Ecology

1.Capella-Gutiérrez, S., Marcet-Houben, M. & Gabaldon, T. Phylogenomics supports microsporidia as the earliest diverging clade of sequenced fungi. BMC Biol. 10, 47. https://doi.org/10.1186/1741-7007-10-47 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
2.Corsaro, D. et al. Filling gaps in the microsporidian tree: rDNA phylogeny of Chytridiopsis typographi (Microsporidia: Chytridiopsida). Parasitol. Res. 118, 169–180. https://doi.org/10.1007/s00436-018-6130-1 (2019).Article 
PubMed 

Google Scholar 
3.Corsaro, D. et al. Molecular identification of Nucleophaga terricolae sp. nov. (Rozellomycota), and new insights on the origin of the Microsporidia. Parasitol. Res. 115, 3003–3011 (2016).Article 

Google Scholar 
4.James, T. Y. et al. Reconstructing the early evolution of Fungi using a six-gene phylogeny. Nature 443, 818–822 (2006).ADS 
CAS 
Article 

Google Scholar 
5.Sprague, V. & Becnel, J. J. in The Microsporidia and Microsporidiosis (eds M. Wittner & L. M. Weiss) 517–530 (ASM Press, 1999).6.Dunn, A. M., Terry, R. S. & Smith, J. E. Transovarial transmission in the microsporidia. Adv. Parasitol. 48, 57–100. https://doi.org/10.1016/S0065-308X(01)48005-5 (2001).CAS 
Article 
PubMed 

Google Scholar 
7.Goertz, D. & Hoch, G. Vertical transmission and overwintering of Microsporidia in the gypsy moth, Lymantria dispar. J. Invertebr. Pathol. 99, 43–48. https://doi.org/10.1016/j.jip.2008.03.008 (2008).Article 
PubMed 

Google Scholar 
8.Becnel, J. J. & Andreadis, T. G. in Microsporidia: Pathogens of Opportunity (eds L. M. Weiss & J. J. Becnel) 521–570 (Wiley, 2014).9.Kellen, W. R. & Lindegren, J. E. Modes of transmission of Nosema plodiae Kellen and Lindegren, a pathogen of Plodia interpunctella (Hübner). J. Stored Prod. Res. 7, 31–34. https://doi.org/10.1016/0022-474X(71)90035-X (1971).Article 

Google Scholar 
10.Vávra, J. & Larsson, R. J. in Microsporidia: Pathogens of Opportunity (eds L. M. Weiss & J. J. Becnel) 1–70 (Wiley, 2014).11.Mudasar, M., Mathivanan, V., Shah, G. N., Mir, G. M. & Selvisabhanayakam, M. Nosemosis and its effect on performance of honey bees: A review. Int. J. Pharm. Bio. Sci. 4, 923–937 (2013).
Google Scholar 
12.Wolf, S. et al. So near and yet so far: Harmonic radar reveals reduced homing ability of Nosema infected honeybees. PLoS ONE 9, e103989. https://doi.org/10.1371/journal.pone.0103989 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Naug, D. & Gibbs, A. Behavioral changes mediated by hunger in honeybees infected with Nosema ceranae. Apidologie 40, 595–599 (2009).Article 

Google Scholar 
14.Dussaubat, C. et al. Flight behavior and pheromone changes associated to Nosema ceranae infection of honey bee workers (Apis mellifera) in field conditions. J. Invertebr. Pathol. 113, 42–51 (2013).CAS 
Article 

Google Scholar 
15.Goblirsch, M., Huang, Z. Y. & Spivak, M. Physiological and behavioral changes in honey bees (Apis mellifera) induced by Nosema ceranae infection. PLoS ONE 8, 6 (2013).
Google Scholar 
16.Lipsitch, M., Nowak, M. A., Ebert, D. & May, R. M. The population dynamics of vertically and horizontally transmitted parasites. Proc. R. Soc. Lond. B 260, 321–327. https://doi.org/10.1098/rspb.1995.0099 (1995).ADS 
CAS 
Article 

Google Scholar 
17.Goertz, D., Solter, L. F. & Linde, A. Horizontal and vertical transmission of a Nosema sp. (Microsporidia) from Lymantria dispar (L.) (Lepidoptera: Lymantriidae). J. Invertebr. Pathol. 95, 9–16. https://doi.org/10.1016/j.jip.2006.11.003 (2007).Article 
PubMed 

Google Scholar 
18.Kellen, W. R., Chapman, H. C., Clark, T. B. & Lindegren, J. E. Host-parasite relationships of some Thelohania from mosquitoes (Nosematidae: Microsporidia). J. Invertebr. Pathol. 7, 161–166. https://doi.org/10.1016/0022-2011(65)90030-3 (1965).CAS 
Article 
PubMed 

Google Scholar 
19.Dunn, A. M. & Smith, J. E. Microsporidian life cycles and diversity: the relationship between virulence and transmission. Microbes Infect. 3, 381–388. https://doi.org/10.1016/S1286-4579(01)01394-6 (2001).CAS 
Article 
PubMed 

Google Scholar 
20.Terry, R. S. et al. Widespread vertical transmission and associated host sex–ratio distortion within the eukaryotic phylum Microspora. Proc. R. Soc. Lond. B 271, 1783–1789. https://doi.org/10.1098/rspb.2004.2793 (2004).Article 

Google Scholar 
21.Mercer, C. & Wigley, P. A microsporidian pathogen of the poroporo stem borer, Sceliodes cordalis (Dbld)(Lepidoptera: Pyralidae): Effects on adult reproductive success. J. Invertebr. Pathol. 49, 108–115. https://doi.org/10.1016/0022-2011(87)90132-7 (1987).Article 

Google Scholar 
22.Bauer, L. S. & Nordin, G. L. Effect of Nosema fumiferanae (Microsporida) on fecundity, fertility, and progeny performance of Choristoneura fumiferana (Lepidoptera: Tortricidae). Environ. Entomol. 18, 261–265. https://doi.org/10.1093/ee/18.2.261 (1989).Article 

Google Scholar 
23.Futerman, P. et al. Fitness effects and transmission routes of a microsporidian parasite infecting Drosophila and its parasitoids. Parasitology 132, 479–492. https://doi.org/10.1017/S0031182005009339 (2006).CAS 
Article 
PubMed 

Google Scholar 
24.Goertz, D., Golldack, J. & Linde, A. Two different and sublethal isolates of Nosema lymantriae (Microsporidia) reduce the reproductive success of their host, Lymantria dispar. Biocontrol Sci. Technol. 18, 419–430. https://doi.org/10.1080/09583150801993212 (2008).Article 

Google Scholar 
25.Lockwood, J. A., Bomar, C. R. & Ewen, A. B. The history of biological control with Nosema locustae: Lessons for locust management. Int. J. Trop. Insect Sci. 19, 333–350. https://doi.org/10.1017/S1742758400018968 (1999).Article 

Google Scholar 
26.Kiritani, K. & Yamamura, K. in Invasive Species: Vectors and Management Strategies. (ed J. Carlton) 44–67 (Island Press, 2003).27.Walsh, D. B. et al. Drosophila suzukii (Diptera: Drosophilidae): invasive pest of ripening soft fruit expanding its geographic range and damage potential. J. Integr. Pest Manage. 2, G1–G7. https://doi.org/10.1603/IPM10010 (2011).Article 

Google Scholar 
28.Cini, A., Ioriatti, C. & Anfora, G. A review of the invasion of Drosophila suzukii in Europe and a draft research agenda for integrated pest management. Bull. Insectol. 65, 149–160 (2012).
Google Scholar 
29.Tochen, S. et al. Temperature-related development and population parameters for Drosophila suzukii (Diptera: Drosophilidae) on cherry and blueberry. Environ. Entomol. 43, 501–510. https://doi.org/10.1603/en13200 (2014).Article 
PubMed 

Google Scholar 
30.Chabert, S., Allemand, R., Poyet, M., Eslin, P. & Gibert, P. Ability of European parasitoids (Hymenoptera) to control a new invasive Asiatic pest, Drosophila suzukii. Biol. Control 63, 40–47. https://doi.org/10.1016/j.biocontrol.2012.05.005 (2012).Article 

Google Scholar 
31.Gabarra, R., Riudavets, J., Rodríguez, G., Pujade-Villar, J. & Arnó, J. Prospects for the biological control of Drosophila suzukii. Biocontrol 60, 331–339. https://doi.org/10.1007/s10526-014-9646-z (2015).Article 

Google Scholar 
32.Cuthbertson, A. G. S. & Audsley, N. Further screening of entomopathogenic fungi and nematodes as control agents for Drosophila suzukii. Insects 7, 24. https://doi.org/10.3390/insects7020024 (2016).Article 
PubMed Central 

Google Scholar 
33.Woltz, J. M., Donahue, K. M., Bruck, D. J. & Lee, J. C. Efficacy of commercially available predators, nematodes and fungal entomopathogens for augmentative control of Drosophila suzukii. J. Appl. Entomol. 139, 759–770. https://doi.org/10.1111/jen.12200 (2015).Article 

Google Scholar 
34.Haye, T. et al. Current SWD IPM tactics and their practical implementation in fruit crops across different regions around the world. J. Pest. Sci. 89, 643–651. https://doi.org/10.1007/s10340-016-0737-8 (2016).Article 

Google Scholar 
35.Biganski, S., Jehle, J. A. & Kleespies, R. G. Bacillus thuringiensis serovar israelensis has no effect on Drosophila suzukii Matsumura. J. Appl. Entomol. 142, 33–36. https://doi.org/10.1111/jen.12415 (2018).CAS 
Article 

Google Scholar 
36.Carrau, T., Hiebert, N., Vilcinskas, A. & Lee, K.-Z. Identification and characterization of natural viruses associated with the invasive insect pest Drosophila suzukii. J. Invertebr. Pathol. 154, 74–78. https://doi.org/10.1016/j.jip.2018.04.001 (2018).CAS 
Article 
PubMed 

Google Scholar 
37.Medd, N. C. et al. The virome of Drosophila suzukii, an invasive pest of soft fruit. BioRxiv 4, 190322. https://doi.org/10.1093/ve/vey009 (2017).Article 

Google Scholar 
38.Kaur, R., Siozios, S., Miller, W. J. & Rota-Stabelli, O. Insertion sequence polymorphism and genomic rearrangements uncover hidden Wolbachia diversity in Drosophila suzukii and D. subpulchrella. Sci. Rep. 7, 14815. https://doi.org/10.1038/s41598-017-13808-z (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Biganski, S. et al. Molecular and morphological characterisation of a novel microsporidian species, Tubulinosema suzukii, infecting Drosophila suzukii (Diptera: Drosophilidae). J. Invertebr. Pathol. 107440 (2020).40.Anderson, R. M. & May, R. M. Coevolution of hosts and parasites. Parasitology 85, 411–426. https://doi.org/10.1017/S0031182000055360 (1982).Article 
PubMed 

Google Scholar 
41.Aigaki, T. & Ohba, S. Effect of mating status on Drosophila virilis lifespan. Exp. Gerontol. 19, 267–278. https://doi.org/10.1016/0531-5565(84)90022-6 (1984).CAS 
Article 
PubMed 

Google Scholar 
42.Partridge, L., Green, A. & Fowler, K. Effects of egg-production and of exposure to males on female survival in Drosophila melanogaster. J. Insect Physiol. 33, 745–749. https://doi.org/10.1016/0022-1910(87)90060-6 (1987).Article 

Google Scholar 
43.Bretman, A., Westmancoat, J. D., Gage, M. J. & Chapman, T. Costs and benefits of lifetime exposure to mating rivals in male Drosophila melanogaster. Evolution 67, 2413–2422. https://doi.org/10.1111/evo.12125 (2013).Article 
PubMed 

Google Scholar 
44.Armstrong, E. & Bass, L. K. Nosema kingi: Effects on fecundity, fertility, and longevity of Drosophila melanogaster. J. Exp. Zool. 250, 82–86. https://doi.org/10.1002/jez.1402500111 (1989).Article 

Google Scholar 
45.Armstrong, E. Transmission and infectivity studies on Nosema kingi in Drosophila willistoni and other Drosophilids. Z. Parasitenkd. 50, 161–165. https://doi.org/10.1007/BF00380520 (1976).Article 

Google Scholar 
46.Armstrong, E., Bass, L., Staker, K. & Harrell, L. A comparison of the biology of a Nosema in Drosophila melanogaster to Nosema kingi in Drosophila willistoni. J. Invertebr. Pathol. 48, 124–126. https://doi.org/10.1016/0022-2011(86)90151-5 (1986).Article 

Google Scholar 
47.Vijendravarma, R. K., Godfray, H. C. J. & Kraaijeveld, A. R. Infection of Drosophila melanogaster by Tubulinosema kingi: Stage-specific susceptibility and within-host proliferation. J. Invertebr. Pathol. 99, 239–241. https://doi.org/10.1016/j.jip.2008.02.014 (2008).Article 
PubMed 

Google Scholar 
48.Niehus, S., Giammarinaro, P., Liégeois, S., Quintin, J. & Ferrandon, D. Fly culture collapse disorder: Detection, prophylaxis and eradication of the microsporidian parasite Tubulinosema ratisbonensis infecting Drosophila melanogaster. Fly 6, 193–204. https://doi.org/10.4161/fly.20896 (2012).CAS 
Article 
PubMed 

Google Scholar 
49.Franchet, A., Niehus, S., Caravello, G. & Ferrandon, D. Phosphatidic acid as a limiting host metabolite for the proliferation of the microsporidium Tubulinosema ratisbonensis in Drosophila flies. Nat Microbiol 4, 645–655 (2019).CAS 
Article 

Google Scholar 
50.Robertson, F. W. & Sang, J. H. The ecological determinants of population growth in a Drosophila culture. I. Fecundity of adult flies. Proc. R. Soc. Lond. B 132, 258–277. https://doi.org/10.1098/rspb.1944.0017 (1944).ADS 
Article 

Google Scholar 
51.Vijendravarma, R. K., Kraaijeveld, A. R. & Godfray, H. C. J. Experimental evolution shows Drosophila melanogaster resistance to a microsporidian pathogen has fitness costs. Evolution 63, 104–114. https://doi.org/10.1111/j.1558-5646.2008.00516.x (2009).Article 
PubMed 

Google Scholar 
52.Rousset, F., Bouchon, D., Pintureau, B., Juchault, P. & Solignac, M. Wolbachia endosymbionts responsible for various alterations of sexuality in arthropods. Proc. R. Soc. Lond. B 250, 91–98. https://doi.org/10.1098/rspb.1992.0135 (1992).ADS 
CAS 
Article 

Google Scholar 
53.Saeed, N., Battisti, A., Martinez-Sañudo, I. & Mori, N. Combined effect of temperature and Wolbachia infection on the fitness of Drosophila suzukii. Bull. Insectol. 71, 161–169 (2018).
Google Scholar 
54.Hamm, C. A. et al. Wolbachia do not live by reproductive manipulation alone: infection polymorphism in Drosophila suzukii and D. subpulchrella. Mol. Ecol. 23, 4871–4885. https://doi.org/10.1111/mec.12901 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
55.Mazzetto, F., Gonella, E. & Alma, A. Wolbachia infection affects female fecundity in Drosophila suzukii. Bull. Insectol. 68, 153–157 (2015).
Google Scholar 
56.Hurst, G. D., Johnson, A. P., vd Schulenburg, J. H. G. & Fuyama, Y. Male-killing Wolbachia in Drosophila: a temperature-sensitive trait with a threshold bacterial density. Genetics 156, 699–709 (2000).57.Markow, T. A. Parents without partners: Drosophila as a model for understanding the mechanisms and evolution of parthenogenesis. G3 3, 757–762. https://doi.org/10.1534/g3.112.005421 (2013).CAS 
Article 
PubMed 

Google Scholar 
58.Wolfner, M. F. The gifts that keep on giving: physiological functions and evolutionary dynamics of male seminal proteins in Drosophila. Heredity 88, 85–93. https://doi.org/10.1038/sj.hdy.6800017 (2002).CAS 
Article 
PubMed 

Google Scholar 
59.Blaser, M. & Schmid-Hempel, P. Determinants of virulence for the parasite Nosema whitei in its host Tribolium castaneum. J. Invertebr. Pathol. 89, 251–257. https://doi.org/10.1016/j.jip.2005.04.004 (2005).Article 
PubMed 

Google Scholar 
60.Solter, L. F. in Microsporidia: Pathogens of Opportunity (eds L. M. Weiss & J. J. Becnel) 165–194 (Wiley, 2014).61.Eberle, K. E., Wennmann, J. T., Kleespies, R. G. & Jehle, J. A. in Manual of Techniques in Invertebrate Pathology (ed L. A. Lacey) 15–74 (Academic Press, 2012).62.Hughes, P. & Wood, H. A synchronous peroral technique for the bioassay of insect viruses. J. Invertebr. Pathol. 37, 154–159. https://doi.org/10.1016/0022-2011(81)90069-0 (1981).Article 

Google Scholar 
63.Abbott, W. A method of computing the effectiveness of an insecticide. J. Econ. Entomol. 18, 265–267 (1925).CAS 
Article 

Google Scholar 
64.Software for the statistical analysis of biotests (ToxRat GmbH, Alsdorf, Germany, 2003).65.Pan, G. et al. Invertebrate host responses to microsporidia infections. Dev. Comp. Immunol. 83, 104–113. https://doi.org/10.1016/j.dci.2018.02.004 (2018).Article 
PubMed 

Google Scholar 
66.Roxström-Lindquist, K., Terenius, O. & Faye, I. Parasite-specific immune response in adult Drosophila melanogaster: A genomic study. EMBO Rep. 5, 207–212. https://doi.org/10.1038/sj.embor.7400073 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Kraaijeveld, A. R. & Godfray, H. C. J. Selection for resistance to a fungal pathogen in Drosophila melanogaster. Heredity 100, 400–406. https://doi.org/10.1038/sj.hdy.6801092 (2008).CAS 
Article 
PubMed 

Google Scholar  More

1.Seeley, T. D. Honey bee colonies are group-level adaptive units. Am. Nat. 150, S22–S41 (1997).PubMed 
Article 

Google Scholar 
2.Negroni, M. A., Jongepier, E., Feldmeyer, B., Kramer, B. H. & Foitzik, S. Life history evolution in social insects: A female perspective. Curr. Opin. Insect Sci. 16, 51–57 (2016).PubMed 
Article 

Google Scholar 
3.Wilson, E. O. The Insect Societies. (Belknap Press, 1971).4.Boomsma, J. J., Huszár, D. B. & Pedersen, J. S. The evolution of multiqueen breeding in eusocial lineages with permanent physically differentiated castes. Anim. Behav. 92, 241–252 (2014).Article 

Google Scholar 
5.Ratnieks, F. L. W., Vetter, R. S. & Visscher, P. K. A polygynous nest of Vespula pensylvanica from California with a discussion of possible factors influencing the evolution of polygyny in Vespula. Insect. Soc. 43, 401–410 (1996).Article 

Google Scholar 
6.Wilson, E. E., Mullen, L. M. & Holway, D. A. Life history plasticity magnifies the ecological effects of a social wasp invasion. Proc. Natl. Acad. Sci. USA. 106, 12809–12813 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Gambino, P. Reproductive plasticity of Vespula pensylvanica (Hymenoptera: Vespidae) on Maui and Hawaii Islands, USA. N. Z. J. Zool. 18, 139–149 (1991).Article 

Google Scholar 
8.Hanna, C. et al. Colony social structure in native and invasive populations of the social wasp Vespula pensylvanica. Biol. Invasions 16, 283–294 (2014).Article 

Google Scholar 
9.Ross, K. G. & Matthews, R. W. Two polygynous overwintered Vespula squamosa colonies from the southeastern US (Hymenoptera: Vespidae). Florida Entomol. 65, 176–184 (1982).Article 

Google Scholar 
10.Visscher, P. K. & Vetter, R. S. Annual and multi-year nests of the western yellowjacket, Vespula pensylvanica, in California. Insect. Soc. 50, 160–166 (2003).Article 

Google Scholar 
11.Plunkett, G. M., Moller, H., Hamilton, C., Clapperton, B. K. & Thomas, C. D. Overwintering colonies of German (Vespula germanica) and common wasps (Vespula vulgaris) (Hymenoptera: Vespidae) in New Zealand. N. Z. J. Zool. 16, 345–353 (1989).Article 

Google Scholar 
12.Goodisman, M. A., Matthews, R. W., Spradbery, J. P., Carew, M. E. & Crozier, R. H. Reproduction and recruitment in perennial colonies of the introduced wasp Vespula germanica. J. Hered. 92, 346–349 (2001).CAS 
PubMed 
Article 

Google Scholar 
13.Gambino, P. & Loope, L. L. Yellowjacket (Vespula pensylvanica): Biology and abatement in the National Parks of Hawaii.  Technical report of the Cooperatuve National Parks Resources Study Unit, Honolulu (1992).14.Wilson, E. E. & Holway, D. A. Multiple mechanisms underlie displacement of solitary Hawaiian Hymenoptera by an invasive social wasp. Ecology 91, 3294–3302 (2010).CAS 
PubMed 
Article 

Google Scholar 
15.Wilson Rankin, E. E. Diet subsidies and climate may contribute to Vespula invasion impacts. In 17th Congress of the International Union for the Study of Social Insects (IUSSI), Cairns, Australia, 13-18 July 2014 (2014).16.Seeley, T. D. & Tarpy, D. R. Queen promiscuity lowers disease within honeybee colonies. Proc. R. Soc. B Biol. Sci. 274, 67–72 (2007).Article 

Google Scholar 
17.Berthoud, H., Imdorf, A., Haueter, M., Radloff, S. & Neumann, P. Virus infections and winter losses of honey bee colonies (Apis mellifera). J. Apic. Res. 49, 60–65 (2010).Article 

Google Scholar 
18.Otti, O. & Schmid-Hempel, P. A field experiment on the effect of Nosema bombi in colonies of the bumblebee Bombus terrestris. Ecol. Entomol. 33, 577–582 (2008).Article 

Google Scholar 
19.Cremer, S., Pull, C. D. & Fürst, M. A. Social immunity: Emergence and evolution of colony-level disease protection. Annu. Rev. Entomol. 63, 105–123 (2018).CAS 
PubMed 
Article 

Google Scholar 
20.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 (2013).PubMed 
Article 

Google Scholar 
21.Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.McMahon, D. P. et al. A sting in the spit: widespread cross-infection of multiple RNA viruses across wild and managed bees. J. Anim. Ecol. 84, 615–624 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Alger, S. A., Alexander Burnham, P., Boncristiani, H. F. & Brody, A. K. RNA virus spillover from managed honeybees (Apis mellifera) to wild bumblebees (Bombus spp.). PLoS One 14, 1–13 (2018).24.Dobelmann, J. et al. Fitness in invasive social wasps: The role of variation in viral load, immune response and paternity in predicting nest size and reproductive output. Oikos 126, 1208–1218 (2017).CAS 
Article 

Google Scholar 
25.Torchin, M. E., Lafferty, K. D., Dobson, A. P., McKenzie, V. J. & Kuris, A. M. Introduced species and their missing parasites. Nature 421, 628–630 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Lester, P. J. et al. No evidence of enemy release in pathogen and microbial communities of common wasps (Vespula vulgaris) in their native and introduced range. PLoS One 10, e0121358 (2015).27.Mordecai, G. J. et al. Moku virus; a new Iflavirus found in wasps, honey bees and Varroa. Sci. Rep. 6, srep34983 (2016).28.Loope, K. J., Baty, J. W., Lester, P. J. & Wilson Rankin, E. E. Pathogen shifts in a honeybee predator following the arrival of the Varroa mite. Proc. R. Soc. B Biol. Sci. 286 (2019).29.Brettell, L. E., Schroeder, D. C. & Martin, S. J. RNAseq analysis reveals virus diversity within hawaiian apiary insect communities. Viruses 11 (2019).30.Moret, Y. & Schmid-Hempel, P. Immune responses of bumblebee workers as a function of individual and colony age: Senescence versus plastic adjustment of the immune function. Oikos 118, 371–378 (2009).Article 

Google Scholar 
31.Budge, G. E. et al. Identifying bacterial predictors of honey bee health. J. Invertebr. Pathol. 141, 41–44 (2016).PubMed 
Article 

Google Scholar 
32.Schmid-Hempel, R. & Tognazzo, M. Molecular divergence defines two distinct lineages of Crithidia bombi (Trypanosomatidae), parasites of bumblebees. J. Eukaryot. Microbiol. 57, 337–345 (2010).CAS 
PubMed 
Article 

Google Scholar 
33.Akre, R. D., Hill, W. B., Donald, J. F. M. & Garnett, W. B. Foraging distances of Vespula pensylvanica workers (Hymenoptera: Vespidae). J. Kansas Entomol. Soc. 48, 12–16 (1975).
Google Scholar 
34.Seeley, T. D. & Smith, M. L. Crowding honeybee colonies in apiaries can increase their vulnerability to the deadly ectoparasite Varroa destructor. Apidologie 46, 716–727 (2015).Article 

Google Scholar 
35.McArt, S. H., Koch, H., Irwin, R. E. & Adler, L. S. Arranging the bouquet of disease: Floral traits and the transmission of plant and animal pathogens. Ecol. Lett. 17, 624–636 (2014).PubMed 
Article 

Google Scholar 
36.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS ONE 14, 1–14 (2019).Article 
CAS 

Google Scholar 
37.Yañez, O. et al. Bee viruses: Routes of infection in Hymenoptera. Front. Microbiol. 11, 1–22 (2020).Article 

Google Scholar 
38.Malham, J. P., Rees, J. S., Alspach, P. A., Beggs, J. R. & Moller, H. Traffic rate as an index of colony size in Vespula wasps. N. Z. J. Zool. 18, 105–109 (1991).Article 

Google Scholar 
39.Brettell, L. et al. A comparison of deformed wing virus in deformed and asymptomatic honey bees. Insects 8, 28 (2017).PubMed Central 
Article 
PubMed 

Google Scholar 
40.Garigliany, M. et al. Moku virus in invasive Asian Hornets, Belgium, 2016. Emerg. Infect. Dis. 23, 2109–2112 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Garigliany, M., El Agrebi, N., Franssen, M., Hautier, L. & Saegerman, C. Moku virus detection in honey bees, Belgium, 2018. Transbound. Emerg. Dis. 66, 43–46 (2019).PubMed 
Article 

Google Scholar 
42.Highfield, A. et al. Detection and replication of Moku virus in honey bees and social wasps. Viruses 12, 607 (2020).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
43.Felden, A. et al. Viral and fungal pathogens associated with Pneumolaelaps niutirani (Acari: Laelapidae): A mite found in diseased nests of Vespula wasps. Insect. Soc. 67, 83–93 (2020).Article 

Google Scholar 
44.Lindström, A., Korpela, S. & Fries, I. Horizontal transmission of Paenibacillus larvae spores between honey bee (Apis mellifera) colonies through robbing. Apidologie 39, 515–522 (2008).Article 

Google Scholar 
45.Smith, M. L. The honey bee parasite Nosema ceranae: Transmissible via food exchange?. PLoS ONE 7, 1–6 (2012).
Google Scholar 
46.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 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Loope, K. J., Millar, J. G. & Wilson Rankin, E. E. Weak nestmate discrimination behavior in native and invasive populations of a yellowjacket wasp (Vespula pensylvanica). Biol. Invasions 20, 3431–3444 (2018).48.Yañez, O., Gauthier, L., Chantawannakul, P. & Neumann, P. Endosymbiotic bacteria in honey bees: Arsenophonus spp. are not transmitted transovarially. FEMS Microbiol. Lett. 363, fnw147 (2016).49.McNally, L. C. & Schneider, S. S. Spatial distribution and nesting biology of colonies of the African honey bee Apis mellifera scutellata (Hymenoptera: Apidae) in Botswana, Africa. Environ. Entomol. 25, 643–652 (1996).Article 

Google Scholar 
50.Seeley, T. D. Honey bees of the Arnot forest: A population of feral colonies persisting with Varroa destructor in the northeastern United States. Apidologie 38, 19–29 (2007).Article 

Google Scholar 
51.Arundel, J., Oldroyd, B. P. & Winter, S. Modelling estimates of honey bee (Apis spp.) colony density from drones. Ecol. Model. 267, 1–10 (2013).52.Graystock, P., Goulson, D. & Hughes, W. O. H. Parasites in bloom: Flowers aid dispersal and transmission of pollinator parasites within and between bee species. Proc. R. Soc. B Biol. Sci. 282, 20151371 (2015).Article 

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

Google Scholar 
54.Benaets, K. et al. Covert deformed wing virus infections have long-term deleterious effects on honeybee foraging and survival. Proc. R. Soc. B Biol. Sci. 284, 20162149 (2017).Article 

Google Scholar 
55.Natsopoulou, M. E. et al. The virulent, emerging genotype B of deformed wing virus is closely linked to overwinter honeybee worker loss. Sci. Rep. 7, 5242 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Gambino, P., Medeiros, A. C. & Loope, L. L. Invasion and colonization of upper elevations on East Maui (Hawaii) by Vespula pensylvanica (Hymenoptera: Vespidae). Ann. Entomol. Soc. Am. 83, 1088–1095 (1990).Article 

Google Scholar 
57.Akre, R. D. & Reed, H. C. Population cycles of yellowjackets (Hymenoptera: Vespinae) in the Pacific Northwest. Environ. Entomol. 10, 267–274 (1981).Article 

Google Scholar 
58.Giambelluca, T. W. et al. Online rainfall atlas of Hawai’i. Bull. Am. Meteorol. Soc. 94, 313–316 (2013).ADS 
Article 

Google Scholar 
59.Marion, G. M. et al. Open-top designs for manipulating field temperature in high-latitude ecosystems. Glob. Chang. Biol. 3, 20–32 (1997).Article 

Google Scholar 
60.de Miranda, J. R. et al. Standard methods for virus research in Apis mellifera. J. Apic. Res. 52, 1–56 (2013).ADS 
Article 
CAS 

Google Scholar 
61.Johnson, D. H. Estimating nest success : The Mayfield method and an alternative. Auk 96, 651–661 (1979).
Google Scholar 
62.R Core Team. R: A Language and Environment for Statistical Computing. (2020).63.Therneau, T. A Package for Survival Analysis in S. (2015).64.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
MATH 
Article 

Google Scholar 
65.Kahle, D. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J. 5, 144–161 (2013).Article 

Google Scholar  More

1.Croston, R., Branch, C. L., Kozlovsky, D. Y., Dukas, R. & Pravosudov, V. V. The importance of heritability estimates for understanding the evolution of cognition: A response to comments on Croston et al. Behav. Ecol. 26, 1463–1464 (2015).Article 

Google Scholar 
2.Langley, E. J. G. et al. Heritability and correlations among learning and inhibitory control traits. Behav. Ecol. 1, 1–9 (2020).
Google Scholar 
3.Boogert, N. J., Madden, J. R., Morand-Ferron, J. & Thornton, A. Measuring and understanding individual differences in cognition. Philos. Trans. R. Soc. B. 373, 2017080 (2018).
Google Scholar 
4.Sonnenberg, B. R., Branch, C. L., Pitera, A. M., Bridge, E. & Pravosudov, V. V. Natural selection and spatial cognition in wild food-caching mountain chickadees. Curr. Biol. 29, 1–7 (2019).Article 
CAS 

Google Scholar 
5.Benedict, L. M. et al. Elevation-related differences in annual survival of adult food-caching mountain chickadees are consistent with natural selection on spatial cognition. Behav. Ecol. Sociobiol. 74, 2817 (2020).Article 

Google Scholar 
6.Shaw, R. C., MacKinlay, R. D., Clayton, N. S. & Burns, K. C. Memory performance influences male reproductive success in a wild bird. Curr. Biol. 29, 1498–1502 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Cauchoix, M. & Chaine, A. S. How can we study the evolution of animal minds?. Front. Psychol. 7, 1–18 (2016).Article 

Google Scholar 
8.Janmaat, K. R. L. et al. Spatio-temporal complexity of chimpanzee food: How cognitive adaptations can counteract the ephemeral nature of ripe fruit. Am. J. Primatol. 78, 626–645 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Collett, M., Chittka, L. & Collett, T. S. Spatial memory in insect navigation. Curr. Biol. 23, R789–R800 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Hampton, R. R. & Shettleworth, S. J. Hippocampus and memory in a food-storing and in a nonstoring bird species. Behav. Neurosci. 110, 946–964 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.LaDage, L. D., Roth, T. C., Cerjanic, A. M., Sinervo, B. & Pravosudov, V. V. Spatial memory: Are lizards really deficient?. Biol. Lett. 8, 939–941 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Milton, K. Distribution patterns of tropical plant foods as an evolutionary stimulus to primate mental development. Am. Anthropol. 83, 534–548 (1981).Article 

Google Scholar 
13.Thornton, A. & Boogert, N. J. Animal cognition: The benefits of remembering. Curr. Biol. 29, R324–R327 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Pravosudov, V. V. & Clayton, N. S. A test of the adaptive specialization hypothesis: Population differences in caching, memory, and the hippocampus in black-capped chickadees (Poecile atricapilla). Behav. Neurosci. 116, 515–522 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Morand-Ferron, J., Hermer, E., Jones, T. B. & Thompson, M. J. Environmental variability, the value of information, and learning in winter residents. Anim. Behav. 147, 137–145 (2019).Article 

Google Scholar 
16.Hermer, E., Cauchoix, M., Chaine, A. S. & Morand-Ferron, J. Elevation-related difference in serial reversal learning ability in a nonscatter hoarding passerine. Behav. Ecol. 29, 840–847 (2018).Article 

Google Scholar 
17.Boyle, A. W., Sandercock, B. K. & Martin, K. Patterns and drivers of intraspecific variation in avian life history along elevational gradients: A meta-analysis. Biol. Rev. 91, 469–482 (2016).Article 

Google Scholar 
18.Roth, T. C. II. & Pravosudov, V. V. Hippocampal volumes and neuron numbers increase along a gradient of environmental harshness: A large-scale comparison. Proc. R. Soc. B 276, 401–405 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Körner, C. The use of ‘altitude’ in ecological research. Trends Ecol. Evol. 22, 569–574 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Roth, T. C. II., LaDage, L. D. & Pravosudov, V. V. Learning capabilities enhanced in harsh environments: A common garden approach. Proc. R. Soc. B 277, 3187–3193 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Tello-Ramos, M. C., Branch, C. L., Kozlovsky, D. Y., Pitera, A. M. & Pravosudov, V. V. Spatial memory and cognitive flexibility trade-offs: to be or not to be flexible, that is the question. Anim. Behav. 1, 1–8 (2018).
Google Scholar 
22.Gonzalez, R. C., Behrend, E. R. & Bitterman, M. E. Reversal learning and forgetting in bird and fish. Science 158, 519–521 (1967).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
23.Strang, C. G. & Sherry, D. F. Serial reversal learning in bumblebees (Bombus impatiens). Anim. Cogn. 17, 723–734 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Herszage, J. & Censor, N. Modulation of learning and memory: A shared framework for interference and generalization. Neuroscience 392, 270–280 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Squier, L. H. Reversal learning improvement in the fish Astronotus ocellatus (Oscar). Psychon. Sci. 14, 143–144 (1969).Article 

Google Scholar 
26.Miyashita, Y., Nakajima, S. & Imada, H. Differential outcome effect in the horse. J. Exp. Anal. Behav. 74, 245–253 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Missaire, M. et al. Long-term effects of interference on short-term memory performance in the rat. PLoS ONE 12, 1–18 (2017).Article 
CAS 

Google Scholar 
28.Bublitz, A., Weinhold, S. R., Strobel, S., Dehnhardt, G. & Hanke, F. D. Reconsideration of serial visual reversal learning in octopus (Octopus vulgaris) from a methodological perspective. Front. Physiol. 8, 1–11 (2017).Article 

Google Scholar 
29.Chittka, L. Sensorimotor learning in bumblebees: Long-term retention and reversal training. J. Exp. Biol. 201, 515–524 (1998).Article 

Google Scholar 
30.Chrobak, J. J., Hinman, J. R. & Sabolek, H. R. Revealing past memories: Proactive interference and ketamine-induced memory deficits. J. Neurosci. 28, 4512–4520 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Malleret, G. et al. Bidirectional regulation of hippocampal long-term synaptic plasticity and its influence on opposing forms of memory. J. Neurosci. 30, 3813–3825 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Joseph, M. A. et al. Differential involvement of the dentate gyrus in adaptive forgetting in the rat. PLoS ONE 10, 1–17 (2015).
Google Scholar 
33.Shiflett, M. W., Rankin, A. Z., Tomaszycki, M. L. & DeVoogd, T. J. Cannabinoid inhibition improves memory in food-storing birds, but with a cost. Proc. R. Soc. B. 271, 2043–2048 (2004).CAS 
PubMed 
Article 

Google Scholar 
34.Meck, W. H. & Williams, C. L. Choline supplementation during prenatal development reduces proactive interference in spatial memory. Dev. Brain Res. 118, 51–59 (1999).CAS 
Article 

Google Scholar 
35.Clayton, N. S. & Krebs, J. R. One-trial associative memory: Comparison of food-storing and nonstoring species of birds. Anim. Learn. Behav. 22, 366–372 (1994).Article 

Google Scholar 
36.McGregor, A. & Healy, S. D. Spatial accuracy in food-storing and nonstoring birds. Anim. Behav. 58, 727–734 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Healy, S. D. Memory for objects and positions: Delayed non-matching-to-sample in storing and non-storing tits. Q. J. Exp. Psychol. Sect. B 48, 179–191 (1995).
Google Scholar 
38.Healy, S. D. & Krebs, J. R. Delayed-matching-to-sample by marsh tits and great tits. Q. J. Exp. Psychol. B 45, 33–47 (1992).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Hampton, R. R., Shettleworth, S. J. & Westwood, R. P. Proactive interference, recency, and associative strength: Comparisons of black-capped chickadees and dark-eyed juncos. Anim. Learn. Behav. 26, 475–485 (1998).Article 

Google Scholar 
40.Tello-Ramos, M. C. et al. Memory in wild mountain chickadees from different elevations: Comparing first-year birds with older survivors. Anim. Behav. 137, 149–160 (2018).Article 

Google Scholar 
41.Croston, R. et al. Predictably harsh environment is associated with reduced cognitive flexibility in wild food-caching mountain chickadees. Anim. Behav. 123, 139–149 (2017).Article 

Google Scholar 
42.Careau, V. & Wilson, R. S. Of uberfleas and krakens: Detecting trade-offs using mixed models. Integr. Comp. Biol. 57, 362–371 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Niemelä, P. T. & Dingemanse, N. J. On the usage of single measurements in behavioural ecology research on individual differences. Anim. Behav. 145, 99–105 (2018).Article 

Google Scholar 
44.Gosler, A. G. The Great Tit (Hamlyn, 1993).
Google Scholar 
45.Lejeune, L. et al. Environmental effects on parental care visitation patterns in blue tits Cyanistes caeruleus. Front. Ecol. Evol. 7, 1–15 (2019).Article 

Google Scholar 
46.Bründl, A. C. et al. Experimentally induced increases in fecundity lead to greater nestling care in blue tits. Proc. R. Soc. B. 286, 20191013 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Thompson, M. J. & Morand-Ferron, J. Food caching in city birds: Urbanization and exploration do not predict spatial memory in scatter hoarders. Anim. Cogn. 22, 743–756 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Roth, T. C. II., LaDage, L. D., Freas, C. A. & Pravosudov, V. V. Variation in memory and the hippocampus across populations from different climates: A common garden approach. Proc. R. Soc. B 279, 402–410 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Griffin, A. S., Guillette, L. M. & Healy, S. D. Cognition and personality: An analysis of an emerging field. Trends Ecol. Evol. 30, 207–214 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Ashton, B. J., Thornton, A. & Ridley, A. R. An intraspecific appraisal of the social intelligence hypothesis. Philos. Trans. R. Soc. B. 373, 20170288 (2018).Article 

Google Scholar 
51.Croston, R., Branch, C. L., Kozlovsky, D. Y., Dukas, R. & Pravosudov, V. V. Heritability and the evolution of cognitive traits. Behav. Ecol. 26, 1447–1459 (2015).Article 

Google Scholar 
52.Bründl, A. C. et al. Elevational gradients as a model for understanding associations among temperature, breeding phenology and success. Front. Ecol. Evol. 8, 56377 (2020).Article 

Google Scholar 
53.Freas, C. A., LaDage, L. D., Roth, T. C. II. & Pravosudov, V. V. Elevation-related differences in memory and the hippocampus in mountain chickadees, Poecile gambeli. Anim. Behav. 84, 121–127 (2012).Article 

Google Scholar 
54.Pravosudov, V. V. & Roth, T. C. II. Cognitive ecology of food hoarding: The evolution of spatial memory and the hippocampus. Annu. Rev. Ecol. Evol. Syst. 44, 173–193 (2013).Article 

Google Scholar 
55.Croston, R. et al. Potential mechanisms driving population variation in spatial memory and the hippocampus in food-caching chickadees. Integr. Comp. Biol. 55, 354–371 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Kozlovsky, D. Y., Weissgerber, E. A. & Pravosudov, V. V. What makes specialized food-caching mountain chickadees successful city slickers?. Proc. R. Soc. B 284, 20162613 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Izquierdo, A., Brigman, J. L., Radke, A. K., Rudebeck, P. H. & Holmes, A. The neural basis of reversal learning: An updated perspective. Neuroscience 345, 12–26 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Cauchoix, M. et al. The repeatability of cognitive performance: A meta-analysis. Neuroscience 373, 20170281 (2018).
Google Scholar 
59.Croston, R. et al. Individual variation in spatial memory performance in wild mountain chickadees from different elevations. Anim. Behav. 111, 225–234 (2016).Article 

Google Scholar 
60.Svensson, L. Identification Guide to European Passerines (British Trust for Ornithology, 1992).
Google Scholar 
61.Friard, O. & Gamba, M. BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
62.Tillé, Y., Newman, J. A. & Healy, S. D. New tests for departures from random behavior in spatial memory experiments. Anim. Learn. Behav. 24, 327–340 (1996).Article 

Google Scholar 
63.Bates, D. et al. Linear Mixed-Effects using ‘Eigen’ and S4 1–113 (Springer, 2016).
Google Scholar 
64.Kuznetsova, A. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
65.R Core Team. A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).66.Warton, D. I., Lyons, M., Stoklosa, J. & Ives, A. R. Three points to consider when choosing a LM or GLM test for count data. Methods Ecol. Evol. 7, 882–890 (2016).Article 

Google Scholar 
67.Wilson, A. J. How should we interpret estimates of individual repeatability?. Evol. Lett. 2, 4–8 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).Article 

Google Scholar 
69.Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
70.Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948–952 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
71.Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. Improving bioscience research reporting: The arrive guidelines for reporting animal research. PLoS Biol. 8, 6–10 (2010).Article 
CAS 

Google Scholar  More

An Nasim consists of deep and narrow interdunal basin in which a sequence of aeolian sands overlain by bedded lacustrine marl is preserved (Figs. 2, S1). In the central part of the An Nasim basin, outcrops of these deposits are exposed extending approximately 800 m north–south and 350 m east–west. The marl outcrops are, however, fragmented and discontinuous, occurring at several distinct altitudes (Fig. S1). The thickest visible exposures of marl are found along the basin’s eastern edge (Figs. 2, 3, S1). At the base of these exposures, the deposits express the morphology of the former interdune depression in which they accumulated, in the form of a concave surface dipping steeply away from the edge of the observable outcrops towards the centre of the basin within which they formed. The stratigraphy of the deposits also dips towards the centre of this palaeobasin, indicative of sediments being deposited in a quiescent water body and draping across the existing topography. The western edge of the deposit is at ~ 930 m above sea level (MASL) and has been deeply eroded, forming a small cliff (maximum of 4 m high) providing a thick exposure of lake sediments. Large ‘boulders’ of sediment at the base of this cliff have been dislodged and transported down-slope towards the centre of the current interdune depression. The marls are thickest at the western edge, which likely lay towards the centre of their contemporary interdune palaeobasin, and thin in an easterly direction towards its edges (0.5 m at their thinnest). The thickness of the marl deposits in the central area is exceptional in comparison to previously excavated comparable late Middle and Late Pleistocene deposits found elsewhere in the western Nefud22,24,25. An additional area of palaeolake deposit exists immediately to the south of the primary exposure at the same altitude, likely a continuation of the same deposit in an area that has experienced differential erosion.Figure 2Stratigraphic sequence of An Nasim and artefact distributions. (a) stratigraphy with the locations of the sediment samples dated by luminescence; (b) Lower Palaeolithic artefacts at An Nasim, mapped through systematic survey of the current interdune and recorded using a differential GPS system. The stratigraphic sequence was drawn from the location of the handaxe in Layer 12. Produced using ArcMap version 10.2. Basemap from Bing Maps Aerial, (c) 2010 Microsoft Corporation and its data suppliers.Full size imageFigure 3Different handaxe forms from An Nasim. Credit: Ian Cartwright.Full size imageThe undulating lower contact and complex bedding geometry of the lake sediments reflect the accumulation of these sediments over a pre-existing aeolian dune topography. In this context, the marl sediment precipitates from the water column, falls out of suspension and, consequently, accumulates in thick beds that drape over the sand dune forms that are preserved on the lake bed. These beds consequently dip into the centre of the basin and undulate throughout the exposure. The dip of the marl beds means that units that occur several meters below the surface at the western section edge are found at the land surface on the eastern basin margin. Of particular relevance to this study is the fact that the marl-rich sand bed that is found near the surface of the marl unit at the outcrop edge, containing lithics in stratigraphic position, can be traced laterally and is found to occur 3 m below the surface towards the centre of the basin (Fig. 3).The massive marl beds at the base of the section (Fig. 2a) indicate deep water conditions, while towards the top of the sequence the interdigitation of beds of marl and sand, with associated desiccation cracks, are typical of a shallower water body that experienced episodic drying (Figs. 2a, S1). The upper layers 11 and 12 are laterally extensive and contain lithics in stratigraphic position within horizontally bedded sands (Layer 11) overlain by a thin bed of marl (Layer 12—Fig. S1). This sequence suggests falling water level and sheet wash deposition of sands from the surrounding landscape, followed by a small subsequent rise in water level. The sedimentology of the upper part of the primary marl sequence, and in particular that of unit 11, within which a stratified lithic was found, is therefore consistent with the occupation of the site during a drier phase featuring low lake levels.In arid environments, where reworking is widespread, it is often difficult to demonstrate that lithic artefacts are contemporaneous with the age of the deposit. However, at An Nasim, three observations are important. Firstly, that diagnostic artefacts have been recovered from within the marls and can therefore be directly related to specific strata. Secondly, the size of the lithics (pebble/cobble) is significantly coarser than the grain size of any of the sediments within the host deposits, which are dominated by sands and silts. This observation demonstrates that the processes responsible for depositing these sediments were incapable of transporting and reworking the artefacts. Finally, the surface of the main marl bed is the highest point at the site, meaning that there are no older, higher deposits from which the lithics can be eroded and redeposited in the marl sequence. When these observations are considered the most likely source of the lithics that are found across the surface of the marl bed is the uppermost layers of this unit where stratified archaeology has been directly recovered.At lower altitudes within the current interdune area, additional marl deposits are visible, all of which are much less distinct and appear more degraded than the primary deposit discussed above. Three small exposures of marl exist on the northern flank of the basin between approximately 930 and 923 MASL, potentially peripheral exposures of the massive marls, whilst at the basin centre two distinct large mounds of eroded marl material are present. Mound 1, the northernmost of these, has a curved upper surface, again suggestive of a lake bed deposited in an interdune basin, this time at around 921 MASL (Fig. 2b). Mound 2 (Fig. 2b), to the south, has an indistinct heavily eroded upper surface at ~ 916 MASL, while its relationship to Mound 1 (Fig. 2b) is unclear. Both are eroded, preserved as inverted relief features above the current interdune floor (which lies at 910 MASL) possessing flanks covered with the deflated remnants of the palaeolake deposits. The stratigraphic relationship of these lower deposits to the primary deposit remains unclear due to deflation having created an unconformity between them. However, the morphology of Mound 1, and the lower altitude of these sediments relative to the primary deposit, strongly indicates that they belong to a lacustrine phase distinct from that of the primary deposit. It is likely that they formed in the floor of a later interdune depression, prior to the more recent deflation that created the present interdune area that they lie within. An Nasim thus preserves several discrete phases of lake basin development separated by episodes of aeolian deflation related to cyclic climate change within the western Nefud.The sedimentological observations at An Nasim are in keeping with the picture observed across the wider western Nefud Desert, where the repeated raising of regional groundwater levels during discrete humid intervals produced lakes and wetlands in the interdune depressions13,24. Previous analyses have indicated the these palaeolakes were widespread across the western Nefud, and that despite an absence of evidence for large-scale fluvial activity within the region, the high density of such interdune lakes facilitated hominin dispersals through it11,13.At An Nasim, two discrete concentrations of Lower Palaeolithic artefacts were discovered distributed across the surfaces of the primary deposit, and the lower mounds (Fig. 3). Systematic collection recovered 354 artefacts, primarily handaxes, together with various flakes that included clearly identifiable bifacial thinning flakes (Table 1). The artefacts were found in two main clusters at the site (Fig. 2b) and appear to be eroding out of the marl deposits. All visible artefacts were systematically collected and their locations recorded using a differential GPS (DGPS). However, it should be noted that ever-shifting sands likely hid other artefacts from view, and were therefore not collected. We acknowledge that the assemblage may therefore be biased towards handaxes, which are larger and thicker and therefore less easily buried than flakes. The results of this survey, mapped in Fig. 3, illustrate the close association between the artefacts and the lake.Table 1 Breakdown of artefact classes from An Nasim. Flake numbers are likely an underestimate from the site, as shifting sands hid smaller artefacts from view.Full size tableThe lithic tools are similar to previously reported Acheulean sites in the Nefud Desert21 and consist of relatively thick and finely flaked bifaces (typically triangular and pointed). The artefacts represent the entire bifacial manufacturing sequence, all of which were constructed by thinning out large tabular blocks of ferruginious quartzitic sandstone26. The presence of minimally flaked pieces of these tabular blocks indicate that the raw material was brought to the site, some of it apparently discarded after having been ‘tested’ by the removal of one or two flakes along an edge. Other flaked pieces were very roughly shaped before being abandoned. Many of the handaxes retained the last vestiges of the flat, tabular cortical surface at their centre, often on both faces. The base of the handaxes also frequently retained the thick, flat cortical edge of the tabular block, perhaps to aid grasping. None of the bifaces were made from flakes and there was no evidence of large flake manufacture, perhaps due to the small, tabular nature of the local raw material. Indeed, broader surveys in the Nefud Desert indicate that this local tabular quartzite was frequently used at other undated Acheulean surface assemblages, all of which lacked evidence for large flake manufacture21. This suggests the local raw material impeded this approach to handaxe manufacture.The surface artefacts exhibited a similar high degree of weathering, while the artefacts from buried or recently exposed contexts were fresh. The handaxes were diverse in form, ranging from ovate to cordiform and triangular forms, as at other Acheulean sites in the Nefud21, and variable in size (Fig. 3). All handaxes with observable flake scars showed fine flaking, regardless of form. 2D Geometric Morphometric (GMM) analysis of a random sub-sample of fifty handaxes showed that this form variation was not continuous (Figs. 4, S1, Tables S2–S4). However, no spatial relationship between discrete forms and findspots was observed in the sample.Figure 4Canonical Variates Analysis of Biface form (n = 50) at An Nasim, showing discrete shape groupings corresponding to triangular, ovate and cordiform forms. See Tables S2–S4 for eigenvalues and distances.Full size imageSurvey revealed one face of a stratified handaxe visible in the section in the top 10 cm of the primary marl deposit (Layer 12—Fig. S1). Small-scale excavation in the form of a shallow 1 × 1 m test trench around this location allowed the subsequent recovery of this firmly embedded handaxe. This handaxe was included in the 2D GMM analysis shown in Fig. 3, where it clustered with the cordiform group found on the surface. The tight, shape-based clustering of the cordiform handaxes, along with the similarity of manufacture and raw material indicates that these forms at least, may be regarded as contemporary with each other in the marl. The similarity of manufacture among all the handaxe forms represented at An Nasim may also indicate broad contemporaneity. Digging for a sediment sample for dating purposes also permitted the recovery of a bifacial thinning flake cemented within the sandy Layer 11.A sample for luminescence dating was collected from Layer 11 (NSM1-2017), where archaeology was also recovered (Fig. 2a, See SI), and additional samples were collected beneath the lithic horizon in Layer 8 (NSM1-OSL4) and Layer 7 (NSM1-OSL3). Dose rates for these samples were determined by thick source alpha and beta counting, while gamma dose rates were measured using a field gamma spectrometer (See Table 2, SI, Table S5).Table 2 IR-RF age results.Full size tableThe K-feldspar grains were isolated and then analysed using the infrared-radiofluorescence protocol at controlled temperature (RF70) (See SI 3)27, using the same parameters as described previously19. IR-RF dose and age estimate are reported in Table 1. The overdispersion values (OD) are less than 20%, which is consistent with our prediction for such sediment. The three samples yield ages of 310 ± 17 (NSM1-OSL3), 243 ± 23 ka (NSM1-OSL4) and 330 ± 23 ka (NSM1-2017). These ages are coherent at 2 sigma, however sample NSM1-OSL4 is much younger than the other two samples, which yield very similar ages. The two older ages also have lower overdispersion values than the younger one, possibly suggesting that they are more reliable.To further contextualize these age determinations, we compared the ages with mean summer insolation at the latitude of the Nefud Desert, (Fig. 5), the driver of ‘Green Arabia’ humid phases14. The buried handaxe is associated with a thick marl sequence overlying the dated sediments. Sedimentological analysis indicates these marls were produced by significant wet conditions. Both the MIS 9 and MIS 7 insolation peaks are modulated by high eccentricity (Fig. 5) and are equal or greater in intensity to that of MIS 5a, which is known to have been wet enough to enable large perennial deep lake formation24. As can be seen, the MIS 9 insolation peaks lie closest to the older age estimates and correspond to a time when other lakes in the An Nasim area are known to have formed23 (Fig. 5). Taken together, this evidence is consistent with a MIS 9 date for the formation of the An Nasim deposits, though the possibility a younger MIS 7 age cannot be completely discounted.Figure 5Luminescence ages from the An Nasim site, displayed above the orbital parameters (derived from44) which produced humid episodes in the Arabian Peninsula (eccentricity [green] modulation of precession [turquoise], with a corresponding influence upon summer [JJA] insolation at the latitude of the Nefud [black], driving monsoon incursion). Marine Isotope Stages of the last 700 ka are displayed for reference. Navy blue bar data are from23 and are displayed as follows. Solid bars indicate lake formation occurred during this range (a direct date or paired bracketing ages). Dashed lines with endcaps and thick bars to the left indicate maximum (underlying, no unconformities) ages for lake formation—which likely occurred either before (i.e. older than) the endcap, or during the period denoted by a thick bar. Dashed lines with endcaps and thick bars to the right indicate minimum (overlying, no unconformities) ages for lake formation—which likely occurred after (i.e. younger than) the endcap, or during the period denoted by a thick bar. The hashed area shows the high concurrence of data suggesting lake formation in MIS 9. Produced using Microsoft Excel.Full size image More

Location of potential larval habitats and probability of occurrenceGenerally, Anopheles arabiensis mosquito takes around 15 days to develop from egg to adult, but the duration can be as short as 10 days due to selection pressures from the stressed environment such as drought, temperature anomaly, or limited food resources48,49. In this regard, we considered areas with WI exceeding 10 and 15 days to be potential larval habitats under critical and normal conditions, respectively.To determine the probability of potential larval habitat occurrence, we computed the probability of ponding occurring longer than 10 and 15 days, P(WI  > T), as shown in Eq. (2). P(WI  > T) is defined as the ratio of D(WI(x,y,t)  > T), the number of cumulated days for which the WI (i.e. persistence of ponding) of a grid cell (x,y) at time t that exceeded T days, to Dperiod, the number of days within a defined period of simulation.$$Pleft( {WI > T} right) = frac{{D(WIleft( {x,y,t} right) > T)}}{{D_{period} }},,T in left{ {10,15} right}$$
(2)
Figure 5 shows the results for the spatial distribution of P(WI  > T) over the three periods of simulation, namely the entire year of 2018, the dry season (i.e. January to April and November to December) and the rainy season (i.e. May to October). It can be observed that ponding was persistent throughout the year around the stream edges and the vicinity. P(WI  > 10) and P(WI  > 15) were consistently close to 1, reflecting a high potential of these areas as larval habitats.Figure 5Spatial distribution for the probability of potential larval habitat occurrence. (a–d) represent the probability of WI exceeding 10 days and 15 days for the baseline scenario and the irrigation scenario for the entire year. Similarly, (e–h) represent the probability of WI exceeding 10 days and 15 days during the dry season, and (i–l) represent the probability of WI exceeding 10 days and 15 days during the rainy season. Areas where the simulated surface water flowrate exceeded 0.01 m3/s for 90% of the time in the simulated year were masked out for all the sub-figures since Anopheles larvae have a lower chance of surviving in fast-moving water61.Full size imageFor the baseline scenario shown in Fig. 5a,b, the P(WI  > T) for the areas outside of the streams was predominantly determined by soil type. The areas characterized by Usterts (see Supplementary Fig S2) with the lowest hydraulic conductivity in the model domain were the next most at risk, with a P(WI  > T) of about 0.4–0.5. In the remaining areas, P(WI  > T) was generally 0.2 or less. Comparing Fig. 5a,b, the differences were minimal except for the steep areas at the watershed upstream boundary where P(WI  > 15) was predominantly zero. The surface water ponding was unable to last more than 15 days due to the terrain gradient.Figure 5c,d show the results for the irrigation scenario. Compared to the baseline scenario, the year-round persistent ponding around the streams and the vicinity was wider in coverage and more noticeable. Irrigation also increased P(WI  > 10) in Fig. 5c and P(WI  > 15) in Fig. 5d from 0.4–0.5 to about 0.7 and 0.6 respectively for Farm #1, Farm #2, and a significant portion of Farm #3 and Farm #4. The P(WI  > T) for the remaining area within the farms remained relatively unchanged at 0.2 and this could be attributed to the Ustoll soil type which drains easily. The increase in the probability of potential larval habitat occurrence from the baseline was more pronounced for P(WI  > 10) than P(WI  > 15) since the interval of irrigation was set at 10 days, after which the farm drained without replenishment until the next irrigation cycle.For the dry season, it can be observed in Fig. 5e,f that the stream edges were the only areas with high potential of larval habitat occurrence. In Fig. 5g,h, P(WI  > T) increased visibly in the farms after irrigation, with a distinct similarity between Farms #1/#3 and Farms #2/#4 that points to the irrigation schedule. While irrigation was alternated evenly between the two groups, Farms#1 and #3 showed a higher P(WI  > T) than Farms #2 and #4, possibly due to the timing of the irrigation relative to the rainfall. Irrigation could either coincide with rainfall or act as a supplement when there was no rainfall to augment soil moisture. Noticeably, there was an area to the northeast straddling both Farm #3 and Farm #4 where P(WI  > 10) was around 0.1 but P(WI  > 15) was almost zero, indicating that irrigation only allowed for larval habitats under critical conditions in that area during the dry season.For the rainy season, it can be observed in the baseline scenario (Fig. 5i,j) that the areas characterized by Ustert exhibited a high potential of larval habitat occurrence, apart from the stream edges. Particularly, there was an area to the north where P(WI  > T) was lower than the other parts which could be due to the relatively steeper terrain. In the irrigation scenario (Fig. 5k,l), there was no visible difference in P(WI  > T) as compared to the baseline scenario, apart from a minor increase around the western part of Farm #4.As a summary, we present the results in boxplots as shown in Fig. 6 to illustrate the effect of irrigation in different seasons for the areas inside and outside farms. The relevant statistics can be found in Table 1. The P(WI  > T) had a highly asymmetrical distribution because it was very low in most of the model domain but could be very high in the remaining areas due to the streams. For the following comparison, we will use the median as it was more representative of the distribution.Figure 6Box plots for the probability of potential larval habitat occurrence for the whole year, dry, and rainy season. Probability of WI exceeding (a) 10 days and 15 days (b) for the area inside farms and the area outside farms. The line within each box is the sample median and the top and bottom of each box are the 25th and 75th percentiles. The whiskers were drawn from the two ends of the box and demarcate the observations which were within 1.5 times the interquartile range from the top and bottom of the box.Full size imageTable 1 Summary statistics of the probability of potential larval habitat occurence for the whole year, dry season, and rainy season. Mean, 25th percentile (P25), median and 75th percentile (P75) of the probability of WI exceeding 10 days and 15 days for the (a) areas inside farms and (b) areas outside farms. The p value was derived from the Wilcoxon Rank-Sum test under the null hypothesis that irrigation did not increase the median probability of exceedance from the baseline scenario.Full size tableIn the baseline scenario, there was a higher potential for larval habitats to form inside the farms, with a median P(WI  > 10) of 0.427 and a median P(WI  > 15) of 0.400, than outside the farms, with a median P(WI  > 10) of 0.06 and a median P(WI  > 15) of 0.019. This is expected because the farms are located in an area with relatively flat terrain and a higher concentration of streams. The difference in the median P(WI  > T) inside and outside the farms was bigger in the rainy season compared to the dry season, as the higher rainfall received intensified ponding.Irrigation increased the median P(WI  > T) inside the farms drastically in the dry season, with the median P(WI  > 10) increasing from 0 to 0.442 and the median P(WI  > 15) increasing from 0 to 0.282. Although irrigation was only applied over the dry season, there was also a statistically significant increase in the median P(WI  > T) during the rainy season (p  10) increased from 0.848 to 0.864 while the median P(WI  > 15) increased from 0.794 to 0.810. This was due to irrigation contributing to the antecedent soil moisture before the onset of the rainy season, which shortened the time for the soil to become saturated and ponding to occur. On the other hand, there was no strong evidence outside the farms of an increase in the median P(WI  > T) due to irrigation (p  > 0.01). This applied to both rainy and dry seasons.Stability of larval habitatsIn the previous section, we showed that irrigation did not have a significant impact on areas outside the farms. Here, we evaluated the stability of the potential larval habitats specifically for the areas inside farms based on the distribution of the maximum duration of ponding for each grid cell within the year as shown in the histogram (Fig. 7a). The total number of cells corresponding to each bin interval of 15 days was expressed as a fraction of the total number of cells in the area inside farms.Figure 7The fraction of area inside the irrigated farms for each potential larval habitat types under the baseline and irrigation scenarios. (a) Shows the histogram of the maximum duration of ponding within the year for the grid cells in each type of habitats expressed as a fraction of the total area of the farms. The bin size was 15 days. Temporary, semi-permanent, and permanent larval habitats were typically characterized by ponding duration of 15–90 days, 90–180 days, and 180 days and above, respectively. The baseline scenario is represented in blue and the irrigation scenario is represented in orange. The darker orange bin is a result of the two overlapping. (b) Shows the comparison of the fractional area occupied by non-habitats (less than 15 days) as well as potential temporary, semi-permanent, and permanent larval habitats inside the farms. Each grid cell within the farm was categorized based on its maximum ponding duration.Full size imageFrom the baseline scenario, 13.2% of the area was not favorable for larval habitats because the maximum duration of ponding in those areas was less than 15 days. The most common maximum ponding duration was between 150 and 165 days, which accounted for more than 20% of the area. This was followed by 15–30 days and 360 days and above which made up 17.6% and 13.8% of the area respectively. With irrigation, there was a general increase in the maximum ponding durations. The most common maximum ponding duration was 360 days and above, accounting for 18% of the area. Noticeably, the area with maximum ponding duration between 210–225 days increased fourfold to 10%. The remaining increase was for 285 days and above. Counter-intuitively, the area that was not conducive as larval habitats (i.e. maximum ponding duration less than 15 days) also increased slightly by 0.6%. This was because irrigation raised the overland flowrate in these areas, mostly near streams, and made them unfavorable for breeding.In Fig. 7b, we grouped the maximum ponding durations into stability periods corresponding to temporary (2 weeks to 3 months), semi-permanent (3–6 months), and permanent (6 months and above) habitats based on field observations from a study at the site35. Temporary habitats such as puddles retain water for a short period while permanent habitats such as stream edges and swamps hold water much longer and are more stable. For the baseline scenario, semi-permanent habitats were the most common, occupying 33.1% of the area, while permanent and temporary habitats also accounted for 29.6% and 24.1% of the area respectively. After irrigation, there was a significant shift from semi-permanent habitats, which reduced to 22.9% of the area, to permanent habitats which increased to 41% of the area. There was also a slight reduction in the extent of temporary habitats to 22.4% of the area.Temporal pattern of potential larval habitatsTo shed light on the temporal patterns, we evaluated F(WI  > T), the fractional coverage of potential larval habitats inside farm, for each day throughout the year. We only focused on the area inside farms since irrigation does not have a significant impact on the area outside farms. As shown in Eq. (3), F(WI  > T) is defined as the ratio of C(WI  > T), the number of cells for which the WI (i.e. persistence of ponding) exceeded T days, to Cfarm, the number of cells within the farm area. T is set as 10 days and 15 days, corresponding to critical and normal conditions respectively.$$Fleft( {WI > T} right) = frac{{Cleft( { WIleft( {x,y,t} right) ge T} right)}}{{C_{farm} }},,T in left{ {10,15} right}$$
(3)
In Fig. 8a, F(WI  > 10) increased steeply on January 10 as WI started increasing from 0 at the beginning of the year. For the baseline scenario, the fractional coverage decreased minimally from 0.18 throughout the dry season despite the sporadic spike in precipitation. At the onset of the rainy season, the peak rainfall event of the year from May 5th to May 11th caused a sharp increase in F(WI  > 10) from 0.15 to 0.61 and thereafter, the relentless rainfall maintained the fractional coverage at about 0.6. Throughout the rainy season, there were four recurring peaks at a frequency of about 2 months. Post-rainy season, F(WI  > 10) dropped gradually to below 0.2 after the last peak at the end of October.Figure 8Daily variations in the extent of the potential larval habitats for the year. Time series of the fractional coverage of areas with Wetness Index (WI) exceeding (a) 10 days and (b) 15 days.Full size imageFor the irrigation scenario, F(WI  > 10) increased during the dry season from January to March with visible cyclical variations between 0.2 and 0.4 due to the rotation of irrigation among the four farms. Subsequently, the spike in rainfall at the end of March combined with the higher antecedent soil moisture from irrigation brought forward the step increase in the fractional coverage to April from May in the baseline scenario. As irrigation stopped at the end of April, F(WI  > 10) gradually dropped back to the same level as the baseline scenario at the end of June. In the dry season from November to December, the fractional coverage started to deviate from the baseline scenario again with cyclical fluctuations, gradually decreasing towards the end of the year.In Fig. 8b, F(WI  > 15) remained largely the same for the dry season but the peaks were moderated in the rainy season, compared to F(WI  > 10). There was one less peak at the end of May in the early rainy season because the watershed did not accumulate enough rainfall for the persistence of the ponded areas to exceed 15 days. Specifically, for the irrigation scenario, the increase in fractional coverage during the dry season was moderated and less sensitive to the spikes in rainfall. Similarly, irrigation resulted in the early onset of the steep increase in F(WI  > 15) in April following the spike in rainfall at the end of March. Also, it took two months after the end of irrigation in April for the fractional coverage to return to the same level as the baseline.From F(WI  > 10) and F(WI  > 15), we calculated the corresponding monthly mean, MF(WI  > 10), and MF(WI  > 15) as well as the 95th confidence interval as shown in Fig. 9. In Fig. 9a, MF(WI  > 10) in the baseline was the highest for the months between June and September, constituting a four-month window in which at least 50% of the area was conducive for larval habitat formation. Of the four months, the highest monthly mean fractional coverage was in July at 79.9%. Irrigation extended the window to include the months of April and May. The monthly mean fractional coverage increased 4.5 times to 64.3% in April and 1.4 times to 63.7% in May. The MF(WI  > 10) for the rest of the months in the window (i.e. June to September) remained one of the highest but the increase due to irrigation was not statistically significant (p  > 0.01). July remained as the month with the highest monthly mean fractional coverage at 80.0%. In Fig. 9b, MF(WI  > 15) was generally slightly lower than MF(WI  > 10) for both the baseline and irrigation scenarios but the general trends were the same.Figure 9Monthly variation in the extent of the potential larval habitats for the year. Monthly mean fractional coverage of areas with a probability of WI exceeding 10 days (a) and 15 days (b). The 95% confidence interval is indicated at the top of each bar chart. The asterisks (*) next to the month on the x-axis indicate that irrigation increased the fractional coverage of the potential larval habitats for the month from the baseline scenario based on a 2-sample t-test (p  More

AffiliationsDepartment of Earth System Science, University of California, Irvine, CA, USAJonathan A. Wang & James T. RandersonDepartment of Earth and Environment, Boston University, Boston, MA, USAJonathan A. Wang, Alessandro Baccini & Mark A. FriedlThe Woodwell Climate Research Center, Falmouth, MA, USAAlessandro Baccini & Mary FarinaDepartment of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USAMary FarinaAuthorsJonathan A. WangAlessandro BacciniMary FarinaJames T. RandersonMark A. FriedlCorresponding authorCorrespondence to
Jonathan A. Wang. More

1.Clarke, G. et al. Minireview: gut microbiota: the neglected endocrine organ. Mol. Endocrinol. 28, 1221–1238. https://doi.org/10.1210/me.2014-1108 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
2.Cryan, J. F. & O’Mahony, S. M. The microbiome-gut-brain axis: from bowel to behavior. Neurogastroenterol. Motil. 23, 187–192. https://doi.org/10.1111/j.1365-2982.2010.01664.x (2011).CAS 
Article 
PubMed 

Google Scholar 
3.Hollister, E. B. et al. Structure and function of the healthy pre-adolescent pediatric gut microbiome. Microbiome 3, 36. https://doi.org/10.1186/s40168-015-0101-x (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Cheng, J. et al. Discordant temporal development of bacterial phyla and the emergence of core in the fecal microbiota of young children. ISME J. 10, 1002–1014. https://doi.org/10.1038/ismej.2015.177 (2016).Article 
PubMed 

Google Scholar 
5.Lim, M. Y., Song, E.-J., Kang, K. S. & Nam, Y.-D. Age-related compositional and functional changes in micro-pig gut microbiome. GeroScience 41, 935–944. https://doi.org/10.1007/s11357-019-00121-y (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Kim, J., Nguyen, S. G., Guevarra, R. B., Lee, I. & Unno, T. Analysis of swine fecal microbiota at various growth stages. Arch. Microbiol. 197, 753–759. https://doi.org/10.1007/s00203-015-1108-1 (2015).CAS 
Article 
PubMed 

Google Scholar 
7.Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439. https://doi.org/10.1126/science.1237439 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.de Weerth, C. Do bacteria shape our development? Crosstalk between intestinal microbiota and HPA axis. Neurosci. Biobehav. Rev. 83, 458–471. https://doi.org/10.1016/j.neubiorev.2017.09.016 (2017).CAS 
Article 
PubMed 

Google Scholar 
9.Heijtz, R. D. et al. Normal gut microbiota modulates brain development and behavior. Proc. Natl. Acad. Sci. U.S.A. 108, 3047–3052. https://doi.org/10.1073/pnas.1010529108 (2011).ADS 
Article 
PubMed Central 

Google Scholar 
10.Sudo, N. et al. Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice. J. Physiol. 558, 263–275. https://doi.org/10.1113/jphysiol.2004.063388 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Rutsch, A., Kantsjö, J. B. & Ronchi, F. The gut-brain axis: how microbiota and host inflammasome influence brain physiology and pathology. Front. Immunol. https://doi.org/10.3389/fimmu.2020.604179 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Bravo, J. A. et al. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc. Natl. Acad. Sci. U.S.A. 108, 16050–16055. https://doi.org/10.1073/pnas.1102999108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Kamada, N., Seo, S.-U., Chen, G. Y. & Núñez, G. Role of the gut microbiota in immunity and inflammatory disease. Nat. Rev. Immunol. 13, 321–335. https://doi.org/10.1038/nri3430 (2013).CAS 
Article 
PubMed 

Google Scholar 
14.Dinan, T. G. & Cryan, J. F. Regulation of the stress response by the gut microbiota: implications for psychoneuroendocrinology. Psychoneuroendocrinology 37, 1369–1378. https://doi.org/10.1016/j.psyneuen.2012.03.007 (2012).CAS 
Article 
PubMed 

Google Scholar 
15.Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From Dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345. https://doi.org/10.1016/j.cell.2016.05.041 (2016).CAS 
Article 
PubMed 

Google Scholar 
16.Parker, A., Fonseca, S. & Carding, S. R. Gut microbes and metabolites as modulators of blood-brain barrier integrity and brain health. Gut Microb. 11, 135–157. https://doi.org/10.1080/19490976.2019.1638722 (2020).CAS 
Article 

Google Scholar 
17.Silva, Y. P., Bernardi, A. & Frozza, R. L. The role of short-chain fatty acids from gut microbiota in gut-brain communication. Front. Endocrinol. https://doi.org/10.3389/fendo.2020.00025 (2020).Article 

Google Scholar 
18.Neufeld, K. M., Kang, N., Bienenstock, J. & Foster, J. A. Reduced anxiety-like behavior and central neurochemical change in germ-free mice. Neurogastroenterol. Motil. 23, 255-e119. https://doi.org/10.1111/j.1365-2982.2010.01620.x (2011).CAS 
Article 
PubMed 

Google Scholar 
19.Clarke, G. et al. The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic system in a sex-dependent manner. Mol. Psychiatry 18, 666–673. https://doi.org/10.1038/mp.2012.77 (2013).CAS 
Article 
PubMed 

Google Scholar 
20.Foster, J. A. & McVeyNeufeld, K.-A. Gut–brain axis: how the microbiome influences anxiety and depression. Trends Neurosci. 36, 305–312. https://doi.org/10.1016/j.tins.2013.01.005 (2013).CAS 
Article 
PubMed 

Google Scholar 
21.O’Mahony, S. M. et al. Early life stress alters behavior, immunity, and microbiota in rats: implications for irritable bowel syndrome and psychiatric illnesses. Biol. Psychiatry 65, 263–267. https://doi.org/10.1016/j.biopsych.2008.06.026 (2009).Article 
PubMed 

Google Scholar 
22.Schmidt, B. et al. Establishment of normal gut microbiota is compromised under excessive hygiene conditions. PLoS ONE 6, e28284. https://doi.org/10.1371/journal.pone.0028284 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Mulder, I. E. et al. Environmentally-acquired bacteria influence microbial diversity and natural innate immune responses at gut surfaces. BMC Biol. 7, 79. https://doi.org/10.1186/1741-7007-7-79 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Quigley, E. M. M. Probiotics in functional gastrointestinal disorders: what are the facts?. Curr. Opin. Pharmacol. 8, 704–708. https://doi.org/10.1016/j.coph.2008.08.007 (2008).CAS 
Article 
PubMed 

Google Scholar 
25.Dowarah, R., Verma, A. K. & Agarwal, N. The use of Lactobacillus as an alternative of antibiotic growth promoters in pigs: a review. Anim. Nutr. 3, 1–6. https://doi.org/10.1016/j.aninu.2016.11.002 (2017).Article 
PubMed 

Google Scholar 
26.Cussotto, S., Sandhu, K. V., Dinan, T. G. & Cryan, J. F. The neuroendocrinology of the microbiota-gut-brain axis: a behavioural perspective. Front. Neuroendocrinol. 51, 80–101. https://doi.org/10.1016/j.yfrne.2018.04.002 (2018).CAS 
Article 
PubMed 

Google Scholar 
27.Barros-Santos, T. et al. Effects of chronic treatment with new strains of Lactobacillus plantarum on cognitive, anxiety- and depressive-like behaviors in male mice. PLoS ONE https://doi.org/10.1371/journal.pone.0234037 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Liu, W.-H. et al. Alteration of behavior and monoamine levels attributable to Lactobacillus plantarum PS128 in germ-free mice. Behav. Brain Res. 298, 202–209. https://doi.org/10.1016/j.bbr.2015.10.046 (2016).CAS 
Article 
PubMed 

Google Scholar 
29.Davis, D. J. et al. Lactobacillus plantarum attenuates anxiety-related behavior and protects against stress-induced dysbiosis in adult zebrafish. Sci. Rep. 6, 33726. https://doi.org/10.1038/srep33726 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Jang, H. M., Lee, K. E. & Kim, D. H. The preventive and curative effects of Lactobacillus reuteri NK33 and bifidobacterium adolescentis NK98 on immobilization stress-induced anxiety/depression and colitis in mice. Nutrients https://doi.org/10.3390/nu11040819 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Buffington, S. A. et al. Microbial reconstitution reverses maternal diet-induced social and synaptic deficits in offspring. Cell 165, 1762–1775. https://doi.org/10.1016/j.cell.2016.06.001 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Zhang, N. et al. Efficacy of probiotics on stress in healthy volunteers: a systematic review and meta-analysis based on randomized controlled trials. Brain Behav. 10, e01699. https://doi.org/10.1002/brb3.1699 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Liu, R. T., Walsh, R. F. L. & Sheehan, A. E. Prebiotics and probiotics for depression and anxiety: a systematic review and meta-analysis of controlled clinical trials. Neurosci. Biobehav. Rev. 102, 13–23. https://doi.org/10.1016/j.neubiorev.2019.03.023 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Lyte, M. Microbial endocrinology: host-microbiota neuroendocrine interactions influencing brain and behavior. Gut Microb. 5, 381–389. https://doi.org/10.4161/gmic.28682 (2014).Article 

Google Scholar 
35.Tabouy, L. et al. Dysbiosis of microbiome and probiotic treatment in a genetic model of autism spectrum disorders. Brain Behav. Immun. 73, 310–319. https://doi.org/10.1016/j.bbi.2018.05.015 (2018).Article 
PubMed 

Google Scholar 
36.Mao, J.-H. et al. Genetic and metabolic links between the murine microbiome and memory. Microbiome 8, 53. https://doi.org/10.1186/s40168-020-00817-w (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Mendl, M., Burman, O. H. P. & Paul, E. S. An integrative and functional framework for the study of animal emotion and mood. Proc. Biol. Sci. 277, 2895–2904. https://doi.org/10.1098/rspb.2010.0303 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Kraimi, N. et al. Influence of the microbiota-gut-brain axis on behavior and welfare in farm animals: a review. Physiol. Behav. 210, 112658. https://doi.org/10.1016/j.physbeh.2019.112658 (2019).CAS 
Article 
PubMed 

Google Scholar 
39.Xiao, L. et al. A reference gene catalogue of the pig gut microbiome. Nat. Microbiol. 1, 16161. https://doi.org/10.1038/nmicrobiol.2016.161 (2016).CAS 
Article 
PubMed 

Google Scholar 
40.Douglas, C., Bateson, M., Walsh, C., Bédué, A. & Edwards, S. A. Environmental enrichment induces optimistic cognitive biases in pigs. Appl. Anim. Behav. Sci. 139, 65–73. https://doi.org/10.1016/j.applanim.2012.02.018 (2012).Article 

Google Scholar 
41.Brydges, N. M., Leach, M., Nicol, K., Wright, R. & Bateson, M. Environmental enrichment induces optimistic cognitive bias in rats. Anim. Behav. 81, 169–175. https://doi.org/10.1016/j.anbehav.2010.09.030 (2011).Article 

Google Scholar 
42.Paul, E. S., Harding, E. J. & Mendl, M. Measuring emotional processes in animals: the utility of a cognitive approach. Neurosci. Biobehav. Rev. 29, 469–491. https://doi.org/10.1016/j.neubiorev.2005.01.002 (2005).Article 
PubMed 

Google Scholar 
43.Crump, A., Arnott, G. & Bethell, E. Affect-driven attention biases as animal welfare indicators: review and methods. Animals 8, 136 (2018).Article 

Google Scholar 
44.Hutton, S. B. Cognitive control of saccadic eye movements. Brain Cogn. 68, 327–340. https://doi.org/10.1016/j.bandc.2008.08.021 (2008).CAS 
Article 
PubMed 

Google Scholar 
45.Dolan, R. J. & Vuilleumier, P. Amygdala automaticity in emotional processing. Ann. N. Y. Acad. Sci. 985, 348–355. https://doi.org/10.1111/j.1749-6632.2003.tb07093.x (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
46.Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J. & Van Ijzendoorn, M. H. Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study. Psychol. Bull. 133, 1–24. https://doi.org/10.1037/0033-2909.133.1.1 (2007).Article 
PubMed 

Google Scholar 
47.Verbeek, E., Colditz, I., Blache, D. & Lee, C. Chronic stress influences attentional and judgement bias and the activity of the HPA axis in sheep. PLoS ONE https://doi.org/10.1371/journal.pone.0211363 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Lee, C., Verbeek, E., Doyle, R. & Bateson, M. Attention bias to threat indicates anxiety differences in sheep. Biol. Lett. https://doi.org/10.1098/rsbl.2015.0977 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Brilot, B. O. & Bateson, M. Water bathing alters threat perception in starlings. Biol. Lett. 8, 379–381. https://doi.org/10.1098/rsbl.2011.1200 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Luo, L., Reimert, I., de Haas, E. N., Kemp, B. & Bolhuis, J. E. Effects of early and later life environmental enrichment and personality on attention bias in pigs (Sus scrofa domesticus). Anim. Cogn. 22, 959–972. https://doi.org/10.1007/s10071-019-01287-w (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Bögels, S. M. & Mansell, W. Attention processes in the maintenance and treatment of social phobia: hypervigilance, avoidance and self-focused attention. Clin. Psychol. Rev. 24, 827–856. https://doi.org/10.1016/j.cpr.2004.06.005 (2004).Article 
PubMed 

Google Scholar 
52.Bethell, E. J., Holmes, A., MacLarnon, A. & Semple, S. Evidence that emotion mediates social attention in Rhesus Macaques. PLoS ONE https://doi.org/10.1371/journal.pone.0044387 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
53.Cisler, J. M. & Koster, E. H. W. Mechanisms of attentional biases towards threat in anxiety disorders: an integrative review. Clin. Psychol. Rev. 30, 203–216 (2010).Article 

Google Scholar 
54.Koster, E. H. W., Crombez, G., Verschuere, B., Van Damme, S. & Wiersema, J. R. Components of attentional bias to threat in high trait anxiety: facilitated engagement, impaired disengagement, and attentional avoidance. Behav. Res. Ther. 44, 1757–1771. https://doi.org/10.1016/j.brat.2005.12.011 (2006).Article 
PubMed 

Google Scholar 
55.Mogg, K., Bradley, B., Miles, F. & Dixon, R. Brief report time course of attentional bias for threat scenes: testing the vigilance-avoidance hypothesis. Cogn. Emot. 18, 689–700. https://doi.org/10.1080/02699930341000158 (2004).Article 

Google Scholar 
56.Mogg, K. & Bradley, B. P. A cognitive-motivational analysis of anxiety. Behav. Res. Ther. 36, 809–848. https://doi.org/10.1016/S0005-7967(98)00063-1 (1998).CAS 
Article 
PubMed 

Google Scholar 
57.Ellenbogen, M. A., Schwartzman, A. E., Stewart, J. & Walker, C. D. Stress and selective attention: the interplay of mood, cortisol levels, and emotional information processing. Psychophysiology 39, 723–732. https://doi.org/10.1017/s0048577202010739 (2002).Article 
PubMed 

Google Scholar 
58.Koster, E. H. W., Verschuere, B., Crombez, G. & Van Damme, S. Time-course of attention for threatening pictures in high and low trait anxiety. Behav. Res. Ther. 43, 1087–1098. https://doi.org/10.1016/j.brat.2004.08.004 (2005).Article 
PubMed 

Google Scholar 
59.Richards, H. J., Benson, V., Donnelly, N. & Hadwin, J. A. Exploring the function of selective attention and hypervigilance for threat in anxiety. Clin. Psychol. Rev. 34, 1–13. https://doi.org/10.1016/j.cpr.2013.10.006 (2014).Article 
PubMed 

Google Scholar 
60.McLeman, M. A., Mendl, M., Jones, R. B., White, R. & Wathes, C. M. Discrimination of conspecifics by juvenile domestic pigs, Sus scrofa. Anim. Behav. 70, 451–461. https://doi.org/10.1016/j.anbehav.2004.11.013 (2005).Article 

Google Scholar 
61.Kristensen, H. H., Jones, R. B., Schofield, C. P., White, R. P. & Wathes, C. M. The use of olfactory and other cues for social recognition by juvenile pigs. Appl. Anim. Behav. Sci. 72, 321–333. https://doi.org/10.1016/S0168-1591(00)00209-4 (2001).Article 
PubMed 

Google Scholar 
62.Nores, C., Llaneza, L. & Álvarez, Á. Wild boar “Sus scrofa” mortality by hunting and wolf “Canis lupus” predation: an example in northern Spain. Wildlife Biol. 14, 44–51 (2008).Article 

Google Scholar 
63.Verbeek, E., Ferguson, D. & Lee, C. Are hungry sheep more pessimistic? The effects of food restriction on cognitive bias and the involvement of ghrelin in its regulation. Physiol. Behav. 123, 67–75 (2014).CAS 
Article 

Google Scholar 
64.Forkman, B., Boissy, A., Meunier-Salaün, M. C., Canali, E. & Jones, R. B. A critical review of fear tests used on cattle, pigs, sheep, poultry and horses. Physiol. Behav. 92, 340–374. https://doi.org/10.1016/j.physbeh.2007.03.016 (2007).CAS 
Article 
PubMed 

Google Scholar 
65.Ruis, M. A. W. et al. Adaptation to social isolation: acute and long-term stress responses of growing gilts with different coping characteristics. Physiol. Behav. 73, 541–551. https://doi.org/10.1016/S0031-9384(01)00548-0 (2001).CAS 
Article 
PubMed 

Google Scholar 
66.Stolba, A. & Wood-Gush, D. G. M. The behaviour of pigs in a semi-natural environment. Anim. Prod. 48, 419–425. https://doi.org/10.1017/S0003356100040411 (1989).Article 

Google Scholar 
67.Fleming, S. A. & Dilger, R. N. Young pigs exhibit differential exploratory behavior during novelty preference tasks in response to age, sex, and delay. Behav. Brain Res. 321, 50–60. https://doi.org/10.1016/j.bbr.2016.12.027 (2017).Article 
PubMed 

Google Scholar 
68.Bethell, E. J., Cassidy, L. C., Brockhausen, R. R. & Pfefferle, D. Toward a standardized test of fearful temperament in primates: a sensitive alternative to the human intruder task for laboratory-housed Rhesus Macaques (Macaca mulatta). Front. Psychol. https://doi.org/10.3389/fpsyg.2019.01051 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
69.du Sert, N. P. et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. PLOS Biol. 18, e3000410. https://doi.org/10.1371/journal.pbio.3000410 (2020).CAS 
Article 

Google Scholar 
70.European Union. Council Directive 2008/120/EC of 18 December 2008 laying down minimum standards for the protection of pigs. Off. J. Eur. Union (2018).71.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (R Core Team, 2021).
Google Scholar 
72.Tremblay, A. & Ransijn, J. LMERConvenienceFunctions: Model Selection and Post-Hoc Analysis for (G)LMER Models. R package version 3.0. https://CRAN.R-project.org/package=LMERConvenienceFunctions (2020).73.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. 67, 48. https://doi.org/10.18637/jss.v067.i01 (2015).74.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. 82, 26. https://doi.org/10.18637/jss.v082.i13 (2017).75.Russell, L. Emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.1. https://CRAN.R-project.org/package=emmeans (2020).76.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. https://ggplot2.tidyverse.org (2016). More

Women in northern Mumbai, India, have planted mangrove saplings to protect the area against rising sea levels.Credit: Mahendra Parikh/Hindustan Times via Getty

Projects that manage, protect and restore ecosystems are widely viewed as win–win strategies for addressing two of this century’s biggest global challenges: climate change and biodiversity loss. Yet the potential contribution of such nature-based solutions to mitigating climate change remains controversial.Decision-makers urgently need to know: what role do nature-based solutions have in the race to net-zero emissions and stop further global temperature increases?Analyses of nature-based solutions often focus on how much carbon they can remove from the atmosphere. Here, we provide a new perspective by modelling how these solutions will affect global temperatures — a crucial metric as humanity attempts to limit global warming.Our analysis shows that nature-based solutions can have a powerful role in reducing temperatures in the long term. Land-use changes will continue to act long past the point at which net-zero emissions are achieved and global temperatures peak (known as peak warming), and will have an important role in planetary cooling in the second half of this century. Before then, nature-based solutions can provide real but limited mitigation benefits. Crucially, the more ambitious the climate target, the shorter the time frame for such solutions to have an effect on peak warming.In other words, nature-based solutions must be designed for longevity. This means paying closer attention to their long-term carbon-sink potential, as well as their impacts on biodiversity, equity and sustainable development goals. It also means continuing to limit global warming through other methods, from decarbonization to geological storage of carbon dioxide.Our model reinforces the conclusion that an ambitious scaling-up of nature-based solutions needs to be implemented fast and thoughtfully — and not at the expense of other measures.Win–winsThe world is currently likely to hit 3 °C of warming above pre-industrial levels by 2100 (although recent policy announcements from the United States and China could reduce this). The 2015 Paris climate agreement aims to limit the global temperature rise this century to well below 2 °C, and, ideally, to 1.5 °C. There is no date for either goal, beyond the “end of this century”. The metric that matters most is the peak temperature, with more-aggressive efforts required to stay below 1.5 °C of warming than for the 2 °C target.
Emissions: world has four times the work or one-third of the time
It is impossible to achieve the needed reduction in peak warming solely through cuts to greenhouse gases, because emissions from certain sectors, such as agriculture and some heavy industry, cannot be driven to zero any time soon. For this reason, we also need to remove greenhouse gases from the atmosphere on an unprecedented scale1.There are various options for doing this. For example, when biomass vegetation is burnt for energy, the emitted CO2 can be retained and stored underground. This process, known as bioenergy with carbon capture and storage (BECCS), requires vast areas of land — compromising food security and biodiversity — as well as time to develop on a large scale. Other options involve industrial machines that capture CO2 from the air; these are currently nascent, expensive technologies.A subset of nature-based solutions can be used specifically to limit warming. These ‘natural climate solutions’ aim to reduce atmospheric greenhouse-gas concentrations in three ways. One is to avoid emissions by protecting ecosystems and thus reducing carbon release; this includes efforts to limit deforestation. Another is to restore ecosystems, such as wetlands, so that they sequester carbon. The third is to improve land management — for timber, crops and grazing — to reduce emissions of carbon, methane and nitrous oxide, as well as to sequester carbon (see ‘Three steps to natural cooling’).

Source: S. Jenkins et al. Geophys. Res. Lett. 45, 2795–2804 (2018).

Decades of work provide strong evidence that nature-based solutions can deliver many local ecological and socio-economic benefits2. Restoring a forest next to a stream, for example, might reduce flooding, improve carbon storage and support fisheries. Growing recognition of such benefits means that interest in nature-based solutions is soaring: they can help people adapt to climate change, achieve sustainable development goals, protect biodiversity and mitigate climate change3.Quantifying nature’s roleThere is still debate around how much nature-based solutions can contribute to achieving net-zero targets by mid-century. This is because results have been estimated across a range of objectives, time frames and model assumptions4,5 (see Supplementary information; SI). Some researchers say that tree restoration is the most effective climate-change solution we have available6 (this in itself has been robustly contested); others argue that nature-based solutions won’t be nearly as fast or as effective as is often stated7.Part of the reason for the impasse is this: many well-known papers discuss the annual carbon uptake possibilities of nature-based solutions; they do not discuss their cooling impact year on year. Because the Paris agreement is framed in terms of temperature, we argue that this gap is critical: researchers need to know how nature-based solutions will affect global temperature.To model this, we consider an ambitious but realistic scenario — an update to previous estimates by one of our co-authors (B.W.G)4,8,9. This scenario considers only those projects for nature-based solutions that are constrained by many factors: they are cost-effective (costing less than US$100 per tonne of CO2 equivalent); ensure adequate global production of food and wood-based products; and involve sufficient biodiversity conservation. They also respect land tenure rights and don’t change the amount of sunlight reflected from Earth, or albedo (see SI). In our scenario, nature-based solutions that avoid emissions ramp up quickly — by 2025 — and absorb carbon while avoiding emissions at a rate of 10 gigatonnes of CO2 per year (Gt CO2 yr−1). This rises to 20 Gt CO2 yr−1 in the most ambitious scenario (peak warming of 1.5 °C by 2055), in which we assume a higher price of carbon. The 10-Gt value is cost-contained. But we also account for 30 years of higher-priced nature-based solutions in the 1.5 °C scenario (up to $200 per tonne of CO2 equivalent; see SI). For comparison, 10 Gt CO2 yr−1 is more than the emissions from the entire global transportation sector.

Instituto Terra, an initiative in Aimorés, Brazil, is restoring a devastated ecosystem.Credit: Christian Ender/Getty

Achieving 10 Gt CO2 yr−1 of mitigation in this way would involve stopping the destruction of ecosystems worldwide (including 270 million hectares of deforestation); restoring 678 million hectares of ecosystems (more than twice the size of India); and improving the management of around 2.5 billion hectares of land by mid-century4. This is ambitious, but it is important to note that the bulk of land required (85%) comes from improving management of existing lands for agriculture, grazing and production forest without displacing yields of food, wood-based products or fuel (see ‘Three steps to natural cooling’).These estimates come with caveats (see SI). The role of nature-based solutions could be larger if one considers, for example, their impacts on other greenhouse gases besides CO2. This could represent an additional amount of roughly 1–3 Gt CO2 equivalent yr−1 of climate mitigation. Alternatively, the contribution of such solutions might be smaller in the long term, if the carbon drawdown from land-based interventions decreased over time. This could happen if these natural sinks became saturated or were affected by climate impacts such as forest fires. These caveats are not included in our estimates.We then modelled how this level of nature-based solutions would affect global temperature up to 2100 (see ‘The long game’ and SI). We looked at illustrative pathways from the Intergovernmental Panel on Climate Change, in which peak warming is constrained to 1.5 °C or 2 °C, and ran these scenarios with the added contribution of nature-based solutions as described. These pathways include BECCS, but no nature-based solutions beyond some avoided deforestation.Taking the temperatureOur analysis shows that implementing this level of nature-based solutions could reduce the peak warming by an additional 0.1 °C under a scenario consistent with a 1.5 °C rise by 2055; 0.3 °C under a scenario consistent with a 2 °C rise by 2085; and 0.3 °C under a 3 °C-by-2100 scenario (see ‘The long game’).

Adapted from Fig. SPM.1 of Ref. 1

The most significant contribution nature-based solutions can make to mitigating the peak temperature is in the 2 °C scenario. In a more ambitious 1.5 °C scenario, there isn’t enough time for nature-based solutions to have as great an impact on peak warming. In the 3 °C scenario, several issues constrain the impact of nature-based solutions, including the limited ability of ecosystems to absorb carbon in a warmer world.Overall, the mitigation potential of nature-based solutions remains small compared to what can be achieved by decarbonizing the economy. Yet, assuming that decarbonization takes place, nature-based solutions can still suppress a chunk of the warming (see SI).Crucially, nature-based solutions cool the planet long after the peak temperature is reached. In the 1.5 °C scenario, they take a total of 0.4 °C off warming by 2100 — four times their suppression to the 2055 peak temperature (see SI, Table S2).
Restoring natural forests is the best way to remove atmospheric carbon
Achieving these significant long-term benefits requires several things. Nature-based solutions of good quality must be scaled up rapidly — and not at the expense of other robust strategies. Long-term geological storage of CO2, for example, will need to be ramped up significantly in the next decade as technologies mature and prices fall. The long-term benefits of nature-based solutions also depend on warming being held in check. The increased frequency and intensity of impacts such as wildfires can undermine ecosystems and their capacity to store carbon or provide other benefits to society.Ecosystems that are protected and carefully managed — such as intact peatlands and old-growth tropical rainforests — are very likely to continue to store carbon for thousands of years. These are also more resilient to climate extremes and pathogens.The right metricsRestoration of forest cover is widely considered the most viable near-term opportunity for carbon removal. Unfortunately, some of this enthusiasm has been used to promote plantation forestry — growing trees of a limited variety of ages and species (for example, in monoculture plantations) does not have the same carbon benefits as maintaining an intact forest ecosystem10.One serious problem is that some nature-based solutions, as currently implemented, can have unintended and unwanted consequences. For example, an area of 34,007 hectares of intact forest ecosystem in Cambodia became a logging concession, with much of it replaced with an acacia monoculture. This was the first large-scale reforestation project to be funded in Cambodia in the context of climate-change mitigation. The project resulted in unethical ecological devastation, affecting 1,900 families in the area11.Similarly, Chilean government subsidies for new plantations of pine and eucalyptus have resulted in plantations expanding by 1.3 million hectares since 1986, with an associated sequestration of about 5.6 million tonnes of carbon. However, regulations stating that expansion cannot happen at the expense of native biodiverse forests were not enforced, resulting in large-scale reductions in native forest cover. Clearing of the original forest has resulted in a net decrease of approximately 0.05 million tonnes of stored carbon since 198612.These examples show how a singular focus on rapid carbon sequestration as the metric of success for land-based climate mitigation can result in perverse outcomes. Activities should be evaluated and monitored with the right metrics, to account for the multitude of benefits they provide in the long term.
Adopt a carbon tax to protect tropical forests
To ensure long-term resilience, projects involving nature-based solutions should adhere to four high-level principles (see nbsguidelines.info). First, nature-based solutions are not an alternative to decarbonization; second, they need to involve a wide range of ecosystems; third, they should be designed in partnership with local communities while respecting Indigenous and other rights; and, finally, they must support biodiversity, from the level of the gene to the ecosystem. In addition, the Oxford principles13 for high-quality offsets call for safe and durable CO2 removal and storage for every tonne of CO2 emitted. Metrics of success should include those for carbon dynamics, biodiversity across multiple trophic levels, and socio-economic factors such as women’s empowerment and youth employment.There are many examples of good-practice projects (see also case studies by the University of Oxford’s Nature-based Solutions Initiative, where N.S. and C.A.J.G. work). For example, mangrove forests in eastern India that have been protected from deforestation since 1985 have been shown to protect coastal regions from the negative impacts of cyclones much better than artificial defences do, while also soaking up carbon14. In Sierra Leone’s tropical rainforest, cocoa agroforestry — where cocoa is planted with trees for shade, alongside pineapples, chillies and maize (corn) as an additional source of food and income — has been shown to produce cocoa sustainably while diminishing forest clearance. One agroforestry project in the Gola Rainforest National Park, initiated 30 years ago, has increased biodiversity and the profitability of crops while saving an estimated 500,000 tonnes of carbon each year through sequestration and avoiding deforestation.Invest wiselyThis much is clear: we urgently need to increase investment in high-quality nature-based solutions. They currently receive a small proportion of existing climate-mitigation financing4,15, which does not reflect their potential.Carbon markets are increasingly relied on to finance nature-based solutions. But carbon offsets on the voluntary market are of variable quality. It can be unclear whether projects really represent a carbon sink, whether they are permanent or if they safeguard social and ecological factors. Offsets that adhere to standards can allow organizations to deliver lower-cost and hence larger climate-mitigation outcomes through nature-based solutions; however, budgets to emit fossil fuels should be ratcheted down rapidly to avoid delaying decarbonization from continued greenhouse-gas emissions.
Account for depreciation of natural capital
Nature-based solutions need both public and private finance; in particular, governments need to reward ecosystem stewardship while taxing polluters and ramping up regulation to ensure that companies meet strict social and environmental safeguards.The United Nations Framework Convention on Climate Change (UNFCCC) needs to provide clear guidelines on national-level accounting for nature-based solutions. This will guide the targets set in the Paris agreement’s Nationally Determined Contributions, and the monitoring, reporting and verification methodologies required to comply with these targets.The next UNFCCC meeting, COP26, is due to be held in Glasgow, UK, this November and provides an opportunity for national reporting systems to tighten national carbon accounting related to nature-based solutions. This would ensure that such solutions make a real, long-term contribution to carbon mitigation and could set metrics to ensure high biodiversity levels and maximize human well-being. One pressing issue for COP26 is Article 6 of the Paris agreement, which established a “mechanism to contribute to the mitigation of greenhouse gas emissions and support sustainable development”. A tightly regulated compliance market defined in Article 6 will provide the grounding for a tightly regulated voluntary offsetting market.COP26 also presents the chance to harmonize the goals of the UNFCCC and those of the Convention on Biological Diversity. For example, nature-based solutions projects are likely to be required to adhere to the principle of free prior informed consent of local people: local communities need to be involved at all stages of project planning and management. Similarly, nature-based solutions should be required to protect and enhance biodiversity. This work can build on existing social and biodiversity standards3.Our economy must be decarbonized at unprecedented rates to achieve net-zero targets by mid-century. Carbon must also be removed from the atmosphere to counter emissions that are hard to eliminate, using nature-based solutions and other means. To transform social and economic systems to deliver resilience in the face of ongoing climate impacts, the world must invest now in nature-based solutions that are ecologically sound, socially equitable and designed to pay dividends over a century or more. Properly managed, these could benefit many generations to come. More