Philippa Kaur

arising from Y. Wang et al. Nature https://doi.org/10.1038/s41586-021-04016-x (2021)A unique challenge for environmental DNA (eDNA)-based palaeoecological reconstructions and extinction estimates is that organisms can contribute DNA to sediments long after their death. Recently, Wang et al.1 discovered mammoth eDNA in sediments that are between approximately 4.6 and 7 thousand years (kyr) younger than the most recent mammoth fossils in North America and Eurasia, which they interpreted as mammoths surviving on both continents into the Middle Holocene epoch. Here we present an alternative explanation for these offsets: the slow decomposition of mammoth tissues on cold Arctic landscapes is responsible for the release of DNA into sediments for thousands of years after mammoths went extinct. eDNA records are important palaeobiological archives, but the mixing of undatable DNA from long-dead organisms into younger sediments complicates the interpretation of eDNA, particularly from cold and high-latitude systems.All animal tissues, including faeces, contribute DNA to eDNA records2, but the durations across which tissues can contribute genetic information must vary depending on tissue type and local rates of destruction and decomposition. On high-latitude landscapes, soft tissues and skeletal remains of large mammals may persist, unburied, for millennia3,4,5. For example, unburied antlers of caribou (Rangifer tarandus) from Svalbard (Norway) and Ellesmere Island (Canada) have been dated3,4 to between 1 and 2 cal kyr bp (calibrated kyr before present). Elephant seal (Mirounga leonina) remains near the Antarctic coastline5,6 can persist for more than 5,000 years. This is in contrast to bones in warmer settings, which persist for only centuries or decades7,8. Because bones are particularly resistant to decay, quantifying how their persistence changes across environments enables us to constrain the durations that dead individuals generally contribute to eDNA archives. To do this, we consolidated data on the oldest radiocarbon-dated surface-collected bones from different ecosystems. We included bones that we are reasonably confident persisted without being completely buried (‘never buried’), and bones for which exhumation cannot be confidently excluded (‘potentially never buried’). Pairing bone persistence with mean annual temperatures (MAT) from their sample localities, we find a strong link between the local temperature and the logged duration of bone persistence (Fig. 1, never buried bones: R2 = 0.94, P  More

Author notesMikayla M. ClarkPresent address: University of Tennessee, Knoxville, TN, USAMichael W. SneddonPresent address: Predicine, Inc., Hayward, CA, USARoman SutorminPresent address: Google, Inc., San Francisco, CA, USAAuthors and AffiliationsLawrence Berkeley National Laboratory, Berkeley, CA, USADylan Chivian, Sean P. Jungbluth, Paramvir S. Dehal, Elisha M. Wood-Charlson, Richard S. Canon, Gavin A. Price, William J. Riehl, Michael W. Sneddon, Roman Sutormin & Adam P. ArkinOak Ridge National Laboratory, Oak Ridge, TN, USABenjamin H. Allen, Mikayla M. Clark, Miriam L. Land & Robert W. CottinghamArgonne National Laboratory, Lemont, IL, USATianhao Gu, Qizhi Zhang & Chris S. HenryAuthorsDylan ChivianSean P. JungbluthParamvir S. DehalElisha M. Wood-CharlsonRichard S. CanonBenjamin H. AllenMikayla M. ClarkTianhao GuMiriam L. LandGavin A. PriceWilliam J. RiehlMichael W. SneddonRoman SutorminQizhi ZhangRobert W. CottinghamChris S. HenryAdam P. ArkinCorresponding authorsCorrespondence to
Dylan Chivian or Adam P. Arkin. More

von Humboldt, A., and A. Bonpland. Essai sur la geographiedes plantes. Chez Levrault, Schoell et Campagnie, Libraries, Paris.(1805).Malhi, Y. et al. Introduction: elevation gradients in the tropics: laboratories for ecosystem ecology and global change research. Glob. Change Biol. 16, 3171–3175 (2010).Article 

Google Scholar 
Nottingham, A. T. et al. Climate warming and soil carbon in tropical forests: insights from an elevation gradient in the Peruvian Andes. BioScience 65, 906–921 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Malhi, Y. et al. The variation of productivity and its allocation along a tropical elevation gradient: a whole carbon budget perspective. N. Phytologist 214, 1019–1032 (2017).Article 
CAS 

Google Scholar 
Nottingham, A. T. et al. Soil microbial nutrient constraints along a tropical forest elevation gradient: a belowground test of a biogeochemical paradigm. Biogeosciences 12, 6071–6083 (2015).Article 

Google Scholar 
Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).Article 
PubMed 

Google Scholar 
Jenny, H., Bingham, F. & Padillasaravia, B. Nitrogen and organic matter contents of equatorial soils of Colombia, South-America. Soil Sci. 66, 173–186 (1948).Article 
CAS 

Google Scholar 
Tanner, E., Vitousek, P. & Cuevas, E. Experimental investigation of nutrient limitation of forest growth on wet tropical mountains. Ecology 79, 10–22 (1998).Article 

Google Scholar 
Vitousek, P. M., Matson, P. A. & Turner, D. R. Elevational and age gradients in Hawaiian montane rainforest: foliar and soil nutrients. Oecologia 77, 565–570 (1988).Article 
PubMed 

Google Scholar 
Vitousek, P. M. & Sanford, R. L. Nutrient cycling in moist tropical forest. Annu. Rev. Ecol. Syst. 17, 137–167 (1986).Article 

Google Scholar 
Krishnaswamy, J., John, R. & Joseph, S. Consistent response of vegetation dynamics to recent climate change in tropical mountain regions. Glob. Change Biol. 20, 203–215 (2014).Article 

Google Scholar 
Duque, A. et al. Mature Andean forests as globally important carbon sinks and future carbon refuges. Nat. Commun. 12, 2138 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fadrique, B. et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature 564, 207–212 (2018).Article 
CAS 
PubMed 

Google Scholar 
Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).Article 
PubMed 

Google Scholar 
Marrs, R. H., Proctor, J., Heaney, A. & Mountford, M. D. Changes in soil nitrogen-mineralization and nitrification along an altitudinal transect in tropical rain forest in Costa Rica. J. Ecol. 76, 466–482 (1988).Grubb, P. J. Control of forest growth and distribution on wet tropical mountains: with special reference to mineral nutrition. Annu. Rev. Ecol. Syst. 8, 83–107 (1977).Article 
CAS 

Google Scholar 
Wolf, K., Veldkamp, E., Homeier, J. & Martinson, G. O. Nitrogen availability links forest productivity, soil nitrous oxide and nitric oxide fluxes of a tropical montane forest in southern Ecuador. Glob. Biogeochem. Cycles 25, GB4009 (2011).Barthel, M. et al. Low N2O and variable CH4 fluxes from tropical forest soils of the Congo Basin. Nat. Commun. 13, 330 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brookshire, E. N. J., Hedin, L. O., Newbold, J. D., Sigman, D. M. & Jackson, J. K. Sustained losses of bioavailable nitrogen from montane tropical forests. Nat. Geosci. 5, 123–126 (2012).Article 
CAS 

Google Scholar 
Rütting, T. et al. Leaky nitrogen cycle in pristine African montane rainforest soil. Glob. Biogeochem. Cycles 29, 1754–1762 (2015).Article 

Google Scholar 
Batjes, N. H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 47, 151–163 (1996).Article 
CAS 

Google Scholar 
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL 7, 217–240 (2021).Article 
CAS 

Google Scholar 
Bauters, M. et al. Parallel functional and stoichiometric trait shifts in South American and African forest communities with elevation. Biogeosciences 14, 5313–5321 (2017).Article 
CAS 

Google Scholar 
Dalling, J. W., Heineman, K., González, G. & Ostertag, R. Geographic, environmental and biotic sources of variation in the nutrient relations of tropical montane forests. J. Tropical Ecol. 32, 368–383 (2016).Article 

Google Scholar 
Porder, S., Vitousek, P., Chadwick, O., Chamberlain, C. & Hilley, G. Uplift, erosion, and phosphorus limitation in terrestrial ecosystems. Ecosystems 10, 158–170 (2007).Article 
CAS 

Google Scholar 
Houlton, B. Z., Morford, S. L. & Dahlgren, R. A. Convergent evidence for widespread rock nitrogen sources in Earth’s surface environment. Science 360, 58–62 (2018).Article 
CAS 
PubMed 

Google Scholar 
Hilton, R. G., Galy, A., West, A. J., Hovius, N. & Roberts, G. G. Geomorphic control on the delta N-15 of mountain forests. Biogeosciences 10, 1693–1705 (2013).Article 
CAS 

Google Scholar 
Vitousek, P. M., Van Cleve, K., Balakrishnan, N. & Mueller-Dombois, D. Soil development and nitrogen turnover in montane rainforest soils on Hawai’i. Biotropica 268–274 (1983).Taylor, P. G. et al. Temperature and rainfall interact to control carbon cycling in tropical forests. Ecol. Lett. 20, 779–788 (2017).Article 
PubMed 

Google Scholar 
Houlton, B. & Bai, E. Imprint of denitrifying bacteria on the global terrestrial biosphere. Proc. Natl Acad. Sci. USA 106, 21713–21716 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, Z. et al. The age distribution of global soil carbon inferred from radiocarbon measurements. Nat. Geosci. 13, 555–559 (2020).Article 
CAS 

Google Scholar 
Craine, J. M. et al. Ecological interpretations of nitrogen isotope ratios of terrestrial plants and soils. Plant and Soil 396, 1–26 (2015).Högberg, P. Tansley Review No. 95. 15N Natural Abundance in Soil-Plant Systems. N. Phytologist 137, 179–203 (1997).Article 

Google Scholar 
Martinelli, L. et al. Nitrogen stable isotopic composition of leaves and soil: Tropical versus temperate forests. Biogeochemistry 46, 45–65 (1999).Article 
CAS 

Google Scholar 
Amundson, R. et al. Global patterns of the isotopic composition of soil and plant nitrogen. Glob. Biogeochem. Cycles 17, (2003).Craine, J. M. et al. Convergence of soil nitrogen isotopes across global climate gradients. Sci. Rep. 5, 8280 (2015).Mooshammer, M. et al. Adjustment of microbial nitrogen use efficiency to carbon:nitrogen imbalances regulates soil nitrogen cycling. Nat. Commun. 5, 3694 (2014).Camenzind, T., Hättenschwiler, S., Treseder, K. K., Lehmann, A. & Rillig, M. C. Nutrient limitation of soil microbial processes in tropical forests. Ecol. Monogr. 88, 4–21 (2018).Article 

Google Scholar 
Mariotti, A., Pierre, D., Vedy, J. C., Bruckert, S. & Guillemot, J. The abundance of natural nitrogen 15 in the organic matter of soils along an altitudinal gradient (Chablais, Haute Savoie, France). Catena 7, 293–300 (1980).Article 
CAS 

Google Scholar 
Sena‐Souza, J. P., Houlton, B. Z., Martinelli, L. A. & Nardoto, G. B. Reconstructing continental-scale variation in soil δ15N: a machine learning approach in South America. Ecosphere 11, e03223 (2020).Article 

Google Scholar 
Nottingham, A. T., Bååth E., Reischke, S., Salinas, N. & Meir, P. Adaptation of soil microbial growth to temperature: Using a tropical elevation gradient to predict future changes. Glob. change Biol. 25, 827–838 (2019).Liu, Y. et al. A global synthesis of the rate and temperature sensitivity of soil nitrogen mineralization: latitudinal patterns and mechanisms. Glob. Change Biol. 23, 455–464 (2017).Article 

Google Scholar 
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).Article 
CAS 
PubMed 

Google Scholar 
Zimmermann, M. & Bird, M. I. Temperature sensitivity of tropical forest soil respiration increase along an altitudinal gradient with ongoing decomposition. Geoderma 187–188, 8–15 (2012).Article 

Google Scholar 
Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Change Biol. 17, 798–818 (2011).Article 

Google Scholar 
Wright, S. J. Plant responses to nutrient addition experiments conducted in tropical forests. Ecol. Monogr. 89, e01382 (2019).Article 

Google Scholar 
Brookshire, E. N. J., Gerber, S., Menge, D. N. L. & Hedin, L. O. Large losses of inorganic nitrogen from tropical rainforests suggest a lack of nitrogen limitation. Ecol. Lett. 15, 9–16 (2012).Article 
CAS 
PubMed 

Google Scholar 
Corrales, A., Henkel, T. W. & Smith, M. E. Ectomycorrhizal associations in the tropics—biogeography, diversity patterns and ecosystem roles. N. Phytologist 220, 1076–1091 (2018).Article 

Google Scholar 
Zeng, Z. et al. Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat. Geosci. 1–7 https://doi.org/10.1038/s41561-020-00666-0 (2020).Nogués-Bravo, D., Araújo, M. B., Errea, M. P. & Martínez-Rica, J. P. Exposure of global mountain systems to climate warming during the 21st Century. Glob. Environ. Change 17, 420–428 (2007).Article 

Google Scholar 
Weintraub, S. R., Cole, R. J., Schmitt, C. G. & All, J. D. Climatic controls on the isotopic composition and availability of soil nitrogen across mountainous tropical forest. Ecosphere 7, e01412 (2016).Article 

Google Scholar 
Brookshire, E. N. J. & Thomas, S. A. Ecosystem consequences of tree monodominance for nitrogen cycling in lowland tropical forest. PLoS ONE 8, e70491 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kitayama, K. & Iwamoto, K. Patterns of natural 15N abundance in the leaf-to-soil continuum of tropical rain forests differing in N availability on Mount Kinabalu, Borneo. Plant Soil 229, 203–212 (2001).Article 
CAS 

Google Scholar 
Bauters, M. et al. Contrasting nitrogen fluxes in African tropical forests of the Congo Basin. Ecol. Monogr. 89, e01342 (2019).Article 

Google Scholar 
Proctor, J., Edwards, I. D., Payton, R. W. & Nagy, L. Zonation of forest vegetation and soils of Mount Cameroon, West Africa. Plant Ecol. 192, 251–269 (2007).Article 

Google Scholar 
Grubb, P. J. & Stevens, P. F. The Forests of the Fatima Basin and Mt Kerigomna, Papua New Guinea with a Review of Montane and Subalpine Rainforests in Papuasia (Department of Human Geography, Research School of Pacific Studies…, 2017).Dieleman, W. I. J., Venter, M., Ramachandra, A., Krockenberger, A. K. & Bird, M. I. Soil carbon stocks vary predictably with altitude in tropical forests: Implications for soil carbon storage. Geoderma 204–205, 59–67 (2013).Article 

Google Scholar 
Kapos, V., Rhind, J., Edwards, M., Price, M. F. & Ravilious, C. in Forests in sustainable mountain development: a state of knowledge report for 2000. Task Force on Forests in Sustainable Mountain Development. 4–19 (CABI, 2000). https://doi.org/10.1079/9780851994468.0004.Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digital Earth 6, 427–448 (2013).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org (2022).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48, https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Bartoń K. MuMIn: Multi-Model Inference. R package version 1.43.17 (2020).Grömping, U. Relative Importance for Linear Regression in R: The Package Relaimpo. J. Stat. Softw. 17, 1–27 (2006).Article 

Google Scholar 
Baty, F. et al. A Toolbox for Nonlinear Regression in R: The Package nlstools. J. Stat. Softw. 66, 1–21 (2015).Article 

Google Scholar  More

Department of Zoology, University of Cambridge, Cambridge, UKYucheng Wang, Bianca De Sanctis, Ruairidh Macleod, Daniel Money & Eske WillerslevLundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, DenmarkYucheng Wang, Ana Prohaska, Jialu Cao, Antonio Fernandez-Guerra, James Haile, Kurt H. Kjær, Thorfinn Sand Korneliussen, Nicolaj Krog Larsen, Ruairidh Macleod, Hugh McColl, Mikkel Winther Pedersen, Fernando Racimo, Alexandra Rouillard, Anthony H. Ruter, Lasse Vinner, David J. Meltzer & Eske WillerslevALPHA, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research (ITPCAS), Chinese Academy of Sciences (CAS), Beijing, ChinaYucheng WangKey Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Science, Lanzhou University, Lanzhou, ChinaHaoran DongGénomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, FranceAdriana Alberti, France Denoeud & Patrick WinckerInstitute for Integrative Biology of the Cell (I2BC), Université Paris-Saclay, CEA, CNRS, Gif-sur-Yvette, FranceAdriana AlbertiThe Arctic University Museum of Norway, UiT—The Arctic University of Norway, Tromsø, NorwayInger Greve Alsos, Eric Coissac, Galina Gusarova, Youri Lammers & Marie Kristine Føreid MerkelDepartment of Geography and Environment, University of Hawaii, Honolulu, HI, USADavid W. BeilmanDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, DenmarkAnders A. BjørkInstitute of Earth Sciences, St Petersburg State University, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovArctic and Antarctic Research Institute, St Petersburg, RussiaAnna A. Cherezova & Grigory B. FedorovUniversité Grenoble-Alpes, Université Savoie Mont Blanc, CNRS, LECA, Grenoble, FranceEric CoissacDepartment of Genetics, University of Cambridge, Cambridge, UKBianca De Sanctis & Richard DurbinCarlsberg Research Laboratory, Copenhagen V, DenmarkChristoph Dockter & Birgitte SkadhaugeSchool of Geography and Environmental Science, University of Southampton, Southampton, UKMary E. EdwardsAlaska Quaternary Center, University of Alaska Fairbanks, Fairbanks, AK, USAMary E. EdwardsSchool of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UKNeil R. Edwards & Philip B. HoldenCenter for the Environmental Management of Military Lands, Colorado State University, Fort Collins, CO, USAJulie EsdaleDepartment of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, CanadaDuane G. FroeseFaculty of Biology, St Petersburg State University, St Petersburg, RussiaGalina GusarovaDepartment of Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen K, DenmarkKristian K. KjeldsenDepartment of Earth Science, University of Bergen, Bergen, NorwayJan Mangerud & John Inge SvendsenBjerknes Centre for Climate Research, Bergen, NorwayJan Mangerud & John Inge SvendsenDepartment of Geology, Quaternary Sciences, Lund University, Lund, SwedenPer MöllerCenter for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen Ø, DenmarkDavid Nogués-Bravo, Hannah Lois Owens & Carsten RahbekCentre d’Anthropobiologie et de Génomique de Toulouse, Faculté de Médecine Purpane, Université Paul Sabatier, Toulouse, FranceLudovic OrlandoCenter for Global Mountain Biodiversity, Globe Institute, University of Copenhagen, Copenhagen, DenmarkHannah Lois Owens & Carsten RahbekGates of the Arctic National Park and Preserve, US National Park Service, Fairbanks, AK, USAJeffrey T. RasicDepartment of Geosciences, UiT—The Arctic University of Norway, Tromsø, NorwayAlexandra RouillardZoological Institute, Russian academy of sciences, St Petersburg, RussiaAlexei TikhonovResource and Environmental Research Center, Chinese Academy of Fishery Sciences, Beijing, ChinaYingchun XingCollege of Plant Science, Jilin University, Changchun, Jilin, ChinaYubin ZhangDepartment of Anthropology, Southern Methodist University, Dallas, TX, USADavid J. MeltzerWellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UKEske WillerslevMARUM, University of Bremen, Bremen, GermanyEske WillerslevAll authors contributed to the conception of the presented ideas. Y.W. and H.D. analysed the data. Y.W., D.J.M., A.P. and E.W. wrote the paper with inputs from all authors. More

Olszewska-Guizzo, A., Fogel, A., Benjumea, D. & Tahsin, N. Sustainable Policies and Practices in Energy, Environment and Health Research 223–243 (Springer, 2022).Book 

Google Scholar 
Gascon, M. et al. Mental health benefits of long-term exposure to residential green and blue spaces: A systematic review. Int. J. Environ. Res. Public Health 12, 4354–4379. https://doi.org/10.3390/ijerph120404354 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Houlden, V., Weich, S., Porto-de-Albuquerque, J., Jarvis, S. & Rees, K. The relationship between greenspace and the mental wellbeing of adults: A systematic review. PLoS ONE 13, 3000 (2018).Article 

Google Scholar 
Hung, S.-H. & Chang, C.-Y. Health benefits of evidence-based biophilic-designed environments: A review. J. People Plants Env. 24, 1–16 (2021).Article 

Google Scholar 
Berman, M. G., Jonides, J. & Kaplan, S. The cognitive benefits of interacting with nature. Psychol. Sci. 19, 1207–1212. https://doi.org/10.1111/j.1467-9280.2008.02225.x (2008).Article 
PubMed 

Google Scholar 
Kaplan, S. Meditation, restoration, and the management of mental fatigue. Environ. Behav. 33, 480–506. https://doi.org/10.1177/00139160121973106 (2001).Article 

Google Scholar 
Ulrich, R. S. et al. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 11, 201–230 (1991).Article 

Google Scholar 
Kellert, S. R. & Wilson, E. O. The Biophilia Hypothesis (Island Press, 1993).
Google Scholar 
Stack, K. & Shultis, J. Implications of attention restoration theory for leisure planners and managers. Leisure/Loisir 37, 1–16 (2013).Article 

Google Scholar 
Steel, Z. et al. The global prevalence of common mental disorders: A systematic review and meta-analysis 1980–2013. Int. J. Epidemiol. 43, 476–493. https://doi.org/10.1093/ije/dyu038 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Mueller, D. P. The current status of urban-rural differences in psychiatric disorder. An emerging trend for depression. J. Nerv. Ment. Dis. 169, 18–27 (1981).Article 
CAS 
PubMed 

Google Scholar 
Peen, J., Schoevers, R. A., Beekman, A. T. & Dekker, J. The current status of urban-rural differences in psychiatric disorders. Acta Psychiatr. Scand. 121, 84–93. https://doi.org/10.1111/j.1600-0447.2009.01438.x (2010).Article 
CAS 
PubMed 

Google Scholar 
Taylor, L. & Hochuli, D. F. Defining greenspace: Multiple uses across multiple disciplines. Landsc. Urban Plan. 158, 25–38 (2017).Article 

Google Scholar 
en K Staats, H. Restorative Environments The Oxford Handbook of Environmental and Conservation Psychology 445th edn. (Oxford University Press, 2012).
Google Scholar 
Wood, L., Hooper, P., Foster, S. & Bull, F. Public green spaces and positive mental health–investigating the relationship between access, quantity and types of parks and mental wellbeing. Health Place 48, 63–71 (2017).Article 
PubMed 

Google Scholar 
Tsunetsugu, Y. et al. Physiological and psychological effects of viewing urban forest landscapes assessed by multiple measurements. Landsc. Urban Plan. 113, 90–93 (2013).Article 

Google Scholar 
Gidlow, C. J. et al. Where to put your best foot forward: Psycho-physiological responses to walking in natural and urban environments. J. Environ. Psychol. 45, 22–29 (2016).Article 

Google Scholar 
Lee, J. Experimental study on the health benefits of garden landscape. Int. J. Environ. Res. Public Health 14, 829 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Fuller, R. A., Irvine, K. N., Devine-Wright, P., Warren, P. H. & Gaston, K. J. Psychological benefits of greenspace increase with biodiversity. Biol. Lett. 3, 390–394 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Thompson, C. W., Aspinall, P. & Bell, S. Innovative Approaches to Researching Landscape and Health: Open Space: People Space 2 (Routledge, 2010).Book 

Google Scholar 
Tsutsumi, M., Nogaki, H., Shimizu, Y., Stone, T. E. & Kobayashi, T. Individual reactions to viewing preferred video representations of the natural environment: A comparison of mental and physical reactions. Jpn. J. Nurs. Sci. 14, 3–12 (2017).Article 
PubMed 

Google Scholar 
Grazuleviciene, R. et al. Tracking restoration of park and urban street settings in coronary artery disease patients. Int. J. Environ. Res. Public Health 13, 550 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Bostancı, S. H. In New Approaches to Spatial Planning and Design (ed Murat Özyavuz) Ch. 32, 435–451 (Peter Lang, 2019).Daniel, T. C. Measuring Landscape Esthetics: The Scenic Beauty Estimation Method, vol. 167 (Department of Agriculture, Forest Service, Rocky Mountain Forest and Range…, 1976).Bacon, W. R. In (eds Elsner G. H. et al) Technical Coordinators. Proceedings of our national landscape: A conference on applied techniques for analysis and management of the visual resource [Incline Village, Nev., April 23–25, 1979]. Gen. Tech. Rep. PSW-GTR-35. Berkeley, CA. Pacific Southwest Forest and Range Exp. Stn., Forest Service, US Department of Agriculture 660–665 (1979).Gavrilidis, A. A., Ciocănea, C. M., Niţă, M. R., Onose, D. A. & Năstase, I. I. Urban landscape quality index—planning tool for evaluating urban landscapes and improving the quality of life. Procedia Environ. Sci. 32, 155–167. https://doi.org/10.1016/j.proenv.2016.03.020 (2016).Article 

Google Scholar 
Knobel, P. et al. Development of the urban green space quality assessment tool (RECITAL). Urban For. Urban Green. 57, 126895 (2021).Article 

Google Scholar 
Bacon, W. R. & Dell, J. National Forest Landscape Management (Forest Service, US Department of Agriculture, 1973).Kaplan, R., Kaplan, S. & Ryan, R. With People in Mind: Design and Management of Everyday Nature (Island Press, 1998).
Google Scholar 
Smardon, R., Palmer, J. & Felleman, J. P. Foundations for Visual Project Analysis (Wiley, 1986).
Google Scholar 
Jung, C. G. Man and His Symbols Garden City (Doubleday and Co, 1964).
Google Scholar 
Olszewska, A., Marques, P. F., Ryan, R. L. & Barbosa, F. What makes a landscape contemplative?. Env. Plan. B Urban Anal. City Sci. 45, 7–25. https://doi.org/10.1177/0265813516660716 (2016).Article 

Google Scholar 
Tarkka, I. M. & Hallett, M. Cortical topography of premotor and motor potentials preceding self-paced, voluntary movement of dominant and non-dominant hands. Electroencephalogr. Clin. Neurophysiol. 75, 36–43 (1990).Article 
CAS 
PubMed 

Google Scholar 
Olszewska-Guizzo, A., Paiva, T. O. & Barbosa, F. Effects of 3D contemplative landscape videos on brain activity in a passive exposure EEG experiment. Front. Psychiatry 9, 317. https://doi.org/10.3389/fpsyt.2018.00317 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Bradley, M. M. & Lang, P. J. Measuring emotion: The self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25, 49–59. https://doi.org/10.1016/0005-7916(94)90063-9 (1994).Article 
CAS 
PubMed 

Google Scholar 
Beck, A. T., Steer, R. A. & Brown, G. K. Beck depression inventory-II. San Antonio 78, 490–498 (1996).
Google Scholar 
Ferree, T. C., Luu, P., Russell, G. S. & Tucker, D. M. Scalp electrode impedance, infection risk, and EEG data quality. Clin. Neurophysiol. 112, 536–544. https://doi.org/10.1016/S1388-2457(00)00533-2 (2001).Article 
CAS 
PubMed 

Google Scholar 
Stroganova, T. A. & Orekhova, E. V. EEG and infant states. Infant EEG Event-Relat. Potentials 251, 280 (2007).
Google Scholar 
Cacioppo, J. T., Tassinary, L. G. & Berntson, G. Handbook of Psychophysiology (Cambridge University Press, 2007).
Google Scholar 
Ulrich, R. S. Natural versus urban scenes: Some psychophysiological effects. Environ. Behav. 13, 523–556 (1981).Article 

Google Scholar 
Choi, Y., Kim, M. & Chun, C. Measurement of occupants’ stress based on electroencephalograms (EEG) in twelve combined environments. Build. Environ. 88, 65–72 (2015).Article 

Google Scholar 
Gorji, M. A. H., Davanloo, A. A. & Heidarigorji, A. M. The efficacy of relaxation training on stress, anxiety, and pain perception in hemodialysis patients. Indian J. Nephrol. 24, 356 (2014).Article 

Google Scholar 
Cahn, B. R. & Polich, J. Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol. Bull. 132, 180 (2006).Article 
PubMed 

Google Scholar 
Gruzelier, J. A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cogn. Process. 10, 101–109 (2009).Article 

Google Scholar 
Vecchiato, G. et al. Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cogn. Process. 16, 425–429 (2015).Article 
PubMed 

Google Scholar 
Lagopoulos, J. et al. Increased theta and alpha EEG activity during nondirective meditation. J. Altern. Complement. Med. 15, 1187–1192 (2009).Article 
PubMed 

Google Scholar 
Wascher, E. et al. Frontal theta activity reflects distinct aspects of mental fatigue. Biol. Psychol. 96, 57–65 (2014).Article 
PubMed 

Google Scholar 
Kabat-Zinn, J. Mindfulness. Mindfulness 6, 1481–1483 (2015).Article 

Google Scholar 
McGarrigle, T. & Walsh, C. A. Mindfulness, self-care, and wellness in social work: Effects of contemplative training. J. Relig. Spiritual. Soc. Work Soc. Thought 30, 212–233 (2011).
Google Scholar 
Grossman, P., Niemann, L., Schmidt, S. & Walach, H. Mindfulness-based stress reduction and health benefits: A meta-analysis. J. Psychosom. Res. 57, 35–43 (2004).Article 
PubMed 

Google Scholar 
Bailey, A. W., Allen, G., Herndon, J. & Demastus, C. Cognitive benefits of walking in natural versus built environments. World Leisure J. 60, 293–305 (2018).Article 

Google Scholar 
Qin, J., Zhou, X., Sun, C., Leng, H. & Lian, Z. Influence of green spaces on environmental satisfaction and physiological status of urban residents. Urban For. Urban Green. 12, 490–497 (2013).Article 

Google Scholar 
Kolb, B. & Whishaw, I. Q. Fundamentals of Human Neuropsychology (Freeman, 1990).
Google Scholar 
Milner, B. Visual recognition and recall after right temporal-lobe excision in man. Neuropsychologia 6, 191–209 (1968).Article 

Google Scholar 
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215. https://doi.org/10.1038/nrn755 (2002).Article 
CAS 
PubMed 

Google Scholar 
Chang, C.-Y. & Chen, P.-K. Human response to window views and indoor plants in the workplace. HortScience 40, 1354–1359 (2005).Article 

Google Scholar 
Herzog, T. R., Black, A. M., Fountaine, K. A. & Knotts, D. J. Reflection and attentional recovery as distinctive benefits of restorative environments. J. Environ. Psychol. 17, 165–170 (1997).Article 

Google Scholar 
Baehr, E., Rosenfeld, J. P. & Baehr, R. Clinical use of an alpha asymmetry neurofeedback protocol in the treatment of mood disorders: Follow-up study one to five years post therapy. J. Neurother. 4, 11–18 (2001).Article 

Google Scholar 
Sia, A. et al. Nature-based activities improve the well-being of older adults. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Olszewska-Guizzo, A., Sia, A., Fogel, A. & Ho, R. Can exposure to certain urban green spaces trigger frontal alpha asymmetry in the brain?—Preliminary findings from a passive task EEG study. Int. J. Environ. Res. Public Health 17, 394 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Olszewska-Guizzo, A. et al. Therapeutic garden with contemplative features induces desirable changes in mood and B rain activity in depressed adults. Front. Psychiatry https://doi.org/10.3389/fpsyt.2022.757056 (2021).Article 

Google Scholar 
Tan, S. B., Vignesh, L. N. & Donald, L. Public Housing in Singapore: Examining Fundamental Shifts (Lee Kuan Yew School of Public Policy, National University of Singapore, 2014).Tan, P. Y. Nature, Place & People: Forging Connections Through Neighbourhood Landscape Design (World Scientific Publishing Co., 2018).Book 

Google Scholar 
Peirce, J. et al. PsychoPy2: Experiments in behavior made easy. Behav. Res. Methods 51, 195–203. https://doi.org/10.3758/s13428-018-01193-y (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Edwards, A. L. Balanced Latin-square designs in psychological research. Am. J. Psychol. 64, 598–603 (1951).Article 
CAS 
PubMed 

Google Scholar 
Korpela, K. M., Ylén, M., Tyrväinen, L. & Silvennoinen, H. Determinants of restorative experiences in everyday favorite places. Health Place 14, 636–652 (2008).Article 
PubMed 

Google Scholar 
Ojala, A., Korpela, K., Tyrväinen, L., Tiittanen, P. & Lanki, T. Restorative effects of urban green environments and the role of urban-nature orientedness and noise sensitivity: A field experiment. Health Place 55, 59–70 (2019).Article 
PubMed 

Google Scholar 
Tyrväinen, L. et al. The influence of urban green environments on stress relief measures: A field experiment. J. Environ. Psychol. 38, 1–9 (2014).Article 

Google Scholar 
Herzog, T. R. & Barnes, G. J. Tranquility and preference revisited. J. Environ. Psychol. 19, 171–181 (1999).Article 

Google Scholar 
Neale, C. et al. The impact of walking in different urban environments on brain activity in older people. Cities Health 4, 94–106. https://doi.org/10.1080/23748834.2019.1619893 (2020).Article 

Google Scholar 
Kaplan, R. & Kaplan, S. The Experience of Nature: A Psychological Perspective (CUP Archive, 1989).
Google Scholar 
Treib, M. In Contemporary Landscapes of Contemplation (ed Rebecca Krinke) 27–49 (Routledge, 2005).Appleton, J. The Experience of Landscape (Wiley Chichester, 1996).
Google Scholar 
Grahn, P., Ottosson, J. & Uvnäs-Moberg, K. The oxytocinergic system as a mediator of anti-stress and instorative effects induced by nature: The calm and connection theory. Front. Psychol. 2021, 12 (2021).
Google Scholar 
Hartig, T., Mang, M. & Evans, G. W. Restorative effects of natural environment experiences. Environ. Behav. 23, 3–26. https://doi.org/10.1177/0013916591231001 (1991).Article 

Google Scholar 
Stamps Iii, A. E. Use of photographs to simulate environments: A meta-analysis. Percept. Mot. Skills 71, 907–913 (1990).Article 

Google Scholar 
Menardo, E., Brondino, M., Hall, R. & Pasini, M. Restorativeness in natural and urban environments: A meta-analysis. Psychol. Rep. 124, 417–437 (2021).Article 
PubMed 

Google Scholar  More

Area-based sub-targetWe found that 13.2% of Asian terrestrial landscapes were covered by PAs by the target date for Aichi 11 based on our in-country sources. However, it was 17.4% lower based on WDPA data (10.9%). The average increase in coverage across Asia during the 2010s was 0.4% ± SE 0.1% per year. PA coverage at the level of individual countries increased from a mean 11.1% in 2010 (SE = 1.4%) to 14.1% by 2020 (SE = 1.8%) based on our in-country sources, which was 16.5% higher than WDPA data (12.1 ± SE 1.6%). However, these overall figures concealed considerable country-level and sub-regional heterogeneity.A total of 8,673,433 km2 across 10 countries, equaling 19.6% of Asian terrestrial landscapes was managed as hunting concessions, governed by governments, communities or private sectors, but these areas have not been included in the countries’ report to the Protected Planet Initiative databases. Most of these areas are locally important in terms of biodiversity conservation and local socioeconomic outcomes which may qualify them as examples of “other effective area-based conservation measures” (OECMs). The increase in area-based conservation coverage represented by these areas, above the current Protected Planet Initiative statistic, ranged from 0.2% (Iran) to 41.4% (Russia). With that update incorporated, a total of 32.9% of Asian terrestrial landscapes are under protection, either as protected areas or hunting concessions (potentially as one type of OECMs).We found that 40% of Asian countries met a target of 17% coverage for PAs by 2020 based on our in-country sources, mainly in East and some South Asia, whereas West and Central Asian countries had generally not achieved this target (Figs. 1 and 2). We did not find any statistically significant association between the proportions of highly at-risk (CR/EN) mammalian species range outside PAs and the % PA extent in 2020 (β = −0.22 ± SE 0.15, t = −1.51, P = 0.14 in a Generalized Linear Model). The highest proportions of the highly at-risk (CR/EN) mammalian species range outside PAs were seen in West (βCR/EN_outsidePA = 1.77 ± SE 0.46, t = 3.86, P 10%, but Kuwait lost area. In East Asia, all countries showed at least some PA expansion (South Korea and Japan by >10%) whereas in Central Asia, almost no change was seen. It is also noteworthy that between 2010 and 2015, agricultural lands increased by 2.0% across the continent, averaging 0.51 ± SE 0.03% per year at country level, although 18 counties (45.0%) had agricultural land loss, mainly in West and Central Asia (12 out of 18 countries with agricultural land loss; Fig. 2).In our attempt to model the variation in achievement of area-based target (% PA extent), we found a single model with a ΔAICc weight of 1.0 (R2adj = 0.66; Table 1). There was no evidence to reject the null hypothesis that the model fits well (P = 0.99). This model included the predictors % agricultural extent in 2015, % PA extent in 2010, and sub-region (Table 1). Specifically, the coefficients suggested that countries with greater PA extent in 2010 and a smaller percentage of agricultural lands in 2015 were more likely to achieve higher percentage of PA extent by 2020 (βPAExtent2020 = 0.58 ± SE 0.10, t = 5.74, P  0.05).Table 2 Results of generalized linear models testing different hypotheses on the association between the percentage of ecoregions protected by the PA network in 2020 and ecological and geopolitical factors in Asian countries.Full size tableFor the coverage of highly at-risk (CR/EN) mammalian species, a single statistical model was also selected, with non-significant deviance goodness of fit (P = 0.83), which included only the % PA extent by 2020 and Region as predictors (R2adj = 0. 27). Although there was no evidence for association between the % PA extent by 2020 and the coverage of threatened species (βPAExtent2020 = −0.23 ± SE 0.15, t = −1.57, P = 0.13). However, the coverage of threatened species varied geographically, with high intercept differences for East Asia (βEastAsia = −0.23 ± SE 0.15, t = −1.57, P = 0.13), implying the largest median of range of highly at-risk (CR/EN) mammalian species outside the current network of PAs within each country.PA management effectiveness sub-targetFor the level of PAME assessment, we found that out of 22781 PAs within the 40 studied Asian countries, only 7.0% have been assessed based on PAME criteria (n = 1599), averaging 17.4% ± of PAs per country (SE = 2.5%). Israel, Japan, Lao, Bahrain, Oman and Qatar had no PA assessed based on the PAME criteria while over 1/3 of PAs in Indonesia, Cambodia, Bhutan, Jordan, Nepal, Turkey, Singapore and the UAE were PAME assessed. When modeling the level of PAME assessment, three best supported models were averaged (Table 3), with the averaged model including GDP2019, % PA extent 2020 and the Region as predictors. The averaged model coefficients would be non-significant under a hypothesis-testing approach (βGDP2019 = −0.18 ± SE 0.12, t = 1.47, P = 0.14 and βPAExtent2020 = −0.15 ± SE 0.11, t = 1.31, P = 0.19). Similarly, there was no evidence for the association between the ratio of PAs with PAME and Asian regions (P  > 0.05).Table 3 Results of generalized linear models testing different hypotheses on the association between the ratio of PAs with management effectiveness (PAME) in 2020 and ecological and geopolitical factors in Asian countries.Full size table More

As humans warm the planet, biodiversity is plummeting. These two global crises are connected in multiple ways. But the details of the intricate feedback loops between biodiversity decline and climate change are astonishingly under-studied.It is well known that climate extremes such as droughts and heatwaves can have devastating impacts on ecosystems and, in turn, that degraded ecosystems have a reduced capacity to protect humanity against the social and physical impacts of such events. Yet only a few such relationships have been probed in detail. Even less well known is whether biodiversity-depleted ecosystems will also have a negative effect on climate, provoking or exacerbating weather extremes.For us, a group of researchers living and working mainly in Central Europe, the wake-up call was the sequence of heatwaves of 2018, 2019 and 2022. It felt unreal to watch a floodplain forest suffer drought stress in Leipzig, Germany. Across Germany, more than 380,000 hectares of trees have now been damaged (see go.nature.com/3etrrnp; in German), and the forestry sector is struggling with how to plan restoration activities over the coming decades1. What could have protected these ecosystems against such extremes? And how will the resultant damage further impact our climate?
Nature-based solutions can help cool the planet — if we act now
In June 2021, the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published their first joint report2, acknowledging the need for more collaborative work between these two domains. And some good policy moves are afoot: the new EU Forest Strategy for 2030, released in July 2021, and other high-level policy initiatives by the European Commission, formally recognize the multifunctional value of forests, including their role in regulating atmospheric processes and climate. But much more remains to be done.To thoroughly quantify the risk that lies ahead, ecologists, climate scientists, remote-sensing experts, modellers and data scientists need to work together. The upcoming meeting of the United Nations Convention on Biological Diversity in Montreal, Canada, in December is a good opportunity to catalyse such collaboration.Buffers and responsesWhen lamenting the decline in biodiversity, most people think first about the tragedy of species driven to extinction. There are more subtle changes under way, too.For instance, a study across Germany showed that over the past century, most plant species have declined in cover, with only a few increasing in abundance3. Also affected is species functionality4 — genetic diversity, and the diversity of form and structure that can make communities more or less efficient at taking up nutrients, resisting heat or surviving pathogen attacks.When entire ecosystems are transformed, their functionality is often degraded. They are left with less capacity to absorb pollution, store carbon dioxide, soak up water, regulate temperature and support vital functions for other organisms, including humans5. Conversely, higher levels of functional biodiversity increase the odds of an ecosystem coping with unexpected events, including climate extremes. This is known as the insurance effect6.The effect is well documented in field experiments and modelling studies. And there is mounting evidence of it in ecosystem responses to natural events. A global synthesis of various drought conditions showed, for instance, that forests were more resilient when trees with a greater diversity of strategies for using and transporting water lived together7.

Dead trees near Iserlohn, Germany, in April 2020 (left) and after felling in June 2021 (right).Credit: Ina Fassbender/AFP via Getty

However, biodiversity cannot protect all ecosystems against all kinds of impacts. In a study this year across plots in the United States and Canada, for example, mortality was shown to be higher in diverse forest ecosystems8. The proposed explanation for this unexpected result was that greater biodiversity could also foster more competition for resources. When extreme events induce stress, resources can become scarce in areas with high biomass and competition can suddenly drive mortality, overwhelming the benefits of cohabitation. Whether or not higher biodiversity protects an ecosystem from an extreme is highly site-specific.Some plants respond to drought by reducing photosynthesis and transpiration immediately; others can maintain business as usual for much longer, stabilizing the response of the ecosystem as a whole. So the exact response of ecosystems to extremes depends on interactions between the type of event, plant strategies, vegetation composition and structure.Which plant strategies will prevail is hard to predict and highly dependent on the duration and severity of the climatic extreme, and on previous extremes9. Researchers cannot fully explain why some forests, tree species or individual plants survive in certain regions hit by extreme climate conditions, whereas entire stands disappear elsewhere10. One study of beech trees in Germany showed that survival chances had a genomic basis11, yet it is not clear whether the genetic variability present in forests will be sufficient to cope with future conditions.And it can take years for ecosystem impacts to play out. The effects of the two consecutive hot drought years, 2018 and 2019, were an eye-opener for many of us. In Leipzig, tree growth declined, pathogens proliferated and ash and maple trees died. The double blow, interrupted by a mild winter, on top of the long-term loss of soil moisture, led to trees dying at 4–20 times the usual rate throughout Germany, depending on the species (see go.nature.com/3etrrnp; in German). The devastation peaked in 2020.Ecosystem changes can also affect atmospheric conditions and climate. Notably, land-use change can alter the brightness (albedo) of the planet’s surface and its capacity for heat exchange. But there are more-complex mechanisms of influence.Vegetation can be a source or sink for atmospheric substances. A study published in 2020 showed that vegetation under stress is less capable of removing ozone than are unstressed plants, leading to higher levels of air pollution12. Pollen and other biogenic particles emitted from certain plants can induce the freezing of supercooled cloud droplets, allowing ice in clouds to form at much warmer temperatures13, with consequences for rainfall14. Changes to species composition and stress can alter the dynamics of these particle emissions. Plant stress also modifies the emission of biogenic volatile organic gases, which can form secondary particles. Wildfires — enhanced by drought and monocultures — affect clouds, weather and climate through the emission of greenhouse gases and smoke particles. Satellite data show that afforestation can boost the formation of low-level, cooling cloud cover15 by enhancing the supply of water to the atmosphere.Research prioritiesAn important question is whether there is a feedback loop: will more intense, and more frequent, extremes accelerate the degradation and homogenization of ecosystems, which then, in turn, promote further climate extremes? So far, we don’t know.One reason for this lack of knowledge is that research has so far been selective: most studies have focused on the impacts of droughts and heatwaves on ecosystems. Relatively little is known about the impacts of other kinds of extremes, such as a ‘false spring’ caused by an early-season bout of warm weather, a late spring frost, heavy rainfall events, ozone maxima, or exposure to high levels of solar radiation during dry, cloudless weather.Researchers have no overview, much less a global catalogue, of how each dimension of biodiversity interacts with the full breadth of climate extremes in different combinations and at multiple scales. In an ideal world, scientists would know, for example, how the variation in canopy density, vegetation age, and species diversity protects against storm damage; and whether and how the diversity of canopy structures controls atmospheric processes such as cloud formation in the wake of extremes. Researchers need to link spatiotemporal patterns of biodiversity with the responses of ecosystem processes to climate extremes.
Biodiversity needs every tool in the box: use OECMs
Creating such a catalogue is a huge challenge, particularly given the more frequent occurrence of extremes with little or no precedent16. Scientists will also need to account for the increasing likelihood of pile-ups of climate stressors. The ways in which ecosystems respond to compound events17 could be quite different. Researchers will have to study which facets of biodiversity (genetic, physiological, structural) are required to stabilize ecosystems and their functions against these onslaughts.There is at least one piece of good news: tools for data collection and analysis are improving fast, with huge advances over the past decade in satellite-based observations for both climate and biodiversity monitoring. The European Copernicus Earth-observation programme, for example — which includes the Sentinel 1 and 2 satellite fleet, and other recently launched missions that cover the most important wavelengths of the electromagnetic spectrum — offer metre-scale resolution observations of the biochemical status of plants and canopy structure. Atmospheric states are recorded in unprecedented detail, vertically and in time.Scientists must now make these data interoperable and integrate them with in situ observations. The latter is challenging. On the ground, a new generation of data are being collected by researchers and by citizen scientists18. For example, unique insights into plant responses to stress are coming from time-lapse photography of leaf orientation; accelerometer measures of movement patterns of stems have been shown to provide proxies for the drought stress of trees19.High-quality models are needed to turn these data into predictions. The development of functional ‘digital twins’ of the climate system is now in reach. These models replicate hydrometeorological processes at the metre scale, and are fast enough to allow for rapid scenario development and testing20. The analogous models for ecosystems are still in a more conceptual phase. Artificial-intelligence methods will be key here, to study links between climate extremes and biodiversity.Researchers can no longer afford to track global transformations of the Earth system in disciplinary silos. Instead, ecologists and climate scientists need to establish a joint agenda, so that humanity is properly forewarned: of the risks of removing biodiversity buffers against climate extremes, and of the risk of thereby amplifying these extremes. More

Zimmer, H. D. et al. Memory for Action: A Distinct Form of Episodic Memory? (Oxford University Press, 2001).
Google Scholar 
Goswami, U. The Wiley-Blackwell Handbook of Childhood Cognitive Development (Wiley, 2013).
Google Scholar 
Fujita, K., Morisaki, A., Takaoka, A., Maeda, T. & Hori, Y. Incidental memory in dogs (Canis familiaris): Adaptive behavioral solution at an unexpected memory test. Anim. Cogn. 15, 1055–1063 (2012).Article 
PubMed 

Google Scholar 
Lind, J., Enquist, M. & Ghirlanda, S. Animal memory: A review of delayed matching-to-sample data. Behav. Processes 117, 52–58 (2015).Article 
PubMed 

Google Scholar 
Kuczaj, S. A. II. & Eskelinen, H. C. (2014) The “creative dolphin” revisited: What do dolphins do when asked to vary their behavior. Anim. Behav. Cogn. 1, 66–77 (2014).Article 

Google Scholar 
Tulving, E. Episodic and semantic memory. Organ. Mem. 1, 381–403 (1972).
Google Scholar 
Tulving, E. How many memory systems are there?. Am. Psychol. 40, 385 (1985).Article 

Google Scholar 
Fugazza, C., Pongrácz, P., Pogány, Á., Lenkei, R. & Miklósi, Á. Mental representation and episodic-like memory of own actions in dogs. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: What is it, who has it, and how did it evolve?. Science 298, 1569–1579 (2002).Article 
PubMed 

Google Scholar 
Conway, M. A. Memory and the self. J. Mem. Lang. 53, 594–628 (2005).Article 

Google Scholar 
Scagel, A. & Mercado, E. III. Do that again! Memory for self-performed actions in dogs (Canis familiaris). J. Comp. Psychol. 20, 25 (2022).
Google Scholar 
Mercado, E., Murray, S. O., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for recent actions in the bottlenosed dolphin (Tursiops truncatus): Repetition of arbitrary behaviors using an abstract rule. Learn. Behav. 26, 210–218 (1998).Article 

Google Scholar 
Paukner, A., Anderson, J. R., Donaldson, D. I. & Ferrari, P. F. Cued repetition of self-directed behaviors in macaques (Macaca nemestrina). J. Exp. Psychol. Anim. Behav. Process. 33, 139 (2007).Article 
PubMed 

Google Scholar 
Smeele, S. Q. et al. Memory for own behaviour in pinnipeds. Anim. Cogn. 20, 1–12 (2019).
Google Scholar 
Clayton, N. S. Episodic-like memory and mental time travel in animals. (2017).Clayton, N. S., Griffiths, D. P. & Dickinson, A. Declarative and episodic-like memory in animals: Personal musings of a Scrub Jay (2000).Clayton, N. S. & Dickinson, A. Episodic-like memory during cache recovery by scrub jays. Nature 395, 272–274 (1998).Article 
PubMed 

Google Scholar 
Tulving, E. Episodic memory and autonoesis: Uniquely human. Missing Link Cogn. Orig. Self-Reflect. Conscious 20, 3–56 (2005).
Google Scholar 
Suddendorf, T. & Corballis, M. C. Mental time travel and the evolution of the human mind. Genet. Soc. Gen. Psychol. Monogr. 123, 133–167 (1997).PubMed 

Google Scholar 
Suddendorf, T. & Corballis, M. C. The evolution of foresight: What is mental time travel, and is it unique to humans?. Behav. Brain Sci. 30, 299–313 (2007).Article 
PubMed 

Google Scholar 
Crystal, J. D. Evaluating evidence from animal models of episodic memory. J. Exp. Psychol. Anim. Learn. Cogn. 47, 337 (2021).Article 
PubMed 

Google Scholar 
Mercado, E. III., Uyeyama, R. K., Pack, A. A. & Herman, L. M. Memory for action events in the bottlenosed dolphin. Anim. Cogn. 2, 17–25 (1999).Article 

Google Scholar 
Zentall, T. R. Coding of stimuli by animals: Retrospection, prospection, episodic memory and future planning. Learn. Motiv. 41, 225–240 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Fugazza, C., Pogány, Á. & Miklósi, Á. Recall of others’ actions after incidental encoding reveals episodic-like memory in dogs. Curr. Biol. 26, 3209–3213 (2016).Article 
PubMed 

Google Scholar 
Lambert, M. L., Jacobs, I., Osvath, M. & von Bayern, A. M. Birds of a feather? Parrot and corvid cognition compared. Behaviour 156, 505–594 (2019).Article 

Google Scholar 
Emery, N. J. Cognitive ornithology: The evolution of avian intelligence. Philos. Trans. R. Soc. B Biol. Sci. 361, 23–43 (2006).Article 

Google Scholar 
Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl. Acad. Sci. 113, 7255–7260 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Emery, N. J. & Clayton, N. S. Evolution of the avian brain and intelligence. Curr. Biol. 15, R946–R950 (2005).Article 
PubMed 

Google Scholar 
Bradbury, J. W. & Balsby, T. J. The functions of vocal learning in parrots. Behav. Ecol. Sociobiol. 70, 293–312 (2016).Article 

Google Scholar 
Baciadonna, L., Cornero, F. M., Emery, N. J. & Clayton, N. S. Convergent evolution of complex cognition: Insights from the field of avian cognition into the study of self-awareness. Learn. Behav. 49, 9–22 (2021).Article 
PubMed 

Google Scholar 
Osvath, M., Kabadayi, C. & Jacobs, I. Independent evolution of similar complex cognitive skills (2014).Zentall, T. R., Clement, T. S., Bhatt, R. S. & Allen, J. Episodic-like memory in pigeons. Psychon. Bull. Rev. 8, 685–690 (2001).Article 
PubMed 

Google Scholar 
Zentall, T. R., Singer, R. A. & Stagner, J. P. Episodic-like memory: Pigeons can report location pecked when unexpectedly asked. Behav. Processes 79, 93–98 (2008).Article 
PubMed 

Google Scholar 
Healy, S. D. & Hurly, T. A. Spatial learning and memory in birds. Brain. Behav. Evol. 63, 211–220 (2004).Article 
PubMed 

Google Scholar 
Taylor, A. H. Corvid cognition. Wiley Interdiscip. Rev. Cogn. Sci. 5, 361–372 (2014).Article 
PubMed 

Google Scholar 
Boeckle, M. & Bugnyar, T. Long-term memory for affiliates in ravens. Curr. Biol. 22, 801–806 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Marzluff, J. M., Walls, J., Cornell, H. N., Withey, J. C. & Craig, D. P. Lasting recognition of threatening people by wild American crows. Anim. Behav. 79, 699–707 (2010).Article 

Google Scholar 
Pepperberg, I. M. & Pepperberg, I. M. The Alex Studies: Cognitive and Communicative Abilities of Grey Parrots (Harvard University Press, 2009).Book 

Google Scholar 
Emery, N. J. & Clayton, N. S. Effects of experience and social context on prospective caching strategies by scrub jays. Nature 414, 443–446 (2001).Article 
PubMed 

Google Scholar 
Herzog, S. K. et al. First systematic sampling approach to estimating the global population size of the Critically Endangered Blue-throated Macaw Ara glaucogularis. Bird Conserv. Int. 31, 293–311 (2021).Article 

Google Scholar 
Auersperg, A. M. & von Bayern, A. M. Who’sa clever bird—now? A brief history of parrot cognition. Behaviour 156, 391–407 (2019).Article 

Google Scholar 
Tassin de Montaigu, C., Durdevic, K., Brucks, D., Krasheninnikova, A. & von Bayern, A. Blue-throated macaws (Ara glaucogularis) succeed in a cooperative task without coordinating their actions. Ethology 126, 267–277 (2020).Article 

Google Scholar 
Auersperg, A. M. et al. Social transmission of tool use and tool manufacture in Goffin cockatoos (Cacatua goffini). Proc. R. Soc. B Biol. Sci. 281, 20140972 (2014).Article 

Google Scholar 
Brucks, D. & von Bayern, A. M. Parrots voluntarily help each other to obtain food rewards. Curr. Biol. 30, 292–297 (2020).Article 
PubMed 

Google Scholar 
Krasheninnikova, A., Höner, F., O’Neill, L., Penna, E. & von Bayern, A. M. Economic decision-making in parrots. Sci. Rep. 8, 1–10 (2018).Article 

Google Scholar 
Jarvis, E. D. et al. Avian brains and a new understanding of vertebrate brain evolution. Nat. Rev. Neurosci. 6, 151–159 (2005).Article 
PubMed 

Google Scholar 
Gutiérrez-Ibáñez, C., Iwaniuk, A. N. & Wylie, D. R. Parrots have evolved a primate-like telencephalic-midbrain-cerebellar circuit. Sci. Rep. 8, 1–11 (2018).Article 

Google Scholar 
Smeele, S. Q. et al. Coevolution of relative brain size and life expectancy in parrots. Proc. R. Soc. B 289, 20212397 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Kirsch, J. A., Güntürkün, O. & Rose, J. Insight without cortex: Lessons from the avian brain. Conscious. Cogn. 17, 475–483 (2008).Article 
PubMed 

Google Scholar 
Dunbar, R. I. & Shultz, S. Evolution in the social brain. Science 317, 1344–1347 (2007).Article 
PubMed 

Google Scholar 
Wright, A. A. & Katz, J. S. Mechanisms of same/different concept learning in primates and avians. Behav. Processes 72, 234–254 (2006).Article 
PubMed 

Google Scholar 
Smirnova, A. A., Obozova, T. A., Zorina, Z. A. & Wasserman, E. A. How do crows and parrots come to spontaneously perceive relations-between-relations?. Curr. Opin. Behav. Sci. 37, 109–117 (2021).Article 

Google Scholar 
Schusterman, R. J. & Kastak, D. A California sea lion (Zalophus californianus) is capable of forming equivalence relations. Psychol. Rec. 43, 823–839 (1993).Article 

Google Scholar 
Kastak, D. & Schusterman, R. J. Transfer of visual identity matching-to-sample in two California sea lions (Zalophus californianus). Anim. Learn. Behav. 22, 427–435 (1994).Article 

Google Scholar 
Zentall, T. R., Wasserman, E. A., Lazareva, O. F., Thompson, R. K. & Rattermann, M. J. Concept learning in animals. Comp. Cogn. Behav. Rev. 20, 25 (2008).
Google Scholar 
Marino, L. Convergence of complex cognitive abilities in cetaceans and primates. Brain. Behav. Evol. 59, 21–32 (2002).Article 
PubMed 

Google Scholar 
Huber, L., Range, F. & Virányi, Z. Dog imitation and its possible origins. In Domestic dog Cognition and Behavior 79–100 (Springer, 2014).Chapter 

Google Scholar 
Schmidjell, T., Range, F., Huber, L. & Virányi, Z. Do owners have a Clever Hans effect on dogs? Results of a pointing study. Front. Psychol. 3, 558 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Hare, B., Brown, M., Williamson, C. & Tomasello, M. The domestication of social cognition in dogs. Science 298, 1634–1636 (2002).Article 
PubMed 

Google Scholar 
Lindqvist, C. & Jensen, P. Domestication and stress effects on contrafreeloading and spatial learning performance in red jungle fowl (Gallus gallus) and White Leghorn layers. Behav. Processes 81, 80–84 (2009).Article 
PubMed 

Google Scholar 
Pack, A. A., Herman, L. M. & Roitblat, H. L. Generalization of visual matching and delayed matching by a California sea lion (Zalophus californianus). Anim. Learn. Behav. 19, 37–48 (1991).Article 

Google Scholar 
Bennett, M. S. Five breakthroughs: A first approximation of brain evolution from early bilaterians to humans. Front. Neuroanat. 15, 25 (2021).Article 

Google Scholar 
Cisek, P. Resynthesizing behavior through phylogenetic refinement. Atten. Percept. Psychophys. 81, 2265–2287 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Toft, C. A. & Wright, T. F. Parrots of the wild. Nat. Hist. World’s Most Captiv. Birds 20, 25 (2015).
Google Scholar 
Merkle, J. A., Sigaud, M. & Fortin, D. To follow or not? How animals in fusion–fission societies handle conflicting information during group decision-making. Ecol. Lett. 18, 799–806 (2015).Article 
PubMed 

Google Scholar 
Stevens, J. R. & Gilby, I. C. A conceptual framework for nonkin food sharing: Timing and currency of benefits. Anim. Behav. 67, 603–614 (2004).Article 

Google Scholar 
Kamil, A. C. & Roitblat, H. L. The ecology of foraging behavior—Implications for animal learning and memory. Annu. Rev. Psychol. 36, 141–169 (1985).Article 
PubMed 

Google Scholar 
Ortiz, S. T., Castro, A. C., Balsby, T. J. S. & Larsen, O. N. Problem-solving in a cooperative task in peach-fronted conures (Eupsittula aurea). Anim. Cogn. 23, 265–275 (2020).Article 

Google Scholar 
Krasheninnikova, A., Brucks, D., Blanc, S. & von Bayern, A. M. Assessing African grey parrots’ prosocial tendencies in a token choice paradigm. R. Soc. Open Sci. 6, 190696 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Krasheninnikova, A. et al. Parrots do not show inequity aversion. Sci. Rep. 9, 1–12 (2019).Article 

Google Scholar 
Clayton, N. S., Griffiths, D. P., Emery, N. J. & Dickinson, A. Elements of episodic–like memory in animals. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356, 1483–1491 (2001).Article 
PubMed 
PubMed Central 

Google Scholar 
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall, 2020).Book 

Google Scholar  More