Philippa Kaur

CAREER Q&A
22 February 2022

Marching in the streets for climate-crisis action

Conservationist Charlie Gardner explains why he joined Scientists for Extinction Rebellion and its civil-disobedience protests.

Christine Ro

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Christine Ro

Christine Ro is a freelance journalist based in Buenos Aires.

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Charlie Gardner speaks at an Extinction Rebellion protest.Credit: Louise Jasper Photography

Conservationist, consultant and activist Charlie Gardner is a lecturer in conservation biology at the Durrell Institute of Conservation and Ecology at the University of Kent in Canterbury, UK. He regularly participates in protests with Scientists for Extinction Rebellion, an offshoot of a broader movement that uses nonviolent civil disobedience to push for action on the climate and biodiversity crises. He has also advised on legislation such as the UK Climate and Ecological Emergency Bill, which seeks to curb UK greenhouse-gas emissions and biodiversity loss, and is currently making its way through Parliament. What drove you to activism? Teaching. Five or six years ago, I was standing in front of a lecture theatre, full of young people who are going to suffer the consequences of climate change much more than I am. I couldn’t stand that I wasn’t doing everything I could. When Extinction Rebellion (XR) was launched in the United Kingdom in October 2018, it felt like the answer. As conservationists, we silently wish that members of the general public cared more about the destruction of nature. Now they are taking to the streets and I have this moral obligation to be there in support.How have you been working with Scientists for XR?In October 2019, a group of scientists came together to create Scientists for XR, which has carried out many actions. These include pasting scientific papers to the walls of the London headquarters of News Corp in 2021 in protest against inadequate climate-change coverage in the company’s newspapers. The group has different functions. One is to provide scientific support for the wider XR movement, so that it remains founded on solid scientific ground. And a second is to advocate. Scientists vocally supporting XR sends a powerful message. Society trusts scientists. A third function is direct action. Scientists for XR groups have been involved in a number of XR events, such as marches and roadblocks. For example, at the 2021 opening of a London Science Museum exhibition sponsored by oil and gas company Shell, some scientists locked themselves to parts of the exhibition in protest against the sponsorship, while our scientist group set up a table outside to demonstrate principles of atmospheric cooling to engage with the public. Events such as this serve to highlight the issue of science museums accepting sponsorship from fossil-fuel companies.How can scientists dip their toes into this type of work?What the public sees of these direct actions is the tip of the iceberg. For every person out on the streets, there are 20 more behind the scenes involved in other tasks: organizing, producing press releases, baking cakes for marchers. Whatever you enjoy doing and have skills in, there is a role for you. Taking part does not have to involve engaging in civil disobedience yourself, or putting yourself in a risky position. One of the most important jobs at a protest is for people to stand at the edges, engaging the public in conversations. That’s a role that scientists can perform fantastically.How have your advocacy and activism benefited you?There’s this crazy notion that scientists shouldn’t speak out because it will damage their reputations. But activism has had the opposite effect on my career. My research is based on conservation in Madagascar; it’s fairly niche. I previously had no global reputation. Since becoming a vocal scientist-activist, my reputation and my visibility as a scientist have soared. Also, activism is great for my mental health. Knowing I’m doing what I can is important to me. There are simply the best people in these movements, and there’s a sense of community. Does being a vocal activist diminish your scientific credibility?Popular perception holds that scientists must be neutral purveyors of information and not speak up about what that information means. Somehow, if we do so, it could damage our credibility.But when scientists take personal risks and make personal sacrifices, that communicates the urgency of the situation in an important way. If scientists are saying that it’s time for action, but not acting themselves, that undermines their own arguments. How do you balance your academic responsibilities with advocacy?For five years, I worked half-time at the University of Kent. I did this deliberately, to allow me the freedom to engage in other activities, including conservation consultancy, activism and writing popular non-fiction. I left that post last year, partly to focus on activism and writing, and partly out of frustration with the precarity of academic life.There are things that enable me to be less single-minded in the pursuit of my career: I come from a position of relative privilege; I’m not interested in accumulating money; and I don’t have children. So I think academia has been a good fit for me, but only because it doesn’t fill my life.

doi: https://doi.org/10.1038/d41586-022-00518-4This interview has been edited for length and clarity.

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Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).
Google Scholar 
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Muscatine, L., Pool, R. R. & Trench, R. K. Symbiosis of algae and invertebrates: Aspects of the symbiont surface and the host-symbiont interface. Trans. Am. Microsc. Soc. 94, 450–469 (1975).CAS 
PubMed 

Google Scholar 
Muscatine, L. & Porter, J. W. Reef corals: Mutualistic symbioses adapted to nutrient-poor environments. Bioscience 27, 454–460 (1977).
Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. https://doi.org/10.1016/j.cub.2018.07.008 (2018).Article 
PubMed 

Google Scholar 
Colombo-Pallotta, M. F., Rodríguez-Román, A. & Iglesias-Prieto, R. Calcification in bleached and unbleached Montastraea faveolata: Evaluating the role of oxygen and glycerol. Coral Reefs 29, 899–907 (2010).ADS 

Google Scholar 
Hoegh-Guldberg, O. & Smith, G. J. The effect of sudden changes in temperature, light and salinity on the population density and export of zooxanthellae from the reef corals Stylphora pistillata Esper and Seriatopora hystrix Dana. J. Exp. Mar. Biol. Ecol. 129, 279–303 (1989).
Google Scholar 
Iglesias-Prieto, R., Matta, J. L., Robins, W. A. & Trench, R. K. Photosynthetic response to elevated temperature in the symbiotic dinoflagellate Symbiodinium microadriaticum in culture. Proc. Natl. Acad. Sci. 89, 10302–10305 (1992).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scheufen, T., Krämer, W. E., Iglesias-Prieto, R. & Enríquez, S. Seasonal variation modulates coral sensibility to heat-stress and explains annual changes in coral productivity. Sci. Rep. 7, 1–15 (2017).CAS 

Google Scholar 
Enríquez, S., Méndez, E. R. & Iglesias-Prieto, R. Multiple scattering on coral skeletons enhances light absorption by symbiotic algae. Limnol. Oceanogr. 50, 1025–1032 (2005).ADS 

Google Scholar 
Terán, E., Méndez, E. R., Enríquez, S. & Iglesias-Prieto, R. Multiple light scattering and absorption in reef-building corals. Appl. Opt. 49, 5032 (2010).ADS 
PubMed 

Google Scholar 
Swain, T. D. et al. Skeletal light-scattering accelerates bleaching response in reef-building corals. BMC Ecol. 16, 1–18 (2016).
Google Scholar 
Rodríguez-Román, A., Hernández-Pech, X., E Thome, P., Enríquez, S. & Iglesias-Prieto, R. Photosynthesis and light utilization in the Caribbean coral Montastraea faveolata recovering from a bleaching event. Limnol. Oceanogr. 51, 2702–2710 (2006).ADS 

Google Scholar 
Kemp, D. W., Hernandez-Pech, X., Iglesias-Prieto, R., Fitt, W. K. & Schmidt, G. W. Community dynamics and physiology of Symbiodinium spp. before, during, and after a coral bleaching event. Limnol. Oceanogr. 59, 788–797 (2014).ADS 
CAS 

Google Scholar 
Thornhill, D. J., LaJeunesse, T. C., Kemp, D. W., Fitt, W. K. & Schmidt, G. W. Multi-year, seasonal genotypic surveys of coral-algal symbioses reveal prevalent stability or post-bleaching reversion. Mar. Biol. 148, 711–722 (2006).
Google Scholar 
Schoepf, V. et al. Annual coral bleaching and the long-term recovery capacity of coral. Proc. R. Soc. B Biol. Sci. 282, 20151887 (2015).
Google Scholar 
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. https://doi.org/10.1071/MF99078 (1999).Article 

Google Scholar 
Scheufen, T., Iglesias-Prieto, R. & Enríquez, S. Changes in the number of symbionts and Symbiodinium cell pigmentation modulate differentially coral light absorption and photosynthetic performance. Front. Mar. Sci. 4, 309 (2017).
Google Scholar 
Warner, M. E., Fitt, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: A determinant of coral bleaching. Proc. Natl. Acad. Sci. U. S. A. 96, 8007–8012 (1999).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Takahashi, S., Nakamura, T., Sakamizu, M., van Woesik, R. & Yamasaki, H. Repair machinery of symbiotic photosynthesis as the primary target of heat stress for reef-building corals. Plant Cell Physiol. 45, 251–255 (2004).CAS 
PubMed 

Google Scholar 
Bollati, E. et al. Optical feedback loop involving dinoflagellate symbiont and scleractinian host drives colorful coral bleaching. Curr. Biol. https://doi.org/10.1016/j.cub.2020.04.055 (2020).Article 
PubMed 

Google Scholar 
Dove, S. G., Hoegh-Guldberg, O. & Ranganathan, S. Major colour patterns of reef-building corals are due to a family of GFP-like proteins. Coral Reefs 19, 197–204 (2001).
Google Scholar 
Salih, A., Larkum, A., Cox, G., Kühl, M. & Hoegh-Guldberg, O. Fluorescent pigments in corals are photoprotective. Nature 408, 850–853 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Fine, M. & Loya, Y. Endolithic algae: An alternative source of photoassimilates during coral bleaching. Proc. Biol. Sci. 269, 1205–1210 (2002).PubMed 
PubMed Central 

Google Scholar 
Carilli, J. E., Godfrey, J., Norris, R. D., Sandin, S. A. & Smith, J. E. Periodic endolithic algal blooms in Montastraea faveolata corals may represent periods of low-level stress. Bull. Mar. Sci. 86, 10 (2010).
Google Scholar 
Le Campion-Alsumard, T., Golubic, S. & Hutchings, P. Microbial endoliths in skeletons of live and dead corals: Porites lobata (Moorea, French Polynesia). Mar. Ecol. Prog. Ser. 117, 149–157 (1995).ADS 

Google Scholar 
Schlichter, D., Kampmann, H. & Conrady, S. Trophic potential and photoecology of endolithic algae living within coral skeletons. Mar. Ecol. 18, 299–317 (1997).ADS 

Google Scholar 
Sangsawang, L. et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R. Soc. Open Sci. 4, 171201 (2017).PubMed 
PubMed Central 

Google Scholar 
Yamazaki, S. S., Nakamura, T. & Yamasaki, H. Photoprotective role of endolithic algae colonized in coral skeleton for the host photosynthesis. In Photosynthesis. Energy from the Sun (eds. Allen, J. F., et al.) 1391–1395 (Springer Netherlands, 2008). https://doi.org/10.1007/978-1-4020-6709-9_300.Halldal, P. Photosynthetic capacities and photosynthetic action spectra of endozoic algae of the massive coral Favia. Biol. Bull. 134, 411–424 (1968).CAS 

Google Scholar 
Koehne, B., Elli, G., Jennings, R. C., Wilhelm, C. & Trissl, H.-W. Spectroscopic and molecular characterization of a long wavelength absorbing antenna of Ostreobium sp. Biochim. Biophys. Acta BBA Bioenerg. 1412, 94–107 (1999).CAS 

Google Scholar 
Wangpraseurt, D. et al. In vivo microscale measurements of light and photosynthesis during coral bleaching: Evidence for the optical feedback loop?. Front. Microbiol. 8, 59 (2017).PubMed 
PubMed Central 

Google Scholar 
Lukas, K. J. Two species of the chlorophyte genus Ostreobium from skeletons of Atlantic and Caribbean reef corals. J. Phycol. 10, 331–335 (1974).
Google Scholar 
Fork, D. C. & Larkum, A. W. D. Light harvesting in the green alga Ostreobium sp., a coral symbiont adapted to extreme shade. Mar. Biol. 103, 381–385 (1989).
Google Scholar 
Massé, A., Domart-Coulon, I., Golubic, S., Duché, D. & Tribollet, A. Early skeletal colonization of the coral holobiont by the microboring Ulvophyceae Ostreobium sp. Sci. Rep. 8, 1–11 (2018).
Google Scholar 
Godinot, C., Tribollet, A., Grover, R. & Ferrier-Pagès, C. Bioerosion by euendoliths decreases in phosphate-enriched skeletons of living corals. Biogeosci. Discuss. 9, 2425–2444 (2012).ADS 

Google Scholar 
Vásquez-Elizondo, R. M. et al. Absorptance determinations on multicellular tissues. Photosynth. Res. 132, 311–324 (2017).PubMed 

Google Scholar 
Tribollet, A. The boring microflora in modern coral reef ecosystems: A review of its roles. In Current Developments in Bioerosion (eds. Wisshak, M. & Tapanila, L.) 67–94 (Springer Berlin Heidelberg, 2008). https://doi.org/10.1007/978-3-540-77598-0_4.Fine, M., Meroz-Fine, E. & Hoegh-Guldberg, O. Tolerance of endolithic algae to elevated temperature and light in the coral Montipora monasteriata from the southern Great Barrier Reef. J. Exp. Biol. 208, 75–81 (2005).PubMed 

Google Scholar 
Pernice, M. et al. Down to the bone: The role of overlooked endolithic microbiomes in reef coral health. ISME J. 14, 325–334 (2020).PubMed 

Google Scholar 
Schlichter, D., Zscharnack, B. & Krisch, H. Transfer of photoassimilates from endolithic algae to coral tissue. Naturwissenschaften 82, 564–567 (1995).ADS 

Google Scholar 
Kühl, M., Cohen, Y., Dalsgaard, T., Barker Jorgersen, B. & Revsbech, N. P. Microenvironment and photosynthesis of zooxanthellae in scleractinian corals studied with microsensors for O2, pH and light. Mar. Ecol. Prog. Ser. 117, 159–172 (1995).ADS 

Google Scholar 
Marcelino, L. A. et al. Modulation of light-enhancement to symbiotic algae by light-scattering in corals and evolutionary trends in bleaching. PLoS One 8, e61492 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wangpraseurt, D. et al. Lateral light transfer ensures efficient resource distribution in symbiont-bearing corals. J. Exp. Biol. 217, 489–498 (2014).PubMed 

Google Scholar 
Wangpraseurt, D., Jacques, S. L., Petrie, T. & Kühl, M. Monte Carlo modeling of photon propagation reveals highly scattering coral tissue. Front. Plant Sci. 7, 1404 (2016).PubMed 
PubMed Central 

Google Scholar 
Carilli, J., Donner, S. D. & Hartmann, A. C. Historical temperature variability affects coral response to heat stress. PLoS One 7, e34418 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Marcelino, V. R. & Verbruggen, H. Multi-marker metabarcoding of coral skeletons reveals a rich microbiome and diverse evolutionary origins of endolithic algae. Sci. Rep. 6, 1–9 (2016).
Google Scholar 
del Campo, J., Pombert, J.-F., Šlapeta, J., Larkum, A. & Keeling, P. J. The ‘other’ coral symbiont: Ostreobium diversity and distribution. ISME J. 11, 296–299 (2017).PubMed 

Google Scholar 
Massé, A. et al. Functional diversity of microboring Ostreobium algae isolated from corals. Environ. Microbiol. 22, 4825–4846 (2020).PubMed 

Google Scholar 
Iglesias-Prieto, R., Beltran, V. H., LaJeunesse, T. C., Reyes-Bonilla, H. & Thome, P. E. Different algal symbionts explain the vertical distribution of dominant reef corals in the eastern Pacific. Proc. R. Soc. B Biol. Sci. 271, 1757–1763 (2004).CAS 

Google Scholar 
Fisher, P. L., Malme, M. K. & Dove, S. The effect of temperature stress on coral–Symbiodinium associations containing distinct symbiont types. Coral Reefs 31, 473–485 (2012).ADS 

Google Scholar 
Jeffrey, S. W. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. BPP 167, 191–194 (1975).CAS 

Google Scholar 
Marsh, J. A. Primary productivity of reef-building calcareous red algae. Ecology 51, 255–263 (1970).
Google Scholar 
Shibata, K. Pigments and a UV-absorbing substance in corals and a blue-green alga living in the Great Barrier Reef1. Plant Cell Physiol. https://doi.org/10.1093/oxfordjournals.pcp.a074411 (1969).Article 

Google Scholar 
López-Londoño, T. et al. Physiological and ecological consequences of the water optical properties degradation on reef corals. Coral Reefs 40, 1243–1256 (2021).
Google Scholar  More

Byrne, R. W. & Bates, L. A. Sociality, evolution and cognition. Curr. Biol. 17, R714–R723 (2007).CAS 
PubMed 

Google Scholar 
Wittig, R. M. & Boesch, C. Food competition and linear dominance hierarchy among female chimpanzees of the Tai National Park. Int. J. Primatol. 24, 847–867 (2003).
Google Scholar 
Mitani, J. C. Male chimpanzees form enduring and equitable social bonds. Anim. Behav. 77, 633–640 (2009).
Google Scholar 
Van Hooff, J. A. & Van Schaik, C. P. Male bonds: Afilliative relationships among nonhuman primate males. Behaviour 130, 309–337 (1994).
Google Scholar 
Herbinger, I., Papworth, S., Boesch, C. & Zuberbühler, K. Vocal, gestural and locomotor responses of wild chimpanzees to familiar and unfamiliar intruders: A playback study. Anim. Behav. 78, 1389–1396 (2009).
Google Scholar 
Watts, D. P., Muller, M., Amsler, S. J., Mbabazi, G. & Mitani, J. C. Lethal intergroup aggression by chimpanzees in Kibale National Park, Uganda. Am. J. Primatol. Off. J. Am. Soc. Primatol. 68, 161–180 (2006).
Google Scholar 
Watts, D. & Mitani, J. Boundary patrols and intergroup encounters in wild chimpanzees. Behaviour 138, 299–327 (2001).
Google Scholar 
Silk, J. B. et al. Strong and consistent social bonds enhance the longevity of female baboons. Curr. Biol. 20, 1359–1361 (2010).CAS 
PubMed 

Google Scholar 
Schülke, O., Bhagavatula, J., Vigilant, L. & Ostner, J. Social bonds enhance reproductive success in male macaques. Curr. Biol. 20, 2207–2210 (2010).PubMed 

Google Scholar 
Surbeck, M. et al. Males with a mother living in their group have higher paternity success in bonobos but not chimpanzees. Curr. Biol. 29, R354–R355 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Aureli, F. & Schaffner, C. Relationship assessment through emotional mediation. Behaviour 139, 393–420 (2002).
Google Scholar 
Aureli, F. et al. Fission-fusion dynamics: New research frameworks. Curr. Anthropol. 49, 627–654 (2008).
Google Scholar 
Zuberbühler, K. Audience effects. Curr. Biol. 18, R189–R190 (2008).PubMed 

Google Scholar 
Wittig, R. M., Crockford, C., Langergraber, K. E. & Zuberbühler, K. Triadic social interactions operate across time: A field experiment with wild chimpanzees. Proc. R. Soc. B Biol. Sci. 281, 20133155 (2014).
Google Scholar 
Slocombe, K. E. & Zuberbühler, K. Chimpanzees modify recruitment screams as a function of audience composition. Proc. Natl. Acad. Sci. 104, 17228–17233 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Crockford, C., Wittig, R. M., Mundry, R. & Zuberbühler, K. Wild chimpanzees inform ignorant group members of danger. Curr. Biol. 22, 142–146 (2012).CAS 
PubMed 

Google Scholar 
Townsend, S. W. & Zuberbuhler, K. Audience effects in chimpanzee copulation calls. Commun. Integr. Biol. 2, 282–284 (2009).PubMed 
PubMed Central 

Google Scholar 
Laporte, M. N. & Zuberbühler, K. Vocal greeting behaviour in wild chimpanzee females. Anim. Behav. 80, 467–473 (2010).
Google Scholar 
Kreibig, S. D. Autonomic nervous system activity in emotion: A review. Biol. Psychol. 84, 394–421 (2010).PubMed 

Google Scholar 
Crockford, C. et al. Urinary oxytocin and social bonding in related and unrelated wild chimpanzees. Proc. R. Soc. B Biol. Sci. 280, 20122765 (2013).CAS 

Google Scholar 
Samuni, L. et al. Oxytocin reactivity during intergroup conflict in wild chimpanzees. Proc. Natl. Acad. Sci. 114, 268–273 (2017).CAS 
PubMed 

Google Scholar 
Crockford, C., Deschner, T., Ziegler, T. E. & Wittig, R. M. Endogenous peripheral oxytocin measures can give insight into the dynamics of social relationships: A review. Front. Behav. Neurosci. 8, 68 (2014).PubMed 
PubMed Central 

Google Scholar 
Harrap, M. J., Hempel de Ibarra, N., Whitney, H. M. & Rands, S. A. Reporting of thermography parameters in biology: A systematic review of thermal imaging literature. R. Soc. Open Sci. 5, 181281 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ioannou, S., Gallese, V. & Merla, A. Thermal infrared imaging in psychophysiology: Potentialities and limits. Psychophysiology 51, 951–963 (2014).PubMed 
PubMed Central 

Google Scholar 
Vianna, D. M. & Carrive, P. Changes in cutaneous and body temperature during and after conditioned fear to context in the rat. Eur. J. Neurosci. 21, 2505–2512 (2005).PubMed 

Google Scholar 
Dezecache, G., Zuberbühler, K., Davila-Ross, M. & Dahl, C. D. Skin temperature changes in wild chimpanzees upon hearing vocalizations of conspecifics. R. Soc. Open Sci. 4, 160816 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dezecache, G., Wilke, C., Richi, N., Neumann, C. & Zuberbühler, K. Skin temperature and reproductive condition in wild female chimpanzees. PeerJ 5, e4116 (2017).PubMed 
PubMed Central 

Google Scholar 
Dunbar, R. I. Functional significance of social grooming in primates. Folia Primatol. (Basel) 57, 121–131 (1991).
Google Scholar 
Bekoff, M. & Allen, C. The evolution of social play: Interdisciplinary analyses of cognitive processes. In The cognitive animal: empirical and theoretical perspectives on animal cognition (eds Bekoff, M. et al.) 429–435 (The MIT Press, 2002).
Google Scholar 
Muller, M. N. & Mitani, J. C. Conflict and cooperation in wild chimpanzees. Adv. Study Behav. 35, 275–331 (2005).
Google Scholar 
Slocombe, K. E. & Zuberbühler, K. Agonistic screams in wild chimpanzees (Pan troglodytes schweinfurthii) vary as a function of social role. J. Comp. Psychol. 119, 67 (2005).PubMed 

Google Scholar 
Hosaka, K. Intimidation display. In Mahale Chimpanzees: 50 Years of Research (eds Hosaka, K. et al.) 435–447 (Cambridge University Press, 2015).
Google Scholar 
Muller, M. N. & Wrangham, R. W. Dominance, aggression and testosterone in wild chimpanzees: A test of the ‘challenge hypothesis’. Anim. Behav. 67, 113–123 (2004).
Google Scholar 
Wrangham, R. W. The cost of sexual attraction in female Pan. In Behavioural Diversity in Chimpanzees and Bonobos (eds Boesch, C. et al.) 204-215 (Cambridge University Press, 2002).
Google Scholar 
Townsend, S. W., Slocombe, K. E., Thompson, M. E. & Zuberbühler, K. Female-led infanticide in wild chimpanzees. Curr. Biol. 17, R355–R356 (2007).CAS 
PubMed 

Google Scholar 
Herborn, K. A. et al. Skin temperature reveals the intensity of acute stress. Physiol. Behav. 152, 225–230 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kuraoka, K. & Nakamura, K. The use of nasal skin temperature measurements in studying emotion in macaque monkeys. Physiol. Behav. 102, 347–355 (2011).CAS 
PubMed 

Google Scholar 
Ermatinger, F. A., Brügger, R. K. & Burkart, J. M. The use of infrared thermography to investigate emotions in common marmosets. Physiol. Behav. 211, 112672 (2019).CAS 
PubMed 

Google Scholar 
Manson, J. H. et al. Intergroup aggression in chimpanzees and humans [and comments and replies]. Curr. Anthropol. 32, 369–390 (1991).
Google Scholar 
Tamioso, P. R., Rucinque, D. S., Taconeli, C. A., da Silva, G. P. & Molento, C. F. M. Behavior and body surface temperature as welfare indicators in selected sheep regularly brushed by a familiar observer. J. Vet. Behav. 19, 27–34 (2017).
Google Scholar 
Grandi, L. C. & Heinzl, E. Data on thermal infrared imaging in laboratory non-human primates: Pleasant touch determines an increase in nasal skin temperature without affecting that of the eye lachrymal sites. Data Brief 9, 536–539 (2016).PubMed 
PubMed Central 

Google Scholar 
Brügger, R. K., Willems, E. P. & Burkart, J. M. Do marmosets understand others’ conversations? A thermography approach. Sci. Adv. 7, e8790 (2021).ADS 

Google Scholar 
Salazar-López, E. et al. The mental and subjective skin: Emotion, empathy, feelings and thermography. Conscious. Cogn. 34, 149–162 (2015).PubMed 

Google Scholar 
Muller, M. N., Thompson, M. E. & Wrangham, R. W. Male chimpanzees prefer mating with old females. Curr. Biol. 16, 2234–2238 (2006).CAS 
PubMed 

Google Scholar 
Watts, D. P. Coalitionary mate guarding by male chimpanzees at Ngogo, Kibale National Park, Uganda. Behav. Ecol. Sociobiol. 44, 43–55 (1998).
Google Scholar 
Heinrichs, M. & Domes, G. Neuropeptides and social behaviour: Effects of oxytocin and vasopressin in humans. Prog. Brain Res. 170, 337–350 (2008).CAS 
PubMed 

Google Scholar 
Surbeck, M., Mundry, R. & Hohmann, G. Mothers matter! Maternal support, dominance status and mating success in male bonobos (Pan paniscus). Proc. R. Soc. B Biol. Sci. 278, 590–598 (2011).
Google Scholar 
Reddy, R. B. & Sandel, A. A. Social relationships between chimpanzee sons and mothers endure but change during adolescence and adulthood. Behav. Ecol. Sociobiol. 74, 1–14 (2020).
Google Scholar 
Kosonogov, V. et al. Facial thermal variations: A new marker of emotional arousal. PLoS One 12, e0183592 (2017).PubMed 
PubMed Central 

Google Scholar 
Stanley, R. O. & Burrows, G. D. Varieties and functions of human emotion. Emot. Work Theory Res. Appl. Manag. 3–19 (2001).Fredrickson, B. L. The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am. Psychol. 56, 218 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Or, C. K. & Duffy, V. G. Development of a facial skin temperature-based methodology for non-intrusive mental workload measurement. Occup. Ergon. 7, 83–94 (2007).
Google Scholar 
Reynolds, V. The Chimpanzees of the Budongo Forest: Ecology, Behaviour and Conservation (OUP, 2005).
Google Scholar 
Steketee, J. Spectral emissivity of skin and pericardium. Phys. Med. Biol. 18, 686 (1973).CAS 
PubMed 

Google Scholar 
Chotard, H., Ioannou, S. & Davila-Ross, M. Infrared thermal imaging: Positive and negative emotions modify the skin temperatures of monkey and ape faces. Am. J. Primatol. 80, e22863 (2018).PubMed 

Google Scholar 
Newton-Fisher, N. Association by male chimpanzees: A social tactic?. Behaviour 136, 705–730 (1999).
Google Scholar 
Kano, F., Hirata, S., Deschner, T., Behringer, V. & Call, J. Nasal temperature drop in response to a playback of conspecific fights in chimpanzees: A thermo-imaging study. Physiol. Behav. 155, 83–94 (2016).CAS 
PubMed 

Google Scholar 
Hobaiter, C. & Byrne, R. W. The gestural repertoire of the wild chimpanzee. Anim. Cogn. 14, 745–767 (2011).PubMed 

Google Scholar 
Nishida, T., Kano, T., Goodall, J., McGrew, W. C. & Nakamura, M. Ethogram and ethnography of Mahale chimpanzees. Anthropol. Sci. 107, 141–188 (1999).
Google Scholar 
Muller, M.N. Agonistic relations among Kanyawara chimpanzees. In Behavioural Diversity in Chimpanzees and Bonobos (eds Boesch, C. et al.) 212–220 (Cambridge University Press, 2002).
Google Scholar 
Goodall, J. The Chimpanzees of Gombe: Patterns of Behavior (Harvard University Press, 1986).
Google Scholar 
Wallis, J. Chimpanzee genital swelling and its role in the pattern of sociosexual behavior. Am. J. Primatol. 28, 101–113 (1992).PubMed 

Google Scholar 
Davila-Ross, M., Allcock, B., Thomas, C. & Bard, K. A. Aping expressions? Chimpanzees produce distinct laugh types when responding to laughter of others. Emotion 11, 1013 (2011).PubMed 

Google Scholar 
Schel, A. M., Townsend, S. W., Machanda, Z., Zuberbühler, K. & Slocombe, K. E. Chimpanzee alarm call production meets key criteria for intentionality. PLoS One 8, e76674 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vardasca, R. The influence of angles and distance on assessing inner-canthi of the eye skin temperature. Thermol. Int. 27, 130–135 (2017).
Google Scholar 
Josse, J. & Husson, F. missMDA: A package for handling missing values in multivariate data analysis. J. Stat. Softw. 70, 1–31 (2016).
Google Scholar 
Neumann, C. et al. Assessing dominance hierarchies: Validation and advantages of progressive evaluation with Elo-rating. Anim. Behav. 82, 911–921 (2011).
Google Scholar 
Noë, R., de Waal, F. B. & van Hooff, J. A. Types of dominance in a chimpanzee colony. Folia Primatol. (Basel) 34, 90–110 (1980).
Google Scholar 
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K. (2019). cluster: Cluster Analysis Basics and Extensions. R package version 2.1.0.Barton, K. MuMIn: Multi-model inference, R package version 0.12. 0. Httpr-Forge R-Proj. Orgprojectsmumin (2020).Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).ADS 

Google Scholar 
Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).
Google Scholar 
Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.4. (2021).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2017). More

CORRESPONDENCE
22 February 2022

Apply Singapore Index on Cities’ Biodiversity at scale

Lena Chan

 ORCID: http://orcid.org/0000-0001-7930-7678

0
,

Kenneth Er

 ORCID: http://orcid.org/0000-0003-4485-7260

1
&

Elizabeth Maruma Mrema

2

Lena Chan

National Parks Board, Singapore Botanic Gardens, Singapore.

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Kenneth Er

National Parks Board, Singapore Botanic Gardens, Singapore.

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Elizabeth Maruma Mrema

Secretariat of the Convention on Biological Diversity, Montreal, Canada.

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In the run-up to the 15th meeting of the Conference of the Parties to the Convention on Biological Diversity, the Singapore Index on Cities’ Biodiversity has been updated to align with the post-2020 global biodiversity framework to halt biodiversity loss (see Nature 601, 298; 2022) and for application at scale (see go.nature.com/3cqrknw).

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Nature 602, 578 (2022)
doi: https://doi.org/10.1038/d41586-022-00476-x

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Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Murphy, R. R., Kemp, W. M. & Ball, W. P. Long-term trends in Chesapeake Bay seasonal hypoxia, stratification, and nutrient loading. Estuar. Coast. 34, 1293–1309 (2011).CAS 

Google Scholar 
Nixon, S. W. et al. The impact of changing climate on phenology, productivity, and benthic–pelagic coupling in Narragansett Bay. Estuar. Coast. Shelf S. 82, 1–18 (2009).ADS 
CAS 

Google Scholar 
Testa, J. M., Murphy, R. R., Brady, D. C. & Kemp, W. M. Nutrient-and climate-induced shifts in the phenology of linked biogeochemical cycles in a temperate estuary. Front. Mar. Sci. 5, 114 (2018).
Google Scholar 
Scanes, E., Scanes, P. R. & Ross, P. M. Climate change rapidly warms and acidifies Australian estuaries. Nat. Commun. 11, 1–11 (2020).
Google Scholar 
Statham, P. J. Nutrients in estuaries—An overview and the potential impacts of climate change. Sci. Total Environ. 434, 213–227 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Kolber, Z. S. et al. Contribution of aerobic photoheterotrophic bacteria to the carbon cycle in the ocean. Science 292, 2492–2495 (2001).CAS 
PubMed 

Google Scholar 
Giovannoni, S. J. & Vergin, K. L. Seasonality in ocean microbial communities. Science 335, 671–676 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Sul, W. J., Oliver, T. A., Ducklow, H. W., Amaral-Zettler, L. A. & Sogin, M. L. Marine bacteria exhibit a bipolar distribution. Proc. Natl. Acad. Sci. 110, 2342–2347 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).PubMed 

Google Scholar 
Ghiglione, J.-F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl. Acad. Sci. 109, 17633–17638 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Techtmann, S. M. et al. The unique chemistry of eastern Mediterranean water masses selects for distinct microbial communities by depth. PLoS ONE 10, e0120605 (2015).PubMed 
PubMed Central 

Google Scholar 
Han, D. et al. Bacterial communities along stratified water columns at the Chukchi Borderland in the western Arctic Ocean. Deep-Sea Res. Pt. II 120, 52–60 (2015).
Google Scholar 
Han, D. et al. Bacterial communities of surface mixed layer in the Pacific sector of the western Arctic ocean during sea-ice melting. PLoS ONE 9, e86887 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Hernando-Morales, V., Ameneiro, J. & Teira, E. Water mass mixing shapes bacterial biogeography in a highly hydrodynamic region of the Southern Ocean. Environ. Microbiol. 19, 1017–1029 (2017).CAS 
PubMed 

Google Scholar 
Han, D., Kang, H. Y., Kang, C.-K., Unno, T. & Hur, H.-G. Seasonal mixing-driven system in Estuarine-Coastal Zone triggers an ecological shift in bacterial assemblages involved in phytoplankton-derived DMSP degradation. Microb. Ecol. 79, 12–20 (2020).CAS 
PubMed 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
Higashi, K., Suzuki, S., Kurosawa, S., Mori, H. & Kurokawa, K. Latent environment allocation of microbial community data. PLoS Comput. Biol. 14, e1006143 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kim, D. et al. Water quality assessment at Jinhae Bay and Gwangyang Bay, South Korea. Ocean Sci. J. 49, 251–264 (2014).CAS 

Google Scholar 
Chen, M., Kim, D., Liu, H. & Kang, C.-K. Variability in copepod trophic levels and feeding selectivity based on stable isotope analysis in Gwangyang Bay of the southern coast of the Korean Peninsula. Biogeosciences 15, 2055–2073 (2018).ADS 
CAS 

Google Scholar 
Lee, J. H. et al. The effects of different environmental factors on the biochemical composition of particulate organic matter in Gwangyang Bay, South Korea. Biogeosciences 14, 1903–1917 (2017).ADS 
CAS 

Google Scholar 
Fine, P. V. & Kembel, S. W. Phylogenetic community structure and phylogenetic turnover across space and edaphic gradients in western Amazonian tree communities. Ecography 34, 552–565 (2011).
Google Scholar 
Stegen, J. C., Lin, X., Konopka, A. E. & Fredrickson, J. K. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 6, 1653–1664 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tripathi, B. M. et al. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J. 12, 1072–1083 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng, Y. et al. Two key features influencing community assembly processes at regional scale: Initial state and degree of change in environmental conditions. Mol. Ecol. 27, 5238–5251 (2018).PubMed 

Google Scholar 
Stegen, J. C., Lin, X., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Micorbiol. 6, 370 (2015).
Google Scholar 
Dini-Andreote, F., Stegen, J. C., van Elsas, J. D. & Salles, J. F. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl. Acad. Sci. 112, E1326–E1332 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, J. et al. Phylogenetic beta diversity in bacterial assemblages across ecosystems: Deterministic versus stochastic processes. ISME J. 7, 1310–1321 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lindemann, S. R. et al. The epsomitic phototrophic microbial mat of Hot Lake, Washington: Community structural responses to seasonal cycling. Front. Micorbiol. 4, 323 (2013).
Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Amaral-Zettler, L. A. et al. Microbial community structure across the tree of life in the extreme Rio Tinto. ISME J. 5, 42–50 (2011).PubMed 

Google Scholar 
Han, D. et al. Survey of bacterial phylogenetic diversity during the glacier melting season in an Arctic Fjord. Microb. Ecol. 81, 579–591 (2021).CAS 
PubMed 

Google Scholar 
Brand, L. E., Campbell, L. & Bresnan, E. Karenia: The biology and ecology of a toxic genus. Harmful Algae 14, 156–178 (2012).
Google Scholar 
Escalera, L., Pazos, Y., Moroño, Á. & Reguera, B. Noctiluca scintillans may act as a vector of toxigenic microalgae. Harmful Algae 6, 317–320 (2007).
Google Scholar 
Tada, K., Pithakpol, S., Yano, R. & Montani, S. Carbon and nitrogen content of Noctiluca scintillans in the Seto Inland Sea, Japan. J. Plankton. Res. 22, 1203–1211 (2000).
Google Scholar 
Hyun, B. et al. Effects of increased CO2 and temperature on the growth of four diatom species (Chaetoceros debilis, Chaetoceros didymus, Skeletonema costatum and Thalassiosira nordenskioeldii) in laboratory experiments. J. Environ. Sci. Int. 23, 1003–1012 (2014).
Google Scholar 
Park, J. S., Lee, S. D. & Lee, J. H. Taxonomic study on the euryhaline Cyclotella (Bacillariophyta) species in Korea. J. Ecol. Environ. 36, 407–409 (2013).
Google Scholar 
Yun, S. M. & Lee, J. H. Morphology and distribution of some marine diatoms, Family Rhizosoleniaceae, in Korean coastal waters: A genus Rhizosolenia. Algae 25, 173–182 (2010).
Google Scholar 
Park, J. S., Jung, S. W., Lee, S. D., Yun, S. M. & Lee, J. H. Species diversity of the genus Thalassiosira (Thalassiosirales, Bacillariophyta) in South Korea and its biogeographical distribution in the world. Phycologia 55, 403–423 (2016).
Google Scholar 
Dupont, C. L. et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 6, 1186–1199 (2012).CAS 
PubMed 

Google Scholar 
Landa, M. et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 13, 2536–2550 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Müller, A. L. et al. Bacterial interactions during sequential degradation of cyanobacterial necromass in a sulfidic arctic marine sediment. Environ. Microbiol. 20, 2927–2940 (2018).PubMed 
PubMed Central 

Google Scholar 
Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Giovannoni, S. J. SAR11 bacteria: The most abundant plankton in the oceans. Annu. Rev. Mar. Sci. 9, 231–255 (2017).ADS 

Google Scholar 
Mühlenbruch, M., Grossart, H. P., Eigemann, F. & Voss, M. Mini-review: Phytoplankton-derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ. Microbiol. 20, 2671–2685 (2018).PubMed 

Google Scholar 
Stock, W. et al. Host specificity in diatom–bacteria interactions alleviates antagonistic effects. FEMS Microbiol. Ecol. 95, 171 (2019).
Google Scholar 
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Illumina. 16S Metagenomic sequencing library preparation. Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System (2013).Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).CAS 
PubMed 

Google Scholar 
R Core Team. The R Stats Package (R Core Team, 2002).
Google Scholar 
Oksanen, J. & Blanchet, F. G. Package ‘vegan’ (2017).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. 1 (2017).Roberts, D. W. & Roberts, M. D. W. Package ‘labdsv’. (2016).Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 
PubMed 

Google Scholar 
Gustavsen, J. A., Pai, S., Isserlin, R., Demchak, B. & Pico, A. R. RCy3: Network biology using cytoscape from within R. F1000Research 8, 1774 (2019).PubMed 
PubMed Central 

Google Scholar 
Kahle, D., Wickham, H. & Kahle, M. D. Package ‘ggmap’ (2019). More

DataSpatial gridWe created a grid whose units measure 250 m by 250 m based on the census tract layer for the city of Rio de Janeiro from the Instituto Brasileiro de Geografia e Estatística [Brazilian Institute of Geography and Statistics] (IBGE) website https://www.ibge.gov.br/geociencias/organizacao-do-territorio/malhas-territoriais. Uninhabited locations were excluded.Dengue cases on the gridDengue is a disease of compulsory notification in Brazil, and cases are notified at the Sistema de Informação de Agravos de Notificação [Information System on Diseases of Compulsory Declaration] (SINAN). Dengue cases notified in Rio de Janeiro between January 2010 and March 2015 were geocoded according to address of residency, and then counted for each grid unit by the Secretariat of Health of the city. We obtained the monthly dengue cases data aggregated at the grid level.Population on the gridThe population data is obtained from the Census 2010 (IBGE) (https://www.ibge.gov.br/estatisticas/downloads-estatisticas.html) and it is available at the census tract level. The census tract areas vary in size and can be bigger than the unit of the grid, primarily in the least densely populated zones of the city. To overcome this issue, we cropped from the census tract layer the areas classified as non-urbanized (such as water bodies, swamps, agricultural areas, green areas, beaches, rocky outcrops) in 2010 by the City Hall of Rio de Janeiro (layer available at http://www.data.rio/datasets/uso-do-solo-2010). The population of each census tract is distributed randomly (uniformly) in the areas obtained after deleting the non-urban areas. The population within the units is computed by adding the grid layer. To create the grid and edit the census tract layer we used QGIS (version 3.6.3)45, and to obtain the population in the grid we used the R software46 with the packages tidyverse47 and sf48. We verify the accuracy of our estimated population by comparison with the WordPop dataset49 (see detailed description and Supplementary Fig. 12 and Supplementary Note 2). We chose the WorldPop dataset because: (i) the estimates are also calculated based on census data and are available for 2010, (ii) the pixel size is 100 m, smaller than the size of our grid unit, and (iii) it is open access.Since the units are in fact small and most of them conserve their area of 250 m by 250 m (Supplementary Fig. 1A), we consider population density as the population of each unit. For consistency, we do not consider units with small effective areas and/or populations sizes less than, or equal to, 10 in our analysis. In total, 8954/20212 units were so excluded. This choice circumvents the problem of high sensitivity to random population distribution, and urban vs. non-urban classification, in very small and/or sparsely populated areas. It also facilitates model simulation and does not affect the peak ratio pattern (Supplementary Fig. 1B).Peak ratio and spatial aggregationSince units are small, we binned them into G groups and aggregated their times series of reported cases. The groups were generated according to two aspects: (1) the geographical location of the units as determined by the administrative divisions of the city (10 areas, 33 regions, and 160 neighborhoods); and (2) the population of the units based on quantiles in order to obtain equal size groups. We considered specifically four different partition levels, resulting in 12, 25, 50, and 100 groups with about 900, 450, 225, and 100 units, respectively (from a total number of 11,247 units for the whole city). Groups of unequal size can introduce different statistical effects (it is not the same, for example, to calculate a mean value using 1000 or 10 elements). To compare quantities across groups it is therefore prudent to define groups with the same number of elements. In particular, this consideration becomes important for a large number of groups. Since the population density distribution (number of individuals per unit) is not uniform, groups defined with “equidistant” boundaries would exhibit very different numbers of elements.Given a unit u, we define its time series ({{{{{{bf{v}}}}}}}_{{{{{{bf{u}}}}}}}={{c}_{u}({t}_{1}),{c}_{u}({t}_{2}),…,{c}_{u}({t}_{f})}), where ({c}_{u}({t}_{i})) is the number of reported cases of dengue at time ({t}_{i}) (i = 1, 2, …f) (and the bold symbol is used to indicate a vector). Thus, the aggregated time series is given by$${{{{{{bf{V}}}}}}}_{{{{{{bf{g}}}}}}}=mathop{sum}limits_{uin g}{{{{{{bf{v}}}}}}}_{{{{{{bf{u}}}}}}}={{C}_{g}({t}_{1})=mathop{sum}limits_{uin g}{c}_{u}({t}_{1}),{C}_{g}({t}_{2})=mathop{sum}limits_{uin g}{c}_{u}({t}_{2}),…,{C}_{g}({t}_{f})=mathop{sum}limits_{uin g}{c}_{u}({t}_{f})},$$with (g=1,2,…,G). Then, for each ({{{{{{bf{V}}}}}}}_{{{{{{bf{g}}}}}}}) we computed the ratio between the sizes of the second and first DENV4 peaks, that is$${{{{{rm{peakrati}}}}}}{{{{{{rm{o}}}}}}}_{{{{{{rm{g}}}}}}}=frac{{ma}{x}_{tin {season}2}{{C}_{g}({t}_{1}),{C}_{g}({t}_{2}),…,{C}_{g}({t}_{f})}}{{ma}{x}_{tin {season}1}{{C}_{g}({t}_{1}),{C}_{g}({t}_{2}),…,{C}_{g}({t}_{f})}}$$
(1)
(Supplementary Fig. 2).The deterministic SIR modelAlthough dengue is a vector-borne disease, for simplicity we omitted the explicit representation of the dynamics of the mosquito population, and treated vector transmission via the seasonality of the transmission rate26. Thus, for each unit u, the deterministic SIR model is based on the following traditional differential equations:$$frac{d{S}_{u}}{{dt}}=mu {N}_{u}-beta {S}_{u}frac{{I}_{u}}{{N}_{u}}-mu {S}_{u}$$$$frac{d{I}_{u}}{{dt}}=beta {S}_{u}frac{{I}_{u}}{{N}_{u}}-gamma {I}_{u}-mu {I}_{u}$$
(2)
$$frac{d{R}_{u}}{{dt}}={gamma I}_{u}-mu {R}_{u},$$where ({S}_{u},{I}_{u},{R}_{u}), are, respectively, the number of susceptible, infected, and recovered individuals, and ({N}_{u}) the number of inhabitants, of the spatial unit u. Parameter (mu) is the mortality rate (equal to the birth rate), and (gamma) is the recovery rate. The seasonal transmission rate is specified as (beta (t)={beta }_{0}(1+delta {{sin }},(omega t+phi ))). The units are considered independent of each other, and the initial conditions establish that the whole population of each unit is susceptible to the virus (({S}_{u}(t=0)={N}_{u}) and ({I}_{u}left(t=0right)={R}_{u}left(t=0right)=0forall u)). Transmission begins with one infected individual at a time ({t}_{0u}ge t=0) where ({t}_{0u}) is obtained from the data.Since the goal of this model is to examine the representative dynamics of different population densities, we binned the units according to their population into 12 groups, and computed the mean value of their number of inhabitants ({N}_{g}=langle {N}_{uin g}rangle) and of their arrival times of the infection ({t}_{0g}sim langle {t}_{0uin g}rangle) (where g = 1, …, 12). We then simulated the system considering the 12 sets ({{N}_{g},{t}_{0g}}) as given.The stochastic modelSince units will suffer local extinction of transmission, a major component of a stochastic implementation is the description of the local reintroduction of the virus, namely the arrival of a ‘spark’ or imported infection, in analogy to fire spread. Because space is described by a highly-resolved lattice, we considered that well-mixed transmission applies within each unit. Moreover, in lieu of  explicit spatial coupling between units, we postulated  the importation of infection through the specification of a spark rate.For this purpose, we constructed a binary representation of the time series of cases per month by defining the spatial units either as positive or negative according to whether they reported cases or not (Supplementary Fig. 3). Then, to derive a spark rate we explored the dynamics of the number of positive units as follows,$${U}^{{{mbox{+}}}}(t+{dt})={U}^{{{mbox{+}}}}(t)+{U}_{{{{{{{mathrm{new}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})-{U}_{{{{{{{mathrm{extinct}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})$$
(3)
The number of positive units at time ({t+dt}) is equal to the number of positive units at time t, plus the number of units that have been infected ({{U}_{{{{{{{mathrm{new}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})) between t and t + dt, minus the number of units that were infected at t but are no longer infected at t + dt (i.e., the number of ‘extinctions’ between t and t + dt, ({{U}_{{{{{{{mathrm{extinct}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt}))).Since uninfected units (i.e., negative units) require the arrival of a spark to become positive, the following equation specifies the mean of ({{U}_{{{{{{{mathrm{new}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})) under the assumption that a small unit is unlikely to receive more than a single spark in a period of time dt$${{langle }}{U}_{{{{{{{mathrm{new}}}}}}}}^{{+}}(t,t+{dt}){{rangle }}simeq {N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})frac{{U}^{{-}}(t)}{U},$$
(4)
where ({N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})) is the number of sparks produced between t and t + dt, ({U}^{{{{-}}}}(t)) is the number of negative units at a time t, and (U) is the total number of units in the city ((U={U}^{{{mbox{+}}}}+{U}^{{{{-}}}})).By introducing Eq. (4) into Eq. (3) we obtain,$${U}^{{{mbox{+}}}}(t+{dt})simeq {U}^{{{mbox{+}}}}(t)+{N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})frac{{U}^{{{{-}}}}(t)}{U}-{{U}_{{{{{{{mathrm{extinct}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})$$
(5)
From Eq. (5) we can now compute the spark rate per unit ({{sigma }_{u}}^{{emp}}(t,t+{dt})) from the high-resolution incidence data as$${{sigma }_{u}}^{{emp}}(t,t+{dt})=frac{{N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})}{U}simeq frac{{U}^{{{mbox{+}}}}(t+{dt})-{U}^{{{mbox{+}}}}(t)+{U}_{{{{{{{mathrm{extinct}}}}}}}}(t,t+{dt})}{{U}^{{{{-}}}}(t)}$$
(6)
In order to address the effects of human density on the spark rate, we binned the spatial units according to their population into G groups. To avoid statistical effects due to group size, we considered population quantiles. Then, by applying Eq. (6) to each of these groups, we obtained an empirical spark rate per unit that depends on human density,$${sigma }_{uin g}^{{emp}}(t,t+{dt})={sigma }_{u}^{{emp}}(t,t+{dt}{{{{{rm{;}}}}}}{N}_{g}),$$
(7)
where ({N}_{g}={{langle }}{N}_{uin g}{{rangle }}) with g = 1, 2, …, G.SimulationsThe associated differential equations of the stochastic model are those shown on Eq. (2) but the transmission component has now an additional term ({sigma }_{u}) to describe the importation of infections.$$frac{d{S}_{u}}{{dt}}=mu {N}_{u}-left(beta {S}_{u}frac{{I}_{u}}{{N}_{u}}+{sigma }_{u}right)-mu {S}_{u}$$$$frac{d{I}_{u}}{{dt}}=left(beta {S}_{u}frac{{I}_{u}}{{N}_{u}}+{sigma }_{u}right)-gamma {I}_{u}-mu {I}_{u}$$
(8)
$$frac{d{R}_{u}}{{dt}}={gamma I}_{u}-mu {R}_{u}$$Since the inferred spark rate from the data (Eq. (7)) is obtained from observed infections, we computed the spark rate ({sigma }_{u}) as:$${sigma }_{uin g}={{{{{{mathrm{Poisson}}}}}}}({{sigma }_{uin g}}^{{emp}}/rho )$$
(9)
where (rho) is the reporting rate.The model shown on Eq. (8) was formulated as stochastic by incorporating demographic noise (with the different events represented as Poisson processes). It was implemented in R with the package pomp50. We also considered measurement error by assuming that the observed number of cases ({{C}_{u}}^{{obs}}) during a period of time T is,$${{C}_{u}}^{{obs}}left(Tright)={{{{{{mathrm{binomial}}}}}}}left(rho ,{C}_{u}left(Tright)right),$$
(10)
where ({C}_{u}(T)) is the number of cases computed in the unit u. We simulated the 11,247 units that compose the city of Rio de Janeiro, and aggregated the resulting time series as for the empirical data (see Peak ratio section).The parameters of the model are given in Supplementary Table 1. We relied on parameters estimated for dengue transmission in Rio de Janeiro by ref. 26. Those estimates were obtained for the aggregated city and for the emergence of DENV1. We use these parameters here as a sufficiently realistic set for illustrating and exploring the behavior of the stochastic model with population density. Moreover, with the exception of the spark rate, the model parameters were considered the same for all units. In particular, we applied a uniform reporting rate because access to the nearest public healthcare clinic does not show a dependency on population density (see Supplementary Note 1).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

PlantProbs.net. Nickel in plants and soil https://plantprobs.net/plant/nutrientImbalances/sodium.html (accessed Apr 28, 2021).Guodong Liu, E. H. Simonne, and Y. L. Nickel Nutrition in Plants | EDIS. EDis 2011.Liu, G. D. “A New Essential Mineral Element–Nickel.” Plants Nutr. Fertil. Sci. 2001.Kabata-Pendias, A.; Mukherjee, A. Trace Elements from Soil to Human; 2007.Kasprzak, K. S. Nickel advances in modern environmental toxicology. Environ. Toxicol. 11, 145–183 (1987).CAS 

Google Scholar 
Cempel, M. & Nikel, G. Nickel: A review of its sources and environmental toxicology. Polish J. Environ. Stud. 15, 375–382 (2006).CAS 

Google Scholar 
Bradl, H. B. Chapter Sources and origins of heavy metals. Interface Sci. Technol. 6, 1–27 (2005).CAS 
Article 

Google Scholar 
Von Burg, R. Nickel and some nickel compounds. J. Appl. Toxicol. 17, 425–431 (1997).Article 

Google Scholar 
Freedman, B. & Hutchinson, T. C. Pollutant inputs from the atmosphere and accumulations in soils and vegetation near a nickel–copper smelter at Sudbury, Ontario, Canada. Can. J. Bot. 58(1), 108–132. https://doi.org/10.1139/b80-014 (1980).CAS 
Article 

Google Scholar 
Manyiwa, T. et al. Heavy metals in soil, plants, and associated risk on grazing ruminants in the vicinity of Cu–Ni mine in Selebi-Phikwe, Botswana. Environ. Geochem. Health https://doi.org/10.1007/s10653-021-00918-x (2021).Article 
PubMed 

Google Scholar 
Kabata-Pendias. Kabata-Pendias A. 2011. Trace elements in soils and… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Kabata-Pendias+A.+2011.+Trace+elements+in+soils+and+plants.+4th+ed.+New+York+%28NY%29%3A+CRC+Press&btnG= (accessed Nov 24, 2020).Almås, A., Singh, B., Agricultural, T. S.-N. J. of & 1995, undefined. The impact of nickel industry in Russia on concentrations of heavy metals in agricultural soils and grass in Soer-Varanger, Norway. agris.fao.org.Nielsen, G. D. et al. Absorption and retention of nickel from drinking water in relation to food intake and nickel sensitivity. Toxicol. Appl. Pharmacol. 154, 67–75 (1999).CAS 
Article 

Google Scholar 
Costa, M. & Klein, C. B. Nickel carcinogenesis, mutation, epigenetics, or selection. Environ. Health Perspect. 107, 2 (1999).Article 

Google Scholar 
Agyeman, P. C.; Ahado, S. K.; Borůvka, L.; Biney, J. K. M.; Sarkodie, V. Y. O.; Kebonye, N. M.; Kingsley, J. Trend Analysis of Global Usage of Digital Soil Mapping Models in the Prediction of Potentially Toxic Elements in Soil/Sediments: A Bibliometric Review. Environmental Geochemistry and Health. Springer Science and Business Media B.V. 2020. https://doi.org/10.1007/s10653-020-00742-9.Minasny, B. & McBratney, A. B. Digital soil mapping: A brief history and some lessons. Geoderma 264, 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017 (2016).ADS 
Article 

Google Scholar 
McBratney, A. B., Mendonça Santos, M. L. & Minasny, B. On digital soil mapping. Geoderma 117(1–2), 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4 (2003).ADS 
Article 

Google Scholar 
Deutsch.C.V. Geostatistical Reservoir Modeling,… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=C.V.+Deutsch%2C+2002%2C+Geostatistical+Reservoir+Modeling%2C+Oxford+University+Press%2C+376+pages.+&btnG= (accessed Apr 28, 2021).Olea, R. A. Geostatistics for engineers & earth scientists. Stoch. Environ. Res. Risk Assess. 14(3), 207–209. https://doi.org/10.1007/pl00009782 (2000).Article 

Google Scholar 
Gumiaux, C., Gapais, D. & Brun, J. P. Geostatistics applied to best-fit interpolation of orientation data. Tectonophysics 376(3–4), 241–259. https://doi.org/10.1016/j.tecto.2003.08.008 (2003).ADS 
Article 

Google Scholar 
Wadoux, A. M. J. C., Minasny, B. & McBratney, A. B. Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth-Sci Rev. https://doi.org/10.1016/j.earscirev.2020.103359 (2020).Article 

Google Scholar 
Tan, K. et al. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 382, 120987. https://doi.org/10.1016/j.jhazmat.2019.120987 (2020).CAS 
Article 
PubMed 

Google Scholar 
Sakizadeh, M., Mirzaei, R. & Ghorbani, H. Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran. Neural Comput. Appl. 28(11), 3229–3238. https://doi.org/10.1007/s00521-016-2231-x (2017).Article 

Google Scholar 
Vega, F. A., Matías, J. M., Andrade, M. L., Reigosa, M. J. & Covelo, E. F. Classification and regression trees (CARTs) for modelling the sorption and retention of heavy metals by soil. J. Hazard. Mater. 167(1–3), 615–624. https://doi.org/10.1016/j.jhazmat.2009.01.016 (2009).CAS 
Article 
PubMed 

Google Scholar 
Sun, H. et al. Prediction of distribution of soil cd concentrations in Guangdong Province, China. Huanjing Kexue/Environmental Sci. 38(5), 2111–2124. https://doi.org/10.13227/j.hjkx.201611006 (2017).Article 

Google Scholar 
Woodcock, C. E. & Gopal, S. Fuzzy set theory and thematic maps: accuracy assessment and area estimation. Int. J. Geogr. Inf. Sci. 14(2), 153–172. https://doi.org/10.1080/136588100240895 (2000).Article 

Google Scholar 
Finke, P. A. Chapter 39 Quality assessment of digital soil maps: producers and users perspectives. Dev. Soil Sci. https://doi.org/10.1016/S0166-2481(06)31039-2 (2006).Article 

Google Scholar 
Pontius, R. G. & Cheuk, M. L. A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. Int. J. Geogr. Inf. Sci. 20(1), 1–30. https://doi.org/10.1080/13658810500391024 (2006).Article 

Google Scholar 
Grunwald, S. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152(3–4), 195–207. https://doi.org/10.1016/j.geoderma.2009.06.003 (2009).ADS 
Article 

Google Scholar 
Nelson, M. A., Bishop, T. F. A., Triantafilis, J. & Odeh, I. O. A. An error budget for different sources of error in digital soil mapping. Eur. J. Soil Sci. 62, 417–430 (2011).Article 

Google Scholar 
McBratney, A. B., Minasny, B. & ViscarraRossel, R. Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis. Geoderma 136, 272–278 (2006).ADS 
CAS 
Article 

Google Scholar 
Stumpf, F. et al. Uncertainty-guided sampling to improve digital soil maps. CATENA 153, 30–38 (2017).Article 

Google Scholar 
Legates, D. R. & McCabe, G. J. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999).ADS 
Article 

Google Scholar 
Sergeev, A. P. et al. High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging. AIP Conf. Proc. 2017, 1836. https://doi.org/10.1063/1.4981963 (2017).CAS 
Article 

Google Scholar 
Subbotina, I. E. et al. Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area. AIP Conf. Proc. https://doi.org/10.1063/1.5045410 (2018).Article 

Google Scholar 
Tarasov, D. A., Buevich, A. G., Sergeev, A. P. & Shichkin, A. V. High variation topsoil pollution forecasting in the Russian subarctic: using artificial neural networks combined with residual kriging. Appl. Geochemistry 88, 188–197. https://doi.org/10.1016/j.apgeochem.2017.07.007 (2018).CAS 
Article 

Google Scholar 
Tarasov, D.; Buevich, A.; Shichkin, A.; Subbotina, I.; Tyagunov, A.; Baglaeva, E. Chromium Distribution Forecasting Using Multilayer Perceptron Neural Network and Multilayer Perceptron Residual Kriging. In AIP Conference Proceedings; American Institute of Physics Inc., 2018; Vol. 1978, p 440019. https://doi.org/10.1063/1.5044048.John, K. et al. Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur. CATENA 206, 2 (2021).Article 

Google Scholar 
Gribov, A. & Krivoruchko, K. Empirical Bayesian Kriging Implementation and Usage. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137290 (2020).Article 
PubMed 

Google Scholar 
Samsonova, V. P., Blagoveshchenskii, Y. N. & Meshalkina, Y. L. Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands. Eurasian Soil Sci. 50(3), 305–311. https://doi.org/10.1134/S1064229317030103 (2017).ADS 
CAS 
Article 

Google Scholar 
Fabijańczyk, P., Zawadzki, J. & Magiera, T. Magnetometric assessment of soil contamination in problematic area using empirical bayesian and indicator kriging: a case study in upper Silesia, Poland. Geoderma 308, 69–77. https://doi.org/10.1016/j.geoderma.2017.08.029 (2017).ADS 
CAS 
Article 

Google Scholar 
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 2, 1–16. https://doi.org/10.1007/s13762-020-03089-x (2021).CAS 
Article 

Google Scholar 
Li, T. et al. Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes. Sci. Total Environ. 628–629, 1446–1459. https://doi.org/10.1016/j.scitotenv.2018.02.163 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.114065 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hossain Bhuiyan, M. A., Chandra Karmaker, S., Bodrud-Doza, M., Rakib, M. A. & Saha, B. B. Enrichment, sources and ecological risk mapping of heavy metals in agricultural soils of dhaka district employing SOM PMF and GIS Methods. Chemosphere https://doi.org/10.1016/j.chemosphere.2020.128339 (2021).Article 
PubMed 

Google Scholar 
Kebonye, N. M. et al. Self-organizing map artificial neural networks and sequential gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils. J. Geochemical Explor. 222, 106680. https://doi.org/10.1016/j.gexplo.2020.106680 (2021).CAS 
Article 

Google Scholar 
Weather Spark. Average Weather in Frýdek-Místek, Czechia, Year Round – Weather Spark https://weatherspark.com/y/83671/Average-Weather-in-Frýdek-Místek-Czechia-Year-Round (accessed Sep 14, 2020).Kozák, J. Soil Atlas of the Czech Republic. 2010, 150.Vacek, O., Vašát, R. & Borůvka, L. Quantifying the pedodiversity-elevation relations. Geoderma 373, 114441. https://doi.org/10.1016/j.geoderma.2020.114441 (2020).ADS 
Article 

Google Scholar 
Krivoruchko, K. Empirical Bayesian Kriging; 2012; Vol. Fall 2012.Vapnik, V. The nature of statistical learning theory. Technometrics 38(4), 409. https://doi.org/10.2307/1271324 (1995).Article 
MATH 

Google Scholar 
Li, Z., Zhou, M., Xu, L. J., Lin, H. & Pu, H. Training sparse SVM on the core sets of fitting-planes. Neurocomputing 130, 20–27. https://doi.org/10.1016/j.neucom.2013.04.046 (2014).Article 

Google Scholar 
Cherkassky, V.; Mulier, F. Learning from Data: Concepts, Theory, and Methods: Second Edition; 2006. https://doi.org/10.1002/9780470140529.John, K. et al. Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land 9(12), 1–20. https://doi.org/10.3390/land9120487 (2020).CAS 
Article 

Google Scholar 
Vohland, M., Besold, J., Hill, J. & Fründ, H. C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma 166(1), 198–205. https://doi.org/10.1016/j.geoderma.2011.08.001 (2011).ADS 
CAS 
Article 

Google Scholar 
Fraser, S. J.; Dickson, B. L. A New Method for Data Integration and Integrated Data Interpretation: Self-Organising Maps; 2007.Melssen, W. J.; Smits, J. R. M.; Buydens, L. M. C.; Kateman, G. Using Artificial Neural Networks for Solving Chemical Problems Part II. Kohonen Self-Organising Feature Maps and Hopfield Networks. Chemometrics and Intelligent Laboratory Systems. Elsevier, Amsterdam, 1, 1994, pp 267–291. https://doi.org/10.1016/0169-7439(93)E0036-4.Kooistra, L. et al. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal. Chim. Acta 484(2), 189–200. https://doi.org/10.1016/S0003-2670(03)00331-3 (2003).CAS 
Article 

Google Scholar 
Li, L. et al. Methods for estimating leaf nitrogen concentration of winter oilseed rape (Brassica Napus L.) using in situ leaf spectroscopy. Ind. Crops Prod. 91, 194–204. https://doi.org/10.1016/j.indcrop.2016.07.008 (2016).CAS 
Article 

Google Scholar 
Różański, S. Ł, Kwasowski, W., Castejón, J. M. P. & Hardy, A. Heavy metal content and mobility in urban soils of public playgrounds and sport facility areas, Poland. Chemosphere 212, 456–466. https://doi.org/10.1016/j.chemosphere.2018.08.109 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bretzel, F. & Calderisi, M. Metal contamination in urban soils of coastal Tuscany (Italy). Environ. Monit. Assess. 118(1–3), 319–335. https://doi.org/10.1007/s10661-006-1495-5 (2006).CAS 
Article 
PubMed 

Google Scholar 
Jim, C. Y. Urban soil characteristics and limitations for landscape planting in hong kong. Landsc. Urban Plan. 40(4), 235–249. https://doi.org/10.1016/S0169-2046(97)00117-5 (1998).Article 

Google Scholar 
Birke, M.; Rauch, U.; Chmieleski, J. Environmental Geochemical Survey of the City of Stassfurt: An Old Mining and Industrial Urban Area in Sachsen-Anhalt, Germany. In Mapping the Chemical Environment of Urban Areas; John Wiley and Sons, 2011; pp 269–306. https://doi.org/10.1002/9780470670071.ch18.Khodadoust, A. P., Reddy, K. R. & Maturi, K. Removal of nickel and phenanthrene from kaolin soil using different extractants. Environ. Eng. Sci. 21(6), 691–704. https://doi.org/10.1089/ees.2004.21.691 (2004).CAS 
Article 

Google Scholar 
Jakovljevic, M.; Kostic, N.; Antic-Mladenovic, S. The Availability of Base Elements (Ca, Mg, Na, K) in Some Important Soil Types in Serbia; 2003. https://doi.org/10.2298/zmspn0304011j.Orzechowski, M.; Smolczynski, S. IN SOILS DEVELOPED FROM THE HOLOCENE DEPOSITS IN NORTH-EASTERN POLAND*; -, 2007; Vol. 15.Pongrac, P. et al. Mineral element composition of cabbage as affected by soil type and phosphorus and zinc fertilisation. Plant Soil 434(1–2), 151–165. https://doi.org/10.1007/s11104-018-3628-3 (2019).CAS 
Article 

Google Scholar 
Kingston, G.; Anink, M. C.; Clift, B. M.; Beattie, R. N. Potassium Management for Sugarcane on Base Saturated Soils in Northern New South Wales; 2009; Vol. 31.Santo, L. T., Nakahata, M. H., & Schell, V. P. Santo LT, Nakahata MH, Ito GP and Schell VP (2000)…. – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Santo+LT%2C+Nakahata+MH%2C+Ito+GP+and+Schell+VP+%282000%29.+Calcium+and+liming+trials+from+1994+to+1998+at+HC%26S.+Technical+supplement+to+Agronomy+Report+83%2C+Hawaiian+Agricultural+Research+Centre. (accessed May 16, 2021).Burgos, P., Madejón, E., Pérez-de-Mora, A. & Cabrera, F. Horizontal and vertical variability of soil properties in a trace element contaminated area. Int. J. Appl. Earth Obs. Geoinf. 10(1), 11–25. https://doi.org/10.1016/j.jag.2007.04.001 (2008).ADS 
Article 

Google Scholar 
Olinic, T. & Olinic, E. The effect of quicklime stabilization on soil properties. Agric. Agric. Sci. Procedia 10, 444–451. https://doi.org/10.1016/j.aaspro.2016.09.013 (2016).Article 

Google Scholar 
Madaras, M.; Lipavský, J. Interannual Dynamics of Available Potassium in a Long-Term Fertilization Experiment; 2009; Vol. 55. https://doi.org/10.17221/34/2009-pse.Madaras, M., Koubova, M. & Lipavský, J. Stabilization of available potassium across soil and climatic conditions of the Czech Republic. Arch. Agron. Soil Sci. 56(4), 433–449. https://doi.org/10.1080/03650341003605750 (2010).CAS 
Article 

Google Scholar 
Pulkrabová, J. et al. Is the long-term application of sewage sludge turning soil into a sink for organic pollutants?: Evidence from field studies in the Czech Republic. J. Soils Sedim. 19(5), 2445–2458. https://doi.org/10.1007/s11368-019-02265-y (2019).CAS 
Article 

Google Scholar 
Asare, M. O., Horák, J., Šmejda, L., Janovský, M. & Hejcman, M. A medieval hillfort as an island of extraordinary fertile archaeological dark earth soil in the Czech Republic. Eur. J. Soil Sci. 72(1), 98–113. https://doi.org/10.1111/ejss.12965 (2021).CAS 
Article 

Google Scholar 
Zádorová, T. et al. Identification of Neolithic to Modern Erosion-Sedimentation Phases Using Geochemical Approach in a Loess Covered Sub-Catchment of South Moravia Czech Republic. Geoderma 195–196, 56–69. https://doi.org/10.1016/j.geoderma.2012.11.012 (2013).ADS 
CAS 
Article 

Google Scholar 
Tlustoš, P. et al. Nutrient status of soil and winter wheat (Triticum Aestivum L.) in response to long-term farmyard manure application under different climatic and soil physicochemical conditions in the Czech Republic. Arch. Agron. Soil Sci. 64(1), 70–83. https://doi.org/10.1080/03650340.2017.1331297 (2018).Article 

Google Scholar 
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. 260, 2 (2020).
Google Scholar 
Yan, P., Peng, H., Yan, L. & Lin, K. Spatial variability of soil physical properties based on GIS and geo-statistical methods in the red beds of the Nanxiong Basin, China. Polish J. Environ. Stud. 28, 2961–2972 (2019).Article 

Google Scholar 
Beguin, J., Fuglstad, G. A., Mansuy, N. & Paré, D. Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches. Geoderma 306, 195–205 (2017).ADS 
CAS 
Article 

Google Scholar 
Adhikary, P. P., Dash, C. J., Bej, R. & Chandrasekharan, H. Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India. Environ. Monit. Assess. 176, 663–676 (2011).CAS 
Article 

Google Scholar 
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 18, 3327–3342 (2021).CAS 
Article 

Google Scholar 
Eldeiry, A. A. & Garcia, L. A. Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing. Soil Sci. Soc. Am. J. 72, 201–211 (2008).ADS 
CAS 
Article 

Google Scholar  More

Plant materials, growth conditions and salt treatmentsSoil samples were performed as in previous experiments with S. europaea25, seeds were collected at two maternal sites, the first of which represents natural salinity related to inland salt springs at the health resort of Ciechocinek (Cie) (52°53′N, 18°47′E) characterised by a high soil salinity of ca 100 dS m−1 (~ 1000 mM NaCl), and the second of which is associated with soda factory waste that affects the local environment in Inowrocław-Mątwy (Inw) (52°48′N, 18°15′E) and with a lower salinity of ca 55 dS m−1 (~ 550 mM NaCl). The complete soil description is reported in Piernik et al.51 and Szymanska et al.52,53. Populations are isolated by a distance of ca 40 km without any saline environment between them, however, they were somehow connected due to the presence of salt springs in the nineteenth century. The seeds came from one generation and were collected in early November 2018. The seeds were germinated and grown according to the same steps reported in Cárdenas-Pérez et al.25 with a slight modification in the number of salt treatments at 0, 200, 400, 600, 800 and 1000 mM NaCl. In total, 144 plants were cultivated, and, therefore, a complete randomised factorial design 26 was used, which included (12 plants × 6 treatments × 2 populations) with 14 response variables. After 2 months of development, anatomical analysis such as cell area (A), roundness (R) and maximum cell diameter (Cdiam) were estimated in 12 samples, whereas high and low methyl esterified pectins (HM-HGs and LM-HGs), proline (P), hydrogen peroxide (HP), total soluble protein (Prot), catalase activity (CAT), peroxidase activity (POD), chlorophyll a, b and total (Cha, Chb and TC), carotenoid (Carot) contents, as well as SeNHX1 and SeSOS1 gene expression, were all determined per triplicate (plants were randomly selected). The collection of plant material, comply with relevant institutional, national, and international guidelines and legislation, IUCN Policy Statement on Research Involving Species at Risk of Extinction and Convention on the Trade in Endangered Species of Wild Fauna and Flora. The voucher specimen of the plant material has been deposited in a publicly available herbarium of the Nicolaus Copernicus University in Toruń (Index Herbarium code TRN), deposition number not available (dr. hab. Agnieszka Piernik, prof. NCU undertook the formal identification of plant species, and permission to work with the seeds was provided by the Regional Director of Environmental Protection in Bydgoszcz, WOP.6400.12.2020.JC).Anatomical image analysisFrom the middle primary branch (fleshy segment shoot) of S. europaea plant treatments (0, 200, 400, 600, 800 and 1000 mM NaCl), slices of fresh tissue were obtained by cutting them with a sharp bi-shave blade. The thinner slices of approximately 0.5 mm were selected and used in the microstructure analysis. The size and shape of the stem-cortex cells from the fresh water-storing tissue were characterised by a light microscope (Olympus BX51, USA) connected to a digital camera (DP72 digital microscope camera) and digital acquisition software (DP2-BSW). The microscope images were captured at a magnification of 10 ×/0.30 in RGB scale and stored in TIFF format at 1280 × 1024 pixels. A total of 300 ± 50 cells from five individuals per treatment were analysed. Finally, the shape and size of the cells were obtained from the captured images. Cell image analysis (IA) was performed in ImageJ v. 1.47 (National Institutes of Health, Bethesda, MD, USA). The following anatomical parameters were obtained. Firstly, the cell area (A) was estimated as the number of pixels within the boundary. Secondly, the maximum cell’s diameter (Cdiam) was determined by the distance between the two points separated by the largest coordinates in different orientations, and the cell roundness (R) was obtained through the equation R = (4 A)/(π (Cdiam)2)—where a perfectly round cell has R = 1.0, while elongated cells will show an R → 0. Finally, the degree of succulence (S) in stem was calculated according to24 with slight change S = (Fresh Weight-Dry Weight)/stem Area, where the Area of the stem (As) was calculated as: As = π × r2, the diameter of the stems was obtained according to Cárdenas-Pérez et al.25.Immunolocalisation experimentsThe samples dissected from the middle segment of the shoot (3 individuals per treatment) were prepared for embedding in BMM resin (butyl methacrylate, methyl methacrylate, 0.5% benzoyl ethyl ether (Sigma) with 10 mM DDT (Thermo Fisher Scientific) according to Niedojadło et al.54. Next, specimens were cut on a Leica UCT ultramicrotome into serial semi-thin cross sections (1.5 µm) that were collected on Thermo Scientific Polysine adhesion microscope slides. Before immunocytochemical reaction, the resin was removed with two changes of acetone and washed in distilled water and PBS pH 7.2. After incubation with blocking solution containing 2% BSA (bovine serum albumin, Sigma) in PBS pH 7.2 for 30 min at room temperature, the sections were incubated with anti-pectin rat monoclonal primary antibody JIM7 (recognises partially methylesterified epitopes of homogalacturonan [HG] but does not bind to fully de-esterified HGs) or antibody LM19 (recognises partially methylesterified epitopes of HG and binds strongly to de-esterified HGs) (Plant Probes) diluted 1:50 in 0.2% BSA in PBS pH 7.2 overnight at 4 °C. After washing with PBS pH 7.2, the material was incubated with AlexaFluor 488-conjugated goat anti-rat secondary antibody (Thermo Fisher Scientific) diluted 1:1000 in 0.2% BSA in PBS pH 7.2 for 1 h at 37 °C. Finally, the sections were washed in PBS pH 7.2, dried at room temperature and covered with ProLongTMGold antifade reagent (Thermo Fisher Scientific). The control reactions were performed with the omission of incubation with primary antibodies. Semithin sections were analysed with an Olympus BX50 fluorescence microscope, with an UPlanFI 1009 (N.A. 1.3) oil immersion lens and narrow band filters (U-MNU, U-MNG). The results were recorded with an Olympus XC50 digital colour camera and CellB software (Olympus Soft Imaging Solutions GmbH, Germany).Fluorescence quantitative evaluationFor the quantitative measurement, each experiment was performed using consistent temperatures, incubation times and concentrations of antibodies. The aforementioned ImageJ (1.47v) software was used for image processing and analysis. The fluorescence intensity was measured for five semi-thin sections for each experimental population (Inowrocław and Ciechocinek) at the same magnification (100 ×) and the constant exposure time to ensure comparable results. The threshold fluorescence in the sample was established based on the autofluorescence of the control reaction. The level of signal intensity was expressed in arbitrary units (a.u.) as the mean intensity per μm2 according to Niedojadło et al.54.Biochemical analysisProline content (P) was measured according to Ábrahám et al.55. Five hundred milligrams of fresh stem material was minced on ice and homogenised with 3% aqueous sulfosalicylic acid solution (5 μl mg−1 fresh plant material), centrifuged at 18,000×g, 10 min at 4 °C, and the supernatant was collected. The reaction mixture: 100 μl of 3% sulphosalicylic acid, 200 μl of glacial acetic acid, 200 μl of acidic ninhydrin reagent and 100 μl of supernatant. Acidic ninhydrin reagent was prepared according to Bates et al.56. The standard curve for proline in the concentration range of 0 to 40 μg ml−1. The standard curve equation was y = 0.0467x − 0.0734, R2 = 0.963. P was expressed in mg of proline per gram of fresh weight. Hydrogen peroxide (HP) levels were determined according to the methods described by Velikova et al.57, and 500 mg of stem tissues were homogenised with 5 ml trichloroacetic acid 0.1% (w:v) in an ice bath. The homogenate was centrifuged (12,000×g, 4 °C, 15 min) and 0.5 ml of the supernatant was added to potassium phosphate buffer (0.5 ml) (10 mM, pH 7.0) and 2 ml of 1 M KI. The absorbance was read at 390 nm, and the HP content was given on a standard curve from 0 to 40 mM. The standard curve equation was y = 0.0188x + 0.046, R2 = 0.987. HP concentrations were expressed in nM per gram of fresh weight. Chlorophylls (Cha and Chb) and carotenoids were extracted from fresh plant stems (100 mg) using 80% acetone for 6 h in darkness, and then centrifuged at 10,000 rpm, 10 min. Supernatants were quantified spectrophotometrically. Absorbance was determined at 646, 663 and 470 nm and calculations were performed according to Lichtenthaler and Wellburn58, when 80% of acetone is used as dissolvent. Total chlorophyll content was calculated as the sum of chlorophyll a and b contents.Total CAT activity was determined spectrophotometrically by following the decline in A240 as H2O2 (ε = 39.9 M−1 cm−1) was catabolised, according to the method of Beers and Sizer59. Decrease in absorbance of the reaction at 240 nm was recorded after every 20 s. One unit CAT was defined as an absorbance change of 0.01 units min−1. Total POD activity was determined spectrophotometrically by monitoring the formation of tetraguaiacol (ε = 26.6 mM−1 cm−1) from guaiacol at A470 in the presence of H2O2 by the method of Chance and Maehly60. Increase in absorbance of the reaction solution at 470 nm was recorded after every 20 s. One unit of POD activity was defined as an absorbance change of 0.01 units min−1. Total soluble protein (Prot) content was measured according to Bradford61 using bovine serum albumin (BSA) as a protein standard. Fresh leaf samples (1 g) were homogenised with 4 ml Na-phosphate buffer (pH 7.2) and then centrifuged at 4 °C. Supernatant and dye were pipetted in spectrophotometer cuvettes and absorbances were measured using a UV–vis spectrophotometer (PG instruments T80) at 595 nm62. Prot was determined based on the standard curve y = 1.6565x + 0.0837, R2 = 0.982, for total soluble protein in the concentration range of 0 to 1.2 mg ml−1 BSA. Triplicates per treatment were used for each analysis.Total RNA isolationAfter 2 months of salt treatment, shoots of S. europaea plants (3 individuals per treatment) were washed several times with tap water and then three times with miliQ water. After drying, plant material was frozen in liquid nitrogen, and stored at − 80 °C. Total RNA isolation was performed using RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The quality and quantity of RNA was checked on 1.5% agarose gels in TAE (Tris–HCl, acetic acid, EDTA, pH 8.3) buffer stained with ethidium bromide, and by spectrophotometric measurement (NanoDrop Lite, Thermo Fisher Scientific, Waltham, MA, USA).Cloning of SOS1 gene from S. europaea (SeSOS1)One microgram (1 µg) of total RNA isolated from shoots of S. europaea was primed with 0.5 µg of oligo (dT)20 primer for 5 min at 70 °C. Then 4 µl of ImProm-II 5 × reaction buffer, 2 mM MgCl2, 0.5 mM each dNTP, 20 U of recombinant RNasin ribonuclease inhibitor, and 1 µl of ImProm-II reverse transcriptase (Promega, Madison, WI, USA) were added to a final volume of 20 µl. The reaction was performed at 42 °C for 60 min. To design degenerate primers for SOS1, cDNA sequences from Arabidopsis thaliana (NM_126259.4), Lycopersicon esculentum (AJ717346.1), Mesembryanthemum crystallinum (EF207776.1), Oryza sativa (AY785147.1), Triticum aestivum (AY326952.3), Salicornia brachiata (EU879059.1) were obtained from NCBI GeneBank. The sequences were aligned using the Clustal Omega tool (https://www.ebi.ac.uk/Tools/msa/clustalo/) and three pairs of degenerate primes were designed (listed in Table 4). The PCR reaction mixture includes cDNA, 0.2 µM each primer, 0.2 mM each dNTP, 4 µl of 5 × HF buffer, and 0.5 U of Phusion High-Fidelity DNA polymerase (Thermo Fisher Scientific, Waltham, MA, USA) in a total volume of 20 µl. The thermal conditions were as follows: 98 °C for 30 s, 98 °C for 10 s, gradient between 48 °C and 56 °C for 20 s, 72 °C for 60 s, 32 cycle, final extension for 10 min at 72 °C. A pair of primers deg2_F and deg2_R yielded a PCR product with expected size. The PCR product was purified from agarose gel, cloned into pJET1.2 vector (Thermo Fisher Scientific, Waltham, MA, USA) according to manufacturer’s protocol and sequenced (Genomed, Warsaw, Poland). The obtained partial cDNA sequence was named SeSOS1 and deposited in NCBI GeneBank (acc. no. MZ707082).Table 4 Sequences of the primers used for cloning of SeSOS1 and quantitative real-time PCR.Full size tableReverse transcription reaction and quantitative real-time PCR (qPCR) SeNHX1 and SeSOS1 gene expression analysisPrior to reverse transcription reaction, RNA was treated with DNaseI (Thermo Fisher Scientific, Waltham, MA, USA). The cDNA was synthesised from 1.5 µg of total RNA using a mixture of 2.5 µM oligo(dT)20 primer and 0.2 µg of random hexamers with NG dART RT Kit (Eurx, Gdańsk, Poland) according to the manufacturer’s protocol. The reaction was performed at 25 °C for 10 min, followed by 50 min at 50 °C. The cDNA was stored at − 20 °C.The PCR reaction mixture includes 4 µl of 1/20 diluted cDNA, 0.5 µM gene-specific primers (Table 4) and 5 µl of LightCycler 480 SYBR Green I Master (Roche, Penzberg, Germany) in a total volume of 10 µl. Clathrin adaptor complexes (CAC) was used as a reference gene63. The reaction was performed in triplicate (technical replicates) in LightCycler 480 Instrument II (Roche, Penzberg, Germany). The thermal cycling conditions were as follows: 95 °C for 5 min, 95 °C for 10 s, 60 °C for 20 s, 72 °C for 20 s, 40 cycles. The SYBR Green I fluorescence signal was recorded at the end of the extension step in each cycle. The specificity of the assay was confirmed by the melt curve analysis i.e., increasing the temperature from 55 to 95 °C at a ramp rate 0.11 °C/s. The fold-change in gene expression was calculated using LightCycler 480 Software release 1.5.1.62 (Roche, Penzberg, Germany).Statistical and multivariate analysisIn order to determine the projection of the effect of salt treatment in plants we followed Cárdenas-Pérez et al.25 methodology. A principal component analysis (PCA) was developed using XLSTAT software version 2019.4.165. For this analysis, 14 variables were used, (A, Cdiam, R, Prot, CAT, POD, HM-HGs, LM-HGs, P, HP, Cha, Chb, TC, Carot), arranged in a matrix with the average values obtained from replicates of each treatment and population. A two-way ANOVA comparing treatments within populations and populations within treatments was conducted for all the results with the Holm–Sidak method. The data was fit with a modified three parameter exponential decay using SigmaPlot version 11.066. The relationships between variables were performed using a Pearson analysis, while a significance test (Kaisere Meyere Olkin) was performed in order to determine which variables had a significant correlation with each other (α = 0.05). Then, a 3D plot was developed using the three principal component factors according to the Kaiser criterion which stated that the factors below the unit are irrelevant. The three main factorial scores of the PCA from each sample were used to calculate the distance (D) between the two points (populations) under the same treatment P1 = (x1, y1, z1) and P2 = (x2, y2, z2) in 3D space of the PCA (Eq. 1).$$D ( {P_{1} ,, P_{2} } ) = sqrt {( {x_{2} – x_{1} } )^{2} + ( {y_{2} – y_{1} } )^{2} + ( {z_{2} – z_{1} } )^{2} }$$
(1)
where x, y, and z are the three main factorial scores in the PCA corresponding to the evaluated treatment in Inw and in Cie. Distances were used to evaluate and determine in which salt treatment the greatest differences between the populations were recorded. More