Ecology

Loreau, M. et al. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808 (2001).Article 
ADS 
CAS 

Google Scholar 
Pimm, S. L., Russell, G. J., Gittleman, J. L. & Brooks, T. M. The future of biodiversity. Science 269, 347–350 (1995).Article 
ADS 
CAS 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50. https://doi.org/10.1038/nature14324 (2015).Article 
ADS 
CAS 

Google Scholar 
Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426. https://doi.org/10.1016/j.biocon.2020.108426 (2020).Article 

Google Scholar 
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809. https://doi.org/10.1371/journal.pone.0185809 (2017).Article 
CAS 

Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674. https://doi.org/10.1038/s41586-019-1684-3 (2019).Article 
ADS 
CAS 

Google Scholar 
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Cons. 232, 8–27. https://doi.org/10.1016/j.biocon.2019.01.020 (2019).Article 

Google Scholar 
Yang, L. H. & Gratton, C. Insects as drivers of ecosystem processes. Curr. Opin. Insect Sci. 2, 26–32. https://doi.org/10.1016/j.cois.2014.06.004 (2014).Article 

Google Scholar 
Bowler, D. E., Heldbjerg, H., Fox, A. D., de Jong, M. & Böhning-Gaese, K. Long-term declines of European insectivorous bird populations and potential causes. Conserv. Biol. 33, 1120–1130. https://doi.org/10.1111/cobi.13307 (2019).Article 

Google Scholar 
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354. https://doi.org/10.1126/science.1127863 (2006).Article 
ADS 
CAS 

Google Scholar 
Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity – ecosystem service management. Ecol. Lett. 8, 857–874. https://doi.org/10.1111/j.1461-0248.2005.00782.x (2005).Article 

Google Scholar 
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556. https://doi.org/10.1038/nature09492 (2010).Article 
ADS 
CAS 

Google Scholar 
Siemann, E., Tilman, D. & Haarstad, J. Insect species diversity, abundance and body size relationships. Nature 380, 704–706. https://doi.org/10.1038/380704a0 (1996).Article 
ADS 
CAS 

Google Scholar 
Borer, E. T., Seabloom, E. W. & Tilman, D. Plant diversity controls arthropod biomass and temporal stability. Ecol. Lett. 15, 1457–1464. https://doi.org/10.1111/ele.12006 (2012).Article 

Google Scholar 
Ebeling, A. et al. Plant diversity effects on arthropods and arthropod-dependent ecosystem functions in a biodiversity experiment. Basic Appl. Ecol. 26, 50–63. https://doi.org/10.1016/j.baae.2017.09.014 (2018).Article 

Google Scholar 
Ebeling, A. et al. Plant diversity induces shifts in the functional structure and diversity across trophic levels. Oikos 127, 208–219. https://doi.org/10.1111/oik.04210 (2018).Article 

Google Scholar 
Ebeling, A. et al. Plant diversity impacts decomposition and herbivory via changes in aboveground arthropods. PLoS ONE 9, e106529. https://doi.org/10.1371/journal.pone.0106529 (2014).Article 
ADS 
CAS 

Google Scholar 
Marquard, E. et al. Plant species richness and functional composition drive overyielding in a six-year grassland experiment. Ecology 90, 3290–3302 (2009).Article 

Google Scholar 
Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845. https://doi.org/10.1126/science.1060391 (2001).Article 
ADS 
CAS 

Google Scholar 
Simons, N. K. et al. Resource-mediated indirect effects of grassland management on arthropod diversity. PLoS ONE 9, e107033. https://doi.org/10.1371/journal.pone.0107033 (2014).Article 
ADS 
CAS 

Google Scholar 
Wardle, D. A., Nicholson, K. S., Bonner, K. I. & Yeates, G. W. Effects of agricultural intensification on soil-associated arthropod population dynamics, community structure, diversity and temporal variability over a seven-year period. Soil Biol. Biochem. 31, 1691–1706 (1999).Article 
CAS 

Google Scholar 
Luff, M. L. & Rushton, S. P. The ground beetle and spider fauna of managed and unimproved upland pasture. Agr. Ecosyst. Environ. 25, 195–206 (1989).Article 

Google Scholar 
Dennis, P., Young, M. R., Howard, C. L. & Gordon, I. J. The response of epigeal beetles (Col, Carabidae, Staphylinidae) to varied grazing regimes on upland Nardus stricta grasslands. J. Appl. Ecol. 34, 433–443 (1997).Article 

Google Scholar 
Murdoch, W. W., Evans, F. C. & Peterson, C. H. Diversity and pattern in plants and insects. Ecology 53, 819–829 (1972).Article 

Google Scholar 
Siemann, E., Tilman, D., Haarstad, J. & Ritchie, M. Experimental tests of the dependence of arthropod diversity on plant diversity. Am. Nat. 152, 738–750 (1998).Article 
CAS 

Google Scholar 
Joern, A. & Laws, A. N. Ecological mechanisms underlying arthropod species diversity in grasslands. Annu. Rev. Entomol. 58, 19–36. https://doi.org/10.1146/annurev-ento-120811-153540 (2013).Article 
CAS 

Google Scholar 
Hunter, M. D. & Price, P. W. Playing chutes and ladders: Heterogeneity and relative roles of bottom-up and top-down forces in natural communities. Ecology 73, 724–732 (1992).Article 

Google Scholar 
Knops, J. M. H. et al. Effects of plant species richness on invasion dynamics, disease outbreaks, insect abundances and diversity. Ecol. Lett. 2, 286–293 (1999).Article 
CAS 

Google Scholar 
Thiele, H. U. Carabid beetles in their environment. A study on habitat selection by adaptions in physiology and behaviour. (Springer- Verlag, 1977).Harvey, J. A., van der Putten, W. H., Turin, H., Wagenaar, R. & Bezemer, T. M. Effects of changes in plant species richness and community traits on carabid assemblages and feeding guilds. Agr. Ecosyst. Environ. 127, 100–106 (2008).Article 

Google Scholar 
Luff, M. L. Use of Carabids as environmental indicators in grasslands and cereals. Ann. Zool. Fenn. 33, 185–195 (1996).
Google Scholar 
Kotze, D. J. et al. Forty years of carabid beetle research in Europe—from taxonomy, biology, ecology and population studies to bioindication, habitat assessment and conservation. ZooKeys https://doi.org/10.3897/zookeys.100.1523 (2011).Article 

Google Scholar 
Barnes, A. D. et al. Biodiversity enhances the multitrophic control of arthropod herbivory. Sci. Adv. 6, eabb6603. https://doi.org/10.1126/sciadv.abb6603 (2020).Article 
ADS 

Google Scholar 
Bianchi, F. J. J. A., Booij, C. J. H. & Tscharntke, T. Sustainable pest regulation in agricultural landscapes: A review on landscape composition, biodiversity and natural pest control. Proc. R. Soc. B: Biol. Sci. 273, 1715–1727. https://doi.org/10.1098/rspb.2006.3530 (2006).Article 
CAS 

Google Scholar 
Lövei, G. L. & Magura, T. Ground beetle (Coleoptera: Carabidae) diversity is higher in narrow hedges composed of a native compared to non-native trees in a Danish agricultural landscape. Insect Conserv. Divers. 10, 141–150. https://doi.org/10.1111/icad.12210 (2017).Article 

Google Scholar 
Loreau, M. Consumers as maximizers of matter and energy flow in ecosystems. Am. Nat. 145, 22–42. https://doi.org/10.1086/285726 (1995).Article 

Google Scholar 
Mielke, L. et al. Nematode grazing increases the allocation of plant-derived carbon to soil bacteria and saprophytic fungi, and activates bacterial species of the rhizosphere. Pedobiologia 90, 150787. https://doi.org/10.1016/j.pedobi.2021.150787 (2022).Article 

Google Scholar 
Holland, J. M. & Luff, M. L. The effects of agricultural practices on Carabidae in temperate agroecosystems. Integr. Pest Manag. Rev. 5, 109–129. https://doi.org/10.1023/A:1009619309424 (2000).Article 

Google Scholar 
Roscher, C. et al. The role of biodiversity for element cycling and trophic interactions: An experimental approach in a grassland community. Basic Appl. Ecol. 5, 107–121 (2004).Article 

Google Scholar 
Weisser, W. W. et al. Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: Patterns, mechanisms, and open questions. Basic Appl. Ecol. https://doi.org/10.1016/j.baae.2017.06.002 (2017).Article 

Google Scholar 
Freude, H., Harde, K. W. & Lohse, G. A. Die Käfer Mitteleuropas Bd.1–11. (Goecke & Evers, 1965–83).Koch, K. Die Käfer Mitteleuropas. Ökologie Bd.1–6. (Goecke & Evers, 1989–95).R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Schmid, B., Baruffol, M., Wang, Z. & Niklaus, P. A. A guide to analyzing biodiversity experiments. J. Plant Ecol. 10, 91–110. https://doi.org/10.1093/jpe/rtw107 (2017).Article 

Google Scholar 
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. (Springer, 2009).Oksanen, J. et al. vegan: Community Ecology Package v. 2.6–2 (2022).Lenth, R. et al., emmeans: Estimated Marginal Means, aka Least-Squares Means v. 1.8.1-1 (2022).Lovei, G. L. & Sunderland, K. D. Ecology and behavior of ground beetles (Coleoptera: Carabidae). Annu. Rev. Entomol. 41, 231–256 (1996).Article 
CAS 

Google Scholar 
Ravenek, J. M. et al. Long-term study of root biomass in a biodiversity experiment reveals shifts in diversity effects over time. Oikos 123, 1528–1536. https://doi.org/10.1111/oik.01502 (2014).Article 

Google Scholar 
Root, R. Organization of a plant -arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

Google Scholar 
Duelli, P. & Obrist, M. K. Regional biodiversity in an agricultural landscape: The contribution of seminatural habitat islands. Basic Appl. Ecol. 4, 129–138 (2003).Article 

Google Scholar 
Perner, J. & Malt, S. Assessment of changing agricultural land use: Response of vegetation, ground-dwelling spiders and beetles to the conversion of arable land into grassland. Agr. Ecosyst. Environ. 98, 169–181 (2003).Article 

Google Scholar 
Purtauf, T., Dauber, J. & Wolters, V. Carabid communities in the spatio-temporal mosaic of a rural landscape. Landsc. Urban Plan. 67, 185–193 (2004).Article 

Google Scholar 
Eisenhauer, N. et al. Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Res. Ideas Outcomes https://doi.org/10.3897/rio.5.e47042 (2019).Article 

Google Scholar 
Guerrero-Ramirez, N. R. et al. Diversity-dependent temporal divergence of ecosystem functioning in experimental ecosystems. Nat. Ecol. Evol. 1, 1639–1642. https://doi.org/10.1038/s41559-017-0325-1 (2017).Article 

Google Scholar 
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592. https://doi.org/10.1126/science.1217909 (2012).Article 
ADS 
CAS 

Google Scholar 
Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: Patterns and processes. Ecol. Lett. 12, 443–451. https://doi.org/10.1111/j.1461-0248.2009.01299.x (2009).Article 

Google Scholar 
Blake, S., Foster, G. N., Fisher, G. E. J. & Ligertwood, G. L. Effects of management practices on the carabid faunas of newly established wildflower meadows in southern Scotland. Ann. Zool. Fenn. 33, 139–147 (1996).
Google Scholar 
Boetzl, F. A., Krimmer, E., Krauss, J. & Steffan-Dewenter, I. Agri-environmental schemes promote ground-dwelling predators in adjacent oilseed rape fields: Diversity, species traits and distance-decay functions. J. Appl. Ecol. 56, 10–20. https://doi.org/10.1111/1365-2664.13162 (2019).Article 

Google Scholar 
Knapp, M., Seidl, M., Knappová, J., Macek, M. & Saska, P. Temporal changes in the spatial distribution of carabid beetles around arable field-woodlot boundaries. Sci. Rep. 9, 8967. https://doi.org/10.1038/s41598-019-45378-7 (2019).Article 
ADS 
CAS 

Google Scholar  More

Global Forest Resources Assessment 2020 (FAO, 2020).Taubert, F. et al. Global patterns of tropical forest fragmentation. Nature 554, 519–522 (2018).ADS 
CAS 

Google Scholar 
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).ADS 

Google Scholar 
Foley, J. A. et al. Amazonia revealed: Forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front. Ecol. Environ. 5, 25–32 (2007).
Google Scholar 
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).ADS 
CAS 

Google Scholar 
Brandon, K. Ecosystem services from tropical forests: Review of current science. SSRN J. https://doi.org/10.2139/ssrn.2622749 (2014). Article 

Google Scholar 
Indarto, J. & Mutaqin, D. J. An overview of theoretical and empirical studies on deforestation. MPRA. Paper No. 70178 (2016).Geist, H. J. & Lambin, E. F. Proximate causes and underlying driving forces of tropical deforestation: Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. Bioscience 52, 143–150 (2002).
Google Scholar 
Angelsen, A. & Kaimowitz, D. Rethinking the causes of deforestation: Lessons from economic models. World Bank Res. Obs. 14, 73–98 (1999).CAS 

Google Scholar 
Contreras-Hermosilla, A. The Underlying Causes of Forest Decline (Center for International Forestry Research, 2000).
Google Scholar 
Turner, B. L. et al. Two types of global environmental change: Definitional and spatial-scale issues in their human dimensions. Glob. Environ. Change 1, 14–22 (1990).
Google Scholar 
Meyer, W. B. & Turner, B. L. Human population growth and global land-use/cover change. Ann. Rev. Ecol. Syst. 2, 39–61 (1992).
Google Scholar 
Miyamoto, M., Mohd Parid, M., Noor Aini, Z. & Michinaka, T. Proximate and underlying causes of forest cover change in Peninsular Malaysia. For. Policy Econ. 44, 18–25 (2014).
Google Scholar 
Lim, C. L., Prescott, G. W., De Alban, J. D. T., Ziegler, A. D. & Webb, E. L. Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv. Biol. 31, 1362–1372 (2017).
Google Scholar 
Carodenuto, S. et al. A methodological framework for assessing agents, proximate drivers and underlying causes of deforestation: Field test results from southern cameroon. Forests 6, 203–224 (2015).
Google Scholar 
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).ADS 
CAS 

Google Scholar 
Hosonuma, N. et al. An assessment of deforestation and forest degradation drivers in developing countries. Environ. Res. Lett. 7, 044009 (2012).ADS 

Google Scholar 
Köthke, M., Leischner, B. & Elsasser, P. Uniform global deforestation patterns—An empirical analysis. For. Policy Econ. 28, 23–37 (2013).
Google Scholar 
Busch, J. & Ferretti-Gallon, K. What drives deforestation and what stops it? A meta-analysis. Rev. Environ. Econ. Policy 11, 3–23 (2017).
Google Scholar 
Ferrer Velasco, R. F., Köthke, M., Lippe, M. & Günter, S. Scale and context dependency of deforestation drivers: Insights from spatial econometrics in the tropics. PLoS One 15, e0226830 (2020).CAS 

Google Scholar 
Lambin, E. F. et al. Effectiveness and synergies of policy instruments for land use governance in tropical regions. Glob. Environ. Change 28, 129–140 (2014).
Google Scholar 
Börner, J., Schulz, D., Wunder, S. & Pfaff, A. The effectiveness of forest conservation policies and programs. Ann. Rev. Resour. Econ. 12, 45–64 (2020).
Google Scholar 
Bemelmans-Videc, M.-L., Rist, R. C. & Vedung, E. Carrots, Sticks & Sermons: Policy Instruments and their Evaluation (Transaction Publishers, 1998).
Google Scholar 
Seymour, F. & Harris, N. L. Reducing tropical deforestation. Science 365, 756–757 (2019).ADS 
CAS 

Google Scholar 
Lambin, E. F. et al. The role of supply-chain initiatives in reducing deforestation. Nat. Clim. Change 8, 109–116 (2018).ADS 

Google Scholar 
Wolff, S. & Schweinle, J. Effectiveness and economic viability of forest certification: A systematic review. Forests 13, 798 (2022).
Google Scholar 
Müller, R., Pistorius, T., Rohde, S., Gerold, G. & Pacheco, P. Policy options to reduce deforestation based on a systematic analysis of drivers and agents in lowland Bolivia. Land Use Policy 30, 895–907 (2013).
Google Scholar 
Tegegne, Y. T., Lindner, M., Fobissie, K. & Kanninen, M. Evolution of drivers of deforestation and forest degradation in the Congo Basin forests: Exploring possible policy options to address forest loss. Land Use Policy 51, 312–324 (2016).
Google Scholar 
Hoffmann, C., García Márquez, J. R. & Krueger, T. A local perspective on drivers and measures to slow deforestation in the Andean-Amazonian foothills of Colombia. Land Use Policy 77, 379–391 (2018).
Google Scholar 
Henders, S., Ostwald, M., Verendel, V. & Ibisch, P. Do national strategies under the UN biodiversity and climate conventions address agricultural commodity consumption as deforestation driver?. Land Use Policy 70, 580–590 (2018).
Google Scholar 
Salvini, G. et al. How countries link REDD+ interventions to drivers in their readiness plans: implications for monitoring systems. Environ. Res. Lett. 9, 074004 (2014).ADS 

Google Scholar 
Bos, A. B. et al. Integrated assessment of deforestation drivers and their alignment with subnational climate change mitigation efforts. Environ. Sci. Policy 114, 352–365 (2020).CAS 

Google Scholar 
Fritz, S. et al. A continental assessment of the drivers of tropical deforestation with a focus on protected areas. Front. Conserv. Sci. https://doi.org/10.3389/fcosc.2022.830248 (2022).Article 

Google Scholar 
Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).ADS 

Google Scholar 
Fedele, G., Locatelli, B., Djoudi, H. & Colloff, M. J. Reducing risks by transforming landscapes: Cross-scale effects of land-use changes on ecosystem services. PLoS One 13, e0195895 (2018).
Google Scholar 
Yackulic, C. B. et al. Biophysical and socioeconomic factors associated with forest transitions at multiple spatial and temporal scales. Ecol. Soc. https://doi.org/10.5751/ES-04275-160315 (2011).Article 

Google Scholar 
Loran, C., Ginzler, C. & Bürgi, M. Evaluating forest transition based on a multi-scale approach: Forest area dynamics in Switzerland 1850–2000. Reg. Environ. Change 16, 1807–1818 (2016).
Google Scholar 
Moonen, P. C. et al. Actor-based identification of deforestation drivers paves the road to effective REDD+in DR Congo. Land Use Policy 58, 123–132 (2016).
Google Scholar 
Strassburg, B. The tragedy of the tropics: A dynamic, cross-scale analysis of deforestation incentives. Working Paper—Centre for Social and Economic Research on the Global Environment No. 07-02 (2007).López-Carr, D. et al. Space versus place in complex human–natural systems: Spatial and multi-level models of tropical land use and cover change (LUCC) in Guatemala. Ecol. Model. 229, 64–75 (2012).
Google Scholar 
Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nat. Ecol. Evol. 5, 845–853 (2021).
Google Scholar 
Pendrill, F. et al. Agricultural and forestry trade drives large share of tropical deforestation emissions. Glob. Environ. Change 56, 1–10 (2019).
Google Scholar 
Ferrer Velasco, R. et al. Towards accurate mapping of forest in tropical landscapes: A comparison of datasets on how forest transition matters. Remote Sens. Environ. 274, 112997 (2022).ADS 

Google Scholar 
Jayathilake, H. M., Prescott, G. W., Carrasco, L. R., Rao, M. & Symes, W. S. Drivers of deforestation and degradation for 28 tropical conservation landscapes. Ambio 50, 215–228 (2021).
Google Scholar 
Minang, P. A. et al. REDD+Readiness progress across countries: Time for reconsideration. Clim. Policy 14, 685–708 (2014).
Google Scholar 
Current pledges | Bonn challenge. https://www.bonnchallenge.org/pledges. Accessed: 15th August 2022.Nansikombi, H. et al. Can de facto governance influence deforestation drivers in the Zambian Miombo?. For. Policy Econ. 120, 102309 (2020).
Google Scholar 
Sullivan, A., York, A., White, D., Hall, S. & Yabiku, S. D. Jure versus de facto institutions: Trust, information, and collective efforts to manage the invasive mile-a-minute weed (Mikania micrantha). Int. J. Commons 11, 171–199 (2017).
Google Scholar 
Busch, J. & Amarjargal, O. Authority of second-tier governments to reduce deforestation in 30 tropical countries. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2020.00001 (2020).Article 

Google Scholar 
Sandström, C., Eckerberg, K. & Raitio, K. Studying conflicts, proposing solutions—Towards multi-level approaches to the analyses of forest conflicts. For. Policy Econ. 33, 123–127 (2013).
Google Scholar 
Hoogstra-Klein, M. A., Permadi, D. B. & Yasmi, Y. The value of cultural theory for participatory processes in natural resource management. For. Policy Econ. 20, 99–106 (2012).
Google Scholar 
de Jong, W., Ruiz, S. & Becker, M. Conflicts and communal forest management in northern Bolivia. For. Policy Econ. 8, 447–457 (2006).
Google Scholar 
Eckerberg, K. & Sandström, C. Forest conflicts: A growing research field. For. Policy Econ. 33, 3–7 (2013).
Google Scholar 
Sierra, R., Calva, O. & Guevara, A. La Deforestación en el Ecuador, 1990–2018. Factores promotores y tendencias recientes, 216 (2021).Wasserstrom, R. & Southgate, D. Deforestation, agrarian reform and oil development in Ecuador, 1964–1994. Nat. Resour. 04, 31 (2013).
Google Scholar 
Wiebe, P. C., Zhunusova, E., Lippe, M., Ferrer Velasco, R. & Günter, S. What is the contribution of forest-related income to rural livelihood strategies in the Philippines’ remaining forested landscapes?. For. Policy Econ. 135, 102658 (2022).
Google Scholar 
Le, H. D., Smith, C. & Herbohn, J. What drives the success of reforestation projects in tropical developing countries? The case of the Philippines. Glob. Environ. Change 24, 334–348 (2014).
Google Scholar 
Carandang, A. P. et al. Analysis of key drivers of deforestation and forest degradation in the Philippines. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) (2013).Phiri, D., Morgenroth, J. & Xu, C. Four decades of land cover and forest connectivity study in Zambia—An object-based image analysis approach. Int. J. Appl. Earth Obs. Geoinf. 79, 97–109 (2019).ADS 

Google Scholar 
Nansikombi, H., Fischer, R., Kabwe, G. & Günter, S. Exploring patterns of forest governance quality: Insights from forest frontier communities in Zambia’s Miombo ecoregion. Land Use Policy 99, 104866 (2020).
Google Scholar 
Zhang, H., Wang, P. & Wood, J. Does institutional quality matter for the nexus between environmental quality and economic growth?: A tropics perspective. In Business, Industry, and Trade in the Tropics (eds Wood, J. et al.) (Routledge, 2022).
Google Scholar 
Reed, J., Van Vianen, J., Deakin, E. L., Barlow, J. & Sunderland, T. Integrated landscape approaches to managing social and environmental issues in the tropics: Learning from the past to guide the future. Glob. Change Biol. 22, 2540–2554 (2016).ADS 

Google Scholar 
Fischer, R. et al. Interplay of governance elements and their effects on deforestation in tropical landscapes: Quantitative insights from Ecuador. World Dev. 148, 105665 (2021).
Google Scholar 
Torres, B., Vasco, C., Günter, S. & Knoke, T. Determinants of agricultural diversification in a hotspot area: Evidence from colonist and indigenous communities in the Sumaco biosphere reserve Ecuadorian Amazon. Sustainability 10, 1432 (2018).
Google Scholar 
Ojeda Luna, T., Zhunusova, E., Günter, S. & Dieter, M. Measuring forest and agricultural income in the Ecuadorian lowland rainforest frontiers: Do deforestation and conservation strategies matter?. For. Policy Econ. 111, 102034 (2020).
Google Scholar 
Kazungu, M. et al. Effects of household-level attributes and agricultural land-use on deforestation patterns along a forest transition gradient in the Miombo landscapes Zambia. Ecol. Econ. 186, 107070 (2021).
Google Scholar 
Kleemann, J. et al. Deforestation in continental ecuador with a focus on protected areas. Land 11, 268 (2022).
Google Scholar 
Mulenga, M. M. & Roos, A. Assessing the awareness and adoptability of pellet cookstoves for low-income households in Lusaka, Zambia. J. Energy South. Afr. 32, 52–61 (2021).
Google Scholar 
Eguiguren, P., Ojeda Luna, T., Torres, B., Lippe, M. & Günter, S. Ecosystem service multifunctionality: Decline and recovery pathways in the amazon and chocó lowland rainforests. Sustainability 12, 7786 (2020).CAS 

Google Scholar 
Vasco, C., Torres, B., Pacheco, P. & Griess, V. The socioeconomic determinants of legal and illegal smallholder logging: Evidence from the Ecuadorian Amazon. For. Policy Econ. 78, 133–140 (2017).
Google Scholar 
van der Ploeg, J., van Weerd, M., Masipiqueña, A. B. & Persoon, G. A. Illegal logging in the Northern Sierra Madre Natural Park, the Philippines. Conserv. Soc. 9, 202–215 (2011).
Google Scholar 
Liu, D. S., Iverson, L. R. & Brown, S. Rates and patterns of deforestation in the Philippines: Application of geographic information system analysis. For. Ecol. Manag. 57, 1–16 (1993).
Google Scholar 
Boquet, Y. Environmental challenges in the Philippines. In The Philippine Archipelago (ed. Boquet, Y.) 779–829 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-51926-5_22.Chapter 

Google Scholar 
MAGAP. ATPA: Reconversión Agro productiva Sostenible en la Amazonía Ecuatoriana (2014).Jones, K. W. et al. Forest conservation incentives and deforestation in the Ecuadorian Amazon. Environ. Conserv. 44, 56–65 (2017).
Google Scholar 
Lindsey, P. A. et al. Underperformance of African protected area networks and the case for new conservation models: Insights from Zambia. PLoS One 9, e94109 (2014).ADS 

Google Scholar 
Fischer, R. et al. Effectiveness of policy instrument mixes for forest conservation in the tropics – a stakeholder perspective from Ecuador, the Philippines and Zambia. Land Use Policy https://doi.org/10.1016/j.landusepol.2023.106546 (2022).Article 

Google Scholar 
Gurney, G. G. et al. Biodiversity needs every tool in the box: Use OECMs. Nature 595, 646–649 (2021).ADS 
CAS 

Google Scholar 
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).ADS 
CAS 

Google Scholar 
Priebe, J. et al. Transformative change in context—Stakeholders’ understandings of leverage at the forest–climate nexus. Sustain. Sci. 17, 1921–1938 (2022).
Google Scholar 
Höhl, M. et al. Forest landscape restoration—What generates failure and success?. Forests 11, 938 (2020).
Google Scholar 
Köthke, M., Ahimbisibwe, V. & Lippe, M. The evidence base on the environmental, economic and social outcomes of agroforestry is patchy—An evidence review map. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2022.925477 (2022).Article 

Google Scholar 
Fischer, R., Giessen, L. & Günter, S. Governance effects on deforestation in the tropics: A review of the evidence. Environ. Sci. Policy 105, 84–101 (2020).
Google Scholar 
Bare, M., Kauffman, C. & Miller, D. C. Assessing the impact of international conservation aid on deforestation in sub-Saharan Africa. Environ. Res. Lett. 10, 125010 (2015).ADS 

Google Scholar 
Vuohelainen, A. J., Coad, L., Marthews, T. R., Malhi, Y. & Killeen, T. J. The effectiveness of contrasting protected areas in preventing deforestation in Madre de Dios. Peru. Environ. Manag. 50, 645–663 (2012).ADS 

Google Scholar 
Hull, V. & Liu, J. Telecoupling: A new frontier for global sustainability. Ecol. Soc. 23, 41 (2018).
Google Scholar 
Aitchison, J. The statistical analysis of compositional data. J. Roy. Stat. Soc. 44, 139–160 (1982).MathSciNet 
MATH 

Google Scholar 
Norman, G. Likert scales, levels of measurement and the “laws” of statistics. Adv. Health Sci. Educ. 15, 625–632 (2010).
Google Scholar 
Day, M., Gumbo, D., Moombe, K. B., Wijaya, A. & Sunderland, T. Zambia Country Profile: Monitoring, Reporting and Verification for REDD+ Vol. 113 (CIFOR, 2014).
Google Scholar 
Piotrowski, M. Nearing the tipping point. Drivers of Deforestation in the Amazon Region (2019).Sarker, P. K., Fischer, R., Tamayo, F., Navarrete, B. T. & Günter, S. Analyzing forest policy mixes based on the coherence of policies and the consistency of legislative policy instruments: A case study from Ecuador. For. Policy Econ. 144, 102838 (2022).
Google Scholar 
Likert, R. A technique for the measurement of attitudes. Arch. Psychol. 22(140), 55–55 (1932).
Google Scholar 
Altinsoy, M. et al. Ambulatory ECG monitoring for syncope and collapse in United States, Europe, and Japan: The patients’ viewpoint. J. Arrhythm. 37, 1023–1030 (2021).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/. (R Foundation for Statistical Computing, Vienna, Austria, 2022).Kassambara, A. rstatix: Pipe-friendly framework for basic statistical tests. R package version 0.7.0 (2021).Kassambara, A. & Mundt, F. factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.7 (2020).Komsta, L. & Novometsky, F. moments: Moments, cumulants, skewness, kurtosis and related tests. R package version 0.14.1 (2022).Zhang, Y., Zhou, M. & Shao, Y. mvnormalTest: Powerful tests for multivariate normality. R package version 1.0.0 (2020).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0 (2020).Wickham, H. et al. Welcome to the Tidyverse. JOSS 4, 1686 (2019).ADS 

Google Scholar 
Bache, S. M. & Wickham, H. magrittr: A Forward-Pipe Operator for R. R package version 2.0.3 (2022).Ushey, K., Allaire, J., Wickham, H. & Ritchie, G. rstudioapi: Safely Access the RStudio API. R package version 0.13 (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-24277-4.Book 
MATH 

Google Scholar 
Wilkins, D. treemapify: Draw Treemaps in ‘ggplot2’. R package version 2.5.5 (2021).Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).MathSciNet 
MATH 

Google Scholar 
Mardia, K. V. Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519–530 (1970).MathSciNet 
MATH 

Google Scholar 
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).MATH 

Google Scholar 
Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964).
Google Scholar 
Conover, W. J. & Iman, R. L. Rank transformations as a bridge between parametric and nonparametric statistics. Am. Stat. 35, 124–129 (1981).MATH 

Google Scholar 
Student,. The probable error of a mean. Biometrika 6, 1–25 (1908).MATH 

Google Scholar 
Tukey, J. W. Comparing individual means in the analysis of variance. Biometrics 5, 99–114 (1949).MathSciNet 
CAS 

Google Scholar 
Jolliffe, I. T. Principal Component Analysis (Springer, 2002).MATH 

Google Scholar  More

The growth behaviour of Linnemannia is strain-specificMost strains showed comparable morphological characteristics on both media as well as in pure and co-culture. However, Linnemannia solitaria and Entomortierella galaxiae produced more aerial mycelium on PDA compared to LcA. There was more/less aerial mycelium in co-cultures with P. helmanticensis compared to pure cultures depending on the strain (Fig. 1, SI Fig. S3).The comparison of Linnemannia and E. galaxiae daily radial growth rates did not support a difference between these genera (p ≥ 0.3). The overall linear model indicated that the fungal daily growth rates mainly differed among species (Table 1). In addition, the effect of strains highlighted the heterogeneity among strains within species (Fig. 2, SI Figs. S4, S5). Although there was no relevant main effect of medium on the daily radial growth rate of the fungi, the medium did affect the fungi in a strain-specific manner (Table 1, Fig. 2, SI Figs. S4, S5). On nutrient poor LcA, the fungal daily radial growth rates were reduced for all species, except for L. solitaria, which grew better on LcA (SI Figs. S3, S4).Table 1 The effect of experimental factors on the fungal daily radial growth rate.Full size tableFigure 2Daily radial growth rate of pure Linnemannia and Entomortierella cultures as well as co-cultures with P. helmanticensis on nutrient rich PDA medium. (a) L. exigua, (b) L. gamsii, (c) L. hyalina, (d) L. sclerotiella, (e) L. solitaria, (f) E. galaxiae.Full size imageThe main effect of co-plating P. helmanticensis on radial growth rate was small, yet significant (0.7%, p  More

Guidetti, R. & Bertolani, R. B. Tardigrade taxonomy: An updated check list of the taxa and a list of characters for their identification. Zootaxa 845, 1–46. https://doi.org/10.11646/zootaxa.845.1.1 (2005).Article 

Google Scholar 
Degma, P. & Guidetti, R. Notes to the current checklist of Tardigrada. Zootaxa 1579, 41–53. https://doi.org/10.11646/zootaxa.1579.1.2 (2007)Article 

Google Scholar 
Vicente, F. & Bertolani, R. Considerations on the taxonomy of the phylum Tardigrada. Zootaxa 3626, 245–248. https://doi.org/10.11646/zootaxa.3626.2.2 (2013).Article 

Google Scholar 
Degma, P. & Guidetti, R. Actual checklist of Tardigrada species. (Version 41: Edition: 16-05-2022). (2009–2022).Ramazzotti, G. & Maucci, W. Il phylum Tardigrada. III edizione riveduta e aggiornata. Mem. Ist. Ital. Idrobiol. 41, 1–1012 (1983).
Google Scholar 
Beasley, C. W. The phylum Tardigrada. in English Translation P. 3rd edn (eds Ramazzotti, G. & Maucci, W.) 1–1014 (Abilene, USA, 1995).Nelson, D. R., Guidetti, R., Rebecchi, L., Kaczmarek, Ł. & McInnes, S. Phylum Tardigrada. in Thorp and Covich’s Freshwater Invertebrates 505–522 (Elsevier, 2020). https://doi.org/10.1016/B978-0-12-804225-0.00015-0.Da Cunha, A. X. & do Nascimento-Ribeiro, F. A fauna de Tardígrados da Ilha da Madeira. Mem. Estud. Mus. Zool. Univ. Coimbra 1–24 (1962).Fontoura, P., Pilato, G. & Lisi, O. Tardigrada from Santo Antão Island (Archipelago of Cape Verde, West Africa) with the description of a new species. Zootaxa 2838, 30–40. https://doi.org/10.11646/zootaxa.2838.1.2 (2011).Article 

Google Scholar 
Gąsiorek, P., Vončina, K. & Michalczyk, Ł. Echiniscus testudo (Doyère, 1840) in New Zealand: Anthropogenic dispersal or evidence for the ‘Everything is Everywhere’ hypothesis?. N. Z. J. Zool. 46, 174–181. https://doi.org/10.1080/03014223.2018.1503607 (2019).Article 

Google Scholar 
Guidetti, R., Schill, R. O., Bertolani, R., Dandekar, T. & Wolf, M. New molecular data for tardigrade phylogeny, with the erection of Paramacrobiotus gen. nov. J. Zool. Syst. Evol. 47, 315–321. https://doi.org/10.1111/j.1439-0469.2009.00526.x (2009).Article 

Google Scholar 
Kaczmarek, Ł, Gawlak, M., Bartels, P. J., Nelson, D. R. & Roszkowska, M. Revision of the genus Paramacrobiotus Guidetti et al., 2009 with the description of a new species, re-descriptions and a key. Ann. Zool. 67, 627–656. https://doi.org/10.3161/00034541ANZ2017.67.4.001 (2017).Article 

Google Scholar 
Marley, N. J. et al. A clarification for the subgenera of Paramacrobiotus Guidetti, Schill, Bertolani, Dandekar and Wolf, 2009, with respect to the International Code of Zoological Nomenclature. Zootaxa 4407, 130–134. https://doi.org/10.11646/zootaxa.4407.1.9 (2018).Article 
CAS 

Google Scholar 
Guidetti, R., Cesari, M., Bertolani, R., Altiero, T. & Rebecchi, L. High diversity in species, reproductive modes and distribution within the Paramacrobiotus richtersi complex (Eutardigrada, Macrobiotidae). Zool. Lett. 5, 1–28. https://doi.org/10.1186/s40851-018-0113-z (2019).Article 

Google Scholar 
Stec, D., Krzywański, Ł, Zawierucha, K. & Michalczyk, Ł. Untangling systematics of the Paramacrobiotus areolatus species complex by an integrative redescription of the nominal species for the group, with multilocus phylogeny and species delineation in the genus Paramacrobiotus. Zool. J. Linn. Soc. 188, 694–716. https://doi.org/10.1093/zoolinnean/zlz163 (2020).Article 

Google Scholar 
Murray, J. Scottish Tardigrada, a review of our present knowledge. Ann. Scot. Nat. Hist. 78, 88–95 (1911).
Google Scholar 
Murray, J. XXV.—Arctic Tardigrada, collected by Wm. S. Bruce. Trans. R. Soc. Edinb. 45, 669–681 (1907).Article 

Google Scholar 
Ramazzotti, G. Tre nouve specie di Tardigradi ed altre specie poco comuni. Atti Soc. Nat. Milano 10, 284–291 (1956).
Google Scholar 
Schill, R. O., Förster, F., Dandekar, T. & Wolf, M. Using compensatory base change analysis of internal transcribed spacer 2 secondary structures to identify three new species in Paramacrobiotus (Tardigrada). Org. Divers. Evol. 10, 287–296. https://doi.org/10.1007/s13127-010-0025-z (2010).Article 

Google Scholar 
Kaczmarek, Ł et al. Integrative description of bisexual Paramacrobiotus experimentalis sp. Nov. (Macrobiotidae) from republic of Madagascar (Africa) with microbiome analysis. Mol. Phylogenet. Evol. 145, 106730. https://doi.org/10.1016/j.ympev.2019.106730 (2020).Article 

Google Scholar 
Bertolani, R. Partenogenesi geografica triploide in un Tardigrado (Macrobiotus richtersi). Rend. Acc. Naz. Lincei. Ser. 8, 487–489 (1971).
Google Scholar 
Bertolani, R. Sex ratio and geographic parthenogenesis in Macrobioutus (Tardigrada). Experientia 28, 94–95. https://doi.org/10.1007/BF01928285 (1972).Article 

Google Scholar 
Bertolani, R. L. partenogenesi nei Tardigradi. Boll. Zool. 39, 577–581. https://doi.org/10.1080/11250007209431414 (1972).Article 

Google Scholar 
Bertolani, R. Cytology and Reproductive Mechanisms in Tardigrades. I. 93–114 (East Tennesse State University Press, Johnson City, 1982).
Google Scholar 
Lemloh, M., Brümmer, F. & Schill, R. O. Life-history traits of the bisexual tardigrades Paramacrobiotus tonollii and Macrobiotus sapiens. J. Zool. Syst. Evol. Res. 49, 58–61. https://doi.org/10.1111/j.1439-0469.2010.00599.x (2011).Article 

Google Scholar 
Guil, N. & Giribet, G. A comprehensive molecular phylogeny of tardigrades-adding genes and taxa to a poorly resolved phylum-level phylogeny. Cladistics 28, 21–49. https://doi.org/10.1111/j.1096-0031.2011.00364.x (2012).Article 

Google Scholar 
Kosztyła, P. et al. Experimental taxonomy confirms the environmental stability of morphometric traits in a taxonomically challenging group of microinvertebrates. Zool. J. Linn. Soc. 178, 765–775. https://doi.org/10.1111/zoj.12409 (2016).Article 

Google Scholar 
Kaczmarek, Ł et al. New records of Antarctic Tardigrada with comments on iterpopulation variability of the Paramacrobiotus fairbanksi Schill, Förster, Dandekar and Wolf, 2010. Diversity 12, 108. https://doi.org/10.3390/d12030108 (2020).Article 

Google Scholar 
Stec, D., Vecchi, M., Calhim, S. & Michalczyk, Ł. New multilocus phylogeny reorganises the family Macrobiotidae (Eutardigrada) and unveils complex morphological evolution of the Macrobiotus hufelandi group. Mol. Phylogenet. Evol. 160, 106987. https://doi.org/10.1016/j.ympev.2020.106987 (2021).Article 

Google Scholar 
Stec, D., Smolak, R., Kaczmarek, Ł & Michalczyk, Ł. An integrative description of Macrobiotus paulinae sp. Nov. (Tardigrada: Eutardigrada: Macrobiotidae: hufelandi group) from Kenya. Zootaxa 4052, 501–526. https://doi.org/10.11646/zootaxa.4052.5.1 (2015).Article 

Google Scholar 
Bryce, D. On some moss-dwelling Cathypnadae; with descriptions of five new species. Sci. Gossip Lond. 28, 271–275 (1892).
Google Scholar 
Casquet, J., Thebaud, C. & Gillespie, R. G. Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders. Mol. Ecol. Resour. 12(1), 136–141. https://doi.org/10.1111/j.1755-0998.2011.03073.x (2012).Article 
CAS 

Google Scholar 
Stec, D., Kristensen, R. M. & Michalczyk, Ł. An integrative description of Minibiotus ioculator sp. nov. from the Republic of South Africa with notes on Minibiotus pentannulatus Londoño et al., 2017 (Tardigrada: Macrobiotidae). Zool. Anz. 286, 117–134. https://doi.org/10.1016/j.jcz.2020.03.007 (2020).Article 

Google Scholar 
Stec, D., Zawierucha, K. & Michalczyk, Ł. An integrative description of Ramazzottius subanomalus (Biserov, 1985 (Tardigrada) from Poland. Zootaxa 4300, 403–420. https://doi.org/10.11646/zootaxa.4300.3.4 (2017).Article 

Google Scholar 
Mironov, S. V., Dabert, J. & Dabert, M. A new feather mite species of the genus Proctophyllodes Robin, 1877 (Astigmata: Proctophyllodidae) from the Long-tailed Tit Aegithalos caudatus (Passeriformes: Aegithalidae)—Morphological description with DNA barcode data. Zootaxa 3253, 54–61. https://doi.org/10.11646/zootaxa.3253.1.2 (2012).Article 

Google Scholar 
White, T. J., Bruns, T., Lee, S. & Taylor, J. PCR Protocols: A Guide to Methods and Application 315–322. https://doi.org/10.1016/B978-0-12-372180-8.50042-1 (Academic Press, 1990).Book 

Google Scholar 
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. Phylogenetic uncertainty. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 

Google Scholar 
Vecchi, M. & Stec, D. Integrative descriptions of two new Macrobiotus species (Tardigrada, Eutardigrada, Macrobiotidae) from Mississippi (USA) and Crete (Greece). ZSE 97, 281–306. https://doi.org/10.3897/zse.97.65280 (2021).Article 

Google Scholar 
Thulin, G. Über die phylogenie und das system der. Hereditas 11, 207–266. https://doi.org/10.1111/j.1601-5223.1928.tb02488.x (1928).Article 

Google Scholar 
Stec, D. Mesobiotus datanlanicus sp. nov., a new tardigrade species (Macrobiotidae: Mesobiotus harmsworthi group) from Lâm Đồng Province in Vietnam. Zootaxa 4679, 164–180. https://doi.org/10.11646/zootaxa.4679.1.10 (2019).Article 

Google Scholar 
Katoh, K. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. NAR 30, 3059–3066. https://doi.org/10.1093/nar/gkf436 (2002).Article 
CAS 

Google Scholar 
Katoh, K. & Toh, H. Recent developments in the MAFFT multiple sequence alignment program. Brief. Bioinform. 9, 286–298. https://doi.org/10.1093/bib/bbn013 (2008).Article 
CAS 

Google Scholar 
Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: Concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180. https://doi.org/10.1111/j.1096-0031.2010.00329.x (2011).Article 

Google Scholar 
Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29(6), 1695–1701. https://doi.org/10.1093/molbev/mss020 (2012).Article 
CAS 

Google Scholar 
Xia, X., Xie, Z., Salemi, M., Chen, L. & Wang, Y. An index of substitution saturation and its application. Mol. Phylogenet. Evol. 26, 1–7. https://doi.org/10.1016/S1055-7903(02)00326-3 (2003).Article 
CAS 

Google Scholar 
Xia, X. & Lemey, P. Assessing substitution saturation with DAMBE. In The Phylogenetic Handbook (eds Lemey, P. et al.) 615–630. https://doi.org/10.1017/CBO9780511819049.022 (Cambridge University Press, 2009).Chapter 

Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574. https://doi.org/10.1093/bioinformatics/btg180 (2003).Article 
CAS 

Google Scholar 
Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v1. 6. 2014. (2015).Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 (2014).Article 
CAS 

Google Scholar 
Bandelt, H., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).Article 
CAS 

Google Scholar 
Ehrenberg, C. G. Beitrag zur Bestimmung des Stationären Mikroskopischen Lebens in bis 20,000 Fuss Alpenhöhe. (1859).Guil, N. & Guidetti, R. A new species of Tardigrada (Eutardigrada: Macrobiotidae) from Iberian Peninsula and Canary Islands (Spain). Zootaxa 889, 1–11. https://doi.org/10.11646/zootaxa.889.1.1 (2005).Article 

Google Scholar 
Plate, L. H. Beiträge zur Naturgeschichte der Tardigraden. Zool. Jahrb. Abteilung Anat. Ontog. Tiere 3, 487–550. https://doi.org/10.5962/bhl.part.1265 (1889).Article 

Google Scholar 
Kaczmarek, Ł, Kayastha, P., Roszkowska, M., Gawlak, M. & Mioduchowska, M. Integrative redescription of the Minibiotus intermedius (Plate, 1888)—The type species of the genus Minibiotus R.O. Schuster, 1980. Diversity 14, 356. https://doi.org/10.3390/d14050356 (2022).Article 
CAS 

Google Scholar 
Londoño, R., Daza, A., Lisi, O. & Quiroga, S. New species of waterbear Minibiotus pentannulatus (Tardigrada: Macrobiotidae) from Colombia. Rev. Mex. Biodivers. 88, 807–814. https://doi.org/10.1016/j.rmb.2017.10.040 (2017).Article 

Google Scholar 
Vecchi, M. et al. Macrobiotus naginae sp. nov., a new Xerophilous Tardigrade species from Rokua Sand Dunes (Finland). Zool. Stud. 61, e22 (2022).
Google Scholar 
Stec, D., Dudziak, M. & Michalczyk, Ł. Integrative descriptions of two new Macrobiotidae species (Tardigrada: Eutardigrada: Macrobiotoidea) from French Guiana and Malaysian Borneo. Zool. Stud. 59, e23 (2020).
Google Scholar 
Stec, D., Roszkowska, M., Kaczmarek, Ł & Michalczyk, Ł. Paramacrobiotus lachowskae, a new species of Tardigrada from Colombia (Eutardigrada: Parachela: Macrobiotidae). N. Z. J. Zool. 45, 43–60. https://doi.org/10.1080/03014223.2017.1354896 (2018).Article 

Google Scholar 
Sugiura, K., Matsumoto, M. & Kunieda, T. Description of a model tardigrade Paramacrobiotus metropolitanus sp. nov. (Eutardigrada) from Japan with a summary of its life history, reproduction and genomics. Zootaxa 5134, 92–112. https://doi.org/10.11646/zootaxa.5134.1.4 (2022).Article 

Google Scholar 
Tumanov, D. V. Three new species of Macrobiotus (Eutardigrada, Macrobiotidae, tenuis-group) from Tien Shan (Kirghizia) and Spitsbergen. J. Limnol. 66, 40. https://doi.org/10.4081/jlimnol.2007.s1.40 (2007).Article 

Google Scholar 
Zawierucha, K., Kolicka, M. & Kaczmarek, Ł. Re-description of the Arctic tardigrade Tenuibiotus voronkovi (Tumanov, 2007 (Eutardigrada; Macrobiotidea), with the first molecular data for the genus. Zootaxa 4196, 498. https://doi.org/10.11646/zootaxa.4196.4.2 (2016).Article 

Google Scholar 
Stec, D., Tumanov, D. T. & Kristensen, R. M. Integrative taxonomy identifies two new tardigrade species (Eutardigrada: Macrobiotidae) from Greenland. EJT 614, 1–40. https://doi.org/10.5852/ejt.2020.614 (2020).Article 

Google Scholar 
Fontaneto, D., Flot, J.-F. & Tang, C. Q. Guidelines for DNA taxonomy, with a focus on the meiofauna. Mar. Biodiv. 45, 433–451. https://doi.org/10.1007/s12526-015-0319-7 (2015).Article 

Google Scholar 
Zhang, J., Kapli, P., Pavlidis, P. & Stamatakis, A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29, 2869–2876. https://doi.org/10.1093/bioinformatics/btt499 (2013).Article 
CAS 

Google Scholar 
Puillandre, N., Brouillet, S. & Achaz, G. ASAP: Assemble species by automatic partitioning. Mol. Ecol. Resour. 21, 609–620. https://doi.org/10.1111/1755-0998.13281 (2021).Article 

Google Scholar 
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34(3), 772–773. https://doi.org/10.1093/molbev/msw260 (2017).Article 
CAS 

Google Scholar 
Roszkowska, M., Stec, D., Gawlak, M. & Kaczmarek, Ł. An integrative description of a new tardigrade species Mesobiotus romani sp. nov. (Macrobiotidae: harmsworthi group) from the Ecuadorian Pacific coast. Zootaxa 4450, 550–564. https://doi.org/10.11646/zootaxa.4450.5.2 (2018).Article 

Google Scholar 
Pilato, G. & Binda, M. G. Definition of families, subfamilies, genera and subgenera of the Eutardigrada, and keys to their identification. Zootaxa 2404, 1–54. https://doi.org/10.11646/zootaxa.2404.1.1 (2010).Article 

Google Scholar 
Kaczmarek, Ł & Michalczyk, Ł. The Macrobiotus hufelandi group (Tardigrada) revisited. Zootaxa 4363, 101–123. https://doi.org/10.11646/zootaxa.4363.1.4 (2017).Article 

Google Scholar 
Michalczyk, Ł & Kaczmarek, Ł. A description of the new tardigrade Macrobiotus reinhardti (Eutardigrada: Macrobiotidae, harmsworthi group) with some remarks on the oral cavity armature within the genus Macrobiotus Schultze. Zootaxa 331, 1–24. https://doi.org/10.11646/zootaxa.331.1.1 (2003).Article 

Google Scholar 
Kaczmarek, Ł, Cytan, J., Zawierucha, K., Diduszko, D. & Michalczyk, Ł. Tardigrades from Peru (South America), with descriptions of three new species of Parachela. Zootaxa 3790, 357–379. https://doi.org/10.11646/zootaxa.3790.2.5 (2014).Article 

Google Scholar 
Kiosya, Y., Pogwizd, J., Matsko, Y., Vecchi, M. & Stec, D. Phylogenetic position of two Macrobiotus species with a revisional note on Macrobiotus sottilei Pilato, Kiosya, Lisi & Sabella, 2012 (Tardigrada: Eutardigrada: Macrobiotidae). Zootaxa 4933, 113–135. https://doi.org/10.11646/zootaxa.4933.1.5 (2021).Article 

Google Scholar 
Pilato, G. Analisi di nuovi caratteri nello studio degli Eutardigradi. Animalia 8, 51–57 (1981).
Google Scholar 
Michalczyk, Ł & Kaczmarek, Ł. The Tardigrada Register: a comprehensive online data repository for tardigrade taxonomy. J. Limnol. 72, e22. https://doi.org/10.4081/jlimnol.2013.s1.e22 (2013).Article 

Google Scholar 
Bertolani, R. et al. Phylogeny of Eutardigrada: New molecular data and their morphological support lead to the identification of new evolutionary lineages. Mol. Phylogenet. Evol. 76, 110–126. https://doi.org/10.1016/j.ympev.2014.03.006 (2014).Article 

Google Scholar 
Perry, E., Miller, W. R. & Kaczmarek, Ł. Recommended abbreviations for the names of genera of the phylum Tardigrada. Zootaxa 4608, 145. https://doi.org/10.11646/zootaxa.4608.1.8 (2019).Article 

Google Scholar 
Degma, P., Michalczyk, Ł & Kaczmarek, Ł. Macrobiotus derkai, a new species of Tardigrada (Eutardigrada, Macrobiotidae, huziori group) from the Colombian Andes (South America). Zootaxa 1731, 1–23. https://doi.org/10.11646/zootaxa.1731.1.1 (2008).Article 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & Diduszko, D. Some tardigrades from Siberia (Russia, Baikal region) with a description of Macrobiotus garynahi sp. nov. (Eutardigrada: Macrobiotidae: richtersi group). Zootaxa 1053, 35–45. https://doi.org/10.11646/zootaxa.1053.1.3 (2005).Article 

Google Scholar 
Michalczyk, Ł & Kaczmarek, Ł. Macrobiotus huziori, a new species of Tardigrada (Eutardigrada: Macrobiotidae) from Costa Rica (Central America). Zootaxa 1169, 47–59. https://doi.org/10.11646/zootaxa.1169.1.3 (2006).Article 

Google Scholar 
Michalczyk, L. & Kaczmarek, L. A new species Macrobiotus magdalenae (Tardigrada: Eutardigrada: Macrobiotidae, richtersi group) from Costa Rican rain forest (Central America). N. Z. J. Zool. 33, 189–196. https://doi.org/10.1080/03014223.2006.9518444 (2006).Article 

Google Scholar 
Michalczyk, Ł, Kaczmarek, Ł & Węglarska, B. Macrobiotus sklodowskae sp. nov. (Tardigrada: Eutardigrada: Macrobiotidae, richtersi group) from Cyprus. Zootaxa 1371, 45–56. https://doi.org/10.11646/zootaxa.1371.1.4 (2006).Article 

Google Scholar 
Tumanov, D. V. Notes on the Tardigrada of Thailand, with a description of Macrobiotus alekseevi sp. nov. (Eutardigrada, Macrobiotidae). Zootaxa 999, 1–6. https://doi.org/10.11646/zootaxa.999.1.1 (2005).Article 

Google Scholar 
Doyère, M. Memoire sur les tardigrades. Ann. Sci. Nat Zool. Ser. 2, 269–362 (1840).
Google Scholar 
Richters, F. Tardigrada. In Handbuch der Zoologie Vol. 3 (eds Kükenthal, W. & Krumbach, T.) 58–61 (Walter de Gruyter & Co., Berlin and Leipzig, 1926).
Google Scholar 
Stec, D., Cancellario, T. & Fontaneto, D. Diversification rates in Tardigrada indicate a decreasing tempo of lineage splitting regardless of reproductive mode. Org. Divers. Evol. 22(4), 965–974. https://doi.org/10.1007/s13127-022-00578-4 (2022).Article 

Google Scholar 
Dellicour, S. & Flot, J.-F. The hitchhiker’s guide to single-locus species delimitation. Mol. Ecol. Resour. 18, 1234–1246. https://doi.org/10.1111/1755-0998.12908 (2018).Article 

Google Scholar 
Magoga, G., Fontaneto, D. & Montagna, M. Factors affecting the efficiency of molecular species delimitation in a species-rich insect family. Mol. Ecol. Resour. 21, 1475–1489. https://doi.org/10.1111/1755-0998.13352 (2021).Article 
CAS 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & McInnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part I: Central America. Zootaxa 3763, 1–62. https://doi.org/10.11646/zootaxa.3763.1.1 (2014).Article 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & McInnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part II: South America. Zootaxa 3923, 1–107. https://doi.org/10.11646/zootaxa.3923.1.1 (2015).Article 

Google Scholar 
Kaczmarek, Ł, Michalczyk, Ł & Mcinnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part III: North America and Greenland. Zootaxa 4203, 1–249. https://doi.org/10.11646/zootaxa.4203.1.1 (2016).Article 

Google Scholar 
Mcinnes, S. J., Michalczyk, Ł & Kaczmarek, Ł. Annotated zoogeography of non-marine Tardigrada. Part IV: Africa. Zootaxa 4284, 1. https://doi.org/10.11646/zootaxa.4284.1.1 (2017).Article 

Google Scholar 
Michalczyk, Ł, Kaczmarek, Ł & Mcinnes, S. J. Annotated zoogeography of non-marine Tardigrada. Part V: Australasia. Zootaxa 5107, 1–119. https://doi.org/10.11646/zootaxa.5107.1.1 (2022).Article 

Google Scholar 
Pilato, G., Claxton, S. & Binda, M. G. Tardigrades from Australia. III. Echiniscus marcusi and Macrobiotus peteri, new species of tardigrades from New South Wales. Animalia 16, 43–48 (1989).
Google Scholar 
Pilato, G., Binda, M. G. & Lisi, O. Eutardigrada from New Zealand, with descriptions of two new species. N. Z. J. Zool. 33, 49–63. https://doi.org/10.1080/03014223.2006.9518430 (2006).Article 

Google Scholar 
Bartels, P. J., Pilato, G., Lisi, O. & Nelson, D. R. Macrobiotus (Eutardigrada, Macrobiotidae) from the Great Smoky Mountains National Park, Tennessee/North Carolina, USA (North America): Two new species and six new records. Zootaxa 2022, 45–57. https://doi.org/10.11646/zootaxa.2022.1.4 (2009).Article 

Google Scholar 
Binda, M. G., Pilato, G., Moncada, E. & Napolitano, A. Some tardigrades from Central Africa with the description of two new species: Macrobiotus ragonesei and M. priviterae (Eutardigrada Macrobiotidae). Trop. Zool. 14, 233–242. https://doi.org/10.1080/03946975.2001.10531155 (2001).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Lissi, O. Notes on tardigrades of the Seychelles with the description of two new species. Ital. J. Zool. 71, 171–178 (2004).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Lisi, O. Three new species of eutardigrades from the Seychelles. N. Z. J. Zool. 33, 39–48. https://doi.org/10.1080/03014223.2006.9518429 (2006).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Lisi, O. Notes on tardigrades of the Seychelles with the description of three new species. Ital. J. Zool. 71, 171–178. https://doi.org/10.1080/11250000409356569 (2004).Article 

Google Scholar 
Pilato, G., Binda, M. G. & Catanzaro, R. Remarks on some tardigrades of the African fauna with the description of three new species of Macrobiotus Schultze 1834. Trop. Zool. 4, 167–178. https://doi.org/10.1080/03946975.1991.10539487 (1991).Article 

Google Scholar 
Maucci, W. & Durante Pasa, M. V. Tardigradi muscicoli delle Isole Andamane. Boll. Mus. Civ. St. Nat. Verona 7, 281–291 (1980).
Google Scholar 
Iharos, G. Neuere Daten zur Kenntnis der Tardigraden-Fauna von Neuguinea. Opusc. Zool. Bp. 11, 65–73 (1973).
Google Scholar 
Binda, M. G. & Pilato, G. Macrobiotus savai and Macrobiotus humilis, two new species of tardigrades from Sri Lanka. Boll. Accad. Gioenia Sci. Nat. Catania 34, 101–111 (2001).
Google Scholar 
Pilato, G. Macrobiotus centesimus, new species of eutardigrade from the South America. Boll. Accad. Gioenia Sci. Nat. Catania 33, 97–101 (2000).
Google Scholar 
Daza, A., Caicedo, M., Lisi, O. & Quiroga, S. New records of tardigrades from Colombia with the description of Paramacrobiotus sagani sp. nov. and Doryphoribius rosanae sp. nov. Zootaxa 4362, 29–50. https://doi.org/10.11646/zootaxa.4362.1.2 (2017).Article 

Google Scholar 
Claps, M. C. & Rossi, G. C. Tardígrados de Uruguay, con descripción de dos nuevas especies (Echiniscidae, Macrobiotidae). Iheringia Sér. Zool. 83, 17–22 (1997).
Google Scholar 
Iharos, G. Neue tardigraden-arten aus ungarn (neuere beitrage zur kenntnis der tardigraden-fauna ungarns. 6.). Acta Zool. Acad. Sci. Hung. 12(1–2), 111 (1966).
Google Scholar 
Pilato, G., Kiosya, Y., Lisi, O. & Sabella, G. New records of Eutardigrada from Belarus with the description of three new species. Zootaxa 3179, 39–60. https://doi.org/10.11646/zootaxa.3179.1.2 (2012).Article 

Google Scholar 
Pasa, D. & Maucci, W. Moss Tardigrada from the Scandinavian Peninsula. in Second International Symposium on Tardigrada, Vol. 79(25). 47–85 (1979).Lisi, O., Binda, M. G. & Pilato, G. Eremobiotus ginevrae sp. nov. and Paramacrobiotus pius sp. nov., two new species of Eutardigrada. Zootaxa 4103, 344–360. https://doi.org/10.11646/zootaxa.4103.4.3 (2016).Article 

Google Scholar 
Biserov, V. I. Macrobiotus lorenae sp. n., a new species of Tardigrada (Eutardigrada Macrobiotidae) from the Russian Far East. Arthr Sel. 5, 145–149 (1996).
Google Scholar 
Biserov, V. I. Tardigrades of the Caucasus with a taxonomic analysis of genus Ramazzottius. Zool. Anz. 236, 139–159 (1997).
Google Scholar 
Morek, W. et al. Redescription of Milnesium alpigenum Ehrenberg, 1853 (Tardigrada: Apochela) and a description of Milnesium inceptum sp. nov., a tardigrade laboratory model. Zootaxa 4586(1), 35. https://doi.org/10.11646/zootaxa.4586.1.2 (2019).Article 

Google Scholar 
Morek, W., Surmacz, B., López-López, A. & Michalczyk, Ł. “Everything is not everywhere”: Time-calibrated phylogeography of the genus Milnesium (Tardigrada). Mol. Ecol. 30, 3590–3609. https://doi.org/10.1111/mec.15951 (2021).Article 

Google Scholar 
Mogle, M. J., Kimball, S. A., Miller, W. R. & McKown, R. D. Evidence of avian-mediated long-distance dispersal in American tardigrades. PeerJ 6, e5035. https://doi.org/10.7717/peerj.5035 (2018).Article 

Google Scholar 
Vuori, T., Calhim, S. & Vecchi, M. A lift in snail’s gut provides an efficient colonization route for tardigrades. Ecology 103, e3702. https://doi.org/10.1002/ecy.3702 (2022).Article 

Google Scholar 
Książkiewicz, Z. & Roszkowska, M. Experimental evidence for snails dispersing tardigrades based on Milnesium inceptum and Cepaea nemoralis species. Sci. Rep. 12(4421), 1–10. https://doi.org/10.1038/s41598-022-08265-2 (2022).Article 
ADS 
CAS 

Google Scholar  More

Data reportingThe data underpinning this study is a compilation of existing datasets and therefore, no statistical methods were used to predetermine sample size, the experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. The measurements were taken from distinct samples, repeated measurements from the same sites were averaged in the main analysis.Inclusion & ethicsData were primarily collected from individual archives of contributing co-authors. The data collection initiative was openly announced via the mailing list of the 10th International Seminar on Apterygota and via social media (Twitter, Researchgate). In addition, colleagues from less explored regions (Africa, South America) were contacted via personal networks of the initial authors group and literature search. All direct data providers who collected and standardised the data were invited as co-authors with defined minimum role (data provision and cleaning, manuscript editing and approval). For unpublished data, people who were directly involved in sorting and identification of springtails, including all local researchers, were invited as co-authors. Principal investigators were normally not included as co-authors, unless they contributed to conceptualisation and writing of the manuscript. All co-authors were informed and invited to contribute throughout the research process—from the study design and analysis to writing and editing. The study provided an inclusive platform for researchers around the globe to network, share and test their research ideas.Data acquisitionBoth published and unpublished data were collected, using raw data whenever possible entered into a common template. In addition, data available from Edaphobase47 was included. The following minimum set of variables was collected: collectors, collection method (including sampling area and depth), extraction method, identification precision and resources, collection date, latitude and longitude, vegetation type (generalized as grassland, scrub, woodland, agriculture and other for the analysis), and abundances of springtail taxa found in each soil sample (or sampling site). Underrepresented geographical areas (Africa, South America, Australia and Southeast Asia) were specifically targeted by a literature search in the Web of Science database using the keywords ‘springtail’ or ‘Collembola’, ‘density’ or ‘abundance’ or ‘diversity’, and the region of interest; data were acquired from all found papers if the minimum information listed above was provided. All collected datasets were cleaned using OpenRefine v3.3 (https://openrefine.org) to remove inconsistencies and typos. Geographical coordinates were checked by comparing the dataset descriptions with the geographical coordinates. In total, 363 datasets comprising 2783 sites were collected and collated into a single dataset (Supplementary Fig. 1).Calculation of community parametersCommunity parameters were calculated at the site level. Here, we defined a site as a locality that hosts a defined springtail community, is covered by a certain vegetation type, with a certain management, and is usually represented by a sampling area of up to a hundred metres in diameter, making species co-occurrence and interactions plausible. To calculate density, numerical abundance in all samples was averaged and recalculated per square metre using the sampling area. Springtail communities were assessed predominantly during active vegetation periods (i.e., spring, summer and autumn in temperate and boreal biomes, and summer in polar biomes). Our estimations of community parameters therefore refer to the most favourable conditions (peak yearly densities). This seasonal sampling bias is likely to have little effect on our conclusions, since most springtails survive during cold periods38,48. Finally, we used mean annual soil temperatures49 to estimate the seasonal mean community metabolism (described below) and tested for the seasonal bias in additional analysis (see Linear mixed-effects models).All data analyses were conducted in R v. 4.0.250 with RStudio interface v. 1.4.1103 (RStudio, PBC). Data was transformed and visualised with tidyverse packages51,52, unless otherwise mentioned. Background for the global maps was acquired via the maps package53,54. To calculate local species richness, we used data identified to species or morphospecies level (validated by the expert team). Since the sampling effort varied among studies, we extrapolated species richness using rarefaction curves based on individual samples with the Chao estimator51,52 in the vegan package53. For some sites, sample-level data were not available in the original publications, but site-level averages were provided, and an extensive sampling effort was made. In such cases, we predicted extrapolated species richness based on the completeness (ratio of observed to extrapolated richness) recorded at sites where sample-level data were available (only sites with 5 or more samples were used for the prediction). We built a binomial model to predict completeness in sites where no sample-level data were available using latitude and the number of samples taken at a site as predictors: glm(Completeness~N_samples*Latitude). We found a positive effect of the number of samples (Chisq = 1.97, p = 0.0492) and latitude (Chisq = 2.07, p = 0.0391) on the completeness (Supplementary Figs. 17–19). We further used this model to predict extrapolated species richness on the sites with pooled data (435 sites in Europe, 15 in Australia, 6 in South America, 4 in Asia, and 3 in Africa).To calculate biomass, we first cross-checked all taxonomic names with the collembola.org checklist55 using fuzzy matching algorithms (fuzzyjoin R package56) to align taxonomic names and correct typos. Then we merged taxonomic names with a dataset on body lengths compiled from the BETSI database57, a personal database of Matty P. Berg, and additional expert contributions. We used average body lengths for the genus level (body size data on 432 genera) since data at the species level were not available for many morphospecies (especially in tropical regions), and species within most springtail genera had similar body size ranges. Data with no genus-level identifications were excluded from the analysis. Dry and fresh body masses were calculated from body length using a set of group-specific length-mass regressions (Supplementary Table 1)58,59 and the results of different regressions applied to the same morphogroup were averaged. Dry mass was recalculated to fresh mass using corresponding group-specific coefficients58. We used fresh mass to calculate individual metabolic rates60 and account for the mean annual topsoil (0–5 cm) temperature at a given site61. Group-specific metabolic coefficients for insects (including springtails) were used for the calculation: normalization factor (i0) ln(21.972) [J h−1], allometric exponent (a) 0.759, and activation energy (E) 0.657 [eV]60. Community-weighted (specimen-based) mean individual dry masses and metabolic rates were calculated for each sample and then averaged by site after excluding 10% of maximum and 10% of minimum values to reduce impact of outliers. To calculate site-level biomass and community metabolism, we summed masses or metabolic rates of individuals, averaged them across samples, and recalculated them per unit area (m2).Parameter uncertaintiesOur biomass and community metabolism approximations contain several assumptions. To account for the uncertainty in the length-mass and mass-metabolism regression coefficients, in addition to the average coefficients, we also used maximum (average + standard error) and minimum coefficients (average—standard error; Supplementary Table 1) in all equations to calculate maximum and minimum estimations of biomass and community metabolism reported in the main text. Further, we ignored latitudinal variation in body sizes within taxonomic groups62. Nevertheless, latitudinal differences in springtail density (30-fold), environmental temperature (from −16.0 to +27.6 °C in the air and from −10.2 to +30.4 °C in the soil), and genus-level community compositions (there are only few common genera among polar regions and the tropics)55 are higher than the uncertainties introduced by indirect parameter estimations, which allowed us to detect global trends. Although most springtails are concentrated in the litter and uppermost soil layers20, their vertical distribution depends on the particular ecosystem63. Since sampling methods are usually ecosystem-specific (i.e. sampling is done deeper in soils with developed organic layers), we treated the methods used by the original data collectors as representative of a given ecosystem. Under this assumption, we might have underestimated the number of springtails in soils with deep organic horizons, so our global estimates are conservative and we would expect true global density and biomass to be slightly higher. To minimize these effects, we excluded sites where the estimations were likely to be unreliable (see data selection below).Data selectionOnly data collection methods allowing for area-based recalculation (e.g. Tullgren or Berlese funnels) were used for analysis. Data from artificial habitats, coastal ecosystems, caves, canopies, snow surfaces, and strong experimental manipulations beyond the bounds of naturally occurring conditions were excluded (Supplementary Fig. 1). To ensure data quality, we performed a two-step quality check: technical selection and expert evaluation. Collected data varied according to collection protocols, such as sampling depth and the microhabitats (layers) considered. To technically exclude unreliable density estimations, we explored data with a number of diagnostic graphs (Supplementary Table 2; Supplementary Figs. 12–20) and filtered it, excluding the following: (1) All woodlands where only soil or only litter was considered; (2) All scrub ecosystems where only ground cover (litter or mosses) was considered; (3) Agricultural sites in temperate zones where only soil with sampling depth 90% of cases were masked on the main maps; for the map with density-species richness visualisation, two corresponding masks were applied (Fig. 2).To estimate spatial variability of our predictions while accounting for the spatial sampling bias in our data (Fig. 1a) we performed a spatially stratified bootstrapping procedure. We used the relative area of each IPBES79 region (i.e., Europe and Central Asia, Asia and the Pacific, Africa, and the Americas) to resample the original dataset, creating 100 bootstrap resamples. Each of these resamples was used to create a global map, which was then reduced to create mean, standard deviation, 95% confidence interval, and coefficient of variation maps (Supplementary Figs. 4–7).Global biomass, abundance, and community metabolism of springtails were estimated by summing predicted values for each 30 arcsec pixel10. Global community metabolism was recalculated from joule to mass carbon by assuming 1 kg fresh mass = 7 × 106 J80, an average water proportion in springtails of 70%58, and an average carbon concentration of 45% (calculated from 225 measurements across temperate forest ecosystems)81. We repeated the procedure of global extrapolation and prediction for biomass and community metabolism using minimum and maximum estimates of these parameters from regression coefficient uncertainties (see Parameter uncertainties).Path analysisTo reveal the predictors of springtail communities at the global scale, we performed a path analysis. After filtering the selected environmental variables (see above) according to their global availability and collinearity, 13 variables were used (Supplementary Fig. 9b): mean annual air temperature, mean annual precipitation (CHELSA database67), aridity (CGIAR database68), soil pH, sand and clay contents combined (sand and clay contents were co-linear in our dataset), soil organic carbon content (SoilGrids database73), NDVI (MODIS database72), human population density (GPWv4 database74), latitude, elevation69, and vegetation cover reported by the data providers following the habitat classification of European Environment Agency (woodland, scrub, agriculture, and grasslands; the latter were coded as the combination of woodland, scrub, and agriculture absent). Before running the analysis, we performed the Rosner’s generalized extreme Studentized deviate test in the EnvStats package82 to exclude extreme outliers and we z-standardized all variables (Supplementary R Code).Separate structural equation models were run to predict density, dry biomass, community metabolism, and local species richness in the lavaan package83. To account for the spatial clustering of our data in Europe, instead of running a model for the entire dataset, we divided the data by the IPBES79 geographical regions and selected a random subset of sites for Eurasia, such that only twice the number of sites were included in the model as the second-most represented region. We ran the path analysis 99 times for each community parameter with different Eurasian subsets (density had n = 723 per iteration, local species richness had n = 352, dry biomass had n = 568, and community metabolism had n = 533). We decided to keep the share of the Eurasian dataset larger than other regions to increase the number of sites per iteration and validity of the models. The Eurasian dataset also had the best data quality among all regions and a substantial reduction in datasets from Eurasia would result in a low weight for high-quality data. We additionally ran a set of models in which the Eurasian dataset was represented by the same number of sites as the second-most represented region, which yielded similar effect directions for all factors, but slightly higher variations and fewer consistently significant effects. In the paper, only the first version of analysis is presented. To illustrate the results, we averaged effect sizes for the paths across all iterations and presented the distribution of these effect sizes using mirrored Kernel density estimation (violin) plots. We marked and discussed effects that were significant at p  More

Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 

Google Scholar 
Spehn, E. M., Rudmann-Maurer, K. & Körner, C. Mountain biodiversity. Plant Ecol. Divers. 4, 301–302 (2011).
Google Scholar 
Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp Bot. 127, 1–15 (2017).
Google Scholar 
Hoorn, C. et al. (eds) Mountains, Climate and Biodiversity (Wiley, 2018).
Google Scholar 
Huang, S., Meijers, M. J. M., Eyres, A., Mulch, A. & Fritz, S. A. Unravelling the history of biodiversity in mountain ranges through integrating geology and biogeography. J. Biogeogr. 46, 1777–1791 (2019).
Google Scholar 
Perrigo, A., Hoorn, C. & Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 47, 315–325 (2020).
Google Scholar 
Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).ADS 
CAS 

Google Scholar 
Antonelli, A. et al. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. USA 115, 6034–6039 (2018).ADS 
CAS 

Google Scholar 
Merckx, V. S. F. T. et al. Evolution of endemism on a young tropical mountain. Nature 524, 347–350 (2015).ADS 
CAS 

Google Scholar 
Fjeldsa, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).
Google Scholar 
Badgley, C. et al. Biodiversity and topographic complexity: Modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211–226 (2017).
Google Scholar 
Körner, C. Mountain biodiversity, its causes and function. Ambio 33, 11 (2004).
Google Scholar 
Antonelli, A. et al. An engine for global plant diversity: Highest evolutionary turnover and emigration in the American tropics. Front. Genet. 6, 130 (2015).
Google Scholar 
Chazot, N. et al. Into the Andes: Multiple independent colonizations drive montane diversity in the Neotropical clearwing butterflies Godyridina. Mol. Ecol. 25, 5765–5784 (2016).
Google Scholar 
Stebbins, G. L. Flowering Plants: Evolution Above the Species Level (Belknap Press of Harvard University Press, 1974).
Google Scholar 
Rangel, T. F. et al. Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves. Science 361, 5452 (2018).
Google Scholar 
Chapman, F. M. The relationships and distribution of the warblers of the genus Compsothlypis: A contribution to the study of the origin of Andean bird life. Auk 42(2), 193–208 (1925).
Google Scholar 
Endler, J. A. Geographic variation, speciation, and clines. Genet. Res. 1, b1–b3 (1978).
Google Scholar 
Baert, L. & Maelfait, J. P. A contribution to the knowledge of the spider fauna of Galápagos (Ecuador). Bull. Koninklijk Belg. Instit. Nat. Entomol. 56, 93–123 (1986).
Google Scholar 
Desender, K., Baert, L. & Maelfait, J. P. Distribution and speciation of carabid beetles in the Galápagos Archipelago (Ecuador). Bull. Inst. R. Sci. Natl. Belg. 62, 57–65 (1992).
Google Scholar 
Patton, J. L. & Smith, M. F. mtDNA phylogeny of Andean mice: A test of diversification across ecological gradients. Evolution 46, 174 (1992).CAS 

Google Scholar 
Nevado, B., Contreras-Ortiz, N., Hughes, C. & Filatov, D. A. Pleistocene glacial cycles drive isolation, gene flow and speciation in the high-elevation Andes. New Phytol 219, 779–793 (2018).
Google Scholar 
Winger, B. M. & Bates, J. M. The tempo of trait divergence in geographic isolation: Avian speciation across the Marañon Valley of Peru. Evolution 69, 772–787 (2015).
Google Scholar 
Hazzi, N. A., Moreno, J. S., Ortiz-Movliav, C. & Palacio, R. D. Biogeographic regions and events of isolation and diversification of the endemic biota of the tropical Andes. Proc. Natl. Acad. Sci. USA. 115, 7985–7990 (2018).ADS 
CAS 

Google Scholar 
Palma, R. E., Marquet, P. A. & Boric-Bargetto, D. Inter-and intraspecific phylogeography of small mammals in the Atacama Desert and adjacent areas of northern Chile. J. Biogeogr. 32(11), 1931–1941 (2005).
Google Scholar 
Beckman, E. J. & Witt, C. C. Phylogeny and biogeography of the New World siskins and goldfinches: Rapid, recent diversification in the Central Andes. Mol. Phylogenet. Evol. 87, 28–45 (2015).
Google Scholar 
Drummond, C. S., Eastwood, R. J., Miotto, S. T. S. & Hughes, C. E. Multiple continental radiations and correlates of diversification in Lupinus (Leguminosae): Testing for key innovation with incomplete taxon sampling. Syst. Biol. 61, 443–460 (2012).
Google Scholar 
Hutter, C. R., Lambert, S. M. & Wiens, J. J. Rapid diversification and time explain amphibian richness at different scales in the tropical Andes, Earth’s most biodiverse hotspot. Am. Nat. 190, 828–843 (2017).
Google Scholar 
Toussaint, E. F. A. et al. Flight over the Proto-Caribbean seaway: Phylogeny and macroevolution of Neotropical Anaeini leafwing butterflies. Mol. Phylogenet. Evol. 137, 86–103 (2019).
Google Scholar 
Acevedo, A. A. Historical biogeography, phylogenetic diversity and evolution of body size in Pristimantis, the world’s most diverse amphibian genus. Doctoral thesis, Fac. Ciencias Biológicas, Pontificia Universidad Católica de Chile (2021).Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in A ndean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442 (2016).
Google Scholar 
Fjeldsa, J. & Rahbek, C. Diversification of tanagers, a species rich bird group, from lowlands to montane regions of South America. Integr. Comp. Biol. 46(1), 72–81 (2006).CAS 

Google Scholar 
Struwe, L., Haag, S., Heiberg, E. & Grant, J. R. Andean speciation and vicariance in Neotropical Macrocarpaea (Gentianaceae-Helieae). Ann. Mol. Bot. Gard. 96, 450–469 (2009).
Google Scholar 
Hutter, C. R., Guayasamin, J. M. & Wiens, J. J. Explaining Andean megadiversity: The evolutionary and ecological causes of glassfrog elevational richness patterns. Ecol. Lett. 16, 1135–1144 (2013).
Google Scholar 
Santos, J. C. et al. Amazonian amphibian diversity is primarily derived from late Miocene Andean lineages. PLoS Biol. 7, e1000056 (2009).
Google Scholar 
Luebert, F. & Weigend, M. Phylogenetic insights into Andean plant diversification. Front. Ecol. Evol. 2, 27 (2014).
Google Scholar 
Chazot, N. et al. Renewed diversification following Miocene landscape turnover in a Neotropical butterfly radiation. Glob. Ecol. Biogeogr. 28, 1118–1132 (2019).
Google Scholar 
Esquerré, D., Brennan, I. G., Catullo, R. A., Torres-Pérez, F. & Keogh, J. S. How mountains shape biodiversity: The role of the Andes in biogeography, diversification, and reproductive biology in South America’s most species-rich lizard radiation (Squamata: Liolaemidae). Evolution 73, 214–230 (2019).
Google Scholar 
Garzione, C. N. et al. Rise of the Andes. Science 320, 1304–1307 (2008).ADS 
CAS 

Google Scholar 
Hoorn, C. et al. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science 330, 927–931 (2010).ADS 
CAS 

Google Scholar 
Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: A Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61, 346–367 (2007).CAS 

Google Scholar 
Pennington, R. T. et al. Contrasting plant diversification histories within the Andean biodiversity hotspot. Proc. Natl. Acad. Sci. USA. 107, 13783–13787 (2010).ADS 
CAS 

Google Scholar 
Antonelli, A. & Sanmartín, I. Why are there so many plant species in the Neotropics?. Taxon 60, 403–414 (2011).
Google Scholar 
Hughes, C. & Eastwood, R. Island radiation on a continental scale: Exceptional rates of plant diversification after uplift of the Andes. Proc. Natl. Acad. Sci. USA. 103, 10334–10339 (2006).ADS 
CAS 

Google Scholar 
Madriñán, S., Cortés, A. J. & Richardson, J. E. Páramo is the world’s fastest evolving and coolest biodiversity hotspot. Front. Genet. 4, 192 (2013).
Google Scholar 
Upham, N. S., Ojala-Barbour, R., Brito, M. J., Velazco, P. M. & Patterson, B. D. Transitions between Andean and Amazonian centers of endemism in the radiation of some arboreal rodents. BMC Evol. Biol. 13, 191 (2013).
Google Scholar 
Hughes, C. E. & Atchison, G. W. The ubiquity of alpine plant radiations: From the Andes to the Hengduan mountains. New Phytol. 207, 275–282 (2015).
Google Scholar 
Givnish, T. J. et al. Adaptive radiation, correlated and contingent evolution, and net species diversification in Bromeliaceae. Mol. Phylogenet. Evol. 71, 55–78 (2014).
Google Scholar 
Horton, B. K. Sedimentary record of Andean Mountain building. Earth Sci. Rev. 178, 279–309 (2018).ADS 
CAS 

Google Scholar 
Gianni, G. M. et al. Northward propagation of Andean genesis: Insights from Early Cretaceous synorogenic deposits in the Aysén-Río Mayo basin. Gondwana Res. 77, 238–259 (2020).ADS 
CAS 

Google Scholar 
Boschman, L. M. Andean Mountain building since the Late Cretaceous: A paleoelevation reconstruction. Earth Sci. Rev. 220, 103640 (2021).
Google Scholar 
Gentry, A. H. Patterns of neotropical plant species diversity. Evol. Biol. 15, 1–84 (1982).
Google Scholar 
Gregory-Wodzicki, K. M. Uplift history of the Central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105 (2000).ADS 

Google Scholar 
Pérez-Escobar, O. A. et al. Recent origin and rapid speciation of Neotropical orchids in the world’s richest plant biodiversity hotspot. New Phytol. 215(2), 891–905 (2017).
Google Scholar 
Alhajeri, B. H., Schenk, J. J. & Steppan, S. J. Ecomorphological diversification following continental colonization in muroid rodents (Rodentia: Muroidea). Biol. J. Linn. Soc. 117, 463–481 (2016).
Google Scholar 
Burgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there?. J. Mammal. 99, 1–14 (2018).
Google Scholar 
Parada, A., Pardiñas, U. F. J., Salazar-Bravo, J., D’Elía, G. & Palma, R. E. Dating an impressive Neotropical radiation: Molecular time estimates for the Sigmodontinae (Rodentia) provide insights into its historical biogeography. Mol. Phylogenet. Evol. 66, 960–968 (2013).
Google Scholar 
Schenk, J. J. & Steppan, S. J. The role of geography in adaptive radiation. Am. Nat. 192, 415–431 (2018).
Google Scholar 
Pardiñas, U. F. J. et al. Morphological disparity in a hyperdiverse mammal clade: A new morphotype and tribe of Neotropical cricetids. Zool. J. Linn. Soc. 196, 1013–1038 (2022).
Google Scholar 
Reig, O. A. Distribuição geográfica e história evolutiva dos roedores muroideos sulamericanos (Cricetidae: Sigmodontinae). Rev. Bras. Genét. 7, 333–365 (1984).
Google Scholar 
Reig, O. A. Diversity Patterns and Differentiation of High Andean Rodents. High Altitude Tropical Biogeography 404–438 (Oxford University Press, 1986).
Google Scholar 
Maestri, R., Upham, N. S. & Patterson, B. D. Tracing the diversification history of a Neogene rodent invasion into South America. Ecography 42, 683–695 (2019).
Google Scholar 
Engel, S. R., Hogan, K. M., Taylor, J. F. & Davis, S. K. Molecular systematics and paleobiogeography of the South American sigmodontine rodents. Mol. Biol. Evol. 15(1), 35–49 (1998).CAS 

Google Scholar 
Parada, A., D’Elía, G. & Palma, R. E. The influence of ecological and geographical context in the radiation of Neotropical sigmodontine rodents. BMC Evol. Biol. 15(1), 1–17 (2015).
Google Scholar 
Leite, R. N. et al. In the wake of invasion: Tracing the historical biogeography of the South American cricetid radiation (Rodentia, Sigmodontinae). PLoS ONE 9, e100687 (2014).ADS 

Google Scholar 
Vilela, J. F., Mello, B., Voloch, C. M. & Schrago, C. G. Sigmodontine rodents diversified in South America prior to the complete rise of the Panamanian Isthmus. J. Zool. Syst. Evol. Res. 52, 249–256 (2014).
Google Scholar 
Ronez, C., Martin, R. A., Kelly, T. S., Barbière, F. & Pardiñas, U. F. J. A brief critical review of sigmodontine rodent origins, with emphasis on paleontological data. Mastozool. Neotrop 28, 001–026 (2021).
Google Scholar 
Maestri, R. & Patterson, B. D. Patterns of species richness and turnover for the South American Rodent Fauna. PLoS ONE 11, e0151895 (2016).
Google Scholar 
Smith, M. F. & Patton, J. L. Phylogenetic relationships and the radiation of sigmodontine rodents in South America: Evidence from cytochrome b. J. Mamm. Evol. 6(2), 89–128 (1999).
Google Scholar 
Udvardy, M. D. & Udvardy, M. D. F. A Classification of the Biogeographical Provinces of the World Vol. 8 (International Union for Conservation of Nature and Natural Resources, 1975).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).
Google Scholar 
Kreft, H. & Jetz, W. A framework for delineating biogeographical regions based on species distributions: Global quantitative biogeographical regionalizations. J. Biogeogr. 37, 2029–2053 (2010).
Google Scholar 
Patton, J. L. et al. (eds) Mammals of South America, Volume 2: Rodents (University of Chicago Press, 2015).
Google Scholar 
Marsh, C. J. et al. Expert range maps of global mammal distributions harmonised to three taxonomic authorities. J. Biogeogr. 49, 979–992 (2022).
Google Scholar 
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 

Google Scholar 
Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).CAS 

Google Scholar 
Parada, A., Hanson, J. & D’Eiía, G. Ultraconserved elements improve the resolution of difficult nodes within the rapid radiation of neotropical Sigmodontine Rodents (Cricetidae: Sigmodontinae). Syst. Biol. 70, 1090–1100 (2021).
Google Scholar 
Steppan, S. J. & Schenk, J. J. Muroid rodent phylogenetics: 900-species tree reveals increasing diversification rates. PLoS ONE 12, e0183070 (2017).
Google Scholar 
Gonçalves, P. R. et al. Unraveling deep branches of the Sigmodontinae Tree (Rodentia: Cricetidae) in Eastern South America. J Mammal Evol 27, 139–160 (2020).
Google Scholar 
Steppan, S. J., Adkins, R. M. & Anderson, J. Phylogeny and divergence-date estimates of rapid radiations in muroid rodents based on multiple nuclear genes. Syst. Biol. 53, 533–553 (2004).
Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 

Google Scholar 
Chernomor, O., von Haeseler, A. & Minh, B. Q. Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol. 65, 997–1008 (2016).
Google Scholar 
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 

Google Scholar 
Bouckaert, R. et al. BEAST 2: A software platform for bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).
Google Scholar 
Gavryushkina, A., Welch, D., Stadler, T. & Drummond, A. J. Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration. PLoS Comput. Biol. 10, e1003919 (2014).ADS 

Google Scholar 
Heath, T. A., & Moore, B. R. Bayesian inference of species divergence times. Bayesian phylogenetics: Methods, algorithms, and applications, 277–318 (2014).Douglas, J., Zhang, R. & Bouckaert, R. Adaptive dating and fast proposals: Revisiting the phylogenetic relaxed clock model. PLoS Comput Biol 17, e1008322 (2021).ADS 
CAS 

Google Scholar 
Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17, 42 (2017).
Google Scholar 
Barido-Sottani, J., Aguirre-Fernández, G., Hopkins, M. J., Stadler, T. & Warnock, R. Ignoring stratigraphic age uncertainty leads to erroneous estimates of species divergence times under the fossilized birth–death process. Proc. R. Soc. B. 286, 20190685 (2019).
Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 

Google Scholar 
Maliet, O., Hartig, F. & Morlon, H. A model with many small shifts for estimating species-specific diversification rates. Nat. Ecol. Evol. 3, 1086–1092 (2019).
Google Scholar 
Maliet, O. & Morlon, H. Fast and accurate estimation of species-specific diversification rates using data augmentation. Syst. Biol. 71, 353–366 (2022).CAS 

Google Scholar 
Gelman, A. & Rubin, D. B. A single series from the Gibbs sampler provides a false sense of security. Bayesian Stat. 4(1), 625–631 (1992).
Google Scholar 
Rabosky, D. L. et al. BAMMtools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).
Google Scholar 
Ree, R. H., Moore, B. R., Webb, C. O. & Donoghue, M. J. A likelihood framework for inferring the evolution of geographic range on phylogenetic trees. Evolution 59, 2299–2311 (2005).
Google Scholar 
Ree, R. H. & Sanmartín, I. Conceptual and statistical problems with the DEC +J model of founder-event speciation and its comparison with DEC via model selection. J. Biogeogr. 45, 741–749 (2018).
Google Scholar 
Matzke, N. J. Model selection in historical biogeography reveals that founder-event speciation is a crucial process in Island Clades. Syst. Biol. 63, 951–970 (2014).
Google Scholar 
Matzke, N. J. Statistical comparison of DEC and DEC +J is identical to comparison of two ClaSSE submodels, and is therefore valid. J. Biogeogr. 49, 1805–1824 (2022).
Google Scholar 
Matzke, N. J. Probabilistic Historical Biogeography: New Models for Founder-Event Speciation, Imperfect Detection, and Fossils Allow Improved Accuracy and Model-Testing (University of California, 2013).
Google Scholar 
Tripp, E. A. & McDade, L. A. A rich fossil record yields calibrated phylogeny for acanthaceae (lamiales) and evidence for marked biases in timing and directionality of intercontinental disjunctions. Syst. Biol. 63, 660–684 (2014).
Google Scholar 
Matos-Maraví, P. et al. Mesoamerica is a cradle and the Atlantic Forest is a museum of Neotropical butterfly diversity: Insights from the evolution and biogeography of Brassolini (Lepidoptera: Nymphalidae). Biol. J. Lin. Soc. 133, 704–724 (2021).
Google Scholar 
Caetano, D. S., O’Meara, B. C. & Beaulieu, J. M. Hidden state models improve state-dependent diversification approaches, including biogeographical models. Evolution 72, 2308–2324 (2018).
Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
Google Scholar 
Schenk, J. J., Rowe, K. C. & Steppan, S. J. Ecological opportunity and incumbency in the diversification of repeated continental colonizations by muroid rodents. Syst. Biol. 62(6), 837–864 (2013).
Google Scholar 
Percequillo, A. R. et al. Tempo and mode of evolution of oryzomyine rodents (Rodentia, Cricetidae, Sigmodontinae): A phylogenomic approach. Mol. Phylogenet. Evol. 159, 107120 (2021).
Google Scholar 
Pacheco, V. R., Patton, J. L. & D’elía, G. Tribe Thomasomyini Steadman and Ray, 1982. In Mammals of South America Vol. 2 (eds Patton, J. L. et al.) 571–574 (The University of Chicago Press, 2015).
Google Scholar 
Salazar-Bravo, J., Pardiñas, U. F., Zeballos, H., & Teta, P. Description of a new tribe of sigmodontine rodents (Cricetidae: Sigmodontinae) with an updated summary of valid tribes and their generic contents. Museum of Texas Tech University 338 (2016).Pardiñas, U. F. et al. Morphological disparity in a hyperdiverse mammal clade: A new morphotype and tribe of Neotropical cricetids. Zool. J. Linnean Soc. 196, 1013–1038 (2022).
Google Scholar 
Pardiñas, U. F., Voglino, D. & Galliari, C. A. Miscellany on Bibimys (Rodentia, Sigmodontinae), a unique akodontine cricetid. Mastozool. Neotrop. 24(1), 241–250 (2017).
Google Scholar 
Salazar-Bravo, J., Pardiñas, U. F. J. & D’Elía, G. A phylogenetic appraisal of Sigmodontinae (Rodentia, Cricetidae) with emphasis on phyllotine genera: Systematics and biogeography. Zool. Scr. 42, 250–261 (2013).
Google Scholar 
Pardiñas, U. F. J., Lessa, G., Teta, P., Salazar-Bravo, J. & Câmara, E. M. V. C. A new genus of sigmodontine rodent from eastern Brazil and the origin of the tribe Phyllotini. J. Mamm. 95, 201–215 (2014).
Google Scholar 
Edler, D., Guedes, T., Zizka, A., Rosvall, M. & Antonelli, A. Infomap bioregions: Interactive mapping of biogeographical regions from species distributions. Syst. Biol. 1, 087 (2016).
Google Scholar 
Johnson, T. C. et al. Late pleistocene desiccation of lake victoria and rapid evolution of cichlid fishes. Science 273, 1091–1093 (1996).ADS 
CAS 

Google Scholar 
Azevedo, J. A. R. et al. Museums and cradles of diversity are geographically coincident for narrowly distributed Neotropical snakes. Ecography 43, 328–339 (2020).
Google Scholar 
Rosauer, D. F. & Jetz, W. Phylogenetic endemism in terrestrial mammals: Mammal phylogenetic endemism. Glob. Ecol. Biogeogr. 24, 168–179 (2015).
Google Scholar 
Peyton, B. Ecology, distribution, and food habits of spectacled bears, Tremarctos ornatus, in Peru. J. Mammal. 61, 639–652 (1980).
Google Scholar 
Patterson, B. D., Solari, S. & Velazco, P. M. The role of the Andes in the diversification and biogeography of Neotropical mammals. In Bones, Clones, and Biomes: The History and Geography of Recent Neotropical Mammals (eds Patterson, B. D. & Costa, L. P.) (Springer, 2012).
Google Scholar 
Tribe, C. J. The Neotropical Rodent Genus’ Rhipidomys’(Cricetidae: Sigmodontinae): A Taxonomic Revision (University of London, 1996).
Google Scholar 
Percequillo, A. R. Sistemática de Oryzomys Baird, 1858: Definição dos Grupos de Espécies e Revisão do Grupo Albigularis (Rodentia, Sigmodontinae) (Doctoral dissertation, Tese de Doutorado) (Universidade de São Paulo, 2003).
Google Scholar 
Brito, J. et al. A new genus of oryzomyine rodents (Cricetidae, Sigmodontinae) with three new species from montane cloud forests, western Andean cordillera of Colombia and Ecuador. PeerJ 8, e10247 (2020).
Google Scholar 
Valencia-Pacheco, E., Avaria-Llautureo, J., Munoz-Escobar, C., Boric-Bargetto, D. & Hernandez, C. E. Geographic patterns of richness distribution of rodents species from the Oryzomyini tribe (Rodentia: Sigmodontinae) in South America: Evaluating the importance of colonization and extinction processes. Rev. Chil. Hist. Nat. 84(3), 365–377 (2011).
Google Scholar 
Pine, R. H., Timm, R. M. & Weksler, M. A newly recognized clade of trans-Andean Oryzomyini (Rodentia: Cricetidae), with description of a new genus. J. Mammal. 93(3), 851–870 (2012).
Google Scholar 
Prado, J. R. & Percequillo, A. R. Geographic distribution of the genera of the tribe Oryzomyini (Rodentia: Cricetidae: Sigmodontinae) in South America: Patterns of distribution and diversity. Arq. Zool. 44(1), 1–120 (2013).
Google Scholar 
Prado, J. R. et al. Species richness and areas of endemism of oryzomyine rodents (Cricetidae, Sigmodontinae) in South America: An NDM/VNDM approach. J. Biogeogr. 42(3), 540–551 (2015).
Google Scholar 
Voss, R. S. A new species of Thomasomys (Rodentia: Muridae) from eastern Ecuador, with remarks on mammalian diversity and biogeography in the Cordillera Oriental. Am. Mus. Novit. 2003(3421), 1–47 (2003).
Google Scholar 
Brito, J. et al. Diversidad insospechada en los Andes de Ecuador: Filogenia del grupo “cinereus” de Thomasomys y descripción de una nueva especie (Rodentia, Cricetidae). Mastozool. Neotrop. 26(2), 308–330 (2019).
Google Scholar 
Rodríguez-Serrano, E., Palma, R. E. & Hernández, C. E. The evolution of ecomorphological traits within the Abrothrichini (Rodentia: Sigmodontinae): A Bayesian phylogenetics approach. Mol. Phylogenet. Evol. 48(2), 473–480 (2008).
Google Scholar 
Villagrán, C. & Hinojosa, L. F. Historia de los bosques del sur de Sudamérica, II: Análisis fitogeográfico. Rev. Chil. Hist. Nat. 70(2), 1–267 (1997).ADS 

Google Scholar 
Pardinas, U. F., Teta, P., D’elía, G. & Lessa, E. P. The evolutionary history of sigmodontine rodents in Patagonia and Tierra del Fuego. Biol. J. Lin. Soc. 103(2), 495–513 (2011).
Google Scholar 
Yepes, J. Consideraciones sobre el género “Andinomys” (Cricetinae) y descripción de una forma nueva. In Anales del Museo Argentino de Ciencias Naturales “Bernardino Rivadavia” (Vol. 38, 333–348) (1935).Salazar-Bravo, J. & Jayat, J. P. Genus Andinomys Thomas, 1902. Mamm. S. Am. 2, 75–77 (2015).
Google Scholar 
Pacheco, V. & Patton, J. L. A new species of the Puna mouse, genus Punomys Osgood, 1943 (Muridae, Sigmodontinae) from the Southeastern Andes of Peru. Z. Saugetierkunde 60(2), 85–96 (1995).
Google Scholar 
Salazar-Bravo, J., Miralles-Salazar, J., Rico-Cernohorska, A. & Vargas, J. First record of Punomys (Rodentia: Sigmodontinae) in Bolivia. Mastozool. Neotrop. 18(1), 143–146 (2011).
Google Scholar 
Rolland, J., Condamine, F. L., Beeravolu, C. R., Jiguet, F. & Morlon, H. Dispersal is a major driver of the latitudinal diversity gradient of C arnivora. Glob. Ecol. Biogeogr. 24(9), 1059–1071 (2015).
Google Scholar 
Pyron, R. A. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280(1770), 20131622 (2013).
Google Scholar 
Meseguer, A. S. et al. Reconstructing deep-time palaeoclimate legacies in the clusioid Malpighiales unveils their role in the evolution and extinction of the boreotropical flora. Glob. Ecol. Biogeogr. 27(5), 616–628 (2018).
Google Scholar 
Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: A Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61(2), 346–367 (2007).CAS 

Google Scholar 
Ribas, C. C., Moyle, R. G., Miyaki, C. Y. & Cracraft, J. The assembly of montane biotas: Linking Andean tectonics and climatic oscillations to independent regimes of diversification in Pionus parrots. Proc. R. Soc. B 274(1624), 2399–2408 (2007).
Google Scholar 
Bonaccorso, E. Historical biogeography and speciation in the Neotropical highlands: Molecular phylogenetics of the jay genus Cyanolyca. Mol. Phylogenet. Evol. 50(3), 618–632 (2009).CAS 

Google Scholar 
McGuire, J. A., Witt, C. C., Altshuler, D. L. & Remsen, J. V. Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy. Syst. Biol. 56(5), 837–856 (2007).CAS 

Google Scholar 
Rheindt, F. E., Christidis, L. & Norman, J. A. Habitat shifts in the evolutionary history of a Neotropical flycatcher lineage from forest and open landscapes. BMC Evol. Biol. 8(1), 1–18 (2008).
Google Scholar 
Percequillo, A. R., Weksler, M. & Costa, L. P. Comments on oryzomyine biogeography. Zool. J. Linn. Soc. 161(2), 357–390 (2011).
Google Scholar 
Weksler, M. Tribe Oryzomyini Vorontsov, 1959. Mamm. S. Am. 2, 291–293 (2015).
Google Scholar 
Haag, T. et al. Phylogenetic relationships among species of the genus Calomys with emphasis on South American lowland taxa. J. Mammal. 88(3), 769–776 (2007).
Google Scholar 
Badgley, C. Tectonics, topography, and mammalian diversity. Ecography 33(2), 220–231 (2010).
Google Scholar 
Simpson, G. G. Species density of North American recent mammals. Syst. Zool. 13(2), 57–73 (1964).
Google Scholar  More

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

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

Google Scholar 
Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7, eabe9829 (2021).Article 
CAS 

Google Scholar 
Betts, R. A. et al. The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global climate warming. Theor. Appl. Climatol. 78, 157–175 (2004).Article 

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

Google Scholar 
Rammig, A. et al. Estimating the risk of Amazonian forest dieback. N. Phytol. 187, 694–706 (2010).Article 
CAS 

Google Scholar 
Huntingford, C. et al. Towards quantifying uncertainty in predictions of Amazon ‘dieback’. Philos. Trans. R. Soc. B Biol. Sci. 363, 1857–1864 (2008).Article 

Google Scholar 
Galbraith, D. et al. Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change. N. Phytol. 187, 647–665 (2010).Article 

Google Scholar 
Kumar, D., Pfeiffer, M., Gaillard, C., Langan, L. & Scheiter, S. Climate change and elevated CO2 favor forest over savanna under different future scenarios in South Asia. Biogeosciences 18, 2957–2979 (2021).Article 
CAS 

Google Scholar 
Huntingford, C. et al. Simulated resilience of tropical rainforests to CO2-induced climate change. Nat. Geosci. 6, 268–273 (2013).Article 
CAS 

Google Scholar 
Brienen, R. J. W. et al. Forest carbon sink neutralized by pervasive growth-lifespan trade-offs. Nat. Commun. 11, 4241 (2020).Article 
CAS 

Google Scholar 
Koch, A., Hubau, W. & Lewis, S. L. Earth system models are not capturing present-day tropical forest carbon dynamics. Earths Future 9, e2020EF001874 (2021).Article 
CAS 

Google Scholar 
Negrón-Juárez, R. I., Koven, C. D., Riley, W. J., Knox, R. G. & Chambers, J. Q. Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models. Environ. Res. Lett. 10, 064017 (2015).Article 

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

Google Scholar 
Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).Article 
CAS 

Google Scholar 
Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. 106, 20610–20615 (2009).Article 
CAS 

Google Scholar 
Zelazowski, P., Malhi, Y., Huntingford, C., Sitch, S. & Fisher, J. B. Changes in the potential distribution of humid tropical forests on a warmer planet. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 369, 137–160 (2011).
Google Scholar 
Huang, L. et al. Drought dominates the interannual variability in global terrestrial net primary production by controlling semi-arid ecosystems. Sci. Rep. 6, 24639 (2016).Article 
CAS 

Google Scholar 
Castanho, A. D. A. et al. Potential shifts in the aboveground biomass and physiognomy of a seasonally dry tropical forest in a changing climate. Environ. Res. Lett. 15, 034053 (2020).Article 

Google Scholar 
Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3. NERC EDS Centre for Environmental Data Analysis https://doi.org/10.5285/5f331c418e9f4935b8eb1b836f8a91b8 (2021).Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).Article 
CAS 

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

Google Scholar 
Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393 (2021).Article 
CAS 

Google Scholar 
Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

Google Scholar 
Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).Article 
CAS 

Google Scholar 
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).Article 
CAS 

Google Scholar 
Phillips, O. L. et al. Drought sensitivity of the amazon rainforest. Science 323, 1344–1347 (2009).Article 
CAS 

Google Scholar 
Ross, C. W. et al. Woody-biomass projections and drivers of change in sub-Saharan Africa. Nat. Clim. Change 11, 449–455 (2021).Article 

Google Scholar 
Larjavaara, M., Lu, X., Chen, X. & Vastaranta, M. Impact of rising temperatures on the biomass of humid old-growth forests of the world. Carbon Balance Manag. 16, 31 (2021).Article 

Google Scholar 
Romps, D. M., Seeley, J. T., Vollaro, D. & Molinari, J. Projected increase in lightning strikes in the United States due to global warming. Science 346, 851–854 (2014).Article 
CAS 

Google Scholar 
Gora, E. M., Bitzer, P. M., Burchfield, J. C., Gutierrez, C. & Yanoviak, S. P. The contributions of lightning to biomass turnover, gap formation and plant mortality in a tropical forest. Ecology 102, e03541 (2021).Article 

Google Scholar 
Magnabosco Marra, D. et al. Windthrows control biomass patterns and functional composition of Amazon forests. Glob. Change Biol. 24, 5867–5881 (2018).Article 

Google Scholar 
Negrón-Juárez, R. I. et al. Windthrow variability in central amazonia. Atmosphere 8, 28 (2017).Article 

Google Scholar 
Silva Junior, C. H. L. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, 40 (2020).Article 

Google Scholar 
Yin, Y. et al. Fire decline in dry tropical ecosystems enhances decadal land carbon sink. Nat. Commun. 11, 1900 (2020).Article 
CAS 

Google Scholar 
Koch, A. & Kaplan, J. O. Tropical forest restoration under future climate change. Nat. Clim. Change 12, 279–283 (2022).Article 

Google Scholar 
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).Article 
CAS 

Google Scholar 
Case, M. F. & Staver, A. C. Fire prevents woody encroachment only at higher-than-historical frequencies in a South African savanna. J. Appl. Ecol. 54, 955–962 (2017).Article 
CAS 

Google Scholar 
Mau, A. C., Reed, S. C., Wood, T. E. & Cavaleri, M. A. Temperate and tropical forest canopies are already functioning beyond their thermal thresholds for photosynthesis. Forests 9, 47 (2018).Article 

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

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

Google Scholar 
Doughty, C. E. & Goulden, M. L. Are tropical forests near a high temperature threshold?. J. Geophys. Res. Biogeosciences 113, G00B07 (2008).Article 

Google Scholar 
Doughty, C. E. & Goulden, M. L. Seasonal patterns of tropical forest leaf area index and CO2 exchange. J. Geophys. Res. Biogeosciences 113, G00B06 (2008).Article 

Google Scholar 
Langenbrunner, B., Pritchard, M. S., Kooperman, G. J. & Randerson, J. T. Why does amazon precipitation decrease when tropical forests respond to increasing CO2? Earths Future 7, 450–468 (2019).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).Article 

Google Scholar 
Maurer, E. P., Brekke, L., Pruitt, T. & Duffy, P. B. Fine-resolution climate projections enhance regional climate change impact studies. EOS Trans. Am. Geophys. Union 88, 504–504 (2007).Article 

Google Scholar 
Reclamation. Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Hydrology Projections, Comparison with preceding Information, and Summary of User Needs. https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/techmemo/BCSD5HydrologyMemo.pdf (2014).Silva de Miranda, P. L. et al. Using tree species inventories to map biomes and assess their climatic overlaps in lowland tropical South America. Glob. Ecol. Biogeogr. 27, 899–912 (2018).Article 

Google Scholar 
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 
Wright, M. N. & Ziegler, A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, 1–17 (2017).Article 

Google Scholar 
Middleton, N., Thomas, D. & UNEP. World Atlas of Desertification (Arnold, 1997).Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of Savanna and forest as alternative biome states. Science 334, 230–232 (2011).Article 
CAS 

Google Scholar 
ESRI Data & Maps. World Continents Version 10.3. (2015).Uribe, M. R. et al. Net loss of biomass predicted for tropical biomes in a changing climate. Dryad https://doi.org/10.7280/D1D124 (2023). More

Plant growth model without environmental forcingThe model without environmental forcing closely follows the original description of the Thornley transport resistance (TTR) model29. A summary of the model parameters is provided in Supplementary Table 2. The shoot and root mass pools (MS and MR, in kg structural dry matter) change as a function of growth and loss (equations (1) and (2)). The litter (kL) and maintenance respiration (r) loss rates (in kg kg−1 d−1) are treated as constants. In the original model description29 r = 0. The parameter KM (units kg) describes how loss varies with mass (MS or MR). Growth (Gs and Gr, in kg d−1) varies as a function of the carbon and nitrogen concentrations (equations (3) and (4)). CS, CR, NS and NR are the amounts (kg) of carbon and nitrogen in the roots and shoots. These assumptions yield the following equations for shoot and root dry matter,$${mathrm{MS}}[t+1]={mathrm{MS}}[t]+{G}_{{mathrm{S}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MS}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MS}}[t]}},$$
(1)
$${mathrm{MR}}[t+1]={mathrm{MR}}[t]+{G}_{{mathrm{R}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MR}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MR}}[t]}},$$
(2)
where GS and GR are$${G}_{{mathrm{S}}}=gfrac{{mathrm{CS}}times {mathrm{NS}}}{{mathrm{MS}}},$$
(3)
$${G}_{{mathrm{R}}}=gfrac{{mathrm{CR}}times {mathrm{NR}}}{{mathrm{MR}}},$$
(4)
and g is the growth coefficient (in kg kg−1 d−1).Carbon uptake UC is determined by the net photosynthetic rate (a, in kg kg−1 d−1) and the shoot mass (equation (5)). Similarly, nitrogen uptake (UN) is determined by the nitrogen uptake rate (b, in kg kg−1 d−1) and the root mass. The parameter KA (units kg) forces both photosynthesis and nitrogen uptake to be asymptotic with mass. The second terms in the denominators of equations (5) and (6) model product inhibitions of carbon and nitrogen uptake, respectively; that is, the parameters JC and JN (in kg kg−1) mimic the inhibition of source activity when substrate concentrations are high,$${U}_{{mathrm{C}}}=frac{a{mathrm{MS}}}{left(1+frac{{mathrm{MS}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{CS}}}{{mathrm{MS}}times {J}_{{mathrm{C}}}}right)},$$
(5)
$${U}_{{mathrm{N}}}=frac{b{mathrm{MR}}}{left(1+frac{{mathrm{MR}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{NR}}}{{mathrm{MR}}times {J}_{{mathrm{N}}}}right)}.$$
(6)
The substrate transport fluxes of C and N (τC and τN, in kg d−1) between roots and shoots are determined by the concentration gradients between root and shoot and by the resistances. In the original model description29, these resistances are defined flexibly, but we simplify and assume that they scale linearly with plant mass,$${tau }_{{mathrm{C}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{CS}}}{{mathrm{MS}}}-frac{{mathrm{CR}}}{{mathrm{MR}}}right)$$
(7)
$${tau }_{{mathrm{N}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{NR}}}{{mathrm{MR}}}-frac{{mathrm{NS}}}{{mathrm{MS}}}right)$$
(8)
The changes in mass of carbon and nitrogen in the roots and shoots are then$${mathrm{CS}}[t+1]={mathrm{CS}}[t]+{U}_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{s}}}[t]-{tau }_{{mathrm{C}}}[t]$$
(9)
$${mathrm{CR}}[t+1]={mathrm{CR}}[t]+{tau }_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{r}}}[t]$$
(10)
$${mathrm{NS}}[t+1]={mathrm{NS}}[t]+{tau }_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{s}}}[t]$$
(11)
$${mathrm{NR}}[t+1]={mathrm{NR}}[t]+{U}_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{r}}}[t]-{tau }_{{mathrm{N}}}[t]$$
(12)
where fC and fN (in kg kg−1) are the fractions of structural carbon and nitrogen in dry matter.Adding environmental forcing to the plant growth modelIn this section, we describe how the net photosynthetic rate (a), the nitrogen uptake rate (b), the growth rate (g) and the respiration rate (r) are influenced by environmental-forcing factors. These environmental-forcing effects are described in equations (13)–(17) and summarized graphically in Extended Data Fig. 1. All other model parameters are treated as constants. Previous work that implemented the TTR model as a species distribution model30 is used as a starting point for adding environmental forcing. As in this previous work30, we assume that parameters a, b and g are co-limited by environmental factors in a manner analogous to Liebig’s law of the minimum, which is a crude but pragmatic abstraction. The implementation here differs in some details.Unlike previous work30, we use the Farquhar model of photosynthesis47,48 to represent how solar radiation, atmospheric CO2 concentration and air temperature co-limit photosynthesis35. We assume that the Farquhar model parameters are universal and that all vegetation in our study uses the C3 photosynthetic pathway. The Farquhar model photosynthetic rates are rescaled to [0,amax] to yield afqr. The effects of soil moisture (Msoil) on photosynthesis are represented as an increasing step function ({{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})=max left{min left(frac{M_{mathrm{soil}}-{beta }_{1}}{{beta }_{2}-{beta }_{1}},1right),0right}). This allows us to redefine a as,$$a={a}_{{mathrm{fqr}}} {{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})$$
(13)
The processes influencing nitrogen availability are complex, and global data products on plant available nitrogen are uncertain. We therefore assume that nitrogen uptake will vary with soil temperature and soil moisture. That is, the nitrogen uptake rate b is assumed to have a maximum rate (bmax) that is co-limited by soil temperature Tsoil and soil moisture Msoil,$$b={b}_{{mathrm{max}}} {{{{S}}}}({T}_{soil},{beta }_{3},{beta }_{4}) {{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8}).$$
(14)
In equation (14), we have assumed that the nitrogen uptake rate is a simple increasing and saturating function of temperature. We have also assumed that the nitrogen uptake rate is a trapezoidal function of soil moisture with low uptake rates in dry soils, higher uptake rates at intermediate moisture levels and lower rates once soils are so moist as to be waterlogged. The trapezoidal function is ({{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8})=max left{min left(frac{M_{mathrm{soil}}-{{{{{beta }}}}}_{5}}{{{{{{beta }}}}}_{6}-{{{{{beta }}}}}_{5}},1,frac{{{{{{beta }}}}}_{8}-M_{mathrm{soil}}}{{beta }_{8}-{beta }_{7}}right),0right}).The previous sections describe how the assimilation of carbon and nitrogen by a plant are influenced by environmental factors. The TTR model describes how these assimilate concentrations influence growth (equations (3) and (4)). In our implementation, we additionally allow the growth rate to be co-limited by temperature (soil temperature, Tsoil) and soil moisture (Msoil),$$g={g}_{{mathrm{max}}} {{{{Z}}}}({T}_{{mathrm{soil}}},{beta }_{9},{beta }_{10},{beta }_{11},{beta }_{12}) {{{{S}}}}(M_{mathrm{soil}},{beta }_{13},{beta }_{14}).$$
(15)
We use Tsoil since we assume that growth is more closely linked to soil temperature, which varies slower than air temperature. The respiration rate (r, equations (1) and (2)) increases as a function of air temperature (Tair) to a maximum rmax,$$r={r}_{{mathrm{max}}}{{{{S}}}}({T}_{{mathrm{air}}},{beta }_{15},{beta }_{16}).$$
(16)
The parameter r is best interpreted as a maintenance respiration. Growth respiration is not explicitly considered; it is implicitly incorporated in the growth rate parameter (g, equation (15)), and any temperature dependence in growth respiration is therefore assumed to be accommodated by equation (15).Fire can reduce the structural shoot mass MS as follows,$${mathrm{MS}}[t+1]={mathrm{MS}}[t](1-{{{{S}}}}(F,{beta }_{17},{beta }_{18})).$$
(17)
where F is an indicator of fire severity at a point in time (for example, burnt area) and the function S(F, β17, β18) allows MS to decrease when the fire severity indicator F is high. If F = 0, this process plays no role. This fire impact equation was used in preliminary analyses, but the data on fire activity did not provide sufficient information to estimate β17 and β18; we therefore excluded this process from the final analyses.We further estimate two additional β parameters (βa and βb) so that each site can have unique maximum carbon and nitrogen uptake rates. Specifically, we redefine a as ({a}^{{prime} }={beta }_{a} a) and b as ({b}^{{prime} }={beta }_{b} b).Data sources and preparationTo describe vegetation activity, we use the GIMMS 3g v.1 NDVI data26,27 and the MODIS EVI28 data. The GIMMS data product has been derived from the AVHRR satellite programme and controls for atmospheric contamination, calibration loss, orbital drift and volcanic eruptions26,27. The data provide 24 NDVI raster grids for each year, starting in July 1981 and ending in December 2015. The spatial resolution is 1/12° (~9 × 9 km). The EVI data used are from the MODIS programme’s Terra satellite; it is a 1 km data product provided at a 16-day interval. We use data from the start of the record (February 2000) to December 2019. The MODIS data product (MOD13A2) uses a temporal compositing algorithm to produce an estimate every 16 days that filters out atmospheric contamination. The EVI is designed to reduce the effects of atmospheric, bare-ground and surface water on the vegetation index28.For environmental forcing, we use the ERA5-Land data31,32 (European Centre for Medium-Range Weather Forecasts Reanalysis v. 5; hereafter, ERA5). The ERA5 products are global reanalysis products based on hourly estimates of atmospheric variables and extend from present back to 1979. The data products are supplied at a variety of spatial and temporal resolutions. We used the monthly averages from 1981 to 2019 at a 0.1° spatial resolution (~11 km). The ERA5 data provide air temperature (2 m surface air temperature), soil temperature (0–7 cm soil depth), surface solar radiation and volumetric soil water (0–7 cm soil depth). Fire data were taken from the European Space Agency Fire Disturbance Climate Change Initiative’s AVHRR Long-Term Data Record Grid v.1.0 product49. This product provides gridded (0.25° resolution) data of monthly global (from 1982 to 2017) burned area derived from the AVHRR satellite programme. As mentioned, the fire data did not enrich our analysis, and the analyses we present here therefore exclude further consideration of the fire data.All data were resampled to the GIMMS grid. The mean pixel EVI was then calculated for each GIMMS cell for each time point in the MODIS EVI data. We used linear interpolation on the NDVI, EVI and ERA5 environmental-forcing data to estimate each variable on a weekly time step. This served to set the time step of the TTR difference equations to one week and to synchronize the different time series.Site selectionThe GIMMS grid cells define the spatial resolution of our sample points. GIMMS grid cells are large (1/12°, ~9 km), meaning that most grid cells contain multiple land-cover types. We focused on wilderness landscapes, and our aim was to find multiple grid cells for the major ecosystems of the world. We used the following classification of ecosystem types to guide the stratification of our grid-cell selection: tropical evergreen forest (RF), boreal forest (BF), temperate evergreen and temperate deciduous forest (TF), savannah (SA), shrubland (SH), grassland (GR), tundra (TU) and Mediterranean-type ecosystems (MT).We used the following criteria to select grid cells. (1) Selected grid cells should contain relatively homogeneous vegetation. Small-scale heterogeneity was allowed (for example, catenas, drainage lines, peatlands) as long as many of these elements are repeated in the scene (for example, rolling hills were accepted, but elevation gradients were rejected). (2) Sites should have no signs of transformative human activity (for example, tree harvesting, crop cultivation, paved surfaces). We used the Time Tool in Google Earth Pro, which provides annual satellite imagery of the Earth from 1984 onwards, to ensure that no such activity occurred during the observation period (note that the GIMMS record starts in July 1981; however, it is likely that evidence of transformative activity between July 1981 and 1984 would be visible in 1984). Grid cells with extensive livestock holding on non-improved pasture were included. In some cases, a small agricultural field or pasture was present, and such grid cells were used as long as the field or pasture was small and remained constant in size. (3) Grid cells should not include large water bodies, but small drainage lines or ponds were accepted as long as they did not violate the first criterion. (4) Grid cells should be independent (neighbouring grid cells were not selected) and cover the major ecosystems of the world. Using these criteria, we were able to include 100 sites in the study (Extended Data Figs. 2 and 3 and Supplementary Table 4).State-space modelWe used a Bayesian state-space approach. Conceptually, the analysis takes the form,$$M[t]=f(M[t-1],{boldsymbol{beta}},{boldsymbol{theta}}_{t-1},{epsilon }_{t-1})$$
(18)
$${mathrm{VI}}[t]=m M[t]+eta .$$
(19)
Here M[t] is the plant growth model’s prediction of biomass (M = MS + MR) at time t, and ϵt−1 is the process error associated with the state variables. In the model, each underlying state variable (MS, MR, CS, CR, NS and NR) has an associated process error term. The function f(M[t − 1], β, θt−1, ϵt−1) summarizes that the development of M is influenced by the state variables MS, MR, CS, CR, NS and NR, the environmental-forcing data θt−1 and the β parameters. The observation equation (equation (19)) uses the parameter m to link the VI (vegetation index, either NDVI or EVI) observations to the modelled state M. The parameter η is the observation error. Equation (19) assumes that there is a linear relationship between modelled biomass (M) and VI, which is a simplification of reality50,51,52. The observation error η is structured by our simplification of the data products quality scores (coded Q = 0, 1, 2, with 0 being good and 2 being poor; Supplementary Table 3) to allow the error to increase with each level of the quality score. Specifically, we define η = e0 + e1 × Q.The model was formulated using the R package LaplacesDemon53. All β parameters are given vague uniform priors. The parameter m is given a vague normal prior (truncated to be >0). The process error terms are modelled using normal distributions, and the variances of the error terms are given vague half-Cauchy priors. The ex parameters are given vague normal priors. The model also requires the parameterization of M[0], the initial vegetation biomass; M[0] is given a vague uniform prior. We used the twalk Markov chain Monte Carlo (MCMC) algorithm as implemented in LaplacesDemon53 and its default control parameters to estimate the posterior distributions of the model parameters. We further fitted the model using DEoptim54,55, which is a robust genetic algorithm that is known to perform stably on high-dimensional and multi-modal problems56, to verify that the MCMC algorithm had not missed important regions of the parameter space. The models estimated with MCMC had significantly lower log root-mean-square error than models estimated with DEoptim (paired t-test NDVI analysis: t = –2.9806, degrees of freedom (d.f.) = 99, P = 0.00362; EVI analysis: t = –4.6229, d.f. = 99, P = 1.144 × 10–5), suggesting that the MCMC algorithm performed well compared with the genetic algorithm.Anomaly extraction and trend estimationWe use the ‘seasonal and trend decomposition using Loess’ (STL57) as implemented in the R58 base function stl. STL extracts the seasonal component s of a time series using Loess smoothing. What remains after seasonal extraction (the anomaly or remainder, r) is the sum of any long-term trend and stochastic variation. We estimate the trend in two ways. First, we estimate the trend by fitting a quadratic polynomial (r = a + bx + cx2) to the remainder (r is the remainder, x is time and a, b and c are regression coefficients). The use of polynomials allows the data to specify whether a trend exists, whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. As a second method, we estimate the trend by fitting a bent-cable regression to the remainder. Bent-cable regression is a type of piecewise linear regression for estimating the point of transition between two linear phases in a time series59,60. The model takes the form r = b0 + b1x + b2 q(x, τ, γ)60. Here r is the remainder, x is time, b0 is the initial intercept, b1 is the slope in phase 1, the slope in phase 2 is b2 − b1 and q is a function that defines the change point: (q(x,tau ,gamma )=frac{{(x-tau +gamma )}^{2}}{4gamma }I(tau -gamma < tau +gamma )+(x-tau )I(x > tau +gamma )); τ represents the location of the change point and γ the span of the bent cable that joins the two linear phases; I(A) is an indicator function that returns 1 if A is true and 0 if A is false. The bent-cable model allows the data to specify whether a trend exists and whether there has been a switch in the trend, thereby allowing the identification of whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. Both the polynomial and bent-cable models were estimated using LaplacesDemon’s53 Adaptive Metropolis MCMC algorithm and vague priors, although for the bent-cable model we constrained τ to be in the middle 70% of the time series and γ to be at most two years.The STL extraction of the seasonal components in the air temperature, soil temperature, soil moisture and solar radiation data (there is no stochasticity or seasonal trend in the CO2 data we used) allows us to simulate detrended time series d of these forcing variables as (d=bar{y}+s+{{{{N}}}}(mu ,sigma )) where N(μ, σ) is a normally distributed random variable with mean and standard deviation estimated from the remainder r (we verified that r was well described by the normal distribution), (bar{y}) is the mean of the data over the time series and s is the seasonal component extracted by STL. For CO2, the detrended time series is simply the average CO2 over the time series. More