eye? Comparative image analysis on the external eye morphology of human and nonhuman great apes. Evol. Hum. Behav. https://doi.org/10.1016/j.evolhumbehav.2021.12.004 (2021).
Google Scholar 
Caves, E. M. & Johnsen, S. AcuityView: An r package for portraying the effects of visual acuity on scenes observed by an animal. Methods Ecol. Evol. 9, 793–797 (2018).
Google Scholar 
Osorio, D. & Vorobyev, M. Photoreceptor spectral sensitivities in terrestrial animals: Adaptations for luminance and colour vision. Proc. R. Soc. B Biol. Sci. 272, 1745–1752 (2005).CAS 

Google Scholar 
Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).PubMed 
PubMed Central 

Google Scholar 
Stevens, M., Párraga, C. A., Cuthill, I. C., Partridge, J. C. & Troscianko, T. S. Using digital photography to study animal coloration. Biol. J. Linn. Soc. 90, 211–237 (2007).
Google Scholar 
Whitham, W., Schapiro, S. J., Troscianko, J. & Yorzinski, J. L. The gaze of a social monkey is perceptible to conspecifics and predators but not prey. Proc. R. Soc. B Biol. Sci. 20, 10 (2002).
Google Scholar 
Bethell, E. J., Vick, S. & Bard, K. A. Measurement of eye-gaze in chimpanzees (Pan troglodytes). Am. J. Primatol. 69, 562–575 (2007).PubMed 

Google Scholar 
Sreekar, R. & Quader, S. Influence of gaze and directness of approach on the escape responses of the Indian rock lizard, Psammophilus dorsalis (Gray, 1831). J. Biosci. 38, 829–833 (2013).CAS 
PubMed 

Google Scholar 
Lee, S. et al. Direct look from a predator shortens the risk-assessment time by prey. PLoS One 8, 1–7 (2013).
Google Scholar 
Carter, J., Lyons, N. J., Cole, H. L. & Goldsmith, A. R. Subtle cues of predation risk: Starlings respond to a predator’s direction of eye-gaze. Proc. R. Soc. B Biol. Sci. 275, 1709–1715 (2008).
Google Scholar 
Newton-Fisher, N. E. Chimpanzee hunting. Behav. Handb. Paleoanthropol. https://doi.org/10.1007/978-3-540-33761-4_42. (2007).
Google Scholar 
Caro, T. et al. The evolution of primate coloration revisited. Behav. Ecol. 32, 555–567 (2021).
Google Scholar 
Kilkenny, C., Browne, W., Cuthill, I. C., Emerson, M. & Altman, D. G. Animal research: Reporting in vivo experiments: The ARRIVE guidelines. Br. J. Pharmacol. 160, 1577–1579 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bergman, T. J. & Beehner, J. C. A simple method for measuring colour in wild animals: Validation and use on chest patch colour in geladas (Theropithecus gelada). Biol. J. Linn. Soc. 94, 231–240 (2008).
Google Scholar 
Stevens, M., Stoddard, M. C. & Higham, J. P. Studying primate color: Towards visual system-dependent methods. Int. J. Primatol. 30, 893–917 (2009).
Google Scholar 
van den Berg, C. P., Troscianko, J., Endler, J. A., Marshall, N. J. & Cheney, K. L. Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature. Methods Ecol. Evol. 11, 316–332 (2020).
Google Scholar 
Deeb, S. S., Jorgensen, A. L., Battisti, L., Iwasaki, L. & Motulsky, A. G. Sequence divergence of the red and green visual pigments in great apes and humans. Proc. Natl. Acad. Sci. USA 91, 7262–7266 (1994).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Matsuzawa, T. Form perception and visual acuity. Folia Primatol. Int. J. Primatol. 55, 24–32 (1990).CAS 

Google Scholar 
Jacobs, G. H., Deegan, J. F. & Moran, J. L. ERG measurements of the spectral sensitivity of common chimpanzee (Pan troglodytes). Vis. Res. 36, 2587–2594 (1996).CAS 
PubMed 

Google Scholar 
Jacobs, G. H. & Deegan, J. F. Uniformity of colour vision in Old World monkeys. Proc. R. Soc. B Biol. Sci. 266, 2023–2028 (1999).CAS 

Google Scholar 
Kemp, A. D. & Christopher Kirk, E. Eye size and visual acuity influence vestibular anatomy in mammals. Anat. Rec. 297, 781–790 (2014).
Google Scholar 
Osorio, D., Smith, A. C., Vorobyev, M. & Buchanan-Smith, H. M. Detection of fruit and the selection of primate visual pigments for color vision. Am. Nat. 164, 696–708 (2004).CAS 
PubMed 

Google Scholar 
Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour threshoIds. Proc. R. Soc. B Biol. Sci. 265, 351–358 (1998).CAS 

Google Scholar 
Siddiqi, A., Cronin, T. W., Loew, E. R., Vorobyev, M. & Summers, K. Interspecific and intraspecific views of color signals in the strawberry poison frog Dendrobates pumilio. J. Exp. Biol. 207, 2471–2485 (2004).PubMed 

Google Scholar  More

Laity JJ. Deserts and desert environments. John Wiley & Sons; UK, 2009.Huang J, Yu H, Guan X, Wang G, Guo R. Accelerated dryland expansion under climate change. Nat Clim Chang. 2015;6:166–71.Article 

Google Scholar 
Berdugo M, Delgado-Baquerizo M, Soliveres S, Hernández-Clemente R, Zhao Y, Gaitán JJ, et al. Global ecosystem thresholds driven by aridity. Science. 2020;367:787–90.CAS 
PubMed 
Article 

Google Scholar 
Danin A. Plant adaptations to environmental stresses in desert dunes. In: Cloudsley-Thompson J, Punzo F, editors. Adaptations of desert organisms. Plant of desert dunes. Springer; Verlag Berlin Heidelberg, 1996.Makhalanyane TP, Valverde A, Gunnigle E, Frossard A, Ramond J-B, Cowan DA. Microbial ecology of hot desert edaphic systems. FEMS Microbiol Rev. 2015;39:203–21.CAS 
PubMed 
Article 

Google Scholar 
Fierer N, Leff JWJ, Adams BJ, Nielsen UN, Bates ST, Lauber CL, et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci USA. 2012;109:21390–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ronca S, Ramond J-BB, Jones BE, Seely M, Cowan DA. Namib Desert dune/interdune transects exhibit habitat-specific edaphic bacterial communities. Front Microbiol. 2015;6:1–12.Article 

Google Scholar 
Pointing SB, Belnap J. Microbial colonization and controls in dryland systems. Nat Rev Microbiol. 2012;10:551–62.CAS 
PubMed 
Article 

Google Scholar 
Noy-Meir I. Desert ecosystems: higher trophic levels. Annu Rev Ecol Syst. 1974;5:195–214.Article 

Google Scholar 
Danin A. Plants of desert dunes. In: Cloudsley-Thompson J, editor. Adaptations of desert organisms. Springer; Verlag Berlin Heidelberg, 2000.Roth-Nebelsick A, Ebner M, Miranda T, Gottschalk V, Voigt D, Gorb S, et al. Leaf surface structures enable the endemic Namib Desert grass Stipagrostis sabulicola to irrigate itself with fog water. J R Soc Interface. 2012;9:1965–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ebner M, Miranda T, Roth-Nebelsick A. Efficient fog harvesting by Stipagrostis sabulicola (Namib dune bushman grass). J Arid Environ. 2011;75:524–31.Article 

Google Scholar 
Cartwright J. Ecological islands: conserving biodiversity hotspots in a changing climate. Front Ecol Environ. 2019;17:fee.2058.Article 

Google Scholar 
André HM, Noti MI, Jacobson KM. The soil microarthropods of the Namib Desert: a patchy mosaic. J African Zool. 1997;111:499–517.
Google Scholar 
Marasco R, Mosqueira MJ, Fusi M, Ramond J, Merlino G, Booth JM, et al. Rhizosheath microbial community assembly of sympatric desert speargrasses is independent of the plant host. Microbiome. 2018;6:215.PubMed 
PubMed Central 
Article 

Google Scholar 
Brown LK, George TS, Neugebauer K, White PJ. The rhizosheath—a potential trait for future agricultural sustainability occurs in orders throughout the angiosperms. Plant Soil. 2017;418:115–28.CAS 
Article 

Google Scholar 
Pang J, Ryan MH, Siddique KHMM, Simpson RJ. Unwrapping the rhizosheath. Plant Soil. 2017;418:129–39.CAS 
Article 

Google Scholar 
Marasco R, Fusi M, Mosqueira M, Booth JM, Rossi F, Cardinale M, et al. Rhizosheath–root system changes exopolysaccharide content but stabilizes bacterial community across contrasting seasons in a desert environment. Environ Microbiome. 2022;17:14.PubMed 
PubMed Central 
Article 

Google Scholar 
Moreno-Espíndola IP, Rivera-Becerril F, de Jesús Ferrara-Guerrero M, De León-González F. Role of root-hairs and hyphae in adhesion of sand particles. Soil Biol Biochem. 2007;39:2520–6.Article 
CAS 

Google Scholar 
Wullstein LHH, Pratt SAA. Scanning electron microscopy of rhizosheaths of Oryzopsis hymenoides. Am J Bot. 1981;68:408–19.Article 

Google Scholar 
Young IM. Variation in moisture contents between bulk soil and the rhizosheath of wheat (Triticum aestivum L. cv. Wembley). New Phytol. 1995;130:135–9.Article 

Google Scholar 
Ashraf M, Hasnain S, Berge O, Campus Q. Effect of exo-polysaccharides producing bacterial inoculation on growth of roots of wheat (Triticum aestivum L.) plants grown in a salt-affected soil. Int J Environ Sci Technol. 2006;3:45–53.Article 

Google Scholar 
George TS, Brown LK, Ramsay L, White PJ, Newton AC, Bengough AG, et al. Understanding the genetic control and physiological traits associated with rhizosheath production by barley (Hordeum vulgare). New Phytol. 2014;203:195–205.CAS 
PubMed 
Article 

Google Scholar 
Ndour PMS, Heulin T, Achouak W, Laplaze L, Cournac L. The rhizosheath: from desert plants adaptation to crop breeding. Plant Soil. 2020;456:1–13.CAS 
Article 

Google Scholar 
Othman AA, Amer WM, Fayez M, Monib M, Hegazi NA. Biodiversity of diazotrophs associated to the plant cover of north sinai deserts. Arch Agron Soil Sci. 2003;49:683–705.Article 

Google Scholar 
Bergmann D, Zehfus M, Zierer L, Smith B, Gabel M. Grass rhizosheaths: associated bacterial communities and potential for nitrogen fixation. West North Am Nat. 2009;69:105–14.Article 

Google Scholar 
Marasco R, Rolli E, Ettoumi B, Vigani G, Mapelli F, Borin S, et al. A drought resistance-promoting microbiome is selected by root system under desert farming. PLoS ONE. 2012;7:e48479.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marasco R, Mapelli F, Rolli E, Mosqueira MJ, Fusi M, Bariselli P, et al. Salicornia strobilacea (synonym of Halocnemum strobilaceum) grown under different tidal regimes selects rhizosphere bacteria capable of promoting plant growth. Front Microbiol. 2016;7:1–11.Article 

Google Scholar 
Rolli E, Marasco R, Vigani G, Ettoumi B, Mapelli F, Deangelis ML, et al. Improved plant resistance to drought is promoted by the root-associated microbiome as a water stress-dependent trait. Environ Microbiol. 2015;17:316–31.PubMed 
Article 

Google Scholar 
Alsharif W, Saad MM, Hirt H. Desert microbes for boosting sustainable agriculture in extreme environments. Front Microbiol. 2020;11:1666.Zhang Y, Du H, Xu F, Ding Y, Gui Y, Zhang J, et al. Root-bacteria associations boost rhizosheath formation in moderately dry soil through ethylene responses. Plant Physiol. 2020;183:780–92.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soussi A, Ferjani R, Marasco R, Guesmi A, Cherif H, Rolli E, et al. Plant-associated microbiomes in arid lands: diversity, ecology and biotechnological potential. Plant Soil. 2016;405:357–70.CAS 
Article 

Google Scholar 
Livingston G, Matias M, Calcagno V, Barbera C, Combe M, Leibold MA, et al. Competition-colonization dynamics in experimental bacterial metacommunities. Nat Commun. 2012;3:1–8.Article 
CAS 

Google Scholar 
Smith GR, Steidinger BS, Bruns TD, Peay KG. Competition–colonization tradeoffs structure fungal diversity. ISME J. 2018;12:1758–67.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seely MK. The Namib dune desert: an unusual ecosystem. J Arid Environ. 1978;1:117–28.Article 

Google Scholar 
Klaassen E, Craven P. Checklist of grasses in Namibia. SABONET; Pretoria & Windhoek, 2014. (Produced by National Botanical Research Institute Private Bag 13184).Neilson JW, Califf K, Cardona C, Copeland A, van Treuren W, Josephson KL, et al. Significant impacts of increasing aridity on the arid soil microbiome. mSystems. 2017;2:1–15.Article 

Google Scholar 
Darwin C. On the origin of species. London: Routledge; 1859.Gunnigle E, Frossard A, Ramond J-B, Guerrero L, Seely M, Cowan DA. Diel-scale temporal dynamics recorded for bacterial groups in Namib Desert soil. Sci Rep. 2017;7:40189.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham H. ggplot2: Elegant graphics for data analysis. Media. Springer; New York, NY 2016.RC-Team. R: A language and environment for statistical computing (Version 3.5. 2, R foundation for statistical computing, Vienna, Austria, 2018). R Foundation for Statistical Computing; 2019.Anderson MMJJ, Gorley RNRN, Clarke KRR. PERMANOVA + for PRIMER: guide to software and statistical methods; PRIMER-E. Plymouth, UK: PRIMER-E Ltd.; 2008.Cherif H, Marasco R, Rolli E, Ferjani R, Fusi M, Soussi A, et al. Oasis desert farming selects environment-specific date palm root endophytic communities and cultivable bacteria that promote resistance to drought. Environ Microbiol Rep. 2015;7:668–78.CAS 
PubMed 
Article 

Google Scholar 
Lee KC, Caruso T, Archer SDJ, Gillman LN, Lau MCY, Craig Cary S, et al. Stochastic and deterministic effects of a moisture gradient on soil microbial communities in the McMurdo dry valleys of Antarctica. Front Microbiol. 2018;9:1–12.Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Koljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2014;22:5271–7.Article 
CAS 

Google Scholar 
Ramette A. Multivariate analyses in microbial ecology. FEMS Microbiol Ecol. 2007;62:142–60.CAS 
PubMed 
Article 

Google Scholar 
Clarke KR, Gorley RN. PRIMER v7: user manual/tutorial. Plymouth, UK: PRIMER-E; 2015.Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara B, et al. The vegan R package: community ecology. 2013:0–291Wang Y, Naumann U, Wright ST, Warton DI. mvabund—an R package for model-based analysis of multivariate abundance data. Methods Ecol Evol. 2012;3:471–4.Article 

Google Scholar 
Legendre P. Interpreting the replacement and richness difference components of beta diversity. Glob Ecol Biogeogr. 2014;23:1324–34.Article 

Google Scholar 
Dray S, Blanchet G, Borcard D, Guenard G, Jombart T, Larocque G, et al. Package ‘adespatial’. R package version. 2018.Hammer Ø, Harper DAT, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:1–9.
Google Scholar 
Weiss S, Van Treuren W, Lozupone C, Faust K, Friedman J, Deng Y, et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 2016;10:1669–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.CAS 
PubMed 
Article 

Google Scholar 
Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Third International AAAI Conference on Weblogs and Social Media. 2009;8:361–2.Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.PubMed 
Article 
CAS 

Google Scholar 
Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech Theory Exp. 2008;2008:P10008.Article 

Google Scholar 
de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:3033.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Andrews S. FastQC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom: Babraham Bioinformatics, Babraham Institute; 2010.Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rodriguez-R LM, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. mSystems. 2018;3:1–9.Article 

Google Scholar 
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 2016;32:1088–90.CAS 
PubMed 
Article 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
PubMed 
Article 

Google Scholar 
Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY, et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.PubMed 
Article 
CAS 

Google Scholar 
Vigani G, Rolli E, Marasco R, Dell’Orto M, Michoud G, Soussi A, et al. Root bacterial endophytes confer drought resistance and enhance expression and activity of a vacuolar H+-pumping pyrophosphatase in pepper plants. Environ Microbiol. 2019;21:3212–28.CAS 
Article 

Google Scholar 
Al-Hosni K, Shahzad R, Khan AL, Muhammad Imran Q, Al Harrasi A, Al Rawahi A, et al. Preussia sp. BSL-10 producing nitric oxide, gibberellins, and indole acetic acid and improving rice plant growth. J Plant Interact. 2018;13:112–8.CAS 
Article 

Google Scholar 
Sen D, Paul K, Saha C, Mukherjee G, Nag M, Ghosh S, et al. A unique life-strategy of an endophytic yeast Rhodotorula mucilaginosa JGTA-S1—a comparative genomics viewpoint. DNA Res. 2019;26:131–46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Johnson JM, Ludwig A, Furch ACU, Mithöfer A, Scholz S, Reichelt M, et al. The beneficial root-colonizing fungus Mortierella hyalina promotes the aerial growth of Arabidopsis and activates calcium-dependent responses that restrict Alternaria brassicae–induced disease development in roots. Mol Plant-Microbe Interact. 2019;32:351–63.CAS 
PubMed 
Article 

Google Scholar 
van Dam NM, Bouwmeester HJ. Metabolomics in the rhizosphere: tapping into belowground chemical communication. Trends Plant Sci. 2016;21:256–65.PubMed 
Article 
CAS 

Google Scholar 
Zeng Y, Charkowski AO. The role of ATP-binding cassette transporters in bacterial phytopathogenesis. Phytopathology®. 2021;111:600–10.Article 

Google Scholar 
Louca S, Polz MF, Mazel F, Albright MBN, Huber JA, O’Connor MI, et al. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2:936–43.PubMed 
Article 

Google Scholar 
Balskus EP, Walsh CT. The genetic and molecular basis for sunscreen biosynthesis in cyanobacteria. Science. 2010;329:1653–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Makarova KS, Wolf YI, Iranzo J, Shmakov SA, Alkhnbashi OS, Brouns SJJ, et al. Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol. 2020;18:67–83.CAS 
PubMed 
Article 

Google Scholar 
Smith VH. Effects of resource supplies on the structure and function of microbial communities. Antonie Van Leeuwenhoek. 2002;81:99–106.CAS 
PubMed 
Article 

Google Scholar 
Albalasmeh AA, Ghezzehei TA. Interplay between soil drying and root exudation in rhizosheath development. Plant Soil. 2014;374:739–51.CAS 
Article 

Google Scholar 
Devitt DA, Smith SD. Root channel macropores enhance downward movement of water in a Mojave Desert ecosystem. J Arid Environ. 2002;50:99–108.Article 

Google Scholar 
Othman AA, Amer WM, Fayez M, Hegazi NA. Rhizosheath of sinai desert plants is a potential repository for associative diazotrophs. Microbiol Res. 2004;159:285–93.PubMed 
Article 

Google Scholar 
Naseem H, Ahsan M, Shahid MA, Khan N. Exopolysaccharides producing rhizobacteria and their role in plant growth and drought tolerance. J Basic Microbiol. 2018;58:1009–22.CAS 
PubMed 
Article 

Google Scholar 
Toju H, Peay KG, Yamamichi M, Narisawa K, Hiruma K, Naito K, et al. Core microbiomes for sustainable agroecosystems. Nat Plants. 2018;4:247–57.PubMed 
Article 

Google Scholar 
Banerjee S, Schlaeppi K, van der Heijden MGAA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.CAS 
PubMed 
Article 

Google Scholar 
Agler MT, Ruhe J, Kroll S, Morhenn C, Kim S-TT, Weigel D, et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 2016;14:1–31.Article 
CAS 

Google Scholar 
Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.PubMed 
Article 

Google Scholar 
Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:58.PubMed 
PubMed Central 
Article 

Google Scholar 
Lopez BR, Bacilio M. Weathering and soil formation in hot, dry environments mediated by plant–microbe interactions. Biol Fertil Soils. 2020;56:447–59.CAS 
Article 

Google Scholar 
Hernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. Environmental stress destabilizes microbial networks. ISME J. 2021;15:1722–34.PubMed 
PubMed Central 
Article 

Google Scholar 
Yuan MM, Guo X, Wu L, Zhang Y, Xiao N, Ning D, et al. Climate warming enhances microbial network complexity and stability. Nat Clim Chang. 2021;11:343–8.Article 

Google Scholar 
Safronova VI, Kuznetsova IG, Sazanova AL, Belimov AA, Andronov EE, Chirak ER, et al. Microvirga ossetica sp. nov., a species of rhizobia isolated from root nodules of the legume species Vicia alpestris Steven. Int J Syst Evol Microbiol. 2017;67:94–100.CAS 
PubMed 
Article 

Google Scholar 
Jiménez-Gómez A, Saati-Santamaría Z, Igual J, Rivas R, Mateos P, García-Fraile P. Genome insights into the novel species Microvirga brassicacearum, a rapeseed endophyte with biotechnological potential. Microorganisms. 2019;7:354.PubMed Central 
Article 
CAS 

Google Scholar 
Liu T, Ye N, Wang X, Das D, Tan Y, You X, et al. Drought stress and plant ecotype drive microbiome recruitment in switchgrass rhizosheath. J Integr Plant Biol. 2021;63:1753–74.Blouin M. Chemical communication: an evidence for co-evolution between plants and soil organisms. Appl Soil Ecol. 2018;123:409–15.Article 

Google Scholar 
Sarrocco S, Diquattro S, Baroncelli R, Cimmino A, Evidente A, Vannacci G, et al. A polyphasic contribution to the knowledge of Auxarthron (Onygenaceae). Mycol Prog. 2015;14:112.Macías-Rubalcava ML, Sánchez-Fernández RE. Secondary metabolites of endophytic Xylaria species with potential applications in medicine and agriculture. World J Microbiol Biotechnol. 2017;33:15.Zhang K, Bonito G, Hsu C, Hameed K, Vilgalys R, Liao H-L. Mortierella elongata increases plant biomass among non-leguminous crop species. Agronomy. 2020;10:754.Article 

Google Scholar 
Kobayashi DY, Crouch JA. Bacterial/fungal interactions: from pathogens to mutualistic endosymbionts. Annu Rev Phytopathol. 2009;47:63–82.CAS 
PubMed 
Article 

Google Scholar 
Asmelash F, Bekele T, Birhane E. The potential role of arbuscular mycorrhizal fungi in the restoration of degraded lands. Front Microbiol. 2016;7:1–15.Article 

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

Google Scholar 
Warmink JA, Nazir R, Corten B, van Elsas JD. Hitchhikers on the fungal highway: the helper effect for bacterial migration via fungal hyphae. Soil Biol Biochem. 2011;43:760–5.CAS 
Article 

Google Scholar 
Booth JM, Fusi M, Marasco R, Michoud G, Fodelianakis S, Merlino G, et al. The role of fungi in heterogeneous sediment microbial networks. Sci Rep. 2019;9:7537.Article 
CAS 

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

Google Scholar 
Simon A, Hervé V, Al-Dourobi A, Verrecchia E, Junier P. An in situ inventory of fungi and their associated migrating bacteria in forest soils using fungal highway columns. FEMS Microbiol Ecol. 2017;93:fiw217.PubMed 
Article 
CAS 

Google Scholar 
Faust K, Raes J. CoNet app: inference of biological association networks using Cytoscape. F1000Research. 2016;5:1519.PubMed 
PubMed Central 
Article 

Google Scholar 
Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17.CAS 
PubMed 
Article 

Google Scholar 
Zablocki O, Adriaenssens EM, Cowan D. Diversity and ecology of viruses in hyperarid desert soils. Appl Environ Microbiol. 2016;82:770–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Goethem MW, Swenson TL, Trubl G, Roux S, Northen TR. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. MBio. 2019;10:e02287–19.Lambers H, Mougel C, Jaillard B, Hinsinger P. Plant-microbe-soil interactions in the rhizosphere: an evolutionary perspective. Plant Soil. 2009;321:83–115.CAS 
Article 

Google Scholar 
Ghoul M, Mitri S. The ecology and evolution of microbial competition. Trends Microbiol. 2016;24:833–45.CAS 
PubMed 
Article 

Google Scholar 
Schlatter DC, Kinkel LL. Antibiotics: conflict and communication in microbial communities. Microbe Mag. 2014;9:282–8.Article 

Google Scholar  More

Considering the actual and predicted values, the model generated through the different inputted parameters should be diagnosed satisfactorily. It is pretty understanding that agreement between the actual and predicted values given the effectiveness and accuracy of the generated model, as shown in Fig. 2. The following polynomial regression model equations were obtained:$$begin{aligned} COD;removal , % , & = 76.63 – 0.019*A , + , 0.064*B , – 0.511*C , – 0.405*AB , – 0.153*AC , \ &quad – 0.099*BC , + , 0.263*A^{2} + , 0.479*B^{2} – 0.303*C^{2} \ end{aligned}$$
(1)
$$begin{aligned} Nitrate;Removal , % , & = 72.04 , – 1.881*A – 0.142* , B , + , 2.384*C , + , 2.623*AB , + , 8.579*AC , \ &quad – 2.626*BC , – 10.783*A^{2} + , 0.223*B^{2} + , 0.963*C^{2 } hfill \ end{aligned}$$
(2)
$$begin{aligned} & Phosphate , Removal , % , = \ & 67.179 – 1.215*A , + , 3.539*B , – 1.068*C , + , 1.610*AB , – 2.559*AC , + , 0.392*BC , + , 0.788*A^{2} – 2.943*B^{2} + , 0.564*C^{2} \ end{aligned}$$
(3)
where A is initial pH, B is current time (min), C is MLSS concentration (mg L−1) at which the study was carried out.Figure 2Normal probability versus studentized residuals and predicted versus actual plots for (i) COD removal, (ii) nitrate removal, and (iii) phosphate removal.Full size imageIt has been observed that statistics for the model having low values represent well for the system and its predictions.Statistical analysis of COD, nitrate and phosphate removalIt was seen that 3D surface plots could provide a better understanding of the interactive effects of the parameters. The 3D surface plots are illustrated in Figs. 3, 4, and 5, respectively. It was observed that the maximum removal efficiency for COD, nitrate, and phosphate is in the range of 59% to 74%.Figure 3Model generated surface plot of % COD removal (i) pH versus current time (ii) pH vs. MLSS (iii) MLSS vs. current time.Full size imageFigure 4Model generated surface plot of %nitrate removal (i) pH versus current time (ii) pH vs. MLSS (iii) MLSS vs. current time.Full size imageFigure 5Model generated surface plot of %phosphate removal (i) pH versus current time (ii) pH versus MLSS (iii) MLSS versus current time.Full size imageTable 4 (i) shows the statistics for COD removal. Adeq Precision is desirable, which measures the signal-to-noise ratio and a ratio greater than 4. For the COD removal, Adeq Precision was 19.255, indicating an adequate signal. It was also observed that the adjusted R2 is 0.9118 (difference less than 0.2), and the predicted R2 of 0.8601 was significant, implying that the predictions are in good agreement with experimental values.Table 4 Fit statistics for (i) COD removal, (ii) Nitrate removal, (iii) Phosphate removal.Full size tableFigure 3 illustrates the effect of current flow time and pH concerning the percentage removal of COD. The model predicted values observed were seen to lie in the range of 73.1% at MLSS values of 2500 mg L−1, keeping initial COD values as 200 mg L−1. As the COD load increases, it seems to be predicted that the overloading of bacteria occurs, thereby slowing down the consumption of organics. In Fig. 4, the expected removal efficacy shows upward trends with an increase in the values of MLSS, which also coincided with previous studies. As the value of MLSS increases, the contact time of biomass in the system increases, hence producing more effective results than others.Table 4 (ii) shows the statistics for nitrate removal. The predicted R2 of 0.9164 was in reasonable agreement with the adjusted R2 of 0.9730. For the nitrate removal, Adeq Precision was 29.608, indicating an adequate signal. This model can be used to navigate the design space.Table 4 (iii) shows the statistics for phosphate removal. The predicted R2 of 0.9165 was in reasonable agreement with the adjusted R2 of 0.9720. For the phosphate removal, Adeq Precision was 34.945, indicating an adequate signal. This model can be used to navigate the design space.Figure 5 illustrates that as we reduce the cycle time from 24 to 18 h, the system efficacy, i.e., COD removal effectiveness shows a downward trend due to less contact time with biomass. Meanwhile, if we increase the cycle time, we observe higher efficacy in the system. The model generated surface plot in Fig. 5 illustrated that increasing MLSS values by 3000 mg L−1 will enhance the COD removal by 73.1%, keeping the initial pH constant. This may be due to many microbes that can break down organic matter. In aerobic reactors, pH is an essential factor in the growth of the microbial population. To create granules, the pH of the reactor has a direct impact. Studies have shown that granule formation occurs when bacteria grow at the ideal pH level, whereas mass proliferation of fungus occurs in an acidic environment.COD removal in EBR and tubesettlerThe Influence, effluent, and removal of COD in EBR & tubesettler are illustrated in Fig. 6a,b. Results demonstrate that the COD concentration is consistent and better COD removal efficacy rate. The average removal rate values observed in the EBR were between 74 and 79%, with the initial COD concentration kept around 360–396 mg L−1. It was also observed that tubesettler resulted in approximately 25–36% efficacy when the initial concentration was between 75 and 97 mg L−1. The results of EBR are promising and can be attributed to the fact that electrocoagulation takes place along with the oxidation and biodegradation process. It was also observed that the percentage removal of COD shows downward trends due to electrochemical oxidation and adsorption, thereby resulting in physical entrapment and electrostatic attraction30. It has also been reported in many other studies that COD removal of around 85–90% was observed using composite cathode membrane using MRB/MFC system19 for the specialized treatment of landfill leachate. It was seen with the electrooxidation process having COD removal of around 80–84% and 84–96% with submerged membrane bioreactors, using Iron electrode6. For the Coal industry, it was found to be around 85% using membrane electro bioreactors31.Figure 6(a) Influent, effluent and removal of COD in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of COD in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageIn the current study, results seemed to be lower than the values reported in the previous studies. The main reason might be the employment of a modified EBR system and the production of biomass species. When the overall COD removal with tubesettler is considered, up to 83.58% removal efficiency is observed. The overall COD removal efficiency is significant and is at par with other studies3,4,5. This signifies that EBR performed better than tubesettler in COD removal. The tubesettler’s lower removal efficiency can be attributed to lower influent concentration from already reduced wastewater from EBR.Nitrate removal in EBR and tubesettlerIt was observed in many studies that nitrifying is the leading cause of nitrification, i.e., conversion of NH3-N to nitrate NO3-N10. The indirect method of system nitrification process claudication was to be ascertained using measurements concerning ammonia values32,33. In the current study, the nitrification process was considered using the nitrate concentration measurement from the influent and effluent in both systems, i.e., EBR and tubesettler34,35,36. The nitrate concentration of influent and effluent was observed and illustrated in Fig. 7a,b. The system stabilized and produced enhanced results up to 70% of nitrate removal, and it was seen to be in the range of 40–45% for the tubesettler. It has been observed that EBR produced better results than the tubesettler. The results variation in both the systems were reasonably attributed mainly to two primary reasons (1) low influent concentration in the influent compared to the EBR system and (2) inhibition effect due to the applied DC field, which was absent in tubesettlers.Figure 7(a) Influent, effluent, and removal of nitrate in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of nitrate in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageThe removal efficiency of around 70% was achieved, lower than the values in submerged membrane bioreactors, i.e., 82%6. However, including a membrane would have enhanced the removal efficiency and considered a hybrid EBR system. The results of the current study are close enough to many other studies with a similar system and different operating parameters. Hence, a combined approach can be used for better efficacy. During the weekly analysis, the nitrate concentration during the 1st to 3rd week is lower than in the following weeks. As the concentration of nitrifying bacteria decreased, they had less to work with. Thus, the substrate concentration grew, and so did the removal rate. Nitrate concentrations rose by more than twice the previous week during Week 7. They slowed the bacterial activity, resulting in an efficiency decline to 47% from 70% during the last week’s study period and weeks 6 and 8. A similar pattern emerged for the seventh week in a row in tubesettler. On the other hand, microorganisms overcame differences in engagement because the nitrate content was low in other weeks.Phosphate removal in EBR and tubesettlerMany researchers have looked at nitrate content, but none have looked at phosphate concentration. Eutrophication in receiving water bodies, on the other hand, is predominantly caused by phosphate and nitrate. Additionally, there is a lack of information available on hospital wastewater. The influent and effluent phosphate concentrations in the Electro bioreactor and the tubesettler is shown in Fig. 8a,b. A 75% reduction in the effluent phosphate content in EBR was achieved tubesettler had a 67% effectiveness in phosphate removal but a lower efficiency in nitrate reduction. A previous similar study that used a Submerged Membrane Electro bioreactor claimed a clearance rate of 76% to 95%, which is lower than this study’s results6. Phosphate removal was reported at 50–70% using the electrocoagulation process for different Ph and current6.Figure 8(a) Influent, effluent, and removal of phosphate in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of phosphate in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageIn week 6 and week 8, the EBR’s phosphate removal efficiency fluctuated dependent on the weekly average concentration in EBR. This volatility can be linked to a shift in the composition of hospital wastewater. tubesettler had a modest variation ranging from 5 to 6%. Although phosphate concentrations rose in week two, tubesettler removal efficiency improved. As demonstrated in Fig. 8a,b, the arriving wastewater ingredient exhibited a strong affinity in terms of phosphate reduction.Excess effluent concentration and standard deviation from EBR and tubesettler are shown in Table 5. EBR performed better than tubesettler in COD reduction when nitrate and phosphate were compared. Because tubesettler solely employs a physical process to remove contaminants, this is to be anticipated. Effluent from the secondary treatment facility is sent to a tubesettler, which acts as a polishing unit. EBR eliminated COD by 91%, nitrate by 85%, and Phosphate reduction by 81% compared to tubesettler’ s total efficiency. At the same time, tubesettler reduced COD by 37%, nitrate by 51%, and phosphate by 53%. Hence, EBR primarily removed pollutants from wastewater while tubesettler acted as a polishing unit. Table 5 illustrates the effluent wastewater characteristics of EBR and tubesettler.Table 5 Effluent wastewater characteristics of EBR and tubesettler.Full size tableKinetic models post optimizationFirst-order modelA first-order linear model was analyzed on the experimental data by plotting (So − Se)/Se against hydraulic retention time (HRT), providing K1 and R2. For COD, R2 values were 0.761 with a constant value of 1.213, as shown in Table 6. Henceforth based on the results, the obtained model did not seem to fit well for either of the cases.Table 6 Analyzed kinetic models.Full size tableGrau second-order modelA Grau second-order model was analyzed on the experimental data by plotting HRT/((So − Se)/So) versus HRT. The COD constant obtained was Ks = 10–5, as shown in Table 6. The R2 value of 0.99 suggests a good correlation coefficient. Therefore, the obtained results fit well for AOX and COD.Modified Stover–Kincannon modelSubstrate utilization rate expressed as organic loading in this model is widely used in biological reactor kinetic modelling of wastewater. The developed model can evaluate the performance of the biological system and estimate its efficiency based on the input parameters. The kinetic constant KB and Umax for COD were 0.35 and 1.73 g L−1 d−1, respectively. The R2 was 0.98 for the substrate removal, as presented in Table 6.Monod modelCOD utilization rate was obtained by plotting VX/Q (So − Se) against 1/Se. The value of 1/K (0.421) was obtained from the intercept, while the Ks/K value (1.235) was the slope of the line. COD removal half-saturation values were 0.045 and 0.056 g L−1. These values infer a high affinity of bacteria for the substrate. The R2 value of 0.95 depicted an excellent correlation coefficient in the case of COD. The Monod model fits well for COD, resulting in R2 = 0.98, as shown in Table 6. More

Dollive, S. A tool kit for quantifying eukaryotic rRNA gene sequences from human microbiome samples. Genome Biol 13, 60 (2012).Article 

Google Scholar 
Pausan, M. R. Exploring the archaeome: Detection of archaeal signatures in the human body. Front. Microbiol 10, 2796 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Shkoporov, A. N. & Hill, C. Bacteriophages of the human gut: The “known unknown” of the microbiome. Cell Host Microbe 25, 195–209 (2019).CAS 
PubMed 
Article 

Google Scholar 
Clark, D. P., Pazdernik, N. J. & McGehee, M. R. Plasmids. in Molecular Biology, 712–748 (Elsevier, 2019). https://doi.org/10.1016/B978-0-12-813288-3.00023-9.Meinhardt, F., Schaffrath, R. & Larsen, M. Microbial linear plasmids. Appl. Microbiol. Biotechnol 47, 329–336 (1997).CAS 
PubMed 
Article 

Google Scholar 
Lacroix, B. & Citovsky, V. Transfer of DNA from bacteria to eukaryotes. MBio 7, 00863–16 (2016).Article 

Google Scholar 
Łobocka, M. B. Genome of bacteriophage P1. J. Bacteriol. 186, 7032–7068 (2004).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: Mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Spaziante, M., Oliva, A., Ceccarelli, G. & Venditti, M. What are the treatment options for resistant Klebsiella pneumoniae carbapenemase (KPC)-producing bacteria?. Expert Opin. Pharmacother. 21, 1781–1787 (2020).PubMed 
Article 

Google Scholar 
Kopotsa, K., Osei Sekyere, J. & Mbelle, N. M. Plasmid evolution in carbapenemase-producing Enterobacteriaceae: A review. Ann. N. Y. Acad. Sci. 1457, 61–91 (2019).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Ogilvie, L. A., Firouzmand, S. & Jones, B. V. Evolutionary, ecological and biotechnological perspectives on plasmids resident in the human gut mobile metagenome. Bioengineered 3, 13–31 (2012).Article 

Google Scholar 
Jørgensen, T. S., Xu, Z., Hansen, M. A., Sørensen, S. J. & Hansen, L. H. Hundreds of circular novel plasmids and DNA elements identified in a rat cecum metamobilome. PLoS ONE 9, 87924 (2014).Article 
ADS 
CAS 

Google Scholar 
Kav, A. B. Insights into the bovine rumen plasmidome. Proc. Natl. Acad. Sci. 109, 5452–5457 (2012).CAS 
PubMed Central 
Article 
ADS 

Google Scholar 
Brown Kav, A. Unravelling plasmidome distribution and interaction with its hosting microbiome. Environ. Microbiol. 22, 32–44 (2020).PubMed 
Article 

Google Scholar 
Norman, J. M. et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell 160, 447–460 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krishnamurthy, S. R. & Wang, D. Origins and challenges of viral dark matter. Virus Res. 239, 136–142 (2017).CAS 
PubMed 
Article 

Google Scholar 
Clooney, A. G. et al. Whole-virome analysis sheds light on viral dark matter in inflammatory bowel disease. Cell Host. Microbe 26, 764-778.e5 (2019).CAS 
PubMed 
Article 

Google Scholar 
Sutton, T. D. S., Clooney, A. G. & Hill, C. Giant oversights in the human gut virome. Gut 69, 1357–1358 (2020).PubMed 
Article 

Google Scholar 
Zuo, T. Gut mucosal virome alterations in ulcerative colitis. Gut 68, 1169–1179 (2019).CAS 
PubMed 
Article 

Google Scholar 
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649-662.e20 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tamminen, M., Virta, M., Fani, R. & Fondi, M. Large-scale analysis of plasmid relationships through gene-sharing networks. Mol. Biol. Evol. 29, 1225–1240 (2012).CAS 
PubMed 
Article 

Google Scholar 
Angelakis, E. et al. Treponema species enrich the gut microbiota of traditional rural populations but are absent from urban individuals. New Microbes New Infect 27, 14–21 (2019).CAS 
PubMed 
Article 

Google Scholar 
Mackie, R. I. et al. Ecology of uncultivated oscillospira species in the rumen of cattle, sheep, and reindeer as assessed by microscopy and molecular approaches. Appl. Environ. Microbiol. 69, 6808–6815 (2003).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Konikoff, T. & Gophna, U. Oscillospira: A central, enigmatic component of the human gut microbiota. Trends Microbiol. 24, 523–524 (2016).CAS 
PubMed 
Article 

Google Scholar 
Chen, Y. et al. High Oscillospira abundance indicates constipation and low BMI in the Guangdong Gut Microbiome Project. Sci. Rep. 10, (2020).Bushman, F. D. Multi-omic analysis of the interaction between clostridioides difficile infection and pediatric inflammatory bowel disease. Cell Host Microbe 28, 422–433 (2020).CAS 
PubMed 
Article 

Google Scholar 
Willing, B. P. et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 1844–1854 (2010).PubMed 
Article 

Google Scholar 
Wills, E. S. et al. Fecal microbial composition of ulcerative colitis and Crohn’s disease patients in remission and subsequent exacerbation. PLoS ONE 9, e90981 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Halfvarson, J. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat. Microbiol. 2, 17004 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pascal, V. A microbial signature for Crohn’s disease. Gut 66, 813–822 (2017).CAS 
PubMed 
Article 

Google Scholar 
Nitzan, O., Elias, M., Chazan, B., Raz, R. & Saliba, W. Clostridium difficile and inflammatory bowel disease: Role in pathogenesis and implications in treatment. World J. Gastroenterol. 19, 7577–7585 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clayton, E. M. et al. The vexed relationship between Clostridium difficile and inflammatory bowel disease: an assessment of carriage in an outpatient setting among patients in remission. Am. J. Gastroenterol. 104, 1162–1169 (2009).PubMed 
Article 
ADS 

Google Scholar 
Tariq, R. et al. Efficacy of fecal microbiota transplantation for recurrent C.Marcella, C. Systematic review: The global incidence of faecal microbiota transplantation-related adverse events from 2000 to 2020. Aliment. Pharmacol. Ther. https://doi.org/10.1111/apt.16148 (2020).Article 
PubMed 

Google Scholar 
Shkoporov, A. N. et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe 26, 527-541.e5 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fraser-Liggett, C. Metagenomic analysis of the structure and function of the human gut microbiota in Crohn’s disease. Nat. Preced. [Internet] (2010).Barton, W. et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut (2017).Mira-Pascual, L. Microbial mucosal colonic shifts associated with the development of colorectal cancer reveal the presence of different bacterial and archaeal biomarkers. J. Gastroenterol. 50, 167–179 (2015).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Rampelli, S. Shotgun metagenomics of gut microbiota in humans with up to extreme longevity and the increasing role of xenobiotic degradation. mSystems 5, (2020).Monaghan, T. M. Metagenomics reveals impact of geography and acute diarrheal disease on the Central Indian human gut microbiome. Gut Microbes 12, 1752605 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Chu, D. M. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med. 23, 314–326 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
MD, D. G., K, F., C, C. & EL, C. Whole genome metagenomic analysis of the gut microbiome of differently fed infants identifies differences in microbial composition and functional genes, including an absent CRISPR/Cas9 gene in the formula-fed cohort. Hum. Microbiome J. 12, (2019).Qian, Y. et al. Gut metagenomics-derived genes as potential biomarkers of Parkinson’s disease. Brain J. Neurol. 143, 2474–2489 (2020).Article 

Google Scholar 
Kao, D. Effect of oral capsule- vs colonoscopy-delivered fecal microbiota transplantation on recurrent clostridium difficile infection: A randomized clinical trial. JAMA 318, 1985–1993 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: A new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).Article 
CAS 

Google Scholar 
Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: Interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guerin, E. et al. Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut. (2018). https://doi.org/10.1101/295642.Grazziotin, A. L., Koonin, E. V. & Kristensen, D. M. Prokaryotic Virus Orthologous Groups (pVOGs): A resource for comparative genomics and protein family annotation. Nucleic Acids Res. 45, D491–D498 (2017).CAS 
PubMed 
Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. C. PILER-CR: Fast and accurate identification of CRISPR repeats. BMC Bioinform. 8, 18 (2007).Article 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/. (2019). Accessed Aug 2021–Mar 2022.Wickham, H. Reshaping Data with the reshape Package. J. Stat. Softw. 21, 1–20 (2007).Article 

Google Scholar 
Jari Oksanen et al. vegan: Community Ecology Package. (2019).McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots. (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 
Book 

Google Scholar 
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).CAS 
PubMed 
Article 

Google Scholar 
Flor M. chorddiag: Interactive Chord Diagrams [Internet]. (2020).Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hulsen, T., Vlieg, J. & Alkema, W. BioVenn—A web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genom. 9, (2008).Stothard, P. & Wishart, D. S. Circular genome visualization and exploration using CGView. Bioinform. Oxf. Engl. 21, 537–539 (2005).CAS 
Article 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinform. Oxf. Engl. 30, 2068–2069 (2014).CAS 
Article 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 4.5: A hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293 (2016).CAS 
PubMed 
Article 

Google Scholar 
McArthur, A. G. et al. The comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 57, 3348–3357 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, W. & Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).CAS 
PubMed 
Article 

Google Scholar  More

Von Humboldt, A. Cosmos: A Sketch of a Physical Description of the Universe Vol. 5 (H.G. Bohn Press, 1895).Körner, C. Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits (Springer, 2012).Peñuelas, J., Ogaya, R., Boada, M. & Jump, A. S. Migration, invasion and decline: changes in recruitment and forest structure in a warming‐linked shift of European beech forest in Catalonia (NE Spain). Ecography 30, 829–837 (2007).Article 

Google Scholar 
Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems (Springer, 2021).Körner, C. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115, 445–459 (1998).PubMed 
Article 

Google Scholar 
Körner, C. The cold range limit of trees. Trends Ecol. Evol. 36, 979–989 (2021).PubMed 
Article 

Google Scholar 
Körner, C. & Paulsen, J. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732 (2004).Article 

Google Scholar 
Paulsen, J. & Körner, C. A climate-based model to predict potential treeline position around the globe. Alp. Bot. 124, 1–12 (2014).Article 

Google Scholar 
Feeley, K. J. & Rehm, E. M. Downward shift of montane grasslands exemplifies the dual threat of human disturbances to cloud forest biodiversity. Proc. Natl Acad. Sci. USA 112, E6084–E6084 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lenoir, J. et al. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).CAS 
PubMed 
Article 

Google Scholar 
Macias Fauria, M. & Johnson, E. A. Warming-induced upslope advance of subalpine forest is severely limited by geomorphic processes. Proc. Natl Acad. Sci. USA 110, 8117–8122 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morueta Holme, N. et al. Strong upslope shifts in Chimborazo’s vegetation over two centuries since Humboldt. Proc. Natl Acad. Sci. USA 112, 12741–12745 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Greenwood, S. & Jump, A. S. Consequences of treeline shifts for the diversity and function of high altitude ecosystems. Arct. Antarct. Alp. Res. 46, 829–840 (2014).Article 

Google Scholar 
Körner, C. & Hiltbrunner, E. Why is the alpine flora comparatively robust against climatic warming? Diversity 13, 383 (2021).Article 

Google Scholar 
Miehe, G. et al. Highest treeline in the northern hemisphere found in southern Tibet. Mt. Res. Dev. 27, 169–173 (2007).Article 

Google Scholar 
Myers, N. et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
PubMed 
Article 

Google Scholar 
Wang, F. et al. Add Himalayas’ Grand Canyon to China’s first national parks. Nature 592, 353–353 (2021).CAS 
PubMed 
Article 

Google Scholar 
Zhu, L. et al. Regional scalable priorities for national biodiversity and carbon conservation planning in Asia. Sci. Adv. 7, eabe4261 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Yao, T. et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Change 2, 663–667 (2012).Article 

Google Scholar 
Dirnböeck, T., Essl, F. & Rabitsch, W. Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob. Change Biol. 17, 990–996 (2011).Article 

Google Scholar 
Schickhoff, U. et al. Do Himalayan treelines respond to recent climate change? An evaluation of sensitivity indicators. Earth Syst. Dynam. 6, 245–265 (2015).Article 

Google Scholar 
Singh, S., Sharma, S. & Dhyani, P. Himalayan arc and treeline: distribution, climate change responses and ecosystem properties. Biodivers. Conserv. 28, 1997–2016 (2019).Article 

Google Scholar 
Schickhoff, U. The Upper Timberline in the Himalayas, Hindu Kush and Karakorum: A Review of Geographical and Ecological Aspects (Springer, 2005).Liang, E. et al. Species interactions slow warming-induced upward shifts of treelines on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 113, 4380–4385 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lu, X. et al. Mountain treelines climb slowly despite rapid climate warming. Glob. Ecol. Biogeogr. 30, 305–315 (2021).Article 

Google Scholar 
Harsch, M. A., Hulme, P. E., McGlone, M. S. & Duncan, R. P. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett. 12, 1040–1049 (2009).PubMed 
Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 
Article 

Google Scholar 
Wan, Z. & Li, Z. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens. 35, 980–996 (1997).Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Sigdel, S. R. et al. Moisture-mediated responsiveness of treeline shifts to global warming in the Himalayas. Glob. Change Biol. 24, 5549–5559 (2018).Article 

Google Scholar 
Dolezal, J. et al. Sink limitation of plant growth determines tree line in the arid Himalayas. Funct. Ecol. 33, 553–565 (2019).Article 

Google Scholar 
Dolezal, J. et al. Annual and intra-annual growth dynamics of Myricaria elegans shrubs in arid Himalaya. Trees 30, 761–773 (2016).Article 

Google Scholar 
Malcolm, J. R. et al. Global warming and extinctions of endemic species from biodiversity hotspots. Conserv. Biol. 20, 538–548 (2006).PubMed 
Article 

Google Scholar 
Ding, W., Ree, R. H., Spicer, R. A. & Xing, Y. Ancient orogenic and monsoon-driven assembly of the world’s richest temperate alpine flora. Science 369, 578–581 (2020).CAS 
PubMed 
Article 

Google Scholar 
Pirnat, J. Conservation and management of forest patches and corridors in suburban landscapes. Landsc. Urban Plan. 52, 135–143 (2000).Article 

Google Scholar 
Potapov, P. V. et al. Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM plus data. Remote Sens. Environ. 122, 106–116 (2012).Article 

Google Scholar 
Paulsen, J. & Körner, C. GIS-analysis of tree-line elevation in the Swiss Alps suggests no exposure effect. J. Veg. Sci. 12, 817–824 (2001).Article 

Google Scholar 
FAO. FRA 2000: On Definitions of Forest and Forest Change. Forest Resource Assessment Programme Working Paper, Rome (Food and Agriculture Organization, 2000).Luedeling, E., Siebert, S. & Buerkert, A. Filling the voids in the SRTM elevation model—a TIN-based delta surface approach. ISPRS-J. Photogramm. Remote Sens. 62, 283–294 (2007).Article 

Google Scholar 
Canny, J. Collision detection for moving polyhedra. IEEE Trans. Pattern Anal. Mach. Intell. 8, 200–209 (1986).CAS 
PubMed 
Article 

Google Scholar 
More, J. J. & Sorensen, D. C. Computing a trust region step. SIAM J. Sci. Comput. 4, 553–572 (1983).Article 

Google Scholar 
Theobald, D. M., Harrison-Atlas, D., Monahan, W. B. & Albano, C. M. Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS ONE 10, e0143619 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).Article 

Google Scholar 
Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).Article 

Google Scholar 
Liang, E., Wang, Y., Eckstein, D. & Luo, T. Little change in the fir tree-line position on the southeastern Tibetan Plateau after 200 years of warming. New Phytol. 190, 760–769 (2011).PubMed 
Article 

Google Scholar 
Anderegg, W. R. L. et al. Tree mortality predicted from drought-induced vascular damage. Nat. Geosci. 8, 367–371 (2015).CAS 
Article 

Google Scholar 
Abatzoglou, J. T. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Case, B. S. & Buckley, H. L. Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand’s southern beech treelines. PeerJ 3, e1334 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Bush, M. B. et al. Fire and climate: contrasting pressures on tropical Andean timberline species. J. Biogeogr. 42, 938–950 (2015).Article 

Google Scholar 
Herrero, A., Zamora, R., Castro, J. & Hodar, J. A. Limits of pine forest distribution at the treeline: herbivory matters. Plant Ecol. 213, 459–469 (2012).Article 

Google Scholar 
Wang, Y. et al. The stability of spruce treelines on the eastern Tibetan Plateau over the last century is explained by pastoral disturbance. For. Ecol. Manag. 442, 34–45 (2019).Article 

Google Scholar 
Wei, Y. et al. Chinese caterpillar fungus (Ophiocordyceps sinensis) in China: current distribution, trading, and futures under climate change and overexploitation. Sci. Total Environ. 755, 142548 (2021).CAS 
PubMed 
Article 

Google Scholar 
Miehe, G. et al. How old is the human footprint in the world’s largest alpine ecosystem? A review of multiproxy records from the Tibetan Plateau from the ecologists’ viewpoint. Quat. Sci. Rev. 86, 190–209 (2014).Article 

Google Scholar 
Willemann, R. J. & Storchak, D. A. Data collection at the international seismological centre. Seismol. Res. Lett. 72, 440–453 (2001).Article 

Google Scholar 
Chen, A., Huang, L., Liu, Q. & Piao, S. Optimal temperature of vegetation productivity and its linkage with climate and elevation on the Tibetan Plateau. Glob. Change Biol. 27, 1942–1951 (2021).Article 

Google Scholar 
Lehmkuhl, F. & Owen, L. A. Late Quaternary glaciation of Tibet and the bordering mountains: a review. Boreas 34, 87–100 (2005).Article 

Google Scholar 
Owen, L. A. & Dortch, J. M. Nature and timing of Quaternary glaciation in the Himalayan–Tibetan orogen. Quat. Sci. Rev. 88, 14–54 (2014).Article 

Google Scholar 
Strobl, C. et al. Conditional variable importance for random forests. BMC Bioinform. 9, 307 (2008).Article 
CAS 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Vassallo, D., Krishnamurthy, R. & Fernando, H. J. S. Decreasing wind speed extrapolation error via domain-specific feature extraction and selection. Wind Energy Sci. 5, 959–975 (2020).Article 

Google Scholar 
Ramirez-Villegas, J. & Jarvis, A. Downscaling Global Circulation Model Outputs: The Delta Method Decision and Policy Analysis Working Paper No. 1 (CIAT, 2010).Wu, Z. & Raven, P. Flora of China (Science Press and Missouri Botanical Garden Press, 1994–2006).Wu, Z. Flora of Tibet (Science Press, 1987).Maclean, I. M. D. et al. Microclimates buffer the responses of plant communities to climate change. Glob. Ecol. Biogeogr. 24, 1340–1350 (2015).Article 

Google Scholar 
Randin, C. F. et al. Climate change and plant distribution: local models predict high-elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).Article 

Google Scholar 
Scherrer, D. & Körner, C. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J. Biogeogr. 38, 406–416 (2011).Article 

Google Scholar  More

Experimental dataThe dataset used in this study is the global long-term air quality indicator data of 5577 regions from 2010 to 2014 extracted by Betancourt et al.14 based on the TOAR database (https://gitlab.jsc.fz-juelich.de/esde/machine-learning/aq-bench/-/blob/master/resources/AQbench_dataset.csv)29. As shown in Fig. 3, the monitoring sites include 15 regions, including EUR (Europe), NAM (North America), and EAS (East Asia), and are mainly distributed in NAM (North America), EUR (Europe) and EAS (East Asia). The dataset mainly includes the geographical location information of the monitoring site, such as longitude and latitude, the area to which it belongs, altitude, etc., and the site environment information, such as population density, night light intensity, and vegetation coverage. Since it is difficult to directly quantify factors such as the degree of industrial activity and the degree of human activity, environmental information such as the average light intensity at night and population density are used as proxy variables for the above factors. The ozone indicator records the hourly ozone concentration from air quality observation points in various regions and aggregates the collected ozone time series in units of one year into one indicator. Using a longer aggregation period can be used to average short-term weather fluctuations. The experimental data have a total of 35 input variables, including 4 categorical attributes and 31 continuous attributes. The predictor variable is the average ozone concentration in each region from 2010 to 2014. The specific variable names and descriptions14 are shown in the supplementary materials. A total of 4/5 of the total samples were used as the training set, and 1/5 were used as the test set.Figure 3Global distribution of monitoring sites.Full size imageResults of BO-XGBoost-RFEAccording to the XGBoost-RFE algorithm for feature selection, XGBoost-RFE combined with the cross-validation method is used to calculate the selected feature set in each RFE stage for fivefold cross-validation, and the mean absolute error (MAE) is used as the evaluation criterion to finally determine the number of features with the lowest mean absolute error (MAE). At the same time, the Bayesian optimization algorithm is used to adjust the hyper-parameters of XGBoost-RFE, and then the feature subset with the lowest cross-validation mean absolute error (MAE) is obtained. The main parameters of the XGBoost model in this article include the learning_rate, n_estimators, max_depth, gamma, reg_alpha, reg_lambda, colsample_bytree, and subsample. All parameters used in the model are shown in the supplementary material. Within the given parameter range, the Bayesian optimization algorithm is used, the mean absolute error (MAE) of the XGBoost-RFE fivefold cross-validation is used as the objective function, and the number of iterations is controlled to be 100. We obtained the hyperparameter combination corresponding to the lowest MAE and the corresponding optimal feature subset. The iterative process of Bayesian optimization is shown in Fig. 4.Figure 4Iterative process of Bayesian optimization.Full size imageThe parameter range and optimized value of XGBoost-RFE are shown in Table 1. The XGBoost-RFE feature selection results under the above optimized hyperparameters are shown in Fig. 5. The number of features in the feature subset with the lowest mean absolute error is 22, and the MAE is 2.410.Table 1 Main hyper-parameter range and optimized value.Full size tableFigure 5XGBoost-RFE feature selection results: Cross-validation MAE under optimal hyperparameter combination.Full size imageAdditionally, the XGBoost-RFE feature selection model without Bayesian optimization is compared with the algorithm in this study. The default parameters of the underlying model XGBoost are set to learning_rate as 0.3, max_depth as 6, gamma as 0, colsample_bytree as 1, subsample as 1, reg_alpha as 1, and reg_lambda as 0. The comparison results are shown in Table 2. The results show that the XGBoost-RFE cross-validation MAE without parameter tuning is larger than that of the algorithm in this study, and the dimension of the feature subset obtained is also higher than that of the algorithm in this study.Table 2 Comparison of MAE and feature num before and after BO.Full size tablePrediction resultsTo test the prediction accuracy of the prediction model with the optimal subset obtained by BO-XGBoost-RFE, three indexes, MAE, RMSE and R2, are used to evaluate the prediction results, and the expressions are as follows:$$begin{array}{*{20}c} {MAE = frac{1}{n}mathop sum limits_{i = 1}^{n} left| {left( {y_{i} – widehat{{y_{i} }}} right)} right|} \ end{array}$$
(8)
$$begin{array}{*{20}c} {RMSE = sqrt {frac{1}{n}mathop sum limits_{i = 1}^{n} left( {y_{i} – widehat{{y_{i} }}} right)^{2} } } \ end{array}$$
(9)
$$begin{array}{*{20}c} {R^{2} = 1 – frac{{mathop sum nolimits_{i = 1}^{n} left( {widehat{{y_{i} }} – y_{i} } right)^{2} }}{{mathop sum nolimits_{i = 1}^{n} left( {y_{i} – overline{{y_{i} }} } right)^{2} }}} \ end{array}$$
(10)
n indicates the number of samples, yi is the true value, (widehat{{y_{i} }}) is the predicted value and (overline{{y_{i} }}) indicates the mean value of the predicted value.The XGBoost-RFE feature selection algorithm based on Bayesian optimization in this study is compared with feature selection using full features and features selected by the Pearson correlation coefficient, which measures the correlation between two variables. In this study, the correlation with predictor variables was selected to be less than 0.1, and the variables with correlations greater than 0.9 were deleted to avoid multicollinearity.XGBoost, random forest, support vector regression machine, and KNN algorithms were used to predict ozone concentration with full features, features selected by Pearson’s correlation coefficient, and features based on BO-XGBoost-RFE. According to the evaluation indicators described above, the comparison of the prediction performance results of the three algorithms before and after dimensionality reduction can be obtained. The MAE, RMSE and R2 results of each prediction model are shown in Table 3.Table 3 MAE, RMSE and R2 of each prediction model.Full size tableAmong the four prediction models, random forest has the lowest MAE and RMSE and the highest R2 based on three different dimensions of data and therefore has the best prediction performance. The prediction accuracy of all four prediction models based on Pearson correlation is lower than that based on BO-XGBoost-RFE, indicating that only selecting features by correlation cannot accurately extract important variables. Although the RMSE of the support vector regression model based on BO-XGBoost-RFE is slightly lower than the RMSE based on full features, the prediction accuracy of XGBoost, RF, KNN after feature selection of BO-XGBoost-RFE is higher than that based on full features and Pearson correlation. Among the four prediction models, random forest has obtained the highest prediction accuracy. The MAE based on BO-XGBoost-RFE is 5.0% and 1.4% lower than that based on the Pearson correlation coefficient and the full-feature-based model, and the RMSE is reduced by 5.1%, 1.8%, R2 improved by 4.3%, 1.4%. Additionally, the XGBoost model achieved the greatest improvement in accuracy. The MAE was reduced by 5.9% and 1.7%, the RMSE was reduced by 5.2% and 1.7%, and the R2 was improved by 4.9% and 1.4% compared with the Pearson correlation coefficient-based and full-feature-based models, respectively. This indicates that feature selection based on BO-XGBoost-RFE effectively extracts important features, improves prediction accuracy based on multiple prediction models, and has better dimensionality reduction performance.Figure 6 shows the importance of each feature obtained by using the random forest prediction model, reflecting the degree of influence of each variable on the prediction results of the global multi-year average near-ground ozone concentration. The most important variables that affect the prediction results according to the ranking of feature importance are altitude, relative altitude, and latitude, followed by night light intensity within a radius of 5 km, population density and nitrogen dioxide concentration, while the proxy variables for vegetation cover have a relatively weak effect on the prediction of ozone concentration.Figure 6Feature importance in random forest.Full size image More