Philippa Kaur

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

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

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

Skarpe, C. Dynamics of savanna ecosystems. J. Veg. Sci. 3, 293–300 (1992).
Google Scholar 
Solbrig, O. T. The diversity of the savanna ecosystem. In Biodiversity and Savanna Ecosystem Processes: A Global Perspective Vol. 21 (eds Solbrig, O. T. et al.) 1–27 (Springer, Berlin, 1996).
Google Scholar 
Fynn, R. W. S., Augustine, D. J., Peel, M. J. S. & de Garine-Wichatitsky, M. Strategic management of livestock to improve biodiversity conservation in African savannahs: A conceptual basis for wildlife–livestock coexistence. J Appl Ecol 53, 388–397 (2016).
Google Scholar 
Turner, M. D. & Schlecht, E. Livestock mobility in sub-Saharan Africa: A critical review. Pastoralism 9, 13 (2019).
Google Scholar 
Moreno, G. et al. Exploring the causes of high biodiversity of Iberian dehesas: The importance of wood pastures and marginal habitats. Agrofor. Syst. 90, 87–105 (2015).
Google Scholar 
Spiegel, O. et al. Moving beyond curve fitting: Using complementary data to assess alternative explanations for long movements of three vulture species. Am. Nat. 185, E44–E54 (2015).
Google Scholar 
Houston, D. C. Food searching behaviour in griffon vultures. Afr. J. Ecol. 12, 63–77 (1974).
Google Scholar 
Fryxell, J. M. & Sinclair, A. R. E. Causes and Consequences of Migration by Large Herbivores. Trens Ecol. Evol. 3, 237–241 (1988).CAS 

Google Scholar 
Joly, K. et al. Longest terrestrial migrations and movements around the world. Sci. Rep. 9, 15333 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Naveh, Z. Mediterranean ecosystems and vegetation types in California and Israel. In Transdisciplinary Challenges in Landscape Ecology and Restoration Ecology. Landscape Series, vol 6. (Springer, Dordrecht, 1967), vol 48, pp. 445–459.Pereira, P. M. & Pires da Fonseca, M. Nature vs. nurture: The making of the montado ecosystem. Conserv. Ecol. 7, 7 (2003).
Google Scholar 
Blondel, J. The ‘design’ of Mediterranean landscapes: A millennial story of humans and ecological systems during the historic period. Hum. Ecol. 34, 713–729 (2006).
Google Scholar 
Díaz, M., Campos, P. & Pulido, F. J. The Spanish dehesas: A diversity of land use and wildlife. In Farming and birds in Europe: The Common Agricultural Policy and its implications for bird conservation (eds Pain, D. & Pienkowski, M.) 178–209 (Academic Press, London, 1997).
Google Scholar 
Campos, P. et al. Mediterranean Oak Woodland Working Landscapes: Dehesas of Spain and Ranchlands of California (Springer, 2013).
Google Scholar 
Plieninger, T. & Bieling, C. Resilience and the Cultural Landscape: Understanding and Managing Change in Human-Shaped Environments (Cambridge University Press, 2012).
Google Scholar 
Lomba, A. et al. Back to the future: Rethinking socioecological systems underlying high nature value farmlands. Front. Ecol. Environ. 18, 36–42 (2020).
Google Scholar 
Ogada, D. et al. Another continental vulture crisis: Africa’s vultures collapsing toward extinction. Conserv. Lett. 9, 89–97 (2016).ADS 

Google Scholar 
Buechely, E. & Şekercioğlu, Ç. H. The avian scavenger crisis: Looming extinctions, trophic cascades, and loss of critical ecosystem functions. Biol. Conserv. 198, 220–228 (2016).
Google Scholar 
Safford, R. et al. Vulture conservation: The case for urgent action. Bird Conserv. Int. 29, 1–9 (2019).
Google Scholar 
García-Alfonso, M., Donázar, J. A., Serrano, D. Individual and environmental drivers of resource use in endangered vulture: Integrating movement, spatial and social ecology. PhD Thesis. Universidad de Sevilla, Seville, Spain.Ogada, D. L., Keesing, F. & Virani, M. Z. Dropping dead: Causes and consequences of vulture population declines worldwide. Ann. NY Acad. Sci. 1249, 57–71 (2012).ADS 
PubMed 

Google Scholar 
Blanco, G., Cortés-Avizanda, A., Frías, Ó., Arrondo, E. & Donázar, J. A. Livestock farming practices modulate vulture diet-disease interactions. Glob. Ecol. 17, e00518 (2019).
Google Scholar 
Olea, P. P., Mateo-Tomas, P. & Sánchez Zapata, J. A. Carrion Ecology and Management (Springer Nature, 2019).
Google Scholar 
Montsarrat, S. et al. How predictability of feeding patches affects home range and foraging habitat selection in avian social scavengers?. PLoS ONE 8, e53077 (2013).ADS 

Google Scholar 
Arrondo, E. et al. Invisible barriers: Differential sanitary regulations constrain vulture movements across country borders. Biol. Conserv. 219, 46–52 (2018).
Google Scholar 
Houston, D. C. Breeding of the white-backed and Rüppell’s griffon vultures, Gyps africanus and G. rueppellii. Ibis 118, 14–40 (1976).
Google Scholar 
Houston, D. C. A change in the breeding season of Rüppell’s griffon vultures Gyps rueppellii in the Serengeti in response to changes in ungulate populations. Ibis 132, 36–41 (1990).
Google Scholar 
Kendall, C. J., Virani, M. Z., Hopcraft, J. G. C., Bildstein, K. L. & Rubenstein, D. I. African vultures don’t follow migratory herds: Scavenger habitat use is not mediated by prey abundance. PLoS ONE 9, e83470 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Carrete, M. & Donázar, J. A. Application of central-place foraging theory shows the importance of Mediterranean dehesas for the conservation of the cinereous vulture, Aegypius monachus. Biol. Conserv. 126, 582–590 (2005).
Google Scholar 
Martín-Díaz, P. et al. Rewilding processes shape the use of Mediterranean landscapes by an avian top scavenger. Sci. Rep. 10, 2853 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Botha, A. et al. Multi-species action plan to conserve African-Eurasian vultures (vulture MSAP). CMS Raptors MOU Technical Publication 5, 2–162 (2017).
Google Scholar 
Cortés-Avizanda, A. et al. Supplementary feeding and endangered avian scavengers: Benefits, caveats, and controversies. Front. Ecol. Environ. 14, 191–199 (2016).
Google Scholar 
Fluhr, J., Benhamou, S., Riotte-Lambert, L. & Duriez, O. Assessing the risk for an obligate scavenger to be dependent on predictable feeding sources. Biol. Conserv. 215, 92–98 (2017).
Google Scholar 
Schabo, D. G. et al. Long-term data indicates that supplementary food enhances the number of breeding pairs in a Cape Vulture Gyps coprotheres colony. Bird Conserv. Int. 27, 1–13 (2016).
Google Scholar 
Louzao, M. et al. Conserving pelagic habitats: Seascape modelling of an oceanic predator. J. Appl. Ecol. 48, 121–132 (2011).
Google Scholar 
Buechley, E. R. et al. Identifying critical migratory bottlenecks and high-use areas for an endangered migratory soaring bird across three continents. J. Avian Biol. 49, e01629 (2018).
Google Scholar 
Morales-Reyes, Z. et al. Supplanting ecosystem services provided by scavengers raises greenhouse gas emissions. Sci. Rep. 5, 7811 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mateo-Tomás, P., Olea, P. P., Moleón, M., Selva, N. & Sánchez-Zapata, J. A. Both rare and common species support ecosystem services in scavenger communities. Glob. Ecol. 26, 1459–1470 (2017).
Google Scholar 
Aguilera-Alcalá, N., Morales-Reyes, Z., Martín-López, B., Moléon, M. & Sánchez-Zapata, J. A. Role of scavengers in providing non-material contributions to people. Ecol. Indic. 117, 106643 (2020).
Google Scholar 
Margalida, A. et al. Uneven large-scale movement patterns in wild and reintroduced pre-adult bearded vultures: Conservation implications. PLoS ONE 8, e65857 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Griesinger, J. Juvenile dispersion and migration among Griffon Vultures Gyps fulvus in Spain. Holartic Birds of Prey (1998).Plieninger, T. et al. Dehesas as high nature value farming systems: A social-ecological synthesis of drivers, pressures, state, impacts, and responses. Ecology 26, 23 (2021).
Google Scholar 
Virani, M. Z., Monadjem, A., Thomsett, S. & Kendall, C. Seasonal variation in breeding Rüppell’s Vultures Gyps rueppellii at Kwenia, southern Kenya and implications for conservation. Bird. Conserv. Int. 22, 260–269 (2012).
Google Scholar 
Morales-Reyes, Z. et al. Evaluation of the network of protection areas for the feeding of scavengers in Spain: From biodiversity conservation to greenhouse gas emission savings. J. Appl. Ecol. 54, 1120–1129 (2017).CAS 

Google Scholar 
Mateo-Tomás, P. & Olea, P. When hunting benefits raptors: A case study of game species and vultures. Eur. J. Wildl. Res. 56, 519–528 (2010).
Google Scholar 
Pereira, H. M. & Navarro, L. M. Rewilding European Landscapes (Springer, 2015).
Google Scholar 
Margalida, A., Carrete, M., Sánchez-Zapata, J. A. & Donázar, J. A. Good news for European vultures. Science 335, 284 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Paredes, R. et al. Proximity to multiple foraging habitats enhances seabirds’ resilience to local food shortages. Mar. Ecol. Prog. Ser. 471, 253–269 (2012).ADS 

Google Scholar 
Gomo, G., Mattisson, J., Hagen, B. R., Moa, P. F. & Willebrand, T. Scavenging on a pulsed resource. Quality matters for corvids but density for mammals. BMC Ecol. 17, 22 (2017).PubMed 
PubMed Central 

Google Scholar 
Navarro, L. M. & Pereira, H. M. Rewilding abandoned landscapes in Europe. Ecosystems 15, 900–912 (2012).
Google Scholar 
Arrondo, E. et al. Rewilding traditional grazing areas affects scavenger assemblages and carcass consumption patterns. Basic Appl. Ecol. 41, 56–66 (2019).
Google Scholar 
Cortés-Avizanda, A., Donázar, J. A. & Pereira, H. M. Top scavengers in a wilder Europe. In Rewilding European Landscapes (eds Pereira, H. M. & Navarro, L.) 85–106 (Springer, Berlin, 2015).
Google Scholar 
Harel, R. et al. Decision-making by a soaring bird: Time, energy and risk considerations at different spatio-temporal scales. Philos. Trans. R. Soc. B 371, 20150397 (2016).
Google Scholar 
Austin, R. E. et al. A sex-influenced flexible foraging strategy in a tropical seabird, the magnificent frigatebird. Mar. Ecol. Prog. Ser. 611, 203–214 (2019).ADS 

Google Scholar 
Weimerskirch, H., Cherel, Y., Cuenot-Chaillet, F. & Ridoux, V. Alternative foraging strategies and resource allocation by maIe and female wandering albatrosses. Ecology 78, 2051–2063 (1997).
Google Scholar 
Gangoso, L. et al. Avian scavengers living in anthropized landscapes have shorter telomeres and higher levels of glucocorticoid hormones. Sci. Total Environ. 782, 146920 (2021).ADS 
CAS 

Google Scholar 
Lambertucci, S. A., Carrete, M., Donázar, J. A. & Hiraldo, F. Large-scale age-dependent skewed sex ratio in a sexually dimorphic avian scavenger. PLoS ONE 7, e46347 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jackson, A. L., Ruxton, G. D. & Houston, D. C. The effect of social facilitation on foraging success in vultures: A modelling study. Biol. Lett. 4, 311–313 (2008).PubMed 
PubMed Central 

Google Scholar 
Deygout, C., Gault, A., Duriez, O., Sarrazin, F. & Bessa-Gomes, C. Impact of food predictability on social facilitation by foraging scavengers. Behav. Ecol. 21, 1131–1139 (2010).
Google Scholar 
Harel, R., Spiegel, O., Getz, W. M. & Nathan, R. Social foraging and individual consistency in following behaviour: Testing the information centre hypothesis in free-ranging vultures. Proc. R. Soc. B 284, 20162654 (2017).PubMed 
PubMed Central 

Google Scholar 
van Overveld, T. et al. Integrating vulture social behavior into conservation practice. The Condor 122, 1–20 (2020).
Google Scholar 
Genero, F., Franchini, M., Fanin, Y. & Filacorda, S. Spatial ecology of non-breeding Eurasian Griffon Vultures Gyps fulvus in relation to natural and artificial food availability. Bird Study 67, 1–18 (2020).
Google Scholar 
Sherub, S., Fiedler, W., Duriez, O. & Wikelski, M. Bio-Logging – New Technologies to study conservation physiology on the move: A case study on annual survival of Himalayan Vultures. J. Comp. Physiol. 203, 531–542 (2017).
Google Scholar 
Olea, P. P. & Mateo-Tomás, P. The role of traditional farming practices in ecosystem conservation: The case of transhumance and vultures. Biol. Conserv. 142, 1844–1853 (2009).
Google Scholar 
Aguilera-Alcalá, N. et al. The value of transhumance for biodiversity conservation: Vulture foraging in relation to livestock movements. (Ambio, 2021).
Google Scholar 
Clement, V. Spanish wood pasture: Origin and durability of an historical wooded landscape in Mediterranean Europe. Environ. Hist. Camb. 14, 67–87 (2008).
Google Scholar 
Arrondo, E. et al. Landscape anthropization shapes the survival of a top avian scavenger. Biodivers. Conserv. 29, 1411–1425 (2020).
Google Scholar 
Block, T. A., Lyon, B. E., Mikalonis, Z., Chaine, A. S. & Shizuka, D. Social migratory connectivity: Do birds that socialize in winter breed together?. BioRxiv 17, 76 (2021).
Google Scholar 
Thaxter, C. B. et al. Seabird foraging ranges as a preliminary tool for identifying candidate Marine Protected Areas. Biol. Conserv. 156, 53–61 (2012).
Google Scholar 
Santangeli, A. et al. Priority areas for conservation of Old World vultures. Conserv. Biol. 33, 1056–1065 (2019).PubMed 
PubMed Central 

Google Scholar 
Costa, A., Pereira, H. & Madeira, M. Landscape dynamics in endangered cork oak woodlands in Southwestern Portugal (1958–2005). Agrofor. Syst. 77, 83–96 (2009).
Google Scholar 
Del Moral, J. C. & Molina, B. (eds) El buitre leonado en España, población reproductora en 2018 y método de censo (SEO/BirdLife, 2018).
Google Scholar 
Zuberogoitia, I. et al. The flight feather molt of griffon vultures (Gyps fulvus) and associated biological consequences. J. Raptor Res. 47, 292–304 (2013).
Google Scholar 
Fluhr, J., Benhamou, S., Peyrusque, D. & Duriez, O. Space use and time budget in two populations of Griffon Vultures in contrasting landscapes. J. Raptor Res. 55, 13 (2021).
Google Scholar 
Arrondo, E. et al. Use of avian GPS tracking to mitigate human fatalities from bird strikes caused by large soaring birds. J. Appl. Ecol. 58, 1411–1420 (2021).
Google Scholar 
Serrano, D. Dispersal in raptors. In Birds of Prey. Biology and Conservation in the XXI Century (eds HernánSarasola, J. et al.) 95–121 (Springer, 2018).
Google Scholar 
Williams, H. J. et al. Vultures respond to challenges of near-ground thermal soaring by varying bank angle. J. Exp. Biol. 221, 174995 (2018).
Google Scholar 
García-Barón, I. et al. How to fit the distribution of apex scavengers into land-abandonment scenarios? The Cinereous vulture in the Mediterranean biome. Divers. Distrib. 24, 1018–1031 (2018).
Google Scholar 
Donázar, J. A., Ceballos, O. & Cortés-Avizanda, A. Tourism in protected areas: Disentangling road and traffic effects on intra-guild scavenging processes. Sci. Total Environ. 630, 600–608 (2018).ADS 
PubMed 

Google Scholar 
Daoud, J. I. Multicollinearity and Regression Analysis. J. Phys. Conf. Ser. 949, 012009 (2017).
Google Scholar 
Burnham, K. P., Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2002).Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).
Google Scholar 
Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models (2020).R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, (2018), https://www.r-project.org/Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, New York, 2002).MATH 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Warnes, G. R., Bolker, B., Lumley, T., Johnson, R. C. gmodels: Various R programming tools for model fitting (2018).Mazerolle, M. J. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 2.2-2 (2019).K. Barton, MuMIn: Multimodel inference. R package version 1.43.6.557. (2019).QGIS.org, QGIS Geographic Information System. QGIS Association, (2019), http://www.qgis.orgBouten, W., Baaij, E. W., Shammoun-Baranes, J. & Camphuysen, K. C. J. A flexible GPS tracking system for studying bird behaviour at multiple scales. J. Ornithol. 154, 571–580 (2012).
Google Scholar 
ASTER GDEM Validation Team, ASTER Global Digital Elevation Model Version 2 ‐ Summary of Validation Results (2011).Ruiz de la Torre, J. Mapa Forestal de España, 1:200.000, Memoria General, (ICONA, Madrid, 1990).INE, Anuario estadístico. Madrid, Spain: Instituto Nacional de Estadística, Ministerio de Economía y Hacienda (2006). More

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

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

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

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