Ecology

Nash, R. Wilderness and the American Mind (Yale University Press, 1982).
Google Scholar 
Kirchhoff, T. & Vicenzotti, V. A historical and systematic survey of European perceptions of wilderness. Environ. Values 23, 443–464 (2014).Article 

Google Scholar 
Aplet, G., Thomson, J. & Wilbert, M. Indicators of wildness: Using attributes of the land to assess the context of wilderness in Wilderness Science in a Time of Change (eds. McCool, S.F., Cole, D.N., Borrie, W.T., O’Loughlin, J.) 89–98 (USDA Forest Service, RMRS-P-15-Vol-2, 2000).Watson, J. E. et al. Catastrophic declines in wilderness areas undermine global environment targets. Curr. Biol. 26, 2929–2934 (2016).CAS 
PubMed 
Article 

Google Scholar 
Watson, J. E. et al. Protect the last of the wild. Nature 563, 27–30 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hayward, M. W. et al. Reintroducing rewilding to restoration: Rejecting the search for novelty. Biol. Conserv. 233, 255–259 (2019).Article 

Google Scholar 
Perino, A. et al. Rewilding complex ecosystems. Science 364, eaav5570 (2019).CAS 
PubMed 
Article 

Google Scholar 
Soulé, M. & Noss, R. Rewilding and biodiversity: Complementary goals for continental conservation. Wild Earth 8, 18–28 (1998).
Google Scholar 
Torres, A. et al. Measuring rewilding progress. Philos. Trans. R. Soc. Lond. B 373, 20170433 (2018).Article 

Google Scholar 
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).ADS 
PubMed 
Article 

Google Scholar 
Fish, R., Church, A. & Winter, M. Conceptualising cultural ecosystem services: A novel framework for research and critical engagement. Ecosyst. Serv. 21B, 208–217 (2016).Article 

Google Scholar 
Nilsson, K. et al. Forests, Trees and Human Health (Springer, 2011).Book 

Google Scholar 
Cheesbrough, A. E., Garvin, T. & Nykiforuk, C. I. J. Everyday wild: Urban natural areas, health, and well-being. Health Place 56, 43–52 (2019).PubMed 
Article 

Google Scholar 
Child, M. F. Wildness, infinity and freedom. Ecol. Econ. 186, 107055 (2021).Article 

Google Scholar 
Lev, E., Kahn, P. H. Jr., Chen, H. & Esperum, G. Relatively wild urban parks can promote human resilience and flourishing: A case study of Discovery Park, Seattle, Wasshington. Front. Sustain. Cities 2, 2 (2020).Article 

Google Scholar 
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).PubMed 
Article 

Google Scholar 
Giergiczny, M., Czajkowski, M., Żylicz, T. & Angelstam, P. Choice experiment assessment of public preferences for forest structural attributes. Ecol. Econ. 119, 8–23 (2015).Article 

Google Scholar 
Sabatini, F. M. et al. Where are Europe’s last primary forests?. Divers. Distrib. 24, 1426–1439 (2018).Article 

Google Scholar 
Kirby, K. & Watkins, C. Europe’s changing woods and forests: from wildwood to managed landscapes. CABI (2015).Schirpke, U., Meisch, C. & Tappeiner, U. Symbolic species as a cultural ecosystem service in the European Alps: Insights and open issues. Landsc. Ecol. 33, 711–730 (2018).Article 

Google Scholar 
Bruskotter, J. T. & Wilson, R. S. Determining where the wild things will be: Using psychological theory to find tolerance for large carnivores. Conserv. Lett. 7, 158–165 (2014).Article 

Google Scholar 
Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cimatti, M. et al. Large carnivore expansion in Europe is associated with human population density and land cover changes. Divers. Distrib. 27, 602–617 (2021).Article 

Google Scholar 
Røskaft, E., Händel, B., Bjerke, T. & Kaltenborn, B. P. Human attitudes towards large carnivores in Norway. Wildl. Biol. 13, 172–186 (2007).Article 

Google Scholar 
Arbieu, U. et al. Attitudes towards returning wolves (Canis lupus) in Germany: Exposure, information sources and trust matter. Biol. Conserv. 234, 202–210 (2019).Article 

Google Scholar 
Gundersen, V. S. & Frivold, L. H. Public preferences for forest structures: A review of quantitative surveys from Finland, Norway and Sweden. Urban For. Urban Green. 7, 241–258 (2008).Article 

Google Scholar 
Filyushkina, A., Agimass, F., Lundhede, T., Strange, N. & Jacobsen, J. B. Preferences for variation in forest characteristics: Does diversity between stands matter?. Ecol. Econ. 140, 22–29 (2017).Article 

Google Scholar 
Lozano, J. et al. Human-carnivore relations: A systematic review. Biol. Conserv. 237, 480–492 (2019).Article 

Google Scholar 
Rode, J., Flinzberger, L., Karutz, R., Berghöfer, A. & Schröter-Schlaack, C. Why so negative? Exploring the socio-economic impacts of large carnivores from a European perspective. Biol. Conserv. 255, 108918 (2021).Article 

Google Scholar 
Gren, M., Häggmark-Svensson, T., Elofsson, K. & Engelmann, M. Economics of wildlife management—An overview. Eur. J. Wildl. Res. 64, 1–6 (2018).Article 

Google Scholar 
Wilson, E. O. Biophilia and the conservation ethic in The Biophilia Hypothesis (eds. Kellert, S.R. & Wilson, E.O.) 31–41 (Island Press, 1993).Thompson, S. C. G. & Barton, M. A. Ecocentric and anthropocentric attitudes toward the environment. J. Environ. Psychol. 14, 149–157 (1994).Article 

Google Scholar 
Kaltenborn, B. P. & Bjerke, T. Associations between environmental value orientations and landscape preferences. Landsc. Urban Plan. 59, 1–11 (2002).Article 

Google Scholar 
Bjerke, T. & Kaltenborn, B. P. The relationship of ecocentric and anthropocentric motives to attitudes toward large carnivores. J. Environ. Psychol. 19, 415–421 (1999).Article 

Google Scholar 
Johansson, M., Ferreira, I. A., Støen, O. G., Frank, J. & Flykt, A. Targeting human fear of large carnivores—Many ideas but few known effects. Biol. Conserv. 201, 261–269 (2016).Article 

Google Scholar 
Bauer, N., Wallner, A. & Hunziker, M. The change of European landscapes: Human–nature relationships, public attitudes towards rewilding, and the implications for landscape management in Switzerland. J. Environ. Manag. 90, 2910–2920 (2009).Article 

Google Scholar 
Arts, K., Fischer, A. & Van der Wal, R. The promise of wilderness between paradise and hell: A cultural-historical exploration of a Dutch National Park. Landsc. Res. 37, 239–256 (2012).Article 

Google Scholar 
De Groot, W. T. & van den Born, R. J. G. Visions of nature and landscape preferences:an exploration in the Netherlands. Landsc. Urban Plan. 63, 127–138 (2003).Article 

Google Scholar 
Bombieri, G. et al. Brown bear attacks on humans: A worldwide perspective. Sci. Rep. 9, 1–10 (2019).CAS 
Article 

Google Scholar 
Johansson, M., Sjöström, M., Karlsson, J. & Brännlund, R. Is human fear affecting public willingness to pay for the management and conservation of large carnivores?. Soc. Nat. Resour. 25, 610–620 (2012).Article 

Google Scholar 
Dressel, S., Sandström, C. & Ericsson, G. A meta-analysis of studies on attitudes toward bears and wolves across Europe 1976–2012. Conserv. Biol. 29, 565–574 (2015).CAS 
PubMed 
Article 

Google Scholar 
Trajçe, A. et al. All carnivores are not equal in the rural people’s view. Should we develop conservation plans for functional guilds or individual species in the face of conflicts?. Glob. Ecol. Conserv. 19, e00677 (2019).Article 

Google Scholar 
Eriksson, M., Sandström, C. & Ericsson, G. Direct experience and attitude change towards bears and wolves. Wildl. Biol. 21, 131–137 (2015).Article 

Google Scholar 
Methorst, J., Arbieu, U., Bonn, A., Böhning-Gaese, K. & Müller, T. Non-material contributions of wildlife to human well-being: A systematic review. Environ. Res. Lett. 15, 093005 (2020).ADS 
Article 

Google Scholar 
Russell, R. et al. Humans and nature: How knowing and experiencing nature affect well-being. Annu. Rev. Environ. Resour. 38, 473–502 (2013).Article 

Google Scholar 
Maller, C., Mumaw, L. & Cooke, B. Health and social benefits of living with ‘wild’ nature in Rewilding (eds. Pettorelli, N., Durant, S. M. & du Toit, J. T.) 165–181 (Cambridge University Press, 2019).Nevin, O. T., Swain, P. & Convery, I. Bears, place-making, and authenticity in British Columbia. Nat. Areas J. 34, 216–221 (2014).Article 

Google Scholar 
Schnitzler, A. Towards a new European wilderness: Embracing unmanaged forest growth and the decolonisation of nature. Landsc. Urban Plan. 126, 74–80 (2014).Article 

Google Scholar 
Hensher, D., Rose, J. & Greene, D. Applied Choice Analysis (Cambridge University Press, 2005).MATH 
Book 

Google Scholar 
Johnston, R. J. et al. Contemporary guidance for stated preference studies. J. Assoc. Environ. Resour. Econ. 4, 319–405 (2017).
Google Scholar 
Riera, P. et al. Non-market valuation of forest goods and services: Good practice guidelines. J. For. Econ. 18, 259–270 (2012).
Google Scholar 
Larsen, J. B. & Nielsen, A. B. Nature-based forest management: Where are we going? Elaborating forest development types in and with practice. For. Ecol. Manag. 238, 107–117 (2007).Article 

Google Scholar 
Ferrini, S. & Scarpa, R. Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study. J. Environ. Econ. Manag. 53, 342–363 (2007).MATH 
Article 

Google Scholar 
McFadden, D. The measurement of urban travel demand. J. Public Econ. 3, 303–328 (1974).Article 

Google Scholar 
Train, K. Discrete Choice Methods with Simulation (Cambridge University Press, 2009).MATH 

Google Scholar  More

Richardson, D. M. et al. Naturalization and invasion of alien plants: concepts and definitions. Divers. Distrib. 6, 93–107 (2000).Article 

Google Scholar 
van Kleunen, M. et al. Global exchange and accumulation of non-native plants. Nature 525, 100–103 (2015).PubMed 
Article 
CAS 

Google Scholar 
Capinha, C., Essl, F., Seebens, H., Moser, D. & Pereira, H. M. The dispersal of alien species redefines biogeography in the Anthropocene. Science 348, 1248–1251 (2015).CAS 
PubMed 
Article 

Google Scholar 
Vilà, M. & Hulme, P. E. in Impact of Biological Invasions on Ecosystem Services Vol. 12 Invading Nature – Springer Series in Invasion Ecology (eds Vilà, M. & Hulme, P. E.) 1–14 (Springer, 2017).Pyšek, P. et al. A global assessment of invasive plant impacts on resident species, communities and ecosystems: the interaction of impact measures, invading species’ traits and environment. Glob. Chang. Biol. 18, 1725–1737 (2012).PubMed Central 
Article 

Google Scholar 
Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95, 1511–1534 (2020).PubMed 
Article 

Google Scholar 
Bacher, S. et al. Socio-economic impact classification of alien taxa (SEICAT). Methods Ecol. Evol. 9, 159–168 (2018).Article 

Google Scholar 
Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seebens, H. et al. Projecting the continental accumulation of alien species through to 2050. Glob. Chang. Biol. 27, 970–982 (2021).CAS 
Article 

Google Scholar 
Kriticos, D. J., Sutherst, R. W., Brown, J. R., Adkins, S. W. & Maywald, G. F. Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia. J. Appl. Ecol. 40, 111–124 (2003).Article 

Google Scholar 
Thuiller, W., Richardson, D. M. & Midgley, G. F. in Biological Invasions (ed. Nentwig, W.) 197–211 (Springer, 2007).Hobbs, R. J. in Invasive Species in a Changing World (eds Mooney, H. A. & Hobbs, R. J.) 55–64 (Island Press, 2000).Seebens, H. et al. Global trade will accelerate plant invasions in emerging economies under climate change. Glob. Chang. Biol. 21, 4128–4140 (2015).PubMed 
Article 

Google Scholar 
Razanajatovo, M. et al. Plants capable of selfing are more likely to become naturalized. Nat. Commun. 7, 13313 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bucharova, A. & van Kleunen, M. Introduction history and species characteristics partly explain naturalization success of North American woody species in Europe. J. Ecol. 97, 230–238 (2009).Article 

Google Scholar 
Ordonez, A., Wright, I. J. & Olff, H. Functional differences between native and alien species: a global-scale comparison. Funct. Ecol. 24, 1353–1361 (2010).Article 

Google Scholar 
van Kleunen, M., Weber, E. & Fischer, M. A meta-analysis of trait differences between invasive and non-invasive plant species. Ecol. Lett. 13, 235–245 (2010).PubMed 
Article 

Google Scholar 
van Kleunen, M., Dawson, W. & Maurel, N. Characteristics of successful alien plants. Mol. Ecol. 24, 1954–1968 (2015).PubMed 
Article 

Google Scholar 
Essl, F. et al. Drivers of the relative richness of naturalized and invasive plant species on Earth. AoB Plants 11, plz051 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Winkler, D. E., Gremer, J. R., Chapin, K. J., Kao, M. & Huxman, T. E. Rapid alignment of functional trait variation with locality across the invaded range of Sahara mustard (Brassica tournefortii). Am. J. Bot. 105, 1188–1197 (2018).PubMed 
Article 

Google Scholar 
Divíšek, J. et al. Similarity of introduced plant species to native ones facilitates naturalization, but differences enhance invasion success. Nat. Commun. 9, 4631 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Banerjee, A. K., Prajapati, J., Bhowmick, A. R., Huang, Y. & Mukherjee, A. Different factors influence naturalization and invasion processes – a case study of Indian alien flora provides management insights. J. Environ. Manag. 294, 113054 (2021).Article 

Google Scholar 
Ni, M. et al. Invasion success and impacts depend on different characteristics in non-native plants. Divers. Distrib. 27, 1194–1207 (2021).Article 

Google Scholar 
Fristoe, T. S. et al. Dimensions of invasiveness: links between local abundance, geographic range size, and habitat breadth in Europe’s alien and native floras. Proc. Natl Acad. Sci. USA 118, e2021173118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Omer, A. et al. Characteristics of the naturalized flora of Southern Africa largely reflect the non-random introduction of alien species for cultivation. Ecography 44, 1812–1825 (2021).Article 

Google Scholar 
Pyšek, P. et al. Naturalization of central European plants in North America: species traits, habitats, propagule pressure, residence time. Ecology 96, 762–774 (2015).PubMed 
Article 

Google Scholar 
Omer, A., Kordofani, M., Gibreel, H. H., Pyšek, P. & van Kleunen, M. The alien flora of Sudan and South Sudan: taxonomic and biogeographical composition. Biol. Invasions 23, 2033–2045 (2021).Article 

Google Scholar 
Duncan, R. P. & Williams, P. A. Darwin’s naturalization hypothesis challenged. Nature 417, 608–609 (2002).CAS 
PubMed 
Article 

Google Scholar 
Daehler, C. C. Darwin’s naturalization hypothesis revisited. Am. Nat. 158, 324–330 (2001).CAS 
PubMed 
Article 

Google Scholar 
Pyšek, P. Is there a taxonomic pattern to plant invasions? Oikos 82, 282–294 (1998).Article 

Google Scholar 
Tan, J., Pu, Z., Ryberg, W. A. & Jiang, L. Resident–invader phylogenetic relatedness, not resident phylogenetic diversity, controls community invasibility. Am. Nat. 186, 59–71 (2015).PubMed 
Article 

Google Scholar 
Thuiller, W. et al. Resolving Darwin’s naturalization conundrum: a quest for evidence. Divers. Distrib. 16, 461–475 (2010).Article 

Google Scholar 
Loiola, P. P. et al. Invaders among locals: alien species decrease phylogenetic and functional diversity while increasing dissimilarity among native community members. J. Ecol. 106, 2230–2241 (2018).Article 

Google Scholar 
Lososová, Z. et al. Alien plants invade more phylogenetically clustered community types and cause even stronger clustering. Glob. Ecol. Biogeogr. 24, 786–794 (2015).Article 

Google Scholar 
Marx, H. E., Giblin, D. E., Dunwiddie, P. W. & Tank, D. C. Deconstructing Darwin’s naturalization conundrum in the San Juan Islands using community phylogenetics and functional traits. Divers. Distrib. 22, 318–331 (2016).Article 

Google Scholar 
Darwin, C. On the Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).Procheş, Ş., Wilson, J. R. U., Richardson, D. M. & Rejmánek, M. Searching for phylogenetic pattern in biological invasions. Glob. Ecol. Biogeogr. 17, 5–10 (2008).
Google Scholar 
Diez, J. M., Sullivan, J. J., Hulme, P. E., Edwards, G. & Duncan, R. P. Darwin’s naturalization conundrum: dissecting taxonomic patterns of species invasions. Ecol. Lett. 11, 674–681 (2008).PubMed 
Article 

Google Scholar 
Cadotte, M. W., Campbell, S. E., Li, S. P., Sodhi, D. S. & Mandrak, N. E. Preadaptation and naturalization of nonnative species: Darwin’s two fundamental insights into species invasion. Annu Rev. Plant Biol. 69, 661–684 (2018).CAS 
PubMed 
Article 

Google Scholar 
van Kleunen, M., Bossdorf, O. & Dawson, W. The ecology and evolution of alien plants. Annu. Rev. Ecol. Evol. Syst. 49, 25–47 (2018).Article 

Google Scholar 
Park, D. S., Feng, X., Maitner, B. S., Ernst, K. C. & Enquist, B. J. Darwin’s naturalization conundrum can be explained by spatial scale. Proc. Natl Acad. Sci. USA 117, 10904–10910 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Diez, J. M. et al. Learning from failures: testing broad taxonomic hypotheses about plant naturalization. Ecol. Lett. 12, 1174–1183 (2009).PubMed 
Article 

Google Scholar 
Malecore, E. M., Dawson, W., Kempel, A., Müller, G. & van Kleunen, M. Nonlinear effects of phylogenetic distance on early-stage establishment of experimentally introduced plants in grassland communities. J. Ecol. 107, 781–793 (2019).Article 

Google Scholar 
Schaefer, H., Hardy, O. J., Silva, L., Barraclough, T. G. & Savolainen, V. Testing Darwin’s naturalization hypothesis in the Azores. Ecol. Lett. 14, 389–396 (2011).PubMed 
Article 

Google Scholar 
Strauss, S. Y., Webb, C. O. & Salamin, N. Exotic taxa less related to native species are more invasive. Proc. Natl Acad. Sci. USA 103, 5841–5845 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, S.-p. et al. The effects of phylogenetic relatedness on invasion success and impact: deconstructing Darwin’s naturalisation conundrum. Ecol. Lett. 18, 1285–1292 (2015).PubMed 
Article 

Google Scholar 
Pellock, S., Thompson, A., He, K., Mecklin, C. & Yang, J. Validity of Darwin’s naturalization hypothesis relates to the stages of invasion. Community Ecol. 14, 172–179 (2013).Article 

Google Scholar 
Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).PubMed 
Article 

Google Scholar 
van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Broennimann, O. et al. Distance to native climatic niche margins explains establishment success of alien mammals. Nat. Commun. 12, 2353 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Carboni, M. et al. What it takes to invade grassland ecosystems: traits, introduction history and filtering processes. Ecol. Lett. 19, 219–229 (2016).PubMed 
Article 

Google Scholar 
Milbau, A. & Stout, J. C. Factors associated with alien plants transitioning from casual, to naturalized, to invasive. Conserv. Biol. 22, 308–317 (2008).PubMed 
Article 

Google Scholar 
Dawson, W., Burslem, D. F. R. P. & Hulme, P. E. Factors explaining alien plant invasion success in a tropical ecosystem differ at each stage of invasion. J. Ecol. 97, 657–665 (2009).Article 

Google Scholar 
Rejmánek, M. in Invasive Species and Biodiversity Management (eds Schei, P. J. & Vilken, A.) 79–102 (Kluwer Academic, 1998).Rejmánek, M. A theory of seed plant invasiveness: the first sketch. Biol. Conserv. 78, 171–181 (1996).Article 

Google Scholar 
Maurel, N., Hanspach, J., Kuhn, I., Pysek, P. & van Kleunen, M. Introduction bias affects relationships between the characteristics of ornamental alien plants and their naturalization success. Glob. Ecol. Biogeogr. 25, 1500–1509 (2016).Article 

Google Scholar 
Glen, H. F. Cultivated Plants of Southern Africa: Botanical Names, Common Names, Origins, Literature (National Botanical Institute, 2002).Reichard, S. H. & White, P. Horticulture as a pathway of invasive plant introductions in the United States. Bioscience 51, 103–113 (2001).Article 

Google Scholar 
Faulkner, K. T., Robertson, M. P., Rouget, M. & Wilson, J. R. U. Understanding and managing the introduction pathways of alien taxa: South Africa as a case study. Biol. Invasions 18, 73–87 (2016).Article 

Google Scholar 
Dodd, A. J., Burgman, M. A., McCarthy, M. A. & Ainsworth, N. The changing patterns of plant naturalization in Australia. Divers. Distrib. 21, 1038–1050 (2015).Article 

Google Scholar 
Lambdon, P.-W. et al. Alien flora of Europe: species diversity, temporal trends, geographical patterns and research needs. Preslia 80, 101–149 (2008).
Google Scholar 
Bennett, B. M. Naturalising Australian trees in South Africa: climate, exotics and experimentation. J. South. Afr. Stud. 37, 265–280 (2011).Article 

Google Scholar 
Richardson, D. M. et al. in Biological Invasions in South Africa (eds van Wilgen, B. W. et al.) 67–96 (Springer, 2020).Li, S.-p. et al. Contrasting effects of phylogenetic relatedness on plant invader success in experimental grassland communities. J. Appl. Ecol. 52, 89–99 (2015).CAS 
Article 

Google Scholar 
Duarte, M., Verdú, M., Cavieres, L. A. & Bustamante, R. O. Plant–plant facilitation increases with reduced phylogenetic relatedness along an elevation gradient. Oikos 130, 248–259 (2021).Article 

Google Scholar 
Verdú, M., Rey, P. J., Alcántara, J. M., Siles, G. & Valiente-Banuet, A. Phylogenetic signatures of facilitation and competition in successional communities. J. Ecol. 97, 1171–1180 (2009).Article 

Google Scholar 
Valiente-Banuet, A. & Verdu, M. Plant facilitation and phylogenetics. Annu. Rev. Ecol. Evol. Syst. 44, 347–366 (2013).Article 

Google Scholar 
Anacker, B. L. & Strauss, S. Y. Ecological similarity is related to phylogenetic distance between species in a cross-niche field transplant experiment. Ecology 97, 1807–1818 (2016).PubMed 
Article 

Google Scholar 
Dostál, P. Plant competitive interactions and invasiveness: searching for the effects of phylogenetic relatedness and origin on competition intensity. Am. Nat. 177, 655–667 (2011).PubMed 
Article 

Google Scholar 
Levin, S. C., Crandall, R. M., Pokoski, T., Stein, C. & Knight, T. M. Phylogenetic and functional distinctiveness explain alien plant population responses to competition. Proc. R. Soc. B 287, 20201070 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, E. W., Zeldin, J., Semski, W. R., Hipp, A. L. & Larkin, D. J. Phylogenetic distance and resource availability mediate direction and strength of plant interactions in a competition experiment. Oecologia 197, 459–469 (2021).PubMed 
Article 

Google Scholar 
Bezeng, S. B., Davies, J. T., Yessoufou, K., Maurin, O. & Van der Bank, M. Revisiting Darwin’s naturalization conundrum: explaining invasion success of non-native trees and shrubs in Southern Africa. J. Ecol. 103, 871–879 (2015).Article 

Google Scholar 
Trotta, L. B., Siders, Z. A., Sessa, E. B. & Baiser, B. The role of phylogenetic scale in Darwin’s naturalization conundrum in the critically imperilled pine rockland ecosystem. Divers. Distrib. 27, 618–631 (2021).Article 

Google Scholar 
Sol, D. et al. A test of Darwin’s naturalization conundrum in birds reveals enhanced invasion success in the presence of close relatives. Ecol. Lett. 25, 661–672 (2022).PubMed 
Article 

Google Scholar 
Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).PubMed 
Article 

Google Scholar 
Henderson, L. Comparisons of invasive plants in Southern Africa originating from southern temperate, northern temperate and tropical regions. Bothalia 36, 201–222 (2006).Article 

Google Scholar 
Cayuela, L., Stein, A. & Oksanen, J. Taxonstand: Taxonomic Standardization of Plant Species Names. R package version 2.2. https://CRAN.R-project.org/package=Taxonstand (R Foundation for Statistical Computing, Vienna, 2019).Weigelt, P., König, C. & Kreft, H. GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

Google Scholar 
van Kleunen, M. et al. The Global Naturalized Alien Flora (GloNAF) database. Ecology 100, e02542 (2019).PubMed 
Article 

Google Scholar 
Zengeya, T. A. & Wilson, J. R. (eds) The Status of Biological Invasions and Their Management in South Africa in 2019 (South African National Biodiversity Institute and DSI-NRF Centre of Excellence for Invasion Biology, 2021).Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing v.3.6.1 (R Foundation for Statistical Computing, 2019).Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R Vol. 574 (Springer, 2009).Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

Google Scholar 
Nagelkerke, N. J. D. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692 (1991).Article 

Google Scholar 
rcompanion: Functions to support extension education program evaluation v. 2.4.1 (R Foundation for Statistical Computing, 2021).Tung Ho, L. S. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).Article 

Google Scholar  More

Peñuelas, J. & Filella, I. Phenology. Responses to a warming world. Science 294, 793–795 (2001).PubMed 
Article 

Google Scholar 
Peñuelas, J., Rutishauser, T. & Filella, I. Ecology. Phenology feedbacks on climate change. Science 324, 887–888 (2009).PubMed 
Article 

Google Scholar 
Ramos-Jiliberto, R., Moisset de Espanés, P., Franco-Cisterna, M., Petanidou, T. & Vázquez, D. P. Phenology determines the robustness of plant-pollinator networks. Sci. Rep. 8, 14873 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Chuine, I. Why does phenology drive species distribution? Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3149–3160 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Chmielewski, F.-M. in Phenology: An Integrative Environmental Science 2nd edn (ed. Schwartz M. D.) 539–561 (Springer, 2013).Morellato, L. P. C. et al. Linking plant phenology to conservation biology. Biol. Conserv. 195, 60–72 (2016).Article 

Google Scholar 
Katelaris, C. H. & Beggs, P. J. Climate change: allergens and allergic diseases. Intern. Med. J. 48, 129–134 (2018).PubMed 
Article 

Google Scholar 
Schwartz, M. D. (ed.) Phenology: An Integrative Environmental Science 2nd edn (Springer, 2013).Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).PubMed 
Article 

Google Scholar 
Fu, Y. H. et al. Recent spring phenology shifts in western Central Europe based on multiscale observations. Glob. Ecol. Biogeogr. 23, 1255–1263 (2014).Article 

Google Scholar 
Jeong, S.-J., Ho, C.-H., Gim, H.-J. & Brown, M. E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008. Glob. Change Biol. 17, 2385–2399 (2011).Article 

Google Scholar 
Liu, Q. et al. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Glob. Change Biol. 22, 3702–3711 (2016).Article 

Google Scholar 
Vitasse, Y. et al. Leaf phenology sensitivity to temperature in European trees: do within-species populations exhibit similar responses. Agric. For. Meteorol. 149, 735–744 (2009).Article 

Google Scholar 
Wang, S. et al. Temporal trends and spatial variability of vegetation phenology over the Northern Hemisphere during 1982-2012. PLoS ONE 11, e0157134 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 
PubMed 
Article 

Google Scholar 
Huang, M. et al. Velocity of change in vegetation productivity over northern high latitudes. Nat. Ecol. Evol. 1, 1649–1654 (2017).PubMed 
Article 

Google Scholar 
Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nat. Commun. 10, 5388 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zohner, C. M., Mo, L., Pugh, T. A. M., Bastin, J.-F. & Crowther, T. W. Interactive climate factors restrict future increases in spring productivity of temperate and boreal trees. Glob. Change Biol. https://doi.org/10.1111/gcb.15098 (2020).Montgomery, R. A., Rice, K. E., Stefanski, A., Rich, R. L. & Reich, P. B. Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range. Proc. Natl Acad. Sci. USA 117, 10397–10405 (2020).Zohner, C. M., Benito, B. M., Svenning, J.-C. & Renner, S. S. Day length unlikely to constrain climate-driven shifts in leaf-out times of northern woody plants. Nat. Clim. Change 6, 1120–1123 (2016).Article 

Google Scholar 
Peñuelas, J. et al. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytol. 161, 837–846 (2004).PubMed 
Article 

Google Scholar 
Papagiannopoulou, C. et al. Vegetation anomalies caused by antecedent precipitation in most of the world. Environ. Res. Lett. 12, 74016 (2017).Article 

Google Scholar 
Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948 (2009).Article 

Google Scholar 
Fu, Y. H. et al. Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates. Tree Physiol. 39, 1277–1284 (2019).CAS 
PubMed 
Article 

Google Scholar 
Seyednasrollah, B., Swenson, J. J., Domec, J.-C. & Clark, J. S. Leaf phenology paradox: why warming matters most where it is already warm. Remote Sens. Environ. 209, 446–455 (2018).Article 

Google Scholar 
Chuine, I., Morin, X. & Bugmann, H. Warming, photoperiods, and tree phenology. Science 329, 277–278 (2010).PubMed 
Article 

Google Scholar 
Vitasse, Y. & Basler, D. What role for photoperiod in the bud burst phenology of European beech. Eur. J. For. Res 132, 1–8 (2013).Article 

Google Scholar 
Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ. 38, 1725–1736 (2015).PubMed 
Article 

Google Scholar 
Caffarra, A., Donnelly, A. & Chuine, I. Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Clim. Res. 46, 159–170 (2011).Article 

Google Scholar 
Körner, C. & Basler, D. Plant science. Phenology under global warming. Science 327, 1461–1462 (2010).PubMed 
Article 

Google Scholar 
Fu, Y. H. et al. Daylength helps temperate deciduous trees to leaf-out at the optimal time. Glob. Change Biol. 25, 2410–2418 (2019).Article 

Google Scholar 
Singh, R. K., Svystun, T., AlDahmash, B., Jönsson, A. M. & Bhalerao, R. P. Photoperiod- and temperature-mediated control of phenology in trees – a molecular perspective. New Phytol. 213, 511–524 (2017).CAS 
PubMed 
Article 

Google Scholar 
Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).CAS 
PubMed 
Article 

Google Scholar 
Brelsford, C. C., Nybakken, L., Kotilainen, T. K. & Robson, T. M. The influence of spectral composition on spring and autumn phenology in trees. Tree Physiol. 39, 925–950 (2019).CAS 
PubMed 
Article 

Google Scholar 
Strømme, C. B. et al. UV-B and temperature enhancement affect spring and autumn phenology in Populus tremula. Plant Cell Environ. 38, 867–877 (2015).PubMed 
Article 

Google Scholar 
Fu, Y. H. et al. Increased heat requirement for leaf flushing in temperate woody species over 1980-2012: effects of chilling, precipitation and insolation. Glob. Change Biol. 21, 2687–2697 (2015).Article 

Google Scholar 
Huang, Y., Jiang, N., Shen, M. & Guo, L. Effect of preseason diurnal temperature range on the start of vegetation growing season in the Northern Hemisphere. Ecol. Indic. 112, 106161 (2020).Article 

Google Scholar 
Meng, F. et al. Opposite effects of winter day and night temperature changes on early phenophases. Ecology 100, e02775 (2019).PubMed 
Article 

Google Scholar 
Zhang, S., Isabel, N., Huang, J.-G., Ren, H. & Rossi, S. Responses of bud-break phenology to daily-asymmetric warming: daytime warming intensifies the advancement of bud break. Int. J. Biometeorol. 63, 1631–1640 (2019).PubMed 
Article 

Google Scholar 
Meng, L. et al. Divergent responses of spring phenology to daytime and nighttime warming. Agric. For. Meteorol. 281, 107832 (2020).Article 

Google Scholar 
Bigler, C. & Vitasse, Y. Daily maximum temperatures induce lagged effects on leaf unfolding in temperate woody species across large elevational gradients. Front. Plant Sci. 10, 398 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Fu, Y. H. et al. Three times greater weight of daytime than of night-time temperature on leaf unfolding phenology in temperate trees. New Phytol. 212, 590–597 (2016).CAS 
PubMed 
Article 

Google Scholar 
Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).CAS 
PubMed 
Article 

Google Scholar 
Vitasse, Y. et al. Impact of microclimatic conditions and resource availability on spring and autumn phenology of temperate tree seedlings. New Phytol. https://doi.org/10.1111/nph.17606 (2021).Azeez, A. et al. EARLY BUD-BREAK 1 and EARLY BUD-BREAK 3 control resumption of poplar growth after winter dormancy. Nat. Commun. 12, 1123 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hamer, P. The heat balance of apple buds and blossoms. Part I. Heat transfer in the outdoor environment. Agric. For. Meteorol. 35, 339–352 (1985).Article 

Google Scholar 
Landsberg, J. J., Butler, D. R. & Thorpe, M. R. Apple bud and blossom temperatures. J. Horticultural Sci. 49, 227–239 (1974).Article 

Google Scholar 
Grace, J. The temperature of buds may be higher than you thought. N. Phytol. 170, 1–3 (2006).Article 

Google Scholar 
Muir, C. D. tealeaves: an R package for modelling leaf temperature using energy budgets. AoB Plants 11, plz054 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Knohl, A., Schulze, E.-D., Kolle, O. & Buchmann, N. Large carbon uptake by an unmanaged 250-year-old deciduous forest in Central Germany. Agric. For. Meteorol. 118, 151–167 (2003).Article 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
PubMed 
Article 

Google Scholar 
Bailey, B. N., Stoll, R., Pardyjak, E. R. & Miller, N. E. A new three-dimensional energy balance model for complex plant canopy geometries: Model development and improved validation strategies. Agric. For. Meteorol. 218-219, 146–160 (2016).Article 

Google Scholar 
Michaletz, S. T. & Johnson, E. A. A heat transfer model of crown scorch in forest fires. Can. J. For. Res. 36, 2839–2851 (2006).Article 

Google Scholar 
Sanchez‐Lorenzo, A. et al. Reassessment and update of long‐term trends in downward surface shortwave radiation over Europe (1939–2012). J. Geophys. Res. Atmos. 120, 9555–9569 (2015).Pfeifroth, U., Sanchez‐Lorenzo, A., Manara, V., Trentmann, J. & Hollmann, R. Trends and variability of surface solar radiation in Europe based on surface‐ and satellite-based data records. J. Geophys. Res. Atmos. 123, 1735–1754 (2018).Article 

Google Scholar 
Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Change Biol. 18, 566–584 (2012).Article 

Google Scholar 
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ma, Q., Huang, J.-G., Hänninen, H. & Berninger, F. Divergent trends in the risk of spring frost damage to trees in Europe with recent warming. Glob. Change Biol. 25, 351–360 (2019).Article 

Google Scholar 
Zohner, C. M. et al. Late-spring frost risk between 1959 and 2017 decreased in North America but increased in Europe and Asia. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1920816117 (2020).Xiao, L. et al. Estimating spring frost and its impact on yield across winter wheat in China. Agric. For. Meteorol. 260–261, 154–164 (2018).Article 

Google Scholar 
Unterberger, C. et al. Spring frost risk for regional apple production under a warmer climate. PLoS ONE 13, e0200201 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Leolini, L. et al. Late spring frost impacts on future grapevine distribution in Europe. Field Crops Res. 222, 197–208 (2018).Article 

Google Scholar 
Greco, S. et al. Late spring frost in mediterranean beech forests: extended crown dieback and short-term effects on moth communities. Forests 9, 388 (2018).Article 

Google Scholar 
Augspurger, C. K. Spring 2007 warmth and frost: phenology, damage and refoliation in a temperate deciduous forest. Funct. Ecol. 23, 1031–1039 (2009).Article 

Google Scholar 
Dong, N., Prentice, I. C., Harrison, S. P., Song, Q. H. & Zhang, Y. P. Biophysical homoeostasis of leaf temperature: a neglected process for vegetation and land-surface modelling. Glob. Ecol. Biogeogr. 26, 998–1007 (2017).Article 

Google Scholar 
Jones, H. G. Plants and Microclimate. A Quantitative Approach to Environmental Plant Physiology (Cambridge Univ. Press, 2013).University Of East Anglia Climatic Research Unit (CRU) & Harris, I. C. CRU JRA v1.1: a forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901–Dec.2017, 2019; https://catalogue.ceda.ac.uk/uuid/13f3635174794bb98cf8ac4b0ee8f4edDupleix, A., Sousa Meneses, D., de, Hughes, M. & Marchal, R. Mid-infrared absorption properties of green wood. Wood Sci. Technol. 47, 1231–1241 (2013).CAS 
Article 

Google Scholar 
Howard, R. & Stull, R. IR radiation from trees to a ski run: a case study. J. Appl. Meteorol. Climatol. 52, 1525–1539 (2013).Article 

Google Scholar 
Monteith, J. L. & Unsworth, M. H. Principles of Environmental Physics. Plants, Animals, and the Atmosphere 4th edn (Elsevier/Academic Press, 2013).Bergman, T. L., Incropera, F. P. & Lavine, A. S. Fundamentals of Heat and Mass Transfer (J. Wiley & Sons, 2011).Jacobs, A., Heusinkveld, B. G. & Kessel, G. Simulating of leaf wetness duration within a potato canopy. NJAS Wagening. J. Life Sci. 53, 151–166 (2005).Article 

Google Scholar 
Gerlein-Safdi, C. et al. Dew deposition suppresses transpiration and carbon uptake in leaves. Agric. For. Meteorol. 259, 305–316 (2018).Article 

Google Scholar 
Muñoz Sabater, J. Copernicus Climate Change Service: ERA5-Land hourly data from 1981 to present, 2019; https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-landKusch, E. & Davy, R. KrigR – A tool for downloading and statistically downscaling climate reanalysis data. Environ. Res. Lett. 17, 024005 (2022).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018); https://www.R-project.org/ More

The procedure began by obtaining the boundaries of Brazil’s municipalities, which are the most precise spatial reference units available from the Brazilian Ministry of Health of data records on diseases and health events. The boundaries were obtained from the geographic database of the Brazilian Institute of Geography and Statistics (IBGE)11, corresponding to the territorial grid of 2015, with a total of 5,570 Brazilian municipalities.A broad and diverse set of thematic data was used to compose the datasets, spanning a range of time periods (from 1981 to 2021) according to the temporal regularity of individual layers (annual, quinquennial, atemporal, or without temporal regularity), thus covering spatial and temporal variations over Brazil’s territory. It is worth noticing that during the period of 1981 to 2021 the number of municipalities grew from 3991 to 557012, which of course led to major changes to their boundaries, in addition to the creation of the state of Tocantins in 1988 as a result of the division of the state of Goiás13. Most of the changes, though, are subdivisions of one municipality into two or more municipalities. To provide statistics that are invariant over the period we would have to resort to using clusters of municipalities (“artificial municipalities”) by means of the Minimum Comparable Areas (MCA) strategy14. Due to the time-consuming process we preferred to characterize only the current territorial division, thus providing the most refined statistical characterization of Brazil’s municipalities. Still, one can find it useful to aggregate our characterization according to an MCA territorial division; for that we refer the reader to the article by Ehrl14.A total of 19 thematic layers were used, obtained from different Brazilian government and international agencies (Tables 1 and 2, illustrated by Figs. 1–4). Each layer may have multiple thematic classes or variables, depending on the nature of the theme, totaling 642 thematic classes or variables. For each class, 18 descriptive statistics were calculated (9 raw statistics plus 9 normalized by municipality’s area–Table 3) for all the available years, totaling 11,556 attributes per municipality.Table 1 Thematic layers comprising the dataset collection.Full size tableTable 2 Original data format, resulting geometry, unit and scale/resolution of the thematic layers.Full size tableFig. 1Examples of thematic layers with annual temporality in the territorial extension of the municipality of Rio de Janeiro.Full size imageFig. 2Examples of atemporal and no temporal regularity thematic layers in the territorial extension of the municipality of Rio de Janeiro.Full size imageFig. 3Examples of bioclimatic variables from Worldclim in the territorial extension of the municipality of Rio de Janeiro.Full size imageFig. 4Climate data for total precipitation, maximum, mean and minimum temperature from Worldclim in the territorial extension of the municipality of Rio de Janeiro for the month of January.Full size imageTable 3 Statistics calculated for the features/variables in the scope of the municipalities.Full size tableThe annual thematic layers for land use and land cover include 25 thematic classes from 1985 to 2020 for the entire Brazilian territory with spatial resolution of 30 m. (Except for the Fernando de Noronha archipelago, municipality geocode 260545, for which there is no land user/cover data due to the absence of historical series Landsat satellite images for that region.) These layers were produced and made available by the online platform MapBiomas15, collection 6.0. Annual land use and land cover maps were produced via automatic classification processes applied to Landsat satellite images16. The MapBiomas Project is a multi-institutional initiative coordinated by the Greenhouse Gas Emissions Estimation System (SEEG) from the Climate Observatory’s and consists of a collaborative network of cocreators including nongovernmental organizations (NGOs), universities, and companies. The objective is to produce annual land cover and land use maps of Brazil from 1985 to the present.The annual temperature and precipitation layers include 19 different types of data from 1981 to 2020 for the entire land surface, with spatial resolution of 5 km (0.05°). These fields were derived from two different observational gridded datasets, one for precipitation and another for temperature. The observed precipitation came from the Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS)17, with a daily temporal resolution and a spatial resolution of approximately 5 km (0.05°). The observed temperature drawn from the NCEP Climate Forecast System Reanalysis (NCEP/CFSR)18 at a 6-hour temporal resolution and a spatial resolution of approximately 50 km (0.5°). The NCEP/CFSR gridded dataset was spatially downscaled to a higher spatial resolution of 5 km (0.05°) using bilinear interpolation in order to have the same spatial resolution as CHIRPS. (As with land use and land cover, there is no temperature/precipitation data for the Fernando de Noronha archipelago (geocode 260545).)The quinquennial layers for Population Count and Population Density were obtained from the Socioeconomic Data and Applications Center (SEDAC)19 through NASA’s Earth Observing System Data and Information System (EOSDIS), and is hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. This dataset estimates the population count for the years 2000, 2005, 2010, 2015 and 2020, based on national censuses and population records, and is available in raster graphics with spatial resolution of 1 km. The official population demographics data from IBGE census is not used because it is available only as a tabular data aggregate count per census sector or municipality and therefore cannot yield meaningful descriptive statistics.Atemporal data include the following themes: Climatological Normals for Temperature; Altitude; Geomorphology; Soils; Phytophysiognomies; and Biome boundaries. Climatological Normals for Temperature came from Worldclim20 and correspond to observational data, representative of 1950 to 2000, which were interpolated to a resolution of 1 km. These temperature values are in degree Celsius, but for historical reasons they are scaled by a factor of 10. The used mean, minimum and maximum values of temperature include information from different remote sensors onboard the MODIS and NOAA satellites which operate to jointly capture surface temperature and air humidity values. Besides the annual temperature data, we also included climatological normal data because they provide monthly mean values for temperature. These values complement the annual information (considerably influenced by climate events like El Niño and La Niña) and serve as an important reference on seasonal temperature variation patterns, a factor that directly influences the reproduction and survival dynamics of species such as vectors. The altitude data came from NASA’s Shuttle Radar Topography Mission digital elevation model (SRTM) 1 ArcSecond Global, conceived to provide consistent high-quality near-global elevation data21. The original data are radar images with spatial resolution of 30 m, version 3, reprocessed to fix inconsistencies and fill missing data (“voids”). The other themes–Geomorphology, Soils, Phytophysiognomies, and Biome boundaries–were obtained from IBGE22. These provides regional details, and were constructed from interpretation of satellite images and various field studies throughout Brazil beginning in 199023.The layers without temporal regularity include: Mining Areas; Roads; Railways; Waterways or watercourses; Hydroelectric Plants; Dams; Conservation Units; Indigenous Lands; and Zone Climates and Regional Subunits. The Mining Areas layer has 336 classes, representing the different types of minerals explored in Brazil’s territory, provided by the Brazilian National Mining Agency (ANM). The boundaries of Conservation Units were provided by the Brazilian Ministry of Environment (MMA). The other layers are single classes of Roads, Railways, Waterways/watercourses, Hydroelectric Plants, Dams, obtained from the Continuous Cartographic Bases24 and Indigenous lands and Quilombola territories25, all this datasets from IBGE. The roads category comprises all its available classifications, covering data from subcategories such as highways and dirt roads. The same unification was adopted for the railways and waterways categories. The layer on Zone Climates and Regional Subunits represents the different climate zones in Brazil’s territory, grouped by temperature and humidity. This layer also identifies the climate types, characterized by shades and hues: tropical, subtropical, mild mesothermal, and median mesothermal26.Considering the heterogeneity of the data sources and the structural particularities of the thematic layers acquired, it was essential to conduct a pre-processing and structuring stage with the datasets in order to proceed with the calculation of the descriptive statistics. All the raw data, whose total size amounted to 195 GB, were pre-processed in QGIS v3.1027. This stage required standardizing the geospatial data’s cartographic characteristics, correcting topological errors, eliminating duplicate information, and uniformizing the attribute tables. The data were generally organized in two major groups: vector data and matrix data (raster).To be able to process the Land Use and Land Cover features at the original 30 m spatial resolution, we had first to break down each annual raster (1985 to 2020) into 5,569 smaller raster pieces, one for each municipality, by using the gdalwarp tool from the Geospatial Data Abstraction Library (GDAL). Next, we converted all the resulting rasters to vector format (geopackage) via the script gdal_polygonize.py, also from GDAL. The conversion was necessary because the vector format (geopackage) allowed the calculation of the polygons’ statistics for all the Land Use and Land Cover features, which is not possible with the raster format with the techniques and functions used (described in the Code availability section). All that pre-processing took about 600 hours running in parallel on an Intel Core i7 computer with 8 physical CPU cores and 64 GB of RAM.The data on Temperature, Precipitation, Population Count/Density, Altitude, and Climatological Normals, also provided in matrix format, were converted to point geometry, since they are inherently points but which had been interpolated by their sources before making them available. The conversion of Altitude from raster to vector was the most computationally demanding operation due to the need to process 10.6 billion points (spread across 821 tiles of 3601 × 3601 points each) at the resolution of 30 m. It took about one month of uninterrupted parallel processing on a 20-core Intel Xeon E5-2690 machine with 128 GB of RAM.For the vector data, it was first necessary to homogenize the cartographic references using South America Albers Equal Area Conic (EPSG:102033) for data requiring calculation of areas (polygons), South America Equidistant Conic (EPSG:102032) for data requiring calculation of distances (lines), and SIRGAS 2000 Geodetic Reference (EPSG:4674) for data with restricted localization (points)28. It was also necessary to correct some topological errors in the vector data regarding the line and polygon geometries, which are artifacts introduced during the data construction/vectorization stage. The vector data correspond to the following themes: Geomorphology; Soils; Phytophysiognomies; Biome Boundaries; Mining Areas; Roads; Railways; Waterways or watercourses; Hydroelectric Plants; Dams; Conservation Units; Indigenous lands and Quilombola territories; Zone Climates and Regional Subunits.For the statistical description of the municipalities’ socioenvironmental characteristics, we calculated the measures of central tendency such as mean and median, and measures of dispersion such as maximum and minimum values, standard deviation, and percentiles. For each descriptive statistic we also calculated a corresponding normalized statistic, simply dividing the original statistics value by the municipality’s area. The values were normalized due to the wide variation in the territorial area of Brazil’s municipalities. For example, Altamira, in the state of Pará, is Brazil’s largest municipality, with an area of 159,533 km2, while Santa Cruz de Minas, in the state of Minas Gerais, is the smallest one, with only 3,565 km2 29. This wide territorial variability might otherwise skew the modeling towards the identification of distorted correlations, such as the identification of relations between higher proportions of natural or anthropic features and higher concentration of cases, which is merely due to the municipality’s larger territorial dimensions.Based on structuring of the graphic, we executed a spatial data intersection with the municipal boundaries by means of different routines from PostGIS30, an extension that adds spatial and geographic objects to the PostgreSQL object-relational database.Calculation of the descriptive statisticsThe meaning of the statistics described in Table 3 actually depends on both feature’s geometry and unit of measurement, which are reported in Table 2 for each thematic layer.For polygons, such as conservation units, the area of each unit is computed in square meters and the set of all conservation units’ areas in the municipality forms the statistical population upon which the descriptive statistics will be calculated for that municipality. This means that the minimum statistic will refer to the smallest area among the conservation units in the municipality, the mean statistic to the average area, the count statistic will refer to the number of conservation units in the municipality, and so forth. Analogously, when the feature type is line, e.g. roads, the set of all road stretches’ lengths (in meters) is the statistical population.The procedure differs a bit for point features, such as altitude and temperature. In this case, except for the count statistic (which refers to the number of points in the municipality), the actual value at each feature point is taken; for instance, the altitude and temperature at a given location. Differently from the polygons and line cases, the associated unit cannot be predefined (in square meters or meters), and it will depend on the actual unit of the underlying layer–for altitude it is meters, but for temperature it could be either Celsius or Kelvin. Some point-type features, such as hydroelectric plants, do not have a unit per se, i.e. they merely refer to a quantity. Once the set of all point-type feature values are taken, we have a statistical population of values and the calculation of the statistics proceeds exactly as described with the other two feature types.For each descriptive statistic, there is a corresponding normalized one which is calculated by dividing the statistic by the municipality’s area (in m2). Those normalized statistics complement the set of descriptive information and provide the notion of proportion or density. As an example, the statistic sum_normalized corresponds to the percentage of occupation of a given polygon-type thematic layer in the municipality, or an estimation of density for line-type layers such as roads. More

Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique Evolution, Orsay, FranceChristophe Diagne, Anne-Charlotte Vaissière & Franck CourchampUnité Biologie des Organismes et Ecosystèmes Aquatiques (BOREA, UMR 7208), Muséum national d’Histoire naturelle, Sorbonne Université, Université de Caen Normandie, CNRS, IRD, Université des Antilles, Paris, FranceBoris LeroyISEM, Univ. Montpellier, CNRS, IRD, Montpellier, FranceRodolphe E. GozlanMIVEGEC, Univ. Montpellier, IRD, CNRS, Montpellier, FranceDavid RoizInstitute of Hydrobiology, Biology Centre of the Czech Academy of Sciences, České Budějovice, Czech RepublicIvan JarićDepartment of Ecosystem Biology, Faculty of Science, University of South Bohemia, České Budějovice, Czech RepublicIvan JarićCEE-M, UMR5211, Univ. Montpellier, CNRS, INRAE, Institut Agro, Montpellier, FranceJean-Michel SallesGlobal Ecology, College of Science and Engineering, Flinders University, Adelaide, South Australia, AustraliaCorey J. A. Bradshaw More

Vitousek, P. M. Litterfall, nutrient cycling, and nutrient limitation in tropical forests. Ecology 65, 285–298 (1984).CAS 
Article 

Google Scholar 
Wright, S. J. et al. Plant responses to fertilization experiments in lowland, species rich, tropical forests. Ecology 99, 1129–1138 (2018).PubMed 
Article 

Google Scholar 
Turner, B. L. et al. Pervasive phosphorus limitation of tree species but not communities in tropical forests. Nature 555, 367–370 (2018).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Fleischer, K. et al. Amazon forest response to CO2 fertilization depend on plant phosphorus acquisition. Nat. Geosci. 12, 736–741 (2019).CAS 
Article 
ADS 

Google Scholar 
Goll, D. S. et al. Nutrient limitation reduces land carbon uptake in simulations with a model of combined carbon, nitrogen and phosphorus cycling. Biogeosciences 9, 3547–3569 (2012).CAS 
Article 
ADS 

Google Scholar 
Sun, Y. et al. Diagnosing phosphorus limitation in natural terrestrial ecosystems in carbon cycle models. Earths Future 5, 730–749 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Zhang, Q. et al. Nitrogen and phosphorus limitations significantly reduce allowable CO2 emissions. Geophys. Lett. 41, 632–637 (2014).CAS 
Article 
ADS 

Google Scholar 
Luo, Y., Hui, D. & Zhang, D. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystem: a meta analysis. Ecology 87, 53–63 (2006).PubMed 
Article 

Google Scholar 
Jordan, C. F. The nutrient balance of an Amazonian rainforest. Ecology 63, 647–654 (1982).CAS 
Article 

Google Scholar 
Walker, T. W. & Syers, J. K. The fate of phosphorus during pedogenesis. Geoderma 15, 1–19 (1976).CAS 
Article 
ADS 

Google Scholar 
Crews, T. E. et al. Changes in soil phosphorus fractions and ecosystem dynamics across a long chronosequence in Hawaii. Ecology 76, 1408–1424 (1995).Article 

Google Scholar 
Hedin, L. O. et al. Nutrient losses over four million years of tropical forest development. Ecology 84, 2231–2255 (2003).Article 

Google Scholar 
Dalling, J. W. et al. in Tropical Tree Physiology (Springer, 2016).Herrera, R. R. & Medina, E. Amazon ecosystems, their structure and functioning with particular emphasis on nutrients. Interciencia 3, 223–231 (1978).
Google Scholar 
Quesada, C. A. et al. Variations in chemical and physical properties of Amazon forest soils in relation to their genesis. Biogeosciences 7, 1515–1541 (2010).CAS 
Article 
ADS 

Google Scholar 
Quesada, C. A. et al. Basin wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9, 2203–2246 (2012).Article 
ADS 

Google Scholar 
Mercado, L. et al. Variations in Amazon forest productivity correlated with foliar nutrients and modelled rates of photosynthetic carbon supply. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 3316–3329 (2011).PubMed 
PubMed Central 
Article 

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

Google Scholar 
Yang, X. et al. The effects of phosphorus cycle dynamics carbon sources and sink in the Amazon region: a modelling study using ELM v1. J. Geophys. Res. Biogeosci. 124, 3686–3698 (2019).CAS 
Article 

Google Scholar 
Sollins, P. Factors influencing species composition in tropical lowland rain forest: does soil matter? Ecology 79, 23–30 (1998).Article 

Google Scholar 
Alvarez-Clare, S. et al. A direct test of nitrogen and phosphorus limitation to net primary productivity in a lowland tropical wet forest. Ecology 94, 1540–1551 (2013).CAS 
PubMed 
Article 

Google Scholar 
Wright, S. J. et al. Potassium, phosphorus, or nitrogen limit root allocation, tree growth, or litter production in a lowland tropical forest. Ecology 92, 1616–1625 (2011).PubMed 
Article 

Google Scholar 
Sayer, E. J. et al. Variable responses of lowland tropical forest nutrient status to fertilization and litter manipulation. Ecosystems 15, 387–400 (2012).CAS 
Article 

Google Scholar 
Ganade, G. & Brown, V. Succession in old pastures of central Amazonia: role of soil fertility and plant litter. Ecology 83, 743–754 (2002).Article 

Google Scholar 
Markewitz, D. et al. Soil and tree response to P fertilization in a secondary tropical forest supported by an Oxisol. Biol. Fertil. Soils 48, 665–678 (2012).Article 

Google Scholar 
Davidson, E. et al. Nitrogen and phosphorus limitation of biomass growth in a tropical secondary forest. Ecol. Appl. 14, 150–163 (2004).Article 

Google Scholar 
Massad, T. et al. Interactions between fire, nutrients, and insect herbivores affect the recovery of diversity in the southern Amazon. Oecologia 172, 219–229 (2013).PubMed 
Article 
ADS 

Google Scholar 
Newbery, D. M. et al. Does low phosphorus supply limit seedling establishment and tree growth in groves of ectomycorrhizal trees in a central African rainforest? New Phytol. 156, 297–311 (2002).CAS 
PubMed 
Article 

Google Scholar 
Mirmanto, E. et al. Effects of nitrogen and phosphorus fertilization in a lowland evergreen rainforest. Philos. Trans. R. Soc. Lond. B Biol. Sci. 354, 1825–1829 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lugli, L. F. et al. Rapid responses of root traits and productivity to phosphorus and cation additions in a tropical lowland forest in Amazonia. New Phytol. 230, 116–128 (2020).Article 
CAS 

Google Scholar 
Quesada, C. A. et al. Soils of Amazonia with particular reference to the rainfor sites. Biogeosciences 8, 1415–1440 (2011).CAS 
Article 
ADS 

Google Scholar 
Giardina, C. et al. Primary production and carbon allocation in relation to nutrient supply in a tropical experiment forest. Glob. Change Biol. 9, 1438–1450 (2003).Article 
ADS 

Google Scholar 
Rowland, L. et al. Scaling leaf respiration with nitrogen and phosphorus in tropical forests across two continents. New Phytol. 214, 1064–1077 (2017).CAS 
PubMed 
Article 

Google Scholar 
Vicca, S. et al. Fertile forests produce biomass more efficiently. Ecol. Lett. 15, 520–526 (2012).CAS 
PubMed 
Article 

Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–826 (2004).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Hinsinger, P. How do plant roots acquire mineral nutrients? Chemical processes involved in the rhizosphere. Adv. Agron. 64, 225–265 (1998).CAS 
Article 

Google Scholar 
Van Langehove, L. et al. Rapid root assimilation of added phosphorus in a lowland tropical rainforest of French Guiana. Soil Biol. Biochem. 140, 107646 (2019).Article 
CAS 

Google Scholar 
Martins, N. P. et al. Fine roots stimulate nutrient release during early stages of litter decomposition in a central Amazon rainforest. Plant Soil 469, 287–303 (2021).CAS 
Article 

Google Scholar 
Cordeiro, A. L. et al. Fine root dynamics vary with soil and precipitation in a low-nutrient tropical forest in the central Amazonia. Plant Environ. Interact. 220, 3–16 (2020).Article 

Google Scholar 
Yavitt, J. Soil fertility and fine root dynamics in response to four years of nutrient (N,P, K) fertilization in a lowland tropical moist forest, Panamá. Austral. Ecol. 36, 433–445 (2011).Article 

Google Scholar 
Wurzburger, N. & Wright, S. J. Fine root responses to fertilization reveal multiple nutrient limitation in a lowland tropical forest. Ecology 96, 2137–2146 (2015).PubMed 
Article 

Google Scholar 
Waring, B. G., Aviles, D. P., Murray, J. G. & Powers, J. S. Plant community responses to stand level nutrient fertilization in a secondary tropical dry forest. Ecology 100, e02691 (2019).PubMed 
Article 

Google Scholar 
Jansens, I. A. et al. Reductions of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).Article 
ADS 
CAS 

Google Scholar 
Alvarez Claire, S. et al. Do foliar, litter, and root nitrogen and phosphorus concentration reflect nutrient limitation in a lowland tropical wet forest? PLoS ONE 10, e0123796 (2015).Article 
CAS 

Google Scholar 
Bouma, T. in Advances in Photosynthesis and Respiration Vol. 18 (eds Lambers, H. & Ribas-Carbo, M.) 177–194 (Springer, 2005).Malhi, Y. et al. Comprehensive assessment of carbon productivity, allocation and storage in three Amazonian forests. Glob. Change Biol. 15, 1255–1274 (2009).Article 
ADS 

Google Scholar 
Aragão, L. E. O. et al. Above and below ground net primary productivity across ten Amazonian forests on contrasting soils. Biogeosciences 6, 2759–2778 (2009).Article 
ADS 

Google Scholar 
Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Quesada, C. A. & Lloyd, J. in Interactions Between Biosphere, Atmosphere and Human Land Use in the Amazon Basin (eds Nagy, L. et al.) 267–299 (Springer, 2016).Girardin, C. A. J. et al. Seasonal trends of Amazonian rainforest phenology, net primary production, and carbon allocation. Glob. Biogeochem. Cycles 30, 700–715 (2016).CAS 
Article 
ADS 

Google Scholar 
Laurance, W. F. et al. An Amazonian rainforest and its fragments as a laboratory of global change. Biol. Rev. 93, 223–247 (2018).PubMed 
Article 

Google Scholar 
De Oliveira, A. & Mori, S. A. A central Amazonia terra firme forest. I. High tree species richness on poor soils. Biodivers. Conserv. 8, 1219–1244 (1999).Article 

Google Scholar 
Ferreira, S. J. F., Luizão, F. J. & Dallarosa, R. L. G. Throughfall and rainfall interception by an upland forest submitted to selective logging in Central Amazonia [Portuguese]. Acta Amaz. 35, 55–62 (2005).Article 

Google Scholar 
Tanaka, L. D. S., Satyamurty, P. & Machado, L. A. T. Diurnal variation of precipitation in central Amazon Basin. Int. J. Climatol. 34, 3574–3584 (2014).Article 

Google Scholar 
Duque, A. et al. Insights into regional patterns of Amazonian forest structure and dominance from three large terra firme forest dynamics plots. Biodivers. Conserv. 26, 669–686 (2017).Article 

Google Scholar 
Martins, D. L. et al. Soil induced impacts on forest structure drive coarse wood debris stocks across central Amazonia. Plant Ecol. Divers. 8, 229–241 (2014).Article 

Google Scholar 
Metcalfe, D. B. et al. A method for extracting plant roots from soil which facilitates rapid sample processing without compromising measurent accuracy. New Phytol. 174, 697–703 (2007).CAS 
PubMed 
Article 

Google Scholar 
Chave, J. et al. Improved allometric to estimate the above ground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).Article 
ADS 

Google Scholar 
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).PubMed 
Article 

Google Scholar 
Zanne, A. E. et al. Global Wood Density Database https://doi.org/10.5061/dryad.234 (2009).Higuchi, N. & Carvalho, J. A. in Anais do Seminário: Emissão e Sequestro de CO2—Uma Nova Oportunidade de Negócios para o Brasil (CVRD, 1994).Brienen, R. J. W., Philips, O. L. & Zagt, R. J. Long term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Malhado, A. C. M. et al. Seasonal leaf dynamics in an Amazonian tropical forest. Forest Ecol. Manag. 258, 1161–1165 (2009).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 
Bates, D., Marcher, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Moraes, A. C. M. et al. Fine Litterfall Production and Nutrient Composition Data from a Fertilized Site in Central Amazon, Brazil (NERC, 2020).Cunha, H. F. V. et al. Fine Root Biomass in Fertilised Plots in the Central Amazon, 2017–2019 (NERC Environmental Information Data Centre, 2021).Cunha, H. F. V. et al. Tree Census and Diameter Increment in Fertilised Plots in the Central Amazon, 2017–2020 (NERC Environmental Information Data Centre, 2021).Cunha, H. F. V. et al. Leaf Area Index (LAI) in Fertilised Plots in the Central Amazon, 2017–2018 (NERC Environmental Information Data Centre, 2021). More

International Energy Agency. Offshore Wind Outlook 2019. https://iea.blob.core.windows.net/assets/495ab264-4ddf-4b68-b9c0-514295ff40a7/Offshore_Wind_Outlook_2019.pdf (2019).United Nations. Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. (E/CN.3/2016/2/Rev.1). 49. (New York: United Nations Economic and Social Council, 2016).Copping, A. et al. Annex IV State of the Science Report: Environmental Effects of Marine Renewable Energy Development Around the World. https://tethys.pnnl.gov/sites/default/files/publications/Annex-IV-2016-State-of-the-Science-Report_MR.pdf. Accessed 27 Feb 2020. (2016).Dean, N. Performance factors. Nature Energy 5, 5–5 (2020).Article 

Google Scholar 
Global Wind Energy Council. Globarl offshore wind report 2020. https://gwec.net/wp-content/uploads/dlm_uploads/2020/08/GWEC-offshore-wind-2020-5.pdf (2020).Jansen, M. et al. Offshore wind competitiveness in mature markets without subsidy. Nat. Energy 5, 614–622 (2020).Article 

Google Scholar 
IRENA. Global Renewables Outlook: Energy transformation 2050 (Edition: 2020), International Renewable Energy Agency, Abu Dhabi. ISBN 978-92-9260-238-3. www.irena.org/publications (2020).Wiser, R. et al. Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050. Nat. Energy 6, 555–565 (2021).Article 

Google Scholar 
IRENA. Future of wind: Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper), International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf (2019).European Commission. Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions. The European Green Deal. Brussels, 11.12.2019 COM(2019) 640 final. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2019%3A640%3AFIN (2019).European Commission. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. An EU Strategy to harness the potential of offshore renewable energy for a climate neutral future. Brussels, 19.11.2020 COM(2020) 741 final. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2020%3A741%3AFIN (2020).European Parliament. European Parliament resolution of 14 March 2019 on climate change – a European strategic long-term vision for a prosperous, modern, competitive and climate neutral economy in accordance with the Paris Agreement (2019/2582(RSP)). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52019IP0217 (2019).Arneth, A. et al. Post-2020 biodiversity targets need to embrace climate change. Proc. Natl. Acad. Sci. 117, 30882–30891 (2020).CAS 
Article 

Google Scholar 
Copping, A. E., Freeman, M. C., Gorton, A. M. & Hemery, L. G. Risk Retirement—Decreasing Uncertainty and Informing Consenting Processes for Marine Renewable Energy Development. J. Marine Sci. Eng. 8, 172 (2020).Article 

Google Scholar 
WWF. Environmental Impacts of Offshore Wind Power Production in the North Sea. A Literature Overview. https://tethys.pnnl.gov/sites/default/files/publications/WWF-OSW-Environmental-Impacts.pdf (2014).Cook, A. S. C. P., Humphreys, E. M., Bennet, F., Masden, E. A. & Burton, N. H. K. Quantifying avian avoidance of offshore wind turbines: Current evidence and key knowledge gaps. Marine Environ. Res. 140, 278–288 (2018).CAS 
Article 

Google Scholar 
Willsteed, E. A., Jude, S., Gill, A. B. & Birchenough, S. N. R. Obligations and aspirations: A critical evaluation of offshore wind farm cumulative impact assessments. Renew. Sustain. Energy Rev. 82, 2332–2345 (2018).Article 

Google Scholar 
Stelzenmüller, V. et al. Operationalizing risk-based cumulative effect assessments in the marine environment. Sci. Total Environ. 724, 138118 (2020).Article 
CAS 

Google Scholar 
Ehler, C. & Douvere, F. in Intergovernmental Oceanographic Commission and Man and the Biosphere Programme. IOC Manual and Guides No. 53, ICAM Dossier No. 6. Paris: UNESCO. 99pp. (2009).Borja, A. et al. Good Environmental Status of marine ecosystems: What is it and how do we know when we have attained it? Marine Pollut. Bull. 76, 16–27 (2013).CAS 
Article 

Google Scholar 
Peters, J. L., Remmers, T., Wheeler, A. J., Murphy, J. & Cummins, V. A systematic review and meta-analysis of GIS use to reveal trends in offshore wind energy research and offer insights on best practices. Renew. Sustain. Energy Rev. 128, 109916 (2020).Article 

Google Scholar 
Gasparatos, A., Doll, C. N. H., Esteban, M., Ahmed, A. & Olang, T. A. Renewable energy and biodiversity: Implications for transitioning to a Green Economy. Renew. Sustain. Energy Rev. 70, 161–184 (2017).Article 

Google Scholar 
Xiao, Y. & Watson, M. Guidance on Conducting a Systematic Literature Review. J. Plan. Education Res. 39, 93–112 (2017).Article 

Google Scholar 
Mengist, W., Soromessa, T. & Legese, G. Method for conducting systematic literature review and meta-analysis for environmental science research. MethodsX 7, 100777 (2020).Article 

Google Scholar 
Pullin, A. & Stewart, G. Guidelines for Systematic Review in Environmental Management. Conserv. Biol. 20, 1647–1656 (2007).Article 

Google Scholar 
van der Molen, J., Smith, H. C. M., Lepper, P., Limpenny, S. & Rees, J. Predicting the large-scale consequences of offshore wind turbine array development on a North Sea ecosystem. Continental Shelf Res. 85, 60–72 (2014).Article 

Google Scholar 
De Backer, A., Van Hoey, G., Coates, D., Vanaverbeke, J. & Hostens, K. Similar diversity-disturbance responses to different physical impacts: Three cases of small-scale biodiversity increase in the Belgian part of the North Sea. Marine Pollut. Bull. 84, 251–262 (2014).Article 
CAS 

Google Scholar 
Floeter, J. et al. Pelagic effects of offshore wind farm foundations in the stratified North Sea. Prog. Oceanograph. 156, 154–173 (2017).Article 

Google Scholar 
Lindeboom, H. J. et al. Short-term ecological effects of an offshore wind farm in the Dutch coastal zone; A compilation. Environ. Res. Lett. 6, 035101 (2011).Article 

Google Scholar 
Bray, L. et al. Expected effects of offshore wind farms on Mediterranean Marine Life. J. Marine Sci. Eng. 4, 18 (2016).Article 

Google Scholar 
Dannheim, J. et al. Benthic effects of offshore renewables: identification of knowledge gaps and urgently needed research. ICES J. Marine Sci. 77, 1092–1108 (2019).Article 

Google Scholar 
Wilson, J. C. & Elliott, M. The habitat-creation potential of offshore wind farms. Wind Energy 12, 203–212 (2009).Article 

Google Scholar 
Hall, R., João, E. & Knapp, C. W. Environmental impacts of decommissioning: Onshore versus offshore wind farms. Environ. Impact Assess. Rev. 83, 106404 (2020).Article 

Google Scholar 
Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).Article 

Google Scholar 
Korpinen, S. & Andersen, J. H. A Global Review of Cumulative Pressure and Impact Assessments in Marine Environments. Front. Marine Sci. 3, 00153 (2016).Article 

Google Scholar 
Nõges, P. et al. Quantified biotic and abiotic responses to multiple stress in freshwater, marine and ground waters. Sci. Total Environ. 540, 43–52 (2016).Article 
CAS 

Google Scholar 
Gissi, E. et al. A review of the combined effects of climate change and other local human stressors on the marine environment. Sci. Total Environ. 755, 142564 (2021).CAS 
Article 

Google Scholar 
Gușatu, L. F. et al. Spatial and temporal analysis of cumulative environmental effects of offshore wind farms in the North Sea basin. Sci. Rep. 11, 10125 (2021).Article 
CAS 

Google Scholar 
Gissi, E. et al. Addressing uncertainty in modelling cumulative impacts within maritime spatial planning in the Adriatic and Ionian region. PLoS ONE 12, e0180501 (2017).Article 
CAS 

Google Scholar 
Vaissière, A. C., Levrel, H., Pioch, S. & Carlier, A. Biodiversity offsets for offshore wind farm projects: The current situation in Europe. Marine Policy 48, 172–183 (2014).Article 

Google Scholar 
Iglesias, G., Tercero, J. A., Simas, T., Machado, I. & Cruz, E. Environmental Effects. In Wave and Tidal Energy (eds Greaves, D. & Iglesias, G.). https://doi.org/10.1002/9781119014492.ch9 (2018).Causon, P. D. & Gill, A. B. Linking ecosystem services with epibenthic biodiversity change following installation of offshore wind farms. Environ. Sci. Policy 89, 340–347 (2018).Article 

Google Scholar 
Copping, A. E. & Hemery, L. G. OES-Environmental 2020 State of the Science Report: Environmental Effects of Marine Renewable Energy Development Around the World. Report for Ocean Energy Systems (OES). 323 pp., (2020).Gill, A. B. Offshore renewable energy: ecological implications of generating electricity in the coastal zone. J. Appl. Ecol. 42, 605–615 (2005).Article 

Google Scholar 
Scheidat, M. et al. Harbour porpoises (Phocoena phocoena) and wind farms: A case study in the Dutch North Sea. Environ. Res. Lett. 6, 025102 (2011).Article 

Google Scholar 
Skov, H. et al. Patterns of migrating soaring migrants indicate attraction to marine wind farms. Biol. Lett. 12, 20160804 (2016).Article 

Google Scholar 
Vanermen, N. et al. Attracted to the outside: a meso-scale response pattern of lesser black-backed gulls at an offshore wind farm revealed by GPS telemetry. ICES J. Marine Sci. 77, 701–710 (2020).Article 

Google Scholar 
Frank, B. Research on marine mammals summary and discussion of research results. In Offshore Wind Energy: Research on Environmental Impacts. 77–86 https://doi.org/10.1007/978-3-540-34677-7_8 (2006).Thaxter, C. B. et al. Bird and bat species’ global vulnerability to collision mortality at wind farms revealed through a trait-based assessment. Proc. Royal Soc. B.: Biol Sci. 284, 20170829 (2017).Article 

Google Scholar 
Wilson, J. C. et al. Coastal and Offshore Wind Energy Generation: Is It Environmentally Benign? Energies 3, 1383–1422 (2010).Article 

Google Scholar 
Busch, M., Kannen, A., Garthe, S. & Jessopp, M. Consequences of a cumulative perspective on marine environmental impacts: Offshore wind farming and seabirds at North Sea scale in context of the EU Marine Strategy Framework Directive. Ocean Coastal Manag. 71, 213–224 (2013).Article 

Google Scholar 
Garthe, S., Markones, N. & Corman, A.-M. Possible impacts of offshore wind farms on seabirds: a pilot study in Northern Gannets in the southern North Sea. J. Ornithol. 158, 345–349 (2017).Article 

Google Scholar 
Brandt, M. J., Diederichs, A., Betke, K. & Nehls, G. Responses of harbour porpoises to pile driving at the Horns Rev II offshore wind farm in the Danish North Sea. Marine Ecol. Prog. Ser. 421, 205–216 (2011).Article 

Google Scholar 
Wilhelmsson, D., Malm, T. & Öhman, M. C. The influence of offshore windpower on demersal fish. ICES J. Marine Sci. 63, 775–784 (2006).Article 

Google Scholar 
Bergström, L., Sundqvist, F. & Bergström, U. Effects of an offshore wind farm on temporal and spatial patterns in the demersal fish community. Marine Ecol. Progr. Ser. 485, 199–210 (2013).Article 

Google Scholar 
van Hal, R., Griffioen, A. B. & van Keeken, O. A. Changes in fish communities on a small spatial scale, an effect of increased habitat complexity by an offshore wind farm. Marine Environ. Res. 126, 26–36 (2017).Article 
CAS 

Google Scholar 
Degraer, S. et al. Offshore wind farm artificial reefs affect ecosystem structure and functioning: A synthesis. Oceanography 33, 48–57 (2020).Article 

Google Scholar 
Zettler, M. L. & Pollehne, F. The Impact of Wind Engine Constructions on Benthic Growth Patterns in the Western Baltic. In Offshore Wind Energy: Research on Environmental Impacts (eds Köller, J., Köppel, J. & Peters, W.). 201–222 (Springer Berlin Heidelberg, 2006).Wilhelmsson, D. Marine environmental aspects of offshore wind power development. (Nova Science Publishers, Inc, 2010).Teilmann, J. & Carstensen, J. Negative long term effects on harbour porpoises from a large scale offshore wind farm in the Baltic – Evidence of slow recovery. Environ. Res. Lett. 7, 045101 (2012).Article 

Google Scholar 
Halouani, G. et al. A spatial food web model to investigate potential spillover effects of a fishery closure in an offshore wind farm. J. Marine Syst. 212, 103434 (2020).Article 

Google Scholar 
Reubens, J. T., Degraer, S. & Vincx, M. The ecology of benthopelagic fishes at offshore wind farms: a synthesis of 4 years of research. Hydrobiologia 727, 121–136 (2014).CAS 
Article 

Google Scholar 
Wilber, D. H., Carey, D. A. & Griffin, M. Flatfish habitat use near North America’s first offshore wind farm. J. Sea Res. 139, 24–32 (2018).Article 

Google Scholar 
Welcker, J. & Nehls, G. Displacement of seabirds by an offshore wind farm in the North Sea. Marine Ecol. Prog. Ser. 554, 173–182 (2016).Article 

Google Scholar 
Vallejo, G. C. et al. Responses of two marine top predators to an offshore wind farm. Ecol. Evol. 7, 8698–8708 (2017).Article 

Google Scholar 
Tougaard, J., Henriksen, O. D. & Miller, L. A. Underwater noise from three types of offshore wind turbines: Estimation of impact zones for harbor porpoises and harbor seals. J. Acoustical Soc. Am. 125, 3766–3773 (2009).Article 

Google Scholar 
Kastelein, R. A., Jennings, N., Kommeren, A., Helder-Hoek, L. & Schop, J. Acoustic dose-behavioral response relationship in sea bass (Dicentrarchus labrax) exposed to playbacks of pile driving sounds. Marine Environ. Res. 130, 315–324 (2017).CAS 
Article 

Google Scholar 
Vanermen, N. et al. Assessing seabird displacement at offshore wind farms: power ranges of a monitoring and data handling protocol. Hydrobiologia 756, 155–167 (2015).Article 

Google Scholar 
Wahlberg, M. & Westerberg., H. Hearing in fish and their reactions to sounds from offshore wind farms. Marine Ecol. Prog. Ser. 288, 295–309 (2005).Article 

Google Scholar 
Desholm, M. Avian sensitivity to mortality: Prioritising migratory bird species for assessment at proposed wind farms. J. Environ. Manag. 90, 2672–2679 (2009).Article 

Google Scholar 
Vanermen, N. et al. Seabird avoidance and attraction at an offshore wind farm in the Belgian part of the North Sea. Hydrobiologia 756, 51–61 (2015).Article 

Google Scholar 
Brandt, M. J. et al. Disturbance of harbour porpoises during construction of the first seven offshore wind farms in Germany. Marine Ecol. Prog. Ser. 596, 213–232 (2018).Article 

Google Scholar 
Masden, E. A., Haydon, D. T., Fox, A. D. & Furness, R. W. Barriers to movement: Modelling energetic costs of avoiding marine wind farms amongst breeding seabirds. Marine Pollut. Bull. 60, 1085–1091 (2010).CAS 
Article 

Google Scholar 
Lloret, J. et al. Unravelling the ecological impacts of large-scale offshore wind farms in the Mediterranean Sea. Sci. Total Environ. 824, 153803 (2022).CAS 
Article 

Google Scholar 
Everaert, J. Collision risk and micro-avoidance rates of birds with wind turbines in Flanders. Bird Study 61, 220–230 (2014).Article 

Google Scholar 
Rice, J. et al. Indicators for Sea-floor Integrity under the European Marine Strategy Framework Directive. Ecol. Indicators 12, 174–184 (2012).Article 

Google Scholar 
Teixeira, H. et al. A Catalogue of Marine Biodiversity Indicators. Front. Marine Sci. 3, 00207 (2016).Article 

Google Scholar 
Brabant, R., Vanermen, N., Stienen, E. & Degraer, S. Towards a cumulative collision risk assessment of local and migrating birds in North Sea offshore wind farms. Hydrobiologia 756, 63–74 (2015).Article 

Google Scholar 
Desholm, M. & Kahlert, J. Avian collision risk at an offshore wind farm. Biol. Lett. 1, 296–298 (2005).Article 

Google Scholar 
Kelsey, E. C., Felis, J. J., Czapanskiy, M., Pereksta, D. M. & Adams, J. Collision and displacement vulnerability to offshore wind energy infrastructure among marine birds of the Pacific Outer Continental Shelf. J. Environ. Manag. 227, 229–247 (2018).Article 

Google Scholar 
Graham, I. et al. Harbour porpoise responses to pile-driving diminish over time. R. Soc. Open Sci. 6, 190335 (2019).Article 

Google Scholar 
Lindeboom, H. J. & Degraer, S. In Long-term Research Challenges in Wind Energy—A Research Agenda by the European Academy of Wind Energy (eds Gijs van Kuik & Joachim Peinke) 77–81 (Springer International Publishing, 2016).Stenberg, C. et al. Long-term effects of an offshore wind farm in the North Sea on fish communities. Marine Ecol. Prog. Ser. 528, 257–265 (2015).Article 

Google Scholar 
Salvador, S., Gimeno, L. & Sanz Larruga, F. J. The influence of regulatory framework on environmental impact assessment in the development of offshore wind farms in Spain: Issues, challenges and solutions. Ocean Coastal Manag. 161, 165–176 (2018).Article 

Google Scholar 
Bailey, H., Brookes, K. L. & Thompson, P. M. Assessing environmental impacts of offshore wind farms: lessons learned and recommendations for the future. Aquatic Biosyst. 10, 8 (2014).Article 

Google Scholar 
Apolonia, M., Fofack-Garcia, R., Noble, D. R., Hodges, J. & Correia da Fonseca, F. X. Legal and Political Barriers and Enablers to the Deployment of Marine Renewable Energy. Energies 14, 4896 (2021).Article 

Google Scholar 
Borja, A. et al. Moving Toward an Agenda on Ocean Health and Human Health in Europe. Front. Marine Sci. 7, 00037 (2020).Article 

Google Scholar 
European Commission, Directorate-General for Environment, Guidance document on wind energy developments and EU nature legislation, Publications Office of the European Union https://data.europa.eu/doi/10.2779/095188 (2021).O’Hagan, A. M. & Lewis, A. W. The existing law and policy framework for ocean energy development in Ireland. Marine Policy 35, 772–783 (2011).Article 

Google Scholar 
Long, R. D., Charles, A. & Stephenson, R. L. Key principles of marine ecosystem-based management. Marine Policy 57, 53–60 (2015).Article 

Google Scholar 
Borgwardt, F. et al. Exploring variability in environmental impact risk from human activities across aquatic ecosystems. Sci. Total Environ. 652, 1396–1408 (2019).Article 
CAS 

Google Scholar 
Copping, A., Hanna, L., Van Cleve, B., Blake, K. & Anderson, R. M. Environmental Risk Evaluation System-an Approach to Ranking Risk of Ocean Energy Development on Coastal and Estuarine Environments. Estuaries Coasts 38, S287–S302 (2015).Article 

Google Scholar 
Lüdeke, J. Offshore Wind Energy: Good Practice in Impact Assessment, Mitigation and Compensation. J. Environ. Assess. Policy Manag. 19, 1750005 (2017).Article 

Google Scholar 
Boehlert, G. W. & Gill, A. B. Environmental and ecological effects of ocean renewable energy development: a current synthesis. J. Oceanograph. 23, 68–81 (2010).Article 

Google Scholar 
Hammar, L., Wikström, A. & Molander, S. Assessing ecological risks of offshore wind power on Kattegat cod. Renew. Energy 66, 414–424 (2014).Article 

Google Scholar 
Nunneri, C., Lenhart, H. J., Burkhard, B. & Windhorst, W. Ecological risk as a tool for evaluating the effects of offshore wind farm construction in the North Sea. Reg Environ. Change 8, 31–43 (2008).Article 

Google Scholar 
Hutchison, Z. L. et al. Offshore Wind Energy and Benthic Habitat Changes: Lessons from Block Island Wind Farm. Oceanography 33, 58–69 (2020).Article 

Google Scholar 
Pirttimaa, P. & Cruz, E. Ocean energy and the environment: Research and strategic actions. European Technology and Innovation Platform for Ocean Energy (ETIP Ocean), pp.36. https://www.etipocean.eu/assets/Uploads/ETIP-Ocean-Ocean-energy-and-the-environment.pdf (2020).Hooper, T., Beaumont, N. & Hattam, C. The implications of energy systems for ecosystem services: A detailed case study of offshore wind. Renew. Sustain. Energy Rev. 70, 230–241 (2017).Article 

Google Scholar 
Mangi, S. C. The Impact of Offshore Wind Farms on Marine Ecosystems: A Review Taking an Ecosystem Services Perspective. Proceedings of the IEEE 101, 999–1009, (2013).Pınarbaşı, K. et al. A modelling approach for offshore wind farm feasibility with respect to ecosystem-based marine spatial planning. Sci. Total Environ. 667, 306–317 (2019).Article 
CAS 

Google Scholar 
Maldonado, A. D. et al. A Bayesian Network model to identify suitable areas for offshore wave energy farms, in the framework of ecosystem approach to marine spatial planning. Sci. Total Environ. 838, 156037 (2022).CAS 
Article 

Google Scholar 
Stelzenmüller, V., Gimpel, A., Letschert, J., Kraan, C. & DÖRING, R. Research for PECH Committee – Impact of the use of offshore wind and other marine renewables on European fisheries. European Parliament, Policy Department for Structural and Cohesion Policies, Brussels. https://www.europarl.europa.eu/RegData/etudes/STUD/2020/652212/IPOL_STU(2020)652212_EN.pdf (2020).Galparsoro, I. et al. A new framework and tool for ecological risk assessment of wave energy converters projects. Renew. Sustain. Energy Rev. 151, 111539 (2021).Article 

Google Scholar 
Kaikkonen, L., Parviainen, T., Rahikainen, M., Uusitalo, L. & Lehikoinen, A. Bayesian Networks in Environmental Risk Assessment: A Review. Integr. Environ. Assess. Manag. 17, 62–78 (2020).Article 

Google Scholar 
González, D. A., Gleeson, J. & McCarthy, E. Designing and developing a web tool to support Strategic Environmental Assessment. Environ. Modell. Softw. 111, 472–482 (2019).Article 

Google Scholar 
Pınarbaşı, K. et al. Decision support tools in marine spatial planning: Present applications, gaps and future perspectives. Marine Policy 83, 83–91 (2017).Article 

Google Scholar 
Pınarbaşı, K., Galparsoro, I. & Borja, Á. End users’ perspective on decision support tools in marine spatial planning. Marine Policy 108, 103658 (2019).Article 

Google Scholar  More

Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Chang. 4, 598–604 (2014).CAS 
Article 
ADS 

Google Scholar 
Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Finzi, A. C. et al. Carbon budget of the Harvard Forest Long-Term Ecological Research site: pattern, process, and response to global change. Ecol. Monogr. 90, e01423 (2020).Article 

Google Scholar 
Keeling, C. D., Chin, J. F. S. & Whorf, T. P. Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature 382, 146–149 (1996).CAS 
Article 
ADS 

Google Scholar 
Dragoni, D. et al. Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA. Glob. Chang. Biol. 17, 886–897 (2011).Article 
ADS 

Google Scholar 
Zhou, S. et al. Explaining inter-annual variability of gross primary productivity from plant phenology and physiology. Agric. For. Meteorol. 226–227, 246–256 (2016).Article 
ADS 

Google Scholar 
Fu, Z. et al. Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange. Glob. Chang. Biol. 25, 3381–3394 (2019).PubMed 
Article 
ADS 

Google Scholar 
Savage, J. A. & Chuine, I. Coordination of spring vascular and organ phenology in deciduous angiosperms growing in seasonally cold climates. New Phytol. 230, 1700–1715 (2021).PubMed 
Article 

Google Scholar 
Delpierre, N. et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73, 5–25 (2016).Article 

Google Scholar 
Xue, B.-L. et al. Global patterns of woody residence time and its influence on model simulation of aboveground biomass. Global Biogeochem. Cycles 31, 821–835 (2017).CAS 
Article 
ADS 

Google Scholar 
Russell, M. B. et al. Residence times and decay rates of downed woody debris biomass/carbon in eastern US forests. Ecosystems 17, 765–777 (2014).CAS 
Article 

Google Scholar 
Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Chang. Biol. 18, 566–584 (2012).Article 
ADS 

Google Scholar 
Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021).Article 
ADS 

Google Scholar 
Pugh, T. A. M. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Ahlström, A., Schurgers, G., Arneth, A. & Smith, B. Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environ. Res. Lett. 7, 044008 (2012).Article 
ADS 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).Article 
ADS 

Google Scholar 
Fatichi, S., Leuzinger, S. & Körner, C. Moving beyond photosynthesis: from carbon source to sink-driven vegetation modeling. New Phytol. 201, 1086–1095 (2014).CAS 
PubMed 
Article 

Google Scholar 
Lu, X. & Keenan, T. F. No evidence for a negative effect of growing season photosynthesis on leaf senescence timing. Glob. Chang. Biol. 28, 3083–3093 (2022).CAS 
PubMed 
Article 

Google Scholar 
Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Oishi, A. C. et al. Warmer temperatures reduce net carbon uptake, but do not affect water use, in a mature southern Appalachian forest. Agric. For. Meteorol. 252, 269–282 (2018).Article 
ADS 

Google Scholar 
Delpierre, N., Berveiller, D., Granda, E. & Dufrêne, E. Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest. New Phytol. 210, 459–470 (2016).CAS 
PubMed 
Article 

Google Scholar 
Huang, J.-G. et al. Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers. Proc. Natl Acad. Sci. USA 117, 20645–20652 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rossi, S. et al. Critical temperatures for xylogenesis in conifers of cold climates. Global Ecol. Biogeogr. 17, 696–707 (2008).Article 

Google Scholar 
Babst, F. et al. Twentieth century redistribution in climatic drivers of global tree growth. Sci. Adv. 5, eaat4313 (2019).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Gao, S. et al. An earlier start of the thermal growing season enhances tree growth in cold humid areas but not in dry areas. Nat. Ecol. Evol. 6, 397–404 (2022).PubMed 
Article 

Google Scholar 
Zweifel, R. et al. Why trees grow at night. New Phytol. 231, 2174–2185 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Tumajer, J., Scharnweber, T., Smiljanic, M. & Wilmking, M. Limitation by vapour pressure deficit shapes different intra-annual growth patterns of diffuse- and ring-porous temperate broadleaves. New Phytol. 233, 2429–2441 (2022).PubMed 
Article 

Google Scholar 
Etzold, S. et al. Number of growth days and not length of the growth period determines radial stem growth of temperate trees. Ecol. Lett. 25, 427–439 (2022).PubMed 
Article 

Google Scholar 
Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Zohner, C. M., Renner, S. S., Sebald, V. & Crowther, T. W. How changes in spring and autumn phenology translate into growth-experimental evidence of asymmetric effects. J. Ecol. 109, 2717–2728 (2021).Article 

Google Scholar 
Cabon, A. et al. Cross-biome synthesis of source versus sink limits to tree growth. Science 376, 758–761 (2022).CAS 
PubMed 
Article 

Google Scholar 
D’Orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Chang. Biol. 24, 2339–2351 (2018).PubMed 
Article 
ADS 

Google Scholar 
Helcoski, R. et al. Growing season moisture drives interannual variation in woody productivity of a temperate deciduous forest. New Phytol. 223, 1204–1216 (2019).CAS 
PubMed 
Article 

Google Scholar 
McMahon, S. M. & Parker, G. G. A general model of intra-annual tree growth using dendrometer bands. Ecol. Evol. 5, 243–254 (2015).PubMed 
Article 

Google Scholar 
D’Orangeville, L. et al. Peak radial growth of diffuse-porous species occurs during periods of lower water availability than for ring-porous and coniferous trees. Tree Physiol. 42, 304–316 (2022).PubMed 
Article 

Google Scholar 
Richardson, A. D. et al. Seasonal dynamics and age of stemwood nonstructural carbohydrates in temperate forest trees. New Phytol. 197, 850–861 (2013).CAS 
PubMed 
Article 

Google Scholar 
Elmore, A. J., Nelson, D. M. & Craine, J. M. Earlier springs are causing reduced nitrogen availability in North American eastern deciduous forests. Nat. Plants 2, 16133 (2016).CAS 
PubMed 
Article 

Google Scholar 
Cuny, H. E. et al. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nat. Plants 1, 15160 (2015).CAS 
PubMed 
Article 

Google Scholar 
Tardif, J. C. & Conciatori, F. Influence of climate on tree rings and vessel features in red oak and white oak growing near their northern distribution limit, southwestern Quebec, Canada. Can. J. For. Res. 36, 2317–2330 (2006).Article 

Google Scholar 
Roibu, C.-C. et al. The climatic response of tree ring width components of ash (Fraxinus excelsior L.) and common oak (Quercus robur L.) from eastern Europe. Forests 11, 600 (2020).Article 

Google Scholar 
Kern, Z. et al. Multiple tree-ring proxies (earlywood width, latewood width and δ13C) from pedunculate oak (Quercus robur L.), Hungary. Quat. Int. 293, 257–267 (2013).Article 

Google Scholar 
Trumbore, S., Gaudinski, J. B., Hanson, P. J. & Southon, J. R. Quantifying ecosystem-atmosphere carbon exchange with a 14C label. Eos. Trans. Am. Geophys. Union 83, 265–268 (2002).Article 
ADS 

Google Scholar 
Del Mar Delgado, M. et al. Differences in spatial versus temporal reaction norms for spring and autumn phenological events. Proc. Natl Acad. Sci. USA 117, 31249–31258 (2020).Article 
CAS 

Google Scholar 
Anderson-Teixeira, K. J. et al. Joint effects of climate, tree size, and year on annual tree growth derived from tree-ring records of ten globally distributed forests. Glob. Chang. Biol. 28, 245–266 (2022).CAS 
PubMed 
Article 

Google Scholar 
Banbury Morgan, R. et al. Global patterns of forest autotrophic carbon fluxes. Glob. Chang. Biol. 27, 2840–2855 (2021).CAS 
PubMed 
Article 

Google Scholar 
Churkina, G., Schimel, D., Braswell, B. H. & Xiao, X. Spatial analysis of growing season length control over net ecosystem exchange. Glob. Chang. Biol. 11, 1777–1787 (2005).Article 
ADS 

Google Scholar 
Liu, H. et al. Phenological mismatches between above- and belowground plant responses to climate warming. Nat. Clim. Chang. 12, 97–102 (2022).CAS 
Article 
ADS 

Google Scholar 
Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Chang. 6, 1023–1027 (2016).CAS 
Article 
ADS 

Google Scholar 
Zhang, J. et al. Drought limits wood production of Juniperus przewalskii even as growing seasons lengthens in a cold and arid environment. CATENA 196, 104936 (2021).Article 

Google Scholar 
Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2022).Article 
ADS 

Google Scholar 
Bourg, N. A., McShea, W. J., Thompson, J. R., McGarvey, J. C. & Shen, X. Initial census, woody seedling, seed rain, and stand structure data for the SCBI SIGEO Large Forest Dynamics Plot. Ecology 94, 2111–2112 (2013).Article 

Google Scholar 
Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Chang. Biol. 21, 528–549 (2015).PubMed 
Article 
ADS 

Google Scholar 
Davies, S. J. et al. ForestGEO: understanding forest diversity and dynamics through a global observatory network. Biol. Conserv. 253, 108907 (2021).Article 

Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 
ADS 

Google Scholar 
Herrmann, V. et al. Tree circumference dynamics in four forests characterized using automated dendrometer bands. PLoS ONE 11, e0169020 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Friedl, M., Gray, J. & Sulla-Menashe, D. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006. LAADS DAAC https://doi.org/10.5067/MODIS/MCD12Q2.006 (2019).Anderson-Teixeira, K. et al. Forestgeo/Climate: initial release. Zenodo https://doi.org/10.5281/ZENODO.4041609 (2020).Benestad, R. E., Hanssen-Bauer, I. & Chen, D. Empirical-Statistical Downscaling (World Scientific, 2008).Boose, E. & Gould, E. Shaler Meteorological Station at Harvard Forest 1964–2002. Environmental Data Initiative https://doi.org/10.6073/PASTA/213335F5DAA17222A738C105B9FA60C4 (2021).Boose, E. Fisher Meteorological Station at Harvard Forest since 2001. Environmental Data Initiative https://doi.org/10.6073/PASTA/69E92642B512897032446CFE795CFFB8 (2021).Beguería, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

Google Scholar 
van de Pol, M. et al. Identifying the best climatic predictors in ecology and evolution. Methods Ecol. Evol. 7, 1246–1257 (2016).Article 

Google Scholar 
Gabry, J. et al. Rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.1 https://mc-stan.org/rstanarm (2020).Stan Development Team. Stan modeling language users guide and reference manual, 2.28. https://mc-stan.org/users/documentation/ (2019).Stokes, M. A. & Smiley, T. L. An Introduction to Tree-ring Dating (Univ. Arizona Press, 1968).Speer, J. H. Fundamentals of Tree-ring Research (Univ. Arizona Press, 2010).Alexander, M. R. et al. The potential to strengthen temperature reconstructions in ecoregions with limited tree line using a multispecies approach. Quat. Res. 92, 583–597 (2019).Article 

Google Scholar 
Dye, A. et al. Comparing tree-ring and permanent plot estimates of aboveground net primary production in three eastern U.S. forests. Ecosphere 7, e01454 (2016).Article 

Google Scholar 
Pederson, N. Climatic Sensitivity and Growth of Southern Temperate Trees in the Eastern United States: Implications for the Carbon Cycle—ProQuest (Columbia Univ., 2005).Maxwell, J. T. et al. Sampling density and date along with species selection influence spatial representation of tree-ring reconstructions. Clim. Past 16, 1901–1916 (2020).Article 

Google Scholar 
Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology: Applications in the Environmental Sciences (Springer Netherlands, 1990).Cook, E. R. A Time Series Analysis Approach to Tree Ring Standardization (Univ. Arizona, 1985).Cook, E. R. & Peters, K. Calculating unbiased tree-ring indices for the study of climatic and environmental change. Holocene 7, 361–370 (1997).Article 
ADS 

Google Scholar 
Jones, P. D., Osborn, T. J. & Briffa, K. R. Estimating sampling errors in large-scale temperature averages. J. Clim. 10, 2548–2568 (1997).Article 
ADS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

Google Scholar 
Zang, C. & Biondi, F. Dendroclimatic calibration in R: the bootRes package for response and correlation function analysis. Dendrochronologia 31, 68–74 (2013).Article 

Google Scholar  More