Philippa Kaur

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

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

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

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

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

General observation and methods for ploidy analysis on aphid bacteriome cellsConsistent with previous observations9,21,22,40, the bacteriome of viviparous aphids consisted of two types of cells: bacteriocytes and sheath cells (Fig. 2). Bacteriocytes contained Buchnera cells and were much larger than sheath cells. Sheath cells exhibited a flattened morphology and surrounded the bacteriocytes. Both cell types possessed a single nucleus. Bacteriocytes had a single prominent nucleolus, which was not stained using DAPI, but using “Nucleolus Bright Red” staining (Fig. 2). Most sheath cells also had a single nucleolus, yet a small number had two. “Nucleolus Bright Red” also stained the peripheral region of Buchnera, probably because of the richness of RNA around Buchnera cells.Figure 2Morphology of bacteriocytes and sheath cells from each morph of aphids visualized using DAPI/Phalloidin/Nucleolus Bright Red staining. DNA and F-actin were stained by DAPI (gray or blue) and Phalloidin (green), respectively. The nucleolus, which is the site of ribosome biogenesis, was visualized by Nucleolus Bright Red (red). This dye binds RNA electrostatically, therefore the cytoplasm of bacteriocytes and Buchnera cells were also stained. Bacteriocytes (white arrows) had single prominent nucleolus, and the cell sizes were much larger than sheath cells (white arrowheads) in all aphid morphs.Full size imageTo determine the most suitable methods for ploidy analysis of aphid bacteriocytes, three types of methods, flow cytometry, Feulgen densitometry, and fluorometry were compared. First, flow cytometry successfully detected the nuclei of bacteriome cells and heads, and distinct peaks were present (Fig. S3). There were several peaks, which can be categorized as ploidy classes based on head peaks, assuming that the smallest peaks correspond to a diploid population. We recognized peaks up to 256C (256-ploidy) cells but could not distinguish cell types (i.e., bacteriocytes or sheath cells) in this method due to a lack of cytological information. Note that “C” means haploid genome size, for example, 2C = diploid and 8C = octoploid. Second, Feulgen densitometry also showed several ploidy levels of up to 128C (Fig. S4) in bacteriocytes. Sheath cells mainly consisted of 16-32C cells. However, we found that many cells were lost during the experimental procedures, probably due to the repeated washing processes and the long incubation time.We found the third method, image-based fluorometry for isolated nuclei, the best for quantitative ploidy analysis of aphid bacteriocytes (Fig. 3). Fluorometry showed distinct peaks of integrated fluorescence intensity, and they could be categorized as each ploidy class based on the intensity of the smallest peak in head cells (diploid population). The results were consistent with other methods; ploidy levels were 32C-256C in bacteriocytes and 16C-32C in sheath cells. In this analysis, the nucleolus size was used to discriminate between cell types. During cytological observation, we obtained the size distribution of the nucleolus, and it was revealed that the nucleolus of bacteriocytes was always larger than that of sheath cells (Fig. S5). Based on the results, we determined the threshold of the size of the nucleolus. More specifically, in viviparous females, nuclei that have nucleoli larger than 20 μm2 were categorized into bacteriocytes. Note that the peaks of sheath cells were not distinct or reliable for categorizing their ploidy class; therefore, we showed results focusing on bacteriocytes in the following sections.Figure 3Ploidy analysis of aphid bacteriocytes using DAPI-fluorometry. A representative result from the analysis of adult viviparous females is presented. An image of DAPI-stained nuclei was also shown (the blue channel was extracted). Isolated nuclei of bacteriome cells were stained using DAPI, image-captured with a CCD camera, and their integrated fluorescence intensity was measured using ImageJ software. Nuclei were categorized into “bacteriocytes” or “sheath cells,” based on the size distribution of nucleolus (see “Materials and Methods”). Relative ploidy levels were calculated based on the data from head cells which are mainly diploid. Bacteriocytes of adult viviparous aphids consisted of 16C-256C cells, and 64–128 cells were dominant, while sheath cells exhibited lower ploidy levels (mainly 16C). “C” means haploid genome size, for example, 2C = diploid and 8C = octoploid.Full size imageCellular features of bacteriome cells in viviparous and oviparous females, and malesThe cellular features were generally consistent among young adults (within 5 days of adult eclosion) of three morphs, viviparous and oviparous females, and males (Fig. 2). Nevertheless, Buchnera-absence zones in the cytoplasm of bacteriocytes, which are considered to be degeneration of Buchnera45, and bacteriocytes degeneration46 were both observed more frequently in male bacteriocytes than in females (Fig. 2). The cell size of bacteriocytes was significantly different among morphs (LM with type II test, F = 286.15, df = 2, p  More

Swedish environment minister Annika Strandhäll before the start of the Stockholm +50 Climate Summit. Few world leaders will be attending.Credit: Fredrik Persson/TT News Agency/AFP/Getty

Sustainability is now a household term, but it wasn’t always so.Fifty years ago, the United Nations held its Conference on the Human Environment in Stockholm. This landmark event gave the concept of sustainable development its first international recognition. Sweden and the UN are marking the occasion this week with Stockholm+50, an international meeting that serves as both commemoration and call to action.The world is deep in planetary and human crises, with the UN’s Sustainable Development Goals off track and multilateral agreements on climate change and biodiversity behind schedule. Governments need to integrate sustainability into economic planning — and listen to researchers, who are ready with evidence-based arguments and tools to help them do so.Fifty years ago, the time was ripe for an environmental agenda to enter the world stage. Optimistic ideas of economic growth as a driver of progress, propelled by the Industrial Revolution, needed to accommodate concerns over damage to the natural environment. Books such as Rachel Carson’s Silent Spring (1962) — which raised awareness about harms caused by pesticides — brought scientific information about environmental risks into the mainstream.In March 1972, a team of researchers and policymakers sounded another alarm in The Limits to Growth, one of the first reports to forecast catastrophic consequences if humans kept exploiting Earth’s limited supply of natural resources. The conference in Stockholm followed a few months later, steered to success by its secretary-general, Canadian industrialist Maurice Strong. That set crucial institutions in motion, starting with the establishment of the UN Environment Programme (UNEP), based in Nairobi — the first UN body to be headquartered in a developing country. UNEP went on to facilitate a new international law — the 1987 Montreal Protocol to phase out ozone-depleting substances — and co-founded the Intergovernmental Panel on Climate Change (IPCC). It assisted in establishing the first action plans for sustainable development through landmark international agreements on biodiversity, climate and desertification.But there were mistakes and missed opportunities. The establishment of multiple agencies and policy instruments created a disjointed governance system. Newly created environment ministers wielded little power. In national budgets, environmental protection was siloed away from economic development and social concerns. For a long time, action on climate change remained unfocused. And the economic drivers of environmental change were overlooked.And so, 50 years after that momentous conference, the world remains in crisis. With impending climate and biodiversity crises, the warnings issued by visionaries now hit even closer.Stockholm+50 promises “clear and concrete recommendations and messages for action at all levels”. More than 90 ministers are expected to attend, but only 10 heads of government. That’s a missed opportunity for high-level action. World leaders are needed because their presence signals that sustainability remains at the top of their agendas.Awareness of the need to embed sustainability into policymaking has broken into the mainstream, although much of it is still talk. City governments around the world are implementing ambitious climate action plans through the C40 Cities network. Some companies, too, are adopting sustainability principles, from reporting (and reducing) their carbon footprints to ensuring that investments, as far as possible, do not harm the environment.But this urgency has not ascended to heads of state and government. With a handful of exceptions — such as Finland, Iceland, New Zealand, Scotland and Wales — most nations seem unwilling to systemically integrate their economic, environmental and social policymaking.Doing so is not only good for the environment; it is also sound economics and good for well-being. The food and energy crisis driving poverty and diminishing living standards around the world might have been triggered by the shocks of a pandemic and war on Ukraine — but it is driven just as much by the depletion of natural resources.Ahead of the 1972 conference, 2,200 environmental scientists signed a letter — called the Menton Message — to then UN secretary-general U Thant. The signatories had a sense that the world was moving towards multiple crises. They urged “massive research into the problems that threaten the survival of mankind”, such as hunger, wars, environmental degradation and natural-resource depletion. The UN system went on to play a big part in building the body of knowledge that has shown why sustainability is necessary, and in creating the policy architecture to make it happen. But to do the Stockholm vision justice, there must be bolder action from heads of government and from the UN system. The planned creation of a board of science advisers to UN secretary-general António Guterres needs to be accelerated. Once established, the board must find a way to bring joined-up action on sustainability closer to world leaders.Researchers can now join a successor to the Menton Message that has been organized by the International Science Council, the global science network Future Earth and the Stockholm Environment Institute. In an open letter addressed to world citizens, the authors write: “After 50 years, pro-environmental action seems like one step forward and two back. The world produces more food than needed, yet many people still go hungry. We continue to subsidize and invest in fossil fuels, even though renewable energy is increasingly cost-effective. We extract resources where the price is lowest, often in direct disregard of local rights and values.”World leaders must listen to the research community, and accept the evidence and narrative offered to help them to navigate meaningful change. Environmental sustainability does not impede prosperity and well-being — in fact, it is vital to them. People in power need to sit up and take notice. More

Di Giacomo, G., Hadrich, J., Hutchison, W. D., Peterson, H. & Rogers, M. Economic impact of spotted wing drosophila (Diptera: Drosophilidae) yield loss on minnesota raspberry farms: A grower survey. J. Integr. Pest Manag. https://doi.org/10.1093/jipm/pmz006 (2019).Article 

Google Scholar 
Farnsworth, D. et al. Economic analysis of revenue losses and control costs associated with the spotted wing drosophila, Drosophila suzukii (Matsumura), in the California raspberry industry. Pest. Manag. Sci. 73, 1083–1090. https://doi.org/10.1002/ps.4497 (2017).CAS 
Article 
PubMed 

Google Scholar 
Beers, E. H., Van Steenwyk, R. A., Shearer, P. W., Coates, W. W. & Grant, J. A. Developing Drosophila suzukii management programs for sweet cherry in the western United States. Pest Manag. Sci. 67, 1386–1395. https://doi.org/10.1002/ps.2279 (2011).CAS 
Article 
PubMed 

Google Scholar 
Tait, G. et al. Drosophila suzukii (Diptera: Drosophilidae): A decade of research towards a sustainable integrated pest management program. J. Econ. Entomol. 114, 1950–1974. https://doi.org/10.1093/jee/toab158 (2021).Article 
PubMed 

Google Scholar 
Daane, K. M. et al. First exploration of parasitoids of Drosophila suzukii in South Korea as potential classical biological agents. J. Pest Sci. 89, 823–835. https://doi.org/10.1007/s10340-016-0740-0 (2016).ADS 
Article 

Google Scholar 
Abram, P. K. et al. New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. J. Hymenoptera Res. 78, 1–17. https://doi.org/10.3897/jhr.78.55026 (2020).Article 

Google Scholar 
Chabert, S., Allemand, R., Poyet, M., Eslin, P. & Gibert, P. Ability of European parasitoids (Hymenoptera) to control a new invasive Asiatic pest, Drosophila suzukii. Biol. Control 63, 40–47. https://doi.org/10.1016/j.biocontrol.2012.05.005 (2012).Article 

Google Scholar 
Gonzalez-Cabrera, J., Moreno-Carrillo, G., Sanchez-Gonzalez, J. A., Mendoza-Ceballos, M. Y. & Arredondo-Bernal, H. C. Single and Combined Release of Trichopria drosophilae (Hymenoptera: Diapriidae) to Control Drosophila suzukii (Diptera: Drosophilidae). Neotrop. Entomol. 48, 949–956. https://doi.org/10.1007/s13744-019-00707-3 (2019).CAS 
Article 
PubMed 

Google Scholar 
Rossi Stacconi, M. V., Grassi, A., Ioriatti, C. & Anfora, G. Augmentative releases of Trichopria drosophilae for the suppression of early season Drosophila suzukii populations. Biocontrol 64, 9–19. https://doi.org/10.1007/s10526-018-09914-0 (2018).CAS 
Article 

Google Scholar 
Poyet, M. et al. The wide potential trophic niche of the asiatic fruit fly Drosophila suzukii: The key of its invasion success in temperate Europe?. PLoS ONE 10, e0142785. https://doi.org/10.1371/journal.pone.0142785 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mazzetto, F. et al. Drosophila parasitoids in northern Italy and their potential to attack the exotic pest Drosophila suzukii. J. Pest Sci. 89, 837–850. https://doi.org/10.1007/s10340-016-0746-7 (2016).Article 

Google Scholar 
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Foraging efficiency and outcomes of interactions of two pupal parasitoids attacking the invasive spotted wing drosophila. Biol. Control 96, 64–71. https://doi.org/10.1016/j.biocontrol.2016.02.004 (2016).Article 

Google Scholar 
Rossi Stacconi, M. V. et al. Host location and dispersal ability of the cosmopolitan parasitoid Trichopria drosophilae released to control the invasive spotted wing Drosophila. Biol. Control 117, 188–196. https://doi.org/10.1016/j.biocontrol.2017.11.013 (2018).Article 

Google Scholar 
Esteban-Santiago, J. M., Rodríguez-Leyva, E., Lomeli-Flores, J. R. & González-Cabrera, J. Demographic parameters of Trichopria drosophilae in three host species. Entomol. Exp. Appl. 169, 330–337. https://doi.org/10.1111/eea.13026 (2021).CAS 
Article 

Google Scholar 
Häussling, B. J. M., Lienenluke, J. & Stokl, J. The preference of Trichopria drosophilae for pupae of Drosophila suzukii is independent of host size. Sci. Rep. 11, 995. https://doi.org/10.1038/s41598-020-80355-5 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Life-history and host preference of Trichopria drosophilae, a pupal parasitoid of spotted wing drosophila. Biocontrol 61, 387–397. https://doi.org/10.1007/s10526-016-9720-9 (2016).CAS 
Article 

Google Scholar 
Woltz, J. M. & Lee, J. C. Pupation behavior and larval and pupal biocontrol of Drosophila suzukii in the field. Biol. Control 110, 62–69. https://doi.org/10.1016/j.biocontrol.2017.04.007 (2017).Article 

Google Scholar 
Ballman, E. S., Collins, J. A. & Drummond, F. A. Pupation behavior and predation on Drosophila suzukii (Diptera: Drosophilidae) Pupae in Maine wild blueberry fields. J. Econ. Entomol. 110, 2308–2317. https://doi.org/10.1093/jee/tox233 (2017).Article 
PubMed 

Google Scholar 
Guillén, L., Aluja, M. N., Equihua, M. & Sivinski, J. Performance of two fruit fly (Diptera: Tephritidae) pupal parasitoids (Coptera haywardi [Hymenoptera: Diapriidae] and Pachycrepoideus vindemiae [Hymenoptera: Pteromalidae]) under different environmental soil conditions. Biol. Control 23, 219–227. https://doi.org/10.1006/bcon.2001.1011 (2002).Article 

Google Scholar 
Yi, C. et al. Life history and host preference of Trichopria drosophilae from Southern China, one of the effective pupal parasitoids on the Drosophila species. Insects 11, 103. https://doi.org/10.3390/insects11020103 (2020).Article 
PubMed Central 

Google Scholar 
BoychevaWoltering, S., Romeis, J. & Collatz, J. Influence of the rearing host on biological parameters of Trichopria drosophilae, a potential biological control agent of Drosophila suzukii. Insects. https://doi.org/10.3390/insects10060183 (2019).Article 

Google Scholar 
Otto, M. & Mackauer, M. The developmental strategy of an idiobiont ectoparasitoid, Dendrocerus carpenteri: Influence of variations in host quality on offspring growth and fitness. Oecologia 117, 353–364. https://doi.org/10.1007/s004420050668 (1998).ADS 
Article 
PubMed 

Google Scholar 
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (Vienna, Austria, 2008).Johnson, S. N. & Gregory, P. J. Chemically-mediated host-plant location and selection by root-feeding insects. Physiol. Entomol. 31, 1–13. https://doi.org/10.1111/j.1365-3032.2005.00487.x (2006).CAS 
Article 

Google Scholar 
Bezerra Da Silva, C. S., Park, K. R., Blood, R. A. & Walton, V. M. Intraspecific competition affects the pupation behavior of spotted-wing drosophila (Drosophila suzukii). Sci. Rep. 9, 7775. https://doi.org/10.1038/s41598-019-44248-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Renkema, J. M. & Devkota, S. Pupation depth of spotted wing drosophila (Drosophila suzukii) and effects of field sanitation in Florida strawberries. Viii Int. Strawberry Symp. 1156, 849–855. https://doi.org/10.17660/ActaHortic.2017.1156.125 (2017).Article 

Google Scholar 
Tsitsipis, J. A. & Papanicolaou, E. P. Pupation depth in artificially reared olive fruits-flies Dacus-oleae (Diptera, Tephritidae), as affected by several physical characteristics of the substrates. Annales De Zoologie Ecologie Animale 11, 31–40 (1979).
Google Scholar 
Dimou, I., Koutsikopoulos, C., Economopoulos, A. P. & Lykakis, J. Depth of pupation of the wild olive fruit fly, Bactrocera (Dacus) oleae (Gmel.) (Dipt., Tephritidae), as affected by soil abiotic factors. J. Appl. Entomol. 127, 12–17. https://doi.org/10.1046/j.1439-0418.2003.00686.x (2003).Article 

Google Scholar 
de Belle, J. S., Hilliker, A. J. & Sokolowski, M. B. Genetic localization of foraging (for): A major gene for larval behavior in Drosophila melanogaster. Genetics 123, 157–163. https://doi.org/10.1093/genetics/123.1.157 (1989).Article 
PubMed 

Google Scholar 
Sokolowski, M. B. et al. Ecological genetics and behaviour of Drosophila melanogaster larvae in nature. Anim. Behav. 34, 403–408. https://doi.org/10.1016/S0003-3472(86)80109-9 (1986).Article 

Google Scholar 
Rodriguez, L., Sokolowski, M. B. & Shore, J. S. Habitat selection by Drosophila melanogaster larvae. J. Evol. Biol. 5, 61–70. https://doi.org/10.1046/j.1420-9101.1992.5010061.x (1992).Article 

Google Scholar 
McIntosh, H., Atucha, A., Townsend, P. A., Hills, W. B. & Guédot, C. Plastic mulches reduce adult and larval populations of Drosophila suzukii in fall-bearing raspberry. J. Pest. Sci. 95, 525–536. https://doi.org/10.1007/s10340-021-01456-2 (2021).Article 

Google Scholar 
Ballman, E. & Drummond, F. Larval movement of spotted wing drosophila, Drosophila suzukii (Matsumura) (Diptera: Drosophilidae). J. Kansas Entomol. Soc. 92, 412–421. https://doi.org/10.2317/0022-8567-92.1.412 (2019).Article 

Google Scholar  More