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    AnimalTraits – a curated animal trait database for body mass, metabolic rate and brain size

    Westoby, M. & Wright, I. J. Land-plant ecology on the basis of functional traits. Trends Ecol. Evol. 21, 261–268 (2006).Article 

    Google Scholar 
    Chown, S. L. & Gaston, K. J. Body size variation in insects: a macroecological perspective. Biol. Rev. Camb. Philos. Soc. 85, 139–169 (2010).Article 

    Google Scholar 
    Parr, C. L. et al. GlobalAnts: a new database on the geography of ant traits (Hymenoptera: Formicidae). Insect Conserv. Divers. 10, 5–20 (2017).Article 

    Google Scholar 
    Wolff, J. O., Wierucka, K., Uhl, G. & Herberstein, M. E. Building behavior does not drive rates of phenotypic evolution in spiders. Proceedings of the National Academy of Sciences 118, e2102693118 (2021).CAS 
    Article 

    Google Scholar 
    Le Boulch, M., Déhais, P., Combes, S. & Pascal, G. The MACADAM database: a MetAboliC pAthways DAtabase for Microbial taxonomic groups for mining potential metabolic capacities of archaeal and bacterial taxonomic groups. Database 2019 (2019).Madin, J. S. et al. A synthesis of bacterial and archaeal phenotypic trait data. Scientific Data 7, 170 (2020).CAS 
    Article 

    Google Scholar 
    Lowe, E. C., Wolff, J. O. & Aceves-Aparicio, A. Towards establishment of a centralized spider traits database. The Journal of Arachnology (2020).Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 
    Article 

    Google Scholar 
    Mizerek, T. L., Baird, A. H. & Madin, J. S. Species traits as indicators of coral bleaching. Coral Reefs 37, 791–800 (2018).ADS 
    Article 

    Google Scholar 
    De Meester, G. & Huyghe, K. & Van Damme, R. Brain size, ecology and sociality: a reptilian perspective. Biol. J. Linn. Soc. Lond. 126, 381–391 (2019).Article 

    Google Scholar 
    Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Chang. 8, 224–228 (2018).ADS 
    Article 

    Google Scholar 
    Makarieva, A. M. et al. Mean mass-specific metabolic rates are strikingly similar across life’s major domains: Evidence for life’s metabolic optimum. Proceedings of the National Academy of Sciences 105, 16994 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    Gallagher, R. V. et al. Open Science principles for accelerating trait-based science across the Tree of Life. Nat Ecol Evol 4, 294–303 (2020).Article 

    Google Scholar 
    R Core Team. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. (2020).Chamberlain, S. A. & Szöcs, E. taxize: taxonomic search and retrieval in R [version 2; peer review: 3 approved]. F1000Res. 2, (2013).Pebesma, E., Mailund, T. & Hiebert, J. Measurement Units in R. R J. 8, 486–494 (2016).Article 

    Google Scholar 
    Hiebert, J. udunits-2 bindings for R. (2016).Iwaniuk, A. N. & Nelson, J. E. Can endocranial volume be used as an estimate of brain size in birds? Canadian Journal of Zoology-Revue Canadienne De Zoologie 80, 16–23 (2002).Article 

    Google Scholar 
    Taylor, G. M., Nol, E. & Boire, D. Brain regions and encephalization in anurans: adaptation or stability? Brain Behav. Evol. 45, 96–109, https://doi.org/10.1159/000113543 (1995).CAS 
    Article 
    PubMed 

    Google Scholar 
    McLean, D. J. AnimalTraits (v1.0.7). Zenodo. https://doi.org/10.5281/zenodo.6468938 (2022).Christian, K. & Conley, K. Activity and Resting Metabolism of Varanid Lizards Compared With Typical Lizards. Aust. J. Zool. 42, 185–193, https://doi.org/10.1071/ZO9940185 (1994).Article 

    Google Scholar 
    Hadley, N. F., Ahearn, G. A. & Howarth, F. G. Water and metabolic relations of cave-adapted and epigean lycosid spiders in Hawaii. J. Arachnol., 215–222 (1981).Wang, L. C., Jones, D. L., MacArthur, R. A. & Fuller, W. A. Adaptation to cold: energy metabolism in an atypical lagomorph, the arctic hare (Lepus arcticus). Can. J. Zool. 51, 841–846, https://doi.org/10.1139/z73-125 (1973).CAS 
    Article 
    PubMed 

    Google Scholar 
    Nevo, E. & Shkolnik, A. Adaptive metabolic variation of chromosome forms in mole rats, Spalax. Experientia 30, 724–726, https://doi.org/10.1007/bf01924150 (1974).CAS 
    Article 
    PubMed 

    Google Scholar 
    Haim, A. Adaptive variations in heat production within Gerbils (genus Gerbillus) from different habitats. Oecologia 61, 49–52, https://doi.org/10.1007/bf00379087 (1984).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Kamel, S. & Gatten, R. E. J. Aerobic and Anaerobic Activity Metabolism of Limbless and Fossorial Reptiles. Physiol. Zool. 56, 419–429, https://doi.org/10.1086/physzool.56.3.30152607 (1983).Article 

    Google Scholar 
    Gatten, R. E. Jr. Aerobic metabolism in snapping turtles, Chelydra serpentina, after thermal acclimation. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 61, 325–337, https://doi.org/10.1016/0300-9629(78)90116-0 (1978).Article 

    Google Scholar 
    Coelho, J. R. & Moore, A. J. Allometry of resting metabolic rate in cockroaches. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 94, 587–590, https://doi.org/10.1016/0300-9629(89)90598-7 (1989).CAS 
    Article 

    Google Scholar 
    Lighton, J. & Garrigan, D. Ant breathing: testing regulation and mechanism hypotheses with hypoxia. J. Exp. Biol. 198, 1613–1620 (1995).CAS 
    Article 

    Google Scholar 
    Pettit, T. N., Ellis, H. I. & Whittow, G. C. Basal metabolic rate in tropical seabirds. The Auk 102, 172–174, https://doi.org/10.2307/4086838 (1985).Article 

    Google Scholar 
    Bozinovic, F. & Contreras, L. C. Basal rate of metabolism and temperature regulation of two desert herbivorous octodontid rodents: Octomys mimax and Tympanoctomys barrerae. Oecologia 84, 567–570, https://doi.org/10.1007/bf00328175 (1990).ADS 
    Article 
    PubMed 

    Google Scholar 
    Morrison, P. & Middleton, E. H. Body temperature and metabolism in the pigmy marmoset. Folia Primatol. 6, 70–82, https://doi.org/10.1159/000155068 (1967).CAS 
    Article 

    Google Scholar 
    Bartholomew, G. A. & Casey, T. M. Body temperature and oxygen consumption during rest and activity in relation to body size in some tropical beetles. J. Therm. Biol. 2, 173–176, https://doi.org/10.1016/0306-4565(77)90026-2 (1977).Article 

    Google Scholar 
    Cortés, A., Báez, C., Rosenmann, M. & Pino, C. Body temperature, activity cycle and metabolic rate in a small nocturnal Chilean lizard, Garthia gaudichaudi (Sauria: Gekkonidae). Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 109, 967–973, https://doi.org/10.1016/0300-9629(94)90245-3 (1994).Article 

    Google Scholar 
    Leitner, P. & Nelson, J. E. Body temperature, oxygen consumption and heart rate in the Australian false vampire bat, Macroderma gigas. Comp. Biochem. Physiol. 21, 65–74, https://doi.org/10.1016/0010-406X(67)90115-6 (1967).CAS 
    Article 
    PubMed 

    Google Scholar 
    Whittow, G. C., Gould, E. & Rand, D. Body temperature, oxygen consumption, and evaporative water loss in a primitive insectivore, the moon rat, Echinosorex gymnurus. J. Mammal. 58, 233–235, https://doi.org/10.2307/1379582 (1977).CAS 
    Article 
    PubMed 

    Google Scholar 
    Weathers, W. W., Koenig, W. D. & Stanback, M. T. Breeding energetics and thermal ecology of the acorn woodpecker in central coastal California. Condor, 341–359, https://doi.org/10.2307/1368232 (1990).Shelton, T. G. & Appel, A. G. Carbon dioxide release in Coptotermes formosanus Shiraki and Reticulitermes flavipes (Kollar): effects of caste, mass, and movement. J. Insect Physiol. 47, 213–224, https://doi.org/10.1016/S0022-1910(00)00111-6 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bradley, T. J., Brethorst, L., Robinson, S. & Hetz, S. Changes in the Rate of CO2 Release following Feeding in the Insect Rhodnius prolixus. Physiol. Biochem. Zool. 76, 302–309, https://doi.org/10.1086/367953 (2003).Article 
    PubMed 

    Google Scholar 
    Herreid, C. F. & Full, R. J. Cockroaches on a treadmill: aerobic running. J. Insect Physiol. 30, 395–403, https://doi.org/10.1016/0022-1910(84)90097-0 (1984).Article 

    Google Scholar 
    Arends, A. & McNab, B. K. The comparative energetics of ‘caviomorph’ rodents. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 130, 105–122, https://doi.org/10.1016/S1095-6433(01)00371-3 (2001).CAS 
    Article 

    Google Scholar 
    McNab, B. K. The comparative energetics of rigid endothermy: the Arvicolidae. J. Zool. 227, 585–606, https://doi.org/10.1111/j.1469-7998.1992.tb04417.x (1992).Article 

    Google Scholar 
    Bozinovic, F. & Rosenmann, M. Comparative energetics of South American cricetid rodents. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 91, 195–202, https://doi.org/10.1016/0300-9629(88)91616-7 (1988).CAS 
    Article 

    Google Scholar 
    Haim, A. & Skinner, J. D. A comparative study of metabolic rates and thermoregulation of two African antelopes, the steenbok Raphicerus campestris and the blue duiker Cephalophus monticola. J. Therm. Biol. 16, 145–148, https://doi.org/10.1016/0306-4565(91)90036-2 (1991).Article 

    Google Scholar 
    Else, P. L. & Hulbert, A. J. Comparison of the “mammal machine” and the “reptile machine”: energy production. Am. J. Physiol. Regul. Integr. Comp. Physiol. 240, R3–R9, https://doi.org/10.1152/ajpregu.1981.240.1.R3 (1981).CAS 
    Article 

    Google Scholar 
    Duncan, F. D. & Crewe, R. M. A comparison of the energetics of foraging of three species of Leptogenys (Hymenoptera, Formicidae). Physiol. Entomol. 18, 372–378, https://doi.org/10.1111/j.1365-3032.1993.tb00610.x (1993).Article 

    Google Scholar 
    Kurta, A. & Ferkin, M. The correlation between demography and metabolic rate: a test using the beach vole (Microtus breweri) and the meadow vole (Microtus pennsylvanicus). Oecologia 87, 102–105, https://doi.org/10.1007/bf00323786 (1991).ADS 
    Article 
    PubMed 

    Google Scholar 
    Chown, S. L. & Holter, P. Discontinuous gas exchange cycles in Aphodius fossor (Scarabaeidae): a test of hypotheses concerning origins and mechanisms. J. Exp. Biol. 203, 397–403, https://doi.org/10.1242/jeb.203.2.397 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    Duncan, F. D. & Byrne, M. J. Discontinuous gas exchange in dung beetles: patterns and ecological implications. Oecologia 122, 452–458, https://doi.org/10.1007/s004420050966 (2000).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Rezende, E. L., Silva-Durán, I., Novoa, F. F. & Rosenmann, M. Does thermal history affect metabolic plasticity?: a study in three Phyllotis species along an altitudinal gradient. J. Therm. Biol. 26, 103–108, https://doi.org/10.1016/S0306-4565(00)00029-2 (2001).Article 
    PubMed 

    Google Scholar 
    Chown, S. L., Scholtz, C. H., Klok, C. J., Joubert, F. J. & Coles, K. S. Ecophysiology, range contraction and survival of a geographically restricted African dung beetle (Coleoptera: Scarabaeidae). Funct. Ecol. 9, 30–39, https://doi.org/10.2307/2390087 (1995).Article 

    Google Scholar 
    Rübsamen, U., Hume, I. D. & Rübsamen, K. Effect of ambient temperature on autonomic thermoregulation and activity patterns in the rufous rat-kangaroo (Aepyprymnus rufescens: Marsupialia). J. Comp. Physiol. 153, 175–179, https://doi.org/10.1007/bf00689621 (1983).Article 

    Google Scholar 
    Lewis, L. C., Mutchmor, J. A. & Lynch, R. E. Effect of Perezia pyraustae on oxygen consumption by the European corn borer, Ostrinia nubilalis. J. Insect Physiol. 17, 2457–2468, https://doi.org/10.1016/0022-1910(71)90093-X (1971).Article 

    Google Scholar 
    Louw, G., Young, B. & Bligh, J. Effect of thyroxine and noradrenaline on thermoregulation, cardiac rate and oxygen consumption in the monitor lizard Varanus albigularis albigularis. J. Therm. Biol. 1, 189–193, https://doi.org/10.1016/0306-4565(76)90013-9 (1976).CAS 
    Article 

    Google Scholar 
    Full, R. J., Zuccarello, D. A. & Tullis, A. Effect of variation in form on the cost of terrestrial locomotion. J. Exp. Biol. 150, 233–246 (1990).CAS 
    Article 

    Google Scholar 
    Bennett, A. F., Dawson, W. R. & Bartholomew, G. A. Effects of activity and temperature on aerobic and anaerobic metabolism in the Galapagos marine iguana. J. Comp. Physiol. 100, 317–329, https://doi.org/10.1007/bf00691052 (1975).CAS 
    Article 

    Google Scholar 
    Thompson, G. G. & Withers, P. C. Effects of body mass and temperature on standard metabolic rates for two Australian varanid lizards (Varanus gouldii and V. panoptes). Copeia, 343–350, https://doi.org/10.2307/1446195 (1992).Hack, M. A. The effects of mass and age on standard metabolic rate in house crickets. Physiol. Entomol. 22, 325–331, https://doi.org/10.1111/j.1365-3032.1997.tb01176.x (1997).ADS 
    Article 

    Google Scholar 
    Gatten, R. E. Jr. Effects of temperature and activity on aerobic and anaerobic metabolism and heart rate in the turtles Pseudemys scripta and Terrapene ornata. Comp. Biochem. Physiol., A: Mol. Integr. Physiol, https://doi.org/10.1016/0300-9629(74)90606-9 (1974).Gleeson, T. T. The effects of training and captivity on the metabolic capacity of the lizard Sceloporus occidentalis. J. Comp. Physiol. 129, 123–128, https://doi.org/10.1007/bf00798176 (1979).CAS 
    Article 

    Google Scholar 
    Bartholomew, G. A. & Lighton, J. R. Endothermy and energy metabolism of a giant tropical fly, Pantophthalmus tabaninus thunberg. J. Comp. Physiol., B 156, 461–467, https://doi.org/10.1007/bf00691031 (1986).Article 

    Google Scholar 
    Bailey, W. J., Withers, P. C., Endersby, M. & Gaull, K. The energetic costs of calling in the bushcrisket Requena verticalis (Orthoptera: Tettigoniidae: Listroscelidinae). J. Exp. Biol. 178, 21–37 (1993).Article 

    Google Scholar 
    Kotiaho, J. S. et al. Energetic costs of size and sexual signalling in a wolf spider. Proc. R. Soc. B: Biol. Sci. 265, 2203–2209, https://doi.org/10.1098/rspb.1998.0560 (1998).Article 

    Google Scholar 
    Chaplin, S. B. The energetic significance of huddling behavior in common bushtits (Psaltriparus minimus). The Auk, 424-430 (1982).Seymour, R. S., Withers, P. C. & Weathers, W. W. Energetics of burrowing, running, and free-living in the Namib Desert golden mole (Eremitalpa namibensis). J. Zool. 244, 107–117 (1998).Article 

    Google Scholar 
    Herreid, C. F., Full, R. J. & Prawel, D. A. Energetics of Cockroach Locomotion. J. Exp. Biol. 94, 189–202 (1981).Article 

    Google Scholar 
    Bartholomew, G. A., Lighton, J. R. & Louw, G. N. Energetics of locomotion and patterns of respiration in tenebrionid beetles from the Namib Desert. J. Comp. Physiol., B 155, 155–162, https://doi.org/10.1007/bf00685208 (1985).Article 

    Google Scholar 
    Lighton, J. R. B. & Gillespie, R. G. The energetics of mimicry: the cost of pedestrian transport in a formicine ant and its mimic, a clubionid spider. Physiol. Entomol. 14, 173–177, https://doi.org/10.1111/j.1365-3032.1989.tb00949.x (1989).Article 

    Google Scholar 
    Marhold, S. & Nagel, A. The energetics of the common mole rat Cryptomys, a subterranean eusocial rodent from Zambia. J. Comp. Physiol., B 164, 636–645, https://doi.org/10.1007/bf00389805 (1995).CAS 
    Article 

    Google Scholar 
    Pauls, R. W. Energetics of the red squirrel: a laboratory study of the effects of temperature, seasonal acclimatization, use of the nest and exercise. J. Therm. Biol. 6, 79–86, https://doi.org/10.1016/0306-4565(81)90057-7 (1981).ADS 
    Article 

    Google Scholar 
    Brush, A. H. Energetics, temperature regulation and circulation in resting, active and defeathered California quail, Lophortyx californicus. Comp. Biochem. Physiol. 15, 399–421, https://doi.org/10.1016/0010-406X(65)90141-6 (1965).Article 

    Google Scholar 
    Bailey, C. G. & Riegert, P. W. Energy dynamics of Encoptolophus sordidus costalis (Scudder) (Orthoptera: Acrididae) in a grassland ecosystem. Can. J. Zool. 51, 91–100, https://doi.org/10.1139/z73-014 (1973).Article 

    Google Scholar 
    Prinzinger, R., Lübben, I. & Schuchmann, K.-L. Energy metabolism and body temperature in 13 sunbird species (Nectariniidae). Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 92, 393–402, https://doi.org/10.1016/0300-9629(89)90581-1 (1989).Article 

    Google Scholar 
    Baudinette, R. V. Energy metabolism and evaporative water loss in the California ground squirrel. J. Comp. Physiol. 81, 57–72, https://doi.org/10.1007/bf00693550 (1972).Article 

    Google Scholar 
    May, M. L. Energy metabolism of dragonflies (Odonata: Anisoptera) at rest and during endothermic warm-up. J. Exp. Biol. 83, 79–94 (1979).Article 

    Google Scholar 
    Baudinette, R. V., Churchill, S. K., Christian, K. A., Nelson, J. E. & Hudson, P. J. Energy, water balance and the roost microenvironment in three Australian cave-dwelling bats (Microchiroptera). J. Comp. Physiol., B 170, 439–446, https://doi.org/10.1007/s003600000121 (2000).CAS 
    Article 

    Google Scholar 
    Withers, P. C. Energy, Water, and Solute Balance of the Ostrich Struthio camelus. Physiol. Zool. 56, 568–579, https://doi.org/10.1086/physzool.56.4.30155880 (1983).Article 

    Google Scholar 
    Hadley, N. F., Quinlan, M. C. & Kennedy, M. L. Evaporative Cooling in the Desert Cicada: Thermal Efficiency and Water/Metabolic Costs. J. Exp. Biol. 159, 269–283, https://doi.org/10.1242/jeb.159.1.269 (1991).Article 

    Google Scholar 
    Dunson, W. A. & Bramham, C. R. Evaporative Water Loss and Oxygen Consumption of Three Small Lizards from the Florida Keys: Sphaerodactylus cinereus, S. notatus, and Anolis sagrei. Physiol. Zool. 54, 253–259, https://doi.org/10.1086/physzool.54.2.30155827 (1981).Article 

    Google Scholar 
    Wunder, B. A. Evaporative water loss from birds: effects of artificial radiation. Comp. Biochem. Physiol. 63, 493–494, https://doi.org/10.1016/0300-9629(79)90180-4 (1979).Article 

    Google Scholar 
    Maclean, G. S. Factors influencing the composition of respiratory gases in mammal burrows. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 69, 373–380, https://doi.org/10.1016/0300-9629(81)92992-3 (1981).Article 

    Google Scholar 
    Campbell, K. L., McIntyre, I. W. & MacArthur, R. A. Fasting metabolism and thermoregulatory competence of the star-nosed mole, Condylura cristata (Talpidae: Condylurinae). Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 123, 293–298, https://doi.org/10.1016/S1095-6433(99)00065-3 (1999).CAS 
    Article 

    Google Scholar 
    Weathers, W. W., Paton, D. C. & Seymour, R. S. Field Metabolic Rate and Water Flux of Nectarivorous Honeyeaters. Aust. J. Zool. 44, 445–460, https://doi.org/10.1071/ZO9960445 (1996).Article 

    Google Scholar 
    Fewell, J. H., Harrison, J. F., Lighton, J. R. B. & Breed, M. D. Foraging energetics of the ant, Paraponera clavata. Oecologia 105, 419–427, https://doi.org/10.1007/bf00330003 (1996).ADS 
    Article 
    PubMed 

    Google Scholar 
    Greenstone, M. H. & Bennett, A. F. Foraging strategy and metabolic rate in spiders. Ecology 61, 1255–1259, https://doi.org/10.2307/1936843 (1980).Article 

    Google Scholar 
    Schmitz, A. Functional morphology of the respiratory organs in the cellar spider Pholcus phalangioides (Arachnida, Araneae, Pholcidae). J. Comp. Physiol., B 185, 637–646, https://doi.org/10.1007/s00360-015-0914-8 (2015).CAS 
    Article 

    Google Scholar 
    Marder, J. & Bernstein, R. Heat balance of the partridge Alectoris chukar exposed to moderate, high and extreme thermal stress. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 74, 149–154, https://doi.org/10.1016/0300-9629(83)90726-0 (1983).CAS 
    Article 

    Google Scholar 
    Lovegrove, B. G., Raman, J. & Perrin, M. R. Heterothermy in elephant shrews, Elephantulus spp. (Macroscelidea): daily torpor or hibernation? J. Comp. Physiol., B 171, 1–10, https://doi.org/10.1007/s003600000139 (2001).CAS 
    Article 

    Google Scholar 
    Zari, T. The influence of body mass and temperature on the standard metabolic rate of the herbivorous desert lizard, Uromastyx microlepis. J. Therm. Biol. 16, 129–133, https://doi.org/10.1016/0306-4565(91)90033-X (1991).Article 

    Google Scholar 
    Jensen, T. F. & Nielsen, M. G. The influence of body size and temperature on worker ant respiration. Nat. Jutl. 18, 21–25 (1975).
    Google Scholar 
    McNab, B. K. The Influence of Body Size on the Energetics and Distribution of Fossorial and Burrowing Mammals. Ecology 60, 1010–1021, https://doi.org/10.2307/1936869 (1979).Article 

    Google Scholar 
    Shillington, C. Inter-sexual differences in resting metabolic rates in the Texas tarantula, Aphonopelma anax. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 142, 439–445, https://doi.org/10.1016/j.cbpa.2005.09.010 (2005).CAS 
    Article 

    Google Scholar 
    Nespolo, R. F., Lardies, M. A. & Bozinovic, F. Intrapopulational variation in the standard metabolic rate of insects: repeatability, thermal dependence and sensitivity (Q10) of oxygen consumption in a cricket. J. Exp. Biol. 206, 4309–4315, https://doi.org/10.1242/jeb.00687 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    Hailey, A. & Davies, P. M. C. Lifestyle, latitude and activity metabolism of natricine snakes. J. Zool. 209, 461–476, https://doi.org/10.1111/j.1469-7998.1986.tb03604.x (1986).Article 

    Google Scholar 
    Richter, T. A., Webb, P. I. & Skinner, J. D. Limits to the distribution of the southern African ice rat (Otomys sloggetti): thermal physiology or competitive exclusion? Funct. Ecol. 11, 240–246, https://doi.org/10.1046/j.1365-2435.1997.00078.x (1997).Article 

    Google Scholar 
    Putnam, R. W. & Murphy, R. W. Low metabolic rate in a nocturnal desert lizard, Anarbylus switaki Murphy (Sauria: Gekkonidae). Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 71, 119–123 (1982).Article 

    Google Scholar 
    Lighton, J. R. B. & Fielden, L. J. Mass Scaling of Standard Metabolism in Ticks: A Valid Case of Low Metabolic Rates in Sit-and-Wait Strategists. Physiol. Zool. 68, 43–62, https://doi.org/10.1086/physzool.68.1.30163917 (1995).Article 

    Google Scholar 
    Jones, D. L. & Wang, L. C.-H. Metabolic and cardiovascular adaptations in the western chipmunks, genus Eutamias. J. Comp. Physiol. 105, 219–231, https://doi.org/10.1007/bf00691124 (1976).Article 

    Google Scholar 
    Casey, T. M., Withers, P. C. & Casey, K. K. Metabolic and respiratory responses of arctic mammals to ambient temperature during the summer. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 64, 331–341, https://doi.org/10.1016/0300-9629(79)90452-3 (1979).Article 

    Google Scholar 
    Grant, G. S. & Whittow, G. C. Metabolic cost of incubation in the Laysan albatross and Bonin petrel. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 74, 77–82, https://doi.org/10.1016/0300-9629(83)90715-6 (1983).CAS 
    Article 

    Google Scholar 
    Bennett, A. F. & Gleeson, T. T. Metabolic expenditure and the cost of foraging in the lizard Cnemidophorus murinus. Copeia, 573-577, https://doi.org/10.2307/1443864 (1979).Withers, P. C., Thompson, G. G. & Seymour, R. S. Metabolic physiology of the north-western marsupial mole. Notoryctes caurinus (Marsupialia: Notoryctidae). Aust. J. Zool. 48, 241–258, https://doi.org/10.1071/ZO99073 (2000).Article 

    Google Scholar 
    Thurling, D. J. Metabolic rate and life stage of the mites Tetranychus cinnabarinus boisd. (Prostigmata) and Phytoseiulus persimilis A-H. (Mesostigmata). Oecologia 46, 391–396, https://doi.org/10.1007/BF00346269 (1980).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Vleck, C. M. & Vleck, D. Metabolic rate in five tropical bird species. Condor 81, 89–91, https://doi.org/10.2307/1367864 (1979).Article 

    Google Scholar 
    Terblanche, J. S., Jaco Klok, C., Marais, E. & Chown, S. L. Metabolic rate in the whip-spider, Damon annulatipes (Arachnida: Amblypygi). J. Insect Physiol. 50, 637-645, j.jinsphys.2004.04.010 (2004).Boyce, A. J., Mouton, J. C., Lloyd, P., Wolf, B. O. & Martin, T. E. Metabolic rate is negatively linked to adult survival but does not explain latitudinal differences in songbirds. Ecol. Lett. 23, 642–652, https://doi.org/10.1111/ele.13464 (2020).Article 
    PubMed 

    Google Scholar 
    Worthen, G. L. & Kilgore, D. L. Metabolic rate of pine marten in relation to air temperature. J. Mammal. 62, 624–628, https://doi.org/10.2307/1380410 (1981).Article 

    Google Scholar 
    Hails, C. J. The metabolic rate of tropical birds. Condor, 61–65, https://doi.org/10.2307/1367889 (1983).Terblanche, J. S., Klok, C. J. & Chown, S. L. Metabolic rate variation in Glossina pallidipes (Diptera: Glossinidae): gender, ageing and repeatability. J. Insect Physiol. 50, 419–428, https://doi.org/10.1016/j.jinsphys.2004.02.009 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Schmitz, A. Metabolic rates during rest and activity in differently tracheated spiders (Arachnida, Araneae): Pardosa lugubris (Lycosidae) and Marpissa muscosa (Salticidae). J. Comp. Physiol., B 174, 519–526, https://doi.org/10.1007/s00360-004-0440-6 (2004).CAS 
    Article 

    Google Scholar 
    Anderson, J. F. Metabolic rates of resting salticid and thomisid spiders. J. Arachnol. 129–134 (1996).Adams, N. J. & Brown, C. R. Metabolic rates of sub-Antarctic Procellariiformes: a comparative study. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 77, 169–173, https://doi.org/10.1016/0300-9629(84)90030-6 (1984).Article 

    Google Scholar 
    Morrison, P. & Ryser, F. A. Metabolism and body temperature in a small hibernator, the meadow jumping mouse, Zapus hudsonius. J. Cell. Compar. Physl. 60, 169–180, https://doi.org/10.1002/jcp.1030600206 (1962).CAS 
    Article 

    Google Scholar 
    Bieńkowski, P. & Marszałek, U. Metabolism and energy budget in the snow vole. Acta Theriol. 19, 55–67 (1974).Article 

    Google Scholar 
    Lardies, M. A., Catalán, T. P. & Bozinovic, F. Metabolism and life-history correlates in a lowland and highland population of a terrestrial isopod. Can. J. Zool. 82, 677–687, https://doi.org/10.1139/z04-033 (2004).Article 

    Google Scholar 
    Król, E. Metabolism and thermoregulation in the eastern hedgehog Erinaceus concolor. J. Comp. Physiol., B 164, 503–507, https://doi.org/10.1007/bf00714589 (1994).Article 

    Google Scholar 
    Hennemann, W. W., Thompson, S. D. & Konecny, M. J. Metabolism of Crab-Eating Foxes, Cerdocyon thous: Ecological Influences on the Energetics of Canids. Physiol. Zool. 56, 319–324, https://doi.org/10.1086/physzool.56.3.30152596 (1983).Article 

    Google Scholar 
    Lovegrove, B. G. The metabolism of social subterranean rodents: adaptation to aridity. Oecologia 69, 551–555, https://doi.org/10.1007/bf00410361 (1986).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Prinzinger, R. & Hänssler, I. Metabolism-weight relationship in some small nonpasserine birds. Experientia 36, 1299–1300, https://doi.org/10.1007/bf01969600 (1980).Article 

    Google Scholar 
    Hill, R. W. Metabolism, thermal conductance, and body temperature in one of the largest species of Peromyscus, P. pirrensis. J. Therm. Biol. 1, 109–112, https://doi.org/10.1016/0306-4565(76)90029-2 (1976).Article 

    Google Scholar 
    Saarela, S. & Hissa, R. Metabolism, thermogenesis and daily rhythm of body temperature in the wood lemming, Myopus schisticolor. J. Comp. Physiol., B 163, 546–555, https://doi.org/10.1007/bf00302113 (1993).CAS 
    Article 

    Google Scholar 
    MacMillen, R. E. Nonconformance of standard metabolic rate with body mass in Hawaiian Honeycreepers. Oecologia 49, 340–343, https://doi.org/10.1007/bf00347595 (1981).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Krog, H. & Monson, M. Notes on the metabolism of a mountain goat. Am. J. Physiol. 178, 515–516 (1954).CAS 
    Article 

    Google Scholar 
    Du Toit, J. T., Jarvis, J. U. M. & Louw, G. N. Nutrition and burrowing energetics of the Cape mole-rat Georychus capensis. Oecologia 66, 81–87, https://doi.org/10.1007/bf00378556 (1985).ADS 
    Article 
    PubMed 

    Google Scholar 
    Farrell, D. J. & Wood, A. J. The nutrition of the female mink (Mustela vison). I. The metabolic rate of the mink. Can. J. Zool. 46, 41–45, https://doi.org/10.1139/z68-008 (1968).Article 

    Google Scholar 
    Hennemann, W. W. & Konecny, M. J. Oxygen consumption in large spotted genets, Genetta tigrina. J. Mammal. 61, 747–750, https://doi.org/10.2307/1380332 (1980).Article 

    Google Scholar 
    May, M. L., Pearson, D. L. & Casey, T. M. Oxygen consumption of active and inactive adult tiger beetles. Physiol. Entomol. 11, 171–179, https://doi.org/10.1111/j.1365-3032.1986.tb00403.x (1986).Article 

    Google Scholar 
    Bartholomew, G. A. & Casey, T. M. Oxygen Consumption of Moths During Rest, Pre-Flight Warm-Up, and Flight In Relation to Body Size and Wing Morphology. J. Exp. Biol. 76, 11–25 (1978).Article 

    Google Scholar 
    MacMillen, R. E., Whittow, G. C., Christopher, E. A. & Ebisu, R. J. Oxygen consumption, evaporative water loss, and body temperature in the sooty tern. The Auk, 72–79 (1977).Francis, C. & Brooks, G. R. Oxygen consumption, rate of heart beat and ventilatory rate in parietalectomized lizards, Sceloporus occidentalis. Comp. Biochem. Physiol. 35, 463–469, https://doi.org/10.1016/0010-406X(70)90609-2 (1970).Article 

    Google Scholar 
    Tucker, V. A. Oxygen consumption, thermal conductance, and torpor in the California pocket mouse Perognathus californicus. J. Cell. Physiol. 65, 393–403, https://doi.org/10.1002/jcp.1030650313 (1965).CAS 
    Article 
    PubMed 

    Google Scholar 
    McNab, B. K. Physiological convergence amongst ant-eating and termite-eating mammals. J. Zool. 203, 485–510, https://doi.org/10.1111/j.1469-7998.1984.tb02345.x (1984).Article 

    Google Scholar 
    Genoud, M., Bonaccorso, F. J. & Anends, A. Rate of metabolism and temperature regulation in two small tropical insectivorous bats (Peropteryx macrotis and Natalus tumidirostris). Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 97, 229–234, https://doi.org/10.1016/0300-9629(90)90177-T (1990).Article 

    Google Scholar 
    Genoud, M. & Ruedi, M. Rate of metabolism, temperature regulations, and evaporative water loss in the lesser gymnure Hylomys suillus (Insectivora, Mammalia). J. Zool. 240, 309–316, https://doi.org/10.1111/j.1469-7998.1996.tb05287.x (1996).Article 

    Google Scholar 
    Ricklefs, R. E. & Matthew, K. K. Rates of oxygen consumption in four species of seabird at Palmer Station, Antarctic peninsula. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 74, 885–888, https://doi.org/10.1016/0300-9629(83)90363-8 (1983).CAS 
    Article 

    Google Scholar 
    Lasiewski, R. C. & Dawson, W. R. A Re-Examination of the Relation between Standard Metabolic Rate and Body Weight in Birds. Condor 69, 13–23, https://doi.org/10.2307/1366368 (1967).Article 

    Google Scholar 
    Goldstein, R. B. Relation of metabolism to ambient temperature in the Verdin. Condor 76, 116–119, https://doi.org/10.2307/1365995 (1974).Article 

    Google Scholar 
    Mispagel, M. E. Relation of oxygen consumption to size and temperature in desert arthropods. Ecol. Entomol. 6, 423–431, https://doi.org/10.1111/j.1365-2311.1981.tb00634.x (1981).Article 

    Google Scholar 
    Bryant, D. M., Hails, C. J. & Tatner, P. Reproductive energetics of two tropical bird species. The Auk, 25–37 (1984).Holter, P. Resource utilization and local coexistence in a guild of scarabaeid dung beetles (Aphodius spp.). Oikos 39, 213–227, https://doi.org/10.2307/3544488 (1982).Article 

    Google Scholar 
    Goldstein, D. L. & Nagy, K. A. Resource Utilization by Desert Quail: Time and Energy, Food and Water. Ecology 66, 378–387, https://doi.org/10.2307/1940387 (1985).Article 

    Google Scholar 
    Louw, G. N., Nicolson, S. W. & Seely, M. K. Respiration beneath desert sand: carbon dioxide diffusion and respiratory patterns in a tenebrionid beetle. J. Exp. Biol. 120, 443–446 (1986).Article 

    Google Scholar 
    Anderson, J. F. & Prestwich, K. N. Respiratory Gas Exchange in Spiders. Physiol. Zool. 55, 72–90, https://doi.org/10.1086/physzool.55.1.30158445 (1982).Article 

    Google Scholar 
    Meyer, E. & Phillipson, J. Respiratory metabolism of the isopod Trichoniscus pusillus provisorius. Oikos, 69–74, https://doi.org/10.2307/3544200 (1983).Duncan, F. D. & Dickman, C. R. Respiratory patterns and metabolism in tenebrionid and carabid beetles from the Simpson Desert, Australia. Oecologia 129, 509–517, https://doi.org/10.1007/s004420100772 (2001).ADS 
    Article 
    PubMed 

    Google Scholar 
    Nielsen, M. G. Respiratory rates of ants from different climatic areas. J. Insect Physiol. 32, 125–131, https://doi.org/10.1016/0022-1910(86)90131-9 (1986).Article 

    Google Scholar 
    Calder, W. A. III & Dawson, T. J. Resting metabolic rates of ratite birds: the kiwis and the emu. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 60, 479–481 (1978).Article 

    Google Scholar 
    Kawamoto, T. H., Machado, Fd. A., Kaneto, G. E. & Japyassu, H. F. Resting metabolic rates of two orbweb spiders: A first approach to evolutionary success of ecribellate spiders. J. Insect Physiol. 57, 427–432, https://doi.org/10.1016/j.jinsphys.2011.01.001 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lehmann, F. O., Dickinson, M. H. & Staunton, J. The scaling of carbon dioxide release and respiratory water loss in flying fruit flies (Drosophila spp.). J. Exp. Biol. 203, 1613–1624 (2000).CAS 
    Article 

    Google Scholar 
    Chown, S. L. et al. Scaling of insect metabolic rate is inconsistent with the nutrient supply network model. Funct. Ecol. 21, 282–290, https://doi.org/10.1111/j.1365-2435.2007.01245.x (2007).Article 

    Google Scholar 
    Bartholomew, G. A. & Lighton, J. R. B. Short Communication: Ventilation and Oxygen Consumption During Rest and Locomotion in a Tropical Cockroach, Blaberus Giganteus. J. Exp. Biol. 118, 449–454 (1985).Article 

    Google Scholar 
    Stahel, C. D., Megirian, D. & Nicol, S. C. Sleep and metabolic rate in the little penguin, Eudyptula minor. J. Comp. Physiol., B 154, 487–494, https://doi.org/10.1007/bf02515153 (1984).Article 

    Google Scholar 
    Lighton, J. R. Slow Discontinuous Ventilation in the Namib Dune-sea Ant Camponotus Detritus (Hymenoptera, Formicidae). J. Exp. Biol. 151, 71–82 (1990).Article 

    Google Scholar 
    Bech, C., Chappell, M. A., Astheimer, L. B., Londoño, G. A. & Buttemer, W. A. A ‘slow pace of life’ in Australian old-endemic passerine birds is not accompanied by low basal metabolic rates. J. Comp. Physiol., B 186, 503–512, https://doi.org/10.1007/s00360-016-0964-6 (2016).CAS 
    Article 

    Google Scholar 
    Young, S. R. & Block, W. Some factors affecting metabolic rate in an Antarctic mite. Oikos, 178–185, https://doi.org/10.2307/3544180 (1980).Wang, L. C.-H. & Hudson, J. W. Some physiological aspects of temperature regulation in the normothermic and torpid hispid pocket mouse, Perognathus hispidus. Comp. Biochem. Physiol. 32, 275–293, https://doi.org/10.1016/0010-406X(70)90941-2 (1970).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bedford, G. S. & Christian, K. A. Standard metabolic rate and preferred body temperatures in some Australian pythons. Aust. J. Zool. 46, 317–328, https://doi.org/10.1071/ZO98019 (1999).Article 

    Google Scholar 
    Vogt, J. T. & Appel, A. G. Standard metabolic rate of the fire ant, Solenopsis invicta Buren: effects of temperature, mass, and caste. J. Insect Physiol. 45, 655–666, https://doi.org/10.1016/S0022-1910(99)00036-0 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    Thompson, G., Heger, N., Heger, T. & Withers, P. Standard metabolic rate of the largest Australian lizard, Varanus giganteus. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 111, 603–608, https://doi.org/10.1016/0300-9629(95)00055-C (1995).Article 

    Google Scholar 
    Vitali, S. D., Withers, P. C. & Richardson, K. C. Standard metabolic rates of three nectarivorous meliphagid passerine birds. Aust. J. Zool. 47, 385–391, https://doi.org/10.1071/ZO99023 (1999).Article 

    Google Scholar 
    Dawson, T. J., Grant, T. R. & Fanning, D. Standard Metabolism of Monotremes and the Evolution of Homeothermy. Aust. J. Zool. 27, 511–515, https://doi.org/10.1071/ZO9790511 (1979).Article 

    Google Scholar 
    Al-Sadoon, M. K. & Abdo, N. M. Temperature effects on oxygen consumption of two nocturnal geckos, Ptyodactylus hasselquistii (Donndorff) and Bunopus tuberculatus (Blanford) (Reptilia: Gekkonidae) in Saudi Arabia. J. Comp. Physiol., B 159, 1–4, https://doi.org/10.1007/bf00692676 (1989).ADS 
    Article 

    Google Scholar 
    Roxburgh, L. & Perrin, M. R. Temperature regulation and activity pattern of the round-eared elephant shrew Macroscelides proboscideus. J. Therm. Biol. 19, 13–20, https://doi.org/10.1016/0306-4565(94)90004-3 (1994).Article 

    Google Scholar 
    Wang, L. C.-H. & Hudson, J. W. Temperature regulation in normothermic and hibernating eastern chipmunk, Tamias striatus. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 38, 59–90, https://doi.org/10.1016/0300-9629(71)90098-3 (1971).CAS 
    Article 

    Google Scholar 
    Rfinking, L. N., Kilgore, D. L. Jr, Fairbanks, E. S. & Hamilton, J. D. Temperature regulation in normothermic black-tailed prairie dogs, Cynomys ludovicianus. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 57, 161–165, https://doi.org/10.1016/0300-9629(77)90368-1 (1977).Article 

    Google Scholar 
    Chew, R. M., Lindberg, R. G. & Hayden, P. Temperature regulation in the little pocket mouse, Perognathus longimembris. Comp. Biochem. Physiol. 21, 487–505, https://doi.org/10.1016/0010-406X(67)90447-1 (1967).CAS 
    Article 
    PubMed 

    Google Scholar 
    Ebisu, R. J. & Whittow, G. C. Temperature regulation in the small Indian mongoose (Herpestes auropunctatus). Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 54, 309–313, https://doi.org/10.1016/S0300-9629(76)80117-X (1976).CAS 
    Article 

    Google Scholar 
    Whittow, G. C., Scammell, C. A., Leong, M. & Rand, D. Temperature regulation in the smallest ungulate, the lesser mouse deer (Tragulus javanicus). Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 56, 23–26, https://doi.org/10.1016/0300-9629(77)90436-4 (1977).CAS 
    Article 

    Google Scholar 
    Fusari, M. H. Temperature responses of standard, aerobic metabolism by the California legless lizard, Anniella pulchra. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 77, 97–101, https://doi.org/10.1016/0300-9629(84)90018-5 (1984).CAS 
    Article 

    Google Scholar 
    Dawson, T. J. & Fanning, F. D. Thermal and energetic problems of semiaquatic mammals: a study of the Australian water rat, including comparisons with the platypus. Physiol. Zool. 54, 285–296 (1981).Article 

    Google Scholar 
    Campbell, K. L. & Hochachka, P. W. Thermal biology and metabolism of the American shrew-mole, Neurotrichus gibbsii. J. Mammal. 81, 578-585, 10.1644/1545-1542(2000)0812.0.CO;2 (2000).Hosken, D. J. Thermal Biology and Metabolism of the Greater Long-eared Bat. Nyctophilus major (Chiroptera:Vespertilionidae). Aust. J. Zool. 45, 145–156, https://doi.org/10.1071/ZO96043 (1997).Article 

    Google Scholar 
    Duxbury, K. J. & Perrin, M. Thermal biology and water turnover rate in the Cape gerbil, Tatera afra (Gerbillidae). J. Therm. Biol. 17, 199–208, https://doi.org/10.1016/0306-4565(92)90056-L (1992).Article 

    Google Scholar 
    Downs, C. T. & Perrin, M. R. The thermal biology of the white-tailed rat Mystromys albicaudatus, a cricetine relic in southern temperate African grassland. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 110, 65–69, https://doi.org/10.1016/0300-9629(94)00147-L (1995).CAS 
    Article 

    Google Scholar 
    Downs, C. T. & Perrin, M. R. The thermal biology of three southern African elephant-shrews. J. Therm. Biol. 20, 445–450, https://doi.org/10.1016/0306-4565(95)00003-F (1995).Article 

    Google Scholar 
    Maloiy, G. M. O., Kamau, J. M. Z., Shkolnik, A., Meir, M. & Arieli, R. Thermoregulation and metabolism in a small desert carnivore: the Fennec fox (Fennecus zerda)(Mammalia). J. Zool. 198, 279–291, https://doi.org/10.1111/j.1469-7998.1982.tb02076.x (1982).Article 

    Google Scholar 
    Maskrey, M. & Hoppe, P. P. Thermoregulation and oxygen consumption in Kirk’s dik-dik (Madoqua kirkii) at ambient temperatures of 10–45 °C. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 62, 827–830, https://doi.org/10.1016/0300-9629(79)90010-0 (1979).Article 

    Google Scholar 
    Kamau, J. M., Johansen, K. & Maloiy, G. Thermoregulation and standard metabolism of the slender mongoose (Herpestes sanguineus). Physiol. Zool. 52, 594–602 (1979).Article 

    Google Scholar 
    Knight, M. H. Thermoregulation in the largest African cricetid, the giant rat Cricetomys gambianus. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 89, 705–708, https://doi.org/10.1016/0300-9629(88)90856-0 (1988).CAS 
    Article 

    Google Scholar 
    Bennett, N. C., Aguilar, G. H., Jarvis, J. U. M. & Faulkes, C. G. Thermoregulation in three species of Afrotropical subterranean mole-rats (Rodentia: Bathyergidae) from Zambia and Angola and scaling within the genus Cryptomys. Oecologia 97, 222–227, https://doi.org/10.1007/bf00323153 (1994).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Casey, T. M. & Casey, K. K. Thermoregulation of Arctic Weasels. Physiol. Zool. 52, 153–164, https://doi.org/10.1086/physzool.52.2.30152560 (1979).Article 

    Google Scholar 
    Layne, J. N. & Dolan, P. G. Thermoregulation, metabolism, and water economy in the golden mouse (Ochrotomys nuttalli). Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 52, 153–163, https://doi.org/10.1016/S0300-9629(75)80146-0 (1975).CAS 
    Article 

    Google Scholar 
    Roberts, J. R. & Baudinette, R. V. Thermoregulation, Oxygen Consumption and Water Turnover in Stubble Quail, Coturnix pectoralis, and King Quail, Coturnix chinensis. Aust. J. Zool. 34, 25–33, https://doi.org/10.1071/ZO9860025 (1986).Article 

    Google Scholar 
    du Plessis, A., Erasmus, T. & Kerley, G. I. Thermoregulatory patterns of two sympatric rodents: Otomys unisulcatus and Parotomys brantsii. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 94, 215–220, https://doi.org/10.1016/0300-9629(89)90538-0 (1989).Article 

    Google Scholar 
    Bradley, W. & Yousef, M. Thermoregulatory responses in the plains pocket gopher, Geomys bursarius. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 52, 35–38, https://doi.org/10.1016/S0300-9629(75)80122-8 (1975).CAS 
    Article 

    Google Scholar 
    Drent, R. H. & Stonehouse, B. Thermoregulatory responses of the Peruvian penguin, Spheniscus humboldti. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 40, 689–710, https://doi.org/10.1016/0300-9629(71)90254-4 (1971).CAS 
    Article 

    Google Scholar 
    El-Nouty, F. D., Yousef, M. K., Magdub, A. B. & Johnson, H. D. Thyroid hormones and metabolic rate in burros, Equus asinus, and llamas, Lama glama: effects of environmental temperature. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 60, 235–237, https://doi.org/10.1016/0300-9629(78)90238-4 (1978).Article 

    Google Scholar 
    Krüger, K., Prinzinger, R. & Schuchmann, K.-L. Torpor and metabolism in hummingbirds. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 73, 679–689 (1982).
    Google Scholar 
    Bartholomew, G. A. & Barnhart, M. C. Tracheal Gases, Respiratory Gas Exchange, Body Temperature and Flight in Some Tropical Cicadas. J. Exp. Biol. 111, 131–144 (1984).Article 

    Google Scholar 
    Zachariassen, K. E., Andersen, J., Maloiy, G. M. & Kamau, J. M. Transpiratory water loss and metabolism of beetles from arid areas in East Africa. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 86, 403–408, https://doi.org/10.1016/0300-9629(87)90515-9 (1987).Article 

    Google Scholar 
    Bucher, T. L. Ventilation and oxygen consumption in Amazona viridigenalis. J. Comp. Physiol., B 155, 269–276, https://doi.org/10.1007/bf00687467 (1985).ADS 
    Article 

    Google Scholar 
    Bickler, P. E. & Anderson, R. A. Ventilation, Gas Exchange, and Aerobic Scope in a Small Monitor Lizard, Varanus gilleni. Physiol. Zool. 59, 76–83, https://doi.org/10.1086/physzool.59.1.30156093 (1986).Article 

    Google Scholar 
    Seid, M. A., Castillo, A. & Wcislo, W. T. The allometry of brain miniaturization in ants. Brain Behav. Evol. 77, 5–13, https://doi.org/10.1159/000322530 (2011).Article 
    PubMed 

    Google Scholar 
    Quesada, R. et al. The allometry of CNS size and consequences of miniaturization in orb-weaving and cleptoparasitic spiders. Arthropod Struct. Dev. 40, 521–529, https://doi.org/10.1016/j.asd.2011.07.002 (2011).Article 
    PubMed 

    Google Scholar 
    Mares, S., Ash, L. & Gronenberg, W. Brain allometry in bumblebee and honey bee workers. Brain Behav. Evol. 66, 50–61, https://doi.org/10.1159/000085047 (2005).Article 
    PubMed 

    Google Scholar 
    Mlikovsky, J. Brain size and forearmen magnum area in crows and allies (Aves: Corvidae). Acta Soc. Zool. Bohem. 67, 203–211 (2003).
    Google Scholar 
    Mlikovsky, J. Brain size in birds: 4. Passeriformes. Acta Soc. Zool. Bohem. 54, 27–37 (1990).
    Google Scholar 
    Bronson, R. T. Brain weight-body weight relationships in 12 species of nonhuman primates. Am. J. Phys. Anthropol. 56, 77–81, https://doi.org/10.1002/ajpa.1330560109 (1981).Article 

    Google Scholar 
    Guay, P., Weston, M., Symonds, M. & Glover, H. Brains and bravery: Little evidence of a relationship between brain size and flightiness in shorebirds. Austral Ecol. 38, 516–522, https://doi.org/10.1111/j.1442-9993.2012.02441.x (2013).Article 

    Google Scholar 
    Boddy, A. M. et al. Comparative analysis of encephalization in mammals reveals relaxed constraints on anthropoid primate and cetacean brain scaling. J. Evol. Biol. 25, 981–994, https://doi.org/10.1111/j.1420-9101.2012.02491.x (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Stankowich, T. & Romero, A. N. The correlated evolution of antipredator defences and brain size in mammals. Proc. R. Soc. B: Biol. Sci. 284, https://doi.org/10.1098/rspb.2016.1857 (2017).Sheehan, Z. B. V., Kamhi, J. F., Seid, M. A. & Narendra, A. Differential investment in brain regions for a diurnal and nocturnal lifestyle in Australian Myrmecia ants. J. Comp. Neurol. 0, https://doi.org/10.1002/cne.24617.Bauchot, R. & Stephan, H. Données nouvelles sur l’encéphalisation des insectivores et des prosimiens. Mammalia 30, 160–196, https://doi.org/10.1515/mamm.1966.30.1.160 (1966).Article 

    Google Scholar 
    Rosenzweig, M. & Bennett, E. L. Effects of differential environments on brain weights and enzyme activities in gerbils, rats, and mice. Dev. Psychobiol. 2, 87–95, https://doi.org/10.1002/dev.420020208 (1969).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pirlot, P. & Stephan, H. Encephalization in Chiroptera. Can. J. Zool. 48, 433–444, https://doi.org/10.1139/z70-075 (1970).Article 

    Google Scholar 
    Ashwell, K. W. S. Encephalization of Australian and New Guinean marsupials. Brain Behav. Evol. 71, 181–199, https://doi.org/10.1159/000114406 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Hoops, D. et al. Evidence for concerted and mosaic brain evolution in dragon lizards. Brain Behav. Evol. 90, 211–223, https://doi.org/10.1159/000478738 (2017).Article 
    PubMed 

    Google Scholar 
    Pasquet, A., Toscani, C. & Anotaux, M. Influence of aging on brain and web characteristics of an orb web spider. J. Ethol. 36, 85–91, https://doi.org/10.1007/s10164-017-0530-z (2018).Article 
    PubMed 

    Google Scholar 
    Warnke, P. Mitteilung neuer Gehirn-und Körpergewichtsbestimmungen bei Saugern. J. Psychol. Neurol. 13, 355–403 (1908).
    Google Scholar 
    Naccarati, S. On the relation between the weight of the internal secretory glands and the body weight and brain weight. Anat. Rec. 24, 254–260, https://doi.org/10.1002/ar.1090240408 (1922).Article 

    Google Scholar 
    Crile, G. & Quiring, D. P. A record of the body weight and certain organ and gland weights of 3690 animals. Ohio J. Sci. (1940).Franklin, D. C., Garnett, S. T., Luck, G. W., Gutierrez-Ibanez, C. & Iwaniuk, A. N. Relative brain size in Australian birds. Emu 114, 160–170, https://doi.org/10.1071/MU13034 (2014).Article 

    Google Scholar 
    Hrdlička, A. Weight of the brain and of the internal organs in American monkeys. With data on brain weight in other apes. Am. J. Phys. Anthropol. 8, 201–211, https://doi.org/10.1002/ajpa.1330080207 (1925).Article 

    Google Scholar 
    Stöckl, A. L., Ribi, W. A. & Warrant, E. J. Adaptations for nocturnal and diurnal vision in the hawkmoth lamina. J. Comp. Neurol. 524, 160–175, https://doi.org/10.1002/cne.23832 (2016).Article 
    PubMed 

    Google Scholar 
    Napiorkowska, T. & Kobak, J. The allometry of the central nervous system during the postembryonic development of the spider Eratigena atrica. Arthropod Struct. Dev. 46, 805–814, https://doi.org/10.1016/j.asd.2017.08.005 (2017).Article 
    PubMed 

    Google Scholar 
    El Jundi, B., Huetteroth, W., Kurylas, A. E. & Schachtner, J. Anisometric brain dimorphism revisited: Implementation of a volumetric 3D standard brain in Manduca sexta. J. Comp. Neurol. 517, 210–225, https://doi.org/10.1002/cne.22150 (2009).Article 
    PubMed 

    Google Scholar 
    Krieger, J., Sandeman, R. E., Sandeman, D. C., Hansson, B. S. & Harzsch, S. Brain architecture of the largest living land arthropod, the Giant Robber Crab Birgus latro (Crustacea, Anomura, Coenobitidae): evidence for a prominent central olfactory pathway? Front. Zool. 7, 25, https://doi.org/10.1186/1742-9994-7-25 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Powell, B. J. & Leal, M. Brain Organization and Habitat Complexity in Anolis Lizards. Brain Behav. Evol. 84, 8–18, https://doi.org/10.1159/000362197 (2014).Article 
    PubMed 

    Google Scholar 
    Platel, R. in Biology of the Reptilia 10 (eds Gans, C. G., Northcutt, R. G & Ulinski, P. S.) 147–171 (Academic Press, 1979).Van Der Woude, E., Smid, H. M., Chittka, L. & Huigens, M. E. Breaking Haller’s rule: brain-body size isometry in a minute parasitic wasp. Brain Behav. Evol. 81, 86–92, https://doi.org/10.1159/000345945 (2013).Article 
    PubMed 

    Google Scholar 
    Guay, P.-J. & Iwaniuk, A. N. Captive breeding reduces brain volume in waterfowl (Anseriformes). Condor 110, 276–284, https://doi.org/10.1525/cond.2008.8424 (2008).Article 

    Google Scholar 
    Robinson, C. D., Patton, M. S., Andre, B. M. & Johnson, M. A. Convergent evolution of brain morphology and communication modalities in lizards. Current Zoology 61, 281–291, https://doi.org/10.1093/czoolo/61.2.281 (2015).Article 

    Google Scholar 
    Kvello, P., Løfaldli, B., Rybak, J., Menzel, R. & Mustaparta, H. Digital, three-dimensional average shaped atlas of the Heliothis virescens brain with integrated gustatory and olfactory neurons. Front. Syst. Neurosci. 3, https://doi.org/10.3389/neuro.06.014.2009 (2009).Montgomery, S. H. & Merrill, R. M. Divergence in brain composition during the early stages of ecological specialization in Heliconius butterflies. J. Evol. Biol. 30, 571–582, https://doi.org/10.1111/jeb.13027 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gordon, D. G., Zelaya, A., Arganda-Carreras, I., Arganda, S. & Traniello, J. F. A. Division of labor and brain evolution in insect societies: Neurobiology of extreme specialization in the turtle ant Cephalotes varians. PLOS ONE 14, e0213618, https://doi.org/10.1371/journal.pone.0213618 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rein, K., Zöckler, M., Mader, M. T., Grübel, C. & Heisenberg, M. The Drosophila Standard Brain. Curr. Biol. 12, 227–231, https://doi.org/10.1016/S0960-9822(02)00656-5 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    Shen, J.-M., Li, R.-D. & Gao, F.-Y. Effects of ambient temperature on lipid and fatty acid composition in the oviparous lizards, Phrynocephalus przewalskii. Comp. Biochem. Physiol. B: Biochem. Mol. Biol. 142, 293–301, https://doi.org/10.1016/j.cbpb.2005.07.013 (2005).CAS 
    Article 

    Google Scholar 
    Muscedere, M. L., Gronenberg, W., Moreau, C. S. & Traniello, J. F. A. Investment in higher order central processing regions is not constrained by brain size in social insects. Proc. R. Soc. B: Biol. Sci. 281, https://doi.org/10.1098/rspb.2014.0217 (2014).Platel, R. L’encéphalisation chez le Tuatara de Nouvelle-Zélande Sphenodon punctatus Gray (Lepidosauria, Sphenodonta). Etude quantifiée des principales subdivisions encéphaliques. J. Hirnforsch. 30, 325–337 (1989).CAS 
    PubMed 

    Google Scholar 
    Makarova, A. A. & Polilov, A. A. Peculiarities of the brain organization and fine structure in small insects related to miniaturization. 1. The smallest Coleoptera (Ptiliidae). Entomol. Rev. 93, 703–713, https://doi.org/10.1134/S0013873813060043 (2013).Article 

    Google Scholar 
    Bininda‐Emonds, O. R. P. Pinniped brain sizes. Mar. Mamm. Sci. 16, 469–481 (2000).Article 

    Google Scholar 
    Stafstrom, J. A., Michalik, P. & Hebets, E. A. Sensory system plasticity in a visually specialized, nocturnal spider. Sci. Rep. 7, 46627, https://doi.org/10.1038/srep46627 (2017).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    O’Donnell, S., Bulova, S. J., Barrett, M. & Fiocca, K. Size constraints and sensory adaptations affect mosaic brain evolution in paper wasps (Vespidae: Epiponini). Biol. J. Linn. Soc. 123, 302–310, https://doi.org/10.1093/biolinnean/blx150 (2018).Article 

    Google Scholar 
    Kamhi, J. F., Gronenberg, W., Robson, S. K. A. & Traniello, J. F. A. Social complexity influences brain investment and neural operation costs in ants. Proc. R. Soc. B: Biol. Sci. 283, 20161949, https://doi.org/10.1098/rspb.2016.1949 (2016).Article 

    Google Scholar 
    Kurylas, A. E., Rohlfing, T., Krofczik, S., Jenett, A. & Homberg, U. Standardized atlas of the brain of the desert locust, Schistocerca gregaria. Cell Tissue Res. 333, 125, https://doi.org/10.1007/s00441-008-0620-x (2008).Article 
    PubMed 

    Google Scholar 
    O’Donnell, S. et al. A test of neuroecological predictions using paperwasp caste differences in brain structure (Hymenoptera: Vespidae). Behav. Ecol. Sociobiol. 68, 529–536, https://doi.org/10.1007/s00265-013-1667-6 (2014).Article 

    Google Scholar 
    Weltzien, P. & Barth, F. G. Volumetric measurements do not demonstrate that the spider brain “central body” has a special role in web building. J. Morphol. 208, 91–98, https://doi.org/10.1002/jmor.1052080104 (1991).Article 
    PubMed 

    Google Scholar  More

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    Climate change reshuffles northern species within their niches

    Species DataWe analyse long-term high-quality monitoring data on 1,478 species of birds, mammals, small rodents, butterflies, moths, forest understory vascular plants and freshwater phytoplankton sampled across 6,504 individual sites along an ~1,200 km latitudinal gradient in Finland. Because of differences in sampling methods and in spatial and temporal coverage, each dataset was analysed separately. We note that most species have a distributional range larger than Finland and that for the current purposes, niche space was estimated based on empirical data compiled within Finland alone.BirdsBird data have been collected using line transect censuses in Finland since the 1970s21. The data are collected yearly based on a one‐visit census in which birds are counted along transects with lengths typically 3–6 km. Transects are previously established (that is, with known locations) and not all transects are sampled every year. The census period is June, with observations typically carried between 3:00 and 9:00 am, when the singing activity of birds is highest in dry weather conditions. The observer walks alone at a speed of approximately 1 km h−1 depending on the density of birds along the transect using a map, compass or global positioning system. The census is carried out earlier in southern Finland (June 1–20) compared with northern Finland (June 10–30) due to later breeding phenology in northern latitudes. The line transect is divided into a main belt and a supplementary belt. The main belt is 50 m wide (25 m on each side of the transect line), and the supplementary belt represents the area beyond the main belt as far as birds can be detected. Every observation is assigned to either the main or the supplementary belt. Birds crossing the main belt belong to the supplementary belt even if first observed above the main belt. Species‐specific annual proportions of displaying birds and birds in the main belt remained stable between 1987–2010, indicating that there have been no major changes in species detectability36. The data are curated by the Finnish Museum of Natural History. We used records between 1978 and 2017, including a total of 189 species sampled in 1,105 transects after applying our selection criteria (‘Study design and data preparation’ below).MammalsA systematic monitoring programme of counts of mammal snow tracks was established in 1989 by the Natural Resources Institute Finland (Luke; Game triangle data37,38). The wildlife triangle scheme is based on a network of 4 + 4 + 4 km triangle-shaped transects (totalling 12 km per triangle) with fixed locations, covering the entire country. The triangles are located in forested areas covering the main forest types and are usually situated in hunting areas with the observations carried out by volunteers (mainly hunters). Around 2,000 triangles have been established and about half of these are counted annually. In the winter count, the transect is walked or skied during one day and all snow tracks of 24 mammal species crossing the count line are recorded, usually from mid-January to mid-March (when snow cover conditions are good). The number of crossings are typically related to the transect length and number of days since last snowfall, when snow tracks have been accumulating. Snowfall can be replaced with a pre-count, where the existing tracks are marked or erased, to be disregarded during the actual count. We used records between 1989 and 2017, including a total of 18 species sampled in 1,958 sites after applying our selection criteria (‘Study design and data preparation’ below).Small rodentsSince the 1960s, the Natural Resources Institute Finland (Luke) carries out an inventory of vole species to support forest management planning (small rodents data39). Data were collected biannually in spring (mid‐March to mid‐June) and autumn (mid‐August to mid‐October) in forested and field habitats in 34 locations throughout Finland. Individual trapping sites within locations were often not constant over the study period, primarily due to changes in land use. New sites were selected to be as close and as similar as possible to earlier sites regarding habitat characteristics. Trapping was conducted in two habitat types in almost all locations: spruce (Picea abies) or mountain birch (Betula pubescens) forests and in open grassland habitats, primarily old agricultural land no longer in use. We used records between 1978 and 2017, including a total of 15 species sampled in 19 sites after applying our selection criteria (‘Study design and data preparation’ below).ButterfliesWe combined two similar surveys of butterflies conducted in agricultural landscapes in Finland. The first is a butterfly monitoring network based on volunteer transects initiated by the Finnish Environment Institute (SYKE)40. Between 1999 and 2017, the network included 101 sites, with an average of 47 sites recorded annually (range 30–59). At each site, the walking route (transect) is kept constant from year to year and walked repeatedly during the summer. Along each transect, the number of individuals for each species is recorded from a 5 × 5 × 5 m3 cube ahead of the observer41. The transects are monitored by volunteer butterfly enthusiasts with high species identification skills, who are asked to conduct a minimum of seven annual visits per transect, approximately once a fortnight from late May to late August. Weekly counts are recommended and are usually carried out on nearly half of the transects. The sampling period is typically no longer than 16 weeks and less than ten weeks in the northernmost transects. The second survey type spans between 2001 and 2014 and consists of 68 standardized transects of 1 km length in southern Finland and the Åland islands. These transects were sampled by researchers with a constant sampling frequency of seven counts per summer42. The median transect length of the combined data is 1.95 km (mean = 2.41 km). We used records between 1999 and 2017, including a total of 68 species sampled in 98 transects after applying our selection criteria (‘Study design and data preparation’ below).MothsData on moths have been collected under the National Moth Monitoring scheme (Nocturna) between 1993 and 201743,44, which is coordinated by the Finnish Environment Institute (SYKE). Nocturnal moths were sampled using ‘Jalas’ light traps45 equipped with 125 W Hg vapour or 160 W mixed-light bulbs, located mainly in forested areas across Finland. Traps are located in the same location from year to year and are usually emptied weekly. Sampling occurred every night from early spring to late autumn (usually between April and October). Sampling effort (that is, trapping period) was constant across years for each trap, but given that sampling aimed to cover the entire moth activity period at each location, the trapping period was longer in more southern traps. Volunteers empty the traps and identify the specimens43, with a variable number of traps being sampled per year. The taxonomic skills of the volunteer lepidopterists were typically excellent, and data quality control and cross checking was carried out by the monitoring coordinators43,44,46. The data used here consist of species records collected from 65 traps with at least eight years of sampling. We used records between 1993 and 2017, including a total of 615 species after applying our selection criteria (‘Study design and data preparation’ below).Understory vascular plants of forestsUnderstory vegetation was surveyed on a systematic network of 1,700 sites established on mineral soil in forested land between 1985 and 1986 (as part of the 8th Finnish National Forest Inventory47). This network consists of clusters, each including four sites with 400 m intervals. The clusters were located 16 km apart from each other in southern Finland, and 24 km and 32 km apart in northern Finland along east–west and north–south axes, respectively. All 1,700 sites were resurveyed in 1995, and a subset of 443 sites were resurveyed in 2006. The spatial extent of sampling was comparable across surveys covering the whole country. In all three surveys, vascular plant species (dwarf shrubs, herbs, ferns and graminoids, including also tree and shrub seedlings and saplings up to 50 cm tall) were identified and species’ cover (0.1–100%) was visually estimated; this was based on three to six permanent square‐shaped sampling plots of 2 m2, located 5 m apart from each other within each site. The data are curated by the Natural Resources Institute Finland (Luke). For this analysis, we selected all sites with four sampling plots. The average of species cover across these four sampling plots is used as an estimate of species abundance at each site. This included occurrence records from 1,518, 1,673 and 443 sites in years 1985, 1995 and 2006, respectively. After applying the selection criteria (‘Study design and data preparation’ below), the data included a total of 109 species sampled in 1,712 sites.PhytoplanktonThe National Finnish Phytoplankton Monitoring Database maintained by the Finnish Environment Institute (SYKE; open data portal http://www.syke.fi/en-US/Open_information) comprises nationwide phytoplankton community data of lake surface water samples. We used data collected in the late summer months with samples taken during early July to late August, reflecting the peak productivity season of lake phytoplankton communities. To ensure consistent sampling methodology, we included only data between the years 1977 and 2017. All phytoplankton samples were preserved with acid Lugol’s solution and analysed using the standard Utermöhl technique48. We used records between 1978 and 2017, including a total of 464 species sampled in 1,544 sites after applying our selection criteria (‘Study design and data preparation’ below).Study design and data preparationBefore running the joint species distribution models (below), we converted abundance data into presence records. Each site was assigned to one of the four bioclimatic zones in Finland49—from south to north: hemiboreal (HB), southern boreal (SB), middle boreal (MB) and northern boreal (NB). We combined the two southernmost regions by pooling the occurrence records to obtain a better distribution and number of samples given the much smaller extent of the HB zone. Each occurrence record was also assigned to a different decade based on the year of sampling: decade 1 (1978–1987), decade 2 (1988–1997), decade 3 (1998–2007) and decade 4 (2008–2017) (Fig. 1). Scarce records before 1978 were excluded. Splitting the data into discrete zone and decade subsets allowed us to use independent (and computationally manageable) data to jointly model species responses within each taxonomic group and to disentangle any contrasting imprints of climatic changes between regions and periods. Each subset therefore covered a wide range of climatic conditions for each taxon, with the majority including ten years of data. While the number of sites varied over decades, zones and taxa, the change was not systematic, neither over time nor across taxonomic groups—that is, there was no consistent pattern of more sampling in later decades, and for each group, there could be more sampling sites in a given zone in a later or in an earlier decade (Supplementary Table S2). In addition, the frequency distribution of the pairwise distances between all sites remained similar across decades in each zone (Supplementary Fig. S4), suggesting no changes in the spatial aggregation of sites over time. Furthermore, our analyses are model based and thus explicitly account for the number and distribution of sampling sites, while making inference on both environmental covariates and spatial random effects50. Thus, the variation in the number and distribution of sampling sites affects the uncertainty on trend estimates, rather than affecting the estimates themselves.Species were included if they had a minimum of ten occurrences in each zone × decade combination. Finally, due to the difficulty of obtaining reliable model estimates from very sparse data, we set two additional criteria, including only data subsets with at least 20 samples and at least six species; this meant that despite data being available, some subsets were not included in the analyses (for instance, small rodents in NB).Environmental dataFor each site across the different taxonomic datasets and for each year sampled, we extracted values of daily mean temperature, daily precipitation sum and daily snow depth from the Finnish Meteorological Institute (https://etsin.fairdata.fi/datasets/fmi?keys=Finnish%20Meteorological%20Insitute&terms=organization_name_en.keyword&p=1&sort=best; first accessed in April 2019 and updated in May 2020). These datasets are part of the Finnish Meteorological Institute ClimGrid, which is a gridded daily climatology dataset of Finland, with a spatial resolution of 10 × 10 km 51. From these, we calculated values of annual mean temperature, total precipitation and number of days with snow cover. We extracted annual NAO values from the Climate Analysis Section, National Center for Atmospheric Research (NCAR), Boulder, Colorado, United States52. The NAO index is calculated based on surface sea level pressure difference between the Subtropical (Azores) High and the Subpolar (Iceland) Low, with a high index (NAO +) indicating cool summers and mild and wet winters, whereas low values (NAO −) indicate cold dry winters53. We explored whether other climatic variables might also be relevant for our analysis. Specifically, we additionally calculated January temperature, July temperature, temperature range, mean temperature standard deviation, temperature seasonality, growing degree days (over 5 °C), summer precipitation, precipitation range and mean snow depth. For calculating these additional variables, we extracted daily maximum and minimum temperature data from the ClimGrid data. We evaluated the correlation patterns among these variables and found they were highly correlated, particularly with annual values (Supplementary Fig. S5). As such, we included annual mean and summed values in our models because the relevance of the more detailed variables is likely to vary among taxa. Using annual values also facilitates comparisons with other studies and climate scenarios and allows overcoming issues regarding the overlap between some variables and seasonal sampling of the different species surveys.Quantifying climatic change patternsWe used two approaches to quantify changes in the different climatic variables. First, we used linear regressions with an interaction term between decade and bioclimatic zone to test whether changes over time differed between the different zones in our analysis framework. Second, for a more spatio-temporally resolved assessment of changing patterns we used the k-means clustering method to characterize regions of common climatic profiles for each variable considering all available data over space and time. We set k = 4, resulting in four groups of respectively similar variable conditions. Subsequently we calculated the mean and standard deviation of each resulting cluster for all variables and highlighted the average climate regimes over the whole latitudinal gradient and decades.Joint species distribution modellingWe used a joint species distribution modelling framework to (1) determine how the different climatic variables affected species occurrence patterns and (2) assess whether their relative importance in structuring assemblages has changed over time or across latitude. We fitted separate spatially explicit models for each combination of taxonomic group × bioclimatic zone × decade, yielding a total of 63 models. We modelled the probability of species presence in response to temperature, precipitation and snow cover with quadratic and to NAO with a linear function. Because we model presence data, we used probit regression models. Towards (1), we used the species responses to the climatic variables to quantify the proportion of species at the lower end of their niche (that is, occurrences increasing along the climatic gradient), at the upper end of their niche (that is, occurrences decreasing along the gradient) or at the optimum of their niche (that is, occurrences peaking within the gradient; Fig. 2b; ‘Scoring species’ position within niche domains’ below) within each bioclimatic zone and decade. Towards (2), we compared the proportion of explained variance attributed to each variable and examined whether their relative contribution shifted through time and/or space.For each taxon × bioclimatic zone × decade combination, we fitted latent-variable joint species distribution models using the Hierarchical Modelling of Species Communities (HMSC) framework. HMSC is a multi-variate Bayesian generalized linear mixed-effect model framework, which allows joint modelling of the responses of entire species assemblages and explicit modelling of spatial and temporal autocorrelation18,54,55. We used spatially structured latent variables which were originally proposed by Ovaskainen et al. 56. and later expanded to big spatial data by Tikhonov et al. 57. We fitted the models with the ‘Hmsc’ v 3.0.9 package55 in R58 with a probit link function and assuming the default prior distributions. As fixed effects, we included the climatic variables described above, estimating a second-order polynomial term for all covariates except for NAO, for which we estimated a linear term only. To account for variation in other (unmeasured) environmental variables and potential year-to-year variation not captured by the climatic covariates, we included the random effects of site and year, respectively. All models had the same structure for all the taxon × zone × decade subsets, except for the understory vegetation data for which we did not include the covariate NAO nor the random effect of year because these data were collected only in three individual years, corresponding to a single year per decade in our analytical framework (Fig. 1). Finally, due to computational bottlenecks for large data subsets, some model runs failed to complete with available resources; when this was the case, we randomly subsetted 1,000 records before re-fitting the models (specifically, bird and phytoplankton subsets for the last decade in SB and six mammal subsets for MB and SB in the second, third and fourth decades). We performed posterior sampling using four Markov Chain Monte Carlo chains, each collecting 250 samples, yielding a total of 1,000 samples. We used a thinning interval of 100 and excluded the first 12,500 iterations as burn-in, only sampling the subsequent 25,000 iterations per chain. For phytoplankton in the southernmost region in decade 4, we used a thinning of 10 due to computational constraints due to large site and species numbers. To evaluate Markov Chain Monte Carlo convergence, we examined the distribution of the potential scale reduction factor over the parameters related to the fixed effects and the random effects (equivalent to the Gelman-Rubin statistic59). We assessed model fit via the Area Under the Curve (AUC) statistic60 and model discriminatory power was quantified by Tjur’s R2, which is recommended as a standard measure of discriminatory power for binary outcomes61.To quantify shifts in the explanatory power associated with each covariate, we assessed variance component estimates, that is, the relative explanatory power of each environmental covariate in the HMSC models18,54. We estimated how the relative importance of the covariates in explaining species occurrences varied over time by fitting linear regression models to the species variance component estimate values as a function of decade (using the function ‘lm’ in R) and then compared these changes across zones for the different taxonomic groups (Fig. 4). These model comparisons were carried out after weighing the variance component values by each model’s ability to explain species occurrence patterns (that is, discriminatory power quantified using Tjur’s R2 values).Scoring species’ position within niche domainsTo analyse whether a species occurred at the lower end, at the optimum or at the upper end of its climatic niche within a particular bioclimatic zone and decade, we assessed the species’ responses to each of the climatic variables as follows. First, we classified a species as non-responsive to a specific climate variable within the measured range of that variable if the posterior distribution of the corresponding beta parameter estimates included zero with a probability of more than 10% (corresponding to having less than 90% posterior probability for the response). The non-zero responses were then classified as positive, negative or ‘bell-shaped’ based on the sign of the derivative of the response over the observed environmental gradient. A positive response corresponds to a species being at the lower end of its niche, ‘bell-shaped’ response corresponds to a species being at the optimum of its niche, and a negative response corresponds to a species being at the upper end of its niche. In cases where the derivative is positive/negative at both ends of the environmental gradient, responses were classified as either positive or negative, respectively. Cases where the derivative changed from positive to negative required subsequent evaluation. More specifically, if the derivative was positive or negative over less than 20% of the gradient, we classified the response as negative or positive, respectively. If the derivative was positive or negative over more than 80% of the gradient, we classified the response as positive or negative, respectively. Finally, if the derivative was positive or negative over at most 60% of the environmental gradient, we classified the response as bell-shaped. We evaluated whether this threshold affected the overall results by implementing the same classification using two other criteria for the derivatives being positive or negative: over less than 10% and more than 90% of the environmental gradient, and over less than 30% and more than 70% of the gradient. This showed that our classification of species’ responses was robust to these choices (Supplementary Fig. S6). This classification procedure did not apply to the beta parameters for NAO because we did not include a polynomial term for this covariate, as explained above. Thus, we obtained the number of species for each taxon × bioclimatic zone × decade model that showed responses to the different covariates and calculated the proportion of these species relative to the total number of species in each model (Fig. 3 and Extended Data Fig. 2).Comparing overall species composition between decadesWe compiled the species list present in each decade and each zone for the different taxonomic groups and compared these lists between consecutive decades, that is, comparing decades 1 and 2, 2 and 3, and 3 and 4. For each comparison, we noted how many species were present in both decades (‘shared’ = A), were present only in the first decade in the comparison (‘unique to earlier decade’ = C) or were present only in the last decade in the comparison (‘unique to later decade’ = B). We did this exercise for all taxa in all zones, plotting the sum of ‘shared’ and ‘unique species in each decade’ (Supplementary Fig. S2) and all the consecutive decade comparisons for each taxon (Supplementary Fig. S1). To quantify these patterns over a larger temporal extent, we implemented the same procedure but only comparing the first and last decades sampled for each taxon (Supplementary Figs. S1 and S2; note that for butterflies, these analyses are identical, because this taxon was sampled only in decades 3 and 4).Quantifying community dissimilarityTo assess how community composition changed over space and time, we calculated overall dissimilarity among all the sampling units within a given zone and decade—that is, we quantified variation in composition among sites within a given spatio-temporal extent, regardless of their location. Dissimilarity indices range between 0 and 1, representing cases where all or no species are shared between sites, respectively. We used the same occurrence matrices that were analysed with the HMSC models, that is, the raw species data matrices. We used the function ‘beta.sample’ in the ‘betapart’ package v 1.5.262,63 to calculate total dissimilarity (Sørensen index), which can be additively decomposed into the turnover (Simpson index) and nestedness components64. ‘beta.sample’ randomly selects a specified number of sites to generate distributions of the multiple‐site dissimilarity measures. This is important because the number of sites affects the estimated compositional change values. For each taxonomic group, we first determined the minimum number of sites among the different zone × decade combinations, which was used to define the number of sites to be randomly sampled from the original occurrence matrix, performing this subsampling 1,000 times. We then plotted the mean and standard deviation of these distributions to compare compositional change for each taxonomic group across the different zones and decades. We focus on the turnover metric (that is, species replacement among sites independent of changes in species richness; Extended Data Fig. 3), as it was systematically the main component of dissimilarity except for the small rodent data where the nestedness component had a relatively higher contribution to total dissimilarity. We show the results for the three metrics in Supplementary Fig. S3.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Synchrony and idiosyncrasy in the gut microbiome of wild baboons

    Kolodny, O. et al. Coordinated change at the colony level in fruit bat fur microbiomes through time. Nat. Ecol. Evol. 3, 116–124 (2019).PubMed 

    Google Scholar 
    Schlomann, B. H. & Parthasarathy, R. Timescales of gut microbiome dynamics. Curr. Opin. Microbiol. 50, 56–63 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Koch, H. & Schmid-Hempel, P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc. Natl Acad. Sci. USA 108, 19288–19292 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Finnicum, C. T. et al. Cohabitation is associated with a greater resemblance in gut microbiota which can impact cardiometabolic and inflammatory risk. BMC Microbiol. 19, 230 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Bashan, A. et al. Universality of human microbial dynamics. Nature 534, 259–262 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Miller, E. T., Svanback, R. & Bohannan, B. J. Microbiomes as metacommunities: understanding host-associated microbes through metacommunity ecology. Trends Ecol. Evol. 33, 926–935 (2018).PubMed 

    Google Scholar 
    Bjork, J., Díez-Vives, C., Astudillo-García, C., Archie, E. A. & Montoya, J. M. Vertical transmission of sponge microbiota is inconsistent and unfaithful. Nat. Ecol. Evol. 3, 1172–1183 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Sieber, M. et al. Neutrality in the metaorganism. PLoS Biol. 17, e3000298 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Tredennick, A. T., de Mazancourt, C., Loreau, M. & Adler, P. B. Environmental responses, not species interactions, determine synchrony of dominant species in semiarid grasslands. Ecology 98, 971–981 (2017).PubMed 

    Google Scholar 
    Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).PubMed 

    Google Scholar 
    Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451 (2009).PubMed 

    Google Scholar 
    Hector, A. et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology 91, 2213–2220 (2010).CAS 
    PubMed 

    Google Scholar 
    de Mazancourt, C. et al. Predicting ecosystem stability from community composition and biodiversity. Ecol. Lett. 16, 617–625 (2013).PubMed 

    Google Scholar 
    Gross, K. et al. Species richness and the temporal stability of biomass production: a new analysis of recent biodiversity experiments. Am. Nat. 183, 1–12 (2014).PubMed 

    Google Scholar 
    Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 

    Google Scholar 
    Rainey, P. B. & Quistad, S. D. Toward a dynamical understanding of microbial communities. Philos. Trans. R. Soc. B 375, 20190248 (2020).CAS 

    Google Scholar 
    Martiny, J. B., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).PubMed 

    Google Scholar 
    Debray, R. et al. Priority effects in microbiome assembly. Nat. Rev. Microbiol. 20, 109–121 (2022).CAS 
    PubMed 

    Google Scholar 
    Risely, A., Wilhelm, K., Clutton-Brock, T., Manser, M. B. & Sommer, S. Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat. Commun. 12, 6017 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Natl Acad. Sci. USA 112, E2930–E2938 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Flores, G. E. et al. Temporal variability is a personalized feature of the human microbiome. Genome Biol. 15, 531 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Johnson, A. J. et al. Daily sampling reveals personalized diet–microbiome associations in humans. Cell Host Microbe 25, 789–802 (2019).CAS 
    PubMed 

    Google Scholar 
    Smits, S. A., Marcobal, A., Higginbottom, S., Sonnenburg, J. L. & Kashyap, P. C. Individualized responses of gut microbiota to dietary intervention modeled in humanized mice. mSystems 1, e00098 (2016).Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).CAS 
    PubMed 

    Google Scholar 
    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).CAS 
    PubMed 

    Google Scholar 
    Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grieneisen, L. et al. Gut microbiome heritability is nearly universal but environmentally contingent. Science 373, 181–186 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alberts S. C. & Altmann, J. in Long-Term Field Studies of Primates (eds Kappeler, P. & Watts, D. P.) 261–287 (Springer, 2012).Ren, T., Grieneisen, L., Alberts, S. C., Archie, E. A. & Wu, M. Development, diet, and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ. Microbiol. 18, 1312–1325 (2016).PubMed 

    Google Scholar 
    Grieneisen, L. et al. Genes, geology, and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species. Proc. R. Soc. B 286, 20190431 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Hicks, A. L. et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9, 1786 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Orkin, J. D. et al. Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest. ISME J. 13, 183–196 (2019).CAS 
    PubMed 

    Google Scholar 
    Baniel, A. et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 9, 26 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mellard, J. P., Audoye, P. & Loreau, M. Seasonal patterns in species diversity across biomes. Ecology 100, e02627 (2019).PubMed 

    Google Scholar 
    Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).PubMed 

    Google Scholar 
    Sloan, W. T., Woodcock, S., Lunn, M., Head, I. M. & Curtis, T. P. Modeling taxa-abundance distributions in microbial communities using environmental sequence data. Microb. Ecol. 53, 443–455 (2007).PubMed 

    Google Scholar 
    Tung, J. et al. Social networks predict gut microbiome composition in wild baboons. eLife 4, e05224 (2015).PubMed Central 

    Google Scholar 
    Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Amato, K. R. et al. Patterns in gut microbiota similarity associated with degree of sociality among sex classes of a neotropical primate. Microb. Ecol. 74, 250–258 (2017).PubMed 

    Google Scholar 
    Amato, K. R. et al. The role of gut microbes in satisfying the nutritional demands of adult and juvenile wild, black howler monkeys (Alouatta pigra). Am. J. Phys. Anthropol. 155, 652–664 (2014).PubMed 

    Google Scholar 
    Perofsky, A. C., Leriw, R. J., Abondano, L. A., Di Fiore, A. & Meyers, L. A. Hierarchical social networks shape gut microbial composition in wild Verreaux’s sifaka. Proc. R. Soc. B 284, 20172274 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Silk, J. B. Activities and feeding behavior of free-ranging pregnant baboons. Int. J. Primatol. 8, 593–613 (1987).
    Google Scholar 
    Altmann, S. A. Foraging for Survival: Yearling Baboons in Africa (Univ. Chicago Press, 1998).Bronikowski, A. M. & Altmann, J. Foraging in a variable environment: weather patterns and the behavioral ecology of baboons. Behav. Ecol. Sociobiol. 39, 11–25 (1996).
    Google Scholar 
    Muruthi, P., Altmann, J. & Altmann, S. Resource base, parity and reproductive condition affect females’ feeding time and nutrient intake within and between groups of a baboon population. Oecologia 87, 467–472 (1991).PubMed 

    Google Scholar 
    Shopland, J. M. Food quality, spatial deployment, and the intensity of feeding interference in yellow baboons (Papio cynocephalus). Behav. Ecol. Sociobiol. 21, 149–156 (1987).
    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 

    Google Scholar 
    Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
    PubMed 

    Google Scholar 
    Sprockett D. tyRa: Build Models for Microbiome Data. R package version 0.1.0 https://danielsprockett.github.io/tyRa/articles/tyRa.html (2020).Oksanen J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Vieira-Silva, S. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1, 16088 (2016).CAS 
    PubMed 

    Google Scholar 
    Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. 99, 673–686 (2004).
    Google Scholar 
    Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).
    Google Scholar 
    Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn (CRC Press, 2017).Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796–798 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Personalized microbiomes in social baboons

    Sarkar, A. et al. Nat. Ecol. Evol. 4, 1020–1035 (2020).Article 

    Google Scholar 
    Tung, J. et al. eLife 4, e05224 (2015).Article 

    Google Scholar 
    Bennett, G. et al. Am. J. Primatol. 78, 883–892 (2016).CAS 
    Article 

    Google Scholar 
    Moeller, A. H. et al. Sci. Adv. 2, e1500997 (2016).Article 

    Google Scholar 
    Perofsky, A. C., Ancel Meyers, L., Abondano, L. A., Di Fiore, A. & Lewis, R. J. Mol. Ecol. 30, 6759–6775 (2021).Article 

    Google Scholar 
    Yarlagadda, K., Razik, I., Malhi, R. S. & Carter, G. G. Biol. Lett. 17, 20210389 (2021).Article 

    Google Scholar 
    Björk, J. R. et al. Nat. Ecol. Evol., https://doi.org/10.1038/s41559-022-01773-4 (2022).Blekhman, R. et al. Genome Biol. 16, 191 (2015).Article 

    Google Scholar 
    Grieneisen, L. et al. Science 186, 181–186 (2021).Article 

    Google Scholar 
    Lloyd-Price, J. et al. Nature 550, 61–66 (2017).CAS 
    Article 

    Google Scholar  More

  • in

    Metagenomic assembled plasmids of the human microbiome vary across disease cohorts

    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

  • in

    Dental macrowear reveals ecological diversity of Gorilla spp.

    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

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    Characterization of intestinal microbiota in normal weight and overweight Border Collie and Labrador Retriever dogs

    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

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    Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm

    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