Philippa Kaur

We agree that economic valuations of the ecosystem services provided by natural environments can be a powerful tool to aid conservation (see Nature 591, 178; 2021), but we suggest that they are subject to six caveats.First, they are automatically weighted towards countries with strong currencies and high gross domestic products, undervaluing ecosystems and people in low-income nations. Second, current protocols (see P. Dasgupta The Economics of Biodiversity: the Dasgupta Review; HM Treasury, 2021) are incomplete and should take into account mental health, which has cash consequences for employers, insurers, governments and societies (R. Buckley et al. Nature Commun. 10, 5005; 2019). Third, they apply at different scales, physically and politically: global or cross-border for some, but local for most. Fourth, they are most powerful for ecosystem services that are scarce, in demand, rival (one user prevents others from using it) and excludable (it is possible to stop someone from using it). Fifth, their political power depends on the focus and distribution of costs and benefits: health outweighs conservation, for instance. Finally, they depend on human institutions, such as carbon prices.Protocols to account for ecosystem services should therefore be scalable, to match political decisions, and modular, allowing for future adjustments. It would be premature to solidify standards now. More

1.Wong, M. K., Guénard, B. & Lewis, O. T. Trait-based ecology of terrestrial arthropods. Biol. Rev. 94, 999–1022. https://doi.org/10.1111/brv.12488 (2019).Article 
PubMed 

Google Scholar 
2.McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185. https://doi.org/10.1016/j.tree.2006.02.002 (2006).Article 
PubMed 

Google Scholar 
3.Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892. https://doi.org/10.1111/j.0030-1299.2007.15559.x (2007).Article 

Google Scholar 
4.Forrest, J. R. K., Thorp, R. W., Kremen, C. & Williams, N. M. Contrasting patterns in species and functional-trait diversity of bees in an agricultural landscape. J. Appl. Ecol. 52, 706–715. https://doi.org/10.1111/1365-2664.12433 (2015).Article 

Google Scholar 
5.Williams, N. M. et al. Ecological and life-history traits predict bee species responses to environmental disturbances. Biol. Conserv. 143, 2280–2291. https://doi.org/10.1016/j.biocon.2010.03.024 (2010).Article 

Google Scholar 
6.Woodcock, B. A. et al. Meta-analysis reveals that pollinator functional diversity and abundance enhance crop pollination and yield. Nat. Commun. 10, 1481. https://doi.org/10.1038/s41467-019-09393-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Gagic, V. et al. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices. Proc. R. Soc. B 282, 20142620. https://doi.org/10.1098/rspb.2014.2620 (2015).Article 
PubMed 

Google Scholar 
8.Bartomeus, I., Cariveau, D. P., Harrison, T. & Winfree, R. On the inconsistency of pollinator species traits for predicting either response to land-use change or functional contribution. Oikos 127, 306–315. https://doi.org/10.1111/oik.04507 (2018).Article 

Google Scholar 
9.Paull, S. H. et al. From superspreaders to disease hotspots: Linking transmission across hosts and space. Front. Ecol. Environ. 10, 75–82. https://doi.org/10.1890/110111 (2012).Article 
PubMed 

Google Scholar 
10.Perkins, S. E., Cattadori, I. M., Tagliapietra, V., Rizzoli, A. P. & Hudson, P. J. Empirical evidence for key hosts in persistence of a tick-borne disease. Int. J. Parasitol. 33, 909–917. https://doi.org/10.1016/s0020-7519(03)00128-0 (2003).Article 
PubMed 

Google Scholar 
11.Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957. https://doi.org/10.1126/science.1255957 (2015).CAS 
Article 
PubMed 

Google Scholar 
12.Ravoet, J. et al. Widespread occurrence of honey bee pathogens in solitary bees. J. Invertebr. Pathol. 122, 55–58. https://doi.org/10.1016/j.jip.2014.08.007 (2014).Article 
PubMed 

Google Scholar 
13.Evison, S. E. F. et al. Pervasiveness of parasites in pollinators. PLoS ONE 7, e30641. https://doi.org/10.1371/journal.pone.0030641 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Dolezal, A. G. et al. Honey bee viruses in wild bees: viral prevalence, loads, and experimental inoculation. PLoS ONE 11, e0166190. https://doi.org/10.1371/journal.pone.0166190 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
15.Levitt, A. L. et al. Cross-species transmission of honey bee viruses in associated arthropods. Virus Res. 176, 232–240. https://doi.org/10.1016/j.virusres.2013.06.013 (2013).CAS 
Article 
PubMed 

Google Scholar 
16.Figueroa, L. L. et al. Landscape simplification shapes pathogen prevalence in plant-pollinator networks. Ecol. Lett. 23, 1212–1222. https://doi.org/10.1111/ele.13521 (2020).Article 
PubMed 

Google Scholar 
17.Graystock, P. et al. Dominant bee species and floral abundance drive parasite temporal dynamics in plant-pollinator communities. Nat. Ecol. Evol. 4, 1358–1367. https://doi.org/10.1038/s41559-020-1247-x (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596. https://doi.org/10.1007/s00442-007-0752-9 (2007).ADS 
Article 
PubMed 

Google Scholar 
19.Figueroa, L. L. et al. Bee pathogen transmission dynamics: deposition, persistence and acquisition on flowers. Proc. R. Soc. B 286, 20190603. https://doi.org/10.1098/rspb.2019.0603 (2019).Article 
PubMed 

Google Scholar 
20.Palmer-Young, E. C., Calhoun, A. C., Mirzayeva, A. & Sadd, B. M. Effects of the floral phytochemical eugenol on parasite evolution and bumble bee infection and preference. Sci. Rep. 8, 2074. https://doi.org/10.1038/s41598-018-20369-2 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Manson, J. S., Otterstatter, M. C. & Thomson, J. D. Consumption of a nectar alkaloid reduces pathogen load in bumble bees. Oecologia 162, 81–89. https://doi.org/10.1007/s00442-009-1431-9 (2010).ADS 
Article 
PubMed 

Google Scholar 
22.Otterstatter, M. C. & Thomson, J. D. Within-host dynamics of an intestinal pathogen of bumble bees. Parasitology 133, 749–761. https://doi.org/10.1017/S003118200600120X (2006).CAS 
Article 
PubMed 

Google Scholar 
23.Rutrecht, S. T. & Brown, M. J. F. Within colony dynamics of Nosema bombi infections: disease establishment, epidemiology and potential vertical transmission. Apidologie 39, 504–514. https://doi.org/10.1051/apido:2008031 (2008).Article 

Google Scholar 
24.Roberts, K. E., Evison, S. E. F., Baer, B. & Hughes, W. O. H. The cost of promiscuity: Sexual transmission of Nosema microsporidian parasites in polyandrous honey bees. Sci. Rep. 5, 10982. https://doi.org/10.1038/srep10982 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Schmid-Hempel, P. Parasites in Social Insects (Princeton University Press, Princeton, 1998).
Google Scholar 
26.Wuellner, C. T. Nest site preference and success in a gregarious, ground-nesting bee Dieunomia triangulifera. Ecol. Entomol. 24, 471–479. https://doi.org/10.1046/j.1365-2311.1999.00215.x (1999).Article 

Google Scholar 
27.Potts, S. & Willmer, P. Abiotic and biotic factors influencing nest-site selection by Halictus rubicundus, a ground-nesting halictine bee. Ecol. Entomol. 22, 319–328. https://doi.org/10.1046/j.1365-2311.1997.00071.x (1997).Article 

Google Scholar 
28.Cane, J. H. Soils of ground-nesting bees (Hymenoptera: Apoidea): texture, moisture, cell depth and climate. J. Kans. Entomol. Soc. 64, 406–413 (1991).
Google Scholar 
29.Leonard, R. J. & Harmon-Threatt, A. N. Methods for rearing ground-nesting bees under laboratory conditions. Apidologie 50, 689–703. https://doi.org/10.1007/s13592-019-00679-8 (2019).CAS 
Article 

Google Scholar 
30.Folly, A. J., Koch, H., Stevenson, P. C. & Brown, M. J. F. Larvae act as a transient transmission hub for the prevalent bumblebee parasite Crithidia bombi. J. Invertebr. Pathol. 148, 81–85. https://doi.org/10.1016/j.jip.2017.06.001 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Kappeler, P. M., Cremer, S. & Nunn, C. L. Sociality and health: impacts of sociality on disease susceptibility and transmission in animal and human societies. Philos. T. R. Soc. B 370, 20140116. https://doi.org/10.1098/rstb.2014.0116 (2015).Article 

Google Scholar 
32.Stow, A. et al. Antimicrobial defences increase with sociality in bees. Biol. Lett. 3, 422–424. https://doi.org/10.1098/rsbl.2007.0178 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Spivak, M. & Reuter, G. S. Resistance to American foulbrood disease by honey bee colonies Apis mellifera bred for hygienic behavior. Apidologie 32, 555–565. https://doi.org/10.1051/apido:2001103 (2001).Article 

Google Scholar 
34.Pinilla-Gallego, M. S. et al. Within-colony transmission of microsporidian and trypanosomatid parasites in honey bee and bumble bee colonies. Environ. Entomol. https://doi.org/10.1093/ee/nvaa112 (2020).Article 
PubMed 

Google Scholar 
35.Nunn, C. L., Jordán, F., McCabe, C. M., Verdolin, J. L. & Fewell, J. H. Infectious disease and group size: more than just a numbers game. Philos. T. R. Soc. B 370, 20140111. https://doi.org/10.1098/rstb.2014.0111 (2015).Article 

Google Scholar 
36.Adler, L. S., Barber, N. A., Biller, O. M. & Irwin, R. E. Flowering plant composition shapes pathogen infection intensity and reproduction in bumble bee colonies. Proc. Natl. Acad. Sci. USA 117, 11559–11565. https://doi.org/10.1073/pnas.2000074117 (2020).CAS 
Article 
PubMed 

Google Scholar 
37.Adler, L. S. et al. Disease where you dine: plant species and floral traits associated with pathogen transmission in bumble bees. Ecology 99, 2535–2545. https://doi.org/10.1002/ecy.2503 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Koch, H., Brown, M. J. F. & Stevenson, P. C. The role of disease in bee foraging ecology. Curr. Opin. Insect Sci. 21, 60–67. https://doi.org/10.1016/j.cois.2017.05.008 (2017).Article 
PubMed 

Google Scholar 
39.Giacomini, J. J. et al. Medicinal value of sunflower pollen against bee pathogens. Sci. Rep. 8, 14394. https://doi.org/10.1038/s41598-018-32681-y (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.LoCascio, G. M., Aguirre, L., Irwin, R. E. & Adler, L. S. Pollen from multiple sunflower cultivars and species reduces a common bumblebee gut pathogen. R. Soc. Open Sci. 6, 190279. https://doi.org/10.1098/rsos.190279 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Gegear, R. J., Otterstatter, M. C. & Thomson, J. D. Does parasitic infection impair the ability of bumblebees to learn flower-handling techniques?. Anim. behav. 70, 209–215. https://doi.org/10.1016/j.anbehav.2004.09.025 (2005).Article 

Google Scholar 
42.Gegear, R. J., Otterstatter, M. C. & Thomson, J. D. Bumble-bee foragers infected by a gut parasite have an impaired ability to utilize floral information. Proc. R. Soc. B 273, 1073–1078. https://doi.org/10.1098/rspb.2005.3423 (2006).Article 
PubMed 

Google Scholar 
43.Goulson, D., O’Connor, S. & Park, K. J. The impacts of predators and parasites on wild bumblebee colonies. Ecol. Entomol. 43, 168–181. https://doi.org/10.1111/een.12482 (2018).Article 

Google Scholar 
44.Graystock, P., Meeus, I., Smagghe, G., Goulson, D. & Hughes, W. O. The effects of single and mixed infections of Apicystis bombi and deformed wing virus in Bombus terrestris. Parasitology 143, 358–365. https://doi.org/10.1017/S0031182015001614 (2016).Article 
PubMed 

Google Scholar 
45.Graystock, P., Yates, K., Darvill, B., Goulson, D. & Hughes, W. O. H. Emerging dangers: Deadly effects of an emergent parasite in a new pollinator host. J. Invertebr. Pathol. 114, 114–119. https://doi.org/10.1016/j.jip2013.06.005 (2013).Article 
PubMed 

Google Scholar 
46.Otti, O. & Schmid-Hempel, P. Nosema bombi: a pollinator parasite with detrimental fitness effects. J. Invertebr. Pathol. 96, 118–124. https://doi.org/10.1016/j.jip.2007.03.016 (2007).Article 
PubMed 

Google Scholar 
47.Bramke, K., Müller, U., McMahon, D. P. & Rolff, J. Exposure of larvae of the solitary bee Osmia bicornis to the honey bee pathogen Nosema ceranae affects life history. Insects 10, 380. https://doi.org/10.3390/insects10110380 (2019).Article 
PubMed Central 

Google Scholar 
48.Eiri, D. M., Suwannapong, G., Endler, M. & Nieh, J. C. Nosema ceranae can infect honey bee larvae and reduces subsequent adult longevity. PLoS ONE 10, e0126330. https://doi.org/10.1371/journal.pone.0126330 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Mitchell, T. B. Bees of the Eastern United States: volume I. N. C. Agric. Exp. Sta. Tech. Bull 1, 1–538 (1960).
Google Scholar 
50.Mitchell, T. B. Bees of the Eastern United States: volume II. N. C. Agric. Exp. Sta. Tech. Bull II, 1–557 (1962).
Google Scholar 
51.LaBerge, W. E. A revision of the bees of the genus Andrena of the Western Hemisphere. Part XII. Subgenera Leucandrena, Ptilandrena, Scoliandrena and Melandrena. Trans. Am. Entomol. Soc. 112, 191–248 (1986).
Google Scholar 
52.Gibbs, J. Revision of the metallic Lasioglossum (Dialictus) of Eastern North America (Hymenoptera: Halictidae: Halictini). Zootaxa 3073, 1–216 (2011).Article 

Google Scholar 
53.Rehan, S. M. & Sheffield, C. S. Morphological and molecular delineation of a new species in the Ceratina dupla species-group (Hymenoptera: Apidae: Xylocopinae) of Eastern North America. Zootaxa 2873, 35–50 (2011).Article 

Google Scholar 
54.Gibbs, J., Packer, L., Dumesh, S. & Danforth, B. N. Revision and reclassification of Lasioglossum (Evylaeus), L. (Hemihalictus) and L. (Sphecodogastra) in Eastern North America (Hymenoptera: Apoidea: Halictidae). Zootaxa 3672, 1–117 (2013).Article 

Google Scholar 
55.Coutinho, J. G. D. E., Garibaldi, L. A. & Viana, B. F. The influence of local and landscape scale on single response traits in bees: A meta-analysis. Agr. Ecosyst. Environ. 256, 61–73. https://doi.org/10.1016/j.agee.2017.12.025 (2018).Article 

Google Scholar 
56.Bartomeus, I. et al. Historical changes in northeastern US bee pollinators related to shared ecological traits. Proc. Natl. Acad. Sci. USA 110, 4656–4660. https://doi.org/10.1073/pnas.1218503110 (2013).ADS 
Article 
PubMed 

Google Scholar 
57.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
58.Stevenson, M. et al. epiR: Tools for the analysis of epidemiological data. R package version 0.9–62 (2015).59.Teder, T. & Tammaru, T. Sexual size dimorphism within species increases with body size in insects. Oikos 108, 321–334. https://doi.org/10.1111/j.0030-1299.2005.13609.x (2005).Article 

Google Scholar 
60.Müller, U., McMahon, D. P. & Rolff, J. Exposure of the wild bee Osmia bicornis to the honey bee pathogen Nosema ceranae. Agric. For. Entomol. 65, 191–199. https://doi.org/10.1111/afe.12338 (2019).Article 

Google Scholar 
61.Strobl, V., Yañez, O., Straub, L., Albrecht, M. & Neumann, P. Trypanosomatid parasites infecting managed honeybees and wild solitary bees. Int. J. Parasitol. 49, 605–613. https://doi.org/10.1016/j.ijpara.2019.03.006 (2019).Article 
PubMed 

Google Scholar 
62.Ngor, L. et al. Cross-infectivity of honey and bumble bee-associated parasites across three bee families. Parasitology 147, 1–62. https://doi.org/10.1017/S0031182020001018 (2020).Article 

Google Scholar 
63.Figueroa, L. L., Grincavitch, C. & McArt, S. H. Crithidia bombi can infect two solitary bee species while host survivorship depends on diet. Parasitology 148, 435–442. https://doi.org/10.1017/S0031182020002218 (2021).Article 
PubMed 

Google Scholar 
64.Rhodes, J. R., McAlpine, C. A., Zuur, A., Smith, G. & Ieno, E. Mixed Effects Models and Extensions in Ecology with R Statistics for Biology and Health 469–492 (Springer, New York, 2009).
Google Scholar 
65.Burnham, K. P. & Anderson, D. R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Method. Res. 33, 261–304. https://doi.org/10.1177/0049124104268644 (2004).MathSciNet 
Article 

Google Scholar 
66.Ruiz-González, M. X. et al. Dynamic transmission, host quality, and population structure in a multi-host parasite of bumblebees. Evolution 66, 3053–3066. https://doi.org/10.1111/j.1558-5646.2012.01655.x (2012).Article 
PubMed 

Google Scholar 
67.Cook-Patton, S. C., McArt, S. H., Parachnowitsch, A. L., Thaler, J. S. & Agrawal, A. A. A direct comparison of the consequences of plant genotypic and species diversity on communities and ecosystem function. Ecology 92, 915–923. https://doi.org/10.1890/10-0999.1 (2011).Article 
PubMed 

Google Scholar 
68.Goulson, D. & Sparrow, K. R. Evidence for competition between honeybees and bumblebees; effects on bumblebee worker size. J. Insect Conserv. 13, 177–181. https://doi.org/10.1007/s10841-008-9140-y (2009).Article 

Google Scholar 
69.Grab, H. et al. Habitat enhancements rescue bee body size from the negative effects of landscape simplification. J. Appl. Ecol. 56, 2144–2154. https://doi.org/10.1111/1365-2664.13456 (2019).Article 

Google Scholar 
70.Renauld, M., Hutchinson, A., Loeb, G., Poveda, K. & Connelly, H. Landscape smplification constrains adult size in a native ground-nesting bee. PLoS ONE 11, e0150946. https://doi.org/10.1371/journal.pone.0150946 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.Persson, A. S. & Smith, H. G. Bumblebee colonies produce larger foragers in complex landscapes. Basic Appl. Ecol. 12, 695–702. https://doi.org/10.1016/j.baae.2011.10.002 (2011).Article 

Google Scholar 
72.Bommarco, R. et al. Dispersal capacity and diet breadth modify the response of wild bees to habitat loss. Proc. R. Soc. B 277, 2075–2082. https://doi.org/10.1098/rspb.2009.2221 (2010).Article 
PubMed 

Google Scholar 
73.Yerushalmi, S., Bodenhaimer, S. & Bloch, G. Developmentally determined attenuation in circadian rhythms links chronobiology to social organization in bees. J. Exp. Biol. 209, 1044–1051. https://doi.org/10.1242/jeb.02125 (2006).Article 
PubMed 

Google Scholar 
74.McNeil, D. J. et al. Bumble bees in landscapes with abundant floral resources have lower pathogen loads. Sci. Rep. 10, 22306. https://doi.org/10.1038/s41598-020-78119-2 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
75.Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568. https://doi.org/10.1038/ncomms9568 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
76.Williams, N. M., Minckley, R. L. & Silveira, F. A. Variation in native bee faunas and its implications for detecting community changes. Conserv. Ecol. 5, 7 (2001).
Google Scholar 
77.Bartomeus, I. et al. Climate-associated phenological advances in bee pollinators and bee-pollinated plants. Proc. Natl. Acad. Sci. USA 108, 20645–20649. https://doi.org/10.1073/pnas.1115559108 (2011).ADS 
Article 
PubMed 

Google Scholar 
78.Stemkovski, M. et al. Bee phenology is predicted by climatic variation and functional traits. Ecol. Lett. 23, 1589–1598. https://doi.org/10.1111/ele.13583 (2020).Article 
PubMed 

Google Scholar  More

1.UNFCCC. Adoption of the Paris Agreement. 32 (2015).2.Fuss, S. et al. Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett. 13, 063002. https://doi.org/10.1088/1748-9326/aabf9f (2018).CAS 
Article 
ADS 

Google Scholar 
3.Lawrence, M. G. et al. Evaluating climate geoengineering proposals in the context of the Paris Agreement temperature goals. Nat. Commun. 9, 3734. https://doi.org/10.1038/s41467-018-05938-3 (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
4.Matovic, D. Biochar as a viable carbon sequestration option: Global and Canadian perspective. Energy 36, 2011–2016. https://doi.org/10.1016/j.energy.2010.09.031 (2011).CAS 
Article 

Google Scholar 
5.Osman, A. I., Hefny, M., Abdel Maksoud, M. I. A., Elgarahy, A. M. & Rooney, D. W. Recent advances in carbon capture storage and utilisation technologies: A review. Environ. Chem. Lett. https://doi.org/10.1007/s10311-020-01133-3 (2020).Article 

Google Scholar 
6.Clough, B. J. et al. Testing a new component ratio method for predicting total tree aboveground and component biomass for widespread pine and hardwood species of eastern US. Forestry 91, 575–588. https://doi.org/10.1093/forestry/cpy016 (2018).Article 

Google Scholar 
7.Lam, T. Y., Li, X., Kim, R. H., Lee, K. H. & Son, Y. M. Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea. J. For. Res. 26, 875–885. https://doi.org/10.1007/s11676-015-0089-x (2015).CAS 
Article 

Google Scholar 
8.Sileshi, G. W. A critical review of forest biomass estimation models, common mistakes and corrective measures. For. Ecol. Manag. 329, 237–254. https://doi.org/10.1016/j.foreco.2014.06.026 (2014).Article 

Google Scholar 
9.Ver Planck, N. R. & MacFarlane, D. W. A vertically integrated whole-tree biomass model. Trees 29, 449–460, https://doi.org/10.1007/s00468-014-1123-x (2015).10.Jenkins, J. C., Chojnacky, D. C., Heath, L. S. & Birdsey, R. A. National-scale biomass estimators for United States tree species. For. Sci. 49, 12–35. https://doi.org/10.1093/forestscience/49.1.12 (2003).Article 

Google Scholar 
11.Parresol, B. R. Additivity of nonlinear biomass equations. Can. J. For. Res. 31, 865–878. https://doi.org/10.1139/x00-202 (2001).Article 

Google Scholar 
12.Parresol, B. R. Assessing tree and stand biomass: A review with examples and critical comparisons. For. Sci. 45, 573–593, https://doi.org/10.1093/forestscience/45.4.573 (1999).13.Radtke, P. et al. Improved accuracy of aboveground biomass and carbon estimates for live trees in forests of the eastern United States. Forestry 90, 32–46. https://doi.org/10.1093/forestry/cpw047 (2017).Article 

Google Scholar 
14.Woodall, C. W., Heath, L. S., Domke, G. M. & Nichols, M. C. Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. 30, https://doi.org/10.2737/NRS-GTR-88 (2011).15.Chiou, L.-W., Huang, C.-H., Wu, J.-C. & Hsieh, H.-R. Report of the 4th National Forest Resource Inventory in Taiwan. Taiwan For. J. 41, 3–13 (2015).
Google Scholar 
16.Yang, T.-R., Lam, T. Y. & Kershaw, J. A. Jr. Developing relative stand density index for structurally complex mixed species cypress and pine forests. For. Ecol. Manag. 409, 425–433. https://doi.org/10.1016/j.foreco.2017.11.043 (2018).Article 

Google Scholar 
17.Taiwan Forestry Bureau. The Fourth National Forest Resource Inventory. Vol. 78 (2017).18.Ko, S.-H. Study on the Biomass and Carbon Storage in the Zelkova serrata Plantation. MSc. Thesis, National Chung-Hsing University, https://doi.org/10.6845/NCHU.2006.00871 (2006).19.Lin, J.-C., Jeng, M.-R., Liu, S.-F. & Lee, K. J. Economic benefit evaluation of the potential CO2 sequestration by the National Reforestation Program. Taiwan J. For. Sci. 17, 311–321, https://doi.org/10.7075/TJFS.200209.0311 (2002).20.Lin, K.-C., Huang, C.-M. & Duh, C.-T. Study on estimate of carbon storages and sequestration of planted trees in Zelkova serrata plantations, Taiwan. J. Natl. Park 18, 45–58 (2008).CAS 

Google Scholar 
21.Liao, S.-H. & Wang, Y.-N. Study on carbon dioxide fixation efficiency of Cinnamomum camphora and Zelkova serrata in understory planting. Q. J. Chin. For. 35, 361–373 (2002).
Google Scholar 
22.Lambert, M. C., Ung, C. H. & Raulier, F. Canadian national tree aboveground biomass equations. Can. J. For. Res. 35, 1996–2018. https://doi.org/10.1139/x05-112 (2005).Article 

Google Scholar 
23.Zellner, A. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J. Am. Stat. Assoc. 57, 348–368. https://doi.org/10.2307/2281644 (1962).MathSciNet 
Article 
MATH 

Google Scholar 
24.Henningsen, A. & Hamann, J. D. systemfit: A package for estimating systems of simultaneous equations in R. J. Stat. Softw. 23, 1–40, https://doi.org/10.18637/jss.v023.i04 (2007).25.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).26.Nelson, A. S., Weiskittel, A. R., Wagner, R. G. & Saunders, M. R. Development and evaluation of aboveground small tree biomass models for naturally regenerated and planted species in eastern Maine, U.S.A. Biomass Bioenergy 68, 215–227, https://doi.org/10.1016/j.biombioe.2014.06.015 (2014).27.Poudel, K. P., Temesgen, H., Radtke, P. J. & Gray, A. N. Estimating individual-tree aboveground biomass of tree species in the western U.S.A. Can. J. For. Res. 49, 701–714, https://doi.org/10.1139/cjfr-2018-0361 (2019).28.Carvalho, J. P. & Parresol, B. R. Additivity in tree biomass components of Pyrenean oak (Quercus pyrenaica Willd.). For. Ecol. Manag. 179, 269–276, https://doi.org/10.1016/S0378-1127(02)00549-2 (2003).29.He, H. et al. Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China. PLoS ONE 13, e0186226. https://doi.org/10.1371/journal.pone.0186226 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Cheng, C.-H., Huang, Y.-H., Menyailo, O. V. & Chen, C.-T. Stand development and aboveground biomass carbon accumulation with cropland afforestation in Taiwan. Taiwan J. For. Sci. 31, 105–118 (2016).
Google Scholar 
31.Lee, J.-H., Ko, Y. & McPherson, E. G. The feasibility of remotely sensed data to estimate urban tree dimensions and biomass. Urban For. Urban Green. 16, 208–220. https://doi.org/10.1016/j.ufug.2016.02.010 (2016).Article 

Google Scholar 
32.Park, J. H., Baek, S. G., Kwon, M. Y., Je, S. M. & Woo, S. Y. Volumetric equation development and carbon storage estimation of urban forest in Daejeon, Korea. For. Sci. Technol. 14, 97–104. https://doi.org/10.1080/21580103.2018.1452799 (2018).Article 

Google Scholar 
33.Yoon, T. K. et al. Allometric equations for estimating the aboveground volume of five common urban street tree species in Daegu, Korea. Urban For. Urban Green. 12, 344–349. https://doi.org/10.1016/j.ufug.2013.03.006 (2013).Article 

Google Scholar 
34.Chiu, C. M., Lo-Cho, C.-N. & Suen, M.-Y. Pruning method and knot wound analysis of Taiwan zelkova (Zelkova serrata Hay.) plantations. Taiwan J. For. Sci. 17, 503–513, https://doi.org/10.7075/TJFS.200212.0503 (2002).35.Lo-Cho, C.-N., Chung, H.-H. & Chiu, C.-M. Effects of pruning on the growth and the branch occlusion tendency of Taiwan Zelkova (Zelkova serrata Hay.) young plantations. Bull. Taiwan For. Res. Inst. 10, 315–323, https://doi.org/10.7075/BTFRI.199509.0315 (1995).36.Shepherd, K. R. Plantation Silviculture (Springer, 1986).
Google Scholar 
37.Chiou, C.-R., Lin, J.-C. & Liu, W.-Y. The carbon benefit of thinned wood for bioenergy in Taiwan. Forests 10, 255. https://doi.org/10.3390/f10030255 (2019).Article 

Google Scholar 
38.Liu, W.-Y., Lin, C.-C. & Su, K.-H. Modelling the spatial forest-thinning planning problem considering carbon sequestration and emissions. For. Policy Econ. 78, 51–66. https://doi.org/10.1016/j.forpol.2017.01.002 (2017).Article 

Google Scholar 
39.Rais, A., Poschenrieder, W., van de Kuilen, J.-W.G. & Pretzsch, H. Impact of spacing and pruning on quantity, quality and economics of Douglas-fir sawn timber: Scenario and sensitivity analysis. Eur. J. For. Res. 139, 747–758. https://doi.org/10.1007/s10342-020-01282-8 (2020).Article 

Google Scholar 
40.Kershaw, J. A., Ducey, M. J., Beers, T. W. & Husch, B. Forest Mensuration. (John Wiley & Sons Ltd, 2016). More

1.Ajayi, O. O., Fagbenle, R. O., Katende, J., Aasa, S. A. & Okeniyi, J. O. Wind profile characteristics and turbine performance analysis in Kano, north-western Nigeria. Int. J. Energy Environ. Eng. 4, 1–15 (2013).Article 

Google Scholar 
2.Yan, A., Liu, S. & Dong, X. Variables two stage sampling plans based on the coefficient of variation. J. Adv. Mech. Des. Syst. Manuf. 10, JAMDSM0002 (2016).Article 

Google Scholar 
3.Yen, C.-H., Lee, C.-C., Lo, K.-H., Shiue, Y.-R. & Li, S.-H. A rectifying acceptance sampling plan based on the process capability index. Mathematics 8, 141 (2020).Article 

Google Scholar 
4.Akpinar, E. K. & Akpinar, S. A statistical analysis of wind speed data used in installation of wind energy conversion systems. Energy Convers. Manag. 46, 515–532 (2005).Article 

Google Scholar 
5.Yilmaz, V. & Çelik, H. E. A statistical approach to estimate the wind speed distribution: the case of Gelibolu region. Doğuş Üniversitesi Dergisi 9, 122–132 (2011).
Google Scholar 
6.Ali, S., Lee, S.-M. & Jang, C.-M. Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island-Incheon, South Korea. Renew. Energy 123, 652–663 (2018).Article 

Google Scholar 
7.Arias-Rosales, A. & Osorio-Gómez, G. Wind turbine selection method based on the statistical analysis of nominal specifications for estimating the cost of energy. Appl. Energy 228, 980–998 (2018).Article 

Google Scholar 
8.Akgül, F. G. & Şenoğlu, B. Comparison of wind speed distributions: a case study for Aegean coast of Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–18 (2019).9.Ozay, C. & Celiktas, M. S. Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region. Energy Convers. Manag. 121, 49–54 (2016).Article 

Google Scholar 
10.Qing, X. Statistical analysis of wind energy characteristics in Santiago island, Cape Verde. Renew. Energy 115, 448–461 (2018).Article 

Google Scholar 
11.Mahmood, F. H., Resen, A. K. & Khamees, A. B. Wind Characteristic Analysis Based on Weibull Distribution of Al-Salman site (Iraq, 2019).
Google Scholar 
12.Campisi-Pinto, S., Gianchandani, K. & Ashkenazy, Y. Statistical tests for the distribution of surface wind and current speeds across the globe. Renew. Energy 149, 861–876 (2020).Article 

Google Scholar 
13.ul Haq, M. A., Rao, G. S., Albassam, M. & Aslam, M. Marshall-Olkin Power Lomax distribution for modeling of wind speed data. Energy Rep. 6, 1118–1123 (2020).Article 

Google Scholar 
14.Bludszuweit, H., Domínguez-Navarro, J. A. & Llombart, A. Statistical analysis of wind power forecast error. IEEE Trans. Power Syst. 23, 983–991 (2008).ADS 
Article 

Google Scholar 
15.Brano, V. L., Orioli, A., Ciulla, G. & Culotta, S. Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renew. Energy 36, 1026–1039 (2011).Article 

Google Scholar 
16.Katinas, V., Gecevicius, G. & Marciukaitis, M. An investigation of wind power density distribution at location with low and high wind speeds using statistical model. Appl. Energy 218, 442–451 (2018).Article 

Google Scholar 
17.Zaman, B., Lee, M. H. & Riaz, M. An improved process monitoring by mixed multivariate memory control charts: an application in wind turbine field. Comput. Ind. Eng. 142, 106343 (2020).Article 

Google Scholar 
18.Jamkhaneh, E. B., Sadeghpour-Gildeh, B. & Yari, G. Important criteria of rectifying inspection for single sampling plan with fuzzy parameter. Int. J. Contemp. Math. Sci. 4, 1791–1801 (2009).MATH 

Google Scholar 
19.Jamkhaneh, E. B., Sadeghpour-Gildeh, B. & Yari, G. Inspection error and its effects on single sampling plans with fuzzy parameters. Struct. Multidiscip. Optim. 43, 555–560 (2011).MATH 
Article 

Google Scholar 
20.Sadeghpour Gildeh, B., Baloui Jamkhaneh, E. & Yari, G. Acceptance single sampling plan with fuzzy parameter. Iran. J. Fuzzy Syst. 8, 47–55 (2011).MathSciNet 
MATH 

Google Scholar 
21.Afshari, R. & Sadeghpour Gildeh, B. Designing a multiple deferred state attribute sampling plan in a fuzzy environment. Am. J. Math. Manag. Sci. 36, 328–345 (2017).MATH 

Google Scholar 
22.Tong, X. & Wang, Z. Fuzzy acceptance sampling plans for inspection of geospatial data with ambiguity in quality characteristics. Comput. Geosci. 48, 256–266 (2012).ADS 
Article 

Google Scholar 
23.Uma, G. & Ramya, K. Impact of fuzzy logic on acceptance sampling plans–a review. Autom. Auton. Syst. 7, 181–185 (2015).
Google Scholar 
24.Smarandache, F. Neutrosophy. Neutrosophic probability, set, and logic, proquest information & learning. Ann Arbor, Michigan, USA 105, 118–123 (1998).25.Smarandache, F. & Khalid, H. E. Neutrosophic Precalculus and Neutrosophic Calculus. (Infinite Study, 2015).26.Peng, X. & Dai, J. Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Comput. Appl. 29, 939–954 (2018).Article 

Google Scholar 
27.Abdel-Basset, M., Mohamed, M., Elhoseny, M., Chiclana, F. & Zaied, A.E.-N.H. Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases. Artif. Intell. Med. 101, 101735 (2019).PubMed 
Article 

Google Scholar 
28.Nabeeh, N. A., Smarandache, F., Abdel-Basset, M., El-Ghareeb, H. A. & Aboelfetouh, A. An integrated neutrosophic-topsis approach and its application to personnel selection: a new trend in brain processing and analysis. IEEE Access 7, 29734–29744 (2019).Article 

Google Scholar 
29.Pratihar, J., Kumar, R., Dey, A. & Broumi, S. In Neutrosophic Graph Theory and Algorithms 180–212 (IGI Global, 2020).30.Pratihar, J., Kumar, R., Edalatpanah, S. & Dey, A. Modified Vogel’s approximation method for transportation problem under uncertain environment. Complex Intell. Syst. 7, 1–12 (2020).
Google Scholar 
31.Smarandache, F. Introduction to neutrosophic statistics. (Infinite Study, 2014).32.Chen, J., Ye, J. & Du, S. Scale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statistics. Symmetry 9, 208 (2017).Article 

Google Scholar 
33.Chen, J., Ye, J., Du, S. & Yong, R. Expressions of rock joint roughness coefficient using neutrosophic interval statistical numbers. Symmetry 9, 123 (2017).Article 

Google Scholar 
34.Aslam, M. Introducing Kolmogorov–Smirnov tests under uncertainty: an application to radioactive data. ACS Omega 5, 9914–9917 (2019).
Google Scholar 
35.Aslam, M. A new sampling plan using neutrosophic process loss consideration. Symmetry 10, 132 (2018).Article 

Google Scholar 
36.Aslam, M. Design of sampling plan for exponential distribution under neutrosophic statistical interval method. IEEE Access 6, 64153–64158 (2018).Article 

Google Scholar 
37.Aslam, M. A new attribute sampling plan using neutrosophic statistical interval method. Complex Intell. Syst. 11, 1–6 (2019).
Google Scholar 
38.Aslam, M., Jeyadurga, P., Balamurali, S. & Marshadi, A. H. Time-Truncated Group Plan under a Weibull Distribution based on Neutrosophic Statistics. Mathematics 7, 905 (2019).Article 

Google Scholar 
39.Alhasan, K. F. H. & Smarandache, F. Neutrosophic Weibull distribution and Neutrosophic Family Weibull Distribution. (Infinite Study, 2019).40.Cheema, A. N., Aslam, M., Almanjahie, I. M. & Ahmad, I. Mixture modeling of exponentiated pareto distribution in bayesian framework with applications of wind-speed and tensile strength of carbon fiber. IEEE Access 8, 178514–178525 (2020).Article 

Google Scholar 
41.Deep, S., Sarkar, A., Ghawat, M. & Rajak, M. K. Estimation of the wind energy potential for coastal locations in India using the Weibull model. Renew. Energy 161, 319–339 (2020).Article 

Google Scholar 
42.Gugliani, G., Sarkar, A., Ley, C. & Mandal, S. New methods to assess wind resources in terms of wind speed, load, power and direction. Renew. Energy 129, 168–182 (2018).Article 

Google Scholar  More

Peer review information Nature Ecology & Evolution thanks Nate Mcdowell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. More

1.Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

Google Scholar 
2.Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).CAS 
Article 

Google Scholar 
3.Hodgson, D., McDonald, J. L. & Hosken, D. J. What do you mean, ‘resilient’? Trends Ecol. Evol. 30, 503–506 (2015).Article 

Google Scholar 
4.Lloret, F., Keeling, E. G. & Sala, A. Components of tree resilience: effects of successive low-growth episodes in old ponderosa pine forests. Oikos 120, 1909–1920 (2011).Article 

Google Scholar 
5.Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).Article 

Google Scholar 
6.Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).CAS 
Article 

Google Scholar 
7.Ingrisch, J. & Bahn, M. Towards a comparable quantification of resilience. Trends Ecol. Evol. 33, 251–259 (2018).Article 

Google Scholar 
8.Van der Maaten-Theunissen, M., van der Maaten, E. & Bouriaud, O. pointRes: an R package to analyze pointer years and components of resilience. Dendrochronologia 35, 34–38 (2015).Article 

Google Scholar 
9.Gazol, A. et al. Forest resilience to drought varies across biomes. Glob. Change Biol. 24, 2143–2158 (2018).Article 

Google Scholar 
10.Serra-Maluquer, X., Mencuccini, M. & Martínez-Vilalta, J. Changes in tree resistance, recovery and resilience across three successive extreme droughts in the northeast Iberian Peninsula. Oecologia 187, 343–354 (2018).CAS 
Article 

Google Scholar 
11.DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 545 (2020).CAS 
Article 

Google Scholar 
12.Anderegg, W. R. L., Anderegg, L. D. L., Kerr, K. L. & Trugman, A. T. Widespread drought‐induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Change Biol. 25, 3793–3802 (2019).Article 

Google Scholar 
13.Sperry, J. S. et al. The impact of rising CO2 and acclimation on the response of US forests to global warming. Proc. Natl Acad. Sci. USA 116, 25734–25744 (2019).CAS 
Article 

Google Scholar 
14.Peterson, R. A. & Cavanaugh, J. E. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J. Appl. Stat. 47, 2312–2327 (2020).Article 

Google Scholar  More

Peer review information Nature Ecology & Evolution thanks Nick Isaac, Manu Saunders and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. More

1.Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593 (2014).Article 

Google Scholar 
2.Mishra, U. et al. Empirical estimates to reduce modeling uncertainties of soil organic carbon in permafrost regions: a review of recent progress and remaining challenges. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/8/3/035020 (2013).3.Scharlemann, J. P. W., Tanner, E. V. J., Hiederer, R. & Kapos, V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manage. 5, 81–91 (2014).CAS 
Article 

Google Scholar 
4.Pan, Y. D. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
Article 

Google Scholar 
5.Hugelius, G. et al. The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3–13 (2013).Article 

Google Scholar 
6.Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).CAS 
Article 

Google Scholar 
7.Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).CAS 
Article 

Google Scholar 
8.McGuire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523–555 (2009).Article 

Google Scholar 
9.Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth Sci. Rev. 172, 75–86 (2017).CAS 
Article 

Google Scholar 
10.Koven, C. D., Lawrence, D. M. & Riley, W. J. Permafrost carbon−climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics. Proc. Natl Acad. Sci. USA 112, 3752–3757 (2015).CAS 
Article 

Google Scholar 
11.Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J. & Slater, A. G. Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions. Environ. Res. Lett. 10, 094011 (2015).Article 
CAS 

Google Scholar 
12.McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115, 3882–3887 (2018).Article 
CAS 

Google Scholar 
13.Mekonnen, Z. A., Riley, W. J. & Grant, R. F. 21st century tundra shrubification could enhance net carbon uptake of North America Arctic tundra under an RCP8.5 climate trajectory. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aabf28 (2018).14.Schädel, C. et al. Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils. Nat. Clim. Change 6, 950–953 (2016).Article 
CAS 

Google Scholar 
15.Turetsky, M. R. et al. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat. Geosci. 4, 27–31 (2011).CAS 
Article 

Google Scholar 
16.Veraverbeke, S., Rogers, B. M. & Randerson, J. T. Daily burned area and carbon emissions from boreal fires in Alaska. Biogeosciences 12, 3579–3601 (2015).CAS 
Article 

Google Scholar 
17.Hu, F. S. et al. Arctic tundra fires: natural variability and responses to climate change. Front. Ecol. Environ. 13, 369–377 (2015).Article 

Google Scholar 
18.Rogers, B. M., Balch, J. K., Goetz, S. J., Lehmann, C. E. R. & Turetsky, M. Focus on changing fire regimes: interactions with climate, ecosystems, and society. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ab6d3a (2020).19.Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).CAS 
Article 

Google Scholar 
20.Mack, M. C. et al. Carbon loss from an unprecedented Arctic tundra wildfire. Nature 475, 489–492 (2011).CAS 
Article 

Google Scholar 
21.Harsch, M. A., Hulme, P. E., McGlone, M. S. & Duncan, R. P. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett. 12, 1040–1049 (2009).Article 

Google Scholar 
22.Rocha, A. V. et al. The footprint of Alaskan tundra fires during the past half-century: implications for surface properties and radiative forcing. Environ. Res. Lett. 7, 044039 (2012).Article 

Google Scholar 
23.Chambers, S. D., Beringer, J., Randerson, J. T. & Chapin, F. S. III Fire effects on net radiation and energy partitioning: contrasting responses of tundra and boreal forest ecosystems. J. Geophys. Res. Atmos. https://doi.org/10.1029/2004jd005299 (2005).24.Genet, H. et al. Modeling the effects of fire severity and climate warming on active layer thickness and soil carbon storage of black spruce forests across the landscape in interior Alaska. Environ. Res. Lett. 8, 045016 (2013).Article 
CAS 

Google Scholar 
25.Mekonnen, Z. A., Riley, W. J., Randerson, J. T., Grant, R. F. & Rogers, B. M. Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire. Nat. Plants 5, 952–958 (2019).Article 

Google Scholar 
26.Johnstone, J. F., Hollingworth, T. N., Chapin, F. S. III & Mack, M. C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Change Biol. 16, 1281–1295 (2010).Article 

Google Scholar 
27.Christian, H. J. et al. Global frequency and distribution of lightning as observed from space by the Optical Transient Detector. J. Geophys. Res. Atmos. https://doi.org/10.1029/2002jd002347 (2003).28.Cecil, D. J., Buechler, D. E. & Blakeslee, R. J. Gridded lightning climatology from TRMM-LIS and OTD: dataset description. Atmos. Res. 135, 404–414 (2014).Article 

Google Scholar 
29.Veraverbeke, S. et al. Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Change 7, 529–534 (2017).Article 

Google Scholar 
30.Krause, A., Kloster, S., Wilkenskjeld, S. & Paeth, H. The sensitivity of global wildfires to simulated past, present, and future lightning frequency. J. Geophys. Res. Biogeosci. 119, 312–322 (2014).Article 

Google Scholar 
31.Price, C. Lightning applications in weather and climate research. Surv. Geophys. 34, 755–767 (2013).Article 

Google Scholar 
32.Williams, E. R. Lightning and climate: a review. Atmos. Res. 76, 272–287 (2005).Article 

Google Scholar 
33.Albrecht, R. I., Goodman, S. J., Buechler, D. E., Blakeslee, R. J. & Christian, H. J. Where are the lightning hotspots on Earth? Bull. Am. Meteorol. Soc. 97, 2051–2068 (2016).Article 

Google Scholar 
34.Price, C. & Rind, D. Possible implications of global climate change on global lightning distributions and frequencies. J. Geophys. Res. Atmos. 99, 10823–10831 (1994).Article 

Google Scholar 
35.Jayaratne, E. R. & Kuleshov, Y. The relationship between lightning activity and surface wet bulb temperature and its variation with latitude in Australia. Meteorol. Atmos. Phys. 91, 17–24 (2006).Article 

Google Scholar 
36.Romps, D. M., Seeley, J. T., Vollaro, D. & Molinari, J. Projected increase in lightning strikes in the United States due to global warming. Science 346, 851–854 (2014).CAS 
Article 

Google Scholar 
37.Romps, D. M. Evaluating the future of lightning in cloud-resolving models. Geophys. Res. Lett. 46, 14863–14871 (2019).Article 

Google Scholar 
38.Finney, D. L. et al. A projected decrease in lightning under climate change. Nat. Clim. Change 8, 210–213 (2018).Article 

Google Scholar 
39.Bieniek, P. A. et al. Lightning variability in dynamically downscaled simulations of Alaska’s present and future summer climate. J. Appl. Meteorol. Climatol. 59, 1139–1152 (2020).Article 

Google Scholar 
40.Price, C. & Rind, D. A simple lightning parameterization for calculating global lightning distributions. J. Geophys. Res. Atmos. 97, 9919–9933 (1992).Article 

Google Scholar 
41.Reeve, N. & Toumi, R. Lightning activity as an indicator of climate change. Q. J. R. Meteorol. Soc. 125, 893–903 (1999).Article 

Google Scholar 
42.Petersen, W. A. & Rutledge, S. A. On the relationship between cloud-to-ground lightning and convective rainfall. J. Geophys. Res. Atmos. 103, 14025–14040 (1998).Article 

Google Scholar 
43.Allen, D. J. & Pickering, K. E. Evaluation of lightning flash rate parameterizations for use in a global chemical transport model. J. Geophys. Res. Atmos. 107, ACH 15-1–ACH 15-21 (2002).Article 
CAS 

Google Scholar 
44.IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).45.Price, C. Global surface temperatures and the atmospheric electrical circuit. Geophys. Res. Lett. 20, 1363–1366 (1993).Article 

Google Scholar 
46.Michalon, N., Nassif, A., Saouri, T., Royer, J. F. & Pontikis, C. A. Contribution to the climatological study of lightning. Geophys. Res. Lett. 26, 3097–3100 (1999).Article 

Google Scholar 
47.Peterson, D., Wang, J., Ichoku, C. & Remer, L. A. Effects of lightning and other meteorological factors on fire activity in the North American boreal forest: implications for fire weather forecasting. Atmos. Chem. Phys. 10, 6873–6888 (2010).CAS 
Article 

Google Scholar 
48.Kasischke, E. S., Williams, D. & Barry, D. Analysis of the patterns of large fires in the boreal forest region of Alaska. Int. J. Wildland Fire 11, 131–144 (2002).Article 

Google Scholar 
49.Stocks, B. J. et al. Large forest fires in Canada, 1959–1997. J. Geophys. Res. Atmos. https://doi.org/10.1029/2001jd000484 (2002).50.Rogers, B. M., Soja, A. J., Goulden, M. L. & Randerson, J. T. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).CAS 
Article 

Google Scholar 
51.McGuire, A. D., Chapin, F. S., Walsh, J. E. & Wirth, C. Integrated regional changes in Arctic climate feedbacks: implications for the global climate system. Annu. Rev. Environ. Resour. 31, 61–91 (2006).Article 

Google Scholar 
52.Euskirchen, E. S., McGuire, A. D., Chapin, F. S. III, Yi, S. & Thompson, C. C. Changes in vegetation in northern Alaska under scenarios of climate change, 2003–2100: implications for climate feedbacks. Ecol. Appl. 19, 1022–1043 (2009).CAS 
Article 

Google Scholar 
53.Higuera, P. E. et al. Frequent fires in ancient shrub tundra: implications of paleorecords for Arctic environmental change. PLoS ONE 3, e0001744 (2008).Article 
CAS 

Google Scholar 
54.Trugman, A. et al. Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone. J. Adv. Model. Earth Syst. 8, 1180–1209 (2016).Article 

Google Scholar 
55.Bret-Harte, M. S. et al. The response of Arctic vegetation and soils following an unusually severe tundra fire. Phil. Trans. R. Soc. B https://doi.org/10.1098/rstb.2012.0490 (2013).56.Turner, M. G. Disturbance and landscape dynamics in a changing world. Ecology 91, 2833–2849 (2010).Article 

Google Scholar 
57.Dissing, D. & Verbyla, D. L. Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation. Can. J. For. Res. 33, 770–782 (2003).Article 

Google Scholar 
58.Sedano, F. & Randerson, J. T. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences 11, 3739–3755 (2014).Article 

Google Scholar 
59.Yi, S. H., Woo, M. K. & Arain, M. A. Impacts of peat and vegetation on permafrost degradation under climate warming. Geophys. Res. Lett. 34, L16504 (2007).Article 

Google Scholar 
60.Jones, B. M. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci. Rep. https://doi.org/10.1038/srep15865 (2015).61.Brown, D. R. N. et al. Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing. Remote Sens. https://doi.org/10.3390/rs8080654 (2016).62.Walker, G. A world melting from the top down. Nature 446, 718–721 (2007).CAS 
Article 

Google Scholar 
63.Bonfils, C. J. W. et al. On the influence of shrub height and expansion on northern high latitude climate. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/7/1/015503 (2012).64.McConnell, J. R. et al. 20th-century industrial black carbon emissions altered Arctic climate forcing. Science 317, 1381–1384 (2007).CAS 
Article 

Google Scholar 
65.Swann, A. L., Fung, I. Y., Levis, S., Bonan, G. B. & Doney, S. C. Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect. Proc. Natl Acad. Sci. USA 107, 1295–1300 (2010).CAS 
Article 

Google Scholar 
66.Keuper, F. et al. Carbon loss from northern circumpolar permafrost soils amplified by rhizosphere priming. Nat. Geosci. 13, 560–565 (2020).CAS 
Article 

Google Scholar 
67.Bonan, G. B. & Doney, S. C. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models. Science https://doi.org/10.1126/science.aam8328 (2018).68.Magi, B. I. Global lightning parameterization from CMIP5 climate model output. J. Atmos. Ocean. Technol. 32, 434–452 (2015).Article 

Google Scholar 
69.Kloster, S. & Lasslop, G. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Glob. Planet. Change 150, 58–69 (2017).Article 

Google Scholar 
70.Orville, R. E., Huffines, G. R., Burrows, W. R. & Cummins, K. L. The North American Lightning Detection Network (NALDN)—analysis of flash data: 2001–09. Mon. Weather Rev. 139, 1305–1322 (2011).Article 

Google Scholar 
71.Virts, K. S., Wallace, J. M., Hutchins, M. L. & Holzworth, R. H. Highlights of a new ground-based, hourly global lightning climatology. Bull. Am. Meteorol. Soc. 94, 1381–1391 (2013).Article 

Google Scholar 
72.Pohjola, H. & Makela, A. The comparison of GLD360 and EUCLID lightning location systems in Europe. Atmos. Res. 123, 117–128 (2013).Article 

Google Scholar 
73.Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2013).74.Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464, 1334–1337 (2010).CAS 
Article 

Google Scholar 
75.Chapin, F. S. et al. Role of land-surface changes in Arctic summer warming. Science 310, 657–660 (2005).CAS 
Article 

Google Scholar 
76.Foley, J. A. Tipping points in the tundra. Science 310, 627–628 (2005).CAS 
Article 

Google Scholar 
77.Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).Article 

Google Scholar 
78.Mach, D. M. et al. Performance assessment of the Optical Transient Detector and Lightning Imaging Sensor. J. Geophys. Res. 112, D09210 (2007).
Google Scholar 
79.Mackerras, D., Darveniza, M., Orville, R. E., Williams, E. R. & Goodman, S. J. Global lightning: total, cloud and ground flash estimates. J. Geophys. Res. Atmos. 103, 19791–19809 (1998).Article 

Google Scholar 
80.Farukh, M. A. & Hayasaka, H. Active forest fire occurrences in severe lightning years in Alaska. J. Nat. Disaster Sci. 33, 71–84 (2012).Article 

Google Scholar 
81.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).Article 

Google Scholar 
82.Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).Article 

Google Scholar 
83.Seeley, J. T. & Romps, D. M. The effect of global warming on severe thunderstorms in the United States. J. Clim. 28, 2443–2458 (2015).Article 

Google Scholar 
84.van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).Article 

Google Scholar 
85.Chronis, T. G. et al. Global lightning activity from the ENSO perspective. Geophys. Res. Lett. 35, L19804 (2008).Article 

Google Scholar 
86.Satori, G., Williams, E. & Lemperger, I. Variability of global lightning activity on the ENSO time scale. Atmos. Res. 91, 500–507 (2009).Article 

Google Scholar 
87.Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M. & Morton, D. C. Global burned area and biomass burning emissions from small fires. J. Geophys. Res. Biogeosci. 117, G04012 (2012).Article 
CAS 

Google Scholar 
88.van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).Article 

Google Scholar 
89.Walker, D. A. et al. The circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).Article 

Google Scholar  More