Ecology

Surface energy fluxes and componentsIn our study, we focused on the circumpolar land north of 60° latitude, and specifically on the extent of the circumpolar Arctic vegetation map (CAVM20, Supplementary Fig. 1–3). We obtained half-hourly and hourly in situ observations of energy fluxes and meteorological variables from the monitoring networks FLUXNET28 (fluxnet.org; FLUXNET2015 dataset), AmeriFlux29 (ameriflux.lbl.gov), AON31,32 (aon.iab.uaf.edu), ICOS (icos-cp.eu), GEM35,36 (g-e-m.dk), GC-Net33,34 (cires1.colorado.edu/steffen/gcnet) and PROMICE30; (promice.dk; Supplementary Table 3). We did not include observations from the Baseline Surface Radiation Network (BSRN; bsrn.awi.de) and Global Energy Balance Archive (GEBA; geba.ethz.ch) because they typically lack information on non-radiative energy fluxes. Finally, we did not include observations from the European Flux Database Cluster (EFDC, europe-fluxdata.eu) because these data are largely located outside the domain of the CAVM20.We aggregated surface energy fluxes and components (Supplementary Table 1) to daily resolution as follows: (i) we extracted only directly measured data and excluded gap-filled data by filtering according to quality information; (ii) we performed a basic outlier filtering (excluding shortwave and longwave radiation flux values >1400 Wm−2 and in case of incoming/outgoing radiation More

Allsop, K. A. & Miller, J. B. Honey revisited: A reappraisal of honey in pre-industrial diets. Br. J. Nutr. 75, 513–520 (1996).Article 
CAS 
PubMed 

Google Scholar 
Dams, M. & Dams, L. Spanish rock art depicting honey gathering during the Mesolithic. Nature 268, 228–230 (1977).Article 
ADS 

Google Scholar 
Bradbear, N. Bees and their role in forest livelihoods: A guide to the services provided by bees and the sustainable harvesting, processing and marketing of their products. Non-Wood Forests Products Series, Vol. 19 (FAO, Rome, 2009).
Google Scholar 
Crane, E. The World History of Beekeeping and Honey Hunting (Routledge, 1999).Book 

Google Scholar 
Kritsky, G. Beekeeping from Antiquity through the middle ages. Annu. Rev. Entomol. 62, 249–264 (2017).Article 
CAS 
PubMed 

Google Scholar 
Grüter, C. Stingless Bees: Their Behaviour, Ecology and Evolution (Springer International Publishing, 2020).Book 

Google Scholar 
Weaver, N. & Weaver, E. C. Beekeeping with the stingless bee Melipona beecheii, by the Yucatecan Maya. Bee World 62, 7–19 (1981).Article 

Google Scholar 
Quezada-Euán, J. J. G. Stingless Bees of Mexico: The Biology, Management and Conservation of an Ancient Heritage (Springer, 2018).Book 

Google Scholar 
Medellín Morales, S. Meliponicultura Maya: Perspectivas para su sostenibilidad. Reporte de sostenibilidad Maya no. 2; 67 pp. (1991).González-Acereto, J. A. La meliponicultura yucateca en crisis: Una actividad indígena a punto de desaparecer, 1er Seminario Nacional sobre Abejas sin Aguijón. Boca Río Ver México 9–12 (1999).Russell, P. The History of Mexico: From Pre-conquest to Present (Routledge, 2010).
Google Scholar 
Quezada-Euan, J. J., May-Itzá, W. & González-Acereto, J. Meliponiculture in Mexico: Problems and perspective for development. Bee World 82, 160–167 (2001).Article 

Google Scholar 
Freitas, B. M. et al. Diversity, threats and conservation of native bees in the Neotropics. Apidologie 40, 332–346 (2009).Article 

Google Scholar 
Toledo-Hernández, E. et al. The stingless bees (Hymenoptera: Apidae: Meliponini): A review of the current threats to their survival. Apidologie 53, 8 (2022).Article 

Google Scholar 
Guzman-Novoa, E. et al. The process and outcome of the Africanization of honey bees in Mexico: Lessons and future directions. Front. Ecol. Evol. 8, 404 (2020).Article 

Google Scholar 
Fletcher, M. et al. Stingless bee honey, a novel source of trehalulose: A biologically active disaccharide with health benefits. Sci. Rep. 10, 12128 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rao, P. V., Krishnan, K. T., Salleh, N. & Gan, S. H. Biological and therapeutic effects of honey produced by honey bees and stingless bees: A comparative review. Rev. Bras. Farmacogn. 26, 657–664 (2016).Article 
CAS 

Google Scholar 
Rattanawannee, A. & Duangphakdee, O. Southeast Asian meliponiculture for sustainable livelihood. In Modern Beekeeping – Bases for Sustainable Production (ed. Ranz, R. E. R.) (IntechOpen, 2019).
Google Scholar 
Heard, T. The role of stingless bees in crop pollination. Annu. Rev. Entomol. 44, 183–206 (1999).Article 
CAS 
PubMed 

Google Scholar 
Slaa, E. J., Chaves, L. A. S., Malagodi-Braga, K. S. & Hofstede, F. E. Stingless bees in applied pollination: Practice and perspectives. Apidologie 37, 293–315 (2006).Article 

Google Scholar 
Kendall, L. K., Stavert, J. R., Gagic, V., Hall, M. & Rader, R. Initial floral visitor identity and foraging time strongly influence blueberry reproductive success. Basic Appl. Ecol. https://doi.org/10.1016/j.baae.2022.02.009 (2022).Article 

Google Scholar 
Kiatoko, N. et al. Effective pollination of greenhouse Galia musk melon (Cucumis melo L. var. reticulatus ser.) by afrotropical stingless bee species. J. Apic. Res. https://doi.org/10.1080/00218839.2021.2021641 (2022).Article 

Google Scholar 
Nkoba, K. et al. African endemic stingless bees as an efficient alternative pollinator to honey bees in greenhouse cucumber (Cucumis sativus L.). J. Apic. Res. https://doi.org/10.1080/00218839.2021.2013421 (2022).Article 

Google Scholar 
FAO, A. Good beekeeping practices for sustainable apiculture. (FAO, IZSLT, Apimondia and CAAS, 2020). doi:https://doi.org/10.4060/cb5353en.Patel, V., Pauli, N., Biggs, E., Barbour, L. & Boruff, B. Why bees are critical for achieving sustainable development. Ambio 50, 49–59 (2021).Article 
PubMed 

Google Scholar 
Fuller, D. Q. et al. Convergent evolution and parallelism in plant domestication revealed by an expanding archaeological record. Proc. Natl. Acad. Sci. 111, 6147–6152 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Purugganan, M. D. An evolutionary genomic tale of two rice species. Nat. Genet. 46, 931–932 (2014).Article 
CAS 
PubMed 

Google Scholar 
Kleisner, K. & Stella, M. Monsters we met, monsters we made: On the parallel emergence of phenotypic similarity under domestication. Σημειωτκή – Sign Syst. Stud. 37, 454–476 (2009).Article 

Google Scholar 
Wilkins, A. S., Wrangham, R. W. & Fitch, W. T. The, “Domestication Syndrome” in mammals: A unified explanation based on neural crest cell behavior and genetics. Genetics 197, 795–808 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Lecocq, T. Insects: The disregarded domestication histories. In Animal Domestication (ed. Teletchea, F.) (IntechOpen, 2018).
Google Scholar 
Pollan, M. The botany of desire: A plant’s-eye view of the world. Econ. Bot. 57(1), 146–147 (2002).
Google Scholar 
Chuttong, B., Chanbang, Y., Sringarm, K. & Burgett, M. Physicochemical profiles of stingless bee (Apidae: Meliponini) honey from South East Asia (Thailand). Food Chem. 192, 149–155 (2016).Article 
CAS 
PubMed 

Google Scholar 
Spivak, M. & Danka, R. G. Perspectives on hygienic behavior in Apis mellifera and other social insects. Apidologie 52, 1–16 (2021).Article 

Google Scholar 
Breed, M. D., Guzmán-Novoa, E. & Hunt, G. J. 3. Defensive behavior of honey bees: Organization, genetics, and comparisons with other bees. Annu. Rev. Entomol. 49, 271–298 (2004).Article 
CAS 
PubMed 

Google Scholar 
Hunt, G. J. et al. Behavioral genomics of honeybee foraging and nest defense. Naturwissenschaften 94, 247–267 (2007).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Faegri, K. & van der Pijl,. Principles of Pollination Ecology (Pergamon Press, 1979).
Google Scholar 
Nicolson, S. W. & Thornburg, R. W. Nectar chemistry. In Nectaries and Nectar (eds Nicolson, S. W. et al.) (Springer Netherlands, 2007).Chapter 

Google Scholar 
Abrahamczyk, S. et al. Pollinator adaptation and the evolution of floral nectar sugar composition. J. Evol. Biol. 30, 112–127 (2017).Article 
CAS 
PubMed 

Google Scholar 
Parachnowitsch, A. L., Manson, J. S. & Sletvold, N. Evolutionary ecology of nectar. Ann. Bot. 123, 247–261 (2019).Article 
CAS 
PubMed 

Google Scholar 
Rasmussen, C. & Cameron, S. A. Global stingless bee phylogeny supports ancient divergence, vicariance, and long distance dispersal. Biol. J. Linn. Soc. 99, 206–232 (2010).Article 

Google Scholar 
Bantle, J. P. Dietary fructose and metabolic syndrome and diabetes. J. Nutr. 139, 1263S-1268S (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Erejuwa, O. O., Sulaiman, S. A. & Wahab, M. S. A. fructose might contribute to the hypoglycemic effect of honey. Molecules 17, 1900–1915 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kwakman, P. H. S. & Zaat, S. A. J. Antibacterial components of honey. IUBMB Life 64, 48–55 (2012).Article 
CAS 
PubMed 

Google Scholar 
Viuda-Martos, M., Ruiz-Navajas, Y., Fernández-López, J. & Pérez-Álvarez, J. A. Functional properties of honey, propolis, and royal jelly. J. Food Sci. 73, R117–R124 (2008).Article 
CAS 
PubMed 

Google Scholar 
Machado De-Melo, A. A., de Almeida-Muradian, L. B., Sancho, M. T. & Pascual-Maté, A. Composition and properties of Apis mellifera honey: A review. J. Apic. Res. 57, 5–37 (2018).Article 

Google Scholar 
Nordin, A., Sainik, N. Q. A. V., Chowdhury, S. R., Saim, A. B. & Idrus, R. B. H. Physicochemical properties of stingless bee honey from around the globe: A comprehensive review. J. Food Compos. Anal. 73, 91–102 (2018).Article 
CAS 

Google Scholar 
Viteri, R., Zacconi, F., Montenegro, G. & Giordano, A. Bioactive compounds in Apis mellifera monofloral honeys. J. Food Sci. 86, 1552–1582 (2021).Article 
CAS 
PubMed 

Google Scholar 
Bueno, F. G. B. et al. Stingless bee floral visitation in the global tropics and subtropics. BioRxiv. https://doi.org/10.1101/2021.04.26.440550 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Rasmussen, C. & Cameron, S. A. A molecular phylogeny of the Old World stingless bees (Hymenoptera: Apidae: Meliponini) and the non-monophyly of the large genus Trigona. Syst. Entomol. 32, 26–39 (2007).Article 

Google Scholar 
Mokaya, H. O., Nkoba, K., Ndunda, R. M. & Vereecken, N. J. Characterization of honeys produced by sympatric species of Afrotropical stingless bees (Hymenoptera, Meliponini). Food Chem. 366, 130597 (2022).Article 
CAS 
PubMed 

Google Scholar 
Souza, E. C. A., Menezes, C. & Flach, A. Stingless bee honey (Hymenoptera, Apidae, Meliponini): A review of quality control, chemical profile, and biological potential. Apidologie 52, 113–132 (2021).Article 

Google Scholar 
Ohmenhaeuser, M., Monakhova, Y. B., Kuballa, T. & Lachenmeier, D. W. Qualitative and quantitative control of honeys using NMR spectroscopy and chemometrics. ISRN Anal. Chem. 2013, 1–9 (2013).Article 

Google Scholar 
Mazzoni, V., Bradesi, P., Tomi, F. & Casanova, J. Direct qualitative and quantitative analysis of carbohydrate mixtures using 13C NMR spectroscopy: Application to honey. Magn. Reson. Chem. 35, S81–S90 (1997).Article 
CAS 

Google Scholar 
Consonni, R. & Cagliani, L. R. Geographical characterization of polyfloral and acacia honeys by nuclear magnetic resonance and chemometrics. J. Agric. Food Chem. 56, 6873–6880 (2008).Article 
CAS 
PubMed 

Google Scholar 
Schievano, E., Peggion, E. & Mammi, S. H1 nuclear magnetic resonance spectra of chloroform extracts of honey for chemometric determination of its botanical origin. J. Agric. Food Chem. 58, 57–65 (2010).Article 
CAS 
PubMed 

Google Scholar 
RStudio Team. RStudio: Integrated Development Environment for R. Rstudio, PBC, Boston, MA. URL http://www.rstudio.com (2020).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Oksanen J., et al. Vegan: Community ecology package. McGlinn lab URL https://CRAN.R-project.org/package=vegan (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).Book 
MATH 

Google Scholar 
Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinforma. 69, e96. https://doi.org/10.1002/cpbi.96 (2020).Article 

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

Google Scholar  More

Battaglini, L., Bovolenta, S., Gusmeroli, F., Salvador, S. & Sturaro, E. Environmental sustainability of alpine livestock farms. Ital. J. Anim. Sci. 13, 3155 (2014).
Google Scholar 
Lavorel, S. et al. Historical trajectories in land use pattern and grassland ecosystem services in two European alpine landscapes. Reg. Environ. Change 17, 2251–2264 (2017).PubMed Central 

Google Scholar 
Pan, Y., Wu, J. & Xu, Z. Analysis of the tradeoffs between provisioning and regulating services from the perspective of varied share of net primary production in an alpine grassland ecosystem. Ecol. Complex. 17, 79–86 (2014).
Google Scholar 
Rossi, M. et al. A comparison of the signal from diverse optical sensors for monitoring alpine grassland dynamics. Remote Sens. 11, 296 (2019).ADS 

Google Scholar 
Körner, C. Plant ecology at high elevations. In Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems (ed. Körner, C.) 1–7 (Springer, 2003). https://doi.org/10.1007/978-3-642-18970-8_1.Jonas, T., Rixen, C., Sturm, M. & Stoeckli, V. How alpine plant growth is linked to snow cover and climate variability. J. Geophys. Res. Biogeosci. 113 (2008).Körner, C. Impact of atmospheric changes on high mountain vegetation. In Mountain Environments in Changing Climates 155–166 (Routledge, 1994).Choler, P. Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration. Biogeosciences 12, 3885–3897 (2015).ADS 

Google Scholar 
Schirmer, M., Wirz, V., Clifton, A. & Lehning, M. Persistence in intra-annual snow depth distribution: 1. Measurements and topographic control. Water Resour. Res. 47, 09516 (2011).ADS 

Google Scholar 
Revuelto, J., Jonas, T. & López-Moreno, J.-I. Backward snow depth reconstruction at high spatial resolution based on time-lapse photography. Hydrol. Process. 30, 2976–2990 (2016).ADS 

Google Scholar 
López-Moreno, J. I. et al. Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent. Adv. Water Resour. 55, 40–52 (2013).ADS 

Google Scholar 
Clark, M. P. et al. Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review. Water Resour. Res. 47, (2011).Wayand, N. E., Hamlet, A. F., Hughes, M., Feld, S. I. & Lundquist, J. D. Intercomparison of meteorological forcing data from empirical and mesoscale model sources in the north fork american river basin in northern sierra Nevada, California. J. Hydrometeorol. 14, 677–699 (2013).ADS 

Google Scholar 
Revuelto, J., López-Moreno, J. I., Azorin-Molina, C. & Vicente-Serrano, S. M. Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: Intra- and inter-annual persistence. Cryosphere 8, 1989–2006 (2014).ADS 

Google Scholar 
Winkler, D. E., Butz, R. J., Germino, M. J., Reinhardt, K. & Kueppers, L. M. Snowmelt timing regulates community composition, phenology, and physiological performance of alpine plants. Front. Plant Sci. (2018).Scherrer, D. & Körner, C. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J. Biogeogr. 38, 406–416 (2011).
Google Scholar 
Billings, W. D. Arctic and alpine vegetations: Similarities, differences, and susceptibility to disturbance. Bioscience 23, 697–704 (1973).
Google Scholar 
Hua, X., Ohlemüller, R. & Sirguey, P. Differential effects of topography on the timing of the growing season in mountainous grassland ecosystems. Environ. Adv. 8, 100234 (2022).
Google Scholar 
Xie, J. et al. Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors. Sci. Total Environ. 725, 138380 (2020).ADS 
CAS 

Google Scholar 
Carlson, B. Z., Choler, P., Renaud, J., Dedieu, J.-P. & Thuiller, W. Modelling snow cover duration improves predictions of functional and taxonomic diversity for alpine plant communities. Ann. Bot. 116, 1023–1034 (2015).PubMed Central 

Google Scholar 
Beniston, M. et al. The European mountain cryosphere: A review of its current state, trends, and future challenges. Cryosphere 12, 759–794 (2018).ADS 

Google Scholar 
Stöckli, R. & Vidale, P. L. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int. J. Remote Sens. 25, 3303–3330 (2004).
Google Scholar 
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).ADS 
CAS 

Google Scholar 
Fazeli Farsani, I., Farzaneh, M. R., Besalatpour, A. A., Salehi, M. H. & Faramarzi, M. Assessment of the impact of climate change on spatiotemporal variability of blue and green water resources under CMIP3 and CMIP5 models in a highly mountainous watershed. Theor. Appl. Climatol. 136, 169–184 (2019).ADS 

Google Scholar 
Kharin, V. V., Zwiers, F. W., Zhang, X. & Wehner, M. Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim. Change 119, 345–357 (2013).ADS 

Google Scholar 
Engler, R. et al. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol. 17, 2330–2341 (2011).ADS 

Google Scholar 
Qiao, D. & Wang, N. Relationship between winter snow cover dynamics, climate and spring grassland vegetation phenology in Inner Mongolia, China. ISPRS Int. J. Geo-Inf. 8, 42 (2019).
Google Scholar 
Zong, S. et al. Upward range shift of a dominant alpine shrub related to 50 years of snow cover change. Remote Sens. Environ. 268, 112773 (2022).ADS 

Google Scholar 
Ernakovich, J. G. et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Change Biol. 20, 3256–3269 (2014).ADS 

Google Scholar 
Zheng, J., Jia, G. & Xu, X. Earlier snowmelt predominates advanced spring vegetation greenup in Alaska. Agric. For. Meteorol. 315, 108828 (2022).ADS 

Google Scholar 
Dedieu, J.-P. et al. On the importance of high-resolution time series of optical imagery for quantifying the effects of snow cover duration on alpine plant habitat. Remote Sens. 8, 481 (2016).ADS 

Google Scholar 
Virtanen, T. & Ek, M. The fragmented nature of tundra landscape. Int. J. Appl. Earth Obs. Geoinf. 27, 4–12 (2014).ADS 

Google Scholar 
Fontana, F., Rixen, C., Jonas, T., Aberegg, G. & Wunderle, S. Alpine grassland phenology as seen in AVHRR, VEGETATION, and MODIS NDVI time series—A comparison with in situ measurements. Sensors 8, 2833–2853 (2008).ADS 
PubMed Central 

Google Scholar 
Carlson, B. Z. et al. Observed long-term greening of alpine vegetation—A case study in the French Alps. Environ. Res. Lett. 12, 114006 (2017).ADS 

Google Scholar 
Tomaszewska, M. A., Nguyen, L. H. & Henebry, G. M. Land surface phenology in the highland pastures of montane Central Asia: Interactions with snow cover seasonality and terrain characteristics. Remote Sens. Environ. 240, 111675 (2020).ADS 

Google Scholar 
Rumpf, S. B. et al. From white to green: Snow cover loss and increased vegetation productivity in the European Alps. Science 376, 1119–1122 (2022).ADS 
CAS 

Google Scholar 
Myneni, R. B. & Williams, D. L. On the relationship between FAPAR and NDVI. Remote Sens. Environ. 49, 200–211 (1994).ADS 

Google Scholar 
Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).
Google Scholar 
Asam, S. et al. Relationship between spatiotemporal variations of climate, snow cover and plant phenology over the alps—An earth observation-based analysis. Remote Sens. 10, 1757 (2018).ADS 

Google Scholar 
Rossini, M. et al. Remote sensing-based estimation of gross primary production in a subalpine grassland. Biogeosciences 9, 2565–2584 (2012).ADS 

Google Scholar 
Dozier, J. Spectral signature of alpine snow cover from the landsat thematic mapper. Remote Sens. Environ. 28, 9–22 (1989).ADS 

Google Scholar 
Hall, D. K. & Riggs, G. A. Accuracy assessment of the MODIS snow products. Hydrol. Process. 21, 1534–1547 (2007).ADS 

Google Scholar 
Julitta, T. et al. Using digital camera images to analyse snowmelt and phenology of a subalpine grassland. Agric. For. Meteorol. 198–199, 116–125 (2014).ADS 

Google Scholar 
Francon, L. et al. Assessing the effects of earlier snow melt-out on alpine shrub growth: The sooner the better?. Ecol. Ind. 115, 106455 (2020).
Google Scholar 
Assmann, J. J., Myers-Smith, I. H., Kerby, J. T., Cunliffe, A. M. & Daskalova, G. N. Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites. Environ. Res. Lett. 15, 125002 (2020).ADS 
CAS 

Google Scholar 
Revuelto, J. et al. Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017. Earth Syst. Sci. Data 9, 993–1005 (2017).ADS 

Google Scholar 
Nadal Romero, E. et al. Sediment balance in four small catechumen’s with different land cover in the Central Pyrenes (Spain). (2009).Gartzia, M., Alados, C. L. & Pérez-Cabello, F. Assessment of the effects of biophysical and anthropogenic factors on woody plant encroachment in dense and sparse mountain grasslands based on remote sensing data. Progr. Phys. Geogr. Earth Environ. 38, 201–217 (2014).
Google Scholar 
Fillat, F., González, R. G., García, D. G., Gómez, D. & Reiné, R. Pastos del Pirineo. (Editorial CSIC-CSIC Press, 2008).Gómez-García, D., Ferrández, J. V., Tejero, P. & Font, X. Spatial distribution and environmental analysis of the alpine flora in the Pyrenees. Pirineos 172, e027–e027 (2017).
Google Scholar 
Gascoin, S. et al. A snow cover climatology for the Pyrenees from MODIS snow products. Hydrol. Earth Syst. Sci. 19, 2337–2351 (2015).ADS 

Google Scholar 
López-Moreno, J. I. et al. Different sensitivities of snowpacks to warming in Mediterranean climate mountain areas. Environ. Res. Lett. 12, 074006 (2017).ADS 

Google Scholar 
Cernusca, A. Standörtliche Variabilität in Mikroklima und Energiehaushalt Alpiner Zwergstrauchbestände. In Verhandlungen der Gesellschaft für Ökologie Wien 1975: 5. Jahresversammlung vom 22. bis 24. September 1975 in Wien (ed. Müller, P.) 9–21 (Springer Netherlands, 1976). https://doi.org/10.1007/978-94-015-7168-5_2.Cernusca, A. & Seeber, M. C. Canopy structure, microclimate and the energy budget in different alpine plant communities. In Symposium—British Ecological Society (1981).Kudo, G., Nordenhäll, U. & Molau, U. Effects of snowmelt timing on leaf traits, leaf production, and shoot growth of alpine plants: Comparisons along a snowmelt gradient in northern Sweden. Écoscience 6, 439–450 (1999).
Google Scholar 
Baptist, F. & Choler, P. A simulation of the importance of length of growing season and canopy functional properties on the seasonal gross primary production of temperate alpine meadows. Ann. Bot. 101, 549–559 (2008).PubMed Central 

Google Scholar 
Baptist, F., Flahaut, C., Streb, P. & Choler, P. No increase in alpine snowbed productivity in response to experimental lengthening of the growing season. Plant Biol. 12, 755–764 (2010).CAS 

Google Scholar 
Wipf, S., Rixen, C. & Mulder, C. P. H. Advanced snowmelt causes shift towards positive neighbour interactions in a subarctic tundra community. Glob. Change Biol. 12, 1496–1506 (2006).ADS 

Google Scholar 
Sierra-Almeida, A. & Cavieres, L. A. Summer freezing resistance decreased in high-elevation plants exposed to experimental warming in the central Chilean Andes. Oecologia 163, 267–276 (2010).ADS 

Google Scholar 
Camarero, J. J., Gutiérrez, E. & Fortin, M.-J. Spatial pattern of subalpine forest-alpine grassland ecotones in the Spanish Central Pyrenees. For. Ecol. Manag. 134, 1–16 (2000).
Google Scholar 
Dadic, R., Mott, R., Lehning, M. & Burlando, P. Parameterization for wind-induced preferential deposition of snow. Hydrol. Process. 24, 1994–2006 (2010).
Google Scholar 
Vionnet, V. et al. Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model. Cryosphere 8, 395–415 (2014).ADS 

Google Scholar 
Burns, S. F., Tonkin, P. J. & Thorn, C. E. Soil-geomorphic models and the spatial distribution and development of alpine soils. In Space and Time in Geomorphology: Binghamton Geomorphology Symposium, vol. 12 (2020).Lana-Renault, N. et al. Comparative analysis of the response of various land covers to an exceptional rainfall event in the central Spanish Pyrenees, October 2012. Earth Surf. Proc. Land. 39, 581–592 (2014).ADS 

Google Scholar 
Freppaz, M., Williams, B. L., Edwards, A. C., Scalenghe, R. & Zanini, E. Simulating soil freeze/thaw cycles typical of winter alpine conditions: Implications for N and P availability. Appl. Soil. Ecol. 35, 247–255 (2007).
Google Scholar 
López-Moreno, J. I. et al. Long-term trends (1958–2017) in snow cover duration and depth in the Pyrenees. Int. J. Climatol. 40, 6122–6136 (2020).
Google Scholar 
López-Moreno, J. I., Vicente-Serrano, S. M. & Lanjeri, S. Mapping snowpack distribution over large areas using GIS and interpolation techniques. Clim. Res. 33, 257–270 (2007).
Google Scholar 
Revuelto, J., López-Moreno, J. I. & Alonso-González, E. Light and shadow in mapping alpine snowpack with unmanned aerial vehicles in the absence of ground control points. Water Resour. Res. 57, e2020WR028980 (2021).ADS 

Google Scholar 
Eberhard, L. A. et al. Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping. Cryosphere 15, 69–94 (2021).ADS 

Google Scholar 
Harder, P., Schirmer, M., Pomeroy, J. & Helgason, W. Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle. Cryosphere 10, 2559–2571 (2016).ADS 

Google Scholar 
Stanton, M. L., Rejmánek, M. & Galen, C. Changes in vegetation and soil fertility along a predictable snowmelt gradient in the mosquito range, Colorado, USA. Arct. Alp. Res. 26, 364–374 (1994).
Google Scholar 
Winkler, D. E., Chapin, K. J. & Kueppers, L. M. Soil moisture mediates alpine life form and community productivity responses to warming. Ecology 97, 1553–1563 (2016).
Google Scholar 
Litaor, M. I., Williams, M. & Seastedt, T. R. Topographic controls on snow distribution, soil moisture, and species diversity of herbaceous alpine vegetation, Niwot Ridge, Colorado. J. Geophys. Res. Biogeosci. 113, (2008).Keller, F., Kienast, F. & Beniston, M. Evidence of response of vegetation to environmental change on high-elevation sites in the Swiss Alps. Reg. Environ. Change 1, 70–77 (2000).
Google Scholar 
Running, S. W. Estimating terrestrial primary productivity by combining remote sensing and ecosystem simulation. In Remote Sensing of Biosphere Functioning (eds. Hobbs, R. J. & Mooney, H. A.) 65–86 (Springer, 1990). https://doi.org/10.1007/978-1-4612-3302-2_4.Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Remote Sens. 33, 481–486 (1995).ADS 

Google Scholar 
Huang, S., Tang, L., Hupy, J. P., Wang, Y. & Shao, G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 32, 1–6 (2021).
Google Scholar 
Floyd, D. A. & Anderson, J. E. A comparison of three methods for estimating plant cover. J. Ecol. 75, 221–228 (1987).
Google Scholar 
Peet, R. K. The measurement of species diversity. Annu. Rev. Ecol. Syst. 5, 285–307 (1974).
Google Scholar 
Mouillot, D. & Leprêtre, A. A comparison of species diversity estimators. Res. Popul. Ecol. 41, 203–215 (1999).
Google Scholar  More

The success of biological control agents — organisms used to reduce the success of other, usually non-native invasive species — is complicated by ongoing climate change. Chosen for their host-specificity and introduced into new locations, biological agents can succumb to both direct and indirect climate-related stressors, compromising their biology and activity against target organisms. Adding to this is the fact that environmental stressors often occur in concert, making it hard to predict the efficacy of biological control programs. More

Lerato Shikwambana from the South African National Space Agency and the University of the Witwatersrand, South Africa, and colleagues also from South Africa compared the wildfire emissions of a strong El Niño event in 2015–2016 and a pronounced La Niña event in 2010–2011. They find that both a strong El Niño and La Niña event can increase emissions from wildfires compared with average years, but they affect different regions, with the effect of La Niña reaching farther south than El Niño. Overall, emissions are stronger during the El Niño phase, mainly driven by higher air temperatures. ENSO variability is expected to increase with future warming, which would also make strong El Niño events more likely. Therefore, these findings indicate that the exposure to wildfire air pollution could grow in southern Africa. More

Jolles, J. W., King, A. J. & Killen, S. S. The Role of Individual Heterogeneity in Collective Animal Behaviour. Trends Ecol. Evol. 35, 278–291 (2020).Article 
PubMed 

Google Scholar 
Lang, S. D. J. & Farine, D. R. A multidimensional framework for studying social predation strategies. Nat. Ecol. Evol. 1, 1230–1239 (2017).Article 
PubMed 

Google Scholar 
Kissui, B. M. & Packer, C. Top–down population regulation of a top predator: lions in the Ngorongoro Crater. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 1867–1874 (2004).Article 

Google Scholar 
Atwood, T. C. & Gese, E. M. Coyotes and recolonizing wolves: social rank mediates risk-conditional behaviour at ungulate carcasses. Anim. Behav. 75, 753–762 (2008).Article 

Google Scholar 
Pitman, R. L. & Durban, J. W. Cooperative hunting behavior, prey selectivity and prey handling by pack ice killer whales (Orcinus orca), type B, in Antarctic Peninsula waters. Mar. Mammal. Sci. 28, 16–36 (2012).Article 

Google Scholar 
Machovsky-Capuska, G. E. & Raubenheimer, D. The nutritional ecology of marine apex predators. Ann. Rev. Mar. Sci. 12, 361–387 (2020).Article 
PubMed 

Google Scholar 
Hubel, T. Y. et al. Energy cost and return for hunting in African wild dogs and cheetahs. Nat. Commun. 7, 1–13 (2016).Article 

Google Scholar 
Hubel, T. Y. et al. Additive opportunistic capture explains group hunting benefits in African wild dogs. Nat. Commun. 7, 1–11 (2016).Article 

Google Scholar 
Schaller, G. B. The Serengeti Lion.,(University of Chicago Press: Chicago, IL.). (1972).Packer, C., Pusey, A. E. & Eberly, L. E. Egalitarianism in female African lions. Science 293, 690–693 (2001).Article 
CAS 
PubMed 

Google Scholar 
Tilson, R. L. & Hamilton, W. J. III Social dominance and feeding patterns of spotted hyaenas. Anim. Behav. 32, 715–724 (1984).Article 

Google Scholar 
Frank, L. G. Social organization of the spotted hyaena Crocuta crocuta. II. Dominance and reproduction. Anim. Behav. 34, 1510–1527 (1986).Article 

Google Scholar 
Mech, L. D. & Boitani, L. Wolves: behavior, ecology, and conservation. (University of Chicago Press, 2007).Frame, L. H., Malcolm, J. R., Frame, G. W. & Van Lawick, H. Social Organization of African Wild Dog on the Serengeti Plains. Z. Tierpsychol. 50, 225–249 (1979).Article 

Google Scholar 
Bertram, B. C. R. Social factors influencing reproduction in wild lions. J. Zool. 177, 463–482 (1975).Article 

Google Scholar 
Packer, C. & Pusey, A. Asymmetric contests in social mammals: respect, manipulation and age-specific aspects. Evol. essays honour John Maynard Smith 173–186 (1985).Domenici, P., Batty, R. S., Similä, T. & Ogam, E. Killer whales (Orcinus orca) feeding on schooling herring (Clupea harengus) using underwater tail-slaps: Kinematic analyses of field observations. J. Exp. Biol. 203, 283–294 (2000).Article 
CAS 
PubMed 

Google Scholar 
Benoit-Bird, K. J. & Au, W. W. L. Cooperative prey herding by the pelagic dolphin, Stenella longirostris. J. Acoust. Soc. Am. 125, 125–137 (2009).Article 
PubMed 

Google Scholar 
Gazda, S. K. Driver-barrier feeding behavior in bottlenose dolphins (Tursiops truncatus): New insights from a longitudinal study. Mar. Mammal. Sci. 32, 1152–1160 (2016).Article 

Google Scholar 
Herbert-Read, J. E. et al. Proto-Cooperation: Group hunting sailfish improve hunting success by alternating attacks on grouping prey. Proc. R. Soc. B Biol. Sci. 283, 1–14 (2016).
Google Scholar 
Rieucau, G., Holmin, A. J., Castillo, J. C., Couzin, I. D. & Handegard, N. O. School level structural and dynamic adjustments to risk promote information transfer and collective evasion in herring. Anim. Behav. 117, 69–78 (2016).Article 

Google Scholar 
Rieucau, G., Fernö, A., Ioannou, C. C. & Handegard, N. O. Towards of a firmer explanation of large shoal formation, maintenance and collective reactions in marine fish. Rev. Fish. Biol. Fish. 25, 21–37 (2014).Article 

Google Scholar 
Ford, J. K. B. & Ellis, G. M. Transients: mammal-hunting killer whales of British Columbia, Washington, and southeastern Alaska. (UBC Press, 1999).Bailey, I., Myatt, J. P. & Wilson, A. M. Group hunting within the Carnivora: Physiological, cognitive and environmental influences on strategy and cooperation. Behav. Ecol. Sociobiol. 67, 1–17 (2013).Article 

Google Scholar 
Packer, C., Scheel, D. & Pusey, A. E. Why Lions Form Groups: Food is Not Enough. Am. Nat. 136, 1–19 (1990).Article 

Google Scholar 
Creel, S. & Creel, N. M. The African wild dog: behavior, ecology, and conservation. (Princeton University Press, 2002).Carbone, C. et al. Feeding success of African wild dogs (Lycaon pictus) in the Serengeti: The effects of group size and kleptoparasitism. J. Zool. 266, 153–161 (2005).Article 

Google Scholar 
Chakrabarti, S. & Jhala, Y. V. Selfish partners: Resource partitioning in male coalitions of Asiatic lions. Behav. Ecol. 28, 1532–1539 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Amorós, M., Gil-Sánchez, J. M., López-Pastor, B., de las, N. & Moleón, M. Hyaenas and lions: how the largest African carnivores interact at carcasses. Oikos 129, 1820–1832 (2020).Article 

Google Scholar 
Wilmers, C. C. & Stahler, D. R. Constraints on active-consumption rates in gray wolves, coyotes, and grizzly bears. Can. J. Zool. 80, 1256–1261 (2002).Article 

Google Scholar 
Schmidt, P. A. & Mech, L. D. Wolf pack size and food acquisition. Am. Nat. 150, 513–517 (1997).Article 
CAS 
PubMed 

Google Scholar 
Major, P. F. Predator-prey interactions in two schooling fishes, Caranx ignobilis and Stolephorus purpureus. Anim. Behav. 26, 760–777 (1978).Article 

Google Scholar 
Thiebault, A., Semeria, M., Lett, C. & Tremblay, Y. How to capture fish in a school? Effect of successive predator attacks on seabird feeding success. J. Anim. Ecol. 85, 157–167 (2016).Article 
PubMed 

Google Scholar 
Handegard, N. O. et al. The dynamics of coordinated group hunting and collective information transfer among schooling prey. Curr. Biol. 22, 1213–1217 (2012).Article 
CAS 
PubMed 

Google Scholar 
King, A. J., Fehlmann, G., Biro, D., Ward, A. J. & Fürtbauer, I. Re-wilding Collective Behaviour: An Ecological Perspective. Trends Ecol. Evol. 33, 347–357 (2018).Article 
PubMed 

Google Scholar 
Hansen, M. J. et al. Linking hunting weaponry to attack strategies in sailfish and striped marlin. Proc. R. Soc. B Biol. Sci. 287, 20192228 (2020).Rieucau, G. et al. Using unmanned aerial vehicle (UAV) surveys and image analysis in the study of large surface-associated marine species: a case study on reef sharks Carcharhinus melanopterus shoaling behaviour. J. Fish. Biol. 93, 119–127 (2018).Article 
PubMed 

Google Scholar 
Krause, J. The relationship between foraging and shoal position in a mixed shoal of roach (Rutilus rutilus) and chub (Leuciscus cephalus): a field study. Oecologia 93, 356–359 (1993).Article 
PubMed 

Google Scholar 
DeBlois, E. M. & Rose, G. A. Cross-shoal variability in the feeding habits of migrating Atlantic cod (Gadus morhua). Oecologia 108, 192–196 (1996).Article 
PubMed 

Google Scholar 
Hansen, M. J., Schaerf, T. M. & Ward, A. J. W. The influence of nutritional state on individual and group movement behaviour in shoals of crimson-spotted rainbowfish (Melanotaenia duboulayi). Behav. Ecol. Sociobiol. 69, 1713–1722 (2015).Article 

Google Scholar 
Hansen, M. J., Schaerf, T. M., Krause, J. & Ward, A. J. W. Crimson spotted rainbowfish (Melanotaenia duboulayi) change their spatial position according to nutritional requirement. PLoS One 11, 1–17 (2016).Article 

Google Scholar 
McLean, S., Persson, A., Norin, T. & Killen, S. S. Metabolic costs of feeding predictively alter the spatial distribution of individuals in fish schools. Curr. Biol. 28, 1144–1149 (2018).Article 
CAS 
PubMed 

Google Scholar 
Domenici, P. et al. How sailfish use their bills to capture schooling prey. Proc. R. Soc. B Biol. Sci. 281, 20140444 (2014).Kurvers, R. H. J. M. et al. The Evolution of Lateralization in Group Hunting Sailfish. Curr. Biol. 27, 521–526 (2017).Article 
CAS 
PubMed 

Google Scholar 
Ward, A. & Webster, M. Sociality: The behaviour of group-living animals. (Springer, 2016).Wiley, D. et al. Underwater components of humpback whale bubble-net feeding behaviour Published by: Brill Stable URL: http://www.jstor.org/stable/23034261 REFERENCES Linked references are available on JSTOR for this article: You may need to log in to JSTOR to access th. 148, 575–602 (2017).D’Vincent, C. G., Nilson, R. M. & Hanna, R. E. Vocalization and coordinated feeding behavior of the humpback whale Megaptera novaeangliae in Southeastern Alaska, USA. Sci. Rep. Whale Res. Inst. Tokyo 36, 41–47 (1985).
Google Scholar 
Jurasz, C. M. & Jurasz, V. P. Feeding modes of the humpback whale, Megaptera novaeangliae, in southeast Alaska. Sci. Rep. Whales Res. Inst. 31, 69–83 (1979).
Google Scholar 
Similä, T. & Ugarte, F. Surface and underwater observations of cooperatively feeding killer whales in northern Norway. Can. J. Zool. 71, 1494–1499 (1993).Article 

Google Scholar 
Clua, É. & Grosvalet, F. Mixed-species feeding aggregation of dolphins, large tunas and seabirds in the Azores. Aquat. Living Resour. 14, 11–18 (2001).Article 

Google Scholar 
Uchiyama, J. H. & Kazama, T. K. Updated weight-on-length relationships for pelagic fishes caught in the central North Pacific Ocean and bottomfishes from the northwestern Hawaiian Islands. (2003).Ponce-Díaz, G., Ortega-García, S. & González-Ramírez, P. G. Analysis of sizes and weight-length relation of the striped marlin, Tetrapturus sudax (Philippi, 1887) in Baja California Sur, Mexico. Cienc. Mar. 17, 69–82 (1991).Article 

Google Scholar 
Abitı́a-Cárdenas, L. A., Muhlia-Melo, A., Cruz-Escalona, V. & Galván-Magaña, F. Trophic dynamics and seasonal energetics of striped marlin Tetrapturus audax in the southern Gulf of California, Mexico. Fish. Res. 57, 287–295 (2002).Article 

Google Scholar 
R Core Team. (2021). R: A Language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/Hansen, Matthew Mechanisms prey Div. a Mar. group-Hunt. Predat., Dryad, Dataset https://doi.org/10.5061/dryad.b2rbnzshx (2022).Article 

Google Scholar  More

Vegetation is the main component of terrestrial ecosystems and as an indicator of ecosystem changes. In the world’s land area, forest land accounts for about 30%, grassland accounts for 26%, and cultivated land accounts for about 12%1. China has the second largest grassland area in the world. The total grassland is 392 million hectares in China, which is about 12% of the world’s grassland area and about 41% of China’s national territorial area, which is about twice of China’s arable land2. In China, the type of grassland ranks first in the world, mainly including northern grasslands, southern grassy hills and slopes, coastal beaches, wetlands, and natural grasslands in agricultural areas. It includes 18 major categories, 38 subcategories, and more than 1,000 types. The grassland resources also contain extremely rich biodiversity, with more than 7,000 pastures and thousands of animals, making it as the largest biological gene pool in Asia and also the world.
Grassland plays an important role in ecological environment protection and animal husbandry development. Like the grasslands in Europe, China’s land use forms, management objectives, and use systems are becoming increasingly diversified3. Grassland has not only made great contributions to preventing soil erosion, purifying chemical fertilizers and pesticides, regulating groundwater, and promoting biodiversity, but also as a basic nutrient for herbivores and ruminants, providing environmental benefits for ensuring the health of grassland animal products. In addition, grassland has aesthetic and entertainment functions, and it can provide functions that other agricultural land use types do not have. In addition, grassland also has an important ecological function of regulating climate4,5,6, for example, grasslands can significantly contribute to climate mitigation while providing substantial additional ecosystem services7. Grassland is the only land use type that can accomplish so many tasks and meet so many requirements.Grasslands are highly vulnerable to climate change or human activities8, the research on the relationship between grassland coverage change and its human influencing factors can reflect the scope and degree of influence of natural conditions and human activities on grassland coverage change and has a reference significance for balancing economic development and environmental protection. Grassland is not only an important material basis and means of production for the development of animal husbandry but also an important natural barrier to economic development in southeast china. Zhejiang province is located in the Yangtze River Delta, the transportation is quite convenient, the economic foundation is very well and the economy develops very rapid9. Meanwhile, with the rapid development of industrialization and urbanization, the change in land use form has been breathtaking, and human activities have improved the degree of land exploitation and utilization. The natural grassland area in Zhejiang Province is 3 million hm2, about 30% of the total land area of the province, of which the available grassland area is 600,000 hm2, for about 20% of the total area of natural grassland. Accordingly, there is enormous potential for developing the grassland industry in Zhejiang province10.There are three ways to calculate the grassland coverage, (1) field measurement method, (2) remote sensing estimation method, and (3) integrated measurement method of field measurement and remote sensing estimation11. The field measurement method is not suitable for large-scale measurement and measuring alone in various applications, because the measurement range of this method is limited, it is only suitable for the selected field plot. For remote sensing estimation method does not depend on field measurement data, and can reduce the workload and save time, so it is suitable for large-scale grass coverage estimation. At the same time, the field measurement method is an indispensable auxiliary and verification method for modern measurement methods such as remote sensing. Therefore, the comprehensive measurement method of field measurement and remote sensing can obtain more reliable data.With the rapid development of aerospace science and technology, more and more remote sensing data can be used to monitor land use form12. Currently, the most commonly used remote sensing images include Landsat MSS/TM/ETM+, NOAA/AVHRR, and EOS-MODIS. In recent years, satellite SAR, SPOT, CBERS, and other images have also been widely used in research. For global or state-scale land research, NOAA/AVHRR and MODIS data are mainly used. For regional scale, as long as Landsat TM/ETM+ and other high-resolution data are applied.The change of grassland coverage in Zhejiang Province and its effect factors are of great significance to the development of animal husbandry, the rational development and utilization of land, and the balanced development of the economy and environment. However, there are few studies have been done about this. Therefore, we present the following questions: (1) How did the grassland coverage change in Zhejiang Province from 1980 to 2018? (2) What are the main factors that affect the change in grassland coverage? This study aims to make clear grassland coverage Change and quantitative assessment of its effect factors. Meanwhile, the result of this study will provide a more comprehensive knowledge of the grassland of Zhejiang Province as well as useful suggestions for grassland resource management and sustainable development. More

Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: The hologenome theory of evolution. FEMS Microbiol. Rev. 32, 723–735 (2008).Article 
PubMed 

Google Scholar 
Archie, E. A. & Theis, K. R. Animal behaviour meets microbial ecology. Anim. Behav. 82, 425–436 (2011).Article 

Google Scholar 
McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. 110, 3229–3236 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Cresci, G. A. & Bawden, E. Gut microbiome: What we do and don’t know. Nutr. Clin. Pract. Off. Publ. Am. Soc. Parenter. Enter. Nutr. 30, 734–746 (2015).
Google Scholar 
Martin, C. R., Osadchiy, V., Kalani, A. & Mayer, E. A. The brain–gut–microbiome axis. Cell. Mol. Gastroenterol. Hepatol. 6, 133–148 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Davidson, G. L., Raulo, A. & Knowles, S. C. Identifying microbiome-mediated behaviour in wild vertebrates. Trends Ecol. Evol. 35, 972–980 (2020).Article 
PubMed 

Google Scholar 
Ushida, K., Kock, R. & Sundset, M. A. Wildlife microbiology. Microorganisms 9, 1968 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ezenwa, V. O., Gerardo, N. M., Inouye, D. W., Medina, M. & Xavier, J. B. Animal behavior and the microbiome. Science 338, 198–199 (2012).Article 
ADS 
PubMed 

Google Scholar 
Ezenwa, V. O. & Williams, A. E. Microbes and animal olfactory communication: Where do we go from here?. BioEssays 36, 847–854 (2014).Article 
PubMed 

Google Scholar 
Carthey, A. J. R., Gillings, M. R. & Blumstein, D. T. The extended genotype: Microbially mediated olfactory communication. Trends Ecol. Evol. 33, 885–894 (2018).Article 
PubMed 

Google Scholar 
Maraci, Ö., Engel, K. & Caspers, B. A. Olfactory communication via microbiota: What is known in birds?. Genes 9, 387 (2018).Article 
PubMed Central 

Google Scholar 
Hird, S. M. Evolutionary biology needs wild microbiomes. Front. Microbiol. 8, 725 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Alberdi, A., Martin Bideguren, G. & Aizpurua, O. Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: A meta-analysis. Sci. Rep. 11, 22660 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Leclaire, S., Nielsen, J. F. & Drea, C. M. Bacterial communities in meerkat anal scent secretions vary with host sex, age, and group membership. Behav. Ecol. 25, 996–1004 (2014).Article 

Google Scholar 
Theis, K. R., Schmidt, T. M. & Holekamp, K. E. Evidence for a bacterial mechanism for group-specific social odors among hyenas. Sci. Rep. 2, 615 (2012).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Theis, K. R. et al. Symbiotic bacteria appear to mediate hyena social odors. Proc. Natl. Acad. Sci. 110, 1983219837 (2013).Article 
ADS 

Google Scholar 
Gassett, J. W., Dasher, K. A., Miller, K. V., Osborn, D. A. & Russell, S. M. White-tailed deer tarsal glands: Sex and age-related variation in microbial flora. Mammalia 64, 371–377 (2000).Article 

Google Scholar 
Sin, Y. W., Buesching, C. D., Burke, T. & Macdonald, D. W. Molecular characterization of the microbial communities in the subcaudal gland secretion of the European badger (Meles meles). FEMS Microbiol. Ecol. 81, 648–659 (2012).Article 
PubMed 

Google Scholar 
Albone, E. S., Eglinton, G., Walker, J. M. & Ware, G. C. The anal sac secretion of the red fox (Vulpes vulpes); its chemistry and microbiology. A comparison with the anal sac secretion of the lion (Panthera leo). Life Sci. 14, 387–400 (1974).Article 
PubMed 

Google Scholar 
Greene, L. K. et al. The importance of scale in comparative microbiome research: New insights from the gut and glands of captive and wild lemurs. Am. J. Primatol. 81, e22974 (2019).Article 
PubMed 

Google Scholar 
Leclaire, S., Jacob, S., Greene, L. K., Dubay, G. R. & Drea, C. M. Social odours covary with bacterial community in the anal secretions of wild meerkats. Sci. Rep. 7, 3240 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Grieves, L. A., Gloor, G. B., Kelly, T. R., Bernards, M. A. & MacDougall-Shackleton, E. A. Preen gland microbiota of songbirds differ across populations but not sexes. J. Anim. Ecol. 90, 2202–2212 (2021).Article 
PubMed 

Google Scholar 
Whittaker, D. J. et al. Social environment has a primary influence on the microbial and odor profiles of a chemically signaling songbird. Front. Ecol. Evol. 4, 1–15 (2016).Article 

Google Scholar 
Grieves, L. A., Gloor, G. B., Bernards, M. A. & MacDougall-Shackleton, E. A. Preen gland microbiota covary with major histocompatibility complex genotype in a songbird. R. Soc. Open Sci. 8, 210936 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Whittaker, D. J. et al. Experimental evidence that symbiotic bacteria produce chemical cues in a songbird. J. Exp. Biol. 222, jeb202978 (2019).Article 
PubMed 

Google Scholar 
Martín-Vivaldi, M. et al. Antimicrobial chemicals in hoopoe preen secretions are produced by symbiotic bacteria. Proc. R. Soc. B Biol. Sci. 277, 123–130 (2010).Article 

Google Scholar 
Whittaker, D. J. et al. Intraspecific preen oil odor preferences in dark-eyed juncos (Junco hyemalis). Behav. Ecol. 22, 1256–1263 (2011).Article 

Google Scholar 
Grieves, L. A., Bernards, M. A. & MacDougall-Shackleton, E. A. Behavioural responses of songbirds to preen oil odour cues of sex and species. Anim. Behav. 156, 57–65 (2019).Article 

Google Scholar 
Grieves, L. A., Gloor, G. B., Bernards, M. A. & MacDougall-Shackleton, E. A. Songbirds show odour-based discrimination of similarity and diversity at the major histocompatibility complex. Anim. Behav. 158, 131–138 (2019).Article 

Google Scholar 
Pearce, D. S., Hoover, B. A., Jennings, S., Nevitt, G. A. & Docherty, K. M. Morphological and genetic factors shape the microbiome of a seabird species (Oceanodroma leucorhoa) more than environmental and social factors. Microbiome 5, 146 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Leclaire, S. et al. Plumage microbiota covaries with the major histocompatibility complex in blue petrels. Mol. Ecol. 28, 833–846 (2019).PubMed 

Google Scholar 
Bisson, I.-A., Marra, P. P., Burtt, E. H. Jr., Sikaroodi, M. & Gillevet, P. M. Variation in plumage microbiota depends on season and migration. Microb. Ecol. 58, 212 (2009).Article 
PubMed 

Google Scholar 
Kartzinel, T. R., Hsing, J. C., Musili, P. M., Brown, B. R. & Pringle, R. M. Covariation of diet and gut microbiome in African megafauna. Proc. Natl. Acad. Sci. 116, 23588–23593 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Arcese, P., Sogge, M. K., Marr, A. B. & Patten, M. A. Song sparrow (Melospiza melodia), version 2.0. In The Birds of North America (ed. Rodewald, P. G.) (Cornell Lab of Ornithology, 2002).
Google Scholar 
Breton, J. et al. Ecotoxicology inside the gut: Impact of heavy metals on the mouse microbiome. BMC Pharmacol. Toxicol. 14, 62 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Ruan, Y. et al. High doses of copper and mercury changed cecal microbiota in female mice. Biol. Trace Elem. Res. 189, 134–144 (2019).Article 
PubMed 

Google Scholar 
Lin, X. et al. Acute oral methylmercury exposure perturbs the gut microbiome and alters gut-brain axis related metabolites in rats. Ecotoxicol. Environ. Saf. 190, 110130 (2020).Article 
PubMed 

Google Scholar 
Grieves, L. A. et al. Food stress, but not experimental exposure to mercury, affects songbird preen oil composition. Ecotoxicology 29, 275–285 (2020).Article 
PubMed 

Google Scholar 
Christian, V. J., Miller, K. R. & Martindale, R. G. Food insecurity, malnutrition, and the microbiome. Curr. Nutr. Rep. 9, 356–360 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Genton, L., Cani, P. D. & Schrenzel, J. Alterations of gut barrier and gut microbiota in food restriction, food deprivation and protein-energy wasting. Clin. Nutr. 34, 341–349 (2015).Article 
PubMed 

Google Scholar 
Noguera, J. C., Aira, M., Pérez-Losada, M., Domínguez, J. & Velando, A. Glucocorticoids modulate gastrointestinal microbiome in a wild bird. R. Soc. Open Sci. 5, 171743 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Wienemann, T. et al. The bacterial microbiota in the ceca of Capercaillie (Tetrao urogallus) differs between wild and captive birds. Syst. Appl. Microbiol. 34, 542–551 (2011).Article 
PubMed 

Google Scholar 
Salgado-Flores, A., Tveit, A. T., Wright, A.-D., Pope, P. B. & Sundset, M. A. Characterization of the cecum microbiome from wild and captive rock ptarmigans indigenous to Arctic Norway. PLoS One 14, e0213503 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. T. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Thomas, R. H. et al. Use of TLC-FID and GC-MS⁄FID to examine the effects of migratory state, diet and captivity on preen wax composition in White-throated Sparrows Zonotrichia albicollis. Ibis 152, 782–792 (2010).Article 

Google Scholar 
Xie, Y. et al. Effects of captivity and artificial breeding on microbiota in feces of the red-crowned crane (Grus japonensis). Sci. Rep. 6, 33350 (2016).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
San Juan, P. A., Castro, I. & Dhami, M. K. Captivity reduces diversity and shifts composition of the Brown Kiwi microbiome. Anim. Microbiome 3, 48 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, H., Wu, F.-T., Zhou, Q.-H. & Zhao, D.-P. Comparative analysis of gut microbiota in captive and wild oriental white storks: Implications for conservation biology. Front. Microbiol. 12, 649466 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez-Ruano, S. M. et al. The hoopoe’s uropygial gland hosts a bacterial community influenced by the living conditions of the bird. PLoS One 10, e0139734 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Xenoulis, P. G. et al. Molecular characterization of the cloacal microbiota of wild and captive parrots. Vet. Microbiol. 146, 320–325 (2010).Article 
PubMed 

Google Scholar 
Kelly, T. R., Vinson, A. E., King, G. M. & Lattin, C. R. No guts about it: Captivity, but not neophobia phenotype, influences the cloacal microbiome of house sparrows (Passer domesticus). Integr. Org. Biol. 4, obac010 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Grond, K., Sandercock, B. K., Jumpponen, A. & Zeglin, L. H. The avian gut microbiota: Community, physiology and function in wild birds. J. Avian Biol. 49, e01788 (2018).Article 

Google Scholar 
Videvall, E., Strandh, M., Engelbrecht, A., Cloete, S. & Cornwallis, C. K. Measuring the gut microbiome in birds: Comparison of faecal and cloacal sampling. Mol. Ecol. Resour. 18, 424–434 (2018).Article 
PubMed 

Google Scholar 
McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Kohl, K. D., Skopec, M. M. & Dearing, M. D. Captivity results in disparate loss of gut microbial diversity in closely related hosts. Conserv. Physiol. 2, cou009 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Martínez-Mota, R., Kohl, K. D., Orr, T. J. & Denise Dearing, M. Natural diets promote retention of the native gut microbiota in captive rodents. ISME J. 14, 67–78 (2020).Article 
PubMed 

Google Scholar 
Chatelain, M., Frantz, A., Gasparini, J. & Leclaire, S. Experimental exposure to trace metals affects plumage bacterial community in the feral pigeon. J. Avian Biol. 47, 521–529 (2016).Article 

Google Scholar 
Jacob, S. et al. Uropygial gland size and composition varies according to experimentally modified microbiome in great tits. BMC Evol. Biol. 14, 134 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Jacob, J. & Ziswiler, V. The uropygial gland. In Avian Biology Vol. 6 (eds Farner, D. S. et al.) 199–324 (Academic Press, 1982).Chapter 

Google Scholar 
Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143 (2018).Article 
PubMed 

Google Scholar 
Egert, M. & Simmering, R. The microbiota of the human skin. Microbiota Hum. Body 902, 61–81 (2016).Article 

Google Scholar 
Grice, E. A. et al. A diversity profile of the human skin microbiota. Genome Res. 18, 1043–1050 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, B. et al. Molecular and biochemical characterization of a novel xylanase from Massilia sp. RBM26 isolated from the feces of Rhinopithecus bieti. J. Microbiol. Biotechnol. 26, 9–19 (2016).Article 
PubMed 

Google Scholar 
Rosenberg, E. The Prokaryotes (Springer, 2014).Book 

Google Scholar 
Tang, J., Huang, J., Qiao, Z., Wang, R. & Wang, G. Mucilaginibacter pedocola sp. Nov., isolated from a heavy-metal-contaminated paddy field. Int. J. Syst. Evol. Microbiol. 66, 4033–4038 (2016).Article 
PubMed 

Google Scholar 
Vasconcelos, A. L. et al. Mucilaginibacter sp. strain metal (loid) and antibiotic resistance isolated from estuarine soil contaminated mine tailing from the Fundão dam. Genes 13, 174 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Dewi, G. & Kollanoor Johny, A. Lactobacillus in food animal production—A forerunner for clean label prospects in animal-derived products. Front. Sustain. Food Syst. 6, 831195 (2022).Article 

Google Scholar 
Dworkin, M. The Prokaryotes Proteobacteria: Alpha and Beta Subclasses (Springer Science & Business Media, 2006).Book 

Google Scholar 
Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: A call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B 286, 20182448 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).Article 
PubMed 

Google Scholar 
Sherwin, E., Bordenstein, S. R., Quinn, J. L., Dinan, T. G. & Cryan, J. F. Microbiota and the social brain. Science 366, eaar2016 (2019).Article 
PubMed 

Google Scholar 
Bottini, C. L., MacDougall-Shackleton, S. A., Branfireun, B. A. & Hobson, K. A. Feathers accurately reflect blood mercury at time of feather growth in a songbird. Sci. Total Environ. 775, 145739 (2021).Article 
ADS 
PubMed 

Google Scholar 
Kelly, T. R., Bonner, S. J., MacDougall-Shackleton, S. A. & MacDougall-Shackleton, E. A. Exposing migratory sparrows to Plasmodium suggests costs of resistance, not necessarily of infection itself. J. Exp. Zool. Part Ecol. Integr. Physiol. 329, 5–14 (2018).Article 

Google Scholar 
Whittaker, D. J. & Hagelin, J. C. Female-based patterns and social function in avian chemical communication. J. Chem. Ecol. 47, 53–62 (2020).
Google Scholar 
Griffiths, R., Double, M. C., Orr, K. & Dawson, R. J. A DNA test to sex most birds. Mol. Ecol. 7, 1071–1075 (1998).Article 
PubMed 

Google Scholar 
Canadian Council on Animal Care (CCAC). Three Rs | Trois R :: About the Three Rs. https://3rs.ccac.ca/.Lane, D. J. et al. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Natl. Acad. Sci. 82, 6955–6959 (1985).Article 
ADS 
PubMed 
PubMed Central 

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

Google Scholar 
Gloor, G. B. et al. Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products. PLoS One 5, e15406 (2010).Article 
ADS 
PubMed 
PubMed Central 

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

Google Scholar 
Bian, G. et al. The gut microbiota of healthy aged Chinese is similar to that of the healthy young. Msphere 2, e00327-e417 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genom. Bioinform. 3, lqab019 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).Article 
PubMed 

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).Article 
PubMed 
PubMed Central 

Google Scholar 
Aitchison, J. The Statistical Analysis of Compositional Data (Chapman and Hall, 1986).Book 
MATH 

Google Scholar 
Gloor, G. B. & Reid, G. Compositional analysis: A valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692–703 (2016).Article 
PubMed 

Google Scholar 
Quinn, T. P., Erb, I., Richardson, M. F. & Crowley, T. M. Understanding sequencing data as compositions: An outlook and review. Bioinformatics 34, 2870–2878 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Palarea-Albaladejo, J. & Martin-Fernandez, J. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemom. Intell. Lab. Syst. 143, 85–96 (2015).Article 

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

Google Scholar 
Aitchison, J. & Greenacre, M. Biplots of compositional data. J. R. Stat. Soc. Ser. C Appl. Stat. 51, 375–392 (2002).Article 
MathSciNet 
MATH 

Google Scholar 
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).Article 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Dixon, P. & Palmer, M. W. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar 
Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. & Gloor, G. B. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One 8, e67019 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Nearing, J. T. et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat. Commun. 13, 342 (2022).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Halsey, L. G., Curran-Everett, D., Vowler, S. L. & Drummond, G. B. The fickle P value generates irreproducible results. Nat. Methods 12, 179–185 (2015).Article 
PubMed 

Google Scholar  More