Philippa Kaur

1.Wilson, E. O. The insect societies. (Harvard University Press, Cambridge, Massachusetts, USA, 1971).
Google Scholar 
2.Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Robson, S. K. & Traniello, J. F. Key individuals and the organisation of labor in ants. In Information processing in social insects, 239–259 (Springer, 1999).4.Smith, A. The wealth of nations (London, Methuen & Co, 1776).5.Oster, G. F. & Wilson, E. O. Caste and ecology in the social insects (Princeton University Press, 1978).6.Jeanne, R. L. The evolution of the organization of work in social insects. Italian J. Zool. 20, 119–133 (1986).
Google Scholar 
7.Seeley, T. D. Adaptive significance of the age polyethism schedule in honeybee colonies. Behav. Ecol. Sociobiol. 11, 287–293 (1982).Article 

Google Scholar 
8.Franks, N. R. The organization of working teams in social insects. Trends Ecol. Evol. 2, 72–75 (1987).CAS 
PubMed 
Article 

Google Scholar 
9.Robinson, G. E. Regulation of division of labor in insect societies. Ann. Rev. Entomol. 37, 637–665 (1992).CAS 
Article 

Google Scholar 
10.O’Donnell, S. & Jeanne, R. L. Forager specialization and the control of nest repair in Polybia occidentalis olivier (Hymenoptera: Vespidae). Behav. Ecol. Sociobiol. 27, 359–364 (1990).Article 

Google Scholar 
11.Wahl, L. Evolving the division of labour: generalists, specialists and task allocation. J. Theor. Biol. 219, 371–388 (2002).CAS 
PubMed 
Article 

Google Scholar 
12.Jaffé, R., Kronauer, D. J., Bernhard Kraus, F., Boomsma, J. J. & Moritz, R. F. Worker caste determination in the army ant Eciton burchellii. Biol. Lett. 3, 513–516 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Kuhn, S. L. & Stiner, M. C. What’s a mother to do? The division of labor among Neandertals and modern humans in Eurasia. Curr. Anthropol. 47, 953–981 (2006).Article 

Google Scholar 
14.Wilson, E. O. Caste and division of labor in leaf-cutter ants (hymenoptera: Formicidae: Atta). Behav. Ecol. Sociobiol. 7, 157–165 (1980).Article 

Google Scholar 
15.Mirenda, J. T. & Vinson, S. B. Division of labour and specification of castes in the red imported fire ant solenopsis invicta buren. Animal Behav. 29, 410–420 (1981).Article 

Google Scholar 
16.Detrain, C. & Pasteels, J. Caste differences in behavioral thresholds as a basis for polyethism during food recruitment in the ant, pheidole pallidula (nyl.)(hymenoptera: Myrmicinae). J. Insect behav. 4, 157–176 (1991).Article 

Google Scholar 
17.Theraulaz, G., Bonabeau, E. & Denuebourg, J. Response threshold reinforcements and division of labour in insect societies. Proc. R. Soc. Lond. Ser. B Biol. Sci. 265, 327–332 (1998).Article 

Google Scholar 
18.Johnson, B. R. Organization of work in the honeybee: a compromise between division of labour and behavioural flexibility. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, 147–152 (2003).Article 

Google Scholar 
19.Dukas, R. & Visscher, P. K. Lifetime learning by foraging honey bees. Animal Behav. 48, 1007–1012 (1994).Article 

Google Scholar 
20.Richardson, T. O., Mullon, C., Marshall, J. A., Franks, N. R. & Schlegel, T. The influence of the few: a stable ‘oligarchy’ controls information flow in house-hunting ants. Proc. R. Soc. B 285, 20172726 (2018).PubMed 
Article 

Google Scholar 
21.Trumbo, S. T. & Robinson, G. E. Learning and task interference by corpse-removal specialists in honey bee colonies. Ethology 103, 966–975 (1997).Article 

Google Scholar 
22.Julian, G. E. & Cahan, S. Undertaking specialization in the desert leaf-cutter ant Acromyrmex versicolor. Animal Behav. 58, 437–442 (1999).CAS 
Article 

Google Scholar 
23.Dukas, R. Life history of learning: performance curves of honeybees in settings that minimize the role of learning. Animal Behav. 75, 1125–1130 (2008).Article 

Google Scholar 
24.Charbonneau, D., Sasaki, T. & Dornhaus, A. Who needs ‘lazy’workers? inactive workers act as a ‘reserve’labor force replacing active workers, but inactive workers are not replaced when they are removed. PloS one 12, e0184074 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Crall, J. D. et al. Spatial fidelity of workers predicts collective response to disturbance in a social insect. Nat. Commun. 9, 1201 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Franks, N. R., Pratt, S. C., Mallon, E. B., Britton, N. F. & Sumpter, D. J. Information flow, opinion polling and collective intelligence in house–hunting social insects. Philosop. Trans. R. Soc. Lond. Ser. B Biol. Sci. 357, 1567–1583 (2002).Article 

Google Scholar 
27.Pratt, S. C., Mallon, E. B., Sumpter, D. J. & Franks, N. R. Quorum sensing, recruitment, and collective decision-making during colony emigration by the ant Leptothorax albipennis. Behav. Ecol. Sociobiol. 52, 117–127 (2002).Article 

Google Scholar 
28.Möglich, M. Social organization of nest emigration in Leptothorax (Hym., Form.). Insectes Sociaux 25, 205–225 (1978).Article 

Google Scholar 
29.Visscher, P. K. Group decision making in nest-site selection among social insects. Ann. Rev. Entomol. 52, 255–275 (2007).CAS 
Article 

Google Scholar 
30.McGlynn, T. P. The ecology of nest movement in social insects. Ann. Rev. Entomol. 57, 291–308 (2012).CAS 
Article 

Google Scholar 
31.Franks, N. R. & Richardson, T. Teaching in tandem-running ants. Nature 439, 153–153 (2006).CAS 
PubMed 
Article 

Google Scholar 
32.Richardson, T. O., Sleeman, P. A., McNamara, J. M., Houston, A. I. & Franks, N. R. Teaching with evaluation in ants. Curr. Biol. 17, 1520–1526 (2007).CAS 
PubMed 
Article 

Google Scholar 
33.Franklin, E. L., Richardson, T. O., Sendova-Franks, A. B., Robinson, E. J. & Franks, N. R. Blinkered teaching: tandem running by visually impaired ants. Behav. Ecol. Sociobiol. 65, 569–579 (2011).Article 

Google Scholar 
34.Franks, N. R. et al. Ant search strategies after interrupted tandem runs. J. Exper. Biol. 213, 1697–1708 (2010).Article 

Google Scholar 
35.Flack, J. C., Krakauer, D. C. & de Waal, F. B. M. Robustness mechanisms in primate societies: a perturbation study. Proc. R. Soc. B Biol. Sci. 272, 1091–1099 (2005).Article 

Google Scholar 
36.Pinter-Wollman, N., Hubler, J., Holley, J.-A., Franks, N. R. & Dornhaus, A. How is activity distributed among and within tasks in Temnothorax ants? Behav. Ecol. Sociobiol. 66, 1407–1420 (2012).Article 

Google Scholar 
37.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach (Springer Science & Business Media, 2003).38.Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).Article 

Google Scholar 
39.Grueber, C., Nakagawa, S., Laws, R. & Jamieson, I. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
PubMed 
Article 

Google Scholar 
40.Symonds, M. R. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar 
41.Rosenthal, S. B., Twomey, C. R., Hartnett, A. T., Wu, H. S. & Couzin, I. D. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion. Proc. Natl Acad. Sci. 112, 4690–4695 (2015).CAS 
PubMed 
Article 

Google Scholar 
42.Pratt, S. C., Sumpter, D. J., Mallon, E. B. & Franks, N. R. An agent-based model of collective nest choice by the ant Temnothorax albipennis. Animal Behav. 70, 1023–1036 (2005).Article 

Google Scholar 
43.Volny, V. P. & Gordon, D. M. Genetic basis for queen–worker dimorphism in a social insect. Proc. Natl Acad. Sci. 99, 6108–6111 (2002).CAS 
PubMed 
Article 

Google Scholar 
44.Walsh, J. T., Warner, M. R., Kase, A., Cushing, B. J. & Linksvayer, T. A. Ant nurse workers exhibit behavioural and transcriptomic signatures of specialization on larval stage. Animal Behav. 141, 161–169 (2018).Article 

Google Scholar 
45.Seeley, T. D. Division of labor between scouts and recruits in honeybee foraging. Behav. Ecol. Sociobiol. 12, 253–259 (1983).Article 

Google Scholar 
46.Boesch, C. Cooperative hunting roles among tai chimpanzees. Human Nat. 13, 27–46 (2002).Article 

Google Scholar 
47.Stander, P. E. Cooperative hunting in lions: the role of the individual. Behav. Ecol. Sociobiol. 29, 445–454 (1992).Article 

Google Scholar 
48.Gazda, S. K., Connor, R. C., Edgar, R. K. & Cox, F. A division of labour with role specialization in group–hunting bottlenose dolphins (tursiops truncatus) off cedar key, florida. Proc. R. Soc. B Biol. Sci. 272, 135–140 (2005).Article 

Google Scholar 
49.Nagy, M., Ákos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).CAS 
PubMed 
Article 

Google Scholar 
50.Nagy, M. et al. Context-dependent hierarchies in pigeons. Proc. Natl Acad. Sci. USA 110, 13049–13054 (2013).CAS 
PubMed 
Article 

Google Scholar 
51.Fewell, J. H., Armbruster, D., Ingraham, J., Petersen, A. & Waters, J. S. Basketball teams as strategic networks. PloS One 7, e47445 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Alleman, A., Stoldt, M., Feldmeyer, B. & Foitzik, S. Tandem-running and scouting behaviour are characterized by up-regulation of learning and memory formation genes within the ant brain. Mol. Ecol. 28, 2342–2359 (2019).PubMed 
Article 

Google Scholar 
53.Collett, T. S. & Collett, M. Memory use in insect visual navigation. Nat. Rev. Neurosci. 3, 542 (2002).CAS 
PubMed 
Article 

Google Scholar 
54.Collett, T. S., Graham, P. & Durier, V. Route learning by insects. Curr. Opin. Neurobiol. 13, 718–725 (2003).CAS 
PubMed 
Article 

Google Scholar 
55.Wehner, R. Desert ant navigation: how miniature brains solve complex tasks. J. Comp. Physiol. A 189, 579–588 (2003).CAS 
Article 

Google Scholar 
56.Langridge, E. A., Franks, N. R. & Sendova-Franks, A. B. Improvement in collective performance with experience in ants. Behav. Ecol. Sociobiol. 56, 523–529 (2004).Article 

Google Scholar 
57.Ravary, F., Lecoutey, E., Kaminski, G., Châline, N. & Jaisson, P. Individual experience alone can generate lasting division of labor in ants. Curr. Biol. 17, 1308–1312 (2007).CAS 
PubMed 
Article 

Google Scholar 
58.Chittka, L. & Muller, H. Learning, specialization, efficiency and task allocation in social insects. Commun. Integr. Biol. 2, 151–154 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Franklin, E. L., Robinson, E. J., Marshall, J. A., Sendova-Franks, A. B. & Franks, N. R. Do ants need to be old and experienced to teach? J. Exp. Biol. 215, 1287–1292 (2012).PubMed 
Article 

Google Scholar 
60.Westhus, C., Kleineidam, C. J., Roces, F. & Weidenmüller, A. Behavioural plasticity in the fanning response of bumblebee workers: impact of experience and rate of temperature change. Animal Behav. 85, 27–34 (2013).Article 

Google Scholar 
61.Dukas, R. Animal expertise: mechanisms, ecology and evolution. Animal Behav. 147, 199–210 (2019).Article 

Google Scholar 
62.Carter, C. E. & Grahn, J. A. Optimizing music learning: exploring how blocked and interleaved practice schedules affect advanced performance. Front. Psychol. 7, 1251 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
63.Stroeymeyt, N., Franks, N. R. & Giurfa, M. Knowledgeable individuals lead collective decisions in ants. J. Exp. Biol. 214, 3046–3054 (2011).PubMed 
Article 

Google Scholar 
64.Stroeymeyt, N., Giurfa, M. & Franks, N. R. Information certainty determines social and private information use in ants. Sci. Rep. 7, 43607 (2017).PubMed Central 
Article 
PubMed 

Google Scholar 
65.Hansen, M. J., Schaerf, T. M. & Ward, A. J. The influence of nutritional state on individual and group movement behaviour in shoals of crimson-spotted rainbowfish (Melanotaenia duboulayi). Behav. Ecol. Sociobiol. 69, 1713–1722 (2015).Article 

Google Scholar 
66.Jolles, J. W., Boogert, N. J., Sridhar, V. H., Couzin, I. D. & Manica, A. Consistent individual differences drive collective behavior and group functioning of schooling fish. Curr. Biol. 27, 2862–2868 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Leca, J.-B., Gunst, N., Thierry, B. & Petit, O. Distributed leadership in semifree-ranging white-faced capuchin monkeys. Animal Behav. 66, 1045–1052 (2003).Article 

Google Scholar 
68.McComb, K. et al. Leadership in elephants: the adaptive value of age. Proc. R. Soc. B Biol. Sci. 278, 3270–3276 (2011).Article 

Google Scholar 
69.Jolles, J. W., King, A. J. & Killen, S. S. The role of individual heterogeneity in collective animal behaviour. Trends Ecol. Evol. 35, 278–291 (2020).PubMed 
Article 

Google Scholar 
70.Cook, C. N. et al. Individual differences in learning and biogenic amine levels influence the behavioural division between foraging honeybee scouts and recruits. J. Animal Ecol. 88, 236–246 (2019).Article 

Google Scholar 
71.Eyer, P.-A., Freyer, J. & Aron, S. Genetic polyethism in the polyandrous desert ant cataglyphis cursor. Behav. Ecol. 24, 144–151 (2013).Article 

Google Scholar 
72.Franks, N. R., Mallon, E. B., Bray, H. E., Hamilton, M. J. & Mischler, T. C. Strategies for choosing between alternatives with different attributes: exemplified by house-hunting ants. Animal Behav. 65, 215–223 (2003).Article 

Google Scholar 
73.Dornhaus, A., Franks, N. R., Hawkins, R. & Shere, H. Ants move to improve: colonies of Leptothorax albipennis emigrate whenever they find a superior nest site. Animal Behav. 67, 959–963 (2004).Article 

Google Scholar 
74.Planqué, R., Dechaume-Moncharmont, F.-X., Franks, N. R., Kovacs, T. & Marshall, J. A. Why do house-hunting ants recruit in both directions? Naturwissenschaften 94, 911–918 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

1.Bonizzoni, M. et al. On the origins of medfly invasion and expansion in Australia. Mol. Ecol. 13, 3845–3855 (2004).CAS 
PubMed 
Article 

Google Scholar 
2.Tuda, M., Kagoshima, K., Toquenaga, Y. & Arnqvist, G. Global genetic differentiation in a cosmopolitan pest of stored beans: Effects of geography, host-plant usage and anthropogenic factors. PLoS ONE 9, e106268 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Karsten, M., van Vuuren, B. J., Addison, P. & Terblanche, J. S. Deconstructing intercontinental invasion pathway hypotheses of the Mediterranean fruit fly (Ceratitis capitata) using a Bayesian inference approach: Are port interceptions and quarantine protocols successfully preventing new invasions?. Divers. Distrib. 21, 813–825 (2015).Article 

Google Scholar 
4.Rodriguero, M. S. et al. Out of the forest: past and present range expansion of a parthenogenetic weevil pest, or how to colonize the world successfully. Ecol. Evol. 6, 5431–5445 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Kébé, K. et al. Global phylogeography of the insect pest Callosobruchus maculatus (Coleoptera: Bruchinae) relates to the history of its main host Vigna unguiculata. J. Biogeogr. 44, 2515–2526 (2017).Article 

Google Scholar 
6.Lombaert, E. et al. Colonization history of the western corn rootworm (Diabrotica virgifera virgifera) in North America: insights from random forest ABC using microsatellite data. Biol. Invasions 20, 665–677 (2018).Article 

Google Scholar 
7.Tuda, M., Ronn, J., Buranapanichpan, S., Wasano, N. & Arnqvist, G. Evolutionary diversification of the bean beetle genus Callosobruchus (Coleoptera: Bruchidae): Traits associated with stored-product pest status. Mol. Ecol. 15, 3541–3551 (2006).CAS 
PubMed 
Article 

Google Scholar 
8.Wei, S. J. et al. Population genetic structure and approximate Bayesian computation analyses reveal the southern origin and northward dispersal of the oriental fruit moth Grapholita molesta (Lepidoptera: Tortricidae) in its native range. Mol. Ecol. 24, 4094–4111 (2015).PubMed 
Article 

Google Scholar 
9.Takano, S. et al. Unique clade of alphaproteobacterial endosymbionts induces complete cytoplasmic incompatibility in the coconut beetle. Proc. Natl. Acad. Sci. USA 114, 6110–6115 (2017).CAS 
PubMed 
Article 

Google Scholar 
10.Radcliffe, E. B. & Flanders, K. L. Biological control of alfalfa weevil in North America. Integr. Pest Manag. Rev. 3, 225–242 (1998).Article 

Google Scholar 
11.Kuwata, R., Tokuda, M., Yamaguchi, D. & Yukawa, J. Coexistence of two mitochondrial DNA haplotypes in Japanese populations of Hypera postica (Col., Curculionidae). J. Appl. Entomol. 129, 191–197 (2005).CAS 
Article 

Google Scholar 
12.Skuhrovec, J. Host plants of weevils of the genus Hypera (Coleoptera: Curculionidae) occurring in the Czech Republic. Klapalekiana 41, 215–255 (2005).
Google Scholar 
13.Wood, K. A., Armbrust, E. J., Bartell, D. P. & Irwin, B. J. The literature of arthropods associated with alfalfa. V. A bibliography of the alfalfa weevil, Hypera postica (Gyllenhal), and the Egyptian alfalfa weevil, Hypera brunneipennis (Boheman) (Coleoptera: Curculionidae). Illinois Agricultural Experimental Station, Special Publication, 54 (1978).14.Kimura, H., Okumura, M. & Yoshida, T. Emergence of and recent damage by the alfalfa weevil. Shokubutsu Boeki (Plant Protection) 42, 498–501 (in Japanese) (1988).15.CAB International crop protection compendium. CAB International. http://www.cabicompendium.org/cpc/home.asp (2013).16.Titus, E. G. On the life history of the alfalfa leaf-weevil. J. Econ. Entomol. 3, 459–470 (1910).Article 

Google Scholar 
17.Wehrle, L. P. The discovery of an alfalfa weevil (Hypera brunneipennis Boheman) in Arizona. J. Econ. Entomol. 33, 119–121 (1940).Article 

Google Scholar 
18.Poos, F. W. & Bissell, T. L. The alfalfa weevil in Maryland. J. Econ. Entomol. 46, 178–179 (1953).Article 

Google Scholar 
19.Volker, K. C. & Simpson, R. G. Behavior of alfalfa weevil larvae affecting the establishment of Tetrastichus incertus in Colorado. Environ. Entomol. 4, 742–744 (1975).Article 

Google Scholar 
20.Salt, G. & van den Bosch, R. The defense reactions of three species of Hypera (Coleoptera, Curculionidae) to an Ichneumon wasp. J. Invertebr. Pathol. 9, 164–177 (1967).Article 

Google Scholar 
21.Maund, C. M. & Hsiao, T. H. Differential encapsulation of two Bathyplectes parasitoids among alfalfa weevil strains, Hypera postica (Gyllenhal). Can. Entomol. 123, 197–203 (1991).Article 

Google Scholar 
22.Hsiao, T. H. Studies of interactions between alfalfa weevil strains, Wolbachia endosymbionts and parasitoids. In The ecology of agricultural pests: biochemical approaches (eds, Symondson, W. O. C. & Liddell, J. E.). 57–71 (Chapman & Hall, 1996).23.Hsiao, T. H. & Stutz, J. M. Discrimination of alfalfa weevil strains by allozyme analysis. Entomol. Exp. Appl. 37, 113–121 (1985).CAS 
Article 

Google Scholar 
24.Erney, S. J., Pruess, K. P., Danielson, S. D. & Powers, T. O. Molecular differentiation of alfalfa weevil strains (Coleoptera: Curculionidae). Ann. Entomol. Soc. Am. 89, 804–811 (1996).CAS 
Article 

Google Scholar 
25.Böttger, J. A. A. Phylogenetic analysis of the alfalfa weevil complex (Coleoptera: Curculionidae) in North America. J. Econ. Entomol. 106, 426–436 (2013).PubMed 
Article 
CAS 

Google Scholar 
26.Iwase, S., Nakahira, K., Tuda, M., Kagoshima, K. & Takagi, M. Host-plant dependent population genetics of the invading weevil Hypera postica. Bull. Entomol. Res. 105, 92–100 (2015).CAS 
PubMed 
Article 

Google Scholar 
27.White, C. E., Armbrust, E. J. & Ashley, J. Cross-mating studies of eastern and western strains of alfalfa weevil. J. Econ. Entomol. 65, 85–89 (1972).Article 

Google Scholar 
28.Iwase, S. & Tani, S. New haplotype and inter-strain reproductive compatibility of Wolbachia-uninfected alfalfa weevil, Hypera postica (Coleoptera: Curculionidae), in Japan. Entomol. Sci. 19, 72–76 (2016).Article 

Google Scholar 
29.Werren, J. H. Biology of Wolbachia. Annu. Rev. Entomol. 42, 587–609 (1997).CAS 
PubMed 
Article 

Google Scholar 
30.LePage, D. P. et al. Prophage WO genes recapitulate and enhance Wolbachia-induced cytoplasmic incompatibility. Nature 543, 243–247 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Bailly-Bechet, M. et al. How long does Wolbachia remain on board?. Mol. Biol. Evol. 34, 1183–1193 (2017).CAS 
PubMed 
Article 

Google Scholar 
32.Hale, L. R. & Hoffmann, A. A. Mitochondrial DNA polymorphism and cytoplasmic incompatibility in natural populations of Drosophila simulans. Evolution 44, 1383–1386 (1990).PubMed 
Article 

Google Scholar 
33.Ballard, J. W. O. & Kreitman, M. Unravelling selection in the mitochondrial genome of Drosophila. Genetics 138, 757–772 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Johnstone, R. A. & Hurst, G. D. D. Maternally inherited male-killing microorganisms may confound interpretation of mitochondrial DNA variability. Biol. J. Linn. Soc. 58, 453–470 (1996).Article 

Google Scholar 
35.Jiggins, F. M. Male-killing Wolbachia and mitochondrial DNA: selective sweeps, hybrid introgression and parasite population dynamics. Genetics 164, 5–12 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741–751 (2008).CAS 
PubMed 
Article 

Google Scholar 
37.Shoemaker, D. D., Dyer, K. A., Ahrens, M., McAbee, K. & Jaenike, J. Decreased diversity but increased substitution rate in host mtDNA as a consequence of Wolbachia endosymbiont infection. Genetics 168, 2049–2058 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Cariou, M., Duret, L. & Charlat, S. The global impact of Wolbachia on mitochondrial diversity and evolution. J. Evol. Biol. 30, 2204–2210 (2017).CAS 
PubMed 
Article 

Google Scholar 
39.Teixeira, L., Ferreira, A. & Ashburner, M. The bacterial symbiont Wolbachia induces resistance to RNA viral infections in Drosophila melanogaster. PLoS Biol. 6, 2753–2763 (2008).CAS 
Article 

Google Scholar 
40.Brownlie, J. C. et al. Evidence for metabolic provisioning by a common invertebrate endosymbiont, Wolbachia pipientis, during periods of nutritional stress. PLoS Pathog. 5, e1000368 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Rand, D. M., Haney, R. A. & Fry, A. J. Cytonuclear coevolution: the genomics of cooperation. TRENDS Ecol. Evol. 19, 645–653 (2004).PubMed 
Article 

Google Scholar 
42.Arnqvist, G. et al. The genetic architecture of metabolic rate: environment specific epistasis between mitochondrial and nuclear genes in an insect. Evolution 64, 3354–3363 (2010).CAS 
PubMed 
Article 

Google Scholar 
43.Blickenstaff, C. C. Partial intersterility of eastern and western US strains of the alfalfa weevil. Ann. Entomol. Soc. Am. 58, 523–526 (1965).Article 

Google Scholar 
44.Hsiao, T. H. & Hsiao, C. Hybridization and cytoplasmic incompatibility among alfalfa weevil strains. Entomol. Exp. Appl. 37, 155–159 (1985).Article 

Google Scholar 
45.Laven, H. Eradication of Culex pipiens fatigans through cytoplasmic incompatibility. Nature 216, 383–384 (1967).ADS 
CAS 
PubMed 
Article 

Google Scholar 
46.Iwase, S. et al. Dynamics of infection with Wolbachia in Hypera postica (Coleoptera: Curculionidae) during invasion and establishment. Biol. Invasions 17, 3639–3648 (2015).Article 

Google Scholar 
47.Sanaei, E. et al. Global genetic diversity, lineage distribution and Wolbachia infection of the alfalfa weevil Hypera postica (Coleoptera: Curculionidae). Ecol. Evol. 9, 9546–9563 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Ros, V. I. D., Fleming, V. M., Feil, E. J. & Breeuwer, J. A. J. How diverse is the genus Wolbachia? Multiple-gene sequencing reveals a putatively new Wolbachia supergroup recovered from spider mites (Acari: Tetranychidae). Appl. Environ. Microbiol. 75, 1036–1043 (2009).CAS 
PubMed 
Article 

Google Scholar 
49.Avise, J. C. Phylogeography: The history and formation of species (Harvard University Press, 2000).
Google Scholar 
50.Narita, S., Nomura, M., Kato, Y. & Fukatsu, T. Genetic structure of sibling butterfly species affected by Wolbachia infection sweep: evolutionary and biogeographical implications. Mol. Ecol. 15, 1095–1108 (2006).CAS 
PubMed 
Article 

Google Scholar 
51.Raychoudhury, R. et al. Phylogeography of Nasonia vitripennis (Hymenoptera) indicates a mitochondrial–Wolbachia sweep in North America. Heredity 104, 318–326 (2010).CAS 
PubMed 
Article 

Google Scholar 
52.Jäckel, R., Mora, D. & Dobler, S. Evidence for selective sweeps by Wolbachia infections: phylogeny of Altica leaf beetles and their reproductive parasites. Mol. Ecol. 22, 4241–4255 (2013).PubMed 
Article 
CAS 

Google Scholar 
53.Jiang, W. et al. Wolbachia infection status and genetic structure in natural populations of Polytremis nascens (Lepidoptera: Hesperiidae). Infect. Genet. Evol. 27, 202–211 (2014).PubMed 
Article 

Google Scholar 
54.Jansen, V. A. A., Turelli, M. & Godfray, H. C. J. Stochastic spread of Wolbachia. Proc. R. Soc. Lond. B Biol. Sci. 275, 2769–2776 (2008).
Google Scholar 
55.Clancy, D. J. & Hoffmann, A. A. Environmental effects on cytoplasmic incompatibility and bacterial load in Wolbachia-infected Drosophila simulans. Entomol. Exp. Appl. 86, 13–24 (1998).Article 

Google Scholar 
56.Bordenstein, S. R. & Bordenstein, S. R. Temperature affects the tripartite interactions between bacteriophage WO, Wolbachia, and cytoplasmic incompatibility. PLoS ONE 6, e29106 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Kamo, T. et al. Limited distribution of natural cyanamide in higher plants: Occurrence in Vicia villosa subsp varia, V. cracca, and Robinia pseudo-acacia. Phytochemistry 69, 1166–1172 (2008).CAS 
PubMed 
Article 

Google Scholar 
58.Megías, C., Cortes-Giraldo, I., Giron-Calle, J., Alaiz, M. & Vioque, J. Free amino acids, including canavanine, in the seeds from 32 Vicia species belonging to subgenus Vicilla. Biocatal. Agric. Biotechnol. 8, 126–129 (2016).Article 

Google Scholar 
59.Rosenthal, G. A. & Dahlman, D. L. Incorporation of L-canavanine into proteins and the expression of its antimetabolic effects. J. Agric. Food Chem. 39, 987–990 (1991).CAS 
Article 

Google Scholar 
60.Kamo, T., Tokuoka. Y. & Miyazaki, M. Quantification of canavanine, 2-aminoethanol, and cyanamide in Aphis craccivora and its host plants, Robinia pseudoacacia and Vicia angustifolia: Effects of these compounds on larval survivorship of Harmonia axyridis. J. Chem. Ecol. 38, 1552–1560 (2012).61.Hewitt, G. M. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999).Article 

Google Scholar 
62.Schmitt, T. Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Front. Zool. 4, 11 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
63.Taberlet, P., Fumagalli, L., Wust-Saucy, A. G. & Cosson, J. F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).CAS 
PubMed 
Article 

Google Scholar 
64.Jordal, B. H. & Kambestad, M. DNA barcoding of bark and ambrosia beetles reveals excessive NUMTs and consistent east-west divergence across Palearctic forests. Mol. Ecol. Resour. 14, 7–17 (2013).PubMed 
Article 
CAS 

Google Scholar 
65.Quiros, C. F. & Bauchan, G. R. The genus Medicago and the origin of the Medicago sativa complex. In Alfalfa and alfalfa improvement (eds, Hanson, A. A., Barnes, D. K. & Hill, R. R.). 93–124 (American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 1988).66.Small, E. Alfalfa and Relatives: Evolution and Classification of Medicago (NRC Research Press, 2011).
Google Scholar 
67.FAO Statistics Division. FAOSTAT: Crops and livestock products. http://www.fao.org/faostat/en/#data/TP (2017).68.Simon, C. A. et al. Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).CAS 
Article 

Google Scholar 
69.Kim, C. G. et al. Pattern of morphological diversification in the Leptocarabus ground beetles (Coleoptera: Carabidae) as deduced from mitochondrial ND5 gene and nuclear 28S rDNA sequences. Mol. Biol. Evol. 17, 137–145 (2000).CAS 
PubMed 
Article 

Google Scholar 
70.Holden, P. R., Brookfield, J. F. Y. & Jones, P. Cloning and characterization of an ftsZ homologue from a bacterial symbiont of Drosophila melanogaster. Mol. Gen. Genet. 240, 213–220 (1993).CAS 
PubMed 
Article 

Google Scholar 
71.Kondo, N. I. et al. Wolbachia infections in world populations of bean beetles (Coleoptera: Chrysomelidae: Bruchinae) infesting cultivated and wild legumes. Zool. Sci. 28, 501–508 (2011).ADS 
Article 

Google Scholar 
72.Baldo, L. et al. Multilocus sequence typing system for the endosymbiont Wolbachia pipientis. Appl. Environ. Microbiol. 72, 7098–7110 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Huelsenbeck, J. P. & Ronquist, F. MrBayes: Bayesian inference of phylogeny. Biometrics 17, 754–755 (2001).CAS 

Google Scholar 
74.Nylander, J. A. A. MrAIC.pl. Program distributed by the author. Uppsala: Evolutionary Biology Centre, Uppsala University (2004).75.Rambaut, A. & Drummond, A. J. Tracer v1.5, http://beast.bio.ed.ac.uk/ (2009).76.Tajima, F. Statistical methods to test for nucleotide mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Fu, Y.-X. & Li, W.-H. Statistical tests of neutrality of mutations. Genetics 133, 693–709 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).CAS 
PubMed 
Article 

Google Scholar 
79.Yang, Z. PAML 4: A program package for phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Song, H., Buhay, J. E., Whiting, M. F. & Crandall, K. A. Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proc. Natl. Acad. Sci. USA 105, 13486–13491 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
81.Haran, J., Koutroumpa, F., Magnoux, E., Roques, A. & Roux, G. Ghost mtDNA haplotypes generated by fortuitous NUMTs can deeply disturb infra-specific genetic diversity and phylogeographic pattern. J. Zoolog. Syst. Evol. Res. 53, 109–115 (2015).Article 

Google Scholar 
82.Clement, M., Snell, Q., Walker, P., Posada, D. & Crandall, K. TCS: Estimating gene genealogies. Parallel Distrib. Proces. Symp. Int. Proc. 2, 184 (2002).
Google Scholar 
83.Tajima, F. Evolutionary relationship of DNA sequences in finite populations. Genetics 105, 437–460 (1983).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
Article 

Google Scholar 
85.Wright, S. Isolation by distance. Genetics 28, 114–138 (1943).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).CAS 
PubMed 

Google Scholar 
87.Raymond, M. & Rousset, F. Genepop (version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).Article 

Google Scholar 
88.Nei, M. & Li, W. H. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. USA 76, 5269–5273 (1979).ADS 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
89.Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Comput. Biol. 5, e1000520 (2009).ADS 
MathSciNet 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
90.Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
91.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Bielejec, F. et al. SpreaD3: Interactive visualization of spatiotemporal history and trait evolutionary processes. Mol. Biol. Evol. 33, 2167–2169 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Griscom, B. W. et al. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 
Article 

Google Scholar 
2.Anderson, C. M. et al. Science 363, 933–934 (2019).CAS 
Article 

Google Scholar 
3.Bastin, J.-F. et al. Science 365, 6–9 (2019).Article 

Google Scholar 
4.Cook-Patton, S. C. et al. Nature 585, 545–550 (2020).CAS 
Article 

Google Scholar 
5.Holl, K. D. & Brancalion, P. S. Science 368, 580–582 (2020).CAS 
Article 

Google Scholar 
6.Fargione, J. et al. Front. For. Glob. Change (in the press).7.West, T. A. P., Börner, J., Sills, E. O. & Kontoleon, A. Proc. Natl Acad. Sci. USA 117, 24188–24194 (2020).CAS 
Article 

Google Scholar 
8.Brancalion, P. H. S. et al. L. Degrad. Dev. 32, 830–841 (2020).Article 

Google Scholar 
9.Sills, E. O. et al. PLoS ONE 10, e0132590 (2015).Article 

Google Scholar 
10.Ferraro, P. J. & Hanauer, M. M. Annu. Rev. Environ. Resour. 39, 495–517 (2014).Article 

Google Scholar 
11.Harris, N. L. et al. Nat. Clim. Change 11, 234–240 (2021).Article 

Google Scholar 
12.Reytar, K. et al. The challenge of tracking how a trillion trees grow. World Resources Institute https://www.wri.org/blog/2020/07/trillion-trees-tracking-challenges (2020).13.Shoch, D. et al. Methodology For Improved Forest Management (Family Forest Carbon Program, 2020).14.McDowell, N. G. et al. Science 368, eaaz9463 (2020).CAS 
Article 

Google Scholar 
15.IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018). More

1.Davis, S. J. & Caldeira, K. Consumption-based accounting of CO2 emissions. Proc. Natl Acad. Sci. USA 107, 5687–5692 (2010).CAS 
Article 

Google Scholar 
2.Wiedmann, T. O. et al. The material footprint of nations. Proc. Natl Acad. Sci. USA 112, 6271–6276 (2015).CAS 
Article 

Google Scholar 
3.Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).CAS 
Article 

Google Scholar 
4.Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).Article 

Google Scholar 
5.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).CAS 
Article 

Google Scholar 
6.Bond, W. J. & Keane, R. E. Fires, Ecological Effects of☆. In Reference Module in Life Sciences (Elsevier, 2017); https://doi.org/10.1016/B978-0-12-809633-8.02098-77.Valentini, R. et al. A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities. Biogeosciences 11, 381–407 (2014).Article 
CAS 

Google Scholar 
8.Williams, C. A. et al. Africa and the global carbon cycle. Carbon Balance Manag. 2, 3 (2007).Article 
CAS 

Google Scholar 
9.Hanan, N. P. Agroforestry in the Sahel. Nat. Geosci. 11, 296–297 (2018).Article 
CAS 

Google Scholar 
10.Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).Article 

Google Scholar 
11.Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).CAS 
Article 

Google Scholar 
12.Feng, S. & Fu, Q. Expansion of global drylands under a warming climate. Atmos. Chem. Phys. 13, 10081–10094 (2013).CAS 
Article 

Google Scholar 
13.Anchang, J. Y. et al. Trends in woody and herbaceous vegetation in the savannas of West Africa. Remote Sens. 11, 576 (2019).Article 

Google Scholar 
14.Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M. & McVicar, T. R. Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences 10, 6657–6676 (2013).Article 

Google Scholar 
15.Kaptué, A. T., Prihodko, L. & Hanan, N. P. On regreening and degradation in Sahelian watersheds. Proc. Natl Acad. Sci. USA 112, 12133–12138 (2015).Article 
CAS 

Google Scholar 
16.Schneider, S. H. The greenhouse effect: science and policy. Science 243, 771–781 (1989).CAS 
Article 

Google Scholar 
17.Walsh, J. et al. Climate Change Impacts in the United States: The Third National Climate Assessment Ch. 2 (US Global Change Research Program, 2014); https://doi.org/10.7930/J0KW5CXT18.Filatova, T., Polhill, J. G. & van Ewijk, S. Regime shifts in coupled socio-environmental systems: review of modelling challenges and approaches. Environ. Model. Softw. 75, 333–347 (2016).Article 

Google Scholar 
19.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).CAS 
Article 

Google Scholar 
20.Brandt, M. et al. Reduction of tree cover in West African woodlands and promotion in semi-arid farmlands. Nat. Geosci. 11, 328–333 (2018).CAS 
Article 

Google Scholar 
21.Keys, P. W. et al. Anthropocene risk. Nat. Sustain. 2, 667–673 (2019).Article 

Google Scholar 
22.Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).Article 
CAS 

Google Scholar 
23.Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).Article 
CAS 

Google Scholar 
24.Hanan, N. P., Prihodko, L., Ross, C. W., Bucini, G. & Tredennick, A. T. Gridded Estimates of Woody Cover and Biomass across Sub-Saharan Africa, 2000-2004 (ORNL DAAC, 2020); https://doi.org/10.3334/ORNLDAAC/177725.Bouvet, A. et al. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sens. Environ. 206, 156–173 (2018).Article 

Google Scholar 
26.Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).Article 

Google Scholar 
27.Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).CAS 
Article 

Google Scholar 
28.Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).CAS 
Article 

Google Scholar 
29.Anchang, J. Y. et al. Toward operational mapping of woody canopy cover in tropical savannas using Google Earth Engine. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2020.00004 (2020).30.Kahiu, M. N. & Hanan, N. P. Fire in sub-Saharan Africa: the fuel, cure and connectivity hypothesis. Glob. Ecol. Biogeogr. 27, 946–957 (2018).Article 

Google Scholar 
31.Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
32.Ross, C. W. et al. HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling. Sci. Data 5, 180091 (2018).Article 

Google Scholar 
33.Lüdeke, M. K. B., Moldenhauer, O. & Petschel-Held, G. Rural poverty driven soil degradation under climate change: the sensitivity of the disposition towards the Sahel Syndrome with respect to climate. Environ. Model. Assess. 4, 315–326 (1999).Article 

Google Scholar 
34.Hansfort, S. L. & Mertz, O. Challenging the woodfuel crisis in West African woodlands. Hum. Ecol. 39, 583 (2011).Article 

Google Scholar 
35.Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).CAS 
Article 

Google Scholar 
36.Wei, F. et al. Nonlinear dynamics of fires in Africa over recent decades controlled by precipitation. Glob. Change Biol. 26, 4495–4505 (2020).Article 

Google Scholar 
37.Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 084003 (2016).Article 

Google Scholar 
38.Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).Article 

Google Scholar 
39.Potapov, P. et al. Mapping the World’s intact forest landscapes by remote sensing. Ecol. Soc. 13, 2 (2008).Article 

Google Scholar 
40.Herold, M., Mayaux, P., Woodcock, C. E., Baccini, A. & Schmullius, C. Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ. 112, 2538–2556 (2008).Article 

Google Scholar 
41.Martens, C. et al. Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies. Glob. Change Biol. 27, 340–358 (2021).Article 

Google Scholar 
42.Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).Article 
CAS 

Google Scholar 
43.Reich, P. B., Hobbie, S. E. & Lee, T. D. Plant growth enhancement by elevated CO2 eliminated by joint water and nitrogen limitation. Nat. Geosci. 7, 920–924 (2014).CAS 
Article 

Google Scholar 
44.Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).CAS 
Article 

Google Scholar 
45.Körner, C. A matter of tree longevity. Science 355, 130–131 (2017).Article 

Google Scholar 
46.Olson, D. M. & Dinerstein, E. The Global 200: priority ecoregions for global conservation. Ann. Mo. Bot. Gard. 89, 199–224 (2002).Article 

Google Scholar 
47.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).48.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
49.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
50.Massey, F. J. The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46, 68–78 (1951).Article 

Google Scholar 
51.Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe: version 4: data grid (CGIAR Consortium for Spatial Information, 2008).52.Ross, C. W. et al. Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling (ORNL DAAC, 2018); https://doi.org/10.3334/ORNLDAAC/156653.Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. https://doi.org/10.1029/2011JG001708 (2011).54.Jucker, T. et al. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob. Change Biol. 23, 177–190 (2017).Article 

Google Scholar 
55.Sanderson, E. W. et al. The human footprint and the last of the wild. BioScience 52, 891–904 (2002).Article 

Google Scholar 
56.Molnar, C., Bischl, B. & Casalicchio, G. iml: an R package for interpretable machine learning. J. Open Source Softw. 3, 786 (2018).Article 

Google Scholar 
57.Wickham, H. tidyverse: Easily Install and Load the ‘Tidyverse’ (CRAN, 2017).58.Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling (CRAN, 2016).59.Perpiñán, O. & Hijmans, R. rasterVis (CRAN, 2018).60.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).61.Zeileis, A. et al. colorspace: A toolbox for manipulating and assessing colors and palettes. J. Stat. Soft. https://doi.org/10.18637/jss.v096.i01 (2020).62.Neuwirth, E. RColorBrewer: ColorBrewer Palettes (CRAN, 2014).63.Auguie, B. gridExtra: Miscellaneous Functions for ‘Grid’ Graphics (CRAN, 2017).64.Pebesma, E. Simple features for R: standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

Google Scholar 
65.Ross, C. W., Hanan, N. P. & Prihodko, L. Prediction Maps: Woody-Biomass Projections and Drivers of Change in Sub-Saharan Africa (Figshare, 2021); https://doi.org/10.6084/M9.FIGSHARE.14150210.V266.Ross, C. W. R Code for Woody-Biomass Projections and Drivers of Change in Sub-Saharan Africa (Figshare, 2021); https://doi.org/10.6084/M9.FIGSHARE.14143799.V1 More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Gruber, N. & Galloway, J. N. An Earth-system perspective of the global nitrogen cycle. Nature 451, 293–296 (2008).Article 

Google Scholar 
2.Zimmerman, J. B., Mihelcic, J. R. & Smith, J. Global stressors on water quality and quantity. Environ. Sci. Technol. 42, 4247–4254 (2008).Article 

Google Scholar 
3.Banwart, S. A., Nikolaidis, N. P., Zhu, Y.-G., Peacock, C. L. & Sparks, D. L. Soil functions: connecting Earth’s critical zone. Annu. Rev. Earth Planet. Sci. Lett. 47, 333–359 (2019).Article 

Google Scholar 
4.Hartmann, D. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 2 (Cambridge Univ. Press, 2013).5.Knorr, K. H., Lischeid, G. & Blodau, C. Dynamics of redox processes in a minerotrophic fen exposed to a water table manipulation. Geoderma 153, 379–392 (2009).Article 

Google Scholar 
6.McClain, M. E. et al. Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems 6, 301–312 (2003).Article 

Google Scholar 
7.Yabusaki, S. B. et al. Water table dynamics and biogeochemical cycling in a shallow, variably-saturated floodplain. Environ. Sci. Technol. 51, 3307–3317 (2017).Article 

Google Scholar 
8.Krause, S. et al. Ecohydrological interfaces as hot spots of ecosystem processes. Water Resour. Res. 53, 6359–6376 (2017).Article 

Google Scholar 
9.Stumm W. & Morgan J. J. Aquatic Chemistry, Chemical Equilibria and Rates in Natural Waters 3rd edn (John Wiley & Sons, 1996).10.Aeschbacher, M., Vergari, D., Schwarzenbach, R. P. & Sander, M. Electrochemical analysis of proton and electron transfer equilibria of the reducible moieties in humic acids. Environ. Sci. Technol. 45, 8385–8394 (2011).Article 

Google Scholar 
11.Thamdrup, B. Bacterial manganese and iron reduction in aquatic sediments. Adv. Microb. Ecol. 16, 41–84 (2000).Article 

Google Scholar 
12.Kostka, J. E. & Nealson, K. H. Dissolution and reduction of magnetite by bacteria. Environ. Sci. Technol. 29, 2535–2540 (1995).Article 

Google Scholar 
13.Piepenbrock, A., Dippon, U., Porsch, K., Appel, E. & Kappler, A. Dependence of microbial magnetite formation on humic substance and ferrihydrite concentrations. Geochim. Cosmochim. Acta 75, 6844–6858 (2011).Article 

Google Scholar 
14.Amstaetter, K., Borch, T., Larese-Casanova, P. & Kappler, A. Redox transformation of arsenic by Fe(II)-activated goethite (α-FeOOH). Environ. Sci. Technol. 44, 102–108 (2010).Article 

Google Scholar 
15.Ilgen, A. G., Foster, A. L. & Trainor, T. P. Role of structural Fe in nontronite NAu-1 and dissolved Fe(II) in redox transformations of arsenic and antimony. Geochim. Cosmochim. Acta 94, 128–145 (2012).Article 

Google Scholar 
16.Lan, S. et al. Efficient catalytic As(III) oxidation on the surface of ferrihydrite in the presence of aqueous Mn(II). Water Res. 128, 92–101 (2018).Article 

Google Scholar 
17.Lovley, D. R. et al. Humic substances as a mediator for microbially catalyzed metal reduction. Acta Hydroch. Hydrob. 26, 152–157 (1998).Article 

Google Scholar 
18.Lovley, D. R., Fraga, J. L., Coates, J. D. & Blunt-Harris, E. L. Humics as an electron donor for anaerobic respiration. Environ. Microbiol. 1, 89–98 (1999).Article 

Google Scholar 
19.Peretyazhko, T. & Sposito, G. Reducing capacity of terrestrial humic acids. Geoderma 137, 140–146 (2006).Article 

Google Scholar 
20.Heitmann, T. & Blodau, C. Oxidation and incorporation of hydrogen sulfide by dissolved organic matter. Chem. Geol. 235, 12–20 (2006).Article 

Google Scholar 
21.Yu, Z. G., Peiffer, S., Goettlicher, J. & Knorr, K. H. Electron transfer budgets and kinetics of abiotic oxidation and incorporation of aqueous sulfide by dissolved organic matter. Environ. Sci. Technol. 49, 5441–5449 (2015).Article 

Google Scholar 
22.Rose, A. L. & Waite, T. D. Kinetics of iron complexation by dissolved natural organic matter in coastal waters. Mar. Chem. 84, 85–103 (2003).Article 

Google Scholar 
23.Bauer, I. & Kappler, A. Rates and extent of reduction of Fe(III) compounds and O2 by humic substances. Environ. Sci. Technol. 43, 4902–4908 (2009).Article 

Google Scholar 
24.Uchimiya, M. & Stone, A. T. Reversible redox chemistry of quinones: impact on biogeochemical cycles. Chemosphere 77, 451–458 (2009).Article 

Google Scholar 
25.Borch, T. et al. Biogeochemical redox processes and their impact on contaminant dynamics. Environ. Sci. Technol. 44, 15–23 (2010).Article 

Google Scholar 
26.Ilgen, A. G., Kukkadapu, R. K., Leung, K. & Washington, R. E. ‘Switching on’ iron in clay minerals. Environ. Sci. Nano 6, 1704–1715 (2019).Article 

Google Scholar 
27.Peiffer, S., dos Santos Afonso, M., Wehrli, B. & Gaechter, R. Kinetics and mechanism of the reaction of hydrogen sulfide with lepidocrocite. Environ. Sci. Technol. 26, 2408–2413 (1992).Article 

Google Scholar 
28.Poulton, S. W., Krom, M. D. & Raiswell, R. A revised scheme for the reactivity of iron (oxyhydr)oxide minerals towards dissolved sulfide. Geochim. Cosmochim. Acta 68, 3703–3715 (2004).Article 

Google Scholar 
29.Hellige, K., Pollok, K., Larese-Casanova, P., Behrends, T. & Peiffer, S. Pathways of ferrous iron mineral formation upon sulfidation of lepidocrocite surfaces. Geochim. Cosmochim. Acta 81, 69–81 (2012).Article 

Google Scholar 
30.Wan, M., Shchukarev, A., Lohmayer, R., Planer-Friedrich, B. & Peiffer, S. Occurrence of surface polysulfides during the interaction between ferric (hydr)oxides and aqueous sulfide. Environ. Sci. Technol. 48, 5076–5084 (2014).Article 

Google Scholar 
31.Hedderich, R. et al. Anaerobic respiration with elemental sulfur and with disulfides. FEMS Microbiol. Rev. 22, 353–381 (1998).Article 

Google Scholar 
32.Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012).Article 

Google Scholar 
33.Poser, A. et al. Disproportionation of elemental sulfur by haloalkaliphilic bacteria from soda lakes. Extremophiles 17, 1003–1012 (2013).Article 

Google Scholar 
34.Aeppli, M. et al. Decreases in iron oxide reducibility during microbial reductive dissolution and transformation of ferrihydrite. Environ. Sci. Technol. 53, 8736–8746 (2019).Article 

Google Scholar 
35.Aeppli, M. et al. Electrochemical analysis of changes in iron oxide reducibility during abiotic ferrihydrite transformation into goethite and magnetite. Environ. Sci. Technol. 53, 3568–3578 (2019).Article 

Google Scholar 
36.Klüpfel, L., Piepenbrock, A., Kappler, A. & Sander, M. Humic substances as fully regenerable electron acceptors in recurrently anoxic environments. Nat. Geosci. 7, 195–200 (2014).Article 

Google Scholar 
37.Blodau, C. Carbon cycling in peatlands—a review of processes and controls. Environ. Rev. 10, 111–134 (2002).Article 

Google Scholar 
38.Gao, C., Sander, M., Agethen, S. & Knorr, K.-H. Electron accepting capacity of dissolved and particulate organic matter control CO2 and CH4 formation in peat soils. Geochim. Cosmochim. Acta 245, 266–277 (2019).Article 

Google Scholar 
39.Schaefer, M. V., Gorski, C. A. & Scherer, M. M. Spectroscopic evidence for interfacial Fe(II)–Fe(III) electron transfer in a clay mineral. Environ. Sci. Technol. 45, 540–545 (2011).Article 

Google Scholar 
40.Pentrakova, L., Su, K., Pentrak, M. & Stucko, J. W. A review of microbial redox interactions with structural Fe in clay minerals. Clay Miner. 48, 543–560 (2013).Article 

Google Scholar 
41.Kostka, J. E., Dalton, D. D., Skelton, H., Dollhopf, S. & Stucki, J. W. Growth of iron(III)-reducing bacteria on clay minerals as the sole electron acceptor and comparison of growth yields on a variety of oxidized iron forms. Appl. Environ. Microbiol. 68, 6256–6262 (2002).Article 

Google Scholar 
42.Li, Y. L. et al. Iron reduction and alteration of nontronite NAu-2 by a sulfate-reducing bacterium. Geochim. Cosmochim. Acta 68, 3251–3260 (2004).Article 

Google Scholar 
43.Liu, D. et al. Reduction of structural Fe(III) in nontronite by methanogen Methanosarcina barkeri. Geochim. Cosmochim. Acta 75, 1057–1071 (2011).Article 

Google Scholar 
44.Zhang, J., Dong, H., Liu, D. & Agrawal, A. Microbial reduction of Fe(III) in smectite minerals by thermophilicmethanogen Methanothermobacter thermautotrophicus. Geochim. Cosmochim. Acta 106, 203–215 (2013).Article 

Google Scholar 
45.Shelobolina, E. et al. Microbial lithotrophic oxidation of structural Fe(II) in biotite. Appl. Environ. Microbiol. 78, 5746–5752 (2012).Article 

Google Scholar 
46.Gorski, C. A. et al. Redox properties of structural Fe in clay minerals. 1. Electrochemical quantification of electron-donating and -accepting capacities of smectites. Environ. Sci. Technol. 46, 9360–9368 (2012).Article 

Google Scholar 
47.Blodau, C., Mayer, B., Peiffer, S. & Moore, T. R. Support for an anaerobic sulfur cycle in two Canadian peatland soils. J. Geophys. Res. 112, G000364 (2007).
Google Scholar 
48.Gauci, V., Dise, N. & Fowler, D. Controls on suppression of methane flux from a peat bog subjected to simulated acid rain sulfate deposition. Glob. Biogeochem. Cycles 16, GB001370 (2002).Article 

Google Scholar 
49.Pester, M., Knorr, K. H., Friedrich, M. W., Wagner, M. & Loy, A. Sulfate-reducing microorganisms in wetlands—fameless actors in carbon cycling and climate change. Front. Microbiol. 3, 72 (2012).Article 

Google Scholar 
50.Hansel, C. M., Ferdelman, T. G. & Tebo, B. M. Cryptic cross-linkages among biogeochemical cycles: novel insights from reactive intermediates. Elements 11, 409–414 (2015).Article 

Google Scholar 
51.Kappler, A. & Bryce, C. Cryptic biogeochemical cycles: unravelling hidden redox reactions. Environ. Microbiol. 19, 842–846 (2017).Article 

Google Scholar 
52.Holmkvist, L., Ferdelman, T. G. & Jørgensen, B. B. A cryptic sulfur cycle driven by iron in the methane zone of marine sediment (Aarhus Bay, Denmark). Geochim. Cosmochim. Acta 75, 3581–3599 (2011).Article 

Google Scholar 
53.Hansel, C. M. et al. Dominance of sulfur-fueled iron oxide reduction in low-sulfate freshwater sediments. ISME J. 9, 2400–2412 (2015b).Article 

Google Scholar 
54.Findlay, A. J. Microbial impact on polysulfide dynamics in the environment. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnw103(2016).55.Berg, J. S. et al. Intensive cryptic microbial iron cycling in the low iron water column of the meromictic Lake Cadagno. Environ. Microbiol. 18, 5288–5302 (2016).Article 

Google Scholar 
56.Peng, C., Bryce, C., Sundman, A. & Kappler, A.Cryptic cycling of complexes containing Fe(III) and organic matter by phototrophic Fe(II)-oxidizing bacteria. Appl. Environ. Microbiol. 85, e02826-18 (2019).Article 

Google Scholar 
57.Bethke, C. M., Sanford, R. A., Kirk, M. F., Jin, Q. & Flynn, T. M. The thermodynamic ladder in geomicrobiology. Am. J. Sci. 311, 183–210 (2011).Article 

Google Scholar 
58.Otte, J. M. et al. The distribution of active iron cycling bacteria in marine and freshwater sediments is decoupled from geochemical gradients. Environ. Microbiol. 20, 2483–2499 (2018).Article 

Google Scholar 
59.Steefel, C. I. & van Cappellen, P. A new kinetic approach to modeling water–rock interaction: the role of nucleation, precursors, and Ostwald ripening. Geochim. Cosmochim. Acta 54, 2657–2677 (1990).Article 

Google Scholar 
60.Vinson, D. S., Block, S. E., Crossey, L. J. & Dahm, C. N. Biogeochemistry at the zone of intermittent saturation: field-based study of the shallow alluvial aquifer, Rio Grande, New Mexico. Geosphere 3, 366–380 (2007).Article 

Google Scholar 
61.Frei, S., Knorr, K., Peiffer, S. & Fleckenstein, J. Surface micro-topography causes hot spots of biogeochemical activity in wetland systems: a virtual modeling experiment. J. Geophys. Res. Biogeosciences 117, G00N12 (2012).Article 

Google Scholar 
62.Briggs, M. A. et al. A physical explanation for the development of redox microzones in hyporheic flow. Geophys. Res. Lett. 42, 4402–4410 (2015).Article 

Google Scholar 
63.Stockdale, A., Davison, W. & Zhang, H. Micro-scale biogeochemical heterogeneity in sediments: a review of available technology and observed evidence. Earth Sci. Rev. 92, 81–97 (2009).Article 

Google Scholar 
64.Sawyer, A. H. Enhanced removal of groundwater-borne nitrate in heterogeneous aquatic sediments. Geophys. Res. Lett. 42, 403–410 (2015).Article 

Google Scholar 
65.Arora, B., Dwivedi, D., Hubbard, S. S., Steefel, C. I. & Williams, K. H. Identifying geochemical hot moments and their controls on a contaminated river floodplain system using wavelet and entropy approaches. Environ. Model. Softw. 85, 27–41 (2016).Article 

Google Scholar 
66.Sawyer, A. H., Kaplan, L. A., Lazareva, O. & Michael, H. A. Hydrologic dynamics and geochemical responses within a floodplain aquifer and hyporheic zone during Hurricane Sandy. Water Resour. Res. 50, 4877–4892 (2014).Article 

Google Scholar 
67.Posth, N., Canfield, D. E. & Kappler, A. Biogenic Fe(III) minerals: from formation to diagenesis and preservation in the rock record. Earth Sci. Rev. 135, 103–121 (2014).Article 

Google Scholar 
68.Tomaszewski, E. J., Cronk, S. S., Gorski, C. A. & Ginder-Vogel, M. The role of dissolved Fe(II) concentration in the mineralogical evolution of Fe (hydr)oxides during redox cycling. Chem. Geol. 438, 163–170 (2016).Article 

Google Scholar 
69.Bishop, M. E. et al. Reactivity of redox cycled Fe-bearing subsurface sediments towards hexavalent chromium reduction. Geochim. Cosmochim. Acta 252, 88–106 (2019).Article 

Google Scholar 
70.Bartsch, S. et al. River–aquifer exchange fluxes under monsoonal climate conditions. J. Hydrol. 509, 601–614 (2014).Article 

Google Scholar 
71.McAllister, S. M. et al. Dynamic hydrologic and biogeochemical processes drive microbially enhanced iron and sulfur cycling within the intertidal mixing zone of a beach aquifer. Limnol. Oceanogr. 60, 329–345 (2015).Article 

Google Scholar 
72.Goldberg, S. D., Knorr, K. ‐H., Blodau, C., Lischeid, G. & Gebauer, G. Impact of altering the water table height of an acidic fen on N2O and NO fluxes and soil concentrations. Glob. Change Biol. 16, 220–233 (2010).Article 

Google Scholar 
73.Moore, T. R. et al. A multi-year record of methane flux at the Mer Bleue bog, Southern Canada. Ecosystems 14, 646–657 (2011).Article 

Google Scholar 
74.Brown, M. G., Humphreys, E. R., Moore, T. R., Roulet, N. T. & Lafleur, P. M. Evidence for a nonmonotonic relationship between ecosystem-scale peatland methane emissions and water table depth. J. Geophys. Res. Biogeosciences 119, 826–835 (2014).Article 

Google Scholar 
75.Estop-Aragonés, C., Zając, K. & Blodau, C. Effects of extreme experimental drought and rewetting on CO2 and CH4 exchange in mesocosms of 14 European peatlands with different nitrogen and sulfur deposition. Glob. Change Biol. 22, 2285–2300 (2016).Article 

Google Scholar 
76.Chamberlain, S. D. et al. Soil properties and sediment accretion modulate methane fluxes from restored wetlands. Glob. Change Biol. 24, 4107–4121 (2018).Article 

Google Scholar 
77.Arora, B. et al. Influence of hydrological, biogeochemical and temperature transients on subsurface carbon fluxes in a flood plain environment. Biogeochemistry 127, 367–396 (2016).Article 

Google Scholar 
78.Frei, S. & Peiffer, S. Exposure times rather than residence times control redox transformation efficiencies in Riparian Wetlands. J. Hydrol. 543, 182–196 (2016).Article 

Google Scholar 
79.Dwivedi, D., Arora, B., Steefel, C. I., Dafflon, B. & Versteeg, R. Hot spots and hot moments of nitrogen in a riparian corridor. Water Resour. Res. 54, 205–222 (2018).Article 

Google Scholar 
80.Peiffer, S., Klemm, O., Pecher, K. & Hollerung, R. Redox measurements in aqueous solutions—a theoretical approach to data interpretation, based on electrode kinetics. J. Contam. Hydrol. 10, 1–18 (1992).Article 

Google Scholar 
81.Wainwright, H. M. et al. Hierarchical Bayesian method for mapping biogeochemical hot spots using induced polarization imaging. Water Resour. Res. 52, 533–551 (2016).Article 

Google Scholar 
82.Mellage, A. et al. Sensing coated iron-oxide nanoparticles with spectral induced polarization (SIP): experiments in natural sand packed flow-through columns. Environ. Sci. Technol. 52, 14256–14265 (2018).Article 

Google Scholar 
83.Revil, A., Florsch, N. & Mao, D. Induced polarization response of porous media with metallic particles—part 1: a theory for disseminated semiconductors. Geophysics 80, D525–D538 (2015).Article 

Google Scholar 
84.Revil, A., Abdel Aal, G. Z., Atekwana, E. A., Mao, D. & Florsch, N. Induced polarization response of porous media with metallic particles—part 2: comparison with a broad database of experimental data. Geophysics 80, D539–D552 (2015).Article 

Google Scholar 
85.Pausch, J. & Kuzyakov, Y. Carbon input by roots into the soil: quantification of rhizodeposition from root to ecosystem scale. Glob. Change Biol. 24, 1–12 (2018).Article 

Google Scholar 
86.Dwivedi, D. et al. Geochemical exports to river from the intrameander hyporheic zone under transient hydrologic conditions: East River mountainous watershed, Colorado. Water Resour. Res. 54, 8456–8477 (2018).Article 

Google Scholar 
87.Jin, Q. & Bethke, C. M. The thermodynamics and kinetics of microbial metabolism. Am. J. Sci. 307, 643–677 (2007).Article 

Google Scholar 
88.Nitzsche, K. S. et al. Arsenic removal from drinking water by a household sand filter in Vietnam—effect of filter usage practices on arsenic removal efficiency and microbiological water quality. Sci. Total Environ. 502, 526–536 (2015).Article 

Google Scholar 
89.Appelo, C. A. J. & Postma, D. Geochemistry, Groundwater and Pollution (CRC Press, 2004).90.Brazhkin, V. V. Metastable phases and ‘metastable’ phase diagrams. J. Phys. Condens. Matter 18, 9643–9650 (2006).Article 

Google Scholar 
91.Cornell, R. M. & Schwertmann, U. The Iron Oxides: Structure, Properties, Reactions, Occurrences and Uses (Wiley-VCH, 2006).92.Ahmed, I. A. M. & Maher, B. A. Identification and paleoclimatic significance of magnetite nanoparticles in soils. Proc. Natl Acad. Sci. USA 115, 1736–1741 (2018).Article 

Google Scholar 
93.Engel, M. H. & Macko, S. A. Organic Geochemistry. Principles and Applications (Springer, 1993).94.Lovley, D. R. & Phillips, E. J. P. Novel mode of microbial energy metabolism: organic carbon oxidation coupled to dissimilatory reduction of iron or manganese. Appl. Environ. Microbiol. 54, 1472–1480 (1988).Article 

Google Scholar 
95.Gorski, C. A. & Scherer, M. M. in Aquatic Redox Chemistry (eds Tratnyek, P. G. et al.) 315–343 (ACS, 2011).96.Orsetti, S., Laskov, C. & Haderlein, S. B. Electron transfer between iron minerals and quinones: estimating the reduction potential of the Fe(II)–goethite surface from AQDS speciation. Environ. Sci. Technol. 47, 14161–14168 (2013).Article 

Google Scholar 
97.Gorski, C. A., Edwards, R., Sander, M., Hofstetter, T. B. & Stewart, S. M. Thermodynamic characterization of iron oxide–aqueous Fe2+ redox couples. Environ. Sci. Technol. 50, 8538–8547 (2016).Article 

Google Scholar 
98.Byrne, J. M. et al. Redox cycling of Fe(II) and Fe(III) in magnetite by Fe-metabolizing bacteria. Science 347, 1473–1476 (2015).Article 

Google Scholar  More

Lisa Palmer is a journalist and author of Hot, Hungry Planet: The Fight to Stop a Global Food Crisis in the Face of Climate Change (St. Martin’s Press, 2017), and the National Geographic Visiting Professor of Science Communication at the George Washington University in Washington DC. More

Containing over 500 billion tonnes (Pg) of carbon (C)1,2,3, peatlands are an important part of the global C cycle, and there is considerable interest in how C accumulation in these systems has varied in the past and how it might respond to future changes in climate and land management4,5,6,7,8. Carbon accumulates in a peatland because more plant material is added to it than is lost via decay. Although rapid in the near surface (often called the acrotelm), decay rates in waterlogged deeper peat (the catotelm) are much lower, allowing peat to build up. However, the reverse can happen at times; more material can be lost than is added, resulting in a decrease in a peatland’s C store and a net release of C to the atmosphere (e.g.6,9).To reconstruct the C accumulation history of a peatland10, scientists calculate what is often called ‘aCAR’11: the apparent Carbon Accumulation Rate. To estimate aCAR, it is necessary first to establish the age of the peat down a peat core; this is done by dating samples of peat from a number of depths and fitting a curve through the data12,13. The C content of contiguous or regularly-spaced layers of peat down the core also needs to be measured. aCAR is then the amount of C (per unit area) in a layer divided by the difference in age between the top and bottom of the layer. aCAR is usually plotted against time to infer how rates of C accumulation have varied during a peatland’s developmental history. Although aCAR is a widely-used metric, problems with its interpretation have been discussed over many years (e.g.10,11,14,15,16,17,18,19).aCAR has a number of problems as an indicator of the net C accumulation rate of a peatland. First, because it is a measure of the amount of C found within a peat layer at the time of coring it is dependent on the overall age of the layer; this is because decay of the layer will continue as the layer gets older—the layer loses mass and C over time since its formation. For example, the aCAR for peat that is 3,000 years old may be greater than for peat in the same profile that is 4,000 years old. However, this difference in aCAR does not mean the peatland was necessarily accumulating C more rapidly 3,000 years ago than it was 4,000 years ago: when the layer of 4,000-year old peat was only 3,000 years old, its aCAR may have been the same as that of the layer of peat that is currently 3,000 years old. This ‘ageing’ problem makes it impossible to use aCAR to determine the true net rate of C accumulation of a peatland over time. A second problem is that aCAR does not account for what else may be happening to other layers in the peat profile10,11. When a new peat layer is being formed at the peatland surface, new C is being added to the profile, but a greater amount of older C may be being lost via decay from the rest of the peat profile below the new layer. Therefore, despite new C being added, the peatland as a whole may be losing C. This net loss of C is not part of the calculation of aCAR, which is a measure solely of how much C has been added to a peatland over a period of time (notwithstanding ongoing losses from the layer because of decay). Therefore, aCAR can only ever give positive values of C accumulation within the time period spanning the layer of interest11.Further problems in the interpretation of aCAR arise when it is calculated for near-surface peat (i.e., peat from recent decades), and these problems have been termed the ‘acrotelm effect’. We recently explained10 why aCAR from near-surface peat cannot be reliably compared to the long-term rate of C accumulation obtained from a deeper layer within a peat core, which may be several centuries, or more, old. Greater values of aCAR found in the near surface of a peatland are an artefact that arises because recently-added plant litter has decomposed much less than older, deeper, peat. The artefact is another example of the peat-ageing problem noted above, but it is exacerbated in the acrotelm because of the higher decay rates in this aerated part of the peat profile. As a result, many peatland scientists choose to ignore aCAR in the upper parts of the peat profile; they are aware of the problem and do not use or attempt to interpret the increase in aCAR in progressively younger peat (e.g.20). However, misinterpretation of the effect is common in the recent literature (e.g.21,22,23,24). These studies also mistakenly assume that aCAR, when calculated for the acrotelm as a whole (by treating the acrotelm as a single layer), gives a multi-decadal average net rate of peat C accumulation for the entire peat profile, when in fact it merely describes the C content of the acrotelm. This erroneous assumption is perhaps most prominently made by Rydin and Jeglum25, who suggest that:
“… it may be useful to have a measure of peat accumulation over the last few decades …This measure is the recent rate of carbon accumulation (RERCA), which is obtained from the bulk density [which gives the mass of peat and C] down to a dated level not far from the surface. Given the recent developments in precise and accurate dating of young peat…, this is now quite possible” [text in brackets added].
As defined by Rydin and Jeglum25 RERCA is simply aCAR calculated for a single layer of peat at the peatland surface, and, as noted above, this layer may comprise all of the acrotelm. Rydin and Jeglum25 note that RERCA may be used instead of direct measurement of the C fluxes to and from a peatland (see below); in doing so, it is clear they assume RERCA is a measure of the C budget of the peatland in its entirety.Data from peat cores are, of course, essential for understanding peatlands’ C accumulation histories, but it is their use to calculate aCAR or RERCA that we wish to challenge. In discussions with peatland scientists and policy makers we have realised that the problems with aCAR and RERCA are still not fully appreciated; in particular there is confusion over why RERCA cannot be used to give an average net C accumulation rate for a peatland as a whole over recent decades (e.g.26). To address these misunderstandings, and to expand on the explanations given in10, we present and discuss here the results from a simple numerical model based on Clymo’s14 work and a more detailed computer model of peatland development. The simple peatland model is used to illustrate, from first principles, how the acrotelm effect arises. We show how an increase in aCAR in the uppermost part of the peat profile arises even when: (1) actual rates of net C accumulation for the peatland as a whole decline over time, (2) net C accumulation rates for the peatland are steady (constant) over time, and (3) net C accumulation rates are negative (there is a net loss of C from the peatland as a whole). We also show why, except in one unusual case, RERCA is not equal to the net rate of C accumulation of the peatland. Specifically, we show how the method used to calculate RERCA is based on a misapplication of the mass balance equation. In the second part of the paper, we use the DigiBog peatland development model10,27 to show how the mismatch between aCAR and actual rates of net C accumulation, explained by our simple peatland model, applies over millennial timescales. DigiBog’s outputs enable us to calculate aCAR and actual (‘true’) rates of net C accumulation for the thousands of years over which our simulated peatlands develop. Because climate over such timescales is rarely constant, we also explore the effect of changes to temperature and net rainfall (precipitation minus evapotranspiration) on net C accumulation and aCAR.In the remainder of the paper we use a third acronym to describe the rate of C accumulation in peatlands: NCB or net carbon balance11,28. aCAR and RERCA (which, as noted above, is a special case of aCAR) are both calculated for layers of peat. NCB, in contrast, includes all C additions to, and all C losses from, a peatland and may be thought of as the true rate of net C accumulation for the whole peatland (or the whole peat column) at a particular time. NCB may be obtained directly by measuring atmosphere-peatland C exchanges using flux towers and by measuring C losses in water discharging from a peatland (e.g.29,30).Conceptualising the acrotelm effectTo illustrate how the acrotelm effect arises we first use a very simple numerical model where litter produced by peatland plants is added to the peatland surface as cohorts or layers. The rate of litter production has dimensions of mass (addition) per unit area of peatland per time (M L−2 T−1). Past cohorts (M L−2) decay at a specified proportionate rate (proportion per time—T−1) in accordance with Clymo14. We consider three scenarios. In Scenario 1, which we term the ‘establishment phase’ of a peatland, new peat forms on, for example, a bare mineral surface. This phase is illustrated for a column of peat in Fig. 1. In the model shown in the figure, the peatland grows over a period of five notional timesteps (Δt1–Δt5). Throughout this period, litter production is constant at a rate of 1.0 per timestep (arbitrary mass units per unit area), while decay occurs at a fixed proportionate rate of 0.33 per timestep. The peat column is shown for each timestep and its height is proportional to the total mass of peat (M L−2) (Fig. 1). During Δt1, 1.0 mass unit of litter (per unit area) is added and no peat is lost to decay. Therefore, the net mass accumulation rate is 1.0. During Δt2 1.0 mass unit of new litter is again added, but 0.33 mass units of old litter or peat are lost from the pre-existing cohort (formed during Δt1), so that its mass is reduced to 0.67. Therefore, by the end of Δt2 the peatland contains 1.67 mass units compared to 1.0 at the end of Δt1. In other words, the net gain or accumulation rate has reduced to 0.67 in Δt2 from 1.0 in Δt1. This pattern continues and the peatland continues to grow, but at a decreasing rate, during the remaining timesteps (Δt3–Δt5) as shown in the figure. The actual C balance (NCB) of the peatland is also shown in the figure, where it is assumed that C comprises half of the mass of the litter/peat (see below).Figure 1Scenario 1 showing the establishment phase of a peatland. Changes to a single column of peat of unit area are shown for five separate timesteps (Δt). Newly-added litter/peat is shown in pale green. Older peat is shown in pale brown/orange. The numbers in each layer of peat show the mass of peat and, in brackets, the mass of carbon, both per unit area (arbitrary units). NCB denotes the actual or real rate of net C accumulation and is given by the gains of C (in new litter) minus the loss of C from the decay of the older peat cohorts for each Δt, as shown in the boxes above the columns. The arrows below the boxes represent gains (down-pointing) and losses (up-pointing) of C. aCAR is calculated for each cohort or layer at Δt5. The graph shows NCB and aCAR for each Δt. In Scenario 1, the water table always resides at the base of the peat column; there is no catotelm, and all of the peat is aerated—it is acrotelm peat.Full size imageA scientist wishing to know rates of net peat and C accumulation (NCB), and how these change over time, during the establishment phase (i.e., between Δt1 and Δt5), might measure, at each time step, C fluxes to and from the peatland directly (see “Introduction” and reference to29,30). Alternatively, they could core the whole peat profile at each of the time steps and measure the peatland’s total C content on each occasion and see how it changes over time. In practice, neither approach may be practicable because of the time span involved. Most research projects last a few years (typically three to five years), and even long-term monitoring programmes rarely exceed one or two decades. Therefore, if Δt1–Δt5 spanned more than a few decades, the period would be much too long for most studies. Because of these practical difficulties, an alternative used by some peatland scientists is to take a peat core from the peatland in its current state (Δt5 in Fig. 1) and to use the core to reconstruct the apparent C accumulation history of the peatland. This reconstruction is done by dating the peat in the profile and by measuring the mass of layers of peat for which the ages of the upper and lower boundaries of the layers are known (i.e., the duration of the interval represented by the layer is known). For the simple case in Fig. 1, we may assume that the layers for which aCAR is calculated are coincident with the cohorts of litter added every timestep (Δt).If we assume that the proportion of the peat mass that is C is 50%17, NCB is simply half the value of the mass accumulation rate discussed above. Therefore, for Δt1, 0.5 mass units of C are added and none are lost. For Δt2, 0.5 mass units of C are again added, but 0.17 C units are lost from the existing layer formed during Δt1 (loss being the mass in the layer times the decay rate: 0.5 × 0.33 = 0.17), giving a net rate of C accumulation of 0.34 (Fig. 1). As noted above, the peatland continues to gain mass and C but the rate of gain decreases with time. This decrease in rate of net peat and C accumulation is shown by the grey dashed line connecting the top of peat columns in Fig. 1 and is what would be indicated by direct measurement. In contrast, aCAR erroneously suggests that the rate of growth is increasing over time because it does not consider decay other than in the dated layer of interest. The uppermost layer of peat representing the last timestep (Δt5) appears to accumulate at a greater rate than the older cohorts that have undergone progressively more decay with age. For example, the first or deepest cohort, formed in Δt1 (layer 1 in the figure), has an initial C content of 0.5, which, through decay, becomes 0.34, 0.22, 0.15, and finally 0.1 by the end of Δt5. Ascending from this deepest layer, aCAR increases to the surface, giving a pattern that is the opposite of the real rate (NCB), as shown by the graph in the lower right of Fig. 1.Two fundamental differences between aCAR and NCB are revealed here. First, NCB is measured in ‘real time’ (the fluxes are estimated for the time period in which they occur), whereas aCAR is measured between dated layers in a peat core that may have been taken many decades or centuries after the layer was first formed (at the end of Δt5 in our example). This ‘delay’ means that aCAR does not take account of changes to a cohort of peat after its initial formation. For example, if the peatland in Fig. 1 had been cored at the end of Δt4, rather than at the end of Δt5, the aCAR calculated for the lowermost cohort of peat in the profile (layer 1) would be 0.15 and not 0.1. Therefore, aCAR is dependent on the time at which the peatland is cored. This dependency is the ageing effect noted in the Introduction.Secondly, and more importantly, unlike NCB, aCAR does not consider what happens in the whole profile, which is necessary when constructing a whole-peatland C budget. For example, NCB during Δt4 comprises litter addition but also decay losses from the litter laid down in the previous three timesteps, giving a value of 0.15 (inputs of 0.5 minus decomposition losses of 0.35—see Fig. 1). When aCAR is calculated for the same time period—i.e., Δt4—it considers only the remaining mass of new peat laid down during Δt4, giving a value of 0.34 (for a core taken at the end of Δt5).If aCAR is calculated for the column of peat as a whole at Δt5 to give RERCA (i.e., the C mass of the whole core, given by 0.5 + 0.34 + 0.22 + 0.15 + 0.10 = 1.31 units of C, divided by 5 [timesteps]), it will give the correct average NCB (0.26) for the period between Δt1 to Δt5. This correspondence in values may be regarded as unusual because the acrotelm comprises the whole peatland in Scenario 1; using the whole peat profile means that all additions and losses are accounted for, making it impossible for RERCA to give anything other than the right value. However, in most situations the acrotelm sits atop a catotelm and there won’t be a correspondence of values between RERCA and NCB, as we show below in the next two scenarios and also in the next section (‘RERCA and net C balance are not comparable in near surface peat: the peatland mass balance equation and its misuse’).It may be argued that the situation in Fig. 1 is too simple because there is no lower zone of waterlogged peat; there is no catotelm, as seen in most peatlands. In Scenario 1, the water table resides at the bottom of the peat profile—all of the peat is in the acrotelm—and all litter/peat decay occurs at the same (oxic) proportionate rate. We can, however, extend the model by assuming that older, more decayed peat, is less permeable14 and that water drains less readily through it, causing the water table to rise. Figure 2 shows one realisation of this possibility (Scenario 2). In the figure, the acrotelm is in a dynamic equilibrium: its mass remains constant, but new litter continues to be added to it at a constant rate, while the oldest cohorts at the bottom become part of the waterlogged catotelm as the water table rises above them. In Scenario 2, the cohorts that have reached a specified degree of decay (80%, or cohorts with a remaining mass of 0.2) are transferred to, or become part of, the catotelm. Therefore, cohorts added to the top of the acrotelm are buried under new litter and continue to decay until they become part of the catotelm. This situation is similar to that modelled by Clymo14, with the main difference being that peat below the water table in Scenario 2 is assumed not to decay at all (Clymo14 allowed for a low rate of decay). The peatland accumulates mass at a constant rate, as shown by the straight dashed lines fitted to the top of the peat profile in Fig. 2.Figure 2Scenario 2 showing a dynamic acrotelm of constant mass, and a steadily-thickening catotelm (blue-grey shading representing waterlogged peat). Layers 1–4 become submerged by the rising water table, and by Δt9 the acrotelm comprises layers 5–9.Full size imageWhat happens if we core the peatland in Scenario 2 at the end of Δt9 and calculate aCAR for each of layers 1–9? We see that aCAR for layers 1–5 has now changed to 0.1 compared to the ascending values obtained when the peat was cored at Δt5 (Fig. 1). This difference is because these layers have now decomposed further before becoming part of the catotelm when decay ceased. An apparent increase in rates of C accumulation is still evident, however, but now in the layers of peat formed between Δt5 and Δt9 (layers 5–9) that lie above the water table and form the acrotelm.Both aCAR and NCB are plotted against time in the graph in the lower right of Fig. 2. aCAR shows a pattern similar to that from many real peat cores: a low and relatively stable aCAR in the older parts of the peat core, with an increase as one approaches the peatland surface10. It is instructive to compare these values with NCB. NCB was high initially when the peatland first formed (Scenario 1: Δt1 to Δt5) and declined to a steady value (Scenario 2: Δt6 to Δt9). As with Scenario 1, aCAR erroneously suggests that the net rate of C accumulation has increased to the present, and only one of the aCAR values corresponds to NCB (0.1), now for Δt5 (layer 5). In contrast to Scenario 1, RERCA (again applied to the acrotelm as a whole) is now wrong, giving a value of 0.26 ((0.5 + 0.34 + 0.22 + 0.15 + 0.1)/5 [timesteps]), instead of the correct value of 0.1.Finally, in Scenario 3 we may consider what happens if the peatland experiences a drought that causes the water table to fall so that layers that were in the catotelm and below the water table are now exposed above it and undergo renewed or ‘secondary’ oxic decay9. A realisation of this situation is shown in Fig. 3, where the peatland, overall, loses mass during Δt10. During the timestep, the peatland gains 1.0 mass units of litter but loses 1.06 mass units via decay of existing layers of peat above the drought water table (the layers laid down during Δt2–Δt9), giving a net rate of accumulation of − 0.06. For C the figures are a gain of 0.5 and a loss of 0.53, giving a net loss of 0.03 mass units of C per unit area (Fig. 3). If the peatland is cored at the end of Δt10 and aCAR calculated, the same problems as identified before are evident. aCAR suggests that C accumulation is increasing over time to the present. In addition, in this scenario not only does RERCA, when applied to the acrotelm as a whole, give the wrong value of net C accumulation, it also gives the wrong sign. In Scenario 3, RERCA suggests a net C accumulation rate of 0.18 (when calculated for the now deeper acrotelm incorporating the cohorts formed between Δt2 and Δt10), when in fact the peatland as a whole has become a net source of C. Here, we repeat an important point made by11: aCAR cannot be negative.Figure 3Scenario 3 showing a net loss of peat mass caused by the secondary decay of previously waterlogged layers of peat. During the drought in Δt10 the water table falls to the top of layer 1, exposing previously ‘protected’ peat in layers 2–5 to oxic decay (secondary decay).Full size imageOther scenarios in addition to the three discussed here are possible, such as ones that include changes in rates of litter production as well as changes in decay in response to drought (a modification of Scenario 3), and these may even lead to a decrease in aCAR towards the top of a core. However, the three scenarios have, between them, sufficient generality for revealing why aCAR is an unsatisfactory measure of C accumulation in peatlands. All peatlands are expressions of the balance equation: organic matter is added via litter production and is lost via decay (and sometimes erosion—not considered here). Therefore, regardless of differences in specific production and decay rates, scenarios akin to those considered above may arise in all types of peatland. In other words, the problems identified with the use of aCAR in each scenario apply regardless of the values of litter production and the decay coefficient that are used. It is clear that aCAR is misnamed; it is not a measure of net C accumulation rate—it never can be because of the way it is calculated. In the next section we extend our analysis to show that the calculation of aCAR is based on an erroneous version of the peatland mass balance equation. For simplicity, we confine our analysis to RERCA, which, as we note severally above, is aCAR applied to the uppermost part of the peat profile spanning the most recent decades in a peatland’s history; often, the acrotelm as a whole.RERCA and NCB are not comparable in near surface peat: the peatland mass balance equation and its misuseAn advantage of the simple peatland model is that the problems associated with calculating aCAR for near-surface peat become readily apparent. While it is not difficult to grasp intuitively how the artefact of an apparent increase in rates of net C accumulation arises, the exact cause of the apparent increase can be easily identified when cohorts of litter or peat are tracked over time as in Scenarios 1 and 2 (Figs. 1, 2). The simple model is, however, even more useful when considering the problem of RERCA. Without recourse to the simple model, it seems reasonable to suggest that the mass of the acrotelm divided by its overall age (obtained by dating the peat at its base), gives a reliable ‘bulk’, or time-averaged, estimate of the net rate of C accumulation for the peatland as a whole. In Fig. 1 we show that this suggestion is correct for a newly-formed acrotelm (because, in this case, all additions and all losses are considered), and it is tempting to think that it also applies to other situations. After all, the acrotelm contains new peat added in the years since the date of the acrotelm-catotelm boundary, so this would appear to be a net gain to the peatland, especially if there is little or no decay in the underlying catotelm.Our simple peatland model shows why this apparently reasonable view is mistaken in more typical situations (more typical than Scenario 1) where the acrotelm is already in existence and does not develop from scratch, and where a catotelm is present. Scenario 2 is one such situation. Here, the extant acrotelm has a fixed mass, but is dynamic: mass is added to it via litter production and mass is lost from it via decay and transfer to the catotelm (the latter caused by water-table rise). Conceptually, the acrotelm can be thought of as a simple store. To obtain an estimate of the rate of net mass addition or loss, it is necessary to look at the change in the store’s mass over time. In equation form, where Ia is litter input rate to the acrotelm (M L−2 T−1), Oa is output rate from the acrotelm (decay as well as transfer of peat to the catotelm) (M L−2 T−1), Sa is the amount of mass in the acrotelm store (M L−2), t is time (T), and i is time level, we can write the balance equation thus:$${I}_{a}-{O}_{a}=Delta {S}_{a}/Delta t=left({S}_{a,i}-{S}_{a, i-1}right)/left({t}_{i}-{t}_{i-1}right)$$
(1)
What this equation shows is that, if we measure ΔSa/Δt, we can obtain the rate of net mass addition (Ia − Oa) in the acrotelm. In Scenario 2 (Fig. 2), we see that ΔSa between any of the time steps is zero, meaning that Ia − Oa is also zero; there is no net accumulation of peat or C in the acrotelm. For example, at the end of Δt7 Sa,7 is 2.62 (1.0 + 0.67 + 0.45 + 0.30 + 0.20) (for C, the figure is half of this). At the end of Δt8 Sa,8 has the same value (although some different cohorts are now involved because the acrotelm has migrated upwards). Therefore, the right hand side of Eq. (1) gives (2.62–2.62)/(8–7) = 0. ΔSa/Δt is zero, as is Ia − Oa.Equation 1 may be rendered wrongly as follows:$${I}_{a}-{O}_{a}={S}_{a}/Delta t={S}_{a, i}/left({t}_{i}-{t}_{i-1}right)$$
ΔSa/Δt has been replaced by Sa/Δt. Here, net peat and C accumulation is being estimated from the mass in the acrotelm at one time only. This erroneous version of Eq. (1) is what is used when calculating RERCA, where Sa,i is the current mass of the acrotelm (i.e., at ti) and ti-1 now represents the age at the base of the acrotelm. If we apply this version of Eq. (1) to Scenario 2, we obtain 2.62 [the mass of peat per unit area held in the acrotelm]/5 [the difference in age between the peat at the top and bottom of the acrotelm] = 0.524 mass units per unit area per timestep, instead of the correct ΔSa/Δt value of zero. In C terms, the value is 0.262 C units per unit area per timestep (again, instead of the correct value of zero). This erroneous version of the equation can generally only produce the right result in the specific and unusual case where the mass in the acrotelm at ti−1 (Sa,t−1) is 0 (i.e., Scenario 1).However, there is a further problem here; the change in mass of the catotelm has been ignored. As noted above, the acrotelm loss term (Oa) includes two components: the loss of peat to decay and the transfer of peat from the acrotelm to the catotelm. Only the former represents a loss from the peatland; the latter remains part of the peatland and should not, therefore, be included in the loss term when calculating the net C balance of the peatland. In other words, it is not enough to look at the acrotelm alone when estimating the C budget of the peatland as a whole, even when decay in the catotelm is zero. When estimating the net rate of C accumulation for the whole peatland, a balance equation that includes both the acrotelm and the catotelm is needed:$${I}_{a}+{I}_{c}-{O}_{a}-{O}_{c}=left(Delta {S}_{a}+{Delta S}_{c}right)/Delta t=left({S}_{a,i}-{S}_{a, i-1}+{S}_{c,i}-{S}_{c,i-1}right)/left({t}_{i}-{t}_{i-1}right)$$
(2)
where the subscript c denotes the catotelm.Calculated correctly, the net C balance of the peatland in Scenario 2 between t7 and t8, for example (see above), is therefore (1.31 − 1.31 + 0.3 − 0.2)/(8 − 7) = 0.1 as shown in Fig. 2.In Scenario 2 we could have allowed the catotelm to decay slowly at an anoxic rate, which would have meant that the rate of peat accumulation would decrease very slightly over time, but this would not alter our main finding that aCAR wrongly suggests a rapid increase in rates of accumulation. In fact, the discrepancy between aCAR and NCB would be even larger in such a situation from t5 onwards; therefore, our assumption is conservative. What this simple analysis shows is that measurements in the acrotelm alone cannot, except in special cases, be used to provide information on the overall C balance of a peatland. In other words, RERCA is based on a misuse of the balance equation: to estimate the mass balance of the peatland as a whole, it is necessary to measure all of its components.Equation (2) allows for situations where the catotelm gains mass and C and where it is a net loser. However, it can sometimes be unclear where the boundary of the acrotelm and catotelm should be drawn. For example, in Scenario 3, should the acrotelm include layers 2–5 or not? It may be preferable to think of ‘acrotelm’ and ‘catotelm’ as somewhat contrived entities31, in which case the peatland should be considered a single store, giving:$${I}_{p}-{O}_{p}={Delta S}_{p}/Delta t=left({S}_{p,i}-{S}_{p, i-1}right)/left({t}_{i}-{t}_{i-1}right)$$
(3)
where the subscript p denotes ‘peatland’.Our simple peatland model and mass balance equations demonstrate why aCAR, and the special case of RERCA (aCAR for recent peat accumulation), cannot be used to understand changes to peatland C accumulation. However, peatlands develop over millennia and include a wide range of processes including feedbacks32 that can mediate their response to climate and land use, which are not represented in the simple model. We therefore used a more detailed process-based model (DigiBog) to simulate peatland development over thousands of years and to explore the dynamics of aCAR and NCB in response to perturbations to our model’s driving data.Simulating the effect on aCAR and NCB of changes in climateWe used the DigiBog peatland development model10,27 to ‘grow’ a sloping blanket peatland from the north of England over six millennia (see “Methods”). Our model simulates the peatland as a series of linked columns of peat. These can gain or lose mass (including C) depending on the climate inputs, simulated land uses and the autogenic mechanisms of the virtual peatland. And because the model records during the simulation the height of each peat column (based on the addition of mass to the peatland surface and the change in mass of each sub-surface peat layer), we can calculate the rate of change in the mass of C at each time step ((Delta t))—i.e. we know a peat column’s or the peatland’s NCB throughout the whole developmental history of the peatland (see “Methods”). At the end of a simulation we can also take a virtual core for a column and, as previously described, use the difference in age between the top and base of the layers within it, to calculate aCAR (see “Methods”). Because NCB must be calculated at the time the C fluxes occur, it is only possible to compare these past long-term dynamics of aCAR and NCB by using a peatland model.Here we show aCAR and NCB from four simulations of the single blanket peatland (see “Methods” for details of the model set up). The results are shown in Figs. 4, 5 and 6. We used the net rainfall (precipitation minus evapotranspiration) and temperature inputs from10 for a baseline simulation (Figs. 4 and 5) and ran three modifications to the same dataset: (1) a 0.4 m reduction in annual net rainfall to simulate a long-term drought; (2) a 1.5 °C increase in air temperature to simulate a warming climate; and (3) the inputs from 1 and 2 combined (Fig. 6). All other input parameters remained unchanged from the baseline simulation (see “Methods”). To create the perturbations in driving data we linearly increased or decreased the input(s) over 100 years and allowed the simulation to continue using the modified data for a further 200 years before reversing the increase to use the original time series for the remainder of the model run (the total time of a modification—400 years—is henceforth known as the perturbation). We implemented the temperature perturbation (Fig. 6a) earlier than the one for net rainfall (Fig. 6b) so that we could see the effect of later events on aCAR and NCB (Fig. 6c) (see “Methods” for the details and timings of the driving data perturbations).Figure 4Development of the virtual blanket peatland over six millennia. (a) Water-table depth and (b) the peat surface from the virtual core at the centre of the peatland from the baseline simulation (see the main text and “Methods”).Full size imageFigure 5aCAR and NCB (20 year moving average) for the baseline simulation (see “Methods”). The aCAR values are for a virtual core taken from halfway down the modelled hillslope at the end of the simulation. NCB is calculated during each year of the simulation (i.e. at the time peat is gained or lost) and is akin to measuring C fluxes. The inset shows the typical ‘uptick’ of aCAR in recently-accumulated peat layers seen in many peat cores.Full size imageFigure 6The effect of climate perturbations on simulated aCAR and NCB (20 year moving average). (a) Increase in temperature of 1.5 °C, (b) reduction in annual net rainfall of 0.4 m, and (c) both perturbations combined. The light grey vertical bars indicate the timing and duration of the perturbations and the dark grey dashed line is where C accumulation equals zero. The increases in NCB near to the beginning and end of the net rainfall perturbation (a and b) are due to the peatland water tables falling and later rising into the zone of maximum litter addition in DigiBog’s litter production equation.Full size imageOur more detailed model shows aCAR and NCB (Fig. 5) conform to a pattern similar to the one given by our simple peatland model in Scenario 2 (Fig. 2). These dynamics are also predicted by the models used by11,15, and clearly show that aCAR is not the same as NCB. The modelled ‘uptick’ in aCAR in recently accumulated peat (towards the right-hand side of Fig. 5) is also seen in real cores taken from peatlands in a wide range of environments10. The uptick is due to the ‘acrotelm effect’ explained earlier.The climate perturbations in Fig. 6 further illustrate why aCAR should not be used to represent NCB. Although they don’t have the same values as each other, aCAR and NCB in Fig. 6a increase and decrease similarly during the period in which the temperature perturbation occurs. This correspondence is because warming has shifted the peatland’s mass balance to be more in favour of plant litter production than the losses from decomposition. In this instance it might seem reasonable that aCAR can be used to indicate NCB. However, in Fig. 6b, the picture is more complicated and aCAR and NCB produce very different responses. The reduction in net rainfall deepens the peatland’s water tables, shifting the mass balance in favour of decomposition (i.e. all losses exceed all gains), but the changes in aCAR do not coincide with the timing of the perturbation. Whilst NCB is affected at the time of the climatic drying and becomes negative (there is an overall loss of C), the effects on aCAR are offset, but at no time is aCAR negative (it cannot be, as we explain earlier and as explained by11). aCAR suggests that C accumulation has reduced before the perturbation takes place but NCB has not actually changed at this time. This mismatch is because, as well as continuing to decompose, a peat layer can be altered by events that take place many years after it was originally formed. The reduction in aCAR shown in Fig. 6b is known as secondary decomposition or decay9,33. There follows a significant increase in aCAR ‘apparently’ indicating that C accumulation is also increasing when, in fact, the peatland is losing C as shown by NCB. This apparent increase in C is because a shift to deeper water tables can increase plant production; i.e., the mass added to the peatland increases. But because aCAR does not include the C fluxes from the whole peat column it does not take account of the increase in decomposition (the mass lost), and so the total change in C stored is not seen. Our simple peatland model in Fig. 3 also demonstrates how this difference between aCAR and NCB occurs.Finally, when the perturbations are combined (Fig. 6c), the increase in aCAR around 1,600 years ago, caused by an increase in temperature, is partially wiped away by secondary decomposition before sharply declining, but NCB remains unchanged. Whilst it is likely that an assessment of aCAR would conclude that C accumulation had reduced during the time when the temperature was perturbed, the interpretation of both the timing and the magnitude of the reduction would be wrong. And the increase in aCAR starting around 600 years ago would also be misinterpreted as an increase in C accumulation rather than an overall reduction in the peatland’s C store.Implications for assessing changes in peatland C accumulation ratesOur simple peatland model, mass balance equations and DigiBog simulations, along with evidence from previous studies10,11,14,15,17,19 show that aCAR and RERCA cannot generally be used to assess changes to the rate of peatland C accumulation (NCB). Therefore, studies that use aCAR to indicate changes in peatland carbon balance processes over time (acrotelm effect) or to estimate NCB are unreliable and should be viewed with considerable circumspection.As our simple peatland model scenarios show, in general, aCAR does not equal NCB11. Because all peatlands accumulate C according to the mass balance equation—i.e. assessing a peatland’s C balance requires that all of the peatland profile is taken into account and not just a dated section of it—our results apply to all peatlands in all circumstances. The only instance when we can be sure that aCAR equals NCB is when it is calculated for the whole of a peatland’s developmental history11. However, an average C accumulation rate for the entire history of the peatland is of limited use; land managers, researchers and policy makers are usually interested in how NCB has changed over time in response to climate and land-use. Although in some other circumstances (e.g. Fig. 6a) it appears that aCAR and NCB are sufficiently similar for aCAR to be useful (and sometimes they coincide—see Fig. 6 and11), this assessment can only be made because we can calculate NCB from our model outputs and compare the two quantities. But, unless NCB is known from C flux measurements or model simulations of peatland development, the correspondence of aCAR to NCB cannot be established.The results of our simulations, and those from other studies10,11, also show how some land uses or changes to the climate may cause further mismatches in the timing, magnitude and sign of aCAR and NCB. Although acknowledging that aCAR gives an erroneous estimate of past rates of NCB, Frolking et al.11 do not advocate abandoning its use. Based on our evidence, and that provided by other studies, we suggest there is a need to go further. Given that aCAR is based on a mistaken use of the balance equation and can give the wrong sign of NCB as well as the opposite trend, we believe that it should no longer be acceptable to use aCAR to indicate changes in NCB.Our simulations produce virtual and not real peat cores, and, by necessity, all models are simplifications of reality. The perturbations to our driving data are at the high end of what might be experienced naturally but are not implausible. They allow us to see more clearly how such events might affect the timing, magnitude and sign of aCAR in comparison to NCB. If our model is configured for a different type of peatland (e.g.10 simulated a raised bog) with different driving data or changes to land use, the results for aCAR and NCB will likely be different from the ones we show here. But despite these differences we would still not be able to reliably predict NCB from aCAR.The challenge of understanding if C accumulation rates have been altered by external forcing has been discussed in the literature since14, but the implications of using aCAR to indicate NCB have recently been brought to the fore because of the imperative to assess the impact of climate change and land use on peatland C cycling. By highlighting and explaining the deficiencies of aCAR, our aim is to encourage the use of more robust and reliable approaches for calculating past actual C accumulation rates (NCB). Ideally, direct measurements of C fluxes would be used10, but such observations are not available for many sites and, where they do exist, they will cover only the last few decades at most (see above). Therefore, for C accumulation histories extending to centuries and millennia, we propose that C balance models fitted to peatland age-depth (or age-mass) curves are used to estimate if NCB has changed over time. Simple models—for example, that of14—are already used in this regard and are worthy of further investigation19,28,34,35,36. For example, several studies have derived peatland NCB at the global28,37 regional35, and local17,19 scales. The authors back-calculate NCB from the net C pool using empirical models that consider autogenic long-term peat decomposition14. In a further step, with the aim of understanding if contemporary C accumulation rates were different from past rates17 and19 compared the calculated NCB from the catotelm to predictions of peat C mass transfer at the acrotelm-catotelm boundary, using a forward model of acrotelm peat decay. That being said, these approaches cannot differentiate the effects of long-term autogenic decay on peat versus that of secondary decomposition, which could be brought about by land-use or climate change.Given the limitations of such approaches, we encourage exploration of the potential of fitting more complete ecosystem models like the Holocene Peat Model38, MILLENNIA39 and DigiBog to data from peat cores to help estimate changes in peatland function over time. Observations of peat depth and downcore humification along with the inclusion of proxy data from the peatland in question—often shown in palaeoecological studies—are also important for contextualising model outputs10.In conclusion, aCAR is an unsuitable proxy for the actual C accumulation rates of peatlands. Approaches that conceptualise peatlands as dynamic C stores—the balance of all mass additions and losses – are needed. And, as we have noted, some studies, recognising the problems of aCAR, have provided potential alternatives. However, to be useful, it is likely that existing models will need to be modified, tested and their suitability assessed, or new ones developed so that credible comparisons of the effect of climate change or land uses on peatland C accumulation rates can be made. More