Philippa Kaur

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

Fossey, D. & Harcourt, D. H. Feeding ecology of free-ranging mountain gorilla (Gorilla gorilla beringei). In Primate ecology (ed. Clutton-Brock, T. H.) 415–447 (Academy Press, New York, 1977).Watts, D. P. Composition and variability of mountain gorilla diets in the central Virungas. Am. J. Primatol. 7, 323–356 (1984).PubMed 
Article 

Google Scholar 
Watts, D. Comparative socio-ecology of gorillas. In Great Ape Societies (eds. McGrew, W. C., Marchant, L. F. & Nishida, T.) 16–28 (Cambridge University Press, Cambridge, 1986).Doran, D. M. & McNeilage, A. Gorilla ecology and behavior. Evol. Anthropol. 6, 120–131 (1988).Article 

Google Scholar 
Xue, Y. et al. Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding. Science 348, 242–245 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mittermeier, R. A., Rylands, A. B. & Wilson, D. E. Handbook of the mammals of the world. Primates Vol. 3 (Lynx Edicions, 2013).
Google Scholar 
Groves, C. Primate Taxonomy (Smithsonian Institution Press, 2001).
Google Scholar 
Cooksey, K. E. & Morgan, D. B. Gorilla (Gorilla). In The International encyclopedia of primatology, Vol. 1 (ed. Fuentes, A.) 472–477 (Wiley, Hoboken, 2017).McFarland, K. L. Ecology of cross river gorillas (Gorilla gorilla diehli) on Afi mountain, Cross River State, Nigeria. Ph.D. Dissertation. City University of New York, USA (2007).Rogers, M. E., Maisels, F., Wiliamson, E. A., Fernandez, M. & Tutin, C. E. G. Gorilla diet in the Lopé Reserve, Gabon: a nutritional analysis. Oecologia 84, 326–339 (1990).ADS 
Article 

Google Scholar 
van Casteren, A., Wright, E., Kupczik, K. & Robbins, M. M. Unexpected hard-object feeding in Western lowland gorillas. Am. J. Phys. Anthropol. 170, 433–438 (2019).PubMed 
Article 

Google Scholar 
Masi, S., Cipolletta, C. & Robbins, M. M. Western lowland gorillas (Gorilla gorilla gorilla) change their activity patterns in response to frugivory. Am. J. Primatol. 71, 91–100 (2009).PubMed 
Article 

Google Scholar 
Yamagiwa, J., Basabose, A. K., Kaleme, K. & Yumoto, T. Diet of grauer’s gorillas in montane forest of Kahuzi, Democratic Republic of Congo. Int. J. Primatol. 26, 1345–1373 (2005).Article 

Google Scholar 
Grueter, C. C. et al. Long-term temporal and spatial dynamics of food availability for endangered mountain gorillas in Volcanoes National Park, Rwanda. Am. J. Primatol. 75, 267–280 (2013).PubMed 
Article 

Google Scholar 
Ostrofsky, K. R. & Robbins, M. M. Fruit-feeding and activity patterns of mountain gorillas (Gorilla beringei beringei) in Bwindi Impenetrable National Park, Uganda. Am. J. Phys. Anthropol. 173, 3–20 (2020).PubMed 
Article 

Google Scholar 
Berthaume, M. A. Tooth cusp sharpness as a dietary correlate in great apes. Am. J. Phys. Anthropol. 153, 226–235 (2014).PubMed 
Article 

Google Scholar 
King, S. J. et al. Dental senescence in a long-lived primate links infant survival to rainfall. Proc. Natl. Acad. Sci. USA 102, 16579–16583 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Berthaume, M. A. & Schroer, K. Extant ape dental topography and its implications for reconstructing the emergence of early Homo. J. Hum. Evol. 112, 15–29 (2017).PubMed 
Article 

Google Scholar 
Sheine, W. S. & Kay, R. F. An analysis of chewed food particle size and its relationship to molar structure in the primates Cheirogaleus medius and Galago senegalensis and the insectivoran Tupaia glis. Am. J. Phys. Anthropol. 47, 15–20 (1977).Article 

Google Scholar 
Galbany, J., Estebaranz, F., Martínez, L. M. & Pérez-Pérez, A. Buccal dental microwear variability in extant African Hominoidea: taxonomy versus ecology. Primates 50, 221–230 (2009).PubMed 
Article 

Google Scholar 
Scott, R. S., Teaford, M. F. & Ungar, P. S. Dental microwear texture and anthropoid diets. Am. J. Phys. Anthropol. 147, 551–579 (2012).PubMed 
Article 

Google Scholar 
Teaford, M. F. & Oyen, O. J. In vivo and in vitro turnover in dental microwear. Am. J. Phys. Anhtropol. 80, 447–460 (1989).CAS 
Article 

Google Scholar 
Stuhlträger, J. et al. Dental wear patterns reveal dietary ecology and season of death in a historical chimpanzee population. PLoS ONE 16, e0251309 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Elgart, A. A. Dental wear, wear rate, and dental disease in the African apes. Am. J. Primatol. 72, 481–491 (2010).PubMed 

Google Scholar 
Berthaume, M. A. Food mechanical properties and dietary ecology. Am. J. Phys. Anhtropol. 159, 79–104 (2016).Article 

Google Scholar 
Galbany, J. et al. Tooth wear and feeding ecology in mountain gorillas from Volcanoes National Park, Rwanda. Am. J. Phys. Anhtropol. 159, 457–465 (2016).Article 

Google Scholar 
Janis, C. M. The correlation between diet and dental wear in herbivorous mammals, and its relationship to the determination of diets of extinct species, in Evolutionary paleobiology of behavior and coevolution (ed. Boucot, A. J.) 241–259 (Elsevier, Amsterdam, 1990).Knight-Sadler, J. & Fiorenza, L. Tooth wear inclination in great ape molars. Folia Primatol. 88, 223–236 (2017).Article 

Google Scholar 
Kullmer, O. et al. Technical note: Occlusal fingerprint analysis: Quantification of tooth wear pattern. Am. J. Phys. Anthropol. 139, 600–605 (2009).PubMed 
Article 

Google Scholar 
Fiorenza, L. et al. Molar macrowear reveals Neanderthal eco-geographical dietary variation. PLoS ONE 6, e14769 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fiorenza, L., Benazzi, S., Oxilia, G. & Kullmer, O. Functional relationship between dental macrowear and diet in Late Pleistocene and recent modern human populations. Int. J. Osteoarchaeol. 28, 153–161 (2018).Article 

Google Scholar 
Fiorenza, L. et al. The functional role of the Carabelli trait in early and late hominins. J. Hum. Evol. 145, 102816 (2020).PubMed 
Article 

Google Scholar 
Fiorenza, L. & Kullmer, O. Occlusion in an adult male gorilla with a supernumerary maxillary premolar. Int. J. Primatol. 37, 762–777 (2016).Article 

Google Scholar 
Kullmer, O., Menz, U., & Fiorenza, L. Occlusal fingerprint analysis (OFA) reveal dental occlusal behaviour in primate teeth. In T. Martin & W. von Koenigswald (Eds.), T Martin, W von Koenigswald, K-H Südekum), Mammalian teeth: form and function. (pp. 25–43). Munich, Germany: Dr. F. Pfeil (2020)Stuhlträger, J. et al. Dental wear patterns reveal dietary ecology and season of death in a historical chimpanzee population. PLoS ONE 16, e0251309 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Fiorenza, L., Benazzi, S., Estalrrich, A., & Kullmer, O. Diet and cultural diversity in Neanderthals and modern humans from dental macrowear analyses. In C. Schmidt & J. T. Watson (Eds.), Dental wear in evolutionary and biocultural contexts (pp. 39–72). London, UK: Academic Press (2020).M’Kirera, F. & Ungar, P. S. Occlusal relief changes with molar wear in Pan troglodytes troglodytes and Gorilla gorilla gorilla. Am. J. Primatol. 60, 31–41 (2003).PubMed 
Article 

Google Scholar 
Galbany, J. et al. Age-related tooth wear differs between forest and savanna primates. PLoS ONE 9, e94938 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leigh, S. R. & Shea, B. T. Ontogeny and the evolution of adult body size dimorphism in apes. Am. J. Primatol. 36, 37–60 (1995).PubMed 
Article 

Google Scholar 
Watts, D. P. Environmental influences on mountain gorilla time budgets. Am. J. Primatol. 15, 195–211 (1988).PubMed 
Article 

Google Scholar 
Doran, D. M. et al. Western lowland gorilla diet and resource availability: New evidence, cross-site comparisons, and reflections on indirect sampling methods. Am. J. Primatol. 58, 91–116 (2002).PubMed 
Article 

Google Scholar 
Zanolli, C. et al. Evidence of increased hominid diversity in the Early and Middle Pleistocene of Indonesia. Nat. Ecol. Evol. 3, 755–764 (2019).PubMed 
Article 

Google Scholar 
Krueger, K. L., Scott, J. R., Kay, R. F. & Ungar, P. S. Dental microwear textures of “phase I” and “phase II” facets. Am. J. Phys. Anthropol. 137, 485–490 (2008).PubMed 
Article 

Google Scholar 
Kay, R. F. & Hiiemae, K. M. Jaw movement and tooth use in recent and fossil primates. Am. J. Phys. Anthropol. 40, 227–256 (1974).CAS 
PubMed 
Article 

Google Scholar 
Wall, C. E., Vinyard, C. J., Johnson, K. R., Williams, S. H. & Hylander, W. L. Phase II jaw movements and masseter muscle activity during chewing in Papio anubis. Am. J. Phys. Anthropol. 129, 215–224 (2006).PubMed 
Article 

Google Scholar 
Glowacka, H. et al. Toughness of the Virunga mountain gorilla (Gorilla beringei beringei) diet across an altitudinal gradient. Am. J. Primatol. 79, e22661 (2017).Article 

Google Scholar 
Cooper, J. E. & Hull, G. Gorilla pathology and health (Academic Press, 2017).
Google Scholar 
Hammerton, R., Hunt, K. A. & Riley, L. M. An investigation into keeper opinions of great apes diet and abnormal behaviour. J. Zoo Aquar. Res. 7, 170–178 (2019).
Google Scholar 
Kay, R. F. Mastication, molar tooth structure and diet in primates. Ph.D. thesis, Yale University, New Haven, CT (1973).Smith, B. H. Patterns of molar wear in hunter-gatherers and agriculturalists. Am. J. Phys. Anhtropol. 63, 39–56 (1984).CAS 
Article 

Google Scholar 
Maier, W. & Schneck, G. Konstruktionsmorphologische Untersuchungen am Gebiß der hominoiden Primaten. Z. Morphol. Anthropol. 72, 127–169 (1981).CAS 
PubMed 
Article 

Google Scholar 
Fiorenza, L., Benazzi, S., Tausch, J., Kullmer, O. & Schrenk, F. Identification reassessment of the isolated tooth Krapina D58 through Occlusal Fingerprint Analysis. Am. J. Phys. Anthropol. 143, 306–312 (2010).PubMed 
Article 

Google Scholar 
Kullmer, O., Huck, M., Engel, K., Schrenk, F. & Bromage, T. Hominid Tooth Pattern Database (HOTPAD) derived from optical 3D topometry. In Three-dimensional imaging in paleoanthropology and prehistoric archaeology (eds. Mafart, B. & Delingette, H.) 71–82 (Acts of the XIVth UISPP Congress, BAR Int. Ser.1049, 2002).Hammer, Ø. & Harper, D. Paleontological data analysis (Blackwell Publishing, 2006).
Google Scholar 
Brown, M. B. & Forsythe, A. B. Robust tests for the equality of variances. J. Am. Stat. Assoc. 69, 364–367 (1974).MATH 
Article 

Google Scholar 
Noguchi, K. & Gel, Y. R. Combination of Levene-type tests and a finite-intersection method for testing equality of variances against ordered alternatives. J. Nonparam. Stat. 22, 897–913 (2010).MathSciNet 
MATH 
Article 

Google Scholar 
Gastwirth, J. L., et al. Lawstat: tools for biostatistics, public policy, and law. R package version 3.4. https://CRAN.R-project.org/package=lawstat (2020).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. (2021)Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.5–7. https://CRAN.R-project.org/package=vegan (2020).Martinez Arbizu, P. PairwiseAdonis: pairwise multilevel comparison using adonis. R package version 0.4 (2017).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Palaeontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar  More

Lund, E. M., Armstrong, P. J., Kirk, C. A. & Klausner, J. S. Prevalence and risk factors for obesity in adult dogs from private US veterinary practices. Int. J. Appl. Res. Vet. Med. 4(2), 177 (2006).
Google Scholar 
German, A. J. The growing problem of obesity in dogs and cats. J. Nutr. 136(7), 1940S-1946S (2006).CAS 
PubMed 
Article 

Google Scholar 
Courcier, E. A., Thomson, R. M., Mellor, D. J. & Yam, P. S. An epidemiological study of environmental factors associated with canine obesity. J. Small Anim. Pract. 51(7), 362–367 (2010).CAS 
PubMed 
Article 

Google Scholar 
Mao, J., Xia, Z., Chen, J. & Yu, J. Prevalence and risk factors for canine obesity surveyed in veterinary practices in Beijing, China. Prev. Vet. Med. 112(3–4), 438–442 (2013).PubMed 
Article 

Google Scholar 
Payan-Carreira, R., Sargo, T. & Nascimento, M. M. Canine obesity in Portugal: Perceptions on occurrence and treatment determinants. Acta Vet. Scand. 57(1), 1–1 (2015).Article 

Google Scholar 
Chandler, M. et al. Obesity and associated comorbidities in people and companion animals: A one health perspective. J. Comp. Pathol. 156(4), 296–309 (2017).CAS 
PubMed 
Article 

Google Scholar 
Montoya-Alonso, J. A. et al. Prevalence of canine obesity, obesity-related metabolic dysfunction, and relationship with owner obesity in an obesogenic region of Spain. Front. Vet. Sci. 4, 59 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Muñoz-Prieto, A. et al. European dog owner perceptions of obesity and factors associated with human and canine obesity. Sci. Rep. 8(1), 1–10 (2018).Article 
CAS 

Google Scholar 
Marshall, W. G., Bockstahler, B. A., Hulse, D. A. & Carmichael, S. A review of osteoarthritis and obesity: Current understanding of the relationship and benefit of obesity treatment and prevention in the dog. Vet. Comp. Orthop. Traumatol. 22(05), 339–345 (2009).CAS 
PubMed 
Article 

Google Scholar 
Zoran, D. L. Obesity in dogs and cats: A metabolic and endocrine disorder. Vet. Clin. N. Am. Small Anim. Pract. 40(2), 221–239 (2010).Article 

Google Scholar 
Tvarijonaviciute, A. et al. Obesity-related metabolic dysfunction in dogs: A comparison with human metabolic syndrome. BMC Vet. Res. 8(1), 147 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hoenig, M. Comparative aspects of human, canine, and feline obesity and factors predicting progression to diabetes. Vet. Sci. 1(2), 121–135 (2014).Article 

Google Scholar 
Yam, P. S. et al. Impact of canine overweight and obesity on health-related quality of life. Prev. Vet. Med. 127, 64–69 (2016).CAS 
PubMed 
Article 

Google Scholar 
Sandøe, P., Palmer, C., Corr, S., Astrup, A. & Bjørnvad, C. R. Canine and feline obesity: A One Health perspective. Vet. Rec. 175(24), 610–616 (2014).PubMed 
Article 

Google Scholar 
Salt, C., Morris, P. J., Wilson, D., Lund, E. M. & German, A. J. Association between life span and body condition in neutered client-owned dogs. J. Vet. Intern. Med. 33(1), 89–99 (2019).PubMed 

Google Scholar 
Switonski, M. & Mankowska, M. Dog obesity—The need for identifying predisposing genetic markers. Res. Vet. Sci. 95(3), 831–836 (2013).CAS 
PubMed 
Article 

Google Scholar 
Mankowska, M. et al. Sequence analysis of three canine adipokine genes revealed an association between TNF polymorphisms and obesity in Labrador dogs. Anim. Genet. 47(2), 245–249 (2016).CAS 
PubMed 
Article 

Google Scholar 
Raffan, E. et al. A deletion in the canine POMC gene is associated with weight and appetite in obesity-prone Labrador retriever dogs. Cell Metab. 23(5), 893–900 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Suchodolski, J. S. Intestinal microbiota of dogs and cats: A bigger world than we thought. Anim. Pract. 41(2), 261–272 (2011).
Google Scholar 
Barko, P. C., McMichael, M. A., Swanson, K. S. & Williams, D. A. The gastrointestinal microbiome: A review. J. Vet. Intern. Med. 32(1), 9–25 (2018).CAS 
PubMed 
Article 

Google Scholar 
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122), 1027–1031 (2006).PubMed 
Article 
ADS 

Google Scholar 
Bäckhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl. Acad. Sci. 101(44), 15718–15723 (2004).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Ghazalpour, A., Cespedes, I., Bennett, B. J. & Allayee, H. Expanding role of gut microbiota in lipid metabolism. Curr. Opin. Lipidol. 27(2), 141 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Losasso, C. et al. Assessing the influence of vegan, vegetarian and omnivore oriented westernized dietary styles on human gut microbiota: A cross sectional study. Front. Microbiol. 9, 317 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Pizarroso, N. A., Fuciños, P., Gonçalves, C., Pastrana, L. & Amado, I. R. A Review on the role of food-derived bioactive molecules and the microbiota—Gut–brain axis in satiety regulation. Nutrients 13(2), 632. https://doi.org/10.3390/nu13020632 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boulangé, C. L., Neves, A. L., Chilloux, J., Nicholson, J. K. & Dumas, M. E. Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Med. 8(1), 1–12 (2016).Article 
CAS 

Google Scholar 
Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. USA 102(31), 11070–11075 (2005).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Human gut microbes associated with obesity. Nature 444(7122), 1022–1023 (2006).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Zhi, C. et al. Connection between gut microbiome and the development of obesity. Eur. J. Clin. Microbiol. Infect. Dis. 38(11), 1987–1998 (2019).PubMed 
Article 

Google Scholar 
Huang, Z., Pan, Z., Yang, R., Bi, Y. & Xiong, X. The canine gastrointestinal microbiota: Early studies and research frontiers. Gut Microbes 11(4), 635–654 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Swanson, K. S. et al. Phylogenetic and gene-centric metagenomics of the canine intestinal microbiome reveals similarities with humans and mice. ISME J. 5(4), 639–649 (2011).CAS 
PubMed 
Article 

Google Scholar 
Coelho, L. P. et al. Similarity of the dog and human gut microbiomes in gene content and response to diet. Microbiome 6(1), 1–11 (2018).Article 

Google Scholar 
Hand, D., Wallis, C., Colyer, A. & Penn, C. W. Pyrosequencing the canine faecal microbiota: Breadth and depth of biodiversity. PLoS ONE 8(1), e53115 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Handl, S. et al. Faecal microbiota in lean and obese dogs. FEMS Microbiol. Ecol. 84(2), 332–343 (2013).CAS 
PubMed 
Article 

Google Scholar 
Park, H. J. et al. Association of obesity with serum leptin, adiponectin, and serotonin and gut microflora in beagle dogs. J. Vet. Intern. Med. 29(1), 43–50 (2015).PubMed 
Article 

Google Scholar 
Park, H. J. et al. Fecal microbiota analysis of obese dogs with underlying diseases: A pilot study. Korean J. Vet. Res. 55(3), 205–208 (2015).Article 

Google Scholar 
Beloshapka, A. N., Forster, G. M., Holscher, H. D., Swanson, K. S. & Ryan, E. P. Fecal microbial communities of overweight and obese client-owned dogs fed cooked bean powders as assessed by 454-pyrosequencing. J. Vet. Sci. Technol. 7(366), 2 (2016).
Google Scholar 
Li, Q., Lauber, C. L., Czarnecki-Maulden, G., Pan, Y. & Hannah, S. S. Effects of the dietary protein and carbohydrate ratio on gut microbiomes in dogs of different body conditions. MBio 8(1), e01703-e1716 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Kieler, I. N. et al. Gut microbiota composition may relate to weight loss rate in obese pet dogs. Vet. Med. Sci. 3(4), 252–262 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Forster, G. M. et al. A comparative study of serum biochemistry, metabolome and microbiome parameters of clinically healthy, normal weight, overweight, and obese companion dogs. Top. Companion Anim. Med. 33(4), 126–135 (2018).PubMed 
Article 

Google Scholar 
Salas-Mani, A. et al. Fecal microbiota composition changes after a BW loss diet in beagle dogs. J. Anim. Sci. 96(8), 3102–3111 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Alexander, C. et al. Effects of prebiotic inulin-type fructans on blood metabolite and hormone concentrations and faecal microbiota and metabolites in overweight dogs. Br. J. Nutr. 120(6), 711–720 (2018).CAS 
PubMed 
Article 

Google Scholar 
Herstad, K. M. et al. A diet change from dry food to beef induces reversible changes on the faecal microbiota in healthy, adult client-owned dogs. BMC Vet. Res. 13(1), 147 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kim, Y. S., Unno, T., Kim, B. Y. & Park, M. S. Sex differences in gut microbiota. World J. Mens Health 38(1), 48–60 (2020).PubMed 
Article 

Google Scholar 
Xu, J. et al. The response of canine faecal microbiota to increased dietary protein is influenced by body condition. BMC Vet. Res. 13(1), 374 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Masuoka, H. et al. Transition of the intestinal microbiota of dogs with age. PLoS ONE 12, e0181739 (2016).Article 
CAS 

Google Scholar 
Mizukami, K. et al. Age-related analysis of the gut microbiome in a purebred dog colony. FEMS Microbiol. Lett. 366(8), fnz095 (2019).CAS 
PubMed 
Article 

Google Scholar 
Alessandri, G. et al. Metagenomic dissection of the canine gut microbiota: Insights into taxonomic, metabolic and nutritional features. Environ. Microbiol. 21(4), 1331–1343 (2019).CAS 
PubMed 
Article 

Google Scholar 
Xu, H. et al. Oral administration of compound probiotics improved canine feed intake, weight gain, immunity and intestinal microbiota. Front. Immunol. 10, 666 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reddy, K. E. et al. Impact of breed on the fecal microbiome of dogs under the same dietary condition. J. Microbiol. Biotechnol. 29(12), 1947–1956 (2019).CAS 
PubMed 
Article 

Google Scholar 
O’Neill, D. G., Church, D. B., McGreevy, P. D., Thomson, P. C. & Brodbelt, D. C. Prevalence of disorders recorded in dogs attending primary-care veterinary practices in England. PLoS ONE 9(3), e90501 (2014).PubMed Central 
Article 
ADS 

Google Scholar 
Vilson, Å. et al. Disentangling factors that shape the gut microbiota in German Shepherd dogs. PLoS ONE 13(3), e0193507 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Song, S. J. et al. Cohabiting family members share microbiota with one another and with their dogs. Elife 2, e00458 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Guard, B. C. et al. Characterization of the fecal microbiome during neonatal and early pediatric development in puppies. PLoS ONE 12(4), e0175718 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Greer, K. A., Canterberry, S. C. & Murphy, K. E. Statistical analysis regarding the effects of height and weight on life span of the domestic dog. Res. Vet. Sci. 82(2), 208–214 (2007).PubMed 
Article 

Google Scholar 
Fleming, J. M., Creevy, K. E. & Promislow, D. E. L. Mortality in North American dogs from 1984 to 2004: An investigation into age-, size-, and breed-related causes of death. J. Vet. Int. Med. 25(2), 187–198 (2011).CAS 
Article 

Google Scholar 
Oberbauer, A. M., Belanger, J. & Famula, T. R. A review of the impact of neuter status on expression of inherited conditions in dogs. Front. Vet. Sci. 6, 397 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pilla, R. & Suchodolski, J. S. The role of the canine gut microbiome and metabolome in health and gastrointestinal disease. Front. Vet. Sci. 6, 498 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Bermingham, E. N., Maclean, P., Thomas, D. G., Cave, N. J. & Young, W. Key bacterial families (Clostridiaceae, Erysipelotrichaceae and Bacteroidaceae) are related to the digestion of protein and energy in dogs. PeerJ 5, e3019 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kim, J., An, J. U., Kim, W., Lee, S. & Cho, S. Differences in the gut microbiota of dogs (Canis lupus familiaris) fed a natural diet or a commercial feed revealed by the Illumina MiSeq platform. Gut Pathog. 9, 68–68 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Mori, A. et al. Comparison of the effects of four commercially available prescription diet regimens on the fecal microbiome in healthy dogs. J. Vet. Med. Sci. 81, 1783–1790 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Apper, E. et al. Relationships between gut microbiota, metabolome, body weight, and glucose homeostasis of obese dogs fed with diets differing in prebiotic and protein content. Microorganisms 8(4), 513 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Wernimont, S. M. et al. The effects of nutrition on the gastrointestinal microbiome of cats and dogs: Impact on health and disease. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.01266 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Schauf, S. et al. Effect of dietary fat to starch content on fecal microbiota composition and activity in dogs. J. Anim. Sci. 96(9), 3684–3698 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Bresciani, F. et al. Effect of an extruded animal protein-free diet on fecal microbiota of dogs with food-responsive enteropathy. J. Vet. Intern. Med. 32(6), 1903–1910 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Madsen, L., Myrmel, L. S., Fjære, E., Liaset, B. & Kristiansen, K. Links between dietary protein sources, the gut microbiota, and obesity. Front. Physiol. 8, 1047 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
EU law and publications. Regulation (EC) No 767/2009 of the European parliament and of the council of 13 July 2009 on the placing on the market and use of feed, amending European Parliament and council regulation (EC) No 1831/2003 and repealing council directive 79/373/EEC, commission directive 80/511/EEC, council directives 82/471/EEC, 83/228/EEC, 93/74/EEC, 93/113/EC and 96/25/EC and commission decision 2004/217/EC. OJEC L229, 1–28 (2009).
Google Scholar 
Paßlack, N. et al. Impact of the dietary inclusion of dried food residues on the apparent nutrient digestibility and the intestinal microbiota of dogs. Arch. Anim. Nutr. 75(4), 311–327 (2021).PubMed 
Article 

Google Scholar 
Macedo, H. T. et al. Weight-loss in obese dogs promotes important shifts in fecal microbiota profile to the extent of resembling microbiota of lean dogs. Anim. Microbiome 4(1), 1–13 (2022).Article 
CAS 

Google Scholar 
Zhang, X. et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS ONE 8(8), e71108 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Remely, M. et al. Microbiota and epigenetic regulation of inflammatory mediators in type 2 diabetes and obesity. Benef. Microbes 5(1), 33–43 (2014).CAS 
PubMed 
Article 

Google Scholar 
Tamanai-Shacoori, Z. et al. Roseburia spp.: A marker of health?. Future Microbiol. 12(2), 157–170 (2017).CAS 
PubMed 
Article 

Google Scholar 
Herrmann, E. et al. RNA-based stable isotope probing suggests Allobaculum spp. as particularly active glucose assimilators in a complex murine microbiota cultured in vitro. BioMed Res. Int. 5, 1. https://doi.org/10.1155/2017/1829685 (2017).CAS 
Article 

Google Scholar 
Wang, J., Wang, P., Li, D., Hu, X. & Chen, F. Beneficial effects of ginger on prevention of obesity through modulation of gut microbiota in mice. Eur. J. Nutr. 59(2), 699–718 (2020).CAS 
PubMed 
Article 

Google Scholar 
Garcia-Mazcorro, J. F., Ivanov, I., Mills, D. A. & Noratto, G. Influence of whole-wheat consumption on fecal microbial community structure of obese diabetic mice. PeerJ 4, e1702 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, K. et al. Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Rep. 26(1), 222–235 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wu, T. R. et al. Gut commensal Parabacteroides goldsteinii plays a predominant role in the anti-obesity effects of polysaccharides isolated from Hirsutella sinensis. Gut 68(2), 248–262 (2019).CAS 
PubMed 
Article 

Google Scholar 
Karl, J. P. et al. Effects of psychological, environmental and physical stressors on the gut microbiota. Front. Microbiol. 9, 2013 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Gallè, F. et al. Exploring the association between physical activity and gut microbiota composition: a review of current evidence. Ann. Ig. Med. Prev. Comunita 31(6), 582–589 (2019).
Google Scholar 
Laflamme, D. R. P. C. Development and validation of a body condition score system for dogs. Canine Practice (Santa Barbara, Calif.: 1990, USA) (1997).FEDIAF. Nutritional Guidelines for Complete and Complementary Pet Food for Cats and Dogs https://fediaf.org/self-regulation/nutrition.html#guidelines (2021).Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41(1), e1–e1 (2013).CAS 
PubMed 
Article 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7(5), 335–336 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
Google Scholar 
Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000Research 5, 2122 (2016).PubMed 
PubMed Central 

Google Scholar 
Gong, W., Kwak, I. Y., Pota, P., Koyano-Nakagawa, N. & Garry, D. J. DrImpute: Imputing dropout events in single cell RNA sequencing data. BMC Bioinform. 19(1), 1–10 (2018).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), D590–D596 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Finotello, F., Mastrorilli, E. & Di Camillo, B. Measuring the diversity of the human microbiota with targeted next-generation sequencing. Brief. Bioinform. 19(4), 679–692 (2018).PubMed 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. Software http://CRAN.R-project.org/package=vegan (2012). 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

The datasetWe curated and explored 695 published 16S rRNA gene samples from cryospheric ecosystems (Methods section and Supplementary Table 7), including polar ice sheets, mountain glaciers and their proglacial lakes, permafrost soils and the coastal ocean under the influence of glacier runoff, and compared these to 3552 published 16S rRNA gene samples from non-cryospheric ecosystems, including temperate and tropical lakes and soils (Supplementary Table 7). This approach allowed us to identify and explore features specific to the cryospheric microbiome and compare it to other environmental microbiomes. However, we note a geographical bias towards polar regions in current publicly available repositories, and the paucity of alpine samples specifically highlights the need to further characterise these habitats given that they are among the most endangered cryospheric ecosystems globally. This bias is further compounded by the inconsistent methodologies applied across studies (e.g. primer pairs and sequencers used). To account for potential primer biases, we analysed two 16S rRNA primer pairs (Primer Pair 1, PP1: 341f-785r; Primer Pair 2, PP2: 515f-806r)12,13 commonly used in amplicon high-throughput sequencing. In total, this dataset contains 241,502,708 paired sequence reads, resulting in 530,254 and 410,931 amplicon sequence variants (ASVs) for PP1 and PP2, respectively. Moreover, all taxonomic analyses were performed at the genus level, to account for the limitations of 16s rRNA amplicon data. To gain deeper insights into the functional space of the cryospheric microbiome, we compared 34 published metagenomes from cryospheric ecosystems with 56 metagenomes from similar but non-cryospheric ecosystems (Fig. 1A). Given the difficulty of obtaining high-quality metagenomes from cryospheric ecosystems, we restricted our analyses to glacier surfaces, ice-covered lakes, and Antarctic soils. Although our analyses were limited to samples where raw sequence data are available (Methods section), the breadth of habitats covered are representative of the most abundant cryospheric ecosystems, e.g., glacier ice, cryoconites, subglacial lakes and sea ice. On the other hand, several niches such as glacier snow, glacier-fed rivers/streams, and the full-breadth of permafrost may not entirely be represented due to data unavailability. We reanalysed all metagenomes using the same bioinformatic pipeline (IMP3; see Methods section) to avoid analytical biases. Overall, the metagenomic analyses from 2,427,818,072 paired reads yielded 41,068,842 gene sequences. Thus, we here present a catalogue representing a snapshot of the functional diversity in the cryospheric microbiome, integrating across diverse habitats. This represents what we believe to be the first global overview of the functional repertoire of the Earth’s cryosphere compared to other ecosystems.Fig. 1: A unique cryospheric microbiome.A Geographic distribution of the 16 S rRNA gene samples for the two primer pairs (PP) and metagenomes for both cryospheric and non-cryospheric ecosystems, where GPS coordinates were available on NCBI. Symbol size denotes the number of samples per site (see Supplementary Table 7). B Phylogenetic tree based on abundant ASVs ( >0.5% relative abundance in at least one sample) in the PP1 dataset. The heatmap (inner rings) shows the presence (at a  > 0.5% relative abundance threshold) of ASVs in the four ecosystem types of the cryosphere (ice and snow, terrestrial, coastal ocean and freshwater). The barplot (outer ring) represents the coefficient for the SVM classifier analysis, highlighting discriminating ASVs. C Sorensen’s phylogenetic index of β-diversity (n1 = n2 = 84,461 for PP1, and n1 = n2 = 99,000 for PP2) and D β-MNTD calculated across pairs of samples in the cryospheric samples (Cryo-Cryo), pairs of cryospheric and non-cryospheric samples (Cryo-Others) and pairs of non-cryospheric (Others-Others) samples (sample sizes are listed in Supplementary Table 2). The top panel (shades of blue) is for PP1, the bottom one (shades of red) for PP2; two-sided Wilcoxon tests were performed to assess significance in panels C and D; the Holm method was used to correct for multiple testing (****: 0–0.0001). Boxplots depict the median and the 25th and 75th quartiles, whiskers extend to values within 1.5 times the interquartile range, and the remaining points are outliers. Effect sizes and exact p-values are available in Supplementary Table 2. Source data are provided as a Source Data file.Full size imageA cryospheric microbiomeGiven the communal constraints imposed by the harsh environment of cryospheric ecosystems (e.g., low temperature, oligotrophy), we expected them to harbour a specific microbiome. Accordingly, machine-learning classification (logistic regression models, Methods) based on community composition was able to differentiate between cryospheric and non-cryospheric microbiomes with high accuracy (balanced accuracy >0.96, Supplementary Table 1). Both primer pairs consistently yielded a high classification accuracy and especially a high precision. Interestingly, many of the discriminating cryospheric ASVs were spread widely across the bacterial tree of life (Fig. 1A and Supplementary Fig. 1).The notion that a part of the microbiome is specific to the cryosphere is also strongly supported by phylogenetic analyses of the 16 S rRNA gene amplicon dataset. First, we found higher pairwise phylogenetic overlap among cryospheric samples than among cryospheric/non-cryospheric or non-cryospheric samples (Sorensen’s index, Fig. 1C; Wilcoxon test, Holm adj. p  More

In the present work, we extend the base model for the spatial mosquito population dynamics24 to include wild male mosquitoes and genetically modified male mosquitoes. Thus, five populations will be considered: the aquatic mosquito population, including larvae and pupae, the egg mosquito population, the reproductive female mosquito population, the wild male mosquito population, and the genetically modified male population. Similar approaches can be found in the literature25,26.In the following system, we represent mosquito population densities (mosquitoes per m(^2)) by: E – in the egg phase, A – in the aquatic phase, F – female in the reproductive phase, M – wild males, and G – genetically modified male mosquitoes. Due to the very high resistance of the egg phase (up to 450 days27) and as we are interested in an urban spatial macro-scale modeling, we do not consider the mortality in the egg phase. The model is described by the following system of partial differential equations:$$begin{aligned} {left{ begin{array}{ll} partial _t E &{} = alpha beta F M -e E, \ partial _t A &{} = e left( 1 – dfrac{A}{k} right) E -(eta _a+{mu _a})A, \ partial _t F &{} = nabla cdot (D_m nabla F) -mu _f F + reta _{a} A, \ partial _t M &{} = nabla cdot (D_m nabla M) -mu _m M + (1-r)eta _{a} A, \ partial _t G &{} = nabla cdot (D_g nabla G) -mu _{g}G + l, end{array}right. } end{aligned}$$
(1)
where ( alpha ) represents the proportion of wild male mosquitoes to the total number of male mosquitoes (wild males + genetically modified males); (beta ) represents the expected quantity of eggs from the successful encounter between wild females and males; e is the egg hatching rate; k is the carrying capacity of the aquatic phase; ( eta _a ) is the emergence rate for mosquitoes from the aquatic phase to the female or male phases; ( mu _a), (mu _f), (mu _m), and (mu _{g}) are the mortality rates of mosquitoes in the aquatic phase, females, males, and genetically modified males, respectively; r is the proportion of females to males (typically (r=0.5)); (l=l(x,y,t)) is the function representing the number of genetically modified mosquitoes released in a unit of time at any point of the domain; (D_m) is the diffusion coefficient of wild mobiles females and males; (D_g) is the diffusion coefficient of genetically modified males. The proposed model (1) can naturally deal with heterogeneous parameters, such as mortality, diffusion, and carrying capacity coefficients. Thus it is possible to model the influence of rain, wind, and human action. In the context of this work, we are considering that the city neighborhood is divided into two environments: houses and streets. Due to lack of data, we restrict the investigated heterogeneity only to the carrying capacity coefficient.The proposed model can be regarded as an extension of other “economic” models20,24 in the effort to qualitatively reproduce the complex phenomena by using as few parameters as possible. Following this idea, the carrying capacity was neglected in the egg phase because of the skip oviposition phenomenon28 i.e., the female lays the number of eggs that the place holds, without more space, she migrates to other environments to finish laying the eggs. We also do not consider this coefficient in the winged phase as limitations in the winged phase were not reported in any study. On the other hand, we consider it in the aquatic phases (larvae and pupae), where it is effective29.The term ( alpha ), which multiplies the probability of encounters between male and female, represents the impact of the insertion of genetically modified males in the mosquito population to the immobile phase and is defined as$$begin{aligned} alpha = left{ begin{array}{cc} 1, &{} text{ if } M=G= 0, \ dfrac{M}{M + G}, &{} text{ otherwise }. end{array} right. end{aligned}$$
(2)
Similar modeling approach can be found in the literature16. As the release rate of genetically modified males increases, the alpha value decreases, and, consequently, the probability of encounter between females and wild males also decreases. Thus, there is a greater probability of encounter between genetically modified males and females. This approach presents an advantage, when compared to the models found in the literature25, as System  (1) does not present singularities at the equilibrium states, allowing mathematical analysis and numerical simulations. From the biological point of view, the increment of male wild mosquitoes over some critical value does not affect the egg deposition. At first glance, the term FM can lead to a misunderstanding that such property is not satisfied in the presented model. However, in Section “Equilibrium points considering the application of genetically modified male mosquitoes,” we argue that both male and female populations possess mathematical attractor equilibria, blocking the wild male population from growing beyond this value.Finally, any acceptable population model should be invariant in the definition domain, meaning its solution does not present senseless values. Setting the variable domain as$$begin{aligned} 0 le E(x,y,t)< infty ,;; 0 le A(x,y,t) le k, ;; 0 le F(x,y,t)< infty ,;; 0 le M(x,y,t)< infty ,;; 0 le G(x,y,t) < infty , end{aligned}$$ (3) we can verify that it is invariant under the time evolution by the System (1). To prove this statement, it is sufficient to verify that the vector field defined by the right side of (1) points into the domain when (E, A, F, M, G) approaches the domain boundary. When E approaches zero, the right side of the first equation in (1) is not negative. When A approaches zero, the right side of the second equation in (1) is not negative. When A approaches k (bottom), the first term on the right side of the second equation in (1) tends to zero, while the second term remains negative. Since the term ( nabla cdot (D_m nabla F) ) cannot change the F sign, when F approaches zero, the right side of the third equation in (1) is not negative Since the term ( nabla cdot (D_m nabla M) ) cannot change the M sign, when M approaches zero, the right side of the fourth equation in (1) is not negative. Since the term ( nabla cdot (D_g nabla G) ) cannot change the G sign, when G approaches zero, the right side of the fifth equation in (1) is not negative. In the rest of this section, let us explain how to estimate one-by-one all the parameters used in this model from experimental data available in the literature. It is a challenging task as, typically, the development of the Ae. aegypti mosquito depends on food variation30, temperature variations14,15 and rainfall31. This data is not available in the literature in the organized and systematic form. Because of that, we assume the environment is under optimal conditions of temperature, availability of food, and humidity.How to estimate emergence rate ((eta _a)) The emergence rate describes the rate at which the aquatic phase of the mosquito emerges into the adult phases. In the present model, for simplicity, it was considered that no mosquito from the crossing between genetically modified males and females reaches adulthood. Thus, the emergence rate is calculated on the crossing between females and wild males. Under optimal conditions and feeding distribution, based on the literature30, the emergence rate is 0.5596 (text{ day}^{-1}).How to estimate diffusion coefficients ((D_m,D_g)) The diffusion coefficient is one of the most important parameters describing the mosquitoes’ movement. We use the methodology proposed in the previous work24 to obtain the diffusion coefficient of adult mosquitoes (females and males) and genetically modified males.The estimate is done by assuming that all mosquitoes are released at (0, 0), and their movement is described by the corresponding equation in (1) neglecting other terms than diffusion. The population starts spreading in all directions. We define the spreading distance R(t) as the radius of the region centered in (0, 0) where (90%) of the initial mosquitoes population density is present. In Silva et al.24 it is shown that$$begin{aligned} R(t) = sqrt{4Dt} ;text {erf}^{-1}(0.9). end{aligned}$$ (4) Now corresponding diffusion coefficient is estimated by using the average flight distance of the mosquitoes and the characteristic time related to their life expectancy. Under favorable weather conditions, the average lifetime flight distance of females and males is approximately32,33 65 m, while the same for GM males is34 67.3 m. Based on the literature, we consider that the characteristic time for wild females and males32 is 7 days, and the same for genetically modified males is34 2.17 days. Using (4) we estimate the values for (D_m) and (D_g) summarized in Table 1. It would be natural to consider that the mosquitoes’ movement changes in different environments. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (D_m) and (D_g) are the same in streets and house blocks.How to estimate mortality rates ((mu _a), (mu _f), (mu _m), (mu _{g}))The mortality coefficient represents an average quantity of mosquitoes in the corresponding phase dying each day. As mentioned before, we disregard the mortality rate in the egg phase, as it is negligible due to its great durability27, it does not affect the numerical results, and it complicates analytical estimates. Thus, the aquatic phase mortality rate coefficient is equal to the same for larvae’s coefficient, which is approximately29 (mu _a = 0.025) (1/day).There is no solid agreement on the mortality rate of male and female wild mosquitoes in the literature. Although some results29,30 suggest they are similar, we follow these authors and consider them equal. Considering both natural death and accidental ones, approximately (10%) of females and male mosquitoes in the adult phase die at each day35. Under optimal conditions, the mortality coefficient can be estimated from this data by using the proposed model (1) by neglecting diffusion and emergence terms in the corresponding equation; details can be found in the previous work24. The resulting parameter values are summarized in Table 1.It would be natural to consider that the mosquitoes mortality rate depends on the environment. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (mu _a), (mu _f), (mu _m), and (mu _{g}) are the same in streets and house blocks.How to estimate the expected egg number ((beta ))This coefficient represents the average quantity of eggs a wild female lays per day, assuming a successful meeting with a wild male. Considering the number of times a female lays eggs in its lifetime36, the average quantity of eggs per lay and the mosquito’s life expectancy, under favorable conditions, this coefficient is estimated as (beta = 34).How to estimate the hatching rate (e)This coefficient determines the average number of eggs hatching in one day. Experimental data37 suggest that, under optimal humidity conditions, the mean value of the hatch rate coefficient is 0.24 given a temperature of 28 ((^{circ })C), which is considered ideal for mosquito development. This is the value used in the present work.How to estimate carrying capacity coefficient (k)The carrying capacity k represents the space limitation of one phase due to situations present in the environment37,38, such as competition for food among the larvae39. In general, it depends on external factors such as food availability, climate, terrain properties, making direct estimation almost impossible. In the Analytical results section, we show how to estimate this coefficient for each grid block. When considering spatial population dynamics in a heterogeneous environment, carrying capacity is one of the most influential parameters as it varies significantly. For example, house block offer more food and a shelter against natural predators resulting to a larger carrying capacity when compared with street environment. Following the literature32 we assume that the 80% of the mosquito’s breeding places are in houses resulting in the relation (k_h=5k_s), where (k_h) and (k_s) are the carrying capacities of the house blocks and in the streets.Genetically modified mosquitoes release rate (l)Function l(x, y, t) determines how many genetically modified mosquitoes are released in the location (x, y) at time t.In a normal situation, the sex ratio between males and females is 1 : 1. The increment of this proportion favoring GM males increases the probability of females to mate with these mosquitoes. As reported in the literature12,30 the initial launch size is 11 times larger than the adult female population, and it is done in some spots in the city. In this work, we analyze different release strategies maintaining the (11times 1) proportion in some scenarios.Table 1 All parameter values are directly taken or estimated from the literature as explained in section Modeling.Full size table More

Marzluff, J. M. Worldwide urbanization and its effects on birds. In Avian Ecology and Conservation in an Urbanizing World (eds Marzluff, J. et al.) 19–47 (Springer, Boston, 2001).Chapter 

Google Scholar 
McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).Article 

Google Scholar 
Luniak, M. Synurbization–adaptation of animal wildlife to urban development in Proceedings 4th international urban wildlife symposium (eds. Shaw, W., Harris, L.,Vandruff, L.) 50–55 (University of Arizona, Tucson, ARI, 2004).Isaksson, C. Impact of urbanization on birds in Bird Species how they arise, modify and vanish (ed. Tietze D. T.) 235–257 (Springer, 2018).Minias, P. Successful colonization of a novel urban environment is associated with an urban behavioural syndrome in a reed-nesting waterbird. Ethology 121, 1178–1190 (2015).Article 

Google Scholar 
Møller, A. P. et al. Urban habitats and feeders both contribute to flight initiation distance reduction in birds. Behav. Ecol. 26, 861–865 (2015).Article 

Google Scholar 
Jokimäki, J. & Suhonen, J. Distribution and habitat selection of wintering birds in urban environments. Landsc. Urban Plan. 39, 253–263 (1998).Article 

Google Scholar 
Francis, R. A. & Chadwick, M. A. What makes a species synurbic?. Appl. Geogr. 32, 514–521 (2012).Article 

Google Scholar 
Møller, A. P. et al. High urban population density of birds reflects their timing of urbanization. Oecologia 170, 867–875 (2012).PubMed 
Article 
ADS 

Google Scholar 
Tella, J. L. et al. Offspring body condition and immunocompetence are negatively affected by high breeding densities in a colonial seabird: A multiscale approach. Proc. R. Soc. B 268, 1455–1461 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Savoca, M. S., Bonter, D. N., Zuckerberg, B., Dickinson, J. L. & Ellis, J. C. Nesting density is an important factor affecting chick growth and survival in the Herring Gull. Condor 113, 565–571 (2011).Article 

Google Scholar 
Minias, P., Włodarczyk, R. & Janiszewski, T. Opposing selective pressures may act on the colony size in a waterbird species. Evol. Ecol. 29, 283–297 (2015).Article 

Google Scholar 
Kamiński, M. et al. Density-dependence of nestling immune function and physiological condition in semi-precocial colonial bird: A cross-fostering experiment. Front. Zool. 18, 7 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ward, P. & Zahavi, A. The importance of certain assemblages of birds as “information-centres” for food-finding. Ibis 115, 517–534 (1973).Article 

Google Scholar 
Danchin, E. & Wagner, R. H. The evolution of coloniality: The emergence of new perspectives. Trends Ecol. Evol. 12, 342–347 (1997).CAS 
PubMed 
Article 

Google Scholar 
Brown, C. R. & Brown, M. B. Coloniality in the Cliff Swallow: The Effect of Group Size on Social Behavior (University of Chicago Press, 1996).
Google Scholar 
Evans, J. C., Votier, S. C. & Dall, S. R. Information use in colonial living. Biol. Rev. 91, 658–672 (2016).PubMed 
Article 

Google Scholar 
Brown, C. R. & Brown, M. B. Avian coloniality. In Current Ornithology (eds Brown, C. R. & Brown, M. B.) 1–82 (Springer, Boston, 2001).
Google Scholar 
Coulson, J. C., Duncan, N. & Thomas, C. Changes in the breeding biology of the herring gull (Larus argentatus) induced by reduction in the size and density of the colony. J. Anim. Ecol. 51, 739–756 (1982).Article 

Google Scholar 
Ots, I. & Horak, P. Great tits Parus major trade health for reproduction. Proc. R. Soc. B. 263, 1443–1447 (1996).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Richner, H. & Tripet, F. Ectoparasitism and the trade-off between current and future reproduction. Oikos 86, 535–538 (1999).Article 

Google Scholar 
Fokkema, R. W., Ubels, R. & Tinbergen, J. M. Great tits trade off future competitive advantage for current reproduction. Behav. Ecol. 27, 1656–1664 (2016).
Google Scholar 
Horak, P. & Leberton, J. D. Survival of adult Great Tits Parus major in relation to sex and habitat; a comparison of urban and rural populations. Ibis 140, 205–209 (1998).Article 

Google Scholar 
Stracey, C. M. & Robinson, S. K. Are urban habitats ecological traps for a native songbird? Season-long productivity, apparent survival, and site fidelity in urban and rural habitats. J. Avian Biol. 43, 50–60 (2012).Article 

Google Scholar 
Sepp, T., McGraw, K. J., Kaasik, A. & Giraudeau, M. A review of urban impacts on avian life-history evolution: Does city living lead to slower pace of life?. Glob. Change Biol. 24, 1452–1469 (2018).Article 
ADS 

Google Scholar 
Phillips, J. N., Gentry, K. E., Luther, D. A. & Derryberry, E. P. Surviving in the city: Higher apparent survival for urban birds but worse condition on noisy territories. Ecosphere 9, e02440 (2018).Article 

Google Scholar 
Johnston, R. F. & Janiga, M. Feral Pigeons (Oxford University Press on Demand, 1995).
Google Scholar 
Giunchi, D., Mucci, N., Bigi, D., Mengoni, C. & Baldaccini, N. E. Feral pigeon populations: Their gene pool and links with local domestic breeds. Zoology 142, 125817 (2020).PubMed 
Article 

Google Scholar 
Sol, D. Artificial selection, naturalization, and fitness: Darwin’s pigeons revisited. Biol. J. Linn. Soc. 93, 657–665 (2008).Article 

Google Scholar 
Giunchi, D., Albores-Barajas, Y. V., Baldaccini, N. E., Vanni, L. & Soldatini, C. Feral pigeons: Problems, dynamics and control methods. In Integrated Pest Management and Pest Control. Current and Future Tactics (eds Soloneski, S. & Larramendy, M.) 215–240 (InTechOpen, London, 2012).
Google Scholar 
Senar, J. C., Navalpotro, H., Pascual, J. & Montalvo, T. Nicarbazin has no effect on reducing feral pigeon populations in Barcelona. Pest Manag. Sci. 77, 131–137 (2021).CAS 
PubMed 
Article 

Google Scholar 
Rose, E., Nagel, P. & Haag-Wackernagel, D. Spatio-temporal use of the urban habitat by feral pigeons (Columba livia). Behav. Ecol. Sociobiol. 60, 242–254 (2006).Article 

Google Scholar 
Corbel, H. et al. Stress response varies with plumage colour and local habitat in feral pigeons. J. Ornithol. 157, 825–837 (2016).Article 

Google Scholar 
Møller, A. P., Merino, S., Brown, C. R. & Robertson, R. J. Immune defense and host sociality: A comparative study of swallows and martins. Am. Nat. 158, 136–145 (2001).PubMed 
Article 

Google Scholar 
Drzewińska-Chańko, J. et al. Immunocompetent birds choose larger breeding colonies. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13540 (2021).Article 
PubMed 

Google Scholar 
Saino, N., Suffritti, C., Martinelli, R., Rubolini, D. & Møller, A. P. Immune response covaries with corticosterone plasma levels under experimentally stressful conditions in nestling barn swallows (Hirundo rustica). Behav. Ecol. 14, 318–325 (2003).Article 

Google Scholar 
Goutte, A. et al. Long-term survival effect of corticosterone manipulation in black-legged kittiwakes. Gen. Comp. Endocrinol. 167, 246–251 (2010).CAS 
PubMed 
Article 

Google Scholar 
Møller, A. P., Christe, P., Erritzøe, J. & Mavarez, J. Condition, disease and immune defence. Oikos 83, 301–306 (1998).Article 

Google Scholar 
Navarro, C., Marzal, A., De Lope, F. & Møller, A. P. Dynamics of an immune response in house sparrows Passer domesticus in relation to time of day, body condition and blood parasite infection. Oikos 101, 291–298 (2003).Article 

Google Scholar 
Toïgo, C., Gaillard, J. M., Van Laere, G., Hewison, M. & Morellet, N. How does environmental variation influence body mass, body size, and body condition? Roe deer as a case study. Ecography 29, 301–308 (2006).Article 

Google Scholar 
Jacquin, L. et al. A potential role for parasites in the maintenance of color polymorphism in urban birds. Oecologia 173, 1089–1099 (2013).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Meillère, A., Brischoux, F., Parenteau, C. & Angelier, F. Influence of urbanization on body size, condition, and physiology in an urban exploiter: A multi-component approach. PLoS ONE https://doi.org/10.1371/journal.pone.0135685 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).Article 

Google Scholar 
Jacquin, L. et al. Melanin-based coloration is related to parasite intensity and cellular immune response in an urban free living bird: The feral pigeon Columba livia. J. Avian Biol. 42, 11–15 (2011).Article 

Google Scholar 
Liker, A., Papp, Z., Bókony, V. & Lendvai, A. Z. Lean birds in the city: Body size and condition of house sparrows along the urbanization gradient. J. Anim. Ecol. 77, 789–795 (2008).CAS 
PubMed 
Article 

Google Scholar 
Audet, J. N., Ducatez, S. & Lefebvre, L. The town bird and the country bird: Problem solving and immunocompetence vary with urbanization. Behav. Ecol. 27, 637–644 (2016).Article 

Google Scholar 
Kurucz, K., Purger, J. J. & Batáry, P. Urbanization shapes bird communities and nest survival, but not their food quantity. Glob. Ecol. Conserv. 26, e01475 (2021).Article 

Google Scholar 
Partecke, J., Schwabl, I. & Gwinner, E. Stress and the city: Urbanization and its effects on the stress physiology in European blackbirds. Ecology 87, 1945–1952 (2006).PubMed 
Article 

Google Scholar 
Bailly, J. et al. Negative impact of urban habitat on immunity in the great tit Parus major. Oecologia 182, 1053–1062 (2016).PubMed 
Article 
ADS 

Google Scholar 
Glądalski, M. et al. Differences in use of bryophyte species in tit nests between two contrasting habitats: An urban park and a forest. Eur. Zool. J. 88, 807–815 (2021).Article 

Google Scholar 
Tella, J. L., Scheuerlein, A. & Ricklefs, R. E. Is cell–mediated immunity related to the evolution of life-history strategies in birds?. Proc. R. Soc. B 269, 1059–1066 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
Brown, C. R. & Brown, M. B. Empirical measurement of parasite transmission between groups in a colonial bird. Ecology 85, 1619–1626 (2004).Article 

Google Scholar 
O’Brien, V. A. & Brown, C. R. Group size and nest spacing affect Buggy Creek virus (Togaviridae: Alphavirus) infection in nestling house sparrows. PLoS ONE 6, e25521 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Wilcoxen, T. E. et al. Effects of bird-feeding activities on the health of wild birds. Conserv. Physiol. 3, cov058 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Moyers, S. C., Adelman, J. S., Farine, D. R., Thomason, C. A. & Hawley, D. M. Feeder density enhances house finch disease transmission in experimental epidemics. Philos. Trans. R. Soc. B 373, 20170090 (2018).Article 
CAS 

Google Scholar 
Møller, A. P. Successful city dwellers: A comparative study of the ecological characteristics of urban birds in the Western Palearctic. Oecologia 159, 849–858 (2009).PubMed 
Article 
ADS 

Google Scholar 
Watson, H., Videvall, E., Andersson, M. N. & Isaksson, C. Transcriptome analysis of a wild bird reveals physiological responses to the urban environment. Sci. Rep. 7, 44180 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Hasselquist, D. & Nilsson, J. Å. Physiological mechanisms mediating costs of immune responses: What can we learn from studies of birds?. Anim. Behav. 83, 1303–1312 (2012).Article 

Google Scholar 
Biard, C., Monceau, K., Motreuil, S. & Moreau, J. Interpreting immunological indices: The importance of taking parasite community into account. An example in blackbirds Turdus merula. Methods Ecol. Evol. 6, 960–972 (2015).Article 

Google Scholar 
Leclaire, S., Czirják, G. Á., Hammouda, A. & Gasparini, J. Feather bacterial load shapes the trade-off between preening and immunity in pigeons. BMC Evol. Biol. 15, 60 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Vinkler, M., Adelman, J. S. & Ardia, D. R. Evolutionary and ecological immunology. In Avian Immunology 3rd edn (eds Kaspers, B. et al.) 519–558 (Academic Press, London, 2021).
Google Scholar 
Davis, A. K., Maney, D. L. & Maerz, J. C. The use of leukocyte profiles to measure stress in vertebrates: A review for ecologists. Funct. Ecol. 22, 760–772 (2008).Article 

Google Scholar 
Indykiewicz, P., Podlaszczuk, P., Kamiński, M., Włodarczyk, R. & Minias, P. Central–periphery gradient of individual quality within a colony of Black-headed Gulls. Ibis 161, 744–758 (2019).Article 

Google Scholar 
Vleck, C. M., Vertalino, N., Vleck, D. & Bucher, T. L. Stress, corticosterone, and heterophil to lymphocyte ratios in free-living Adélie penguins. Condor 102, 392–400 (2000).Article 

Google Scholar 
Davis, A. K., Cook, K. C. & Altizer, S. Leukocyte profiles in wild house finches with and without mycoplasmal conjunctivitis, a recently emerged bacterial disease. EcoHealth 1, 362–373 (2004).Article 

Google Scholar 
Lobato, E., Moreno, J., Merino, S., Sanz, J. J. & Arriero, E. Haematological variables are good predictors of recruitment in nestling pied flycatchers (Ficedula hypoleuca). Ecoscience 12, 27–34 (2005).Article 

Google Scholar 
Bobby Fokidis, H., Greiner, E. C. & Deviche, P. Interspecific variation in avian blood parasites and haematology associated with urbanization in a desert habitat. J. Avian Biol. 39, 300–310 (2008).Article 

Google Scholar 
Padgett, D. A. & Glaser, R. How stress influences the immune response. Trends Immunol. 24, 444–448 (2003).CAS 
PubMed 
Article 

Google Scholar 
Dimitrov, S. et al. Cortisol and epinephrine control opposing circadian rhythms in T cell subsets. Blood 113, 5134–5143 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ilmonen, P., Hasselquist, D., Langefors, Å. & Wiehn, J. Stress, immunocompetence and leukocyte profiles of pied flycatchers in relation to brood size manipulation. Oecologia 136, 148–154 (2003).PubMed 
Article 
ADS 

Google Scholar 
Minias, P., Gach, K., Włodarczyk, R. & Janiszewski, T. Colony size affects nestling immune function: A cross-fostering experiment in a colonial waterbird. Oecologia 190, 333–341 (2019).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Cyr, N. E., Earle, K., Tam, C. & Romero, L. M. The effect of chronic psychological stress on corticosterone, plasma metabolites, and immune responsiveness in European starlings. Gen. Comp. Endocrinol. 154, 59–66 (2007).CAS 
PubMed 
Article 

Google Scholar 
Schoech, S. J., Bowman, R. & Reynolds, S. J. Food supplementation and possible mechanisms underlying early breeding in the Florida Scrub-Jay (Aphelocoma coerulescens). Horm. Behav. 46, 565–573 (2004).CAS 
PubMed 
Article 

Google Scholar 
Ibáñez-Álamo, J. D. et al. Physiological stress does not increase with urbanization in European blackbirds: Evidence from hormonal, immunological and cellular indicators. Sci. Total Environ. 721, 137332 (2020).PubMed 
Article 
ADS 
CAS 

Google Scholar 
Bonier, F. Hormones in the city: Endocrine ecology of urban birds. Horm. Behav. 61, 763–772 (2012).CAS 
PubMed 
Article 

Google Scholar 
Valdebenito, J. O. et al. Seasonal variation in sex-specific immunity in wild birds. Sci. Rep. 11, 1349 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Hetmański, T. Timing of breeding in the Feral Pigeon Columba livia f. domestica in Słupsk (NW Poland). Acta Ornithol. 39, 105–110 (2004).Article 

Google Scholar 
Dijkstra, C. et al. An adaptive annual rhythm in the sex of first pigeon eggs. Behav. Ecol. Sociobiol. 64, 1393–1402 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Swanson, D. L. Seasonal variation of vascular oxygen transport in the dark-eyed junco. Condor 92, 62–66 (1990).Article 

Google Scholar 
Niedojadlo, J., Bury, A., Cichoń, M., Sadowska, E. T. & Bauchinger, U. Lower haematocrit, haemoglobin and red blood cell number in zebra finches acclimated to cold compared to thermoneutral temperature. J. Avian Biol. 49, e01596 (2018).Article 

Google Scholar 
Roulin, A. Condition-dependence, pleiotropy and the handicap principle of sexual selection in melanin-based colouration. Biol. Rev. 91, 328–348 (2016).PubMed 
Article 

Google Scholar 
Statistics Poland. https://stat.gov.pl/en/ (2021).Sol, D. & Senar, J. C. Urban pigeon populations: Stability, home range, and the effect of removing individuals. Can. J. Zool. 73, 1154–1160 (1995).Article 

Google Scholar 
Minias, P. Reproduction and survival in the city: Which fitness components drive urban colonization in a reed-nesting waterbird?. Curr. Zool. 62, 79–87 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Meissner, W. & Fischer, I. Sexing of common gull, Larus canus, using linear measurements. Folia Zool. 66, 183–188 (2017).Article 

Google Scholar 
Haag-Wackernagel, D., Heeb, P. & Leiss, A. Phenotype-dependent selection of juvenile urban feral pigeons Columba livia. Bird Study 53, 163–170 (2006).Article 

Google Scholar 
Harter, T. S., Reichert, M., Brauner, C. J. & Milsom, W. K. Validation of the i-STAT and HemoCue systems for the analysis of blood parameters in the bar-headed goose, Anser indicus. Conserv. Physiol. 3, cov021 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Minias, P. The use of haemoglobin concentrations to assess physiological condition in birds: A review. Conserv. Physiol. 3, cov007 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Martin, L. B. et al. Phytohemagglutinin-induced skin swelling in birds: Histological support for a classic immunoecological technique. Funct. Ecol. 20, 290–299 (2006).Article 

Google Scholar 
Brown, G. P., Shilton, C. M. & Shine, R. Measuring amphibian immunocompetence: Validation of the phytohemagglutinin skin-swelling assay in the cane toad, Rhinella marina. Methods Ecol. Evol. 2, 341–348 (2011).Article 

Google Scholar 
Kennedy, M. W. & Nager, R. G. The perils and prospects of using phytohaemagglutinin in evolutionary ecology. Trends Ecol. Evol. 21, 653–655 (2006).PubMed 
Article 

Google Scholar 
Vinkler, M., Bainová, H. & Albrecht, T. Functional analysis of the skin-swelling response to phytohaemagglutinin. Funct. Ecol. 24, 1081–1086 (2010).Article 

Google Scholar 
Turmelle, A. S., Ellison, J. A., Mendonça, M. T. & McCracken, G. F. Histological assessment of cellular immune response to the phytohemagglutinin skin test in Brazilian free-tailed bats (Tadarida brasiliensis). J. Comp. Physiol. B 180, 1155–1164 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Griffiths, R., Double, M. C., Orr, K. & Dawson, R. J. A DNA test to sex most birds. Mol. Ecol. 7, 1071–1075 (1998).CAS 
PubMed 
Article 

Google Scholar 
Çakmak, E., Akın Pekşen, Ç. & Bilgin, C. C. Comparison of three different primer sets for sexing birds. J. Vet. Diagn. Investig. 29, 59–63 (2017).Article 
CAS 

Google Scholar 
Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Jaeger, B. C., Edwards, L. J., Das, K. & Sen, P. K. An R 2 statistic for fixed effects in the generalized linear mixed model. J. Appl. Stat. 44, 1086–1105 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
Johnson, P. C. Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods Ecol. Evol. 5, 944–946 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Bartoń, K. MuMIn: Multi-model inference. R package ver. 1.43.17. CRAN: The Comprehensive R Archive Network, Berkeley, CA, USA. https://CRAN.R-project.org/package=MuMIn (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
Kahle, D. J. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J. 5, 144–161 (2013).Article 

Google Scholar  More

Mariotte, P. et al. Plant–soil feedback: Bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).PubMed 
Article 

Google Scholar 
Daryanto, S., Fu, B., Wang, L., Jacinthe, P. A. & Zhao, W. Quantitative synthesis on the ecosystem services of cover crops. Earth-Sci. Rev. 185, 357–373 (2018).ADS 
CAS 
Article 

Google Scholar 
Shackelford, G. E., Kelsey, R. & Dicks, L. V. Effects of cover crops on multiple ecosystem services: Ten meta-analyses of data from arable farmland in California and the Mediterranean. Land Use Policy 88, 104204 (2019).Article 

Google Scholar 
McDaniel, M. D., Tiemann, L. K. & Grandy, A. S. Does agricultural crop diversity enhance soil microbial biomass and organic matter dynamics? A meta-analysis. Ecol. Appl. 24, 560–570 (2014).CAS 
PubMed 
Article 

Google Scholar 
Wittwer, R. A., Dorn, B., Jossi, W. & van der Heijden, M. G. A. A. Cover crops support ecological intensification of arable cropping systems. Sci. Rep. 7, 41911 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chahal, I. & Van Eerd, L. L. Cover crops increase tomato productivity and reduce nitrogen losses in a temperate humid climate. Nutr. Cycl. Agroecosyst. 119, 195–211 (2021).CAS 
Article 

Google Scholar 
Belfry, K. D., Trueman, C., Vyn, R. J., Loewen, S. A. & Van Eerd, L. L. Winter cover crops on processing tomato yield, quality, pest pressure, nitrogen availability, and profit margins. PLoS ONE 12, 1–17 (2017).Article 
CAS 

Google Scholar 
Wall, L. G. et al. Changes of paradigms in agriculture soil microbiology and new challenges in microbial ecology. Acta Oecologica 95, 68–73 (2019).ADS 
Article 

Google Scholar 
Schmidt, R., Gravuer, K., Bossange, A. V., Mitchell, J. & Scow, K. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS ONE 13, 1–19 (2018).
Google Scholar 
Schmidt, R., Mitchell, J. & Scow, K. Cover cropping and no-till increase diversity and symbiotroph:saprotroph ratios of soil fungal communities. Soil Biol. Biochem. 129, 99–109 (2019).CAS 
Article 

Google Scholar 
Ali, A. et al. Hiseq base molecular characterization of soil microbial community, diversity structure, and predictive functional profiling in continuous cucumber planted soil affected by diverse cropping systems in an intensive greenhouse region of Northern China. Int. J. Mol. Sci. 20, 2619 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
Kim, N., Zabaloy, M. C., Guan, K. & Villamil, M. B. Do cover crops benefit soil microbiome? A meta-analysis of current research. Soil Biol. Biochem. 142, 107701 (2020).CAS 
Article 

Google Scholar 
Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R. & Hart, M. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agron. Sustain. Dev. 36, 1–14 (2016).CAS 
Article 

Google Scholar 
Nevins, C. J., Nakatsu, C. & Armstrong, S. Characterization of microbial community response to cover crop residue decomposition. Soil Biol. Biochem. 127, 39–49 (2018).CAS 
Article 

Google Scholar 
Peralta, A. L., Sun, Y., McDaniel, M. D. & Lennon, J. T. Crop rotational diversity increases disease suppressive capacity of soil microbiomes. Ecosphere 9, e02235 (2018).Article 

Google Scholar 
Cloutier, M. L. et al. Fungal community shifts in soils with varied cover crop treatments and edaphic properties. Sci. Rep. 10, 1–15 (2020).Article 
CAS 

Google Scholar 
Finney, D. M., Buyer, J. S. & Kaye, J. P. Living cover crops have immediate impacts on soil microbial community structure and function. J. Soil Water Conserv. 72, 361–373 (2017).Article 

Google Scholar 
Calderón, F. J., Nielsen, D., Acosta-Martínez, V., Vigil, M. F. & Lyon, D. Cover crop and irrigation effects on soil microbial communities and enzymes in semiarid agroecosystems of the central great plains of North America. Pedosphere 26, 192–205 (2016).Article 
CAS 

Google Scholar 
Romdhane, S. et al. Cover crop management practices rather than composition of cover crop mixtures affect bacterial communities in no-till agroecosystems. Front. Microbiol. 10, 1–11 (2019).Article 

Google Scholar 
Blanco-Canqui, H. & Lal, R. Crop residue removal impacts on soil productivity and environmental quality. CRC. Crit. Rev. Plant Sci. 28, 139–163 (2009).CAS 
Article 

Google Scholar 
Turmel, M. S., Speratti, A., Baudron, F., Verhulst, N. & Govaerts, B. Crop residue management and soil health: A systems analysis. Agric. Syst. 134, 6–16 (2015).Article 

Google Scholar 
Yang, Q., Wang, X. & Shen, Y. Comparison of soil microbial community catabolic diversity between rhizosphere and bulk soil induced by tillage or residue retention. J. Soil Sci. Plant Nutr. https://doi.org/10.4067/S0718-95162013005000017 (2013).Article 

Google Scholar 
Tang, H. et al. Tillage and crop residue incorporation effects on soil bacterial diversity in the double-cropping paddy field of southern China. Arch. Agron. Soil Sci. 67, 435–446 (2021).CAS 
Article 

Google Scholar 
Zhang, Y. et al. Long-term harvest residue retention could decrease soil bacterial diversities probably due to favouring oligotrophic lineages. Microb. Ecol. 76, 771–781 (2018).CAS 
PubMed 
Article 

Google Scholar 
Zhang, C. et al. Straw retention efficiently improves fungal communities and functions in the fallow ecosystem. BMC Microbiol. 21, 52 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chahal, I. & Van Eerd, L. L. Cover crop and crop residue removal effects on temporal dynamics of soil carbon and nitrogen in a temperate, humid climate. PLoS ONE 15, e0235665 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chahal, I. & Van Eerd, L. L. Evaluation of commercial soil health tests using a medium-term cover crop experiment in a humid, temperate climate. Plant Soil 427, 351–367 (2018).CAS 
Article 

Google Scholar 
Ruis, S. J. & Blanco-Canqui, H. Cover crops could offset crop residue removal effects on soil carbon and other properties: A review. Agron. J. 109, 1785–1805 (2017).CAS 
Article 

Google Scholar 
Zhao, M. et al. Intercropping affects genetic potential for inorganic nitrogen cycling by root-associated microorganisms in Medicago sativa and Dactylis glomerata. Appl. Soil Ecol. 119, 260–266 (2017).ADS 
Article 

Google Scholar 
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science (80-). 304, 1629–1633 (2004).ADS 
CAS 
Article 

Google Scholar 
Xiong, C. et al. Host selection shapes crop microbiome assembly and network complexity. New Phytol. 229, 1091–1104 (2021).CAS 
PubMed 
Article 

Google Scholar 
McDaniel, M. D., Grandy, A. S., Tiemann, L. K. & Weintraub, M. N. Eleven years of crop diversification alters decomposition dynamics of litter mixtures incubated with soil. Ecosphere 7, e01426 (2016).Article 

Google Scholar 
Buyer, J. S., Teasdale, J. R., Roberts, D. P., Zasada, I. A. & Maul, J. E. Factors affecting soil microbial community structure in tomato cropping systems. Soil Biol. Biochem. 42, 831–841 (2010).CAS 
Article 

Google Scholar 
Fernandez-Gnecco, G. et al. Microbial community analysis of soils under different soybean cropping regimes in the Argentinean south-eastern Humid Pampas. FEMS Microbiol. Ecol. 97, 1–14 (2021).Article 
CAS 

Google Scholar 
Semenov, M. V., Krasnov, G. S., Semenov, V. M. & van Bruggen, A. H. C. Long-term fertilization rather than plant species shapes rhizosphere and bulk soil prokaryotic communities in agroecosystems. Appl. Soil Ecol. 154, 103641 (2020).Article 

Google Scholar 
White, C. M. & Weil, R. R. Forage radish cover crops increase soil test phosphorus surrounding radish taproot holes. Soil Sci. Soc. Am. J. 75, 121–130 (2011).ADS 
CAS 
Article 

Google Scholar 
Schulz, M., Marocco, A., Tabaglio, V., Macias, F. A. & Molinillo, J. M. G. Benzoxazinoids in rye allelopathy—From discovery to application in sustainable weed control and organic farming. J. Chem. Ecol. 39, 154–174 (2013).CAS 
PubMed 
Article 

Google Scholar 
Cheng, F. & Cheng, Z. Research progress on the use of plant allelopathy in agriculture and the physiological and ecological mechanisms of allelopathy. Front. Plant Sci. 6, 1020 (2015).PubMed 
PubMed Central 

Google Scholar 
Thapa, V. R., Ghimire, R., Acosta-Martínez, V., Marsalis, M. A. & Schipanski, M. E. Cover crop biomass and species composition affect soil microbial community structure and enzyme activities in semiarid cropping systems. Appl. Soil Ecol. 157, 103735 (2021).Article 

Google Scholar 
Drost, S. M., Rutgers, M., Wouterse, M., de Boer, W. & Bodelier, P. L. E. Decomposition of mixtures of cover crop residues increases microbial functional diversity. Geoderma 361, 114060 (2020).ADS 
CAS 
Article 

Google Scholar 
Di Rauso Simeone, G., Müller, M., Felgentreu, C. & Glaser, B. Soil microbial biomass and community composition as affected by cover crop diversity in a short-term field experiment on a podzolized Stagnosol-Cambisol. J. Plant Nutr. Soil Sci. 183, 539–549 (2020).Article 
CAS 

Google Scholar 
Maul, J. E. et al. Microbial community structure and abundance in the rhizosphere and bulk soil of a tomato cropping system that includes cover crops. Appl. Soil Ecol. 77, 42–50 (2014).Article 

Google Scholar 
Huang, J. et al. Allocation and turnover of rhizodeposited carbon in different soil microbial groups. Soil Biol. Biochem. 150, 107973 (2020).CAS 
Article 

Google Scholar 
Strickland, M. S. & Rousk, J. Considering fungal:bacterial dominance in soils—Methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).CAS 
Article 

Google Scholar 
Leff, J. W. et al. Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits. ISME J. 12, 1794–1805 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Milcu, A. et al. Functionally and phylogenetically diverse plant communities key to soil biota. Ecology 94, 1878–1885 (2013).PubMed 
Article 

Google Scholar 
Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73, 1576–1585 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lay, C.-Y., Hamel, C. & St-Arnaud, M. Taxonomy and pathogenicity of Olpidium brassicae and its allied species. Fungal Biol. 122, 837–846 (2018).PubMed 
Article 

Google Scholar 
Liu, L., Zhu, K., Wurzburger, N. & Zhang, J. Relationships between plant diversity and soil microbial diversity vary across taxonomic groups and spatial scales. Ecosphere 11, e02999 (2020).
Google Scholar 
Hartwright, L. M., Hunter, P. J. & Walsh, J. A. A comparison of Olpidium isolates from a range of host plants using internal transcribed spacer sequence analysis and host range studies. Fungal Biol. 114, 26–33 (2010).CAS 
PubMed 
Article 

Google Scholar 
Barel, J. M. et al. Winter cover crop legacy effects on litter decomposition act through litter quality and microbial community changes. J. Appl. Ecol. 56, 132–143 (2019).CAS 
Article 

Google Scholar 
Austin, E. E., Wickings, K., McDaniel, M. D., Robertson, G. P. & Grandy, A. S. Cover crop root contributions to soil carbon in a no-till corn bioenergy cropping system. GCB Bioenergy 9, 1252–1263 (2017).CAS 
Article 

Google Scholar 
Bai, Z., Liang, C., Bodé, S., Huygens, D. & Boeckx, P. Phospholipid 13C stable isotopic probing during decomposition of wheat residues. Appl. Soil Ecol. 98, 65–74 (2016).Article 

Google Scholar 
Põlme, S. et al. FungalTraits: A user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

Google Scholar 
Pepe, A., Giovannetti, M. & Sbrana, C. Lifespan and functionality of mycorrhizal fungal mycelium are uncoupled from host plant lifespan. Sci. Rep. 8, 1–10 (2018).
Google Scholar 
Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).Article 

Google Scholar 
Saleem, M., Hu, J. & Jousset, A. More than the sum of its parts: Microbiome biodiversity as a driver of plant growth and soil health. Annu. Rev. Ecol. Evol. Syst. 50, 145–168 (2019).Article 

Google Scholar 
Wei, Z. et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci. Adv. 5, 1–12 (2019).
Google Scholar 
Ozimek, E. & Hanaka, A. Mortierella species as the plant growth-promoting fungi present in the agricultural soils. Agriculture 11, 7 (2020).Article 
CAS 

Google Scholar 
Li, F. et al. Mortierella elongata’s roles in organic agriculture and crop growth promotion in a mineral soil. L. Degrad. Dev. 29, 1642–1651 (2018).Article 

Google Scholar 
Sansinenea, E. Bacillus spp.: As plant growth-promoting bacteria. in Secondary Metabolites of Plant Growth Promoting Rhizomicroorganisms: Discovery and Applications 225–237 (Springer, 2019). https://doi.org/10.1007/978-981-13-5862-3_11.Palaniyandi, S. A., Yang, S. H., Zhang, L. & Suh, J.-W. Effects of actinobacteria on plant disease suppression and growth promotion. Appl. Microbiol. Biotechnol. 97, 9621–9636 (2013).CAS 
PubMed 
Article 

Google Scholar 
Jung, M.-Y. et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 16, 272–283 (2022).CAS 
PubMed 
Article 

Google Scholar 
Zhong, Y. et al. Microbial community assembly and metabolic function during wheat straw decomposition under different nitrogen fertilization treatments. Biol. Fertil. Soils 56, 697–710 (2020).CAS 
Article 

Google Scholar 
Liu, X. et al. Decomposing cover crops modify root-associated microbiome composition and disease tolerance of cash crop seedlings. Soil Biol. Biochem. 160, 108343 (2021).CAS 
Article 

Google Scholar 
Larkin, R. P., Griffin, T. S. & Honeycutt, C. W. Rotation and cover crop effects on soilborne potato diseases, tuber yield, and soil microbial communities. Plant Dis. 94, 1491–1502 (2010).PubMed 
Article 

Google Scholar 
van der Putten, W. H., Bradford, M. A., Brinkman, E. P., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).Article 

Google Scholar 
Menalled, U. D., Seipel, T. & Menalled, F. D. Farming system effects on biologically mediated plant–soil feedbacks. Renew. Agric. Food Syst. 36, 1–7 (2021).Article 

Google Scholar 
Fierer, N. & Jackson, J. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl. Environ. Microbiol. 71, 4117 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vainio, E. J. & Hantula, J. Direct analysis of wood-inhabiting fungi using denaturing gradient gel electrophoresis of amplified ribosomal DNA. Mycol. Res. 104, 927–936 (2000).CAS 
Article 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
White, T. J., Bruns, T., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, 1990).

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rivers, A. R., Weber, K. C., Gardner, T. G., Liu, S. & Armstrong, S. D. ITSxpress: Software to rapidly trim internally transcribed spacer sequences with quality scores for marker gene analysis. F1000Research 7, 1418 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Abarenkov, K. et al. UNITE QIIME release for Fungi. https://doi.org/10.15156/bio/786385 (2020).R Core Team. R: A Language and Environment for Statistical Computing. (2020).Oksanen, J. et al. vegan: Community Ecology Package. (2020).Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. (PRIMER-E, 2008).Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).Article 

Google Scholar 
Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 
Article 

Google Scholar 
Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).PubMed 
PubMed Central 
Article 

Google Scholar  More