Ecology

1.IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC (IPCC, 2014).2.Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).CAS 
PubMed 
ADS 

Google Scholar 
3.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
PubMed 
ADS 

Google Scholar 
4.Peñuelas, J. & Filella, I. Responses to a warming world. Science (80-). 294, 793–795 (2001).
Google Scholar 
5.Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: Altered species interactions are more important than direct effects. Glob. Change Biol. 20, 2221–2229 (2014).ADS 

Google Scholar 
6.Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
Google Scholar 
7.Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).CAS 
PubMed 
ADS 

Google Scholar 
8.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).
Google Scholar 
9.Vanbergen, A. J. et al. Threats to an ecosystem service: Pressures on pollinators. Front. Ecol. Environ. 11, 251–259 (2013).
Google Scholar 
10.Hung, K. L. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B Biol. Sci. 285, 20172140 (2018).
Google Scholar 
11.Watanabe, M. E. Pollination worries rise as honey bees decline. Science (80-). 265, 1170 (1994).CAS 
ADS 

Google Scholar 
12.Chauzat, M.-P. et al. Demographics of the European apicultural industry. PLoS ONE 8, e79018 (2013).PubMed 
PubMed Central 
ADS 

Google Scholar 
13.Conte, Y. L. & Navajas, M. Climate change: Impact on honey bee populations and diseases. OIE Rev. Sci. Tech. 27, 485–510 (2008).
Google Scholar 
14.Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363 (2010).
Google Scholar 
15.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. Seasonal timing in honey bee colonies: Phenology shifts affect honey stores and Varroa infestation levels. Oecologia 189, 1121–1131 (2019).PubMed 
ADS 

Google Scholar 
16.Traynor, K. S. et al. Multiyear survey targeting disease incidence in US honey bees. Apidologie https://doi.org/10.1007/s13592-016-0431-0 (2016).Article 

Google Scholar 
17.Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. U. S. A. 116, 1792–1801 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119 (2010).PubMed 

Google Scholar 
19.Switanek, M., Crailsheim, K., Truhetz, H. & Brodschneider, R. Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate. Sci. Total Environ. 579, 1581–1587 (2017).CAS 
PubMed 
ADS 

Google Scholar 
20.Genersch, E. et al. The German bee monitoring project: A long term study to understand periodically high winter losses of honey bee colonies. Apidologie 41, 332–352 (2010).CAS 

Google Scholar 
21.van Dooremalen, C. et al. Winter survival of individual honey bees and honey bee colonies depends on level of Varroa destructor infestation. PLoS One 7, e36285 (2012).PubMed 
PubMed Central 
ADS 

Google Scholar 
22.Morawetz, L. et al. Health status of honey bee colonies (Apis mellifera) and disease-related risk factors for colony losses in Austria. PLoS One 14, e0219293 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Fries, I., Imdorf, A. & Rosenkranz, P. Survival of mite infested (Varroa destructor) honey bee (Apis mellifera) colonies in a Nordic climate. Apidologie 37, 564–570 (2006).
Google Scholar 
24.Guzmán-Novoa, E. et al. Varroa destructor is the main culprit for the death and reduced populations of overwintered honey bee (Apis mellifera) colonies in Ontario, Canada. Apidologie 41, 443–450 (2010).
Google Scholar 
25.Giacobino, A. et al. Environment or beekeeping management: What explains better the prevalence of honey bee colonies with high levels of Varroa destructor?. Res. Vet. Sci. 112, 1–6 (2017).PubMed 

Google Scholar 
26.van de Pol, M. et al. Identifying the best climatic predictors in ecology and evolution. Methods Ecol. Evol. 7, 1246–1257 (2016).
Google Scholar 
27.Leza, M. M., Miranda-Chueca, M. A. & Purse, B. V. Patterns in Varroa destructor depend on bee host abundance, availability of natural resources, and climate in Mediterranean apiaries. Ecol. Entomol. 41, 542–553 (2016).
Google Scholar 
28.Dietemann, V. et al. Standard methods for Varroa research. J. Apic. Res. 52, 1–54 (2013).
Google Scholar 
29.Branco, M. R., Kidd, N. A. C. & Pickard, R. S. A comparative evaluation of sampling methods for Varroa destructor (Acari: Varroidae) population estimation. Apidologie 37, 452–461 (2006).
Google Scholar 
30.Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, D20119 (2008).ADS 

Google Scholar 
31.Bailey, L. D. & van de Pol, M. climwin: An R toolbox for climate window analysis. PLoS One 11, 1–27 (2016).
Google Scholar 
32.Hartig, F. Residual Diagnostics for Hierachical (Multi-Level/Mixed) Regression Models. (2021).33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 51 (2014).
Google Scholar 
34.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar 
35.R Core Team. R: A Language and Environment for Statistical Computing. (2021).36.Seeley, T. D. & Morse, R. A. The nest of the honey bee (Apis mellifera L.). Insectes Soc. 23, 495–512 (1976).
Google Scholar 
37.Calis, J. N. M., Fries, I. & Ryrie, S. C. Population modelling of Varroa jacobsoni Oud. Apidologie 30, 111–124 (1999).
Google Scholar 
38.Fries, I., Hansen, H., Imdorf, A. & Rosenkranz, P. Swarming in honey bees (Apis mellifera) and Varroa destructor population development in Sweden. Apidologie 34, 389–397 (2003).
Google Scholar 
39.Wilde, J., Fuchs, S., Bratkowski, J. & Siuda, M. Distribution of Varroa destructor between swarms and colonies. J. Apic. Res. 44, 190–194 (2005).
Google Scholar 
40.Loftus, J. C., Smith, M. L. & Seeley, T. D. How honey bee colonies survive in the wild: Testing the importance of small nests and frequent swarming. PLoS One 11, 1–11 (2016).
Google Scholar 
41.Moretto, G., Goncalves, L. S., De Jong, D. & Bichuette, M. Z. The effects of climate and bee race on Varroa jacobsoni Oud infestations in Brazil. Apidologie 22, 197–203 (1991).
Google Scholar 
42.Guzmán-Novoa, E., Vandame, R. & Arechavaleta, M. E. Susceptibility of European and Africanized honey bees (Apis mellifera L.) to Varroa jacobsoni Oud. in Mexico. Apidologie 30, 173–182 (1999).
Google Scholar 
43.Ruttner, F. Biogeography and Taxonomy of Honeybees (Springer, 1988). https://doi.org/10.1007/978-3-642-72649-1.Book 

Google Scholar 
44.Adam, B. Breeding the Honeybee: A Contribution to the Science of Bee Breeding (Northern Bee Books, 2013).
Google Scholar 
45.Tarpy, D. R., Hatch, S. & Fletcher, D. J. C. The influence of queen age and quality during queen replacement in honeybee colonies. Anim. Behav. 59, 97–101 (2000).CAS 
PubMed 

Google Scholar 
46.Simeunovic, P. et al. Nosema ceranae and queen age influence the reproduction and productivity of the honey bee colony. J. Apic. Res. 53, 545–554 (2014).
Google Scholar 
47.Akyol, E., Yeninar, H., Karatepe, M., Karatepe, B. & Özkök, D. Effects of queen ages on Varroa (Varroa destructor) infestation level in honey bee (Apis mellifera caucasica) colonies and colony performance. Ital. J. Anim. Sci. 6, 143–149 (2007).
Google Scholar 
48.Harris, J. W., Harbo, J. R., Villa, J. D. & Danka, R. G. Variable population growth of Varroa destructor (Mesostigmata: Varroidae) in colonies of honey bees (Hymenoptera: Apidae) during a 10-year period. Environ. Entomol. 32, 1305–1312 (2003).
Google Scholar 
49.Kruuk, L. E. B., Osmond, H. L. & Cockburn, A. Contrasting effects of climate on juvenile body size in a Southern Hemisphere passerine bird. Glob. Change Biol. 21, 2929–2941 (2015).ADS 

Google Scholar 
50.Dainat, B., Evans, J. D., Chen, Y. P., Gauthier, L. & Neumann, P. Predictive markers of honey bee colony collapse. PLoS One 7, e32151 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
51.Peck, D. T., Smith, M. L. & Seeley, T. D. Varroa destructor mites can nimbly climb from flowers onto foraging honey bees. PLoS One 11, e0167798 (2016).PubMed 
PubMed Central 

Google Scholar 
52.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS One 14, e0218392 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Seeley, T. D. & Smith, M. L. Crowding honeybee colonies in apiaries can increase their vulnerability to the deadly ectoparasite Varroa destructor. Apidologie 46, 716–727 (2015).
Google Scholar 
54.Vetharaniam, I. Predicting reproduction rate of Varroa. Ecol. Model. 224, 11–17 (2012).
Google Scholar 
55.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. The influence of temperature and photoperiod on the timing of brood onset in hibernating honey bee colonies. PeerJ 6, e4801. https://doi.org/10.7717/peerj.4801 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Seeley, T. D. & Visscher, P. K. Survival of honeybees in cold climates: The critical timing of colony growth and reproduction. Ecol. Entomol. 10, 81–88 (1985).
Google Scholar 
57.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in worker brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. https://doi.org/10.1007/BF00055033 (1994).Article 

Google Scholar 
58.Martin, S. J. Reproduction of Varroa jacobsoni in cells of Apis mellifera containing one or more mother mites and the distribution of these cells. J. Apic. Res. 34, 187–196 (1995).
Google Scholar 
59.Sparks, T. H. et al. Advances in the timing of spring cleaning by the honeybee Apis mellifera in Poland. Ecol. Entomol. 35, 788–791 (2010).
Google Scholar 
60.Langowska, A. et al. Long-term effect of temperature on honey yield and honeybee phenology. Int. J. Biometeorol. 61, 1125–1132 (2017).PubMed 
ADS 

Google Scholar 
61.Bordier, C. et al. Colony adaptive response to simulated heat waves and consequences at the individual level in honeybees (Apis mellifera). Sci. Rep. 7, 1–11 (2017).CAS 

Google Scholar 
62.Fahrenholz, L., Lamprecht, I. & Schricker, B. Thermal investigations of a honey bee colony: Thermoregulation of the hive during summer and winter and heat production of members of different bee castes. J. Comp. Physiol. B 159, 551–560 (1989).
Google Scholar 
63.Villa, J. D., Gentry, C. & Taylor, O. R. Jr. Preliminary observations on thermoregulation, clustering, and energy utilization in African and European Honey Bees. J. Kansas Entomol. Soc. 60, 4–14 (1987).
Google Scholar 
64.Anderson, D. L. & Trueman, J. W. H. Varroa jacobsoni (Acari: Varroidae) is more than one species. Exp. Appl. Acarol. 24, 165–189 (2000).CAS 
PubMed 

Google Scholar 
65.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen–Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).ADS 

Google Scholar 
66.Schmickl, T. & Crailsheim, K. Cannibalism and early capping: Strategy of honeybee colonies in times of experimental pollen shortages. J. Comp. Physiol. A Sens. Neural Behav. Physiol. 187, 541–547 (2001).CAS 

Google Scholar 
67.Requier, F., Odoux, J. F., Henry, M. & Bretagnolle, V. The carry-over effects of pollen shortage decrease the survival of honeybee colonies in farmlands. J. Appl. Ecol. 54, 1161–1170 (2017).
Google Scholar 
68.Seeley, T. D. Honeybee Ecology. A Study of Adaptation in Social Life (Princeton University Press, 1985).
Google Scholar 
69.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in drone brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. 19, 199–210 (1995).ADS 

Google Scholar 
70.Amiri, E., Strand, M. K., Rueppell, O. & Tarpy, D. R. Queen quality and the impact of honey bee diseases on queen health: Potential for interactions between two major threats to colony health. Insects 8, 48 (2017).PubMed Central 

Google Scholar 
71.Giacobino, A. et al. Risk factors associated with failures of Varroa treatments in honey bee colonies without broodless period. Apidologie 46, 573–582 (2015).
Google Scholar 
72.Locke, B. Natural Varroa mite-surviving Apis mellifera honeybee populations. Apidologie 47, 467–482 (2016).
Google Scholar 
73.FAO. Good beekeeping practices: Practical manual on how to identify and control the main diseases of the honeybee (Apis mellifera). TECA—Technologies and practices for small agricultural producers. (2020).74.Harbo, J. R. Effect of population size on brood production, worker survival and honey gain in colonies of honeybees. J. Apic. Res. 25, 22–29 (1986).
Google Scholar 
75.Döke, M. A., McGrady, C. M., Otieno, M., Grozinger, C. M. & Frazier, M. Colony size, rather than geographic origin of stocks, predicts overwintering success in honey bees (Hymenoptera: Apidae) in the Northeastern United States. J. Econ. Entomol. 112, 525–533 (2019).PubMed 

Google Scholar 
76.Martin, S. J. The role of Varroa and viral pathogens in the collapse of honeybee colonies: A modelling approach. J. Appl. Ecol. 38, 1082–1093 (2001).
Google Scholar  More

1.Knox, G. A. Biology of the Southern Ocean (CRC Press, 2006). https://doi.org/10.1201/9781420005134Book 

Google Scholar 
2.Thomas, D. N. et al. The Biology of Polar Regions: The Biology of Polar Regions (Oxford University Press, 2008).
Google Scholar 
3.Trathan, P. N. & Hill, S. L. The Importance of Krill Predation in the Southern Ocean. In Biology and Ecology of Antarctic Krill (ed. Siegel, V.) 321–350 (Springer, 2016). https://doi.org/10.1007/978-3-319-29279-3_9.Chapter 

Google Scholar 
4.Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).ADS 
CAS 

Google Scholar 
5.Siegel, V. & Watkins, J. L. Distribution, biomass and demography of antarctic krill, Euphausia superba. In Biology and Ecology of Antarctic Krill (ed. Siegel, V.) 21–100 (Springer, 2016). https://doi.org/10.1007/978-3-319-29279-3_2.Chapter 

Google Scholar 
6.Reiss, C. S. et al. Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: Implications for top predators and fishery management. Mar. Ecol. Prog. Ser. 568, 1–16 (2017).ADS 
CAS 

Google Scholar 
7.Andrews-Goff, V. et al. Humpback whale migrations to Antarctic summer foraging grounds through the southwest Pacific Ocean. Sci. Rep. 8, 12333 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Ribic, C. A., Ainley, D. G. & Fraser, W. R. Habitat selection by marine mammals in the marginal ice zone. Antarct. Sci. 3, 181–186 (1991).ADS 

Google Scholar 
9.Takahashi, A. et al. Migratory movements and winter diving activity of Adélie penguins in East Antarctica. Mar. Ecol. Prog. Ser. 589, 227–239 (2018).ADS 

Google Scholar 
10.Hückstädt, L. A. et al. Projected shifts in the foraging habitat of crabeater seals along the Antarctic Peninsula. Nat. Clim. Change 10, 472–477 (2020).ADS 

Google Scholar 
11.Lowther, A. D., Staniland, I., Lydersen, C. & Kovacs, K. M. Male Antarctic fur seals: Neglected food competitors of bioindicator species in the context of an increasing Antarctic krill fishery. Sci. Rep. 10, 18436 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Forcada, J. & Staniland, I. J. Antarctic fur seal Arctocephalus gazella. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 36–42 (Academic Press, 2009).
Google Scholar 
13.Boyd, I. L., McCafferty, D. J., Reid, K., Taylor, R. & Walker, T. R. Dispersal of male and female Antarctic fur seals (Arctocephalus gazella). Can. J. Fish. Aquat. Sci. https://doi.org/10.1139/f97-314 (1998).Article 

Google Scholar 
14.Cherel, Y., Kernaléguen, L., Richard, P. & Guinet, C. Whisker isotopic signature depicts migration patterns and multi-year intra- and inter-individual foraging strategies in fur seals. Biol. Lett. 5, 830–832 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Kernaléguen, L. et al. Long-term species, sexual and individual variations in foraging strategies of fur seals revealed by stable isotopes in whiskers. PLoS ONE 7, e32916 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
16.Kernaléguen, L., Arnould, J. P. Y., Guinet, C. & Cherel, Y. Determinants of individual foraging specialization in large marine vertebrates, the Antarctic and subantarctic fur seals. J. Anim. Ecol. 84, 1081–1091 (2015).PubMed 

Google Scholar 
17.Arthur, B. et al. Winter habitat predictions of a key Southern Ocean predator, the Antarctic fur seal (Arctocephalus gazella). Deep Sea Res. Part II Top. Stud. Oceanogr. 140, 171–181 (2017).ADS 

Google Scholar 
18.Arthur, B. et al. Managing for change: Using vertebrate at sea habitat use to direct management efforts. Ecol. Indic. 91, 338–349 (2018).
Google Scholar 
19.Reisinger, R. R. et al. Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean. Divers. Distrib. 24, 535–550 (2018).MathSciNet 

Google Scholar 
20.Wege, M., de Bruyn, P. J. N., Hindell, M. A., Lea, M.-A. & Bester, M. N. Preferred, small-scale foraging areas of two Southern Ocean fur seal species are not determined by habitat characteristics. BMC Ecol. 19, 36 (2019).PubMed 
PubMed Central 

Google Scholar 
21.Jones, K. A. et al. Intra-specific niche partitioning in antarctic fur seals, Arctocephalus gazella. Sci. Rep. 10, 3238 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Siniff, D. B., Garrott, R. A., Rotella, J. J., Fraser, W. R. & Ainley, D. G. Opinion: Projecting the effects of environmental change on Antarctic seals. Antarct. Sci. 20, 425–435 (2008).ADS 

Google Scholar 
23.Raymond, B. et al. Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking. Ecography 38, 121–129 (2015).
Google Scholar 
24.Bestley, S., Jonsen, I. D., Hindell, M. A., Harcourt, R. G. & Gales, N. J. Taking animal tracking to new depths: Synthesizing horizontal–vertical movement relationships for four marine predators. Ecology 96, 417–427 (2015).PubMed 

Google Scholar 
25.Kernaléguen, L. et al. Early-life sexual segregation: Ontogeny of isotopic niche differentiation in the Antarctic fur seal. Sci. Rep. 6, 33211 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
26.Payne, M. R. Growth in the Antarctic fur seal Arctocephalus gazella. J. Zool. 187, 1–20 (1979).
Google Scholar 
27.Costa, D., Goebel, M. E. & Sterling, J. T. Foraging energetics and diving behavior of the Antarctic fur seal, Arctocephalus gazzella at Cape Shirreff, Livingston Island. In Antarctic Ecosystems: Models for Wider Ecological Understanding (eds Davision, W. et al.) 77–84 (New Zealand Natural Science Press, 2000).
Google Scholar 
28.Staniland, I. J. et al. Geographical variation in the behaviour of a central place forager: Antarctic fur seals foraging in contrasting environments. Mar. Biol. 157, 2383–2396 (2010).
Google Scholar 
29.Blanchet, M.-A. et al. At-sea behaviour of three krill predators breeding at Bouvetøya—Antarctic fur seals, macaroni penguins and chinstrap penguins. Mar. Ecol. Prog. Ser. 477, 285–302 (2013).ADS 

Google Scholar 
30.Jeanniard-du-Dot, T., Trites, A. W., Arnould, J. P. Y. & Guinet, C. Reproductive success is energetically linked to foraging efficiency in Antarctic fur seals. PLoS ONE 12, e0174001 (2017).PubMed 
PubMed Central 

Google Scholar 
31.Favilla, A. B. & Costa, D. P. Thermoregulatory strategies of diving air-breathing marine vertebrates: A review. Front. Ecol. Evol. 8, 292 (2020).
Google Scholar 
32.Staniland, I. J. & Robinson, S. L. Segregation between the sexes: Antarctic fur seals, Arctocephalus gazella, foraging at South Georgia. Anim. Behav. 75, 1581–1590 (2008).
Google Scholar 
33.Reid, K. The diet of Antarctic fur seals (Arctocephalus gazella Peters 1875) during winter at South Georgia. Antarct. Sci. 7, 241–249 (1995).ADS 

Google Scholar 
34.Kirkman, S. P., Wilson, W., Klages, N. T. W., Bester, M. N. & Isaksen, K. Diet and estimated food consumption of Antarctic fur seals at Bouvetøya during summer. Polar Biol. 23, 745–752 (2000).
Google Scholar 
35.Casaux, R., Baroni, A., Arrighetti, F., Ramón, A. & Carlini, A. Geographical variation in the diet of the Antarctic fur seal Arctocephalus gazella. Polar Biol. 26, 753–758 (2003).
Google Scholar 
36.Casaux, R., Baroni, A. & Ramón, A. Diet of Antarctic fur seals Arctocephalus gazella at the Danco Coast, Antarctic Peninsula. Polar Biol. 26, 49–54 (2003).
Google Scholar 
37.Davis, D., Staniland, I. J. & Reid, K. Spatial and temporal variability in the fish diet of Antarctic fur seal (Arctocephalus gazella) in the Atlantic sector of the Southern Ocean. Can. J. Zool. https://doi.org/10.1139/z06-071 (2006).Article 

Google Scholar 
38.Casaux, R., Juares, M., Carlini, A. & Corbalán, A. The diet of the Antarctic fur seal Arctocephalus gazella at the South Orkney Islands in ten consecutive years. Polar Biol. 39, 1197–1206 (2016).
Google Scholar 
39.Tarroux, A., Lowther, A. D., Lydersen, C. & Kovacs, K. M. Temporal shift in the isotopic niche of female Antarctic fur seals from Bouvetøya. Polar Res. 35, 31335 (2016).
Google Scholar 
40.Garcia-Garin, O. et al. No evidence of microplastics in Antarctic fur seal scats from a hotspot of human activity in Western Antarctica. Sci. Total Environ. 737, 140210 (2020).ADS 
CAS 
PubMed 

Google Scholar 
41.Boyd, I. L. Estimating food consumption of marine predators: Antarctic fur seals and macaroni penguins. J. Appl. Ecol. 39, 103–119 (2002).
Google Scholar 
42.Wilson, D. E. & Mittermeier, R. A. Handbook of the mammals of the world : vol. 4 : Sea mammals. (2014).43.Melin, S. R. et al. Reversible immobilization of free-ranging adult male California sea lions (Zalophus californianus). Mar. Mammal Sci. 29, E529–E536 (2013).
Google Scholar 
44.Pussini, N. & Goebel, M. E. A safer protocol for field immobilization of leopard seals (Hydrurga leptonyx). Mar. Mammal Sci. 31, 1549–1558 (2015).
Google Scholar 
45.Spelman, L. H. Reversible anesthesia of captive California sea lions (Zalophus californianus) with medetomidine, midazolam, butorphanol, and isoflurane. J. Zoo Wildl. Med. Off. Publ. Am. Assoc. Zoo Vet. 35, 65–69 (2004).
Google Scholar 
46.Cook, T. A. Butorphanol tartrate: An intravenous analgesic for outpatient surgery. Otolaryngol. Head Neck Surg. J. Am. Acad. Otolaryngol. Head Nexk Surg. 91, 251–254 (1983).CAS 

Google Scholar 
47.Ropert-Coudert, Y. et al. The retrospective analysis of Antarctic tracking data project. Sci. Data 7, 94 (2020).PubMed 
PubMed Central 

Google Scholar 
48.Freitas, C., Lydersen, C., Fedak, M. A. & Kovacs, K. M. A simple new algorithm to filter marine mammal Argos locations. Mar. Mammal Sci. 24, 315–325 (2008).
Google Scholar 
49.Bonadonna, F., Lea, M.-A., Dehorter, O. & Guinet, C. Foraging ground fidelity and route-choice tactics of a marine predator: The Antarctic fur seal Arctocephalus gazella. Mar. Ecol. Prog. Ser. 223, 287–297 (2001).ADS 

Google Scholar 
50.Lea, M.-A. & Dubroca, L. Fine-scale linkages between the diving behaviour of Antarctic fur seals and oceanographic features in the southern Indian Ocean. ICES J. Mar. Sci. 60, 990–1002 (2003).
Google Scholar 
51.Jonsen, I. D. et al. Movement responses to environment: Fast inference of variation among southern elephant seals with a mixed effects model. Ecology 100, e02566 (2019).CAS 
PubMed 

Google Scholar 
52.Jonsen, I. D. et al. A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags. Mov. Ecol. 8, 31 (2020).PubMed 
PubMed Central 

Google Scholar 
53.Hazen, E. L. et al. Where did they not go? Considerations for generating pseudo-absences for telemetry-based habitat models. Mov. Ecol. 9, 5 (2021).PubMed 
PubMed Central 

Google Scholar 
54.O’Toole, M., Queiroz, N., Humphries, N. E., Sims, D. W. & Sequeira, A. M. M. Quantifying effects of tracking data bias on species distribution models. Methods Ecol. Evol. 12, 170–181 (2021).
Google Scholar 
55.Lee, J. F., Friedlaender, A. S., Oliver, M. J. & DeLiberty, T. L. Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula. Anim. Biotelemetry 5, 23 (2017).
Google Scholar 
56.Labrousse, S. et al. Under the sea ice: Exploring the relationship between sea ice and the foraging behaviour of southern elephant seals in East Antarctica. Prog. Oceanogr. 156, 17–40 (2017).ADS 

Google Scholar 
57.Hazen, E. L. et al. A dynamic ocean management tool to reduce bycatch and support sustainable fisheries. Sci. Adv. 4, eaar3001 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
58.Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).ADS 
CAS 
PubMed 

Google Scholar 
59.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).
Google Scholar 
60.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Google Scholar 
61.Hijmans, R. J., Phillips, S. & Elith, J. L. dismo: Species Distribution Modeling. (2020).62.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
PubMed 

Google Scholar 
63.Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).
Google Scholar 
64.Scales, K. L. et al. Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. Ecol. Appl. 27, 2313–2329 (2017).PubMed 

Google Scholar 
65.Pya, N. & Wood, S. N. Shape constrained additive models. Stat. Comput. 25, 543–559 (2015).MathSciNet 
MATH 

Google Scholar 
66.R Core Team. R: A Language and Environment for Statistical Computing. (2019).67.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).ADS 

Google Scholar 
68.Brodie, S. et al. Integrating dynamic subsurface habitat metrics into species distribution models. Front. Mar. Sci. (2018).69.Becker, E. A. et al. Moving Towards dynamic ocean management: How well do modeled ocean products predict species distributions?. Remote Sens. 8, 149 (2016).ADS 

Google Scholar 
70.Lellouche, J.-M. et al. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system. Ocean Sci. 14, 1093–1126 (2018).ADS 

Google Scholar 
71.Handcock, M. S. & Raphael, M. N. Modeling the annual cycle of daily Antarctic sea ice extent. Cryosphere 14, 2159–2172 (2020).ADS 

Google Scholar 
72.Smith, G. C. et al. Polar ocean observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Front. Mar. Sci. (2019).73.March, D., Boehme, L., Tintoré, J., Vélez-Belchi, P. J. & Godley, B. J. Towards the integration of animal-borne instruments into global ocean observing systems. Glob. Change Biol. 26, 586–596 (2020).ADS 

Google Scholar 
74.Santora, J. A. Dynamic intra-seasonal habitat use by Antarctic fur seals suggests migratory hotspots near the Antarctic Peninsula. Mar. Biol. 160, 1383–1393 (2013).
Google Scholar 
75.Vergani, D. F. & Coria, N. R. Increase in numbers of male fur seals Arctocephalus gazella during the summer autumn period at Mossman Peninsula (Laurie Island). Polar Biol. 9, 487–488 (1989).
Google Scholar 
76.Rutishauser, M. R., Costa, D. P., Goebel, M. E. & Williams, T. M. Ecological implications of body composition and thermal capabilities in young antarctic fur seals (Arctocephalus gazella). Physiol. Biochem. Zool. PBZ 77, 669–681 (2004).PubMed 

Google Scholar 
77.Vales, D. G., Cardona, L., García, N. A., Zenteno, L. & Crespo, E. A. Ontogenetic dietary changes in male South American fur seals Arctocephalus australis in Patagonia. Mar. Ecol. Prog. Ser. 525, 245–260 (2015).ADS 
CAS 

Google Scholar 
78.Cardona, L., Vales, D., Aguilar, A., Crespo, E. & Zenteno, L. Temporal variability in stable isotope ratios of C and N in the vibrissa of captive and wild adult South American sea lions Otaria byronia: More than just diet shifts. Mar. Mammal Sci. 33, 975–990 (2017).CAS 

Google Scholar 
79.Costa, D. P., Gales, N. J. & Goebel, M. E. Aerobic dive limit: How often does it occur in nature?. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 129, 771–783 (2001).CAS 
PubMed 

Google Scholar 
80.Biuw, M., Krafft, B. A., Hofmeyr, G. J. G., Lydersen, C. & Kovacs, K. M. Time budgets and at-sea behaviour of lactating female Antarctic fur seals Arctocephalus gazella at Bouvetøya. Mar. Ecol. Prog. Ser. 385, 271–284 (2009).ADS 

Google Scholar 
81.Lascara, C. M., Hofmann, E. E., Ross, R. M. & Quetin, L. B. Seasonal variability in the distribution of Antarctic krill, Euphausia superba, west of the Antarctic Peninsula. Deep Sea Res. Part Oceanogr. Res. Pap. 46, 951–984 (1999).ADS 

Google Scholar 
82.Lea, M.-A., Hindell, M., Guinet, C. & Goldsworthy, S. Variability in the diving activity of Antarctic fur seals, Arctocephalus gazella, at Iles Kerguelen. Polar Biol. 25, 269–279 (2002).
Google Scholar 
83.Vaughan, D. G. et al. Recent rapid regional climate warming on the antarctic peninsula. Clim. Change 60, 243–274 (2003).
Google Scholar 
84.Forcada, J., Trathan, P. N., Reid, K. & Murphy, E. J. The effects of global climate variability in pup production of antarctic fur seals. Ecology 86, 2408–2417 (2005).
Google Scholar 
85.Forcada, J. & Hoffman, J. I. Climate change selects for heterozygosity in a declining fur seal population. Nature 511, 462–465 (2014).ADS 
CAS 
PubMed 

Google Scholar 
86.Schwarz, L. K., Goebel, M. E., Costa, D. P. & Kilpatrick, A. M. Top-down and bottom-up influences on demographic rates of Antarctic fur seals Arctocephalus gazella. J. Anim. Ecol. 82, 903–911 (2013).PubMed 

Google Scholar 
87.Hoffman, J. I. & Forcada, J. Extreme natal philopatry in female Antarctic fur seals (Arctocephalus gazella). Mamm. Biol. 77, 71–73 (2012).
Google Scholar 
88.Hucke-Gaete, R., Osman, L. P., Moreno, C. A. & Torres, D. Examining natural population growth from near extinction: The case of the Antarctic fur seal at the South Shetlands, Antarctica. Polar Biol. 27, 304–311 (2004).
Google Scholar  More

1.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 

Google Scholar 
2.Riggio, J. et al. The size of savannah Africa: A lion’s (Panthera leo) view. Biodivers. Conserv. 22, 17–35 (2013).
Google Scholar 
3.Carbone, C. & Gittleman, J. L. A common rule for the scaling of carnivore density. Science 295, 2273–2276 (2002).ADS 
CAS 
PubMed 

Google Scholar 
4.Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).ADS 
CAS 
PubMed 

Google Scholar 
5.Palomares, F. & Caro, T. M. Interspecific killing among mammalian carnivores. Am. Nat. 153, 492–508 (1999).CAS 
PubMed 

Google Scholar 
6.Tanner, E. et al. Wolves contribute to disease control in a multi-host system. Sci. Rep. 9, 1–12 (2019).ADS 

Google Scholar 
7.O’Bryan, C. J. et al. The contribution of predators and scavengers to human well-being. Nat. Ecol. Evol. 2, 229–236 (2018).PubMed 

Google Scholar 
8.Prugh, L. R. & Sivy, K. J. Enemies with benefits: integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. 23, 902–918 (2020).PubMed 

Google Scholar 
9.Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).ADS 
CAS 
PubMed 

Google Scholar 
10.Wilfred, P. Towards sustainable wildlife management areas in Tanzania. Trop. Conserv. Sci. 3, 103–116 (2010).
Google Scholar 
11.Sinclair, A. R., Metzger, K. L., Mduma, S. A. & Fryxell, J. M. Serengeti IV: Sustaining Biodiversity in a Coupled Human-Natural System (University of Chicago Press, 2015).
Google Scholar 
12.Crooks, K. R. & Sanjayan, M. Connectivity Conservation Vol. 14 (Cambridge University Press, 2006).
Google Scholar 
13.Balme, G. A., Slotow, R. & Hunter, L. T. Edge effects and the impact of non-protected areas in carnivore conservation: Leopards in the Phinda-Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).
Google Scholar 
14.Lindsey, P. et al. The performance of African protected areas for lions and their prey. Biol. Conserv. 209, 137–149 (2017).
Google Scholar 
15.Elliot, N. B. & Gopalaswamy, A. M. Toward accurate and precise estimates of lion density. Conserv. Biol. 31, 934–943 (2017).PubMed 

Google Scholar 
16.Masenga, E. et al. Strychnine poisoning in African wild dogs (Lycaon pictus) in the Loliondo game controlled area, Tanzania. Int. J. Biodivers. Conserv. 5, 367–370 (2013).
Google Scholar 
17.Metzger, K., Sinclair, A., Hilborn, R., Hopcraft, J. G. C. & Mduma, S. A. Evaluating the protection of wildlife in parks: The case of African buffalo in Serengeti. Biodivers. Conserv. 19, 3431–3444 (2010).
Google Scholar 
18.Mogensen, N. L., Ogutu, J. O. & Dabelsteen, T. The effects of pastoralism and protection on lion behaviour, demography and space use in the Mara Region of Kenya. Afr. Zool. 46, 78–87 (2011).
Google Scholar 
19.Kiffner, C., Meyer, B., Mühlenberg, M. & Waltert, M. Plenty of prey, few predators: what limits lions Panthera leo in Katavi National Park, western Tanzania?. Oryx 43, 52–59 (2009).
Google Scholar 
20.Kiffner, C., Stoner, C. & Caro, T. Edge effects and large mammal distributions in a national park. Anim. Conserv. 16, 97–107 (2013).
Google Scholar 
21.Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).
Google Scholar 
22.Hofer, H. & East, M. Population dynamics, population size, and the commuting system of Serengeti spotted hyenas. Serengeti II Dyn. Manag. Conserv. Ecosyst. 2, 332 (1995).
Google Scholar 
23.Holekamp, K. E. & Dloniak, S. M. Intraspecific variation in the behavioral ecology of a tropical carnivore, the spotted hyena. Adv. Study Behav. 42, 189–229 (2010).
Google Scholar 
24.Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. biol. 16, 488–502 (2002).
Google Scholar 
25.Martin, J. et al. Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach. Methods Ecol. Evol. 2, 595–601 (2011).ADS 

Google Scholar 
26.Prins, H. H., Grootenhuis, J. G. & Dolan, T. T. Wildlife Conservation by Sustainable Use Vol. 12 (Springer Science & Business Media, 2012).
Google Scholar 
27.Knapp, E. J. Why poaching pays: a summary of risks and benefits illegal hunters face in Western Serengeti, Tanzania. Trop. Conserv. Sci. 5, 434–445 (2012).ADS 

Google Scholar 
28.Revilla, E., Palomares, F. & Delibes, M. Edge-core effects and the effectiveness of traditional reserves in conservation: Eurasian badgers in Doñana National Park. Conserv. Biol. 15, 148–158 (2001).
Google Scholar 
29.Lindsey, P. A. et al. The bushmeat trade in African savannas: Impacts, drivers, and possible solutions. Biol. Conserv. 160, 80–96 (2013).
Google Scholar 
30.Ikanda, D. & Packer, C. Ritual vs. retaliatory killing of African lions in the Ngorongoro Conservation Area, Tanzania. Endanger. Species Res. 6, 67–74 (2008).
Google Scholar 
31.Belant, J. L. et al. Estimating lion abundance using N-mixture models for social species. Sci. Rep. 6, 1–9 (2016).
Google Scholar 
32.Hofer, H. & East, M. L. The commuting system of Serengeti spotted hyaenas: how a predator copes with migratory prey I. Social organization. Anim. Behav. 46, 547–557 (1993).
Google Scholar 
33.Durant, S. M. et al. Long-term trends in carnivore abundance using distance sampling in Serengeti National Park, Tanzania. J. Appl. Ecol. 48, 1490–1500 (2011).
Google Scholar 
34.Swanson, A. B. Living with Lions: Spatiotemporal Aspects of Coexistence in Savanna Carnivores (University of Minnesota, 2014).
Google Scholar 
35.Masenga, E. H., Lyamuya, R. D., Mjingo, E. E., Fyumagwa, R. D. & Røskaft, E. Communal knowledge and perceptions of African wild dog (Lycaon pictus) reintroduction in the western part of Serengeti National Park, Tanzania. Int. J. Biodivers. Conserv. 9, 122–129 (2017).
Google Scholar 
36.Hopcraft, J. G. C., Sinclair, A. & Packer, C. Planning for success: Serengeti lions seek prey accessibility rather than abundance. J. Anim. Ecol. 74, 559–566 (2005).
Google Scholar 
37.Packer, C. & Pusey, A. E. Adaptations of female lions to infanticide by incoming males. Am. Nat. 121, 716–728 (1983).
Google Scholar 
38.Kruuk, H. & Turner, M. Comparative notes on predation by lion, leopard, cheetah and wild dog in the Serengeti area, East Africa. Mammalia 31, 1–27 (1967).
Google Scholar 
39.Green, D. S., Johnson-Ulrich, L., Couraud, H. E. & Holekamp, K. E. Anthropogenic disturbance induces opposing population trends in spotted hyenas and African lions. Biodiver. Conserv. 27, 871–889. https://doi.org/10.1007/s10531-017-1469-7 (2018).Article 

Google Scholar 
40.Kolowski, J. M., Katan, D., Theis, K. R. & Holekamp, K. E. Daily patterns of activity in the spotted hyena. J. Mamm. 88, 1017–1028 (2007).
Google Scholar 
41.Šálek, M., Kreisinger, J., Sedláček, F. & Albrecht, T. Do prey densities determine preferences of mammalian predators for habitat edges in an agricultural landscape?. Landsc. Urban Plan. 98, 86–91 (2010).
Google Scholar 
42.Mosser, A., Fryxell, J. M., Eberly, L. & Packer, C. Serengeti real estate: density vs. fitness-based indicators of lion habitat quality. Ecol. Lett. 12, 1050–1060 (2009).PubMed 

Google Scholar 
43.Schmitt, J. A. Improving Conservation Efforts in the Serengeti Ecosystem, Tanzania: An Examination of Knowledge, Benefits, Costs, and Attitudes (University of Minnesota, 2010).
Google Scholar 
44.Makacha, S., Msingwa, M. J. & Frame, G. W. Threats to the Serengeti herds. Oryx 16, 437–444 (1982).
Google Scholar 
45.Crosmary, W.-G. et al. Lion densities in selous game reserve, Tanzania. Afr. J. Wildl. Res. 48, 1–6 (2018).
Google Scholar 
46.Belant, J. L. et al. Track surveys do not provide accurate or precise lion density estimates in serengeti. Glob. Ecol. 19, e00651 (2019).
Google Scholar 
47.Midlane, N., O’Riain, M. J., Balme, G. A. & Hunter, L. T. B. To track or to call: comparing methods for estimating population abundance of African lions Panthera leo in Kafue National Park. Biodiver. Conserv. 24, 1311–1327. https://doi.org/10.1007/s10531-015-0858-z (2015).Article 

Google Scholar 
48.Ogutu, J. O. & Dublin, H. T. The response of lions and spotted hyaenas to sound playbacks as a technique for estimating population size. Afr. J. Ecol. 36, 83–95. https://doi.org/10.1046/j.1365-2028.1998.113-89113.x (1998).Article 

Google Scholar 
49.Belant, J. L. et al. Temporal and spatial variation of broadcasted vocalizations does not reduce lion Panthera leo habituation. Wildl. Biol. wlb. 00287 (2017).50.Cozzi, G., Broekhuis, F., McNutt, J. & Schmid, B. Density and habitat use of lions and spotted hyenas in northern Botswana and the influence of survey and ecological variables on call-in survey estimation. Biodiver. Conserv. 22, 2937–2956 (2013).
Google Scholar 
51.M’soka, J., Creel, S., Becker, M. S. & Droge, E. Spotted hyaena survival and density in a lion depleted ecosystem: The effects of prey availability, humans and competition between large carnivores in African savannahs. Biol. Conserv. 201, 348–355 (2016).
Google Scholar 
52.Croes, B. et al. The impact of trophy hunting on lions (Panthera leo) and other large carnivores in the Bénoué Complex, northern Cameroon. Biol. Conserv. 144, 3064–3072 (2011).
Google Scholar 
53.Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).ADS 
CAS 
PubMed 

Google Scholar 
54.National Bureau of Statistics. Tanzania in Figures 2012 (The United Republic of Tanzania, 2013).55.McNaughton, S. Serengeti grassland ecology: The role of composite environmental factors and contingency in community organization. Ecol. Monograph. 53, 291–320 (1983).
Google Scholar 
56.Reed, D., Anderson, T., Dempewolf, J., Metzger, K. & Serneels, S. The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics. J. Biogeogr. 36, 770–782 (2009).
Google Scholar 
57.Sollmann, R., Gardner, B., Belant, J. L., Wilton, C. M. & Beringer, J. Habitat associations in a recolonizing, low‐density black bear population. Ecosphere 7 (2016).58.Royle, J. A. & Dorazio, R. M. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities (Elsevier, 2008).
Google Scholar 
59.Chandler, R. B., Royle, J. A. & King, D. I. Inference about density and temporary emigration in unmarked populations. Ecology 92, 1429–1435 (2011).PubMed 

Google Scholar 
60.Royle, J. A. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108–115 (2004).MathSciNet 
PubMed 
MATH 

Google Scholar 
61.Kellner, K. & Meredith, M. Package ‘jagsUI’. (2021).62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).63.Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).MathSciNet 
MATH 

Google Scholar 
64.Kuo, L. & Mallick, B. Variable selection for regression models. Indian J. Stat. 65–81 (1998).65.Congdon, P. Bayesian Models for Categorical Data (John Wiley and Sons, 2005).MATH 

Google Scholar  More

1.Mirzabaev, A., Sacande, M., Motlagh, F., Shyrokaya, A. & Martucci, A. Nat. Sustain. https://doi.org/10.1038/s41893-021-00801-8 (2021).2.Barbier, E. B. & Hochard, J. P. Nat. Sustain. 1, 623–631 (2018).Article 

Google Scholar 
3.Deininger, K. & Jin, S. Eur. Econ. Rev. 50, 1245–1277 (2006).Article 

Google Scholar 
4.Barbier, E. B. Environ. Dev. Econ. 15, 635–660 (2010).Article 

Google Scholar 
5.Nkonya, E., Mirzabaev, A. & von Braun, J. in Economics of Land Degradation and Improvement—A Global Assessment for Sustainable Development (eds. Nkonya, E., Mirzabaev, A. & von Braun, J.) 1–14 (Springer, Cham, 2016).6.Barbier, E. B. & Hochard, J. P. Rev. Environ. Econ. Policy 12, 26–47 (2018).Article 

Google Scholar  More

1.IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (in the press).2.Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Meltofte, H. (ed.) Arctic Biodiversity Assessment: Status and Trends in Arctic Biodiversity (CAFF International Secretariat, 2013).4.Wassmann, P. & Reigstad, M. Future Arctic Ocean seasonal ice zones and implications for pelagic-benthic coupling. Oceanography 24, 220–231 (2011).
Google Scholar 
5.Bunse, C. & Pinhassi, J. Marine bacterioplankton seasonal succession dynamics. Trends Microbiol. 25, 494–505 (2017).CAS 
PubMed 

Google Scholar 
6.Olli, K. et al. Seasonal variation in vertical flux of biogenic matter in the marginal ice zone and the central Barents Sea. J. Mar. Syst. 38, 189–204 (2002).
Google Scholar 
7.Riedel, A., Michel, C., Gosselin, M. & LeBlanc, B. Winter–spring dynamics in sea-ice carbon cycling in the coastal Arctic Ocean. J. Mar. Syst. 74, 918–932 (2008).
Google Scholar 
8.Joli, N., Monier, A., Logares, R. & Lovejoy, C. Seasonal patterns in Arctic prasinophytes and inferred ecology of Bathycoccus unveiled in an Arctic winter metagenome. ISME J. 11, 1372–1385 (2017).PubMed 
PubMed Central 

Google Scholar 
9.Alonso-Sáez, L., Sánchez, O., Gasol, J. M., Balagué, V. & Pedrós-Alio, C. Winter-to-summer changes in the composition and single-cell activity of near-surface Arctic prokaryotes. Environ. Microbiol. 10, 2444–2454 (2008).PubMed 

Google Scholar 
10.Alonso-Sáez, L. et al. Role for urea in nitrification by polar marine Archaea. Proc. Natl Acad. Sci. USA 109, 17989–17994 (2012).PubMed 
PubMed Central 

Google Scholar 
11.Boetius, A., Anesio, A. M., Deming, J. W., Mikucki, J. A. & Rapp, J. Z. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat. Rev. Microbiol. 13, 677–690 (2015).CAS 
PubMed 

Google Scholar 
12.Circumpolar Biodiversity Monitoring Program, Conservation of Arctic Flora and Fauna. State of the Arctic Marine Biodiversity Report (Conservation of Arctic Flora and Fauna International Secretariat, 2017).13.Kirchman, D. L., Cottrell, M. T. & Lovejoy, C. The structure of bacterial communities in the western Arctic Ocean as revealed by pyrosequencing of 16S rRNA genes. Environ. Microbiol. 12, 1132–1143 (2010).CAS 
PubMed 

Google Scholar 
14.Galand, P. E., Casamayor, E. O., Kirchman, D. L., Potvin, M. & Lovejoy, C. Unique archaeal assemblages in the Arctic Ocean unveiled by massively parallel tag sequencing. ISME J. 3, 860–869 (2009).CAS 
PubMed 

Google Scholar 
15.Pedrós-Alió, C., Potvin, M. & Lovejoy, C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog. Oceanogr. 139, 233–243 (2015).
Google Scholar 
16.Amaral-Zettler, L. et al. in Life in the World’s Oceans: Diversity, Distribution, and Abundance (ed. McIntyre, A. D.) 221–245 (Blackwell Publishing Ltd, 2010).17.Christman, G. D., Cottrell, M. T., Popp, B. N., Gier, E. & Kirchman, D. L. Abundance, diversity, and activity of ammonia-oxidizing prokaryotes in the coastal Arctic Ocean in summer and winter. Appl. Environ. Microbiol. 77, 2026–2034 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Alonso-Sáez, L., Galand, P. E., Casamayor, E. O., Pedrós-Alió, C. & Bertilsson, S. High bicarbonate assimilation in the dark by Arctic bacteria. ISME J. 4, 1581–1590 (2010).PubMed 

Google Scholar 
19.Galand, P. E., Lovejoy, C., Pouliot, J., Garneau, M.-È. & Vincent, W. F. Microbial community diversity and heterotrophic production in a coastal Arctic ecosystem: a stamukhi lake and its source waters. Limnol. Oceanogr. 53, 813–823 (2008).
Google Scholar 
20.Nguyen, D. et al. Winter diversity and expression of proteorhodopsin genes in a polar ocean. ISME J. 9, 1835–1845 (2015).PubMed 
PubMed Central 

Google Scholar 
21.Cifuentes-Anticevic, J. et al. Proteorhodopsin phototrophy in Antarctic coastal waters. mSphere 6, e00525–21 (2021).CAS 
PubMed Central 

Google Scholar 
22.Ghiglione, J.-F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl Acad. Sci. USA 109, 17633–17638 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083.e21 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Kraemer, S., Ramachandran, A., Colatriano, D., Lovejoy, C. & Walsh, D. A. Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J. 14, 79–90 (2020).PubMed 

Google Scholar 
25.Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome 8, 47 (2020).PubMed 
PubMed Central 

Google Scholar 
26.Sunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. 18, 428–445 (2020).CAS 
PubMed 

Google Scholar 
27.Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).CAS 
PubMed 

Google Scholar 
29.Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Aagaard, K., Swift, J. H. & Carmack, E. C. Thermohaline circulation in the Arctic Mediterranean Seas. J. Geophys. Res. Oceans 90, 4833–4846 (1985).
Google Scholar 
32.Dupont, C. L. et al. Genomes and gene expression across light and productivity gradients in eastern subtropical Pacific microbial communities. ISME J. 9, 1076–1092 (2015).CAS 
PubMed 

Google Scholar 
33.Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Jones, S. E. & Lennon, J. T. Dormancy contributes to the maintenance of microbial diversity. Proc. Natl Acad. Sci. USA 107, 5881–5886 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Mestre, M. & Höfer, J. The microbial conveyor belt: connecting the globe through dispersion and dormancy. Trends Microbiol. 29, 482–492 (2021).CAS 
PubMed 

Google Scholar 
36.Ciufo, S. et al. Using average nucleotide identity to improve taxonomic assignments in prokaryotic genomes at the NCBI. Int. J. Syst. Evol. Microbiol. 68, 2386–2392 (2018).PubMed 
PubMed Central 

Google Scholar 
37.Chaumeil, P-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

Google Scholar 
38.Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).PubMed 
PubMed Central 

Google Scholar 
39.Alneberg, J. et al. Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun. Biol. 3, 119 (2020).PubMed 
PubMed Central 

Google Scholar 
40.Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Christensen, M. & Nilsson, A. E. Arctic sea ice and the communication of climate change. Pop. Commun. 15, 249–268 (2017).
Google Scholar 
42.Jaffe, A. L., Castelle, C. J., Dupont, C. L. & Banfield, J. F. Lateral gene transfer shapes the distribution of RuBisCO among candidate phyla radiation bacteria and DPANN Archaea. Mol. Biol. Evol. 36, 435–446 (2019).CAS 
PubMed 

Google Scholar 
43.Kono, T. et al. A RuBisCO-mediated carbon metabolic pathway in methanogenic archaea. Nat. Commun. 8, 14007 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Sato, T., Atomi, H. & Imanaka, T. Archaeal type III RuBisCOs function in a pathway for AMP metabolism. Science 315, 1003–1006 (2007).CAS 
PubMed 

Google Scholar 
45.Tabita, F. R., Satagopan, S., Hanson, T. E., Kreel, N. E. & Scott, S. S. Distinct form I, II, III, and IV Rubisco proteins from the three kingdoms of life provide clues about Rubisco evolution and structure/function relationships. J. Exp. Bot. 59, 1515–1524 (2008).CAS 
PubMed 

Google Scholar 
46.Yelton, A. P. et al. Global genetic capacity for mixotrophy in marine picocyanobacteria. ISME J. 10, 2946–2957 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Cordero, P. R. F. et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 13, 2868–2881 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.King, G. M. & Weber, C. F. Distribution, diversity and ecology of aerobic CO-oxidizing bacteria. Nat. Rev. Microbiol. 5, 107–118 (2007).CAS 
PubMed 

Google Scholar 
49.Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

Google Scholar 
50.Sul, W. J., Oliver, T. A., Ducklow, H. W., Amaral-Zettler, L. A. & Sogin, M. L. Marine bacteria exhibit a bipolar distribution. Proc. Natl Acad. Sci. USA 110, 2342–2347 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Levins, R. Evolution in Changing Environments: Some Theoretical Explorations (Princeton Univ. Press, 1968).
Google Scholar 
53.Colwell, R. K. & Futuyma, D. J. On the measurement of niche breadth and overlap. Ecology 52, 567–576 (1971).PubMed 

Google Scholar 
54.Massana, R. & Logares, R. Eukaryotic versus prokaryotic marine picoplankton ecology. Environ. Microbiol. 15, 1254–1261 (2013).PubMed 

Google Scholar 
55.Székely, A. J., Berga, M. & Langenheder, S. Mechanisms determining the fate of dispersed bacterial communities in new environments. ISME J. 7, 61–71 (2013).PubMed 

Google Scholar 
56.Brooks, J. P. et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol. 15, 66 (2015).PubMed 
PubMed Central 

Google Scholar 
57.Logares, R. et al. Biogeography of bacterial communities exposed to progressive long-term environmental change. ISME J. 7, 937–948 (2013).CAS 
PubMed 

Google Scholar 
58.Ruiz-González, C. et al. Higher contribution of globally rare bacterial taxa reflects environmental transitions across the surface ocean. Mol. Ecol. 28, 1930–1945 (2019).PubMed 

Google Scholar 
59.Staley, J. T. & Gosink, J. J. Poles apart: biodiversity and biogeography of sea ice bacteria. Annu. Rev. Microbiol. 53, 189–215 (1999).CAS 
PubMed 

Google Scholar 
60.Chaffron, S. et al. Environmental vulnerability of the global ocean epipelagic plankton community interactome. Sci. Adv. 7, eabg1921 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Estrada, E. Characterization of topological keystone species: local, global and “meso-scale” centralities in food webs. Ecol. Complex. 4, 48–57 (2007).
Google Scholar 
62.Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
PubMed 

Google Scholar 
63.Tully, B. J., Sachdeva, R., Graham, E. D. & Heidelberg, J. F. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ 2017, e3558 (2017).
Google Scholar 
64.Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun. Biol. 4, 604 (2021).65.Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 170093 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
68.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 
PubMed Central 

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

Google Scholar 
70.Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
71.Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).PubMed 
PubMed Central 

Google Scholar 
72.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Huang, X. & Madan, A. CAP3: a DNA sequence assembly program. Genome Res. 9, 868–877 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 

Google Scholar 
77.Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. Bioinformatics 29, 2487–2489 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Jain, C., Rodriguez-R, L. M., Phillipy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).PubMed 
PubMed Central 

Google Scholar 
79.Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
PubMed Central 

Google Scholar 
81.Pertea, G. & Pertea, M. GFF utilities: GffRead and GffCompare. F1000Res. 9, ISCB Comm J-304 (2020).PubMed 
PubMed Central 

Google Scholar 
82.Aylward, F. O. & Santoro, A. E. Heterotrophic Thaumarchaeota with ultrasmall genomes are widespread in the ocean. mSystems 5, e00415–20 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).PubMed 
PubMed Central 

Google Scholar 
84.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2––approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).PubMed 
PubMed Central 

Google Scholar 
85.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Louca, S., Doebeli, M. & Parfrey, L. W. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome 6, 41 (2018).PubMed 
PubMed Central 

Google Scholar  More

1.Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUVIG). Nat Biotechnol 2019;37:29–37.PubMed 

Google Scholar 
2.Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.PubMed 

Google Scholar 
3.Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23.PubMed 
PubMed Central 

Google Scholar 
4.Kavagutti VS, Andrei AŞ, Mehrshad M, Salcher MM, Ghai R. Phage-centric ecological interactions in aquatic ecosystems revealed through ultra-deep metagenomics. Microbiome. 2019;7:1–15.
Google Scholar 
5.Schulz F, Alteio L, Goudeau D, Ryan EM, Yu FB, Malmstrom RR, et al. Hidden diversity of soil giant viruses. Nat Commun 2018;9:1–9.
Google Scholar 
6.Trubl G, Jang H Bin, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 2018;3:e00076–18.PubMed 
PubMed Central 

Google Scholar 
7.Guerin E, Shkoporov A, Stockdale SR, Clooney AG, Ryan FJ, Sutton TDS, et al. Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut. Cell Host Microbe. 2018;24:653–664.e6.PubMed 

Google Scholar 
8.Martinez-Hernandez F, Fornas O, Lluesma Gomez M, Bolduc B, de la Cruz Peña MJ, Martínez JM, et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat Commun 2017;8:1–13.
Google Scholar 
9.Aguirre de Cárcer D, Angly FE, Alcamí A. Evaluation of viral genome assembly and diversity estimation in deep metagenomes. BMC Genomics. 2014;15:1–12.
Google Scholar 
10.Roux S, Emerson JB, Eloe-Fadrosh EA, Sullivan MB. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ. 2017;5:e3817.PubMed 
PubMed Central 

Google Scholar 
11.Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. Genomic island variability facilitates Prochlorococcus-virus coexistence. Nature. 2011;474:604–8.PubMed 

Google Scholar 
12.Rodriguez-Valera F, Martin-Cuadrado A-B, Rodriguez-Brito B, Pasic L, Thingstad TF, Rohwer F, et al. Explaining microbial population genomics through phage predation. Nat Rev Microbiol 2009;7:828–36.PubMed 

Google Scholar 
13.Marston MF, Pierciey FJ, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA 2012;109:4544–9.PubMed 
PubMed Central 

Google Scholar 
14.Enav H, Kirzner S, Lindell D, Mandel-Gutfreund Y, Béjà O. Adapt sub-Optim hosts is a Driv viral Diversif ocean Nat Comm 2018;9:1–11.
Google Scholar 
15.Boon M, Holtappels D, Lood C, van Noort V, Lavigne R. Host range expansion of pseudomonas virus LUZ7 is driven by a conserved tail fiber mutation. PHAGE. 2020;1:87–90.
Google Scholar 
16.Bernheim A, Sorek R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat Rev Microbiol 2020;18:113–9.PubMed 

Google Scholar 
17.Sørensen MA, Kurland CG, Pedersen S. Codon usage determines translation rate in Escherichia coli. J Mol Biol 1989;207:365–77.PubMed 

Google Scholar 
18.Varenne S, Buc J, Lloubes R, Lazdunski C. Translation is a non-uniform process. Effect of tRNA availability on the rate of elongation of nascent polypeptide chains. J Mol Biol 1984;180:549–76.PubMed 

Google Scholar 
19.Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, et al. Codon Usage Influences the Local Rate of Translation Elongation to Regulate Co-translational Protein Folding. Mol Cell. 2015;59:744–54.PubMed 
PubMed Central 

Google Scholar 
20.Plotkin JB, Kudla G. Synonymous but not the same: The causes and consequences of codon bias. Nat Rev Genet 2011;12:32–42.PubMed 

Google Scholar 
21.Chu D, Wei L. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer. 2019;19:359.PubMed 
PubMed Central 

Google Scholar 
22.Deng L, Ignacio-Espinoza JC, Gregory AC, Poulos BT, Weitz JS, Hugenholtz P, et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature. 2014;513:242–5.PubMed 

Google Scholar 
23.Edwards RA, Vega AA, Norman HM, Ohaeri M, Levi K, Dinsdale EA, et al. Global phylogeography and ancient evolution of the widespread human gut virus crAssphage. Nat Microbiol 2019;4:1727–36.PubMed 
PubMed Central 

Google Scholar 
24.Ignacio-Espinoza JC, Ahlgren NA, Fuhrman JA. Long-term stability and Red Queen-like strain dynamics in marine viruses. Nat. Microbiol. 2019;5:1–7.25.Coutinho FH, Rosselli R, Rodríguez-Valera F. Trends of microdiversity reveal depth-dependent evolutionary strategies of viruses in the Mediterranean. mSystems. 2019;4:1–17.
Google Scholar 
26.Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.PubMed 
PubMed Central 

Google Scholar 
27.Martinez-Hernandez F, Fornas Ò, Lluesma Gomez M, Garcia-Heredia I, Maestre-Carballa L, López-Pérez M, et al. Single-cell genomics uncover Pelagibacter as the putative host of the extremely abundant uncultured 37-F6 viral population in the ocean. ISME J. 2019;13:232–6.PubMed 

Google Scholar 
28.McMullen A, Martinez‐Hernandez F, Martinez‐Garcia M. Absolute quantification of infecting viral particles by chip‐based digital polymerase chain reaction. Environ Microbiol Rep. 2019;11:855–60.PubMed 

Google Scholar 
29.Marston MF, Amrich CG. Recombination and microdiversity in coastal marine cyanophages. Environ Microbiol. 2009;11:2893–903.PubMed 

Google Scholar 
30.Marston MF, Martiny JBH. Genomic diversification of marine cyanophages into stable ecotypes. Environ Microbiol 2016;18:4240–53.PubMed 

Google Scholar 
31.Cordero OX. Endemic cyanophages and the puzzle of phage-bacteria coevolution. Environ Microbiol 2017;19:420–2.PubMed 

Google Scholar 
32.Shannon CE. The mathematical theory of communication. 1963. MD Comput. 1997;14:306–17.PubMed 

Google Scholar 
33.Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.PubMed 

Google Scholar 
34.Bobay L-M, Ochman H. Biological species in the viral world. Proc Natl Acad Sci USA 2018;115:6040–5.PubMed 
PubMed Central 

Google Scholar 
35.Henson MW, Lanclos VC, Faircloth BC, Thrash JC. Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J. 2018;12:1846–60.PubMed 
PubMed Central 

Google Scholar 
36.Paez-Espino D, Roux S, Chen I-MA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes. Nucleic Acids Res. 2019;47:D678–D686.PubMed 

Google Scholar 
37.Brum JR, Ignacio-Espinoza JC, Kim E-H, Trubl G, Jones RM, Roux S, et al. Illuminating structural proteins in viral ‘dark matter’ with metaproteomics. Proc Natl Acad Sci USA 2016;113:2436–41.PubMed 
PubMed Central 

Google Scholar 
38.Sakowski EG, Arora-Williams K, Tian F, Zayed AA, Zablocki O, Sullivan MB, et al. Interaction dynamics and virus–host range for estuarine actinophages captured by epicPCR. Nat. Microbiol. 2021;6:1–13.39.Alonso-Sáez L, Morán XAG, Clokie MR. Low activity of lytic pelagiphages in coastal marine waters. ISME J. 2018;12:2100–2.PubMed 
PubMed Central 

Google Scholar 
40.Martinez‐Hernandez F, Luo E, Tominaga K, Ogata H, Yoshida T, DeLong EF, et al. Diel cycling of the cosmopolitan abundant Pelagibacter virus 37‐F6: one of the most abundant viruses in Earth. Environ Microbiol Rep. 2020;12:214–21941.Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.PubMed 

Google Scholar 
42.de Avila e Silva S, Echeverrigaray S, Gerhardt GJL. BacPP: bacterial promoter prediction-A tool for accurate sigma-factor specific assignment in enterobacteria. J Theor Biol 2011;287:92–99.PubMed 

Google Scholar 
43.Sampaio M, Rocha M, Oliveira H, Dias O. Predicting promoters in phage genomes using PhagePromoter. Bioinformatics. 2019;35:5301–2.PubMed 

Google Scholar 
44.Allert M, Cox JC, Hellinga HW. Multifactorial determinants of protein expression in prokaryotic open reading frames. J Mol Biol. 2010;402:905–18.PubMed 
PubMed Central 

Google Scholar 
45.Dressaire C, Picard F, Redon E, Loubière P, Queinnec I, Girbal L, et al. Role of mRNA stability during bacterial adaptation. PLoS ONE 2013;8:e59059.PubMed 
PubMed Central 

Google Scholar 
46.Deana A, Belasco JG. Lost in translation: The influence of ribosomes on bacterial mRNA decay. Genes Dev. 2005;19:2526–33.PubMed 

Google Scholar 
47.Zhao Y, Temperton B, Thrash JC, Schwalbach MS, Vergin KL, Landry ZC, et al. Abundant SAR11 viruses in the ocean. Nature. 2013;494:357–60.PubMed 

Google Scholar 
48.Zhang Z, Qin F, Chen F, Chu X, Luo H, Zhang R, et al. Culturing novel and abundant pelagiphages in the ocean. Environ Microbiol 2020;1462-2920:15272.
Google Scholar 
49.Zhao Y, Qin F, Zhang R, Giovannoni SJ, Zhang Z, Sun J, et al. Pelagiphages in the Podoviridae family integrate into host genomes. Environ Microbiol. 2018;21:1989–2001.50.Morris RM, Cain KR, Hvorecny KL, Kollman JM. Lysogenic host–virus interactions in SAR11 marine bacteria. Nat Microbiol 2020;5:1011–5.PubMed 
PubMed Central 

Google Scholar 
51.Konstantinidis KT, Ramette A, Tiedje JM. The bacterial species definition in the genomic era. Philos Trans R Soc Lond, B, Biol Sci 2006;361:1929–40.
Google Scholar 
52.Rosselló-Mora R. Updating prokaryotic taxonomy. J Bacteriol. 2005;187:6255–7.PubMed 
PubMed Central 

Google Scholar 
53.Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2017;2:1533–42.PubMed 

Google Scholar 
54.Richter M, Rossello-Mora R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci 2009;106:19126–31.PubMed 
PubMed Central 

Google Scholar 
55.Pope WH, Bowman CA, Russell DA, Jacobs-Sera D, Asai DJ, Cresawn SG, et al. Whole genome comparison of a large collection of mycobacteriophages reveals a continuum of phage genetic diversity. eLife 2015;4:e06416.PubMed 
PubMed Central 

Google Scholar 
56.Gregory AC, Solonenko SA, Ignacio-Espinoza JC, LaButti K, Copeland A, Sudek S, et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC genomics. 2016;17:930.PubMed 
PubMed Central 

Google Scholar 
57.Martinez-Hernandez F, Garcia-Heredia I, Lluesma Gomez M, Maestre-Carballa L, Martínez Martínez J, Martinez-Garcia M. Droplet digital PCR for estimating absolute abundances of widespread Pelagibacter viruses. Front Microbiol 2019;10:1226.PubMed 
PubMed Central 

Google Scholar 
58.Warwick-Dugdale J, Solonenko N, Moore K, Chittick L, Gregory AC, Allen MJ, et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ. 2019;7:e6800.PubMed 
PubMed Central 

Google Scholar 
59.Beaulaurier J, Luo E, Eppley JM, Uyl P Den, Dai X, Burger A, et al. Assembly-free single-molecule sequencing recovers complete virus genomes from natural microbial communities. Genome Res. 2020;30:437–46.PubMed 
PubMed Central 

Google Scholar 
60.Murigneux V, Rai SK, Furtado A, Bruxner TJC, Tian W, Harliwong I, et al. Comparison of long-read methods for sequencing and assembly of a plant genome. GigaScience 2020;9:giaa146.61.Wenger AM, Peluso P, Rowell WJ, Chang PC, Hall RJ, Concepcion GT, et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat Biotechnol 2019;37:1155–62.PubMed 
PubMed Central 

Google Scholar 
62.Martínez Martínez J, Martinez-Hernandez F, Martinez-Garcia M. Single-virus genomics and beyond. Nat Rev Microbiol. 2020;18:705–16.PubMed 

Google Scholar 
63.Labonté JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, et al. Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J. 2015;9:2386–99.PubMed 
PubMed Central 

Google Scholar 
64.Mizuno CM, Rodriguez-Valera F, Kimes NE, Ghai R. Expanding the marine virosphere using metagenomics. PLoS Genet. 2013;9:e1003987.PubMed 
PubMed Central 

Google Scholar 
65.Mizuno CM, Ghai R, Saghaï A, López-García P, Rodriguez-Valera F. Genomes of abundant and widespread viruses from the deep ocean. mBio. 2016;7:e00805–16.PubMed 
PubMed Central 

Google Scholar 
66.Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinforma. 2012;13:134.
Google Scholar 
67.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.PubMed 
PubMed Central 

Google Scholar 
68.Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.PubMed 

Google Scholar 
69.Philosof A, Yutin N, Flores-Uribe J, Sharon I, Koonin EV, Béjà O. Novel abundant oceanic viruses of uncultured marine group II Euryarchaeota. Curr Biol. 2017;27:1362–8.PubMed 
PubMed Central 

Google Scholar 
70.Vik DR, Roux S, Brum JR, Bolduc B, Emerson JB, Padilla CC, et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ. 2017;5:e3428.PubMed 
PubMed Central 

Google Scholar 
71.Bin Jang H, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol 2019;37:632–9.
Google Scholar 
72.Bobay L-M, Ellis BS-H, Ochman H. ConSpeciFix: classifying prokaryotic species based on gene flow. Bioinformatics. 2018;34:3738–40.PubMed 
PubMed Central 

Google Scholar 
73.Bobay L-M, Ochman H. Biological species are universal across life’s domains. Genome Biol Evol. 2017;9:491–501.PubMed Central 

Google Scholar 
74.Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.
Google Scholar 
75.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.PubMed 
PubMed Central 

Google Scholar 
76.Harris CD, Torrance EL, Raymann K, Bobay L-M. CoreCruncher: Fast and robust construction of core genomes in large prokaryotic data sets. Mol. Biol. Evol. 2020;38:727–734.77.Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.PubMed 
PubMed Central 

Google Scholar 
78.Rice P, Longden L, Bleasby A EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 2000. Elsevier Ltd., 16: 276–779.Džunková M, Low SJ, Daly JN, Deng L, Rinke C, Hugenholtz P. Defining the human gut host–phage network through single-cell viral tagging. Nat Microbiol 2019;4:2192–203.PubMed 

Google Scholar 
80.Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 

Google Scholar 
81.Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.PubMed 
PubMed Central 

Google Scholar 
82.Swan BK, Ehrhardt CJ, Reifel KM, Moreno LI, Valentine DL. Archaeal and bacterial communities respond differently to environmental gradients in anoxic sediments of a california hypersaline lake, the Salton Sea. Appl Environ Microbiol 2010;76:757–68.PubMed 

Google Scholar 
83.Baran N, Goldin S, Maidanik I, Lindell D. Quantification of diverse virus populations in the environment using the polony method. Nat Microbiol 2018;3:62–72.PubMed 

Google Scholar  More

Study area and samplingSmall mammals (murid rodents and shrews) were captured using mouse-type snap traps in Tatarstan, Russian Federation (Fig. 1, Table S1). Area type (urban or rural), vegetation (forest or field) and distance from trapping points to the nearest human settlement were recorded. The distinction between forest and field was made based on the UN Food and Agriculture Organization’s criteria23,24. Each administrative division in the Tatarstan was defined to be urban or rural by the Federal Service of State Statistics of Russian Federation25. Based on these criteria, Kazan city and Naberezhnye Chelny city were classified as urban districts and Vysokogorsky district, Yelabuzhsky district, Laishevsky district, Mamadyshsky district, Nizhnekamsky district, Pestrechinsky district and Tukayevsky district were classified as rural districts. Small mammals were captured during the spring and fall periods of 2016 and 2017. Fifty traps were placed in a line every 5 m in one place. Traps were baited and left for one night. Animal suffering was minimized as snap traps cause rapid death in murid rodents and shrews. Each captured small mammal’s species, age, and sex were morphologically identified using a reference guide26, and the animals were then stored at − 20 °C until their brains were isolated.EthicsAll experiments were performed in compliance with relevant Russian and Japanese and institutional laws and guidelines and were approved by the Ministry of Health of the Russian Federation and the Animal Research Committee of Gifu University (Permit Nos. MU 3.1.1029-01, and 17060, respectively). Study was carried out in compliance with the ARRIVE guidelines (https://arriveguidelines.org).DNA extraction and PCRBrain tissue samples were prepared as described previously12. Brain samples stored at − 20 °C were transferred to a − 86 °C deep freezer. Each deep-frozen whole brain sample was homogenized in 1 ml of a 0.9% saline solution. Total DNA was extracted from the brain tissues of each small mammal using a Genomic DNA Purification Kit (Promega, Madison, WI, USA), following the manufacturer’s instructions. Nested PCR was performed with the Takara PCR Amplification Kit (Takara Bio Inc., Foster City, California, USA) according to the manufacturer’s instructions. The primer sets and PCR conditions used to detect the B1 gene from T. gondii were those described previously12.MappingSpatial referencing of the sampling sites was conducted using global positioning system navigation with a Garmin eTrex 10 device. Visualization of cartographic data and measurements of the distances from the trapping points to the nearest human settlements were performed using QGIS 3.12 software27. Geodetic coordinates were projected into planar rectangular coordinates in the Universal Transverse Mercator projection on the WGS-84 ellipsoid (Universal Transverse Mercator, zone 39N). The overview map of the European part of Russia was made in the Lambert Conformal Conic Projection. Map coordinates are represented as geodetic coordinates (WGS-84, degrees and minutes north latitude and east longitude). To visualize thematic objects (administrative boundaries, forests, agricultural lands, and water bodies), a set of vector data layers, NextGIS (Russia), was purchased from OpenStreetMap and contributors, 2021 (https://data.nextgis.com). Data license: ODbL.Dataset and statistical analysesMultivariate logistic regression was performed using the R statistical software package (version 3.6.3)28 to assess the trapping point area (urban or rural), vegetation (forest or field), small mammal species type (alien or non-alien species), age (0–2 months-old juveniles, 3–6 months-old adults or ≧ 6 months old), sex (male or female) and distance from trapping points to the nearest human settlements as risk factors for PCR positivity. According to previous reports2,13,16,17,18, four species, Mi. arvalis, A. flavicollis, A. agrarius, A. uralensis, and three species, My. glareolus, S. araneus and D. nitedula are considered alien and non-alien species, respectively. Quantitative data were replaced with 0 or 1 dummy variables, and age data were replaced by 0, 1 and 2 for juveniles, adults and elders, respectively. Multicollinearity of the explanatory variables was evaluated using Spearman’s coefficient29 calculated using dplyr, FSA and psych packages30,31,32. None of the Spearman’s coefficients were  > 0.6. To find the best fit model, a forward selection procedure was used. Predictive performance and model fitting were assessed using the area under the receiver operating characteristic (ROC) curve, area under the curve (AUC) and corrected Akaike’s information criterion (AICc) with Akaike weight (Wi). AICc and Wi were calculated using the MuMIN package33, and the AUC was calculated using the R pROC package34. P-values of  More

The micro-CT results from Arthrorhynchus agree perfectly with the previously known light microscope and transmission electron microscope images2. This emphasizes that microtomography is a good technique to visualize the type of fungal attachment to the host and especially the penetration of the cuticle, apart from the study of thallus in amber fossils17. As Jensen et al. (2019) demonstrated the presence of a haustorium in Arthrorhynchus using scanning electron microscopy, we are confident that the lack of penetration and haustorium in Rickia found by micro-CT is real. This is also in agreement with results from the scanning electron microscopical investigation of the attachment sites of R. gigas, which exhibits no indication of penetration and are very similar to those of R. wasmannii previously shown18.Despite the absence of a haustorium, and hence without any obvious means of obtaining nutrition, Rickia gigas is quite a successful fungus, being often abundant on several species of Afrotropical millipedes of the family Spirostreptidae10. It was originally described from Archispirostreptus gigas, and Tropostreptus (= ‘Spirostreptus’) hamatus20, and was subsequently reported from several other Tropostreptus species19.A further challenge for Laboulbeniales growing on millipedes is that infected millipedes, in some species even adults, may moult, shedding the exuviae with the fungus, as has been observed by us on an undescribed Rickia species on a millipede of the genus Spirobolus (family Spirobolidae).The question of how non-haustoriate Laboulbeniales obtain nutrients has been discussed by several authors18, including staining experiments using fungi of the non-haustoriate genus Laboulbenia on various beetles21. Whereas the surface of the main thallus was almost impenetrable to the dye applied (Nile Blue), the smaller appendages could sometimes be penetrated21. The dye injection into the beetle elytra upon which the fungi were sitting, actually spread from the elytron into the fungus, thus indicating that in spite of the lack of a haustorium, the fungus is able to extract nutrients from the interior of its host21.Such experiments have not been performed on Rickia species, but the possibility that nutrients may pass from the host into the basis of the fungus cannot be excluded. For this genus, or at least R. gigas, there may, however, be an alternative way to obtain nutrients: the small opening in the circular wall by which the thallus is attached to the host may allow nutrients from the surface of the millipede or from the environment to seep into the foot of the fungus. However, further experiments are needed in order to evaluate this hypothesis. Moreover, we should not exclude a potential role of primary and secondary appendages in Laboulbeniales nutrition, as we still do not understand exactly their functional role on the fungus life cycle11.The predominant position of the Laboulbeniales on the host might be related to the absence or presence of a haustorium. Thus, the haustoriate species of the genus Arthrorhynchus are most frequently encountered in large numbers on the arthrodial membranes of the host’s abdomen, although some thalli are found on legs2,22. At the arthrodial membranes the cuticle is more flexible and therefore might be easier to penetrate by a parasite. Furthermore, most tissues providing/storing nutrition (e.g., fat body) are located within the abdomen. In contrast, non-haustoriate fungi as are often located on more stiff and sclerotized body-parts like the genus Rickia on the legs or body-rings of millipedes7,20,23 or the genus Laboulbenia on the elytra of beetles21,24. A reason for this might be that the non-haustoriate forms, which are only superficially attached to the host need a more or less smooth surface for adherence and can easily become detached from a flexible surface, which is movable in itself, like the arthrodial membrane, while the haustoriate forms are firmly anchored within the hosts’ cuticle.Whereas the vast majority of the more than 2000 described species of Laboulbeniales show no sign of host penetration, haustoria have been reported from some other genera18, including Trenomyces parasitizing bird lice25,26, Hesperomyces growing on coccinellid beetles and Herpomyces on cockroaches (formerly a Laboulbeniales and now in the order Herpomycetales10), with pernicious consequences on the hosts’ fitness18,27. Micro-CT studies on these genera could help to understand the host penetration. In order to fully understand how Laboulbeniales obtain nourishment, although other approaches are, also needed—for the time being it remains a mystery how the non-haustoriate Laboulbeniales sustain themselves. More