Ecology

Mogollón JM, Bouwman AF, Beusen AH, Lassaletta L, van Grinsven HJ, Westhoek H. More efficient phosphorus use can avoid cropland expansion. Nat Food. 2021;2:509–18.Article 

Google Scholar 
Goldhammer T, Brüchert V, Ferdelman TG, Zabel M. Microbial sequestration of phosphorus in anoxic upwelling sediments. Nat Geosci. 2010;3:557–61.CAS 
Article 

Google Scholar 
Oliverio AM, Bissett A, McGuire K, Saltonstall K, Turner BL, Fierer N. The role of phosphorus limitation in shaping soil bacterial communities and their metabolic capabilities. mBio. 2020;11:e01718–20.CAS 
Article 

Google Scholar 
Wu X, Rensing C, Han D, Xiao KQ, Dai Y, Tang Z, et al. Genome-resolved metagenomics reveals distinct phosphorus acquisition strategies between soil microbiomes. mSystems. 2022;7:e01107–21.PubMed Central 

Google Scholar 
Long XE, Yao H, Huang Y, Wei W, Zhu YG. Phosphate levels influence the utilisation of rice rhizodeposition carbon and the phosphate-solubilizing microbial community in a paddy soil. Soil Bio Biochem. 2018;118:103–14.CAS 
Article 

Google Scholar 
Dai Z, Liu G, Chen H, Chen C, Wang J, Ai S, et al. Long-term nutrient inputs shift soil microbial functional profiles of phosphorus cycling in diverse agroecosystems. ISME J. 2020;14:757–70.CAS 
Article 

Google Scholar 
Liang J, Liu J, Jia P, Yang T, Zeng Q, Zhang S, et al. Novel phosphate-solubilizing bacteria enhance soil phosphorus cycling following ecological restoration of land degraded by mining. ISME J. 2020;14:1600–13.CAS 
Article 

Google Scholar 
Willsky GR, Bennett RL, Malamy MH. Inorganic phosphate transport in Escherichia coli: involvement of two genes which play a role in alkaline phosphatase regulation. J Bacteriol. 1973;113:529–39.CAS 
Article 

Google Scholar 
Wanner BL. Gene regulation by phosphate in enteric bacteria. J Cell Biochem. 1993;51:47–54.CAS 
Article 

Google Scholar 
Li J, Lu J, Wang H, Fang Z, Wang X, Feng S, et al. A comprehensive synthesis unveils the mysteries of phosphate‐solubilizing microbes. Biol Rev. 2021;96:2771–93.Article 

Google Scholar 
Hessen DO, Jeyasingh PD, Neiman M, Weider LJ. Genome streamlining and the elemental costs of growth. Trends Ecol Evol. 2010;25:75–80.Article 

Google Scholar 
Li J, Mau RL, Dijkstra P, Koch BJ, Schwartz E, Liu XA, et al. Predictive genomic traits for bacterial growth in culture versus actual growth in soil. ISME J. 2019;13:2162–72.Article 

Google Scholar 
Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.Article 

Google Scholar 
Ye L, Mei R, Liu WT, Ren H, Zhang XX. Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes. Microbiome. 2020;8:1–13.Article 

Google Scholar 
Farhat MB, Boukhris I, Chouayekh H. Mineral phosphate solubilization by Streptomyces sp. CTM396 involves the excretion of gluconic acid and is stimulated by humic acids. FEMS Microbiol Lett. 2015;362:1–8.Article 

Google Scholar 
Bücking H, Shachar-Hill Y. Phosphate uptake, transport and transfer by the arbuscular mycorrhizal fungus Glomus intraradices is stimulated by increased carbohydrate availability. New Phytol. 2005;165:899–912.Article 

Google Scholar 
Zhang L, Xu M, Liu Y, Zhang F, Hodge A, Feng G. Carbon and phosphorus exchange may enable cooperation between an arbuscular mycorrhizal fungus and a phosphate‐solubilizing bacterium. New Phytol. 2016;210:1022–32.CAS 
Article 

Google Scholar 
Spohn M, Kuzyakov Y. Phosphorus mineralization can be driven by microbial need for carbon. Soil Biol Biochem. 2013;61:69–75.CAS 
Article 

Google Scholar 
Huang Y, Dai Z, Lin J, Li D, Ye H, Dahlgren RA, et al. Labile carbon facilitated phosphorus solubilization as regulated by bacterial and fungal communities in Zea mays. Soil Biol Biochem. 2021;163:108465.CAS 
Article 

Google Scholar 
Yao Q, Li Z, Song Y, Wright SJ, Guo X, Tringe SG, et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat Ecol Evol. 2018;2:499–509.Article 

Google Scholar  More

Geoffrey, E. Sur les Phyllostomes et les Megadermes, deux Genres de la famille des Chauve-souris. in Annales du Museum d’histoire (ed. Dufour, G.) vol. 15, 181 (d’Ocagne, 1810).Wilson, D. E. & Mittermeier, R. A. Bats. in Handbook of the Mammals of the World. Vol. 9. (eds. Wilson, D. E. & Mittermeier, R. A.) 1008 (Springer International Publishing, 2019).Hilaire, É. G. S., Pupuya, I. D. E., Del, R. & Higgins, L. B. O. Ampliación del rango de distribución sur de Desmodus rotundus. Boletín del Mus. Nac. Hist. Nat. 68, 5–12 (2019).
Google Scholar 
Kwon, M. & Gardner, A. L. Subfamily Desmodontinae. in Mammals of South America, Volume 1: Marsupials, Xenarthrans, Shrews and Bats (ed. Gardner, A. L.) 218–223 (The University of Chicago Press, 2008).Arellano-Sota, C. Vampire bat-transmitted rabies in cattle. Rev. Infect. Dis. 10, 707–709 (1988).
Google Scholar 
Fernandes, M. E. B., Da Costa, L. J. C., De Andrade, F. A. G. & Silva, L. P. Rabies in humans and non-human in the state of Pará, Brazilian Amazon. Brazilian J. Infect. Dis. 17, 251–253 (2013).
Google Scholar 
Andrade, F. A. G., Franca, E. S., Souza, V. P., Barreto, M. S. O. D. & Fernandes, M. E. B. Spatial and temporal analysis of attacks by common vampire bats (Desmodus rotundus) on humans in the rural Brazilian Amazon basin. Acta Chiropterologica 17, 393–400 (2015).
Google Scholar 
Greenhall, A. M., Joermann, G. & Schmidt, U. Desmodus rotundus. Mamm. Species 202, 1–6 (1983).
Google Scholar 
Herrera, L. G., Fleming, T. H. & Sternberg, L. S. Trophic relationships in a neotropical bat community: A preliminary study using carbon and nitrogen isotopic signatures. Trop. Ecol. 39, 23–29 (1998).
Google Scholar 
Dantas Torres, F., Valença, C. & De Andrade Filho, G. V. First record of Desmodus rotundus in urban area from the city of Olinda, Pernambuco, Northeastern Brazil: A case report. Rev. Inst. Med. Trop. Sao Paulo 47, 107–108 (2005).PubMed 

Google Scholar 
Flores-Crespo, R. & Arellano-Sota, C. Biology and control of the vampire bat. in The natural history of rabies (ed. Baer, G. M.) 461–476 (CRC Press Inc, 1991).Flores-Crespo, R. & Arellano-Sota, C. Biology and control of the vampire bat. Nat. Hist. Rabies, 2nd Ed. 10, 461–476 (2017).
Google Scholar 
Bolívar-Cimé, B., Flores-Peredo, R., García-Ortíz, S. A., Murrieta-Galindo, R. & Laborde, J. Influence of landscape structure on the abundance of Desmodus rotundus (Geoffroy 1810) in Northeastern Yucatan, Mexico. Ecosistemas y Recur. Agropecu. 6, 263 (2019).
Google Scholar 
Koopman, K. F. Systematics and distribution. in Natural History of Vampire Bats (eds. Greenhall, A. M. & Schmidt, U.) 4–28 (CRC Press, 1988).Dalquest, W. W. Natural history of the vampire bats of Eastern Mexico. Am. Midl. Nat. 53, 79–87 (1955).
Google Scholar 
Kalko, E. K. V. & Handley, C. O. Neotropical bats in the canopy: Diversity, community structure, and implications for conservation. Plant Ecol. 153, 319–333 (2001).
Google Scholar 
García-Morales, R., Badano, E. I. & Moreno, C. E. Response of neotropical bat assemblages to human land use. Conserv. Biol. 27, 1096–1106 (2013).PubMed 

Google Scholar 
Barquez, R.M., Perez, S., Miller, B. & Diaz, M. M. Desmodus rotundus. The IUCN Red List of Threatened Species 2015 e.T6510A21979045, https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T6510A21979045.en (2015).Becker, D. J. et al. Genetic diversity, infection prevalence, and possible transmission routes of Bartonella spp. in vampire bats. PLoS Negl. Trop. Dis. 12, e0006786 (2018).PubMed 
PubMed Central 

Google Scholar 
Brandão, P. E. et al. A coronavirus detected in the vampire bat Desmodus rotundus. Brazilian J. Infect. Dis. 12, 466–468 (2008).
Google Scholar 
Alves, R. S. et al. Detection of coronavirus in vampire bats (Desmodus rotundus) in southern Brazil. Transbound. Emerg. Dis. 00, 1–6 (2021).
Google Scholar 
Rocha, F. & Dias, R. A. The common vampire bat Desmodus rotundus (Chiroptera: Phyllostomidae) and the transmission of the rabies virus to livestock: A contact network approach and recommendations for surveillance and control. Prev. Vet. Med. 174, e104809 (2020).
Google Scholar 
Raoult, D. et al. Diagnosis of 22 new cases of Bartonella endocarditis. Ann. Intern. Med. 125, 646–652 (1996).CAS 
PubMed 

Google Scholar 
Raoult, D. et al. Outcome and treatment of Bartonella endocarditis. Arch. Int. Med. 163, 226–230 (2003).
Google Scholar 
Neely, B. A. et al. Surveying the vampire bat (Desmodus rotundus) serum proteome: A resource for identifying immunological proteins and detecting pathogens. J. Proteome Res. 20, 2547–2559 (2021).CAS 
PubMed 

Google Scholar 
Rupprecht, C. E., Hanlon, C. A. & Hemachudha, T. Rabies re-examined. Lancet Infect. Dis. 2, 327–343 (2002).PubMed 

Google Scholar 
World Health Organization. Rabies. WHO https://www.who.int/news-room/fact-sheets/detail/rabies (2020).Lee, D. N., Papeş, M. & Van Den Bussche, R. A. Present and potential future distribution of common vampire bats in the Americas and the associated risk to cattle. PLoS One 7, e42466 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Acha, P. N. & Malaga-Alba, A. Economic losses due to Desmodus rotundus. in Natural History of Vampire Bats (eds. Greenhall, A. M. & Schmidt, U.) 207–214 (CRC Press, 1968).Kotait, I. & Gonçalves, C. Manual Técnico MAPA – Controle da raiva dos herbívoros in Manual técnico dos herbívoros (Ministério da Agricultura, Pecuária e Abastecimento, 2009).Johnson, N., Aréchiga-Ceballos, N. & Aguilar-Setien, A. Vampire bat rabies: Ecology, epidemiology and control. Viruses 6, 1911–1928 (2014).PubMed 
PubMed Central 

Google Scholar 
Blackwood, J. C., Streicker, D. G., Altizer, S. & Rohani, P. Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats. Proc. Natl. Acad. Sci. 110, 20837–20842 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Streicker, D. G. et al. Host-pathogen evolutionary signatures reveal dynamics and future invasions of vampire bat rabies. Proc. Natl. Acad. Sci. USA 113, 10926–10931 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zarza, H., Martínez-Meyer, E., Suzán, G. & Ceballos, G. Geographic distribution of Desmodus rotundus in Mexico under current and future climate change scenarios: Implications for bovine paralytic rabies infection. Vet. Mex. 4, 3–16 (2017).
Google Scholar 
Hayes, M. A. & Piaggio, A. J. Assessing the potential impacts of a changing climate on the distribution of a rabies virus vector. PLoS One 13, e0192887 (2018).PubMed 
PubMed Central 

Google Scholar 
Nunez, G. B., Becker, D. J., Lawrence, R. L. & Plowright, R. K. Synergistic effects of grassland fragmentation and temperature on bovine rabies emergence. EcoHealth 17, 203–216 (2020).PubMed Central 

Google Scholar 
da Rosa, E. S. T. et al. Bat-transmitted human rabies outbreaks, Brazilian Amazon. Emerg. Infect. Dis. 12, 1197–1202 (2006).PubMed 
PubMed Central 

Google Scholar 
Rocha, S. M., de Oliveira, S. V., Heinemann, M. B. & Gonçalves, V. S. P. Epidemiological profile of wild rabies in Brazil (2002–2012). Transbound. Emerg. Dis. 64, 624–633 (2017).CAS 
PubMed 

Google Scholar 
Schneider, M. C. et al. Rabies transmitted by vampire bats to humans: An emerging zoonotic disease in Latin America? Pan Am. J. Public Health. 25, 260–269 (2009).
Google Scholar 
VERA. Vigilancia epidemiológica de la rabia en las Américas. Organ. Panam. la Salud. 34, 14–42 (2020).
Google Scholar 
World Health Organization. WHO expert consultation on rabies, second report. WHO Tech. Rep. Ser. 982, 1–139 (2013).
Google Scholar 
Gilbert, A. T. et al. Evidence of rabies virus exposure among humans in the Peruvian Amazon. Am. J. Trop. Med. Hyg. 87, 206–215 (2012).
Google Scholar 
Fahl, W. O. et al. Desmodus rotundus and Artibeus spp. bats might present distinct rabies virus lineages. Brazilian J. Infect. Dis. 16, 545–551 (2012).
Google Scholar 
Berger, F. et al. Rabies risk: Difficulties encountered during management of grouped cases of bat bites in 2 isolated villages in French Guiana. PLoS Negl. Trop. Dis. 7, e2258 (2013).PubMed 
PubMed Central 

Google Scholar 
Linhart, S. B., Flores Crespo, R. & Mitchell, G. C. Control de murciélagos vampiros por medio de un anticoagulante. Bull Pan Am Health Organ. 73, 100–109 (1972).CAS 

Google Scholar 
Streicker, D. G. et al. Ecological and anthropogenic drivers of rabies exposure in vampire bats: Implications for transmission and control. Proc. R. Soc. B Biol. Sci. 279, 3384–3392 (2012).
Google Scholar 
Henry, M., Cosson, J. F. & Pons, J. M. Modelling multi-scale spatial variation in species richness from abundance data in a complex neotropical bat assemblage. Ecol. Modell. 221, 2018–2027 (2010).
Google Scholar 
Bárcenas-Reyes, I. et al. Comportamiento epidemiológico de la rabia paralítica bovina en la región central de México, 2001-2013. Pan Am. J. Public Health. 38, 396–402 (2015).
Google Scholar 
Benavides, J. A., Valderrama, W. & Streicker, D. G. Spatial expansions and travelling waves of rabies in vampire bats. Proc. R. Soc. B 283, e20160328 (2016).
Google Scholar 
Van de Vuurst, P. et al. Desmodus rotundus Occurrence Record Database. figshare https://doi.org/10.6084/m9.figshare.15025296.v6 (2021).Wieczorek, J. et al. Darwin core: An evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Robertson, T. et al. The GBIF integrated publishing toolkit: Facilitating the efficient publishing of biodiversity data on the internet. PLoS One 9, e102623 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Marcial, L. H. & Hemminger, B. M. Scientific data repositories on the web: An initial survey. J. Am. Soc. Inf. Sci. Technol. 61, 2029–2048 (2010).
Google Scholar 
GBIF.org. GBIF Occurrence Download. Global Biodiversity Information Facility. GBIF https://doi.org/10.15468/dl.my64ap (2020).Grattarola, F. et al. Biodiversidata: An open-access biodiversity database for Uruguay. Biodivers. Data J. 7, e36226 (2019).PubMed 
PubMed Central 

Google Scholar 
CRIA. speciesLink Data Download. Centro de Referência em Informação Ambiental. https://specieslink.net/search/download/20210909104533-0016416 (2021).Cambon, J. Package ‘ tidygeocoder’. CRAN 2–13 (2021).Wickham, H., Francois, R., Henry, L. & Muller, K. Package ‘ dplyr’: A grammar of data manipulation. CRAN 3–88 (2020).R Core Team. R: A language and environment for statisitical computing. https://www.R-project.org/ (2019).Zizka, A. et al. Package ‘ CoordinateCleaner’. CRAN 13-152 (2019).Wickham, H. ggplot2: Elegant graphics for data analysis. (Springer-Verlag, 2016).ESRI Inc. ArcGIS Desktop Pro, version 2.4.3. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (2019). 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

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,

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

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Hernani Fernandes Magalhaes de Oliveira

Federal University of Paraná, Curitiba, Brazil.

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Daiana Cardoso Silva

Mato Grosso State University, Nova Xavantina, Brazil.

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Priscilla Lora Zangrandi

Toronto, Canada.

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Fabricius Maia Chaves Bicalho Domingos

Federal University of Paraná, Curitiba, Brazil.

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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.

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

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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

Study areaThe QTP is located in southwestern China (25° ~ 40°N, 75° ~ 103°E), with a total area of 2.5 million km2 and an average elevation above 4000 m (Fig. 7). The QTP is mainly covered with permafrost and grassland, with areas of glacier and desert48. The QTP, also known as the “Asian Water Tower”49, is the source of 13 major Asian rivers (e.g., the Indus, Ganges, Brahmaputra, Yangtze, and Yellow Rivers). The QTP has a clod, arid climate, with an annual average temperature below 0 °C and an annual mean precipitation of 400 mm. The seasonal distribution of precipitation is uneven, with most precipitation concentrated in the period June to September. There is a decreasing trend in precipitation from the southeast to the northwest of the plateau50. Known as the “Roof of the World” and “Third Pole”, the QTP is also an area that is sensitive to global climate change, showing increasing warming and humidification in recent decades51. In addition, the QTP contains a diversity of ecosystems and fosters a historic ecological security barrier, which nurtures the development of animal husbandry and diverse cultures.Figure 7Geographical location of the QTP. The map was created using ArcMap 10.2, URL: http://www.esri.com.Full size imageData sourcesRCP scenarios and climate change datasetThe RCP scenarios released by the IPCC 5th Assessment Report52 supply a forecasting standard for climate change research. RCP values ranging from 2.6 to 8.5 reflect radiation forcing values in 2100 relative to the beginning of the Industrial Revolution in 175053. Different radiative forcing scenarios represent different future climate scenarios. RCPs consist of one high-emission scenario (8.5 ({text{W}} cdot {text{m}}^{ – 2}), RCP8.5), two medium-emission scenarios (6.0 ({text{W}} cdot {text{m}}^{ – 2}), RCP6.0; 4.5 ({text{W}} cdot {text{m}}^{ – 2}), RCP4.5), and one low-emission scenario (2.6 ({text{W}} cdot {text{m}}^{ – 2}), RCP2.6)54. In this study, we adopted the RCP2.6, RCP4.5 and RCP8.5 climate change scenarios choosing RCP4.5 to represent the medium emission scenario in consideration of increasing activity through global initiatives in response to climate change. Specific descriptions of each scenario are shown in Table 1.Table 1 The characteristics of each RCP scenario.Full size tableWe adopted the climate change dataset outputs from five global circulation models(GCMs) (namely GFDL-ESM2M, HadGEM2-ES, IPSLCM5ALR, MIROC-ESM-CHEM, and NorESM1-M) within the fifth phase of the Coupled Model Intercomparison Project (CMIP5)55. The dataset outputs from GCMs were downscaled to a resolution of 0.5° and bias-corrected with Water and Global Change (WATCH) data (Integrated Project Water and Global Change, http:/www.eu-watch.org/data_availability)56. The baseline period of the dataset is 1950–2005 and the forecast period is 2006–2099.The climate change dataset included daily precipitation, air pressure, solar radiation, air temperature, maximum air temperature, minimum air temperature, wind speed, and relative humidity.Auxiliary dataThe auxiliary data for our research include the following. (1) The land use and land cover (LULC) map was obtained from the Resource and Environment Science and Data Center (RESDC), Chinese Academy of Sciences (https://www.resdc.cn) for 1980, 1990, 1995, 2000, 2005, 2010, 2015 and 2020 at a 1 km resolution. The LULC data have six major classes: cropland, grassland, forestland, water, built-up land and barren land. (2) The spatial distribution of soil type data, digital elevation model (DEM), watershed boundaries and normalized difference vegetation index (NDVI) data with a resolution of 1 km were obtained from the RESDC. (3) Soil physical and chemical property data (available soil water capacity, absolute depth to bedrock, silt content, clay content, sand content and soil organic carbon content) were obtained from the International Soil Reference and Information Centre (ISRIC Data Hub) (https://data.isric.org) with a 1 km spatial resolution. (4) During 1986–2005 and 1986–2098 (RCP2.6; RCP4.5; RCP8.5), the permafrost datasets in the Northern Hemisphere (https://doi.org/10.12072/ncdc.CCI.db0032.2020) and the response of the alpine grassland ecosystem to climate change (RCP2.6, RCP4.5, and RCP8.5) in the permafrost region of the Qinghai-Tibet Plateau from 1981 to 2099 (https://doi.org/10.12072/ncdc.CCI.db0006.2020) were provided by the National Cryosphere Desert Data Center (https://www.ncdc.ac.cn).Future land use simulation and validationIn this study, we used the Future Land Use Simulation model (FLUS) to simulate the LULC in 2030, 2050 and 2100 under the three RCP scenarios. This model was developed by57 and is available for download at (www.geosimulation.cn/flus.html). The FLUS model is an efficient land use simulation tool and has been widely used58,59. We selected the DEM, slope, precipitation, temperature, soil type, and permafrost distribution to calculate the suitability probability. Based on the land use transfer from 2010 to 2015, we calculated the total land use in 2030, 2050 and 2100 under three RCP scenarios by the Markov model. To validate the FLUS model, we set 2010 as the starting year and simulated the land use in 2015. The output results were compared with the real 2015 land use data, and we calculated the Kappa coefficient as follows:$$begin{array}{*{20}c} {Kappa = frac{{P_{0} – P_{C} }}{{P_{P} – P_{C} }}} \ end{array}$$
(1)
where (P_{0}) is the number of pixels converted correctly,(P_{C}) is the correct number of pixels to be converted in the random case, and (P_{P}) is the correct number of pixels to convert under ideal conditions.Assessment of ecosystem services under different RCP scenariosThis study assessed four ESs namely WY, SR, CS, and RMP, under climate change scenarios in 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2030 (short-term); 2050 (medium-term); and 2100 (long-term). We adopted the Integrated Valuation of Environmental Service and Tradeoffs (InVEST)60 model to assess the WY, SR, and CS ecosystem services. The InVEST model developed by the Natural Capital Project(www.naturalcapitalproject.org) is an effective model to evaluate ESs61 and is widely used in ES research on the QTP22,23,24,25. All spatial data were processed into a 1 km resolution and Albers projection by ArcGIS 10.2 before input into the InVEST model. The data requirements of the InVEST model and its processing are shown in Table S1. We use net primary productivity (NPP) to evaluate the RMP, and NPP can be used to represent the richness of biomass and the supply of organic materials. We adopted the Carnegie-Ames-Stanford Approach (CASA)62 model to estimate NPP.Water yieldWater yield is a key ecosystem service. It refers to the annual quantity of water available for human use, as measured by the supply of surface water per unit area63. We adopted the InVEST 3.9.0 water yield model to estimate WY services in the QTP region. The water yield model is based on the water balance principle64. The biophysical parameter table required by the model is shown in Table S2. The parameters in the biophysical table come from the published literature26,63,65. The annual WY is calculated as follows:$$begin{array}{*{20}{c}} {{Y_{xj}} = left( {1 – frac{{AE{T_{xj}}}}{{{P_x}}}} right){P_x}} end{array}$$
(2)
where (Y_{xj}) is the annual WY of land cover type j in pixel x; (P_{x}) is the annual average precipitation of pixel x; and (AET_{xj}) is the actual evapotranspiration of land cover type j in pixel x.$$begin{array}{*{20}c} {frac{{AET_{xj} }}{{P_{x} }} = frac{{1 + omega_{x} R_{xj} }}{{1 + omega_{x} R_{xj} + frac{1}{{R_{xj} }}}}} \ end{array}$$
(3)
where (omega_{x}) is a dimensionless nonphysical parameter representing soil properties under natural climate conditions. The calculation method is as follows:$$begin{array}{*{20}c} {omega_{x} = Zfrac{{AWC_{x} }}{{P_{x} }}} \ end{array}$$
(4)
where Z is a seasonal rainfall factor representing the regional precipitation distribution and other hydrogeological characteristics. The higher the Z value is, the less the seasonal constant Z affects the model results66. Since the QTP region belongs to the arid and cold climate zone in China, the Z value is set as 9. (AWC_{x}) is the soil effective water content of pixel X, which is determined by the soil depth and physical and chemical properties. (R_{xj}) is the Budyko dryness index, which is calculated as follows:$$begin{array}{*{20}c} {R_{xj} = frac{{K_{xj} cdot ET_{0} }}{{P_{x} }}} \ end{array}$$
(5)
where, (K_{xj}) is the reference crop evapotranspiration and (ET_{0}) is the reference evapotranspiration in pixel x. We adopted the modified Hargreaves method to calculate (ET_{0}).$$ET_{0} = 0.0013 times 0.408 times RA times (T_{av} + 17) times (TD – 0.0123P)^{0.76}$$
(6)
In the above formula, (T_{av}) represents the average daily maximum temperature and minimum temperature, (TD) represents the difference between the daily maximum temperature and minimum temperature, (RA) represents astronomical radiation (MJm-2d-1) and P represents precipitation (mm/month).Soil retentionSoil retention refers to the ability of various land cover types to prevent soil erosion. The InVEST 3.9.0 sediment delivery ratio (SDR) was employed to estimate SR services in the QTP region. The SDR model is based on the Revised Universal Soil Loss Equation (RUSLE)67, and the model is calculated as follows:$$begin{array}{*{20}c} {SR = R*K*LS – R*K*LS*C*P} \ end{array}$$
(7)
$$begin{array}{*{20}c} {L = left( {frac{gamma }{22.3}} right)^{{frac{beta }{1 + beta }}} } \ end{array}$$
(8)
$$begin{array}{*{20}c} {beta = frac{{sin frac{theta }{0.0896}}}{{left[ {3.0, *,left( {sin theta } right)^{0.8} +, 0.56} right]}}} \ end{array}$$
(9)
$$begin{array}{*{20}c} {S = 65.41*sin^{2} theta + 4.56*sin theta + 0.065} \ end{array}$$
(10)
where SR is the total amount of soil retention (tons ha-1 a-1), LS is the topographic factor, and LS is calculated from the slope length factor (L) and slope steepness factor (S). C is the vegetation and management factor. P is the support practice factor. C and P are shown in Table S2. R is the rainfall erosivity index(MJ mm ha-1 h-1 a-1), which was calculated via monthly precipitation28. K is the soil erodibility, which was calculated from the sand, silt, clay and organic soil moisture contents68. R and K are calculated as follows:$$begin{array}{*{20}c} {R = mathop sum limits_{i = 1}^{12} left( { – 1.5527 + 0.179P_{i} } right)} \ end{array}$$
(11)
$$begin{array}{*{20}c} {K = 0.1317*left{ {0.2 + 0.3*exp left[ { – 0.0256*SANleft( {1 – frac{SIL}{{100}}} right)} right]} right}} \ {*left( {frac{SIL}{{CLA – SIL}}} right)^{0.3} *left( {1 – frac{0.25*SOM}{{SOM + exp 3.72 – 2.95*SOM}}} right)} \ {quad quad*left( {1 – frac{{0.7*1 – frac{SAN}{{100}}}}{{begin{array}{*{20}c} {1 – frac{SAN}{{100}} + exp left( { – 5.51 + 22.9*left( {1 – frac{SAN}{{100}}} right)} right)} \ end{array} }}} right)} \ end{array}$$
(12)
where Pi is the precipitation in month i. SAN, SIL, CLA, and SOM are the contents of sand, silt, clay and organic moisture, respectively. Other parameters are shown in Table S1.Carbon storageCarbon storage services refer to the carbon that ecosystems store in vegetation, soil and debris. The InVEST 3.9.0 carbon model uses a simple method to estimate CS based on land use data. The carbon pools in this model include four categories: aboveground carbon, belowground carbon, soil organic carbon and dead organic matter. This model simplifies the carbon cycle, and the change in carbon storage is mainly caused by change in land use69. The carbon pools for land use types were set according to published literature70,71,72. The carbon storage is calculated as follows:$$begin{array}{*{20}c} {{text{C}}_{{{text{total}}}} = C_{above} + C_{below} + C_{soil} + C_{dead} } \ end{array}$$
(13)
where ({text{C}}_{{{text{total}}}}), (C_{above}), (C_{below}), (C_{soil}) and (C_{dead}) are the total carbon storage, aboveground carbon, belowground carbon, soil organic carbon and dead organic matter, respectively.Raw material provisionRaw material supply refers to the organic matter provided by the ecosystem for human production and life, such as pasture and wood. In this study, RMP was quantified by the annual NPP. The NPP in the QTP region is calculated by the CASA model, which is a light use efficiency model driven by climate and remote sensing data73,74. The CASA model has been widely used to estimate NPP in terrestrial ecosystems75,76. In the CASA model, NPP is calculated as follows:$$begin{array}{*{20}c} {NPPleft( {x,t} right) = APARleft( {x,t} right) times varepsilon left( {x,t} right)} \ end{array}$$
(14)
where, (APARleft( {x,t} right)) is the photosynthetically active radiation(MJ m-2) absorbed by pixel x in month t, (varepsilon left( {x,t} right)) is the actual light energy utilization rate(gC MJ-1), and the (APARleft( {x,t} right)) calculation method is as follows:$$begin{array}{*{20}c} {APARleft( {x,t} right) = SOLleft( {x,t} right) times FPARleft( {x,t} right) times 0.5} \ end{array}$$
(15)
In the formula, (SOLleft( {x,t} right)) is the total solar radiation in pixel x in month t(MJ M-2); (FPARleft( {x,t} right)) is the absorption ratio of photosynthetically active radiation by vegetation, which is determined by the normalized difference vegetation index (NDVI); and the constant 0.5 is the proportion of photosynthetically active radiation to the total radiation. (SOLleft( {x,t} right)) can be calculated by the solar shortwave radiation as follows:$$begin{array}{*{20}c} {SOLleft( {x,t} right) = a_{s} + b_{s} frac{n}{N}R_{s} } \ end{array}$$
(16)
where, (R_{s}) is the solar shortwave radiation(MJ M-2 d-1), n is the actual sunshine time(hours), N is the time of day(hours), and (frac{n}{N}) is the relative sunshine time; The constants (a_{s} = 0.25) and (b_{s} = 0.5).And the (varepsilon left( {x,t} right)) is calculated as follows:$$begin{array}{*{20}c} {varepsilon left( {x,t} right) = T_{varepsilon 1} left( {x,t} right) times T_{varepsilon 2} left( {x,t} right) times W_{varepsilon } left( {x,t} right) times varepsilon_{max} } \ end{array}$$
(17)
where, (T_{varepsilon 1}) and (T_{varepsilon 2}) are the stress factors of cold and heat, respectively; (W_{varepsilon }) is the water stress factor, reflecting the influence of water conditions; (varepsilon_{max}) is the maximum light use efficiency(gC MJ-1) under the optimal conditions, in this study, (varepsilon_{max}) is 0.389.Trend analysisThe Mann–Kendall nonparametric test and Sen’s slope estimator were used to analyze the trend of ESs in the QTP region. The Mann–Kendall method is widely used to analyze climatic and hydrological time series variation trends77. The advantage of the Mann–Kendall test is that it does not require the sample to follow a certain distribution, allows the existence of missing values, is not affected by a small number of outliers, and has strong quantitative ability78. The Mann–Kendall test is as follows:$$begin{array}{*{20}c} {S = mathop sum limits_{i}^{n – 1} mathop sum limits_{j = i + 1}^{n} sgnleft( {x_{j} – x_{i} } right)} \ end{array}$$
(18)
For time series data, i.e., {x1, x2, …, xn}, n is the length of the data, and (sgnleft( {x_{j} – x_{i} } right)) is derived as:$$begin{array}{*{20}c} {sgnleft( {x_{j} – x_{i} } right) = left{ {begin{array}{*{20}c} { + 1,x_{j} – x_{i} > 0} \ {0,x_{j} – x_{i} = 0} \ { – 1,x_{j} – x_{i} < 0} \ end{array} } right.} \ end{array}$$ (19) In this study, we set the significance level of (alpha = 0.05), when (left| Z right| le Z_{1 - alpha /2}) accepts the null hypothesis. Otherwise, the null hypothesis is rejected, and the trend is statistically significant.$$begin{array}{*{20}c} {Z = left{ {begin{array}{*{20}l} frac{S - 1}{{sqrt {VARleft( S right)} }},&quad S > 0 \ 0,&quad S = 0 \ frac{S + 1}{{sqrt {VARleft( S right)} }},&quad S < 0 \ end{array} } right.} \ end{array}$$ (20) $$begin{array}{*{20}c} {VARleft( S right) = left{ {nleft( {n - 1} right)left( {2n + 5} right) - mathop sum limits_{j = 1}^{p} t_{j} left( {t_{j} - 1} right)left( {2t_{j} + 5} right)} right} div 18} \ end{array}$$ (21) where p is the number of nodes in the dataset and (t_{j}) is the length of the nodes.Sen’s slope estimator is an estimation method based on the median and its insensitivity to outliers78.$$begin{array}{*{20}c} {beta = Medianleft( {frac{{x_{j} - x_{i} }}{j - i}} right)} \ end{array}$$ (22) Trade-offs and synergy analysisSynergies and trade-offs were used to describe the relationships among the ESs. A trade-off analysis was conducted to reflect the difference in ESs and their responses to climate change. Trade-offs are when ESs change in the opposite direction. Synergies are when ESs change in the same direction79. Correlation analysis is often used to evaluate trade-offs and synergies between ESs2. To analyze the trade-offs and synergies of ESs at different administrative and natural scales, we allocated the ES values at the 10 km (pixel), county and watershed scales by the “zonal statistic” module of ArcGIS 10.2, and conducted minimum–maximum normalization in R4.0.3 (www.R-project.com). To analyze the relationship between any two of the four ES types, the R package PerformanceAnalytics was adopted to measure the Spearman correlation matrix at different scales. More

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