More stories

  • in

    Adult mosquito predation and potential impact on the sterile insect technique

    World Health Organization. World malaria report 2020: 20 years of global progress and challenges. 299 https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2020 (2020).Bhanot, K., Schroeder, D., Llewellyn, I., Luczak, N. & Munasinghe, T. Dengue spread information system (DSIS). In Proceedings of the 4th International Conference on Medical and Health Informatics 150–159 (Association for Computing Machinery, 2020). https://doi.org/10.1145/3418094.3418133.Wilson, A. L. et al. The importance of vector control for the control and elimination of vector-borne diseases. PLoS Negl. Trop. Dis. 14, e0007831 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Carrasco, D. et al. Behavioural adaptations of mosquito vectors to insecticide control. Curr. Opin. Insect Sci. 34, 48–54 (2019).PubMed 

    Google Scholar 
    Sokhna, C., Ndiath, M. O. & Rogier, C. The changes in mosquito vector behaviour and the emerging resistance to insecticides will challenge the decline of malaria. Clin. Microbiol. Infect. 19, 902–907 (2013).CAS 
    PubMed 

    Google Scholar 
    Flint, M. L. & Dreistadt, S. H. Natural Enemies Handbook: The Illustrated Guide to Biological Pest Control Vol. 3386 (Univ of California Press, 1998).
    Google Scholar 
    Chandra, G., Bhattacharjee, I., Chatterjee, S. N. & Ghosh, A. Mosquito control by larvivorous fish. Indian J. Med. Res. 127, 13–27 (2008).CAS 
    PubMed 

    Google Scholar 
    Dambach, P. The use of aquatic predators for larval control of mosquito disease vectors: Opportunities and limitations. Biol. Control 150, 104357 (2020).CAS 

    Google Scholar 
    Sebastian, A., Sein, M. M., Thu, M. M. & Corbet, P. S. Suppression of Aedes aegypti (Diptera: Culicidae) using augmentative release of dragonfly larvae (Odonata: Libellulidae) with community participation in Yangon, Myanmar1. Bull. Entomol. Res. 80, 223–232 (1990).
    Google Scholar 
    Harrington, R. W. & Harrington, E. S. Effects on fishes and their forage organisms of impounding a Florida salt marsh to prevent breeding by salt marsh mosquitoes. Bull. Mar. Sci. 32, 523–531 (1982).
    Google Scholar 
    Mk, D. & Rn, P. Evaluation of mosquito fish Gambusia affinis in the control of mosquito breeding in rice fields. Indian J. Malariol. 28, 171–177 (1991).
    Google Scholar 
    Rk, S., Rc, D. & Sp, S. Laboratory studies on the predatory potential of dragon-fly nymphs on mosquito larvae. J. Commun. Dis. 35, 96–101 (2003).
    Google Scholar 
    Focks, D. A., Sackett, S. R., Dame, D. A. & Bailey, D. L. Effect of weekly releases of Toxorhynchites amboinensis (Doleschall) on Aedes aegypti (L.) (Diptera: Culicidae) in New Orleans, Louisiana. J. Econ. Entomol. 78, 622–626 (1985).CAS 
    PubMed 

    Google Scholar 
    Brodman, R. & Dorton, R. The effectiveness of pond-breeding salamanders as agents of larval mosquito control. J. Freshw. Ecol. 21, 467–474 (2006).
    Google Scholar 
    Vu, S. N., Nguyen, T. Y., Kay, B. H., Marten, G. G. & Reid, J. W. Eradication of Aedes aegypti from a village in Vietnam, using copepods and community participation. Am. J. Trop. Med. Hyg. 59, 657–660 (1998).CAS 
    PubMed 

    Google Scholar 
    Canyon, D. V. & Hii, J. L. K. The gecko: An environmentally friendly biological agent for mosquito control. Med. Vet. Entomol. 11, 319–323 (1997).CAS 
    PubMed 

    Google Scholar 
    Strickman, D., Sithiprasasna, R. & Southard, D. Bionomics of the spider, Crossopriza lyoni (Araneae, Pholcidae), a predator of dengue vectors in Thailand. J. Arachnol. 25, 194–201 (1997).
    Google Scholar 
    Tkaczenko, G., Fischer, A. & Weterings, R. Prey preference of the common house geckos Hemidactylus frenatus and Hemidactylus platyurus. Herpetol. Notes 7, 482–488 (2014).
    Google Scholar 
    Weterings, R., Umponstira, C. & Buckley, H. L. Landscape variation influences trophic cascades in dengue vector food webs. Sci. Adv. 4, eaap9534 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Weterings, R., Umponstira, C. & Buckley, H. L. Predation on mosquitoes by common Southeast Asian house-dwelling jumping spiders (Salticidae). Argy 16, 122–127 (2014).
    Google Scholar 
    Puig-Montserrat, X. et al. Bats actively prey on mosquitoes and other deleterious insects in rice paddies: Potential impact on human health and agriculture. Pest Manag. Sci. 76, 3759–3769 (2020).CAS 
    PubMed 

    Google Scholar 
    May, M. L. Odonata: Who they are and what they have done for us lately: Classification and ecosystem services of dragonflies. Insects 10, 62 (2019).PubMed Central 

    Google Scholar 
    Raghavendra, K., Sharma, P. & Dash, A. P. Biological control of mosquito populations through frogs: Opportunities & constrains. Indian J. Med. Res. 128, 22–25 (2008).CAS 
    PubMed 

    Google Scholar 
    Poulin, B., Lefebvre, G. & Paz, L. Red flag for green spray: adverse trophic effects of Bti on breeding birds. Journal
    of Applied Ecology 47, 884–889 (2010).
    Google Scholar 
    Korichi, R. et al. Ecological impact of trophic diet of mantids in Ghardaïa (Algerian Sahara). Ponte Int. Sci. Res. J. 72, 94–106 (2016).
    Google Scholar 
    Prete, F. R. The Praying Mantids (Johns Hopkins University Press, 1999).
    Google Scholar 
    Dyck, V. A., Hendrichs, J. & Robinson, A. S. Sterile Insect Technique: Principles And Practice In Area-Wide Integrated Pest Management (CRC Press, 2021).
    Google Scholar 
    Bouyer, J. & Vreysen, M. J. B. Yes, irradiated sterile male mosquitoes can be sexually competitive!. Trends Parasitol. 36, 877–880 (2020).CAS 
    PubMed 

    Google Scholar 
    Parker, A., Vreysen, M., Bouyer, J. & Calkins, C. Sterile insect quality control/assurance. In Sterile Insect Technique: Principles And Practice In Area-Wide Integrated Pest Management 399–440 (2021).Lees, R., Carvalho, D. O. & Bouyer, J. Potential impact of integrating the sterile insect technique into the fight against disease-transmitting mosquitoes. In Sterile Insect Technique. Principles and Practice in Area-Wide Integrated Pest Management 2nd edn (eds Dyck, A. V. et al.) 1082–1118 (CRC Press, 2021).
    Google Scholar 
    Bimbilé Somda, N. S. et al. Cost-effective larval diet mixtures for mass rearing of Anopheles arabiensis Patton (Diptera: Culicidae). Parasit. Vectors 10, 619 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Bimbilé Somda, N. S. B. et al. Insects to feed insects-feeding Aedes mosquitoes with flies for laboratory rearing. Sci. Rep. 9, 1–13 (2019).
    Google Scholar 
    Maïga, H. et al. Assessment of a novel adult mass-rearing cage for Aedes albopictus (Skuse) and Anopheles arabiensis (Patton). Insects 11, 801 (2020).PubMed Central 

    Google Scholar 
    Maïga, H. et al. Reducing the cost and assessing the performance of a novel adult mass-rearing cage for the dengue, chikungunya, yellow fever and Zika vector, Aedes aegypti (Linnaeus). PLOS Negl. Trop. Dis. 13, e0007775 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Mamai, W. et al. Black soldier fly (Hermetia illucens) larvae powder as a larval diet ingredient for mass-rearing Aedes mosquitoes. Parasite 26, 57 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Mamai, W. et al. Optimization of mass-rearing methods for Anopheles arabiensis larval stages: Effects of rearing water temperature and larval density on mosquito life-history traits. J. Econ. Entomol. 111, 2383–2390 (2018).PubMed 

    Google Scholar 
    Bellini, R., Puggioli, A., Balestrino, F., Carrieri, M. & Urbanelli, S. Exploring protandry and pupal size selection for Aedes albopictus sex separation. Parasites Vectors 11, 650 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Yamada, H. et al. Genetic sex separation of the malaria vector, Anopheles arabiensis, by exposing eggs to dieldrin. Malar J. 11, 208 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Yamana, T. K. & Eltahir, E. A. B. Projected impacts of climate change on environmental suitability for malaria transmission in West Africa. Environ. Health Perspect. 121, 1179–1186 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Zacarés, M. et al. Exploring the potential of computer vision analysis of pupae size dimorphism for adaptive sex sorting systems of various vector mosquito species. Parasites Vectors 11, 656 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Culbert, N. J., Gilles, J. R. L. & Bouyer, J. Investigating the impact of chilling temperature on male Aedes aegypti and Aedes albopictus survival. PLoS ONE 14, e0221822 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Helinski, M. E., Parker, A. G. & Knols, B. G. Radiation-induced sterility for pupal and adult stages of the malaria mosquito Anopheles arabiensis. Malar J. 5, 41 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    Yamada, H. et al. Identification of critical factors that significantly affect the dose-response in mosquitoes irradiated as pupae. Parasit. Vectors 12, 1–13 (2019).CAS 

    Google Scholar 
    Culbert, N. J. et al. A rapid quality control test to foster the development of the sterile insect technique against Anopheles arabiensis. Malar. J. 19, 1–10 (2020).
    Google Scholar 
    Culbert, N. J. et al. A rapid quality control test to foster the development of genetic control in mosquitoes. Sci. Rep. 8, 1–9 (2018).CAS 

    Google Scholar 
    Bouyer, J. et al. Field performance of sterile male mosquitoes released from an uncrewed aerial vehicle. Sci. Robot. 5, eaba6251 (2020).PubMed 

    Google Scholar 
    Somda, N. S. B. et al. Ecology of reproduction of Anopheles arabiensis in an urban area of Bobo-Dioulasso, Burkina Faso (West Africa): Monthly swarming and mating frequency and their relation to environmental factors. PLoS ONE 13, e0205966 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Bellini, R., Medici, A., Puggioli, A., Balestrino, F. & Carrieri, M. Pilot field trials with Aedes albopictus irradiated sterile males in Italian urban areas. J. Med. Entomol. 50, 317–325 (2013).CAS 
    PubMed 

    Google Scholar 
    Vavassori, L., Saddler, A. & Müller, P. Active dispersal of Aedes albopictus: A mark-release-recapture study using self-marking units. Parasites Vectors 12, 583 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Zheng, X. et al. Incompatible and sterile insect techniques combined eliminate mosquitoes. Nature 572, 56–61 (2019).CAS 
    PubMed 

    Google Scholar 
    Dor, A. & Liedo, P. Survival ability of Mexican fruit fly males from different strains in presence of the predatory orb-weaving spider Argiope argentata (Araneae: Araneidae). Bull. Entomol. Res. 109, 279–286 (2019).CAS 
    PubMed 

    Google Scholar 
    Rathnayake, D. N., Lowe, E. C., Rempoulakis, P. & Herberstein, M. E. Effect of natural predators on Queensland fruit fly, Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) control by sterile insect technique (SIT). Pest Manag. Sci. 75, 3356–3362 (2019).CAS 
    PubMed 

    Google Scholar 
    Kral, K. The functional significance of mantis peering behaviour. Eur. J. Entomol. 109, 295–301 (2012).
    Google Scholar 
    Bond, J. G. et al. Optimization of irradiation dose to Aedes aegypti and Ae. albopictus in a sterile insect technique program. PLoS ONE 14, e0212520 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Helinski, M. E., Parker, A. G. & Knols, B. G. Radiation biology of mosquitoes. Malar. J. 8, S6 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Hurd, L. E. et al. Cannibalism reverses male-biased sex ratio in adult mantids: Female strategy against food limitation?. Oikos 69, 193–198 (1994).
    Google Scholar 
    Lawrence, S. E. Sexual cannibalism in the praying mantid, Mantis religiosa: A field study. Anim. Behav. 43, 569–583 (1992).
    Google Scholar 
    Trujillo-Jiménez, P., Castro-Franco, R., Zagal, M. & Corona, Y. The Asian house gecko Hemidactylus frenatus. (2018).Tyler, M. J. On the diet and feeding habits of Hemidactylus frenatus (Dumeril and Bibron) (Reptilia:Gekkonidae) at Rangoon, Burma. Trans. R. Soc. S. Aust. 84, 45–49 (1961).
    Google Scholar 
    Dor, A., Valle-Mora, J., Rodríguez-Rodríguez, S. E. & Liedo, P. Predation of Anastrepha ludens (Diptera: Tephritidae) by Norops serranoi (Reptilia: Polychrotidae): Functional response and evasion ability. Environ. Entomol. 43, 706–715 (2014).PubMed 

    Google Scholar 
    Schmidt, J. M., Sebastian, P., Wilder, S. M. & Rypstra, A. L. The nutritional content of prey affects the foraging of a generalist arthropod predator. PLoS ONE 7, e49223 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Turesson, H., Persson, A. & Brönmark, C. Prey size selection in piscivorous pikeperch (Stizostedion lucioperca) includes active prey choice. Ecol. Freshw. Fish 11, 223–233 (2002).
    Google Scholar 
    Collins, C. M., Bonds, J. A. S., Quinlan, M. M. & Mumford, J. D. Effects of the removal or reduction in density of the malaria mosquito, Anopheles gambiae s.l., on interacting predators and competitors in local ecosystems. Med. Vet. Entomol. 33, 1 (2019).CAS 
    PubMed 

    Google Scholar 
    FAO/IAEA. Guidelines for mark-release-recapture procedures of Aedes mosquitoes. Version 1.0. In (eds Bouyer, J. et al.) 22 (Food and Agriculture Organization of the United Nations International Atomic Energy Agency, 2020). More

  • in

    Social behavior mediates the use of social and personal information in wild jays

    Gil, M. A., Hein, A. M., Spiegel, O., Baskett, M. L. & Sih, A. Social information links individual behavior to population and community dynamics. Trends Ecol. Evol. 33, 535–548 (2018).PubMed 

    Google Scholar 
    Shettleworth, S. J. Cognition, Evolution, and Behavior (Oxford University Press, 2010).
    Google Scholar 
    Papini, M. Pattern and process in the evolution of learning. Psychol. Rev. 109, 186–201 (2002).PubMed 

    Google Scholar 
    Wagner, R. H. & Danchin, E. A taxonomy of biological information. Oikos 119, 203–209 (2010).
    Google Scholar 
    Heyes, C. Social learning in animals: Categories and mechanisms. Biol. Rev. Camb. Philos. Soc. 69, 207–231 (1994).CAS 
    PubMed 

    Google Scholar 
    Ladds, Z., Hoppitt, W. & Boogert, N. J. Social learning in otters. R. Soc. Open Sci. 4, 170489 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morand-Ferron, J., Cole, E. F., Rawles, J. E. C. & Quinn, J. L. Who are the innovators? A field experiment with 2 passerine species. Behav. Ecol. 22, 1241–1248 (2011).
    Google Scholar 
    Coussi-Korbel, S. & Fragaszy, D. M. On the relation between social dynamics and social learning. Anim. Behav. 50, 1441–1453 (1995).
    Google Scholar 
    Giraldeau, L.-A. & Lefebvre, L. Is social learning an adaptive specialization? In Social Learning in Animals: The Roots of Culture (eds Heyes, C. M. & Galef, B. G.) 107–128 (Academic Press, inc., 1996).
    Google Scholar 
    Giraldeau, L.-A., Valone, T. J. & Templeton, J. J. Potential disadvantages of using socially acquired information. Philos. Trans. R. Soc. B Biol. Sci. 357, 1559–1566 (2002).
    Google Scholar 
    Reader, S. M. & Biro, D. Experimental identification of social learning in wild animals. Learn. Behav. 38, 265–283 (2010).PubMed 

    Google Scholar 
    Laland, K. N. COMMENTARIES is social learning always locally adaptive? Anim. Behav. 52, 637–640 (1996).
    Google Scholar 
    Whitehead, H. Conserving and managing animals that learn socially and share cultures. Learn. Behav. 38, 329–336 (2010).PubMed 

    Google Scholar 
    Kenward, B., Rutz, C., Weir, A. A. S. & Kacelnik, A. Development of tool use in New Caledonian crows: Inherited action patterns and social influences. Anim. Behav. 72, 1329–1343 (2006).
    Google Scholar 
    Mann, J., Stanton, M. A., Patterson, E. M., Bienenstock, E. J. & Singh, L. O. Social networks reveal cultural behaviour in tool-using dolphins. Nat. Commun. 3, 980 (2012).ADS 
    PubMed 

    Google Scholar 
    Musgrave, S., Morgan, D., Lonsdorf, E., Mundry, R. & Sanz, C. Tool transfers are a form of teaching among chimpanzees. Sci. Rep. 6, 34783 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thornton, A. & McAuliffe, K. Teaching in wild meerkats. Science 313, 227–229 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Faegre, S., Nietmann, L., Hannon, P., Ha, J. C. & Ha, R. R. Age-related differences in diet and foraging behavior of the critically endangered Mariana Crow (Corvus kubaryi), with notes on the predation of Coenobita hermit crabs. J. Ornithol. 161, 149–158 (2019).
    Google Scholar 
    Laland, K. N. Social learning strategies. Learn. Behav. 32, 4–14 (2004).PubMed 

    Google Scholar 
    Byrne, R. W. Machiavellian intelligence. Evol. Anthropol. 5, 172–180 (1997).
    Google Scholar 
    Heyes, C. What’s social about social learning? J. Comp. Psychol. 126, 193–202 (2012).PubMed 

    Google Scholar 
    Dawson, E. H., Avarguès-Weber, A., Chittka, L. & Leadbeater, E. Learning by observation emerges from simple associations in an insect model. Curr. Biol. 23, 727–730 (2013).CAS 
    PubMed 

    Google Scholar 
    Coolen, I., Giraldeau, L.-A. & Lavoie, M. Head position as an indicator of producer and scrounger tactics in a ground-feeding bird. Anim. Behav. 61, 895–903 (2001).
    Google Scholar 
    Scheid, C., Range, F. & Bugnyar, T. When, what, and whom to watch? Quantifying attention in ravens (Corvus corax) and jackdaws (Corvus monedula). J. Comp. Psychol. 121, 380–386 (2007).PubMed 

    Google Scholar 
    Hoppitt, W. & Laland, K. N. Social Learning: An Introduction to Mechanisms, Methods, and Models (Princeton University Press, 2013).
    Google Scholar 
    Whiten, A. The burgeoning reach of animal culture. Science 372 (2021).Penn, D. C. & Povinelli, D. J. On the lack of evidence that non-human animals possess anything remotely resembling a ‘theory of mind’. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 731–744 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    Whiten, A. Humans are not alone in computing how others see the world. Anim. Behav. 86, 213–221 (2013).
    Google Scholar 
    Zentall, T. R. Social learning mechanisms: Implications for a cognitive theory of imitation. Interact. Stud. 12, 233–261 (2011).
    Google Scholar 
    Akins, C. K. & Zentall, T. R. Imitative learning in male Japanese quail (Coturnix japonica) using the two-action method. J. Comp. Psychol. 110, 316–320 (1996).CAS 
    PubMed 

    Google Scholar 
    Heyes, C. & Saggerson, A. Testing for imitative and nonimitative social learning in the budgerigar using a two-object/two-action test. Anim. Behav. 64, 851–859 (2002).
    Google Scholar 
    McGrew, W. C. Social and cognitive capabilities of nonhuman primates: Lessons from the wild to captivity. Int. J. Study Anim. Probl. 2, 138–149 (1981).
    Google Scholar 
    Chapman, B. B., Ward, A. J. W. & Krause, J. Schooling and learning: Early social environment predicts social learning ability in the guppy, Poecilia reticulata. Anim. Behav. 76, 923–929 (2008).
    Google Scholar 
    Arnold, C. & Taborsky, B. Social experience in early ontogeny has lasting effects on social skills in cooperatively breeding cichlids. Anim. Behav. 79, 621–630 (2010).
    Google Scholar 
    McCune, K. B., Jablonski, P. G., Lee, S. & Ha, R. R. Captive jays exhibit reduced problem-solving performance compared to wild conspecifics. R. Soc. Open Sci. 6, 181311 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wilkinson, A., Kuenstner, K., Mueller, J. & Huber, L. Social learning in a non-social reptile (Geochelone carbonaria). Biol. Lett. 6, 614–616 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Leadbeater, E. What evolves in the evolution of social learning? J. Zool. 295, 4–11 (2015).
    Google Scholar 
    Doody, J. S. et al. Aggregated drinking behavior of radiated tortoises (Astrochelys radiata) in arid southwestern Madagascar. Chelonian Conserv. Biol. 10, 145–146 (2011).
    Google Scholar 
    Wendland, L. D. et al. Social behavior drives the dynamics of respiratory disease in threatened tortoises. Ecology 91, 1257–1262 (2010).PubMed 

    Google Scholar 
    Whiten, A. & Mesoudi, A. Establishing an experimental science of culture: Animal social diffusion experiments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3477–3488 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    Slagsvold, T. & Wiebe, K. L. Social learning in birds and its role in shaping a foraging niche. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 969–977 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Galef, B. G. & Whiten, A. The comparative psychology of social learning. In APA Handbook of Comparative Psychology: Vol. 2. Evolution, Development, and Neural Substrate (ed. Call, J.) 411–439 (American Psychological Association, 2017). https://doi.org/10.1037/0000012-019.Chapter 

    Google Scholar 
    Prum, R. O., Robinson, S. K. & Gill, F. B. Ornithology (Macmillan Learning, 2019).
    Google Scholar 
    Curry, R. L., Townsend Peterson, A. & Langen, T. A. California scrub-jay (Aphelocoma californica). In Birds of North America (eds Poole, A. & Gill, F.) (The Birds of North America, Inc, 2017).
    Google Scholar 
    Brown, J. L. Mexican jay (Aphelocoma ultramarina). In The Birds of North America (eds Poole, A. & Gill, F.) (The Birds of North America, Inc, 1994).
    Google Scholar 
    Rice, N. H., Martínez-Meyer, E. & Peterson, A. T. Ecological niche differentiation in the Aphelocoma jays: A phylogenetic perspective. Biol. J. Linn. Soc. 80, 369–383 (2003).
    Google Scholar 
    de Kort, S. R. & Clayton, N. S. An evolutionary perspective on caching by corvids. Proc. R. Soc. B Biol. Sci. 273, 417–423 (2006).
    Google Scholar 
    Pesendorfer, M. B. & Koenig, W. D. Competing for seed dispersal: Evidence for the role of avian seed hoarders in mediating apparent predation among oaks. Funct. Ecol. 31, 622–631 (2017).
    Google Scholar 
    Zentall, T. R. Perspectives on observational learning in animals. J. Comp. Psychol. 126, 114–128 (2012).PubMed 

    Google Scholar 
    Aplin, L. M. et al. Experimentally induced innovations lead to persistent culture via conformity in wild birds. Nature 518, 538–541 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    McCormack, J. E., Jablonski, P. G. & Brown, J. L. Producer-scrounger roles and joining based on dominance in a free-living group of Mexican jays (Aphelocoma ultramarina). Behaviour 144, 967–982 (2007).
    Google Scholar 
    Logan, C. J., Breen, A. J., Taylor, A. H., Gray, R. D. & Hoppitt, W. How New Caledonian crows solve novel foraging problems and what it means for cumulative culture. Learn. Behav. 44, 18–28 (2016).PubMed 

    Google Scholar 
    Ashton, B. J., Thornton, A. & Ridley, A. R. Larger group sizes facilitate the emergence and spread of innovations in a group-living bird. Anim. Behav. 158, 1–7 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Griffin, A. S. & Diquelou, M. C. Innovative problem solving in birds: A cross-species comparison of two highly successful passerines. Anim. Behav. 100, 84–94 (2015).
    Google Scholar 
    Therneau, T. M., Crowson, C. & Atkinson, E. Using time dependent covariates and time dependent coefficents in the Cox model. Survival Vignettes, 2, 3 (2017).
    Google Scholar 
    Barrett, B. J., McElreath, R. L. & Perry, S. E. Pay-off-biased social learning underlies the diffusion of novel extractive foraging traditions in a wild primate. Proc. R. Soc. B Biol. Sci. 284, 20170358 (2017).
    Google Scholar 
    Therneau, T. M. Coxme: Mixed Effects Cox Models (R Package, 2018).
    Google Scholar 
    Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974).ADS 
    MathSciNet 
    MATH 

    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002). https://doi.org/10.1016/j.ecolmodel.2003.11.004.Book 
    MATH 

    Google Scholar 
    Clayton, N. S., Dally, J. M. & Emery, N. J. Social cognition by food-caching corvids. The western scrub-jay as a natural psychologist. Philos. Trans. R. Soc. B Biol. Sci. 362, 507–522 (2007).
    Google Scholar 
    Hare, B., Call, J., Agnetta, B. & Tomasello, M. Chimpanzees know what conspecifics do and do not see. Anim. Behav. 59, 771–785 (2000).CAS 
    PubMed 

    Google Scholar 
    Emery, N. J., Seed, A. M., von Bayern, A. M. P. & Clayton, N. S. Cognitive adaptations of social bonding in birds. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 489–505 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    Westcott, P. W. Relationships among three species of jays wintering in southeastern Arizona. Condor 71, 353–359 (1969).
    Google Scholar 
    Kulahci, I. G. et al. Social networks predict selective observation and information spread in ravens. R. Soc. Open Sci. 3, 160256 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boucherie, P. H., Loretto, M. C., Massen, J. J. M. & Bugnyar, T. What constitutes “social complexity” and “social intelligence” in birds? Lessons from ravens. Behav. Ecol. Sociobiol. 73, 12 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Emery, N. J. Cognitive ornithology: The evolution of avian intelligence. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 23–43 (2006).PubMed 

    Google Scholar 
    Maclean, E. L. et al. How does cognition evolve? Phylogenetic comparative psychology. Anim. Cogn. 15, 223–238 (2012).PubMed 

    Google Scholar 
    Edwards, S. V. & Naeem, S. The phylogenetic component of cooperative breeding in perching birds. Am. Nat. 141, 754–789 (1993).CAS 
    PubMed 

    Google Scholar 
    Berg, E. C., Aldredge, R. A., Peterson, A. T. & McCormack, J. E. New phylogenetic information suggests both an increase and at least one loss of cooperative breeding during the evolutionary history of Aphelocoma jays. Evol. Ecol. 26, 43–54 (2012).
    Google Scholar 
    Ekman, J. & Ericson, P. G. P. Out of Gondwanaland; the evolutionary history of cooperative breeding and social behaviour among crows, magpies, jays and allies. Proc. R. Soc. B Biol. Sci. 273, 1117–1125 (2006).
    Google Scholar 
    Midford, P., Hailman, J. & Woolfenden, G. E. Social learning of a novel foraging patch in families of free-living Florida scrub-jays. Anim. Behav. 59, 1199–1207 (2000).CAS 
    PubMed 

    Google Scholar 
    Alcock, J. Animal Behavior (Sinauer Associates, Inc, 2009).
    Google Scholar 
    Burkart, J. M., Kupferberg, A., Glasauer, S. & van Schaik, C. P. Even simple forms of social learning rely on intention attribution in marmoset monkeys (Callithrix jacchus). J. Comp. Psychol. 126, 129–138 (2012).PubMed 

    Google Scholar 
    Burkart, J. M. & van Schaik, C. P. Cognitive consequences of cooperative breeding in primates? Anim. Cogn. 13, 1–19 (2010).PubMed 

    Google Scholar 
    Danchin, E., Giraldeau, L.-A., Valone, T. J. & Wagner, R. H. Public information: From nosy neighbors to cultural evolution. Science 305, 487–491 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gil, M. A., Emberts, Z., Jones, H. & St. Mary, C. M. Social information on fear and food drives animal grouping and fitness. Am. Nat. 189, 227–241 (2017).PubMed 

    Google Scholar 
    Call, J. & Tomasello, M. Does the chimpanzee have a theory of mind? 30 years later. Trends Cogn. Sci. 12, 187–192 (2008).PubMed 

    Google Scholar 
    Seyfarth, R. M. & Cheney, D. L. Social cognition. Anim. Behav. 103, 191–202 (2015).
    Google Scholar 
    Huber, L., Rechberger, S. & Taborsky, M. Social learning affects object exploration and manipulation in keas, Nestor notabilis. Anim. Behav. 62, 945–954 (2001).
    Google Scholar 
    Gajdon, G. K., Fijn, N. & Huber, L. Testing social learning in a wild mountain parrot, the kea (Nestor notabilis). Learn. Behav. 32, 62–71 (2004).PubMed 

    Google Scholar 
    McCowan, B., Anderson, K., Heagarty, A. & Cameron, A. Utility of social network analysis for primate behavioral management and well-being. Appl. Anim. Behav. Sci. 109, 396–405 (2008).
    Google Scholar 
    Williams, E., Bremner-Harrison, S. & Ward, S. Can we meet the needs of social species in zoos? An overview of the impact of group housing on welfare in socially housed zoo mammals. In Zoo Animals: Husbandry, Welfare and Public Interactions (eds. Berger, M. & Corbett, S.) (Nova Science Publishers, 2018).
    Google Scholar 
    Hoppitt, W., Samson, J., Laland, K. N. & Thornton, A. Identification of learning mechanisms in a wild Meerkat population. PLoS ONE 7, 1–11 (2012).
    Google Scholar 
    Kendal, R. L., Galef, B. G. & van Schaik, C. P. Social learning research outside the laboratory: How and why? Learn. Behav. 38, 187–194 (2010).PubMed 

    Google Scholar 
    Thornton, A. & Lukas, D. Individual variation in cognitive performance: Developmental and evolutionary perspectives. Philos. Trans. R. Soc. B Biol. Sci. 367, 2773–2783 (2012).
    Google Scholar 
    Herrmann, E., Call, J., Hernandez-Lloreda, M. V., Hare, B. & Tomasello, M. Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science 317, 1360–1366 (2007).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Balda, R. P. & Kamil, A. C. Spatial and social cognition in corvids: An evolutionary approach. In The Cognitive Animal: Empirical and Theoretical Perspectives on Animal Cognition (eds. Bekoff, M., Burghardt, G. & Allen, C.) (Bradford Book, 2002).
    Google Scholar 
    Greggor, A. L., Thornton, A. & Clayton, N. S. Harnessing learning biases is essential for applying social learning in conservation. Behav. Ecol. Sociobiol. 71, 1–12 (2017).
    Google Scholar 
    Brakes, P. et al. A deepening understanding of animal culture suggests lessons for conservation. Proc. R. Soc. B Biol. Sci. 288, 20202718 (2021).
    Google Scholar 
    Brakes, P. et al. Animal cultures matter for conservation. Science 363, 1032–1034 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Barrett, B. J., Zepeda, E., Pollack, L., Munson, A. & Sih, A. Counter-culture: Does social learning help or hinder adaptive response to human-induced rapid environmental change? Front. Ecol. Evol. 7, 1–18 (2019).
    Google Scholar 
    Rushworth, M. F. S., Mars, R. B. & Sallet, J. Are there specialized circuits for social cognition and are they unique to humans? Curr. Opin. Neurobiol. 23, 436–442 (2013).CAS 
    PubMed 

    Google Scholar 
    van Schaik, C. P. & Burkart, J. M. Social learning and evolution: The cultural intelligence hypothesis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 1008–1016 (2011).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Global patterns in functional rarity of marine fish

    We used AquaMaps18,32 to obtain species occurrence data, and extracted eleven ecologically and biologically relevant traits (seven continuous and four categorical) from FishBase19. All calculations were done separately for the two group of fishes (bony fishes and cartilaginous fishes). Our workflow is in Supplementary Fig. 1.Step 1- InputOccupancy Data (assemblage matrix)We extracted occupancy data from AquaMaps18,32. This online database provides half-degree grid cell species occurrences based on data from GBIF44 and OBIS45 complemented with information from FishBase19 and SeaLifeBase46. AquaMaps gives probabilities of the occurrence of a given species between 0 (based on the environmental envelope indicating there is no chance of finding the species in that grid cell) to 1 (highest probability to find the species in that grid cell)32.These occurrence data are then combined with an algorithm implemented by AquaMaps using “estimates of environmental preferences with respect to depth, water temperature, salinity, primary productivity, and association with sea ice or coastal areas”. For more information see Kesner-Reyes, et al.32.In our analysis, we selected occurrence data (the presence of a given species in a certain half degree grid cell) with a probability >0.9. To ensure our results were not simply an artefact of using a high probability of occurrence we also examined probabilities higher than 0.7 and 0.5. Further analyses were repeated independently for each of these probabilities. After this initial step, we allocated each half-degree grid cell to a 2° grid cell. We created the 2° grid cells using features available in ArcGIS47. These occupancy data also allowed us to compute the species richness of each 2° grid cell.We applied all analyses described here (see Supplementary Fig. 1) separately for the Coastal Systems and High Seas of each of the Seven Oceanic Regions (see Supplementary Fig. 3). This gives us seven Coastal Systems and seven High Seas, a total of 14 assemblage matrices for each of the probabilities (probability  > 0.9, 0.7 and 0.5).Trait Data Compilation (trait matrix)Our final occurrence data (each of the 14 systems) were split between marine bony fishes (11,961 Actinopterygii species) and cartilaginous fishes (866 Elasmobranchii species). For each species in both groups of fish we assembled biologically and ecologically relevant traits from the most recent version of the FishBase database19. This gave us seven continuous and four categorical traits that are largely uncorrelated with one another, (see Supplementary Fig. 9a and b):Environmental Traits: (1) Position in Water Column—The vertical position of a species indicates its feeding habitat19 and its influence on the process of transferring nutrients through the water column48,49. This trait has 8 categories. (2) Maximum Depth (m)—This trait reflects the environmental conditions that each species occur27. (3) Mean Temperature Preference (°C)—Thermal variations in preferences indicates the species tolerances to changes in temperature50,51,52.Life History Traits: (4) Growth (k)—This coefficient parameter (k = 1 year−1) is derived from the von Bertalanffy growth function (Lt = L ∞ (1-exp(−K(t−t0)))). Faster growth rates are associated with higher k values19. (5) (Q/B)—this trait represents the proportional ratio between food consumption (Q) and biomass (B) and can be used as a proxy for trophic interactions, evidencing the flow of energy in the system53,54. (6) Trophic Level—the position of a given species in the food chain is expressed as its trophic level and as discussed by Froese and Pauly19 can be assessed as the amount of “energy-transfer steps to that level”. The trophic level also gives information on interactions between species, for example predator-prey and trophic cascades55.Morphological Traits: (7) Body Shape—The body shape of a species relates to ecological behaviours, such as migration patterns56. We divided this trait into 38 categories (see Supplementary Table 1). (8) Swimming Mode—the mobile strategy adopted by fish species has a direct relationship with ecology and behaviour57. Following FishBase we used 12 different swimming modes (see Supplementary Table 1) based on anatomical and morphological features; these traits provide information on the functional role that each species plays.Reproductive Traits: (9) Generation Time—defined by FishBase as “the time period from birth to average age of reproduction”. (10) Length of First Maturity (mm)—body length when around 50% of a given species becomes mature58,59. (11) Reproductive Guild—15 categories of reproductive guild as defined by FishBase (see Supplementary Table 1). Reproductive traits have a direct influence on population dynamics and species resilience60,61, and are therefore commonly used in fisheries management62.These traits were selected for their ecological and biological relevance as described above. We tested the correlations of the traits to ensure complementarity, and as shown in the (Supplementary Fig. 9a and b), these traits are largely uncorrelated. We also took into consideration the data gaps inevitable in a large data set such as FishBase (traits were selected if a maximum of 30% of data were missing). To overcome this limitation we applied random forest algorithms to fill the missing traits63, by using the package “missForest”64.Step 2—Rarity indicesSpecies restrictednessWe calculated species restrictedness (Resi) by dividing the species geographical extent (GE = number of 2° grid cells that a species occurs in based on the assemblage matrix compiled from AquaMaps) by the total number of grid cells (TOT), minus one (see Supplementary Fig. 2). We scaled the values between 0 (species occurring in all 2° grid cells) and 1 (the most restricted species). We used the function “restrictedness” in the “funrar” package to do this calculation16.Functional distinctivenessFunctional distinctiveness (Disi) quantifies the level of dissimilarity in trait combination between species16,22 (see Supplementary Fig. 2). This index is the average of functional distance of a given species compared with all other species in the assemblage16.We calculated how distinct or common each species is by using the function “distinctiveness_global” available in the “funrar” package16. We then scaled the values found between 0 (species with common combinations of traits) and 1 (species with the most dissimilar combination of traits). This analysis was conducted using presence/absence data.Functional uniquenessFunctional uniqueness quantifies the level of species isolation in the multidimensional functional space16,17. This index is calculated by quantifying the distance of each species in relation to its nearest neighbour16. This mathematical approach applied to multidimensional functional space was adapted from the mean nearest neighbour distance developed initially to calculate the phylogenetic distance between species65 (see equation descriptions at the Supplementary Fig. 2).Step 3—Selecting rare species & Step 4—Rarity biogeographyQuartile analysisWe examined the distribution of values for species restrictedness and functional distinctiveness (or species restrictedness and functional uniqueness) and used the quartile criterion (performed using the base R function “quantile” from the package “stats” in R Core Team66) as proposed by Gaston20 to identify the rare species. By this definition the species considered rare lie in the top quartile of both metrics (i.e. values between 0 (less restricted) and 1 (more restricted)). We next assigned the observed number of rare species (Step 4), as defined above, to each 2° grid cell. The analysis was undertaken separately for Actinopterygii and Elasmobranchii.Step 5—Null modelDoes the number of rare species in a given grid cell differ from the null expectation? To answer this question we applied a null model approach based on the curveball algorithm67. This algorithm keeps constant the total number of species (rare + non-rare) and the number of grid cells that each species occurs. It then randomizes the presence and absence of all species following these thresholds. We ran the model for 2000 iterations; in each loop it randomizes the occurrences of all species, identifies where the rare species are falling and then counts the total number of rare species in each grid cell.To quantify how the observed number of rare species differ from the null expectation we then use Standardized Effect Sizes (SES) as follows:$${{{{{rm{SES}}}}}}=({{{{{rm{X}}}}}}-{{{{{rm{Y}}}}}})/{{{{{rm{Z}}}}}}$$X as the number of rare species observed in each grid cell,Y as the average of rare species found from the null model after 2000 interactions andZ as the standard deviation from Y.A positive SES indicates more rare species than would be expected by chance and a negative SES fewer than expected.We are using 14 different systems (7 Coastal Systems and 7 High Seas (see Supplementary Fig. 3)) for 2 groups of organisms (bony and cartilaginous fish), 2 functional rarity indices (distinctiveness and uniqueness), and using 3 different probabilities of occurrences (prob. >0.9, >0.7 and >0.5).We then have the following “roadmap”:

    i.

    Scales—7 Coastal Systems and 7 High Seas.

    ii.

    Groups—bony and cartilaginous fish.

    iii.

    Indices—distinctiveness and uniqueness.

    iv.

    Probability of occurrences— >0.9, >0.7 and >0.5.

    v.

    Total—168 independent cases analysis (each having its own assemblage and trait matrices.

    Therefore, as mentioned above, the null model ran for 2000 iterations in each of those independent cases. The final matrices from these initial steps contain grid cells as rows and as columns we have the raw number of rare species along with the SES values for each. These matrices were important to map our results.Step 6—Mapping the resultsAfter the above steps (and using the matrices with the results), to visualise the results for Coastal Systems we plotted the geographic distribution of rarity, measured using the observed number of rare species, based on species Restrictedness and functional Distinctiveness using Fig. 1a, c, and the results from the SES using Fig. 1b, d. Meanwhile in Fig. 2 we constructed the same plots for the High Seas. The complementary results of the alternative approach using species Restrictedness and functional Uniqueness are shown in Supplementary Fig. 4 (for Coastal Systems) and Supplementary Fig. 5 (for High Seas).The flow chart in Supplementary Fig. 1 provides step by step details of what was done for each of the 168 independent cases explained at the “roadmap” above. The comprehensive list of all rare fish species found for each system is available in Supplementary Table 2.Further analysesLatitudinal rarity biogeographyWe then produced the density plots of the positive SES values (using the function geom_density from the package ggplot268) to further understand these patterns in relation to latitude (Fig. 3, from c to j). These were compared with the latitudinal gradient of species richness (Fig. 3a, b). The main text discusses results focused on rarity measured using the probability of occurrence higher than 0.9 (Fig. 3). We also examined density of positive SES values across the latitudinal distribution using the probability of occurrences higher than 0.7 and 0.5 (see Supplementary Fig. 6a–d for probability >0.7 and Supplementary Fig. 6e–f for probability >0.5).Spatial autocorrelationWe constructed distance decay plots to examine spatial autocorrelation, and fitted a quantile regression to these relationships. The results are illustrated in Supplementary Fig. 7, which shows the distance decay calculated by pairwise differences (Supplementary Fig. 7a—Coastal Systems and b—High Seas for bony fish, and Supplementary Fig. 7c—Coastal Systems and d—High Seas for cartilaginous fish) between a given grid cell and all other grid cells present in the Northwest Pacific Ocean. These plots provide reassurance that spatial autocorrelation is not obscuring the results we report.Sensitivity analysisWe performed a sensitivity analysis to ensure that the environmental traits “Depth” and “Mean Temperature Preference” had no major influence on determining the level of distinctiveness and uniqueness of the species. We did this by excluding each trait in turn from the analysis (each of those were removed individually and a third time without both together) and compared the results with the full analysis. We found strong correlations in rarity estimates in all cases (see Supplementary Fig. 8a, b (for bony fish (distinctiveness and uniqueness) respectively, c and d (for cartilaginous fish (distinctiveness and uniqueness)).Trait correlation analysisWe tested the correlation between traits to ensure that those were largely uncorrelated, as shown in Supplementary Fig. 9a, b.Supplementary analysisWe tested the possible influence of sampling effect on the rarity hotspots observed by creating random fill matrices and comparing those with the observed matrices from four scenarios: Northwest Pacific Coast (bony and cartilaginous fish species) and Southwest Pacific Coast (bony and cartilaginous species). The subsequent results showed no evidence of sampling effect (see Supplementary Fig. 11).Mapping marine protected areas (MPAs)We used the MPAs shapefiles provided by the UNEP-WCMC and IUCN69 to measure the level of congruence between marine protected areas and hotspots of rarity. The distances between each MPA and the centroid of each grid cell were calculated using the spatial analysis tool in ArcGIS (the unit of the distance calculated is in decimal degrees). We then assigned each MPA to its nearest 2° degree grid cell centroid (the distance cut point used was < 0.75 decimal degrees (the distance from a given MPA to a grid cell centroid)). We plotted these global spatial patterns from the 2° grid cells indicating either congruence or mismatches between Marine Protected Areas (MPAs) and Rarity Hotspots (species rare in both dimensions of biodiversity; taxonomically—highest restrictedness and functionally—highest distinctiveness) (Fig. 4a and b, bony and cartilaginous fish respectively).Habitat specializationAll species were classified according to their position in the water column (bathydemersal, bathypelagic, benthopelagic, demersal, pelagic neritic, pelagic oceanic and reef associated (as categorized in FishBase19)); here we are using this trait as a proxy for “habitat specialization”. We then used a G test, and Cramer’s V (using the functions GTest and CramerV from the package DescTools70) to compare the frequency distribution in habitat specialization between rare and non-rare species (see Supplementary Fig. 10 for all frequency distributions and statistical results).Forms of functional rarity classification schemeIn their 2017 paper Violle et al.17, suggested 12 forms of functional rarity. We believe that the approach applied here is similar to the classification scheme they described as: “Rare traits irrespective of the scale and the species pool”. The authors pointed to two possible extremes: rare traits (exhibited by range-restricted species) and common traits (supported by many widespread species). In this case, our approach identifies species that are both geographically restricted within each of the 14 systems (coastal and high seas systems) and present a distinct (or unique) combination of traits. Our approach to the classification of rarity differs slightly, however, in that we follow Gaston’s approach20 of quantile distribution as illustrated in Supplementary Fig. 1, step 3 QUARTILES.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

  • in

    Niche partitioning of the ubiquitous and ecologically relevant NS5 marine group

    Hutchinson GE. Concluding remarks. Cold Spring Harb Symp Quant Biol. 1957;22:415–27.
    Google Scholar 
    Hutchinson GE. An introduction to population biology. New Haven, CT: Yale University Press; 1978.Larkin AA, Martiny AC. Microdiversity shapes the traits, niche space, and biogeography of microbial taxa. Environ Microbiol Rep. 2017;9:55–70.CAS 
    PubMed 

    Google Scholar 
    Mena C, Reglero P, Balbín R, Martín M, Santiago R, Sintes E. Seasonal niche partitioning of surface temperate open ocean prokaryotic communities. Front Microbiol. 2020;11:1749.PubMed 
    PubMed Central 

    Google Scholar 
    Sarmento H, Morana C, Gasol JM. Bacterioplankton niche partitioning in the use of phytoplankton-derived dissolved organic carbon: quantity is more important than quality. ISME J. 2016;10:2582–92.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Auladell A, Barberán A, Logares R, Garcés E, Gasol JM, Ferrera I. Seasonal niche differentiation among closely related marine bacteria. ISME J. 2022;16:178–89.CAS 
    PubMed 

    Google Scholar 
    Avcı B, Krüger K, Fuchs BM, Teeling H, Amann RI. Polysaccharide niche partitioning of distinct Polaribacter clades during North Sea spring algal blooms. ISME J. 2020;14:1369–83.PubMed 
    PubMed Central 

    Google Scholar 
    Ghiglione J-F, Galand PE, Pommier T, Pedrós-Alió C, Maas EW, Bakker K, et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc Natl Acad Sci USA. 2012;109:17633–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Johnson ZI, Zinser ER, Coe A, McNulty NP, Woodward EMS, Chisholm SW. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science. 2006;311:1737–40.CAS 
    PubMed 

    Google Scholar 
    Wang Z, Juarez DL, Pan J-F, Blinebry SK, Gronniger J, Clark JS, et al. Microbial communities across nearshore to offshore coastal transects are primarily shaped by distance and temperature. Environ Microbiol. 2019;21:3862–72.CAS 
    PubMed 

    Google Scholar 
    Herlemann DP, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappé MS, et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife. 2019;8:e46497.PubMed 
    PubMed Central 

    Google Scholar 
    Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife. 2016;5:e11888.PubMed 
    PubMed Central 

    Google Scholar 
    Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
    PubMed 

    Google Scholar 
    Alonso C, Warnecke F, Amann R, Pernthaler J. High local and global diversity of flavobacteria in marine plankton. Environ Microbiol. 2007;9:1253–66.CAS 
    PubMed 

    Google Scholar 
    Ngugi DK, Stingl U. High-quality draft single-cell genome sequence of the NS5 Marine Group from the Coastal Red Sea. Genome Announc. 2018;26:e00565-18.
    Google Scholar 
    Meziti A, Kormas KA, Moustaka-Gouni M, Karayanni H. Spatially uniform but temporally variable bacterioplankton in a semi-enclosed coastal area. Syst Appl Microbiol. 2015;38:358–67.
    Google Scholar 
    Milici M, Vital M, Tomasch J, Badewien TH, Giebel H-A, Plumeier I, et al. Diversity and community composition of particle-associated and free-living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: evidence of dispersal limitation in the Bransfield Strait. Limnol Oceanogr. 2017;62:1080–95.
    Google Scholar 
    Beman JM, Vargas SM, Vazquez S, Wilson JM, Yu A, Cairo A, et al. Biogeochemistry and hydrography shape microbial community assembly and activity in the eastern tropical North Pacific Ocean oxygen minimum zone. Environ Microbiol. 2020;23:2765–81.PubMed 

    Google Scholar 
    Rapp JZ, Fernández-Méndez M, Bienhold C, Boetius A. Effects of ice-algal aggregate export on the connectivity of bacterial communities in the Central Arctic Ocean. Front Microbiol. 2018;9:01035.
    Google Scholar 
    Gómez-Pereira PR, Fuchs BM, Alonso C, Oliver MJ, van Beusekom JEE, Amann R. Distinct flavobacterial communities in contrasting water masses of the North Atlantic Ocean. ISME J. 2010;4:472–87.PubMed 

    Google Scholar 
    Choi DH, An SM, Yang EC, Lee H, Shim J, Jeong J, et al. Daily variation in the prokaryotic community during a spring bloom in shelf waters of the East China Sea. FEMS Microbiol Ecol. 2018;94:fiy134.CAS 
    PubMed Central 

    Google Scholar 
    Yang C, Li Y, Zhou B, Zhou Y, Zheng W, Tian Y, et al. Illumina sequencing-based analysis of free-living bacterial community dynamics during an Akashiwo sanguine bloom in Xiamen sea, China. Sci Rep. 2015;5:8476.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Díez‐Vives C, Nielsen S, Sánchez P, Palenzuela O, Ferrera I, Sebastián M, et al. Delineation of ecologically distinct units of marine Bacteroidetes in the Northwestern Mediterranean Sea. Mol Ecol. 2019;28:2846–59.PubMed 

    Google Scholar 
    Seo J-H, Kang I, Yang S-J, Cho J-C. Characterization of spatial distribution of the bacterial community in the South Sea of Korea. PLoS ONE. 2017;12:e0174159.PubMed 
    PubMed Central 

    Google Scholar 
    Alonso‐Sáez L, Díaz‐Pérez L, Morán XAG. The hidden seasonality of the rare biosphere in coastal marine bacterioplankton. Environ Microbiol. 2015;17:3766–80.PubMed 

    Google Scholar 
    Priest T, Orellana LH, Huettel B, Fuchs BM, Amann R. Microbial metagenome-assembled genomes of the Fram Strait from short and long read sequencing platforms. PeerJ. 2021;9:e11721.PubMed 
    PubMed Central 

    Google Scholar 
    Zhou J, Bruns MA, Tiedje JM. DNA recovery from soils of diverse composition. Appl Environ Microbiol. 1996;62:316–22.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, et al. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
    PubMed 

    Google Scholar 
    Kolmogorov M, Bickhart DM, Behsaz B, Gurevich A, Rayko M, Shin SB, et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods. 2020;17:1103–10.CAS 
    PubMed 

    Google Scholar 
    Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
    PubMed 

    Google Scholar 
    Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
    PubMed Central 

    Google Scholar 
    Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
    PubMed 

    Google Scholar 
    Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.CAS 

    Google Scholar 
    Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.CAS 
    PubMed 

    Google Scholar 
    Krüger K, Chafee M, Ben Francis T, Glavina del Rio T, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
    PubMed Central 

    Google Scholar 
    Francis TB, Bartosik D, Sura T, Sichert A, Hehemann J-H, Markert S, et al. Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom. ISME J. 2021;15:2336–50.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
    PubMed Central 

    Google Scholar 
    Winkelmann N, Harder J. An improved isolation method for attached-living Planctomycetes of the genus Rhodopirellula. J Microbiol Methods. 2009;77:276–84.CAS 
    PubMed 

    Google Scholar 
    Hahnke RL, Bennke CM, Fuchs BM, Mann AJ, Rhiel E, Teeling H, et al. Dilution cultivation of marine heterotrophic bacteria abundant after a spring phytoplankton bloom in the North Sea. Environ Microbiol. 2015;17:3515–26.PubMed 

    Google Scholar 
    Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27:722–36.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:1–6.
    Google Scholar 
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.PubMed 
    PubMed Central 

    Google Scholar 
    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 
    Seeman T. Barrnap 0.9 (version 3): rapid ribosomal RNA prediction. 2017. https://github.com/tseemann/barrnap.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Amann RI, Krumholz L, Stahl DA. Fluorescent-oligonucleotide probing of whole cells for determinative, phylogenetic, and environmental studies in microbiology. J Bacteriol. 1990;172:762–70.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pesant S, Not F, Picheral M, Kandels-Lewis S, Le Bescot N, Gorsky G, et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci Data. 2015;2:150023.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bushnell B. BBTools software package. 2017. https://sourceforge.net/projects/bbmap/.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–8.CAS 
    PubMed 

    Google Scholar 
    RStudio Team. RStudio: integrated development of R. Boston, MA: RStudio Inc.; 2015.South A. rnaturalearth: World map data from Natural Earth. R packag version 0.1.0; 2017.Pebesma E. Simple features for R: standardized support for spatial vector data. R J. 2018;10:439–46.
    Google Scholar 
    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.Orellana LH, Francis TB, Ferraro M, Hehemann J-H, Fuchs BM, Amann RI. Verrucomicrobiota are specialist consumers of sulfated methyl pentoses during diatom blooms. ISME J. 2021.Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.PubMed 

    Google Scholar 
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan community ecology package version 2.5, 7 November. 2020.Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36:2251–2.CAS 
    PubMed 

    Google Scholar 
    Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res. 2014;42:D206–14.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
    PubMed 

    Google Scholar 
    Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:95–101.
    Google Scholar 
    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 

    Google Scholar 
    Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:490–5.
    Google Scholar 
    Barbeyron T, Brillet-Guéguen L, Carré W, Carrière C, Caron C, Czjzek M, et al. Matching the diversity of sulfated biomolecules: creation of a classification database for sulfatases reflecting their substrate specificity. PLoS ONE. 2016;11:e0164846.PubMed 
    PubMed Central 

    Google Scholar 
    Rawlings ND, Barrett AJ, Thomas PD, Huang X, Bateman A, Finn RD. The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res. 2018;46:624–32.
    Google Scholar 
    Wilkins D. gggenes: Draw Gene Arrow Maps in ‘ggplot2’. R package version 0.4.1; 2020.de Vries A, Ripley BD. ggdendro: create dendrograms and tree diagrams using ‘ggplot2’. 2020.Kappelmann L, Krüger K, Hehemann J-H, Harder J, Markert S, Unfried F, et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 2019;13:76–91.CAS 
    PubMed 

    Google Scholar 
    Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Letunic I, Bork P. Interactive tree of life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:256–9.
    Google Scholar 
    Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer K-H, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12:635–45.CAS 
    PubMed 

    Google Scholar 
    Konstantinidis KT, Rosselló-Móra R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
    PubMed Central 

    Google Scholar 
    Bjursell MK, Martens EC, Gordon JI. Functional genomic and metabolic studies of the adaptations of a prominent adult human gut symbiont, Bacteroides thetaiotaomicron, to the suckling period. J Biol Chem. 2006;281:36269–79.CAS 
    PubMed 

    Google Scholar 
    Ficko-Blean E, Préchoux A, Thomas F, Rochat T, Larocque R, Zhu Y, et al. Carrageenan catabolism is encoded by a complex regulon in marine heterotrophic bacteria. Nat Commun. 2017;8:1685.PubMed 
    PubMed Central 

    Google Scholar 
    Johnson ET, Baron DB, Naranjo B, Bond DR, Schmidt-Dannert C, Gralnick JA. Enhancement of survival and electricity production in an engineered bacterium by light-driven proton pumping. Appl Environ Microbiol. 2010;76:4123–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dubinsky V, Haber M, Burgsdorf I, Saurav K, Lehahn Y, Malik A, et al. Metagenomic analysis reveals unusually high incidence of proteorhodopsin genes in the ultraoligotrophic Eastern Mediterranean Sea. Environ Microbiol. 2017;19:1077–90.CAS 
    PubMed 

    Google Scholar 
    Fernández-Gómez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013;7:1026–37.PubMed 
    PubMed Central 

    Google Scholar 
    Heins A, Reintjes G, Amann RI, Harder J. Particle collection in Imhoff sedimentation cones enriches both motile chemotactic and particle-attached bacteria. Front Microbiol. 2021;12:643730.PubMed 
    PubMed Central 

    Google Scholar 
    Unfried F, Becker S, Robb CS, Hehemann J-H, Markert S, Heiden SE, et al. Adaptive mechanisms that provide competitive advantages to marine Bacteroidetes during microalgal blooms. ISME J. 2018;12:2894–906.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bauer M, Kube M, Teeling H, Richter M, Lombardot T, Allers E, et al. Whole genome analysis of the marine Bacteroidetes ‘Gramella forsetii’ reveals adaptations to degradation of polymeric organic matter. Environ Microbiol. 2006;8:2201–13.CAS 
    PubMed 

    Google Scholar 
    Kabisch A, Otto A, König S, Becher D, Albrecht D, Schüler M, et al. Functional characterization of polysaccharide utilization loci in the marine Bacteroidetes ‘Gramella forsetii’ KT0803. ISME J. 2014;8:1492–502.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Reintjes G, Arnosti C, Fuchs B, Amann R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: a biogeographical study of bacterial substrate utilisation. ISME J. 2019;13:1119–32.CAS 
    PubMed 

    Google Scholar 
    Thomas F, Barbeyron T, Tonon T, Génicot S, Czjzek M, Michel G. Characterization of the first alginolytic operons in a marine bacterium: from their emergence in marine Flavobacteriia to their independent transfers to marine Proteobacteria and human gut Bacteroides. Environ Microbiol. 2012;14:2379–94.CAS 
    PubMed 

    Google Scholar 
    Hehemann J-H, Arevalo P, Datta MS, Yu X, Corzett CH, Henschel A, et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat Commun. 2016;7:12860.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Deniaud-Bouët E, Hardouin K, Potin P, Kloareg B, Hervé C. A review about brown algal cell walls and fucose-containing sulfated polysaccharides: cell wall context, biomedical properties and key research challenges. Carbohydr Polym. 2017;175:395–408.PubMed 

    Google Scholar 
    Sichert A, Corzett CH, Schechter MS, Unfried F, Markert S, Becher D, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5:1026–39.CAS 
    PubMed 

    Google Scholar 
    Duerschlag J, Mohr W, Ferdelman TG, LaRoche J, Desai D, Croot PL, et al. Niche partitioning by photosynthetic plankton as a driver of CO2-fixation across the oligotrophic South Pacific Subtropical Ocean. ISME J. 2022;15:465–76.
    Google Scholar 
    Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.PubMed 

    Google Scholar 
    Raes J, Letunic I, Yamada T, Jensen LJ, Bork P. Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data. Mol Syst Biol. 2011;7:473.PubMed 
    PubMed Central 

    Google Scholar 
    Baas Becking L. G. M.. Geobiologie of inleiding tot de milieukunde. WP Van Stock Zoon, Den Haag; 1934.Gibbons SM, Caporaso JG, Pirrung M, Field D, Knight R, Gilbert JA. Evidence for a persistent microbial seed bank throughout the global ocean. Proc Natl Acad Sci USA. 2013;110:4651–5.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lennon JT, Jones SE. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9:119–30.CAS 
    PubMed 

    Google Scholar  More

  • in

    Physiological response and proteomics analysis of Reaumuria soongorica under salt stress

    Effects of NaCl concentrations on growth indicators of R. soongorica seedlingsAs shown in Table 1, when compared with control A (i.e., 0 mM NaCl), both the fresh weight and root/shoot ratio of R. soongorica in group B (i.e., 200 mM NaCl) were significantly higher. However, both fresh weight and root/shoot ratio gradually decreased in group C (i.e., 500 mM NaCl). When the NaCl concentration reached that of group C (i.e., 500 mM NaCl), the growth of R. soongorica was significantly inhibited. The fresh weight of above-ground and root tissues was respectively 43.82% and 50.99% that of the control, and these differences were significant (P  More

  • in

    Human ignitions on private lands drive USFS cross-boundary wildfire transmission and community impacts in the western US

    Zald, H. S. J. & Dunn, C. J. Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape. Ecol. Appl. 2, 1–13 (2018).
    Google Scholar 
    Schoennagel, T. et al. Adapt to more wildfire in western North American forests as climate changes. Proc. Natl. Acad. Sci. 114, 4582–4590 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Johnstone, J. F. et al. Changing disturbance regimes, ecological memory, and forest resilience. Front. Ecol. Environ. 14, 369–378 (2016).
    Google Scholar 
    Radeloff, V. C., Helmers, D. P., Kramer, H. A., Mockrin, M. H. & Alexandre, P. M. Rapid growth of the US wildland-urban interface raises wildfire risk. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1718850115 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase western U.S. forest wildfire activity. Science (80-.). 313, 940–943 (2006).ADS 
    CAS 

    Google Scholar 
    Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 1–11 (2015).CAS 

    Google Scholar 
    Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. U. S. A. 113, 11770–11775 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Agee, J. K. The landscape ecology of western forest fire regimes. Northwest Sci. 72, 7569 (1993).
    Google Scholar 
    Whitehair, L., Fulé, P. Z., Meador, A. S., Azpeleta, T. A. & Kim, Y. S. Fire regime on a cultural landscape: Navajo Nation. Ecol. Evol. 8, 9848–9858 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Hessburg, P. F. et al. Restoring fire-prone Inland Pacific landscapes: seven core principles. Landsc. Ecol. 30, 1805–1835 (2015).
    Google Scholar 
    Calkin, D. E., Thompson, M. P. & Finney, M. A. Negative consequences of positive feedbacks in US wildfire management. For. Ecosyst. 2, 1–10 (2015).
    Google Scholar 
    Mietkiewicz, N. et al. In the line of fire: consequences of human-ignited wildfires to homes in the U.S. (1992–2015). Fire 3, 1–20 (2020).
    Google Scholar 
    USDA Forest Service & Department of the Interior. 2014 Quadrennial Fire Review: Final Report. (2015).Fischer, A. P. et al. Wildfire risk as a socioecological pathology. Front. Ecol. Environ. 14, 276–284 (2016).
    Google Scholar 
    Hamilton, M., Fischer, A. P. & Ager, A. A social-ecological network approach for understanding wildfire risk governance. Glob. Environ. Chang. 54, 113–123 (2019).
    Google Scholar 
    Syphard, A. D. et al. Human influence on California fire regimes. Ecol. Appl. 17, 1388–1402 (2007).PubMed 

    Google Scholar 
    Balch, J. K. et al. Human-started wildfires expand the fire niche across the USA. Proc. Natl. Acad. Sci. U. S. A. 114, 2946–2951 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoover, K. Federal wildfire management: Ten-year funding trends and issues (FY2011-FY2020). Congressional Research Service (2020).Brown, H. The Camp Fire tragedy of 2018 in California. Fire Manag. Today 78, 11–22 (2020).
    Google Scholar 
    Wang, D., Guan, D., Kinnon, M. M., Geng, G. & Davis, S. J. Economic footprint of California wildfires in 2018. Nat. Sustain. https://doi.org/10.1038/s41893-020-00646-7 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Higuera, P. E. & Abatzoglou, J. T. Record-setting climate enabled the extraordinary 2020 fire season in the western USA. Glob. Chang. Biol. 27, 1–2 (2021).ADS 
    PubMed 

    Google Scholar 
    NIFC. National Report of Wildland Fires and Acres Burned by State. Natl. Interag. Fire Cent. 64–75 (2018).Ager, A. A. et al. Wildfire exposure to the wildland urban interface in the western US. Appl. Geogr. 111, 102059 (2019).
    Google Scholar 
    Palaiologou, P., Ager, A. A., Evers, C. R., Nielsen-Pincus, M. & Day, M. A. Fine-scale assessment of cross-boundary wildfire events in the western USA. Nat. Hazards Earth Syst. Sci. 6, 1755–1777 (2019).ADS 

    Google Scholar 
    Evers, C. R., Ager, A. A., Nielsen-pincus, M., Palaiologou, P. & Bunzel, K. Archetypes of community wildfire exposure from national forests of the western USA. Landsc. Urban Plan. 182, 55–66 (2019).
    Google Scholar 
    Artley, D. K. Wildland fire protection and response in the United States: the responsibilities, authorities, and roles of federal, state, local, and tribal government. Int. Assoc. Fire Chiefs 5, 1–117 (2009).
    Google Scholar 
    USDA Forest Service. National action plan: An implementation framework for the National Cohesive Wildland Fire Management Strategy. USDA For. Serv. (2014).Ager, A. A. et al. Network analysis of wildfire transmission and implications for risk governance. PLoS One https://doi.org/10.1371/journal.pone.0172867 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fleming, C. J., Mccartha, E. B. & Steelman, T. A. Conflict and collaboration in wildfire management: the role of mission alignment. Public Adm. Rev. 75, 445–454 (2015).
    Google Scholar 
    Dunn, C. J. et al. Wildfire risk science facilitates adaptation of fire-prone social-ecological systems to the new fire reality. Environ. Res. Lett. 15, 25001 (2020).
    Google Scholar 
    Calkin, D. E., Cohen, J. D., Finney, M. A. & Thompson, M. P. How risk management can prevent future wildfire disasters in the wildland-urban interface. Proc. Natl. Acad. Sci. U. S. A. 111, 746–751 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Whitman, E. et al. The climate space of fire regimes in north-western North America. J. Biogeogr. 42, 1736–1749 (2015).
    Google Scholar 
    Littell, J. S., Mckenzie, D., Peterson, D. L. & Westerling, A. L. Climate and wildfire area burned in western U.S.A ecoprovinces, 1916–2003. Ecol. Appl. 19, 1003–1021 (2009).PubMed 

    Google Scholar 
    Syphard, A. D., Keeley, J. E., Pfaff, A. H. & Ferschweiler, K. Human presence diminishes the importance of climate in driving fire activity across the USA. Proc. Natl. Acad. Sci. U. S. A. 114, 13750–13755 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parisien, M. A. et al. The spatially varying influence of humans on fire probability in North America. Environ. Res. Lett. 11, 1089 (2016).
    Google Scholar 
    Scott, J. H. et al. Wildfire risk to communities: spatial datasets of landscape-wide widlfire risk components for the USA. Fort Collins CO For. Serv. Res. Data Arch. 3, 159–1089 (2020).
    Google Scholar 
    Smith, A. M. S. et al. The science of firescapes: achieving fire-resilient communities. Bioscience 66, 130–146 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Moritz, M. A. et al. Learning to coexist with wildfire. Nature 515, 58–66 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ager, A. A. et al. Predicting paradise: modeling future wildfire disasters in the western USA. Sci. Total Environ. 784, 147057 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ager, A. A. et al. Wildfire exposure and fuel management on western USA national forests. J. Environ. Manag. 145, 54–70 (2014).
    Google Scholar 
    Haas, J. R., Calkin, D. E. & Thompson, M. P. Wildfire risk transmission in the Colorado Front Range, USA. Risk Anal. 35, 226–240 (2015).PubMed 

    Google Scholar 
    Stephens, S. L. & Ruth, L. W. Federal forest-fire policy in the USA. Ecol. Appl. 15, 532–542 (2005).
    Google Scholar 
    Harrell, A. All California’s national forests, including Tahoe’s, to close as fires rage (San Francisco Chronicle, 2020).Thompson, M. P., Gannon, B. M. & Caggiano, M. D. Forest roads and operational wildfire response planning. Forests 12, 1–11 (2021).
    Google Scholar 
    Parks, S. A., Parisien, M. A., Miller, C. & Dobrowski, S. Z. Fire activity and severity in the western US vary along proxy gradients representing fuel amount and fuel moisture. PLoS ONE 9, 1–8 (2014).
    Google Scholar 
    Scott, J. H. & Burgan, R. E. Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA For. Serv. Gen. Tech. Rep. RMRS GTR 2, 1–76. https://doi.org/10.2737/RMRS-GTR-153 (2005).Article 

    Google Scholar 
    Keeley, J. E. & Syphard, A. D. Climate change and future fire regimes: examples from California. Geosciences 6, 129 (2016).
    Google Scholar 
    Thompson, M. P., Dunn, C. J. & Calkin, D. E. Wildfire: systemic changes required. Science (80-.) 20, 63 (2015).
    Google Scholar 
    North, M. et al. Reform forest fire management. Science (80-.) 3, 7–1459 (2015).
    Google Scholar 
    Williams, J. Exploring the onset of high-impact mega-fires through a forest land management prism. For. Ecol. Manag. 294, 4–10 (2013).
    Google Scholar 
    Safford, H. D., Stevens, J. T., Merriam, K., Meyer, M. D. & Latimer, A. M. Fuel treatment effectiveness in California yellow pine and mixed conifer forests. For. Ecol. Manag. 274, 17–28 (2012).
    Google Scholar 
    Prichard, S. J., Povak, N. A., Kennedy, M. C. & Peterson, D. W. Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires. Ecol. Appl. 30, 1–22 (2020).
    Google Scholar 
    Thompson, M. P., Riley, K. L., Loeffler, D. & Haas, J. R. Modeling fuel treatment leverage: encounter rates, risk reduction, and suppression cost impacts. Forests 8, 1–26 (2017).
    Google Scholar 
    Boer, M. M., Price, O. F. & Bradstock, R. A. Wildfires: weigh policy effectiveness. Science (80-.) 250, 919 (2015).
    Google Scholar 
    Barnett, K., Parks, S. A., Miller, C. & Naughton, H. T. Beyond fuel treatment effectiveness: characterizing interactions between fire and treatments in the USA. Forests 7, 7569 (2016).
    Google Scholar 
    Brenkert-Smith, H., Champ, P. A. & Flores, N. Insights into wildfire mitigation decisions among wildland-urban interface residents. Soc. Nat. Resour. 19, 759–768 (2006).
    Google Scholar 
    Reams, M. A., Haines, T. K., Renner, C. R., Wascom, M. W. & Kingre, H. Goals, obstacles and effective strategies of wildfire mitigation programs in the Wildland-Urban Interface. For. Policy Econ. 7, 818–826 (2005).
    Google Scholar 
    Cohen, J. The wildland-urban interface fire problem: a consequence of the fire exclusion paradigm. For. Hist. Today 2008, 20–26 (2008).
    Google Scholar 
    Caggiano, M. D., Hawbaker, T. J., Gannon, B. M. & Hoffman, C. M. Building loss in WUI disasters: evaluating the core components of the wildland–urban interface definition. Fire 3, 1–17 (2020).
    Google Scholar 
    Steelman, T. A. & Burke, C. A. Is wildfire policy in the USA sustainable?. J. For. 105, 67–72 (2007).
    Google Scholar 
    Syphard, A. D. & Keeley, J. E. Factors associated with structure loss in the 2013–2018 California wildfires. Fire 2, 1–15 (2019).
    Google Scholar 
    Keeley, J. E. & Syphard, A. D. Historical patterns of wildfire ignition sources in California ecosystems. Int. J. Wildl. Fire 27, 781–799 (2018).
    Google Scholar 
    Scott, J. H., Thompson, M. P. & Calkin, D. E. A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315 US. Dep. Agric. For. Serv. Rocky Mt. Res. Stn. P 83, 59–67 (2013).
    Google Scholar 
    Rodrıguez y Silva, F., O’Connor, C. D., Thompson, M. P., Ramon Molina Martinez, J. & Calkin, D. E. Modelling suppression difficulty: current and future applications. Int. J. Wildl. Fire (2020).O’Connor, C. D., Calkin, D. E. & Thompson, M. P. An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. Int. J. Wildl. Fire 2, 587–597 (2017).
    Google Scholar 
    Thompson, M. P. et al. Application of wildfire risk assessment results to wildfire response planning in the Southern Sierra Nevada, California, USA. Forests 7, 542 (2016).
    Google Scholar 
    Thompson, M. P. et al. Prototyping a geospatial atlas for wildfire planning and management. Forests 2, 1–17 (2020).
    Google Scholar 
    Paveglio, T. B. et al. Urban interface: adaptive capacity for wildfire. For. Sci. 61, 298–310 (2015).
    Google Scholar 
    Haas, J. R., Calkin, D. E. & Thompson, M. P. A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the USA. Landsc. Urban Plan. 119, 44–53 (2013).
    Google Scholar 
    Mockrin, M. H., Stewart, S. I., Radeloff, V. C., Hammer, R. B. & Alexandre, P. M. Adapting to wildfire: rebuilding after home loss. Soc. Nat. Resour. 28, 839–856 (2015).
    Google Scholar 
    Haire, S. L. & McGarigal, K. Effects of landscape patterns of fire severity on regenerating ponderosa pine forests (Pinus ponderosa) in New Mexico and Arizona, USA. Landsc. Ecol. 25, 1055–1069 (2010).
    Google Scholar 
    Coop, J. D. et al. Wildfire-driven forest conversion in western North American landscapes. Bioscience 70, 659–673 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Syphard, A. D., Brennan, T. J. & Keeley, J. E. Drivers of chaparral type conversion to herbaceous vegetation in coastal Southern California. Sci. Rep. 2, 90–101. https://doi.org/10.1111/ddi.12827 (2019).Article 

    Google Scholar 
    Steelman, T. U. S. wildfire governance as a socio-ecological problem. Ecol. Soc. 21, 386–408 (2016).
    Google Scholar 
    Short, K. C. Spatial wildfire occurrence data for the United States, 1992-2018 [FPA_FOD_20210617], 5th edn. https://doi.org/10.2737/RDS-2013-0009.5 (Forest Service Research Data Archive, Fort Collins, CO, 2021).
    Google Scholar 
    Short, K. C. A spatial database of wildfires in the USA, 1992–2011. Earth Syst. Sci. Data 6, 1–27 (2014).ADS 

    Google Scholar 
    PRISM. (PRISM Climate Group, Oregon State University. http://www.prism.oregonstate.edu, 2020).USGS. Protected areas database of the United States (PAD-US) 2.1: U.S. Geological Survey data release. (2020). https://doi.org/10.5066/P92QM3NT. Accessed 15 Nov 2020.Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography (Cop.) 35, 879–888 (2012).
    Google Scholar 
    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2, 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x (2008).Article 

    Google Scholar 
    Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).
    Google Scholar 
    Greenwell, B., Boehmke, B., Cunningham, J. & GBM-developers. gmb: Generalized boosted regression models. R Packag. version 2.1.8. https//CRAN.R-project.org/package=gbm (2020).Hijmans, R. J., Philips, S., Leathwick, J. & Elith, J. dismo: Species distribution modeling. R Packag. version 1.3–3. https//CRAN.R-project.org/package=dismo (2020). More

  • in

    Brazil opens highly protected caves to mining, risking fauna

    CORRESPONDENCE
    15 February 2022

    Brazil opens highly protected caves to mining, risking fauna

    Hernani Fernandes Magalhaes de Oliveira

     ORCID: http://orcid.org/0000-0001-7040-8317

    0
    ,

    Daiana Cardoso Silva

     ORCID: http://orcid.org/0000-0003-1612-6452

    1
    ,

    Priscilla Lora Zangrandi

     ORCID: http://orcid.org/0000-0003-1406-944X

    2
    &

    Fabricius Maia Chaves Bicalho Domingos

     ORCID: http://orcid.org/0000-0003-2069-9317

    3

    Hernani Fernandes Magalhaes de Oliveira

    Federal University of Paraná, Curitiba, Brazil.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Daiana Cardoso Silva

    Mato Grosso State University, Nova Xavantina, Brazil.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Priscilla Lora Zangrandi

    Toronto, Canada.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Fabricius Maia Chaves Bicalho Domingos

    Federal University of Paraná, Curitiba, Brazil.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Twitter

    Facebook

    Email

    Brazil’s government has changed the designation of caves that warrant top priority for conservation (see go.nature.com/3gy5). Constituting some 13–30% of the country’s 22,000 protected caves, these will now be open to commercial exploitation, which could seriously affect their vulnerable fauna.

    Access options

    Access through your institution

    Change institution

    Buy or subscribe

    /* style specs start */
    style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
    /* style specs end */Subscribe to nature+Get immediate online access to the entire Nature family of 50+ journals$29.99monthlySubscribeSubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00BuyAll prices are NET prices.

    Additional access options:

    Log in

    Learn about institutional subscriptions

    Nature 602, 386 (2022)
    doi: https://doi.org/10.1038/d41586-022-00406-x

    Competing Interests
    The authors declare no competing interests.

    Related Articles

    See more letters to the editor

    Subjects

    Conservation biology

    Government

    Industry

    Latest on:

    Government

    China: reform research-evaluation criteria
    Correspondence 15 FEB 22

    Biden needs scientists with policy chops
    World View 11 FEB 22

    Africa is bringing vaccine manufacturing home
    Editorial 09 FEB 22

    Industry

    Start-ups create career opportunities for scientists
    Career Feature 07 FEB 22

    Climate pledges from top companies crumble under scrutiny
    News 07 FEB 22

    Theranos’s lesson for investors: speak to lab workers
    Correspondence 25 JAN 22

    Jobs

    Hodi Research Fellow

    Dana-Farber Cancer Institute (DFCI)
    Boston, MA, United States

    Research Associate / Postdoctoral Researcher-Carbon

    Woodwell Climate Research Center
    Falmouth, MA, United States

    Postdoc Bioinformatics in (single cell) transcriptomic and (epi)genomic analyses of pediatric brain tumors

    Prinses Máxima Centrum
    Utrecht, Netherlands

    Chief Editor, Nature Reviews Cancer

    Springer Nature
    London, United Kingdom More

  • in

    Retention of deposited ammonium and nitrate and its impact on the global forest carbon sink

    Study sitesThe paired 15N-tracer experiments were conducted in 13 forest sites, of which nine were in China, two in Europe and two in the USA. These sites vary in mean annual precipitation (MAP) from 700 to 2500 mm, in mean annual temperature (MAT) from 3 to > 20 °C, and in soil types (Fig. 1, Supplementary Table 1, Supplementary Table 2). Ambient N deposition (bulk/throughfall NH4+ plus NO3−) at the sites ranged from 6 to 54 kg N ha−1 yr−1. Forest types at the experimental sites include tropical forests in southern China, subtropical forests in central China, and temperate forests in northeastern China, Europe, and the USA. Data from the sites in Europe, the USA, and six of the nine sites in China have been reported previously. Detailed descriptions of these sites and the related data source references are summarized in Supplementary Table 1. Data for forests at the other three sites in China (Xishuangbanna, Wuyishan, and Maoershan) are originally presented here. The Xishuangbanna sites, which is located Xishuangbanna National Forest Reserve in Menglun, Mengla County, Yunnan Province, is a primary mixed forest dominated by the typical tropical forest tree species Terminalia myriocarpa and Pometia tomentosa. The Wuyishan forest, which is located in the Wuyi mountains in Jiangxi Province, is also a mature subtropical forest with Tsuga chinensis var. tchekiangensis as the dominant tree species in the canopy layer. Other common tree species in the forest include Betula luminifera and Cyclobalanopsis multinervis. Maoershan is a relatively young (45 years) larch (Larix gmelinii) plantation located at Laoshan Forest Research Station of Northeast Forestry University, Heilongjiang Province. A few tree species- Juglans mandshurica, Quercus mongolica, and Betula platyphylla- coexist with Larix gmelinii in the canopy. More information about these sites is also presented in Supplementary Table 1.
    15N-tracer experimentAt all sites, small amounts of 15NH4+ or 15NO3− tracers (generally  20% in a 1-km pixel was defined as forest. Based on this, we estimated the total global forest area to be ≈42 million km2.Calculation of N-induced C sinkThe N-induced C sink was estimated via the stoichiometric upscaling method19, i.e., by multiplying the N retention in woody tissues of stems, branches, and coarse roots and in the soil with the C/N ratios in these compartments. The C sink due to NHx and or NOy deposition was calculated separately using Eq. (4) as follows:$${{{{{{mathrm{C}}}}}}}_{{{{{{mathrm{sink}}}}}}}={{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{dep}}}}}}}times left(,{!}^{15}{{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{org}}}}}}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{org}}}}}}}+{{,}^{15}}{{{{{{{mathrm{N}}}}}}}_{{{min }}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{min }}}+{{,}^{15}}{{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{wood}}}}}}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{wood}}}}}}}times {{{{{mathrm{f}}}}}}right)$$
    (4)
    where Ndep is NHx or NOy deposition (kg N ha−1 yr−1); ({}^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{org}}}}}}}^{{{{{{rm{R}}}}}}}), ({}^{15}{{{{{{rm{N}}}}}}}_{{{min }}}^{{{{{{rm{R}}}}}}}) and ({}^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{wood}}}}}}}^{{{{{{rm{R}}}}}}}) indicate the fraction of deposited NHx or NOy allocated to organic layer, mineral soil, and woody biomass, respectively; and ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{{{{rm{org}}}}}}}), ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{min }}}), and ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{{{{rm{wood}}}}}}}) indicate C/N ratios in the soil organic layer, soil mineral layer and woody plant biomass, respectively. f is the fraction we applied to account for flexible C/N in response to elevated N deposition. At elevated N deposition, wood C/N ratio may decrease, and N accumulates without stimulating additional ecosystem C storage. To account for this scenario, we adopted a flexible stoichiometry51, in which the effects of N deposition on wood C/N ratios are accounted for by multiplying the C/N ratios of wood with a fraction f (from 1 to 0) depending on plant growth response to different rates of N deposition level (kg N ha−1 yr−1). Results of growth responses to experimental N addition and field N gradient studies show plant growth increased with increasing N deposition, flattening near 15–30 kg N ha−1 yr−1 and a reversal toward no enhanced growth response at about 100 kg N ha−1 yr−1 (ref. 36,52). Therefore, for N deposition More